Asta Literature Retrieval: Pathophysiology and clinical mechanisms of PCWH syndrome. Core disease mechanisms, molecular and cellular pathways, i...

This report is retrieval-only and is generated directly from Asta results.

• Papers retrieved: 20
• Snippets retrieved: 20

Relevant Papers

[1] Modeling psychiatric disorders: from genomic findings to cellular phenotypes

• Authors: Anna Falk, Vivi M. Heine, A. Harwood, Patrick F. Sullivan, M. Peitz et al.
• Year: 2016
• Venue: Molecular Psychiatry
• URL: https://www.semanticscholar.org/paper/235b41240d78140de7ab06a3ad8a7d0b1bdff1a5
• DOI: 10.1038/mp.2016.89
• PMID: 27240529
• PMCID: 4995546
• Citations: 77
• Influential citations: 2
• Summary: The challenges for modeling of psychiatric disorders, potential solutions and how iPSC technology can be used to develop an analytical framework for the evaluation and therapeutic manipulation of fundamental disease processes are critically reviewed.
• Evidence snippets:
• Snippet 1 (score: 0.431) > The key challenge for iPSC-based disease modeling is to identify one or more relevant cellular phenotypes that accurately represent the disease pathophysiology. Increasing numbers of reports have demonstrated that for many diseases specific pathophysiology can be captured in human iPSC-based disease models. These range from cardiovascular disease, 44,45 cancer, 46,47 ocular disease, 48,49 diabetes mellitus 50,51 and neurological disorders of the brain. 52,53 Can the same approach be applied to complex psychiatric disorders? > The problem is that almost all psychiatric disorders are characterized by clinical signs and symptoms, but lack independent verification from objective biomarkers. Thus, how might these clinical phenotypes manifest themselves in terms of cell behavior? The identity of robust cellular 'readouts', which typify any psychiatric disorder, is a crucial unsolved problem and an area of intense study 54 (Table 2). When satisfactorily answered, this will herald a new degree of biological objectivity and quantification for the study of psychiatric disorders. > The aim is to find a single or small number of cell phenotypes or parameters that strongly associate with psychiatric disorders, and establish a cellular profile characteristic of cells derived from the general patient population. Although a consensus set of cellular phenotypes for psychiatric disorder is yet to be established, we can define some of their desired characteristics. First, cellular phenotypes have to relate to the biological pathways identified by genetics. Second, although there are many risk genes in disparate biological pathways, at some level, phenotypes should converge onto a much smaller grouping. Third, phenotypes need to be quantifiable. Finally, to be useful for drug development cellular phenotypes should be reversed by pharmacological treatment, although not necessarily by drugs in current use. > Although human iPSC-based approaches underrepresent the complexity of the human central nervous system, cellular phenotypes are likely to lie more proximal to molecular disease mechanisms than phenotypes seen at the level of a tissue or organism, 55 and thus may bypass compensatory homeostatic (2) Gene expression profiles of SCZ human iPSC neurons identified altered expression of many components of the cyclic AMP and WNT signaling pathways. > (3

[2] Investigating the role of NPR1 in dilated cardiomyopathy and its potential as a therapeutic target for glucocorticoid therapy

• Authors: Yaomeng Huang, Tongxin Li, Shichao Gao, Shuyu Li, Xiaoran Zhu et al.
• Year: 2023
• Venue: Frontiers in Pharmacology
• URL: https://www.semanticscholar.org/paper/be229f6f2059faab4c97ec0a04bd055adab9dfe1
• DOI: 10.3389/fphar.2023.1290253
• PMID: 38026943
• PMCID: 10662320
• Citations: 3
• Summary: Natriuretic peptide receptor 1 (NPR1) was identified as a core gene associated with DCM through bioinformatics analysis and led to substantial improvements in cardiac and renal function, accompanied by an upregulation of NPR1 expression.
• Evidence snippets:
• Snippet 1 (score: 0.424) > Multiple pathways and molecules are involved in this process; however, the detailed underlying mechanisms remain unclear. In recent years, with the development of high-throughput sequencing and gene chip technologies, the use of bioinformatics technology to explore the occurrence, development, and prognosis of diseases has become a hot topic for scholars worldwide (Hwang et al., 2018;Nayor et al., 2019;Rinschen et al., 2019;Sturm et al., 2019;Montaner et al., 2020). > The present study aimed to use bioinformatics technology to screen for DCM-related genes and investigate their mechanisms, with the purpose of revealing the pathogenesis of DCM and seeking treatment methods. The GSE3586 dataset, containing expression profiles related to DCM, was selected from the Gene Expression Omnibus (GEO) database. This study aimed to predict the core genes that may play crucial roles in disease progression at the molecular level through the enrichment of relevant molecular pathways associated with DCM. Furthermore, the phenotype of the core genes was validated to further support the results of the bioinformatics analysis through basic and clinical experiments. Additionally, the role of glucocorticoids in DCM treatment is discussed in this article with the purpose of providing a theoretical and experimental basis for exploring the pathogenesis of DCM and elucidating therapeutic methods. This study also provides a theoretical reference for the interpretation, early diagnosis, and treatment of DCM.

[3] Molecular Mechanisms and Risk Factors for the Pathogenesis of Hydrocephalus

• Authors: Jing-wen Li, Xinjie Zhang, Jianfeng Guo, Chen Yu, Jun Yang
• Year: 2022
• Venue: Frontiers in Genetics
• URL: https://www.semanticscholar.org/paper/d53bdf5f73f54a6d5a8be8777d23c465a13e9185
• DOI: 10.3389/fgene.2021.777926
• PMID: 35047005
• PMCID: 8762052
• Citations: 15
• Influential citations: 2
• Summary: Some possible fundamental molecular mechanisms and facilitating risk factors involved in the pathogenesis of hydrocephalus are elicited, and knowledge could be used to improve patient care in different ways, such as early precise diagnosis and effective therapeutic regimens.
• Evidence snippets:
• Snippet 1 (score: 0.417) > Cwh43 modifies the glycosylphosphatidylinositol-anchored proteins on the ependymal cells, and the mutant Cwh43 is related to iNPH in both humans and mice. The clinical features manifest as late-onset communicating hydrocephalus with symptoms of gait and balance dysfunction (Yang et al., 2021a). > The clinical manifestation and progression, as well as experimental investigations, indicate that hydrocephalus is a complex disease with polygenic involvement, rather than a simple CSF accumulation disorder. Although the current studies have revealed that some genetic mutations are involved in the pathogenesis of hydrocephalus, how these mutations are associated with the disorder of CSF circulation and their pathogenic roles in the pathological progression of hydrocephalus still remain largely unknown. Previous studies indicated that a lot of genetic mutations were relevant to the disorders of ciliary and/or centrosome, resulting in the dysfunction of the glymphatic system. However, how these mutations and their interactions contribute to the pathogenesis of hydrocephalus needs to be further elucidated. Moreover, there is still a lack of basic knowledge on the mechanisms underlying the cognitive functional impairment of hydrocephalus. Therefore, further extensive studies should be conducted to explore the underlying molecular mechanisms of identified and/or unidentified genes in the pathophysiology of hydrocephalus. Based on our knowledge, we propose that the genetic mutations relevant to ciliary and centrosomal proteins and the interaction between glymphatic system and ciliary/ centrosomal structures/functions may be a critical molecular mechanism in the pathophysiology of hydrocephalus. In addition, based on these fundamental molecular mechanisms, it is noteworthy that environmental and other acquired risks or etiological factors are also involved in the facilitation of ventricular enlargement.

[4] Cardiomyocytes Derived from Induced Pluripotent Stem Cells as a Disease Model for Propionic Acidemia

• Authors: Esmeralda Alonso-Barroso, B. Pérez, L. Desviat, E. Richard
• Year: 2021
• Venue: International Journal of Molecular Sciences
• URL: https://www.semanticscholar.org/paper/da649a0f04477c53b448c5ac5f873f8762235290
• DOI: 10.3390/ijms22031161
• PMID: 33503868
• PMCID: 7865492
• Citations: 16
• Influential citations: 1
• Summary: The novel results show that PA iPSC-cardiomyocytes represent a promising model for investigating the pathological mechanisms underlying PA cardiomyopathies, also serving as an ex vivo platform for therapeutic evaluation.
• Evidence snippets:
• Snippet 1 (score: 0.412) > The study of the mechanisms involved in disease physiopathology has been mainly performed using the hypomorphic PA mouse model that mimics the biochemical and clinical phenotype [5]. Using this model, bioenergetic failure, oxidative damage and deregulation of miRNAs induced by accumulating propionyl-CoA have been described as potential mechanisms contributing to PA physiopathology [6][7][8]. The limitations of animal models for the study of cardiac energy metabolism [9] and of the commonly available cellular human models such as fibroblasts, underline the importance of generating new relevant cell models to provide deeper insight into the underlying mechanisms of disease. The use of in vitro models with human cellular context is highly recommended and, in this sense, induced pluripotent stem cells (iPSCs) have certain advantages since they provide the genetic background of the patient and represent an unlimited source of biological material for the study of pathophysiology and treatment effectiveness [10]. We have previously generated an iPSC line from a PA patient with defects in the PCCA gene that showed full pluripotency, differentiation capacity and genetic stability [11]. > In the present study, we aimed to establish a platform that served as a disease model to study the cellular and molecular alterations operating in cardiac tissue affected by PA disease. We described the characterization of cardiomyocytes derived from the PCCA iPSC line (PCCA iPSC-CMs) and the analysis of specific pathways potentially involved in cardiac PA physiopathology.

[5] Transcriptional profiling of Hutchinson-Gilford progeria patients identifies primary target pathways of progerin

• Authors: Sandra Vidak, Sohyoung Kim, Tom Misteli
• Year: 2026
• Venue: Nucleus
• URL: https://www.semanticscholar.org/paper/4bd99b0875508364d8672b6da5a50d024d485a53
• DOI: 10.1080/19491034.2025.2611484
• PMID: 41489464
• PMCID: 12773485
• Summary: To probe the clinical relevance of previously implicated cellular pathways and to address the extent of gene expression heterogeneity between patients, transcriptomic analysis of a comprehensive set of HGPS patients finds misexpression of several cellular pathways, including multiple signaling pathways, the UPR and mesodermal cell fate specification.
• Evidence snippets:
• Snippet 1 (score: 0.395) > Oxidative stress represents another key pathogenic mechanism in HGPS, as impaired NRF2 activity or increased reactive oxygen species (ROS) levels are sufficient to recapitulate HGPSassociated phenotypes [17,32,60]. Collectively, these findings underscore the multifactorial nature of HGPS pathogenesis, implicating interconnected signaling cascades involved in inflammation, oxidative stress, proteostasis, and vascular remodeling. Reassuringly, our findings indicate that many of the major pathways that have been described to contribute to HGPS phenotypes in mouse and cellular disease models are also misregulated in progeria patients, and targeting these pathways may provide therapeutic avenues to mitigate disease severity and improve outcomes in HGPS. > Although individuals with HGPS typically exhibit a characteristic set of clinical features, such as craniofacial abnormalities, growth retardation, and cardiovascular complications, there is notable variability in the age of onset, severity, and progression of symptoms between patients [7,9]. At the cellular level, HGPS is associated with several hallmark abnormalities, including nuclear envelope defects, decreased expression of several nuclear proteins and epigenetic marks, mitochondrial dysfunction, and increased cellular senescence [1,11,30,31,61]. These cellular phenotypes also exhibit considerable variation between patients, possibly contributing to differences in clinical outcomes. Our results indicate that even though some degree of transcriptional heterogeneity between the individual patients exists, the majority of patients exhibit misregulation of a set of shared pathways, suggesting that these pathways are universal driver mechanisms in HGPS. Further work is needed to understand the molecular and genetic factors that underlie inter-individual variability in disease expression and progression. > A limitation of pathway analysis of HGPS patient samples is to distinguish the pathways which are directly targeted by the disease-causing progerin protein and the emergence of adaptive secondary response pathways during progression of the disease in patients during their lifetime. The same caveat applies to the use of cell-based models used in the study of HGPS disease mechanisms.

[6] Recent Evidences of Epigenetic Alterations in Chronic Obstructive Pulmonary Disease (COPD): A Systematic Review

• Authors: R. Ragusa, Pasquale Bufano, A. Tognetti, M. Laurino, Chiara Caselli
• Year: 2025
• Venue: International Journal of Molecular Sciences
• URL: https://www.semanticscholar.org/paper/2660cdbbe1f205c631fe890e5c6a3c8d9b81ce5f
• DOI: 10.3390/ijms26062571
• PMID: 40141213
• PMCID: 11942187
• Citations: 4
• Summary: A systematic review of the latest knowledge on epigenetic modifications that characterize COPD, summarizing epigenetic factors that could serve as potential novel biomarkers and therapeutic targets for the treatment of COPD patients.
• Evidence snippets:
• Snippet 1 (score: 0.391) > The papers included were clustered according to epigenetic mechanisms involved in COPD (molecular and cellular processes, as biomarker or therapeutic target). Tables 4-9 describe the extracted information, including the following: Study = name of first author et al., year; Country (Region) = where the study took place; Number of participants = sample size; Type of sample = biological sample employed; Gene affected = gene or group of genes whose expression can be "regulated" by epigenetic mechanisms; Epigenetic alteration = type of epigenetic alteration observed in the presence of disease; Activity in COPD = involvement of epigenetic elements in different molecular and cellular mechanisms associated with COPD; and Role of epigenetic mechanisms = epigenetic modifications that can be used to explain the pathophysiology of COPD or as biomarkers and therapeutic targets.

[7] Human Dermal Fibroblast: A Promising Cellular Model to Study Biological Mechanisms of Major Depression and Antidepressant Drug Response

• Authors: P. Mesdom, R. Colle, É. Lebigot, S. Trabado, Eric Deflesselle et al.
• Year: 2020
• Venue: Current Neuropharmacology
• URL: https://www.semanticscholar.org/paper/79368e365458486de96794333613c12a6063bf54
• DOI: 10.2174/1570159X17666191021141057
• PMID: 31631822
• PMCID: 7327943
• Citations: 12
• Summary: This review highlights the great and still underused potential of HDF, which stands out as a very promising tool in the understanding of MDD and AD mechanisms of action.
• Evidence snippets:
• Snippet 1 (score: 0.391) > Background: Human dermal fibroblasts (HDF) can be used as a cellular model relatively easily and without genetic engineering. Therefore, HDF represent an interesting tool to study several human diseases including psychiatric disorders. Despite major depressive disorder (MDD) being the second cause of disability in the world, the efficacy of antidepressant drug (AD) treatment is not sufficient and the underlying mechanisms of MDD and the mechanisms of action of AD are poorly understood. Objective The aim of this review is to highlight the potential of HDF in the study of cellular mechanisms involved in MDD pathophysiology and in the action of AD response. Methods The first part is a systematic review following PRISMA guidelines on the use of HDF in MDD research. The second part reports the mechanisms and molecules both present in HDF and relevant regarding MDD pathophysiology and AD mechanisms of action. Results HDFs from MDD patients have been investigated in a relatively small number of works and most of them focused on the adrenergic pathway and metabolism-related gene expression as compared to HDF from healthy controls. The second part listed an important number of papers demonstrating the presence of many molecular processes in HDF, involved in MDD and AD mechanisms of action. Conclusion The imbalance in the number of papers between the two parts highlights the great and still underused potential of HDF, which stands out as a very promising tool in our understanding of MDD and AD mechanisms of action

[8] Exploring the molecular mechanisms of subarachnoid hemorrhage and potential therapeutic targets: insights from bioinformatics and drug prediction

• Authors: Yi Liu, Yang Zhang, Huan Wei, Li Wang, Lishang Liao
• Year: 2025
• Venue: Scientific Reports
• URL: https://www.semanticscholar.org/paper/19a91d9c8cabec6a5a186729d545077e252ecb67
• DOI: 10.1038/s41598-025-97642-8
• PMID: 40229542
• PMCID: 11997208
• Summary: The findings not only elucidate the molecular mechanisms underlying SAH but also provide robust bioinformatics and experimental evidence supporting IRN as a promising therapeutic candidate, offering novel insights for future intervention strategies in SAH.
• Evidence snippets:
• Snippet 1 (score: 0.390) > involved in SAH pathology. As a result, our understanding of the cellular composition and microenvironment in SAH remains incomplete 8 . > Advances in bioinformatics provide powerful tools to analyze large-scale gene expression data and understand complex biological processes. By integrating transcriptomic data with immune cell infiltration analysis, we can gain a deeper understanding of the molecular mechanisms underlying SAH and identify potential key genes as therapeutic targets 9,10 . Previous studies have indicated that inflammation, oxidative stress, and cell death play crucial roles in the development of SAH, processes that are often closely associated with changes in specific cell types and immune responses 11 . > The goal of this study is to explore the molecular mechanisms of SAH, with a focus on immune cell infiltration and its role in disease progression. We aim to identify key genes and signaling pathways associated with SAH and investigate potential therapeutic strategies. Specifically, we will examine Isorhynchophylline (IRN) as a potential treatment for SAH and analyze its effects on relevant targets and signaling pathways. Through a comprehensive understanding of the pathological features of SAH, this study aims to provide valuable insights into future clinical interventions and treatment strategies.

[9] Computational drug discovery approaches identify mebendazole as a candidate treatment for autosomal dominant polycystic kidney disease

• Authors: P. Brownjohn, A. Zoufir, Daniel J O’Donovan, Saatviga Sudhahar, A. Syme et al.
• Year: 2024
• Venue: Frontiers in Pharmacology
• URL: https://www.semanticscholar.org/paper/a595e78572ca02b8cb2897bfc4a989a2b021b279
• DOI: 10.3389/fphar.2024.1397864
• PMID: 38846086
• PMCID: 11154008
• Citations: 3
• Summary: It is determined that the anthelmintic mebendazole was a potent anti-cystic agent in human cellular and in vivo models of ADPKD, and is likely acting through the inhibition of microtubule polymerisation and protein kinase activity.
• Evidence snippets:
• Snippet 1 (score: 0.387) > Targets and molecules were ultimately filtered for validation based on biological and chemical insights, and the potential for clinical translation.Earlier this year, Wilk et al., 2023 applied a similar transcriptomic approach to us, in that case making use of publicly available transcriptomic datasets to create Pkd2-specific ADPKD disease signatures, from which signature reversion was sought from the Library of Integrated Network-based Cellular Signatures (LINCs) drug signature database in order to identify drug repurposing candidates.While one group has previously made use of a knowledge graph-based approach to prioritise preclinically active compounds with the highest chance of clinical translation (Malas et al., 2019), to our knowledge, the current study provides the first combined application of transcriptomic and machine-learning approaches to identify and prioritise putative treatments for ADPKD, and further deconvolute potential mechanisms of action for experimental validation. > In summary we report, using computational, in vitro and in vivo approaches, that the anthelmintic drug mebendazole ameliorates disease-relevant phenotypes in cellular and animal models of ADPKD.We further show that this effect is likely primarily due to the inhibitory effect of mebendazole on the polymerisation of microtubules, which underlie cellular processes important in ADPKD, including cell proliferation, transport, and cilia signalling, and extends previous work linking the importance of the microtubule network to ADPKD pathophysiology.We also describe the inhibitory profile of mebendazole on known and novel protein kinase targets, some of which have previously been implicated in ADPKD, suggesting mebendazole may be acting via polypharmacology to impact disease mechanisms.We acknowledge that further experimental efforts will be required to confirm the actions of mebendazole on these putative targets in relevant disease model systems.It would be particularly informative to investigate these mechanisms in dedicated in vivo studies, where the effects of mebendazole on a wider range of ADPKD-relevant cell types and phenotypes could be evaluated.

[10] Mechanistic Models of Signaling Pathways Reveal the Drug Action Mechanisms behind Gender-Specific Gene Expression for Cancer Treatments

• Authors: C. Çubuk, F. Can, M. Peña-Chilet, J. Dopazo
• Year: 2020
• Venue: Cells
• URL: https://www.semanticscholar.org/paper/e40f7a3b8f72ba01374ba00fbf308a47a3fa5dd4
• DOI: 10.3390/cells9071579
• PMID: 32610626
• PMCID: 7408716
• Citations: 9
• Summary: Despite the existence of differences in gene expression across numerous genes between males and females having been known for a long time, these have been mostly ignored in many studies, including drug development and its therapeutic use. In fact, the consequences of such differences over the disease mechanisms or the drug action mechanisms are completely unknown. Here we applied mechanistic mathematical models of signaling activity to reveal the ultimate functional consequences that gender-s...
• Evidence snippets:
• Snippet 1 (score: 0.387) > Therefore, a proper interpretation of the effect that differences in gene expression have over phenotypes, such as drug response or disease progression, involves understanding the mechanisms of the disease or the mode of action of drugs, which can be interpreted through mechanistic models of cell signaling [12] or cell metabolism [13]. Mechanistic models have helped to understand the disease mechanisms behind different cancers [14,15], including neuroblastoma [16,17], breast cancer [18], rare diseases [19], complex diseases [20], the mechanisms of action of drugs [21,22], and other biologically interesting scenarios such as the molecular mechanisms that explain how stress-induced activation of brown adipose tissue prevents obesity [23] or the molecular mechanisms of death and the post-mortem ischemia of a tissue [24]. Among the few available proposals of mechanistic modeling algorithms that model different aspects of signaling pathway activity, Hipathia has demonstrated having superior sensitivity and specificity [12]. > Here, we propose the use of mechanistic models [13,14] of signaling activity related with cancer hallmarks [25], other cancer-related signaling pathways, and some extra relevant cellular functions to understand the functional consequences of the gender bias in gene expression. Such mechanistic models use gene expression data to produce an estimation of profiles of signaling or metabolic circuit activity within pathways [13,14]. An interesting property of mechanistic models is that they can be used not only to understand molecular mechanisms of disease or of drug action but also to predict the potential consequences of gene perturbations over the circuit activity in a given condition [26]. Actually, in a recent work, our group has successfully predicted therapeutic targets in cancer cell lines with a precision over 60% [15]. Therefore, we will use this mechanistic framework to understand what is the molecular basis of the different effects of cancer drugs by directly simulating their effect in the patients. This approach has recently been used by us to understand the generation of resistances in cancer at the single cell level in glioblastoma [27].

[11] Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?

• Authors: E. Courcelles, J. Boissel, J. Massol, I. Klingmann, R. Kahoul et al.
• Year: 2022
• Venue: Frontiers in Medical Technology
• URL: https://www.semanticscholar.org/paper/877d5b1b75599745f704a9c8371f74601ff17e2f
• DOI: 10.3389/fmedt.2022.810315
• PMID: 35281671
• PMCID: 8907708
• Citations: 6
• Summary: Light is shed on different stakeholder's contributions and needs in the appraisal phase and how mechanistic modeling strategies and reporting can contribute to this effort to implement mechanistic models central in the evidence generation, synthesis, and appraisal of HTA so that the totality of mechanistic and clinical evidence can be leveraged by all relevant stakeholders.
• Evidence snippets:
• Snippet 1 (score: 0.385) > A second limitation in HTA is the fact that currently population (and sometimes stratified) medicine is pursued during clinical Uncertainty not completely addressed in competent authority assessment report Example use of MIDD relevant to address uncertainty potentially also during HTA What is the optimal dosage in the clinical context? > Physiologically based pharmacokinetic models can investigate dosing-regimens relevant for regulatory review and product labels (9) and can also mimic real-life adherence to prescribed treatment regimens (see also below) or pharmacology-relevant characteristics of special populations as well as drug-drug interactions. > What is the duration of the effectiveness, especially with chronic use of a treatment? > Mechanistic models can predict the long-term disease progression by extrapolation of shorter-term findings under the constraints of how the components of the system function (and these constraints convey biological plausibility by design). An example is the use of a mechanism-based disease progression model for comparison of long-term effects of pioglitazone, metformin, and gliclazide on disease processes underlying Type 2 Diabetes Mellitus (10). Another example is prediction of long-term outcomes by short-term marker data as demonstrated by a semi-mechanistic approach in context of osteoporosis treatment (11). > What is the efficacy for relevant clinical outcomes? > Mechanistic models combined with pharmacometric approaches can translate findings for one outcome to a range of other outcomes. An example of survival modeling on the back of a mechanistic description is the modeling framework for CD19-Specific CAR-T cell immunotherapy using a quantitative systems pharmacology model (12). > What is the size of the clinical effect dependent on patient characteristics and extrinsic factors? > Data-driven modeling techniques can capture correlation within clinical data. Describing the clinical effect of a drug can also be based on mechanistic considerations. Such models either (a) link disease phenotypes to increasingly granular mathematical representations of pathophysiologic processes (top-down approach) or (b) derive functional, computable cellular networks from the molecular building blocks of genes and proteins to elucidate the impact of pathologic or therapeutic alterations on network operating states and hence clinical phenotype (bottom-up) [

[12] New therapeutic targets in rare genetic skeletal diseases

• Authors: M. Briggs, Peter A. Bell, M. Wright, K. A. Pirog
• Year: 2015
• Venue: Expert Opinion on Orphan Drugs
• URL: https://www.semanticscholar.org/paper/1363107f71ae6d2d60abca471cddf3da5d13644b
• DOI: 10.1517/21678707.2015.1083853
• PMID: 26635999
• PMCID: 4643203
• Citations: 37
• Influential citations: 1
• Summary: An overview of disease mechanisms that are shared amongst groups of different GSDs and potential therapeutic approaches that are under investigation are described to generate critical mass for the identification and validation of novel therapeutic targets and biomarkers.
• Evidence snippets:
• Snippet 1 (score: 0.383) > proteins of the cartilage ECM such as type II collagen [50]. However, emerging knowledge suggests that the primary genetic defect may be less important than the cells' response to the expression of the mutant gene product [107]. Moreover, the largely overlooked response of a cell (i.e. chondrocyte) to the abnormal extracellular environment is also important for disease progression as illustrated by several GSDs discussed in this review. > It is important that 'omics'-based approaches and technologies are systematically applied to the study of rare GSDs so that definitive reference profiles and disease signatures are generated for each phenotype. These can then be used in a Systems Biology approach to identify both common and dissimilar pathological signatures and disease mechanisms. This approach is entirely dependent upon relevant in vitro and in vivo models (and also novel 'disease-mechanism phenocopies' [107]) for testing new diagnostic and prognostic tools and for determining the molecular mechanisms that underpin the pathophysiology so that effective therapeutic treatments can be developed and validated. This approach will eventually lead to personalized treatments and care strategies centred on shared disease mechanisms with the use of relevant biomarkers to monitor the efficacy of treatment and disease progression. > It is vital that all relevant stakeholders are involved from the outset in defining the appropriate outcomes of any potential therapeutic regime. The perceptions of a successful therapy can differ widely between the clinical academic community and the relevant patient-support groups and it is vital that there is engagement on all these issues. > In summary, the identification of causative genes and mutations for GSDs over the last 20 years, coupled with the generation and in-depth analysis of a plethora of relevant cell and mouse models, has derived new knowledge on disease mechanisms and suggested potential therapeutic targets. The fast-evolving hypothesis that clinically disparate diseases can share common disease mechanisms is a powerful concept that will generate critical mass for the identification and validation of novel therapeutic targets and biomarkers.

[13] Proteomic analysis of pulmonary arterial hypertension

• Authors: Xiaohan Qin, Tianhao Li, Wei Sun, Xiaoxiao Guo, Q. Fang
• Year: 2021
• Venue: Therapeutic Advances in Chronic Disease
• URL: https://www.semanticscholar.org/paper/00b9e0c61187941d6a5ca6e198e664469b927f53
• DOI: 10.1177/20406223211047304
• PMID: 34729151
• PMCID: 8482352
• Citations: 8
• Summary: This article reviews published literature on proteomic biomarkers and underlying molecular mechanisms in PAH and their value for disease management, aiming to deepen the understanding of the disease and, ultimately, pave the way for clinical application.
• Evidence snippets:
• Snippet 1 (score: 0.383) > Except for HPAH, PAH subtypes are mainly classified by clinical features, which are not relevant to molecular biology pathogenesis.Although they all belong to PAH based on the WSPH classification, different subtypes of PAH have both common and specific mechanisms.According to the abovementioned proteomic studies, we summarized the differences and similarities of the altered proteins and pathways among IPAH, HPAH, CHD-PAH, and SSc-PAH (Supplementary Table 1).Funded by the National Heart, Lung, and Blood Institute (NHLBI), the pulmonary vascular disease phenomics program (PVDOMICS, NCT02980887) aims to perform reclassification and deep phenotyping of PH based on molecular and cellular information using multiomics approaches (genomics, transcriptomics, proteomics, metabolomics, coagulomics, cell biomics), which could journals.sagepub.com/home/taj13 assist with personalized diagnoses, prevention and treatment in the long term.In the future, we expect more innovative findings from currently undergoing programs in this area will be published in article form.

[14] Changes in Serum Proteomic Profiles at Different Stages of Pregnancy Toxemia in Goats

• Authors: M. Uzti̇mür, C. N. Ünal, Gurler Akpinar
• Year: 2025
• Venue: Journal of Veterinary Internal Medicine
• URL: https://www.semanticscholar.org/paper/4b9c488b5dbd65d7b26fd2ad9aed70e8c4b59942
• DOI: 10.1111/jvim.70139
• PMID: 40492724
• PMCID: 12150350
• Summary: Understanding the serum proteome profiles of goats with pregnancy toxemia might help identify the proteomes and pathways responsible for the development of this disease and improve diagnosis and treatment.
• Evidence snippets:
• Snippet 1 (score: 0.383) > The pathophysiology and progression of this disease are not fully understood. > Traditional biomedical research has focused on the analysis of single genes, proteins, metabolites, or metabolic pathways in diseases. This molecular reductionist approach is based on the assumption that identifying genetic variations and molecular components will lead to new treatments for diseases [13][14][15][16]. However, many diseases are complex and multifactorial, and in order to determine the phenotype of such diseases, it is necessary to understand the changes that occur in more than one gene, pathway, protein, or metabolite at the cellular, tissue, and organismal levels [17][18][19]. Therefore, in recent years, proteomics, as one field of multi-omics technologies, has helped in evaluating the complex pathogenetic mechanisms of different diseases from a broad perspective and has made substantial contributions [20,21]. In veterinary medicine, proteomic analysis of metabolic diseases such as ketosis [16], hypocalcemia [22], and fatty liver [23] in dairy cows has contributed valuable insights for the definition of new pathophysiological pathways and new diagnosis and treatment protocols for these diseases. The proteomic approach can contribute importantly to a broad and detailed understanding of the changes that occur at the organismal level associated with the increase in BHBA concentration in goats with pregnancy toxemia. Our aim was to evaluate the serum protein profiles of goats with SPT or CPT using proteomic techniques to determine the proteomic profiles of these animals and to identify the relevant pathophysiological mechanisms.

[15] The hyperornithinemia–hyperammonemia-homocitrullinuria syndrome

• Authors: D. Martinelli, D. Diodato, Emanuela Ponzi, M. Monné, S. Boenzi et al.
• Year: 2015
• Venue: Orphanet Journal of Rare Diseases
• URL: https://www.semanticscholar.org/paper/ed033868ee677da141e5c926bc7c93cac242ea06
• DOI: 10.1186/s13023-015-0242-9
• PMID: 25874378
• PMCID: 4358699
• Citations: 92
• Influential citations: 5
• Summary: The clinical phenotype of HHH syndrome is extremely variable and its severity does not correlate with the genotype or with recorded ammonium/ornithine plasma levels, suggesting the need for a better understanding of the still unsolved pathophysiology of the disease.
• Evidence snippets:
• Snippet 1 (score: 0.382) > Although the disease responds well to treatment with low risk of relapse of hyperammonemia [38], slowly progressive pyramidal signs characterize the chronic course, as also seen in argininemia [89]. However, the mechanism(s) of pyramidal dysfunction in HHH syndrome still remains to be elucidated. Creatine deficiency may contribute to the pathogenetic mechanism of the syndrome, as creatine is relevant for mitochondrial energy metabolism, regulation of glycolysis, proteins synthesis, membrane stabilization and neuromodulation [77,78,85]. This could be in line with the finding of abnormally shaped mitochondria at electron microscopy studies in skin fibroblasts, hepatocytes and muscle biopsy from HHH syndrome patients [11,23,82]. Furthermore, a mitochondrial dysfunction has been recently related to the effects of ornithine and homocitrulline in causing oxidative stress and disturbed mitochondrial homeostasis [79,80]. > A further mechanism that can be involved in the pathophysiology of HHH syndrome is related to polyamines metabolism. Shimizu and colleagues reported increased total and fractional (putrescine, cadaverine, spermine, spermidine) polyamines in one HHH syndrome patient [30]. Indeed, the clinical similarities between HHH syndrome and argininemia, which has been associated to an abnormal polyamine metabolism [91,92], may suggest a common pathogenetic mechanism causing pyramidal dysfunction. > Overall, the pathogenesis of HHH syndrome is complex and not completely understood. It is likely that different mechanisms, including the impact of low mitochondrial ornithine on UC flux, the presence of hyperammonemic crises and the disturbance of other pathways in major organs play a role in determining the heterogeneous clinical presentation of ORC1 deficiency. > In addition, as molecular studies failed to disclose a correlation between type of mutations or ornithine transport capacity and disease severity, an effect of genetic modifiers, such as ORC genes redundancy, seems to be likely, but further studies are certainly needed to clarify this point.

[16] Renal ciliopathies: promising drug targets and prospects for clinical trials

• Authors: L. Devlin, Praveen Dhondurao Sudhindar, J. Sayer
• Year: 2023
• Venue: Expert Opinion on Therapeutic Targets
• URL: https://www.semanticscholar.org/paper/ab2155b6e12caba53d57ac0e8ce28860d69ec9fd
• DOI: 10.1080/14728222.2023.2218616
• PMID: 37243567
• Citations: 10
• Summary: The advances in basic science and clinical research into renal ciliopathies which have yielded promising small compounds and drug targets are reviewed, within both preclinical studies and clinical trials.
• Evidence snippets:
• Snippet 1 (score: 0.382) > Although renal ciliopathies can be classified into distinct syndromes, causative mutations in genes encoding proteins involved in the primary cilium or centrosome mean they may share overlapping mechanisms of disease, which may be amenable for therapeutic intervention (Figure 2). Abnormal functioning of proteins involved in ciliogenesis, such as CEP164, can prevent proper cilia formation, which will effect a myriad of downstream ciliary signaling pathways. Additionally, mutations in genes encoding for proteins involved in cargo trafficking or regulation, such as CEP290, will have implications for signal pathway transduction, as well as mutations in components of signaling pathways themselves, such as PKD1. In regard to renal ciliopathies, abnormalities in signaling pathways such as cAMP, Shh, Wnt, mTOR, and AMPK, likely cause misoriented cellular divisions, increased proliferation, increased fluid secretion and subsequent cystogenesis, consequently leading to further kidney damage. Ciliary and centriolar proteins which have roles in DDR and cell cycle regulation may also be driving a renal cystogenesis phenotype alongside increased fibrosis and apoptosis. Increased inflammation and dysfunctional mitochondria are also byproducts of dysregulated signaling pathways have been shown to contribute to the progression of renal ciliopathies. Extensive reviews of mechanisms of renal ciliopathy diseases have recently been performed [23,24]. Importantly, due to the wide range of cellular processes that primary cilia regulate, it is likely that in each syndrome there are multiple pathogenic drivers of disease. In some ways, this is advantageous as it offers many points for potential therapeutic targets. However, the cross talk between pathways and feedback loops introduces complications of changing one pathway without negatively affecting another. Further challenges arise with core biological pathways, such as Shh signaling, in which modification in vitro may be beneficial, but systemic treatment is unrealistic due to the expected severe side effects [18,24,116].

[17] Molecular insights into the premature aging disease progeria

• Authors: Sandra Vidak, R. Foisner
• Year: 2016
• Venue: Histochemistry and Cell Biology
• URL: https://www.semanticscholar.org/paper/60fb3b46bb7e42d5d08cc3b7cbc783b118300c31
• DOI: 10.1007/s00418-016-1411-1
• PMID: 26847180
• PMCID: 4796323
• Citations: 105
• Influential citations: 3
• Summary: Changes in mechanosignaling, altered chromatin organization and impaired genome stability, and changes in signaling pathways, leading to impaired regulation of adult stem cells, defective extracellular matrix production and premature cell senescence are discussed.
• Evidence snippets:
• Snippet 1 (score: 0.380) > The number of molecular biological studies aiming at the identification of lamin-mediated molecular disease mechanisms involved in HGPS increased tremendously following the surprising discovery that LMNA is causally linked to the premature aging disease HGPS in 2003. Despite numerous cellular pathways that were identified to be affected by the expression of the mutant lamin A protein (Fig. 2), the mechanistic details behind these effects are still unclear in most cases. Knowledge based on what was already known on lamin biology before the protein was linked to HGPS and findings on novel roles of lamins in diverse pathways in recent years allowed the launch of translational studies and the efficient search for drug targets and therapeutic approaches within a short time period. The results of the first clinical trials taught us that some improvements of the disease phenotypes can be achieved by FTI treatment, but they also made clear that we need a much better understanding of the underlying disease mechanisms to be able to tackle specific aspects of the disease in a more focused approach. It will also be important to elucidate which of the numerous pathways found to be impaired in HGPS are most relevant for and causally involved in the pathologies, and which ones are just bystanders.

[18] Baseline urinary metabolites predict albuminuria response to spironolactone in type 2 diabetes.

• Authors: S. Mulder, P. Perco, C. Oxlund, Uzma F Mehdi, T. Hankemeier et al.
• Year: 2020
• Venue: Translational research : the journal of laboratory and clinical medicine
• URL: https://www.semanticscholar.org/paper/e5f7ffaa67fba1fe09d82c2d3787ad398d175bb6
• DOI: 10.1016/j.trsl.2020.04.010
• PMID: 32438071
• Citations: 9
• Summary: The data suggest that urinary metabolites may be used as a tool to tailor optimal therapy and move in the direction of personalized medicine.
• Evidence snippets:
• Snippet 1 (score: 0.379) > In-silico modeling of spironolactone mechanism of action and DKD pathophysiology. Network-based molecular models reflecting spironolactone mechanism of action as well as DKD pathophysiology were generated following previously described and successfully applied computational workflows. 8,9 In brief, molecular features associated with spironolactone were consolidated from 3 data sources, namely scientific literature, DrugBank, and a transcriptomics data set from DrugMatrix. Molecular features were defined as genes, transcripts, or proteins. Scientific articles annotated with spironolactone as major MeSH term were retrieved and genes were extracted using NCBI's gene2pubmed file. This set of genes was complemented by drug targets listed in DrugBank for spironolactone. 10 We further extracted transcripts being differentially expressed between spironolactone-treated and untreated kidney samples of animal models as stored in DrugMatrix. 11 The unique set of spironolactone associated molecular features was mapped onto a hybrid interaction network including protein-protein interaction data from IntAct, BioGrid, and Reactome together with computationally inferred relations. 12 Interactions between members of the spironolactone feature set were extracted and the MCODE algorithm was used to identify clusters of highly interconnected proteins. 13 A previously published DKD molecular model was used which was constructed following the same logic using data from scientific literature as well as from Omics datasets in the context of DKD. 7 Proteomics data of the published CKD273 proteomics classifier were used in order to identify DKD processes linked with DKD progression by mapping the set of proteins in the CKD273 classifier onto the DKD molecular network thus defining progression-associated process units. 14 Network interference analysis and identification of candidate metabolites. Network alignment method was used to identify DKD molecular processes linked to DKD prognosis affected by spironolactone treatment on the molecular level. Metabolites linked to proteins in affected DKD molecular processes were identified via enzyme-metabolite associations as stored in the Human Metabolome Database and forwarded to measurements in clinical samples. 15 A

[19] Pulmonary fibrosis: pathogenesis and therapeutic strategies

• Authors: Jianhai Wang, Kuan Li, De Hao, Xue Li, Yu Zhu et al.
• Year: 2024
• Venue: MedComm
• URL: https://www.semanticscholar.org/paper/27d52cce107cbf87fe7b61df145de94a94bc4167
• DOI: 10.1002/mco2.744
• PMID: 39314887
• PMCID: 11417429
• Citations: 57
• Summary: This review thoroughly examines the diverse etiological factors, cellular and molecular mechanisms, and key signaling pathways involved in PF, such as TGF‐β, WNT/β‐catenin, and PI3K/Akt/mTOR and discusses current therapeutic strategies.
• Evidence snippets:
• Snippet 1 (score: 0.379) > This review highlights that PF involves multiple factors, including epithelial cells, mesenchymal cells, immune responses, and microorganisms. These elements interact with and modify various pathways simultaneously, necessitating a systematic and integrative research approach. Future research on the mechanisms, diagnostics, and therapies should incorporate advanced technologies, such as single-cell sequencing, organoid cultures, and metabolomics (Figure 3). Single-cell sequencing can be used to identify the unique contributions of specific cell types to the lung microenvironment. Organoid cultures replicate the three-dimensional structure and function of the lung tissue, providing a more physiologically relevant model for studying disease mechanisms and testing treatments. Metabolomics can reveal changes in metabolic pathways that contribute to disease progression, whereas microbiology can elucidate the role of microorganisms in PF. These studies should be integrated within a systems biology framework to capture the intricate interactions and regulatory networks involved in PF. > Early and accurate diagnosis is crucial for effective management of PF. Future efforts should focus on the discovery and clinical application of new biomarkers to detect this disease in its early stages. Advanced imaging techniques and molecular diagnostics can be used to monitor disease progression and evaluate treatment responses. Reliable biomarkers can facilitate personalized treatment strategies, allowing timely and targeted interventions to slow or halt disease progression. > Because of the multifactorial nature of PF, a single therapeutic approach is often inadequate. Therefore, a combination of treatments that target multiple pathways and cellular interactions should be considered. Combining antifibrotic drugs with cell and gene therapies, as well as leveraging nanoparticles and gene-editing technologies, can enhance treatment precision and efficacy. Exploring the synergistic effects of various therapies can improve therapeutic outcomes and reduce adverse effects. Supportive measures such as lifestyle modifications, pulmonary rehabilitation, and oxygen therapy should be incorporated to improve the overall quality of life of patients. > In summary, the pathogenic mechanisms underlying PF are complex and involve numerous cellular interactions and pathways. Future research should adopt a systematic and integrative approach to uncover the intricate details of PF pathogenesis. Early diagnosis using novel biomarkers and advanced imaging techniques coupled with multimodal treatment strategies holds promise for significantly improving patient outcomes.

[20] 18O-assisted dynamic metabolomics for individualized diagnostics and treatment of human diseases

• Authors: E. Nemutlu, Song Zhang, N. Juranic, A. Terzic, S. Macura et al.
• Year: 2012
• Venue: Croatian Medical Journal
• URL: https://www.semanticscholar.org/paper/880f053c7f060db4b990e447d0a22c4b69372ddb
• DOI: 10.3325/cmj.2012.53.529
• PMID: 23275318
• PMCID: 3541579
• Citations: 28
• Summary: The potential use of dynamic phosphometabolomic platform for disease diagnostics currently under development at Mayo Clinic is described and discussed briefly.
• Evidence snippets:
• Snippet 1 (score: 0.376) > Living cells represent an integrated and interacting network of genes, transcripts, proteins, small signaling molecules, and metabolites that define cellular phenotype and function. Traditionally the focus of biomedical research was on individual genes, single protein targets, single metabolites, and metabolic or signaling pathways. This "molecular reductionist" paradigm was based on the assumption that identifying genetic variations and molecular components would lead to discovery of cures for human diseases. However, most of diseases are complex and multi-factorial and the disease phenotype is determined by the alterations of multiple genes, pathways, proteins and metabolites (at cellular, tissue, and organismal levels). Therefore, an integrated "omics" approach is more viable direction for uncovering alterations in metabolic networks, disease mechanisms, and mechanisms of drug effects. > Recent advent of large-scale metabolomics and fluxomic (metabolite dynamics and metabolic flux analysis) completed the "omics revolution" (Figure 1), where genomics, transcriptomics, proteomics, metabolomics, and fluxomics all together complement phenotype determination of living organism. Such integrated "omics" cascades provide a framework for advances in system and network biology, integrative physiology, and system medicine as well as system pharmacology and regenerative medicine. Noteworthy is the "reverse omic" approach or "metabolomicsinformed pharmacogenomics, " where discovery of specific metabolite changes have led to discovery of genetic alterations (2). Therefore, bringing new "omics" technologies to clinical practice will improve disease diagnostics and treatment by targeting drugs and procedures for each unique transcriptomic and metabolomic profiles.

Notes

• This provider combines search_papers_by_relevance with snippet_search.
• No synthesis or second-stage model call is performed.

Question: You are an expert researcher providing comprehensive, well-cited information.

Provide detailed information focusing on: 1. Key concepts and definitions with current understanding 2. Recent developments and latest research (prioritize 2023-2024 sources) 3. Current applications and real-world implementations 4. Expert opinions and analysis from authoritative sources 5. Relevant statistics and data from recent studies

Format as a comprehensive research report with proper citations. Include URLs and publication dates where available. Always prioritize recent, authoritative sources and provide specific citations for all major claims.

Disease Characteristics Research Template

Target Disease

• Disease Name: PCWH syndrome
• MONDO ID: (if available)
• Category: Mendelian

Research Objectives

Please provide a comprehensive research report on PCWH syndrome covering all of the disease characteristics listed below. This report will be used to populate a disease knowledge base entry. Be thorough and cite primary literature (PMID preferred) for all claims.

For each section, suggested databases/resources are listed. These are the first places you should search for information on each topic.

1. Disease Information

Search first: OMIM, Orphanet, ICD-10/ICD-11, MeSH, PubMed

• What is the disease? Provide a concise overview.
• What are the key identifiers? (OMIM, Orphanet, ICD-10/ICD-11, MeSH, Mondo)
• What are the common synonyms and alternative names?
• Is the information derived from individual patients (e.g., EHR) or aggregated disease-level resources?

2. Etiology

• Disease Causal Factors: What are the primary causes? (genetic, environmental, infectious, mechanistic)
• Risk Factors:

  Search first: PubMed, Cochrane Library, UpToDate, clinical guidelines, ClinVar, ClinGen, GWAS Catalog, PheGenI, CTD, CDC, WHO, epidemiological databases

• Genetic risk factors (causal variants, susceptibility loci, modifier genes)
• Environmental risk factors (toxins, lifestyle, occupational exposures, age, sex, family history)
• Protective Factors:

  Search first: PubMed, Cochrane Library, clinical trial databases, GWAS Catalog, gnomAD, WHO, CDC, nutrition databases

• Genetic protective factors (protective variants, modifier alleles)
• Environmental protective factors (diet, lifestyle, exposures that reduce risk)
• Gene-Environment Interactions: How do genetic and environmental factors interact to influence disease?

  Search first: CTD, PubMed, PheGenI, GxE databases

3. Phenotypes

Search first: HPO (Human Phenotype Ontology), OMIM, Orphanet, PubMed, clinicaltrials.gov, MedDRA, SNOMED CT, DECIPHER, LOINC

For each phenotype, provide: - Phenotype type: symptoms, clinical signs, physical manifestations, behavioral changes, or laboratory abnormalities

For symptoms/signs: HPO, OMIM, Orphanet, PubMed For behavioral changes: HPO, DSM, RDoC (Research Domain Criteria), PubMed For laboratory abnormalities: LOINC, SNOMED CT, LabTests Online, PubMed - Phenotype characteristics: Search first: OMIM, Orphanet, HPO, PubMed - Age of symptom onset (neonatal, childhood, adult-onset, late-onset) - Symptom severity (mild, moderate, severe, variable) - Symptom progression (stable, progressive, episodic, fluctuating) - Frequency among affected individuals (percentage or qualitative) - Quality of life impact: Effects on daily functioning and well-being (per-phenotype when possible) Search first: EQ-5D database, SF-36, WHO QOL databases, PubMed - Suggest HPO (Human Phenotype Ontology) terms for each phenotype

4. Genetic/Molecular Information

• Causal Genes: Gene mutations or chromosomal abnormalities responsible for disease (gene symbols, OMIM IDs)

  Search first: OMIM, ClinVar, HGMD, Ensembl, NCBI Gene

• Pathogenic Variants:
• Affected genes (gene symbols, HGNC IDs) > Search first: OMIM, NCBI Gene, Ensembl, HGNC, UniProt, GeneCards
• Variant classification (pathogenic, likely pathogenic, VUS per ACMG/AMP guidelines) > Search first: ClinVar, ClinGen, ACMG/AMP guidelines, VarSome
• Variant type/class (missense, frameshift, nonsense, splice-site, structural)
• Allele frequency in population databases > Search first: gnomAD, 1000 Genomes, ExAC, TOPMed, dbSNP
• Somatic vs germline origin > Search first: COSMIC (somatic), ClinVar, ICGC, TCGA
• Functional consequences (loss of function, gain of function, dominant negative)
• Modifier Genes: Genes that modify disease severity or expression
• Epigenetic Information: DNA methylation, histone modifications, chromatin changes affecting disease

  Search first: ENCODE, Roadmap Epigenomics, MethBase, DiseaseMeth

• Chromosomal Abnormalities: Large-scale genetic changes (aneuploidy, translocations, inversions)

  Search first: DECIPHER, ClinVar, ECARUCA, UCSC Genome Browser

5. Environmental Information

• Environmental Factors: Non-genetic contributing factors (toxins, radiation, pollution, occupational exposure)

  Search first: CTD (Comparative Toxicogenomics Database), TOXNET, PubMed, EPA databases

• Lifestyle Factors: Behavioral factors (smoking, diet, exercise, alcohol consumption)

  Search first: CDC databases, WHO, PubMed, NHANES

• Infectious Agents: If applicable, pathogens causing or triggering disease (bacteria, viruses, fungi, parasites)

  Search first: NCBI Taxonomy, ViPR, BV-BRC, MicrobeDB, GIDEON

6. Mechanism / Pathophysiology

• Molecular Pathways: Specific signaling cascades or biochemical pathways involved (Wnt, MAPK, mTOR, PI3K-AKT, etc.)

  Search first: KEGG, Reactome, WikiPathways, PathBank, BioCyc

• Cellular Processes: Cell-level mechanisms (apoptosis, autophagy, cell cycle dysregulation, inflammation, etc.)

  Search first: Gene Ontology (GO), Reactome, KEGG, PubMed

• Protein Dysfunction: How protein structure or function is altered (misfolding, aggregation, loss of function, gain of function)

  Search first: UniProt, PDB (Protein Data Bank), InterPro, Pfam, AlphaFold

• Metabolic Changes: Alterations in metabolic processes (energy metabolism, lipid metabolism, amino acid metabolism)

  Search first: KEGG, BioCyc, HMDB (Human Metabolome Database), BRENDA

• Immune System Involvement: Role of immune response (autoimmunity, immunodeficiency, chronic inflammation)

  Search first: ImmPort, Immunome Database, IEDB, Gene Ontology

• Tissue Damage Mechanisms: How tissues/ are injured (oxidative stress, ischemia, fibrosis, necrosis)

  Search first: PubMed, Gene Ontology, Reactome

• Biochemical Abnormalities: Specific molecular defects (enzyme deficiencies, receptor dysfunction, ion channel defects)

  Search first: BRENDA, UniProt, KEGG, OMIM, PubMed

• Epigenetic Changes: DNA methylation, histone modifications affecting gene expression in disease

  Search first: ENCODE, Roadmap Epigenomics, MethBase, DiseaseMeth

• Molecular Profiling (if available):
• Transcriptomics/gene expression changes > Search first: GEO (Gene Expression Omnibus), ArrayExpress, GTEx, Human Cell Atlas, SRA
• Proteomics findings > Search first: PRIDE, ProteomeXchange, Human Protein Atlas, STRING, BioGRID
• Metabolomics signatures > Search first: MetaboLights, Metabolomics Workbench, HMDB, METLIN
• Lipidomics alterations > Search first: LIPID MAPS, SwissLipids, LipidHome, Metabolomics Workbench
• Genomic structural features > Search first: UCSC Genome Browser, Ensembl, NCBI, dbVar, DGV
• Advanced Technologies (if applicable):
• Single-cell analysis findings (cell-type specific mechanisms, cellular heterogeneity) > Search first: Human Cell Atlas, Single Cell Portal, GEO, CELLxGENE
• Spatial transcriptomics findings > Search first: GEO, Spatial Research, Vizgen, 10x Genomics data
• Multi-omics integration results > Search first: TCGA, ICGC, cBioPortal, LinkedOmics, PubMed
• Functional genomics screens (CRISPR, RNAi) > Search first: DepMap, GenomeRNAi, PubMed, BioGRID ORCS

For each mechanism, describe: - The causal chain from initial trigger to clinical manifestation - Which mechanisms are upstream vs downstream - What cell types and biological processes are involved - Suggest GO terms for biological processes and CL terms for cell types

7. Anatomical Structures Affected

• Organ Level:
• Primary organs directly affected
• Secondary organ involvement (complications, secondary effects)
• Body systems involved (cardiovascular, nervous, digestive, respiratory, endocrine, etc.)

  Search first: Uberon, FMA (Foundational Model of Anatomy), OMIM, HPO, ICD-11, MeSH, SNOMED CT

• Tissue and Cell Level:
• Specific tissue types affected (epithelial, connective, muscle, nervous)
• Specific cell populations targeted (with Cell Ontology terms)

  Search first: Uberon, Human Protein Atlas, Cell Ontology, Human Cell Atlas, CellMarker, PanglaoDB

• Subcellular Level:
• Cellular compartments involved (mitochondria, nucleus, ER, lysosomes) (with GO Cellular Component terms)

  Search first: Gene Ontology (Cellular Component), UniProt, Human Protein Atlas

• Localization:
• Specific anatomical sites (with UBERON terms) > Search first: FMA, Uberon, NeuroNames (for brain), SNOMED CT
• Lateralization (unilateral, bilateral, asymmetric) > Search first: HPO, clinical literature, imaging databases

8. Temporal Development

• Onset:
• Typical age of onset (congenital, pediatric, adult, geriatric)
• Onset pattern (acute, subacute, chronic, insidious)

  Search first: OMIM, Orphanet, HPO, PubMed

• Progression:
• Disease stages (early, intermediate, advanced, end-stage) > Search first: Cancer Staging Manual (AJCC), WHO classifications, PubMed
• Progression rate (rapid, slow, variable)
• Disease course pattern (episodic, relapsing-remitting, progressive, stable)
• Disease duration (self-limited, chronic lifelong)

  Search first: Disease registries, longitudinal cohort databases, natural history studies, PubMed, Orphanet, OMIM

• Patterns:
• Remission patterns (spontaneous, treatment-induced) > Search first: Clinical trial databases, disease registries, PubMed
• Critical periods (time windows of vulnerability or opportunity for intervention) > Search first: PubMed, developmental biology databases, clinical guidelines

9. Inheritance and Population

• Epidemiology:
• Prevalence (cases per 100,000 at given time)
• Incidence (new cases per 100,000 per year)

  Search first: Orphanet, CDC, WHO, GBD (Global Burden of Disease), national registries, SEER, disease registries

• For Genetic Etiology:
• Inheritance pattern (AD, AR, X-linked, mitochondrial, multifactorial, polygenic) > Search first: OMIM, Orphanet, ClinVar, GTR (Genetic Testing Registry)
• Penetrance (complete, incomplete, age-dependent) > Search first: ClinVar, OMIM, PubMed, ClinGen
• Expressivity (variable, consistent) > Search first: OMIM, ClinVar, PubMed
• Genetic anticipation (increasing severity in successive generations) > Search first: OMIM, PubMed (especially for repeat expansion disorders)
• Germline mosaicism > Search first: ClinVar, OMIM, genetic counseling literature, PubMed
• Founder effects (population-specific mutations) > Search first: gnomAD, population genetics databases, PubMed
• Consanguinity role > Search first: OMIM, population studies, genetic counseling resources
• Carrier frequency > Search first: gnomAD, carrier screening databases, GeneReviews, GTR
• Population Demographics:
• Affected populations (ethnic or demographic groups with higher prevalence) > Search first: gnomAD, 1000 Genomes, PAGE Study, PubMed, population registries
• Geographic distribution (endemic areas, regional variation) > Search first: WHO, CDC, GBD, Orphanet, geographic epidemiology databases
• Geographic distribution of specific variants
• Sex ratio (male:female) > Search first: Disease registries, OMIM, PubMed, epidemiological databases
• Age distribution of affected individuals > Search first: CDC, disease registries, SEER, Orphanet

10. Diagnostics

• Clinical Tests:
• Laboratory tests (blood, urine, tissue chemistry, specific enzyme assays) > Search first: LOINC, LabTests Online, PubMed
• Biomarkers (proteins, metabolites, genetic markers, circulating biomarkers) > Search first: FDA Biomarker List, BEST (Biomarkers, EndpointS, and other Tools), PubMed
• Imaging studies (X-ray, CT, MRI, PET, ultrasound) > Search first: RadLex, DICOM, Radiopaedia, imaging databases
• Functional tests (pulmonary function, cardiac stress tests) > Search first: LOINC, clinical guidelines, PubMed
• Electrophysiology (EEG, EMG, ECG, nerve conduction studies) > Search first: LOINC, clinical neurophysiology databases, PubMed
• Biopsy findings (histopathology, immunohistochemistry) > Search first: SNOMED CT, College of American Pathologists resources, PubMed
• Pathology findings (microscopic examination) > Search first: SNOMED CT, Digital Pathology databases, PubMed
• Genetic Testing:

  Search first: GTR (Genetic Testing Registry), GeneReviews, ClinGen

• Overview of recommended genetic testing approach
• Whole genome sequencing (WGS) utility > Search first: GTR, ClinVar, GEL (Genomics England), gnomAD
• Whole exome sequencing (WES) utility > Search first: GTR, ClinVar, OMIM, GeneMatcher
• Gene panels (which panels, which genes) > Search first: GTR, ClinVar, laboratory-specific databases
• Single gene testing > Search first: GTR, ClinVar, OMIM, GeneReviews
• Chromosomal microarray (CMA) > Search first: DECIPHER, ClinVar, dbVar, ECARUCA
• Karyotyping > Search first: Chromosome Abnormality Database, ClinVar, cytogenetics resources
• FISH > Search first: ClinVar, cytogenetics databases, PubMed
• Mitochondrial DNA testing > Search first: MITOMAP, MSeqDR, ClinVar, GTR
• Repeat expansion testing > Search first: GTR, ClinVar, repeat expansion databases, PubMed
• Omics-Based Diagnostics (if applicable):
• RNA sequencing / transcriptomics > Search first: GEO, ArrayExpress, GTEx, RNA-seq databases
• Proteomics > Search first: PRIDE, ProteomeXchange, FDA Biomarker database
• Metabolomics > Search first: MetaboLights, Metabolomics Workbench, HMDB
• Epigenomics > Search first: GEO, ENCODE, Roadmap Epigenomics, MethBase
• Liquid biopsy > Search first: COSMIC, ClinVar, liquid biopsy databases, PubMed
• Clinical Criteria:
• Standardized diagnostic criteria (DSM, ICD, society guidelines) > Search first: DSM-5, ICD-11, clinical society guidelines, UpToDate
• Differential diagnosis (other conditions to rule out, with distinguishing features) > Search first: DynaMed, UpToDate, clinical decision support systems
• Screening:
• Screening methods for asymptomatic individuals (newborn screening, carrier screening, cascade screening) > Search first: ACMG recommendations, CDC newborn screening, GTR

11. Outcome/Prognosis

• Survival and Mortality:
• Survival rate (5-year, 10-year, overall) > Search first: SEER, cancer registries, disease-specific registries, PubMed
• Life expectancy (with and without treatment if applicable) > Search first: Orphanet, disease registries, actuarial databases, PubMed
• Mortality rate > Search first: CDC, WHO, GBD, national mortality databases
• Disease-specific mortality (deaths directly attributable to disease) > Search first: Disease registries, CDC Wonder, GBD, PubMed
• Morbidity and Function:
• Morbidity (disease-related disability and health impacts) > Search first: GBD, WHO, disability databases, PubMed
• Disability outcomes (long-term functional impairments) > Search first: ICF (International Classification of Functioning), disability registries
• Quality of life measures (EQ-5D, SF-36, PROMIS, disease-specific tools) > Search first: EQ-5D database, SF-36, PROMIS, PubMed
• Disease Course:
• Complications (secondary problems: infections, organ failure, etc.) > Search first: ICD codes, disease registries, clinical databases, PubMed
• Recovery potential (likelihood and extent of recovery, with vs without treatment) > Search first: Natural history studies, rehabilitation databases, PubMed
• Prediction:
• Prognostic factors (age, disease severity, biomarkers, treatment response) > Search first: Prognostic models databases, clinical calculators, PubMed
• Prognostic biomarkers (molecular markers predicting disease course) > Search first: FDA Biomarker database, PubMed, cancer prognostic databases

12. Treatment

• Pharmacotherapy:
• Pharmacological treatments (drug names, drug classes, mechanisms of action) > Search first: DrugBank, RxNorm, ATC classification, DailyMed, FDA databases
• Pharmacogenomics (how genetic variants affect drug metabolism, efficacy, toxicity) > Search first: PharmGKB, CPIC (Clinical Pharmacogenetics), FDA Table of PGx Biomarkers
• Advanced Therapeutics:
• Gene therapy (viral vectors, CRISPR, gene replacement, gene editing) > Search first: ClinicalTrials.gov, FDA gene therapy database, ASGCT resources
• Cell therapy (stem cell transplant, CAR-T, cellular therapeutics) > Search first: ClinicalTrials.gov, FDA cell therapy database, FACT standards
• RNA-based therapies (ASOs, siRNA, mRNA therapies) > Search first: ClinicalTrials.gov, FDA approvals, PubMed
• Targeted therapies (treatments directed at specific molecular targets) > Search first: My Cancer Genome, OncoKB, ClinicalTrials.gov, FDA approvals
• Immunotherapies (checkpoint inhibitors, monoclonal antibodies) > Search first: Cancer Immunotherapy Database, FDA approvals, ClinicalTrials.gov
• Surgical and Interventional:
• Surgical interventions (types of surgery, timing, outcomes) > Search first: CPT codes, surgical registries, clinical guidelines, PubMed
• Supportive and Rehabilitative:
• Supportive care (symptom management, pain control, nutrition) > Search first: Clinical guidelines, Cochrane Library, PubMed
• Rehabilitation (physical therapy, occupational therapy, speech therapy) > Search first: Rehabilitation medicine databases, clinical guidelines, PubMed
• Experimental:
• Experimental treatments in clinical trials (with NCT identifiers if available) > Search first: ClinicalTrials.gov, EU Clinical Trials Register, WHO ICTRP
• Treatment Outcomes:
• Treatment response rates > Search first: Clinical trial databases, FDA reviews, systematic reviews, PubMed
• Side effects and adverse events > Search first: FDA Adverse Event Reporting System (FAERS), MedWatch, PubMed
• Treatment Strategy:
• Treatment algorithms (clinical pathways, decision trees) > Search first: Clinical practice guidelines, NCCN Guidelines, UpToDate
• Combination therapies > Search first: ClinicalTrials.gov, treatment guidelines, PubMed
• Personalized medicine approaches (genotype-guided treatment) > Search first: My Cancer Genome, CIViC, PharmGKB, precision medicine databases

For each treatment, suggest MAXO (Medical Action Ontology) terms where applicable.

13. Prevention

• Prevention Levels:
• Primary prevention (preventing disease occurrence: vaccination, risk factor modification) > Search first: CDC, WHO, USPSTF recommendations, Cochrane Library
• Secondary prevention (early detection and treatment: screening programs, early intervention) > Search first: USPSTF, CDC screening guidelines, WHO
• Tertiary prevention (preventing complications in those with disease) > Search first: Clinical guidelines, disease management protocols, PubMed
• Immunization: Vaccine strategies (if applicable)

  Search first: CDC vaccine schedules, WHO immunization, FDA vaccine database

• Screening and Early Detection:
• Screening programs (population-based: newborn screening, cancer screening) > Search first: CDC screening programs, USPSTF, cancer screening databases
• Genetic screening (carrier screening, preimplantation genetic diagnosis, prenatal testing) > Search first: ACMG recommendations, ACOG guidelines, GTR
• Risk stratification (identifying high-risk individuals for targeted prevention) > Search first: Risk prediction models, clinical calculators, PubMed
• Behavioral Interventions: Lifestyle modifications to reduce risk

  Search first: CDC, WHO, behavioral intervention databases, Cochrane Library

• Counseling: Genetic counseling (risk assessment, family planning guidance)

  Search first: NSGC resources, ACMG guidelines, GeneReviews

• Public Health:
• Public health interventions (sanitation, vector control, health education) > Search first: CDC, WHO, public health databases, PubMed
• Environmental interventions (reducing environmental risk factors) > Search first: EPA databases, WHO environmental health, PubMed
• Prophylaxis: Preventive medications or procedures

  Search first: Clinical guidelines, FDA approvals, PubMed

14. Other Species / Natural Disease

• Taxonomy: Species affected (with NCBI Taxon identifiers)

  Search first: NCBI Taxonomy

• Breed: Specific breeds affected (with VBO identifiers if applicable)

  Search first: VBO (Vertebrate Breed Ontology)

• Gene: Orthologous genes in other species (with NCBI Gene IDs)

  Search first: NCBI Gene

• Natural Disease:
• Naturally occurring disease in other species (companion animals, wildlife) > Search first: OMIA (Online Mendelian Inheritance in Animals), VetCompass, PubMed
• Veterinary relevance and importance in animal health > Search first: OMIA, veterinary databases, PubMed
• Comparative Biology:
• Comparative pathology (similarities and differences across species) > Search first: OMIA, comparative pathology databases, PubMed
• Evolutionary conservation of disease mechanisms > Search first: HomoloGene, OrthoMCL, Alliance of Genome Resources
• Transmission (if applicable):
• Zoonotic potential > Search first: CDC zoonotic diseases, WHO zoonoses, GIDEON
• Cross-species susceptibility > Search first: NCBI Taxonomy, veterinary databases, PubMed

15. Model Organisms

• Model Types:
• Model organism type (mammalian, invertebrate, cellular, in vitro) > Search first: Alliance of Genome Resources, model organism databases
• Specific model systems (mouse, rat, zebrafish, Drosophila, C. elegans, yeast, cell lines, organoids, iPSCs) > Search first: MGI, RGD, ZFIN, FlyBase, WormBase, SGD, ATCC, Cellosaurus
• Induced models (drug treatment, surgical intervention, environmental manipulation) > Search first: MGI, model organism databases, PubMed
• Genetic Models:
• Types available (knockout, knock-in, transgenic, conditional, humanized) > Search first: MGI, IMPC, KOMP, EuMMCR, IMSR
• Model Characteristics:
• Phenotype recapitulation (how well model reproduces human disease features) > Search first: Model organism databases, comparative studies, PubMed
• Model limitations (aspects of human disease not captured) > Search first: Model organism databases, PubMed, review articles
• Applications:
• Research applications (what aspects of disease can be studied) > Search first: Model organism databases, PubMed
• Resources:
• Model databases > Search first: MGI, RGD, ZFIN, FlyBase, WormBase, IMSR, EMMA, MMRRC

Citation Requirements

• Cite primary literature (PMID preferred) for all mechanistic and clinical claims
• Prioritize recent reviews and landmark papers
• Include direct quotes from abstracts where possible to support key statements
• Distinguish evidence source types: human clinical, model organism, in vitro, computational

Output Format

Structure your response as a comprehensive narrative organized by the sections above. For each section, provide: - Factual content with specific details (numbers, percentages, gene names, variant nomenclature) - Ontology term suggestions (HPO, GO, CL, UBERON, CHEBI, MAXO, MONDO) where applicable - Evidence citations with PMIDs - Direct quotes from abstracts to support key claims - Clear indication when information is not available or not applicable for this disease

This report will be used to populate a disease knowledge base entry with: - Pathophysiology descriptions with causal chains - Gene/protein annotations (HGNC, GO terms) - Phenotype associations (HP terms) with frequencies - Cell type involvement (CL terms) - Anatomical locations (UBERON terms) - Chemical entities (CHEBI terms) - Treatment annotations (MAXO terms) - Evidence items with PMIDs and exact abstract quotes - Epidemiology, prognosis, diagnostic, and prevention information - Animal model descriptions with phenotype recapitulation details

Warning: no contexts were retrieved, so this answer is not grounded in evidence but is instead a direct response from the agent model.

PCWH syndrome — Limitations-First Disease Characteristics Report

Evidence Status: Unresolved Disease Concept

This report summarizes an exhaustive but unsuccessful attempt to retrieve indexed, citable sources for the disease entity “PCWH syndrome” using the available tools. Multiple searches across literature (paper_search), clinical trials (clinical_trials_search), patents (patent_search), and the OpenTargets database (open_targets_search) returned zero results. This strongly suggests that “PCWH syndrome” is either a non-standard or ambiguous acronym, or that the entity is not indexed in the queried sources. Consequently, it is not possible to provide an evidence-based summary of its characteristics.

Note: This report does not contain citations, PMIDs, URLs, or specific data because no citable sources were retrieved during this session.

I. Retrieval Log

To ensure transparency, the following table documents the extensive search queries performed and their outcomes.

Table: This table summarizes the tool-based searches performed for 'PCWH syndrome' across literature, trials, patents, and OpenTargets. It is useful for documenting that the acronym could not be confidently resolved with available indexed sources and that further disambiguation is needed.

II. Report Completeness

Due to the lack of retrievable sources, all sections of the requested disease knowledge base entry remain undetermined. The following table shows the status for each section and the minimum information anchor required to unlock evidence-based curation in a future search.

Table: This table summarizes the current completeness state of the 15 requested disease-report sections for PCWH syndrome. Each section remains undetermined because no retrievable sources resolved the disease concept; the final column shows the minimal anchor most likely to unlock evidence-based curation in a follow-up search.

III. Disambiguation Workflow for Future Curation

To resolve the “PCWH syndrome” entity and enable the completion of this report, at least one of the following informational anchors is required. This workflow provides a structured checklist for follow-up curation.

Table: This table provides a fill-in workflow for resolving the ambiguous acronym “PCWH syndrome.” It helps map a minimal set of anchors to the next resources to query and the expected outputs needed to identify the correct disease concept.

IV. Structured Knowledge Base Template

The following is a complete, but currently unfilled, template for the PCWH syndrome knowledge base entry. It is provided as a deliverable to be populated once the disease entity is successfully disambiguated using the workflow above.

# Comprehensive Research Report Template: PCWH syndrome

## Evidence status
- **Current finding:** No indexed sources for **“PCWH syndrome”** were retrievable using the available tools in this session.
- **Implication:** The disease entity could not be confidently resolved, so identifiers, causal genes, phenotypes, mechanisms, diagnostics, prognosis, and treatments are **undetermined in this evidence state**.
- **Curation rule:** Do **not** infer disease identity from acronym similarity alone.

## Retrieval log summary
- Literature search queries attempted: `"PCWH syndrome"`, `"PCWH"`, `PCWH syndrome OMIM`, `PCWH mendelian syndrome`, `PCWH syndrome case report`, `PCWH gene`, `PCWH cardiomyopathy`, `PCWH woolly hair`, and phenotype-oriented variants.
- Clinical trials search attempted: `"PCWH" OR "PCWH syndrome"`.
- Patent search attempted: `PCWH syndrome`, `PCWH`.
- OpenTargets lookup attempted for disease names: `PCWH syndrome`, `PCWH`.
- **Outcome across tools:** No retrievable indexed papers, no trials, no patents; OpenTargets lookup failed to resolve a disease entity.

## Disease characteristics report

### 1. Disease Information
- **Disease name:** PCWH syndrome
- **Category:** Mendelian
- **What is the disease?**
  - Unresolved disease concept; unable to provide an evidence-based overview.
- **Key identifiers:**
  - OMIM: Not identified
  - Orphanet: Not identified
  - ICD-10: Not identified
  - ICD-11: Not identified
  - MeSH: Not identified
  - MONDO: Not identified
- **Common synonyms / alternative names:** Not identified
- **Evidence source type:** No patient-level or disease-aggregation source retrievable
- **Fill-in fields:**
  - Expanded disease name: `________________`
  - Canonical synonym: `________________`
  - OMIM ID: `________________`
  - Orphanet ID: `________________`
  - MONDO ID: `________________`

### 2. Etiology
- **Primary causes:** Undetermined
- **Genetic risk factors:** Undetermined
- **Environmental risk factors:** Undetermined
- **Protective factors:** Undetermined
- **Gene-environment interactions:** Undetermined
- **Fill-in fields:**
  - Causal gene(s): `________________`
  - Inheritance mechanism: `________________`
  - Known pathogenic mechanism: `________________`
  - Environmental modifiers: `________________`

### 3. Phenotypes
- **Phenotypic spectrum:** Undetermined
- **Age of onset:** Undetermined
- **Severity:** Undetermined
- **Progression:** Undetermined
- **Frequency among affected individuals:** Undetermined
- **Quality-of-life impact:** Undetermined
- **Suggested HPO terms:** Deferred pending disease resolution
- **Fill-in phenotype table skeleton:**
  - Phenotype 1: `________________`
    - Type: `symptom / sign / lab / behavioral / physical`
    - HPO: `________________`
    - Onset: `________________`
    - Severity: `________________`
    - Progression: `________________`
    - Frequency: `________________`
    - QoL impact: `________________`
  - Phenotype 2: `________________`
    - Type: `________________`
    - HPO: `________________`
    - Onset: `________________`
    - Severity: `________________`
    - Progression: `________________`
    - Frequency: `________________`
    - QoL impact: `________________`

### 4. Genetic / Molecular Information
- **Causal genes:** Undetermined
- **Pathogenic variants:** Undetermined
- **Variant class:** Undetermined
- **Allele frequencies:** Undetermined
- **Somatic vs germline:** Undetermined
- **Functional consequences:** Undetermined
- **Modifier genes:** Undetermined
- **Epigenetic information:** Undetermined
- **Chromosomal abnormalities:** Undetermined
- **Fill-in fields:**
  - Gene symbol: `________________`
  - HGNC ID: `________________`
  - OMIM gene ID: `________________`
  - Variant(s): `________________`
  - ACMG class: `________________`
  - Consequence: `________________`
  - Population frequency source: `________________`

### 5. Environmental Information
- **Environmental factors:** Undetermined
- **Lifestyle factors:** Undetermined
- **Infectious agents:** Undetermined / likely not applicable unless evidence emerges
- **Fill-in fields:**
  - Exposure factor: `________________`
  - Evidence type: `________________`
  - Mechanistic link: `________________`

### 6. Mechanism / Pathophysiology
- **Molecular pathways:** Undetermined
- **Cellular processes:** Undetermined
- **Protein dysfunction:** Undetermined
- **Metabolic changes:** Undetermined
- **Immune involvement:** Undetermined
- **Tissue damage mechanisms:** Undetermined
- **Biochemical abnormalities:** Undetermined
- **Molecular profiling:** No data retrievable
- **Advanced technologies:** No data retrievable
- **Suggested ontology placeholders:**
  - GO biological process: `________________`
  - GO cellular component: `________________`
  - CL cell type: `________________`
- **Causal chain template:**
  - Trigger / variant: `________________`
  - Molecular defect: `________________`
  - Cell type affected: `________________`
  - Tissue consequence: `________________`
  - Clinical manifestation: `________________`

### 7. Anatomical Structures Affected
- **Organ level:** Undetermined
- **Tissue level:** Undetermined
- **Cell level:** Undetermined
- **Subcellular level:** Undetermined
- **Localization / laterality:** Undetermined
- **Ontology placeholders:**
  - UBERON term: `________________`
  - CL term: `________________`
  - GO cellular component: `________________`

### 8. Temporal Development
- **Onset:** Undetermined
- **Progression:** Undetermined
- **Disease stages:** Undetermined
- **Course pattern:** Undetermined
- **Critical periods:** Undetermined
- **Fill-in fields:**
  - Typical onset age: `________________`
  - Pattern: `acute / chronic / congenital / insidious / other`
  - Course: `stable / progressive / episodic / relapsing`

### 9. Inheritance and Population
- **Prevalence:** Undetermined
- **Incidence:** Undetermined
- **Inheritance pattern:** Undetermined
- **Penetrance:** Undetermined
- **Expressivity:** Undetermined
- **Anticipation:** Undetermined
- **Mosaicism:** Undetermined
- **Founder effects:** Undetermined
- **Consanguinity role:** Undetermined
- **Carrier frequency:** Undetermined
- **Affected populations / geography / sex ratio / age distribution:** Undetermined
- **Fill-in fields:**
  - Inheritance: `________________`
  - Penetrance: `________________`
  - Population notes: `________________`
  - Geographic enrichment: `________________`

### 10. Diagnostics
- **Clinical tests:** Undetermined
- **Biomarkers:** Undetermined
- **Imaging:** Undetermined
- **Functional tests:** Undetermined
- **Electrophysiology:** Undetermined
- **Biopsy / pathology findings:** Undetermined
- **Genetic testing approach:** Undetermined
- **WGS/WES/panel/single-gene/CMA/karyotype/FISH utility:** Undetermined
- **Omics-based diagnostics:** Undetermined
- **Diagnostic criteria:** Undetermined
- **Differential diagnosis:** Undetermined
- **Screening methods:** Undetermined
- **Fill-in fields:**
  - Recommended test: `________________`
  - Diagnostic biomarker: `________________`
  - Differential diagnosis list: `________________`
  - Gene panel / assay: `________________`

### 11. Outcome / Prognosis
- **Survival / mortality:** Undetermined
- **Life expectancy:** Undetermined
- **Morbidity / disability:** Undetermined
- **Quality of life:** Undetermined
- **Complications:** Undetermined
- **Recovery potential:** Undetermined
- **Prognostic factors / biomarkers:** Undetermined
- **Fill-in fields:**
  - Major complication: `________________`
  - Prognostic factor: `________________`
  - Outcome measure: `________________`

### 12. Treatment
- **Pharmacotherapy:** Undetermined
- **Advanced therapeutics:** Undetermined
- **Surgery / interventions:** Undetermined
- **Supportive care / rehabilitation:** Undetermined
- **Experimental treatments / trials:** No retrievable trial evidence under this disease label
- **Treatment outcomes / adverse events:** Undetermined
- **Treatment algorithms / personalized medicine:** Undetermined
- **Suggested MAXO placeholders:**
  - MAXO term: `________________`
  - Intervention details: `________________`
- **Fill-in fields:**
  - Drug / intervention: `________________`
  - Mechanism of action: `________________`
  - Evidence level: `________________`
  - Response rate: `________________`

### 13. Prevention
- **Primary prevention:** Undetermined
- **Secondary prevention:** Undetermined
- **Tertiary prevention:** Undetermined
- **Immunization relevance:** Undetermined / likely not applicable unless evidence emerges
- **Screening / early detection:** Undetermined
- **Genetic counseling:** Likely relevant for Mendelian disease once identity is resolved
- **Public health / environmental interventions:** Undetermined
- **Prophylaxis:** Undetermined
- **Fill-in fields:**
  - Counseling recommendation: `________________`
  - Screening strategy: `________________`
  - Prevention measure: `________________`

### 14. Other Species / Natural Disease
- **Species affected:** Undetermined
- **Breed-specific disease:** Undetermined
- **Orthologous genes:** Undetermined
- **Natural disease in other species:** Undetermined
- **Comparative biology:** Undetermined
- **Transmission / zoonotic potential:** Undetermined / likely not applicable unless evidence emerges
- **Fill-in fields:**
  - Species: `________________`
  - Ortholog gene: `________________`
  - Comparative phenotype: `________________`

### 15. Model Organisms
- **Model types:** Undetermined
- **Specific model systems:** Undetermined
- **Genetic models:** Undetermined
- **Phenotype recapitulation:** Undetermined
- **Limitations:** Undetermined
- **Research applications:** Undetermined
- **Resources:** Undetermined
- **Fill-in fields:**
  - Organism / system: `________________`
  - Model type: `________________`
  - Key phenotype reproduced: `________________`
  - Limitation: `________________`

## Minimal disambiguation checklist
- Expanded disease name: `________________`
- PMID or article title: `________________`
- Suspected causal gene: `________________`
- Core phenotype triad: `________________ / ________________ / ________________`
- Suspected synonym or legacy name: `________________`
- Inheritance clue: `________________`
- Population / ancestry clue: `________________`

## Next recommended curation actions
1. Search OMIM, Orphanet, MONDO, and MeSH using the **expanded disease name** once known.
2. If a **gene symbol** is known, search ClinVar, ClinGen, GeneReviews, and GTR gene-first.
3. If only phenotype clues are known, search OMIM/Orphanet/HPO using the **phenotype triad**.
4. Record matched identifiers and return to populate each section above with evidence-backed citations.

## Safe-stop statement
- Until the acronym **PCWH** is disambiguated, all disease-specific assertions should remain blank or marked **undetermined** to avoid incorrect knowledge-base entries.

Code_block: This artifact is a single code-block report template for PCWH syndrome that explicitly documents the absence of retrievable evidence and provides a structured fill-in framework aligned to the requested disease characteristics schema.

Conclusion

Without a confirmed disease concept, no assertions can be made regarding the etiology, phenotype, genetics, or clinical aspects of “PCWH syndrome.” All fields in a corresponding knowledge base should be marked as undetermined to prevent the propagation of unverified information. The recommended next step is to use the provided disambiguation workflow to resolve the disease entity.

Artifacts

• Edison artifact artifact-00
• Edison artifact artifact-01
• Edison artifact artifact-02
• Edison artifact artifact-03
• Edison artifact artifact-04
• Edison artifact artifact-05