Front Artif Intell. 2025 Apr 2;8:1559461. doi: 10.3389/frai.2025.1559461. eCollection 2025.

ABSTRACT

Adeno-associated virus (AAV) vectors have emerged as powerful tools in gene therapy, potentially treating various genetic disorders. Engineering the AAV capsids through computational methods enables the customization of these vectors to enhance their effectiveness and safety. This engineering allows for the development of gene therapies that are not only more efficient but also personalized to unique genetic profiles. When developing, it is essential to understand the structural biology and the vast techniques used to guide vector designs. This review covers the fundamental biology of the Parvoviridae capsids, focusing on modern structural study techniques, including (a) Cryo-electron microscopy and X-ray Crystallography studies and (b) Comparative analysis of capsid structures across different Parvoviridae species. Along with the structure and evolution of the Parvoviridae capsids, computational methods have provided significant insights into the design of novel AAV vector techniques, which include (a) Structure-guided design of AAV capsids with improved properties, (b) Directed Evolution of AAV capsids for specific applications, and (c) Computational prediction of AAV capsid-receptor interactions. Further discussion addressed the ongoing challenges in the AAV vector design and proposed future directions for exploring enhanced computational tools, such as artificial intelligence/machine learning and deep learning.

PMID:40242328 | PMC:PMC12000042 | DOI:10.3389/frai.2025.1559461

Comput Struct Biotechnol J. 2025 Apr 3;27:1416-1430. doi: 10.1016/j.csbj.2025.04.005. eCollection 2025.

ABSTRACT

The accurate prediction of protein structures remains a cornerstone challenge in structural bioinformatics, essential for understanding the intricate relationship between protein sequence, structure, and function. Recent advancements in Machine Learning (ML) and Deep Learning (DL) have revolutionized this field, offering innovative approaches to tackle one- dimensional (1D) protein structure annotations, including secondary structure, solvent accessibility, and intrinsic disorder. This review highlights the evolution of predictive methodologies, from early machine learning models to sophisticated deep learning frameworks that integrate sequence embeddings and pretrained language models. Key advancements, such as AlphaFold's transformative impact on structure prediction and the rise of protein language models (PLMs), have enabled unprecedented accuracy in capturing sequence-structure relationships. Furthermore, we explore the role of specialized datasets, benchmarking competitions, and multimodal integration in shaping state-of-the-art prediction models. By addressing challenges in data quality, scalability, interpretability, and task-specific optimization, this review underscores the transformative impact of ML, DL, and PLMs on 1D protein prediction while providing insights into emerging trends and future directions in this rapidly evolving field.

PMID:40242292 | PMC:PMC12002955 | DOI:10.1016/j.csbj.2025.04.005

Front Oncol. 2025 Apr 2;15:1557486. doi: 10.3389/fonc.2025.1557486. eCollection 2025.

ABSTRACT

BACKGROUND: Cervical cancer remains a significant global health concern, particularly for women. Accurate preoperative staging is crucial for treatment planning and long-term prognosis. Traditional staging methods rely on manual imaging analysis, which is subjective and time-consuming. Deep learning-based automated staging models offer a promising approach to enhance both accuracy and efficiency.

METHODS: This study retrospectively analyzed preoperative MRI scans (T1 and T2 stages) from 112 cervical cancer patients. Seven deep learning models-DenseNet, FBNet, HRNet, RegNet, ResNet50, ShuffleNet, and ViT-were trained and validated using standardized preprocessing, data augmentation, and manual annotation techniques. Convolutional neural networks were employed to extract multidimensional imaging features, forming the basis of an automated staging prediction model.

RESULTS: Among all tested models, HRNet demonstrated the best performance, achieving an accuracy of 69.70%, recall of 68.89%, F1-score of 68.98%, and precision of 69.62%. ShuffleNet ranked second, with slightly lower performance, while ViT exhibited the weakest predictive ability. The ROC curve analysis confirmed HRNet's superior classification capability, with an AUC of 0.7778, highlighting its effectiveness in small-sample datasets.

CONCLUSION: This study confirms that deep learning models utilizing MRI images can enable automated cervical cancer staging with improved accuracy and efficiency. HRNet, in particular, demonstrates strong potential as a clinical decision-support tool, contributing to the advancement of precision medicine and personalized treatment strategies for cervical cancer.

PMID:40242247 | PMC:PMC11999846 | DOI:10.3389/fonc.2025.1557486

Ecol Evol. 2025 Apr 15;15(4):e71200. doi: 10.1002/ece3.71200. eCollection 2025 Apr.

ABSTRACT

Strategies of coexistence with large carnivores should integrate scientific evidence, population monitoring providing an opportunity for advancing outdated management paradigms. We estimated wolf population density and social dynamics across a 1400 km2 area in a data-poor region of the Romanian Carpathians. Across three consecutive years (2017-2018 until 2019-2020), we collected and genotyped 505 noninvasive DNA wolf samples (scat, hair and urine) to identify individuals, reconstruct pedigrees, and check for the presence of hybridization with domestic dogs. We identified 27 males, 20 females, and one F1 wolf-dog hybrid male. We delineated six wolf packs, with pack size varying between two and seven individuals, and documented yearly changes in pack composition. Using a spatial capture-recapture approach, we estimated population density at 2.35 wolves/100 km2 (95% BCI = 1.68-3.03) and population abundance at 70 individuals (95% BCI = 49-89). Noninvasive DNA data collection coupled with spatial capture-recapture has the potential to inform on wolf population size and dynamics at broader spatial scales, across different sampling areas representative of the diverse Carpathian landscapes, and across different levels of human impact, supporting wildlife decision making in one of Europe's main strongholds for large carnivores.

PMID:40242802 | PMC:PMC12000540 | DOI:10.1002/ece3.71200

Front Genet. 2025 Apr 2;16:1528935. doi: 10.3389/fgene.2025.1528935. eCollection 2025.

ABSTRACT

High-altitude polycythemia (HAPC) represents a pathological escalation of the physiological erythrocytosis induced by chronic hypoxia exposure. While moderate erythroid expansion enhances oxygen delivery, HAPC manifests as hematologic disorder characterized by hemoglobin thresholds (≥21 g/dL males; ≥19 g/dL females) and multi-organ complications including microcirculatory thrombosis, right ventricular hypertrophy, and uric acid dysmetabolism. This review critically evaluates the continuum between adaptive and maladaptive polycythemia through multiscale analysis of erythrocyte biology. We integrate genomic predisposition patterns, bone marrow erythroid kinetic studies, and peripheral erythrocyte pathophenotypes revealed by multi-omics profiling (iron-redox proteome, hypoxia-metabolome crosstalk). Current diagnostic limitations are highlighted, particularly the oversimplification of hemoglobin cutoffs that neglect transitional dynamics in erythrocyte turnover. By reconstructing the erythroid life cycle-from hypoxia-sensitive progenitor commitment to senescent cell clearance-we propose a phase transition model where cumulative epigenetic-metabolic derangements overcome homeostatic buffers, triggering pathological erythroid amplification. These insights reframe HAPC as a systems biology failure of erythroid adaptation, informing predictive biomarkers and targeted interventions to preserve hematological homeostasis in hypoxic environments.

PMID:40242475 | PMC:PMC12000012 | DOI:10.3389/fgene.2025.1528935

JHEP Rep. 2025 Jan 30;7(5):101338. doi: 10.1016/j.jhepr.2025.101338. eCollection 2025 May.

ABSTRACT

BACKGROUND & AIMS: Acute liver failure (ALF) is defined as rapid onset coagulopathy and encephalopathy in patients without a prior history of liver disease. We performed untargeted and targeted serum proteomics to delineate processes occurring in adult patients with ALF and to identify potential biomarkers.

METHODS: Sera of 319 adult patients with ALF (∼50% acetaminophen [APAP]-related cases) were randomly selected from admission samples of the multicenter USA Acute Liver Failure Study Group consortium and subdivided into discovery/validation cohorts. They were analyzed using untargeted proteomics with mass spectroscopy and a serum cytokine profiling and compared with 30 healthy controls. The primary clinical outcome was 21-day transplant-free survival. Single-cell RNAseq data mapped biomarkers to cells of origin; functional enrichment analysis provided mechanistic insights. Novel prognostic scores were compared with the model for end-stage liver disease and ALFSG prognostic index scores.

RESULTS: In the discovery cohort, 117 proteins differed between patients with ALF and healthy controls. There were 167 proteins associated with APAP-related ALF, with the majority being hepatocyte-derived. Three hepatocellular proteins (ALDOB, CAT, and PIGR) robustly and reproducibly discriminated APAP from non-APAP cases (AUROCs ∼0.9). In the discovery cohort, 37 proteins were related to 21-day outcome. The key processes associated with survival were acute-phase response and hepatocyte nuclear factor 1α signaling. SERPINA1 and LRG1 were the best individual discriminators of 21-day transplant-free survival in both cohorts. Two models of blood-based proteomic biomarkers outperformed the model for end-stage liver disease and ALFSG prognostic index and were reproduced in the validation cohort (AUROCs 0.83-0.86) for 21-day transplant-free survival.

CONCLUSIONS: Proteomics and cytokine profiling identified new, reproducible biomarkers associated with APAP etiology and 21-day outcome. These biomarkers may improve prognostication and understanding of the etiopathogenesis of ALF but need to be independently validated.

IMPACT AND IMPLICATIONS: Acute liver failure (ALF) is a sudden, and severe condition associated with high fatality. More sensitive and specific prognostic scores are urgently needed to facilitate decision-making regarding liver transplantation in patients with ALF. Our proteomic analysis uncovered marked differences between acetaminophen and non-acetaminophen-related ALF. The identification of routinely measurable biomarkers that are associated with 21-day transplant-free survival and the derivation of novel prognostic scores may facilitate clinical management as well as decisions for/against liver transplantation. Further studies are needed to quantify less abundant proteins. Although we used two cohorts, our findings still need to be independently and prospectively validated.

PMID:40242314 | PMC:PMC11998117 | DOI:10.1016/j.jhepr.2025.101338

Front Cell Neurosci. 2025 Apr 2;19:1548252. doi: 10.3389/fncel.2025.1548252. eCollection 2025.

ABSTRACT

The dorsal horn of the spinal cord represents the first site in the central nervous system (CNS) where nociceptive signals are integrated. As a result, there has been a rapid growth in the number of studies investigating the ionic mechanisms regulating the excitability of dorsal horn neurons under normal and pathological conditions. We believe that it is time to look back and to critically examine what picture emerges from this wealth of studies. What are the actual types of neurons described in the literature based on electrophysiological criteria? Are these electrophysiologically-defined subpopulations strongly linked to specific morphological, functional, or molecular traits? Are these electrophysiological properties stable, or can they change during development or in response to peripheral injury? Here we provide an in-depth overview of both early and recent publications that explore the factors influencing dorsal horn neuronal excitability (including intrinsic membrane properties and synaptic transmission), how these factors vary across distinct subtypes of dorsal horn neurons, and how such factors are altered by peripheral nerve or tissue damage. The meta-research presented below leads to the conclusion that the dorsal horn is comprised of highly heterogeneous subpopulations in which the observed electrophysiological properties of a given neuron often fail to easily predict other properties such as biochemical phenotype or morphology. This highlights the need for future studies which can more fully interrogate the properties of dorsal horn neurons in a multi-modal manner.

PMID:40241846 | PMC:PMC12001243 | DOI:10.3389/fncel.2025.1548252

iScience. 2025 Feb 27;28(4):112111. doi: 10.1016/j.isci.2025.112111. eCollection 2025 Apr 18.

ABSTRACT

Drosophila fat body-derived SPARC acts as a chaperone for collagen IV (Col(IV)), enabling their diffusion and incorporation into distal tissue basement membranes (BMs). Disruption of SPARC or Col(IV) production by the fat body is lethal, despite expression by other tissues such as imaginal discs. Wing disc-derived SPARC does not associate with Col(IV) in BMs and is not essential for survival. We show that differential association of fat body- and wing disc-derived SPARC with Col(IV) is not due to differences in SPARC glycosylation nor to the absence of SPARC and Col(IV) co-expression. Further, we demonstrate that SPARC domain II/III produced by the fat body is sufficient for Col(IV) diffusion to both proximal and distal BMs, and rescues lethality associated with loss of SPARC. However, SPARC domain II/III does not diffuse beyond the hemolymph. Thus, the essential Col(IV) chaperone-like activity specific to fat body-derived SPARC is not required beyond the hemolymph.

PMID:40241767 | PMC:PMC12002606 | DOI:10.1016/j.isci.2025.112111

iScience. 2025 Mar 22;28(4):112266. doi: 10.1016/j.isci.2025.112266. eCollection 2025 Apr 18.

ABSTRACT

Hepatocellular carcinoma (HCC) resists immunotherapy due to its immunosuppressive microenvironment. Sarcoma homology 2 domain-containing protein tyrosine phosphatase-1 (SHP-1) inhibits T cell receptor signaling, and its pharmacological inhibition is limited by poor selectivity and membrane permeability. Here, we generated CRISPR-edited SHP-1-knockout (KO) CD8+ T cells to enhance adoptive therapy against HCC. Single-cell RNA sequencing of HCC patient T cells revealed elevated SHP-1 in exhausted subsets. SHP-1-KO T cells exhibited increased effector memory T cells (TEM) proportions and enhanced IFN-γ/Granzyme B/perforin secretion, improving cytotoxicity against HCC lines. In humanized PDX models, SHP-1-KO T cells demonstrated superior tumor-killing activity. Transcriptomics identified upregulated lipid metabolism pathways, with HMGCR as a hub gene. Combining SHP-1-KO T cells with simvastatin (HMGCR inhibitor) synergistically amplified anti-HCC efficacy. This study proposes a dual strategy combining SHP-1-targeted cell therapy and metabolic modulation to overcome immunotherapy resistance, offering a translatable approach for HCC treatment.

PMID:40241752 | PMC:PMC12003012 | DOI:10.1016/j.isci.2025.112266

Front Physiol. 2025 Apr 2;16:1520826. doi: 10.3389/fphys.2025.1520826. eCollection 2025.

ABSTRACT

Over the past decades, MDV has dramatically evolved towards more virulent strains and remains a persistent threat to the world's poultry industry. We performed genome-wide gene expression analysis in the spleen, thymus, and bursa tissues from MD-resistant line and susceptible line to explore the mechanism of MD resistance and susceptibility. We identified genes and pathways associated with the transcriptional response to MDV infection using the robust RNA sequencing approach. The transcriptome analysis revealed a tissue-specific expression pattern among immune organs when confronting MDV. At pathway and network levels, MDV infections influenced cytokine-cytokine receptor interaction and cellular development in resistant and susceptible chicken lines. Meanwhile, we also observed different genetic responses between the two chicken lines: some pathways like herpes simplex infection and influenza A were found in MD resistant line spleen tissues, whereas metabolic-related pathways and DNA replication could only be observed in MD susceptible line chickens. In summary, our research renders new perceptions of the MD progression mechanism and beckons further gene function studies into MD resistance.

PMID:40241721 | PMC:PMC12000659 | DOI:10.3389/fphys.2025.1520826

New Phytol. 2025 Apr 16. doi: 10.1111/nph.70150. Online ahead of print.

NO ABSTRACT

PMID:40241401 | DOI:10.1111/nph.70150

J Agric Food Chem. 2025 Apr 16. doi: 10.1021/acs.jafc.5c00903. Online ahead of print.

ABSTRACT

Retrograde signaling regulates plant senescence, but the role of vacuoles in this process remains unclear. Here, we demonstrate that rice vacuolar H+-ATPase subunit A (OsVHA-A) localizes to both the cytoplasm and nucleus. Sucrose treatment increased OsVHA-A expression and nuclear accumulation, while darkness reduced it. Methyl jasmonate (MeJA) initially promoted OsVHA-A nuclear translocation but decreased it upon prolonged exposure. Downregulation of OsVHA-A expression accelerated MeJA-induced rice leaf senescence but delayed darkness-induced senescence. MeJA treatment also significantly upregulated the expression of OsMYC2 and OsMAPK6 in OsVHA-A-RNAi plants compared to wild-type plants. Moreover, OsVHA-A downregulation notably increased the level of expression of genes associated with sugar signaling and transport under dark conditions. Immunoprecipitation-mass spectrometry and molecular docking analyses identified interactions between OsVHA-A and OsTPR1, OsMed14, sucrose transporters, and enzymes involved in sucrose metabolism. The binding of OsVHA-A with OsTPR1 and OsSUS1 was confirmed by BiFC. These findings highlight the multifunctional role of OsVHA-A in coordinating organelles and nuclear signaling, providing new insights and potential strategies for manipulating senescence to improve rice yield and quality.

PMID:40241244 | DOI:10.1021/acs.jafc.5c00903

Case Rep Rheumatol. 2025 Apr 9;2025:9276592. doi: 10.1155/crrh/9276592. eCollection 2025.

ABSTRACT

Background: Hydralazine is a commonly used arteriolar vasodilator that is associated with autoimmune side effects, including drug-induced lupus. A less well-recognized drug-induced vasculitis can be seen, often accompanying drug-induced lupus. This syndrome can cause long-standing vague symptoms, leading to missed diagnoses, and can result in permanent end-organ damage. We describe here such a case of hydralazine-induced vasculitis and lupus overlap syndrome. Case Presentation: An 85-year old male presented with chronic fatigue and weight loss associated with anemia, leukopenia, and acute renal injury in the setting of longstanding hydralazine use. Serologic studies were notable for a positive antinuclear antibody, antihistone antibody, along with anti-myeloperoxidase (MPO) and anti-proteinase 3 (PR3) antibodies. Hydralazine was discontinued, and treatment was initiated with high-dose prednisone. A renal biopsy revealed antineutrophil cytoplasmic antibody (ANCA)-associated focal necrotizing pauci-immune glomerulonephritis. The patient's clinical course was complicated by the development of oral ulcerations and recurrent hydrocele secondary to serositis. Rituximab was then employed without clinical improvement, with eventual progression to end-stage renal disease requiring hemodialysis. Conclusions: This case report helps highlight the vague symptoms that can be associated with hydralazine-induced vasculitis/lupus overlap syndrome. This case will increase clinician awareness for early recognition of such a syndrome, prompting early diagnosis, preventing end-organ damage, reducing hospitalizations and improving quality of life.

PMID:40242048 | PMC:PMC12003037 | DOI:10.1155/crrh/9276592

Pharmacol Res Perspect. 2025 Jun;13(3):e70093. doi: 10.1002/prp2.70093.

ABSTRACT

A key challenge when identifying opportunities and prioritizing strategies for quality improvement of healthcare services is an accurate design specification against which clinical performance can be assessed. This study aimed to explore evidence-based methods as a more effective framework for quality improvement in pharmacotherapeutic practices. A stakeholder management matrix was adapted to differentiate the dimensions of the theoretical construct for quality of care and establish a design specification for healthcare practice. A review of drug-related problems (DRPs) associated with preventable medication errors was carried out on individual cases of adverse drug events (ADRs) reported in the EudraVigilance database system. The potential impact of strategies aimed at preventing the underlying root cause medication error (RCME) was evaluated according to the relative frequency and severity of patient harm identified with DRPs. Out of 1750 medication errors reported in the EudraVigilance database, 1300 cases of preventable DRPs were identified, of which 531 (41%) were classified as prescribing errors, 260 (20%) as dispensing errors, and 509 (39%) as errors encountered in drug administration. The highest risk scores were associated with case-based prescribing errors and rule-based drug administration errors. The research builds on a quality risk management approach to assess how targeted interventions may reduce the risk of medication errors. The theoretical model provided a basis for establishing the strategic domains of quality in health care and comparing quality improvement strategies in drug therapy. The measures required to mitigate the highest risk of error include medication review of prescribing practices, unit dose dispensing systems and in-line quality control to avoid treatment administration errors, and patient education in dispensing practice. This framework is independent of the healthcare or institutional setting and may be applied on a broad scale for sharing best practices, harmonization of standards, and elimination of disparities in treatment outcomes.

PMID:40241378 | DOI:10.1002/prp2.70093

BMC Med Educ. 2025 Apr 16;25(1):545. doi: 10.1186/s12909-025-07149-z.

ABSTRACT

BACKGROUND: Rare diseases (RDs) affect 10% of the global population but have inadequate medical resources. Early detection and treatment are crucial, yet many emergency physicians lack awareness of RDs. This study aims to evaluate the effects of continuing medical education (CME) on the knowledge and attitude of emergency physicians.

METHODS: This retrospective study was conducted from April to June 2023, involving 218 Chinese emergency physicians. The online questionnaire consisted of four groups and 30 questions, covering demographic data, knowledge, and attitudes regarding RDs. Respondents were divided into two groups based on their recent CME training experience with RDs.

RESULTS: Two hundred and eighteen emergency physicians completed the questionnaire, of which 108 received RD CME training and 110 did not receive RD CME training. Most respondents (98.2%) felt their knowledge about RDs was insufficient. The CME training group showed increased awareness of RD incidence (p = 0.047) and improved case analysis after training, but only slight improvement in knowledge of RD professional websites. Among the CME training group, CME was identified as the most prominent avenue for acquiring knowledge about RDs, with 72 respondents (66.7%, p < 0.001). In contrast, in the non-training group, clinical work was identified as the primary source of learning, with 47 respondents (42.7%, p < 0.001).

CONCLUSION: Emergency physicians generally lacked knowledge about rare diseases. CME training can improve their awareness and knowledge of RDs.

PMID:40241072 | DOI:10.1186/s12909-025-07149-z

BMC Biol. 2025 Apr 16;23(1):101. doi: 10.1186/s12915-025-02206-x.

ABSTRACT

BACKGROUND: Drug-disease association (DDA) prediction aims to identify potential links between drugs and diseases, facilitating the discovery of new therapeutic potentials and reducing the cost and time associated with traditional drug development. However, existing DDA prediction methods often overlook the global relational information provided by other biological entities, and the complex association structure between drug diseases, limiting the potential correlations of drug and disease embeddings.

RESULTS: In this study, we propose HNF-DDA, a subgraph contrastive-driven transformer-style heterogeneous network embedding model for DDA prediction. Specifically, HNF-DDA adopts all-pairs message passing strategy to capture the global structure of the network, fully integrating multi-omics information. HNF-DDA also proposes the concept of subgraph contrastive learning to capture the local structure of drug-disease subgraphs, learning the high-order semantic information of nodes. Experimental results on two benchmark datasets demonstrate that HNF-DDA outperforms several state-of-the-art methods. Additionally, it shows superior performance across different dataset splitting schemes, indicating HNF-DDA's capability to generalize to novel drug and disease categories. Case studies for breast cancer and prostate cancer reveal that 9 out of the top 10 predicted candidate drugs for breast cancer and 8 out of the top 10 for prostate cancer have documented therapeutic effects.

CONCLUSIONS: HNF-DDA incorporates all-pairs message passing and subgraph capture strategies into heterogeneous network embedding, enabling effective learning of drug and disease representations enriched with heterogeneous information, while also demonstrating significant potential for applications in drug repositioning.

PMID:40241152 | DOI:10.1186/s12915-025-02206-x

J Transl Med. 2025 Apr 16;23(1):448. doi: 10.1186/s12967-025-06431-4.

ABSTRACT

BACKGROUND: Oral Lichen Planus (OLP) is a chronic inflammatory disorder that may progress to Oral Squamous Cell Carcinoma (OSCC). Lipid metabolism dysregulation has been implicated in tumor development and immune response modulation. This study aims to explore the role of lipid metabolism, particularly the lipids diacylglycerol (DAG), triacylglycerol (TAG), and phosphatidylcholine (PC), in the progression from OLP to OSCC, and to identify potential therapeutic targets for prevention and treatment.

METHODS: We performed a Mendelian randomization (MR) analysis to investigate the causal relationships between lipid metabolism and the risk of OLP and OSCC. Differential gene expression analysis was conducted to identify key genes related to lipid metabolism. The interactions of lipid species and key genes were examined using drug databases (DrugBank, DGIdb, and TCMSP) to explore potential drug candidates. Enrichment analysis of signaling pathways, including PPAR signaling, was also conducted to understand the underlying mechanisms.

RESULTS: Our MR analysis revealed that DAG exerts a protective effect in OLP (OR < 1), but its role shifts to a risk factor in OSCC (OR > 1), potentially by altering the tumor immune microenvironment. TAG and PI dysregulation also plays a critical role in tumorigenesis. Gene expression analysis identified several key lipid metabolism-related genes, including SLC27A6, FABP3, FABP4, ADIPOQ, and PLIN1, whose expression differed between OLP and OSCC, highlighting their importance in tumor progression. These genes were enriched in the PPAR signaling pathway, suggesting its involvement in tumor growth and immune modulation. Potential drug candidates, such as palm acid (PA), Imatinib, and Curcumin, were identified through drug-repurposing strategies.

CONCLUSION: Lipid metabolism dysregulation plays a crucial role in the progression of OLP to OSCC. Targeting key lipid metabolism pathways and genes, such as DAG, TAG, PI, and the PPAR pathway, may offer promising strategies for early diagnosis and therapeutic intervention. This study provides novel insights into the molecular mechanisms of OLP-to-OSCC progression and suggests potential drug candidates, including natural compounds, for future clinical applications. Further research is needed to validate these findings in clinical settings.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40241125 | DOI:10.1186/s12967-025-06431-4

NPJ Parkinsons Dis. 2025 Apr 16;11(1):77. doi: 10.1038/s41531-025-00933-0.

ABSTRACT

Many drug targets in ongoing Parkinson's disease (PD) clinical trials have strong genetic links. While genome-wide association studies (GWAS) nominate regions associated with disease, pinpointing causal genes is challenging. Our aim was to prioritize additional druggable genes underlying PD GWAS signals. The polygenic priority score (PoPS) integrates genome-wide information from MAGMA gene-level associations and over 57,000 gene-level features. We applied PoPS to East Asian and European PD GWAS data and prioritized genes based on PoPS, distance to the GWAS signal, and non-synonymous credible set variants. We prioritized 46 genes, including well-established PD genes (SNCA, LRRK2, GBA1, TMEM175, VPS13C), genes with strong literature evidence supporting a mechanistic link to PD (RIT2, BAG3, SCARB2, FYN, DYRK1A, NOD2, CTSB, SV2C, ITPKB), and genes relatively unexplored in PD. Many hold potential for drug repurposing or development. We prioritized high-confidence genes with strong links to PD pathogenesis that may represent our next-best candidates for developing disease-modifying therapeutics.

PMID:40240380 | DOI:10.1038/s41531-025-00933-0

Annu Rev Biomed Data Sci. 2025 Apr 16. doi: 10.1146/annurev-biodatasci-101424-120950. Online ahead of print.

ABSTRACT

Drug target identification is the first step in drug development, and its importance is underscored by the fact that, even when using genetic evidence to improve success rates, only a small fraction of lead targets end up approved for use in the clinic. One of the reasons for this is the lack of in-depth understanding of the complexity of human diseases.In this review we argue that network-based approaches, which are able to capture relationships between relevant genes and proteins, and diverse data modalities have high potential for improving drug target identification and drug repurposing. We present the evolution of network-based methods that have been developed for this purpose and discuss the limitations of these approaches that are holding them back from making an impact in the clinic. We finish by presenting our recommendations for overcoming these limitations, for example, by leveraging emerging technologies such as artificial intelligence and knowledge graphs.

PMID:40239307 | DOI:10.1146/annurev-biodatasci-101424-120950