J Appl Clin Med Phys. 2025 Apr 23:e70098. doi: 10.1002/acm2.70098. Online ahead of print.

ABSTRACT

Prostate Stereotactic Ablative Body Radiotherapy (SABR) is an ultra-hypofractionated treatment where small setup errors can lead to higher doses to organs at risk (OARs). Although bowel and bladder preparation protocols reduce inter-fraction variability, inconsistent patient adherence still results in OAR variability. At many centers without online adaptive machines, radiation therapists use decision trees (DTs) to visually assess patient setup, yet their application varies. To evaluate our center's DTs, we employed deep learning-generated cone-beam computed tomography (CBCT) contours to estimate daily doses to the rectum and bladder, comparing these with planned dose-volume metrics to guide future personalized DT development. Two hundred pretreatment CBCT scans from 40 prostate SABR patients (each receiving 40 Gy in five fractions) were auto-contoured retrospectively, and daily rectum and bladder doses were estimated by overlaying the planned dose on the CBCT using online rigid registration data. Dose-volume metrics were classified as "no", "minor", or "major" violations based on meeting preferred or mandatory goals. Twenty-seven percent of fractions exhibited at least one major bladder violation (with an additional 34% minor), while 14% of fractions had a major rectum violation (10% minor). Across treatments, five patients had recurring bladder V37 Gy major violations and two had rectum V36 Gy major violations. Bowel and bladder preparation significantly influenced OAR position and volume, leading to unmet mandatory goals. Our retrospective analysis underscores the significant impact of patient preparation on dosimetric outcomes. Our findings highlight that DTs based solely on visual assessment miss dose metric violations due to human error; only 23 of 59 under-filled bladder fractions were flagged. In addition to the insensitivity of visual assessments, variability in DT application further compromises patient setup evaluation. These analyses confirm that reliance on visual inspection alone can overlook deviations, emphasizing the need for automated tools to ensure adherence to dosimetric constraints in prostate SABR.

PMID:40265325 | DOI:10.1002/acm2.70098

Front Med (Lausanne). 2025 Apr 8;12:1503229. doi: 10.3389/fmed.2025.1503229. eCollection 2025.

ABSTRACT

Deoxyribonucleic acid (DNA) serves as fundamental genetic blueprint that governs development, functioning, growth, and reproduction of all living organisms. DNA can be altered through germline and somatic mutations. Germline mutations underlie hereditary conditions, while somatic mutations can be induced by various factors including environmental influences, chemicals, lifestyle choices, and errors in DNA replication and repair mechanisms which can lead to cancer. DNA sequence analysis plays a pivotal role in uncovering the intricate information embedded within an organism's genetic blueprint and understanding the factors that can modify it. This analysis helps in early detection of genetic diseases and the design of targeted therapies. Traditional wet-lab experimental DNA sequence analysis through traditional wet-lab experimental methods is costly, time-consuming, and prone to errors. To accelerate large-scale DNA sequence analysis, researchers are developing AI applications that complement wet-lab experimental methods. These AI approaches can help generate hypotheses, prioritize experiments, and interpret results by identifying patterns in large genomic datasets. Effective integration of AI methods with experimental validation requires scientists to understand both fields. Considering the need of a comprehensive literature that bridges the gap between both fields, contributions of this paper are manifold: It presents diverse range of DNA sequence analysis tasks and AI methodologies. It equips AI researchers with essential biological knowledge of 44 distinct DNA sequence analysis tasks and aligns these tasks with 3 distinct AI-paradigms, namely, classification, regression, and clustering. It streamlines the integration of AI into DNA sequence analysis tasks by consolidating information of 36 diverse biological databases that can be used to develop benchmark datasets for 44 different DNA sequence analysis tasks. To ensure performance comparisons between new and existing AI predictors, it provides insights into 140 benchmark datasets related to 44 distinct DNA sequence analysis tasks. It presents word embeddings and language models applications across 44 distinct DNA sequence analysis tasks. It streamlines the development of new predictors by providing a comprehensive survey of 39 word embeddings and 67 language models based predictive pipeline performance values as well as top performing traditional sequence encoding-based predictors and their performances across 44 DNA sequence analysis tasks.

PMID:40265190 | PMC:PMC12011883 | DOI:10.3389/fmed.2025.1503229

Front Med (Lausanne). 2025 Apr 8;12:1562818. doi: 10.3389/fmed.2025.1562818. eCollection 2025.

ABSTRACT

OBJECTIVE: This study conducts a bibliometric analysis of artificial intelligence (AI) applications in cervical cancer to provide a comprehensive overview of the research landscape and current advancements.

METHODS: Relevant publications on cervical cancer and AI were retrieved from the Web of Science Core Collection. Bibliometric analysis was performed using CiteSpace and VOSviewer to assess publication trends, authorship, country and institutional contributions, journal sources, and keyword co-occurrence patterns.

RESULTS: From 1996 to 2024, our analysis of 770 publications on cervical cancer and AI showed a surge in research, with 86% published in the last 5 years. China (315 pubs, 32%) and the US (155 pubs, 16%) were the top contributors. Key institutions were the Chinese Academy of Sciences, Southern Medical University, and Huazhong University of Science and Technology. Research hotspots included disease prediction, image analysis, and machine learning in cervical cancer. Schiffman led in publications (12) and citations (207). China had the highest citations (3,819). Top journals were "Diagnostics," "Scientific Reports," and "Frontiers in Oncology." Keywords like "machine learning" and "deep learning" indicated current research trends. This study maps the field's growth, highlighting key contributors and topics.

CONCLUSION: This bibliometric analysis provides valuable insights into research trends and hotspots, guiding future studies and fostering collaboration to enhance AI applications in cervical cancer.

PMID:40265176 | PMC:PMC12011737 | DOI:10.3389/fmed.2025.1562818

J Natl Cancer Cent. 2024 Dec 27;5(2):113-131. doi: 10.1016/j.jncc.2024.12.006. eCollection 2025 Apr.

ABSTRACT

Upper gastrointestinal cancers, mainly comprising esophageal and gastric cancers, are among the most prevalent cancers worldwide. There are many new cases of upper gastrointestinal cancers annually, and the survival rate tends to be low. Therefore, timely screening, precise diagnosis, appropriate treatment strategies, and effective prognosis are crucial for patients with upper gastrointestinal cancers. In recent years, an increasing number of studies suggest that artificial intelligence (AI) technology can effectively address clinical tasks related to upper gastrointestinal cancers. These studies mainly focus on four aspects: screening, diagnosis, treatment, and prognosis. In this review, we focus on the application of AI technology in clinical tasks related to upper gastrointestinal cancers. Firstly, the basic application pipelines of radiomics and deep learning in medical image analysis were introduced. Furthermore, we separately reviewed the application of AI technology in the aforementioned aspects for both esophageal and gastric cancers. Finally, the current limitations and challenges faced in the field of upper gastrointestinal cancers were summarized, and explorations were conducted on the selection of AI algorithms in various scenarios, the popularization of early screening, the clinical applications of AI, and large multimodal models.

PMID:40265096 | PMC:PMC12010392 | DOI:10.1016/j.jncc.2024.12.006

Front Oncol. 2025 Apr 8;15:1567577. doi: 10.3389/fonc.2025.1567577. eCollection 2025.

ABSTRACT

The segmentation and classification of breast ultrasound (BUS) images are crucial for the early diagnosis of breast cancer and remain a key focus in BUS image processing. Numerous machine learning and deep learning algorithms have shown their effectiveness in the segmentation and diagnosis of BUS images. In this work, we propose a multi-task learning network with an object contextual attention module (MTL-OCA) for the segmentation and classification of BUS images. The proposed method utilizes the object contextual attention module to capture pixel-region relationships, enhancing the quality of segmentation masks. For classification, the model leverages high-level features extracted from unenhanced segmentation masks to improve accuracy. Cross-validation on a public BUS dataset demonstrates that MTL-OCA outperforms several current state-of-the-art methods, achieving superior results in both classification and segmentation tasks.

PMID:40265029 | PMC:PMC12011763 | DOI:10.3389/fonc.2025.1567577

Front Oncol. 2025 Apr 8;15:1549803. doi: 10.3389/fonc.2025.1549803. eCollection 2025.

ABSTRACT

BACKGROUND: Uterine broad ligament fibroids present unique surgical challenges due to their proximity to vital pelvic structures. This study aimed to evaluate artificial intelligence (AI)-guided MRI instance segmentation for optimizing laparoscopic myomectomy outcomes.

METHODS: In this trial, 120 patients with MRI-confirmed broad ligament fibroids were allocated to either AI-assisted group (n=60) or conventional MRI group (n=60). A deep learning model was developed to segment fibroids, uterine walls, and uterine cavity from preoperative MRI.

RESULT: Compared to conventional MRI guidance, AI assistance significantly reduced operative time (118 [112.25-125.00] vs. 140 [115.75-160.75] minutes; p<0.001). The AI group also demonstrated lower intraoperative blood loss (50 [50-100] vs. 85 [50-100] ml; p=0.01) and faster postoperative recovery (first flatus within 24 hours: (15[25.00%] vs. 29[48.33%], p=0.01).

CONCLUSION: This multidisciplinary AI system enhances surgical precision through millimeter-level anatomical delineation, demonstrating transformative potential for complex gynecologic oncology procedures. Clinical adoption of this approach could reduce intraoperative blood loss and iatrogenic complications, thereby promoting postoperative recovery.

PMID:40265020 | PMC:PMC12011577 | DOI:10.3389/fonc.2025.1549803

Front Oncol. 2025 Apr 8;15:1538854. doi: 10.3389/fonc.2025.1538854. eCollection 2025.

ABSTRACT

PURPOSE: This study aimed to establish and evaluate a model utilizing bi-parametric ultrasound-based deep learning radiomics (DLR) in conjunction with clinical factors to anticipate clinically significant prostate cancer (csPCa).

METHODS: We retrospectively analyzed 232 participants from our institution who underwent both B-mode ultrasound and shear wave elastography (SWE) prior to prostate biopsy between June 2022 and December 2023. A random allocation placed the participants into training and test cohorts with a 7:3 distribution. We developed a nomogram that integrates DLR with clinical factors within the training cohort, which was subsequently validated using the test cohort. The diagnostic performance and clinical applicability were evaluated with receiver operating characteristic (ROC) curve analysis and decision curve analysis.

RESULTS: In our study, the bi-parametric ultrasound-based DLR model demonstrated an area under the curve (AUC) of 0.80 (95%CI: 0.70-0.91) in the test set, surpassing the performance of both the radiomics and deep learning models individually. By integrating clinical factors, a composite model, presented as the nomogram, was developed and exhibited superior diagnostic performance, achieving an AUC of 0.87 (95%CI: 0.77-0.95) in the test set. The performance exceeded that of the DLR (P = 0.049) and the clinical model (AUC = 0.79, 95%CI: 0.69-0.86, P = 0.041). Furthermore, the decision curve analysis indicated that the composite model provided a greater net benefit across a various high-risk threshold than the DLR or the clinical model alone.

CONCLUSION: To our knowledge, this is the first proposal of a nomogram integrating ultrasound-based DLR with clinical indicators for predicting csPCa. This nomogram can improve the accuracy of csPCa prediction and may help physicians make more confident decisions regarding interventions, particularly in settings where MRI is unavailable.

PMID:40265019 | PMC:PMC12011619 | DOI:10.3389/fonc.2025.1538854

Patterns (N Y). 2025 Mar 3;6(4):101186. doi: 10.1016/j.patter.2025.101186. eCollection 2025 Apr 11.

ABSTRACT

The ever-increasing stream of big data offers potential for deep decarbonization in the transportation sector but also presents challenges in extracting interpretable insights due to its complexity and volume. This overview discusses the application of transportation big data to help understand carbon dioxide emissions and introduces how artificial intelligence models, including machine learning (ML) and deep learning (DL), are used to assimilate and understand these data. We suggest using ML to interpret low-dimensional data and DL to enhance the predictability of data with spatial connections across multiple timescales. Overcoming challenges related to algorithms, data, and computation requires interdisciplinary collaboration on both technology and data.

PMID:40264962 | PMC:PMC12010448 | DOI:10.1016/j.patter.2025.101186

Patterns (N Y). 2025 Apr 11;6(4):101228. doi: 10.1016/j.patter.2025.101228. eCollection 2025 Apr 11.

ABSTRACT

Perturbation analysis in single-cell RNA sequencing (scRNA-seq) data is challenging due to the complexity of cellular responses. To address this, Xu and Fleming et al. developed CellCap, a generative deep-learning model that decodes the perturbation effect on a particular cell state. CellCap extracts interpretable latent representations of perturbation response modules, identifying key cellular pathways activated under various conditions. This allows for a deeper understanding of cell-state-specific responses to genetic, chemical, or biological perturbations.

PMID:40264958 | PMC:PMC12010446 | DOI:10.1016/j.patter.2025.101228

Front Mol Biosci. 2025 Apr 8;12:1542267. doi: 10.3389/fmolb.2025.1542267. eCollection 2025.

ABSTRACT

Intrinsically Disordered Proteins (IDPs) challenge traditional structure-function paradigms by existing as dynamic ensembles rather than stable tertiary structures. Capturing these ensembles is critical to understanding their biological roles, yet Molecular Dynamics (MD) simulations, though accurate and widely used, are computationally expensive and struggle to sample rare, transient states. Artificial intelligence (AI) offers a transformative alternative, with deep learning (DL) enabling efficient and scalable conformational sampling. They leverage large-scale datasets to learn complex, non-linear, sequence-to-structure relationships, allowing for the modeling of conformational ensembles in IDPs without the constraints of traditional physics-based approaches. Such DL approaches have been shown to outperform MD in generating diverse ensembles with comparable accuracy. Most models rely primarily on simulated data for training and experimental data serves a critical role in validation, aligning the generated conformational ensembles with observable physical and biochemical properties. However, challenges remain, including dependence on data quality, limited interpretability, and scalability for larger proteins. Hybrid approaches combining AI and MD can bridge the gaps by integrating statistical learning with thermodynamic feasibility. Future directions include incorporating physics-based constraints and learning experimental observables into DL frameworks to refine predictions and enhance applicability. AI-driven methods hold significant promise in IDP research, offering novel insights into protein dynamics and therapeutic targeting while overcoming the limitations of traditional MD simulations.

PMID:40264953 | PMC:PMC12011600 | DOI:10.3389/fmolb.2025.1542267

J Mass Spectrom. 2025 May;60(5):e5138. doi: 10.1002/jms.5138.

ABSTRACT

Pulmonary fibrosis (PF) is a chronic and progressive lung disease with fatal consequences. The study of PF is challenging due to the complex mechanism involved, the need to understand the heterogeneity and spatial organization within lung tissues. In this study, we investigate the metabolic heterogeneity between two forms of lung fibrosis: idiopathic pulmonary fibrosis (IPF) and silicosis, using advanced spatially-resolved metabolomics techniques. Employing high-resolution mass spectrometry imaging, we spatially mapped and identified over 260 metabolites in lung tissue sections from mouse models of IPF and silicosis. Histological analysis confirmed fibrosis in both models, with distinct pathological features: alveolar destruction and collagen deposition in IPF, and nodule formation in silicosis. Metabolomic analysis revealed significant differences between IPF and silicosis in key metabolic pathways, including phospholipid metabolism, purine/pyrimidine metabolism, and the TCA cycle. Notably, phosphocholine was elevated in silicosis but reduced in IPF, while carnitine levels decreased in both conditions. Additionally, glycolytic activity was increased in both models, but TCA cycle intermediates showed opposing trends. These findings highlight the spatial metabolic heterogeneity of PF and suggest potential metabolic targets for therapeutic intervention. Further investigation into the regulatory mechanisms behind these metabolic shifts may open new avenues for fibrosis treatment.

PMID:40264277 | DOI:10.1002/jms.5138

Comput Struct Biotechnol J. 2025 Apr 3;27:1511-1517. doi: 10.1016/j.csbj.2025.04.006. eCollection 2025.

ABSTRACT

Within the biopharmaceutical industry, the cell line development (CLD) process generates recombinant mammalian cell lines for the expression of therapeutic proteins. Analytical methods for the extensive characterisation of the protein product are well established; however, over recent years, next-generation sequencing (NGS) technologies have rapidly become an integral part of the CLD workflow. NGS can be used for different applications to characterise the genome, epigenome and transcriptome of cell lines. The resulting extensive datasets, especially when integrated with systems biology models, can give comprehensive insights that can be applied to optimize cell lines, media, and fermentation processes. NGS also provides comprehensive methods to monitor genetic variability during CLD. High coverage NGS experiments can indeed be used to ensure the integrity of plasmids, identify integration sites, and verify monoclonality of the cell lines. This review summarises the role of NGS in advancing biopharmaceutical production to ensure safety and efficacy of therapeutic proteins.

PMID:40265158 | PMC:PMC12013335 | DOI:10.1016/j.csbj.2025.04.006

Front Genet. 2025 Apr 8;16:1603498. doi: 10.3389/fgene.2025.1603498. eCollection 2025.

NO ABSTRACT

PMID:40264451 | PMC:PMC12011714 | DOI:10.3389/fgene.2025.1603498

Food Res Int. 2025 May;208:116288. doi: 10.1016/j.foodres.2025.116288. Epub 2025 Mar 16.

ABSTRACT

Induction of amyloid-like morphology in food proteins offers high potential to induce new techno-functional properties in food products (e.g. use as emulsifier, thickener or gelling agent in e.g. bakery and confectionery products). However, the health impact of amyloid-like fibril (ALF) consumption remains widely understudied and merits additional research. The aim of this study was to (partially) elucidate the general health impact of food-borne ALF consumption, using egg white ovalbumin as a case study. Based on in vitro cell culture models it was demonstrated that ovalbumin ALFs (i) do not induce direct cytotoxic effects on intestinal (Caco-2, IPEC-J2) and neuronal (SH-SY5Y) cell lines, but (ii) are able to induce a Toll-like-receptor-mediated innate immune response, similar to endogenous amyloids, in activated THP-1 cells. Furthermore, the consecutive in vitro digestion and absorption (enterocyte and M-cell) experiments demonstrated that ovalbumin ALFs (i) do not completely lose their ALF morphology upon in vitro gastrointestinal digestion, and that (ii) the ALF core sequences, located at the center of the ALF structure, are transported across Caco-2 based cell models, suggesting aggregate transport. In vivo, intestinal translocation of ingested ALFs would imply potential cross-seeding of endogenous, disease-related precursor proteins. The ability of ovalbumin ALFs to induce aggregation of a disease-related precursor protein, αSyn, was evaluated in a precursor overexpressing cell model. Here, it was illustrated that only homologous (αSyn) - but not heterologous (ovalbumin) - seeding resulted in intracellular aggregation bodies of (phosphorylated) αSyn. The lack of cross-seeding supports the assumption that ovalbumin ALF consumption is not a risk factor for the development of α-synucleinopathies like Parkinson's disease.

PMID:40263866 | DOI:10.1016/j.foodres.2025.116288

Food Res Int. 2025 May;208:116170. doi: 10.1016/j.foodres.2025.116170. Epub 2025 Mar 22.

NO ABSTRACT

PMID:40263834 | DOI:10.1016/j.foodres.2025.116170

Front Immunol. 2025 Apr 8;16:1582050. doi: 10.3389/fimmu.2025.1582050. eCollection 2025.

ABSTRACT

OBJECTIVE: Advanced cervical cancer remains associated with high mortality rates. While pembrolizumab has improved clinical outcomes in cervical cancer, the therapeutic efficacy in advanced stages is often compromised by immune-related adverse events (irAEs). This study aimed to systematically analyze pembrolizumab-associated adverse events (AEs) in cervical cancer using the FDA Adverse Event Reporting System (FAERS) database, providing new insights for optimizing clinical practice.

METHODS: AE reports related to pembrolizumab in cervical cancer were extracted from the FAERS database (Q1 2016 to Q4 2024). Disproportionality analyses were performed using multiple algorithms, including the reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS). AEs were classified by system organ class (SOC) and preferred term (PT) based on the Medical Dictionary for Regulatory Activities (MedDRA), then ranked by frequency and signal strength.

RESULTS: A total of 646 pembrolizumab-related AE reports in cervical cancer were identified. Age distribution peaked at 45-65 years cohort (32.75%), followed by 18-44 years (12.85%), 66-75 years (11.76%), and >75 years (4.64%). Among 270 AE reports with documented onset timelines, events predominantly occurred 3-6 months after pembrolizumab initiation (n=114, 41.36%). Clinical outcomes were categorized as other (52.80%), hospitalization (27.00%), death (10.25%), unknown (6.06%), life-threatening (2.77%), and disability (1.12%). Predominant AEs involved hematologic, endocrine, dermatologic, neurologic, gastrointestinal, urinary, and reproductive systems.

CONCLUSION: This real-world pharmacovigilance study systematically characterizes pembrolizumab-associated AEs in cervical cancer, identifying high-signal events such as hematologic disorders, endocrine dysfunction, and dermatologic toxicities. These findings provide critical evidence for risk stratification and safety monitoring in clinical practice, emphasizing the need for organ-specific vigilance during the 3-6 months treatment window.

PMID:40264768 | PMC:PMC12011867 | DOI:10.3389/fimmu.2025.1582050

Acta Med Indones. 2025 Jan;57(1):74-80.

ABSTRACT

BACKGROUND: Geriatric patients are often subject to polypharmacy, increasing their risk of adverse drug reactions (ADRs). This study evaluated polypharmacy practices, ADR incidence, predictive factors, and the applicability of the GerontoNet Score at a tertiary referral teaching hospital in Indonesia.

METHODS: This retrospective study included 340 geriatric inpatients at Dr. Cipto Mangunkusumo Hospital, Jakarta, in 2023. The relationship between demographic data, comorbidities, number of drugs used, and ADR events was analyzed using the Chi-square test. The association between GerontoNet ADR scores and ADR events was also assessed.

RESULTS: The study included 182 (53.5%) male and 158 (46.5%) female patients, with a mean age of 71.9±6.1 years. Of these, 70.9% were aged 65 to 74. A total of 78.8% of patients had ≥ 4 comorbidities. The number of drugs ranged from 3 to 28, with a mean of 10.7 drugs and a median of 10 drugs. ADRs were detected in 26 patients (7.6%), with 17 cases in females and 9 in males (p=0.044). Insulin- and diuretic-induced hypokalemia were the most frequent ADR (13 patients), followed by heparin-induced thrombocytopenia (3 patients). No significant correlation was found between ADRs and age (p=0.505), number of comorbidities (p=0.425), number of drugs (p=0.576), or GerontoNet ADR Score (p=0.530).

CONCLUSION: Polypharmacy is prevalent at Dr. Cipto Mangunkusumo Hospital, yet the incidence of ADRs is relatively low. Most ADRs were related to high-alert drugs, while no significant correlations were found between age, polypharmacy, comorbidities, or GerontoNet Score with ADR events.

PMID:40263686

Sci Rep. 2025 Apr 22;15(1):13975. doi: 10.1038/s41598-025-88388-4.

ABSTRACT

The 3C-like protease (3CLpro) is essential in the SARS-CoV-2 life cycle and a promising target for antiviral drug discovery, as no similar proteases exist in humans. This study aimed to identify effective SARS-CoV-2 inhibitors among FDA-approved drugs. Previous computational analysis revealed several drugs with high binding affinity to the 3CLpro active site. In vitro enzymatic assays confirmed that ten of these drugs effectively inhibited the enzyme. To evaluate their impact on viral replication, we used non-infectious SARS-CoV-2 sub-genomic replicons in lung and intestinal cells. Amcinonide, eltrombopag, lumacaftor, candesartan, and nelfinavir inhibited replication at low micromolar concentrations. Lumacaftor showed IC50 values of 964 nM in Caco-2 cells and 458 nM in Calu-3 cells, while candesartan had IC50 values of 714 nM and 1.05 µM, respectively. Furthermore, dual combination experiments revealed that amcinonide, pimozide, lumacaftor, and eltrombopag acted as potent inhibitors at nanomolar concentrations when combined with candesartan. This study highlights lumacaftor, candesartan, and nelfinavir as effective inhibitors of SARS-CoV-2 replication in vitro and emphasizes their potential for repurposing as antiviral treatments. These findings support future clinical trials and may lead to breakthroughs in COVID-19 treatment strategies.

PMID:40263343 | DOI:10.1038/s41598-025-88388-4

Pharmacogenomics J. 2025 Apr 23;25(3):10. doi: 10.1038/s41397-025-00373-2.

ABSTRACT

We examined the association of selected candidate pharmacodynamic (PD) genes in MDD with treatment outcomes, defined according to remission thresholds for Hamilton Depression Rating Scale (HDRS) with 6- and 17- items. To this end, we recruited 158 individuals living with MDD followed in an academic community mental health center. We reconstructed their clinical history and tested the association of a selected panel of pharmacodynamic genes with clinical remission. Our multivariate models were corrected for illness duration, substance use, lifetime stressful events, and sex. We found partially concordant associations for candidate biomarkers and clinical remission defined with HDRS-6 and HDRS-17. In the logistic regression model, two polymorphisms were statistically significantly associated with HDRS-17 remission: namely rs10975641 and rs11628713. Our results suggest that polymorphisms in PD genes might influence clinical response in MDD. Interestingly, we showed some degree of concordance of the association depending on the definition of the response.

PMID:40263270 | DOI:10.1038/s41397-025-00373-2