Sci Rep. 2025 Jan 23;15(1):2982. doi: 10.1038/s41598-024-82517-1.

ABSTRACT

Recent research has identified sex-dependent links between risk taking behaviors, approach-avoidance bias and alcohol intake. However, preclinical studies have typically assessed alcohol drinking using a singular dimension of intake (i.e. drinking level), failing to capture the multidimensional pattern of aberrant alcohol-seeking observed in alcohol use disorder. In this study, we sought to further explore individual and sex differences in the relationship between approach-avoidance bias, frontloading (bingeing and onset skew) and multiple addiction-like indices of ethanol seeking that included motivation for ethanol, persistence despite its absence (extinction), and ethanol-taking in the face of mild footshock. We found that female rats displayed more addiction-like phenotypes than males overall, and that frontloading patterns differed by sex, with females outdrinking males in the early part of access sessions (bingeing), but males strongly concentrating their lever pressing for ethanol in that period (onset skew). Multiple regression analyses revealed that bingeing was a strong positive predictor and onset skew a negative predictor of motivational breakpoint. Cued-conflict preference - a measure of approach-avoidance bias towards a mixed-valence conflict cue - was predictive of both extinction and footshock in males, but not females. Our data highlight key sex differences, and the relevance of both frontloading patterns and conflict preference in predicting future addiction-like phenotypes.

PMID:39848982 | DOI:10.1038/s41598-024-82517-1

Free Radic Biol Med. 2025 Jan 21:S0891-5849(25)00052-8. doi: 10.1016/j.freeradbiomed.2025.01.042. Online ahead of print.

ABSTRACT

Cuproptosis, a copper-dependent form of regulated cell death, has been implicated in the progression and treatment of various tumors. The copper ionophores, such as Disulfiram (DSF), an FDA-approved drug previously used to treat alcohol dependence, have been found to induce cuproptosis. However, the limited solubility and effectiveness of the combination of DSF and copper ion restrict its widespread application. In this study, through a random screening of our in-house compound library, we identified a novel cuproptosis inducer, YL21, comprising a naphthoquinone core substituted by two dithiocarbamate groups. The combination of YL21 with copper ion induces cuproptosis by disrupting mitochondrial function and promoting the oligomerization of lipoylated protein DLAT. Further, this combination induces endoplasmic reticulum (ER) stress and oxidative stress, triggering immunogenic cell death (ICD) and subsequently promoting the activation of antitumor immune responses to suppress tumor growth in the mice breast cancer model. Notably, the combination of YL21 and copper ion demonstrated improved solubility and increased antitumor activity compared to the combination of DSF and copper ion. Thus, YL21 functions as a novel cuproptosis inducer and may serve as a promising candidate for antitumor immunotherapy.

PMID:39848344 | DOI:10.1016/j.freeradbiomed.2025.01.042

J Psychiatr Res. 2025 Jan 20;182:329-337. doi: 10.1016/j.jpsychires.2025.01.039. Online ahead of print.

ABSTRACT

Major depressive disorder (MDD) is a highly prevalent and debilitating disorder, yet its pathophysiology has not been fully elucidated. The aim of this study is to identify novel potential proteins and biological processes associated with MDD through a systems biology approach. Original articles involving the measurement of proteins in the blood of patients diagnosed with MDD were selected. Data on the differentially expressed proteins (DEPs) in each article were extracted and imported into R, and the pathfindR package was used to identify the main gene ontology terms involved. Data from the STRING database were combined with the DEPs identified in the original studies to create expanded networks of protein-protein interactions (PPIs). An R script was developed to obtain the five most reliable connections from each DEP and to create the networks, which were visualized through Cytoscape software. Out of 510 articles found, eight that contained all the values necessary for the analysis were selected, including 1112 adult patients with MDD and 864 controls. A total of 240 DEPs were identified, with the most significant gene ontology term being "endoplasmic reticulum lumen" (46 DEPs, p-value = 5.5x10-13). An extended PPI network was obtained, where Anoctamin-8 was the most central protein. Using systems biology contributed to the interpretation of data obtained in proteomic studies on MDD and expanded the findings of these studies. The combined use of these methodologies can provide new insights into the pathophysiology of psychiatric disorders, identifying novel biomarkers to improve diagnostic, prognostic, and treatment strategies in MDD.

PMID:39848100 | DOI:10.1016/j.jpsychires.2025.01.039

Int Immunopharmacol. 2025 Jan 22;148:114090. doi: 10.1016/j.intimp.2025.114090. Online ahead of print.

ABSTRACT

INTRODUCTION: Biomarkers play a crucial role across various fields by providing insights into biological responses to interventions. High-throughput gene expression profiling technologies facilitate the discovery of data-driven biomarkers through extensive datasets. This study focuses on identifying biomarkers in gene expression data related to chemical injuries by mustard gas, covering a spectrum from healthy individuals to severe injuries.

MATERIALS AND METHODS: The study utilized RNA-Seq data comprising 52 expression data samples for 54,583 gene transcripts. These samples were categorized into four classes based on the GOLD classification for chemically injured individuals: Severe (n = 14), Moderate (n = 11), Mild (n = 16), and healthy controls (n = 11). Data preparation involved examining an Excel file created in the R programming environment using MLSeq and devtools packages. Feature selection was performed using Genetic Algorithm and Simulated Annealing, with Random Forest algorithm employed for classification. Ab initio methods ensured computational efficiency and result accuracy, while molecular dynamics simulation acted as a virtual experiment bridging the gap between experimental and theoretical experiences.

RESULTS: A total of 12 models were created, each introducing a list of differentially expressed genes as potential biomarkers. The performance of models varied across group comparisons, with the Genetic Algorithm generally outperforming Simulated Annealing in most cases. For the Severe vs. Moderate group, GA achieved the best performance with an accuracy of 94.38%, recall of 91.64%, and specificity of 97.10%. The results highlight the effectiveness of GA in most group comparisons, while SA performed better in specific cases involving Moderate and Mild groups. These biomarkers were evaluated against the gene expression data to assess their expression changes between different groups of chemically injured individuals. Four genes were selected based on level expression for further investigation: CXCR1, EIF2B2, RAD51, and RXFP2. The expression levels of these genes were analyzed to determine their differential expression between the groups.

CONCLUSION: This study was designed as a computational effort to identify diagnostic biomarkers in basic biological system research. Our findings proposed a list of discriminative biomarkers capable of distinguishing between different groups of chemically injured individuals. The identification of key genes highlights the potential for biomarkers to serve as indicators of chemical injury severity, warranting further investigation to validate their clinical relevance and utility in diagnosis and treatment.

PMID:39847951 | DOI:10.1016/j.intimp.2025.114090

J Invest Dermatol. 2025 Jan 21:S0022-202X(25)00034-X. doi: 10.1016/j.jid.2024.12.025. Online ahead of print.

ABSTRACT

TRIAL DESIGN: This two-part, double-blinded trial assessed the truncated retinoic acid-related orphan receptor γ (RORγt) inhibitor BI 730357 in plaque psoriasis.

METHODS: Part 1: patients were randomized 2:2:2:2:1 to BI 730357 25, 50, 100, 200 mg, or placebo once daily (qd; fasting conditions); non-responders switched to higher doses. Part 2: a separate patient set was randomized 4:4:1 to BI 730357 400 mg qd, 200 mg twice daily, or placebo (fed conditions). Patients from Parts 1 and 2 could enter a long-term extension (LTE) trial. Co-primary endpoints: ≥75% reduction from baseline in Psoriasis Area Severity Index (PASI 75) and static physician's global assessment (sPGA) score 0/1 (clear/almost clear) at week 12.

RESULTS: 274 patients were treated (178 [Part 1]; 96 [Part 2]). Part 1: 12 (30.0%) patients achieved PASI 75 (P=0.0062) and 11 (27.5%) achieved sPGA 0/1 (P=0.0095) with BI 730357 200 mg versus none receiving placebo. Exposure-response plateaued at BI 730357 ≥200 mg qd. Drug-related adverse events occurred in ≤15.8% of patients. Of 165 patients who entered the LTE, 93 (56.4%) achieved PASI 75 during treatment and ≤18.5% experienced a drug-related adverse event.

CONCLUSIONS: BI 730357 was well-tolerated with moderate efficacy versus placebo in plaque reduction.

PMID:39848568 | DOI:10.1016/j.jid.2024.12.025

Curr Res Transl Med. 2025 Jan 17;73(2):103496. doi: 10.1016/j.retram.2025.103496. Online ahead of print.

ABSTRACT

Drug repurposing is a promising strategy for managing cardiovascular disease (CVD) in geriatric populations, offering efficient and cost-effective solutions. CVDs are prevalent across all age groups, with a significant increase in prevalence among geriatric populations. The middle-age period (40-65 years) is critical due to factors like obesity, sedentary lifestyle, and psychosocial stress. In individuals aged 65 and older, the incidence of CVDs is highest due to age-related physiological changes and prolonged exposure to risk factors. In this review we find that certain drugs, such as non-cardiovascular drugs like anakinra, probenecid, N-acetyl cysteine, quercetin, resveratrol, rapamycin, colchicine, bisphosphonates, hydroxychloroquine, SGLT-2i drugs, GLP-1Ras drugs and sildenafil are recommended for drug repurposing to achieve cardiovascular benefits in geriatric patients. However, agents such as canakinumab, methotrexate, ivermectin, erythromycin, capecitabine, carglumic acid, chloroquine, and furosemide are constrained in their therapeutic use and warrant meticulous consideration, rendering them less favorable for this specific application. This review emphasizes the importance of exploring alternative therapeutic strategies to improve outcomes in geriatric populations and suggests drug repurposing as a promising avenue to enhance treatment efficacy.

PMID:39847829 | DOI:10.1016/j.retram.2025.103496

JMIR Res Protoc. 2025 Jan 23;14:e65137. doi: 10.2196/65137.

ABSTRACT

BACKGROUND: Injectable platelet-rich fibrin (i-PRF) has the capacity to release great amounts of several growth factors, as well as to stimulate increased fibroblast migration and the expression of collagen, transforming growth factor β, and platelet-derived growth factor. Consequently, i-PRF can be used as a bioactive agent to promote periodontal tissue regeneration.

OBJECTIVE: We aim to compare and evaluate the effectiveness of i-PRF in periodontal tissue regeneration.

METHODS: We will conduct an electronic search in the following databases: PubMed, Cochrane Library, Google Scholar, Semantic Scholar, Scopus, and Web of Science. Papers will be restricted to those in English and to those that are randomized controlled trials comparing PRF or any other biomaterial with i-PRF for periodontal regeneration during dental treatment. The included papers in this review and the reference lists of pertinent reviews will be manually searched. The selection of studies, data extraction, and assessment will be carried out separately by 2 reviewers using the Risk of Bias 2 tool for the included research.

RESULTS: The success of i-PRF will be evaluated by comparing the mean difference in periodontal regeneration of soft and hard tissues in terms of gingival recession, probing pocket depth, clinical attachment level, bone gain, and gingival width. The combined effect size measurements and the associated 95% CIs will be estimated using a random-effects model. The synthesis or work for this systematic review started in October 2023 and will last until December 2025.

CONCLUSIONS: i-PRF may play a role in dentistry and could enhance soft and hard tissue regeneration.

TRIAL REGISTRATION: PROSPERO CRD42023464250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=464250.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/65137.

PMID:39847766 | DOI:10.2196/65137

Annu Rev Pharmacol Toxicol. 2025 Jan;65(1):111-130. doi: 10.1146/annurev-pharmtox-061724-080718.

ABSTRACT

Pharmacogenetic variation is common and an established driver of response for many drugs. There has been tremendous progress in pharmacogenetics knowledge over the last 30 years and in clinical implementation of that knowledge over the last 15 years. But there have also been many examples where translation has stalled because of the lack of available data sets for discovery or validation research. The recent availability of data from very large cohorts with linked genetic, electronic health record, and other data promises new opportunities to advance pharmacogenetics research. This review presents the stages from pharmacogenetics discovery to widespread clinical adoption using prominent gene-drug pairs that have been implemented into clinical practice as examples. We discuss the opportunities that the All of Us Research Program and other large biorepositories with genomic and linked electronic health record data present in advancing and accelerating the translation of pharmacogenetics into clinical practice.

PMID:39847465 | DOI:10.1146/annurev-pharmtox-061724-080718

Soft comput. 2024;28(Suppl 1):67. doi: 10.1007/s00500-024-09993-5. Epub 2024 Jul 22.

ABSTRACT

[This retracts the article DOI: 10.1007/s00500-020-05275-y.].

PMID:39847670 | PMC:PMC11753128 | DOI:10.1007/s00500-024-09993-5

Soft comput. 2024;28(Suppl 1):295. doi: 10.1007/s00500-024-10107-4. Epub 2024 Aug 27.

ABSTRACT

[This retracts the article DOI: 10.1007/s00500-023-08313-7.].

PMID:39847665 | PMC:PMC11753125 | DOI:10.1007/s00500-024-10107-4

Soft comput. 2024;28(Suppl 1):65. doi: 10.1007/s00500-024-09992-6. Epub 2024 Jul 22.

ABSTRACT

[This retracts the article DOI: 10.1007/s00500-020-05424-3.].

PMID:39847664 | PMC:PMC11753127 | DOI:10.1007/s00500-024-09992-6

J Phys Chem B. 2025 Jan 23. doi: 10.1021/acs.jpcb.4c06190. Online ahead of print.

ABSTRACT

PGLa, an antimicrobial peptide (AMP), primarily exerts its antibacterial effects by disrupting bacterial cell membrane integrity. Previous theoretical studies mainly focused on the binding mechanism of PGLa with membranes, while the mechanism of water pore formation induced by PGLa peptides, especially the role of structural flexibility in the process, remains unclear. In this study, using all-atom simulations, we investigated the entire process of membrane deformation caused by the interaction of PGLa with an anionic cell membrane composed of dimyristoylphosphatidylcholine (DMPC) and dimyristoylphosphatidylglycerol (DMPG). Using a deep learning-based key intermediate identification algorithm, we found that the C-terminal tail plays a crucial role for PGLa insertion into the membrane, and that with its assistance, a variety of water pores formed inside the membrane. Mutation of the tail residues revealed that, in addition to electrostatic and hydrophobic interactions, the flexibility of the tail residues is crucial for peptide insertion and pore formation. The full extension of these flexible residues enhances peptide-peptide and peptide-membrane interactions, guiding the transmembrane movement of PGLa and the aggregation of PGLa monomers within the membrane, ultimately leading to the formation of water-filled pores in the membrane. Overall, this study provides a deep understanding of the transmembrane mechanism of PGLa and similar AMPs, particularly elucidating for the first time the importance of C-terminal flexibility in both insertion and oligomerization processes.

PMID:39847609 | DOI:10.1021/acs.jpcb.4c06190

PLoS One. 2025 Jan 23;20(1):e0317619. doi: 10.1371/journal.pone.0317619. eCollection 2025.

ABSTRACT

This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). ET achieved the highest performance among ML models, with an R-squared value of 0.7231 and a RMSE of 0.1512. Among DL models, ANN demonstrated the best performance, achieving an R-squared value of 0.7248 and a RMSE of 0.1516. The results show that DL models, especially ANN, did slightly better than the best ML models. This means that they are better at modeling non-linear dependencies in multivariate data. Preprocessing techniques, including feature scaling and parameter tuning, improved model performance by enhancing data consistency and optimizing hyperparameters. When compared to previous benchmarks, the performance of both ANN and ET demonstrates significant predictive accuracy gains in WT power output forecasting. This study's novelty lies in directly comparing a diverse range of ML and DL algorithms while highlighting the potential of advanced computational approaches for renewable energy optimization.

PMID:39847588 | DOI:10.1371/journal.pone.0317619

PLoS Comput Biol. 2025 Jan 23;21(1):e1012708. doi: 10.1371/journal.pcbi.1012708. eCollection 2025 Jan.

ABSTRACT

The applications of artificial intelligence (AI) and deep learning (DL) are leading to significant advances in cancer research, particularly in analysing histopathology images for prognostic and treatment-predictive insights. However, effective translation of these computational methods requires computational researchers to have at least a basic understanding of histopathology. In this work, we aim to bridge that gap by introducing essential histopathology concepts to support AI developers in their research. We cover the defining features of key cell types, including epithelial, stromal, and immune cells. The concepts of malignancy, precursor lesions, and the tumour microenvironment (TME) are discussed and illustrated. To enhance understanding, we also introduce foundational histopathology techniques, such as conventional staining with hematoxylin and eosin (HE), antibody staining by immunohistochemistry, and including the new multiplexed antibody staining methods. By providing this essential knowledge to the computational community, we aim to accelerate the development of AI algorithms for cancer research.

PMID:39847582 | DOI:10.1371/journal.pcbi.1012708

PLoS One. 2025 Jan 23;20(1):e0318164. doi: 10.1371/journal.pone.0318164. eCollection 2025.

ABSTRACT

[This corrects the article DOI: 10.1371/journal.pone.0272423.].

PMID:39847555 | DOI:10.1371/journal.pone.0318164

Science. 2025 Jan 24;387(6732):eadn7277. doi: 10.1126/science.adn7277. Epub 2025 Jan 24.

ABSTRACT

Vascular inflammation regulates endothelial pathophenotypes, particularly in pulmonary arterial hypertension (PAH). Dysregulated lysosomal activity and cholesterol metabolism activate pathogenic inflammation, but their relevance to PAH is unclear. Nuclear receptor coactivator 7 (NCOA7) deficiency in endothelium produced an oxysterol and bile acid signature through lysosomal dysregulation, promoting endothelial pathophenotypes. This oxysterol signature overlapped with a plasma metabolite signature associated with human PAH mortality. Mice deficient for endothelial Ncoa7 or exposed to an inflammatory bile acid developed worsened PAH. Genetic predisposition to NCOA7 deficiency was driven by single-nucleotide polymorphism rs11154337, which alters endothelial immunoactivation and is associated with human PAH mortality. An NCOA7-activating agent reversed endothelial immunoactivation and rodent PAH. Thus, we established a genetic and metabolic paradigm that links lysosomal biology and oxysterol processes to endothelial inflammation and PAH.

PMID:39847635 | DOI:10.1126/science.adn7277

Science. 2025 Jan 23:eadk9688. doi: 10.1126/science.adk9688. Online ahead of print.

ABSTRACT

Genomes contain mosaics of discordant evolutionary histories, challenging the accurate inference of the tree of life. While genome-wide data are routinely used for discordance-aware phylogenomic analyses, due to modeling and scalability limitations, the current practice leaves out large chunks of genomes. As more high-quality genomes become available, we urgently need discordance-aware methods to infer the tree directly from a multiple genome alignment. Here, we introduce CASTER, a theoretically justified site-based method that eliminates the need to predefine recombination-free loci. CASTER is scalable to hundreds of mammalian whole genomes. We demonstrate the accuracy and scalability of CASTER in simulations that include recombination and apply CASTER to several biological datasets, showing that its per-site scores can reveal both biological and artefactual patterns of discordance across the genome.

PMID:39847611 | DOI:10.1126/science.adk9688

PLoS Pathog. 2025 Jan 23;21(1):e1012903. doi: 10.1371/journal.ppat.1012903. Online ahead of print.

ABSTRACT

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to persist, demonstrating the risks posed by emerging infectious diseases to national security, public health, and the economy. Development of new vaccines and antibodies for emerging viral threats requires substantial resources and time, and traditional development platforms for vaccines and antibodies are often too slow to combat continuously evolving immunological escape variants, reducing their efficacy over time. Previously, we designed a next-generation synthetic humanized nanobody (Nb) phage display library and demonstrated that this library could be used to rapidly identify highly specific and potent neutralizing heavy chain-only antibodies (HCAbs) with prophylactic and therapeutic efficacy in vivo against the original SARS-CoV-2. In this study, we used a combination of high throughput screening and machine learning (ML) models to identify HCAbs with potent efficacy against SARS-CoV-2 viral variants of interest (VOIs) and concern (VOCs). To start, we screened our highly diverse Nb phage display library against several pre-Omicron VOI and VOC receptor binding domains (RBDs) to identify panels of cross-reactive HCAbs. Using HCAb affinity for SARS-CoV-2 VOI and VOCs (pre-Omicron variants) and model features from other published data, we were able to develop a ML model that successfully identified HCAbs with efficacy against Omicron variants, independent of our experimental biopanning workflow. This biopanning informed ML approach reduced the experimental screening burden by 78% to 90% for the Omicron BA.5 and Omicron BA.1 variants, respectively. The combined approach can be applied to other emerging viruses with pandemic potential to rapidly identify effective therapeutic antibodies against emerging variants.

PMID:39847604 | DOI:10.1371/journal.ppat.1012903

PLoS Comput Biol. 2025 Jan 23;21(1):e1012198. doi: 10.1371/journal.pcbi.1012198. Online ahead of print.

ABSTRACT

Systems biology tackles the challenge of understanding the high complexity in the internal regulation of homeostasis in the human body through mathematical modelling. These models can aid in the discovery of disease mechanisms and potential drug targets. However, on one hand the development and validation of knowledge-based mechanistic models is time-consuming and does not scale well with increasing features in medical data. On the other hand, data-driven approaches such as machine learning models require large volumes of data to produce generalisable models. The integration of neural networks and mechanistic models, forming universal differential equation (UDE) models, enables the automated learning of unknown model terms with less data than neural networks alone. Nevertheless, estimating parameters for these hybrid models remains difficult with sparse data and limited sampling durations that are common in biological applications. In this work, we propose the use of physiology-informed regularisation, penalising biologically implausible model behavior to guide the UDE towards more physiologically plausible regions of the solution space. In a simulation study we show that physiology-informed regularisation not only results in a more accurate forecasting of model behaviour, but also supports training with less data. We also applied this technique to learn a representation of the rate of glucose appearance in the glucose minimal model using meal response data measured in healthy people. In that case, the inclusion of regularisation reduces variability between UDE-embedded neural networks that were trained from different initial parameter guesses.

PMID:39847592 | DOI:10.1371/journal.pcbi.1012198

PLoS Biol. 2025 Jan 23;23(1):e3002707. doi: 10.1371/journal.pbio.3002707. eCollection 2025 Jan.

ABSTRACT

Antibodies are extensively used in biomedical research, clinical fields, and disease treatment. However, to enhance the reproducibility and reliability of antibody-based experiments, it is crucial to have a detailed understanding of the antibody's target specificity and epitope. In this study, we developed a high-throughput and precise epitope analysis method, DECODE (Decoding Epitope Composition by Optimized-mRNA-display, Data analysis, and Expression sequencing). This method allowed identifying patterns of epitopes recognized by monoclonal or polyclonal antibodies at single amino acid resolution and predicted cross-reactivity against the entire protein database. By applying the obtained epitope information, it has become possible to develop a new 3D immunostaining method that increases the penetration of antibodies deep into tissues. Furthermore, to demonstrate the applicability of DECODE to more complex blood antibodies, we performed epitope analysis using serum antibodies from mice with experimental autoimmune encephalomyelitis (EAE). As a result, we were able to successfully identify an epitope that matched the sequence of the peptide inducing the disease model without relying on existing antigen information. These results demonstrate that DECODE can provide high-quality epitope information, improve the reproducibility of antibody-dependent experiments, diagnostics and therapeutics, and contribute to discover pathogenic epitopes from antibodies in the blood.

PMID:39847587 | DOI:10.1371/journal.pbio.3002707