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

Mol Biol Evol. 2025 Jan 23:msaf005. doi: 10.1093/molbev/msaf005. Online ahead of print.

ABSTRACT

Copy-number variants (CNVs) are an important class of genetic variation that can mediate rapid adaptive evolution. Whereas CNVs can increase the relative fitness of the organism, they can also incur a cost due to the associated increased gene expression and repetitive DNA. We previously evolved populations of Saccharomyces cerevisiae over hundreds of generations in glutamine-limited (Gln-) chemostats and observed the recurrent evolution of CNVs at the GAP1 locus. To understand the role that gene expression plays in adaptation, both in relation to the adaptation of the organism to the selective condition and as a consequence of the CNV, we measured the transcriptome, translatome, and proteome of 4 strains of evolved yeast, each with a unique CNV, and their ancestor in Gln- conditions. We find CNV-amplified genes correlate with higher mRNA abundance; however, this effect is reduced at the level of the proteome, consistent with post-transcriptional dosage compensation. By normalizing each level of gene expression by the abundance of the preceding step we were able to identify widespread differences in the efficiency of each level of gene expression. Genes with significantly different translational efficiency were enriched for potential regulatory mechanisms including either upstream open reading frames (uORFs), RNA binding sites for Ssd1, or both. Genes with lower protein expression efficiency were enriched for genes encoding proteins in protein complexes. Taken together, our study reveals widespread changes in gene expression at multiple regulatory levels in lineages containing adaptive CNVs highlighting the diverse ways in which genome evolution shapes gene expression.

PMID:39847535 | DOI:10.1093/molbev/msaf005

Plant Cell. 2024 Dec 23;37(1):koaf004. doi: 10.1093/plcell/koaf004.

NO ABSTRACT

PMID:39847516 | DOI:10.1093/plcell/koaf004

Cell Rep. 2025 Jan 21;44(2):115224. doi: 10.1016/j.celrep.2024.115224. Online ahead of print.

ABSTRACT

The redox state of proteins is essential for their function and guarantees cell fitness. Peroxiredoxins protect cells against oxidative stress, maintain redox homeostasis, act as chaperones, and transmit hydrogen peroxide signals to redox regulators. Despite the profound structural and functional knowledge of peroxiredoxins action, information on how the different functions are concerted is still scarce. Using global proteomic analyses, we show here that the yeast peroxiredoxin Tsa1 interacts with many proteins of essential biological processes, including protein turnover and carbohydrate metabolism. Several of these interactions are of a covalent nature, and we show that failure of peroxiredoxinylation of Gnd1 affects its phosphogluconate dehydrogenase activity and impairs recovery upon stress. Thioredoxins directly remove TSA1-formed mixed disulfide intermediates, thus expanding the role of the thioredoxin-peroxiredoxin redox cycle pair to buffer the redox state of proteins.

PMID:39847483 | DOI:10.1016/j.celrep.2024.115224

Medicines (Basel). 2024 Dec 31;12(1):1. doi: 10.3390/medicines12010001.

ABSTRACT

Cystic fibrosis (CF) is a rare genetic disorder commonly affecting multiple organs such as the lungs, pancreas, liver, kidney, and intestine. Our search focuses on the pathophysiological changes that affect the drugs' absorption, distribution, metabolism, and excretion (ADME). This review aims to identify the ADME data that compares the pharmacokinetics (PK) of different drugs in CF and healthy subjects. The published data highlight multiple factors that affect absorption, such as the bile salt precipitation and the gastrointestinal pH. Changes in CF patients' protein binding and body composition affected the drug distribution. The paper also discusses the factors affecting metabolism and renal elimination, such as drug-protein binding and metabolizing enzyme capacity. The majority of CF patients are on multidrug therapy, which increases the risk of drug-drug interactions (DDI). This is particularly true for those receiving the newly developed transmembrane conductance regulator (CFTR), as they are at a higher risk for CYP-related DDI. Our research highlights the importance of meticulously evaluating PK variations and DDIs in drug development and the therapeutic management of CF patients.

PMID:39846711 | DOI:10.3390/medicines12010001

Noncoding RNA. 2025 Jan 12;11(1):3. doi: 10.3390/ncrna11010003.

ABSTRACT

The discovery of the involvement of microRNAs (miRNAs) in cystic fibrosis (CF) has generated increasing interest in the past years, due to their possible employment as a novel class of drugs to be studied in pre-clinical settings of therapeutic protocols for cystic fibrosis. In this narrative review article, consider and comparatively evaluate published laboratory information of possible interest for the development of miRNA-based therapeutic protocols for cystic fibrosis. We consider miRNAs involved in the upregulation of CFTR, miRNAs involved in the inhibition of inflammation and, finally, miRNAs exhibiting antibacterial activity. We suggest that antago-miRNAs and ago-miRNAs (miRNA mimics) can be proposed for possible validation of therapeutic protocols in pre-clinical settings.

PMID:39846681 | DOI:10.3390/ncrna11010003