Phytother Res. 2025 May 26. doi: 10.1002/ptr.8490. Online ahead of print.

ABSTRACT

Growing rates of overweight and obesity worldwide call for novel approaches to treatment, and plant-derived natural products present a promising therapeutic option. Evaluate the efficacy of plant-derived natural products as dietary interventions for overweight and obesity through a systematic review and network meta-analysis. We conduct a systematic review and network meta-analysis following PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines. We searched from five databases and registries up to March 2024, selecting randomized controlled trials examining dietary interventions with plant-derived natural products for adults with obesity or overweight. The frequentist approach was used for the network meta-analysis, assessing 13 metabolic and obesity-related outcomes. Our review included 39 studies with 2513 participants with PROSPERO registration ID CRD42024520305. African Mango emerged as the most effective intervention, reducing body weight (MD: -10.00 kg, 95% CI: -16.74 to -3.26), waist circumference (MD: -11.70 cm, 95% CI: -17.15 to -6.25), total cholesterol (MD: -44.01 mg/dL, 95% CI: -58.95 to -29.08), triglycerides (MD: -42.65 mg/dL, 95% CI: -79.70 to -5.60), and random blood glucose (MD: -14.95 mg/dL, 95% CI: -18.60 to -11.30). Green coffee led to the largest reduction in body fat percentage (MD: -2.90%, 95% CI: -4.88 to -0.92) and BMI (MD: -3.08 kg/m2, 95% CI: -6.35 to 0.19). Ephedra was most effective in reducing fasting blood glucose (MD: -4.60 mg/dL, 95% CI: -5.49 to -3.71) and HOMA-IR (MD: -16.20, 95% CI: -18.66 to -13.74). There were too few direct comparisons between various metabolites; thus, most of the comparisons were indirect comparisons through placebo. Plant-derived natural products significantly impact obesity management, notably in body weight, waist circumference, and lipid profile reduction; however, further high-quality and rigorous studies were needed to establish the clinical efficacy of the plant-derived natural products.

PMID:40420357 | DOI:10.1002/ptr.8490

Hum Mol Genet. 2025 May 27:ddaf082. doi: 10.1093/hmg/ddaf082. Online ahead of print.

ABSTRACT

Rare variants affecting the epigenetic regulator KDM2B cause a recently delineated neurodevelopmental disorder. Interestingly, we previously identified both a general KDM2B-associated episignature and a subsignature specific to variants in the DNA-binding CxxC domain. In light of the existence of a distinct subsignature, we set out to determine if KDM2B CxxC variants are associated with a unique phenotype and disease mechanism. We recruited individuals with heterozygous CxxC variants and assessed the variants' effect on protein expression and DNA-binding ability. We analyzed clinical data from 19 individuals, including ten previously undescribed individuals with seven novel CxxC variants. The core phenotype of the KDM2B-CxxC cohort is more extensive as compared to that of individuals with KDM2B haploinsufficiency. All individuals with CxxC variants presented with developmental delay, mainly in the speech and motor domain, in addition to variable intellectual disability and mild facial dysmorphism. Congenital heart defects were observed in up to 78% of individuals, with additional common findings including musculoskeletal, ophthalmological, and urogenital anomalies, as well as behavioral challenges and feeding difficulties. Functional assays revealed that while mutant KDM2B protein with CxxC variants can be expressed in vitro, its DNA-binding ability is significantly reduced compared to wildtype. This study shows that KDM2B CxxC variants cause a distinct neurodevelopmental syndrome, possibly through a molecular mechanism different from haploinsufficiency.

PMID:40420380 | DOI:10.1093/hmg/ddaf082

Drug Metab Pers Ther. 2025 May 23. doi: 10.1515/dmpt-2025-0008. Online ahead of print.

ABSTRACT

OBJECTIVES: Macrophages play a major role in the inflammation. Recently, the expression of some cytochrome P450 (CYP450) 4 family enzymes was identified in the macrophages including CYP4V2, which metabolizes saturated fatty acids. Lauric acid is unsaturated fatty acid, which can induce inflammation. Its effect on the expression of CYP4V2 and the inflammatory mediators in macrophages is still unknown. This study aims to investigate the effect of lauric acid on the expression of CYP4V2 and cyclo-oxygenase 2 (COX2) in the human monocytes and macrophage THP1 cell line.

METHODS: The THP1 monocyte cell line was differentiated into macrophages using 100 ng/mL PMA. Then, the cells were treated with 10 µM lauric acid for 72 h. The mRNA and protein expression of human CYP4V2 and COX2 were analyzed using real-time and western blot techniques, respectively.

RESULTS: It was found that the mRNA and protein expression of CYP4V2 was upregulated after treatment of the macrophages with lauric acid in a dose-dependent manner. This upregulation was correlated with the expression of COX2.

CONCLUSIONS: It can be concluded from the results of this study that mRNA and protein expression of CYP4V2 are upregulated by lauric acid in correlation with the induction of inflammation. CYP4V2 can play a role in the inflammation process managed by macrophages.

PMID:40421601 | DOI:10.1515/dmpt-2025-0008

Pharmacogenet Genomics. 2025 May 27. doi: 10.1097/FPC.0000000000000570. Online ahead of print.

ABSTRACT

Propofol is commonly used to sedate patients, but variations in how individuals metabolize the drug may affect dosing requirements. The objective of this study was to explore how genetic variations in CYP450 enzymes, particularly CYP2B6, influence propofol metabolism in ICU patients receiving mechanical ventilation. Genetic variants of CYP2B6, CYP2C9, CYP2C19, and CYP3A5 were collected from an institutional genetic data repository. Patients were dichotomized into low and high metabolic activity for each enzyme, and the mean weight- and time-normalized propofol dose administered was compared between groups via t test. There was no significant difference in average daily propofol dose between patients with low and high CYP2B6 activity (11 vs. 11 mg/kg/h, P = 0.78), or any of the other CYP enzymes analyzed (all P > 0.05). This study could not replicate previous studies indicating that patients carrying genetic variants with diminished CYP2B6 activity required lower propofol doses. Future studies with prospectively collected dosing and outcomes data, and measurement of plasma drug concentrations, may provide insights into personalized propofol dosing strategies.

PMID:40421567 | DOI:10.1097/FPC.0000000000000570

Front Cardiovasc Med. 2025 May 12;12:1572389. doi: 10.3389/fcvm.2025.1572389. eCollection 2025.

ABSTRACT

BACKGROUND: Variability in responses to clopidogrel and aspirin therapy for coronary artery disease has driven interest in pharmacogenomics. This study investigates the role of genetic variants in CYP2C19, ABCB1, and PON1 in predicting adverse cardiovascular events and guiding personalised antiplatelet therapy.

METHODS: A retrospective cohort study designed to compare the effectiveness and safety of the risk levels from CYP2C19 (*2, *3, *17), ABCB1 C3435T, and PON1 Q192R polymorphisms. The primary outcome was the incidence of haemorrhage and major adverse cardiovascular events (MACE). Kaplan Merir curves and Cox regression with IPTW adjustments were used for analysis.

RESULTS: The results of this study indicate that patients in Group A, who received treatment consistent with multigene testing (CYP2C19, ABCB1, and PON1), experienced significantly lower major adverse cardiovascular events (MACE) compared to Group B. Multigene testing proved to be more accurate in predicting clopidogrel effectiveness and reducing adverse events without an increased risk of haemorrhage (HR 0.671, 95% CI: 0.526-0.855, P = 0.001). Patients in Group A showed no significant difference in haemorrhage risk compared to Group B, with an HR of 0.831 (95% CI: 0.598-1.155, P = 0.271) after adjustment.

CONCLUSION: Multigene-guided antiplatelet therapy is more effective in reducing adverse cardiovascular events. Further prospective studies are needed to validate these findings, incorporating genetic, environmental, and lifestyle factors for a comprehensive personalised medicine approach.

PMID:40421189 | PMC:PMC12104257 | DOI:10.3389/fcvm.2025.1572389

Brief Bioinform. 2025 May 1;26(3):bbaf226. doi: 10.1093/bib/bbaf226.

ABSTRACT

Pharmacogenomics studies are attracting an increasing amount of interest from researchers in precision medicine. The advances in high-throughput experiments and multiplexed approaches allow the large-scale quantification of drug sensitivities in molecularly characterized cancer cell lines (CCLs), resulting in a number of open drug sensitivity datasets for drug biomarker discovery. However, a significant inconsistency in drug sensitivity values among these datasets has been noted. Such inconsistency indicates the presence of substantial noise, subsequently hindering downstream analyses. To address the noise in drug sensitivity data, we introduce a robust and scalable deep learning framework, Residual Thresholded Deep Matrix Factorization (RT-DMF). This method takes a single drug sensitivity data matrix as its sole input and outputs a corrected and imputed matrix. Deep matrix factorization (DMF) excels at uncovering subtle patterns, due to its minimal reliance on data structure assumptions. This attribute significantly boosts DMF's ability to identify complex hidden patterns among nuisance effects in the data, thereby facilitating the detection of signals that are therapeutically relevant. Furthermore, RT-DMF incorporates an iterative residual thresholding procedure, which plays a crucial role in retaining signals more likely to hold therapeutic importance. Validation using simulated datasets and real pharmacogenomics datasets demonstrates the effectiveness of our approach in correcting noise and imputing missing data in drug sensitivity datasets (open-source package available at https://github.com/tomwhoooo/rtdmf).

PMID:40420482 | DOI:10.1093/bib/bbaf226

IUBMB Life. 2025 May;77(5):e70027. doi: 10.1002/iub.70027.

ABSTRACT

Nonsense mutations in gene coding regions introduce an in-frame premature termination codon (PTC) in the mRNA transcript, resulting in the early termination of translation and the production of a truncated, nonfunctional protein. The absence of protein expression and the consequent loss of essential cellular functions are responsible for the severe phenotypes in the so-called genetic nonsense-related diseases (NRDs), such as cystic fibrosis, hemophilia, Duchenne muscular dystrophy, Fabry disease, Choroideremia, Usher syndrome, Shwachman-Diamond syndrome, and even certain types of cancer. Nonsense mutations pose a significant challenge in the treatment of NRDs, as a specific approach directly addressing this genetic defect is currently unavailable. Developing new therapeutic strategies for nonsense suppression is a crucial goal of precision medicine. This review describes some of the most promising therapeutic approaches and emerging strategies for treating NRDs. It considered both the use of small molecules to interfere with molecular mechanisms related to nonsense mutations, such as translational readthrough-inducing drugs (TRIDs) or inhibitors of the nonsense-mediated decay (NMD) pathway, and also innovative approaches involving nucleic acids, such as gene editing, anticodon engineered-tRNA (ACE-tRNA), or mRNA-based therapy. Future research should focus on refining these approaches and exploring integrated and personalized treatments to enhance therapeutic outcomes and ensure continuous improvement in the quality of care.

PMID:40420818 | DOI:10.1002/iub.70027

Neurol Res. 2025 May 27:1-15. doi: 10.1080/01616412.2025.2507323. Online ahead of print.

ABSTRACT

OBJECTIVES: Epilepsy is a disorder causing repeated seizures because of unusual brain activity recorded using electroencephalography. Nevertheless, conventional epilepsy seizure detection approaches face difficulties such as poor epilepsy seizure detection accuracy and higher computational complexity. To overcome these limitations, this work proposes a novel TransseizNet framework for epilepsy seizure detection from the electroencephalography signal.

METHODS: The electroencephalography data from three datasets are pre-processed using the Savitzky-Golay filter. The proposed framework utilizes the Empirical Tunable Q-Wavelet Transform for signal decomposition, which is the combination of the Empirical Wavelet Transform and the Tunable Q-factor Wavelet Transform. This enhances time-frequency resolution and adaptively captures localized oscillatory patterns critical for precise seizure detection. The proposed framework utilizes a Wavelet-Graph Convolutional Network Vision Transformer for epilepsy seizure detection and classification. The integration of wavelet-driven attention with graph-based learning enhances spatial-temporal feature representation, which makes seizure detection more accurate, interpretable, and computationally efficient than the baseline approaches.

RESULTS: The TransseizNet model is trained and validated on three datasets and achieves an average accuracy of 98.65% a precision of 98.59%, a F1-score of 98.45%, a recall of 98.30%, a specificity of 98.52%, a computational time of 17 sec, and the detection latency of 2.5 sec, which outperforms the performance of baseline approaches in the detection of epileptic seizures.

DISCUSSION: TransseizNet framework provides superior performance in seizure detection by efficiently integrating adaptive frequency decomposition and hybrid deep learning. Its minimal detection latency, higher accuracy, and interpretability make it suitable for practical healthcare uses.

PMID:40421487 | DOI:10.1080/01616412.2025.2507323

ScientificWorldJournal. 2025 May 18;2025:1671766. doi: 10.1155/tswj/1671766. eCollection 2025.

ABSTRACT

The investigation and diagnosis of hematologic malignancy using blood cell image analysis are major and emerging subjects that lie at the intersection of artificial intelligence and medical research. This survey systematically examines the state-of-the-art in blood cancer detection through image-based analysis, aimed at identifying the most effective computational strategies and highlighting emerging trends. This review focuses on three principal objectives, namely, to categorize and compare traditional machine learning (ML), deep learning (DL), and hybrid learning approaches; to evaluate performance metrics such as accuracy, precision, recall, and area under the ROC curve; and to identify methodological gaps and propose directions for future research. Methodologically, we organize the literature by categorizing the malignancy types-leukemia, lymphoma, and multiple myeloma-and particularizing the preprocessing steps, feature extraction techniques, network architectures, and ensemble strategies employed. For ML methods, we discuss classical classifiers including support vector machines and random forests; for DL, we analyze convolutional neural networks (e.g., AlexNet, VGG, and ResNet) and transformer-based models; and for hybrid systems, we examine combinations of CNNs with attention mechanisms or traditional classifiers. Our synthesis reveals that DL models consistently outperform ML baselines, achieving classification accuracies above 95% in benchmark datasets, with hybrid models pushing peak accuracy to 99.7%. However, challenges remain in data scarcity, class imbalance, and generalizability to clinical settings. We conclude by recommending the integration of multimodal data, semisupervised learning, and rigorous external validation to advance toward deployable diagnostic tools. This survey also provides a comprehensive roadmap for researchers and clinicians striving to harness AI for reliable hematologic cancer detection.

PMID:40421320 | PMC:PMC12103971 | DOI:10.1155/tswj/1671766

J Med Signals Sens. 2025 May 1;15:14. doi: 10.4103/jmss.jmss_51_24. eCollection 2025.

ABSTRACT

BACKGROUND: This study aimed to evaluate the effectiveness of clinical, dosimetric, and radiomic features from computed tomography (CT) scans in predicting the probability of heart failure in breast cancer patients undergoing chemoradiation treatment.

MATERIALS AND METHODS: We selected 54 breast cancer patients who received left-sided chemoradiation therapy and had a low risk of natural heart failure according to the Framingham score. We compared echocardiographic patterns and ejection fraction (EF) measurements before and 3 years after radiotherapy for each patient. Based on these comparisons, we evaluated the incidence of heart failure 3 years postchemoradiation therapy. For machine learning (ML) modeling, we first segmented the heart as the region of interest in CT images using a deep learning technique. We then extracted radiomic features from this region. We employed three widely used classifiers - decision tree, K-nearest neighbor, and random forest (RF) - using a combination of radiomic, dosimetric, and clinical features to predict chemoradiation-induced heart failure. The evaluation criteria included accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (area under the curve [AUC]).

RESULTS: In this study, 46% of the patients experienced heart failure, as indicated by EF. A total of 873 radiomic features were extracted from the segmented area. Out of 890 combined radiomic, dosimetric, and clinical features, 15 were selected. The RF model demonstrated the best performance, with an accuracy of 0.85 and an AUC of 0.98. Patient age and V5 irradiated heart volume were identified as key predictors of chemoradiation-induced heart failure.

CONCLUSION: Our quantitative findings indicate that employing ML methods and combining radiomic, dosimetric, and clinical features to identify breast cancer patients at risk of cardiotoxicity is feasible.

PMID:40421235 | PMC:PMC12105806 | DOI:10.4103/jmss.jmss_51_24

Digit Health. 2025 May 25;11:20552076251337848. doi: 10.1177/20552076251337848. eCollection 2025 Jan-Dec.

ABSTRACT

OBJECTIVE: Integrating IoT technologies into the healthcare system has significantly raised the prospects for patient monitoring and disease prediction. However, the present-day models have failed to effectively encompass spatial-temporal data samples.

METHODS: This paper presents a novel hybrid machine-learning model by amalgamating Convolutional Neural Networks (CNNs) with Long Short-Term Memory models (LSTMs) to boost prediction accuracy. Whereas the CNNs extract spatial features from medical images, the LSTMs model the temporal patterns of wearable sensor data. Such a configuration increases the prediction accuracy by 10% more than that achieved by the individual models. For better feature extraction, the proposed method implements Physiological Event Extraction (PEE), which is aimed at identifying important physiological events such as heart rate variability and respiratory changes from raw sensor data samples.

RESULTS: This method helps render the features interpretable, providing another 15% improvement in prediction performance. Anomaly detection employed ensemble techniques that combined the Isolation Forest and One-Class SVM, reducing false positives by 20%, thus outperforming conventional approaches. It further enhanced the True Positive Rate (TPR) by 25% through using an online learning algorithm with Incremental Gradient Descent with Momentums. Robust statistical methods based on M-estimator theory had been integrated for the treatment of outliers and missing data, which helped in reducing bias in estimation by 30% and increasing the False Positive Rate (FPR) by 12%.

CONCLUSION: All these enhancements constitute a major step towards improving the IoT healthcare data processing chain, thereby providing a trusted and accurate system for real-time health monitoring and anomaly detection. In this regard, the research also paves the way for designing next-gen IoT healthcare analytics and their actual clinical applications.

PMID:40421178 | PMC:PMC12104608 | DOI:10.1177/20552076251337848

Front Neurol. 2025 May 12;16:1502382. doi: 10.3389/fneur.2025.1502382. eCollection 2025.

ABSTRACT

OBJECTIVES: To harness the U2-Net deep learning framework for automated quantification of collateral circulation in acute ischemic stroke (AIS) via computed tomography angiography (CTA) images, comparing its performance against traditional visual collateral scores (vCS).

METHODS: A cohort of 118 confirmed AIS cases was assembled and stratified into 94 development and 24 test cases. CTA images underwent preprocessing and annotation. The U2-Net was trained to segment collateral vessels, yielding a quantitative collateral score (qCS) based on vessel volume ratios between affected and healthy hemispheres. Performance was assessed via Dice Similarity Coefficient (DSC), Spearman correlation, Intraclass Correlation Coefficient (ICC), and accuracy, with comparisons to vCS (Tan and Menon score) and ground truth.

RESULT: The U2-Net demonstrated robust segmentation capabilities, achieving a mean DSC of 0.75 in the test set. The qCS showed a strong correlation with vCS with ρ ranging from 0.78 to 0.92. When compared to the more refined six-class Menon score, the qCS exhibited stronger consistency (development set: ICC = 0.83, test set: ICC = 0.93) than when compared to the four-class Tan score (development set: ICC = 0.76, test set: ICC = 0.79). In terms of classification accuracy, the AI model achieved 0.83 and 0.71 against ground truth and vCS, respectively, for four-class classification. This accuracy escalated to 0.88 and 0.83 for binary classification, emphasizing its proficiency in differentiating collateral status.

CONCLUSION: Our U2-Net AI model offers a reliable, objective tool for quantifying collateral circulation in AIS. The qCS aligns well with vCS and demonstrates the feasibility of automated collateral assessment, which may enhance diagnostic accuracy and therapeutic decision-making.

PMID:40421138 | PMC:PMC12104720 | DOI:10.3389/fneur.2025.1502382

Front Oncol. 2025 May 12;15:1558915. doi: 10.3389/fonc.2025.1558915. eCollection 2025.

ABSTRACT

BACKGROUND: Colorectal cancer is the third most common malignant tumor with the third highest incidence rate. Distant metastasis is the main cause of death in colorectal cancer patients. Early detection and prognostic prediction of colorectal cancer has improved with the widespread use of artificial intelligence technologies.

PURPOSE: The aim of this study was to comprehensively evaluate the accuracy and validity of AI-based imaging data for predicting distant metastasis in colorectal cancer patients.

METHODS: A systematic literature search was conducted to find relevant studies published up to January, 2024, in different databases. The quality of articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. The predictive value of AI-based imaging data for distant metastasis in colorectal cancer patients was assessed using pooled sensitivity, specificity. To explore the reasons for heterogeneity, subgroup analyses were performed using different covariates.

RESULTS: Seventeen studies were included in the systematic evaluation. The pooled sensitivity, specificity, and AUC of AI-based imaging data for predicting distant metastasis in colorectal cancer patients were 0.86, 0.82, and 0.91. Based on QUADAS-2, risk of bias was detected in patient selection, diagnostic tests to be evaluated, and gold standard. Based on the results of subgroup analyses, found that the duration of follow-up, site of metastasis, etc. had a significant impact on the heterogeneity.

CONCLUSION: Imaging data images based on artificial intelligence algorithms have good diagnostic accuracy for predicting distant metastasis in colorectal cancer patients and have potential for clinical application.

SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42024516063).

PMID:40421093 | PMC:PMC12104061 | DOI:10.3389/fonc.2025.1558915

Brief Bioinform. 2025 May 1;26(3):bbaf226. doi: 10.1093/bib/bbaf226.

ABSTRACT

Pharmacogenomics studies are attracting an increasing amount of interest from researchers in precision medicine. The advances in high-throughput experiments and multiplexed approaches allow the large-scale quantification of drug sensitivities in molecularly characterized cancer cell lines (CCLs), resulting in a number of open drug sensitivity datasets for drug biomarker discovery. However, a significant inconsistency in drug sensitivity values among these datasets has been noted. Such inconsistency indicates the presence of substantial noise, subsequently hindering downstream analyses. To address the noise in drug sensitivity data, we introduce a robust and scalable deep learning framework, Residual Thresholded Deep Matrix Factorization (RT-DMF). This method takes a single drug sensitivity data matrix as its sole input and outputs a corrected and imputed matrix. Deep matrix factorization (DMF) excels at uncovering subtle patterns, due to its minimal reliance on data structure assumptions. This attribute significantly boosts DMF's ability to identify complex hidden patterns among nuisance effects in the data, thereby facilitating the detection of signals that are therapeutically relevant. Furthermore, RT-DMF incorporates an iterative residual thresholding procedure, which plays a crucial role in retaining signals more likely to hold therapeutic importance. Validation using simulated datasets and real pharmacogenomics datasets demonstrates the effectiveness of our approach in correcting noise and imputing missing data in drug sensitivity datasets (open-source package available at https://github.com/tomwhoooo/rtdmf).

PMID:40420482 | DOI:10.1093/bib/bbaf226

Stroke. 2025 May 27. doi: 10.1161/STROKEAHA.124.050033. Online ahead of print.

NO ABSTRACT

PMID:40421566 | DOI:10.1161/STROKEAHA.124.050033

PeerJ. 2025 May 23;13:e19487. doi: 10.7717/peerj.19487. eCollection 2025.

ABSTRACT

OBJECTIVE: Transfer RNA-derived small RNAs (tsRNAs) are emerging regulators of gene expression in response to abiotic stress. This review aims to summarize recent advances in the classification, biogenesis, and biological functions of tsRNAs, with a focus on their roles in plant stress responses and the methodologies for investigating these molecules.

METHODS: We conducted a comprehensive literature search across PubMed, Web of Science, and Google Scholar using keywords such as "tRNA-derived small RNAs", "abiotic stress", "plant gene regulation", and "RNA sequencing". Studies were selected based on their relevance to tsRNA biogenesis pathways, stress-responsive mechanisms, and functional validation in plant systems. Classification of tsRNAs was performed according to cleavage site specificity and nucleotide length. Bioinformatic tools and experimental approaches for tsRNA identification, target prediction, and functional validation were evaluated.

RESULTS: tsRNAs are categorized into two main types: tRNA-derived stress-induced RNAs (tiRNAs; 29-50 nt) and tRNA-derived fragments (tRFs; 14-40 nt). tiRNAs arise from anticodon loop cleavage by RNase A/T2, while tRFs are generated via Dicer-dependent or -independent pathways. These molecules regulate gene expression at transcriptional, post-transcriptional, and translational levels by interacting with AGO proteins, displacing translation initiation factors, and modulating stress granule assembly. In plants, tsRNAs respond dynamically to abiotic stresses (e.g., drought, salinity, heat), influencing stress signaling pathways and epigenetic modifications. Advanced sequencing techniques (e.g., cP-RNA-seq, RtcB sRNA-seq) and databases (PtRFdb, tRFanalyzer) have facilitated tsRNA discovery and functional annotation.

CONCLUSIONS: tsRNAs represent a versatile class of regulatory molecules in plant stress biology. Their ability to fine-tune gene expression underpins adaptive responses to environmental challenges. Future research should prioritize standardized methodologies for tsRNA profiling, elucidation of stress-specific biogenesis mechanisms, and exploration of their potential as biomarkers or therapeutic targets for crop improvement. Integrating tsRNA research with systems biology approaches will deepen our understanding of plant resilience mechanisms.

PMID:40421365 | PMC:PMC12105621 | DOI:10.7717/peerj.19487

Small Methods. 2025 May 27:e2500572. doi: 10.1002/smtd.202500572. Online ahead of print.

ABSTRACT

Single-cell technologies have revolutionized the understanding of cellular dynamics by allowing researchers to investigate individual cell responses under various conditions, such as comparing diseased versus healthy states. Many differential abundance methods have been developed in this field, however, the understanding of the gene signatures obtained from those methods is often incomplete, requiring the integration of cell type information and other biological factors to yield interpretable and meaningful results. To better interpret the gene signatures generated in the differential abundance analysis, iDAS is developed to classify the gene signatures into multiple categories. When applied to melanoma single-cell data with multiple cell states and treatment phenotypes, iDAS identified cell state- and treatment phenotype-specific gene signatures, as well as interaction effect-related gene signatures with meaningful biological interpretations. The iDAS model is further applied to a longitudinal study and spatially resolved omics data to demonstrate its versatility in different analytical contexts. These results demonstrate that the iDAS framework can effectively identify robust, cell-state specific gene signatures and is versatile enough to accommodate various study designs, including multi-factor longitudinal and spatially resolved data.

PMID:40420636 | DOI:10.1002/smtd.202500572

Mol Plant. 2025 May 26:S1674-2052(25)00171-6. doi: 10.1016/j.molp.2025.05.012. Online ahead of print.

NO ABSTRACT

PMID:40420481 | DOI:10.1016/j.molp.2025.05.012

CPT Pharmacometrics Syst Pharmacol. 2025 May 26. doi: 10.1002/psp4.70041. Online ahead of print.

ABSTRACT

The unprecedented effort to cope with the COVID-19 pandemic has unlocked the potential of mRNA vaccines as a powerful technology, set to become increasingly pervasive in the years to come. As in other areas of drug development, mathematical modeling is a pivotal tool to support and expedite the mRNA vaccine development process. This study introduces a Quantitative Systems Pharmacology (QSP) model that captures key immune responses following mRNA vaccine administration, encompassing both tissue-level and molecular-level events. The model mechanistically describes the biological processes from the uptake of mRNA by antigen-presenting cells at the injection site to the subsequent release of antibodies into the bloodstream. This two-layer model represents a first attempt to link the molecular mechanisms leading to antigen expression with the immune response, paving the way for the future integration of specific vaccine attributes, such as mRNA sequence features and nanotechnology-based delivery systems. Calibrated specifically for the BNT162b2 SARS-CoV-2 vaccine, the model has undergone successful validation across various dosing regimens and administration schedules. The results underscore the model's effectiveness in optimizing dosing strategies and highlighting critical differences in immune responses, particularly among low-responder groups such as the elderly. Furthermore, the model's adaptability has been demonstrated through its calibration for other mRNA vaccines, such as the Moderna mRNA-1273 vaccine, emphasizing its versatility and broad applicability in mRNA vaccine research and development.

PMID:40420402 | DOI:10.1002/psp4.70041

Genome Med. 2025 May 26;17(1):61. doi: 10.1186/s13073-025-01491-z.

ABSTRACT

Many people with rare diseases cannot access personalized therapies because they do not have a confirmed genetic diagnosis. Promising technologies including proteomics are underutilized in routine diagnostic practice. It is time to incorporate proteomics into the diagnostic workflow to shorten time to diagnosis and expand treatment options for rare disease.

PMID:40420250 | DOI:10.1186/s13073-025-01491-z