Biomicrofluidics. 2025 May 1;19(3):034101. doi: 10.1063/5.0255847. eCollection 2025 May.

ABSTRACT

Remote health monitoring has the potential to enable individuals to take control of their own health and well-being and to facilitate a transition toward preventative and personalized healthcare. Sweat can be sampled non-invasively and contains a wealth of information about the metabolic state of an individual, making it an excellent candidate for remote health monitoring. An accurate, rapid, and low-cost biofluid characterization technique is required to enable the widespread use of remote health monitoring. We previously introduced microfluidic impedance spectroscopy for the detection of electrolyte concentration in fluids, whereby a novel device architecture, measurement method, and analysis technique were presented for the characterization of cationic species. The purely electrical nature of this measurement technique removes the intermediate steps inherent in common rival technologies such as optical and electrochemical sensing, offering a range of advantages. In this work, we investigate the effect of temperature on microfluidic impedance spectroscopy of ionic species commonly present in biofluids. We find that the impedance spectra and concentration determination are temperature-dependent; remote health monitoring devices must be calibrated appropriately as they are likely to experience temperature fluctuations. Importantly, we demonstrate the ability of the method to measure the concentration of anionic species alongside that of cationic species, enabling the detection of chloride and lactate, which are useful biomarkers for hydration, cystic fibrosis, fatigue, sepsis, and hypoperfusion. We show that the presence of neutral species does not impair accurate determination of ionic concentration, thus, demonstrating the suitability of microfluidic impedance spectroscopy for non-invasive biofluid characterization.

PMID:40322639 | PMC:PMC12048175 | DOI:10.1063/5.0255847

Open Respir Med J. 2025 Feb 12;19:e18743064341009. doi: 10.2174/0118743064341009241210045737. eCollection 2025.

ABSTRACT

Despite medical science advancements in recent years, pulmonary diseases are still hard to control and can be potentially life-threatening. These include asthma, COPD, lung cancer, cystic fibrosis, pneumonia, pleurisy, and sarcoidosis. These illnesses often cause severe breathing problems, which can be fatal if not treated properly. While some chemical drugs are used to treat these conditions, they can cause side effects and are not always effective. Herbal medicine offers an alternative treatment option with fewer side effects and has shown promise in treating respiratory issues. Certain medicinal plants, such as garlic (Allium sativum), hawthorn (Crataegus rhipidophylla), moringa (Moringa oleifera), and ashwagandha (Withania somnifera), may help manage lung diseases. Natural compounds found in plants, like apple polyphenol, ligustrazine, salidroside, resveratrol, and quercetin, can also help reduce symptoms. These plants and compounds work by reducing cell overgrowth, fighting oxidative stress, lowering inflammation, stopping tumor growth, improving blood flow, and relaxing the airways. This review outlines the types of plants and compounds that can be utilized in treating pulmonary conditions, along with their respective mechanisms of action.

PMID:40322495 | PMC:PMC12046236 | DOI:10.2174/0118743064341009241210045737

Bioinformation. 2025 Feb 28;21(2):173-176. doi: 10.6026/973206300210173. eCollection 2025.

ABSTRACT

Artificial intelligence (AI) driven automated image analysis accurately tracks orthodontic tooth movement by reducing reliance on time-consuming manual assessments. AI achieved 92% precision with a 0.25 mm error margin and a strong correlation (r = 0.94, p < 0.001) to manual measurements in a study of 100 patients. AI analysis took 3 seconds per image set, significantly faster than the 7-minute manual process (p < 0.001). Orthodontists rated AI reliability at 4.7/5, with 86% preferring AI-assisted monitoring. Thus, AI enhances treatment efficiency, standardization, and clinical decision-making.

PMID:40322709 | PMC:PMC12044183 | DOI:10.6026/973206300210173

Bioinformation. 2025 Feb 28;21(2):105-109. doi: 10.6026/973206300210105. eCollection 2025.

ABSTRACT

The integration of Artificial Intelligence (AI) in to the field of medicine is offering a new-age of updated diagnostics, prediction and treatment across multiple fields, addressing systemic disease including viral infections and cancer. The fields of Oral Pathology, Dermatology, Psychiatry and Cardiology are shifting towards integrating these algorithms to improve health outcomes. AI trained on biomarkers (e.g. salivary cf DNA) has shown to uncover the genetic linkage to disease and symptom susceptibility. AI-enhanced imaging has increased sensitivity in cancer and lesion detection, as well as detecting functional abnormalities not clinically identified. The integration of AI across fields enables a systemic approach to understanding chronic inflammation, a central driver in conditions like cardiovascular disease, diabetes and neuropsychiatric disorders. We propose that through the use of imaging data with biomarkers like cytokines and genetic variants, AI models can better trace the effects of inflammation on immune and metabolic disruptions. This can be applied to the pandemic response, where AI can model the cascading effects of systemic dysfunctions, refine predictions of severe outcomes and guide targeted interventions to mitigate the multi-systemic impacts of pathogenic diseases.

PMID:40322698 | PMC:PMC12044186 | DOI:10.6026/973206300210105

Cureus. 2025 May 3;17(5):e83421. doi: 10.7759/cureus.83421. eCollection 2025 May.

ABSTRACT

Breast cancer remains one of the leading causes of mortality among women, particularly in low- and middle-income countries, where limited healthcare access and delayed diagnosis contribute to poor outcomes. Deep learning, especially convolutional neural networks (CNNs), has shown remarkable efficacy in breast cancer detection through automated image analysis, reducing reliance on manual interpretation. This study provides a comprehensive review of recent advancements in CNN-based breast cancer detection, evaluating deep learning architectures, feature extraction techniques, and optimization strategies. A comparative analysis of CNNs, recurrent neural networks (RNNs), and hybrid models highlights their strengths, limitations, and applicability in medical image classification. Using a dataset of 569 instances with 33 tumor morphology features, various deep learning architectures - including CNNs, long short-term memory networks (LSTMs), and multilayer perceptrons (MLPs) - were implemented, achieving classification accuracies between 89% and 98%. The study underscores the significance of data augmentation, transfer learning, and feature selection in improving model performance. Hybrid CNN-based models demonstrated superior predictive accuracy by capturing spatial and sequential dependencies within tumor feature sets. The findings support the potential of AI-driven breast cancer detection in clinical applications, reducing diagnostic errors and improving early detection rates. Future research should explore transformer-based models, federated learning, and explainable AI techniques to enhance interpretability, robustness, and generalization across diverse datasets.

PMID:40322605 | PMC:PMC12049196 | DOI:10.7759/cureus.83421

Clin Cosmet Investig Dermatol. 2025 Apr 29;18:1033-1044. doi: 10.2147/CCID.S508580. eCollection 2025.

ABSTRACT

BACKGROUND: Melasma is a prevalent pigmentary disorder characterized by treatment resistance and high recurrence. Existing assessment methods like the Melasma Area and Severity Index (MASI) are subjective and prone to inter-observer variability.

OBJECTIVE: This study aimed to develop an AI-assisted, real-time melasma severity classification framework based on deep learning and clinical facial images.

METHODS: A total of 1368 anonymized facial images were collected from clinically diagnosed melasma patients. After image preprocessing and MASI-based labeling, six CNN architectures were trained and evaluated using PyTorch. Model performance was assessed through accuracy, precision, recall, F1-score, AUC, and interpretability via Layer-wise Relevance Propagation (LRP).

RESULTS: GoogLeNet achieved the best performance, with an accuracy of 0.755 and an F1-score of 0.756. AUC values across severity levels reached 0.93 (mild), 0.86 (moderate), and 0.94 (severe). LRP analysis confirmed GoogLeNet's superior feature attribution.

CONCLUSION: This study presents a robust, interpretable deep learning model for melasma severity classification, offering enhanced diagnostic consistency. Future work will integrate multimodal data for more comprehensive assessment.

PMID:40322508 | PMC:PMC12049110 | DOI:10.2147/CCID.S508580

Biomed Opt Express. 2025 Apr 1;16(4):1732-1741. doi: 10.1364/BOE.557748. eCollection 2025 Apr 1.

ABSTRACT

This study evaluates the role of differential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) for treatment outcome prediction of diabetic macular edema (DME). Deep learning AV segmentation in OCTA enabled the robust extraction of quantitative AV features, including perfusion intensity density (PID), blood vessel density (BVD), vessel skeleton density (VSD), vessel area flux (VAF), blood vessel caliber (BVC), blood vessel tortuosity (BVT), and vessel perimeter index (VPI). Support vector machine (SVM) classifiers were employed to predict changes in best-corrected visual acuity (BCVA) and central retinal thickness (CRT). Comparative analysis revealed that differential AV analysis significantly enhanced prediction performance, with BCVA accuracy improved from 70.45% to 86.36% and CRT accuracy enhanced from 68.18% to 79.55% compared to traditional OCTA analysis. These findings underscore the potential of AV analysis as a transformative tool for advancing personalized therapeutic strategies and improving clinical decision-making in managing DME.

PMID:40322014 | PMC:PMC12047724 | DOI:10.1364/BOE.557748

Biomed Opt Express. 2025 Mar 26;16(4):1582-1601. doi: 10.1364/BOE.549880. eCollection 2025 Apr 1.

ABSTRACT

Diffuse optical tomography (DOT) performed using deep-learning allows high-speed reconstruction of tissue optical properties and could thereby enable image-guided scanning, e.g., to enhance clinical breast imaging. Previously published models are geometry-specific and, therefore, require extensive data generation and training for each use case, restricting the scanning protocol at the point of use. A transformer-based architecture is proposed to overcome these obstacles that encode spatially unstructured DOT measurements, enabling a single trained model to handle arbitrary scanning pathways and measurement density. The model is demonstrated with breast tissue-emulating simulated and phantom data, yielding - for 24 mm-deep absorptions (μ a ) and reduced scattering (μ s ') images, respectively - average RMSEs of 0.0095±0.0023 cm-1 and 1.95±0.78 cm-1, Sørensen-Dice coefficients of 0.55±0.12 and 0.67±0.1, and anomaly contrast of 79±10% and 93.3±4.6% of the ground-truth contrast, with an effective imaging speed of 14 Hz. The average absolute μ a and μ s ' values of homogeneous simulated examples were within 10% of the true values.

PMID:40322000 | PMC:PMC12047716 | DOI:10.1364/BOE.549880

Biomed Opt Express. 2025 Mar 21;16(4):1528-1545. doi: 10.1364/BOE.558675. eCollection 2025 Apr 1.

ABSTRACT

Recent advancements in cellular-resolution optical coherence tomography (OCT) have opened up possibilities for high-resolution and non-invasive clinical diagnosis. This study uses deep learning-based models on cross-sectional OCT images for in vivo human skin layers and keratinocyte nuclei segmentation. With U-Net as the basic framework, a 5-class segmentation model is developed. With deeply supervised learning objective functions, the global (skin layers) and local (nuclei) features were separately considered in designing our multi-class segmentation model to achieve an > 85% Dice coefficient accuracy through 5-fold cross-validation, enabling quantitative measurements for the healthy human skin structure. Specifically, we calculate the thickness of the stratum corneum, epidermis, and the cross-sectional area of keratinocyte nuclei as 22.71 ± 17.20 µm, 66.44 ± 11.61 µm, and 17.21 ± 9.33 µm2, respectively. These measurements align with clinical findings on human skin structures and can serve as standardized metrics for clinical assessment using OCT imaging. Moreover, we enhance the segmentation accuracy by addressing the limitations of microscopic system resolution and the variability in human annotations.

PMID:40321995 | PMC:PMC12047727 | DOI:10.1364/BOE.558675

ArXiv [Preprint]. 2025 Apr 16:arXiv:2504.12480v1.

ABSTRACT

Reservoir computers (RCs) provide a computationally efficient alternative to deep learning while also offering a framework for incorporating brain-inspired computational principles. By using an internal neural network with random, fixed connections$-$the 'reservoir'$-$and training only the output weights, RCs simplify the training process but remain sensitive to the choice of hyperparameters that govern activation functions and network architecture. Moreover, typical RC implementations overlook a critical aspect of neuronal dynamics: the balance between excitatory and inhibitory (E-I) signals, which is essential for robust brain function. We show that RCs characteristically perform best in balanced or slightly over-inhibited regimes, outperforming excitation-dominated ones. To reduce the need for precise hyperparameter tuning, we introduce a self-adapting mechanism that locally adjusts E/I balance to achieve target neuronal firing rates, improving performance by up to 130% in tasks like memory capacity and time series prediction compared with globally tuned RCs. Incorporating brain-inspired heterogeneity in target neuronal firing rates further reduces the need for fine-tuning hyperparameters and enables RCs to excel across linear and non-linear tasks. These results support a shift from static optimization to dynamic adaptation in reservoir design, demonstrating how brain-inspired mechanisms improve RC performance and robustness while deepening our understanding of neural computation.

PMID:40321946 | PMC:PMC12047930

Heart Rhythm O2. 2025 Jan 21;6(4):473-480. doi: 10.1016/j.hroo.2025.01.008. eCollection 2025 Apr.

ABSTRACT

BACKGROUND: Rotors and focal ectopies, or "drivers," are hypothesized mechanisms of persistent atrial fibrillation (AF). Machine learning algorithms have been used to identify these drivers, but the limited size of current driver data sets constrains their performance.

OBJECTIVE: We proposed that pretraining using unsupervised learning on a substantial data set of unlabeled electrograms could enhance classifier accuracy when applied to a smaller driver data set.

METHODS: We used a SimCLR-based framework to pretrain a residual neural network on 113,000 unlabeled 64-electrode measurements from a canine model of AF. The network was then fine-tuned to identify drivers from intracardiac electrograms. Various augmentations, including cropping, Gaussian blurring, and rotation, were applied during pretraining to improve the robustness of the learned representations.

RESULTS: Pretraining significantly improved driver detection accuracy compared with a non-pretrained network (80.8% vs 62.5%). The pretrained network also demonstrated greater resilience to reductions in training data set size, maintaining higher accuracy even with a 30% reduction in data. Gradient-weighted Class Activation Mapping analysis revealed that the network's attention aligned well with manually annotated driver regions, suggesting that the network learned meaningful features for driver detection.

CONCLUSION: This study demonstrates that contrastive pretraining can enhance the accuracy of driver detection algorithms in AF. The findings support the broader application of transfer learning to other electrogram-based tasks, potentially improving outcomes in clinical electrophysiology.

PMID:40321744 | PMC:PMC12047512 | DOI:10.1016/j.hroo.2025.01.008

Pol J Radiol. 2025 Mar 14;90:e124-e137. doi: 10.5114/pjr/200628. eCollection 2025.

ABSTRACT

PURPOSE: Tuberculosis (TB) continues to be a major cause of death from infectious diseases globally. TB is treatable with antibiotics, but it is often misdiagnosed or left untreated, particularly in rural and resource-constrained regions. While chest X-rays are a key tool in TB diagnosis, their effectiveness is hindered by the variability in radiological presentations and the lack of trained radiologists in high-prevalence areas. Deep learning-based imaging techniques offer a promising approach to computer-aided diagnosis for TB, enabling precise and timely detection while alleviating the burden on healthcare professionals. This study aims to enhance TB detection in chest X-ray images by developing deep learning models. We have observed upper and lower lobe consolidation, pleural effusion, calcification, cavity formation and military nodules. A proposed preprocessing technique has been also introduced in our work based on gamma correction and gradient based technique for contrast enhancement. We leverage the Res-UNet architecture for image segmentation and introduce a novel deep learning network for classification, targeting improved accuracy and precision in diagnostic performance.

MATERIAL AND METHODS: A Res-UNet segmentation model was trained using 704 chest X-ray images sourced from the Montgomery County and Shenzhen Hospital datasets. Following training, the model was applied to segment lung regions in 1400 chest X-ray scans, encompassing both TB cases and normal controls, obtained from the National Institute of Allergy and Infectious Diseases (NIAID) TB Portal program dataset. The segmented lung regions were subsequently classified as either TB or normal using a deep learning model. A gradient based technique was used for contrast enhancement by capturing intensity changes in image by comparing each pixel with its neighbour with pyramid reduction unique mapping and histogram matching along with gamma correction is used. This integrated approach of segmentation and classification aims to enhance the accuracy and precision of TB detection in chest X-ray images. Classification of segmented images was done using customised convolutional neural network, and visualisation was done using Grad-CAM.

RESULTS: The Res-UNet model demonstrated excellent performance for segmentation, achieving an accuracy of 98.18%, recall of 98.40%, precision of 97.45%, F1-score of 97.97%, Dice coefficient of 96.33%, and Jaccard index of 96.05%. Similarly, the classification model exhibited outstanding results, with a classification accuracy of 99.45%, precision of 99.29%, recall of 99.29%, F1-score of 99.29%, and an AUC of 99.9%. Enhanced gradient based method showed ambe of 16.51, entropy of 6.7370, CII of 86.80, psnr of 28.71, ssim of 86.83 which are quite satisfactory.

CONCLUSIONS: The findings demonstrate the efficiency of our system in diagnosing TB from chest X-rays, potentially surpassing clinician-level precision. This underscores its effectiveness as a diagnostic tool, particularly in resourcelimited settings with restricted access to radiological expertise. Additionally, the modified Res-UNet model demonstrated superior performance compared to the standard U-Net, highlighting its potential for achieving greater diagnostic accuracy.

PMID:40321710 | PMC:PMC12049158 | DOI:10.5114/pjr/200628

Mol Clin Oncol. 2025 Apr 23;22(6):59. doi: 10.3892/mco.2025.2854. eCollection 2025 Jun.

ABSTRACT

Although nintedanib, an anti-fibrotic drug, relieves the chronological worsening of pulmonary function and prevents acute exacerbations of interstitial pneumonia, the perioperative safety and efficacy of nintedanib remains to be elucidated. The present study aimed to examine the safety and efficacy of nintedanib in patients with interstitial pneumonia. This study included 12 patients who underwent lung resection, including bilobectomy (n=2), lobectomy (n=7), segmentectomy (n=2) and wedge resection (n=1) between January 2020 and August 2023 at Juntendo University Nerima Hospital (Tokyo, Japan). Nintedanib was administered preoperatively to 10 male and two female patients with idiopathic pulmonary fibrosis and stage I to III lung cancer. The nintedanib dosing period ranged from 14 to 43 days. None of the patients canceled or postponed surgery because of side effects of nintedanib. Although prolonged air leak (n=3), surgical site infection (n=2), pyothorax (n=1), heart failure (n=1) and pleurisy (n=1) were observed postoperatively, the 30-day mortality rate was 0, with no acute exacerbation of interstitial pneumonia. These results encourage further investigation into the potential of nintedanib treatment in a larger patient cohort through prospective verification.

PMID:40322548 | PMC:PMC12046624 | DOI:10.3892/mco.2025.2854

J Exp Bot. 2025 May 4:eraf187. doi: 10.1093/jxb/eraf187. Online ahead of print.

ABSTRACT

Tomato (Solanum lycopersicum L.) serves as a major food source and a model crop for understanding plant responses to stress. Abiotic and biotic stresses, exacerbated by climate change, threaten global tomato production. Stress hormones, including abscisic acid (ABA), ethylene (ET), jasmonates (JAs), and salicylic acid (SA), orchestrate intricate signaling pathways that mediate plant immunity and metabolism. This review synthesizes the roles of these hormones in tomato stress responses. We discuss the biosynthesis and signalling cascades of these stress hormones, and focus on the cellular and metabolic reprogramming they cause and the crosstalk that occurs between them. Increased understanding of these molecular events and interactions provides insights to improve tomato resilience and productivity under environmental challenges.

PMID:40322793 | DOI:10.1093/jxb/eraf187

Zookeys. 2025 Apr 24;1236:19-39. doi: 10.3897/zookeys.1236.143336. eCollection 2025.

ABSTRACT

Exploring genetic diversity is essential for precise species delimitation, especially within taxonomically complex groups like passerine birds. Traditional morphological methods often fail to resolve species boundaries; however, DNA barcoding, particularly through the mitochondrial cytochrome c oxidase subunit I (COI) gene, provides a powerful complementary method for accurate species identification. This study establishes a comprehensive DNA barcode library for Iranian passerine birds, analyzing 546 COI sequences from 94 species across 23 families and 53 genera. There is a pronounced barcode gap, with average intraspecific divergence at 0.41% and interspecific divergence at 18.6%. Notable intraspecific variation emerged in the Persian nuthatch (Sittatephronota) and the Lesser whitethroat (Currucacurruca), while the European goldfinch (Cardueliscarduelis) and the grey-crowned goldfinch (Cardueliscaniceps) showed limited genetic differentiation despite marked morphological distinctions. Phylogenetic analysis revealed significant east-west genetic splits in C.curruca and S.tephronota, reflecting Iran's geographic and zoogeographic boundaries. These findings demonstrate the effectiveness of DNA barcoding in elucidating biogeographic patterns, emphasizing Iran's key role as an ornithological crossroads for avian biodiversity. Moreover, our results suggest that much of the genetic variation in the COI gene arises from synonymous mutations, highlighting the role of purifying selection in shaping mtDNA diversity across species.

PMID:40322611 | PMC:PMC12046340 | DOI:10.3897/zookeys.1236.143336

One Health. 2025 Apr 12;20:101035. doi: 10.1016/j.onehlt.2025.101035. eCollection 2025 Jun.

ABSTRACT

BACKGROUND AND OBJECTIVE: To assess the association of pulmonary tuberculosis (PTB) recurrence with fine particulate matter (PM2.5) and residential greenness using a population-based retrospective study design.

METHODS: All incident PTB patients, registered in Tuberculosis Information Management System (TBIMS) from 2015 to 2019 in Quzhou City, China, were included. The data on PM2.5 exposure was extracted from the China High Air Pollutants dataset and the level of greenness was estimated using the Normalized Difference Vegetation Index (NDVI) values around the patient's residence. The Cox proportional hazards models were used to quantify the risk of PTB recurrence.

RESULTS: 6732 Eligible PTB incident patients were included in the study with a mean age of 56.86 years and a median follow-up time of 750 days. Recurrence was observed in 554 patients (8.2 %). Exposure to NDVI was observed to be negatively associated with PTB recurrence (HR: 0.86, 95 % CI: 0.75-0.98 per 0.1-unit increase). The strength of the association between higher PM2.5 and the risk of PTB recurrence was greater than that of lower PM2.5 concentrations in both low and high NDVI groups (HR:6.62 and 4.35, p-interaction <0.001).

CONCLUSIONS: Our findings suggest that higher PM2.5 exposure might increase the risk of PTB recurrence, while residential greenness might have a protective effect. Like other chronic respiratory diseases, prevention and control of PTB will also benefit from comprehensive environmental management.

PMID:40321627 | PMC:PMC12047573 | DOI:10.1016/j.onehlt.2025.101035

ACS Omega. 2025 Apr 15;10(16):16339-16354. doi: 10.1021/acsomega.4c10653. eCollection 2025 Apr 29.

ABSTRACT

INTRODUCTION: Primary Sjogren's Syndrome (pSS) is a chronic inflammatory autoimmune disease that manifests as dry mouth and eyes. Luteolin can repress immuno-inflammation and improve the function of exocrine glands.

METHODS: Bibliometrics was used to visualize pSS-related key indicators. The efficacy of traditional Chinese medicines (TCMs) in pSS treatment was analyzed with the internal database containing the clinical records of pSS. Using the network pharmacology technology to identify involved pathways. Additionally, molecular docking and cell experiments were performed to screen and verify the therapeutic effect of luteolin on pSS.

RESULTS: Key indicators that were selected according to the bibliometrics were worse in pSS and had certain compatibility and correlation with laboratory and immunoinflammatory indicators. After treatment, pSS patients showed improvements in the above indicators. The results of risk analyses revealed that TCMs were protective factors for laboratory indicators and key indicators. The main effective TCMs for pSS treatment and TNF pathways were identified with network pharmacology. Cell experiments validated that luteolin indeed improved the secretion dysfunction and inflammation of modeled human submandibular gland epithelial cells through the TNF/NF-κB pathway.

CONCLUSIONS: TCMs may effectively improve transcription factors and immuno-inflammatory markers in pSS patients. Moreover, we hypothesized and verified the potential mechanism of action of luteolin in HSG cells.

PMID:40321503 | PMC:PMC12044447 | DOI:10.1021/acsomega.4c10653

Ann N Y Acad Sci. 2025 May 5. doi: 10.1111/nyas.15352. Online ahead of print.

ABSTRACT

The human gut microbiome is a complex ecosystem that plays a vital role in maintaining health and contributing to the pathogenesis of various diseases. This review proposes a transformative approach that involves engineering artificial microbial consortia-precisely designed communities of microorganisms-for personalized modulation of the gut microbiome and targeted therapeutic interventions. By integrating synthetic biology, systems biology, and advanced culturing techniques, tailored microbial consortia can be developed to perform specific functions within the gut, including the production of therapeutic molecules, modulation of immune responses, and competition against pathogenic bacteria. In vitro and in vivo studies indicate that these engineered consortia can effectively restore microbial balance and enhance host resilience. This personalized approach holds immense potential to revolutionize healthcare by addressing the root causes of diseases such as metabolic disorders, inflammatory conditions, and gastrointestinal infections through precise manipulation of the gut microbiome. Future research should focus on rigorous clinical trials to evaluate the safety, efficacy, and long-term impacts of these engineered consortia in diverse human populations, paving the way for innovative microbial therapies that promote overall health and well-being.

PMID:40320966 | DOI:10.1111/nyas.15352

Clin Exp Allergy. 2025 May 4. doi: 10.1111/cea.70071. Online ahead of print.

NO ABSTRACT

PMID:40320688 | DOI:10.1111/cea.70071

Immunology. 2025 May 4. doi: 10.1111/imm.13940. Online ahead of print.

ABSTRACT

Our previous findings demonstrated that naïve B cells elicit suppressive CD4+ regulatory T (Treg) cells, named as Treg-of-B cells. However, the capability of antigen-specific B cells in that process remains unclear. Using ovalbumin (OVA) as a model antigen, the present study showed that B cells from OVA-immunised mice decreased that ability. Instead, OVA-activated OVA-specific (OB1) B cells induced effector-like T-of-OB1 cells without regulatory function. Phenotypically, Treg-of-B cells reduced the production of interferon (IFN)-γ, interleukin (IL)-17 and IL-2 and expressed CD62L, PD1 and endothelial cell adhesion molecule 1 (PECAM1). Functionally, adoptive transfer of Treg-of-B cells significantly attenuated Th1 cell-mediated delayed-type hypersensitivity (DTH) responses and inhibited IFN-γ-producing Th1 cells, while T-of-OB1 cells did not. Mechanistically, activated antigen-specific B cells increased the expression of costimulatory molecules and promoted higher T cell activation, contributing to effector T cell phenotype. Conversely, Treg-of-B cells exhibited lower T cell activation, possibly mediated through the expression of PECAM1, Dusp2, Dusp5, Ptpn7, Ptpn22 and Ms4a4b. These findings suggest that non-antigen-specific B cells elicit CD4+ Treg cells, potentially via attenuating T cell activation, whereas that capacity is absent in antigen-specific B cells. This distinction underscores the critical role of B cell antigen specificity in immune regulation and inflammation.

PMID:40320632 | DOI:10.1111/imm.13940