Front Immunol. 2025 Apr 10;16:1543454. doi: 10.3389/fimmu.2025.1543454. eCollection 2025.

ABSTRACT

Gamma delta (γδ) T cells represent a unique and distinct population of lymphocytes that bridge the innate and adaptive immune responses. This functional duality positions them as one of the pivotal elements in the evolution and development of the human body's defense mechanisms. This review aims to provide a comprehensive and in-depth overview of γδ T cells, covering their origins, development, classification, and functional roles in immunology. Special attention is given to their involvement in the pathogenesis of autoimmune and cancer-related diseases-areas that remain subjects of intensive research with many unanswered questions. Additionally, this article explores the therapeutic potential of γδ T cells, which hold promise as a novel approach to treating various difficult-to-manage diseases. The review also presents an analysis of the latest clinical studies utilizing γδ T cells, emphasizing their emerging role in modern medicine. The ultimate goal of this work is to offer a holistic perspective on the current state of research on γδ T cells and their prospective applications in immunotherapy and cancer treatment, highlighting their potential to become a groundbreaking tool in future medical interventions.

PMID:40276509 | PMC:PMC12018481 | DOI:10.3389/fimmu.2025.1543454

Cureus. 2025 Mar 25;17(3):e81151. doi: 10.7759/cureus.81151. eCollection 2025 Mar.

ABSTRACT

This clinical overview reflects on a case of a nine-month-old boy presenting with mild bronchiolitis and persistently elevated transaminases. A total creatine kinase (CK) was requested to assess for dystrophinopathies, which was significantly elevated at 3000 U/L on repeat samples. Molecular testing confirmed the diagnosis of Becker's muscle dystrophy (BMD). During molecular testing, two cystic fibrosis (CF) mutations were incidentally detected, a p.Phe312del mutation and the classic CF-causing mutation p.Phe508del. Sweat chloride testing was repeatedly elevated in keeping with the diagnosis of CF. Despite the significantly elevated sweat chloride and molecular genetic profile showing heterozygosity for p.Phe508del and p.Phe312del mutations, the patient did not show any clinical manifestation of CF. During the newborn screening, immunoreactive trypsinogen (IRT) was 26 ng/mL, below the upper limit value used for screening (54 ng/mL) at that time. This case illustrates two important points: firstly, patients heterozygous for p.Phe312del and p.Phe508del mutations may not be detected during newborn screening and may not have clinical manifestations of cystic fibrosis despite having unequivocally elevated sweat chloride. Secondly, an unexplained elevation of transaminases should trigger creatine kinase testing to check for dystrophinopathies.

PMID:40276426 | PMC:PMC12020654 | DOI:10.7759/cureus.81151

JHLT Open. 2025 Mar 18;8:100252. doi: 10.1016/j.jhlto.2025.100252. eCollection 2025 May.

ABSTRACT

Non-White patients with interstitial lung disease (ILD) experience racial disparities in lung transplant waitlist mortality. Race-specific equations for spirometry may contribute by underestimating restriction severity in non-White candidates. We analyzed US lung transplant candidates to assess for disparities in forced vital capacity (FVC) at listing, comparing absolute and adjusted values using race-specific and race-neutral equations. We identified 17,457 adults with ILD listed May 4, 2005 to September 31, 2023. At listing, mean absolute FVC was higher for White patients (2.03 ± 0.80 liters) than Black patients (1.61 ± 0.67 liters) and Asian patients (1.49 ± 0.86 liters). Differences were attenuated after applying race-specific equations (White patients 50.0 ± 17.5%, Black patients 47.7 ± 17.9%, Asian patients 46.2 ± 24.2%). Compared with race-neutral equations, race-specific equations had higher odds of classifying FVC as severe (≤40%) requiring listing in White patients (OR 1.37, 95% CI 1.28-1.40) but lower odds in Black patients (OR 0.82, 95% CI 0.74-0.90). Using race-neutral equations might help improve racial disparities for lung transplant candidates with ILD.

PMID:40276319 | PMC:PMC12019416 | DOI:10.1016/j.jhlto.2025.100252

J Res Pharm Pract. 2025 Mar 11;13(3):72-77. doi: 10.4103/jrpp.jrpp_54_24. eCollection 2024 Jul-Sep.

ABSTRACT

OBJECTIVE: This study investigated the efficacy of adding the oral N-acetyl cysteine (NAC) supplement to the cystic fibrosis (CF) treatment regimen compared to adding a placebo. It also studied the quality of life and respiratory indicators of patients aged 6-18 with mild-to-moderate pulmonary involvement.

METHODS: This clinical trial was a randomized, quasi-experimental pilot and add-on therapy controlled with a placebo for 3 months. The case group received 200 mg of oral NAC three times a day. In contrast, the control group had a placebo in the same way. From the 2021 fall to the summer of 2022, 38 CF patients referred to Imam Hossein Children's Hospital Clinic were finally examined. They were clinically stable with a forced expiratory volume in the first second (FEV1) level of more than 50% and no history of underlying cardiovascular and renal diseases.

FINDINGS: The differences between the groups were not significant. In the placebo group, key measures remained unchanged, whereas the NAC group had an improvement in the CF Questionnaire-Revised score but no notable changes in other indices. Overall, comparisons of forced vital capacity (FVC) between the groups showed no variation.

CONCLUSION: The indicators of FEV1, FVC, FEV1/FVC, forced expiratory flow between 25% and 75% of vital capacity, and the quality of life of the case group were not significantly different from those of the placebo group, and no significant differences were observed between this medicine and placebo.

PMID:40275972 | PMC:PMC12017402 | DOI:10.4103/jrpp.jrpp_54_24

J Clin Transl Endocrinol. 2025 Apr 7;40:100392. doi: 10.1016/j.jcte.2025.100392. eCollection 2025 Jun.

ABSTRACT

CONTEXT: Diabetes and bone disease are common in cystic fibrosis (CF) and primarily occur alongside exocrine pancreatic insufficiency (PI). "Incretins," glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide 1 (GLP-1), augment insulin secretion and regulate bone metabolism. In CF, PI dampens the incretin response. Loss of the insulinotropic effect of GIP in CF was recently identified, but effects on bone are unknown.

OBJECTIVE: Determine effects of incretins on bone resorption markers in adults with PI-CF.

DESIGN: Secondary analysis of a mechanistic double-blinded randomized placebo-controlled crossover trial including adults ages 18-40 years with PI-CF (n = 25).

INTERVENTION: Adults with PI-CF received either GIP (4 pmol/kg/min) or GLP-1 (1.5 pmol/kg/min) infusion, followed by double-blind randomization to either incretin or placebo infusion. Non-CF healthy controls received double-blind GIP (4 pmol/kg/min) or placebo. Serum C-terminal telopeptide (CTX), a bone resorption marker, was assessed during the infusion over 80 (GIP) or 60 (GLP-1) minutes.

MAIN OUTCOME MEASURES: CTX (mg/dL) concentrations.

RESULTS: In PI-CF, CTX decreased during GIP infusion, but not during placebo (time-by-treatment interaction P < 0.01). GLP-1 did not affect CTX. In non-CF healthy controls, time-by-treatment interaction was not significant (P = 0.23), but CTX decreased during GIP (P = 0.02) but not placebo (P = 0.47).

CONCLUSIONS: GIP evokes a bone anti-resorptive effect in people with PI-CF. Since the incretin response is perturbed in PI-CF, and an infusion of GIP lowers bone resorption, the "gut-bone axis" in CF-related bone disease requires attention.

PMID:40275940 | PMC:PMC12019020 | DOI:10.1016/j.jcte.2025.100392

Front Med (Lausanne). 2025 Apr 10;12:1574428. doi: 10.3389/fmed.2025.1574428. eCollection 2025.

ABSTRACT

BACKGROUND: Brain tumor categorization on MRI is a challenging but crucial task in medical imaging, requiring high resilience and accuracy for effective diagnostic applications. This study describe a unique multimodal scheme combining the capabilities of deep learning with ensemble learning approaches to overcome these issues.

METHODS: The system integrates three new modalities, spatial feature extraction using a pre-trained VGG19 network, sequential dependency learning using a Bidirectional LSTM, and classification efficiency through a LightGBM classifier.

RESULTS: The combination of both methods leverages the complementary strengths of convolutional neural networks and recurrent neural networks, thus enabling the model to achieve state-of-the-art performance scores. The outcomes confirm the efficacy of this multimodal approach, which achieves a total accuracy of 97%, an F1-score of 0.97, and a ROC AUC score of 0.997.

CONCLUSION: With synergistic harnessing of spatial and sequential features, the model enhances classification rates and effectively deals with high-dimensional data, compared to traditional single-modal methods. The scalable methodology has the possibility of greatly augmenting brain tumor diagnosis and planning of treatment in medical imaging studies.

PMID:40276738 | PMC:PMC12020513 | DOI:10.3389/fmed.2025.1574428

Front Aging Neurosci. 2025 Apr 10;17:1547727. doi: 10.3389/fnagi.2025.1547727. eCollection 2025.

ABSTRACT

INTRODUCTION: Frontotemporal dementia (FTD) is a neurodegenerative disorder characterized by progressive degeneration of the frontal and temporal lobes, leading to significant changes in personality, behavior, and language abilities. Early and accurate differential diagnosis between FTD, its subtypes, and other dementias, such as Alzheimer's disease (AD), is crucial for appropriate treatment planning and patient care. Machine learning (ML) techniques have shown promise in enhancing diagnostic accuracy by identifying complex patterns in clinical and neuroimaging data that are not easily discernible through conventional analysis.

METHODS: This systematic review, following PRISMA guidelines and registered in PROSPERO, aimed to assess the strengths and limitations of current ML models used in differentiating FTD from other neurological disorders. A comprehensive literature search from 2013 to 2024 identified 25 eligible studies involving 6,544 patients with dementia, including 2,984 with FTD, 3,437 with AD, 103 mild cognitive impairment (MCI) and 20 Parkinson's disease dementia or probable dementia with Lewy bodies (PDD/DLBPD).

RESULTS: The review found that Support Vector Machines (SVMs) were the most frequently used ML technique, often applied to neuroimaging and electrophysiological data. Deep learning methods, particularly convolutional neural networks (CNNs), have also been increasingly adopted, demonstrating high accuracy in distinguishing FTD from other dementias. The integration of multimodal data, including neuroimaging, EEG signals, and neuropsychological assessments, has been suggested to enhance diagnostic accuracy.

DISCUSSION: ML techniques showed strong potential for improving FTD diagnosis, but challenges like small sample sizes, class imbalance, and lack of standardization limit generalizability. Future research should prioritize the development of standardized protocols, larger datasets, and explainable AI techniques to facilitate the integration of ML-based tools into real-world clinical practice.

SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024520902.

PMID:40276595 | PMC:PMC12018464 | DOI:10.3389/fnagi.2025.1547727

Head Neck Tumor Segm MR Guid Appl (2024). 2025;15273:179-190. doi: 10.1007/978-3-031-83274-1_13. Epub 2025 Mar 3.

ABSTRACT

The superior soft tissue differentiation provided by MRI may enable more accurate tumor segmentation compared to CT and PET, potentially enhancing adaptive radiotherapy treatment planning. The Head and Neck Tumor Segmentation for MR-Guided Applications challenge (HNTSMRG-24) comprises two tasks: segmentation of primary gross tumor volume (GTVp) and metastatic lymph nodes (GTVn) on T2-weighted MRI volumes obtained at (1) pre-radiotherapy (pre-RT) and (2) mid-radiotherapy (mid-RT). The training dataset consists of data from 150 patients, including MRI volumes of pre-RT, mid-RT, and pre-RT registered to the corresponding mid-RT volumes. Each MRI volume is accompanied by a label mask, generated by merging independent annotations from a minimum of three experts. For both tasks, we propose adopting the nnU-Net V2 framework by the use of a 15-fold cross-validation ensemble instead of the standard number of 5 folds for increased robustness and variability. For pre-RT segmentation, we augmented the initial training data (150 pre-RT volumes and masks) with the corresponding mid-RT data. For mid-RT segmentation, we opted for a three-channel input, which, in addition to the mid-RT MRI volume, comprises the registered pre-RT MRI volume and the corresponding mask. The mean of the aggregated Dice Similarity Coefficient for GTVp and GTVn is computed on a blind test set and determines the quality of the proposed methods. These metrics determine the final ranking of methods for both tasks separately. The final blind testing (50 patients) of the methods proposed by our team, RUG_UMCG, resulted in an aggregated Dice Similarity Coefficient of 0.81 (0.77 for GTVp and 0.85 for GTVn) for Task 1 and 0.70 (0.54 for GTVp and 0.86 for GTVn) for Task 2.

PMID:40276554 | PMC:PMC12018675 | DOI:10.1007/978-3-031-83274-1_13

Phys Imaging Radiat Oncol. 2025 Mar 8;34:100740. doi: 10.1016/j.phro.2025.100740. eCollection 2025 Apr.

ABSTRACT

BACKGROUND AND PURPOSE: Pancreatic gross tumor volume (GTV) delineation is challenging due to their variable morphology and uncertain ground truth. Previous deep learning-based auto-segmentation methods have struggled to handle tasks with uncertain ground truth and have not accommodated stylistic customizations. We aim to develop a human-in-the-loop pancreatic GTV segmentation tool using Tversky ensembles by leveraging uncertainty estimation techniques.

MATERIAL AND METHODS: In this study, we utilized a total of 282 patients from the pancreas task of the Medical Segmentation Decathlon. Thirty patients were randomly selected to form an independent test set, while the remaining 252 patients were divided into an 80-20 % training-validation split. We incorporated Tversky loss layer during training to train a five-member segmentation ensemble with varying contouring tendencies. The Tversky ensemble predicted probability maps by estimating pixel-level segmentation uncertainties. Probability thresholding was employed on the resulting probability maps to generate the final contours, from which eleven contours were extracted for quantitative evaluation against ground truths, with variations in the threshold values.

RESULTS: Our Tversky ensemble achieved DSC of 0.47, HD95 of 12.70 mm and MSD of 3.24 mm respectively using the optimal thresholding configuration. We outperformed the Swin-UNETR configuration that achieved the state-of-the-art result in the pancreas task of the medical segmentation decathlon.

CONCLUSIONS: Our study demonstrated the effectiveness of employing an ensemble-based uncertainty estimation technique for pancreatic tumor segmentation. The approach provided clinicians with a consensus probability map that could be fine-tuned in line with their preferences, generating contours with greater confidence.

PMID:40276495 | PMC:PMC12019452 | DOI:10.1016/j.phro.2025.100740

JHEP Rep. 2025 Feb 19;7(5):101356. doi: 10.1016/j.jhepr.2025.101356. eCollection 2025 May.

ABSTRACT

BACKGROUND & AIMS: Accurate prediction of disease severity and prognosis are challenging in patients with cirrhosis. We evaluated whether the deep learning-based AI-Cirrhosis-ECG (ACE) score could detect hepatic decompensation and predict clinical outcomes in cirrhosis.

METHODS: We analyzed 2,166 ECGs from 472 patients in a retrospective Mayo Clinic cohort, 420 patients in a prospective Mayo transplant cohort, and 341 patients in an external validation cohort from Hospital Clínic de Barcelona. The ACE score's performance was assessed using receiver-operating characteristic analysis for decompensation detection and competing risks Cox regression for outcome prediction.

RESULTS: The ACE score showed high accuracy in detecting hepatic decompensation (area under the curve 0.933, 95% CI: 0.923-0.942) with 88.0% sensitivity and 84.3% specificity at an optimal threshold of 0.25. In multivariable analysis, each 0.1-point increase in ACE score was independently associated with increased risk of liver-related death (hazard ratio [HR] 1.44, 95% CI 1.32-1.58, p <0.001). Adding ACE to model for end-stage liver disease-sodium significantly improved prediction of adverse outcomes across all cohorts (c-statistics: retrospective cohort 0.903 vs. 0.844; prospective cohort 0.779 vs. 0.735; external validation 0.744 vs. 0.732; all p <0.001).

CONCLUSIONS: The ACE score accurately identifies hepatic decompensation and independently predicts liver-related outcomes in cirrhosis. This non-invasive tool enhances current prognostic models and may improve risk stratification in cirrhosis management.

IMPACT AND IMPLICATIONS: This study demonstrates the potential of artificial intelligence to enhance prognostication in liver disease, addressing the critical need for improved risk stratification in cirrhosis management. The AI-Cirrhosis-ECG (ACE) score, derived from widely available ECGs, shows promise as a non-invasive tool for detecting hepatic decompensation and predicting liver-related outcomes, which could significantly impact clinical decision-making and resource allocation in hepatology. These findings are particularly important for hepatologists, transplant surgeons, and patients with cirrhosis, as they offer a novel approach to complement existing prognostic models such as model for end-stage liver disease-sodium. In practical terms, the ACE score could be integrated into routine clinical assessments to provide more accurate risk predictions, potentially improving the timing of interventions, optimizing transplant listing decisions, and ultimately enhancing patient outcomes. However, further validation in diverse populations and integration with other established predictors is necessary before widespread clinical implementation.

PMID:40276480 | PMC:PMC12018547 | DOI:10.1016/j.jhepr.2025.101356

Front Public Health. 2025 Apr 10;13:1540618. doi: 10.3389/fpubh.2025.1540618. eCollection 2025.

ABSTRACT

INTRODUCTION: Preventive healthcare policies are critical for improving public health outcomes and reducing the socioeconomic burden of diseases, aligning closely with the theme of enhancing residents' health welfare through robust social security systems. However, traditional approaches often overlook the dynamic interplay between economic factors and health outcomes, limiting their effectiveness in designing sustainable interventions.

METHODS: To address these gaps, this study leverages corporate financial monitoring as a novel lens for assessing the effectiveness of preventive healthcare policies. Utilizing the Advanced Financial Monitoring Neural Framework (AFMNF) and the Dynamic Risk-Adaptive Framework (DRAF), we integrate deep learning techniques with dynamic risk modeling to analyze the financial and health impacts of such policies. Our methodology involves monitoring corporate financial metrics, anomaly detection, and trend analysis to identify correlations between policy implementation and economic indicators.

RESULTS AND DISCUSSION: The results demonstrate that integrating financial insights with health policy evaluation improves prediction accuracy of socioeconomic outcomes by 40% and enhances anomaly detection in policy performance by 30%. This adaptive framework offers a scalable, real-time approach to monitoring, providing actionable insights for policymakers to optimize preventive healthcare strategies. This study underscores the importance of interdisciplinary methods in advancing public health outcomes through innovative, data-driven frameworks.

PMID:40276349 | PMC:PMC12019879 | DOI:10.3389/fpubh.2025.1540618

Alzheimers Dement (N Y). 2025 Apr 24;11(2):e70084. doi: 10.1002/trc2.70084. eCollection 2025 Apr-Jun.

ABSTRACT

INTRODUCTION: Alzheimer's disease (AD) clinical trials with therapeutic interventions require hundreds of subjects to be studied over many months/years due to variable and slow disease progression. This article presents a novel screening paradigm integrating disease progression models to improve trial efficiency by identifying appropriate candidates for early phase clinical studies.

METHODS: A traditional screening funnel is enhanced using machine learning models, including 3D convolutional neural networks and ensemble models, which integrate neuroimaging, demographic, genetic, and clinical data.

RESULTS: This approach predicts clinical progression (2-year Clinical Dementia Rating Sum of Boxes change > 1) with an area under the curve of 0.836. Incorporating it into trials (with maximized sensitivity/specificity optimization) could reduce the number of subjects required by 55%, shorten recruitment by 13 months, and reduce screening amyloid positron emission tomography scans by 72%.

DISCUSSION: By reducing patient burden and shortening timelines in clinical trials, this enhanced screening funnel could accelerate the development of AD therapies.

HIGHLIGHTS: An innovative screening funnel was developed to improve Alzheimer's disease clinical trial efficiency.The funnel incorporates machine learning (ML)-based disease progression models.The ML model identifies patients with progression rate optimal for clinical trials.Unsuitable patients would fail early in the funnel before burdensome imaging procedures.This screening funnel is customizable to specific study needs.

PMID:40276322 | PMC:PMC12019303 | DOI:10.1002/trc2.70084

Comput Struct Biotechnol J. 2025 Apr 2;27:1538-1549. doi: 10.1016/j.csbj.2025.04.002. eCollection 2025.

ABSTRACT

MOTIVATION: Assessing the potential toxicity of proteins is crucial for both therapeutic and agricultural applications. Traditional experimental methods for protein toxicity evaluation are time-consuming, expensive, and labor-intensive, highlighting the requirement for efficient computational approaches. Recent advancements in language models and deep learning have significantly improved protein toxicity prediction, yet current models often lack the ability to integrate evolutionary and structural information, which is crucial for accurate toxicity assessment of proteins.

RESULTS: In this study, we present ToxDL 2.0, a novel multimodal deep learning model for protein toxicity prediction that integrates both evolutionary and structural information derived from a pretrained language model and AlphaFold2. ToxDL 2.0 consists of three key modules: (1) a Graph Convolutional Network (GCN) module for generating protein graph embeddings based on AlphaFold2-predicted structures, (2) a domain embedding module for capturing protein domain representations, and (3) a dense module that combines these embeddings to predict the toxicity. After constructing a comprehensive toxicity benchmark dataset, we obtained experimental results on both an original non-redundant test set (comprising pre-2022 protein sequences) and an independent non-redundant test set (a holdout set of post-2022 protein sequences), demonstrating that ToxDL 2.0 outperforms existing state-of-the-art methods. Additionally, we utilized Integrated Gradients to discover known toxic motifs associated with protein toxicity. A web server for ToxDL 2.0 is publicly available at www.csbio.sjtu.edu.cn/bioinf/ToxDL2/.

PMID:40276117 | PMC:PMC12018212 | DOI:10.1016/j.csbj.2025.04.002

J Med Imaging (Bellingham). 2025 Mar;12(2):024506. doi: 10.1117/1.JMI.12.2.024506. Epub 2025 Apr 22.

ABSTRACT

PURPOSE: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and in-silico studies. However, such models are computationally expensive when applied directly to high-resolution 3D images and, therefore, require image dimensionality reduction (DR) to efficiently process the data. The goal of this work was to compare how different DR methods affect counterfactual neuroimage generation.

APPROACH: Five DR techniques [2D principal component analysis (PCA), 2.5D PCA, 3D PCA, autoencoder, and Vector Quantised-Variational AutoEncoder] were applied to 23,692 3D brain images to create low-dimensional representations for causal DL model training. Convolutional neural networks were used to quantitatively evaluate age and sex changes on the counterfactual neuroimages. Age alterations were measured using the mean absolute error (MAE), whereas sex changes were assessed via classification accuracy.

RESULTS: The 2.5D PCA technique achieved the lowest MAE of 4.16 when changing the age variable of an original image. When sex was changed, the autoencoder embedding led to the highest classification accuracy of 97.84% while also significantly impacting the age variable predictions, increasing the MAE to 5.24 years. Overall, 3D PCA provided the best balance, with an age prediction MAE of 4.57 years while maintaining 94.01% sex classification accuracy when altering the age variable and 94.73% sex classification accuracy and the lowest age prediction MAE (3.84 years) when altering the sex variable.

CONCLUSIONS: 3D PCA appears to be the best-suited DR method for causal neuroimage analysis.

PMID:40276097 | PMC:PMC12014944 | DOI:10.1117/1.JMI.12.2.024506

Data Brief. 2025 Apr 6;60:111532. doi: 10.1016/j.dib.2025.111532. eCollection 2025 Jun.

ABSTRACT

This study focuses on the urgent need to increase detection of diseases in Malabar Spinach, a valuable leaf vegetable crop which is at risk from several disease types including Anthracous leaf spot and Straw mite infestation. There is still a lack of research focused on Malabar spinach, although advances in machine vision have considerably increased the detection of largescale crop diseases. By developing and evaluating machine vision algorithms specifically designed for accurate detection of diseases in Malabar spinach, this research aims to fill this gap. To achieve this, a comprehensive dataset comprising images of both healthy and diseased Malabar Spinach plants is utilized for training, testing, and validation purposes. This study seeks to develop reliable disease detection models through the examination of different image processing techniques and deep learning algorithms such as ResNet50. In particular, the performance of these models is rigorously evaluated on the basis of a set of standardized evaluation metrics which aim to achieve an overall test accuracy of 94%. The results of this research will have a major impact on the cultivation of Malabar spinach in terms of precision farming techniques and effective crop management practices. This study will contribute to the wider objectives of agricultural sustainability and food security, through increasing crop productivity and reducing yield losses. In the end, it is intended to strengthen the resilience of farming communities dependent on Malabar Spinach crops by providing farmers and experts with efficient tools for detecting diseases.

PMID:40275977 | PMC:PMC12019838 | DOI:10.1016/j.dib.2025.111532

iScience. 2025 Apr 2;28(5):112333. doi: 10.1016/j.isci.2025.112333. eCollection 2025 May 16.

ABSTRACT

Overexpression of the antiapoptotic oncogene BCL-2 predicts poor prognosis in diffuse large B cell lymphoma (DLBCL) treated with anthracycline-based chemoimmunotherapy. Anthracyclines exert antitumor effects by multiple mechanisms including inhibition of ribosome biogenesis (RiBi) through rRNA synthesis blockade. RiBi inhibitors induce p53 stabilization through the ribosomal proteins-MDM2-p53 pathway, with stabilized p53 levels depending on baseline rRNA synthesis rate. We found that the BH3-mimetic venetoclax could not fully reverse BCL-2-mediated resistance to RiBi inhibitors in DLBCL cells. BCL-2 overexpression was associated with decreased baseline rRNA synthesis rate, attenuating p53 stabilization by RiBi inhibitors. Drugs stabilizing p53 irrespective of RiBi inhibition reversed BCL-2-induced resistance in vitro and in vivo, restoring p53 activation and apoptosis. A small nucleolar size, indicative of low baseline rRNA synthesis, correlated with high BCL-2 levels and poor outcomes in DLBCL patients. These findings uncover alternative BCL-2-dependent chemoresistance mechanisms, providing a rationale for specific combination strategies in BCL-2 positive lymphomas.

PMID:40276769 | PMC:PMC12020883 | DOI:10.1016/j.isci.2025.112333

iScience. 2025 Mar 30;28(5):112321. doi: 10.1016/j.isci.2025.112321. eCollection 2025 May 16.

ABSTRACT

Sleep deprivation is one of concomitant symptoms of cancer patients, particularly those with non-small cell lung cancer (NSCLC). The potential effect of sleep deprivation on tumor progression and underlying mechanisms remain to be fully investigated. Using a sleep-deprived tumor-bearing mouse model, we found that sleep deprivation altered immune cell composition and regulated pro-tumoral M2 macrophage polarization by the sympathetic nervous system. Furthermore, we identified a role of catecholaminergic neurons in the rostral ventrolateral medulla (RVLM) in influencing NSCLC metastasis. Clinical analyses revealed a correlation between sympathetic-related indicators and poor prognosis. Mechanistically, our findings indicate that sleep deprivation facilitates the polarization of pro-tumoral macrophages by upregulating β2-adrenergic receptor (ADRB2), which subsequently enhances the expression of Kruppel-like transcription factor 4 (KLF4) through the JAK1/STAT6 phosphorylation pathway. These findings highlight a neuro-immune mechanism linking sleep deprivation to NSCLC metastasis, suggesting that targeting the ADRB2/KLF4 axis could improve outcomes for sleep-deprived NSCLC patients.

PMID:40276761 | PMC:PMC12018092 | DOI:10.1016/j.isci.2025.112321

iScience. 2025 Apr 2;28(5):112343. doi: 10.1016/j.isci.2025.112343. eCollection 2025 May 16.

ABSTRACT

Mesenchymal stem cells (MSCs) hold potential in cancer therapy; however, insufficient tumor homing ability and heterogeneity limit their therapeutic benefits. Obviously, the homogeneous induced pluripotent stem cell (iPSC)-derived mesenchymal stem cells (iMSCs) with enhanced ability of tumor targeting could be the solution. In this study, a CAR containing the NKG2D extracellular domain was targeted at the B2M locus of iPSCs to generate NKG2D-CAR-iPSCs, which were subsequently differentiated into NKG2D-CAR-iMSCs. In vitro, NKG2D-CAR significantly enhanced migration and adhesion of iMSCs to a variety of solid tumor cells expressing NKG2D ligands. RNA sequencing (RNA-seq) revealed significant upregulation of genes related to cell adhesion, migration, and binding in NKG2D-CAR-iMSCs. In A549 xenograft model, NKG2D-CAR-iMSCs demonstrated a 57% improvement in tumor-homing ability compared with iMSCs. In conclusion, our findings demonstrate enhanced targeting specificity of NKG2D-CAR-iMSCs to tumor cells expressing NKG2D ligands in vitro and in vivo, facilitating future investigation of iMSCs as an off-the-shelf living carrier for targeted delivery of anti-tumor agents.

PMID:40276759 | PMC:PMC12020857 | DOI:10.1016/j.isci.2025.112343

J Biochem Mol Toxicol. 2025 May;39(5):e70280. doi: 10.1002/jbt.70280.

ABSTRACT

The toxicity of chemicals and drugs is a common crisis worldwide. Therefore, the search for protective compounds is growing. Natural compounds such as indole-3-carbinol (I3C) derived from cruciferous vegetables are preferred since they are safe for humans and the environment. This review focuses on I3C potential role in preventing and repairing damage caused by chemicals and drugs. Interestingly, I3C ameliorates hepatotoxicity induced by carbon tetrachloride (CCl4), diethylnitrosamine (DENA), alcohol, gold nanoparticles, and microbial toxins. Additionally, it inhibits carcinogenesis induced by different chemicals and prevents the deleterious effects of different antineoplastic drugs including cisplatin, doxorubicin (DOX), and trabectidin on normal tissues. Moreover, it reduces fetal malformation and protects against micronuclei formation and calstogenecity induced by cyclophosphamide (CP) in bone marrow cells. It also attenuates methotrexate (MTX)-induced hepatotoxicity, mitigates neurotoxicity caused by thioacetamide and clonidine, and protects against aspirin side effects in gastric mucosa. Furthermore, its nanoparticles inhibit neuronal damage caused by glutamate and rotenone. Thus, I3C prevents the toxicities caused by chemicals in the surrounding environment as well as those of consumed drugs.

PMID:40269607 | DOI:10.1002/jbt.70280