Curr Top Med Chem. 2025 Jul 30. doi: 10.2174/0115680266382257250721051440. Online ahead of print.

ABSTRACT

Precision medicine's quick development has transformed the way cancer is treated, and because small-molecule kinase inhibitors can specifically block the abnormal signaling pathways that cause tumor growth and progression, they are now a key component of targeted therapy. This review explores the most recent advancements in kinase inhibitor design and optimization, with a focus on novel drug scaffolds, improved structure-activity relationships (SARs), and molecular modification techniques meant to improve target selectivity, potency, and pharmacokinetic profiles. Emerging strategies to combat resistance mechanisms are heavily emphasized, such as the use of dual-target inhibitors that block parallel signaling cascades, allosteric modulators that bind to non-ATP sites, and combination therapies that work in concert to increase efficacy while reducing resistance. A thorough summary of the kinase inhibitors that are now FDA-approved for use in treating different forms of cancer is also included in the review, along with information on their safety profiles, clinical effectiveness, and changing indications of usage. Additionally, it examines encouraging results from preclinical research and ongoing clinical studies assessing nextgeneration kinase inhibitors, which have the potential to further customize cancer treatment. In order to improve patient outcomes, address therapeutic resistance, and broaden the therapeutic scope of kinase-targeted interventions in oncology, the review concludes by highlighting future research directions, such as drug repurposing, computational drug discovery, and advanced precision oncology approaches.

PMID:40754874 | DOI:10.2174/0115680266382257250721051440

Pharmacogenomics. 2025 Aug 3:1-15. doi: 10.1080/14622416.2025.2541402. Online ahead of print.

ABSTRACT

RATIONALE: Preemptive pharmacogenetic (PGx) testing offers a promising approach to personalized antidepressant treatment by identifying genetic variations influencing drug metabolism. By focusing on CYP2D6 and CYP2C19 genes, this strategy aims to improve treatment response, minimize adverse effects, and optimize dosing in patients with depression.

OBJECTIVES AND METHODS: This systematic review evaluates the effectiveness of preemptive PGx testing, primarily for CYP2D6 and CYP2C19, in enhancing antidepressant treatment outcomes. A comprehensive search of databases, including PubMed and Embase, was conducted to identify relevant studies. The review included randomized controlled trials and meta-analyses that assessed PGx testing in relation to treatment response and remission. Data on clinical outcomes were extracted and analyzed.

RESULTS: PGx testing led to improved antidepressant response rates and remission at 8- and 12-week follow-ups compared to treatment-as-usual (TAU). However, where data were available, benefits were less pronounced after six months of follow-up. The findings suggest that PGx testing plays an important role in achieving earlier remission, while TAU requires a longer time to achieve remission.

CONCLUSION: Preemptive pharmacogenetic testing for CYP2D6 and CYP2C19 could enhance early antidepressant treatment outcomes, offering a valuable tool for personalized medicine. Further research is required to explore implementation challenges in diverse clinical settings.

PMID:40754894 | DOI:10.1080/14622416.2025.2541402

Br J Pharmacol. 2025 Aug 3. doi: 10.1111/bph.70164. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Statins are widely prescribed for the prevention of cardiovascular disease, yet recent studies suggest an increased risk of new-onset Type 2 diabetes mellitus. This study aimed to identify genetic variants associated with statin-associated new-onset Type 2 diabetes mellitus from the UK Biobank and All of Us.

EXPERIMENTAL APPROACH: Among statin users, cases were defined as those diagnosed with Type 2 diabetes mellitus after statin initiation and controls as those never having diabetes mellitus. A genome-wide association study (GWAS) was performed using logistic regression analysis with an additive model using the UK Biobank. We conducted a replication analysis in the All of Us cohort using the lead variants identified in the UK Biobank. Additionally, we tested interaction analyses between statin use and lead variants.

KEY RESULTS: The GWAS identified four significant lead variants. The most significant, TCF7L2 rs7903146, increased risk of new-onset Type 2 diabetes mellitus by 1.3-fold. Similarly, POU5F1 rs879882 was associated with a higher risk. By contrast, CCND2 rs76895963 and ADCY5 rs35841686 were associated with a lower risk. In the replication analysis of the All of Us cohort, TCF7L2 rs7903146, CCND2 rs76895963 and POU5F1 rs879882 remained significant. In the interaction analyses, those three lead variants also exhibited additive interactions with statin use.

CONCLUSION AND IMPLICATIONS: These findings provide insights that may support personalized statin therapy to mitigate diabetic risk.

PMID:40754711 | DOI:10.1111/bph.70164

Cancer Med. 2025 Aug;14(15):e71077. doi: 10.1002/cam4.71077.

ABSTRACT

BACKGROUND AND PURPOSE: Prognostic stratification in non-small cell lung cancer (NSCLC) presents considerable challenges due to tumor heterogeneity. Emerging evidence has proposed that adipose tissue may play a prognostic role in oncological outcomes. This study investigates the integration of deep learning (DL)-derived computed tomography (CT) imaging biomarkers with mediastinal adiposity metrics to develop a multimodal prognostic model for postoperative survival prediction in NSCLC patients.

METHODS: A retrospective cohort of 702 surgically resected NSCLC patients was analyzed. Tumor radiomic features were extracted using a DenseNet121 convolutional neural network architecture, while mediastinal fat area (MFA) was quantified through semiautomated segmentation using ImageJ software. A multimodal survival prediction model was developed through feature-level fusion of DL-extracted tumor characteristics and MFA measurements. Model performance was evaluated using Harrell's concordance index (C-index) and receiver operating characteristic (ROC) analysis. Risk stratification was performed using an optimal threshold derived from training data, with subsequent Kaplan-Meier survival curve comparison between high- and low-risk cohorts.

RESULTS: The DL-based tumor model achieved C-indices of 0.787 (95% CI: 0.742-0.832) for disease-free survival (DFS) and 0.810 (95% CI: 0.768-0.852) for overall survival (OS) in internal validation. Integration of MFA with DL-derived tumor features yielded a multimodal model demonstrating enhanced predictive performance, with C-indices of 0.823 (OS) and 0.803 (DFS). Kaplan-Meier analysis revealed significant survival divergence between risk-stratified groups (log-rank p < 0.05).

CONCLUSION: The multimodal fusion of DL-extracted tumor radiomics and mediastinal adiposity metrics represents a significant advancement in postoperative survival prediction for NSCLC patients, demonstrating superior prognostic capability compared to unimodal approaches.

PMID:40755324 | DOI:10.1002/cam4.71077

Can Assoc Radiol J. 2025 Aug 4:8465371251357790. doi: 10.1177/08465371251357790. Online ahead of print.

ABSTRACT

PURPOSE: In pelvic MRI, Turbo Spin Echo (TSE) pulse sequences are used for T2-weighted imaging. However, its lengthy acquisition time increases the potential for artifacts. Deep learning (DL) reconstruction achieves reduced scan times without the degradation in image quality associated with other accelerated techniques. Unfortunately, a comprehensive assessment of DL-reconstruction in pelvic MRI has not been performed. The objective of this prospective study was to compare the performance of DL-TSE and conventional TSE pulse sequences in a broad spectrum of pelvic MRI indications.

METHODS: Fifty-five subjects (33 females and 22 males) were scanned at 3 T using DL-TSE and conventional TSE sequences in axial and/or oblique acquisition planes. Two radiologists independently assessed image quality in 6 categories: edge definition, vessel margin sharpness, T2 Contrast Dynamic Range, artifacts, overall image quality, and lesion features. The contrast ratio was calculated for quantitative assessment. A two-tailed sign test was used for assessment.

RESULTS: The 2 readers found DL-TSE to deliver equal or superior image quality than conventional TSE in most cases. There were only 3 instances out of 24 where conventional TSE was scored as providing better image quality. Readers agreed on DL-TSE superiority/inferiority/equivalence in 67% of categories in the axial plane and 75% in the oblique plane. DL-TSE also demonstrated a better contrast ratio in 75% of cases. DL-TSE reduced scan time by approximately 50%.

CONCLUSION: DL-accelerated TSE sequences generally provide equal or better image quality in pelvic MRI than standard TSE with significantly reduced acquisition times.

PMID:40755270 | DOI:10.1177/08465371251357790

Thorac Cancer. 2025 Aug;16(15):e70144. doi: 10.1111/1759-7714.70144.

ABSTRACT

BACKGROUND: While established biomarkers predict immunotherapy response in advanced nonsmall cell lung cancer (NSCLC), additional noninvasive imaging biomarkers may enhance treatment selection. Pretreatment computed tomography (CT) texture analysis may provide tumor characterization to predict survival outcomes.

METHODS: We conducted a systematic review and meta-analysis following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Cochrane Library databases were searched. Study quality was assessed using the quality in prognosis studies (QUIPS) tool. Hazard ratios (HRs) with 95% confidence intervals (CIs) were pooled using random-effects models.

RESULTS: Ten retrospective studies involving 2400 patients were included. Patients stratified as low-risk based on CT texture features demonstrated significantly improved survival outcomes compared to high-risk patients. The included studies used diverse radiomic features for risk stratification, including texture features from gray-level co-occurrence matrix (GLCM) such as entropy and dissimilarity, first-order statistical parameters including skewness and kurtosis, gray-level run-length matrix (GLRLM) features, and deep learning-derived features. Meta-analysis of five studies (n = 1102) revealed that patients stratified as low-risk based on these quantitative CT texture signatures had substantially better overall survival (OS) (p < 0.0001) with minimal heterogeneity (I2 = 0.0%). Similarly, progression-free survival (PFS) analysis of five studies (n = 1799) showed significant benefit for low-risk patients (p < 0.0001), though with moderate heterogeneity (I2 = 71.7%).

CONCLUSIONS: Pretreatment quantitative CT texture analysis effectively predicts survival outcomes in advanced NSCLC patients receiving immunotherapy, providing clinically meaningful risk stratification. This noninvasive imaging approach may serve as an additional tool to complement established pathological and molecular biomarkers, including liquid biopsy, for enhanced personalized treatment selection.

PMID:40755255 | DOI:10.1111/1759-7714.70144

Ultrasonography. 2025 Jun 15. doi: 10.14366/usg.25074. Online ahead of print.

ABSTRACT

PURPOSE: This study evaluates the feasibility of combining deep learning (DL) and conventional computer vision techniques to classify kidney ultrasound (US) images for the presence or absence of chronic kidney disease (CKD).

METHODS: A retrospective analysis was conducted on 258 kidneys (124 normal and 134 with CKD). A DL model was trained using midsagittal US images of the right kidney and corresponding contour maps to automate measurements of parenchymal thickness and parenchyma-to-sinus ratios. These features were integrated with a convolutional neural network for classification. The ground truth was determined based on clinical CKD diagnosis and laboratory data.

RESULTS: The combined DL and conventional feature extraction model achieved an accuracy of 82%, with a specificity of 93% and a negative predictive value of 97%. This approach outperformed models that relied solely on raw US images using DL, which achieved an accuracy of 64%. The inclusion of contour-based parenchymal measurements enhanced classification performance.

CONCLUSION: The integration of DL with automated feature extraction enables accurate classification of CKD using minimal user input. This proof-of-concept study highlights the potential of combining artificial intelligence-driven analysis with traditional metrics to serve as a noninvasive adjunct for CKD diagnosis and monitoring.

PMID:40755093 | DOI:10.14366/usg.25074

J Glob Health. 2025 Aug 4;15:04213. doi: 10.7189/jogh.15.04213.

ABSTRACT

BACKGROUND: Respiratory viral infections pose a substantial disease burden worldwide. Spatiotemporal techniques help identify transmission patterns of these infections, thereby supporting timely control and prevention efforts. We aimed to synthesise the current state of evidence on quantitative methodologies for investigating the spatiotemporal characteristics of respiratory viral infections.

METHODS: We conducted a scoping review using the PRISMA-ScR guidelines. We searched three biomedical bibliographic databases, EMBASE, MEDLINE, and Web of Science, identifying studies that analysed spatiotemporal transmission of viral respiratory infectious diseases (published before 1 March 2023).

RESULTS: We identified 8466 articles from database searches, of which 152 met our inclusion criteria and were qualitatively synthesised. Most included articles (n = 140) were published during the COVID-19 pandemic, with 131 articles specifically analysing COVID-19. Exploratory research (n = 77) investigated the spatiotemporal transmission characteristics of respiratory infectious diseases, focussing on transmission patterns (n = 16), and influencing factors (n = 61). Forecasting research (n = 75) aimed to predict the disease trends using either univariate (n = 57) or multivariate models (n = 18), predominantly using machine learning methods (n = 41). The application of advanced deep learning models (n = 20) in disease forecasting analysis was often constrained by the quality of the available disease data.

CONCLUSIONS: There is a growing body of research on spatiotemporal analyses of respiratory viral infections, particularly during the COVID-19 pandemic. The acquisition of high-quality data remains important for effectively leveraging sophisticated models in disease forecasting research. Concurrently, although advanced modelling techniques are widely applied, future studies should consider capturing the complex spatiotemporal interactions in disease trajectory modelling.

PMID:40755019 | DOI:10.7189/jogh.15.04213

ACS Sens. 2025 Aug 3. doi: 10.1021/acssensors.5c01225. Online ahead of print.

ABSTRACT

Raman spectroscopy is an enticing tool for the rapid identification of pathogenic bacteria and has the potential to meet the demand for early diagnosis and timely treatment of patients. However, it remains a challenge to devise a reliable Raman detection platform to obtain reproducible signals from single bacterial cells. Herein, we utilize a reflective Ag/SiO2 film that enhances the intrinsically weak Raman signals by re-excitation of the bacteria and reflection of downward-scattered photons, with maximum Raman intensities recorded by exciting the central edge of each single cell. The reflection-based configuration is simple, and its reliability as a sensing platform is validated by deep learning analysis. Importantly, given the positional dependence of the laser light on the Raman intensity, we employ capillarity-assisted particle assembly (CAPA) to selectively position single bacterial cells into a reflective topographical template to align the most Raman active region of the cell per the trap site geometry. Moreover, CAPA is utilized to directly isolate single cells from a suspension of artificial urine, eradicating any additional steps previously required to separate bacteria from biological samples. The proposed system has positive implications for future clinical settings that require simple, accurate, and reproducible detection of bacteria at the single-cell level.

PMID:40754993 | DOI:10.1021/acssensors.5c01225

Psychiatry Investig. 2025 Aug 5. doi: 10.30773/pi.2025.0133. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to improve the prediction of treatment response in patients with posttraumatic stress disorder (PTSD) by applying a variational autoencoder (VAE)-based data augmentation (DA) approach to electroencephalogram (EEG) data.

METHODS: EEG spectrograms were collected from patients diagnosed with PTSD. A VAE model was pretrained on the original spectrograms and used to generate augmented data samples. These augmented spectrograms were then utilized to train a deep neural network (DNN) classifier. The performance of the model was evaluated by comparing the area under the receiver operating characteristic curve (AUC) between models trained with and without DA.

RESULTS: The DNN trained with VAE-augmented EEG data achieved an AUC of 0.85 in predicting treatment response, which was 0.11 higher than the model trained without augmentation. This reflects a significant improvement in classification performance and model generalization.

CONCLUSION: VAE-based DA effectively addresses the challenge of limited EEG data in clinical settings and enhances the performance of DNN models for treatment response prediction in PTSD. This approach presents a promising direction for future EEG-based neuropsychiatric research involving small datasets.

PMID:40754940 | DOI:10.30773/pi.2025.0133

Curr Med Imaging. 2025 Jul 30. doi: 10.2174/0115734056392711250718201911. Online ahead of print.

ABSTRACT

INTRODUCTION: Early-stage Brain tumor detection is critical for timely diagnosis and effective treatment. We propose a hybrid deep learning method, Convolutional Neural Network (CNN) integrated with YOLO (You Only Look once) and SAM (Segment Anything Model) for diagnosing tumors.

METHOD: A novel hybrid deep learning framework combining a CNN with YOLOv11 for real-time object detection and the SAM for precise segmentation. Enhancing the CNN backbone with deeper convolutional layers to enable robust feature extraction, while YOLOv11 localizes tumor regions, SAM is used to refine the tumor boundaries through detailed mask generation.

RESULTS: A dataset of 896 MRI brain images is used for training, testing, and validating the model, including images of both tumors and healthy brains. Additionally, CNN-based YOLO+SAM methods were utilized successfully to segment and diagnose brain tumors.

DISCUSSION: Our suggested model achieves good performance of Precision as 94.2%, Recall as 95.6% and mAP50(B) score as 96.5% demonstrating and highlighting the effectiveness of the proposed approach for early-stage brain tumor diagnosis Conclusion: The validation is demonstrated through a comprehensive ablation study. The robustness of the system makes it more suitable for clinical deployment.

PMID:40754882 | DOI:10.2174/0115734056392711250718201911

Curr Med Imaging. 2025 Jul 30. doi: 10.2174/0115734056361649250717162910. Online ahead of print.

ABSTRACT

INTRODUCTION: Magnetic Resonance Imaging (MRI) is a crucial method for clinical diagnosis. Different abdominal MRI sequences provide tissue and structural information from various perspectives, offering reliable evidence for doctors to make accurate diagnoses. In recent years, with the rapid development of intelligent medical imaging, some studies have begun exploring deep learning methods for MRI sequence recognition. However, due to the significant intra-class variations and subtle inter-class differences in MRI sequences, traditional deep learning algorithms still struggle to effectively handle such types of complex distributed data. In addition, the key features for identifying MRI sequence categories often exist in subtle details, while significant discrepancies can be observed among sequences from individual samples. In contrast, current deep learning based MRI sequence classification methods tend to overlook these fine-grained differences across diverse samples.

METHODS: To overcome the above challenges, this paper proposes a fine-grained prototype network, SequencesNet, for MRI sequence classification. A network combining convolutional neural networks (CNNs) with improved vision transformers is constructed for feature extraction, considering both local and global information. Specifically, a Feature Selection Module (FSM) is added to the visual transformer, and fine-grained features for sequence discrimination are selected based on fused attention weights from multiple layers. Then, a Prototype Classification Module (PCM) is proposed to classify MRI sequences based on fine-grained MRI representations.

RESULTS: Comprehensive experiments are conducted on a public abdominal MRI sequence classification dataset and a private dataset. Our proposed SequencesNet achieved the highest accuracy with 96.73% and 95.98% in two sequence classification datasets, respectively, and outperfom the comparative prototypes and fine-grained models. The visualization results exhibit that our proposed sequencesNet can better capture fine-grained information.

DISCUSSION: The proposed SequencesNet shows promising performance in MRI sequence classification, excelling in distinguishing subtle inter-class differences and handling large intra-class variability. Specifically, FSM enhances clinical interpretability by focusing on fine-grained features, and PCM improves clustering by optimizing prototype-sample distances. Compared to baselines like 3DResNet18 and TransFG, SequencesNet achieves higher recall and precision, particularly for similar sequences like DCE-LAP and DCE-PVP.

CONCLUSION: The proposed new MRI sequence classification model, SequencesNet, addresses the problem of subtle inter-class differences and significant intraclass variations existing in medical images. The modular design of SequencesNet can be extended to other medical imaging tasks, including but not limited to multimodal image fusion, lesion detection, and disease staging. Future work can be done to decrease the computational complexity and increase the generalization of the model.

PMID:40754881 | DOI:10.2174/0115734056361649250717162910

Curr Neuropharmacol. 2025 Jul 30. doi: 10.2174/011570159X353595250303064846. Online ahead of print.

ABSTRACT

Neurological disorders are marked by neurodegeneration, leading to impaired cognition, psychosis, and mood alterations. These symptoms are typically associated with functional changes in both emotional and cognitive processes, which are often correlated with anatomical variations in the brain. Hence, brain structural magnetic resonance imaging (MRI) data have become a critical focus in research, particularly for predictive modeling. The involvement of large MRI data consortia, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), has facilitated numerous MRI-based classification studies utilizing advanced artificial intelligence models. Among these, convolutional neural networks (CNNs) and non-convolutional artificial neural networks (NC-ANNs) have been prominently employed for brain image processing tasks. These deep learning models have shown significant promise in enhancing the predictive performance for the diagnosis of neurological disorders, with a particular emphasis on Alzheimer's disease (AD). This review aimed to provide a comprehensive summary of these deep learning studies, critically evaluating their methodologies and outcomes. By categorizing the studies into various sub-fields, we aimed to highlight the strengths and limitations of using MRI-based deep learning approaches for diagnosing brain disorders. Furthermore, we discussed the potential implications of these advancements in clinical practice, considering the challenges and future directions for improving diagnostic accuracy and patient outcomes. Through this detailed analysis, we seek to contribute to the ongoing efforts in harnessing AI for better understanding and management of AD.

PMID:40754866 | DOI:10.2174/011570159X353595250303064846

Expert Rev Clin Immunol. 2025 Aug 3. doi: 10.1080/1744666X.2025.2543473. Online ahead of print.

ABSTRACT

INTRODUCTION: This network meta-analysis (NMA) of randomized controlled trials (RCTs) aimed to evaluate the efficacy and safety of pharmacotherapies for progressive fibrotic-interstitial lung diseases (PF-ILDs) to identify optimal treatments.

METHODS: We searched for RCTs on PF-ILD [idiopathic pulmonary fibrosis (IPF), connective tissue disease-ILD (CTD-ILD), chronic hypersensitivity pneumonitis (CHP), and pulmonary sarcoidosis] pharmacotherapies until 5 June 2025. NMA assessed efficacy [forced vital capacity, diffusing capacity of lungs for carbon monoxide, 6-minute-walk distance] and safety [serious adverse events (SAEs) and all-cause mortality] (PROSPERO: CRD42024554475).

RESULTS: We included sixty-five studies (13,521 participants) for forty-eight drugs in IPF, ten studies (1,508 participants) for eight drugs in CTD-ILD, four studies (259 participants) for three drugs in CHP, and nine studies (525 participants) for nine drugs in pulmonary sarcoidosis. In IPF, pirfenidone, nintedanib, and IFNγ-1b slowed lung function decline and reduced mortality. In CTD-ILD, pirfenidone, nintedanib, tocilizumab, and cyclophosphamide improved lung function and reduced mortality, with higher SAEs for nintedanib and cyclophosphamide. Pirfenidone and prednisolone benefited CHP, while budesonide improved lung function in pulmonary sarcoidosis.

CONCLUSIONS: Anti-fibrotic drugs - Pirfenidone and nintedanib effectively slow disease progression and reduce mortality in PF-ILDs. Emerging therapies like IFNγ-1b warrant further research, underscoring the need for large, high-quality RCTs.

PMID:40754799 | DOI:10.1080/1744666X.2025.2543473

Gut Microbes. 2025 Dec;17(1):2539450. doi: 10.1080/19490976.2025.2539450. Epub 2025 Aug 3.

ABSTRACT

The mucosal virome is increasingly recognized for its potential role in shaping intestinal health and disease. Building on previous findings, we analyzed the mucosal virome from 51 individuals, including newly diagnosed treatment naïve participants with ulcerative colitis (UC), Crohn's disease (CD), and non-inflammatory bowel disease (non-IBD) controls, incorporating longitudinal sampling for a subset of the participants. Viromes were highly individualized, with no shared or core components across participants. Unlike fecal virome studies, we observed no significant associations between mucosal virome diversity and mucosal inflammation, disease subtype, or sampling site. However, there was positive correlation between virome and bacteriome diversity, particularly in CD, suggesting the presence of dynamic interactions that influence microbial community structure. Crassvirales was abundant in the mucosa layer and, consistent with prior studies, Crassvirales abundance was reduced in IBD, irrespective of inflammation status or IBD subtype. These findings highlight their potential as biomarkers of virome health. Our data also revealed the potential presence of altered bacteriome-virome interactions and longitudinal sampling revealed a persistent subset of viruses, potentially shaping disease progression and remission dynamics. Our study underscores the importance of distinguishing microbial community dynamics across IBD subtypes and highlights Crassvirales as key players in mucosal immunity.

PMID:40754936 | DOI:10.1080/19490976.2025.2539450

Gut Microbes. 2025 Dec;17(1):2526719. doi: 10.1080/19490976.2025.2526719. Epub 2025 Aug 3.

ABSTRACT

The associations of the gut microbiome and virome with human health and disease are increasingly numerous and clear. The mechanistic roles of bacteriophages (phages) in the microbiome, however, are especially unclear, as their cultivation is exceedingly difficult and their diversity so immense. We use viral tagging (VT), a technique wherein fluorescently stained uncultivated viruses are allowed to adsorb to host cells and then host cells are singly sorted. This method identifies interacting phage-bacteria pairs to better sample and characterize the phages in human stool samples from healthy and inflammatory bowel disease (IBD)-affected patients. First, we apply VT to uncultivated bacteria from a healthy human sample, demonstrating far-reaching ability to observe diverse bacteria and phages alike. We also use VT with a cultured Faecalibacterium prausnitzii isolate, a bacterial host of interest due to its anti-inflammatory effects and strong negative correlation with IBD. Comparing VT with virome sequencing and phage identification from single amplified genomes shows that it is a practical technique for phage discovery, especially when it is used to focus on individual bacterial cultivars for which genomes have been sequenced. VT can detect phages so rare as to be undetectable in standard virome sequencing, which is biased toward the most abundant phage species even at high sequencing depth. Remarkably, VT also identified novel prophage integration events in F. prausnitzii, demonstrating that VT interactions can extend beyond the level of surface attachment and constitute active infection events. In total, VT identified at least 328 unique and highly diverse phage-host pairs, almost all of which are entirely uncharacterized, and several phages that are differentially abundant in IBD patients compared to healthy controls. Taken together, we show that VT is an extremely powerful tool to move beyond the cultivation and abundance biases inherent to current techniques and suggest that the phage-host pairs identified by VT here are crucial first step to enable future mechanistic studies of phage-bacteria-human interactions.

PMID:40754853 | DOI:10.1080/19490976.2025.2526719

Immunology. 2025 Aug 3. doi: 10.1111/imm.70024. Online ahead of print.

ABSTRACT

Tissue-resident memory T (TRM) cells are a specialised subset of immune cells that remain within tissues, playing a vital role in localised immune defence and long-term immunity. Unlike circulating memory T cells, TRM cells do not recirculate to provide rapid and effective responses against previously encountered pathogens at the tissue level. The formation of TRM cells is driven by tissue-specific cues, guiding their differentiation and retention within organs such as the skin, lungs and gut. They are characterised by the expression of unique markers, including CD69 and CD103, which facilitate their retention and longevity in tissues. TRM cells are essential for immune surveillance, effectively detecting and responding to different infections and contributing to tumour suppression. However, TRM cells are also implicated in chronic inflammatory and autoimmune diseases, where persistent activation by resident and autoantigens can lead to tissue damage. This pathogenic role is evident in chronic inflammatory conditions such as psoriasis, vitiligo and inflammatory bowel disease (IBD), where TRM cells may drive persistent localised inflammation and contribute to disease progression and severity. Emerging therapeutic strategies seek to modulate TRM cells to balance their protective and pathogenic roles in these inflammatory diseases. Approaches such as checkpoint inhibitors, cytokine modulation and cell-depletion therapies aim to enhance TRM cells' beneficial immune functions while minimising their role in autoimmunity. A deeper understanding of TRM cell development, maintenance and functional diversity is critical for advancing treatments for infectious diseases, chronic inflammation, autoimmune conditions and cancer.

PMID:40754695 | DOI:10.1111/imm.70024

Immunopharmacol Immunotoxicol. 2025 Aug 3:1-13. doi: 10.1080/08923973.2025.2542130. Online ahead of print.

ABSTRACT

OBJECTIVE: The aim of the study is to evaluate the long term outcome following renal transplantation with a combination of low dose quadruple immunosuppression.

METHODS: Group-I (n = 25) comprised of low dose everolimus (0.5 mg/bd) (EVR), low dose Tacrolimus (1mg/bd), low dose mycophenolate sodium (360mg/bd) and prednisolone. Group II (n = 29) consisted of standard triple drug regimen of tacrolimus (3mg/bd), mycophenolate sodium (720mg/bd) and prednisolone. Renal function, rejection episodes, adverse events, graft and patient survival were analyzed.Results and discussion: There was an improvement in the renal function from 1-year post transplant to the end of the study period in Group I. The mean serum creatinine at 10 years was1.54 ± 0.44 and in Group II it was 2.1 ± 0.7mg/dl with a statistical significance of p = 0.005. Mean eGFR at 10 years in Group-I was 57.8 and in Group II it was 46.7ml/mt/1.73m2 (p= <0.05). There was no statistical difference between the two groups in rejection rates (Group I-12% Group-II -17.24% (p = 0.65), graft loss (Group-I-12% vs Group II-27%(p= 0.26) and the patient loss was (Group I -12% vs Group-II 24%(p = 0.36). Drug related adverse events were insignificant. Proteinuria and hyperlipidemia were comparable between the groups.

CONCLUSION: The low dose quadruple immunosuppression protocol was a better option for long-term graft survival with fewer complications.

PMID:40754854 | DOI:10.1080/08923973.2025.2542130

Int Dent J. 2025 Aug 2;75(5):100940. doi: 10.1016/j.identj.2025.100940. Online ahead of print.

ABSTRACT

Previously, automated cropping and a reasonable classification accuracy for distinguishing impacted and non-impacted canines were demonstrated. This study evaluates multiple convolutional neural network (CNN) architectures for improving accuracy as a step towards a fully automated software for identification of impacted maxillary canines (IMCs) in panoramic radiographs (PRs). Eight CNNs (SqueezeNet, GoogLeNet, NASNet-Mobile, ShuffleNet, VGG-16, ResNet 50, DenseNet 201, and Inception V3) were compared in terms of their ability to classify 2 groups of PRs (impacted: n = 91; and non-impacted: n = 91 maxillary canines) before pre-processing and after applying automated cropping. For the PRs with impacted and non-impacted maxillary canines, GoogLeNet achieved the highest classification performance among the tested CNN architectures. Area under the curve (AUC) values of the Receiver Operating Characteristic (ROC) analysis without preprocessing and with preprocessing were 0.9 and 0.99 respectively, compared to 0.84 and 0.96 respectively with SqueezeNet. Among the tested CNN architectures, GoogLeNet achieved the highest performance on this dataset for the automated identification of impacted maxillary canines on both cropped and uncropped PRs.

PMID:40753865 | DOI:10.1016/j.identj.2025.100940

Bioorg Chem. 2025 Jul 30;164:108797. doi: 10.1016/j.bioorg.2025.108797. Online ahead of print.

ABSTRACT

The rising prevalence of antifungal resistance in Candida species poses a significant challenge to public health, necessitating the exploration of novel therapeutic strategies. This review highlights advancements in molecular innovations targeting Candida infections, emphasizing novel drug discovery approaches, including high-throughput screening, structure-based drug design, and synthetic modifications of existing molecules. We discuss emerging drug candidates in preclinical and clinical development, targeting key fungal pathways such as ergosterol biosynthesis, β-(1,3)-D-glucan synthesis, and novel metabolic regulators. Furthermore, drug repurposing strategies, leveraging known pharmacokinetics and pharmacodynamics of existing drugs, provide accelerated routes to new antifungal treatments. Collaborative efforts integrating pharmaceutical research, clinical insights, and technological advancements are imperative for the development of next-generation antifungal therapeutics. This review underscores the need for an interdisciplinary approach to antifungal drug discovery, ensuring effective and sustainable treatment options against resistant Candida strains.

PMID:40753878 | DOI:10.1016/j.bioorg.2025.108797