Clin Transplant. 2025 Aug;39(8):e70270. doi: 10.1111/ctr.70270.

ABSTRACT

BACKGROUND: En bloc heart-lung transplantation (HLTx) has been utilized for the past 50 years for the treatment of end-stage heart and lung disease, with significant evolution in the field over that time. This is a systematic review of HLTx and a description of the evolution and outcomes in this patient population.

METHODS: Pubmed and Embase were searched for all articles on HLTx from the time of database inception. A total of 1513 articles were screened, and after exclusion, 29 were included in this systematic review.

RESULTS: Reported cases of HLTx were more common in the early era (before 2000), for the indications of cystic fibrosis, Eisenmenger's syndrome, and pulmonary hypertension. In the contemporary era (2000-present), patients were not as commonly transplanted for cystic fibrosis, with pulmonary hypertension and congenital heart disease comprising the majority of cases. Rates of short-term mortality tended to be lower in more recent studies, with only recent studies reporting long-term survival.

DISCUSSION: HLTx has evolved substantially. In tandem with isolated heart and lung transplantation, the indications for transplant, medical therapy, and outcomes have changed over time. While HLTx is used less frequently in contemporary times compared to the early days of cardiothoracic transplantation, indications for HLTx continue to exist, and the use of HLTx will continue to be indicated. Centers with experience in HLTx should continue to report trends in patient management and outcomes, to continue to guide continued refinement in the field of HLTx.

PMID:40788177 | DOI:10.1111/ctr.70270

AACE Endocrinol Diabetes. 2025 Apr 11;12(2):67-70. doi: 10.1016/j.aed.2025.03.011. eCollection 2025 Jul-Aug.

ABSTRACT

BACKGROUND/OBJECTIVE: Cystic fibrosis (CF)-related diabetes (CFRD) is a common extrapulmonary complication of CF, with increasing prevalence. As individuals with CF live longer, obesity rates are increasing, leading to an emerging phenotype called CFRD with metabolic syndrome. The objective of this report is to describe the use of semaglutide in an adult with CFRD, obesity, and clinical insulin resistance.

CASE REPORT: A 32-year-old man with CF, pancreatic insufficiency, obesity, and poorly controlled CFRD presented with worsening blood sugar control, increasing insulin requirements, and a strong family history of metabolic syndrome. His body mass index was 38.5 kg/m2, and his hemoglobin A1c level ranged from 9.4% to 11.4%. He reported difficulty adhering to insulin therapy and concerns regarding weight and body image. A continuous glucose monitor was initiated; however, it did not significantly improve glycemic control. Given his metabolic profile and desire to lose weight, semaglutide was introduced and gradually increased over 5 months. This improved the hemoglobin A1c level by 5.7%, lowered the mean glucose levels, reduced the body mass index to 33.4 kg/m2, and decreased insulin requirements without adverse effects.

DISCUSSION: Although insulin is the primary treatment for CFRD, glucagon-like peptide 1 receptor agonists may provide additional benefits in carefully selected patients.

CONCLUSION: This case highlights the potential benefits of glucagon-like peptide 1 receptor agonists in CFRD with metabolic syndrome and emphasizes the need for further investigation.

PMID:40786988 | PMC:PMC12332434 | DOI:10.1016/j.aed.2025.03.011

Abdom Radiol (NY). 2025 Aug 11. doi: 10.1007/s00261-025-05149-7. Online ahead of print.

ABSTRACT

BACKGROUND: Fully automated AI-based algorithms can quantify adipose tissue on abdominal CT images. The aim of this study was to investigate the clinical value of these biomarkers by determining the association between adipose tissue measures and all-cause mortality.

METHODS: This retrospective study included 151,141 patients who underwent abdominal CT for any reason between 2000 and 2021. A validated AI-based algorithm quantified subcutaneous (SAT) and visceral (VAT) adipose tissue cross-sectional area. A visceral-to-subcutaneous adipose tissue area ratio (VSR) was calculated. Clinical data (age at the time of CT, sex, date of death, date of last contact) was obtained from a database search of the electronic health record. Hazard ratios (HR) and Kaplan-Meier curves assessed the relationship between adipose tissue measures and mortality. The endpoint of interest was all-cause mortality, with additional subgroup analysis including age and gender.

RESULTS: 138,169 patients were included in the final analysis. Higher VSR was associated with increased mortality; this association was strongest in younger women (highest compared to lowest risk quartile HR 3.32 in 18-39y). Lower SAT was associated with increased mortality regardless of sex or age group (HR up to 1.63 in 18-39y). Higher VAT was associated with increased mortality in younger age groups, with the trend weakening and reversing with age; this association was stronger in women.

CONCLUSION: AI-based CT measures of SAT, VAT, and VSR are predictive of mortality, with VSR being the highest performing fat area biomarker overall. These metrics tended to perform better for women and younger patients. Incorporating AI tools can augment patient assessment and management, improving outcome.

PMID:40788576 | DOI:10.1007/s00261-025-05149-7

Indian Dermatol Online J. 2025 Aug 11. doi: 10.4103/idoj.idoj_1055_24. Online ahead of print.

ABSTRACT

Psoriasis is a chronic inflammatory skin condition affecting millions of people globally, with prevalence varying significantly between countries. Conventional treatments, including topical agents, phototherapy, and systemic medications, often fail to account for individual variability, leading to suboptimal outcomes and potential adverse effects. Artificial intelligence (AI) has emerged as a promising approach to enhance precision and personalization in psoriasis management, potentially transforming diagnostic accuracy and treatment selection. This review examines the integration of AI across multiple domains of psoriasis treatment: (1) machine learning algorithms for phototherapy outcome prediction, (2) deep learning techniques for lesion segmentation and severity assessment, (3) AI-enhanced remote photographic monitoring systems, and (4) predictive modeling for response to systemic therapies and biologics. The analysis encompasses various AI methodologies, including random forest classifiers, convolutional neural networks, multiscale superpixel clustering, and gradient-boosted decision trees applied to clinical datasets, imaging analysis, and multi-omic patient data. AI-driven models demonstrate significant clinical utility with phototherapy outcome prediction, achieving high sensitivity (>84%) and accuracy (75-85%). Automated lesion segmentation reaches 86.99%-pixel accuracy, while remote AI assessments strongly correlate with clinical evaluations (Intraclass Correlation Coefficient [ICC] = 0.78-0.99). Notably, predictive models can forecast biologic therapy responses with > 95% accuracy within 2-4 weeks of treatment initiation, substantially reducing evaluation timelines from the conventional 12-week assessment period. AI technologies offer transformative potential in psoriasis management by enabling precise diagnosis, outcome prediction, and personalized therapy selection. Current implementations show promising results across diverse clinical applications, from phototherapy optimization to biologic response prediction. While challenges in dataset diversity, standardization, and validation remain, these represent opportunities for further advancement toward precision medicine in dermatology.

PMID:40788101 | DOI:10.4103/idoj.idoj_1055_24

Adv Sci (Weinh). 2025 Aug 11:e04079. doi: 10.1002/advs.202504079. Online ahead of print.

ABSTRACT

The precise matching of medication regimens to individual patients, known as personalized medication, is critical for the effective management of chronic diseases. Traditional machine learning-based models for personalized medication regimens typically rely solely on either clinical macro-phenotypes or molecular-level drug characteristics. It remains challenging to capture the patient-medication relationship from a comprehensive perspective that integrates individual patient characteristics with macro- and micro-level properties of the medication. Determining patient-medication relationships constitutes a three-stage sequential decision process from a clinical decision-making perspective. Therefore, inspired by Chain-of-Thought prompting, which simulates the decision-making process of human experts, a Multimodal Data-Driven Chain-of-Decisions (MDD-CoD) framework is proposed, where three-stage deep learning tasks are sequentially organized to reflect upstream-downstream logical dependencies, thereby forming a coherent clinical decision-making process. The model incorporates multimodal clinical phenotype data, multi-attribute medication data, and insights from clinical experts. Performance evaluation of the model involved comprehensive experiments utilizing five datasets covering four chronic diseases sourced from three hospitals. The dataset comprises information from chronic kidney disease (CKD), membranous nephropathy (MN), rheumatoid arthritis (RA), colorectal cancer (CRC), and knee osteoarthritis (KOA), totaling 3173 unimodal, 502 multimodal, and 2187 medication records from 3675 patients. Experimental results demonstrate that the framework achieves enhanced predictive performance in personalized medication decision-making based on individual patient disease characteristics, surpassing the strongest baseline across all tasks. This framework serves as a foundational model for clinical mixed data, with improved generalization and interpretability in cross-disease personalized decision-making tasks. It offers a scalable solution for the implementation of personalized medication regimens for chronic diseases.

PMID:40788064 | DOI:10.1002/advs.202504079

Adv Sci (Weinh). 2025 Aug 11:e07189. doi: 10.1002/advs.202507189. Online ahead of print.

ABSTRACT

Surface-Enhanced Raman Scattering (SERS) shows promise for monitoring health during space missions, particularly in assessing the effects of microgravity and radiation. However, traditional SERS sensors struggle with precise interfacial engineering, leading to a relatively poor assembly efficiency, and are unable to meet the practical needs of extreme spaceflight environments. To address this, it is designed and fabricated precision-arranged DNA origami plasmonic nanoantennas. By leveraging DNA origami's addressability, it is built a 3 × 4 antenna array with a controlled spacing of 21.76 nm, enhancing assembly efficiency fourfold compared to disordered systems. The ordered system enabled accurate detection of calcium ions, interleukin-6, and microRNA-214 in serum from mice exposed to microgravity and radiation, with intraclass correlation coefficients > 0.75, comparable to ELISA and qPCR. More importantly, integrating the system with a convolutional neural network enabled precise bone health prediction. This platform provides a promising tool for astronaut health monitoring.

PMID:40788053 | DOI:10.1002/advs.202507189

Adv Sci (Weinh). 2025 Aug 11:e07044. doi: 10.1002/advs.202507044. Online ahead of print.

ABSTRACT

Peptide drugs have revolutionized modern therapeutics, offering novel treatment avenues for various diseases. Nevertheless, low design efficacy, time consumption, and high cost still hinder peptide drug design and discovery. Here, an efficient approach that integrates deep learning-based protein design with functional screening is presented, enabling the rapid design of biotechnologically important peptides with improved stability and efficacy. 10,000 de novo glucagon-like peptide-1 receptor agonists (GLP-1RAs) are designed, 60 of these satisfied the stability, efficacy, and diversity criteria in the virtual functional screening. In vitro validations reveal a 52% success rate, and in vivo experiments demonstrate that two lead GLP-1RAs (D13 and D41) exhibit extended half-lives, approximately three times longer than that of Semaglutide. In diabetic mouse models, candidate D13 results in significantly lower blood glucose levels than Semaglutide. In the obesity mouse model, D13 induces weight loss efficacy comparable to that of Semaglutide. The AI-driven peptide design pipeline-which integrates protein design, functional screening, and experimental validation-reduces the number of iterations required to find novel peptide candidates. The entire process, from design to screening, can be completed in a single cycle within two weeks.

PMID:40787887 | DOI:10.1002/advs.202507044

Compr Rev Food Sci Food Saf. 2025 Sep;24(5):e70248. doi: 10.1111/1541-4337.70248.

ABSTRACT

Cereal quality significantly influences human health, requiring thorough evaluation of authenticity, nutritional composition, and food safety hazards. Conventional detection methods are often characterized by limitations, including time-consuming intricacy, complexity, and limited sensitivity. Recently, optical imaging and spectroscopy have emerged as rapid, nondestructive, and high-throughput alternatives for assessing cereal quality. The integration of chemometrics and artificial intelligence (AI), particularly deep learning algorithms, is paramount in the processing and analysis of optical data, which is indispensable for extracting key features from large datasets. In this work, the advanced spectroscopy and optical imaging techniques are comprehensively introduced, and their recent progress in applied research is outlined, emphasizing the major innovations and practical applications of these techniques. Besides, the latest developments of these techniques and AI-driven data processing methods in various aspects of cereal quality assessment have been summarized in order to highlight the potential research directions and future trends for practical application.

PMID:40787808 | DOI:10.1111/1541-4337.70248

ACS Omega. 2025 Jul 22;10(30):33662-33674. doi: 10.1021/acsomega.5c04524. eCollection 2025 Aug 5.

ABSTRACT

The interaction between circular RNAs (circRNAs) and RNA-binding proteins (RBPs) plays a crucial role in gene regulation; however, experimental identification is costly and inefficient. Current computational methods often overlook the structural features of circRNAs, thereby limiting prediction accuracy. To address these challenges, we propose GGCRB, a deep learning framework that integrates both sequence and structural features for predicting circRNA-RBP binding sites. Sequence features are captured through five encoding schemes (HFN, ND, NCP, DPCP, and Doc2Vec), followed by convolutional layers for local pattern extraction. Structural features are derived from base-pairing adjacency matrices generated by RNAstructure and modeled using graph convolutional networks and graph attention networks to learn topological dependencies. The fused representations are further processed by bidirectional LSTM and multihead attention modules to capture global interactions. Final predictions are made through pooling and softmax layers. Extensive experiments on 16 benchmark data sets demonstrate that GGCRB significantly outperforms existing models. Ablation studies and motif analyses further confirm its effectiveness, underscoring the importance of integrating structural and sequence information for accurate prediction of circRNA-RBP interactions.

PMID:40787315 | PMC:PMC12332793 | DOI:10.1021/acsomega.5c04524

ACS Omega. 2025 Jul 23;10(30):33031-33044. doi: 10.1021/acsomega.5c01924. eCollection 2025 Aug 5.

ABSTRACT

DNA methylation is an epigenetic modification that plays a crucial role in genome stability and cellular specialization, essential for maintaining normal cellular function and development, also a manifestation indicator of some diseases. Various tools have been proposed for methylation detection, typically leveraging a third-generation sequencing technology called nanopore sequencing, which provides more accurate DNA sequencing data. However, existing tools have their own limitations and advantages in terms of computational resources and information processing, without achieving a good balance. In this situation, we developed EDNTOM (Ensemble Deep Network Tool Of Methylation), a DNA methylation detection tool based on deep learning technology. We employed ensemble learning techniques, integrating predictions from multiple pretrained single models, and introduced an attention weight mechanism to provide accurate and reliable detection, reducing the consumption of computational resources. Results demonstrate that EDNTOM outperforms individual models. Additionally, in cross-species transfer experiments, EDNTOM exhibits strong transfer learning capabilities. We hope this work can provide a more powerful and reliable solution for methylation detection, contributing to the fields of biological science and medicine. The project code is available at https://github.com/ViceMusic/EDNTOM.

PMID:40787313 | PMC:PMC12332607 | DOI:10.1021/acsomega.5c01924

Front Bioeng Biotechnol. 2025 Jul 25;13:1556748. doi: 10.3389/fbioe.2025.1556748. eCollection 2025.

ABSTRACT

INTRODUCTION: Echocardiography is a first-line noninvasive test for diagnosing coronary artery disease (CAD), but it depends on time-consuming visual assessments by experts.

METHODS: This study constructed an echocardiographic video-driven multi-task learning model, denoted Intelligent echo for CAD (IE-CAD), to facilitate CAD screening and stenosis grading. A 3DdeeplabV3+ backbone and multi-task learning were simultaneously incorporated into the core frame of the IE-CAD model to capture the dynamic myocardial contours. Multifarious features reflecting local semantic structures were extracted and integrated to yield echocardiographic metrics such as ejection fraction, strain, and myocardial work. For model training and testing, we used a total of 870 echocardiographic videos from 290 patients with clinically suspected CAD at Beijing Hospital (Beijing, China), split at an 8:2 ratio. To evaluate the model's generalizability, we used an external dataset comprising 450 echocardiographic videos from 150 patients at Fuwai Hospital (Beijing, China).

RESULTS: The IE-CAD model achieved an AUC of 0.78 and a sensitivity of 0.85 for detecting significant or severe CAD, with a pearson correlation coefficient of 0.545 for predicting the Gensini score. When applied to the external dataset, the model achieved an AUC of 0.77 and a sensitivity of 0.78 for detecting significant or severe CAD.

DISCUSSION: Thus, the IE-CAD model demonstrated effective CAD diagnosis and grading in patients with clinical suspicion.

TRIAL REGISTRATION: This work was registered at ClinicalTrials.gov on 05 April 2019 (registration number: NCT03905200).

PMID:40787200 | PMC:PMC12331746 | DOI:10.3389/fbioe.2025.1556748

Int J Biomed Imaging. 2025 Aug 1;2025:2149042. doi: 10.1155/ijbi/2149042. eCollection 2025.

ABSTRACT

Brain tumors are complex clinical lesions with diverse morphological characteristics, making accurate segmentation from MRI scans a challenging task. Manual segmentation by radiologists is time-consuming and susceptible to human error. Consequently, automated approaches are anticipated to accurately delineate tumor boundaries and quantify tumor burden, addressing these challenges efficiently. The presented work integrates a convolutional block attention module (CBAM) into a deep learning architecture to enhance the accuracy of MRI-based brain tumor segmentation. The deep learning network is built upon a VGG19-based U-Net model, augmented with depthwise and pointwise convolutions to improve feature extraction and processing efficiency during brain tumor segmentation. Furthermore, the proposed framework enhances segmentation precision while simultaneously incorporating tumor area measurement, making it a comprehensive tool for early-stage tumor analysis. Several qualitative assessments are used to assess the performance of the model in terms of tumor segmentation analysis. The qualitative metrics typically analyze the overlap between predicted tumor masks and ground truth annotations, providing information on the segmentation algorithms' accuracy and dependability. Following segmentation, a new approach is used to compute the extent of segmented tumor areas in MRI scans. This involves counting the number of pixels within the segmented tumor masks and multiplying by their area or volume. The computed tumor areas offer quantifiable data for future investigation and clinical interpretation. In general, the proposed methodology is projected to improve segmentation accuracy, efficiency, and clinical relevance compared to existing methods, resulting in better diagnosis, treatment planning, and monitoring of patients with brain tumors.

PMID:40786983 | PMC:PMC12334286 | DOI:10.1155/ijbi/2149042

FASEB J. 2025 Aug 15;39(15):e70920. doi: 10.1096/fj.202501665R.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive form of interstitial lung disease and is pathologically featured by excessive deposition of extracellular matrix in response to repetitive epithelial injury. Burgeoning evidence suggests that ketone body exerts a beneficial effect on oxidative stress and on different types of fibrotic diseases, including cardiac fibrosis, hepatic fibrosis, and renal fibrosis. However, its effect on IPF is largely unknown. In vitro in alveolar epithelial cells type II (AECII) exposed to bleomycin, β-hydroxybutyrate (BHB) treatment substantially mitigated cellular ferroptosis, as evidenced by enhanced cell viability, reduced iron content, and reduced lipid peroxidation. This beneficial action of BHB coincided with a reinforced de novo glutathione synthesis and increased glutathione peroxidase 4 (GPX4) antioxidant response. Mechanistically, BHB promoted the expression of glutamate-cysteine ligase catalytic subunit (GCLC), the rate-limiting enzyme in de novo glutathione synthesis. Indeed, RSL3, a selective inhibitor of GPX4, or knockdown of GCLC abolished, whereas selective activation of GPX4 was sufficient for the antiferroptosis and AECII protective effects of BHB. In murine models of bleomycin-induced IPF, BHB therapy promoted the expression of GCLC and reinforced GPX4 activity in AECII, resulting in lessened AECII ferroptosis and improved lung injury and fibrosis. Thus, our findings may pave the way for developing a BHB-based novel approach to therapeutic ketosis for treating IPF.

PMID:40787805 | DOI:10.1096/fj.202501665R

Ann Thorac Med. 2025 Jul-Sep;20(3):145-152. doi: 10.4103/atm.atm_132_25. Epub 2025 Jul 14.

ABSTRACT

This updated systematic review and meta-analysis pooled the results of previous clinical trials assessing the effects of pirfenidone and nintedanib on patients with pulmonary fibrosis. Scopus, the Cochrane Library, PubMed, and Web of Science were searched from the inception to April 12, 2025, to identify randomized controlled trials measuring the effect of pirfenidone and nintedanib on pulmonary fibrosis. Because of high methodological heterogeneity, we utilized a random-effects model (DerSimonian-Laird) to perform this meta-analysis. Finally, 18 articles with 20 randomized controlled trials were included in this meta-analysis. We found that compared to placebo, treatment with the two antifibrotic drugs increased forced vital capacity (FVC) predicted (weighted mean difference [WMD] 3.12%, 95% confidence interval [CI] [1.41, 4.82], I 2 = 53.30%), FVC volume (WMD 87.44 ml, 95% CI [59.32, 115.57], I 2 = 99.4%), and the distance walked in the 6-minute walk test (WMD 24.63 m, 95% CI [16.05, 33.22], I 2 = 0.00%). However, compared to placebo, treatment with the two antifibrotic drugs did not significantly change the diffusing capacity of the lungs for carbon monoxide (WMD 1.38 ml/min/mmHg, 95% CI [-9.42, 12.18], I 2 = 0.00%). Therapeutic benefits were observed for both pirfenidone and nintedanib and for both idiopathic pulmonary fibrosis (IPF) and non-IPF. Pirfenidone and nintedanib can improve lung function and functional capacity in patients with different types of pulmonary fibrosis.

PMID:40786886 | PMC:PMC12333965 | DOI:10.4103/atm.atm_132_25

Quant Imaging Med Surg. 2025 Aug 1;15(8):6604-6615. doi: 10.21037/qims-2025-380. Epub 2025 Jul 23.

ABSTRACT

BACKGROUND: In patients with idiopathic pulmonary fibrosis (IPF), computed tomography (CT) quantification using artificial intelligence (AI) has been explored as a method to assess the therapeutic response to antifibrotic agents; however, studies evaluating long-term follow-up outcomes remain scarce. We investigated AI-driven quantitative analysis for long-term follow-up chest CT of IPF patients undergoing pirfenidone treatment.

METHODS: Among the 2,223 patients diagnosed with interstitial lung disease by chest CT at Jeonbuk National University Hospital, 36 patients with a multidisciplinary diagnosis of IPF were included in the study after excluding those who had not undergone surgical lung biopsy or did not have available pulmonary function tests (PFTs). These 36 patients underwent high-resolution computed tomography (HRCT) along with concurrent PFTs over a 10-year period and were categorized into two groups: those treated with pirfenidone (n=17) and those not treated with pirfenidone (n=19). Quantitative texture analysis was performed using a deep convolutional neural network to calculate fibrotic scores, defined as the combined mean percentage of two fibrotic components-reticulation and honeycombing, with or without accompanying ground-glass opacities-across the entire lung. This analysis aimed to assess treatment response in IPF patients receiving pirfenidone. Repeated measures analysis of variance (ANOVA) was used to evaluate the correlation between changes in pulmonary function and fibrotic scores over time.

RESULTS: The final study population comprised 36 patients, with a mean age of 67.1±7.7 years. Patients (DLCO: 58.0%±21.0%) who received pirfenidone (n=17) exhibited lower DLCO values at the final follow-up compared to the untreated group (n=19) (DLCO: 69.0%±21.7%), although the difference was not statistically significant (P=0.260). However, in the treated group (n=17), patients with progression despite pirfenidone treatment (n=6) (fibrotic score: 27.1%±12.1%) showed a markedly greater increase in mean AI fibrotic scores at the final follow-up compared to those with no or little change (n=11) (fibrotic score: 10.9%±8.7%), with the difference approaching statistical significance (P=0.076). There was a significant correlation between the decrease in DLCO values and the increase in AI fibrotic score in patients with pirfenidone on long-term follow-up (P<0.01).

CONCLUSIONS: AI-driven quantitative analysis of HRCT images in patients with IPF enables objective monitoring of the effects of pirfenidone on the progression of pulmonary fibrosis on long-term follow-up.

PMID:40785882 | PMC:PMC12332572 | DOI:10.21037/qims-2025-380

J Cell Biol. 2025 Nov 3;224(11):e202506102. doi: 10.1083/jcb.202506102. Epub 2025 Aug 11.

ABSTRACT

Segmenting multidimensional microscopy data requires high accuracy across many images (e.g., time points or Z slices) and is thus a labor-intensive part of biological image processing pipelines. We present ReSCU-Nets, recurrent convolutional neural networks that use the segmentation results from the previous image in a sequence as a prompt to segment the current image. We demonstrate that ReSCU-Nets outperform state-of-the-art image segmentation models, including nnU-Net and the Segment Anything Model, in different segmentation tasks on time-lapse microscopy sequences. Furthermore, ReSCU-Nets enable human-in-the loop corrections that prevent propagation of segmentation errors throughout image sequences. Using ReSCU-Nets, we investigate the role of gap junctions during Drosophila embryonic wound healing. We show that pharmacological blocking of gap junctions slows down wound closure by disrupting cytoskeletal polarity and cell shape changes necessary to repair the wound. Our results demonstrate that ReSCU-Nets enable the analysis of the molecular and cellular dynamics of tissue morphogenesis from multidimensional microscopy data.

PMID:40788207 | DOI:10.1083/jcb.202506102

Microbiol Spectr. 2025 Aug 11:e0219124. doi: 10.1128/spectrum.02191-24. Online ahead of print.

ABSTRACT

This study integrated multisource longitudinal data with trajectory modeling to delineate the heterogeneity of humoral immune dynamics induced by inactivated SARS-CoV-2 vaccinations and their clinical implications for subsequent SARS-CoV-2 infection outcomes in 205 individuals from Sichuan Province, China. We found that preexisting infection status served as the dominant stratifier of antibody trajectory divergence across the cohort. Additionally, among individuals without prior infection before the vaccine cohort, we identified five distinct immune response patterns with clinical implications. An age-associated "minimal response" subtype was associated with an increased risk of prolonged recovery time, sore throat, and limb pain in subsequent infections. In contrast, a subtype characterized by a marked increase in S-Igs titers after the booster dose exhibited fewer symptoms, with a lower likelihood of experiencing fever and fatigue. What is more, for those with prior infection, clinical data-including viral shedding duration and total antibody levels 15 days after discharge from the initial infection-could provide valuable insights into the subsequent risk of reinfection.

IMPORTANCE: To better identify vulnerable populations in epidemic surveillance and predict their clinical manifestations post-infection for accurate diagnosis and effective management, it is vital to understand the intrinsic dynamic immune patterns among individuals and how these trajectory patterns relate to future infections and associated symptoms. In the post-COVID-19 era, conducting nuanced analyses remains of great significance, especially as longer-term observational data become available. This is the first finding from China that illustrates the dynamic characteristics of the immune response following inactivated COVID-19 vaccination over an extended observation period, including information on following infection and symptoms. These data are particularly valuable as no participants experienced COVID-19 infection during the vaccine cohort follow-ups, meaning their antibody levels solely reflect the intrinsic dynamic immune patterns triggered by the inactivated COVID-19 vaccines.

PMID:40788170 | DOI:10.1128/spectrum.02191-24

Clin Pharmacol Ther. 2025 Aug 11. doi: 10.1002/cpt.70026. Online ahead of print.

ABSTRACT

Pharmacomicrobiomics explores the role of the gut microbiota in pharmacokinetics and pharmacodynamics, paving the way for new biomarkers and intervention strategies to reduce interindividual variability in drug response. Emerging mechanistic insights and tools enable microbiome-aware approaches as a promising new facet of personalized medicine.

PMID:40788067 | DOI:10.1002/cpt.70026

ACS Omega. 2025 Jul 27;10(30):32728-32743. doi: 10.1021/acsomega.4c10983. eCollection 2025 Aug 5.

ABSTRACT

Leptospirosis, a re-emerging zoonotic disease, is challenging human and animal health due to the lack of early and rapid diagnostic tools and effective vaccines. The exoproteome of the pathogen expressed under pathogenic conditions possesses a rational diagnostic significance due to its consistent presence in body fluids. were challenged to conditions simulating infection using physiological temperatures and osmolarity. Using state-of-the-art extraction techniques, efficient enrichment of nonabundant proteins, and high-resolution LC-MS/MS, we identified 1575 exoproteins from both the pathogen surface and culture supernatant. The results indicate a significant upregulation of 155 exoproteins, of which 41 were predicted to have moonlighting properties, 35 were identified as adhesins, and several proteins were components of the type 2 secretion system (T2SS). Additionally, 10 proteins showed extracellular matrix (ECM) binding properties, out of which 4 orthologs were found using the T2SS. The overall characteristics of upregulated proteins show that they can help Leptospira establish infection, invasion, and protection from the host defense, thereby providing new insights into the pathogen to confront the host via an increased energy level, secretion system, and host ECM binding molecules. Furthermore, the study suggests potential candidates for efficient antileptospiral countermeasures.

PMID:40787326 | PMC:PMC12332550 | DOI:10.1021/acsomega.4c10983

NPJ Biol Phys Mech. 2025;2(1):20. doi: 10.1038/s44341-025-00025-3. Epub 2025 Aug 6.

ABSTRACT

This review focuses on the roles of chemical species transport and biochemical and biophysical transformation within the gastrointestinal and immune systems and interactions with tissue structure and biomechanics in the mechanisms of pathophysiological conditions including gastrointestinal reflux disease and allergic responses. Combinations of biophysical and biochemical techniques are needed to unravel the complex interplay between transport and transformation to develop more effective interventions and ultimately improve patient outcomes.

PMID:40786563 | PMC:PMC12328227 | DOI:10.1038/s44341-025-00025-3