Lancet Respir Med. 2025 May 28:S2213-2600(25)00159-6. doi: 10.1016/S2213-2600(25)00159-6. Online ahead of print.

NO ABSTRACT

PMID:40449514 | DOI:10.1016/S2213-2600(25)00159-6

Soc Sci Med. 2025 May 15;380:118175. doi: 10.1016/j.socscimed.2025.118175. Online ahead of print.

ABSTRACT

Individuals living with rare diseases have conventionally been understood as being particularly vulnerable, which often promotes a negative and stigmatising interpretation of vulnerability. In this article, we embrace the framework of structural (health) vulnerability to gain a deeper understanding of the circumstances and factors contributing to adverse outcomes in the specific context of a Global South country, India, and a particular rare disease, Cystic Fibrosis (CF). By drawing on published materials and preliminary data from an evolving ethnographic research project, we contend that it is crucial to examine global power dynamics and the unequal distribution of resources to contextualize the precarious conditions experienced by Indians living with CF. Epistemologically, this stems from pervasive racialised assumptions ingrained in CF knowledge production, constituting a form of hermeneutic injustice, while economically, India's position in the global bioeconomy restricts access to potentially beneficial treatments derived from advanced clinical research. Moreover, reduced investment in healthcare by the Indian Central Government, notably evident in its rare disease policy, leaves CF patients reliant on philanthropy, which is susceptible to shifting interests and priorities. Therefore, we argue that focusing on structural (health) vulnerability is essential for shedding light on the distinct challenges faced by individuals living with CF in India, as well as in other locations in the Global South.

PMID:40449408 | DOI:10.1016/j.socscimed.2025.118175

Patient Educ Couns. 2025 May 21;138:109183. doi: 10.1016/j.pec.2025.109183. Online ahead of print.

ABSTRACT

OBJECTIVES: We explored the impact of taking part in a medication discontinuation study for people with cystic fibrosis (CF) on subsequent clinical conversations and what interviewees valued as characteristics of these conversations.

METHODS: This analysis is part of the Qualitative Understanding of the Experience of SIMPLIFY Trial (QUEST), a qualitative companion study to a discontinuation trial of two commonly prescribed medications for people with CF. We interviewed 109 total individuals (87 people with CF and 22 caregivers). The interviews were analyzed to explore the influence of participation in a discontinuation study on clinical conversations.

RESULTS: Respect emerged as an overarching theme of these interviews: how much the interviewees respected their clinicians and how they appreciated having their autonomy respected too. Other desirable attributes of clinical conversations surfaced including the importance of reciprocity, empowerment, sensitivity, partnership, empathy, consideration and transparency; (R.E.S.P.E.C.T).

CONCLUSIONS: Communication is a fundamental aspect of chronic disease management. This population study focused on patient perspectives of clinical conversations after sharing the experience of being in a novel discontinuation study. Since the acronym R.E.S.P.E.C.T. emerged from the data, we believe it has value as a framework for clinical conversations with people who have chronic conditions that require active selfmanagement.

PMID:40449206 | DOI:10.1016/j.pec.2025.109183

FEBS Lett. 2025 May 31. doi: 10.1002/1873-3468.70083. Online ahead of print.

ABSTRACT

Cryo-electron microscopy has yielded high-resolution structural data of the multidrug efflux transporter P-glycoprotein (ABCB1), but its direct and indirect interactions within the native membrane environment have remained largely unexplored. Here, we compared the fluidity gradients of plasma membranes of the drug-sensitive CHO cell line AuxB1 and its P-glycoprotein overexpressing derivative B30 by fluorescence anisotropy of embedded n-(9-anthroyloxy) fatty acid probes (n = 2, 7, 9, 12, 16) in the temperature range of 10-50 °C. The shape of the temperature profiles of probe mobility was comparable in AuxB1 and B30 membranes, but did not match. Overexpression of P-glycoprotein smoothened the transversal gradient of the out-of-plane mode of rotation of the probes, which may facilitate the partitioning of hydrophobic drugs into the membrane and thereby increase the speed of P-glycoprotein to pump the drug out of the cell.

PMID:40448544 | DOI:10.1002/1873-3468.70083

Alzheimers Res Ther. 2025 May 31;17(1):126. doi: 10.1186/s13195-025-01773-z.

ABSTRACT

BACKGROUND: Magnetic resonance imaging (MRI), combined with artificial intelligence techniques, has improved our understanding of brain structural change and enabled the estimation of brain age. Neurodegenerative disorders, such as Alzheimer's disease (AD), have been linked to accelerated brain aging. In this study, we aimed to develop a deep-learning framework that processes and integrates MRI images to more accurately predict brain age, cognitive function, and amyloid pathology.

METHODS: In this study, we aimed to develop a deep-learning framework that processes and integrates MRI images to more accurately predict brain age, cognitive function, and amyloid pathology.We collected over 10,000 T1-weighted MRI scans from more than 7,000 individuals across six cohorts. We designed a multi-modal deep-learning framework that employs 3D convolutional neural networks to analyze MRI and additional neural networks to evaluate demographic data. Our initial model focused on predicting brain age, serving as a foundational model from which we developed separate models for cognition function and amyloid plaque prediction through transfer learning.

RESULTS: The brain age prediction model achieved the mean absolute error (MAE) for cognitive normal population in the ADNI (test) datasets of 3.302 years. The gap between predicted brain age and chronological age significantly increases while cognition declines. The cognition prediction model exhibited a root mean square error (RMSE) of 0.334 for the Clinical Dementia Rating (CDR) regression task, achieving an area under the curve (AUC) of approximately 0.95 in identifying ing dementia patients. Dementia related brain regions, such as the medial temporal lobe, were identified by our model. Finally, amyloid plaque prediction model was trained to predict amyloid plaque, and achieved an AUC about 0.8 for dementia patients.

CONCLUSIONS: These findings indicate that the present predictive models can identify subtle changes in brain structure, enabling precise estimates of brain age, cognitive status, and amyloid pathology. Such models could facilitate the use of MRI as a non-invasive diagnostic tool for neurodegenerative diseases, including AD.

PMID:40450379 | DOI:10.1186/s13195-025-01773-z

Med Phys. 2025 May 31. doi: 10.1002/mp.17909. Online ahead of print.

ABSTRACT

BACKGROUND: Proton resonance frequency (PRF)-based magnetic resonance (MR) thermometry plays a critical role in thermal ablation therapies through focused ultrasound (FUS). For clinical applications, accurate and rapid temperature feedback is essential to ensure both the safety and effectiveness of these treatments.

PURPOSE: This work aims to improve temporal resolution in dynamic MR temperature map reconstructions using an enhanced deep-learning method, thereby supporting the real-time monitoring required for effective FUS treatments.

METHODS: Five classical neural network architectures-cascade net, complex-valued U-Net, shift window transformer for MRI, real-valued U-Net, and U-Net with residual blocks-along with training-optimized methods were applied to reconstruct temperature maps from 2-fold and 4-fold undersampled k-space data. The training enhancements included pre-training/training-phase data augmentations, knowledge distillation, and a novel amplitude-phase decoupling loss function. Phantom and ex vivo tissue heating experiments were conducted using a FUS transducer. Ground truth was the complex MR images with accurate temperature changes, and datasets were manually undersampled to simulate such acceleration here. Separate testing datasets were used to evaluate real-time performance and temperature accuracy. Furthermore, our proposed deep learning-based rapid reconstruction approach was validated on a clinical dataset obtained from patients with uterine fibroids, demonstrating its clinical applicability.

RESULTS: Acceleration factors of 1.9 and 3.7 were achieved for 2× and 4× k-space under samplings, respectively. The deep learning-based reconstruction using ResUNet incorporating the four optimizations, showed superior performance. For 2-fold acceleration, the RMSE of temperature map patches were 0.89°C and 1.15°C for the phantom and ex vivo testing datasets, respectively. The DICE coefficient for the 43°C isotherm-enclosed regions was 0.81, and the Bland-Altman analysis indicated a bias of -0.25°C with limits of agreement of ±2.16°C. In the 4-fold under-sampling case, these evaluation metrics showed approximately a 10% reduction in accuracy. Additionally, the DICE coefficient measuring the overlap between the reconstructed temperature maps (using the optimized ResUNet) and the ground truth, specifically in regions where the temperature exceeded the 43°C threshold, were 0.77 and 0.74 for the 2× and 4× under-sampling scenarios, respectively.

CONCLUSION: This study demonstrates that deep learning-based reconstruction significantly enhances the accuracy and efficiency of MR thermometry, particularly in the context of FUS-based clinical treatments for uterine fibroids. This approach could also be extended to other applications such as essential tremor and prostate cancer treatments where MRI-guided FUS plays a critical role.

PMID:40450352 | DOI:10.1002/mp.17909

Respir Med. 2025 May 29:108185. doi: 10.1016/j.rmed.2025.108185. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Comorbid depression in idiopathic pulmonary fibrosis (IPF) has been linked to reduced quality of life and worsened symptoms. However, the incidence of depression and its association with mortality in IPF, based on large-scale epidemiological data, remains unclear. This study investigated the clinical significance of depression in patients with IPF using the National Database of Health Insurance Claims and Specific Health Checkups of Japan.

METHODS: We analysed data from 31,386 patients with IPF. The prevalence of depression at IPF diagnosis, its post-diagnosis incidence rates and its associations with mortality and anti-fibrotic therapy were evaluated using propensity score matching, landmark analysis and a Cox model with time-dependent covariates.

RESULTS: The prevalence of depression at IPF diagnosis was 6.7% and the annual incidence rate post-diagnosis was 32.4 cases per 1,000 person-years. Pre-existing depression was not associated with mortality (hazard ratio [HR] 0.978; 95% confidence interval [CI] 0.887-1.080). Conversely, depression developing post-IPF diagnosis significantly increased the mortality risk (HR 2.71; 95% CI 2.55-2.87). While pre-existing depression was not associated with the cumulative initiation rate of anti-fibrotic therapy, depression developing post-IPF diagnosis was associated with a lower cumulative initiation rate.

CONCLUSIONS: Depression arising after an IPF diagnosis was associated with both a lower cumulative initiation rate of anti-fibrotic therapy and increased mortality, emphasising the need for the early detection and management of mental health issues in patients with IPF. Given the high incidence of depression post-IPF diagnosis, integrating mental health care into comprehensive management strategies is crucial for improving patient outcomes.

PMID:40449566 | DOI:10.1016/j.rmed.2025.108185

J Cheminform. 2025 May 31;17(1):90. doi: 10.1186/s13321-025-01028-x.

ABSTRACT

We introduce PoLiGenX, a novel generative model for de novo ligand design that employs latent-conditioned, target-aware equivariant diffusion. Our approach leverages the conditioning of the ligand generation process on reference molecules located within a specific protein pocket. By doing so, PoLiGenX generates shape-similar ligands that are adapted to the target pocket, enabling effective applications in target-aware hit expansion and hit optimization. Our experimental results underscore the efficacy of PoLiGenX in advancing ligand design. Notably, docking analyses reveal that the ligands generated by PoLiGenX show enhanced binding affinities relative to their reference molecules, all while retaining a similar molecular shape, but also retaining better poses with lower strain energies and less steric clashes. Furthermore, the model promotes substantial chemical diversity, facilitating the exploration of broader and more varied chemical spaces. Importantly, the generated ligands were assessed for drug-likeness using Lipinski's rule of five, demonstrating superior adherence to drug-likeness criteria compared to the reference dataset. This work represents a step forward in the controlled and precise generation of therapeutically relevant de novo ligands tailored for specific protein targets, contributing to progress in computational drug discovery and ligand design.

PMID:40450349 | DOI:10.1186/s13321-025-01028-x

Int Immunopharmacol. 2025 May 30;160:114971. doi: 10.1016/j.intimp.2025.114971. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Pulmonary fibrosis, a pathological process where the extracellular matrix overly deposits in lung tissue because of various pathogenic factors, leads to lung structure damage and function decline. Idiopathic pulmonary fibrosis (IPF) has a poor prognosis and high mortality, lacking effective drug treatments. Mefunidone (MFD), a new small-molecule compound, showed therapeutic effects on it in previous studies, but its specific molecular target is unknown. This study aims to clarify MFD's target and its potential mechanism. By exploring this, we hope to offer new insights and potential solutions for treating IPF and improving patients' outcomes.

METHODS: Mice with pulmonary fibrosis induced by bleomycin (BLM) were used as experimental models. MFD was administered by gavage. The changes in inflammation and fibrosis were evaluated through histopathological examinations. Subsequently, single-cell sequencing technology was used to explore how MFD affects the phenotype of pro-fibrotic macrophages, and verification was carried out in vitro to prove that MFD treats pulmonary fibrosis by influencing the phenotype of pro-fibrotic macrophages.

RESULT: MFD can inhibit the generation of succinate by binding and inhibiting the activity of succinate dehydrogenase (SDH). MFD can also inhibit the transformation of MMP12+CCL2+ profibrotic macrophages in the BLM pulmonary fibrosis model. Treatment with succinate can induce the transformation of macrophages into MMP12+CCL2+ profibrotic macrophages, and this induction depends on the succinate-specific receptor GPR91.

CONCLUSION: Our research results have revealed for the first time that MFD can treat pulmonary fibrosis by targeting SDH and regulating the transformation of MMP12+CCL2+ profibrotic macrophages.

PMID:40449267 | DOI:10.1016/j.intimp.2025.114971

Sci Rep. 2025 May 31;15(1):19141. doi: 10.1038/s41598-025-04643-8.

ABSTRACT

The growing adoption of intelligent transportation systems and connected vehicle networks has raised significant cybersecurity concerns due to their vulnerability to cyberattacks such as spoofing, message tampering, and denial-of-service. Traditional intrusion detection systems struggle to cope with the dynamic and high-volume nature of vehicular data, often leading to high false positives and limited adaptability. To address this problem, this study proposes an enhanced deep learning-based optimization framework for detecting cyberattacks in vehicle networks. The methodology employs the UNSW-NB15 dataset, with data preprocessed using Maximum-Minimum Normalization. Feature extraction is performed using the Discrete Fourier Transform (DFT), capturing frequency-domain patterns indicative of anomalies. Detection is executed through an Improved Long Short-Term Memory (ILSTM) model, whose parameters are optimized using the Crocodile Optimization Algorithm (COA), aiming to maximize classification accuracy. Experimental results demonstrate that the proposed ILSTM-COA model significantly outperforms existing techniques, achieving 98.9% accuracy and showing notable improvements across sensitivity, specificity, and other performance metrics. This model offers a robust, scalable, and real-time solution for safeguarding vehicular networks against evolving cyber threats.

PMID:40450183 | DOI:10.1038/s41598-025-04643-8

Sci Rep. 2025 May 31;15(1):19155. doi: 10.1038/s41598-025-04746-2.

ABSTRACT

Congenital heart disease affects approximately 1% of children worldwide, with a number of cases in resource-limited settings remaining undiagnosed through school age. While cardiac auscultation is a key screening method, its effectiveness varies widely, depending on practitioner expertise. This study introduces an innovative artificial intelligence (AI) approach combining conventional machine learning and deep learning techniques to improve heart murmur detection in pediatric populations. By developing an integrated Residual-Recurrent Neural Networks model and analyzing heart sound recordings from 500 pediatric participants, we achieved remarkable diagnostic performance in real-world pediatric clinical settings. At the single recording-level, the model achieved an accuracy of 88.5%, sensitivity of 85.5%, and specificity of 90.7%. Performance improved at the participant-level, with an accuracy of 90.0%, sensitivity of 88.8%, and specificity of 91.2%. The model showed particularly strong results when tested against the PhysioNet database (accuracy 95.2%, sensitivity 91.6%, and specificity 99.1%). This research provides a compelling proof-of-concept for AI-assisted cardiac screening, potentially revolutionizing early detection strategies in pediatric cardiac diseases.

PMID:40450176 | DOI:10.1038/s41598-025-04746-2

Med Biol Eng Comput. 2025 May 31. doi: 10.1007/s11517-025-03385-z. Online ahead of print.

ABSTRACT

Subclinical atrial fibrillation (SCAF), also known as atrial high-rate episodes (AHREs), refers to asymptomatic heart rate elevations associated with increased risks of atrial fibrillation and cardiovascular events. Although deep learning (DL) models leveraging echocardiographic images from ultrasound are widely used for cardiac function analysis, their application to AHRE prediction remains unexplored. This study introduces a novel DL-based framework for automatic AHRE detection using echocardiograms. The approach encompasses left atrium (LA) segmentation, LA strain feature extraction, and AHRE classification. Data from 117 patients with cardiac implantable electronic devices undergoing echocardiography were analyzed, with 80% allocated to the development set and 20% to the test set. LA segmentation accuracy was quantified using the Dice coefficient, yielding scores of 0.923 for the LA cavity and 0.741 for the LA wall. For AHRE classification, metrics such as area under the curve (AUC), accuracy, sensitivity, and specificity were employed. A transformer-based model integrating patient characteristics demonstrated robust performance, achieving mean AUC of 0.815, accuracy of 0.809, sensitivity of 0.800, and specificity of 0.783 for a 24-h AHRE duration threshold. This framework represents a reliable tool for AHRE assessment and holds significant potential for early SCAF detection, enhancing clinical decision-making and patient outcomes.

PMID:40450156 | DOI:10.1007/s11517-025-03385-z

Sci Rep. 2025 May 31;15(1):19140. doi: 10.1038/s41598-025-04263-2.

ABSTRACT

Parkinson's disease (PD) is a neurodegenerative condition characterized by frequently changing motor symptoms, necessitating continuous symptom monitoring for more targeted treatment. Classical time series classification and deep learning techniques have demonstrated limited efficacy in monitoring PD symptoms using wearable accelerometer data due to complex PD movement patterns and the small size of available datasets. We investigate InceptionTime and RandOm Convolutional KErnel Transform (ROCKET) as they are promising for PD symptom monitoring. InceptionTime's high learning capacity is well-suited to modeling complex movement patterns, while ROCKET is suited to small datasets. With random search methodology, we identify the highest-scoring InceptionTime architecture and compare its performance to ROCKET with a ridge classifier and a multi-layer perceptron on wrist motion data from PD patients. Our findings indicate that all approaches can learn to estimate tremor severity and bradykinesia presence with moderate performance but encounter challenges in detecting dyskinesia. Among the presented approaches, ROCKET demonstrates higher scores in identifying dyskinesia, whereas InceptionTime exhibits slightly better performance in tremor and bradykinesia estimation. Notably, both methods outperform the multi-layer perceptron. In conclusion, InceptionTime can classify complex wrist motion time series and holds potential for continuous symptom monitoring in PD with further development.

PMID:40450120 | DOI:10.1038/s41598-025-04263-2

BMJ Open. 2025 May 31;15(5):e099167. doi: 10.1136/bmjopen-2025-099167.

ABSTRACT

OBJECTIVES: To develop and validate an automated diabetic macular oedema (DME) classification system based on the images from different three-dimensional optical coherence tomography (3-D OCT) devices.

DESIGN: A multicentre, platform-based development study using retrospective and cross-sectional data. Data were subjected to a two-level grading system by trained graders and a retina specialist, and categorised into three types: no DME, non-centre-involved DME and centre-involved DME (CI-DME). The 3-D convolutional neural networks algorithm was used for DME classification system development. The deep learning (DL) performance was compared with the diabetic retinopathy experts.

SETTING: Data were collected from Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Chaozhou People's Hospital and The Second Affiliated Hospital of Shantou University Medical College from January 2010 to December 2023.

PARTICIPANTS: 7790 volumes of 7146 eyes from 4254 patients were annotated, of which 6281 images were used as the development set and 1509 images were used as the external validation set, split based on the centres.

MAIN OUTCOMES: Accuracy, F1-score, sensitivity, specificity, area under receiver operating characteristic curve (AUROC) and Cohen's kappa were calculated to evaluate the performance of the DL algorithm.

RESULTS: In classifying DME with non-DME, our model achieved an AUROCs of 0.990 (95% CI 0.983 to 0.996) and 0.916 (95% CI 0.902 to 0.930) for hold-out testing dataset and external validation dataset, respectively. To distinguish CI-DME from non-centre-involved-DME, our model achieved AUROCs of 0.859 (95% CI 0.812 to 0.906) and 0.881 (95% CI 0.859 to 0.902), respectively. In addition, our system showed comparable performance (Cohen's κ: 0.85 and 0.75) to the retina experts (Cohen's κ: 0.58-0.92 and 0.70-0.71).

CONCLUSIONS: Our DL system achieved high accuracy in multiclassification tasks on DME classification with 3-D OCT images, which can be applied to population-based DME screening.

PMID:40449950 | DOI:10.1136/bmjopen-2025-099167

Bone Joint J. 2025 Jun 1;107-B(6):582-586. doi: 10.1302/0301-620X.107B6.BJJ-2024-1567.R1.

ABSTRACT

The deployment of AI in medical imaging, particularly in areas such as fracture detection, represents a transformative advancement in orthopaedic care. AI-driven systems, leveraging deep-learning algorithms, promise to enhance diagnostic accuracy, reduce variability, and streamline workflows by analyzing radiograph images swiftly and accurately. Despite these potential benefits, the integration of AI into clinical settings faces substantial barriers, including slow adoption across health systems, technical challenges, and a major lag between technology development and clinical implementation. This commentary explores the role of AI in healthcare, highlighting its potential to enhance patient outcomes through more accurate and timely diagnoses. It addresses the necessity of bridging the gap between AI innovation and practical application. It also emphasizes the importance of implementation science in effectively integrating AI technologies into healthcare systems, using frameworks such as the Consolidated Framework for Implementation Research and the Knowledge-to-Action Cycle to guide this process. We call for a structured approach to address the challenges of deploying AI in clinical settings, ensuring that AI's benefits translate into improved healthcare delivery and patient care.

PMID:40449898 | DOI:10.1302/0301-620X.107B6.BJJ-2024-1567.R1

Bone. 2025 May 29:117550. doi: 10.1016/j.bone.2025.117550. Online ahead of print.

ABSTRACT

OBJECTIVE: Despite the established association between chronic obstructive pulmonary disease (COPD) severity and risk of osteoporosis, even after accounting for the known shared confounding variables (e.g., age, smoking, history of exacerbations, steroid use), there is paucity of data on bone loss among mild to moderate COPD, which is more prevalent in the general population.

METHODS: We conducted a longitudinal analysis using data from the Multi-Ethnic Study of Atherosclerosis. Participants with chest CT at Exam 5 (2010-2012) and Exam 6 (2016-2018) were included. Mild to moderate COPD was defined as forced expiratory volume in 1 s (FEV1) to forced vital capacity ratio of <0.70 and FEV1 of 50 % or higher. Vitamin D deficiency was defined as serum vitamin D < 20 ng/mL. We utilized a validated deep learning algorithm to perform automated multilevel segmentation of vertebral bodies (T1-T10) from chest CT and derive 3D volumetric thoracic vertebral BMD measurements at Exam 5 and 6.

RESULTS: Of the 1226 participants, 173 had known mild to moderate COPD at baseline, while 1053 had no known COPD. After adjusting for age, race/ethnicity, sex, body mass, index, bisphosphonate use, alcohol consumption, smoking, diabetes, physical activity, C-reactive protein and vitamin D deficiency, mild to moderate COPD was associated with faster decline in BMD (estimated difference, β = -0.38 g/cm3/year; 95 % CI: -0.74, -0.02). A significant interaction between COPD and vitamin D deficiency (p = 0.001) prompted stratified analyses. Among participants with vitamin D deficiency (47 % of participants), COPD was associated with faster decline in BMD (-0.64 g/cm3/year; 95 % CI: -1.17 to -0.12), whereas no significant association was observed among those with normal vitamin D in both crude and adjusted models.

CONCLUSIONS: Mild to moderate COPD is associated with longitudinal declines in vertebral BMD exclusively in participants with vitamin D deficiency over 6-year follow-up. Vitamin D deficiency may play a crucial role in bone loss among patients with mild to moderate COPD.

PMID:40449861 | DOI:10.1016/j.bone.2025.117550

Cell Commun Signal. 2025 May 31;23(1):256. doi: 10.1186/s12964-025-02240-w.

ABSTRACT

INTRODUCTION: Cardiac mitochondrial function is intricately regulated by various processes, ultimately impacting metabolic performance. Additionally, protein turnover is crucial for sustained metabolic homeostasis in cardiomyocytes.

OBJECTIVE: Here, we studied the role of mTOR in OPA-1 cleavage and its consequent effects on mitochondrial dynamics and energetics in cardiomyocytes.

RESULTS: Cultured rat cardiomyocytes treated with rapamycin for 6-24 h showed a significant reduction in phosphorylation of p70S6K, indicative of sustained inhibition of mTOR. Structural and functional analysis revealed increased mitochondrial fragmentation and impaired bioenergetics characterized by decreases in ROS production, oxygen consumption, and cellular ATP. Depletion of either the mitochondrial protease OMA1 or the mTOR regulator TSC2 by siRNA, coupled with an inducible, cardiomyocyte-specific knockout of mTOR in vivo, suggested that inhibition of mTOR promotes mitochondrial fragmentation through a mechanism involving OMA1 processing of OPA-1. Under homeostatic conditions, OMA1 activity is kept under check through an interaction with microdomains in the inner mitochondrial membrane that requires prohibitin proteins (PHB). Loss of these microdomains releases OMA1 to cleave its substrates. We found that rapamycin both increased ubiquitination of PHB1 and decreased its abundance, suggesting proteasomal degradation. Consistent with this, the proteasome inhibitor MG-132 maintained OPA-1 content in rapamycin-treated cardiomyocytes. Using pharmacological activation and inhibition of AMPK our data supports the hypothesis that this mTOR-PHB1-OMA-OPA-1 pathway impacts mitochondrial morphology under stress conditions, where it mediates dynamic changes in metabolic status.

CONCLUSIONS: These data suggest that mTOR inhibition disrupts mitochondrial integrity in cardiomyocytes by promoting the degradation of prohibitins and OPA-1, leading to mitochondrial fragmentation and metabolic dysfunction, particularly under conditions of metabolic stress.

PMID:40450326 | DOI:10.1186/s12964-025-02240-w

Sci Rep. 2025 May 31;15(1):19148. doi: 10.1038/s41598-025-03339-3.

ABSTRACT

This cross-sectional observational study aimed to identify the predictors of sleep bruxism (SB) in patients with temporomandibular disorder (TMD) and to comprehensively investigate its association with clinical, sleep-related, psychological, and hematological factors. Seventy-nine patients with TMD (69 females and 10 males; mean age 45.46 ± 14.46 years) were divided into two groups based on the presence or absence of SB: TMD_nonbruxer and TMD_bruxer. Descriptive statistics, correlation analyses, and multivariate stepwise logistic regression were conducted; p < 0.05 was considered statistically significant. In Cramer's V, SB was correlated with several clinical and sleep-related factors, including TMJ noise (r = 0.52), TMD pain (r = 0.48), craniomandibular index (r = 0.32), limited mouth opening (r = 0.29), tinnitus (r = 0.29), an increase in the Pittsburgh sleep quality index (PSQI) global score (r = 0.24), and poor sleep quality, defined as a PSQI global score ≥ 5 (r = 0.19) (all p < 0.05). SB was also associated with psychological distress. Regarding hematological factors, elevated levels of cortisol (r = 0.30), adrenocorticotropic hormone (ACTH) (r = 0.34), and cortisol/ACTH ratio (r = 0.35) were also associated with SB (all p < 0.05). The factors associated with an increased likelihood of SB ranked in terms of the odds ratio (OR) were: craniomandibular index (OR = 18.400, p = 0.006), poor sleep quality with a PSQI global score ≥ 5 (OR = 11.425, p = 0.027), depression (OR = 1.189, p = 0.014), cortisol/ACTH ratio (OR = 1.151, p = 0.007), anxiety (OR = 1.081, p = 0.040), and adrenocorticotropic hormone (OR = 1.073, p = 0.019). Notably, an increase in age was associated with a decreased likelihood of SB (OR = 0.905, p = 0.006), with a cut-off value of 50 years (AUC = 0.259, 95% CI: 0.149-0.368, p = 0.024), indicating a significant decrease in bruxism occurrence in individuals aged ≥ 50 years. Further analysis revealed complex interconnections between SB and its predictors. In conclusion, SB in TMD patients was associated with age < 50 years, various clinical factors, such as TMD pain and TMJ noise, poor sleep quality, psychological deterioration, and elevated cortisol and ACTH levels.

PMID:40450081 | DOI:10.1038/s41598-025-03339-3

Nat Commun. 2025 May 31;16(1):5061. doi: 10.1038/s41467-025-59653-x.

ABSTRACT

Base editors enable the direct conversion of target nucleotides without introducing DNA double strand breaks, making them a powerful tool for creating point mutations in a human genome. However, current Cas9-derived base editing technologies have limited ability to simultaneously edit multiple loci with base-pair level precision, hindering the generation of polygenic phenotypes. Here, we test the ability of six Cas12a-derived base editing systems to process multiple gRNAs from a single transcript. We identify base editor variants capable of multiplexed base editing and improve the design of the respective gRNA array expression cassette, enabling multiplexed editing of 15 target sites in multiple human cell lines, increasing state-of-the-art in multiplexing by three-fold in the field of mammalian genome engineering. To reduce bystander mutations, we also develop a Cas12a gRNA engineering approach that directs editing outcomes towards a single base-pair conversion. We combine these advances to demonstrate that both strategies can be combined to drive multiplex base editing with greater precision and reduced bystander mutation rates. Overcoming these key obstacles of mammalian genome engineering technologies will be critical for their use in studying single nucleotide variant-associated diseases and engineering synthetic mammalian genomes.

PMID:40449999 | DOI:10.1038/s41467-025-59653-x

J Invest Dermatol. 2025 May 29:S0022-202X(25)00540-8. doi: 10.1016/j.jid.2025.04.041. Online ahead of print.

ABSTRACT

The sebaceous gland is essential for skin homeostasis by producing sebum to lubricate and protect the skin. Dysfunctions in sebaceous gland activity are associated with skin disorders such as acne, seborrheic dermatitis, and alopecia. However, its cellular and molecular mechanisms in humans remain poorly understood as most studies have been conducted in mouse models. This study provides a comprehensive molecular analysis of the human sebaceous gland, focusing on cellular interactions, sebocyte differentiation, and, to our knowledge, previously unreported gene markers. By integrating Stereo-seq spatial transcriptomics, single-cell RNA sequencing, and validation by MERFISH, we identified four distinct stages of sebocyte differentiation, each characterized by unique gene signatures. These results reveal that sebocyte differentiation is a dynamic and complex process. Our findings enhance the understanding of sebaceous gland biology and provide a valuable reference for future research and the development of therapies for sebaceous gland-related disorders, including acne.

PMID:40449655 | DOI:10.1016/j.jid.2025.04.041