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

J Physiol. 2025 May 31. doi: 10.1113/JP288757. Online ahead of print.

NO ABSTRACT

PMID:40448977 | DOI:10.1113/JP288757

Mamm Genome. 2025 May 31. doi: 10.1007/s00335-025-10136-w. Online ahead of print.

ABSTRACT

Innate immunity, the primary defence mechanism, encompasses a range of protective processes like anatomical barriers, cytokine secretion, and the action of various immune cells. Cattle breeds might differ in these processes because of their genetic differences such as copy number variations (CNVs). Therefore, the present investigation employed an array comparative genomic hybridisation (aCGH) approach on breed representative pooled DNA samples to evaluate CNVs across six cattle breeds: four indigenous Indian breeds, Kangayam (KNG), Tharparkar (TP), Sahiwal (SW), Gir (GIR), one crossbred Karan Fries (KF), and one exotic breed, Holstein Friesian (HF). In aCGH, HF DNA was used as control, while test DNA was from the other breeds. Each pooled test DNA sample was a representative of 18 animals belonging to three distinct geographical locations of India. The study using Aberration Detection Method 2 (ADM-2) of Agilent Genomic Workbench revealed the highest number of duplications in KNG (1189 genes), followed by TP (534 genes), and the greatest number of deletions in SW (774 genes). Among these genes, 183 and 76 innate immune genes with hub genes TGF-β1, CD79A, and IL4 showed duplications in KNG and TP, respectively. In SW, 113 innate immune genes with hub genes PSMC5, MAPK1, and AXIN1 showed deletions. In contrast, KF and HF showed no genes with deletions and fewer duplicated innate immunity genes, reflecting either lower genetic variability in their immune gene repertoire or a potential bias due to HF DNA as a control in aCGH. Functional enrichment of innate immune genes revealed duplications in KNG enriched in interleukin-1 receptor (IL1R) activity (p = 9.9 × 10-3) and nucleobase metabolism (p = 2.88 × 10⁻11), while duplications in TP were linked to DNA-binding transcription factor activity (p = 2.34 × 10⁻14). The KEGG pathway analysis highlighted Th17 cell differentiation (p = 1.3 × 10⁻4) in KNG and Hippo signalling (p = 3.7 × 10-2) in TP. Overall, these findings highlight the importance of genetic diversity in shaping innate immunity in indigenous Indian cattle breeds, favouring a balanced crossbreeding to sustain the Indian dairy sector.

PMID:40448838 | DOI:10.1007/s00335-025-10136-w

Mol Biol Rep. 2025 May 31;52(1):529. doi: 10.1007/s11033-025-10644-7.

ABSTRACT

BACKGROUND: Multidrug-resistant and biofilm-forming pathogens have become a global health challenge, contributing to persistent and hard-to-treat infections. The objective of this study was to characterize an active metabolite produced by a novel halo-thermophilic Streptomyces sp. CBN-1 that exhibits potent antibacterial and antibiofilm activities using a combined in-silico and experimental approach.

METHODS & RESULTS: In this study, a halo-thermophilic Streptomyces sp. CBN-1 strain was selected for its ability to grow in 10% NaCl at 40 °C. This strain was identified using phenotypic characterizations and 16S rRNA gene sequence analysis as Streptomyces rochei NRRL B-2410 with 99.15% similarity. An active metabolite, CBNa-1, was extracted using n-butanol solvent from ISP2 broth medium and purified by HPLC. Structural characterization using electrospray ionization mass spectrometry and NMR spectroscopy identified CBNa-1 as an aromatic heterocyclic compound regulated by non-ribosomal peptide synthetase (NRPS) and type II polyketide synthase (PKS) genes. It exhibited potent activity with minimum inhibitory concentrations (MIC) ranging from 4 to 5 µg/mL and minimum biofilm inhibitory concentrations (MBIC50%) at ½ MIC. Additionally, in-silico docking analyses showed that CBNa-1 had stronger binding affinities from - 8.7 to -8.1 kcal/mol with isoleucyl-tRNA synthetase, glucosamine-6-phosphate synthase, penicillin-binding protein 1a, type II DNA topoisomerases, and quorum sensing compared to antibiotics (-5.7 to -7.9 kcal/mol). Furthermore, molecular dynamic (MD) simulation showed the stability of the protein-ligand complex under physiological conditions.

CONCLUSION: This study reports the first identification of CBNa-1, a metabolite from prokaryotic cells, with potent antibacterial and anti-biofilm properties to combat nosocomial infections caused by MDR pathogens, including bacteria resistant to third-generation cephalosporins.

PMID:40448741 | DOI:10.1007/s11033-025-10644-7

ISME J. 2025 May 31:wraf109. doi: 10.1093/ismejo/wraf109. Online ahead of print.

ABSTRACT

Microbial consortia play pivotal roles in nutrient cycling across diverse ecosystems, where the functionality and composition of microbial communities are shaped by metabolic interactions. Despite the critical importance of understanding these interactions, accurately mapping and manipulating microbial interaction networks to achieve specific outcomes remains challenging. Genome-scale metabolic models (GEMs) offer significant promise for predicting microbial metabolic functions from genomic data; however, traditional community GEMs typically rely on species abundance information, which may limit their predictive accuracy due to the absence of condition-specific gene expression or protein abundance data. Here, we introduce the Integration of Metatranscriptomes Into Community GEMs (IMIC) approach, which utilizes metatranscriptomic data to construct context-specific community models for predicting individual growth rates and metabolic interactions. By incorporating metatranscriptomic profiles, which reflect both gene expression activity and partially encode abundance information, IMIC could predict condition-specific flux distributions that enable the investigation of metabolite interactions among community members. Our results show that growth rates predicted by IMIC correlate strongly with relative as well as absolute abundance of species and offer a streamlined, automated procedure for estimating the single intrinsic parameter. Specifically, IMIC results in improved predictions of measured metabolite concentration changes compared with other approaches in our case study. We further demonstrate that this improvement is driven by the network-wide adjustment of flux bounds based on gene expression profiles. In conclusion, IMIC approach enables the accurate prediction of individual growth rates and improves the model performance of predicting metabolite interactions, facilitating a deeper understanding of metabolic interdependencies within microbial communities.

PMID:40448581 | DOI:10.1093/ismejo/wraf109

Vet Rec. 2025 May 30:e5325. doi: 10.1002/vetr.5325. Online ahead of print.

ABSTRACT

BACKGROUND: Bovine viral diarrhoea (BVD) is an endemic disease in the UK. In England, a voluntary control and eradication scheme, BVDFree England, has been running since 2016.

METHODS: We analysed test results from 7005 herds that were submitted to BVDFree England between 2016 and 2023 to investigate changes in the prevalence of BVD in participating herds and engagement by farmers since the previously published analysis covering the period up to 2020.

RESULTS: Herds that tested for multiple consecutive years were more likely to be BVD negative in later testing years than when starting. Few herds were still positive after 5 years of testing. Overall, the prevalence of BVD-positive herds in the dataset declined between 2020 and 2023; however, fewer farmers joined the scheme for the first time each year since 2019 (214 in 2023 compared with 2614 in 2019).

LIMITATIONS: This dataset represents the herds that submit tests to BVDFree England and is not representative of all cattle herds in England.

CONCLUSION: Herds that tested for multiple consecutive years in the scheme were less likely to be BVD positive in later years of testing, and the prevalence of BVD in participating herds has continued to fall since 2020.

PMID:40448356 | DOI:10.1002/vetr.5325

J Affect Disord. 2025 May 29:119435. doi: 10.1016/j.jad.2025.119435. Online ahead of print.

ABSTRACT

BACKGROUND: There is insufficient understanding of the long-term studies on adverse events (ADEs) in major depressive disorder (MDD) treated with atypical antipsychotics (AAPs), risks in patients with different psychiatric disorders, and differences between male and female patients.

METHODS: This study retrieved ADE reports for aripiprazole, quetiapine XR, brexpiprazole, and cariprazine from the FDA Adverse Event Reporting System (FAERS) for the time periods of FDA approval for MDD in the first quarter (Q1) of 2007, the Q1 of 2009, the Q1 of 2015, and the Q1 of 2022 respectively to the Q1 of 2024. Four algorithms (ROR, PRR, BCPNN, and MGPS) assessed ADE signals. We compared positive signal rates between MDD and non-MDD, and assessed sex differences in drug-related risks by ROR.

RESULTS: Patients with MDD had significantly higher rates of impulse control disorders (ICDs), obsessive-compulsive disorder (OCD), weight gain, extrapyramidal symptoms, and metabolic disorders compared to non-MDD (P < 0.05). Restless legs syndrome was associated with aripiprazole (P < 0.01), brexpiprazole (P < 0.01), and quetiapine XR. Serotonin syndrome, eosinophilic myocarditis, and angle closure glaucoma were new signals of aripiprazole in patients with MDD (P < 0.05). Female patients were more likely to gain weight (P < 0.05) with using aripiprazole, quetiapine XR, and brexpiprazole, whereas male patients with aripiprazole (P < 0.01) or brexpiprazole (P < 0.05) reported higher rates of ICDs and OCD.

CONCLUSION: It is suggesting a potential increased risk of various ADEs in patients with MDD when taking AAPs. The causal relationship and the exact mechanism between drugs and ADEs remains unclear, requiring further research.

PMID:40449747 | DOI:10.1016/j.jad.2025.119435

J Diabetes Sci Technol. 2025 May 30:19322968251333441. doi: 10.1177/19322968251333441. Online ahead of print.

ABSTRACT

This consensus report evaluates the potential role of continuous glucose monitoring (CGM) in screening for stage 2 type 1 diabetes (T1D). CGM offers a minimally invasive alternative to venous blood testing for detecting dysglycemia, facilitating early identification of at-risk individuals for confirmatory blood testing. A panel of experts reviewed current evidence and addressed key questions regarding CGM's diagnostic accuracy and screening protocols. They concluded that while CGM cannot yet replace blood-based diagnostics, it holds promise as a screening tool that could lead to earlier, more effective intervention. Metrics such as time above range >140 mg/dL could indicate progression risk, and artificial intelligence (AI)-based modeling may enhance predictive capabilities. Further research is needed to establish CGM-based diagnostic criteria and refine screening strategies to improve T1D detection and intervention.

PMID:40444471 | PMC:PMC12125016 | DOI:10.1177/19322968251333441

BMC Health Serv Res. 2025 May 30;25(1):773. doi: 10.1186/s12913-025-12898-0.

ABSTRACT

The high prevalence of multimorbidity in the aging population necessitates complex medication regimens, increasing the risk of adverse drug events (ADEs) and hospital admissions. This paper evaluates an intervention aimed at improving medication safety for northeastern and western Germany under real-world conditions, thereby providing a pragmatic approach to the challenges of multi-center studies with staggered intervention starts and voluntary participation. The analysis utilizes iterative Propensity Score Matching (PSM) followed by a Difference-in-Differences (DiD) estimator to navigate the methodological complexities and assess the intervention's effectiveness and cost-effectiveness. Results reveal a significant reduction in ADE-related hospital admissions by 27.5% and overall hospital admissions by 17.5%. We find that the intervention is cost-effective at an incremental cost-effectiveness ratio (ICER) of €15,169.66 per averted ADE and €4,180.61 per averted hospital admission. This study illustrates for evaluating complex health interventions in real-world settings and underscores the importance of balancing health outcomes improvements with economic considerations in aging populations.

PMID:40448133 | DOI:10.1186/s12913-025-12898-0

Plant Methods. 2025 May 30;21(1):75. doi: 10.1186/s13007-025-01393-6.

ABSTRACT

Wheat, a major global cereal crop, is prone to quality degradation from early sprouting when stored improperly, resulting in substantial economic losses. Traditional methods for detecting early sprouting are labor-intensive and destructive, underscoring the need for rapid, non-destructive alternatives. Terahertz (THz) technology provides a promising solution due to its ability to perform non-invasive imaging of internal structures. However, current THz imaging techniques are limited by low image resolution, which restricts their practical application. We address these challenges by proposing an advanced deep learning framework for THz image classification of early sprouting wheat. We first develop an Enhanced Super-Resolution Generative Adversarial Network (AESRGAN) to improve the resolution of THz images, integrating an attention mechanism to focus on critical image regions. This model achieves a 0.76 dB improvement in Peak Signal-to-Noise Ratio (PSNR). Subsequently, we introduce the EfficientViT-based YOLO V8 classification model, incorporating a Depthwise Separable Attention (C2F-DSA) module, and further optimize the model using the Gazelle Optimization Algorithm (GOA). Experimental results demonstrate the GOA-EViTDSA-YOLO model achieves an accuracy of 97.5% and a mean Average Precision (mAP) of 0.962. The model is efficient and significantly enhances the classification of early sprouting wheat compared to other deep learning models.

PMID:40448208 | DOI:10.1186/s13007-025-01393-6

BMC Med Imaging. 2025 May 30;25(1):200. doi: 10.1186/s12880-025-01746-6.

ABSTRACT

PURPOSE: To compare the image quality and pulmonary nodule detectability and measurement accuracy between deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) of chest ultra-low-dose CT (ULDCT).

MATERIALS AND METHODS: Participants who underwent chest standard-dose CT (SDCT) followed by ULDCT from October 2020 to January 2022 were prospectively included. ULDCT images reconstructed with HIR and DLR were compared with SDCT images to evaluate image quality, nodule detection rate, and measurement accuracy using a commercially available deep learning-based nodule evaluation system. Wilcoxon signed-rank test was used to evaluate the percentage errors of nodule size and nodule volume between HIR and DLR images.

RESULTS: Eighty-four participants (54 ± 13 years; 26 men) were finally enrolled. The effective radiation doses of ULDCT and SDCT were 0.16 ± 0.02 mSv and 1.77 ± 0.67 mSv, respectively (P < 0.001). The mean ± standard deviation of the lung tissue noises was 61.4 ± 3.0 HU for SDCT, 61.5 ± 2.8 HU and 55.1 ± 3.4 HU for ULDCT reconstructed with HIR-Strong setting (HIR-Str) and DLR-Strong setting (DLR-Str), respectively (P < 0.001). A total of 535 nodules were detected. The nodule detection rates of ULDCT HIR-Str and ULDCT DLR-Str were 74.0% and 83.4%, respectively (P < 0.001). The absolute percentage error in nodule volume from that of SDCT was 19.5% in ULDCT HIR-Str versus 17.9% in ULDCT DLR-Str (P < 0.001).

CONCLUSION: Compared with HIR, DLR reduced image noise, increased nodule detection rate, and improved measurement accuracy of nodule volume at chest ULDCT.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40448068 | DOI:10.1186/s12880-025-01746-6

Sci Rep. 2025 May 30;15(1):19103. doi: 10.1038/s41598-025-04061-w.

ABSTRACT

The Internet of Vehicles (IoV) is crucial in facilitating secure and efficient vehicle-infrastructure communication. Nevertheless, with an increasing reliance on the IoV in modern logistics and intelligent fleet systems, cyberattacks on vital supply chain information pose a far greater threat. This research presents the ASCON, a low-power cryptographic algorithm, with the Message Queued Telemetry Transport (MQTT) protocol for secure IoV communications. Integration of a deep learning model that is suited for real-time anomaly detection and breach prediction. The novelty of this study is the hybrid framework that uses lightweight cryptographic methods coupled with deep learning-based threat protection. Therefore, it is resilient against a wide range of cyber-attacks, including password cracking, authentication compromises, brute-force attacks, differential cryptanalysis, and Zig-Zag attacks. The system employs Raspberry Pi boards with authentic industrial vehicluar dataset and offers a remarkable encryption rate of 0.025 s, takes 0.003 s for hash generation, and detection of tampering takes 0.002 s. By bridging the gap between high-level cryptography and proactive and smart security analytics, this work not only fortifies fleet management systems but also makes substantial contributions to the overall objectives of enhancing safety, sustainability, and operational robustness in autonomous vehicle networks.

PMID:40447743 | DOI:10.1038/s41598-025-04061-w