BMJ Open. 2025 Aug 3;15(8):e093124. doi: 10.1136/bmjopen-2024-093124.

ABSTRACT

OBJECTIVE: This systematic review and meta-analysis aimed to determine the pooled prevalence of and factors associated with work-related musculoskeletal disorders (WMSDs) among low- and middle-income countries.

METHODS AND DESIGN: Databases such as PubMed/MEDLINE, CINAHL, LIVIVO, African Journals Online, African Index Medicus (AIM), HINARI, Science Direct, Web of Science, Cochrane Library, Google Scholar, Semantic Scholar and Google were used to retrieve all the relevant articles. The search was carried out from 22 April 2024 to 26 June 2024. Data were analysed via STATA 17 software. With a 95% CI, this meta-analysis with a random-effects model was carried out to determine the pooled prevalence.

SETTING: The study was conducted in low- and middle-income countries.

PARTICIPANTS: Weavers of low- and middle-income countries.

OUTCOME MEASURES: The primary outcome of this study was the prevalence of WMSD.

RESULT: In this meta-analysis, a total of 21 articles with 7322 study participants were included. The pooled prevalence of WMSDs was 72.20%. Working more than 8 hours per day, working in a chair with no back support, working in an uncomfortable posture, not performing regular physical exercise, lacking knowledge of the causes of WMSD and lacking job satisfaction were factors significantly associated with WMSDs.

CONCLUSION: A high prevalence of WMSDs among weavers in low- and middle-income countries was recorded. This indicates the need to take effective intervention measures. Rigorous ergonomic training, providing lengthy breaks and building centres for physical exercise, improving workplace ergonomic design and increasing job satisfaction are recommended.

PROSPERO REGISTRATION NUMBER: CRD42024561064.

PMID:40754326 | DOI:10.1136/bmjopen-2024-093124

Biomed Pharmacother. 2025 Aug 2;190:118392. doi: 10.1016/j.biopha.2025.118392. Online ahead of print.

ABSTRACT

Cardiovascular diseases (CVDs) are a leading cause of morbidity and mortality worldwide. Genetics factors play a significant role in the development of CVDs, which may result from monogenic or polygenic influences. These genetic contributions can lead to various life-threatening conditions such as cardiomyopathies, aortopathies, dyslipidemias, and arrhythmias, many of which are associated with sudden cardiac death (SCD) in adults. Early genetic screening and diagnosis enable timely intervention, not only for affected individuals but also for at-risk family members. Advances in molecular genetics and pharmacogenomic are transforming the management of cardiovascular diseases. By identifying genetic variants that influence drug metabolism, clinicians can tailor drug selection and dosing to individual patients, paving the way for personalized treatment strategies. This review will explore the role of genetics and pharmacogenetics in cardiovascular medicine, with a focus on how genetic insights inform risk stratification and guide therapy. In particular, the review will examine the pharmacogenetic considerations in the use of anticoagulant, antiplatelet agents, lipid-lowering therapies, direct-acting vasodilators, antiarrhythmics, renin-angiotensin system inhibitors, and diuretics ultimately supporting the implementation of personalized clinical interventions to achieve optimal patient care outcomes.

PMID:40753939 | DOI:10.1016/j.biopha.2025.118392

Mutat Res Rev Mutat Res. 2025 Aug 2;796:108556. doi: 10.1016/j.mrrev.2025.108556. Online ahead of print.

ABSTRACT

Epilepsy is a multifaceted and heterogenous neurological disorder that affects an estimated 70 million people worldwide and is identified by recurrent or unprovoked seizure activity. Although there have been advances in pharmacotherapeutic treatments, approximately one-third of patients with epilepsy remain drug resistant, highlighting the need for personalised and mechanism-based strategies. Neurogenetic biomarkers are emerging as valuable instruments for translating the genetic findings to the bedside and may provide new opportunities within a more precise treatment paradigm in epilepsy. Neurogenetic biomarkers include single-nucleotide polymorphisms (SNPs), copy number variants (CNVs), and mutations in disease-specific genes that inform our knowledge about the genetic architecture of seizure susceptibility, seizure progression and therapeutic response. The main genes, such as SCN1A, KCNQ2, GRIN2A, LGI1, GABRA1, and CHRNA4, impact neuronal excitability, ion channel dynamics, and synaptic interactions. Variations of mTOR signaling pathways (TSC1, TSC2, DEPDC5) and mutations in epigenetic regulators (MECP2, CDKL5) implicated a multilayered structure in the mechanistic underpinnings of epileptogenesis. Neurogenetic biomarkers are increasingly relevant to clinical practice for refining diagnosis, predicting seizure onset, guiding drug selection, and determining surgical intervention. The integration of neurogenetic sampling with neuroimaging, electrophysiological, inflammatory, and molecular signatures can improve diagnostic precision and provide an evidence-based framework towards therapeutic stratification. Although challenges remain-such as genetic heterogeneity, variant interpretation, cost barriers, and ethical considerations, advances in next-generation sequencing, pharmacogenomics, and artificial intelligence are rapidly transforming these limitations into opportunities. Neurogenetic biomarkers hold transformative potential to redefine epilepsy care, enabling earlier diagnosis, individualized therapy, and improved long-term outcomes. As the field advances, they are poised to shift epilepsy management from reactive to predictive, and from generalized to precision-driven, initiating a new era of neurology.

PMID:40753872 | DOI:10.1016/j.mrrev.2025.108556

Drug Metab Dispos. 2025 Jul 12;53(8):100121. doi: 10.1016/j.dmd.2025.100121. Online ahead of print.

ABSTRACT

Although racial differences in drug response have been well documented, the mechanisms underlying these variations remain incompletely understood. The racial differences may be partially attributed to variations in the expression of drug-metabolizing enzymes (DMEs) between racial groups. We conducted a proteomics analysis of selected clinically relevant DMEs, including 12 cytochrome P450s, 10 UDP-glucuronosyltransferases (UGTs), 19 transferases, and 11 hydrolases, in liver samples from White and Black Americans and compared the protein expression levels between the 2 groups. Among the DMEs examined, CYP2C9, CYP3A5, CYP2E1, UGT1A6, UGT2B15, UGT2B4, UGT2B10, GSTA1, GSTA2, MGST1, TPMT, SULT1B1, ALB, and PON3 exhibited significantly different expression levels between the 2 populations. Genetic analysis of CYP2C9 and CYP3A5 was performed to assess the impact of genetic polymorphisms on the differential protein expression. CYP2C9 protein expression levels were significantly lower in carriers of the ∗8 and ∗11 alleles, which were found exclusively in Black Americans. Although CYP2C9 expression levels were also lower in Black subjects within the ∗1/∗1 and ∗2 or ∗3 carrier groups, the differences were not statistically significant. These results indicate that the lower CYP2C9 protein expression in Black is due to both population-specific genetic variants (ie, ∗8 and ∗11) and potentially unknown genetic or nongenetic regulators. CYP3A5 protein levels were significantly higher in Black Americans compared to White counterparts, mainly due to the greater prevalence of the functional ∗1 allele in the Black population. Our findings provide crucial insights into racial differences in hepatic DME expression and support the development of ancestry-informed personalized pharmacotherapy strategies. SIGNIFICANCE STATEMENT: This study, to our knowledge, provides the first comprehensive protein-level analysis of drug-metabolizing enzymes across racial groups, revealing differences in key enzyme expression between White and Black Americans and the associated genetic polymorphisms. These findings advance our understanding of racial differences in drug metabolism, supporting the development of an ancestry-informed personalized pharmacotherapy approach.

PMID:40753786 | DOI:10.1016/j.dmd.2025.100121

J Cyst Fibros. 2025 Aug 2:S1569-1993(25)01538-3. doi: 10.1016/j.jcf.2025.07.015. Online ahead of print.

ABSTRACT

BACKGROUND: Data from cystic fibrosis (CF) animal models and case studies suggests that in utero administration of CF transmembrane conductance regulator (CFTR) modulators (variant specific therapies, VST) can rescue CFTR-related pathophysiology in the fetus. Use of VST during pregnancy to prevent disease in infants has not been systematically studied. Through stakeholder engagement, we sought to determine if formal research evaluation is warranted.

METHODS: We surveyed CF care center directors to assess their awareness of the potential off-label use of VST for in utero treatment of a fetus with CF. We then conducted a one-day, international multidisciplinary workshop to review available pre-clinical and clinical data, embryology principles and federal drug regulation considerations, identify knowledge gaps, and consider future clinical study designs.

RESULTS: Sixty-two unique individuals responded to the survey; 92% were aware of use of VST to treat pregnant females who are CF carriers for the prevention of CF complications in the fetus. Expert workshop presentations suggested that use of VST in pregnant females carrying a fetus with CF to mitigate complications of CF is relatively safe and effective in animal models and human case series to date. Further research is needed to understand the optimal timing of VST initiation during pregnancy to improve clinical outcomes, to understand VST pharmacokinetics, and optimize dosing of VST during pregnancy and lactation, and to evaluate the long-term infant safety among those exposed to VST in utero.

CONCLUSIONS: Based on available data and knowledge gaps, stakeholders agreed that formal evaluation of in utero and early life VST therapy in a prospective trial is warranted.

PMID:40754574 | DOI:10.1016/j.jcf.2025.07.015

Sci Rep. 2025 Aug 3;15(1):28305. doi: 10.1038/s41598-025-10661-3.

ABSTRACT

With the growth of social media, people are sharing more content than ever, including X posts that reflect a variety of emotions and opinions. AI-generated synthetic text, known as deepfake text, is used to imitate human writing to disseminate misleading information and fake news. However, as deepfake technology continues to grow, it becomes harder to accurately understand people's opinions on deepfake posts. Existing sentiment analysis algorithms frequently fail to capture the domain-specific, misleading, and context-sensitive characteristics of deepfake-related content. This study proposes a hybrid deep learning (DL) approach and novel transfer learning (TL)-based feature extraction approach for deepfake posts' sentiment analysis. The transfer learning-based approach combines the strengths of the hybrid DL technique to capture global and local contextual information. In this study, we compare the proposed approach with a range of machine learning algorithms, as well as, DL techniques for validation. Different feature extraction techniques, such as a bag of words (BOW), term frequency-inverse document frequency (TF-IDF), word embedding features, and novel TL features that combine the LSTM and DT, are used to build the models. The ML models are fine-tuned with extensive hyperparameter tuning to enhance performance and efficiency. The sentiment analysis performance of each applied method is validated using the k-fold cross-validation. The experimental results indicate that the proposed LGR (LSTM+GRU+RNN) approach with novel TL features performs well with a 99% accuracy. The proposed approach helps detect and prevent the spread of deepfake content, keeping people and organizations safe from its negative effects. This study covers a crucial gap in evaluating deepfake-specific social media sentiment by providing a comprehensive, scalable mechanism for monitoring and reducing the effect of fake content online.

PMID:40754634 | DOI:10.1038/s41598-025-10661-3

Sci Rep. 2025 Aug 3;15(1):28321. doi: 10.1038/s41598-025-14111-y.

ABSTRACT

Energy Harvesting Wireless Sensor Networks (EH-WSNs) are widely adopted for their ability to harvest ambient energy. However, these networks face significant challenges due to the limited and continuously varying energy availability at individual nodes, which depends on unpredictable environmental sources. To operate effectively in such conditions, energy fluctuations need to be regulated. This requires continuous monitoring of each node's energy level over time and adaptively adjusting operations. State-of-the-art mechanisms often categorize nodes or discretize energy levels, leading to issues such as the inability to select appropriate actions based on the actual energy states of the nodes. This discretization simplifies the representation of energy states and reduces complexity, making it easier to design and implement. However, it overlooks subtle variations in energy levels, leading to inaccurate assessments and suboptimal performance. To overcome this limitation, this paper proposes an energy-aware transmission method based on the Deep Reinforcement Learning (DRL) algorithm that integrates Q-learning with Deep Neural Networks (DNNs). This method enables each node to adaptively select transmission actions based on its real-time energy state, improving responsiveness to dynamic network conditions. Simulation results show that the proposed method improves throughput by 11.79% compared to traditional methods. These findings demonstrate the effectiveness of DRL-based control in enhancing performance and energy efficiency in EH-WSNs.

PMID:40754616 | DOI:10.1038/s41598-025-14111-y

Sci Rep. 2025 Aug 3;15(1):28295. doi: 10.1038/s41598-025-13289-5.

ABSTRACT

Electroencephalography (EEG) signals based emotion brain computer interface (BCI) is a significant field in the domain of affective computing where EEG signals are the cause of reliable and objective applications. Despite these advancements, significant challenges persist, including individual differences in EEG signals across subjects during emotion recognition. To cope this challenge, current study introduces a cutting-edge cross subject contrastive learning (CSCL) scheme for EEG signals representation of brain region. The proposed scheme addresses the generalisation across subjects directly, which is a primary challenge in EEG signals-based emotions recognition. The proposed CSCL scheme captures the complex patterns effectively by employing emotions and stimulus contrastive losses within hyperbolic space. CSCL is designed primarily to learn representations that can effectively distinguish signals originating from different brain regions. Further, we evaluate the significance of our proposed CSCL scheme on five different datasets, including SEED, CEED, FACED and MPED, and obtain 97.70%, 96.26%, 65.98%, and 51.30% respectively. The experimental results show that our proposed CSCL scheme demonstrates strong effectiveness while addressing the challenges related to cross subject variability and label noise in the EEG-based emotion recognition system.

PMID:40754610 | DOI:10.1038/s41598-025-13289-5

Mil Med Res. 2025 Aug 4;12(1):42. doi: 10.1186/s40779-025-00633-z.

ABSTRACT

Conventional diagnostic and therapeutic approaches in orthopedics are frequently time intensive and associated with elevated rates of diagnostic error, underscoring the urgent need for more efficient tools to improve the current situation. Recently, artificial intelligence (AI) has been increasingly integrated into orthopedic practice, providing data-driven approaches to support diagnostic and therapeutic processes. With the continuous advancement of AI technologies and their incorporation into routine orthopedic workflows, a comprehensive understanding of AI principles and their clinical applications has become increasingly essential. The review commences with a summary of the core concepts and historical evolution of AI, followed by an examination of machine learning and deep learning frameworks designed for orthopedic clinical and research applications. We then explore various AI-based applications in orthopedics, including image analysis, disease diagnosis, and treatment approaches such as surgical assistance, drug development, rehabilitation support, and personalized therapy. These applications are designed to help researchers and clinicians gain a deeper understanding of the current applications of AI in orthopedics. The review also highlights key challenges and limitations that affect the practical use of AI, such as data quality, model generalizability, and clinical validation. Finally, we discuss possible future directions for improving AI technologies and promoting their safe and effective integration into orthopedic care.

PMID:40754583 | DOI:10.1186/s40779-025-00633-z

Sci Rep. 2025 Aug 3;15(1):28314. doi: 10.1038/s41598-025-13742-5.

ABSTRACT

Intracerebral hemorrhage (ICH) is a medical emergency that demands rapid and accurate diagnosis for optimal patient management. Hemorrhagic lesions' segmentation on CT scans is a necessary first step for acquiring quantitative imaging data that are becoming increasingly useful in the clinical setting. However, traditional manual segmentation is time-consuming and prone to inter-rater variability, creating a need for automated solutions. This study introduces a novel approach combining advanced deep learning models to segment extensive and morphologically variable ICH lesions in non-contrast CT scans. We propose a two-step methodology that begins with a user-defined loose bounding box around the lesion, followed by a fine-tuned YOLOv8-S object detection model to generate precise, slice-specific bounding boxes. These bounding boxes are then used to prompt the Medical Segment Anything Model for accurate lesion segmentation. Our pipeline achieves high segmentation accuracy with minimal supervision, demonstrating strong potential as a practical alternative to task-specific models. We evaluated the model on a dataset of 252 CT scans demonstrating high performance in segmentation accuracy and robustness. Finally, the resulting segmentation tool is integrated into a user-friendly web application prototype, offering clinicians a simple interface for lesion identification and radiomic quantification.

PMID:40754551 | DOI:10.1038/s41598-025-13742-5

BMC Biol. 2025 Aug 4;23(1):236. doi: 10.1186/s12915-025-02359-9.

ABSTRACT

BACKGROUND: Prediction of protein-protein interactions (PPIs) is fundamental for identifying drug targets and understanding cellular processes. The rapid growth of PPI studies necessitates the development of efficient and accurate tools for automated prediction of PPIs. In recent years, several robust deep learning models have been developed for PPI prediction and have found widespread application in proteomics research. Despite these advancements, current computational tools still face limitations in modeling both the pairwise interactions and the hierarchical relationships between proteins.

RESULTS: We present HI-PPI, a novel deep learning method that integrates hierarchical representation of PPI network and interaction-specific learning for protein-protein interaction prediction. HI-PPI extracts the hierarchical information by embedding structural and relational information into hyperbolic space. A gated interaction network is then employed to extract pairwise features for interaction prediction. Experiments on multiple benchmark datasets demonstrate that HI-PPI outperforms the state-of-the-art methods; HI-PPI improves Micro-F1 scores by 2.62%-7.09% over the second-best method. Moreover, HI-PPI offers explicit interpretability of the hierarchical organization within the PPI network. The distance between the origin and the hyperbolic embedding computed by HI-PPI naturally reflects the hierarchical level of proteins.

CONCLUSIONS: Overall, the proposed HI-PPI effectively addresses the limitations of existing PPI prediction methods. By leveraging the hierarchical structure of PPI network, HI-PPI significantly enhances the accuracy and robustness of PPI predictions.

PMID:40754535 | DOI:10.1186/s12915-025-02359-9

Enferm Infecc Microbiol Clin (Engl Ed). 2025 Aug-Sep;43(7):402-410. doi: 10.1016/j.eimce.2025.06.004.

ABSTRACT

OBJECTIVE: Glucocorticoids are vital in treating COVID-19, but standard dosage for noncritical patients remain controversial. To determine the optimal glucocorticoid dosage for noncritical COVID-19 patients, we analyzed factors influencing dosage and developed a predictive model.

METHODS: We retrospectively analyzed 273 noncritical COVID-19 pneumonia patients underwent pulmonary CT and treated with glucocorticoids in a tertiary hospital (12/2022-01/2023). Patients were divided into low and high glucocorticoid dosage groups based on a daily 40mg methylprednisolone or equivalent. Artificial intelligence (AI)-based deep learning was utilized to assess pulmonary CT images for accurate lesion area, which then analyzed through multivariable logistic regression to explore their correlation with glucocorticoid dosage. A predictive model was developed and validated for dosage prediction.

RESULTS: The primary analysis included 243 patients, with 168 in the training set and 75 in the validation set. High-dose treatment was administered to 139 patients (82.7%) and low-dose to 29 patients (17.3%) in the training cohort. A predictive model incorporating normally inflated ratio, ground-glass opacity (GGO) ratio, and consolidation ratio accurately predicted selection of high- or low-dose, in both training (AUC=0.803) and validation cohorts (AUC=0.836), respectively. In 30 patients with post-CT adjusted dosages, the predicted dosages highly matched with the actual adjusted dosages.

CONCLUSION: Glucocorticoid dosages for noncritical COVID-19 pneumonia treatment are influenced by pulmonary CT features. Our predictive model can predict glucocorticoid dosage, however, should be validated by larger, prospective studies.

PMID:40754353 | DOI:10.1016/j.eimce.2025.06.004

Ann Oncol. 2025 Aug 1:S0923-7534(25)00910-X. doi: 10.1016/j.annonc.2025.07.013. Online ahead of print.

NO ABSTRACT

PMID:40754034 | DOI:10.1016/j.annonc.2025.07.013

Neuropsychologia. 2025 Aug 2:109242. doi: 10.1016/j.neuropsychologia.2025.109242. Online ahead of print.

ABSTRACT

Action suppresses the neural responses to its sensory feedback. The phenomenon, termed action-induced suppression, highlights the predictive processes in sensorimotor integration but remains controversial regarding the underlying mechanisms. The predictive coding framework posits that action-induced suppression is a general, non-action-specific process driven by predictions. In contrast, the Dual-Stream Prediction Model (DSPM) argues that motor-based and memory-based predictions are mediated by distinct processes - motor predictions rely on precise action-perception mappings and temporal synchrony, whereas memory predictions are based on learned associations. To test these competing theories, we compared auditory ERP responses elicited by self-initiated keypresses (motor-based) and visually cued auditory events (memory-based) in a matching judgment task. Results revealed significant suppression at the P2 component, when the prediction matched the auditory feedback only in the motor-auditory task but not in the visual-auditory task. The findings qualitatively replicated common observations of action-induced suppression; the suppression effects are at a later component rather than N1, indicating the interaction between prediction and perception at a higher level, such as syllable categorization in the current experimental design. Surprisingly, we observed N1 enhancement to the auditory probe in both conditions, with greater enhancement in the motor-auditory task compared to the visual-auditory task. The enhancement effects likely reflect a prediction-induced attentional-like modulation at an early auditory processing stage, potentially driven by the demands of the matching judgment task. Together, these findings support the DSPM by demonstrating functional dissociable mechanisms of motor-based and memory-based predictions.

PMID:40754023 | DOI:10.1016/j.neuropsychologia.2025.109242

Comput Biol Med. 2025 Aug 2;196(Pt B):110860. doi: 10.1016/j.compbiomed.2025.110860. Online ahead of print.

ABSTRACT

Leukemia is a hematologic tumor that proliferates in bone marrow and seriously affects the survival of patients. Early and accurate diagnosis is crucial for effective leukemia treatment. Traditional diagnostic methods rely on experts' subjective analysis of bone marrow smears microscopic images. This approach is time-consuming and complex. Despite recent advances in deep learning, automated leukemia detection remains limited due to the scarcity of high-quality datasets, the prevailing focus on single-cell image classification rather than precise cell-level detection in whole slide images, along with challenges such as morphological heterogeneity, uneven staining, scale variation, and occluded cell boundary in bone marrow smears. To address these challenges, we construct a novel dataset comprising 1794 high-quality microscopic images, establishing a new benchmark for lymphocytic leukemia detection. Additionally, we develop a fully automated diagnostic method based on spatially-guided learning (SGLNet), enabling rapid whole slide analysis of leukemia. Specifically, we introduce several innovative enhancements to the baseline algorithm, including the spatially-guided learning framework, scale-aware fusion module, small object-enhancing mechanisms, and efficient intersection over union loss function. These improvements effectively address the impact of morphological similarity and complex backgrounds in leukemia detection, significantly enhancing detection accuracy. Finally, the results show that SGLNet achieves mean average precision scores of 95.9 % and 98.6 % in detecting acute lymphoblastic leukemia and chronic lymphocytic leukemia, respectively. These results demonstrate the efficiency and accuracy of our method in identifying lymphoblastic leukemia cells, significantly enhancing large-scale clinical diagnosis, and supporting clinicians in developing personalized treatment plans.

PMID:40753948 | DOI:10.1016/j.compbiomed.2025.110860

Respir Investig. 2025 Aug 2;63(5):983-990. doi: 10.1016/j.resinv.2025.07.018. Online ahead of print.

ABSTRACT

BACKGROUND: Unilateral upper lung field pulmonary fibrosis (upper-PF) that is radiologically consistent with pleuroparenchymal fibroelastosis occasionally develops after lung cancer surgery in the operated side. However, the incidence and perioperative risk factors for unilateral upper-PF development remain unclear in lung cancer patients with interstitial lung disease (ILD).

METHODS: All consecutive lung cancer patients with ILD who underwent complete resection from 2008 to 2020 were investigated retrospectively. Pre-/postoperative characteristics were compared between patients with and without unilateral upper-PF. Cumulative incidence curves were estimated using competing risk analysis.

RESULTS: Among the 110 included patients, 20 patients (18.2 %) were diagnosed as developing unilateral upper-PF. The median interval from lung cancer surgery to unilateral upper-PF diagnosis was 16.5 months. The 3-, 5- and 10-year cumulative incidence was 15.5 %, 16.7 % and 20.1 %. In multivariable analysis, the presence of pulmonary apical cap and idiopathic pulmonary fibrosis (IPF) were independent perioperative risk factors. The 3-year cumulative incidence of unilateral upper-PF was 38.7 % in patients with pulmonary apical cap and 37.5 % in IPF patients. Pleural effusion at 6 months postoperatively was more frequent in patients who developed unilateral upper-PF later. During the clinical courses of 20 patients with unilateral upper-PF, 19 patients suffered from subsequent respiratory symptoms related to upper-PF and 14 patients died. Overall survival after unilateral upper-PF diagnosis was poor with a median of 33 months.

CONCLUSIONS: Thoracic surgeons and pulmonologists should recognize that unilateral upper-PF sometimes develops mostly within 3 years after lung cancer surgery as a poor prognostic late complication in ILD patients.

PMID:40753729 | DOI:10.1016/j.resinv.2025.07.018

Biol Pharm Bull. 2025;48(8):1165-1171. doi: 10.1248/bpb.b25-00330.

ABSTRACT

One of the most significant conceptual changes brought about by the discovery of clock genes and development of circadian-clock mutant mice is the recognition that impaired circadian rhythmicity extends its impact far beyond sleep, driving pathogenesis of a wide variety of disorders such as cancer, obesity, and hypertension. However, despite this growing clinical evidence, chronobiology still lacks a coherent answer to the converse question: can restoration of circadian rhythms ameliorate-or even reverse-such diseases? In this review, three complementary pharmacological strategies-each still in preclinical development-are explored. First, direct modulation of the transcription-translation feedback loop (TTFL)-the core gene-regulatory circuit that generates 24-h rhythms in almost all nucleated cells-is reviewed as an approach to manipulation of cellular circadian biology. Second, the suprachiasmatic nucleus (SCN)-enriched G-protein-coupled receptor Gpr176 is highlighted as a central-clock target, given its ligand-independent, Gz-mediated control of cAMP signaling and demonstrated ability to reset the master pacemaker. Third, the concept of rhythmic enhancement of output function is introduced and exemplified by describing re-activation of circadian oxidized form of nicotinamide adenine dinucleotide (NAD+)-dependent 3β-hydroxy-steroid dehydrogenase (3β-HSD) activity in the meibomian gland-using nicotinamide mononucleotide (NMN)-to restore peripheral clock-driven steroidogenesis in this tissue, which leads to amelioration of meibomian gland dysfunction, a leading cause of dry eye disease. This review aims to highlight the molecular logic of each strategy; both mechanistic insights and safety/efficacy considerations are discussed.

PMID:40754455 | DOI:10.1248/bpb.b25-00330

Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Aug 1:S2451-9022(25)00226-5. doi: 10.1016/j.bpsc.2025.07.010. Online ahead of print.

ABSTRACT

BACKGROUND: Neuroanatomical variation in individuals with bipolar disorder (BD) has been previously described in observational studies. However, the causal dynamics of these relationships remain unexplored.

METHODS: We performed Mendelian Randomization of 297 structural and functional neuroimaging phenotypes from the UK Biobank and BD using GWAS summary statistics. We carried out a suite of sensitivity analyses and examined phenotypic categories with the greatest effect on BD. We applied a novel inverse sparse regression model which accounts for covariance between sets of correlated effects to estimate 'direct causal effects' (DCE), representing the effect of one phenotype conditional on all other effects. We used DCE weights to create causal scores for BD using neuroimaging data from three clinical cohorts.

RESULTS: We found 28 significant causal relationship pairs after multiple testing corrections containing BD as a term, 27 of which described neuroimaging phenotype effects on BD. White matter tract phenotypes have larger absolute effects on BD than vice versa in MR tests and estimated direct causal effect solutions. We found that white matter phenotypes had significantly larger out-degrees than non-white matter tract phenotypes across network solutions. A causal score constructed using neuroimaging causal estimates was a significant predictor of BD in an adolescent cohort (O.R.=0.79).

CONCLUSION: Mendelian randomization analyses suggest that neuroanatomical variation, specifically in white matter tracts such as the longitudinal fasciculi, is likely a cause rather than a consequence of BD. Verification of estimated causal relationships requires replication and triangulation of evidence approaches using other study designs.

PMID:40754166 | DOI:10.1016/j.bpsc.2025.07.010

J Mol Biol. 2025 Aug 1:169370. doi: 10.1016/j.jmb.2025.169370. Online ahead of print.

ABSTRACT

Polyprotein processing is a common strategy in many positive sense single-stranded RNA ((+)ssRNA) viruses. This highly regulated process is crucial for viral progeny and ensures the release of functional replicase proteins in the correct location and at the right time. Coronaviruses (CoVs) have one of the largest genomes on average among (+)ssRNA viruses requiring a unique replication-transcription complex (RTC) with proofreading function that prevents error catastrophe. Two thirds of the CoV genome encode for the non-structural proteins (nsps) that drive replication. These are directly synthesized by RNA genome translation after infection as two large polyproteins pp1a and pp1ab. A regulated polyprotein proteolytic auto-processing is essential for viral growth and always has been an interesting target for therapeutics. Here, we present an overview of polyprotein processing and RTC research in CoVs in vitro and in vivo over the last 30 years. We highlight cutting-edge methodologies such as super resolution microscopy or structural mass spectrometry approaches and demonstrate how these have contributed to polyprotein research, e.g. by providing comprehensive structural models. We illustrate exciting examples of polyprotein processing in other viruses that could be transferred to CoVs, too. Additionally, we identify critical knowledge gaps in polyprotein processing and RTC assembly, proposing future perspectives to address these limitations.

PMID:40754154 | DOI:10.1016/j.jmb.2025.169370

Adv Gerontol. 2025;38(2):171-180.

ABSTRACT

Osteoporosis of the jawbones is a significant concern in dental practice, particularly for implant treatment planning. This review summarizes current diagnostic approaches with a focus on the use of artificial intelligence (AI) algorithms, including convolutional neural networks, for analyzing panoramic radiographs and cone-beam computed tomography. The findings demonstrate that AI models achieve high diagnostic accuracy in the automated classification of radiographic images, comparable to dual-energy X-ray absorptiometry. AI reduces subjectivity in image interpretation, although further standardization, dataset expansion, and development of explainable models are necessary. The review highlights comparative metrics of various neural network architectures and their potential for integration into clinical workflows.

PMID:40753551