Redox Biol. 2025 Mar 29;82:103624. doi: 10.1016/j.redox.2025.103624. Online ahead of print.

ABSTRACT

Humans exposed to chlorine (Cl2) due to industrial accidents or acts of terrorism may develop lung injury culminating to Acute Respiratory Distress syndrome and death from respiratory failure. Early molecular targets of inhaled oxidant gases suitable for pharmacologic modulation have not been established. Because the mitochondrial genome is highly sensitive to oxidant stress, we tested the hypothesis that mice exposure to Cl2 gas causes oxidative damage to the mitochondrial DNA (mtDNA) that triggers the development of acute and chronic lung injury. Cl2 gas-exposed C57BL/6 mice and returned to room air, developed progressive loss of lung DNA glycosylase OGG1, followed by oxidative mtDNA damage. This resulted in activation of inflammatory pathways by circulating DNA, progressive increased airway resistance, alveolar injury and acute pulmonary edema due to loss of epithelial amiloride-sensitive sodium channels. Mice not succumbing acutely displayed a delayed syndrome of progressive increase in airway resistance and emphysematous-like changes in lung morphology. Global proteomics of lungs harvested 24 h post Cl2 exposure revealed alterations in over 1500 lung proteins, including 14 key mitochondrial proteins. Intranasal instillation of a recombinant protein targeting OGG1 to mitochondria (mitoOGG1) at 1 h post exposure decreased oxidized lung mtDNA, alterations to the lung and mitochondrial proteomes, severity of the acute and delayed lung injury and increased survival. These data show that injury to the mt-genome is a key contributor to the development of acute and chronic lung injury after Cl2 gas exposure and point to mtDNA oxidation as a target for pharmacologic intervention.

PMID:40209617 | DOI:10.1016/j.redox.2025.103624

Drug Ther Bull. 2025 Apr 10:dtb-2025-000013. doi: 10.1136/dtb.2025.000013. Online ahead of print.

NO ABSTRACT

PMID:40210451 | DOI:10.1136/dtb.2025.000013

Drug Ther Bull. 2025 Apr 10:dtb-2025-000014. doi: 10.1136/dtb.2025.000014. Online ahead of print.

NO ABSTRACT

PMID:40210450 | DOI:10.1136/dtb.2025.000014

Clin Lab. 2025 Apr 1;71(4). doi: 10.7754/Clin.Lab.2024.241019.

ABSTRACT

BACKGROUND: This case report presents the history, findings, and diagnostic workup of a 28-year-old woman who presented to the hospital with a yellowish, parasite-like structure in her stool.

METHODS: The patient had no significant gastrointestinal complaints other than weight loss and decreased appetite, and no parasites or leukocytes were detected on direct examination of the stool.

RESULTS: No eosinophilia, elevated C-reactive protein or leukocytosis was detected in laboratory tests. It was suggested that the structure in the stool of the patient whose symptoms occurred after taking extended-release metformin could be a ghost tablet, and further investigation was not considered necessary.

CONCLUSIONS: The report emphasizes the importance of considering drug-related side effects, especially ghost pills, in patients with atypical stool findings to avoid unnecessary investigations and anxiety.

PMID:40209786 | DOI:10.7754/Clin.Lab.2024.241019

IEEE J Biomed Health Inform. 2025 Apr 10;PP. doi: 10.1109/JBHI.2025.3559570. Online ahead of print.

ABSTRACT

Drug repositioning, which identifies new therapeutic potential of approved drugs, is instrumental in accelerating drug discovery. Recently, to alleviate the effect of data sparsity on predicting possible drug-disease associations (DDAs), graph contrastive learning (GCL) has emerged as a promising paradigm for learning discriminative representations of drugs and diseases through distilling informative self-supervised signals. However, existing GCLbased methods devised for DDA prediction still encounter two limitations. Firstly, the crucial heterogeneous property, which allows for capturing nuanced interaction semantics between biological entities, is overlooked. The second is how to perform contrastive view augmentation without relying on stochastic perturbation. In this study, we propose a novel multi-view contrastive learning approach for DDA prediction, namely MICLE. To handle the first issue, proteinrelated bipartite graphs are integrated with the original DDA network in advance, thereby composing a heterogeneous biological network (HBN). Besides, heterogeneous graph neural network is applied to mine the rich connectivity patterns implicit in the above HBN. For the second limitation, we design the complementary inter-view and intra-view contrastive learning tasks. Specifically, the former ensures that the mutual information between paired nodes across views is maximized, the latter enhances the agreement between each node and its first-order neighbors on similarity networks. Extensive experiments conducted on three benchmarks under 10-fold cross-validation demonstrate the model effectiveness. Source code and datasets are available at https://github.com/OleCui/paper MICLE.

PMID:40208759 | DOI:10.1109/JBHI.2025.3559570

Pharmacogenomics. 2025 Apr 10:1-14. doi: 10.1080/14622416.2025.2490464. Online ahead of print.

ABSTRACT

Systemic lupus erythematosus (SLE) is a complex autoimmune disease requiring immunosuppressive medications to control symptoms and prevent organ damage. This review explores the influence of genetic polymorphisms on the pharmacokinetics and therapeutic responses of immunosuppressants in SLE. A total of 37 studies were reviewed, focusing on mycophenolic acid, tacrolimus, azathioprine, glucocorticoids, and cyclophosphamide. Genetic variants in UGT1A9, UGT2B7, CYP3A5, ABCB1,ABCC2 and TPMT significantly affect drug metabolism, efficacy, and toxicity. For instance, ABCB1 polymorphisms influence drug transport and bioavailability, impacting tacrolimus and glucocorticoid response, while ABCC2 variants alter MPA clearance, potentially affecting therapeutic outcomes, UGT1A9 and UGT2B7 variants influence mycophenolic acid metabolism, CYP3A5 impacts tacrolimus dosing, TPMT determines azathioprine metabolism, and CYP2C19 and CYP2B6 affect cyclophosphamide processing. These genetic differences can alter treatment effectiveness and risk of adverse effects. However, most pharmacogenetic studies focus on organ transplantation, leaving a critical gap in SLE-specific research, particularly among diverse populations. Addressing this gap is essential to optimizing personalized treatment for SLE. Integrating pharmacogenetics into clinical practice holds great potential to enhance the safety, efficacy, and outcomes of immunosuppressive therapy in SLE. This review highlights the urgent need for further studies to advance precision medicine for SLE patients.

PMID:40208755 | DOI:10.1080/14622416.2025.2490464

Am J Respir Crit Care Med. 2025 Apr 10. doi: 10.1164/rccm.202411-2231OC. Online ahead of print.

ABSTRACT

RATIONALE: Clinical and real-world studies show elexacaftor/tezacaftor/ivacaftor (ELX/TEZ/IVA) is efficacious and safe in people with cystic fibrosis (CF) ≥12 years of age with at least one F508del allele.

OBJECTIVES: Given the potential for life-long ELX/TEZ/IVA use, long-term safety and efficacy of ELX/TEZ/IVA was assessed.

METHODS: In this phase 3, open-label, single-arm extension study, participants with F508del-minimal function genotypes (from 24-week parent study 445-102 [n = 399]) or with the F508del-F508del genotype (from 4-week parent study 445-103 [n = 107]) received ELX/TEZ/IVA (ELX 200 mg once daily, TEZ 100 mg once daily, and IVA 150 mg every 12 hours) over 192 weeks.

MEASUREMENTS AND MAIN RESULTS: Primary endpoint was safety and tolerability. Mean exposure to ELX/TEZ/IVA was 172.6 weeks. Most participants had adverse events classified as mild (12.8%) or moderate (60.7%) in severity. Eighteen participants (3.6%) had adverse events that led to treatment discontinuation. After starting ELX/TEZ/IVA, participants had consistent increases in percent predicted FEV1 (ppFEV1), Cystic Fibrosis Questionnaire-Revised respiratory domain score, and body mass index, with decreases in sweat chloride concentration and pulmonary exacerbations rates; these improvements were maintained through 192 weeks. The mean annualized rate of change in ppFEV1 was 0.02 percentage points (95% CI, -0.14 to 0.19) after initiation of ELX/TEZ/IVA.

CONCLUSIONS: During this 192-week open label extension study, the longest clinical study of a CFTR modulator to date, ELX/TEZ/IVA remained generally safe and well-tolerated. Participants had sustained improvements in lung function, respiratory symptoms, CFTR function, pulmonary exacerbation rates, and nutritional status. The estimated annualized rate of change in ppFEV1 suggests no evidence of pulmonary function loss across the study population over the 4-year treatment period. These results confirm the favorable long-term safety profile and durable disease-modifying clinical benefits of ELX/TEZ/IVA in adolescents and adults with CF. Clinical trial registration available at www.

CLINICALTRIALS: gov, ID: NCT03525574.

PMID:40209082 | DOI:10.1164/rccm.202411-2231OC

Comput Med Imaging Graph. 2025 Apr 7;123:102538. doi: 10.1016/j.compmedimag.2025.102538. Online ahead of print.

ABSTRACT

Patent foramen ovale (PFO) is one of the main causes of ischemic stroke. Due to the complex characteristics of contrast transthoracic echocardiography (cTTE), PFO classification is time-consuming and laborious in clinical practice. For this reason, a variety of PFO diagnostic models have been presented based on machine learning in recent years. However, existing models have lower diagnostic accuracy due to similar gray values of microbubbles and surrounding myocardial tissue in cTTE. Meanwhile, the greater volume of right-to-left shunt (RLS) volume leads to a higher incidence of migraine and stroke. Existing models do not quantify the severity of RLS, which affects the use of treatment methods in later clinical treatment. To solve these problems, we propose TVUNet++ for left ventricular segmentation and ULSAM-ResNet for PFO classification. More specifically, TVUNet++ can distinguish various local features in cTTE through learnable affinity maps and implicitly capture the semantic relationship between the left heart cavity and the background region. In addition, we provide a benchmark cTTE dataset to evaluate the performance of the proposed model through various experiments. Experimental results show that the average Dice Coefficient of the proposed model can reach 92.11%. Moreover, ULSAM-ResNet can realize multi-scale and multi-frequency feature learning through multiple subspaces and learn cross-channel information for accurate grade classification efficiently. The average recall of static cTTE can reach 84.27%. Furthermore, the proposed model outperforms state-of-the-art models in the grade classification of PFO.

PMID:40209281 | DOI:10.1016/j.compmedimag.2025.102538

J Med Internet Res. 2025 Apr 10;27:e67883. doi: 10.2196/67883.

ABSTRACT

BACKGROUND: Ocular myasthenia gravis (OMG) is a neuromuscular disorder primarily affecting the extraocular muscles, leading to ptosis and diplopia. Effective patient education is crucial for disease management; however, in China, limited health care resources often restrict patients' access to personalized medical guidance. Large language models (LLMs) have emerged as potential tools to bridge this gap by providing instant, AI-driven health information. However, their accuracy and readability in educating patients with OMG remain uncertain.

OBJECTIVE: The purpose of this study was to systematically evaluate the effectiveness of multiple LLMs in the education of Chinese patients with OMG. Specifically, the validity of these models in answering patients with OMG-related questions was assessed through accuracy, completeness, readability, usefulness, and safety, and patients' ratings of their usability and readability were analyzed.

METHODS: The study was conducted in two phases: 130 choice ophthalmology examination questions were input into 5 different LLMs. Their performance was compared with that of undergraduates, master's students, and ophthalmology residents. In addition, 23 common patients with OMG-related patient questions were posed to 4 LLMs, and their responses were evaluated by ophthalmologists across 5 domains. In the second phase, 20 patients with OMG interacted with the 2 LLMs from the first phase, each asking 3 questions. Patients assessed the responses for satisfaction and readability, while ophthalmologists evaluated the responses again using the 5 domains.

RESULTS: ChatGPT o1-preview achieved the highest accuracy rate of 73% on 130 ophthalmology examination questions, outperforming other LLMs and professional groups like undergraduates and master's students. For 23 common patients with OMG-related questions, ChatGPT o1-preview scored highest in correctness (4.44), completeness (4.44), helpfulness (4.47), and safety (4.6). GEMINI (Google DeepMind) provided the easiest-to-understand responses in readability assessments, while GPT-4o had the most complex responses, suitable for readers with higher education levels. In the second phase with 20 patients with OMG, ChatGPT o1-preview received higher satisfaction scores than Ernie 3.5 (Baidu; 4.40 vs 3.89, P=.002), although Ernie 3.5's responses were slightly more readable (4.31 vs 4.03, P=.01).

CONCLUSIONS: LLMs such as ChatGPT o1-preview may have the potential to enhance patient education. Addressing challenges such as misinformation risk, readability issues, and ethical considerations is crucial for their effective and safe integration into clinical practice.

PMID:40209226 | DOI:10.2196/67883

PLoS One. 2025 Apr 10;20(4):e0320006. doi: 10.1371/journal.pone.0320006. eCollection 2025.

ABSTRACT

Obtaining the traces and the characteristics of elongated structures is an important task in computer vision pipelines. In biomedical applications, the analysis of traces of vasculature, nerves or fibres of the extracellular matrix can help characterise processes like angiogenesis or the effect of a certain treatment. This paper presents an objective comparison of six existing methodologies (Edge detection, CT Fire, Scale Space, Twombli, U-Net and Graph Based) and one novel approach called Trace Ridges to trace biomedical images with fibre-like structures. Trace Ridges is a fully automatic and fast algorithm that combines a series of image-processing algorithms including filtering, watershed transform and edge detection to obtain an accurate delineation of the fibre-like structures in a rapid time. To compare the algorithms, four biomedical data sets with vastly distinctive characteristics were selected. Ground truth was obtained by manual delineation of the fibre-like structures. Three pre-processing filtering options were used as a first step: no filtering, Gaussian low-pass and DnCnn, a deep-learning filtering. Three distance error metrics (total, average and maximum distance from the obtained traces to the ground truth) and processing time were calculated. It was observed that no single algorithm outperformed the others in all metrics. For the total distance error, which was considered the most significative, Trace Ridges ranked first, followed by Graph Based, U-Net, Twombli, Scale Space, CT Fire and Edge Detection. In terms of speed, Trace Ridges ranked second, only slightly slower than Edge Detection. Code is freely available at github.com/youssefarafat/Trace_Ridges.

PMID:40209168 | DOI:10.1371/journal.pone.0320006

Int J Med Robot. 2025 Apr;21(2):e70065. doi: 10.1002/rcs.70065.

ABSTRACT

BACKGROUND: Pancreatic diseases such as cancer and pancreatitis pose significant health risks. Early detection requires precise segmentation results. Fully automatic segmentation algorithms cannot integrate clinical expertise and correct output errors, while interactive methods can offer a better chance for higher accuracy and reliability.

METHODS: We proposed a new network-RRM-TransUNet for the interactive pancreas segmentation task in CT images aiming to provide more reliable and precise results. The network incorporates Rotary Position Embedding, Root Mean Square Normalisation, and a Mixture of Experts mechanism. An intuitive interface is constructed for user-aided pancreas segmentation.

RESULTS: RRM-TransUNet achieves outstanding performance on multiple datasets, with a Dice Similarity Coefficient (DSC) of 93.82% and an Average Symmetric Surface Distance error (ASSD) of 1.12 mm on MSD, 93.79%/1.15 mm on AMOS, and 93.68%/1.18 mm on AbdomenCT-1K.

CONCLUSION: Our method outperforms previous methods and provides doctors with an efficient and user-friendly interactive pancreas segmentation experience through the intuitive interface.

PMID:40209153 | DOI:10.1002/rcs.70065

PLoS Genet. 2025 Apr 10;21(4):e1011659. doi: 10.1371/journal.pgen.1011659. Online ahead of print.

ABSTRACT

Genome-wide association studies (GWAS) performed on large cohort and biobank datasets have identified many genetic loci associated with Alzheimer's disease (AD). However, the younger demographic of biobank participants relative to the typical age of late-onset AD has resulted in an insufficient number of AD cases, limiting the statistical power of GWAS and any downstream analyses. To mitigate this limitation, several trait imputation methods have been proposed to impute the expected future AD status of individuals who may not have yet developed the disease. This paper explores the use of imputed AD status in nonlinear transcriptome/proteome-wide association studies (TWAS/PWAS) to identify genes and proteins whose genetically regulated expression is associated with AD risk. In particular, we considered the TWAS/PWAS method DeLIVR, which utilizes deep learning to model the nonlinear effects of expression on disease. We trained transcriptome and proteome imputation models for DeLIVR on data from the Genotype-Tissue Expression (GTEx) Project and the UK Biobank (UKB), respectively, with imputed AD status in UKB participants as the outcome. Next, we performed hypothesis testing for the DeLIVR models using clinically diagnosed AD cases from the Alzheimer's Disease Sequencing Project (ADSP). Our results demonstrate that nonlinear TWAS/PWAS trained with imputed AD outcomes successfully identifies known and putative AD risk genes and proteins. Notably, we found that training with imputed outcomes can increase statistical power without inflating false positives, enabling the discovery of molecular exposures with potentially nonlinear effects on neurodegeneration.

PMID:40209152 | DOI:10.1371/journal.pgen.1011659

Br J Dermatol. 2025 Apr 10:ljaf133. doi: 10.1093/bjd/ljaf133. Online ahead of print.

NO ABSTRACT

PMID:40209097 | DOI:10.1093/bjd/ljaf133

JMIR Hum Factors. 2025 Apr 10;12:e58377. doi: 10.2196/58377.

ABSTRACT

BACKGROUND: The global increase in the Internet of Things (IoT) adoption has sparked interest in its application within the educational sector, particularly in colleges and universities. Previous studies have often focused on individual attitudes toward IoT without considering a multiperspective approach and have overlooked the impact of IoT on the technology acceptance model outside the educational domain.

OBJECTIVE: This study aims to bridge the research gap by investigating the factors influencing IoT adoption in educational settings, thereby enhancing the understanding of collaborative learning through technology. It seeks to elucidate how IoT can facilitate learning processes and technology acceptance among college and university students in the United Arab Emirates.

METHODS: A questionnaire was distributed to students across various colleges and universities in the United Arab Emirates, garnering 463 participants. The data collected were analyzed using a hybrid approach that integrates structural equation modeling (SEM) and artificial neural network (ANN), along with importance-performance map analysis to evaluate the significance and performance of each factor affecting IoT adoption.

RESULTS: The study, involving 463 participants, identifies 2 primary levels at which factors influence the intention to adopt IoT technologies. Initial influences include technology optimism (TOP), innovation, and learning motivation, crucial for application engagement. Advanced influences stem from technology acceptance model constructs, particularly perceived ease of use (PE) and perceived usefulness (PU), which directly enhance adoption intentions. Detailed statistical analysis using partial least squares-SEM reveals significant relationships: TOP and innovativeness impact PE (β=.412, P=.04; β=.608, P=.002, respectively), and PU significantly influences TOP (β=.381, P=.04), innovativeness (β=.557, P=.003), and learning motivation (β=.752, P<.001). These results support our hypotheses (H1, H2, H3, H4, and H5). Further, the intention to use IoT is significantly affected by PE and usefulness (β=.619, P<.001; β=.598, P<.001, respectively). ANN modeling enhances these findings, showing superior predictive power (R2=89.7%) compared to partial least squares-SEM (R2=86.3%), indicating a more effective identification of nonlinear associations. Importance-performance map analysis corroborates these results, demonstrating the importance and performance of PU as most critical, followed by technology innovativeness and optimism, in shaping behavioral intentions to use IoT.

CONCLUSIONS: This research contributes methodologically by leveraging deep ANN architecture to explore nonlinear relationships among factors influencing IoT adoption in education. The study underscores the importance of both intrinsic motivational factors and perceived technological attributes in fostering IoT adoption, offering insights for educational institutions considering IoT integration into their learning environments.

PMID:40209037 | DOI:10.2196/58377

Science. 2025 Apr 11;388(6743):eadq1521. doi: 10.1126/science.adq1521. Epub 2025 Apr 11.

ABSTRACT

Human pelvic evolution following the human-chimpanzee divergence is thought to result in an obstetrical dilemma, a mismatch between large infant brains and narrowed female birth canals, but empirical evidence has been equivocal. By using deep learning on 31,115 dual-energy x-ray absorptiometry scans from UK Biobank, we identified 180 loci associated with seven highly heritable pelvic phenotypes. Birth canal phenotypes showed sex-specific genetic architecture, aligning with reproductive function. Larger birth canals were linked to slower walking pace and reduced back pain but increased hip osteoarthritis risk, whereas narrower birth canals were associated with reduced pelvic floor disorder risk but increased obstructed labor risk. Lastly, genetic correlation between birth canal and head widths provides evidence of coevolution between the human pelvis and brain, partially mitigating the dilemma.

PMID:40208988 | DOI:10.1126/science.adq1521

PLoS Biol. 2025 Apr 10;23(4):e3003099. doi: 10.1371/journal.pbio.3003099. Online ahead of print.

ABSTRACT

Understanding how specific secretory cargoes are targeted to distinct domains of the plasma membrane in epithelial cells requires analyzing the trafficking of post-Golgi vesicles to their sites of secretion. We used the RUSH (retention using selective hooks) system to synchronously release an apical cargo, Cadherin 99C (Cad99C), and a basolateral cargo, the ECM protein Nidogen, from the endoplasmic reticulum and follow their movements to the plasma membrane. We also developed an interactive vesicle tracking framework, MSP-tracker and viewer, that exploits developments in computer vision and deep learning to determine vesicle trajectories in a noisy environment without the need for extensive training data. MSP-tracker outperformed other tracking software in detecting and tracking post-Golgi vesicles, revealing that Cad99c vesicles predominantly move apically with a mean speed of 1.1µm/sec. This is reduced to 0.85 µm/sec by a dominant slow dynein mutant, demonstrating that dynein transports Cad99C vesicles to the apical cortex. Furthermore, both the dynein mutant and microtubule depolymerization cause lateral Cad99C secretion. Thus, microtubule organization plays a central role in targeting apical secretion, suggesting that Drosophila does not have distinct apical versus basolateral vesicle fusion machinery. Nidogen vesicles undergo planar-polarized transport to the leading edge of follicle cells as they migrate over the ECM, whereas most Collagen is secreted at trailing edges. The follicle cells therefore bias secretion of different ECM components to opposite sides of the cell, revealing that the secretory pathway is more spatially organized than previously thought.

PMID:40208901 | DOI:10.1371/journal.pbio.3003099

PLoS One. 2025 Apr 10;20(4):e0320892. doi: 10.1371/journal.pone.0320892. eCollection 2025.

ABSTRACT

BACKGROUND: The allelic variations of the apolipoprotein E (APOE) gene play a critical role in regulating lipid metabolism and significantly impact cardiovascular disease risk (CVD). This study aimed to evaluate the impact of exercise on cardiac structure and function in mouse models expressing different APOE genotypes using photon-counting computed tomography (PCCT) and deep learning-based segmentation.

METHODS: A total of 140 mice were grouped based on APOE genotype (APOE2, APOE3, APOE4), sex, and exercise regimen. All mice were maintained on a controlled diet to isolate the effects of exercise. Low dose cardiac photon counting micro-CT imaging with intrinsic gating was performed using a custom-built micro-PCCT system and data was reconstructed with an iterative algorithm incorporating both temporal and spectral dimensions. A liposomal-iodine nanoparticle contrast agent was intravenously administered to uniformly opacify cardiovascular structures. Cardiac structures were segmented using a 3D U-Net deep learning model that was trained and validated on manually labeled data. Statistical analyses, including ANOVA, post-hoc analysis, and stratified group comparisons, were used to assess the effects of genotype, sex, and exercise on key cardiac metrics, including ejection fraction and cardiac index.

RESULTS: The PCCT imaging pipeline provided high-resolution images with enhanced contrast between blood compartment and myocardium allowing for precise segmentation of cardiac features. Deep learning-based segmentation achieved high accuracy with an average Dice coefficient of 0.85. Exercise significantly improved cardiac performance, with ejection fraction increasing by up to 18% and cardiac index by 46% in exercised males, who generally benefited more from exercise. Females, particularly those with the APOE4 genotype, also showed improvements, with a 31% higher ejection fraction in exercised versus non-exercised mice. Stratified analyses confirmed that both sexes benefited from exercise, with males showing larger effect sizes. APOE3 and APOE4 genotypes derived the greatest benefit, while APOE2 mice showed no significant improvement.

CONCLUSIONS: This study demonstrates the utility of PCCT combined with deep learning segmentation in assessing the cardioprotective effects of exercise in APOE mouse models. These findings highlight the importance of genotype-specific approaches in understanding and potentially mitigating the impact of CVD through lifestyle interventions such as exercise.

PMID:40208877 | DOI:10.1371/journal.pone.0320892

Science. 2025 Apr 11;388(6743):eadq1709. doi: 10.1126/science.adq1709. Epub 2025 Apr 11.

ABSTRACT

We used Hi-C, imaging, proteomics, and polymer modeling to define rules of engagement for SMC (structural maintenance of chromosomes) complexes as cells refold interphase chromatin into rod-shaped mitotic chromosomes. First, condensin disassembles interphase chromatin loop organization by evicting or displacing extrusive cohesin. Second, condensin bypasses cohesive cohesins, thereby maintaining sister chromatid cohesion as sisters separate. Studies of mitotic chromosomes formed by cohesin, condensin II, and condensin I alone or in combination lead to refined models of mitotic chromosome conformation. In these models, loops are consecutive and not overlapping, implying that condensins stall upon encountering each other. The dynamics of Hi-C interactions and chromosome morphology reveal that during prophase, loops are extruded in vivo at ∼1 to 3 kilobases per second by condensins as they form a disordered discontinuous helical scaffold within individual chromatids.

PMID:40208986 | DOI:10.1126/science.adq1709

Science. 2025 Apr 11;388(6743):176-180. doi: 10.1126/science.ads3888. Epub 2025 Apr 10.

ABSTRACT

Denisovans are an extinct hominin group defined by ancient genomes of Middle to Late Pleistocene fossils from southern Siberia. Although genomic evidence suggests their widespread distribution throughout eastern Asia and possibly Oceania, so far only a few fossils from the Altai and Tibet are confidently identified molecularly as Denisovan. We identified a hominin mandible (Penghu 1) from Taiwan (10,000 to 70,000 years ago or 130,000 to 190,000 years ago) as belonging to a male Denisovan by applying ancient protein analysis. We retrieved 4241 amino acid residues and identified two Denisovan-specific variants. The increased fossil sample of Denisovans demonstrates their wider distribution, including warm and humid regions, as well as their shared distinct robust dentognathic traits that markedly contrast with their sister group, Neanderthals.

PMID:40208980 | DOI:10.1126/science.ads3888

PLoS One. 2025 Apr 10;20(4):e0321015. doi: 10.1371/journal.pone.0321015. eCollection 2025.

ABSTRACT

BACKGROUND: HIV/AIDS remains a global health challenge, with significant prevalence in sub-Saharan Africa. Highly active antiretroviral therapy (HAART) is the mainstay treatment for HIV, and the number of people living with HIV (PLWHIV) on HAART has considerably increased worldwide. The use of HAART has led to improved patient outcomes; however, it is associated with adverse drug reactions (ADRs) and drug-drug interactions (DDIs), which pose serious concerns in the management of patients with HIV. The aim of the study was to determine the prevalence and factors associated with ADRs among patients on HAART.

METHODOLOGY: This was a hospital-based cross-sectional study carried out among 312 HIV patients on HAART attending HIV clinics at Mbarara Regional Hospital. Data was collected using an interviewer-administered, semi-structured questionnaire and a review of patient charts. ADRs were assessed for causality and categorized using Naranjo ADR assessment scale into probable, possible and definite, for severity using the modified Hartwig and Siegel criteria into mild, moderate and Severe, and for preventability using Schumock and Thornton criteria into definite, probable and non-preventable. Lexicomp® Drug Interaction Checker software was used to identify and rate clinically significant drug-drug interactions. The prevalence of ADRs and potential DDI was analyzed using descriptive statistics while logistic regression analysis was used to establish the association of variables.

RESULTS: 312 patients were interviewed and their records reviewed. The prevalence of ADRs during this study was 76.0%. On assessment, 78.3% of the ADRs were mild and 76.6% of ADRs were definitely preventable. CD4 count below 200 cells/mm3 (AOR = 1.00, 95% CI: 1.00-1.02; p value = 0.04), primary education level (AOR = 3.27, 95% CI: 1.34-7.95; p value = 0.009), and secondary education level (AOR = 3.64, 95% CI: 1.39-9.52; p value = 0.009) were identified as independent risk factors. Patients who experienced a significant DDI were 5.66 times more likely to experience an ADR (p value = 0.02, 95% CI: 1.32-24.18).

CONCLUSION: There is a high prevalence of adverse drug reactions among patients with HIV on HAART. Low CD4 count and lower education levels are risk factors for ADRs in this population; therefore, tailored interventions to these subgroups should be implemented for early ADR identification and management. Significant drug-drug interactions are highly associated with the occurrence of ADRs among HIV patients on HAART, which calls for intensified pharmacovigilance and pharmaceutical care in this population.

PMID:40208898 | DOI:10.1371/journal.pone.0321015