Proc Biol Sci. 2025 Feb;292(2041):20242805. doi: 10.1098/rspb.2024.2805. Epub 2025 Feb 19.

ABSTRACT

Music and rhythm are typical features of all human cultures, but their biological origins remain unclear. Recent investigations suggest that rhythmic features of human music are shared with animal vocalizations. Moreover, arousal is known to influence the structure of both human speech and animal sounds. We investigated coppery titi monkeys' (Plecturocebus cupreus) duet rhythms to assess adherence to rhythmic patterns previously observed only in Old World primates and to deepen our understanding of the proximate causes of non-human primate song rhythm. Titis' songs were remarkably isochronous, but their tempo depended on the social context: songs sung during territorial confrontations have a slower pace than during early morning singing. Songs had a faster tempo and were less regular when infants were present, suggesting a speed-accuracy trade-off. Finally, we found that pair-mates perform isochronous songs with the same precision, suggesting that isochrony plays a role in boosting pair coordination, as it does in other singing primates. Our investigation sheds light on the ultimate and proximate causes of primates' isochronous rhythm, to our knowledge confirming its presence for the first time in a New World monkey and highlighting the role of social factors in shaping its timing and regularity in the short term.

PMID:39968619 | DOI:10.1098/rspb.2024.2805

Med J Islam Repub Iran. 2024 Nov 6;38:129. doi: 10.47176/mjiri.38.129. eCollection 2024.

ABSTRACT

BACKGROUND: Diffuse large B-cell lymphoma (DLBL) and systemic lupus erythematosus (SLE) are complex autoimmune disorders that present unique clinical challenges. These conditions may share underlying genetic and signaling pathways despite their distinct manifestations. Uncovering these commonalities could offer invaluable insights into disease pathogenesis, paving the way for more targeted and effective therapeutic interventions. This study embarks on a comprehensive investigation of the common genes and signaling pathways between SLE and DLBL.

METHODS: The researchers scoured the Gene Expression Omnibus database, meticulously gathering microarray datasets for SLE (GSE61635) and DLBL (GSE56315). Differential expression analysis was performed, allowing the team to identify the genes that were commonly dysregulated across these 2 autoimmune conditions. To delve deeper into the biological significance of these shared genes, the researchers conducted functional enrichment analysis, network analysis, and core gene identification. Notably, the diagnostic potential of the identified hub genes was assessed using a cutting-edge neural network model.

RESULTS: The data analysis revealed a remarkable 146 genes that were shared between SLE and DLBL, of which 111 were upregulated and 45 downregulated. Functional enrichment analysis unveiled the involvement of these shared genes in vital immune system-related processes-such as defense response to viruses, interferon signaling, and broader immune system pathways. Network analysis pinpointed 5 hub genes (IFIT3, IFIT1, DDX58, CCL2, and OASL) that emerged as central players, exhibiting a high degree of centrality and predicted to hold crucial roles in the underlying molecular mechanisms. Remarkably, the neural network model demonstrated exceptional diagnostic accuracy in distinguishing between the disease states (DLBL and SLE) based solely on the expression patterns of these hub genes.

CONCLUSION: The identified hub genes and their associated pathways hold immense potential as diagnostic biomarkers and may serve as valuable targets for future therapeutic explorations.

PMID:39968469 | PMC:PMC11835410 | DOI:10.47176/mjiri.38.129

Front Cell Dev Biol. 2025 Feb 4;12:1526795. doi: 10.3389/fcell.2024.1526795. eCollection 2024.

ABSTRACT

Glucose metabolism is a key factor characterizing the cellular state during multicellular development. In metazoans, the metabolic state of undifferentiated cells correlates with growth/differentiation transition and cell fate determination. Notably, the cell fate of the Amoebozoa species Dictyostelium discoideum is biased by the presence of glucose and is also correlated with early differences in intracellular ATP. However, the relationship between early cell-cell heterogeneity, cell differentiation, and the metabolic state is unclear. To address the link between glucose metabolism and cell differentiation in D. discoideum, we studied the role of phosphoenolpyruvate carboxylase (PEPC), a key enzyme in the PEP-oxaloacetate-pyruvate node, a core junction that dictates the metabolic flux of glycolysis, the TCA cycle, and gluconeogenesis. We demonstrate that there is cell-cell heterogeneity in PEPC promoter activity in vegetative cells, which depends on nutrient conditions, and that cells with high PEPC promoter activity differentiate into spores. The PEPC null mutant exhibited an aberrantly high prestalk/prespore ratio, and the spore mass of the fruiting body was glassy and consisted of immature spores. Furthermore, the PEPC null mutant had high ATP levels and low mitochondrial membrane potential. Our results suggest the importance of cell-cell heterogeneity in the levels of metabolic enzymes during early cell fate priming.

PMID:39968235 | PMC:PMC11832675 | DOI:10.3389/fcell.2024.1526795

Front Pain Res (Lausanne). 2025 Feb 4;6:1501932. doi: 10.3389/fpain.2025.1501932. eCollection 2025.

ABSTRACT

INTRODUCTION: Degenerative joint disease (DJD) occurs very commonly in cats and can be associated with pain. Almost 70% of cats with DJD-associated pain suffer the co-morbidity of chronic kidney disease (CKD). There are currently very limited treatment or management options. A greater understanding of the systems biology of DJD, DJD-associated pain, and CKD may contribute to identifying disease specific biomarkers and relevant targets for the development of therapeutics for the control of these conditions in cats, and help inform human pain therapeutic development.

METHODS: Using mass spectrometry-based proteomic profiling of the serum of 200 highly phenotyped cats with varying burdens of DJD, pain, and CKD, we identified significant individual proteins and pathways.

RESULTS: Functional pathway analysis, based on differentially abundant proteins across individual disease states (DJD, pain, CKD), identified pathways playing a role in DJD and DJD-associated pain including acute phase response signaling, LXR/RXR and FXR/RXR activation and the complement system. With the added co-morbidity of CKD, similar pathways were identified, with the addition of IL-12 signaling and production in macrophages.

DISCUSSION: We identified differentially abundant proteins associated with DJD, pain and CKD and future work should evaluate these proteins as potential biomarkers of disease (individually or as clusters). Further, these data could be leveraged to identify novel therapeutic targets to address the gap in our ability to manage DJD, pain, and CKD in cats. Given that our work was in cats with naturally occurring DJD, these results may have translational applicability to human health.

PMID:39968160 | PMC:PMC11832531 | DOI:10.3389/fpain.2025.1501932

Ecol Evol. 2025 Feb 17;15(2):e71023. doi: 10.1002/ece3.71023. eCollection 2025 Feb.

ABSTRACT

Estimating species extinction risk is crucial to reverse biodiversity loss and to adopt proper conservation measures. Different sources may play a pivotal role in prioritising species conservation. Recently, citizen science demonstrated a substantial role, especially when it comes to butterflies. This study examines species records and richness in Aosta Valley, which represents one of the highest mountain areas in Europe. Through 30,351 data points from 1825 to 2022, the impact and efficiency of three groups of data sources were investigated: literature (i.e., publications and collections), sampling (butterfly experts' recording), and citizen science (open-source databases). The study also aims to assess the extinction potential of the butterflies in relation to functional traits. The results showed that even if there were significant differences in the number of records between the three sources, there were no significant differences for species recorded. Moreover, 2.9% of the butterfly community risks extinction, and it is related to some response traits. Indeed, extinction risks increase when the altitudinal range decreases and for multivoltines. In conclusion, citizen science has a strong impact on the amount of data and could be exploited to fill data gaps at low/medium altitudes. However, professional sampling is needed to focus on species no longer reported, and in particular on species that are difficult to identify, have specific distributions or particular traits (e.g., limited altitudinal range). Using different data sources, extinction risk estimation, and trait analysis, it is possible to prioritise studies on some species using different efforts (sampling and/or citizen sciences).

PMID:39967758 | PMC:PMC11832908 | DOI:10.1002/ece3.71023

Ecol Evol. 2025 Feb 17;15(2):e70984. doi: 10.1002/ece3.70984. eCollection 2025 Feb.

ABSTRACT

In shared environments, where different species interact depending on overlapping resources, complex interspecific interactions emerge, with human activities impacting these dynamics and influencing wildlife abundance and distribution. In the Alps, the presence of multiple species of ungulates, such as roe deer and red deer, and a predator, the wolf, creates a web of spatial and behavioral interactions in an area where farming, hunting and tourism have persisted over time, with tourism recently experiencing a substantial growth. Accounting for these multiple interactions, we modelled the co-occurrence probabilities of roe deer, red deer and wolves in an area of the Maritime Alps using data derived from 60 camera traps. We applied multi-species occupancy models to investigate (i) the role of species co-occurrences in explaining the occupancy of model species across the landscape, (ii) the role of human presence and activities on species occupancy and (iii) the potential effect of the hunting season on the species detection probabilities. Among the identified species, roe deer reported the highest frequency of recorded events and were the most widespread species. We provided important evidence of interspecific dependence, revealing that pairwise interactions among species had a greater impact than only considering individual environmental effects. We documented that the setting of cameras on trails increased the likelihood of detecting wolves but decreased the likelihood of detecting ungulates. Most importantly, the hunting season significantly reduced the likelihood of capturing roe deer, while having no effect on either red deer or wolves. Our results confirmed the relevance of including prey, predators, and human dynamics as a whole. Since the sharing of habitat makes human activities significantly important in defining predator-prey mechanisms, our insights are particularly relevant for defining solutions to optimize human-wildlife coexistence, especially in a highly anthropogenic system such as Europe.

PMID:39967757 | PMC:PMC11832906 | DOI:10.1002/ece3.70984

Brief Bioinform. 2024 Nov 22;26(1):bbaf029. doi: 10.1093/bib/bbaf029.

ABSTRACT

Modelling biological systems depends on the availability of data and components of the system at hand. As our understanding of these systems evolves, the ability to gradually refine models by adding new components of different formalisms covering stochastic, discrete, deterministic, and uncertainty without starting from scratch becomes essential. However, there remains a significant gap in the availability of methodologies and tool support for incrementally modelling and analysing complex biological systems in a flexible and intuitive manner. In this paper, we employ fuzzy hybrid Petri nets as a powerful expressive tool for presenting an incremental modelling and analysis protocol of biological systems. We demonstrate the utility of our protocol through a case study on cholesterol and lipoprotein metabolism and hypercholesterolemia therapy. Our model not only captures the underlying biochemical processes, but also quantitatively analyses how cholesterol levels are regulated, offering insights into potential therapeutic strategies for diseases associated with elevated cholesterol levels. The results confirm the validity and flexibility of our approach in representing complex biological processes and therapeutic interventions.

PMID:39967018 | DOI:10.1093/bib/bbaf029

Case Rep Dent. 2025 Feb 11;2025:8318894. doi: 10.1155/crid/8318894. eCollection 2025.

ABSTRACT

Background: Pemphigus vulgaris (PV) is a chronic autoimmune disorder affecting mucous membranes and skin, with potential life-threatening risks. It is typically characterized by blisters within the oral cavity with or without subsequent skin involvement. Given the importance of timely intervention, dental professionals are responsible for diagnosing this condition, as prompt detection and intervention greatly influence the disease progression and prognosis. Case Description: A 44-year-old male patient presented with swollen and bleeding gums, accompanied by multiple chronic ulcers in the oral cavity. He was initially diagnosed with PV in 2018; his case posed significant challenges, including drug-influenced gingival enlargement and the psychological burden of managing a chronic, relapsing condition. Management and Prognosis: The patient received treatment with an immunosuppressive medication (cyclosporin) along with long-term systemic steroids (prednisolone). In November 2022, cyclosporin was replaced with a steroid-sparing medication (methotrexate) to control drug-influenced gingival enlargement. The periodontal condition improved after 3 months of changing the medication regimen, nonsurgical periodontal therapy, and reinforced oral hygiene practices. The patient undergoes regular medical evaluations every 6 months with the dermatology department. Clinical Implications: Effective management of PV necessitates long-term systemic steroid therapy, often supplemented with immunosuppressive agents, to control the disease and minimize relapse risks. Regular clinical assessments are essential for patients receiving steroid and immunosuppressive treatment to monitor potential side effects, including cyclosporin-induced gingival enlargement. If gingival enlargement is compounded by periodontal disease, it can further complicate the management of PV. Drug-induced gingival enlargement has a favorable prognosis and is reversible upon discontinuation or substitution of the causative medication. An interdisciplinary approach involving primary clinicians, dentists, and the healthcare team is crucial to addressing the patient's signs and symptoms effectively.

PMID:39968166 | PMC:PMC11835479 | DOI:10.1155/crid/8318894

Redox Biol. 2025 Apr;81:103551. doi: 10.1016/j.redox.2025.103551. Epub 2025 Feb 14.

ABSTRACT

Iron overload and related oxidative damage are seen in many rare diseases, due to mutation of iron homeostasis-related genes. As a core regulator on cellular antioxidant reaction, Nrf2 can also decrease systemic and cellular iron levels by regulating iron-related genes and pathways, making Nrf2 activators very good candidates for the treatment of iron overload disorders. Successful examples include the clinical use of omaveloxolone for Friedreich's Ataxia and dimethyl fumarate for relapsing-remitting multiple sclerosis. Despite these uses, the therapeutic potentials of Nrf2 activators for iron overload disorders may be overlooked in clinical practice. Therefore, this study talks about the potential use, possible mechanisms, and precautions of Nrf2 activators in treating rare iron overload diseases. In addition, a combination therapy with Nrf2 activators and iron chelators is proposed for clinical reference, aiming to facilitate the clinical use of Nrf2 activators for more iron overload disorders.

PMID:39965404 | PMC:PMC11876910 | DOI:10.1016/j.redox.2025.103551

Sci Rep. 2025 Feb 18;15(1):5912. doi: 10.1038/s41598-025-89249-w.

ABSTRACT

A brain tumor is an abnormal growth of cells within the brain or surrounding tissues, which can be either benign or malignant. Brain tumors develop in various regions of the brain, each affecting different functions such as movement, speech, and vision, depending on their location. Early prediction of brain tumors is crucial for improving survival rates and treatment outcomes. Advanced techniques, including medical imaging and machine learning, are widely used for early diagnosis. However, conventional machine learning and deep learning detection models face challenges in achieving high accuracy in brain tumor disease prediction while minimizing time complexity. To address this, a novel Linear Regressive Weighted Gaussian Kernel Liquid Neural Network (LRWGKLNN) model is developed. The proposed LRWGKLNN model comprises four major steps, namely data acquisition, preprocessing, feature selection, and classification. In the initial step, a large volume of time-series data samples is collected from a comprehensive dataset. Following data collection, preprocessing is performed, involving two key processes: handling missing data and outlier detection. First, the proposed LRWGKLNN model handles missing values using a linear regression method. After the imputation process, outlier data is identified and removed using the Generalized Extreme Studentized Deviation test. Once preprocessing is complete, the Cosine Congruence Weighted Majority Algorithm is employed to select significant features from the dataset while removing irrelevant features. This step helps minimize the brain tumor disease prediction time. Finally, the classification process is performed using the selected significant features with the Gaussian Kernelized Liquid Neural Network. This approach enhances the accuracy of brain tumor disease prediction using time-series data samples. The experimental evaluation is carried out using various performance metrics such as accuracy, precision, recall, F1 score, and disease prediction time with respect to the number of time-series data samples. The obtained results demonstrate that the proposed LRWGKLNN model achieves higher 4%, 4% 5%, 4% and 4% accuracy, precision, recall, specificity and F1 score in brain tumor disease prediction. Furthermore, the LRWGKLNN model realizes a substantial reduction in time consumption with feature selection by 16% compared to existing deep learning methods.

PMID:39966518 | DOI:10.1038/s41598-025-89249-w

Sci Rep. 2025 Feb 18;15(1):5846. doi: 10.1038/s41598-025-88822-7.

ABSTRACT

Magnetic resonance imaging (MRI) has changed veterinary diagnosis but its long-sequence time can be problematic, especially because animals need to be sedated during the exam. Unfortunately, shorter scan times implies a fall in overall image quality and diagnosis reliability. Therefore, we developed a Generative Adversarial Net-based denoising algorithm called HawkAI. In this study, a Standard-Of-Care (SOC) MRI-sequence and then a faster sequence were acquired and HawkAI was applied to the latter. Radiologists were then asked to qualitatively evaluate the two proposed images based on different factors using a Likert scale (from 1 being strong preference for HawkAI to 5 being strong preference for SOC). The aim was to prove that they had at least no preference between the two sequences in terms of Signal-to-Noise Ratio (SNR) and diagnosis. They slightly preferred HawkAI in terms of SNR (confidence interval (CI) being [1.924-2.176]), had no preference in terms of Artifacts Presence, Diagnosis Pertinence and Lesion Conspicuity (respective CIs of [2.933-3.113], [2.808-3.132] and [2.941-3.119]), and a very slight preference for SOC in terms of Spatial Resolution and Image Contrast (respective CIs of [3.153-3.453] and [3.072-3.348]). This shows the possibility to acquire images twice as fast without any major drawback compared to a longer acquisition.

PMID:39966480 | DOI:10.1038/s41598-025-88822-7

Environ Sci Pollut Res Int. 2025 Feb 18. doi: 10.1007/s11356-025-36018-x. Online ahead of print.

ABSTRACT

The health of an ecosystem and the quality of water can be determined by the clarity of the water. The Persian Gulf and Oman Sea have a unique ecosystem, and monitoring their water clarity is necessary for sustainable development. Here, various criteria such as hue angle, chlorophyll-a, Forel-Ule index, organic carbon (OC), precipitation, sea surface salinity (SSS), Secchi disk depth (SDD), and sea surface temperature (SST) were analyzed from 2002 to 2018 using MODIS-Aqua Imagery, statistical tests, and deep learning (DL) models to monitor the water clarity of the Persian Gulf and the Oman Sea. The study found differences in criteria across different regions, with coastal areas showing higher Forel-Ule index and chlorophyll-a values. Positive trends in the Persian Gulf and the Oman Sea were attributed to the Forel-Ule index and OC, while negative trends were seen in SSS and SST in the Persian Gulf. The convolutional neural network (CNN) model was found to perform better than long short-term memory (LSTM) in predicting water clarity. Interpretation techniques were used to determine the importance of criteria in monitoring water clarity, with the Forel-Ule index, hue angle, and OC showing the greatest interaction. Sensitivity analysis revealed that chlorophyll-a and SSS had the most significant impact on water clarity prediction. Overall, this study using DL models and MODIS-Aqua Imagery can help improve water quality and protect the environment.

PMID:39966320 | DOI:10.1007/s11356-025-36018-x

Int Ophthalmol. 2025 Feb 18;45(1):79. doi: 10.1007/s10792-025-03439-z.

ABSTRACT

OBJECTIVE: Hyperreflective foci (HRF) are small, punctate lesions ranging from 20 to 50 μ m and exhibiting high reflective intensity in optical coherence tomography (OCT) images of patients with diabetic retinopathy (DR). The purpose of the model proposed in this paper is to precisely identify and segment the HRF in OCT images of patients with DR. This method is essential for assisting ophthalmologists in the early diagnosis and assessing the effectiveness of treatment and prognosis. In this study, we introduce an HRF segmentation algorithm based on KiU-Net, the algorithm that comprises the Kite-Net branch using up-sampling coding to collect more detailed information and a three-layer U-Net branch to extract high-level semantic information. To enhance the capacity of a single-branch network, we also design a cross-attention block (CAB) which combines the information extracted from two branches. The experimental results demonstrate that the number of parameters of our model is significantly reduced, and the sensitivity (SE) and the dice similarity coefficient (DSC) are respectively improved to 72.90 % and 66.84 % . Considering the SE and precision(P) of the segmentation, as well as the recall ratio and recall P of HRF, we believe that this model outperforms most advanced medical image segmentation algorithms and significantly relieves the strain on ophthalmologists.

PURPOSE: Hyperreflective foci (HRF) are small, punctate lesions ranging from 20 to 50 μm with high reflective intensity in optical coherence tomography (OCT) images of patients with diabetic retinopathy (DR). This study aims to develop a model that precisely identifies and segments HRF in OCT images of DR patients. Accurate segmentation of HRF is essential for assisting ophthalmologists in early diagnosis and in assessing the effectiveness of treatment and prognosis.

METHODS: We introduce an HRF segmentation algorithm based on the KiU-Net architecture. The model comprises two branches: a Kite-Net branch that uses up-sampling coding to capture detailed information, and a three-layer U-Net branch that extracts high-level semantic information. To enhance the capacity of the network, we designed a cross-attention block (CAB) that combines the information extracted from both branches, effectively integrating detail and semantic features.

RESULTS: Experimental results demonstrate that our model significantly reduces the number of parameters while improving performance. The sensitivity (SE) and Dice Similarity Coefficient (DSC) of our model are improved to 72.90% and 66.84%, respectively. Considering the SE and precision (P) of the segmentation, as well as the recall ratio and precision of HRF detection, our model outperforms most advanced medical image segmentation algorithms CONCLUSION: The proposed HRF segmentation algorithm effectively identifies and segments HRF in OCT images of DR patients, outperforming existing methods. This advancement can significantly alleviate the burden on ophthalmologists by aiding in early diagnosis and treatment evaluation, ultimately improving patient outcomes.

PMID:39966317 | DOI:10.1007/s10792-025-03439-z

NPJ Antimicrob Resist. 2025 Feb 18;3(1):9. doi: 10.1038/s44259-024-00070-3.

ABSTRACT

Antimicrobial resistance (AMR) poses a growing global health threat. Nanomedicine, combined with drug repurposing, may help extend the effective lifespan of current and new antimicrobials. This review, presents an Australian perspective on nanotechnology-based therapies, highlighting scientific and clinical challenges. Early consideration of the potential barriers to market access may help to accelerate research translation, regulatory approval and patient access to nano-antimicrobial (NAM) drugs for resistant pathogens, not only in Australia, but globally.

PMID:39966608 | DOI:10.1038/s44259-024-00070-3

Neurotherapeutics. 2025 Feb 17:e00553. doi: 10.1016/j.neurot.2025.e00553. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) presents significant challenges in drug discovery and development due to its complex and poorly understood pathology and etiology. Digital twins (DTs) are recently developed virtual real-time representations of physical entities that enable rapid assessment of the bidirectional interaction between the virtual and physical domains. With recent advances in artificial intelligence (AI) and the growing accumulation of multi-omics and clinical data, application of DTs in healthcare is gaining traction. Digital twin technology, in the form of multiscale virtual models of patients or organ systems, can track health status in real time with continuous feedback, thereby driving model updates that enhance clinical decision-making. Here, we posit an additional role for DTs in drug discovery, with particular utility for complex diseases like AD. In this review, we discuss salient challenges in AD drug development, including complex disease pathology and comorbidities, difficulty in early diagnosis, and the current high failure rate of clinical trials. We also review DTs and discuss potential applications for predicting AD progression, discovering biomarkers, identifying new drug targets and opportunities for drug repurposing, facilitating clinical trials, and advancing precision medicine. Despite significant hurdles in this area, such as integration and standardization of dynamic medical data and issues of data security and privacy, DTs represent a promising approach for revolutionizing drug discovery in AD.

PMID:39965994 | DOI:10.1016/j.neurot.2025.e00553

Int J Biol Macromol. 2025 Feb 16:141057. doi: 10.1016/j.ijbiomac.2025.141057. Online ahead of print.

ABSTRACT

Of all isoforms, human carbonic anhydrase II (PF00194; EC 4.2.1.1), which is mostly found in red cells, kidneys, and the eyes, plays a pivotal role in numerous physiological processes, and its dysregulation has been linked to the wide range of illnesses, such as glaucoma. Finding new inhibitors that target Carbonic anhydrase II, therefore has great potential in drug discovery. Using drug repurposing approach, this study focused on the investigation of different drugs as Carbonic anhydrase II inhibitors and their structural studies using X-ray crystallography. For this purpose, 100 different drugs were evaluated for bovine and human carbonic anhydrase II inhibitory activity. Among all, two drugs, i.e. acetohexamide (1) and levosulpiride (54) were found to be active, with IC50 = 437.0 ± 0.2 and 1128 ± 0.75 μM, respectively. Mechanistic studies suggested that both drugs are competitive inhibitors of the human carbonic anhydrase II enzyme. The X-ray crystal structure analysis revealed that acetohexamide (1) interacts via terminal acetyl group with the active site residues of the carbonic anhydrase II enzyme, and showed strong hydrogen bonding with Zn, His94, His119, and Asn67. The sulfonamide group of levosulpiride was involved in strong hydrogen bonding with Zn, His94, His119, and Thr199. From in vivo studies, we found that carbonic anhydrase activity was significantly inhibited by the intraperitoneal administration of levosulpiride for up to 5 h. Our findings provide comprehensive insights for the optimization of the pharmacological profile of these drugs, and provide avenues for the exploration of different derivatives of these drugs with enhanced efficacy and fewer adverse effects.

PMID:39965680 | DOI:10.1016/j.ijbiomac.2025.141057

Nat Genet. 2025 Feb 18. doi: 10.1038/s41588-025-02087-4. Online ahead of print.

ABSTRACT

Hypertrophic cardiomyopathy (HCM) is an important cause of morbidity and mortality with both monogenic and polygenic components. Here, we report results from a large genome-wide association study and multitrait analysis including 5,900 HCM cases, 68,359 controls and 36,083 UK Biobank participants with cardiac magnetic resonance imaging. We identified 70 loci (50 novel) associated with HCM and 62 loci (20 novel) associated with relevant left ventricular traits. Among the prioritized genes in the HCM loci, we identify a novel HCM disease gene, SVIL, which encodes the actin-binding protein supervillin, showing that rare truncating SVIL variants confer a roughly tenfold increased risk of HCM. Mendelian randomization analyses support a causal role of increased left ventricular contractility in both obstructive and nonobstructive forms of HCM, suggesting common disease mechanisms and anticipating shared response to therapy. Taken together, these findings increase our understanding of the genetic basis of HCM, with potential implications for disease management.

PMID:39966646 | DOI:10.1038/s41588-025-02087-4

Pharmacogenomics J. 2025 Feb 18;25(2):1. doi: 10.1038/s41397-025-00362-5.

ABSTRACT

Patients carrying specific HLA risk alleles are at higher risk for developing drug hypersensitivity reactions, yet pre-therapeutic screening is uncommon. We examined whether patients with a history of drug allergies have more HLA risk alleles to assess whether these patients are potential candidates for pre-therapeutic HLA screening. We performed a case-control study with patients who had a self-reported history of drug allergy (N = 94) and patients without such a history (N = 185). HLA regions were sequenced by use of Alloseq Tx for HLA-A -B, -C, -DP, -DQ and -DR genotypes. A logistic regression was performed to investigate whether the number of HLA risk alleles differed between cases and controls. Sequencing data of 279 patients were available for this analysis. There was no statistically significant difference in the mean number of unique HLA risk alleles between the cases and controls (5.31 vs 5.31, p = 0.9397). Therefore, patients with a self-reported history of drug allergy do not form a suitable group for pre-therapeutic screening for HLA risk alleles to prevent future drug allergies.

PMID:39966354 | DOI:10.1038/s41397-025-00362-5

Mycoses. 2025 Feb;68(2):e70034. doi: 10.1111/myc.70034.

ABSTRACT

BACKGROUND: Post-tuberculosis lung disease (PTLD) is a precursor to Aspergillus-related lung diseases. While Chronic Pulmonary Aspergillosis (CPA) has been extensively studied in the background of tuberculosis, Allergic Bronchopulmonary Aspergillosis (ABPA) has been reported sporadically with limited information on its prevalence, clinical-radiological features, and treatment outcomes.

OBJECTIVE: This study, conducted in a high TB burden setting, aimed to address this knowledge gap by systematically evaluating ABPA in PTLD patients.

METHODS: This retrospective cohort study screened PTLD patients presenting with respiratory or constitutional symptoms persisting for more than 3 months. The objective was to report the prevalence, clinical-radiological-laboratory data, and outcomes of ABPA-PTLD compared to a cohort of CPA (CPA-PTLD) and patients with PTLD (PTLD only).

RESULTS: Out of a total of 1012 PTLD patients, ABPA was seen in 2.27%, CPA in 20.75% and Aspergillus sensitization in 0.7%. ABPA patients primarily presented with breathlessness (91.3%) and cough (82.6%) while haemoptysis (43.5%), weight loss (13%), and anorexia (21.7%) were also observed, albeit less commonly than in CPA-PTLD. Bronchiectasis (100%) and nodules (87%) were more frequent in ABPA-PTLD patients, whereas consolidation (21.7%), cavities (30.4%), pleural thickening (8.7%), and 'fungal ball' (9.1%) were also seen, although less commonly than in CPA-PTLD. Most patients received azoles (78%) as first-line therapy, with symptomatic improvement (partial/complete) observed in ~78%.

CONCLUSION: ABPA may occur in PTLD patients, with specific clinical (e.g., haemoptysis) and radiological (e.g., cavity and fungal ball) features uncommon in other types of ABPA, but resembling other PTLD conditions. Future studies should focus on identifying differences in the natural course and appropriate treatment paradigms of ABPA-PTLD patients compared to ABPA occurring in asthma and cystic fibrosis patients.

PMID:39966329 | DOI:10.1111/myc.70034

Sci Rep. 2025 Feb 18;15(1):5954. doi: 10.1038/s41598-025-89770-y.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is an age-related interstitial lung disease of unknown cause. Oxidative stress, an imbalance between oxidants and antioxidants, is implicated in IPF pathogenesis and prognosis but needs further study. We used transcriptome sequencing data (GSE70866) and oxidative stress-related genes from GeneCards. A prognostic risk model for IPF patients was constructed using LASSO. Functional and pathway differences were analyzed between risk score groups, along with comparisons of immune cells and functions. An IPF rat model with vitamin D3 (VD3) intervention was also established. Finally, we used IL-4 to induce M2 macrophages to explore the mechanism of action of CCL2. We identified 483 DEGs and 50 oxidative stress-related DEGs (OSDEGs). Single-factor COX regression identified 34 prognostic OSDEGs, and LASSO identified an 8-gene signature for the risk model. The high-risk group had more CD8 + T cells, macrophages, APC costimulation, and cytokine-cytokine receptor activity. CCL2 was significantly correlated with macrophages in IPF. VD3 inhibited the TGF-β signaling pathway and reduced macrophage M2 infiltration in the rat model. In the IL-4 induced M2 macrophage model, we found that M2 macrophages produced more CCL2, and the production of CCL2 was significantly reduced after VD3 intervention. We established prognostic markers of eight oxidative stress-related genes. The risk score effectively predicts adverse outcomes in IPF. VD3 may alleviate IPF by reducing macrophage infiltration and inhibiting the TGF-β signaling pathway.

PMID:39966531 | DOI:10.1038/s41598-025-89770-y