HGG Adv. 2025 May 31:100463. doi: 10.1016/j.xhgg.2025.100463. Online ahead of print.

ABSTRACT

The human X-chromosome contains hundreds of genes and has well-established impacts on sex differences and traits. However, the X-chromosome is often excluded from many genetic analyses, limiting broader understanding of variant effects. In particular, the functional impact of rare variants on the X-chromosome is understudied. To investigate functional rare variants on the X-chromosome, we use observations of outlier gene expression from GTEx consortium data. We show outlier genes are enriched for having nearby rare variants on the X-chromosome, and this enrichment is stronger for males. Using the RIVER model, we identified 733 rare variants in 450 genes predicted to have functional differences between males and females. We examined the pharmacogenetic implications of these variants and observed that 25% of drugs with a known sex difference in adverse drug reactions were connected to genes that contained a sex-biased rare variant. We further identify that sex-biased rare variants preferentially impact transcription factors with predicted sex-differential binding, such as the XIST-modulated SIX1. Overall, we observed more within-sex variation than between-sex variation. Combined, our study investigates functional rare variants on the X-chromosome, and further details how sex-stratification of variant effect prediction improves identification of rare variants with predicted sex-biased effects, transcription factor biology, and pharmacogenomic impacts.

PMID:40452186 | DOI:10.1016/j.xhgg.2025.100463

J Surg Case Rep. 2025 May 30;2025(5):rjaf315. doi: 10.1093/jscr/rjaf315. eCollection 2025 May.

ABSTRACT

Diverticula of the appendix have been the subject of study since the early 1900s. Appendiceal diverticulitis has varying presentations that can be acute or chronic and has features that differentiate it from classical acute appendicitis. We present the case of a 61-year-old female who was incidentally found to have appendiceal diverticulitis. Appendiceal diverticula are divided into two types, congenital and acquired. This patient developed an acquired appendiceal diverticula. The exact etiology and pathogenesis of acquired appendiceal diverticulosis is unknown. Risk factors associated with acquired appendiceal diverticulosis include male gender, older adults (>30 years old), Hirschsprung's disease, and cystic fibrosis. They were not found to be associated with colonic diverticulosis. Of these known risk factors, our patient only met the criteria of being an older adult. With the post-operative diagnosis of appendiceal diverticulitis, the patient does not require any further intervention beyond the appendectomy which was already completed.

PMID:40453748 | PMC:PMC12122285 | DOI:10.1093/jscr/rjaf315

Front Immunol. 2025 May 16;16:1583460. doi: 10.3389/fimmu.2025.1583460. eCollection 2025.

ABSTRACT

INTRODUCTION: Extracorporeal photopheresis (ECP) is a viable treatment that slows the progression of chronic lung allograft dysfunction. Despite its immunoregulatory potential, data on extracorporeal photopheresis as an induction therapy remain rather limited.

METHODS: We conducted a pilot randomized controlled study on ECP as induction therapy in cystic fibrosis patients undergoing primary lung transplantation. Primary endpoints included safety, assessed based on the incidence of adverse events, treatment-related toxicity, and procedure-related complication rates; and feasibility, evaluated through the completion rate of scheduled ECP sessions, patient tolerability, and treatment discontinuation rates. Secondary endpoint consisted of an exploratory assessment of efficacy, using a composite measure that included three key components: freedom from biopsy-proven acute rejection within the first 12 months, absence of chronic lung allograft dysfunction at 36 months, and optimal graft function, defined as a predicted forced expiratory volume in the first second ≥ 90% at 36 months. Finally, exploratory endpoints included cell phenotypic and functional analyses, secreted immune protein profiling, and gene expression analysis for mechanistic insights. Patients were randomly assigned to receive either standard immunosuppressive therapy alone or standard therapy plus six sessions of extracorporeal photopheresis, with a follow-up period of 36 months.

RESULTS: Among 36 cystic fibrosis patients who underwent lung transplantation between 2018 and 2021 and met the eligibility criteria, 21 were randomized (9 to the study group and 12 to the control group). No patients in the treatment group experienced adverse events. The enrollment rate was 61%, and the treatment discontinuation rate was 22%. The clinical composite endpoint was achieved by 28.6% of patients in the treatment group and 16.7% in the control group. Exploratory endpoint analyses revealed significant decreases in pro-inflammatory cytokines, degranulating CD8+ T lymphocytes, and NK cells in the treatment group. Moreover, significant increases in Treg lymphocytes, IL-10-producing NK cells, and anti-inflammatory cytokines appeared to be associated with improved pulmonary function in the treatment group.

CONCLUSIONS: Induction therapy with extracorporeal photopheresis is safe and feasible in lung transplantation for cystic fibrosis. Some clinical benefits appear to persist for the first 36 months of follow-up. Interestingly, a correlation between immunological modulation induced by extracorporeal photopheresis and pulmonary function was observed.

CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/study/NCT03500575?cond=NCT03500575&rank=1, identifier NCT03500575.

PMID:40453071 | PMC:PMC12122324 | DOI:10.3389/fimmu.2025.1583460

Expert Rev Clin Immunol. 2025 Jun 1. doi: 10.1080/1744666X.2025.2514604. Online ahead of print.

ABSTRACT

INTRODUCTION: Chronic airway inflammatory diseases mainly comprise chronic rhinosinusitis (CRS), allergic rhinitis (AR), asthma, cystic fibrosis (CF), and chronic obstructive pulmonary disease (COPD). Epithelial cells fulfill a protective role as a barrier; however, when stimulated, these cells also release a variety of cytokines that attract and activate immune cells, including macrophages, neutrophils, and T-lymphocytes. Excessive activation and aggregation of immune cells disrupts the balance of the cellular microenvironment, and leads to impaired immune defense of the airway mucosa, which can further exacerbate an inflammatory response.

AREAS COVERED: In this article, we discuss the key cytokines and immune pathways involved in epithelial-immune cell interactions, and we detail discoveries in the emerging field of single-cell sequencing and summarize monoclonal antibody-targeted therapies. A comprehensive search was conducted using the search terms 'epithelial cell,' 'immune,' 'interaction,' 'cytokines,' 'asthma,' 'chronic sinusitis,' 'allergic rhinitis,' 'monoclonal antibodies,' and 'single-cell sequencing' by querying Google Scholar and PubMed.

EXPERT OPINION: The intricate pathophysiology of airway inflammation remains to be fully elucidated. Emerging technologies, such as single-cell sequencing, have led to a more comprehensive characterization of the immune mechanisms underlying the pathophysiology of airway inflammatory diseases, which points the way to further precision medicine in the future.

PMID:40452271 | DOI:10.1080/1744666X.2025.2514604

J Proteome Res. 2025 Jun 2. doi: 10.1021/acs.jproteome.5c00063. Online ahead of print.

ABSTRACT

A fundamental challenge in mass spectrometry-based proteomics is determining which peptide generated a given MS2 spectrum. Peptide sequencing typically relies on matching spectra against a known sequence database, which in some applications is not available. Deep learning-based de novo sequencing can address this limitation by directly predicting peptide sequences from MS2 data. We have seen the application of the transformer architecture to de novo sequencing produce state-of-the-art results on the so-called nine-species benchmark. In this study, we propose an improved transformer encoder inspired by the heuristics used in the manual interpretation of spectra. We modify the attention mechanism with a learned bias based on pairwise mass differences, termed Pairwise Attention (PA). Adding PA improves average peptide precision at 100% coverage by 12.7% (5.9 percentage points) over our base transformer on the original nine-species benchmark. We have also achieved a 7.4% increase over the previously published model Casanovo. Our MS2 encoding strategy is largely orthogonal to other transformer-based models encoding MS2 spectra, enabling straightforward integration into existing deep-learning approaches. Our results show that integrating domain-specific knowledge into transformers boosts de novo sequencing performance.

PMID:40454436 | DOI:10.1021/acs.jproteome.5c00063

Nano Lett. 2025 Jun 2. doi: 10.1021/acs.nanolett.5c01460. Online ahead of print.

ABSTRACT

Moiré patterns in van der Waals bilayer materials complicate the analysis of atomic-resolution images, hindering the atomic-scale insight typically attainable with scanning transmission electron microscopy. Here, we report a method to detect the positions and identities of atoms in each of the individual layers that compose twisted bilayer heterostructures. We developed a deep learning model, Gomb-Net, which identifies the coordinates and atomic species in each layer, deconvoluting the moiré pattern. This enables layer-specific mapping of atomic positions and dopant distributions, unlike other commonly used segmentation models which struggle with moiré-induced complexity. Using this approach, we explored the Se atom substitutional site distribution in a twisted fractional Janus WS2-WS2(1-x)Se2x heterostructure and found that layer-specific implantation sites are unaffected by the moiré pattern's local energetic or electronic modulation. This advancement enables atom identification within material regimes where it was not possible before, opening new insights into previously inaccessible material physics.

PMID:40454431 | DOI:10.1021/acs.nanolett.5c01460

Biomicrofluidics. 2025 May 29;19(3):031304. doi: 10.1063/5.0252554. eCollection 2025 May.

ABSTRACT

Microfluidic biochips (MBs) are transforming diagnostics, healthcare, and biomedical research. However, their rapid deployment has exposed them to diverse security threats, including structural tampering, material degradation, sample-level interference, and intellectual property (IP) theft, such as counterfeiting, overbuilding, and piracy. This perspective highlights emerging attack vectors and countermeasures aimed at mitigating these risks. Structural attacks, such as stealthy design code modifications, can result in faulty diagnostics. To address this, deep learning -based anomaly detection leverages microstructural changes, including optical changes such as shadows or reflections, to identify and resolve faults. Material-level countermeasures, including mechano-responsive dyes and spectrometric watermarking, safeguard against subtle chemical alterations during fabrication. Sample-level protections, such as molecular barcoding, ensure bio-sample integrity by embedding unique DNA sequences for authentication. At the IP level, techniques like watermarking, physically unclonable functions, fingerprinting, and obfuscation schemes provide robust defenses against reverse engineering and counterfeiting. Together, these approaches offer a multi-layered security framework to protect MBs, ensuring their reliability, safety, and trustworthiness in critical applications.

PMID:40454326 | PMC:PMC12124908 | DOI:10.1063/5.0252554

HSS J. 2025 May 28:15563316251339660. doi: 10.1177/15563316251339660. Online ahead of print.

ABSTRACT

As artificial intelligence (AI) advances in healthcare, encompassing robust applications for the diagnosis and prognostication of musculoskeletal diseases, clinicians must increasingly understand the implications of machine learning and deep learning in their practice. This review article explores computer vision algorithms and patient-specific, multimodal prediction models; provides a simple framework to guide discussion on the limitations of AI model development; and introduces the field of generative AI.

PMID:40454292 | PMC:PMC12119539 | DOI:10.1177/15563316251339660

Biol Methods Protoc. 2025 May 5;10(1):bpaf034. doi: 10.1093/biomethods/bpaf034. eCollection 2025.

ABSTRACT

This study introduces an approach to classifying histopathological images for detecting dysplasia in oral cancer through the fusion of support vector machine (SVM) classifiers trained on deep learning features extracted from InceptionResNet-v2 and vision transformer (ViT) models. The classification of dysplasia, a critical indicator of oral cancer progression, is often complicated by class imbalance, with a higher prevalence of dysplastic lesions compared to non-dysplastic cases. This research addresses this challenge by leveraging the complementary strengths of the two models. The InceptionResNet-v2 model, paired with an SVM classifier, excels in identifying the presence of dysplasia, capturing fine-grained morphological features indicative of the condition. In contrast, the ViT-based SVM demonstrates superior performance in detecting the absence of dysplasia, effectively capturing global contextual information from the images. A fusion strategy was employed to combine these classifiers through class selection: the majority class (presence of dysplasia) was predicted using the InceptionResNet-v2-SVM, while the minority class (absence of dysplasia) was predicted using the ViT-SVM. The fusion approach significantly outperformed individual models and other state-of-the-art methods, achieving superior balanced accuracy, sensitivity, precision, and area under the curve. This demonstrates its ability to handle class imbalance effectively while maintaining high diagnostic accuracy. The results highlight the potential of integrating deep learning feature extraction with SVM classifiers to improve classification performance in complex medical imaging tasks. This study underscores the value of combining complementary classification strategies to address the challenges of class imbalance and improve diagnostic workflows.

PMID:40454251 | PMC:PMC12122209 | DOI:10.1093/biomethods/bpaf034

Asia Pac J Sports Med Arthrosc Rehabil Technol. 2025 May 5;42:1-7. doi: 10.1016/j.asmart.2025.04.005. eCollection 2025 Oct.

ABSTRACT

OBJECTIVE: To evaluate the performance of a code free deep learning (CFDL) application in diagnosing supraspinatus tendon pathologies on shoulder magnetic resonance imaging (MRI) images.

DESIGN: This retrospective cross-sectional study included patients with supraspinatus MRI showing partial or full-thickness tears and tendinosis, with patients having normal findings as the control group. MRI images were processed in the LobeAI application using transfer learning with ResNet-50 V2 for model development. Models were built to differentiate each pathology from normal and full-thickness tears from partial tears.

RESULTS: The ML models developed using the LobeAI application demonstrated the ability to differentiate between normal shoulder MRI images and partial tears, full-thickness tears, and tendinosis with sensitivities of 93.75 %, 100 %, and 100 %, respectively, and specificities of 43.75 %, 62.5 %, and 18.75 %. The model designed to classify partial vs. full-thickness tears achieved an accuracy of 34.38 %. The model incorporating all pathological images compared to normal MRI images exhibited an accuracy of 37.50 % and a weighted F1 score of 0.32.

CONCLUSION: The results of the study suggest that, although CFDL applications may be promising for the initial detection of supraspinatus pathologies, their current iteration has limitations that must be resolved before they can be reliably integrated into clinical practice.

PMID:40454208 | PMC:PMC12124677 | DOI:10.1016/j.asmart.2025.04.005

Med Hypothesis Discov Innov Ophthalmol. 2025 May 10;14(1):255-272. doi: 10.51329/mehdiophthal1517. eCollection 2025 Spring.

ABSTRACT

BACKGROUND: By leveraging the imaging-rich nature of ophthalmology and optometry, artificial intelligence (AI) is rapidly transforming the vision sciences and addressing the global burden of ocular diseases. The ability of AI to analyze complex imaging and clinical data allows unprecedented improvements in diagnosis, management, and patient outcomes. In this narrative review, we explore the current and emerging opportunities of utilizing AI in the vision sciences, critically examine the associated challenges, and discuss the ethical implications of integrating AI into clinical practice.

METHODS: We searched PubMed/MEDLINE and Google Scholar for English-language articles published from January 1, 2005, to March 31, 2025. Studies on AI applications in ophthalmology and optometry, focusing on diagnostic performance, clinical integration, and ethical considerations, were included, irrespective of study design (clinical trials, observational studies, validation studies, systematic reviews, and meta-analyses). Articles not related to the use of AI in vision care were excluded.

RESULTS: AI has achieved high diagnostic accuracy across different ocular domains. In terms of the cornea and anterior segment, AI models have detected keratoconus with sensitivity and accuracy exceeding 98% and 99.6%, respectively, including in subclinical cases, by analyzing Scheimpflug tomography and corneal biomechanics. For cataract surgery, machine learning-based intraocular lens power calculation formulas, such as the Kane and ZEISS AI formulas, reduce refractive errors, achieving mean absolute errors below 0.30 diopters and performing particularly well in highly myopic eyes. AI-based retinal screening systems, such as the EyeArt and IDx-DR, can autonomously detect diabetic retinopathy with sensitivities above 95%, while deep learning models can predict age-related macular degeneration progression with an area under the receiver operating characteristic curve exceeding 0.90. In glaucoma detection, fundus and optical coherence tomography-based AI models have reached pooled sensitivity and specificity exceeding 90%, although performance varies with disease stage and population diversity. AI has also advanced strabismus detection, amblyopia risk prediction, and myopia progression forecasting by using facial analysis and biometric data. Currently, key challenges in implementing AI in ophthalmology include dataset bias, limited external validation, regulatory hurdles, and ethical issues, such as transparency and equitable access.

CONCLUSIONS: AI is rapidly transforming vision sciences by improving diagnostic accuracy, streamlining clinical workflow, and broadening access to quality eye care, particularly in underserved regions. Its integration into ophthalmology and optometry thus holds significant promise for enhancing patient outcomes and optimizing healthcare delivery. However, to harness the transformative potential of AI fully, sustained multidisciplinary collaboration, involving clinicians, data scientists, ethicists, and policymakers, is essential. Rigorous validation processes, transparency in algorithm development, and strong ethical oversight are equally important to mitigate risks such as bias, data misuse, and unequal access. Responsible implementation of AI in the vision sciences is essential to ensure that all populations are served equitably.

PMID:40453785 | PMC:PMC12121673 | DOI:10.51329/mehdiophthal1517

Natl Sci Rev. 2025 May 14;12(6):nwaf188. doi: 10.1093/nsr/nwaf188. eCollection 2025 Jun.

NO ABSTRACT

PMID:40453638 | PMC:PMC12125967 | DOI:10.1093/nsr/nwaf188

J Med Imaging (Bellingham). 2025 Nov;12(Suppl 2):S22010. doi: 10.1117/1.JMI.12.S2.S22010. Epub 2025 May 29.

ABSTRACT

PURPOSE: Breast cancer risk depends on an accurate assessment of breast density due to lesion masking. Although governed by standardized guidelines, radiologist assessment of breast density is still highly variable. Automated breast density assessment tools leverage deep learning but are limited by model robustness and interpretability.

APPROACH: We assessed the robustness of a feature selection methodology (RFE-SHAP) for classifying breast density grades using tissue-specific radiomic features extracted from raw central projections of digital breast tomosynthesis screenings ( n I = 651 , n II = 100 ). RFE-SHAP leverages traditional and explainable AI methods to identify highly predictive and influential features. A simple logistic regression (LR) classifier was used to assess classification performance, and unsupervised clustering was employed to investigate the intrinsic separability of density grade classes.

RESULTS: LR classifiers yielded cross-validated areas under the receiver operating characteristic (AUCs) per density grade of [ A : 0.909 ± 0.032 , B : 0.858 ± 0.027 , C : 0.927 ± 0.013 , D : 0.890 ± 0.089 ] and an AUC of 0.936 ± 0.016 for classifying patients as nondense or dense. In external validation, we observed per density grade AUCs of [ A : 0.880, B : 0.779, C : 0.878, D : 0.673] and nondense/dense AUC of 0.823. Unsupervised clustering highlighted the ability of these features to characterize different density grades.

CONCLUSIONS: Our RFE-SHAP feature selection methodology for classifying breast tissue density generalized well to validation datasets after accounting for natural class imbalance, and the identified radiomic features properly captured the progression of density grades. Our results potentiate future research into correlating selected radiomic features with clinical descriptors of breast tissue density.

PMID:40453545 | PMC:PMC12120562 | DOI:10.1117/1.JMI.12.S2.S22010

medRxiv [Preprint]. 2025 Apr 7:2025.04.05.25325276. doi: 10.1101/2025.04.05.25325276.

ABSTRACT

BACKGROUND: Malnutrition significantly impacts surgical outcomes yet is difficult to identify preoperatively. Few studies have investigated the association between comprehensive body composition assessment and malnutrition in males and females separately. This study evaluates sex-specific associations between preoperative imaging-derived body composition features and malnutrition in abdominal surgery patients.

METHODS: We retrospectively analyzed patients who underwent computed tomography (CT) scans and elective abdominal surgery at a single institution (2018-2021). Preoperative CT scans were assessed using deep learning to quantify five muscle groups and two fat depots. Malnutrition was diagnosed by registered dietitians using standardized criteria. Sex-specific associations with malnutrition were evaluated using logistic regression.

RESULTS: Among 1,143 patients (52% female), clinical malnutrition was diagnosed in 20.2% of patients, with prevalence varying by procedure type (3.5-38.2%). Malnutrition was associated with reduced muscle volume for both sexes; in contrast, malnutrition was associated with myosteatosis in 3 of 5 muscle groups for females only. In males, malnutrition was associated with decreased psoas volume (OR 0.59 SD, p<0.01), decreased quadratus lumborum volume (OR 0.59 SD, p<0.01), and reduced erector spinae attenuation (OR 0.66 SD, p=0.048). In females, decreased psoas volume (OR 0.55 SD, p<0.001) and attenuation (OR 0.64 SD, p<0.01) were associated with malnutrition. Both sexes demonstrated increased subcutaneous fat attenuation associated with malnutrition (males: OR 1.51 SD, p<0.01; females: OR 1.73 SD, p<0.001), while increased visceral fat attenuation (OR 1.4 SD, p=0.027) was associated with malnutrition only in females.

CONCLUSIONS: Males and females differ in baseline body composition and features associated with clinical malnutrition. Comprehensive deep learning analysis of muscle and fat characteristics from cross-sectional imaging provides insight into the sex-specific relationships between body composition and malnutrition in the preoperative setting and provides an opportunity for early identification of patients with greater nutrition-related surgical risk.

PMID:40453372 | PMC:PMC12124193 | DOI:10.1101/2025.04.05.25325276

iScience. 2025 Apr 18;28(5):112488. doi: 10.1016/j.isci.2025.112488. eCollection 2025 May 16.

ABSTRACT

Steroid hormones regulate cell physiology and immune function, with dysregulated steroidogenesis promoting cancer progression by supporting tumor growth and suppressing anti-tumor immunity. Targeting CYP11A1, the first and rate-limiting enzyme in steroid biosynthesis, has shown promise in cancer therapy, but safe and effective inhibitors remain an unmet need. Undertaking in silico structure-based drug repurposing approach, we found posaconazole as an inhibitor of CYP11A1. The docking pose analysis showed that posaconazole can form multiple hydrogen bonds and hydrophobic interactions with the key residues at the binding site and the cofactor, stabilizing the protein-ligand complex. We validated its inhibition efficiency in cell-based assays. In a mouse model of lung metastasis, we demonstrated that posaconazole restricts metastasis by stimulating anti-tumor immunity. These findings highlight posaconazole's potential as a research tool to study steroidogenesis and as a candidate for further preclinical and clinical evaluation in pathologies associated with local steroidogenesis, such as steroidogenic tumors.

PMID:40454094 | PMC:PMC12124671 | DOI:10.1016/j.isci.2025.112488

iScience. 2025 Apr 23;28(5):112505. doi: 10.1016/j.isci.2025.112505. eCollection 2025 May 16.

ABSTRACT

Gallbladder cancer (GBC) is a highly aggressive tumor associated with risk factors, such as chronic infection, gallstones, and exposure to harmful chemicals. Our study explores the role of polycyclic aromatic hydrocarbons (PAHs) and long noncoding RNA HEGBC in GBC progression. We found that PAHs activate the aryl hydrocarbon receptor (AhR), which enhances HEGBC expression and promotes GBC cell proliferation, invasion, and metastasis both in vitro and in vivo. Mechanistically, AhR binds to the HEGBC promoter, establishing a positive feedback loop that further activates AhR transcription. Moreover, CYP1A1 was identified as a key downstream effector of PAHs-AhR/HEGBC-mediated proliferation, migration, and invasion of GBC cells in vitro and in vivo. These findings provide the integrative view of a molecular mechanism loop for regulating the malignant progression of GBC centered by PAHs/AhR/HEGBC, which represents a promising strategy for the treatment of GBC.

PMID:40454092 | PMC:PMC12124655 | DOI:10.1016/j.isci.2025.112505

Vet World. 2025 Apr;18(4):763-772. doi: 10.14202/vetworld.2025.763-772. Epub 2025 Apr 7.

ABSTRACT

BACKGROUND AND AIM: Electroejaculation (EE) is widely used for semen collection in bulls but raises concerns about animal welfare due to potential pain and stress. The physiological impact of EE on bulls remains a topic of debate, with previous studies yielding inconclusive results. This study aims to objectively evaluate pain and stress responses in bulls subjected to EE using electroencephalography (EEG) alongside hormonal, behavioral, and metabolite profiling.

MATERIALS AND METHODS: Eight bulls were subjected to EE in three replicates, with physiological and behavioral data collected before, during, and after the procedure. EEG parameters, including median frequency (MF) and total power (Ptot), were analyzed to assess cortical activity indicative of pain and stress. Blood samples were evaluated for stress-related hormones (adrenaline, noradrenaline, β-endorphin, and dopamine), while metabolomic analysis was conducted to identify biochemical alterations associated with stress. Behavioral indicators, including vocalization and muscle spasms, were recorded.

RESULTS: EE induced significant increases (p < 0.05) in stress hormones at ejaculation, which gradually returned to baseline 20 min post-procedure. EEG metrics, such as MF and Ptot, significantly increased during EE (p < 0.05), indicating heightened cortical activity associated with nociception. Metabolomic analysis revealed distinct biochemical shifts, with variations in glucose, taurine, and norepinephrine profiles across baseline, stimulation, and recovery phases. Behavioral observations corroborated physiological findings, with bulls exhibiting signs of discomfort, such as struggling, arched back posture, and excessive salivation.

CONCLUSION: The combined EEG, hormonal, and metabolomic findings confirm that EE is a stressful and painful procedure for bulls. The study provides robust evidence of neurophysiological and biochemical responses indicative of pain. These findings highlight the need for alternative semen collection methods to minimize animal distress and improve welfare standards.

PMID:40453932 | PMC:PMC12123290 | DOI:10.14202/vetworld.2025.763-772

Blood Neoplasia. 2024 Feb 15;1(1):100004. doi: 10.1016/j.bneo.2024.100004. eCollection 2024 Mar.

ABSTRACT

TP53 mutation predicts adverse prognosis in many cancers, including myeloid neoplasms, but the mechanisms by which specific mutations affect disease biology, and whether they differ between disease categories, remain unknown. We analyzed TP53 mutations in 4 myeloid neoplasm subtypes (myelodysplastic syndrome [MDS], acute myeloid leukemia [AML], AML with myelodysplasia-related changes [AML-MRC], and therapy-related AML), and identified differences in mutation types, spectrum, and hot spots between disease categories and in comparison to solid tumors. Missense mutations in the DNA-binding domain were most common across all categories, whereas inactivating mutations and mutations outside the DNA binding domain were more common in AML-MRC than in MDS. TP53 mutations in MDS were more likely to retain transcriptional activity, and comutation profiles were distinct between disease categories and mutation types. Our findings suggest that mutated TP53 contributes to initiation and progression of neoplasia via distinct mechanisms, and support the utility of specific identification of TP53 mutations in myeloid malignancies.

PMID:40453522 | PMC:PMC12082110 | DOI:10.1016/j.bneo.2024.100004

Comput Struct Biotechnol J. 2025 May 1;27:1754-1771. doi: 10.1016/j.csbj.2025.04.040. eCollection 2025.

ABSTRACT

BACKGROUND: The interaction between HIV-1 and host immune cells, particularly macrophages, is crucial in understanding viral persistence and pathogenesis. This study aims to explore the impact of HIV-1 infection on macrophage microRNA (miRNA) expression profiles using a systems biology approach to uncover the potential role of miRNAs in modulating macrophage functionality and identify key miRNA targets that may serve as therapeutic avenues.

METHODS: PMA-differentiated THP-1 cells were used to model macrophage infection with HIV-1. A custom miRNA microarray was performed to identify dysregulated miRNAs following infection. miRTarBase was utilized for miRNA target identification, revealing gene targets associated with the dysregulated miRNAs. A protein-protein interaction (PPI) map of miRNA targets and their first interactors was constructed, with key nodes identified based on a calculated disease score, which considered degree, betweenness centrality, average shortest path length, and clustering coefficient. Gene Ontology molecular function analysis was also conducted on the identified targets.

RESULTS: The miRNA microarray identified 23 dysregulated miRNAs in HIV-1-infected macrophages, with 8 upregulated and 15 downregulated. Among these, the top 10 dysregulated miRNAs targeted over 2000 unique genes. PPI analysis revealed key nodes in the upregulated miRNA network, including APP, MYC, ESR2, RAF1, and HIST1H4A, while ZRANB1, HSPA8, TGOLN2, HSPA5, and BRD4 were prominent in the downregulated miRNA network. Notably, KRAS, CUL3, TP53, ESR1, and PARP1 were influenced by both upregulated and downregulated miRNAs. Gene Ontology analysis indicated that the targeted genes were involved in processes such as protein and RNA binding, ATPase activity, and ribosomal function.

CONCLUSIONS: HIV-1 infection induces significant dysregulation of miRNAs in macrophages, impacting a wide array of gene targets and molecular functions. These findings suggest that miRNA-mediated regulation may play a crucial role in HIV-1 pathogenesis within macrophages and present potential targets for miRNA-based therapeutic strategies.

PMID:40453371 | PMC:PMC12124685 | DOI:10.1016/j.csbj.2025.04.040

Blood Neoplasia. 2025 Mar 3;2(2):100083. doi: 10.1016/j.bneo.2025.100083. eCollection 2025 May.

ABSTRACT

Loss of all or part of chromosome 7 [-7/del(7q)] is recurrent in myeloid neoplasms and associated with a poor response to chemotherapy. Chromosome 7-encoded genes driving drug resistance and the consequences of combinatorial 7q tumor suppressor gene loss have remained unclear, the latter question largely because of the challenges of modeling aneuploidy. Here, we use in silico data mining to uncover 7q genes involved in chemotherapy resistance. We establish murine models of del(7q) clonal hematopoiesis and drug resistance with multiplex CRISPR-Cas9 (CRISPR-associated protein 9)-mediated inactivation of 4 genes, Cux1, Ezh2, Kmt2c, and Kmt2e. Postgenotoxic exposure, combined deficiency of Cux1 and Ezh2 preferentially promotes clonal myeloid expansion in vivo, with compounding defects in DNA damage recognition and repair. Human acute myeloid leukemia cell lines similarly illustrate central roles for CUX1 and EZH2 loss in survival and DNA damage resolution after chemotherapy exposure. Transcriptome analysis reveals combined Cux1 and Ezh2 loss recapitulates gene signatures of -7 patients and defective DNA damage response pathways, to a greater extent than single gene loss. This work reveals a genetic interaction between CUX1 and EZH2, and sheds light on how -7/del(7q) contributes to leukemogenesis and drug resistance characteristic of these adverse-risk neoplasms. These data support the concept of 7q as a contiguous gene syndrome region, in which combined loss of multiple gene drives pathogenesis. Furthermore, our CRISPR-based approach may serve as a framework for interrogating other recurrent aneuploid events in cancer.

PMID:40453147 | PMC:PMC12067884 | DOI:10.1016/j.bneo.2025.100083