Acad Radiol. 2025 Mar 28:S1076-6332(25)00187-4. doi: 10.1016/j.acra.2025.02.043. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: Ultrasound (US)-based deep learning (DL) models for grading the severity of carpal tunnel syndrome (CTS) are scarce. We aimed to advance CTS grading by developing a joint-DL model integrating clinical information and multimodal US features.

MATERIALS AND METHODS: A retrospective dataset of CTS patients from three hospitals was randomly divided into the training (n=680) and internal validation (n=173) sets. An external validation set was prospectively recruited from another hospital (n=174). To further test the model's generalizability, cross-vendor testing was conducted at three additional hospitals utilizing different US systems in the external validation set 2 (n=224). An US-based model was developed to grade CTS severity utilizing multimodal sonographic features, including cross-sectional area [CSA], echogenicity, longitudinal nerve appearance, and intraneural vascularity. A joint-DL model (CTSGrader) was constructed integrating sonographic features and clinical information. Diagnostic performance of both models was verified based on electrophysiological results. In the validation sets, the better-performing model was compared to two junior and two senior radiologists. Additionally, the radiologists' diagnostic performance with artificial intelligence (AI) assistance was evaluated in external validation sets.

RESULTS: CTSGrader achieved areas under the curve (AUCs) of 0.951, 0.910, and 0.897 in the validation sets. The accuracies of CTSGrader were 0.849, 0.833, and 0.827, which were higher than those of US-based model (all p<.05). It outperformed two junior and one senior radiologists (all p<.05) and was equivalent to 1 senior radiologist (all p>.05). With its assistance, the accuracies of two junior and one senior radiologists were improved (all p<.05).

CONCLUSION: The joint-DL model (CTSGrader) developed in our study outperformed the single-modality model. The AI-aided strategy suggested its potential to support clinical decision-making for grading CTS severity.

PMID:40157849 | DOI:10.1016/j.acra.2025.02.043

Spine J. 2025 Mar 27:S1529-9430(25)00165-2. doi: 10.1016/j.spinee.2025.03.011. Online ahead of print.

ABSTRACT

BACKGROUND CONTEXT: Lumbar foraminal stenosis (LFS) is a common spinal condition that requires accurate assessment. Current magnetic resonance imaging (MRI) reporting processes are often inefficient, and while deep learning has potential for improvement, challenges in generalization and interpretability limit its diagnostic effectiveness compared to physician expertise.

PURPOSE: The present study aimed to leverage a multimodal large language model to improve the accuracy and efficiency of LFS image classification, thereby enabling rapid and precise automated diagnosis, reducing the dependence on manually annotated data, and enhancing diagnostic efficiency.

STUDY DESIGN/SETTING: Retrospective study conducted from April 2017 to March 2023.

PATIENT SAMPLE: Sagittal T1-weighted MRI data for the lumbar spine were collected from 1,200 patients across three medical centers. A total of 810 patient cases were included in the final analysis, with data collected from seven different MRI devices.

OUTCOME MEASURES: Automated classification of LFS using the multi modal large language model. Accuracy, sensitivity, Specificity and Cohen's Kappa coefficient were calculated.

METHODS: An advanced multimodal fusion framework GPT4LFS was developed with the primary objective of integrating imaging data and natural language descriptions to comprehensively capture the complex LFS features. The model employed a pre-trained ConvNeXt as the image processing module for extracting high-dimensional imaging features. Concurrently, medical descriptive texts generated by the multimodal large language model GPT-4o and encoded and feature-extracted using RoBERTa were utilized to optimize the model's contextual understanding capabilities. The Mamba architecture was implemented during the feature fusion stage, effectively integrating imaging and textual features and thereby enhancing the performance of the classification task. Finally, the stability of the model's detection results was validated by evaluating classification task metrics, such as the accuracy, sensitivity, specificity, and Kappa coefficients.

RESULTS: The training set comprised 6,299 images from 635 patients, the internal test set included 820 images from 82 patients, and the external test set was composed of 930 images from 93 patients. The GPT4LFS model demonstrated an overall accuracy of 93.7%, sensitivity of 95.8%, and specificity of 94.5% in the internal test set (Kappa = 0.89,95% confidence interval (CI): 0.84-0.96, p<.001). In the external test set, the overall accuracy was 92.2%, with a sensitivity of 92.2% and a specificity of 97.4% (Kappa = 0.88, 95% CI: 0.84-0.89, p<.001). Both the internal and external test sets showed excellent consistency in the model. After the article is published, we will make the full code publicly available on GitHub.

CONCLUSIONS: Using the GPT4LFS model for LFS image categorization demonstrated accuracy and the capacity for feature description at a level commensurate with that of professional clinicians.

PMID:40157428 | DOI:10.1016/j.spinee.2025.03.011

Eur Heart J. 2025 Mar 30:ehaf073. doi: 10.1093/eurheartj/ehaf073. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Accurate near-term prediction of life-threatening ventricular arrhythmias would enable pre-emptive actions to prevent sudden cardiac arrest/death. A deep learning-enabled single-lead ambulatory electrocardiogram (ECG) may identify an ECG profile of individuals at imminent risk of sustained ventricular tachycardia (VT).

METHODS: This retrospective study included 247 254, 14 day ambulatory ECG recordings from six countries. The first 24 h were used to identify patients likely to experience sustained VT occurrence (primary outcome) in the subsequent 13 days using a deep learning-based model. The development set consisted of 183 177 recordings. Performance was evaluated using internal (n = 43 580) and external (n = 20 497) validation data sets. Saliency mapping visualized features influencing the model's risk predictions.

RESULTS: Among all recordings, 1104 (.5%) had sustained ventricular arrhythmias. In both the internal and external validation sets, the model achieved an area under the receiver operating characteristic curve of .957 [95% confidence interval (CI) .943-.971] and .948 (95% CI .926-.967). For a specificity fixed at 97.0%, the sensitivity reached 70.6% and 66.1% in the internal and external validation sets, respectively. The model accurately predicted future VT occurrence of recordings with rapid sustained VT (≥180 b.p.m.) in 80.7% and 81.1%, respectively, and 90.0% of VT that degenerated into ventricular fibrillation. Saliency maps suggested the role of premature ventricular complex burden and early depolarization time as predictors for VT.

CONCLUSIONS: A novel deep learning model utilizing dynamic single-lead ambulatory ECGs accurately identifies patients at near-term risk of ventricular arrhythmias. It also uncovers an early depolarization pattern as a potential determinant of ventricular arrhythmias events.

PMID:40157386 | DOI:10.1093/eurheartj/ehaf073

Comput Biol Med. 2025 Mar 28;190:110019. doi: 10.1016/j.compbiomed.2025.110019. Online ahead of print.

ABSTRACT

Communication involves exchanging information between individuals or groups through various media sources. However, limitations such as hearing loss can make it difficult for some individuals to understand the information delivered during speech communication. Conventional methods, including sign language, written text, and manual lipreading, offer some solutions; however, emerging software-based tools using artificial intelligence (AI) are introducing more effective approaches. Many approaches rely on AI to improve communication quality, with the current trend of leveraging deep learning being a particularly effective tool. This paper presents a comprehensive Systematic Literature Review (SLR) of research trends in automatic lipreading technologies, a critical field in enhancing communication among individuals with hearing impairments. The SLR, which followed the Preferred Reporting Items for Systematic Literature Review and Meta-Analysis (PRISMA) protocol, identified 114 original research articles published between 2014 and mid-2024. The essential information from these articles was summarized, including the trends in automatic lipreading research, dataset availability, task categories, existing approaches, and architectures for automatic lipreading systems. The results showed that various techniques and advanced deep learning models achieved convincing performance to become state-of-the-art in automatic lipreading tasks. However, several challenges, such as insufficient data quantity, inadequate environmental conditions, and language diversity, must be resolved in the future. Furthermore, many improvements have been made to the deep learning models to overcome these challenges and become a massive solution, particularly for automatic lipreading tasks in the near future.

PMID:40157316 | DOI:10.1016/j.compbiomed.2025.110019

Comput Biol Med. 2025 Mar 28;190:110048. doi: 10.1016/j.compbiomed.2025.110048. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Accurate medical tumor segmentation is critical for early diagnosis and treatment planning, significantly improving patient outcomes. This study aims to enhance liver and tumor segmentation from CT and liver images by developing a novel model, ResTransUNet, which combines convolutional and transformer blocks to improve segmentation accuracy.

METHODS: The proposed ResTransUNet model is a custom implementation inspired by the TransUNet architecture, featuring a Standalone Transformer Block and ResNet50 as the backbone for the encoder. The hybrid architecture leverages the strengths of Convolutional Neural Networks (CNNs) and Transformer blocks to capture both local features and global context effectively. The encoder utilizes a pre-trained ResNet50 to extract rich hierarchical features, with key feature maps to preserved it as skip connections. The Standalone Transformer Block, integrated into the model, employs multi-head attention mechanisms to capture long-range dependencies across the image, enhancing segmentation performance in complex cases. The decoder reconstructs the segmentation mask by progressively upsampling encoded features while integrating skip connections, ensuring both semantic information and spatial details are retained. This process culminates in a precise binary segmentation mask that effectively distinguishes liver and tumor regions.

RESULTS: The ResTransUNet model achieved superior Dice Similarity Coefficient (DSC) for liver segmentation (98.3% on LiTS and 98.4% on 3D-IRCADb-01) and for tumor segmentation from CT images (94.7% on LiTS and 89.8% on 3D-IRCADb-01) as well as from liver images (94.6% on LiTS and 91.1% on 3D-IRCADb-01). The model also demonstrated high precision, sensitivity, and specificity, outperforming current state-of-the-art methods in these tasks.

CONCLUSIONS: The ResTransUNet model demonstrates robust and accurate performance in complex medical image segmentation tasks, particularly in liver and tumor segmentation. These findings suggest that ResTransUNet has significant potential for improving the precision of surgical interventions and therapy planning in clinical settings.

PMID:40157314 | DOI:10.1016/j.compbiomed.2025.110048

Commun Eng. 2025 Mar 29;4(1):59. doi: 10.1038/s44172-025-00399-1.

NO ABSTRACT

PMID:40158063 | DOI:10.1038/s44172-025-00399-1

Sci Rep. 2025 Mar 29;15(1):10897. doi: 10.1038/s41598-025-95633-3.

ABSTRACT

Lung cancer is the leading cause of cancer-related deaths globally, with radiotherapy as a key treatment modality for inoperable cases. Lactate, once considered a by-product of anaerobic cellular metabolism, is now considered critical for cancer progression. Lactate dehydrogenase B (LDHB) converts lactate to pyruvate and supports mitochondrial metabolism. In this study, a re-analysis of our previous transcriptomic data revealed that LDHB silencing in the NSCLC cell lines A549 and H358 dysregulated 1789 genes, including gene sets associated with cell cycle and DNA repair pathways. LDHB silencing increased H2AX phosphorylation, a surrogate marker of DNA damage, and induced cell cycle arrest at the G1/S or G2/M checkpoint depending on the p53 status. Long-term LDHB silencing sensitized A549 cells to radiotherapy, resulting in increased DNA damage and genomic instability as evidenced by increased H2AX phosphorylation levels and micronuclei accumulation, respectively. The combination of LDHB silencing and radiotherapy increased protein levels of the senescence marker p21, accompanied by increased phosphorylation of Chk2, suggesting persistent DNA damage. Metabolomics analysis revealed that LDHB silencing decreased nucleotide metabolism, particularly purine and pyrimidine biosynthesis, in tumor xenografts. Nucleotide supplementation partially attenuated DNA damage caused by combined LDHB silencing and radiotherapy. These findings suggest that LDHB supports metabolic homeostasis and DNA damage repair in NSCLC, while its silencing enhances the effects of radiotherapy by impairing nucleotide metabolism and promoting persistent DNA damage.

PMID:40158058 | DOI:10.1038/s41598-025-95633-3

Sci Rep. 2025 Mar 29;15(1):10856. doi: 10.1038/s41598-025-95738-9.

ABSTRACT

SF3B1 gene mutations are prevalent in myelodysplastic syndrome (MDS) and define a distinct disease subtype. These mutations are associated with dysregulated genes and pathways, offering potential for novel therapeutic approaches. However, the aberrant mRNA alternative splicing landscape in SF3B1-deficient MDS cells remains underexplored. In this study, we investigated the influence of SF3B1 gene alterations on the pre-mRNA splicing landscape in MDS cells using transcriptomic data from two independent MDS cohorts. we identified over 5000 significant differential alternative splicing events associated with SF3B1 mutation. This work corroborates previous studies, showing significant enrichment of MYC activity and heme metabolism in SF3B1 mutant cells. A key novel finding of this study is the identification of a gene expression signature driven by SF3B1 mutations, centered on protein post-translational modifications. Notably, we discovered aberrant alternative splicing of the tumor suppressor gene UBA7, leading to significantly reduced gene expression. This dysregulation implicates UBA7 as a critical player in MDS pathogenesis. Importantly, the clinical relevance of this finding is underscored by the observation that low UBA7 gene expression was associated with poor overall survival in chronic lymphocytic leukemia (CLL), another hematological malignancy with frequent SF3B1 mutations. Furthermore, a similar association between low UBA7 gene expression and poor survival outcomes was observed across multiple tumor types in the TCGA database, highlighting the broader implications of UBA7 dysregulation in cancer biology. These findings provide new insights into the mechanisms by which SF3B1 mutations reshape the pre-mRNA splicing landscape and drive disease pathogenesis in MDS. Furthermore, they underscore the potential of UBA7 as a biomarker to stratify SF3B1-mutant MDS and CLL patients, offering a refined approach for risk assessment and highlighting opportunities for targeted therapeutic interventions.

PMID:40158006 | DOI:10.1038/s41598-025-95738-9

Nat Commun. 2025 Mar 29;16(1):3065. doi: 10.1038/s41467-025-58259-7.

ABSTRACT

Enzymes are essential catalysts in biological systems. Substrate inhibition, once dismissed, is now observed in 20% of enzymes1 and is attributed to the formation of an unproductive enzyme-substrate complex, with no structural evidence of unproductivity provided to date1-6. This study uncovers the molecular mechanism of substrate inhibition in tobacco glucosyltransferase NbUGT72AY1, which transfers glucose to phenols for plant protection. The peculiarity that β-carotene strongly attenuates the substrate inhibition of NbUGT72AY1, despite being a competitive inhibitor, allows to determine the conformational changes that occur during substrate binding in both active and substrate-inhibited complexes. Crystallography reveals structurally different ternary enzyme-substrate complexes that do not conform to classical mechanisms. An alternative pathway suggests substrates bind randomly, but the reaction occurs only if a specific order is followed (asymmetric cooperativity). This unreported paradigm explains substrate inhibition and reactivation by competitive inhibitors, opening new research avenues in metabolic regulation and industrial applications.

PMID:40157902 | DOI:10.1038/s41467-025-58259-7

Biotechnol Adv. 2025 Mar 27:108568. doi: 10.1016/j.biotechadv.2025.108568. Online ahead of print.

ABSTRACT

Curcumin, a natural compound found in turmeric, has shown promise in treating brain-related diseases and conditions associated with aging. Curcumin has shown multiple anti-inflammatory and brain-protective effects, but its clinical use is limited by challenges like poor absorption, specificity and delivery to the right tissues. A range of contemporary approaches at the intersection with bioengineering and systems biology are being explored to address these challenges. Data from preclinical and human studies highlight various neuroprotective actions of curcumin, including the inhibition of neuroinflammation, modulation of critical cellular signaling pathways, promotion of neurogenesis, and regulation of dopamine levels. However, curcumin's multifaceted effects - such as its impact on microRNAs and senescence markers - suggest novel therapeutic targets in neurodegeneration. Tetrahydrocurcumin, a primary metabolite of curcumin, also shows potential due to its presence in circulation and its anti-inflammatory properties, although further research is needed to elucidate its neuroprotective mechanisms. Recent advancements in delivery systems, particularly brain-targeting nanocarriers like polymersomes, micelles, and liposomes, have shown promise in enhancing curcumin's bioavailability and therapeutic efficacy in animal models. Furthermore, the exploration of drug-laden scaffolds and dermal delivery may extend the pharmacological applications of curcumin. Studies reviewed here indicate that engineered dermal formulations and devices could serve as viable alternatives for neuroprotective treatments and to manage skin or musculoskeletal inflammation. This work highlights the need for carefully designed, long-term studies to better understand how curcumin and its bioactive metabolites work, their safety, and their effectiveness.

PMID:40157560 | DOI:10.1016/j.biotechadv.2025.108568

Gastroenterology. 2025 Mar 27:S0016-5085(25)00540-2. doi: 10.1053/j.gastro.2025.03.018. Online ahead of print.

NO ABSTRACT

PMID:40157433 | DOI:10.1053/j.gastro.2025.03.018

Cell Metab. 2025 Mar 25:S1550-4131(25)00104-4. doi: 10.1016/j.cmet.2025.03.001. Online ahead of print.

ABSTRACT

Aging affects reproductive capabilities in males through physiological and behavioral alterations, including endocrine changes and decreased libido. In this study, we investigated the influence of intermittent fasting (IF) on these aging-related declines, using male C57BL/6J mice. Our findings revealed that IF significantly preserved reproductive success in aged mice, not by improving traditional reproductive metrics such as sperm quality or endocrine functions but by enhancing mating behavior. This behavioral improvement was attributed to IF's ability to counter age-dependent increases in serotonergic inhibition, primarily through the decreased supply of the serotonin precursor tryptophan from the periphery to the brain. Our research underscores the potential of dietary interventions like IF in mitigating age-associated declines in male reproductive health and suggests a novel approach to managing conditions related to reduced sexual desire, highlighting the complex interplay between diet, metabolism, and reproductive behavior.

PMID:40157367 | DOI:10.1016/j.cmet.2025.03.001

Sci Total Environ. 2025 Mar 28;974:179243. doi: 10.1016/j.scitotenv.2025.179243. Online ahead of print.

ABSTRACT

Deforestation is one of the main drivers of environmental degradation around the world. Slash-and-burn is a common practice, performed in tropical forests to create new agricultural lands for local communities. In Madagascar, this practice affects many natural areas that host lemur habitats. Reforestation within nature reserves including fast-growing native species is desirable, for example in this area using native bamboo with the aim of restoring the habitat increased plantation success. In this context, the extensive detection of forest disturbances can effectively support restoration actions, providing an overall framework to address priorities and maximizing ecological benefits. In this work and with respect to a study area located around the Maromizaha New Protected Area (Madagascar), an analysis was conducted based on a time series of NDVI maps from Landsat missions (GSD = 30 m). The period between 1991 and 2022 was investigated to detect the location and moment of forest disturbances with the additional aim of quantifying the level of damage and of the recovery process at every disturbed location. It is worth noting that the Maromizaha New Protected Area currently hosts 12 species of endangered lemurs, highlighting its pivotal role as a critical conservation and restoration priority due to the ecological significance of preserving habitat integrity to sustain these threatened species. Detection was operated at pixel level by analyzing the local temporal profile of Normalized Difference Vegetation Index - NDVI (yearly step). Time of the eventual detected disturbance was found within the profile looking for the first derivative minimum. Significance of NDVI change was evaluated testing the Chebyshev condition and the following parameters mapped: i) year of disturbance; ii) significance of NDVI change; iii) level of damage; (iv) year of vegetation recovery; (v) rate of recovery. Accordingly, the level of the damage and the rate of recovery were used to estimate resistance and resilience indices of lemurs' habitat (inherently forested areas). Finally, temporal trends of both forest loss and recovery were analyzed to investigate potential impacts onto local lemur populations and, more in general, to the entire Reserve.

PMID:40157089 | DOI:10.1016/j.scitotenv.2025.179243

Curr Opin Biotechnol. 2025 Mar 28;93:103297. doi: 10.1016/j.copbio.2025.103297. Online ahead of print.

ABSTRACT

Oleaginous yeast are remarkably versatile organisms, distinguished by their natural capacities to accumulate high levels of neutral lipids and broad substrate range. With recent growing interests in engineering non-model organisms as superior biomanufacturing platforms, oleaginous yeasts have emerged as promising chassis for oleochemicals, terpenoids, organic acids, and other valuable products. Advancement in systems biology along with genetic tool development have significantly expanded our understanding of the metabolism in these species and enabled engineering efforts to produce biofuels and bioproducts from diverse feedstocks. This review examines the latest technical advances in oleaginous yeast research toward sustainable biomanufacturing. We cover recent developments in systems biology-enabled metabolism understanding, genetic tools, feedstock utilization, and strain engineering approaches for the production of various valuable chemicals.

PMID:40157044 | DOI:10.1016/j.copbio.2025.103297

Br J Surg. 2025 Mar 28;112(4):znaf051. doi: 10.1093/bjs/znaf051.

NO ABSTRACT

PMID:40156895 | DOI:10.1093/bjs/znaf051

BioData Min. 2025 Mar 28;18(1):27. doi: 10.1186/s13040-025-00442-z.

ABSTRACT

The integration of multi-omics data from diverse high-throughput technologies has revolutionized drug discovery. While various network-based methods have been developed to integrate multi-omics data, systematic evaluation and comparison of these methods remain challenging. This review aims to analyze network-based approaches for multi-omics integration and evaluate their applications in drug discovery. We conducted a comprehensive review of literature (2015-2024) on network-based multi-omics integration methods in drug discovery, and categorized methods into four primary types: network propagation/diffusion, similarity-based approaches, graph neural networks, and network inference models. We also discussed the applications of the methods in three scenario of drug discovery, including drug target identification, drug response prediction, and drug repurposing, and finally evaluated the performance of the methods by highlighting their advantages and limitations in specific applications. While network-based multi-omics integration has shown promise in drug discovery, challenges remain in computational scalability, data integration, and biological interpretation. Future developments should focus on incorporating temporal and spatial dynamics, improving model interpretability, and establishing standardized evaluation frameworks.

PMID:40155979 | DOI:10.1186/s13040-025-00442-z

BMC Cancer. 2025 Mar 28;25(1):560. doi: 10.1186/s12885-025-13845-2.

ABSTRACT

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, particularly due to advanced-stage metastasis. P-cadherin (CDH3), a potential therapeutic target, is highly expressed in CRC tissues and associated with poor prognosis and metastasis. However, the mechanisms underlying its role in CRC progression and its translational potential remain poorly understood.

MATERIALS AND METHODS: This study integrated multiple public databases (TCGA, HCMDB, UALCAN, HPA, UniProt, cBioPortal, and GEO) to evaluate CDH3 expression, construct a prognostic model, and perform functional and translational analyses. Immunohistochemistry was used to validate CDH3 protein expression in clinical samples. Additional analyses included correlations with clinicopathological parameters, immune infiltration (TIDE, TISIDB), functional enrichment (KEGG, GSEA), drug sensitivity (GSCA), and molecular docking (MOE). Single-cell sequencing (CancerSEA, HPA) was also conducted to explore CDH3's role at the single-cell level.

RESULTS: CDH3 expression was significantly elevated in CRC tissues and correlated with poor prognosis, recurrence, and metastasis. CDH3 expression was associated with the infiltration of resting immune cells, particularly dendritic cells, and enrichment analysis revealed its critical role in CRC metastasis through extracellular matrix (ECM) and local adhesion pathways. Notably, afatinib emerged as a promising candidate for targeting CDH3 via "drug repositioning," a process involving the repurposing of existing drugs for new therapeutic applications.

CONCLUSION: This study provides novel insights into CDH3's role in CRC metastasis and its potential as a therapeutic target. The translational potential of CDH3, including its integration with immunotherapy and drug repositioning strategies, offers a promising avenue for the treatment of metastatic CRC.

PMID:40155851 | DOI:10.1186/s12885-025-13845-2

Br J Pharmacol. 2025 Mar 28. doi: 10.1111/bph.70018. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Opioid use disorder (OUD) is a pressing public health concern marked by frequent relapse during periods of abstinence, perpetuated by negative affective states. Classical antidepressants or the currently prescribed opioid pharmacotherapies have limited efficacy to reverse the negative affect or prevent relapse.

EXPERIMENTAL APPROACH: Using mouse models, we investigated the effects of ketamine's metabolite (2R,6R)-hydroxynorketamine (HNK) on reversing conditioning to sub-effective doses of morphine in stress-susceptible mice, preventing conditioned-place aversion and alleviating acute somatic abstinence symptoms in opioid-dependent mice. Additionally, we evaluated its effects on anhedonia, anxiety-like behaviours and cognitive impairment during protracted opioid abstinence, while mechanistic studies examined cortical EEG oscillations and synaptic plasticity markers.

KEY RESULTS: (2R,6R)-HNK reversed conditioning to sub-effective doses of morphine in stress-susceptible mice and prevented conditioned-place aversion and acute somatic abstinence symptoms in opioid-dependent mice. In addition, (2R,6R)-HNK reversed anhedonia, anxiety-like behaviours and cognitive impairment emerging during protracted opioid abstinence plausibly via a restoration of impaired cortical high-frequency EEG oscillations, through a GluN2A-NMDA receptor-dependent mechanism. Notably, (2R,6R)-HNK facilitated the extinction of opioid conditioning, prevented stress-induced reinstatement of opioid-seeking behaviours and reduced the propensity for enhanced morphine self-consumption in mice previously exposed to opioids.

CONCLUSIONS AND IMPLICATIONS: These findings emphasize the therapeutic potential of (2R,6R)-HNK, which is currently in Phase II clinical trials, in addressing stress-related opioid responses. Reducing the time and cost required for development of new medications for the treatment of OUDs via drug repurposing is critical due to the opioid crisis we currently face.

PMID:40155780 | DOI:10.1111/bph.70018

Laryngoscope. 2025 Mar 29. doi: 10.1002/lary.32155. Online ahead of print.

ABSTRACT

OBJECTIVES: Olfactory dysfunction (OD) is common among people with cystic fibrosis (PwCF) and chronic rhinosinusitis (CRS). OD is associated with impaired quality of life (QOL) and dietary alterations in certain non-CF populations. This study explored relationships between OD, QOL, and modulator use in PwCF.

METHODS: This is a cross-sectional analysis of an ongoing multicenter, prospective study (2019-2023) investigating PwCF with comorbid CRS. Participants completed the 40-Question Smell Identification Test (SIT-40), 22-question SinoNasal Outcome Test-(SNOT-22), Questionnaire of Olfactory Disorders (QOD-NS), and Cystic Fibrosis Questionnaire-Revised (CFQ-R). Clinical and sinus CT data were collected. After stratification by SIT-40 score, data was analyzed by chi-square, Kruskal-Wallis, Spearman correlation, and logistic regression to identify factors associated with OD.

RESULTS: Of 59 participants, those with anosmia (n = 12) had worse eating-related QOL (CFQ-R eating) compared to individuals with normosmia (n = 16) and hyposmia (n = 31). Participants with anosmia had worse sinus CT scores than those with hyposmia. Although PwCF treated with highly effective modulator therapy (HEMT; n = 30) had better CT scores vs. non-HEMT individuals (n = 23), rates of OD in both groups were comparable. Higher SNOT-22 total scores were associated with increased odds of hyposmia or anosmia. In an eating-related QOD-NS subscore, those with worse CFQ-R eating had 2.38 times higher odds of having OD. Each point decrease in CFQ-R eating domain score was associated with 10% increased odds of OD.

CONCLUSION: In PwCF, OD was associated with increased CRS severity, impaired olfactory QOL, and decreased CFQ-R eating. There were no differences in SIT-40 or QOD-NS scores based on HEMT status.

TRIAL REGISTRATION: NCT04469439.

PMID:40156369 | DOI:10.1002/lary.32155

Int J Tuberc Lung Dis. 2025 Mar 31;29(4):153-158. doi: 10.5588/ijtld.24.0544.

ABSTRACT

<sec><title>BACKGROUND</title>Post-infectious bronchiolitis obliterans (PIBO) is a complication of severe childhood respiratory infection resulting in small airway injury, bronchiectasis, and prolonged respiratory consequences. Risk factors for PIBO and PIBO-associated bronchiectasis are unclear.</sec><sec><title>METHODS</title>This retrospective study identified all children with PIBO at a South African tertiary hospital between 1 January 2016 and 31 December 2022. The clinical characteristics, chest CT findings, and details of prior hospitalisation for respiratory infection were collected, and the characteristics of those with and without bronchiectasis were compared.</sec><sec><title>RESULTS</title>A total of 59 children were included (median age at primary lung insult: 10 months, IQR 6-17; median age at PIBO diagnosis: 16 months, IQR 11-28). Twenty-three had comorbidities, most frequently premature birth (30.5%) and HIV infection (6.8%). The most common pathogen was adenovirus (n = 41; 69.5%). At initial lung insult, 19 (32.2%) required mechanical ventilation. Mosaic attenuation on the chest CT was present in all. Thirty-three (55.9%) had bronchiectasis. The clinical characteristics, ventilation, causative pathogen, and comorbidity were similar in those with and without bronchiectasis.</sec><sec><title>CONCLUSION</title>Bronchiectasis occurs frequently in paediatric PIBO and is present within months of initial respiratory insult with no identified risk factors. Premature birth is common and may contribute to PIBO development.</sec>.

PMID:40155792 | DOI:10.5588/ijtld.24.0544