Front Neurol. 2025 May 19;16:1516168. doi: 10.3389/fneur.2025.1516168. eCollection 2025.

ABSTRACT

INTRODUCTION: While the adjustment of intracranial volume (ICV) is reported to have a significant influence in the outcomes of the analyses of brain structural measures, our study offers a paradigm shift, positing that adjusting for lateral ventricle (LV) inter-individual variability may reveal more atrophic patterns that might be overlooked in analyses without this adjustment,-and such LV-adjusted atrophic patterns may reduce discrepancies observed in earlier studies and better elucidate complex conditions associated with HIV, such as HAND.

METHODS: To test this hypothesis, we employed a number of adjustment strategies on MRI T1-image-derived data extracted using deep learning models and compared their ability to identify the presence and extent of HIV-specific atrophic patterns based on statistical measures and strength.

RESULTS: Our results show that both ICV adjustments may be effective to identify atrophic patterns associated with either aging or HIV in areas of the thalamus, basal ganglia, ventral DC and lateral ventricle, some of which may be overlooked without these adjustments. We also report that LV adjustmenst detect most atrophic patterns associated with HIV and HAND across multiple subcortical regions with more strong statistical strengths, especially the areas of the basal ganglia (putamen, pallidum, caudate nucleus), hippocampus, thalamus, ventral DC, basal forebrain, third ventricle, fourth ventricle, and inferior lateral ventricle. The analyses of LV-adjusted metrics also show that atrophic patterns observed in the hippocampus, thalamus and pallidum were strongly correlated with HAND(especially dysfunction in executive function) and clinical markers (i.e., CD4/CD8 ratio).

CONCLUSION: We conclude that models that control for individual variability in intracranial and ventricular volumes have the potential to minimize discrepancies and variations in structural reports of HIV, improving the diagnostic power of identified patterns and fostering greater consistency across research studies. More importantly, adjusting for LV may not only detect atrophic patterns that could be overlooked in analyses performed without any adjustments, but the outcomes obtained from the adjustments may better explain HIV-associated conditions such as HAND and underlying immunological issues often observed in subjects with HIV treated with combination antiretroviral therapy, considering that the adjustments account for certain aspects of regional interaction.

PMID:40458466 | PMC:PMC12127162 | DOI:10.3389/fneur.2025.1516168

Front Plant Sci. 2025 May 19;16:1541982. doi: 10.3389/fpls.2025.1541982. eCollection 2025.

ABSTRACT

The temporal and spatial irregularity of plant diseases results in insufficient image data for certain diseases, challenging traditional deep learning methods that rely on large amounts of manually annotated data for training. Few-shot learning has emerged as an effective solution to this problem. This paper proposes a method based on the Feature Adaptation Score (FAS) metric, which calculates the FAS for each feature layer in the Swin-TransformerV2 structure. By leveraging the strict positive correlation between FAS scores and test accuracy, we can identify the Swin-Transformer V2-F6 network structure suitable for few-shot plant disease classification without training the network. Furthermore, based on this network structure, we designed the Plant Disease Feature Calibration (PDFC) algorithm, which uses extracted features from the PlantVillage dataset to calibrate features from other datasets. Experiments demonstrate that the combination of the Swin-Transformer V2F6 network structure and the PDFC algorithm significantly improves the accuracy of few-shot plant disease classification, surpassing existing state-of-the-art models. Our research provides an efficient and accurate solution for few-shot plant disease classification, offering significant practical value.

PMID:40458225 | PMC:PMC12127352 | DOI:10.3389/fpls.2025.1541982

Front Public Health. 2025 May 19;13:1585481. doi: 10.3389/fpubh.2025.1585481. eCollection 2025.

ABSTRACT

OBJECTIVE: This study investigates the relationship between vaginal radiation dose and radiation-induced vaginal injury in cervical cancer patients, with the aim of developing a risk prediction model to support personalized treatment strategies.

METHODS: A retrospective analysis was performed on the clinical data of 66 cervical cancer patients treated between December 2022 and December 2023. The Synthetic Minority Over-sampling Technique (SMOTE) was employed for data augmentation. Univariate and multivariate analyses were conducted to identify key factors influencing radiation-induced vaginal injury, and five distinct algorithms were applied to develop predictive models. The AUC/ROC metric was used to assess the performance of the models.

RESULTS: Univariate analysis revealed significant associations between the posterior-inferior border of the symphysis (PIBS) point dose and brachytherapy dose with radiation-induced vaginal injury (p < 0.05). Multivariate analysis confirmed PIBS point dose, brachytherapy dose, age, external beam radiation dose, and vaginal involvement as significant factors. A neural network algorithm was chosen to construct the radiation-induced vaginal injury model, which was subsequently developed into an online tool.

CONCLUSION: The developed predictive model can assess the risk of radiation-induced vaginal injury, thereby facilitating the development of individualized radiotherapy plans.

PMID:40458090 | PMC:PMC12128087 | DOI:10.3389/fpubh.2025.1585481

Trends Hear. 2025 Jan-Dec;29:23312165251347773. doi: 10.1177/23312165251347773. Epub 2025 Jun 3.

ABSTRACT

Auditory brainstem response (ABR) interpretation in clinical practice often relies on visual inspection by audiologists, which is prone to inter-practitioner variability. While deep learning (DL) algorithms have shown promise in objectifying ABR detection in controlled settings, their applicability to real-world clinical data is hindered by small datasets and insufficient heterogeneity. This study evaluates the generalizability of nine DL models for ABR detection using large, multicenter datasets. The primary dataset analyzed, Clinical Dataset I, comprises 128,123 labeled ABRs from 13,813 participants across a wide range of ages and hearing levels, and was divided into a training set (90%) and a held-out test set (10%). The models included convolutional neural networks (CNNs; AlexNet, VGG, ResNet), transformer-based architectures (Transformer, Patch Time Series Transformer [PatchTST], Differential Transformer, and Differential PatchTST), and hybrid CNN-transformer models (ResTransformer, ResPatchTST). Performance was assessed on the held-out test set and four external datasets (Clinical II, Southampton, PhysioNet, Mendeley) using accuracy and area under the receiver operating characteristic curve (AUC). ResPatchTST achieved the highest performance on the held-out test set (accuracy: 91.90%, AUC: 0.976). Transformer-based models, particularly PatchTST, showed superior generalization to external datasets, maintaining robust accuracy across diverse clinical settings. Additional experiments highlighted the critical role of dataset size and diversity in enhancing model robustness. We also observed that incorporating acquisition parameters and demographic features as auxiliary inputs yielded performance gains in cross-center generalization. These findings underscore the potential of DL models-especially transformer-based architectures-for accurate and generalizable ABR detection, and highlight the necessity of large, diverse datasets in developing clinically reliable systems.

PMID:40457875 | DOI:10.1177/23312165251347773

Cancer. 2025 Jun 1;131(11):e35883. doi: 10.1002/cncr.35883.

NO ABSTRACT

PMID:40457864 | DOI:10.1002/cncr.35883

J Phys Chem A. 2025 Jun 2. doi: 10.1021/acs.jpca.5c01881. Online ahead of print.

ABSTRACT

The aqueous diffusion coefficients of organic compounds are among the most important topics of interest for chemical research and engineering, particularly for the dispersal of organic pollutants in water environments. The experimental determination of the diffusion coefficients is time-consuming, and the experimental data are lacking for most newly synthesized chemicals. Our study developed a multimodal deep learning model incorporating molecular images, molecular descriptors, and temperatures to predict aqueous diffusion coefficients at varying temperatures. The deep learning model made accurate predictions on the test set (R2 = 0.986) and had a considerably lower error than previous empirical equations. Besides, the model interpretation indicated that the deep learning model correctly captured the effects of molecular features and temperature on the prediction of diffusion coefficients. Thus, our study provided a valuable tool for the rapid and accurate prediction of organic compounds diffusion coefficients in water at varying temperatures and also facilitated a better understanding of how different molecular features and water temperatures influence diffusion-controlled processes in both the environmental and engineered systems.

PMID:40457760 | DOI:10.1021/acs.jpca.5c01881

Int J Cancer. 2025 Jun 3. doi: 10.1002/ijc.35499. Online ahead of print.

ABSTRACT

Recent studies show that the dysregulation of the transcription factor SOX10 is essential for the development and progression of melanoma. MicroRNAs (miRNAs) can regulate the expression of transcription factors at the post-transcriptional level. The interactions between SOX10 and its targeting miRNAs form network motifs such as feedforward and feedback loops. Such motifs can result in nonlinear dynamics in gene expression levels, therefore playing a crucial role in regulating tumor proliferation and metastasis as well as the tumor's responses to therapies. Here, we reviewed and discussed the intricate interplay between SOX10 and miRNAs in melanoma biology including melanogenesis, phenotype switch, and therapy resistance. Additionally, we investigated the gene regulatory interactions in melanoma, identifying crucial network motifs that involve SOX10, MITF, and miRNAs. We also analyzed the expression levels of the components within these motifs. From a control theory perspective, we explained how these dynamics are linked to the phenotypic plasticity of melanoma cells. In summary, we underscored the importance of employing a data-driven network biology approach to elucidate the complex regulatory mechanisms and identify driver network motifs within the melanoma network. This methodology facilitates a deeper understanding of the regulation of SOX10 and MITF by miRNAs in melanoma. The insight gained could potentially contribute to the development of miRNA-based treatments, thereby enhancing the clinical management of this malignancy.

PMID:40458894 | DOI:10.1002/ijc.35499

Front Pharmacol. 2025 May 19;16:1570158. doi: 10.3389/fphar.2025.1570158. eCollection 2025.

ABSTRACT

Protein glycosylation, particularly O-GlcNAcylation, is a critical post-translational modification (PTM) that regulates cardiac and vascular functions by modulating protein stability, localization, and interactions. Dysregulated glycosylation is generally believed as a key driver in the pathogenesis of cardiovascular diseases (CVDs), contributing to adverse cardiac remodeling, mitochondrial dysfunction, metabolic dysregulation, and vascular inflammation. This review highlights the mechanistic roles of glycosylation in CVD progression, including its regulation of cardiac remodeling, mitochondrial dysfunction, and vascular inflammation. This study explored the dual role of O-GlcNAcylation in acute protection and chronic injury, emphasizing its potential as a biomarker for early diagnosis and risk stratification. Therapeutic strategies targeting glycosylation pathways, particularly O-GlcNAc transferase (OGT), and O-GlcNAcase (OGA), hold promise for addressing myocardial ischemia-reperfusion injury, diabetic cardiomyopathy, and atherosclerosis. Advances in glycosylation profiling and interdisciplinary collaboration are essential to overcome challenges such as tissue specificity and off-target effects, advancing precision cardiovascular medicine.

PMID:40458788 | PMC:PMC12127152 | DOI:10.3389/fphar.2025.1570158

Biofilm. 2025 May 3;9:100284. doi: 10.1016/j.bioflm.2025.100284. eCollection 2025 Jun.

ABSTRACT

Synthetic communities (SynComs) are valuable tools for addressing microbial community assembly and function, towards their manipulation for clinical, biotechnological and agricultural applications. However, SynCom design is complicated since interactions between microbes cannot be predicted based on their individual properties. Here we aimed to assemble a functionally cohesive SynCom displaying high-order interactions, as a model to study the community-level beneficial functions of seed-endophytic bacteria from native maize landraces, including strains from the Bacilli class, and the Burkholderia and Pseudomonas genera. We developed a partial combinatorial, bottom-up strategy that was followed by the detection of complex colony architecture as an emergent property in co-cultures. Using this simplified approach, we tested less than 400 co-cultures from a pool of 27 strains, resulting in the assembly the Xilonen SynCom, which includes Bacillus pumilus NME155, Burkholderia contaminans XM7 and Pseudomonas sp. GW6. In this community, higher-order interactions result in complex colony architecture, which is considered a proxy of biofilm formation. Additionally, we generated protocols for absolute quantification of each member from a complex mixture. The Xilonen SynCom will serve as a model to study biofilm formation in community settings, and will aid in the study of the molecular and ecological basis mediating maize fertility.

PMID:40458266 | PMC:PMC12127623 | DOI:10.1016/j.bioflm.2025.100284

Mol Biol Evol. 2025 Jun 3:msaf127. doi: 10.1093/molbev/msaf127. Online ahead of print.

ABSTRACT

How viruses evolve largely depends on their hosts. To quantitatively characterize this dependence, we introduce Maximum Entropy Nucleotide Bias models (MENB) learned from single, di- and tri- nucleotide usage of viral sequences that infect a given host. We first use MENB to classify the viral family and the host of a virus from its genome, among four families of ssRNA viruses and three hosts. We show that both the viral family and the host leave a fingerprint in nucleotide motif usages that MENB models decode. Benchmarking our approach against state-of-the-art methods based on deep neural networks, such as VIDHOP, shows that MENB is rapid, interpretable and robust. Our approach is able to predict, with good accuracy, both the viral family and the host from a whole genomic sequence or a portion of it. MENB models also display promising out of sample generalization ability on viral sequences of new host taxa or new viral families. Our approach is also capable of identifying, within the limitations imposed by the three-host setting, intermediate hosts for well-known pathogenic strains of Influenza A subtypes and Human Coronavirus and recombinations and reassortments on specific genomic regions. Finally MENB models can be used to track the adaptation to the new host, to shed light on the more relevant selective pressures that acted on motif usage during this process and to design new sequences with altered nucleotide usage at fixed amino-acid content.

PMID:40458044 | DOI:10.1093/molbev/msaf127

Basic Clin Pharmacol Toxicol. 2025 Jul;137(1):e70063. doi: 10.1111/bcpt.70063.

ABSTRACT

Prescribing cascades occur when an adverse drug reaction (ADR) to one medication is treated with additional medication. Most literature focusses on this simplistic singular concept of one medication followed by another. However, ADRs in clinical practice may appear less straightforward, making prescribing cascades difficult to identify and deprescribe. More insight is needed into the real-world complexity of prescribing cascades, since they may negatively impact both patients and the healthcare system. This article aims to provide exemplary cases of the real-world complexity of prescribing cascades and explores strategies to identify, mitigate and prevent them. The real-world cases discussed highlight the multifaceted nature of prescribing cascades in clinical practice. They show several factors contributing to the challenges in recognizing ADRs and preventing prescribing cascades, including misinterpretation of ADRs, fragmented healthcare systems and accumulation of pharmacological effects and comorbidities within an individual patient. Several strategies are recommended to improve identification, mitigation and prevention of prescribing cascades. Although educating patients and healthcare providers (HCPs) can help bridge the knowledge gap, additional strategies are needed. Implementing supportive tools to deprescribe, enhanced communication among HCPs and patients regarding ADRs and rationale for medication changes, and monitoring patients for ADRs are considered the most promising strategies.

PMID:40457590 | DOI:10.1111/bcpt.70063

Orphanet J Rare Dis. 2025 Jun 2;20(1):266. doi: 10.1186/s13023-025-03756-7.

ABSTRACT

BACKGROUND: In the European Union (EU), the orphan legislation, aiming to increase the number of pharmacotherapies available for rare diseases, came into force in April 2000. This study examined the development of the selection of orphan medicines granted marketing authorisation, their approved indications, and the number of orphan medicines developed for paediatric use in EU during 2000-2022. This study also examined the availability of the orphan medicines with a marketing authorisation in the Finnish market in order to demonstrate their country level uptake in a single member state.

METHODS: The material on orphan medicines' marketing authorisations and their introduction were collected from the European Commission's Community Registers in June 2022 and analysed with a qualitative document analysis. This study covered the period 2000-2022 since the introduction of the orphan legislation, and comparisons were made in 10-year periods of, 2001-2010 and 2011-2020.

RESULTS: By May 2022, there were 213 novel orphan medicines approved in Europe during the observation period. Of them, 67% (n = 142) were on the market in Finland in May 2024. The number of new orphan medicines approved in Europe doubled from 63 products in 2001-2010 to 127 products in 2011-2020. Several orphan medicines were developed for certain type of rare diseases, such as haematological cancers. The proportion of orphan medicines approved for paediatric use decreased from 55% in 2001-2010 to 42% in 2011-2020.

CONCLUSION: The number of orphan medicines available within EU increased significantly after the orphan legislation came into force. The development of orphan medicines seemed to often focus on diseases or disease groups that already have available treatment options, while several rare diseases remain without available treatment. Even though rare diseases are more common in children, orphan medicines have not been developed for paediatric use in the same proportion.

PMID:40457478 | DOI:10.1186/s13023-025-03756-7

BMC Bioinformatics. 2025 Jun 2;26(1):149. doi: 10.1186/s12859-025-06158-5.

ABSTRACT

BACKGROUND: The volume and complexity of biological data have significantly increased in recent years, often represented as network models continue to increase at a rapid pace. However, drug discovery in the context of complex phenotypes are hampered by the difficulties inherent in producing machine learning algorithms that can integrate molecular-genetic, biochemical, physiological, and other diverse datasets. Recent developments have expanded network analysis techniques, such as network embedding, to effectively explore multilayer network structures. Multilayer networks, which incorporate various nodes and connections in formats such as multiplex, heterogeneous, and bipartite networks, provide an effective framework for merging diverse and multi-scale biological data sources. However, current network embedding methods face challenges and limitations in addressing the heterogeneity and diversity of these networks. Therefore, there is an essential need for the development of new network embedding methods to manage the complexity and diversity of multi-omics biological information effectively.

RESULTS: Here, we report a universal multilayer network embedding method MultiXVERSE, which is to the best of our knowledge the first one capable of handling any kind of multilayer network. We applied it to a molecular-drug-disease multiplex-heterogeneous network. Our model made new predictions about a link between GABA and cancer that we verified experimentally in the Xenopus laevis model.

CONCLUSIONS: The development of MultiXVERSE represents a significant advancement in the integration and analysis of multilayer networks for biological research. By providing a universal, scalable framework for multilayer network embedding, MultiXVERSE enables the systematic exploration of molecular and phenotypic interactions across diverse biological contexts. Our experimental validation of the predicted link between GABA and cancer using Xenopus laevis underscores its capability to generate biologically meaningful hypotheses and accelerate breakthroughs in multi-omics research. Future directions include applying MultiXVERSE to additional multi-omics datasets and integrating it with high-throughput experimental pipelines for systematic hypothesis generation and validation, particularly in drug discovery. Beyond its biological applications, MultiXVERSE is a versatile tool that can be utilized for analyzing multilayer networks in a wide range of fields, including social sciences and other complex systems. By offering a universal framework, MultiXVERSE paves the way for novel insights and interdisciplinary collaborations in multilayer network research.

PMID:40457205 | DOI:10.1186/s12859-025-06158-5

J Pharm Health Care Sci. 2025 Jun 2;11(1):46. doi: 10.1186/s40780-025-00453-2.

ABSTRACT

BACKGROUND: Edoxaban is used as an anti-coagulant to prevent cardioembolic infarction, deep vein thrombosis, and pulmonary embolism. Edoxaban pharmacokinetics have been reported to be affected by several factors such as renal function, age, body weight, and the concomitant use of P-glycoprotein inhibitors. However, the relationship between genetic polymorphisms in drug metabolizing enzymes and transporters and the inter-individual variability of edoxaban pharmacokinetics in patients with atrial fibrillation (AF) remains unclear. Additionally, there is little information concerning PPK analysis using real world data. In this study a population pharmacokinetic and pharmacogenomic analysis was conducted to clarify covariate factors affecting the edoxaban pharmacokinetics in Japanese adult AF patients.

METHODS: One hundred and thirty-one blood samples were collected from 131 patients. The edoxaban pharmacokinetic profile was described by a one-compartment model, and pharmacogenomic data were stratified according to CYP3A5 (CYP3A5*3) and ABCB1 (ABCB1 1236 C > T, 2677G > T/A, and 3435 C > T) polymorphisms. A non-linear mixed-effects modeling software (NONMEM™) was used to evaluate the effects of patient characteristics and genetic polymorphisms on the edoxaban pharmacokinetics.

RESULTS: The apparent oral clearance (CL/F) of edoxaban was estimated, and the apparent volume of distribution was fixed at the reported value. The CL/F of edoxaban was correlated non-linearly with creatinine clearance (CLcr), wherein the population mean CL/F for a typical patient (CLcr = 61.8 mL/min) was estimated to be 28.2 L/h. Other clinical laboratory data and genetic polymorphisms, excluding CLcr, did not affect the edoxaban pharmacokinetics.

CONCLUSIONS: These results suggest that genetic polymorphisms of CYP3A5 and ABCB1 are not considered intrinsic factors affecting edoxaban pharmacokinetics in Japanese adult AF patients. Similarly to previous studies, renal function affects its pharmacokinetics. These findings may provide useful information for individualized anticoagulant therapy with edoxaban to prevent adverse events without reference to genetic polymorphisms of CYP3A5 and ABCB1.

PMID:40457359 | DOI:10.1186/s40780-025-00453-2

BMC Infect Dis. 2025 Jun 2;25(1):788. doi: 10.1186/s12879-025-11135-7.

ABSTRACT

BACKGROUND: The global HIV prevalence estimate at the end of 2022 was 39 million. This has led to mass testing to diagnose new cases, and the conduction of prognostic tests for monitoring HIV progression. This is being done to meet the targets of the 95%: 95%: 95% UN AIDS goal which is to be achieved by 2025. Therefore, there is the need to discover other diagnostic and prognostic biomarkers to be supplemented with the currently used ones. Studies indicate that micro RNAs have their levels influenced by the presence and replication of aetiologic agents, thus the micro RNA (miRNA) expression profile test may serve the dual purpose of HIV diagnosis and prognosis. Therefore, this study assessed the HIV-1 diagnostic and prognostic potential of the expression patterns of circulating miRNAs in HIV positive persons in Cape Coast, Ghana.

METHODS: A cross-sectional research design was used to assess the expression patterns of seven miRNAs - hsa-mir-146a, hsa-mir-155, hsa-mir-34a, hsa-mir-29a, hsa-mir-29b, hsa-mir-223 and hsa-mir-27a in 72 plasma samples. The samples were obtained from six antiretroviral therapy naïve persons, 37 ART-experienced persons and 29 healthy controls. Total RNA was extracted, afterwards, cDNA synthesis and RT-qPCR was done.

RESULTS: There were low levels of hsa-mir-155, and elevated levels of two miRNAs - hsa-mir-34a, and hsa-mir-27a in ART-experienced persons relative to healthy control. The sensitivities of the above miRNAs were 82.14%, 72.73% and 81.25 respectively, and specificities were 86.21%, 70.59% and 87.50% respectively. Upregulated levels of hsa-mir-223 was associated with HIV-1 infection in ART-naïve persons. Hsa-mir-223, hsa-mir-155, hsa-mir-29a and hsa-mir29b were present in quantities that distinguished between viral load categories classified as very low and high. These miRNAs were correlated with viral load. Low levels of hsa-mir-155 in HIV-1 persons with viral load of less than 7cps/ml distinguished them from HIV-negative control. Increased levels of hsa-mir-146a correlated with ART-resistance.

CONCLUSION: Decreased levels of hsa-mir-155, and elevated levels of hsa-mir-34a, hsa-mir-223 and hsa-mir-27a could be biomarkers of HIV-1. Hsa-mir-155, hsa-mir-29a, hsa-mir29b and hsa-mir-223 could be good biomarkers of HIV-1 progression and Low levels of hsa-mir-155 may possess the potential to detect minute HIV-1 RNA copies. Elevated levels of hsa-mir-146a could be a potential biomarker of ART-resistance.

PMID:40457240 | DOI:10.1186/s12879-025-11135-7

Sci Rep. 2025 Jun 2;15(1):19347. doi: 10.1038/s41598-025-99541-4.

ABSTRACT

MET exon 14 skipping is an oncogenic driver observed in 1 to 4% of non-small cell lung cancer (NSCLC). MET exon 14 mutations affect splice sites and are highly heterogeneous which makes them difficult to detect. Because of the approval of capmatinib for patients with MET exon 14 mutated tumors and the related poor response to immunotherapy (ICI) for a subset of patients with MET mutated tumors, MET screening has become mandatory for first line treatment decision. Here we report our testing experience based on 1143 consecutive NSCLC addressed for molecular diagnosis. Two strategies using either DNA sequencing (NGS) and fragment analysis or DNA-RNA sequencing (NGS) were developed and validated to accurately detect MET exon 14 alterations including large deletions. For patients with MET tumors (n = 46), demographic characteristics, treatments and outcomes were obtained from medical records and discussed. 46 MET exon 14 alterations were identified, 4 were not called by DNA sequencing and rescued by fragment analysis or RNA sequencing. Sixty-seven percent tumors had a high PD-L1 expression > 50 and 42% of cases had co-occurring alterations, mainly TP53 mutations (24%) and PIK3CA mutations (9%). Response to MET inhibitors (Crizotinib and Capmatinib) was evaluated for 15 patients. The ORR (Objective Response Rate) and the median of PFS (Progression Free Survival) were 44% and 5.5 months [1.6-18.2 months] respectively. Thirteen patients were treated by immunotherapy, ORR and median PFS (Progression Free Survival) median were 30% and 4 months [0.7-55.5 months] respectively. The response to immunotherapy was not correlated with PD-L1 status but smokers seemed to better respond to ICIs. This study highlights that a multimodal approach may be necessary to detect MET exon 14 mutations as large deletions may not be detected by DNA sequencing. Targeted DNA-ARN sequencing strategies broadly interrogate the diverse druggable genomic variations and permits direct detection of altered splicing or gene fusions. Because patients with MET exon 14 mutated tumors, demonstrate low response to immunotherapy despite high PDL1 and because MET exon 14 is druggable the detection of MET mutations is mandatory to optimize treatment.

PMID:40456875 | DOI:10.1038/s41598-025-99541-4

J Biol Chem. 2025 May 31:110327. doi: 10.1016/j.jbc.2025.110327. Online ahead of print.

ABSTRACT

Obesity is a major risk factor for multiple metabolic diseases, including type 2 diabetes mellitus (T2DM) and metabolic dysfunction-associated steatotic liver disease (MASLD). The cholesterol sulfotransferase SULT2B1 is best known for its function in converting cholesterol to cholesterol sulfate. Here, by using the high fat diet (HFD)-induced obesity model and the genetic obese ob/ob mice, we showed that genetic ablation of Sult2b1 protected mice from developing obesity and related insulin resistance, hepatic steatosis, and adipose tissue inflammation. Loss of Sult2b1 increased energy expenditure without affecting food intake or locomotive activity. The cold exposure test revealed that loss of Sult2b1 promoted thermogenesis in brown adipose tissue, which may have contributed to increased energy expenditure. In vivo reconstitution experiments suggested that the loss of Sult2b1 in extrahepatic tissues might have been responsible for the metabolic benefit. Mechanistically, our in vivo lipid uptake and metabolomic analyses showed that the Sult2b1KO mice exhibited suppression of intestinal dietary lipid absorption and the consequent downregulation of both systemic fatty acid level and fatty acid metabolism. Our results suggest that targeting SULT2B1 may represent a novel strategy to combat obesity and related metabolic syndrome.

PMID:40456448 | DOI:10.1016/j.jbc.2025.110327

Biol Psychiatry. 2025 May 31:S0006-3223(25)01219-3. doi: 10.1016/j.biopsych.2025.05.020. Online ahead of print.

ABSTRACT

Bipolar disorder (BD) is a highly heritable mental disorder that affects millions of people worldwide. Our understanding of the genetic etiology and biological processes underlying BD have greatly increased in recent years. Extensive progress has been made in identifying common variant signal for BD, and the PGS from the latest GWAS may provide some clinical utility if combined with other risk factors for BD. The role of rare variation in bipolar disorder remains to be determined, although genes annotated to common variant loci are shown to be enriched for rare variation. BD subtypes are shown to differ in their genetic architecture, and as such, genetic studies across the subtypes of the BD spectrum will identify subtype-specific signals and reveal subtype-specific biological mechanisms. Despite this, subtype-specific GWAS sample sizes have not increased at the same rate as BD cases and more concerted efforts are required to obtain this information for participants included in future BD GWAS studies. Moreover, assessment of culture, geography and other systematic differences that might impact patient assessment will be necessary to ensure accurate inclusion of diverse ancestral groups and global representation in genetic studies of BD moving forward.

PMID:40456304 | DOI:10.1016/j.biopsych.2025.05.020

BMC Pulm Med. 2025 Jun 2;25(1):276. doi: 10.1186/s12890-025-03745-3.

ABSTRACT

BACKGROUND: Severe respiratory failure in patients with cystic fibrosis (CF) requiring invasive mechanical ventilation is associated with poor clinical outcomes. The purpose of this study was to evaluate the role of extracorporeal membrane oxygenation (ECMO) in this clinical setting.

METHODS: In this descriptive retrospective monocentric cohort study, we collected data by using electronic medical records from all patients with CF who received ECMO therapy during the period 2012-2021.

SETTING: A monocentric setting at the non-surgical intensive care unit of the University Hospital of Frankfurt, Germany (tertiary care level center and nationally certified CF center).

RESULTS: During the study period 72 cases of CF patients with intensive care treatment were detected. Of these, 46 cases required mechanical ventilation. Nine patients received ECMO therapy for severe respiratory failure due to pulmonary exacerbation. Eight of the nine patients died in the hospital. This corresponds to an in-hospital mortality rate of 88.9%. None of the patients underwent lung transplantation. The most common CF mutation was the p.Phe508del homo- or heterozygous genotype. Pseudomonas aeruginosa colonization was significantly associated with the in-hospital mortality.

CONCLUSIONS: ECMO support in CF patients and severe hypoxemic failure is associated with high mortality and its use must take into account the increased risk and poor patient outcome in this clinical setting.

CLINICAL TRIAL NUMBER: This was a retrospective, unregistered analysis. A clinical trial number is not applicable.

PMID:40457261 | DOI:10.1186/s12890-025-03745-3