Stud Health Technol Inform. 2025 May 15;327:914-918. doi: 10.3233/SHTI250505.

ABSTRACT

This study investigates the application of a Long Short-Term Memory (LSTM) architecture for classifying hypertension using accelerometer data, specifically focusing on physical activity and sleep from the publicly available NHANES 2011-2012 dataset. The LSTM model captures the sequential patterns in this data, providing insights into behavioral patterns related to hypertension. The performance of the LSTM model is compared to traditional machine learning methods as well as other commonly used sequence models, including Recurrent Neural Networks (RNN), Transformers (TF), and 1D Convolutional Networks (Conv1D). The results show that the LSTM model achieves superior accuracy at 96.37%, outperforming the RNN (75.67%), TF (77.10%), and Conv1D (89.34%), as well as the other machine learning models, which range from 60.92% to 64.75%. These findings underscore the potential of LSTM models for integration into wearable health monitoring systems, enabling early detection or management of hypertension.

PMID:40380612 | DOI:10.3233/SHTI250505

Stud Health Technol Inform. 2025 May 15;327:884-885. doi: 10.3233/SHTI250491.

ABSTRACT

AI-driven "audiomics" leverages voice and respiratory sounds as non-invasive biomarkers to diagnose and manage pulmonary conditions, including COVID-19, tuberculosis, ILD, asthma, and COPD. By analyzing acoustic features, machine and deep learning enhance diagnostic accuracy and track disease progression. Key applications include cough-based TB detection, smartphone COVID-19 screening, and speech analysis for asthma and COPD monitoring. Ethical challenges like data privacy and standardization remain barriers to clinical adoption. With ongoing research, audiomics holds promise for transforming respiratory diagnostics and personalized care.

PMID:40380599 | DOI:10.3233/SHTI250491

Stud Health Technol Inform. 2025 May 15;327:657-661. doi: 10.3233/SHTI250430.

ABSTRACT

Predicting whether a patient will develop cancer using nuclear features on pathological images is important for decision making regarding patient treatment after liver transplantation or hepatectomy. Unlike manual segmentation to extract nuclei parts from pathology images, we performed the entire process of predicting patient survival automatically. In addition, we established a method to correctly predict survival even in cases where the amount of data is small. After segmenting nuclei from pathological images extracted from patients who underwent liver transplantation, we trained a deep learning model to distinguish survival/death by overlapping the segmented mask image and the original volume image. The cohort was collected from the liver transplantation group (n=67). Approximately two pathological images were collected from each patient, and one of the large pathological images was split into an average of 30 small-sized images to train the classification model. The VIT (Vision Transformer) model provided by the python timm library was used to classify whether the pathological images had recurred cancer. The methods used for survival analysis were CoxPH and Kaplan-Meier models, and survival results obtained from deep learning models were compared with other patient variables to determine how well they predicted patient survival. The indicators measured for comparison were C-index and AUC, NRI and HR were calculated. As the number of patients being diagnosed and the number of images resulting from them become more complex and larger, experts may make misjudgments. Artificial intelligence technology quickly and accurately judges this complex and large amount of data.

PMID:40380539 | DOI:10.3233/SHTI250430

Stud Health Technol Inform. 2025 May 15;327:587-588. doi: 10.3233/SHTI250405.

ABSTRACT

Deep learning methods achieve high performance, while often lacking explainability, hindering application in the field. We propose the use of a logistic regression classifier based on temporal aligned Electrocardiograms, and the utilisation of interpretable feature importance. This work suggests that non-deep learning based classifiers achieve comparable performance, and introduce new opportunities to on-the-fly counterfactual explanations. The code, pretrained model, and extracted kernels are available under github.com/imi-ms/rlign.

PMID:40380515 | DOI:10.3233/SHTI250405

Stud Health Technol Inform. 2025 May 15;327:487-491. doi: 10.3233/SHTI250385.

ABSTRACT

BACKGROUND: Patient similarity analysis is pivotal in cancer research and clinical oncology, aiding in identifying patterns among patients with similar clinical and molecular profiles to guide therapeutic decisions, particularly in Molecular Tumor Boards (MTB), where therapy decisions are frequently informed by the treatment experiences of previously treated similar patients. However, the lack of standardized tools for automation and visualization limits efficiency here, especially in individualized MTB decisions.

OBJECTIVE: This study aims to develop a graphical user interface that aligns with clinician preferences to enhance patient similarity assessments and support decision-making in MTBs.

METHODS: Visualization concepts were developed through iterative design and evaluation cycles involving clinical experts. Mock-ups were created to represent various approaches for displaying patient similarities, focusing on molecular data relevant to MTB decisions.

RESULTS: Various designs were developed for visualizing patient similarity in cBioPortal. These include tabular views, network representations, and radar plots.

CONCLUSIONS: These visualizations offer promise in enhancing decision-making in MTBs by making patient similarity assessments more accessible. Future development will focus on additional functionalities and better integration into clinical workflows.

PMID:40380495 | DOI:10.3233/SHTI250385

Stud Health Technol Inform. 2025 May 15;327:98-102. doi: 10.3233/SHTI250281.

ABSTRACT

Molecular tumor boards present special challenges when it comes to information collection for case preparation. It is one of the most time-consuming tasks participating pathologists and oncologists face, limiting the number of cases that can be discussed in these specialized tumor boards and in turn can profit from a potential highly personalized therapy. Digital support is a necessity to enable medical professionals to efficiently make use of the vast amount of data available for each patient and their genomic and clinical profile. This includes historically recommended therapies for patients with molecularly similar tumors. To combat this issue, we developed an extension for the MTB-cBioPortal in collaboration with clinicians, enabling users to access previously documented therapy recommendations combined with corresponding follow-up data based on HL7 FHIR profiles and modules established in the Medical Informatics Initiative (MII). The information is made available through an additional annotation in the MTB-cBioPortal patient view. In doing so we intend to improve the efficiency of the case preparation process for molecular tumor boards and lay the groundwork for a potential multicentric exchange of therapy recommendations and follow-up data.

PMID:40380393 | DOI:10.3233/SHTI250281

Stud Health Technol Inform. 2025 May 15;327:1255-1259. doi: 10.3233/SHTI250599.

ABSTRACT

Pharmacist interventions significantly reduce medication errors and improve drug-therapy safety, with IT-supported workflows, including Clinical Decision Support Systems (CDSS), enhancing real-time decision-making. To address challenges in Germany's federated hospital system, the INTERPOLAR study platform integrates real-world data into routine care workflows. Developed under the Medical Informatics Initiative (MII), the platform leverages a CDS Toolchain with FHIR-based data items, predefined triggers, real-time feedback, and a robust data security framework. The platform supports diverse study designs, aligning technical capabilities with clinical workflows through process mapping and requirements engineering. A centralized health data warehouse enables large-scale analysis while ensuring GDPR compliance. Initial results demonstrate harmonized documentation, improved detection of drug-related problems (DRPs), and efficiency gains via automation. Current studies include medication adherence, ADEs, and pharmacist intervention impacts. By embedding IT-supported workflows into routine care, INTERPOLAR provides a scalable, federated solution for drug-therapy safety research, contributing to global pharmacovigilance and advancing evidence-based medicine.

PMID:40380703 | DOI:10.3233/SHTI250599

Stud Health Technol Inform. 2025 May 15;327:892-893. doi: 10.3233/SHTI250495.

ABSTRACT

Monitoring adverse drug events (ADEs) is critical for pharmacovigilance and patient safety. However, identifying ADEs remains challenging, as suspected or confirmed side effects are often documented solely in the unstructured text of electronic health records (EHRs). Manually reviewing clinical notes to detect ADEs is labor-intensive and time-consuming, highlighting the need for automated methods capable of analyzing and extracting ADE-related information from clinical documentation. In this short communication, we describe our ongoing research on fine-tuning and evaluating a large language model (LLM) for the detection of ADEs in clinical notes. Preliminary descriptive result of this study indicates that ADEs are poorly documented in discharge notes, with less than 15% explicitly linking ADEs to specific drugs, which highlights the need for improved reporting practices.

PMID:40380603 | DOI:10.3233/SHTI250495

Stud Health Technol Inform. 2025 May 15;327:778-782. doi: 10.3233/SHTI250465.

ABSTRACT

Adverse drug event (ADE) detection in social media texts poses significant challenges due to the informal nature of the text and the limited availability of annotations. The scarcity of ADE named entity recognition (NER) datasets for social media hinders the development of robust ADE detection models for this type of corpus. In this paper, we leveraged the generative capabilities of large language models (LLMs) to create synthetic data, addressing this dataset gap. Specifically, we generated 17,000 tweets with ADE annotations and pre-trained NER models on this synthetic data. Our evaluations on an out-of-sample collection of 915 manually annotated tweets revealed that these models outperform state-of-the-art lexico-based and massively pre-trained open NER models. We also show that fine-tuning our synthetically pre-trained models on human-annotated data surpasses the current state-of-the-art in ADE detection on tweets. These findings suggest that synthetic data generated by LLMs can enhance ADE detection performance, offering a promising avenue to explore in response to the scarcity of annotated ADE datasets. The synthetic dataset is available at https://huggingface.co/datasets/anthonyyazdaniml/synthetic-ner-ade-tweets-v1.

PMID:40380573 | DOI:10.3233/SHTI250465

Stud Health Technol Inform. 2025 May 15;327:245-246. doi: 10.3233/SHTI250320.

ABSTRACT

One approach to preventing adverse drug events (ADEs), such as harmful drug interactions, is the implementation of clinical decision support systems (CDSS). In an ongoing project, we are investigating the accuracy of the rule-based CDSS currently utilized in Swedish healthcare for predicting ADEs and exploring whether machine learning (ML) can improve these predictions. By analyzing real-world healthcare data from a Swedish region spanning a 10-year period, we show that ML has potential to improve ADE predictions compared to existing rule-based CDSS.

PMID:40380432 | DOI:10.3233/SHTI250320

BMC Microbiol. 2025 May 16;25(1):299. doi: 10.1186/s12866-025-03973-x.

ABSTRACT

BACKGROUND: The urgent need for new antibacterial drugs has driven interest in repurposing therapies to combat Gram-positive biofilms and persisters. Fingolimod, an Food and Drug Administration (FDA)-approved drug for multiple sclerosis, shows bactericidal activity, particularly against Methicillin-resistant Staphylococcus aureus (MRSA) and biofilm-related infections. With a well-documented safety profile and strong translational potential, it aligns with World Health Organization's goals for antimicrobial repurposing. However, the action mode and mechanism of Fingolimod against gram-positive bacteria remain elusive.

METHODS: This study utilized clinical Staphylococcus aureus (S. aureus), Enterococcus faecalis (E. faecalis), Streptococcus agalactiae (S. agalactiae). And their susceptibility to Fingolimod and other antibiotics was tested via Minimum Inhibitory Concentration (MIC) assays. Biofilm inhibition and hemolytic activity were evaluated using crystal violet staining, Confocal Laser Scanning Microscopy (CLSM), and hemolysis assays, respectively, while the effect of phospholipids on Fingolimod efficacy was assessed with checkerboard assays. Membrane permeability and integrity were measured using SYTOX green staining and transmission electron microscopy. Whole-genome sequencing was performed on Fingolimod-resistant S. aureus isolates to identify Single Nucleotide Polymorphisms (SNPs) linked to resistance.

RESULTS: Our data indicated that Fingolimod exerted bactericidal activity against a wide spectrum of gram-positive bacteria, including S. aureus, E. faecalis, S. agalactiae. Moreover, Fingolimod could significantly eliminate the persisters, inhibit biofilm formation and eradicate in-vitro mature biofilms of S. aureus. The mechanism by which Fingolimod rapidly eradicated S. aureus involved a pH-dependent disruption of bacterial cell permeability and envelope integrity. Concomitantly, exogenous supplementation of phospholipids in the culture medium resulted in a dose-dependent increase in the MIC of Fingolimod. Specifically, the addition of 64 μg/mL of cardiolipin (CL) and phosphatidylethanolamine (PE) completely nullified the bactericidal activity of Fingolimod at a concentration of 4 times the MIC. After four months of Fingolimod exposure, the MIC values of S. aureus showed a slight increase, indicating that it is not prone to developing drug resistance.

CONCLUSION: Fingolimod exhibits bactericidal activity against diverse gram-positive bacteria, with remarkable effects on S. aureus (including MRSA), disrupting bacterial cell structural integrity in a pH-dependent way and eradicating biofilms and persisters of S. aureus.

PMID:40380090 | DOI:10.1186/s12866-025-03973-x

Int J Biol Macromol. 2025 May 14:144230. doi: 10.1016/j.ijbiomac.2025.144230. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD), the most common neurodegenerative disease in humans, has been a major medical challenge. Lactoferrin (Ltf) in salivary glands might be identified as a potential detectable biomarker in AD and a therapeutic target for AD. Pharmaceutical studies directly addressing this biomarker, though, are scarce. Using a computational strategy for drug repurposing, we explored the proximal neighborhood of Ltf by exploring its interactome and regulatory constellations. We aimed to focus on the discovery of potential therapeutic agents for AD. Based on extensive analytical evaluation comprising structural congruence scales, profiling disease clusters, pathway enrichment analyses as well as molecular docking, SPR, in vivo studies, and immunofluorescence assays, our research identified three candidate repurposed drugs: Lovastatin, SU-11652, and SB-239063. Taken together, these results highlight strong binding affinities of the drug candidates to Ltf. In vitro studies showed that such compounds decrease β-amyloid (Aβ) production by increasing the fluorescence signal emitted by Ltf in N2a-sw cells, and that they act by modulating the expression of amyloidogenic pathway-associated enzymes (BACE1 and APH1α). In addition, in vivo studies showed a concomitant reduction in the expression levels of amyloidogenic pathway-related enzymes (BACE1 or APH1α). Thus, computational studies have focused on Ltf interactions that may recommend drug repurposing strategies and options for AD.

PMID:40379164 | DOI:10.1016/j.ijbiomac.2025.144230

Pharmacol Res. 2025 May 14:107779. doi: 10.1016/j.phrs.2025.107779. Online ahead of print.

ABSTRACT

Pregnane X receptor (PXR) and constitutive androstane receptor (CAR) are so-called xenobiotic nuclear receptors that play pivotal roles in xenobiotic metabolism and detoxification. Both receptors, highly expressed in the liver and intestine, also have endobiotic functions by regulating the homeostasis of endogenous chemicals. While their hepatic functions are well-documented, the functional roles of PXR and CAR in the gastrointestinal tract are less understood. This review highlights the intestinal functions of PXR and CAR, focusing on their involvement in colon cancer, host-microbiome interactions, inflammation, and gut barrier integrity. PXR exhibits dual roles in colon cancer, acting either as a tumor suppressor by inducing cell-cycle arrest or as a promoter of cancer aggressiveness through activating the FGF19 signaling. CAR, on the other hand, regulates intestinal barrier integrity and immune responses, particularly in the context of inflammatory bowel disease (IBD). Both PXR and CAR interact with gut microbiota, modulating microbial composition and the production of metabolites, such as indole-3-propionic acid (IPA) that influences the gut barrier function and inflammation. Activation of PXR also mitigates intestinal inflammation by antagonizing the NF-κB signaling, while CAR activation affects bile acid metabolism and T-cell homeostasis. These findings underscore the complex and context-dependent roles of PXR and CAR in the intestinal tracts, offering potential therapeutic targets for gastrointestinal diseases.

PMID:40378938 | DOI:10.1016/j.phrs.2025.107779

Cell Genom. 2025 May 13:100878. doi: 10.1016/j.xgen.2025.100878. Online ahead of print.

ABSTRACT

Emerging evidence suggests that MYC interacts with RNAs. Here, we performed an integrative characterization of MYC as an RNA-binding protein in six cell lines. We found that MYC binds to a myriad of RNAs with high affinity for guanosine-rich RNAs. Global and specific depletion of RNAs reduces MYC chromatin occupancy. Mechanistically, two highly conserved sequences, amino acids 355-357 KRR and 364-367 RQRR, within the basic region of MYC are necessary for its RNA binding. Notably, alanine substitution of KRR abolishes MYC's RNA-binding ability both in vitro and in vivo, without affecting its ability to bind E-box DNA as part of the MYC:MAX dimer in vitro. The loss of RNA-binding function decreases MYC chromatin binding in vivo and attenuates its ability to promote gene expression, cell-cycle progression, and proliferation. Our study lays a foundation for future investigation into the role of RNAs in MYC-mediated transcriptional activation and oncogenic functions.

PMID:40378850 | DOI:10.1016/j.xgen.2025.100878

AAPS PharmSciTech. 2025 May 16;26(5):135. doi: 10.1208/s12249-025-03131-6.

ABSTRACT

Cystic fibrosis is a serious life-threatening hereditary disease that occurs due to a mutation in the cystic fibrosis transmembrane conductance regulator gene (CFTR). Ivacaftor (IVA) is a drug that targets the mutated CFTR protein. IVA is highly hydrophobic (log P = 5.6) with poor aqueous solubility (0.05 µg/mL) and is formulated as an amorphous solid dispersion tablet under the brand name Kalydeco®. The recommended daily dose of Kalydeco® is twice per day with a high fat meal to aid in IVA's absorption. In this research, we studied the application of cyclodextrins (CDs) to improve the dissolution of IVA. Phase solubility studies between IVA and four different CDs (α-, β-, γ-, and hydroxypropyl-β-CD [HP-β-CD]) were conducted and a significant improvement in IVA's aqueous solubility with HP-β-CD was observed. Solid state characterizations confirmed the formation of IVA/HP-β-CD inclusion complexes. In vitro dissolution studies were conducted at pH = 6.8 and showed improvement in IVA's rate and extent of dissolution with IVA/HP-β-CD (1:2) complexes in comparison to uncomplexed IVA. In vivo pharmacokinetics in mice showed a 2-fold increase in area under the curve (AUC) after the oral administration of the IVA/HP-β-CD complex in comparison to Kalydeco tablets. In addition, HP-β-CD extended the release of IVA from the IVA/HP-β-CD complexes with a longer Tmax of 7.05 h compared to 2.96 h with Kalydeco® tablets. These results demonstrate that CD inclusion complexes of IVA using HP-β-CD can be a successful alternative approach to improving the solubility of IVA while extending its release.

PMID:40379997 | DOI:10.1208/s12249-025-03131-6

J Cyst Fibros. 2025 May 15:S1569-1993(25)01467-5. doi: 10.1016/j.jcf.2025.04.012. Online ahead of print.

ABSTRACT

BACKGROUND: The cystic fibrosis (CF) transmembrane conductance regulator modulator (CFTRm) elexacaftor/tezacaftor/ivacaftor (ELX/TEZ/IVA) has demonstrated efficacy and safety in clinical trials and emerging observational studies in people with CF. This study evaluated the real-world impact of ELX/TEZ/IVA in a large cohort of people with CF in the UK.

METHODS: LONGITUDE is an observational, registry-based cohort study using data from the UK CF Registry to evaluate outcomes of ELX/TEZ/IVA in people aged ≥6 years who initiated ELX/TEZ/IVA from August 2019. Key outcomes included percent predicted forced expiratory volume in 1 s (ppFEV1), body mass index (BMI), pulmonary exacerbations (PEx), lung infections, transplants, deaths, and treatment discontinuation. We report results of people ≥12 years with data up to December 31, 2022.

RESULTS: A total of 5187 people were included (mean follow-up 19.1 months). ppFEV1 improvements were observed at 2 years (10.2; 95 % CI: 9.6, 10.8; n = 1448). A clinically meaningful difference in the annual change of ppFEV1 between ELX/TEZ/IVA-treated people and historical CFTRm-naïve controls was observed, with those treated with ELX/TEZ/IVA having less of a decline in lung function over time by 1.1 percentage points (95 % CI: 0.9, 1.4). A 64.7 % reduction in the rate of PEx, increase in BMI by 1.7 kg/m2 (SD: 2.3), reduced lung infections, and low number of lung transplants and deaths were also observed.

CONCLUSIONS: People with CF aged ≥12 years in the UK who initiated ELX/TEZ/IVA had sustained improvements in multiple CF-related health outcomes, consistent with results from clinical trials. These results support the positive impact of ELX/TEZ/IVA on the lives of people with CF.

PMID:40379539 | DOI:10.1016/j.jcf.2025.04.012

An Pediatr (Engl Ed). 2025 May 15:503836. doi: 10.1016/j.anpede.2025.503836. Online ahead of print.

ABSTRACT

INTRODUCTION: Treatments with CFTR protein modulators have improved respiratory and digestive health in patients with cystic fibrosis.

OBJECTIVE: To assess changes in intestinal inflammation through the analysis of fecal calprotectin in patients with cystic fibrosis during treatment with CFTR modulators.

MATERIAL AND METHODS: Prospective multicenter study of changes in fecal calprotectin in patients with cystic fibrosis treated with CFTR modulators, comparing double combinations (lumacaftor/ivacaftor or tezacaftor/ivacaftor) and triple combinations (elexacaftor/tezacaftor/ivacaftor). We collected aata before treatment initiation and at 6 and 12 months.

RESULTS: Analysis of 117 patients (69% with F508del/F508del). The median baseline fecal calprotectin level was 49 µg/g (IQR, 23-108); 48.7% had median levels greater than 50 µg/g and 11% levels greater than 250 µg/g. Fecal calprotectin decreased in both groups, with a greater decrease in patients treated with elexacaftor/tezacaftor/ivacaftor. We found a progressive decrease in abnormal values (>50 µg/g) at 6 months (48.7% vs 33.1%; P = .0067) and at 12 months (54% vs 33.5%; P = .0218). In the elexacaftor/tezacaftor/ivacaftor group, only two patients at 6 months and one patient at 12 months had levels greater than 250 µg/g. The estimated change at 12 months in the triple therapy group compared to the other group was -133 µg/g (95% CI, -254 to -13; P = .030); and, adjusting for sex, probiotics and Pseudomonas aeruginosa, -130 µg/g (-259 to -1; P = .049).

CONCLUSIONS: Treatment with CFTR modulators reduces intestinal inflammation in patients with cystic fibrosis, with a greater decrease in patients treated with triple therapy.

PMID:40379512 | DOI:10.1016/j.anpede.2025.503836

Lancet Child Adolesc Health. 2025 Jun;9(6):371-382. doi: 10.1016/S2352-4642(25)00099-9.

ABSTRACT

BACKGROUND: People with cystic fibrosis can have impaired insulin secretion and hyperglycaemia before meeting the diagnostic criteria for cystic fibrosis-related diabetes during an oral glucose tolerance test (OGTT). Insulin therapy given to such patients was associated with improved weight and lung function in several small, uncontrolled trials but might increase treatment burden and cause hypoglycaemia. We aimed to assess whether insulin treatment improves weight and lung function when given to patients with cystic fibrosis with early glycaemic abnormality.

METHODS: CF-IDEA was a multicentre, randomised controlled trial conducted at five children's hospitals in Australia and one in the USA. Eligible participants were children with cystic fibrosis aged 5-18 years without cystic fibrosis-related diabetes and with peak glucose concentration on a five-point OGTT of 8·2-11·0 mmol/L (cystic fibrosis insulin deficiency stage 1) or ≥11·1 mmol/L (cystic fibrosis insulin deficiency stage 2). Participants were randomly assigned (1:1) to insulin or observation. Randomisation was done using the biased coin method, followed by minimisation when the study groups became imbalanced by chance. Randomisation was stratified by glycaemic category (cystic fibrosis insulin deficiency stage 1 or 2), weight Z score (more than or equal to -0·61 or less than -0·61), and study centre. Participants in the insulin group received once-daily, long-acting insulin detemir by subcutaneous injection before breakfast, commencing at 0·1 units per kg per day, adjusted in 0·5-unit increments to achieve all fingerstick blood glucose concentrations between 4 mmol/L and 8 mmol/L. The primary outcomes were absolute changes in weight Z score, percentage predicted forced expiratory volume in 1 s (ppFEV1), and percentage predicted forced vital capacity (ppFVC), derived with generalised estimating equations and presented with two-sided 95% CIs. Severe hypoglycaemic events (defined as requiring outside assistance or causing reduced level of consciousness or seizure), insulin-related adverse events, and continuous glucose monitoring (CGM) percentage time with blood glucose below 3·9 mmol/L were recorded as safety outcomes. This study is registered with ClinicalTrials.gov, NCT01100892, and is completed.

FINDINGS: Between Dec 6, 2010, and Feb 25, 2022, 109 participants were randomly assigned to observation (n=54) or insulin (n=55). Five participants withdrew after the baseline visit, and the analysis therefore included 104 participants (53 observation and 51 insulin); 95 participants completed the 12-month protocol and nine completed only 6 months. Baseline characteristics were similar between the groups; however, the observation group included 30 (57%) boys and 23 (43%) girls, whereas the insulin group included 23 (45%) boys and 28 (55%) girls. The median daily insulin dose at 12 months was 0·12 units per kg per day (range 0·05-0·41). There were no statistically or clinically significant differences between the observation and insulin groups in change in weight Z score (difference insulin minus observation 0·07 [95% CI -0·04 to 0·18]; p=0·20), change in ppFEV1 (1·2 [-2·2 to 4·7]; p=0·48), or change in ppFVC (0·6 [-2·6 to 3·8]; p=0·72). Similarly, there were no significant differences in subgroup analyses by cystic fibrosis insulin deficiency stages 1 and 2. There were no episodes of severe hypoglycaemia or insulin-related adverse events, and we found no evidence of difference between the observation and insulin groups in CGM percentage time less than 3·9 mmol/L.

INTERPRETATION: Insulin treatment did not improve weight or lung function when given to children and adolescents with cystic fibrosis and early glycaemic abnormalities. Insulin treatment should not be given to those who do not meet OGTT criteria for cystic fibrosis-related diabetes.

FUNDING: National Health and Medical Research Council of Australia, Australian Cystic Fibrosis Research Trust, Pfizer Australasian Paediatric Endocrine Care Research Grant, Novo Nordisk Regional Diabetes Support Scheme, Sydney Children's Hospital Foundation.

PMID:40379429 | DOI:10.1016/S2352-4642(25)00099-9

Pharmacol Res. 2025 May 14:107775. doi: 10.1016/j.phrs.2025.107775. Online ahead of print.

ABSTRACT

The roles of gut microbiota and microbially modified bile acids in human health have become widely recognized. In the last five years, various microbially modified bile acids (e.g., proteinogenic amino-conjugated bile acids, polyamine-conjugated bile acids, neuroactive amine-conjugated bile acids, methylcysteamine-conjugated bile acid, acylated bile acids, dicarboxylic acid-conjugated bile acids, lithocholic acid (LCA) derivatives) were identified and evaluated, which greatly enriched the mammalian bile acid pool and the bioactivity of bile acids. The structure, enzyme, function, clinical reports of these bile acids, and the bacteria to produce these bile acids were summarized in this review. These novel bile acids had various functions including immunoregulation, receptor regulator, antimicrobial activity, and microbial communities regulating effect. 70, 4, 1, 11, 19, 41, 43, 9, 10 species were observed to produce proteinogenic amino-conjugated bile acids, neuroactive amine-conjugated bile acids, methylcysteamine-conjugated bile acid, acylated bile acids, dicarboxylic acid-conjugated bile acids, 3-oxoLCA, isoLCA, 3-oxoalloLCA, and isoalloLCA, respectively. The current review has shed new light on discovering new bile acid derivatives as drug candidates. These microbially modified bile acids may play important roles in disease such as sleeve gastrectomy, fatty acid, inflammatory bowel disease, cystic fibrosis, and type 2 diabetes, which may also participate in normal physiological processes such as growth of infants, longevity, and dietary habits.

PMID:40378940 | DOI:10.1016/j.phrs.2025.107775