Ultrason Sonochem. 2025 Feb 12;114:107271. doi: 10.1016/j.ultsonch.2025.107271. Online ahead of print.

ABSTRACT

Javanese turmeric (Curcuma xanthorrhiza Roxb.) is known for its diverse pharmacological activities due to its rich phytoconstituents, including curcuminoids and xanthorrhizol. Typically, these compounds are extracted using organic solvents, which pose health and environmental risks. Therefore, safer and more environmentally friendly green extraction methods are being developed. This study investigated the effect of ultrasound-assisted extraction (UAE) combined with natural deep eutectic solvents (NADES) based on choline chloride and organic acids (lactic, malic, and citric acid) to find the best combination for extracting curcuminoids and xanthorrhizol from Javanese turmeric. Results showed that UAE using choline chloride and malic acid (1:1) (ChCl-MA) yielded the best results. The Box-Behnken Design optimized water addition, solvent-to-powder ratio, and extraction time, with optimal conditions being 25 % water addition, a 20 mL/g ratio, and a 15-minute extraction time. This method yielded 4.58 mg/g of curcuminoids and 12.93 mg/g of xanthorrhizol. Furthermore, the ChCl-MA NADES with UAE extraction showed more cytoselective activity towards the HeLa cancer cell line compared to the non-cancer HaCaT cell line. In contrast, traditional ethanol extraction was non-selective, as indicated by similar cell viability reductions in both HeLa and HaCaT cells at 6.25 ppm. Collectively, this study is the first to report the optimal NADES combination with UAE, based on salts and organic acids, for the extraction of Javanese turmeric rhizomes with selective cytotoxic effects against cancer cells. These findings may contribute to the development of novel, naturally derived anticancer agents using green extraction techniques.

PMID:39955874 | DOI:10.1016/j.ultsonch.2025.107271

J Cyst Fibros. 2025 Feb 16:S1569-1993(25)00054-2. doi: 10.1016/j.jcf.2025.02.005. Online ahead of print.

ABSTRACT

BACKGROUND: Despite the existence of numerous clinical practice guidelines (CPGs) for cystic fibrosis (CF), there is limited understanding of their credibility and consistency. This systematic review aims to comprehensively evaluate the quality of CPGs for CF and its pulmonary complications, focusing on treatment recommendations for pulmonary care.

METHODS: We conducted a comprehensive search across four databases and relevant websites to identify eligible guidelines providing treatment recommendations. The quality of these guidelines was assessed using the Appraisal of Guidelines for Research and Evaluation (AGREE) II tool. Pulmonary treatment recommendations were analyzed and synthesized narratively.

RESULTS: A total of 35 guidelines were identified. Most guidelines were of moderate quality according to the AGREE II instrument, with overall scores ranging from 21·05 to 76·13. Only six guidelines were recommended for use. These guidelines provide 359 pulmonary treatment recommendations for seven primary therapies and others. There was inconsistency in the use of airway clearance therapy, anti-inflammatories, antibiotics, inhaled drugs, and cystic fibrosis transmembrane conductance regulator modulator therapy. Four guidelines conditionally advocated for oral corticosteroids, while six opposed routine inhaled corticosteroids. One guideline discouraged lumacaftor-ivacaftor in the general CF population, two recommended only for children under 12 years old, and another strongly advocated for children between 2 and 5 years of age. However, one guideline noted a lack of evidence to recommend it for children under 6.

CONCLUSION: The quality of CPGs for CF and its pulmonary complications has improved over time, reaching a moderate level generally, but there is still room for further improvement. Future efforts should focus on standardizing methodological frameworks and generating robust clinical evidence to enhance the overall quality and applicability of CF guidelines.

PMID:39956717 | DOI:10.1016/j.jcf.2025.02.005

J Cyst Fibros. 2025 Feb 15:S1569-1993(25)00055-4. doi: 10.1016/j.jcf.2025.02.003. Online ahead of print.

ABSTRACT

BACKGROUND: In individuals with cystic fibrosis (CF), respiratory viral infections frequently result in hospitalization and have been linked to secondary bacterial infection and colonization, highlighting viral infections as possible contributors to CF lung disease progression. We hypothesized that expression of antiviral host defense genes is dysregulated in CF airway epithelia.

METHODS: We infected primary CF and Non-CF airway epithelia with respiratory syncytial virus (RSV) and characterized their responses at 12 hr, 24 hr, 48 hr, 72 hr, and 120 hr post infection (hpi) by RNA sequencing (RNAseq).

RESULTS: Our analysis revealed strikingly different gene expression profiles for the CF and Non-CF epithelia over the course of the infection. While both CF and Non-CF cells exhibited an early signature for interferon signaling and antiviral defense pathways, this response was relatively exaggerated and sustained in CF epithelia. We also observed, in both genotypes, a transient downregulation of cilia-associated genes and loss of ciliary activity by 72 hpi. Interestingly, recovery of cilia activity was delayed in the CF epithelia.

CONCLUSIONS: These findings further our understanding of innate immune dysfunction in the CF airway epithelium and suggest that virus-induced cilia injury may further compromise host defenses in CF airways.

PMID:39956716 | DOI:10.1016/j.jcf.2025.02.003

J Cyst Fibros. 2025 Feb 15:S1569-1993(25)00052-9. doi: 10.1016/j.jcf.2025.02.001. Online ahead of print.

ABSTRACT

BACKGROUND: Cystic fibrosis (CF) has been associated with impaired cardiovascular and endothelial function. CF transmembrane conductance regulator (CFTR) modulator therapy, most recently Elexacaftor/Tezacaftor/Ivacaftor (ETI), has led to improved CFTR function and life expectancy. However, the rising prevalence of obesity in adults is concerning. This study assessed the micro- and macrovascular endothelial function, cardiovascular disease (CVD) risk factors, and physical activity (PA) profiles in people with CF (pwCF) on ETI compared to healthy matched controls.

METHODS: In 15 pwCF and 15 age- and sex-matched controls, microvascular endothelial function (via transdermal delivery of insulin [INS] and acetylcholine [ACh] on the forearm), macrovascular endothelial function (via flow-mediated dilation [FMD] of the brachial artery), central haemodynamic parameters, including heart rate (HR), stroke volume index (SVi) and cardiac output index (Q̇I) (via thoracic impedance cardiography), body mass index (BMI), blood pressure (BP), and accelerometer-assessed PA were measured.

RESULTS: There were no differences in INS or FMD-mediated vasodilation between the groups (P > 0.05). However, a reduced vasodilatory response was evident in pwCF following ACh-mediated vasodilation (P = 0.01) and FMD normalised for shear rate (P = 0.03). No differences in resting HR, SVi, Q̇I, BP, BMI or PA were found (P > 0.05).

CONCLUSION: This study demonstrated reduced micro- and macrovascular function in pwCF. This dysfunction may have potential health implications, particularly regarding long-term cardiovascular risk and further longitudinal assessments are warranted.

PMID:39956715 | DOI:10.1016/j.jcf.2025.02.001

J Cyst Fibros. 2025 Feb 15:S1569-1993(25)00060-8. doi: 10.1016/j.jcf.2025.02.007. Online ahead of print.

ABSTRACT

BACKGROUND & AIMS: Cystic fibrosis hepatobiliary involvement is a heterogeneous and systemic entity. The primary objective was to determine the prevalence of steatosis, by magnetic resonance-proton density fat fraction (MR-PDFF), and liver fibrosis, by magnetic resonance elastography (MRE), in a cohort of adults with cystic fibrosis. The secondary objective was to determine the diagnostic yield of widely available non-invasive liver markers for steatosis and fibrosis, and vibration controlled transitional elastography (VCTE) releasing Control Attenuation Parameter (CAP) (dB/m) and stiffness (kPa), with the aim of proposing a diagnostic algorithm.

METHODS: We conducted a cross-sectional study including 101 adult patients with cystic fibrosis seen in a multidisciplinary unit. The study encompassed a clinical evaluation, morpho-functional assessment, VCTE, non-invasive liver markers and MR-PDFF and MRE. Diagnostic accuracy was assessed using ROC curves and 2 × 2 tables.

RESULTS: MR-PDFF detected hepatic steatosis in 18 of 101 (17.8 %) patients, while MRE detected significant liver fibrosis in 15 of 101 (14.9 %). The VCTE cut-off with the best diagnostic yield, determined by the Youden index, was 222 dB/m for the presence of steatosis (AUC 0.864 (95 % CI 0.768-0.961; p < 0.001) and the VCTE cut-off was 6.65 kPa for liver fibrosis (AUC 0.951(95 % CI 0.81-1; p = 0.053). A screening algorithm for hepatic steatosis was developed using the fatty liver index (FLI) and CAP, with a negative predictive value of 83.3 %. For liver fibrosis, it was outperformed by the Hepamet Fibrosis Score (HFS) and VCTE, with a negative predictive value of 100 %.

CONCLUSIONS: The prevalence of hepatic steatosis and liver fibrosis was 17.8 % and 14.9 %, respectively. VCTE alone or in combination with FLI for steatosis or HFS for fibrosis demonstrated high diagnostic accuracy. This approach effectively allows for the exclusion of steatosis and fibrosis, thereby reducing the need for MR-PDFF and MRE studies.

PMID:39956714 | DOI:10.1016/j.jcf.2025.02.007

Cancer Imaging. 2025 Feb 16;25(1):14. doi: 10.1186/s40644-025-00837-5.

ABSTRACT

BACKGROUND: Immunotherapy has revolutionized the treatment landscape for head and neck squamous cell carcinoma (HNSCC) and PD-L1 combined positivity score (CPS) scoring is recommended as a biomarker for immunotherapy. Therefore, this study aimed to develop an MRI-based deep learning score (DLS) to non-invasively assess PD-L1 expression status in HNSCC patients and evaluate its potential effeciency in predicting prognostic stratification following treatment with immune checkpoint inhibitors (ICI).

METHODS: In this study, we collected data from four patient cohorts comprising a total of 610 HNSCC patients from two separate institutions. We developed deep learning models based on the ResNet-101 convolutional neural network to analyze three MRI sequences (T1WI, T2WI, and contrast-enhanced T1WI). Tumor regions were manually segmented, and features extracted from different MRI sequences were fused using a transformer-based model incorporating attention mechanisms. The model's performance in predicting PD-L1 expression was evaluated using the area under the curve (AUC), sensitivity, specificity, and calibration metrics. Survival analyses were conducted using Kaplan-Meier survival curves and log-rank tests to evaluate the prognostic significance of the DLS.

RESULTS: The DLS demonstrated high predictive accuracy for PD-L1 expression, achieving an AUC of 0.981, 0.860 and 0.803 in the training, internal and external validation cohort. Patients with higher DLS scores demonstrated significantly improved progression-free survival (PFS) in both the internal validation cohort (hazard ratio: 0.491; 95% CI, 0.270-0.892; P = 0.005) and the external validation cohort (hazard ratio: 0.617; 95% CI, 0.391-0.973; P = 0.040). In the ICI-treated cohort, the DLS achieved an AUC of 0.739 for predicting durable clinical benefit (DCB).

CONCLUSIONS: The proposed DLS offered a non-invasive and accurate approach for assessing PD-L1 expression in patients with HNSCC and effectively stratified HNSCC patients to benefit from immunotherapy based on PFS.

PMID:39956910 | DOI:10.1186/s40644-025-00837-5

Neurol Res Pract. 2025 Feb 17;7(1):11. doi: 10.1186/s42466-025-00367-2.

ABSTRACT

BACKGROUND: Artificial Intelligence is influencing medicine on all levels. Neurology, one of the most complex and progressive medical disciplines, is no exception. No longer limited to neuroimaging, where data-driven approaches were initiated, machine and deep learning methodologies are taking neurologic diagnostics, prognostication, predictions, decision making and even therapy to very promising potentials.

MAIN BODY: In this review, the basic principles of different types of Artificial Intelligence and the options to apply them to neurology are summarized. Examples of noteworthy studies on such applications are presented from the fields of acute and intensive care neurology, stroke, epilepsy, and movement disorders. Finally, these potentials are matched with risks and challenges jeopardizing ethics, safety and equality, that need to be heeded by neurologists welcoming Artificial Intelligence to their field of expertise.

CONCLUSION: Artificial intelligence is and will be changing neurology. Studies need to be taken to the prospective level and algorithms undergo federated learning to reach generalizability. Neurologists need to master not only the benefits but also the risks in safety, ethics and equity of such data-driven form of medicine.

PMID:39956906 | DOI:10.1186/s42466-025-00367-2

J Transl Med. 2025 Feb 16;23(1):188. doi: 10.1186/s12967-025-06183-1.

ABSTRACT

BACKGROUND: Predicting long-term outcomes in liver transplantation remain a challenging endeavor. This research aims to harness the power of deep learning to develop an advanced prognostic model for assessing long-term outcomes, with a specific focus on distinguishing between deceased and living donor transplantation.

METHODS: A comprehensive dataset from UNOS encompassing clinical, demographic, and transplant-related variables of liver transplant recipients from deceased and living donors was utilized. The main dataset has been transformed into Deceased Donor-Recipient and Living Donor-Recipient dataset. After manual extraction, the dimensionality reduction was performed with Principal component analysis in both datasets and top ranked 23 attributes were collected. A Deeplearning4j Multilayer Perceptron classifier has been employed and long-term survival analysis has been conducted with the help of liver follow-up data. The performance evaluation is done separately in datasets and evaluated the survival probabilities of 23 years.

RESULTS: UNOS database comprises 410 attributes and 353,589 records from 1998 to 2023. The outcome from the deep learning model was compared with actual graft survival to ensure the accuracy. The model trained 23 attributes and obtained Sensitivity, Specificity and accuracy values were 99.9, 99.9 and 99.91% using R-Living donor dataset. The Sensitivity, Specificity and Accuracy value obtained using R-Deceased donor dataset were 99.7, 99.7 and 99.86%. The short term and long-term survival prediction after liver transplantation has been done successfully with Dl4jMLP classifier with appropriate selection of attributes irrespective of donor type. This study's finding suggesting that the distinction between deceased and living donor transplantation does not significantly affect survival prediction after liver transplantation is noteworthy.

CONCLUSIONS: The utility of the Deeplearning4j model in survival prediction after liver transplantation has been validated in this study. Based on the findings, deceased donor transplantation could be promoted over living donor transplantation.

PMID:39956905 | DOI:10.1186/s12967-025-06183-1

Commun Eng. 2025 Feb 16;4(1):22. doi: 10.1038/s44172-025-00370-0.

ABSTRACT

Sports helmets provide incomplete protection against brain injuries. Here we aim to improve helmet liner efficiency by employing a novel approach that optimizes their properties. By exploiting a finite element model that simulates head impacts, we developed deep learning models that predict the peak rotational velocity and acceleration of a dummy head protected by various liner materials. The deep learning models exhibited a remarkable correlation coefficient of 0.99 within the testing dataset with mean absolute error of 0.8 rad.s-1 and 0.6 krad.s-2 respectively, highlighting their predictive ability. Deep learning-based material optimization demonstrated a significant reduction in the risk of brain injuries, ranging from -5% to -65%, for impact energies between 250 and 500 Joules. This result emphasizes the effectiveness of material design to mitigate sport-related brain injury risks. This research introduces promising avenues for optimizing helmet designs to enhance their protective capabilities.

PMID:39956866 | DOI:10.1038/s44172-025-00370-0

Sci Rep. 2025 Feb 16;15(1):5685. doi: 10.1038/s41598-025-89557-1.

ABSTRACT

Glaucoma is characterised by progressive vision loss due to retinal ganglion cell deterioration, leading to gradual visual field (VF) impairment. The standard VF test may be impractical in some cases, where optical coherence tomography (OCT) can offer predictive insights into VF for multimodal diagnoses. However, predicting VF measures from OCT data remains challenging. To address this, five regression models were developed to predict VF measures from OCT, Shapley Additive exPlanations (SHAP) analysis was performed for interpretability, and a clinical software tool called OCT to VF Predictor was developed. To evaluate the models, a total of 268 glaucomatous eyes (86 early, 72 moderate, 110 advanced) and 226 normal eyes were included. The machine learning models outperformed recent OCT-based VF prediction deep learning studies, with correlation coefficients of 0.76, 0.80 and 0.76 for mean deviation, visual field index and pattern standard deviation, respectively. Introducing the pointwise normalisation and step-size concept, a mean absolute error of 2.51 dB was obtained in pointwise sensitivity prediction, and the grayscale prediction model yielded a mean structural similarity index of 77%. The SHAP-based analysis provided critical insights into the most relevant features for glaucoma diagnosis, showing promise in assisting eye care practitioners through an explainable AI tool.

PMID:39956834 | DOI:10.1038/s41598-025-89557-1

Commun Biol. 2025 Feb 16;8(1):247. doi: 10.1038/s42003-025-07694-9.

ABSTRACT

Accurate computational determination of RNA-protein interactions remains challenging, particularly when encountering unknown RNAs and proteins. The limited number of RNAs and their flexibility constrained the effectiveness of the deep-learning models for RNA-protein interaction prediction. Here, we introduce ZHMolGraph, which integrates graph neural network and unsupervised large language models to predict RNA-protein interaction. We validate ZHMolGraph predictions on two benchmark datasets and outperform the current best methods. For the dataset of entirely unknown RNAs and proteins, ZHMolGraph shows an improvement in achieving high AUROC of 79.8% and AUPRC of 82.0%. This represents a substantial improvement of 7.1%-28.7% in AUROC and 4.6%-30.0% in AUPRC over other methods. We utilize ZHMolGraph to enhance the challenging SARS-CoV-2 RPI and unbound RNA-protein complex predictions. Such enhancements make ZHMolGraph a reliable option for genome-wide RNA-protein prediction. ZHMolGraph holds broad potential for modeling and designing RNA-protein complexes.

PMID:39956833 | DOI:10.1038/s42003-025-07694-9

Sci Data. 2025 Feb 16;12(1):276. doi: 10.1038/s41597-025-04454-6.

ABSTRACT

Automated Optical Inspection (AOI) technology is crucial for industrial defect detection but struggles with shadows and surface reflectivity, resulting in false positives and missed detections, especially on non-planar parts. To address these issues, a novel defect detection technique based on deep learning and photometric stereo vision was proposed, along with the creation of the Metal Surface Defect Dataset (MSDD). The proposed Stroboscopic Illuminant Image Acquisition (SIIA) method uses a specially arranged illuminant setup and a Taylor Series Channel Mixer (TSCM) to blend multi-angle illumination images into pseudo-color images. This approach enables end-to-end defect detection using universal object detectors. The method involves mapping color space transformations to spatial domain transformations and utilizing hue randomization for data augmentation. Four object detection methods (FCOS, YOLOv5, YOLOv8, and RT-DETR) were validated on the MSDD, achieving an mAP of 86.1%, surpassing traditional methods. The MSDD includes 138,585 single-channel images and 9,239 mixed images, covering eight defect types. This dataset is essential for automated visual inspection of metal surfaces and is freely accessible for research purposes.

PMID:39956811 | DOI:10.1038/s41597-025-04454-6

Nat Commun. 2025 Feb 16;16(1):1691. doi: 10.1038/s41467-025-57027-x.

ABSTRACT

Single-cell ATAC-seq technology advances our understanding of single-cell heterogeneity in gene regulation by enabling exploration of epigenetic landscapes and regulatory elements. However, low sequencing depth per cell leads to data sparsity and high dimensionality, limiting the characterization of gene regulatory elements. Here, we develop scAGDE, a single-cell chromatin accessibility model-based deep graph representation learning method that simultaneously learns representation and clustering through explicit modeling of data generation. Our evaluations demonstrated that scAGDE outperforms existing methods in cell segregation, key marker identification, and visualization across diverse datasets while mitigating dropout events and unveiling hidden chromatin-accessible regions. We find that scAGDE preferentially identifies enhancer-like regions and elucidates complex regulatory landscapes, pinpointing putative enhancers regulating the constitutive expression of CTLA4 and the transcriptional dynamics of CD8A in immune cells. When applied to human brain tissue, scAGDE successfully annotated cis-regulatory element-specified cell types and revealed functional diversity and regulatory mechanisms of glutamatergic neurons.

PMID:39956806 | DOI:10.1038/s41467-025-57027-x

Acad Radiol. 2025 Feb 15:S1076-6332(25)00101-1. doi: 10.1016/j.acra.2025.01.046. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: Immunotherapy combined with chemotherapy has improved outcomes for some esophageal squamous cell carcinoma (ESCC) patients, but accurate pre-treatment risk stratification remains a critical gap. This study constructed a deep learning (DL) model to predict survival outcomes in ESCC patients receiving immunotherapy combined with chemotherapy.

MATERIALS AND METHODS: A DL model was developed to predict survival outcomes in ESCC patients receiving immunotherapy and chemotherapy. Retrospective data from 482 patients across three institutions were split into training (N=322), internal test (N=79), and external test (N=81) sets. Unenhanced computed tomography (CT) scans were processed to analyze tumor and peritumoral regions. The model evaluated multiple input configurations: original tumor regions of interest (ROIs), ROI subregions, and ROIs expanded by 1 and 3 pixels. Performance was assessed using Harrell's C-index and receiver operating characteristic (ROC) curves. A multimodal model combined DL-derived risk scores with five key clinical and laboratory features. The Shapley Additive Explanations (SHAP) method elucidated the contribution of individual features to model predictions.

RESULTS: The DL model with 1-pixel peritumoral expansion achieved the best accuracy, yielding a C-index of 0.75 for the internal test set and 0.60 for the external test set. Hazard ratios for high-risk patients were 1.82 (95% CI: 1.19-2.46; P=0.02) in internal test set. The multimodal model achieved C-indices of 0.74 and 0.61 for internal and external test sets, respectively. Kaplan-Meier analysis revealed significant survival differences between high- and low-risk groups (P<0.05). SHAP analysis identified tumor response, risk score, and age as critical contributors to predictions.

CONCLUSION: This DL model demonstrates efficacy in stratifying ESCC patients by survival risk, particularly when integrating peritumoral imaging and clinical features. The model could serve as a valuable pre-treatment tool to facilitate the implementation of personalized treatment strategies for ESCC patients undergoing immunotherapy and chemotherapy.

PMID:39956748 | DOI:10.1016/j.acra.2025.01.046

J Formos Med Assoc. 2025 Feb 15:S0929-6646(25)00059-2. doi: 10.1016/j.jfma.2025.02.017. Online ahead of print.

NO ABSTRACT

PMID:39956680 | DOI:10.1016/j.jfma.2025.02.017

Biomed Pharmacother. 2025 Feb 15;184:117891. doi: 10.1016/j.biopha.2025.117891. Online ahead of print.

ABSTRACT

Cyclin-dependent kinase 8 (CDK8) plays a crucial role in the transforming growth factor beta (TGF-β) signaling pathway, which is critical to the pathology of idiopathic pulmonary fibrosis (IPF). CDK8 promotes the epithelial-mesenchymal transition (EMT) and excessive extracellular matrix (ECM) deposition, making it a promising target for IPF treatment. This study focused on optimizing F059-1017, a previously identified CDK8 inhibitor, to enhance its potency. Through integrated structure-based modifications, a series of compounds was synthesized, and their inhibitory effects on CDK8 were tested. Results indicated that substituting with cyclopentanone significantly improved the inhibitory activity, and compound 4j demonstrated the best potency (IC50 = 16 nM). Notably, compared to F059-1017, its potency increased 35-fold, and kinase profiling revealed that the compound was selective for CDK8. Compound 4j inhibited the TGF-β1-induced EMT, cell migration, and morphological changes in A549 cells at a concentration of 0.1 μM and inhibited ECM and EMT protein expressions. In addition, the compound blocked TGF-β1-induced transcriptional changes and inhibited Smad3 and RNA polymerase II phosphorylation. These results highlight the potential of the optimized CDK8 inhibitor as a prospective drug for IPF treatment.

PMID:39955852 | DOI:10.1016/j.biopha.2025.117891

J Math Biol. 2025 Feb 16;90(3):31. doi: 10.1007/s00285-025-02191-3.

ABSTRACT

In this paper we introduce a formalism that allows to describe the response of a part of a biochemical system in terms of renewal equations. In particular, we examine under which conditions the interactions between the different parts of a chemical system, described by means of linear ODEs, can be represented in terms of renewal equations. We show also how to apply the formalism developed in this paper to some particular types of linear and non-linear ODEs, modelling some biochemical systems of interest in biology (for instance, some time-dependent versions of the classical Hopfield model of kinetic proofreading). We also analyse some of the properties of the renewal equations that we are interested in, as the long-time behaviour of their solution. Furthermore, we prove that the kernels characterising the renewal equations derived by biochemical system with reactions that satisfy the detail balance condition belong to the class of completely monotone functions.

PMID:39956846 | DOI:10.1007/s00285-025-02191-3

J Allergy Clin Immunol. 2025 Feb 14:S0091-6749(25)00170-8. doi: 10.1016/j.jaci.2025.01.044. Online ahead of print.

NO ABSTRACT

PMID:39956282 | DOI:10.1016/j.jaci.2025.01.044

Reg Anesth Pain Med. 2025 Feb 16:rapm-2024-105955. doi: 10.1136/rapm-2024-105955. Online ahead of print.

ABSTRACT

BACKGROUND/IMPORTANCE: Driving under the influence of drugs (DUID) refers to operating a vehicle after consuming drugs or medications other than alcohol that impair the ability to drive safely. There is no consensus on legal limits for drug intoxication while driving in the USA. Balancing the benefits of prescription medications, such as opioids, with traffic safety remains an ongoing public health challenge.

OBJECTIVE: This article examines DUID policy and provides recommendations for policy improvement and unification grounded in scientific evidence on opioid-related impairment and driving risks.

EVIDENCE REVIEW: A literature review of epidemiologic data, psychomotor effects, and public policy related to opioid use and driving was conducted. A total of 38 epidemiological studies, 21 studies on psychomotor effects, and pertinent laws and policies were reviewed.

FINDINGS: Epidemiological data reveal an increasing prevalence of opioid-positive drivers and an association between opioid use and elevated risk of motor vehicle collisions. Psychomotor studies show mixed results, with some indicating impairment in opioid users and others suggesting minimal effects on driving ability. State laws regarding DUID remain heterogeneous, with trends toward expanded testing powers, lower impairment thresholds, and limitations on prescription-based defenses. The lack of standardized opioid testing limits and inconsistent policy approaches across states hinder effective management of opioid-related impaired driving.

CONCLUSIONS: A balanced public health approach can reduce opioid-involved crashes through education, prevention, enhanced enforcement tools, and rehabilitation. In drafting future DUID laws, policymakers must analyze evolving opioid research when balancing the pain relief of opioids with public roadway safety.

PMID:39956556 | DOI:10.1136/rapm-2024-105955

Trop Med Int Health. 2025 Feb 16. doi: 10.1111/tmi.14091. Online ahead of print.

ABSTRACT

BACKGROUND: Drug-related adverse events cause poorer treatment outcomes amongst people with multi-drug-resistant tuberculosis, exacerbating a major global public health problem. The Harnessing new mHealth technologies to Strengthen the Management of Multi-Drug-Resistant Tuberculosis in Vietnam (V-SMART) trial tests whether a mobile health (mHealth) application (app) can optimise management of drug-related adverse events, within routine health services in Vietnam. Implementation of digital health within routine services is complex and driven by behaviour change as well as a range of health system factors. Understanding implementation is key to informing the evidence base for digital health prior to scale up, despite its potential appeal.

METHODS: Through a process evaluation of the V-SMART trial, we aim to (i) understand the multi-drug-resistant tuberculosis service delivery context and how trial procedures are implemented within services; (ii) describe 'dose' and 'reach' of the app; and (iii) understand health worker and patient perspectives of app implementation and identify areas for improvement. To achieve this, we will (i) conduct process maps (patient flow maps) to describe implementation of the mHealth intervention within routine multi-drug-resistant tuberculosis health services including adverse event management pathways at different levels of the health system; (ii) measure app usage by all participating health workers and people with multi-drug-resistant tuberculosis over time; and (iii) conduct a total of up to 45 semi-structured interviews in seven provinces, with people with multi-drug-resistant tuberculosis, health workers, and policymakers, to identify determinants of app uptake and suggestions for future person-centred app design. Interview topic guides are informed by the Theoretical Framework for Acceptability, Normalisation Process Theory, and the Tailored Implementation of Chronic Diseases framework respectively.

DISCUSSION: The process evaluation will strongly complement the parent trial impact evaluation, and the economic evaluation. Moreover, it will inform future tailored approaches to scaling up digital health as part of broader health system strengthening initiatives.

PMID:39956136 | DOI:10.1111/tmi.14091