Annu Rev Genomics Hum Genet. 2025 Mar 27. doi: 10.1146/annurev-genom-121323-104008. Online ahead of print.

ABSTRACT

Genetic variation is a major determinant of drug response across populations. Owing to advances in sequencing technologies over the last two decades, several clinically actionable variants or haplotypes have been characterized in genes that encode proteins mediating drug pharmacokinetics or pharmacodynamics. Therefore, clinical application of pharmacogenomics has gained significant traction as a promising tool for enabling drug therapy optimization to mitigate adverse drug reactions while promoting drug efficacy. However, the implementation of pharmacogenetics testing has been slow in African settings and other resource-limited global regions. Moreover, there is a need to address gaps in various pharmacogenomics knowledgebases, especially regarding the genetic diversity in underrepresented populations. It is also important to ensure that emerging assays and technologies do not heighten existing healthcare disparities affecting African populations. We present the status of pharmacogenomics in Africa, highlighting its potential to impact health outcomes in the safe and efficacious use of medicines.

PMID:40146997 | DOI:10.1146/annurev-genom-121323-104008

PLoS Med. 2025 Mar 27;22(3):e1004565. doi: 10.1371/journal.pmed.1004565. eCollection 2025 Mar.

ABSTRACT

BACKGROUND: Adverse drug reactions (ADRs) harm patients and are costly for healthcare systems. Genetic variation contributes to variability in medication response and prospective knowledge of these variants can decrease risk of ADRs, as shown in the PREPARE trial. Reduction in ADRs would affect only those reactions to drugs contained in well-validated pharmacogene-drug pairs. The scope of ADRs represented by these drugs on a population scale is unclear. The objective of this study was to characterize the pharmacogene-drug-associated ADR reporting landscape from a national regulatory pharmacovigilance dataset to elucidate the scale of potential ADR mitigation by pharmacogenomics (PGx) implementation.

METHODS AND FINDINGS: All publicly available Yellow Card ADR reports to the United Kingdom Medicines and Healthcare Products Regulatory Agency, from 1963 to 2024, were compiled using programmatic data extraction. The ADRs were analysed with descriptive statistics, stratified by PGx status and by associated genes. Prescribing prevalence from the literature was compared with age range matched ADR reports for PGx-associated drugs. There were 1,345,712 ADR reports, attributed to 2,499 different substances. 115,789 adverse drug reports (9%) were associated with drugs for which ADR risk can be modified based on pharmacogenomic prescribing guidance. Seventy-five percent of these (n = 87,339) were due to medicines which interact with only three pharmacokinetic pharmacogenes (CYP2C19, CYP2D6, SLCO1B1). Forty-seven percent of all the PGx mitigatable ADRs identified were attributed to psychiatric medications (n = 54,846), followed by 24% attributed to cardiovascular medications (n = 28,279). Those experiencing PGx mitigatable ADRs, as compared with non-PGx mitigatable ADRs, were older and the ADRs more often consisted of severe non-fatal reactions. Many PGx-associated psychiatric drug ADRs were overrepresented as compared with prescribing prevalence, but fatal cardiac arrhythmias were uncommon consequences, comprising only 0.4% of these ADRs (n = 172 of n = 48,315 total ADRs). Limitations of this data source include under reporting of ADRs and reporting bias. These findings are based on analysis of the Yellow Card dataset described and may not represent all ADRs from a generalised patient population.

CONCLUSIONS: Nine percent of all reported ADRs are associated with drugs where a genetic variant can cause heightened risk of an ADR and inform prescribing. A panel of only three pharmacogenes could potentially mitigate three in every four PGx modifiable ADRs. Based on our findings, Psychiatry may be the single highest impact specialty to pilot PGx to reduce ADRs and associated morbidity, mortality and costs.

PMID:40146782 | DOI:10.1371/journal.pmed.1004565

Clin Transl Sci. 2025 Apr;18(4):e70173. doi: 10.1111/cts.70173.

ABSTRACT

Lurbinectedin is an oncogenic transcription inhibitor indicated for the treatment of small cell lung cancer (SCLC), which has also shown activity against other malignancies. In this work, two independent cohorts of 180 (discovery cohort) and 719 (validation cohort) cancer patients receiving lurbinectedin in Phases I, II, or III clinical trials were enrolled. Using a population pharmacokinetic (popPK) model of the discovery cohort, patients with extremely high (n = 10, cohort 1) and low (n = 10, cohort 2) etaCL values (i.e., a variable used as a surrogate of unexplained CL interindividual variability) were identified. They were sequenced for 42 candidate genes involved in lurbinectedin pharmacokinetics. A total of 34 variants located in 20 genes were significantly associated with lurbinectedin etaCL; the best nine hits (located in CYP3A5, CYP3A4, ABCB1, ARNT, NR5A2, NR1H4, and FOXA3) were subsequently genotyped in the validation cohort. A strong additive association between CYP3A4 and CYP3A5 genotypes (informed as a CYP3A activity score [AS] variable) and lurbinectedin clearance (CL) and exposure was confirmed, for example, patients with an AS of 3, 2, or 1 showed a 2.3-, 1.6-, and 1.5-fold higher total lurbinectedin CL compared to those with an AS of 0 and 2.3-, 1.8-, and 1.6-fold higher unbound lurbinectedin CL. In conclusion, preemptive CYP3A genotyping may offer a valuable approach for personalizing treatment with lurbinectedin in cancer patients.

PMID:40146606 | DOI:10.1111/cts.70173

Med Ultrason. 2025 Mar 8. doi: 10.11152/mu-4495. Online ahead of print.

ABSTRACT

AIM: This study aimed to develop a deep learning (DL) model for automatic detection and diagnosis of gouty arthritis (GA) in the first metatarsophalangeal joint (MTPJ) using ultrasound (US) images.

MATERIALS AND METHODS: A retrospective study included individuals who underwent first MTPJ ultrasonography between February and July 2023. A five-fold cross-validation method (training set = 4:1) was employed. A deep residual convolutional neural network (CNN) was trained, and Gradient-weighted Class Activation Mapping (Grad-CAM) was used for visualization. Different ResNet18 models with varying residual blocks (2, 3, 4, 6) were compared to select the optimal model for image classification. Diagnostic decisions were based on a threshold proportion of abnormal images, determined from the training set.

RESULTS: A total of 2401 US images from 260 patients (149 gout, 111 control) were analyzed. The model with 3 residual blocks performed best, achieving an AUC of 0.904 (95% CI: 0.887~0.927). Visualization results aligned with radiologist opinions in 2000 images. The diagnostic model attained an accuracy of 91.1% (95% CI: 90.4%~91.8%) on the testing set, with a diagnostic threshold of 0.328.

CONCLUSION: The DL model demonstrated excellent performance in automatically detecting and diagnosing GA in the first MTPJ.

PMID:40146981 | DOI:10.11152/mu-4495

JMIR Diabetes. 2025 Mar 27;10:e66831. doi: 10.2196/66831.

ABSTRACT

Type 2 diabetes mellitus has seen a continuous rise in prevalence in recent years, and a similar trend has been observed in the increased availability of glucose-lowering drugs. There is a need to understand the variation in treatment response to these drugs to be able to predict people who will respond well or poorly to a drug. Electronic health records, clinical trials, and observational studies provide a huge amount of data to explore predictors of drug response. The use of artificial intelligence (AI), which includes machine learning and deep learning techniques, has the capacity to improve the prediction of treatment response in patients. AI can assist in the analysis of vast datasets to identify patterns and may provide valuable information on selecting an effective drug. Predicting an individual's response to a drug can aid in treatment selection, optimizing therapy, exploring new therapeutic options, and personalized medicine. This viewpoint highlights the growing evidence supporting the potential of AI-based methods to predict drug response with accuracy. Furthermore, the methods highlight a trend toward using ensemble methods as preferred models in drug response prediction studies.

PMID:40146874 | DOI:10.2196/66831

IEEE Trans Neural Netw Learn Syst. 2025 Mar 27;PP. doi: 10.1109/TNNLS.2025.3549816. Online ahead of print.

ABSTRACT

This article introduces the deep learning-based nonlinear model predictive controller with scene dynamics (DL-NMPC-SD) method for autonomous navigation. DL-NMPC-SD uses an a priori nominal vehicle model in combination with a scene dynamics model learned from temporal range sensing information. The scene dynamics model is responsible for estimating the desired vehicle trajectory, as well as to adjust the true system model used by the underlying model predictive controller. We propose to encode the scene dynamics model within the layers of a deep neural network, which acts as a nonlinear approximator for the high-order state space of the operating conditions. The model is learned based on temporal sequences of range-sensing observations and system states, both integrated by an Augmented Memory component. We use inverse reinforcement learning (IRL) and the Bellman optimality principle to train our learning controller with a modified version of the deep Q-learning (DQL) algorithm, enabling us to estimate the desired state trajectory as an optimal action-value function. We have evaluated DL-NMPC-SD against the baseline dynamic window approach (DWA), as well as against two state-of-the-art End2End and RL methods, respectively. The performance has been measured in three experiments: 1) in our GridSim virtual environment; 2) on indoor and outdoor navigation tasks using our RovisLab autonomous mobile test unit (AMTU) platform; and 3) on a full-scale autonomous test vehicle driving on public roads.

PMID:40146653 | DOI:10.1109/TNNLS.2025.3549816

Mol Inform. 2025 Mar;44(3):e202400325. doi: 10.1002/minf.202400325.

ABSTRACT

The blood brain barrier (BBB) is an endothelial-derived structure which restricts the movement of certain molecules between the general somatic circulatory system to the central nervous system (CNS). While the BBB maintains homeostasis by regulating the molecular environment induced by cerebrovascular perfusion, it also presents significant challenges in developing therapeutics intended to act on CNS targets. Many drug development practices rely partly on extensive cell and animal models to predict, to an extent, whether prospective therapeutic molecules can cross the BBB. In interest to reduce costs and improve prediction accuracy, many propose using advanced computational modeling of BBB permeability profiles leveraging empirical data. Given the scale of growth in machine learning and deep learning, we review the most recent machine learning approaches in predicting BBB permeability.

PMID:40146590 | DOI:10.1002/minf.202400325

Am J Physiol Lung Cell Mol Physiol. 2025 Mar 27. doi: 10.1152/ajplung.00286.2024. Online ahead of print.

ABSTRACT

INTRODUCTION: Idiopathic pulmonary fibrosis(IPF) is a chronic progressive lung disease that leads to destruction of alveoli and replacement by fibrotic tissue. Metabolic profiling of lung tissue and serum from IPF patients has revealed that levels of tricarboxylic acid (TCA) cycle metabolites such as succinate are altered in patients with IPF. In our study, we aim to evaluate the role of succinate and its receptor- succinate receptor 1 (SUCNR1) in the pathogenesis of lung fibrosis, with a focus on fibroblasts, a central cell in IPF.

METHODS: SUCNR1 expression was investigated using Western blots, qPCR, and FISH. In vitro assays with IPF and normal human lung fibroblasts(NHLF) were used to evaluate the effect of succinate treatment on the expression of fibrotic markers, fibroblast-myofibroblast transition, apoptosis and signaling mechanisms. Studies with the bleomycin mouse model of PF were used to evaluate the effect of succinate in vivo.

RESULTS: Several cell types in the lung express SUCNR1 including ATII cells, fibroblasts, and macrophages. In IPF patient fibroblasts, succinate treatment increased expression of fibrosis associated markers such as alpha smooth muscle actin and collagen. Moreover, succinate exaggerated TGF-β-mediated fibroblast-to-myofibroblast transition in NHLF. In vivo, succinate treatment significantly increased collagen accumulation in the lung and enhanced weight loss in bleomycin-treated mice. Importantly, succinate-mediated elevation of fibrosis-associated markers was lost upon knockdown of SUCNR1 or inhibition of ERK activation in IPF patient-derived fibroblasts.

CONCLUSION: Succinate exerted pro-fibrotic effects in vitro and in vivo. Thus, SUCNR1 antagonism may be a potential therapeutic target for the treatment of IPF.

PMID:40146935 | DOI:10.1152/ajplung.00286.2024

JMIR Med Inform. 2025 Mar 27;13:e67591. doi: 10.2196/67591.

ABSTRACT

BACKGROUND: Integrated clinical databases from national biobanks have advanced the capacity for disease research. Data quality and completeness filters are used when building clinical cohorts to address limitations of data missingness. However, these filters may unintentionally introduce systemic biases when they are correlated with race and ethnicity.

OBJECTIVE: In this study, we examined the race and ethnicity biases introduced by applying common filters to 4 clinical records databases. Specifically, we evaluated whether these filters introduce biases that disproportionately exclude minoritized groups.

METHODS: We applied 19 commonly used data filters to electronic health record datasets from 4 geographically varied locations comprising close to 12 million patients to understand how using these filters introduces sample bias along racial and ethnic groupings. These filters covered a range of information, including demographics, medication records, visit details, and observation periods. We observed the variation in sample drop-off between self-reported ethnic and racial groups for each site as we applied each filter individually.

RESULTS: Applying the observation period filter substantially reduced data availability across all races and ethnicities in all 4 datasets. However, among those examined, the availability of data in the white group remained consistently higher compared to other racial groups after applying each filter. Conversely, the Black or African American group was the most impacted by each filter on these 3 datasets: Cedars-Sinai dataset, UK Biobank, and Columbia University dataset. Among the 4 distinct datasets, only applying the filters to the All of Us dataset resulted in minimal deviation from the baseline, with most racial and ethnic groups following a similar pattern.

CONCLUSIONS: Our findings underscore the importance of using only necessary filters, as they might disproportionally affect data availability of minoritized racial and ethnic populations. Researchers must consider these unintentional biases when performing data-driven research and explore techniques to minimize the impact of these filters, such as probabilistic methods or adjusted cohort selection methods. Additionally, we recommend disclosing sample sizes for racial and ethnic groups both before and after data filters are applied to aid the reader in understanding the generalizability of the results. Future work should focus on exploring the effects of filters on downstream analyses.

PMID:40146917 | DOI:10.2196/67591

Science. 2025 Mar 28;387(6741):1362. doi: 10.1126/science.ads8799. Epub 2025 Mar 27.

NO ABSTRACT

PMID:40146823 | DOI:10.1126/science.ads8799

PLoS Med. 2025 Mar 27;22(3):e1004565. doi: 10.1371/journal.pmed.1004565. eCollection 2025 Mar.

ABSTRACT

BACKGROUND: Adverse drug reactions (ADRs) harm patients and are costly for healthcare systems. Genetic variation contributes to variability in medication response and prospective knowledge of these variants can decrease risk of ADRs, as shown in the PREPARE trial. Reduction in ADRs would affect only those reactions to drugs contained in well-validated pharmacogene-drug pairs. The scope of ADRs represented by these drugs on a population scale is unclear. The objective of this study was to characterize the pharmacogene-drug-associated ADR reporting landscape from a national regulatory pharmacovigilance dataset to elucidate the scale of potential ADR mitigation by pharmacogenomics (PGx) implementation.

METHODS AND FINDINGS: All publicly available Yellow Card ADR reports to the United Kingdom Medicines and Healthcare Products Regulatory Agency, from 1963 to 2024, were compiled using programmatic data extraction. The ADRs were analysed with descriptive statistics, stratified by PGx status and by associated genes. Prescribing prevalence from the literature was compared with age range matched ADR reports for PGx-associated drugs. There were 1,345,712 ADR reports, attributed to 2,499 different substances. 115,789 adverse drug reports (9%) were associated with drugs for which ADR risk can be modified based on pharmacogenomic prescribing guidance. Seventy-five percent of these (n = 87,339) were due to medicines which interact with only three pharmacokinetic pharmacogenes (CYP2C19, CYP2D6, SLCO1B1). Forty-seven percent of all the PGx mitigatable ADRs identified were attributed to psychiatric medications (n = 54,846), followed by 24% attributed to cardiovascular medications (n = 28,279). Those experiencing PGx mitigatable ADRs, as compared with non-PGx mitigatable ADRs, were older and the ADRs more often consisted of severe non-fatal reactions. Many PGx-associated psychiatric drug ADRs were overrepresented as compared with prescribing prevalence, but fatal cardiac arrhythmias were uncommon consequences, comprising only 0.4% of these ADRs (n = 172 of n = 48,315 total ADRs). Limitations of this data source include under reporting of ADRs and reporting bias. These findings are based on analysis of the Yellow Card dataset described and may not represent all ADRs from a generalised patient population.

CONCLUSIONS: Nine percent of all reported ADRs are associated with drugs where a genetic variant can cause heightened risk of an ADR and inform prescribing. A panel of only three pharmacogenes could potentially mitigate three in every four PGx modifiable ADRs. Based on our findings, Psychiatry may be the single highest impact specialty to pilot PGx to reduce ADRs and associated morbidity, mortality and costs.

PMID:40146782 | DOI:10.1371/journal.pmed.1004565

Curr Psychiatry Rep. 2025 Mar 27. doi: 10.1007/s11920-025-01606-8. Online ahead of print.

ABSTRACT

Bipolar Disorder (BD) is a chronic mental health condition characterized by significant mood swings, including periods of mania or hypomania and depression. Affecting approximately 1-2% of the global population, BD is associated with impaired social functioning, decreased quality of life, and an increased risk of suicide. The disorder presents a substantial burden on healthcare systems and imposes significant economic costs due to lost productivity and the need for extensive treatment and support services. This comprehensive review synthesizes global trends in BD pharmacotherapy over the past 1 to 3 years, focusing on emerging medications, novel treatment protocols, and ongoing debates within the field. Additionally, the review explores differences in prescribing patterns across developed and developing countries, introduces the impact of pharmacogenomics and personalized medicine on treatment outcomes. PURPOSE OF REVIEW: The primary purpose of this review is to provide a comprehensive and up-to-date synthesis of the global trends in the use of medications for the treatment of BD over the past 1 to 3 years. This review aims to outline the latest studies, clinical trials, and meta-analyses relevant to BD pharmacotherapy, highlighting new discoveries and advancements. Furthermore, this review will address ongoing debates and controversies in the field, such as the role of antidepressants in BD treatment and the long-term use of antipsychotics, aiming to bridge knowledge gaps and guide future research directions. RECENT FINDINGS: Studies continue to reinforce the efficacy of lithium in mood stabilization and reduction of suicidal behavior, despite its declining use due to safety concerns. Mood stabilizing anticonvulsants like valproate and carbamazepine continue to be vital alternatives, each with distinct side effect profiles necessitating careful patient monitoring. The approval and increasing use of novel atypical antipsychotics, such as lurasidone (2013) and cariprazine (2015), has expanded treatment options, offering efficacy in different phases of BD with relatively favorable side effect profiles. Antidepressants remain contentious, with evidence suggesting their benefits primarily when used in combination with mood stabilizers. Emerging agents like lumateperone (Dec 2021) and esketamine show promise, while pharmacogenomic research is paving the way for more personalized treatments. The landscape of BD pharmacotherapy is marked by significant advancements and ongoing challenges. Lithium and mood stabilizing anticonvulsants remain foundational treatments, albeit with adherence challenges and side effect concerns. The advent of new atypical antipsychotics and novel agents offers promising therapeutic options, while antidepressants continue to be debated. Personalized medicine and pharmacogenomics could emerge as transformative approaches, allowing for more tailored and effective treatments. However, disparities in medication accessibility between developed and developing countries highlight the need for global collaboration to optimize BD management. Continued research and innovation are essential to addressing the complexities of BD and improving patient outcomes worldwide.

PMID:40146356 | DOI:10.1007/s11920-025-01606-8

Clin Transl Sci. 2025 Apr;18(4):e70210. doi: 10.1111/cts.70210.

ABSTRACT

Monitoring whole blood tacrolimus concentrations is standard in clinical practice; however, it may not fully reflect its therapeutic effects, as tacrolimus primarily acts within lymphocytes. While various intracellular quantification methods have been developed, many involve complex procedures such as evaporation, reconstitution, or specialized tools (e.g., magnetic beads, online solid-phase extraction), limiting their accessibility. This study aimed to develop and validate a streamlined, sensitive method for measuring intracellular tacrolimus concentrations using 5×105 peripheral blood mononuclear cells (PBMCs). Tacrolimus concentrations were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS). PBMCs were aliquoted into 50 μL volumes containing 5×105 cells and prepared via acetonitrile-based protein precipitation. Chromatographic separation was performed using a Luna C18 column with a gradient mobile phase consisting of water with 20 mM ammonium acetate, 0.1% formic acid, and methanol at a flow rate of 0.4 mL/min. The method demonstrated excellent linearity between 0.1 and 25 ng/mL, corresponding to intracellular concentrations of 1-250 pg/5×105 cells (r2 = 0.999). Intra- and interday accuracy ranged from 98.1% to 109.8%, with precision between 2.08% and 8.70% across validation runs. Extraction recovery was high (93.0%-97.2%), with minimal matrix effects (100.9% at low QC and 111.6% at high QC). This validated LC-MS/MS method provides a rapid, reliable, and sensitive approach for pharmacokinetic studies and clinical applications, facilitating intracellular tacrolimus monitoring in transplant patients.

PMID:40145774 | DOI:10.1111/cts.70210

R I Med J (2013). 2025 Apr 1;108(4):26-31.

ABSTRACT

BACKGROUND: Novel therapies have increased life expectancy for people with cystic fibrosis (pwCF), shifting care to adult providers with limited CF experience. Hospitalizations expose trainees to CF care, but educational tools for managing CF exacerbations are scarce.

METHODS: A six-year longitudinal study assessed internal medicine resident physician trainees' (RPTs) experience managing hospitalized pwCF. A mixed-method survey of RPTs informed the creation of educational and workflow tools, including a video didactic and order set. A targeted knowledge assessment measured the effectiveness of these tools. Multidisciplinary team meetings also tracked challenges and outcomes.

RESULTS: Among 48 RPTs surveyed, 83% cared for pwCF during training. Most reported less comfort managing pwCF compared to diseases like chronic obstructive pulmonary disease (COPD). Immediately after implementing targeted educational tools, knowledge scores improved, particularly in CF-related diabetes management (38.4% correct pre vs 61.5% correct post, p=0.04).

CONCLUSION: Rare disease education requires focused assessments of learners' needs, sustained reinforcement, and adaptable tools to maintain effectiveness.

PMID:40146222

Tuberk Toraks. 2025 Mar;73(1):11-19. doi: 10.5578/tt.202501999.

ABSTRACT

INTRODUCTION: Early diagnosis with newborn screening programs and prolonged life expectancy with new treatment strategies have made cardiovascular disease (CVD) one of the important issues in cystic fibrosis (CF). In the early stages of CVD, it is difficult to recognize and follow-up increased arterial stiffness with conventional methods. Different measurement methods are needed. Therefore, in this study, we aimed to use arterial stiffness measurements in the follow-up of children with CF.

MATERIALS AND METHODS: This is a follow-up study examining the changes in arterial stiffness in children with CF by repeating hemodynamic measurements [augmentation index (AIx) and pulse wave velocity (PWV)]. We repeated hemodynamic measurements and CF-related CVD risk factors (Atherosclerosis risk factors: Fasting blood sugar, lipid profiles, and HbA1c) and systemic inflammation markers [C-reactive protein (CRP) and immunoglobulin G and pulmonary function tests] in children undergoing routine annual complication evaluation and examined changes during follow-up.

RESULT: Hemodynamic measurements could be repeated in 37 of 52 patients due to inclusion criteria. Mean age of the study group was 12 ± 4.5 years and 48.6% were female. There was a statistically significant increase in high density lipoprotein, HbA1c, and CRP and a decrease in low density lipoprotein and FEV1 at the follow-up. Heart rate, central blood pressure, augmented pressure, and PWV were similar. AIx, peripheral systolic blood pressure (SBP), and mean arterial pressure were increased significantly (p< 0.05). The increase in AIx was greater than expected for age and greater in female patients and in those with low body mass index, moderate-severe disease, and high CRP levels. Also, the change in AIx was positively correlated with changes in peripheral SBP and CRP.

CONCLUSIONS: This is the first study to evaluate the use of PWV and AIx in the follow-up of children with CF and showed that arterial stiffness measured with AIx increased at follow-up. The use of markers of arterial stiffness in CF from childhood onwards may enable early detection and monitoring of CVD risk and future prevention.

PMID:40145819 | DOI:10.5578/tt.202501999

Cochrane Database Syst Rev. 2025 Mar 27;3:CD016039. doi: 10.1002/14651858.CD016039.

ABSTRACT

RATIONALE: Cystic fibrosis (CF) is a common genetic condition in which progressive lung disease leads to morbidity and mortality. Non-tuberculous mycobacteria (NTM) are mycobacteria, other than those in the Mycobacterium tuberculosis complex, and are commonly found in the environment. NTM pulmonary infections affect a significant proportion of people with CF worldwide, which may be associated with a more rapid decline in lung function and even death in certain circumstances. Although there are guidelines for the antimicrobial treatment of NTM lung disease, there is no specific evidence from studies of people with CF to inform recommendations for their treatment. It is not clear which antibiotic regimen may be the most effective in the treatment of people with CF. This is an update of a previous review.

OBJECTIVES: To compare antibiotic treatment to no antibiotic treatment, or to compare different combinations of antibiotic treatment, for suppressing or eradicating non-tuberculous mycobacteria (NTM) lung infections in people with cystic fibrosis (CF).

SEARCH METHODS: We searched Cochrane's Cystic Fibrosis Trials Register, online databases (MEDLINE, Embase and PubMed) and online trials registries (www.

CLINICALTRIALS: gov and the World Health Organization International Clinical Trials Registry). We also searched the reference lists of included studies and relevant reviews. The date of the last search was 14 October 2024.

ELIGIBILITY CRITERIA: Randomised controlled trials (RCTs) or quasi-RCTs with a parallel design; non-randomised studies of interventions (NRSIs) with the following designs: instrumental variables; regression discontinuity; interrupted time series; difference-in-differences and fixed-effect designs. These should have compared antibiotic treatment to no antibiotic treatment, or different combinations of antibiotic treatment, in people with CF of any age with NTM pulmonary infection.

OUTCOMES: We aimed to assess the critical outcomes of microbiological clearance of NTM in sputum, quality of life, adverse events, lung function and pulmonary exacerbations. Further, we planned to assess important outcomes of mortality, nutritional parameters, hospitalisations and use of additional oral antibiotics.

RISK OF BIAS: We planned to use the recommended Cochrane tools for RCTs or NRSIs. These were not suitable for the included study, so we assessed the risk of bias using a tool for case series developed by the Joanna Briggs Institute.

SYNTHESIS METHODS: We were only able to report the limited results from the single included study narratively. We assessed the certainty of the results using GRADE.

INCLUDED STUDIES: Due to a lack of studies of the types planned, we were only able to include a single retrospective case review, which presented data as the change from baseline for some outcomes. It was conducted in Sweden in 2003 and included 11 participants with CF and NTM infection (three males) aged between 10 and 36 years. The study identified the specific cystic fibrosis transmembrane conductance regulator (CFTR) mutation for 10 participants. All participants were chronically colonised with Pseudomonas aeruginosa; 10 participants had been vaccinated with the Bacillus Calmette-Guérin vaccine. Antibiotic selection differed amongst participants and was determined according to in vitro susceptibility testing. Antibiotics included isoniazid, ethambutol, rifampicin (or rifabutin), amikacin, clarithromycin, ciprofloxacin, streptomycin and clofazimine. Of note, at the start of the study, isoniazid was the standard treatment for NTM, and three participants received this drug; however, investigators stated that following severe adverse effects, the drug was excluded in the latter part of the 1980s. Investigators reported data for lung function, weight and adverse events one year before NTM diagnosis, at baseline, at completion of therapy and at the latest follow-up (ranging from one to 14 years). Treatment was considered effective if NTM was cleared and cultures remained negative throughout treatment; it was considered to have failed if there were continued or sporadic positive cultures.

SYNTHESIS OF RESULTS: We graded all the evidence as very low and are very uncertain of the effects of the different antibiotic regimens on any of the outcomes reported. The study reported that in 10/11 participants, microbiological cultures turned negative. They also stated that five participants reported adverse events; three reported photosensitivity to ciprofloxacin, while each of the following events was reported by one of the five participants: impaired hearing, convulsions, neuropathy and lupus erythematous. There was no consistent effect on lung function. Investigators reported that forced expiratory volume in one second increased by between 1% predicted and 46% predicted in six participants, decreased between 2% predicted and 31% predicted in four participants and remained the same in one participant. They also reported that forced vital capacity increased in eight participants by between 3% predicted and 53% predicted, and decreased in three participants by between 4% predicted and 21% predicted. Two participants died as a result of progression of CF respiratory disease two years after completion of therapy. A further participant died of gastrointestinal bleeding and renal insufficiency eight years after lung transplant which followed clearance of NTM infection (negative NTM cultures were maintained until death). Eight participants gained weight (range 3.30 kg to 14.00 kg), while three participants lost weight (range -0.90 kg to -6.00 kg). Investigators additionally reported body mass index values in three participants, which decreased minimally in two participants and increased slightly in the third participant.

AUTHORS' CONCLUSIONS: The very low-certainty evidence identified in this review suggests that antimicrobial treatment may lead to sputum clearance of NTM in people with CF, but may result in variable clinical response in terms of lung function. Very low-certainty evidence also suggests that adverse events may be common, necessitating close monitoring. This review highlights the need for larger, more standardised studies in order to make meaningful comparisons between treatment regimens. Although microbiological clearance seems feasible, studies should be powered to detect relevant clinical outcomes as well.

FUNDING: Cochrane CF received funding from the Cystic Fibrosis Foundation for a series of reviews on NTM, of which the update of this review is one.

REGISTRATION: The protocol for this updated version of the review was registered at PROSPERO in November 2023.

PMID:40145528 | DOI:10.1002/14651858.CD016039

Ophthalmol Ther. 2025 Mar 27. doi: 10.1007/s40123-025-01127-w. Online ahead of print.

ABSTRACT

INTRODUCTION: Existing deep learning (DL) algorithms lack the capability to accurately identify patients in immediate need of treatment for proliferative diabetic retinopathy (PDR). We aimed to develop a DL segmentation model to detect active PDR in six-field retinal images by the annotation of new retinal vessels and preretinal hemorrhages.

METHODS: We identified six-field retinal images classified at level 4 of the International Clinical Diabetic Retinopathy Disease Severity Scale collected at the Island of Funen from 2009 to 2019 as part of the Danish screening program for diabetic retinopathy (DR). A certified grader (grader 1) manually dichotomized the images into active or inactive PDR, and the images were then reassessed by two independent certified graders. In cases of disagreement, the final classification decision was made in collaboration between grader 1 and one of the secondary graders. Overall, 637 images were classified as active PDR. We then applied our pre-established DL segmentation model to annotate nine lesion types before training the algorithm. The segmentations of new vessels and preretinal hemorrhages were corrected for any inaccuracies before training the DL algorithm. After the classification and pre-segmentation phases the images were divided into training (70%), validation (10%), and testing (20%) datasets. We added 301 images with inactive PDR to the testing dataset.

RESULTS: We included 637 images of active PDR and 301 images of inactive PDR from 199 individuals. The training dataset had 1381 new vessel and preretinal hemorrhage lesions, while the validation dataset had 123 lesions and the testing dataset 374 lesions. The DL system demonstrated a sensitivity of 90% and a specificity of 70% for annotation-assisted classification of active PDR. The negative predictive value was 94%, while the positive predictive value was 57%.

CONCLUSIONS: Our DL segmentation model achieved excellent sensitivity and acceptable specificity in distinguishing active from inactive PDR.

PMID:40146482 | DOI:10.1007/s40123-025-01127-w

MAGMA. 2025 Mar 27. doi: 10.1007/s10334-025-01242-6. Online ahead of print.

ABSTRACT

OBJECTIVES: Despite prevalent use of chemical exchange saturation transfer (CEST) MRI, standardization remains elusive. Imaging depends heavily on parameters dictating radiofrequency (RF) events, gradients, and apparent diffusion coefficient (ADC). We present the Pulseq-CEST Library, a repository of CEST preparation and simulation definitions, including example data and evaluations, that provides a common basis for reproducible research, rapid prototyping, and in silico deep learning training data generation.

MATERIALS AND METHODS: A Pulseq-CEST experiment requires (i) a CEST preparation sequence, (ii) a Bloch-McConnell parameter set, (iii) a Bloch-McConnell simulation, and (iv) an evaluation script. Pulseq-CEST utilizes the Bloch-McConnell equations to model in vitro and in vivo conditions. Using this model, a candidate sequence or environment can be held constant while varying other inputs, enabling robust testing.

RESULTS: Data were compared for amide proton transfer weighted (APTw) and water shift and B1 (WASABI) protocols using a five-tube phantom and simulated environments. Real and simulated data matched anticipated spectral shapes and local peak characteristics. The Pulseq-CEST Library supports similar experiments with common sequences and environments to assess new protocols and sample data.

DISCUSSION: The Pulseq-CEST Library provides a flexible mechanism for standardizing and prototyping CEST sequences, facilitating collaborative development. With the capability for expansion, including open-source incorporation of new sequences and environments, the library accelerates the invention and spread of novel CEST and other saturation transfer approaches, such as relayed NOEs (rNOEs) and semisolid magnetization transfer contrast (MTC) methods.

PMID:40146474 | DOI:10.1007/s10334-025-01242-6

Brain Imaging Behav. 2025 Mar 27. doi: 10.1007/s11682-025-00998-8. Online ahead of print.

ABSTRACT

The morphological fingerprint in the brain is capable of identifying the uniqueness of an individual. However, whether such individual patterns are present in perinatal brains, and which morphological attributes or cortical regions better characterize the individual differences of neonates remain unclear. In this study, we proposed a deep learning framework that projected three-dimensional spherical meshes of three morphological features (i.e., cortical thickness, mean curvature, and sulcal depth) onto two-dimensional planes through quasi-conformal mapping, and employed the ResNet18 and contrastive learning for individual identification. We used the cross-sectional structural MRI data of 461 infants, incorporating with data augmentation, to train the model and fine-tuned the parameters based on 41 infants who had longitudinal scans. The model was validated on a fold of 20 longitudinal scanned infant data, and remarkable Top1 and Top5 accuracies of 85.90% and 92.20% were achieved, respectively. The sensorimotor and visual cortices were recognized as the most contributive regions in individual identification. Moreover, morphological fingerprints successfully predicted the long-term development of cognition and behavior. Furthermore, the folding morphology demonstrated greater discriminative capability than the cortical thickness. These findings provided evidence for the emergence of morphological fingerprints in the brain at the beginning of the third trimester, which may hold promising implications for understanding the formation of individual uniqueness, and predicting long-term neurodevelopmental risks in the brain during early development.

PMID:40146450 | DOI:10.1007/s11682-025-00998-8

Interdiscip Sci. 2025 Mar 27. doi: 10.1007/s12539-025-00694-7. Online ahead of print.

ABSTRACT

circRNAs are a type of single-stranded non-coding RNA molecules, and their unique feature is their closed circular structure. The interaction between circRNAs and RNA-binding proteins (RBPs) plays a key role in biological functions and is crucial for studying post-transcriptional regulatory mechanisms. The genome-wide circRNA binding event data obtained by cross-linking immunoprecipitation sequencing technology provides a foundation for constructing efficient computational model prediction methods. However, in existing studies, although machine learning techniques have been applied to predict circRNA-RBP interaction sites, these methods still have room for improvement in accuracy and lack interpretability. We propose CR-deal, which is an interpretable joint deep learning network that predicts the binding sites of circRNA and RBP through genome-wide circRNA data. CR-deal utilizes a graph attention network to unify sequence and structural features into the same view, more effectively utilizing structural features to improve accuracy. It can infer marker genes in the binding site through integrated gradient feature interpretation, thereby inferring functional structural regions in the binding site. We conducted benchmark tests on CR-deal on 37 circRNA datasets and 7 lncRNA datasets, respectively, and obtained the interpretability of CR-deal and discovered functional structural regions through 5 circRNA datasets. We believe that CR-deal can help researchers gain a deeper understanding of the functions and mechanisms of circRNA in living organisms and its critical role in the occurrence and development of diseases. The source code of CR-deal is provided free of charge on https://github.com/liuliwei1980/CR .

PMID:40146403 | DOI:10.1007/s12539-025-00694-7