Pharmaceuticals (Basel). 2025 Apr 1;18(4):515. doi: 10.3390/ph18040515.

ABSTRACT

Background/Objectives: Fluoropyrimidine derivatives, 5-fluorouracil (5-FU) and its prodrugs (capecitabine and tegafur), are widely used in patients suffering from colorectal cancer. The enzyme responsible for their metabolization, dihydropyrimidine dehydrogenase (DPD), is encoded by the DPYD gene, which is highly polymorphic and may contain polymorphisms which could severely compromise its function. This article aims to describe the prevalence of the four main DPYD polymorphisms in the Galician population (Spain) and to compare these frequencies with data obtained from European cohorts in genetic databases and a Spanish study. Methods: Galician data frequencies for the four main DPYD polymorphisms recommended by the European Medicine Agency (EMA) and the Spanish Agency for Medicines and Health Products (AEMPS) (rs3918290 (c.1905+1G>A), rs55886062 (c.1679T>G), rs56038477 (c.1236G>A) and rs67376798 (c.2846A>T)) were collected, as well as data from the genomic databases 1000 Genomes and gnomAD. Additionally, the results from a Spanish DPYD study were included. Results: Significant differences in DPYD variant allele frequencies were observed in the Galician population compared to the frequencies reported in the European population, as well as in the Spanish PhotoDPYD study. Specifically, the rs56038477-T variant (most prevalent) along with the rs3918290-T variant, exhibited significantly lower frequencies than anticipated in the Galician cohort, with a high degree of statistical significance. Conclusions: Observed allele frequencies for the four DPYD variants suggest that Europeans and Spanish frequencies may not be fully applicable to the Galician population. These results emphasize the emerging need for incorporating the genetic information of populations that might be underrepresented into populational databases available worldwide.

PMID:40283950 | DOI:10.3390/ph18040515

Pharmaceuticals (Basel). 2025 Mar 29;18(4):499. doi: 10.3390/ph18040499.

ABSTRACT

Lysergic acid diethylamide (LSD) is gaining renewed interest as a potential treatment for anxiety, depression, and alcohol use disorder, with clinical trials reporting significant symptom reductions and long-lasting effects. LSD modulates serotonin (5-HT2A) receptors, which, in turn, influence dysfunctional brain networks involved in emotional processing and cognition. It has also shown promise in psychedelic-assisted psychotherapy, where mystical-type experiences are linked to improved psychological well-being. This review examines LSD's pharmacokinetics, neurobiological mechanisms, and safety considerations, including cardiovascular risks, emotional vulnerability, and the potential for hallucinogen-persisting perception disorder (HPPD). Challenges such as small sample sizes, variable dosing protocols, and regulatory restrictions limit large-scale trials. Future research should focus on standardization, pharmacogenetic influences, and personalized treatment strategies to ensure its safe and effective integration into clinical practice.

PMID:40283936 | DOI:10.3390/ph18040499

Genes (Basel). 2025 Apr 15;16(4):453. doi: 10.3390/genes16040453.

ABSTRACT

BACKGROUND/OBJECTIVES: Lung cancer has a high mortality rate worldwide, with non-small cell lung cancer (NSCLC) being the most prevalent. Carboplatin and paclitaxel are key treatments for NSCLC; however, adverse drug reactions (ADRs) pose significant challenges. This study examined the impact of genetic variations in ABCB1 and ABCC2 genes on the incidence of ADRs and survival in NSCLC patients treated with carboplatin and paclitaxel.

METHODS: Variants were identified using RT-PCR, and ADRs classified according to the Common Toxicity Criteria for Adverse Events, Version 4.03.

RESULTS: The ABCB1 rs1128503 (c.1236C>T) CC genotype was associated with a higher chance of nausea (OR: 3.5, 95% CI 1.367-9.250, p = 0.0093), vomiting (OR: 13.553, 95% CI 1.705-107.723, p = 0.0137), and a higher risk of death in CT or TT genotypes (HR: 1.725, 95% CI 1.036-2.871, p = 0.0361). The ABCC2 rs717620 (c.-24C>T) TT genotype was associated with increased ALP levels (OR: 14.6, 95% CI 1.234-174.236, p = 0.0335). The ABCB1 rs2032582 non-CC genotypes (TT+AA+TA+CA+CT) were associated with an increased risk of death (HR: 1.922, 95% CI 1.093-3.377, p = 0.0232). Patients with hypocalcemia (HR: 2.317, 95% IC 1.353-3.967, p = 0.022), vomiting (HR: 3.047, 95% IC 1.548-5.997, p = 0.0013), and diarrhea (HR: 2.974, 95% IC 1.590-5.562, p = 0.0006) were associated with lower overall survival.

CONCLUSIONS: The data suggest that ABCB1 variants may influence gastrointestinal ADRs and patient survival, highlighting the importance of pharmacogenomics in predicting ADRs and drug resistance. This approach offers more precise pharmacotherapy, reduces ADRs, and enhances the patients' quality of life and survival.

PMID:40282412 | DOI:10.3390/genes16040453

Genes (Basel). 2025 Mar 30;16(4):403. doi: 10.3390/genes16040403.

ABSTRACT

BACKGROUND/OBJECTIVES: This review examines the role of pharmacogenomics in individual responses to the pharmacotherapy of various drugs of abuse, including alcohol, cocaine, and opioids, to identify genetic variants that contribute to variability in substance use disorder treatment outcomes. In addition, it explores the pharmacomicrobiomic aspects of substance use, highlighting the impact of the gut microbiome on bioavailability, drug metabolism, pharmacodynamics, and pharmacokinetics.

RESULTS: Research on pharmacogenetics has identified several promising genetic variants that may contribute to the individual variability in responses to existing pharmacotherapies for substance addiction. However, the interpretation of these findings remains limited. It is estimated that genetic factors may account for 20-95% of the variability in individual drug responses. Therefore, genetic factors alone cannot fully explain the differences in drug responses, and factors such as gut microbiome diversity may also play a significant role. Drug microbial biotransformation is produced by microbial exoenzymes that convert low molecular weight organic compounds into analogous compounds by oxidation, reduction, hydrolysis, condensation, isomerization, unsaturation, or by the introduction of heteroatoms. Despite significant advances in pharmacomicrobiomics, challenges persist including the lack of standardized methodologies, inter-individual variability, limited understanding of drug biotransformation mechanisms, and the need for large-scale validation studies to develop microbiota-based biomarkers for clinical use.

CONCLUSIONS: Progress in the pharmacogenomics of substance use disorders has provided biological insights into the pharmacological needs associated with common genetic variants in drug-metabolizing enzymes. The gut microbiome and its metabolites play a pivotal role in various stages of drug addiction including seeking, reward, and biotransformation. Therefore, integrating pharmacogenomics with pharmacomicrobiomics will form a crucial foundation for significant advances in precision and personalized medicine.

PMID:40282363 | DOI:10.3390/genes16040403

Genes (Basel). 2025 Mar 24;16(4):372. doi: 10.3390/genes16040372.

ABSTRACT

BACKGROUND/OBJECTIVES: The CYP2C9 enzyme is involved in the metabolism of warfarin. The CYP2C9 gene harbors several single-nucleotide polymorphisms (SNPs), including CYP2C9*2 (rs1799853), which is known to affect warfarin's therapeutic response. So, it is important to develop analytical tools capable of genotyping these SNPs to adjust warfarin's therapeutic outcomes. In this work, an electrochemical DNA-based sensor was constructed and optimized for the detection of the CYP2C9*2 polymorphism.

METHODS: Using bioinformatic database platforms, two 71 base pair DNA target probes with the polymorphic variants A and G were chosen and designed. A DNA-based sensor was composed by mercaptohexanol and the CYP2C9*2 DNA capture probe in a self-assembled monolayer connected to screen-printed gold electrodes. Two independent hybridization events of the CYP2C9*2 allele were designed using complementary fluorescein-labeled DNA signaling to improve selectivity and avoid secondary structures. Three human samples with the homozygous variant (G/G) and non-variant (A/A) and heterozygous (G/A) genotypes were amplified by PCR and then applied to the developed genosensor.

RESULTS: Chronoamperometry measurements were performed for both polymorphic probes. A calibration curve in the 0.25 to 2.50 nM (LOD of 13 pM) and another in the 0.15 to 5.00 nM range (LOD of 22.6 pM) were obtained for the homozygous non-variant and variant probes, respectively. This innovative tool was capable of identifying the hybridization reaction between two complementary strands of immobilized DNA, representing a genotyping alternative to the classical PCR methodology.

CONCLUSIONS: The developed electrochemical DNA-based sensor was able to discriminate two synthetic SNP target sequences (Target-A and Target-G) and detect, with specificity, the three patients' genotypes (G/G, G/A, and A/A). This tool is therefore a promising, sensitive, and cost-effective analytical way to determine and discriminate an individual's genotype and predict the appropriate warfarin dose.

PMID:40282332 | DOI:10.3390/genes16040372

Genes (Basel). 2025 Mar 24;16(4):371. doi: 10.3390/genes16040371.

ABSTRACT

Neuropsychiatric disorders are complex conditions with multifactorial etiologies, in which genetics play a pivotal role. Despite significant advancements in psychiatric research, traditional treatment options remain largely symptomatic, focusing on clinical signs without fully addressing the underlying biological causes. However, recent developments in precision medicine-an approach that tailors treatments based on genetic, environmental, and lifestyle factors-hold great promise for transforming the treatment of these disorders. By identifying specific genetic markers and understanding gene-environment interactions, precision medicine can offer more personalized and effective treatments, leading to better patient outcomes. Our primary aim was to explore how integrating genetic data with environmental factors could enhance the understanding and treatment of neuropsychiatric conditions such as schizophrenia, bipolar disorder, and depression. The secondary aim was to examine the potential of pharmacogenomics and gene therapy in improving therapeutic strategies. The results indicate that while significant progress has been made, challenges remain, including the complexity of genetic interactions and the need for more granular phenotypic data. In conclusion, precision medicine has the potential to revolutionize neuropsychiatric treatment by providing individualized care that considers genetic makeup, environmental influences, and lifestyle factors, paving the way for more effective therapies and improved patient outcomes.

PMID:40282331 | DOI:10.3390/genes16040371

Microorganisms. 2025 Mar 24;13(4):730. doi: 10.3390/microorganisms13040730.

ABSTRACT

This study examines the impact of inhaled tobramycin therapy on the within-host changes in P. aeruginosa strains isolated from Bulgarian patients with CF prior to and post treatment. Genotypic comparison by RAPD-PCR indicated that most of the pre-treatment isolates had a high similarity and were genetically comparatively close to strains from other countries with known increased morbidity or treatment requirements. Most of the post-treatment isolates were, however, genetically distant from their pre-treatment counterparts, showing genotypic diversification after the treatment. Phenotypic comparisons showed a lower ODmax reached during groswth and an increased lag-time in the post-treatment isolates. All strains were capable of invasion and intracellular reproduction within A549 cultured cells. The addition of sub-inhibitory amounts (1/4 or 1/2 MIC) of tobramycin during growth showed the higher relative fitness (as a percentage of the untreated control) of the post-treatment strains. The effects of sub-MICs on biofilm growth did not show such a pronounced trend. However, when a resazurin-based viability test was applied, the advantage of the post-treatment strains was confirmed for both broth and biofilm cultures. In spite of that, according to the determined MIC values, all isolates were tobramycin-sensitive, and the data from this study imply the development of tolerance to the antibiotic in the strains that survived the treatment.

PMID:40284567 | DOI:10.3390/microorganisms13040730

J Clin Med. 2025 Apr 18;14(8):2807. doi: 10.3390/jcm14082807.

ABSTRACT

Background/Objective: Cystic fibrosis (CF) is a chronic genetic disease affecting multiple body systems and having a significant impact on mental health and sleep. Patients with CF frequently suffer from anxiety, depression, and sleep disturbances, but non-pharmacological strategies are understudied. Although progressive muscle relaxation (PMR) has recognized benefits, its impact on CF remains insufficiently explored. The study aimed to analyze the effect of integrating PMR into a standard pulmonary rehabilitation (PR) program on mental health, sleep quality, and quality of life in adults with CF. Methods: A total of 22 adult patients with CF were randomly assigned to either the intervention group (PR + PMR) or the control group (PR only). Assessments were performed at baseline, after 21 days of intervention, and at the 48-day follow-up. Outcome measures included the CFQ-R for quality of life, the HADS for mental health, and the PSQI for sleep. Results: Compared to the control group, participants who practiced PMR experienced significant reductions in anxiety (p = 0.05) and depression (p = 0.02) at the final assessment. A significant improvement in sleep quality was also observed (p < 0.01). No relevant differences were found in pulmonary function or performance on the six-minute walk test. Conclusions: Integrating PMR into pulmonary rehabilitation programs may be an effective strategy for improving mental health and sleep in patients with CF. Due to its accessibility and ease of implementation, PMR could be adopted as a complementary method in the holistic care of these patients.

PMID:40283637 | DOI:10.3390/jcm14082807

J Clin Med. 2025 Apr 9;14(8):2583. doi: 10.3390/jcm14082583.

ABSTRACT

Background/Objectives: Olfactory dysfunction (OD) is a common symptom among people with cystic fibrosis (PwCF) and contributes to environmental safety concerns, nutritional challenges, and an overall diminished quality of life. OD is perceived to progress along the lifespan in PwCF, often due to worsening sinonasal disease. Among children with cystic fibrosis (CwCF), OD is poorly characterized as limited resources and tolerance contribute to challenges in psychophysical olfactory evaluation among pediatric populations. The Children's Personal Significance of Olfaction (ChiPSO) questionnaire was recently proposed as a tool to assess olfaction and the importance of olfactory stimulation among children. This pilot study aimed to evaluate the utility of ChiPSO among a cohort of ethnically diverse CwCF. Methods: Individuals aged 7-17 with physician-diagnosed CF were asked to complete questionnaires, including ChiPSO and the brief questionnaire on olfactory dysfunction (bQOD-NS), prior to undergoing psychophysical olfactory evaluation with the U-Sniff Identification test. Potential associations between questionnaires and olfactory performance, pulmonary function, and demographic characteristics were evaluated using Pearson and Spearman correlations, independent-sample t-tests, Wilcoxon rank sum tests, and multiple linear regression. Results: U-Sniff Identification score positively correlated with the overall ChiPSO total score [r(13) = 0.640, p = 0.010] and its environmental subdomain score [r(13) = 0.774, p < 0.001], though not with the food subdomain [r(13) = 0.450, p = 0.093], the social subdomain [r(13) = 0.343, p = 0.2], or bQOD-NS score [r(11) = -0.125, p = 0.7]. Hispanic ethnicity is associated with ChiPSO (p = 0.041). Conclusions: In this preliminary study, olfactory importance increases with olfactory function among an ethnically diverse sample of CwCF, with a preferential influence of olfactory function on personal importance of environmental olfactory information. While these results should be interpreted with limitations imposed by the pilot nature of our sample size, our pilot data highlights associations with early adolescent development of importance of olfaction that can be disrupted in the setting of progressive disease among CwCF.

PMID:40283411 | DOI:10.3390/jcm14082583

Medicina (Kaunas). 2025 Apr 2;61(4):653. doi: 10.3390/medicina61040653.

ABSTRACT

Background and Objectives: In the present systematic review and meta-analysis, we aimed to discover the overall efficacy of azithromycin in children with cystic fibrosis (CF) and with or without Pseudomonas aeruginosa infection, specifically regarding its effect on respiratory parameters such as forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) in addition to its effect on exacerbations and the need to use additional antibiotics. Materials and Method: We conducted this systematic review and meta-analysis by searching for all eligible articles on PubMed, Web of Science, and Scopus published between inception and September 2024. We used the following search strategy for our searching process: "Cystic fibrosis" AND "Azithromycin" and "Children" OR "Pediatric" OR "Infant". We conducted the meta-analysis by pooling the mean difference (MD) and comparing the continuous variables and odds ratio (OR) for dichotomous variables at 95% confidence intervals (CI), at a p-value of 0.05. Results: Azithromycin was observed to be associated with increased FEV1 compared with the control, showing an MD of 1.91 (95% CI: 1.09, 2.74, p < 0.00001) and non-significant heterogeneity. However, no significant difference was observed between azithromycin and control groups regarding FVC with MD = 0.62 (95% CI: -0.01, 1.25, p = 0.06). Compared with the control group, azithromycin was significantly associated with lower risk and a lower number of exacerbations, with OR = 0.48 (95% CI: 0.34, 0.67, p < 0.0001) and MD = -0.82 (95% CI: -1.32, -0.33, p = 0.001), respectively, with non-significant heterogeneity. Regarding the need for new antibiotic usage, azithromycin showed a significantly lower need, with OR = 0.35 (95% CI: 0.13, 0.94, p = 0.04), I2 = 75%, p = 0.02. No significant difference was observed between both groups regarding hospitalization rate, with OR = 0.88 (95% CI: 0.55, 1.4, p = 0.59). Conclusions: This systematic review and meta-analysis showed the efficacy of azithromycin in pediatric patients with CF, as it improved lung function by increasing FEV1, reduced exacerbations of CF, which is the most common symptom of CF that leads to mortality, and reduced the number of antibiotics that needed to be administered to patients with CF, which reduces the risk of antibiotic resistance. Therefore, the long-term use of azithromycin is recommended for pediatric patients with CF as part of their treatment regimen.

PMID:40282944 | DOI:10.3390/medicina61040653

Genes (Basel). 2025 Apr 7;16(4):439. doi: 10.3390/genes16040439.

ABSTRACT

BACKGROUND: Psychosis, particularly schizophrenia (SZ), is influenced by genetic and environmental factors. The neurodevelopmental hypothesis suggests that genetic factors affect neuronal circuit connectivity during perinatal periods, hence causing the onset of the diseases. In this study, we performed a genome-wide association study (GWAS) in a sample of the first episode of psychosis (FEP).

METHODS: A sample of 147 individuals diagnosed with non-affective psychosis and 102 controls were recruited and assessed. After venous blood and DNA extraction, the samples were genotyped. Genetic data underwent quality controls, genotype imputation, and a case-control genome-wide association study (GWAS). After the GWAS, results were investigated using an in silico functional mapping and annotation approach.

RESULTS: Our GWAS showed the association of 27 variants across 13 chromosomes at genome-wide significance (p < 1 × 10-7) and a total of 1976 candidate variants across 188 genes at suggestive significance (p < 1 × 10-5), mostly mapping in non-coding or intergenic regions. Gene-based tests reported the association of the SUFU (p = 4.8 × 10-7) and NCAN (p = 1.6 × 10-5) genes. Gene-sets enrichment analyses showed associations in the early stages of life, spanning from 12 to 24 post-conception weeks (p < 1.4 × 10-3) and in the late prenatal period (p = 1.4 × 10-3), in favor of the neurodevelopmental hypothesis. Moreover, several matches with the GWAS Catalog reported associations with strictly related traits, such as SZ, as well as with autism spectrum disorder, which shares some genetic overlap, and risk factors, such as neuroticism and alcohol dependence.

CONCLUSIONS: The resulting genetic associations and the consequent functional analysis displayed common genetic liability between the non-affective psychosis, related traits, and risk factors. In sum, our investigation provided novel hints supporting the neurodevelopmental hypothesis in SZ and-in general-in non-affective psychoses.

PMID:40282399 | DOI:10.3390/genes16040439

Genes (Basel). 2025 Mar 30;16(4):402. doi: 10.3390/genes16040402.

ABSTRACT

Cystic fibrosis (CF), a genetic disorder characterized by mutations in the CFTR gene, has seen significant advances in treatment through cutting-edge approaches such as gene therapy and personalized medicine. This review examines the current and emerging strategies shaping CF care, focusing on novel therapies that target the root cause of CF and optimize patient outcomes. CFTR modulators have transformed cystic fibrosis management by enhancing protein function for specific mutations, leading to improved lung function and quality of life. Concurrently, gene therapy offers transformative potential by aiming to correct CFTR mutations using tools like CRISPR/Cas9 or prime editing, though challenges remain in delivery and long-term efficacy. The integration of precision medicine, facilitated by genomic and computational technologies, allows for personalized treatment plans that account for genetic variability and disease severity. Complementing these approaches, holistic management emphasizes the importance of psychological support and nutritional optimization, acknowledging CF's multi-system impact. Future directions include exploring anti-inflammatory agents and microbiome modulation to further mitigate disease morbidity. However, global disparities in treatment access continue to challenge equitable healthcare delivery, underscoring the need for policy reform and international cooperation. By synthesizing these developments, this review highlights the transformative potential of modern CF treatments, advocating for continued innovation and global healthcare equity, with the ultimate goal of dramatically improving life expectancy and quality of life for individuals with CF.

PMID:40282362 | DOI:10.3390/genes16040402

Healthcare (Basel). 2025 Apr 17;13(8):923. doi: 10.3390/healthcare13080923.

ABSTRACT

Pediatric gastroenterology is entering a pivotal phase marked by significant challenges and emerging opportunities in treating conditions like celiac disease (CeD), eosinophilic esophagitis (EoE), inflammatory bowel disease (IBD), and autoimmune hepatitis (AIH) pose significant clinical hurdles, but new therapeutic avenues are emerging. Advances in precision medicine, particularly proteomics, are reshaping care by tailoring treatments to individual patient characteristics. For CeD, therapies like gluten-degrading enzymes (latiglutenase, Kuma030) and zonulin inhibitors (larazotide acetate) show promise, though clinical outcomes are inconsistent. Immunotherapy and microbiota modulation, including probiotics and fecal microbiota transplantation (FMT), are also under exploration, with potential benefits in symptom management. Transglutaminase 2 inhibitors like ZED-1227 could help prevent gluten-induced damage. Monoclonal antibodies targeting immune pathways, such as AMG 714 and larazotide acetate, require further validation in pediatric populations. In EoE, biologics like dupilumab, cendakimab, dectrekumab (IL-13 inhibitors), and mepolizumab, reslizumab, and benralizumab (IL-5/IL-5R inhibitors) show varying efficacy, while thymic stromal lymphopoietin (TSLP) inhibitors like tezepelumab are also being investigated. These therapies require more pediatric-specific research to optimize their use. For IBD, biologics like vedolizumab, ustekinumab, and risankizumab, as well as small molecules like tofacitinib, etrasimod, and upadacitinib, are emerging treatments. New medications for individuals with refractory or steroid-dependent AIH have been explored. Personalized therapy, integrating precision medicine, therapeutic drug monitoring, and lifestyle changes, is increasingly guiding pediatric IBD management. This narrative review explores recent breakthroughs in treating CeD, EoE, IBD, and AIH, with a focus on pediatric studies when available, and discusses the growing role of proteomics in advancing personalized gastroenterological care.

PMID:40281872 | DOI:10.3390/healthcare13080923

Int J Tuberc Lung Dis. 2025 May 25;29(5):199-201. doi: 10.5588/ijtld.25.0157.

ABSTRACT

Until relatively recently, bronchiectasis (not due to cystic fibrosis) was considered an orphan disease, lacking clinical and commercial interest, and was rarely diagnosed. Since the 2000s, several working groups have emerged in Europe and the US - with the first register for bronchiectasis launching in Spain - and these have demonstrated the impact bronchiectasis has on health. Today, bronchiectasis is considered the third most common chronic inflammatory disease of the airways, after COPD and asthma, and represents a significant economic burden. We make the case for further characterization of these registries to better understand the heterogeneous epidemiology of bronchiectasis.

PMID:40281677 | DOI:10.5588/ijtld.25.0157

Jpn J Radiol. 2025 Apr 26. doi: 10.1007/s11604-025-01790-w. Online ahead of print.

ABSTRACT

PURPOSE: This study aimed to evaluate the efficacy of deep learning-based reconstruction (DLR) in improving pancreatic diffusion-weighted imaging (DWI) quality.

MATERIALS AND METHODS: In total, 117 patients (mean age of 68.0 ± 12.9 years) suspected of pancreatic diseases underwent magnetic resonance imaging (MRI) between July and December 2023. MRI sequences included respiratory-gated conventional diffusion-weighted images (RGC-DWIs), respiratory-gated diffusion-weighted images with deep learning-based reconstruction (DLR) (RGDLR-DWIs), and breath-hold diffusion-weighted images with DLR (BHDLR-DWIs) (short TE and long TE equal to other DWIs) at a 3 T MR system. Among these patients, 27 had solid lesions. Two radiologists qualitatively assessed pancreatic shape, main pancreatic duct (MPD) visualization, and solid lesion conspicuity using a 5-point scale. Quantitative analysis included apparent diffusion coefficient (ADC) values for pancreatic parenchyma and solid lesions, signal-to-noise ratio (SNR), pancreas-to-muscle signal-intensity ratio (PM-SIR) and lesion-to-pancreas signal-intensity ratio (LP-SIR). Differences among DWI sequences were analyzed using Friedman's and Bonferroni's tests.

RESULTS: Qualitatively, BHDLR-DWIs (short TE) had the highest scores for pancreatic shape and MPD but lowest for solid lesions visibility, whereas RGDLR-DWIs had the highest score for solid lesions. Quantitatively, BHDLR-DWIs (short TE) had the lowest ADC values for pancreatic parenchyma and solid lesions, with the highest PM-SIR. There was no significant difference between BHDLR-DWIs (short TE) and RGDLR-DWIs for solid lesion ADC values. RGC-DWIs had the highest SNR, though differences from RGDLR-DWIs and BHDLR-DWIs (short TE) were not significant. Although LP-SIR in RGDLR-DWIs were the lowest, the difference was not significant.

CONCLUSION: BHDLR-DWIs (short TE) provided the best pancreatic morphology image quality, whereas RGDLR-DWIs were superior for solid lesion detection.

PMID:40285832 | DOI:10.1007/s11604-025-01790-w

Abdom Radiol (NY). 2025 Apr 26. doi: 10.1007/s00261-025-04949-1. Online ahead of print.

ABSTRACT

PURPOSE: To establish and validate a model based on deep learning (DL), integrating radiomic features with relevant clinical features to generate nomogram, for predicting preoperative serosal invasion in gastric cancer (GC).

METHODS: This retrospective study included 335 patients from dual centers. T staging (T1-3 or T4) was used to assess serosal invasion. Radiomic features were extracted from primary GC lesions in the venous phase CT, and DL features from 8 transfer learning models were combined to create the Hand-crafted Radiomics and Deep Learning Radiomics (HCR-DLR) model. The Clinical (CL) model was built using clinical features, and both were combined into the Clinical and Radiomics Combined (CRC) model. In total, 15 predictive models were developed using 5 machine learning algorithms. The best-performing models were visualized as nomograms.

RESULTS: The total of 14 radiomic features, 13 DL features, and 2 clinical features were considered valuable through dimensionality reduction and selection. Among the constructed models: CRC model (AUC, training cohort: 0.9212; internal test cohort: 0.8743; external test cohort: 0.8853) than HCR-DLR model (AUC, training cohort: 0.8607; internal test cohort: 0.8543; external test cohort: 0.8824) and CL model (AUC, training cohort: 0.7632; internal test cohort: 0.7219; external test cohort: 0.7294) showed better performance. A nomogram based on the logistic CL model was drawn to facilitate the usage and showed its excellent predictive performance.

CONCLUSION: The predictive performance of the CRC Model, which integrates clinical features, radiomic features, and DL features, exhibits robust predictive capability and can serve as a simple, non-invasive, and practical tool for predicting the serosal invasion status of GC.

PMID:40285792 | DOI:10.1007/s00261-025-04949-1

Chem Res Toxicol. 2025 Apr 26. doi: 10.1021/acs.chemrestox.4c00560. Online ahead of print.

ABSTRACT

Transthyretin (TTR) plays a vital role in thyroid hormone transport and homeostasis in both the blood and target tissues. Interactions between exogenous compounds and TTR can disrupt the function of the endocrine system, potentially causing toxicity. In the Tox24 challenge, we leveraged the data set provided by the organizers to develop a deep learning-based consensus model, integrating sPhysNet, KANO, and GGAP-CPI for predicting TTR binding affinity. Each model utilized distinct levels of molecular information, including 2D topology, 3D geometry, and protein-ligand interactions. Our consensus model achieved favorable performance on the blind test set, yielding an RMSE of 20.8 and ranking fifth among all submissions. Following the release of the blind test set, we incorporated the leaderboard test set into our training data, further reducing the RMSE to 20.6 in an offlineretrospective study. These results demonstrate that combining three regression models across different modalities significantly enhances the predictive accuracy. Furthermore, we employ the standard deviation of the consensus model's ensemble outputs as an uncertainty estimate. Our analysis reveals that both the RMSE and interval error of predictions increase with rising uncertainty, indicating that the uncertainty can serve as a useful measure of prediction confidence. We believe that this consensus model can be a valuable resource for identifying potential TTR binders and predicting their binding affinity in silico. The source code for data preparation, model training, and prediction can be accessed at https://github.com/xiaolinpan/tox24_challenge_submission_yingkai_lab.

PMID:40285676 | DOI:10.1021/acs.chemrestox.4c00560

Adv Mater. 2025 Apr 26:e2419096. doi: 10.1002/adma.202419096. Online ahead of print.

ABSTRACT

Shear-stiffening materials, renowned for their rate-dependent behavior, hold immense potential for impact-resistant applications but are often constrained by limited load-bearing capacity under extreme conditions. In this study, a novel hybrid additive manufacturing strategy that successfully achieves anisotropic structural design of shear-stiffening materials is proposed. In this strategy, fused deposition modeling (FDM) is synergistically combined with direct ink writing (DIW) to fabricate lattice-structured soft-hard phase elastomer composites (TPR-SSE composites) with enhanced mechanical properties. Through quasistatic characterization and dynamic impact experiments, complemented by noncontact optical measurement and finite element simulation, the mechanical enhancement mechanisms imparted by the lattice architecture are systematically uncovered. The resulting composites exhibit exceptional load-bearing capacity under quasistatic conditions and superior energy dissipation under dynamic impacts, making them ideal for advanced protective systems. Building on this, smart sports shoes featuring a deep-learning-based smart sensing module that integrates structural customizability, buffering capacity, and gait recognition, are developed. This work provides a transformative structure design approach to shear-stiffening materials systems, paving the way for next-generation intelligent wearable protection applications.

PMID:40285578 | DOI:10.1002/adma.202419096

Adv Sci (Weinh). 2025 Apr 26:e2417682. doi: 10.1002/advs.202417682. Online ahead of print.

ABSTRACT

The increasing demand for automation in livestock farming scenarios highlights the need for effective noncontact measurement methods. The current methods typically require either fixed postures and specific positions of the target animals or high computational demands, making them difficult to implement in practical situations. In this study, a novel dual-network framework is presented that extracts accurate contour information instead of segmented images from unconstrained pigs and then directly employs this information to obtain precise liveweight estimates. The experimental results demonstrate that the developed framework achieves high accuracy, providing liveweight estimates with an R2 value of 0.993. When contour information is used directly to estimate the liveweight, the real-time performance of the framework can reach 1131.6 FPS. This achievement sets a new benchmark for accuracy and efficiency in non-contact liveweight estimation. Moreover, the framework holds significant practical value, equipping farmers with a robust and scalable tool for precision livestock management in dynamic, real-world farming environments. Additionally, the Liveweight and Instance Segmentation Annotation of Pigs dataset is introduced as a comprehensive resource designed to support further advancements and validation in this field.

PMID:40285549 | DOI:10.1002/advs.202417682

Brief Bioinform. 2025 Mar 4;26(2):bbaf186. doi: 10.1093/bib/bbaf186.

ABSTRACT

Deep learning (DL) methods have drastically advanced structure-based drug discovery by directly predicting protein structures from sequences. Recently, these methods have become increasingly accurate in predicting complexes formed by multiple protein chains. We evaluated these advancements to predict and accurately model the largest receptor family and its cognate peptide hormones. We benchmarked DL tools, including AlphaFold 2.3 (AF2), AlphaFold 3 (AF3), Chai-1, NeuralPLexer, RoseTTAFold-AllAtom, Peptriever, ESMFold, and D-SCRIPT, to predict interactions between G protein-coupled receptors (GPCRs) and their endogenous peptide ligands. Our results showed that structure-aware models outperformed language models in peptide binding classification, with the top-performing model achieving an area under the curve of 0.86 on a benchmark set of 124 ligands and 1240 decoys. Rescoring predicted structures on local interactions further improved the principal ligand discovery among decoy peptides, whereas DL-based approaches did not. We explored a competitive tournament approach for modeling multiple peptides simultaneously on a single GPCR, which accelerates the performance but reduces true-positive recovery. When evaluating the binding poses of 67 recent complexes, AF2 reproduced the correct binding modes in nearly all cases (94%), surpassing those of both AF3 and Chai-1. Confidence scores correlate with structural binding mode accuracy, which provides a guide for interpreting interface predictions. These results demonstrated that DL models can reliably rediscover peptide binders, aid peptide drug discovery, and guide the selection of optimal tools for GPCR-targeted therapies. To this end, we provided a practical guide for selecting the best models for specific applications and an independent benchmarking set for future model evaluation.

PMID:40285358 | DOI:10.1093/bib/bbaf186