Plant Dis. 2025 Apr 3. doi: 10.1094/PDIS-02-25-0251-PDN. Online ahead of print.

ABSTRACT

In July 2024, a pooled leaf sample (D760/24) was collected from several plants of three watermelon cultivars (Citrullus lanatus cvs. Crimson Sweet, Asahi Miyako Hybrid F1 and Top Gun) grown in an open field (approx. 0.5ha) in Dombrava, Slovenia. The plants which were included in the pooled sample showed virus-like symptoms, such as leaf mosaic, wilting and necrosis (eXtra Supplementary material Fig. S1). The disease incidence was estimated at 10%. DNA and RNA were extracted following Mehle et al. (2013) and RNeasy Plant Mini Kit (Qiagen, Germany) protocols, respectively. The sample was tested positive by reverse-transcription (RT)-PCR for watermelon crinkle leaf-associated virus 1 (WCLaV-1) and WCLaV-2 ( Hernandez et al. 2021) and negative for other viruses (details on viruses tested and primers used are available in eXtra Table S1). The obtained amplicons of expected sizes of WCLaV-1 and WCLaV-2 movement protein (MP) and RNA-dependent RNA polymerase (RdRp) genes (eXtra Fig S2) were then subjected to Sanger sequencing (Eurofins Genomics, Germany) and BLAST analysis. The MP (PQ570004, PQ570006) and the RdRp (PQ570005, PQ570007) sequences exhibited 100% identity with multiple accessions of WCLaV-1, such as PP792977 and PP792976, and WCLaV-2, such as LC636073 and LC636074. Illumina high-throughput sequencing (HTS, Novogene, Germany, NovaSeq X Plus, PE150) identified WCLaV-1 (PV012703-04) and WCLaV-2 (PV012705-06) reads, along with cucumis melo amalgavirus 1 (CmAV1, PV012707) and solanum nigrum ilarvirus 1 reads (insufficient reads to reconstruct genome segments, it may originate from pollen contamination of nearby infected plants in the field (Rivarez et al. 2023)). HTS data were analyzed in CLC Genomics Workbench v. 24 (Qiagen, USA) using the pipeline by (Pecman et al. 2022). Consensus genome sequences were reconstructed by iterative read mapping to the most similar reference sequence of the virus obtained from NCBI GenBank. To check for WCLaVs in watermelon seeds sold in Slovenia, we tested five seed samples from Sugar Baby, Crimstar F1, and Crimson Sweet (three lots) by RT-PCR. We also tested four leaf samples from plants grown from these seeds at 3-5 true leaves stage. Both viruses were found in all seed and leaf extracts. However, mechanical inoculations with the sap of two samples (plants grown from infected seed sample and sample D760/24) on several commonly used indicator plants including Chenopodium quinoa, Capsicum annuum, Nicotiana clevelandii, Nicotiana glutinosa, Nicotiana benthamiana, Nicotiana tabacum cv. White Burley, Nicotiana rustica, Datura stramonium, Cucurbita pepo cv. Bianca di Trieste, and Cucurbita maxima did not result in their infection. Retrospective analyses of our HTS data of two watermelon and 84 other cucurbits samples from previous years showed WCLaV-1 and WCLaV-2 reads in two pooled samples (containing equal amount of RNA of each sample): one from 2018 and another from 2019. RT-PCR confirmed the presence of WCLaVs only in watermelons. The pool from 2018 was sequenced at GATC (Germany, NovaSeq 6000 S2, PE 150) and from 2019 in-house using Oxford Nanopore Technologies (UK, MinION Mk1B device, SQK-PCS108, R9 flow cell). All HTS reads are deposited in the NCBI Short Reads Archive (PRJNA1202089). This is the first report of WCLaV-1 and WCLaV-2 in Slovenia and Europe, the two viruses which were included to the Alert list of the European and Mediterranean Plant Protection Organization, due to limited knowledge about their epidemiology (EPPO 2023). Further research is necessary to determine the incidence of these viruses in Europe, elucidate their epidemiology, symptoms association and their potential impact on the production of watermelons in the region.

PMID:40178537 | DOI:10.1094/PDIS-02-25-0251-PDN

Plant Cell. 2025 Apr 3:koaf059. doi: 10.1093/plcell/koaf059. Online ahead of print.

ABSTRACT

The significance of research conducted on Arabidopsis thaliana cannot be overstated. This focus issue showcases how insights from Arabidopsis have opened new areas of biology and directly advanced our understanding of crops. Here, experts intimately involved in bridging between Arabidopsis and crops share their perspectives on the challenges and opportunities for translation. First, we examine the translatability of genetic modules from Arabidopsis into maize, emphasizing the need to publish well-executed translational experiments, regardless of outcome. Second, we highlight the landmark success of HB4, the first GM wheat cultivar on the market, whose abiotic tolerance is borne from direct translation and based on strategies first outlined in Arabidopsis. Third, we discuss the decades-long journey to engineer oilseed crops capable of producing omega-3 fish oils, with Arabidopsis serving as a critical intermediary. Fourth, we explore how direct translation of starch synthesizing proteins characterised in Arabidopsis helped uncover novel mechanisms and functions in crops, with potential valuable applications. Finally, we illustrate how shared molecular factors between Arabidopsis and barley exhibit distinct molecular wiring as exemplified in cuticular and stomatal development. Together, these vignettes underscore the pivotal role of Arabidopsis as a foundational model plant while highlighting the challenges of translating discoveries into field-ready, commercial cultivars with enhanced knowledge-based traits.

PMID:40178150 | DOI:10.1093/plcell/koaf059

Int J Clin Pharm. 2025 Apr 3. doi: 10.1007/s11096-025-01904-4. Online ahead of print.

ABSTRACT

BACKGROUND: Methotrexate (MTX) is the cornerstone of rheumatoid arthritis (RA) treatment. However, patients using MTX often experience drug-related problems (DRPs), negatively affecting adherence and persistence.

AIM: To identify the number and type of DRPs experienced by RA patients during the first 3 months of MTX treatment.

METHOD: A longitudinal observational study was conducted in the Sint Maartenskliniek, The Netherlands, between March and August 2023. Adult RA patients were interviewed at 2, 6 and 12 weeks after MTX initiation using the United Kingdom's New Medicines Service interview guide. DRPs were categorized using a classification system for patient-reported DRPs, and analyzed descriptively.

RESULTS: All fifty participants (median age 62 years (IQR 51-68), 66% female) reported a DRP, with a median of 6 (IQR 3-8) DRPs per patient and a total of 301 DRPs. The top 5 most frequently reported DRPs were concerns about (long-term) side-effects, nausea, fatigue, remembering intake and information needs regarding dose instructions. Of the DRPs reported at weeks 2 and 6, 33% were unresolved at week 12.

CONCLUSION: Patients with RA experience numerous DRPs in the first 3 months of MTX use. Resolving DRPs soon after occurrence may reduce the burden of drug treatment and improve adherence and/or persistence.

PMID:40178797 | DOI:10.1007/s11096-025-01904-4

Front Oncol. 2025 Mar 19;15:1450602. doi: 10.3389/fonc.2025.1450602. eCollection 2025.

ABSTRACT

AIM: Statins have been shown to improve the possibility of a pathological complete response (pCR) in patients with locally advanced rectal cancer when given in combination with neo-adjuvant chemo-radiation (NACTRT) in observational studies. The primary objective of this phase II randomized controlled trial (RCT) is to determine the impact of rosuvastatin in improving pCR rates in patients with locally advanced rectal cancer who are undergoing NACTRT. The secondary objectives are to compare adverse events, postoperative morbidity and mortality, disease-free survival (DFS), and overall survival in the two arms and to identify potential prognostic and predictive factors determining outcomes. If the study is positive, we plan to proceed to a phase III RCT with 3-year DFS as the primary endpoint.

METHODS: This is a prospective, randomized, open-label phase II/III study. The phase II study has a sample size of 316 patients (158 in each arm) to be accrued over 3 years to have 288 evaluable patients. The standard arm will receive NACTRT while the intervention group will receive 20 mg rosuvastatin orally once daily along with NACTRT for 6 weeks followed by rosuvastatin alone for 6-10 weeks until surgery. All patients will be reviewed after repeat imaging by a multidisciplinary tumor board at 12-16 weeks after starting NACTRT and operable patients will be planned for surgery. The pathological response rate, tumor regression grade (TRG), and post-surgical complications will be recorded.

CONCLUSION: The addition of rosuvastatin to NACTRT may improve the oncological outcomes by increasing the likelihood of pCR in patients with locally advanced rectal cancer undergoing NACTRT. This would be a low-cost, low-risk intervention that could potentially lead to the refinement of strategies, such as "watch and wait", in a select subgroup of patients.

CLINICAL TRIAL REGISTRATION: Clinical Trials Registry of India, identifier CTRI/2018/11/016459.

PMID:40177244 | PMC:PMC11961435 | DOI:10.3389/fonc.2025.1450602

Front Pharmacol. 2025 Mar 19;16:1565890. doi: 10.3389/fphar.2025.1565890. eCollection 2025.

NO ABSTRACT

PMID:40176905 | PMC:PMC11961997 | DOI:10.3389/fphar.2025.1565890

Brain Commun. 2025 Mar 17;7(2):fcaf113. doi: 10.1093/braincomms/fcaf113. eCollection 2025.

ABSTRACT

Somatic mosaic variants contribute to focal epilepsy, with variants often present only in brain tissue and not in blood or other samples typically assayed for genetic testing. Thus, genetic analysis for mosaic variants in focal epilepsy has been limited to patients with drug-resistant epilepsy who undergo surgical resection and have resected brain tissue samples available. Stereo-EEG (sEEG) has become part of the evaluation for many patients with focal drug-resistant epilepsy, and sEEG electrodes provide a potential source of small amounts of brain-derived DNA. We aimed to identify, validate, and assess the distribution of deleterious mosaic variants in epilepsy-associated genes in DNA extracted from trace brain tissue on individual sEEG electrodes. We enrolled a prospective cohort of 10 paediatric patients with drug-resistant epilepsy who had sEEG electrodes implanted for invasive monitoring. We extracted unamplified DNA and in parallel performed whole-genome amplification from trace brain tissue on each sEEG electrode. We also extracted DNA from resected brain tissue and blood/saliva samples where available. We performed deep sequencing (panel and exome) and analysis for candidate germline and mosaic variants. We validated candidate mosaic variants and assessed the variant allele fraction in amplified and unamplified electrode-derived DNA and across electrodes. We extracted unamplified DNA and performed whole-genome amplification from >150 individual electrodes from 10 individuals. Immunohistochemistry confirmed the presence of neurons in the brain tissue on electrodes. Deep sequencing and analysis demonstrated similar depth of coverage between amplified and unamplified DNA samples but significantly more potential mosaic variants in amplified samples. We validated four deleterious mosaic variants in epilepsy-associated genes in electrode-derived DNA in three patients who underwent laser ablation and did not have resected brain tissue samples available. Three of the four variants were detected in both amplified and unamplified electrode-derived DNA, with higher variant allele fraction observed in DNA from electrodes in closest proximity to the electrical seizure focus in one case. We demonstrate that mosaic variants can be identified and validated from DNA extracted from trace brain tissue on individual sEEG electrodes in patients with drug-resistant focal epilepsy, from both unamplified and amplified electrode-derived DNA. Our findings support a relationship between the extent of regional genetic abnormality and electrophysiology and suggest that with further optimization, this minimally invasive diagnostic approach holds promise for advancing precision medicine for patients with drug-resistant epilepsy as part of the surgical evaluation.

PMID:40177531 | PMC:PMC11961356 | DOI:10.1093/braincomms/fcaf113

Acta Pharm Sin B. 2025 Feb;15(2):963-972. doi: 10.1016/j.apsb.2024.11.002. Epub 2024 Nov 7.

ABSTRACT

One of the leading therapies for acute lymphoblastic leukemia (ALL) is the chemotherapeutic agent PEGylated E. coli-derived-l-asparaginase (PEG-ASNase). Due to the high risk of dose-limiting liver injury, characterized by clinically elevated levels of hepatic transaminases, PEG-ASNase therapy is generally avoided in adult patients. Our preclinical investigations have indicated that PEG-ASNase-induced liver injury is associated with the release of free fatty acids (FFAs) from white adipose tissue (WAT), suggesting potential lipotoxic effects. However, it remains uncertain whether PEG-ASNase directly induces hepatotoxicity or sensitizes hepatocytes to FFA-induced toxicity. Our results show that PEG-ASNase treatment results in hepatocyte apoptosis and lipid peroxidation. Ex vivo and in vitro studies in mouse and human WAT suggest that PEG-ASNase induces the expression of adipose triglyceride lipase (ATGL), activates the lipase, and stimulates adipose tissue lipolysis, suggesting that the FFAs from WAT may contribute to the observed liver injury. Moreover, treatment with PEG-ASNase sensitizes hepatocytes to FFA-induced lipotoxicity. Mechanistically, our RNA-sequencing (RNA-seq) analyses reveal that PEG-ASNase-induced sensitization to lipotoxicity is accompanied by the induction of Cyp2e1. We demonstrated that this sensitization effect is attenuated by both pharmacological and genetic inhibition of Cyp2e1. Our findings suggest that PEG-ASNase therapy induces WAT lipolysis and sensitizes hepatocytes to hepatic lipotoxicity in a Cyp2e1-dependent manner.

PMID:40177540 | PMC:PMC11959929 | DOI:10.1016/j.apsb.2024.11.002

Front Pharmacol. 2025 Mar 19;16:1538432. doi: 10.3389/fphar.2025.1538432. eCollection 2025.

ABSTRACT

BACKGROUND: Tacrolimus, an approved first-line calcineurin inhibitor, is widely prescribed in organ transplantation to prevent allograft rejection. Its narrow therapeutic index requires precise management to achieve optimal dosing and to minimize adverse drug events (ADEs) while ensuring its therapeutic efficacy. Among several factors, genetic differences contribute significantly to the inter-individual and inter-ethnic variability in pharmacokinetics (PK) of tacrolimus in kidney transplant recipients. As a result, investigating the role of genetic variation in Greek transplant recipients becomes crucial to optimizing therapeutic strategies and enhancing the efficacy of immunosuppressive treatment.

HYPOTHESIS: Genetic variants which are known to influence the activity of enzymes or drug-transporters critical to tacrolimus pharmacokinetics, may significantly affect the required kidney post-transplant tacrolimus daily dose.

AIM: To assess the correlation of ABCB1 genetic variants (rs1128503, rs2229109) and CYP3A4 (rs2242480, rs4986910) with tacrolimus dose-adjusted trough concentration (C0/D), in Greek kidney transplant recipients.

METHODS: Ninety-four unrelated Greek kidney transplant recipients were included in this study from the Department of Nephrology and Kidney Transplantation of the University General Hospital of Patras. Patients' dose-adjusted trough levels were measured at five distinct time points after transplantation and analyzed in relation to the possible influence of CYP3A4 and correlated with the abovementioned ABCB1 genetic variants using standard genotyping analysis and Sanger sequencing.

RESULTS: The genetic variants rs1128503, rs2229109, rs2242480, rs4986910 did not show any significant association with the daily dosing requirements of tacrolimus for at least 1 year, in Greek patients who have undergone kidney transplant.

CONCLUSION: It remains uncertain whether these genetic variants influence the assessment of the appropriate tacrolimus dosing 1 year after transplantation in Greek kidney transplant recipients.

PMID:40176889 | PMC:PMC11962430 | DOI:10.3389/fphar.2025.1538432

Curr Rev Clin Exp Pharmacol. 2025 Mar 28. doi: 10.2174/0127724328356429250315163111. Online ahead of print.

ABSTRACT

INTRODUCTION: Statin-induced myopathy (SIM) is a prevalent adverse event impacting treatment adherence. Despite extensive exploration of genotypes, conflicting evidence obscures their role in SIM incidence, prompting this network meta-analysis.

METHODS: Observational studies meeting eligibility criteria (patients on any statin with reported SNPs and SIM details) were systematically reviewed. Severe SIM was defined as creatine kinase elevations exceeding 10 times the upper limit of normal. Mixed treatment comparison pooled estimates were generated from direct and indirect pooled estimates, represented by odds ratios (OR) with 95% confidence intervals (CI), and validated via bootstrap analysis.

RESULTS: Thirty-four studies (26,152 participants) examining genotypes spanning drug transporters, metabolizing enzymes, reactive oxygen species production, and myopathy-related genes were analyzed. Significant associations were observed with drug transporters (OR: 1.4; 95% CI: 1.04, 1.5). Notably, solute carrier organic anion transporter 1B1 (SLCO1B1) (rs4149056) exhibited a moderate association with SIM (OR: 2.1; 95% CI: 1.7, 2.6), validated by bootstrap analysis (OR: 2.1; 95% CI: 1.7, 2.8). Similar associations were found for severe SIM with SLCO1B1 (rs4149056) (OR: 3.8; 95% CI: 1.4, 10.4) and ATP Binding Cassette Subfamily B Member 1 (ABCB1) (rs2373588) (OR: 2.8; 95% CI: 1.4, 5.4). Intraclass differences in genetic predictor risks were noted among statins.

CONCLUSION: Our meta-analysis underscores the significant association of SLCO1B1 with SIM, supporting its clinical utility. Further research is warranted to clarify additional genetic predictors. These findings endorse current guidelines advocating for SLCO1B1 genotyping in statin therapy decisions.

PMID:40176697 | DOI:10.2174/0127724328356429250315163111

J Antimicrob Chemother. 2025 Apr 3:dkaf101. doi: 10.1093/jac/dkaf101. Online ahead of print.

ABSTRACT

OBJECTIVES: Mycobacterium abscessus is the most frequent of the rapidly growing mycobacteria responsible for lung infections in patients suffering from cystic fibrosis and COPD. Imipenem is currently recommended in the treatment of these infections in spite of β-lactamase production. Since the targets of β-lactams include transpeptidases of both the l,d and d,d specificities, we tested, in vitro, intracellularly and in vivo, a combination of two β-lactams active on these enzymes, amoxicillin and imipenem, alone or in combination with the β-lactamase inhibitor relebactam.

METHODS: Drug combinations were evaluated against M. abscessus CIP 104536T and clinical isolates (n = 35) by determining MICs, FIC indices and time-killing. Drug combinations were also evaluated in macrophages and in mice.

RESULTS: In the presence of relebactam, synergy between amoxicillin and imipenem was observed against both M. abscessus CIP 104536T and the clinical isolates. Against M. abscessus CIP 104536T, the addition of 1 mg/L imipenem and 4 mg/L relebactam led to a decrease in the MIC of amoxicillin from 64 to 1 mg/L. The triple combination was active in vitro and intracellularly (a 4.30 decrease in the log10 cfu/mL and 82% killing, respectively). The triple combination was effective in reducing log10 cfu in mouse organs and mouse spleen weights, and in preventing losses in mouse weights.

CONCLUSIONS: The amoxicillin/imipenem/relebactam combination was synergistic in vitro and effective in vivo against M. abscessus. Since these drugs are clinically available, the triple combination should be considered by clinicians and further evaluated based on the reporting of the patient outcomes.

PMID:40177837 | DOI:10.1093/jac/dkaf101

JAC Antimicrob Resist. 2025 Apr 2;7(2):dlaf047. doi: 10.1093/jacamr/dlaf047. eCollection 2025 Apr.

ABSTRACT

Mycobacterium abscessus (MAB) is a rapidly growing, non-tuberculous mycobacterium that has emerged as a significant pathogen in both pulmonary and extrapulmonary infections. It is rising in prevalence, especially among individuals with underlying lung conditions such as cystic fibrosis and chronic obstructive pulmonary disease, highlighting its growing clinical importance. The treatment of MAB infections is notoriously challenging due to intrinsic resistance to many antibiotics and low cure rates, typically <50%. Macrolides are a cornerstone in the treatment of MAB infections because regimens that include effective macrolide therapy are associated with higher cure rates. However, MAB possesses intrinsic and acquired drug resistance mechanisms against macrolides, complicating drug susceptibility testing and selection of highly effective treatment regimens. This review aims to provide a summary of the current understanding of macrolide resistance mechanisms in MAB. We explored the epidemiology of resistance in different countries and the molecular mechanisms involved. We have highlighted the variability in sensitivity of existing markers to predict phenotypic macrolide drug resistance across different countries, suggesting the involvement of unknown resistance mechanisms. By synthesizing current knowledge and identifying gaps in the literature, this review seeks to inform clinical practice and guide future research efforts in the fight against MAB drug resistance.

PMID:40177306 | PMC:PMC11961302 | DOI:10.1093/jacamr/dlaf047

Pharmacol Res Perspect. 2025 Apr;13(2):e70083. doi: 10.1002/prp2.70083.

ABSTRACT

The combination of Elexacaftor/Tezacaftor/Ivacaftor (ETI) has resulted in a significant improvement in lung function and global clinical parameters, which have not been previously achieved with other CFTR modulators. However, there is a paucity of evidence in the literature on the long-term use of ETI in adolescents and patients with severe pulmonary impairment. Furthermore, the response to ETI may differ between homozygotes and heterozygotes, as well as between naïve patients and those previously treated with other CFTR modulators. A retrospective study was conducted to examine changes in percent predicted forced expiratory volume in 1 s (ppFEV1), body-mass index (BMI), and sweat chloride concentration (SwCl) at baseline and at 6, 12 and 24 months after the initiation of ETI. Secondary outcomes included the number of pulmonary exacerbations, Cystic Fibrosis Questionnaire-Revised (CFQ-R) score, adverse events, mortality and transplantation rates. 139 subjects were included and followed up for up to 2 years after starting ETI. The results demonstrated a significant improvement in ppFEV1 and BMI after 12 months of therapy (respectively, 16%, p < 0.001; +1.5 kg/m2, p = 0.005), with a slight decline in the values after 24 months. This effect was independent of genotype and showed a different degree of response in naïve subjects compared to patients previously treated with other CFTR modulators. SwCl decreased from 84 to 37 mmol/L over 24 months (p < 0.001). 58.3% reduction of PEx rate was observed compared to the number of exacerbations prior to ETI. Overall, lung function, SwCl, PEx rate, CFQ-R scores and BMI improved after 24 months of ETI treatment. ETI was well tolerated, and none of the patients interrupted the treatment due to toxicity.

PMID:40176392 | DOI:10.1002/prp2.70083

Lancet Reg Health West Pac. 2025 Mar 18;56:101517. doi: 10.1016/j.lanwpc.2025.101517. eCollection 2025 Mar.

ABSTRACT

BACKGROUND: As global aging intensifies, urological cancers pose increasing health and economic burdens. In China, home to one-fifth of the world's population, monitoring the distribution and determinants of these cancers and simulating the effects of health interventions are crucial for global and national health.

METHODS: With Global Burden of Disease (GBD) China database, the present study analyzed age-sex-specific patterns of incidence, prevalence, mortality, disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs) in China and its 34 provinces as well as the association between gross domestic product per capita (GDPPC) and these patterns. Importantly, a multi-attentive deep learning pipeline (iTransformer) was pioneered to model the spatiotemporal patterns of urological cancers, risk factors, GDPPC, and population, to provide age-sex-location-specific long-term forecasts of urological cancer burdens, and to investigate the impacts of risk-factor-directed interventions on their future burdens.

FINDINGS: From 1990 to 2021, the incidence and prevalence of urological cancers in China has increased, leading to 266,887 new cases (95% confidence interval: 205,304-346,033) and 159,506,067 (12,236,0000-207,447,070) cases in 2021, driven primarily by males aged 55+ years. In 2021, Taiwan, Beijing, and Zhejiang had the highest age-standardized incidence rate (ASIR) and age-standardized prevalence rates of urological cancer in China, highlighting significant regional disparities in the disease burden. Conversely, the national age-standardized mortality rate (ASMR) has declined from 6.5 (5.1-7.8) per 100,000 population in 1990 to 5.6 (4.4-7.2) in 2021, notably in Jilin [-166.7% (-237 to -64.6)], Tibet [-135.4% (-229.1 to 4.4)], and Heilongjiang [-118.5% (-206.5 to -4.6)]. Specifically, the national ASMR for bladder and testicular cancers reduced by -32.1% (-47.9 to 1.9) and -31.1% (-50.2 to 7.2), respectively, whereas prostate and kidney cancers rose by 7.9% (-18.4 to 43.6) and 9.2% (-12.2 to 36.5). Age-standardized DALYs, YLDs, and YLLs for urological cancers were consistent with ASMR. Males suffered higher burdens of urological cancers than females in all populations, except those aged <5 years. Regionally and provincially, high GDPPC provinces have the highest burden of prostate cancer, while the main burden in other provinces is bladder cancer. The main risk factors for urological cancers in 2021 are smoking [accounting for 55.1% (42.7-67.4)], high body mass index [13.9% (5.3-22.4)], and high fasting glycemic index [5.9% (-0.8 to 13.4)] for both males and females, with smoking remarkably affecting males and high body mass index affecting females. Between 2022 and 2040, the ASIR of urological cancers increased from 10.09 (9.19-10.99) to 14.42 (14.30-14.54), despite their ASMR decreasing. Notably, prostate cancer surpassed bladder cancer as the primary subcategory, with those aged 55+ years showing the highest increase in ASIR, highlighting the aging-related transformation of the urological cancer burden. Following the implementation of targeted interventions, smoking control achieved the greatest reduction in urological cancer burden, mainly affecting male bladder cancer (-45.8% decline). In females, controlling smoking and high fasting plasma glucose reduced by 5.3% and 5.8% ASMR in urological cancers. Finally, the averaged mean-square-Percentage-Error, absolute-Percentage-Error, and root-mean-square Logarithmic-Error of the forecasting model are 0.54 ± 0.22, 1.51 ± 1.26, and 0.15 ± 0.07, respectively, indicating that the model performs well.

INTERPRETATION: Urological cancers exhibit an aging trend, with increased incidence rates among the population aged 55+ years, making prostate cancer the most burdensome subcategory. Moreover, urological cancer burden is imbalanced by age, sex, and province. Based on our findings, authorities and policymakers could refine or tailor population-specific health strategies, including promoting smoking cessation, weight reduction, and blood sugar control.

FUNDING: Bill & Melinda Gates Foundation.

PMID:40177596 | PMC:PMC11964562 | DOI:10.1016/j.lanwpc.2025.101517

Front Med (Lausanne). 2025 Mar 19;12:1514931. doi: 10.3389/fmed.2025.1514931. eCollection 2025.

ABSTRACT

Computed tomography pulmonary angiography (CTPA) is an essential diagnostic tool for identifying pulmonary embolism (PE). The integration of AI has significantly advanced CTPA-based PE detection, enhancing diagnostic accuracy and efficiency. This review investigates the growing role of AI in the diagnosis of pulmonary embolism using CTPA imaging. The review examines the capabilities of AI algorithms, particularly deep learning models, in analyzing CTPA images for PE detection. It assesses their sensitivity and specificity compared to human radiologists. AI systems, using large datasets and complex neural networks, demonstrate remarkable proficiency in identifying subtle signs of PE, aiding clinicians in timely and accurate diagnosis. In addition, AI-powered CTPA analysis shows promise in risk stratification, prognosis prediction, and treatment optimization for PE patients. Automated image interpretation and quantitative analysis facilitate rapid triage of suspected cases, enabling prompt intervention and reducing diagnostic delays. Despite these advancements, several limitations remain, including algorithm bias, interpretability issues, and the necessity for rigorous validation, which hinder widespread adoption in clinical practice. Furthermore, integrating AI into existing healthcare systems requires careful consideration of regulatory, ethical, and legal implications. In conclusion, AI-driven CTPA-based PE detection presents unprecedented opportunities to enhance diagnostic precision and efficiency. However, addressing the associated limitations is critical for safe and effective implementation in routine clinical practice. Successful utilization of AI in revolutionizing PE care necessitates close collaboration among researchers, medical professionals, and regulatory organizations.

PMID:40177281 | PMC:PMC11961422 | DOI:10.3389/fmed.2025.1514931

Front Med (Lausanne). 2025 Mar 19;12:1505530. doi: 10.3389/fmed.2025.1505530. eCollection 2025.

ABSTRACT

BACKGROUND: Macular edema (ME) is an ophthalmic disease that poses a serious threat to human vision. Anti-vascular endothelial growth factor (anti-VEGF) therapy has become the first-line treatment for ME due to its safety and high efficacy. However, there are still cases of refractory macular edema and non-responding patients. Therefore, it is crucial to develop automated and efficient methods for predicting therapeutic outcomes.

METHODS: We have developed a predictive model for the surgical efficacy in ME patients based on deep learning and optical coherence tomography (OCT) imaging, aimed at predicting the treatment outcomes at different time points. This model innovatively introduces group convolution and multiple convolutional kernels to handle multidimensional features based on traditional attention mechanisms for visual recognition tasks, while utilizing spatial pyramid pooling (SPP) to combine and extract the most useful features. Additionally, the model uses ResNet50 as a pre-trained model, integrating multiple knowledge through model fusion.

RESULTS: Our proposed model demonstrated the best performance across various experiments. In the ablation study, the model achieved an F1 score of 0.9937, an MCC of 0.7653, an AUC of 0.9928, and an ACC of 0.9877 in the test conducted on the first day after surgery. In comparison experiments, the ACC of our model was 0.9930 and 0.9915 in the first and the third months post-surgery, respectively, with AUC values of 0.9998 and 0.9996, significantly outperforming other models. In conclusion, our model consistently exhibited superior performance in predicting outcomes at various time points, validating its excellence in processing OCT images and predicting postoperative efficacy.

CONCLUSION: Through precise prediction of the response to anti-VEGF therapy in ME patients, deep learning technology provides a revolutionary tool for the treatment of ophthalmic diseases, significantly enhancing treatment outcomes and improving patients' quality of life.

PMID:40177270 | PMC:PMC11961644 | DOI:10.3389/fmed.2025.1505530

Front Oncol. 2025 Mar 19;15:1468232. doi: 10.3389/fonc.2025.1468232. eCollection 2025.

ABSTRACT

OBJECTIVES: Radiotherapy is a fundamental cancer treatment method, and pre-treatment patient-specific quality assurance (prePSQA) plays a crucial role in ensuring dose accuracy and patient safety. Artificial intelligence model for measurement-free prePSQA have been investigated over the last few years. While these models stack successive pooling layers to carry out sequential learning, directly splice together different modalities along channel dimensions and feed them into shared encoder-decoder network, which greatly reduces the anatomical features specific to different modalities. Furthermore, the existing models simply take advantage of low-dimensional dosimetry information, meaning that the spatial features about the complex dose distribution may be lost and limiting the predictive power of the models. The purpose of this study is to develop a novel deep learning model for measurement-guided therapeutic-dose (MDose) prediction from head and neck cancer radiotherapy data.

METHODS: The enrolled 310 patients underwent volumetric-modulated arc radiotherapy (VMAT) were randomly divided into the training set (186 cases, 60%), validation set (62 cases, 20%), and test set (62 cases, 20%). The effective prediction model explicitly integrates the multi-scale features that are specific to CT and dose images, takes into account the useful spatial dose information and fully exploits the mutual promotion within the different modalities. It enables medical physicists to analyze the detailed locations of spatial dose differences and to simultaneously generate clinically applicable dose-volume histograms (DVHs) metrics and gamma passing rate (GPR) outcomes.

RESULTS: The proposed model achieved better performance of MDose prediction, and dosimetric congruence of DVHs, GPR with the ground truth compared with several state-of-the-art models. Quantitative experimental predictions show that the proposed model achieved the lowest values for the mean absolute error (37.99) and root mean square error (4.916), and the highest values for the peak signal-to-noise ratio (52.622), structural similarity (0.986) and universal quality index (0.932). The predicted dose values of all voxels were within 6 Gy in the dose difference maps, except for the areas near the skin or thermoplastic mask indentation boundaries.

CONCLUSIONS: We have developed a feasible MDose prediction model that could potentially improve the efficiency and accuracy of prePSQA for head and neck cancer radiotherapy, providing a boost for clinical adaptive radiotherapy.

PMID:40177241 | PMC:PMC11961879 | DOI:10.3389/fonc.2025.1468232

Front Robot AI. 2025 Mar 19;12:1520374. doi: 10.3389/frobt.2025.1520374. eCollection 2025.

ABSTRACT

A significant number of individuals have been affected by pandemic diseases, such as COVID-19 and seasonal influenza. Nucleic acid testing is a common method for identifying infected patients. However, manual sampling methods require the involvement of numerous healthcare professionals. To address this challenge, we propose a novel transoral swab sampling robot designed to autonomously perform nucleic acid sampling using a visual-tactile fusion approach. The robot comprises a series-parallel hybrid flexible mechanism for precise distal posture adjustment and a visual-tactile perception module for navigation within the subject's oral cavity. The series-parallel hybrid mechanism, driven by flexible shafts, enables omnidirectional bending through coordinated movement of the two segments of the bendable joint. The visual-tactile perception module incorporates a camera to capture oral images of the subject and recognize the nucleic acid sampling point using a deep learning method. Additionally, a force sensor positioned at the distal end of the robot provides feedback on contact force as the swab is inserted into the subject's oral cavity. The sampling robot is capable of autonomously performing transoral swab sampling while navigating using the visual-tactile perception algorithm. Preliminary experimental trials indicate that the designed robot system is feasible, safe, and accurate for sample collection from subjects.

PMID:40177224 | PMC:PMC11961991 | DOI:10.3389/frobt.2025.1520374

Exp Biol Med (Maywood). 2025 Mar 19;250:10359. doi: 10.3389/ebm.2025.10359. eCollection 2025.

ABSTRACT

Opioids exert their analgesic effect by binding to the µ opioid receptor (MOR), which initiates a downstream signaling pathway, eventually inhibiting pain transmission in the spinal cord. However, current opioids are addictive, often leading to overdose contributing to the opioid crisis in the United States. Therefore, understanding the structure-activity relationship between MOR and its ligands is essential for predicting MOR binding of chemicals, which could assist in the development of non-addictive or less-addictive opioid analgesics. This study aimed to develop machine learning and deep learning models for predicting MOR binding activity of chemicals. Chemicals with MOR binding activity data were first curated from public databases and the literature. Molecular descriptors of the curated chemicals were calculated using software Mold2. The chemicals were then split into training and external validation datasets. Random forest, k-nearest neighbors, support vector machine, multi-layer perceptron, and long short-term memory models were developed and evaluated using 5-fold cross-validations and external validations, resulting in Matthews correlation coefficients of 0.528-0.654 and 0.408, respectively. Furthermore, prediction confidence and applicability domain analyses highlighted their importance to the models' applicability. Our results suggest that the developed models could be useful for identifying MOR binders, potentially aiding in the development of non-addictive or less-addictive drugs targeting MOR.

PMID:40177220 | PMC:PMC11961360 | DOI:10.3389/ebm.2025.10359

World J Hepatol. 2025 Mar 27;17(3):101721. doi: 10.4254/wjh.v17.i3.101721.

ABSTRACT

BACKGROUND: In recent years, the utilization of artificial intelligence (AI) technology has gained prominence in the field of liver disease.

AIM: To analyzes AI research in the field of liver disease, summarizes the current research status and identifies hot spots.

METHODS: We searched the Web of Science Core Collection database for all articles and reviews on hepatopathy and AI. The time spans from January 2007 to August 2023. We included 4051 studies for further collection of information, including authors, countries, institutions, publication years, keywords and references. VOS viewer, CiteSpace, R 4.3.1 and Scimago Graphica were used to visualize the results.

RESULTS: A total of 4051 articles were analyzed. China was the leading contributor, with 1568 publications, while the United States had the most international collaborations. The most productive institutions and journals were the Chinese Academy of Sciences and Frontiers in Oncology. Keywords co-occurrence analysis can be roughly summarized into four clusters: Risk prediction, diagnosis, treatment and prognosis of liver diseases. "Machine learning", "deep learning", "convolutional neural network", "CT", and "microvascular infiltration" have been popular research topics in recent years.

CONCLUSION: AI is widely applied in the risk assessment, diagnosis, treatment, and prognosis of liver diseases, with a shift from invasive to noninvasive treatment approaches.

PMID:40177211 | PMC:PMC11959664 | DOI:10.4254/wjh.v17.i3.101721

J Med Imaging (Bellingham). 2025 Mar;12(2):024004. doi: 10.1117/1.JMI.12.2.024004. Epub 2025 Apr 1.

ABSTRACT

PURPOSE: The issue of delivered doses during computed tomography (CT) scans encouraged sparser sets of X-ray projection, severely degrading reconstructions from conventional methods. Although most deep learning approaches benefit from large supervised datasets, they cannot generalize to new acquisition protocols (geometry, source/detector specifications). To address this issue, we developed a method working without training data and independently of experimental setups. In addition, our model may be initialized on small unsupervised datasets to enhance reconstructions.

APPROACH: We propose a conditioned generative latent optimization (cGLO) in which a decoder reconstructs multiple slices simultaneously with a shared objective. It is tested on full-dose sparse-view CT for varying projection sets: (a) without training data against Deep Image Prior (DIP) and (b) with training datasets of multiple sizes against state-of-the-art score-based generative models (SGMs). Peak signal-to-noise ratio (PSNR) and structural SIMilarity (SSIM) metrics are used to quantify reconstruction quality.

RESULTS: cGLO demonstrates better SSIM than SGMs (between + 0.034 and + 0.139 ) and has an increasing advantage for smaller datasets reaching a + 6.06 dB PSNR gain. Our strategy also outperforms DIP with at least a + 1.52 dB PSNR advantage and peaks at + 3.15 dB with fewer angles. Moreover, cGLO does not create artifacts or structural deformations contrary to DIP and SGMs.

CONCLUSIONS: We propose a parsimonious and robust reconstruction technique offering similar to better performances when compared with state-of-the-art methods regarding full-dose sparse-view CT. Our strategy could be readily applied to any imaging reconstruction task without any assumption about the acquisition protocol or the quantity of available data.

PMID:40177097 | PMC:PMC11961077 | DOI:10.1117/1.JMI.12.2.024004