Soft Matter. 2025 Apr 23. doi: 10.1039/d5sm00141b. Online ahead of print.

ABSTRACT

Granular materials are defined as collections of macroscopic dissipative particles. Although these systems are ubiquitous in our lives, the nature and the causes of their non-trivial collective dynamics still remain elusive and have attracted significant interest in non-equilibrium physics. Here, we focus on the vibrational dynamics of granular materials. While the vibrational dynamics of random packings have been examined concerning the jamming transition, previous research has overlooked the role of contact dissipations. We conducted numerical and analytical investigations into the vibrational dynamics of random packings influenced by the normal dissipative force, which is the simplest model for contact dissipations. Our findings reveal that the kinetic energy per mode diverges in the low-frequency range, following the scaling law with the frequency ωl, indicating that low-frequency modes experience strong excitation and that the equipartition of energy is violated. Additionally, the spatial structure factor of the velocity field displays the scaling law Sv(q) ∝ q-2 with the wavenumber q, which signifies that the velocity field has an infinitely long range. We demonstrate that these phenomena arise from the effects of weaker damping on softer modes, where the particle displacements parallel to the contacts are minimal in the low-frequency modes, rendering normal dissipation ineffective at damping these modes.

PMID:40265522 | DOI:10.1039/d5sm00141b

JAMA. 2025 Apr 23. doi: 10.1001/jama.2025.3810. Online ahead of print.

ABSTRACT

IMPORTANCE: Electronic cigarette use (vaping) among adolescents and young adults is common. Few treatments have been tested in this population.

OBJECTIVE: To evaluate the efficacy of varenicline for nicotine vaping cessation in youth who do not smoke tobacco regularly.

DESIGN, SETTING, AND PARTICIPANTS: A 3-group randomized clinical trial compared 12 weeks of double-blind varenicline vs placebo, each added to brief, remotely delivered behavioral counseling and compared with single-blind enhanced usual care, with monthly follow-up to 24 weeks. The trial was conducted among youth, aged 16 to 25 years, who vaped nicotine daily or near daily, did not regularly smoke tobacco, and wanted to reduce or quit vaping, in a single US state from June 2022 to May 2024. Data collection ended May 28, 2024.

INTERVENTIONS: Participants were randomized (1:1:1) to 12 weeks of varenicline titrated to 1 mg twice daily over 7 days (standard titration), weekly counseling, and referral to text messaging vaping cessation support (This is Quitting [TIQ]) (n = 88); identical placebo, weekly counseling, and referral to TIQ (n = 87); or enhanced usual care (referral to TIQ only) (n = 86).

MAIN OUTCOMES AND MEASURES: Biochemically verified continuous vaping abstinence for the last 4 weeks of varenicline treatment vs placebo (primary outcome). Secondary outcomes included bioverified continuous abstinence from weeks 9 through 24 in the varenicline and placebo groups. Additional analyses compared varenicline group and placebo group with enhanced usual care.

RESULTS: Of 261 randomized participants (mean age, 21.4 years; 53% female), 254 completed the trial (97.3%). For varenicline and placebo, continuous abstinence rates were 51% vs 14% during weeks 9 through 12 (adjusted odds ratio [aOR], 6.5 [95% CI, 3.0-14.1]; P < .001) and 28% vs 7% during weeks 9 through 24 (aOR, 6.0 [95% CI, 2.1-16.9]; P < .001). Varenicline had higher continuous abstinence rates vs enhanced usual care during weeks 9 through 12 (51% vs 6%; aOR, 16.9 [95% CI, 6.2-46.3]) and during weeks 9 through 24 (28% vs 4%; aOR, 11.0 [95% CI, 3.1-38.8]). Continuous abstinence rates were not significantly different between the placebo and enhanced usual care groups. Study medication was generally well tolerated. Two varenicline participants (2%) and 1 placebo participant (1%) discontinued study medications due to adverse events. No drug-related serious adverse events occurred. Treatment-emergent adverse events were reported by 76 (86%) in the varenicline group, 68 (79%) in the placebo group, and 68 (79%) in the enhanced usual care group.

CONCLUSIONS AND RELEVANCE: Varenicline, combined with behavioral counseling, increased vaping abstinence in youth who vape nicotine and do not regularly smoke tobacco.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05367492.

PMID:40266580 | DOI:10.1001/jama.2025.3810

J Nurs Adm. 2025 May 1;55(5):267-273. doi: 10.1097/NNA.0000000000001574.

ABSTRACT

OBJECTIVE: This study aimed to identify predictors of adverse drug reaction (ADR) monitoring practices among hospital nurses.

BACKGROUND: ADR monitoring is crucial for patient safety but remains insufficient in healthcare institutions.

METHODS: A cross-sectional descriptive study was conducted with 165 RNs from 4 tertiary hospitals. Data were collected using self-report questionnaires between August 5 and September 16, 2022.

RESULTS: Regression analysis revealed significant associations between ADR monitoring practices and nurses' attitudes, workload intensity, and work units. Of the nurses, 61.2% observed ADRs in the past year, and 31.5% had received ADR education. However, only 51.5% reported all ADR cases. Major barriers to reporting included time constraints and insufficient knowledge.

CONCLUSION: The findings highlight the need for educational programs to enhance nurses' knowledge and attitudes toward ADRs and the importance of strategies to support nursing units and reduce workload intensity to ensure safe medication administration.

PMID:40266099 | DOI:10.1097/NNA.0000000000001574

BMC Pharmacol Toxicol. 2025 Apr 22;26(1):91. doi: 10.1186/s40360-025-00920-4.

ABSTRACT

BACKGROUND: Ganirelix, a third-generation GnRH antagonist, is widely used in assisted reproductive technology (ART) for rapid pituitary suppression to prevent premature luteinizing hormone (LH) surges. Despite its extensive clinical use, real-world evidence on its safety in large populations remains scarce. This study aimed to evaluate the safety profile of ganirelix by comprehensively analyzing adverse drug events (ADEs) using real-world data from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) and the Japan Adverse Drug Event Reporting (JADER) database.

METHODS: We extracted ADE data from FAERS (Q1 2004-Q2 2024) and JADER (Q1 2009-Q1 2024). Disproportionality analyses, including reporting odds ratios (ROR), proportional reporting ratios (PRR), Bayesian Confidence Propagation Neural Networks (BCPNN), and Multi-item Gamma Poisson Shrinkage (MGPS), were employed to identify significant associations between ganirelix and ADEs.

RESULTS: In the FAERS database, we identified 1,096 ganirelix-related ADE reports, spanning 26 system organ classes (SOCs). A total of 65 positive signals were detected, including ADEs consistent with drug label such as ovarian hyperstimulation syndrome (OHSS) (n = 290, ROR 2462.76, PRR 2168.48, EBGM05 1655.59, IC025 9.18), injection site pain (n = 54, ROR 3.99, PRR 3.93, EBGM05 3.13, IC025 0.31), and fetal death (n = 6, ROR 21.05, PRR 21.00, EBGM05 10.72, IC025 2.72). Additionally, unexpected signals not listed in the drug label were identified, including ectopic pregnancy (n = 7, ROR 33.02, PRR 32.93, EBGM05 17.64, IC025 3.37), maternal exposure before pregnancy (n = 30, ROR 76.09, PRR 75.16, EBGM05 74.72, IC025 6.22), dermatitis allergic (n = 4, ROR 7.98, PRR 7.97, EBGM05 3.50, IC025 1.33), and bladder tamponade (n = 4, ROR 771.47, PRR 770.3, EBGM05 311.57, IC025 7.80). The median time to ADE onset was 13 days. External validation using the JADER database (62 ganirelix-related ADE reports) confirmed four signals, including abortion (n = 19), OHSS (n = 17), missed abortion (n = 9), and fetal death (n = 8), aligning with FAERS findings.

CONCLUSION: This study provides a robust real-world safety evaluation of ganirelix, with findings corroborated by two independent pharmacovigilance databases. While consistent with clinical observations, the identification of unexpected signals warrants further pharmacoepidemiological investigations to confirm these results.

PMID:40264185 | DOI:10.1186/s40360-025-00920-4

Front Pharmacol. 2025 Apr 8;16:1539032. doi: 10.3389/fphar.2025.1539032. eCollection 2025.

ABSTRACT

Background: Parkinson's disease (PD), a prevalent and progressive neurodegenerative disorder, currently lacks effective and satisfactory pharmacological treatments. Computational drug repurposing represents a promising and efficient strategy for drug discovery, aiming to identify new therapeutic indications for existing pharmaceuticals. Methods: We employed a drug-target network approach to computationally repurpose FDA-approved drugs from databases such as DrugBank. A literature review was conducted to select candidates not previously reported as pharmacoprotective against PD. Subsequent in vitro evaluation utilized Cell Counting Kit-8 (CCK8) assays to assess the neuroprotective effects of the selected compounds in the SH-SY5Y cell model of Parkinson's disease induced by 1-methyl-4-phenylpyridinium (MPP+). Furthermore, an in vivo mouse model of Parkinson's disease induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) was developed to investigate the mechanisms of action and therapeutic potential of the identified drug candidates. Results: Our approach identified 176 drug candidates, with 28 selected for their potential anti-Parkinsonian effects and lack of prior PD-related reporting. CCK8 assays showed significant neuroprotection in SH-SY5Y cells for Omaveloxolone and Cyproheptadine. In the MPTP-induced mouse model, Cyproheptadine inhibited interleukin-6 (IL-6) expression and prevented Tyrosine Hydroxylase (TH) downregulation via the MAPK/NFκB pathway, while Omaveloxolone alleviated TH downregulation, potentially through the Kelch-like ECH-associated protein 1 (KEAP1)-NF-E2-related factor 2 (Nrf2)/antioxidant response element (ARE) pathway. Both drugs preserved dopaminergic neurons and improved neurological deficits in the PD model. Conclusion: This study elucidates potential drug candidates for the treatment of Parkinson's disease through the application of computational repurposing, thereby underscoring its efficacy as a drug discovery strategy.

PMID:40264664 | PMC:PMC12011821 | DOI:10.3389/fphar.2025.1539032

Curr Drug Res Rev. 2025 Apr 21. doi: 10.2174/0125899775365699250408091801. Online ahead of print.

ABSTRACT

Computational drug repositioning has emerged as an efficient approach to discovering new indications for existing drugs, offering lower risk and cost compared to traditional drug discovery methods. Various computational approaches have been developed, including targetbased, gene-expression-based, phenome-based, and multi-omics-based methods. Recent advancements leverage diverse data sources, such as biomedical databases and online healthrelated information. Techniques incorporating drug structure and target information have shown promising results in predicting new drug indications. Despite significant progress, challenges remain, including data noise reduction, method ensemble, negative sample selection, and data sparseness. Overall, computational drug repositioning continues to be a valuable tool in drug discovery and development.

PMID:40264316 | DOI:10.2174/0125899775365699250408091801

RSC Adv. 2025 Apr 22;15(16):12866-12875. doi: 10.1039/d5ra00709g. eCollection 2025 Apr 16.

ABSTRACT

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, with amyloid-beta (Aβ) plaques and acetylcholine deficits being central pathological features. Inhibition of dual targets including acetylcholinesterase (AChE) and beta-site amyloid precursor protein cleaving enzyme 1 (BACE-1) represents a promising strategy to address cholinergic deficits and amyloid pathology. In this study, we used computational approaches to evaluate 8000 tripeptides as potential dual inhibitors of AChE and BACE-1. Machine learning models revealed the four top-lead tripeptides including WHM, HMW, WMH, and HWM. Molecular docking simulations indicated that WHM possessed the most favorable interactions through hydrogen bonds, π-π stacking, and salt bridges with key catalytic residues in both enzymes. Molecular dynamics simulations confirmed the stability of the protein-ligand complexes, with WHM exhibiting the most consistent conformations and significant disruption of catalytic residue geometries. Free energy perturbation analysis further supported WHM's superior stability across both targets. ADMET predictions suggested moderate oral absorption and limited brain penetration, consistent with the typical behavior of peptide-based compounds. Overall, WHM demonstrated the strongest potential as a dual inhibitor of AChE and BACE-1, offering a promising lead for future therapeutic development in AD.

PMID:40264872 | PMC:PMC12013280 | DOI:10.1039/d5ra00709g

Front Psychiatry. 2025 Apr 8;16:1584673. doi: 10.3389/fpsyt.2025.1584673. eCollection 2025.

ABSTRACT

INTRODUCTION: Treatment-resistant depression (TRD) affects approximately 30% of patients with major depressive disorder (MDD), for whom effective treatment options are limited. Repetitive transcranial magnetic stimulation (rTMS) has shown efficacy in alleviating depressive symptoms in TRD. However, it remains unclear if these improvements are driven or mediated by changes in cognitive function or biological markers, such as brain-derived neurotrophic factor (BDNF).

METHODS: This study examines the effects of rTMS on depressive symptoms, cognition, and BDNF levels, as well as the potential moderating role of lifetime suicidal attempts (LSA) on cognition and the predictive value of baseline BDNF for clinical outcomes. Twenty-five TRD patients were included, with 13 in the rTMS treatment group (receiving 20 sessions of rTMS over four weeks) and 12 as control group. Depression severity, cognitive function (Mini-Mental State Examination, Verbal Fluency, Digit Span), and serum BDNF levels were measured pre- and post-treatment. Mixed-effects linear regression models assessed clinical and biological associations.

RESULTS: rTMS significantly reduced HAM-D (p < 0.001) and CGI (p < 0.001) scores compared to controls. Cognitive performance improved significantly in MMSE (p = 0.049) and Digit Span (p = 0.04), with no significant changes in BDNF levels (p = 0.39). LSA did not moderate cognitive outcomes, and baseline BDNF did not predict clinical improvement (p = 0.68).

DISCUSSION: rTMS reduced depressive symptoms in TRD patients, with modest cognitive benefits. Baseline BDNF did not predict outcomes, though the lack of significant change suggests complex neuroplastic responses. Future studies should include larger samples and refined biomarker assessments.

PMID:40264519 | PMC:PMC12011827 | DOI:10.3389/fpsyt.2025.1584673

Clin Exp Allergy. 2025 Apr 22. doi: 10.1111/cea.70065. Online ahead of print.

NO ABSTRACT

PMID:40263895 | DOI:10.1111/cea.70065

J Pediatr Gastroenterol Nutr. 2025 Apr 23. doi: 10.1002/jpn3.70050. Online ahead of print.

ABSTRACT

OBJECTIVES: Cystic fibrosis hepato-biliary involvement (CFHBI) is a common comorbidity in patients with CF and is associated with increased morbidity and mortality. The effect of the new and highly potent CF transmembrane conductance regulator modulator therapy, elexacaftor-tezacaftor-ivacaftor (ETI), on CFHBI, is still unclear. This study aimed to investigate the impact of ETI on liver stiffness in children with CF, as measured using two-dimensional (2D) shear wave elastography (SWE).

METHODS: Twenty-one children with CF were included in this retrospective study at the CF centre, Skåne University Hospital, Lund, Sweden. Twelve children of our cohort had CFHBI; none had advanced CF liver disease. 2D SWE data from annual assessments, clinical data and liver enzymes were analysed.

RESULTS: We found a significant reduction in liver stiffness after starting treatment with ETI in the total cohort. This reduction in liver stiffness could even be seen in children with CFHBI. Liver enzymes were within the normal range in both pre- and post-ETI therapy in the total cohort. In children with CFHBI, a decline in aspartate aminotransferase activity was observed after ETI was initiated. Lung function and lung clearance index improved significantly after ETI treatment commenced.

CONCLUSION: ETI treatment could positively affect CFHBI in children with CF, as demonstrated by reduced liver stiffness during treatment.

PMID:40264362 | DOI:10.1002/jpn3.70050

Biomater Sci. 2025 Apr 23. doi: 10.1039/d5bm00322a. Online ahead of print.

ABSTRACT

The rapid advancement of mRNA therapeutics, exemplified by COVID-19 vaccines, underscores the transformative potential of non-viral delivery systems. However, achieving efficient and targeted mRNA delivery to the lungs remains a critical challenge due to biological barriers such as pulmonary mucus, nanoparticle instability, and off-target accumulation particularly in the liver. Addressing these challenges is crucial for advancing treatments for respiratory diseases, including cystic fibrosis, primary ciliary dyskinesia, and lung cancers. This review highlights emerging strategies to enhance lung-targeted mRNA delivery, focusing on lipid nanoparticles, polymeric nanoparticles, lipid-polymer hybrids, and peptide/protein conjugates. By discussing advances in bioinspired design and nanoparticle reformulation, this review provides a roadmap for overcoming current delivery limitations and accelerating the clinical translation of lung-targeted mRNA therapies.

PMID:40264303 | DOI:10.1039/d5bm00322a

J Appl Clin Med Phys. 2025 Apr 23:e70098. doi: 10.1002/acm2.70098. Online ahead of print.

ABSTRACT

Prostate Stereotactic Ablative Body Radiotherapy (SABR) is an ultra-hypofractionated treatment where small setup errors can lead to higher doses to organs at risk (OARs). Although bowel and bladder preparation protocols reduce inter-fraction variability, inconsistent patient adherence still results in OAR variability. At many centers without online adaptive machines, radiation therapists use decision trees (DTs) to visually assess patient setup, yet their application varies. To evaluate our center's DTs, we employed deep learning-generated cone-beam computed tomography (CBCT) contours to estimate daily doses to the rectum and bladder, comparing these with planned dose-volume metrics to guide future personalized DT development. Two hundred pretreatment CBCT scans from 40 prostate SABR patients (each receiving 40 Gy in five fractions) were auto-contoured retrospectively, and daily rectum and bladder doses were estimated by overlaying the planned dose on the CBCT using online rigid registration data. Dose-volume metrics were classified as "no", "minor", or "major" violations based on meeting preferred or mandatory goals. Twenty-seven percent of fractions exhibited at least one major bladder violation (with an additional 34% minor), while 14% of fractions had a major rectum violation (10% minor). Across treatments, five patients had recurring bladder V37 Gy major violations and two had rectum V36 Gy major violations. Bowel and bladder preparation significantly influenced OAR position and volume, leading to unmet mandatory goals. Our retrospective analysis underscores the significant impact of patient preparation on dosimetric outcomes. Our findings highlight that DTs based solely on visual assessment miss dose metric violations due to human error; only 23 of 59 under-filled bladder fractions were flagged. In addition to the insensitivity of visual assessments, variability in DT application further compromises patient setup evaluation. These analyses confirm that reliance on visual inspection alone can overlook deviations, emphasizing the need for automated tools to ensure adherence to dosimetric constraints in prostate SABR.

PMID:40265325 | DOI:10.1002/acm2.70098

Front Med (Lausanne). 2025 Apr 8;12:1503229. doi: 10.3389/fmed.2025.1503229. eCollection 2025.

ABSTRACT

Deoxyribonucleic acid (DNA) serves as fundamental genetic blueprint that governs development, functioning, growth, and reproduction of all living organisms. DNA can be altered through germline and somatic mutations. Germline mutations underlie hereditary conditions, while somatic mutations can be induced by various factors including environmental influences, chemicals, lifestyle choices, and errors in DNA replication and repair mechanisms which can lead to cancer. DNA sequence analysis plays a pivotal role in uncovering the intricate information embedded within an organism's genetic blueprint and understanding the factors that can modify it. This analysis helps in early detection of genetic diseases and the design of targeted therapies. Traditional wet-lab experimental DNA sequence analysis through traditional wet-lab experimental methods is costly, time-consuming, and prone to errors. To accelerate large-scale DNA sequence analysis, researchers are developing AI applications that complement wet-lab experimental methods. These AI approaches can help generate hypotheses, prioritize experiments, and interpret results by identifying patterns in large genomic datasets. Effective integration of AI methods with experimental validation requires scientists to understand both fields. Considering the need of a comprehensive literature that bridges the gap between both fields, contributions of this paper are manifold: It presents diverse range of DNA sequence analysis tasks and AI methodologies. It equips AI researchers with essential biological knowledge of 44 distinct DNA sequence analysis tasks and aligns these tasks with 3 distinct AI-paradigms, namely, classification, regression, and clustering. It streamlines the integration of AI into DNA sequence analysis tasks by consolidating information of 36 diverse biological databases that can be used to develop benchmark datasets for 44 different DNA sequence analysis tasks. To ensure performance comparisons between new and existing AI predictors, it provides insights into 140 benchmark datasets related to 44 distinct DNA sequence analysis tasks. It presents word embeddings and language models applications across 44 distinct DNA sequence analysis tasks. It streamlines the development of new predictors by providing a comprehensive survey of 39 word embeddings and 67 language models based predictive pipeline performance values as well as top performing traditional sequence encoding-based predictors and their performances across 44 DNA sequence analysis tasks.

PMID:40265190 | PMC:PMC12011883 | DOI:10.3389/fmed.2025.1503229

Front Med (Lausanne). 2025 Apr 8;12:1562818. doi: 10.3389/fmed.2025.1562818. eCollection 2025.

ABSTRACT

OBJECTIVE: This study conducts a bibliometric analysis of artificial intelligence (AI) applications in cervical cancer to provide a comprehensive overview of the research landscape and current advancements.

METHODS: Relevant publications on cervical cancer and AI were retrieved from the Web of Science Core Collection. Bibliometric analysis was performed using CiteSpace and VOSviewer to assess publication trends, authorship, country and institutional contributions, journal sources, and keyword co-occurrence patterns.

RESULTS: From 1996 to 2024, our analysis of 770 publications on cervical cancer and AI showed a surge in research, with 86% published in the last 5 years. China (315 pubs, 32%) and the US (155 pubs, 16%) were the top contributors. Key institutions were the Chinese Academy of Sciences, Southern Medical University, and Huazhong University of Science and Technology. Research hotspots included disease prediction, image analysis, and machine learning in cervical cancer. Schiffman led in publications (12) and citations (207). China had the highest citations (3,819). Top journals were "Diagnostics," "Scientific Reports," and "Frontiers in Oncology." Keywords like "machine learning" and "deep learning" indicated current research trends. This study maps the field's growth, highlighting key contributors and topics.

CONCLUSION: This bibliometric analysis provides valuable insights into research trends and hotspots, guiding future studies and fostering collaboration to enhance AI applications in cervical cancer.

PMID:40265176 | PMC:PMC12011737 | DOI:10.3389/fmed.2025.1562818

J Natl Cancer Cent. 2024 Dec 27;5(2):113-131. doi: 10.1016/j.jncc.2024.12.006. eCollection 2025 Apr.

ABSTRACT

Upper gastrointestinal cancers, mainly comprising esophageal and gastric cancers, are among the most prevalent cancers worldwide. There are many new cases of upper gastrointestinal cancers annually, and the survival rate tends to be low. Therefore, timely screening, precise diagnosis, appropriate treatment strategies, and effective prognosis are crucial for patients with upper gastrointestinal cancers. In recent years, an increasing number of studies suggest that artificial intelligence (AI) technology can effectively address clinical tasks related to upper gastrointestinal cancers. These studies mainly focus on four aspects: screening, diagnosis, treatment, and prognosis. In this review, we focus on the application of AI technology in clinical tasks related to upper gastrointestinal cancers. Firstly, the basic application pipelines of radiomics and deep learning in medical image analysis were introduced. Furthermore, we separately reviewed the application of AI technology in the aforementioned aspects for both esophageal and gastric cancers. Finally, the current limitations and challenges faced in the field of upper gastrointestinal cancers were summarized, and explorations were conducted on the selection of AI algorithms in various scenarios, the popularization of early screening, the clinical applications of AI, and large multimodal models.

PMID:40265096 | PMC:PMC12010392 | DOI:10.1016/j.jncc.2024.12.006

Front Oncol. 2025 Apr 8;15:1567577. doi: 10.3389/fonc.2025.1567577. eCollection 2025.

ABSTRACT

The segmentation and classification of breast ultrasound (BUS) images are crucial for the early diagnosis of breast cancer and remain a key focus in BUS image processing. Numerous machine learning and deep learning algorithms have shown their effectiveness in the segmentation and diagnosis of BUS images. In this work, we propose a multi-task learning network with an object contextual attention module (MTL-OCA) for the segmentation and classification of BUS images. The proposed method utilizes the object contextual attention module to capture pixel-region relationships, enhancing the quality of segmentation masks. For classification, the model leverages high-level features extracted from unenhanced segmentation masks to improve accuracy. Cross-validation on a public BUS dataset demonstrates that MTL-OCA outperforms several current state-of-the-art methods, achieving superior results in both classification and segmentation tasks.

PMID:40265029 | PMC:PMC12011763 | DOI:10.3389/fonc.2025.1567577

Front Oncol. 2025 Apr 8;15:1549803. doi: 10.3389/fonc.2025.1549803. eCollection 2025.

ABSTRACT

BACKGROUND: Uterine broad ligament fibroids present unique surgical challenges due to their proximity to vital pelvic structures. This study aimed to evaluate artificial intelligence (AI)-guided MRI instance segmentation for optimizing laparoscopic myomectomy outcomes.

METHODS: In this trial, 120 patients with MRI-confirmed broad ligament fibroids were allocated to either AI-assisted group (n=60) or conventional MRI group (n=60). A deep learning model was developed to segment fibroids, uterine walls, and uterine cavity from preoperative MRI.

RESULT: Compared to conventional MRI guidance, AI assistance significantly reduced operative time (118 [112.25-125.00] vs. 140 [115.75-160.75] minutes; p<0.001). The AI group also demonstrated lower intraoperative blood loss (50 [50-100] vs. 85 [50-100] ml; p=0.01) and faster postoperative recovery (first flatus within 24 hours: (15[25.00%] vs. 29[48.33%], p=0.01).

CONCLUSION: This multidisciplinary AI system enhances surgical precision through millimeter-level anatomical delineation, demonstrating transformative potential for complex gynecologic oncology procedures. Clinical adoption of this approach could reduce intraoperative blood loss and iatrogenic complications, thereby promoting postoperative recovery.

PMID:40265020 | PMC:PMC12011577 | DOI:10.3389/fonc.2025.1549803

Front Oncol. 2025 Apr 8;15:1538854. doi: 10.3389/fonc.2025.1538854. eCollection 2025.

ABSTRACT

PURPOSE: This study aimed to establish and evaluate a model utilizing bi-parametric ultrasound-based deep learning radiomics (DLR) in conjunction with clinical factors to anticipate clinically significant prostate cancer (csPCa).

METHODS: We retrospectively analyzed 232 participants from our institution who underwent both B-mode ultrasound and shear wave elastography (SWE) prior to prostate biopsy between June 2022 and December 2023. A random allocation placed the participants into training and test cohorts with a 7:3 distribution. We developed a nomogram that integrates DLR with clinical factors within the training cohort, which was subsequently validated using the test cohort. The diagnostic performance and clinical applicability were evaluated with receiver operating characteristic (ROC) curve analysis and decision curve analysis.

RESULTS: In our study, the bi-parametric ultrasound-based DLR model demonstrated an area under the curve (AUC) of 0.80 (95%CI: 0.70-0.91) in the test set, surpassing the performance of both the radiomics and deep learning models individually. By integrating clinical factors, a composite model, presented as the nomogram, was developed and exhibited superior diagnostic performance, achieving an AUC of 0.87 (95%CI: 0.77-0.95) in the test set. The performance exceeded that of the DLR (P = 0.049) and the clinical model (AUC = 0.79, 95%CI: 0.69-0.86, P = 0.041). Furthermore, the decision curve analysis indicated that the composite model provided a greater net benefit across a various high-risk threshold than the DLR or the clinical model alone.

CONCLUSION: To our knowledge, this is the first proposal of a nomogram integrating ultrasound-based DLR with clinical indicators for predicting csPCa. This nomogram can improve the accuracy of csPCa prediction and may help physicians make more confident decisions regarding interventions, particularly in settings where MRI is unavailable.

PMID:40265019 | PMC:PMC12011619 | DOI:10.3389/fonc.2025.1538854

Patterns (N Y). 2025 Mar 3;6(4):101186. doi: 10.1016/j.patter.2025.101186. eCollection 2025 Apr 11.

ABSTRACT

The ever-increasing stream of big data offers potential for deep decarbonization in the transportation sector but also presents challenges in extracting interpretable insights due to its complexity and volume. This overview discusses the application of transportation big data to help understand carbon dioxide emissions and introduces how artificial intelligence models, including machine learning (ML) and deep learning (DL), are used to assimilate and understand these data. We suggest using ML to interpret low-dimensional data and DL to enhance the predictability of data with spatial connections across multiple timescales. Overcoming challenges related to algorithms, data, and computation requires interdisciplinary collaboration on both technology and data.

PMID:40264962 | PMC:PMC12010448 | DOI:10.1016/j.patter.2025.101186

Patterns (N Y). 2025 Apr 11;6(4):101228. doi: 10.1016/j.patter.2025.101228. eCollection 2025 Apr 11.

ABSTRACT

Perturbation analysis in single-cell RNA sequencing (scRNA-seq) data is challenging due to the complexity of cellular responses. To address this, Xu and Fleming et al. developed CellCap, a generative deep-learning model that decodes the perturbation effect on a particular cell state. CellCap extracts interpretable latent representations of perturbation response modules, identifying key cellular pathways activated under various conditions. This allows for a deeper understanding of cell-state-specific responses to genetic, chemical, or biological perturbations.

PMID:40264958 | PMC:PMC12010446 | DOI:10.1016/j.patter.2025.101228