Category: Nevin Manimala

Radiol Phys Technol. 2025 Aug 7. doi: 10.1007/s12194-025-00948-5. Online ahead of print.

ABSTRACT

In acute ischemic stroke (AIS), where the shortest possible assessment is required to minimize time to mechanical thrombectomy (MT). With recent advancements in MRI reconstruction technology, MRI has also become valuable in the decision-making process for AIS treatment planning. In this study, we compared the examination times of our MRI protocol with those of a standard CT protocol for evaluating AIS through phantom simulations to obtain timing information directly relevant to treatment strategies, and evaluated the utility of MRI for MT. Ten radiological technologists performed scans using the same phantom for each modality. Evaluation items included time for hemorrhage detection, time for penumbra evaluation, and time for brain artery evaluation, and total examination time. The total examination time was slightly shorter with CT (696.2 ± 52.7 s) compared to MRI (701.8 ± 15.8 s), although this difference was not statistically significant (p = 0.4). For other parameters, MRI demonstrated significantly faster detection times: hemorrhage detection (CT, 80.9 ± 12.8 s; MRI, 66.3 ± 1.7 s; p = 0.0002), penumbra evaluation (CT, 696.2 ± 52.7 s; MRI, 262.1 ± 9.3 s; p = 0.0002), and brain artery evaluation (CT, 592.1 ± 32.3 s; MRI, 367.8 ± 8.3 s; p = 0.0002). The coefficient of variation (CV) was lower for MRI compared to CT, indicating less variability in examination times with MRI. This study demonstrates that MRI protocols, including perfusion imaging, can more rapidly visualize factors essential for MT decision-making and do not delay time to MT.

PMID:40775486 | DOI:10.1007/s12194-025-00948-5

Int J Clin Pharm. 2025 Aug 7. doi: 10.1007/s11096-025-01972-6. Online ahead of print.

ABSTRACT

INTRODUCTION: Integrating community pharmacies into primary care via digital infrastructure is crucial to enhancing continuity, coordination, and safety of care. Historically, community pharmacies have not had full access to general practice electronic health records (EHRs), limiting their ability to provide informed interventions. The introduction of shared, interoperable EHRs has the potential to address this limitation and redefine the clinical role of community pharmacists.

AIM: This study aimed to evaluate the feasibility, acceptability, and impact of granting community pharmacies read-and-write access to a shared EHR system (SystmOne) across selected sites in the East of England.

METHOD: A 12-month mixed-methods pilot (Jan-Dec 2023) was conducted using an explanatory sequential and convergent approach. Data were collected from 35 community pharmacies and 31 general practices via activity logs, surveys, and semi-structured interviews. Descriptive statistics was used to analyse quantitative data and thematic coding used for analysing qualitative data. Data was then integrated to evaluate service delivery, communication, and user experience.

RESULTS: Thirteen community pharmacies actively used the EHR, documenting over 19,000 appointments and 16,000 clinical entries. Usage varied, with barriers including workload, technical complexity, and duplicated documentation requirements. However, users reported improvements in patient safety, interprofessional communication, and service efficiency. Appointment booking and task-sharing functions fostered collaborative working, while access to real-time clinical information supported clinical decision-making. Training support, trust between sectors, and policy alignment were identified as critical enablers for system uptake.

CONCLUSION: Providing community pharmacies with read-and-write access to a shared EHR is feasible and contributes to safer, more integrated patient care. Improved communication, clinical documentation, and task delegation between pharmacists and general practice staff represent a major shift in digital collaboration. However, successful scale-up requires investment in interoperability, national IT infrastructure alignment, and streamlined reimbursement processes to prevent duplication of effort. These findings support the evolving clinical role of community pharmacists and suggest that integrated digital systems are essential to realising the full potential of community pharmacy in the modern NHS to improve patient care.

PMID:40775484 | DOI:10.1007/s11096-025-01972-6

Int J Clin Pharm. 2025 Aug 7. doi: 10.1007/s11096-025-01977-1. Online ahead of print.

ABSTRACT

BACKGROUND: The potentially inappropriate medications (PIM)-China criteria, published in 2017, require updates to reflect new therapeutic evidence and address limitations such as outdated medications and condition-specific considerations.

AIM: This study aimed to develop an updated version of the PIM-China criteria through a modified Delphi consensus methodology, ensuring evidence-based and clinically relevant recommendations for older adults in China.

METHOD: A literature review of six PIM criteria (Beers, STOPP, FORTA, EU(7)-PIM, Japan and Korea criteria) and relevant literature (2018-2023) informed a preliminary list of PIMs. A multidisciplinary panel of 33 experts, comprising 12 physicians and 21 pharmacists, evaluated 210 candidate criteria over three Delphi rounds. Statistical measures were used to validate consensus, including Kendall’s W, coefficient of variation (CV), and expert authority coefficient (Cr). Cr values ≥ 0.80 indicated high reliability, while Kendall’s W > 0.20 signified moderate to strong agreement.

RESULTS: The updated criteria consist of 154 items, a 57% increase from 2017, including 100 individual medications or drug classes and 54 condition-specific PIMs. Notable additions include recommendations addressing drug-drug interactions, renal function adjustments, and alternative treatments. Consensus improved significantly across rounds, with Kendall’s W increasing from 0.145 to 0.271 for individual PIMs and 0.118 to 0.360 for condition-specific PIMs (P < 0.05). Cr reached 0.85, reflecting the panel’s high authority.

CONCLUSION: The updated 2024 PIM-China criteria enhance prescribing safety and clinical relevance by incorporating new evidence and expert consensus. These criteria are vital for reducing adverse drug events, optimizing prescribing practices, and improving healthcare for older adults in China.

PMID:40775482 | DOI:10.1007/s11096-025-01977-1

Surg Endosc. 2025 Aug 7. doi: 10.1007/s00464-025-12021-8. Online ahead of print.

ABSTRACT

BACKGROUND: Traditional intraoperative navigation methods show insufficient adaptability in dynamic surgical environments. The rapid development of Artificial Intelligence (AI) presents an opportunity to overcome these limitations, making the construction of real-time, adaptive intraoperative navigation systems a key research goal. This study, based on Edge-AI and multimodal AI technologies, aims to develop and evaluate a foundational system for achieving real-time, offline intraoperative navigation and warnings during minimally invasive liver surgery.

METHODS: 161 minimally invasive liver resection videos collected from the Medtechshare platform were finely annotated. The dataset was divided into training (60%), validation (20%), and independent test (20%) sets. The self-developed ONE-PEACE-SZYYv3 model was used for training and validation, followed by quantitative evaluation using accuracy, recall, precision, Intersection over Union (IoU), and Dice coefficient on the independent test set. For qualitative evaluation, an innovative Turing-like test method, the Humanlike Test, was proposed. In this test, experienced surgeons blindly evaluated and scored AI-generated warnings and manually delineated warnings created by junior surgeons. Two groups of data from different processing sources were blindly evaluated by experts, combined with Mann-Whitney U and TOST (Δ = ± 1.5 points) tests to assess whether they were equivalent within an acceptable range. This ultimately tested whether the current AI possesses judgment capabilities plausible enough to be comparable to human levels during surgery.

RESULTS: In quantitative evaluation on the independent test set, under mAP50, the model’s mean average precision reached as high as 99.5%, and under the stricter mAP50-95, it achieved 89.9% precision. IoU and Dice were 0.63 and 0.79, respectively. In qualitative evaluation, the average score of the “AI guided results, Experimental group” was slightly lower than the “Manually delineated results, Control group,” but the difference was not statistically significant (p > 0.05). Equivalence testing confirmed that the scores were statistically equivalent within a pre-defined narrow margin.

CONCLUSION: The proposed model exhibits high precision and real-time capability on a curated, retrospective dataset. It can accurately and effectively provide multimodal guidance (image-text-sound) and warnings in complex anatomical structures with multiple types of interference elements during surgery. The innovative Humanlike Test shows its intraoperative judgment capability can, to a certain extent, produce outputs that are indistinguishable from those of human surgeons. While this study demonstrates foundational feasibility, prospective clinical trials are required to validate its clinical utility and impact on surgical outcomes. Such technology holds promise for elevating the surgical field to a new paradigm of digital intelligence.

PMID:40775470 | DOI:10.1007/s00464-025-12021-8

Br J Cancer. 2025 Aug 7. doi: 10.1038/s41416-025-03141-y. Online ahead of print.

ABSTRACT

BACKGROUND: Integrating genome-wide association study (GWAS) and transcriptomic datasets can identify mediators for genetic risk of cancer. Traditional methods often are insufficient as they rely on total gene expression measures and overlook alternative splicing, which generates different transcript-isoforms with potentially distinct effects.

METHODS: We integrate multi-tissue isoform expression data from the Genotype Tissue-Expression Project with GWAS summary statistics (all N > ~20,000 cases) to identify isoform- and gene-level associations with six cancers (breast, endometrial, colorectal, lung, ovarian, prostate) and six related cancer subtype classifications (N = 12 total).

RESULTS: Directly modeling isoforms using transcriptome-wide association studies (isoTWAS) significantly improves discovery of genetic associations compared to gene-level approaches, identifying 164% more significant associations (6163 vs. 2336) with isoTWAS-prioritized genes enriched 4-fold for evolutionarily-constrained genes. isoTWAS tags transcriptomic associations at 52% more independent GWAS loci across the six cancers. Isoform expression mediates an estimated 63% greater proportion of cancer risk SNP heritability compared to gene expression. We highlight several isoTWAS associations that demonstrate GWAS colocalization at the isoform level but not at the gene level, including CLPTM1L (lung cancer), LAMC1 (colorectal), and BABAM1 (breast).

CONCLUSION: These results underscore the importance of modeling isoforms to maximize discovery of genetic risk mechanisms for cancers.

PMID:40775447 | DOI:10.1038/s41416-025-03141-y

Br J Cancer. 2025 Aug 7. doi: 10.1038/s41416-025-03110-5. Online ahead of print.

ABSTRACT

BACKGROUND: One-third of all lung cancer cases globally are reported in China. This study evaluated the symptom management efficacy of an electronic patient-reported outcomes (ePRO)-based intervention for postoperative symptoms like pain and psychological distress after lung cancer surgery.

METHODS: We included lung cancer surgery patients (April 2022-October 2023; age, 18-75 years) with ECOG scores of 0-2 and expected survival of >6 months and randomized them into control and intervention groups. The latter completed MDASI-LC and QLQ-C30 questionnaires, wherein high symptom scores prompted treatment recommendations; the former received routine care. Changes in symptom scores, daily function, and quality of life were evaluated over 12 weeks and 1 year through surveys and interviews for ePRO-based symptom management efficacy assessments.

RESULTS: Herein, 355 participants comprised intervention (n = 182) and control groups (n = 173). At 12 weeks, the former had significantly lower symptoms threshold [0 (0-1) vs. 1 (0-3)], lower symptom scores [adjusted mean difference, -0.527 (95% CI: -0.788 to -0.266)], and higher QOL scores (emotional function: 2.908; 95% CI: 0.600-5.216, P = 0.014; global health: 6.775; 95% CI: 3.967-9.583).

CONCLUSIONS: ePRO-based collaborative management effectively lessened postoperative burden and improved QOL beyond 6 months.

PMID:40775446 | DOI:10.1038/s41416-025-03110-5

Sci Rep. 2025 Aug 7;15(1):28949. doi: 10.1038/s41598-025-14320-5.

ABSTRACT

With the growing demand for English language teaching, the efficient and accurate evaluation of students’ writing ability has become a key focus in English education. This study introduces a Hybrid Feature-based Cross-Prompt Automated Essay Scoring (HFC-AES) model that leverages deep learning for intelligent text analysis. Building on traditional deep neural networks (DNNs), the model incorporates text structure features and attention mechanisms, while adversarial training is employed to optimize feature extraction and enhance cross-prompt adaptability. In the topic-independent stage, statistical methods and DNNs extract shared features for preliminary scoring. In the topic-specific stage, topic information is integrated into a hierarchical neural network to improve semantic understanding and topic alignment. Compared with existing Transformer-based scoring models, HFC-AES demonstrates superior robustness and semantic modeling capabilities. Experimental results show that HFC-AES achieves strong cross-prompt scoring performance, with an average Quadratic Weighted Kappa (QWK) of 0.856, outperforming mainstream models. Ablation studies further highlight the critical role of text structure features and attention mechanisms, particularly in improving argumentative writing assessment. Overall, HFC-AES offers effective technical support for automated essay grading, contributing to more reliable and efficient evaluation in English language teaching.

PMID:40775439 | DOI:10.1038/s41598-025-14320-5

Sci Rep. 2025 Aug 7;15(1):28990. doi: 10.1038/s41598-025-14276-6.

ABSTRACT

Straightlining (uniform responses across items), poses a risk in surveys. Among adolescents, previous studies have investigated the prevalence and impact of straightlining in shorter questionnaires within larger surveys. A typical finding is that straightlining is more common among younger respondents, and particularly among boys. A better understanding of straightlining is important for improving data quality. The present study aims to estimate the prevalence of straightlining among adolescents filling out a survey covering different aspects of social media use across 64 items. Additionally, it seeks to assess the impact of straightlining on internal consistency and the associations between six domains of social media use and symptoms of anxiety and depression. Data from the «LifeOnSoMe»-study (N = 3,285), collected from adolescents (aged 16+) in Bergen, Norway. Descriptive and inferential statistics. In total, 5.4% of participants were straightliners, (8.6% of the boys vs. 2.9% of the girls (p < 0.001)). There were no differences in age between straightliners and the remainder of the sample. Overall, the prevalence and impact of straightlining was limited in the present sample. However, there were large discrepancies in terms of both internal consistency, correlations between domains of social media use, and associations with symptoms of anxiety and depression between straightliners and the remainder of the sample. Straightlining behavior had minimal effects on this sample’s analytical epidemiological conclusions. While boys were more prone to straightlining than girls, overall prevalence was low. However, significant discrepancies between straightliners and other respondents suggest potential risks in samples with higher straightlining prevalence.

PMID:40775438 | DOI:10.1038/s41598-025-14276-6

Sci Rep. 2025 Aug 7;15(1):28969. doi: 10.1038/s41598-025-13240-8.

ABSTRACT

This study assesses the practical implications of ChatGPT’s ability to identify hotspots by comparing its performance to Geographical Information System (GIS) software in detecting CO₂ sources and sinks observed by the Orbiting Carbon Observatory-3 (OCO-3) satellite. ChatGPT exhibited performance comparable to ArcGIS in both z-score statistics and spatial distribution patterns of XCO₂ hot and cold spots. The results generated by ChatGPT showed a strong correlation with ArcGIS-generated hotspots, demonstrating a z-score correlation coefficient of R²=0.82 and a cosine similarity score of 0.90. As multimodal artificial intelligence becomes more prevalent in earth monitoring, ChatGPT is expected to be a valuable tool for identifying CO₂ emission patterns, particularly for users who lack specialized GIS expertise. These findings establish a significant benchmark for ChatGPT’s potential in this field, offering a novel approach to identifying area-wide spatial patterns of CO₂ emissions compared to conventional GIS software.

PMID:40775428 | DOI:10.1038/s41598-025-13240-8

Sci Rep. 2025 Aug 7;15(1):28925. doi: 10.1038/s41598-025-13380-x.

ABSTRACT

This study used data from a large dam site to model changes in groundwater quality variables. Several indicators were investigated to check the quality of water sources for the site for different purposes. The factor analysis results displayed that four factors control 87.58% of water quality changes. The primary factor responsible for approximately half of the impact on water quality, accounting for 55.12% of the total variance, includes the EC, Ca²⁺, SAR, SO₄^–, Na⁺, CO₃^–, %Na, Cl^–, and TDS parameters. These parameters are directly related to water quality and are influenced by the natural characteristics of the region. Considering that the main control factor for water quality is the first factor mentioned, these factors were used in multivariate analysis and intelligent modeling. Therefore, Na⁺, Cl⁺, Na%, CO₃^–, and SO₄^2- were used as input variables (independent variables), and EC, TDS, and SAR were used as output variables (dependent variables). Support vector machine (SVM) with various kernel functions, multilayer perceptron artificial neural network (MLP-ANN) with various training algorithms, random forest algorithm (RFA), Gaussian process regression (GPR), and statistical analysis methods were used for modeling. Among the kernel functions used in SVM, the radial basis function (RBF) kernel provided the most accurate results. On the other hand, among the learning algorithms used in neural networks, the Levenberg-Marquardt algorithm demonstrated the highest level of accuracy. Modeling results based on error value, Wilmot agreement index, A₂₀ index, determination coefficient, and violin diagrams showed that the SVM (R² > 0.99, RMSE < 0.04, A₂₀ = 1.00, WAI = 1.00) achieved better than the other models. The results of Kruskal-Wallis’s test disclosed that there is no substantial difference between the water quality parameters obtained from the models and the measured values.

PMID:40775421 | DOI:10.1038/s41598-025-13380-x