Respir Investig. 2025 Mar 6;63(3):314-321. doi: 10.1016/j.resinv.2025.02.001. Online ahead of print.

ABSTRACT

BACKGROUND: The interobserver agreement regarding airway-centered fibrosis (ACF), the key diagnostic feature of fibrotic hypersensitivity pneumonitis (fHP) has not been sufficiently addressed to date. We applied digital image analysis to investigate this issue and extracted histological features of ACF to correlate with fHP diagnosis.

METHODS: A total of 111 selected glass slides from 17 fHP and 30 idiopathic pulmonary fibrosis (IPF) were scanned and seven expert pulmonary pathologists were tasked with digital annotation of ACF. Interobserver agreement on annotated ACF was assessed using Fleiss' kappa value. ACF recognized by majority of pathologists (4 or more) were considered as consensus ACF (cACF), and their frequencies were compared between fHP and IPF cases.

RESULTS: Fleiss' kappa agreement in ACF recognition was 0.32 among seven pathologists. A significant difference between cryobiopsy and VATS specimens regarding an average ACF count per slide (p = 0.012) was found. The number of cACFs in a single case ranged from 0 to 20 (mean 5.71) for fHP cases and 0 to 13 (mean 1.80) for IPF cases (p = 0.011). When limited to surgical biopsies, the average number of cACF was 10.3 for fHP vs. 1.68 for IPF (p < 0.001). The common characteristic features of cACF in fHP were their confinement to the vicinity of respiratory bronchioles, frequent association with peribronchiolar metaplasia, and mild to moderate lymphocytic infiltration.

CONCLUSIONS: The recognition of ACF varies widely among pathologists. We identified common histologic features of ACF in fHP cases, proposing criteria for ACF recognition in fHP.

PMID:40054038 | DOI:10.1016/j.resinv.2025.02.001

J Agric Food Chem. 2025 Mar 7. doi: 10.1021/acs.jafc.4c09501. Online ahead of print.

ABSTRACT

Betalains, a group of pigments widely distributed in various plants, are extensively applied in the food, beverage, and medicinal industries. The biosynthesis of betalains involves the enzymatic action of 4,5-DOPA-dioxygenase, which catalyzes the key ring-opening reaction of DOPA to produce betalamic acid, a crucial intermediate in the pathway. The crystal structure of a 4,5-DOPA-dioxygenase from Beta vulgaris (BvDOD) was determined in this study. The structural analysis revealed that BvDOD exhibited a structural fold similar to that of other members of the extradiol dioxygenase family. Moreover, the Fe-ligand residues His15, His53, and His229 indicated the enzyme's reliance on nonheme iron for catalyzing the ring-opening reaction. Molecular docking and mutational analysis identified two conserved residues, His119 and His175, in the active site essential for the catalytic reaction. In addition, Thr17, Asp254, and Tyr260 contributed to properly positioning the substrate in the active site. This study has provided structural insights into substrate recognition and catalytic mechanisms of BvDOD, which can be applied to develop enzymes for improved betalain production.

PMID:40055856 | DOI:10.1021/acs.jafc.4c09501

BMC Gastroenterol. 2025 Mar 7;25(1):148. doi: 10.1186/s12876-025-03732-2.

ABSTRACT

OBJECTIVE: With the rising incidence of MASLD, extensive drug research has been conducted in clinical trials. The study examined the design principles and research objectives of MASLD therapeutics, in order to offer guidance to clinical trial participants and decision makers.

METHODS: By searching the clinical research trial data registered on clinicaltrials.gov platform, 1209 interventional clinical trials were screened. These trials were subsequently evaluated based on clinical stage, trial design, intervention modalities, outcome metrics, and other pertinent factors.

RESULTS: A total of 1,209 trials were included, of which 199 were registered from 2000 to 2012 (16.46%) and 1010 were registered from 2013 to 2024 (83.54%), reflecting the growing body of research on MASLD. Regarding the intervention model type, single-group designs were employed in 232 (19.19%) trials, and parallel designs were employed in 873(72.21%). A total of 13 trials were early phase 1 (1.08%), 152 (12.57%) were phase 1, 34 (2.81%) were phase 1/phase 2, 301 were phase 2 (24.90%), 19 (1.57%) were phase 2/phase 3, 72 (5.96%) were phase 3, and 84 (6.95%) were phase 4. Within these trials, the three primary clinical outcomes for drug interventions were hepatic histological improvement, hepatic fat content and adverse events. Furthermore, 140 drug interventional trials with results for therapeutic purposes (This accounted for 88.61% of the 158 drug interventional trials with results) primarily aimed to improve MASLD through mechanisms such as metabolic and energy balance, inflammatory and immunomodulatory, and lipid reduction, targeting primarily PPAR, FXR, ACC and GLP-1.

CONCLUSION: This study suggests the basic characteristics of global MASLD clinical trial design, and the current global interventional clinical trials are mainly focused on drug-related treatments, and drugs to improve inflammation and metabolism are still the first choice for MASLD drug intervention studies.

PMID:40055604 | DOI:10.1186/s12876-025-03732-2

J Proteome Res. 2025 Mar 7. doi: 10.1021/acs.jproteome.4c01068. Online ahead of print.

ABSTRACT

Computational toolkits for data exploration and visualization from widely used omics platforms often lack flexibility and customization. While many tools generate standardized output, advanced programming skills are necessary to create high-quality visualizations. Therefore, user-friendly tools that simplify this crucial, yet time-consuming, step are essential. We developed EasyPubPlot (Easy Publishable Plotting), a straightforward, easy-to-use, no-coding, user experience-oriented, open-source, and shiny web application along with its associated R package to streamline data exploration and visualization for functional omics-empowered research. EasyPubPlot generates publishable scores plots, volcano plots, heatmaps, box plots, dot plots, and bubble plots with minimal necessary steps. The tool was designed to guide new users to accurate and efficient navigation. Step-by-step tutorials for each type of plot are also provided. Herein, we demonstrated EasyPubPlot's competent functionality and versatility by showcasing metabolomics, proteomics, and transcriptomics data. Collectively, EasyPubPlot reduces the gap between data analysis and stunning visualization, thereby diminishing friction and focusing on science. The app can be downloaded and installed locally (https://github.com/Pharmaco-OmicsLab/EasyPubPlot) or used through a web application (https://pharmaco-omicslab.shinyapps.io/EasyPubPlot).

PMID:40053871 | DOI:10.1021/acs.jproteome.4c01068

J Particip Med. 2025 Mar 6;17:e59686. doi: 10.2196/59686.

ABSTRACT

BACKGROUND: Newborn screening aims to identify babies affected by rare but serious genetic conditions. As technology advances, there is the potential to expand the newborn screening program following evaluation of the likely benefits and drawbacks. To inform these decisions, it is important to consider the family experience of screening and the views of the public. Engaging in public dialogue can be difficult. The conditions, screening processes, and associated moral and ethical considerations are complex.

OBJECTIVE: This study aims to develop a stand-alone online resource to enable a range of stakeholders to understand whether and how next-generation sequencing should be incorporated into the CF screening algorithm.

METHODS: Around 4 development workshops with policymakers, parents, and other stakeholders informed the design of an interactive activity, including the structure, content, and questions posed. Stakeholders were recruited to take part in the development workshops via purposeful and snowball sampling methods to achieve a diversity of views across roles and organizations, with email invitations sent to representative individuals with lived, clinical, and academic experience related to CF and screening. Ten stakeholders informed the development process including those with lived experience of CF (2/10, 20%), clinicians (2/10, 20%), and representatives from relevant government, charity, and research organizations (6/10, 60%). Vignettes constructed using interview data and translated into scripts were recorded to provide short films to represent and provoke consideration of families' experiences. Participants were recruited (n=6, adults older than 18 years) to test the resulting resource. Study advertisements were circulated via physical posters and digital newsletters to recruit participants who self-identified as having a reading difficulty or having English as a second language.

RESULTS: An open access online resource, "Cystic Fibrosis Newborn Screening: You Decide," was developed and usability and acceptability tested to provide the "user" (eg, a parent, the general public, or a health care professional) with an interactive scenario-based presentation of the potential outcomes of extended genetic testing, allowing them to visualize the impact on families. This included a learning workbook that explains key concepts and processes. The resulting tool facilitates public engagement with and understanding of complex genetic and screening concepts.

CONCLUSIONS: Online resources such as the one developed during this work have the potential to help people form considered views and facilitate access to the perspectives of parents and the wider public on genetic testing. These may be otherwise difficult to obtain but are of importance to health care professionals and policymakers.

TRIAL REGISTRATION: ClinicalTrials.gov NCT06299566; https://clinicaltrials.gov/study/NCT06299566.

PMID:40053726 | DOI:10.2196/59686

J Neurosurg. 2025 Mar 7:1-9. doi: 10.3171/2024.10.JNS241060. Online ahead of print.

ABSTRACT

OBJECTIVE: Preoperative workup of trigeminal neuralgia (TN) consists of identification of neurovascular features on MRI. In this study, the authors apply and evaluate the performance of deep learning models for segmentation of the trigeminal nerve and surrounding vasculature to quantify anatomical features of the nerve and vessels.

METHODS: Six U-Net-based neural networks, each with a different encoder backbone, were trained to label constructive interference in steady-state MRI voxels as nerve, vasculature, or background. A retrospective dataset of 50 TN patients at the authors' institution who underwent preoperative high-resolution MRI in 2022 was utilized to train and test the models. Performance was measured by the Dice coefficient and intersection over union (IoU) metrics. Anatomical characteristics, such as surface area of neurovascular contact and distance to the contact point, were computed and compared between the predicted and ground truth segmentations.

RESULTS: Of the evaluated models, the best performing was U-Net with an SE-ResNet50 backbone (Dice score = 0.775 ± 0.015, IoU score = 0.681 ± 0.015). When the SE-ResNet50 backbone was used, the average surface area of neurovascular contact in the testing dataset was 6.90 mm2, which was not significantly different from the surface area calculated from manual segmentation (p = 0.83). The average calculated distance from the brainstem to the contact point was 4.34 mm, which was also not significantly different from manual segmentation (p = 0.29).

CONCLUSIONS: U-Net-based neural networks perform well for segmenting trigeminal nerve and vessels from preoperative MRI volumes. This technology enables the development of quantitative and objective metrics for radiographic evaluation of TN.

PMID:40053933 | DOI:10.3171/2024.10.JNS241060

Mol Pharm. 2025 Mar 7. doi: 10.1021/acs.molpharmaceut.4c01086. Online ahead of print.

ABSTRACT

The optimization of absorption, distribution, metabolism, and excretion (ADME) profiles of compounds is critical to the drug discovery process. As such, machine learning (ML) models for ADME are widely used for prioritizing the design and synthesis of compounds. The effectiveness of ML models for ADME depends on the availability of high-quality experimental data for a diverse set of compounds that is relevant to the emerging chemical space being explored by the drug discovery teams. To that end, ADME data sets from Genentech and Roche were combined to evaluate the impact of expanding the chemical space on the performance of ML models, a first experiment of its kind for large-scale, historical ADME data sets. The combined ADME data set consisted of over 1 million individual measurements distributed across 11 assay end points. We utilized a multitask (MT) neural network architecture that enables the modeling of multiple end points simultaneously and thereby exploits information transfer between interconnected ADME end points. Both single- and cross-site MT models were trained and compared against single-site, single-task baseline models. Given the differences in assay protocols across the two sites, the data for corresponding end points across sites were modeled as separate tasks. Models were evaluated against test sets representing varying degrees of extrapolation difficulty, including cluster-based, temporal, and external test sets. We found that cross-site MT models appeared to provide a greater generalization capacity compared to single-site models. The performance improvement of the cross-site MT models was more pronounced for the relatively "distant" external and temporal test sets, suggesting an expanded applicability domain. The data exchange exercise described here demonstrates the value of expanding the learning from ADME data from multiple sources without the need to aggregate such data when the experimental methods are disparate.

PMID:40053846 | DOI:10.1021/acs.molpharmaceut.4c01086

JMIR Med Inform. 2025 Mar 4;13:e68863. doi: 10.2196/68863.

ABSTRACT

BACKGROUND: Patients' oral expressions serve as valuable sources of clinical information to improve pharmacotherapy. Natural language processing (NLP) is a useful approach for analyzing unstructured text data, such as patient narratives. However, few studies have focused on using NLP for narratives in the Japanese language.

OBJECTIVE: We aimed to develop a high-performance NLP system for extracting clinical information from patient narratives by examining the performance progression with a gradual increase in the amount of training data.

METHODS: We used subjective texts from the pharmaceutical care records of Keio University Hospital from April 1, 2018, to March 31, 2019, comprising 12,004 records from 6559 cases. After preprocessing, we annotated diseases and symptoms within the texts. We then trained and evaluated a deep learning model (bidirectional encoder representations from transformers combined with a conditional random field [BERT-CRF]) through 10-fold cross-validation. The annotated data were divided into 10 subsets, and the amount of training data was progressively increased over 10 steps. We also analyzed the causes of errors. Finally, we applied the developed system to the analysis of case report texts to evaluate its usability for texts from other sources.

RESULTS: The F1-score of the system improved from 0.67 to 0.82 as the amount of training data increased from 1200 to 12,004 records. The F1-score reached 0.78 with 3600 records and was largely similar thereafter. As performance improved, errors from incorrect extractions decreased significantly, which resulted in an increase in precision. For case reports, the F1-score also increased from 0.34 to 0.41 as the training dataset expanded from 1200 to 12,004 records. Performance was lower for extracting symptoms from case report texts compared with pharmaceutical care records, suggesting that this system is more specialized for analyzing subjective data from pharmaceutical care records.

CONCLUSIONS: We successfully developed a high-performance system specialized in analyzing subjective data from pharmaceutical care records by training a large dataset, with near-complete saturation of system performance with about 3600 training records. This system will be useful for monitoring symptoms, offering benefits for both clinical practice and research.

PMID:40053805 | DOI:10.2196/68863

J Med Internet Res. 2025 Mar 7;27:e69068. doi: 10.2196/69068.

ABSTRACT

BACKGROUND: Tuberculosis (TB) remains a significant health concern, contributing to the highest mortality among infectious diseases worldwide. However, none of the various TB diagnostic tools introduced is deemed sufficient on its own for the diagnostic pathway, so various artificial intelligence (AI)-based methods have been developed to address this issue.

OBJECTIVE: We aimed to provide a comprehensive evaluation of AI-based algorithms for TB detection across various data modalities.

METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020 guidelines, we conducted a systematic review to synthesize current knowledge on this topic. Our search across 3 major databases (Scopus, PubMed, Association for Computing Machinery [ACM] Digital Library) yielded 1146 records, of which we included 152 (13.3%) studies in our analysis. QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies version 2) was performed for the risk-of-bias assessment of all included studies.

RESULTS: Radiographic biomarkers (n=129, 84.9%) and deep learning (DL; n=122, 80.3%) approaches were predominantly used, with convolutional neural networks (CNNs) using Visual Geometry Group (VGG)-16 (n=37, 24.3%), ResNet-50 (n=33, 21.7%), and DenseNet-121 (n=19, 12.5%) architectures being the most common DL approach. The majority of studies focused on model development (n=143, 94.1%) and used a single modality approach (n=141, 92.8%). AI methods demonstrated good performance in all studies: mean accuracy=91.93% (SD 8.10%, 95% CI 90.52%-93.33%; median 93.59%, IQR 88.33%-98.32%), mean area under the curve (AUC)=93.48% (SD 7.51%, 95% CI 91.90%-95.06%; median 95.28%, IQR 91%-99%), mean sensitivity=92.77% (SD 7.48%, 95% CI 91.38%-94.15%; median 94.05% IQR 89%-98.87%), and mean specificity=92.39% (SD 9.4%, 95% CI 90.30%-94.49%; median 95.38%, IQR 89.42%-99.19%). AI performance across different biomarker types showed mean accuracies of 92.45% (SD 7.83%), 89.03% (SD 8.49%), and 84.21% (SD 0%); mean AUCs of 94.47% (SD 7.32%), 88.45% (SD 8.33%), and 88.61% (SD 5.9%); mean sensitivities of 93.8% (SD 6.27%), 88.41% (SD 10.24%), and 93% (SD 0%); and mean specificities of 94.2% (SD 6.63%), 85.89% (SD 14.66%), and 95% (SD 0%) for radiographic, molecular/biochemical, and physiological types, respectively. AI performance across various reference standards showed mean accuracies of 91.44% (SD 7.3%), 93.16% (SD 6.44%), and 88.98% (SD 9.77%); mean AUCs of 90.95% (SD 7.58%), 94.89% (SD 5.18%), and 92.61% (SD 6.01%); mean sensitivities of 91.76% (SD 7.02%), 93.73% (SD 6.67%), and 91.34% (SD 7.71%); and mean specificities of 86.56% (SD 12.8%), 93.69% (SD 8.45%), and 92.7% (SD 6.54%) for bacteriological, human reader, and combined reference standards, respectively. The transfer learning (TL) approach showed increasing popularity (n=89, 58.6%). Notably, only 1 (0.7%) study conducted domain-shift analysis for TB detection.

CONCLUSIONS: Findings from this review underscore the considerable promise of AI-based methods in the realm of TB detection. Future research endeavors should prioritize conducting domain-shift analyses to better simulate real-world scenarios in TB detection.

TRIAL REGISTRATION: PROSPERO CRD42023453611; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023453611.

PMID:40053773 | DOI:10.2196/69068

J Med Internet Res. 2025 Mar 5;27:e54543. doi: 10.2196/54543.

ABSTRACT

BACKGROUND: Chronic obstructive pulmonary disease (COPD) ranks among the leading causes of global mortality, and COVID-19 has intensified its challenges. Beyond the evident physical effects, the long-term psychological effects of COVID-19 are not fully understood.

OBJECTIVE: This study aims to unveil the long-term psychological trends and patterns in populations with COPD throughout the COVID-19 pandemic and beyond via large-scale Twitter mining.

METHODS: A 2-stage deep learning framework was designed in this study. The first stage involved a data retrieval procedure to identify COPD and non-COPD users and to collect their daily tweets. In the second stage, a data mining procedure leveraged various deep learning algorithms to extract demographic characteristics, hashtags, topics, and sentiments from the collected tweets. Based on these data, multiple analytical methods, namely, odds ratio (OR), difference-in-difference, and emotion pattern methods, were used to examine the psychological effects.

RESULTS: A cohort of 15,347 COPD users was identified from the data that we collected in the Twitter database, comprising over 2.5 billion tweets, spanning from January 2020 to June 2023. The attentiveness toward COPD was significantly affected by gender, age, and occupation; it was lower in females (OR 0.91, 95% CI 0.87-0.94; P<.001) than in males, higher in adults aged 40 years and older (OR 7.23, 95% CI 6.95-7.52; P<.001) than in those younger than 40 years, and higher in individuals with lower socioeconomic status (OR 1.66, 95% CI 1.60-1.72; P<.001) than in those with higher socioeconomic status. Across the study duration, COPD users showed decreasing concerns for COVID-19 and increasing health-related concerns. After the middle phase of COVID-19 (July 2021), a distinct decrease in sentiments among COPD users contrasted sharply with the upward trend among non-COPD users. Notably, in the post-COVID era (June 2023), COPD users showed reduced levels of joy and trust and increased levels of fear compared to their levels of joy and trust in the middle phase of COVID-19. Moreover, males, older adults, and individuals with lower socioeconomic status showed heightened fear compared to their counterparts.

CONCLUSIONS: Our data analysis results suggest that populations with COPD experienced heightened mental stress in the post-COVID era. This underscores the importance of developing tailored interventions and support systems that account for diverse population characteristics.

PMID:40053739 | DOI:10.2196/54543

J Med Internet Res. 2025 Feb 28;27:e67576. doi: 10.2196/67576.

ABSTRACT

BACKGROUND: In-hospital cardiac arrest (IHCA) is a severe and sudden medical emergency that is characterized by the abrupt cessation of circulatory function, leading to death or irreversible organ damage if not addressed immediately. Emergency department (ED)-based IHCA (EDCA) accounts for 10% to 20% of all IHCA cases. Early detection of EDCA is crucial, yet identifying subtle signs of cardiac deterioration is challenging. Traditional EDCA prediction methods primarily rely on structured vital signs or electrocardiogram (ECG) signals, which require additional preprocessing or specialized devices. This study introduces a novel approach using image-based 12-lead ECG data obtained at ED triage, leveraging the inherent richness of visual ECG patterns to enhance prediction and integration into clinical workflows.

OBJECTIVE: This study aims to address the challenge of early detection of EDCA by developing an innovative deep learning model, the ECG-Image-Aware Network (EIANet), which uses 12-lead ECG images for early prediction of EDCA. By focusing on readily available triage ECG images, this research seeks to create a practical and accessible solution that seamlessly integrates into real-world ED workflows.

METHODS: For adult patients with EDCA (cases), 12-lead ECG images at ED triage were obtained from 2 independent data sets: National Taiwan University Hospital (NTUH) and Far Eastern Memorial Hospital (FEMH). Control ECGs were randomly selected from adult ED patients without cardiac arrest during the same study period. In EIANet, ECG images were first converted to binary form, followed by noise reduction, connected component analysis, and morphological opening. A spatial attention module was incorporated into the ResNet50 architecture to enhance feature extraction, and a custom binary recall loss (BRLoss) was used to balance precision and recall, addressing slight data set imbalance. The model was developed and internally validated on the NTUH-ECG data set and was externally validated on an independent FEMH-ECG data set. The model performance was evaluated using the F1-score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC).

RESULTS: There were 571 case ECGs and 826 control ECGs in the NTUH data set and 378 case ECGs and 713 control ECGs in the FEMH data set. The novel EIANet model achieved an F1-score of 0.805, AUROC of 0.896, and AUPRC of 0.842 on the NTUH-ECG data set with a 40% positive sample ratio. It achieved an F1-score of 0.650, AUROC of 0.803, and AUPRC of 0.678 on the FEMH-ECG data set with a 34.6% positive sample ratio. The feature map showed that the region of interest in the ECG was the ST segment.

CONCLUSIONS: EIANet demonstrates promising potential for accurately predicting EDCA using triage ECG images, offering an effective solution for early detection of high-risk cases in emergency settings. This approach may enhance the ability of health care professionals to make timely decisions, with the potential to improve patient outcomes by enabling earlier interventions for EDCA.

PMID:40053733 | DOI:10.2196/67576

J Chem Inf Model. 2025 Mar 7. doi: 10.1021/acs.jcim.4c02216. Online ahead of print.

ABSTRACT

Proteins, as the fundamental macromolecules of life, play critical roles in various biological processes. Recent advancements in intelligent protein function prediction methods leverage sequences, structures, and biomedical literature data. Among them, function prediction methods for protein sequences remain an enduring and popular research direction. Existing studies have failed to effectively utilize the multilevel attribute features reflected in protein sequences. This limitation hinders the enrichment of protein descriptions needed for high-precision prediction of protein functions. To address this, we propose DeepMVD, a novel deep learning model that enhances prediction accuracy by dynamically fusing multiview features. DeepMVD employs specialized modules to extract unique features from each view and utilizes an adaptive fusion mechanism for optimal integration. Evaluation of the CAFA4 data set shows that DeepMVD significantly outperforms existing state-of-the-art models in terms of BP, MF, and CC terminology, all obtaining the highest Fmax (0.523, 0.712, 0.740). Ablation studies confirm the model's robustness. Source code and data sets are available at http://swanhub.co/scl/DeepMVD.

PMID:40053671 | DOI:10.1021/acs.jcim.4c02216

IEEE J Biomed Health Inform. 2025 Mar 7;PP. doi: 10.1109/JBHI.2025.3548940. Online ahead of print.

ABSTRACT

MiRNA subcellular localizations (MSLs) are essential for uncovering and understanding miRNA functions in various biological processes. Several computational methods have been proposed for measuring MSL. However, existing methods only rely on manually crafted features based on sequence without considering RNA 3D structure information, and most methods often rely on single-model approaches, which fail to capture the full complexity of biological systems, further hindering predictive accuracy and performance. In this study, we introduce a deep learning-based approach, MMFmiRLocEL, which integrates multi-model fusion and ensemble learning for MSL identification. To the best of our knowledge, MMFmiRLocEL is the first method to combine sequence, structure, and function three information for MSL prediction. Specifically, it employs RNA 3D structure generated by the predicted structural model to construct a structure-based approach for MSL prediction. It also develops a sequence-based prediction method using sequence features and convolutional neural networks, while constructing a function-based prediction method using miRNA-disease association networks and deep residual neural networks. Furthermore, a multi-model fusion approach, employing weighted ensemble strategies, integrates sequence, structure, and function models to enhance the robustness and accuracy of MSL identification. Experimental results demonstrate that MMFmiRLocEL outperforms existing state-of-the-art methods, and then ablation analysis confirmed the significant contribution of the multi-model fusion mechanism to improve the prediction performance.

PMID:40053625 | DOI:10.1109/JBHI.2025.3548940

PLoS One. 2025 Mar 7;20(3):e0317240. doi: 10.1371/journal.pone.0317240. eCollection 2025.

ABSTRACT

BACKGROUND: Breast cancer (BC) continues to pose a substantial global health concern, necessitating continuous advancements in therapeutic approaches. Neoadjuvant chemotherapy (NAC) has gained prominence as a key therapeutic strategy, and there is growing interest in the predictive utility of Background Parenchymal Enhancement (BPE) in evaluating the response of breast tumors to NAC. However, the analysis of BPE as a predictive biomarker, along with the techniques used to model BPE changes for accurate and timely predictions of treatment response presents several obstacles. This systematic review aims to thoroughly investigate recent advancements in the analytical methodologies for BPE analysis, and to evaluate their reliability and effectiveness in predicting breast tumor response to NAC, ultimately contributing to the development of personalized and effective therapeutic strategies.

METHODS: A comprehensive and structured literature search was conducted across key electronic databases, including Cochrane Database of Systematic Reviews, Google Scholar, PubMed, and IEEE Xplore covering articles published up to May 10, 2024. The inclusion criteria targeted studies focusing on breast cancer cohorts treated with NAC, involving both pre-treatment and at least one post-treatment breast dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) scan, and analyzing BPE utility in predicting breast tumor response to NAC. Methodological quality assessment and data extraction were performed to synthesize findings and identify commonalities and differences among various BPE analytical approaches.

RESULTS: The search yielded a total of 882 records. After meticulous screening, 78 eligible records were identified, with 13 studies ultimately meeting the inclusion criteria for the systematic review. Analysis of the literature revealed a significant evolution in BPE analysis, from early studies focusing on single time-point BPE analysis to more recent studies adopting longitudinal BPE analysis. The review uncovered several gaps that compromise the accuracy and timeliness of existing longitudinal BPE analysis methods, such as missing data across multiple imaging time points, manual segmentation of the whole-breast region of interest, and over reliance on traditional statistical methods like logistic regression for modeling BPE and pathological complete response (pCR).

CONCLUSION: This review provides a thorough examination of current advancements in analytical approaches for BPE analysis in predicting breast tumor response to NAC. The shift towards longitudinal BPE analysis has highlighted significant gaps, suggesting the need for alternative analytical techniques, particularly in the realm of artificial intelligence (AI). Future longitudinal BPE research work should focus on standardization in longitudinal BPE measurement and analysis, through integration of deep learning-based approaches for automated tumor segmentation, and implementation of advanced AI technique that can better accommodate varied breast tumor responses, non-linear relationships and complex temporal dynamics in BPE datasets, while also handling missing data more effectively. Such integration could lead to more precise and timely predictions of breast tumor responses to NAC, thereby enhancing personalized and effective breast cancer treatment strategies.

PMID:40053513 | DOI:10.1371/journal.pone.0317240

J Phys Chem B. 2025 Mar 7. doi: 10.1021/acs.jpcb.4c07794. Online ahead of print.

ABSTRACT

Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by the existence of over 80 FDA-approved small-molecule kinase inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused by mutations in the kinase therapeutic target. The advent of clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes by pairing optimal therapies with tumor mutation profiles. However, modern precision oncology efforts are hindered by lack of sufficient biochemical or clinical evidence to classify each mutation as resistant or sensitive to existing inhibitors. Structure-based methods show promising accuracy in retrospective benchmarks at predicting whether a kinase mutation will perturb inhibitor binding, but comparisons are made by pooling disparate experimental measurements across different conditions. We present the first prospective benchmark of structure-based approaches on a blinded dataset of in-cell kinase inhibitor affinities to Abl kinase mutants using a NanoBRET reporter assay. We compare NanoBRET results to structure-based methods and their ability to estimate the impact of mutations on inhibitor binding (measured as ΔΔG). Comparing physics-based simulations, Rosetta, and previous machine learning models, we find that structure-based methods accurately classify kinase mutations as inhibitor-resistant or inhibitor-sensitizing, and each approach has a similar degree of accuracy. We show that physics-based simulations are best suited to estimate ΔΔG of mutations that are distal to the kinase active site. To probe modes of failure, we retrospectively investigate two clinically significant mutations poorly predicted by our methods, T315A and L298F, and find that starting configurations and protonation states significantly alter the accuracy of our predictions. Our experimental and computational measurements provide a benchmark for estimating the impact of mutations on inhibitor binding affinity for future methods and structure-based models. These structure-based methods have potential utility in identifying optimal therapies for tumor-specific mutations, predicting resistance mutations in the absence of clinical data, and identifying potential sensitizing mutations to established inhibitors.

PMID:40053698 | DOI:10.1021/acs.jpcb.4c07794

STAR Protoc. 2025 Mar 5;6(1):103671. doi: 10.1016/j.xpro.2025.103671. Online ahead of print.

ABSTRACT

Reverse vaccine technology, supported by advancements in immunoinformatics, facilitates the development of multi-epitope vaccines for rapidly evolving pathogens, thereby strengthening the immune defense. Here, we present a protocol for a peptide-based multi-epitope vaccine targeting monkeypox virus (MPXV) using an open-source approach. We describe steps for evaluating physicochemical properties and allergenicity. We then detail procedures for validating pattern recognition receptor (PRR)-binding affinity and stable major histocompatibility complex (MHC) I/II presentation. Molecular dynamics (MD) simulations confirm immune receptor interactions, enhancing vaccine stability. For complete details on the use and execution of this protocol, please refer to Kaur et al.1.

PMID:40053448 | DOI:10.1016/j.xpro.2025.103671

Med Lett Drugs Ther. 2025 Mar 17;67(1724):41-43. doi: 10.58347/tml.2025.1724a.

NO ABSTRACT

PMID:40053374 | DOI:10.58347/tml.2025.1724a

Curr Gastroenterol Rep. 2025 Mar 7;27(1):20. doi: 10.1007/s11894-025-00969-5.

ABSTRACT

PURPOSE OF REVIEW: Cystic fibrosis is a chronic condition that has significant effects on the nutritional status of pediatric patients. Malnutrition is frequently encountered in this population and has been shown to contribute to poor pulmonary and overall disease outcomes. This article will provide an overview of the physiologic and psychosocial challenges toward attaining optimal nutrition in pediatric cystic fibrosis patients.

RECENT FINDINGS: Newer therapies such as CFTR modulators have played significant roles in improving the nutritional status of patients with cystic fibrosis. There is also a greater focus on becoming more aware of psychosocial and cultural barriers in the care of cystic fibrosis patients. Many challenges exist in optimizing nutritional support including but not limited to the patient's clinical manifestations and disease severity, caregiver ability, and access to care. Both gastrointestinal and non-gastrointestinal disorders lead to insufficient caloric intake, increased loss and metabolic needs, and micronutrient and macronutrient deficiency. Social factors including stressful patient and caregiver relationships and altered body image also contribute to poor nutritional status.

PMID:40053205 | DOI:10.1007/s11894-025-00969-5

Environ Sci Technol. 2025 Mar 7. doi: 10.1021/acs.est.4c14209. Online ahead of print.

ABSTRACT

Water, as a finite and vital resource, necessitates water quality monitoring to ensure its sustainable use. A key aspect of this process is the accurate measurement of critical parameters such as chemical oxygen demand (COD). However, current spectroscopic methods struggle with accurately and consistently measuring COD in large-scale, complex water environments due to an insufficient understanding of water spectra and limited generalizability. To address these limitations, we introduce the physicochemical-informed spectral Transformer (PIST) model, combined with ultraviolet-visible-shortwave-near-infrared (UV-vis-SWNIR) spectroscopy for water quality sensing. To the best of our knowledge, this is the first approach to combine Transformer with spectroscopy for water quality sensing. PIST integrates a physicochemical-informed block to incorporate existing physical and chemical information into the spectral encoding for domain adaptation, along with a feature embedding block for comprehensive spectral features extraction. We validated PIST using an actual surface water spectral data set with extensive geographic coverage including the Yangtze River and Poyang Lake. PIST demonstrated notable performance in COD sensing within complex water environments, achieving an impressive R2 value of 0.9008 and reducing root mean squared error (RMSE) by 45.20% and 29.38% compared to benchmark models such as support vector regression (SVR) and convolutional neural network (CNN). These results emphasize PIST's accuracy and generalizability, marking a significant advancement in multidisciplinary approaches that combine spectroscopy with deep learning for rapid water quality sensing.

PMID:40053333 | DOI:10.1021/acs.est.4c14209

Jpn J Radiol. 2025 Mar 7. doi: 10.1007/s11604-025-01759-9. Online ahead of print.

ABSTRACT

PURPOSE: This is a preliminary analysis of one of the secondary endpoints in the prospective study cohort. The aim of this study is to assess the image quality and diagnostic confidence for lung cancer of CT images generated by using cadmium-zinc-telluride (CZT)-based photon-counting-detector-CT (PCD-CT) and comparing these super-high-resolution (SHR) images with conventional normal-resolution (NR) CT images.

MATERIALS AND METHODS: Twenty-five patients (median age 75 years, interquartile range 66-78 years, 18 men and 7 women) with 29 lung nodules overall (including two patients with 4 and 2 nodules, respectively) were enrolled to undergo PCD-CT. Three types of images were reconstructed: a 512 × 512 matrix with adaptive iterative dose reduction 3D (AIDR 3D) as the NRAIDR3D image, a 1024 × 1024 matrix with AIDR 3D as the SHRAIDR3D image, and a 1024 × 1024 matrix with deep-learning reconstruction (DLR) as the SHRDLR image. For qualitative analysis, two radiologists evaluated the matched reconstructed series twice (NRAIDR3D vs. SHRAIDR3D and SHRAIDR3D vs. SHRDLR) and scored the presence of imaging findings, such as spiculation, lobulation, appearance of ground-glass opacity or air bronchiologram, image quality, and diagnostic confidence, using a 5-point Likert scale. For quantitative analysis, contrast-to-noise ratios (CNRs) of the three images were compared.

RESULTS: In the qualitative analysis, compared to NRAIDR3D, SHRAIDR3D yielded higher image quality and diagnostic confidence, except for image noise (all P < 0.01). In comparison with SHRAIDR3D, SHRDLR yielded higher image quality and diagnostic confidence (all P < 0.01). In the quantitative analysis, CNRs in the modified NRAIDR3D and SHRDLR groups were higher than those in the SHRAIDR3D group (P = 0.003, <0.001, respectively).

CONCLUSION: In PCD-CT, SHRDLR images provided the highest image quality and diagnostic confidence for lung tumor evaluation, followed by SHRAIDR3D and NRAIDR3D images. DLR demonstrated superior noise reduction compared to other reconstruction methods.

PMID:40053285 | DOI:10.1007/s11604-025-01759-9