Commun Med (Lond). 2025 Feb 2;5(1):32. doi: 10.1038/s43856-025-00749-2.

ABSTRACT

BACKGROUND: Deep learning methods on standard, 12-lead electrocardiograms (ECG) have resulted in the ability to identify individuals at high-risk for the development of atrial fibrillation. However, the process remains a "black box" and does not help clinicians in understanding the electrocardiographic changes at an individual level. we propose a nonparametric feature extraction approach to identify features that are associated with the development of atrial fibrillation (AF).

METHODS: We apply functional principal component analysis to the raw ECG tracings collected in the Chronic Renal Insufficiency Cohort (CRIC) study. We define and select the features using ECGs from participants enrolled in Phase I (2003-2008) of the study. Cox proportional hazards models are used to evaluate the association of selected ECG features and their changes with the incident risk of AF during study follow-up. The findings are then validated in ECGs from participants enrolled in Phase III (2013-2015).

RESULTS: We identify four features that are related to the P-wave amplitude, QRS complex and ST segment. Both their initial measurement and 3-year changes are associated with the development of AF. In particular, one standard deviation in the 3-year decline of the P-wave amplitude is independently associated with a 29% increased risk of incident AF in the multivariable model (HR: 1.29, 95% CI: [1.16, 1.43]).

CONCLUSIONS: Compared with deep learning methods, our features are intuitive and can provide insights into the longitudinal ECG changes at an individual level that precede the development of AF.

PMID:39894874 | DOI:10.1038/s43856-025-00749-2

Med Phys. 2025 Feb 2. doi: 10.1002/mp.17636. Online ahead of print.

ABSTRACT

BACKGROUND: Sparse-view CT shortens scan time and reduces radiation dose but results in severe streak artifacts due to insufficient sampling data. Deep learning methods can now suppress these artifacts and improve image quality in sparse-view CT reconstruction.

PURPOSE: The quality of sparse-view CT reconstructed images can still be improved. Additionally, the interpretability of deep learning-based optimization methods for these reconstruction images is lacking, and the role of different network layers in artifact removal requires further study. Moreover, the optimization capability of these methods for reconstruction images from various sparse views needs enhancement. This study aims to improve the network's optimization ability for sparse-view reconstructed images, enhance interpretability, and boost generalization by establishing multiple network structures and datasets.

METHODS: In this paper, we developed a sparse-view CT reconstruction images improvement network (SRII-Net) based on U-Net. We added a copy pathway in the network and designed a residual image output block to boost the network's performance. Multiple networks with different connectivity structures were established using SRII-Net to analyze the contribution of each layer to artifact removal, improving the network's interpretability. Additionally, we created multiple datasets with reconstructed images of various sampling views to train and test the proposed network, investigating how these datasets from different sampling views affect the network's generalization ability.

RESULTS: The results show that the proposed method outperforms current networks, with significant improvements in metrics like PSNR and SSIM. Image optimization time is at the millisecond level. By comparing the performance of different network structures, we've identified the impact of various hierarchical structures. The image detail information learned by shallow layers and the high-level abstract feature information learned by deep layers play a crucial role in optimizing sparse-view CT reconstruction images. Training the network with multiple mixed datasets revealed that, under a certain amount of data, selecting the appropriate categories of sampling views and their corresponding samples can effectively enhance the network's optimization ability for reconstructing images with different sampling views.

CONCLUSIONS: The network in this paper effectively suppresses artifacts in reconstructed images with different sparse views, improving generalization. We have also created diverse network structures and datasets to deepen the understanding of artifact removal in deep learning networks, offering insights for noise reduction and image enhancement in other imaging methods.

PMID:39894762 | DOI:10.1002/mp.17636

J Voice. 2025 Feb 1:S0892-1997(24)00486-7. doi: 10.1016/j.jvoice.2024.12.048. Online ahead of print.

ABSTRACT

PURPOSE: Voice pathology detection is a rapidly evolving field of scientific research focused on the identification and diagnosis of voice disorders. Early detection and diagnosis of these disorders is critical, as it increases the likelihood of effective treatment and reduces the burden on medical professionals.

METHODS: The objective of this scientific paper is to develop a comprehensive model that utilizes various deep learning techniques to improve the detection of voice pathology. To achieve this, the paper employs several techniques to extract a set of sensitive features from the original voice signal by analyzing the time-frequency characteristics of the signal. In this regard, as a means of extracting these features, a state-of-the-art approach combining Gammatonegram features with Scalogram Teager_Kaiser Energy Operator (TKEO) features is proposed, and the proposed feature extraction scheme is named Combine Gammatonegram with (TKEO) Scalogram (CGT Scalogram). In this study, ResNet deep learning is used to recognize healthy voices from pathological voices. To evaluate the performance of the proposed model, it is trained and tested using the Saarbrucken voice database.

RESULTS: In the end, the proposed system yielded impressive results with an accuracy of 96%, a precision of 96.3%, and a recall of 96.1% for binary classification and an accuracy of 94.4%, a precision of 94.5%, and a recall of 94% for multi-class.

CONCLUSION: The results of the experiments demonstrate the effectiveness of the feature selection technique in maximizing the prediction accuracy in both binary and multi-class classifications.

PMID:39894721 | DOI:10.1016/j.jvoice.2024.12.048

Lab Invest. 2025 Jan 31:104101. doi: 10.1016/j.labinv.2025.104101. Online ahead of print.

ABSTRACT

Most gastric cancer (GC) patients exhibit microsatellite stability (MSS), yet comprehensive subtyping for prognostic prediction and clinical treatment decisions for MSS GC is lacking. In this work, RNA-sequencing gene expression data and clinical information of MSS GC patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. We employed several machine learning methods to develop and validate a signature based on immune-related genes (IRGs) for subtyping MSS GC patients. Moreover, two deep learning models based on the Vision Transformer (ViT) architecture were developed to predict GC tumor tiles and identify MSS GC subtypes from digital pathology slides. Microsatellite status was evaluated by immunohistochemistry, and prognostic data as well as H&E whole slide images were collected from 105 MSS GC patients to serve as an independent validation cohort. A signature comprising five IRGs was established and validated, stratifying MSS GC patients into high-risk (MSS-HR) and low-risk (MSS-LR) groups. This signature demonstrated consistent performance, with areas under the receiver operating characteristic (ROC) curve (AUC) of 0.65, 0.70, and 0.70 at 1, 3, and 5 years in the TCGA cohort, and 0.70, 0.60, and 0.62 in the GEO cohort, respectively. The MSS-HR subtype exhibited higher levels of tumor immune dysfunction and exclusion, suggesting a greater potential for immune escape compared to the MSS-LR subtype. Moreover, the MSS-HR/LR subtypes showed differential sensitivities to various therapeutic drugs. Leveraging morphological differences, the tumor recognition segmentation model (TRSM) achieved an impressive AUC of 0.97, while the MSS-HR/LR identification model (MSSIM) effectively classified MSS-HR/LR subtypes with an AUC of 0.94. Both models demonstrated promising results in classifying MSS GC patients in the external validation cohort, highlighting the strong ability to accurately differentiate between MSS GC subtypes. The IRGs-related MSS-HR/LR subtypes had potential in enhancing outcome prediction accuracy and guide treatment strategies. This research may optimize precision treatment and improve the prognosis for MSS GC patients.

PMID:39894411 | DOI:10.1016/j.labinv.2025.104101

Acta Biomater. 2025 Jan 31:S1742-7061(25)00079-0. doi: 10.1016/j.actbio.2025.01.059. Online ahead of print.

ABSTRACT

The surface modification of titanium (Ti) and its alloys is crucial for improving their osteogenic capability, as their bio-inert nature limits effective osseointegration despite their prevalent use in orthopedic implants. However, these modification methods produce varied surface properties, making it challenging to standardize criteria for assessing the osteogenic capacity of implant surfaces. Additionally, traditional evaluation experiments are time-consuming and inefficient. To overcome these limitations, this study introduced a high-throughput, efficient screening method for assessing the osteogenic capability of implant surfaces based on early cell morphology and deep learning. The Orthopedic Implants-Osteogenic Differentiation Network (OIODNet) was developed using early cell morphology images and corresponding alkaline phosphatase (ALP) activity values from cells cultured on Ti and its alloy surfaces, achieving performance metrics exceeding 0.98 across all six evaluation parameters. Validation through metal-polyphenol network (MPN) coatings and cell experiments demonstrated a strong correlation between OIODNet's predictions and actual ALP activity outcomes, confirming its accuracy in predicting osteogenic potential based on early cell morphology. The Osteogenic Predictor application offers an intuitive tool for predicting the osteogenic capacity of implant surfaces. Overall, this research highlights the potential to accelerate progress at the intersection of artificial intelligence and biomaterials, paving the way for more efficient screening of osteogenic capabilities in orthopedic implants. STATEMENT OF SIGNIFICANCE: By leveraging deep learning, this study introduces the Orthopedic Implants-Osteogenic Differentiation Network (OIODNet), which utilizes early cell morphology data and alkaline phosphatase (ALP) activity values to provide a high-throughput, accurate method for predicting osteogenic capability. With performance metrics exceeding 0.98, OIODNet's accuracy was further validated through experiments involving metal-polyphenol network (MPN) coatings, showing a strong correlation between the model's predictions and experimental outcomes. This research offers a powerful tool for more efficient screening of implant surfaces, marking a transformative step in the integration of artificial intelligence and biomaterials, while opening new avenues for advancing orthopedic implant technologies.

PMID:39894326 | DOI:10.1016/j.actbio.2025.01.059

Spine J. 2025 Jan 31:S1529-9430(25)00053-1. doi: 10.1016/j.spinee.2025.01.020. Online ahead of print.

ABSTRACT

BACKGROUND CONTEXT: Accurate and consistent measurement of sagittal alignment is challenging, particularly in patients with severe coronal deformities, including degenerative lumbar scoliosis (DLS).

PURPOSE: This study aimed to develop and validate an artificial intelligence (AI)-based system for automating the measurement of key sagittal parameters, including lumbar lordosis, pelvic incidence, pelvic tilt, and sacral slope, with a focus on its applicability across a wide range of deformities, including severe coronal deformities, such as DLS.

DESIGN: Retrospective observational study.

PATIENT SAMPLE: A total of 1,011 standing lumbar lateral radiographs, including DLS.

OUTCOME MEASURE: Interclass and intraclass correlation coefficients (CC), and Bland-Altman plots.

METHODS: The model utilizes a deep-learning framework, incorporating a U-Net for segmentation and a Keypoint Region-based Convolutional Neural Network for keypoint detection. The ground truth masks were annotated by an experienced orthopedic specialist. The performance of the model was evaluated against ground truth measurements and assessments from two expert raters using interclass and intraclass CC, and Bland-Altman plots.

RESULTS: In the test set of 113 patients, 39 (34.5%) had DLS, with a mean Cobb's angle of 14.8° ± 4.4°. The AI model achieved an intraclass CC of 1.00 across all parameters, indicating perfect consistency. Interclass CCs comparing the AI model to ground truth ranged from 0.96 to 0.99, outperforming experienced orthopedic surgeons. Bland-Altman analysis revealed no significant systemic bias, with most differences falling within clinically acceptable ranges. A 5-fold cross-validation further demonstrated robust performance, with interclass CCs ranging from 0.96 to 0.99 across diverse subsets.

CONCLUSION: This AI-based system offers a reliable and efficient automated measurement of sagittal parameters in spinal deformities, including severe coronal deformities. The superior performance of the model compared with that of expert raters highlights its potential for clinical applications.

PMID:39894276 | DOI:10.1016/j.spinee.2025.01.020

Radiother Oncol. 2025 Jan 31:110770. doi: 10.1016/j.radonc.2025.110770. Online ahead of print.

ABSTRACT

PURPOSE: This multicenter study aimed to develop and validate a multiscale deep learning radiomics nomogram for predicting recurrence-free survival (RFS) in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS: A total of 469 PDAC patients from four hospitals were divided into training and test sets. Handcrafted radiomics and deep learning (DL) features were extracted from optimal regions of interest, encompassing both intratumoral and peritumoral areas. Univariate Cox regression, LASSO regression, and multivariate Cox regression selected features for three image signatures (intratumoral, peritumoral radiomics, and DL). A multiscale nomogram was constructed and validated against the 8th AJCC staging system.

RESULTS: The 4 mm peritumoral VOI yielded the best radiomics prediction, while a 15 mm expansion was optimal for deep learning. The multiscale nomogram demonstrated a C-index of 0.82 (95 % CI: 0.78-0.85) in the training set and 0.70 (95 % CI: 0.64-0.76) in the external test 1 (high-volume hospital), with the external test 2 (low-volume hospital) showing a C-index of 0.78 (95 % CI: 0.65-0.91). These outperformed the AJCC system's C-index (0.54-0.57). The area under the curve (AUC) for recurrence prediction at 0.5, 1, and 2 years was 0.89, 0.94, and 0.89 in the training set, with AUC values ranging from 0.75 to 0.97 in the external validation sets, consistently surpassing the AJCC system across all sets.. Kaplan-Meier analysis confirmed significant differences in prognosis between high- and low-risk groups (P < 0.01 across all cohorts).

CONCLUSION: The multiscale nomogram effectively stratifies recurrence risk in PDAC patients, enhancing presurgical clinical decision-making and potentially improving patient outcomes.

PMID:39894259 | DOI:10.1016/j.radonc.2025.110770

Neuroimage. 2025 Jan 31:121045. doi: 10.1016/j.neuroimage.2025.121045. Online ahead of print.

ABSTRACT

INTRODUCTION: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian compressed-sense acquisition enables fast high-resolution metabolic imaging but has lengthy reconstruction times that limits throughput and needs expert user interaction. Here, we present a robust and efficient Deep Learning reconstruction embedded in a physical model within an end-to-end automated processing pipeline to obtain high-quality metabolic maps.

METHODS: Fast high-resolution whole-brain metabolic imaging was performed at 3.4 mm3 isotropic resolution with acquisition times between 4:11-9:21 min:s using ECCENTRIC pulse sequence on a 7T MRI scanner. Data were acquired in a high-resolution phantom and 27 human participants, including 22 healthy volunteers and 5 glioma patients. A deep neural network using recurring interlaced convolutional layers with joint dual-space feature representation was developed for deep learning ECCENTRIC reconstruction (Deep-ER). 21 subjects were used for training and 6 subjects for testing. Deep-ER performance was compared to iterative compressed sensing Total Generalized Variation reconstruction using image and spectral quality metrics.

RESULTS: Deep-ER demonstrated 600-fold faster reconstruction than conventional methods, providing improved spatial-spectral quality and metabolite quantification with 12%-45% (P<0.05) higher signal-to-noise and 8%-50% (P<0.05) smaller Cramer-Rao lower bounds. Metabolic images clearly visualize glioma tumor heterogeneity and boundary. Deep-ER generalizes reliably to unseen data.

CONCLUSION: Deep-ER provides efficient and robust reconstruction for sparse-sampled MRSI. The accelerated acquisition-reconstruction MRSI is compatible with high-throughput imaging workflow. It is expected that such improved performance will facilitate basic and clinical MRSI applications for neuroscience and precision medicine.

PMID:39894238 | DOI:10.1016/j.neuroimage.2025.121045

Ann Rheum Dis. 2025 Feb 1:S0003-4967(25)00078-0. doi: 10.1016/j.ard.2025.01.022. Online ahead of print.

ABSTRACT

OBJECTIVES: To analyse the immune mechanisms of diffuse cutaneous systemic sclerosis (dcSSc) skin disease focusing on CD8+ T-cell responses in the affected skin of patients because chronic inflammation, vasculopathy, and extensive cutaneous fibrosis are prominent features of dcSSc skin disease, causing pain and disability in patients, with no effective therapy.

METHODS: Single-cell transcriptomics and epigenomics were applied to well-characterised patient skin samples to identify transcriptomes and key regulators of skin-resident CD8+ T-cell subsets. Multicolor immunofluorescence miscoscopy was used to validate molecular findings. Ex vivo skin explant assays were used to functionally characterise dysfunctional CD8+ T-cell subsets on nonlesional autologous skin.

RESULTS: We identified 2 major developmentally connected CD8+ T-cell subpopulations that were expanded in SSc skin lesions compared with healthy control skin. The first was a heterogeneous subset of effector-memory CD8+KLRB1+IL7R+ cells characterised by increased cytolytic and Tc2/Tc17 effector functions that appear to induce tissue damage and fibrosis in early-stage dcSSc skin lesions. The second, found primarily in patients with late-stage disease, was an exhausted CD8+KLRG1+IL7R- subset that exhibited transcriptional features of long-lived effector cells, likely contributing to chronic inflammation. Significantly, both subsets were also expanded in other benign dermatoses, implicating these cell populations in the pathogenesis of chronic human skin inflammation.

CONCLUSIONS: This study provides new insight into core regulatory programmes modulating skin-resident CD8+ T-cell plasticity and identifies distinct CD8+ T-cell subpopulations that contribute to initiation and chronicity of inflammatory responses in systemic sclerosis skin lesions. These findings reveal prospective molecular targets for new therapeutic strategies against this incurable disease.

PMID:39894688 | DOI:10.1016/j.ard.2025.01.022

J Adv Res. 2025 Jan 31:S2090-1232(25)00071-2. doi: 10.1016/j.jare.2025.01.052. Online ahead of print.

ABSTRACT

BACKGROUND: The development of pangenomes has revolutionized genomic studies by capturing the complete genetic diversity within a species. Pangenome assembly integrates data from multiple individuals to construct a comprehensive genomic landscape, revealing both core and accessory genomic elements. This approach enables the identification of novel genes, structural variations, and gene presence-absence variations, providing insights into species evolution, adaptation, and trait variation. Representing pangenomes requires innovative visualization formats that effectively convey the complex genomic structures and variations.

AIM: This review delves into contemporary methodologies and recent advancements in constructing pangenomes, particularly in plant genomes. It examines the structure of pangenome representation, including format comparison, conversion, visualization techniques, and their implications for enhancing crop improvement strategies.

KEY SCIENTIFIC CONCEPTS OF REVIEW: Earlier comparative studies have illuminated novel gene sequences, copy number variations, and presence-absence variations across diverse crop species. The concept of a pan-genome, which captures multiple genetic variations from a broad spectrum of genotypes, offers a holistic perspective of a species' genetic makeup. However, constructing a pan-genome for plants with larger genomes poses challenges, including managing vast genome sequence data and comprehending the genetic variations within the germplasm. To address these challenges, researchers have explored cost-effective alternatives to encapsulate species diversity in a single assembly known as a pangenome. This involves reducing the volume of genome sequences while focusing on genetic variations. With the growing prominence of the pan-genome concept in plant genomics, several software tools have emerged to facilitate pangenome construction. This review sheds light on developing and utilizing software tools tailored for constructing pan-genomes in plants. It also discusses representation formats suitable for downstream analyses, offering valuable insights into the genetic landscape and evolutionary dynamics of plant species. In summary, this review underscores the significance of pan-genome construction and representation formats in resolving the genetic architecture of plants, particularly those with complex genomes. It provides a comprehensive overview of recent advancements, aiding in exploring and understanding plant genetic diversity.

PMID:39894347 | DOI:10.1016/j.jare.2025.01.052

Int J Biol Macromol. 2025 Jan 31:140567. doi: 10.1016/j.ijbiomac.2025.140567. Online ahead of print.

ABSTRACT

BACKGROUND: Staphylococcus aureus (S. aureus) is a globally prevalent foodborne pathogen responsible for significant public health concerns. Staphylococcal food poisoning (SFP) results from staphylococcal enterotoxins (SEs) produced by specific strains of S. aureus. Rapid and effective detection of SEs remains a significant challenge for public health authorities. Aptamers, short single-stranded DNA(ssDNA), RNA, or synthetic xeno nucleic acid (XNA) molecules, exhibit high affinity for binding to their specific targets. Due to their unique properties, including low production costs, ease of chemical modification, high thermal stability, and reproducibility, aptamers present a viable alternative to antibodies for diagnostic and therapeutic applications.

OBJECTIVES: This research aimed to isolate high-affinity ssDNA aptamers with specificity for staphylococcal enterotoxin D (SED).

METHODS: The systematic evolution of ligands by the exponential enrichment (SELEX) method was utilized to identify specific aptamers. These aptamers were then validated using enzyme-linked apta-sorbent assay (ELASA) and surface plasmon resonance (SPR) to assess their binding characteristics and affinity.

RESULTS: SELEX successfully identified aptamers with strong binding affinity to SED. Among the identified candidates, one aptamer, Aptamer 1, exhibited the highest specificity for SED with a dissociation constant (KD) of 4.4 ± 2.26 nM. The limit of detection (LOD) for SED using this aptamer was determined to be 45 nM.

CONCLUSIONS: The findings indicate that the ELASA system designed using the aptamer developed in this study demonstrates higher specificity, sensitivity, and reproducibility in detecting enterotoxin D. This novel aptamer offers significant potential for applications in diagnostic platforms targeting S. aureus enterotoxins.

PMID:39894103 | DOI:10.1016/j.ijbiomac.2025.140567

SLAS Discov. 2025 Jan 31:100220. doi: 10.1016/j.slasd.2025.100220. Online ahead of print.

ABSTRACT

Protein-nucleic acid phase separation has been implicated in many diseases such as viral infections, neurodegeneration, and cancer. There is great interest in identifying condensate modulators (CMODs), which are small molecules that alter the dynamics and functions of phase-separated condensates, as a potential therapeutic modality. Most CMODs were identified in cellular high-content screens (HCS) where micron-scale condensates were characterized by fluorescence microscopy. These approaches lack information on protein dynamics, are limited by microscope resolution, and are insensitive to subtle condensation phenotypes missed by overfit analysis pipelines. Here, we evaluate two alternative cell-based assays: high-throughput single molecule tracking (htSMT) and proximity-based condensate biosensors using NanoBIT (split luciferase) and NanoBRET (bioluminescence resonance energy transfer) technologies. We applied these methods to evaluate condensation of the SARS-CoV-2 nucleocapsid (N) protein under GSK3 inhibitor treatment, which we had previously identified in our HCS campaign to induce condensation with well-defined structure-activity relationships (SAR). Using htSMT, we observed robust changes in N protein diffusion as early as 3 hours post GSK3 inhibition. Proximity-based N biosensors also reliably reported on condensation, enabling the rapid assaying of large compound libraries with a readout independent of imaging. Both htSMT and proximity-based biosensors performed well in a screening format and provided information on CMOD activity that was complementary to HCS. We expect that this expanded toolkit for interrogating phase-separated proteins will accelerate the identification of CMODs for important therapeutic targets.

PMID:39894078 | DOI:10.1016/j.slasd.2025.100220

Hum Vaccin Immunother. 2025 Dec;21(1):2458948. doi: 10.1080/21645515.2025.2458948. Epub 2025 Feb 2.

ABSTRACT

The utilization of immune-checkpoint inhibitors (ICIs) in cancer immunotherapy frequently leads to the occurrence of immune-related adverse events (irAEs), making it generally not recommended for patients with preexisting autoimmune diseases. Hence, we conducted a meta-analysis on safety and efficacy of ICIs in cancer patients with preexisting autoimmune diseases to provide further insights. PubMed, EMBASE, and Cochrane Library were systematically searched until December 20, 2024. The main summary measures used were pooled rate and risk ratio (RR) with 95% confidential interval (CI), which were analyzed using R statistic software. A total of 52 articles were included in the study. When cancer patients with preexisting autoimmune diseases received ICIs treatment, the overall incidence was 0.610 (95% CI: 0.531-0.686) for any grade irAEs, 0.295 (95% CI: 0.248-0.343) for flares, 0.325 (95% CI: 0.258-0.396) for de novo irAEs, 0.238 (95% CI: 0.174-0.309) for grade ≥3 irAEs, and 0.143 (95% CI: 0.109-0.180) for discontinuation due to immunotoxicity. Compared with those without autoimmune diseases, cancer patients with autoimmune diseases experienced a higher risk of any-grade irAEs (RR: 1.23, 95% CI: 1.12-1.35) and discontinuation due to immunotoxicity (1.40, 95% CI: 1.11-1.78). However, no statistically significant differences were observed in the incidence of grade ≥3 irAEs, objective response rate (ORR), disease control rate (DCR), overall survival (OS), and progression-free survival (PFS) between the two groups. During ICIs treatment, irAEs are common among cancer patients with autoimmune diseases, but severe irAEs is relatively low. ICIs are effective in this population, but should be strictly monitored when used to avoid immunotoxicity.

PMID:39894761 | DOI:10.1080/21645515.2025.2458948

Hum Vaccin Immunother. 2025 Dec;21(1):2458831. doi: 10.1080/21645515.2025.2458831. Epub 2025 Feb 2.

ABSTRACT

To comprehensively assess the safety and difference of two types of EV71 vaccines: EV71-Vero, produced using Vero cells and EV71-H2, using human diploid cells. Our research included children of the recommended age who voluntarily received the EV71 vaccine in Hebei Province from 2019 to 2023. Detailed data on adverse events following immunization (AEFI) were collected, analyzed and compared for EV71-Vero and EV71-H2 vaccines. With 477 AEFI reported, the reported rate was 14.21 per 100,000 doses. Most cases occurred in infants under one year of age (45.91%). No significant differences in the AEFI reported rate were found between two types of EV71 vaccines across various demographic. However, a higher number of AEFI was reported in children under 1-year old following EV71-Vero compared to EV71-H2 with a reversal in 4-5 years- group (χ2 = 13.90, p = .01). The prognosis of cured took higher proportion for EV71-Vero than for EV71-H2 while inversely with improved outcome. The EV71 vaccine is advisable recommend to the appropriate age children to prevent EV7l infection. Both the EV71-Vero and EV71-H2 vaccines have good safety profiles. The reported AEFI, primarily high fever and allergic reactions, showed no significant differences in reported rates or case characteristics between the two types.

PMID:39894458 | DOI:10.1080/21645515.2025.2458831

Cell Rep. 2025 Feb 1;44(2):115240. doi: 10.1016/j.celrep.2025.115240. Online ahead of print.

ABSTRACT

Despite the broad use of single-cell/nucleus RNA sequencing in plant research, accurate cluster annotation in less-studied plant species remains a major challenge due to the lack of validated marker genes. Here, we generated a single-cell RNA sequencing atlas of soil-grown wheat roots and annotated cluster identities by transferring annotations from publicly available datasets in wheat, rice, maize, and Arabidopsis. The predictions from our orthology-based annotation approach were next validated using untargeted spatial transcriptomics. These results allowed us to predict evolutionarily conserved tissue-specific markers and generate cell type-specific gene regulatory networks for root tissues of wheat and the other species used in our analysis. In summary, we generated a single-cell and spatial transcriptomics resource for wheat root apical meristems, including numerous known and uncharacterized cell type-specific marker genes and developmental regulators. These data and analyses will facilitate future cell type annotation in non-model plant species.

PMID:39893633 | DOI:10.1016/j.celrep.2025.115240

STAR Protoc. 2025 Jan 31;6(1):103593. doi: 10.1016/j.xpro.2024.103593. Online ahead of print.

ABSTRACT

Here, we present a protocol for the rapid functional screening of gene editing and addition strategies in patient-derived organoids using the deep-learning-based tool DETECTOR (detection of targeted editing of cystic fibrosis transmembrane conductance regulator [CFTR] in organoids). We describe steps for wet-lab experiments, image acquisition, and CFTR function analysis by DETECTOR. We also detail procedures for applying pre-trained models and training custom models on new customized datasets. For complete details on the use and execution of this protocol, refer to Bulcaen et al.1.

PMID:39893642 | DOI:10.1016/j.xpro.2024.103593

Mar Pollut Bull. 2025 Feb 1;213:117608. doi: 10.1016/j.marpolbul.2025.117608. Online ahead of print.

ABSTRACT

Motivated by the need of rapid detection of living microalgae cells in ship ballast water, this study is intended to determine the activities of microalgae using stained microscopic images and detect the living cells with image processing algorithms. The staining selectivity on living cells of neutral red dye is utilized to distinguish the activities of microalgae. A deep-learning-based detection model was designed and tested using the microscopic images of stained microalgae cells. The results showed that the deep learning model achieved high accuracies without considering the activities of microalgae: The model's average precisions (APs) on Platymonas helgolandica tsingtaoensis and Alexandrium catenella were 99.3 % and 98.3 %, respectively. In contrast, the detection accuracies of living microalgae cells were slightly lower: The model's APs on living Platymonas helgolandica tsingtaoensis and Alexandrium catenella were 91.7 % and 91.9 %, respectively. The model achieved high detection accuracy and determined the activities of microalgae cells.

PMID:39893717 | DOI:10.1016/j.marpolbul.2025.117608

Environ Sci Technol. 2025 Feb 2. doi: 10.1021/acs.est.4c10595. Online ahead of print.

ABSTRACT

Aqueous film-forming foams (AFFFs) are complex product mixtures that often contain per- and polyfluorinated alkyl substances (PFAS) to enhance fire suppression and protect firefighters. However, PFAS have been associated with a range of adverse health effects (e.g., liver and thyroid disease and cancer), and innovative approach methods to better understand their toxicity potential and identify safer alternatives are needed. In this study, we investigated a set of 30 substances (e.g., AFFF, PFAS, and clinical drugs) using differentiated cultures of human hepatocytes (HepaRG, 2D), high-throughput transcriptomics, deep learning of cell morphology images, and liver enzyme leakage assays with benchmark dose analysis to (1) predict the potency ranges for human liver injury, (2) delineate gene- and pathway-level transcriptomic points-of-departure for molecular hazard characterization and prioritization, (3) characterize human hepatocellular response similarities to inform regulatory read-across efforts, and (4) introduce an innovative approach to translate mechanistic hepatocellular response data to predict the potency ranges for PFAS-induced hepatomegaly in vivo. Collectively, these data fill important mechanistic knowledge gaps with PFAS/AFFF and represent a scalable platform to address the thousands of PFAS in commerce for greener chemistries and next-generation risk assessments.

PMID:39893674 | DOI:10.1021/acs.est.4c10595

STAR Protoc. 2025 Jan 31;6(1):103593. doi: 10.1016/j.xpro.2024.103593. Online ahead of print.

ABSTRACT

Here, we present a protocol for the rapid functional screening of gene editing and addition strategies in patient-derived organoids using the deep-learning-based tool DETECTOR (detection of targeted editing of cystic fibrosis transmembrane conductance regulator [CFTR] in organoids). We describe steps for wet-lab experiments, image acquisition, and CFTR function analysis by DETECTOR. We also detail procedures for applying pre-trained models and training custom models on new customized datasets. For complete details on the use and execution of this protocol, refer to Bulcaen et al.1.

PMID:39893642 | DOI:10.1016/j.xpro.2024.103593

Anal Chem. 2025 Feb 2. doi: 10.1021/acs.analchem.4c05113. Online ahead of print.

ABSTRACT

Mass spectrometry is used to determine infectious microbial species in thousands of clinical laboratories across the world. The vast amount of data allows modern data analysis methods that harvest more information and potentially answer new questions. Here, we present an end-to-end deep learning model for predicting antibiotic resistance using raw matrix assisted laser desorption ionization mass spectrometry (MALDI-MS) data. We used a 1-dimensional convolutional neural network to model (almost) raw data, skipping conventional peak-picking and directly predict resistance. The model's performance is state-of-the-art, having AUCs between 0.93 and 0.99 in all antimicrobial resistance phenotypes and validates across time and location. Feature attribution values highlight important insights into the model and how the end-to-end workflow can be improved further. This study showcases that reliable resistance phenotyping using MALDI-MS data is attainable and highlights the gains of using end-to-end deep learning for spectrometry data.

PMID:39893590 | DOI:10.1021/acs.analchem.4c05113