Transl Anim Sci. 2025 Feb 5;9:txae168. doi: 10.1093/tas/txae168. eCollection 2025.

ABSTRACT

The aim of the study was to deploy computer vision models for real-time detection of digital dermatitis (DD) lesions in cows using Android or iOS mobile applications. Early detection of DD lesions in dairy cows is crucial for prompt treatment and animal welfare. Android and iOS apps could facilitate routine and early DD detection in cows' feet on dairy and beef farms. Upon detecting signs of DD, dairy farmers could implement preventive and treatment methods, including foot baths, topical treatment, hoof trimming, or quarantining cows affected by DD to prevent its spread. We applied transfer-learning to DD image data for 5 lesion classes, M0, M4H, M2, M2P, and M4P, on pretrained YOLOv5 model architecture using COCO-128 pretrained weights. The combination of localization loss, classification loss, and objectness loss was used for the optimization of prediction performance. The custom DD detection model was trained on 363 images of size 416 × 416 pixels and tested on 46 images. During model training, data were augmented to increase model robustness in different environments. The model was converted into TFLite format for Android devices and CoreML format for iOS devices. Techniques such as quantization were implemented to improve inference speed in real-world settings. The DD models achieved a mean average precision (mAP) of 0.95 on the test dataset. When tested in real-time, iOS devices resulted in Cohen's kappa value of 0.57 (95% CI: 0.49 to 0.65) averaged across the 5 lesion classes denoting the moderate agreement of the model detection with human investigators. The Android device resulted in a Cohen's kappa value of 0.38 (95% CI: 0.29 to 0.47) denoting fair agreement between model and investigator. Combining M2 and M2P classes and M4H and M4P classes resulted in a Cohen's kappa value of 0.65 (95% CI: 0.54 to 0.76) and 0.46 (95% CI: 0.35 to 0.57), for Android and iOS devices, respectively. For the 2-class model (lesion vs. non-lesion), a Cohen's kappa value of 0.74 (95% CI: 0.63 to 0.85) and 0.65 (95% CI: 0.52 to 0.78) was achieved for iOS and Android devices, respectively. iOS achieved a good inference time of 20 ms, compared to 57 ms on Android. Additionally, we deployed models on Ultralytics iOS and Android apps giving kappa scores of 0.56 (95% CI: 0.48 to 0.64) and 0.46 (95% CI: 0.37 to 0.55), respectively. Our custom iOS app surpassed the Ultralytics apps in terms of kappa score and confidence score.

PMID:39959562 | PMC:PMC11829201 | DOI:10.1093/tas/txae168

Heliyon. 2025 Jan 25;11(3):e42257. doi: 10.1016/j.heliyon.2025.e42257. eCollection 2025 Feb 15.

ABSTRACT

Congenital heart disease (CHD), impacting around 1 % of infants worldwide, constitutes a significant healthcare challenge. Early detection is crucial, however constrained by the intricacies of conventional diagnostic techniques such as auscultation and echocardiography. This research presents a tailored one-dimensional convolutional neural network (1D-CNN) for the classification of phonocardiogram (PCG) signals into normal or abnormal categories, providing an automated and efficient solution for congenital heart disease (CHD) diagnosis. The model was trained on a composite dataset consisting of local pediatric PCG signals and publicly accessible dataset. Preprocessing methods, such as low- and high-pass filtering (60-650 Hz), resampling, and noise reduction, were utilized to enhance signal quality. Data augmentation techniques, including chunking, padding, and pitch-shifting, were employed to rectify dataset imbalances and improve model efficacy. Experimental results indicate substantial enhancements, attaining an accuracy of 98.56 %, precision of 98.56 %, F1 score of 98.55 %, sensitivity of 0.98, and specificity of 0.99. The comparative analysis demonstrates the proposed approach's superiority over current methods regarding accuracy and reliability. The research highlights the promise of combining modern signal processing with deep learning for efficient CHD screening. The suggested model exhibits outstanding performance yet, issues like dataset variability and noise persist. Future endeavors involve extending to multiclass categorization and assessing performance across a wider range of medical problems. This study represents a significant advancement in accessible, automated CHD diagnoses, enhancing clinical competence to elevate pediatric treatment.

PMID:39959496 | PMC:PMC11830292 | DOI:10.1016/j.heliyon.2025.e42257

Front Plant Sci. 2025 Jan 31;16:1533206. doi: 10.3389/fpls.2025.1533206. eCollection 2025.

ABSTRACT

INTRODUCTION: Ratoon rice, as a high-efficiency rice cultivation mode, is widely applied around the world. Mechanical righting of rolled rice stubble can significantly improve yield in regeneration season, but lack of automation has become an important factor restricting its further promotion.

METHODS: In order to realize automatic navigation of the righting machine, a method of fusing an instance segmentation model and a monocular depth prediction model was used to realize monocular localization of the rolled rice stubble rows in this study.

RESULTS: To achieve monocular depth prediction, a depth estimation model was trained on training set we made, and absolute relative error of trained model on validation set was only 7.2%. To address the problem of degradation of model's performance when migrated to other monocular cameras, based on the law of the input image's influence on model's output results, two optimization methods of adjusting inputs and outputs were used that decreased the absolute relative error from 91.9% to 8.8%. After that, we carried out model fusion experiments, which showed that CD (chamfer distance) between predicted 3D coordinates of navigation points obtained by fusing the results of the two models and labels was only 0.0990. The CD between predicted point cloud of rolled rice stubble rows and label was only 0.0174.

PMID:39959348 | PMC:PMC11825797 | DOI:10.3389/fpls.2025.1533206

ACS Omega. 2025 Jan 28;10(5):5105-5110. doi: 10.1021/acsomega.4c11266. eCollection 2025 Feb 11.

ABSTRACT

The integration of artificial intelligence (AI) into natural product science is an exciting and rapidly evolving area of research. By combining classical chemistry and biology with deep learning, these technologies have significantly improved research efficiency, particularly in overcoming laborious and time-consuming processes. Recently, there has been growing interest in leveraging multimodal algorithms to integrate biologically relevant yet mathematically disparate data sets in order to reorganize knowledge graphs. However, to the best of our knowledge, no studies have yet applied this approach specifically within the natural product field. This is largely because correlating multimodal natural product data is challenging due to their high degree of fragmentation. Here, we present MultiT2, an algorithm that connects these disparate data from bacterial aromatic polyketides, which form a medically important natural product family, as a showcase. Through a large-scale causal inference process, this approach aims to transcend mere prediction, unlocking new dimensions in the natural product discovery and research domains.

PMID:39959056 | PMC:PMC11822507 | DOI:10.1021/acsomega.4c11266

Arch Acad Emerg Med. 2024 Dec 26;13(1):e23. doi: 10.22037/aaemj.v13i1.2479. eCollection 2025.

ABSTRACT

INTRODUCTION: Identifying the people who try to hide illegal substances in the body for smuggling is of considerable importance in forensic medicine and poisoning. This study aimed to develop a new diagnostic method using artificial intelligence to detect body packs in real-time Abdominal computed tomography (CT) scans.

METHODS: In this cross-sectional study, abdominal CT scan images were employed to create a machine learning-based model for detecting body packs. A single-step object detection called RetinaNet using a modified neck (Proposed Model) was performed to achieve the best results. Also, an angled Bbox (oriented bounding box) in the training dataset played an important role in improving the results.

RESULTS: A total of 888 abdominal CT scan images were studied. Our proposed Body Packs Detection (BPD) model achieved a mean average precision (mAP) value of 86.6% when the intersection over union (IoU) was 0.5, and a mAP value of 45.6% at different IoU thresholds (from 0.5 to 0.95 in steps of 0.05). It also obtained a Recall value of 58.5%, which was the best result among the standard object detection methods such as the standard RetinaNet.

CONCLUSION: This study employed a deep learning network to identify body packs in abdominal CT scans, highlighting the importance of incorporating object shape and variability when leveraging artificial intelligence in healthcare to aid medical practitioners. Nonetheless, the development of a tailored dataset for object detection, like body packs, requires careful curation by subject matter specialists to ensure successful training.

PMID:39958959 | PMC:PMC11829241 | DOI:10.22037/aaemj.v13i1.2479

Phys Imaging Radiat Oncol. 2025 Jan 26;33:100708. doi: 10.1016/j.phro.2025.100708. eCollection 2025 Jan.

ABSTRACT

BACKGROUND AND PURPOSE: The development of Magnetic Resonance Imaging (MRI)-only Radiotherapy (RT) represents a significant advancement in the field. This study introduces a Deep Learning (DL) algorithm designed to quickly generate synthetic CT (sCT) images from low-field MR images in the brain, an area not yet explored.

METHODS: Fifty-six patients were divided into training (32), validation (8), and test (16) groups. A conditional Generative Adversarial Network (cGAN) was trained on pre-processed axial paired images. sCTs were validated using mean absolute error (MAE) and mean error (ME) calculated within the patient body. Intensity Modulated Radiation Therapy (IMRT) plans were optimised on simulation MRI and calculated considering sCT and original CT as electron density (ED) map. Dose distributions using sCT and CT were compared using global gamma analysis at different tolerance criteria (2 %/2mm and 3 %/3mm) and evaluating the difference in estimating different Dose Volume Histogram (DVH) parameters for target and organs at risk (OARs).

RESULTS: The network generated sCTs of each single patient in less than two minutes (mean time = 103 ± 41 s). For test patients, the MAE was 62.1 ± 17.7 HU, and the ME was -7.3 ± 13.4 HU. Dose parameters on sCTs were within 0.5 Gy of those on original CTs. Gamma passing rates 2 %/2mm, and 3 %/3mm criteria were 99.5 %±0.5 %, and 99.7 %±0.3 %, respectively.

CONCLUSION: The proposed DL algorithm generates in less than 2 min accurate sCT images in the brain for online adaptive radiotherapy, potentially eliminating the need for CT simulation in MR-only workflows for brain treatments.

PMID:39958708 | PMC:PMC11830347 | DOI:10.1016/j.phro.2025.100708

Front Immunol. 2025 Jan 27;16:1530279. doi: 10.3389/fimmu.2025.1530279. eCollection 2025.

ABSTRACT

BACKGROUND: This study aimed to develop a multi-modality model by incorporating pretreatment computed tomography (CT) radiomics and pathomics features along with clinical variables to predict pathologic complete response (pCR) to neoadjuvant chemoimmunotherapy in patients with locally advanced esophageal cancer (EC).

METHOD: A total of 223 EC patients who underwent neoadjuvant chemoimmunotherapy followed by surgical intervention between August 2021 and December 2023 were included in this study. Radiomics features were extracted from contrast-enhanced CT images using PyrRadiomics, while pathomics features were derived from whole-slide images (WSIs) of pathological specimens using a fine-tuned deep learning model (ResNet-50). After feature selection, three single-modality prediction models and a combined multi-modality model integrating two radiomics features, 11 pathomics features, and two clinicopathological features were constructed using the support vector machine (SVM) algorithm. The performance of the models were evaluated using receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA). Shapley values were also utilized to explain the prediction model.

RESULTS: The predictive capability of the multi-modality model in predicting pCR yielded an area under the curve (AUC) of 0.89 (95% confidence interval [CI], 0.75-1.00), outperforming the radiomics model (AUC 0.70 [95% CI 0.54-0.85]), pathomics model (AUC 0.77 [95% CI 0.53-1.00]), and clinical model (AUC 0.63 [95% CI 0.46-0.80]). Additionally, both the calibration plot and DCA curves support the clinical utility of the integrated multi-modality model.

CONCLUSIONS: The combined multi-modality model we propose can better predict the pCR status of esophageal cancer and help inform clinical treatment decisions.

PMID:39958355 | PMC:PMC11827421 | DOI:10.3389/fimmu.2025.1530279

J Inflamm Res. 2025 Feb 11;18:1993-2009. doi: 10.2147/JIR.S489210. eCollection 2025.

ABSTRACT

BACKGROUND: Studies suggest that immune and inflammation processes may be involved in the development of idiopathic pulmonary fibrosis (IPF); however, their roles remain unclear. This study aims to identify key genes associated with immune response and inflammation in IPF using bioinformatics.

METHODS: We identified differentially expressed genes (DEGs) in the GSE93606 dataset and GSE28042 dataset, then obtained differentially expressed immune- and inflammation-related genes (DE-IFRGs) by overlapping DEGs. Two machine learning algorithms were used to further screen key genes. Genes with an area under curve (AUC) of > 0.7 in receiver operating characteristic (ROC) curves, significant expression and consistent trends across datasets were considered key genes. Based on these key genes, we carried out nomogram construction, enrichment and immune analyses, regulatory network mapping, drug prediction, and expression verification.

RESULTS: 27 DE-IFRGs were identified by intersecting 256 DEGs, 1793 immune-related genes, and 1019 inflammation-related genes. Three genes (RNASE3, S100A12, S100A8) were obtained by crossing two machine algorithms (Boruta and LASSO),which had good diagnostic performance with AUC values. These key genes were all enriched in the same pathways, such as GOCC_azurophil_granule, IL-12 signalling and production in macrophages is the pathway with the strongest role for key genes. Six distinct immune cells, including naive CD4 T cells, T cells CD4 memory resting, T cells regulatory (Tregs), Monocytes, Macrophages M2, Neutrophils were identified. Real-time quantitative polymerase chain reaction (RT-qPCR) results were consistent with the training and validation sets, and the expression of these key genes was significantly upregulated in the IPF samples.

CONCLUSION: This study identified three key genes (RNASE3, S100A12 and S100A8) associated with immune response and inflammation in IPF, providing valuable insights into the diagnosis and treatment of IPF.

PMID:39959639 | PMC:PMC11829586 | DOI:10.2147/JIR.S489210

Cureus. 2025 Jan 16;17(1):e77522. doi: 10.7759/cureus.77522. eCollection 2025 Jan.

ABSTRACT

Bird fancier's lung (BFL) is a subtype of hypersensitivity pneumonitis (HP), an immune-mediated interstitial lung disease (ILD) resulting from the repeated inhalation of avian proteins found in bird droppings, feathers, and serum. Diagnosing BFL is challenging due to nonspecific symptoms that overlap with other ILDs like idiopathic pulmonary fibrosis and sarcoidosis. This complexity is heightened during pandemics such as coronavirus disease 2019 (COVID-19), where respiratory symptoms may be misattributed to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, leading to diagnostic anchoring and delays in appropriate management. High-resolution computed tomography (HRCT) is pivotal in detecting subtle pulmonary changes, characteristic of HP, surpassing standard chest radiographs. We present the case of a 43-year-old male pigeon keeper with an eight-week history of progressive dyspnea on exertion and intermittent chest pain. Despite unremarkable chest X-rays, HRCT revealed bilateral diffuse centrilobular nodules, patchy ground-glass opacities, and a mosaic attenuation pattern without fibrosis, consistent with acute HP. A thorough occupational history uncovered significant avian antigen exposure, and a family history suggested genetic susceptibility. The patient was diagnosed with BFL and treated with a tapering regimen of oral corticosteroids, starting at 40 mg/day. He was advised to cease pigeon keeping and avoid future avian exposure. Significant symptomatic improvement occurred within three months. Follow-up imaging over one year confirmed stable lung parenchyma with no disease progression or recurrence. This case underscores the importance of incorporating detailed occupational histories and utilizing advanced imaging modalities like HRCT when standard imaging is inconclusive. Early identification and intervention are crucial to prevent progression to chronic HP and irreversible fibrosis. Management should focus on reducing inflammation with corticosteroids and implementing strict environmental controls to prevent re-exposure. Long-term follow-up is essential to monitor for recurrence and maintain remission. Clinicians should remain vigilant for alternative diagnoses during pandemics to avoid diagnostic anchoring. This case contributes to the evidence supporting HRCT's critical role in early HP detection and emphasizes heightened clinical awareness of occupational lung diseases. A multidisciplinary approach involving pulmonologists, radiologists, and occupational medicine specialists is key to optimizing outcomes in HP and other ILDs.

PMID:39958101 | PMC:PMC11830419 | DOI:10.7759/cureus.77522

Annu Rev Cancer Biol. 2024 Jun;8(1):15-33. doi: 10.1146/annurev-cancerbio-062722-013740. Epub 2023 Dec 6.

ABSTRACT

The non-receptor tyrosine phosphatase SHP2 has been at the center of cell signaling research for three decades. SHP2 is required to fully activate the RTK-RAS-ERK cascade, although the underlying mechanisms are not completely understood. PTPN11, coding for SHP2, is the first identified proto-oncogene that encodes a tyrosine phosphatase, with dominantly activating mutations detected in leukemias and solid tumors. However, SHP2 has been shown to have pro- and anti-oncogenic effects, and the most recent data reveal opposite activities of SHP2 in tumor cells and microenvironment cells. Allosteric SHP2 inhibitors show promising anti-tumor effects and overcome resistance to inhibitors of RAS-ERK signaling in animal models. Many clinical trials with orally bioactive SHP2 inhibitors, alone or combined with other regimens, are ongoing for a variety of cancers worldwide, with therapeutic outcomes yet unknown. This review discusses the multi-faceted SHP2 functions in oncogenesis, preclinical studies and clinical trials with SHP2 inhibitors in oncological treatment.

PMID:39959686 | PMC:PMC11824402 | DOI:10.1146/annurev-cancerbio-062722-013740

Liver Res. 2024 Dec 5;8(4):218-236. doi: 10.1016/j.livres.2024.12.001. eCollection 2024 Dec.

ABSTRACT

Liver cancer ranks as the sixth most common cancer globally, with hepatocellular carcinoma (HCC) accounting for approximately 75%-85% of cases. Most patients present with moderately advanced disease, while those with advanced HCC face limited and ineffective treatment options. Despite diagnostic efforts, no ideal tumor marker exists to date, highlighting the urgent clinical need for improved early detection of HCC. A key research objective is the development of assays that target specific pathways involved in HCC progression. This review explores the pathological origin and development of HCC, providing insights into the mechanistic rationale, clinical statistics, and the advantages and limitations of commonly used diagnostic tumor markers. Additionally, it discusses the potential of emerging biomarkers for early diagnosis and offers a brief overview of relevant assay methodologies. This review aims to summarize existing markers and investigate new ones, providing a basis for subsequent research.

PMID:39958920 | PMC:PMC11771281 | DOI:10.1016/j.livres.2024.12.001

MicroPubl Biol. 2025 Jan 31;2025. doi: 10.17912/micropub.biology.001485. eCollection 2025.

ABSTRACT

DNA fiber assays are indispensable tools for studying DNA damage and replication stress responses in vivo at the single replication fork level. These assays typically rely on antibodies recognizing IdU and CldU. Historically, the availability of CldU-reactive antibodies has been limited to one reagent (clone BU1/75(ICR1)). We validated two alternative antibodies for CldU detection in DNA fiber assays. One of these antibodies can be readily paired with a common IdU-reactive antibody, and we confirmed that it produces quantitatively similar CldU track length results vis-à-vis the BU1/75 antibody. The new reagents should boost versatility of DNA fiber assays, facilitating DNA replication research.

PMID:39958910 | PMC:PMC11829213 | DOI:10.17912/micropub.biology.001485

Biodivers Data J. 2025 Feb 6;13:e134717. doi: 10.3897/BDJ.12.e134717. eCollection 2025.

ABSTRACT

BACKGROUND: The section Cryptogamia-Lichenes of the Herbarium Universitatis Taurinensis (TO) includes ca. 34,600 lichen specimens, organised in the historical (ca. 30,700 specimens, mostly from the 19th century) and modern (ca. 3,900 specimens collected from 1978, out of which ca. 3400 from Italy) collections. Specimens from the administrative regions of Piemonte and Valle d'Aosta (NW Italy) are the core of the modern collection, documenting floristic and vegetation studies, as well as biomonitoring campaigns and investigations on the biodeterioration of the stone cultural heritage.

NEW INFORMATION: The dataset of the Italian materials of the modern lichenological collection of TO, with 3,365 samples, is fully georeferenced and accessible in the Global Biodiversity Information Facility (GBIF), in the Jointly Administered Herbarium Management System and Specimen Database (JACQ) and in the Information System of Italian Lichens (ITALIC). With regard to the historical collection, only a set of 59 recently revised specimens is available on the mentioned platforms, but most of the materials are accessible as digital images on the website of the project HERB-TO-CHANGE.

PMID:39958908 | PMC:PMC11826221 | DOI:10.3897/BDJ.12.e134717

Curr Res Microb Sci. 2025 Jan 23;8:100352. doi: 10.1016/j.crmicr.2025.100352. eCollection 2025.

ABSTRACT

A sustainable approach to microbial polyhydroxyalkanoate (PHA) production involves utilizing waste as a substrate, which can include toxic pollutants like phenol as a carbon feedstock. Phenol-contaminated effluents offer cost-effective and readily available resources for PHA production, while simultaneously addressing phenol contamination issues. Understanding the metabolic conversion of phenol to PHA is crucial to enhance its efficiency, especially considering phenol's toxicity to microbial cells and the substrate-dependent nature of microbial PHA production. In this review, the mechanisms of phenol biodegradation and PHA biosynthesis are first independently elucidated to comprehend the role of bacteria in these processes. Phenol can be metabolized aerobically via various pathways, including catechol meta-cleavage I and II, catechol ortho-cleavage, protocatechuate ortho-cleavage, and protocatechuate meta-cleavage, as well as anaerobically via 4-hydrozybenzoate and/or n-caproate formation. Meanwhile, PHA can be synthesized through the acetoacetyl-CoA (pathway I), de novo fatty acids synthesis (pathway II), β-oxidation (pathway III), and the tricarboxylic acid (TCA) cycle, with the induction of these pathways are highly dependent on the substrate. Given that the link between these two mechanisms was not comprehensively reported before, the second part of the review delve into understanding phenol conversion into PHA, specifically polyhydroxybutyrate (PHB). While phenol toxicity can inhibit bacterial performance, it can be alleviated through the utilization of microbial mixed culture (MMC), which offers a wider range of metabolic capabilities. Utilizing phenol as a carbon feedstock for PHB accumulation could offer a viable approach to boost PHA's commercialization while addressing the issue of phenol pollution.

PMID:39958774 | PMC:PMC11830346 | DOI:10.1016/j.crmicr.2025.100352

Expert Rev Proteomics. 2025 Feb 16. doi: 10.1080/14789450.2025.2468300. Online ahead of print.

ABSTRACT

INTRODUCTION: Rare diseases (RDs) are a heterogeneous group of diseases recognized as a relevant global health priority but posing aspects of complexity such as: geographical scattering of affected individuals, improper/late diagnosis, limited awareness, difficult surveillance and monitoring, limited understanding of natural history, and lack of treatment. Usually, RDs have a pediatric onset and are life-long, multisystemic, and associated with a poor prognosis.

AREAS COVERED: In this work, we review how high-throughput omics technologies such as genomics, transcriptomics, proteomics, metabolomics, epigenomics, and other well-established omics, which are increasingly more affordable and efficient, can be applied to the study of RDs promoting diagnosis, understanding of pathological mechanisms, biomarker discovery and identification of treatments.

EXPERT OPINION: RDs, despite their challenges, offer a niche where collaborative efforts and personalized treatment strategies might be feasible using omics technologies. Specialized consortia fostering multidisciplinary collaboration, data sharing, and the development of biobanks and registries can be built; multi-omics approaches, including so far less exploited omics technologies, along with the implementation of AI tools can be undertaken to deepen our understanding of RDs, driving biomarker discovery and clinical interventions. Nevertheless, technical, ethical, legal and societal issues must be clearly defined and addressed.

PMID:39956998 | DOI:10.1080/14789450.2025.2468300

Am J Transl Res. 2025 Jan 15;17(1):685-692. doi: 10.62347/EHTT9544. eCollection 2025.

ABSTRACT

OBJECTIVE: This study aimed to assess the efficacy and safety of sacral nerve root magnetic stimulation (SNRMS) combined with solifenacin in female patients with overactive bladder (OAB) symptoms.

METHODS: A total of 183 female patients with OAB symptoms were prospectively randomized into 2 groups. Ninety-two patients in the combination group accepted SNRMS and solifenacin therapy and 91 patients serving as control accepted only solifenacin therapy. The lower urinary tract symptoms, OAB questionnaire (OAB-q) health-related quality of life (HRQoL), symptom bother score, and overactive bladder syndrome score (OABSS) were compared between the two groups at the end of the second, fourth, and eighth weeks.

RESULTS: The incidence of lower urinary tract symptoms, including urgency, frequent urination, and incontinence episodes, was significantly lower in the fourth and eighth weeks in patients of the combination treatment group than those in the solifenacin group (P < 0.05). The incidence of drug-related adverse events in the two groups was similar, with no statistically significant difference (P > 0.05). The OAB-q HRQoL score in the combination group was significantly higher than that in the solifenacin group between the fourth and eighth weeks (P < 0.05). Meanwhile, the OAB-q symptom bother score and OABSS were both lower in the combination group than those in the solifenacin group from the fourth to eighth weeks (P < 0.05).

CONCLUSIONS: The combination therapy of SNRMS and solifenacin demonstrated significant improvements over solifenacin monotherapy in reducing OAB symptoms in female patients, providing a higher QoL without increasing bothersome adverse effects.

PMID:39959226 | PMC:PMC11826160 | DOI:10.62347/EHTT9544

Liver Res. 2024 Oct 24;8(4):295-303. doi: 10.1016/j.livres.2024.10.001. eCollection 2024 Dec.

ABSTRACT

BACKGROUND AND AIMS: Antiviral therapy is essential for hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). No data are available on the long-term prognosis or safety of tenofovir alafenamide (TAF), tenofovir disoproxil fumarate (TDF), or entecavir (ETV) in treating HBV-ACLF globally. This study was conducted to investigate the long-term efficacy and safety of the three nucleos(t)ide analogs in the treatment of HBV-ACLF.

METHODS: In this prospective, real-world cohort study, patients with HBV-ACLF were assigned to the TAF, TDF, and ETV groups. A total of 199 patients completed the 144-week follow-up. After propensity score matching (PSM), 44 patients remained in each group for further analysis of survival status, incidence of hepatocellular carcinoma (HCC), virological response, and liver and renal function indicators.

RESULTS: In the original cohort, HCC developed in one patient in each group. No serious drug-related adverse events were observed. In the PSM cohort, the 144-week survival rates were 56.82%, 75.00%, and 59.09% in the TAF, TDF, and ETV groups, respectively (P = 0.118). When stratified into noncirrhosis and cirrhosis subgroups at baseline, the survival rate of the ETV group was slightly lower than that of the TAF and TDF group in noncirrhosis patients (P = 0.338), and the survival rate of the TAF group was slightly lower than that of the TDF and ETV group in cirrhosis patients (P = 0.052), but the differences were not statistically significant. The long-term overall survival rates in the TAF, TDF, and ETV groups were comparable. After 144 weeks, no significant difference in the virological response rate or liver or renal function indicators was found among the three groups, except for the level of aspartate aminotransferase, which was significantly higher in the TDF group than in the ETV group at week 144 (P = 0.001).

CONCLUSIONS: There were no significant differences in the survival rate, incidence of HCC, efficacy or safety associated with the use of these three nucleos(t)ide analogs in treating HBV-ACLF.

TRIAL REGISTRATION: ClinicalTrials.gov NCT03920618.

PMID:39958923 | PMC:PMC11771274 | DOI:10.1016/j.livres.2024.10.001

Discoveries (Craiova). 2024 Dec 31;12(4):e201. doi: 10.15190/d.2024.20. eCollection 2024 Oct-Dec.

ABSTRACT

Lomotil (diphenoxylate-atropine) toxicity in the pediatric population remains a significant concern particularly in low and lower middle-income countries. This may result from accidental ingestion or inappropriate therapeutic administration which can lead to life threatening complications including respiratory and central nervous system depression. A 2-year-old child presented to the pediatric emergency room in an altered state of consciousness. Clinical examination revealed dry mucous membranes, and a prolonged capillary refill time with weak radial pulses. Keeping in view the one-day history of 10-12 episodes of acute onset loose, watery stools, patient was initially treated as a case of hypovolemic shock. With rehydration therapy, his perfusion improved. However, the Glasgow Coma Scale score remained 8, as was observed on initial presentation. Upon further probing, it was revealed by the parents that the child had been given Lomotil by a local general practitioner for unresolved watery diarrhea. Pinpoint pupils and slow shallow vesicular breathing confirmed this diagnosis of Lomotil overdose. Administration of 0.1mg/kg/dose Naloxone repeated once, completely reversed the toxic effects. The child was able to make a full recovery and was discharged the following day. This case highlights the importance of recognizing and managing diphenoxylate toxicity in children, emphasizing the need for increased clinical awareness. A lack of consensus regarding the toxic dose of this drug reveals a gap warranting further research and establishment of standardized guidelines to ensure accurate dosing and improved patient safety.

PMID:39958914 | PMC:PMC11830492 | DOI:10.15190/d.2024.20

Phys Med. 2025 Feb 15;131:104935. doi: 10.1016/j.ejmp.2025.104935. Online ahead of print.

ABSTRACT

PURPOSE: To assess the lower [18F]FDG limit in administered activity and/or scan time reduction capabilities of a digital-BGO 32-cm axial field-of-view PET system while being compliant with current and updated EANM Research Ltd Fluorine-18 accreditation specifications (EARL1 and EARL2).

METHODS: EARL1 and EARL2 compliance of the digital-BGO system (Omni Legend 32 cm) was tested for several reconstructions, including those that apply precision deep learning-based image enhancement (PDL) as postprocessing, using the calibration QC and NEMA IEC phantom measurements. The image quality QC scan was repeated every hour for 7 h, with each subsequent hour representing a lower administered activity, and reconstructed for various times per bed position, i.e. 30, 60, 120, 180, and 300 s. For each of the image quality QC images, coefficient of variation (COV) of the background compartment, and mean, maximum and peak activity concentration recovery coefficients (RCmean, RCmax and RCpeak) of differently-sized spheres were calculated and compared to current and updated EARL accreditation specifications.

RESULTS: When we apply 1 min per bed position for PET acquisition, [18F]FDG administration can be reduced by a factor of ∼ 4 for EARL1, by a factor of ∼ 8 for EARL2 (2 mm voxels) and by a factor of ∼ 4 for EARL2 (4 mm voxels) using both standard reconstructions and PDL post-processing compared to current EANM recommendations for [18F]FDG administration (7 MBqminbed-1kg-1).

CONCLUSIONS: Reduction in [18F]FDG administered activity is possible by at least a factor 4 for 1 min/bed with the Omni Legend 32 cm PET/CT while maintaining EARL1 and EARL2 compliance.

PMID:39956005 | DOI:10.1016/j.ejmp.2025.104935

BMJ Open. 2025 Feb 16;15(2):e089418. doi: 10.1136/bmjopen-2024-089418.

ABSTRACT

INTRODUCTION: Rare disease treatments (RDTs) promise considerable patient benefit but the evidence to demonstrate their value in health technology assessment (HTA) is often limited. HTA outcomes for RDTs vary across countries and there are differences in how uncertainty is dealt with by HTA agencies. Yet, there is limited comparative research assessing how different HTA agencies consider issues affecting evidence quality and uncertainty in RDT appraisals. This protocol describes a systematic and consistent approach for data extraction from RDT appraisal documents produced to inform decisions by HTA agencies. By documenting data extraction rules transparently, we ensure reproducibility and reliability of analyses of the extracted data.

METHODS AND ANALYSIS: We will select RDT appraisals issued by the National Institute for Health and Care Excellence (NICE) in England and the Federal Joint Committee (GBA) in Germany, using predefined inclusion criteria. We will extract data from appraisal documents in accordance with the rules set out in this protocol. We will analyse the extracted data to investigate how issues affecting evidence quality and uncertainty as documented in appraisals are considered, highlighting the similarities and differences between countries and identifying factors that are associated with HTA outcomes.

ETHICS AND DISSEMINATION: This study was approved by the Ethics Committee of the London School of Hygiene & Tropical Medicine (reference number 29156). Study results will be submitted for publication in peer-reviewed journals.

PMID:39956595 | DOI:10.1136/bmjopen-2024-089418