Deep learning-based quantitative morphological study of anteroposterior digital radiographs of the lumbar spine.

Background: Morphological parameters of the lumbar spine are valuable in assessing lumbar spine diseases. However, manual measurement of lumbar morphological parameters is time-consuming. Deep learning has automatic quantitative and qualitative analysis capabilities. To develop a deep learning-based model for the automatic quantitative measurement of morphological parameters from anteroposterior digital radiographs of the lumbar spine and to evaluate its performance. Methods: This study used 1,368 anteroposterior digital radiographs of the lumbar spine to train a deep learning model to measure the quantitative morphological indicators, including L1 to L5 vertebral body height (VBH) and L1-L2 to L4-L5 intervertebral disc height (IDH). The means of the manual measurements by three radiologists were used as the reference standard. The parameters predicted by the model were analyzed against the manual measurements using paired t-tests. Percentage of correct key points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and Bland-Altman plots were performed to assess the performance of the model. Results: Within the 3-mm distance threshold, the model had a PCK range of 99.77-99.46% for the L1 to L4 vertebrae and 77.37% for the L5 vertebrae. Except for VBH-L5 and IDH_L3-L4, IDH_L4-L5 (P<0.05), the estimated values of the model in the remaining parameters were not statistically significant compared with the reference standard (P>0.05). Except for VBH-L5 and IDH_L4-L5, the model showed good correlation and consistency with the reference standard (ICC =0.84-0.96, r=0.85-0.97, MAE =0.5-0.66, RMSE =0.66-0.95). The model outperformed other models (EfficientDet + Unet, EfficientDet + DarkPose, HRNet, and Unet) in predicting landmarks within a distance threshold of 1.5 to 5 mm. Conclusions: The model developed in this study can automatically measure the morphological parameters of the L1 to L4 vertebrae from anteroposterior digital radiographs of the lumbar spine. Its performance is close to the level of radiologists.

Apigenin protects against ischemic stroke by increasing DNA repair.

Background and Objective: Oxidative stress is an important pathological process in ischemic stroke (IS). Apigenin (APG) is a natural product with favorable antioxidative effects, and some studies have already demonstrated the antioxidative mechanism of APG in the treatment of IS. However, the mechanism of APG on DNA damage and repair after IS is not clear. The aim of this study was to investigate the mechanism of APG on DNA repair after IS. Methods: Male Sprague-Dawley rats were used to establish a model of permanent middle cerebral artery occlusion (pMCAO) on one side, and were pre-treated with gavage of APG (30, 60, or 120 mg/kg) for 7 days. One day after pMCAO, the brain tissues were collected. Cerebral infarct volume, brain water content, HE staining and antioxidant index were analyzed to evaluated the brain damage. Molecular Docking, molecular dynamics (MD) simulation, immunohistochemistry, and Western blot were used to explore the potential proteins related to DNA damage repair. Results: APG has a low binding score with DNA repair-related proteins. APG treatment has improved the volume of cerebral infarction and neurological deficits, reduced brain edema, and decreased parthanatos and apoptosis by inhibiting PARP1/AIF pathway. In addition, APG improved the antioxidative capacity through reducing reactive oxygen species and malondialdehyde, and increasing glutathione and superoxide dismutase. Also, APG has reduced DNA damage- and cell death-related proteins such as PARP1, γH2A.X, 53BP1, AIF, cleaved caspase3, Cytochrome c, and increased DNA repair by BRCA1 and RAD51 through homologous recombination repair, and reduced non-homologous end link repair by KU70. Conclusion: APG can improve nerve damage after IS, and these protective effects were realized by reducing oxidative stress and DNA damage, and improving DNA repair.

MetagenomicKG: a knowledge graph for metagenomic applications.

Motivation: The sheer volume and variety of genomic content within microbial communities makes metagenomics a field rich in biomedical knowledge. To traverse these complex communities and their vast unknowns, metagenomic studies often depend on distinct reference databases, such as the Genome Taxonomy Database (GTDB), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), for various analytical purposes. These databases are crucial for genetic and functional annotation of microbial communities. Nevertheless, the inconsistent nomenclature or identifiers of these databases present challenges for effective integration, representation, and utilization. Knowledge graphs (KGs) offer an appropriate solution by organizing biological entities and their interrelations into a cohesive network. The graph structure not only facilitates the unveiling of hidden patterns but also enriches our biological understanding with deeper insights. Despite KGs having shown potential in various biomedical fields, their application in metagenomics remains underexplored. Results: We present MetagenomicKG, a novel knowledge graph specifically tailored for metagenomic analysis. MetagenomicKG integrates taxonomic, functional, and pathogenesis-related information from widely used databases, and further links these with established biomedical knowledge graphs to expand biological connections. Through several use cases, we demonstrate its utility in enabling hypothesis generation regarding the relationships between microbes and diseases, generating sample-specific graph embeddings, and providing robust pathogen prediction. Availability and Implementation: The source code and technical details for constructing the MetagenomicKG and reproducing all analyses are available at Github: https://github.com/KoslickiLab/MetagenomicKG. We also host a Neo4j instance: http://mkg.cse.psu.edu:7474 for accessing and querying this graph.

Predictive cues elicit a liminal confirmation bias in the moral evaluation of real-world images.

Previous research suggested that predictive cues enhance the preference and reduce the response time for congruent targets during bivalent food evaluation, indicating a confirmation bias. Less is known about how prior processing affects subjective moral evaluation. Here, we used three different types of predictive cues to elicit directional vs. non-predictive prior processing and then asked the participants to perform moral evaluations on a continuous scale from -10 ("very immoral") to +10 ("very moral") with a diverse set of real-world images. Our experimental image database balanced the morality of image content and the volatility of the ratings based on the means and standard deviations in a preliminary study. Ratings, response times, and gaze positions were measured to examine the effects of predictive cues on the moral evaluation of real-world images. We found that the moral ratings were in line with the expectations induced by the cues. Compared to the non-predictive condition, the moral evaluation in the directional conditions was more polarized. For neutral images, the predictive cues tilted the evaluations to positive vs. negative, indicating a decisive liminal influence. High-volatility images were impacted more than low-volatility images in ratings as well as response times. Furthermore, the gaze positions during the interval between the predictive cue and the image showed a spatial displacement in line with the cue instruction, indicating a response bias. Together, the results show that predictive cues elicit a liminal confirmation bias in moral image evaluation, much in the same way as in bivalent food evaluation.

Efficacy and safety of auricular therapy in the treatment of post-stroke constipation: A protocol for systematic review and meta-analysis.

BACKGROUND: Constipation is one of the common gastrointestinal complications after stroke. It not only aggravates the condition of stroke, but also brings huge medical burden to patients, and has a negative impact on the quality of life of patients. Auricular therapy, as a part of Chinese traditional acupuncture and moxibustion, has been found to be effective in the clinical treatment of constipation. However, no systematic review has investigated the efficacy and safety of auricular therapy in the treatment of post-stroke constipation. Therefore, the aim of this systematic review is to assess the effectiveness and safety of auricular therapy for post-stroke constipation. METHODS AND ANALYSIS: Eight electronic databases including PubMed, Cochrane Library/Cochrane Central Register of Controlled Trials, Web of Science, Embase, China National Knowledge Internet, Chinese Biomedical Literature Database, Wanfang, and VIP databases, will be searched for relevant studies published from inception to February 2023. Two reviewers will independently conduct research selection, data extraction, and evaluation of research quality. Only randomized controlled trials (RCTs) that assess the efficacy and safety of auricular therapy for the treatment of post-stroke constipation will be included in this study. We will use the Cochrane risk of bias assessment tool to evaluate the methodological qualities (including bias risk). If possible, a meta-analysis will be performed after screening. RESULTS: This study may provide high-quality evidence for the efficacy and safety of auricular therapy in treating post-stroke constipation. CONCLUSION: The conclusions of our study will provide an evidence to judge whether auricular therapy is an effective and safe intervention for patients with post-stroke constipation. ETHICS AND DISSEMINATION: Ethical approval is not required, as this study was based on a review of published research. This review will be published in a peer-reviewed journal and disseminated electronically and in print. TRIAL REGISTRATION: Registration number: PROSPERO CRD42023402242.

YACHT: an ANI-based statistical test to detect microbial presence/absence in a metagenomic sample.

MOTIVATION: In metagenomics, the study of environmentally associated microbial communities from their sampled DNA, one of the most fundamental computational tasks is that of determining which genomes from a reference database are present or absent in a given sample metagenome. Existing tools generally return point estimates, with no associated confidence or uncertainty associated with it. This has led to practitioners experiencing difficulty when interpreting the results from these tools, particularly for low-abundance organisms as these often reside in the "noisy tail" of incorrect predictions. Furthermore, few tools account for the fact that reference databases are often incomplete and rarely, if ever, contain exact replicas of genomes present in an environmentally derived metagenome. RESULTS: We present solutions for these issues by introducing the algorithm YACHT: Yes/No Answers to Community membership via Hypothesis Testing. This approach introduces a statistical framework that accounts for sequence divergence between the reference and sample genomes, in terms of ANI, as well as incomplete sequencing depth, thus providing a hypothesis test for determining the presence or absence of a reference genome in a sample. After introducing our approach, we quantify its statistical power and how this changes with varying parameters. Subsequently, we perform extensive experiments using both simulated and real data to confirm the accuracy and scalability of this approach. AVAILABILITY AND IMPLEMENTATION: The source code implementing this approach is available via Conda and at https://github.com/KoslickiLab/YACHT. We also provide the code for reproducing experiments at https://github.com/KoslickiLab/YACHT-reproducibles.

Dilute Povidone-Iodine Solution Soaking Is Ineffective in Improving Outcomes of Necrotizing Fasciitis Caused by Diabetic Foot.

ABSTRACT: Wound soaking is a physical debridement method that helps reduce bacterial colonization and consequently promotes wound healing. Although soaking in povidone-iodine solution was ineffective in reducing bacterial colonization in acute trauma wounds, there is still a lack of evidence supporting the efficacy of this method in treating severe soft tissue infection. This study aimed to explore the effects of wound soaking in 1% dilute povidone-iodine solution on necrotizing fasciitis caused by diabetic foot ulcers. We retrospectively reviewed and finally included 153 patients who were admitted because of diabetic foot ulcers after undergoing fasciotomy for necrotizing infection from January 2018 to December 2021. Results showed no statistical difference in the outcomes between patients in the soaking and nonsoaking groups. End-stage renal disease (P = 0.029) and high serum C-reactive protein level (P = 0.007) were the only independent factors for below-knee amputation in the univariate and multivariate logistic regression analyses. Therefore, soaking diabetic wounds with severe infection in 1% dilute povidone-iodine solution may not reduce the hospital length of stay, risk of below-knee amputation, and readmission rate.