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Introduction: COVID-19 severity has been linked to immune factors, with excessive immune responses like cytokine storms contributing to mortality. However, the genetic basis of these immune responses is not well understood. This study aimed to explore the genetic connection between COVID-19 severity and blood cell traits, given their close relationship with immunity. Materials and methods: GWAS summary statistics for COVID-19 and blood cell counts were analyzed using Linkage Disequilibrium Score Regression (LDSC) to estimate genetic correlations and heritabilities. For traits with significant correlations, a Multi-Trait GWAS Analysis (MTAG) was performed to identify pleiotropic loci shared between COVID-19 and blood cell counts. Results: Our MTAG analysis identified four pleiotropic loci associated with COVID-19 severity, five loci linked to hospitalized cases, and one locus related to general patients. Among these, two novel loci were identified in the high-risk population, with rs55779981 located near RAVER1 and rs73009538 near CARM1. In hospitalized patients, two previously unrecognized loci were detected, namely, rs115545251 near GFI1 and rs3181049 near RAVER1 , while in general patients, rs11065822 near CUX2 emerged as a newly identified locus. We also identified potential target genes, including those involved in inflammation signaling (CARM1), endothelial dysfunction (INTS12), and antiviral immune response (RAVER1), which may require further investigation. Conclusion: Our study offers insights into the genetic overlap between COVID-19 and immune factors, suggesting potential directions for future research and clinical exploration. [ABSTRACT FROM AUTHOR]

Digestive and psychiatric disorders tend to co-occur, yet mechanisms remain unclear. Leveraging genetic and transcriptomic data integration, we conduct multi-trait analysis of GWAS (MTAG) and weighted gene co-expression network analysis (WGCNA) to explore shared mechanism between psychiatric and gastrointestinal disorders. Significant genetic correlations were found between these disorders, especially in irritable bowel syndrome (IBS), gastroesophageal reflux disease (GERD), depression (DEP), and neuroticism (NE). MTAG identify 60 novel pleiotropic loci for IBS and 14 for GERD, predominantly located near genes associated with neurological pathways. Further WGCNA identifies multiple co-expression modules enriched with genes involved in neurological pathways in digestive tissues, with some modules strongly preserved across brain and digestive tissues. Moreover, our network analysis suggests BSN, CELF4, and NRXN1 as central players in the regulation of the gut-brain axis (GBA). This study enhances our understanding of the GBA and underscores BSN, CELF4, and NRXN1 as crucial targets for future research. Genetics and transcriptomics reveal shared mechanisms of psychiatric and gastrointestinal disorders, highlighting neurological pathways and pleiotropic loci. Genes BSN, CELF4, NRXN1 regulate gut-brain axis, revealing potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging (fMRI) has been widely used in studying the neural mechanisms of pain in the human brain, primarily focusing on where in the brain pain‐elicited neural activities occur (i.e., the spatial distribution of pain‐related brain activities). However, the temporal dynamics of pain‐elicited hemodynamic responses (HDRs) measured by fMRI may also contain information specific to pain processing but have been largely neglected. Using high temporal resolution fMRI (TR = 0.8 s) data acquired from 62 healthy participants, in the present study we aimed to test whether pain‐distinguishing information could be decoded from the spatial pattern of the temporal dynamics (i.e., the spatiotemporal pattern) of HDRs elicited by painful stimuli. Specifically, the peak latency and the response duration were used to characterize the temporal dynamics of HDRs to painful laser stimuli and non‐painful electric stimuli, and then were compared between the two conditions (i.e., pain and no‐pain) using a voxel‐wise univariate analysis and a multivariate pattern analysis. Furthermore, we also tested whether the two temporal characteristics of pain‐elicited HDRs and their spatial patterns were associated with pain‐related behaviors. We found that the spatial patterns of HDR peak latency and response duration could successfully discriminate pain from no‐pain. Interestingly, we also observed that the Pain Vigilance and Awareness Questionnaire (PVAQ) scores were correlated with the average response duration in bilateral insula and secondary somatosensory cortex (S2) and could also be predicted from the across‐voxel spatial patterns of response durations in the middle cingulate cortex and middle frontal gyrus only during painful condition but not during non‐painful condition. These findings indicate that the spatiotemporal pattern of pain‐elicited HDRs may contain pain‐specific information and highlight the importance of studying the neural mechanisms of pain by taking advantage of the high sensitivity of fMRI to both spatial and temporal information of brain responses. [ABSTRACT FROM AUTHOR]

For high-quality growth to occur, new-type urbanization and environmental preservation must coexist and advance at the same time. The focus has shifted to maintain a balance between ecological quality and urbanization growth. This study focuses on the Yangtze River Delta (YRD) in China, utilizing panel data from 41 cities in the YRD spanning from 2009 to 2021 to construct evaluation index systems for new (type of) urbanization and ecological environment. To analyze spatial-temporal evolutionary aspects and determine the causes of the degree of coupling coordination between new-type urbanization and the ecological environment, methodologies such as the entropy weight method, coupled coordination degree model, and Tobit regression approach were used. The results show that (1) economic urbanization has experienced the most growth in the level of new-type urbanization in the YRD, which has been steadily increasing. Moreover, the ecological environment evaluation score increased from 0.581 in 2009 to 0.701 in 2021, revealing a cyclical pattern of increase and decrease in its evolutionary trajectory. (2) Within the scope of the study, the overall coupling coordination degree between new-type urbanization and ecological environment has increased, with the average value rising from 0.512 in 2009 to 0.540 in 2021. In comparison to Lishui, Huaibei, Huainan, Ningbo, Chuzhou, and Bozhou saw a greater increase in coupling and coordination degree, with pronounced variations and clustering patterns visible in their spatial distribution. (3) According to the Tobit regression analysis, the level of economic development, technological progress, industrial concentration, global openness, and educational investment had significant positive effects on the degree of coupled coordination between new-type urbanization and the ecological environment in the YRD, whereas the level of information technology did not reach the significance threshold. The findings of the study are crucial for establishing a regional framework for green and sustainable development, as well as for facilitating the coordinated growth of new-type urbanization and ecological environment. These findings hold great potential for driving positive change in both urban development and environmental conservation efforts. [ABSTRACT FROM AUTHOR]

Since its first detection in March 2013, avian influenza A(H7N9) virus has caused 1,534 human infections that, as of November 30, 2017, had resulted in 608 deaths. Recurrent waves of [...]

Human infections with avian influenza A(H7N9) virus have been documented in China since 2013 (1). Among 1,220 confirmed H7N9 case-patients during 2013-2017, a total of 73% reported poultry exposure, and [...]

Since its first notification on March 30, 2013 (1), avian influenza A(H7N9) virus caused 3 complete epidemic waves of human infection in China, comprising 670 laboratory-confirmed clinical cases and 274 [...]

Objective: The purpose of this study was to develop a deep learning-based system to automatically predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma in 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). Methods: Three hundred and one lung adenocarcinoma patients with EGFR mutation status were enrolled in this study. Two deep learning models (SECT and SEPET) were developed with Squeeze-and-Excitation Residual Network (SE-ResNet) module for the prediction of EGFR mutation with CT and PET images, respectively. The deep learning models were trained with a training data set of 198 patients and tested with a testing data set of 103 patients. Stacked generalization was used to integrate the results of SECT and SEPET. Results: The AUCs of the SECT and SEPET were 0.72 (95% CI, 0.62–0.80) and 0.74 (95% CI, 0.65–0.82) in the testing data set, respectively. After integrating SECT and SEPET with stacked generalization, the AUC was further improved to 0.84 (95% CI, 0.75–0.90), significantly higher than SECT (p<0.05). Conclusion: The stacking model based on 18F-FDG PET/CT images is capable to predict EGFR mutation status of patients with lung adenocarcinoma automatically and non-invasively. The proposed model in this study showed the potential to help clinicians identify suitable advanced patients with lung adenocarcinoma for EGFR‐targeted therapy. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to develop a classification method based on support vector machine (SVM) to improve the diagnostic performance of 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) to detect the lymph node (LN) metastasis in non-small cell lung cancer (NSCLC).Method: Two hundred nineteen lymph nodes (37 metastatic) from 71 patients were evaluated in this study. SVM models were developed with 7 LN features. The area under the curve (AUC) and accuracy of 9 models were compared to select the best model. The best SVM model was simplified on the basis of the feature weights and value distribution to further suit the clinical application.Results: The maximum, minimum, and mean accuracy of the best model was 91.89% (68/74, 95% CI 83.11~96.54%), 66.22% (49/74, 95% CI 54.85~75.98%), and 80.09% (59,266/74,000, 95% CI 70.27~89.19%), respectively, with an AUC of 0.94, 0.66, and 0.81, respectively. The best SVM model was finally simplified into a score rule: LNs with scores more than 3.0 were considered as malignant ones, whereas LNs with scores less than 1.5 tended to be benign ones. For the LNs with scores within a range of 1.5-3.0, metastasis was suspected.Conclusion: An SVM model based on 18F-FDG PET/CT images was able to predict the metastatic LNs for patients with NSCLC. The ratio of the maximum of standard uptake value of LNs to aortic arch played a major role in the model. After simplification, the model could be transferred into a scoring method which may partly help clinicians determine the clinical staging of patients with NSCLC relatively easier.Key Points: • The SVM model based on 18F-FDG PET/CT features may help clinicians to make a decision for metastatic mediastinal lymph nodes in patients with NSCLC. • The SURblood plays a major role in the SVM model. • The score rule based on the SVM model simplified the complexity of the model and may partly help clinicians determine the clinical staging of patients with NSCLC relatively easier. [ABSTRACT FROM AUTHOR]