Enhancing Retinal Fundus Image Quality Assessment With Swin-Transformer-Based Learning Across Multiple Color-Spaces.

Purpose: The assessment of retinal image (RI) quality holds significant importance in both clinical trials and large datasets, because suboptimal images can potentially conceal early signs of diseases, thereby resulting in inaccurate medical diagnoses. This study aims to develop an automatic method for Retinal Image Quality Assessment (RIQA) that incorporates visual explanations, aiming to comprehensively evaluate the quality of retinal fundus images (RIs). Methods: We developed an automatic RIQA system, named Swin-MCSFNet, utilizing 28,792 RIs from the EyeQ dataset, as well as 2000 images from the EyePACS dataset and an additional 1,000 images from the OIA-ODIR dataset. After preprocessing, including cropping black regions, data augmentation, and normalization, a Swin-MCSFNet classifier based on the Swin-Transformer for multiple color-space fusion was proposed to grade the quality of RIs. The generalizability of Swin-MCSFNet was validated across multiple data centers. Additionally, for enhanced interpretability, a Score-CAM-generated heatmap was applied to provide visual explanations. Results: Experimental results reveal that the proposed Swin-MCSFNet achieves promising performance, yielding a micro-receiver operating characteristic (ROC) of 0.93 and ROC scores of 0.96, 0.81, and 0.96 for the "Good," "Usable," and "Reject" categories, respectively. These scores underscore the accuracy of RIQA based on Swin-MCSF in distinguishing among the three categories. Furthermore, heatmaps generated across different RIQA classification scores and various color spaces suggest that regions in the retinal images from multiple color spaces contribute significantly to the decision-making process of the Swin-MCSFNet classifier. Conclusions: Our study demonstrates that the proposed Swin-MCSFNet outperforms other methods in experiments conducted on multiple datasets, as evidenced by the superior performance metrics and insightful Score-CAM heatmaps. Translational Relevance: This study constructs a new retinal image quality evaluation system, which will contribute to the subsequent research of retinal images.

Genetic architecture of hippocampus subfields volumes in Alzheimer's disease.

BACKGROUND: The hippocampus is a heterogeneous structure, comprising histologically and functionally distinguishable hippocampal subfields. The volume reductions in hippocampal subfields have been demonstrated to be linked with Alzheimer's disease (AD). The aim of our study is to investigate the hippocampal subfields' genetic architecture based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set. METHODS: After preprocessing the downloaded genetic variants and imaging data from the ADNI database, a co-sparse reduced rank regression model was applied to analyze the genetic architecture of hippocampal subfields volumes. Homology modeling, docking, molecular dynamics simulations, and Co-IP experiments for protein-protein interactions were used to verify the function of target protein on hippocampal subfields successively. After that, the association analysis between the candidated genes on the hippocampal subfields volume and clinical scales were performed. RESULTS: The results of the association analysis revealed five unique genetic variants (e.g., ubiquitin-specific protease 10 [USP10]) changed in nine hippocampal subfields (e.g., the granule cell and molecular layer of the dentate gyrus [GC-ML-DG]). Among five genetic variants, USP10 had the strongest interaction effect with BACE1, which affected hippocampal subfields verified by MD and Co-IP experiments. The results of association analysis between the candidated genes on the hippocampal subfields volume and clinical scales showed that candidated genes influenced the volume and function of hippocampal subfields. CONCLUSIONS: Current evidence suggests that hippocampal subfields have partly distinct genetic architecture and may improve the sensitivity of the detection of AD.

The association between genetic variations and morphology-based brain networks changes in Alzheimer's disease.

Alzheimer's disease (AD) is a highly heritable disease. The morphological changes of cortical cortex (such as, cortical thickness and surface area) in AD always accompany by the change of the functional connectivity to other brain regions and influence the short- and long-range brain network connections, causing functional deficits of AD. In this study, the first hypothesis is that genetic variations might affect morphology-based brain networks, leading to functional deficits; the second hypothesis is that protein-protein interaction (PPI) between the candidate proteins and known interacting proteins to AD might exist and influence AD. 600 470 variants and structural magnetic resonance imaging scans from 175 AD patients and 214 healthy controls were obtained from the Alzheimer's Disease Neuroimaging Initiative-1 database. A co-sparse reduced-rank regression model was fit to study the relationship between non-synonymous mutations and morphology-based brain networks. After that, PPIs between selected genes and BACE1, an enzyme that was known to be related to AD, are explored by using molecular dynamics (MD) simulation and co-immunoprecipitation (Co-IP) experiments. Eight genes affecting morphology-based brain networks were identified. The results of MD simulation showed that the PPI between TGM4 and BACE1 was the strongest among them and its interaction was verified by Co-IP. Hence, gene variations influence morphology-based brain networks in AD, leading to functional deficits. This finding, validated by MD simulation and Co-IP, suggests that the effect is robust.

Rare Variant Analysis and Molecular Dynamics Simulation in Alzheimer's Disease Identifies Exonic Variants in FLG.

BACKGROUND: Although an increasing number of common variants contributing to Alzheimer's disease (AD) are uncovered by genome-wide association studies, they can only explain less than half of the heritability of AD. Rare variant association studies (RVAS) has become an increasingly important area to explain the risk or trait variability of AD. METHOD: To investigate the potential rare variants that cause AD, we screened 70,209 rare variants from two cohorts of a 175 AD cohort and a 214 cognitively normal cohort from the Alzheimer's Disease Neuroimaging Initiative database. MIRARE, a novel RVAS method, was performed on 232 non-synonymous variants selected by ANNOVAR annotation. Molecular docking and molecular dynamics (MD) simulation were adopted to verify the interaction between the chosen functional variants and BACE1. RESULTS: MIRAGE analysis revealed significant associations between AD and six potential pathogenic genes, including PREX2, FLG, DHX16, NID2, ZnF585B and ZnF875. Only interactions between FLG (including wild type and rs3120654(SER742TYR)) and BACE1 were verified by molecular docking and MD simulation. The interaction of FLG(SER742TYR) with BACE1 was greater than that of wildtype FLG with BACE1. CONCLUSIONS: According to the literature search, bio-informatics analysis, and molecular docking and MD simulation, we find non-synonymous rare variants in six genes, especially FLG(rs3120654), that may play key roles in AD.

Erector Spinae Plane Block for Perioperative Pain Control and Short-term Outcomes in Lumbar Laminoplasty: A Randomized Clinical Trial.

BACKGROUND: Spine surgery causes severe pain and can be associated with significant opioid utilization; however, the evidence for opioid-sparing analgesic techniques such as erector spinae plane (ESP) block from controlled studies is limited. We aimed to investigate the analgesic effects of ESP block in lumbar laminoplasty. METHODS: In this prospective, double-blind, controlled single-center trial, 62 consecutive elective lumbar laminoplasty patients were randomized into either a control group (Group G, N=32) or a treatment group (Group E, N=30). Group G received general anesthesia and multimodal analgesia, similar to group E, while Group E received additional bilateral ESP block after induction of general anesthesia. The primary outcome was postoperative pain scores for the first 48 h after surgery, and the secondary outcomes analyzed included intraoperative anesthetic usage, perioperative analgesic consumption, return of bowel function and satisfaction for acute pain management indicated by overall benefit of analgesia score (OBAS). RESULTS: Significant differences in pain scores over time were found between the two groups (P=0.010), with Group E patients having significantly lower pain scores than Group G during the first six hours (P=0.000). The opioid consumption in Group G was significantly higher than in Group E both intraoperatively (P=0.000) and postoperatively (P=0.0005). Group E patients had lower intraoperative sevoflurane requirement, improved satisfaction with pain management, and earlier return of bowel function than Group G patients. CONCLUSION: ESP block is effective in reducing postoperative pain scores and lowering opioid utilization (both intraoperatively and postoperatively), resulting in improved patient satisfaction for pain management in lumbar laminoplasty.

Prolonged symptom onset to admission time is associated with severe Coronavirus disease: A meta combined propensity-adjusted analysis.

BACKGROUND: Patients with severe COVID-19 are more likely to develop adverse outcomes with a huge medical burden. We aimed to investigate whether a shorter symptom onset to admission time (SOAT) could improve outcomes of COVID-19 patients. METHODS: A single-center retrospective study combined with a meta-analysis was performed. The meta-analysis identified studies published between 1 December 2019 and 15 April 2020. Additionally, clinical data of COVID-19 patients diagnosed between January 20 and February 20, 2020, at the First Affiliated Hospital of the University of Science and Technology of China were retrospectively analyzed. SOAT and severity of illness in patients with COVID-19 were used as effect measures. The random-effects model was used to analyze the heterogeneity across studies. Propensity score matching was applied to adjust for confounding factors in the retrospective study. Categorical data were compared using Fisher's exact test. We compared the differences in laboratory characteristic varied times using a two-way nonparametric, Scheirer-Ray-Hare test. RESULTS: In a meta-analysis, we found that patients with adverse outcomes had a longer SOAT (I2 = 39%, mean difference 0.88, 95% confidence interval = 0.47-1.30). After adjusting for confounding factors, such as age, complications, and treatment options, the retrospective analysis results also showed that severe patients had longer SOAT (mean difference 1.13 [1.00, 1.27], p = 0.046). Besides, most biochemical marker levels improved as the hospitalization time lengthened without the effect of disease severity or associated treatment (p < 0.001). CONCLUSION: Shortening the SOAT may help reduce the possibility of mild patients with COVID-19 progressing to severe illness.

Identifying genetic risk variants associated with brain volumetric phenotypes via K-sample Ball Divergence method.

Regional human brain volumes including total area, average thickness, and total volume are heritable and associated with neurological disorders. However, the genetic architecture of brain structure and function is still largely unknown and worthy of exploring. The Pediatric Imaging, Neurocognition, and Genetics (PING) data set provides an excellent resource with genome-wide genetic data and related neuroimaging data. In this study, we perform genome-wide association studies (GWAS) of 315 brain volumetric phenotypes from the PING data set including 1036 samples with 539,865 single-nucleotide polymorphisms (SNPs). We introduce a nonparametric test based on K-sample Ball Divergence (KBD) to identify genetic risk variants that influence regional brain volumes. We carry out simulations to demonstrate that KBD is a powerful test for identifying significant SNPs associated with multivariate phenotypes although controlling the type I error rate. We successfully identify nine SNPs below a significance level of 5 × 10-5 for the PING data. Among the nine identified genetic variants, two SNPs rs486179 and rs562110 are located in the ADRA1A gene that is a well-known risk factor of mental illness, such as schizophrenia and attention deficit hyperactivity disorder. Our study suggests that the nonparametric test KBD is an effective method for identifying genetic variants associated with complex diseases in large-scale GWAS of multiple phenotypes.

Risk factors on admission associated with hospital length of stay in patients with COVID-19: a retrospective cohort study.

Treating patients with COVID-19 is expensive, thus it is essential to identify factors on admission associated with hospital length of stay (LOS) and provide a risk assessment for clinical treatment. To address this, we conduct a retrospective study, which involved patients with laboratory-confirmed COVID-19 infection in Hefei, China and being discharged between January 20 2020 and March 16 2020. Demographic information, clinical treatment, and laboratory data for the participants were extracted from medical records. A prolonged LOS was defined as equal to or greater than the median length of hospitable stay. The median LOS for the 75 patients was 17 days (IQR 13-22). We used univariable and multivariable logistic regressions to explore the risk factors associated with a prolonged hospital LOS. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated. The median age of the 75 patients was 47 years. Approximately 75% of the patients had mild or general disease. The univariate logistic regression model showed that female sex and having a fever on admission were significantly associated with longer duration of hospitalization. The multivariate logistic regression model enhances these associations. Odds of a prolonged LOS were associated with male sex (aOR 0.19, 95% CI 0.05-0.63, p = 0.01), having fever on admission (aOR 8.27, 95% CI 1.47-72.16, p = 0.028) and pre-existing chronic kidney or liver disease (aOR 13.73 95% CI 1.95-145.4, p = 0.015) as well as each 1-unit increase in creatinine level (aOR 0.94, 95% CI 0.9-0.98, p = 0.007). We also found that a prolonged LOS was associated with increased creatinine levels in patients with chronic kidney or liver disease (p < 0.001). In conclusion, female sex, fever, chronic kidney or liver disease before admission and increasing creatinine levels were associated with prolonged LOS in patients with COVID-19.

Co-sparse Non-negative Matrix Factorization.

Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible interpretability with non-negativity property. Nowadays, especially in the neuroimaging field, it is common to have at least thousands of voxels while the sample size is only hundreds. The non-negative matrix factorization encounters both computational and theoretical challenge with such high-dimensional data, i.e., there is no guarantee for a sparse and part-based representation of data. To this end, we introduce a co-sparse non-negative matrix factorization method to high-dimensional data by simultaneously imposing sparsity in both two decomposed matrices. Instead of adding some sparsity induced penalty such as l 1 norm, the proposed method directly controls the number of non-zero elements, which can avoid the bias issues and thus yield more accurate results. We developed an alternative primal-dual active set algorithm to derive the co-sparse estimator in a computationally efficient way. The simulation studies showed that our method achieved better performance than the state-of-art methods in detecting the basis matrix and recovering signals, especially under the high-dimensional scenario. In empirical experiments with two neuroimaging data, the proposed method successfully detected difference between Alzheimer's patients and normal person in several brain regions, which suggests that our method may be a valuable toolbox for neuroimaging studies.

Association of degree of loss aversion and grey matter volume in superior frontal gyrus by voxel-based morphometry.

Loss-aversion behaviors reflect individuals' personal preference bias when they meet uncertainties and measure the potential gains and losses of the uncertain situations before making a decision. Such behaviors are common and well documented in daily life; one example is irrational financial investments. The exact neural mechanisms for these loss-aversion behaviors have been widely discussed. In this study, we explored the neural mechanisms of loss-aversion behaviors by using voxel-based morphometry of brain regions based on two datasets. In the behavioral analysis, the degree of individual behavioral loss aversion was measured. Voxel-based morphometry analysis revealed positive correlations between the degree of individual behavioral loss aversion and grey matter volume in the superior frontal gyrus, which may be crucial neural structures for individual loss-aversion behaviors.

Whole genome association study of brain-wide imaging phenotypes: A study of the ping cohort.

Neuropsychological disorders have a biological basis rooted in brain function, and neuroimaging data are expected to better illuminate the complex genetic basis of neuropsychological disorders. Because they are biological measures, neuroimaging data avoid biases arising from clinical diagnostic criteria that are subject to human understanding and interpretation. A challenge with analyzing neuroimaging data is their high dimensionality and complex spatial relationships. To tackle this challenge, we introduced a novel distance covariance tests that can assess the association between genetic markers and multivariate diffusion tensor imaging measurements, and analyzed a genome-wide association study (GWAS) dataset collected by the Pediatric Imaging, Neurocognition, and Genetics (PING) study. We also considered existing approaches as comparisons. Our results showed that, after correcting for multiplicity, distance covariance tests of the multivariate phenotype yield significantly greater power at detecting genetic markers affecting brain structure than standard mass univariate GWAS of individual neuroimaging biomarkers. Our results underscore the usefulness of utilizing the distance covariance to incorporate neuroimaging data in GWAS.

B-CAN: a resource sharing platform to improve the operation, visualization and integrated analysis of TCGA breast cancer data.

Breast cancer is a high-risk heterogeneous disease with myriad subtypes and complicated biological features. The Cancer Genome Atlas (TCGA) breast cancer database provides researchers with the large-scale genome and clinical data via web portals and FTP services. Researchers are able to gain new insights into their related fields, and evaluate experimental discoveries with TCGA. However, it is difficult for researchers who have little experience with database and bioinformatics to access and operate on because of TCGA's complex data format and diverse files. For ease of use, we build the breast cancer (B-CAN) platform, which enables data customization, data visualization, and private data center. The B-CAN platform runs on Apache server and interacts with the backstage of MySQL database by PHP. Users can customize data based on their needs by combining tables from original TCGA database and selecting variables from each table. The private data center is applicable for private data and two types of customized data. A key feature of the B-CAN is that it provides single table display and multiple table display. Customized data with one barcode corresponding to many records and processed customized data are allowed in Multiple Tables Display. The B-CAN is an intuitive and high-efficient data-sharing platform.

Main Effects of Diagnoses, Brain Regions, and their Interaction Effects for Cerebral Metabolites in Bipolar and Unipolar Depressive Disorders.

Previous studies suggested patients with bipolar depressive disorder (BDd) or unipolar depressive disorder (UDd) have cerebral metabolites abnormalities. These abnormalities may stem from multiple sub-regions of gray matter in brain regions. Thirteen BDd patients, 20 UDd patients and 20 healthy controls (HC) were enrolled to investigate these abnormalities. Absolute concentrations of 5 cerebral metabolites (glutamate-glutamine (Glx), N-acetylaspartate (NAA), choline (Cho), myo-inositol (mI), creatine (Cr), parietal cortex (PC)) were measured from 4 subregions (the medial frontal cortex (mPFC), anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and parietal cortex (PC)) of gray matter. Main and interaction effects of cerebral metabolites across subregions of gray matter were evaluated. For example, the Glx was significantly higher in BDd compared with UDd, and so on. As the interaction analyses showed, some interaction effects existed. The concentrations of BDds' Glx, Cho, Cr in the ACC and HCs' mI and Cr in the PC were higher than that of other interaction effects. In addition, the concentrations of BDds' Glx and Cr in the PC and HCs' mI in the ACC were statistically significant lower than that of other interaction effects. These findings point to region-related abnormalities of cerebral metabolites across subjects with BDd and UDd.

Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation.

Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC) has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI)-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.