Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods.

Multiple types of COVID-19 vaccines have been shown to be highly effective in preventing SARS-CoV-2 infection and in reducing post-infection symptoms. Almost all of these vaccines induce systemic immune responses, but differences in immune responses induced by different vaccination regimens are evident. This study aimed to reveal the differences in immune gene expression levels of different target cells under different vaccine strategies after SARS-CoV-2 infection in hamsters.

Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods.

To date, COVID-19 remains a serious global public health problem. Vaccination against SARS-CoV-2 has been adopted by many countries as an effective coping strategy. The strength of the body's immune response in the face of viral infection correlates with the number of vaccinations and the duration of vaccination.

Identification of Genes Associated with the Impairment of Olfactory and Gustatory Functions in COVID-19 via Machine-Learning Methods.

The coronavirus disease 2019 (COVID-19), as a severe respiratory disease, affects many parts of the body, and approximately 20-85% of patients exhibit functional impairment of the senses of smell and taste, some of whom even experience the permanent loss of these senses. These symptoms are not life-threatening but severely affect patients' quality of life and increase the risk of depression and anxiety. The pathological mechanisms of these symptoms have not been fully identified.

Identification of genes related to immune enhancement caused by heterologous ChAdOx1-BNT162b2 vaccines in lymphocytes at single-cell resolution with machine learning methods.

The widely used ChAdOx1 nCoV-19 (ChAd) vector and BNT162b2 (BNT) mRNA vaccines have been shown to induce robust immune responses. Recent studies demonstrated that the immune responses of people who received one dose of ChAdOx1 and one dose of BNT were better than those of people who received vaccines with two homologous ChAdOx1 or two BNT doses. However, how heterologous vaccines function has not been extensively investigated.

Characterization of chromatin accessibility patterns in different mouse cell types using machine learning methods at single-cell resolution.

Chromatin accessibility is a generic property of the eukaryotic genome, which refers to the degree of physical compaction of chromatin. Recent studies have shown that chromatin accessibility is cell type dependent, indicating chromatin heterogeneity across cell lines and tissues. The identification of markers used to distinguish cell types at the chromosome level is important to understand cell function and classify cell types.

Identification of Methylation Signatures and Rules for Sarcoma Subtypes by Machine Learning Methods.

Sarcoma, the second common type of solid tumor in children and adolescents, has a wide variety of subtypes that are often not properly diagnosed at an early stage, leading to late metastases and causing serious loss of life and property to patients and families. It exhibits a high degree of heterogeneity at the cellular, molecular, and epigenetic levels, where DNA methylation has been proposed to play a role in the diagnosis of sarcoma subtypes. Thus, this study is aimed at finding potential biomarkers at the DNA methylation level to distinguish different sarcoma subtypes.

Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods.

Individuals with the SARS-CoV-2 infection may experience a wide range of symptoms, from being asymptomatic to having a mild fever and cough to a severe respiratory impairment that results in death. MicroRNA (miRNA), which plays a role in the antiviral effects of SARS-CoV-2 infection, has the potential to be used as a novel marker to distinguish between patients who have various COVID-19 clinical severities. In the current study, the existing blood expression profiles reported in two previous studies were combined for deep analyses.

Characterization of spleen and lymph node cell types CITE-seq and machine learning methods.

The spleen and lymph nodes are important functional organs for human immune system. The identification of cell types for spleen and lymph nodes is helpful for understanding the mechanism of immune system. However, the cell types of spleen and lymph are highly diverse in the human body.

Follicular fluid lipidomic profiling reveals potential biomarkers of polycystic ovary syndrome: A pilot study.

Background: Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder associated with multiple metabolic conditions including obesity, insulin resistance, and dyslipidemia. PCOS is the most common cause of anovulatory infertility; however, the molecular diversity of the ovarian follicle microenvironment is not fully understood. This study aimed to investigate the follicular fluid (FF) lipidomic profiles in different phenotypes of PCOS and to explore novel lipid biomarkers.

Identification of Human Cell Cycle Phase Markers Based on Single-Cell RNA-Seq Data by Using Machine Learning Methods.

The cell cycle is composed of a series of ordered, highly regulated processes through which a cell grows and duplicates its genome and eventually divides into two daughter cells. According to the complex changes in cell structure and biosynthesis, the cell cycle is divided into four phases: gap 1 (G1), DNA synthesis (S), gap 2 (G2), and mitosis (M). Determining which cell cycle phases a cell is in is critical to the research of cancer development and pharmacy for targeting cell cycle.

Identification of DNA Methylation Signature and Rules for SARS-CoV-2 Associated with Age.

Background: COVID-19 displays an increased mortality rate and higher risk of severe symptoms with increasing age, which is thought to be a result of the compromised immunity of elderly patients. However, the underlying mechanisms of aging-associated immunodeficiency against (SARS-CoV-2) remains unclear. Epigenetic modifications show considerable changes with age, causing altered gene regulations and cell functions during the aging process.

Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using a Machine Learning Method.

SARS-CoV-2 shows great evolutionary capacity through a high frequency of genomic variation during transmission. Evolved SARS-CoV-2 often demonstrates resistance to previous vaccines and can cause poor clinical status in patients. Mutations in the SARS-CoV-2 genome involve mutations in structural and nonstructural proteins, and some of these proteins such as spike proteins have been shown to be directly associated with the clinical status of patients with severe COVID-19 pneumonia.

Correlation between decreased plasma miR-29a and vascular endothelial injury induced by hyperlipidemia.

Background: Hyperlipidemia is a major risk factor for vascular endothelial injury and atherosclerosis leading to cardiovascular diseases. Early diagnosis of vascular endothelial injury is important for the prevention and prognosis of cardiovascular diseases. This study aimed to investigate sensitive circulating microRNA (miRNA) as a potential diagnostic biomarker of vascular endothelial injury in a hyperlipidemic rat model.

Identifying Key MicroRNA Signatures for Neurodegenerative Diseases With Machine Learning Methods.

Neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease, and many other disease types, cause cognitive dysfunctions such as dementia via the progressive loss of structure or function of the body's neurons. However, the etiology of these diseases remains unknown, and diagnosing less common cognitive disorders such as vascular dementia (VaD) remains a challenge. In this work, we developed a machine-leaning-based technique to distinguish between normal control (NC), AD, VaD, dementia with Lewy bodies, and mild cognitive impairment at the microRNA (miRNA) expression level.

Recognition of Immune Cell Markers of COVID-19 Severity with Machine Learning Methods.

COVID-19 is hypothesized to be linked to the host's excessive inflammatory immunological response to SARS-CoV-2 infection, which is regarded to be a major factor in disease severity and mortality. Numerous immune cells play a key role in immune response regulation, and gene expression analysis in these cells could be a useful method for studying disease states, assessing immunological responses, and detecting biomarkers. Here, we developed a machine learning procedure to find biomarkers that discriminate disease severity in individual immune cells (B cell, CD4 cell, CD8 cell, monocyte, and NK cell) using single-cell gene expression profiles of COVID-19.

Follicular free fatty acid metabolic signatures and their effects on oocyte competence in non-obese PCOS patients.

In Brief: Polycystic ovary syndrome (PCOS) is a common cause of anovulatory infertility in women. This study identified changes in free fatty acids profiles in the follicular fluid that may lead to better diagnosis and management of infertility in PCOS women.

Abstract: Polycystic ovary syndrome (PCOS) is a heterogeneous disease characterized by various endocrine/metabolic disorders and impaired reproductive potential.

Detecting Blood Methylation Signatures in Response to Childhood Cancer Radiotherapy via Machine Learning Methods.

Radiotherapy is a helpful treatment for cancer, but it can also potentially cause changes in many molecules, resulting in adverse effects. Among these changes, the occurrence of abnormal DNA methylation patterns has alarmed scientists. To explore the influence of region-specific radiotherapy on blood DNA methylation, we designed a computational workflow by using machine learning methods that can identify crucial methylation alterations related to treatment exposure.

Lupus enhancer risk variant causes dysregulation of IRF8 through cooperative lncRNA and DNA methylation machinery.

Despite strong evidence that human genetic variants affect the expression of many key transcription factors involved in autoimmune diseases, establishing biological links between non-coding risk variants and the gene targets they regulate remains a considerable challenge. Here, we combine genetic, epigenomic, and CRISPR activation approaches to screen for functional variants that regulate IRF8 expression. We demonstrate that the locus containing rs2280381 is a cell-type-specific enhancer for IRF8 that spatially interacts with the IRF8 promoter.

Exploring the Genomic Patterns in Human and Mouse Cerebellums Via Single-Cell Sequencing and Machine Learning Method.

In mammals, the cerebellum plays an important role in movement control. Cellular research reveals that the cerebellum involves a variety of sub-cell types, including Golgi, granule, interneuron, and unipolar brush cells. The functional characteristics of cerebellar cells exhibit considerable differences among diverse mammalian species, reflecting a potential development and evolution of nervous system.

Horizontal transfer of antibiotic resistance genes within the bacterial communities in aquacultural environment.

Very little is known about how microbiome interactions shape the horizontal transfer of antibiotic resistance genes in aquacultural environment. To this end, we first conducted 16S rRNA gene amplicon sequencing to monitor the dynamics of bacterial community compositions in one shrimp farm from 2019 to 2020. Next, co-occurrence analysis was then conducted to reveal the interactions network between Vibrio spp.

Genome-wide analysis of DNA methylation in 106 schizophrenia family trios in Han Chinese.

Background: Schizophrenia (SCZ) is a severe psychiatric disorder that affects approximately 0.75% of the global population. Both genetic and environmental factors contribute to development of SCZ.

One health pathogen surveillance demonstrated the dissemination of gut pathogens within the two coastal regions associated with intensive farming.

Background: Intensive aquaculture farming has caused significant degradation of coastal wetlands and has been proposed as a reservoir for pathogenic Vibrio spp.

Results: Gut pathogens including Vibrio spp., Salmonella spp.

Distinct dynamics of Vibrio parahaemolyticus populations in two farming models.

Aims: Despite the recent prosperity of shrimp cultivation in China, very little is known about how different shrimp farming models influence the dynamics of Vibrio parahaemolyticus populations and the antibiotic resistance of this bacterium.

Methods And Results: To this end, we conducted continuous surveillance of V. parahaemolyticus on four farms over 3 years: two traditional shrimp farms with daily water exchange and two farms operated in the recirculating aquaculture systems (RAS).

Semi-In-Vivo Pull-Down Assay for Blue Light-Dependent Protein Interactions.

Cryptochromes are photolyase-like blue-light receptors found in all major evolutionary lineages (Ahmad and Cashmore, Nature 366:162-166, 1993; Lin, Plant Physiol 110:1047, 1996; Cashmore, Cell 114:537-543, 2003; Partch and Sancar, Methods Enzymol 393:726-745, 2005). Arabidopsis cryptochrome 1 (CRY1) and cryptochrome 2 (CRY2) mediate primarily blue-light inhibition of hypocotyl elongation and photoperiodic control of floral initiation (Ahmad and Cashmore, Nature 366:162-166, 1993; Somers et al., Science, 282:1488-1490, 1998; Guo et al.

Express Arabidopsis Cryptochrome in Sf9 Insect Cells Using the Baculovirus Expression System.

The Bac-to-Bac Baculovirus Expression System provides a rapid and efficient method to generate recombinant cryptochrome (CRY) proteins with chromophore flavin (FAD), which showed blue light response in vitro.