LIMB MOVEMENT OF BASKETBALL ATHLETES IN SPORTS EXERCISE

ABSTRACT Introduction: The performance of basketball players is based on physical function and quality. In addition to genetic factors, physical function can also be improved through acquired training. Objective: The article analyzes the concept of body movement through literature data and a questionnaire survey. Methods: This article analyzes the mechanical characteristics of basketball technology from the perspective of physiology and proposes methods to develop the strength of basketball players. Results: Through the activation of different training actions, controlling the muscles that maintain the stability of the limbs to adjust body balance is beneficial to improvement of the coordination and sensitivity of the muscles. Conclusion: Pay attention to the principle of incremental load, the SAID principle, and comprehensiveness in strength training. The training method adopted is helpful to the improvement of the athlete’s aerobic metabolism. Level of evidence II; Therapeutic studies - investigation of treatment results.

Genetic factors affecting the composition and quality of cow’s milk

This chapter reviews the genetic factors affecting the composition and quality of cow's milk. It starts by discussing how different breeds of cattle can affect the composition of milk. The chapter then goes on to examine milk proteins, genetic variants and post-translational modifications. It then discusses milk coagulation and other functional properties, before highlighting the genetic influence of fatty acids on minor milk components. The chapter also discusses using mid-infrared spectroscopy for genetic parameter estimation, before concluding with a section on the possibilities for genetic improvement in relation to dairy milk.

A Predictor Combining Clinical and Genetic Factors for AML1-ETO Leukemia Patients

Core Binding Factor (CBF)-AML is one of the most common somatic mutations in acute myeloid leukemia (AML). t(8;21)/AML1-ETO-positive acute myeloid leukemia accounts for 5-10% of all AMLs. In this study, we consecutively included 254 AML1-ETO patients diagnosed and treated at our institute from December 2009 to March 2020, and evaluated molecular mutations by 185-gene NGS platform to explore genetic co-occurrences with clinical outcomes. Our results showed that high KIT VAF(≥15%) correlated with shortened overall survival compared to other cases with no KIT mutation (3-year OS rate 26.6% vs 59.0% vs 69.6%, HR 1.50, 95%CI 0.78-2.89, P=0.0005). However, no difference was found in patients’ OS whether they have KIT mutation in two or three sites. Additionally, we constructed a risk model by combining clinical and molecular factors; this model was validated in other independent cohorts. In summary, our study showed that c-kit other than any other mutations would influence the OS in AML1-ETO patients. A proposed predictor combining both clinical and genetic factors is applicable to prognostic prediction in AML1-ETO patients.

Ethnic Pharmacogenomic Differences in the Management of Asian Patients with Metastatic Prostate Cancer

Progression to metastatic disease occurs in about half of all men who develop prostate cancer (PC), one of the most common cancers in men worldwide. Androgen deprivation therapy has been the mainstay therapy for patients with metastatic PC (mPC) since the 1940s. In the last decade, there has been unprecedented advancement in systemic therapies, e.g., taxane, androgen-signalling pathway inhibitors, and biomarker-driven targeted therapies for various stages of disease, resulting in overall survival improvement. Adding to ongoing controversies over how best to treat these patients is the recognition that ethnicity may influence prognosis and outcomes. This review discusses recent evidence for the impacts of Asian ethnicity specifically, which includes environmental, sociocultural, and genetic factors, on the approach to pharmacological management of mPC. Clear inter-ethnic differences in drug tolerability, serious adverse events (AEs), and genetic heterogeneity must all be considered when dosing and scheduling for treatment, as well as designing future precision studies in PC.

Human Microbiota in Esophageal Adenocarcinoma: Pathogenesis, Diagnosis, Prognosis and Therapeutic Implications

Esophageal adenocarcinoma (EAC) is one of the main subtypes of esophageal cancer. The incidence rate of EAC increased progressively while the 5-year relative survival rates were poor in the past two decades. The mechanism of EAC has been studied extensively in relation to genetic factors, but less so with respect to human microbiota. Currently, researches about the relationship between EAC and the human microbiota is a newly emerging field of study. Herein, we present the current state of knowledge linking human microbiota to esophageal adenocarcinoma and its precursor lesion—gastroesophageal reflux disease and Barrett’s esophagus. There are specific human bacterial alternations in the process of esophageal carcinogenesis. And bacterial dysbiosis plays an important role in the process of esophageal carcinogenesis via inflammation, microbial metabolism and genotoxicity. Based on the human microbiota alternation in the EAC cascade, it provides potential microbiome-based clinical application. This review is focused on novel targets in prevention, diagnosis, prognosis, and therapy for esophageal adenocarcinoma.

A Reevaluation of the 1990 “Minnesota Study of Twins Reared Apart” IQ Study

In 1990, Thomas J. Bouchard, Jr. and colleagues published the widely cited 1990 “Minnesota Study of Twins Reared Apart” (MISTRA) Science IQ study. To arrive at the conclusion that “IQ is strongly affected by genetic factors,” Bouchard and colleagues omitted their control group reared-apart dizygotic twin (“DZA”) IQ-score correlations. Near-full-sample correlations published after the study’s 2000 endpoint show that the reared-apart monozygotic twin (“MZA”) and DZA group IQ correlations did not differ at a statistically significant level, suggesting that the study failed the first step in determining that IQ scores are influenced by heredity. After bypassing the model-fitting technique they used in most non-IQ MISTRA studies, the researchers assumed that the MZA group IQ-score correlation alone “directly estimates heritability.” This method was based on unsupported assumptions by the researchers, and they largely overlooked the confounding influence of cohort effects. Bouchard and colleagues then decided to count most environmental influences they did recognize as genetic influences. I conclude that the MISTRA IQ study failed to discover genetic influences on IQ scores and cognitive ability across the studied population, and that the study should be evaluated in the context of psychology’s replication problem.

What Have We Learned (or Expect to) From Analysis of Murine Genetic Models Related to Substance Use Disorders?

The tremendous public health problem created by substance use disorders (SUDs) presents a major opportunity for mouse genetics. Inbred mouse strains exhibit substantial and heritable differences in their responses to drugs of abuse (DOA) and in many of the behaviors associated with susceptibility to SUD. Therefore, genetic discoveries emerging from analysis of murine genetic models can provide critically needed insight into the neurobiological effects of DOA, and they can reveal how genetic factors affect susceptibility drug addiction. There are already indications, emerging from our prior analyses of murine genetic models of responses related to SUDs that mouse genetic models of SUD can provide actionable information, which can lead to new approaches for alleviating SUDs. Lastly, we consider the features of murine genetic models that enable causative genetic factors to be successfully identified; and the methodologies that facilitate genetic discovery.

The activity of human enhancers is modulated by the splicing of their associated lncRNAs

Pervasive enhancer transcription is at the origin of more than half of all long noncoding RNAs in humans. Transcription of enhancer-associated long noncoding RNAs (elncRNA) contribute to their cognate enhancer activity and gene expression regulation in cis. Recently, splicing of elncRNAs was shown to be associated with elevated enhancer activity. However, whether splicing of elncRNA transcripts is a mere consequence of accessibility at highly active enhancers or if elncRNA splicing directly impacts enhancer function, remains unanswered. We analysed genetically driven changes in elncRNA splicing, in humans, to address this outstanding question. We showed that splicing related motifs within multi-exonic elncRNAs evolved under selective constraints during human evolution, suggesting the processing of these transcripts is unlikely to have resulted from transcription across spurious splice sites. Using a genome-wide and unbiased approach, we used nucleotide variants as independent genetic factors to directly assess the causal relationship that underpin elncRNA splicing and their cognate enhancer activity. We found that the splicing of most elncRNAs is associated with changes in chromatin signatures at cognate enhancers and target mRNA expression. We provide evidence that efficient and conserved processing of enhancer-associated elncRNAs contributes to enhancer activity.

An explainable model of host genetic interactions linked to COVID-19 severity

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with training of multiple supervised classifiers, to predict severity on the basis of screened features. Feature importance analysis from tree-based models allowed to identify a handful of 16 variants with highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with good accuracy (ACC=81.88%; ROC_AUC=96%; MCC=61.55%). Principal Component Analysis (PCA) and clustering of patients on important variants orthogonally identified two groups of individuals with a higher fraction of severe cases. Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response, such as JAK-STAT, Cytokine, Interleukin, and C-type lectin receptor signaling. It also identified additional processes cross-talking with immune pathways, such as GPCR signalling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as “Respiratory or thoracic disease”, confirming their link with COVID-19 severity outcome. Taken together, our analysis suggests that curated genetic information can be effectively integrated along with other patient clinical covariates to forecast COVID-19 disease severity and dissect the underlying host genetic mechanisms for personalized medicine treatments.