Racial, ethnic, and sex differences in heavy drinking and negative alcohol-related consequences in a national sample of NCAA student-athlete drinkers.

Objective: Athletic involvement is linked to increased risk for heavy alcohol use among college students. We examined whether student-athletes from diverse racial/ethnic backgrounds differ with respect to heavy drinking and related consequences. Method: Participants were 15,135 student-athlete drinkers (50.7% female) from 170 NCAA member institutions who participated in an online study. Results: Findings from our hierarchical linear models indicated that being a male student-athlete was associated with an increased likelihood of high intensity drinking (10/8 + drinks/per sitting for males/females) for White, Asian American/Pacific Islander, and Black student-athletes, but not for Hispanic student-athletes. Additionally, being a female student-athlete was associated with higher levels of negative alcohol-related consequences across all racial/ethnic groups. Finally, at similar drink quantities, compared to being a White student-athlete, being an Asian American/Pacific Islander student-athlete was associated with higher levels of alcohol-related consequences. Conclusions: Student-athlete drinkers are not homogeneous with respect to heavy drinking and related consequences.

A National Study on Drinking Game Behaviors and Related Consequences Among NCAA Student-Athletes: Racial/Ethnic and Sex Differences.

OBJECTIVE: Research indicates that college student-athletes report more alcohol use and negative drinking consequences than non-student-athletes. One drinking practice that has been linked to heavy alcohol use and related consequences is playing drinking games. In the present study, we investigated which segment of the student-athlete population is most at risk for frequent drinking game participation, elevated alcohol consumption while playing drinking games, and negative drinking game consequences. We examined sex and racial/ethnic differences in behaviors and consequences associated with drinking games in a national sample of White, Hispanic, Black, and Asian American/Pacific Islander (AAPI) student-athletes. METHOD: A total of 11,839 student-athletes (51.4% women) from 165 National Collegiate Athletic Association (NCAA) member institutions who endorsed lifetime participation in drinking games completed a confidential online survey. RESULTS: Hierarchical linear modeling revealed that being a White (vs. Black or Hispanic) student-athlete was associated with more frequent drinking game participation, and among AAPI and Black (but not White or Hispanic) student-athletes, men played drinking games more frequently than women. Being a Black (vs. White) student-athlete was associated with more drinking game consumption; no sex differences in drinking game consumption were found among Black student-athletes. Among White, AAPI, and Hispanic student-athletes, being a male student-athlete was associated with more drinking game consumption. Finally, female student-athletes had a higher likelihood of experiencing one or more negative consequences from drinking games than did male student-athletes. The association between drinking game participation and negative drinking game consequences was also stronger for women compared with men. CONCLUSIONS: Student-athletes are heterogeneous with regard to drinking game behaviors and related consequences. Knowing who is at greatest risk for drinking game participation and related outcomes is an important first step in developing targeted intervention approaches for student-athletes.

Deep learning-based classification of multi-categorical Alzheimer's disease data.

It is urgent to find the appropriate technology for the early detection of Alzheimer's disease (AD) due to the unknown AD etiopathologies that bring about serious social problems. Early detection of mild cognitive impairment (MCI) has pivotal importance in delaying or preventing the AD onset. Herein, we utilize deep learning (DL) techniques for the purpose of multiclass classification between normal control, MCI, and AD subjects. We used multi-categorical data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) including brain imaging measurements, cognitive test results, cerebrospinal fluid measures, ApoE4 status, and age. We achieved an overall accuracy of 87.197% for our artificial neural network classifier and a similar overall accuracy of 88.275% for our 1D convolutional neural network classifier. We conclude that DL-based techniques are powerful tools in analyzing ADNI data although further method refinements are needed.

Alzheimer's Disease and Its Potential Alternative Therapeutics.

Alzheimer's Disease (AD) is a chronic neurodegenerative disease that affects over 5 million individuals in the United States alone. Currently, there are only two kinds of pharmacological interventions available for symptomatic relief of AD; Acetyl Cholinesterase Inhibitors (AChEI) and N-methyl-D-aspartic Acid (NMDA) receptor antagonists and these drugs do not slow down or stop the progression of the disease. Several molecular targets have been implicated in the pathophysiology of AD, such as the tau (τ) protein, Amyloid-beta (Aß), the Amyloid Precursor Protein (APP) and more and several responses have also been observed in the advancement of the disease, such as reduced neurogenesis, neuroinflammation, oxidative stress and iron overload. In this review, we discuss general features of AD and several small molecules across different experimental AD drug classes that have been studied for their effects in the context of the molecular targets and responses associated with the AD progression. These drugs include: Paroxetine, Desferrioxamine (DFO), N-acetylcysteine (NAC), Posiphen/-(-)Phenserine, JTR-009, Carvedilol, LY450139, Intravenous immunoglobulin G 10%, Indomethacin and Lithium Carbonate (Li2CO3).

Blockchain in Healthcare: A Patient-Centered Model.

A blockchain is a system for storing and sharing information that is secure because of its transparency. Each block in the chain is both its own independent unit containing its own information, and a dependent link in the collective chain, and this duality creates a network regulated by participants who store and share the information, rather than a third party. Blockchain has many applications in healthcare, and can improve mobile health applications, monitoring devices, sharing and storing of electronic medical records, clinical trial data, and insurance information storage. Research about blockchain and healthcare is currently limited, but blockchain is on the brink of transforming the healthcare system; through its decentralized principles, blockchain can improve accessibility and security of patient information, and can therefore overturn the healthcare hierarchy and build a new system in which patients manage their own care.

Identification and Analysis of Alzheimer's Candidate Genes by an Amplitude Deviation Algorithm.

BACKGROUND: Alzheimer's disease (AD) is the most common form of senile dementia. However, its pathological mechanisms are not fully understood. In order to comprehend AD pathological mechanisms, researchers employed AD-related DNA microarray data and diverse computational algorithms. More efficient computational algorithms are needed to process DNA microarray data for identifying AD-related candidate genes. METHODS: In this paper, we propose a specific algorithm that is based on the following observation: When an acrobat walks along a steel-wire, his/her body must have some swing; if the swing can be controlled, then the acrobat can maintain the body balance. Otherwise, the acrobat will fall. Based on this simple idea, we have designed a simple, yet practical, algorithm termed as the Amplitude Deviation Algorithm (ADA). Deviation, overall deviation, deviation amplitude, and 3δ are introduced to characterize ADA. RESULTS: 52 candidate genes for AD have been identified via ADA. The implications for some of the AD candidate genes in AD pathogenesis have been discussed. CONCLUSIONS: Through the analysis of these AD candidate genes, we believe that AD pathogenesis may be related to the abnormality of signal transduction (AGTR1 and PTAFR), the decrease in protein transport capacity (COL5A2 (221729_at), COL5A2 (221730_at), COL4A1), the impairment of axon repair (CNR1), and the intracellular calcium dyshomeostasis (CACNB2, CACNA1E). However, their potential implication for AD pathology should be further validated by wet lab experiments as they were only identified by computation using ADA.

Modulation of SPARC/Hevin Proteins in Alzheimer's Disease Brain Injury.

Alzheimer's disease (AD) is an age-related progressive form of dementia that features neuronal loss, intracellular tau, and extracellular amyloid-ß (Aß) protein deposition. Neurodegeneration is accompanied by neuroinflammation mainly involving microglia, the resident innate immune cell population of the brain. During AD progression, microglia shift their phenotype, and it has been suggested that they express matricellular proteins such as secreted protein acidic and rich in cysteine (SPARC) and Hevin protein, which facilitate the migration of other immune cells, such as blood-derived dendritic cells. We have detected both SPARC and Hevin in postmortem AD brain tissues and confirmed significant alterations in transcript expression using real-time qPCR. We suggest that an infiltration of myeloid-derived immune cells occurs in the areas of diseased tissue. SPARC is highly expressed in AD brain and collocates to Aß protein deposits, thus contributing actively to cerebral inflammation and subsequent tissue repair, and Hevin may be downregulated in the diseased state. However, further research is needed to reveal the exact roles of SPARC and Hevin proteins and associated signaling pathways in AD-related neuroinflammation. Nevertheless, normalizing SPARC/Hevin protein expression such as interdicting heightened SPARC protein expression may confer a novel therapeutic opportunity for modulating AD progression.

Deep learning and virtual drug screening.

Current drug development is still costly and slow given tremendous technological advancements in drug discovery and medicinal chemistry. Using machine learning (ML) to virtually screen compound libraries promises to fix this for generating drug leads more efficiently and accurately. Herein, we explain the broad basics and integration of both virtual screening (VS) and ML. We then discuss artificial neural networks (ANNs) and their usage for VS. The ANN is emerging as the dominant classifier for ML in general, and has proven its utility for both structure-based and ligand-based VS. Techniques such as dropout, multitask learning and convolution improve the performance of ANNs and enable them to take on chemical meaning when learning about the drug-target-binding activity of compounds.

Network Medicine for Alzheimer's Disease and Traditional Chinese Medicine.

Alzheimer's Disease (AD) is a neurodegenerative condition that currently has no known cure. The principles of the expanding field of network medicine (NM) have recently been applied to AD research. The main principle of NM proposes that diseases are much more complicated than one mutation in one gene, and incorporate different genes, connections between genes, and pathways that may include multiple diseases to create full scale disease networks. AD research findings as a result of the application of NM principles have suggested that functional network connectivity, myelination, myeloid cells, and genes and pathways may play an integral role in AD progression, and may be integral to the search for a cure. Different aspects of the AD pathology could be potential targets for drug therapy to slow down or stop the disease from advancing, but more research is needed to reach definitive conclusions. Additionally, the holistic approaches of network pharmacology in traditional Chinese medicine (TCM) research may be viable options for the AD treatment, and may lead to an effective cure for AD in the future.