Who would benefit from memory training? A pilot study examining the ceiling effect of concurrent cognitive stimulation

Diverse effects of memory training were observed in the literature. One possible factor is the amount of concurrent cognitive training received during the training program. In this pilot study, we recruited 24 elderly adults with or without concurrent cognitive stimulations to attend a memory-training program. Findings suggested that elderly people without concurrent cognitive stimulation could benefit from a memory-training program in the form of improved initiation and memory functioning. Self-rated quality of life measure also showed improvements alongside the cognitive benefits. Elderly people with regular concurrent cognitive stimulation, on the other hand, seemed to plateau in their level of performance and did not show any significant change. Our preliminary findings suggested nonlinear concurrent cognitive stimulation in the elderly

Genotype Distribution and Sequence Variation of Hepatitis E Virus, Hong Kong

Most acute cases of infection with hepatitis E virus (HEV) in Hong Kong were autochthonous, sporadic, and occurred in older adults. All except 1 isolate belonged to genotype 4; most were phylogenetically related to swine isolates. The epidemiology is similar to that in industrialized countries, where zoonosis is the major source of HEV infection in humans

A Panel of Serum MicroRNAs as Specific Biomarkers for Diagnosis of Compound- and Herb-Induced Liver Injury in Rats

Drug-induced liver injury (DILI) has been a public, economic and pharmaceutical issue for many years. Enormous effort has been made for discovering and developing novel biomarkers for diagnosing and monitoring both clinical and preclinical DILI at an early stage, though progress has been relatively slow. Additionally, herb-induced liver injury is an emerging cause of liver disease because herbal medicines are increasingly being used worldwide. Recently, circulating microRNAs (miRNAs) have shown potential to serve as novel, minimally invasive biomarkers to diagnose and monitor human cancers and other diseases at early stages.In order to identify candidate miRNAs as diagnostic biomarkers for DILI, miRNA expression profiles of serum and liver tissue from two parallel liver injury Sprague-Dawley rat models induced by a compound (acetaminophen, APAP) or an herb (Dioscorea bulbifera, DB) were screened in this study. The initial screens were performed on serum using a MicroRNA TaqMan low-density qPCR array and on liver tissue using a miRCURY LNA hybridization array and were followed by a TaqMan probe-based quantitative reverse transcription-PCR (qRT-PCR) assay to validate comparison with serum biochemical parameters and histopathological examination. Two sets of dysregulated miRNA candidates in serum and liver tissue were selected in the screening phase. After qRT-PCR validation, a panel of compound- and herb- related serum miRNAs was identified.We have demonstrated that this panel of serum miRNAs provides potential biomarkers for diagnosis of DILI with high sensitivity and specificity

RUVBL1/2 Complex Regulates Pro-Inflammatory Responses in Macrophages via Regulating Histone H3K4 Trimethylation

Macrophages play an important role in the host defense mechanism. In response to infection, macrophages activate a genetic program of pro-inflammatory response to kill any invading pathogen, and initiate an adaptive immune response. We have identified RUVBL2 - an ATP-binding protein belonging to the AAA+ (ATPase associated with diverse cellular activities) superfamily of ATPases - as a novel regulator in pro-inflammatory response of macrophages. Gene knockdown of Ruvbl2, or pharmacological inhibition of RUVBL1/2 activity, compromises type-2 nitric oxide synthase (Nos2) gene expression, nitric oxide production and anti-bacterial activity of mouse macrophages in response to lipopolysaccharides (LPS). RUVBL1/2 inhibitor similarly inhibits pro-inflammatory response in human monocytes, suggesting functional conservation of RUVBL1/2 in humans. Transcriptome analysis further revealed that major LPS-induced pro-inflammatory pathways in macrophages are regulated in a RUVBL1/2-dependent manner. Furthermore, RUVBL1/2 inhibition significantly reduced the level of histone H3K4me3 at the promoter region of Nos2 and Il6, two prototypical pro-inflammatory genes, and diminished the recruitment of NF-kappaB to the corresponding enhancers. Our study reveals RUVBL1/2 as an integral component of macrophage pro-inflammatory responses through epigenetic regulations, and the therapeutic potentials of RUVBL1/2 inhibitors in the treatment of diseases caused by aberrant activation of pro-inflammatory pathways

Evolutionary and Transmission Dynamics of Reassortant H5N1 Influenza Virus in Indonesia

H5N1 highly pathogenic avian influenza (HPAI) viruses have seriously affected the Asian poultry industry since their recurrence in 2003. The viruses pose a threat of emergence of a global pandemic influenza through point mutation or reassortment leading to a strain that can effectively transmit among humans. In this study, we present phylogenetic evidences for the interlineage reassortment among H5N1 HPAI viruses isolated from humans, cats, and birds in Indonesia, and identify the potential genetic parents of the reassorted genome segments. Parsimony analyses of viral phylogeography suggest that the reassortant viruses may have originated from greater Jakarta and surroundings, and subsequently spread to other regions in the West Java province. In addition, Bayesian methods were used to elucidate the genetic diversity dynamics of the reassortant strain and one of its genetic parents, which revealed a more rapid initial growth of genetic diversity in the reassortant viruses relative to their genetic parent. These results demonstrate that interlineage exchange of genetic information may play a pivotal role in determining viral genetic diversity in a focal population. Moreover, our study also revealed significantly stronger diversifying selection on the M1 and PB2 genes in the lineages preceding and subsequent to the emergence of the reassortant viruses, respectively. We discuss how the corresponding mutations might drive the adaptation and onward transmission of the newly formed reassortant viruses

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Developments of electric cars and fuel cell hydrogen electric cars

The world continues to strive in the search for clean power sources to run the millions of different vehicles on the road on daily basis as they are the main contributors to toxic emissions releases from internal combustion engines to the atmosphere. These toxic emissions contribute to climate change and air pollution and impact negatively on people's health. Fuel cell devices are gradually replacing the internal combustion engines in the transport industry. Some notable challenges of the PEMFC technology are discussed in this paper. High costs, low durability and hydrogen storage problems are some of the major obstacles being examined in this investigation. The paper explores the latest advances in electric cars technology and their design specifications. The study also compares the characteristics and the technologies of the three types of electric cars now available in the market.interna

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Application of machine learning models on predicting the length of hospital stay in fragility fracture patients

Abstract Background The rate of geriatric hip fracture in Hong Kong is increasing steadily and associated mortality in fragility fracture is high. Moreover, fragility fracture patients increase the pressure on hospital bed demand. Hence, this study aims to develop a predictive model on the length of hospital stay (LOS) of geriatric fragility fracture patients using machine learning (ML) techniques. Methods In this study, we use the basic information, such as gender, age, residence type, etc., and medical parameters of patients, such as the modified functional ambulation classification score (MFAC), elderly mobility scale (EMS), modified Barthel index (MBI) etc, to predict whether the length of stay would exceed 21 days or not. Results Our results are promising despite the relatively small sample size of 8000 data. We develop various models with three approaches, namely (1) regularizing gradient boosting frameworks, (2) custom-built artificial neural network and (3) Google’s Wide & Deep Learning technique. Our best results resulted from our Wide & Deep model with an accuracy of 0.79, with a precision of 0.73, with an area under the receiver operating characteristic curve (AUC-ROC) of 0.84. Feature importance analysis indicates (1) the type of hospital the patient is admitted to, (2) the mental state of the patient and (3) the length of stay at the acute hospital all have a relatively strong impact on the length of stay at palliative care. Conclusions Applying ML techniques to improve the quality and efficiency in the healthcare sector is becoming popular in Hong Kong and around the globe, but there has not yet been research related to fragility fracture. The integration of machine learning may be useful for health-care professionals to better identify fragility fracture patients at risk of prolonged hospital stays. These findings underline the usefulness of machine learning techniques in optimizing resource allocation by identifying high risk individuals and providing appropriate management to improve treatment outcome