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Maintenance of lung function is an often underappreciated, yet critical component of healthy aging. Given the unprecedented shift in the average age of Canadians over the next half century, it will be important to investigate the determinants of lung function in the elderly. In the following study, we estimated the association between lung function and a broad array of factors related to sociodemographics, lifestyle, chronic medical conditions and psychosocial factors in older adults aged 45-86 years old using cross-sectional data from the Canadian Longitudinal Study of Aging (n = 21,338). In addition to examining the entire cohort, we also performed stratified analyses within men/women, adults aged 45-64/65+, and healthy/comorbid. In multivariable regression, our explanatory factors (excluding age, sex, height and ethnicity) were able to explain 17% and 11% of the total variance in FEV1 and FEV1/FVC, respectively. Notable and significant contributions were observed for respiratory disease, smoking, obesity, income, and physical activity, while psychosocial factors mainly exhibited non-significant associations. Generally, these associations were stronger for males than females, and adults 65 and older as compared to those aged 45-64. Our findings indicate that there are pervasive and generally under-recognized sociodemographic and lifestyle factors that exhibit significant associations with FEV1 and FEV1/FVC in older adults. While implication of causality in these relationships is not possible due to the cross-sectional nature of the study, future work aiming to investigate determinants of lung health in older adults may choose to target these factors, given that many are modifiable.
The outbreak of COVID-19 caused by SARS-CoV-2 has been a pandemic. The objective of our study was to explore the association between sex and clinical outcomes in patients with COVID-19.

Detailed clinical data including clinical characteristics, laboratory tests, imaging features and treatments of 1190 cases of adult patients with confirmed COVID-19 were retrospectively analyzed. Associations between sex and clinical outcomes were identified by multivariable Cox regression analysis.

There were 635 (53.4%) male and 555 (46.6%) female patients in this study. Higher rates of acute kidney injury (5.5% vs. 2.9%, p=0.026), acute cardiac injury (9.1% vs. 4.3%, p=0.001), and disseminated intravascular coagulation (2.5% vs. 0.7%, P=0.024) were observed in males. Compared with female patients, male patients with COVID-19 had a higher inhospital mortality rate (15.7% vs. 10.3%, p=0.005). However, Cox regression analysis showed that sex did not influence inhospital mortality of COVID-19 patients.

Male sex was associated with a worse prognosis of COVID-19, but it seems not to be an independent prognostic factor.
Male sex was associated with a worse prognosis of COVID-19, but it seems not to be an independent prognostic factor.
To build a machine-learning model that predicts laboratory test results and provides a promising lab test reduction strategy, using spatial-temporal correlations.

We developed a global prediction model to treat laboratory testing as a series of decisions by considering contextual information over time and across modalities. We validated our method using a critical care database (MIMIC III), which includes 4,570,709 observations of 12 standard laboratory tests, among 38,773 critical care patients. Our deep-learning model made real-time laboratory reduction recommendations and predicted the properties of lab tests, including values, normal/abnormal (whether labs were within the normal range) and transition (normal to abnormal or abnormal to normal from the latest lab test). We reported area under the receiver operating characteristic curve (AUC) for predicting normal/abnormal, evaluated accuracy and absolute bias on prediction vs. observation against lab test reduction proportion. We compared our model agaitory tests may be omitted.
This work demonstrates a machine-learning model that assists physicians in determining which laboratory tests may be omitted.
Parkinson's disease (PD) is a neurodegenerative disease of the elderly, which leads to patients' motor and non-motor disabilities and affects patients' quality of daily life. Timely and effective detection of PD is a key step to medical intervention. Recently, computer aided methods for PD detection have gained lots of attention in artificial intelligence domain.

This paper proposed a novel ensemble learning model fusing Random Forest (RF) classifiers and Principal Component Analysis (PCA) technique to differentiate PD patients from healthy controls (HC). Six different RF models were separately constructed to generate the corresponding class probability vectors which represent an individual's category predictions on 6 different handwritten exams, and the final prediction result for an individual was obtained through voting strategy of all RF models. Stratified k-fold cross validation was performed to split the exam datasets and evaluate the classification performances.

Experimental results prove that our proposed ensemble model on six handwritten exams has achieved better classification performances than a single RF based method on a single handwritten exam. Our ensemble of RF model based on multiple handwritten exams has promising accuracy (89.4 %), specificity (93.7 %), sensitivity (84.5 %) and F1-score (87.7 %). Compared with Logistic Regression (LR) and Support Vector Machines (SVM), the ensemble model based on RF can achieve better classification results.

A computer-assisted PD diagnosis model on small handwritten dynamics dataset is proposed, and it provides a potential way for assisting diagnosis of PD in clinical setting.
A computer-assisted PD diagnosis model on small handwritten dynamics dataset is proposed, and it provides a potential way for assisting diagnosis of PD in clinical setting.Based on the osteogenic effect, triiodothyronine (T3) plays an important role in bone growth and development. see more Autophagy contributes to osteoblast formation and subsequent osteogenesis. Our study aims to explore the relationship among T3, autophagy and osteogenesis. In this study, cranial primary osteoblasts were obtained from 2 to 3 weeks-old Sprague Dawley (SD) rat fetuses. Osteoblasts were treated with T3, and then the autophagic parameters of Osteoblasts (including autophagic proteins, LC3 conversion rate and autophagosome formation) were observed through Western Blotting and Transmission Electron Microscopy. Next, after using autophagic pharmacological inhibitors (3-MA and chloroquine) and silencing vectors of autophagic genes (BECN1, Atg5 and Atg7) to downregulate autophagic activity, osteoblast proliferation and osteoblastic gene expression were detected using cell counting kit-8 (CCK-8) and quantitative real-time PCR (qRT-PCR) assays, respectively. Ultimately, the mice treated with partial thyroidectomy (PTx mice) were used to further observe the effect of T3 on the formation and autophagy of osteoblasts in trabecular bone in vivo.
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