A model for predicting physical function upon discharge of hospitalized older adults in Taiwan—a machine learning approach based on both electronic health records and comprehensive geriatric assessment

BackgroundPredicting physical function upon discharge among hospitalized older adults is important. This study has aimed to develop a prediction model of physical function upon discharge through use of a machine learning algorithm using electronic health records (EHRs) and comprehensive geriatrics assessments (CGAs) among hospitalized older adults in Taiwan.MethodsData was retrieved from the clinical database of a tertiary medical center in central Taiwan. Older adults admitted to the acute geriatric unit during the period from January 2012 to December 2018 were included for analysis, while those with missing data were excluded. From data of the EHRs and CGAs, a total of 52 clinical features were input for model building. We used 3 different machine learning algorithms, XGBoost, random forest and logistic regression.ResultsIn total, 1,755 older adults were included in final analysis, with a mean age of 80.68 years. For linear models on physical function upon discharge, the accuracy of prediction was 87% for XGBoost, 85% for random forest, and 32% for logistic regression. For classification models on physical function upon discharge, the accuracy for random forest, logistic regression and XGBoost were 94, 92 and 92%, respectively. The auROC reached 98% for XGBoost and random forest, while logistic regression had an auROC of 97%. The top 3 features of importance were activity of daily living (ADL) at baseline, ADL during admission, and mini nutritional status (MNA) during admission.ConclusionThe results showed that physical function upon discharge among hospitalized older adults can be predicted accurately during admission through use of a machine learning model with data taken from EHRs and CGAs

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

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

Long-term atherosclerotic cardiovascular disease risk in patients with cancer: a population-based study.

The long-term risk of incident atherosclerotic cardiovascular diseases (ASCVD) among cancer patients remains incompletely defined. This study aimed to evaluate the long-term ASCVD risk in cancer patients compared with the non-cancer population. This was a prospective population-based study using data from the Kailuan cohort, 6,204 individuals with newly diagnosed cancer, free of ASCVD, were matched in a 1:1 ratio to non-cancer controls for age (±1) and sex, from June 2006 to December 2020. Multivariable competing risk analyses were performed to evaluate the association between cancer diagnosis and risk of incident ASCVD events (including myocardial infarction, ischemic stroke, heart failure, and revascularization with coronary artery bypass graft surgery or percutaneous coronary intervention). During a median follow-up of 5.3 (1.7, 9.7) years, 1,019 incident ASCVD events were observed. Compared to participants without cancer, there was a similar risk for incident ASCVD events among cancer patients within the first few years after cancer diagnosis, and the risk declined over time. Overall, cancer patients showed lower risks of incident ASCVD compared to the non-cancer patients over the long term, with a hazard ratio (95% confidence interval) of 0.52 (0.45-0.60) for composite ASCVD events, 0.43 (0.35-0.53) for ischemic stroke, 0.63 (0.42-0.95) for myocardial infarction, 0.63 (0.48-0.83) for heart failure, and 0.82 (0.60-1.11) for coronary revascularization. Baseline level of low-density lipoprotein cholesterol, fasting blood glucose, blood pressure, and high-sensitivity C-reactive protein could independently predict the incident ASCVD among the study population. Subgroup analyses according to cancer types revealed a significantly lower risk of ASCVD events among patients with digestive cancer or respiratory cancer compared with non-cancer controls, but not for urologic or genital cancer. Multiple sensitivity analyses yielded similar results to the primary analysis. Long-term ASCVD risk among cancer survivors is not increased compared with the non-cancer individuals, probably driven by a favorable profile of baseline risk factor in cancer population. [Abstract copyright: Copyright © 2023 Elsevier Ltd. All rights reserved.

Glycemia Lowering Effect of an Aqueous Extract of Hedychium coronarium Leaves in Diabetic Rodent Models

Hedychium coronarium has a long history of use worldwide as a food and in folk medicine. In this study, we aimed to investigate the effect of an aqueous extract of H. coronarium leaves (HC) on type 2 diabetes mellitus (T2DM). Two types of animal models were used in this study: Streptozotocin (STZ)-induced T2DM (Wistar rats; N = 8) and C57BKSdb/db mice (N = 5). After treatment with HC for 28 days, glucose tolerance improved in both of the diabetic animal models. As significant effects were shown after 14 days of treatment in the STZ-induced T2DM model, we carried out the experiments with it. After 28 days of treatment with HC, the levels of cholesterol, triglyceride, high-density lipoprotein, and low-density lipoprotein were significantly improved in the STZ-induced T2DM model. The lesions degree of islet β-cells was decreased after the HC treatment. Although the insulin level increased moderately, the aldosterone level was significantly decreased in the HC-treated groups, suggesting that aldosterone might play an important role in this effect. In summary, HC is a natural product and it is worth exploring its effect on T2DM

Evaluation of Acute 13-Week Subchronic Toxicity and Genotoxicity of the Powdered Root of Tongkat Ali (Eurycoma longifolia Jack)

is an indigenous traditional herb in Southern Asia. Its powdered root has been processed to produce health supplements, but no detailed toxicology report is available. In this study, neither mutagenicity nor clastogenicity was noted, and acute oral LD 50 was more than 6 g/kg b.w. After 4-week subacute and 13-week subchronic exposure paradigms (0, 0.6, 1.2, and 2 g/kg b.w./day), adverse effects attributable to test compound were not observed with respect to body weight, hematology, serum biochemistry, urinalysis, macropathology, or histopathology. However, the treatment significantly reduced prothrombin time, partial thromboplastin time, blood urea nitrogen, creatinine, aspartate aminotransferase, creatine phosphate kinase, lactate dehydrogenase, and cholesterol levels, especially in males ( < 0.05). These changes were judged as pharmacological effects, and they are beneficial to health. The calculated acceptable daily intake (ADI) was up to 1.2 g/adult/day. This information will be useful for product development and safety management

High throughput and high yield nanofabrication of precisely designed gold nanohole arrays for fluorescence enhanced detection of biomarkers

Fluorescence excitation enhancement by plasmonic nanostructures such as gold nanohole arrays has been a hot topic in biosensing and bioimaging in recent years. However, the high throughput and high yield fabrication of precisely designed metal nanostructures for optimized fluorescence excitation remains a challenge. Our work is the first report combining nanopattern nickel mould fabrication and UV imprinting for gold nanostructure mass fabrication in high yield. We report our successful gold nanohole array mass fabrication on a 4′′ glass wafer, by first fabricating a high fidelity nickel mould, then using the mould for UV nanoimprinting on a polymer coated on the glass, evaporating the gold film on the glass wafer, and lifting off the polymer to obtain a gold nanohole array on the glass. Our optimized process for wafer fabrication can achieve almost 100% yield from nanoimprinting to gold lift-off, while the fabricated nickel mould has >70% defect-free area with the rest having a few scattered defects. In our work, the size and pitch of the gold nanohole array are designed to enhance the fluorescent dye Alexa 647. When the fabricated gold nanohole array is used for prostate specific antigen (PSA) detection by establishing a sandwiched fluorescence assay on the gold surface, a detection limit of 100 pg ml−1 is achieved, while with a same thickness of gold film, only 1 ng ml−1 is detected.ASTAR (Agency for Sci., Tech. and Research, S’pore

Specific drug delivery efficiently induced human breast tumor regression using a lipoplex by non-covalent association with anti-tumor antibodies

Abstract Background A cationic liposome-PEG-PEI complex (LPPC) was employed as a carrier for achieving targeted delivery of drug to human epidermal growth factor receptor-2 (HER2/neu)-expressing breast cancer cells. LPPC can be easily loaded with an anti-tumor drug and non-covalently associated with an anti-tumor antibody such as Herceptin that is clinically used to rapidly form immunoparticles within 1 h. Results Drug-loaded LPPC have an average size about 250 nm and a zeta potential of about 40 mV. Herceptin was complexed onto surface of the LPPC to form the drug/LPPC/Herceptin complexes. The size of curcumin/LPPC/Herceptin complexes were 280 nm and the zeta potentials were about 23 mV. Targeting ability of this delivery system was demonstrated through specific binding on surface of cells and IVIS images in vivo, which showed specific binding in HER2-positive SKBR3 cells as compared to HER2-negative Hs578T cells. Only the drug/LPPC/Herceptin complexes displayed dramatically increased the cytotoxic activity in cancer cells. Both in vitro and in vivo results indicated that Herceptin adsorbed on LPPC directed the immunocomplex towards HER2/neu-positive cells but not HER2/neu-negative cells. The complexes with either component (curcumin or doxorubicin) used in the LPPC-delivery system provided a better therapeutic efficacy compared to the drug treatment alone and other treatment groups, including clinical dosages of Herceptin and LipoDox, in a xenografted model. Conclusions LPPC displays important clinical implications by easily introducing a specific targeting characteristic to drugs utilized for breast cancer therapy