High symptom burden in patients undergoing gastrectomy for cancer.

Background: There is little data describing symptom burden before or after gastrectomy for patients with cancer. We aimed to examine the perioperative patterns of symptom severity in patients undergoing gastrectomy.

Methods: In this single-institution prospective cohort study, patients scheduled to undergo gastrectomy for cancer completed serial symptom measurement questionnaires preoperatively, at postoperative day (POD) 1-3, and POD 4-7.

A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses.

A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events.

Antibiotic perceptions, adherence, and disposal practices among parents of pediatric patients.

Antibiotics are frequently prescribed for children in the outpatient setting. Although sometimes necessary, antibiotic use is associated with important downstream effects including the development of antimicrobial resistance among human and environmental microorganisms. Current outpatient stewardship efforts focus on guiding appropriate antibiotic prescribing practices among providers, but little is known about parents' understanding of antibiotics and appropriate disposal of leftover antibiotics.

An On-Device Learning System for Estimating Liquid Consumption from Consumer-Grade Water Bottles and Its Evaluation.

A lightweight on-device liquid consumption estimation system involving an energy-aware machine learning algorithm is developed in this work. This system consists of two separate on-device neural network models that carry out liquid consumption estimation with the result of two tasks: the detection of sip from gestures with which the bottle is handled by its user and the detection of first sips after a bottle refill. This predictive volume estimation framework incorporates a self-correction mechanism that can minimize the error after each bottle fill-up cycle, which makes the system robust to errors from the sip classification module.