Digital remote monitoring for screening and early detection of urinary tract infections.

Urinary Tract Infections (UTIs) are one of the most prevalent bacterial infections in older adults and a significant contributor to unplanned hospital admissions in People Living with Dementia (PLWD), with early detection being crucial due to the predicament of reporting symptoms and limited help-seeking behaviour. The most common diagnostic tool is urine sample analysis, which can be time-consuming and is only employed where UTI clinical suspicion exists. In this method development and proof-of-concept study, participants living with dementia were monitored via low-cost devices in the home that passively measure activity, sleep, and nocturnal physiology. Using 27828 person-days of remote monitoring data (from 117 participants), we engineered features representing symptoms used for diagnosing a UTI. We then evaluate explainable machine learning techniques in passively calculating UTI risk and perform stratification on scores to support clinical translation and allow control over the balance between alert rate and sensitivity and specificity. The proposed UTI algorithm achieves a sensitivity of 65.3% (95% Confidence Interval (CI) = 64.3-66.2) and specificity of 70.9% (68.6-73.1) when predicting UTIs on unseen participants and after risk stratification, a sensitivity of 74.7% (67.9-81.5) and specificity of 87.9% (85.0-90.9). In addition, feature importance methods reveal that the largest contributions to the predictions were bathroom visit statistics, night-time respiratory rate, and the number of previous UTI events, aligning with the literature. Our machine learning method alerts clinicians of UTI risk in subjects, enabling earlier detection and enhanced screening when considering treatment.

SARS-CoV-2 and Obesity: "CoVesity"-a Pandemic Within a Pandemic.

Individuals who are overweight or suffering from obesity are in a chronic state of low-grade inflammation, making them particularly susceptible to developing severe forms of respiratory failure. Studies conducted in past pandemics link obesity with worse health outcomes. This population is thus of particular concern within the context of the COVID-19 pandemic, considering the cessation of obesity management services. This systematic review highlights [1] the reciprocal link between the obesity and COVID-19 pandemics, [2] obesity as a risk factor for more severe disease in past pandemics, [3] potential mechanisms that make individual's suffering from obesity more susceptible to severe disease and higher viral load, and [4] the need to safely resume bariatric services as recommended by expert guidelines, in order to mitigate the health outcomes of an already vulnerable population.

The origins of G12P[6] rotavirus strains detected in Lebanon.

The G12 rotaviruses are an increasingly important cause of severe diarrhoea in infants and young children worldwide. Seven human G12P[6] rotavirus strains were detected in stool samples from children hospitalized with gastroenteritis in Lebanon during a 2011-2013 surveillance study. Complete genomes of these strains were sequenced using VirCapSeq-VERT, a capture-based high-throughput viral-sequencing method, and further characterized based on phylogenetic analyses with global RVA and vaccine strains. Based on the complete genomic analysis, all Lebanese G12 strains were found to have Wa-like genetic backbone G12-P[6]-I1-R1-C1-M1-A1-N1-T1-E1-H1. Phylogenetically, these strains fell into two clusters where one of them might have emerged from Southeast Asian strains and the second one seems to have a mixed backbone between North American and Southeast Asian strains. Further analysis of these strains revealed high antigenic variability compared to available vaccine strains. To our knowledge, this is the first report on the complete genome-based characterization of G12P[6] emerging in Lebanon. Additional studies will provide important insights into the evolutionary dynamics of G12 rotaviruses spreading in Asia.

Electrocautery, Diathermy, and Surgical Energy Devices: Are Surgical Teams at Risk During the COVID-19 Pandemic?

OBJECTIVE: The aim of the study was to provide a rapid synthesis of available data to identify the risk posed by utilizing surgical energy devices intraoperatively due to the generation of surgical smoke, an aerosol. Secondarily it aims to summarize methods to minimize potential risk to operating room staff. SUMMARY BACKGROUND DATA: Continuing operative practice during the coronavirus disease-19 (COVID-19) pandemic places the health of operating theatre staff at potential risk. SARS-CoV2 is transmitted through inhaled droplets and aerosol particles, thus posing an inhalation threat even at considerable distance. Surgical energy devices generate an aerosol of biological particular matter during use. The risk to healthcare staff through use of surgical energy devices is unknown. METHODS: This review was conducted utilizing a rapid review methodology to enable efficient generation and dissemination of information useful for concurrent clinical practice. RESULTS: There are conflicting stances on the use of energy devices and laparoscopy by different surgical governing bodies and societies. There is no definitive evidence that aerosol generated by energy devices may carry active SARS-CoV2 virus. However, investigations of other viruses have demonstrated aerosolization through energy devise use. Measures to reduce potential transmission include appropriate personal protective equipment, evacuation and filtration of surgical plume, limiting energy device use if appropriate, and adjusting endoscopic and laparoscopic practice (low CO2 pressures, evacuation through ultrafiltration systems). CONCLUSIONS: The risk of transmission of SARS-CoV2 through aerosolized surgical smoke associated with energy device use is not fully understood, however transmission is biologically plausible. Caution and appropriate measures to reduce risk to healthcare staff should be implemented when considering intraoperative use of energy devices.