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Abstract The ambulatory arterial stiffness index (AASI) is a novel measure of both blood pressure (BP) variability and arterial stiffness. This systematic review and meta‐analysis was designed to evaluate the strength of the association between AASI and mortality and major adverse cardiovascular events (MACE). PubMed, Scopus, CINAHL, Google Scholar. and the Cochrane library were searched for relevant studies to July 31, 2023. Two investigators independently extracted data. The Newcastle‐Ottawa Scale (NOS) was used to assess the quality of all included articles. The relationship between baseline AASI and outcomes were examined using relative risk (RR) ratios with 95% confidence intervals (CI) with RevMan web. Thirteen studies were included and representing 28 855 adult patients who were followed up from 2.2 to 15.2 years. A 1‐standard deviation (1‐SD) increase in AASI was associated with a significant increase in all‐cause death (RR 1.12; 95% CI: 0.95‐1.32), stroke (RR 1.25; 95% CI: 1.09‐1.44), and MACE (RR 1.07; 95% CI: 1.01‐1.13; [I2 = 32%]). Higher dichotomized AASI (above vs. below researcher defined cut‐offs) was associated with a significant increase in all‐cause mortality (RR 1.19; 95% CI: 1.06‐1.32), cardiovascular death (RR 1.29; 95% CI: 1.14‐1.46), stroke (RR 1.57; 95% CI: 1.33‐1.85), and MACE (RR1.29; 95% CI: 1.16‐1.44). There was a significant risk of bias in more than 50% of studies with no evidence of significant publication bias. Higher AASI is associated with an increased risk of all‐cause and cardiovascular death, stroke, and MACE. Further high‐quality studies are warranted to determine reproducible AASI cut‐offs to enhance its clinical risk precision.

Thin TiO2 films were produced on single-crystal Si-wafers by magnetron sputtering. Subsequently, they were annealed in air at different temperatures. We researched the structure, the phase composition, the morphology, and the dimensional characteristics of the films before and after annealing using X-ray diffraction, energy-dispersive and spectrophotometric analysis, scanning electron microscopy, and small-angle X-ray scattering. The analysis of the influence of annealing parameters on the characteristics of TiO2 films is carried out. The technique for determining the qualitative and quantitative phase composition of TiO2 during its polymorphic transformations at high temperature heating has been developed. It was found that TiO2 annealing at 400 ºC leads to crystallization of the anatase phase, and annealing at 600 ºC leads to transformations into the rutile phase. The optical band gap decreases with increasing temperature and with increasing annealing time.

ABSTRACT: Objectives: Following the emergence of the Delta variant of SARS-CoV-2 in Singapore, our hospital experienced a Delta-linked ward cluster. In this study, we review the enhanced strategies in preventing nosocomial transmission of COVID-19 following widespread community transmission of the Delta variant. Methods: We conducted a cohort study on exposures to unexpected COVID-19 cases for which contact tracing was initiated from June 2021 to October 2021. Strategies evaluated included upgraded personal protective equipment (PPE) and rostered routine testing (RRT) for staff and patients, surveillance of staff with acute respiratory illness (ARI), and expanded quarantining and testing for contacts of identified cases. Results: From 193 unexpected COVID-19 exposures, 2,573 staff, 542 patients, and 128 visitor contacts were traced. Four staff contacts subsequently had SARS-CoV-2 infection. Two were likely from exposure in community settings, whereas 2 had exposure to the same COVID-19 positive staff in the hospital, forming the only hospital cluster. One inpatient had a nosocomial infection, possibly from visitors. The SARS-CoV-2 detection rate among staff was 0.3% (of 11,200 staff) from biweekly RRT and 2.5% (of 3,675 staff) from ARI surveillance. Conclusion: Enhanced hospital measures, including upgraded PPE and RRT for staff and patients, staff sickness surveillance, and more rigorous management of contacts of COVID-19 cases, were likely to have reduced nosocomial transmission amid the Delta variant.

Abstract Background Appropriate antibiotic prescribing is key to combating antimicrobial resistance. Upper respiratory tract infections (URTIs) are common reasons for emergency department (ED) visits and antibiotic use. Differentiating between bacterial and viral infections is not straightforward. We aim to provide an evidence-based clinical decision support tool for antibiotic prescribing using prediction models developed from local data. Methods Seven hundred-fifteen patients with uncomplicated URTI were recruited and analysed from Singapore’s busiest ED, Tan Tock Seng Hospital, from June 2016 to November 2018. Confirmatory tests were performed using the multiplex polymerase chain reaction (PCR) test for respiratory viruses and point-of-care test for C-reactive protein. Demographic, clinical and laboratory data were extracted from the hospital electronic medical records. Seventy percent of the data was used for training and the remaining 30% was used for validation. Decision trees, LASSO and logistic regression models were built to predict when antibiotics were not needed. Results The median age of the cohort was 36 years old, with 61.2% being male. Temperature and pulse rate were significant factors in all 3 models. The area under the receiver operating curve (AUC) on the validation set for the models were similar. (LASSO: 0.70 [95% CI: 0.62–0.77], logistic regression: 0.72 [95% CI: 0.65–0.79], decision tree: 0.67 [95% CI: 0.59–0.74]). Combining the results from all models, 58.3% of study participants would not need antibiotics. Conclusion The models can be easily deployed as a decision support tool to guide antibiotic prescribing in busy EDs.

The development of a non-invasive method for measuring the internal fluid behavior and dynamics of microchannels in microfluidics poses critical challenges to biological research, such as understanding the impact of wall shear stress (WSS) in the growth of a bone-forming osteoblast. This study used the General Defocus Particle Tracking (GDPT) technique to develop a non-invasive method for quantifying the fluid velocity profile and calculated the WSS within a microfluidic chip. The GDPT estimates particle motion in a three-dimensional space by analyzing two-dimensional images and video captured using a single camera. However, without a lens to introduce aberration, GDPT is prone to error in estimating the displacement direction for out-of-focus particles, and without knowing the exact refractive indices, the scaling from estimated values to physical units is inaccurate. The proposed approach addresses both challenges by using theoretical knowledge on laminar flow and integrating results obtained from multiple analyses. The proposed approach was validated using computational fluid dynamics (CFD) simulations and experimental video of a microfluidic chip that can generate different WSS levels under steady-state flow conditions. By comparing the CFD and GDPT velocity profiles, it was found that the Mean Pearson Correlation Coefficient is 0.77 (max = 0.90) and the Mean Intraclass Correlation Coefficient is 0.66 (max = 0.82). The densitometry analysis of osteoblast cells cultured on the designed microfluidic chip for four days revealed that the cell proliferation rate correlates positively with the measured WSS values. The proposed analysis can be applied to quantify the laminar flow in microfluidic chip experiments without specialized equipment. [ABSTRACT FROM AUTHOR]

BackgroundIn early 2020, coronavirus disease (COVID-19) emerged and spread by community and nosocomial transmission. Effective contact tracing of potentially exposed health care workers is crucial for the prevention and control of infectious disease outbreaks in the health care setting. ObjectiveThis study aimed to evaluate the comparative effectiveness of contact tracing during the COVID-19 pandemic through the real-time locating system (RTLS) and review of the electronic medical record (EMR) at the designated hospital for COVID-19 response in Singapore. MethodsOver a 2-day study period, all admitted patients with COVID-19, their ward locations, and the health care workers rostered to each ward were identified to determine the total number of potential contacts between patients with COVID-19 and health care workers. The numbers of staff-patient contacts determined by EMR reviews, RTLS-based contact tracing, and a combination of both methods were evaluated. The use of EMR-based and RTLS-based contact tracing methods was further validated by comparing their sensitivity and specificity against self-reported staff-patient contacts by health care workers. ResultsOf 796 potential staff-patient contacts (between 17 patients and 162 staff members), 104 (13.1%) were identified by both the RTLS and EMR, 54 (6.8%) by the RTLS alone, and 99 (12.4%) by the EMR alone; 539 (67.7%) were not identified through either method. Compared to self-reported contacts, EMR reviews had a sensitivity of 47.2% and a specificity of 77.9%, while the RTLS had a sensitivity of 72.2% and a specificity of 87.7%. The highest sensitivity was obtained by including all contacts identified by either the RTLS or the EMR (sensitivity 77.8%, specificity 73.4%). ConclusionsRTLS-based contact tracing showed higher sensitivity and specificity than EMR review. Integration of both methods provided the best performance for rapid contact tracing, although technical adjustments to the RTLS and increasing user compliance with wearing of RTLS tags remain necessary.

Background: Upper respiratory tract infections (URTIs) account for substantial non-urgent ED attendances. Hence, we explored the reasons for such attendances using a mixed-methods approach.Methods: We interviewed adult patients with URTI who visited the second busiest adult ED in Singapore from June 2016 to November 2018 on their expectations and reasons for attendance. A structured questionnaire, with one open-ended question was used. Using the Andersen's Behavioural Model for Healthcare Utilisation, the topmost reasons for ED attendances were categorised into (1) contextual predisposing factors (referral by primary care physician, family, friends or coworkers), (2) contextual enabling factors (convenience, accessibility, employment requirements), (3) individual enablers (personal preference and trust in hospital-perceived care quality and efficiency) and (4) individual needs (perceived illness severity and non-improvement). Multivariable multinomial logistic regression was used to assess associations between sociodemographic and clinical factors, patient expectations for ED visits and the drivers for ED attendance.Results: There were 717 patients in the cohort. The mean age of participants was 40.5 (SD 14.7) years, 61.2% were males, 66.5% without comorbidities and 40.7% were tertiary educated. Half had sought prior medical consultation (52.4%) and expected laboratory tests (55.7%) and radiological investigations (46.9%). Individual needs (32.8%) and enablers (25.1%) were the main drivers for ED attendance. Compared with ED attendances due to contextual enabling factors, attendances due to other drivers were more likely to be aged ≥45 years, had prior medical consultation and expected radiological investigations. Having a pre-existing medical condition (adjusted OR (aOR) 1.78, 95% CI 1.05 to 3.04) and an expectation for laboratory tests (aOR 1.64, 95% CI 1.01 to 2.64) were associated with individual needs while being non-tertiary educated (aOR 2.04, 95% CI 1.22 to 3.45) and having pre-existing comorbidities (aOR 1.79, 95% CI 1.04 to 3.10) were associated with individual enablers.Conclusions: Meeting individual needs of perceived illness severity or non-improvement was the topmost driver of ED visits for URTI, while contextual enabling factors such as convenience was the lowest. Patients' sociodemographic and clinical factors and visit expectations influence their motivations for ED attendances. Addressing these factors and expectations can alleviate the overutilisation of ED services. [ABSTRACT FROM AUTHOR]