Search

Mask usage remains low across many parts of the world during the COVID- 19 pandemic, and strategies to increase mask-wearing remain untested. Our objectives were to identify strategies that can persistently increase mask-wearing and assess the impact of increasing mask-wearing on symptomatic SARS-CoV-2 infections. We conducted a cluster-randomized trial of community-level mask promotion in rural Bangladesh from November 2020 to April 2021 (N=600 villages, N=342,126 adults). We cross-randomized mask promotion strategies at the village and household level, including cloth vs. surgical masks. All intervention arms received free masks, information on the importance of masking, role modeling by community leaders, and in-person reminders for 8 weeks. The control group did not receive any interventions. Neither participants nor field staff were blinded to intervention assignment. Outcomes included symptomatic SARS-CoV-2 seroprevalence (primary) and prevalence of proper mask-wearing, physical distancing, and symptoms consistent with COVID-19 (secondary). Mask-wearing and physical distancing were assessed through direct observation at least weekly at mosques, markets, the main entrance roads to villages, and tea stalls. At 5 and 9 weeks follow-up, we surveyed all reachable participants about COVID-related symptoms. Blood samples collected at 10-12 weeks of follow-up for symptomatic individuals were analyzed for SARS-CoV-2 IgG antibodies. There were 178,288 individuals in the intervention group and 163,838 individuals in the control group. The intervention increased proper mask-wearing from 13.3% in control villages (N=806,547 observations) to 42.3% in treatment villages (N=797,715 observations) (adjusted percentage point difference = 0.29 [0.27, 0.31]). This tripling of mask usage was sustained during the intervention period and two weeks after. Physical distancing increased from 24.1% in control villages to 29.2% in treatment villages (adjusted percentage point difference = 0.05 [0.04, 0.06]). After 5 months, the impact of the intervention faded, but mask-wearing remained 10 percentage points higher in the intervention group. The proportion of individuals with COVID-like symptoms was 7.62% (N=13,273) in the intervention arm and 8.62% (N=13,893) in the control arm. Blood samples were collected from N=10,952 consenting, symptomatic individuals. Adjusting for baseline covariates, the intervention reduced symptomatic seroprevalence by 9.3% (adjusted prevalence ratio (aPR) = 0.91 [0.82, 1.00]; control prevalence 0.76%; treatment prevalence 0.68%). In villages randomized to surgical masks (n = 200), the relative reduction was 11.2% overall (aPR = 0.89 [0.78, 1.00]) and 34.7% among individuals 60+ (aPR = 0.65 [0.46, 0.85]). No adverse events were reported.

The human liver exhibits variable characteristics and anatomical information, which is often ambiguous in radiological images. Machine learning can be of great assistance in automatically segmenting the liver in radiological images, which can be further processed for computer-aided diagnosis. Magnetic resonance imaging (MRI) is preferred by clinicians for liver pathology diagnosis over volumetric abdominal computerized tomography (CT) scans, due to their superior representation of soft tissues. The convenience of Hounsfield unit (HoU) based preprocessing in CT scans is not available in MRI, making automatic segmentation challenging for MR images. This study investigates multiple state-of-the-art segmentation networks for liver segmentation from volumetric MRI images. Here, T1-weighted (in-phase) scans are investigated using expert-labeled liver masks from a public dataset of 20 patients (647 MR slices) from the Combined Healthy Abdominal Organ Segmentation grant challenge (CHAOS). The reason for using T1-weighted images is that it demonstrates brighter fat content, thus providing enhanced images for the segmentation task. Twenty-four different state-of-the-art segmentation networks with varying depths of dense, residual, and inception encoder and decoder backbones were investigated for the task. A novel cascaded network is proposed to segment axial liver slices. The proposed framework outperforms existing approaches reported in the literature for the liver segmentation task (on the same test set) with a dice similarity coefficient (DSC) score and intersect over union (IoU) of 95.15 % and 92.10 % , respectively. [ABSTRACT FROM AUTHOR]

Background: Surgical site infections (SSIs) pose a global challenge, impacting patients and healthcare expenditures. This second-order meta-analysis endeavors to assess the efficacy of antibiotic sutures in averting SSIs by amalgamating data from various meta-studies., Materials and Methods: This research adhered to the PRISMA 2020 guidelines. The quality and comprehensiveness of the encompassed meta-analyses were assessed through the QUOROM checklist and AMSTAR techniques. The primary study overlap was evaluated via measures such as pairwise intersection heat maps, corrected covered area, and the citation matrix of evidence. The statistical power at the study-level was determined utilizing the meta-meta package. Data synthesis employed random and fixed effects models at a 95% CI. A meta-regression analysis was conducted to explore potential correlations between the CDC classification of SSIs, trial types, and the observed effect sizes in the studies., Results: This investigation revealed a significant reduction in SSI rates due to antimicrobial-coated sutures, evidenced by a relative risk (RR) of 0.68 (95% CI: 0.59-0.76), with a prediction interval of 0.38-1.19. The analysis encompassed 18 studies with 22 meta-analyses, demonstrating a median QUOROM score of 13.6 out of 18 and an AMSTAR score of 9.1 out of 11. The presence of moderate heterogeneity was noted ( Q =106.611, I2 =54.038%), with nonrandomized controlled trials exhibiting an RR of 0.56 (95% CI: 0.39-0.80), and RCTs displaying an RR of 0.71 (95% CI: 0.63-0.81). Subgroup analysis unveiled variable RR reductions for specific surgical procedures., Conclusion: Antimicrobial-coated sutures offer a promising approach to mitigating SSIs risk. However, their efficacy is optimally realized when employed in conjunction with other robust practices., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

The aim of this study was to determine the prevalence of amoebic infection in non-human primates (NHPs) from six Zoological gardens in the United Kingdom. Initially, 126 faecal samples were collected from 37 individually identified NHPs at Twycross Zoo, UK, and were subjected to microscopic examination. A subsequent, nationwide experiment included 350 faecal samples from 89 individually identified NHPs and 73 unidentified NHPs from a number of UK captive wildlife facilities: Twycross Zoo (n = 60), Colchester Zoo (n = 3), Edinburgh Zoo (n = 6), Port Lympne Wild Animal Park (n = 58), Howletts Wild Animal Park (n = 31), and Cotswold Wildlife Park (n = 4). Samples were examined by PCR and sequencing using four specific primer sets designed to differentiate between the pathogenic E. histolytica, the non-pathogenic E. dispar, and non-pathogenic uninucleate cyst-producing Entamoeba species. In the first experiment, Entamoeba was detected in 30 primates (81.1%). Six (16.2%) primates were infected with E. histolytica species complex. The highest carriage of Entamoeba species was found in Old World Colobinae primates. In the nationwide experiment, molecular analysis of faecal samples revealed notable rates of Entamoeba infection (101 samples, 28.9%), including one sample infected with E. histolytica, 14 samples with E. dispar, and 86 samples with uninucleated-cyst producing Entamoeba species. Sequences of positive uninucleated-cyst producing Entamoeba samples from Twycross Zoo clustered with the E. polecki reference sequences ST4 reported in Homo sapiens, and are widely separated from other Entamoeba species. These findings suggest a low prevalence of the pathogenic Entamoeba infection, but notable prevalence of non-pathogenic E. polecki infection in NHPs in the UK.