Search

Background: Pregnant women with their infants are considered at higher risk for influenza-associated complications, and the World Health Organization (WHO) recommends influenza vaccination during pregnancy to protect them, including their infants (0-6 months). There are limited data on the influenza burden among pregnant women and their infants (0-6 months), and there are no routine influenza vaccinations in Bangladesh., Methods: Five annual cohorts (2013-2017) of pregnant women were enrolled from the eight sub-districts of Bangladesh before the influenza season (May-September); they were contacted weekly to identify new onset of influenza-like illness (ILI) (subjective or measured fever and cough) and acute respiratory illness (ARI) (at least two of these symptoms: cough, rhinorrhea, or difficulty in breathing) among their infants from birth to 6 months of age. We collected nasopharyngeal swabs from ILI and ARI cases, tested by real-time reverse transcription polymerase chain reaction (rRT-PCR) for influenza virus (including types and subtypes) and estimated influenza incidence (95% CI)/10000 pregnant women-months or infant-months, respectively., Results: We enrolled 9020 pregnant women, followed for 26,709 pregnancy-months, and detected 1241 ILI episodes. We also followed 8963 infants for 51,518 infant-months and identified 5116 ARI episodes. Influenza positivity was 23% for ILI and 3% for ARI cases. The overall incidence (2013-2017) of influenza among pregnant women was 158.5/10000 pregnant women-months (95% CI: 141.4-177.6) and that among infants was 21.9/10000 infant-months (95% CI: 18.2-26.5)., Conclusions: Although the data was collected more than 5 years ago, as the only baseline data, our findings illustrate evidence of influenza burden among pregnant women and infants (0-6 months), which may support preventive policy decisions in Bangladesh., Competing Interests: The authors have no competing interest to declare., (© 2023 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Importance: Associations between prenatal SARS-CoV-2 exposure and neurodevelopmental outcomes have substantial public health relevance. A previous study found no association between prenatal SARS-CoV-2 infection and parent-reported infant neurodevelopmental outcomes, but standardized observational assessments are needed to confirm this finding., Objective: To assess whether mild or asymptomatic maternal SARS-CoV-2 infection vs no infection during pregnancy is associated with infant neurodevelopmental differences at ages 5 to 11 months., Design, Setting, and Participants: This cohort study included infants of mothers from a single-site prospective cross-sectional study (COVID-19 Mother Baby Outcomes [COMBO] Initiative) of mother-infant dyads and a multisite prospective cohort study (Epidemiology of Severe Acute Respiratory Syndrome Coronavirus 2 in Pregnancy and Infancy [ESPI]) of pregnant individuals. A subset of ESPI participants was subsequently enrolled in the ESPI COMBO substudy. Participants in the ongoing COMBO study were enrolled beginning on May 26, 2020; participants in the ESPI study were enrolled from May 7 to November 3, 2021; and participants in the ESPI COMBO substudy were enrolled from August 2020 to March 2021. For the current analysis, infant neurodevelopment was assessed between March 2021 and June 2022. A total of 407 infants born to 403 mothers were enrolled (204 from Columbia University Irving Medical Center in New York, New York; 167 from the University of Utah in Salt Lake City; and 36 from the University of Alabama in Birmingham). Mothers of unexposed infants were approached for participation based on similar infant gestational age at birth, date of birth, sex, and mode of delivery to exposed infants., Exposures: Maternal symptomatic or asymptomatic SARS-CoV-2 infection., Main Outcomes and Measures: Infant neurodevelopment was assessed using the Developmental Assessment of Young Children, second edition (DAYC-2), adapted for telehealth assessment. The primary outcome was age-adjusted standard scores on 5 DAYC-2 subdomains: cognitive, gross motor, fine motor, expressive language, and receptive language., Results: Among 403 mothers, the mean (SD) maternal age at delivery was 32.1 (5.4) years; most mothers were of White race (240 [59.6%]) and non-Hispanic ethnicity (253 [62.8%]). Among 407 infants, 367 (90.2%) were born full term and 212 (52.1%) were male. Overall, 258 infants (63.4%) had no documented prenatal exposure to SARS-CoV-2 infection, 112 (27.5%) had confirmed prenatal exposure, and 37 (9.1%) had exposure before pregnancy or at an indeterminate time. In adjusted models, maternal SARS-CoV-2 infection during pregnancy was not associated with differences in cognitive (β = 0.31; 95% CI, -2.97 to 3.58), gross motor (β = 0.82; 95% CI, -1.34 to 2.99), fine motor (β = 0.36; 95% CI, -0.74 to 1.47), expressive language (β = -1.00; 95% CI, -4.02 to 2.02), or receptive language (β = 0.45; 95% CI, -2.15 to 3.04) DAYC-2 subdomain scores. Trimester of exposure and maternal symptom status were not associated with DAYC-2 subdomain scores., Conclusions and Relevance: In this study, results of a novel telehealth-adapted observational neurodevelopmental assessment extended a previous finding of no association between prenatal exposure to maternal SARS-CoV-2 infection and infant neurodevelopment. Given the widespread and continued high prevalence of COVID-19, these data offer information that may be helpful for pregnant individuals who experience asymptomatic or mild SARS-CoV-2 infections.

Accurate patient disease classification and detection through deep-learning (DL) models are increasingly contributing to the area of biomedical imaging. The most frequent gastrointestinal (GI) tract ailments are peptic ulcers and stomach cancer. Conventional endoscopy is a painful and hectic procedure for the patient while Wireless Capsule Endoscopy (WCE) is a useful technology for diagnosing GI problems and doing painless gut imaging. However, there is still a challenge to investigate thousands of images captured during the WCE procedure accurately and efficiently because existing deep models are not scored with significant accuracy on WCE image analysis. So, to prevent emergency conditions among patients, we need an efficient and accurate DL model for real-time analysis. In this study, we propose a reliable and efficient approach for classifying GI tract abnormalities using WCE images by applying a deep Convolutional Neural Network (CNN). For this purpose, we propose a custom CNN architecture named GI Disease-Detection Network (GIDD-Net) that is designed from scratch with relatively few parameters to detect GI tract disorders more accurately and efficiently at a low computational cost. Moreover, our model successfully distinguishes GI disorders by visualizing class activation patterns in the stomach bowls as a heat map. The Kvasir-Capsule image dataset has a significant class imbalance problem, we exploited a synthetic oversampling technique BORDERLINE SMOTE (BL-SMOTE) to evenly distribute the image among the classes to prevent the problem of class imbalance. The proposed model is evaluated against various metrics and achieved the following values for evaluation metrics: 98.9%, 99.8%, 98.9%, 98.9%, 98.8%, and 0.0474 for accuracy, AUC, F1-score, precision, recall, and loss, respectively. From the simulation results, it is noted that the proposed model outperforms other state-of-the-art models in all the evaluation metrics.

Background: There are limited data on influenza vaccine effectiveness (IVE) in preventing laboratory-confirmed influenza illness among healthcare personnel (HCP)., Methods: HCP with direct patient contact working full-time in hospitals were followed during three influenza seasons in Israel (2016-2017 to 2018-2019) and Peru (2016 to 2018). Trivalent influenza vaccines were available at all sites, except during 2018-2019 when Israel used quadrivalent vaccines; vaccination was documented by electronic medical records, vaccine registries, and/or self-report (for vaccinations outside the hospital). Twice-weekly active surveillance identified acute respiratory symptoms or febrile illness (ARFI); self-collected respiratory specimens were tested by real-time reverse transcription polymerase chain reaction (PCR) assay. IVE was 100 × 1-hazard ratio (adjusted for sex, age, occupation, and hospital)., Results: Among 5,489 HCP who contributed 10,041 person-seasons, influenza vaccination coverage was 47% in Israel and 32% in Peru. Of 3,056 ARFIs in Israel and 3,538 in Peru, A or B influenza virus infections were identified in 205 (7%) in Israel and 87 (2.5%) in Peru. IVE against all viruses across seasons was 1% (95% confidence interval [CI] = -30%, 25%) in Israel and 12% (95% CI = -61%, 52%) in Peru., Conclusion: Estimates of IVE were null using person-time models during six study seasons in Israel and Peru., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021. Published by Elsevier Ltd.)