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Introduction: The present study aimed to explore the epidemiologic threats and factors associated with the coronavirus disease 2019 (COVID-19)-associated mucormycosis (CAM) epidemic that emerged in Egypt during the second COVID-19 wave. The study also aimed to explore the diagnostic features and the role of surgical interventions of CAM on the outcome of the disease in a central referral hospital.

Methodology: The study included 64 CAM patients from a referral hospital for CAM and a similar number of matched controls from COVID-19 patients who did not develop CAM.

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Background: Innovation in crop establishment is crucial for wheat productivity in drought-prone climates. Seedling establishment, the first stage of crop productivity, relies heavily on root and coleoptile system architecture for effective soil water and nutrient acquisition, particularly in regions practicing deep planting. Root phenotyping methods that quickly determine coleoptile lengths are vital for breeding studies.

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As the earliest measure of social communication in rodents, ultrasonic vocalizations (USVs) in response to maternal separation are critical in preclinical research on neurodevelopmental disorders (NDDs). While sex differences in both USV production and behavioral outcomes are reported, many studies overlook sex as a biological variable in preclinical NDD models. We aimed to evaluate sex differences in USV call parameters and determine if USVs are differently impacted based on sex in the preclinical maternal immune activation (MIA) model.

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An automatic cervical cell classification model based on improved DenseNet121.

Sci Rep

January 2025

Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.

The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.

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This study aimed to explore the diagnostic value of the two cytology techniques, including liquid-based cytology of mammary ductal lavage fluid and nipple discharge smear cytology, in the intraductal lesions in patients with pathological nipple discharge (PND). This retrospective analysis included 119 patients with PND who underwent surgical treatment. At the same time, they all underwent fiberoptic ductoscopy (FDS), nipple discharge smear cytology and liquid-based cytology of ductal lavage fluid before surgery.

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