The mental health impacts of the COVID-19 pandemic on frontline, patient-facing healthcare staff have been described in several studies, but the effects of the COVID-19 response on the US public health workforce have not been well characterized. In early 2021, we conducted interviews with a subset of public health practitioners in the United States who participated in a cross-sectional survey and indicated their willingness to participate in a follow-up interview. An interview guide was developed to collect information about professional roles since the start of the pandemic, aspects of the individual COVID-19 response that impacted mental health, and aspects of the organizational/institutional COVID-19 response that impacted mental health, as well as the strengths and weaknesses of, opportunities for, and threats to public health professionals and organizations going forward. Interviews were transcribed and inductively coded to identify themes. Of the 48 people invited to participate, 24 completed an interview between January 28 and February 23, 2021. Five key themes were identified through inductive coding of interview transcripts: (1) teamwork and workplace camaraderie, (2) potential for growth in the field of public health, (3) considerations for adaptive work environments (eg, remote work, work out of jurisdiction, transition to telework), (4) politicization of response, and (5) constrained hiring capacity and burnout. After more than a year of public health emergency response to the COVID-19 pandemic, it is critically important to understand the detrimental and supportive factors of good mental health among the public health workforce.
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http://dx.doi.org/10.1089/hs.2021.0132 | DOI Listing |
In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance.
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January 2025
Department of Surgery, Laboratory of Tumor Immunology and Immunotherapy, Morehouse School of Medicine, Atlanta, GA 30310, USA.
Immunology advances have increased our understanding of autoimmune, auto-inflammatory, immunodeficiency, infectious, and other immune-mediated inflammatory diseases (IMIDs). Furthermore, evidence is growing for the immune involvement in aging, metabolic and neurodegenerative diseases, and different cancers. However, further research has indicated sex/gender-based immune differences, which further increase higher incidences of various autoimmune diseases (AIDs), such as systemic lupus erythematosus (SLE), myasthenia gravis, and rheumatoid arthritis (RA) in females.
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January 2025
Department of Obstetrics and Gynecology, Zhongda Hospital, School of Medicine, Southeast University, 210000 Nanjing, Jiangsu, China.
Background: Pre-eclampsia (PE) is a gestational disorder that significantly endangers maternal and fetal health. Transfer ribonucleic acid (tRNA)-derived small RNAs (tsRNAs) are important in the progression and diagnosis of various diseases. However, their role in the development of PE is unclear.
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