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Article: Editorial: New evidence on the psychological impacts and consequences of COVID-19 on mental workload healthcare workers in diverse regions in the world.

Zhang, Stephen X / Kowalczuk, Krystyna

2023 Volume 14, Page(s) 1226793

Language	English
Publishing date	2023-06-13
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2564218-2
ISSN	1664-0640
ISSN	1664-0640
DOI	10.3389/fpsyt.2023.1226793
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Where does mating drive come from?

Zhang, Stephen X / Glantz, Ethan H / Miner, Lauren E / Rogulja, Dragana / Crickmore, Michael A

EMBO reports

2023 Volume 24, Issue 10, Page(s) e57771

Abstract: Comment on "Asexuality in Drosophila juvenile males is organizational and independent of juvenile hormone" by Ji et al. ...

Abstract	Comment on "Asexuality in Drosophila juvenile males is organizational and independent of juvenile hormone" by Ji et al.
Language	English
Publishing date	2023-08-02
Publishing country	England
Document type	Letter
ZDB-ID	2020896-0
ISSN	1469-3178 ; 1469-221X
ISSN (online)	1469-3178
ISSN	1469-221X
DOI	10.15252/embr.202357771
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Zs.A 5433: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MG 41: Show issues

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Article ; Online: Mental Health and well-being During the COVID-19 Pandemic and After the Russian Invasion of Ukraine.

Pavlova, Iuliia / Rogowska, Aleksandra M / Zhang, Stephen X

Journal of community health

2023 Volume 49, Issue 1, Page(s) 173–182

Abstract: The study compared the mental health of university students during the COVID-19 pandemic and the Russian invasion of Ukraine. A repeated online cross-sectional study was performed twice among university students in Ukraine: in November 2020 (Pre-war ... ...

Abstract	The study compared the mental health of university students during the COVID-19 pandemic and the Russian invasion of Ukraine. A repeated online cross-sectional study was performed twice among university students in Ukraine: in November 2020 (Pre-war sample, n = 752) and September-October 2022 (During-war sample, n = 862). The survey measured life satisfaction (SWLS), perceived stress (PSS-10), anxiety (GAD-7), and depression (PHQ-9). Stress, anxiety, and life satisfaction levels were similar during the war and pandemic, while symptoms of depression decreased during the war, compared to the pandemic. Network analysis showed that anxiety has a crucial effect on depression and stress, and stress is most closely related to life satisfaction. The association of life satisfaction with anxiety and depression disappeared in wartime, in contrast to the pandemic. The priority of prevention and intervention programs in wartime Ukraine should focus on anxiety as the most influential factor in other mental health and well-being problems. The results showed high resistance and adaptation to war conditions among university students in Ukraine.
MeSH term(s)	Humans ; COVID-19/epidemiology ; Pandemics ; Cross-Sectional Studies ; Mental Health ; Ukraine/epidemiology ; Anxiety/epidemiology ; Russia/epidemiology ; Depression/epidemiology
Language	English
Publishing date	2023-08-25
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	426631-6
ISSN	1573-3610 ; 0094-5145
ISSN (online)	1573-3610
ISSN	0094-5145
DOI	10.1007/s10900-023-01273-x
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Zs.A 1386: Show issues

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Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Simultaneous Measurement of Changes in Mitochondrial and Endoplasmic Reticulum Free Calcium in Pancreatic Beta Cells.

Jeyarajan, Sivakumar / Zhang, Irina X / Arvan, Peter / Lentz, Stephen I / Satin, Leslie S

Biosensors

2023 Volume 13, Issue 3

Abstract: The free calcium ( ... ...

Abstract	The free calcium (Ca
MeSH term(s)	Insulin-Secreting Cells/metabolism ; Calcium/metabolism ; Calcium/pharmacology ; Mitochondria/metabolism ; Endoplasmic Reticulum ; Insulin Secretion
Chemical Substances	Calcium (SY7Q814VUP)
Language	English
Publishing date	2023-03-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662125-3
ISSN	2079-6374 ; 2079-6374
ISSN (online)	2079-6374
ISSN	2079-6374
DOI	10.3390/bios13030382
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Article ; Online: Scientific evidence on mental health in key regions under the COVID-19 pandemic - meta-analytical evidence from Africa, Asia, China, Eastern Europe, Latin America, South Asia, Southeast Asia, and Spain.

Zhang, Stephen X / Chen, Jiyao

European journal of psychotraumatology

2021 Volume 12, Issue 1, Page(s) 2001192

Abstract: This systematic review aims to summarize the prevalence of anxiety, depression, and insomnia in the general adult population and healthcare workers (HCWs) in several key regions worldwide during the first year of the COVID pandemic. Several literature ... ...

Abstract	This systematic review aims to summarize the prevalence of anxiety, depression, and insomnia in the general adult population and healthcare workers (HCWs) in several key regions worldwide during the first year of the COVID pandemic. Several literature databases were systemically searched for meta-analyses published by 22 September 2021 on the prevalence rates of mental health symptoms worldwide. The prevalence rates of mental health symptoms were summarized based on 388 empirical studies with a total of 1,067,021 participants from six regions and four countries. Comparatively, Africa and South Asia had the worse overall mental health symptoms, followed by Latin America. The research effort on mental health during COVID-19 has been highly skewed in terms of the scope of countries and mental health outcomes. The mental health symptoms are highly prevalent yet differ across regions, and such evidence helps to enable prioritization of mental health assistance efforts to allocate attention and resources based on the regional differences in mental health.
MeSH term(s)	Adult ; Africa/epidemiology ; Asia/epidemiology ; Asia, Southeastern/epidemiology ; COVID-19/epidemiology ; COVID-19/psychology ; China/epidemiology ; Europe, Eastern/epidemiology ; Global Health/statistics & numerical data ; Health Personnel/psychology ; Humans ; Latin America/epidemiology ; Mental Disorders/epidemiology ; Pandemics ; Prevalence ; Spain/epidemiology
Language	English
Publishing date	2021-12-06
Publishing country	United States
Document type	Journal Article ; Meta-Analysis ; Systematic Review
ZDB-ID	2586642-4
ISSN	2000-8066 ; 2000-8066
ISSN (online)	2000-8066
ISSN	2000-8066
DOI	10.1080/20008198.2021.2001192
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Article ; Online: mTOR regulates aerobic glycolysis through NEAT1 and nuclear paraspeckle-mediated mechanism in hepatocellular carcinoma.

Zhang, Hong / Su, Xiaoyang / Burley, Stephen K / Zheng, X F Steven

Theranostics

2022 Volume 12, Issue 7, Page(s) 3518–3533

Abstract: Background: ...

Abstract	Background:
MeSH term(s)	Carcinoma, Hepatocellular/pathology ; Cell Line, Tumor ; Cell Proliferation/genetics ; Glucose ; Glycolysis ; Humans ; Liver Neoplasms/genetics ; Mechanistic Target of Rapamycin Complex 1/metabolism ; Paraspeckles ; RNA, Long Noncoding/genetics ; RNA, Long Noncoding/metabolism ; Sirolimus ; TOR Serine-Threonine Kinases/metabolism
Chemical Substances	RNA, Long Noncoding ; MTOR protein, human (EC 2.7.1.1) ; Mechanistic Target of Rapamycin Complex 1 (EC 2.7.11.1) ; TOR Serine-Threonine Kinases (EC 2.7.11.1) ; Glucose (IY9XDZ35W2) ; Sirolimus (W36ZG6FT64)
Language	English
Publishing date	2022-04-24
Publishing country	Australia
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2592097-2
ISSN	1838-7640 ; 1838-7640
ISSN (online)	1838-7640
ISSN	1838-7640
DOI	10.7150/thno.72581
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Article ; Online: Mental health symptoms and coping strategies among Ukrainians during the Russia-Ukraine war in March 2022.

Xu, Wen / Pavlova, Iuliia / Chen, Xi / Petrytsa, Petro / Graf-Vlachy, Lorenz / Zhang, Stephen X

The International journal of social psychiatry

2023 Volume 69, Issue 4, Page(s) 957–966

Abstract: Context: The Russian attack on Ukraine has been ongoing since February 24, 2022. Nevertheless, no research has documented the mental health of Ukrainians during the biggest land war in Europe after the Second World War, or how Ukrainians cope with the ... ...

Abstract	Context: The Russian attack on Ukraine has been ongoing since February 24, 2022. Nevertheless, no research has documented the mental health of Ukrainians during the biggest land war in Europe after the Second World War, or how Ukrainians cope with the impact of the war. Objectives: To provide the prevalence rates of symptoms of psychological distress, anxiety, depression, and insomnia; and to link them with Ukrainians' productive coping strategies during the war. Design, setting, and participants: Online survey conducted in Ukraine during the initial period of the Russian invasion (March 19-31, 2022), using a quota sampling method, of 1,400 Ukrainians aged 18 years or older, with a total of 801 valid responses for a response rate of 57.2%. Main outcome measures: Psychological distress assessed by the Kessler Psychological Distress scale (K6); anxiety assessed by Generalized Anxiety Disorder-2 (GAD-2) scale; depression assessed by Patient Health Questionnaire-2 (PHQ-2); insomnia assessed by Insomnia Severity Index-4 (ISI-4); modes of coping assessed by Brief COPE. Results: Of 801 Ukrainian adults, 52.7% had symptoms of psychological distress (mean = 13.3 [ Conclusions: Prevalence rates of symptoms of psychological distress, anxiety, depression, and insomnia were high. These findings underscore the need for healthcare and productive coping strategies for Ukrainians during the war.
MeSH term(s)	Adult ; Aged ; Child ; Humans ; Mental Health ; Ukraine/epidemiology ; Sleep Initiation and Maintenance Disorders ; Depression/psychology ; Adaptation, Psychological ; Anxiety/psychology ; Russia/epidemiology
Language	English
Publishing date	2023-01-04
Publishing country	England
Document type	Journal Article
ZDB-ID	3062-4
ISSN	1741-2854 ; 0020-7640
ISSN (online)	1741-2854
ISSN	0020-7640
DOI	10.1177/00207640221143919
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Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Predicting Managers' Mental Health Across Countries

Lun Li / Stephen X. Zhang / Lorenz Graf-Vlachy

Frontiers in Public Health, Vol

Using Country-Level COVID-19 Statistics

2022 Volume 10

Abstract: BackgroundThere is limited research focusing on publicly available statistics on the Coronavirus disease 2019 (COVID-19) pandemic as predictors of mental health across countries. Managers are at risk of suffering from mental disorders during the pandemic ...

Abstract	BackgroundThere is limited research focusing on publicly available statistics on the Coronavirus disease 2019 (COVID-19) pandemic as predictors of mental health across countries. Managers are at risk of suffering from mental disorders during the pandemic because they face particular hardship.ObjectiveWe aim to predict mental disorder (anxiety and depression) symptoms of managers across countries using country-level COVID-19 statistics.MethodsA two-wave online survey of 406 managers from 26 countries was performed in May and July 2020. We used logistic panel regression models for our main analyses and performed robustness checks using ordinary least squares regressions. In the sample, 26.5% of managers reached the cut-off levels for anxiety (General Anxiety Disorder-7; GAD-7) and 43.5% did so for depression (Patient Health Questionnaire-9; PHQ-9) symptoms.FindingsWe found that cumulative COVID-19 statistics (e.g., cumulative cases, cumulative cases per million, cumulative deaths, and cumulative deaths per million) predicted managers' anxiety and depression symptoms positively, whereas daily COVID-19 statistics (daily new cases, smoothed daily new cases, daily new deaths, smoothed daily new deaths, daily new cases per million, and smoothed daily new cases per million) predicted anxiety and depression symptoms negatively. In addition, the reproduction rate was a positive predictor, while stringency of governmental lockdown measures was a negative predictor. Individually, we found that the cumulative count of deaths is the most suitable single predictor of both anxiety and depression symptoms.ConclusionsCumulative COVID-19 statistics predicted managers' anxiety and depression symptoms positively, while non-cumulative daily COVID-19 statistics predicted anxiety and depression symptoms negatively. Cumulative count of deaths is the most suitable single predictor of both anxiety and depression symptoms. Reproduction rate was a positive predictor, while stringency of governmental lockdown measures was a negative ...
Keywords	managers ; mental disorders ; cumulative deaths ; COVID-19 ; cross-country ; Public aspects of medicine ; RA1-1270
Subject code	310 ; 150
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Predicting Managers' Mental Health Across Countries: Using Country-Level COVID-19 Statistics.

Li, Lun / Zhang, Stephen X / Graf-Vlachy, Lorenz

Frontiers in public health

2022 Volume 10, Page(s) 791977

Abstract: Background: There is limited research focusing on publicly available statistics on the Coronavirus disease 2019 (COVID-19) pandemic as predictors of mental health across countries. Managers are at risk of suffering from mental disorders during the ... ...

Abstract	Background: There is limited research focusing on publicly available statistics on the Coronavirus disease 2019 (COVID-19) pandemic as predictors of mental health across countries. Managers are at risk of suffering from mental disorders during the pandemic because they face particular hardship. Objective: We aim to predict mental disorder (anxiety and depression) symptoms of managers across countries using country-level COVID-19 statistics. Methods: A two-wave online survey of 406 managers from 26 countries was performed in May and July 2020. We used logistic panel regression models for our main analyses and performed robustness checks using ordinary least squares regressions. In the sample, 26.5% of managers reached the cut-off levels for anxiety (General Anxiety Disorder-7; GAD-7) and 43.5% did so for depression (Patient Health Questionnaire-9; PHQ-9) symptoms. Findings: We found that cumulative COVID-19 statistics (e.g., cumulative cases, cumulative cases per million, cumulative deaths, and cumulative deaths per million) predicted managers' anxiety and depression symptoms positively, whereas daily COVID-19 statistics (daily new cases, smoothed daily new cases, daily new deaths, smoothed daily new deaths, daily new cases per million, and smoothed daily new cases per million) predicted anxiety and depression symptoms negatively. In addition, the reproduction rate was a positive predictor, while stringency of governmental lockdown measures was a negative predictor. Individually, we found that the cumulative count of deaths is the most suitable single predictor of both anxiety and depression symptoms. Conclusions: Cumulative COVID-19 statistics predicted managers' anxiety and depression symptoms positively, while non-cumulative daily COVID-19 statistics predicted anxiety and depression symptoms negatively. Cumulative count of deaths is the most suitable single predictor of both anxiety and depression symptoms. Reproduction rate was a positive predictor, while stringency of governmental lockdown measures was a negative predictor.
MeSH term(s)	COVID-19/epidemiology ; Communicable Disease Control ; Depression/epidemiology ; Depression/psychology ; Humans ; Mental Health ; SARS-CoV-2
Language	English
Publishing date	2022-05-19
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2711781-9
ISSN	2296-2565 ; 2296-2565
ISSN (online)	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2022.791977
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Article ; Online: CXR-Net: A Multitask Deep Learning Network for Explainable and Accurate Diagnosis of COVID-19 Pneumonia From Chest X-Ray Images.

Zhang, Xin / Han, Liangxiu / Sobeih, Tam / Han, Lianghao / Dempsey, Nina / Lechareas, Symeon / Tridente, Ascanio / Chen, Haoming / White, Stephen / Zhang, Daoqiang

IEEE journal of biomedical and health informatics

2023 Volume 27, Issue 2, Page(s) 980–991

Abstract: ... X-Ray (CXR) is the first-line imaging technique for COVID-19 pneumonia diagnosis as it is fast, cheap ...

Abstract	Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first-line imaging technique for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Currently, many deep learning (DL) models have been proposed to detect COVID-19 pneumonia from CXR images. Unfortunately, these deep classifiers lack the transparency in interpreting findings, which may limit their applications in clinical practice. The existing explanation methods produce either too noisy or imprecise results, and hence are unsuitable for diagnostic purposes. In this work, we propose a novel explainable CXR deep neural Network (CXR-Net) for accurate COVID-19 pneumonia detection with an enhanced pixel-level visual explanation using CXR images. An Encoder-Decoder-Encoder architecture is proposed, in which an extra encoder is added after the encoder-decoder structure to ensure the model can be trained on category samples. The method has been evaluated on real world CXR datasets from both public and private sources, including healthy, bacterial pneumonia, viral pneumonia and COVID-19 pneumonia cases. The results demonstrate that the proposed method can achieve a satisfactory accuracy and provide fine-resolution activation maps for visual explanation in the lung disease detection. Compared to current state-of-the-art visual explanation methods, the proposed method can provide more detailed, high-resolution, visual explanation for the classification results. It can be deployed in various computing environments, including cloud, CPU and GPU environments. It has a great potential to be used in clinical practice for COVID-19 pneumonia diagnosis.
MeSH term(s)	Humans ; COVID-19/diagnostic imaging ; Deep Learning ; X-Rays ; Thorax/diagnostic imaging ; Pneumonia, Viral/diagnostic imaging ; COVID-19 Testing
Language	English
Publishing date	2023-02-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2022.3220813
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