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ABSTRACT Propolis, a bee product, is renowned globally for its diverse pharmacological activities attributed to its phenolic compounds, flavonoids, and terpenes. This study aims to identify the chemical compounds in Algerian raw propolis and evaluate the protective potential of its inclusion complex. Gas chromatography–mass spectrometry (GC/MS), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA) were used for chemical characterization. Additionally, molecular docking against SARS‐COVID‐2 main proteases and ADMET profiling were conducted. The analysis revealed α‐pinene (25.29%–11.04%) and limonene (22.46%–12.88%) as the major components in raw propolis and its complex. DSC/TGA analysis demonstrated the protective effect of encapsulated molecules, with limonene and α‐pinene showing high activity against SARS‐COVID‐2 main proteases 7N5Z, 6LU7, and 7E6L, with docking scores of −5.71, −5.61, and −5.10 kcal/mol, respectively. This study highlights the potential of propolis constituents as drug candidates against the SARS‐COVID‐2 pandemic. [ABSTRACT FROM AUTHOR]

Aim: Depression is a frequent outcome of long‐term stress, but no studies have examined depression rates among Japanese healthcare workers fighting the COVID‐19 pandemic. Therefore, we conducted a web‐based interview of hospital employees to assess depression prevalence and factors. Methods: This observational cohort study was conducted from July to August, 2020, as part of a mandatory health checkup of Juntendo University Hospital employees (Tokyo, Japan). A total of 4239 participants completed a web‐based questionnaire on medical history and current health status. The Center for Epidemiologic Studies Depression Scale (CES‐D) was used for self‐assessment, with a score of ≥16 considered to indicate depression. Results: Among all employees, the proportion of depression was 31.3% in 2020, the highest measured in the last 10 years and substantially greater than the pre‐pandemic value in 2019 (27.5%). The proportion of depression for 2020 was significantly higher in new recruits than in employees with more than 2 years of experience (47.0% vs 29.9%, respectively, P <.0001) and in new recruits in 2019 (26.4%, P <.0001). When subdivided by occupation, nurses demonstrated the highest depression rate (43.2%), followed by paramedics (35.1%) and clerks (31.6%), whereas residents (22.9%), doctors (20.4%), teaching staff (18.0%), and part‐time staff (15.3%) reported lower depression rates. The positive CES‐D score significantly correlated with age (P <.0001). Conclusions: Younger and newer employees demonstrated the highest rates of depression independent of occupation. Therefore, mental healthcare programs focusing on these vulnerable groups need to be established. [ABSTRACT FROM AUTHOR]

The current pandemic represents unprecedented times in medical education. In addition to the already strenuous demands of medical school, the SARS-CoVid-2 pandemic introduced a new source of ethical and moral pressure on students. Medical students navigated finishing their didactic years in isolation and initiated their clinical rotations in a pandemic environment. Many medical students found themselves in the frustrating position of being non-essential healthcare workers but still wanting to help. This paper follows the personal and shared experiences of a second-year medical student transitioning to their third year. In particular, this paper examines the author's personal ties to the disability community through their family, and how this impacted their approach in striving to aid in the pandemic. [ABSTRACT FROM AUTHOR]

COVID‐19 has developed into a worldwide pandemic; early identification of severe illness is critical for controlling it and improving the prognosis of patients with limited medical resources. The present study aimed to analyze the characteristics of severe COVID‐19 and identify biomarkers for differential diagnosis and prognosis prediction. In total, 27 consecutive patients with COVID‐19 and 75 patients with flu were retrospectively enrolled. Clinical parameters were collected from electronic medical records. The disease course was divided into four stages: initial, progression, peak, and recovery stages, according to computed tomography (CT) progress. to mild COVID‐19, the lymphocytes in the severe COVID‐19 progressively decreased at the progression and the peak stages, but rebound in the recovery stage. The levels of C‐reactive protein (CRP) in the severe group at the initial and progression stages were higher than those in the mild group. Correlation analysis showed that CRP (R =.62; P <.01), erythrocyte sedimentation rate (R =.55; P <.01) and granulocyte/lymphocyte ratio (R =.49; P <.01) were positively associated with the CT severity scores. In contrast, the number of lymphocytes (R = −.37; P <.01) was negatively correlated with the CT severity scores. The receiver‐operating characteristic analysis demonstrated that area under the curve of CRP on the first visit for predicting severe COVID‐19 was 0.87 (95% CI 0.10–1.00) at 20.42 mg/L cut‐off, with sensitivity and specificity 83% and 91%, respectively. CRP in severe COVID‐19 patients increased significantly at the initial stage, before CT findings. Importantly, CRP, which was associated with disease development, predicted early severe COVID‐19. Highlights: Compared with COVID'19, Patients with flu had higher WBC and granulocyte. Lymphocyte progressively decreased in the progress of COVID'19. CRP level associated with CT scores and disease development of COVID'19. CRP increased at the initial stage of severe COVID'19, prior to CT findings. CRP showed good performance in prediction of severe COVID'19. [ABSTRACT FROM AUTHOR]

[Display omitted] • The recent emerging-technology applications for the detection of SARS-COVID-2 were discussed. • Specialties were analyzed using CiteSpace bibliometric and network analyses. • The top authors, organizations, co-authors, co-citation, and co-occurrence analyses are highlighted. • Emerging technology-based COVID-19 detection challenges and future perspectives were outlined. In light of the constantly changing terrain of the COVID outbreak, medical specialists have implemented proactive schemes for vaccine production. Despite the remarkable COVID-19 vaccine development, the virus has mutated into new variants, including delta and omicron. Currently, the situation is critical in many parts of the world, and precautions are being taken to stop the virus from spreading and mutating. Early identification and diagnosis of COVID-19 are the main challenges faced by emerging technologies during the outbreak. In these circumstances, emerging technologies to tackle Coronavirus have proven magnificent. Artificial intelligence (AI), big data, the internet of medical things (IoMT), robotics, blockchain technology, telemedicine, smart applications, and additive manufacturing are suspicious for detecting, classifying, monitoring, and locating COVID-19. Henceforth, this research aims to glance at these COVID-19 defeating technologies by focusing on their strengths and limitations. A CiteSpace-based bibliometric analysis of the emerging technology was established. The most impactful keywords and the ongoing research frontiers were compiled. Emerging technologies were unstable due to data inconsistency, redundant and noisy datasets, and the inability to aggregate the data due to disparate data formats. Moreover, the privacy and confidentiality of patient medical records are not guaranteed. Hence, Significant data analysis is required to develop an intelligent computational model for effective and quick clinical diagnosis of COVID-19. Remarkably, this article outlines how emerging technology has been used to counteract the virus disaster and offers ongoing research frontiers, directing readers to concentrate on the real challenges and thus facilitating additional explorations to amplify emerging technologies. [ABSTRACT FROM AUTHOR]

The article presents the discussion on SDG indicators such as poverty reduction, hunger, and economic growth being dependent on labor market conditions.