BACKGROUND: To date, multiple cases of adverse reactions to COVID-19 vaccines have been reported worldwide. Alopecia areata (AA) is an uncommon type of adverse reaction reported in some articles and has a significant social and psychological impact on patients. Our study aimed to review the AA and COVID-19 vaccine literature.
METHODS: This systematic review was conducted by searching for articles on AA following COVID-19 vaccines in international databases such as Embase, MEDLINE, PubMed, Web of Knowledge, and Ovid from December 2019 to December 30, 2023. We included studies that provided data for AA patients following COVID-19 vaccination with at least one dose. Data on sex, age, country/region of origin, vaccine type, days between vaccination and symptom presentation, manifestations of AA, trichoscopy and histopathological findings, treatment, and outcomes were included.
RESULTS: In total, 579 explored studies were identified and assessed, and 25 articles with a total of 51 patients were included in the review. Twenty-seven (52.9%) patients developed new-onset AA following receiving the COVID-19 vaccine, and AA recurrence or exacerbation occurred after receiving the COVID-19 vaccine in 24 (47.1%) patients with preexisting disease. Five vaccines were reported to cause AA in all cases. The Pfizer vaccine (45.1%) was the most frequently reported, followed by the ChAdOx1 nCoV-19 vaccine (27.5%), Moderna mRNA-1273 (19.6%), Sinopharm (3.9%) and SinoVac (3.9%). AA occurred most frequently within one month after the 1st dose, and then, the incidence decreased gradually with time. Topical or systemic corticosteroids were used in 38 patients. Eleven patients were treated with a Janus Kinase inhibitor (jakinib) inhibitor, eight with tofacitinib, and three with an unspecified jakinib. However, 3 of the 11 patients experienced exacerbations after treatment.
CONCLUSIONS: AA after COVID-19 vaccination is rare, and physicians should be aware of this phenomenon to improve early diagnosis and appropriate treatment.

BACKGROUND: A previous study reported significant excess mortality among non-COVID-19 patients due to disrupted surgical care caused by resource prioritization for COVID-19 cases in France. The primary objective was to investigate if a similar impact occurred for medical conditions and determine the effect of hospital saturation on non-COVID-19 hospital mortality during the first year of the pandemic in France.
METHODS: We conducted a nationwide population-based cohort study including all adult patients hospitalized for non-COVID-19 acute medical conditions in France between March 1, 2020 and 31 May, 2020 (1st wave) and September 1, 2020 and December 31, 2020 (2nd wave). Hospital saturation was categorized into four levels based on weekly bed occupancy for COVID-19: no saturation (< 5%), low saturation (> 5% and ≤ 15%), moderate saturation (> 15% and ≤ 30%), and high saturation (> 30%). Multivariate generalized linear model analyzed the association between hospital saturation and mortality with adjustment for age, sex, COVID-19 wave, Charlson Comorbidity Index, case-mix, source of hospital admission, ICU admission, category of hospital and region of residence.
RESULTS: A total of 2,264,871 adult patients were hospitalized for acute medical conditions. In the multivariate analysis, the hospital mortality was significantly higher in low saturated hospitals (adjusted Odds Ratio/aOR = 1.05, 95% CI [1.34-1.07], P < .001), moderate saturated hospitals (aOR = 1.12, 95% CI [1.09-1.14], P < .001), and highly saturated hospitals (aOR = 1.25, 95% CI [1.21-1.30], P < .001) compared to non-saturated hospitals. The proportion of deaths outside ICU was higher in highly saturated hospitals (87%) compared to non-, low- or moderate saturated hospitals (81-84%). The negative impact of hospital saturation on mortality was more pronounced in patients older than 65 years, those with fewer comorbidities (Charlson 1-2 and 3 vs. 0), patients with cancer, nervous and mental diseases, those admitted from home or through the emergency room (compared to transfers from other hospital wards), and those not admitted to the intensive care unit.
CONCLUSIONS: Our study reveals a noteworthy \"dose-effect\" relationship: as hospital saturation intensifies, the non-COVID-19 hospital mortality risk also increases. These results raise concerns regarding hospitals\' resilience and patient safety, underscoring the importance of identifying targeted strategies to enhance resilience for the future, particularly for high-risk patients.

BACKGROUND: Redondoviridae is a newly discovered virus family linked to oral and respiratory conditions in people, while there is still debate about whether it is also coinfected with other respiratory viruses. This study aimed to determine the frequency of Redondovirus (ReDoV) in nasopharyngeal samples and to investigate any possible links to SARS-CoV-2 infections.
METHODS: A polymerase chain reaction (PCR) test was conducted on 731 nasopharyngeal samples from individuals referred to medical centers in Tehran, Iran, for SARS-CoV-2 testing to investigate the prevalence of ReDoV. An oral interview was performed to complete information on dental issues and the individuals\' demographics, symptoms, and vaccination history.
RESULTS: The prevalence of ReDoV was 25.99%, and 15.26% had a coinfection with SARS-CoV-2. No notable correlation was found regarding ReDoVs and SARS-CoV-2 infections (p > 0.05). Women had a higher ReDoV positivity rate of 18.47% compared to men at 7.52% (p = 0.12), and there was no significant correlation between age groups and ReDoV presence. Nonetheless, a significant association was noted between ReDoVs and dental/gum issues (p < 0.0001, OR: 13.0326). A phylogenetic analysis showed that ReDoVs originated from various human-related clusters.
CONCLUSIONS: These results highlight the potential for detecting ReDoVs in nasopharyngeal samples of people with gum or dental issues. Additionally, conducting more ReDoV epidemiological research and proposing oral health as a possible marker for ReDoV infections is important.

BACKGROUND: The COVID-19 pandemic period (2020 to 2022) challenged and overstretched the capacity of primary health care services to deliver health care globally. The sector faced a highly uncertain and dynamic period that encompassed anticipation of a new, unknown, lethal and highly transmissible infection, the introduction of various travel restrictions, health workforce shortages, new government funding announcements and various policies to restrict the spread of the COVID-19 virus, then vaccination and treatments. This qualitative study aims to document and explore how the pandemic affected primary health care utilisation and delivery in remote and regional Aboriginal and Torres Strait Islander communities.
METHODS: Semi-structured interviews were conducted with staff working in 11 Aboriginal Community-Controlled Health Services (ACCHSs) in outer regional, remote and very remote Australia. Interviews were transcribed, inductively coded and thematically analysed.
RESULTS: 248 staff working in outer regional, remote and very remote primary health care clinics were interviewed between February 2020 and June 2021. Participants reported a decline in numbers of primary health care presentations in most communities during the initial COVID-19 lock down period. The reasons for the decline were attributed to community members apprehension to go to the clinics, change in work priorities of primary health care staff (e.g. more emphasis on preventing the virus entering the communities and stopping the spread) and limited outreach programs. Staff forecasted a future spike in acute presentations of various chronic diseases leading to increased medical retrieval requirements from remote communities to hospital. Information dissemination during the pre-vaccine roll-out stage was perceived to be well received by community members, while vaccine roll-out stage information was challenged by misinformation circulated through social media.
CONCLUSIONS: The ability of ACCHSs to be able to adapt service delivery in response to the changing COVID-19 strategies and policies are highlighted in this study. The study signifies the need to adequately fund ACCHSs with staff, resources, space and appropriate information to enable them to connect with their communities and continue their work especially in an era where the additional challenges created by pandemics are likely to become more frequent. While the PHC seeking behaviour of community members during the COVID-19 period were aligned to the trends observed across the world, some of the reasons underlying the trends were unique to outer regional, remote and very remote populations. Policy makers will need to give due consideration to the potential effects of newly developed policies on ACCHSs operating in remote and regional contexts that already battle under resourcing issues and high numbers of chronically ill populations.

OBJECTIVE: In this study, we present DeepVirusClassifier, a tool capable of accurately classifying Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) viral sequences among other subtypes of the coronaviridae family. This classification is achieved through a deep neural network model that relies on convolutional neural networks (CNNs). Since viruses within the same family share similar genetic and structural characteristics, the classification process becomes more challenging, necessitating more robust models. With the rapid evolution of viral genomes and the increasing need for timely classification, we aimed to provide a robust and efficient tool that could increase the accuracy of viral identification and classification processes. Contribute to advancing research in viral genomics and assist in surveilling emerging viral strains.
METHODS: Based on a one-dimensional deep CNN, the proposed tool is capable of training and testing on the Coronaviridae family, including SARS-CoV-2. Our model\'s performance was assessed using various metrics, including F1-score and AUROC. Additionally, artificial mutation tests were conducted to evaluate the model\'s generalization ability across sequence variations. We also used the BLAST algorithm and conducted comprehensive processing time analyses for comparison.
RESULTS: DeepVirusClassifier demonstrated exceptional performance across several evaluation metrics in the training and testing phases. Indicating its robust learning capacity. Notably, during testing on more than 10,000 viral sequences, the model exhibited a more than 99% sensitivity for sequences with fewer than 2000 mutations. The tool achieves superior accuracy and significantly reduced processing times compared to the Basic Local Alignment Search Tool algorithm. Furthermore, the results appear more reliable than the work discussed in the text, indicating that the tool has great potential to revolutionize viral genomic research.
CONCLUSIONS: DeepVirusClassifier is a powerful tool for accurately classifying viral sequences, specifically focusing on SARS-CoV-2 and other subtypes within the Coronaviridae family. The superiority of our model becomes evident through rigorous evaluation and comparison with existing methods. Introducing artificial mutations into the sequences demonstrates the tool\'s ability to identify variations and significantly contributes to viral classification and genomic research. As viral surveillance becomes increasingly critical, our model holds promise in aiding rapid and accurate identification of emerging viral strains.

With devastating health and socioeconomic impact worldwide, much work is left to understand the Coronavirus Disease 2019 (COVID-19), with emphasis in the severely affected elderly population. Here, we present a proteomics study of lung tissue obtained from aged vs. young rhesus macaques (Macaca mulatta) and olive baboons (Papio Anubis) infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Using age as a variable, we identified common proteomic profiles in the lungs of aged infected non-human primates (NHPs), including key regulators of immune function, as well as cell and tissue remodeling, and discuss the potential clinical relevance of such parameters. Further, we identified key differences in proteomic profiles between both NHP species, and compared those to what is known about SARS-CoV-2 in humans. Finally, we explored the translatability of these animal models in the context of aging and the human presentation of the COVID-19.

This study, leveraging search engine data, investigates the dynamics of China\'s domestic tourism markets in response to the August 2022 epidemic outbreak in Xinjiang. It focuses on understanding the reaction mechanisms of tourist-origin markets during destination crises in the post-pandemic phase. Notably, the research identifies a continuous rise in the potential tourism demand from tourist origin cities, despite the challenges posed by the epidemic. Further analysis uncovers a regional disparity in the growth of tourism demand, primarily influenced by the economic stratification of origin markets. Additionally, the study examines key tourism attractions such as Duku Road, highlighting its resilient competitive system, which consists of distinctive tourism experiences, economically robust tourist origins, diverse tourist markets, and spatial pattern stability driven by economic factors in source cities, illustrating an adaptive response to external challenges such as crises. The findings provide new insights into the dynamics of tourism demand, offering a foundation for developing strategies to bolster destination resilience and competitiveness in times of health crises.

Vaccine hesitancy is an inevitable risk for societies as it contributes to outbreaks of diseases. Prior research suggests that vaccination decisions of individuals tend to spread within social networks, resulting in a tendency to vaccination homophily. The clustering of individuals resistant to vaccination can substantially make the threshold necessary to achieve herd immunity harder to reach. In this study, we examined the extent of vaccination homophily among social contacts and its association with vaccine uptake during the COVID-19 pandemic in Hungary using a contact diary approach in two cross-sectional surveys. The results indicate strong clustering among both vaccinated and unvaccinated groups. The most powerful predictor of vaccine uptake was the perceived vaccination rate within the egos\' social contact network. Vaccination homophily and the role of the interpersonal contact network in vaccine uptake were particularly pronounced in the networks of close relationships, including family, kinship, and strong social ties of the ego. Our findings have important implications for understanding COVID-19 spread dynamics by showing that the strong clustering of unvaccinated individuals posed a great risk in preventing the spread of the disease.

Many waterborne diseases are related with viruses, and COVID-19 worldwide has raised the concern of virus security in water into the public horizon. Compared to other conventional water treatment processes, membrane technology can achieve satisfactory virus removal with fewer chemicals, and prevent the outbreaks of viruses to a maximal extent. Researchers developed new modification methods to improve membrane performance. This review focused on the membrane modifications that enhance the performance in virus removal. The characteristics of viruses and their removal by membrane filtration were briefly generalized, and membrane modifications were systematically discussed through different virus removal mechanisms, including size exclusion, hydrophilic and hydrophobic interactions, electronic interactions, and inactivation. Advanced functional materials for membrane modification were summarized based on their nature. Furthermore, it is suggested that membranes should be enhanced through different mechanisms mainly based on their ranks of pore size. The current review provided theoretical support regarding membrane modifications in the enhancement of virus removal and avenues for practical application.

Interdigitated electrodes (IDEs) enable electrochemical signal enhancement through repeated reduction and oxidation of the analyte molecule. Porosity on these electrodes is often used to lower the impedance background. However, their high capacitive current and signal interferences with oxygen reduction limit electrochemical detection ability. We present utilization of alkanethiol modification on nanoporous gold (NPG) electrodes to lower their background capacitance and chemically passivate them from interferences due to oxygen reduction, while maintaining their fast electron transfer rates, as validated by lower separation between anodic and cathodic peaks (ΔE) and lower charge transfer resistance (Rct) values in comparison to planar gold electrodes. Redox amplification based on this modification enables sensitive detection of various small molecules, including pyocyanin, p-aminophenol, and selective detection of dopamine in the presence of ascorbic acid. Alkanethiol NPG arrays are applied as a multiplexed sensor testbed within a well plate to screen binding of various peptide receptors to the SARS COV2 S-protein by using a sandwich assay for conversion of PAPP (4-aminophenyl phosphate) to PAP (p-aminophenol), by the action of AP (alkaline phosphatase), which is validated against optical ELISA screens of the peptides. Such arrays are especially of interest in small volume analytical settings with complex samples, wherein optical methods are unsuitable.