Prevalence and associated influential factors of mental health problems among Chinese college students during different stages of COVID-19 pandemic: A systematic review.

This systematic review aims to take China as an example to determine the prevalence of mental health problems and associated influential factors of college students in different stages of the COVID-19 pandemic and provide a reference for effective intervention in the future. A systematic search was conducted on PubMed, Web of Science, Scopus, Science Direct, and Google scholar. A total of 30 articles were included. 1,477,923 Chinese college students were surveyed. In the early stage, the prevalence rates of depression, anxiety, stress, and PTSD ranged from 9.0% to 65.2%, 6.88%-41.1%, 8.53%-67.05%, and 2.7%-30.8%, respectively. Major risk factors were being a female, a medical student, isolation or quarantine, having family members or friends infected with COVID-19, and challenges of online learning. During the normalization stage, depression, anxiety, and insomnia prevalence rates ranged from 8.7% to 50.2%, 4.2%-34.6%, and 6.1%-35.0%, respectively. The main risk factors were self-quarantined after school reopening, regular taking temperature, and wearing face masks. The prevalence rates of mental health problems and associated influential factors unveiled in both stages showed that the students' mental health status was greatly affected. Therefore, a combination of efforts from the government, universities, and families or communities is highly needed to alleviate the mental health sufferings of students.

Zoonotic Spillover in an Era of Rapid Deforestation of Tropical Areas and Unprecedented Wildlife Trafficking: Into the Wild

Rapid deforestation and unprecedented wildlife trafficking are important factors triggering the rate of zoonotic spillover from animals to humans. Consequently, this leads to the emergence and re-emergence of zoonotic infectious diseases among the human population. Deforestation is an important ecological disruption that leads to the loss of biodiversity. The loss of biodiversity results in the persistence of highest-quality hosts of zoonotic pathogens dominating the low-diversity communities, a process termed the dilution effect. Activities like intensive farming and logging that resulted in deforestation bring vulnerable people in close contact with these highest-quality reservoir hosts (wildlife). As a result of this vulnerability, there is an increased risk of spillover, leading to zoonotic infection in humans and eventually disease outbreaks during human–human transmission. One prominent example of a disease of wildlife origin is the ongoing SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus 2), even though the original source has not been found. Another important factor facilitating the risk of spillover and emergence of zoonotic infectious diseases is wildlife trafficking. This involves illegal hunting and trading of wildlife and their products, which increases the risk of spillover as a result of exchange of bodily fluids and bloodmeals between humans and wildlife during the hunting and butchering of animals' carcasses. Consequently, little or no hygiene protocol and poor handling practices during the wildlife-trade chain expose poachers, consumers, and local market sellers to the risk of zoonotic diseases. Despite the interventions on deforestation-induced spillover and wildlife trafficking-associated spillover, there are still knowledge and research gaps that need to be addressed towards preventing the outbreaks of future zoonotic infectious diseases. In response to this, there is a need for interdisciplinary and intersectoral collaborations among researchers from various fields as well as sectors in minimizing the risk of zoonotic spillover driven by deforestation and wildlife trafficking at the human–animal–environmental nexus. In addition, there is a need for integrated and unified evidence-based policy formulation that puts an end to deforestation and wildlife trafficking, especially in tropical areas such as Africa and Asia.

Lung-brain axis.

The appreciation of human microbiome is gaining strong grounds in biomedical research. In addition to gut-brain axis, is the lung-brain axis, which is hypothesised to link pulmonary microbes to neurodegenerative disorders and behavioural changes. There is a need for analysis based on emerging studies to map out the prospects for lung-brain axis. In this review, relevant English literature and researches in the field of 'lung-brain axis' is reported. We recommend all the highlighted prospective studies to be integrated with an interdisciplinary approach. This might require conceptual research approaches based on physiology and pathophysiology. Multimodal aspects should include experimental animal units, while exploring the research gaps and making reference to the already existing human data. The overall microbiome medicine is gaining more ground. Aetiological paths and experimental recommendations as per prospective studies in this review will be an important guideline to develop effective treatments for any lung induced neurodegenerative diseases. An in-depth knowledge of the bi-directional communication between host and microbiome in the lung could help treatment to respiratory infections, alleviate stress, anxiety and enhanced neurological effects. The timely prevention and treatment of neurodegenerative diseases requires paradigm shift of the aetiology and more innovative experimentation.Impact statementThe overall microbiome medicine is gaining more ground. An in-depth knowledge of the bi-directional communication between host and microbiome in the lung could confer treatment to respiratory infections, alleviate stress, anxiety and enhanced neurological effects. Based on this review, we recommend all the highlighted prospective studies to be integrated and be given an interdisciplinary approach. This might require conceptual research approaches based on physiology and pathophysiology. Multimodal aspects should include experimental animal units; while exploring the research gaps and making reference to the already existing human data.