Lung cancer is the most commonly diagnosed of all cancers and one of the leading causes of cancer deaths among men and women worldwide, causing 1.5 million deaths every year. Despite developments in cancer treatment technologies and new pharmaceutical products, high mortality and morbidity remain major challenges for researchers. More than 75% of lung cancer patients are diagnosed in advanced stages, leading to poor prognosis. Lung cancer is a multistep process associated with genetic and epigenetic abnormalities. Rapid, accurate, precise, and reliable detection of lung cancer biomarkers in biological fluids is essential for risk assessment for a given individual and mortality reduction. Traditional diagnostic tools are not sensitive enough to detect and diagnose lung cancer in the early stages. Therefore, the development of novel bioanalytical methods for early-stage screening and diagnosis is extremely important. Recently, biosensors have gained tremendous attention as an alternative to conventional methods because of their robustness, high sensitivity, inexpensiveness, and easy handling and deployment in point-of-care testing. This review provides an overview of the conventional methods currently used for lung cancer screening, classification, diagnosis, and prognosis, providing updates on research and developments in biosensor technology for the detection of lung cancer biomarkers in biological samples. Finally, it comments on recent advances and potential future challenges in the field of biosensors in the context of lung cancer diagnosis and point-of-care applications.

The COVID-19 pandemic has severely impacted normal human life worldwide. Due to its rapid community spread and high mortality statistics, the development of prompt diagnostic tests for a massive number of samples is essential. Currently used traditional methods are often expensive, time-consuming, laboratory-based, and unable to handle a large number of specimens in resource-limited settings. Because of its high contagiousness, efficient identification of SARS-CoV-2 carriers is crucial. As the advantages of adopting biosensors for efficient diagnosis of COVID-19 increase, this narrative review summarizes the recent advances and the respective reasons to consider applying biosensors. Biosensors are the most sensitive, specific, rapid, user-friendly tools having the potential to deliver point-of-care diagnostics beyond traditional standards. This review provides a brief introduction to conventional methods used for COVID-19 diagnosis and summarizes their advantages and disadvantages. It also discusses the pathogenesis of COVID-19, potential diagnostic biomarkers, and rapid diagnosis using biosensor technology. The current advancements in biosensing technologies, from academic research to commercial achievements, have been emphasized in recent publications. We covered a wide range of topics, including biomarker detection, viral genomes, viral proteins, immune responses to infection, and other potential proinflammatory biomolecules. Major challenges and prospects for future application in point-of-care settings are also highlighted.

Background: Sleep hygiene habits are essential to maintaining a healthy lifestyle. Sleep deprivation is a prevalent problem worldwide. Multiple studies have shown a high prevalence of sleep insufficiency among medical students due to high academic load, caffeine intake, and high-stress levels. Objectives: This study explores the relationship between sleep quality, academic performance, and psychological distress among medical students at Alfaisal University, Riyadh, Saudi Arabia. Methods: This cross-sectional study was conducted on medical students of all academic years at Alfaisal University, Riyadh. Two popular surveys – the Pittsburgh sleep quality index (PSQI) and K10 - were used to assess sleep quality and psychological distress, respectively. In addition, the survey contained questions on socio-demographics and academic performance. Sociodemographic characteristics were assessed across groups having poor and good sleep quality. In multivariate analysis, we employed conscious variable selection to develop models. All statistical tests were two-sided at P < 0.05 as statistically significant. Results: A total of 241 respondents participated in the study. Most respondents reported poor sleep quality (75.93%). The highest psychological distress was reported among respondents with poor sleep quality (48.63%). The relationship between Kessler's Psychological Distress score and sleep quality score was significantly positive and linear (P < 0.0001). The mean PSQI score was higher in students with lower GPAs (1.50 - 2.99) than in those with higher GPAs (3.00 - 4.00). This shows that those students who sleep well perform academically better. Conclusions: The quality of sleep among medical students is poor, which is associated with psychological distress and poor academic performance. It is highly suggested that the awareness of sleep hygiene with healthy sleep habits be campaigned among medical students. Also, academic mentors, program directors, and coordinators play a crucial role in ensuring that medical students are not academically overwhelmed.

SARS-CoV-2 infects the respiratory tract and leads to the disease entity, COVID-19. Accordingly, the lungs bear the greatest pathologic burden with the major cause of death being respiratory failure. However, organs remote from the initial site of infection (e.g., kidney, heart) are not spared, particularly in severe and fatal cases. Emerging evidence indicates that an excessive inflammatory response coupled with a diminished antiviral defense is pivotal in the initiation and development of COVID-19. A common finding in autopsy specimens is the presence of thrombi in the lungs as well as remote organs, indicative of immunothrombosis. Herein, the role of SARS-CoV-2 in lung inflammation and associated sequelae are reviewed with an emphasis on immunothrombosis. In as much as vitamin D is touted as a supplement to conventional therapies of COVID-19, the impact of this vitamin at various junctures of COVID-19 pathogenesis is also addressed.

Physical inactivity has been identified as a major factor in developing and progressing chronic non-communicable diseases such as obesity. The Kingdom of Saudi Arabia ranks high worldwide in rates of obesity. During the coronavirus disease 2019 (COVID-19) pandemic, public health measures have been enforced. These included social distancing, masking, reduction of workplace daily hours, prevention of social gatherings, and home quarantine measures. These ultimately restricted the ability to perform regular physical health activities. The aim of this study is to understand the impact of COVID-19 on physical activity among adults in the Kingdom of Saudi Arabia. A cross-sectional study was conducted among the Saudi population. An online survey was sent through social media to gather data regarding individual physical activity before and after the start of the COVID-19 restrictions. The data were collected from March 20, 2021, until May 20, 2021, and analyzed using chi-square and paired t-test using the SAS software version 9.4. In total, 433 participants completed the survey. There were 183 (42.3%) males, and the majority of the participants were Saudi nationals (284, 65.6%). Most of the participants (181, 41.8%) were in the age group 25-35 years and 253 (58%) had bachelor’s degrees. Although the results did not show a statistically significant difference between pre- and post-COVID-19 respondents in terms of physical activity, married participants, participants from the eastern province, and participants who did not exercise regularly were all significantly impacted by lack of exercise compared to their counterparts (p < 0.05). Taking measures to prevent the spread of COVID-19 is essential. Nonetheless, recommendations should be sought for physical activity during lockdowns, and large-scale research should be conducted to better understand what causes the exaggeration of sedentary lifestyles during lockdowns and how to prevent them. Further studies need to be conducted, and national guidelines should be made available in case of a future lockdown.

Recent advances in human blastoids have opened new avenues for modeling early human development and implantation. Human blastoids can be generated in large numbers, making them well-suited for high-throughput screening. However, automated methods for evaluating and characterizing blastoid morphology are lacking. We developed a deep-learning model—deepBlastoid—for automated classification of live human blastoids using only brightfield images. The model processes 273.6 images per second with an average accuracy of 87%, which is further improved to 97% by integrating a Confidence Rate metric. deepBlastoid outperformed human experts in throughput while matching accuracy in blastoid classification. We demonstrated the utility of the model in two use cases: (i) systematic assessment of the effect of lysophosphatidic acid (LPA) on blastoid formation and (ii) evaluating the impact of dimethyl sulfoxide (DMSO) on blastoid formation. The evaluation results of deepBlastoid using over 10,000 images were consistent with the known drug effects and showed subtle but significant effects that might have been overlooked in manual assessments. The publicly available deepBlastoid model enables researchers to train customized models based on their imaging and protocols, providing an efficient, automated tool for blastoid classification with broad applications in research, drug screening, and in-vitro-fertilization applications.