Greenway of Digital Health Technology During COVID-19 Crisis: Bibliometric Analysis, Challenges, and Future Perspective.

Digital health has transformed the healthcare landscape by leveraging technology to improve patient outcomes and access to medical services. The COVID-19 pandemic has highlighted the urgent need for digital healthcare solutions that can mitigate the impact of the outbreak while ensuring patient safety. In this chapter, we delve into how digital health technologies such as telemedicine, mobile apps, and wearable devices can provide personalized care, reduce healthcare provider burden, and lower healthcare costs. We also explore the creation of a greenway of digital healthcare that safeguards patient confidentiality, enables efficient communication, and ensures cost-effective payment systems. This chapter showcases the potential of digital health to revolutionize healthcare delivery while ensuring patient well-being and medical staff satisfaction.

Development and Validation of a Deep Learning Model for Histopathological Slide Analysis in Lung Cancer Diagnosis.

Lung cancer is the leading cause of cancer-related deaths worldwide. Two of the crucial factors contributing to these fatalities are delayed diagnosis and suboptimal prognosis. The rapid advancement of deep learning (DL) approaches provides a significant opportunity for medical imaging techniques to play a pivotal role in the early detection of lung tumors and subsequent monitoring during treatment. This study presents a DL-based model for efficient lung cancer detection using whole-slide images. Our methodology combines convolutional neural networks (CNNs) and separable CNNs with residual blocks, thereby improving classification performance. Our model improves accuracy (96% to 98%) and robustness in distinguishing between cancerous and non-cancerous lung cell images in less than 10 s. Moreover, the model's overall performance surpassed that of active pathologists, with an accuracy of 100% vs. 79%. There was a significant linear correlation between pathologists' accuracy and years of experience (r Pearson = 0.71, 95% CI 0.14 to 0.93, p = 0.022). We conclude that this model enhances the accuracy of cancer detection and can be used to train junior pathologists.

Psychological distress among healthcare students in Poland from COVID-19 to war on Ukraine: a cross-sectional exploratory study.

Objectives: Healthcare students went through a rough time in March 2022 due to extreme changes in the educational system (moving from online to stationary learning) and Ukrainian-Russian war circumstances. Our study aims to update knowledge about psychological distress and its impact on healthcare students in Poland after two years of the COVID-19 pandemic, followed by intense and political instability in Europe. Methods: We conducted a cross-sectional study on healthcare students from Poznan University of Medical Sciences, Poland, from March to April 2022. The questionnaire included subjective retrospective 5-point Likert-scales ratings of anxiety, stress, and depression and self-reported information on various psychological distress predictors. Results: The anxiety levels at the beginning of the COVID-19 pandemic were higher than in April 2022. There was no significant reduction in stress and depression. Females had higher initial anxiety levels than post-pandemic levels. Higher reported levels of anxiety, stress, and depression were significantly correlated with political instability in Eastern Europe (Spearman ranxiety = 0.178, rstress = 0.169, rdepression = 0.154, p ≤ 0.001, respectively). The concern about moving towards online education showed a significant association only with stress level (rstress = 0.099, p = 0.034). We also observed a positive correlation between anxiety, stress, and depression and deteriorating sleep quality (Spearman ranxiety,=0.325, rstress = 0.410, rdepression = 0.440, p < 0.001), the feeling of worsening relationships with family and peers (ranxiety = 0.325, rstress = 0.343, rdepression = 0.379, p < 0.001), and the sense of loss of efficient time management (ranxiety = 0.321, rstress = 0.345, rdepression = 0.410, p < 0.001). Conclusion: Throughout the progression of the Ukrainian war and the COVID-19 pandemic, females reported improved (lower levels) levels of anxiety. Nevertheless, the current levels of self-reported anxiety post-pandemic remain alarming, while stress and depression levels remained unchanged. Mental, psychological, and social support activities are required for healthcare students, especially those away from their families. Time management, academic performance, and coping skills in relation to the additional stressors of war and the global pandemic require further research in this group of students.

Perception of Pathologists in Poland of Artificial Intelligence and Machine Learning in Medical Diagnosis-A Cross-Sectional Study.

BACKGROUND: In the past vicennium, several artificial intelligence (AI) and machine learning (ML) models have been developed to assist in medical diagnosis, decision making, and design of treatment protocols. The number of active pathologists in Poland is low, prolonging tumor patients' diagnosis and treatment journey. Hence, applying AI and ML may aid in this process. Therefore, our study aims to investigate the knowledge of using AI and ML methods in the clinical field in pathologists in Poland. To our knowledge, no similar study has been conducted. METHODS: We conducted a cross-sectional study targeting pathologists in Poland from June to July 2022. The questionnaire included self-reported information on AI or ML knowledge, experience, specialization, personal thoughts, and level of agreement with different aspects of AI and ML in medical diagnosis. Data were analyzed using IBM® SPSS® Statistics v.26, PQStat Software v.1.8.2.238, and RStudio Build 351. RESULTS: Overall, 68 pathologists in Poland participated in our study. Their average age and years of experience were 38.92 ± 8.88 and 12.78 ± 9.48 years, respectively. Approximately 42% used AI or ML methods, which showed a significant difference in the knowledge gap between those who never used it (OR = 17.9, 95% CI = 3.57-89.79, p < 0.001). Additionally, users of AI had higher odds of reporting satisfaction with the speed of AI in the medical diagnosis process (OR = 4.66, 95% CI = 1.05-20.78, p = 0.043). Finally, significant differences (p = 0.003) were observed in determining the liability for legal issues used by AI and ML methods. CONCLUSION: Most pathologists in this study did not use AI or ML models, highlighting the importance of increasing awareness and educational programs regarding applying AI and ML in medical diagnosis.

Potential Use of Thalidomide in Glioblastoma Treatment: An Updated Brief Overview.

Glioblastoma is the most common malignant primary brain tumor in adults. Thalidomide is a vascular endothelial growth factor inhibitor that demonstrates antiangiogenic activity, and may provide additive or synergistic anti-tumor effects when co-administered with other antiangiogenic medications. This study is a comprehensive review that highlights the potential benefits of using thalidomide, in combination with other medications, to treat glioblastoma and its associated inflammatory conditions. Additionally, the review examines the mechanism of action of thalidomide in different types of tumors, which may be beneficial in treating glioblastoma. To our knowledge, a similar study has not been conducted. We found that thalidomide, when used in combination with other medications, has been shown to produce better outcomes in several conditions or symptoms, such as myelodysplastic syndromes, multiple myeloma, Crohn's disease, colorectal cancer, renal failure carcinoma, breast cancer, glioblastoma, and hepatocellular carcinoma. However, challenges may persist for newly diagnosed or previously treated patients, with moderate side effects being reported, particularly with the various mechanisms of action observed for thalidomide. Therefore, thalidomide, used alone, may not receive significant attention for use in treating glioblastoma in the future. Conducting further research by replicating current studies that show improved outcomes when thalidomide is combined with other medications, using larger sample sizes, different demographic groups and ethnicities, and implementing enhanced therapeutic protocol management, may benefit these patients. A meta-analysis of the combinations of thalidomide with other medications in treating glioblastoma is also needed to investigate its potential benefits further.

Changes in psychological distress among Polish medical university teachers during the COVID-19 pandemic.

Our study aims to update knowledge about psychological distress and its changes in the Polish group of academic medical teachers after two years of a global pandemic. During the coronavirus disease, teachers were challenged to rapidly transition into remote teaching and adapt new assessment and evaluation systems for students, which might have been a completely novel situation that was not addressed before, especially in medical universities in Poland. We conducted a cross-sectional study at Poznan University of Medical Sciences from March to April 2022. The questionnaire included self-reported information on anxiety, stress, and depression. We found that post-pandemic levels of anxiety, stress, and depression have significantly (p<0.001) improved compared to initial levels at the beginning of coronavirus disease. In multivariate models, females had higher odds of improving levels of anxiety (OR = 0.46; 95% CI = -1.58-(-0.03); p = 0.04), stress (OR = 0.36; 95% CI = -1.83-(-0.22); p = 0.01), and depression (OR = 0.0.37; 95% CI = -1.58-(-0.12); p = 0.03). Anxiety, stress, or depression were not significantly associated with years of experience, the number of taught subjects, and weekly teaching hours, but only with the academic work during COVID-19 (Spearman ranxiety = 0.37, rstress = 0.32, rdepression = 0.37, p<0.001). For the virtual learning concerns, 79% of teachers reported that students might engage less; and it was correlated with higher weekly teaching hours (r = 0.19, p<0.05). Even though only 29.8% reported cheating as a concern, it was correlated with a higher number of taught subjects (r = 0.2, p<0.05). Levels of anxiety, stress, and depression have improved as time passed, not affecting teachers' academic performance. Concerns about virtual learning have been raised, suggesting it may be conjoined with classroom learning but not as an alternative. Universities should highlight the importance of seeking psychological support and provide essential programs to employees. Teachers' coping skills with psychological distress should be further studied.

Deep Learning Approaches in Histopathology.

The revolution of artificial intelligence and its impacts on our daily life has led to tremendous interest in the field and its related subtypes: machine learning and deep learning. Scientists and developers have designed machine learning- and deep learning-based algorithms to perform various tasks related to tumor pathologies, such as tumor detection, classification, grading with variant stages, diagnostic forecasting, recognition of pathological attributes, pathogenesis, and genomic mutations. Pathologists are interested in artificial intelligence to improve the diagnosis precision impartiality and to minimize the workload combined with the time consumed, which affects the accuracy of the decision taken. Regrettably, there are already certain obstacles to overcome connected to artificial intelligence deployments, such as the applicability and validation of algorithms and computational technologies, in addition to the ability to train pathologists and doctors to use these machines and their willingness to accept the results. This review paper provides a survey of how machine learning and deep learning methods could be implemented into health care providers' routine tasks and the obstacles and opportunities for artificial intelligence application in tumor morphology.

Attitudes toward Receiving COVID-19 Booster Dose in the Middle East and North Africa (MENA) Region: A Cross-Sectional Study of 3041 Fully Vaccinated Participants.

COVID-19 vaccines are crucial to control the pandemic and avoid COVID-19 severe infections. The rapid evolution of COVID-19 variants such as B.1.1.529 is alarming, especially with the gradual decrease in serum antibody levels in vaccinated individuals. Middle Eastern countries were less likely to accept the initial doses of vaccines. This study was directed to determine COVID-19 vaccine booster acceptance and its associated factors in the general population in the MENA region to attain public herd immunity. We conducted an online survey in five countries (Egypt, Iraq, Palestine, Saudi Arabia, and Sudan) in November and December 2021. The questionnaire included self-reported information about the vaccine type, side effects, fear level, and several demographic factors. Kruskal−Wallis ANOVA was used to associate the fear level with the type of COVID-19 vaccine. Logistic regression was performed to confirm the results and reported as odds ratios (ORs) and 95% confidence intervals. The final analysis included 3041 fully vaccinated participants. Overall, 60.2% of the respondents reported willingness to receive the COVID-19 booster dose, while 20.4% were hesitant. Safety uncertainties and opinions that the booster dose is not necessary were the primary reasons for refusing the booster dose. The willingness to receive the booster dose was in a triangular relationship with the side effects of first and second doses and the fear (p < 0.0001). Females, individuals with normal body mass index, history of COVID-19 infection, and influenza-unvaccinated individuals were significantly associated with declining the booster dose. Higher fear levels were observed in females, rural citizens, and chronic and immunosuppressed patients. Our results suggest that vaccine hesitancy and fear in several highlighted groups continue to be challenges for healthcare providers, necessitating public health intervention, prioritizing the need for targeted awareness campaigns, and facilitating the spread of evidence-based scientific communication.

Telehealth and Artificial Intelligence Insights into Healthcare during the COVID-19 Pandemic.

Soon after the coronavirus disease 2019 pandemic was proclaimed, digital health services were widely adopted to respond to this public health emergency, including comprehensive monitoring technologies, telehealth, creative diagnostic, and therapeutic decision-making methods. The World Health Organization suggested that artificial intelligence might be a valuable way of dealing with the crisis. Artificial intelligence is an essential technology of the fourth industrial revolution that is a critical nonmedical intervention for overcoming the present global health crisis, developing next-generation pandemic preparation, and regaining resilience. While artificial intelligence has much potential, it raises fundamental privacy, transparency, and safety concerns. This study seeks to address these issues and looks forward to an intelligent healthcare future based on best practices and lessons learned by employing telehealth and artificial intelligence during the COVID-19 pandemic.

New approach in SARS-CoV-2 surveillance using biosensor technology: a review.

Biosensors are analytical tools that transform the bio-signal into an observable response. Biosensors are effective for early detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection because they target viral antigens to assess clinical development and provide information on the severity and critical trends of infection. The biosensors are capable of being on-site, fast, and extremely sensitive to the target viral antigen, opening the door for early detection of SARS-CoV-2. They can screen individuals in hospitals, airports, and other crowded locations. Microfluidics and nanotechnology are promising cornerstones for the development of biosensor-based techniques. Recently, due to high selectivity, simplicity, low cost, and reliability, the production of biosensor instruments have attracted considerable interest. This review article precisely provides the extensive scientific advancement and intensive look of basic principles and implementation of biosensors in SARS-CoV-2 surveillance, especially for human health. In this review, the importance of biosensors including Optical, Electrochemical, Piezoelectric, Microfluidic, Paper-based biosensors, Immunosensors, and Nano-Biosensors in the detection of SARS-CoV-2 has been underscored. Smartphone biosensors and calorimetric strips that target antibodies or antigens should be developed immediately to combat the rapidly spreading SARS-CoV-2. Wearable biosensors can constantly monitor patients, which is a highly desired feature of biosensors. Finally, we summarized the literature, outlined new approaches and future directions in diagnosing SARS-CoV-2 by biosensor-based techniques.

Arbidol targeting influenza virus A Hemagglutinin; A comparative study.

Influenza (flu) is a serious global health threat. The Hemagglutinin (HA) protein binds the flu virus to the sialic acids at the surface of the host cells' membrane which allows the endocytosis of the virus. Therefore, potential inhibitors can attach to the active site of HA and block the virus life-cycle. In this study, the antiviral drug arbidol (ARB) and 16 HA-subtypes were docked and analyzed to represent different approaches in predicting the conformation of protein-ligand, protein-protein, and protein-glycan complex and its binding energy. Our findings show that ARB interacts with all HA subtypes, and H7 possesses the best affinity. The next influenza pandemic could be caused by H4, H5, H6, and H14 subtypes, which prompts further studies in investigating the interaction between these particular HA subtypes and other antiviral drugs to obtain higher efficacy.