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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.