Objective: This study aimed to assess and characterize the neurological manifestations of MIS-C in pediatric patients with COVID-19.

Methods: A retrospective cross-sectional cohort study was conducted on 303 pediatric patients admitted to a pediatric intensive care unit in Upper Egypt between March 2020 and March 2023. Patients were divided into three groups: Group I (MIS-C, PCR-positive), Group II (MIS-C, serologypositive), and Group III (non-MIS-C, PCR-positive). Neurological symptoms, laboratory markers, and outcomes were analyzed.

Results and Discussion: Neurological manifestations were most frequent in Group II (53.5%), followed by Group I (45.0%) and Group III (14.3%). Convulsions, decreased consciousness, headaches, and weakness were significantly more common in Group I. Inflammatory markers (CRP, Ddimer, ferritin, and troponin) were elevated in Groups I and II. Radiological abnormalities were infrequent, and neuroimaging findings showed no significant intergroup differences. Hospital stays were longest for Group I (mean 7.8 days), and mortality was highest in Group I (21.7%). Severe neurological symptoms correlated with adverse outcomes.

Conclusion: This study emphasizes the high prevalence of neurological symptoms in pediatric patients with COVID-19-associated MIS-C, especially in those with positive serology. Symptoms like convulsions, decreased consciousness, headaches, and weakness were more common, with severe cases linked to elevated ferritin and D-dimer levels and abnormal neuroimaging findings. It highlights the importance of thorough neurological assessments in these patients and calls for further research into the long-term neurological effects of COVID-19 in children.

Aims: This study aimed to control the spread of infectious diseases and offer safe flights. The government of Jordan has implemented various measures at its international airport during and after the COVID-19 pandemic. Such measures had a significant impact on travel behavior. While many travelers’ decisions were affected by the pandemic, it became a priority to assess their experiences at international airports and onboard during the health crisis to predict their future decisions.

Methods: The cross-sectional questionnaire was created through Google Forms and distributed to travelers through emails, social media channels, and official websites during the spread of COVID- 19. Questionnaires were distributed through a purposive sampling technique to flight travelers who departed from Queen Alia International Airport (QAIA) to different destinations. Finally, 61 responses were collected and analyzed through SPSS 23.

Results: This study revealed that most respondents agreed it was easy to get information about available flights, fares, and new travel regulations and restrictions, with an arithmetic mean of 4.02. In addition, results showed that airport staff were wearing face masks all the time, with an arithmetic mean of 4.39. However, the waiting time at the airport was incredibly long. In addition, responses illustrated that the airplane cabin was thoroughly cleaned and sterilized with an arithmetic mean of 4.05, but the meals were neither wholesome nor properly packaged.

Discussion: Risks perception at airport and onboard significantly influenced traveler behavior during the pandemic. Quality of services, safety protocols, and operational efficiency are crucial for restoring traveler confidence and preparing for future health crises.

Conclusion: The results of this study may help plan air travel based on the various experiences and levels of satisfaction of passengers. With this knowledge, stakeholders, health agencies, international airports, and airlines might make better plans for their safety management during pandemics.

Methods: A retrospective cross-sectional study was conducted involving adult patients diagnosed with COVID-19. Patient-related information was extracted from electronic medical records using standardized data collection forms. A univariate logistic regression model was employed to determine the factors associated with COVID-19 outcomes while controlling for confounders.

Results and Discussion: Among the 634 patients studied, 44.37% were over 60 years old, and women outnumbered men by 53.47%. Based on the available patient data, 53.5% had at least one underlying disease, and 15% of these patients died. Radiologically, a lung score greater than 50 was observed in 30.59% of all patients, multilobular infiltration (bilateral) in 71.29%, wide nodules in 3.31%, and ground-glass opacities in 75.87% of cases. The results of the univariate logistic regression indicated a significant association between age, lung score, laboratory biomarkers (including WBC, creatinine, neutrophils, LDH, CRP, ESR, lymphocytes, and monocytes), and the mortality rate among these patients.

Conclusion: This study found that older age and specific laboratory biomarkers significantly correlated with increased mortality among COVID-19 patients. Identifying these factors can facilitate the prompt diagnosis of patients at higher risk for unfavorable outcomes. Furthermore, the findings can provide essential insights for effective clinical management and informed decision-making regarding the health conditions of COVID-19 patients, particularly in cases involving disease severity.

Methods: A descriptive cross-sectional study was conducted using convenience sampling among 111 cancer patients admitted to the oncology units of a tertiary care center in central India. Data were collected through a well-validated and reliable structured questionnaire covering sociodemographic characteristics, stress, anxiety, depression, and challenges faced during the pandemic. Data were analyzed using the Statistical Package for the Social Sciences (SPSS) software (IBM Corp., Armonk, NY).

Results and Discussion: A high prevalence rate of moderate-to-severe stress (100%), anxiety (78.4%), and depression (64.9%) was reported among participants. The study identified several key challenges that affected patient care delivery during pandemics, including transportation issues, nonavailability of telecommunication services, difficulty in adhering to preventive measures, delays in surgery scheduling, and financial hardships due to lockdowns. Depression was significantly associated with transportation problems (p = 0.028) and surgery delays (p = 0.020), whereas anxiety was significantly associated with a lack of teleconsultation services (p = 0.014) and difficulty in maintaining preventive measures (p = 0.040).

Conclusion: This study highlights the complex difficulties experienced by cancer patients during COVID-19, emphasizing the need for improved healthcare strategies, including digital health interventions, such as teleconsultation, remote monitoring, and virtual psychological support, to ensure uninterrupted cancer care during public health crises. Addressing these challenges can help to mitigate the emotional burden on patients with cancer and improve overall treatment outcomes.

Materials and Methods: This work utilized four publicly available CT scan datasets: SARS-CoV-2- CTScan (Brazil), COVID-CTset (Iran), MosMedData (Russia), and BIMCV-COVID19+ (Spain), totaling 77,050 CT images from over 2,100 patients. The datasets were processed through various preprocessing and augmentation techniques, including CLAHE, noise filtering, and 3D transformations. The model’s segmentation was evaluated using the Dice Similarity Coefficient (DSC), while classification accuracy was measured using standard metrics like precision, recall, and F1-score.

Results: The proposed model achieved an average DSC of 0.91 across the datasets, with classification accuracy reaching 95.2%, 92.5%, 93.4%, and 94.1% for each dataset, respectively. Precision for COVID-19 detection was 96.4% on the SARS-CoV-2-CTScan dataset, with corresponding recall and F1-scores of 94.8% and 95.6%. Cross-dataset evaluations maintained consistency with an overall accuracy of 95.0% and a DSC of 0.90. Key novel metrics like the COVID-19 Severity Grading Index (97%) and Temporal Progression Index (96%) demonstrated the clinical relevance of the model.

Discussion: The model demonstrated exceptional generalization ability across diverse datasets and imaging conditions. Augmentation techniques, such as MixUp and CutMix, significantly improved model robustness, with a 14% reduction in Dice loss and a 7% improvement in classification accuracy for rare cases. The explainability of the model, through attention heatmaps and feature attribution, further enhanced its clinical applicability by providing transparency and aiding decision-making.

Conclusion: This study presents a robust deep-learning framework for COVID-19 detection from lung CT scans, demonstrating high accuracy, reliability, and interpretability across multiple datasets. Despite strong performance, there is room for refinement, particularly in handling noisy or atypical cases. The model is poised for clinical deployment, providing an effective tool for automated COVID-19 diagnosis and monitoring, with the potential for further improvement in real-world applications.

Materials and Methods: This correlational study was conducted on 247 patients during the COVID- 19 outbreak. The data collection tools included the demographic characteristics questionnaire, the Templer death anxiety scale (DAS), the coronavirus disease anxiety scale (CDAS), the trust in nurses scale, and the nurse quality of communication with patient questionnaire (NQCPQ).

Results and Discussion: Of the participants, 10.9% and 69.6% had high COVID-19 anxiety and death anxiety, respectively. There was no significant correlation between death anxiety, trust (r = -0.02; p = 0.73), and communication (r = -0.09; p = 0.13). A negative significant correlation was found between COVID-19 anxiety and communication (r = -0.26; p < 0.001), while a positive significant correlation was found between trust and communication (r = 0.35; p < 0.001).

Conclusion: The results of the present study showed that patients hospitalized during the COVID-19 outbreak experienced high levels of death anxiety and moderate levels of COVID-19 anxiety. These findings highlight the importance of providing professional help to patients to reduce their anxiety and improve the quality of communication with nurses during pandemics such as COVID-19.

Materials and Methods: In this study, the COVID-19 Chest X-Ray Database on Kaggle, which comprises 36,116 images classified as normal, viral pneumonia, and COVID-19, was employed. Largescale preprocessing operations, including the resizing, normalizing, and data augmentation operations, were carried out to generalize these models. Pretraining and fine-tuning the PVT model on the dataset included SSL, dropout regularization, and attention mechanisms. The primary metrics considered during the evaluation were the Measurement of Lung Severity Score (LSS), segmentation accuracy, Severity Detection Precision (SDP), Detection Sensitivity of Opacity (ODS), Time-to-Severity Detection (TSD), and focal AUC-ROC score.

Results: Fine-tuning the PVT model significantly improved performance across multiple metrics. LSS increased from 15% (pretrained) to 17% (fine-tuned), while segmentation accuracy improved from 88% to 91%. Dropout regularization slightly reduced LSS to 16% but enhanced SDP (80% to 90%) and ODS (78% to 85%). TSD decreased from 4.5s (pretrained) to 3.8s (fine-tuned), improving detection speed. The focal AUC-ROC score improved from 0.92 to 0.95 with fine-tuning and dropout, while the Misclassification Visualization Score (MVS) increased from 0.85 to 0.91, reducing misclassification rates. Data augmentation further enhanced accuracy (88% to 94%), precision (85% to 91%), and recall (83% to 90%).

Discussion: This study demonstrates the effectiveness of SSL pretraining, dropout regularization, and data augmentation in improving COVID-19 detection performance. The significant improvements in precision, recall, and robustness highlight the model's potential for clinical deployment. Attention map visualizations further enhance trust and interpretability by illustrating key lung regions that the model focuses on, ensuring transparency in decision-making.

Conclusion: The PVT-based model, integrated with SSL, fine-tuning, and attention mechanisms, provides a robust, interpretable, and efficient solution for COVID-19 detection from chest X-ray images. The results validate its potential for real-world clinical use, offering improved diagnostic accuracy, reduced misclassification, and enhanced detection speed.