Impact of vaccination on morbidity and mortality in adults hospitalized with COVID-19 during the omicron wave in the Jazan Region, Saudi Arabia.

OBJECTIVES: To evaluate the impact of coronavirus disease-19 (COVID-19) vaccination on morbidity and mortality in adults hospitalized with COVID-19 during the omicron wave in the Jazan Region, Saudi Arabia. METHODS: A 6-month record-based historical prospective study enrolled COVID-19 adult patients admitted between January and June 2022. Individuals were classified into 3 groups according to their immunity status (immunized, partially immunized, and not immunized). Death, intensive care unit (ICU) admission, and mechanical ventilation were identified as the primary outcomes, collectively referred to as "serious outcomes". On the other hand, the length of hospital stays longer than 5 days was categorized as a secondary outcome. Multiple logistic regression analysis was used to evaluate independent factors and the relationship between the outcomes and vaccination status. RESULTS: Among the 634 COVID-19 patients admitted to Jazan hospitals, 46.4% were fully immunized, 19.7% were partially immunized, and 33.9% were not immunized. Not being immunized was significantly associated with ICU admission (odds ratio [OR]=1.91, 95% confidence interval [CI]: [1.17-3.11]; p=0.009), mechanical ventilation (OR=2.11, 95% CI: [1.25-3.56]; p=0.005), increased length of hospital stays (OR=1.79, 95% CI: [1.24-2.59]; p=0.002), and death (OR=3.03, 95% CI: [1.85-4.98]; p<0.001). CONCLUSION: Our study underscores the importance of a comprehensive approach for managing COVID-19 patients that includes vaccination against the disease.

Sturge-Weber syndrome with massive macroglossia and anterior neck space infection- a case report and review of literature.

Sturge- Weber syndrome (SWS), is a rare neuro-cutaneous angiomatosis which affects male and females alike. The clinical manifestations include angiomas, haemangiomas of the lips, tongue and palatine region. The oral manifestations are usually unilateral and are susceptible to bleed. Patients can also present with macroglossia and maxillary bone hypertrophy which can lead to malocclusion of the oral cavity. Food accumulation due to occlusion can cause growth of bacteria which can intensify infections and can cause gingival hyperplasia. A case of a middle-aged 39 year old female was reported in the Ziauddin Hospital, Karachi on 2nd of February,2022 with the presenting complaints of intermittent fever and drowsiness for 10 days. On examination she had massive tongue enlargement, drooling, malocclusion, difficulty in eating and breathing. She was a known case of Sturgeweber syndrome. Based on the clinical and radiological findings, she was managed along the lines of prelaryngeal soft tissue and submandibular infection.

COVID-19 Pandemic Lessons for Creating Effective Mental Health Safety Nets in Lower Middle-Income Countries.

The coronavirus disease 2019 (COVID-19) pandemic has posed unprecedented challenges to mental health globally, particularly in low- and middle-income countries (LMICs) such as Pakistan. This narrative review aims to synthesize the literature on the impact of the pandemic on mental health in LMICs, the challenges and opportunities for mental health system reform, and the role of safety nets in promoting mental health. A comprehensive search was conducted in several electronic databases, resulting in 35 articles being included for review. Data were extracted and analyzed to identify key themes and trends. The COVID-19 pandemic has led to a significant increase in the prevalence of mental health problems in LMICs, particularly anxiety and depression. This burden is disproportionately borne by vulnerable populations, including women, front-line workers, and those living in poverty. The pandemic has highlighted pre-existing weaknesses in mental health systems in LMICs, including inadequate funding, lack of trained mental health professionals, and stigmatization of mental illness. However, it has also presented opportunities for reform, such as increased awareness and political will, and the use of technology to expand access to mental health services. Building effective safety nets, including social protection programs and community-based interventions, can promote mental health and address social determinants of mental illness. The COVID-19 pandemic has underscored the urgent need for mental health system reform and the development of effective safety nets in LMICs. Policymakers should prioritize investment in mental health and address the social determinants of mental illness to build more resilient societies.

A Survey of Traumatic Brain Injuries from Road Traffic Collisions in a Lower Middle-Income Country.

The burden of traumatic brain injury (TBI) from road traffic collisions (RTCs) is great in low-and middle-income countries (LMICs) due to shortfalls in preventative measures, and the lack of relevant, accurate data collection. To address this gap, we sought to study the epidemiology of TBI from RTCs in two LMIC neurosurgical centres in order to identify factors amenable to preventative strategies. A prospective survey of all adult and paediatric cases of TBI from RTCs admitted to Northwest General Hospital (NWGH) and Hayatabad Medical Complex (HMC) over a four-week period was carried out. Data on patient demographics, risk factors, injury details, pre-hospitalisation details, admission details and post-acute care was collected and analysed. A total of 68 patients were included in the study. 18 (26%) of the patients were male and in the 30 to 39 age group. Fifty-two percent were two-wheeler riders and/or passengers. 51 (75%) of the RTCs occurred between 12 noon and 12 midnight and in rural areas (66.2%). The most commonly documented risk factor that led to the RTC was speeding (35.3%). Pre-hospital care was either absent or undocumented. Up to two-thirds of patients were not direct transfers, and most were transported in private vehicles (48.5%) arriving later than an hour after injury (94.1%). Less than half with documented disabilities were referred for rehabilitation (38.5%). There are still gaps in the prevention of TBI from RTCs and in relevant data collection. Data collection systems must be strengthened, and further exploratory research carried out in order to improve the prevention of TBI from RTCs.

An Insight into the Machine-Learning-Based Fileless Malware Detection.

In recent years, massive development in the malware industry changed the entire landscape for malware development. Therefore, cybercriminals became more sophisticated by advancing their development techniques from file-based to fileless malware. As file-based malware depends on files to spread itself, on the other hand, fileless malware does not require a traditional file system and uses benign processes to carry out its malicious intent. Therefore, it evades conventional detection techniques and remains stealthy. This paper briefly explains fileless malware, its life cycle, and its infection chain. Moreover, it proposes a detection technique based on feature analysis using machine learning for fileless malware detection. The virtual machine acquired the memory dumps upon executing the malicious and non-malicious samples. Then the necessary features are extracted using the Volatility memory forensics tool, which is then analyzed using machine learning classification algorithms. After that, the best algorithm is selected based on the k-fold cross-validation score. Experimental evaluation has shown that Random Forest outperforms other machine learning classifiers (Decision Tree, Support Vector Machine, Logistic Regression, K-Nearest Neighbor, XGBoost, and Gradient Boosting). It achieved an overall accuracy of 93.33% with a True Positive Rate (TPR) of 87.5% at zeroFalse Positive Rate (FPR) for fileless malware collected from five widely used datasets (VirusShare, AnyRun, PolySwarm, HatchingTriage, and JoESadbox).