Explore the vast range of titles available.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a severe pandemic of the current century. The vicious tentacles of the disease have been disseminated worldwide with unknown complications and repercussions. Advanced COVID-19 syndrome is characterized by the uncontrolled and elevated release of pro-inflammatory cytokines and suppressed immunity, leading to the cytokine storm. The uncontrolled and dysregulated secretion of inflammatory and pro-inflammatory cytokines is positively associated with the severity of the viral infection and mortality rate. The secretion of various pro-inflammatory cytokines such as TNF-α, IL-1, and IL-6 leads to a hyperinflammatory response by recruiting macrophages, T and B cells in the lung alveolar cells. Moreover, it has been hypothesized that immune cells such as macrophages recruit inflammatory monocytes in the alveolar cells and allow the production of large amounts of cytokines in the alveoli, leading to a hyperinflammatory response in severely ill patients with COVID-19. This cascade of events may lead to multiple organ failure, acute respiratory distress, or pneumonia. Although the disease has a higher survival rate than other chronic diseases, the incidence of complications in the geriatric population are considerably high, with more systemic complications. This review sheds light on the pivotal roles played by various inflammatory markers in COVID-19-related complications. Different molecular pathways, such as the activation of JAK and JAK/STAT signaling are crucial in the progression of cytokine storm; hence, various mechanisms, immunological pathways, and functions of cytokines and other inflammatory markers have been discussed. A thorough understanding of cytokines’ molecular pathways and their activation procedures will add more insight into understanding immunopathology and designing appropriate drugs, therapies, and control measures to counter COVID-19. Recently, anti-inflammatory drugs and several antiviral drugs have been reported as effective therapeutic drug candidates to control hypercytokinemia or cytokine storm. Hence, the present review also discussed prospective anti-inflammatory and relevant immunomodulatory drugs currently in various trial phases and their possible implications.

Background Air pollution is a serious environmental factor associated with higher rates of illness and death from respiratory, cardiovascular, and other non-communicable diseases. Accurate prediction is vital for assessing health risks, which would guide the public health experts and shape sustainable policies. Aim This study aims to provide a modelling approach by comparing traditional, non-parametric, and machine learning based hybrid models, the first of its kind to forecast PM 2.5 levels across the Gulf Cooperation Council (GCC) nations, to inform data-driven environmental health strategies and public policy development. Method The study utilized the annual PM 2.5 dataset from the World Bank database for GCC countries covering the span of 1991–2021. The traditional models, like ARIMA, Naïve, exponential smoothing, non-parametric model, NPAR, and machine learning based hybrid models, were applied. The model accuracy was evaluated by RMSE, MAE, MAPE, nRMSE, and Diebold–Mariano (DM) test. A Rolling Cross-validation procedure was performed to validate the models. Key results The study showed that Qatar consistently showed the highest PM 2.5 levels at 87.90 ± 5.78 µg/m³, followed by Bahrain (67.42 ± 4.59 µg/m³) and Kuwait (58.00 ± 5.95 µg/m³). The modelling approach concluded that machine learning based hybrid models performed well across all competing models, with NPAR-NNAR showing the lowest RMSE (KSA = 2.0181, Qatar = 2.2852, Bahrain = 1.3145, and Kuwait = 2.299), while NAIVE-NNAR performed best for UAE = 1.0462 and Oman = 1.6522. The forecasting results showed that GCC countries may experience variations in PM2.5 levels, with Qatar and Bahrain facing the highest concentrations among them in the coming decades. Major Implication This is the first multi-country study across the GCC to forecast PM 2.5 , showing a significant step forward for environmental health planning. The higher accuracy of modelling approaches is important for improving early warning capabilities, anticipating pollution trends that directly affect respiratory and cardiovascular health outcomes.

Artificial intelligence (AI) is a branch of science and engineering that focuses on the computational understanding of intelligent behavior. Many human professions, including clinical diagnosis and prognosis, are greatly useful from AI. Antimicrobial resistance (AMR) is among the most critical challenges facing Pakistan and the rest of the world. The rising incidence of AMR has become a significant issue, and authorities must take measures to combat the overuse and incorrect use of antibiotics in order to combat rising resistance rates. The widespread use of antibiotics in clinical practice has not only resulted in drug resistance but has also increased the threat of super-resistant bacteria emergence. As AMR rises, clinicians find it more difficult to treat many bacterial infections in a timely manner, and therapy becomes prohibitively costly for patients. To combat the rise in AMR rates, it is critical to implement an institutional antibiotic stewardship program that monitors correct antibiotic use, controls antibiotics, and generates antibiograms. Furthermore, these types of tools may aid in the treatment of patients in the event of a medical emergency in which a physician is unable to wait for bacterial culture results. AI’s applications in healthcare might be unlimited, reducing the time it takes to discover new antimicrobial drugs, improving diagnostic and treatment accuracy, and lowering expenses at the same time. The majority of suggested AI solutions for AMR are meant to supplement rather than replace a doctor’s prescription or opinion, but rather to serve as a valuable tool for making their work easier. When it comes to infectious diseases, AI has the potential to be a game-changer in the battle against antibiotic resistance. Finally, when selecting antibiotic therapy for infections, data from local antibiotic stewardship programs are critical to ensuring that these bacteria are treated quickly and effectively. Furthermore, organizations such as the World Health Organization (WHO) have underlined the necessity of selecting the appropriate antibiotic and treating for the shortest time feasible to minimize the spread of resistant and invasive resistant bacterial strains.

Saudi Arabia increased spending on research and development from$4.6 Billion to $ 6.5 Billion between 2010 and 2013.2 Three years ago, the 2030 vision had been established, and one of the aims of this vision is to expand research activity, and to improve the country’s position in the scientific world.3 One of the crucial objectives that Saudi 2030 vision tries to accomplish is to improve the Saudi rank to be on the top 10 countries in Global Competitiveness Index.3 Research and development are 2 of the important subjects that can increase the Saudi position in science. According to SCimago Journal Ranking (SJR) there are 21 Saudi indexed journals, and the top 10 journals are indicated in Table 1. There should be support from the private sector in health care and education to reach the vision’s goal. [...]public and private sectors should work on creating more Saudi journals in different fields and attract local and international researchers to publish in Saudi journals.

Epidemiological features characterization of COVID-19 is highly important for developing and implementing effective control measures. In Saudi Arabia mortality rate varies between 0.6% to 1.26%. The purpose of the study was to investigate whether demographic characteristics (age and gender) and non-communicable diseases (Hypertension and Diabetes mellitus) have a significant association with mortality in COVID-19 patients. Prior to data collection, an expedite approval was obtained from Institutional Review Board (IRB Log No: RC. RC20.09.10) in Al Habib Research Center at Dr. Sulaiman Al-Habib Medical Group, Riyadh, Saudi Arabia. This is a retrospective design where we used descriptive and inferential analysis to analyse the data. Binary logistic regression was done to study the association between comorbidities and mortality of COVID-19. 43 (86%) of the male patients were non-survivors while 7 (14%) of the female patients were survivors. The odds of non-survivors among hypertensive patients are 3.56 times higher than those who are not having a history of Hypertension (HTN). The odds of non-survivors among diabetic patients are 5.17 times higher than those who are not having a history of Diabetes mellitus (DM). The odds of non-survivors are 2.77 times higher among those who have a history of HTN and DM as compared to those who did not have a history of HTN and DM. Those patients that had a history of Hypertension and Diabetes had a higher probability of non-survival in contrast to those who did not have a history of Diabetes and hypertension. Further studies are required to study the association of comorbidities with COVID-19 and mortality.

This study aims to evaluate the overall emotional wellbeing and emotional predictors of the Saudi population during COVID-19. A cross-sectional design was employed; the data were collected by using the Arabic version of the Mental Health Inventory. A total of 5041 participants were successfully recruited over 1 week. The participants scored moderately on Anxiety, Depression, Loss of Behavioral/Emotional Control, General Positive Affect, Emotional Ties, and Life Satisfaction. The results indicated that age, gender, marital status, socioeconomic status, and having chronic health conditions are major predictors of emotional wellbeing during the COVID-19 pandemic. A rehabilitation program should be initiated to restore the community function and the wellbeing of individuals who have been impacted by the COVID-19 pandemic.

The SARS-CoV-2 virus, which caused the COVID-19 infection, was discovered two and a half years ago. It caused a global pandemic, resulting in millions of deaths and substantial damage to the worldwide economy. Currently, only a few vaccines and antiviral drugs are available to combat SARS-CoV-2. However, there has been an increase in virus-related research, including exploring new drugs and their repurposing. Since discovering penicillin, natural products, particularly those derived from microbes, have been viewed as an abundant source of lead compounds for drug discovery. These compounds treat bacterial, fungal, parasitic, and viral infections. This review incorporates evidence from the available research publications on isolated and identified natural products derived from microbes with anti-hepatitis, anti-herpes simplex, anti-HIV, anti-influenza, anti-respiratory syncytial virus, and anti-SARS-CoV-2 properties. About 131 compounds with in vitro antiviral activity and 1 compound with both in vitro and in vivo activity have been isolated from microorganisms, and the mechanism of action for some of these compounds has been described. Recent reports have shown that natural products produced by the microbes, such as aurasperone A, neochinulin A and B, and aspulvinone D, M, and R, have potent in vitro anti-SARS-CoV-2 activity, targeting the main protease (Mpro). In the near and distant future, these molecules could be used to develop antiviral drugs for treating infections and preventing the spread of disease.

Acne vulgaris is a common skin condition that significantly impacts both physical appearance and mental well-being. Acne, being a chronic skin condition, often requires continuous treatment. This study aims to evaluate the efficacy and safety of clindamycin phosphate 1.2%/benzoyl peroxide 3% compared to clindamycin phosphate 1.2%/adapalene 0.1% combinations for treating acne vulgaris. A systematic review and meta-analysis of randomized controlled trials were carried out following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, and three databases were searched to identify RCTs comparing CLIN/BPO with CLIN/ADAP. Primary outcomes included treatment-emergent adverse events, inflammatory and non-inflammatory lesion counts, and application site side effects. Statistical analyses were conducted using RevMan 5.3. The study included a total of 800 participants across three RCTs. The meta-analysis of three RCTs demonstrated a significantly lower risk of TEAEs with CLIN/BPO (OR = 0.49, 95% CI: 0.35–0.86, p  < 0.001). CLIN/BPO also resulted in fewer application site side effects (OR = 0.33, 95% CI: 0.23–0.47, p  < 0.001). However, no significant differences were observed between the groups for reducing inflammatory (MD = 1.34, 𝑝 = 0.121) or non-inflammatory lesion counts (MD = 0.04, 𝑝 = 0.98). The study concluded that although CLIN/BPO was associated with fewer side effects, both treatments were equally effective in reducing acne lesions. The favorable safety profile of CLIN/BPO, particularly regarding treatment-emergent and application-site adverse events, suggests it may be the more tolerable option for patients. Future studies with larger, more diverse populations are recommended to confirm these findings and explore long-term efficacy.

As medical science and technology progress towards the era of “big data”, a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC’s diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.

Tuberculosis (TB) caused by the bacterial pathogen Mycobacterium tuberculosis (Mtb) remains a threat to mankind, with over a billion of deaths in the last two centuries. Recent advancements in science have contributed to an understanding of Mtb pathogenesis and developed effective control tools, including effective drugs to control the global pandemic. However, the emergence of drug resistant Mtb strains has seriously affected the TB eradication program around the world. There is, therefore, an urgent need to develop new drugs for TB treatment, which has grown researchers’ interest in small molecule-based drug designing and development. The small molecules-based treatments hold significant potential to overcome drug resistance and even provide opportunities for multimodal therapy. In this context, various natural and synthetic flavonoids were reported for the effective treatment of TB. In this review, we have summarized the recent advancement in the understanding of Mtb pathogenesis and the importance of both natural and synthetic flavonoids against Mtb infection studied using in vitro and in silico methods. We have also included flavonoids that are able to inhibit the growth of non-tubercular mycobacterial organisms. Hence, understanding the therapeutic properties of flavonoids can be useful for the future treatment of TB.