Heat Stroke in Emergency Department: Diagnosis and Management

Background: Heat stroke is a severe health concern with the potential for multi-organ failure, necessitating rapid and effective management. With rising global temperatures, there is increasing concern regarding the vulnerability of populations in high-heat areas, notably in Saudi Arabia, especially during the annual Hajj pilgrimage. Objective: This paper aims to review the epidemiology, evaluation and management techniques of heat stroke, emphasizing the situation during Hajj pilgrimages in Saudi Arabia, and to outline the best practices for emergency management. Methodology: A comprehensive review of literature and studies related to heat stroke, both globally and specific to Saudi Arabia, was undertaken. An in-depth analysis of emergency management, including initial assessment, cooling methods, organ support, medication, and prevention strategies, was conducted. Results: Heat stroke remains a significant cause of emergency department visits, with specific groups, such as men and the elderly, being more susceptible. During the Hajj in 2016, 267 patients were diagnosed with heat-related illnesses, with heatstroke accounting for 29% of these cases. With the threat of global warming, studies indicate a potential tenfold increase in heat stroke risk with a 2°C rise in temperatures. Swift and comprehensive cooling is pivotal for recovery. Management emphasizes rapid recognition, assessment, and varied cooling methods, along with targeted treatments for organ dysfunctions. Prevention strategies play a vital role, given the higher efficacy and practicality over treating organ dysfunctions. Conclusion: Heat stroke is a pressing health challenge, particularly in high-risk environments like Saudi Arabia during the Hajj pilgrimage. While effective emergency management protocols exist, an emphasis on prevention is crucial. It is imperative to incorporate a comprehensive approach to address both the immediate threat and long-term risks of heat stroke, especially with the looming challenge of global warming

Unveiling the therapeutic potential of exogenous β-hydroxybutyrate for chronic colitis in rats: novel insights on autophagy, apoptosis, and pyroptosis

Ulcerative colitis (UC) is a chronic relapsing inflammatory disease of the colorectal area that demonstrates a dramatically increasing incidence worldwide. This study provides novel insights into the capacity of the exogenous β-hydroxybutyrate and ketogenic diet (KD) consumption to alleviate dextran sodium sulfate (DSS)-induced UC in rats. Remarkably, both interventions attenuated disease activity and colon weight-to-length ratio, and improved macro and microstructures of the damaged colon. Importantly, both β-hydroxybutyrate and KD curbed the DSS-induced aberrant NLRP3 inflammasome activation as observed in mRNA and protein expression analysis. Additionally, inhibition of the NLRP3/NGSDMD-mediated pyroptosis was detected in response to both regimens. In parallel, these modalities attenuated caspase-1 and its associated consequences of IL-1β and IL-18 overproduction. They also mitigated apoptosis as indicated by the inactivation of caspase-3. The anti-inflammatory effects of BHB and KD were confirmed by the reported decline in the levels of inflammatory markers including MPO, NFκB, IL-6, and TNF-α. Moreover, these interventions exhibited antioxidative properties by reducing ROS production and improving antioxidative enzymes. Their effectiveness in mitigating UC was also evident in the renovation of normal intestinal epithelial barrier function, as shown by correcting the discrepancies in the levels of tight junction proteins ZO-1, OCLN, and CLDN5. Furthermore, their effects on the intestinal microbiota homeostasis were investigated. In terms of autophagy, exogenous β-hydroxybutyrate upregulated BECN-1 and downregulated p62, which may account for its superiority over KD in attenuating colonic damage. In conclusion, this study provides experimental evidence supporting the potential therapeutic use of β-hydroxybutyrate or β-hydroxybutyrate-boosting regimens in UC

Fuzzy Neural Network Expert System with an Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm for Early Diagnosis of Breast Cancer in Saudi Arabia

Breast cancer is one of the common malignancies among females in Saudi Arabia and has also been ranked as the one most prevalent and the number two killer disease in the country. However, the clinical diagnosis process of any disease such as breast cancer, coronary artery diseases, diabetes, COVID-19, among others, is often associated with uncertainty due to the complexity and fuzziness of the process. In this work, a fuzzy neural network expert system with an improved gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia was proposed to address the uncertainty and ambiguity associated with the diagnosis of breast cancer and also the heavier burden on the overlay of the network nodes of the fuzzy neural network system that often happens due to insignificant features that are used to predict or diagnose the disease. An Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm was used to select the five fittest features of the diagnostic wisconsin breast cancer database out of the 32 features of the dataset. The logistic regression, support vector machine, k-nearest neighbor, random forest, and gaussian naïve bayes learning algorithms were used to develop two sets of classification models. Hence, the classification models with full features (32) and models with the 5 fittest features. The two sets of classification models were evaluated, and the results of the evaluation were compared. The result of the comparison shows that the models with the selected fittest features outperformed their counterparts with full features in terms of accuracy, sensitivity, and sensitivity. Therefore, a fuzzy neural network based expert system was developed with the five selected fittest features and the system achieved 99.33% accuracy, 99.41% sensitivity, and 99.24% specificity. Moreover, based on the comparison of the system developed in this work against the previous works that used fuzzy neural network or other applied artificial intelligence techniques on the same dataset for diagnosis of breast cancer using the same dataset, the system stands to be the best in terms of accuracy, sensitivity, and specificity, respectively. The z test was also conducted, and the test result shows that there is significant accuracy achieved by the system for early diagnosis of breast cancer

Fuzzy Neural Network Expert System with an Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm for Early Diagnosis of Breast Cancer in Saudi Arabia

Breast cancer is one of the common malignancies among females in Saudi Arabia and has also been ranked as the one most prevalent and the number two killer disease in the country. However, the clinical diagnosis process of any disease such as breast cancer, coronary artery diseases, diabetes, COVID-19, among others, is often associated with uncertainty due to the complexity and fuzziness of the process. In this work, a fuzzy neural network expert system with an improved gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia was proposed to address the uncertainty and ambiguity associated with the diagnosis of breast cancer and also the heavier burden on the overlay of the network nodes of the fuzzy neural network system that often happens due to insignificant features that are used to predict or diagnose the disease. An Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm was used to select the five fittest features of the diagnostic wisconsin breast cancer database out of the 32 features of the dataset. The logistic regression, support vector machine, k-nearest neighbor, random forest, and gaussian naïve bayes learning algorithms were used to develop two sets of classification models. Hence, the classification models with full features (32) and models with the 5 fittest features. The two sets of classification models were evaluated, and the results of the evaluation were compared. The result of the comparison shows that the models with the selected fittest features outperformed their counterparts with full features in terms of accuracy, sensitivity, and sensitivity. Therefore, a fuzzy neural network based expert system was developed with the five selected fittest features and the system achieved 99.33% accuracy, 99.41% sensitivity, and 99.24% specificity. Moreover, based on the comparison of the system developed in this work against the previous works that used fuzzy neural network or other applied artificial intelligence techniques on the same dataset for diagnosis of breast cancer using the same dataset, the system stands to be the best in terms of accuracy, sensitivity, and specificity, respectively. The z test was also conducted, and the test result shows that there is significant accuracy achieved by the system for early diagnosis of breast cancer

Spectroscopic and Molecular Docking Analysis of π-Acceptor Complexes with the Drug Barbital

The drug barbital (Bar) has a strong sedative–hypnotic effect. The intermolecular charge transfer compounds associated with the chemical reactions between Bar and some π acceptors, such as 2,6-dibromoquinone-4-chloroimide (DBQ), tetracyanoquinodimethane (TCNQ), chloranil (CHL), and chloranilic acid (CLA), have been synthesized and isolated in solid state. The synthesized products have the molecular formulas (Bar–DBQ), (Bar–TCNQ), (Bar–CHL), and (Bar–CLA) with 1:1 stoichiometry based on Raman, IR, TG, 1H NMR, XRD, SEM, and UV-visible analysis techniques. Additionally, the comparative analysis of molecular docking between the donor reactant moiety, Bar, and its four CT complexes was conducted using two neurotransmitter receptors (dopamine and serotonin). The docking results obtained from AutoDockVina software were investigated by a molecular dynamics simulation technique with 100ns run. The molecular mechanisms behind receptor–ligand interactions were also looked into. The DFT computations were conducted using theory at the B3LYP/6-311G++ level. In addition, the HOMO LUMO electronic energy gap and the CT complex’s optimal geometry and molecule electrostatic potential were examined

Multipotent Cholinesterase Inhibitors for the Treatment of Alzheimer’s Disease: Synthesis, Biological Analysis and Molecular Docking Study of Benzimidazole-Based Thiazole Derivatives

Twenty-four analogues of benzimidazole-based thiazoles (1–24) were synthesized and assessed for their in vitro acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitory potential. All analogues were found to exhibit good inhibitory potential against cholinesterase enzymes, having IC50 values in the ranges of 0.10 ± 0.05 to 11.10 ± 0.30 µM (for AChE) and 0.20 ± 0.050 µM to 14.20 ± 0.10 µM (for BuChE) as compared to the standard drug Donepezil (IC50 = 2.16 ± 0.12 and 4.5 ± 0.11 µM, respectively). Among the series, analogues 16 and 21 were found to be the most potent inhibitors of AChE and BuChE enzymes. The number (s), types, electron-donating or -withdrawing effects and position of the substituent(s) on the both phenyl rings B & C were the primary determinants of the structure-activity relationship (SAR). In order to understand how the most active derivatives interact with the amino acids in the active site of the enzyme, molecular docking studies were conducted. The results obtained supported the experimental data. Additionally, the structures of all newly synthesized compounds were elucidated by using several spectroscopic methods like 13C-NMR, 1H-NMR and HR EIMS

Ganetespib (STA-9090) augments sorafenib efficacy via necroptosis induction in hepatocellular carcinoma: Implications from preclinical data for a novel therapeutic approach

Sorafenib, a multikinase inhibitor, is a first-line treatment for advanced hepatocellular carcinoma, but its long-term effectiveness is limited by the emergence of resistance mechanisms. One such mechanism is the reduction of microvessel density and intratumoral hypoxia caused by prolonged sorafenib treatment. Our research has demonstrated that HSP90 plays a critical role in conferring resistance to sorafenib in HepG2 cells under hypoxic conditions and N-Nitrosodiethylamine-exposed mice as well. This occurs through the inhibition of necroptosis on the one hand and the stabilization of HIF-1α on the other hand. To augment the effects of sorafenib, we investigated the use of ganetespib, an HSP90 inhibitor. We found that ganetespib activated necroptosis and destabilized HIF-1α under hypoxia, thus enhancing the effectiveness of sorafenib. Additionally, we discovered that LAMP2 aids in the degradation of MLKL, which is the mediator of necroptosis, through the chaperone-mediated autophagy pathway. Interestingly, we observed a significant negative correlation between LAMP2 and MLKL. These effects resulted in a reduction in the number of surface nodules and liver index, indicating a regression in tumor production rates in mice with HCC. Furthermore, AFP levels decreased. Combining ganetespib with sorafenib showed a synergistic cytotoxic effect and resulted in the accumulation of p62 and inhibition of macroautophagy. These findings suggest that the combined therapy of ganetespib and sorafenib may offer a promising approach for the treatment of hepatocellular carcinoma by activating necroptosis, inhibiting macroautophagy, and exhibiting a potential antiangiogenic effect. Overall, continued research is critical to establish the full therapeutic potential of this combination therapy

The prevalence of sedentary behavior among university students in Saudi Arabia

Abstract Background A considerable body of research has demonstrated that reducing sitting time benefits health. Therefore, the current study aimed to explore the prevalence of sedentary behavior (SB) and its patterns. Methods A total of 6975 university students (49.1% female) were chosen randomly to participate in a face-to-face interview. The original English version of the sedentary behavior questionnaire (SBQ) was previously translated into Arabic. Then, the validated Arabic version of the SBQ was used to assess SB. The Arabic SBQ included 9 types of SB (watching television, playing computer/video games, sitting while listening to music, sitting and talking on the phone, doing paperwork or office work, sitting and reading, playing a musical instrument, doing arts and crafts, and sitting and driving/riding in a car, bus or train) on weekdays and weekends. Results SBQ indicated that the total time of SB was considerably high (478.75 ± 256.60 and 535.86 ± 316.53 (min/day) during weekdays and weekends, respectively). On average, participants spent the most time during the day doing office/paperwork (item number 4) during weekdays (112.47 ± 111.11 min/day) and weekends (122.05 ± 113.49 min/day), followed by sitting time in transportation (item number 9) during weekdays (78.95 ± 83.25 min/day) and weekends (92.84 ± 100.19 min/day). The average total sitting time of the SBQ was 495.09 ± 247.38 (min/day) and 58.4% of the participants reported a high amount of sitting time (≥ 7 hours/day). Independent t-test showed significant differences (P ≤ 0.05) between males and females in all types of SB except with doing office/paperwork (item number 4). The results also showed that male students have a longer daily sitting time (521.73 ± 236.53 min/day) than females (467.38 ± 255.28 min/day). Finally, 64.1% of the males reported a high amount of sitting time (≥ 7 hours/day) compared to females (52.3%). Conclusion In conclusion, the total mean length of SB in minutes per day for male and female university students was considerably high. About 58% of the population appeared to spend ≥7 h/day sedentary. Male university students are likelier to sit longer than female students. Our findings also indicated that SB and physical activity interventions are needed to raise awareness of the importance of adopting an active lifestyle and reducing sitting time

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population