Background. Youth populations are vulnerable to substance use particularly in developing countries where circumstances may be favorable for it. There is no published data on substance use among the youth in Sudan other than on tobacco use. Objectives. The aim of this study was to investigate the prevalence, circumstances, and factors associated with substance use. Methods. An institution-based survey was conducted on a sample of 500 students. Data was collected using a questionnaire designed by the WHO for student drug surveys and analyzed using IBM SPSS version 20. Results. The overall prevalence of substance use is 31%. The current prevalence of tobacco, cannabis, alcohol, amphetamines, tranquilizers, inhalants, opiates, cocaine, and heroin use was 13.7%, 4.9%, 2.7%, 2.4%, 3.2%, 1%, 1.2%, 0.7%, and 0.5%, respectively. Curiosity (33.1%) was the main reason for initiation of substance use. The main adverse effects reported were health problems (19.7%) and theft (19.7%). Peers (40.9%) were the prime source of substance use. On multivariate analysis, male sex was the principle predictor for substance use (AOR: 5.55; 95% CI: 3.38, 9.17). Conclusion. Strategies to control substance use should encompass the role of the university and parents in observing and providing education to improve awareness of substances and their consequences.

High-flow nasal cannula (HFNC) is an open oxygen delivery system, which provides heated and humidified oxygen at a high flow (up to 60 L/min). This effect can improve mucociliary function, airway clearance, and level of comfort to the patient. It can provide controlled and adequate fraction of inspired oxygen (FiO 2 ) between 21% and 100%. Generation of end-expiratory pressure helps in carbon dioxide washout, reduction of anatomical dead space, and recruitment of collapsed alveoli, ultimately improving tissue oxygenation. The use of HFNC in acute hypoxemic respiratory failure, post-extubation period, pre-intubation period, respiratory infection, and obstructive airway disease has been extensively studied, but there are very few studies regarding its use in cardiogenic pulmonary edema. This review provides the current understanding of the physiological effect of HFNC and its application in acute cardiogenic pulmonary edema (ACPE). We conducted a literature search on PubMed using appropriate terms and reviewed relevant articles published within the last 10 years. We found that initial therapy with HFNC in ACPE patients can improve oxygenation and respiratory rate. HFNC can potentially be an alternative to non-invasive positive-pressure ventilation in terms of initial oxygen therapy in patients with ACPE. There is a need for larger prospective studies to evaluate and develop guidelines to consider the use of HFNC in patients with ACPE. We also highlight the fact that if there is no improvement in arterial blood gas parameters after HFNC therapy, initiation of invasive ventilation should not be delayed.

Introduction: Acute kidney injury is a common cause of morbidity after congenital heart disease surgery. Progress on diagnosis and therapy remains limited, however, in part due to poor mechanistic understanding and lack of relevant translational models. Metabolomic approaches could help identify novel mechanisms of injury and potential therapeutic targets. Methods: We used a piglet model of cardiopulmonary bypass with deep hypothermic circulatory arrest (CPB/DHCA) and targeted metabolic profiling of kidney tissue, urine, and serum to evaluate metabolic changes specific to animals with histologic acute kidney injury. CPB/DHCA animals with acute kidney injury were compared to those without acute kidney injury and mechanically ventilated controls. Results: Acute kidney injury occurred in 10/20 CPB/DHCA animals 4hrs after CPB/DHCA and 0/7 control animals. Injured kidneys showed a distinct tissue metabolic profile compared to uninjured kidneys (R2=0.90, Q2=0.52), with evidence of dysregulated tryptophan and purine metabolism. Nine urine metabolites differed significantly in animals with acute kidney injury with a pattern suggestive of increased aerobic glycolysis. Dysregulated metabolites in kidney tissue and urine did not overlap. CPB/DHCA strongly affected the serum metabolic profile, with only one metabolite that differed significantly with acute kidney injury (pyroglutamic acid, a marker of oxidative stress). Conclusions: Based on these findings, kidney tryptophan and purine metabolism are candidates for further mechanistic and therapeutic investigation. Urine biomarkers of aerobic glycolysis could help diagnose early acute kidney injury after CPB/DHCA and warrant further evaluation. The serum metabolites measured at this early time point did not strongly differentiate based on acute kidney injury.

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Acute respiratory distress syndrome (ARDS) accounts for 10% of all diagnoses in the Intensive Care Unit, and about 40% of the patients succumb to the disease. Clinical methods alone can result in the under-recognition of this heterogeneous syndrome. The purpose of this study is to evaluate the role that big data and machine learning (ML) have played in understanding the heterogeneity of the disease and the development of various prediction algorithms. Most of the work in the field of ML in ARDS has been in the development of prediction models that have comparable efficacies to that of traditional models. Prediction algorithms have been useful in identifying new variables that may be important to consider in the future, supplementing the unknown information with the help of available noninvasive parameters, as well as predicting mortality. Phenotype identification using an unsupervised ML algorithm has been pivotal in classifying the heterogeneous population into more homogenous classes. Big data generated from ventilators in the form of ventilator waveform analysis and images in the form of radiomics have also been leveraged for the identification of the syndrome and can be incorporated into a clinical decision support system. Although the results are promising, lack of generalizability, “black box” nature of algorithms and concerns about “alarm fatigue” should be addressed for more mainstream adoption of these models.