SOUTHERN JOURNAL OF SCIENCES

Diabetes mellitus (DM) is a clinical disease correlated with a deficiency of insulin secretion or action.It is one of the leading causes of morbidity and mortality worldwide. The global burden of diabetes is rising dueto increasing obesity and population aging. Urinary tract infections (UTI) are common microbial infections knownto affect the different parts of the urinary tract accounting for major antibacterial drug consumption. About 150million UTI cases were diagnosed every year. Urinary tract infections are the most important and most commonsite of infections in a diabetic patient. Diabetic patients have been found to have a 5-fold frequency of acutepyelonephritis at autopsy than non-diabetics. Most of the urinary tract infections in patients with diabetes arerelatively asymptomatic. The presence of this syndrome predisposes to much more severe infections,particularly in patients with acute ketoacidosis, poor diabetic control, diabetic complications such as neuropathy,vasculopathy, and nephropathy. The Gram-negative aerobic bacilli are the large group of bacterial pathogensthat cause UTI with few species of Gram-positive bacteria. However, some fungi, parasites, and viruses havealso been reported to invade the urinary tract. Urinary tract infection affects women more than men due toseveral factors such as proximity of the genital tract to the urethra, anatomy of the female urethra, sexualactivity, menopause, and pregnancy. Other possible risk factors of UTI include allergy, obesity, diabetes, pasthistory of UTI, contraceptive use, catheter use, and family history. 

Background: COVID-19, caused by SARS-CoV-2, has unresolved mortality risk factors and clinical course, highlighting the need for further research. Aims: The study aimed to asses D-dimer and C-Reactive Protein (CRP) as the risk factors for severity covid-19 and who are less capable of surviving. Methods: A retrospective study conduct of COVID-19 in adult inpatients aged >20 at Al-sadder and Alamal Hospital in Iraq. Demographics, clinical trials, treatments, and viral RNA samples were analyzed. The study involved 100 patients, with 67 discharged and 33 hospitalized died. The majority of the participants 45% were aged < 40, but 55% were aged >40 years. Results: A significant and 57% were male 37(55.2%) Survivor vs. 20 (60.6%) non-survivor, p=0.024), more than 43% were female (30(44.8%) Survivor vs. 13(39.4%) non-survivor, p=0.010. Patients had underlying comorbidities (66%), survivor 37(55%), and non-survivor 29(87%). The most prominent comorbidity in non-survivors more than survivors was diabetic mellitus 85%, asthma 58%, stroke 48%, renal failure 42%, heart strake 33%, and hypertension 18%. The study found significant differences in WBC, lymphocyte count, D-dimer, Ferritin, CRP, and LDH levels in non-survivors compared to survivor patients, with a positive correlation between D- dimer and these parameters. The ROC analysis curve showed CRP with a high AUC of 80.2%, 87.9% sensitivity, and 37.3% specificity, while D-dimer and LDH had AUCs of 0.74.9 and 70%, respectively. Discussion: The study found that older age, higher d-dimer, ferritin, CRP, and LDH are associated with disease severity and higher mortality risk in adult COVID-19 patients. Conclusions: These biomarkers could aid in early detection of disease progression signs and better patient management

Background: MQ-series gas sensors belong to the metal oxide semiconductor (MOS) family of sensors that can sense the presence of many gases. These sensors find their application in gas alarm systems as key components. While necessary sensor circuit output voltage value for alarm point in a stand-alone gas alarm system is desirable, but what exact combination of the sensor circuit parameters is required? Hitherto, the determination of these circuit parameters has not been given much attention in the research community. Aim: the purpose of this work is to explore a structured graphical approach of determination of MQ series gas sensor circuit parameters for a stand-alone gas alarm system that yields desired sensor circuit output voltage value for the alarm point; the main objective of the study was to develop mathematical model equations that relate the: (i) sensor resistance (RS) with the gas concentration (x) and the sensor resistance at standard calibration concentration of the sensor base gas in the clean air (Ro) and (ii) sensor circuit output voltage (VRL), load resistance (RL) and sensor resistance (RS). It is expected from the model equations developed that graphical correlations of the sensor circuits parameters will be generated. Using these graphs for a particular case of an MQ-4 gas sensor under the influence of LPG, the parameters that yield desired sensor circuit output voltage of 2V for 1000 ppm of LPG alarm point will be determined. Methods: Model equations were developed for the sensor dynamics, and based on these model equations, graphs for the determination of required sensor parameters were plotted for a case of MQ-4 gas sensor response to LPG. Results and Discussion: The results yielded optimal values for R_O,R_S and R_L of 20 kΩ, 30 kΩ and 20 kΩ respectively, for alarm settings of 1000 ppm and a desired sensor circuit output voltage of 2 V. Based on determined parameters, the calibration equation for determination of best concentration value for a given value of emulated LPG concentration was developed. Using the method proposed in this study makes the process of determining the MQ-series gas sensor circuit parameters less cumbersome as their value can easily be obtained from the resulting graphs. Conclusions: a structured graphical approach for determination of MQ-series gas sensor circuit parameters for alarm points in a stand-alone gas alarm system showed that using MQ-4 gas sensor and LPG as the target gas, and for a sensor circuit output voltage of 2 V for alarm point at 1000 ppm of LPG, the corresponding value of R_O, R_S and R_L obtained were 20 kΩ, 30 kΩ, and 20 kΩ respectively. Hence, a structured graphical approach is suitable for determining MQ series gas sensor circuit parameters for a stand-alone gas alarm system under the influence of its associated gases.