SOUTHERN JOURNAL OF SCIENCES

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.

Background: A crucial aspect of electrochemical enzymatic biosensor development is the immobilization of the enzymes, as it directly influences the sensitivity of the bioelectrode. Among the different methods used to incorporate enzymes on the surface of the transducers, layer-by-layer (LbL) self-assembly based on electrostatic interaction with polyelectrolytes of opposite charge stands out due to its simplicity and reproducibility. Aims: The aim of the work was to develop an electrochemical glucose biosensor by LbL assembly of a new functionalized chitosan polycation and the enzyme glucose oxidase (GOx). Methods: Chitosan was chemically functionalized with glucose by the Maillard reaction. The resulting polycation, named G-Chit, is soluble in the medium compatible with the enzyme. The bioelectrode was obtained by alternating adsorption of G-Chit and GOx onto carbon paste electrodes. By selecting the number of bilayer of G-Chit/GOX, the enzyme concentration, and the pH, the electroanalytical performance of the biosensor was optimized. The electrochemical responses were characterized by cyclic voltammetry and chronoamperometry. Results: Under optimized experimental conditions, the biosensor exhibited a sensitivity of (0.81 ± 0.03) µA mM-1 in a glucose concentration range of (0.18 to 1.75) mM. Discussion: Results indicated that catalytic response increases both with the number of G-Chit/GOx bilayers and the enzyme concentration, obtaining the best responses for 3 bilayers and 2 mg mL-1, respectively, while the optimum working pH value was 7.0. Conclusions: The analytical response of the biosensor was tested in milk samples with negligible matrix effects, suggesting a potential application in other dairy products. Results show that G-Chit appears promising for the immobilization of enzymes.