Comparisons of biochemical parameters and diabetic ketoacidosis severity in adult patients with type 1 and type 2 diabetes

Objective The aim of this study was to determine the differences in biochemical parameters and diabetic ketoacidosis (DKA) severity in adult patients with type 1 and type 2 diabetes and utilization of serum BHB as a biomarker for DKA resolution was also evaluated. Materials and methods This prospective observational study of type 1 or type 2 diabetes mellitus who were diagnosed with DKA between 01 October 2018 and 30 September 2020. The correlations between serum BHB, measured by the Ranbut assay, and pH, bicarbonate, and anion gap were examined. Results A total of 99 diabetes patients were diagnosed with DKA (mean age 39.4 years, 63.4% female, 53.6% T2DM). while infection was the most common precipitating factor in T2DM (43.4%), non-compliance with treatment was the most common precipitating factor in T1DM (43.5%). T1DM patients had more severe DKA more hypokalemia during treatment. However, there was no significant difference in mortality between type1 and type2 diabetes. The initial laboratories evaluation of patients did not significant differ between type1 and type2 diabetes. Serum BHB during treatment of DKA was significantly correlated with changes in serum bicarbonate (r = − 0.64), serum anion gap (r = 0.84), and venous pH (r = − 0.6). The serum BHB levels corresponding to HCO3 levels for DKA severity were 4.5, 5.7, and 5.9 mmol/L in mild, moderate, and severe DKA, respectively. The serum BHB level of < 1 mmol/L had 73.7% sensitivity and 100% specificity to predict DKA resolution. Median time to resolution of DKA was 12 h with an optimized BHB cut-off value of < 1 mmol/L. There were no significant difference in time to resolution of DKA in the patients with type 1 and type 2 diabetes. Conclusions There are no differences in DKA-related biochemical parameters between type 1 and type 2 diabetes patients. The present findings suggest that DKA should be assessed and treated similarly, regardless of its occurrence in type 1 or type 2 diabetes patients.

RHINO-ORBITO-CEREBRAL MUCORMYCOSIS (BLACK FUNGUS) IN COVID 19 PATIENTS

ABSTRACT BACKGROUND Mucormycosis (Black fungus) is a designated as a rare, rapidly progressive fatal disease of immunocompromised caused by saprophytic fungus of family mucorales. Early diagnosis with prompt medical and surgical treatment is the only tool available. Rhino-orbito-cerebral is the most common subtype. In India we saw a sudden rise in mucormycosis cases during second wave of COVID 19. This necessitated a systematic review of epidemic of mucormycosis in COVID 19. METHODS A Retrospective multi-centric study was conducted at various Government and Private Hospitals of Western UP comprising of 51 cases of Rhino-orbitocerebral mucormycosis with present or recent COVID19 positive status presenting to us during 14th April 2021- 31st May 2021. RESULT Either Type2 Diabetes Mellitus or history of recent use of steroids in high doses was present in all the patients. Contribution of virulence of the Delta strain B1.617.2 is significant. FESS with sino-nasal debridement contributes significantly towards mortality reduction and cost of total treatment by significantly reducing days of Liposomal Amphotericin B therapy. CONCLUSION Early diagnosis with prompt medical and surgical management along with blood sugar control and avoiding use of high dose of steroids remain to key to mortality and morbidity reduction. Keywords: Black fungus, mucor, mucormycosis, rhino-orbito-cerebral, causes, treatment, covid 19, India, sugar, steroids, steam, oxygen, surgery

Rhino-orbito-Cerebral mucormycosis during COVID 19 pandemic in western Uttar Pradesh India

Mucormycosis (Black fungus) is a designated as a rare, rapidly progressive fatal disease of immunocompromised caused by saprophytic fungus of family mucorales. Early diagnosis with prompt medical and surgical treatment is the only tool available. Rhino-orbito-cerebral is the most common subtype. In India we saw a sudden rise in mucormycosis cases during second wave of COVID 19. This necessitated a systematic review of epidemic of mucormycosis in COVID 19.A Retrospective multi-centric study was conducted comprising of 51 cases of Rhino-orbito-cerebral mucormycosis with present or recent COVID19 in Western Uttar Pradesh positive status presenting to us during 14 April 2021- 31 May 2021.Either Type2 Diabetes Mellitus or history of recent use of steroids in high doses was present in all the patients. Contribution of virulence of the Delta strain B1.617.2 is significant. FESS with sino-nasal debridement contributes significantly towards mortality reduction and cost of total treatment by significantly reducing days of Liposomal Amphotericin B therapy.Early diagnosis with prompt medical and surgical management along with blood sugar control and avoiding use of high dose of steroids remain to key to mortality and morbidity reduction.

Predict Diabetes Mellitus Using Machine Learning Algorithms

Diabetes mellitus has become a very frequent disease that affects totally different organs of human body. Diabetes cause diverge depending on genetic, family history, health and environmental factors. Diabetes mellitus refers to a gaggle of diseases that affect how your body uses blood glucose. The underlying reason behind diabetes varies by type. But, despite what kind of diabetes you’ve got, it will cause excess sugar in your blood. Diabetes will be of two types, they are Type1 Diabetes and Type2 Diabetes. Early prediction will help in society a lot. It will provides the humanlife in safe way. The aim of this analysis is to develop a system that predicts the diabetes with a better accuracy. Parameters used to predict the type of Diabetes Mellitus are Glucose, Pregnancies, skin thickness, Blood pressure, Insulin, BMI, Diabetes pedigree function, age and upshot. In this we are with different machine learning algorithms, namely SVM, ANN, Decision tree, Logistic regression and Farthest first to predict the accuracy. Our experimental results show that farthest first attain superior correctness compare to dissimilar machine learning techniques.

Forgone care in patients with type 2 diabetes: a cross-sectional study

Background and objective Diabetes mellitus is a complex chronic disease requiring appropriate continuous medical care and delayed, or forgone care may exacerbate the severity of the disease. This study aimed to investigate the factors affecting forgone care in patients with type2 diabetes. Materials and methods This was a cross-sectional study involving 1139 patients with type 2 diabetes aged> 18 years in 2019 in Tabriz, Iran. The researcher-made questionnaire was used for data collection. Data were analyzed using IBM SPSS software version 22 and IBM AMOS 22. Exploratory Factor Analysis (EFA) was performed for dimension reduction of the questionnaire, and Confirmatory Factor Analysis (CFA) used to verify the result of EFA. We applied the binary logistic regression model to assess the factors affecting forgone care. Results Of the 1139 patients, 510 patients (45%) reported forgone care during the last year. The percentage of forgoing care was higher in patients without supplementary insurance coverage (P = 0.01), those with complications (P = 0.01) and those with a history of hospitalization (P = 0.006). The majority of patients (41.5%) reported that the most important reason for forgoing care is financial barriers resulting from disease treatment costs. Of the main four factors affecting, quality of care had the highest impact on forgone care at 61.28 (of 100), followed by accessibility (37.01 of 100), awareness and attitude towards disease (18.52 of 100) and social support (17.22 of 100). Conclusion The results showed that, despite the implementation of the Islamic Republic of Iran on a fast-track to beating non-communicable diseases (IraPEN), a considerable number of patients with type2 diabetes had a history of forgoing care, and the most important reasons for forgoing care were related to the financial pressure and dissatisfaction with the quality of care. Therefore, not only more financial support programs should be carried out, but the quality of care should be improved.