Symptom Triggered Therapy for Alcohol withdrawal Symptoms using Revised Clinical Institute withdrawal Assessment of Alcohol Scale (CIWA-Ar) – A Randomized control study.

BACKGROUND : Alcohol related withdrawal symptoms are common problems encountered in alcohol dependent patients. Withdrawal symptoms exist in a continuum and there is need for timely treatment of these withdrawal symptoms in order to prevent complicated withdrawal states like Delirium tremens and withdrawal seizures. The treatment is by administration of benzodiazepines and the usual practice is to start patients on an empirical dose, “Fixed schedule treatment”. But there is another method called the “Symptom triggered treatment” for administering benzodiazepines which utilizes a rating scale called the Clinical Institute Withdrawal Assessment Scale (CIWA-Ar), which helps in giving medications according to severity of the withdrawal rating. AIMS OF THE STUDY : To compare between the two regimens for alcohol detoxification namely, the “Symptom triggered treatment” and the “Fixed schedule treatment” for outcome variables: Dose of benzodiazepine, duration of treatment and severity of withdrawal. METHODS : Prospectively we randomized consenting, consecutive patients, admitted with the diagnosis of alcohol dependence syndrome according to DSM-IV criteria into either of the 2 detoxification treatment methods: 1)Fixed schedule treatment or 2)Symptom triggered treatment. RESULTS : The “Symptom triggered treatment” group required 93 mg lesser mean benzodiazepine dose than the “Fixed schedule treatment” group. The mean duration of treatment in the “Symptom triggered treatment” was 2.12 days lesser than the “Fixed schedule treatment.” There was a group of patients who did not require any benzodiazepine during detoxification and they were significantly higher in the “Symptom triggered treatment” group (24%) when compared with the “Fixed schedule treatment” group (4%). No incidences of any complicated withdrawal symptoms during course of detoxification in the sample. CONCLUSION : The Symptom triggered treatment using CIWA-Ar scale may be a safe and better treatment option for patients during alcohol detoxification in terms of lower benzodiazepine dose and shorter duration of treatment

Serum adenosine deaminase as oxidative stress marker in type 2 diabetes mellitus

Background: Oxidative stress markers are increased in type 2 diabetes mellitus and its estimation helps in predicting the long term complications. In present study comparison and correlation of the levels of serum adenosine deaminase, serum malondialdehyde, and serum total antioxidant capacity in type 2 diabetes mellitus and in age and sex matched healthy controls.Methods: Study group consisted of 100 individuals between the age group of 35-65 years of age. Of which 50 individuals with type 2 diabetes mellitus were considered as cases. The control group consisted of 50 age and sex matched healthy individuals. Study was approved by institutional ethical committee. By aseptic precautions 2 ml of venous blood was collected in a plain vacutainer tube, after 8-12 hours of fasting. Serum adenosine deaminase, serum malondialdehyde, and serum total antioxidant capacity were estimated in all groups.Results: The study observed an increased level of serum adenosine deaminase, malondialdehyde and decreased levels of total antioxidant capacity in type 2 diabetes mellitus compared to controls. Serum adenosine deaminase levels in type 2 diabetics were 50.77 ± 6.95 and in controls was 17.86 ± 4.04. Serum Malondialdehyde levels in type 2 diabetics was 512.13 ± 70.15 and in controls was 239.32 ± 23.97. Serum total antioxidant levels in type 2 diabetics was 0.39±0.15 and in controls was 1.66±0.25. Positive correlation was seen between serum adenosine deaminase and malondialdehyde and it was statistically significant. Statistically significant negative correlation was seen between serum adenosine deaminase and total antioxidant capacity.Conclusion: Adenosine deaminase can be used as oxidative stress marker. Their increased levels indicate oxidative stress in type 2 diabetes mellitus. Therefore, estimation of serum adenosine deaminase levels help in early prediction and prevention of long term complications occurring due to oxidative stress in diabetics, thereby decreasing the mortality and morbidity in them.

Serum electrolytes levels in patients with type 2 diabetes mellitus: a cross-sectional study

BACKGROUND: Diabetes Mellitus (DM) is a common metabolic disease worldwide. Electrolyte played significant roles in the normal functioning of the body, and deregulation is indicative of different types of disease and electrolyte disturbances are often reported in type 2 DM (T2DM). AIM: The aim of the study was to estimate the levels of serum electrolytes in outpatients with T2DM and correlate serum electrolytes with random blood sugar (RBS). MATERIALS AND METHODS: Patients with T2DM visiting the outpatient Departments of Medicine, between April 2016 and March 2017 were included. Of 148 diagnosed T2DM cases, 74 were had RBS level >300mg/dL (group-1) and 74 had RBS level ≤300mg/dL (group-2). Serum sodium (Na+), potassium (K+), chloride (Cl-) levels were measured by using the Roche 9180 electrolyte analyzer. RESULTS: In this study, there was a significant decrease in serum Na+ levels in group 1 (131.83±4.36 mmol/L) compared to group 2 (134.15±4.90 mmol/L).The serum levels of K+ was found to be increased in group 1 (4.51±0.61 mmol/L) in comparison with group 2 (4.26±0.52 mmol/L). In group-1, an inverse relationship was present between serum Na+ (r=-0.342) and Cl- (r=-0.538) with RBS which was statistically significant. In group-2, a significant correlation was present between serum K+ and RBS (r=0.356, p<0.05). CONCLUSIONS: The study showed lower levels of Na+ and higher K+ levels in group-1 compared to group-2 subjects. This study showed that the distribution of serum Na+ and K+ levels is dependent on plasma glucose levels in patients with DM and also suggests that monitoring the electrolyte levels in hyperglycemia is pertinent in the management of diabetes

Análisis de series temporales de conjuntos de datos clínicos utilizando el clasificador ImageNet

Deep learning is a bunch of calculations in AI that endeavor to learn in numerous levels, comparing to various degrees of deliberation. It regularly utilizes counterfeit brain organizations. The levels in these learned factual models compare to unmistakable degrees of ideas, where more significant level ideas are characterized from lower-level ones, and a similar lower level ideas can assist with characterizing numerous more elevated level ideas. As of late, an AI (ML) region called profound learning arose in the PC vision field and turned out to be exceptionally famous in many fields. It began from an occasion in late 2018, when a profound learning approach in light of a convolutional brain organization (CNN) won a mind-boggling triumph in the most popular overall com management rivalry, ImageNet Characterization. From that point forward, scientists in many fields, including clinical picture examination, have begun effectively partaking in the dangerously developing field of profound learning. In this section, profound learning procedures and their applications to clinical picture examination are studied. This study outlined 1) standard ML procedures in the PC vision field, 2) what has changed in ML when the presentation of profound learning, 3) ML models in profound learning, and 4) uses of profound figuring out how to clinical picture examination. Indeed, even before the term existed, profound learning, in particular picture input ML, was applied to an assortment of clinical picture examination issues, including harm and non-harm characterization, harm type grouping, harm or organ division, and sore location.El aprendizaje profundo es un conjunto de cálculos en IA que intentan aprender en numerosos niveles, en comparación con varios grados de deliberación. Utiliza regularmente organizaciones cerebrales falsificadas. Los niveles en estos modelos fácticos aprendidos se comparan con grados inconfundibles de ideas, donde las ideas de niveles más significativos se diferencian de las de niveles inferiores, y las ideas de niveles inferiores similares pueden ayudar a caracterizar muchas ideas de niveles más elevados. Últimamente, surgió una región de IA (ML) llamada aprendizaje profundo en el campo de la visión de la PC y resultó ser excepcionalmente famosa en muchos campos. Todo comenzó a partir de una ocasión a finales de 2018, cuando un enfoque de aprendizaje profundo basado en una organización cerebral convolucional (CNN) obtuvo un triunfo alucinante en la rivalidad general de gestión de comunicaciones más popular, ImageNet Characterization. A partir de ese momento, los científicos de muchos campos, incluido el examen del cuadro clínico, han comenzado a participar de manera efectiva en el campo del aprendizaje profundo, en peligroso desarrollo. En esta sección se estudian los procedimientos de aprendizaje profundo y sus aplicaciones al examen del cuadro clínico. Este estudio describió 1) procedimientos estándar de ML en el campo de visión de la PC, 2) qué ha cambiado en ML cuando se presenta el aprendizaje profundo, 3) modelos de ML en el aprendizaje profundo y 4) usos del aprendizaje profundo en el examen del cuadro clínico. De hecho, incluso antes de que existiera el término, el aprendizaje profundo, en particular el ML de entrada de imágenes, se aplicaba a una variedad de cuestiones de examen de cuadros clínicos, incluida la caracterización de daño y no daño, agrupación de tipos de daño, división de órganos o daños y ubicación del dolor

Computing and Monitoring various Biopotential signals using Machine Learning algorithms

Nowadays health care units play a vital role of the human existence after the pandemic periods. It is very essential to monitor the potential signals of the human body for survival on regular basis. In this paper extracting the values of different biopotential signals produced in human body, monitoring and analysing them using various machine learning algorithms. Monitoring involves observing and checking the progress or quality of data over a period of time and keeping it under system review. The beauty of effective computing is to make machine more emphatic to the user. Machine with the capability of human electrical signal recognition can look inside the user’s body. This paper generalises the view of training of the bio potentials signals data in the MATLAB software as well in python software. Analysis with different machine learning algorithms like K-Nearest Neighbours (KNN), Decision tree (DT), Logistic Regression (LR), Support Vector Machine(SVM) are used in the training ,testing and validation of the data. Better performance is achieved with these algorithms

Clinical utility of serum holotranscobalamin in the assessment of Vitamin B12 deficiency in patients with Hypothyroidism

Thyroid disorder is the second most frequently encountered endocrinological condition after diabetes mellitus. When vitamin B12 deficiency coexists with hypothyroidism, neurological symptoms and signs are more pronounced. Holotranscobalamin (Active B12) may be a more sensitive marker in the early diagnosis of Vitamin B12 deficiency than total B12. The study aimed to evaluate the serum levels of active B12 in patients with clinical hypothyroidism and to correlate active B12 and thyroid profiles. The case-control study was carried out in a tertiary hospital on 80 study subjects, comprising 40 confirmed hypothyroidism patients and 40 age- and gender-matched healthy controls. Serum thyroid profile and active B12 assays were performed by Chemiluminescent Microparticle Immunoassay. Statistical methods such as independent t-test and Pearson’s correlation were used to compare and correlate quantitative data. A significant percentage (90%) of hypothyroid patients had vitamin B12 deficiency, with a mean value of 17.39 ± 5.73 pmol/L. Active B12 showed a positive correlation with T3 (r = 0.818; P < 0.001) and T4 (r = 0.851; P < 0.001) and a negative correlation with TSH (r = -0.930; P < 0.001). Vitamin B12 deficiency was found in patients with hypothyroidism. This vitamin B12 deficiency may be caused by inadequate malabsorption, as seen in hypothyroidism. HoloTC (Active B12) may be a promising marker for early detection and management of B12 deficiency, which may be beneficial in preventing irreversible neurological damage at an early stage

Impact of Diet on Serum Lipids, Atherogenic Index of Plasma and Non HDL-c in Pre and Postmenopausal Women

Introduction: Menopause is an inevitable phase of a woman’s natural ageing process, marked by cessation of ovarian function. Hormonal changes during the phase causes derangement of lipid metabolism and thereby increasing cardiovascular risk in postmenopausal women. Diet plays a major role in influencing serum lipids. Aim: To determine and compare lipid profile, Atherogenic Index of Plasma (AIP) and non High-Density Lipoprotein-cholesterol (HDL-c) in pre and postmenopausal women based on vegetarian and non vegetarian diet. Materials and Methods: This cross-sectional study was comprised of 92 women (46 were premenopausal and 46 were postmenopausal) carried out at AJ Institute of Medical Sciences and Research Centre, Mangaluru, Karnataka, India between December 2019-May 2020. The groups were further divided into vegetarian and non vegetarians. Fasting lipid profile was determined by enzymatic methods. AIP and non HDL levels were calculated. Comparison of means between two groups was done using student t-test. Association between categorical variables was analysed using Chi-square test. Statistical significance was considered at p<0.05. Results: Serum Total Cholesterol (TC), Triglycerides (TG), Low-Density Lipoprotein-cholesterol (LDL-c), Very Low-Density Lipoprotein-cholesterol (VLDL-c), AIP and non HDL-c levels were LDL-c and HDL-c was high (184.09±17.49, 131.96±9.49, 106.00±20.92, 26.46±1.96, 0.05±0.07, 132.45±22.39 and 51.64±5.88, respectively) in vegetarians compared to non vegetarians in premenopausal women. In postmenopausal women, similar pattern was observed with regards to serum TC, TG, LDL-c, VLDL-c, AIP, non HDL-c and HDL-c in vegetarians and non vegetarians (p<0.05). An alarming proportion of non vegetarian postmenopausal women showed “very high” TC (91.3%), “low” HDL-c (56.5%), “very high” LDL-c (69.6%) and “high-risk” AIP (91.3%). Conclusion: The findings of this study indicated that all lipid parameters, AIP and non HDL-c were higher in non vegetarians except HDL-c in pre and postmenopausal women. Relevant dietary recommendations can be given to premenopausal women to promote positive health outcomes and alleviate cardiovascular risk