Study of maternal, fetal and neonatal outcomes in patients with gestational diabetes mellitus in a tertiary care hospital

Background: GDM is defined as carbohydrate intolerance of variable severity with onset or first recognition during pregnancy. Women with gestational diabetes are characterized by a relatively diminished insulin secretion and pregnancy induced insulin resistance primarily present in the skeletal muscle tissue. Normal pregnancy is a diabetogenic state characterized by exaggerated rate and amount of insulin release, associated with decreased sensitivity to insulin at cellular levels. The objective of the study was to study the maternal, the fetal and the neonatal outcomes of treated patients of GDM in present study.Methods: It was a hospital based clinical study. 1000 patients were enrolled between 24-28 weeks of gestation and DIPSI test was performed. Diagnosis of GDM was done using DIPSI criteria. 80 patients were diagnosed with GDM and followed till delivery to study the maternal, fetal and neonatal outcome.Results: Elderly patients, patients with previous history of GDM, patients with family history of diabetes, patients with high BMI and patients with polyhydramnios are at high risk for GDM.Conclusions: Hypertensive disorders and preterm birth are known to be higher with GDM are similar to the non-GDM group suggesting that early diagnosis and prompt treatment and maintaining strict glycemic control by patient may be beneficial. GDM can be managed well on MNT and lifestyle modifications, only few patients required insulin therapy. In spite of appropriate glycemic control, the incidence of macrosomia found to be high in GDM group. Sudden unexplained stillbirth can occur in spite of strict glycemic control. Neonatal complications have occurred despite well glycemic control

Comparative study of capillary blood glucose estimation by glucometer and venous plasma glucose estimation in women undergoing the one step DIPSI test (diabetes in pregnancy study group India) for screening and diagnosis of gestational diabetes mellitus

Background: Gestational diabetes mellitus (GDM) is defined as carbohydrate intolerance of variable severity with onset or first recognition during pregnancy. The importance of GDM is that two generations are at risk of developing diabetes in the future. Aim was to study the merits and demerits of capillary blood glucose estimation by glucometer over venous plasma glucose estimation while performing DIPSI test.Methods: It was a hospital based clinical study. 1000 patients were enrolled between 24-28 weeks of gestation and DIPSI test was performed. Patient was instructed to come irrespective of fasting. 75 g glucose dissolved in 200-400 ml of water and patient was asked to drink in 5 minutes. Venous blood was drawn after 2 hours, capillary blood sugar also was measured at the same time by glucometer.Results: Sensitivity of capillary blood sugar (CBS) method in detecting GDM is 100% as compared to venous plasma glucose (VPG) and specificity is 99.46% as compared to VPG. Considering the agreement between two methods for diagnosis of GDM, equal sensitivity of both methods and small number of false positive cases detected by CBS method, due to almost equal specificity (99.46%), CBS method by glucometer can be recommended as an alternative to VPG method as a screening and diagnostic test for GDM.Conclusions: It is appropriate and feasible to offer capillary blood sugar sampling by DIPSI test for screening and diagnosis of GDM. The prevalence of GDM in our study is 8% by capillary blood sugar sampling and 7.5% by venous plasma glucose sampling according to DIPSI test.

Brain Tumor Classification, Segmentation, and Detection using Deep Learning - A Review

V.Vapnik in 1965 proposed Vector methods. Kimeldorf presented a technique for creating kernel space based on support vectors in 1971. Support Vector Machine (SVM) techniques were initially presented in the 1990s by V. Vapnik in the field of statistical learning.  Since then, pattern recognition, natural language processing, image processing and other areas have seen extensive use of SVM. By converting non-linear sample space into linear space via a kernel approach, the algorithm's complexity is reduced. Image classification is a well-known issue in image processing.  Predicting the input image categories using the features is the main objective of image classification. There are several different classifiers, including Artificial Neural Networks, Support Vector Machines, and Random Forests, Decision Forests, k-NNs (k Nearest Neighbors), and Adaptive Boost. SVM is one of the best techniques for categorizing any image or pattern. A common non-invasive technique used in the medical sector for the analysis, diagnosis, treatment of brain tissues is magnetic resonance imaging.  When a brain tumor is discovered early, the patient's life can be saved by receiving the appropriate care.  It becomes difficult to accurately identify tumors in the MRI slices, which requires fussy work.

ABSORPTION CORRECTION METHOD FOR THE SIMULTANEOUS ESTIMATION OF N-ACETYL-L-CYSTEINE AND AMBROXOL HYDROCHLORIDE IN BULK AND IN COMBINED TABLET DOSAGE FORM

Objective: The objective of this research was to estimate the concentrations of N-Acetyl-L-cysteine (NAC) and Ambroxol Hydrochloride (AMB) simultaneously, in bulk and combined tablet formulation using a new, simple, precise and accurate absorption correction method using UV Spectrophotometer.Methods: The concentrations of both the drugs (NAC and AMB) were determined using absorption correction method as; at 244 nm only AMB gave substantial absorbance and at 220 nm both NAC and AMB gave absorbance. Distilled water was used as a common solvent for both the drugs and the method was developed. Further statistical evaluations were carried out and the method was validated.Results: In the range of 3-18 µg/ml for AMB at 244 nm and 20-120 µg/ml for NAC at 220 nm, the Beer's law was obeyed. Percentage recovery for AMB was in the range 100.50-101.10% and for NAC it was 99.85-100.20%. The % RSD values reported were less than 2. The developed method was validated and was found to be linear, accurate, precise and also rugged.Conclusion: The results obtained clearly demonstrated that the proposed method of analysis was simple, sensitive, accurate, precise, rapid and also economical and could be applied successfully for the simultaneous estimation of NAC and AMB in bulk and combined tablet formulation.Keywords: N-Acetyl-L-cysteine, Ambroxol Hydrochloride, Absorption correction method, Mucolytic, UV spectrophotomete

Automated Video and Audio based Stress Detection using Deep Learning Techniques

In today's world, stress has become an undoubtedly severe problem that affects people's health. Stress can modify a person's behavior, ideas, and feelings in addition to having an impact on mental health. Unchecked stress can contribute to chronic illnesses including high blood pressure, diabetes, and obesity. Early stress detection promotes a healthy lifestyle in society. This work demonstrates a deep learning-based method for identifying stress from facial expressions and speech signals.An image dataset formed by collecting images from the web is used to construct and train the model Convolution Neural Network (CNN), which then divides the images into two categories: stressed and normal. Recurrent Neural Network (RNN), which is used to categorize speech signals into stressed and normal categories based on the features extracted by the MFCC (Mel Frequency Cepstral Coefficient), is thought to perform better on sequential data since it maintains the past results to determine the final output

Design and development of a novel tunable electrorheological fluid (ERF) damper-foundation to attenuate residual vibrations in machine tools

Residual vibrations in machine tools hamper accuracy and productivity. The attenuation of residual vibrations has been an industrial concern for decades. Meanwhile, the residual vibrations' vibration pattern reveals that the support foundation's damping capabilities predominantly influence them. Therefore, inserting dampers in any other location on a machine tool (such as a machine column) is ineffective. Hence, the scope of inserting the damper into the machine foundation needs to be verified. However, conventional machine mounting systems (concrete foundation and rubber mounts) equally respond to all variable inputs. Both these flocks resulted in inadequate dampening and perhaps poor accuracy. This paper provides a first-generation model of a semiactive-viscous damper (ERF damper-foundation) with tunable damping facilitating machine installation. Controlled experimentation by exposing the developed damper foundation to excitations of medium duty lathe machine confirms its effectiveness and obtains over 48% attenuation compared to a conventional concrete foundation

CROWD ABNORMAL BEHAVIOUR DETECTION USING DEEP LEARNING

Crowd analysis has become an extremely famous research point in the territory of computer vision. Computerized examination of group exercises utilizing reconnaissance recordings is a significant issue for public security since it permits the identification of hazardous groups and where they’re going. We all see how many problems are faced because of the crowd. In our country, many terrorists are there. They plant a bomb in a crowded area which causes a lot of injuries. Thieves are mostly found or always leave in crowded areas so they can easily get an advantage of the crowd. In that situation, crowd analysis is very important. This paper presents the design of the deep learning architecture that provides control over the crowd behavior that will help to avoid violence or any other act which occurs because of the crowd which causes harmful effects to the society. So we are proposing a system that detects abnormal behavior of crowds using deep learning techniques

An analysis of pulmonary function tests in construction workers.

In construction workers, repeated and constant exposure to noxious materials generated at construction sites often increases the risk of respiratory illness. Pulmonary function test (PFT) is an important tool utilized for both diagnosis the cause of unknown or unexplained respiratory symptoms and monitoring prognosis of patients with known respiratory pathology. In the present study, PFT of construction workers was assessed using spirometry. A total of 100 male construction workers (working for >1 year) belonging age group 21 to 60 years were included in the study. Additionally, equal number of age matched healthy individuals without any exposure to construction work was recruited as controls. Indices of pulmonary functions included forced vital capacity (FVC), peak expiratory flow rate (PEFR), forced expiratory volume (FEV1), FEV1/FVC and maximal voluntary ventilation (MVV). Maximum workers belonged to age group 21 to 40 years. A total of 37 had habit of smoking. Construction workers also suffered from respiratory ailments like cough, dyspnea, sorethroat etc. All indices of PFT were significantly decreased in construction workers compared to controls. Construction workers are at high risk of developing respiratory ailments due to continuous long term exposure to noxious material used in construction. Habit of smoking and consumption of alcohol also adds on to risk of developing respiratory disorders. Spirometry can be recommended as an effective tool for screening of construction workers for respiratory ailments. The present study highlights the importance of regular health camps, use of proper protective wears and initiation of awareness program to prevent respiratory ailments in construction workers

Nutritional and Medicinal Knowledge of Wild Edible Flowers Amongst Rural Women

The study investigates the nutritional and medicinal knowledge of wild edible flowers among rural women. It identifies the diverse use of these flowers, their nutritional composition, and their traditional culinary applications. The research also highlights the cultural significance of these flowers. Challenges in awareness and perception highlight the need for education and promotion. The study suggests that sustainable use of these flowers can improve nutrition, health, and well-being in rural communities, honoring local traditions