Application of Binary Logistic Regression Model for Assessing the Caesarean Risk Factors in Bangladesh: A Case Study of Khulna and Gopalganj District

The main focus of this study is to investigate the caesarean risk factors in a particular area of Bangladesh. The caesarean delivery rate is increasing day by day in most developing countries like Bangladesh and number of caesarean births has almost doubled in the last eight years in Bangladesh largely due to maternal, socio-economic and demographic factors. Instead of many disadvantages, caesarean deliveries are most common among women but it is not clinically justified. For improving the maternal health status, it is essential to determine the risk factors of caesarean delivery. For this study some hospitals have selected from Khulna and Gopalganj district. Our population is the total number of pregnant women admitted for delivery in the hospitals and 600 respondents were taken as sample. After collecting data, information were arranged in tables and analyzed. For the analysis, chi-square test and fisher’s exact test were performed to identify the significant association between delivery type (caesarean/non-caesarean) and maternal, socio-demographic and socio-economic factor’s respectively. A stepwise binary logistic regression analysis was carried out to identify the most impact factors on caesarean delivery. We found that 14 risk factors were statistically associated with delivery type out of 21 risk factors. From this study, it is clear to us that above influential factors may affects the mother’s health status in Bangladesh as well as Khulna and Gopalganj district

Status of deep learning for EEG-based brain–computer interface applications

In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain–computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research

Efficiency of Polyphenol Extraction from Artificial Honey Using C18 Cartridges and Amberlite„ XAD-2 Resin: A Comparative Study

A comparative study of the extraction efficiency of nine known polyphenols [phenolic acids (benzoic acid, dihydroxybenzoic acid, gallic acid, trans-cinnamic acid, and vanillic acid) and flavonoids (naringenin, naringin, quercetin, and rutin)] was conducted by deliberately adding the polyphenols to an artificial honey solution and performing solid phase extraction (SPE). Two SPE methods were compared: one using Amberlite XAD-2 resin and another one using a C18 cartridge. A gradient high performance liquid chromatography system with an RP18 column and photodiode array detector was utilized to analyze the extracted polyphenols. The mean percent of recovery from the C18 cartridges was 74.2%, while that from the Amberlite XAD-2 resin was 43.7%. The recoveries of vanillic acid, naringin, and rutin were excellent (>90%); however, gallic acid was not obtained when C18 cartridges were used. Additionally, the reusability of Amberlite XAD-2 resin was investigated, revealing that the mean recovery of polyphenols decreased from 43.7% (1st extraction) to 29.3% (3rd extraction). It was concluded that although Amberlite XAD-2 resin yielded a higher number of compounds, C18 cartridges gave a better extraction recovery. The lower recovery seen for the Amberlite XAD-2 resin also cannot be compensated by repeated extractions due to the gradual decrease of extraction recovery when reused

AI powered asthma prediction towards treatment formulation : An android app approach

Asthma is a disease which attacks the lungs and that affects people of all ages. Asthma prediction is crucial since many individuals already have asthma and increasing asthma patients is continuous. Machine learning (ML) has been demonstrated to help individuals make judgments and predictions based on vast amounts of data. Because Android applications are widely available, it will be highly beneficial to individuals if they can receive therapy through a simple app. In this study, the machine learning approach is utilized to determine whether or not a person is affected by asthma. Besides, an android application is being cre-ated to give therapy based on machine learning predictions. To collect data, we enlisted the help of 4,500 people. We collect information on 23 asthma-related characteristics. We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. TensorFlow is utilized to integrate machine learning with an Android application. We accomplished asthma therapy using an Android application developed in Java and running on the Android Studio platform

On the Quantitative Potential of Viscoelastic Response (VisR) Ultrasound Using the One-Dimensional Mass-Spring-Damper Model

Viscoelastic Response (VisR) ultrasound is an acoustic radiation force (ARF)-based imaging method that fits induced displacements to a one-dimensional (1D) mass-spring-damper (MSD) model to estimate the ratio of viscous to elastic moduli, τ, in viscoelastic materials. Error in VisR τ estimation arises from inertia and acoustic displacement underestimation. These error sources are herein evaluated using finite element method (FEM) simulations, error correction methods are developed, and corrected VisR τ estimates are compared to true simulated τ values to assess VisR’s relevance to quantifying viscoelasticity. In regards to inertia, adding a mass term in series with the Voigt model, to achieve the MSD model, accounts for inertia due to tissue mass when ideal point force excitations are used. However, when volumetric ARF excitations are applied, the induced complex system inertia is not described by the single-degree-of-freedom MSD model, causing VisR to overestimate τ. Regarding acoustic displacement underestimation, associated deformation of ARF-induced displacement profiles further distorts VisR τ estimates. However, median error in VisR τ is reduced to approximately −10% using empirically derived error correction functions applied to simulated viscoelastic materials with viscous and elastic properties representative of tissue. The feasibility of corrected VisR imaging is then demonstrated in vivo in the rectus femoris muscle of an adult with no known neuromuscular disorders. These results suggest VisR’s potential relevance to quantifying viscoelastic properties clinically

Physicochemical and biochemical properties of pepsin-solubilized collagen isolated from the integument of Sea Cucumber (Stichopus vastus)

The integument (high collagen content) of sea cucumber Stichopus vastus is generally wasted after harvesting, whereas only its stomach and intestines are eaten in few Asian countries. Amino acid composition, thermal transition temperature (Tm), zeta potential, solubility, moisture absorption and retention capacities, proximate composition and morphology of pepsin-solubilized collagen (PSC) isolated from the integument of S. vastus were studied. Amino acid composition revealed that glycine was dominant in the isolated collagen. PSC was found to have an isoelectric pH of 4.67, good moisture absorption and retention capacity at higher humidity, a sharp effect of pH and NaCl concentration on solubility, and an inverse relationship between temperature and viscosity. PSC also showed the maximum Tm to be 37.3C, very high protein content and ultrastructural characteristics. Hence, the PSC has the potential to be used as a functional ingredient in food, cosmetics and nutraceutical products