Search

Background: Dyslipidemia was defined according to the third report of the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III, USA, 2001), TC level ≥200 mg/dl, HDL-C level

Study habits have an impact on students' capacity for learning. Amongst these, the technology use has implicit a controversial part. This study aimed to assess the study habits of university students and the role of technology in learning. A cross sectional survey was conducted. A simple random sampling technique of 301 students from the different colleges of the selected university was used. A self-administered developed questionnaire about the students’ demographic data, their study habits including time management, study environment, study techniques, test preparation, device used for studying, number of hours per day spent on technology and how technology enhances learning. The findings revealed that most of students preferred to use laptop for learning. Around half of them spent 2-5 hours every day on technology for studying purpose. There was a statistical significant difference between gender, nationality and different element of the students study habits as time management and methods of studying as teacher notes and attending the revision classes. More than textbooks, students rely on technology for their academic work. The use of technology to enhance university students' psychomotor and linguistic abilities, including communication skills that are crucial for the health care team, can be the subject of further research.

In the research of bone tissue engineering and regeneration, nano-hydroxyapatite (nHAp), chitosan (CS), and gelatin (GeL)-based scaffold shows promising result because of their potentials of tailored properties by manipulating 3D networks. In this work, nHAp was synthesized by wet chemical precipitation method using calcium nitrate tetrahydrate [Ca(NO3)2∙4H2O] and di-ammonium hydrogen phosphate (NH4)2HPO4. Glutaraldehyde was used as the cross-linking agent to prepare 3D networks through a freeze-drying technique. X-ray diffraction (XRD) analysis confirmed the formation of single-phase nHAp. Fourier Transformation Infrared Spectroscopy (FTIR) spectra revealed the presence of hydroxyl (-OH) and phosphate $$\left( {{\text{PO}}_{4}^{3- } } \right)$$ groups into the sample which confirmed the formation of hydroxyapatite. Raman Spectroscopy analysis elicited the conservancy of the nHAp and scaffold structure. Scanning Electron Microscopy (SEM) images revealed the formation of a 3D interconnected porous scaffold with a pore size in the range of 30–250 μm. Energy Dispersive X-ray Spectroscopy (EDS) of the scaffold affirmed that the prepared nHAp is calcium abundant nHAp. The cytotoxicity of the above scaffolds was studied by VERO cells, which revealed that the prepared samples were non-cytotoxic. Mechanical testing demonstrated that inclusion of higher nHAp concentration leads to increase the mechanical properties of the scaffolds.

not available J. Asiat. Soc. Bangladesh, Sci. 47(1): 91-97, June 2021

Pancytopenia is a condition where all three types of blood cell counts simultaneously become low. There are multiple factors for which pancytopenia might occur, however pancytopenia because of Covid-19 have been reported rarely. Here, we discussed a case of pancytopenia, which presented to us as a consequence of Covid-19 infection. Bangladesh Crit Care J September 2021; 9 (2): 118-120

For just about 30 years, researchers have considered the likelihood to utilize nucleic acids as antiviral therapeutics. In principle, small single-stranded nucleotide sequence (oligonucleotide) could hybridize to a particular gene or messenger RNA and diminish transcription or translation, respectively, in this manner decreasing the amount of protein that is synthesized. Until now, an incredible number of antisense oligonucleotides, double-stranded oligonucleotides, aptamers, ribozymes, deoxyribozymes, interfering RNAs, chimeric RNA-DNA molecules, antibody genes has been created artificially and applied effectively for comprehension and manipulating biological processes and in clinical preliminaries to treat a variety of diseases. Their versatility and potency make them similarly fit candidates for fighting viral infections. However, troubles with their efficiency, off-target effects, toxicity, delivery, and stability halted the development of nucleic acid-based therapeutics that can be utilized in the clinic. The potential for nucleic acid therapeutic agents is significant and is quite recently beginning to be realized. In this review, we have summarized some of the recent advancements made in the area of nucleic acid based therapeutics and focused on the methods of their delivery and associated challenges.

Music has the ability to evoke different emotions in people, which is reflected in their physiological signals. Advances in affective computing have introduced computational methods to analyse these signals and understand the relationship between music and emotion in greater detail. We analyse Electrodermal Activity (EDA), Blood Volume Pulse (BVP), Skin Temperature (ST) and Pupil Dilation (PD) collected from 24 participants while they listen to 12 pieces from 3 different genres of music. A set of 34 features were extracted from each signal and 6 different feature selection methods were applied to identify useful features. Empirical analysis shows that a neural network (NN) with a set of features extracted from the physiological signals can achieve 99.2% accuracy in differentiating among the 3 music genres. The model also reaches 98.5% accuracy in classification based on participants’ subjective rating of emotion. The paper also identifies some useful features to improve accuracy of the classification models. Furthermore, we introduce a new technique called ’Gingerbread Animation’ to visualise the physiological signals we record as a video, and to make these signals more comprehensible to the human eye, and also appropriate for computer vision techniques such as Convolutional Neural Networks (CNNs). Our results overall provide a strong motivation to investigate the relationship between physiological signals and music, which can lead to improvements in music therapy for mental health care and musicogenic epilepsy reduction (our long term goal).

Acute myocardial infarction is one of the most important reason of death in the world. There are some established risk factors of acute myocardial infarction (AMI), but measurement of trace element like zinc can be helpful in the prevention and better management of AMI. The aim of this study was to estimate serum zinc in AMI patients. This is a case control type of study carried out in the Department of Biochemistry, Dhaka Medical College, Dhaka during the period of January 2015 to December 2015 with a total number of 100 study subjects. Acute myocardial infarction patients were selected as case (50) from coronary care unit (CCU), Department of Cardiology, Dhaka Medical College Hospital. Normal healthy individuals were selected as control (50) from the attendants of patients, relatives and doctors. Serum level of zinc was assessed for both case and control groups. The mean values of the variable were compared between them by statistical analysis using SPSS version 16. For all the statistical analysis p

Background: The development of improved health and nutrition in adulthood is significantly influenced by early nutrition. It is, however, almost ever investigated how to determine the nutritional condition of Bangladeshi children using anthropometric measurements including height, weight, biepicondylar width of the humerus and femur, BMI, and BSA.Methods: A descriptive cross-sectional study was conducted among 400 government elementary school students aged between 9 and 12 years in Dhaka, Bangladesh. Data was collected using a semi-structured questionnaire and analyzed using SPSS.Results: For the B1 and B2 groups, heights ranged from 124.00 to 151.00 cm and 129.00 to 157.00 cm, respectively. Height ranges for the C1 and C2 groups were respectively 129.50-153.00 cm and 129.00-160.00 cm. The B2 study group had a greater weight than the B1 study group (p

Emotion monitoring can play a vital role in investigating mental health disorders that contribute to 14% of global diseases. Currently, the mental healthcare system is struggling to cope with the increasing demand. Robot-assisted mental health monitoring tools can take the enormous strain off the system. The current study explored existing state-of-art machine learning (ML) models and signal data from different bio-sensors assessed the suitability of robotic devices for surveilling different physiological and physical traits related to human emotions and discussed their potential applicability for mental health monitoring. Among the selected 80 articles, we subdivided our findings in terms of two different emotional categories, namely—discrete and valence-arousal (VA). By examining two different types of signals (physical and physiological) from 10 different signal sources, we found that RGB images and CNN models outperformed all other data sources and models, respectively, in both categories. Out of the 27 investigated discrete imaging signals, 25 reached higher than 80% accuracy, while the highest accuracy was observed from facial imaging signals (99.90%). Besides imaging signals, brain signals showed better potentiality than other data sources in both emotional categories, with accuracies of 99.40% and 96.88%. For both discrete and valence-arousal categories, neural network-based models illustrated superior performances. The majority of the neural network models achieved accuracies of over 80%, ranging from 80.14% to 99.90% in discrete, 83.79% to 96.88% in arousal, and 83.79% to 99.40% in valence. We also found that the performances of fusion signals (a combination of two or more signals) surpassed that of the individual ones in most cases, showing the importance of combining different signals for future model development. Overall, the potential implications of the survey are discussed, considering both human computing and mental health monitoring. The current study will definitely serve as the base for research in the field of human emotion recognition, with a particular focus on developing different robotic tools for mental health monitoring.

One of the leading cause of hyponatremia is syndrome of inappropriate antidiuretic hormone secretion (SIADH). Various etiologies of hyponatraemia have been observed till today but its association with Covid leading to SIADH is rare. Therefore, we present a case where SIADH was associated with Covid-19 pneumonia. This was a case of a 66 years old male with multiple co-morbidities presenting with symptoms of Covid infection including generalized weakness. After confirming Covid 19 infection management was started accordingly but patient’s weakness seemed to increase. He was found to have low sodium level of 105 mmol /L and investigations confirmed that he was having SIADH. He was treated with hypertonic saline, fluid restriction and his symptoms and laboratory parameters gradually improved. Bangladesh Crit Care J September 2020; 8(2): 126-128