Impact of synbiotic on growth performance, histo-architectural modulation of lymphoid organ, hematology, blood biochemistry and humoral immune response in naked neck chicken.

Synbiotics, which synergistically enhance the development and activity of beneficial bacteria in the gastrointestinal tract, play a crucial role in the growth and production of chickens. However, their effects on lymphoid organs and immunity in Naked Neck (NN) chickens are not well understood. This experiment aimed to investigate the effects of synbiotics on growth performance, histo-architecture of lymphoid organs, hematology, serum biochemistry, and immunity in NN chickens in Bangladesh.

Proximal tubular dysfunction as a predictor of AKI in Hospitalized COVID-19 patients.

Background: High concentration of Angiotensin converting enzyme receptors in the proximal tubules make kidneys an early target in COVID-19. Proximal tubular dysfunction (PTD) may act as an early predictor of acute kidney injury (AKI) and more severe disease.

Methods: This prospective observational study was conducted in the COVID unit, Bangabandhu Sheikh Mujib Medical University.

Exploring post-COVID-19 health effects and features with advanced machine learning techniques.

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state.

Digital health interventions for cervical cancer care: A systematic review and future research opportunities.

Background: Cervical cancer is a malignancy among women worldwide, which is responsible for innumerable deaths every year. The primary objective of this review study is to offer a comprehensive and synthesized overview of the existing literature concerning digital interventions in cervical cancer care. As such, we aim to uncover prevalent research gaps and highlight prospective avenues for future investigations.

A systematic review and future research agenda on detection of polycystic ovary syndrome (PCOS) with computer-aided techniques.

Polycystic Ovary Syndrome (PCOS) is among the most prevalent endocrinological abnormalities seen in reproductive female bodies posing serious health hazards. The correctness of interpreting this condition depends heavily on the wide spectrum of associated symptoms and the doctor's expertise, making real-time clinical detection quite challenging. Thus, investigations on computer-aided PCOS detection systems have recently been explored by several researchers worldwide as a potential replacement for manual assessment.

Clinical manifestations and outcomes of COVID-19 in maintenance hemodialysis patients of a high infectious epidemic country: a prospective cross-sectional study.

Introduction: The outbreak of COVID-19 poses great challenges for patients on maintenance haemodialysis. Here, we reported the clinical characteristics and laboratory features of maintenance haemodialysis (MHD) patients with COVID-19 in Bangladesh.

Methods: Altogether, 67 MHD patients were enroled in the study from two dedicated tertiary-level hospitals for COVID-19 after the prospective cross-sectional execution of selection criteria.

Exploring usability problems of mHealth applications developed for cervical cancer: An empirical study.

Objectives: Nowadays, mobile health applications are developed to raise awareness and facilitate screening and treatment of cervical cancer, while a very few studies have been conducted focusing on the measurement and assurance of usability and exploring the acceptable user experience of such applications. Usability issues become a crucial concern for such cervical-cancer-related applications because users with diverse backgrounds in terms of education, information technology literacy, and geographic reasons are required to access those applications. The objective of this research is to evaluate the usability of mobile health applications developed for cervical cancer patients.

Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique.

Polycystic ovary syndrome (PCOS) is the most frequent endocrinological anomaly in reproductive women that causes persistent hormonal secretion disruption, leading to the formation of numerous cysts within the ovaries and serious health complications. But the real-world clinical detection technique for PCOS is very critical since the accuracy of interpretations being substantially dependent on the physician's expertise. Thus, an artificially intelligent PCOS prediction model might be a feasible additional technique to the error prone and time-consuming diagnostic technique.

Predictis: an IoT and machine learning-based system to predict risk level of cardio-vascular diseases.

Background: Despite technological advancement in the field of healthcare, the worldwide burden of illness caused by cardio-vascular diseases (CVDs) is rising, owing mostly to a sharp increase in developing nations that are undergoing fast health transitions. People have been experimenting with techniques to extend their lives since ancient times. Despite this, technology is still a long way from attaining the aim of lowering mortality rates.

Leveraging machine learning to analyze sentiment from COVID-19 tweets: A global perspective.

Since the advent of the worldwide COVID-19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision-makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state-of-the-art technologies has been focused on during the COVID-19 pandemic, the reasons behind the variations in public sentiment are yet to be explored.

An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image.

Polycystic ovary syndrome (PCOS) is the most prevalent endocrinological abnormality and one of the primary causes of anovulatory infertility in women globally. The detection of multiple cysts using ovary ultrasonograpgy (USG) scans is one of the most reliable approach for making an accurate diagnosis of PCOS and creating an appropriate treatment plan to heal the patients with this syndrome. Instead of depending on error-prone manual identification, an intelligent computer-aided cyst detection system can be a viable approach.

A deep learning based multimodal interaction system for bed ridden and immobile hospital admitted patients: design, development and evaluation.

Background: Hospital cabins are a part and parcel of the healthcare system. Most patients admitted in hospital cabins reside in bedridden and immobile conditions. Though different kinds of systems exist to aid such patients, most of them focus on specific tasks like calling for emergencies, monitoring patient health, etc.

COVID-19 analytics: Towards the effect of vaccine brands through analyzing public sentiment of tweets.

The COVID-19 outbreak has created effects on everyday life worldwide. Many research teams at major pharmaceutical companies and research institutes in various countries have been producing vaccines since the beginning of the outbreak. There is an impact of gender on vaccine responses, acceptance, and outcomes.

UVC-PURGE: A Novel Cost-Effective Disinfection Robot for Combating COVID-19 Pandemic.

During the COVID-19 pandemic, surface disinfection using prevailing chemical disinfection methods had several limitations. Due to cost-inefficiency and the inability to disinfect shaded places, static UVC lamps cannot address these limitations properly. Moreover, the average market price of the prevailing UVC robots is huge, approximately 55,165 USD.

Machine learning to predict pregnancy outcomes: a systematic review, synthesizing framework and future research agenda.

Machine Learning (ML) has been widely used in predicting the mode of childbirth and assessing the potential maternal risks during pregnancy. The primary aim of this review study is to explore current research and development perspectives that utilizes the ML techniques to predict the optimal mode of childbirth and to detect various complications during childbirth. A total of 26 articles (published between 2000 and 2020) from an initial set of 241 articles were selected and reviewed following a Systematic Literature Review (SLR) approach.

COVID-19 and black fungus: Analysis of the public perceptions through machine learning.

While COVID-19 is ravaging the lives of millions of people across the globe, a second pandemic "black fungus" has surfaced robbing people of their lives especially people who are recovering from coronavirus. Thus, the objective of this article is to analyze public perceptions through sentiment analysis regarding black fungus during the COVID-19 pandemic. To attain the objective, first, a support vector machine (SVM) model, with an average AUC of 82.

A Systematic Review of the Digital Interventions for Fighting COVID-19: The Bangladesh Perspective.

The objective of this paper is to synthesize the digital interventions initiatives to fight against COVID-19 in Bangladesh and compare with other countries. In order to obtain our research objective, we conducted a systematic review of the online content. We first reviewed the digital interventions that have been used to fight against COVID-19 across the globe.

A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic.

Artificial intelligence (AI) and machine learning (ML) have caused a paradigm shift in healthcare that can be used for decision support and forecasting by exploring medical data. Recent studies have shown that AI and ML can be used to fight COVID-19. The objective of this article is to summarize the recent AI- and ML-based studies that have addressed the pandemic.

Towards developing a secure medical image sharing system based on zero trust principles and blockchain technology.

Background: Data security has been a critical topic of research and discussion since the onset of data sharing in e-health systems. Although digitalization of data has increased efficiency and speed, it has also made data vulnerable to cyber attacks. Medical records in particular seem to be the regular victims of hackers.

A Review on the Mobile Applications Developed for COVID-19: An Exploratory Analysis.

The objective of this research is to explore the existing mobile applications developed for the COVID-19 pandemic. To obtain this research objective, firstly the related applications were selected through the systematic search technique in the popular application stores. Secondly, data related to the app objectives, functionalities provided by the app, user ratings, and user reviews were extracted.

Investigating usability of mobile health applications in Bangladesh.

Background: Lack of usability can be a major barrier for the rapid adoption of mobile services. Therefore, the purpose of this paper is to investigate the usability of Mobile Health applications in Bangladesh.

Method: We followed a 3-stage approach in our research.

Seasonal morphological changes in the ovary of the Jungle crow (Corvus macrorhynchos).

Morphometric and histological studies were conducted to examine the seasonal ovarian changes in the Jungle crow of the Kanto area, Japan, from December to June. The ovary weights, largest diameters and atresias of the ovarian follicles and steroid-producing cells were examined. Hematoxylin and eosin-stained ovary sections and ImageJ software were used.

Histological and morphometric analyses of seasonal testicular variations in the Jungle Crow (Corvus macrorhynchos).

A histological and morphometric study was conducted to examine the seasonal testicular variations in the Jungle Crow (Corvus macrorhynchos) of the Kanto area, Japan, from January to July. The paired testes mass, diameter and number of germ cells of the seminiferous tubules, and proportion of seminiferous tubule area and interstitium were examined. Hematoxylin and eosin-stained testis sections and ImageJ Software were used.