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BackgroundUric acid is associated with noncommunicable diseases such as cardiovascular diseases, chronic kidney disease, coronary artery disease, stroke, diabetes, metabolic syndrome, vascular dementia, and hypertension. Therefore, uric acid is considered to be a risk factor for the development of noncommunicable diseases. Most studies on uric acid have been performed in developed countries, and the application of machine-learning approaches in uric acid prediction in developing countries is rare. Different machine-learning algorithms will work differently on different types of data in various diseases; therefore, a different investigation is needed for different types of data to identify the most accurate algorithms. Specifically, no study has yet focused on the urban corporate population in Bangladesh, despite the high risk of developing noncommunicable diseases for this population. ObjectiveThe aim of this study was to develop a model for predicting blood uric acid values based on basic health checkup test results, dietary information, and sociodemographic characteristics using machine-learning algorithms. The prediction of health checkup test measurements can be very helpful to reduce health management costs. MethodsVarious machine-learning approaches were used in this study because clinical input data are not completely independent and exhibit complex interactions. Conventional statistical models have limitations to consider these complex interactions, whereas machine learning can consider all possible interactions among input data. We used boosted decision tree regression, decision forest regression, Bayesian linear regression, and linear regression to predict personalized blood uric acid based on basic health checkup test results, dietary information, and sociodemographic characteristics. We evaluated the performance of these five widely used machine-learning models using data collected from 271 employees in the Grameen Bank complex of Dhaka, Bangladesh. ResultsThe mean uric acid level was 6.63 mg/dL, indicating a borderline result for the majority of the sample (normal range

Education is one of the most important factors in producing human resources. University education can be influenced by many factors that affect students' quality of academic performance. The main focus of this study is identifying the influencing factors that are significantly associated with the academic performance of universities students. Data were extracted from the various departments of two public universities. Data analysis by Contingency and logistic regression were used to identify the determinants of academic performance of the students. Among the students, 56.8% were male and 43.2% were female most of them are Muslims and unmarried. 75.1% of students came from rural areas, only 15% of students' mothers were higher educated and 65% of student's family status was middle class. Higher educated mother, HSC and SSC results, admitted to an expected university, admitted present department by their own choice, study environment in the department and use of the internet had significant effects on the results of 1st year of the students. The findings concluded that increase and improve students' academic performance are some crucial steps regarding securing admission first choice of the department and advising about peer influence should be taken into consideration.

BackgroundThe prevalence of non-communicable diseases is increasing throughout the world, including developing countries. ObjectiveThe intent was to conduct a study of a preventive medical service in a developing country, combining eHealth checkups and teleconsultation as well as assess stratification rules and the short-term effects of intervention. MethodsWe developed an eHealth system that comprises a set of sensor devices in an attaché case, a data transmission system linked to a mobile network, and a data management application. We provided eHealth checkups for the populations of five villages and the employees of five factories/offices in Bangladesh. Individual health condition was automatically categorized into four grades based on international diagnostic standards: green (healthy), yellow (caution), orange (affected), and red (emergent). We provided teleconsultation for orange- and red-grade subjects and we provided teleprescription for these subjects as required. ResultsThe first checkup was provided to 16,741 subjects. After one year, 2361 subjects participated in the second checkup and the systolic blood pressure of these subjects was significantly decreased from an average of 121 mmHg to an average of 116 mmHg (P

Aim: Calcium hydroxide (CH) has been considered as a direct pulp capping materials (DPC) for the last decades despite having some limitations. Phosphorylate pullulan (PPL) incorporated with CH (CHPPL) is a novel biomaterial that was introduced as a promising DPC material. Thus, the aim of the study was to evaluate the inflammatory response and mineralized tissue formation (MTF) ability of PPL‐based CH formulations on rat molars after DPC. Methodology: This study consisted of six groups: CH with 1% PPL (CHPPL‐1); 3% PPL (CHPPL‐3); 5% PPL (CHPPL‐5); Dycal and NEX MTA Cement (N‐MTA) as the positive control, and no capping materials (NC). One hundred twenty maxillary first molar cavities were prepared on Wistar rats. After capping, all the cavities were restored with 4‐META/MMA‐TBB resin and pulpal responses were evaluated at days 1, 7, and 28. Kruskal–Wallis followed by Mann–Whitney U‐test was performed with a significance level of 0.05. Immunohistochemical expression of IL‐6, Nestin, and DMP‐1 was observed. Results: At day 1, CHPPL‐1, N‐MTA, and Dycal exhibited no to mild inflammation, whilst CHPPL‐3, CHPPL‐5, and NC showed mild to moderate inflammation, and the results were significantly different (p <.05). At day 7, mild to moderate inflammation was observed in CHPPL‐1, N‐MTA, and Dycal, whereas CHPPL‐3, CHPPL‐5, and NC exhibited moderate to severe inflammation. Significant differences were observed between CHPPL‐1 and N‐MTA with NC (p <.05), CHPPL‐1 and CHPPL‐3 with CHPPL‐5 and Dycal (p <.05), and CHPPL‐3 with N‐MTA (p <.05). A thin layer of mineralized tissue formation (MTF) was observed in all groups. At day 28, CHPPL‐1, Dycal, and N‐MTA showed no to mild inflammation, whilst CHPPL‐3, CHPPL‐5, and NC exhibited mild to severe inflammation, and statistically significant difference was detected (p <.05). CHPPL‐1, Dycal, and N‐MTA exhibited continuous MTF, whilst CHPPL‐3, CHPPL‐5, and NC had thicker and interrupted MTF. Significant differences were observed between CHPPL‐1, CHPPL‐3, and N‐MTA with NC group (p <.05). Variable expressions of IL‐6, Nestin, and DMP‐1 indicated differences in the materials' impact on odontoblast‐like cell formation and tissue mineralization. Conclusions: These findings suggest that CHPPL‐1 has the potential to minimize pulpal inflammation and promote MTF and had similar efficacy as MTA cement. [ABSTRACT FROM AUTHOR]

The present study aimed to identify novel biostimulatory compounds in boar seminal gel (SG), saliva and semen using Gas chromatography-mass spectrometry (GC-MS). The bio-stimulatory effect of SG, SG + saliva and SG + semen on young boar for semen collection as well were employed to study bio-stimulatory effects on gilts and sows. Distilled water (DW) exposure was kept as control. SG, saliva and semen were screened for total 105, 96 and 89 compounds. The highest concentration was of alkanes followed by sugar alcohols, then hydrocarbons, amino acids and fatty acids. Elaidic acid was the novel compound identified in pigs. Significantly higher (p < 0.05) number of males got trained in exposure to SG (80%), SG + saliva (75%) and SG + semen (75%) than control (0%). The time (hrs) taken by young boars to get trained on exposure to combination of SG + saliva (244 ± 22.19) and SG + semen (216 ± 13.14) was lesser (p < 0.05) than SG (356 ± 61.85) alone. Interval (hrs) from initiation of exposure for exhibition of different sexual behaviour by males on exposure to SG, saliva and semen was lesser (p < 0.05) than control. Significantly (p < 0.05) higher number of females showed estrus response to exposure of SG (72.72%), SG + saliva (69.23%) and SG + semen (76.92%) than control (0). Interval (hrs) taken to exhibit estrus was shorter (p < 0.05) in females exposed to SG + saliva (201.88 ± 12.66), SG + semen (198.20 ± 9.42) than SG (262.14 ± 20.06) alone. Interval (hrs) for exhibition of different sexual behaviour by females on exposure to SG + saliva and SG + semen was lesser (p < 0.05) than control. In conclusion, novel compounds were identified in boar seminal gel, saliva and semen with biostimulatory properties have been identified in boar SG, saliva and semen. The combined exposure of SG with saliva and semen has more intense biostimulation effect than SG alone for training of young boars and estrus induction in gilts and sows. Such compounds biostimulatory effects can be exploited for augmenting reproductive efficiency in pigs. [ABSTRACT FROM AUTHOR]

Cinchona alkaloid-derived sulfonamides and ester dimers containing chiral hyperbranched polymers have been successfully synthesized and applied as catalysts in asymmetric reactions. Several hyperbranched polymers derived from cinchona alkaloids, incorporating sulfonamides and esters, were synthesized through Mizoroki--Heck coupling polymerization. These polymers were subsequently applied in enantioselective Michael addition reactions. As the prepared polymers are not soluble in frequently used organic solvents, they act as efficient catalysts in the enantioselective reaction of β-ketoesters to nitroolefins, achieving up to 99% enantioselectivity with good yields. The insoluble property allows them to better satisfy "green chemistry" requirements and be used several times without losing the enantioselectivity. [ABSTRACT FROM AUTHOR]

Objective: The purpose of the current study was to examine the effectiveness of green macroalgae as a novel, natural feed additive for broilers that have a greater concentration of active ingredients. Materials and Methods: Four experimental groups of 180-day-old male broiler chicks (Cobb-500) were randomly assigned, with three replicates in each group: the control group [T0, maintained only with basal diet] and three treatment groups supplemented with macroalgae for 35 days along with basal diet [T1 = 0.05% (w/w); T2 = 0.1% (w/w); T3 = 0.2% (w/w) macroalgae]. Live weight, carcass weight, and organs' weight were noted at the conclusion of the experiment. The meat quality was examined using the muscles of the thighs and breasts, and blood serum was obtained for biochemical assessment. Results: The results revealed that dietary supplementation of green macroalgae (0.1%) in broiler rations significantly (p < 0.05) improved the growth performance compared to other treated groups and controls. With increasing weight, it enhanced meat quality traits assessed by increased water holding capacity, ultimate pH, redness and yellowness, and decreased lightness of muscles in the thighs and breasts. Both the levels of serum cholesterol and abdominal fat decreased and showed no unwholesome effects on liver and kidney functions. Conclusions: For the production of safe and high-quality poultry meat, marine green macroalgae (Enteromorpha intestinalis) could be used as a potential feed additive. It enhanced the growth rate in broilers and improved meat quality and serum biochemical parameters for supplying healthy meat in the human food chain. [ABSTRACT FROM AUTHOR]

Prostate cancer is a common cancer with significant implications for global health. Prompt and precise identification is crucial for efficient treatment strategizing and enhanced patient results. This research study investigates the utilization of machine learning techniques to diagnose prostate cancer. It emphasizes utilizing deep learning models, namely VGG16, VGG19, ResNet50, and ResNet50V2, to extract relevant features. The random forest approach then uses these features for classification. The study begins by doing a thorough comparison examination of the deep learning architectures outlined above to evaluate their effectiveness in extracting significant characteristics from prostate cancer imaging data. Key metrics such as sensitivity, specificity, and accuracy are used to assess the models' efficacy. With an accuracy of 99.64%, ResNet50 outperformed other tested models when it came to identifying important features in images of prostate cancer. Furthermore, the analysis of understanding factors aims to offer valuable insights into the decision‐making process, thereby addressing a critical problem for clinical practice acceptance. The random forest classifier, a powerful ensemble learning method renowned for its adaptability and ability to handle intricate datasets, then uses the collected characteristics as input. The random forest model seeks to identify patterns in the feature space and produce precise predictions on the presence or absence of prostate cancer. In addition, the study tackles the restricted availability of datasets by utilizing transfer learning methods to refine the deep learning models using a small amount of annotated prostate cancer data. The objective of this method is to improve the ability of the models to generalize across different patient populations and clinical situations. This study's results are useful because they show how well VGG16, VGG19, ResNet50, and ResNet50V2 work for extracting features in the field of diagnosing prostate cancer, when used with random forest's classification abilities. The results of this work provide a basis for creating reliable and easily understandable machine learning‐based diagnostic tools for detecting prostate cancer. This will enhance the possibility of an early and precise diagnosis in clinical settings such as index terms deep learning, machine learning, prostate cancer, cancer identification, and cancer classification. [ABSTRACT FROM AUTHOR]

Brain tumors are critical neurological ailments caused by uncontrolled cell growth in the brain or skull, often leading to death. An increasing patient longevity rate requires prompt detection; however, the complexities of brain tissue make early diagnosis challenging. Hence, automated tools are necessary to aid healthcare professionals. This study is particularly aimed at improving the efficacy of computerized brain tumor detection in a clinical setting through a deep learning model. Hence, a novel thresholding-based MRI image segmentation approach with a transfer learning model based on contour (ContourTL-Net) is suggested to facilitate the clinical detection of brain malignancies at an initial phase. The model utilizes contour-based analysis, which is critical for object detection, precise segmentation, and capturing subtle variations in tumor morphology. The model employs a VGG-16 architecture priorly trained on the "ImageNet" collection for feature extraction and categorization. The model is designed to utilize its ten nontrainable and three trainable convolutional layers and three dropout layers. The proposed ContourTL-Net model is evaluated on two benchmark datasets in four ways, among which an unseen case is considered as the clinical aspect. Validating a deep learning model on unseen data is crucial to determine the model's generalization capability, domain adaptation, robustness, and real-world applicability. Here, the presented model's outcomes demonstrate a highly accurate classification of the unseen data, achieving a perfect sensitivity and negative predictive value (NPV) of 100%, 98.60% specificity, 99.12% precision, 99.56% F 1 -score, and 99.46% accuracy. Additionally, the outcomes of the suggested model are compared with state-of-the-art methodologies to further enhance its effectiveness. The proposed solution outperforms the existing solutions in both seen and unseen data, with the potential to significantly improve brain tumor detection efficiency and accuracy, leading to earlier diagnoses and improved patient outcomes. [ABSTRACT FROM AUTHOR]

Since the settlement of Rohingya people in Cox's Bazar, national and international NGO s have undertaken initiatives such as trust-building and community relationships for establishing social cohesion between the Rohingyas and host communities. However, cohabitation between the two communities is far-behind, and social conflict between the two communities has been a reality for the host society. By applying multiple approaches – perception survey among the local people, focus group discussion (FGD) among the Rohingyas and key informant interviews, this article finds that Rohingya settlement has resulted many challenges such as scarcity of resources, competition over livelihood options, price hikes, decaying of the physical environment, and security complexities. These challenges have been a source of generating conflict instead of building social cohesion in Cox's Bazar. Survey results outline that local people are anxious about population growth, social unrest, illicit business and drugs, and potential political conflict. The FGD among the Rohingyas explores the uncertain future of the refugee, frustration over livelihood, health, and education for their children. Overall results highlight that social, economic, environmental and political changes in the post-settlement phase post-2017 have been a potential source of conflict. Analysis suggests that the conflict potential for the host society stemming from the Rohingya displaced population is significant enough that the host government and international organisations should consider actions, policies, and diplomatic actions to address the social tension and remedy the emergent problems in the area. Such measures include those that strengthen efforts at repatriation initiatives for the refugee population, and those that can ensure livelihood opportunities for them without compromising such opportunities for the host population. [ABSTRACT FROM AUTHOR]