Synthesis of Pectin and Eggshell Biowaste-Mediated Nano-hydroxyapatite (nHAp), Their Physicochemical Characterizations, and Use as Antibacterial Material.

The current study reports the synthesis of sustainable nano-hydroxyapatite (nHAp) using a wet chemical precipitation approach. The materials used in the green synthesis of nHAp were obtained from environmental biowastes such as HAp from eggshells and pectin from banana peels. The physicochemical characterization of obtained nHAp was carried out using different techniques. For instance, X-ray diffractometer (XRD) and FTIR spectroscopy were used to study the crystallinity and synthesis of nHAp respectively. In addition, the morphology and elemental composition of nHAP were studied using FESEM equipped with EDX. HRTEM showed the internal structure of nHAP and calculated its grain size which was 64 nm. Furthermore, the prepared nHAp was explored for its antibacterial and antibiofilm activity which has received less attention previously. The obtained results showed the potential of pectin-bound nHAp as an antibacterial agent for various biomedical and healthcare applications.

Physical Activity Maintain Immune Response Through TLR-2/TLR-4 Gene Expression in Type-2 Diabetes Mellitus Patient at Medan City.

Background: The Increasing in type-2 diabetes mellitus (T2DM) needs to solve comprehensively and holistically. Patients with T2DM should have self-coping due to lifestyle modification. Abdominal fat accumulation can release pro-inflammatory cytokine that leads TLR-2 and TLR-4 to the response. These two kinds of toll-like receptors exist on the monocyte surface membrane which is an innate immunity cell. Objective: The aims of this study were to get the profile of physical activity, metabolic state, and mononuclear cell response to the expression of the TLR2 and TLR4 genes in T2DM patients. Methods: It was a descriptive-analytic study with a cross-sectional study design. Thirty-two eligible patients with inclusion criteria participated as subjects. All subjects answered questions by IPAQ, and checked metabolic state with body composition analysis. The TLR2 and TLR4 gene expression was determined with quantitative Real- Time PCR. Results: This study result found that most T2DM patients were in a highly active category in which most of their activity was walking (light intensity). The average abdominal circumferences were 91.81 ± 15.4 cm, body fat percentage was 29.5 ± 8.8%, and fasting blood sugar was 187.07 ± 67.03 mg/dl. Mononuclear cells number were normal. The expression of the TLR2 gene was lower by 0.71 fold and TLR4 gene expression was lower by 0.9 fold compared with non-DM (p<0.05). By chi-square test, there was a positive correlation between TLR2 gene expression with fasting blood glucose (p=0.011, and a positive correlation between the abdominal circumference and TLR4 gene expression (p=0.011). Conclusion: Type-2 Diabetes mellitus patients in primary health care keep walking as their physical activity to maintain blood glucose. Patients need to do moderate to vigorous exercise regularly to reduce body fat percentage especially abdominal fat to reduce Toll-like receptor gene expression, so insulin resistance and blood glucose level might decline to normal.

Multi-scale feature fusion-based lightweight dual stream transformer for detection of paddy leaf disease.

Traditionally, rice leaf disease identification relies on a visual examination of abnormalities or an analytical result obtained by growing bacteria in the research lab. This method of visual evaluation is qualitative and error-prone. On the other hand, an artificial neural network system is fast and more accurate. Several pieces of research using traditional machine learning and deep convolution neural networks (CNN) have been utilized to overcome the issues. Still, these methods need more semantic contextual global and local feature extraction. Due to this, efficiency is less. Hence, in the present study, a multi-scale feature fusion-based RDTNet has been designed. The RDTNet contains two modules, and the first module extracts feature via three scales from the local binary pattern (LBP), gray, and a histogram of orient gradient (HOG) image. The second module extracts semantic global and local features through the transformer and convolution block. Furthermore, the computing cost is reduced by dividing the query into two parts and feeding them to convolution and the transformer block. The results indicate that the proposed method has a very high average precision, f1-score, and accuracy of 99.55%, 99.54%, and 99.53%, respectively. It is suggestive of improved classification accuracy using multi-scale features and the transformer. The model has also been validated on other datasets confirming that the present model can be used for real-time rice disease diagnosis. In the future, such models can be used for monitoring other crops, including wheat, tomato, and potato.