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.

Metagenomics in the fight against zoonotic viral infections: A focus on SARS-CoV-2 analogues.

Zoonotic viral infections continue to pose significant threats to global public health, as highlighted by the COVID-19 pandemic caused by the SARS-CoV-2 virus. The emergence of SARS-CoV-2 served as a stark reminder of the potential for zoonotic transmission of viruses from animals to humans. Understanding the origins and dynamics of zoonotic viruses is critical for early detection, prevention, and effective management of future outbreaks. Metagenomics has emerged as a powerful tool for investigating the virome of diverse ecosystems, shedding light on the diversity of viral populations, their hosts, and potential zoonotic spillover events. We provide an in-depth examination of metagenomic approaches, including, NGS metagenomics, shotgun metagenomics, viral metagenomics, and single-virus metagenomics, highlighting their strengths and limitations in identifying and characterizing zoonotic viral pathogens. This review underscores the pivotal role of metagenomics in enhancing our ability to detect, monitor, and mitigate zoonotic viral infections, using SARS-CoV-2 analogues as a case study. We emphasize the need for continued interdisciplinary collaboration among virologists, ecologists, and bioinformaticians to harness the full potential of metagenomic approaches in safeguarding public health against emerging zoonotic threats.

Development of chemically synthesized hydroxyapatite composite with reduced graphene oxide for enhanced mechanical properties.

A successful attempt has been made to improve the mechanical properties of Hydroxyapatite (HAp) and reduced graphene oxide (rGO) composite nanoparticles (NPs). Various proportions of HAp and rGO were synthesized to improve the mechanical properties. HAp NPs were prepared using the wet precipitation method and further calcined to form crystalline particles. The physicochemical characterization of the HAp NPs revealed that the crystalline size and percentage of crystallinity were calculated to be 42.49 ± 1.2 nm and 44% post calcination. Furthermore, the rGO-HA composites were prepared using ball milling and obtained in the shape of pellets with different ratios of rGO (10, 20, 30, 40, 50% wt.). The mechanical properties have been evaluated through a Universal testing machine. Compared to calcined HAp (cHAp), the strength of variants significantly enhanced with the increased concentration of rGO. The compressive strength of HA-rGO with the ratio of the concentration of 60:40% by weight is a maximum of about 10.39 ± 0.43 MPa. However, the porosity has also been bolstered by increasing the concentration of rGO, which has been evaluated through the liquid displacement method. The mean surface roughness of the composites has also been evaluated from the images through Image J (an image analysis program).

Identification of Plausible Candidates in Prostate Cancer Using Integrated Machine Learning Approaches.

Background: Currently, prostate-specific antigen (PSA) is commonly used as a prostate cancer (PCa) biomarker. PSA is linked to some factors that frequently lead to erroneous positive results or even needless biopsies of elderly people. Objectives: In this pilot study, we undermined the potential genes and mutations from several databases and checked whether or not any putative prognostic biomarkers are central to the annotation. The aim of the study was to develop a risk prediction model that could help in clinical decision-making. Methods: An extensive literature review was conducted, and clinical parameters for related comorbidities, such as diabetes, obesity, as well as PCa, were collected. Such parameters were chosen with the understanding that variations in their threshold values could hasten the complicated process of carcinogenesis, more particularly PCa. The gathered data was converted to semi-binary data (-1, -0.5, 0, 0.5, and 1), on which machine learning (ML) methods were applied. First, we cross-checked various publicly available datasets, some published RNA-seq datasets, and our whole-exome sequencing data to find common role players in PCa, diabetes, and obesity. To narrow down their common interacting partners, interactome networks were analysed using GeneMANIA and visualised using Cytoscape, and later cBioportal was used (to compare expression level based on Z scored values) wherein various types of mutation w.r.t their expression and mRNA expression (RNA seq FPKM) plots are available. The GEPIA 2 tool was used to compare the expression of resulting similarities between the normal tissue and TCGA databases of PCa. Later, top-ranking genes were chosen to demonstrate striking clustering coefficients using the Cytoscape-cytoHubba module, and GEPIA 2 was applied again to ascertain survival plots. Results: Comparing various publicly available datasets, it was found that BLM is a frequent player in all three diseases, whereas comparing publicly available datasets, GWAS datasets, and published sequencing findings, SPFTPC and PPIMB were found to be the most common. With the assistance of GeneMANIA, TMPO and FOXP1 were found as common interacting partners, and they were also seen participating with BLM. Conclusion: A probabilistic machine learning model was achieved to identify key candidates between diabetes, obesity, and PCa. This, we believe, would herald precision scale modeling for easy prognosis.