Patient Record Maintenance Among Private Dental Practitioners in Bangalore City

Aim and Objectives: The aim of the study was to obtain information on documentation of patient record maintenance among private dental practitioners in Bangalore city”. The objective was to assess the knowledge, attitude and behavior regarding documentation and patient record maintenance among private dental practitioners in Bangalore city. Method: A self- administered questionnaire survey was conducted in August-September 2021 among Dentists engaged in active clinical practices in private clinics/hospitals in Bangalore city, India [ N=470]. Results: A response rate of 86.4% [n=411] was obtained.73% of them were aware of the documentation of patient records as per Laws, Ethics and Jurisprudence act of 1997. 53% of them documented patient records in their clinical practice and 64% mentioned that only they have access to patient records.13% mentioned that they include patient consent as a part of documentation. Although 73% of them said that they were aware of all the guidelines, a large proportion of respondents lack knowledge about the minimum time period for which patient records should be maintained in their possession. 21% of them felt that patient record keeping is not necessary and the most common problem cited for documentation of patient records was lack of time to give attention to the records. Conclusion: Dentists needs more information and should spare more time for documentation of patient records

Supply Chain Optimization in Industry 5.0: An Experimental Investigation Using Al

This experimental study examines the use of AI-driven supply chain management solutions in the framework of Industry 5.0. An analysis of fictitious data that represented product inventory, supplier details, customer orders, and transportation details showed significant cost savings in transportation logistics (10%), improvements in supplier cost efficiency (20%), and significant reductions in excess inventory (10%). In Industry 5.0, artificial intelligence (AI) emerges as a key technology that can promote effective, customer-focused, and sustainable supply chains

Screening of recombinant inbred lines for resistance to bacterial leaf blight pathotypes in rice (Oryza sativa L.)

In the present investigation 16 recombinant inbred lines (RIL’s) developed from the intra-specific cross between YH3 and AKDRMS 21-54 through Marker Assisted Pedigree Breeding Method were screened along with their parents and the checks, namely, BPT 5204, TN1 and Improved Samba Mahsuri (ISM) against IxoPt-20 pathotype at the ICAR-Indian Institute of Rice Research, Hyderabad during Rabi 2021-22 and a new pathotype of Xanthomonas oryzae pv. oryzae causing Bacterial Leaf Blight disease in rice at Regional Agricultural Research Station, Maruteru during Kharif 2022 to identify pathotype specific resistant sources.  Morpho-Molecular screening was adopted to evaluate the recombinant inbred lines over two locations in the consecutive seasons of Rabi 2021-22 and Kharif 2022. Based on per cent diseased leaf area, the genotypes were scored and categorised as per the Standard Evaluation System (SES) scale provided by International Rice Research Institute (IRRI). The results revealed all 16 RIL’s to be either resistant (10) or moderately resistant (6) to IxoPt-20 pathotype. However, only five RIL’s were found to be resistant, while four RIL’s were moderately resistant for the new virulent pathotype. Seven RIL’s with resistant to moderately resistant reaction for IxoPt-20 pathotype, showed moderately susceptible reaction for the new virulent pathotype.  Among the resistant RIL’s identified for each pathotype, BPT-1901-72-10-6, BPT-1901-108-4-1 and BPT-1901-111-3-2 were found to be uniformly resistant, while, BPT-1901-45-8-6 and BPT-1901-163-1-18 were uniformly moderately resistant to both IXoPt-20 and the new virulent pathotype at Hyderabad and Maruteru, respectively, indicating their potential as genetic stocks for development of new cultivars resistant to bacterial leaf blight disease

Data-Intensive Traffic Management: Real-Time Insights from the Traffic Management Simulation Test

This research examined the effectiveness of data-intensive traffic management in urban settings using real-time insights from traffic management simulation experiments. The examination of data on traffic flow revealed a noteworthy decrease in congestion, with a 25% increase in traffic velocity during peak hours. Real-time information led to a 40% drop in the severity of traffic accidents and a 50% reduction in reaction times. Improved road safety was aided by a 30% decrease in accidents during inclement weather thanks to real-time weather data. To further optimize urban traffic flow, dynamic traffic management operations based on real-time information also resulted in a 20% reduction in congestion. These results highlight the revolutionary potential of data-intensive traffic management, offering safer and more effective urban transportation solutions by incorporating real-time information into traffic control plans

Edge Computing and AI: Advancements in Industry 5.0- An Experimental Assessment

This empirical research evaluated, via experimentation, how Edge Computing and Artificial Intelligence (AI) work together in the context of Industry 5.0. With a high satisfaction rating of 88%, participants in the Edge Computing condition saw an astonishing 18% decrease in task completion times. Similarly, in the AI integration scenario, participants rated AI's value at 86%, and they saw a significant 12% reduction in task completion times and a noteworthy 7% drop in mistake rates. Significantly, with an astounding 21% gain in work completion times, the Edge Computing and AI combo had the largest performance boost. These results highlight how Edge Computing and AI may dramatically improve industrial efficiency and performance in the context of Industry 5.0, providing insightful information for businesses looking to use these technologies to streamline processes and spur innovation

Influence of plant growth-promoting rhizobacteria inoculation on nutrient availability, soil microbial properties and defence enzymes in rice (Oryza sativa) crop

Soil organic carbon, available N, P, K, Zn, Fe and Cu in soil at crop harvest stage significantly increased due to the integrated inoculation of PGPR and Multani mitti based BGA with application of compost and chemical N fertilizer (80 and 120 kg/ha) over N control. Status of SOC and available N, P, K, Fe, Zn and Cu in soil at crop maturity stage was highest at 2/3N+BI+CI+C@ 5.0 tonnes/ha. Soil chlorophyll, dehydrogenase and ARA activity were significantly higher at 2/3N+BI+CI+C@ 5.0 tonnes/ha compared to other treatments. Plant enzymatic activity of POD and PPO at 60 DAT also was significantly higher in 2/3N+BI+CI+Compost@ 5.0 tonnes/ha. Application of 2/ 3N with inoculation of bacterial and cyanobacterial PGPR along with 5.0 tonnes/ha compost not only improved nutrient availability in soil but also enhanced soil microbial, plant enzymatic activity and crop yield

Machine larning-based intelligent wireless communication system for solving real-world security issues

The intelligent wireless system focuses on integrating with the advanced technologies like machine learning and related approaches in order to enhance the performance, productivity, and output. The implementation of machine learning approaches is mainly applied in order to enhance the efficient communication system, enable creation of variable node locations, support collection of data and information, analyze the pattern, and forecast so as to provide better services to the end users. The efficiency of using these technologies tend to lower the cost and support in deploying the resources effectively. The wireless network system tends to enhance the bandwidth, and the application of novel machine learning approaches supports detection of unrelated data and information and enables analysis of latency at each part of the communication channel. The study involves critically analyzing the key determinants of machine learning approaches in supporting enhanced intelligent network communication in the industries. The researchers are aimed at gathering both primary data and secondary data for the study. The respondents are chosen in the industry so that they can provide better inputs and insights related to the area of research. The key determinants considered for the study are machine learning-influenced management of hotspots, identification of critical congestion points, spectrum availability, and management. The analysis is made using SPSS data analysis package based on which it is noted that all the factors make major influences towards the intelligent communication, and hence machine learning supports critically in enhancing the user experience effectively.Campus At

Using ICP-OES and SEM-EDX in biosorption studies

We have compared the analytical results obtained by inductively coupled plasma optical emission spectroscopy (ICP-OES) and by scanning electron microscopy with an energy dispersive X-ray analytical system (SEM-EDX) in order to explore the mechanism of metal ions biosorption by biomass using two independent methods. The marine macroalga Enteromorpha sp. was enriched with Cu(II), Mn(II), Zn(II), and Co(II) ions via biosorption, and the biosorption capacity of alga determined from the solution and biomass composition before and after biosorption process was compared. The first technique was used to analyze the composition of the natural and metal-loaded biomass, and additionally the composition of the solution before and after biosorption. The second technique was used to obtain a picture of the surface of natural and metal ion-loaded macroalgae, to map the elements on the cell wall of dry biomass, and to determine their concentration before and after biosorption. ICP-OES showed a better precision and lower detection limit than EDX, but SEM-EDX gave more information regarding the sample composition of Enteromorpha sp. Both techniques confirmed that biosorption is a surface phenomenon, in which alkali and alkaline earth metal ions were exchanged by metal ions from aqueous solution

Association analysis of ADPRT1, AKR1B1, RAGE, GFPT2 and PAI-1 gene polymorphisms with chronic renal insufficiency among Asian Indians with type-2 diabetes

<p>Abstract</p> <p>Background</p> <p>To determine association of nine single nucleotide polymorphisms (SNPs) in ADP ribosyltransferase-1 (ADPRT1), aldo-keto reductase family 1 member B1 (AKR1B1), receptor for advanced glycation end-products (RAGE), glutamine:fructose-6-phosphate amidotransferase-2 (GFPT2), and plasminogen activator inhibitor-1 (PAI-1) genes with chronic renal insufficiency (CRI) among Asian Indians with type 2 diabetes; and to identify epistatic interactionss between genes from the present study and those from renin-angiotensin-aldosterone system (RAAS), and chemokine-cytokine, dopaminergic and oxidative stress pathways (previously investigated using the same sample set).</p> <p>Methods</p> <p>Type 2 diabetes subjects with CRI (serum creatinine ≥3.0 mg/dl) constituted the cases (n = 196), and ethnicity and age matched individuals with diabetes for a duration of ≥ 10 years, normal renal functions and normoalbuminuria recruited as controls (n = 225). Allelic and genotypic constitution of 10 polymorphisms (SNPs) from five genes namely- <it>ADPRT1</it>, <it>AKR1B1, RAGE, GFPT2 </it>and <it>PAI-1 </it>with diabetic CRI was investigated. The genetic associations were evaluated by computation of odds ratio and 95% confidence interval. Multiple logistic regression analysis was carried out to correlate various clinical parameters with genotypes, and to study epistatic interactions between SNPs in different genes.</p> <p>Results</p> <p>Single nucleotide polymorphisms -429 T>C in <it>RAGE </it>and rs7725 C>T SNP in 3' UTR in <it>GFPT2 </it>gene showed a trend towards association with diabetic CRI. Investigation using miRBase statistical tool revealed that rs7725 in <it>GFPT2 </it>was a perfect target for predicted miRNA (hsa miR-378) suggesting the presence of the variant 'T' allele may result in an upregulation of GFPT2 contributing to diabetic renal complication. Epistatic interaction between SNPs in transforming growth factor <it>TGF-β1 </it>(investigated using the same sample set and reported elsewhere) and <it>GFPT2 </it>genotype was observed.</p> <p>Conclusions</p> <p>Association of SNPs in <it>RAGE </it>and <it>GFPT2 </it>suggest that the genes involved in modulation of oxidative pathway could be major contributor to diabetic chronic renal insufficiency. In addition, GFPT2 mediated overproduction of TGF-β1 leading to endothelial expansion and thereby CRI seems likely, suggested by our observation of a significant interaction between GFPT2 with TGF-β1 genes. Further, identification of predicted miRNA targets spanning the associated SNP in <it>GFPT2 </it>implicates the rs7725 SNP in transcriptional regulation of the gene, and suggests <it>GFPT2 </it>could be a relevant target for pharmacological intervention. Larger replication studies are needed to confirm these observations.</p