09, 95% CI = 1.24-3.54; male, adjusted HR 1.87, 95% CI = 0.97-3.60). People with HTN, hyperlipidemia, asthma/COPD, and chronic liver disease were respectively 1.73, 2.3, 2.2, and 1.69 times more likely to suffer from depression than those without these comorbidities (HTN, adjusted HR 0.75, 95% CI = 0.41-1.42; hyperlipidemia, adjusted HR 1.48, 95% CI = 0.78-2.82; asthma/COPD, adjusted HR 1.4, 95% CI = 0.72-2.69; and chronic liver disease, adjusted HR 1.61, 95% CI = 1.07-2.43). There was a significant association between pemphigus and increased risk of depression. Female patients had a higher incidence of depression.The rising prevalence and global burden of diabetes fortify the need for more comprehensive and effective management to prevent, monitor, and treat diabetes and its complications. Applying artificial intelligence in complimenting the diagnosis, management, and prediction of the diabetes trajectory has been increasingly common over the years. This study aims to illustrate an inclusive landscape of application of artificial intelligence in diabetes through a bibliographic analysis and offers future direction for research.  https://www.selleckchem.com/products/sc-43.html  Bibliometrics analysis was combined with exploratory factor analysis and latent Dirichlet allocation to uncover emergent research domains and topics related to artificial intelligence and diabetes. Data were extracted from the Web of Science Core Collection database. The results showed a rising trend in the number of papers and citations concerning AI applications in diabetes, especially since 2010. The nucleus driving the research and development of AI in diabetes is centered around developed countries, mainly consisting of the United States, which contributed 44.1% of the publications. Our analyses uncovered the top five emerging research domains to be (i) use of artificial intelligence in diagnosis of diabetes, (ii) risk assessment of diabetes and its complications, (iii) role of artificial intelligence in novel treatments and monitoring in diabetes, (iv) application of telehealth and wearable technology in the daily management of diabetes, and (v) robotic surgical outcomes with diabetes as a comorbid. Despite the benefits of artificial intelligence, challenges with system accuracy, validity, and confidentiality breach will need to be tackled before being widely applied for patients' benefits.Porcine circovirus 2 (PCV-2) is one of the most impactful and widespread pathogens of the modern swine industry. Unlike other DNA viruses, PCV-2 is featured by a remarkable genetic variability, which has led to the emergence and recognition of different genotypes, some of which (PCV-2a, 2b, and 2d) have alternated over time. Currently, PCV-2d is considered the most prevalent genotype, and some evidence of differential virulence and vaccine efficacy have been reported. Despite the potential practical relevance, the data on PCV-2 epidemiology in Italy are quite outdated and do not quantify the actual circulation of this genotype in Italy. In the present study, 82 complete ORF2 sequences were obtained from domestic pigs and wild boars sampled in Northern Italy in the period 2013-2018 and merged with those previously obtained from Italy and other countries. A combination of phylogenetic, haplotype network, and phylodynamic analyses were used to genotype the collected strains and evaluate the temporal trend and the spatial and host spread dynamics. A rising number of PCV-2d detections was observed in domestic pigs, particularly since 2013, reaching a detection frequency comparable to PCV-2b. A similar picture was observed in wild boars, although a lower sequence number was available. Overall, the present study demonstrates the extreme complexity of PCV-2 molecular epidemiology in Italy, the significant spread across different regions, the recurrent introduction from foreign countries, and the frequent occurrence of recombination events. Although a higher viral flux occurred from domestic to wild populations than vice versa, wild boars seem to maintain PCV-2 infection and spread it over relatively long distances.Background Marijuana use is increasing among adolescents and young adults. Long-term marijuana use magnifies the risk of a wide variety of behavioral, cognitive-emotional, and neurological problems, and can be a gateway to use of other drugs. In the present study, we investigated the cognitive-emotional and behavioral predictors of marijuana use. To this end, young Iranian adults answered questions based on an extended Theory of Planned Behavior (TPB) and related it to marijuana use. We hypothesized that cognitive-emotional and behavioral factors would predict intention to use marijuana, and that this, in turn, would predict actual consumption. Methods A total of 166 young Iranian adults (mean age 20.51 years; 15.7% females) attending a walk-in center for drug use took part in this cross-sectional study. Participants completed questionnaires covering sociodemographic information, frequency of marijuana use per week, along with questionnaires assessing the following dimensions of the TPB attitude towards marijed to be the best predictors of actual use. It follows that prevention programs aimed at improving problem-solving skills and raising self-efficacy, along with educational interventions aimed at highlighting the negative effects of marijuana might decrease the risk of its use among young adults in Iran.This study aimed to investigate the concentrations of Cr, Cd and Pb in the water, sediment and experimental hybrid catfish muscles, and to compare the genetic differentiation and the levels of oxidative stress biomarkers (malondialdehyde and protein carbonyl) between the catfish from the contaminated reservoir near a municipal landfill and the reference area after chronic exposure. The concentrations of all metals in the water and the concentration of Cd in the sediment exceeded Thailand's surface water quality and soil quality standards, respectively, whereas the concentrations of these metals in fish muscles did not exceed Thailand's food quality standards. Dendrogram results in terms of genetic similarity values of the catfish from the reference and the landfill areas were 0.90 to 0.96 and 0.79 to 0.86, respectively, implying that the genetic differentiation of the fish from the landfill was greater than of those from the reference area. The fish in the landfill reservoir had slightly increased protein carbonyl levels.