Effect of Dietary Simulating Solvents on the CAD-CAM Provisional Restorative Materials' Microhardness and Color Stability Properties: An in vitro Study.

Purpose: This in vitro study investigated the effects of dietary solvents on the microhardness and color stability of CAD/CAM provisional restorations compared to conventional materials. Methods: Disc-shaped specimens (n=200) were fabricated from self-cured acrylic resin, two 3D-printing resins (FormLabs, NextDent), and a milled material (TelioCAD). Randomization assigned specimens (n=10/group) to immersion solutions: artificial saliva, citric acid, heptane, coffee, and tea. Microhardness and color stability were evaluated. One-way and three-way ANOVA with Tukey's post hoc test analyzed the data. Results: Dietary solvents significantly reduced the surface microhardness of all tested materials (p<0.05). Unpolished surfaces exhibited greater color changes compared to polished ones (p<0.05) across all materials. Coffee and tea induced the most substantial reductions in hardness and the most significant color alterations (p<0.05), whereas saliva and citric acid had minimal effects. Conclusion: Milled provisional restorations exhibited superior hardness and color stability. Dietary solvents significantly affected material properties over time, highlighting the importance of material selection for clinical applications.

Short Variable Regions flaA Gene (SVR-flaA) Diversity and Virulence Profile of Multidrug-Resistant Campylobacter from Poultry and Poultry Meat in India.

Human gastrointestinal infections caused by Campylobacter species is the second most important foodborne illness after salmonellosis worldwide. Poultry represent one of the main sources of Campylobacter organisms. In the present study, the short variable region of flagellin gene (SVR-flaA) typing was carried out to determine the variation among the circulating strains of Campylobacter jejuni and Campylobacter coli. The C. jejuni and C. coli isolated from poultry and poultry meat were screened for the presence of virulence determinants like cadF, flaA, cdtB, and wlaN gene. The screening for wlaN gene is crucial in view of the fact that most patients with Guillian Barre's (GB) syndrome with a preceding history of diarrheal illness have been found to harbor wlaN gene-positive C jejuni strains. Out of the 200 samples comprising poultry meat and cloacal swabs, 21.5% of samples were found to harbor Campylobacter spp. of which 2.5% were Campylobacter jejuni, and 19% were confirmed as Campylobacter coli. The cadF, flaA, cdtB virulence genes were detected in all the Campylobacter spp. isolated in the present study. The presence of the wlaN gene in the Campylobacter jejuni isolated in the present study may pose a public health threat with long-term human health implications. The SVR-flaA typing of twelve Campylobacter isolates obtained in the present study revealed that Campylobacter coli flaA sequence OL471375 is a new strain with a novel allele type 1,675 and peptide sequence 5 which stands deposited in pubMLST database for Campylobacter. The other flaA-SVR gene sequences identified in this study were OL471369, OL471370, OL471371, OL471372, OL471373, and OL471374. Among twelve Campylobacter spp., three distinct DdeI-RFLP patterns were observed, each varying in size from 100 to 1,000 base pairs. Antimicrobial profiling of the Campylobacter spp. isolated in the present study revealed that 50% of the strains were multidrug resistant. All the Campylobacter spp. were resistant to ciprofloxacin (CIP), ampicillin (AMP), penicillin (PEN), and nalidixic acid (NAL) whereas 57.1% of strains were resistant to tetracycline (TET) and erythromycin (ERY) 28% to amoxicillin (AMX) and enrofloxacin (ENO), 85% to amikacin (AMK). The high degree of resistance to fluoroquinolones observed in the present study is crucial in view of fluoroquinolones being drugs of choice for the treatment of human Campylobacter infections.

Application of artificial intelligence in active assisted living for aging population in real-world setting with commercial devices - A scoping review.

BACKGROUND: The aging population is steadily increasing, posing new challenges and opportunities for healthcare systems worldwide. Technological advancements, particularly in commercially available Active Assisted Living devices, offer a promising alternative. These readily accessible products, ranging from smartwatches to home automation systems, are often equipped with Artificial Intelligence capabilities that can monitor health metrics, predict adverse events, and facilitate a safer living environment. However, there is no review exploring how Artificial Intelligence has been integrated into commercially available Active Assisted Living technologies, and how these devices monitor health metrics and provide healthcare solutions in a real-world environment for healthy aging. This review is essential because it fills a knowledge gap in understanding AI's integration in Active Assisted Living technologies in promoting healthy aging in real-world settings, identifying key issues that require to be addressed in future studies. OBJECTIVE: The aim of this overview is to outline current understanding, identify potential research opportunities, and highlight research gaps from published studies regarding the use of Artificial Intelligence in commercially available Active Assisted Living technologies that assists older individuals aging at home. METHODS: A comprehensive search was conducted in six databases-PubMed, CINAHL, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science-to identify relevant studies published over the past decade from 2013 to 2024. Our methodology adhered to the PRISMA extension for scoping reviews to ensure rigor and transparency throughout the review process. After applying predefined inclusion and exclusion criteria on 825 retrieved articles, a total of 64 papers were included for analysis and synthesis. RESULTS: Several trends emerged from our analysis of the 64 selected papers. A majority of the work (39/64, 61%) was published after the year 2020. Geographically, most of the studies originated from East Asia and North America (36/64, 56%). The primary application goal of Artificial Intelligence in the reviewed literature was focused on activity recognition (34/64, 53%), followed by daily monitoring (10/64, 16%). Methodologically, tree-based and neural network-based approaches were the most prevalent Artificial Intelligence algorithms used in studies (32/64, 50% and 31/64, 48% respectively). A notable proportion of the studies (32/64, 50%) carried out their research using specially designed smart home testbeds that simulate the conditions in real-world. Moreover, ambient technology was a common thread (49/64, 77%), with occupancy-related data (such as motion and electrical appliance usage logs) and environmental sensors (indicators like temperature and humidity) being the most frequently used. CONCLUSION: Our results suggest that Artificial Intelligence has been increasingly deployed in the real-world Active Assisted Living context over the past decade, offering a variety of applications aimed at healthy aging and facilitating independent living for the older adults. A wide range of smart home indicators were leveraged for comprehensive data analysis, exploring and enhancing the potentials and effectiveness of solutions. However, our review has identified multiple research gaps that need further investigation. First, most research has been conducted in controlled testbed environments, leaving a lack of real-world applications that could validate the technologies' efficacy and scalability. Second, there is a noticeable absence of research leveraging cloud technology, an essential tool for large-scale deployment and standardized data collection and management. Future work should prioritize these areas to maximize the potential benefits of Artificial Intelligence in Active Assisted Living settings.

Validating part of the social media infodemic listening conceptual framework using structural equation modelling.

Background: The literature has identified various factors that promote or hinder people's intentions towards COVID-19 vaccination, and structural equation modelling (SEM) is a common approach to validate these associations. We propose a conceptual framework called social media infodemic listening (SoMeIL) for public health behaviours. Hypothesizing parameters retrieved from social media platforms can be used to infer people's intentions towards vaccination behaviours. This study preliminarily validates several components of the SoMeIL conceptual framework using SEM and Twitter data and examines the feasibility of using Twitter data in SEM research. Methods: A total of 2420 English tweets in Toronto or Ottawa, Ontario, Canada, were collected from March 8 to June 30, 2021. Confirmatory factor analysis and SEM were applied to validate the SoMeIL conceptual framework in this cross-sectional study. Findings: The results showed that sentiment scores, the log-numbers of favourites and retweets of a tweet, and the log-numbers of a user's favourites, followers, and public lists had significant direct associations with COVID-19 vaccination intention. The sentiment score of a tweet had the strongest relationship, whereas a user's number of followers had the weakest relationship with the intention of COVID-19 vaccine uptake. Interpretation: The findings preliminarily validate several components of the SoMeIL conceptual framework by testing associations between self-reported COVID-19 vaccination intention and sentiment scores and the log-numbers of a tweet's favourites and retweets as well as users' favourites, followers, and public lists. This study also demonstrates the feasibility of using Twitter data in SEM research. Importantly, this study preliminarily validates the use of these six components as online reaction behaviours in the SoMeIL framework to infer the self-reported COVID-19 vaccination intentions of Canadian Twitter users in two cities. Funding: This study was supported by the 2023-24 Ontario Graduate Scholarship.

Health trends in Canada 1990-2019: An analysis for the Global Burden of Disease Study.

OBJECTIVE: Monitoring trends in key population health indicators is important for informing health policies. The aim of this study was to examine population health trends in Canada over the past 30 years in relation to other countries. METHODS: We used data on disability-adjusted life years (DALYs), years of life lost (YLL), years lived with disability, life expectancy (LE), and child mortality for Canada and other countries between 1990 and 2019 provided by the Global Burden of Disease Study. RESULTS: Life expectancy, age-standardized YLL, and age-standardized DALYs all improved in Canada between 1990 and 2019, although the rate of improvement has leveled off since 2011. The top five causes of all-age DALYs in Canada in 2019 were neoplasms, cardiovascular diseases, musculoskeletal disorders, neurological disorders, and mental disorders. The greatest increases in all-age DALYs since 1990 were observed for substance use, diabetes and chronic kidney disease, and sense organ disorders. Age-standardized DALYs declined for most conditions, except for substance use, diabetes and chronic kidney disease, and musculoskeletal disorders, which increased by 94.6%, 14.6%, and 7.3% respectively since 1990. Canada's world ranking for age-standardized DALYs declined from 9th place in 1990 to 24th in 2019. CONCLUSION: Canadians are healthier today than in 1990, but progress has slowed in Canada in recent years in comparison with other high-income countries. The growing burden of substance abuse, diabetes/chronic kidney disease, and musculoskeletal diseases will require continued action to improve population health.

RéSUMé: OBJECTIF: La surveillance des tendances des indicateurs clés de la santé de la population est importante pour éclairer les politiques de santé. Dans cette étude, nous avons examiné les tendances de la santé de la population au Canada au cours des 30 dernières années par rapport à d'autres pays. MéTHODES: Nous avons utilisé des données sur les années de vie ajustées en fonction de l'incapacité (DALY), les années de vie perdues (YLL), les années vécues avec un handicap, l'espérance de vie (LE) et la mortalité infantile pour le Canada et d'autres pays entre 1990 et 2019, fournies par l'Étude mondiale sur le fardeau de la maladie. RéSULTATS: L'espérance de vie, les YLL ajustées selon l'âge et les DALY ajustées selon l'âge ont tous connu une amélioration au Canada entre 1990 et 2019, bien que le taux d'amélioration se soit stabilisé depuis 2011. Les cinq principales causes des DALY pour tous les âges au Canada en 2019 étaient les néoplasmes, les maladies cardiovasculaires, les affections musculosquelettiques, les affections neurologiques et les troubles mentaux. Les plus fortes augmentations des DALY pour tous les âges depuis 1990 ont été observées pour l'usage de substances, le diabète et les maladies rénales chroniques, ainsi que les troubles des organes sensoriels. Les DALY ajustées selon l'âge ont diminué pour la plupart des conditions, à l'exception de l'usage de substances, du diabète et des maladies rénales chroniques, ainsi que des troubles musculosquelettiques, qui ont augmenté de 94,6 %, 14,6 % et 7,3 % respectivement depuis 1990. Le classement mondial du Canada pour les DALY ajustées selon l'âge est diminué de la 9ième place en 1990 à la 24ième place en 2019. CONCLUSION: Les Canadiens sont en meilleure santé aujourd'hui qu'en 1990, mais les progrès se sont ralentis ces dernières années par rapport à d'autres pays à revenu élevé. La croissance du fardeau lié à l'abus de substances, au diabète/maladies rénales chroniques et aux affections musculosquelettiques exigera des actions continues pour améliorer la santé de la population.

National burden of rheumatoid arthritis in Canada, 1990-2019: findings from the Global Burden of Disease Study 2019 - a GBD collaborator-led study.

OBJECTIVE: The objectives of this study were: (1) to describe burden of rheumatoid arthritis (RA) and trends from 1990 to 2019 using the Global Burden of Diseases, Injuries and Risk Factors Study (GBD) data, (2) to describe age and sex differences in RA and (3) to compare Canada's RA burden to that of other countries. METHODS: Disease burden indicators included prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs) and disability-adjusted life-years (DALYs). GBD estimated fatal and non-fatal outcomes using published literature, survey data and health insurance claims. Data were analysed by Bayesian meta-regression, cause of death ensemble model and other statistical methods. DALYs for Canada were compared with DALYs of countries with similarly high Socio-Demographic Index values. RESULTS: In Canada, the RA prevalence rate increased by 27% between 1990 and 2019, mortality rate decreased by 27%, YLL rate decreased by 30%, YLD increased by 27% and DALY rate increased by 13%, all age standardised. The decline in RA mortality and YLL rates was especially pronounced after 2002. The disease burden was higher in females for all indicators, and DALY rates were higher among older age groups, peaking at age 75-79 years. Prevalence and DALYs were higher in Canada compared with global rates. CONCLUSION: Trends in RA burden indicators over time and differences by age and sex have important implications for Canadian policy-makers, researchers and care providers. Early identification and management of RA in women may help reduce the overall burden of RA in Canada.

Experimental investigation and multi-performance optimization of the leachate recirculation based sustainable landfills using Taguchi approach and an integrated MCDM method.

Landfill leachates contain harmful substances viz. chemicals, heavy metals, and pathogens, that pose a threat to human health and the environment. Unattended leachate can also cause ground water contamination, soil pollution and air pollution. This study focuses on management of leachate, by recirculating the rich, nutrient-filled fluid back into the landfills, turning it to a bioreactor, thereby maximising the performance parameters of landfills favourable for electricity production by the waste to energy plants. This study demonstrates a sustainable alternative method for utilising the fluid, rather than treating it using an extremely expensive treatment process. Further, it also experimentally investigates the effect of varying levels of five input parameters of the landfill including waste particle size, waste addition, inorganic content in waste, leachate recirculation rate, and landfill age, each at five levels, on the multiple performance of the landfill using Taguchi's L25 standard orthogonal array. Experimental results are analysed using an integrated MCDM approach i.e. MEREC-PIV method and statistical techniques such as analysis of mean (ANOM) and analysis of variance (ANOVA). The results indicate that the optimal setting of the input parameters is waste particle size at 9 ppm, waste addition at 80 Ktoe, inorganic content in waste at 2%, leachate recirculation rate at 250 l/day and landfill age at 3 years. Further, inorganic content waste is found to be the most significant parameter for the multiple performance of the landfill. This study presents a novel approach to produce input parameters for power plants which may enhance their profitability and sustainability.

Selectivity of Sol-Gel and Hydrothermal TiO2 Nanoparticles towards Photocatalytic Degradation of Cationic and Anionic Dyes.

Titanium dioxide (TiO2) nanoparticles have been extensively studied for catalyzing the photo-degradation of organic pollutants, the photocatalyst being nonselective to the substrate. We, however, found that TiO2 nanoparticles prepared via the sol-gel and hydrothermal synthetic routes each possess a definite specificity to the charge of the substrate for photodegradation. The nanoparticles were characterized by SEM, FTIR, XRD, TGA, and UV-visible spectra, and the photocatalytic degradation under UV-B (285 nm) irradiation of two model compounds, anionic methyl Orange (MO) and cationic methylene blue (MB) was monitored by a UV-visible spectrophotometer. Untreated sol-gel TiO2 nanoparticles (Tsg) preferentially degraded MO over MB (90% versus 40% in two hours), while after calcination at 400 °C for two hours (Tsgc) they showed reversed specificity (50% MO versus 90% MB in one hour). The as-prepared hydrothermal TiO2 nanoparticles (Tht) behaved in the opposite sense of Tsg (41% MO versus 91% MB degraded in one and a half hours); calcination at 400 °C (Thtc) did not reverse the trend but enhanced the efficiency of degradation. The study indicates that TiO2 nanoparticles can be made to degrade a specific class of organic pollutants from an effluent facilitating the recycling of a specific class of pollutants for cost-effective effluent management.

Investigating Canadian Public Attitudes Toward COVID-19 Vaccine Mandates with a Nested Analysis Framework.

BACKGROUND: Social media is an important medium for studying public attitudes toward COVID-19 vaccine mandates in Canada, and Reddit network communities are a good source for this. METHODS: This study applied a "nested analysis" framework. We collected 20378 Reddit comments via the Pushshift API and developed a BERT-based binary classification model to screen for relevance to COVID-19 vaccine mandates. We then used a Guided Latent Dirichlet Allocation (LDA) model on relevant comments to extract key topics and assign each comment to its most relevant topic. RESULTS: There were 3179 (15.6%) relevant and 17199 (84.4%) irrelevant comments. Our BERT-based model achieved 91% accuracy trained with 300 Reddit comments after 60 epochs. The Guided LDA model had an optimal coherence score of 0.471 with four topics: travel, government, certification, and institutions. Human evaluation of the Guided LDA model showed an 83% accuracy in assigning samples to their topic groups. CONCLUSION: We develop a screening tool for filtering and analyzing Reddit comments on COVID-19 vaccine mandates through topic modelling. Future research could develop more effective seed word-choosing and evaluation methods to reduce the need for human judgment.

Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada.

BACKGROUND: Emerging Infectious Diseases (EID) are a significant threat to population health globally. We aimed to examine the relationship between internet search engine queries and social media data on COVID-19 and determine if they can predict COVID-19 cases in Canada. METHODS: We analyzed Google Trends (GT) and Twitter data from 1/1/2020 to 3/31/2020 in Canada and used various signal-processing techniques to remove noise from the data. Data on COVID-19 cases was obtained from the COVID-19 Canada Open Data Working Group. We conducted time-lagged cross-correlation analyses and developed the long short-term memory model for forecasting daily COVID-19 cases. RESULTS: Among symptom keywords, "cough," "runny nose," and "anosmia" were strong signals with high cross-correlation coefficients >0.8 ( rCough = 0.825, t - 9; rRunnyNose = 0.816, t - 11; rAnosmia = 0.812, t - 3 ), showing that searching for "cough," "runny nose," and "anosmia" on GT correlated with the incidence of COVID-19 and peaked 9, 11, and 3 days earlier than the incidence peak, respectively. For symptoms- and COVID-related Tweet counts, the cross-correlations of Tweet signals and daily cases were rTweetSymptoms = 0.868, t - 11 and tTweetCOVID = 0.840, t - 10, respectively. The LSTM forecasting model achieved the best performance (MSE = 124.78, R2 = 0.88, adjusted R2 = 0.87) using GT signals with cross-correlation coefficients >0.75. Combining GT and Tweet signals did not improve the model performance. CONCLUSION: Internet search engine queries and social media data can be used as early warning signals for creating a real-time surveillance system for COVID-19 forecasting, but challenges remain in modelling.

Monkeypox: a review of epidemiological modelling studies and how modelling has led to mechanistic insight.

Human monkeypox (mpox) virus is a viral zoonosis that belongs to the Orthopoxvirus genus of the Poxviridae family, which presents with similar symptoms as those seen in human smallpox patients. Mpox is an increasing concern globally, with over 80,000 cases in non-endemic countries as of December 2022. In this review, we provide a brief history and ecology of mpox, its basic virology, and the key differences in mpox viral fitness traits before and after 2022. We summarize and critique current knowledge from epidemiological mathematical models, within-host models, and between-host transmission models using the One Health approach, where we distinguish between models that focus on immunity from vaccination, geography, climatic variables, as well as animal models. We report various epidemiological parameters, such as the reproduction number, R0, in a condensed format to facilitate comparison between studies. We focus on how mathematical modelling studies have led to novel mechanistic insight into mpox transmission and pathogenesis. As mpox is predicted to lead to further infection peaks in many historically non-endemic countries, mathematical modelling studies of mpox can provide rapid actionable insights into viral dynamics to guide public health measures and mitigation strategies.

Assessment of Proinflammatory Cytokines Among Patients with Middle East Respiratory Syndrome Coronavirus Infection.

Middle East respiratory syndrome coronavirus (MERS-CoV) is associated with significant morbidity and mortality. This study was performed to assess the proinflammatory cytokines profile among MERS-CoV patients. A total of 46 MERS-CoV-infected patients (27 symptomatic and 19 asymptomatic) were assessed and compared with 52 normal healthy controls for plasma levels of interleukin (IL)-1ß, tumor necrosis factor (TNF)-α, IL-17, IL-7, IL-6, interferon (IFN)-α, and IL-15 using a customized luminex kit. Whereas asymptomatic MERS-CoV patients and controls were no different; the mean plasma levels among MERS-CoV symptomatic patients were significantly higher than the normal controls: IL-1ß (16.89 ± 1.23 vs. 12.80 ± 0.59 pg/mL; p < 0.001), TNF-α (14.04 ± 0.93 vs. 10.35 ± 0.29 pg/mL; p < 0.0001), IL-17 (14.3 ± 0.89 vs. 11.47 ± 0.61 pg/mL; p < 0.001), IL-7 (21.56 ± 1.00 vs. 16.31 ± 0.30 pg/mL; p < 0.0001), IL-6 (156.5 ± 37.90 vs. 18.60 ± 1.59 pg/mL; p < 0.0001), and IFN-α (68.73 ± 13.06 vs. 23.57 ± 1.05 pg/mL; p < 0.0001). The mean plasma levels of IL-7 (24.81 ± 1.63 vs. 19.79 ± 0.94 pg/mL; p < 0.01), IL-6 (312.7 ± 94.67 vs. 101.2 ± 25.67 pg/mL; p < 0.01), and IFN-α (89.00 ± 18.97 vs. 51.05 ± 8.68 pg/mL; p < 0.05) were significantly elevated among MERS-CoV symptomatic patients with fatal outcome compared with MERS-CoV symptomatic patients who survived. Only IL-7 was found to have a higher risk ratio of mortality (4.76, 95% confidence interval: 1.5-14.94; p < 0.01). No differences were observed in IL-15 levels among the groups. Significantly elevated proinflammatory cytokines among symptomatic MERS-CoV-infected patients may contribute to manifestations of cytokine storm frequently observed among critically ill MERS-CoV patients and IL-7 may serve as a marker for disease activity.

Evaluating CNC Milling Performance for Machining AISI 316 Stainless Steel with Carbide Cutting Tool Insert.

The present study investigates the CNC milling performance of the machining of AISI 316 stainless steel using a carbide cutting tool insert. Three critical machining parameters, namely cutting speed (v), feed rate (f) and depth of cut (d), each at three levels, are chosen as input machining parameters. The face-centred central composite design (FCCCD) of the experiment is based on response surface methodology (RSM), and machining performances are measured in terms of material removal rate (MRR) and surface roughness (SR). Analysis of variance, response graphs, and three-dimensional surface plots are used to analyse experimental results. Multi-response optimization using the data envelopment analysis based ranking (DEAR) approach is used to find the ideal configuration of the machining parameters for milling AISI 316 SS. The variables v = 220 m/min, f = 0.20 mm/rev and d = 1.2 mm were obtained as the optimal machine parameter setting. Study reveals that MRR is affected dominantly by d followed by v. For SR, f is the dominating factor followed by d. SR is found to be almost unaffected by v. Finally, it is important to state that this work made an attempt to successfully machine AISI 316 SS with a carbide cutting tool insert, to investigate the effect of important machining parameters on MRR and SR and also to optimize the multiple output response using DEAR method.

Age- and Sex-Specific Association Between Vegetation Cover and Mental Health Disorders: Bayesian Spatial Study.

BACKGROUND: Despite growing evidence that reduced vegetation cover could be a putative risk factor for mental health disorders, the age- and the sex-specific association between vegetation and mental health disorder cases in urban areas is poorly understood. However, with rapid urbanization across the globe, there is an urgent need to study this association and understand the potential impact of vegetation loss on the mental well-being of urban residents. OBJECTIVE: This study aims to analyze the spatial association between vegetation cover and the age- and sex-stratified mental health disorder cases in the neighborhoods of Toronto, Canada. METHODS: We used remote sensing to detect urban vegetation and Bayesian spatial hierarchical modeling to analyze the relationship between vegetation cover and mental health disorder cases. Specifically, an Enhanced Vegetation Index was used to detect urban vegetation, and Bayesian Poisson lognormal models were implemented to study the association between vegetation and mental health disorder cases of males and females in the 0-19, 20-44, 45-64, and ≥65 years age groups, after controlling for marginalization and unmeasured (latent) spatial and nonspatial covariates at the neighborhood level. RESULTS: The results suggest that even after adjusting for marginalization, there were significant age- and sex-specific effects of vegetation on the prevalence of mental health disorders in Toronto. Mental health disorders were negatively associated with the vegetation cover for males aged 0-19 years (-7.009; 95% CI -13.130 to -0.980) and for both males (-4.544; 95% CI -8.224 to -0.895) and females (-3.513; 95% CI -6.289 to -0.681) aged 20-44 years. However, for older adults in the 45-64 and ≥65 years age groups, only the marginalization covariates were significantly associated with mental health disorder cases. In addition, a substantial influence of the unmeasured (latent) and spatially structured covariates was detected in each model (relative contributions>0.7), suggesting that the variations in area-specific relative risk were mainly spatial in nature. CONCLUSIONS: As significant and negative associations between vegetation and mental health disorder cases were found for young males and females, investments in urban greenery can help reduce the future burden of mental health disorders in Canada. The findings highlight the urgent need to understand the age-sex dynamics of the interaction between surrounding vegetation and urban dwellers and its subsequent impact on mental well-being.

Characteristics of Conventional and Microwave Sintered Iron Ore Preform.

In this study, compacted hematite (Fe2O3) preforms were made and sintered at various temperatures, such as 1250 °C and 1300 °C, using both conventional and microwave sintering methods. The density, porosity, microhardness, cold crushing strength, microphotographs, and X-ray diffraction (XRD) analysis of the sintered preforms were used to evaluate the performance of the two sintering methods. It was found that microwave sintered preforms possessed lesser porosity and higher density than conventionally sintered preforms owing to uniform heating of the powdered ore in microwave sintering method. Furthermore, it was also observed that microwave sintered preforms exhibited relatively higher cold crushing strength and hardness than conventionally sintered preforms. Thus, the overall results revealed that microwave sintering yielded better properties considered in the present study.

Closed Repair Technique: Innovative Surface Design for Polymethylmethacrylate Denture Base Repair.

PURPOSE: This study aimed to evaluate the repair strength of a newly introduced repair technique involving zero-gap repair width. MATERIALS AND METHODS: A total of 36 rectangular prism specimens with dimensions of 64 × 10 × 3.3 mm were prepared from heat-polymerized acrylic resin. Nine specimens were kept intact. The other specimens were sectioned into halves and modified to create repair gaps of 2.5-mm beveled (2.5B) as control, 0-mm beveled (ZB), and 0-mm inverse bi-beveled (ZIBB). The ZIBB group was prepared with a V-shaped internal groove on both halves (repair tunnel), while the intaglio and cameo surfaces were kept intact except for two small holes at the cameo surface for repair resin injection. The 2.5B and ZB groups were repaired conventionally while the ZIBB group was repaired by injecting repair resin into the tunnel through one of the holes until excess material oozed from the other hole. Repaired specimens were thermally cycled at 5 and 55°C for 10,000 cycles with 1 min dwell time. A 3-point bending test was conducted using a universal testing machine for flexural strength and elastic modulus measurement. Kruskal-Wallis/Mann-Whitney tests and ANOVA/post hoc Tukey tests were applied for data analysis (α = 0.05). RESULTS: The flexural strength of repaired specimens was substantially lower than that of intact specimens, and significant differences were present between repaired groups (p ˂ 0.05). ZB and ZIBB had higher flexural strength (p ˂ 0.001) and elastic modulus (p ˂ 0.05) than 2.5B. Among the ZB and ZIBB groups, ZB showed the highest flexural strength, and ZIBB had the highest elastic modulus. CONCLUSION: The closed repair technique improved the flexural strength and elastic modulus of repaired acrylic denture base.

Artificial Intelligence against COVID-19 Pandemic: A Comprehensive Insight.

COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain, which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infectious disease, yet there are no well-defined absolute treatments available at present. Several vaccination drives using emergency use authorisation vaccines have been held across many countries; however, their long-term efficacy and side-effects studies are yet to be studied. Various analytical and statistical models have been developed, however, their outcome rate is prolonged. Thus, modern science stresses the application of state-of-the-art methods to combat COVID-19. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in the battle against the COVID-19 pandemic. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e., > 95%, as reported in various studies. The extensive literature reviewed in this paper is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing. The application of Artificial Intelligence (AI) and AI-driven tools are proving to be useful in managing and fighting against the COVID-19 pandemic, especially by analysing the X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions, etc.

Exploring the dynamic transitions of polysubstance use patterns among Canadian youth using Latent Markov Models on COMPASS data.

Background: Understanding what factors lead to youth polysubstance use (PSU) patterns and how the transitions between use patterns can inform the design and implementation of PSU prevention programs. We explore the dynamics of PSU patterns from a large cohort of Canadian secondary school students using machine learning techniques. Methods: We employed a multivariate latent Markov model (LMM) on COMPASS data, with a linked sample (N = 8824) of three-annual waves, Wave I (WI, 2016-17, as baseline), Wave II (WII, 2017-18), and Wave III (WIII, 2018-19). Substance use indicators, i.e., cigarette, e-cigarette, alcohol and marijuana, were self-reported and were categorized into never/occasional/current use. Outcomes: Four distinct use patterns were identified: no-use (S1), single-use of alcohol (S2), dual-use of e-cigarettes and alcohol (S3), and multi-use (S4). S1 had the highest prevalence (60.5%) at WI, however, S3 became the prominent use pattern (32.5%) by WIII. Most students remained in the same subgroup over time, particularly S4 had the highest transition probability (0.87) across the three-wave. With time, those who transitioned typically moved towards a higher use pattern, with the most and least likely transition occurring S2→S3 (0.45) and S3→S2 (<0.01), respectively. Among all covariates being examined, truancy, being measured by the # of classes skipped, significantly affected transition probabilities from any low→high (e.g., ORS2→S4 = 2.41, 95% CI [2.11, 2.72], p < 0.00001) and high→low (e.g., ORS3→S1 = 0.38, 95% CI [0.33, 0.44], p < 0.00001) use directions over time. Older students, blacks (vs. whites), and breakfast eaters were less likely to transition from low→high use direction. Students with more weekly allowance, with more friends that smoked, longer sedentary time, and attended attended school unsupportive to resist or quit drug/alcohol were more likely to transition from low→high use direction. Except for truancy, all other covariates had inconsistent effects on the transition probabilities from the high→low use direction. Interpretation: This is the first study to ascertain the dynamics of use patterns and factors in youth PSU utilizing LMM with population-based longitudinal health surveys, providing evidence in developing programs to prevent youth PSU. Funding: The Applied Health Sciences scholarship; the Microsoft AI for Good grant; the Canadian Institutes of Health Research, Health Canada, the Canadian Centre on Substance Abuse, the SickKids Foundation, the Ministère de la Santé et des Services sociaux of the province of Québec.