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Zero Waste: Consumers’ Perception of the Use of Eco-friendly Toothpaste Tablets—A Quasi-experimental Study

Development and evaluation of a training module for people with lived experience of mental illness using social contact strategy for stigma reduction: A study protocol

Social contact strategy or social contact based anti-stigma intervention, where a person with lived experience (PWLE) of mental illness shares his/her lived experiences with the target group, has been found to be effective in reducing stigma and discrimination. A culturally appropriate social contact based anti-stigma intervention training module would be helpful in training PWLE. Since there is no culturally appropriate training module available in India, there is a need to develop a training module for PWLE of mental illness to deliver a social contact based anti-stigma intervention. Thus, the proposed mixed-methods study aims to develop and test the efficacy of a training module for PWLE of mental illness, using social contact strategy to reduce stigma and discrimination towards people with mental illness amongst undergraduate students. The proposed study will be carried out in three phases; Phase-I: formative work will be conducted using an explorative research design. After a formative and extensive literature review, the culturally appropriate training module will be developed and subsequently reviewed and validated by mental health experts and service users. Phase-II: PWLE of mental illness will be trained using the developed manual adopting a case series design. Phase-III: To test the efficacy of the training, a quasi-experimental research design will be used, in which the target group’s knowledge, attitudes and behaviour towards mental illness will be assessed pre- and post and at three-month follow-up. Socio-demographic data will be analysed using descriptive statistics. Qualitative data (Phase-I and Phase-III) will be analysed through thematic analysis. Based on normality distribution, a parametric test like RMANOVA or an equivalent non parametric test will be adopted during phase III for efficacy testing. In addition, the outcomes amongst the PWLE, i.e., self-stigma and self-esteem, will be assessed and use of the training module will be analysed using thematic analysis.

Comparative Evaluation of Enamel Microfracture and Adhesive Remnant Index of Adhesive Precoated Flash-free System vs Conventional Bonding Using Different Debonding Techniques: An In Vitro Study

Incorporating sparse labels into hidden Markov models using weighted likelihoods improves accuracy and interpretability in biologging studies

Ecologists often use a hidden Markov model to decode a latent process, such as a sequence of an animal’s behaviours, from an observed biologging time series. Modern technological devices such as video recorders and drones now allow researchers to directly observe an animal’s behaviour. Using these observations as labels of the latent process can improve a hidden Markov model’s accuracy when decoding the latent process. However, many wild animals are observed infrequently. Including such rare labels often has a negligible influence on parameter estimates, which in turn does not meaningfully improve the accuracy of the decoded latent process. We introduce a weighted likelihood approach that increases the relative influence of labelled observations. We use this approach to develop hidden Markov models to decode the foraging behaviour of killer whales (Orcinus orca) off the coast of British Columbia, Canada. Using cross-validated evaluation metrics and a detailed simulation study, we show that our weighted likelihood approach produces more accurate and understandable decoded latent processes compared to existing hidden Markov models and single-frame machine learning methods. Thus, our method effectively leverages sparse labels to enhance researchers’ ability to accurately decode hidden processes across various fields.

Assessment of the Accuracy and Reliability of the Three Different Devices Measuring Dental Implant Stability: A Comparative Study

Hidden Markov model for acoustic pesticide exposure detection and hive identification in stingless bees

Pollinator populations are declining globally at an unprecedented rate, driven by factors such as pathogens, habitat loss, climate change, and the widespread application of pesticides. Although colony losses remain difficult to prevent, precision beekeeping has introduced non-invasive strategies for monitoring hive conditions. Acoustic data, combined with machine learning techniques, has proven effective in detecting stressors and specific events in honeybee colonies; however, such methodologies remain underexplored for stingless bees, a group of native pantropical pollinators. Meliponiculture, the practice of keeping stingless bees, is an expanding field that offers significant economic and conservation benefits. Stingless bees are particularly susceptible to pesticide toxicity, even at residual concentrations, underscoring the critical need to prevent hive losses and to understand the impacts of sub-lethal pesticide exposure on these species. This study addresses the challenge of detecting airborne pesticide exposure by aiming to identify stress responses in hives of the stingless bee Tetragonisca fiebrigi when exposed to chlorpyrifos, a commonly used insecticide. We employed a Hidden Markov Model (HMM) with MATLAB’s Hidden Markov Model Toolkit (MATLABHTK) to analyze acoustic data from eight hives under both exposed and unexposed conditions, assessing the potential of acoustic monitoring as an indicator of pesticide-related stress. Initial analysis across multiple hives indicated moderate model performance. However, hive-specific analyses yielded higher performance in detecting pesticide exposure. Furthermore, the model accurately classified individual hives, suggesting the presence of a distinct acoustic ’fingerprint’ for each hive. These findings advance the field of stingless bee bioacoustics and provide initial evidence that acoustic monitoring of stingless bee hives could be a useful and non-invasive tool to detect airborne pesticide contamination.

Knowledge, Perception and Awareness of Clear Aligner Treatment among General Dentists of Saudi Arabia: A Cross-sectional Study

Antenatal identification of early- and late-onset fetal growth restriction and the possible impact of the introduction of cerebroplacental ratio: Effect on perinatal and childhood outcome

Objective To investigate the effect of antenatal identification of small for gestational age (SGA) fetuses on perinatal and childhood outcomes, separately analyzing early- and late-onset fetal growth restriction (FGR). Materials and methods A register-based cohort study of all newborns born SGA, delivered in Stockholm in 2014 and 2017, n = 5499. Ultrasound reports of fetuses born SGA were reviewed and fetuses identified as SGA with ultrasound before birth were further defined as early- or late-onset FGR according to established criteria. Data from the medical chart for maternity and delivery was linked to nationwide Swedish registers. Adverse outcomes for antenatally non-identified SGA/FGR newborns and fetuses identified as early- or late-onset FGR were compared using logistic regression models. A composite outcome, severe adverse outcome, was constructed and defined as at least one of the following: stillbirth, severe newborn distress, severe neonatal outcome, severe childhood outcome. Individual components of the composite outcome were analyzed as secondary outcomes. Results Identified early-onset FGR fetuses had an increased risk for severe adverse outcome, compared to non-identified SGA/FGR (aOR 1.81, 95% CI 1.25–2.61), in contrast to late-onset FGR fetuses (aOR 1.14, 95% CI 0.78–1.67). Identified early-onset FGR had a decreased risk of stillbirth (aOR 0.47, 95% CI 0.23–0.96), an increased risk of severe newborn distress (aOR 2.80, 95% CI 1.79–4.39) and severe childhood outcome (aOR 3.00, 95% CI 1.51–5.94), compared to non-identified SGA/FGR. Identified late-onset FGR was only associated with an increased risk of severe childhood outcome (aOR 1.91, 95% CI 1.04–3.52). Conclusion Identified early-onset FGR fetuses benefited from identification with a decreased risk of stillbirth at the price of an increased risk for severe newborn and childhood outcomes. For late-onset FGR the advantages were undetectable; identification was associated with an increased risk for severe childhood outcome, while the negative association with stillbirth did not reach significance.

An Evaluation of the Relationship between Condylar Guidance, Occlusal Plane Orientation, Cuspal Inclination, and Compensatory Curves in Permanent Dentition

Comprehensive framework for thyroid disorder diagnosis: Integrating advanced feature selection, genetic algorithms, and machine learning for enhanced accuracy and other performance matrices

Thyroid hormones control crucial physiological activities, such as metabolism, oxidative stress, erythropoiesis, thermoregulation, and organ development. Hormonal imbalances may cause serious conditions like cognitive impairment, depression, and nervous system damage. Traditional diagnostic techniques, based on hormone level measurements (TSH, T3, FT4, T4, and FTI), are usually lengthy and laborious. This study uses machine learning (ML) algorithms and feature selection based on GA to improve the accuracy and efficiency of diagnosing thyroid disorders using the UCI thyroid dataset. Five ML algorithms-LR, RF, SVM, AB, and DT- were tested using two paradigms: (1) default classifiers and (2) hybrid GA-ML models- GA-RF, GA-LR, GA-SVM, GA-DT, and GA-AB. The data pre-processed included handling missing values, feature scaling, and correlation analysis. In this case, the performance metrics used for model evaluation are accuracy, F1 Score, sensitivity, specificity, precision, and Cohen’s Kappa with 80% of the dataset to train the model and the rest 20% used to test it. Among the non-hybrid models, RF achieved the highest accuracy, which was 93.93%. The hybrid GA-RF model outperformed all others, achieving a remarkable accuracy of 97.21%, along with superior metrics across all the evaluated parameters. These findings highlight the diagnostic potential of the GA-RF model in providing faster, more accurate, and reliable thyroid disorder detection. The research illustrated the potential of the hybrid GA-ML approaches to improving the clinical diagnostic process while proposing a strong and scalable approach towards thyroid disorder identification.

Assessment of Imaging Accuracy Using Different Intraoral Scanner Streaming Modes: An In Vitro Study

Relationship network, knowledge transfer and open innovation behavior of SMEs from China

In the context of globalization and digital transformation, small and medium-sized enterprises (SMEs) face increasing resource constraints that limit traditional innovation models. This study investigates how Chinese SMEs’ relational networks influence their open innovation behaviors, examining the mediating role of knowledge transfer and the moderating effect of innovation strategic orientation. Through questionnaire data collected from 329 SMEs’ middle and senior managers, we find that: (1) Firm relational networks significantly enhance knowledge transfer; (2) Innovation strategic orientation positively moderates the relationship between networks and knowledge transfer; and (3) Both networks and knowledge transfer exhibit strong positive impacts on SMEs’ open innovation behaviors, with knowledge transfer acting as a partial mediator. The research reveals the mechanism through which relational networks drive open innovation via knowledge mobilization while highlighting the critical regulatory role of strategic innovation focus. These findings contribute to understanding open innovation dynamics in emerging markets by demonstrating the sequential pathway: network development → knowledge acquisition → innovation outcomes. For practitioners, the study underscores the strategic value of cultivating robust relational networks aligned with innovation objectives, enhancing internal knowledge circulation capabilities, and maintaining adaptive strategic orientations to capitalize on external opportunities. The research provides theoretical and practical guidance for SMEs seeking sustainable competitive advantages through open innovation strategies in knowledge-based economies.

Comparison of Injectable Platelet-rich Fibrin and Semilunar Flap with Tuberosity Graft for Gingival Black Triangles: A Randomized Clinical Trial

Psychological dynamics among Chinese and British negotiators and translators: A text-analytical study of translation manipulation in the English - Chinese translation of the Treaty of Nanking

This paper examines translation manipulation strategies employed by Chinese and British negotiators and translators in the English Chinese translation of the Treaty of Nanking (1842) and explores the psychological dynamics behind these practices. From the perspective of Lefevere’s translation manipulation theory, the study uncovers how translation served as a tool to mediate power asymmetries and cultural differences between the two parties through a detailed text-analytical approach. The findings highlight deliberate textual manipulations, including rhetorical adjustments omissions, and additions, which reflect the translators’ dual role as mediators and ideological agents. For instance, the Chinese text consistently elevated the Qing emperor’s authority through honorific terms and omitted critical language, aligning the treaty with Qing norms while softening its unfavorable terms. Conversely, the British version emphasized precision and claims of justice, aligning with imperial objectives. Additions in the Chinese version, such as references to imperial approval and justifications for concessions, illustrate attempts to preserve Qing dignity and promote acceptance of the treaty. Overall, the study demonstrates how psychological pressures and power dynamics shaped translation outcomes, offering new insights into translation manipulation, cultural mediation, and the role of translators in constructing historical narratives.

Phytochemical Screening, Antioxidant, and Anti-inflammatory Activity of Luffa Cylindrica Extract in Oral Carcinoma: An In Vitro and In Silico Analysis

The Chilean burden of disability-adjusted life years due to cardiovascular diseases: Results from the Global Burden of Disease Study 2021

Cardiovascular disease (CVD) remains a leading cause of death and disability worldwide, including Chile. While mortality rates from CVD are well-documented, the associated burden of disability-adjusted life years (DALYs) and the impact of key modifiable risk factors have yet to be fully explored. This study aims to describe the temporal trends of CVD-related DALYs in Chile, stratified by gender, and to identify the primary risk factors contributing to this burden. An ecological study was conducted using data from the Global Burden of Disease Study 2021. We analysed age-standardised DALYs rates for overall and specific CVDs in Chile from 1990 to 2021. For the year 2021, we calculated the percentage distribution of DALYs by CVD type. Where applicable, CVDs were examined in relation to behavioural, metabolic, and environmental risk factors, and relative changes in DALYs over time. Over the last three decades, the overall CVD DALYs rate in Chile decreased substantially. Ischaemic heart disease and stroke accounted for most of the CVD burden, with gender differences observed. Stroke predominated in females and ischaemic heart disease in males. Metabolic risk factors, particularly high systolic blood pressure (SBP), were the most significant contributors to CVD DALYs, followed by behavioural and environmental risk factors. Although the rates of CVD DALYs have declined significantly in Chile during the past three decades, the burden remains substantial and with gender-specific differences. Metabolic risk factors, especially high SBP, followed by behavioural and environmental factors, remain key contributors to CVD, highlighting the need for continued public health efforts focused on multi-level interventions to reduce the impact of these risk factors on cardiovascular health, such as adopting lower blood pressure goals among older people.

Technological Advancements in Dentistry: A Step Towards Successful Treatment

Covid-19 in hospitals: Studying influencing factors through agent-based modelling

Hospitals are highly dynamic environments where Covid-19 is highly transmissible if effective measures are not taken. General preventive policies are not necessarily effective; however, agent-based modelling can offer a way to tailor policies in such specific settings. In this paper, we develop an agent-based model to simulate a hospital as a multi-agent system and examine the influence of well-established Covid-19 factors on transmission, based on the Susceptible-Exposed-Infected-Recovered epidemic model. These factors include the mask efficacy, the mask-wearing percentage, the vaccination percentage, the room ventilation, the distancing rule, and the screening policy. This study, conducted in 2023, simulates agent interactions in a hospital setting using Python. We apply different parametrisations and employ a local sensitivity analysis to isolate the effect of each factor on transmission. We perform statistical analysis using independent two-sample t-tests, with a significance threshold of p<0.05. When worn by the entire population, N95 masks effectively mitigate transmission. They are more effective than surgical masks and substantially more effective than not wearing masks. Surgical masks, especially when worn by all individuals, also have a significant impact. Vaccination is highly effective, particularly when the entire population is vaccinated, and becomes critical when mask protection is weaker. Ventilation and distancing show minimal impact when N95 masks are used entirely within the hospital; alternatively, their effect is noteworthy but not highly decisive. Finally, the screening policy substantially affects transmission when a high percentage of entrants are tested, especially when surgical masks or no masks are worn. The findings highlight the need for prioritising mask-wearing and vaccination compliance, combined with a comprehensive screening policy. The model is highly parameterisable and can be adjusted to simulate other hospital settings or other infectious diseases, serving as a decision-making tool. Future studies could explore the combined effects of multiple interventions and validate the model with empirical data.

An In Vitro Comparative Evaluation of Three Different Solvents in the Retreatment of Teeth Obturated using Bioceramic Sealer

Protection of LPS-induced intestinal injury in goslings by polysaccharide of Atractylodes macrocephala Koidz based on 16S rRNA and metabolomics analysis

The protective effects of the polysaccharide of Atractylodes macrocephala Koidz (PAMK) against lipopolysaccharide (LPS)-induced intestinal injury in goslings was determined using 16S rRNA analysis of cecal contents and serum metabolomics analysis. PAMK was administered to goslings following LPS-induced intestinal injury, and its effects were assessed. PAMK significantly reduced the serum levels of inflammatory factors including interleukin (IL)-6 and C-reactive protein and decreased the expression of pro-inflammatory cytokines including interleukin(IL)-1β, IL-6, and toll-like receptor 2 in jejunal tissues. Moreover, PAMK significantly upregulated the relative mRNA expression levels of the tight junction proteins Zonula occludens-1, Occludin, Claudin, and Mucin-2, enhancing the integrity of the intestinal barrier and alleviating LPS-induced intestinal injury. 16S rRNA sequencing revealed that PAMK could alleviate LPS-induced disruption of the intestinal microbiota structure and improve microbial diversity. Metabolomics analysis revealed that PAMK could influence key metabolic pathways, including the mTOR, PI3K-Akt, and FoxO signaling pathways, and regulate metabolites such as L-aspartic acid and S-adenosylmethionine. Integrated analysis indicated that PAMK could promote the enrichment of beneficial bacteria (e.g., Allobaculum and Peptococcus) while alleviating LPS-induced microbial dysbiosis by modulating the correlation between key metabolites and specific microbial populations. Overall, PAMK could alleviate LPS induced intestinal injury by enhancing intestinal barrier function, optimizing gut microbiota composition, and regulating metabolic signaling pathways. Our findings provide a novel strategy for maintaining the intestinal health of poultry and preventing intestinal diseases.