High-performing cross-dataset machine learning reveals robust microbiota alteration in secondary apical periodontitis

Multiple research groups have consistently underscored the intricate interplay between the microbiome and apical periodontitis. However, the presence of variability in experimental design and quantitative assessment have added a layer of complexity, making it challenging to comprehensively assess the relationship. Through an unbiased methodological refinement analysis, we re-analyzed 4 microbiota studies including 120 apical samples from infected teeth (with/without root canal treatment), healthy teeth, using meta-analysis and machine learning. With high-performing machine-learning models, we discover disease signatures of related species and enriched metabolic pathways, expanded understanding of apical periodontitis with potential therapeutic implications. Our approach employs uniform computational tools across datasets to leverage statistical power and define a reproducible signal potentially linked to the development of secondary apical periodontitis (SAP)

Exploring the optimal impact force for chronic skeletal muscle injury induced by drop-mass technique in rats

Introduction: Skeletal muscle injuries are widespread in sports, traffic accidents and natural disasters and some of them with poor prognoses can lead to chronic skeletal muscle damage in the clinic. We induced a chronic skeletal muscle injury by controlling time and contusion force using an acute blunt trauma model that will help us better comprehend the pathological features of chronic skeletal muscle injury.Methods: Several levels of injury were induced by repeatedly striking in 5, 10, and 15 times the gastrocnemius muscle from the same height with 200 g weights. After injury, the markers of muscle injury were assessed at 2 and 4 weeks by serum elisa. Electron microscopy, histologic and immunohistochemical staining, and mRNA analysis were used to evaluate the ultrastructure, inflammation, extracellular matrix decomposition, and anabolism of injured muscle in 2 and 4 weeks.Results: All three different kinetic energies can result in skeletal muscle injuries. However, the injured skeletal muscles of rats in each group could not recover within 2 weeks. After 4 weeks, tissue self-repair and reconstruction caused the damage induced by 5 J kinetic energy to almost return to normal. In contrast, damage induced by 10 J kinetic energy displayed slight improvement compared to that at 2 weeks. Despite this, collagen fibers on the surface of the tissue were disorganized, directionally ambiguous, and intertwined with each other. Myofilaments within the tissue were also arranged disorderly, with blurry and broken Z-lines. Damage caused by 15 J kinetic energy was the most severe and displayed no improvements at 4 weeks compared to 2 weeks. At 4 weeks, IL-1β, IL-6, Collagen I, and Collagen III, MMP2 expressions in the 10 J group were lower than those at 2 weeks, showing a tendency towards injury stabilization.Conclusion: After 4 weeks of remodeling and repair, the acute skeletal muscle injury model induced by 10 J kinetic energy can stabilize pathological manifestations, inflammatory expression, and extracellular matrix synthesis and catabolism, making it an appropriate model for studying chronic skeletal muscle injuries caused by acute injury

Morphology and transverse alignment of the patella have no effect on knee gait characteristics in healthy Chinese adults over the age of 40 years

Background: The influence of patella morphology and horizontal alignment on knee joint kinematics and kinetics remains uncertain. This study aimed to assess patella morphology and transverse alignment in relation to knee kinetics and kinematics in individuals without knee conditions. A secondary objective was to investigate the impact of femur and tibia alignment and shape on knee gait within this population.Patients and methods: We conducted a prospective collection of data, including full-leg anteroposterior and skyline X-ray views and three-dimensional gait data, from a cohort comprising 54 healthy individuals aged 40 years and older. Our study involved correlation and logistic regression analyses to examine the influence of patella, femur, and tibia morphology and alignment on knee gait.Results: The patellar tilt angle or the patella index did not show any significant relationships with different aspects of gait in the knee joint, such as velocity, angle, or moment (p > 0.05, respectively). Using multivariate logistic regression analysis, we found that the tibiofemoral angle and the Q angle both had a significant effect on the adduction angle (OR = 1.330, 95%CI 1.033–1.711, p = 0.027; OR = 0.475, 95%CI 0.285–0.792, p = 0.04; respectively). The primary variable influencing the knee adduction moment was the tibiofemoral angle (OR = 1.526, 95% CI 1.125–2.069, p = 0.007).Conclusion: In healthy Chinese individuals aged over 40, patella morphology and transverse alignment do not impact knee gait. However, the femoral-tibial angle has a big impact on the knee adduction moment

Additional file 1 of Metabolomics reveals high fructose-1,6-bisphosphate from fluoride-resistant Streptococcus mutans

Supplementary Material

Predicting the effect of street environment on residents' mood states in large urban areas using machine learning and street view images

Background: Researchers have demonstrated that the built environment is associated with mental health outcomes. However, evidence concerning the effects of street environments on mood in fast-growing Asian cities is scarce. Traditional questionnaires and interview methods are labor intensive and time consuming and pose challenges for accurately and efficiently evaluating the impact of urban-scale street environments on mood. Objective: This study aims to use street view images and machine learning methods to model the impact of street environments on mood states in a large urban area in Guangzhou, China, and to assess the effect of different street view elements on mood. Methods: A total of 199,754 street view images of Guangzhou were captured from Tencent Street View, and street elements were extracted by pyramid scene parsing network. Data on six mood state indicators (motivated, happy, positive-social emotion, focused, relaxed, and depressed) were collected from 1590 participants via an online platform called Assessing the Effects of Street Views on Mood. A machine learning approach was proposed to predict the effects of street environment on mood in large urban areas in Guangzhou. A series of statistical analyses including stepwise regression, ridge regression, and lasso regression were conducted to assess the effects of street view elements on mood. Results: Streets in urban fringe areas were more likely to produce motivated, happy, relaxed, and focused feelings in residents than those in city center areas. Conversely, areas in the city center, a high-density built environment, were more likely to produce depressive feelings. Street view elements have different effects on the six mood states. “Road” is a robust indicator positively correlated with the “motivated” indicator and negatively correlated with the “depressed” indicator. “Sky” is negatively associated with “positive-social emotion” and “depressed” but positively associated with “motivated”. “Building” is a negative predictor for the “focused” and “happy” indicator but is positively related to the “depressed” indicator, while “vegetation” and “terrain” are the variables most robustly and positively correlated with all positive moods. Conclusion: Our findings can help urban designers identify crucial areas of the city for optimization, and they have practical implications for urban planners seeking to build urban environments that foster better mental health

Attitude and influencing factors to receive the COVID‐19 vaccine among university students in Sichuan Province, China: A cross‐sectional study

Abstract Aims To explore the university students' attitude and the potential influencing factors to receive the coronavirus disease 2019 (COVID‐19) vaccine in Sichuan Province, China. Design A cross‐sectional study. Methods The self‐designed questionnaire was distributed among university students online in June 2021. SPSS software was used for statistical analysis of the data. Descriptive statistics, Chi‐square, two independent samples t‐tests, one‐way analysis of variance (ANOVA), multivariate linear regression, and content analysis were performed. Results A total of 397 questionnaires were analysed, involving 316 (79.6%) respondents have received at least one dose of a COVID‐19 vaccine and 81 (20.4%) have not taken the vaccine. The total mean score of university students' vaccination attitude was 25.97 (standard deviation [SD] = 3.720), and the total scoring rate was 74.2%. Main factors influencing students' attitude included education level, major, living style, with chronic disease or not, self‐reported vaccination status, and number of medical units that can provide vaccination within 3 km of residence. Students were more willing to choose Chinese‐manufactured vaccines (66.8%) and participate in collective vaccination programs organized by the school (71.3%). The desired vaccine protection period was 5–10 years (42.1%). The top three reasons for refusing the vaccine or vaccine hesitancy were as follows: concern about the side effects of vaccine (44.8%), lack of information about vaccine (31.0%), and concern about the efficacy of vaccine (29.3%). Conclusion In general, most of the participants had relatively high level of positive attitude to receive the COVID‐19 vaccine. Nevertheless, more attention should be paid to postgraduate students, non‐medical students, those living alone, those with chronic disease, those have not received the COVID‐19 vaccine, and those living far away from the vaccination medical units. Findings of this study can help educational institutions in developing effective interventions to improve the vaccination rate in the university student population

NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were learnable through provided pairs of low and high resolution train images. The tracks had 145, 114, 101, and 113 registered participants, resp., and 31 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution