Explore the vast range of titles available.

Artificial intelligence (AI) algorithms have been applied to diagnose temporomandibular disorders (TMDs). However, studies have used different patient selection criteria, disease subtypes, input data, and outcome measures. Resultantly, the performance of the AI models varies. This study aimed to systematically summarize the current literature on the application of AI technologies for diagnosis of different TMD subtypes, evaluate the quality of these studies, and assess the diagnostic accuracy of existing AI models. The study protocol was carried out based on the preferred reporting items for systematic review and meta-analysis protocols (PRISMA). The PubMed, Embase, and Web of Science databases were searched to find relevant articles from database inception to June 2022. Studies that used AI algorithms to diagnose at least one subtype of TMD and those that assessed the performance of AI algorithms were included. We excluded studies on orofacial pain that were not directly related to the TMD, such as studies on atypical facial pain and neuropathic pain, editorials, book chapters, and excerpts without detailed empirical data. The risk of bias was assessed using the QUADAS-2 tool. We used Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) to provide certainty of evidence. A total of 17 articles for automated diagnosis of masticatory muscle disorders, TMJ osteoarthrosis, internal derangement, and disc perforation were included; they were retrospective studies, case-control studies, cohort studies, and a pilot study. Seven studies were subjected to a meta-analysis for diagnostic accuracy. According to the GRADE, the certainty of evidence was very low. The performance of the AI models had accuracy and specificity ranging from 84% to 99.9% and 73% to 100%, respectively. The pooled accuracy was 0.91 (95% CI 0.76-0.99), I.sup.2 = 97% (95% CI 0.96-0.98), p < 0.001. Various AI algorithms developed for diagnosing TMDs may provide additional clinical expertise to increase diagnostic accuracy. However, it should be noted that a high risk of bias was present in the included studies. Also, certainty of evidence was very low. Future research of higher quality is strongly recommended.

This study aimed to use artificial intelligence to determine whether biological and psychosocial factors, such as stress, socioeconomic status, and working conditions, were major risk factors for temporomandibular disorders (TMDs). Data were retrieved from the fourth Korea National Health and Nutritional Examination Survey (2009), with information concerning 4744 participants’ TMDs, demographic factors, socioeconomic status, working conditions, and health-related determinants. Based on variable importance observed from the random forest, the top 20 determinants of self-reported TMDs were body mass index (BMI), household income (monthly), sleep (daily), obesity (subjective), health (subjective), working conditions (control, hygiene, respect, risks, and workload), occupation, education, region (metropolitan), residence type (apartment), stress, smoking status, marital status, and sex. The top 20 determinants of temporomandibular disorders determined via a doctor’s diagnosis were BMI, age, household income (monthly), sleep (daily), obesity (subjective), working conditions (control, hygiene, risks, and workload), household income (subjective), subjective health, education, smoking status, residence type (apartment), region (metropolitan), sex, marital status, and allergic rhinitis. This study supports the hypothesis, highlighting the importance of obesity, general health, stress, socioeconomic status, and working conditions in the management of TMDs.

Regulated in development and DNA damage-response 1 (REDD1) is a stress-induced protein that controls various cellular functions, including metabolism, oxidative stress, autophagy, and cell fate, and contributes to the pathogenesis of metabolic and inflammatory disorders, neurodegeneration, and cancer. REDD1 usually exerts deleterious effects, including tumorigenesis, metabolic inflammation, neurodegeneration, and muscle dystrophy; however, it also exhibits protective functions by regulating multiple intrinsic cell activities through either an mTORC1-dependent or -independent mechanism. REDD1 typically regulates mTORC1 signaling, NF-κB activation, and cellular pro-oxidant or antioxidant activity by interacting with 14-3-3 proteins, IκBα, and thioredoxin-interacting protein or 75 kDa glucose-regulated protein, respectively. The diverse functions of REDD1 depend on cell type, cellular context, interaction partners, and cellular localization (e.g., mitochondria, endomembrane, or cytosol). Therefore, comprehensively understanding the molecular mechanisms and biological roles of REDD1 under pathophysiological conditions is of utmost importance. In this review, based on the published literature, we highlight and discuss the molecular mechanisms underlying the REDD1 expression and its actions, biological functions, and pathophysiological roles. Cell stress: Damaging activities of a stress-response protein The gene that codes for the REDD1 protein is activated by a variety of cellular stresses, including metabolic imbalance and DNA damage; REDD1’s effect on various aspects of cellular activities contributes to the pathogenesis of many diseases. Researchers in South Korea led by Young-Myeong Kim at Kangwon National University, Chuncheon, review the cellular functions, molecular mechanisms, and disease-causing actions of REDD1. They assess the extensive evidence on the mechanisms by which REDD1 acts as a detrimental factor in serious conditions, including metabolic disorders, cancer, muscle atrophy, neurological diseases, and autoimmune diseases. However, some of the evidence is uncertain and controversial, and the involvement of REDD1 in disease may depend on complex interactions with other factors, meaning further research is needed to improve understanding. Drugs that regulate the activity of the REDD1 protein or its gene could have therapeutic potential.

Osteoporosis is the most common disease of the musculoskeletal system in old age. Therefore, research on osteoporosis risk factors is actively being conducted. However, whether socioeconomic inequality is associated with the prevalence and diagnosis experience of osteoporosis remains largely unexplored. This study aims to investigate whether socioeconomic inequality can be a risk factor for osteoporosis in postmenopausal women. Cross-sectional data of 1,477 postmenopausal women aged over 50 obtained from the Korea National Health and Nutrition Examination Survey V-2 were analyzed. Univariate analyses were performed to calculate the prevalence of osteoporosis and the rate of osteoporosis diagnosis experience according to the risk factor categories. Multivariate logistic regression analysis was performed to identify the independent variables’ associations with osteoporosis prevalence and diagnosis experience. The prevalence of osteoporosis was 34.8%, while the diagnosis experience rate was 22.1%. The higher the age, the higher the probability of osteoporosis presence and diagnosis experience. The lowest household income level was associated with a 1.63 times higher risk of osteoporosis. On the contrary, this factor was not significant for diagnosis experience. These results were similar for the 50–59 and 60–69 age groups. Among postmenopausal women, those who are older and have low socioeconomic levels are at a high risk of developing osteoporosis. Moreover, the lower the socioeconomic level, the lower the awareness of osteoporosis. Therefore, there is a need to develop more proactive preventive measures in postmenopausal women with low socioeconomic levels.

This study investigated the association between tongue and hyoid position, tongue volume, and pharyngeal airway dimensions with craniofacial growth patterns in the sagittal, vertical, and transverse planes. Cone beam computed tomography was used to assess 185 non-growing subjects (mean age, 28.7 ± 9.5 years). Multivariate linear regression analyses evaluated relationships between tongue and airway variables, and cephalometric/dental arch measurements. Class III skeletal patterns-reflected by lower ANB and higher APDI-were significantly correlated with anteriorly positioned hyoids (ANB: β = 0.249; APDI: β = -0.291), and lower tongue positions at the tongue tip (ANB: β = -0.231; APDI: β = 0.166) and in the posterior area (ANB: β = -0.186; APDI: β = 0.196), and greater tongue volume (APDI: β = 0.174). Hyperdivergent vertical patterns-indicated by a lower ODI-were significantly correlated with a lower tongue tip position (β = -0.311) and posterior tongue position (β = -0.230). Regarding transverse dimensions, tongue volume showed positive correlations with upper intermolar width (β = 0.349), lower intercanine width (β = 0.130), lower intermolar width (β = 0.311), and a negative correlation with upper intercanine width (β = -0.299). Sagittal and vertical craniofacial patterns are interrelated and show associations with tongue and hyoid position, as well as tongue volume. Transverse dental arch dimensions are correlated not only with tongue position and volume but also with pharyngeal airway volume.

Background Genome-wide dysregulation of CpG methylation accompanies tumor progression and characteristic states of cancer cells, prompting a rationale for biomarker development. Understanding how the archetypic epigenetic modification determines systemic contributions of immune cell types is the key to further clinical benefits. Results In this study, we characterized the differential DNA methylome landscapes of peripheral blood mononuclear cells (PBMCs) from 76 canines using methylated CpG-binding domain sequencing (MBD-seq). Through gene set enrichment analysis, we discovered that genes involved in the growth and differentiation of T- and B-cells are highly methylated in tumor PBMCs. We also revealed the increased methylation at single CpG resolution and reversed expression in representative marker genes regulating immune cell proliferation (BACH2, SH2D1A, TXK, UHRF1). Furthermore, we utilized the PBMC methylome to effectively differentiate between benign and malignant tumors and the presence of mammary gland tumors through a machine-learning approach. Conclusions This research contributes to a better knowledge of the comprehensive epigenetic regulation of circulating immune cells responding to tumors and suggests a new framework for identifying benign and malignant cancers using genome-wide methylome.

Orthognathic surgery, or corrective jaw surgery, is performed to correct severe dentofacial deformities and is increasingly sought for cosmetic purposes. Accurate prediction of surgical outcomes is essential for selecting the optimal treatment plan and ensuring patient satisfaction. Here, we present GPOSC-Net, a generative prediction model for orthognathic surgery that synthesizes post-operative lateral cephalograms from pre-operative data. GPOSC-Net consists of two key components: a landmark prediction model that estimates post-surgical cephalometric changes and a latent diffusion model that generates realistic synthesizes post-operative lateral cephalograms images based on predicted landmarks and segmented profile lines. We validated our model using diverse patient datasets, a visual Turing test, and a simulation study. Our results demonstrate that GPOSC-Net can accurately predict cephalometric landmark positions and generate high-fidelity synthesized post-operative lateral cephalogram images, providing a valuable tool for surgical planning. By enhancing predictive accuracy and visualization, our model has the potential to improve clinical decision-making and patient communication. Accurate prediction of surgical outcomes is essential for selecting treatment plans and ensuring patient satisfaction in corrective jaw surgery (orthognathic surgery). Here, the authors present and validate GPOSC-Net, a generative model for orthognathic surgery planning, to predict surgical movement and synthesize post-operative cephalograms from pre-operative data using a diffusion model.

The incidence of metabolic syndrome (MetS), which causes heart disease and stroke, has increased significantly worldwide. Although many studies have revealed the relationship between heavy metals (cadmium, mercury, and lead), the sum of metabolites of di(2-ethylhexyl) phthalate (DEHP), and MetS, the results remain inconsistent. No study has reported the association between various volatile organic compounds (VOCs) and phthalate metabolites with MetS. This cross-sectional study of a representative sample of adult South Koreans aimed to evaluate the relationship between heavy metals, VOC metabolites, phthalate metabolites, bisphenol A and MetS after adjusting for demographic variables. Data from the Korean National Environmental Health Survey II (2012–2014) (n = 5251) were used in the analysis. Multiple logistic regression analysis was performed for MetS with log-transformed hazardous material quartiles after covariate adjustment. Urine muconic acid (MuA) and mono- (2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) levels were significantly associated with MetS after adjusting for confounders (odds ratio: 1.34 and 1.39, respectively). Urine MuA and MEHHP levels were significantly associated with MetS. Because of the rarity of this study, which investigated the relationship between each VOC and phthalate metabolite with MetS and the strict definition of all indirect measures of MetS components, further research is needed.

The demand for rhizoremediation technology that can minimize greenhouse gas emissions while effectively removing pollutants in order to mitigate climate change has increased. The inoculation effect of N 2 O-reducing Pseudomonas sp. TF716 on N 2 O emissions and on remediation performance during the rhizoremediation of diesel-contaminated soil planted with tall fescue ( Festuca arundinacea ) or maize ( Zea mays ) was investigated. Pseudomonas sp. TF716 was isolated from the rhizosphere soil of tall fescue. The maximum N 2 O reduction rate of TF716 was 18.9 mmol N 2 O g dry cells −1  h −1 , which is superior to the rates for previously reported Pseudomonas spp. When Pseudomonas sp. TF716 was added to diesel-contaminated soil planted with tall fescue, the soil N 2 O-reduction potential was 2.88 times higher than that of soil with no inoculation during the initial period (0–19 d), and 1.08–1.13 times higher thereafter. However, there was no enhancement in the N 2 O-reduction potential for the soil planted with maize following inoculation with strain TF716. In addition, TF716 inoculation did not significantly affect diesel degradation during rhizoremediation, suggesting that the activity of those microorganisms involved in diesel degradation was unaffected by TF716 treatment. Analysis of the dynamics of the bacterial genera associated with N 2 O reduction showed that Pseudomonas had the highest relative abundance during the rhizoremediation of diesel-contaminated soil planted with tall fescue and treated with strain TF716. Overall, these results suggest that N 2 O emissions during the rhizoremediation of diesel-contaminated soil using tall fescue can be reduced with the addition of Pseudomonas sp. TF716.

Medial and lateral hypothalamic loci are known to suppress and enhance appetite, respectively, but the dynamics and functional significance of their interaction have yet to be explored. Here we report that, in larval zebrafish, primarily serotonergic neurons of the ventromedial caudal hypothalamus (cH) become increasingly active during food deprivation, whereas activity in the lateral hypothalamus (LH) is reduced. Exposure to food sensory and consummatory cues reverses the activity patterns of these two nuclei, consistent with their representation of opposing internal hunger states. Baseline activity is restored as food-deprived animals return to satiety via voracious feeding. The antagonistic relationship and functional importance of cH and LH activity patterns were confirmed by targeted stimulation and ablation of cH neurons. Collectively, the data allow us to propose a model in which these hypothalamic nuclei regulate different phases of hunger and satiety and coordinate energy balance via antagonistic control of distinct behavioral outputs. How soon after a meal do you start feeling hungry again? The answer depends on a complex set of processes within the brain that regulate appetite. A key player in these processes is the hypothalamus, a small structure at the base of the brain. The hypothalamus consists of many different subregions, some of which are responsible for increasing or decreasing hunger. Wee, Song et al. now show how two of these subregions interact to regulate appetite and feeding, by studying them in hungry zebrafish larvae. The brains of zebrafish have many features in common with the brains of mammals, but they are smaller and transparent, which makes them easier to study. Wee, Song et al. show that as larvae become hungry, an area called the caudal hypothalamus increases its activity. But when the larvae find food and start feeding, activity in this area falls sharply. It then remains low while the hungry larvae eat as much as possible. Eventually the larvae become full and start eating more slowly. As they do so, the activity of the caudal hypothalamus goes back to normal levels. While this is happening, activity in a different area called the lateral hypothalamus shows the opposite pattern. It has low activity in hungry larvae, which increases when food becomes available and feeding begins. When the larvae finally reduce their rate of feeding, the activity in the lateral hypothalamus drops back down. The authors posit that by inhibiting each other’s activity, the caudal and lateral hypothalamus work together to ensure that animals search for food when necessary, but switch to feeding behavior when food becomes available. Serotonin – which is produced by the caudal hypothalamus – and drugs that act like it have been proposed to suppress appetite, but they have varied and complex effects on food intake and weight gain. By showing that activity in the caudal hypothalamus changes depending on whether food is present, the current findings may provide insights into this complexity. More generally, they show that mapping the circuits that regulate appetite and feeding in simple organisms could help us understand the same processes in humans.