Citrate Improves Biomimetic Mineralization Induced by Polyelectrolyte-Cation Complexes Using PAsp-Ca&Mg Complexes.

Magnesium ions are highly enriched in early stage of biological mineralization of hard tissues. Paradoxically, hydroxyapatite (HAp) crystallization is inhibited significantly by high concentration of magnesium ions. The mechanism to regulate magnesium-doped biomimetic mineralization of collagen fibrils has never been fully elucidated. Herein, it is revealed that citrate can bioinspire the magnesium-stabilized mineral precursors to generate magnesium-doped biomimetic mineralization as follows: Citrate can enhance the electronegativity of collagen fibrils by its absorption to fibrils via hydrogen bonds. Afterward, electronegative collagen fibrils can attract highly concentrated electropositive polyaspartic acid-Ca&Mg (PAsp-Ca&Mg) complexes followed by phosphate solution via strong electrostatic attraction. Meanwhile, citrate adsorbed in/on fibrils can eliminate mineralization inhibitory effects of magnesium ions by breaking hydration layer surrounding magnesium ions and thus reduce dehydration energy barrier for rapid fulfillment of biomimetic mineralization. The remineralized demineralized dentin with magnesium-doped HAp possesses antibacterial ability, and the mineralization mediums possess excellent biocompatibility via cytotoxicity and oral mucosa irritation tests. This strategy shall shed light on cationic ions-doped biomimetic mineralization with antibacterial ability via modifying collagen fibrils and eliminating mineralization inhibitory effects of some cationic ions, as well as can excite attention to the neglected multiple regulations of small biomolecules, such as citrate, during biomineralization process.

Metabolomics and lipidomics in non-small cell lung cancer.

Due to its insidious nature, lung cancer remains a leading cause of cancer-related deaths worldwide. Therefore, there is an urgent need to identify sensitive/specific biomarkers for early diagnosis and monitoring. The current study was designed to provide a current metabolic profile of non-small cell lung cancer (NSCLC) by systematically reviewing and summarizing various metabolomic/ lipidomic studies based on NSCLC blood samples, attempting to find biomarkers in human blood that can predict or diagnose NSCLC, and investigating the involvement of key metabolites in the pathogenesis of NSCLC. We searched all articles on lung cancer published in Elsevier, PubMed, Web of Science and the Cochrane Library between January 2012 and December 2022. After critical selection, a total of 31 studies (including 2768 NSCLC patients and 9873 healthy individuals) met the inclusion criteria, and 22 were classified as "high quality". Forty-six metabolites related to NSCLC were repeatedly identified, involving glucose metabolism, amino acid metabolism, lipid metabolism and nucleotide metabolism. Pyruvic acid, carnitine, phenylalanine, isoleucine, kynurenine and 3-hydroxybutyrate showed upward trends in all studies, citric acid, glycine, threonine, cystine, alanine, histidine, inosine, betaine and arachidic acid showed downward trends in all studies. This review summarizes the existing metabolomic/lipidomic studies related to the identification of blood biomarkers in NSCLC, examines the role of key metabolites in the pathogenesis of NSCLC, and provides an important reference for the clinical diagnosis and treatment of NSCLC. Due to the limited size and design heterogeneity of the existing studies, there is an urgent need for standardization of future studies, while validating existing findings with more studies.

The correlation between periodontitis and uncontrolled hypertension is mediated by inflammatory markers: results from a cross-sectional study of urban elderly population in southeast China.

BACKGROUND: Periodontal diseases is considered the most important global oral health burden according to the world health organization (WHO) (Oral health. https://www.who.int/news-room/fact-sheets/detail/oral-health#Overviewth (who.int). Accessed 21 Sep 2023). It is a common local inflammatory disease associated with hypertension, this study aims to explore the relationship between periodontitis and uncontrolled hypertension and whether inflammation indication such as white blood cell (WBC) count or neutrophil count is a mediator of this relationship. METHODS: One thousand four hundred eighty-eight elders attending annual physical and oral examinations in Zhejiang province were included in this study. The staging of Periodontitis was classified as none, mild-moderate and severe. Participants are categorized into two groups based on blood pressure: hypertensive( positive high blood pressure( HBP) history or underwent HBP medication or blood pressure( BP) ≥ 140/90 mmHg) and uncontrolled hypertensive (systolic blood pressure( SBP) ≥ 140 mmHg or distolic blood pressure( DBP) ≥ 90 mmHg). Peripheral blood samples were collected, information on hypertension history and potential confounders (age, sex, smoking, alcohol consumption, exercise frequency, diabetes) was collected in questionnaires. The correlation between periodontitis and hypertension was investigated using logistics regression analyses, mediation analysis was assessed for the effect of inflammation on hypertension. RESULTS: The study population includes 1,488 participants aged 55-90 years. Odds of uncontrolled hypertension increased significantly along with periodontitis in the regression models both in unadjusted model (odds ratio( OR): 1.407, 95% confidence intervals( CI): 1.037 ~ 1.910) and fully adjusted model (OR: 1.950, 95% CI: 1.127 ~ 3.373). Mediation analysis confirmed that WBC and neutrophic count function as a full mediator of the association between periodontitis and uncontrolled hypertension either in the unadjusted or the adjusted model. CONCLUSIONS: In a study of urban elderly population in southeast China, periodontitis is found to be significantly associated with uncontrolled hypertension, such relation is mediated by WBC and neutrophil count. Periodontitis can increase the difficulty of controlling hypertension. Promotion of periodontal health strategies in the dental setting could help reduce the burden of hypertension and its complications.

Development of a thiostrepton-free system for stable production of PLD in Streptomyces lividans SBT5.

BACKGROUND: Phospholipase D (PLD) is highly valuable in the food and medicine industries, where it is used to convert low-cost phosphatidylcholine into high-value phospholipids (PLs). Despite being overexpressed in Streptomyces, PLD production requires expensive thiostrepton feeding during fermentation, limiting its industrialization. To address this issue, we propose a new thiostrepton-free system. RESULTS: We developed a system using a combinatorial strategy containing the constitutive promoter kasOp* and PLD G215S mutation fused to a signal peptide sigcin of Streptoverticillium cinnamoneum pld. To find a candidate vector, we first expressed PLD using the integrative vector pSET152 and then built three autonomously replicating vectors by substituting Streptomyces replicons to increase PLD expression. According to our findings, replicon 3 with stability gene (sta) inserted had an ideal result. The retention rate of the plasmid pOJ260-rep3-pld* was 99% after five passages under non-resistance conditions. In addition, the strain SK-3 harboring plasmid pOJ260-rep3-pld* produced 62 U/mL (3.48 mg/g) of PLD, which further improved to 86.8 U/mL (7.51 mg/g) at 32 °C in the optimized medium, which is the highest activity achieved in the PLD secretory expression to date. CONCLUSIONS: This is the first time that a thiostrepton-free PLD production system has been reported in Streptomyces. The new system produced stable PLD secretion and lays the groundwork for the production of PLs from fermentation stock. Meanwhile, in the Streptomyces expression system, we present a highly promising solution for producing other complex proteins.

Changes in root chemical diversity along an elevation gradient of Changbai Mountain, China.

Root chemical traits play a critical role in plant resource use strategies and ecosystem nutrient cycling; however, the chemical diversity of multiple elements of fine root and community chemical assembly belowground are poorly understood. Here, we measured 13 elements (C, N, K, Ca, Mg, S, P, Al, Fe, Na, Mn, Zn, and Cu) in the fine roots of 204 plant species along elevational transect from 540 to 2357 m of Changbai Mountain, China to explore the variation, diversity, and community assembly of root chemical traits. At the species level, the concentrations of macronutrients (N, K, Ca, Mg, S, and P) decreased, whereas the trace metals (Fe, Mn, and Zn) increased with elevation. Root chemical traits at the community level systematically shifted along elevational gradients showing a pattern similar to that at the species level, which were mainly influenced by climate and soil rather than species diversity. In general, the interactions of climate and soil were the main drivers of root chemical assembly for woody layers, whereas soil factors played significant role for root chemical assembly for herb layer. The chemical assembly of rock-derived element P was mainly driven by soil factors. Meanwhile, root chemical diversities were mainly regulated by species diversity, the interactions of climate and soil, and soil factors in the tree, shrub, and herb layers, respectively. A better understanding of plant root chemical diversity and community chemical assembly will help to reveal the role of chemical traits in ecosystem functioning.

Storage stability and in vitro digestion of apigenin encapsulated in Pickering emulsions stabilized by whey protein isolate-chitosan complexes.

Few studies have investigated the encapsulation of apigenin in solid particle-stabilized emulsions. In this work, Pickering emulsions containing apigenin and stabilized by whey protein isolate-chitosan (WPI-CS) complexes were created to enhance the bioavailability of apigenin. Different lipids including medium-chain triglycerides (MCTs), ethyl oleate (EO), and corn oil (CO) were selected to fabricate lipid-based delivery systems. The microstructure of the Pickering emulsions, as revealed by optical and cryo-scanning electron microscopies, showed that the oil droplets were dispersed evenly and trapped by a three-dimensional network formed by the WPI-CS complexes, which was further confirmed by rheology properties. After 30 days of storage, Pickering emulsions with MCTs achieved the highest apigenin retention rate, exhibiting 95.05 ± 1.45% retention when stored under 4°C. In vitro gastrointestinal tract experiments indicated that the lipid types of the emulsions also affected the lipid digestion and release rate of apigenin. Pickering emulsions with MCTs achieved a higher bioaccessibility compared to that of the other two emulsions (p < 0.01). These results indicate that the delivery system of Pickering emulsions with MCTs stabilized by WPI-CS complexes offers good storage stability and improved bioaccessibility of apigenin.

Related Risk Factors of Early Childhood Caries in Zhejiang Province, China During the COVID-19 Pandemic.

Objectives: This work aims to examining the latest early childhood caries situation in children aged 3 and 5 and its related risk factors in Zhejiang Province during the COVID-19 pandemic. Method: There are 3,537 children and their main caregivers participate in this study. We used chi square test or U-test to analyze whether there were differences in the prevalence of dental caries under different variables. The risk factors on ECC were determined by multivariate logistic analysis. Results: The ECC rate of children in this study was 57.51%. The mean decayed missing filled teeth (dmft) scores were 3.01. The result of multivariate logistic analysis showed higher ECC prevalence was found in children as age increasing, with lower district economic level, with high frequency of confectionary consumption, having oral medical treatment behavior and bad evaluation of children's oral health by parents. Conclusion: In general, the prevalence rate of ECC in this study was lower than five years ago, but still higher than those developed countries. And it was associated with age, district economic level, frequency of confectionary consumption, oral medical treatment behavior and evaluation of children's oral health by parents.

Association between Smoking and Sugar-Sweetened Beverage Consumption, Tooth Brushing among Adolescents in China.

The study aimed to investigate the relationship between smoking, Sugar-Sweetened Beverage (SSB) consumption and tooth brushing among adolescents in China. A valid sample of 6084 middle school students from the Zhejiang province was included. Participants were questioned about smoking status, SSB consumption, tooth brushing, and oral health-related quality of life (OHRQoL). Among the participants, smoking prevalence was 1.9% and nearly half of the students consumed SSBs. The demographic factors associated with smoking were gender, place of residence, and parental level of education. There are co-variations between smoking status, SSB consumption, and tooth brushing. Logistic regression showed that smoking adolescents were more likely to brush their teeth less than once per day (OR = 1.74, p < 0.05), consume soft drinks once or more per day (OR = 2.18, p < 0.01) and have a higher score on the Child Oral Health Impact Profile (OR = 1.05, p < 0.05) after adjusting for demographic factors. The findings provide compelling evidence for governments and related stakeholders to intervene in the lifestyle of adolescents. Future studies are needed to understand the interaction effects of such behaviors, and should help to inform appropriate interventions.

Assessment of the predictive ability of the Johns Hopkins Fall Risk Assessment Tool (Chinese Version) in inpatient settings.

AIMS: This study was to assess the predictive ability of the Johns Hopkins Fall Risk Assessment Tool (Chinese Version) in inpatient settings. DESIGN: A case-control study. METHODS: This study was conducted in a tertiary hospital based on 2019 data. With a case-control design in a 1:2 ratio, the predictive ability of the Johns Hopkins Fall Risk Assessment Tool (Chinese Version) was determined by ROC curve. The best cut point was identified based on sensitivity, specificity, positive predict value and negative predict value. Conditional logistical regression analysis was conducted to test the predictive ability of each indicator. RESULTS: The study included 309 patients, with 103 in the case group and 206 in the control groups. Generally, the predictive ability was acceptable with the area under ROC curve value at 0.73 (95% CI: 0.67-0.79). Positive predict value and negative predict value performed best at the cut point of 13. Sensitivity at cut point 6 was much higher than that at cut point 13, though specificity was lower. Except for age, all indicators in the Johns Hopkins Fall Risk Assessment Tool (Chinese Version) demonstrated significant predictive ability as to occurrence of fall. CONCLUSION: The Johns Hopkins Fall Risk Assessment Tool (Chinese Version) is a reliable assessment instrument in the inpatient settings. IMPACT: This is the first study that evaluated the predictive ability of the Johns Hopkins Fall Risk Assessment Tool (Chinese version) in the inpatient settings, and proved that the instrument is reliable for assessing inpatient fall risks. Further studies could be carried out to assess the predict ability of Johns Hopkins Fall Risk Assessment Tool (Chinese version) among specific populations.

Correlation between periodontitis and prostate-specific antigen levels in the elderly Chinese male population.

BACKGROUND: The comparison of prostate-specific antigen (PSA) levels among older individuals with different periodontal statuses has not been fully investigated. Here we aimed to explore the correlation between the staging and grading of periodontitis and PSA levels in an elderly Chinese male population, which may lead to a biopsy recommendation and prevent prostate cancer as early as possible. METHODS: The study included 996 individuals aged ≥ 55 years who participated in routine postretirement physical examinations. Periodontal data included probing depth and gingival recession on four sites/tooth and on two diagonal quadrants (1-3 or 2-4) while excluding third molars, and clinical attachment loss was calculated. Periodontal status was classified as none, mild-moderate or severe periodontitis according to the Centers for Disease Control and Prevention and the American Academy of Periodontology case definition. Blood samples, oral health status and sociodemographic characteristics were collected by using general and oral examinations and questionnaires. Linear and logistic regressions were used to estimate the correlation between periodontitis severity and PSA levels, respectively. RESULTS: A total of 479 men had mild-moderate periodontitis and 355 had severe periodontitis; 162 men were periodontally healthy individuals. After adjusting for potential confounders, PSA levels were significantly lower in the individuals without periodontitis than in those with mild-moderate (P = 0.04) or severe (P = 0.03) periodontitis. However, PSA levels did not significantly correlate with periodontitis severity (P = 0.06). Although the ORs of elevated PSA were not significant, individuals with PSA ≥ 4.0 ng/mL were more likely to have periodontitis. CONCLUSIONS: In a sample of an elderly Chinese male population, after adjusting for possible confounders, serum PSA levels in individuals with periodontitis were significantly higher than those in individuals without periodontitis, but serum PSA did not significantly correlate with periodontitis severity.

A two-stage deep learning architecture for radiographic staging of periodontal bone loss.

BACKGROUND: Radiographic periodontal bone loss is one of the most important basis for periodontitis staging, with problems such as limited accuracy, inconsistency, and low efficiency in imaging diagnosis. Deep learning network may be a solution to improve the accuracy and efficiency of periodontitis imaging staging diagnosis. This study aims to establish a comprehensive and accurate radiological staging model of periodontal alveolar bone loss based on panoramic images. METHODS: A total of 640 panoramic images were included, and 3 experienced periodontal physicians marked the key points needed to calculate the degree of periodontal alveolar bone loss and the specific location and shape of the alveolar bone loss. A two-stage deep learning architecture based on UNet and YOLO-v4 was proposed to localize the tooth and key points, so that the percentage of periodontal alveolar bone loss was accurately calculated and periodontitis was staged. The ability of the model to recognize these features was evaluated and compared with that of general dental practitioners. RESULTS: The overall classification accuracy of the model was 0.77, and the performance of the model varied for different tooth positions and categories; model classification was generally more accurate than that of general practitioners. CONCLUSIONS: It is feasible to establish deep learning model for assessment and staging radiographic periodontal alveolar bone loss using two-stage architecture based on UNet and YOLO-v4.

Guardian Factors Affecting High Prevalence of Dental Caries in Preschool Children.

PURPOSE: This study analysed the relationship between caregiver-related factors (dental knowledge, attitude, behaviour, and health status) and early childhood caries. ?It aimed to explore better intervention methods for reducing caries prevalence in preschool children. MATERIALS AND METHODS: This cross-sectional investigation was carried out in Zhejiang, China. A total of 1344 guardians (parents and grandparents) paired with their children aged 3-5 years old were enrolled. The guardians completed structured questionnaires, which included their attitude, knowledge level and oral health status. The children received dental examinations. All of the data ?were analysed with R software. Chi-squared and Fisher's exact tests were used to analyse different variables. Multinomial logistic regression with stepwise procedures and curve fitting was used to explore the relationship between guardians' risk factors and the level of early childhood caries. RESULTS: Guardians have a great influence on the incidence of early childhood dental caries. ?When guardians pay attention to oral health and have a good command of relevant knowledge, then the risk of early dental caries in children is low (p = 0.027). The guardians' dental problems?, e.g. dental caries (p = 0.0002), gingival bleeding problems (p = 0.049) and chewing discomfort experience (p = 0.049), demonstrated ?statistically significant correlations with early childhood caries levels?. CONCLUSION: Guardians' attitudes, knowledge, and oral health status ?had a statistically significant relationship with the level of early childhood caries? in their children/grandchildren. Instead of instructing schoolchildren about oral health, multiple-level dental knowledge instruction of guardians is needed to prevent early childhood caries.

Association of high-density lipoprotein cholesterol and periodontitis severity in Chinese elderly: a cross-sectional study.

OBJECTIVES: Periodontitis is a local inflammatory disease of high prevalence worldwide. Increasing evidence has shown its association with cardiovascular diseases. While high-density lipoprotein is an important protective factor in preventing cardiovascular diseases, this study aims to examine whether high-density lipoprotein cholesterol (HDL-C) level is associated with different status of periodontitis. MATERIALS AND METHODS: A total of 874 Chinese retirees (≥ 60 years of age) with different statuses of periodontitis were enrolled. Periodontal clinical data were collected to define periodontal disease severity (no, mild-moderate, severe). Peripheral blood was collected for serum lipid profile analysis. Linear and logistic regression analysis with adjustment for potential confounders (gender, age, BMI, alcohol intake, exercise frequency, smoking habits) were used to determine the association of periodontitis with HDL-C. RESULTS: After adjustments for confounders, linear regression analyses revealed a significant relationship between the decreased HDL-C and periodontitis severity (p < 0.05). Although the multivariable-adjusted ORs of decreased HDL-C were not statistically significant, logistic regression analyses showed Chinese elderly with severe periodontitis had higher odds of exhibiting clinically abnormal HDL-C levels than those without periodontitis. CONCLUSIONS: The elderly population with periodontitis showed HDL-C levels significantly lower than those without periodontitis. The severity of periodontitis was positively correlated with serum HDL-C levels. CLINICAL RELEVANCE: Periodontitis reduces HDL-C level in the elderly population, indicating that oral health should be paid attention to in the prevention and treatment of dyslipidemia.

Automatic segmentation and detection of ectopic eruption of first permanent molars on panoramic radiographs based on nnU-Net.

AIM: The purpose of this research was to present an artificial intelligence (AI) model, which can automatically segment and detect ectopic eruption of first permanent molars (EMMs) in early mixed dentition on panoramic radiographs using the no-new-Net (nnU-Net) model. DESIGN: A total of 438 EMMs obtained from 285 panoramic radiographs were included in this study. An AI model based on nnU-Net was trained to segment and detect EMMs. The performance of the model was evaluated by the intersection over union (IoU), precision, F1-score, accuracy and FROC. Furthermore, the detecting performance of nnU-Net was compared with that of three dentists with different years of experience using the McNemar chi-squared test. The reliability of different dentists was evaluated by intraclass correlation coefficients (ICCs). RESULTS: The nnU-Net yielded an IoU of 0.834, a precision of 0.845, an F1-score of 0.902 and an accuracy of 0.990, whereas the dentists yielded a mean IoU of 0.530, a mean precision of 0.539, a mean F1-score of 0.699 and a mean accuracy of 0.811. The ICC of different dentists was 0.776. The statistical analysis of the McNemar chi-squared test showed that the nnU-Net results were statistically significant and superior to those of dentists (p < .05). CONCLUSION: This study validated an AI model based on nnU-Net for automatically segmenting and detecting EMMs more consistently and accurately on panoramic radiography.

Caries segmentation on tooth X-ray images with a deep network.

OBJECTIVES: Deep learning has been a promising technology in many biomedical applications. In this study, a deep network was proposed aiming for caries segmentation on the clinically collected tooth X-ray images. METHODS: The proposed network inherited the skip connection characteristic from the widely used U-shaped network, and creatively adopted vision Transformer, dilated convolution, and feature pyramid fusion methods to enhance the multi-scale and global feature extraction capability. It was then trained on the clinically self-collected and augmented tooth X-ray image dataset, and the dice similarity and pixel classification precision were calculated for the network's performance evaluation. RESULTS: Experimental results revealed an average dice similarity of 0.7487 and an average pixel classification precision of 0.7443 on the test dataset, which outperformed the compared networks such as UNet, Trans-UNet, and Swin-UNet, demonstrating the remarkable improvement of the proposed network. CONCLUSIONS: This study contributed to the automatic caries segmentation by using a deep network, and highlighted the potential clinical utility value.

CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image.

Dental caries has been a common health issue throughout the world, which can even lead to dental pulp and root apical inflammation eventually. Timely and effective treatment of dental caries is vital for patients to reduce pain. Traditional caries disease diagnosis methods like naked-eye detection and panoramic radiograph examinations rely on experienced doctors, which may cause misdiagnosis and high time-consuming. To this end, we propose a novel deep learning architecture called CariesNet to delineate different caries degrees from panoramic radiographs. We firstly collect a high-quality panoramic radiograph dataset with 3127 well-delineated caries lesions, including shallow caries, moderate caries, and deep caries. Then we construct CariesNet as a U-shape network with the additional full-scale axial attention module to segment these three caries types from the oral panoramic images. Moreover, we test the segmentation performance between CariesNet and other baseline methods. Experiments show that our method can achieve a mean 93.64% Dice coefficient and 93.61% accuracy in the segmentation of three different levels of caries.

Comparison of Positioning Accuracy Between 2 Different Implant Systems Using Mucosa-Supported Surgical Templates: A Retrospective Clinical Study.

Although guided implant surgery is widely practiced, clinical studies examining the differences in accuracy between implanting systems that use the same surgical guide are currently lacking. This study aimed to evaluate and compare the effects of different dental implanting systems on positioning accuracy using a uniform type of stereolithographic surgical guide to account for cumulative errors in guide production. One hundred BEGO Semados S implants (group A) and 91 NobelActive implants (group B) were inserted into patients using the same type of guide. The accuracy was assessed by matching the preoperative and postoperative cone-beam computerized tomography. The implant shoulder, tip, depth, and angular deviation were registered. Statistically significant differences between groups were determined using Student t test, bivariate correlation test, and generalized estimating equation. The angular deviation was 3.16 ± 1.74° in group A and 2.58 ± 1.41° in group B (P = .013); the depth deviation was 0.44 ± 0.23 mm in group A and 0.51 ± 0.22 mm in group B (P = .032). In terms of vertical accuracy, the Bego implant system is superior to the Nobel implant system using the same type of surgical guide, while the angle accuracy is opposite. Therefore, it is important to control the depth when using the template-guided surgery with the Nobel implant system. Similarly, angle control should be emphasized in the Bego implant system. Measurements of the deviations provide the basis for a clinical reference that will be useful in preoperative analysis for improving the safety and accuracy of guided implant surgical procedures.

Anxiety, depression and post-traumatic stress disorder and related factors among Chinese population during the COVID-19 pandemic: A cross-sectional study.

The sudden outbreak of coronavirus disease 2019 (COVID-19) has deep and wide negative mental impacts on the public, and studies on the impact of COVID-19 on social and mental well-being are necessary. This study aimed to evaluate mental distress, including anxiety, depression, and post-traumatic stress disorder (PTSD), and its related risk factors in Chinese adults in the early stages of the COVID-19 pandemic. This study used a large-scale cross-sectional design. A total of 2067 adult participants completed the online survey via REDcap from 1st to 15th of March 2020 during the COVID-19 outbreak in China. Anxiety, depression, PTSD, and related risk factors, including self-efficacy, coping style, and social support, were measured using valid and reliable instruments. The data were analyzed using multiple linear regression. We found that 201 (9.7%) participants reported moderate-to-severe anxiety, 669 (33.8%) reported depression, and 368 (17.8%) reported symptoms of PTSD. Self-efficacy, coping style, and social support significantly affected anxiety, depression, and PTSD symptoms. Participants' sociodemographic characteristics, COVID-19 pandemic-related factors, low self-efficacy, low social support, and negative coping were predictors of mental distress during the COVID-19 pandemic. Our study will help healthcare professionals carry out early predictions and identification of high-risk groups and provide appropriate interventions to target groups during public health emergencies that plague the world.

Artificial Intelligence Model to Detect Real Contact Relationship between Mandibular Third Molars and Inferior Alveolar Nerve Based on Panoramic Radiographs.

This study aimed to develop a novel detection model for automatically assessing the real contact relationship between mandibular third molars (MM3s) and the inferior alveolar nerve (IAN) based on panoramic radiographs processed with deep learning networks, minimizing pseudo-contact interference and reducing the frequency of cone beam computed tomography (CBCT) use. A deep-learning network approach based on YOLOv4, named as MM3-IANnet, was applied to oral panoramic radiographs for the first time. The relationship between MM3s and the IAN in CBCT was considered the real contact relationship. Accuracy metrics were calculated to evaluate and compare the performance of the MM3-IANnet, dentists and a cooperative approach with dentists and the MM3-IANnet. Our results showed that in comparison with detection by dentists (AP = 76.45%) or the MM3-IANnet (AP = 83.02%), the cooperative dentist-MM3-IANnet approach yielded the highest average precision (AP = 88.06%). In conclusion, the MM3-IANnet detection model is an encouraging artificial intelligence approach that might assist dentists in detecting the real contact relationship between MM3s and IANs based on panoramic radiographs.

Deep Learning for Caries Detection and Classification.

OBJECTIVES: Deep learning methods have achieved impressive diagnostic performance in the field of radiology. The current study aimed to use deep learning methods to detect caries lesions, classify different radiographic extensions on panoramic films, and compare the classification results with those of expert dentists. METHODS: A total of 1160 dental panoramic films were evaluated by three expert dentists. All caries lesions in the films were marked with circles, whose combination was defined as the reference dataset. A training and validation dataset (1071) and a test dataset (89) were then established from the reference dataset. A convolutional neural network, called nnU-Net, was applied to detect caries lesions, and DenseNet121 was applied to classify the lesions according to their depths (dentin lesions in the outer, middle, or inner third D1/2/3 of dentin). The performance of the test dataset in the trained nnU-Net and DenseNet121 models was compared with the results of six expert dentists in terms of the intersection over union (IoU), Dice coefficient, accuracy, precision, recall, negative predictive value (NPV), and F1-score metrics. RESULTS: nnU-Net yielded caries lesion segmentation IoU and Dice coefficient values of 0.785 and 0.663, respectively, and the accuracy and recall rate of nnU-Net were 0.986 and 0.821, respectively. The results of the expert dentists and the neural network were shown to be no different in terms of accuracy, precision, recall, NPV, and F1-score. For caries depth classification, DenseNet121 showed an overall accuracy of 0.957 for D1 lesions, 0.832 for D2 lesions, and 0.863 for D3 lesions. The recall results of the D1/D2/D3 lesions were 0.765, 0.652, and 0.918, respectively. All metric values, including accuracy, precision, recall, NPV, and F1-score values, were proven to be no different from those of the experienced dentists. CONCLUSION: In detecting and classifying caries lesions on dental panoramic radiographs, the performance of deep learning methods was similar to that of expert dentists. The impact of applying these well-trained neural networks for disease diagnosis and treatment decision making should be explored.