Highly Porous Metal-Organic Framework Entrapped by Cobalt Telluride-Manganese Telluride as an Efficient Bifunctional Electrocatalyst.

The electrochemical conversion of oxygen holds great promise in the development of sustainable energy for various applications, such as water electrolysis, regenerative fuel cells, and rechargeable metal-air batteries. Oxygen electrocatalysts are needed that are both highly efficient and affordable, since they can serve as alternatives to costly precious-metal-based catalysts. This aspect is particularly significant for their practical implementation on a large scale in the future. Herein, highly porous polyhedron-entrapped metal-organic framework (MOF)-assisted CoTe2/MnTe2 heterostructure one-dimensional nanorods were initially synthesized using a simple hydrothermal strategy and then transformed into ZIF-67 followed by tellurization which was used as a bifunctional electrocatalyst for both the oxygen evolution reaction (OER) and oxygen reduction reaction (ORR). The designed MOF CoTe2/MnTe2 nanorod electrocatalyst exhibited superior activity for both OER (η = 220 mV@ 10 mA cm-2) and ORR (E1/2 = 0.81 V vs RHE) and outstanding stability. The exceptional achievement could be primarily credited to the porous structure, interconnected designs, and deliberately created deficiencies that enhanced the electrocatalytic activity for the OER/ORR. This improvement was predominantly due to the enhanced electrochemical surface area and charge transfer inherent in the materials. Therefore, this simple and cost-effective method can be used to produce highly active bifunctional oxygen electrocatalysts.

Single dopaminergic neuron DAN-c1 in Drosophila larval brain mediates aversive olfactory learning through D2-like receptors.

The dopaminergic system plays critical roles in Drosophila olfactory associative learning. In this study, we identified DAN-c1, a single dopaminergic neuron (DAN) in each brain hemisphere, that is both necessary and sufficient for Drosophila larval aversive associative learning. Compared to well-known roles of excitatory D1-like receptors in learning, the role of D2-like receptors (D2Rs) has not been fully investigated. We observed that D2Rs were expressed in DANs and the mushroom body (MB) in third instar larval brains. Knockdown of D2Rs in DAN-c1 by microRNA impaired aversive learning. Optogenetic activation of DAN-c1 during training led to an aversive learning deficit as well, indicating that D2R achieves its functions via autoreceptor inhibition. Interestingly, knockdown of D2R in MB impaired both appetitive and aversive learning. These results reveal that D2Rs in different brain structures play important but distinct roles in Drosophila larval olfactory learning, providing new insights into molecular mechanisms underlying associative learning.

Diagnosing oral and maxillofacial diseases using deep learning.

The classification and localization of odontogenic lesions from panoramic radiographs is a challenging task due to the positional biases and class imbalances of the lesions. To address these challenges, a novel neural network, DOLNet, is proposed that uses mutually influencing hierarchical attention across different image scales to jointly learn the global representation of the entire jaw and the local discrepancy between normal tissue and lesions. The proposed approach uses local attention to learn representations within a patch. From the patch-level representations, we generate inter-patch, i.e., global, attention maps to represent the positional prior of lesions in the whole image. Global attention enables the reciprocal calibration of path-level representations by considering non-local information from other patches, thereby improving the generation of whole-image-level representation. To address class imbalances, we propose an effective data augmentation technique that involves merging lesion crops with normal images, thereby synthesizing new abnormal cases for effective model training. Our approach outperforms recent studies, enhancing the classification performance by up to 42.4% and 44.2% in recall and F1 scores, respectively, and ensuring robust lesion localization with respect to lesion size variations and positional biases. Our approach further outperforms human expert clinicians in classification by 10.7 % and 10.8 % in recall and F1 score, respectively.

Activation of cyclic AMP signaling pathway in dopaminergic neurons rescues locomotion defects in a Drosophila larval model of Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disease showing uncontrollable motor symptoms that are primarily caused by the progressive loss of dopaminergic neurons in the brain. Currently no treatment exists to prevent PD progression. Therefore, discovery of new neuroprotective strategies still has great potential to benefit PD patients. A handful of studies show that activation of cAMP pathways is neuroprotective against PD progression. However, the neuroprotective role of this signaling cascade specifically in DA neurons has not been explored. In this study, fruit fly Drosophila melanogaster was used because of its sophisticated and powerful genetic approaches, especially with related to cAMP signaling pathway. We have investigated molecular mechanisms of neuroprotection in a fly larval model of PD by administering an environmental PD toxin rotenone. Increased cAMP signaling in the dunce mutant fly carrying defects in phosphodiesterase (PDE) gene, is neuroprotective against rotenone-induced locomotion deficits. Furthermore, the neuroprotective role of cAMP signaling specifically in DA neurons has been studied as it has not been explored. By using transgenic flies expressing designer receptors exclusively activated by designer drugs (DREADDs), we have shown that an increase of cAMP levels in DA neurons rescues rotenone-induced locomotion deficits. We also showed that this neuroprotection is mediated by activation of Gαs and PKA-C1 subunits. The results provide novel findings that expand our knowledge of neuroprotective mechanisms in DA neurons affecting PD progression, which could contribute to the development of new therapeutic treatments against PD. An important future study will explore downstream targets of cAMP-PKA signaling.

Assessment of a novel deep learning-based software developed for automatic feature extraction and grading of radiographic knee osteoarthritis.

BACKGROUND: The Kellgren-Lawrence (KL) grading system is the most widely used method to classify the severity of osteoarthritis (OA) of the knee. However, due to ambiguity of terminology, the KL system showed inferior inter- and intra-observer reliability. For a more reliable evaluation, we recently developed novel deep learning (DL) software known as MediAI-OA to extract each radiographic feature of knee OA and to grade OA severity based on the KL system. METHODS: This research used data from the Osteoarthritis Initiative for training and validation of MediAI-OA. 44,193 radiographs and 810 radiographs were set as the training data and used as validation data, respectively. This AI model was developed to automatically quantify the degree of joint space narrowing (JSN) of medial and lateral tibiofemoral joint, to automatically detect osteophytes in four regions (medial distal femur, lateral distal femur, medial proximal tibia and lateral proximal tibia) of the knee joint, to classify the KL grade, and present the results of these three OA features together. The model was tested by using 400 test datasets, and the results were compared to the ground truth. The accuracy of the JSN quantification and osteophyte detection was evaluated. The KL grade classification performance was evaluated by precision, recall, F1 score, accuracy, and Cohen's kappa coefficient. In addition, we defined KL grade 2 or higher as clinically significant OA, and accuracy of OA diagnosis were obtained. RESULTS: The mean squared error of JSN rate quantification was 0.067 and average osteophyte detection accuracy of the MediAI-OA was 0.84. The accuracy of KL grading was 0.83, and the kappa coefficient between the AI model and ground truth was 0.768, which demonstrated substantial consistency. The OA diagnosis accuracy of this software was 0.92. CONCLUSIONS: The novel DL software known as MediAI-OA demonstrated satisfactory performance comparable to that of experienced orthopedic surgeons and radiologists for analyzing features of knee OA, KL grading and OA diagnosis. Therefore, reliable KL grading can be performed and the burden of the radiologist can be reduced by using MediAI-OA.

Impaction of multiple dilacerated primary incisors: a case report.

Dental complications such as defective alveolar bone development, delayed eruption, and tooth impaction are related to neonatal oral intubation. This case report presents an example of potential complications that occur in children who have undergone oral intubation as neonates. A 20-month-old girl visited our pediatric clinic. We observed delayed, non-erupted teeth #51, #71, and #81 and determined a history of intubation during the neonatal period as a related factor. After 22 months of observation, tooth #71 erupted spontaneously. After 40 months of monitoring, teeth #51 and #81 were extracted surgically, and normal permanent teeth erupted six months later. This study is helpful for pediatric anesthesiologists, pediatricians, and dentists who diagnose and treat eruption disorders of the primary dentition.

Prediction Model for Future Success of Early Orthopedic Treatment of Class III Malocclusion.

This study aimed to identify predictors for successful post-treatment outcomes in early orthopedic class III malocclusion treatment with a facemask and hyrax expander appliance. The study was performed on lateral cephalograms from 37 patients at the start of treatment (T0), post-treatment (T1), and a minimum of three years after treatment (T2). The patients were grouped as stable or unstable according to the existence of a 2-mm overjet at T2. For statistical analysis, independent t-tests were used to compare the baseline characteristics and measurements of the two groups, considering a significance level of < 0.05. Thirty variables of pretreatment cephalograms were considered during logistic regression analysis to identify predictors. A discriminant equation was established using a stepwise method. The success rate and area under the curve were calculated, with AB to the mandibular plane, ANB, ODI, APDI, and A-B plane angles as predictors. The A-B plane angle was the most significantly different between the stable and unstable groups. In terms of the A-B plane angle, the success rate of early class III treatment with a facemask and hyrax expander appliance was 70.3%, and the area under the curve indicated a fair grade.

Deep learning for osteoarthritis classification in temporomandibular joint.

OBJECTIVES: This study aimed to develop a diagnostic support tool using pretrained models for classifying panoramic images of the temporomandibular joint (TMJ) into normal and osteoarthritis (OA) cases. SUBJECTS AND METHODS: A total of 858 panoramic images of the TMJ (395 normal and 463 TMJ-OA) were obtained from 518 individuals from January 2015 to December 2018. The data were randomly divided into training, validation, and testing sets (6:2:2). We used pretrained Resnet152 and EfficientNet-B7 as transfer learning models. The accuracy, specificity, sensitivity, area under the curve, and gradient-weighted class activation mapping (grad-CAM) of both trained models were evaluated. The performances of the trained models were compared to that of dentists (both TMD specialists and general dentists). RESULTS: The classification accuracies of ResNet-152 and EfficientNet-B7 were 0.87 and 0.88, respectively. The trained models exhibited the highest accuracy in OA classification. In the grad-CAM analysis, the trained models focused on specific areas in osteoarthritis images where erosion or osteophyte were observed. CONCLUSIONS: The artificial intelligence model improved the diagnostic power of TMJ-OA when trained with two-dimensional panoramic condyle images and can be effectively applied by dentists as a screening diagnostic tool for TMJ-OA.

Effectiveness of Human-Artificial Intelligence Collaboration in Cephalometric Landmark Detection.

Detection of cephalometric landmarks has contributed to the analysis of malocclusion during orthodontic diagnosis. Many recent studies involving deep learning have focused on head-to-head comparisons of accuracy in landmark identification between artificial intelligence (AI) and humans. However, a human-AI collaboration for the identification of cephalometric landmarks has not been evaluated. We selected 1193 cephalograms and used them to train the deep anatomical context feature learning (DACFL) model. The number of target landmarks was 41. To evaluate the effect of human-AI collaboration on landmark detection, 10 images were extracted randomly from 100 test images. The experiment included 20 dental students as beginners in landmark localization. The outcomes were determined by measuring the mean radial error (MRE), successful detection rate (SDR), and successful classification rate (SCR). On the dataset, the DACFL model exhibited an average MRE of 1.87 ± 2.04 mm and an average SDR of 73.17% within a 2 mm threshold. Compared with the beginner group, beginner-AI collaboration improved the SDR by 5.33% within a 2 mm threshold and also improved the SCR by 8.38%. Thus, the beginner-AI collaboration was effective in the detection of cephalometric landmarks. Further studies should be performed to demonstrate the benefits of an orthodontist-AI collaboration.

Clinical diagnosis and treatment of temporomandibular disorders in children and adolescents: a case series.

The early diagnosis of temporomandibular disorders (TMDs) in children and adolescents is important because they can affect oral and maxillofacial growth and development. This case series introduces patients with various clinical features of TMDs and demonstrates how symptoms were reduced through appropriate interventions in collaboration with oral medicine specialists and pediatric dentists. TMDs symptoms in children are often mild and difficult to express accurately; therefore, diagnosis through clinical evaluation is important. Pediatric dentists should be aware of TMDs in children and adolescents, and should diagnose, treat, and refer to specialists in a timely manner.

Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network.

Recently, the estimation of bone maturation using deep learning has been actively conducted. However, many studies have considered hand-wrist radiographs, while a few studies have focused on estimating cervical vertebral maturation (CVM) using lateral cephalograms. This study proposes the use of deep learning models for estimating CVM from lateral cephalograms. As the second, third, and fourth cervical vertebral regions (denoted as C2, C3, and C4, respectively) are considerably smaller than the whole image, we propose a stepwise segmentation-based model that focuses on the C2-C4 regions. We propose three convolutional neural network-based classification models: a one-step model with only CVM classification, a two-step model with region of interest (ROI) detection and CVM classification, and a three-step model with ROI detection, cervical segmentation, and CVM classification. Our dataset contains 600 lateral cephalogram images, comprising six classes with 100 images each. The three-step segmentation-based model produced the best accuracy (62.5%) compared to the models that were not segmentation-based.

Dental Management of Congenital Granular Cell Lesion and Neonatal Teeth: A Case Report.

Congenital granular cell lesion (CGCL) is a rare benign oral cavity tumor in infants. Neonatal teeth are also rare dental anomalies that appear during the first month of life. This report describes a case of eruption of neonatal teeth after surgical excision of CGCL. Surprisingly, residual neonatal teeth erupted after extraction of the neonatal teeth. If neonatal teeth are mobile, they should be carefully extracted with curettage of the underlying tissues of the dental papilla; failure to curette the socket might result in eruption of odontogenic remnants. If neonatal teeth were exfoliated, parents should be informed of the need for regular checkups with a dentist due to possibility of development of residual neonatal teeth.

Activity-dependent release of phosphorylated human tau from Drosophila neurons in primary culture.

Neuronal activity can enhance tau release and thus accelerate tauopathies. This activity-dependent tau release can be used to study the progression of tau pathology in Alzheimer's disease (AD), as hyperphosphorylated tau is implicated in AD pathogenesis and related tauopathies. However, our understanding of the mechanisms that regulate activity-dependent tau release from neurons and the role that tau phosphorylation plays in modulating activity-dependent tau release is still rudimentary. In this study, Drosophila neurons in primary culture expressing human tau (hTau) were used to study activity-dependent tau release. We found that hTau release was markedly increased by 50 mM KCl treatment for 1 h. A similar level of release was observed using optogenetic techniques, where genetically targeted neurons were stimulated for 30 min using blue light (470 nm). Our results showed that activity-dependent release of phosphoresistant hTauS11A was reduced when compared with wildtype hTau. In contrast, release of phosphomimetic hTauE14 was increased upon activation. We found that released hTau was phosphorylated in its proline-rich and C-terminal domains using phosphorylation site-specific tau antibodies (e.g., AT8). Fold changes in detectable levels of total or phosphorylated hTau in cell lysates or following immunopurification from conditioned media were consistent with preferential release of phosphorylated hTau after light stimulation. This study establishes an excellent model to investigate the mechanism of activity-dependent hTau release and to better understand the role of phosphorylated tau release in the pathogenesis of AD since it relates to alterations in the early stage of neurodegeneration associated with increased neuronal activity.

EARLY STARVATION 1 Is a Functionally Conserved Protein Promoting Gravitropic Responses in Plants by Forming Starch Granules.

Starch granules in the endodermis of plant hypocotyls act as statoliths that promote hypocotyl negative gravitropism-the directional growth of hypocotyls against gravity-in the dark. To identify the molecular components that regulate hypocotyl negative gravitropism, we performed a mutagenesis screen and isolated reduced gravitropic 1 (rgv1) mutants that lack starch granules in their hypocotyl endodermis and show reduced hypocotyl negative gravitropism in the dark. Using whole genome sequencing, we identified three different rgv1 mutants that are allelic to the previously reported early starvation 1 mutant, which is rapidly depleted of starch just before the dawn. ESV1 orthologs are present in starch-producing green organisms, suggesting ESV1 is a functionally conserved protein necessary for the formation of starch granules. Consistent with this, we found that liverwort and rice ESV1 can complement the Arabidopsis ESV1 mutant phenotype for both starch granules and hypocotyl negative gravitropism. To further investigate the function of ESV1 in other plants, we isolated rice ESV1 mutants and found that they show reduced levels of starch in their leaves and loosely packed starch granules in their grains. Both Arabidopsis and rice ESV1 mutants also lack starch granules in root columella and show reduced root gravitropism. Together, these results indicate ESV1 is a functionally conserved protein that promotes gravitropic responses in plants via its role in starch granule formation.

Risk Factors for Early Childhood Caries: An Umbrella Review.

Purpose: The purpose of this umbrella review was to summarize and evaluate the available evidence concerning risk factors for early childhood caries (ECC). Methods: Literature searches were performed until July 2020 in Medline, Web of Science, and EMBASE databases. Systematic reviews were selected, and their methodological qualities were assessed using the AMSTAR 2 tool (16 items). Results: A total of 977 articles for possible inclusion were identified, and 15 studies were finally selected for qualitative analysis. In these 15 studies, the risk of bias varied from critically low to high. The included studies reported risk factors for ECC such as dietary factors, mutans streptococci, low maternal education, maternal age, low family income and parental education level, low social class, enamel defects, obesity, the presence of dentinal caries, presence of Candida albicans, moderate to late preterm birth, and prenatal maternal cigarette smoking. Conclusions: The potential risk factors for early childhood caries are enamel defects, high levels of mutans streptococci, the presence of dentinal caries, increased consumption of soda, daily intake of sugary snacks, and obesity. In the future, longitudinal studies are needed to explore the potential relationship between these risk factors and ECC.

Transcriptomic Analysis of Rice Plants Overexpressing PsGAPDH in Response to Salinity Stress.

In plants, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is a main enzyme in the glycolytic pathway. It plays an essential role in glycerolipid metabolism and response to various stresses. To examine the function of PsGAPDH (Pleurotus sajor-caju GAPDH) in response to abiotic stress, we generated transgenic rice plants with single-copy/intergenic/homozygous overexpression PsGAPDH (PsGAPDH-OX) and investigated their responses to salinity stress. Seedling growth and germination rates of PsGAPDH-OX were significantly increased under salt stress conditions compared to those of the wild type. To elucidate the role of PsGAPDH-OX in salt stress tolerance of rice, an Illumina HiSeq 2000 platform was used to analyze transcriptome profiles of leaves under salt stress. Analysis results of sequencing data showed that 1124 transcripts were differentially expressed. Using the list of differentially expressed genes (DEGs), functional enrichment analyses of DEGs such as Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed. KEGG pathway enrichment analysis revealed that unigenes exhibiting differential expression were involved in starch and sucrose metabolism. Interestingly, trehalose-6-phosphate synthase (TPS) genes, of which expression was enhanced by abiotic stress, showed a significant difference in PsGAPDH-OX. Findings of this study suggest that PsGAPDH plays a role in the adaptation of rice plants to salt stress.

Dentist Job Satisfaction: A Systematic Review and Meta-analysis.

OBJECTIVES: Because of the heterogeneous nature of the evidence regarding dentists' job satisfaction, an overview was necessary to examine dentists' level of job satisfaction and to determine related work environmental factors. MATERIALS AND METHODS: A literature search was conducted using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Electronic database searches of PubMed/MEDLINE, EMBASE, and Web of Science were performed until March 1, 2020. Two independent authors collected data and assessed the methodological quality of primary studies using the Newcastle Ottawa Scale. RESULTS: Nine studies were included from the 1987 initially retrieved. Among the included studies, 5 exhibited a neutral level of satisfaction and originated from China, South Korea, Egypt, and the United States, and 3 studies from Canada, Lithuania, and the United States showed a high level of satisfaction. Only 1 study did not report the mean job satisfaction score. According to bias evaluation, 9 studies were considered low risk. CONCLUSION: The findings showed that dentists were satisfied with their jobs at a moderate to high level, and specialists were more satisfied than general dentists. Regarding work environmental factors, the 6 most satisfied factors were patient relationships, respect, delivery of care, staff, professional relationship, and professional environment. Five of the least satisfied factors were personal time, stress, income, practice management, and professional time. However, longitudinal studies would be required to identify changes in these factors. Further studies should be performed in middle- and low-income countries using the Dentist Satisfaction Survey, including stress evaluation.

Influence of mouth breathing on atopic dermatitis risk and oral health in children: A population-based cross-sectional study.

BACKGROUND/PURPOSE: Previous studies have suggested that mouth breathing has harmful effects on atopic dermatitis (AD) and oral health in children, but the evidence has been insufficient. To investigate the association of mouth breathing with AD and oral health in Korean schoolchildren aged 8-11 years. MATERIALS AND METHODS: Cross-sectional data were obtained from March to April 2016. A questionnaire was used to investigate children's mouth breathing habits and personal/family histories related to allergic disease. Oral health status was determined through a clinical oral examination. Data were analyzed with multivariable logistic regression. RESULTS: In total, 1507 children were included. A moderate relationship was observed between mouth breathing and AD (adjusted odds ratio, 1.05; 95% confidence interval, 1.00-1.10; p-value, 0.035), whereas no association was found between mouth breathing and dental caries in children. Mouth breathing during sleep (MBS) was closely related to allergic diseases and other respiratory diseases. Furthermore, mouth breathing was associated with child's tonsillitis and was identified as a possible risk factor for class II dental malocclusion. CONCLUSION: We confirmed the positive association between mouth breathing (especially during sleep) and allergic diseases, including the AD in school-aged children. The influence of mouth breathing on dental caries remains uncertain. An intervention trial is required to evaluate whether the prevention of mouth breathing can reduce the risk of allergic diseases.

Multiple Idiopathic Internal Root Resorption in an Adolescent: A Case R Multiple Idiopathic Internal Root Resorption in an Adolescent: A Case Report.

Internal root resorption (IRR) refers to a slow or rapid progressive resorption process that occurs in the pulp cavity of the tooth or the dentin of the root. IRR occurs as result of odontoclast action; in many cases, the pulp tissue exhibits chronic inflammation, and odontoblasts and predentin do not appear on the dentin wall near the pulp. Exact predisposing factors have not been clearly elucidated; therefore, it is difficult to identify reliable data on the prevalence of IRR because of its scarce occurrence and pathology. Reports have indicated that IRR is more common in the primary than in the permanent teeth. This case report discusses a 17-year-old girl with multiple idiopathic internal root resorptions of anterior permanent teeth in a short period of a time and its management.

Deep Anatomical Context Feature Learning for Cephalometric Landmark Detection.

In the past decade, anatomical context features have been widely used for cephalometric landmark detection and significant progress is still being made. However, most existing methods rely on handcrafted graphical models rather than incorporating anatomical context during training, leading to suboptimal performance. In this study, we present a novel framework that allows a Convolutional Neural Network (CNN) to learn richer anatomical context features during training. Our key idea consists of the Local Feature Perturbator (LFP) and the Anatomical Context loss (AC loss). When training the CNN, the LFP perturbs a cephalometric image based on prior anatomical distribution, forcing the CNN to gaze relevant features more globally. Then AC loss helps the CNN to learn the anatomical context based on spatial relationships between the landmarks. The experimental results demonstrate that the proposed framework makes the CNN learn richer anatomical representation, leading to increased performance. In the performance comparisons, the proposed scheme outperforms state-of-the-art methods on the ISBI 2015 Cephalometric X-ray Image Analysis Challenge.