Foci-Xpress: Automated and Fast Nuclear Foci Counting Tool.

In the nucleus, distinct, discrete spots or regions called "foci" have been identified, each harboring a specific molecular function. Accurate and efficient quantification of these foci is essential for understanding cellular dynamics and signaling pathways. In this study, we present an innovative automated image analysis method designed to precisely quantify subcellular foci within the cell nucleus. Manual foci counting methods can be tedious and time-consuming. To address these challenges, we developed an open-source software that automatically counts the number of foci from the indicated image files. We compared the foci counting efficiency, velocity, accuracy, and convenience of Foci-Xpress with those of other conventional methods in foci-induced models. We can adjust the brightness of foci to establish a threshold. The Foci-Xpress method was significantly faster than other conventional methods. Its accuracy was similar to that of conventional methods. The most significant strength of Foci-Xpress is automation, which eliminates the need for analyzing equipment while counting. This enhanced throughput facilitates comprehensive statistical analyses and supports robust conclusions from experiments. Furthermore, automation completely rules out biases caused by researchers, such as manual errors or daily variations. Thus, Foci-Xpress is a convincing, convenient, and easily accessible focus-counting tool for cell biologists.

A novel approach to predicting the synergy of anti-cancer drug combinations using document-based feature extraction.

BACKGROUND: To reduce drug side effects and enhance their therapeutic effect compared with single drugs, drug combination research, combining two or more drugs, is highly important. Conducting in-vivo and in-vitro experiments on a vast number of drug combinations incurs astronomical time and cost. To reduce the number of combinations, researchers classify whether drug combinations are synergistic through in-silico methods. Since unstructured data, such as biomedical documents, include experimental types, methods, and results, it can be beneficial extracting features from documents to predict anti-cancer drug combination synergy. However, few studies predict anti-cancer drug combination synergy using document-extracted features. RESULTS: We present a novel approach for anti-cancer drug combination synergy prediction using document-based feature extraction. Our approach is divided into two steps. First, we extracted documents containing validated anti-cancer drug combinations and cell lines. Drug and cell line synonyms in the extracted documents were converted into representative words, and the documents were preprocessed by tokenization, lemmatization, and stopword removal. Second, the drug and cell line features were extracted from the preprocessed documents, and training data were constructed by feature concatenation. A prediction model based on deep and machine learning was created using the training data. The use of our features yielded higher results compared to the majority of published studies. CONCLUSIONS: Using our prediction model, researchers can save time and cost on new anti-cancer drug combination discoveries. Additionally, since our feature extraction method does not require structuring of unstructured data, new data can be immediately applied without any data scalability issues.

Early Prediction of the Need for Orthognathic Surgery in Patients With Repaired Unilateral Cleft Lip and Palate Using Machine Learning and Longitudinal Lateral Cephalometric Analysis Data.

ABSTRACT: The purpose of this study was to determine the cephalometric predictors of the future need for orthognathic surgery in patients with repaired unilateral cleft lip and palate (UCLP) using machine learning. This study included 56 Korean patients with UCLP, who were treated by a single surgeon and a single orthodontist with the same treatment protocol. Lateral cephalograms were obtained before the commencement of orthodontic/orthopedic treatment (T0; mean age, 6.3 years) and at at least of 15 years of age (T1; mean age, 16.7 years). 38 cephalometric variables were measured. At T1 stage, 3 cephalometric criteria (ANB ≤ -3°; Wits appraisal ≤ -5 mm; Harvold unit difference ≥34 mm for surgery group) were used to classify the subjects into the surgery group (n = 10, 17.9%) and non-surgery group (n = 46, 82.1%). Independent t-test was used for statistical analyses. The Boruta method and XGBoost algorithm were used to determine the cephalometric variables for the prediction model. At T0 stage, 2 variables exhibited a significant intergroup difference (ANB and facial convexity angle [FCA], all P < 0.05). However, 18 cephalometric variables at the T1 stage and 14 variables in the amount of change (ΔT1-T0) exhibited significant intergroup differences (all, more significant than P < 0.05). At T0 stage, the ANB, PP-FH, combination factor, and FCA were selected as predictive parameters with a cross-validation accuracy of 87.4%. It was possible to predict the future need for surgery to correct sagittal skeletal discrepancy in UCLP patients at the age of 6 years.

FGFR1 is associated with c-MYC and proangiogenic molecules in metastatic renal cell carcinoma under anti-angiogenic therapy.

AIMS: This study aimed to investigate the clinicopathological significance of FGFR1 and c-MYC expression, particularly in relation to angiogenesis in clear cell renal cell carcinoma (CCRCC). METHODS AND RESULTS: Immunohistochemistry and fluorescence in-situ hybridisation were conducted with tissue microarrays from 91 metastatic CCRCC patients who received VEGF receptor tyrosine kinase inhibitors (VEGFR-TKIs). The expression of angiogenic molecules, FGFR1 and c-MYC, and tumoral vascular density (TVD) and mRNA expression and TVD of 533 CCRCCs in The Cancer Genome Atlas (TCGA) were analysed. FGFR1, pFGFR1 and c-MYC expression was observed in 29.1, 74.4 and 30.8% of tumours, respectively. FGFR1high was an independent worse prognostic factor for overall (HR = 1.871, P = 0.032) and progression-free (HR = 1.976, P = 0.016) survival. FGFR1high was significantly related to VEGFR-TKI responsiveness (P = 0.011). The presence of FGFR1high /c-MYChigh showed a positive correlation with proangiogenic markers, including VEGF (P = 0.018) and HIF-1α (P < 0.0001). FGFR1high /c-MYChigh tumours showed higher TVDs together with higher VEGFR2 and PDGFR-ß expression (both P < 0.0001). FGFR1 and c-MYC expression was also positively correlated with the expression of hypoxia-related and proangiogenic-related genes in the TCGA data. CONCLUSIONS: FGFR1 and c-MYC may be involved in tumour angiogenesis and FGFR1 may represent a promising therapeutic target in metastatic CCRCC.

Dishevelled segment polarity protein 3 (DVL3): a novel and easily applicable recurrence predictor in localised prostate adenocarcinoma.

OBJECTIVE: To identify new biomarkers for biochemical recurrence (BCR) of prostate adenocarcinoma. PATIENTS AND METHODS: Clinical information of 500 patients with prostate adenocarcinoma and their 152 RNA-sequencing and protein-array data from The Cancer Genome Atlas (TCGA) were separated into a discovery set and a validation set. Each dataset was analysed according to the Gleason grade groups reflecting BCR. The results obtained from the analysis using TCGA dataset were confirmed by immunohistochemistry analyses of a confirmation cohort composed of 395 patients with localised prostate adenocarcinoma. RESULTS: TCGA discovery set was subgrouped into lower- and higher-risk groups for recurrence-free survival (RFS) (P < 0.001). Cyclin B1 (CCNB1), dishevelled segment polarity protein 3 (DVL3), paxillin (PXN), RAF1, transferrin, X-ray repair cross complementing 5 (XRCC5) and BIM had lower expression in the lower-risk group than that in the higher-risk group (all, P < 0.05). In TCGA validation set, CCNB1, DVL3, transferrin, XRCC5 and BIM were also differently expressed between the two groups. Immunohistochemically, DVL3 positivity was associated with high prostate-specific antigen (PSA) levels, resection margin involvement, and BCR (all, P < 0.05). A high Gleason score indicated a marginal relationship (P = 0.055). BIM positivity was related to high PSA levels, lymphovascular invasion, and BCR (all, P < 0.05). Both DVL3 positivity (P = 0.010) and BIM positivity (P = 0.024) were associated with shorter RFS, but statistical significance was lost when the multivariate Cox regression model included all patients. In the lower-risk group, the multivariate Cox model confirmed that DVL3 was an independent predictor for poor RFS (hazard ratio 1.80, P = 0.040), and the concordance index (C-index) was 0.805. CONCLUSIONS: DVL3 and BIM were expressed in patients with a higher risk of BCR. DVL3 may be a novel and easily applicable recurrence predictor of localised prostate adenocarcinoma.

Clinicopathologic Analysis of PD-L1 and PD-L2 Expression in Renal Cell Carcinoma: Association with Oncogenic Proteins Status.

BACKGROUND: Immune checkpoint blockade therapy targeting programmed death (PD)-1 or PD-ligand 1 (L1) has shown promising results in renal cell carcinoma (RCC);however, the prognostic implications and clinicopathological features of PD-L1 and PD-L2 expression in RCC remain unclear. METHODS: PD-L1 and PD-L2 expression was immunohistochemically evaluated in 425 resected RCCs of variable histologic subtypes and analyzed according to the clinicopathological status and oncogenic proteins status. RESULTS: PD-L1 expression was observed in 9.4 % with no difference between histologic subtypes, but PD-L2 was observed in 49.6 % with highest frequency in papillary RCC (PRCC) (P<0.001). In clear cell RCC (CCRCC), PD-L1 expression was associated with adverse features,including higher nuclear grade, necrosis, sarcomatoid transformation, c-MET expression (all, P<0.001) and VEGF expression (P = 0.002), whereas PD-L2 expression was related with c-MET and VEGF expression (P = 0.008 and P<0.001). In PRCC, positive correlations between PD-L1 and EGFR expression (P = 0.007) or between PDL2 and VEGF expression (P<0.001) were observed. In CCRCC, PD-L1 and PD-L2 positivity were significantly associated with shorter progression-free survival (P<0.001; P = 0.033) and cancer-specific survival (P<0.001; P = 0.010), but not in PRCC. CONCLUSIONS: PD-L1 and PD-L2 expression predict poor prognosis in CCRCC. Thus, PD-1/PD-L pathway-targeted immunotherapy may be useful for treatment of patients with CCRCC.

Clinicopathological analysis of programmed cell death 1 and programmed cell death ligand 1 expression in the tumour microenvironments of diffuse large B cell lymphomas.

AIMS: To investigate the clinicopathological characteristics of programmed cell death ligand 1 (PD-L1) and programmed cell death 1 (PD-1) expression in the tumour microenvironments of diffuse large B cell lymphoma (DLBCL). METHODS AND RESULTS: Tumour tissues from 126 DLBCL patients were immunostained for PD-L1 and PD-1. The expression of PD-L1 by tumour cells and/or tumour-infiltrating immune cells (mainly macrophages) was evaluated, and the number of tumour-infiltrating PD-1(+) cells was assessed. PD-L1 expression in tumour cells was observed in 61.1% of DLBCLs, with a weak intensity in 29.4%, moderate intensity in 21.4% and strong intensity in 10.3% of cases. Strong PD-L1 expression in tumour cells was associated significantly with the presence of B symptoms (adjusted P = 0.005) and Epstein-Barr virus (EBV) infection (adjusted P = 0.015), and tended to be higher in activated B cell-like immunophenotype (16.7%) than germinal centre B cell-like immunophenotype (2.5%) (adjusted P = 0.271). DLBCLs with PD-L1 expression in tumour cells/macrophages showed similar clinicopathological characteristics. The quantity of PD-1(+) tumour-infiltrating lymphocytes correlated positively with the level of PD-L1 expression in tumour cells (P = 0.042) or in tumour cells/macrophages (P = 0.03). Increased infiltration of PD-1(+) cells was associated with prolonged progression-free survival (P = 0.005) and overall survival (P = 0.026) in DLBCL patients treated with rituximab-cyclophosphamide, doxorubicin, vincristine, prednisone (R-CHOP), whereas PD-L1 expression had no prognostic significance. CONCLUSIONS: PD-L1 and PD-1 were expressed variably in DLBCLs by tumour cells and tumour-infiltrating immune cells and might be potential therapeutic targets using PD-1/PD-L1 blockade.

Usefulness of permutation entropy as an anesthetic depth indicator in children.

Permutation entropy (PE) as a complexity measure has been introduced to monitor anesthetic depth for adult. However, PE has not yet been evaluated for its clinical applicability as an indicator of anesthetic depth in children. Therefore, in order to investigate the validity of PE, we compared PE with BIS using pharmacodynamic (PD) modeling in children. Electroencephalogram (EEG) was obtained from BIS monitor during sevoflurane deepening and lightening protocol. End-tidal sevoflurane concentration (Etsevo) and BIS were measured simultaneously. PE was calculated from the processed EEG with the scale ranging from 0 to 100. NONMEM software was used to investigate the PD relationship between Etsevo with BIS and PE. Adjusted PE (APE) values were decreased as anesthesia deepened. APE and BIS showed significant linear correlation (P < 0.001), indicating that PE also reflects anesthesia depth. PD parameters for APE and BIS were estimated with a sigmoid Emax model which describes the relationship between Etsevo and APE/BIS (E o : 78, E max : 17.6, C e50 : 2.5 vol%; γ: 13.1, k eo : 0.47 min(-1) for APE; E o : 89.4; E max : 15.7; C e50 : 2.2 vol%; γ: 6.6, keo: 0.52 min(-1) for BIS). PE seems to be a useful indicator of anesthetic depth, which is comparable to BIS in children.

Prediction of the Need for Orthognathic Surgery in Patients With Cleft Lip and/or Palate.

The purpose of this study was to determine the cephalometric variables that can predict the future need for orthognathic surgery or distraction osteogenesis in Korean male patients with nonsyndromic cleft lip and alveolus (CLA) and unilateral (UCLP) and bilateral cleft lip and palate (BCLP). A total of 131 patients who were treated by one surgeon and one orthodontist using identical protocol were divided into CLA group (n = 35), UCLP group (n = 56), and BCLP group (n = 40). Lateral cephalograms were taken before secondary alveolar bone graft (T0; mean age, 9.3 years) and at the minimum of 15 years of age (T1; mean age, 17.3 years). The cephalometric variables of these cephalograms were measured. At T1 stage, 3 cephalometric criteria were used to divide the subjects into surgery and nonsurgery groups (ANB ≤ -3 degrees; Wits appraisal ≤ -5 mm; Harvold unit difference ≥ 34 mm for surgery group). The feature wrapping method was used to determine the cephalometric variables at T0 stage for a prediction model. At T1 stage, 27 (20.6%) of 131 subjects required surgical intervention to correct their sagittal skeletal discrepancies. Frequency was significantly different among the CLA, UCLP, and BCLP groups (8.5%, 21.4%, and 30.0%, respectively; P < 0.05; [CLA, UCLP] < [UCLP, BCLP]). A total of 10 cephalometric variables of T0 stage were selected as predictors, and weighted classification accuracy was 77.3%. The frequency of surgical intervention increased with cleft severity. Ten cephalometric variables might be regarded as effective predictors of the future need for surgery to correct their sagittal skeletal discrepancies.

The cumulative survival rate of dental implants with micro-threads: a long-term retrospective study.

PURPOSE: This study aimed to evaluate the long-term cumulative survival rate (CSR) of dental implants with micro-threads in the neck over a 10-year follow-up period and to examine the factors influencing the survival rate of dental implants. METHODS: This retrospective study was based on radiographic and dental records. In total, 151 patients received 490 Oneplant® dental implants with an implant neck micro-thread design during 2006-2010 in the Department of Periodontology of Seoul National University Dental Hospital. Implant survival was evaluated using Kaplan-Meier analysis. Cox proportional hazard regression analysis was used to identify the factors influencing implant failure. RESULTS: Ten out of 490 implants (2.04%) failed due to fixture fracture. The CSR of the implants was 97.9%, and no significant difference was observed in the CSR between external- and internal-implant types (98.2% and 97.6%, respectively, P=0.670). In Cox regression analysis, 2-stage surgery significantly increased the risk of implant failure (hazard ratio: 4.769, P=0.039). There were no significant differences in influencing factors, including sex, age, implant diameter, length, fixture type, location, surgical procedure, bone grafting, and restoration type. CONCLUSIONS: Within the limitations of this retrospective study, the micro-thread design of the implant neck was found to be favorable for implant survival, with stable clinical outcomes.

A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma.

This study aimed to develop a deep learning (DL) model for predicting the recurrence risk of lung adenocarcinoma (LUAD) based on its histopathological features. Clinicopathological data and whole slide images from 164 LUAD cases were collected and used to train DL models with an ImageNet pre-trained efficientnet-b2 architecture, densenet201, and resnet152. The models were trained to classify each image patch into high-risk or low-risk groups, and the case-level result was determined by multiple instance learning with final FC layer's features from a model from all patches. Analysis of the clinicopathological and genetic characteristics of the model-based risk group was performed. For predicting recurrence, the model had an area under the curve score of 0.763 with 0.750, 0.633 and 0.680 of sensitivity, specificity, and accuracy in the test set, respectively. High-risk cases for recurrence predicted by the model (HR group) were significantly associated with shorter recurrence-free survival and a higher stage (both, p < 0.001). The HR group was associated with specific histopathological features such as poorly differentiated components, complex glandular pattern components, tumor spread through air spaces, and a higher grade. In the HR group, pleural invasion, necrosis, and lymphatic invasion were more frequent, and the size of the invasion was larger (all, p < 0.001). Several genetic mutations, including TP53 (p = 0.007) mutations, were more frequently found in the HR group. The results of stages I-II were similar to those of the general cohort. DL-based model can predict the recurrence risk of LUAD and identify the presence of the TP53 gene mutation by analyzing histopathologic features.

Application of the water subtraction algorithm in determining the degree of conversion of self-etching adhesives.

PURPOSE: This study evaluated the effect of residual water included within the FT-IR spectra on the calculation of the degree of conversion (DC) of a self-etching adhesive (SEA). MATERIALS AND METHODS: FT-IR spectra of a SEA (Adper Prompt SE, 3M ESPE) were obtained for different amounts of dentin powder, agitation times, and light-curing times. The measured DC (mDC) obtained from the IR spectra was compared to the apparent DC (aDC) from the adjusted IR spectra using the water subtraction algorithm (WSA), by which the water absorption band was subtracted from the obtained IR spectrum. RESULTS: When the SEA was mixed with 10 mg of dentin powder, the aDC was significantly higher than the mDC immediately after light curing (paired t-test, p < 0.001). With the increase in the amount of dentin powder, the mDC immediately after curing and the difference between the mDC and the aDC gradually increased. The amount of dentin powder, light-curing time, square of light-curing time, and time until measurement were the variables that significantly affected the aDC (linear mixed model, p < 0.05). However, the agitation time did not affect the aDC or the difference between the mDC and the aDC, except at 20 s. CONCLUSION: When the DC of SEA itself is measured with FT-IR, the mDC needs to be adjusted with the water subtraction algorithm. Clinically, SEA needs to be applied under conditions of prolonged contact with the dentin substrate and vigorous and prolonged drying.

Graph approaches for analysis of brain connectivity during dexmedetomidine sedation.

Sedation is commonly used to relieve fear and anxiety during procedures. Dexmedetomidine (DEX), approved by the US Food and Drug Administration in 1999 for short-term sedation, is a selective alpha2-adrenoreceptor agonist. The use of DEX is increasing due to minimal respiratory depression and easy and quick awakening from sedation. Its sedative mechanisms are suggested to be related to changes in the interaction between brain regions. In this study, we used graph theory to investigate whether the altered network connection is associated with sedation. Electroencephalogram (EEG) recordings of 32 channels were acquired during awake and DEX-induced sedation for 20 participants. We extracted EEG epochs from the awake and the DEX sedation state. Using the graph theory, we compared the changes in the network connection parameters with the awake state. We observed that the slopes in 1/f dynamics, which indicate overall brain network characteristics, were greater during DEX-induced sedation compared to the awake state, suggesting a transition towards a random network behavior. In addition, network connections from the perspective of information processing were significantly disturbed in the alpha frequency band, unlike other frequency bands augmenting network connections. The alpha frequency band plays a prominent role in the function and interaction of cognitive activities. These results collectively indicate that changes in the brain network critical to cognition during DEX administration may also be related to the mechanism of sedation.

A retrospective study of the long-term survival of RESTORE® dental implants with resorbable blast media surface.

PURPOSE: The aim of this study was to retrospectively evaluate the survival and failure rates of RESTORE® implants over a follow-up period of 10-15 years at a university dental hospital and to investigate the factors affecting the survival rate of these dental implants. METHODS: A total of 247 RESTORE® dental implants with a resorbable blast media (RBM) surface inserted in 86 patients between March 2006 and April 2011 at the Department of Periodontology of Seoul National University Dental Hospital were included. Patients with follow-up periods of less than 10 years were excluded, and data analysis was conducted based on dental records and radiographs. RESULTS: Over a 10- to 15-year period, the cumulative survival rate of the implants was 92.5%. Seventeen implants (6.88%) were explanted due to implant fracture (n=10, 4.05%), peri-implantitis (n=6, 2.43%), and screw fracture (n=1, 0.4%). The results of univariate regression analysis using a Cox proportional hazards model demonstrated that implants placed in male patients (hazard ratio [HR], 4.542; 95% confidence interval [CI], 1.305-15.807; P=0.017) and implants that supported removable prostheses (HR, 15.498; 95% CI, 3.105-77.357; P=0.001) showed statistically significant associations with implant failure. CONCLUSIONS: Within the limitations of this retrospective study, the RESTORE® dental implant with an RBM surface has a favorable survival rate with stable clinical outcomes.

How general anesthetics work: from the perspective of reorganized connections within the brain.

General anesthesia is critical for various procedures and surgeries. Despite the widespread use of anesthetics, their precise mechanisms remain poorly understood. Anesthetics inevitably act on the brain, primarily through the modulation of target receptors. Even if the action is specific to an individual neuron, however, long-range effects can occur due to the tremendous interconnectedness of neuronal activity. The strength of this connectivity can be understood using mathematical models that allow for the study of neuronal connectivity dynamics. These models also allow researchers to develop hypotheses on the candidate mechanisms of action of different types of anesthesia. This review highlights the theoretical background associated with the study of the mechanisms of action of anesthetics. We propose a candidate framework that describes how anesthetics act on the brain and consciousness in general.

Gene-centric multi-omics integration with convolutional encoders for cancer drug response prediction.

MOTIVATION: Tumor heterogeneity, including genetic and transcriptomic characteristics, can reduce the efficacy of anticancer pharmacological therapy, resulting in clinical variability in patient response to therapeutic medications. Multi-omics integration can allow in silico models to provide an additional perspective on a biological system. METHODS: In this study, we propose a gene-centric multi-channel (GCMC) architecture to integrate multi-omics for predicting cancer drug response. GCMC transformed multi-omics profiles into a three-dimensional tensor with an additional dimension for omics types. GCMC's convolutional encoders captures multi-omics profiles for each gene and yields gene-centric features to predict drug responses. RESULTS: We evaluated GCMC on various datasets, including The Cancer Genome Atlas (TCGA) patients, patient-derived xenografts (PDX) mice models, and the Genomics of Drug Sensitivity in Cancer (GDSC) cell line datasets. GCMC achieved better performance than baseline models, including single-omics models, in more than 75% of 265 drugs from GDSC cell line datasets. Furthermore, as for the clinical applicability of GCMC, it achieved the best performance on TCGA and PDX datasets in terms of both AUPR and AUC. We also analyzed models' capability of integrating multi-omics profiles by measuring the contribution ratio of omics types. GCMC can incorporate multi-omics profiles in various manners to enhance performance for each drug type. These results suggested that GCMC can improve performance and feature extraction capability by integrating multi-omics profiles in a gene-centric manner.

Dental implant proximity to adjacent teeth: A retrospective study.

BACKGROUND AND PURPOSE: The purpose of this study was to assess the occurrence and prognosis of dental implant proximity or direct contact with the adjacent tooth and to evaluate the symptoms and complications in both the implant and the adjacent tooth. We then elaborate on treatment modalities and discuss the prevention of this phenomenon. MATERIALS AND METHODS: This retrospective study was conducted based on the dental clinical and radiographic records of 43 patients with implant-tooth proximity of <1.0 mm or direct implant-tooth contact. Multivariate Bayesian logistic regression was performed to examine the influence of individual variables on correcting major clinical variables. RESULTS: In the multivariate regression analysis, the rate of occurrence of tooth symptom decreased by about 95% with every increase of 1.0 mm distance between implant and tooth (odds ratio [OR] = 0.1, 95% confidence interval [CI]: 0.004-0.680, p = 0.024). In the case of implant-tooth root proximity in the anterior area, the OR of peri-implantitis occurrence was 30.4-fold greater than in posterior sites (OR = 30.4, 95% CI: 1.189-785.914, p = 0.040). CONCLUSION: Implant-tooth root proximity or direct implant-tooth contact is an iatrogenic factor that causes various complications, including devitalization of the adjacent tooth and peri-implantitis. Proactive prevention with surgical stents and intra-operative periapical radiographs is needed. If proximity or contact is found during surgery, repositioning of the fixture to the correct location is recommended in order to maintain peri-implant health and prevent complications.

Prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data.

The purpose of this study is to apply a machine learning approach to predict whether patients with burning mouth syndrome (BMS) respond to the initial approach and clonazepam therapy based on clinical data. Among the patients with the primary type of BMS who visited the clinic from 2006 to 2015, those treated with the initial approach of detailed explanation regarding home care instruction and use of oral topical lubricants, or who were prescribed clonazepam for a minimum of 1 month were included in this study. The clinical data and treatment outcomes were collected from medical records. Extreme Gradient-Boosted Decision Trees was used for machine learning algorithms to construct prediction models. Accuracy of the prediction models was evaluated and feature importance calculated. The accuracy of the prediction models for the initial approach and clonazepam therapy was 67.6% and 67.4%, respectively. Aggravating factors and psychological distress were important features in the prediction model for the initial approach, and intensity of symptoms before administration was the important feature in the prediction model for clonazepam therapy. In conclusion, the analysis of treatment outcomes in patients with BMS using a machine learning approach showed meaningful results of clinical applicability.

Differences in the heritability of craniofacial skeletal and dental characteristics between twin pairs with skeletal Class I and II malocclusions.

OBJECTIVE: To investigate differences in the heritability of skeletodental characteristics between twin pairs with skeletal Class I and Class II malocclusions. METHODS: Forty Korean adult twin pairs were divided into Class I (C-I) group (0° ≤ angle between point A, nasion, and point B [ANB]) ≤ 4°; mean age, 40.7 years) and Class II (C-II) group (ANB > 4°; mean age, 43.0 years). Each group comprised 14 monozygotic and 6 dizygotic twin pairs. Thirty-three cephalometric variables were measured using lateral cephalograms and were categorized as the anteroposterior, vertical, dental, mandible, and cranial base characteristics. The ACE model was used to calculate heritability (A > 0.7, high heritability). Thereafter, principal component analysis (PCA) was performed. RESULTS: Twin pairs in C-I group exhibited high heritability values in the facial anteroposterior characteristics, inclination of the maxillary and mandibular incisors, mandibular body length, and cranial base angles. Twin pairs in C-II group showed high heritability values in vertical facial height, ramus height, effective mandibular length, and cranial base length. PCA extracted eight components with 88.3% in the C-I group and seven components with 91.0% cumulative explanation in the C-II group. CONCLUSIONS: Differences in the heritability of skeletodental characteristics between twin pairs with skeletal Class I and II malocclusions might provide valuable information for growth prediction and treatment planning.

Transcriptome and methylome analysis of periodontitis and peri-implantitis with tobacco use.

Peri-implantitis is similar to periodontitis in both symptoms and treatment; however, their level of similarity remains controversial. Here, we compared multiple cases of periodontitis and peri-implantitis through transcriptome and methylome profiling, and analyzed the effects of smoking as a typical risk factor. Human gingival tissues were obtained from 20 patients with periodontitis or peri-implantitis via periodontal surgical procedures. Total RNA and genomic DNA were isolated, and transcriptome and methylome datasets were generated. Comprehensive analysis of differential gene expression, DNA methylation, and protein-protein interactions indicated that periodontitis and peri-implantitis share biological similarities; however, hierarchical clustering between the two disease groups revealed distinct molecular characteristics. These differences might be related to structural differences in natural tooth-bone and implant-bone. Additionally, smoking differentially affected periodontitis and peri-implantitis in terms of host-defense mechanism impairment. Within the limitations of this study, the results provide evidence that peri-implantitis is distinct from periodontitis and that smoking potentially affects disease progression. Our study provides a foundation for the rational design of a large-scale study in the future for a more comprehensive analysis that includes microbiome and clinical data.