Integrated correlation analysis of the thickness of buccal bone and gingiva of maxillary incisors

Abstract Objective This study aimed to validate the integrated correlation between the buccal bone and gingival thickness of the anterior maxilla, and to gain insight into the reference plane selection when measuring these two tissues before treatment with implants. Methodology Cone beam computed tomography (CBCT) and model scans of 350 human subjects were registered in the coDiagnostiX software to obtain sagittal maxillary incisor sections. The buccal bone thickness was measured at the coronal (2, 4, and 6 mm apical to the cementoenamel junction [CEJ]) and apical (0, 2, and 4 mm coronal to the apex plane) regions. The buccal gingival thickness was measured at the supra-CEJ (0, 1mm coronal to the CEJ) and sub-CEJ regions (1, 2, 4, and 6 mm apical to the CEJ). Canonical correlation analysis was performed for intergroup correlation analysis and investigation of key parameters. Results The mean thicknesses of the buccal bone and gingiva at different levels were 0.64~1.88 mm and 0.66~1.37 mm, respectively. There was a strong intergroup canonical correlation between the thickness of the buccal bone and that of the gingiva (r=0.837). The thickness of the buccal bone and gingiva at 2 mm apical to the CEJ are the most important indices with the highest canonical correlation coefficient and loadings. The most and least prevalent subgroups were the thin bone and thick gingiva group (accounting for 47.6%) and the thick bone and thick gingiva group (accounting for 8.6%). Conclusion Within the limitations of this retrospective study, the thickness of the buccal bone is significantly correlated with that of the buccal gingiva, and the 2 mm region apical to the CEJ is a vital plane for quantifying the thickness of these two tissues

Measuring the binary thickness of buccal bone of anterior maxilla in low-resolution cone-beam computed tomography via a bilinear convolutional neural network.

Background: The thickness of the buccal bone of the anterior maxilla is an important aesthetic-determining factor for dental implant, which is divided into the thick (≥1 mm) and thin type (<1 mm). However, as a micro-scale structure that is evaluated through low-resolution cone-beam computed tomography (CBCT), its thickness measurement is error-prone under the circumstance of enormous patients and relatively inexperienced primary dentists. Further, the challenges of deep learning-based analysis of the binary thickness of buccal bone include the substantial real-world variance caused by pixel error, the extraction of fine-grained features, and burdensome annotations. Methods: This study built bilinear convolutional neural network (BCNN) with 2 convolutional neural network (CNN) backbones and a bilinear pooling module to predict the binary thickness of buccal bone (thick or thin) of the anterior maxilla in an end-to-end manner. The methods of 5-fold cross-validation and model ensemble were adopted at the training and testing stages. The visualization methods of Gradient Weighted Class Activation Mapping (Grad-CAM), Guided Grad-CAM, and layer-wise relevance propagation (LRP) were used for revealing the important features on which the model focused. The performance metrics and efficacy were compared between BCNN, dentists of different clinical experience (i.e., dental student, junior dentist, and senior dentist), and the fusion of BCNN and dentists to investigate the clinical feasibility of BCNN. Results: Based on the dataset of 4,000 CBCT images from 1,000 patients (aged 36.15±13.09 years), the BCNN with visual geometry group (VGG)16 backbone achieved an accuracy of 0.870 [95% confidence interval (CI): 0.838-0.902] and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.924 (95% CI: 0.896-0.948). Compared with the conventional CNNs, BCNN precisely located the buccal bone wall over irrelevant regions. The BCNN generally outperformed the expert-level dentists. The clinical diagnostic performance of the dentists was improved with the assistance of BCNN. Conclusions: The application of BCNN to the quantitative analysis of binary buccal bone thickness validated the model's excellent ability of subtle feature extraction and achieved expert-level performance. This work signals the potential of fine-grained image recognition networks to the precise quantitative analysis of micro-scale structures.

Quantification of the apical palatal bone index for maxillary incisor immediate implant assessment: A retrospective cross-sectional study.

BACKGROUND: Apical palatal bone is important in immediate implant evaluation. Current consensus gives qualitative suggestions regarding it, limiting its clinical decision-making value. OBJECTIVES: To quantify the apical palatal bone dimension in maxillary incisors and reveal its quantitative correlation with other implant-related hard tissue indices to give practical advice for pre-immediate implant evaluation and design. MATERIAL AND METHODS: A retrospective analysis of immediate implant-related hard tissue indices in maxillary incisors obtained by cone beam computed tomography (CBCT) was conducted. Palatal bone thickness at the apex level (Apical-P) on the sagittal section was selected as a parameter reflecting the apical palatal bone. Its quantitative correlation with other immediate implant-related hard tissue indices was revealed. Clinical advice of pre-immediate implant assessment was given based on the quantitative classification of Apical-P and its other correlated immediate implant-related hard tissue indices. RESULTS: Apical-P positively correlated with cervical palatal bone, whole cervical buccal-palatal bone, sagittal root angle, and basal bone width indices. while negatively correlated with apical buccal bone, cervical buccal bone, and basal bone length indices. Six quantitative categories of Apical-P are proposed. Cases with Apical-P below 4 mm had an insufficient apical bone thickness to accommodate the implant placement, while Apical-P beyond 12 mm should be cautious about the severe implant inclination. Cases with Apical-P of 4-12 mm can generally achieve satisfying immediate implant outcomes via regulating the implant inclination. CONCLUSIONS: Quantification of the apical palatal bone index for maxillary incisor immediate implant assessment can be achieved, providing a quantitative guide for immediate implant placement in the maxillary incisor zone.

Abnormal maxillary sinus diagnosing on CBCT images via object detection and 'straight-forward' classification deep learning strategy.

BACKGROUND: Pathological maxillary sinus would affect implant treatment and even result in failure of maxillary sinus lift and implant surgery. However, the maxillary sinus abnormalities are challenging to be diagnosed through CBCT images, especially for young dentists or dentists in grassroots medical institutions without systematical education of general medicine. OBJECTIVES: To develop a deep-learning-based screening model incorporating object detection and 'straight-forward' classification strategy to screen out maxillary sinus abnormalities on CBCT images. METHODS: The large area of background noise outside maxillary sinus would affect the generalisation and prediction accuracy of the model, and the diversity and imbalanced distribution of imaging manifestations may bring challenges to intellectualization. Thus we adopted an object detection to limit model's observation zone and 'straight-forward' classification strategy with various tuning methods to adapt to dental clinical need and extract typical features of diverse manifestations so that turn the task into a 'normal-or-not' classification. RESULTS: We successfully constructed a deep-learning model consist of well-trained detector and diagnostor module. This model achieved ideal AUROC and AUPRC of 0.953 and 0.887, reaching more than 90% accuracy at optimal cut-off. McNemar and Kappa test verified no statistical difference and high consistency between the prediction and ground truth. Dentist-model comparison test showed the model's statistically higher diagnostic performance than dental students. Visualisation method confirmed the model's effectiveness in region recognition and feature extraction. CONCLUSION: The deep-learning model incorporating object detection and straightforward classification strategy could achieve satisfying predictive performance for screening maxillary sinus abnormalities on CBCT images.

Novel Muscle-Homing Peptide FGF1 Conjugate Based on AlphaFold for Type 2 Diabetes Mellitus.

Drugs for metabolic diseases usually require systemic administration and act on multiple tissues, which may produce some unpredictable side effects. There have been many successful studies on targeted drugs, especially antitumor drugs. However, there is still little research on metabolic disease drugs targeting specific tissues. Fibroblast growth factor 1 (FGF1) is a potential therapy for type 2 diabetes (T2D) without the risk of hypoglycemia. However, the major impediment to the clinical application of FGF1 is its mitogenic potential. We previously engineered an FGF1 variant (named FGF1ΔHBS) to tune down its mitogenic activity via reducing the heparin-binding ability. However, other notable side effects still remained, including severe appetite inhibition, pathogenic loss of body weight, and increase in fatality rate. In this study, we used AlphaFold2 and PyMOL visualization tools to construct a novel FGF1ΔHBS conjugate fused with skeletal muscle-targeted (MT) peptide through a flexible peptide linker termed MT-FGF1ΔHBS. We found that MT-FGF1ΔHBS specifically homed to skeletal muscle tissue after systemic administration and induced a potent glucose-lowering effect in T2D mice without hypoglycemia. Mechanistically, MT-FGF1ΔHBS elicits the glucose-lowering effect via AMPK activation to promote the GLUT4 expression and translocation in skeletal muscle cells. Notably, compared with native FGF1ΔHBS, MT-FGF1ΔHBS had minimal effects on food intake and body weight and did not induce any hyperplasia in major tissues of both T2D and normal mice, indicating that this muscle-homing protein may be a promising candidate for T2D treatment. Our targeted peptide strategy based on computer-aided structure prediction in this study could be effectively applied for delivering agents to functional tissues to treat metabolic or other diseases, offering enhanced efficacy and reducing systemic off-target side effects.

Host-induced gene silencing of PcCesA3 and PcOSBP1 confers resistance to Phytophthora capsici in Nicotiana benthamiana through NbDCL3 and NbDCL4 processed small interfering RNAs.

Host-induced gene silencing (HIGS) is a RNA-based system depend on the biological macromolecules generated in plants to control diseases. However, the effector proteins active in the HIGS are uncertain, which impedes its further application, especially for oomycete that lack efficient HIGS targets. Phytophthora capsici is an important oomycete causes blight in over 70 crops. Here, we comprehensively screened efficient HIGS vectors targeting PcCesA3 or PcOSBP1 in P. capsici to better control it and explore the characteristics of efficient HIGS vectors. Among the 26 vectors with different lengths and structures, we found that hairpin vectors with a 70 nt loop and ~ 500 bp stem showed the highest control efficacy, with the expressing of the screened vectors, the infection and fertility of P. capsici were greatly inhibited in transgenic Nicotiana benthamiana. Based on these efficient vectors, we demonstrated that the amount of HIGS vector generated small interfering RNAs (siRNAs) was positively related to gene silencing efficiency and resistance, and that NbDCL3 and NbDCL4 were the key effectors producing siRNAs. This work discovers the principles for efficient HIGS vectors design, and elucidates the molecular mechanism of HIGS, which could benefit the control of many other plant diseases based on HIGS.

Successful targeting of the NRG1 fusion reveals durable response to afatinib in lung adenocarcinoma: a case report.

The treatments for advanced non-small cell lung cancer (NSCLC) patients have been improved by developing tyrosine kinase inhibitors (TKIs) as targeted therapies. Oncogenic gene fusions resulting from structural DNA rearrangements have been proposed as a unique class of oncogenic drivers and therapeutic targets. Currently approved TKIs mainly focused on a few well-known fusion genes such as anaplastic lymphoma kinase (ALK) and ROS proto-oncogene 1 (ROS1). Fusions involving neuregulin 1 gene (NRG1) have been recently described in a small portion of solid tumors as actionable oncogenic drivers, leading to the activation of the erythroblastic leukemia viral oncogene homolog (ErbB)-mediated pathway. Therefore, gene fusions containing NRG1 could serve as a therapeutic candidate for ErbB-targeted treatment. In the present study, we report a lung adenocarcinoma patient harboring the CD74-NRG1 fusion, which was identified by next-generation sequencing (NGS). The patient received the irreversible pan-ErbB inhibitor, afatinib, as first-line treatment and showed a significant treatment response with a progression-free survival of 8 months. After progressive disease (PD), the second NGS did not identify novel genetic alterations that emerged after afatinib resistance. Our case supports the use of ErbB-targeted treatment for NRG1 fusion-positive NSCLC. Further studies are warranted to understand treatment effects and acquired resistance of afatinib in NGR1 fusion-positive patients.

Urinary exosomal long noncoding RNAs serve as biomarkers for early detection of non-small cell lung cancer.

OBJECTIVE: Increasing the efficiency of early diagnosis using noninvasive biomarkers is crucial for enhancing the survival rate of lung cancer patients. We explore the differential expression of non-small cell lung cancer (NSCLC)-related long noncoding RNAs (lncRNAs) in urinary exosomes in NSCLC patients and normal controls to diagnose lung cancer. METHODS: A differential expression analysis between NSCLC patients and healthy controls was performed using microarrays. Gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to predict potential functions of lncRNAs in NSCLC. quantitative real-time PCR (QT-PCR) was used to verify microarray results. RESULTS: A total of 640 lncRNAs (70 up- and 570 down-regulated) were differentially expressed in NSCLC patients in comparison to healthy controls. Six lncRNAs were detected by QT-PCR. GO term and KEGG pathway analyses showed that differential lncRNAs were enriched in cellular component organization or biogenesis, as well as other biological processes and signaling pathways, such as the PI3K-AKT, FOXO, p53, and fatty acid biosynthesis. CONCLUSIONS: The differential lncRNAs in urinary exosomes are potential diagnostic biomarkers of NSCLC. The lncRNAs enriched in specific pathways may be associated with tumor cell proliferation, tumor cell apoptosis, and the cell cycle involved in the pathogenesis of NSCLC.

Response mechanisms to joint exposure of triclosan and its chlorinated derivatives on zebrafish (Danio rerio) behavior.

Triclosan (TCS), 2,4,6-trichlorophenol (2,4,6-TCP) and 2,4-dichlorophenol (2,4-DCP) frequently co-exist in real-world aquatic environments; the latter two contaminants contributing to TCS photolytic products or chlorinated derivatives. There is a paucity of information regarding their joint toxicity to aquatic organisms leading us to study their effects on the swimming behavior of zebrafish (Danio rerio). Herein, we reported that 0.28 mg/L TDT exposure (mixtures of TCS, 2,4,6-TCP and 2,4-DCP) enhanced 24-hpf embryonic spontaneous movement frequency, 96-hpf larval activity; however, the 0.56 and 1.12 mg/L TDT treatments decreased all of these behavioral endpoints. All adult behavioral tests demonstrated that chronic TDT exposure (0.14 mg/L) led to hyperactivity and restlessness in adult zebrafish. A 0.14 mg/L TD DATE /@ "M/d/yyyy" 11/21/2017T treatment led to anxiety-like behavior in a bottom dwelling test and excessive panic and low hedging capacity in a conditioned place preference test. Social interaction test demonstrated that zebrafish preferred quiet and isolated space in response to TDT stress. Zebrafish memory was significantly decreased in a T-maze experiment. Whole mount in situ hybridization of pax2a and bcl2l11 genes revealed that their differential expression in the brain and skeleton were related to the corresponding phenotypic behavioral abnormality. A series of biomarker and estrogen receptor assays demonstrated that TDT acute exposure caused abnormal energy metabolism and neurological diseases. AO staining revealed that TDT exposure produced vascular ablation in the head, as well as the occurrence of massive apoptosis in the brain. TEM observation showed pyknosis of nucleus following TDT exposure. These results allow assessment of mechanisms for zebrafish abnormal behavior in response to TDT exposure, and are useful for early intervention and gene therapy of contaminant-induced diseases.

Effects of methylglyoxal and glyoxalase I inhibition on breast cancer cells proliferation, invasion, and apoptosis through modulation of MAPKs, MMP9, and Bcl-2.

Emerging evidence indicates that methylglyoxal (MG) can inhibit tumorigenesis. Glyoxalase I (GLOI), a MG degradation enzyme, is implicated in the progression of human malignancies. However, little is known about the roles of MG and GLOI in breast cancer. Our purpose was to investigate the anticancer effects of MG and inhibition of GLOI on breast cancer cells and the underlying mechanisms of these effects. Our findings demonstrate that cell viability, migration, invasion, colony formation, and tubule formation were significantly restrained by addition of MG or inhibition of GLOI, while apoptosis was significantly increased. Furthermore, the expression of p-JNK, p-ERK, and p-p38 was markedly upregulated by addition of MG or inhibition of GLOI, whereas MMP-9 and Bcl-2 expression levels were dramatically decreased. These effects were augmented by combined treatment with MG and inhibition of GLOI. Collectively, these data indicate that MG or inhibition of GLOI induces anticancer effects in breast cancer cells and that these effects are potentiated by combination of the 2. These effects were modulated by activation of the MAPK family and downregulation of Bcl-2 and MMP-9. These findings may provide a new approach for the treatment of breast cancer.

Spirulina phycocyanin induces differential protein expression and apoptosis in SKOV-3 cells.

The present study was designed to determine the effects of phycocyanin (PC) on Human ovarian cancer SKOV-3 cells and the underlying molecular mechanisms of action. The inhibitory effects of PC on the cell proliferation were detected by MTT assay. The IC50 values of PC were 182.0µM and 133.6µM for 24h and 48h exposure, respectively. PC induced apoptosis in SKOV-3 cells was observed by electron microscopy and flow cytometry. The apoptosis rate was increased from 1.6% to 19.8% after PC exposure. The fluorescence intensity of ROS and the activities of Caspase-3, Caspase-8, and Caspase-9 were increased. Differentiated expression protein spots were selected and identified using proteomic techniques. There were 698±73 and 683±79 protein spots resolved in untreated and PC-treated cells, respectively. Forty five differential protein spots were analyzed by MALDI-TOF-MS, including mtSSB, PSME3, and nucleolin. The mRNA expression profiles determined by RT-PCR were consistent with that of the two-dimensional electrophoresis. The decreased proteins such as HSP60, nucleolin, PPase, peroxiredoxin-4 and the increased protein (mtSSB) were identified in SKOV-3 cells after PC treatment, indicating that the effects of PC on tumor cell apoptosis may be relate to multiple target proteins. And the mitochondrial pathway may be the main pathway for PC-induced apoptosis.