The accuracy and reliability of different midsagittal planes in the symmetry assessment using cone-beam computed tomography.

Symmetry is an essential component of esthetic assessment. Accurate assessment of facial symmetry is critical to the treatment plan of orthognathic surgery and orthodontic treatment. However, there is no internationally accepted midsagittal plane (MSP) for orthodontists and orthognathic surgeons. The purpose of this study was to explore a clinically friendly MSP, which is more accurate and reliable than what is commonly used in symmetry assessment. Forty patients with symmetric craniofacial structures were analyzed on cone-beam computed tomography (CBCT) scans. The CBCT data were exported to the Simplant Pro software to build four reference planes that were constructed by nasion (N), basion (Ba), sella (S), odontoid (Dent), or incisive foramen (IF). A total of 31 landmarks were located to determine which reference plane is the most optimal MSP by comparing the asymmetry index (AI). The mean value of AI showed a significant difference (p < 0.05) among four reference planes. Also, the mean value of AI for all landmarks showed that Plane 2 (consisting of N, Ba, and IF) and Plane 4 (consisting of N, IF, and Dent) were more accurate and stable. In conclusion, the MSP consisting of N, Dent, and IF shows more accuracy and reliability than the other planes. Further, it is more clinically friendly because of its significant advantage in landmarking.

Prediction Models for Sleep Quality Among College Students During the COVID-19 Outbreak: Cross-sectional Study Based on the Internet New Media.

BACKGROUND: COVID-19 has been reported to affect the sleep quality of Chinese residents; however, the epidemic's effects on the sleep quality of college students during closed-loop management remain unclear, and a screening tool is lacking. OBJECTIVE: This study aimed to understand the sleep quality of college students in Fujian Province during the epidemic and determine sensitive variables, in order to develop an efficient prediction model for the early screening of sleep problems in college students. METHODS: From April 5 to 16, 2022, a cross-sectional internet-based survey was conducted. The Pittsburgh Sleep Quality Index (PSQI) scale, a self-designed general data questionnaire, and the sleep quality influencing factor questionnaire were used to understand the sleep quality of respondents in the previous month. A chi-square test and a multivariate unconditioned logistic regression analysis were performed, and influencing factors obtained were applied to develop prediction models. The data were divided into a training-testing set (n=14,451, 70%) and an independent validation set (n=6194, 30%) by stratified sampling. Four models using logistic regression, an artificial neural network, random forest, and naïve Bayes were developed and validated. RESULTS: In total, 20,645 subjects were included in this survey, with a mean global PSQI score of 6.02 (SD 3.112). The sleep disturbance rate was 28.9% (n=5972, defined as a global PSQI score >7 points). A total of 11 variables related to sleep quality were taken as parameters of the prediction models, including age, gender, residence, specialty, respiratory history, coffee consumption, stay up, long hours on the internet, sudden changes, fears of infection, and impatient closed-loop management. Among the generated models, the artificial neural network model proved to be the best, with an area under curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 0.713, 73.52%, 25.51%, 92.58%, 57.71%, and 75.79%, respectively. It is noteworthy that the logistic regression, random forest, and naive Bayes models achieved high specificities of 94.41%, 94.77%, and 86.40%, respectively. CONCLUSIONS: The COVID-19 containment measures affected the sleep quality of college students on multiple levels, indicating that it is desiderate to provide targeted university management and social support. The artificial neural network model has presented excellent predictive efficiency and is favorable for implementing measures earlier in order to improve present conditions.

Evaluating stress and displacement in the craniomandibular complex using Twin Block appliances at varied angles: A finite element study.

OBJECTIVES: The objective of this investigation was to assess the stress and displacement pattern of the craniomandibular complex by employing finite element methodology to simulate diverse angulations of inclined planes that are incorporated in the Twin Block appliance. METHODS: A 3D finite element representation was established by use of Cone Beam Computed Tomography (CBCT) scans. This comprehensive structure included craniofacial skeletal components, the articular disc, a posterior disc elastic layer, dental elements, periodontal ligaments, and a Twin Block appliance. This investigation is the first to incorporated inclined planes featuring three distinct angulations (45, 60, and 70°) as the study models. Mechanical impacts were evaluated within the glenoid fossa, tooth, condylar, and articular disc regions. RESULTS: In all simulations, the stress generated by the Twin Block appliance was distributed across teeth and periodontal ligament, facilitating the anterior movement of mandibular teeth and the posterior displacement of maxillary teeth. Within the temporomandibular joint region, compressive forces on the superior and posterior facets of the condyle diminished, coinciding with the stress configuration that fosters condylar and mandibular growth. Stress dispersion homogenized in the condylar anterior facet and articular disc, with considerable tensile stress in the glenoid fossa's posterior aspect conforming to stress distribution that promote fossa reconfiguration. The 70° inclined plane exerts the highest force on the tissues. The condyle's maximum and minimum principal stresses are 0.36 MPa and -0.15 MPa, respectively, while those of the glenoid fossa are 0.54 MPa and -0.23 MPa. CONCLUSION: Three angled appliances serve the purpose of advancing the mandible. A 45° inclined plane relative to the occlusal plane exerts balanced anteroposterior and vertical forces on the mandibular arch. Steeper angles yield greater horizontal forces, which may enhance forward growth and efficient repositioning.

Stress distribution of the modified clear twin-block aligner on the temporomandibular joint, alveolar bone and teeth: A finite element analysis.

OBJECTIVE: The modified clear twin-block aligner (CTBA) was developed to provide a mandibular advancement appliance for the treatment of mandibular retrognathia. The objective of this study was to analyse the stress distribution changes of CTBA with 45°, 60° and 70° bite blocks. MATERIAL AND METHODS: A three-dimensional model of the craniomaxillofacial bones and teeth was generated from a spiral computed tomography (CT) scan. The models of the articular disc, capsule, periodontal ligament and CTBA were constructed mathematically. After assigning the appropriate material properties and the boundary condition using ABAQUS software, we simulated the CTBA with different bite blocks to analyse the mechanical effects. RESULTS: In the temporomandibular joint (TMJ) region, the posterior aspect of the condyle and glenoid fossa experienced tensile stress that was approximately about 22 times greater at 70° than at 45°. The Von Mises stress distribution on the articular disc tended to be uniform. The strain direction of the condyle was backward. In the maxillary bone, the stress on the labial alveolar bone was about 5.83MPa at 70° and greater than that on the lingual side. The resulting displacement of the dentition revealed a tendency for the upper teeth to shift backward and the lower teeth to move forward by 0.46 to 0.49mm. The foregoing stress and displacement rose as the angle of the bite blocks increased. CONCLUSIONS: CTBA with 70° bite blocks constituted an advantageous biomechanical setting for the treatment of mandibular retrognathia in teenagers and provided a superior therapeutic effect.

PHLDA1 modulates microglial response and NLRP3 inflammasome signaling following experimental subarachnoid hemorrhage.

Balancing microglia M1/M2 polarization is an effective therapeutic strategy for neuroinflammation after subarachnoid hemorrhage (SAH). Pleckstrin homology-like domain family A member 1 (PHLDA1) has been demonstrated to play a crucial role in immune response. However, the function roles of PHLDA1 in neuroinflammation and microglial polarization after SAH remain unclear. In this study, SAH mouse models were assigned to treat with scramble or PHLDA1 small interfering RNAs (siRNAs). We observed that PHLDA1 was significantly increased and mainly distributed in microglia after SAH. Concomitant with PHLDA1 activation, nod-like receptor pyrin domain-containing protein 3 (NLRP3) inflammasome expression in microglia was also evidently enhanced after SAH. In addition, PHLDA1 siRNA treatment significantly reduced microglia-mediated neuroinflammation by inhibiting M1 microglia and promoting M2 microglia polarization. Meanwhile, PHLDA1 deficiency reduced neuronal apoptosis and improved neurological outcomes after SAH. Further investigation revealed that PHLDA1 blockade suppressed the NLRP3 inflammasome signaling after SAH. In contrast, NLRP3 inflammasome activator nigericin abated the beneficial effects of PHLDA1 deficiency against SAH by promoting microglial polarization to M1 phenotype. In all, we proposed that PHLDA1 blockade might ameliorate SAH-induced brain injury by balancing microglia M1/M2 polarization via suppression of NLRP3 inflammasome signaling. Targeting PHLDA1 might be a feasible strategy for treating SAH.

Conditional Dnmt3b deletion in hippocampal dCA1 impairs recognition memory.

AIM: Active changes in neuronal DNA methylation and demethylation appear to act as controllers of synaptic scaling and glutamate receptor trafficking in learning and memory formation. DNA methyltransferases (DNMTs), including proteins encoded by Dnmt1, Dnmt3a and Dnmt3b, are dominant enzymes carrying out DNA methylation. Our previous study demonstrated the important roles that DNMT1 and DNMT3a play in synaptic function and memory. In this study, we aim to explore the role of DNMT3b and its-mediated DNA methylation in memory processes. METHODS: Dnmt3b was knocked down specifically in dorsal CA1 neurons of adult mice hippocampus by AAV-syn-Cre-GFP virus injection. Behavioral tests were used to evaluate memory performance. Gene expression microarray analysis followed by quantitative RT-PCR were performed to find differential expression genes. RESULTS: Dnmt3bflox/flox mice receiving Cre-virus infection showed impaired novel object-place recognition (NPR) and normal novel object recognition (NOR), in comparison to mice receiving control GFP-virus infection. Microarray analysis revealed differential expression of K+ channel subunits in the hippocampus of Dnmt3bflox/flox mice receiving Cre-virus injection. Increased Kcne2 expression was confirmed by following qRT-PCR analysis. We also found that NPR training and testing induced up-regulation of hippocampal Dnmt1 and Dnmt3a mRNA expression in control mice, but not in Cre-virus injected mice. Our findings thus demonstrate that conditional Dnmt3b deletion in a sub-region of the hippocampus impairs a specific form of recognition memory that is hippocampus-dependent.

Phenotype Classification using Proteome Data in a Data-Independent Acquisition Tensor Format.

A novel approach for phenotype prediction is developed for data-independent acquisition (DIA) mass spectrometric (MS) data without the need for peptide precursor identification using existing DIA software tools. The first step converts the DIA-MS data file into a new file format called DIA tensor (DIAT), which can be used for the convenient visualization of all the ions from peptide precursors and fragments. DIAT files can be fed directly into a deep neural network to predict phenotypes such as appearances of cats, dogs, and microscopic images. As a proof of principle, we applied this approach to 102 hepatocellular carcinoma samples and achieved an accuracy of 96.8% in distinguishing malignant from benign samples. We further applied a refined model to classify thyroid nodules. Deep learning based on 492 training samples achieved an accuracy of 91.7% in an independent cohort of 216 test samples. This approach surpassed the deep-learning model based on peptide and protein matrices generated by OpenSWATH. In summary, we present a new strategy for DIA data analysis based on a novel data format called DIAT, which enables facile two-dimensional visualization of DIA proteomics data. DIAT files can be directly used for deep learning for biological and clinical phenotype classification. Future research will interpret the deep-learning models emerged from DIAT analysis.