Predicting maximum occlusal force and tongue pressure using decision tree analysis in patients diagnosed with head and neck tumors.

STATEMENT OF PROBLEM: Preserving and restoring oral functions, especially mastication and swallowing, is important to the quality of life of patients being treated for head and neck tumors. Studies that help predict maximum occlusal force and tongue pressure during prosthetic treatment, necessary for providing comprehensive, appropriate treatment and encouraging patient adherence and confidence are lacking. PURPOSE: The purpose of this clinical study was to develop a decision tree model for predicting maximum occlusal force and tongue pressure in patients diagnosed with head and neck tumors that could help both experienced and less experienced prosthodontists and oral surgeons optimize the treatment plan and support patient compliance and their quality of life. MATERIAL AND METHODS: A total of 80 patients who had been treated for head and neck tumors were enrolled in the study. Their maximum occlusal force was measured using a pressure-sensitive film and tongue pressure using a tongue pressure measurement device. Data, including basic characteristics, were transferred to a comma separated values file, which was then imported into a statistical software package to produce a decision tree. The classification and regression tree method was used to construct a predictive model. RESULTS: The number of occlusal contacts associated with not wearing a prosthesis, flap reconstruction, radiotherapy, chemotherapy, the number of teeth present, age, tumor stage, and tumor type were found to be associated with maximum occlusal force, with a prediction accuracy of 96.3%, area under the receiver operating characteristic curve of 0.99, sensitivity of 97%, and specificity of 94%. The number of occlusal contacts associated with wearing and not wearing a prosthesis, tumor stage, age, radiotherapy, and surgery type were found to be associated with tongue pressure, with a prediction accuracy of 96.3%, area under the receiver operating characteristic curve of 0.97, sensitivity of 97%, and specificity of 93%. CONCLUSIONS: The decision tree model can be an effective tool for the prediction of maximum occlusal force and tongue pressure in patients diagnosed with head and neck tumors, helping both experienced and less experienced prosthodontists and oral surgeons to provide early, appropriate, and necessary treatment before starting prosthetic treatment and helping patients with treatment compliance and communication with medical staff.

Photochromic Spiropyran-Based Dual-Emitting Luminescent Hybrid Films for Dynamic Information Anticounterfeiting.

Photoluminescent materials are widely used for information storage and anticounterfeiting, while most of them have the disadvantages of static information performance and weak processability, which is still a challenging task in developing dynamic anticounterfeiting materials with high security levels. Herein, we fabricated a novel photostimuli-responsive dual-emitting luminescent material UPTES-SPn-Tb-hfa, which was obtained by introducing the photochromic molecule spiropyran (SP) and lanthanide complex (Tb-hfa) into a siloxane-polyether matrix using the sol-gel process. Due to the conformation-dependent photochromic fluorescence resonance energy transfer between the Tb-hfa donor and SP acceptor, the ring-closing (SP)/ring-opening (MC) isomerization of the SP unit leads to a reversible luminescence switching in UPTES-SPn-Tb-hfa. This composite material has great potential for advanced anticounterfeiting because of the advantage of rapidly repeatable encryption/decryption for at least 8 times and dynamic luminescent colors within 15 s. In addition, due to its two luminescent centers (Tb3+ and MC), the luminescent color of this material can be regulated by 254 and 365 nm UV-light irradiation, which facilitates the design of multicolored anticounterfeiting labels. Our work presents a novel design methodology to fabricate dynamic anticounterfeiting materials, significantly enhancing the security of anticounterfeiting applications.

Event Graph Guided Compositional Spatial-Temporal Reasoning for Video Question Answering.

Video question answering (VideoQA) is challenging since it requires the model to extract and combine multi-level visual concepts from local objects to global actions from complex events for compositional reasoning. Existing works represent the video with fixed-duration clip features that make the model struggle in capturing the crucial concepts in multiple granularities. To overcome this shortcoming, we propose to represent the video with an Event Graph in a hierarchical structure whose nodes correspond to visual concepts of different levels (object, relation, scene and action) and edges indicate their spatial-temporal relationships. We further propose a H ierarchical S patial- T emporal T ransformer (HSTT) which takes nodes from the graph as visual input to realize compositional reasoning guided by the event graph. To fully exploit the spatial-temporal context delivered from the graph structure, on the one hand, we encode the nodes in the order of their semantic hierarchy (depth) and occurrence time (breadth) with our improved graph search algorithm; On the other hand, we introduce edge-guided attention to combine the spatial-temporal context among nodes according to their edge connections. HSTT then performs QA by cross-modal interactions guaranteed by the hierarchical correspondence between the multi-level event graph and the cross-level question. Experiments on the recent challenging AGQA and STAR datasets show that the proposed method clearly outperforms the existing VideoQA models by a large margin, including those pre-trained with large-scale external data. Our code is available at https://github.com/ByZ0e/HSTT.

CD44 and its implication in neoplastic diseases.

CD44, a nonkinase single span transmembrane glycoprotein, is a major cell surface receptor for many other extracellular matrix components as well as classic markers of cancer stem cells and immune cells. Through alternative splicing of CD44 gene, CD44 is divided into two isoforms, the standard isoform of CD44 (CD44s) and the variant isoform of CD44 (CD44v). Different isoforms of CD44 participate in regulating various signaling pathways, modulating cancer proliferation, invasion, metastasis, and drug resistance, with its aberrant expression and dysregulation contributing to tumor initiation and progression. However, CD44s and CD44v play overlapping or contradictory roles in tumor initiation and progression, which is not fully understood. Herein, we discuss the present understanding of the functional and structural roles of CD44 in the pathogenic mechanism of multiple cancers. The regulation functions of CD44 in cancers-associated signaling pathways is summarized. Moreover, we provide an overview of the anticancer therapeutic strategies that targeting CD44 and preclinical and clinical trials evaluating the pharmacokinetics, efficacy, and drug-related toxicity about CD44-targeted therapies. This review provides up-to-date information about the roles of CD44 in neoplastic diseases, which may open new perspectives in the field of cancer treatment through targeting CD44.

Machine Learning Prediction of Tongue Pressure in Elderly Patients with Head and Neck Tumor: A Cross-Sectional Study.

Background: This investigation sought to cross validate the predictors of tongue pressure recovery in elderly patients' post-treatment for head and neck tumors, leveraging advanced machine learning techniques. Methods: By employing logistic regression, support vector regression, random forest, and extreme gradient boosting, the study analyzed an array of variables including patient demographics, surgery types, dental health status, and age, drawn from comprehensive medical records and direct tongue pressure assessments. Results: Among the models, logistic regression emerged as the most effective, demonstrating an accuracy of 0.630 [95% confidence interval (CI): 0.370-0.778], F1 score of 0.688 [95% confidence interval (CI): 0.435-0.853], precision of 0.611 [95% confidence interval (CI): 0.313-0.801], recall of 0.786 [95% confidence interval (CI): 0.413-0.938] and an area under the receiver operating characteristic curve of 0.626 [95% confidence interval (CI): 0.409-0.806]. This model distinctly highlighted the significance of glossectomy (p = 0.039), the presence of functional teeth (p = 0.043), and the patient's age (p = 0.044) as pivotal factors influencing tongue pressure, setting the threshold for statistical significance at p < 0.05. Conclusions: The analysis underscored the critical role of glossectomy, the presence of functional natural teeth, and age as determinants of tongue pressure in logistics regression, with the presence of natural teeth and the tumor site located in the tongue consistently emerging as the key predictors across all computational models employed in this study.