Exploring the therapeutic potential of urine-derived stem cell exosomes in temporomandibular joint osteoarthritis.

Temporomandibular joint osteoarthritis (TMJOA) is a degenerative ailment that causes slow cartilage degeneration, aberrant bone remodeling, and persistent discomfort, leading to a considerable reduction in the patient's life quality. Current treatment options for TMJOA have limited efficacy. This investigation aimed to explore a potential strategy for halting or reversing the progression of TMJOA through the utilization of exosomes (EXOs) derived from urine-derived stem cells (USCs). The USC-EXOs were obtained through microfiltration and ultrafiltration techniques, followed by their characterization using particle size analysis, electron microscopy, and immunoblotting. Subsequently, an in vivo model of TMJOA induced by mechanical force was established. To assess the changes in the cartilage of TMJOA treated with USC-EXOs, we performed histology analysis using hematoxylin-eosin staining, immunohistochemistry, and histological scoring. Our findings indicate that the utilization of USC-EXOs yields substantial reductions in TMJOA, while concurrently enhancing the structural integrity and smoothness of the compromised condylar cartilage surface. Additionally, USC-EXOs exhibit inhibitory effects on osteoclastogenic activity within the subchondral bone layer of the condylar cartilage, as well as attenuated apoptosis in the rat TMJ in response to mechanical injury. In conclusion, USC-EXOs hold considerable promise as a potential therapeutic intervention for TMJOA.

Tumor Stiffness Measurement at Multifrequency MR Elastography to Predict Lymphovascular Space Invasion in Endometrial Cancer.

Background Pathologic lymphovascular space invasion (LVSI) is associated with poor outcome in endometrial cancer. Its relationship with tumor stiffness, which can be measured with use of MR elastography, has not been extensively explored. Purpose To assess whether MR elastography-based mechanical characteristics can aid in the noninvasive prediction of LVSI in patients with endometrial cancer. Materials and Methods This prospective study included consecutive adult patients with a suspected uterine tumor who underwent MRI and MR elastography between October 2022 and July 2023. A region of interest delineated on T2-weighted magnitude images was duplicated on MR elastography images and used to calculate c (stiffness in meters per second) and φ (viscosity in radians) values. Pathologic assessment of hysterectomy specimens for LVSI served as the reference standard. Data were compared between LVSI-positive and -negative groups with use of the Mann-Whitney U test. Multivariable logistic regression was used to determine variables associated with LVSI positivity and develop diagnostic models for predicting LVSI. Model performance was assessed with use of area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. Results A total of 101 participants were included, 72 who were LVSI-negative (median age, 53 years [IQR, 48-62 years]) and 29 who were LVSI-positive (median age, 54 years [IQR, 49-60 years]). The tumor stiffness in the LVSI-positive group was higher than in the LVSI-negative group (median, 4.1 m/sec [IQR, 3.2-4.6 m/sec] vs 2.2 m/sec [IQR, 2.0-2.8 m/sec]; P < .001). Tumor volume, cancer antigen 125 level, and tumor stiffness were associated with LVSI positivity (adjusted odds ratio range, 1.01-9.06; P range, <.001-.04). The combined model (AUC, 0.93) showed better performance for predicting LVSI compared with clinical-radiologic model (AUC, 0.77; P = .003) and similar performance to the MR elastography-based model (AUC, 0.89; P = .06). Conclusion The addition of tumor stiffness as measured at MR elastography into a clinical-radiologic model improved prediction of LVSI in patients with endometrial cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ehman in this issue.

Excessive load promotes temporomandibular joint chondrocyte apoptosis via Piezo1/endoplasmic reticulum stress pathway.

Excessive load on the temporomandibular joint (TMJ) is a significant factor in the development of TMJ osteoarthritis, contributing to cartilage degeneration. The specific mechanism through which excessive load induces TMJ osteoarthritis is not fully understood; however, mechanically-activated (MA) ion channels play a crucial role. Among these channels, Piezo1 has been identified as a mediator of chondrocyte catabolic responses and is markedly increased in osteoarthritis. Our observations indicate that, under excessive load conditions, endoplasmic reticulum stress in chondrocytes results in apoptosis of the TMJ chondrocytes. Importantly, using the Piezo1 inhibitor GsMTx4 demonstrates its potential to alleviate this condition. Furthermore, Piezo1 mediates endoplasmic reticulum stress in chondrocytes by inducing calcium ion influx. Our research substantiates the role of Piezo1 as a pivotal ion channel in mediating chondrocyte overload. It elucidates the link between excessive load, cell apoptosis, and calcium ion influx through Piezo1. The findings underscore Piezo1 as a key player in the pathogenesis of TMJ osteoarthritis, shedding light on potential therapeutic interventions for this condition.

Expression of CGRP in the Trigeminal Ganglion and Its Effect on the Polarization of Macrophages in Rats with Temporomandibular Arthritis.

Calcitonin gene-related peptide (CGRP) is synthesized and secreted by trigeminal ganglion neurons, and is a key neuropeptide involved in pain and immune regulation. This study investigates the expression of CGRP in the trigeminal ganglion (TG) and its regulatory role in the polarization of macrophages in rats with temporomandibular arthritis. A rat model of temporomandibular arthritis was established using CFA. Pain behavior was then observed. Temporomandibular joint (TMJ) and the TG were collected, and immunohistochemistry, immunofluorescence (IF) staining, and RT-qPCR were used to examine the expression of CGRP and macrophage-related factors. To investigate the impact of CGRP on macrophage polarization, both CGRP and its antagonist, CGRP 8-37, were separately administered directly within the TG. Statistical analysis revealed that within 24 h of inducing temporomandibular arthritis using CFA, there was a significant surge in CD86 positive macrophages within the ganglion. These macrophages peaked on the 7th day before beginning their decline. In this context, it's noteworthy that administering CGRP to the trigeminal ganglion can prompt these macrophages to adopt the M2 phenotype. Intriguingly, this study demonstrates that injecting the CGRP receptor antagonist (CGRP 8-37) to the ganglion counteracts this shift towards the M2 phenotype. Supporting these in vivo observations, we found that in vitro, CGRP indeed fosters the M2-type polarization of macrophages. CGRP can facilitate the conversion of macrophages into the M2 phenotype. The phenotypic alterations of macrophages within the TG could be instrumental in initiating and further driving the progression of TMJ disorders.

Increased adipose tissue is associated with improved overall survival, independent of skeletal muscle mass in non-small cell lung cancer.

BACKGROUND: The prognostic significance of non-cancer-related prognostic factors, such as body composition, has gained extensive attention in oncological research. Compared with sarcopenia, the prognostic significance of adipose tissue for overall survival in non-small cell lung cancer remains unclear. We investigated the prognostic value of skeletal muscle and adipose tissue in patients with non-small cell lung cancer. METHODS: This retrospective study included 4434 patients diagnosed with non-small cell lung cancer between January 2014 and December 2016. Cross-sectional areas of skeletal muscle and subcutaneous fat were measured, and the pericardial fat volume was automatically calculated. The skeletal muscle index and subcutaneous fat index were calculated as skeletal muscle area and subcutaneous fat area divided by height squared, respectively, and the pericardial fat index was calculated as pericardial fat volume divided by body surface area. The association between body composition and outcomes was evaluated using Cox proportional hazards model. RESULTS: A total of 750 patients (501 males [66.8%] and 249 females [33.2%]; mean age, 60.9 ± 9.8 years) were included. Sarcopenia (60.8% vs. 52.7%; P < 0.001), decreased subcutaneous fat index (51.4% vs. 25.2%; P < 0.001) and decreased pericardial fat index (55.4% vs. 16.5%; P < 0.001) were more commonly found in the deceased group than survived group. In multivariable Cox regression analysis, after adjusting for clinical variables, increased subcutaneous fat index (hazard ratio [HR] = 0.56, 95% confidence interval [CI]: 0.47-0.66, P < 0.001) and increased pericardial fat index (HR = 0.47, 95% CI: 0.40-0.56, P < 0.001) were associated with longer overall survival. For stage I-III patients, increased subcutaneous fat index (HR = 0.62, 95% CI: 0.48-0.76, P < 0.001) and increased pericardial fat index (HR = 0.43, 95% CI: 0.34-0.54, P < 0.001) were associated with better 5-year overall survival rate. Similar results were recorded in stage IV patients. For patients with surgery, the prognostic value of increased subcutaneous fat index (HR = 0.60, 95% CI: 0.44-0.80, P = 0.001) and increased pericardial fat index (HR = 0.51, 95% CI: 0.38-0.68, P < 0.001) remained and predicted favourable overall survival. Non-surgical patients showed similar results as surgical patients. No association was noted between sarcopenia and overall survival (P > 0.05). CONCLUSIONS: Increased subcutaneous fat index and pericardial fat index were associated with a higher 5-year overall survival rate, independent of sarcopenia, in non-small cell lung cancer and may indicate a reduced risk of non-cancer-related death.

Quantitative parameters derived from dual-energy computed tomography for the preoperative prediction of early recurrence in patients with esophageal squamous cell carcinoma.

OBJECTIVES: This study aimed to assess the efficacy of quantitative parameters derived from dual-energy computed tomography (DECT) for the preoperative prediction of early recurrence (ER) in patients with esophageal squamous cell carcinoma (ESCC). METHODS: In total, 78 patients with ESCC who underwent radical esophagectomy and DECT from June 2019 to August 2020 were enrolled in this study. Normalized iodine concentration (NIC) and electron density (Rho) in tumors were measured using arterial and venous phase images, whereas unenhanced images were used to determine the effective atomic number (Zeff). Univariate and multivariate Cox proportional hazards models were used to identify independent risk predictors of ER. Receiver operating characteristic curve analysis was performed using the independent risk predictors. ER-free survival curves were constructed using the Kaplan-Meier method. RESULTS: NIC in the arterial phase (A-NIC; hazards ratio [HR], 3.91; 95% confidence interval [CI], 1.79-8.56; p = 0.001) and pathological grade (PG; HR, 2.69; 95% CI, 1.32-5.49; p = 0.007) were identified as significant risk predictors of ER. The area under the curve of A-NIC for predicting ER in patients with ESCC was not significantly higher than that of PG (0.72 vs. 0.66, p = 0.441). In a stratified survival analysis, patients with high A-NIC or poorly differentiated ESCC had a higher rate of ER than those with low A-NIC or highly/moderately differentiated ESCC. CONCLUSIONS: A-NIC derived from DECT can be used to noninvasively predict preoperative ER in patients with ESCC, with an efficacy comparable to that of pathological grade. CLINICAL RELEVANCE STATEMENT: Preoperative quantitative measurement of dual-energy CT parameters can predict the early recurrence of esophageal squamous cell carcinoma and serve as an independent prognostic factor to guide clinical designation of personalized treatment. KEY POINTS: • Normalized iodine concentration in the arterial phase and pathological grade were independent risk predictors of early recurrence in patients with esophageal squamous cell carcinoma. • Normalized iodine concentration in the arterial phase may be a noninvasive imaging marker for preoperatively predicting early recurrence in patients with esophageal squamous cell carcinoma. • The efficacy of normalized iodine concentration in the arterial phase derived from dual-energy computed tomography for predicting early recurrence is comparable to that of pathological grade.

3D convolutional neural network model from contrast-enhanced CT to predict spread through air spaces in non-small cell lung cancer.

PURPOSE: The purpose of this study was to compare the efficacy of five non-invasive models, including three-dimensional (3D) convolutional neural network (CNN) model, to predict the spread through air spaces (STAS) status of non-small cell lung cancer (NSCLC), and to obtain the best prediction model to provide a basis for clinical surgery planning. MATERIALS AND METHODS: A total of 203 patients (112 men, 91 women; mean age, 60 years; age range 22-80 years) with NSCLC were retrospectively included. Of these, 153 were used for training cohort and 50 for validation cohort. According to the image biomarker standardization initiative reference manual, the image processing and feature extraction were standardized using PyRadiomics. The logistic regression classifier was used to build the model. Five models (clinicopathological/CT model, conventional radiomics model, computer vision (CV) model, 3D CNN model and combined model) were constructed to predict STAS by NSCLC. Area under the receiver operating characteristic curves (AUC) were used to validate the capability of the five models to predict STAS. RESULTS: For predicting STAS, the 3D CNN model was superior to the clinicopathological/CT model, conventional radiomics model, CV model and combined model and achieved satisfactory discrimination performance, with an AUC of 0.93 (95% CI: 0.70-0.82) in the training cohort and 0.80 (95% CI: 0.65-0.86) in the validation cohort. Decision curve analysis indicated that, when the probability of the threshold was over 10%, the 3D CNN model was beneficial for predicting STAS status compared to either treating all or treating none of the patients within certain ranges of risk threshold CONCLUSION: The 3D CNN model can be used for the preoperative prediction of STAS in patients with NSCLC, and was superior to the other four models in predicting patients' risk of developing STAS.

Development and Validation of a CT-Based Signature for the Prediction of Distant Metastasis Before Treatment of Non-Small Cell Lung Cancer.

RATIONALE AND OBJECTIVES: To develop and validate a radiomics model, a clinical-semantic model and a combined model by using standard methods for the pretreatment prediction of distant metastasis (DM) in patients with non-small-cell lung cancer (NSCLC) and to explore whether the combined model provides added value compared to the individual models. MATERIALS AND METHODS: This retrospective study involved 356 patients with NSCLC. According to the image biomarker standardization initiative reference manual, we standardized the image processing and feature extraction using in-house software. Finally, 6692 radiomics features were extracted from each lesion based on contrast-enhanced chest CT images. The least absolute shrinkage selection operator and the recursive feature elimination algorithm were used to select features. The logistic regression classifier was used to build the model. Three models (radiomics model, clinical-semantic model and combined model) were constructed to predict DM in NSCLC. Area under the receiver operating characteristic curves were used to validate the ability of the three models to predict DM. A visual nomogram based on the combined model was developed for DM risk assessment in each patient. RESULTS: The receiver operating characteristic curve showed predictive performance for DM of the radiomics model (area under the curve [AUC] values for training and validation were 0.76 [95% CI, 0.704 - 0.820] and 0.76 [95% CI, 0.653 - 0.858], respectively). The combined model had AUCs of 0.78 (95% CI, 0.723 - 0.835) and 0.77 (95% CI, 0.673 - 0.870) in the training and validation cohorts, respectively. Both the radiomics model and combined model performed better than the clinical-semantic model (0.70 [95% CI, 0.634 - 0.760] and 0.67 [95% CI, 0.554 - 0.787] in the training and validation cohorts, respectively). CONCLUSION: The radiomics model and combined model may be useful for the prediction of DM in patients with NSCLC.

Vaccination control of an epidemic model with time delay and its application to COVID-19.

This paper studies an SEIR-type epidemic model with time delay and vaccination control. The vaccination control is applied when the basic reproduction number R 0 > 1 . The vaccination strategy is expressed as a state delayed feedback which is related to the current and previous state of the epidemic model, and makes the model become a linear system in new coordinates. For the presence and absence of vaccination control, we investigate the nonnegativity and boundedness of the model, respectively. We obtain some sufficient conditions for the eigenvalues of the linear system such that the nonnegativity of the epidemic model can be guaranteed when the vaccination strategy is applied. In addition, we study the stability of disease-free equilibrium when R 0 < 1 and the persistent of disease when R 0 > 1 . Finally, we use the obtained theoretical results to simulate the vaccination strategy to control the spread of COVID-19.

Control of a multigroup COVID-19 model with immunity: treatment and test elimination.

This paper introduces a multigroup COVID-19 model with immunity, in which the total population of each group is partitioned into five compartments, that is, susceptible, exposed, infective, infective in treatment and recovered compartment. If the basic reproduction number is less than or equal to one, and the infection graph is strongly connected, then the disease-free equilibrium is globally asymptotically stable and the disease dies out. However, the COVID-19 is already in a pandemic state, and the basic reproduction number is large than one. Hence, in order to make the COVID-19 die out in some groups in an area, we design some appropriate control strategies which reduce the number of exposed people and increase the number of people treated. These two methods have been proved to be the most effective methods at present. An effective algorithm is proposed to identify the groups that need to be controlled. Finally, we use the actual limited data of Hubei, Guangdong and Zhejiang provinces in China to illustrate the effectiveness of the obtained results.