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Background Accurate regulation of gene expression is crucial for normal development and function of cells. The prognostic significance and potential carcinogenic mechanisms of the related gene JARID2 in OSCC are not yet clear, but existing research has indicated a significant association between the two. Methods and materials The relationship between the expression of the JARID2 gene in tumor samples of OSCC patients and clinical pathological factors was analyzed using immunohistochemistry experiments and RT-qPCR analysis. Based on the clinical pathological data of patients, bioinformatics analysis was conducted using public databases to determine the function of JARID2 in OSCC. Knockdown OSCC cell lines were constructed, and the impact of JARID2 on the biological behavior of OSCC cell lines was assessed through CCK-8, wound healing assay, and transwell analysis. Results Immunohistochemistry experiments confirmed the correlation between JARID2 and the prognosis of OSCC patients, while RT-qPCR experiments demonstrated its expression levels in tissue and cells. CKK-8 experiments, wound healing assays, and Transwell experiments indicated that knocking down JARID2 had a negative impact on the proliferation, invasion, and migration of OSCC cells. Bioinformatics analysis results showed that the expression of JARID2 in OSCC is closely associated with patient gene co-expression, gene function enrichment, immune infiltration, and drug sensitivity. Conclusion Our study indicates that JARID2 is a novel prognostic biomarker and potential therapeutic target for OSCC.

Background The tumor suppressor FBW7 is the substrate recognition component of the SCF E3-ubiquitin ligase complex that mediates proteolytic degradation of various oncogenic proteins. However, the role of FBW7 in ovarian cancer progression remains inadequately understood. Methods IP-MASS, co-IP, immunohistochemistry, and western blotting were used to identify the potential substrate of FBW7 in ovarian cancer. The biological effects of FBW7 were investigated using in vitro and in vivo models. LC/MS was used to detect the m 6 A levels in ovarian cancer tissues. MeRIP-Seq and RNA-Seq were used to assess the downstream targets of YTHDF2. Results We unveil that FBW7 is markedly down-regulated in ovarian cancer tissues and its high expression is associated with favorable prognosis and elevated m 6 A modification levels. Consistently, ectopic FBW7 inhibits ovarian cancer cell survival and proliferation in vitro and in vivo, while ablation of FBW7 empowers propagation of ovarian cancer cells. In addition, the m 6 A reader protein, YTHDF2, is identified as a novel substrate for FBW7. FBW7 counteracts the tumor-promoting effect of YTHDF2 by inducing proteasomal degradation of the latter in ovarian cancer. Furthermore, YTHDF2 globally regulates the turnover of m 6 A-modified mRNAs, including the pro-apoptotic gene BMF. Conclusions Our study has demonstrated that FBW7 suppresses tumor growth and progression via antagonizing YTHDF2-mediated BMF mRNA decay in ovarian cancer.

Discontinuous structures in coal mine roadway roofs, such as rock interfaces and joint fractures, are critical factors leading to surrounding rock instability. The use of Measurement While Drilling (MWD) technology to identify geological formations has become a growing trend. However, there is still a lack of rock structure recognition methods that offer high accuracy, efficiency, and strong generalizability. Therefore, this study acquired four drilling parameters including thrust, torque, modulation specific energy (SEM), and rock drillability assessment (RDA) through drilling experiments. By leveraging the Bayes algorithm, which has high precision, efficiency, and low cost, a change point detection model for drilling parameters was established, and a multi-parameter fusion criterion was proposed for identifying rock structures. The results show that for single rock interface identification, the errors of thrust, torque, SEM, and RDA were 13.3 mm, 4.6 mm, 4.4 mm, and 18.3 mm, respectively. For multiple rock interface identification, the recognition rates were 83.3%, 100.0%, 66.7%, and 83.3%, respectively. Moreover, the absolute value of the magnitude index (SLP) at the interface location was generally the highest among all change points. In multi-change-point detection, the SLP threshold should be set at ± 0.2. It is worth noting that the SLP value is correlated with data fluctuation intensity; greater fluctuation leads to higher SLP values at change points. This study contributes significantly to enabling intelligent perception of rock structures and improving the quality of rock mass control.

Objective The aim of this study was to investigate the potential value of dual tracers 18 F-FDG and 18 F-FES PET/CT in predicting response to Cyclin-Dependent 4/6 Kinase (CDK4/6) inhibitors combined with endocrine therapy for metastatic estrogen receptor (ER)-positive breast cancer patients. Methods This retrospective study enrolled 38 ER-positive metastatic breast cancer patients from our center who underwent both 18 F-FDG and 18 F-FES PET/CT scans within 1 month before CDK4/6 inhibitors combined with endocrine therapy. The extracted parameters comprised the maximum standardized uptake value (SUVmax) for both FDG and FES PET, as well as the ratio between FES and FDG SUVmax. Each parameter was dichotomized based on its median threshold. The primary endpoint was progression-free survival (PFS), which was estimated using the Kaplan–Meier method and compared by the log-rank test. Results After a median follow-up of 15.6 months, progressive disease was observed in 23 out of 38 patients, and the median PFS for the whole cohort was 21.0 months [95% confidence interval (CI) 12.7–29.3]. FES and FDG PET identified 6 patients (15.8%) with FES-negative lesions, suggesting ER heterogeneity in metastatic lesions. The median PFS of these patients was only 5.3 months (95% CI 1.7–8.9), which was substantially shorter than that of patients with 100% FES-positive lesions (median PFS 22.9 months, 95% CI 17.1–28.7, P  < 0.001). Patients with 100% FES-positive lesions who had high FES/FDG showed significantly shorter PFS compared to those with low FES/FDG (14.9 vs. 30.5 months, P  = 0.003). Conclusions This study shows that FDG and FES PET imaging may serve as valuable tools for patient selection in the context of CDK4/6 inhibitor therapy combined with endocrine treatment, and have the potential to function as prognostic biomarkers.

Background Carbon-ion radiotherapy (CIRT) for head and neck tumors is typically delivered in the supine posture using fixed beam lines, which limits beam angle selection. Combining upright posture with fixed beam lines offers expanded angular access and potential dosimetric advantages, yet optimal angle configurations remain unclear. This study identifies optimal beam angles in head and neck CIRT by comparing dosimetry and robustness of upright and supine plans for fixed-beam systems, thereby supporting beam angle optimization and clinical implementation of upright treatment in fixed-beam systems. Methods Twenty patients with head and neck cancer were retrospectively robustly optimized using four beam configurations: horizontal beams at 0° (S0) and with a 15° superior-oblique tilt (S15) in the supine posture, anterior beams at 15° (U15) and 45° (U45) in the upright posture. Plans were generated in RayStation (v10B) accounting for ± 3 mm setup and ± 3.5% range uncertainties. Target coverage (D 95% , D 2% , V 95% , conformity index [CI], homogeneity index [HI]), plan robustness (DVH bands, worst-case scenario), and organ-at-risk (OAR) dosimetry (mean dose to cochleae and parotid glands, and brainstem D 1cc ) were compared. Statistical analyses used paired t-tests or Wilcoxon signed-rank tests. Results All plans achieved comparable nominal target coverage and similar CI values. S15 showed significantly improved robustness (DVH band ΔD 95% = 0.5 Gy(RBE), ΔV 95% = 1.4%; worst-case ΔD 95% = 0.3 Gy(RBE), ΔHI = 0.01, ΔCI = 0.02, all p  < 0.05) and lower OAR doses versus S0 (cochlea: 28.4 vs. 30.6 Gy(RBE), parotid: 13.5 vs. 18.5 Gy(RBE), brainstem D 1cc : 40.1 vs. 41.7 Gy(RBE), all p  < 0.001). U15 exhibited comparable robustness to S15 with further reductions in cochlea (18.5 vs. 28.4 Gy(RBE), p  < 0.001) and parotid sparing (11.9 vs. 13.5 Gy(RBE), p  < 0.05). U45 showed the highest robustness and OAR sparing, except for the brainstem, where D 1cc was significantly increased (50.9 Gy(RBE), p  < 0.05). Conclusions The anterior beams at 15°in the upright setup (U15) showed the best balance of robustness and OAR sparing, making it the preferred option. The 15°-angled supine setup (S15) is a practical alternative. S0 and U45 are not recommended due to inferior robustness and higher brainstem dose, respectively.

Purpose of the Report: Accurate clinical axillary evaluation plays an important role in the diagnosis and treatment planning for early-stage breast cancer (BC). This study aimed to develop a scalable, non-invasive and robust machine learning model for predicting of the pathological node status using dedicated-PET integrating the clinical characteristics in early-stage BC. Materials and Methods: A total of 420 BC patients confirmed by postoperative pathology were retrospectively analyzed. 18F-fluorodeoxyglucose (18F-FDG) Mammi-PET, ultrasound, physical examination, Lymph-PET, and clinical characteristics were analyzed. The least absolute shrinkage and selection operator (LASSO) regression analysis were used in developing prediction models. The characteristic curve (ROC) of the area under receiver-operator (AUC) and DeLong test were used to evaluate and compare the performance of the models. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results: A total of 290 patients were enrolled in this study. The AUC of the integrated model diagnosed performance was 0.94 (95% confidence interval (CI), 0.91–0.97) in the training set (n = 203) and 0.93 (95% CI, 0.88–0.99) in the validation set (n = 87) (both p < 0.05). In clinical N0 subgroup, the negative predictive value reached 96.88%, and in clinical N1 subgroup, the positive predictive value reached 92.73%. Conclusions: The use of a machine learning integrated model can greatly improve the true positive and true negative rate of identifying clinical axillary lymph node status in early-stage BC.

The safety valves of powered supports control the maximum working resistance, and their statuses must be known to ensure the safety of both the support and the overlying strata. However, the inspection of powered support valves involves manual or semiautomated operations, the costs of which are high. In this study, an extreme point extraction method was developed for the determination of the characteristic parameters of safety valves using roof pressure data, and a safety valve state monitoring module was constructed. Using the longwall face of 0116306 with top coal caving in the Mindong Mine as an example, the characteristic parameters of the safety valves were extracted, including the peak, reseating, and blowdown pressures, as well as the recovery and unloading durations. The results of the field tests showed the following: (1) The amplitude threshold method based on extreme points can be used to accurately extract characteristic parameters, and the distribution of the characteristic parameters of the safety valves follows either a Gaussian or an exponential distribution. (2) The mining pressure analysis results, derived from the characteristic parameters, closely align with the in situ mining pressure observations. This method can be used for the online monitoring of safety valve conditions, increasing the operational efficiency and quality of safety valve inspections.

The precise detection of stratum interfaces holds significant importance in geological discontinuity recognition and roadway support optimization. In this study, the model for locating rock interfaces through change point detection was proposed, and a drilling test on composite strength mortar specimens was conducted. With the logistic function and the particle swarm optimization algorithm, the drilling specific energy was modulated to detect the stratum interface. The results indicate that the drilling specific energy after the modulation of the logistic function showed a good anti-interference quality under stable drilling and sensitivity under interface drilling, and its average recognition error was 2.83 mm, which was lower than the error of 6.56 mm before modulation. The particle swarm optimization algorithm facilitated the adaptive matching of drive parameters to drilling data features, yielding a substantial 50.88% decrease in the recognition error rate. This study contributes to enhancing the perception accuracy of stratum interfaces and eliminating the potential danger of roof collapse.

Measurement while drilling technology (MWD) has emerged as a pivotal approach for geological exploration. However, the accuracy of existing geological recognition models remains limited, primarily due to data fluctuations that result in high overlap rates and reduced reliability of drilling parameters. This study takes torque data as an example and analyzes the frequency distribution laws of torque responses across rock with varying strengths. A quantitative model of the frequency distribution characteristic interval is established, and a rock information prediction approach based on frequency distribution characteristics is proposed. The results indicate that torque frequency distributions for homogeneous rock exhibit a unimodal pattern, whereas those for composite rocks display multimodal characteristics. The boundaries of the frequency distribution characteristic intervals are mathematically defined as CIS = Tp|(dF/dT) = 0 ± σ and CIM = xli ± 0.5∆xi. The strength prediction model constructed using torque within the characteristic interval achieves an average accuracy of 85.3%. Furthermore, the frequency of torque within the characteristic interval enables the estimation of rock stratum thickness. This research contributes to enhancing the accuracy of rock information identification.

Background The aim of this study was to evaluate the efficacy of fluorine 18 ( 18 F) labeled fibroblast activation protein inhibitor (FAPI) in identifying mediastinal and hilar lymph node metastases and to develop a model to quantitatively and repeatedly identify lymph node status. Methods Twenty-seven patients with 137 lymph nodes were identified by two PET/CT images. The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of lymph node status were analyzed, and the optimal cut-off value was identified by ROC analysis. Results The SUVmax of metastatic lymph nodes on 18 F-FAPI was higher than that on 18 F-FDG PET/CT (10.87 ± 7.29 vs 6.08 ± 5.37, p  < 0.001). 18 F-FAPI presented much greater lymph node detection sensitivity, specificity, accuracy, PPV and NPV than 18 F-FDG PET/CT (84% vs. 71%; 92% vs. 67%; 90% vs. 69%, 84% vs. 52%, and 92% vs. 83%, respectively). Additionally, the diagnostic effectiveness of 18 F-FAPI in small lymph nodes was greater than that of 18 F-FDG PET/CT (specificity: 96% vs. 72%; accuracy: 93% vs. 73%; PPV: 77% vs. 33%, respectively). Notably, the optimal cut-off value for specificity and PPV of 18 F-FAPI SUVmax was 5.3; the optimal cut-off value for sensitivity and NPV was 2.5. Conclusion 18 F-FAPI showed promising diagnostic efficacy in metastatic mediastinal and hilar lymph nodes from lung cancer patients, with a higher SUVmax, especially in small metastatic nodes, compared with 18 F-FDG. In addition, this exploratory work recommended optimal SUVmax cutoff values to distinguish between nonmetastatic and metastatic lymph nodes, thereby advancing the development of image-guided radiation. Trial registration ClinicalTrials.gov identifier: ChiCTR2000036091.