HELQ suppresses migration and proliferation of non-small cell lung cancer cells by repairing DNA damage and inducing necrosis.

HELQ plays a key role in DNA damage response and cell-cycle checkpoint regulation. It has been implicated in ovarian and pituitary tumors and may play a role in germ cell maintenance. This study investigated the role of HELQ in lung cancer. The expression of HELQ in patients with non-small-cell lung cancer (NSCLC) was downregulated compared with normal human lungs. Clinical prognostic analysis of Kaplan-Meier plots revealed that patients with NSCLC with low HELQ levels had a reduced overall survival. Further, we found that HELQ depletion enhanced lung cancer cell malignancy. Furthermore, overexpression of HELQ in lung cancer cells reduced cell migration in vitro, while DNA damage repair was inhibited. Both in vitro and in vivo studies have shown that HELQ induces cell death. Mechanistically, we found that cells overexpressing HELQ showed a tendency to induce necrosis. After analyzing the database of HELQ interactors. we found that RIPK3 may interact with it and proved this conclusion by immunoprecipitation. Our findings identified the tumor suppressive role of HELQ in malignant human lung cancer and unraveled a potential therapeutic strategy for cancer treatment through HELQ activation. Moreover, HELQ may also be a predictive biomarker for the clinical predisposition, progression, and prognosis of lung cancer.

Automated machine learning-based model for the prediction of delirium in patients after surgery for degenerative spinal disease.

OBJECTIVE: This study used machine learning algorithms to identify critical variables and predict postoperative delirium (POD) in patients with degenerative spinal disease. METHODS: We included 663 patients who underwent surgery for degenerative spinal disease and received general anesthesia. The LASSO method was used to screen essential features associated with POD. Clinical characteristics, preoperative laboratory parameters, and intraoperative variables were reviewed and were used to construct nine machine learning models including a training set and validation set (80% of participants), and were then evaluated in the rest of the study sample (20% of participants). The area under the receiver-operating characteristic curve (AUROC) and Brier scores were used to compare the prediction performances of different models. The eXtreme Gradient Boosting algorithms (XGBOOST) model was used to predict POD. The SHapley Additive exPlanations (SHAP) package was used to interpret the XGBOOST model. Data of 49 patients were prospectively collected for model validation. RESULTS: The XGBOOST model outperformed the other classifier models in the training set (area under the curve [AUC]: 92.8%, 95% confidence interval [CI]: 90.7%-95.0%), validation set (AUC: 87.0%, 95% CI: 80.7%-93.3%). This model also achieved the lowest Brier Score. Twelve vital variables, including age, serum albumin, the admission-to-surgery time interval, C-reactive protein level, hypertension, intraoperative blood loss, intraoperative minimum blood pressure, cardiovascular-cerebrovascular disease, smoking, alcohol consumption, pulmonary disease, and admission-intraoperative maximum blood pressure difference, were selected. The XGBOOST model performed well in the prospective cohort (accuracy: 85.71%). CONCLUSION: A machine learning model and a web predictor for delirium after surgery for the degenerative spinal disease were successfully developed to demonstrate the extent of POD risk during the perioperative period, which could guide appropriate preventive measures for high-risk patients.

Development and biomechanical test of a new pedicle screw for thoracolumbar spinal surgery.

Rupture of the medial pedicle wall often occurs during pedicle screw insertion. This allows the pedicle screw to compress or cut the nerve root and/or spinal cord. In this paper, we designed a new double-threaded pedicle screw that has a cylindrical outer diameter and a conical core diameter, with a wide thread at the front, no thread in the middle, and a narrow thread at the rear, according to an analysis of the shortcomings of the conventional pedicle screw and anatomical parameters in 300 healthy adult volunteers. After the screw was placed, the non-threaded portion of the screw was located at the vertebral pedicle. No nerve root cutting occurred if the screw was misplaced and the medial wall of the vertebral pedicle was broken. We then performed biomechanical tests using a static universal testing machine and compared the new double-threaded screws with the conventional full-threaded pedicle screws, however, the differences were not obvious. In compression bending fatigue tests, the novel double-threaded pedicle screws were subjected to 5 million fatigue cycle loads without failure. The current study demonstrated that the new pedicle screw possesses similar biomechanical features as those of the conventional fully threaded pedicle screw. This provides a basis for further clinical applications.