A Novel Mitochondrial-Related Gene Signature for the Prediction of Prognosis and Therapeutic Efficacy in Lower-Grade Glioma.

Lower-grade glioma (LGG) is a common primary brain tumor with a highly heterogeneous clinical presentation, and its prognosis cannot be accurately predicted by current histopathology. It has been found that mitochondria play an important role in hypoxia, angiogenesis, and energy metabolism in glioma, and mitochondrial function may have an important impact on LGG prognosis. The goal of this study was to develop a novel prognostic model based on Mitochondrial-related genes (MRGs). We first analyzed the somatic alterations profiles of MRGs in patients with LGG and found that somatic alterations were common in LGG and correlated with prognosis. Using RNA-seq data from TCGA and CGGA, 12 prognosis-related MRGs were identified to construct a mitochondrial activation score (MiAS) model by combining univariate regression and LASSO regression analysis. The model and nomogram were evaluated using the area under the ROC curve with AUC = 0.910. The model was closely correlated with the clinical characteristics of LGG patients and performed well in predicting the prognosis of LGG patients with significantly shorter overall survival (OS) time in the high-MiAS group. GSVA and GSEA results showed that oxidative stress, pro-cancer, and immune-related pathways were significantly enriched in the high-MiAS group. CIBERSORT results showed that MiAS was significantly associated with immune cell infiltration in LGG. Macrophage M1 and follicular helper T cells had increased infiltration in the high-MiAS group. TIDE predicted a better immunotherapy outcome in patients in the low-MiAS group. Finally, using data from the CTRPv2 and GDSC2 datasets to assess chemotherapy response in LGG, it was predicted that the chemotherapeutic agents AZD6482, MG-132, and PLX-4720 might be potential agents for patients in the high-MiAS group of LGG. In addition, we performed in vitro experiments and found that knockdown of OCIAD2 expression reduced the abilities of glioma cells to proliferate, migrate, and invade. In contrast, overexpression of OCIAD2 enhanced these abilities of glioma cells. This study found that MRGs were correlated with LGG patient prognosis, which is expected to provide new treatment strategies for LGG patients with different MiAS.

Efficacy and Safety of General Anesthesia Induction with Ciprofol in Hip Fracture Surgery of Elderly Patients: A Randomized Controlled Trial.

Background: Ciprofol is a new intravenous sedative / anesthetic drug. In recent years, many clinical studies have also confirmed the sedative effect of ciprofol. However, more clinical research is still needed on its clinical application characteristics in special populations. Objective: The aim of this study was to compare the clinical effects of ciprofol and propofol in general anesthesia induction of elderly patients. Methods: 60 elderly (aged ≥ 75 years) patients underwent hip fracture surgery were randomly into two groups of a 1:1 ratio. Group C (ciprofol group): 0.3mg/kg ciprofol was infused. Group P (propofol group): 1.5mg/kg propofol was infused. The observation period was from the infusion of test drug to 5 min after endotracheal intubation. The primary outcomes included the incidence of severe hypotension and hypotension during the observation period. The secondary outcomes were as follows: the success rate of general anesthesia induction, the number of additional sedation, the time of loss of consciousness (LOC), Δ MAP, Δ HR, adverse events and the frequency of vasoactive drugs used. Results: Finally, 60 subjects completed the study. Compared with Group P, the incidence of severe hypotension in Group C was lower (26.7% vs 53.3%, P = 0.035), the incidence of hypotension was also lower (36.7% vs 63.3%, P = 0.037), Δ MAP in Group C was significantly lower (31.4 ± 11.4 vs 39.6 ± 15.7, P = 0.025), the frequency of ephedrine used and the incidence of injection pain in Group C were also significantly lower. Conclusion: Ciprofol showed similar efficacy to propofol when used for general anesthesia induction in elderly patients underwent hip fracture surgery and could maintain more stable blood pressure.

Association of procalcitonin with voriconazole concentrations: a retrospective cohort study.

Inflammation is a potential risk factor of voriconazole (VCZ) overdose, procalcitonin (PCT) is reported to act as a diagnostic marker for bacterial infections. However, the association of PCT with VCZ trough serum concentrations (VCZ-Cmin) is not fully clear. Our study aims to investigate the associations between PCT and VCZ-Cmin. In this retrospective cohort study, we collected the clinical data of 147 patients who received VCZ and monitored the VCZ concentration of them in our hospital from August 2017 to August 2021. All patients underwent routine clinical examinations on the day or the day before VCZ administration. General information and clinical symptoms of these patients were recorded. Multivariate liner analysis showed that PCT was significantly associated with VCZ-Cmin (p < 0.001). Overall, it was shown that VCZ-Cmin was significantly increased by 0.32 µg/mL for each fold increment in PCT in crude model. In the minor adjusted model (Model 1, adjustment for sex, age, albumin, direct bi1irubin, WBC) and fully adjusted model (Model 2, adjustment for sex, age, albumin, direct bilirubin, WBC, AST and ALT), VCZ-Cmin was significantly increased by 0.23 µg/mL and 0.21 µg/mL, respectively, for each fold increment in PCT. In conclusion, this research reveals the correlation between PCT and VCZ-Cmin, indicating that PCT has the potential to serve as a valuable biomarker for drug monitoring in the treatment of VCZ.

Study on CO dispersion characteristics and isolation door control technology of refuge chamber in coal mine.

To investigate the dispersion process of the underground toxic gas carbon monoxide (CO) into the refuge chamber during a mine disaster and enhance the survival rate of trapped miners, a simplified model of an underground refuge chamber and the main roadway was constructed. The impact of temperature and pressurized air volume on CO dispersion into the refuge chamber has been examined through both analog experiments and numerical simulations, and the reliability of the simulation results was verified. The results indicate that CO dispersion into the refuge chamber through the top of the protective isolation door occurs when the temperature in the refuge chamber is lower than that of the toxic gas. When the temperature of the toxic gas is higher, it tends to enter the refuge chamber through the bottom of the protective isolation door. The evolution of CO concentration in the transition chamber can be divided toxic survival chamber can be categorized into a sudden decline stage and a stable stage. And a flexible isolation door designed to control the entry of toxic gases into the refuge chamber was implemented, and its impact on CO dispersion has been compared and analyzed. When the temperature of the main roadway is 50 °C and the temperature of the refuge chamber is 20 °C, the required pressurized air volume to maintain the CO concentration within the safe threshold (24 ppm) is reduced to 69.6% of that needed without the isolation door, thereby significantly reducing the infiltration of harmful gases from the main roadway into the refuge chamber.

Radiomics predicts the prognosis of patients with clear cell renal cell carcinoma by reflecting the tumor heterogeneity and microenvironment.

PURPOSE: We aimed to develop and externally validate a CT-based deep learning radiomics model for predicting overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients, and investigate the association of radiomics with tumor heterogeneity and microenvironment. METHODS: The clinicopathological data and contrast-enhanced CT images of 512 ccRCC patients from three institutions were collected. A total of 3566 deep learning radiomics features were extracted from 3D regions of interest. We generated the deep learning radiomics score (DLRS), and validated this score using an external cohort from TCIA. Patients were divided into high and low-score groups by the DLRS. Sequencing data from the corresponding TCGA cohort were used to reveal the differences of tumor heterogeneity and microenvironment between different radiomics score groups. What's more, univariate and multivariate Cox regression were used to identify independent risk factors of poor OS after operation. A combined model was developed by incorporating the DLRS and clinicopathological features. The SHapley Additive exPlanation method was used for interpretation of predictive results. RESULTS: At multivariate Cox regression analysis, the DLRS was identified as an independent risk factor of poor OS. The genomic landscape of different radiomics score groups was investigated. The heterogeneity of tumor cell and tumor microenvironment significantly varied between both groups. In the test cohort, the combined model had a great predictive performance, with AUCs (95%CI) for 1, 3 and 5-year OS of 0.879(0.868-0.931), 0.854(0.819-0.899) and 0.831(0.813-0.868), respectively. There was a significant difference in survival time between different groups stratified by the combined model. This model showed great discrimination and calibration, outperforming the existing prognostic models (all p values < 0.05). CONCLUSION: The combined model allowed for the prognostic prediction of ccRCC patients by incorporating the DLRS and significant clinicopathologic features. The radiomics features could reflect the tumor heterogeneity and microenvironment.

Development and validation of a predictive model for atrial fibrillation recurrence post-catheter ablation in patients with nonvalvular atrial fibrillation on the basis of hemodynamic parameters.

BACKGROUND: The influence of hemodynamic parameters on the recurrence of atrial fibrillation (AF) after catheter ablation is not well known, and it remains unclear whether a nomogram combining risk factors and hemodynamic parameters improves prediction accuracy. OBJECTIVE: This study aimed to develop a nomogram on the basis of echocardiographic hemodynamic parameters for predicting AF recurrence after catheter ablation in nonvalvular atrial fibrillation (NVAF). METHODS: A total of 380 consecutive patients with NVAF undergoing AF catheter ablation treatment were prospectively included. Patients were divided into training and validation cohorts in a 7:3 ratio. The follow-up time averaged 9 months with a median of 12 months, during which 132 patients (34.7%) experienced AF recurrence. RESULTS: Least absolute shrinkage and selection operator regression and Cox regression analyses identified 4 significant predictors of AF recurrence: persistent AF (hazard ratio [HR] 1.63; 95% confidence interval [CI] 1.02-2.61; P = .041), S/D ratio of pulmonary vein (HR 0.50; 95% CI 0.30-0.84; P = .009), left atrial acceleration factor α (HR 1.31; 95% CI 1.02-1.68; P = .032), and left atrial appendage peak emptying flow velocity (HR 0.98; 95% CI 0.97-0.99; P = .004). On the basis of these 4 variables, a predictive nomogram was constructed. The nomogram demonstrated C indices of 0.664 and 0.728 for predicting 1- and 2-year AF recurrence, respectively, in the validation cohort. The Kaplan-Meier survival analysis indicated that a Nomo score of >128 was associated with a higher risk of AF recurrence. CONCLUSION: Hemodynamic parameters may offer valuable insight into predicting AF recurrence after catheter ablation. Our study successfully developed a reliable nomogram on the basis of echocardiographic hemodynamic parameters to estimate the risk of AF recurrence after catheter ablation in patients with NVAF.

A machine learning based quantification system for automated diagnosis of lumbar spondylolisthesis on spinal X-rays.

The automated diagnosis of lumbar spondylolisthesis lacks standardized criteria and the diagnostic of lumbar spondylolisthesis itself inherently relies on the subjective judgment of experts, resulting in a lack of standardized criteria. The objective of this study is to develop a novel, fully automated diagnostic system for lumbar spondylolisthesis. A two-stage system was developed, consisting of a Mask R-CNN with Prime Sample Attention (PISA) for vertebral segmentation in the first stage and a Light Gradient Boosting Machine (LGBM) for spondylolisthesis diagnosis in the second stage. The training data was developed by a total of 936 X-ray images including neutral, extension, and flexion lateral radiographs retrospectively gathered from 312 patients diagnosed with lumbar spondylolisthesis between January 2021 and March 2022. From a model perspective, there were a total of 4680 vertebrae, of which 1056 (22.6 %) were spondylolisthesis and the rest were normal. The Bbox mAP50, Bbox mAP75, Segm mAP50, and Segm mAP75 of Mask R-CNN with PISA were 0.9852, 0.9179, 0.9741, and 0.8957, respectively. The Accuracy, AUC, Recall, Precision, and F1-score of LGBM were 0.9660, 0.9843, 0.9020, 0.9020, and 0.9020, respectively. This study presents a robust system for the diagnosis of lumbar spondylolisthesis, providing accurate detection, classification, and localization of lumbar spondylolisthesis.

Prediction of sepsis within 24 hours at the triage stage in emergency departments using machine learning.

BACKGROUND: Sepsis is one of the main causes of mortality in intensive care units (ICUs). Early prediction is critical for reducing injury. As approximately 36% of sepsis occur within 24 h after emergency department (ED) admission in Medical Information Mart for Intensive Care (MIMIC-IV), a prediction system for the ED triage stage would be helpful. Previous methods such as the quick Sequential Organ Failure Assessment (qSOFA) are more suitable for screening than for prediction in the ED, and we aimed to find a light-weight, convenient prediction method through machine learning. METHODS: We accessed the MIMIC-IV for sepsis patient data in the EDs. Our dataset comprised demographic information, vital signs, and synthetic features. Extreme Gradient Boosting (XGBoost) was used to predict the risk of developing sepsis within 24 h after ED admission. Additionally, SHapley Additive exPlanations (SHAP) was employed to provide a comprehensive interpretation of the model's results. Ten percent of the patients were randomly selected as the testing set, while the remaining patients were used for training with 10-fold cross-validation. RESULTS: For 10-fold cross-validation on 14,957 samples, we reached an accuracy of 84.1%±0.3% and an area under the receiver operating characteristic (ROC) curve of 0.92±0.02. The model achieved similar performance on the testing set of 1,662 patients. SHAP values showed that the five most important features were acuity, arrival transportation, age, shock index, and respiratory rate. CONCLUSION: Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage. This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.

Silver-Catalyzed 1,2-Thiosulfonylation of Alkenes: Development of a Nucleophilic d3-Methylthiolating Reagent.

Development of robust d3-methylthiolating reagents represents an attractive synthetic method to access deuterated molecules in the field of drug discovery. Here, we report a straightforward strategy to prepare electrophilic S-methyl-d3 arylsulfonothioates in one-step without column purification. These reagents exhibit good radical reactivity toward silver-catalyzed vicinal thiosulfonylation of alkenes or 1,6-enynes on water. As a result, simultaneous incorporation of both SCD3 and ArSO2 units into unsaturated carbon-carbon bonds with 100% atom economy has been established. Moreover, the ATRA adducts with >99% D incorporation can serve as nucleophilic d3-methylthiolating synthons via retro-Michael addition under mild basic conditions, providing a good alternative in trideuteromethylthiolating alkyl iodides to access corresponding trideuteromethyl sulfides with high efficiency.

RETRACTED: Yang et al. How Does Physical Activity Enhance the Subjective Well-Being of University Students? A Chain Mediation of Cognitive Reappraisal and Resilience. Behav. Sci. 2024, 14, 164.

The journal Behavioral Sciences retracts the article titled, "How Does Physical Activity Enhance the Subjective Well-Being of University Students [...].

From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning.

Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that the model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, in a virtual screening on heat shock protein 90 (HSP90) using Dynaformer, 20 candidates are identified and their binding affinities are further experimentally validated. Dynaformer displayed promising results in virtual drug screening, revealing 12 hit compounds (two are in the submicromolar range), including several novel scaffolds. Overall, these results demonstrated that the approach offer a promising avenue for accelerating the early drug discovery process.

Posterior Release, Reduction, and Intra-Articular Fusion for Irreducible Type III Atlantoaxial Rotary Fixation.

BACKGROUND AND OBJECTIVES: For irreducible atlantoaxial rotary fixation (AARF), anterior or posterior release was often needed before posterior reduction and fusion. Anterior atlantoaxial joint release has potential complications such as retropharyngeal abscess, persistent hoarseness, and infection. This study aims to assess the efficacy of posterior release, reduction, and intra-articular fusion without resecting the C2 nerve root on irreducible type III AARF. METHODS: The data of 9 pediatric patients diagnosed with AARF who underwent posterior atlantoaxial release, reduction, and intra-articular fusion without resecting the C2 nerve root were retrospectively reviewed. Japanese Orthopaedic Association scores and Visual Analog Scale for Neck Pain were used to assess outcomes. The preoperative and follow-up assessments of atlantodens interval (ADI) were documented to evaluate the reduction of atlantoaxial joint. The patient demographics, surgery time, blood loss, bone fusion time, follow-up period, and surgery-related complications were meticulously documented. RESULTS: The mean follow-up duration was 35.1 ± 11.5 months. Complete reduction was achieved in 8 patients, while one patient did not achieve complete reduction. The ADI decreased significantly from 8.7 ± 2.2 mm before surgery to 2.1 ± 1.3 mm at the final follow-up. All patients demonstrated successful bone fusion, with an average fusion period of 3.7 ± 1.3 months. The Visual Analog Scale for Neck Pain at the final follow-up exhibited a significant decrease compared with preoperative values (P < .05), while no significant difference was observed in Japanese Orthopaedic Association scores. There were no complications related to surgery. CONCLUSION: Posterior atlantoaxial release, reduction, and intra-articular fusion with a C2 nerve root preservation technique is effective in the treatment of irreducible type III AARF.

A Single Model for the Thermodynamics and Kinetics of Metal Exsolution from Perovskite Oxides.

Exsolution has emerged as an outstanding route for producing oxide-supported metal nanoparticles. For ABO3-perovskite oxides, various late transition-metal cations can be substituted into the lattice under oxidizing conditions and exsolved as metal nanoparticles after reduction. A consistent and comprehensive description of the point-defect thermodynamics and kinetics of this phenomenon is lacking, however. Herein, supported by hybrid density-functional-theory calculations, we propose a single model that explains diverse experimental observations, such as why substituent transition-metal cations (but not host cations) exsolve from perovskite oxides upon reduction; why different substituent transition-metal cations exsolve under different conditions; why the metal nanoparticles are embedded in the surface; why exsolution occurs surprisingly rapidly at relatively low temperatures; and why the reincorporation of exsolved species involves far longer times and much higher temperatures. Our model's foundation is that the substituent transition-metal cations are reduced to neutral species within the perovskite lattice as the Fermi level is shifted upward within the bandgap upon sample reduction. The calculations also indicate unconventional influences of oxygen vacancies and A-site vacancies. Our model thus provides a fundamental basis for improving existing, and creating new, exsolution-generated catalysts.

A Theoretical Study on Static Gas Pressure Measurement via Circular Non-Touch Mode Capacitive Pressure Sensor.

A circular non-touch mode capacitive pressure sensor can operate in both transverse and normal uniform loading modes, but the elastic behavior of its movable electrode plate is different under the two different loading modes, making its input-output analytical relationships between pressure and capacitance different. This suggests that when such a sensor operates, respectively, in transverse and normal uniform loading modes, the theory of its numerical design and calibration is different, in other words, the theory for the transverse uniform loading mode (available in the literature) cannot be used as the theory for the normal uniform loading mode (not yet available in the literature). In this paper, a circular non-touch mode capacitive pressure sensor operating in normal uniform loading mode is considered. The elastic behavior of the movable electrode plate of the sensor under normal uniform loading is analytically solved with the improved governing equations, and the improved analytical solution obtained can be used to mathematically describe the movable electrode plate with larger elastic deflections, in comparison with the existing two analytical solutions in the literature. This provides a larger technical space for developing the circular non-touch mode capacitive pressure sensors used for measuring the static gas pressure (belonging to normal uniform loading).