PSAT1 prompted cell proliferation and inhibited cell apoptosis in multiple myeloma through regulating PI3K/AKT pathway

Purpose: To identify the biological function of phosphoserine aminotransferase 1 (PSAT1) in regulating cell proliferation and apoptosis in multiple myeloma (MM).Methods: The mRNA and protein levels of PSAT1 were determined using quantitative real-time polymerase chain reaction (PCR) and western blotting, respectively. Cell proliferation was measured using CCK-8 assay.Results: PSAT1 mRNA and protein expression levels were significantly increased in MM cell lines when compared to control cells. Moreover,  downregulation of PSAT1 inhibited MM cell proliferation and induced cell apoptosis, whereas overexpression of PSAT1 promoted MM cell  proliferation and suppressed cell apoptosis. Further analysis demonstrated that the underlying mechanism was via regulation of PI3K/AKT pathway.Conclusion: The results identified a novel role for PSAT1 in the progression of MM, which may provide a therapeutic and a new anticancer target for the therapy of MM. Keywords: Multiple myeloma, PSAT1, Cell proliferation, PI3K/AKT pathwa

Bevacizumab treatment for radiation brain necrosis: mechanism, efficacy and issues

Abstract Vascular damage is followed by vascular endothelial growth factor (VEGF) expression at high levels, which is an important mechanism forradiation brain necrosis development. Bevacizumab alleviates brain edema symptoms caused by radiation brain necrosis through inhibiting VEGF and acting on vascular tissue around the brain necrosis area. Many studies have confirmed that bevacizumab effectively relieves symptoms caused by brain necrosis, improves patients’ Karnofsky performance status (KPS) scores and brain necrosis imaging. However, necrosis is irreversible, and hypoxia and ischemia localized in the brain necrosis area may easily lead to radiation brain necrosis recurrence after bevacizumab is discontinued. Further studies are necessary to investigate brain necrosis diagnoses, bevacizumab indications, and the optimal mode of administration, bevacizumab resistance and necrosis with a residual or recurrent tumor

A Tactile Sensor Decoupling Process

An improved hybrid homotopy method is proposed to decouple the multi-input model of tactile sensors. The time-embedded homotopy algorithm is proved to be very suitable for solving the problem. Three tracking factors that control the efficiency of the algorithm are studied: tracking operator, stepsize, and accuracy. Trust region methods are applied to track the zero paths instead of the traditional differential algorithm, and a periodic sampling method is proposed to improve the efficiency of the algorithm. Numerical experiments show that both the robustness and accuracy have received a huge boost after the hybrid algorithm is applied

Tropical Overshooting Cloud-Top Height Retrieval from Himawari-8 Imagery Based on Random Forest Model

Tropical overshooting convection has a strong impact on both heat budget and moisture distribution in the upper troposphere and lower stratosphere, and it can pose a great risk to aviation safety. Cloud-top height is one of the essential concerns of overshooting convection for both the climate system and the aviation weather forecast. The main purpose of our work is to verify the application of the machine learning method, taking the random forest (RF) model as an instance, in overshooting cloud-top height retrieval from Himawari-8 data. By using collocated CloudSat observations as a reference, we utilize several infrared indicators of Himawari-8 that are commonly recognized to relate to cloud-top height, along with some temporal and geographical parameters (latitude, month, satellite zenith angle, etc.), as predictors to construct and validate the model. Analysis of variable importance shows that the brightness temperature of 6.2 um acts as the dominant predictor, followed by satellite zenith angle, brightness temperature of 13.3 um, latitude, and month. In the comparison between the RF model and the traditional single-channel interpolation method, retrievals from the RF model agree well with observation with a high correlation coefficient (0.92), small RMSE (222 m), and small MAE (164 m), while these metrics from traditional single-channel interpolation method shows lower skills (0.70, 1305 m, and 1179 m). This work presents a new sight of overshooting cloud-top height retrieval based on the machine learning method

The Study of Decoupling Methods for a Novel Tactile Sensor Based on BP Neural Network

This paper proposes a decoupling method for a novel tactile sensor based on improved Back Propagation Neural Network (BPNN). In the numerical experiments, the number of hidden layer nodes of the BPNN is optimized and k-fold-cross-validation (k-CV) method is also applied to construct the dataset. Furthermore, information of the tactile sensor array at different scales is used to construct the BPNN, which enhances the performance greatly. Numerical simulations show that the BPNN with strong nonlinear approximation ability plays an important role in decoupling mapping relationship between resistance and deformation of the tactile sensor, which significantly increases the decoupling accuracy and satisfies the real-time requirements of the multi-dimensional tactile sensor arra

Elevated THBS2, COL1A2, and SPP1 Expression Levels as Predictors of Gastric Cancer Prognosis

Background/Aims: Gastric cancer (GC) is an important health problem. Classification based on molecular subtypes may help to determine the prognosis of patients with GC. Tumor invasion and metastasis are important factors affecting the prognosis of cancer. We aimed to identify genes related to tumor invasion and metastasis, which may serve as indicators of good GC prognosis. Methods: Tumor tissues and adjacent normal tissues were collected from 105 patients with primary GC who were treated by undergoing radical surgery. Samples were used for tissue microarray analysis. Identified genes with altered expression were further analyzed using the Gene Ontology (Go) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The expression levels of THBS2, COL1A2 and SPP1 were analyzed by RT-PCR, western blot and immunohistochemistry. The overall survival curves of patients with high and low expression of each gene of interest were plotted and compared. Results: Forty-three genes were identified. THBS2, COL1A2 and SPP1 were selected for further analysis. Altered expression levels of THBS2, COL1A2 and SPP1 in tumor tissues were confirmed. Patients with low THBS2 expression had a better prognosis; the expression of COL1A2 and SPP1 might not affect the prognosis of patients with GC. Conclusion: THBS2, but not COL1A2 and SPP1, may serve as an indicator of GC prognosis

MRI feature-based radiomics models to predict treatment outcome after stereotactic body radiotherapy for spinal metastases

Abstract Objective This study aimed to extract radiomics features from MRI using machine learning (ML) algorithms and integrate them with clinical features to build response prediction models for patients with spinal metastases undergoing stereotactic body radiotherapy (SBRT). Methods Patients with spinal metastases who were treated using SBRT at our hospital between July 2018 and April 2023 were recruited. We assessed their response to treatment using the revised Response Evaluation Criteria in Solid Tumors (version 1.1). The lesions were categorized into progressive disease (PD) and non-PD groups. Radiomics features were extracted from T1-weighted image (T1WI), T2-weighted image (T2WI), and fat-suppression T2WI sequences. Feature selection involved intraclass correlation coefficients, minimal-redundancy-maximal-relevance, and least absolute shrinkage and selection operator methods. Thirteen ML algorithms were employed to construct the radiomics prediction models. Clinical, conventional imaging, and radiomics features were integrated to develop combined models. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, and the clinical value was assessed using decision curve analysis. Results We included 194 patients with 142 (73.2%) lesions in the non-PD group and 52 (26.8%) in the PD group. Each region of interest generated 2264 features. The clinical model exhibited a moderate predictive value (area under the ROC curve, AUC = 0.733), while the radiomics models demonstrated better performance (AUC = 0.745–0.825). The combined model achieved the best performance (AUC = 0.828). Conclusion The MRI-based radiomics models exhibited valuable predictive capability for treatment outcomes in patients with spinal metastases undergoing SBRT. Critical relevance statement Radiomics prediction models have the potential to contribute to clinical decision-making and improve the prognosis of patients with spinal metastases undergoing SBRT. Key points • Stereotactic body radiotherapy effectively delivers high doses of radiation to treat spinal metastases. • Accurate prediction of treatment outcomes has crucial clinical significance. • MRI-based radiomics models demonstrated good performance to predict treatment outcomes. Graphical Abstrac