Refusal of treatment among HER2-positive breast cancer patients in China: a retrospective analysis

BackgroundThere is a need to update the understanding of treatment refusal among cancer patients in China, taking into account recent developments. This study investigated how public insurance coverage of the first breast cancer targeted therapy contributed to the changes in treatment refusal among HER2-positive breast cancer patients in China. And it intensively examined and discussed additional barriers affecting patient utilization of innovative anticancer medicines based on the types and reasons for treatment refusal.MethodsThis retrospective study included female breast cancer patients diagnosed as HER2-positive who received treatment at a provincial oncology center in southern China between 2014 and 2020. Multivariable analysis was conducted using a binary logistic regression model. Subgroup analysis was performed with the same regression model.ResultsAmong the 1,322 HER2-positive breast cancer patients who received treatment at the study hospital between 2014 and 2020, 327 (24.55%) had ever refused treatment. Economic reasons were reported as the primary cause by 142 patients (43.43%). Patients diagnosed after September 2017, when the first breast cancer targeted therapy was included in the public health insurance, were less likely to refuse treatment (OR = 0.64, 95% CI:0.45 ~ 0.91, p = 0.01) compared to those diagnosed before September 2017. Patients enrolled in the resident health insurance were more likely to refuse treatment (OR = 2.43, 95% CI:1.77 ~ 3.35, p < 0.001) than those enrolled in the employee health insurance.ConclusionThis study reveals a high rate of treatment refusal among HER2-positive breast cancer patients, primarily attributed to financial factors. The disparity in public health insurance benefits resulted in a heavier economic burden for patients with less comprehensive benefits. Furthermore, the study identified challenges faced by patients seeking quality-assured cancer care in underdeveloped regions in China. By addressing economic barriers, promoting accurate health information, and improving cancer care capacity across the country can reduce the rate of treatment refusal

Illustrating the biological functions and diagnostic value of transmembrane protein family members in glioma

BackgroundIt is well-established that patients with glioma have a poor prognosis. Although the past few decades have witnessed unprecedented medical advances, the 5-year survival remains dismally low.ObjectiveThis study aims to investigate the role of transmembrane protein-related genes in the development and prognosis of glioma and provide new insights into the pathogenesis of the diseaseMethodsThe datasets of glioma patients, including RNA sequencing data and relative clinical information, were obtained from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) databases. Prognostic transmembrane protein-related genes were identified by univariate Cox analysis. New disease subtypes were recognized based on the consensus clustering method, and their biological uniqueness was verified via various algorithms. The prognosis signature was constructed using the LASSO-Cox regression model, and its predictive power was validated in external datasets by receiver operating characteristic (ROC) curve analysis. An independent prognostic analysis was conducted to evaluate whether the signature could be considered a prognostic factor independent of other variables. A nomogram was constructed in conjunction with traditional clinical variables. The concordance index (C-index) and Decision Curve Analysis (DCA) were used to assess the net clinical benefit of the signature over traditional clinical variables. Seven different softwares were used to compare the differences in immune infiltration between the high- and low-risk groups to explore potential mechanisms of glioma development and prognosis. Hub genes were found using the random forest method, and their expression was based on multiple single-cell datasets.ResultsFour molecular subtypes were identified, among which the C1 group had the worst prognosis. Principal Component Analysis (PCA) results and heatmaps indicated that prognosis-related transmembrane protein genes exhibited differential expression in all four groups. Besides, the microenvironment of the four groups exhibited significant heterogeneity. The 6 gene-based signatures could predict the 1-, 2-, and 3-year overall survival (OS) of glioma patients. The signature could be used as an independent prognosis factor of glioma OS and was superior to traditional clinical variables. More immune cells were infiltrated in the high-risk group, suggesting immune escape. According to our signature, many genes were associated with the content of immune cells, which revealed that transmembrane protein-related genes might influence the development and prognosis of glioma by regulating the immune microenvironment. TMEM158 was identified as the most important gene using the random forest method. The single-cell datasets consistently showed that TMEM158 was expressed in multiple malignant cells.ConclusionThe expression of transmembrane protein-related genes is closely related to the immune status and prognosis of glioma patients by regulating tumor progression in various ways. The interaction between transmembrane protein-related genes and immunity during glioma development lays the groundwork for future studies on the molecular mechanism and targeted therapy of glioma

Charging load forecasting of electric vehicles based on sparrow search algorithm‐improved random forest regression model

Abstract In order to solve the problem that the current charging load forecasting accuracy is not high, it is difficult to simulate the actual charging load distribution of Electric Vehicles (EVs), and it is impossible to reasonably predict the future load, a charging load forecasting model based on Sparrow Search Algorithm (SSA) improved Random Forest Regression (RFR) is proposed. The SSA is used to enhance the ability of global optimization and local exploration. Combined with the advantages of the RFR model, such as low generalization error, fast convergence speed, and few adjustment parameters, the SSA was used to optimize the parameters of the decision tree number and the number of split nodes in the RFR, and the optimal value of the parameters is obtained, so as to obtain the optimal performance of the RFR. Firstly, based on the concept of travel chain and conditional probability distribution, the user's travel habits are described. Monte Carlo simulation method was used to simulate the driving, parking, and charging behaviours of a large number of EVs in different regions, so as to obtain the charging load of EVs in different regions. Then, a charging load forecasting model based on SSA improved RFR is established. Monte Carlo simulation results are used as sample data to predict the charging load of EVs in different regions. Finally, taking a certain area as an example, the experimental results show that the charging load prediction model based on Sparrow Search Algorithm improved Random Forest Regression (SSA‐RFR) can accurately predict the charging load of EVs in different regions, and the charging load of different regional types is obviously different. Compared with the RFR model and other literature models, the SSA‐RFR model has better prediction accuracy, which verifies the feasibility and superiority of SSA‐RFR model in EVs charging load prediction

Sodium Iodate Influences the Apoptosis, Proliferation and Differentiation Potential of Radial Glial Cells In Vitro

Background/Aims: Sodium iodate (NaIO3)-induced acute retinal injury is typically used as an animal model for degenerative retinal disease; however, how NaIO3 influences the apoptosis, proliferation and differentiation of endogenous retinal stem cells is unknown. Methods: We exposed a radial glial cells (RGCs) line (L2.3) to different NaIO3 concentrations and determined the influence of NaIO3 on apoptosis, proliferation, and differentiation using flow cytometry and immunofluorescence assays. We used a real-time polymerase chain reaction assay to analyze the levels of mRNAs encoding GSK-3β, AXIN2, β-catenin, TGF-β1, SMAD2, SMAD3, NOG (Noggin), and BMP4. Results: Cell density decreased dramatically as a function of the NaIO3 dose. NaIO3 increased apoptosis, inhibited mitosis, proliferation, and the Wnt/β-catenin pathway. CHIR99021 (Wnt agonist) treatment efficiently reversed the effects of NaIO3 on the apoptosis and proliferation of RGCs. The number of neuronal class III β-tubulin-positive cells decreased markedly, whereas that of glial fibrillary acidic protein-positive cells increased significantly when RGCs were exposed to NaIO3. During differentiation, the Nog mRNA level decreased and transforming growth factor-β1 (Tgf-β1) and Smad2/3 mRNA levels increased significantly when RGCs were exposed to NaIO3. Conclusion: NaIO3 increased apoptosis, influenced the proliferation of RGCs and drove them toward astrocytic differentiation, likely through inhibition of the Wnt/β-catenin and noggin pathways and activation of the TGF-β1/SMAD2/3 pathway

Down-Regulation of MiR-30c Promotes the Invasion of Non-Small Cell Lung Cancer by Targeting MTA1

Background: The connection between microRNA expression and lung cancer development has been identified in recent literature. However, the mechanism of microRNA has been poorly elucidated in non-small-cell lung cancer (NSCLC). Methods and Results: Comparing with adjacent tissues (n=75), miR-30c has a lower expression in lung cancer specimens (n=75). The knockdown of miR-30c enhanced the invasion of A549 cells; meanwhile, the overexpression of miR-30c could reverse the effect of the knockdown of miR-30c in vitro. A luciferase assay revealed that miR-30c was directly bound to the 3‘-untranslated regions (3‘-UTR) of MTA1. QRT-PCR and western blot shows MTA1 was up-regulated in mRNA and protein levels. The effect taken on the invasion of NSCLC by overexpression of MTA1 works the same as down-regulated miR-30c. Conclusion: miR-30c may play a pivotal role in controlling lung cancer invasion through regulating MTA1in NSCLC

Auditory Brainstem Responses in Senile Presbycusis Patients over 90 years

AbstractObjectiveTo analyze the characteristics of auditory brainstem response (ABR) in presbycusis patients elder than 90 years.MethodsFourteen presbycusis patients elder than 90 years (presbycusis group, 91.1.4± 1.3 years, 26 ears) and 9 normal-hearing young adults (control group, 22.7±1.2 years, 18 ears) participated in the study. Alternative click-evoked ABRs were recorded in both groups. The peak latency (PL) of peak I, III, and V, and the inter-peak latency (IPI) of I-III, III-V, and I-V were compared between groups.ResultsIn elder presbycusis patients, the occurrence rate of peak I and III were both 76.9%, and that of peak V was 84.6%. In presbycusis group, the peak latencies of I, III, V were significantly longer than that of control group (P<0.001). There was no significant difference between groups in the IPI of peak I-I III (P=0.298, peak III-V (P=0.254) and peak I-V (P=0.364).ConclusionsAuditory brainstem responses in presbycusis patients elder than 90 years showed worse wave differentiatio

Association between Plasma HMGB-1 and Silicosis: A Case-Control Study

High-mobility group box-1 (HMGB-1) has been associated with fibrotic diseases. However, the role of HMGB-1 in silicosis is still uncertain. In this study, we conducted a case-control study involving 74 patients with silicosis and 107 age/gender-matched healthy controls in China. An Enzyme-linked immunosorbent assay (ELISA) was used to examine the concentrations of plasma HMGB-1 among all subjects. A logistic regression model and receiver operating characteristic curve (ROC) analysis were performed to assess the relationships between HMGB-1 and silicosis. We observed that plasma HMGB-1 concentrations were significantly increased in silicosis patients when compared with healthy controls (p &lt; 0.05). Each 1 ng/mL increase in plasma HMGB-1 was positively associated with increased odds of silicosis, and the odds ratio (OR) (95% confidence interval) was 1.86 (1.52, 2.27). Additionally, compared with subjects with lower HMGB-1 concentrations, increased odds of silicosis were observed in those with higher HMGB-1 concentrations, and the OR was 15.33 (6.70, 35.10). Nonlinear models including a natural cubic spline function of continuous HMGB-1 yielded similar results. In ROC analyses, we found that plasma HMGB-1 &gt;7.419 ng/mL had 81.6% sensitivity and 80.4% specificity for silicosis, and the area under the curve (AUC) was 0.84. Our results demonstrated that elevated plasma HMGB-1 was positivity associated with increased OR of silicosis

Rapid Production Biofloc by Inoculating Chlorella pyrenoidosa in a Separate Way

Microalgae play an important role in the formation of biofloc. To demonstrate the feasibility of Chlorella pyrenoidosa in biofloc formation, an experiment was performed with a simple random design consisting of five inoculation levels (in triplicate) of C. pyrenoidosa (0, 1 &times; 108, 1 &times; 109, 5 &times; 109, and 1 &times; 1010 cells&middot;L&minus;1) in the biofloc system. All treatments kept a C:N ratio of approximately 15:1. This study observed the effects of different initial concentrations of C. pyrenoidosa on biofloc formation, water quality and bacterial community in biofloc systems. The results indicated that C. pyrenoidosa had the ability to enhance biofloc development, especially when the C. pyrenoidosa initial concentration reached 5~10 &times; 109 cells&middot;L&minus;1. Too high or too low a concentration of C. pyrenoidosa will adversely affect the formation of biofloc. The effect of C. pyrenoidosa addition on water quality (TAN, NO2&minus;-N, and NO3&minus;-N) was not significant in the final stage. The inoculation of C. pyrenoidosa decreased the species richness and diversity of the bacterial community but increased the domination of Proteobacteria and Bacteroidota in the biofloc system, especially the order of Rhizobiales. The addition of C. pyrenoidosa could maintain water quality by increasing the proportion of several denitrifying bacteria, including Flavobacterium, Chryseobacterium, Pseudomonas, Brevundimonas, Xanthobacter, etc. These above dominant denitrifying bacteria in the biofloc system could play a major role in reducing the concentration of NO2&minus;-N and NO3&minus;-N. So, we recommended the reasonable concentration is 5~10 &times; 109 cells&middot;L&minus;1 if C. pyrenoidosa is used to rapidly produce biofloc