Dynamic effect of economic growth on the persistence of suicide rates.

Positive and negative economic growth is closely related to the suicide rate. To answer the question whether economic development has a dynamic impact on this rate, we used a panel smooth transition autoregressive model to evaluate the threshold effect of economic growth rate on the persistence of suicide. The research period was from 1994 to 2020, and the results show that the suicide rate had a persistent effect, which varied over time depending on the transition variable within different threshold intervals. However, the persistent effect was manifested in different degrees with the change in the economic growth rate and as the lag period of the suicide rate increased, the effect of the influence gradually decreased. We investigated different lag periods and noted that the impact on the suicide rate was the strongest in the first year after an economic change and then reduced to be only marginal after three years. This means that the growth momentum of the suicide rate within the first two years after a change in the economic growth rate, should be included in policy considerations of how to prevent suicides.

Developing a Stacked Ensemble-Based Classification Scheme to Predict Second Primary Cancers in Head and Neck Cancer Survivors.

Despite a considerable expansion in the present therapeutic repertoire for other malignancy managements, mortality from head and neck cancer (HNC) has not significantly improved in recent decades. Moreover, the second primary cancer (SPC) diagnoses increased in patients with HNC, but studies providing evidence to support SPCs prediction in HNC are lacking. Several base classifiers are integrated forming an ensemble meta-classifier using a stacked ensemble method to predict SPCs and find out relevant risk features in patients with HNC. The balanced accuracy and area under the curve (AUC) are over 0.761 and 0.847, with an approximately 2% and 3% increase, respectively, compared to the best individual base classifier. Our study found the top six ensemble risk features, such as body mass index, primary site of HNC, clinical nodal (N) status, primary site surgical margins, sex, and pathologic nodal (N) status. This will help clinicians screen HNC survivors before SPCs occur.

Development of a Longitudinal Diagnosis and Prognosis in Patients with Chronic Kidney Disease: Intelligent Clinical Decision-Making Scheme.

Previous studies on CKD patients have mostly been retrospective, cross-sectional studies. Few studies have assessed the longitudinal assessment of patients over an extended period. In consideration of the heterogeneity of CKD progression. It's critical to develop a longitudinal diagnosis and prognosis for CKD patients. We proposed an auto Machine Learning (ML) scheme in this study. It consists of four main parts: classification pipeline, cross-validation (CV), Taguchi method and improve strategies. This study includes datasets from 50,174 patients, data were collected from 32 chain clinics and three special physical examination centers, between 2015 and 2019. The proposed auto-ML scheme can auto-select the level of each strategy to associate with a classifier which finally shows an acceptable testing accuracy of 86.17%, balanced accuracy of 84.08%, sensitivity of 90.90% and specificity of 77.26%, precision of 88.27%, and F1 score of 89.57%. In addition, the experimental results showed that age, creatinine, high blood pressure, smoking are important risk factors, and has been proven in previous studies. Our auto-ML scheme light on the possibility of evaluation for the effectiveness of one or a combination of those risk factors. This methodology may provide essential information and longitudinal change for personalized treatment in the future.

Shugosin 2 is a biomarker for pathological grading and survival prediction in patients with gliomas.

Glioblastomas are the most common type of adult primary brain neoplasms. Clinically, it is helpful to identify biomarkers to predict the survival of patients with gliomas due to its poor outcome. Shugoshin 2 (SGO2) is critical in cell division and cell cycle progression in eukaryotes. However, the association of SGO2 with pathological grading and survival in patients with gliomas remains unclear. We analyzed the association between SGO2 expression and clinical outcomes from Gene Expression Omnibus (GEO) dataset profiles, The Cancer Genome Atlas (TCGA), and Chinese Glioma Genome Atlas (CGGA). SGO2 mRNA and protein expression in normal brain tissue and glioma cell lines were investigated via quantitative RT-PCR, Western blot, and IHC staining. The roles of SGO2 in proliferation, migration, and apoptosis of GBM cells were studied with wound-healing assay, BrdU assay, cell cycle analysis, and JC-1 assay. The protein-protein interaction (PPI) was analyzed via Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). SGO2 mRNA expression predicted higher grade gliomas than non-tumor brain tissues. Kaplan-Meier survival analysis showed that patients with high-grade gliomas with a higher SGO2 expression had worse survival outcomes. SGO2 mRNA and protein expression were upper regulated in gliomas than in normal brain tissue. Inhibition of SGO2 suppressed cell proliferation and migration. Also, PPI result showed SGO2 to be a potential hub protein, which was related to the expression of AURKB and FOXM1. SGO2 expression positively correlates with WHO pathological grading and patient survival, suggesting that SGO2 is a biomarker that is predictive of disease progression in patients with gliomas.

Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT.

Early detection is important in glaucoma management. By using optical coherence tomography (OCT), the subtle structural changes caused by glaucoma can be detected. Though OCT provided abundant parameters for comprehensive information, clinicians may be confused once the results conflict. Machine learning classifiers (MLCs) are good tools for considering numerous parameters and generating reliable diagnoses in glaucoma practice. Here we aim to compare different MLCs based on Spectralis OCT parameters, including circumpapillary retinal nerve fiber layer (cRNFL) thickness, Bruch's membrane opening-minimum rim width (BMO-MRW), Early Treatment Diabetes Retinopathy Study (ETDRS) macular thickness, and posterior pole asymmetry analysis (PPAA), in discriminating normal from glaucomatous eyes. Five MLCs were proposed, namely conditional inference trees (CIT), logistic model tree (LMT), C5.0 decision tree, random forest (RF), and extreme gradient boosting (XGBoost). Logistic regression (LGR) was used as a benchmark for comparison. RF was shown to be the best model. Ganglion cell layer measurements were the most important predictors in early glaucoma detection and cRNFL measurements were more important as the glaucoma severity increased. The global, temporal, inferior, superotemporal, and inferotemporal sites were relatively influential locations among all parameters. Clinicians should cautiously integrate the Spectralis OCT results into the entire clinical picture when diagnosing glaucoma.

Clinical Significance and Systematic Expression Analysis of the Thyroid Receptor Interacting Protein 13 (TRIP13) as Human Gliomas Biomarker.

The prognosis of malignant gliomas such as glioblastoma multiforme (GBM) has remained poor due to limited therapeutic strategies. Thus, it is pivotal to determine prognostic factors for gliomas. Thyroid Receptor Interacting Protein 13 (TRIP13) was found to be overexpressed in several solid tumors, but its role and clinical significance in gliomas is still unclear. Here, we conducted a comprehensive expression analysis of TRIP13 to determine the prognostic values. Gene expression profiles of the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and GSE16011 dataset showed increased TRIP13 expression in advanced stage and worse prognosis in IDH-wild type lower-grade glioma. We performed RT-PCR and Western blot to validate TRIP13 mRNA expression and protein levels in GBM cell lines. TRIP13 co-expressed genes via database screening were regulated by essential cancer-related upstream regulators (such as TP53 and FOXM1). Then, TCGA analysis revealed that more TRIP13 promoter hypomethylation was observed in GBM than in low-grade glioma. We also inferred that the upregulated TRIP13 levels in gliomas could be regulated by dysfunction of miR-29 in gliomas patient cohorts. Moreover, TRIP13-expressing tumors not only had higher aneuploidy but also tended to reduce the ratio of CD8+/Treg, which led to a worse survival outcome. Overall, these findings demonstrate that TRIP13 has with multiple functions in gliomas, and they may be crucial for therapeutic potential.

Overexpression of Cell-Surface Marker SLC16A1 Shortened Survival in Human High-Grade Gliomas.

Solute carrier family 16 member 1 (SLC16A1) is a crucial transcription factor in modifying cancer progression and metastasis. However, its character in defining the clinical prognosis of human gliomas has not been illuminated. In our analysis from PREdiction of Clinical Outcomes from Genomic Profiles (PRECOG), The Cancer Genome Atlas (TCGA), and Chinese Glioma Genome Atlas (CGGA), we found that SLC16A1 mRNA expression level was significantly increased in high-grade gliomas in contrast to low-grade gliomas and non-tumor controls (P < 0.05). Kaplan-Meier analysis of four independent cohort studies from the Gene Expression Omnibus (GEO) profile, TCGA, and CGGA which consistently presented patients with high SLC16A1 mRNA expression displayed poor overall survival in high-grade glioma patients (P < 0.05 by log-rank test). Based on the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), the protein-protein interaction analysis of SLC16A1-regulated oncogenesis showed SLC16A1 as a potential hub protein. Immunohistochemical staining exhibited that SLC16A1 protein overexpressed in high-grade gliomas compared with low-grade clinical glioma samples. All these findings suggest that SLC16A1 expression has a positive correlation with WHO pathological grading and poor survival. SLC16A1 might be a potential biomarker of prognosis in human gliomas.

Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Breast Cancer Survivors.

Due to the high effectiveness of cancer screening and therapies, the diagnosis of second primary cancers (SPCs) has increased in women with breast cancer. The present study was conducted to develop a novel machine learning-based classification scheme for predicting the risk factors of SPCs in breast cancer survivors. The proposed scheme was based on the XGBoost classifier with the following four comparable strategies: transformation, resampling, clustering, and ensemble learning, to improve the training balanced accuracy. Results suggested that the best prediction accuracy for an empirical case is the XGBoost associated with the strategies of resampling and clustering. The experimental results showed that age, sequence of radiotherapy and surgery, surgical margins of the primary site, human epidermal growth factor, high-dose clinical target volume, and estrogen receptors are relatively more important risk factors associated with SPCs in patients with breast cancer. These risk factors should be monitored for the early detection of breast cancer. In conclusion, the proposed scheme can support the important influence of personality and clinical symptom representations in all phases of the primary treatment trajectory. Our results further suggested that adaptive machine learning techniques require the incorporation of significant variables for optimal predictions.

Superoxide dismutase as a novel macromolecular nitric oxide carrier: preparation and characterization.

Nitric oxide (NO) is an important molecule that exerts multiple functions in biological systems. Because of the short-lived nature of NO, S-nitrosothiols (RSNOs) are believed to act as stable NO carriers. Recently, sulfhydryl (SH) containing macromolecules have been shown to be promising NO carriers. In the present study, we aimed to synthesize and characterize a potential NO carrier based on bovine Cu,Zn-superoxide dismutase (bSOD). To prepare S-nitrosated bSOD, the protein was incubated with S-nitrosoglutathione (GSNO) under varied experimental conditions. The results show that significant S-nitrosation of bSOD occurred only at high temperature (50 °C) for prolonged incubation time (>2 h) S-nitrosation efficiency increased with reaction time and reached a plateau at ~4 h. The maximum amount of NO loaded was determined to be about 0.6 mol SNO/mol protein (~30% loading efficiency). The enzymatic activity of bSOD, however, decreased with reaction time. Our data further indicate that NO functionality can only be measured in the presence of extremely high concentrations of Hg2+ or when the protein was denatured by guanidine. Moreover, mildly acidic pH was shown to favor S-nitrosation of bSOD. A model based on unfolding and refolding of bSOD during preparation was proposed to possibly explain our observation.