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The continuous upsurge in worldwide economic development and human activities has intensified CO2 emissions that highlighted the significant role of eco-innovation and green investment in curbing CO2 emissions. The study aims to explore the impact of eco-innovation and green investment on CO2 emissions by using the China dataset for time period 1990-2019. The study adopts the ARDL approach. The study used two proxies to determine the impact of eco-innovation, namely environment-related technologies and patents. The empirical estimates of the ARDL approach confirm the negative impact of eco-innovation and green investment on CO2 emissions confirming that these determinants result in limiting CO2 emissions in China. Based on these findings, the study suggests strengthening environmentally friendly policies and the advancement of green investment to mitigate CO2 emissions.

Recent studies have proposed that pyruvate dehydrogenase E1 component subunit alpha (PDHA1), a cuproptosis-key gene, is crucial to the glucose metabolism reprogram of tumor cells. However, the functional roles and regulated mechanisms of PDHA1 in multiple cancers are largely unknown. The Cancer Genome Atlas (TCGA), GEPIA2, and cBioPortal databases were utilized to elucidate the function of PDHA1 in 33 tumor types. We found that PDHA1 was aberrantly expressed in most cancer types. Lung adenocarcinoma (LUAD) patients with high PDHA1 levels were significantly correlated with poor prognosis of overall survival (OS) and first progression (FP). Kidney renal clear cell carcinoma (KIRC) patients with low PDHA1 levels displayed poor OS and disease-free survival (DFS). However, for stomach adenocarcinoma (STAD), the downregulated PDHA1 expression predicted a good prognosis in patients. Moreover, we evaluated the mutation diversity of PDHA1 in cancers and their association with prognosis. We also analyzed the protein phosphorylation and DNA methylation of PDHA1 in various tumors. The PDHA1 expression was negatively correlated with tumor-infiltrating immune cells, such as myeloid dendritic cells (DCs), B cells, and T cells in pan-cancers. Mechanically, we used single-cell sequencing to discover that the PDHA1 expression had a close link with several cancer-associated signaling pathways, such as DNA damage, cell invasion, and angiogenesis. At last, we conducted a co-expressed enrichment analysis and showed that aberrantly expressed PDHA1 participated in the regulation of mitochondrial signaling pathways, including oxidative phosphorylation, cellular respiration, and electron transfer activity. In summary, PDHA1 could be a prognostic and immune-associated biomarker in multiple cancers.

Glioma is a highly malignant brain tumor with a poor survival rate. Novel biomarkers that act as prompt indicators of glioma are urgently needed. In this study, we identified and validated prognosis-related differentially expressed genes by datasets of glioma in the GEO and TCGA databases. Ferroptosis is a newly recognized process of cell death playing a vital role in cancer biology. Pearson correlation coefficient were used to discovery the prognosis-related genes which have the highest correlation with ferroptosis. Matrix remodeling-associated protein 8 (MXRA8) was identified as a novel prognosis indicator which may be involved in ferroptosis. The expression of MXRA8 was significantly higher in glioma compared with normal brain tissue, and increased expression of MXRA8 was associated with unfavorable survivals. Furthermore, in vitro analysis showed that knockdown of MXRA8 inhibited the cell viability in T98G and U251 cells and increased the sensitivity of glioma cells to temozolomide. We further observed that downregulation of MXRA8 elevated the levels of intracellular ferrous iron and lipid peroxidation, accompanied by upregulation of NCOA4 and suppression of FTH1. Moreover, co-expression analyses showed that GO term and KEGG pathways were mainly enriched in immunity-related pathways, such as neutrophil-related immunity, adaptive immune response, and cytokine binding. Through ssGSEA algorithm and TISIDB database, immunological analyses showed that MXRA8 was significantly correlated with various immune infiltration cells including NK cells, macrophages, and neutrophils. Meanwhile, MXRA8 was also associated with chemokines and multiple immunoinhibitory molecules, such as TGF-β1, IL-10, PD-L1, and CTLA4. We also found that MXRA8 was positively associated with immune infiltration score, and patients with higher immune score underwent worse overall survivals. Moreover, IHC staining indicated a highly positive correlation of MXRA8 with a macrophage marker CSF1R. The co-cultured models of glioma cells and M2 macrophages showed MXRA8 knockdown glioma cells alleviated the infiltration of M2 macrophage, while the reduced M2 macrophage infiltration generated by MXRA8 could be rescued by Fer-1 treatment. These results suggest that MXRA8 promotes glioma progression and highlight the pivotal role of MXRA8 in ferroptosis and immune microenvironment of glioma. Therefore, MXRA8 may serve as a novel prognostic marker and therapeutic target for glioma.

PurposeGlioma is the most common primary tumor in the brain, accounting for 81% of intracranial malignancies. Nowadays, cancer immunotherapy has become a novel and revolutionary treatment for patients with advanced, highly aggressive tumors. However, to date, there are no effective biomarkers to reflect the response of glioma patients to immunotherapy. In this study, we aimed to assess the clinical predictive value of ITGB2 in patients with glioma.MethodsThe correlation between ITGB2 expression levels and glioma progression was explored and validated using data from CGGA, TCGA, GEO datasets, and patient samples from our hospital. Univariate and multivariate cox regression models were developed to determine the predictive role of ITGB2 on the prognosis of patients with glioma. The relationship between ITGB2 and immune activation was then analyzed. Finally, we predicted the immunotherapy response in both high and low ITGB2 expression subgroups.ResultsITGB2 was significantly elevated in gliomas with higher malignancy and predicted poor prognosis. In multivariate analysis, the hazard ratio for ITGB2 expression (low versus high) was 0.71 with 95% CI (0.59–0.85) (P < 0.001). Furthermore, we found that ITGB2 stratified glioma patients into high and low ITGB2 expression subgroups, exhibiting different clinical outcomes and immune activation status. At last, we demonstrated that glioma patients with high ITGB2 expression levels had better immunotherapy response.ConclusionsThis study demonstrated ITGB2 as a novel predictor for clinical prognosis and response to immunotherapy in gliomas. Assessing expression levels of ITGB2 is a promising method to discover patients that may benefit from immunotherapy.

The strategic optimization of a container terminal’s quayside assets, including the berth and quay cranes, is crucial for maximizing their deployment and utilization. The interrelated and complex challenges of Berth Allocation (BAP) and Quay Crane Scheduling (QCSP) are fundamental to enhancing the resilience of container ports, as berths and quay cranes constitute essential infrastructure. Efficient berth allocation and quay crane scheduling can mitigate operational disruptions, even in the face of maintenance or failures, thereby improving both operational reliability and resilience. However, previous studies have often overlooked the uncertainty associated with quay crane maintenance when planning these operations. This paper aims to minimize vessel turnaround time by accounting for the uncertain in quay crane maintenance activities. To address this novel problem, we propose a proactive-reactive method that incorporates a reliability-based model into the Swarm Optimization with Differential Evolution (SWO-DE) algorithm. Computational results confirm the practical relevance and effectiveness of our proposed solution methods for container terminals.

Background Studies have shown a strong correlation between excess iron and the development of gestational diabetes mellitus (GDM), though iron is an essential trace element during pregnancy. This study aims to investigate the precise relationship between iron storage levels during late pregnancy and the development of GDM, trying to find out ways to meet pregnant iron storage requirements and reduce GDM risk simultaneously. Methods A non-anemic population consisting of 9,512 healthy singleton pregnant women were included in this study. Serum ferritin (SF) levels during the second and third trimesters and other clinical information were retrospectively collected. Restricted cubic splines (RCS) were performed to examined the non-linear associations between SF level and the GDM incidence as well as blood glucose related indicators during the second trimester. Moreover, the association between the variation of HbA1c levels and the fluctuation of SF levels throughout the third trimester was also explored with the method of RCS. Results Overall, women with GDM had slightly higher median SF level than women without GDM 20.5 (13.3, 32.3) vs. 19.8 (12.9, 30.5), P  = 0.017) in the second trimester. A U-shaped relationship between GDM risk and SF levels in the second trimester was established after accounting for other cofounding factors ( P  < 0.001 for nonlinearity). Both GDM and non-GMD women revealed a significant negative relationship between hemoglobin A1c (HbA1c) and SF levels ( P  < 0.001 for nonlinearity for both). The 1-hour post-glucose load plasma glucose showed a positive correlation tendency with SF levels ( P  = 0.748 for nonlinearity) in GDM women, while the relationship between these two variables was not obvious in non-GDM women ( P  = 0.045 for nonlinearity). Generally, the levels of HbA1c rose in the trimester, however, maintaining a high SF level throughout the third trimester would substantially increase the HbA1c level among GDM women with high SF levels (> 30ng/ml) in the second trimester ( P  < 0.001 for nonlinearity). Conclusions GDM might result from high or low SF levels during the second trimester. Iron supplementation during pregnancy should be administered judiciously based on blood glucose level and iron storage capacity to maintain the SF level within an appropriate range.

Rapid urbanization and growth of population in megacities generate severe pressures on urban rail transit (URT) system. The quantity and frequency of disruptive events have increased significantly, which might have obvious adverse impacts. A large number of passengers are stranded at disrupted URT station when a disruptive event occurs. One essential solution for passenger evacuation is the bus bridging service. This paper is aimed at addressing the passenger evacuation problem caused by a disruptive event in the URT network, by proposing a bus bridging service model considering the passengers’ space-time requirements. The model is proposed to minimize the waiting time of passengers and considers factors including bus service capacity limitations, bus stop parking capacity, and the maximum bridging time limit of a single bus. Buses are assumed to provide bridging service on either the local bus route or the direct bus route. The optimal routes and scheduling plans of bridging bus are designed. The model is applied to an example of a disruptive event in Shanghai URT line 9. The results of this example show that the proposed model is capable of reducing the waiting time of passengers and the number of buses used by 3.2% and 24.7%, compared with the traditional bus bridging service. Further analysis of the example shows that it is not a cost-effective solution to reserve a large number of buses for URT disruption. Decision-makers should comprehensively trade off between passengers’ space-time demands and monetary costs of bus bridging service.

Mid-infrared spectral analysis has long been recognized as the most accurate noninvasive blood glucose measurement method, yet no practical compact mid-infrared blood glucose sensor has ever passed the accuracy benchmark set by the USA Food and Drug Administration (FDA): to substitute for the finger-pricking glucometers in the market, a new sensor must first show that 95% of their glucose measurements have errors below 15% of these glucometers. Although recent innovative exploitations of the well-established Fourier-transform infrared (FTIR) spectroscopy have reached such FDA accuracy benchmarks, an FTIR spectrometer is too bulky. The advancements of quantum cascade lasers (QCLs) can lead to FTIR spectrometers of reduced size, but compact QCL-based noninvasive blood glucose sensors are not yet available. This work reports on two compact sensor system designs, both reaching the FDA accuracy benchmark. Each design commonly comprises a mid-infrared QCL for emission, a multiple attenuation total reflection prism (MATR) for data acquisition, and a computer-controlled infrared detector for data analysis. The first design translates the comb-like signals into conventional spectra, and then data-mines the resultant spectra to yield blood glucose concentrations. When a pressure actuator is employed to press the patient’s hypothenar against the MATR, the sensor accuracy is considered to reach the FDA accuracy benchmark. The second design abandons the data processing step of translating combs-to-spectra and directly data-mines the “first-hand” comb signal. Beyond increasing the measurement accuracy to the FDA accuracy benchmark, even without a pressure actuator, direct comb data-mining upgrades the sensor system with speed and data integrity, which can impact the healthcare of diabetic patients. Specifically, the sensor performance is validated with 492 glucose absorption scans in the time domain, each with 20 million datapoints measured from four subjects with glucose concentrations of 3.9–7.9 mM. The sensor data-mines 164 sets of critical singularity strengths, each comprising 4 critical singularity strengths directly from the 9840 million raw signal datapoints, and the 656 critical singularity strengths are subjected to a machine-learning regression model analysis, which yields 164 glucose concentrations. These concentrations are correlated with those measured with a standard finger-pricking glucometer. An accuracy of 99.6% is confirmed from the 164 measurements with errors not more than 15% from the reference of the standard glucometer.

Recent studies have suggested that ferroptosis, a form of iron-dependent regulated cell death, might play essential roles in tumor initiation and progression. Six-transmembrane epithelial antigen of prostate 3 (STEAP3) is a ferrireductase involved in the regulation of intracellular iron homeostasis. However, the clinical significance and biological function of STEAP3 in human cancers remain poorly understood. Through a comprehensive bioinformatics analysis, we found that STEAP3 mRNA and protein expression were up-regulated in GBM, LUAD, and UCEC, and down-regulated in LIHC. Survival analysis indicated that STEAP3 had prognostic significance only in glioma. Multivariate Cox regression analysis revealed that high STEPA3 expression was correlated with poor prognosis. STEAP3 expression was significantly negatively correlated with promoter methylation level, and patients with lower STEAP3 methylation level had worse prognosis than those with higher STEAP3 methylation level. Single-cell functional state atlas showed that STEAP3 regulated epithelial-to-mesenchymal transition (EMT) in GBM. Furthermore, the results of wound healing and transwell invasion assays demonstrated that knocking down STEAP3 inhibited the migration and invasion of T98G and U251 cells. Functional enrichment analysis suggested that genes co-expressed with STEAP3 mainly participated in inflammation and immune-related pathways. Immunological analysis revealed that STEAP3 expression was significantly correlated with immune infiltration cells, including macrophages and neutrophils, especially the M2 macrophages. Individuals with low STEAP3 expression were more likely to respond to immunotherapy than those with high STEAP3 expression. These results suggest that STEAP3 promotes glioma progression and highlight its pivotal role in regulating immune microenvironment.

To combine pharmacovigilance and network toxicology methods to observe the acute pancreatitis (AP) following the use of antipsychotics, and potential toxic mechanisms, and to provide a reference for the safe use of drugs. This study combined pharmacovigilance methods using real-world data and network toxicology methods to investigate AP associated with antipsychotics and the potential toxicological mechanism involved. First, the reports of antipsychotics were extracted from the US FDA Adverse Event Reporting System (FAERS), and the signals of AP were detected by four pharmacovigilance algorithms. The gene targets of drugs were predicted using multiple databases. The disease targets of AP were determined by bioinformatics methods. Protein-protein interaction (PPI) analysis was conducted using STRING database, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis were also performed through R software. Molecular docking was applied to test the molecular affinity using AutoDock. The signal intensity of AP was statistically significant in olanzapine, quetiapine, and fluphenazine. Due to the small number of reports associated with AP AEs on fluphenazine, our subsequent studies mainly focused on olanzapine and quetiapine. The results of stratification analysis suggested robustness of our results. Age ≤65, female, and weight >80 kg were identified as risk factors of the development of AP in patients receiving olanzapine, while weight >80 kg and age ≤65 were risk factors of that in patients receiving quetiapine. Network toxicology analysis and molecular docking suggested that olanzapine and quetiapine may exert their toxic effects through acting on hub genes. The pharmacovigilance analysis investigated the signal intensity, clinical features, risk factors, and onset time of AP associated with olanzapine and quetiapine. Network toxicology analysis suggested that the toxic effects of olanzapine and quetiapine may be related to their hub genes.