Assessment of tumor-associated immune cells in laryngeal squamous cell carcinoma.

PURPOSE: This study investigated the characteristics of tumor-associated immune cells (TAICs) in laryngeal squamous cell carcinoma (LSCC) and their correlation with clinicopathological variables. METHODS: The immune cell infiltrates of 71 specimens of stages I-IV LSCC were examined. The density of TAICs expressing CD3, CD4, CD8, CD68, and CD163 was assessed using immunohistochemical staining and image analysis in peritumoral and intratumoral regions. RESULTS: Higher densities of CD3+ and CD8+ cell and lower densities of CD68+ and CD163+ cell infiltrations were found in early tumor stages than in late tumor stages. A higher percentage of patients with strong CD3+ and CD8+ immune cell infiltration and weak CD68+ cell infiltration in both tumor regions presented with T1 stage tumors compared with T4 stage tumors. Further, strong CD68+ cells infiltration in both regions was observed in a greater number of patients who had a relapse, while a weak CD3+ cells infiltration in both regions was found in a greater number of patients with nodal lymphatic metastasis. The univariate analysis showed that a high density of peritumoral CD3+ and CD8+ immune cells in both regions was significantly associated with a favorable overall survival (OS) (P = 0.004; P = 0.006; P = 0.042). In contrast, a high density of intratumoral CD68+ cells and peritumoral CD163+ cells was significantly associated with poor OS durations (P = 0.026; P = 0.030). The multivariate analysis demonstrated that a high density of peritumoral CD163+ cells correlated with poor OS after adjusting for tumor stage, recurrence, and nodal lymphatic metastasis (P = 0.034). This study found different patterns of TAIC infiltration in LSCC. The density and location of TAICs infiltration correlated with the clinicopathological characteristics of LSCC. CONCLUSION: A combined analysis of the density of TAICs and their location may help predict patient survival and response to checkpoint inhibitors.

An Empirical Study on Low-Carbon: Human Resources Performance Evaluation.

Low-carbon logistics meets the requirements of a low-carbon economy and is the most effective operating model for logistic development to achieve sustainability by coping with severe energy consumption and global warming. Low-carbon logistics aims to reduce carbon intensity rather than simply reduce energy consumption and carbon emissions. Human resources are an important part of the great competition in the logistics market and significantly affect the operations of enterprises. Performance evaluations of human resources are particularly important for low-carbon logistics enterprises with scarce talents. Such evaluations in these enterprises are of great significance for their strategic development. This study constructed a human resource performance evaluation system to assess non-managerial employees' low-carbon job capacity, job performance, and job attitude in the low-carbon logistics sector. The case study results revealed that the investigated company enjoyed initial success after having promoted low-carbon concepts and values to its non-managerial employees, and the success was demonstrated by excellent performance in its employees' job attitude and knowledge. This study adopts the AHP method to reasonably determine an indicator system of performance evaluation and its weight to avoid certain human-caused bias. This study not only fills the gap in the related literature, but can also be applied to industrial practice.

Combining FMEA with DEMATEL models to solve production process problems.

Failure mode and effects analysis (FMEA) is an analysis tool for identifying and preventing flaws or defects in products during the design and process planning stage, preventing the repeated occurrence of problems, reducing the effects of these problems, enhancing product quality and reliability, saving costs, and improving competitiveness. However, FMEA can only analyze one influence factor according to its priority, rendering this method ineffective for systems containing multiple FMs whose effects are simultaneous or interact with one another. Accordingly, when FMEA fails to identify the influence factors and the factors being influenced, the most crucial problems may be placed in lower priority or remain unresolved. Decision-Making Trial and Evaluation Laboratory (DEMATEL) facilitates the determination of cause and effect factors; by identifying the causal factors that should be prioritized, prompt and effective solutions to core problems can be derived, thereby enhancing performance. Using the photovoltaic cell manufacturing industry in China as the research target, the present study combined FMEA with DEMATEL to amend the flaws of FMEA and enhance its effectiveness. First, FMEA was used to identify items requiring improvement. Then, DEMATEL was employed to examine the interactive effects and causal relationships of these items. Finally, the solutions to the problems were prioritized. The proposed method effectively combined the advantages of FMEA and DEMATEL to facilitate the identification of core problems and prioritization of solutions in the Chinese photovoltaic cell industry.

An empirical research on evaluating banks' credit assessment of corporate customers.

BACKGROUND: Under the rapid change of the global financial environment, the risk control of the credit granting is viewed as the foremost task to each bank. With the impact one by one from financial crisis and European debt crisis, the steady bank business is also facing the severe challenge. Banks approve the credits for their customers and then make money from the interest. CASE PRESENTATION: Credit granting is not only the primary job but also the main source of income. The quality of credit granting concerns not just the reclaims of creditor's rights; it also affects the successful running of banks. DISCUSSION AND EVALUATION: To enhance the reliability and usefulness of bank credit risk assessment, we first will delve in the facets and indexes in the bank credit risk assessment. Then, we will examine the different dimensions of cause-effect relationships and correlations in the assessment process. Finally, the study focuses on how to raise the functions and benefits of the bank credit risk assessment. CONCLUSIONS: In those five credit risk evaluation dimensions, A "optional capability" and D "competitiveness" are of high relation and high prominence among those dimensions, influencing other items obviously. By actively focusing on these two dimensions and improving their credit risk assessment ability will solve the foremost problems and also solve other facets of credit risk assessment problems at the same time.

Climate change and the macroeconomic structure in pre-industrial europe: new evidence from wavelet analysis.

The relationship between climate change and the macroeconomy in pre-industrial Europe has attracted considerable attention in recent years. This study follows the combined paradigms of evolutionary economics and ecological economics, in which wavelet analysis (spectrum analysis and coherence analysis) is applied as the first attempt to examine the relationship between climate change and the macroeconomic structure in pre-industrial Europe in the frequency domain. Aside from confirming previous results, this study aims to further substantiate the association between climate change and macroeconomy by presenting new evidence obtained from the wavelet analysis. Our spectrum analysis shows a consistent and continuous frequency band of 60-80 years in the temperature, grain yield ratio, grain price, consumer price index, and real wage throughout the study period. Besides, coherence analysis shows that the macroeconomic structure is shaped more by climate change than population change. In addition, temperature is proven as a key climatic factor that influences the macroeconomic structure. The analysis reveals a unique frequency band of about 20 years (15-35 years) in the temperature in AD1600-1700, which could have contributed to the widespread economic crisis in pre-industrial Europe. Our findings may have indications in re-examining the Malthusian theory.

Epidemics in Ming and Qing China: Impacts of changes of climate and economic well-being.

We investigated the mechanism of epidemics with the impacts of climate change and socio-economic fluctuations in the Ming and Qing Dynasties in China (AD 1368-1901). Using long-term and high-quality datasets, this study is the first quantitative research that verifies the 'climate change â†’ economy â†’ epidemics' mechanism in historical China by statistical methods that include correlation analysis, Granger causality analysis, ARX, and Poisson-ARX modeling. The analysis provides the evidences that climate change could only fundamentally lead to the epidemics spread and occurrence, but the depressed economic well-being is the direct trigger of epidemics spread and occurrence at the national and long term scale in historical China. Moreover, statistical modeling shows that economic well-being is more important than population pressure in the mechanism of epidemics. However, population pressure remains a key element in determining the social vulnerability of the epidemics occurrence under climate change. Notably, the findings not only support adaptation theories but also enhance our confidence to address climatic shocks if economic buffering capacity can be promoted steadily. The findings can be a basis for scientists and policymakers in addressing global and regional environmental changes.

Using the Chinese version of Memorial Delirium Assessment Scale to describe postoperative delirium after hip surgery.

OBJECTIVE: Memorial Delirium Assessment Scale (MDAS) assesses severity of delirium. However, whether the MDAS can be used in a Chinese population is unknown. Moreover, the optimal postoperative MDAS cutoff point for describing postoperative delirium in Chinese remains largely to be determined. We therefore performed a pilot study to validate MDAS in the Chinese language and to determine the optimal postoperative MDAS cutoff point for delirium. METHODS: Eighty-two patients (80 ± 6 years, 21.9% male), who had hip surgery under general anesthesia, were enrolled. The Confusion Assessment Method (CAM) and Mini-Mental State Examination (MMSE) were administered to the patients before surgery. The CAM and MDAS were performed on the patients on the first, second and fourth postoperative days. The reliability and validity of the MDAS were determined. A receiver operating characteristic (ROC) curve was used to determine the optimal Chinese version MDAS cutoff point for the identification of delirium. RESULTS: The Chinese version of the MDAS had satisfactory internal consistency (α = 0.910). ROC analysis obtained an average optimal MDAS cutoff point of 7.5 in describing the CAM-defined postoperative delirium, with an area under the ROC of 0.990 (95% CI 0.977-1.000, P < 0.001). CONCLUSIONS: The Chinese version of the MDAS had good reliability and validity. The patients whose postoperative Chinese version MDAS cutoff point score was 7.5 would likely have postoperative delirium. These results have established a system for a larger scale study in the future.

Multiplexed, quantitative, and targeted metabolite profiling by LC-MS/MRM.

Targeted metabolomics, which focuses on a subset of known metabolites representative of biologically relevant metabolic pathways, is a valuable tool to discover biomarkers and link disease phenotypes to underlying mechanisms or therapeutic modes of action. A key advantage of targeted metabolomics, compared to discovery metabolomics, is its immediate readiness for extracting biological information derived from known metabolites and quantitative measurements. However, simultaneously analyzing hundreds of endogenous metabolites presents a challenge due to their diverse chemical structures and properties. Here we report a method which combines different chromatographic separation conditions, optimal ionization polarities, and the most sensitive triple-quadrupole MS-based data acquisition mode, multiple reaction monitoring (MRM), to quantitatively profile 205 endogenous metabolites in 10 min.

Climate change and macro-economic cycles in pre-industrial europe.

Climate change has been proven to be the ultimate cause of social crisis in pre-industrial Europe at a large scale. However, detailed analyses on climate change and macro-economic cycles in the pre-industrial era remain lacking, especially within different temporal scales. Therefore, fine-grained, paleo-climate, and economic data were employed with statistical methods to quantitatively assess the relations between climate change and agrarian economy in Europe during AD 1500 to 1800. In the study, the Butterworth filter was adopted to filter the data series into a long-term trend (low-frequency) and short-term fluctuations (high-frequency). Granger Causality Analysis was conducted to scrutinize the associations between climate change and macro-economic cycle at different frequency bands. Based on quantitative results, climate change can only show significant effects on the macro-economic cycle within the long-term. In terms of the short-term effects, society can relieve the influences from climate variations by social adaptation methods and self-adjustment mechanism. On a large spatial scale, temperature holds higher importance for the European agrarian economy than precipitation. By examining the supply-demand mechanism in the grain market, population during the study period acted as the producer in the long term, whereas as the consumer in the short term. These findings merely reflect the general interactions between climate change and macro-economic cycles at the large spatial region with a long-term study period. The findings neither illustrate individual incidents that can temporarily distort the agrarian economy nor explain some specific cases. In the study, the scale thinking in the analysis is raised as an essential methodological issue for the first time to interpret the associations between climatic impact and macro-economy in the past agrarian society within different temporal scales.