With COVID-19 prevalent worldwide, current studies have focused on the factors influencing the epidemic. In particular, the built environment deserves immediate attention to produce place-specific strategies to prevent the further spread of coronavirus. This research assessed the impact of the built environment on the incidence rate in King County, US and explored methods of researching infectious diseases in urban areas. Using principal component analysis and the Pearson correlation coefficient to process the data, we built multiple linear regression and geographically weighted regression models at the ZIP code scale. Results indicated that although socioeconomic indicators were the primary factors influencing COVID-19, the built environment affected COVID-19 cases from different aspects. Built environment density was positively associated with incidence rates. Specifically, increased open space was conducive to reducing incidence rates. Within each community, overcrowded households led to an increase in incidence rates. This study confirmed previous research into the importance of socioeconomic variables and extended the discussion on spatial and temporal variation in the impacts of urban density on the spread of COVID, effectively guiding sustainable urban development.

There is little evidence around Camrelizumab combined with cytoreductive nephrectomy (CN) and radiotherapy (RT) as a treatment option for metastatic renal cell carcinoma (mRCC). The influence of CN on immune responses and the abscopal effect are not well understood. In this paper, we report a case of anti-programmed cell death-1 (PD-1) treated with combined RT once CN reduced the primary tumor burden (TB). This patient also encountered an increased response to targeted radiotherapy after immune resistance. We also observed a macrophage-to-lymphocyte ratio (MLR) peak, which may be correlated with subsequent pseudoprogression after thoracic radiotherapy. Consequently, even with the disease, this patient has remained stable. This peculiar instance suggests there is a need to investigate the underlying mechanisms of CN in promoting the abscopal effect during immunotherapy when combined with RT. It also suggests that there is a need for further investigation into the role of RT in overcoming immune resistance, and the value of MLR in predicting pseudoprogression. We hypothesize that a heavy tumor burden might suppress the abscopal effect, thereby ensuring that CN promotes it. However, radiotherapy may overcome immune resistance during oligoprogression.

Aims: The present study was to evaluate the diagnostic value of routine blood test as potential inflammatory markers in early esophageal cancer (EEC) patients. Methods: A matched case-control study was conducted by recruiting 314 patients who were pathologically diagnosed with EEC and then underwent Endoscopic Submucosal Dissection (ESD) from July 2015 to July 2019 in First Affiliated Hospital of Nanjing Medical University. Each EEC patient was matched against one healthy control on the criteria of gender, and age (±2 years). Additionally, a total of 40 subjects (20 cases and 20 controls) were also included in the validation set. Statistical analysis of selected hematological parameters was performed between the two groups. The correlation between preoperative blood indexes and clinicopathological characteristics after ESD in EEC patients were further assessed. Results: Mono-factor analysis showed that the index of monocyte (p<0.001), MCV (p=0.018), MCH (p=0.01), MPV (p=0.022), PT (p=0.003), PT-INR (p=0.003), PDW (p<0.001) and MLR (p<0.001) were statistically significant in EEC patients when compared with those in healthy controls. Multivariate logistic regression analysis further identified that PDW and MLR was independently associated with the risk of early esophageal cancer (both p<0.001). The higher level of NLR (P=0.007) and MLR (P=0.015) were statistically significant with submucosal invasion in EEC patients and the level of MLR were significantly associated with larger tumor size (P=0.030). The results of the validation group were in consistence with the primary group. Conclusions: Hematological parameters of MLR and PDW can be used as an adjuvant tool for the diagnosis of EEC. Moreover, the value of MLR can reflect the invasion depth index.

The present study evaluates the water quality status of 6-km-long Kali River stretch that passes through the Aligarh district in Uttar Pradesh, India, by utilizing high-resolution IRS P6 LISS IV imagery. In situ river water samples collected at 40 random locations were analyzed for seven physicochemical and four heavy metal concentrations, and the water quality index (WQI) was computed for each sampling location. A set of 11 spectral reflectance band combinations were formulated to identify the most significant band combination that is related to the observed WQI at each sampling location. Three approaches, namely multiple linear regression (MLR), backpropagation neural network (BPNN) and gene expression programming (GEP), were employed to relate WQI as a function of most significant band combination. Comparative assessment among the three utilized approaches was performed via quantitative indicators such as R 2, RMSE and MAE. Results revealed that WQI estimates ranged between 203.7 and 262.33 and rated as \"very poor\" status. Results further indicated that GEP performed better than BPNN and MLR approaches and predicted WQI estimates with high R 2 values (i.e., 0.94 for calibration and 0.91 for validation data), low RMSE and MAE values (i.e., 2.49 and 2.16 for calibration and 4.45 and 3.53 for validation data). Moreover, both GEP and BPNN depicted superiority over MLR approach that yielded WQI with R 2 ~ 0.81 and 0.67 for calibration and validation data, respectively. WQI maps generated from the three approaches corroborate the existing pollution levels along the river stretch. In order to examine the significant differences among WQI estimates from the three approaches, one-way ANOVA test was performed, and the results in terms of F-statistic (F = 0.01) and p-value (p = 0.994 > 0.05) revealed WQI estimates as \"not significant,\" reasoned to the small water sample size (i.e., N = 40). The study therefore recommends GEP as more rational and a better alternative for precise water quality monitoring of surface water bodies by producing simplified mathematical expressions.

Improving the interpretability of multivariate QSPR models is a major issue in modern drug discovery. In this study we applied three strategies to model and deconvolute the balance of intermolecular forces governing log KW SDS , a chromatographic descriptor of potential relevance in the prediction of ADME phenomena. A dataset of 77 compounds was set-up and an ad hoc pool of VS+ descriptors calculated. The data matrix was firstly submitted to a PCA run for a preliminary analysis and outliers detection. To model and interpret log KW SDS three chemoinformatic approaches implementing either variable selection or grouping tools were used: a) MLR and GA, b) PLSR combined with BR analysis and c) MBPLSR. Results provided by the three methods were largely superposable both in terms of prediction performances and mechanistic interpretation. Overall, they showed that log KW SDS is a complex descriptor mainly governed by the dimension, polarity and HBD solutes\' properties. Chemoinformatic strategies as those reported in this paper might be applied to any chromatographic system and thus represent a potent tool to exploit the full potential of chromatographic descriptors in pharmaceutical, toxicological and related sciences.

This paper proposes multilayer perceptron neural network (MLPNN) to predict phycocyanin (PC) pigment using water quality variables as predictor. In the proposed model, four water quality variables that are water temperature, dissolved oxygen, pH, and specific conductance were selected as the inputs for the MLPNN model, and the PC as the output. To demonstrate the capability and the usefulness of the MLPNN model, a total of 15,849 data measured at 15-min (15 min) intervals of time are used for the development of the model. The data are collected at the lower Charles River buoy, and available from the US Environmental Protection Agency (USEPA). For comparison purposes, a multiple linear regression (MLR) model that was frequently used for predicting water quality variables in previous studies is also built. The performances of the models are evaluated using a set of widely used statistical indices. The performance of the MLPNN and MLR models is compared with the measured data. The obtained results show that (i) the all proposed MLPNN models are more accurate than the MLR models and (ii) the results obtained are very promising and encouraging for the development of phycocyanin-predictive models.