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Objective. The aim of the study is to examine the efficacy of laparoscopic radical resection of colorectal cancer combined with neoadjuvant chemotherapy and its impact on the overall prognosis of patients with colorectal cancer (CC). Methods. A total of 80 CC patients hospitalized and treated at our hospital between November 2019 and June 2021 were selected at random as research subjects and divided equally into two groups: the surgical group (n = 40) and the combination group (n = 40). Patients in the surgical group were treated with laparoscopic radical resection, while patients in the combination group received laparoscopic radical resection combined with neoadjuvant chemotherapy. The two groups were compared in terms of surgery-related indicators, tumor markers (serum carcinoembryonic antigen (CEA), glycoprotein 199 (CA199), vascular endothelial growth factor (VEGF), and matrix metalloproteinase 9 (MMP9)), postoperative complications, and 1–3 years postoperative survival rate and recurrence rate. Results. The surgical duration of the combination group was significantly shorter than the surgical group (P < 0.05). No significant differences were found in intraoperative blood loss, time to get out of bed, exhaust time, or hospital stay between the two groups (P < 0.05). In the combination group, serum tumor markers (carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), vascular endothelial growth factor (VEGF), and matrix metalloproteinase 9 (MMP9)) were markedly lower than those in the surgical group (P < 0.05). The combination group exhibited fewer postoperative complications than those in the operation group (P < 0.05). In the combination group, the 1–3 years postoperative survival rate was higher, while the 1–3 years postoperative recurrence rate was considerably lower than that in the surgical group (P < 0.05). Conclusion. CC patients benefit well from laparoscopic radical resection coupled with neoadjuvant chemotherapy. The approach is efficient in lowering blood tumor markers in patients and lowering the risk of surgery-related complications. It has the potential to enhance patients' long-term prognoses, allowing them to live longer and lower their chance of recurrence. [ABSTRACT FROM AUTHOR]

Background: The vitamin niacin is used as a lipid-regulating supplement, but it is unknown whether niacin has a positive influence on cancer prognosis. In this study, we examine the relationship between niacin intake and mortality among patients with cancer.Methods: Our study utilized all available continuous data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2014. Multivariable Cox regression models were applied in order to investigate dietary niacin intake's association with mortality. We compared the survival probability between groups of low and high niacin intake by plotting Kaplan-Meier curves. An analysis of subgroups was used to investigate heterogeneity sources.Results: A total of 3504 participants were included in the cohort, with 1054 deaths. One thousand eight hundred forty-seven participants (52.3%) were female, 2548 participants (73.4%) were white, and the mean age (SE) was 65.38 years (0.32). According to multivariate logistic regression analysis, niacin intake was negatively associated with mortality outcomes in patients with cancer, with P values below 0.05 in all models. In subgroup analyses based on sex, age, and BMI, the association persisted. The Kaplan-Meier curves indicate that high niacin intake groups have better survival rates than low intake groups. Niacin supplementation improved cancer mortality but not all-cause mortality.Conclusion: According to our study, higher dietary niacin intake was associated with lower mortality in cancer patients. Niacin supplements improved cancer survival rates, but not all causes of mortality. [ABSTRACT FROM AUTHOR]

• Assessing impacts of climate change on wheat production in climate-suitable area. • Quantifying projected uncertainty of wheat production under future climate change. • SDM dominated projected uncertainty of wheat production in climate-suitable area. • CM was more certain when projecting future wheat production in climate-suitable area. Climate change poses a great challenge to global food security. Recently the combination of crop models (CMs), global climate models (GCMs), and species distribution models (SDMs) has been applied to assess the impacts of climate change on crop production with consideration of changes of crop climate-suitable regions. However, little is known about the uncertainty sources in the wheat production projections with consideration of crop climatic suitability under future climate. In this study, an integration method based on multiple CMs, SDMs, and GCMs was adopted to assess the impacts of climate change on winter wheat production in the Loess Plateau of China. A comprehensive analysis of different uncertainty sources (i.e. CM, GCM, SDM, Emission Scenario or Scen , and their interactions) was conducted through the ANOVA (Analysis of variance) method. Based on the projections of CM ensemble and ensemble-SDMs driven by 27 GCMs, multi-model mean winter wheat production would increase by 14.6% and 19.7% in 2041–2060 and 4.9% and 3.5% in 2081–2100 under SSP245 and SSP585, respectively. We found that the changes in climate-suitable areas of winter wheat caused larger changes in winter wheat production than the changes of per unit yield. SDM was the largest uncertainty contributor among the four main factors of CM, GCM, SDM, and Scen in the projections of winter wheat production under future climate in the Loess Plateau, accounting for about 20.3% of total uncertainty. At the same time, CM was the lowest uncertainty contributor and accounted for only about 3.0% of total uncertainty. Thus, CM was proved more certain in future projections of winter wheat production when considering the changes of crop climate-suitable areas. The efforts in this study could help to rationally integrate the crop modeling, species distribution modeling, and climate models on the projections of global wheat production under future global climate change. [ABSTRACT FROM AUTHOR]

This study systematically assessed the feasibility of replacing ordinary Portland cement (OPC) with a multi–solid waste cementitious material (MSC) composed of ground granulated blast furnace slag (GGBS), steel slag (SS), red mud (RM), fly ash (FA), limestone powder (LP), and desulfurized gypsum (DG) in specific proportions of 28%, 25%, 21%, 11.5%, 9%, and 5.5%, respectively. The hydration characteristics were analyzed using hydration heat, XRD, TG–DTG, FTIR, and SEM–EDX tests. The heavy metal content and leaching concentration of the MSC were determined using inductively coupled plasma mass spectrometry (ICP–MS). The unconfined compressive strength (UCS), indirect tensile strength (ITS), and water stability of the MSC-stabilized clayey sand and stone chips were investigated. The results showed that synergistic hydration among multiple solid wastes generated abundant hydration products, including Ca(OH) 2 , C–(A)–S–H gels, and AFt crystals, which formed dense and compact microstructures. Furthermore, Pb and Cd in the raw material were effectively solidified by MSC hydration, with leaching concentrations as low as 0.181 and 0.002 mg/L, respectively. The contents and leaching concentrations of the other heavy metals were significantly lower than their limits. The pH of the MSC mortar leachate was stable at 11.56 with curing age, which complied with environmental safety standards. Moreover, MSC as a binder demonstrated higher UCS, ITS, and water stability in comparison to OPC. When MSC was added at a 6% dosage to stabilize clayey sand and stone chips, the UCS values measured after 7 days of curing were 2.82 MPa and 4.83 MPa, respectively. Finally, the MSC was successfully applied in practical projects, demonstrating its promising application prospects. • The synergistic hydration characteristics of a multi–solid waste cementitious material (MSC) were revealed. • The environmental impact of MSC was investigated. • The mechanical properties of MSC-stabilized materials were evaluated. [ABSTRACT FROM AUTHOR]

• Two solutions were established with or without R s inputs for daily ET 0 estimation. • FAO56 recommended larger coefficient a but smaller b of the A-P formula in China. • The transfer error from estimated R s to ET 0 was reduced by using the P-M model. • SVM-ET 0 model obtained larger errors than P-M model with estimated R s at test stages. Accurate estimation of reference evapotranspiration (ET 0) is of great importance for regional water resources planning and irrigation scheduling. The FAO56 recommended Penman-Monteith (P-M) model is widely adopted as the standard method for ET 0 estimation, but its application is usually restricted by limited meteorological data worldwide, especially global solar radiation (R s). This study provided two possible solutions to deal with the missing R s data in ET 0 estimation in China mainland. In the first solution, R s data were estimated with the Ångström-Prescott (A-P) formula and daily sunshine hours. The values of two A-P formula fundamental coefficients a and b were obtained through three ways: (1) estimated based on limited R s measurements at 80 solar radiation measurement stations (or site-calibrated); (2) recommended by the FAO-56 manual (or FAO-recommended); and (3) estimated based on the altitude and latitude of each weather station through the support vector machine algorithm (or SVM-estimated). The second solution used the SVM algorithm and available weather variables without R s. The results showed that the FAO-recommended coefficients a and b were separately overestimated and underestimated in China mainland, which generated the largest simulation errors of R s. However, the transfer errors from R s estimations to ET 0 estimations were reduced by using the P-M model for all of the three kinds of coefficients. Compared with the R s -based models, the estimation accuracy of the SVM-ET 0 model yielded the highest accuracy both at the training stage (R2 = 0.979; RMSE = 0.273 mm d−1) and the testing stage (R2 = 0.973; RMSE = 0.302 mm d−1). Generally, both the P-M and the machine-learning-based methods could be used for the ET 0 estimation, when only R s data were missing. However, considering the complexity in the programming, the P-M model combining with the A-P formula with the SVM-estimated A-P coefficients is recommended for daily ET 0 estimation in China. [ABSTRACT FROM AUTHOR]