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Abstract Objective Numerous epidemiological investigations have explored the impact of body composition on the effectiveness of immune checkpoint inhibitors (ICIs) in urological malignancies (UM) patients, yielding conflicting findings. As a result, our study aims to elucidate the influence of baseline body composition on the long-term prognosis of UM patients treated with ICIs. Methods We employed a rigorous systematic search across various databases, including PubMed, Embase, the Cochrane Library, and Google Scholar, to identify studies meeting our inclusion criteria. Our primary endpoints of interest encompassed overall survival (OS) and progression-free survival (PFS). Results This analysis included a total of 10 articles with a combined patient cohort of 707 individuals. Our findings revealed a noteworthy association between several body composition parameters and unfavorable OS outcomes, including low psoas muscle index (PMI; HR: 3.88, p

Abstract Background Intrahepatic cholangiocarcinoma (ICC) has a poor prognosis and is understudied. Based on the clinical features of patients with ICC, we constructed machine learning models to understand their importance on survival and to accurately determine patient prognosis, aiming to develop reference values to guide physicians in developing more effective treatment plans. Methods This study used machine learning (ML) algorithms to build prediction models using ICC data on 1,751 patients from the SEER (Surveillance, Epidemiology, and End Results) database and 58 hospital cases. The models’ performances were compared using receiver operating characteristic curve analysis, C-index, and Brier scores. Results A total of eight variables were used to construct the ML models. Our analysis identified the random survival forest model as the best for prognostic prediction. In the training cohort, its C-index, Brier score, and Area Under the Curve values were 0.76, 0.124, and 0.882, respectively, and it also performed well in the test cohort. Kaplan–Meier survival analysis revealed that the model could effectively determine patient prognosis. Conclusions To our knowledge, this is the first study to develop ML prognostic models for ICC in the high-incidence age group. Of the ML models, the random survival forest model was best at prognosis prediction.

BackgroundMost patients with multiple hepatocellular carcinoma (MHCC) are at advanced stage once diagnosed, so that clinical treatment and decision-making are quite tricky. The AJCC-TNM system cannot accurately determine prognosis, our study aimed to identify prognostic factors for MHCC and to develop a prognostic model to quantify the risk and survival probability of patients.MethodsEligible patients with HCC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and then prognostic models were built using Cox regression, machine learning (ML), and deep learning (DL) algorithms. The model’s performance was evaluated using C-index, receiver operating characteristic curve, Brier score and decision curve analysis, respectively, and the best model was interpreted using SHapley additive explanations (SHAP) interpretability technique.ResultsA total of eight variables were included in the follow-up study, our analysis identified that the gradient boosted machine (GBM) model was the best prognostic model for advanced MHCC. In particular, the GBM model in the training cohort had a C-index of 0.73, a Brier score of 0.124, with area under the curve (AUC) values above 0.78 at the first, third, and fifth year. Importantly, the model also performed well in test cohort. The Kaplan–Meier (K-M) survival analysis demonstrated that the newly developed risk stratification system could well differentiate the prognosis of patients.ConclusionOf the ML models, GBM model could predict the prognosis of advanced MHCC patients most accurately.

Abstract Sarcopenia has been considered an adverse prognostic factor in cancer patients. Intramuscular adipose tissue content, as a new marker of sarcopenia, can effectively reflect skeletal muscle quality. The aim of this study was performed to evaluate the association between high intramuscular adipose tissue content (IMAC) and survival outcomes and postoperative complications in cancer patients. Specific databases, including the Web of Science, Embase and Web of Science, were systematically searched to identify relevant articles evaluating the prognostic value of IMAC in cancer patients. Hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) were utilized for comprehensive analysis. All data analyses were performed using STATA 12.0 software. A total of 25 studies from 24 articles including 5663 patients were enrolled in the study. Meta‐analysis showed that high IMAC was associated with unfavourable overall survival (OS) (HR: 2.21, 95% CI: 1.70–2.86, P

Study region: The Xiying River Basin is a typical inland mountainous watershed located in the Qilian Mountains. Study focus: This article reveals the change of runoff stable isotopes and their control mechanisms based on isotope data of different water bodies (precipitation, runoff, groundwater, and glacial snowmelt) in the Xiying River Basin from 2016 to 2021, combined with relevant meteorological observation data and the Normalized Vegetation Index (NDVI). New hydrological insights for the region: The results show that (1) meteorological factors control runoff isotope variations through controlling the evaporation process of runoff and the isotopic characteristics of recharge sources. (2) In areas with low vegetation cover, surface landscape features influence runoff isotopic composition by altering the isotopic characteristics of recharge sources. (3) In areas with high vegetation cover, surface vegetation cover affects runoff stable isotopes by controlling evapotranspiration processes. This study connects vegetation cover and runoff isotopes to explain the surface landscape control of runoff stable isotopes. It offers a more powerful reference for elucidating the factors influencing runoff isotopes in inland mountain watersheds.

Abstract Background Systemic inflammation is crucial for the development and progression of cancers. The advanced lung cancer inflammation index (ALI) is considered to be a better indicator of systemic inflammation than current biomarkers. However, the prognostic value of the ALI in gastrointestinal neoplasms remains unclear. We performed the first meta-analysis to explore the association between ALI and gastrointestinal oncologic outcomes to help physicians better evaluate the prognosis of those patients. Methods Eligible articles were retrieved using PubMed, the Cochrane Library, EMBASE, and Google Scholar by December 29, 2022. Clinical outcomes were overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), and cancer-specific survival (CSS). Results A total of 18 articles with 6898 patients were included in this meta-analysis. The pooled results demonstrated that a low ALI was correlated with poor OS (HR = 1.914, 95% CI: 1.514–2.419, P

Abstract Inactivation of the p53 pathway is linked to a variety of human cancers. As a critical component of the p53 pathway, ubiquitin‐specific protease 7 (USP7) acts as a deubiquitinase for both p53 and its ubiquitin E3 ligase mouse double minute 2 homolog. Here, myeloid leukemia factor 2 (MLF2) is reported as a new negative regulator of p53. MLF2 interacts with both p53 and USP7. Via these interactions, MLF2 inhibits the binding of USP7 to p53 and antagonizes USP7‐mediated deubiquitination of p53, thereby leading to p53 destabilization. Functionally, MLF2 plays an oncogenic role in colorectal cancer, at least partially, via the negative regulation of p53. Clinically, MLF2 is elevated in colorectal cancer and its high expression is associated with poor prognosis in patients with colorectal cancer. In wild‐type‐p53‐containing colorectal cancer, MLF2 and p53 expressions are inversely correlated. These findings establish MLF2 as an important suppressor of p53 function. The study also reveals a critical role for the MLF2–p53 axis in promoting colorectal carcinogenesis.

Background: M2 macrophages perform an influential role in the progression of pancreatic cancer. This study is dedicated to explore the value of M2 macrophage-related genes in the treatment and prognosis of pancreatic cancer.Methods: RNA-Seq and clinical information were downloaded from TCGA, GEO and ICGC databases. The pancreatic cancer tumour microenvironment was revealed using the CIBERSORT algorithm. Weighted gene co-expression network analysis (WGCNA) was used to detect M2 macrophage-associated gene modules. Univariate Cox regression, Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression were applied to develop the prognostic model. The modelling and validation cohorts were divided into high-risk and low-risk groups according to the median risk score. The nomogram predicting survival was constructed based on risk scores. Correlations between risk scores and tumour mutational load, clinical variables, immune checkpoint blockade, and immune cells were further explored. Finally, potential associations between different risk models and chemotherapeutic agent efficacy were predicted.Results: The intersection of the WGCNA results from the TCGA and GEO data screened for 317 M2 macrophage-associated genes. Nine genes were identified by multivariate COX regression analysis and applied to the construction of risk models. The results of GSEA analysis revealed that most of these genes were related to signaling, cytokine receptor interaction and immunodeficiency pathways. The high and low risk groups were closely associated with tumour mutational burden, immune checkpoint blockade related genes, and immune cells. The maximum inhibitory concentrations of metformin, paclitaxel, and rufatinib lapatinib were significantly differences on the two risk groups.Conclusion: WGCNA-based analysis of M2 macrophage-associated genes can help predict the prognosis of pancreatic cancer patients and may provide new options for immunotherapy of pancreatic cancer.

Summary: The tumor suppressor p53 plays a pivotal role in tumor prevention. The activity of p53 is mainly restrained by the ubiquitin E3 ligase Mdm2. However, it is not well understood how the Mdm2-p53 pathway is intricately regulated. Here we report that the RNA binding protein RALY functions as an oncogenic factor in lung cancer. RALY simultaneously binds to Mdm2 and the deubiquitinating enzyme USP7. Via these interactions, RALY not only stabilizes Mdm2 by stimulating the deubiquitinating activity of USP7 toward Mdm2 but also increases the trans-E3 ligase activity of Mdm2 toward p53. Consequently, RALY enhances Mdm2-mediated ubiquitination and degradation of p53. Functionally, RALY promotes lung tumorigenesis, at least partially, via negative regulation of p53. These findings suggest that RALY destabilizes p53 by modulating the function of Mdm2 at multiple levels. Our study also indicates a critical role for RALY in promoting lung tumorigenesis via p53 inhibition.

BackgroundMany reports have described that abnormal nectin-4 expression may be used as a prognostic marker in many tumors. However, these studies failed to reach a consensus. Here, we performed a meta-analysis to comprehensively evaluate the prognostic value of nectin-4 in cancers.MethodsRelevant studies were identified through a comprehensive search of PubMed, EMBASE and Web of science until August 31, 2022. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were used to evaluate the relationship between nectin-4 expression and overall survival (OS) and disease-free survival/progression-free survival/relapse-free survival (DFS/PFS/RFS). Odds ratios (ORs) with 95% CIs were applied to assess the relationship between nectin-4 expression and clinicopathologic features. Subgroup analysis was performed to explore the sources of heterogeneity. Sensitivity analysis and funnel plot were used to test the reliability of the results. All data analyses were performed using STATA version 12.0 software.ResultsFifteen articles involving 2245 patients were included in the meta-analysis. The pooled analysis showed that high nectin-4 expression was significantly associated with poor OS (HR: 1.75, 95% CI: 1.35–2.28). There was no relationship between high nectin-4 expression and DFS/PFS/RFS (HR: 178, 95% CI: 0.78–4.08).Subgroup analyses revealed that that high nectin-4 expression mainly presented adverse OS in esophageal cancer (EC) (HR: 1.78, 95% CI: 1.30–2.44) and gastric cancer (GC) (HR: 1.92, 95% CI: 1.43–2.58). We also found that high nectin-4 expression was associated with tumor diameter (big vs small) (OR: 1.96, 95% CI: 1.02–3.75), tumor stage (III-IV vs I-II) (OR: 2.04, 95% CI: 1.01–4.12) and invasion depth (T3+T4 vs T2+T1) (OR: 3.95, 95% CI: 2.06–7.57).ConclusionsNectin-4 can be used as an effective prognostic indicator for specific cancers.

BackgroundHepatocellular carcinoma (HCC) has the highest cancer-related mortality rate. This study aims to create a nomogram to predict the cancer-specific survival (CSS) in patients with advanced hepatocellular carcinoma.MethodsPatients diagnosed with advanced HCC (AJCC stage III and IV) during 1975 to 2018 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Qualified patents were randomized into training cohort and validation cohort at a ratio of 7:3. The results of univariate and multivariate Cox regression analyses were used to construct the nomogram. Consistency index (C-index), area under the time-dependent receiver operating characteristic (ROC) curve [time-dependent area under the curve (AUC)], and calibration plots were used to identify and calibrate the nomogram. The net reclassification index (NRI), integrated discrimination improvement (IDI), and C-index, and decision curve analysis DCA were adopted to compare the nomogram’s clinical utility with the AJCC criteria.ResultsThe 3,103 patients with advanced hepatocellular carcinoma were selected (the training cohort: 2,175 patients and the validation cohort: 928 patients). The C-index in both training cohort and validation cohort were greater than 0.7. The AUC for ROC in the training cohort was 0.781, 0.771, and 0.791 at 1, 2, and 3 years CSS, respectively. Calibration plots showed good consistency between actual observations and the 1-, 2-, and 3-year CSS predicted by the nomogram. The 1-, 2-, and 3-year NRI were 0.77, 0.46, and 0.48, respectively. The 1-, 2-, and 3-year IDI values were 0.16, 0.15, and 0.12 (P < 0.001), respectively. DCA curves in both the training and validation cohorts demonstrated that the nomogram showed better predicted 1-, 2-, and 3-year CSS probabilities than AJCC criteria.ConclusionsThis study established a practical nomogram for predicting CSS in patients with advanced HCC and a risk stratification system that provided an applicable tool for clinical management.

Global changes and human activities have significantly altered water cycle processes and water resource patterns in inland river basins in arid zones. New tools are needed to conduct more comprehensive and scientific assessments of basin water cycle processes and water resource patterns. Based on GRACE satellite and Landsat data, this study investigated terrestrial water storage changes and surface water area in the Shiyang River Drainage Basin from 2002 to 2021. It explored the effects of climate change and water conservancy construction on terrestrial water storage changes in the basin. The results of the study show that, although the surface water quantity in the Shiyang River basin has increased in the past 20 years, the overall decreasing trend of terrestrial water storage in the basin of the Shiyang River has an interannual decreasing rate of 0.01 cm/a. The decreasing trend of water storage in the midstream and downstream areas is more prominent. The change in precipitation controls the change in water storage in the Shiyang River Drainage Basin. Artificial water transfer has changed the spatial distribution of water resources in the basin of the Shiyang River. However, it still has not completely reversed the trend of decreasing water storage in the middle and lower reaches of the Shiyang River.

ObjectiveHepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths worldwide. This study aims to construct a novel practical nomogram and risk stratification system to predict cancer-specific survival (CSS) in HCC patients with severe liver fibrosis.MethodsData on 1,878 HCC patients with severe liver fibrosis in the period 1975 to 2017 were extracted from the Surveillance, Epidemiology, and End Results database (SEER). Patients were block-randomized (1,316 training cohort, 562 validation cohort) by setting random seed. Univariate and multivariate COX regression analyses were employed to select variables for the nomogram. The consistency index (C-index), the area under time-dependent receiver operating characteristic curve (time-dependent AUC), and calibration curves were used to evaluate the performance of the nomogram. Decision curve analysis (DCA), the C-index, the net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to compare the nomogram with the AJCC tumor staging system. We also compared the risk stratification of the nomogram with the American Joint Committee on Cancer (AJCC) staging system.ResultsSeven variables were selected to establish the nomogram. The C-index (training cohort: 0.781, 95%CI: 0.767–0.793; validation cohort: 0.793, 95%CI = 95%CI: 0.779–0.798) and the time-dependent AUCs (the training cohort: the values of 1-, 3-, and 5 years were 0.845, 0.835, and 0.842, respectively; the validation cohort: the values of 1-, 3-, and 5 years were 0.861, 0.870, and 0.876, respectively) showed satisfactory discrimination. The calibration plots also revealed that the nomogram was consistent with the actual observations. NRI (training cohort: 1-, 2-, and 3-year CSS: 0.42, 0.61, and 0.67; validation cohort: 1-, 2-, and 3-year CSS: 0.26, 0.52, and 0.72) and IDI (training cohort: 1-, 3-, and 5-year CSS:0.16, 0.20, and 0.22; validation cohort: 1-, 3-, and 5-year CSS: 0.17, 0.26, and 0.30) indicated that the established nomogram significantly outperformed the AJCC staging system (P

The Yellow River Basin is essential to China’s economic and social development and ecological security. In order to assess the temporal and spatial distribution of cloud water in the Yellow River Basin, we analyzed the distribution characteristics of water vapor content and cloud water content using ERA5 monthly averaged data from 1980 to 2019. The results showed that the high-value area of the annual average atmospheric water vapor content distribution was concentrated above the North China Plain in the eastern part of the basin, and the value was mostly between 21 and 24 mm. The low-value areas were mainly centered above the high mountain areas in the western part of the basin, and the value mostly fell between 3 and 6 mm. The seasonal distribution characteristics of the annual average water vapor content were relatively consistent with the annual average distribution characteristics. The high-value cloud ice water content area was in the northeastern part of the Qinghai-Tibet Plateau (4.5 g·m−2), while the low-value area appeared on the Loess Plateau (2 g·m−2). The high-value area of cloud liquid water content was on the north side of the West Qinling Mountains (12 g·m−2). The low-value area appeared on the Loess Plateau and the northern part of the Qinghai-Tibet Plateau (3 g·m−2). The cloud water content was higher in the eastern region than in the western region in the overall spatial distribution, and the content of cloud liquid water was higher than that of cloud ice water. The average annual atmospheric water vapor content was increasing, and the annual average content of cloud ice water and cloud liquid water was declining. The change in the total amount and spatial distribution of cloud water was not obvious in the Yellow River Basin.

Gravity Recovery and Climate Experiment (GRACE) satellite data are widely used in drought studies. In this study, we quantified drought severity based on land terrestrial water storage (TWS) changes in GRACE data. We used the water storage deficit (WSD) and water storage deficit index (WSDI) to identify the drought events and evaluate the drought severity. The WSDI calculated by GRACE provides an effective assessment method when assessing the extent of drought over large areas under a lack of site data. The results show a total of 22 drought events in the central Asian dry zone during the study period. During spring and autumn, the droughts among these incidents occurred more frequently and severely. The longest and most severe drought occurred near the Caspian Sea. In the arid area of central Asia, the north of the region tended to be moist (the WSDI value was 0.04 year−1), and the south, east, and Caspian Sea area tended to be drier (the WSDI values were −0.07 year−1 in the south, −0.11 year−1 in the east, and −0.19 year−1 in the Caspian Sea). These study results can provide a key scientific basis for agricultural development, food security, and climate change response in the Asian arid zone.

High water cut reservoirs and edge and bottom water reservoirs have become the focus of conventional oil and gas production research due to the defects of low recovery efficiency and reservoir dispersion. Based on the reservoir distribution characteristics of edge and bottom water reservoirs, this study studied a low polarity surfactant to study the influence of different factors on the special water drive efficiency of edge and bottom water reservoirs in Huanjiang Oilfield, and analyzed the microscopic mechanism of surfactant enhancing water drive efficiency. The research results show that compared with the traditional water drive, the enhanced water drive with surfactant can increase the oil displacement efficiency to 85%, which is far higher than 60% of the traditional water drive. In addition, the residual oil saturation of the core can be reduced to 0.35 by adding surfactant to the water drive system, which is much lower than 0.48 of the traditional water drive system. Optimizing the cross pattern of injection wells can also improve the water drive efficiency. When the injection wells cross, the displacement efficiency and remaining oil saturation of the edge and bottom water reservoirs reach 85% and 0.36 respectively, which are much higher than those of traditional water flooding. By changing the water drive system composition of edge and bottom water reservoirs, the oil displacement efficiency and crude oil recovery of edge and bottom water reservoirs can be improved to a certain extent, and the basic ideas for the exploration and development of edge and bottom water reservoirs can also be provided. [ABSTRACT FROM AUTHOR]

Taking Chang 6 oil layer in Yanchang, Ordos as an example, this study experimentally evaluates the fractal characteristics of tight sandstone reservoir. The petrology and oil-gas geochemical characteristics of the tight sandstone are analyzed by using high-pressure mercury injection, low-frequency nuclear magnetic resonance and field emission scanning electron microscopy. The pore structure characterization and fractal characteristics calculation of the tight sandstone are carried out. The results show that the rock type of Chang 6 is mainly feldspathic sandstone, the volume fraction of debris is 84.095.0%, and the debris components are mainly feldspar and quartz. The reservoir porosity is between 1.2-15.9% and the permeability is between 0.01x10" 3-3.27x10"3pm2. There is a good positive correlation between reservoir porosity and permeability logarithm of Chang 6 reservoir group. The results of FHH model method show that the fractal dimension of Chang 61 is the largest, followed by Chang 62 and Chang 63. The fractal dimension (Dwop) of all organic pores in FE-SEM images is the largest, followed by the fractal dimension (Dop>5o) of organic pores with pore size greater than 50m and the fractal dimension (Dop4-5o) of organic pores with pore size of 4-50m. The comparison between small layers also shows the same characteristics as the fractal dimension of gas adsorption pores calculated by FHH model method. [ABSTRACT FROM AUTHOR]

Ecosystems in arid areas are fragile and are easily disturbed by various natural and human factors. The purpose of this study is to clarify the hydrological and ecological processes of arid inland river basins in Eurasia and provide a scientific basis for their sustainable development. From 2015 to 2020, we took the Shiyang River Basin, which has the highest utilization rate of water resources and the most prominent contradiction of water use, as a typical demonstration basin to establish and improve the isotope hydrology observation system. The data in the observation system are classified by water type (precipitation, river water, lake water, groundwater, soil water, and plant water). Six observation systems with stable isotopes as the main observation elements, including river source region, oasis region, reservoir channel system region, oasis farmland region, ecological engineering construction region, and salinization process region, have been built, and meteorological and hydrological data have also been collected. We will gradually improve the various observation systems, increase the data of observation sites, and update the data set yearly. We can use these data to carry out hydrometeorological and ecological scientific research and provide a scientific basis for water resources utilization and ecological environment restoration in arid areas. The datasets are available at https://data.mendeley.com/datasets/d5kzm92nn3/1 (Zhu et al., 2021). [ABSTRACT FROM AUTHOR]