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BackgroundBreast cancer (BC) is the most common and prominent deadly disease among women. Predicting BC survival mainly relies on TNM staging, molecular profiling and imaging, hampered by subjectivity and expenses. This study aimed to establish an economical and reliable model using the most common preoperative routine blood tests (RT) data for survival and surveillance strategy management.MethodsWe examined 2863 BC patients, dividing them into training and validation cohorts (7:3). We collected demographic features, pathomics characteristics and preoperative 24-item RT data. BC risk factors were identified through Cox regression, and a predictive nomogram was established. Its performance was assessed using C-index, area under curves (AUC), calibration curve and decision curve analysis. Kaplan-Meier curves stratified patients into different risk groups. We further compared the STAR model (utilizing HE and RT methodologies) with alternative nomograms grounded in molecular profiling (employing second-generation short-read sequencing methodologies) and imaging (utilizing PET-CT methodologies).ResultsThe STAR nomogram, incorporating subtype, TNM stage, age and preoperative RT data (LYM, LYM%, EOSO%, RDW-SD, P-LCR), achieved a C-index of 0.828 in the training cohort and impressive AUCs (0.847, 0.823 and 0.780) for 3-, 5- and 7-year OS rates, outperforming other nomograms. The validation cohort showed similar impressive results. The nomogram calculates a patient’s total score by assigning values to each risk factor, higher scores indicating a poor prognosis. STAR promises potential cost savings by enabling less intensive surveillance in around 90% of BC patients. Compared to nomograms based on molecular profiling and imaging, STAR presents a more cost-effective, with potential savings of approximately $700-800 per breast cancer patient.ConclusionCombining appropriate RT parameters, STAR nomogram could help in the detection of patient anemia, coagulation function, inflammation and immune status. Practical implementation of the STAR nomogram in a clinical setting is feasible, and its potential clinical impact lies in its ability to provide an early, economical and reliable tool for survival prediction and surveillance strategy management. However, our model still has limitations and requires external data validation. In subsequent studies, we plan to mitigate the potential impact on model robustness by further updating and adjusting the data and model.

Abstract Background IL-33 is a multifunctional cytokine with dual functions. However, the clinicopathological and prognostic significance of IL-33 in cancer patients, especially in patients with hepatocellular carcinoma (HCC), remains controversial. Therefore, we conducted a study of 565 patients with HCC and 561 healthy controls and performed a meta-analysis to quantitatively evaluate the above problems. Methods We collected blood from 565 patients with HCC and 561 healthy controls. ELISA was used to detect the concentrations of IL-33 and ST2 in the serum, and RT‒PCR was used to detect the levels of IL-33 and ST2 mRNA. Meanwhile, we collected comprehensive literature on IL-33 and the clinical characteristics of cancer patients retrieved from the PubMed, Web of Science and CNKI databases as of December 2022. An odds ratio (OR) with a 95% confidence interval (CI) was used to estimate the impact through overall and stratified analyses. Results Compared with the healthy control group, the levels of ST2 mRNA and serum in the peripheral blood of HCC patients increased (p 0.05). In the meta-analysis section, at the tissue level, the overall analysis showed that the expression of IL-33 was positively correlated with tumor stage, histological grade, distant metastasis, and tumor size. Compared with patients with low IL-33 expression, the 3-year overall survival (OS) rate (OR = 3.467, p

This study investigates the use of self-produced solid waste in mines to backfill the goaf, with energy saving and emission reduction technology requirements. Using a relevant backfill mixing proportion test and mechanical property test, we analyzed the strength variation law of backfill materials under different concentrations and cementation powder addition. The results showed that, for backfill materials at 7 days with an ash-tailings ratio of 1:6, the strength of specimens with 52% and 49% prepared concentrations, respectively, increased by 1.361 and 1.266 times over specimens with 49% and 46% concentrations. For backfill materials with 49% prepared concentrations and ash-tailings ratios of 1:6, 1:9, and 1:12, the specimen strength of the backfill materials with ratios of 1:6 and 1:9 improved by 0.616 MPa and 0.321 MPa—109.28% and 114.6%, respectively—compared with 1:9 and 1:12 backfill materials at 60 days. It is thus evident that the quantity of binding materials added plays a critical function in improving the strength of backfill materials, and in the subsequent application of goaf backfill. The strength of backfill materials can be enhanced by improving the gradation of backfill aggregates and increasing the amount of binding materials so as to achieve cost reductions and sustainable development.

Three groups of filling materials with different mix proportions were prepared. PO42.5 grade cement was selected as the binding material, mechanized granular coal gangue and fine sand were used as the filling aggregate, and then the specimens were curing at room temperature and 95% moisture. Through the uniaxial compression test, the influence of gangue–sand ratio, the mass fraction, and curing age on the mechanical properties of filling materials was analyzed. The microstructure analysis of SEM was conducted to explore the internal mechanism of the strength difference of filling materials. The results show that the gangue–sand ratio dramatically influences the uniaxial compressive strength. The strength increases first and then decreases with the gangue–sand ratio increase. The mass fraction of filling paste is positively correlated with specimen strength. When the sand–cement ratio, gangue–sand ratio, and the mass fraction remain unchanged, the longer the curing age, the greater the uniaxial compressive strength of specimens. The specimens with a sand–cement ratio of 3.5 : 1, a gangue–sand ratio of 5 : 5, and a mass fraction of 86% reach the maximum value of 11.03 MPa at a curing age of 10 days. It can be seen that when the gangue–sand ratio is 5 : 5, the filling material has the best mechanical properties, so it can be recommended as the optimal mix proportion for goaf filling. Thus, it provides a solid technical guarantee for the treatment of mine surface collapse disasters and the utilization of bulk coal gangue resources.

In the process of continuous and high-intensity mining in soft rock coal mines, high-quality filling treatment is required for the goaf. A nondestructive acoustic velocimetry method is an excellent way to measure the quality of filling body to effectively maintain the rock stability of the mining area and decrease industrial solid waste. In this study, the gangue and fine sand are used as the filling aggregates, and the uniaxial compression test and acoustic wave test are conducted in the cementitious gangue-sand filling body with different gangue-sand ratio. The results show that the longitudinal wave velocity of the filling body is basically between 1.556 km/s and 2.413 km/s. When the gangue-sand ratio is small, and the slurry concentration is high, the early strength of the cementitious gangue-sand filling body is low, but the later strength has a good growth trend. For specimens with higher strength-filled bodies, the propagation speed of sound waves inside them is also faster, indicating a certain positive correlation between the strength of the filled body and the speed of sound waves. A mathematical equation using longitudinal wave velocity to predict the strength of filling body is established. This equation can be used to predict and judge the quality of the filling body in the mining area.

Objectives This study aimed to investigate the differentiation state and clinical significance of colorectal cancer cells, as well as to predict the immune response and prognosis of patients based on differentiation-related genes of colorectal cancer. Introduction Colorectal cancer cells exhibit different differentiation states under the influence of the tumor microenvironment, which determines the cell fates. Methods We combined single-cell sequencing (scRNA-seq) data from The Cancer Genome Atlas source with extensive transcriptome data from the Gene Expression Omnibus database. We obtained colorectal cancer differentiation-related genes using cell trajectory analysis and developed a colorectal cancer differentiation-related gene based molecular typing and prognostic model to predict the immune response and prognosis of patients with colorectal cancer. Results We identified 5 distinct cell differentiation subsets and 620 colorectal cancer differentiation-related genes. Colorectal cancer differentiation-related genes were significantly associated with metabolism, angiogenesis, and immunity. We separated patients into 3 subtypes based on colorectal cancer differentiation-related gene expression in the tumor and found differences among the different subtypes in immune infiltration status, immune checkpoint gene expression, clinicopathological features, and overall survival. Immunotherapeutic interventions involving a highly expressed immune checkpoint blockade may be selectively effective in the corresponding cancer subtypes. We built a risk score prediction model (5-year AUC: .729) consisting of the 4 most important predictors of survival (TIMP1, MMP1, LGALS4, and ITLN1). Finally, we generated and validated a nomogram consisting of the risk score and clinicopathological variables. Conclusion This study highlights the significance of genes involved in cell differentiation for clinical prognosis and immunotherapy in patients and provides prospective therapeutic targets for colorectal cancer.

For the mine of large goaf, there are many factors that affect the filling quality. In order to improve the filling quality, it is necessary to identify the factors that have a significant impact. In this paper, the fuzzy mathematical method was used to analyze the five main factors (PC32.5cement sand ratio, slurry concentration, number of slurry lowering, dehydration and drainage, and tailing particle size grading) affecting the filling quality. The priority of each influence factor was calculated, and the priority set was established. It is found that the slurry launching point and particle size grading of the tailings have an obvious effect on filling quality. After increasing the slurry launching point and optimizing the particle size grading, the strength and uniformity of the filling body are improved.

Under the current pressure of mineral product price and market competition, as well as the tightening of ecological and environmental protection policies, energy saving, emission reduction and cost reduction, and efficiency increase have become inevitable trends. Lead-smelting slag of smelter, as an industrial byproduct, is harmful solid waste to be eliminated. In this research, a series of experiments on substitute for cement were done for filling cost reduction under premise of guaranteeing the filling strength, using PC32.5 cement as reference cementing material. Meanwhile, with Portland cement clinker, water quenching slag of BF ironmaking, and lead-smelting slag of smelter in different ratios, four cementing materials were made and named as cementing material I, cementing material II, cementing material III, and cementing material IV. Then, the above five cementing materials including PC32.5 cement were mixed, respectively, with tailing and water to prepare paste backfill slurry with different binder-tailing ratio and concentration. Backfill block was made and cured at 20°C; their uniaxial compressive strengths in different curing ages were measured. The result shows the strength of backfill test block using cementing material I (cement clinker and water quenching slag) without lead-smelting slag is far higher than that of test block using PC32.5 cement as cementing material. The strength of block with 16% lead-smelting slag increased dramatically, which was higher than the strength of block using PC32.5 as cementing material. However, the strength of test block decreased when the content of lead-smelting slag reached 32% and 40%. The more lead-smelting slag was added, the worse influence on strength of backfill would be. Because of large specific gravity of lead-smelting slag and low content of CaO, the hydration activity of lead-smelting slag is far lower than that of water quenching slag. Therefore, using moderate lead-smelting slag as substitute for cement will lead to increase of backfill strength and decrease filling cost.

In the process of filling the goaf of Huashugou copper mine, the filling aggregate was the mine’s self-produced copper flotation tailings. The physical and chemical property tests showed that the tailings were relatively fine, and the cumulative contents of the 7.5, 15, 80, and 90 μm particles were 56.99, 79.46, 99.88, and 100%, respectively. PSA42.5 Portland slag cement was selected as the cementing material for the tests. The amount of finely ground blast furnace slag added to the cement was 20–50%. The filling test blocks of all proportions were demolded within 24–36 h, and the integrity of the formed filling bodies was satisfactory. Among them, the uniaxial compressive strength of the test block formed by the filling slurry with a tailings-to-cement ratio of 3/1 and a concentration of 74% after 3, 7, 28, and 60 days reached 0.833, 2.026, 4.520, and 6.560 MPa, respectively. The strengths of the other filling blocks also met the engineering design requirements. Based on the statistical regression analysis method, the influences of the slag cement content χ₁ and the tailings water content χ₂ on the filling body’s strength Ϭ were analyzed. The regression calculation also considered the interaction between χ₁ and χ₂, and the obtained multiple nonlinear regression model successfully predicted Ϭ. In production and application, the large density of tailings would accelerate their settling, which could easily cause blockage during the transportation of filling slurry through the pipeline. Regarding a series of existing filling materials and technical conditions, the filling process investigated in this study overcomes several unfavorable conditions. Using advanced and reliable technology and an industrial filling pump with a delivery pressure of 10 MPa, the prepared filling slurry concentration was 68–72% and the flow rate was 56–79 m3/h. The tailings and PSA42.5 Portland slag cement combined quite well. The filling body in the goaf demonstrated excellent homogeneity, and the various industrial indicators met the technical requirements for mining deposits. The preparation of high-quality filling materials with a smooth filling process has played a positive role in the prevention and treatment of surface mountain cracks and collapse during underground mining in high-altitude areas.

Abstract Background Primordial follicular depletion has thought to be a common adverse effect of chemotherapy especially for female of reproductive age. The study aimed to evaluate the protective effect of rapamycin on the primordial follicles and its potential mechanism for patients receiving chemotherapy. Methods 8-week old BALB/c female mice were randomly assigned into four groups (control; rapamycin; cyclophosphamide; and rapamycin combined with cyclophosphamide). Hematoxylin staining, immunohistochemical, TUNEL, western blotting and ELISA were employed to assess inter-group differences using Student’s t-test and Mann-Whitney test. Results Cyclophosphamide depleted the follicular reserve and induced the phosphorylation of the key proteins of PI3K/Akt/mTOR pathway in mice in a dose-dependent manner. Co-treatment with rapamycin significantly reduced primordial follicle loss at all cyclophosphamide dose groups and prevent the follicle growth wave caused by cyclophosphamide treatment (P

DL frameworks are the basis of constructing all DL programs and models, and thus their bugs could lead to the unexpected behaviors of any DL program or model relying on them. Such a wide effect demonstrates the necessity and importance of guaranteeing DL frameworks’ quality. Understanding the characteristics of DL framework bugs is a fundamental step for this quality assurance task, facilitating designing effective bug detection and debugging approaches. Hence, in this work, we conduct the most large-scale study on 1,000 bugs from four popular and diverse DL frameworks (i.e., TensorFlow, PyTorch, MXNet, and DL4J). By analyzing the root causes and symptoms of DL framework bugs associated with five components decomposed from DL frameworks, as well as measuring test coverage achieved by three state-of-the-art testing techniques, we obtain 12 major findings for the comprehensive understanding of DL framework bugs and the current status of existing DL framework testing practice, and then provide a series of actionable guidelines for better DL framework bug detection and debugging. Finally, based on the guidelines, we design and implement a prototype DL-framework testing tool, called TenFuzz, which is evaluated to be effective and finds three unknown bugs on the latest TensorFlow framework in a preliminary study, indicating the significance of our guidelines. [ABSTRACT FROM AUTHOR]