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PURPOSE: Current clinical risk stratification methods for localized prostate cancer are suboptimal, leading to over- and undertreatment. Recently, machine learning approaches using digital histopathology have shown superior prognostic ability in phase III trials. This study aims to develop a clinically usable risk grouping system using multimodal artificial intelligence (MMAI) models that outperform current National Comprehensive Cancer Network (NCCN) risk groups. MATERIALS AND METHODS: The cohort comprised 9,787 patients with localized prostate cancer from eight NRG Oncology randomized phase III trials, treated with radiation therapy, androgen deprivation therapy, and/or chemotherapy. Locked MMAI models, which used digital histopathology images and clinical data, were applied to each patient. Expert consensus on cut points defined low-, intermediate-, and high-risk groups on the basis of 10-year distant metastasis rates of 3% and 10%, respectively. The MMAIs reclassification and prognostic performance were compared with the three-tier NCCN risk groups. RESULTS: The median follow-up for censored patients was 7.9 years. According to NCCN risk categories, 30.4% of patients were low-risk, 25.5% intermediate-risk, and 44.1% high-risk. The MMAI risk classification identified 43.5% of patients as low-risk, 34.6% as intermediate-risk, and 21.8% as high-risk. MMAI reclassified 1,039 (42.0%) patients initially categorized by NCCN. Despite the MMAI low-risk group being larger than the NCCN low-risk group, the 10-year metastasis risks were comparable: 1.7% (95% CI, 0.2 to 3.2) for NCCN and 3.2% (95% CI, 1.7 to 4.7) for MMAI. The overall 10-year metastasis risk for NCCN high-risk patients was 16.6%, with MMAI further stratifying this group into low-, intermediate-, and high-risk, showing metastasis rates of 3.4%, 8.2%, and 26.3%, respectively. CONCLUSION: The MMAI risk grouping system expands the population of men identified as having low metastatic risk and accurately pinpoints a high-risk subset with elevated metastasis rates. This approach aims to prevent both overtreatment and undertreatment in localized prostate cancer, facilitating shared decision making.

BACKGROUND: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life, and there remain no validated predictive models to guide its use. METHODS: We used digital pathology images from pretreatment prostate tissue and clinical data from 5727 patients enrolled in five phase 3 randomized trials, in which treatment was radiotherapy with or without ADT, as our data source to develop and validate an artificial intelligence (AI)–derived predictive patient-specific model that would determine which patients would develop the primary end point of distant metastasis. The model used baseline data to provide a binary output that a given patient will likely benefit from ADT or not. After the model was locked, validation was performed using data from NRG Oncology/Radiation Therapy Oncology Group (RTOG) 9408 (n=1594), a trial that randomly assigned men to radiotherapy plus or minus 4 months of ADT. Fine–Gray regression and restricted mean survival times were used to assess the interaction between treatment and the predictive model and within predictive model–positive, i.e., benefited from ADT, and –negative subgroup treatment effects. RESULTS: Overall, in the NRG/RTOG 9408 validation cohort (14.9 years of median follow-up), ADT significantly improved time to distant metastasis. Of these enrolled patients, 543 (34%) were model positive, and ADT significantly reduced the risk of distant metastasis compared with radiotherapy alone. Of 1051 patients who were model negative, ADT did not provide benefit. CONCLUSIONS: Our AI-based predictive model was able to identify patients with a predominantly intermediate risk for prostate cancer likely to benefit from short-term ADT. (Supported by a grant [U10CA180822] from NRG Oncology Statistical and Data Management Center, a grant [UG1CA189867] from NCI Community Oncology Research Program, a grant [U10CA180868] from NRG Oncology Operations, and a grant [U24CA196067] from NRG Specimen Bank from the National Cancer Institute and by Artera, Inc. ClinicalTrials.gov numbers NCT00767286, NCT00002597, NCT00769548, NCT00005044, and NCT00033631.)

As an essential community of practice which could facilitate teachers' professional development and enrich research in teacher education, the teacher--researcher partnership (TRP) has not been sufficiently investigated in China. As reported in this paper, we conducted an empirical study on TRPs between secondary school mathematics teachers and university researchers by applying activity theory to investigate their collaboration and contradictions, through the process of co-designing lesson studies. We found four levels of contradictions, namely, primary, secondary, tertiary, and quaternary, within which the fourth one occurs less frequently compared to the other three. To ameliorate or even resolve the contradictions, it requires both sides to (1) achieve agreement on understanding the objects, (2) promote equality in communication and mutual respect, (3) facilitate transformation in identity, and (4) construct a united community. In addition, the investigation provided a depiction of how teachers learn and grow in the TRP as well as how teachers and researchers interact in the partnership, which in turn enriches both teacher education and activity theory.

BackgroundProstate cancer exhibits biological and clinical heterogeneity even within established clinico-pathologic risk groups. The Decipher genomic classifier (GC) is a validated method to further risk-stratify disease in patients with prostate cancer, but its performance solely within National Comprehensive Cancer Network (NCCN) high-risk disease has not been undertaken to date.MethodsA multi-institutional retrospective study of 405 men with high-risk prostate cancer who underwent primary treatment with radical prostatectomy (RP) or radiation therapy (RT) with androgen-deprivation therapy (ADT) at 11 centers from 1995 to 2005 was performed. Cox proportional hazards models were used to determine the hazard ratios (HR) for the development of metastatic disease based on clinico-pathologic variables, risk groups, and GC score. The area under the receiver operating characteristic curve (AUC) was determined for regression models without and with the GC score.ResultsOver a median follow-up of 82 months, 104 patients (26%) developed metastatic disease. On univariable analysis, increasing GC score was significantly associated with metastatic disease ([HR]: 1.34 per 0.1 unit increase, 95% confidence interval [CI]: 1.19-1.50, p  0.05). On multivariable analysis, GC score (HR: 1.33 per 0.1 unit increase, 95% CI: 1.19-1.48, p

In Older to solve the difficulty dyeing dark color, the dyeing differences between commercially available PLA dyes and commercially available HA dyes on PLA fabric were compared in the paper, and the effects of dyeing temperature on the bursting strength of PLA fabric were discussed, the effects of dyeing temperature, pH value of dyeing solution, dyeing bath ratio, dyeing time and dye dosage on the depth of PLA fabric using commercially available PLA dyes. After the dyeing temperature effects 115 °C the fabric strength decreases significantly, when the dyeing temperature is 120 °C, the top breaking strength decreases by 11.4%, and the optimized dyeing process is as follows; dyeing temperature 120 °C, pH value 4.5, bath ratio 1:10, holding time 30 min. In order to ensure the level dyeing of fabrics, the temperature should be slowly increased by 0.5-1.0 °C/min between 60-110 °C. Commercially available PLA dyes have good dyeing property, which is suitable for dyeing dark color. The color fastness to rubbing and soaping wash of the polylactic acid fabric after reduction washing are above grade 4, and the standard deviation of K/S value is 0.215, which can meet the color fastness requirements of the garments. [ABSTRACT FROM AUTHOR]

The extensive accumulation of coal-derived solid wastes in China has become a significant environmental challenge. Utilizing these wastes as resources is considered an effective approach. This study investigated activation systems for coal gangue to determine the optimal dosage for incorporation into cementitious materials. The effects of types and dosages of admixtures on the mechanical properties were examined. The results showed that augmenting the coal gangue content within a certain range enhanced the compressive strength of the composite cementitious materials. However, exceeding 20 wt.% led to a decline in compressive strength. The addition of coal gangue led to an increase in porosity. Furthermore, the addition of 4% lime and 4% gypsum resulted in enhanced properties. The performance of the resultant optimal cementitious material surpasses that of P·O 32.5 cement, making it a viable replacement in construction sector and contributing to the realization of resource recycling and ecological civilization construction goals. [ABSTRACT FROM AUTHOR]

Purpose It is clinically challenging to integrate genomic-classifier results that report a numeric risk of recurrence into treatment recommendations for localized prostate cancer, which are founded in the framework of risk groups. We aimed to develop a novel clinical-genomic risk grouping system that can readily be incorporated into treatment guidelines for localized prostate cancer. Materials and Methods Two multicenter cohorts (n = 991) were used for training and validation of the clinical-genomic risk groups, and two additional cohorts (n = 5,937) were used for reclassification analyses. Competing risks analysis was used to estimate the risk of distant metastasis. Time-dependent c-indices were constructed to compare clinicopathologic risk models with the clinical-genomic risk groups. Results With a median follow-up of 8 years for patients in the training cohort, 10-year distant metastasis rates for National Comprehensive Cancer Network (NCCN) low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.3%, 9.2%, 38.0%, and 39.5%, respectively. In contrast, the three-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.5%, 29.4%, and 54.6%, for low-, intermediate-, and high-risk, respectively, which were consistent in the validation cohort (0%, 25.9%, and 55.2%, respectively). C-indices for the clinical-genomic risk grouping system (0.84; 95% CI, 0.61 to 0.93) were improved over NCCN (0.73; 95% CI, 0.60 to 0.86) and Cancer of the Prostate Risk Assessment (0.74; 95% CI, 0.65 to 0.84), and 30% of patients using NCCN low/intermediate/high would be reclassified by the new three-tier system and 67% of patients would be reclassified from NCCN six-tier (very-low- to very-high-risk) by the new six-tier system. Conclusion A commercially available genomic classifier in combination with standard clinicopathologic variables can generate a simple-to-use clinical-genomic risk grouping that more accurately identifies patients at low, intermediate, and high risk for metastasis and can be easily incorporated into current guidelines to better risk-stratify patients.

Purpose To perform the first meta-analysis of the performance of the genomic classifier test, Decipher, in men with prostate cancer postprostatectomy. Methods MEDLINE, EMBASE, and the Decipher genomic resource information database were searched for published reports between 2011 and 2016 of men treated by prostatectomy that assessed the benefit of the Decipher test. Multivariable Cox proportional hazards models fit to individual patient data were performed; meta-analyses were conducted by pooling the study-specific hazard ratios (HRs) using random-effects modeling. Extent of heterogeneity between studies was determined with the I2 test. Results Five studies (975 total patients, and 855 patients with individual patient-level data) were eligible for analysis, with a median follow-up of 8 years. Of the total cohort, 60.9%, 22.6%, and 16.5% of patients were classified by Decipher as low, intermediate, and high risk, respectively. The 10-year cumulative incidence metastases rates were 5.5%, 15.0%, and 26.7% ( P < .001), respectively, for the three risk classifications. Pooling the study-specific Decipher HRs across the five studies resulted in an HR of 1.52 (95% CI, 1.39 to 1.67; I2 = 0%) per 0.1 unit. In multivariable analysis of individual patient data, adjusting for clinicopathologic variables, Decipher remained a statistically significant predictor of metastasis (HR, 1.30; 95% CI, 1.14 to 1.47; P < .001) per 0.1 unit. The C-index for 10-year distant metastasis of the clinical model alone was 0.76; this increased to 0.81 with inclusion of Decipher. Conclusion The genomic classifier test, Decipher, can independently improve prognostication of patients postprostatectomy, as well as within nearly all clinicopathologic, demographic, and treatment subgroups. Future study of how to best incorporate genomic testing in clinical decision-making and subsequent treatment recommendations is warranted.

Hydrogen sulfide (H2S) contamination of food has raised widespread public health concerns, leading to substantial medical and economic burdens. Herein, a 2D/0D heterojunction fluorescent probe (TCTG) with Schottky barriers (SB) is designed and synthesized, utilizing Ti3C2Tx MXene‐loaded graphene quantum dots (GQDs), for the detection of H2S during food spoilage. The microstructures observed through SEM and TEM reveal that uniformly sized GQDs are evenly attached to the surface of a monolayer Ti3C2Tx. The chemisorption between GQDs and Ti3C2Tx facilitates charge transfer and the formation of SB, resulting in intramolecular charge transfer (ICT) effects. With the introduction of H2S, TCTG(50%) exhibits the highest sensitivity, selectivity, and anti‐interference properties, with ultra‐fast fluorescence transient reaction (3s) and remarkably low detection limit of 41.82 ppb as well as noticeable color change. When TCTG(50%) reacted with H2S, the ICT effects are inhibited, leading to the recovery of photoinduced electron transfer (PET) and fluorescence quenching. Notably, probe TCTG is effectively utilized to detect changes in H2S levels in raw foods to assess their quality. Overall, the significance of this study is its potential to revolutionize food spoilage detection, offering a fast, reliable, and sensitive method to ensure food safety and reduce associated health and economic burdens. [ABSTRACT FROM AUTHOR]

The accumulation of phosphogypsum (PG) has become a significant environmental and economic challenge, making PG reuse an urgent concern. This study aims to explore the potential of combining PG with steel slag (SS), ground granulated blast-furnace slag (GGBS), and cement clinker (CC) to create excessive-sulfate phosphogypsum slag cement (EPSC) for use in self-compacting concrete (SCC). However, residual acid and other impurities in PG can significantly slow down the setting time of EPSC. To optimize the setting time, different methods including wet grinding, washing, and the addition of pure gypsum, limestone powder (LP), and aluminum cement (AC) are used. Results show that the wet grinding method and washing method remove soluble phosphorus impurities and reduce fluorine ion levels by 64%. However, nonsoluble impurities that cannot be removed by wet grinding and washing continue to affect EPSC's setting time. Furthermore, the addition of LP shortens the setting time, but not enough. Using a 3% dosage of AC successfully reduces the setting time to a satisfactory level and EPSC SCC with a 28-day strength greater than 40 MPa is produced using the paste rheological threshold method. [ABSTRACT FROM AUTHOR]

Self-compacting concrete (SCC), known for its excellent fluidity and self-compacting ability, is widely used in civil engineering. To enhance the comprehensive performance of SCC, dolomite powder (DP) is integrated as a substitute for cement. This study aims to analyze the impact of DP on the yield stress relationship between self-compacting mortar (SCM) and self-compacting paste (SCP) from a multi-scale perspective. A new predictive model for the yield stress relationship between SCM and SCP incorporating DP is established by improving the n value in the existing ϕ e model, which characterizes the sensitivity of the mortar yield stress relative to changes in the paste yield stress. By conducting mini-slump flow tests on nine sets of cement–DP mixtures, it is found that DP impacts the yield stress relationship between SCM and SCP mainly through changes in the inter-particle filling effect, and the n value in the predictive model is roughly between 2.4 and 3.6. When the DP content is kept constant and the particle size is changed, the n value shows a strong positive linear relationship with the packing density of the paste ( ϕ e , p ). The relationship between n and ϕ e , p is derived using the linear fitting method, which improves the model's predictive accuracy by 95.2%. [ABSTRACT FROM AUTHOR]