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Abstract Background Lymph node metastasis (LNM) is a prognostic biomarker and affects therapeutic selection in colorectal cancer (CRC). Current evaluation methods are not adequate for estimating LNM in CRC. H&E images contain much pathological information, and collagen also affects the biological behavior of tumor cells. Hence, the objective of the study is to investigate whether a fully quantitative pathomics-collagen signature (PCS) in the tumor microenvironment can be used to predict LNM. Methods Patients with histologically confirmed stage I-III CRC who underwent radical surgery were included in the training cohort (n = 329), the internal validation cohort (n = 329), and the external validation cohort (n = 315). Fully quantitative pathomics features and collagen features were extracted from digital H&E images and multiphoton images of specimens, respectively. LASSO regression was utilized to develop the PCS. Then, a PCS-nomogram was constructed incorporating the PCS and clinicopathological predictors for estimating LNM in the training cohort. The performance of the PCS-nomogram was evaluated via calibration, discrimination, and clinical usefulness. Furthermore, the PCS-nomogram was tested in internal and external validation cohorts. Results By LASSO regression, the PCS was developed based on 11 pathomics and 9 collagen features. A significant association was found between the PCS and LNM in the three cohorts (P

Abstract Postoperative adjuvant chemotherapy (AC) for poor responders to neoadjuvant chemoradiotherapy (nCRT) remains debatable among patients with locally advanced rectal cancer (LARC), necessitating biomarkers to accurately predict the benefits of AC. This study aimed to develop a patient‐derived tumor organoid (PDTO) platform to predict the benefit of AC in LARC patients showing poor nCRT response. PDTOs were established using irradiated rectal cancer specimens with poor nCRT responses, and their sensitivity to chemotherapy regimens was tested. The half‐maximal inhibitory concentration (IC50) value for the PDTO drug test was defined based on the clinical outcomes, and the accuracy of the PDTO prognostic predictions was calculated. Predictive models were developed and validated using the PDTO drug test results. Between October 2018 and December 2021, 86 PDTOs were successfully constructed from 138 specimens (success rate 62.3%). The optimal IC50 cut‐off value for the organoid drug test was 39.31 μmol/L, with a sensitivity of 84.75%, a specificity of 85.19%, and an accuracy of 84.88%. Multivariate Cox regression analysis revealed that the PDTO drug test was an independent predictor of prognosis. A nomogram based on the PDTO drug test was developed, showing good prognostic ability in predicting the 2‐year and 3‐year disease‐free survivals (AUC of 0.826 [95% CI, 0.721–0.931] and 0.902 [95% CI, 0.823–0.982], respectively) and overall survivals (AUC of 0.859 [95% CI, 0.745–0.973] and 0.885 [95% CI, 0.792–0.978], respectively). The PDTO drug test can predict the benefit of postoperative AC in poor responders with LARC to nCRT.

ObjectivesThe Immunoscore can categorize patients into high- and low-risk groups for prognostication in colorectal cancer (CRC). Collagen plays an important role in immunomodulatory functions in the tumor microenvironment (TME). However, the correlation between collagen and the Immunoscore in the TME is unclear. This study aimed to construct a collagen signature to illuminate the relationship between collagen structure and Immunoscore.MethodsA total of 327 consecutive patients with stage I-III stage CRC were included in a training cohort. The fully quantitative collagen features were extracted at the tumor center and invasive margin of the specimens using multiphoton imaging. LASSO regression was applied to construct the collagen signature. The association of the collagen signature with Immunoscore was assessed. A collagen nomogram was developed by incorporating the collagen signature and clinicopathological predictors after multivariable logistic regression. The performance of the collagen nomogram was evaluated via calibration, discrimination, and clinical usefulness and then tested in an independent validation cohort. The prognostic values of the collagen nomogram were assessed using Cox regression and the Kaplan−Meier method.ResultsThe collagen signature was constructed based on 16 collagen features, which included 6 collagen features from the tumor center and 10 collagen features from the invasive margin. Patients with a high collagen signature were more likely to show a low Immunoscore (Lo IS) in both cohorts (P

Summary: Patient-derived tumor organoids (PDTOs) have the potential to be used to predict the patient response to chemotherapy. However, the cutoff value of the half-maximal inhibition concentration (IC50) for PDTO drug sensitivity has not been validated with clinical cohort data. We established PDTOs and performed a drug test in 277 samples from 242 CRC patients who received FOLFOX or XELOX chemotherapy. After follow-up and comparison of the PDTO drug test and final clinical outcome results, the optimal IC50 cutoff value for PDTO drug sensitivity was 43.26 μmol/L. This PDTO drug test-defined cutoff value could predict patient response with 75.36% sensitivity, 74.68% specificity, and 75% accuracy. Moreover, this value distinguished groups of patients with significant differences in survival benefit. Our study is the first to define the IC50 cutoff value for the PDTO drug test to effectively distinguish CRC patients with chemosensitivity or nonsensitivity and predict survival benefits.

Summary: The tumor, nodes and metastasis (TNM) classification system provides useful but incomplete prognostic information and lacks the assessment of the tumor microenvironment (TME). Collagen, the main component of the TME extracellular matrix, plays a nonnegligible role in tumor invasion and metastasis. In this cohort study, we aimed to develop and validate a TME collagen signature (CSTME) for prognostic prediction of stage II/III colorectal cancer (CRC) and to compare the prognostic values of “TNM stage + CSTME” with that of TNM stage alone. Results indicated that the CSTME was an independent prognostic risk factor for stage II/III CRC (hazard ratio: 2.939, 95% CI: 2.180–3.962, p

Abstract The current tumor‐node‐metastasis staging system does not provide sufficient prognostic prediction or adjuvant chemotherapy benefit information for stage II‐III colon cancer (CC) patients. Collagen in the tumor microenvironment affects the biological behaviors and chemotherapy response of cancer cells. Hence, in this study, we proposed a collagen deep learning (collagenDL) classifier based on the 50‐layer residual network model for predicting disease‐free survival (DFS) and overall survival (OS). The collagenDL classifier was significantly associated with DFS and OS (P

Given the characteristics of the flexible job-shop scheduling problem and the practical production of a given enterprise, a flexible job-shop scheduling model was proposed to minimize the maximum completion time. A novel algorithm was proposed to solve the model by integrating the dung beetle optimization algorithm and the simulated annealing algorithm. Algorithmic improvements include the design of a single-layer process encoding scheme with machine selection during decoding to ensure a higher level of the initial population. During population update, the dung beetle optimization algorithm was applied for optimization, followed by simulated annealing operations to enhance the convergence speed and solution quality of the algorithm. Through simulation experiments and comparisons with other algorithms, the effectiveness and superiority of the proposed algorithm were validated. In addition, the feasibility of the algorithm was tested through a real-world factory production case. In conclusion, the improvements made in this paper to the algorithms and scheduling models offer valuable insights into the educational aspects of job-shop scheduling. For instance, the single-layer encoding proposed herein simplifies the coding process, making it more accessible for beginners. Additionally, the accompanying decoding strategy yields relatively higher-quality initial solutions, facilitating subsequent optimization processes by accelerating convergence without compromising solution quality. Students were able to gain a better understanding of real workshop conditions through this project, going beyond the sole goal of minimizing completion time. They began to consider more complex situations in the machining process, such as machine breakdowns, changes in machining schedules, and the load on bottleneck machines and total machine load. This allowed students to have a holistic view of a complex production workshop. In terms of education, the project improved students' ability to consider practical aspects when solving problems and provided them with a way to solve problems.

The existing 2D/3D strategies still have limitations in human liver and tumor segmentation efficiency. Therefore, this paper proposes a 2.5D network combing cascaded context module (CCM) and Ladder Atrous Spatial Pyramid Pooling (L-ASPP), named CCLNet, for automatic liver and tumor segmentation from CT. First, we utilize the 2.5D mode to improve the training efficiency; Second, we employ the ResNet-34 as the encoder to enhance the segmentation accuracy. Third, the L-ASPP module is used to enlarge the receptive field. Finally, the CCM captures more local and global feature information. We experimented on the LiTS17 and 3DIRCADb datasets. Experimental results prove that the method skillfully balances accuracy and cost, thus having good prospects in liver and liver segmentation in clinical assistance.

Consumers' time allocation decisions among various activities are fundamental to marketing research and consumer behavior. The authors construct a dynamic panel data model to examine how consumers allocate time to a portfolio of leisure activities over time. The data comprise a longitudinal panel in which the authors tracked 287 U.S. consumers' time use, consumption motives, and expertise measures on a weekly basis from January to June 2011. This is the first empirical research to examine the underlying mechanisms that guide the dynamics of an individual's activity consumption. The authors demonstrate that expertise contributes to the perceived benefits of an activity, which in turn leads to high value associated with it. Expertise also directly influences value obtained from an activity. This expertise, in turn, is acquired over time through past consumption. This finding implies a chain from expertise to value to time use and back to expertise, which may lead consumers to form a lifestyle in which they specialize in a subset of activities they know well. Consequently, expertise can be regarded as a key variable that explains lifestyle choices. [ABSTRACT FROM AUTHOR]

Although miR-5195-3p has been acknowledged for its tumor suppressor role in diverse cancer categories, its precise functions and mechanisms concerning melanoma have not been comprehensively elucidated. In this study, we employed quantitative reverse transcription PCR, Western blot analysis, and immunohistochemistry staining to investigate the expression patterns of miR-5195-3p and poly (rC) binding protein 2 (PCBP2) in melanoma tissues compared to adjacent tissues. Our findings revealed downregulation of miR-5195-3p and upregulation of PCBP2 in melanoma tissues. Through the implementation of a luciferase reporter assay, we successfully identified PCBP2 as a newly discovered target of miR-5195-3p in melanoma cells. Enforced expression of miR-5195-3p via mimics inhibited cell proliferation and migration in A375 and A2058 cells, as demonstrated by CCK-8 and transwell migration assays. In melanoma cells, reintroduction of PCBP2 partially reversed the inhibitory effects of miR-5195-3p overexpression. Treatment with LY294002, an inhibitor of the PI3K/AKT signaling pathway, also reversed the effects of PCBP2 in melanoma cells. Furthermore, our results suggest that miR-5195-3p inhibits the activation of the PI3K/AKT signaling pathway in melanoma by inhibiting PCBP2. In conclusion, our research has identified the miR-5195-3p targeting of the PCBP2-mediated PI3K/AKT signaling pathway as a potential therapeutic target for melanoma treatment.

Abstract Improvement in volumetric performance is growing requirements for rechargeable batteries of lightweight and compact size. Flake graphite (FG) with thin lamellar structure realizes a satisfactory K‐ion storage performance, but its low packing density leads to poor volumetric capacity. Herein, this work fabricates an FG anode with improved packing density by hydrothermal treatment of the FG with graphene oxide and subsequent capillary evaporation‐induced drying process. A three‐dimensional flake graphite/reduced graphene oxide (FG/rGO) hydrogel is formed after hydrothermal treatment, which can be compressed during drying process via the capillary force generated by water evaporation, resulting in a compact structure with enhanced packing density. Compared to the pristine FG electrode (106.0 mAh cm−3), the FG/rGO‐82 anode realizes an ultrahigh volumetric capacity of 218.9 mAh cm−3 and good rate performance with a retained capacity of 152.3 mAh cm−3 at 2 C. Moreover, the FG/rGO‐82 exhibits excellent cycle stability with a capacity retention of 90.1% after 300 cycles. Even under a high mass loading of 7.4 mg cm−2, it still remains a volumetric capacity of 145.4 mAh cm−3 after 300 cycles at 1 C. The results prove that the capillary evaporation induced drying of hydrogel is an effective way to prepare electrode material with high volumetric capacity.

Abstract Objective To identify the plasma protein biomarkers related to the chemoresistance of postoperative recurrence of epithelial ovarian cancers. Methods Forty plasma samples from patients in chemotherapy-sensitive and chemotherapy-resistant groups (20 for each group) were collected at Gynecology Department in the Fourth Hospital of Hebei Medical University from September 2013 to September 2014. The differentially expressed proteins between two groups were screened with two-dimensional gel electrophoresis (2-DE) and further analyzed by matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF–MS). Results Thirty-four differentially expressed spots were identified between the two groups. Compared with the chemo-sensitive group, 21 protein spots were up-regulated and 13 were down-regulated in the chemoresistant group, in which 14 differentially expressed proteins were identified by the Mass spectrometry and Mascot search. Among the 14 proteins, complement C4-A, IgJ chain, clusterin, α-1-antitrypsin and carbonic anhydrase 1 were up-regulated, and transthyretin, haptoglobin, β-2-glycoprotein, Ig γ-2 chain C region, Ig γ-1 chain C region, complement factor I light chain, Igκ chain C region, complement C3 and apolipoprotein E were down-regulated in the chemoresistant group when compared with the chemosensitive group. Conclusion The up-regulated proteins including transthyretin, apolipoprotein E and haptoglobin proteins and the down-regulated proteins such as clusterin, carbonic anhydrase 1, alpha-1-antitrypsin were differentially expressed in the plasma between the chemo-sensitive group and the chemoresistant group, which may be potential biomarkers for predicting the chemotresistance of epithelial ovarian cancer patients.