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Collagen in the tumor microenvironment is recognized as a potential biomarker for predicting treatment response. This study investigated whether the collagen features are associated with pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) and develop and validate a prediction model for individualized prediction of pCR. The prediction model was developed in a primary cohort (353 consecutive patients). In total, 142 collagen features were extracted from the multiphoton image of pretreatment biopsy, and the least absolute shrinkage and selection operator (Lasso) regression was applied for feature selection and collagen signature building. A nomogram was developed using multivariable analysis. The performance of the nomogram was assessed with respect to its discrimination, calibration, and clinical utility. An independent cohort (163 consecutive patients) was used to validate the model. The collagen signature comprised four collagen features significantly associated with pCR both in the primary and validation cohorts (p 0.001). Predictors in the individualized prediction nomogram included the collagen signature and clinicopathological predictors. The nomogram showed good discrimination with area under the ROC curve (AUC) of 0.891 in the primary cohort and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (AUC = 0.908) and good calibration. Decision curve analysis demonstrated that the nomogram was clinically useful. In conclusion, the collagen signature in the tumor microenvironment of pretreatment biopsy is significantly associated with pCR. The nomogram based on the collagen signature and clinicopathological predictors could be used for individualized prediction of pCR in LARC patients before nCRT.

Background Collagen changes in the extracellular matrix caused by neoadjuvant chemoradiotherapy are a potential mechanism of anastomotic leakage. We aimed to construct a fully quantitative collagen score to describe collagen structure changes in the extracellular matrix and then develop and validate a prediction model to identify patients who are at a high risk of postoperative anastomotic leakage. Methods This is a retrospective study in which 372 patients were enrolled, and their baseline clinicopathological characteristics were collected. Anastomotic distal and proximal “doughnut” specimens underwent second harmonic generation imaging, and collagen features were extracted. A LASSO regression was used to select significant predictors, and the collagen score was constructed. A prediction model based on collagen score was developed and internally and externally validated. Results The primary cohort included 214 consecutive patients, and the anastomotic leakage rate was 8.9%. The validation cohort comprised 158 consecutive patients, and the anastomotic leakage rate was 10.1%. The collagen score was significantly related to anastomotic leakage in both cohorts (P Conclusion The collagen score is associated with anastomotic leakage, and the collagen nomogram based on the collagen score is useful for individualized prediction of anastomotic leakage in rectal cancer patients with neoadjuvant chemoradiotherapy after surgery.

Background D3 lymph node dissection is recommended for patients with advanced sigmoid and rectal cancer in Japan. This trial aimed to investigate the feasibility of indocyanine green (ICG) as a tracer to increase the nodal harvest during D3 lymph node dissection in patients with sigmoid and rectal cancer. Methods This prospective randomized clinical trial was performed between May 2021 and April 2022. The inclusion criteria were patients with stage I–III sigmoid or rectal cancer eligible for laparoscopic resection. Patients were 1: 1 randomized to either the ICG group (endoscopic ICG injection at the tumour site and intraoperative imaging to guide dissection) or the control group (routine laparoscopic white-light imaging). All patients were treated with D3 dissection, and the primary outcome was the number of harvested lymph nodes at the D3 level. Results Out of 210 patients screened, a total of 66 patients were enrolled and randomized. Patients in the two groups presented similar ages and clinical stages (ICG group versus control group, median age of 58.0 versus 58.5 years; stage III 36.4 per cent versus 36.4 per cent, whereas the rate of rectal cancer was 27.3 per cent versus 48.5 per cent respectively). ICG imaging was helpful for completely dissecting D3 lymph nodes and could identify a median of more than 2 (range 1–6) D3 lymph nodes neglected by routine laparoscopic white-light imaging during surgery. The median number of D3 lymph nodes harvested in the ICG group was significantly higher than that in the control group (7.0 versus 5.0, P = 0.003); however, there was no significant difference in the median numbers of positive D1, D2, and D3 lymph nodes between the two groups. Conclusion ICG is safe and feasible to guide D3 lymph node dissection and can increase the number of harvested D3 lymph nodes in patients with sigmoid and rectal cancer. Registration number: NCT04848311 (http://www.clinicaltrials.gov).

Anastomotic ischemia can affect healing and eventually lead to anastomotic leakage, and confocal laser endomicroscopy (CLE) can offer detailed observations at the subcellular level. We aimed to evaluate the anastomotic microcirculation in different anastomotic perfusion models using CLE. Anastomotic perfusion models were established using twelve rabbits distributed into two groups: group A (good perfusion, n = 6) and group B (poor perfusion, n = 6). Afterward, intraoperative detection of anastomotic perfusion was carried out using CLE, and quantitative analysis of blood cells was performed. Rabbits that satisfied the criteria underwent a second exploratory operation and specimens were stained by hematoxylin and eosin. Enhanced with fluorescein sodium, capillaries were obviously highlighted in group A, while few capillaries were viewed in group B. Delayed development of fluorescence occurred in group B. The average flow of blood cells was 37.0 ± 5.93 per minute in group A and 6.33 ± 2.16 per minute in group B (p

The relationship between collagen features (CFs) in the tumor microenvironment and the treatment response to neoadjuvant chemoradiotherapy (nCRT) is still unknown. This study aimed to develop and validate a perdition model based on the CFs and clinicopathological characteristics to predict the treatment response to nCRT among locally advanced rectal cancer (LARC) patients. In this multicenter, retrospective analysis, 428 patients were included and randomly divided into a training cohort (299 patients) and validation cohort (129 patients) [7:3 ratio]. A total of 11 CFs were extracted from a multiphoton image of pretreatment biopsy, and a support vector machine (SVM) was then used to construct a CFs-SVM classifier. A prediction model was developed and presented with a nomogram using multivariable analysis. Further validation of the nomogram was performed in the validation cohort. The CFs-SVM classifier, which integrated collagen area, straightness, and crosslink density, was significantly associated with treatment response. Predictors contained in the nomogram included the CFs-SVM classifier and clinicopathological characteristics by multivariable analysis. The CFs nomogram demonstrated good discrimination, with area under the receiver operating characteristic curves (AUROCs) of 0.834 in the training cohort and 0.854 in the validation cohort. Decision curve analysis indicated that the CFs nomogram was clinically useful. Moreover, compared with the traditional clinicopathological model, the CFs nomogram showed more powerful discrimination in determining the response to nCRT. The CFs-SVM classifier based on CFs in the tumor microenvironment is associated with treatment response, and the CFs nomogram integrating the CFs-SVM classifier and clinicopathological characteristics is useful for individualized prediction of the treatment response to nCRT among LARC patients.

Background A significant difference in the anastomotic leakage (AL) rate has been observed between patients with locally advanced rectal cancer who have undergone preoperative chemotherapy and those undergoing preoperative chemoradiotherapy. This study aimed to quantitatively analyse collagen structural changes caused by preoperative chemoradiotherapy and illuminate the relationship between collagen changes and AL. Methods Anastomotic distal and proximal “doughnut” specimens from the Sixth Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) were quantitatively assessed for collagen structural changes between patients with and without preoperative radiotherapy using multiphoton imaging. Then, patients treated with preoperative chemoradiotherapy were used as a training cohort to construct an AL–SVM classifier by the Mann–Whitney U test and support vector machine (SVM). An independent test cohort from the Fujian Province Cancer Hospital (Fuzhou, China) was used to validate the AL–SVM classifier. Results A total of 207 patients were included from the Sixth Affiliated Hospital of Sun Yat-sen University. The AL rate in the preoperative chemoradiotherapy group (n = 107) was significantly higher than that in the preoperative chemotherapy group (n = 100) (21.5% vs 7.0%, P = 0.003). A fully quantitative analysis showed notable morphological and spatial distribution feature changes in collagen in the preoperative chemoradiotherapy group. Then, the patients who received preoperative chemoradiotherapy were used as a training cohort to construct the AL–SVM classifier based on five collagen features and the tumor distance from the anus. The AL–SVM classifier showed satisfactory discrimination and calibration with areas under the curve of 0.907 and 0.856 in the training and test cohorts, respectively. Conclusions The collagen structure may be notably altered by preoperative radiotherapy. The AL–SVM classifier was useful for the individualized prediction of AL in rectal cancer patients undergoing preoperative chemoradiotherapy.

The relationship between collagen features (CFs) in the tumor microenvironment and the treatment response to neoadjuvant chemoradiotherapy (nCRT) is still unknown. This study aimed to develop and validate a perdition model based on the CFs and clinicopathological characteristics to predict the treatment response to nCRT among locally advanced rectal cancer (LARC) patients.In this multicenter, retrospective analysis, 428 patients were included and randomly divided into a training cohort (299 patients) and validation cohort (129 patients) [7:3 ratio]. A total of 11 CFs were extracted from a multiphoton image of pretreatment biopsy, and a support vector machine (SVM) was then used to construct a CFs-SVM classifier. A prediction model was developed and presented with a nomogram using multivariable analysis. Further validation of the nomogram was performed in the validation cohort.The CFs-SVM classifier, which integrated collagen area, straightness, and crosslink density, was significantly associated with treatment response. Predictors contained in the nomogram included the CFs-SVM classifier and clinicopathological characteristics by multivariable analysis. The CFs nomogram demonstrated good discrimination, with area under the receiver operating characteristic curves (AUROCs) of 0.834 in the training cohort and 0.854 in the validation cohort. Decision curve analysis indicated that the CFs nomogram was clinically useful. Moreover, compared with the traditional clinicopathological model, the CFs nomogram showed more powerful discrimination in determining the response to nCRT.The CFs-SVM classifier based on CFs in the tumor microenvironment is associated with treatment response, and the CFs nomogram integrating the CFs-SVM classifier and clinicopathological characteristics is useful for individualized prediction of the treatment response to nCRT among LARC patients.

Background: Collagen in the tumour microenvironment is recognized as a potential biomarker for predicting treatment response. This study investigated whether the collagen features are associated with pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) and develop and validate a prediction model for individualized prediction of pCR. Methods: The prediction model was developed in a primary cohort (353 consecutive patients). In total, 142 collagen features were extracted from the multiphoton image of pretreatment biopsy, and the least absolute shrinkage and selection operator (Lasso) regression was applied for feature selection and collagen signature building. A nomogram was developed by multivariable analysis. The performance of the nomogram was assessed with respect to its discrimination, calibration, and clinical utility. An independent cohort (153 consecutive patients) was used to validate the model. Findings: The collagen signature comprised 4 collagen features significantly associated with pCR both in the two cohorts (P

The feasibility of endoscopic dissection for gastric gastrointestinal stromal tumor (gGIST) between 2 and 5 cm in size has been demonstrated. However, its impact on short-term and long-term outcomes, compared with laparoscopic resection, is unknown. The purpose of this study was to compare short-term and long-term outcomes between laparoscopic resection and endoscopic dissection for 2–5-cm gGIST. A case-matched study was performed using the propensity score. To overcome selection bias, we performed a 1:1 match using six covariates, including age, sex, BMI, ASA score, tumor size, and tumor location. Short-term and long-term outcomes between laparoscopic resection and endoscopic dissection were compared. A total of 210 patients with 2–5-cm gGIST were enrolled between 2006 and 2017 in our gastrointestinal center. According to the intention-to-treat approach, 165 patients underwent laparoscopic resection, and 45 patients underwent endoscopic dissection. After the propensity score, 45 pairs were balanced and analyzed. There was no significant difference in the baseline characteristics between the laparoscopic and endoscopic groups after matching. The rate of complications was significantly higher in the endoscopic group compared with the laparoscopic group (P

In this paper, a new preparation method of metal matrix composites is presented. The W/FeNiMnAlW high-entropy alloy (HEA) matrix composite materials have been prepared by a simple and efficient technology. The HEA matrix consists of a face-centered cubic (FCC) phase, an ordered body-centered cubic (BCC) phase and W2C phase. The volume fraction and average grain size of the reinforcing phase W particles, uniformly distributed in the microstructure of HEA matrix, are 30.9% and 13.57 µm, respectively. Good metallurgical bonding between W particle and matrix is achieved, and the formation mechanism of near spherical tungsten particles is discussed, and the density of the composites is 10.55 g/cm3. The hardness of W-phase, B2 phase and FCC phase was 681.48 HV, 533.82 HV and 286.70 HV, respectively. The yield strength (σ0.2) of the W/FeNiMnAlW composites is 1241 MPa, the maximum compressive strength (σmax) and the maximum plastic strain (ep) are over 2530 MPa and 15% respectively, which showed superior mechanical properties. The effective combination of FCC phase and ordered BCC phase and the uniform distribution of W particles without edges are the main reasons for its good mechanical properties. The volume wear loss and wear rate of the W/FeNiMnAlW composites are respectively 0.42 mm3 and 4.95 × 10−3 mm3/N m, and the worn mechanism is mainly adhesive wear and abrasive wear.

We present a simple one-dimensional process-based morphological model for predicting medium–long term beach profile evolution of a bayside beach with a low tide terrace. Bayside beaches are characterized by low wave energy. In regions with a large tidal range, a beach face is slowly eroded due to erosional waves inside the surf zone which shifts along the beach profile over a wide range during a tidal cycle. The model is developed using an existing model framework proposed by Mariotti and Fagherazzi (2010) , who formulated a model for sediment suspension and transport due to waves and tidal currents in a tidal flat regime. The new developments in this study include an implementation of the bedload sediment transport and modification of the suspended load transport for noncohesive particles. The model parameters were calibrated using beach profile data collected over a one-year span at Xiamen Beach. Numerical experiments indicate that waves suspend sediment in the narrow surf zone, inducing pulse-like high sediment concentration in shallow water. The suspended sediment is advected by the tidal current and wave-induced undertow, causing erosion of the steep beach face at the upper portion and deposition of the flat beach face at the lower portion of a beach profile. The erosion on the upper beach face was not predicted by a bedload-only transport model, suggesting that the sediment suspension associated wave breaking may be the major mechanism for the erosion. Tide-only induced sediment transport is insignificant, but tides play a role in distributing the wave-induced erosion over a wide region on a beach profile.