Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion.

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the image of corporate distribution, further reduce corporate operating costs, and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identifies the locations of primary and secondary logistics nodes but also provides a new perspective for constructing urban last-mile logistics systems, enriching the academic research related to the construction of logistics nodes. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

The Machine Learning Model for Predicting Inadequate Bowel Preparation Before Colonoscopy: A Multicenter Prospective Study.

INTRODUCTION: Colonoscopy is a critical diagnostic tool for colorectal diseases; however, its effectiveness depends on adequate bowel preparation (BP). This study aimed to develop a machine learning predictive model based on Chinese adults for inadequate BP. METHODS: A multicenter prospective study was conducted on adult outpatients undergoing colonoscopy from January 2021 to May 2023. Data on patient characteristics, comorbidities, medication use, and BP quality were collected. Logistic regression and 4 machine learning models (support vector machines, decision trees, extreme gradient boosting, and bidirectional projection network) were used to identify risk factors and predict inadequate BP. RESULTS: Of 3,217 patients, 21.14% had inadequate BP. The decision trees model demonstrated the best predictive capacity with an area under the receiver operating characteristic curve of 0.80 in the validation cohort. The risk factors at the nodes included body mass index, education grade, use of simethicone, diabetes, age, history of inadequate BP, and longer interval. DISCUSSION: The decision trees model we created and the identified risk factors can be used to identify patients at higher risk of inadequate BP before colonoscopy, for whom more polyethylene glycol or auxiliary medication should be used.

Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study.

PURPOSE: To develop a noninvasive radiomics-based nomogram for identification of disagreement in pathology between endoscopic biopsy and postoperative specimens in gastric cancer (GC). MATERIALS AND METHODS: This observational study recruited 181 GC patients who underwent pre-treatment computed tomography (CT) and divided them into a training set (n = 112, single-energy CT, SECT), a test set (n = 29, single-energy CT, SECT) and a validation cohort (n = 40, dual-energy CT, DECT). Radiomics signatures (RS) based on five machine learning algorithms were constructed from the venous-phase CT images. AUC and DeLong test were used to evaluate and compare the performance of the RS. We assessed the dual-energy generalization ability of the best RS. An individualized nomogram combined the best RS and clinical variables was developed, and its discrimination, calibration, and clinical usefulness were determined. RESULTS: RS obtained with support vector machine (SVM) showed promising predictive capability with AUC of 0.91 and 0.83 in the training and test sets, respectively. The AUC of the best RS in the DECT validation cohort (AUC, 0.71) was significantly lower than that of the training set (Delong test, p = 0.035). The clinical-radiomic nomogram accurately predicted pathologic disagreement in the training and test sets, fitting well in the calibration curves. Decision curve analysis confirmed the clinical usefulness of the nomogram. CONCLUSION: CT-based radiomics nomogram showed potential as a clinical aid for predicting pathologic disagreement status between biopsy samples and resected specimens in GC. When practicability and stability are considered, the SECT-based radiomics model is not recommended for DECT generalization. CRITICAL RELEVANCE STATEMENT: Radiomics can identify disagreement in pathology between endoscopic biopsy and postoperative specimen.

Lycium barbarum polysaccharide alleviates DSS-induced chronic ulcerative colitis by restoring intestinal barrier function and modulating gut microbiota.

PURPOSE: This study examined the protective effects and mechanism of Lycium barbarum polysaccharides (LBP) in the context of intestinal barrier function and intestinal microbiota in mice with dextran sulfate sodium (DSS)-induced chronic ulcerative colitis (UC). METHODS: C57BL/6J male mice were assigned to a standard normal diet without DSS (control group), a normal diet with DSS (DSS group, 2% DSS given discontinuously for 3 weeks) or a normal diet supplemented with LBP (1% dry feed weight, LBP group, 2% DSS given discontinuously for 3 weeks) for a total of 8 weeks, at which point colonic tissues and caecal contents were collected. RESULTS: LBP exerted a significant effect against colitis by increasing body weight, colon length, DAI and histopathological scores. LBP inhibited proinflammatory cytokines (IL-1ß, IL-6, iNOS and TNF-α) expression, improved anti-inflammatory cytokine (IL-10) expression, promoted the expression of tight junction proteins (Occludin and ZO-1) via nuclear factor erythroid 2-related factor 2 (Nrf2) activation and decreased Claudin-2 expression to maintain the intestinal mucosal barrier. In addition, the abundances of some probiotics (Ruminococcaceae, Lactobacillus, Butyricicoccus, and Akkermansia) were decreased with DSS treatment but increased obviously with LBP treatment. And LBP reduced the abundance of conditional pathogens associated with UC (Mucispirillum and Sutterella). Furthermore, LBP improved the production of short-chain fatty acids (SCFAs), including acetic acid, propionic acid, butyric acid and isobutyric acid. CONCLUSION: LBP can alleviate DSS-induced UC by regulating inflammatory cytokines and tight junction proteins. Moreover, LBP promotes probiotics, suppresses conditional pathogens and increases SCFAs production, showing a strong prebiotic effect.

A novel prognostic model to predict OS and DFS of stage II/III gastric adenocarcinoma patients in China.

Background: The prognosis of advanced gastric adenocarcinoma (GAC) after radical gastrectomy varies greatly. We aimed to build and validate a novel individualized nomogram based on inflammation index and tumor markers for patients with stage II/III GAC. Methods: A total of 755 individuals with stage II/III GAC who had undergone radical gastrectomy at the First Affiliated Hospital of Zhengzhou University between 2012 and 2017 were included in this retrospective study. The patients were randomly divided into a training cohort (n â€‹= â€‹503) and a validation cohort (n â€‹= â€‹252). Univariate and multivariate analyses were used to determine independent prognostic factors of overall survival (OS) and disease-free survival (DFS). A nomogram was developed based on these independent factors. The concordance index (C-index) and calibration curves were used to evaluate the predictive accuracy of the nomogram. Results: Univariate and multivariate analyses demonstrated that older age, poor differentiation, advanced stage, elevated neutrophil-to-lymphocyte ratio (NLR), lower hemoglobin, and high carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) levels were significantly associated with lower OS and DFS and were independent prognostic factors in stage II/III GAC. The nomogram developed based on these factors in the training cohort showed excellent calibration and discrimination (OS: C-index â€‹= â€‹0.739, 95% CI â€‹= â€‹0.706-0.772; DFS: C-index â€‹= â€‹0.735, 95% CI â€‹= â€‹0.702-0.769). In the internal validation cohort, the nomogram was also well-calibrated for the prediction of OS and DFS; it was superior to the 8th edition UICC/AJCC TNM staging system (for OS: C-index â€‹= â€‹0.746 vs. 0.679, respectively; for DFS: C-index â€‹= â€‹0.736 vs. 0.675, respectively; P â€‹< â€‹0.001). Conclusion: The nomogram model could reliably predict OS and DFS in stage II/III gastric cancer patients with radical gastrectomy. It may help physicians make better treatment decisions.

Analysis on the value of soluble intercellular adhesion molecule-1 (sICAM-1), alpha fetoprotein (AFP), and aspartate aminotransferase/platelet ratio index (APRI) in predicting the prognostic survival of patients with primary liver cancer after radiofrequency ablation.

BACKGROUND: To analyze the value of soluble intercellular adhesion molecule-1 (sICAM-1), alpha fetoprotein (AFP), and aspartate aminotransferase (AST)/platelet (PLT) ratio index (APRI) in predicting the prognostic survival of patients with primary liver cancer after radiofrequency ablation (RFA). METHODS: The data of 115 patients with primary liver cancer admitted to our hospital from June 2016 to June 2018 were retrospectively analyzed as the research group, and 120 healthy people who were examined during the same period were selected as the control group. Multivariate logistic regression analysis was used to analyze the risk factors that affect the prognostic survival of patients with primary liver cancer treated with RFA. Receiver operating characteristic (ROC) curve was used to analyze the value of serum sICAM-1, AFP, and APRI levels in predicting the prognostic survival of patients with primary liver cancer after RFA. RESULTS: The levels of sICAM-1, AFP, and APRI in the control group were significantly lower than those in the study group, and the difference between the 2 groups was statistically significant (P<0.05). After 115 patients were followed up for 2 years, the 2-year survival rate was 55.65% (64/115). Multivariate logistic regression showed that the clinical stage: III + IV, extrahepatic metastasis, abnormal increasing in sICAM-1, AFP, and APRI levels, were independent risk factors affecting the prognosis survival of hepatocellular carcinoma (HCC) patients after RFA treatment (P<0.05). The ROC curve showed that the areas under the curve of sICAM-1, AFP, APRI, and their combination in predicting the prognosis survival of HCC patients after RFA treatment were 0.693, 0.828, 0.901, and 0.947, respectively, with the area under the curve of the combination being the largest. CONCLUSIONS: ICMS-1, AFP, and APRI are closely related to the RFA treatment and prognosis of HCC patients. In clinic, individualized treatment plans can be formulated based on these levels, which can contribute to prolonging the survival of patients.

Anlotinib Combined With Chemotherapy for Recurrence of Pulmonary Sarcomatoid Cancer Previously Surgically Treated: A Case Report and Literature Review.

BACKGROUND: Pulmonary sarcomatoid cancer (PSC) is a very rare subtype of poorly differentiated non-small-lung-cancer (NSCLC) with very poor prognosis. To date, the optimal treatment for PSC has not been elucidated, and the efficacy of anlotinib in PSC has not been previously reported. CASE PRESENTATION: A 77-year-old male patient was admitted with cough, expectoration, and blood-stained sputum for one month. CT showed a soft mass in the inferior lobe of the right lung, which was diagnosed as spindle cell carcinoma (PSC) by histopathology. A videothoracoscopic right lower lobectomy and mediastinal lymph node dissection procedure was performed on the patient, but the disease recurred one month after surgery. The patient was then given first-line chemotherapy with gemcitabine and albumin paclitaxel for one cycle, but the disease continued to progress. The patient then received anlotinib combined with second-line chemotherapy (dacarbazine and cis-platinum) for six cycles, and the response reached complete remission (CR). Then the patient was given maintenance therapy with anlotinib alone, and the disease was still stable at the most recent reexamination. Progression-free survival (PFS) has lasted for more than two years, without any intolerable toxicity. CONCLUSION: This postoperative recurrent PSC patient achieved significant clinical benefits with anlotinib treatment. Our findings provide direct evidence of the efficacy of anlotinib in PSC. More studies are needed to confirm our observation.

Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study.

OBJECTIVE: To build and assess a pre-treatment dual-energy CT-based clinical-radiomics nomogram for the individualized prediction of clinical response to systemic chemotherapy in advanced gastric cancer (AGC). METHODS: A total of 69 pathologically confirmed AGC patients who underwent dual-energy CT before systemic chemotherapy were enrolled from two centers in this retrospective study. Treatment response was determined with follow-up CT according to the RECIST standard. Quantitative radiomics metrics of the primary lesion were extracted from three sets of monochromatic images (40, 70, and 100 keV) at venous phase. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used to select the most relevant radiomics features. Multivariable logistic regression was performed to establish a clinical model, three monochromatic radiomics models, and a combined multi-energy model. ROC analysis and DeLong test were used to evaluate and compare the predictive performance among models. A clinical-radiomics nomogram was developed; moreover, its discrimination, calibration, and clinical usefulness were assessed. RESULT: Among the included patients, 24 responded to the systemic chemotherapy. Clinical stage and the iodine concentration (IC) of the tumor were significant clinical predictors of chemotherapy response (all p < 0.05). The multi-energy radiomics model showed a higher predictive capability (AUC = 0.914) than two monochromatic radiomics models and the clinical model (AUC: 40 keV = 0.747, 70 keV = 0.793, clinical = 0.775); however, the predictive accuracy of the 100-keV model (AUC: 0.881) was not statistically different (p = 0.221). The clinical-radiomics nomogram integrating the multi-energy radiomics signature with IC value and clinical stage showed good calibration and discrimination with an AUC of 0.934. Decision curve analysis proved the clinical usefulness of the nomogram and multi-energy radiomics model. CONCLUSION: The pre-treatment DECT-based clinical-radiomics nomogram showed good performance in predicting clinical response to systemic chemotherapy in AGC, which may contribute to clinical decision-making and improving patient survival.