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Sarcopenic obesity, together with age, open surgery, and combined resection are independent predictors of SPCs. Obesity will significantly increase the risk of SPCs in sarcopenic patient with gastric cancer, but it will not bring higher risk to normal patients. Our nomogram is a simple and practical instrument to identify patients at high risk of surgical complications.

The aims of the present study were to evaluate the predictive value of the platelet-to-lymphocyte ratio for peritoneal metastasis in patients with gastric cancer and to construct an available preoperative prediction system for peritoneal metastasis. A total of 1080 patients with gastric cancer were enrolled in our study. The preoperative platelet-to-lymphocyte ratio and other serum markers and objective clinical tumor characteristics were evaluated by receiver operating characteristic curves. A logistic analysis was performed to determine the independent predictive indicators of peritoneal metastasis. A prediction system that included the independent predictive indicators was constructed and evaluated by receiver operating characteristic curves. Based on the receiver operating characteristic curves, the ideal platelet-to-lymphocyte ratio cutoff value to predict peritoneal metastasis was 131.00. The logistic analysis showed that the platelet-to-lymphocyte ratio was an independent indicator to predict peritoneal metastasis. The area under the receiver operating characteristic curve was 0.599. When integrating all independent indicators (i.e., platelet-to-lymphocyte ratio, invasion depth, lymphatic invasion, pathological type), the prediction system more reliably predicted peritoneal metastasis with a higher area under the receiver operating characteristic curve (0.769). The preoperative platelet-to-lymphocyte ratio was an indicator that could be used to predict peritoneal metastasis. Our prediction system could be a reliable instrument to discriminate between patients with gastric cancer with and those without peritoneal metastasis.

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Visceral obesity defined by VFA effectively predicted PGS. Our scoring system may be a reliable instrument for identifying patients most at risk of PGS.

Slow gait speed is an independent predictor of post-operative medical complications in elderly patients undergoing curative gastrectomy. Those patients should be managed with appropriate perioperative nutritional support and physical exercise which can improve gait speed and reduce the risk of post-operative medical complications.

Background and Purpose The relationships among interleukin (IL)-10 levels, anxiety, and cognitive status after stroke remain controversial. We aimed to determine the associations of serum IL-10 levels with poststroke anxiety (PSA) and poststroke cognitive impairment (PSCI). Methods We recruited 350 patients with stroke, of whom only 151 completed a 1-month follow-up assessment. The Mini Mental State Examination (MMSE) and Hamilton Anxiety Scale (HAMA) were used to assess the cognitive status and anxiety, respectively. Serum IL-10 levels were measured within 24 hours of admission. Results IL-10 levels were significantly lower in the PSA group than in the non-PSA group, and they were negatively associated with HAMA scores ( r =-0.371, p <0.001). After adjusting for all potential confounders, IL-10 levels remained an independent predictor of PSA (odds ratio=0.471, 95% confidence interval=0.237–0.936, p =0.032). IL-10 levels were strongly correlated with behavior during interviews, psychic anxiety, and somatic anxiety. Patients without PSCI had higher IL-10 levels were higher in non-PSCI patients than in PSCI patients, and they were positively associated with MMSE scores in the bivariate correlation analysis ( r =0.169, p =0.038), and also with memory capacity, naming ability, and copying capacity. However, IL-10 did not predict PSCI in the univariable or multivariable logistic regression. Conclusions Low IL-10 levels were associated with increased risks of PSA and PSCI at a 1-month follow-up after stroke. Serum IL-10 levels may therefore be helpful in predicting PSA.