【摘要】 目的 了解在圍手術期術前30 min應用抗生素的情況。 方法 根據2004年衛生部、國家中醫藥管理局、總后勤部發布的《抗菌藥物臨床應用指導原則》中圍手術期抗生素的使用原則，對四川大學華西醫院2010年4-6月500臺手術圍手術期抗生素的使用情況進行分析。 結果 抗生素在麻醉前輸：0臺；抗生素末在術前30 min輸2臺，占0.4%；抗生素末即用即配：0臺；抗生素與麻藥及其他禁忌藥混合輸:0臺；手術3 h后末及時追加抗生素：0臺；特殊患者使用抗生素的注意事項不清楚2臺，占0.4%。 結論 該院99.6%的手術實行在手術室術前30 min輸入抗生素，確保抗生素達到有效濃度，有效控制感染，保證手術的成功，保障患者安全。【Abstract】 Objective To investigate the application of perioperative antibiotics half an hour before operation in West China Hospital of Sichuan University. Methods According to Clinical Guidance of Antibiotics published by Ministry of Health, State Administration of Traditional Chinese Medicine and General Logistics Department in 2004, we investigated the application of perioperative antibiotics in 500 operations between April to June 2010 in our hospital. Results There was no operation with infusion of antibiotics before anesthesia, 2 operations without infusion of antibiotics half an hour before operation (0.4%), no operation without immediate infusion after preparation, no operation with mixed infusion of antibiotics and anesthesia and other contraindicated drugs, no operation without infusion of antibiotics 3 hours after operation, and 2 operations in which cautious items about the children, pregnancy and old patients were unclear (0.4%). Conclusion About 99.6% operations in our hospital have the infusion of antibiotics 30 minutes before the operation, which is the guarantee of antibiotics with effective concentration, inhibition of infection, success of the operation and safety of the patients.
ObjectiveTo compare the clinical efficacy of modified Ivor-Lewis esophagectomy, which preserves azygos vein, thoracic duct and peripheral tissues, and classic Ivor-Lewis esophagectomy, which resects these tissues, in the treatment of esophageal cancer, so as to evaluate whether it is necessary to resect azygos vein, thoracic duct and peripheral tissues in esophagectomy for esophageal cancer.MethodsPatients scheduled for surgical treatment of thoracic esophageal cancer in Department of Thoracic Surgery of Sichuan Cancer Hospital from June 2011 to June 2013 were randomly assigned to the retention group and the resection group, each including 100 patients. The retention group included 87 males and 13 females with an average age of 60.53±7.72 years. In the resection group, there were 80 males and 20 females with an average age of 60.69±7.69 years. Patients in the two groups were compared for the duration of surgery, intraoperative blood loss, postoperative thoracic drainage volume, postoperative complications, and number of dissected lymph nodes, etc. Postoperative relapse and survival rates at 1, 3 and 5 years postoperatively were also followed up and compared for patients in the two groups.ResultsThere was no statistical difference between the two groups in general patient characteristics, number of dissected lymph nodes, or postoperative pathological stage, etc. (P>0.05). Compared to the resection group, there were shorter duration of surgery, less intraoperative blood loss, and less thoracic drainage volume in the first 3 days following surgery in the retention group, with statistical differences (P<0.05). There was no statistical difference between the two groups in type or site of relapse or metastasis (P>0.05). The survival rates at 1, 3, and 5 years postoperatively was 78.7% vs. 81.3%, 39.4% vs. 37.5%, and 23.4% vs. 17.7%, respectively, in the retention group and the resection group, with no statistical difference (P>0.05).ConclusionModified Ivor-Lewis esophagectomy preserving azygos vein, thoracic duct and peripheral tissues could reduce surgical trauma, would not increase postoperative relapse or metastasis, and could produce long-term efficacy comparable to that of extended resection.
With the rising incidence of breast cancer among women, Neoadjuvant Chemotherapy (NAC) is becoming increasingly crucial as a preoperative treatment modality, enabling tumor downstaging and volume reduction. However, its efficacy varies significantly among patients, underscoring the importance of predicting Pathological Complete Response (pCR) following NAC. Early research relied on statistical methods to integrate clinical data for predicting treatment outcomes. With the advent of artificial intelligence (AI), traditional machine learning approaches were subsequently employed for efficacy prediction. Deep learning emerged to dominate this field, and demonstrated the capability to automatically extract imaging features and integrate multimodal data for pCR prediction. This review comprehensively examined the applications and limitations of these three methodologies in predicting breast cancer pCR. Future efforts must prioritize the development of superior predictive models to achieve precise predictions, integrate them into clinical workflows, enhance patient care, and ultimately improve therapeutic outcomes and quality of life.