The impact of SARS-Cov-2 Omicron infection on short-term outcomes after elective surgery in patients with gastrointestinal cancer.

With the emergence of novel variants, Omicron variant caused a different clinical picture than the previous variants and little evidence was reported regarding perioperative outcomes after Omicron variants. The aim of the study was to evaluate the postoperative outcomes of gastrointestinal cancer patients following Omicron variants infection and also to determine the timing of surgery after infection recovery. A total of 124 patients who underwent gastrointestinal cancer surgery with prior SARS-CoV-2 infection between December 2022 and February 2023 were retrospectively reviewed. 174 cases underwent the same operation during December 2018 and February 2019 as control group. SARS-CoV-2-infected patients were further categorized into three groups based on infected time (1-3 weeks; 4-6 weeks; and ≥ 7 weeks). 90.3% of SARS-CoV-2-infected patients had mild symptoms. The COVID-19 vaccination rate was 71.0%, with a full vaccination rate of 48.4%. There were no significant differences in 30-day morbidity and mortality. There was also no significant difference in pulmonary complications, cardiovascular complications, and surgical complications between the three different diagnosis time groups. In conclusion, reducing waiting time for elective surgery was safe for gastrointestinal cancer patients in the context of an increased transmissibility and milder illness severity with Omicron variant.

Management and outcomes of gastric leak after sleeve gastrectomy: results from the 2010-2020 national registry.

BACKGROUND: Management of gastric leak after sleeve gastrectomy (SG) is challenging due to its unpredictable outcomes. We aimed to summarize the characteristics of SG leaks and analyze interventions and corresponding outcomes in a real-world setting. METHODS: To retrospectively review of 15,721 SG procedures from 2010 to 2020 based on a national registry. A cumulative sum analysis was used to identify a fitting curve of gastric leak rate. The Kaplan-Meier method and log-rank tests were performed to calculate and compare the probabilities of relevant outcomes. The logistic regression analysis was conducted to determine the predictors of acute leaks. RESULTS: A total of 78 cases of SG leaks were collected with an incidence of 0.5% (78/15,721) from this registry (6 patients who had the primary SG in non-participating centers). After accumulating 260 cases in a bariatric surgery center, the leak rate decreased to a stably low value of under 1.17%. The significant differences presented in sex, waist circumference, and the proportion of hypoproteinemia and type 2 diabetes at baseline between patients with SG leak and the whole registry population ( P = 0.005, = 0.026, <0.001, and = 0.001, respectively). Moreover, 83.1% (59/71) of the leakage was near the esophagogastric junction region. Leakage healed in 64 (88.9%, 64/72) patients. The median healing time of acute and non-acute leaks was 5.93 months and 8.12 months, respectively. Acute leak (38/72, 52.8%) was the predominant type with a cumulative reoperation rate >50%, whereas the cumulative healing probability in the patients who required surgical treatment was significantly lower than those requring non-surgical treatment ( P = 0.013). Precise dissection in the His angle area was independently associated with a lower acute leak rate, whereas preservation ≥2 cm distance from the His angle area was an independent risk factor. CONCLUSIONS: Male sex, elevated waist circumference, hypoproteinaemia, and type 2 diabetes are risk factors of gastric leaks after SG. Optimizing surgical techniques, including precise dissection of His angle area and preservation of smaller gastric fundus, should be suggested to prevent acute leaks.

Machine learning algorithms to predict intraoperative hemorrhage in surgical patients: a modeling study of real-world data in Shanghai, China.

BACKGROUND: Prediction tools for various intraoperative bleeding events remain scarce. We aim to develop machine learning-based models and identify the most important predictors by real-world data from electronic medical records (EMRs). METHODS: An established database of surgical inpatients in Shanghai was utilized for analysis. A total of 51,173 inpatients were assessed for eligibility. 48,543 inpatients were obtained in the dataset and patients were divided into haemorrhage (N = 9728) and without-haemorrhage (N = 38,815) groups according to their bleeding during the procedure. Candidate predictors were selected from 27 variables, including sex (N = 48,543), age (N = 48,543), BMI (N = 48,543), renal disease (N = 26), heart disease (N = 1309), hypertension (N = 9579), diabetes (N = 4165), coagulopathy (N = 47), and other features. The models were constructed by 7 machine learning algorithms, i.e., light gradient boosting (LGB), extreme gradient boosting (XGB), cathepsin B (CatB), Ada-boosting of decision tree (AdaB), logistic regression (LR), long short-term memory (LSTM), and multilayer perception (MLP). An area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. RESULTS: The mean age of the inpatients was 53 ± 17 years, and 57.5% were male. LGB showed the best predictive performance for intraoperative bleeding combining multiple indicators (AUC = 0.933, sensitivity = 0.87, specificity = 0.85, accuracy = 0.87) compared with XGB, CatB, AdaB, LR, MLP and LSTM. The three most important predictors identified by LGB were operative time, D-dimer (DD), and age. CONCLUSIONS: We proposed LGB as the best Gradient Boosting Decision Tree (GBDT) algorithm for the evaluation of intraoperative bleeding. It is considered a simple and useful tool for predicting intraoperative bleeding in clinical settings. Operative time, DD, and age should receive attention.

Laparoscopic sleeve gastrectomy makes acid reflux symptoms worse or better?: a prospective short-term observational study in patients with morbid obesity.

BACKGROUND: Gastroesophageal reflux symptom (GERS) occur frequently in obese patients. Although some surgeons avoid laparoscopic sleeve gastrectomy (LSG) in these patients for fear of postoperative exacerbation of GERS, this notion is not supported by sufficient medical evidence. OBJECTIVES: This prospective study aimed to evaluate the impact of LSG on GERS. SETTING: Shanghai East Hospital, Shanghai, China. METHODS: Seventy-five LSG candidates were enrolled between April 2020 and October 2021. Only patients with completed preoperative and 6-month postoperative evaluation of GERS with the Reflux Symptom Score (RSS) and the Gastrointestinal Quality of Life index were included. Each patient's characteristics, including sex, age, drinking and smoking history, body mass index (BMI) at the time of surgery, recent BMI, comorbidities, glucose and lipid metabolism-related laboratory results, and uric acid and sex hormone levels were obtained. RESULTS: Sixty-five patients (33.8 ± 9.1 years) were finally included in our study. The mean preoperative BMI was 36.4 ± 6.8 kg/m2. Preoperative GERS were reported in 32 (49.2%) patients (RSS > 13), and 26 of them (81.3%) had dramatic remission at 6 months postoperatively. Four patients (12.1%) developed de novo GERS postoperatively, which were well-controlled with oral proton pump inhibitors. Furthermore, GERS were significantly correlated with preoperative BMI; the risk of developing new or worsening GERS postoperatively was positively associated with preoperative insulin resistance. CONCLUSIONS: A low incidence of de novo GERS and significant alleviation in preoperative GERS occurred in most obese patients after LSG. A patient with preoperative insulin resistance may not be suitable for LSG surgery owing to the increased risk of new or worsening of GERS postoperatively.

Application of a New Retraction Method in Laparoscopic Gastrectomy for Gastric Cancer.

BACKGROUND: Better exposition is important for lymph node dissection in the suprapancreatic region and lesser curvature region of the stomach, and digestive tract reconstruction, especially without excellent assistants. PATIENTS AND METHODS: We developed a new laparoscopic retraction method with the use of two internal retractors (TIRs) punctured along with suture. Clinicopathological data, surgical data, and postoperative outcomes were assessed. RESULTS: Of the 143 patients included, 51 underwent surgery with the double-sling suture method and 92 underwent surgery with the TIRs method. Laparoscopic radical gastrectomy was successfully performed in all patients. There were no significant differences in patient characteristics or preoperative data in the 2 groups. The operative time was significantly shorter in the TIR group, but the amount of bleeding did not differ. No retraction-related complications both in clipped tissue and liver occurred in all patients. CONCLUSIONS: Our new retraction technique provided an optimal surgical field and make surgery lower requirements for assistants.

The Modified Glasgow Prognostic Score Predicts Survival in Gastric Cancer Patients with Normal CEA and CA19-9.

Background: Traditionally, serum CEA and CA19-9 levels are good prognostic factors for gastric cancer. Many gastric cancer patients do not have elevated CEA or CA19-9 levels even at a very advanced stage. This study investigates the significance of the modified Glasgow prognostic score (mGPS) for the survival of gastric cancer patients with normal CEA and CA19-9. Methods: We retrospectively examined 488 curatively resected gastric cancer patients with normal preoperative serum levels of CEA and CA19-9 to evaluate the prognostic ability of mGPS for overall survival. The prognostic significance was analyzed by univariate and multivariate analyses. Results: Age, hemoglobin, white cell count, and neutrophils were each significantly correlated with the mGPS. Multivariate analyses showed that tumor location (HR, 0.803; 95% CI, 0.667-0.966; P=0.020), TNM stage (HR, 2.714; 95% CI, 2.250-3.275; P < 0.001), and mGPS (HR, 1.042; 95% CI, 1.105-1.772; P=0.023) were significantly associated with overall survival. Significant correlations were found between overall survival and mGPS. The Kaplan-Meier analysis demonstrated significant differences among patients with mGPS of 0, 1, and 2 (P < 0.001), with the mortality rate being higher for patients with a higher mGPS. Conclusion: The mGPS can predict survival in gastric cancer patients with normal CEA and CA19-9.

SHMT2 Drives the Progression of Colorectal Cancer by Regulating UHRF1 Expression.

INTRODUCTION: Serine hydroxymethyltransferase 2 (SHMT2) has a critical role in serine-glycine metabolism to drive cancer cell proliferation. Yet, the function of SHMT2 in tumorigenesis, especially in human colorectal cancer (CRC) progression, remains largely unclear. MATERIALS AND METHODS: CRC and paired normal samples were collected in the Department of Colorectal Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, and assessed by real-time polymerase chain reaction (qPCR) analysis, western blot (WB), and immunohistochemistry (IHC). Moreover, SHMT2 expression in human CRC cells was identified by qPCR and WB. The CRC cell proliferation, migration, and invasion after SHMT2 knockdown were explored through in vitro and in vivo assays. mRNA-seq assays were used to investigate the underlying mechanisms behind the SHMT2 function. RESULTS: It was found that SHMT2 mRNA and protein were overexpressed in CRC tissue compared to the levels in normal mucosa. Positive expression of SHMT2 was significantly correlated with TNM stage and lymph node metastasis, and elevated expression of SHMT2 resulted as an independent prognostic factor in patients with CRC. SHMT2 knockdown impaired the proliferation of CRC in vitro and in vivo and induced cell cycle arrest by regulating UHRF1 expression. CONCLUSION: Taken together, our findings reveal that UHRF1 is a novel target gene of SHMT2, which can be used as a potential therapeutic strategy for CRC therapy.

The predictive value of preoperative luteinizing hormone to follicle stimulating hormone ratio for ovulation abnormalities recovery after laparoscopic sleeve gastrectomy: A prospective cohort study.

Introduction: Obesity-related ovulation abnormalities (OA) affect fertility. LSG is the most frequent bariatric operation. However, no research has identified a reliable indicator for predicting OA recovery after LSG. The purpose of this research was to examine the prognostic usefulness of preoperative the luteinizing hormone (LH) to follicle-stimulating hormone (FSH) ratio (LFR). Methods: Our department conducted a prospective study from 2016 to 2021. Venous blood was typically tested 3 days before surgery to get the preoperative LFR. Descriptive data, preoperative and postoperative variables were also collected. Binary logistic regression related preoperative LFR with OA recovery. The receiver operating characteristic (ROC) curve evulated preoperative LFR's predictive capability. Results: A total of 157 women with a complete follow-up of one year were included. LFR was the only factor linked with OA (P < 0.001). AUC (area under the ROC curve) = 0.915, cutoff = 1.782, sensitivity = 0.93, and specificity = 0.82. Discussion: Overall, LSG has a favorable surgical result, with a %TWL of 66.082 ± 12.012 at 12 months postoperatively. Preoperative sexual hormone levels, as expressed by LFR, has the potential to predict the fate of OA following LSG at one year post-operatively.

Machine Learning to Identify Metabolic Subtypes of Obesity: A Multi-Center Study.

Background and objective: Clinical characteristics of obesity are heterogenous, but current classification for diagnosis is simply based on BMI or metabolic healthiness. The purpose of this study was to use machine learning to explore a more precise classification of obesity subgroups towards informing individualized therapy. Subjects and Methods: In a multi-center study (n=2495), we used unsupervised machine learning to cluster patients with obesity from Shanghai Tenth People's hospital (n=882, main cohort) based on three clinical variables (AUCs of glucose and of insulin during OGTT, and uric acid). Verification of the clustering was performed in three independent cohorts from external hospitals in China (n = 130, 137, and 289, respectively). Statistics of a healthy normal-weight cohort (n=1057) were measured as controls. Results: Machine learning revealed four stable metabolic different obese clusters on each cohort. Metabolic healthy obesity (MHO, 44% patients) was characterized by a relatively healthy-metabolic status with lowest incidents of comorbidities. Hypermetabolic obesity-hyperuricemia (HMO-U, 33% patients) was characterized by extremely high uric acid and a large increased incidence of hyperuricemia (adjusted odds ratio [AOR] 73.67 to MHO, 95%CI 35.46-153.06). Hypermetabolic obesity-hyperinsulinemia (HMO-I, 8% patients) was distinguished by overcompensated insulin secretion and a large increased incidence of polycystic ovary syndrome (AOR 14.44 to MHO, 95%CI 1.75-118.99). Hypometabolic obesity (LMO, 15% patients) was characterized by extremely high glucose, decompensated insulin secretion, and the worst glucolipid metabolism (diabetes: AOR 105.85 to MHO, 95%CI 42.00-266.74; metabolic syndrome: AOR 13.50 to MHO, 95%CI 7.34-24.83). The assignment of patients in the verification cohorts to the main model showed a mean accuracy of 0.941 in all clusters. Conclusion: Machine learning automatically identified four subtypes of obesity in terms of clinical characteristics on four independent patient cohorts. This proof-of-concept study provided evidence that precise diagnosis of obesity is feasible to potentially guide therapeutic planning and decisions for different subtypes of obesity. Clinical Trial Registration: www.ClinicalTrials.gov, NCT04282837.