Pediatric incarcerated inguinal hernia: Traditional open or laparoscopic-assisted approach?

The objective of this study was to compare the safety and efficacy of laparoscopic-assisted surgery and traditional open surgery for pediatric incarcerated inguinal hernia. A total of 58 pediatric patients with indirect incarcerated inguinal hernia between January 2014 and January 2020 were included in this study. The patients were divided into 2 groups; observational group who underwent laparoscopic-assisted surgery (n = 36), and a control group who underwent traditional open surgery (n = 22). The overall operation time, intraoperative blood loss, postoperative recovery time, length of hospital stay, occurrence of postoperative scrotal or vulvar hematomas, incidence of postoperative surgical site infection, and hernia recurrence were analyzed and compared between the 2 groups. Compared with the control group, the operation time (38.28 ±â€…5.90) minutes, intraoperative blood loss (1.15 ±â€…0.54 mL), postoperative recovery time (8.39 ±â€…1.42 h), and length of hospital stay (1.64 ±â€…0.59) were significantly lower in the observational group (P < .05). There was no incidence of scrotal or vulvar hematoma or surgical site infection in the observation group, which was significantly lower than that in the control group (P < .05). However, no statistically significant difference was found in the rate of postoperative hernia recurrence between the 2 groups (P > .05). In conclusion, laparoscopic-assisted surgery appears to be a safe and effective alternative approach to traditional open surgery for the treatment of pediatric incarcerated inguinal hernia. Its advantages include reduced trauma, faster recovery, shorter hospital stays, and fewer complications.

Comprehensive analysis of single cell and bulk data develops a promising prognostic signature for improving immunotherapy responses in ovarian cancer.

The tumor heterogeneity is an important cause of clinical therapy failure and yields distinct prognosis in ovarian cancer (OV). Using the advantages of integrated single cell RNA sequencing (scRNA-seq) and bulk data to decode tumor heterogeneity remains largely unexplored. Four public datasets were enrolled in this study, including E-MTAB-8107, TCGA-OV, GSE63885, and GSE26193 cohorts. Random forest algorithm was employed to construct a multi-gene prognostic panel and further evaluated by receiver operator characteristic (ROC), calibration curve, and Cox regression. Subsequently, molecular characteristics were deciphered, and treatments strategies were explored to deliver precise therapy. The landscape of cell subpopulations and functional characteristics, as well as the dynamic of macrophage cells were detailly depicted at single cell level, and then screened prognostic candidate genes. Based on the expression of candidate genes, a stable and robust cell characterized gene associated prognosis signature (CCIS) was developed, which harbored excellent performance at prognosis assessment and patient stratification. The ROC and calibration curves, and Cox regression analysis elucidated CCIS could serve as serve as an independent factor for predicting prognosis. Moreover, a promising clinical tool nomogram was also constructed according to stage and CCIS. Through comprehensive investigations, patients in low-risk group were charactered by favorable prognosis, elevated genomic variations, higher immune cell infiltrations, and superior antigen presentation. For individualized treatment, patients in low-risk group were inclined to better immunotherapy responses. This study dissected tumor heterogeneity and afforded a promising prognostic signature, which was conducive to facilitating clinical outcomes for patients with OV.

A framework for identification and classification of liver diseases based on machine learning algorithms.

Hepatocellular carcinoma (HCC) is one of the most commonly seen liver disease. Most of HCC patients are diagnosed as Hepatitis B related cirrhosis simultaneously, especially in Asian countries. HCC is the fifth most common cancer and the second most common cause of cancer-related death in the World. HCC incidence rates have been rising in the past 3 decades, and it is expected to be doubled by 2030, if there is no effective means for its early diagnosis and management. The improvement of patient's care, research, and policy is significantly based on accurate medical diagnosis, especially for malignant tumor patients. However, sometimes it is really difficult to get access to advanced and expensive diagnostic tools such as computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET-CT)., especially for people who resides in poverty-stricken area. Therefore, experts are searching for a framework for predicting of early liver diseases based on basic and simple examinations such as biochemical and routine blood tests, which are easily accessible all around the World. Disease identification and classification has been significantly enhanced by using artificial intelligence (AI) and machine learning (ML) in conjunction with clinical data. The goal of this research is to extract the most significant risk factors or clinical parameters for liver diseases in 525 patients based on clinical experience using machine learning algorithms, such as regularized regression (RR), logistic regression (LR), random forest (RF), decision tree (DT), and extreme gradient boosting (XGBoost). The results showed that RF classier had the best performance (accuracy = 0.762, recall = 0.843, F1-score = 0.775, and AUC = 0.999) among the five ML algorithms. And the important orders of 14 significant risk factors are as follows: Total bilirubin, gamma-glutamyl transferase (GGT), direct bilirubin, hemoglobin, age, platelet, alkaline phosphatase (ALP), aspartate transaminase (AST), creatinine, alanine aminotransferase (ALT), cholesterol, albumin, urea nitrogen, and white blood cells. ML classifiers might aid medical organizations in the early detection and classification of liver disease, which would be beneficial in low-income regions, and the relevance of risk factors would be helpful in the prevention and treatment of liver disease patients.

Evolution of Endoscopic Thyroidectomy: Will a Novel Single Channel Flexible Endoscopic Approach Change the Treatment Paradigm?

Endoscopic thyroidectomy has evolved from a hybrid approach utilizing a laparoscopic assistant and robotic-assistance to pure flexible endoscopic thyroidectomy without laparoscopic assistance. However, all the hybrid approaches are complicated and results in scar somewhere on the body surface. Current experimental and clinical studies focus on the implementation of new minimally invasive approaches such as flexible endoscopic thyroidectomy which leaves no scar on the body surface and easier to perform. Flexible endoscopic thyroidectomy is seeming to be a relatively safe and feasible technique with good outcomes. However, more research, particularly using newly developed tools to further improve this technique, and large scaled practice is needed to make it more available to patients worldwide. This article summarizes established endoscopic thyroidectomy techniques and highlight the pros and cons of different available endoscopic approaches to thyroid resection, and discussed how flexible endoscopic thyroidectomy compares to the well establish techniques and future perspective.

Effectiveness of mild ovarian stimulation versus GnRH agonist protocol in women undergoing assisted reproductive technology: a meta-analysis.

OBJECTIVE: our meta-analysis was conducted to evaluate the effectiveness of the mild ovulation induction protocol using CC/gonadotropin/GnRH antagonist compared to the conventional GnRH agonist protocol in women undergoing ART. METHOD: Six electronic databases were searched from their date of establishment until August 2016. Outcomes in our analysis were calculated in terms of relative risk (RR) and weighted mean differences (WMD) and standard mean differences (SMD) with 95% confidence intervals (CI) using random effect models or fixed effect models. RESULTS: Six prospective controlled clinical trials with 1543 women comparing the clinical impacts of the two protocols were included. The synthesized results suggested a significant reduction in the quantity of gonadotropins (SMD: -1.96, 95% CI: -2.28 to 1.64, I2 = 78.5%), the incidence of OHSS (RR: 0.16, 95% CI 0.03-0.86, I2 = 0%) and an increase in the cycle cancelation rate (RR: 1.46, 95% CI 1.05-2.03, I2 = 89.4%). While no evidence of statistically significant differences between the groups existed in the other clinical outcomes. CONCLUSION: This study suggested that the probable benefits of the mild protocol, including its less costs and safer process without reducing the overall IVF treatment success rates, seemed to make it a better treatment option. Larger sample prospective trials evaluating live birth, clinical pregnancy, OHSS, multiple pregnancy incidence and so on were desired to establish.