PURPOSE: To evaluate the impact of high body mass index (BMI) on outcomes following robotic laparoendoscopic single-site surgery (R-LESS) robotic-assisted laparoscopic partial nephrectomy (RPN). MATERIALS AND METHODS: Data from 83 Korean patients who had undergone robotic partial nephrectomy from 2006 to 2014 were retrospectively analyzed. The subjects were stratified into two groups according to WHO definitions for the Asian population, consisting of 56 normal range (BMI=18.5-24.99 kg/m2) and 27 obese (> or =25 kg/m2) patients. Outcome measurements included Trifecta achievement and the perioperative and postoperative comparison between high and normal BMI series. The measurements were estimated and analyzed with SPSS version 17. RESULTS: Tumor's complexity characteristics (R.E.N.A.L. score, tumor size) of both groups were similar. No significant differences existed between the two groups with regard to operative time (p=0.27), warm ischemia time (p=0.35) estimated blood loss (p=0.42), transfusion rate (p=0.48) renal function following up for 1 year, positive margins (p=0.24) and postoperative complication rate (p=0.34). Trifecta was achieved in 5 (18.5%) obese and 19 (33.9%) normal weight patients, respectively (p=0.14). In multivariable analysis, only tumor size was significantly correlated with the possibility of Trifecta accomplishment. CONCLUSION: Our findings suggest that R-LESS RPN can be effectively and safely performed in patients with increased BMI, since Trifecta rate, and perioperative and postoperative outcomes are not significantly different in comparison to normal weight subjects.
Adult ; Blood Transfusion ; Body Mass Index ; Female ; Humans ; Kidney Neoplasms/*surgery ; *Laparoscopy/adverse effects ; Male ; Middle Aged ; Nephrectomy/*methods ; Obesity/*complications ; Operative Time ; Outcome Assessment (Health Care) ; Postoperative Complications ; Republic of Korea ; Retrospective Studies ; Robotic Surgical Procedures/*methods ; *Robotics ; Treatment Outcome ; Warm Ischemia

Background/Aims: The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups. Methods: The study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort. Results: In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-tomonocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATSINF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores. Conclusions Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.

OBJECTIVETo observe the effect of combining red yeast rice and Lactobacillus casei (L. casei) in lowering cholesterol in patients with primary hyperlipidemia, the later has also been shown to remove cholesterol in in vitro studies.

METHODSA double-blind clinical trial was conducted to evaluate the cholesterol-lowering effect of the combination of red yeast rice and L. casei. Sixty patients with primary hyperlipidemia were recruited and randomized equally to either the treatment group (red yeast rice + L. casei) or the control group (red yeast rice + placebo). One red yeast rice capsule and two L. casei capsules were taken twice a day. The treatment lasted for 8 weeks, with an extended follow-up period of 4 weeks. The primary endpoint was a difference of serum low-density lipoprotein cholesterol (LDL-C) level at week 8.

RESULTSAt week 8, the LDL-C serum level in both groups was lower than that at baseline, with a decrease of 33.85±26.66 mg/dL in the treatment group and 38.11±30.90 mg/dL in the control group; however, there was no statistical difference between the two groups (P>0.05). The total cholesterol was also lower than the baseline in both groups, yet without a statistical difference between the two groups. The only statistically signifificant difference between the two groups was the average diastolic pressure at week 12, which dropped by 2.67 mm Hg in the treatment group and increased by 4.43 mm Hg in the placebo group (P<0.05). The antihypertensive activity may be associated with L. casei. Red yeast rice can signifificantly reduce LDL-C, total cholesterol and triglyceride.

CONCLUSIONThe combination of red yeast rice and L. casei did not have an additional effect on lipid profifiles.

OBJECTIVESTo investigate whether three strains of probiotics, L. acidophilus, L. rhamnosus, and L. sporogenes, had signifificant inhibitive effects on Helicobacter pylori (H. pylori).

METHODSThis is a 4-week, randomly assigned, parallel-group, doubled-blind, and placebo-controlled study. Fifty patients with a positive H. pylori infection urea breath test (△UBT) result > 10% and without ulcer symptoms were randomized into a treatment group and a placebo group by a computer generated allocation sheet with 1:1. These subjects took one capsule of probiotics or placebo twice daily. The primary measurement was the change in △UBT values.

RESULTSThe △UBT values during the 4-week treatment period and the 2-week follow-up period were not signifificantly different between the treatment group and the placebo group, indicating that the inhibitive effects on H. pylori were comparable between both groups. The monocyte count (%) was 5.77±1.11 in the treatment group versus 5.09±1.12 in the placebo group (P=0.044), and the basophile count was 0.55±0.32 in the treatment group versus 0.36±0.23 in the placebo group (P=0.024) at week 2 of the treatment period, both of which reached statistical signifificance. The monocyte count was 5.75±1.26 in the treatment group and 4.72±0.99 in the placebo group at the end of the follow-up period (P=0.003).

CONCLUSIONThere was no signifificant inhibitive effects of the three probiotic strains (L. acidophilus, L. rhamnosus, and L. sporogenes) on H. pylori. Probiotics can not play the same role as antibiotics in the eradication of H. pylori, the role of probiotics is likely to be important as adjuvant to the triple or quadruple therapy for H. pylori, especially in resistance cases.

Adult ; Aged ; Breath Tests ; Demography ; Double-Blind Method ; Endpoint Determination ; Female ; Helicobacter pylori ; drug effects ; Humans ; Lactobacillus ; metabolism ; Male ; Middle Aged ; Probiotics ; administration & dosage ; adverse effects ; pharmacology ; Urea ; analysis ; Young Adult

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.