A comparative study on laparoscopic and open surgical approaches for perforated peptic ulcer repair: efficacy and outcomes analysis.

PURPOSE: This study aimed to compare the clinical outcomes of the clinical outcomes of laparoscopic and open sutures for peptic ulcer perforation (PPU). MATERIALS AND METHODS: PubMed, EMBASE, and Cochrane Library databases were searched for eligible studies from inception to March 31, 2023. Odds ratios (OR) and 95% confidence intervals (Cl) were also calculated. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the included studies. This study was performed using the Stata (V.16.0) software. RESULTS: A total of 29 studies involving 17,228 patients were included in this study. In terms of postoperative outcomes, the laparoscopic group had a shorter postoperative hospital stay (MD = -0.29, 95%CI = -0.44 to -0.13, P = 0.00), less blood loss (MD = -0.45, 95%CI = -0.82 to -0.08, P = 0.02), fewer wound infection (OR = 0.20, 95%CI = 0.17 to 0.24, P = 0.00), fewer pneumonia (OR = 0.59, 95%CI = 0.41 to 0.87, P = 0.01), fewer respiratory complications (OR = 0.26, 95%CI = 0.13 to 0.55, P = 0.00) and lower postoperative morbidity (OR = 0.51, 95%CI = 0.33 to 0.78, P = 0.00). The laparoscopic group had a lower mortality rate (OR = 0.36, 95%CI = 0.27 to 0.49, P = 0.00) than the open group. We also found that the laparoscopic group had a higher overall complication rate than the open group (OR = 0.45, 95%CI = 0.34 to 0.60, P = 0.00). CONCLUSION: Laparoscopic repair was associated with a lower risk of mortality than open repair in patients with PPU. Laparoscopic repair may be a better option in patients with PPU.

Impact of preoperative type 2 diabetes mellitus on the outcomes of patients with gastric cancer following gastrectomy: Analysis of 834 patients using propensity score matching.

The purpose of the current study was to compare the outcomes of patients with gastric cancer (GC) between the type 2 diabetes mellitus (T2DM) group and the non-T2DM group. The PubMed, Embase and Cochrane Library databases were searched from inception to March 8, 2022, to identify propensity score matching (PSM) studies that analyzed the effect of T2DM on the outcomes of patients with GC. Total complications, overall survival (OS), disease-free survival (DFS) and cancer-specific survival (CSS) were compared between the T2DM group and the non-T2DM group. A total of four PSM studies with 834 patients were included in the current study. There were 311 and 523 patients in the T2DM group and the non-T2DM group, respectively. Baseline characteristics of the two groups were adjusted with PSM in all the four studies, however, no significant difference was found in baseline characteristics (P>0.05). DFS was significantly worse in the T2DM group compared with that in the non-T2DM group [hazard ratio (HR), 1.45; 95% confidence interval (CI), 1.10-1.90; P=0.007)]. However, after pooling up the data, there was no significant difference between the T2DM group and the non-T2DM group in terms of OS (HR, 1.41; 95% CI, 0.92-2.16; P=0.11), CSS (HR, 1.29; 95% CI, 0.92-1.81; P=0.14) and total complications (odds ratio, 1.01; 95% CI, 0.64-1.60; P=0.95). Patients with GC and T2DM are associated with poor DFS. However, there were no significant differences between the T2DM group and the non-T2DM group in terms of OS, CSS and total complications.

Diagnostic accuracy of transient elastography (FibroScan) in detection of esophageal varices in patients with cirrhosis: A meta-analysis.

AIM: To investigate the diagnostic accuracy of FibroScan (FS) in detecting esophageal varices (EV) in cirrhotic patients. METHODS: Through a systemic literature search of multiple databases, we reviewed 15 studies using endoscopy as a reference standard, with the data necessary to calculate pooled sensitivity (SEN) and specificity (SPE), positive and negative LR, diagnostic odds ratio (DOR) and area under receiver operating characteristics (AUROC). The quality of the studies was rated by the Quality Assessment of Diagnostic Accuracy studies-2 tool. Clinical utility of FS for EV was evaluated by a Fagan plot. Heterogeneity was explored using meta-regression and subgroup analysis. All statistical analyses were conducted via Stata12.0, MetaDisc1.4 and RevMan5. RESULTS: In 15 studies (n = 2697), FS detected the presence of EV with the summary sensitivities of 84% (95%CI: 81.0%-86.0%), specificities of 62% (95%CI: 58.0%- 66.0%), a positive LR of 2.3 (95%CI: 1.81-2.94), a negative LR of 0.26 (95%CI: 0.19-0.35), a DOR of 9.33 (95%CI: 5.84-14.92) and an AUROC of 0.8262. FS diagnosed the presence of large EV with the pooled SEN of 0.78 (95%CI: 75.0%-81.0%), SPE of 0.76 (95%CI: 73.0%-78.0%), a positive and negative LR of 3.03 (95%CI: 2.38-3.86) and 0.30 (95%CI: 0.23-0.39) respectively, a summary diagnostic OR of 10.69 (95%CI: 6.81-16.78), and an AUROC of 0.8321. A meta-regression and subgroup analysis indicated different etiology could serve as a potential source of heterogeneity in the diagnosis of the presence of EV group. A Deek's funnel plot suggested a low probability for publication bias. CONCLUSION: Using FS to measure liver stiffness cannot provide high accuracy for the size of EV due to the various cutoff and different etiologies. These limitations preclude widespread use in clinical practice at this time; therefore, the results should be interpreted cautiously given its SEN and SPE.

Clarifying the signal network of salvianolic acid B using proteomic assay and bioinformatic analysis.

Salvianolic acid B (SB) is a natural compound with protective effect against ischemia-reperfusion heart injury. However, the signal network of SB including both direct target proteins and downstream signal-related proteins has not been clarified. In the present study, epidermal growth factor receptor (EGFR) was predicted to be the most possible direct protein target of SB by INVDOCK, a ligand-protein inverse-docking algorithm. Possible signal-related proteins of SB in H9C2 cells, including both under normal condition and under ischemia-reperfusion injury, were searched using 2-DE analysis. Totally, 14 signal-related proteins were found. Finally, signal network from EGFR to the signal-related proteins was established using bioinformatic analysis. Interestingly, 9 of the 14 signal-related proteins could be included in a network together with EGFR through direct interaction or only one intermediate partner. The signal cascade from EGFR to heat shock protein 27 (HSP27) and mitofilin (IMMT, inner membrane mitochondrial protein) might be the most important cascade. The signal network was certified by measuring the binding affinity of SB to EGFR in vitro, the effect of SB on internalization and phosphorylation of EGFR, the effect of SB on viability and proliferation of H9C2 cells, and the expression of inner membrane mitochondrial protein in the presence of EGFR inhibitor AG 1478.

Cancer classification from the gene expression profiles by Discriminant Kernel-PLS.

Cancer diagnosis depending on microarray technology has drawn more and more attention in the past few years. Accurate and fast diagnosis results make gene expression profiling produced from microarray widely used by a large range of researchers. Much research work highlights the importance of gene selection and gains good results. However, the minimum sets of genes derived from different methods are seldom overlapping and often inconsistent even for the same set of data, partially because of the complexity of cancer disease. In this paper, cancer classification was attempted in an alternative way of the whole gene expression profile for all samples instead of partial gene sets. Here, the three common sets of data were tested by NIPALS-KPLS method for acute leukemia, prostate cancer and lung cancer respectively. Compared to other conventional methods, the results showed wide improvement in classification accuracy. This paper indicates that sample profile of gene expression may be explored as a better indicator for cancer classification, which deserves further investigation.

Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data.

BACKGROUND: Recent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduction and classification are considerable challenges in statistical machine learning. We therefore propose a novel approach for dimensionality reduction and tested it using published high-resolution SELDI-TOF data for ovarian cancer. RESULTS: We propose a method based on statistical moments to reduce feature dimensions. After refining and t-testing, SELDI-TOF data are divided into several intervals. Four statistical moments (mean, variance, skewness and kurtosis) are calculated for each interval and are used as representative variables. The high dimensionality of the data can thus be rapidly reduced. To improve efficiency and classification performance, the data are further used in kernel PLS models. The method achieved average sensitivity of 0.9950, specificity of 0.9916, accuracy of 0.9935 and a correlation coefficient of 0.9869 for 100 five-fold cross validations. Furthermore, only one control was misclassified in leave-one-out cross validation. CONCLUSION: The proposed method is suitable for analyzing high-throughput proteomics data.