Independent external validation and comparison of existing pancreatic fistula risk scores after laparoscopic pancreaticoduodenectomy with Bing's pancreaticojejunostomy.

BACKGROUND: The fistula risk score (FRS) is the widely acknowledged prediction model for clinically relevant postoperative pancreatic fistula (CR-POPF). In addition, the alternative FRS (a-FRS) and updated alternative FRS (ua-FRS) have been developed. This study performed external validation and comparison of these 3 models in patients who underwent laparoscopic pancreaticoduodenectomy (LPD) with Bing's pancreaticojejunostomy. METHODS: The FRS total points and predictive probabilities of a-FRS and ua-FRS were retrospectively calculated using patient data from a completed randomized controlled trial. Postoperative pancreatic fistula (POPF) and CR-POPF were defined according to the 2016 International Study Group of Pancreatic Surgery criteria. The correlations of the 4 risk items of the FRS model with CR-POPF and POPF were analyzed and represented using the Cramer V coefficient. The performance of the 3 models was measured using the area under the curve (AUC) and calibration plot and compared using the DeLong test. RESULTS: This study enrolled 200 patients. Pancreatic texture and pathology had discrimination for CR-POPF (Cramer V coefficient: 0.180 vs 0.167, respectively). Pancreatic duct diameter, pancreatic texture, and pathology had discrimination for POPF (Cramer V coefficient: 0.357 vs 0.322 vs 0.257, respectively). Only the calibration of a-FRS predicting CR-POPF was good. The differences among the AUC values of the FRS, a-FRS, and ua-FRS were not statistically significant (CR-POPF: 0.687 vs 0.701 vs 0.710, respectively; POPF: 0.733 vs 0.741 vs 0.750, respectively). After recalibrating, the ua-FRS got sufficient calibration, and the AUC was 0.713 for predicting CR-POPF. CONCLUSION: For LPD cases with Bing's pancreaticojejunostomy, the 3 models predicted POPF with better discrimination than predicting CR-POPF. The recalibrated ua-FRS had sufficient discrimination and calibration for predicting CR-POPF.

Extended pancreatic neck transection versus conventional pancreatic neck transection during laparoscopic pancreaticoduodenectomy (LPDEXCEPT): protocol for a multicentre superiority randomised controlled trial.

INTRODUCTION: Postoperative pancreatic fistula (POPF) remains one of the most severe complications of laparoscopic pancreaticoduodenectomy (LPD). Theoretically, transecting the pancreatic neck more distally has both advantages (more blood supply, and more central pancreatic duct) and disadvantages (maybe smaller the pancreatic duct) in preventing POPF. This theoretical contradiction pushed us to organise this trial to explore the impact of the level of pancreatic transection in clinical practice. We conduct this randomised trial with the hypothesis that extended pancreatic neck transection has superiority to conventional pancreatic neck transection. METHODS AND ANALYSIS: The LPDEXCEPT (Extended pancreatic neck transection versus conventional pancreatic neck transection during laparoscopic pancreaticoduodenectomy) trial is a multicentre, randomised-controlled, open-label, superiority trial in 4 centres whose annual surgical volume for LPD is more than 25 cases with pancreatic surgeons who had completed their learning curve. A total of 154 patients who meet the inclusive and exclusive criteria are randomly allocated to the extended pancreatic neck transection group or conventional pancreatic neck transection group in a 1:1 ratio. The stratified randomised block design will be applied, with stratified factors are surgical centre and the diameter of the main pancreatic duct measured by preoperative CT scan (preMPD). The primary outcome is the incidence of the clinically relevant pancreatic fistula. ETHICS AND DISSEMINATION: Ethics Committee on Biomedical Research of West China Hospital of Sichuan University has approved this trial in March 2023 (approval no. 2023-167). Results of this trial will be published in peer-reviewed journals and conference proceedings. TRIAL REGISTRATION NUMBER: NCT05808894.

Circular RNA hsa_circ_0023404 promotes the proliferation, migration and invasion in endometrial cancer cells through regulating miR-217/MAPK1 axis.

BACKGROUND: Emerging studies indicated that circular RNA hsa_circ_ 0023404 and its target miR-217/MARK1 axis play a critical role in cancer progression such as non-small cell lung cancer and cervical cancer. However, the role of hsa_circ_0023404/miR-217/MARK1 involved in endometrial cancer (EC) was not investigated yet. The aim of this study is to investigate the functions of hsa_circ_0023404 in endometrial cancer (EC) and the potential molecular mechanism. METHODS: We used RT-qPCR and Western blot approach to detect the expressed levels of related genes in EC cell lines. Transfected siRNAs were applied to knockdown the level of related mRNA in cells. Cell proliferation by CCK-8 assay and colony formation assay were applied to detect cell proliferation. Transwell migration and invasion assay was for detecting the migration and invasion of the cells. RESULTS: RT-qPCR showed that the levels of hsa_circ_0023404 and MARK1 mRNA were upregulated, but mirR-217 was decreased in three endometrial cancer cell lines. Knockdown of hsa_circ_0023404 by siRNA markedly increased the level of miR-217 and reduced the proliferation of the Ishikawa cells. It also inhibited the cell migration and invasion. Anti-miR-217 can reverse the promoted proliferation, migrations and invasion of Ishikawa cells mediated by si-circ_0023404. si-MARK1 restored the inhibited cell proliferation, migration and invasion of the co-transfected Ishikawa cells with si- circ_0023404 and anti-miR-217. CONCLUSION: hsa_circ_0023404 exerts a tumor-promoting role in endometrial cancer by regulating miR-217/MARK1 axis. hsa_circ_0023404 inhibit miR-217 as sponge which inhibit endometrial cancer cell growth and metastasis. MARK1 is downstream target of miR217 and upregulated by hsa_circ_ 0023404/miR-217 axis and involved in the endometrial cancer progression.

Surgesture: a novel instrument based on surgical actions for objective skill assessment.

BACKGROUND: Due to varied surgical skills and the lack of an efficient rating system, we developed Surgesture based on elementary functional surgical gestures performed by surgeons, which could serve as objective metrics to evaluate surgical performance in laparoscopic cholecystectomy (LC). METHODS: We defined 14 LC basic Surgestures. Four surgeons annotated Surgestures among LC videos performed by experts and novices. The counts, durations, average action time, and dissection/exposure ratio (D/E ratio) of LC Surgestures were compared. The phase of mobilizing hepatocystic triangle (MHT) was extracted for skill assessment by three professors using a modified Global Operative Assessment of Laparoscopic Skills (mGOALS). RESULTS: The novice operation time was significantly longer than the expert operation time (58.12 ± 19.23 min vs. 26.66 ± 8.00 min, P < 0.001), particularly during MHT phase. Novices had significantly more Surgestures than experts in both hands (P < 0.05). The left hand and inefficient Surgesture of novices were dramatically more than those of experts (P < 0.05). The experts demonstrated a significantly higher D/E ratio of duration than novices (0.79 ± 0.37 vs. 2.84 ± 1.98, P < 0.001). The counts and time pattern map of LC Surgestures during MHT demonstrated that novices tended to complete LC with more types of Surgestures and spent more time exposing the surgical scene. The performance metrics of LC Surgesture had significant but weak associations with each aspect of mGOALS. CONCLUSION: The newly constructed Surgestures could serve as accessible and quantifiable metrics for demonstrating the operative pattern and distinguishing surgeons with various skills. The association between Surgestures and Global Rating Scale laid the foundation for establishing a bridge to automated objective surgical skill evaluation.

Artificial intelligence-based automated laparoscopic cholecystectomy surgical phase recognition and analysis.

BACKGROUND: Artificial intelligence and computer vision have revolutionized laparoscopic surgical video analysis. However, there is no multi-center study focused on deep learning-based laparoscopic cholecystectomy phases recognizing. This work aims to apply artificial intelligence in recognizing and analyzing phases in laparoscopic cholecystectomy videos from multiple centers. METHODS: This observational cohort-study included 163 laparoscopic cholecystectomy videos collected from four medical centers. Videos were labeled by surgeons and a deep-learning model was developed based on 90 videos. Thereafter, the performance of the model was tested in additional ten videos by comparing it with the annotated ground truth of the surgeon. Deep-learning models were trained to identify laparoscopic cholecystectomy phases. The performance of models was measured using precision, recall, F1 score, and overall accuracy. With a high overall accuracy of the model, additional 63 videos as an analysis set were analyzed by the model to identify different phases. RESULTS: Mean concordance correlation coefficient for annotations of the surgeons across all operative phases was 92.38%. Also, the overall phase recognition accuracy of laparoscopic cholecystectomy by the model was 91.05%. In the analysis set, there was an average surgery time of 2195 ± 896 s, with a huge individual variance of different surgical phases. Notably, laparoscopic cholecystectomy in acute cholecystitis cases had prolonged overall durations, and the surgeon would spend more time in mobilizing the hepatocystic triangle phase. CONCLUSION: A deep-learning model based on multiple centers data can identify phases of laparoscopic cholecystectomy with a high degree of accuracy. With continued refinements, artificial intelligence could be utilized in huge data surgery analysis to achieve clinically relevant future applications.

Deep Modeling of Regulating Effects of Small Molecules on Longevity-Associated Genes.

Aging is considered an inevitable process that causes deleterious effects in the functioning and appearance of cells, tissues, and organs. Recent emergence of large-scale gene expression datasets and significant advances in machine learning techniques have enabled drug repurposing efforts in promoting longevity. In this work, we further developed our previous approach-DeepCOP, a quantitative chemogenomic model that predicts gene regulating effects, and extended its application across multiple cell lines presented in LINCS to predict aging gene regulating effects induced by small molecules. As a result, a quantitative chemogenomic Deep Model was trained using gene ontology labels, molecular fingerprints, and cell line descriptors to predict gene expression responses to chemical perturbations. Other state-of-the-art machine learning approaches were also evaluated as benchmarks. Among those, the deep neural network (DNN) classifier has top-ranked known drugs with beneficial effects on aging genes, and some of these drugs were previously shown to promote longevity, illustrating the potential utility of this methodology. These results further demonstrate the capability of "hybrid" chemogenomic models, incorporating quantitative descriptors from biomarkers to capture cell specific drug-gene interactions. Such models can therefore be used for discovering drugs with desired gene regulatory effects associated with longevity.

Safety of laparoscopic pancreaticoduodenectomy in patients with liver cirrhosis using propensity score matching.

Currently, safety of laparoscopic pancreaticoduodenectomy (LPD) in patients with liver cirrhosis is unknown. The aim of this study was to explore postoperative morbidity and mortality and long-term outcomes of cirrhotic patients after LPD. The study was a one-center retrospective study comprising 353 patients who underwent LPD between October 2010 and December 2019. A total of 28 patients had liver cirrhosis and were paired with 56 non-cirrhotic counterparts through propensity score matching (PSM). Baseline data, intra-operative data, postoperative data, and survival data were collected. Postoperative morbidity was considered as primary outcome whereas postoperative mortality, surgical parameters (operative durations, intraoperative blood loss), and long-term overall survival were secondary outcomes. Cirrhotic patients showed postoperative complication rates of 82% compared with rates of patients in the control group (48%) (P = 0.003). Further, Clavien-Dindo ≥III complication rates of 14% and 11% (P = 0.634), Clavien-Dindo I-II complication rates of 68% and 38% (P = 0.009), hospital mortality of 4% and 2% (P = 0.613) were observed for cirrhotic patients and non-cirrhotic patients, respectively. In addition, an overall survival rate of 32 months and 34.5 months (P = 0.991), intraoperative blood loss of 300 (200-400) ml and 150 (100-250) ml (P<0.0001), drain amount of 2572.5 (1023.8-5275) ml and 1617.5 (907.5-2700) ml (P = 0.048) were observed in the cirrhotic group and control group, respectively. In conclusion, LPD is associated with increased risk of postoperative morbidity in patients with liver cirrhosis. However, the incidence of Clavien-Dindo ≥III complications and post-operative mortality showed no significant increase. In addition, liver cirrhosis showed no correlation with poor overall survival in patients who underwent LPD. These findings imply that liver cirrhosis patients can routinely be considered for LPD at high volume centers with rigorous selection and management.

Predicting drug-target interaction network using deep learning model.

BACKGROUND: Traditional methods for drug discovery are time-consuming and expensive, so efforts are being made to repurpose existing drugs. To find new ways for drug repurposing, many computational approaches have been proposed to predict drug-target interactions (DTIs). However, due to the high-dimensional nature of the data sets extracted from drugs and targets, traditional machine learning approaches, such as logistic regression analysis, cannot analyze these data sets efficiently. To overcome this issue, we propose LASSO (Least absolute shrinkage and selection operator)-based regularized linear classification models and a LASSO-DNN (Deep Neural Network) model based on LASSO feature selection to predict DTIs. These methods are demonstrated for repurposing drugs for breast cancer treatment. METHODS: We collected drug descriptors, protein sequence data from Drugbank and protein domain information from NCBI. Validated DTIs were downloaded from Drugbank. A new similarity-based approach was developed to build the negative DTIs. We proposed multiple LASSO models to integrate different combinations of feature sets to explore the prediction power and predict DTIs. Furthermore, building on the features extracted from the LASSO models with the best performance, we also introduced a LASSO-DNN model to predict DTIs. The performance of our newly proposed DNN model (LASSO-DNN) was compared with the LASSO, standard logistic (SLG) regression, support vector machine (SVM), and standard DNN models. RESULTS: Experimental results showed that the LASSO-DNN over performed the SLG, LASSO, SVM and standard DNN models. In particular, the LASSO models with protein tripeptide composition (TC) features and domain features were superior to those that contained other protein information, which may imply that TC and domain information could be better representations of proteins. Furthermore, we showed that the top ranked DTIs predicted using the LASSO-DNN model can potentially be used for repurposing existing drugs for breast cancer based on risk gene information. CONCLUSIONS: In summary, we demonstrated that the efficient representations of drug and target features are key for building learning models for predicting DTIs. The disease-associated risk genes identified from large-scale genomic studies are the potential drug targets, which can be used for drug repurposing.

A favorable tumor size to define papillary thyroid microcarcinoma: an analysis of 1176 consecutive cases.

BACKGROUND: The optimal treatment strategy for papillary thyroid microcarcinoma (PTMC) has remained controversial. The purpose of this study was to provide a new reference value for PTMC to aid the selection of optimal management for minute lesions. PATIENTS AND METHODS: A pool of 1176 consecutive patients who met the inclusion criteria were ultimately enrolled in this study. The correlation of papillary thyroid carcinoma (PTC) tumor size and lymph node metastasis was analyzed. Receiver operating characteristic curve studies were conducted to identify the reference value by determining the optimal cut-off point of size related to lymph node metastasis. To validate our results, all selected patients were divided into two groups according to the cut-off point and some of the prognostic factors were compared. RESULTS: A moderate significant correlation was found between the tumor size and the average number of lymph node metastases (r=0.502, P<0.01) and the percentage of lymph node metastasis (r=0.625, P<0.01). The optimal cut-off reference value was 8.5 mm according to the receiver operating characteristic curves. Significant differences were observed for PTC prognostic factors, for example, extrathyroidal extension, multifocality, pathologic (p) N+ stage, occult metastasis in clinical (c) N- stage, radioactive iodine ablation, and recurrence between the two groups. CONCLUSION: Due to more aggressive behavior and poorer prognosis in larger tumor size (>8.5 mm), a tumor size ≤8.5 mm in diameter may be favorable to discriminate PTMC from PTC and aid the selection of optimal management.

Epigenome-wide DNA methylation profiling of periprostatic adipose tissue in prostate cancer patients with excess adiposity-a pilot study.

Background: Periprostatic adipose tissue (PPAT) has been recognized to associate with prostate cancer (PCa) aggressiveness and progression. Here, we sought to investigate whether excess adiposity modulates the methylome of PPAT in PCa patients. DNA methylation profiling was performed in PPAT from obese/overweight (OB/OW, BMI > 25 kg m-2) and normal weight (NW, BMI < 25 kg m-2) PCa patients. Significant differences in methylated CpGs between OB/OW and NW groups were inferred by statistical modeling. Results: Five thousand five hundred twenty-six differentially methylated CpGs were identified between OB/OW and NW PCa patients with 90.2% hypermethylated. Four hundred eighty-three of these CpGs were found to be located at both promoters and CpG islands, whereas the representing 412 genes were found to be involved in pluripotency of stem cells, fatty acid metabolism, and many other biological processes; 14 of these genes, particularly FADS1, MOGAT1, and PCYT2, with promoter hypermethylation presented with significantly decreased gene expression in matched samples. Additionally, 38 genes were correlated with antigen processing and presentation of endogenous antigen via MHC class I, which might result in fatty acid accumulation in PPAT and tumor immune evasion. Conclusions: Results showed that the whole epigenome methylation profiles of PPAT were significantly different in OB/OW compared to normal weight PCa patients. The epigenetic variation associated with excess adiposity likely resulted in altered lipid metabolism and immune dysregulation, contributing towards unfavorable PCa microenvironment, thus warranting further validation studies in larger samples.

Predictive factors and prognosis for recurrent laryngeal nerve invasion in papillary thyroid carcinoma.

BACKGROUND: Recurrent laryngeal nerve (RLN) invasion in papillary thyroid carcinoma (PTC) is one of the main predictors of poor prognosis. The present study investigated the risk factors for RLN invasion in PTC patients. METHODS: A total of 3,236 patients who received thyroidectomy due to PTC in Thyroid and Parathyroid Surgery Center of West China Hospital of Sichuan University were reviewed. Demographics and clinical factors, imaging examination (ultrasonography) characteristics, surgical details, postoperative pathological details, recurrence, and postoperative complications were recorded. Univariate and multivariate analyses were used to study the risk factors of RLN invasion, Kaplan-Meier method was performed to compare the outcomes of tumor recurrence. RESULTS: Patients with RLN invasion had a higher recurrence rate than those in the control group (p<0.001). Multivariate analyses showed that age greater than 45 years (p<0.001), a largest tumor size bigger than 10 mm (p<0.001), clinical lymph node metastasis (cN1) (p<0.001), posterior focus (p<0.001), extrathyroidal extension (p<0.001), esophageal extension (p<0.001), tracheal extension (p<0.001), and preoperative vocal cord paralysis (p<0.001) were independent predictors for RLN invasion. CONCLUSION: PTC patients with RLN invasion have a negative prognosis and a higher recurrence rate. Meticulous operation and careful follow-up of patients with the above factors is recommended.