Clinical Effect and Effective Rate of Laparoscopic Cholecystectomy for Gallstones Complicated with Gallbladder Polyps

This paperA total of 62 patients with gallstones and gallbladder polyps were selected from May 2019 to May 2020, who were divided into the observation group (n=31, laparoscopic cholecystectomy) and the control group (n=31, open cholecystectomy) in a 1:1 ratio. The clinical indicators, clinical efficacy, level of pain and complication rate of the two groups were recorded and compared. Results Indicators such as the operation duration (38.64±14.42min), blood loss (30.42±8.21ml), length of stay (4.71±1.82 d), first anal exhaust (21.82±6.65min), drainage volume (72.02±4.21ml), length of incision and the time for the recovery of gastrointestinal functions in the observation group were better than the control group (P<0.05). The clinical efficacy of the observation group (96.77%) was higher than that of the control group (80.65%), with statistical value =4.0260 (P<0.05). The level of pain of the observation group was lower than that of control group (P<0.05), while the complication rate in the observation group (3.22%) was also lower than that of the control group (22.58%) (P<0.05). Laparoscopic cholecystectomy is an effective treatment of gallstones complicated with gallbladder polyps, which can alleviate pain and improve the prognosis, and is thus worthy of promotion

Short-Term Speed Prediction Using Remote Microwave Sensor Data: Machine Learning versus Statistical Model

Recently, a number of short-term speed prediction approaches have been developed, in which most algorithms are based on machine learning and statistical theory. This paper examined the multistep ahead prediction performance of eight different models using the 2-minute travel speed data collected from three Remote Traffic Microwave Sensors located on a southbound segment of 4th ring road in Beijing City. Specifically, we consider five machine learning methods: Back Propagation Neural Network (BPNN), nonlinear autoregressive model with exogenous inputs neural network (NARXNN), support vector machine with radial basis function as kernel function (SVM-RBF), Support Vector Machine with Linear Function (SVM-LIN), and Multilinear Regression (MLR) as candidate. Three statistical models are also selected: Autoregressive Integrated Moving Average (ARIMA), Vector Autoregression (VAR), and Space-Time (ST) model. From the prediction results, we find the following meaningful results: (1) the prediction accuracy of speed deteriorates as the prediction time steps increase for all models; (2) the BPNN, NARXNN, and SVM-RBF can clearly outperform two traditional statistical models: ARIMA and VAR; (3) the prediction performance of ANN is superior to that of SVM and MLR; (4) as time step increases, the ST model can consistently provide the lowest MAE comparing with ARIMA and VAR

4-CPA (4-chlorophenoxyacetic acid) induces the formation and development of defective “fenghou” (vitis vinifera × v. labrusca) grape seeds

For some horticultural plants, auxins can not only induce normal fruit setting but also form fake seeds in the induced fruits. This phenomenon is relatively rare, and, so far, the underlying mechanism remains unclear. In this study, “Fenghou” (Vitis vinifera × V. labrusca) grapes were artificially emasculated before flowering and then sprayed with 4-CPA (4-chlorophenoxyacetic acid) to analyze its effect on seed formation. The results show that 4-CPA can induce normal fruit setting in “Fenghou” grapes. Although more seeds were detected in the fruits of the 4-CPA-treated grapevine, most seeds were immature. There was no significant difference in the seed shape; namely, both fruit seeds of the grapevines with and without 4-CPA treatment contained a hard seed coat. However, the immature seeds lacked embryo and endosperm tissue and could not germinate successfully; these were considered defective seeds. Tissue structure observation of defective seeds revealed that a lot of tissue redifferentiation occurred at the top of the ovule, which increased the number of cell layers of the outer integument; some even differentiated into new ovule primordia. The qRT-PCR results demonstrated that 4-CPA application regulated the expression of the genes VvARF2 and VvAP2, which are associated with integument development in “Fenghou” grape ovules. Together, this study evokes the regulatory role of 4-CPA in the division and continuous redifferentiation of integument cells, which eventually develop into defective seeds with thick seed coats in grapes

A feasibility study of multi-electrode high-purity germanium detector for Ge-76 neutrinoless double beta decay searching

Experiments to search for neutrinoless double-beta (0{\nu}\b{eta}\b{eta}) decay of 76Ge using a high-purity germanium (HPGe) detector rely heavily on background suppression technologies to enhance their sensitivities. In this work, we proposed a pulse-shape analysis method based on a neural network (NN) and a light gradient boosting machine (lightGBM; LGB) to discriminate single-electron (background) and double-electrons (0{\nu}\b{eta}\b{eta} signal) events in a multi-electrode HPGe detector. In this paper, we describe a multi-electrode HPGe detector system, a data-processing system, and pulse-shape simulation procedures. We built a fully connected (FC) neural network and an LGB model to classify the single- and double-electron events. The FC network is trained with simulated single- and double-electron-induced pulses and tested in an independent dataset generated by the pulse-shape simulation. The discrimination efficiency of the FC neural network in the test set for the 0{\nu}\b{eta}\b{eta} double-electron events signal was 77.4%, the precision was 57.7%, and the training time was 430 min. The discrimination efficiency of LGB model was 73.1%, the precision was 64.0%, and the training time was 1.5 min. This study demonstrated that it is feasible to realize single- and double-electron discrimination on multi-electrode HPGe detectors using an FC neural network and LGB model. These results can be used as a reference for future 76Ge 0{\nu}\b{eta}\b{eta} experiments.Comment: 16 pages,12 figure

An Efficient Method for Chip-Level Statistical Capacitance Extraction Considering Process Variations with Spatial Correlation

An efficient method is proposed to consider the process variations with spatial correlation, for chip-level capacitance extraction based on the window technique. In each window, an efficient technique of Hermite polynomial collocation (HPC) is presented to extract the statistical capacitance. The capacitance covariances between windows are then calculated to reflect the spatial correlation. The proposed method is practical for chip-level extraction task, and the experiments on full-path extraction exhibit its high accuracy and efficiency

Derivation and validation of a prognostic model for predicting in-hospital mortality in patients admitted with COVID-19 in Wuhan, China:the PLANS (platelet lymphocyte age neutrophil sex) model

Background Previous published prognostic models for COVID-19 patients have been suggested to be prone to bias due to unrepresentativeness of patient population, lack of external validation, inappropriate statistical analyses, or poor reporting. A high-quality and easy-to-use prognostic model to predict in-hospital mortality for COVID-19 patients could support physicians to make better clinical decisions. Methods Fine-Gray models were used to derive a prognostic model to predict in-hospital mortality (treating discharged alive from hospital as the competing event) in COVID-19 patients using two retrospective cohorts (n = 1008) in Wuhan, China from January 1 to February 10, 2020. The proposed model was internally evaluated by bootstrap approach and externally evaluated in an external cohort (n = 1031). Results The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted cumulative incidence curves were close to the observed cumulative incidence curves in patients with different risk profiles. Conclusions The PLANS model based on five routinely collected predictors would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality

The Proinflammatory Cytokines IL-18, IL-21, and IFN-γ Differentially Regulate Liver Inflammation and Anti-Mitochondrial Antibody Level in a Murine Model of Primary Biliary Cholangitis

Primary biliary cholangitis (PBC) is a cholestatic liver disease primarily featured by autoimmune-mediated damage of intrahepatic small- and medium-sized bile ducts. Elevated serum proinflammatory cytokines, serum anti-mitochondrial antibodies (AMAs), liver inflammation, and fibrosis are also hallmarks of PBC disease. However, whether the elevated proinflammatory cytokines play a role in autoimmune cholangitis remains unknown. Herein, we utilized the p40-/-IL-2Rα-/- PBC mouse model to investigate the roles of proinflammatory cytokines IL-18, IL-21, and IFN-γ in the onset and progression of PBC. IL-18-/-, IFN-γ-/-, and IL-21-/- mice were crossed with p40-/-IL-2Ra+/- mice, respectively, to produce corresponding cytokine-deficient PBC models. Autoantibody level, liver inflammation, and bile duct injury were analyzed. We found that livers from p40-/-IL-2Rα-/- mice exhibit similar transcriptomic characters of PBC patients. In p40-/-IL-2Rα-/- mice, deletion of IL-18 has no remarkable effect on disease progression, while deletion of IL-21 indicates that it is necessary for AMA production but independent of liver inflammation and cholangitis. IFN-γ is responsible for both AMA production and liver inflammation in our model. Our results demonstrate that different proinflammatory cytokines can regulate different effector functions in PBC pathogenesis and need to be considered in PBC treatment