Efficacy Of Different Models Research Articles

Acupuncture for rehabilitation after total knee arthroplasty: A systematic review and network meta-analysis

Background: The increasing interest in acupuncture for promoting post-operative rehabilitation has encouraged its use in rehabilitation after total knee arthroplasty (TKA), but its effectiveness remains controversial. Objective: This study aims to assess the efficacy of different models of acupuncture-related therapies on pain relief, motor function, inflammation, and overall rehabilitation of the knee after TKA. Methods: Databases PubMed, Embase, Cochrane, Web of Science, Scopus, and Clinicaltrials.gov were searched to screen eligible randomized controlled trials (RCTs). All RCTs that used acupuncture/moxibustion on TKA patients were included by two researchers independently after rigorous quality evaluation, with data extracted. The statistics were performed by using R 4.2.3 and Stata 17. Results: The network meta-analysis incorporated 28 RCTs, with 21 of them conducted in Chinese mainland and Taiwan region. Evidence of the relation of several common acupuncture /moxibustion treatments was shown by the network meta-analysis (NMA). The results of NMA showed that electroacupuncture was the preferred therapy for soothing pain symptoms (standardized mean difference=0.58, 95% CI=0.36 to 0.81). Auricular acupressure was the best treatment to improve the knee motor function. Electroacupuncture was superior to special acupuncture in relieving knee joint stiffness and inflammation. Conclusion: Acupuncture intervention, especially electroacupuncture, can effectively alleviate pain, improve motion, reduce stiffness, and decrease inflammation in post-TKA patients, with no serious adverse events.

Prediction value study of breast cancer tumor infiltrating lymphocyte levels based on ultrasound imaging radiomics.

Construct models based on grayscale ultrasound and radiomics and compare the efficacy of different models in preoperatively predicting the level of tumor-infiltrating lymphocytes in breast cancer. This study retrospectively collected clinical data and preoperative ultrasound images from 185 breast cancer patients confirmed by surgical pathology. Patients were randomly divided into a training set (n=111) and a testing set (n=74) using a 6:4 ratio. Based on a 10% threshold for tumor-infiltrating lymphocytes (TIL) levels, patients were classified into low-level and high-level groups. Radiomic features were extracted and selected using the training set. The evaluation included assessing the relationship between TIL levels and both radiomic features and grayscale ultrasound features. Subsequently, grayscale ultrasound models, radiomic models, and nomograms combining radiomics score (Rad-score) and grayscale ultrasound features were established. The predictive performance of different models was evaluated through receiver operating characteristic (ROC) analysis. Calibration curves assessed the fit of the nomograms, and decision curve analysis (DCA) evaluated the clinical effectiveness of the models. Univariate analyses and multivariate logistic regression analyses revealed that indistinct margin (P<0.001, Odds Ratio [OR]=0.214, 95% Confidence Interval [CI]: 0.103-1.026), posterior acoustic enhancement (P=0.027, OR=2.585, 95% CI: 1.116-5.987), and ipsilateral axillary lymph node enlargement (P=0.001, OR=4.214, 95% CI: 1.798-9.875) were independent predictive factors for high levels of TIL in breast cancer. In comparison to grayscale ultrasound model (Training set: Area under curve [AUC] 0.795; Testing set: AUC 0.720) and radiomics model (Training set: AUC 0.803; Testing set: AUC 0.759), the nomogram demonstrated superior discriminative ability on both the training (AUC 0.884) and testing (AUC 0.820) datasets. Calibration curves indicated high consistency between the nomogram model's predicted probability of breast cancer TIL levels and the actual occurrence probability. DCA revealed that the radiomics model and the nomogram model achieved higher clinical net benefits compared to the grayscale ultrasound model. The nomogram based on preoperative ultrasound radiomics features exhibits robust predictive capacity for the non-invasive evaluation of breast cancer TIL levels, potentially providing a significant basis for individualized treatment decisions in breast cancer.

Credit Risk Modelling Using RNN-LSTM Hybrid Model for Digital Financial Institutions

In response to the rapidly evolving financial market and the escalating concern surrounding credit risk in digital financial institutions, this project addresses the urgency for accurate credit risk prediction models. Traditional methods such as Neural network models, kernel-based virtual machines, Z-score, and Logit (logistic regression model) have all been used, but their results have proven less than satisfactory. The project focuses on developing a credit scoring model specifically tailored for digital financial institutions, by leveraging a hybrid model that combines long short-term memory (LSTM) networks with recurrent neural networks (RNN). This innovative approach capitalizes on the strengths of the Long-Short Term Memory (LSTM) for long-term predictions and Recurrent Neural Network (RNN) for its recurrent neural network capabilities. A key component of the approach is feature selection, which entails extracting a subset of pertinent features from the credit risk data using RNN in order to help classify loan applications. The researcher chose to use data from Kaggle to study and compare the efficacy of different models. The findings reveal that the RNN-LSTM hybrid model outperforms other RNNs, LSTMs, and traditional models. Specifically, the hybrid model demonstrated distinct advantages, showcasing higher accuracy and a superior Area Under the Curve (AUC) compared to individual RNN and LSTM models. While RNN and LSTM models exhibited slightly lower accuracy individually, their combination in the hybrid model proved to be the optimal choice. In summary, the RNN-LSTM hybrid model developed stands out as the most effective solution for predicting credit risk in digital financial institutions, surpassing the performance of standalone RNN and LSTM models as well as traditional methodologies. This research contributes valuable insights for banks, regulators, and investors seeking robust credit risk assessment tools in the dynamic landscape of digital finance.

Preoperative prediction of lymphovascular invasion of colorectal cancer by radiomics based on 18F-FDG PET-CT and clinical factors.

The purpose of this study was to investigate the value of a clinical radiomics model based on Positron emission tomography-computed tomography (PET-CT) radiomics features combined with clinical predictors of Lymphovascular invasion (LVI) in predicting preoperative LVI in patients with colorectal cancer (CRC). A total of 95 CRC patients who underwent preoperative 18F-fluorodeoxyglucose (FDG) PET-CT examination were retrospectively enrolled. Univariate and multivariate logistic regression analyses were used to analyse clinical factors and PET metabolic data in the LVI-positive and LVI-negative groups to identify independent predictors of LVI. We constructed four prediction models based on radiomics features and clinical data to predict LVI status. The predictive efficacy of different models was evaluated according to the receiver operating characteristic curve. Then, the nomogram of the best model was constructed, and its performance was evaluated using calibration and clinical decision curves. Mean standardized uptake value (SUVmean), maximum tumour diameter and lymph node metastasis were independent predictors of LVI in CRC patients (P < 0.05). The clinical radiomics model obtained the best prediction performance, with an Area Under Curve (AUC) of 0.922 (95%CI 0.820-0.977) and 0.918 (95%CI 0.782-0.982) in the training and validation cohorts, respectively. A nomogram based on the clinical radiomics model was constructed, and the calibration curve fitted well (P > 0.05). The clinical radiomics prediction model constructed in this study has high value in the preoperative individualized prediction of LVI in CRC patients.

A feasibility study for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials and other related visual parameters with machine learning algorithm

BackgroundTo develop machine learning models for objectively evaluating visual acuity (VA) based on pattern-reversal visual evoked potentials (PRVEPs) and other related visual parameters.MethodsTwenty-four volunteers were recruited and forty-eight eyes were divided into four groups of 1.0, 0.8, 0.6, and 0.4 (decimal vision). The relationship between VA, peak time, or amplitude of P100 recorded at 5.7°, 2.6°, 1°, 34′, 15′, and 7′ check sizes were analyzed using repeated-measures analysis of variance. Correlations between VA and P100, contrast sensitivity (CS), refractive error, wavefront aberrations, and visual field were analyzed by rank correlation. Based on meaningful P100 peak time, P100 amplitude, and other related visual parameters, four machine learning algorithms and an ensemble classification algorithm were used to construct objective assessment models for VA. Receiver operating characteristic (ROC) curves were used to compare the efficacy of different models by repeated sampling comparisons and ten-fold cross-validation.ResultsThe main effects of P100 peak time and amplitude between different VA and check sizes were statistically significant (all P < 0.05). Except amplitude at 2.6° and 5.7°, VA was negatively correlated with peak time and positively correlated with amplitude. The peak time initially shortened with increasing check size and gradually lengthened after the minimum value was reached at 1°. At the 1° check size, there were statistically significant differences when comparing the peak times between the vision groups with each other (all P < 0.05), and the amplitudes of the vision reduction groups were significantly lower than that of the 1.0 vision group (all P < 0.01). The correlations between peak time, amplitude, and visual acuity were all highest at 1° (rs = − 0.740, 0.438). VA positively correlated with CS and spherical equivalent (all P < 0.001). There was a negative correlation between VA and coma aberrations (P < 0.05). For different binarization classifications of VA, the classifier models with the best assessment efficacy all had the mean area under the ROC curves (AUC) above 0.95 for 500 replicate samples and above 0.84 for ten-fold cross-validation.ConclusionsMachine learning models established by meaning visual parameters related to visual acuity can assist in the objective evaluation of VA.

MRI-based radiomics model for preoperative prediction of extramural venous invasion of rectal adenocarcinoma.

Extramural venous invasion (EMVI) is an important prognostic factor of rectal adenocarcinoma. However, accurate preoperative assessment of EMVI remains difficult. To assess EMVI preoperatively through radiomics technology, and use different algorithms combined with clinical factors to establish a variety of models in order to make the most accurate judgments before surgery. A total of 212 patients with rectal adenocarcinoma between September 2012 and July 2019 were included and distributed to training and validation datasets. Radiomics features were extracted from pretreatment T2-weighted images. Different prediction models (clinical model, logistic regression [LR], random forest [RF], support vector machine [SVM], clinical-LR model, clinical-RF model, and clinical-SVM model) were constructed on the basis of radiomics features and clinical factors, respectively. The area under the curve (AUC) and accuracy were used to assess the predictive efficacy of different models. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. The clinical-LR model exhibited the best diagnostic efficiency with an AUC of 0.962 (95% confidence interval [CI] = 0.936-0.988) and 0.865 (95% CI = 0.770-0.959), accuracy of 0.899 and 0.828, sensitivity of 0.867 and 0.818, specificity of 0.913 and 0.833, PPV of 0.813 and 0.720, and NPV of 0.940 and 0.897 for the training and validation datasets, respectively. The radiomics-based prediction model is a valuable tool in EMVI detection and can assist decision-making in clinical practice.

Preoperative contrast-enhanced CT-based radiomics signature for predicting hypoxia-inducible factor 1α expression in retroperitoneal sarcoma

To develop and test a contrast-enhanced computed tomography (CECT)-based radiomics signature (RS) to preoperatively predict hypoxia-inducible factor 1α (HIF-1α) expression in retroperitoneal sarcoma (RPS). This study included 129 patients with RPS retrospectively who underwent CECT, including 64 male and 65 female patients (55 [2-84] years). Participants were divided into a training set comprising 85 patients and a test set comprising 44 patients. Clinical data and CECT findings of all patients were collected. RS construction was performed by the minimum redundancy maximum relevance method and least absolute shrinkage and selection operator algorithm. The clinical information was analysed by univariate and multivariate logistic regression analysis. The RS and risk factors were included to build a radiomics nomogram. The predictive efficacy of different models was evaluated by accuracy, area under the receiver operating characteristic curve (AUC), and decision curve analysis. The RS combined signature was constructed on the basis of multi-phase CECT and had an accuracy of 0.795 and an AUC of 0.719 (95% confidence interval [CI], 0.552-0.886) in the test set, which were higher than that of the radiomics nomogram (accuracy: 0.636; AUC: 0.702 [95% CI, 0.547-0.857]) and the clinical model (accuracy: 0.682; AUC: 0.486 [95% CI, 0.324-0.647]). The decision curve analysis showed that the RS combined signature provided better clinical application than the clinical model and radiomics nomogram. The multi-phase CECT-based RS constructed can be used as a powerful tool for predicting HIF-1α expression in patients with RPS.

Machine Learning Based House Price Prediction Using Modified Extreme Boosting

In recent years, machine learning has become increasingly important in everyday voice commands and predictions. Instead, it provides a safer auto system and better customer assistance. As a result of all that has been demonstrated, ML is a technology that is becoming more and more popular in a range of industries. To gauge changes in house values, the House Price Index is frequently employed (HPI). Due to the substantial correlation that exists between property prices and other variables, such as location, region, and population, the HPI on its own is not sufficient to accurately forecast a person's house price. Some studies have successfully predicted house prices using conventional machine learning techniques, but they seldom evaluate the efficacy of different models and ignore the more complicated but less well-known models. We proposed Modified Extreme Gradient Boosting as our model in this study due to its adaptive and probabilistic model selection process. Feature engineering, hyperparameter training and optimization, model interpretation, and model selection and evaluation are all steps in the process. Home price indices, which are frequently used to support real estate policy initiatives and estimate housing costs. In this project, models for forecasting changes in home prices are developed using machine learning methods.

Maximum Standardized Uptake Value of 18F-deoxyglucose PET Imaging Increases the Effectiveness of CT Radiomics in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules.

To investigate whether the maximum standardized uptake value (SUVmax) of 18F-deoxyglucose (FDG) PET imaging can increase the diagnostic efficiency of CT radiomics-based prediction model in differentiating benign and malignant pulmonary ground-glass nodules (GGNs). We retrospectively collected 190 GGNs from 165 patients who underwent 18F-FDG PET/CT examination from January 2012 to March 2020. Propensity score matching (PSM) was performed to select GGNs with similar baseline characteristics. LIFEx software was used to extract 49 CT radiomic features, and the least absolute shrinkage and selection operator (LASSO) algorithm was used to select parameters and establish the Rad-score. Logistic regression analysis was performed combined with semantic features to construct a CT radiomics model, which was combined with SUVmax to establish the PET + CT radiomics model. Receiver operating characteristic (ROC) was used to compare the diagnostic efficacy of different models. After PSM at 1:4, 190 GGNs were divided into benign group (n = 23) and adenocarcinoma group (n = 92). After texture analysis, the Rad-score with three CT texture features was constructed for each nodule. Compared with the Rad-score and CT radiomics model (AUC: 0.704 (95%CI: 0.562-0.845) and 0.908 (95%CI: 0.842-0.975), respectively), PET + CT radiomics model had the best diagnostic efficiency (AUC: 0.940, 95%CI: 0.889-0.990), and there was significant difference between each two of them (P = 0.001-0.030). SUVmax can effectively improve CT radiomics model performance in the differential diagnosis of benign and malignant GGNs. PET + CT radiomics might become a noninvasive and reliable method for differentiating of GGNs.

Prediction of Locoregional Recurrence-Free Survival of Oesophageal Squamous Cell Carcinoma After Chemoradiotherapy Based on an Enhanced CT-Based Radiomics Model.

Background and PurposeChemoradiotherapy is the standard treatment for moderate and advanced oesophageal cancer. The aim of this study was to establish a predictive model based on enhanced computed tomography examination, and to evaluate its clinical value for detecting locoregional recurrence-free survival (LRFS) in cases of oesophageal squamous cell carcinoma after radiotherapy.Materials and MethodsIn total, 218 patients with pathologically diagnosed oesophageal squamous cell carcinoma who received radical chemoradiotherapy from July 2016 to December 2017 were collected in this study. Patients were randomly divided into either a training group (n=153) or a validation group (n=65) in a 7:3 ratio. Clinical patient information was then recorded. The enhanced computed tomography scan images of the patients were imported into 3D-slicer software (version 4.8.1), and the radiomic features were extracted by the Python programme package. In the training group, the dimensionality reduction of the radiomic features was implemented by Lasso regression, and then a radiological label, the model of predicting LRFS, was established and evaluated. To achieve a better prediction performance, the radiological label was combined with clinical risk factor information to construct a radiomics nomogram. A receiver operating characteristic curve was used to evaluate the efficacy of different models. Calibration curves were used to assess the consistency between the predicted and observed recurrence risk, and the Hosmer-Lemeshow method was used to test model fitness. The C-index evaluated the discriminating ability of the prediction model. Decision curve analysis was used to determine the clinical value of the constructed prediction model.ResultsOf the 218 patients followed up in this study, 44 patients (28.8%) in the training group and 21 patients (32.3%) in the validation group experienced recurrence. There was no difference in LRFS between the two groups (χ2 = 0.525, P=0.405). Lasso regression was used in the training group to select six significant radiomic features. The radiological label established using these six features had a satisfactory prediction performance. The C-index was 0.716 (95% CI: 0.645–0.787) in the training group and 0.718 (95% CI: 0.612–0.825) in the validation group. The radiomics nomogram, which included the radiological label and clinical risk factors, achieved a better prediction than the radiological label alone. The C-index was 0.742 (95% CI: 0.674–0.810) in the training group and 0.715 (95% CI: 0.609–0.820) in the validation group. The results of the calibration curve and decision curve analyses indicated that the radiomics nomogram was superior in predicting LRFS of oesophageal carcinoma after radiotherapy.ConclusionsA radiological label was successfully established to predict the LRFS of oesophageal squamous cell carcinoma after radiotherapy. The radiomics nomogram was complementary to the clinical prognostic features and could improve the prediction of the LRFS after radiotherapy for oesophageal cancer.

Early assessment of shear wave elastography parameters foresees the response to neoadjuvant chemotherapy in patients with invasive breast cancer

BackgroundEarly prediction of tumor response to neoadjuvant chemotherapy (NACT) is crucial for optimal treatment and improved outcome in breast cancer patients. The purpose of this study is to investigate the role of shear wave elastography (SWE) for early assessment of response to NACT in patients with invasive breast cancer.MethodsIn a prospective study, 62 patients with biopsy-proven invasive breast cancer were enrolled. Three SWE studies were conducted on each patient: before, at mid-course, and after NACT but before surgery. A new parameter, mass characteristic frequency (fmass), along with SWE measurements and mass size was obtained from each SWE study visit. The clinical biomarkers were acquired from the pre-NACT core-needle biopsy. The efficacy of different models, generated with the leave-one-out cross-validation, in predicting response to NACT was shown by the area under the receiver operating characteristic curve and the corresponding sensitivity and specificity.ResultsA significant difference was found for SWE parameters measured before, at mid-course, and after NACT between the responders and non-responders. The combination of Emean2 and mass size (s2) gave an AUC of 0.75 (0.95 CI 0.62–0.88). For the ER+ tumors, the combination of Emean_ratio1, s1, and Ki-67 index gave an improved AUC of 0.84 (0.95 CI 0.65–0.96). For responders, fmass was significantly higher during the third visit.ConclusionsOur study findings highlight the value of SWE estimation in the mid-course of NACT for the early prediction of treatment response. For ER+ tumors, the addition of Ki-67improves the predictive power of SWE. Moreover, fmass is presented as a new marker in predicting the endpoint of NACT in responders.

Social isolation during the COVID-19 pandemic can increase physical inactivity and the global burden of cardiovascular disease.

Emerging data indicate a substantial decrease in global physical activity levels during the period of social isolation adopted worldwide to contain the spread of the coronavirus disease 2019 (COVID-19). Confinement-induced decreases in physical activity levels and increases in sedentary behavior may provoke a rapid deterioration of cardiovascular health and premature deaths among populations with increased cardiovascular risk. Even short-term (1–4 wk) inactivity has been linked with detrimental effects in cardiovascular function and structure and increased cardiovascular risk factors. In this unprecedented and critical scenario, home-based physical activity programs arise as a clinically relevant intervention to promote health benefits to cardiac patients. Many studies have demonstrated the feasibility, safety, and efficacy of different models of home-based exercise programs in the primary and secondary prevention of cardiovascular diseases and major cardiovascular events among different populations. This body of knowledge can inform evidence-based policies to be urgently implemented to counteract the impact of increased physical inactivity and sedentary behavior during the COVID-19 outbreak, thereby alleviating the global burden of cardiovascular disease.

The role of first trimester fetal heart rate in the prediction of gestational diabetes: A multicenter study

ObjectiveEarly pregnancy models for prediction of GDM have been proposed, mostly using anamnestic and biochemical parameters. The aim of our study was to evaluate the strength of association of first trimester fetal heart rate (FHR) in predicting the development of gestational diabetes (GDM). Study designWe considered in our analysis singleton non-diabetic pregnant women who underwent a first trimester screening at 11–14 weeks. Data on maternal age, BMI, cigarette smoking, NT, FHR, CRL, DV-PVI, β-hCG and PAPP-A were included in the analysis. Multivariate logistic regression analysis was used to estimate the association between maternal characteristics and first-trimester ultrasound measurements and GDM. We evaluated the efficacy of different models for the prediction of GDM. ResultsWe considered 603 women, of whom 199 (33%) were subsequently diagnosed with GDM. ROC analysis showed that first trimester FHR was highly predictive of GDM (AUC 0.809, 95% CI 0.769–0.849, p < 0.001). At FPR of 20%, first trimester FHR had a detection rate of 65.2% for GDM (positive likelihood ratio: 3.26; negative likelihood ratio: 0.43), which increased to 89.5% at FPR of 40% (positive likelihood ratio: 2.24; negative likelihood ratio: 0.17). When considering as threshold 162 bpm, FHR showed detection rate of 76.9%, specificity of 67.1% and negative predictive value of 85.5% for GDM. ConclusionThis is the first study to highlight the potential role of first trimester FHR as early predictor of GDM. In our cohort, a threshold of 162 bpm has shown high detection rate and NPV for GDM.

Diagnostic value of endoscopic ultrasonography combined with liver stiffness measurement and liver histopathology for early liver cirrhosis

Objective To evaluate the value of endoscopic ultrasonography(EUS) alone for early liver cirrhosis and the diagnostic efficacy of EUS combined with liver histopathology (LH) and liver stiffness measurement (LSM) for early liver cirrhosis. Methods Data of 226 patients with chronic liver lesions who underwent endoscopy and EUS at Tianjin Second People's Hospital were collected to assess esophageal and gastric varices. Liver fibrosis was assessed by LH and LSM was determined by Fibroscan. Models of EUS-LSM, EUS-LH, LSM-LH, ELL (EUS, LSM and LH) were constructed to predict early liver cirrhosis. The ROC curve and AUROC were used to evaluate the efficacy of different models in the diagnosis of early liver cirrhosis. Results A total of 149 patients were diagnosed as chronic liver disease and 77 patients were diagnosed as early liver cirrhosis (Child-Pugh A grade) by clinical evaluation. Ratio of varices found by EUS was significantly higher than that by endoscopy [68.8% (53/77) VS 32.5% (25/77), P<0.05]. The cut-off value was 8.65 kPa by LSM to predict early liver cirrhosis. Pseudolobules were confirmed by LH in 42(54.5%) patients in the early liver cirrhosis group. The AUROC of ELL was 0.919 (95%CI: 0.875-0.951), sensitivity=0.792, specificity=0.913, PPV=0.824, NPV=0.895, + LR=9.08, -LR=0.23, accuracy=0.872, and ELL was superior to EUS (P<0.000 1), LSM (P<0.000 1), LH (P<0.000 1), EUS-LSM (P<0.000 1), EUS-LH (P=0.013 4) and LSM-LH (P=0.002 2) in the diagnosis of early liver cirrhosis. Conclusion EUS is superior to endoscopy in detecting the varices for early liver cirrhosis. Combination of EUS with LSM and LH can improve diagnostic efficacy for early liver cirrhosis. Key words: Ultrasonography; Pathology; Liver cirrhosis; Liver stiffness measurement

Comparing community models of COPD care

Introduction: In Gwent, South Wales, different models of community-based chronic obstructive pulmonary disease (COPD) care were developed in different local health boards (LHB) leading up to the year 2009. This gave a unique opportunity to compare the efficacy of different models among patients with similar demographic characteristics. Methods: Hospital admission data were compared for the three years before and after the introduction of COPD services around 2006. Primary care practices without access to services served as control populations. Results: Long-term conditions services where community nurses worked in isolation (Caerphilly), or that were based around primary care practices (Newport and Monmouthshire), showed an increase in COPD hospital admission of 12.3%, 43.4% and 20.8% respectively. This corresponded to an increase of 5.2% and 1.0% respectively in control practices (p &lt; 0.01 for each LHB). In Blaenau Gwent and Torfaen, services integrated between primary and secondary care were associated with a reduction in admissions of 3.3% and 4.6% respectively. (p &lt; 0.01). Conclusion: There is little evidence base to guide the large sums of money invested in community care across the UK for COPD and other long-term conditions. Our data suggest that services with close communication between primary and secondary care may be more effective than those developed around primary care alone.

Comparison of Safety and Efficacy of Different Models of Target Vessel Regional Chemotherapy for Gastric Cancer with Liver Metastases

Background/Aims: We previously demonstrated the safety and efficacy of low-dose, short-interval target vessel regional chemotherapy (TVRC^LDSI) delivered through the hepatic artery with transarterial embolization (TAE) in patients with advanced gastric cancer (AGC). The present study aimed to compare the efficacy of TAE + TVRC^LDSI with that of standard TAE + TVRC in AGC patients with liver metastases who failed to respond to first- or second-line systemic chemotherapy. Methods: This study recruited a total of 58 GC patients with liver metastases after failure of first- or second-line systemic chemotherapy. Twenty-eight patients were assigned to the TAE + TVRC^LDSI group and 30 patients to the TAE + TVRC group. The primary end point was overall survival (OS^TVRC), which was defined as the time from the initiation of TVRC until the last follow-up or death. Results: OS^TVRC, time to progression (TTP) until appearance of intra- and extrahepatic metastases, and overall TTP and treatment periods in the TAE + TVRC^LDSI group were all significantly longer than in the TAE + TVRC group (all p < 0.001). Conclusion: TAE + TVRC^LDSI had a higher efficacy and safety, which was reflected by OS rates, progression-free survival rates, longer duration of treatment and milder side effects compared to standard TAE + TVRC.

Efficacy of different models for explaining the endocrine surgery operative procedure

Efficacy of different models for explaining the endocrine surgery operative procedure

‘What’s So Funny ‘Bout Peace, Love and Understanding?’ Further Reflections on the Limits of Prejudice Reduction as a Model of Social Change

This paper aims to encourage greater reflexivity about the limits of prejudice reduction as a model of social change, particularly when applied to societies characterised by historically entrenched patterns of inequality. We begin by outlining some underlying values and assumptions of this model. We then elaborate how our research on political attitudes in post-apartheid South Africa has led us to question, qualify and sometimes reject those assumptions and move towards a ‘contextualist’ perspective on the efficacy of different models of social change. We agree that the project of getting us to like one another may be crucial for producing change in some contexts. In other contexts, however, it is an epiphenomenon that distracts psychologists from the main causes of, and solutions to, problems such as race, class, or gender discrimination. In still others, with an irony that is evidenced increasingly by research, prejudice reduction may actually contribute to the very problem it is designed to resolve. That is, it may diminish the extent to which social injustice is acknowledged, rejected and challenged.

The efficacy of different models of smoke-free laws in reducing exposure to second-hand smoke: A multi-country comparison

Exposure to second-hand tobacco smoke is a serious public health concern and while all EU Member States have enacted some form of regulation aimed at limiting exposure, the scope of these regulations vary widely and many countries have failed to enact comprehensive legislation creating smoke-free workplaces and indoor public places. To gauge the effectiveness of different smoke-free models we compared fine particles from second-hand smoke in hospitality venues before and after the implementation of smoking bans in France, Greece, Ireland, Italy, Portugal, Turkey, and Scotland.Data on PM2.5 fine particle concentration levels were recorded in 338 hospitality venues across these countries before and after the implementation of smoke-free legislation. Changes in mean PM2.5 concentrations during the period from pre- to post-legislation were then compared across countries.While a reduction in PM2.5 was observed in all countries, those who had enacted and enforced more fully comprehensive smoke-free legislation experienced the greatest reduction in second-hand tobacco smoke.Comprehensive smoke-free laws are more effective than partial laws in reducing exposure to second-hand tobacco smoke. Also, any law, regardless of scope must be actively enforced in order to have the desired impact. There is continued need for surveillance of smoke-free efforts in all countries.

Structured analysis of the ISI Atomic Pair Actions dataset using workflows

Understanding pair-wise activities is an essential step towards studying complex group and crowd behaviors in video. However, such research is often hampered by a lack of datasets that concentrate specifically on Atomic Pair Actions; [Here, we distinguish between the atomic motion of individual objects and the atomic motion of pairs of objects. The term action in Atomic Pair Action means an atomic interaction movement of two objects in video; a pair activity, then, is composed of multiple actions by a pair or multiple pairs of interacting objects (Ahad, 2011; Turaga et al., 2008). Please see Section 1 for details.] in addition, the general dearth in computer vision of a standardized, structured approach for reproducing and analyzing the efficacy of different models limits the ability to compare different approaches. In this paper, we introduce the ISI Atomic Pair Actions dataset, a set of 90 videos that concentrate on the Atomic Pair Actions of objects in video, namely converging, diverging, and moving in parallel. We further incorporate a structured, end-to-end analysis methodology, based on workflows, to easily and automatically allow for standardized testing of state-of-the-art models, as well as inter-operability of varied codebases and incorporation of novel models. We demonstrate the efficacy of our structured framework by testing several models on the new dataset. In addition, we make the full dataset (the videos, along with their associated tracks and ground truth, and the exported workflows) publicly available to the research community for free use and extension at <http://research.sethi.org/ricky/datasets/>.