Notes

Frequency involving subcutaneous implantable cardioverter-defibrillator candidacy according to web template ECG screening process throughout sufferers using hypertrophic cardiomyopathy.
67; 95% CI 0.54-0.84, P=0.0006). The addition of TRT significantly improved OS in patients over 65 years of age (HR =0.55; 95% CI 0.40-0.74, P=0.0001). For patients with only one organ metastasis, there was no significant difference in OS between the two groups (HR =0.61; 95% CI 0.36-1.01, P=0.06). There was no statistical difference in hematologic toxicity (leukopenia, thrombocytopenia, anemia) and non-hematologic toxicity (nausea or vomiting) between the two groups. The incidence of grade ≥3 esophageal toxicity was 4.6% in the TRT group and 0% in the non-TRT group (P=0.0001). Grade ≥3 bronchopulmonary toxicity was 2.9% in the TRT group and 0.8% in the non-TRT group (P=0.02).

TRT improves OS, PFS and LRFS in patients with ES-SCLC, with a low increase in esophageal and bronchopulmonary toxicity. More randomized controlled trials (RCTs) are expected to confirm our conclusions.

CRD42020190575.
CRD42020190575.
To investigate the feasibility of integrating global radiomics and local deep features based on multi-modal magnetic resonance imaging (MRI) for developing a noninvasive glioma grading model.

In this study, 567 patients [211 patients with glioblastomas (GBMs) and 356 patients with low-grade gliomas (LGGs)] between May 2006 and September 2018, were enrolled and divided into training (n=186), validation (n=47), and testing cohorts (n=334), respectively. All patients underwent postcontrast enhanced T1-weighted and T2 fluid-attenuated inversion recovery MRI scanning. Radiomics and deep features (trained by 8,510 3D patches) were extracted to quantify the global and local information of gliomas, respectively. A kernel fusion-based support vector machine (SVM) classifier was used to integrate these multi-modal features for grading gliomas. The performance of the grading model was assessed using the area under receiver operating curve (AUC), sensitivity, specificity, Delong test, and
-test.

The AUC, sensitivity, and specificity of the model based on combination of radiomics and deep features were 0.94 [95% confidence interval (CI) 0.85, 0.99], 86% (95% CI 64%, 97%), and 92% (95% CI 75%, 99%), respectively, for the validation cohort; and 0.88 (95% CI 0.84, 0.91), 88% (95% CI 80%, 93%), and 81% (95% CI 76%, 86%), respectively, for the independent testing cohort from a local hospital. The developed model outperformed the models based only on either radiomics or deep features (Delong test, both of P<0.001), and was also comparable to the clinical radiologists.

This study demonstrated the feasibility of integrating multi-modal MRI radiomics and deep features to develop a promising noninvasive grading model for gliomas.
This study demonstrated the feasibility of integrating multi-modal MRI radiomics and deep features to develop a promising noninvasive grading model for gliomas.
Conflicts in regarding the lateralization of the seizure onset for mesial temporal lobe epilepsy (MTLE) are frequently encountered during presurgical evaluation. As a more elaborate, quantified protocol, indices of diffusion spectrum imaging (DSI) might be sensitive to evaluate the seizure involvement. However, the accuracy was less revealed. Herein, we determined the lateralizing value of the DSI indices among MTLE patients.

Eleven MTLE patients were enrolled together with 11 matched health contrasts. All the participants underwent a DSI scan and with reconstruction of the diffusion scalar, including quantitative anisotropy (QA), isotropic (ISO), and track density imaging (TDI) values. Statistics of these indices were applied to identify the differences between the healthy and ipsilateral sides, and those between the patients and the controls, with special attention to areas of the crura of fornix (FORX), the parahippocampal radiation of the cingulum (PHCR), the hippocampus (HP), parahippocampus (PHC), athe ipsilateral side for MTLE patients. For preliminary exploration, the use of quantitative DSI scalars may help to improve the seizure outcome by increasing the accuracy of localization and lateralization for MTLE.
Rectal cancer accounts for approximately 30-50% of colorectal cancer. Despite its widespread use and convenience, the American Joint Committee on Cancer (AJCC) staging system for predicting survival is prone to inaccuracy, even including a survival paradox for locally advanced rectal cancer (LARC). An accurate risk stratification of LARC is essential for proper treatment selection and prognostic evaluation. Therefore, we aimed to create prognostic nomograms for LARC capable of assessing overall survival (OS) and cancer-specific survival (CSS) precisely and intuitively.

The Surveillance, Epidemiology, and End Results (SEER) database was accessed. All of the significant variables in the multivariate analysis were integrated to build the nomograms.

Data for a total of 23,055 patients with LARC were collected from the SEER database in this study. Based on the multivariate Cox regression analysis, both OS and CSS were significantly associated with 13 variables age, marital status, race, pathological grade, histological type, T stage, N stage, surgery, radiotherapy, chemotherapy, regional nodes examined (RNE), tumor size, and carcinoembryonic antigen (CEA). These were included in the construction of nomograms for OS and CSS. Time-dependent receiver operating characteristic (ROC) curves, decision curve analysis (DCA), concordance index, and calibration curves demonstrated the discriminative superiority of the nomograms.

The nomograms, which effectively solve the issue of the survival paradox in the AJCC staging system regarding LARC, may act as excellent tools for integrating clinical characteristics and to guiding therapeutic choices for LARC patients.
The nomograms, which effectively solve the issue of the survival paradox in the AJCC staging system regarding LARC, may act as excellent tools for integrating clinical characteristics and to guiding therapeutic choices for LARC patients.
To develop an ultrasound-based deep learning model to predict postoperative upgrading of pure ductal carcinoma in situ (DCIS) diagnosed by core needle biopsy (CNB) before surgery.

Of the 360 patients with DCIS diagnosed by CNB and identified retrospectively, 180 had lesions upstaged to ductal carcinoma in situ with microinvasion (DCISM) or invasive ductal carcinoma (IDC) postoperatively. Ultrasound images obtained from the hospital database were divided into a training set (n=240) and validation set (n=120), with a ratio of 21 in chronological order. Four deep learning models, based on the ResNet and VggNet structures, were established to classify the ultrasound images into postoperative upgrade and pure DCIS. We obtained the area under the receiver operating characteristic curve (AUROC), specificity, sensitivity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) to estimate the performance of the predictive models. The robustness of the models was evaluated by a 3-fold cross-validation.

Clinical features were not significantly different between the training set and the test set (P value >0.05). The area under the receiver operating characteristic curve of our models ranged from 0.724 to 0.804. The sensitivity, specificity, and accuracy of the optimal model were 0.733, 0.750, and 0.742, respectively. The three-fold cross-validation results showed that the model was very robust.

The ultrasound-based deep learning prediction model is effective in predicting DCIS that will be upgraded postoperatively.
The ultrasound-based deep learning prediction model is effective in predicting DCIS that will be upgraded postoperatively.
Altered thyroid function and increased N-terminal pro-B-type natriuretic peptide (NT-proBNP) are prognostic factors in acute myocardial infarction (AMI). The study aims to investigate whether free triiodothyronine (fT3) and NT-proBNP are prognostic factors for long-term outcomes in patients with AMI undergoing percutaneous coronary intervention (PCI).

This was an observational, prospective, single-center study of consecutive patients enrolled at Fuwai Hospital between January, 2013 and December, 2013. The patients were divided into two groups according to fT3 levels low fT3 (<2.5 pg/mL) and normal fT3 (2.50-4.09 pg/mL). The primary outcome of this study was the incidence of major adverse cardiovascular events (MACEs).

There were 252 patients with low fT3 and 561 patients with normal fT3. After >2 years of follow-up, patients with low fT3 levels had higher rates of MACEs than those with normal fT3 (27.0%
7.8%, P<0.001). Univariable Cox proportional hazards regression analyses showed that NT-proBNP >802.7 pg/mL [hazard ratio (HR) =5.063, 95% confidence interval (CI) 3.176-8.071, P<0.001] and fT3 <2.5 pg/mL (HR =3.867, 95% CI 2.646-5.651, P<0.001) were the strongest predictors of MACEs. After adjustment for traditional risk predictors, fT3 <2.5 pg/mL (HR =2.570, 95% CI 1.653-3.993, P<0.001) was one of the most important independent predictors of MACEs. Patients with NT-proBNP ≤802.7 pg/mL and fT3 ≥2.5 pg/mL had the best prognosis, while patients with NT-proBNP >802.7 pg/mL and fT3 <2.5 pg/mL had the worst outcomes (P<0.001).

Low fT3 is a strong predictor of poor prognosis after AMI. The fT3+NT-proBNP combination might be a valuable predictor of the long-term outcomes of PCI after AMI.
Low fT3 is a strong predictor of poor prognosis after AMI. The fT3+NT-proBNP combination might be a valuable predictor of the long-term outcomes of PCI after AMI.
The treatment strategies and prognostic factors for uterine cervical adenocarcinoma (UAC) primarily refer to that for squamous cell carcinoma (SCC). https://www.selleckchem.com/products/vtp50469.html However, the biological behavior, treatment outcomes of UAC differ from that of SCC. This study aimed to develop and validate a prognostic nomogram for predicting the probability of 3- and 5-year cancer-specific survival (CSS) in patients with UAC.

A total of 8,991 UAC patients from the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. Patients diagnosed between 1988 and 2010 (n=5,655) were enrolled for model development and internal validation, and those diagnosed between 2011 and 2016 (n=3,336) were used for temporal validation. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to select predictors of CSS. Cox hazard regression analysis was used to construct the model, which was presented as a static nomogram and web-based dynamic nomogram. The nomogram was internally validated usccuracy.

In the form of a static nomogram or an online calculator, an effective and convenient nomogram was developed and validated to help clinicians quantify the risk of mortality, make personalized survival assessments, and create optimal treatment plans for UAC patients.
In the form of a static nomogram or an online calculator, an effective and convenient nomogram was developed and validated to help clinicians quantify the risk of mortality, make personalized survival assessments, and create optimal treatment plans for UAC patients.
Non-alcoholic fatty liver disease (NAFLD), characterized by the accumulation of excess fat in the liver in people who consume little or no alcohol, is becoming increasingly common around the world, especially in developed countries. Extracts from earthworms have been used as alternative therapies for a variety of diseases but not in NAFLD. Therefore, the aim of this study was to investigate the effect of earthworm extract (EE) on diet-induced fatty liver disease in guinea pigs.

EE was extracted, and the effect of EE on the lipid levels and liver damage in guinea pigs fed a high-fat diet (HFD) was assessed. Thirty male guinea pigs at 3 weeks of age were allocated equally to five groups, namely, chow diet, HFD, and HFD with different dosages (0.3, 1.4 and 6.8 µg per kg bodyweight per day) of EE for 4 weeks, and their body weight was monitored throughout the experiment. Liver tissues were examined for gross morphology and histology. Serum levels of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), alanine transaminase (ALT) and aspartate aminotransferase (AST) were determined using an autoanalyser.
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