Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

Sleep plays a vital role in optimum working of the brain and the body.Numerous people suffer from sleep-oriented illnesses like apnea,insomnia,etc.Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording.Sleep stage scoring is mainly based on experts’knowledge which is laborious and time consuming.Hence,it can be essential to design automated sleep stage classification model using machine learning(ML)and deep learning(DL)approaches.In this view,this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification(CMVODL-SSC)model using Electroencephalogram(EEG)signals.The proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG signals.Primarily,data pre-processing is performed to convert the actual data into useful format.Besides,a cascaded long short term memory(CLSTM)model is employed to perform classification process.At last,the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model.In order to report the enhancements of the CMVODL-SSC model,a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%.

Development of a novel staging classification for Siewert Ⅱ adenocarcinoma of the esophagogastric junction after neoadjuvant chemotherapy

BACKGROUND Stage classification for Siewert Ⅱ adenocarcinoma of the esophagogastric junction(AEG)treated with neoadjuvant chemotherapy(NAC)has not been established.AIM To investigate the optimal stage classification for Siewert Ⅱ AEG with NAC.METHODS A nomogram was established based on Cox regression model that analyzed variables associated with overall survival(OS)and disease-specific survival(DSS).The nomogram performance in terms of discrimination and calibration ability was evaluated using the likelihood-ratio test,Akaike information criterion,Harrell concordance index,time-receiver operating characteristic curve,and decision curve analysis.RESULTS Data from 725 patients with Siewert type Ⅱ AEG who underwent neoadjuvant therapy and gastrectomy were obtained from the Surveillance,Epidemiology,and End Results database.Univariate and multivariate analyses revealed that sex,marital status,race,ypT stage,and ypN stage were independent prognostic factors of OS,whereas sex,race,ypT stage,and ypN stage were independent prognostic factors for DSS.These factors were incorporated into the OS and DSS nomograms.Our novel nomogram model performed better in terms of OS and DSS prediction compared to the 8th American Joint Committee of Cancer pathological staging system for esophageal and gastric cancer.Finally,a user-friendly web application was developed for clinical use.CONCLUSION The nomogram established specifically for patients with Siewert type Ⅱ AEG receiving NAC demonstrated good prognostic performance.Validation using external data is warranted before its widespread clinical application.

Clinicopathological features of giant cell carcinoma of the pancreas

BACKGROUND: Giant cell carcinoma of the pancreas (GCCP) as a tumor of high malignancy, large size, and inflammatory reaction occupies 2.1%-12.8% of all cases of pancreatic malignancies. This study was to analyze cases of GCCP collected in 8 years at our hospital in an attempt to describe some features of GCCP in Chinese people. METHODS: The clinicopathological features of 19 patients who had been pathologically diagnosed as having GCCP from 1021 patients with pancreatic malignancies collected by Pancreatic Disease Research Group (PDRG) of Chang- hai Hospital were retrospectively analyzed compared with those of 96 patients with common pancreatic carcinoma (PC) who were randomly selected from 1002 patients with pancreatic carcinoma. The differences of location, clinical symptoms, imagings, laboratory test, operation and the prognosis of these two groups were defined. RESULTS: Tumors in the head of the pancreas were found in 8 patients (42.1%), and those in the body or tail of the pancreas in 11 (57.9%). The initial symptom was abdomi- nal pain in most patients (57.9%). Abdominal pain (73.7%), dyspepsia (63.2%), weight loss (36.8%) but jaun- dice were common at the time of diagnosis. The abnormal rates of routine laboratory tests in the GCCP group were lower than those in the common PC group. The assay of tumor markers between the groups of GCCP and common PC was approximately the same. The sensitivity and accu- racy of ultrasonography, spiral computed tomography and magnetic resonance imaging were considerably high. Large carcinoma in stage was seen in 9 patients or 47.4% of the GCCP group, a rate higher than that in the common PC group. Osteoid formation was found microscopically in some patients, and poorly differentiated tumor cells were found in most patients. The 1-year survival rate was 17.6%, which was lower than that in the common PC group. CONCLUSION: The clinicopathological features of GCCP are different from those of common PC. Imaging tests can be used together with the assay of tumor markers to diag- nose GCCP as early as possible and to improve the progno-sis of GCCP patients.

The altered DNA methylation pattern and its implications in liver cancer

DNA methylation is the most intensively studied epigenetic phenomenon, disturbances of which result in changes ingene transcription, thus exerting drastic imparts onto biological behaviors of cancer. Both the global demethylation andthe local hypermethylation have been widely reported in all types of tumors, providing both challenges and opportunitiesfor a better understanding and eventually controlling of the malignance. However, we are still in the very early stage ofinformation accumulation concerning the tumor associated changes in DNA methylation pattern. A number of excellentrecent reviews have covered this issue in depth. Therefore, this review will summarize our recent data on DNA methy-lation profiling in cancers. Perspectives for the future direction in this dynamic and exciting field will also be given.

Pancreatic Neuroendocrine Neoplasms: Correlation between MR Features and Pathological Tumor Grades

This study investigated the accuracy of MRI features in differentiating the pathological grades of pancreatic neuroendocrine neoplasms（PNENs）. A total of 31 PNENs patients were retrospectively evaluated, including 19 cases in grade 1, 5 in grade 2, and 7 in grade 3. Plain and contrastenhanced MRI was performed on all patients. MRI features including tumor size, margin, signal intensity, enhancement patterns, degenerative changes, duct dilatation and metastasis were analyzed. Chi square tests, Fisher＇s exact tests, one-way ANOVA and ROC analysis were conducted to assess the associations between MRI features and different tumor grades. It was found that patients with older age, tumors with higher TNM stage and without hormonal syndrome had higher grade of PNETs（all P〈0.05）. Tumor size, shape, margin and growth pattern, tumor pattern, pancreatic and bile duct dilatation and presence of lymphatic and distant metastasis as well as MR enhancement pattern and tumor-topancreas contrast during arterial phase were the key features differentiating tumors of all grades（all P〈0.05）. ROC analysis revealed that the tumor size with threshold of 2.8 cm, irregular shape, pancreatic duct dilatation and lymphadenopathy showed satisfactory sensitivity and specificity in distinguishing grade 3 from grade 1 and grade 2 tumors. Features of peripancreatic tissue or vascular invasion, and distant metastasis showed high specificity but relatively low sensitivity. In conclusion, larger size, poorlydefined margin, heterogeneous enhanced pattern during arterial phase, duct dilatation and the presence of metastases are common features of higher grade PNENs. Plain and contrast-enhanced MRI provides the ability to differentiate tumors with different pathological grades.

Criteria for assessing carbon emissions peaks at provincial level in China

China has pledged to peak carbon emissions by 2030 and neutralize emissions by 2060.There is an urgent need to develop a comprehensive and reliable methodology to judge whether a region has reached its carbon emissions peak(CEP),as well as to schedule and prioritize mitigation activities for different regions.In this study,we developed an approach for identifying the CEP status of 30 provincial areas in China,considering both the carbon emissions trends and the main socioeconomic factors that influence these trends.According to the results of the Mann-Kendall(MK)tests,changes in carbon emissions for the 30 provincial areas can be grouped inlo four clusters:those with significant reductions,marginal reductions,marginal increases,and significant increases.Then,total energy consumption(TEC),the proportion of coal consumption(PCC),the proportion of the urban population(PUP),the proportion of secondary industry(PASP),and per capita GDP(PGDP)were further identified as the main factors influencing carbon emissions,by applying Redundancy analysis(RDA)and Monte Carlo permutation tests.To balance efficacy with fairness,we assigned scores from 1 to 4 to trends in carbon emissions,and the Group Analysis results of the main influencing factors above except for TEC;for TEC,main basis is the relevant assessment results.And finally,according to the actual condition of total scores,provincial areas were assigned to the first,second,third and fourth stage of progress toward CEP,using the method of Natural Breaks(Jenks).Based on the method,differentiated plans should be adopted from the perspective of fair development and emissions reduction efficiency,in accordance with the basic principles of Doing the Best within Capacity and Common but Differentiated Responsibilities.This classification method can also be adopted by other developing countries which have not yet achieved CEP.