Enhancing XRF sensor-based sorting of porphyritic copper ore using particle swarm optimization-support vector machine(PSO-SVM)algorithm

X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifications and hindering widespread technology adoption.Accurate classification models are crucial to determine if actual grade exceeds the sorting threshold using localized XRF signals.Previous studies mainly used linear regression(LR)algorithms including simple linear regression(SLR),multivariable linear regression(MLR),and multivariable linear regression with interaction(MLRI)but often fell short attaining satisfactory results.This study employed the particle swarm optimization support vector machine(PSO-SVM)algorithm for sorting porphyritic copper ore pebble.Lab-scale results showed PSO-SVM out-performed LR and raw data(RD)models and the significant interaction effects among input features was observed.Despite poor input data quality,PSO-SVM demonstrated exceptional capabilities.Lab-scale sorting achieved 93.0%accuracy,0.24%grade increase,84.94%recovery rate,57.02%discard rate,and a remarkable 39.62 yuan/t net smelter return(NSR)increase compared to no sorting.These improvements were achieved by the PSO-SVM model with optimized input combinations and highest data quality(T=10,T is XRF testing times).The unsuitability of LR methods for XRF sensor-based sorting of investigated sample is illustrated.Input element selection and mineral association analysis elucidate element importance and influence mechanisms.

Systemic immune-inflammation index combined with pediatric appendicitis score in assessing the severity and prognosis for paediatric appendicitis

BACKGROUND Pediatric appendicitis is a common cause of abdominal pain in children and is recognized as a significant surgical emergency.A prompt and accurate diagnosis is essential to prevent complications such as perforation and peritonitis.AIM To investigate the predictive value of the systemic immune-inflammation index(SII)combined with the pediatric appendicitis score(PAS)for the assessment of disease severity and surgical outcomes in children aged 5 years and older with appendicitis.METHODS Clinical data of 104 children diagnosed with acute appendicitis were analyzed.The participants were categorized into the acute appendicitis group and chronic appendicitis group based on disease presentation and further stratified into the good prognosis group and poor prognosis group based on prognosis.The SII and PAS were measured,and a joint model using the combined SII and PAS was constructed to predict disease severity and surgical outcomes.RESULTS Significant differences were observed in the SII and PAS parameters between the acute appendicitis group and chronic appendicitis group.Correlation analysis showed associations among the SII,PAS,and disease severity,with the combined SII and PAS model demonstrating significant predictive value for assessing disease severity[aera under the curve(AUC)=0.914]and predicting surgical outcomes(AUC=0.857)in children aged 5 years and older with appendicitis.CONCLUSION The study findings support the potential of integrating the SII with the PAS for assessing disease severity and predicting surgical outcomes in pediatric appendicitis,indicating the clinical utility of the combined SII and PAS model in guiding clinical decision-making and optimizing surgical management strategies for pediatric patients with appendicitis.

The combined detection of carcinoembryonic antigen,carcinogenic antigen 125,and carcinogenic antigen 19-9 in colorectal cancer patients

BACKGROUND Hepatic metastases are common and difficult to treat after colorectal cancer(CRC)surgery.The predictive value of carcinoembryonic antigen(CEA),cancer antigen(CA)125 and CA19-9 combined tests for liver metastasis is unclear.AIM To evaluate predictive value of combined tests for CEA,CA125,and CA19-9 levels in patients with liver metastases of CRC.METHODS The retrospective study included patients with CRC alone(50 cases)and patients with CRC combined with liver metastases(50 cases)who were hospitalized between January 2021 and January 2023.Serum CEA,CA125 and CA19-9 levels were compared between the two groups,and binary logistic regression was used to analyze the predictive value of the combination of these tumor markers in liver metastasis.In addition,we performed receiver operating characteristic(ROC)curve analysis to assess its diagnostic accuracy.RESULTS The results showed that the serum CEA,CA125 and CA19-9 levels in the CRC with liver metastasis group were significantly higher than those in the CRC alone group.Specifically,the average serum CEA level in the CRC with liver metastasis group was 162.03±810.01 ng/mL,while that in the CRC alone group was 5.71±9.76 ng/mL;the average serum CA125 levels were 43.47±83.52 U/mL respectively.and 13.5±19.68 U/mL;the average serum CA19-9 levels were 184.46±473.13 U/mL and 26.55±43.96 U/mL respectively.In addition,binary logistic regression analysis showed that CA125 was significant in predicting CRC liver metastasis(P<0.05).ROC curve analysis results showed that the areas under the ROC curves of CEA,CA125 and CA19-9 were 0.607,0.692 and 0.586.CONCLUSION These results suggest that combined detection of these tumor markers may help early detection and intervention of CRC liver metastasis,thereby improving patient prognosis.

Effectiveness of magnetic resonance imaging and spiral computed tomography in the staging and treatment prognosis of colorectal cancer

BACKGROUND Colorectal cancer(CRC)is a prevalent cancer type in clinical settings;its early signs can be difficult to detect,which often results in late-stage diagnoses in many patients.The early detection and diagnosis of CRC are crucial for improving treatment success and patient survival rates.Recently,imaging techniques have been hypothesized to be essential in managing CRC,with magnetic resonance imaging(MRI)and spiral computed tomography(SCT)playing a significant role in enhancing diagnostic and treatment approaches.AIM To explore the effectiveness of MRI and SCT in the preoperative staging of CRC and the prognosis of laparoscopic treatment.METHODS Ninety-five individuals admitted to Zhongshan Hospital Xiamen University underwent MRI and SCT and were diagnosed with CRC.The precision of MRI and SCT for the presurgical classification of CRC was assessed,and pathological staging was used as a reference.Receiver operating characteristic curves were used to evaluate the diagnostic efficacy of blood volume,blood flow,time to peak,permeability surface,blood reflux constant,volume transfer constant,and extracellular extravascular space volume fraction on the prognosis of patients with CRC.RESULTS Pathological biopsies confirmed the following CRC stages:23,23,32,and 17 at T1,T2,T3,and T4,respectively.There were 39 cases at the N0 stage,22 at N1,34 at N2,44 at M0 stage,and 51 at M1.Using pathological findings as the benchmark,the combined use of MRI and SCT for preoperative TNM staging in patients with CRC demonstrated superior sensitivity,specificity,and accuracy compared with either modality alone,with a statistically significant difference in accuracy(P<0.05).Receiver operating characteristic curve analysis revealed the predictive values for laparoscopic treatment prognosis,as indicated by the areas under the curve for blood volume,blood flow,time to peak,and permeability surface,blood reflux constant,volume transfer constant,and extracellular extravascular space volume fraction were 0.750,0.683,0.772,0.761,0.709,0.719,and 0.910,respectively.The corresponding sensitivity and specificity values were also obtained(P<0.05).CONCLUSION MRI with SCT is effective in the clinical diagnosis of patients with CRC and is worthy of clinical promotion.

Prediction of Abdominal Visceral Obesity From Body Mass Index,Waist Circumference and Waist-hip Ratio in Chinese Adults:Receiver Operating Characteristic Curves Analysis

Objective To evaluate the sensitivity and specificity of body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHR) measurements in diagnosing abdominal visceral obesity. Methods BMI, WC, and WHR were assessed in 690 Chinese adults (305 men and 385 women) and compared with magnetic resonance imaging (MRI) measurements of abdominal visceral adipose tissue (VA). Receiver operating characteristic (ROC) curves were generated and used to determine the threshold point for each anthropometric parameter. Results 1) MRI showed that 61.7% of overweight/obese individuals (BMI≥25 kg/m2) and 14.2% of normal weight (BMI<25 kg/m2) individuals had abdominal visceral obesity (VA≥100 cm2). 2) VA was positively correlated with each anthropometric variable, of which WC showed the highest correlation (r=0.73-0.77, P<0.001). 3) The best cut-off points for assessing abdominal visceral obesity were as followed: BMI of 26 kg/m2, WC of 90 cm, and WHR of 0.93, with WC being the most sensitive and specific factor. 4) Among subjects with BMI≥28 kg/m2 or WC≥95 cm, 95% of men and 90% of women appeared to have abdominal visceral obesity. Conclusion Measurements of BMI, WC, and WHR can be used in the prediction of abdominal visceral obesity, of which WC was the one with better accuracy.

Using receiver operating characteristic curves to evaluate the diagnostic value of the combination of multislice spiral CT and alpha-fetoprotein levels for small hepatocellular carcinoma in cirrhotic patients

BACKGROUND： The various combination of multiphase enhancement multislice spiral CT （MSCT） makes the diagno- sis of a small hepatocellular carcinoma （sHCC） on the back- ground of liver cirrhosis possible. This study was to explore whether the combination of MSCT enhancement scan and alpha-fetoprotein （AFP） level ficiency for sHCC. could increase the diagnostic ef- METHODS： This study included 35 sHCC patients and 52 cir- rhotic patients without image evidence of HCC as a control group. The diagnoses were made by three radiologists em- ploying a 5-point rating scale, with postoperative pathologic results as the gold standard. Receiver operating characteristic （ROC） curve analysis was performed to evaluate the diag- nostic value of the three MSCT combination modes （arterial phase＋portal-venous phase, arterial phase＋delayed phase, arterial phase＋portal-venous phase＋delayed phase） and AFP levels for sHCC on the background of liver cirrhosis. RESULTS： The area under ROC curve （AUC）, sensitivity, and specificity of the combination of arterial phase＋portal- venous phase＋delayed phase were 0.93, 93%, and 82%, respectively. The average AUC of the arterial phase＋portal- venous phase＋delayed phase combination was significantly greater than that of the arterial phase＋portal-venous phase （AUC=0.84, P=0.01） and arterial phase＋delayed phase （AUC=0.85, P=0.03）. Arterial phase＋portal-venous phase had a smaller AUC （0.84） than arterial phase＋delayed phase （0.85）, but the difference was insignificant （P=0.15）. After combining MSCT enhancement scan with AFP, the AUC, sensitivity, and specificity were 0.95, 94%, and 83%, respectively, indicating a greatly increased diagnostic efficiency for sHCC. CONCLUSIONS： The combination of AFP and 3 phases MSCT enhancement scan could increase the diagnostic efficiency for sHCC on the background of liver cirrhosis. The application of ROC curve analysis has provided a new method and reference in HCC diagnosis.

Determination of the diagnostic value of the resazurin reduction assay for evaluating boar semen by receiver operating characteristic analysis

Aim： To assess that metabolic status of spermatozoa could provide a useful tool for evaluation of semen quality. Methods： The accuracy of the spectrophotometric application of the resazurin reduction assay was assessed using receiver operating characteristic （ROC） analysis. Results： Areas under ROC curves （AUC） for motile sperm concentration and sperm index （SI） （sperm concentration multiplied by the square root of percentage sperm motility multiplied by the percentage normal sperm morphology） were 0.922. The best discrimination between poor and good semen samples according to the SI was achieved at a cut-off point of A610 = 0.209, where high sensitivity （94.1%） and specificity （91.7%） were calculated. The assay was less accurate when motile sperm concentration was used as the criterion value, yielding sensitivity of 88.2% and specificity of 87.5%, respectively. Likelihood ratios （LR） indicate that absorbances lower than 0.209 were at least 11.3 times as likely to be found in good semen samples than those in poor according to the SI, whereas in the case of motile sperm concentration, the LR was calculated to be 7.06. Conclusion： These results show that the resazurin reduction assay combined with spectrophotometry is an accurate method of assessing the quality of boar semen.

Determination of the diagnostic value of the resazurin reduction assay for evaluating boar semen by receiver operating cha-racteristic analysis

Aim:To assess that metabolic status of spermatozoa could provide a useful tool for evaluation of semen quality. Methods:The accuracy of the spectrophotometric application of the resazurm reduction assay was assessed using receiver operating characteristic(ROC)analysis.Results:Areas under ROC curves(AUC)for motile sperm concen- tration and sperm index(SI)(sperm concentration multiplied by the square root of percentage sperm motility multi- plied by the percentage normal sperm morphology)were 0.922.The best discrimination between poor and good semen samples according to the SI was achieved at a cut-off point of A_(610)=0.209,where high sensitivity(94.1%)and specificity(91.7%)were calculated.The assay was less accurate when motile sperm concentration was used as the criterion value,vielding sensitivity of 88.2% and specificity of 87.5%,respectively,Likelihood ratios(LR)indicate that absorbances lower than 0.209 were at least 11.3 times as likely to be found in good semen samples than those in poor according to the SI,whereas in the case of motile sperm concentration,the LR was calculated to be 7.06.Conclusion: These results show that the resazurin reduction assay combined with spectrophotometry is an accurate method of assessing the quality of boar semen.

Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network:A Case Study of Vietnam

Flash floods are one of the most dangerous natural disasters,especially in hilly terrain,causing loss of life,property,and infrastructures and sudden disruption of traffic.These types of floods are mostly associated with landslides and erosion of roads within a short time.Most of Vietnamis hilly and mountainous;thus,the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management.In this study,three Machine Learning(ML)methods namely Deep Learning Neural Network(DL),Correlation-based FeatureWeighted Naive Bayes(CFWNB),and Adaboost(AB-CFWNB)were used for the development of flash flood susceptibility maps for hilly road section(115 km length)of National Highway(NH)-6 inHoa Binh province,Vietnam.In the proposedmodels,88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental factors.The performance of themodels was evaluated using standard statisticalmeasures including Receiver Operating Characteristic(ROC)Curve,Area Under Curve(AUC)and Root Mean Square Error(RMSE).The results revealed that all the models performed well(AUC>0.80)in predicting flash flood susceptibility zones,but the performance of the DL model is the best(AUC:0.972,RMSE:0.352).Therefore,the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area.

Machine Learning-Based Threatened Species Translocation Under Climate Vulnerability

Climate change is the most serious causes and has a direct impact on biodiversity.According to the world’s biodiversity conservation organization,rep-tile species are most affected since their biological and ecological qualities are directly linked to climate.Due to a lack of time frame in existing works,conser-vation adoption affects the performance of existing works.The proposed research presents a knowledge-driven Decision Support System(DSS)including the assisted translocation to adapt to future climate change to conserving from its extinction.The Dynamic approach is used to develop a knowledge-driven DSS using machine learning by applying an ecological and biological variable that characterizes the model and mitigation processes for species.However,the frame-work demonstrates the huge difference in the estimated significance of climate change,the model strategy helps to recognize the probable risk of threatened spe-cies translocation to future climate change.The proposed system is evaluated using various performance metrics and this framework can comfortably adapt to the decisions support to reintroduce the species for conservation in the future.

Support vector machine for prediction of siRNA silencing efficacy

In order to assist the design of short interfering ribonucleic acids （siRNA）, 573 non-redundant siRNAs were collected from published literatures and the relationship between siRNAs sequences and RNA interference （RNAi） effect is analyzed by a support vector machine （SVM） based algorithm relied on a basebase correlation （BBC） feature. The results show that the proposed algorithm has the highest area under curve （AUC） value （0. 73） of the receive operating characteristic （ROC） curve and the greatest r value （0. 43） of the Pearson＇s correlation coefficient. This indicates that the proposed algorithm is better than the published algorithms on the collected datasets and that more attention should be paid to the base-base correlation information in future siRNA design.

Comparative study of CEA and CA19-9 in esophageal,gastric and colon cancers individually and in combination(ROC curve analysis)

Objective： To determine the clinical serum levels of carcinoembryonic antigen （CEA） and carbohydrate antigen 19-9 （CA19-9）, individually and in combination, for the diagnosis of 50 healthy subjects and 150 cases of esophageal, gastric, and colon cancers. Methods： The sensitivities of the two markers were compared individually and in combination, with specificity set at 100%. Receiver operating characteristic （ROC） curves were plotted. Results： Serum CEA levels were significantly higher in cancer patients than in the control group. The sensitivity of CEA was determined： in esophageal cancer, sensitivity=28%, negative predictive value （NPV）=61.72%, and AUC=0.742 （SE=0.05）, with a significance level of P〈0.0001; in gastric cancer, sensitivity=30%, NPV=58.82%, and AUC=0.734 （SE=0.0S）, with a significance level of P〈0.0001; in colon cancer, sensitivity=74%, NPV=79.36%, and AUC=0.856 （SE=0.04）, with a significance level of P〈0.0001. The sensitivity of CA19-9 was also evaluated： in esophageal cancer, sensitivity=18%, NPV=54.94%, and AUC=0.573 （SE=0.05）, with a significance level of P=0.2054. In gastric cancer, sensitivity=42%, NPV=63.29%, and AUC=0.679 （SE=0.05）, with a significance level of P〈0.0011. In colon cancer, sensitivity=26%, NPV=57.47%, and AUC=0.S80 （SE=0.05）, with a significance level ofP=0.1670. The following were the sensitivities of CEA/CA19-9 combined： in esophageal cancer, sensitivity=42%, NPV=63.29%, SE=0.078 （95% CI： 0.0159-0.322）; gastric cancer, sensitivity=S8%, NPV=70.42%, SE=0.072 （9$% CI： -0.0866-0.198）; and colon cancer, sensitivity=72%, NPV=78.12%, SE=0.070 （9S% CI： 0.137-0.415）. Conclusion： CEA exhibited the highest sensitivity for colon cancer, and CA19-9 exhibited the highest sensitivity for gastric cancer. Combined analysis indicated an increase in diagnostic sensitivity in esophageal and gastric cancer compared with that in colon cancer.

Staging of liver fibrosis in chronic hepatitis B patients with a composite predictive model:A comparative study

AIM:To evaluate the efficacy of 6 noninvasive liver fibrosis models and to identify the most valuable model for the prediction of liver fibrosis stage in chronic hepatitis B(CHB) patients.METHODS:Seventy-eight CHB patients were consecutively enrolled in this study.Liver biopsy was performed and blood serum was obtained at admission.Histological diagnosis was made according to the METAVIR system.Significant fibrosis was defined as stage score ≥ 2,severe fibrosis as stage score ≥ 3.The diagnostic accuracy of 6 noninvasive liver fibrosis models,including serum aspartate aminotransferase(AST) to platelet ratio index(APRI),FIB-4,Forn's index,Fibrometer,Hepascore,and Shanghai Liver Fibrosis Group's index(SLFG),was investigated.RESULTS:The APRI,FIB-4 and Forn's index under receiver operating characteristic curve(AUROC) for sig-nificant fibrosis were 0.71,0.75 and 0.79,respectively,with a diagnosis accuracy of 67%,77% and 80%,respectively,and 0.80,0.87 and 0.86,respectively,under the AUROC for severe fibrosis.The Hepascore,SLFG,and Fibrometer were 0.80,0.83 and 0.85,respectively under the AUROC for significant fibrosis(P < 0.01).The diagnosis accuracy of Hepascore and SLFG was 86% and 88%,respectively.The Hepascore,SLFG,and Fibrometer were 0.95,0.93,and 0.94,respectively,under the AUROC for severe fibrosis(P < 0.01).CONCLUSION:The models containing direct serum markers have a better diagnostic value than those not containing direct serum markers.

Clinical value of serum CA19-9 levels in evaluating resectability of pancreatic carcinoma

AIM:To evaluate the clinical value of serum CA19-9 levels in predicting the respectability of pancreatic carcinoma according to receiver operating characteristic(ROC) curve analysis. METHODS:Serum CA19-9 levels were measured in 104 patients with pancreatic cancer which were possible to be resected according to the imaging. ROC curve was plotted for the CA19-9 levels. The point closest to the upper left-hand corner of the graph were chosen as the cut-off point. The sensitivity,specificity,positive and negative predictive values of CA19-9 at this cut-off point were calculated. RESULTS:Resectable pancreatic cancer was detected in 58(55.77%) patients and unresectable pancreatic cancer was detected in 46(44.23%) patients. The area under the ROC curve was 0.918 and 95% CI was 0.843-0.992. The CA19-9 level was 353.15 U/mL,and the sensitivity and specificity of CA19-9 at this cut-off point were 93.1% and 78.3%,respectively. The positive and negative predictive value was 84.38% and 90%,respectively. CONCLUSION:Preoperative serum CA19-9 level is a useful marker for further evaluating the resectability of pancreatic cancer. Obviously increased serum levels of CA19-9(> 353.15 U/mL) can be regarded as an ancillary parameter for unresectable pancreatic cancer.

Reduced hemoglobin and increased C-reactive protein are associated with upper gastrointestinal bleeding

AIM: To investigate the early upper gastrointestinal endoscopy (endoscopy) significantly reduces mortality resulting from upper gastrointestinal (GI) bleeding.

Serum N-glycan markers for diagnosing liver fibrosis induced by hepatitis B virus

BACKGROUND Hepatitis B virus (HBV) infection is the primary cause of hepatitis with chronic HBV infection,which may develop into liver fibrosis,cirrhosis and hepatocellular carcinoma.Detection of early-stage fibrosis related to HBV infection is of great clinical significance to block the progression of liver lesion.Direct liver biopsy is regarded as the gold standard to detect and assess fibrosis;however,this method is invasive and prone to clinical sampling error.In order to address these issues,we attempted to find more convenient and effective serum markers for detecting HBV-induced early-stage liver fibrosis.AIM To investigate serum N-glycan profiling related to HBV-induced liver fibrosis and verify multiparameter diagnostic models related to serum N-glycan changes.METHODS N-glycan profiles from the sera of 432 HBV-infected patients with liver fibrosis were analyzed.Significant changed N-glycan levels (peaks)(P <0.05) in differentfibrosis stages were selected in the modeling group,and multiparameter diagnostic models were established based on changed N-glycan levels by logistic regression analysis.The receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic efficacy of N-glycans models.These models were then compared with the aspartate aminotransferase to platelet ratio index (APRI),fibrosis index based on the four factors (FIB-4),glutamyltranspeptidase platelet albumin index (S index),GlycoCirrho-test,and GlycoFibro-test.Furthermore,we combined multiparameter diagnostic models with alanine aminotransferase (ALT) and platelet (PLT) tests and compared their diagnostic power.In addition,the diagnostic accuracy of N-glycan models was also verified in the validation group of patients.RESULTS Multiparameter diagnostic models constructed based on N-glycan peak 1,3,4and 8 could distinguish between different stages of liver fibrosis.The area under ROC curves (AUROCs) of Model A and Model B were 0.890 and 0.752,respectively differentiating fibrosis F0-F1 from F2-F4,and F0-F2 from F3-F4,and surpassing other serum panels.However,AUROC (0.747) in Model C used for the diagnosis of F4 from F0-F3 was lower than AUROC (0.795) in FIB-4.In combination with ALT and PLT,the multiparameter models showed better diagnostic power (AUROC=0.912,0.829,0.885,respectively) when compared with other models.In the validation group,the AUROCs of the three combined models (0.929,0.858,and 0.867,respectively) were still satisfactory.We also applied the combined models to distinguish adjacent fibrosis stages of 432patients (F0-F1/F2/F3/F4),and the AUROCs were 0.917,0.720 and 0.785.CONCLUSION Multiparameter models based on serum N-glycans are effective supplementary markers to distinguish between adjacent fibrosis stages of patients caused by HBV,especially in combination with ALT and PLT.

Predictive role of interleukin-6 and CAT score in mechanical ventilation in patients with chronic obstructive pulmonary disease at the acute exacerbation stage in the emergency department

BACKGROUND:The study aimed to evaluate the predictive role of interleukin-6(IL-6)and chronic obstructive pulmonary disease(COPD)assessment test(CAT)score in mechanical ventilation(MV)in COPD patients at the acute exacerbation stage in the emergency department(ED).METHODS:For a one-year period,among adult patients in the ED who met the criteria of acute exacerbation of COPD,158 who received MV within 48 hours after admission were compared to 294 who didn't require MV within the same period after admission.IL-6 level and CAT score were compared between the two groups.The predicted value of IL-6 and CAT score was assessed by logistic regression analysis and a receiver operating characteristic(ROC)curve.RESULTS:The IL-6 and CAT scores in the 158 MV patients were much higher than those without.IL-6 and CAT scores were independent predictors of MV within 48 hours using logistic regression analysis(IL-6:odds ratio[OR]1.053,95%confidence interval[CI]1.039–1.067,P<0.001;CAT score:OR 1.122,95%CI 1.086–1.159,P<0.001).The combination of IL-6 and CAT scores(area under ROC curve[AUC]0.826,95%CI 0.786–0.866,P<0.001)improved the accuracy of predicting MV within 48 hours when compared with IL-6(AUC 0.752,95%CI 0.703–0.800,P<0.001)and CAT scores alone(AUC 0.739,95%CI 0.692–0.786,P<0.001).The sensitivity and specificity were 69.6%,74.1%,75.32%and 63.6%,respectively.CONCLUSION:The combined of IL-6 and CAT scores is useful for evaluating the risk of COPD patients at acute exacerbation in ED,and can provide a predictive value for MV or not within 48 hours.

Prognostic relevance of β-catenin expression in T2-3N0M0 esophageal squamous cell carcinoma

AIM: To study the expression of β-catenin in esophageal squamous cell carcinoma (ESCC) at stage T2-3N0M0 and its relation with the prognosis of ESCC patients. METHODS: Expression of β-catenin in 227 ESCC speci-mens was detected by immunohistochemistry (IHC). A reproducible semi-quantitative method which takes both staining percentage and intensity into account was applied in IHC scoring, and receiver operating char-acteristic curve analysis was used to select the cut-off score for high or low IHC reactivity. Then, correlation of β-catenin expression with clinicopathological features and prognosis of ESCC patients was determined. RESULTS: No significant correlation was observed between β-catenin expression and clinicopathological parameters in terms of gender, age, tumor size, tumor grade, tumor location, depth of invasion and pathologi-cal stage. The Kaplan-Meier survival curve showed that the up-regulated expression of β-catenin indicated a poorer post-operative survival rate of ESCC patients at stage T2-3N0M0 (P = 0.004), especially of those with T3 lesions (P = 0.014) or with stage ⅡB diseases (P = 0.007). Multivariate analysis also confirmed that β-catenin was an independent prognostic factor for the overall survival rate of ESCC patients at stage T2-3N0M0 (relative risk = 1.642, 95% CI: 1.159-2.327, P = 0.005). CONCLUSION: Elevated β-catenin expression level may be an adverse indicator for the prognosis of ESCC patients at stage T2-3N0M0, especially for those with T3 lesions or stage ⅡB diseases.

Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India

Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world.The number of landslides and the level of damage across the globe has been increasing over time.Therefore,landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region.Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study.The prime goal of the study is to prepare landslide susceptibility maps(LSMs)using computer-based advanced machine learning techniques and compare the performance of the models.To properly understand the existing spatial relation with the landslide,twenty factors,including triggering and causative factors,were selected.A deep learning algorithm viz.convolutional neural network model(CNN)and three popular machine learning techniques,i.e.,random forest model(RF),artificial neural network model(ANN),and bagging model,were employed to prepare the LSMs.Two separate datasets including training and validation were designed by randomly taken landslide and nonlandslide points.A ratio of 70:30 was considered for the selection of both training and validation points.Multicollinearity was assessed by tolerance and variance inflation factor,and the role of individual conditioning factors was estimated using information gain ratio.The result reveals that there is no severe multicollinearity among the landslide conditioning factors,and the triggering factor rainfall appeared as the leading cause of the landslide.Based on the final prediction values of each model,LSM was constructed and successfully portioned into five distinct classes,like very low,low,moderate,high,and very high susceptibility.The susceptibility class-wise distribution of landslides shows that more than 90%of the landslide area falls under higher landslide susceptibility grades.The precision of models was examined using the area under the curve(AUC)of the receiver operating characteristics(ROC)curve and statistical methods like root mean square error(RMSE)and mean absolute error(MAE).In both datasets(training and validation),the CNN model achieved the maximum AUC value of 0.903 and 0.939,respectively.The lowest value of RMSE and MAE also reveals the better performance of the CNN model.So,it can be concluded that all the models have performed well,but the CNN model has outperformed the other models in terms of precision.

Serum gastrin-17 concentration for prediction of upper gastrointestinal tract bleeding risk among peptic ulcer patients

BACKGROUND Serum gastrin-17(G-17),pepsinogen I(PGI),and pepsinogen II(PGII)concentrations regulate gastric acid secretion,and hypersecretion of gastric acid increases the risks of peptic ulcer and upper gastrointestinal bleeding.These associations suggest that serum G-17,PGI,and(or)PGII may predict gastrointestinal bleeding risk among peptic ulcer patients.AIM To evaluate the efficacies of serum G-17,PGI,PGII,and PGI/PGII ratio(PGR)for predicting upper gastrointestinal bleeding among peptic ulcer patients.METHODS A total of 199 patients diagnosed with peptic ulcer confirmed by gastroscopy and positivity for Helicobacter pylori by the 14C-urea breath test were recruited,including 107 patients with simple peptic ulcer and 92 cases complicated by upper gastrointestinal bleeding.Serum PGI,PGII,G-17,and PGR were measured by immune methods and compared between bleeding and non-bleeding groups by univariate analysis.The specificity and sensitivity of PGs and G-17 for evaluating upper gastrointestinal bleeding risk were then assessed by constructing receiver operating characteristic(ROC)curves.RESULTS Serum G-17 was significantly higher among peptic ulcer patients with upper gastrointestinal bleeding compared to simple peptic ulcer patients(25.34±14.29 vs 8.84±8.03 pmol/L,t=9.822,P<0.01),whereas serum PGI,PGII,and PGR did not differ significantly between bleeding and non-bleeding groups(all P>0.05).The risk of bleeding was significantly higher among peptic ulcer patients with elevated serum G-17(>15 pmol/L)compared to patients with normal or low serum G-17(73.2%vs 27.4%,χ2=40.72,P<0.01).The area under the ROC curve for serum G-17 was 0.866±0.024,and a cut-off of 9.86 pmol/L yielded 90.2%sensitivity and 68.2%specificity for distinguishing peptic ulcer with and without upper gastrointestinal bleeding.CONCLUSION Serum G-17 is significantly upregulated in peptic ulcer patients and higher levels are predictive of upper gastrointestinal bleeding.Conversely,serum PGI,PGII,and PGR have no predictive value.Further prospective studies are warranted to examine if high G-17 can be used to assess risk of bleeding prior to onset.