Development and validation of a prediction model for early screening of people at high risk for colorectal cancer

BACKGROUND Colorectal cancer(CRC)is a serious threat worldwide.Although early screening is suggested to be the most effective method to prevent and control CRC,the current situation of early screening for CRC is still not optimistic.In China,the incidence of CRC in the Yangtze River Delta region is increasing dramatically,but few studies have been conducted.Therefore,it is necessary to develop a simple and efficient early screening model for CRC.AIM To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC.METHODS Data of 64448 participants obtained from Ningbo Hospital,China between 2014 and 2017 were retrospectively analyzed.The cohort comprised 64448 individuals,of which,530 were excluded due to missing or incorrect data.Of 63918,7607(11.9%)individuals were considered to be high risk for CRC,and 56311(88.1%)were not.The participants were randomly allocated to a training set(44743)or validation set(19175).The discriminatory ability,predictive accuracy,and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic(ROC)curves and calibration curves and by decision curve analysis.Finally,the model was validated internally using a bootstrap resampling technique.RESULTS Seven variables,including demographic,lifestyle,and family history information,were examined.Multifactorial logistic regression analysis revealed that age[odds ratio(OR):1.03,95%confidence interval(CI):1.02-1.03,P<0.001],body mass index(BMI)(OR:1.07,95%CI:1.06-1.08,P<0.001),waist circumference(WC)(OR:1.03,95%CI:1.02-1.03 P<0.001),lifestyle(OR:0.45,95%CI:0.42-0.48,P<0.001),and family history(OR:4.28,95%CI:4.04-4.54,P<0.001)were the most significant predictors of high-risk CRC.Healthy lifestyle was a protective factor,whereas family history was the most significant risk factor.The area under the curve was 0.734(95%CI:0.723-0.745)for the final validation set ROC curve and 0.735(95%CI:0.728-0.742)for the training set ROC curve.The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population.CONCLUSION The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age,BMI,WC,lifestyle,and family history exhibited high accuracy.

Financial crisis early-warning model of listed companies based on predicted value

To establish a financial early-warning model with high accuracy of discrimination and achieve the aim of long-term prediction, principal component analysis (PCA), Fisher discriminant, together with grey forecasting models are used at the same time. 110 A-share companies listed on the Shanghai and Shenzhen stock exchange are selected as research samples. And 10 extractive factors with 89.746% of all the original information are determined by applying PCA, which obtains the goal of dimension reduction without information loss. Based on the index system, the early-warning model is constructed according to the Fisher rules. And then the GM(1,1) is adopted to predict financial ratios in 2004, according to 40 testing samples from 2000 to 2003. Finally, two different methods, a self-validated and a forecasting-validated, are used to test the validity of the financial crisis warning model. The empirical results show that the model has better predictability and feasibility, and GM(1,1) contributes to the ability to make long-term predictions.

Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma

BACKGROUND Surgical resection is the primary treatment for hepatocellular carcinoma(HCC).However,studies indicate that nearly 70%of patients experience HCC recurrence within five years following hepatectomy.The earlier the recurrence,the worse the prognosis.Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data,which are lagging.Hence,developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis.AIM To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC.METHODS The demographic and clinical data of 371 HCC patients were collected for this retrospective study.These data were randomly divided into training and test sets at a ratio of 8:2.The training set was analyzed,and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models.Each model was evaluated,and the bestperforming model was selected for interpreting the importance of each variable.Finally,an online calculator based on the model was generated for daily clinical practice.RESULTS Following machine learning analysis,eight key feature variables(age,intratumoral arteries,alpha-fetoprotein,preoperative blood glucose,number of tumors,glucose-to-lymphocyte ratio,liver cirrhosis,and pre-operative platelets)were selected to construct six different prediction models.The XGBoost model outperformed other models,with the area under the receiver operating characteristic curve in the training,validation,and test datasets being 0.993(95%confidence interval:0.982-1.000),0.734(0.601-0.867),and 0.706(0.585-0.827),respectively.Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value.CONCLUSION The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence.This model may guide surgical strategies and postoperative individualized medicine.

Validation and performance of three scoring systems for predicting primary non-function and early allograft failure after liver transplantation

Background: Primary non-function(PNF) and early allograft failure(EAF) after liver transplantation(LT) seriously affect patient outcomes. In clinical practice, effective prognostic tools for early identifying recipients at high risk of PNF and EAF were urgently needed. Recently, the Model for Early Allograft Function(MEAF), PNF score by King's College(King-PNF) and Balance-and-Risk-Lactate(BAR-Lac) score were developed to assess the risks of PNF and EAF. This study aimed to externally validate and compare the prognostic performance of these three scores for predicting PNF and EAF. Methods: A retrospective study included 720 patients with primary LT between January 2015 and December 2020. MEAF, King-PNF and BAR-Lac scores were compared using receiver operating characteristic(ROC) and the net reclassification improvement(NRI) and integrated discrimination improvement(IDI) analyses. Results: Of all 720 patients, 28(3.9%) developed PNF and 67(9.3%) developed EAF in 3 months. The overall early allograft dysfunction(EAD) rate was 39.0%. The 3-month patient mortality was 8.6% while 1-year graft-failure-free survival was 89.2%. The median MEAF, King-PNF and BAR-Lac scores were 5.0(3.5–6.3),-2.1(-2.6 to-1.2), and 5.0(2.0–11.0), respectively. For predicting PNF, MEAF and King-PNF scores had excellent area under curves(AUCs) of 0.872 and 0.891, superior to BAR-Lac(AUC = 0.830). The NRI and IDI analyses confirmed that King-PNF score had the best performance in predicting PNF while MEAF served as a better predictor of EAD. The EAF risk curve and 1-year graft-failure-free survival curve showed that King-PNF was superior to MEAF and BAR-Lac scores for stratifying the risk of EAF. Conclusions: MEAF, King-PNF and BAR-Lac were validated as practical and effective risk assessment tools of PNF. King-PNF score outperformed MEAF and BAR-Lac in predicting PNF and EAF within 6 months. BAR-Lac score had a huge advantage in the prediction for PNF without post-transplant variables. Proper use of these scores will help early identify PNF, standardize grading of EAF and reasonably select clinical endpoints in relative studies.

The cooling models of Earth’s early mantle

The thermal state of the early Earth’s interior and its way of cooling are crucial for its subsequent evo-lution.Earth is initially hot as it acquired enormous heat in response to violent processes during its formation,e.g.,the Moon-forming giant impact,the segregation and formation of its metallic core,the tidal interaction with the early Moon,and the decay of radioactive elements,etc.In the meantime,the cooling mechanisms of early Earth’s mantle remain elusive despite their importance,and the previously proposed cooling models of the mantle are controversial.In this paper,we first reviewed several prevalent parameter-ized thermal evolution models of the early mantle.The models give unrealistic predictions since they were estab-lished solely based on a single tectonic regime,such as the stagnant-lid regime,or relied on the disputable existence of the plate tectonics prior to-3.5 Ga.Then we argue that the mantle should have started to cool down from a very hot state after the solidification of the ferocious magma ocean.Instead of using one single scaling law to describe a single-stage model,we suggest that an episodic multi-stage cooling model(EMCM)of the early mantle could be more plausible to account for the mantle’s early cooling process.The model reconciles with the fact that the mantle cools down from a hot state prior to*3.5 Ga and can also explain the well-constrained post-3.5 Ga thermal history of the mantle.

Advancing critical care recovery:The pivotal role of machine learning in early detection of intensive care unit-acquired weakness

This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.

Early-warning signals for an outbreak of the influenza pandemic

Over the course of human history, influenza pandemics have been seen as major disasters, so studies on the influenza virus have become an important issue for many experts and scholars. Comprehensive research has been performed over the years on the biological properties, chemical characteristics, external environmental factors and other aspects of the virus, and some results have been achieved. Based on the chaos game representation walk model, this paper uses the time series analysis method to study the DNA sequences of the influenza virus from 1913 to 2010, and works out the early-warning signals indicator value for the outbreak of an influenza pandemic. The variances in the CCR wall〈 sequences for the pandemic years （or ＋ -1 to 2 years） are significantly higher than those for the adjacent years, while those in the non-pandemic years are usually smaller. In this way we can provide an influenza early-warning mechanism so that people can take precautions and be well prepared prior to a pandemic.

Introduction to marine emergency forecasting and early-warning system(MEFES)

Marine emergencies especially oil spill may bring irreversible harm to the marine environment,and will cause immeasurable economic losses.In recent years,the demand for crude oil is increasing year by year in China with the high-speed economic development,leading to the high risk of marine oil spill.Therefore,it is necessary that promoting emergency response on marine oil spill in China and improving oil spill forecasting and early-warning techniques.This paper introduces the Marine Emergency Forecasting and Early-warning System(MEFES)developed by National Marine Data and Information Service(NMDIS).The system consists of one database,two modelling subsystems and a GIS platform.The database is the marine emergency database,and two subsystems include the marine environmental forecasting subsystem and the oil spill behaviour forecasting subsystem.MEFES has been applied in the emergency response of some major oil spill accidents occurred in recent years.The operational applications of the system can provide some theoretical basis and reference for marine oil spill emergency response.

Climate prediction of the seasonal sea-ice early melt onset in the Bering Sea

基于大尺度环流异常对海冰消融的影响过程,本文采用年际增量预测方法研制了白令海季节性海冰早期消融开始日期(EMO)的统计预测模型.预测模型选取了3个具有明确物理意义的预测因子:1月波弗特高压,前期11月东西伯利亚地区海平面气压,以及11月东欧平原积雪覆盖率。1月波弗特高压可以通过海气相互作用影响白令海地区海温异常,该海温异常能够从1月持续到3月,进而影响白令海EMO.11月东西伯利亚地区海平面气压与11月至次年2月北太平洋中纬度东部海温密切相关。伴随着北太平洋中纬度东部冷海温异常的出现,白令海地区会出现暖海温异常,进而导致白令海海冰范围减少,EMO较晚.1月北极偶极子异常是11月东欧平原积雪覆盖率影响次年白令海EMO的桥梁之一.1981-2022年的交叉检验结果表明:统计模型对白令海EMO具有较好的预测能力,预测与观测的EMO之间时间相关系数达到了0.45,超过了99%的置信水平.统计模型对白令海EMO正常年份和异常年份的预测准确率分别为60%和41%.

Studies on Molecular Mechanism of Human Early Cardiogenesis Using the Drosophila as a Model System

Recently,some of the genetic mechanisms of heart specification have been elucidated in Drosophila .However,genes involved in early cardiogenesis of human remain to be identified.Since the pathways that regulate early cardiac fate determination are conserved between Drosophila and vertebrates,flies can be used as a model test system to explore the genetic basis of cardiogenesis in human.In this project,about 3000 reccieve lethal gene lines were produced by P or EMS mutagenesis.With staining of antibodies against heart precussor cells of Drosophila ,about 200 lines were observed to show heart phenotype.In pilot studies of their function with RNAi technique,the RNAi phenotypes of several genes tested were observed,which were very similar to that of their mutants,showing heart tube defects or no heart precursors formation.Taking advantage of the advanced genetic information available in the Drosophila and human systems,we have identified about 50 human transcripts homologous to the Drosophila heart related gene candidates.Northern blot analysis for some of the human candidates showed that several genes were expressed in both adult and early embryonic tissues,which may help in the evaluation of candidate genes for human cardiogenesis.Our further experiments with transgenic flies generated with wild type and mutant forms of these candidate genes to examine for defects in cardiogenesis or cardiac function are under way.The candidate genes producing cardiac specific defects suggestive of similarities to the heart disease syndromes can then be pursued further as likely disease gene candidates.Such an approach is likely to provide a dramatic reduction of possible candidate genes,or to screen and identify mutations that may generate the disease in human.

Nutrient Application Model Affects the Contents of Chlorophyll and Carotenoid in Functional Leaves of Early Rice

The objective of this study was to investigate the effects of different nutri-ent application models on the contents of chlorophyl and carotenoid in the functional leaves of early rice. Using rice cultivar Xiangzaoxian45 as experimental materials, the experiment was performed by designing 6 treatments, i.e., T1 （fertilization without nitrogen）, T2（local conventional fertilization）, T3（fertilization for high yield and high effi-ciency）, T4 （fertilization for super high yield）, T5 （fertilization application for super high yield and high efficiency A） and T6 （fertilization application for super high yield and high efficiency B） in two experimental plots Yiyang and Xiangyin. The results showed that T3 respectively increased the contents of chlorophyl and carotenoid at fil ing stage by 29.27%, 38.20% and 13.16%, 30.12% in Yiyang and Xiangyin, as wel as yield of early rice by 4.20%, 4.80% to T2 on the condition of saving 20% ni-trogen fertilizer. Additional y, T5 and T6 on the condition of saving 16.7% nitrogen fertilizer by T4 increased the contents of chlorophyl and carotenoid of fil ing stage by 53.91%, 53.73% and 35.95%, 37.47% in Yiyang and Xiangyin, as wel as yield of early rice by 16.60%, 18.75% to T2 in Yiyang; increased the contents of chlorophyl and carotenoid at fil ing stage by 57.82%, 56.80% and 54.88%, 57.03% in Yiyang and Xiangyin, as wel as yield of early rice 10.10%, 6.75% to T2 in Xiangyin. More-over, there was a significant correlation or an extremely significant correlation be-tween yield and the contents of chlorophyl and carotenoid at different soil fertility level （P〈0.05 or P〈0.01）. Therefore, nutrient application plays an important role in the contents of chlorophyl and carotenoid in the functional leaves of early rice.

Early warning model for slope debris flow initiation

Early warning model of debris flow is important for providing local residents with reliable and accurate warning information to escape from debris flow hazards. This research studied the debris flow initiation in the Yindongzi gully in Dujiangyan City, Sichuan province, China with scaled-down model experiments. We set rainfall intensity and slope angle as dominating parameters and carried out 20 scaled-down model tests under artificial rainfall conditions. The experiments set four slope angles(32°, 34°, 37°, 42°) and five rainfall intensities(60 mm/h, 90 mm/h, 120 mm/h, 150 mm/h, and 180 mm/h) treatments. The characteristic variables in the experiments, such as, rainfall duration, pore water pressure, moisture content, surface inclination, and volume were monitored. The experimental results revealed the failure mode of loose slope material and the process of slope debris flow initiation, as well as the relationship between the surface deformation and the physical parameters of experimental model. A traditional rainfall intensity-duration early warning model(I-D model) was firstly established by using a mathematical regression analysis, and it was then improved into ISD model and ISM model(Here, I is rainfall Intensity, S is Slope angle, D is rainfall Duration, and M is Moisture content). The warning model can provide reliable early warning of slope debris flow initiation.

Distinct neuronal excitability alterations of medial prefrontal cortex in early-life neglect model of rats

Object:Early-life neglect has irreversible emotional effects on the central nervous system.In this work,we aimed to elucidate distinct functional neural changes in me-dial prefrontal cortex(mPFC)of model rats.Methods:Maternal separation with early weaning was used as a rat model of early-life neglect.The excitation of glutamatergic and GABAergic neurons in rat mPFC was recorded and analyzed by whole-cell patch clamp.Results:Glutamatergic and GABAergic neurons of mPFC were distinguished by typi-cal electrophysiological properties.The excitation of mPFC glutamatergic neurons was significantly increased in male groups,while the excitation of mPFC GABAergic neurons was significant in both female and male groups,but mainly in terms of rest membrane potential and amplitude,respectively.Conclusions:Glutamatergic and GABAergic neurons in medial prefrontal cortex showed different excitability changes in a rat model of early-life neglect,which can contribute to distinct mechanisms for emotional and cognitive manifestations.

A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional Network

Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on society.To achieve early intervention and to prevent outbreaks of disease,we propose a novel warning model that can accurately predict the incidence of HFMD.Methods We propose a spatial-temporal graph convolutional network(STGCN)that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019.The 2011-2018 data served as the training and verification set,while data from 2019 served as the prediction set.Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error.Results As the first application using a STGCN for disease forecasting,we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level,especially for cities of significant concern.Conclusions This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance,which may significantly reduce the morbidity associated with HFMD in the future.

Thermodynamic modeling and elemental migration for the early stage of rodingitization:An example from the Xialu massif of the Xigaze ophiolite,southern Tibet

The analysis of early stage rodingite from the ultramafic rocks of the Xialu Massif in the Xigaze Ophiolite,Tibet,in China shows that the rodingitization involved continuous changes in fluid composition during different stages of subduction.The early stage prehnite-bearing rodingite was produced at low pressures and temperatures along extensional fractures.Samples of rodingite were collected along a profile from the center to the margin of a rodingitized intrusive igneous rock(^10 m×30 m),and they record wide variations in bulk composition,mineralogy,and texture.The mineral assemblages,from center to margin,vary from(1)relics of primary clinopyroxene(Cpx_(r))and primary amphibole(Amp_(r))+newly formed late amphibole(Act)+primary plagioclase(Pl_(r))+clinozoisite+prehnite+albite+chlorite+titanite+ilmenite(R1 rodingite),through(2)relics of primary clinopyroxene(Cpx_(r))+newly formed late clinopyroxene(Cpx_(n))+primary and late amphiboles(Amp_(r)+Act)+clinozoisite+prehnite+albite+chlorite+titanite(R2 rodingite),to(3)newly formed late clinopyroxene(Cpx_(n))and amphibole(Act)+clinozoisite+prehnite+albite+chlorite+titanite(R3 rodingite).As a result of the metasomatic process of rodingitization,the content of CaO in the whole rock chemical composition from R1 to R3 increases,SiO_(2) decreases,and Na_(2)O+K_(2)O is almost completely removed.Massbalance diagrams show enrichments in large ion lithophile elements such as Rb,Cs,Ba,and Pb as well as Ni during rodingitization.The central part of the rodingitized intrusion(R1 rodingite)was only slightly affected by metasomatism.On the other hand,the contents of the rare earth elements(REEs),high field strength elements(HFSEs;e.g.Zr,Nb,Ta,Hf,and Y),and some highly compatible elements such as Cr and Sc decreased slightly during rodingitization.Thermodynamic modeling based on equilibrium mineral assemblages indicates that the rodingite of the Xialu Massif formed in an H_(2)O-saturated,CO_(2)-rich environment.The estimated conditions of metamorphism were-281-323℃and 0.4-3.9 kbar,representing the subgreenschist facies.In this environment,REEs and HFSEs were soluble in the fluids and partly removed.Moreover,these prehnite rodingites formed in a progressively reducing and less alkaline environment,as indicated by decreases in f(O_(2))and bulk-rock Fe^(3+)/Fe^(2+) ratios,and the records of fluidΔpH from the center to the margin of the studied rodingitized intrusion.

Construction and analysis of an ulcer risk prediction model after endoscopic submucosal dissection for early gastric cancer

BACKGROUND Endoscopic submucosal dissection(ESD) has been widely used in the treatment of early gastric cancer(EGC). A personalized and effective prediction method for ESD with EGC is urgently needed.AIM To construct a risk prediction model for ulcers after ESD for EGC based on LASSO regression.METHODS A total of 196 patients with EGC who received ESD treatment were prospectively selected as the research subjects and followed up for one month. They were divided into an ulcer group and a non-ulcer group according to whether ulcers occurred. The general data, pathology, and endoscopic characteristics of the groups were compared, and the best risk predictor subsets were screened by LASSO regression and tenfold cross-validation. Multivariate logistic regression was applied to analyze the risk factors for ulcers after ESD in patients with EGC. A receiver operating characteristic(ROC) curve was used to estimate the predictive model performance.RESULTS One month after the operation, no patient was lost to follow-up. The incidence of ulcers was 20.41%(40/196)(ulcer group), and the incidence of no ulcers was 79.59%(156/196)(non-ulcer group). There were statistically significant differences in the course of disease, Helicobacter pylori infection history, smoking history, tumor number, clopidogrel medication history, lesion diameter, infiltration depth, convergent folds, and mucosal discoloration between the groups. Gray’s medication history, lesion diameter, convergent folds, and mucosal discoloration, which were the 4 nonzero regression coefficients, were screened by LASSO regression analysis. Further multivariate logistic analysis showed that lesion diameter [Odds ratios(OR) = 30.490, 95%CI: 8.584-108.294], convergent folds(OR = 3.860, 95%CI: 1.060-14.055), mucosal discoloration(OR = 3.191, 95%CI: 1.016-10.021), and history of clopidogrel(OR = 3.554, 95%CI: 1.009-12.515) were independent risk factors for ulcers after ESD in patients with EGC(P < 0.05). The ROC curve showed that the area under the curve of the risk prediction model for ulcers after ESD in patients with EGC was 0.944(95%CI: 0.902-0.972).CONCLUSION Clopidogrel medication history, lesion diameter, convergent folds, and mucosal discoloration can predict the occurrence of ulcers after ESD in patients with EGC.

Early-Middle Permian Reef Frameworks and Reef-building Models in the Eastern Kunlun Mountains

Reef frameworks and building models of the Early-Middle Permian in the eastern Kunlun Mountains have been verified through studies of reef-building communities, palaeoecology and carbonate facies. The eastern Kunlun reefs are built mainly by 6 reef-building communities, which include 11 major categories of frame-building organisms and 6 categories of reef-associated organisms. Eight types of reef-frames have been distinguished and eleven kinds of rocks identified to belong to 6 reef facies. Three sorts of reefs classified by previous researchers, namely mudmounds, knoll reefs and walled reefs, are well developed in the study area. Such reef-facies association and reef distribution show that there are 4 models of reef growth and development, i.e. the tidal-bank knoll-reef model, the plateau-margin wall-reef model, the composite wall-reef model and the deep-water mudmound model. The reefs are mainly constructed by calcareous sponge and calcareous algae, which are similar to all Permian reefs in other area

Modeling the Asian Aridity during the Early Cenozoic

Asian aridity can be traced back to the early Cenozoic(e.g.,Guo et al.,2008;Licht et al.,2016;Li et al.,2018a).Previous modelling studies focus mainly on the monsoon climate during the early Cenozoic,and studies for the Asian aridity are still limited(e.g.,Huber and Goldner,2012;Zhang et al.,2012;Li et al.,2018b).Here Asian aridity during the early Cenozoic is investigated through climate modelling by changing atmospheric CO2 concentration,orbital parameters,and topography.

Prediction of lymph node metastasis in early gastric signet-ring cell carcinoma:A real-world retrospective cohort study

BACKGROUND Signet-ring cell carcinoma(SRCC)was previously thought to have a worse prognosis than other differentiated gastric cancer(GC),however,recent studies have shown that the prognosis of SRCC is related to pathological type.We hypothesize that patients with SRCC and with different SRCC pathological components have different probability of lymph node metastasis(LNM).AIM To establish models to predict LNM in early GC(EGC),including early gastric SRCC.METHODS Clinical data from EGC patients who had undergone gastrectomy at the First Affiliated Hospital of Nanjing Medical University from January 2012 to March 2022 were reviewed.The patients were divided into three groups based on type:Pure SRCC,mixed SRCC,and non-signet ring cell carcinoma(NSRC).The risk factors were identified through statistical tests using SPSS 23.0,R,and EmpowerStats software.RESULTS A total of 1922 subjects with EGC were enrolled in this study,and included 249 SRCC patients and 1673 NSRC patients,while 278 of the patients(14.46%)presented with LNM.Multivariable analysis showed that gender,tumor size,depth of invasion,lymphovascular invasion,ulceration,and histological subtype were independent risk factors for LNM in EGC.Establishment and analysis using prediction models of EGC showed that the artificial neural network model was better than the logistic regression model in terms of sensitivity and accuracy(98.0%vs 58.1%,P=0.034;88.4%vs 86.8%,P<0.001,respectively).Among the 249 SRCC patients,LNM was more common in mixed(35.06%)rather than in pure SRCC(8.42%,P<0.001).The area under the ROC curve of the logistic regression model for LNM in SRCC was 0.760(95%CI:0.682-0.843),while the area under the operating characteristic curve of the internal validation set was 0.734(95%CI:0.643-0.826).The subgroups analysis of pure types showed that LNM was more common in patients with a tumor size>2 cm(OR=5.422,P=0.038).CONCLUSION A validated prediction model was developed to recognize the risk of LNM in EGC and early gastric SRCC,which can aid in pre-surgical decision making of the best method of treatment for patients.

The measuring method of early lateral energy fraction in the scale model experiments

This paper introduces a measuring method of early lateral energy fraction in the scale model experiments. According to the interference principle of half wave length making the high frequency figure-8 directional microphone. With the signal-processing technique, a receiving and analyzing system, for the measurements of lateral energy fraction in the scale model is realized.