基于血清淀粉酶和体质量指数预测急性胰腺炎严重程度的模型

<正>【据《Clin Gastroenterol Hepatol》2015年3月报道】题:基于血清淀粉酶和体质量指数预测急性胰腺炎严重程度的模型(作者Kumaravel A等)大多数急性胰腺炎(AP)患者发病轻微,但高达20%的患者进展为重症。许多临床医生监测血清淀粉酶和脂肪酶水平试图预测疾病病程,但实践指南并未推荐这一策略。Kumaravel等实施了一项回顾性研究,以确定淀粉酶和脂肪酶的百分比变化是否与     

奥曲肽注射液治疗急性胰腺炎的临床疗效分析

[目的]探讨奥曲肽注射液在急性胰腺炎的临床治疗效果。[方法]选择急性胰腺炎患者60例,随机分为观察组和对照组,各30例。对照组采取常规治疗,观察组在常规治疗基础上加用奥曲肽,观察2组临床治疗效果。治疗结束后对比2组患者腹痛缓解时间、血尿淀粉酶恢复时间等。[结果]观察组治疗总有效率为96.67%,高于对照组的76.67%(P0.05);观察组血、尿淀粉酶恢复至正常时间和腹痛消失时间均短于对照组(P0.01)。[结论]奥曲肽能快速抑制胰酶的分泌,能及时缓解患者临床症状及体征,减轻胰腺炎症,减少并发症发生,可明显缩短病程、缩短往院时间,有效提高临床治疗效果。     

GP-2在急性胰腺炎诊断中的临床价值

目的对血清GP-2(glycoprotein2)浓度在急性胰腺炎中的诊断价值进行研究。方法通过临床症状、血清酶学、影像学和病理学诊断的48例急性胰腺炎患者分为重症急性胰腺炎(n=28)和轻症急性胰腺炎(n=20)两组,另选择20例非胰腺炎腹痛患者作为对照组,测定他们血清中的GP-2浓度,并和他们的血清酶学结果进行比较。结果GP-2诊断急性胰腺炎的特异性为100%,高于淀粉酶(83.3%)和脂肪酶(89.6%);淀粉酶和脂肪酶水平分别在入院后的第3及第4天降至正常值上限的3倍以下,而在观察的第6天,GP-2水平仍然维持在诊断标准的5倍以上;同时在轻症急性胰腺炎和重症急性胰腺炎患者,GP-2平均水平分别为4.71U和11.30U,后者明显高于前者(P<0.05)。结论GP-2对急性胰腺炎的早期诊断特异性高,持续时间长,同时对病情的判断有一定的帮助,因此有相当的I临床应用价值。     

胆宁片联合奥曲肽治疗急性胰腺炎的临床效果观察

[目的]观察胆宁片联合奥曲肽治疗急性胰腺炎的临床效果。[方法]将28例急性胰腺炎患者随机分为治疗组与对照组,各14例。治疗组以胆宁片联合奥曲肽治疗;对照组单用奥曲肽治疗。观察2组患者治疗后腹痛腹胀缓解时间、肠道功能恢复时间、白细胞计数变化、血淀粉酶、C反应蛋白(CRP)变化,并发症及住院天数等指标。[结果]治疗组在腹痛腹胀缓解时间、肠道功能恢复时间、血CRP、并发症控制等方面均优于对照组(P0.05),但2组血淀粉酶降低程度比较,差异无统计学意义(P0.05)。[结论]胆宁片联合奥曲肽在治疗急性胰腺炎有协同作用,可提高疗效,缩短住院时间。     

流行性腮腺炎合并急性胰腺炎患者血清细胞因子水平的变化

目的：观察流行性腮腺炎合并急性胰腺炎患者的血清细胞因子水平变化。方法应用悬液芯片法检测流行性腮腺炎合并急性胰腺炎及单纯流行性腮腺炎患者的血清IL-2、IL-4、IL-6、IL-8、IL-10、干扰素γ（ INF-γ）、肿瘤坏死因子-α（ TNF-α）水平;血细胞分析仪检测白细胞数,速率法检测血尿淀粉酶及血脂肪酶。收集流行性腮腺炎合并胰腺炎患者临床资料,发病24 h内行APACHEⅡ评分,48 h内行Ranson评分。结果 APACHEⅡ及Ranson评分结果显示急性胰腺炎患者均为轻症。流行性腮腺炎合并急性胰腺炎患者血清IL-6、IL-8、TNF-α、血尿淀粉酶及血脂肪酶高于单纯流行性腮腺炎患者（P均＜0．05）。结论血清高水平的IL-6、IL-8、TNF-α可能参与了流行性腮腺炎感染后急性胰腺炎的形成。     

西米替丁对急性胰腺炎的影响及机制探讨

目的:探讨西米替丁治疗急性胰腺炎的利弊及其机制。方法:对156例急性水肿型胰腺炎患者进行传统疗法与传统疗法加西米替丁的治疗对照研究,并检测了50例消化性溃疡患者服用西米替丁前后的血清胃泌素及24例急性胰腺炎患者急性发病期的血清胃泌素。结果西米替丁组腹痛消失时间、血尿淀粉酶下降时间、住院时间均较对照组明显延长。另有40.16％(51/127)的患者出现病情反复。溃疡病患者服用西米替丁后血清胃泌素明显升高。急性胰腺炎发病期血清胃泌素明显高于正常,发病当日超过正常值4～10余倍。结论:急性胰腺炎的发病可能与血清胃泌素过高有关,西米替丁虽能降低胃酸,但因反馈性升高血清胃泌素而不利于急性胰腺炎的恢复。     

前列腺素E1辅助治疗轻中度急性胰腺炎的临床分析

目的对比观察前列腺素E1在治疗急性胰腺炎(AP)中的临床疗效和安全性。方法前瞻性研究2014年5月-2015年1月吉林大学第一医院收治的轻中度AP患者80例,随机分为2组。对照组44例行胰腺炎常规综合治疗,试验组36例在常规治疗基础上加用前列腺素E1。比较2组患者腹部症状消失时间、血尿淀粉酶、血清脂肪酶、C反应蛋白、降钙素原降至正常水平的时间。计量资料组间比较采用独立样本t检验;计数资料组间比较采用χ2检验或Fisher精确检验。结果 2组血淀粉酶、C反应蛋白、中性粒细胞百分比恢复正常值的时间及住院费用间差异均有统计学意义(P值分别为0.041、0.030、0.012、0.026)。前列腺素E1可明显加速缓解腹痛、腹胀,快速降低血淀粉酶、C反应蛋白和中性粒细胞百分比;缩短住院时间,减少住院费用。结论前列腺素E1在治疗胰腺炎中具有良好的临床疗效、安全性好,可作为胰腺炎药物综合治疗的辅助用药选择。     

以自发性脾破裂出血为首发临床表现的急性胰腺炎1例

<正>急性胰腺炎(acute pancreatitis, AP)是指胰酶损伤胰腺组织,导致腺体乃至其他器官受炎症性损伤。急性胰腺炎全球年发病率为34/10万,首次发作后21%的患者发展为复发性急性胰腺炎,36%的患者在反复发作急性胰腺炎后发展为慢性胰腺炎,约20%的患者会发展为中度或重症胰腺炎^[1]。典型的急性胰腺炎一般以持续性上腹痛为首要症状,疼痛可向背部放射,如伴有血淀粉酶和(或)脂肪酶显著升高且CT提示胰腺坏死和渗出改变,一般不难诊断。     

尿胰蛋白酶原-2鉴别高淀粉酶血症的价值

淀粉酶检测急性胰腺炎实验室指标缺乏特异性。血淀粉酶的测定值要有非常明显的升高才有诊断的意义 [1 ] 。本院采用试纸法检测尿胰蛋白酶原 - 2 ,对怀疑急性胰腺炎患者进行测定并与血淀粉酶比较 ,探讨该方法对高淀粉酶血症的鉴别意义。临床资料与方法一、一般资料2 0 0 0年 7月 ～ 2 0 0 3年 4月 ,初诊疑似急性胰腺炎的急性腹痛患者 32 0例 ,其中男 12 8例 ,女 192例 ,年龄 18～ 78岁 ,平均 4 6岁。发病时间 2 ～ 32 h不等 ,平均 8h。急性胰腺炎经治疗后 1周以上 (平均 12 d)患者复查指标共 35例 ,其中男 16例 ,女 19例。二、检测方法及诊断…     

柴芍承气汤联合丹红治疗重症急性胰腺炎的效果评价

[目的]观察中药柴芍承气汤联合活血化瘀药物丹红注射液治疗重症急性胰腺炎(severe acute pancreatitis,SAP)的临床疗效。[方法]将80例SAP患者分为对照组(n=40)和治疗组(n=40)。对照组给予单纯常规治疗,如:抑制胰腺分泌,降低胰管内压,减少胰液外渗,抗感染;治疗组在常规治疗的同时加用中药柴芍承气汤和丹红注射液。观察2组患者腹痛缓解、消失时间、血淀粉酶恢复正常时间以及住院时间和临床总有效率。[结果]治疗组患者腹痛缓解、消失时间以及血淀粉酶恢复正常时间均短于对照组(P0.05),且临床总有效率明显优于对照组(P0.05)。[结论]中药柴芍承气汤联合丹红治疗可以显著的改善SAP患者各项临床观察指标,缩短住院时间,提高临床疗效。     

Forecasting model for the incidence of hepatitis A based on artificial neural network

AIM: To study the application of artificial neural network (ANN) in forecasting the incidence of hepatitis A, which had an autoregression phenomenon. METHODS: The data of the incidence of hepatitis A in Liaoning Province from 1981 to 2001 were obtained from Liaoning Disease Control and Prevention Center. We used the autoregressive integrated moving average (ARIMA) model of time series analysis to determine whether there was any autoregression phenomenon in the data. Then the data of the incidence were switched into [0,1] intervals as the network theoretical output. The data from 1981 to 1997 were used as the training and verifying sets and the data from 1998 to 2001 were made up into the test set. STATISTICA neural network (ST NN) was used to construct, train and simulate the artificial neural network. RESULTS: Twenty-four networks were tested and seven were retained. The best network we found had excellent performance, its regression ratio was 0.73, and its correlation was 0.69. There were 2 input variables in the network, one was AR(1), and the other was time. The number of units in hidden layer was 3. In ARIMA time series analysis results, the best model was first order autoregression without difference and smoothness. The total sum square error of the ANN model was 9 090.21, the sum square error of the training set and testing set was 8 377.52 and 712.69, respectively, they were all less than that of ARIMA model. The corresponding value of ARIMA was 12 291.79, 8 944.95 and 3 346.84, respectively. The correlation coefficient of nonlinear regression (R(NL)) of ANN was 0.71, while the R(NL) of ARIMA linear autoregression model was 0.66. CONCLUSION: ANN is superior to conventional methods in forecasting the incidence of hepatitis A which has an autoregression phenomenon.     

Prediction of emergency department visits for respiratory symptoms using an artificial neural network

STUDY OBJECTIVES: Accurate prediction of the effect of atmospheric changes, including pollutants, on emergency department (ED) visits for respiratory symptoms would be useful, but has proven difficult. The main difficulty is the limitation of the classical linear models and logistic regression with multiple variables to handle the multifactorial effect. DESIGN AND SETTING: To predict ED visits, we have created a computer-based model called an artificial neural network (ANN) using a back-propagation training algorithm and genetic algorithm optimization. This ANN was fed meteorologic and air pollution input variables and trained to predict the number of patients admitted to the ED with respiratory symptoms of asthma, COPD, and acute and chronic bronchitis on the corresponding day. One thousand twenty data sets were extracted from an ED admittance database at the Barzilai Medical Center (Ashkelon, Israel), and randomized to a network training set (n = 816) and a test set (n = 204). RESULTS: The neural network performed best when the predictor variables used were temperature, relative humidity, barometric pressure, SO(2), and oxidation products of nitric oxide, and the data presented as peak value 24 h prior to ED admission and the average during the 7 days before the ED visit. The neural network was able to predict the test set with an average error of 12%. CONCLUSION: Based on meteorologic and pollution data, the use of an ANN can assist in the prediction of ED visits related to respiratory conditions.     

Application of preoperative artificial neural network based on blood biomarkers and clinicopathological parameters for predicting longterm survival of patients with gastric cancer

BACKGROUND Because of the powerful abilities of self-learning and handling complex biological information, artificial neural network(ANN) models have been widely applied to disease diagnosis, imaging analysis, and prognosis prediction. However, there has been no trained preoperative ANN(preope-ANN) model to preoperatively predict the prognosis of patients with gastric cancer(GC).AIM To establish a neural network model that can predict long-term survival of GC patients before surgery to evaluate the tumor condition before the operation.METHODS The clinicopathological data of 1608 GC patients treated from January 2011 toApril 2015 at the Department of Gastric Surgery, Fujian Medical University Union Hospital were analyzed retrospectively. The patients were randomly divided into a training set(70%) for establishing a preope-ANN model and a testing set(30%). The prognostic evaluation ability of the preope-ANN model was compared with that of the American Joint Commission on Cancer(8 th edition) clinical TNM(c TNM) and pathological TNM(p TNM) staging through the receiver operating characteristic curve, Akaike information criterion index,Harrell's C index, and likelihood ratio chi-square.RESULTS We used the variables that were statistically significant factors for the 3-year overall survival as input-layer variables to develop a preope-ANN in the training set. The survival curves within each score of the preope-ANN had good discrimination(P 0.05). Comparing the preope-ANN model, c TNM, and p TNM in both the training and testing sets, the preope-ANN model was superior to c TNM in predictive discrimination(C index), predictive homogeneity(likelihood ratio chi-square), and prediction accuracy(area under the curve). The prediction efficiency of the preope-ANN model is similar to that of p TNM.CONCLUSION The preope-ANN model can accurately predict the long-term survival of GC patients, and its predictive efficiency is not inferior to that of pTNM stage.     

Evaluation of Steels Susceptibility to Hydrogen Embrittlement: A Thermal Desorption Spectroscopy-Based Approach Coupled with Artificial Neural Network

A novel approach has been developed for quantitative evaluation of the susceptibility of steels and alloys to hydrogen embrittlement. The approach uses a combination of hydrogen thermal desorption spectroscopy (TDS) analysis with recent advances in machine learning technology to develop a regression artificial neural network (ANN) model predicting hydrogen-induced degradation of mechanical properties of steels. We describe the thermal desorption data processing, artificial neural network architecture development, and the learning process beneficial for the accuracy of the developed artificial neural network model. A data augmentation procedure was proposed to increase the diversity of the input data and improve the generalization of the model. The study of the relationship between thermal desorption spectroscopy data and the mechanical properties of steel evidences a strong correlation of their corresponding parameters. A prototype software application based on the developed model is introduced and is openly available. The developed prototype based on TDS analysis coupled with ANN is shown to be a valuable engineering tool for steel characterization and quantitative prediction of the degradation of steel properties caused by hydrogen.     

An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission: Development and validation study

Background:In a pandemic situation (e.g., COVID-19), the most important issue is to select patients at risk of high mortality at an early stage and to provide appropriate treatments. However, a few studies applied the model to predict in-hospital mortality using routine blood samples at the time of hospital admission. This study aimed to develop an app, name predict the mortality of COVID-19 patients (PMCP) app, to predict the mortality of COVID-19 patients at hospital-admission time.Methods:We downloaded patient records from 2 studies, including 361 COVID-19 patients in Wuhan, China, and 106 COVID-19 patients in 3 Korean medical institutions. A total of 30 feature variables were retrieved, consisting of 28 blood biomarkers and 2 demographic variables (i.e., age and gender) of patients. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared with each other across 2 scenarios using

• 1.raw laboratory versus normalized data and
• 2.training vs testing datasets (n = 361 and n = 106/361≅30%) to verify the model performance (e.g., sensitivity [SENS], specificity [SPEC], and area under the receiver operating characteristic curve [AUC]).

An app for predicting the mortality of COVID-19 patients was developed using the model''s estimated parameters for the prediction and classification of PMCP at an earlier stage. Feature variables and prediction results were visualized using the forest plot and category probability curves shown on Google Maps.Results:We observed that

• 1.the normalized dataset gains a relatively higher AUC(>0.9) when compared to that(<0.9) in the raw-laboratory dataset based on training data,
• 2.the normalized dataset in ANN yielded a high AUC of 0.96 that that(=0.91) in CNN based on testing data, and
• 3.a ready and available app, where anyone can access the model to predict mortality, for PMCP was developed in this study.

Conclusions:Our new PMCP app with ANN model accurately predicts the mortality probability for COVID-19 patients. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ classifications against treatment risk.     

Size Effect of a Piezoelectric Patch on a Rectangular Plate with the Neural Network Model

Artificial neural networks have been widely used in many studies, such as the prediction of the piezoelectric effect of the plate of engineering structures in vibration and noise reduction. In this paper, an artificial neural network (ANN) model was employed to explore the piezoelectric patch size and thickness’s effect on the first order natural frequency and displacement amplitude of a plate. With the finite element method (FEM), a rectangular plate actuated by a piezoelectric patch was analyzed with various patch sizes. The FEM data was later used to build an ANN model. The dynamic response of the plate was predicted by the ANN model and validated with FEM in terms of 1st order natural frequency and displacement amplitude. Results from case studies showed that with the input of patch length, width and thickness, ANN model can accurately predict both natural frequency and displacement amplitude. When the input of ANN model was simplified to patch size and thickness or the volume of the patch, the accuracy became worse and worse. The influence of the patch size and thickness on the first order natural frequency was coupled and the maximal and minimal values were predicted based on the ANN model.     

The use of the artificial neural network for three-month prognosis in diabetic foot syndrome

AimsMaking a reliable prognosis in new patients with diabetic foot syndrome (DFS) is challenging. We used the artificial neural network (ANN) to identify the patients who didn't heal in three months. We provided data for an application which helps predict the course of healing in DFS.Methods175 in-patients (213 limbs) with DFS ulcerations were enrolled in this prospective observational study and were followed up for three months. Thirty-five clinical variables were included in the statistical analysis.ResultsSix significant variables predicting the outcome of DFS treatment were identified: probe-to-bone test, presence of blood flow in Doppler probe, prior amputation within the foot, erythrocyte sedimentation rate, the area and duration of the ulceration. ANN was created with nine input neurons, six hidden nodes and two output neurons. The area under the ROC curve was 0.85. The total accuracy was 82.21 %, sensitivity 91.6 %, specificity 66.18 %.ConclusionsANN as a new prognosis method in DFS ulcers can be reliably used in the prediction, helping physicians and patients predict the course and outcome of the treatment. The algorithm can be particularly useful in identifying individuals who fail to be healed.     

Artificial Neural Network with a Cross-Validation Technique to Predict the Material Design of Eco-Friendly Engineered Geopolymer Composites

A material-tailored special concrete composite that uses a synthetic fiber to make the concrete ductile and imposes strain-hardening characteristics with eco-friendly ingredients is known as an “engineered geopolymer composite (EGC)”. Mix design of special concrete is always tedious, particularly without standards. Researchers used several artificial intelligence tools to analyze and design the special concrete. This paper attempts to design the material EGC through an artificial neural network with a cross-validation technique to achieve the desired compressive and tensile strength. A database was formulated with seven mix-design influencing factors collected from the literature. The five best artificial neural network (ANN) models were trained and analyzed. A gradient descent momentum and adaptive learning rate backpropagation (GDX)–based ANN was developed to cross-validate those five best models. Upon regression analysis, ANN [2:16:16:7] model performed best, with 74% accuracy, whereas ANN [2:16:25:7] performed best in cross-validation, with 80% accuracy. The best individual outputs were “tacked-together” from the best five ANN models and were also analyzed, achieving accuracy up to 88%. It is suggested that when these seven mix-design influencing factors are involved, then ANN [2:16:25:7] can be used to predict the mix which can be cross-verified with GDX-ANN [7:14:2] to ensure accuracy and, due to the few mix trials required, help design the SHGC with lower costs, less time, and fewer materials.     

原发性肝癌血清蛋白质谱图人工神经网络诊断模型研究

目的建立早期有效检测原发性肝癌的实验指标。方法利用表面增强激光解析电离飞行时间质谱（SELDI—TOF—MS）技术及其配套的金芯片（GoldChip）检测435份血清蛋白质谱数据,并将其分为训练集和验证集两组。训练集用于筛选原发性肝癌的差异蛋白标志物并建立ANN诊断模型,验证集用于模型诊断效度的盲法验证。结果共发现7个有明显表达差异的标志蛋白。用其建立ANN诊断模型对原发性肝癌进行盲法验证,诊断的灵敏度和特异度分别为84．00％和81．25％,受试者工作特征曲线（ROC曲线）下面积（AUC）为0．847,阴性预测值94．20％,阳性预测值58．33％,准确度为81．90％。结论原发性肝癌患者血清具有明显表达差异的特征蛋白,据其建立的人工神经网络模型可为原发性肝癌的诊断提供新方法,有重要的参考价值。