Establishment of a Predictive Diagnostic Model for Acute Mycoplasma Pneumoniae Infection in Elderly Patients with Community-acquired Pneumonia

We established a diagnostic model to predict acute Mycoplasma pneumoniae （M. pneumonia） infection in elderly Community-acquired pneumonia （CAP） patients. We divided 456 patients into acute and non-acute M. pneumoniae infection groups. Binary logistic regression and receiver operating characteristic （ROC） curves were used to establish a predictive model. The following independent factors were identified： age 〉 70 years; serum cTNT level 〉 0.0S ng/mL; lobar consolidation; mediastinal lymphadenopathy; and antibody titer in the acute phase 〉 1：40. The area under the ROC curve of the model was 0.923 and a score of 2 7 score predicted acute M. pneumoniae infection in elderly patients with CAP. The predictive model developed in this study has high diagnostic accuracy for the identification of elderly acute M. pneumoniae infection.

The calculation of the circulation in South China Sea by a diagnostic model

A high resolved two-dimensional linear global diagnostic model combining with the dynamical calculation is used to calculate velocity field in the South China Sea(SCS). The study of model results shows that eddy diffusion does not change basic structure of circulation in the SCS and does not change the direction of invasive water, but changes the value of transport considerably especially in straits. The velocity field is not changed whether the wind stress is considered or not. This result shows the circulation is largely determined by a density field which well records most of the important contribution of the wind stress effect. Potential vorticity is calculated to testify the dynamics of the model results. The result shows that a good conservation of the nonlinear PV. This indicates most effects of the important nonlinear processes are well recorded in density and the nonlinear term is negligible so that the simplified model is reliable. The model results show the water exchanges between the SCS and open ocean or surrounding seas. Cold deep water invades through Luzon Strait and Warm shallow water is pushed out mainly through Karimata Straits. The model results also reveal the structure of the circulation in the SCS basin. In two circulations of upper and middle layers, a cyclonic one in the north and an anti-cyclonic one in the south, reflect the climatologic average of the circulation driven by monsoon. In the deep or bottom layer, these two circulations reflect the topography of the basin. Above the middle layer, invasive water enters westward in the north but the way of invasion of Kuroshio is not clear. Below the deep layer, a current goes down south near the east basin ,and invasive water enters in the basin from the west Pacific.

A new diagnostic model of primary open angle glaucoma based on FD-OCT parameters

AIM： To build a clinical diagnostic model of primary open angle glaucoma （POAG） using the normal probability chart of frequency-domain optical coherence tomography （FD-OCT）. METHODS： This is a cross-sectional study. Total 133 eyes from 133 healthy subjects and 99 eyes from 99 early POAG patients were included in the study. The retinal nerve fibre layer （RNFL） thickness parameters of optic nerve head （ONH） and RNFL3.45 scan were measured in one randomly selected eye of each subject using RTVue-100 FD-OCT. Then, we used these parameters to establish the diagnostic models. Four different diagnostic models based on two different area partition strategies on ONH and RNFL3.45 parameters, including ONH traditional area partition model （ONH-T）, ONH new area partition model （ONH-N）, RNFL3.45 traditional area partition model （RNFL3.45-T） and RNFL3.45 new area partition model （RNFL3.45-N）, were built and tested by cross-validation. RESULTS： The new area partition models had higher area under the receiver operating characteristic （AROC; ONH-N： 0.990; RNFL3.45-N： 0.939） than corresponding traditional area partition models （ONH-T： 0.979; RNFL3.45-T： 0.881）. There was no statistical difference among AROC of ONH-T, ONH-N, and RNFL3.45-N. Nevertheless, ONH-N was the simplest model. CONCLUSION： The new area partition models had higher diagnostic accuracy than corresponding traditional area partition models, which can improve the diagnostic ability of early POAG. In particular, the simplest ONH-N diagnostic model may be convenient for clinical application.

Diagnostic model construction and example analysis of habitat degradation in enclosed bay: I. diagnostic model construction

Presently, research is lacking regarding the diagnosis and evaluation of habitat degradation in enclosed bay systems. We established a diagnostic model for enclosed bay habitat degradation(EBHD model) using a multi-approach integrated diagnostic method in consideration of driving force-pressurestate-infl uence-response. The model optimizes the indicator standardization with annual average change rate of habitat degradation as the basic element, to refl ect accurately the impact of the change and speed of degradation on the diagnostic results, to quantify reasonably the contribution of individual diagnostic indicator to habitat degradation, and to solve the issue regarding the infl uence of subjective factors on the evaluation results during indicator scoring. We then applied the EBHD model for the Sansha Bay in Fujian Province, China, evaluated comprehensively the situation of habitat degradation in the bay, and screened out the major controlling factors in the study area. Results show that the diagnostic results are consistent in overall with the real situation of the study area. Therefore, the EBHD model is advantageous in terms of objectivity and accuracy, making a breakthrough in diagnosis and evaluation for habitat degradation in enclosed bay systems.

Creating Knowledge-Based Diagnostic Models by Mining Textual Diagnostic Reports of SPECT Scans

Mining rich semantic information hidden in heterogeneous information network is one of the important tasks of data mining. Generally, a nuclear medicine text consists of the description of disease (i.e., lesions) and diagnostic results. However, how to construct a computer-aided diagnostic model with a large number of medical texts is a challenging task. To automatically diagnose diseases with SPECT imaging, in this work, we create a knowledge-based diagnostic model by exploring the association between a disease and its properties. Firstly, an overview of nuclear medicine and data mining is presented. Second, the method of preprocessing textual nuclear medicine diagnostic reports is proposed. Last, the created diagnostic modes based on random forest and SVM are proposed. Experimental evaluation conducted real-world data of diagnostic reports of SPECT imaging demonstrates that our diagnostic models are workable and effective to automatically identify diseases with textual diagnostic reports.

A Clinical Analysis of 293 FUO Patients, A Diagnostic Model Discriminating infectious Diseases from Non-infectious Diseases

Objective A diagnostic model was established to discriminate infectious diseases from non-infectious diseases. Methods The clinical data of patients with fever of unknown origin(FUO) hospitalized in Xiangya Hospital Central South University, from January, 2006 to April, 2011 were retrospectively analyzed. Patients enrolled were divided into two groups. The first group was used to develop a diagnostic model: independent variables were recorded and considered in a logistic regression analysis to identify infectious and non-infectious diseases(αin = 0.05, αout = 0.10). The second group was used to evaluate the diagnostic model and make ROC analysis.Results The diagnostic rate of 143 patients in the first group was 87.4%, the diagnosis included infectious disease(52.4%), connective tissue diseases(16.8%), neoplastic disease(16.1%) and miscellaneous(2.1%). The diagnostic rate of 168 patients in the second group was 88.4%, and the diagnosis was similar to the first group. Logistic regression analysis showed that decreased white blood cell count(WBC < 4.0×109/L), higher lactate dehydrogenase level(LDH > 320 U/L) and lymphadenectasis were independent risk factors associated with non-infectious diseases. The odds ratios were 14.74, 5.84 and 5.11(P ≤ 0.01), respectively. In ROC analysis, the sensitivity and specificity of the positive predictive values was 62.1% and 89.1%, respectively, while that of negative predicting values were 75% and 81.7%, respectively(AUC = 0.76, P = 0.00).Conclusions The combination of WBC < 4.0×109/L, LDH > 320 U/L and lymphadenectasis may be useful in discriminating infectious diseases from non-infectious diseases in patients hospitalized as FUO.

Establishment of a metabolite diagnostic model for the risk of diabetic nephropathy in type 2 diabetic population:Based on a cross-sectional study in China

Introduction:This study aimed to investigate the correlation between various plasma metabolites and the likelihood of developing diabetic nephropathy(DN)and construct a diagnostic model for DN in Chinese patients with type 2 diabetes mellitus(T2DM).Methods:A cross-sectional investigation was conducted in a hospital setting.Based on medical data,a total of 743 patients from a tertiary hospital were selected and categorized into two groups:the diabetic nephropathy group(DN group)and the non-diabetic nephropathy group(non-DN group).Plasma levels of metabolites,including amino acids and acylcarnitines,were determined using a laser counter measurement system(LC-MS).Subsequently,partial least-squares regression was used to assess the significance of these metabolites.Receiver operating characteristic(ROC)curves were generated for factors that ranked highest in terms of relevance.Model performance was assessed using the curve(AUC).Results:Of the 743 patients with T2DM admitted to the hospital,145 had DN.Compared with the non-DN group,the DN group exhibited elevated systolic blood pressure(P=0.001),high-density lipoprotein cholesterol(P=0.01),and low-density lipoprotein cholesterol(P=0.042).Additionally,the DN group had a higher prevalence of stroke patients(P<0.001)and diabetic retinopathy patients(P<0.001).Finally,a risk model that included citrulline,leucine,tyrosine,valine,propionylcarnitine(C3),and palmitoylcarnitine(C16)was developed.This model achieved an AUC of 0.709,with a 95%confidence interval(CI)ranging from 0.626 to 0.793.Conclusions:A diagnostic model consisting of six plasma metabolites to assess the risk of DN in Chinese patients with T2DM may provide clues for future research.

Genetic algorithm-optimized backpropagation neural network establishes a diagnostic prediction model for diabetic nephropathy:Combined machine learning and experimental validation in mice

Background:Diabetic nephropathy(DN)is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease worldwide.Diagnostic biomarkers may allow early diagnosis and treatment of DN to reduce the prevalence and delay the development of DN.Kidney biopsy is the gold standard for diagnosing DN;however,its invasive character is its primary limitation.The machine learning approach provides a non-invasive and specific criterion for diagnosing DN,although traditional machine learning algorithms need to be improved to enhance diagnostic performance.Methods:We applied high-throughput RNA sequencing to obtain the genes related to DN tubular tissues and normal tubular tissues of mice.Then machine learning algorithms,random forest,LASSO logistic regression,and principal component analysis were used to identify key genes(CES1G,CYP4A14,NDUFA4,ABCC4,ACE).Then,the genetic algorithm-optimized backpropagation neural network(GA-BPNN)was used to improve the DN diagnostic model.Results:The AUC value of the GA-BPNN model in the training dataset was 0.83,and the AUC value of the model in the validation dataset was 0.81,while the AUC values of the SVM model in the training dataset and external validation dataset were 0.756 and 0.650,respectively.Thus,this GA-BPNN gave better values than the traditional SVM model.This diagnosis model may aim for personalized diagnosis and treatment of patients with DN.Immunohistochemical staining further confirmed that the tissue and cell expression of NADH dehydrogenase(ubiquinone)1 alpha subcomplex,4-like 2(NDUFA4L2)in tubular tissue in DN mice were decreased.Conclusion:The GA-BPNN model has better accuracy than the traditional SVM model and may provide an effective tool for diagnosing DN.

Study on short－range numerical forecasting of ocean current in the East China Sea—II．Three－dimensional diagnostic model and its application in the Bohai Sea

In this paper,a tbree-dimensional (3-D) baroclinic diagnostic model for short-range numerical forecast is proposed to calculate the monthly averaged flow field in the Bohai Sea. By using the model and monthly averaged tempeature and salinity date, monthly barotropic and baroclinic flow field are calculated,and 2-D and 3-D characteristics of flow are described and demonstrated. On the basis of the analysis of the modelling results and the observed temperature,salinity and wind data,the monthly and seasonal characteristics and generation mechanism of circulation in the Bohai Sea are also discussed. It is pointed out in this paper that in spring and autumn,the monthly averaged flow fields are not representative, for the wind direction varies in a wide range and the averaged wind field is much weaker than the instantaneous one. These results show the reliability of the model for describing the monthly characteristics in numerical forecast of ocean current.

Diagnostic of capacitively coupled radio frequency plasma from electrical discharge characteristics：comparison with optical emission spectroscopy and fluid model simulation

The capacitively coupled radio frequency（CCRF）plasma has been widely used in various fields.In some cases,it requires us to estimate the range of key plasma parameters simpler and quicker in order to understand the behavior in plasma.In this paper,a glass vacuum chamber and a pair of plate electrodes were designed and fabricated,using 13.56 MHz radio frequency（RF）discharge technology to ionize the working gas of Ar.This discharge was mathematically described with equivalent circuit model.The discharge voltage and current of the plasma were measured atdifferent pressures and different powers.Based on the capacitively coupled homogeneous discharge model,the equivalent circuit and the analytical formula were established.The plasma density and temperature were calculated by using the equivalent impedance principle and energy balance equation.The experimental results show that when RF discharge power is 50–300 W and pressure is 25–250 Pa,the average electron temperature is about 1.7–2.1 e V and the average electron density is about 0.5？×10^17–3.6？×10^17m^-3.Agreement was found when the results were compared to those given by optical emission spectroscopy and COMSOL simulation.

Diagnostic Calculation of the Oceanic Circulation

A 2-dimensional global free surface diagnostic model, combined with dynamic calculation, is used to investigate the world ocean circulation; the model has a horizontal resolution of 1/4°×1/4°. The simulated results agree well with the results of other modesl and observations. The distribution of Stream Function suggests that the main circulation systems in the wodd ocean have been represented, including oceanic currents strengthened in the oceanic western. Be close to the observed results, the net mass transport of the Kuroshio axes is estimated about 54Sv; The distribution of the horizontal circulation in each layer shows that the main circulation systems in the world ocean are well simulated, for example, the Kuroshio and the Antarctic Circumpolar Current can go down to the bottom layer, but the Gulf Stream cannot, and its direction reverses at the depths of 1000 to 2 000 m.

Fecal microbial biomarkers combined with multi-target stool DNA test improve diagnostic accuracy for colorectal cancer

BACKGROUND Colorectal cancer(CRC)is a major global health burden.The current diagnostic tests have shortcomings of being invasive and low accuracy.AIM To explore the combination of intestinal microbiome composition and multi-target stool DNA(MT-sDNA)test in the diagnosis of CRC.METHODS We assessed the performance of the MT-sDNA test based on a hospital clinical trial.The intestinal microbiota was tested using 16S rRNA gene sequencing.This case-control study enrolled 54 CRC patients and 51 healthy controls.We identified biomarkers of bacterial structure,analyzed the relationship between different tumor markers and the relative abundance of related flora components,and distinguished CRC patients from healthy subjects by the linear discriminant analysis effect size,redundancy analysis,and random forest analysis.RESULTS MT-sDNA was associated with Bacteroides.MT-sDNA and carcinoembryonic antigen(CEA)were positively correlated with the existence of Parabacteroides,and alpha-fetoprotein(AFP)was positively associated with Faecalibacterium and Megamonas.In the random forest model,the existence of Streptococcus,Escherichia,Chitinophaga,Parasutterella,Lachnospira,and Romboutsia can distinguish CRC from health controls.The diagnostic accuracy of MT-sDNA combined with the six genera and CEA in the diagnosis of CRC was 97.1%,with a sensitivity and specificity of 98.1%and 92.3%,respectively.CONCLUSION There is a positive correlation of MT-sDNA,CEA,and AFP with intestinal microbiome.Eight biomarkers including six genera of gut microbiota,MT-sDNA,and CEA showed a prominent sensitivity and specificity for CRC prediction,which could be used as a non-invasive method for improving the diagnostic accuracy for this malignancy.

Non-invasive model for predicting esophageal varices based on liver and spleen volume

BACKGROUND Upper endoscopy is the gold standard for predicting esophageal varices in China.Guidelines and consensus suggest that patients with liver cirrhosis should undergo periodic upper endoscopy,most patients undergo their first upper endoscopy when esophageal variceal bleeds.Therefore,it is important to develop a non-invasive model to early diagnose esophageal varices.AIM To develop a non-invasive predictive model for esophageal varices based on liver and spleen volume in viral cirrhosis patients.METHODS We conducted a cross-sectional study based on viral cirrhosis crowd in the Second Affiliated Hospital of Xi'an Jiaotong University.By collecting the basic information and clinical data of the participants,we derived the independent risk factors and established the prediction model of esophageal varices.The established model was compared with other models.Area under the receiver operating characteristic curve,calibration plot and decision curve analysis were used to test the discriminating ability,calibration ability and clinical practicability in both the internal and external validation.RESULTS The portal vein diameter,the liver and spleen volume,and volume change rate were the independent risk factors of esophageal varices.We successfully used the factors to establish the predictive model[area under the curve(AUC)0.87,95%CI:0.80-0.95],which showed better predictive value than other models.The model showed good discriminating ability,calibration ability and the clinical practicability in both modelling group and external validation group.CONCLUSION The developed non-invasive predictive model can be used as an effective tool for predicting esophageal varices in viral cirrhosis patients.

Modern Control and Diagnostic System of Traction Vehicle with Hybrid Drive System

In paper it introduced a review of modem traction vehicle drive system with induction motor drive system （PMSM with single or dual rotor drive system） or BLDC motor with different configuration of magnetic circuits. For particular part of drive system proposed a quasi intelligent control system version smart control enables multi criteria predictive control of vehicle work. In the paper presented also a selected diagnostic procedure, enables monitoring exploitation parameters, and prediction of probable failure state. For different vehicle work state realized a simulation models and crash test of exploitations failure models.

Development and external validation of a quantitative diagnostic model for malignant gastric lesions in clinical opportunistic screening:A multicenter real-world study

Background:Clinical opportunistic screening is a cost-effective cancer screening modality.This study aimed to establish an easyto-use diagnostic model serving as a risk stratification tool for identification of individuals with malignant gastric lesions for opportunistic screening.Methods:We developed a questionnaire-based diagnostic model using a joint dataset including two clinical cohorts from northern and southern China.The cohorts consisted of 17,360 outpatients who had undergone upper gastrointestinal endoscopic examination in endoscopic clinics.The final model was derived based on unconditional logistic regression,and predictors were selected according to the Akaike information criterion.External validation was carried out with 32,614 participants from a community-based randomized controlled trial.Results:This questionnaire-based diagnostic model for malignant gastric lesions had eight predictors,including advanced age,male gender,family history of gastric cancer,low body mass index,unexplained weight loss,consumption of leftover food,consumption of preserved food,and epigastric pain.This model showed high discriminative power in the development set with an area under the receiver operating characteristic curve(AUC)of 0.791(95%confidence interval[CI]:0.750-0.831).External validation of the model in the general population generated an AUC of 0.696(95%CI:0.570-0.822).This model showed an ideal ability for enriching prevalent malignant gastric lesions when applied to various scenarios.Conclusion:This easy-to-use questionnaire-based model for diagnosis of prevalent malignant gastric lesions may serve as an effective prescreening tool in clinical opportunistic screening for gastric cancer.

TESTING THE ADEQUACY OF GARCH-TYPE MODELS IN TIME SERIES

In this article a new approach for checking the adequacy of GARCH-type models in time series was proposed. The resulted tests involve weight functions, which provide them with the flexibility in choosing scores to enhance power performance. The choice of weight functions and the power properties of the tests are studied. For a large number of alternatives, asymptotically distribution-free maximin test is constructed. The tests are asymptotically chi-squared under the null hypothesis and easy to implement. Simulation results indicate that the tests perform well.

Blood microRNAs as potential diagnostic markers for hemorrhagic stroke

Proper medical treatment of a stroke victim relies on accurate and rapid differentiation between ischemic and hemorrhagic stroke,which in current practice is performed by computerized tomography（CT） or magnetic resonance imaging（MRI） scans.A panel of micro RNAs could be an extremely useful clinical tool for distinguishing between hemorrhagic and ischemic stroke.This review has shown that blood miRNA profile can distinguish hemorrhagic from ischemic stroke in patients and in experimental animal models.It also seems likely they can differentiate between intracerebral and subarachnoid hemorrhage stroke.The miRNA profile in cerebrospinal fluid could be a useful diagnostic tool for subarachnoid hemorrhagic stroke.Decreased or increased miRNA levels may be needed either as prevention or treatment of stroke.Administration in vivo of miR-130 a inhibitor or miRNA mimic（miR-367,miR-223） in an intracerebral hemorrhage animal model improved neurological outcomes.

The Pathogenetic Concept of the Diagnostic-Treatment Approach for Patients with Purulent-Septic Complications

Background: The significant improvement of immediate and long-term functional results of treating patients is the fundamental problem of modern medical science. A deep understanding of the pathogenesis is the key point in creating the management strategy for patients with various diseases. Information about the mechanisms of origin and development of purulent-inflammatory diseases and sepsis is essential for finding effective ways to prevent and treat them. The aim of the research is to use the method of fluorescence spectroscopy in creating the pathogenetic diagnostic and treatment model for the prevention and treatment of purulent-inflammatory diseases and sepsis, modification of treatment tactics, search for new markers of purulent-septic diseases, as well as monitoring of patients during the treatment. Materials and methods: The proposed approach, along with standard diagnostic methods, was used to organize the treatment process of 100 patients with purulent-inflammatory diseases, including 15 patients with sepsis, 35 with acute inflammatory abdominal pathology, 20 patients with burn injury (main group) and 35 patients with burn injury (comparison group). Results: The behavior of spectral-fluorescent characteristics in their dynamics has been studied, and the new markers for assessing patients’ conditions have been proposed. Their effectiveness for the diagnosis of purulent-septic diseases has been proved, which advances the results of standard research methods by 24 - 48 hours. Conclusions: The proposed diagnostic and treatment approach is fundamentally important for diagnosis and monitoring during the treatment of patients with purulent-septic diseases. Particularly relevant is the proposal to modify the treatment process for these patients, associated with the use of infusion of donor albumin solutions.

ASSESSMENT OF THE POEM2 MODEL FOR SIMULATING TROPICAL INTRASEASONAL OSCILLATION

The intraseasonal oscillation(ISO)of the atmosphere is closely related to weather and climate systems and is also an important aspect of extended numerical weather forecast research.This phenomenon is significant in tropical regions and is one of the key indices for assessing the simulation capability of a climate model.To better evaluate numerical model simulations of the tropical ISO using the 10-year historic data calculated by the POEM2 climate system model developed by the University of Hawaii in the U.S.,we utilized the methods of variance and power spectral analysis to compare and assess the simulation ability of this model for the ISO in tropical regions.Our results showed that the simulated variance results for the 850 h Pa zonal wind and outgoing long-wave radiation(OLR)by POEM2 are overall consistent with the observed distribution pattern,and the simulated variance is relatively larger than the observed in the North Indian Ocean and West Pacific regions.With respect to the summer model,the winter model can better simulate the eastward propagation motion of the Madden-Julian oscillation(MJO)and the 850 h Pa zonal wind.In comparison,the summer model can better simulate the northward propagation motion of MJO and atmospheric precipitation than the winter model.The eastward propagation speed of the simulated MJO signal is faster in the model than in the observation,and the high frequency region for the power spectra of meteorological element anomalies are concentrated in wavenumber 2-3 in the simulation and in wavenumber 1-2 in the observation.The multivariate combined empirical orthogonal function(EOF)results showed that this model can simulate the relationship between high-low level wind distributions and precipitation over the East Indian Ocean and the West Pacific,but the simulated signal is weaker than the observed.The lagging correlation of time coefficients between the first two EOFs from observation and simulation shows a similar cycle.Thus,these results indicate that in the future,the POEM2 climate system model needs to optimize the involved physical processes and parameterization scheme,strengthen the dynamic description of the mixed Rossby gravity wave,and improve the simulated ability of wavenumber 1.

Modeling Inflation in Bangladesh

Inflation has a substantial impact on an economy because it affects the financial value of money and stability in the economy. Government and non-govern- ment policies might be hindered due to the excessive rate of inflation. This paper aims to model and forecast inflation by the Box-Jenkins autoregressive integrated moving average (ARIMA) technique using annual time series data on inflation from 1987 to 2017 in Bangladesh. It is found that ARIMA (2, 1, 0) model is the best optimal to forecast inflation for a period of up to eight years. Graphical tools, as well as theoretical tests such as Ljung-Box, Shapiro-Wilk, and runs tests have been used in the model diagnostics.