To address the challenge of identifying the primary causes of energy consumption fluctuations and accurately assessing the influence of various factors in the converter unit of an iron and steel plant,the focus is pla...To address the challenge of identifying the primary causes of energy consumption fluctuations and accurately assessing the influence of various factors in the converter unit of an iron and steel plant,the focus is placed on the critical components of material and heat balance.Through a thorough analysis of the interactions between various components and energy consumptions,six pivotal factors have been identified—raw material composition,steel type,steel temperature,slag temperature,recycling practices,and operational parameters.Utilizing a framework based on an equivalent energy consumption model,an integrated intelligent diagnostic model has been developed that encapsulates these factors,providing a comprehensive assessment tool for converter energy consumption.Employing the K-means clustering algorithm,historical operational data from the converter have been meticulously analyzed to determine baseline values for essential variables such as energy consumption and recovery rates.Building upon this data-driven foundation,an innovative online system for the intelligent diagnosis of converter energy consumption has been crafted and implemented,enhancing the precision and efficiency of energy management.Upon implementation with energy consumption data at a steel plant in 2023,the diagnostic analysis performed by the system exposed significant variations in energy usage across different converter units.The analysis revealed that the most significant factor influencing the variation in energy consumption for both furnaces was the steel grade,with contributions of−0.550 and 0.379.展开更多
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 t...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.展开更多
BACKGROUND Patients with decompensated liver cirrhosis suffering from esophagogastric variceal bleeding(EGVB)face high mortality.AIM To investigate the risk factors for EGVB in patients with liver cirrhosis and establ...BACKGROUND Patients with decompensated liver cirrhosis suffering from esophagogastric variceal bleeding(EGVB)face high mortality.AIM To investigate the risk factors for EGVB in patients with liver cirrhosis and establish a diagnostic nomogram.METHODS Patients with liver cirrhosis who met the inclusion criteria were randomly divided into training and validation cohorts in a 6:4 ratio in this retrospective research.Univariate analysis,least absolute shrinkage and selection operator regression,and multivariate analysis were employed to establish the nomogram model.Calibration curve,the area under the receiver operating characteristic curve(AUC),and decision curve analysis(DCA)were applied to assess the discrimination,accuracy,and clinical practicability of the nomogram,respectively.RESULTS A total of 1115 patients were enrolled in this study.The nomogram was established based on white blood cells(P<0.001),hemoglobin(P<0.001),fibrinogen(P<0.001),total bilirubin(P=0.007),activated partial thromboplastin time(P=0.002),total bile acid(P=0.012),and ascites(P=0.006).The calibration curve indicated that the actual observation results were in good agreement with the prediction results of the model.The AUC values of the diagnostic model were 0.861 and 0.859 in the training and validation cohorts,respectively,which were higher than that of the aspartate aminotransferase-to-platelet ratio index,fibrosis index based on 4 factors,and aspartate aminotransferase-to-alanine aminotransferase ratio.Additionally,DCA indicated that the net benefit value of the model was higher than that of the other models.CONCLUSION This research constructed and validated a nomogram with perfect performance for predicting EGVB events in patients with liver cirrhosis,which could help clinicians with timely diagnosis,individualized treatment,and follow-up.展开更多
Objective:A risk-based sequential screening strategy,from questionnaire-based assessment to biomarker measurement and then to endoscopic examination,has the potential to enhance gastric cancer(GC)screening efficiency....Objective:A risk-based sequential screening strategy,from questionnaire-based assessment to biomarker measurement and then to endoscopic examination,has the potential to enhance gastric cancer(GC)screening efficiency.We aimed to evaluate the ability of five common stomach-specific serum biomarkers to further enrich high-risk individuals for GC in the questionnaire-identified high-risk population.Methods:This study was conducted based on a risk-based screening program in Ningxia Hui Autonomous Region,China.We first performed questionnaire assessment involving 23,381 individuals(7,042 outpatients and 16,339 individuals from the community),and those assessed as“high-risk”were then invited to participate in serological assays and endoscopic examinations.The serological biomarker model was derived based on logistic regression,with predictors selected via the Akaike information criterion.Model performance was evaluated by the area under the receiver operating characteristic curve(AUC).Results:A total of 2,011 participants were ultimately included for analysis.The final serological biomarker model had three predictors,comprising pepsinogenⅠ(PGI),pepsinogenⅠ/Ⅱratio(PGR),and anti-Helicobacter pylori immunoglobulin G(anti-H.pylori IgG)antibodies.This model generated an AUC of 0.733(95%confidence interval:0.655-0.812)and demonstrated the best discriminative ability compared with previously developed serological biomarker models.As the risk cut-off value of our model rose,the detection rate increased and the number of endoscopies needed to detect one case decreased.Conclusions:PGI,PGR,and anti-H.pylori Ig G could be jointly used to further enrich high-risk individuals for GC among those selected by questionnaire assessment,providing insight for the development of a multi-stage riskbased sequential strategy for GC screening.展开更多
Inf<span style="font-family:Verdana;">lation has </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">substantial impact on ...Inf<span style="font-family:Verdana;">lation has </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">substantial impact on an economy because it affects the financial value of money and stability in the economy. Government </span><span style="font-family:Verdana;">and non-govern</span><span style="font-family:Verdana;">- </span><span style="font-family:Verdana;">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</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> as well as theoretical tests such as Ljung-Box, Shapiro-Wilk, and runs tests have been used in the model diagnostics.</span>展开更多
BACKGROUND Occult pancreaticobiliary reflux(OPBR)is characterized by the absence of congenital anomalies at the pancreaticobiliary junction yet leads to altered bile composition and an increased incidence of gallbladd...BACKGROUND Occult pancreaticobiliary reflux(OPBR)is characterized by the absence of congenital anomalies at the pancreaticobiliary junction yet leads to altered bile composition and an increased incidence of gallbladder stones.AIM To explore the computed tomography(CT)imaging characteristics of gallbladder stones in patients diagnosed with OPBR.METHODS We analyzed 362 patients undergoing cholecystectomy(November 2020 to January 2022).Intraoperative bile samples were assayed for amylase(>110 U/L indicated OPBR).CT features,including stone density and visibility,were compared between 54 OPBR and 308 controls.Stone attenuation(HU)was measured under standardized conditions(uCT-780,120 kVp,160 mAs).Logistic regression and receiver operating characteristic curve analysis identified independent OPBR predictors,forming a validated nomogram.RESULTS OPBR patients exhibited significantly higher rates of CT-invisible stones(35.2%vs 12.3%)and uniform stones(87%vs 73.1%)along with lower overall stone density(P=0.01).Logistic regression identified stone visibility,uniformity,and density as independent predictors.A nomogram integrating these features with patient age achieved high diagnostic accuracy(area under the curve=0.71).CONCLUSION CT imaging distinctly identifies gallbladder stone density,indicating a heightened risk of OPBR in patients with uniform and CT-invisible stones.Such imaging is crucial for preoperative assessments to evaluate potential recurrent biliary pathologies post-cholecystectomy.展开更多
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. pneumon...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.展开更多
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 diffu...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.展开更多
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 enh...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.展开更多
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 unde...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.展开更多
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 D...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.展开更多
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 ...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.展开更多
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 ...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.展开更多
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-ap...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.展开更多
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...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.展开更多
Ovarian cancer has one of the highest mortality rates among gynecological malignancies.This disease has a low early detection rate,a high postoperative recurrence rate,and a 5-year survival rate of only 40%.Hence,ther...Ovarian cancer has one of the highest mortality rates among gynecological malignancies.This disease has a low early detection rate,a high postoperative recurrence rate,and a 5-year survival rate of only 40%.Hence,there is an urgent need to improve the early diagnosis and prognosis of ovarian cancer.Prediction models can effectively estimate the risk of disease occurrence,as well as its prognosis.Recently,many studies have established multiple ovarian cancer prediction models based on different regions and populations.These models can improve the detection rate and optimize the prognosis management to a certain extent.Herein,the construction principle of the ovarian cancer risk prediction model and its validation are summarized;furthermore,comprehensive reviews and comparisons of the different types of these models are made.Therefore,our review may be of great significance for the whole course of ovarian cancer management.展开更多
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>i.e.</i...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>i.e.</i>, 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.展开更多
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 C...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.展开更多
Background: The deep understanding of pathogenesis is a key moment in the formation of the modern strategy of modern medicine. We conducted the thorough analysis of the microscopic processes occurring in the bodies of...Background: The deep understanding of pathogenesis is a key moment in the formation of the modern strategy of modern medicine. We conducted the thorough analysis of the microscopic processes occurring in the bodies of patients with purulent-septic complications. The modified pathogenetic concept of the diagnostic and treatment model of diseases with septic complications is presented. The obtained information about the mechanisms of origin and development of these diseases is fundamentally important for finding the modern effective methods of treating patients. The aim of the research is to modify treatment tactics for patients with sepsis and burn injuries based on the modified pathogenetic concept using modern diagnostics, i.e. the method of fluorescence spectroscopy (MFS) and biomarkers. Materials and Methods: The proposed modified pathogenetic concept of the diagnostic and treatment model of diseases with purulent-septic complications along with standard methods was used successfully for effective treatment of 15 patients with sepsis and 25 with burn injuries. Results: 3 main scenarios of behaviour of spectral-fluorescence characteristics of patients with sepsis are illustrated. Spectral-fluorescence markers of sepsis were studied, which are informative 24 to 48 hours before the appearance of obvious clinical and laboratory signs of significant changes in the general somatic status of patients. Conclusions: The proposed diagnostic and therapeutic approach is new and fundamentally important for diagnostics and monitoring of the process of treatment of patients with purulent-septic diseases and burn injuries. An in-depth understanding of the dynamics of septic complications and the corresponding changes of the main markers of these diseases during treatment is especially relevant. The use of infusion therapy with solutions of donor albumin as an effective pathogenetic treatment is scientifically justified.展开更多
AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally ...AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of RVO.All the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease experts.Swin Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were conducted.The model’s performance was compared to that of the experts.RESULTS:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 respectively.In addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,respectively.The diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was superior.CONCLUSION:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.展开更多
基金financial support from the National Key R&D Program of China(Grant No.2020YFB1711100).
文摘To address the challenge of identifying the primary causes of energy consumption fluctuations and accurately assessing the influence of various factors in the converter unit of an iron and steel plant,the focus is placed on the critical components of material and heat balance.Through a thorough analysis of the interactions between various components and energy consumptions,six pivotal factors have been identified—raw material composition,steel type,steel temperature,slag temperature,recycling practices,and operational parameters.Utilizing a framework based on an equivalent energy consumption model,an integrated intelligent diagnostic model has been developed that encapsulates these factors,providing a comprehensive assessment tool for converter energy consumption.Employing the K-means clustering algorithm,historical operational data from the converter have been meticulously analyzed to determine baseline values for essential variables such as energy consumption and recovery rates.Building upon this data-driven foundation,an innovative online system for the intelligent diagnosis of converter energy consumption has been crafted and implemented,enhancing the precision and efficiency of energy management.Upon implementation with energy consumption data at a steel plant in 2023,the diagnostic analysis performed by the system exposed significant variations in energy usage across different converter units.The analysis revealed that the most significant factor influencing the variation in energy consumption for both furnaces was the steel grade,with contributions of−0.550 and 0.379.
基金Natural Science Foundation of China(41605049,41530531,41475096)Key Special Scientific Research Fund of Meteorological Public Welfare Profession of China(GYHY201506001)Fund for Meteorological Science and Technology of Zhejiang Province,China(2017QN04)
文摘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.
基金Supported by the National Natural Science Foundation of China,No.82270594the National Natural Science Foundation for Youths of China,No.82103151+1 种基金the Fundamental Research Funds for the Central Universities of Central South University,No.2022ZZTS0265the Graduate Research Innovation Project of Hunan Province,No.CX20220347.
文摘BACKGROUND Patients with decompensated liver cirrhosis suffering from esophagogastric variceal bleeding(EGVB)face high mortality.AIM To investigate the risk factors for EGVB in patients with liver cirrhosis and establish a diagnostic nomogram.METHODS Patients with liver cirrhosis who met the inclusion criteria were randomly divided into training and validation cohorts in a 6:4 ratio in this retrospective research.Univariate analysis,least absolute shrinkage and selection operator regression,and multivariate analysis were employed to establish the nomogram model.Calibration curve,the area under the receiver operating characteristic curve(AUC),and decision curve analysis(DCA)were applied to assess the discrimination,accuracy,and clinical practicability of the nomogram,respectively.RESULTS A total of 1115 patients were enrolled in this study.The nomogram was established based on white blood cells(P<0.001),hemoglobin(P<0.001),fibrinogen(P<0.001),total bilirubin(P=0.007),activated partial thromboplastin time(P=0.002),total bile acid(P=0.012),and ascites(P=0.006).The calibration curve indicated that the actual observation results were in good agreement with the prediction results of the model.The AUC values of the diagnostic model were 0.861 and 0.859 in the training and validation cohorts,respectively,which were higher than that of the aspartate aminotransferase-to-platelet ratio index,fibrosis index based on 4 factors,and aspartate aminotransferase-to-alanine aminotransferase ratio.Additionally,DCA indicated that the net benefit value of the model was higher than that of the other models.CONCLUSION This research constructed and validated a nomogram with perfect performance for predicting EGVB events in patients with liver cirrhosis,which could help clinicians with timely diagnosis,individualized treatment,and follow-up.
基金supported by the Tencent Charity Foundationthe Ningxia Hui Autonomous Region Key Research and Development Program(No.2021BEG 02025)+1 种基金the Flexible Introduction of Technological Innovation Teams of Ningxia Hui Autonomous Region(No.2021RXTDLX15)the Natural Science Foundation of China(No.82160644)。
文摘Objective:A risk-based sequential screening strategy,from questionnaire-based assessment to biomarker measurement and then to endoscopic examination,has the potential to enhance gastric cancer(GC)screening efficiency.We aimed to evaluate the ability of five common stomach-specific serum biomarkers to further enrich high-risk individuals for GC in the questionnaire-identified high-risk population.Methods:This study was conducted based on a risk-based screening program in Ningxia Hui Autonomous Region,China.We first performed questionnaire assessment involving 23,381 individuals(7,042 outpatients and 16,339 individuals from the community),and those assessed as“high-risk”were then invited to participate in serological assays and endoscopic examinations.The serological biomarker model was derived based on logistic regression,with predictors selected via the Akaike information criterion.Model performance was evaluated by the area under the receiver operating characteristic curve(AUC).Results:A total of 2,011 participants were ultimately included for analysis.The final serological biomarker model had three predictors,comprising pepsinogenⅠ(PGI),pepsinogenⅠ/Ⅱratio(PGR),and anti-Helicobacter pylori immunoglobulin G(anti-H.pylori IgG)antibodies.This model generated an AUC of 0.733(95%confidence interval:0.655-0.812)and demonstrated the best discriminative ability compared with previously developed serological biomarker models.As the risk cut-off value of our model rose,the detection rate increased and the number of endoscopies needed to detect one case decreased.Conclusions:PGI,PGR,and anti-H.pylori Ig G could be jointly used to further enrich high-risk individuals for GC among those selected by questionnaire assessment,providing insight for the development of a multi-stage riskbased sequential strategy for GC screening.
文摘Inf<span style="font-family:Verdana;">lation has </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">substantial impact on an economy because it affects the financial value of money and stability in the economy. Government </span><span style="font-family:Verdana;">and non-govern</span><span style="font-family:Verdana;">- </span><span style="font-family:Verdana;">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</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> as well as theoretical tests such as Ljung-Box, Shapiro-Wilk, and runs tests have been used in the model diagnostics.</span>
基金Supported by Key Specialty Construction Project of Shanghai Pudong New Area Health Commission,No.PWZzk2022-17Shanghai East Hospital Clinical Research Project,No.DFLC2022019the Featured Clinical Discipline Project of Shanghai Pudong District,No.PWYts2021-06.
文摘BACKGROUND Occult pancreaticobiliary reflux(OPBR)is characterized by the absence of congenital anomalies at the pancreaticobiliary junction yet leads to altered bile composition and an increased incidence of gallbladder stones.AIM To explore the computed tomography(CT)imaging characteristics of gallbladder stones in patients diagnosed with OPBR.METHODS We analyzed 362 patients undergoing cholecystectomy(November 2020 to January 2022).Intraoperative bile samples were assayed for amylase(>110 U/L indicated OPBR).CT features,including stone density and visibility,were compared between 54 OPBR and 308 controls.Stone attenuation(HU)was measured under standardized conditions(uCT-780,120 kVp,160 mAs).Logistic regression and receiver operating characteristic curve analysis identified independent OPBR predictors,forming a validated nomogram.RESULTS OPBR patients exhibited significantly higher rates of CT-invisible stones(35.2%vs 12.3%)and uniform stones(87%vs 73.1%)along with lower overall stone density(P=0.01).Logistic regression identified stone visibility,uniformity,and density as independent predictors.A nomogram integrating these features with patient age achieved high diagnostic accuracy(area under the curve=0.71).CONCLUSION CT imaging distinctly identifies gallbladder stone density,indicating a heightened risk of OPBR in patients with uniform and CT-invisible stones.Such imaging is crucial for preoperative assessments to evaluate potential recurrent biliary pathologies post-cholecystectomy.
基金supported by the Capital Medical Development and Scientific Research Fund(2009-1033)and the Science and Technology Plan of Beijing City(Z101107050210018)
文摘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.
基金Chinese Academy of Sciences under contract No.KZCX2-YW-214the National Nat-ural Science Foundation of China under contract Nos 40476014 and 40346029.
文摘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.
基金supported by a grant from the Research Grants Council of Hong Kong.Jianhong Wu was also supported by a grant from Humanities & Social Sciences in Chinese University (07JJD790154)the Youth Talent Foundation of Zhejiang GongShang University (Q09-12)
文摘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.
基金Supported by Key Research and Development Plan of Shaanxi Province,No.2020SF-222。
文摘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.
基金the National Natural Science Foundation of China(Grant Number:81970631 to W.L.).
文摘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.
文摘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.
基金supported by National Natural Science Foundation of China(Grant No.61378037)the Fundamental Research Funds for the Central Universities(Nos.2013B33614,2017B15214)+1 种基金the Research Funds of Innovation and Entrepreneurship Education Reform for Chinese Universities(No.16CCJG01Z004)the Changzhou Science and Technology Program(No.CJ20160027)
文摘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.
基金Supported by the Projects of Public Science and Technology Research Funds of Ocean Sector of China(No.201205009)the National Natural Science Foundation of China(No.41201569)
文摘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.
文摘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.
基金the Shanghai Municipal Key Clinical Specialty Program(No.shslczdzk06302)。
文摘Ovarian cancer has one of the highest mortality rates among gynecological malignancies.This disease has a low early detection rate,a high postoperative recurrence rate,and a 5-year survival rate of only 40%.Hence,there is an urgent need to improve the early diagnosis and prognosis of ovarian cancer.Prediction models can effectively estimate the risk of disease occurrence,as well as its prognosis.Recently,many studies have established multiple ovarian cancer prediction models based on different regions and populations.These models can improve the detection rate and optimize the prognosis management to a certain extent.Herein,the construction principle of the ovarian cancer risk prediction model and its validation are summarized;furthermore,comprehensive reviews and comparisons of the different types of these models are made.Therefore,our review may be of great significance for the whole course of ovarian cancer management.
文摘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>i.e.</i>, 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.
文摘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.
文摘Background: The deep understanding of pathogenesis is a key moment in the formation of the modern strategy of modern medicine. We conducted the thorough analysis of the microscopic processes occurring in the bodies of patients with purulent-septic complications. The modified pathogenetic concept of the diagnostic and treatment model of diseases with septic complications is presented. The obtained information about the mechanisms of origin and development of these diseases is fundamentally important for finding the modern effective methods of treating patients. The aim of the research is to modify treatment tactics for patients with sepsis and burn injuries based on the modified pathogenetic concept using modern diagnostics, i.e. the method of fluorescence spectroscopy (MFS) and biomarkers. Materials and Methods: The proposed modified pathogenetic concept of the diagnostic and treatment model of diseases with purulent-septic complications along with standard methods was used successfully for effective treatment of 15 patients with sepsis and 25 with burn injuries. Results: 3 main scenarios of behaviour of spectral-fluorescence characteristics of patients with sepsis are illustrated. Spectral-fluorescence markers of sepsis were studied, which are informative 24 to 48 hours before the appearance of obvious clinical and laboratory signs of significant changes in the general somatic status of patients. Conclusions: The proposed diagnostic and therapeutic approach is new and fundamentally important for diagnostics and monitoring of the process of treatment of patients with purulent-septic diseases and burn injuries. An in-depth understanding of the dynamics of septic complications and the corresponding changes of the main markers of these diseases during treatment is especially relevant. The use of infusion therapy with solutions of donor albumin as an effective pathogenetic treatment is scientifically justified.
基金Supported by Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties(No.SZGSP014)Sanming Project of Medicine in Shenzhen(No.SZSM202011015)Shenzhen Science and Technology Planning Project(No.KCXFZ20211020163813019).
文摘AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of RVO.All the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease experts.Swin Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were conducted.The model’s performance was compared to that of the experts.RESULTS:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 respectively.In addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,respectively.The diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was superior.CONCLUSION:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.
