Twenty years of changes in the disease assessment method of the Global Initiative for Chronic Obstructive Lung Disease

The Global Initiative for Chronic Obstructive Lung Disease(GOLD)has been changing for nearly 20 years.GOLD has moved from single assessment using spirometry to a more comprehensive assessment of chronic obstructive pulmonary disease using spirometry,symptoms and exacerbation history.And subsequently,a new assessment system for chronic obstructive pulmonary disease separated spirometric grades from the old assessment system,and classified patients only according to their symptoms and history of exacerbation.The distribution,clinical characteristics,treatment,and prognosis of the new subgroups were different from the old ones.In this review,we will present a brief profile of changes made to the disease assessment method of GOLD,based on the relevant existing literature.

Construction of Risk Assessment Application Model of Epidemic Disease in Large-scale Pig Farms

The application model of epidemic disease assessment technology for Web-based large-scale pig farm was expounded from the identification of epidemic disease risk factors, construction of risk assessment model and development of risk assessment system. The assessed pig farm uploaded the epidemic disease risk data information through on-line answering evaluating questionnaire to get the immediate evaluation report. The model could enhance the risk communication between pig farm veterinarian, manager and veterinary experts to help farm system understand and find disease risk factors, assess and report the potential high risk items of the pig farm in the three systems of engineering epidemic disease prevention technology, biological safety and immune monitoring, and promote the improvement and perfection of epidemic disease prevention and control measures.

Assessing the severity of cotton Verticillium wilt disease from in situ canopy images and spectra using convolutional neural networks

Verticillium wilt(VW)is a common soilborne disease of cotton.It occurs mainly in the seedling and bollopening stages and severely impairs the yield and quality of the fiber.Rapid and accurate identification and evaluation of VW severity(VWS)forms the basis of field cotton VW control,which has great significance to cotton production.Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses,which require abundant time and professional expertise.Remote and proximal sensing using imagery and spectrometry have great potential for this purpose.In this study,we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values,in situ images,and spectra of 361 cotton canopies.To estimate cotton VWS values at the canopy scale,we developed two deep learning approaches that use in situ images and spectra,respectively.For the imagery-based method,given the high complexity of the in situ environment,we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes(CFS)dataset with over 1000 images for each scene-unit type.We performed pretrained convolutional neural networks(CNNs)training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy.The results showed that the Dark Net-19 model achieved satisfactory performance in CFS classification and VWS values estimation(R^(2)=0.91,root-mean-square error(RMSE)=6.35%).For the spectroscopy-based method,we first designed a one-dimensional regression network(1D CNN)with four convolutional layers.After dimensionality reduction by sensitive-band selection and principal component analysis,we fitted the 1D CNN with varying numbers of principal components(PCs).The 1D CNN model with the top 20 PCs performed best(R^(2)=0.93,RMSE=5.77%).These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.

A substrate scaffold for assessment of nerve regeneration and neurodegenerative diseases

Neuroregenerafion is a complex topic in neurosci- ence and includes 3 concepts： neurogenesis, neuro- plasticity, and neurorestoration. After injury of the nervous system, axons have the capacity for self-re- pair, regrowth or proliferation. The peripheral ner- vous system is more effective at restoring damaged axons than the central nervous system （CNS）. This is because formation of scar tissue in the CNS in- fluences neural regrowth or synthesis of growth-in- hibiting proteins, thereby preventing reconstruction of a neural circuit （Silver and Miller, 2004; Enciu et al., 2011）. Parkinson＇s disease （PD） and Alzheimer＇s disease （AD） are two most common degenerative diseases of the CNS among the elderly.

B-type natriuretic peptide in predicting the severity of community-acquired pneumonia

BACKGROUND: Although pneumonia severity index(PSI) is widely used to evaluate the severity of community-acquired pneumonia(CAP), the calculation of PSI is very complicated. The present study aimed to evaluate the role of B-type natriuretic peptide(BNP) in predicting the severity of CAP.METHODS: For 202 patients with CAP admitted to the emergency department, BNP levels, cardiac load indexes, inf lammatory indexes including C-reactive protein(CRP), white blood cell count(WBC), and PSI were detected. The correlation between the indexes and PSI was investigated. BNP levels for survivor and non-survivor groups were compared, and a receiver operating characteristic(ROC) curve analysis was performed on the BNP levels versus PSI.RESULTS: The BNP levels increased with CAP severity(r=0.782, P<0.001). The BNP levels of the high-risk group(PSI classes IV and V) were signifi cantly higher than those of the low-risk group(PSI classes I–III)(P<0.001). The BNP levels were signifi cantly higher in the non-survivor group than in the survivor group(P<0.001). In addition, there were positive correlations between BNP levels and PSI scores(r=0.782, P<0.001). The BNP level was highly accurate in predicting the severity of CAP(AUC=0.952). The optimal cut-off point of BNP level for distinguishing high-risk patients from low-risk ones was 125.0 pg/m L, with a sensitivity of 0.891 and a specifi city of 0.946. Moreover, BNP level was accurate in predicting mortality(AUC=0.823). Its optimal cut-off point for predicting death was 299.0 pg/m L, with a sensitivity of 0.675 and a specifi city of 0.816. Its negative predictive cut-off value was 0.926, and the positive predictive cut-off value was 0.426.CONCLUSION: BNP level is positively correlated with the severity of CAP, and may be used as a biomarker for evaluating the severity of CAP.

New disease severity classification of patients with stable chronic obstructive pulmonary disease in Shanghai

Background The Global Initiative for Chronic Obstructive Lung Disease （GOLD） presented a new ABCD group classification of chronic obstructive pulmonary disease （COPD）.We aimed to examine the association of spirometric classification and the new GOLD classification with exacerbations,and to compare symptoms in different ways.Methods We investigated 848 patients with stable COPD from 24 hospitals.The annual frequencies of acute exacerbation and hospitalization were compared between the old and new classification.The symptom level was assessed using COPD assessment test （CAT） and modified British Medical Research Council （mMRC） questionnaire.Results A total of 848 patients were included in this study.According to spirometric classification,there were 32 patients of grade Ⅰ （3.8％）,315 of grade Ⅱ （37.1％）,366 of grade Ⅲ （43.2％）,and 135 of grade Ⅳ （15.9％）.According to GOLD 2011 classification,there were 59 patients of group A （7.0％）,172 of group B （20.3％）,55 of group C （6.5％）,and 562 of group D （66.3％）.In spirometric classification,the annual frequencies of acute exacerbation and associated hospitalization were respectively 1 （0-3） and 0 （0-2） for grade Ⅰ; 1 （0-5） and 0 （0-2） for grade Ⅱ; 2 （0-6） and 1 （0-3） for grade Ⅲ,and 3 （0-6） and 2 （0-3） for grade Ⅳ.In GOLD 2011,respectively 0 （0-3） and 0 （0-1） （group A）,1 （0-4） and 0 （0-3） （group B）,1 （0-5） and 0 （0-3） （group C）,and 3 （0-6） and 1 （0-3） （group D）.There were no significant difference between group B and C （Z=-1.347,P=0.178; Z=-0.772,P=0.440,respectively）.The coincidence rate using mMRC=1 and CAT=10 as cutoff points was 86.6％ （734/848,x=0.706）,compared with 77.9％ （661/848,K=0.60） using mMRC=2 and CAT=10.Conclusions Lung function test may be a better predictor of acute exacerbation and associated hospitalization of COPD.It is more appropriate to use mMRC=1 as cutoff point for assessing COPD symptoms.