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Objective@#To investigate the spatial clustering characteristics of varicella public health emergency events in Nanjing City from 2017 to 2022, so as to provide the evidence for the development of varicella prevention and control strategies.@*Methods@#Data of varicella public health emergency events in Nanjing City from 2017 to 2022 were collected through Emergency Public Health Management Information System of Chinese Disease Prevention and Control Information System. The software ArcGIS 10.2 was employed for trend-surface analysis, spatial autocorrelation analysis and hotspot analysis to understand the spatial clustering characteristics of varicella public health emergency events.@*Results@#A total of 84 varicella public health emergency events were reported in Nanjing City from 2017 to 2022, with an average attack rate of 2.53% (2 558 cases). Ⅳ-level events were predominant, accounting for 90.48% (76 events). The trend-surface analysis showed lower incidence of varicella public health emergency events in the west of Nanjing City and higher in the east, and lower in the north and higher in the south. The global spatial autocorrelation analysis showed that there was a positive spatial correlation in varicella public health emergency events in 2017 and 2019, indicating spatial clustering (P<0.05). The hot spots were Jiangning District, Gaochun District, Yuhuatai District and Jianye District.@*Conclusion@#The incidence of varicella public health emergency events in Nanjing City from 2017 to 2022 has spatial aggregation, with a concentration in suburban areas such as Jiangning District and Gaochun District.
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Abstract Introduction Wegener granulomatosis (WG) appears with clinical symptoms, including recurrent respiratory infection, renal manifestations, and nonspecific systemic symptoms. Objective To study the clinical manifestations of WG in Iranian ethnicities, and data on 164 patients were recorded from 2013 to 2018. Methods The data included demographics, symptoms, and the Birmingham Vasculitis Activity Score (BVAS). The symptoms involved the following sites: the nose, sinus, glottis, ears, lungs, kidneys, eyes, central nervous system, mucous membranes, skin, heart, stomach, intestine, as well as general symptoms. The clinical manifestations of nine ethnicities were analyzed. Results In total, 48% of the patients were male and 51% were female, with a median age of 51 years. The BVAS was of 15.4, the sites most involved were the sinus (n =155), nose (n = 126), lungs (n = 125), and ears (n =107). Gastrointestinal (n = 14) and cardiac (n = 7) involvement were less common. Among the patients, 48.17% were Persian, 13.41% were Azari, 11.17% were Gilaki, 11.17% were Kurd, and 10.9% were Lor. Conclusion Our findings indicated that the sinus, nose, lungs, and ears were the sites most involved, and gastrointestinal and cardiac involvement were less common. In the present study, involvement of the upper and lower respiratory tract was higher than that reported in Western and Asian case series. Moreover, we report for the first time that, in all patients with ear involvement, the left ear was the first to be affected. The clinical manifestations among Iranian ethnicities were not different, and the Gilaki ethnicity had the highest BVAS, mostly because the weather was humid; therefore, in Iran, in areas with humidity, the rate of the disease was higher.
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AIM To simultaneously determine the contents of neochlorogenic acid,caffeic acid,chlorogenic acid,cryptochlorogenic acid,hydroxysafflor yellow A,ferulic acid,senkyunolide I,senkyunolide H and senkyunolide A in Fuyang Granules,and to make chemical pattern recognition.METHODS The UHPLC was performed on a 35℃thermostatic Waters Acquity UPLC?BEH C18 column(150 mm×2.1 mm,1.7 μm),with the mobile phase comprising of acetonitrile-0.01%phosphoric acid flowing at 0.4 mL/min in a gradient elution manner,and the detection wavelengths were set at 278,322,325,390 nm.Then heatmap clustering analysis and principal component analysis were adopted.RESULTS Nine constituents showed good linear relationships within their own ranges(r>0.999 0),whose average recoveries were 93.89%-102.25%with the RSDs of 0.85%-2.88%.Different batches of samples from the same enterprises demonstrated consistent overall qualities,while the overall qualities of samples from different enterprises exhibited obvious differences.CONCLUSION This simple and accurate method can be used for the quality control of Fuyang Granules.
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@#Objective To investigate the dietary patterns of rural residents in the high-incidence areas of esophageal cancer (EC), and to explore the clustering and influencing factors of risk factors associated with high-incidence characteristics. Methods A special structured questionnaire was applied to conduct a face-to-face survey on the dietary patterns of rural residents in Yanting county of Sichuan Province from July to August 2021. Univariate and multivariate logistic regression models were used to analyze the influencing factors of risk factor clustering for EC. Results There were 838 valid questionnaires in this study. A total of 90.8% of rural residents used clean water such as tap water. In the past one year, the people who ate fruits and vegetables, soybean products, onions and garlic in high frequency accounted for 69.5%, 32.8% and 74.5%, respectively; the people who ate kimchi, pickled vegetables, sauerkraut, barbecue, hot food and mildew food in low frequency accounted for 59.2%, 79.6%, 68.2%, 90.3%, 80.9% and 90.3%, respectively. The clustering of risk factors for EC was found in 73.3% of residents, and the aggregation of two risk factors was the most common mode (28.2%), among which tumor history and preserved food was the main clustering pattern (4.6%). The logistic regression model revealed that the gender, age, marital status and occupation were independent influencing factors for the risk factors clustering of EC (P<0.05). Conclusion A majority of rural residents in high-incidence areas of EC in Yanting county have good eating habits, but the clustering of some risk factors is still at a high level. Gender, age, marital status, and occupation are influencing factors of the risk factors clustering of EC.
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BACKGROUND:The use of blood flow restriction training in clinical rehabilitation is becoming more common,in part due to technological advances and increased interest in research and medicine. OBJECTIVE:To explore the global trend and development prospects of clinical rehabilitation-related blood flow restriction training application,identify research gaps,and propose future research directions. METHODS:Web of Science,PubMed,and Elsevier were searched for literature related to clinical rehabilitation using blood flow restriction training published from 2001 to 2022.A total of 515 articles were included for further review.Bibliometrics,exploratory factor analysis and cluster analysis were used to reveal the emerging research fields and topics. RESULTS AND CONCLUSION:First of all,the overall research trend of the number of published articles shows a steady rise.The United States is the main research area in this field,and universities contribute the most to journals.Secondly,a core group of authors,led by Loenneke JP,Abe T and Dankel SJ,has been formed,with less collaboration among highly productive authors.Finally,research on blood flow restriction training focuses on skeletal muscle rehabilitation,cognitive functional activity,and chronic disease rehabilitation in clinical rehabilitation,with particular attention to the application of blood flow restriction in the recovery of symptoms such as muscle atrophy,knee pain,and joint effusion after anterior cruciate reconstruction surgery.Theoretical research on low-pressure blood flow restriction that is effective in relieving musculoskeletal symptoms has been hot in recent years,which can systematically guide the musculoskeletal rehabilitation in clinical practice.In the future,clinicians will extend follow-up periods,register larger and more diverse sample sizes,and use randomization techniques to determine appropriate prescribing indications for rehabilitating patients,ensure clinical safety,and quantitatively analyze and develop rehabilitation protocols with the maximum effect at the minimum effective dose for different clinical populations.
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Objective To propose a Kaczmarz algorithm-based fast reconstruction method of magnetic particle imaging to solve the problems of magnetic particle tomography in prolonged imaging time and computational complexity.Methods Firstly,the convergence speeds of the classical Kaczmarz algorithm and its variant algorithms were analyzed,and the number of iterations and computational time under arbitrary matrices were calculated.Secondly,the abilities of Euclidean distance and cosine distance in differentiating system data were compared,and the cosine distance-based K-means algorithm was employed to enhance the computational capability of block Kaczmarz algorithm,shorten the system matrix reconstruction time,and ultimately fast reconstruction of magnetic particle imaging was realized.The effectiveness of the proposed algorithm was verified through computer simulation.Results The proposed algorithm reduced greatly the reconstruction time and improved the spatial resolution and quality of the reconstructed images.Conclusion The proposed algorithm enables fast reconstruction of magnetic particle imaging and behaves well in reconstruction of data containing noise.[Chinese Medical Equipment Journal,2024,45(1):9-14]
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Objective:To explore a solution for the construction of the CCI index with an example of cerebral infarction to provide a guide for adjusting the policy of Diagnosis-Intervention Packet.Methods:Lasso regression was used to screen for comorbidities to construct the model,K-means clustering was used for case severity categorization.CCI indices were calculated as one plus the sum of standardized regression coefficients.Results:According to the analysis,five key variables were found,including other disorders of the lungs,non-insulin-dependent diabetes mellitus with ketoacidosis,systemic inflammatory response syndrome of non-infectious etiology with organ failure,acute duodenal ulcer with bleeding,and chronic obstructive pulmonary disease with acute lower respiratory tract in-fection,with CCI indices of 1,1,1.026,1.034,and 1.101.Simulated calculation's result showed a decrease in medical insurance pay-ment losses after applying CCI indices.Conclusion:The CCI index construction scheme based on Lasso regression and K-means clus-tering is reasonable and effective.
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In order to address issues such as the decline in diagnostic performance of deep learning models due to imbalanced data distribution in psoriasis vulgaris,a VGG13-based deep convolutional neural network model is proposed by integrating the processing capability of the improved fuzzy KMeans clustering algorithm for highly clustered complex data and the predictive capability of VGG13 deep convolutional neural network model.The model is applied to the diagnosis of psoriasis vulgaris,and the experimental results indicate that compared with VGG13 and resNet18,the proposed approach based on deep learning and improved fuzzy KMeans is more suitable for identifying psoriasis features.
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Objective To establish the UPLC fingerprint chromatogram combined with chemometric analysis for the quality evaluation of classical formula Linggui Zhugan Decoction.Methods SHIMADZU Shim-Pack GIST C18 column(100 mm×2.1 mm,2.0 μm)was used with acetonitrile-0.1%phosphoric acid aqueous solution as mobile phase,gradient elution;flow rate was 0.2 mL/min;the detection wavelength was 266 nm for the first 30 minutes and 235 nm for the last 36 minutes;the column temperature was 30℃.The UPLC fingerprint of Linggui Zhugan Decoction was established by Similarity Evaluation System for Chromatographic Fingerprint of TCM(2012.130723 version),and the common peak was determined and the similarity evaluation was carried out.Based on the peak area determination results of the common peak of the fingerprint,the quality of different batches of Linggui Zhugan Decoction was evaluated by chemometrics such as clustering analysis and principal component analysis.Results A total of 24 common peaks were confirmed and 14 components were identified by using reference substances.The similarity of 10 batches of Linggui Zhugan Decoction samples was greater than 0.950,which could be divided into two categories by chemometrics,and the principal component 1-4 were the main factors affecting its quality evaluation.OPLS-DA identified 6 differential markers.Conclusion The fingerprint research method established in the study is simple,reliable and reproducible.Through the method of fingerprint combined with chemometrics analysis,the differences between Linggui Zhugan Decoction from different origins of medicinal materials are identified,which provides a reference for the internal quality evaluation of Linggui Zhugan Decoction.
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Objective To discuss the medication law of Professor Gu Shizhe in the treatment of insomnia based on various data mining techniques.Methods Clinical information of patients of Professor Gu was collected from the hospital management information system of outpatient clinic of Beijing University of Chinese Medicine(BUCM)and BUCM Famous Elderly TCM Inheritance Research Integrated Platform from January 2018 to October 2022.Professor Gu's books,such as Zhi Zhen Zhi Yao,and Gu Jisheng,Gu Shizhe Yi An Yi Hua Jing Cui,and the medical records from CNKI were screened.WPS Office 5.2.1 was used to build a database of medicine data for Professor Gu's TCM prescriptions,and R 4.2.1 was used for descriptive analysis and Apriori association rule analysis.SPSS Statistics 26.0 was used for clustering analysis,combined with theoretical analysis,to extract Professor Gu's medication law and characteristics in the treatment of insomnia.Results A total of 307 prescriptions were included,involving 250 kinds of Chinese materia medica,among which 30 were high-frequency drugs.The top 5 most frequently used were Glycyrrhizae Radix et Rhizoma Praeparata Cum Melle,Fossilizid,Poria,Bupleuri Radix,and Ziziphi Spinosae Semen.The meridians were mainly liver and lung meridian,and the properties were mainly warm and cold,and the tastes were mainly sweet and bitter.Five core drug groups were obtained by clustering analysis,including Xiaochaihu Decoction,Erchen Decoction,Huanglian Wendan Decoction,Suanzaoren Decoction,Xiaoyao Powder and other prescriptions.Conclusion The characteristics of Professor Gu's treatment of insomnia are to reconcile Shaoyang,clear the heart and soothe the mind;pay attention to yin and yang,regulate the liver;pay attention to the liver,gallbladder,spleen and stomach;the main idea of syndrome differentiation is to reconcile Shaoyang,run the Shaoyang cardinal,and at the same time regulate qi and benefit qi and nourishes yin.
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Objective To analyze the characteristics and distribution of TCM syndromes of advanced gastric cancer;To provide reference for the standardization and clinical research of TCM syndromes of advanced gastric cancer.Methods The four diagnosis information with advanced gastric cancer was retrospectively collected at Dongzhimen Hospital of Beijing University of Chinese Medicine from January 2010 to December 2020.And the investigation results were analyzed by combining principal component analysis and clustering analysis,so as to explore the distribution pattern of TCM syndromes of advanced gastric cancer.Results Totally 164 patients were included,involving 601 case-times.10 principal components were obtained through principal component analysis on 29 items of four diagnosis information.The four diagnosis information with factor coefficient>0.4 were selected and allocated to the 10 principal components.Then,based on the results of principal component analysis,clustering analysis was conducted to obtain the distribution proportion of the three types of TCM syndromes.According to the syndrome differentiation by professional clinicians,the results were followed by the frequency distribution as cold coagulation and blood stasis(356,59.28%),stomach yin deficiency(145,24.17%)and phlegm-heat accumulation(100,16.55%).Conclusion There are three basic TCM syndromes of advanced gastric cancer,which are cold coagulation and blood stasis,stomach yin deficiency and phlegm-heat accumulation.Cold coagulation and blood stasis occupies the largest proportion,and the treatment should be based on warming the middle to dissipate cold and promoting blood circulation to remove blood stasis.
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Objectives: To examine the association between psychiatric and non-psychiatric comorbidity and 28-day mortality among patients with psychiatric disorders and COVID-19. Methods: Multicenter observational retrospective cohort study of adult patients with psychiatric disorders hospitalized with laboratory-confirmed COVID-19 at 36 Greater Paris university hospitals (January 2020-May 2021) (n=3,768). First, we searched for different subgroups of patients according to their psychiatric and non-psychiatric comorbidities through cluster analysis. Next, we compared 28-day all-cause mortality rates across the identified clusters, while taking into account sex, age, and the number of medical conditions. Results: We found five clusters of patients with distinct psychiatric and non-psychiatric comorbidity patterns. Twenty-eight-day mortality in the cluster of patients with mood disorders was significantly lower than in other clusters. There were no significant differences in mortality across other clusters. Conclusion: All psychiatric and non-psychiatric conditions may be associated with increased mortality in patients with psychiatric disorders and COVID-19. The lower risk of death among patients with mood disorders might be in line with the potential beneficial effect of certain antidepressants in COVID-19, but requires further research. These findings may help identify at-risk patients with psychiatric disorders who should benefit from vaccine booster prioritization and other prevention measures.
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Abstract Four conditions of spatial contiguity of positions were used to assess sequence learning. Two sequences of 16 and 25 positions presented in two matrices of 4x4 and 5x5 respectively were used. Within each matrix, 4 (in the 4x4 matrix) or 6 positions (in the 5x5 matrix) presented spatial contiguity. The place at the sequence in which contiguous positions occurred varied across groups. In this way, spatial contiguity of the 4 or 6 positions was presented at the beginning of the sequence (Group 1), in the middle part (Group 2), at the end of the sequence (Group 3) or it was presented a sequence in which all positions occurred without spatial contiguity (Group 4). 28 undergraduate students participated. Results showed no differences among groups in the number of trials required to reproduce the sequence correctly. Number of errors was lower when contiguous positions were presented at the beginning of the sequence. These findings are explained as a possible effect of accentuation of primacy given by the occurrence of contiguous positions at the beginning of the sequence.
Resumen Cuatro condiciones de contigüidad espacial de posiciones fueron empleadas para evaluar el aprendizaje de secuencias. Se emplearon dos secuencias de 16 y 25 posiciones presentadas en dos matrices de 4x4 y 5x5, respectivamente. Dentro de cada matriz, 4 (en la matriz de 4x4) o 6 posiciones (en la matriz de 5x5) presentaron contigüidad espacial. Entre grupos, se varió el punto de la secuencia en el que se presentaron las posiciones contiguas. De este modo, la contigüidad espacial de las 4 o 6 posiciones se presentó al inicio de la secuencia (Grupo 1), en la parte media (Grupo 2), al final de la secuencia (Grupo 3), o bien, se presentó una secuencia en la que todas las posiciones ocurrieron sin contigüidad espacial (Grupo 4). Participaron 28 estudiantes de licenciatura. Los resultados no mostraron diferencias entre grupos en cuanto al número de ensayos requeridos para reproducir la secuencia correctamente. El número de errores fue menor cuando las posiciones contiguas se presentaron al inicio de la secuencia. Los hallazgos se explican a partir de un posible efecto de acentuación de la primacía, dado por la ocurrencia de posiciones contiguas al inicio de la secuencia.
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SUMMARY: Due to the complexity of head shape, limited 1D or 2D head anthropometry fail to fully capture its shape characteristics. Currently, there is limited research on clustering analysis of head shape from a shape difference perspective, especially for the head shape of Chinese people. Head shape is influenced by factors such as race, sex, and age, making it imperative to create a head shape database for Chinese individuals. In this study, three-dimensional head data of 339 Chinese young adult were collected, and the head shapes were clustered into 7 clusters using an improved k-medoids algorithm. The differences between clusters and the average head shape were further analyzed. It can be foreseen that the head shape database for Chinese young adult constructed in this study has important reference value for the ergonomic design of head-related products and head morphology research, among other fields.
Debido a la complejidad de la forma de la cabeza, la antropometría limitada de ésta, en 1D o 2D, no logra capturar completamente sus características de forma. Actualmente, existen estudios limitados sobre el análisis de agrupamiento de la forma de la cabeza, desde una perspectiva de diferencia de forma, especialmente en el caso de la población china. La forma de la cabeza está influenciada por factores como la raza, el sexo y la edad, por lo que resulta imperativo crear una base de datos sobre la forma de la cabeza de los individuos chinos. En este estudio, se recopilaron datos tridimensionales de la cabeza de 339 adultos jóvenes chinos, y las formas de la cabeza se agruparon en 7 grupos utilizando un algoritmo k-medoids mejorado. Las diferencias entre los grupos y la forma promedio de la cabeza se analizaron a profundidad. Se puede prever que la base de datos sobre la forma de la cabeza de adultos jóvenes chinos construida en este estudio, tiene un valor de referencia importante para el diseño ergonómico de productos relacionados con la morfología de la cabeza, entre otros campos.
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Humans , Adolescent , Adult , Young Adult , Anthropometry , Imaging, Three-Dimensional , Head/anatomy & histologyABSTRACT
ABSTRACT BACKGROUND: Cardiovascular disease (CVD) is the second leading cause of death in sub-Saharan Africa. Globally, there is substantial evidence that modifiable risk factors for CVD are increasing in adolescents. Unfortunately, there is a paucity of information on the prevalence and clustering of these risk factors in adolescents. OBJECTIVES: This study explores the modifiable risk factors for CVD among first-year students at the University of Ibadan, Nigeria. DESIGN AND SETTING: This cross-sectional study was conducted at the University of Ibadan, Nigeria. METHODS: A total of 546 newly admitted students at the University of Ibadan, Nigeria, were recruited using stratified random sampling. An interviewer-administered questionnaire was used to obtain information from study participants between January and February 2016. RESULTS: The mean age of respondents was 19 ± 2.2 years with a male-to-female ratio of 1:1. The reported risk factors for CVD were smoking (1.6%), abdominal obesity (3.3%), alcohol consumption (3.7%), overweight/obesity (20.7%), unhealthy diet (85.3%), and physical inactivity (94.5%). Clustering of ≥ 2 risk factors was reported in 23.4% of students. Female students were twice as probably overweight/obese as male students (adjusted odds ratio [AOR] = 2.2; confidence interval [CI] = 1.41-3.43). Students whose fathers were skilled workers were 3.5 times more likely to be physically inactive (AOR = 1.7; CI = 0.97-2.96). The clustering of ≥ 2 risk factors was significantly higher among women and Muslims in bivariate analysis, whereas no significant association was found in multivariate analysis. CONCLUSIONS: Public health strategies to prevent CVD risk factors should begin in schools and extend to the entire community.
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Recording of peripheral pulse serves as a very important and essential non-invasive tool used widely by doctors for the diagnosis of various diseases. The morphology of pulse is seen to vary as a function of time in a given individual and also from individual to individual. There are many variations in morphological patterns of peripheral pulse in different disease conditions, which lead to difficulty in accurate diagnosis. The peripheral pulse waveforms are extracted from radial arteries as time series data using a peripheral pulse analyzer which is designed on the principle of impedance plethysmography. It was first introduced by Nyober in the mid-nineteen hundreds and ameliorated further by Kubicek. It involves the recording of the instantaneous blood volume by the measurement of electrical impedance as a function of time. Therefore, the study of peripheral pulse morphology has gained much attention in the past few years among researchers. Physiological variability is one of the recent investigations added during the last two decades for the objective assessment of autonomic function and the assessment of prognosis in severe sicknesses namely myocardial infarction, diabetic neuropathy, etc. In addition to heart rate variability studied worldwide, few researchers have studied blood pressure variability and peripheral blood flow variability. In this computer era, artificial intelligence and machine learning techniques have become more important day-by- day, and different types of algorithms were used for the identification of hidden patterns from plethysmographic observations on the radial pulse such as support vector machine as well as crisp and fuzzy clustering. Eight patterns were classified with a yield of 80%–90% and helped with the diagnosis of disorders such as myocardial infarction, pulmonary tuberculosis, coronary artery disorders, cirrhosis of the liver, and bronchial asthma. This paper briefly describes the use of machine learning techniques for the classification of peripheral pulse morphologies.
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Objective:To obtain the relative biological effectiveness (RBE) of DNA double strand breaks (DSB) clusters by tracing the mechanism of radiated DNA damage, and explore the relationship among the biological effectiveness of DNA damage, chromosomal aberrations and germ cell death.Methods:Taking low-energy electrons, protons, and α particles as the research objects, this study simulated the process that cell nuclei were exposed to particle radiation using a radiation-related physicochemical model. On the ground of the DSB density-based spatial clustering of applications with noise (DBSCAN) algorithm, the DSB cluster classification method was improved to weaken the connection between the DSBs and the random distribution assumptions of energy depositions during the simulation. In this manner, the DSB clusters can be much closer to a non-random distribution. Furthermore, this study obtained the yields of DSB clusters and proposed a method to calculate the RBE values of DSB clusters.Results:The calculated RBE value (12.29) of DSB clusters of 2 MeV α particles was similar to the experimental RBE values of chromosomal fragments (15.3±5.9) and cell survival (14.7±5.1).Conclusions:After high-LET ionizing radiation, unlike the single DSB, the RBE of DSB clusters was similar to that of chromosomal aberration and cell survival.
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Objective:To study the improvement of normal tissue region of interest (ROI) segmentation based on clustering-based multi-Atlas segmentation method, thereby achieving better delineation of organs at risk.Methods:CT images of 100 patients with cervical cancer who had completed treatment in Zhejiang Cancer Hospital during 2019-2020 were selected as the Atlas database. According to the volume characteristic parameters of the organs at risk (bladder, rectum and outer contour), the Atlas database was divided into several subsets by k-means clustering algorithm. The image to be segmented was matched to the corresponding Atlas library for multi-Atlas segmentation. The dice similarity coefficient (DSC) was used to evaluate the segmentation results.Results:Using 30 patients as the test set, the sub-Atlas generated by different clustering methods were compared for the improvement of image segmentation results. Compared with general multi-Atlas segmentation methods, clustering-based multi-Atlas segmentation method significantly improve the segmentation accuracy for the bladder (DSC=0.83±0.09 vs. 0.69±0.15, P<0.001) and the rectum (0.7±0.07 vs. 0.56±0.16, P<0.001), but no statistical significance was observed for left and right femoral head (0.92±0.04, 0.91±0.02) and bone marrow (0.91±0.06). The average segmentation time of clustering-based multi-Atlas segmentation method was shorter than that of the general multi-Atlas segmentation method (2.7 min vs. 6.3 min). Conclusion:The clustering-based multi-Atlas segmentation method can not only reduce the number of Atlas images registered with the image to be segmented, but also can be expected to improve the segmentation effect and obtain higher accuracy.
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Objective To evaluate the feasibility of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for identification and cluster analysis of Streptococcus suis. Methods Eighteen clinical isolates biochemically identified as Streptococcus suis were pre-treated by smearing formic acid method and formic acid-acetonitrile extraction method. The identification results and protein profiles of MALDI-TOF-MS method were analyzed and compared. A self-constructed database of profiling with better pretreatment method was established, and cluster analysis was performed on the 18 strains of Streptococcus suis. At the same time, PFGE homology analysis was performed to compare the results of the two genotyping. Results Both pretreatment methods could accurately identify Streptococcus suis with scores above 2.1. The protein fingerprint of formic acid and acetonitrile extraction method had a smoother baseline, fewer miscellaneous peaks and more identifiable ion peaks. Comparison of the results of homology typing showed that the homology results of MALDI-TOF-MS were significantly different from those of PFGE. Conclusion MALDI-TOF-MS can accurately identify Streptococcus suis for the strains pre-treated with formic acid method or formic acid-acetonitrile extraction method, and the formic acid-acetonitrile extraction method can obtain a better protein mapping. MALDI-TOF-MS can play a certain role in typing, but it still has some limitations and cannot completely replace the PFGE.
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Objective To analyze the distribution features of newly reported cases of occupational welder′s pneumoconiosis (OWP) in Guangdong Province from 2013 to 2022. Methods Data of newly reported cases of OWP from National Occupational Disease Network Direct Reporting System of Guangdong Province from 2013 to 2022 was analyzed using conventional data analyzing Methods. Results A total of 294 cases of OWP were reported in Guangdong Province from 2013 to 2022 with an overall downward trend. The average age at diagnosis for new OWP cases was 44±6 years old, and the median of dust exposure duration was 11 years. Males accounted for 99.3% of the cases, and 94.2% of the cases were phase Ⅰ OWP. Guangzhou City had the highest regional distribution with 74.8% of the cases. The industry distribution was mainly manufacturing, accounting for 86.7% of the cases. The scale of enterprises was most common in small enterprises, followed by large and medium-sized enterprises, accounting for 38.4%, 30.6% and 24.5% of the cases respectively. The economic types of enterprises were mostly private enterprises, followed by state-owned enterprises, accounting for 54.1% and 32.0% of the cases respectively. Conclusion In Guangdong Province, the newly reported OWP cases were clustered in terms of disease stage, regional distribution, industry, enterprise scale and enterprise economic type. There was a tendency of younger age at diagnosis and shorter dust exposure duration.