Objective: This study aimed to develop a nomogram to predict the 1-year survival of patients with pancreatic cancer who underwent pancreatectomy following neoadjuvant treatment with preoperatively detectable clinical ...Objective: This study aimed to develop a nomogram to predict the 1-year survival of patients with pancreatic cancer who underwent pancreatectomy following neoadjuvant treatment with preoperatively detectable clinical parameters. Extended pancreatectomy is necessary to achieve complete tumor removal in borderline resectable and locally advanced pancreatic cancer. However, it increases postoperative morbidity and mortality rates, and should be balanced with potential benefit of long-term survival.Methods: The medical records of patients who underwent pancreatectomy following neoadjuvant treatment from January 2005 to December 2016 at Severance Hospital were retrospectively reviewed. Medical records were collected from five international institutions from Japan and Singapore for external validation.Results: A total of 113 patients were enrolled. The nomogram for predicting 1-year disease-specific survival was created based on 5 clinically detectable preoperative parameters as follows: age(year), symptom(no/yes), tumor size at initial diagnostic stage(cm), preoperative serum carbohydrate antigen(CA) 19-9 level after neoadjuvant treatment(<34/≥34 U/m L), and planned surgery [pancreaticoduodenectomy(PD)(pylorus-preserving PD)/distal pancreatectomy(DP)/total pancreatectomy]. Model performance was assessed for discrimination and calibration.The calibration plot showed good agreement between actual and predicted survival probabilities;the the Greenwood-Nam-D’Agostino(GND) goodness-of-fit test showed that the model was well calibrated(χ~2=8.24,P=0.5099). A total of 84 patients were used for external validation. When correlating actual disease-specific survival and calculated 1-year disease-specific survival, there were significance differences according to the calculated probability of 1-year survival among the three groups(P=0.044).Conclusions: The developed nomogram had quite acceptable accuracy and clinical feasibility in the decision-making process for the management of pancreatic cancer.展开更多
Pulmonary diseases are common throughout the world,especially in developing countries.These diseases include chronic obstructive pulmonary diseases,pneumonia,asthma,tuberculosis,fibrosis,and recently COVID-19.In gener...Pulmonary diseases are common throughout the world,especially in developing countries.These diseases include chronic obstructive pulmonary diseases,pneumonia,asthma,tuberculosis,fibrosis,and recently COVID-19.In general,pulmonary diseases have a similar footprint on chest radiographs which makes them difficult to discriminate even for expert radiologists.In recent years,many image processing techniques and artificial intelligence models have been developed to quickly and accurately diagnose lung diseases.In this paper,the performance of four popular pretrained models(namely VGG16,DenseNet201,DarkNet19,and XceptionNet)in distinguishing between different pulmonary diseases was analyzed.To the best of our knowledge,this is the first published study to ever attempt to distinguish all four cases normal,pneumonia,COVID-19 and lung opacity from ChestX-Ray(CXR)images.All models were trained using Chest-X-Ray(CXR)images,and statistically tested using 5-fold cross validation.Using individual models,XceptionNet outperformed all other models with a 94.775%accuracy and Area Under the Curve(AUC)of Receiver Operating Characteristic(ROC)of 99.84%.On the other hand,DarkNet19 represents a good compromise between accuracy,fast convergence,resource utilization,and near real time detection(0.33 s).Using a collection of models,the 97.79%accuracy achieved by Ensemble Features was the highest among all surveyed methods,but it takes the longest time to predict an image(5.68 s).An efficient effective decision support system can be developed using one of those approaches to assist radiologists in the field make the right assessment in terms of accuracy and prediction time,such a dependable system can be used in rural areas and various healthcare sectors.展开更多
Adequate oxygen in red blood cells carrying through the body to the heart and brain is important to maintain life.For those patients requiring blood,blood transfusion is a common procedure in which donated blood or bl...Adequate oxygen in red blood cells carrying through the body to the heart and brain is important to maintain life.For those patients requiring blood,blood transfusion is a common procedure in which donated blood or blood components are given through an intravenous line.However,detecting the need for blood transfusion is time-consuming and sometimes not easily diagnosed,such as internal bleeding.This study considered physiological signals such as electrocardiogram(ECG),photoplethysmogram(PPG),blood pressure,oxygen saturation(SpO2),and respiration,and proposed the machine learning model to detect the need for blood transfusion accurately.For the model,this study extracted 14 features from the physiological signals and used an ensemble approach combining extreme gradient boosting and random forest.The model was evaluated by a stratified five-fold crossvalidation:the detection accuracy and area under the receiver operating characteristics were 92.7%and 0.977,respectively.展开更多
Cervical cancer is screened by pap smear methodology for detection and classification purposes.Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues.In this pa...Cervical cancer is screened by pap smear methodology for detection and classification purposes.Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues.In this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes problem.Pap smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images cells.Automated features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)classifier.The success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%sensitivity.On top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high accuracy.On the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%accuracy.The proposed system is a built-in cascading manner with five models of polynomial(SVM)classifier.The overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.展开更多
Liver cancer is the second leading cause of cancer death worldwide.Early tumor detection may help identify suitable treatment and increase the survival rate.Medical imaging is a non-invasive tool that can help uncover...Liver cancer is the second leading cause of cancer death worldwide.Early tumor detection may help identify suitable treatment and increase the survival rate.Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs.Magnetic Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs tissue.However,the interpretation of medical images requires the subjective expertise of a radiologist and oncologist.Thus,building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses.This paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor images.This paper proposed two models;the first one employed the 3D features while the second exploited the 2D features.The first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor categories.On top of that,the proposed method is applied to 2D slices for comparison purposes.The proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver tumors.On the other hand,the performance is lower in 2D classification.The maximum accuracy reached 96.4%for two classes and 92.1%for four classes.The top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different classes.The novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D classification.In the future,the presented work can be extended to be used in the huge dataset.Then,it can be a reliable,efficient Computer Aided Diagnosis(CAD)system employed in hospitals in rural areas.展开更多
Background:The use of antidepressants in the treatment of bipolar depression remains controversial due to concerns about their potential to induce mood polarity switches.This multinational observational study aims to ...Background:The use of antidepressants in the treatment of bipolar depression remains controversial due to concerns about their potential to induce mood polarity switches.This multinational observational study aims to examine the association between the use of antidepressants and the risk of hypomanic/manic switch among bipolar depressive patients.Methods:Four electronic health record databases(IQVIA Disease Analyzer Germany,IQVIA Disease Analyzer France,IQVIA US Hospital Charge Data Master,and Beijing Anding Hospital)and one administrative claims database(IQVIA US Open Claims)were analyzed,and the study period covered from January 2013 until December 2017.Treatment patterns of patients with bipolar depression were collected.The hazard ratio(HR)was calculated by comparing the incidence of hypomanic/manic switch in patients who received antidepressants(AD group)with that in those who did not receive any antidepressant(non-AD group)in 730 days after the date of the first diagnosis of bipolar depression.Results:The analysis included a total of 122,843 patients from the 5 databases;60.6% of them received antidepressants for bipolar depression.Across the 5 data sources,the mean age at index date ranged from 37.50(15.72)to 52.10(16.22)years.After controlling potential confounders by propensity score matching,the AD group’s manic switch risk was not significantly higher than the non-AD group’s(HR 1.04[95%CI,0.96 to 1.13];P=0.989).Additionally,no statistically significant difference was observed between patients prescribed antimanic drugs and those who were not(HR 0.69[95%CI,0.38 to 1.25];P=0.535).Conclusions:This study indicated that antidepressants were widely used in clinical settings for managing bipolar depression.The use of antidepressants was not associated with the risk of mania/hypomania switch when compared to non-antidepressants treatment.Therefore,antidepressants could be considered a treatment option for bipolar depression.展开更多
Sepsis is a dysregulated immune response to infection that leads to organ dysfunction and is associated with a high incidence and mortality rate.The lack of reliable biomarkers for diagnosing and prognosis of sepsis i...Sepsis is a dysregulated immune response to infection that leads to organ dysfunction and is associated with a high incidence and mortality rate.The lack of reliable biomarkers for diagnosing and prognosis of sepsis is a major challenge in its management.We aimed to investigate the potential of three-dimensional label-free CD8+T cell morphology as a biomarker for sepsis.This study included three-time points in the sepsis recovery cohort(N=8)and healthy controls(N=20).Morphological features and spatial distribution within cells were compared among the patients'statuses.We developed a deep learning model to predict the diagnosis and prognosis of sepsis using the internal cell morphology.Correlation between the morphological features and clinical indices were analysed.Cell morphological features and spatial distribution differed significantly between patients with sepsis and healthy controls and between the survival and non-survival groups.The model for predicting the diagnosis and prognosis of sepsis showed an area under the receiver operating characteristic curve of nearly 100%with only a few cells,and a strong correlation between the morphological features and clinical indices was observed.Our study highlights the potential of three-dimensional label-free CD8+T cell morphology as a promising biomarker for sepsis.This approach is rapid,requires a minimum amount of blood samples,and has the potential to provide valuable information for the early diagnosis and prognosis of sepsis.展开更多
Background Takayasu arteritis(TAK)is a disease associated with increased risk of cardiovascular complications.We aimed to evaluate the incidence,prevalence and risk of stroke in patients with TAK.Methods Data from 106...Background Takayasu arteritis(TAK)is a disease associated with increased risk of cardiovascular complications.We aimed to evaluate the incidence,prevalence and risk of stroke in patients with TAK.Methods Data from 1065 patients were obtained from a national database(2010–2018).The annual incidence and prevalence per 100000 persons were estimated using the registration population in the midst of every year,and the standardised incidence ratio(SIR)of stroke was compared with the general population based on the data from the 2006 national report for cardiovascular and cerebrovascular diseases.Age-adjusted incidence rate ratio(IRR)of stroke based on the time interval after diagnosis was also calculated.A time-dependent Cox regression was conducted to investigate predictive factors of stroke.Results The overall incidence rate of TAK ranged between 0.2 and 0.3/100000 person-years annually;the prevalence of TAK gradually increased,reaching 3.25/100000 person-years in 2018.Seventy-three(6.9%)patients experienced stroke during follow-up,and the risk of developing stroke was higher than the general population(overall SIR 7.39,95%CI 5.79 to 9.29;men:SIR 5.70,95%CI 2.84 to 10.20;women:SIR 7.06,95%CI 5.41 to 9.05).Most stroke events(90.9%)were cerebral infarction for men,whereas the proportion of cerebral infarction was lower(62.9%)in women.Over half of stroke events occurred within 6 months after diagnosis,and stroke was more common within 6 months of diagnosis compared with after 3 years in women(IRR 13.46,95%CI 6.86 to 26.40).In Cox regression analysis,age was the sole predictor of stroke(adjusted HR 1.02,95%CI 1.00 to 1.04,p=0.043).Conclusions The annual incidence of TAK was similar to the previous studies from Asia,and the risk of stroke increased in TAK.Different patterns of subtype and incidence of stroke were found according to sex,although age was the only predictor.展开更多
Dear Editor,Coronavirus disease 2019(COVID-19)is a novel respiratory infectious disease,caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which led to a global pandemic.Although vaccination is the ...Dear Editor,Coronavirus disease 2019(COVID-19)is a novel respiratory infectious disease,caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which led to a global pandemic.Although vaccination is the best measure to overcome a pandemic,the immunogenicity of vaccines can be influenced by diverse factors,including intrinsic(age,sex,genetics,and comorbidities),extrinsic(diet,nutrition,and behavior),and vaccine-associated characteristics.展开更多
In November 2021,the Omicron variant(B.1.1.529)emerged and was designated a variant of concern(VOC)by the World Health Organization.Recently,Omicron was reported to extensively escape neutralizing antibodies elicited ...In November 2021,the Omicron variant(B.1.1.529)emerged and was designated a variant of concern(VOC)by the World Health Organization.Recently,Omicron was reported to extensively escape neutralizing antibodies elicited by COVID-19 vaccination or natural infection[1,2,3].However,whether Omicron evades the T cell immunity elicited by COVID-19 vaccination or natural infection remains to be elucidated.To address this issue,we analyzed the amino acid sequences of T cell epitopes identified from the original SARS-CoV-2 strain(Wuhan-Hu-1)in the Omicron variant(hCoV-19/South Africa/CERI-KRISP-K032284/2021).展开更多
基金supported by a faculty research grant of Yonsei University College of Medicine for 6-2015-0053.
文摘Objective: This study aimed to develop a nomogram to predict the 1-year survival of patients with pancreatic cancer who underwent pancreatectomy following neoadjuvant treatment with preoperatively detectable clinical parameters. Extended pancreatectomy is necessary to achieve complete tumor removal in borderline resectable and locally advanced pancreatic cancer. However, it increases postoperative morbidity and mortality rates, and should be balanced with potential benefit of long-term survival.Methods: The medical records of patients who underwent pancreatectomy following neoadjuvant treatment from January 2005 to December 2016 at Severance Hospital were retrospectively reviewed. Medical records were collected from five international institutions from Japan and Singapore for external validation.Results: A total of 113 patients were enrolled. The nomogram for predicting 1-year disease-specific survival was created based on 5 clinically detectable preoperative parameters as follows: age(year), symptom(no/yes), tumor size at initial diagnostic stage(cm), preoperative serum carbohydrate antigen(CA) 19-9 level after neoadjuvant treatment(<34/≥34 U/m L), and planned surgery [pancreaticoduodenectomy(PD)(pylorus-preserving PD)/distal pancreatectomy(DP)/total pancreatectomy]. Model performance was assessed for discrimination and calibration.The calibration plot showed good agreement between actual and predicted survival probabilities;the the Greenwood-Nam-D’Agostino(GND) goodness-of-fit test showed that the model was well calibrated(χ~2=8.24,P=0.5099). A total of 84 patients were used for external validation. When correlating actual disease-specific survival and calculated 1-year disease-specific survival, there were significance differences according to the calculated probability of 1-year survival among the three groups(P=0.044).Conclusions: The developed nomogram had quite acceptable accuracy and clinical feasibility in the decision-making process for the management of pancreatic cancer.
文摘Pulmonary diseases are common throughout the world,especially in developing countries.These diseases include chronic obstructive pulmonary diseases,pneumonia,asthma,tuberculosis,fibrosis,and recently COVID-19.In general,pulmonary diseases have a similar footprint on chest radiographs which makes them difficult to discriminate even for expert radiologists.In recent years,many image processing techniques and artificial intelligence models have been developed to quickly and accurately diagnose lung diseases.In this paper,the performance of four popular pretrained models(namely VGG16,DenseNet201,DarkNet19,and XceptionNet)in distinguishing between different pulmonary diseases was analyzed.To the best of our knowledge,this is the first published study to ever attempt to distinguish all four cases normal,pneumonia,COVID-19 and lung opacity from ChestX-Ray(CXR)images.All models were trained using Chest-X-Ray(CXR)images,and statistically tested using 5-fold cross validation.Using individual models,XceptionNet outperformed all other models with a 94.775%accuracy and Area Under the Curve(AUC)of Receiver Operating Characteristic(ROC)of 99.84%.On the other hand,DarkNet19 represents a good compromise between accuracy,fast convergence,resource utilization,and near real time detection(0.33 s).Using a collection of models,the 97.79%accuracy achieved by Ensemble Features was the highest among all surveyed methods,but it takes the longest time to predict an image(5.68 s).An efficient effective decision support system can be developed using one of those approaches to assist radiologists in the field make the right assessment in terms of accuracy and prediction time,such a dependable system can be used in rural areas and various healthcare sectors.
基金This work was supported by the Korea Medical Device Development Fund from the Korean government(the Ministry of Science and ICTMinistry of Trade,Indus-try and Energy+2 种基金Ministry of Health and Welfareand Ministry of Food and Drug Safety)(KMDF_PR_20200901_0095)the Soonchunhyang University Research Fund.
文摘Adequate oxygen in red blood cells carrying through the body to the heart and brain is important to maintain life.For those patients requiring blood,blood transfusion is a common procedure in which donated blood or blood components are given through an intravenous line.However,detecting the need for blood transfusion is time-consuming and sometimes not easily diagnosed,such as internal bleeding.This study considered physiological signals such as electrocardiogram(ECG),photoplethysmogram(PPG),blood pressure,oxygen saturation(SpO2),and respiration,and proposed the machine learning model to detect the need for blood transfusion accurately.For the model,this study extracted 14 features from the physiological signals and used an ensemble approach combining extreme gradient boosting and random forest.The model was evaluated by a stratified five-fold crossvalidation:the detection accuracy and area under the receiver operating characteristics were 92.7%and 0.977,respectively.
基金This work was supported by the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme(FRGS/1/2021/SKK0/UNIMAP/02/1).
文摘Cervical cancer is screened by pap smear methodology for detection and classification purposes.Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues.In this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes problem.Pap smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images cells.Automated features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)classifier.The success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%sensitivity.On top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high accuracy.On the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%accuracy.The proposed system is a built-in cascading manner with five models of polynomial(SVM)classifier.The overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.
文摘Liver cancer is the second leading cause of cancer death worldwide.Early tumor detection may help identify suitable treatment and increase the survival rate.Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs.Magnetic Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs tissue.However,the interpretation of medical images requires the subjective expertise of a radiologist and oncologist.Thus,building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses.This paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor images.This paper proposed two models;the first one employed the 3D features while the second exploited the 2D features.The first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor categories.On top of that,the proposed method is applied to 2D slices for comparison purposes.The proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver tumors.On the other hand,the performance is lower in 2D classification.The maximum accuracy reached 96.4%for two classes and 92.1%for four classes.The top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different classes.The novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D classification.In the future,the presented work can be extended to be used in the huge dataset.Then,it can be a reliable,efficient Computer Aided Diagnosis(CAD)system employed in hospitals in rural areas.
基金partially supported by the Beijing Municipal Administration of Hospitals Incubating Program(PX20211903 and PX2019071)Capital’s Funds for Health Improvement and Research(2024-4-2129)+1 种基金the Beijing Municipal Science&Technology Commission(Z221100007422010)the Beijing High Level Public Health Professionals Training Plan(xuekegugan-01-12).
文摘Background:The use of antidepressants in the treatment of bipolar depression remains controversial due to concerns about their potential to induce mood polarity switches.This multinational observational study aims to examine the association between the use of antidepressants and the risk of hypomanic/manic switch among bipolar depressive patients.Methods:Four electronic health record databases(IQVIA Disease Analyzer Germany,IQVIA Disease Analyzer France,IQVIA US Hospital Charge Data Master,and Beijing Anding Hospital)and one administrative claims database(IQVIA US Open Claims)were analyzed,and the study period covered from January 2013 until December 2017.Treatment patterns of patients with bipolar depression were collected.The hazard ratio(HR)was calculated by comparing the incidence of hypomanic/manic switch in patients who received antidepressants(AD group)with that in those who did not receive any antidepressant(non-AD group)in 730 days after the date of the first diagnosis of bipolar depression.Results:The analysis included a total of 122,843 patients from the 5 databases;60.6% of them received antidepressants for bipolar depression.Across the 5 data sources,the mean age at index date ranged from 37.50(15.72)to 52.10(16.22)years.After controlling potential confounders by propensity score matching,the AD group’s manic switch risk was not significantly higher than the non-AD group’s(HR 1.04[95%CI,0.96 to 1.13];P=0.989).Additionally,no statistically significant difference was observed between patients prescribed antimanic drugs and those who were not(HR 0.69[95%CI,0.38 to 1.25];P=0.535).Conclusions:This study indicated that antidepressants were widely used in clinical settings for managing bipolar depression.The use of antidepressants was not associated with the risk of mania/hypomania switch when compared to non-antidepressants treatment.Therefore,antidepressants could be considered a treatment option for bipolar depression.
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)[grant number 2022R1F1A1064578].
文摘Sepsis is a dysregulated immune response to infection that leads to organ dysfunction and is associated with a high incidence and mortality rate.The lack of reliable biomarkers for diagnosing and prognosis of sepsis is a major challenge in its management.We aimed to investigate the potential of three-dimensional label-free CD8+T cell morphology as a biomarker for sepsis.This study included three-time points in the sepsis recovery cohort(N=8)and healthy controls(N=20).Morphological features and spatial distribution within cells were compared among the patients'statuses.We developed a deep learning model to predict the diagnosis and prognosis of sepsis using the internal cell morphology.Correlation between the morphological features and clinical indices were analysed.Cell morphological features and spatial distribution differed significantly between patients with sepsis and healthy controls and between the survival and non-survival groups.The model for predicting the diagnosis and prognosis of sepsis showed an area under the receiver operating characteristic curve of nearly 100%with only a few cells,and a strong correlation between the morphological features and clinical indices was observed.Our study highlights the potential of three-dimensional label-free CD8+T cell morphology as a promising biomarker for sepsis.This approach is rapid,requires a minimum amount of blood samples,and has the potential to provide valuable information for the early diagnosis and prognosis of sepsis.
基金supported by a faculty research grant of Yonsei University College of Medicine(6-2019-0184)a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institutefunded by the Ministry of Health and Welfare,Republic of Korea(HI14C1324).
文摘Background Takayasu arteritis(TAK)is a disease associated with increased risk of cardiovascular complications.We aimed to evaluate the incidence,prevalence and risk of stroke in patients with TAK.Methods Data from 1065 patients were obtained from a national database(2010–2018).The annual incidence and prevalence per 100000 persons were estimated using the registration population in the midst of every year,and the standardised incidence ratio(SIR)of stroke was compared with the general population based on the data from the 2006 national report for cardiovascular and cerebrovascular diseases.Age-adjusted incidence rate ratio(IRR)of stroke based on the time interval after diagnosis was also calculated.A time-dependent Cox regression was conducted to investigate predictive factors of stroke.Results The overall incidence rate of TAK ranged between 0.2 and 0.3/100000 person-years annually;the prevalence of TAK gradually increased,reaching 3.25/100000 person-years in 2018.Seventy-three(6.9%)patients experienced stroke during follow-up,and the risk of developing stroke was higher than the general population(overall SIR 7.39,95%CI 5.79 to 9.29;men:SIR 5.70,95%CI 2.84 to 10.20;women:SIR 7.06,95%CI 5.41 to 9.05).Most stroke events(90.9%)were cerebral infarction for men,whereas the proportion of cerebral infarction was lower(62.9%)in women.Over half of stroke events occurred within 6 months after diagnosis,and stroke was more common within 6 months of diagnosis compared with after 3 years in women(IRR 13.46,95%CI 6.86 to 26.40).In Cox regression analysis,age was the sole predictor of stroke(adjusted HR 1.02,95%CI 1.00 to 1.04,p=0.043).Conclusions The annual incidence of TAK was similar to the previous studies from Asia,and the risk of stroke increased in TAK.Different patterns of subtype and incidence of stroke were found according to sex,although age was the only predictor.
文摘Dear Editor,Coronavirus disease 2019(COVID-19)is a novel respiratory infectious disease,caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which led to a global pandemic.Although vaccination is the best measure to overcome a pandemic,the immunogenicity of vaccines can be influenced by diverse factors,including intrinsic(age,sex,genetics,and comorbidities),extrinsic(diet,nutrition,and behavior),and vaccine-associated characteristics.
基金This study was supported by the National Research Foundation Grant NRF-2018M3A9D3079498the Institute for Basic Science(IBS),Korea,under project code IBS-R801-D2.
文摘In November 2021,the Omicron variant(B.1.1.529)emerged and was designated a variant of concern(VOC)by the World Health Organization.Recently,Omicron was reported to extensively escape neutralizing antibodies elicited by COVID-19 vaccination or natural infection[1,2,3].However,whether Omicron evades the T cell immunity elicited by COVID-19 vaccination or natural infection remains to be elucidated.To address this issue,we analyzed the amino acid sequences of T cell epitopes identified from the original SARS-CoV-2 strain(Wuhan-Hu-1)in the Omicron variant(hCoV-19/South Africa/CERI-KRISP-K032284/2021).
