Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and prediction.However,action recognition currently used in non-human primate(NHP)research ...Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and prediction.However,action recognition currently used in non-human primate(NHP)research relies heavily on intense manual labor and lacks standardized assessment.In this work,we established two standard benchmark datasets of NHPs in the laboratory:Monkeyin Lab(Mi L),which includes 13 categories of actions and postures,and MiL2D,which includes sequences of two-dimensional(2D)skeleton features.Furthermore,based on recent methodological advances in deep learning and skeleton visualization,we introduced the Monkey Monitor Kit(Mon Kit)toolbox for automatic action recognition,posture estimation,and identification of fine motor activity in monkeys.Using the datasets and Mon Kit,we evaluated the daily behaviors of wild-type cynomolgus monkeys within their home cages and experimental environments and compared these observations with the behaviors exhibited by cynomolgus monkeys possessing mutations in the MECP2 gene as a disease model of Rett syndrome(RTT).Mon Kit was used to assess motor function,stereotyped behaviors,and depressive phenotypes,with the outcomes compared with human manual detection.Mon Kit established consistent criteria for identifying behavior in NHPs with high accuracy and efficiency,thus providing a novel and comprehensive tool for assessing phenotypic behavior in monkeys.展开更多
BACKGROUND Acute respiratory distress syndrome(ARDS)is a major cause of death in patients with severe acute pancreatitis(SAP).Although a series of prediction models have been developed for early identification of such...BACKGROUND Acute respiratory distress syndrome(ARDS)is a major cause of death in patients with severe acute pancreatitis(SAP).Although a series of prediction models have been developed for early identification of such patients,the majority are complicated or lack validation.A simpler and more credible model is required for clinical practice.AIM To develop and validate a predictive model for SAP related ARDS.METHODS Patients diagnosed with AP from four hospitals located at different regions of China were retrospectively grouped into derivation and validation cohorts.Statistically significant variables were identified using the least absolute shrinkage and selection operator regression method.Predictive models with nomograms were further built using multiple logistic regression analysis with these picked predictors.The discriminatory power of new models was compared with some common models.The performance of calibration ability and clinical utility of the predictive models were evaluated.RESULTS Out of 597 patients with AP,139 were diagnosed with SAP(80 in derivation cohort and 59 in validation cohort)and 99 with ARDS(62 in derivation cohort and 37 in validation cohort).Four identical variables were identified as independent risk factors for both SAP and ARDS:heart rate[odds ratio(OR)=1.05;95%CI:1.04-1.07;P<0.001;OR=1.05,95%CI:1.03-1.07,P<0.001],respiratory rate(OR=1.08,95%CI:1.0-1.17,P=0.047;OR=1.10,95%CI:1.02-1.19,P=0.014),serum calcium concentration(OR=0.26,95%CI:0.09-0.73,P=0.011;OR=0.17,95%CI:0.06-0.48,P=0.001)and blood urea nitrogen(OR=1.15,95%CI:1.09-1.23,P<0.001;OR=1.12,95%CI:1.05-1.19,P<0.001).The area under receiver operating characteristic curve was 0.879(95%CI:0.830-0.928)and 0.898(95%CI:0.848-0.949)for SAP prediction in derivation and validation cohorts,respectively.This value was 0.892(95%CI:0.843-0.941)and 0.833(95%CI:0.754-0.912)for ARDS prediction,respectively.The discriminatory power of our models was improved compared with that of other widely used models and the calibration ability and clinical utility of the prediction models performed adequately.CONCLUSION The present study constructed and validated a simple and accurate predictive model for SAPrelated ARDS in patients with AP.展开更多
Pattern identification （PI）, also called Bian Zheng （辨证）, syndrome differentiation, pattem diagnosis, or pattem classification, is the basic principle and the key concept of Chinese medicine （CM）. The core of ...Pattern identification （PI）, also called Bian Zheng （辨证）, syndrome differentiation, pattem diagnosis, or pattem classification, is the basic principle and the key concept of Chinese medicine （CM）. The core of PI is CM syndrome, on which CM theory, therapeutic method, prescribing formula and the use of Chinese herbal medicine are basically based. PI, in fact, is another classification method anticipated to improve the clinical efficacy. How to make an exact PI seems to be very important for taking full advantage of PI in clinical practice. Therefore, the establishment of diagnostic criterion of pattern has been the prerequisite for the standardization of PI. In recent years, a lot of diagnostic criteria of different CM patterns have been formulated. Taking the diagnostic criteria for blood-stasis syndrome as a model, the methodologies and considerations in establishing a pattern diagnostic criterion were discussed in this paper, which might be of great reference value in future PI standardization research.展开更多
Objective： To observe the influence of treatment based on Chinese medicine pattem identification on cellular immunophenotype of the myelodysplastic syndrome （MDS）. Methods： Sixty patients with MDS were randomly an...Objective： To observe the influence of treatment based on Chinese medicine pattem identification on cellular immunophenotype of the myelodysplastic syndrome （MDS）. Methods： Sixty patients with MDS were randomly and equally assigned to the treatment group and the control group using a randomized digital table. Thirty patients in each group included 3 risk levels （low, moderate and high risks） with each level 10 patients according to the international prognostic scoring system. The control group was given conventional therapy which was also used in the treatment group. While the treatment group was given Zuogui Pill （左归丸） and Yougui Pill （右归丸） for low risk patients; Qingwen Baidu Decoction （清瘟败毒饮） and Bazhen Decoction （八珍汤） for moderate risk patients; Gexia Zhuyu Decoction （膈下逐瘀汤） and Qinghao Biejia Decoction （青蒿鳖甲汤） combined with Shiquan Dabu Decoction （十全大补汤 ） for high risk patients. After the treatment, the differences of overatl response rate and immunophenotype （CD13, CD14, CD15, CD33 and CD34） of each group were analyzed. Results： The overall response rate of the treatment group was significantly higher than the control group in low risk and moderate risk patients （P=0.029）, there was no statistical differences of overall response rate between the treatment group and the control group in high risk patients （P=0.089）. The expressions of CD13, CD14, CD33 and CD34 in all three risk levels of the treatment group were obviously decreased after the treatment, while CD15 in all three risk levels of the treatment group was obviously increased after the treatment （P〈0.05 or P〈0.01）. Meanwhile, the difference values of CD13 and CD33 in low risk level of the treatment group, CD33 and CD34 in moderate risk level of the treatment group as well as CD34 and CD15 in high risk level of the treatment group, were all greater than the control groups and they were statistically significant （P〈0.05 or P〈0.01）. Conclusions： It shows a better therapeutic effect if the MDS patients treated with Chinese medicine pattern identification in addition to conventional therapy. Since the treatment may inhibit the malignant clones and improve the dysmaturity of granulocyte differentiation, it is a feasible option in clinical practice.展开更多
Objective：To analyze the diagnostic consistency of Chinese medicine（CM） specialists in patients with cardiovascular disease and to study syndrome classification and identification based on the multi-label learning ...Objective：To analyze the diagnostic consistency of Chinese medicine（CM） specialists in patients with cardiovascular disease and to study syndrome classification and identification based on the multi-label learning method.Methods：Using self-developed CM clinical scales to collect cases,inquiry information,complexity,tongue manifestation and pulse manifestation were assessed.The number of cases collected was 2,218.Firstly,each case was differentiated by two CM specialists according to the same diagnostic criteria.The consistency of the diagnosis based on Cohen’s Kappa coefficient was analyzed.Secondly,take the same diagnosis syndromes of two specialists as the results of the cases.According to injury information in the CM scale ＂yes＂ or ＂no＂ was assigned ＂1＂ or ＂0＂,and according to the syndrome type in each case ＂yes＂ or ＂no＂ was assigned ＂1＂ or ＂0＂.CM information data on cardiovascular disease cases were established.We studied CM syndrome classification and identification based on the relevant feature for each label（REAL） leaming method,and the diagnostic rate of the syndrome was studied using the REAL method when the number of features selected was 5,10,15,20,30,50,70,and 100,respectively.Results：The syndromes with good diagnostic consistency were Heart（Xin）-qi deficiency,Heart-yang deficiency,Heart-yin deficiency,phlegm,stagnation of blood and stagnation of qi.Syndromes with poor diagnostic consistency were heartblood deficiency and blood deficiency of Heart and Liver（Gan）.The highest diagnostic rates using the REAL method were Heart-yang deficiency followed by Heart-qi deficiency.A different number of features,such as 5,10,15,20,30,40,50,70,and 100,respectively,were selected and the diagnostic accuracy based on five features showed the highest diagnostic accuracy.The top five features which had a strong correlation with the syndromes were in accordance with the CM theory.Conclnsions：CM syndrome differentiation is strongly subjective and it is difficult to obtain good diagnostic consistency.The REAL method fully considers the relationship between syndrome types and injury symptoms,and is suitable for the establishment of models for CM syndrome classification and identification.This method can probably provide the prerequisite for objectivity and standardization of CM differentiation.展开更多
基金supported by the National Key R&D Program of China (2021ZD0202805,2019YFA0709504,2021ZD0200900)National Defense Science and Technology Innovation Special Zone Spark Project (20-163-00-TS-009-152-01)+4 种基金National Natural Science Foundation of China (31900719,U20A20227,82125008)Innovative Research Team of High-level Local Universities in Shanghai,Science and Technology Committee Rising-Star Program (19QA1401400)111 Project (B18015)Shanghai Municipal Science and Technology Major Project (2018SHZDZX01)Shanghai Center for Brain Science and Brain-Inspired Technology。
文摘Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and prediction.However,action recognition currently used in non-human primate(NHP)research relies heavily on intense manual labor and lacks standardized assessment.In this work,we established two standard benchmark datasets of NHPs in the laboratory:Monkeyin Lab(Mi L),which includes 13 categories of actions and postures,and MiL2D,which includes sequences of two-dimensional(2D)skeleton features.Furthermore,based on recent methodological advances in deep learning and skeleton visualization,we introduced the Monkey Monitor Kit(Mon Kit)toolbox for automatic action recognition,posture estimation,and identification of fine motor activity in monkeys.Using the datasets and Mon Kit,we evaluated the daily behaviors of wild-type cynomolgus monkeys within their home cages and experimental environments and compared these observations with the behaviors exhibited by cynomolgus monkeys possessing mutations in the MECP2 gene as a disease model of Rett syndrome(RTT).Mon Kit was used to assess motor function,stereotyped behaviors,and depressive phenotypes,with the outcomes compared with human manual detection.Mon Kit established consistent criteria for identifying behavior in NHPs with high accuracy and efficiency,thus providing a novel and comprehensive tool for assessing phenotypic behavior in monkeys.
基金Supported by the Chinese Natural Science Foundation,No.32170788.
文摘BACKGROUND Acute respiratory distress syndrome(ARDS)is a major cause of death in patients with severe acute pancreatitis(SAP).Although a series of prediction models have been developed for early identification of such patients,the majority are complicated or lack validation.A simpler and more credible model is required for clinical practice.AIM To develop and validate a predictive model for SAP related ARDS.METHODS Patients diagnosed with AP from four hospitals located at different regions of China were retrospectively grouped into derivation and validation cohorts.Statistically significant variables were identified using the least absolute shrinkage and selection operator regression method.Predictive models with nomograms were further built using multiple logistic regression analysis with these picked predictors.The discriminatory power of new models was compared with some common models.The performance of calibration ability and clinical utility of the predictive models were evaluated.RESULTS Out of 597 patients with AP,139 were diagnosed with SAP(80 in derivation cohort and 59 in validation cohort)and 99 with ARDS(62 in derivation cohort and 37 in validation cohort).Four identical variables were identified as independent risk factors for both SAP and ARDS:heart rate[odds ratio(OR)=1.05;95%CI:1.04-1.07;P<0.001;OR=1.05,95%CI:1.03-1.07,P<0.001],respiratory rate(OR=1.08,95%CI:1.0-1.17,P=0.047;OR=1.10,95%CI:1.02-1.19,P=0.014),serum calcium concentration(OR=0.26,95%CI:0.09-0.73,P=0.011;OR=0.17,95%CI:0.06-0.48,P=0.001)and blood urea nitrogen(OR=1.15,95%CI:1.09-1.23,P<0.001;OR=1.12,95%CI:1.05-1.19,P<0.001).The area under receiver operating characteristic curve was 0.879(95%CI:0.830-0.928)and 0.898(95%CI:0.848-0.949)for SAP prediction in derivation and validation cohorts,respectively.This value was 0.892(95%CI:0.843-0.941)and 0.833(95%CI:0.754-0.912)for ARDS prediction,respectively.The discriminatory power of our models was improved compared with that of other widely used models and the calibration ability and clinical utility of the prediction models performed adequately.CONCLUSION The present study constructed and validated a simple and accurate predictive model for SAPrelated ARDS in patients with AP.
基金Supported by the Twelve Five-year Plan of China(No.2013BA113B01)
文摘Pattern identification （PI）, also called Bian Zheng （辨证）, syndrome differentiation, pattem diagnosis, or pattem classification, is the basic principle and the key concept of Chinese medicine （CM）. The core of PI is CM syndrome, on which CM theory, therapeutic method, prescribing formula and the use of Chinese herbal medicine are basically based. PI, in fact, is another classification method anticipated to improve the clinical efficacy. How to make an exact PI seems to be very important for taking full advantage of PI in clinical practice. Therefore, the establishment of diagnostic criterion of pattern has been the prerequisite for the standardization of PI. In recent years, a lot of diagnostic criteria of different CM patterns have been formulated. Taking the diagnostic criteria for blood-stasis syndrome as a model, the methodologies and considerations in establishing a pattern diagnostic criterion were discussed in this paper, which might be of great reference value in future PI standardization research.
基金Supported by Zhejiang Provincial Administration of Traditional Chinese Medicine Youth Talent Program(No.2012ZQ010),China
文摘Objective： To observe the influence of treatment based on Chinese medicine pattem identification on cellular immunophenotype of the myelodysplastic syndrome （MDS）. Methods： Sixty patients with MDS were randomly and equally assigned to the treatment group and the control group using a randomized digital table. Thirty patients in each group included 3 risk levels （low, moderate and high risks） with each level 10 patients according to the international prognostic scoring system. The control group was given conventional therapy which was also used in the treatment group. While the treatment group was given Zuogui Pill （左归丸） and Yougui Pill （右归丸） for low risk patients; Qingwen Baidu Decoction （清瘟败毒饮） and Bazhen Decoction （八珍汤） for moderate risk patients; Gexia Zhuyu Decoction （膈下逐瘀汤） and Qinghao Biejia Decoction （青蒿鳖甲汤） combined with Shiquan Dabu Decoction （十全大补汤 ） for high risk patients. After the treatment, the differences of overatl response rate and immunophenotype （CD13, CD14, CD15, CD33 and CD34） of each group were analyzed. Results： The overall response rate of the treatment group was significantly higher than the control group in low risk and moderate risk patients （P=0.029）, there was no statistical differences of overall response rate between the treatment group and the control group in high risk patients （P=0.089）. The expressions of CD13, CD14, CD33 and CD34 in all three risk levels of the treatment group were obviously decreased after the treatment, while CD15 in all three risk levels of the treatment group was obviously increased after the treatment （P〈0.05 or P〈0.01）. Meanwhile, the difference values of CD13 and CD33 in low risk level of the treatment group, CD33 and CD34 in moderate risk level of the treatment group as well as CD34 and CD15 in high risk level of the treatment group, were all greater than the control groups and they were statistically significant （P〈0.05 or P〈0.01）. Conclusions： It shows a better therapeutic effect if the MDS patients treated with Chinese medicine pattern identification in addition to conventional therapy. Since the treatment may inhibit the malignant clones and improve the dysmaturity of granulocyte differentiation, it is a feasible option in clinical practice.
基金Supported by the National Natural Science Foundation of China(No.81173199)
文摘Objective：To analyze the diagnostic consistency of Chinese medicine（CM） specialists in patients with cardiovascular disease and to study syndrome classification and identification based on the multi-label learning method.Methods：Using self-developed CM clinical scales to collect cases,inquiry information,complexity,tongue manifestation and pulse manifestation were assessed.The number of cases collected was 2,218.Firstly,each case was differentiated by two CM specialists according to the same diagnostic criteria.The consistency of the diagnosis based on Cohen’s Kappa coefficient was analyzed.Secondly,take the same diagnosis syndromes of two specialists as the results of the cases.According to injury information in the CM scale ＂yes＂ or ＂no＂ was assigned ＂1＂ or ＂0＂,and according to the syndrome type in each case ＂yes＂ or ＂no＂ was assigned ＂1＂ or ＂0＂.CM information data on cardiovascular disease cases were established.We studied CM syndrome classification and identification based on the relevant feature for each label（REAL） leaming method,and the diagnostic rate of the syndrome was studied using the REAL method when the number of features selected was 5,10,15,20,30,50,70,and 100,respectively.Results：The syndromes with good diagnostic consistency were Heart（Xin）-qi deficiency,Heart-yang deficiency,Heart-yin deficiency,phlegm,stagnation of blood and stagnation of qi.Syndromes with poor diagnostic consistency were heartblood deficiency and blood deficiency of Heart and Liver（Gan）.The highest diagnostic rates using the REAL method were Heart-yang deficiency followed by Heart-qi deficiency.A different number of features,such as 5,10,15,20,30,40,50,70,and 100,respectively,were selected and the diagnostic accuracy based on five features showed the highest diagnostic accuracy.The top five features which had a strong correlation with the syndromes were in accordance with the CM theory.Conclnsions：CM syndrome differentiation is strongly subjective and it is difficult to obtain good diagnostic consistency.The REAL method fully considers the relationship between syndrome types and injury symptoms,and is suitable for the establishment of models for CM syndrome classification and identification.This method can probably provide the prerequisite for objectivity and standardization of CM differentiation.
