Objective:To construct and verify a nomogram prediction model basing on the influencing factors of the ac-tivity of daily living(ADL)disorders in ischemic stroke(IS),providing evidences for the early clinical imple-mentation of efficient rehabilitation therapies.Method:We retrospectively collected clinical data of 401 IS patients hospitalized in the department of Rehabili-tation of Guangdong Provincial Hospital of Chinese Medicine from January 2019 to December 2022,focusing on factors that may influence their ADL.These patients were randomly divided into the training(280 cases)and the validation(121 cases)sets according to 7∶3.We used univariate and multivariate logistic regression to analyze the influencing factors for the occurrence of ADL disorders in IS patients,and established the risk pre-diction model for the occurrence of ADL disorders by a visualized nomogram.The performance of this predic-tion model was assessed by the area under the curve(AUC),specificity,sensitivity,and calibration curve.Result:The results of multivariate logistic regression analysis in the training sets showed that age,diabetes mellitus,NIHSS score,FMA score,Hb,PLT,and HDL were independent influencing factors for the occur-rence of ADL disorders in IS patients.The nomogram model was constructed with the above 7 factors.The AUC,specificity,and sensitivity of this model were 0.875,82.8%,and 75.4%in the validation set,respec-tively,indicating that the model has a good discriminative ability.The calibration curve showed that the mod-el agrees well with the actual predicted probability.Conclusion:The nomogram risk prediction model constructed in this study can effectively predict the probabili-ty of ADL disorder in IS patients,aiding medical staff in implementing early rehabilitation intervention as early as possible and reduce the occurrence of ADL disorder.

Diabetic cardiomyopathy(DCM)is one of the complications of diabetes.The onset of DCM is hidden and easy to be ignored.Myocardial injury is serious in the later stage and the prognosis is poor.At present,symptomatic treatment is the main clinical treatment.Cuproptosis is a novel cell death mode caused by imbalanced copper ion concentration in the body,leading to mitochondrial metabolic abnormalities,which is one of the important mechanisms of DCM.Targeted cuproptosis pathway therapy for DCM is currently a focus and hotspot of research.The"Qi Luo Theory"is one of the disciplinary branches of the theory of collateral diseases,which mainly operates the meridian Qi system.The syndrome and treatment system of collateral diseases cardiovascular diseases have important guiding significance for the treatment of DCM.Traditional Chinese medicine believes that deficiency and stagnation of Qi that in the collaterals are the root causes of DCM,with stasis and toxin obstructing collaterals and damage to the heart collaterals being the core of the disease.The ultimate outcome is the deficiency and decline of Qi,Blood,Yin,and Yang in the heart.The"Qi Luo Theory"and cuproptosis have similarities in physiological functions and pathological processes,and cuproptosis can be said to be one of the microscopic manifestations of the"Qi Luo theory".Based on this,the staged treatment principle of tonifying deficiency and promoting stagnation as the norm,attacking and supplementing simultaneously as the principle,and strengthening the body and consolidating the core has been proposed,in order to provide theoretical reference for the clinical treatment of DCM.

Diabetic cardiomyopathy(DCM)is one of the complications of diabetes.The onset of DCM is hidden and easy to be ignored.Myocardial injury is serious in the later stage and the prognosis is poor.At present,symptomatic treatment is the main clinical treatment.Cuproptosis is a novel cell death mode caused by imbalanced copper ion concentration in the body,leading to mitochondrial metabolic abnormalities,which is one of the important mechanisms of DCM.Targeted cuproptosis pathway therapy for DCM is currently a focus and hotspot of research.The"Qi Luo Theory"is one of the disciplinary branches of the theory of collateral diseases,which mainly operates the meridian Qi system.The syndrome and treatment system of collateral diseases cardiovascular diseases have important guiding significance for the treatment of DCM.Traditional Chinese medicine believes that deficiency and stagnation of Qi that in the collaterals are the root causes of DCM,with stasis and toxin obstructing collaterals and damage to the heart collaterals being the core of the disease.The ultimate outcome is the deficiency and decline of Qi,Blood,Yin,and Yang in the heart.The"Qi Luo Theory"and cuproptosis have similarities in physiological functions and pathological processes,and cuproptosis can be said to be one of the microscopic manifestations of the"Qi Luo theory".Based on this,the staged treatment principle of tonifying deficiency and promoting stagnation as the norm,attacking and supplementing simultaneously as the principle,and strengthening the body and consolidating the core has been proposed,in order to provide theoretical reference for the clinical treatment of DCM.

Objective:To construct and verify a nomogram prediction model basing on the influencing factors of the ac-tivity of daily living(ADL)disorders in ischemic stroke(IS),providing evidences for the early clinical imple-mentation of efficient rehabilitation therapies.Method:We retrospectively collected clinical data of 401 IS patients hospitalized in the department of Rehabili-tation of Guangdong Provincial Hospital of Chinese Medicine from January 2019 to December 2022,focusing on factors that may influence their ADL.These patients were randomly divided into the training(280 cases)and the validation(121 cases)sets according to 7∶3.We used univariate and multivariate logistic regression to analyze the influencing factors for the occurrence of ADL disorders in IS patients,and established the risk pre-diction model for the occurrence of ADL disorders by a visualized nomogram.The performance of this predic-tion model was assessed by the area under the curve(AUC),specificity,sensitivity,and calibration curve.Result:The results of multivariate logistic regression analysis in the training sets showed that age,diabetes mellitus,NIHSS score,FMA score,Hb,PLT,and HDL were independent influencing factors for the occur-rence of ADL disorders in IS patients.The nomogram model was constructed with the above 7 factors.The AUC,specificity,and sensitivity of this model were 0.875,82.8%,and 75.4%in the validation set,respec-tively,indicating that the model has a good discriminative ability.The calibration curve showed that the mod-el agrees well with the actual predicted probability.Conclusion:The nomogram risk prediction model constructed in this study can effectively predict the probabili-ty of ADL disorder in IS patients,aiding medical staff in implementing early rehabilitation intervention as early as possible and reduce the occurrence of ADL disorder.

Objective To study the distribution and significance of CD1a and CD83 positive dendritic cells(DCs)in lung tissue of chronic obstructive pulmonary disease(COPD)mice.Methods Twenty C57BL/6 mice were randomly divided into the air control group and the smoked COPD group(n=10 for each group).COPD mouse model was established using cigarette smoking method.Mice were executed within 24 h after the last cigarette smoking,and right lower lung was collected.Body mass changes and lung histopathological changes of mice were observed in two groups.Mean linear intercept(MLI)was measured,and expression levels of CD1a+and CD83+DCs in lung tissue were detected by immunohistochemistry.Results The body mass of mice at 7,14,21 and 28 d after modeling was lower in the smoked COPD group than that in the air control group(P＜0.05).HE staining showed that the normal alveolar structure of lung tissue of mice in the smoked group was disrupted,with multiple alveoli fused with each other to form a larger alveolar lumen,a large number of inflammatory cells infiltrated in alveolar intervals,and walls of the alveoli were thickened.COPD modeling was successful.Compared with the air control group,MLI values(μm)increased in the smoked COPD group(28.30±3.47 vs.50.40±3.60),and the number of CD1a+DCs(per field of view)in lung tissue increased(9.58±2.18 vs.17.08±3.67),while the number of CD83+DCs(per field of view)decreased(19.78±4.95 vs.8.02±3.30)(all P＜0.05).Conclusion The number of CD1a+DCs in lung tissue is increased and the number of CD83+DCs in lung tissue is decreased in the smoked COPD group of mice,and cigarette smoking may have impaired DC maturation.

Objective:To compare the clinical value of digital breast tomosynthesis (DBT) localization and stereotactic positioning biopsy of breast lesions.Methods:This study was a cross-sectional study. Totally of 250 patients who underwent breast biopsy at Shenzhen People′s Hospital, Luohu District People′s Hospital and Peking University Shenzhen Hospital between August 2021 to October 2023 was analyzed retrospectively, including 136 cases of DBT-guided biopsy (DBT-guided group) and 114 cases of stereotactic-guided biopsy (stereotactic-guided group). The stereotactic-guided biopsy methods included core needle biopsy (CNB) and wire positioning. The DBT-guided biopsy methods included CNB, wire positioning and vacuum-assisted breast biopsy (VABB). The χ2 test or Mann-Whitney U test was used to compare the puncture success rate, operation time, localization time, puncture time, number of first valid localization phases obtained, number of exposures, and complications of different biopsy methods between 2 groups. Results:In the wire positioning biopy, the puncture success rate was 100% (33/33) in DBT-guided group and 96% (48/50) in the stereotactic-guided group, with no statistically significant difference between the two groups ( P=0.515). Compared to the stereotactic-guided group, the operation time and localization time were shorter, and the number of first valid localization phases obtained, number of exposures were fewer in the DBT-guided group( P<0.05). The incidence of complications was lower in both the DBT-guided group and the stereotactic-guided group, with no statistically significant difference ( P=0.871). In CNB, both the DBT-guided group and the stereotactic-guided group had higher puncture success rates, with no statistically significant difference ( P=0.080). Compared to the stereotactic-guided group, the operation time, localization time and puncture time were shorter, and the number of first valid localization phases obtained, number of exposures were lower in the DBT-guided group, and the difference between the two groups were statistically significant ( P<0.05). The incidence of complications was lower in both the DBT-guided group and the stereotactic-guided group, with no statistically significant difference ( P=0.627). Twenty-one cases received DBT-guided VABB, with an operation time of (19.90±3.38) min, a localization time of 6.00 (6.00, 7.00) min, a puncture time of (13.42±3.28) min, the number of first effective localization phases obtained was 1.00 (1.00, 1.00) time, the number of exposures was 4.00 (3.50, 5.00) times, and one case experienced severe pain after puncture. Conclusion:Compared with stereotactic-guided biopsy, DBT-guided biopsy can reduce operation time and exposure times, and can target more types of breast lesions, with higher clinical application value.

Objective:Cognitive impairment is one of the common complications of stroke,which can affect the rehabili-tation and their quality of life.It is very important to build reliable risk prediction model tools to detect post-stroke cognitive impairment(PSCI)in advance,but there is still no clinical risk prediction model for PSCI.Our aim was to identify the influencing factors of PSCI in convalescent stroke patients and construct a nomo-gram model for predicting the risk of PSCI based on these factors.Method:We retrospectively collected the demographic characteristics and clinically relevant data of convales-cent stroke patients hospitalized in Guangdong Provincial Hospital of Chinese Medicine from December 2019 to December 2022.Then we randomly divided the whole data set into the training set and the validation set according to 7:3,the former data was used to construct a nomogram model for predicting the risk of PSCI,and the latter data was used to evaluate the model performance.Univariate and multivariate logistic regression were used to analyze the factors affecting PSCI in convalescent stroke patients.Based on these factors,we used the R software to construct a PSCI risk prediction model who was visualized through a nomogram.The model performance was evaluated using the area under the curve(AUC),sensitivity,specificity,calibration curve,and decision curve analysis(DCA).Result:Our prediction model indicated that age,right hemiparesis,hypertension,coronary heart disease,hyper-homocysteinemia,Fugl-Meyer assessment scale(FMA)score,modified Barthel index(MBI)score and,mean cor-puscular hemoglobin were independent factors influencing the occurrence of PSCI in convalescent stroke pa-tients.The AUC,sensitivity and specificity of the model were 0.804,75.5％and 73.7％in the training set,and 0.737,82.9％and 62.8％in the validation set,suggesting that the model had a good discrimination.The calibra-tion curve of the training and validation sets indicated a good consistency between the prediction and the real observation.The decision curve analysis of the training and validation sets showed that the PSCI risk prediction model performed well in terms of the net clinical benefit.Conclusion:The PSCI risk prediction nomogram model constructed in this study can be personalize prediction of cognitive impairment probabilities in convalescent stroke patients,which can help healthcare providers to de-tect and treat PSCI early and improve patient outcome.

Objective:Cognitive impairment is one of the common complications of stroke,which can affect the rehabili-tation and their quality of life.It is very important to build reliable risk prediction model tools to detect post-stroke cognitive impairment(PSCI)in advance,but there is still no clinical risk prediction model for PSCI.Our aim was to identify the influencing factors of PSCI in convalescent stroke patients and construct a nomo-gram model for predicting the risk of PSCI based on these factors.Method:We retrospectively collected the demographic characteristics and clinically relevant data of convales-cent stroke patients hospitalized in Guangdong Provincial Hospital of Chinese Medicine from December 2019 to December 2022.Then we randomly divided the whole data set into the training set and the validation set according to 7:3,the former data was used to construct a nomogram model for predicting the risk of PSCI,and the latter data was used to evaluate the model performance.Univariate and multivariate logistic regression were used to analyze the factors affecting PSCI in convalescent stroke patients.Based on these factors,we used the R software to construct a PSCI risk prediction model who was visualized through a nomogram.The model performance was evaluated using the area under the curve(AUC),sensitivity,specificity,calibration curve,and decision curve analysis(DCA).Result:Our prediction model indicated that age,right hemiparesis,hypertension,coronary heart disease,hyper-homocysteinemia,Fugl-Meyer assessment scale(FMA)score,modified Barthel index(MBI)score and,mean cor-puscular hemoglobin were independent factors influencing the occurrence of PSCI in convalescent stroke pa-tients.The AUC,sensitivity and specificity of the model were 0.804,75.5％and 73.7％in the training set,and 0.737,82.9％and 62.8％in the validation set,suggesting that the model had a good discrimination.The calibra-tion curve of the training and validation sets indicated a good consistency between the prediction and the real observation.The decision curve analysis of the training and validation sets showed that the PSCI risk prediction model performed well in terms of the net clinical benefit.Conclusion:The PSCI risk prediction nomogram model constructed in this study can be personalize prediction of cognitive impairment probabilities in convalescent stroke patients,which can help healthcare providers to de-tect and treat PSCI early and improve patient outcome.

OBJECTIVE: To assess the practical and health economical values of non-invasive prenatal test (NIPT) in Changsha Municipal Public Welfare Program. METHODS: A retrospective analysis was carried out on 149 165 women undergoing NIPT test from April 9, 2018 to December 31, 2019. For pregnant women with high risks, invasive prenatal diagnosis and follow-up of pregnancy outcome were conducted. The cost-benefit of NIPT for Down syndrome was analyzed. RESULTS: NIPT was carried out for 149 165 pregnant women and succeeded in 148 749 cases (99.72%), for which outcome were available in 148 538 (99.86%). 90% of pregnant women from the region accepted the screening with NIPT. 415 (0.27%) were diagnosed as high risk. Among these, 381 (91.81%) accepted amniocentesis, which led to the diagnosis of 212 cases of trisomy 21 (PPV=85.14%), 41 cases with trisomy 18 (PPV=48.81%) and 10 cases with trisomy 13 (PPV=20.83%). The sensitivity and specificity of NIPT for trisomy 21, trisomy 18 and trisomy 13 were (97.70%, 99.98%), (97.62%, 9.97%) and (100%, 99.97%), respectively. In addition, 213 and 30 cases were diagnosed with sex chromosomal aneuploidies (PPV=46.2%) and other autosomal anomalies (PPV=16.57%), respectively. For Down syndrome screening, the cost and benefit of the project was 120.79 million yuan and 1,056.95 million yuan, respectively. The cost-benefit ratio was 1: 8.75, and safety index was 0.0035. CONCLUSION NIPT is a highly accurate screening test for trisomy 21, which was followed by trisomy 18 and sex chromosomal aneuploidies, while it was less accurate for other autosomal aneuploidies. The application of NIPT screening has a high health economical value.
Aneuploidy ; Cost-Benefit Analysis ; Female ; Humans ; Noninvasive Prenatal Testing ; Pregnancy ; Retrospective Studies ; Trisomy 18 Syndrome/genetics*

With the recent upsurge of studies in the field of microbiology, we have learned more about the complexity of the gastrointestinal microecosystem. More than 30 genera and 1000 species of gastrointestinal microflora have been found. The structure of the normal microflora is relatively stable, and is in an interdependent and restricted dynamic equilibrium with the body. In recent years, studies have shown that there is a potential relationship between gastrointestinal microflora imbalance and gastric cancer (GC) and precancerous lesions. So, restoring the balance of gastrointestinal microflora is of great significance. Moreover, intervention in gastric premalignant condition (GPC), also known as precancerous lesion of gastric cancer (PLGC), has been the focus of current clinical studies. The holistic view of traditional Chinese medicine (TCM) is consistent with the microecology concept, and oral TCM can play a two-way regulatory role directly with the microflora in the digestive tract, restoring the homeostasis of gastrointestinal microflora to prevent canceration. However, large gaps in knowledge remain to be addressed. This review aims to provide new ideas and a reference for clinical practice.
Drugs, Chinese Herbal/therapeutic use* ; Gastrointestinal Microbiome ; Humans ; Medicine, Chinese Traditional ; Precancerous Conditions/pathology* ; Stomach Neoplasms/pathology*