Yunling Zhang

A data-driven method for syndrome type identification and classification in traditional Chinese medicine

The efficacy of traditional Chinese medicine (TCM) treatments for Western medicine (WM) diseases relies heavily on the proper classification of patients into TCM syndrome types. The authors developed a data-driven method for solving the classification problem, where syndrome types were identified and quantified based on statistical patterns detected in unlabeled symptom survey data. The new method is a generalization of latent class analysis (LCA), which has been widely applied in WM research to solve a similar problem, i.e., to identify subtypes of a patient population in the absence of a gold standard. A well-known weakness of LCA is that it makes an unrealistically strong independence assumption. The authors relaxed the assumption by first detecting symptom co-occurrence patterns from survey data and used those statistical patterns instead of the symptoms as features for LCA. This new method consists of six steps: data collection, symptom co-occurrence pattern discovery, statistical pattern interpretation, syndrome identification, syndrome type identification and syndrome type classification. A software package called Lantern has been developed to support the application of the method. The method was illustrated using a data set on vascular mild cognitive impairment.

Comparison of the Mini-Mental State Examination and Montreal Cognitive Assessment executive subtests in detecting post-stroke cognitive impairment.

The Montreal Cognitive Assessment (MoCA) has been shown to be more sensitive in detecting executive dysfunction than the Mini‐Mental State Examination (MMSE). However, it is still not known whether all the MoCA executive subtests contribute to the superior sensitivity. Thus, the present study aimed to determine how much executive abnormality was detected by the MMSE and MoCA executive subtests in a population‐based cohort of Chinese post‐stroke patients.

Kudiezi injection mitigates myocardial injury induced by acute cerebral ischemia in rats.

BackgroundKudiezi (KDZ) injection is commonly used in traditional Chinese medicine as treatment for cerebral infarction and angina pectoris. The present study investigated the therapeutic effects of KDZ injection on myocardial injury induced by acute cerebral ischemia and the possibly protective mechanisms.MethodsRats were divided into three groups: sham, 6h-ischemia, and KDZ treatment (KDZ). The neurological deficits were determined by the Garcia score. The cerebral infarct volume was measured by 2,3,5-triphenyltetrazolium chloride (TTC) staining, and brain water content was also evaluated. Serum creatinine kinase (CK), lactate dehydrogenase (LDH), and creatine kinase-myocardial band (CK-MB) activity, myocardial tissue malondialdehyde (MDA) levels, L-Glutathione (GSH) levels, and superoxide dismutase (SOD) activity as well as mitochondrial cytochrome c oxidase (COX) activity were determined. Mitochondrial COX I and COX III mRNA expressions of myocardial tissues were measured by RT-PCR.ResultsImpaired neurological function and brain edema were observed in the 6h-ischemia group. TTC staining showed that the 6h-ischemia group had larger infarct zones than the sham group. Myocardial ischemic changes (widened myocardial cell gap, cracks, and obvious edema) were detected in the 6h-ischemia group compared with the sham group, with elevated serum CK-MB activity and CK and LDH levels. Electrocardiography showed lower medium frequency (MF) and high frequency (HF) in the 6h-ischemia group compared with the sham group. In myocardial tissue, COX activity was elevated in the 6h-ischemia compared with the sham group, while SOD, GSH, and MDA levels, and COX I and COX III mRNA expressions remained unchanged. KDZ injection decreased neurological impairment, brain edema, gaps between cells, and infarct size. Compared with the 6h-ischemia group, it reduced serum CK-MB activity and CK and LDH levels, and MDA levels in myocardial tissue. KDZ significantly increased GSH levels, SOD activity, and mitochondria COX activity and the expression of COX I and COX III mRNA in myocardial tissue compared with the sham group.ConclusionKDZ injection had a protective effect against cerebral ischemia in rats. KDZ injection could also alleviate myocardial injury after acute cerebral ischemia in rats. The possible mechanisms involve the regulation of the oxidative stress/antioxidant capacity after cerebral ischemia.

Improvement in cerebral ischemia–reperfusion injury through the TLR4/NF-κB pathway after Kudiezi injection in rats

Aim: Kudiezi injection (KDZ) can improve the clinical outcomes of patients with stroke, but the mechanisms remain unclear. This study aimed to investigate whether KDZ could modulate the nuclear factor kappa B (NF‐&kgr;B) pathways in rat models of transient middle cerebral artery occlusion (tMCAO). Materials and methods: Male Sprague–Dawley rats were subjected to tMCAO and randomized to Sham (sham‐operated), tMCAO (tMCAO + 0.9% saline), and KDZ (tMCAO + 7.2 mL/kg KDZ) groups. The infarct volume, brain water content, and neurological deficit were assessed 72 h after reperfusion. Immunofluorescence was used to detect the expression of cleaved caspase‐3 and NF‐&kgr;Bp65. The expression of cleaved caspase‐3, NF‐&kgr;B p65, TLR4, MyD88, TRAF6, and p‐I&kgr;B&agr;/I&kgr;B&agr; was determined using Western blotting. The expression levels of TNF‐&agr;, interleukin (IL)‐1&bgr;, and IL‐10 in the ischemic cortex were measured using the enzyme‐linked immunosorbent assay. In vitro ischemic paradigm (oxygen‐glucose deprivation) was performed in SH‐SY5Y cells to evaluate the effects of KDZ. Key findings: Lower brain water content, smaller infarct volume, and better neurologic function were found in the KDZ group compared with the tMCAO group. The expression of activated caspase‐3, TLR‐4, TRAF6, NF‐&kgr;Bp65, and p‐I&kgr;B&agr;/I&kgr;B&agr; reduced and the levels of TNF‐&agr; and IL‐1&bgr; decreased in the KDZ group, with the increased IL‐10 level. In SH‐SY5Y cells, KDZ significantly reduced the expression of p‐I&kgr;B&agr; and I&kgr;B&agr;, lowered the death ratio, and reversed the effects induced by caffeic acid phenethyl ester (a potent NF‐&kgr;B inhibitor). Significance: KDZ may function through downregulating the TLR‐4‐dependent NF‐&kgr;B signaling pathway to protect the brain against ischemic injury.

A Two-Level Model for the Analysis of Syndrome of Acute Ischemic Stroke: From Diagnostic Model to Molecular Mechanism

Prompt and accurate diagnosis of acute ischemic stroke is critical to seek acute therapy. In traditional Chinese medicine (TCM) science, there is a comprehensive system of diagnosis and medical care of acute ischemic stroke. Here we introduce a two-level model for the analysis of TCM syndrome of acute ischemic stroke. Owing to the limitation of sample size and imbalance, we focused on the analysis of wind-phlegm collateral obstruction syndrome (Feng Tan Yu Zu Zheng). Firstly, a Support-Vector-Machine- (SVM-) based diagnostic model was set up through selection of core symptoms. After pairwise undersampling, we improved the performance of prediction and generated the core symptoms-based diagnostic model of wind-phlegm collateral obstruction syndrome. Next, Pathway Pattern-based method and MetaDrug platform were used to shed light on the molecular basis of the significance of core symptoms in three complementary aspects: symptom-gene-pathway multilayer correlation network, enriched pathways, and most relevant interaction network. The integration of diagnostic model and molecular mechanism analysis creates an interesting perspective for better understanding the syndrome. The two-level model would provide a new opportunity for the study of TCM syndromes.

Identification and classification of traditional Chinese medicine syndrome types among senior patients with vascular mild cognitive impairment using latent tree analysis

OBJECTIVE To treat patients with vascular mild cognitive impairment (VMCI) using traditional Chinese medicine (TCM), it is necessary to classify the patients into TCM syndrome types and to apply different treatments to different types. In this paper, we investigate how to properly carry out the classification for patients with VMCI aged 50 or above using a novel data-driven method known as latent tree analysis (LTA). METHOD A cross-sectional survey on VMCI was carried out in several regions in Northern China between February 2008 and February 2012 which resulted in a data set that involves 803 patients and 93 symptoms. LTA was performed on the data to reveal symptom co-occurrence patterns, and the patients were partitioned into clusters in multiple ways based on the patterns. The patient clusters were matched up with syndrome types, and population statistics of the clusters are used to quantify the syndrome types and to establish classification rules. RESULTS Eight syndrome types are identified: Qi deficiency, Qi stagnation, Blood deficiency, Blood stasis, Phlegm-dampness, Fire-heat, Yang deficiency, and Yin deficiency. The prevalence and symptom occurrence characteristics of each syndrome type are determined. Quantitative classification rules are established for determining whether a patient belongs to each of the syndrome types. CONCLUSION A solution for the TCM syndrome classification problem for patients with VMCI and aged 50 or above is established based on the LTA of unlabeled symptom survey data. The results can be used as a reference in clinic practice to improve the quality of syndrome differentiation and to reduce diagnosis variances across physicians. They can also be used for patient selection in research projects aimed at finding biomarkers for the syndrome types and in randomized control trials aimed at determining the efficacy of TCM treatments of VMCI.

Investigating the pattern of syndrome based on the difference of symptom network in depression

In TCM theory, the syndrome is crucial to diagnose diseases and treat patients. In syndrome identification, the relation of symptoms usually correlates with syndrome and represents the pattern of syndrome at symptomatic level. Hence, we learn models for classifying syndromes in depression using 4 different algorithms, which are naive Bayes, Bayes network, SVM and C4.5. From the results of classification, we find that the dependence of symptoms has something to do with the accuracies of syndrome classification. Then, 8 symptom networks corresponding to depression and 7 syndromes are constructed to explore the interaction profile of symptoms under syndrome. By comparing syndrome-specific symptom network to the base network of depression, we discover the enriched edges and different nodes to represent the pattern of each syndrome. Literature and symptom ranking by Fisher score demonstrate the correctness of the different nodes selected through network comparison. After all, the enriched edges and different nodes associated with a given syndrome reveal the pattern of that syndrome at symptomatic level.

Effects of Kudiezi Injection on Serum Inflammatory Biomarkers in Patients with Acute Cerebral Infarction

Background Kudiezi injection is a traditional Chinese medicine for acute cerebral infarction, but the exact mechanisms are poorly understood. Objective To investigate the mechanisms of Kudiezi injection on the inflammatory response in the treatment of acute cerebral infarction. Methods This was a prospective study of patients with acute cerebral infarction within 48 h of onset and treated between July 2012 and July 2016 at three hospitals in China. The patients were randomized to routine treatments (control group) versus routine treatments and Kudiezi injection (Kudiezi group). The National Institutes of Health Stroke Score was assessed on days 1, 3, 5, 7, and 14. The patients were tested for serum levels of pro- and anti-inflammatory cytokines (S100 calcium-binding protein B, neuron-specific enolase, interleukin-6, interleukin-10, interleukin-18, and matrix metaloproteinase-9; by enzyme-linked immunosorbent assay) immediately after admission and on days 3, 5, and 14. Results Stroke scores were improved in both groups from days 1 to 14. On days 5 and 7, stroke scores in the Kudiezi group were lower than in the control group (P < 0.05). Compared with controls, the Kudiezi group had lower serum S100 calcium-binding protein B on day 14; higher interleukin-6 and interleukin-10 on day 3; lower interleukin-6 and interleukin-18 on day 5; and lower interleukin-18 and matrix metaloproteinase-9 on day 14. Conclusion Kudiezi injection could lead to early reduction of interleukin-6, interleukin-18, matrix metaloproteinase-9, neuron-specific enolase, and S100 calcium-binding protein B levels and increases of interleukin-10 levels in patients with acute ischemic stroke. This trial is registered with ClinicalTrials.gov NCT01636154.

A Network-Based Approach to Investigate the Pattern of Syndrome in Depression

In Traditional Chinese Medicine theory, syndrome is essential to diagnose diseases and treat patients, and symptom is the foundation of syndrome differentiation. Thus the combination and interaction between symptoms represent the pattern of syndrome at phenotypic level, which can be modeled and analyzed using complex network. At first, we collected inquiry information of 364 depression patients from 2007 to 2009. Next, we learned classification models for 7 syndromes in depression using naïve Bayes, Bayes network, support vector machine (SVM), and C4.5. Among them, SVM achieves the highest accuracies larger than 0.9 except for Yin deficiency. Besides, Bayes network outperforms naïve Bayes for all 7 syndromes. Then key symptoms for each syndrome were selected using Fishers score. Based on these key symptoms, symptom networks for 7 syndromes as well as a global network for depression were constructed through weighted mutual information. Finally, we employed permutation test to discover dynamic symptom interactions, in order to investigate the difference between syndromes from the perspective of symptom network. As a result, significant dynamic interactions were quite different for 7 syndromes. Therefore, symptom networks could facilitate our understanding of the pattern of syndrome and further the improvement of syndrome differentiation in depression.