SIAP: an intelligent algorithm for multiple prescription pattern recognition based on weighted similarity distances.

BACKGROUND: Clinical practices have demonstrated that disease treatment can be very complex. Patients with chronic diseases often suffer from more than one disease. Complex diseases are often treated with a variety of drugs, including both primary and auxiliary treatments. This complexity and multidimensionality increase the difficulty of extracting knowledge from clinical data. METHODS: In this study, we proposed a subgroup identification algorithm for complex prescriptions (SIAP). We applied the SIAP algorithm to identify the importance level of each drug in complex prescriptions. The algorithm quickly classified and determined valid prescription combinations for patients. The algorithm was validated through classification matching of classical prescriptions in traditional Chinese medicine. We collected 376 formulas and their compositions from a formulary to construct a database of standard prescriptions. We also collected 1438 herbal prescriptions from clinical data for automated prescription identification. The prescriptions were divided into training and test sets. Finally, the parameters of the two sub-algorithms of SIAP and SIAP-All, as well as those of the combination algorithm SIAP + All, were optimized on the training set. A comparison analysis was performed against the baseline intersection set rate (ISR) algorithm. The algorithm for this study was implemented with Python 3.6. RESULTS: The SIAP-All and SIAP + All algorithms outperformed the benchmark ISR algorithm in terms of accuracy, recall, and F1 value. The F1 values were 0.7568 for SIAP-All and 0.7799 for SIAP + All, showing improvements of 8.73% and 11.04% over the existing ISR algorithm, respectively. CONCLUSION: We developed an algorithm, SIAP, to automatically match sub-prescriptions of complex drugs with corresponding standard or classic prescriptions. The matching algorithm weights the drugs in the prescription according to their importance level. The results of this study can help to classify and analyse the drug compositions of complex prescriptions.

In vivo analysis of ciliary muscle in myopic Chinese young adults using ArcScan Insight® 100.

OBJECTIVE: To analyse the morphological characteristics of the ciliary muscle (CM) and to explore its relationship with different ocular biometric parameters in myopic young Chinese adults. METHODS: This observational, cross-sectional study included 50 right eyes from 50 myopic adults. The CM area (CMA), CM thickness (CMT) and CM length (CML) were measured using the ArcScan Insight® 100. CMT was determined at three points: 1.0 mm (CMT-1), 2.0 mm (CMT-2) and 3.0 mm (CMT-3) posterior to the scleral spur. CML was measured on the scleral (CMLs) and vitreous (CMLv) aspects. The spherical equivalent refraction (SER), axial length (AL) and subfoveal choroidal thickness (SFCT) were examined to determine their associations with CM parameters (CMA, CML and CMT). RESULTS: The mean SER and AL were -4.39 ± 2.29 D and 25.61 ± 1.15 mm, respectively. Compared with the nasal CMA, CML and CMT (CMT-1, CMT-2 and CMT-3) findings, the temporal CM parameters (CMA, CMLs, CMLv, CMT-1, CMT-2 and CMT-3) were found to be significantly thicker (all p < 0.001, except CMLv and CMT-1; p < 0.01). The nasal CMA was associated with the average corneal curvature (r = 0.30, p = 0.03) and SER (r = -0.30, p = 0.04). Nasal and temporal CMT-2 were negatively correlated with SER (r = -0.33 and -0.32, respectively, both p < 0.05). There was no correlation between CM parameters (except nasal CMLs, r = 0.31, p = 0.03) and SFCT, or between CM parameters and either the AL or anterior chamber depth (all p > 0.05). CONCLUSION: These results suggest that there is temporal versus nasal asymmetry of the CM. CMA, CMT or CML did not vary with axial growth of the eye. The CM is not simply stretched as the eye elongates in myopic young adults.

Risk factors associated with 31-day unplanned readmission in 50,912 discharged patients after stroke in China.

BACKGROUND: Unplanned readmission within 31 days of discharge after stroke is a useful indicator for monitoring quality of hospital care. We evaluated the risk factors associated with 31-day unplanned readmission of stroke patients in China. METHODS: We identified 50,912 patients from 375 hospitals in 29 provinces, municipalities or autonomous districts across China who experienced an unplanned readmission after stroke between 2015 and 2016, and extracted data from the inpatients' cover sheet data from the Medical Record Monitoring Database. Patients were grouped into readmission within 31 days or beyond for analysis. Chi-squared test was used to analyze demographic information, health system and clinical process-related factors according to the data type. Multilevel logistic modeling was used to examine the effects of patient (level 1) and hospital (level 2) characteristics on an unplanned readmission ≤31 days. RESULTS: Among 50,912 patients, 14,664 (28.8%) were readmitted within 31 days after discharge. The commonest cause of readmissions were recurrent stroke (34.8%), hypertension (22.94%), cardio/cerebrovascular disease (13.26%) and diabetes/diabetic complications (7.34%). Higher risks of unplanned readmissions were associated with diabetes (OR = 1.089, P = 0.001), use of clinical pathways (OR = 1.174, P < 0.001), and being discharged without doctor's advice (OR = 1.485, P < 0.001). Lower risks were associated with basic medical insurances (OR ranging from 0.225 to 0.716, P < 0.001) and commercial medical insurance (OR = 0.636, P = 0.021), compared to self-paying for medical services. And patients aged 50 years old and above (OR ranging from 0.650 to 0.985, P < 0.05), with haemorrhagic stroke (OR = 0.467, P < 0.001), with length of stay more than 7 days in hospital (OR ranging from 0.082 to 0.566, P < 0.001), also had lower risks. CONCLUSIONS: Age, type of stroke, medical insurance status, type of discharge, use of clinical pathways, length of hospital stay and comorbidities were the most influential factors for readmission within 31 days.

Assessing the temporal within-day glycemic variability during hospitalization in patients with type 2 diabetes patients using continuous glucose monitoring: a retrospective observational study.

AIMS: Frequent and extensive within-day glycemic variability (GV) in blood glucose levels may increase the risk of hypoglycemia and long-term mortality in hospitalized patients with diabetes. We aimed to assess the amplitude and frequency of within-day GV in inpatients with type 2 diabetes and to explore the factors influencing within-day GV. METHODS: We conducted a single-center, retrospective observational study by analyzing hospital records and 10-day real-time continuous glucose monitoring data. Within-day GV was assessed using the coefficient of variation (%CV). The primary outcome was the amplitude and frequency of within-day GV. The frequency of within-day GV was assessed by the consecutive days (CD) of maintaining within the target %CV range after first reaching it (CD after first reaching the target) and the maximum consecutive days of maintaining within the target %CV range (Max-CD). The target %CV range was less than 24.4%. We evaluated the factors influencing within-day GV using COX regression and Poisson regression models. RESULTS: A total of 1050 cases were analyzed, of whom 86.57% reduced the amplitude of within-day GV before the sixth day of hospitalization. Of the 1050 hospitalized patients, 66.57% stayed within the target %CV range for less than two days after first reaching the target and 69.71% experienced a Max-CD of fewer than four days. Reducing the average postprandial glucose excursion (hazard ratio [HR]: 0.81, 95% confidence interval [CI]: 0.77-0.85; incidence rate ratios [IRR]: 0.72, 95% CI: 0.69-0.74) and the use of α-glucosidase inhibitors (IRR: 1.1, 95% CI: 1.01-1.18) and glucagon-like peptide-1 agonist (IRR: 1.30, 95% CI: 1.02-1.65) contributed to reducing the amplitude and decreasing the frequency of within-day GV. However, the use of insulin (HR: 0.64, 95% CI: 0.55-0.75; IRR: 0.86, 95% CI: 0.79-0.93) and glinide (HR: 0.47, 95% CI: 0.31-0.73; IRR: 0.84, 95% CI: 0.73-0.97) may lead to an increased frequency of within-day GV. CONCLUSIONS: An increasing frequency of within-day GV was observed during the hospitalization in patients with type 2 diabetes, despite the effective reduction in the amplitude of within-day GV. Using medications designed to lower postprandial blood glucose could contribute to minimize the risk of frequent within-day GV.

Acceptability and adoption of handheld computer data collection for public health research in China: a case study.

BACKGROUND: Handheld computers for data collection (HCDC) and management have become increasingly common in health research. However, current knowledge about the use of HCDC in health research in China is very limited. In this study, we administered a survey to a hard-to-reach population in China using HCDC and assessed the acceptability and adoption of HCDC in China. METHODS: Handheld computers operating Windows Mobile and Questionnaire Development Studio (QDS) software (Nova Research Company) were used for this survey. Questions on tobacco use and susceptibility were drawn from the Global Adult Tobacco Survey (GATS) and other validated instruments, and these were programmed in Chinese characters by local staff. We conducted a half-day training session for survey supervisors and a three-day training session for 20 interviewers and 9 supervisors. After the training, all trainees completed a self-assessment of their skill level using HCDC. The main study was implemented in fall 2010 in 10 sites, with data managed centrally in Beijing. Study interviewers completed a post-survey evaluation questionnaire on the acceptability and utility of HCDC in survey research. RESULTS: Twenty-nine trainees completed post-training surveys, and 20 interviewers completed post-data collection questionnaires. After training, more than 90% felt confident about their ability to collect survey data using HCDC, to transfer study data from a handheld computer to a laptop, and to encrypt the survey data file. After data collection, 80% of the interviewers thought data collection and management were easy and 60% of staff felt confident they could solve problems they might encounter. Overall, after data collection, nearly 70% of interviewers reported that they would prefer to use handheld computers for future surveys. More than half (55%) felt the HCDC was a particularly useful data collection tool for studies conducted in China. CONCLUSIONS: We successfully conducted a health-related survey using HCDC. Using handheld computers for data collection was a feasible, acceptable, and preferred method by Chinese interviewers. Despite minor technical issues that occurred during data collection, HCDC is a promising methodology to be used in survey-based research in China.

The construction of a TCM knowledge graph and application of potential knowledge discovery in diabetic kidney disease by integrating diagnosis and treatment guidelines and real-world clinical data.

Background: The complexity and rapid progression of lesions in diabetic kidney disease pose significant challenges for clinical diagnosis and treatment. The advantages of Traditional Chinese Medicine (TCM) in diagnosing and treating this condition have gradually become evident. However, due to the disease's complexity and the individualized approach to diagnosis and treatment in Traditional Chinese Medicine, Traditional Chinese Medicine guidelines have limitations in guiding the treatment of diabetic kidney disease. Most medical knowledge is currently stored in the process of recording medical records, which hinders the understanding of diseases and the acquisition of diagnostic and treatment knowledge among young doctors. Consequently, there is a lack of sufficient clinical knowledge to support the diagnosis and treatment of diabetic kidney disease in Traditional Chinese Medicine. Objective: To build a comprehensive knowledge graph for the diagnosis and treatment of diabetic kidney disease in Traditional Chinese Medicine, utilizing clinical guidelines, consensus, and real-world clinical data. On this basis, the knowledge of Traditional Chinese Medicine diagnosis and treatment of diabetic kidney disease was systematically combed and mined. Methods: Normative guideline data and actual medical records were used to construct a knowledge graph of Traditional Chinese Medicine diagnosis and treatment for diabetic kidney disease and the results obtained by data mining techniques enrich the relational attributes. Neo4j graph database was used for knowledge storage, visual knowledge display, and semantic query. Utilizing multi-dimensional relations with hierarchical weights as the core, a reverse retrieval verification process is conducted to address the critical problems of diagnosis and treatment put forward by experts. Results: 903 nodes and 1670 relationships were constructed under nine concepts and 20 relationships. Preliminarily a knowledge graph for Traditional Chinese Medicine diagnosis and treatment of diabetic kidney disease was constructed. Based on the multi-dimensional relationships, the diagnosis and treatment questions proposed by experts were validated through multi-hop queries of the graphs. The results were confirmed by experts and showed good outcomes. Conclusion: This study systematically combed the Traditional Chinese Medicine diagnosis and treatment knowledge of diabetic kidney disease by constructing the knowledge graph. Furthermore, it effectively solved the problem of "knowledge island". Through visual display and semantic retrieval, the discovery and sharing of diagnosis and treatment knowledge of diabetic kidney disease were realized.

Multichannel Flexible Pulse Perception Array for Intelligent Disease Diagnosis System.

Pressure sensors with high sensitivity, a wide linear range, and a quick response time are critical for building an intelligent disease diagnosis system that directly detects and recognizes pulse signals for medical and health applications. However, conventional pressure sensors have limited sensitivity and nonideal response ranges. We proposed a multichannel flexible pulse perception array based on polyimide/multiwalled carbon nanotube-polydimethylsiloxane nanocomposite/polyimide (PI/MPN/PI) sandwich-structure pressure sensor that can be applied for remote disease diagnosis. Furthermore, we established a mechanical model at the molecular level and guided the preparation of MPN. At the structural level, we achieved high sensitivity (35.02 kPa-1) and a broad response range (0-18 kPa) based on a pyramid-like bilayer microstructure with different upper and lower surfaces. A 27-channel (3 × 9) high-density sensor array was integrated at the device level, which can extract the spatial and temporal distribution information on a pulse. Furthermore, two intelligent algorithms were developed for extracting six-dimensional pulse information and automatic pulse recognition (the recognition rate reaches 97.8%). The results indicate that intelligent disease diagnosis systems have great potential applications in wearable healthcare devices.

Genetic linkage map construction and QTL mapping of cadmium accumulation in radish (Raphanus sativus L.).

Cadmium (Cd) is a widespread soil pollutant and poses a significant threat to human health via the food chain. Large phenotypic variations in Cd concentration of radish roots and shoots have been observed. However, the genetic and molecular mechanisms of Cd accumulation in radish remain to be elucidated. In this study, a genetic linkage map was constructed using an F(2) mapping population derived from a cross between a high Cd-accumulating cultivar NAU-Dysx and a low Cd-accumulating cultivar NAU-Yh. The linkage map consisted of 523 SRAP, RAPD, SSR, ISSR, RAMP, and RGA markers and had a total length of 1,678.2 cM with a mean distance of 3.4 cM between two markers. All mapped markers distributed on nine linkage groups (LGs) having sizes between 134.7 and 236.8 cM. Four quantitative trait loci (QTLs) for root Cd accumulation were mapped on LGs 1, 4, 6, and 9, which accounted for 9.86 to 48.64 % of all phenotypic variance. Two QTLs associated with shoot Cd accumulation were detected on LG1 and 3, which accounted for 17.08 and 29.53 % of phenotypic variance, respectively. A major-effect QTL, qRCd9 (QTL for root Cd accumulation on LG9), was identified on LG 9 flanked by NAUrp011_754 and EM5me6_286 markers with a high LOD value of 23.6, which accounted for 48.64 % of the total phenotypic variance in Cd accumulation of F(2) lines. The results indicated that qRCd9 is a novel QTL responsible for controlling root Cd accumulation in radish, and the identification of specific molecular markers tightly linked to the major QTL could be further applied for marker-assisted selection (MAS) in low-Cd content radish breeding program.

Study on the TCM Syndromes Evolution and Chinese Herbal Characteristics of Type 2 Diabetes Patients with Different Courses of Disease in TCM "Heat Stage": A Real-World Study.

OBJECTIVE: The purpose of this study is to analyze and summarize the syndrome distribution, syndrome evolution, and Chinese herb medicine characteristics of T2D in heat stage. METHOD: In this study, 228 heat-stage T2D patients were divided into three groups based on the course of disease. Group 1 (the course of disease ≤5 years) included 118 patients. Group 2 (5< the course of disease ≤10 years) had 73 patients. Group 3 (the course of disease >10 years) consisted of 37 patients. The main methods used in our study were complex network community partitioning algorithms and Sankey diagram visualization, based on the clinical electronic medical record data we collected. RESULT: In the three groups, the nodes with the highest node degree are all "heat syndrome." Edge weight between "heat" and "dampness," "qi stagnation," "phlegm," "liver," and "stomach" is the largest. During the whole course of treatment, 60.17%, 63.01%, and 62.16% of the patients' syndromes in groups 1, 2, and 3, respectively, were ascribed to the heat stage all the time. The patients' syndromes in groups 1 and 2 easily transformed to the syndrome of deficiency of both qi and yin of the spleen and stomach. In group 3, 27% of the patients' syndromes were easily transformed into kidney yin deficiency and qi deficiency and blood stasis syndrome. The largest Chinese herb communities of the patients whose syndromes did not change after treatment in the three groups were all heat-clearing drugs. The proportion of blood-activating drugs in patients with syndrome changes increased significantly after treatment. CONCLUSION: (1) The basic syndrome of T2D patients in the heat stage is liver-stomach heat syndrome. (2) T2D patients in the heat stage tend to deteriorate towards the direction of qi and yin deficiency syndrome. However, the longer the course of the disease is, the more likely it is to deteriorate to the direction of kidney yin deficiency syndrome and blood stasis syndrome. (3) Drugs that can help T2D patients in the heat stage to maintain their condition stably are heat-clearing drugs represented by Coptis chinensis, which usually need to be combined with warming interior drugs such as Zingiberis Rhizoma and Pinelliae Rhizoma.

Correlation between TCM Syndromes and Type 2 Diabetic Comorbidities Based on Fully Connected Neural Network Prediction Model.

OBJECTIVE: To predict the major comorbidities of type 2 diabetes based on the distribution characteristics of syndromes, and to explore the relationship between TCM syndromes and comorbidities of type 2 diabetes. METHODS: Based on the electronic medical record data of 3413 outpatient visits from 995 type 2 diabetes patients with comorbidities, descriptive statistical methods were used to analyze the basic characteristics of the population, the distribution characteristics of comorbidities, and TCM syndromes. A neural network model for the prediction of type 2 diabetic comorbidities based on TCM syndromes was constructed. RESULTS: Patients with TCM syndrome of blood amassment in the lower jiao were diagnosed with renal insufficiency with 95% test sensitivity. The patients with spleen deficiency combined with ascending counterflow of stomach qi and cold-damp patterns were diagnosed with gastrointestinal lesions with 92% sensitivity. The patients with TCM syndrome group of spleen heat and exuberance of heart fire were diagnosed as type 2 diabetes complicated with hypertension with a sensitivity of 91%. In addition, the prediction accuracy of combined neuropathy, heart disease, liver disease, and lipid metabolism disorder reached 70∼90% in TCM syndrome groups. CONCLUSION: The fully connected neural network model study showed that syndrome characteristics are highly correlated with type 2 diabetes comorbidities. Syndrome location is commonly in the heart, spleen, stomach, lower jiao, meridians, etc., while syndrome pattern manifests in states of deficiency, heat, phlegm, and blood stasis. The different combinations of disease location and disease pattern reflect the syndrome characteristics of different comorbidities forming the characteristic syndrome group of each comorbidity. Major comorbidities could be predicted with a high degree of accuracy through TCM syndromes. Findings from this study may have further implementations to assist with the diagnosis, treatment, and prevention of diabetic comorbidities at an early stage.

Analysis of disease comorbidity patterns in a large-scale China population.

BACKGROUND: Disease comorbidity is popular and has significant indications for disease progress and management. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set. METHODS: We extracted the diseases from a large-scale anonymized data set derived from 8,572,137 inpatients in 453 hospitals across China. We built a Disease Comorbidity Network (DCN) using correlation analysis and detected the topological patterns of disease comorbidity using both complex network and data mining methods. The comorbidity patterns were further validated by shared molecular mechanisms using disease-gene associations and pathways. To predict the disease occurrence during the whole disease progressions, we applied four machine learning methods to model the disease trajectories of patients. RESULTS: We obtained the DCN with 5702 nodes and 258,535 edges, which shows a power law distribution of the degree and weight. It further indicated that there exists high heterogeneity of comorbidities for different diseases and we found that the DCN is a hierarchical modular network with community structures, which have both homogeneous and heterogeneous disease categories. Furthermore, adhering to the previous work from US and Europe populations, we found that the disease comorbidities have their shared underlying molecular mechanisms. Furthermore, take hypertension and psychiatric disease as instance, we used four classification methods to predicte the disease occurrence using the comorbid disease trajectories and obtained acceptable performance, in which in particular, random forest obtained an overall best performance (with F1-score 0.6689 for hypertension and 0.6802 for psychiatric disease). CONCLUSIONS: Our study indicates that disease comorbidity is significant and valuable to understand the disease incidences and their interactions in real-world populations, which will provide important insights for detection of the patterns of disease classification, diagnosis and prognosis.

Pattern Classification of Enterovirus 71-Associated Hand, Foot, and Mouth Disease in Chinese Medicine: A Retrospective Study in 433 Cases.

OBJECTIVE: To determine whether patterns of enterovirus 71 (EV71)-associated hand, foot, and mouth disease (HFMD) were classified based on symptoms and signs, and explore whether individual characteristics were correlated with membership in particular pattern. METHODS: Symptom-based latent class analysis (LCA) was used to determine whether patterns of EV71-HFMD existed in a sample of 433 cases from a clinical data warehouse system. Logistic regression was then performed to explore whether demographic, and laboratory data were associated with pattern membership. RESULTS: LCA demonstrated a two-subgroup solution with an optimal fit, deduced according to the Bayesian Information Criterion minima. Hot pattern (59.1% of all patients) was characterized by a very high fever and high endorsement rates for classical HFMD symptoms (i.e., rash on the extremities, blisters, and oral mucosa lesions). Non-hot pattern (40.9% of all patients) was characterized by classical HFMD symptoms. The multiple logistic regression results suggest that white blood cell counts and aspartate transaminase were positively correlated with the hot pattern (adjust odds ratio=1.07, 95% confidence interval: 1.006-1.115; adjust odds ratio=1.051, 95% confidence interval: 1.019-1.084; respectively). CONCLUSIONS: LCA on reported symptoms and signs in a retrospective study allowed different subgroups with meaningful clinical correlates to be defined. These findings provide evidence for targeted prevention and treatment interventions.

[Establishing the acupuncture-moxibustion clinical trial registry and improving the transparence of clinical trials of acupuncture and moxibustion].

As a kind of intervention measures of traditional Chinese medicine, acupuncture-moxibustion is highly adopted on global clinical practice. Even though the global clinical trial registration system was established more than 10 years ago, the proportion of acupuncture-moxibustion clinical trial registration is still very low; and it is very problematic on the methodological quality and report quality in the published acupuncture-moxibustion clinical trials. In order to manage particularly the acupuncture-moxibustion clinical trials, China Academy of Chinese Medical Sciences, collaborated with China Association of Acupuncture and Moxibustion and World Federation of Acupuncture Societies, established the Acupuncture-Moxibustion Clinical Trail Registry (AMCTR). AMCTR is a secondary registry platform affiliated to the Chinese Clinical Trial Registry (ChiCTR) and WHO International Clinical Trials Registry Platform (ICTRP), specifically for the acceptance and management of clinical trials in the field of acupuncture and moxibustion. It is a nonprofit academic organization, located in China Academy of Chinese Medical Sciences.

[Effect of glucocorticoid with traditional Chinese medicine in severe acute aespiratory syndrome (SARS)].

OBJECTIVE: To evaluate the effects of traditional Chinese medicine in 461 cases of severe acute respiratory syndrome(SARS) on glucocorticoid's dosage. METHOD: By using the polycentric nonrandomized concurrent controled trial and under the condition of glucocorticoid use, the patierts were divided into two groups: the integrated traditional Chinese and western mendicine(ITCWM) and simplicity western mendicine alone(WM). The observation indexes were time in hospital, pneumonia duration, mortality, glucocorticoid's gross dosage, glucocorticoid's average dosage of days and glucocorticoid use time. RESULT: In the ITCWM group, average time in hospital was shortened (P = 0.058), pneumonia duration was shortened (P = 0.057), mortality fell (P = -0.001). The median of glucocorticoid' s gross dosage was 1,277.0 mg x d(-1) in the ITCWM group, and that was 1,680.0 mg x d(-1) in the WM group (P = 0.083). The median of glucocorticoid's average dosage of days was 84.40 mg x d(-1) in the ITCWM group, and that was 115.33 mg x d(-1) in the WM group (P = 0.025). According to the analysis of 461 cases divided by stages and different ponderance, within 7 days after illness, in the ITCWM group, the glucocorticoid' s average dosage decreased. In the common type, the dosage in the ITCWM and in the WM was 146.43 mg x d(-1), and 183.64 mg x d(-1), respectively (P = 0.057), in the severe type, that was 137.71 and 177.86 mg x d(-1), respectively (P = 0.001). CONCLUSION: Compared with the group of simplicity western mendicine, in the group of integratived Chinese and western mendicine, time in hospital shorten, pneumonia duration shorten, mortality fall, simultaneity, glucocorticoid's average dosage is decreased. The use of TCM in the forepart of treatment can be capable of decreasing glucocorticoid's dosage.

Clinical Features for Mild Hand, Foot and Mouth Disease in China.

BACKGROUND: Mild hand, foot and mouth disease (HFMD) is at a critical stage owing to its ease of communicability and a higher risk of developing severe complications and death. Clinical diagnosis of mild HFMD was made by the presenting symptoms and signs (symptoms in brief) alone. We aim to evaluate the frequencies of symptoms in a retrospective case series study. METHODS: We collected epidemiological, demographic, clinical, and laboratory data from outpatient and inpatient settings on the clinical data warehouse system. We principally described the frequencies of symptoms of mild HFMD. Correlations between symptoms with laboratory-confirmed cases were then analyzed. RESULTS: The clinical data warehouse system included 3649 probable cases, between 2010 and 2012, of which 956 (26.20%) were laboratory confirmed. The peak incidence was identified in children 2 years of age. A total of 370 of the 956 laboratory confirmed cases (38.70%) were associated with enterovirus 71 (EV71). Logistic regression analysis adjusted for geographical variables, age, sex, month of onset, and time from onset to diagnosis showed that the clinical features constipation (P<0.0001; adjusted OR, 95%CI (2.99, 2.28-3.91)), and blisters (P<0.0001; adjusted OR, 95%CI (2.16, 1.82-2.56)) were positively correlated with the confirmed cases. CONCLUSIONS: This is the largest case series study, including all the guideline-mentioned symptoms of mild HFMD. Our findings suggest that blisters and constipation should be considered as potential warning signs while front-line clinicians manage surges of children diagnosed with mild HFMD during a pandemic.

Reevaluation of the effect of Dianxianning on seizure rate of refractory epilepsy as additive treatment in clinical practice.

We observed the effect of Dianxianning, which was used as additive treatment to treat 206 epilepsy patients, on the epilepsy seizure rate. Based on a multicenter, prospective, randomized, and controlled clinical trial design, we used the seizure rate of epilepsy as the main index. For the treatment group comprising 137 patients, we combined Dianxianning with chemical medicine, which is the basic treatment. For the control group with 69 patients, we added placebo. The results showed that 1) Effect on seizure rate: After a three-month treatment, the seizure rate of the treatment group decreased by 37.84% on average, whereas that of the control group decreased by 13.18% on average. Statistically comparing the two groups, there was a significant difference between these groups (P < 0.05). 2) Effect on seizure frequency: As time passed, the frequency in each group gradually decreased. After a three-month treatment, there was a significant difference between the two groups (P < 0.05). 3) Comparison between the before and after treatment of each group: There was a very significant difference between the two groups (P < 0.0001). The results indicated that, as an additive treatment, Dianxianning has a good effect on controlling the epilepsy seizure rate and frequency management. It is more effective than using chemical medicine alone.