Transmission risks of schistosomiasis japonica: extraction from back-propagation artificial neural network and logistic regression model

Prediction of SMILE surgical cutting formula based on back propagation neural network

AIM

To predict cutting formula of small incision lenticule extraction (SMILE) surgery and assist clinicians in identifying candidates by deep learning of back propagation (BP) neural network.

METHODS

A prediction program was developed by a BP neural network. There were 13 188 pieces of data selected as training validation. Another 840 eye samples from 425 patients were recruited for reverse verification of training results. Precision of prediction by BP neural network and lenticule thickness error between machine learning and the actual lenticule thickness in the patient data were measured.

RESULTS

After training 2313 epochs, the predictive SMILE cutting formula BP neural network models performed best. The values of mean squared error and gradient are 0.248 and 4.23, respectively. The scatterplot with linear regression analysis showed that the regression coefficient in all samples is 0.99994. The final error accuracy of the BP neural network is -0.003791±0.4221102 µm.

CONCLUSION

With the help of the BP neural network, the program can calculate the lenticule thickness and residual stromal thickness of SMILE surgery accurately. Combined with corneal parameters and refraction of patients, the program can intelligently and conveniently integrate medical information to identify candidates for SMILE surgery.

Spatiotemporal distribution of schistosomiasis transmission risk in Jiangling County, Hubei Province, P.R. China

OBJECTIVE

This study aims to explore the spatiotemporal distribution of schistosomiasis in Jiangling County, and provide insights into the precise schistosomiasis control.

METHODS

The descriptive epidemiological method and Joinpoint regression model were used to analyze the changes in infection rates of humans, livestock, snails, average density of living snails and occurrence rate of frames with snails in Jiangling County from 2005 to 2021. Spatial epidemiology methods were used to detect the spatiotemporal clustering of schistosomiasis transmission risk in Jiangling county.

RESULTS

The infection rates in humans, livestock, snails, average density of living snails and occurrence rate of frames with snails in Jiangling County decreased from 2005 to 2021 with statistically significant. The average density of living snails in Jiangling County was spatially clustered in each year, and the Moran's I varied from 0.10 to 0.26. The hot spots were mainly concentrated in some villages of Xionghe Town, Baimasi Town and Shagang Town. The mean center of the distribution of average density of living snails in Jiangling County first moved from northwest to southeast, and then returned from southeast to northwest after 2014. SDE azimuth fluctuated in the range of 111.68°-124.42°. Kernal density analysis showed that the high and medium-high risk areas of Jiangling County from 2005 to 2021 were mainly concentrated in the central and eastern of Jiangling County, and the medium-low and low risk areas were mainly distributed in the periphery of Jiangling County.

CONCLUSIONS

The epidemic situation of schistosomiasis decreased significantly in Jiangling County from 2005 to 2021, but the schistosomiasis transmission risk still had spatial clustering in some areas. After transmission interruption, targeted transmission risk intervention strategies can be adopted according to different types of schistosomiasis risk areas.

Systematic review of applied mathematical models for the control of Schistosoma japonicum

BACKGROUND

Schistosoma japonicum remains endemic in China and the Philippines. Substantial progress has been made in the control of Japonicum in both China and the Philippines. China is reaching elimination thanks to a concerted effort of control strategies. Mathematical modelling has been a key tool in the design of control strategies, in place of expensive randomised-controlled trials. We conducted a systematic review to investigate mathematical models of Japonicum control strategies in China and the Philippines.

METHODS

We conducted a systematic review on July 5, 2020, in four electronic bibliographic databases - PubMed, Web of Science, SCOPUS and Embase. Articles were screened for relevance and for meeting the inclusion criteria. Data extracted included authors, year of publication, year of data collection, setting and ecological context, objectives, control strategies, main findings, the form and content of the model including its background, type, representation of population dynamics, heterogeneity of hosts, simulation period, source of parameters, model validation and sensitivity analysis. Results After screening, 19 eligible papers were included in the systematic review. Seventeen considered control strategies in China and two in the Philippines. Two frameworks were identified; the mean-worm burden framework and the prevalence-based framework, the latter of which increasingly common. Most models considered human and bovine definitive hosts. There were mixed additional elements included in the models, such as alternative definitive hosts and the role of seasonality and weather. Models generally agreed upon the need for an integrated control strategy rather than reliance on mass drug administration alone to sustain reductions in prevalence.

CONCLUSIONS

Mathematical modelling of Japonicum has converged from multiple approaches to modelling using the prevalence-based framework with human and bovine definitive hosts and find integrated control strategies to be most effective. Further research could investigate the role of other definitive hosts and model the effect of seasonal fluctuations in transmission.

A stepwise strategy integrating metabolomics and pseudotargeted spectrum–effect relationship to elucidate the potential hepatotoxic components in Polygonum multiflorum

Polygonummultiflorum (PM) Thunb., a typical Chinese herbal medicine with different therapeutic effect in raw and processed forms, has been used worldwide for thousands of years. However, hepatotoxicity caused by PM has raised considerable concern in recent decades. The exploration of toxic components in PM has been a great challenge for a long time. In this study, we developed a stepwise strategy integrating metabolomics and pseudotargeted spectrum–effect relationship to illuminate the potential hepatotoxic components in PM. First, 112 components were tentatively identified using ultraperformance liquid chromatography-quadrupole-time-of-flight-mass spectrometry (UPLC-Q-TOF-MS). Second, based on the theory of toxicity attenuation after processing, we combined the UPLC-Q-TOF-MS method and plant metabolomics to screen out the reduced differential components in PM between raw and processed PM. Third, the proposed pseudotargeted MS of 16 differential components was established and applied to 50 batches of PM for quantitative analysis. Fourth, the hepatocytotoxicity of 50 batches of PM was investigated on two hepatocytes, LO2 and HepG2. Last, three mathematical models, gray relational analysis, orthogonal partial least squares analysis, and back propagation artificial neural network, were established to further identify the key variables affecting hepatotoxicity in PM by combining quantitative spectral information with toxicity to hepatocytes of 50 batches of PM. The results suggested that 16 components may have different degrees of hepatotoxicity, which may lead to hepatotoxicity through synergistic effects. Three components (emodin dianthrones, emodin-8-O-β-D-glucopyranoside, PM 14-17) were screened to have significant hepatotoxicity and could be used as toxicity markers in PM as well as for further studies on the mechanism of toxicity. Above all, the study established an effective strategy to explore the hepatotoxic material basis in PM but also provides reference information for in-depth investigations on the hepatotoxicity of PM.

Association of Salt-Reduction Knowledge and Behaviors and Salt Intake in Chinese Population

Objective

Excessive salt intake is causally associated with an increased risk of cardiovascular disease. Salt-reduction strategies have been rapidly deployed across China since 2017. This study aimed to investigate the association of salt-reduction knowledge and behaviors and salt intake in Chinese population.

Study Design

This study was a national cross-sectional study in China.

Methods

This cross-sectional study was based on data collected during a Chinese adult chronic disease and nutrition surveillance program in 2018 with 7,665 study participants. Salt intake was assessed by calculating 24 h urine sodium from morning urine samples. Logistic regression and mean impact value (MIV) based on the back propagation (BP) artificial neural network were used to screen the potential influencing factors.

Results

A total of 7,665 participants were included in the analysis, with an average age of 54.64 ± 13.26 years, and with men accounting for 42.6%. Only 19.3% of the participants were aware of the Chinese Dietary Guidelines, and only 7.3% of them could accurately identify the level of salt intake recommended in the Chinese Dietary Guidelines. Approximately 41% of the participants adopted salt-reduction behaviors, among whom the number of participants who used less salt when cooking was the highest, and the number of participants who used low sodium salt was the lowest. In the logistic regression, only "No extra salt was added at the table" group showed the effect of salt-reduction, the odds ratio (OR) being 0.78 (95% confidence interval [CI]: 0.64-0.95). The MIV result based on the BP neural network showed that the most important salt-reduction behavior was using less salt when cooking, while reducing eating-out behavior and using salt-limiting tools were the least important.

Conclusion

The research shows that the popularization of salt-reduction knowledge and behaviors can reduce the population's salt intake. However, there is still considerable scope for promoting salt-reduction knowledge and behaviors, while the promotion of salt-reduction tools and low-sodium salt still needs to be strengthened.

Determinants investigation and peak prediction of CO2 emissions in China's transport sector utilizing bio-inspired extreme learning machine

The transport sector is recognized as one of the largest carbon emitters. To achieve China's carbon peak commitment in the Paris Agreement on schedule, it is indispensable to explore the peak carbon emissions and mitigation strategies in the transport sector. Many researches in the past have contextualized in China's total emissions peak, while the study about forecasting China's transport CO₂ emissions peak seldom appeared, especially the application of intelligent prediction model. To further investigate the determinants and forecast the peak of transport CO₂ emissions in China accurately, a novel bio-inspired prediction model is proposed in this paper, namely, the extreme learning machine (ELM) optimized by manta rays foraging optimization (MRFO), hereafter referred as MRFO-ELM. Adhering to this hybrid model, the mean impact value (MIV) method is then employed to evaluate and differentiate the importance of thirteen influencing factors. Additionally, three scenarios are set to conduct prediction of China's transport CO₂ emissions. The empirical results indicate that the proposed MRFO-ELM has excellent performance in terms of the optimization searching velocity and prediction accuracy. Simultaneously the level of vehicle electrification is verified to be one of the emerging major factors affecting China's transport CO₂ emissions. The transport CO₂ emissions in China would peak in 2039 under the baseline model scenario, while the plateau would occur in 2035 or 2043 under sustainable development mode and high growth mode, respectively. The peak years imply much pressure on China's transport carbon emissions abatement currently, whereas active policy adjustments can effectively urge the earlier occurrence of the emission peak. These new findings suggest that it is essential for China to improve the energy mix and encourage the electric energy replacement in line with urbanization pace, so as to achieve CO₂ emissions mitigation in the transport industry.

Using Metabolic and Biochemical Indicators to Predict Diabetic Retinopathy by Back-Propagation Artificial Neural Network

PURPOSE

Timely diagnosis of diabetic retinopathy (DR) can significantly improve the prognosis of patients. In this study, we established a prediction model by analyzing the relationship between diabetic retinopathy and related metabolic and biochemical indicators.

METHODS

A total of 427 type 2 diabetes mellitus (T2DM) patients were selected from the datadryad website data. Logistic regression (MLR) was used to input layer variables of the model were screened. Then, Tan-Sigmoid was selected as the transfer function of the hidden layer node, and the linear function was used as the output layer function to establish the back propagation artificial neural network (BP-ANN) model. The model was applied to 183 patients with type 2 diabetes mellitus (T2DM) in our hospital to predict DR.

RESULTS

A total of 167 patients (39.2%) with DR were obtained from the Datadryad database. Input variables were screened by MLR model, and it was concluded that the age, sex, albumin and creatinine, diabetes course were independently associated with the occurrence of DR. The above variables were used to establish BP-ANN model. The area under receiver operating characteristic curve (AUC) was significantly higher than that of MLR model (0.88 vs 0.74, P<0.05), the probability threshold of the model was 0.3. Type 2 diabetes mellitus (T2DM) were selected in our hospital, including 92 patients with DR (50.2%). The above BP-ANN model was used to predict the incidence of DR, and the AUC area was significantly higher than that of the MLR model (0.77 vs 0.70, P<0.05), the probability threshold was 0.7.

CONCLUSION

We established the BP-ANN model and applied it to diagnose DR. Taking diabetic course, age, sex, albumin and creatinine as the inputs of BP-ANN, the existence of DR could be well predicted. Meanwhile, the generalization ability of the model could be improved by selecting different probability thresholds in different ROC curves.

Prediction of antischistosomal small molecules using machine learning in the era of big data

Schistosomiasis is a neglected tropical disease caused by helminths of the Schistosoma genus. Despite its high morbidity and socio-economic burden, therapeutics are just a handful with praziquantel being the main drug. Praziquantel is an old drug registered for human use in 1982 and has since been administered en masse for chemotherapy, risking the development of resistance, thus the need for new drugs with different mechanisms of action. This review examines the use of machine learning (ML) in this era of big data to aid in the prediction of novel antischistosomal molecules. It first discusses the challenges of drug discovery in schistosomiasis. Explanations are then offered for big data, its characteristics and then, some open databases where large biochemical data on schistosomiasis can be obtained for ML model development are examined. The concepts of artificial intelligence, ML, and deep learning and their drug applications are explored in schistosomiasis. The use of binary classification in predicting antischistosomal compounds and some algorithms that have been applied including random forest and naive Bayesian are discussed. For this review, some deep learning algorithms (deep neural networks) are proposed as novel algorithms for predicting antischistosomal molecules via binary classification. Databases specifically designed for housing bioactivity data on antischistosomal molecules enriched with functional genomic datasets and ontologies are thus urgently needed for developing predictive ML models. This shows the application of machine learning techniques for the discovery of novel antischistosomal small molecules via binary classification in the era of big data.

Identification of the high-risk area for schistosomiasis transmission in China based on information value and machine learning: a newly data-driven modeling attempt

BACKGROUND

Schistosomiasis control is striving forward to transmission interruption and even elimination, evidence-lead control is of vital importance to eliminate the hidden dangers of schistosomiasis. This study attempts to identify high risk areas of schistosomiasis in China by using information value and machine learning.

METHODS

The local case distribution from schistosomiasis surveillance data in China between 2005 and 2019 was assessed based on 19 variables including climate, geography, and social economy. Seven models were built in three categories including information value (IV), three machine learning models [logistic regression (LR), random forest (RF), generalized boosted model (GBM)], and three coupled models (IV + LR, IV + RF, IV + GBM). Accuracy, area under the curve (AUC), and F1-score were used to evaluate the prediction performance of the models. The optimal model was selected to predict the risk distribution for schistosomiasis.

RESULTS

There is a more prone to schistosomiasis epidemic provided that paddy fields, grasslands, less than 2.5 km from the waterway, annual average temperature of 11.5-19.0 °C, annual average rainfall of 1000-1550 mm. IV + GBM had the highest prediction effect (accuracy = 0.878, AUC = 0.902, F1 = 0.920) compared with the other six models. The results of IV + GBM showed that the risk areas are mainly distributed in the coastal regions of the middle and lower reaches of the Yangtze River, the Poyang Lake region, and the Dongting Lake region. High-risk areas are primarily distributed in eastern Changde, western Yueyang, northeastern Yiyang, middle Changsha of Hunan province; southern Jiujiang, northern Nanchang, northeastern Shangrao, eastern Yichun in Jiangxi province; southern Jingzhou, southern Xiantao, middle Wuhan in Hubei province; southern Anqing, northwestern Guichi, eastern Wuhu in Anhui province; middle Meishan, northern Leshan, and the middle of Liangshan in Sichuan province.

CONCLUSIONS

The risk of schistosomiasis transmission in China still exists, with high-risk areas relatively concentrated in the coastal regions of the middle and lower reaches of the Yangtze River. Coupled models of IV and machine learning provide for effective analysis and prediction, forming a scientific basis for evidence-lead surveillance and control.

Effect of Genetic Polymorphisms on the Pharmacokinetics of Deferasirox in Healthy Chinese Subjects and an Artificial Neural Networks Model for Pharmacokinetic Prediction

BACKGROUND AND OBJECTIVE

Deferasirox is an oral iron chelator used to reduce iron levels in iron-overloaded patients with transfusion-dependent anemia or non-transfusion-dependent thalassemia. This study investigated the effects of genetic polymorphisms on the pharmacokinetics of deferasirox in healthy Chinese subjects and constructed a pharmacokinetic prediction model based on physiologic factors and genetic polymorphism data.

METHODS

Twenty-eight subjects were enrolled in a randomized, open-label, two-period crossover study, and they received a single dose of one of two formulations of deferasirox (20 mg/kg) with a 7-day washout interval between the two periods. The plasma defersirox concentration was determined using a validated liquid chromatography-tandem mass spectrometry method, and pharmacokinetic parameters were calculated using the noncompartmental method. The polymorphisms of uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1), UGT1A3, multidrug resistance protein 2 (MRP2), cytochrome P450 1A1 (CYP1A1), and breast cancer resistance protein 1 (BCRP1) were genotyped using Sanger sequencing. A back-propagation artificial neural network (BP-ANN) model was used to predict the pharmacokinetics.

RESULTS

The UGT1A1 rs887829 C > T single-nucleotide polymorphism (SNP) significantly influenced the area under the plasma concentration-time curve and the terminal half-life. Neither the MRP2 rs2273697 G > A SNP nor BCRP1 rs2231142 G > T SNP altered the absorption, disposition, and excretion of the drug. The BP-ANN model had a high goodness-of-fit index and good coherence between the predicted and measured concentrations (R² = 0.921).

CONCLUSION

Metabolic enzyme-related genetic polymorphisms were more strongly associated with the pharmacokinetics of deferasirox than membrane transporter-related genetic polymorphisms in the Chinese population.

TRIAL REGISTRATION

www.Chinadrugtrials.org.cn CTR20191164.

UHPLC-qMS spectrum-effect relationships for Rhizoma Paridis extracts

Rhizoma Paridis (RP) with significant anti-tumor and haemostatic effects, has been used as the raw material of many Traditional Chinese preparations. However, its active ingredients are still unclear. The present study aimed to discover bioactive ingredients from RP based on spectrum-relationship and chemometric methods. Firstly, the saponins extract was prepared by phytochemical methods. Furthermore, UHPLC-QTOF-MS and UHPLC-qMS were incorporated to establish an efficient and sensitive method for obtaining the chemical profiles of RP. A total of 34 saponins were characterized in RP and 13 of them were assigned as common peaks in 25 batches of samples. After evaluation of the anti-tumor and haemostatic activities of samples, spectrum-effect relationships were investigated by the grey relational analysis (GRA), orthogonal projections to latent structures (OPLS) and back propagation artificial neural network (BP-ANN). These analyses showed that polyphyllin VII (P27), polyphyllin II (P30), dioscin (P31) and polyphyllin I (P33) play a role in the haemostatic effects of RP whereas polyphyllin VII (P27), dioscin (P31), polyphyllin I (P33), progenin III (P34) were assigned as candidate ingredients accounting for the anti-tumor activity of RP. The anti-tumor and haemostatic activities of these screened ingredients were subsequently verified in vitro. Collectively, the present study established the spectrum-effect relationship mode of RP and discovered the bioactive compounds of RP, which could be also used for exploration of bioactive compounds in herbal medicines, especially for trace compounds.

Epidemiological big data and analytical tools applied in the control programmes on parasitic diseases in China: NIPD's sustained contributions in 70 years

The analysis of epidemiological data has played an important role for the academic research carried out by the National Institute of Parasitic Diseases, China CDC, since its foundation in 1950s. Those researches, e.g., the temporal-spatial patterns of disease transmission and the identification of risk factors, have contributed significantly to the national parasitic disease control and elimination programmes in China. With the development and application of epidemiological data analysis in the last decade, all research results improve our understanding of parasitic diseases epidemiology and related health issues through the application platform of epidemiological big data and analytical tools. In particular, implementation research on analytical predictions on disease outbreak or epidemic risks have provided references to the scientific guidance on effective preventions and interventions in the parasitic disease elimination in China, such as fliariasis, malaria and schistosomiasis. This review has reflected the function of data accumulation and application of temporospatial tools in parasitic diseases control, and the ways of the NIPD's sustained contributions to the disease control programmes in China.

Contributions and achievements on schistosomiasis control and elimination in China by NIPD-CTDR

Being a zoonotic parasitic disease, schistosomiasis was widely spread in 12 provinces of Southern China in the 1950s, severly harming human health and hindering economic development. The National Institute of Parasitic Diseases at the Chinese Center for Diseases Control and Prevention, and Chinese Center for Tropical Diseases Research (NIPD-CTDR), as the only professional institution focussing on parasitic diseases at the national level, has played an important role in schistosomiasis control in the country. In this article, we look back at the changes of schistosomiasis endemicity and the contribution of NIPD-CTDR to the national schistosomiasis control programme. We review NIPD-CTDR's activities, including field investigations, design of control strategies and measures, development of diagnostics and drugs, surveillance-response of endemic situation, and monitoring & evaluation of the programme. The NIPD-CTDR has mastered the transmission status of schistosomiasis, mapped the snail distribution, and explored strategies and measures suitable for different types of endemic areas in China. With a good understanding of the life cycle of Schistosoma japonicum and transmission patterns of the disease, advanced research carried out in the NIPD-CTDR based on genomics and modern technology has made it possible to explore highly efficient and soft therapeutic drugs and molluscicides, making it possible to develop new diagnostic tools and produce vaccine candidates. In the field, epidemiological studies, updated strategies and targeted intervention measures developed by scientists from the NIPD-CTDR have contributed significantly to the national schistosomiasis control programme. This all adds up to a strong foundation for eliminating schistosomiasis in China in the near future, and recommendations have been put forward how to reach this goal.

Assessment of knowledge, attitude and practices and the analysis of risk factors regarding schistosomiasis among fishermen and boatmen in the Dongting Lake Basin, the People's Republic of China

BACKGROUND

Fishermen and boatmen are a population at-risk for contracting schistosomiasis due to their high frequency of water contact in endemic areas of schistosomiasis in the People's Republic of China (P. R. China). To develop specific interventions towards this population, the present study was designed to assess the knowledge, attitudes and practices (KAPs) towards schistosomiasis of fishermen and boatmen, and to identify the risk factors associated with schistosome infection using a molecular technique in a selected area of Hunan Province in P. R. China.

METHODS

A cross sectional survey was conducted in the Dongting Lake Basin of Yueyang County, Hunan Province. A total of 601 fishermen and boatmen were interviewed between October and November 2017. Information regarding sociodemographic details and KAPs towards schistosomiasis were collected using a standardized questionnaire. Fecal samples of participants were collected and tested by polymerase chain reaction (PCR). Logistic regression analysis was conducted to explore the risk factors related to the positive results of PCR.

RESULTS

Of the 601 respondents, over 90% knew schistosomiasis and how the disease was contracted, the intermediate host of schistosomes and preventive methods. The majority of respondents had a positive attitude towards schistosomiasis prevention. However, only 6.66% (40/601) of respondents had installed a latrine on their boats, while 32.61% (196/601) of respondents defecated in the public toilets on shore. In addition, only 4.99% (30/601) respondents protected themselves while exposed to freshwater. The prevalence of schistosomiasis, as determined by PCR, among fishermen and boatmen in Yueyang County was 13.81% (83/601). Age, years of performing the current job, number of times receiving treatment, and whether they were treated in past three years were the main influencing factors of PCR results among this population.

CONCLUSIONS

Fishermen and boatmen are still at high risk of infection in P. R. China and gaps exist in KAPs towards schistosomiasis in this population group. Chemotherapy, and health education encouraging behavior change in combination with other integrated approaches to decrease the transmission risk in environments should be improved.

Field Testing Integrated Interventions for Schistosomiasis Elimination in the People's Republic of China: Outcomes of a Multifactorial Cluster-Randomized Controlled Trial

Despite significant progress, China faces the challenge of re-emerging schistosomiasis transmission in currently controlled areas due, in part, to the presence of a range of animal reservoirs, notably water buffalo and cattle, which can harbor Schistosoma japonicum infections. Environmental, ecological and social-demographic changes in China, shown to affect the distribution of oncomelanid snails, can also impact future schistosomiasis transmission. In light of their importance in the S. japonicum, lifecycle, vaccination has been proposed as a means to reduce the excretion of egg from cattle and buffalo, thereby interrupting transmission from these reservoir hosts to snails. A DNA-based vaccine (SjCTPI) our team developed showed encouraging efficacy against S. japonicum in Chinese water buffaloes. Here we report the results of a double-blind cluster randomized trial aimed at determining the impact of a combination of the SjCTPI bovine vaccine (given as a prime-boost regimen), human mass chemotherapy and snail control on the transmission of S. japonicum in 12 selected administrative villages around the Dongting Lake in Hunan province. The trial confirmed human praziquantel treatment is an effective intervention at the population level. Further, mollusciciding had an indirect ~50% efficacy in reducing human infection rates. Serology showed that the SjCTPI vaccine produced an effective antibody response in vaccinated bovines, resulting in a negative correlation with bovine egg counts observed at all post-vaccination time points. Despite these encouraging outcomes, the effect of the vaccine in preventing human infection was inconclusive. This was likely due to activities undertaken by the China National Schistosomiasis Control Program, notably the treatment, sacrifice or removal of bovines from trial villages, over which we had no control; as a result, the trial design was compromised, reducing power and contaminating outcome measures. This highlights the difficulties in undertaking field trials of this nature and magnitude, particularly over a long period, and emphasizes the importance of mathematical modeling in predicting the potential impact of control intervention measures. A transmission blocking vaccine targeting bovines for the prevention of S. japonicum with the required protective efficacy would be invaluable in tandem with other preventive intervention measures if the goal of eliminating schistosomiasis from China is to become a reality.

Discovery of Hepatotoxic Equivalent Combinatorial Markers from Dioscorea bulbifera tuber by Fingerprint-Toxicity Relationship Modeling

Due to extremely chemical complexity, identification of potential toxicity-related constituents from an herbal medicine (HM) still remains challenging. Traditional toxicity-guided separation procedure suffers from time- and labor-consumption and neglects the additive effect of multi-components. In this study, we proposed a screening strategy called “hepatotoxic equivalent combinatorial markers (HECMs)” for a hepatotoxic HM, Dioscorea bulbifera tuber (DBT). Firstly, the chemical constituents in DBT extract were globally characterized. Secondly, the fingerprints of DBT extracts were established and their in vivo hepatotoxicities were tested. Thirdly, three chemometric tools including partial least squares regression (PLSR), back propagation-artificial neural network (BP-ANN) and cluster analysis were applied to model the fingerprint-hepatotoxicity relationship and to screen hepatotoxicity-related markers. Finally, the chemical combination of markers was subjected to hepatotoxic equivalence evaluation. A total of 40 compounds were detected or tentatively characterized. Two diterpenoid lactones, 8-epidiosbulbin E acetate (EEA) and diosbulbin B (DIOB), were discovered as the most hepatotoxicity-related markers. The chemical combination of EEA and DIOB, reflecting the whole hepatotoxicity of original DBT extract with considerable confidential interval, was verified as HECMs for DBT. The present study is expected not only to efficiently discover hepatotoxicity-related markers of HMs, but also to rationally evaluate/predict the hepatotoxicity of HMs.

Correlations of Complete Blood Count with Alanine and Aspartate Transaminase in Chinese Subjects and Prediction Based on Back-Propagation Artificial Neural Network (BP-ANN)

BACKGROUND The complete blood count (CBC) is the most common examination used to monitor overall health in clinical practice. Whether there is a relationship between CBC indexes and alanine transaminase (ALT) and aspartate aminotransferase (AST) has been unclear. MATERIAL AND METHODS In this study, 572 normal-weight and 346 overweight Chinese subjects were recruited. The relationship between CBC indexes with ALT and AST were analyzed by Pearson and Spearman correlations according to their sex, then we conducted colinearity diagnostics and multiple linear regression (MLR) analysis. A prediction model was developed by a back-propagation artificial neural network (BP-ANN). RESULTS ALT was related to 4 CBC indexes in the male normal-weight group and 3 CBC indexes in the female group. In the overweight group, ALT had a similar relationship with the normal group, but there was only 1 index related with AST in the normal-weight group and male overweight groups. The ALT regression models were developed in normal-weight and overweight people, which had better correlation coefficient (R>0.3). After training 1000 epochs, the BP-ANN models of ALT achieved higher correlations than MLR models in normal-weight and overweight people. CONCLUSIONS ALT is a more suitable index than AST for developing a regression model. ALT can be predicted by CBC indexes in normal-weight and overweight individuals based on a BP-ANN model, which was better than MLR analysis.

Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model

Background

The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people.

Methods

A total of 302 adult healthy subjects and 273 overweight subjects were enrolled in this study. The levels of fasting indexes of TG (fs-TG), TC (fs-TC), blood glucose, liver function, and kidney function were measured and analyzed by correlation analysis and multiple linear regression (MRL). The back propagation artificial neural network (BP-ANN) was applied to develop prediction models of fs-TG and fs-TC.

Results

The results showed there was significant difference in biochemical indexes between healthy people and overweight people. The correlation analysis showed fs-TG was related to weight, height, blood glucose, and indexes of liver and kidney function; while fs-TC was correlated with age, indexes of liver function (P < 0.01). The MRL analysis indicated regression equations of fs-TG and fs-TC both had statistic significant (P < 0.01) when included independent indexes. The BP-ANN model of fs-TG reached training goal at 59 epoch, while fs-TC model achieved high prediction accuracy after training 1000 epoch.

Conclusions

In conclusions, there was high relationship of fs-TG and fs-TC with weight, height, age, blood glucose, indexes of liver function and kidney function. Based on related variables, the indexes of fs-TG and fs-TC can be predicted by BP-ANN models in overweight people.

Electronic supplementary material

The online version of this article (doi:10.1186/s12944-017-0434-5) contains supplementary material, which is available to authorized users.

Socioeconomic Inequalities in Neglected Tropical Diseases: A Systematic Review

BACKGROUND

Neglected tropical diseases (NTDs) are generally assumed to be concentrated in poor populations, but evidence on this remains scattered. We describe within-country socioeconomic inequalities in nine NTDs listed in the London Declaration for intensified control and/or elimination: lymphatic filariasis (LF), onchocerciasis, schistosomiasis, soil-transmitted helminthiasis (STH), trachoma, Chagas' disease, human African trypanosomiasis (HAT), leprosy, and visceral leishmaniasis (VL).

METHODOLOGY

We conducted a systematic literature review, including publications between 2004-2013 found in Embase, Medline (OvidSP), Cochrane Central, Web of Science, Popline, Lilacs, and Scielo. We included publications in international peer-reviewed journals on studies concerning the top 20 countries in terms of the burden of the NTD under study.

PRINCIPAL FINDINGS

We identified 5,516 publications, of which 93 met the inclusion criteria. Of these, 59 papers reported substantial and statistically significant socioeconomic inequalities in NTD distribution, with higher odds of infection or disease among poor and less-educated people compared with better-off groups. The findings were mixed in 23 studies, and 11 studies showed no substantial or statistically significant inequality. Most information was available for STH, VL, schistosomiasis, and, to a lesser extent, for trachoma. For the other NTDs, evidence on their socioeconomic distribution was scarce. The magnitude of inequality varied, but often, the odds of infection or disease were twice as high among socioeconomically disadvantaged groups compared with better-off strata. Inequalities often took the form of a gradient, with higher odds of infection or disease each step down the socioeconomic hierarchy. Notwithstanding these inequalities, the prevalence of some NTDs was sometimes also high among better-off groups in some highly endemic areas.

CONCLUSIONS

While recent evidence on socioeconomic inequalities is scarce for most individual NTDs, for some, there is considerable evidence of substantially higher odds of infection or disease among socioeconomically disadvantaged groups. NTD control activities as proposed in the London Declaration, when set up in a way that they reach the most in need, will benefit the poorest populations in poor countries.

Reaching the Surveillance-Response Stage of Schistosomiasis Control in The People's Republic of China: A Modelling Approach

With the goal set to eliminate schistosomiasis nationwide by 2020, The People's Republic of China has initiated the surveillance-response stage to identify remaining sources of infection and potential pockets from where the disease could reemerge. Shifting the focus from classical monitoring and evaluation to rapid detection and immediate response, this approach requires modelling to bridge the surveillance and response components. We review here studies relevant to schistosomiasis modelling in a Chinese surveillance-response system with the expectation to achieve a practically useful understanding of the current situation and potential future study directions. We also present useful experience that could tentatively be applied in other endemic regions in the world. Modelling is discussed at length as it plays an essential role, both with regard to the intermediate snail host and in the definitive, mammal hosts. Research gaps with respect to snail infection, animal hosts and sectoral research cooperation are identified and examined against the prevailing background of ecosystem and socioeconomic changes with a focus on coexisting challenges and opportunities in a situation with increasing financial constraints.

Prediction of liver injury using the BP-ANN model with metabolic parameters in overweight and obese Chinese subjects

Nonalcoholic fatty liver disease (NAFLD) is often associated with dyslipidemia. Metabolic disequilibrium, resulting from being overweight and obesity, increases risk to cardiovascular system and chronic liver disease. Alanine aminotransferase (ALT), aspartate aminotransferase (AST) and gamma-glutamyl transferase (GGT) are standard clinical markers for liver injury. In this study, we examined association of body mass index (BMI) and metabolic markers with serum ALT, AST and GGT activity in an overweight and obese Chinese population. A total of 421 overweight and obese Chinese adults (211 males and 210 females) from The First Affiliated Hospital of Wenzhou Medical University were recruited in this study in 2014. All participants underwent anthropometric measures and phlebotomy after an overnight fast. Elevated ALT, AST and GGT levels were found in 17%, 5% and 24%, respectively. There were significant correlations between ALT and BMI, plasma triglycerides (TG), cholesterol, HDL and glucose, and between AST and plasma TG and cholesterol. GGT also correlated with plasma TG, cholesterol and glucose. The levels of ALT, AST and GGT could be predicted by BMI, plasma TG, cholesterol, HDL and glucose using the back propagation artificial neural network model (BP-ANN). These data suggest that abnormal metabolic markers could be used to monitor liver function to determine whether liver damage has occurred in overweight and obese individuals. This approach has clinical utility with respect to early scanning of liver injury or NAFLD based on routinely available metabolic data in overweight and obese population.

A hybrid model for predicting the prevalence of schistosomiasis in humans of Qianjiang City, China

Backgrounds/Objective

Schistosomiasis is still a major public health problem in China, despite the fact that the government has implemented a series of strategies to prevent and control the spread of the parasitic disease. Advanced warning and reliable forecasting can help policymakers to adjust and implement strategies more effectively, which will lead to the control and elimination of schistosomiasis. Our aim is to explore the application of a hybrid forecasting model to track the trends of the prevalence of schistosomiasis in humans, which provides a methodological basis for predicting and detecting schistosomiasis infection in endemic areas.

Methods

A hybrid approach combining the autoregressive integrated moving average (ARIMA) model and the nonlinear autoregressive neural network (NARNN) model to forecast the prevalence of schistosomiasis in the future four years. Forecasting performance was compared between the hybrid ARIMA-NARNN model, and the single ARIMA or the single NARNN model.

Results

The modelling mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model was 0.1869×10⁻⁴, 0.0029, 0.0419 with a corresponding testing error of 0.9375×10⁻⁴, 0.0081, 0.9064, respectively. These error values generated with the hybrid model were all lower than those obtained from the single ARIMA or NARNN model. The forecasting values were 0.75%, 0.80%, 0.76% and 0.77% in the future four years, which demonstrated a no-downward trend.

Conclusion

The hybrid model has high quality prediction accuracy in the prevalence of schistosomiasis, which provides a methodological basis for future schistosomiasis monitoring and control strategies in the study area. It is worth attempting to utilize the hybrid detection scheme in other schistosomiasis-endemic areas including other infectious diseases.

Reduction patterns of acute schistosomiasis in the People's Republic of China

Background

Despite significant, steady progress in schistosomiasis control in the People's Republic of China over the past 50 years, available data suggest that the disease has re-emerged with several outbreaks of acute infections in the early new century. In response, a new integrated strategy was introduced.

Methods

This retrospective study was conducted between Jan 2005 and Dec 2012, to explore the effectiveness of a new integrated control strategy that was implemented by the national control program since 2004.

Results

A total of 1,047 acute cases were recorded between 2005 and 2012, with an annual reduction in prevalence of 97.7%. The proportion of imported cases of schistosomiasis was higher in 2011 and 2012. Nine clusters of acute infections were detected by spatio-temporal analysis between June and November, indicating that the high risk areas located in the lake and marshland regions.

Conclusion

This study shows that the new integrated strategy has played a key role in reducing the morbidity of schistosomiasis in the People's Republic of China.

A retrospective study on the incidence of acute schistosomiasis in the People's Republic of China (P.R. China) was performed, in order to assess the new integrated control strategy that was implemented through the national control program from 2005 to 2012. The lake and marshland regions have been identified as high risk areas as shown by the nine spatio-temporal clusters that we found in the transmission period between June and November each year. When a total of 1,047 reported cases of acute schistosomiasis were analyzed, a reduction in prevalence of 97.7% between 2005 and 2012 was found. In contrast, imported cases of acute schistosomiasis increased between 2011 and 2012. These findings support the approach and effectiveness of the new integrated strategy in the reduction of schistosomiasis morbidity.

Field comparison of circulating antibody assays versus circulating antigen assays for the detection of schistosomiasis japonica in endemic areas of China

BACKGROUND

Schistosomiasis remains a serious public health problem in affected countries, and routine, highly sensitive and cost-effective diagnostic methods are lacking. We evaluated two immunodiagnostic techniques for the detection of Schistosoma japonicum infections: circulating antibody and circulating antigen assays.

METHODS

A total of 1864 individuals (between 6 and 72 years old) residing in five administrative villages in Hubei province were screened by serum examination with an indirect hemagglutination assay (IHA). The positive individuals (titer ≥20 in IHA) were reconfirmed by stool examination with the Kato-Katz method (three slides from a single stool specimen). Samples of good serum quality and a volume above 0.5 ml were selected for further testing with two immunodiagnostic antibody (DDIA and ELISA) and two antigen (ELISA) assays.

RESULTS

The average antibody positive rate in the five villages was 12.7%, while the average parasitological prevalence was 1.50%; 25 of the 28 egg-positive samples were also circulating antigen-positive. Significant differences were observed between the prevalence according to the Kato-Katz method and all three immunodiagnostic antibody assays (P-value <0.0001). Similar differences were observed between the Kato-Katz method and the two immunodiagnostic antigen assays (P-value <0.0001) and between the antigen and antibody assays (P-value <0.0001).

CONCLUSION

Both circulating antibody and circulating antigen assays had acceptable performance characteristics. Immunodiagnostic techniques to detect circulating antigens have potential to be deployed for schistosomiasis japonica screening in the endemic areas.