The prognosis of infectious diseases is determined by host-pathogen interactions. Control of pathogens has been the central dogma of treating infectious diseases in modern medicine, but the pathogen-directed medicine is facing significant challenges, including a lack of effective antimicrobials for newly emerging pathogens, pathogen drug resistance, and drug side effects. Here, a mathematic equation (termed equation of host-pathogen interactions, HPI-Equation) is developed to dissect the key variables of host-pathogen interactions. It shows that control of pathogens does not necessarily lead to host recovery. Instead, a combination of promoting a host's power of self-healing and balancing immune responses provides the best benefit for host. Moreover, the HPI-Equation elucidates the scientific basis of traditional Chinese medicine (TCM), a host-based medicine that treats infectious diseases by promoting self-healing power and balancing immune responses. The importance of self-healing power elucidated in the HPI-Equation is confirmed by recent studies that the tolerance mechanism, which is discovered in plants and animals and conceptually similar to self-healing power, improves host survival without directly attacking pathogens. In summary, the HPI-Equation describes host-pathogen interactions with mathematical logic and precision; it translates the ancient wisdoms of TCM into apprehensible modern sciences and opens a new venue for integrating TCM and modern medicine for a future medicine. Sun J. A mathematic equation derived from host-pathogen interactions elucidates the significance of integrating modern medicine with traditional Chinese medicine to treat infectious diseases. J Integr Med. 2023; 21(4):324-331.
Animals ; Medicine, Chinese Traditional ; Communicable Diseases/drug therapy* ; Mathematics ; Host-Pathogen Interactions ; Drugs, Chinese Herbal/therapeutic use*

OBJECTIVES: To investigate the role of brain functional connectivity and nonlinear dynamic analysis in brain function assessment for infants with controlled infantile spasm (IS). METHODS: A retrospective analysis was performed on 14 children with controlled IS (IS group) who were admitted to the Department of Neurology, Anhui Provincial Children's Hospital, from January 2019 to January 2023. Twelve healthy children, matched for sex and age, were enrolled as the control group. Electroencephalogram (EEG) data were analyzed for both groups to compare the features of brain network, and nonlinear dynamic indicators were calculated, including approximate entropy, sample entropy, permutation entropy, and permutation Lempel-Ziv complexity. RESULTS: Brain functional connectivity showed that compared with the control group, the IS group had an increase in the strength of functional connectivity, and there was a significant difference between the two groups in the connection strength between the Fp2 and F8 channels (P<0.05). The network stability analysis showed that the IS group had a significantly higher network stability than the control group at different time windows (P<0.05). The nonlinear dynamic analysis showed that compared with the control group, the IS group had a significantly lower sample entropy of Fz electrode (P<0.05). CONCLUSIONS Abnormalities in brain network and sample entropy may be observed in some children with controlled IS, and it is suggested that quantitative EEG analysis parameters can serve as neurological biomarkers for evaluating brain function in children with IS.
Child ; Humans ; Infant ; Nonlinear Dynamics ; Spasms, Infantile ; Retrospective Studies ; Brain ; Electroencephalography

In recent years, exploring the physiological and pathological mechanisms of brain functional integration from the neural network level has become one of the focuses of neuroscience research. Due to the non-stationary and nonlinear characteristics of neural signals, its linear characteristics are not sufficient to fully explain the potential neurophysiological activity mechanism in the implementation of complex brain functions. In order to overcome the limitation that the linear algorithm cannot effectively analyze the nonlinear characteristics of signals, researchers proposed the transfer entropy (TE) algorithm. In recent years, with the introduction of the concept of brain functional network, TE has been continuously optimized as a powerful tool for nonlinear time series multivariate analysis. This paper first introduces the principle of TE algorithm and the research progress of related improved algorithms, discusses and compares their respective characteristics, and then summarizes the application of TE algorithm in the field of electrophysiological signal analysis. Finally, combined with the research progress in recent years, the existing problems of TE are discussed, and the future development direction is prospected.
Algorithms ; Brain/physiology* ; Entropy ; Neural Networks, Computer ; Nonlinear Dynamics

The use of non-invasive blood glucose detection techniques can help diabetic patients to alleviate the pain of intrusive detection, reduce the cost of detection, and achieve real-time monitoring and effective control of blood glucose. Given the existing limitations of the minimally invasive or invasive blood glucose detection methods, such as low detection accuracy, high cost and complex operation, and the laser source's wavelength and cost, this paper, based on the non-invasive blood glucose detector developed by the research group, designs a non-invasive blood glucose detection method. It is founded on dual-wavelength near-infrared light diffuse reflection by using the 1 550 nm near-infrared light as measuring light to collect blood glucose information and the 1 310 nm near-infrared light as reference light to remove the effects of water molecules in the blood. Fourteen volunteers were recruited for
Blood Glucose ; Diabetes Mellitus ; Humans ; Nonlinear Dynamics

INTRODUCTION: Approximately 1000 children die each year due to preventable water and sanitation-related diarrheal diseases. Six in 10 people lacked access to safely managed sanitation facilities in 2015. Numerous community- and school-based approaches have been implemented to eradicate open defecation practices, promote latrine ownership, improve situation sanitation, and reduce waterborne disease. OBJECTIVE: Given that current evidence for sanitation interventions seem promising, the aim of this study was to systematically summarize existing research on the effectiveness of community- and school-based randomized controlled sanitation intervention in improving (1) free open defecation (safe feces disposal), (2) latrine usage, (3) latrine coverage or access, and (4) improved latrine coverage or access. METHODS: Eight electronic databases were searched: PubMed, Scopus, WHO Global Health Library (GHL), Virtual Health Library (VHL), POPLINE, Web of Science, Cochrane, and Google Scholar up to 26 April 2019. Original randomized clinical trials addressing community-based or school-based intervention that reported feces disposal and latrine coverage were deemed eligible. More than two researchers independently contributed to screening of papers, data extraction, and bias assessment. We conducted a meta-analysis by random-effects model. The risk of bias was assessed by the Cochrane risk of bias tool. RESULTS: Eighteen papers that matched all criteria and 16 studies were included in the final meta-analysis. Compared to the control, the sanitation intervention significantly increased safe feces disposal (OR 2.19, 95% CI 1.51-3.19, p < 0.05, I CONCLUSION Our study showed strong evidence for both community- and school-based sanitation interventions as effective for the safe disposal of human excreta. The finding suggests major implications for health policy and design of future intervention in developing countries.
Community Participation/statistics & numerical data* ; Randomized Controlled Trials as Topic ; Sanitation/instrumentation* ; School Health Services/statistics & numerical data* ; Toilet Facilities/statistics & numerical data*

Objective To evaluate the influence of temperature on the outpatient visits for urticaria in Lanzhou City and its hysteresis and to find out the sensitive populations by sex and age stratification.Methods We collected the urticaria outpatient data in three grade A class three hospitals as well as the meteorological data and air pollutant data in Lanzhou from January 2011 to December 2017.The distributed lag non-linear model(DLNM)was employed to analyze the influence of daily mean temperature on the outpatient visits for urticaria.Stratification analysis was performed for different age groups(0-14,15-59,≥60 years)and different sex populations.Results Temperature had a non-linear relationship with the outpatient visits for urticaria,and there existed hysteresis.During the research period,the average daily outpatient visits for urticaria at the three hospitals in Lanzhou was 25,ranging from 1 to 76.With the rise in the daily mean temperature within 0-10 ℃,the risk of outpatient visits for urticaria first increased and then decreased.When the daily mean temperature was 2 ℃,hysteresis occurred on the 18th day,and the relative risk(
Adolescent ; Air Pollutants/analysis* ; Child ; Child, Preschool ; China/epidemiology* ; Female ; Humans ; Infant ; Infant, Newborn ; Middle Aged ; Nonlinear Dynamics ; Outpatients ; Temperature ; Urticaria/epidemiology*

BACKGROUND: For the reason that many studies have been inconclusive on the effect of humidity on respiratory disease, we examined the association between absolute humidity and respiratory disease mortality and quantified the mortality burden due to non-optimal absolute humidity in Guangzhou, China. METHODS: Daily respiratory disease mortality including total 42,440 deaths from 1 February 2013 to 31 December 2018 and meteorological data of the same period in Guangzhou City were collected. The distributed lag non-linear model was used to determine the optimal absolute humidity of death and discuss their non-linear lagged effects. Attributable fraction and population attributable mortality were calculated based on the optimal absolute humidity, defined as the minimum mortality absolute humidity. RESULTS: The association between absolute humidity and total respiratory disease mortality showed an M-shaped non-linear curve. In total, 21.57% (95% CI 14.20 ~ 27.75%) of respiratory disease mortality (9154 deaths) was attributable to non-optimum absolute humidity. The attributable fractions due to high absolute humidity were 13.49% (95% CI 9.56 ~ 16.98%), while mortality burden of low absolute humidity were 8.08% (95% CI 0.89 ~ 13.93%), respectively. Extreme dry and moist absolute humidity accounted for total respiratory disease mortality fraction of 0.87% (95% CI - 0.09 ~ 1.58%) and 0.91% (95% CI 0.25 ~ 1.39%), respectively. There was no significant gender and age difference in the burden of attributable risk due to absolute humidity. CONCLUSIONS Our study showed that both high and low absolute humidity are responsible for considerable respiratory disease mortality burden, the component attributed to the high absolute humidity effect is greater. Our results may have important implications for the development of public health measures to reduce respiratory disease mortality.
China/epidemiology* ; Cities/epidemiology* ; Climate ; Humans ; Humidity/adverse effects* ; Models, Theoretical ; Nonlinear Dynamics ; Respiratory Tract Diseases/mortality* ; Sensitivity and Specificity

No abstract available.
Dissent and Disputes ; Statistics as Topic

The Big Data Research Committee of the Korean Society of Gastroenterology conducted activities and researches with three goals. First, it provides the basis for proper and cost-effective treatment of digestive diseases in Korea. Second, it carries out population-level global research by establishing a system of big data analysis related to gastroenterology. Third, it provides the members of the Korean Society of Gastroenterology with the opportunity to plan and assess the public interest related to big data. The studies published by the committee members in this paper were carried out under these objectives, and the findings are believed to have achieved the public interest goals that may be helpful in the current medical and health policy. The construction of the big data infrastructure for digestive drugs is also underway, and we expect to see meaningful results pertaining to important digestive drugs. Research using public health medical big data, such as the National Health Insurance Corporation data base, should ultimately provide a basis for reflecting public messages and policies for the public. To this end, it is necessary for Korean researchers to lead efforts to lower the barriers and to approach relevant information and opportunities using big data research.
Committee Membership ; Gastroenterology ; Health Policy ; Korea ; National Health Programs ; Public Health ; Statistics as Topic

OBJECTIVES: Breast cancer is the second most common cancer among Korean women. Because breast cancer is strongly associated with negative emotional and physical changes, early detection and treatment of breast cancer are very important. As a supporting tool for classifying breast cancer, we tried to identify the best meta-learner model in a stacking ensemble when the same machine learning models for the base learner and meta-learner are used. METHODS: We used machine learning models, such as the gradient boosted model, distributed random forest, generalized linear model, and deep neural network in a stacking ensemble. These models were used to construct a base learner, and each of them was used as a meta-learner again. Then, we compared the performance of machine learning models in the meta-learner to determine the best meta-learner model in the stacking ensemble. RESULTS: Experimental results showed that using the GBM as a meta-learner led to higher accuracy than that achieved with any other model for breast cancer data and using the GLM as a meta learner led to low root-mean-squared error for both sets of breast cancer data. CONCLUSIONS: We compared the performance of every meta-learner model in a stacking ensemble as a supporting tool for classifying breast cancer. The study showed that using specific models as a metalearner resulted in better performance than single classifiers, and using GBM and GLM as a meta-learner is appropriate as a supporting tool for classifying breast cancer data.
Breast Neoplasms ; Breast ; Classification ; Female ; Forests ; Humans ; Linear Models ; Machine Learning ; Medical Informatics ; Statistics as Topic