Building a disease risk model of osteoporosis based on traditional Chinese medicine symptoms and western medicine risk factors

The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review

Background: The development of artificial intelligence (AI) in the medical field has been growing rapidly. As AI models have been introduced in complementary and alternative medicine (CAM), a systematized review must be performed to understand its current status.

Objective: To categorize and seek the current usage of AI in CAM.

Method: A systematic scoping review was conducted based on the method proposed by the Joanna Briggs Institute. The three databases, PubMed, Embase, and Cochrane Library, were used to find studies regarding AI and CAM. Only English studies from 2000 were included. Studies without mentioning either AI techniques or CAM modalities were excluded along with the non-peer-reviewed studies. A broad-range search strategy was applied to locate all relevant studies.

Results: A total of 32 studies were identified, and three main categories were revealed: 1) acupuncture treatment, 2) tongue and lip diagnoses, and 3) herbal medicine. Other CAM modalities were music therapy, meditation, pulse diagnosis, and TCM syndromes. The majority of the studies utilized AI models to predict certain patterns and find reliable computerized models to assist physicians.

Conclusion: Although the results from this review have shown the potential use of AI models in CAM, future research ought to focus on verifying and validating the models by performing a large-scale clinical trial to better promote AI in CAM in the era of digital health.

Prevalence of and Risk Factors for Community-Based Osteoporosis and Associated Fractures in Beijing: Study Protocol for a Cross-Sectional and Prospective Study

Background: Osteoporosis (OP) patients are usually asymptomatic until osteoporotic fractures occur, which makes early diagnosis and prevention difficult, and the associated fractures secondary to OP could be preventable with appropriate management. Therefore, early identification and relevant evidence-based management of OP could guide the prevention of subsequent fractures. This study will investigate the prevalence of OP and the incidence of osteoporotic fractures in Beijing community residents to further explore the related risk factors and put forward suggestions for people aged 45-80 years old. Methods: Over 2 years, this study will conduct an OP screening and a prospective follow-up in the Beijing community to investigate the incidence of osteoporotic fractures. The study will undertake bone mineral density detection, collect biological samples, and record information via questionnaires. Discussion: The study aims to investigate the potential risk factors for osteoporosis and explore syndromes from traditional Chinese medicine that are associated with this condition based on large samples from the Beijing community. Data on the incidence of osteoporotic fractures among community dwellers in Beijing over the two-years will be available on the Chinese clinical trial registry: ChiCTR-SOC-17013090.

Unified approaches to assessing treatment effect of traditional Chinese medicine based on health profiles

Two dissimilarity indices are introduced to measure the disharmony of a human body system by mimicking the population bioequivalence and the individual bioequivalence concepts. Hypotheses for the treatment effect of a traditional Chinese medicine are formulated based on the two indices and then tested under the proposed designs by reverting an approximate confidence upper bound. The proposed methods can also be used when a drug product has multiple components or a trial has multiple endpoints.

A systematic literature review and classification of knowledge discovery in traditional medicine

INTRODUCTION AND OBJECTIVE

Despite the importance of machine learning methods application in traditional medicine there is a no systematic literature review and a classification for this field. This is the first comprehensive literature review of the application of data mining methods in traditional medicine.

METHOD

We reviewed 5 database between 2000 to 2017 based on the Kitchenham systematic review methodology. 502 articles were identified and reviewed for their relevance to application of machine learning methods in traditional medicine, 42 selected papers were classified and categorized on four dimension; 1) application domain of data mining techniques in traditional medicine; 2) the data mining methods most frequently used in traditional medicine; 3) main strength and limitation of data mining techniques in traditional medicine; 4) the performance evaluation methods in data mining methods in traditional medicine.

RESULT

The result obtained showed that main application domain of data mining techniques in traditional medicine was related to syndrome differentiation. Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) were recognized as being the methods most frequently applied in traditional medicine. Furthermore, each data mining techniques has its own strength and limitations when applied in traditional medicine. Single scaler methods were frequently used for performance evaluation of data mining methods.

CONCLUSION

Machine learning methods have become an important research field in traditional medicine. Our research provides information about this methods by examining the related articles.

Inhibition of Osteoclast Differentiation and Bone Resorption by Bisphosphonate-conjugated Gold Nanoparticles

In recent years, gold nanoparticles (GNPs) have been reported to affect the regeneration of bone tissue. The goal of this study was to improve bone tissue regeneration by using targeted GNPs. We fabricated a functionalized GNPs conjugated with alendronate (ALD), of the bisphosphonate group. Subsequently, the ALD, GNPs, and ALD conjugated GNPs (GNPs-ALD) were analyzed by ultraviolet-visible absorbance (UV-vis) spectrophotometer, Attenuated total reflectance Fourier transform infrared spectrometer (ATR-FTIR), and thermo gravimetric analysis (TGA). The prepared GNPs-ALD were used to investigate their inhibitory effects on the receptor activator of nuclear factor- κb ligand (RANKL)-induced osteoclastogenesis in bone marrow-derived macrophages (BMMs). Additionally, the GNPs-ALD were applied to ovariectomy (OVX)-induced osteoporotic mice and the experiments were evaluated. ALD was found to be successfully conjugated to the GNPs surface, and it displayed significant adhesion onto the bone surface. The in-vitro study indicated that the GNPs, ALD and GNPs-ALD suppressed osteoclast formation in a dose-dependent manner. Furthermore, in the OVX mouse model, the mice treated GNPs-ALD had higher bone density as compared to other OVX mice groups. The results from these tests indicated that GNPs-ALD can be useful agents for preventing and treating osteoporosis.

Mining gene expression data of multiple sclerosis

OBJECTIVES

Microarray produces a large amount of gene expression data, containing various biological implications. The challenge is to detect a panel of discriminative genes associated with disease. This study proposed a robust classification model for gene selection using gene expression data, and performed an analysis to identify disease-related genes using multiple sclerosis as an example.

MATERIALS AND METHODS

Gene expression profiles based on the transcriptome of peripheral blood mononuclear cells from a total of 44 samples from 26 multiple sclerosis patients and 18 individuals with other neurological diseases (control) were analyzed. Feature selection algorithms including Support Vector Machine based on Recursive Feature Elimination, Receiver Operating Characteristic Curve, and Boruta algorithms were jointly performed to select candidate genes associating with multiple sclerosis. Multiple classification models categorized samples into two different groups based on the identified genes. Models' performance was evaluated using cross-validation methods, and an optimal classifier for gene selection was determined.

RESULTS

An overlapping feature set was identified consisting of 8 genes that were differentially expressed between the two phenotype groups. The genes were significantly associated with the pathways of apoptosis and cytokine-cytokine receptor interaction. TNFSF10 was significantly associated with multiple sclerosis. A Support Vector Machine model was established based on the featured genes and gave a practical accuracy of ∼86%. This binary classification model also outperformed the other models in terms of Sensitivity, Specificity and F1 score.

CONCLUSIONS

The combined analytical framework integrating feature ranking algorithms and Support Vector Machine model could be used for selecting genes for other diseases.

Looking back into the future: 30 years of metabolomics at TNO

Metabolites have played an essential role in our understanding of life, health, and disease for thousands of years. This domain became much more important after the concept of metabolism was discovered. In the 1950s, mass spectrometry was coupled to chromatography and made the technique more application-oriented and allowed the development of new profiling technologies. Since 1980, TNO has performed system-based metabolic profiling of body fluids, and combined with pattern recognition has led to many discoveries and contributed to the field known as metabolomics and systems biology. This review describes the development of related concepts and applications at TNO in the biomedical, pharmaceutical, nutritional, and microbiological fields, and provides an outlook for the future.

A general binomial regression model to estimate standardized risk differences from binary response data

Estimates of absolute risks and risk differences are necessary for evaluating the clinical and population impact of biomedical research findings. We have developed a linear-expit regression model (LEXPIT) to incorporate linear and nonlinear risk effects to estimate absolute risk from studies of a binary outcome. The LEXPIT is a generalization of both the binomial linear and logistic regression models. The coefficients of the LEXPIT linear terms estimate adjusted risk differences, whereas the exponentiated nonlinear terms estimate residual odds ratios. The LEXPIT could be particularly useful for epidemiological studies of risk association, where adjustment for multiple confounding variables is common. We present a constrained maximum likelihood estimation algorithm that ensures the feasibility of risk estimates of the LEXPIT model and describe procedures for defining the feasible region of the parameter space, judging convergence, and evaluating boundary cases. Simulations demonstrate that the methodology is computationally robust and yields feasible, consistent estimators. We applied the LEXPIT model to estimate the absolute 5-year risk of cervical precancer or cancer associated with different Pap and human papillomavirus test results in 167,171 women undergoing screening at Kaiser Permanente Northern California. The LEXPIT model found an increased risk due to abnormal Pap test in human papillomavirus-negative that was not detected with logistic regression. Our R package blm provides free and easy-to-use software for fitting the LEXPIT model.