Herbal medicine for inflammatory bowel diseases: development of pattern identification algorithms by retrospective analysis of case series data

Discovering the key symptoms for identifying patterns in functional dyspepsia patients: Doctor's decision and machine learning

Background

Pattern identification is a crucial diagnostic process in Traditional East Asian Medicine, classifying patients with similar symptom patterns. This study aims to identify key symptoms for distinguishing patterns in patients with functional dyspepsia (FD) using explicit (doctor's decision-based) and implicit (computational model-based) approaches.

Methods

Data from twenty-one FD patients were collected from local clinics of traditional Korean Medicine and provided to three doctors in a standardized format. Each doctor identified patterns among three types: spleen-stomach weakness, spleen deficiency with qi stagnation/liver-stomach disharmony, and food retention. Doctors evaluated the importance of the symptoms indicated by items in the Standard Tool for Pattern Identification of Functional Dyspepsia questionnaire. Explicit importance was determined through doctors' survey by general evaluation and by selecting specific information used for the diagnosis of patient cases. Implicit importance was assessed by feature importance from the random forest classification models, which classify three types for general differentiation and perform binary classification for specific types.

Results

Key symptoms for distinguishing FD patterns were identified using two approaches. Explicit importance highlighted dietary and nausea-related symptoms, while implicit importance identified complexion or chest tightness as generally crucial. Specific symptoms important for particular pattern types were also identified, and significant correlation between implicit and explicit importance scores was observed for types 1 and 3.

Conclusion

This study showed important clinical information for differentiating FD patients using real patient data. Our findings suggest that these approaches can contribute to developing tools for pattern identification with enhanced accuracy and reliability.

Prediction of Clinical Remission with Adalimumab Therapy in Patients with Ulcerative Colitis by Fourier Transform-Infrared Spectroscopy Coupled with Machine Learning Algorithms

We aimed to develop prediction models for clinical remission associated with adalimumab treatment in patients with ulcerative colitis (UC) using Fourier transform-infrared (FT-IR) spectroscopy coupled with machine learning (ML) algorithms. This prospective, observational, multicenter study enrolled 62 UC patients and 30 healthy controls. The patients were treated with adalimumab for 56 weeks, and clinical remission was evaluated using the Mayo score. Baseline fecal samples were collected and analyzed using FT-IR spectroscopy. Various data preprocessing methods were applied, and prediction models were established by 10-fold cross-validation using various ML methods. Orthogonal partial least squares-discriminant analysis (OPLS-DA) showed a clear separation of healthy controls and UC patients, applying area normalization and Pareto scaling. OPLS-DA models predicting short- and long-term remission (8 and 56 weeks) yielded area-under-the-curve values of 0.76 and 0.75, respectively. Logistic regression and a nonlinear support vector machine were selected as the best prediction models for short- and long-term remission, respectively (accuracy of 0.99). In external validation, prediction models for short-term (logistic regression) and long-term (decision tree) remission performed well, with accuracy values of 0.73 and 0.82, respectively. This was the first study to develop prediction models for clinical remission associated with adalimumab treatment in UC patients by fecal analysis using FT-IR spectroscopy coupled with ML algorithms. Logistic regression, nonlinear support vector machines, and decision tree were suggested as the optimal prediction models for remission, and these were noninvasive, simple, inexpensive, and fast analyses that could be applied to personalized treatments.

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.

Suppression of plasmacytoid dendritic cell migration to colonic isolated lymphoid follicles abrogates the development of colitis

BACKGROUND

Dendritic cells (DCs) play a pivotal role in maintaining immunological homeostasis by orchestrating innate and adaptive immune responses via migration to inflamed sites and the lymph nodes (LNs). Plasmacytoid DCs (pDCs) have been reported to accumulate in the colon of inflammatory bowel disease (IBD) patients and dextran sulfate sodium (DSS)-induced colitis mice. However, the role of pDCs in the progression of colonic inflammation remains unclear.

METHODS

80 compounds in natural medicines were searched for inhibitors of pDC migration using bone marrow-derived pDCs (BMpDCs) and conventional DCs (BMcDCs). BALB/c mice were given 3% DSS in the drinking water to induce acute colitis. Compounds, which specifically inhibited pDC migration, were administrated into DSS-induced colitis mice.

FINDINGS

Astragaloside IV (As-IV) and oxymatrine (Oxy) suppressed BMpDC migration but not BMcDC migration. In DSS-induced colitis mice, the number of pDCs was markedly increased in the colonic lamina propria (LP), and the expression of CCL21 was obviously observed in colonic isolated lymphoid follicles (ILFs). As-IV and Oxy reduced symptoms of colitis and the accumulation of pDCs in colonic ILFs but not in the colonic LP. Moreover, in a BMpDC adoptive transfer model, BMpDC migration to colonic ILFs was significantly decreased by treatment with As-IV or Oxy.

INTERPRETATION

pDCs accumulated in the colon of colitis mice, and As-IV and Oxy ameliorated colitis by suppressing pDC migration to colonic ILFs. Accordingly, the selective inhibition of pDC migration may be a potential therapeutic approach for treating colonic inflammatory diseases.