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The inference of associations between environmental factors and microbes and among microbes is critical to interpreting metagenomic data, but compositional bias, indirect associations resulting from common factors, and variance within metagenomic sequencing data limit the discovery of associations. To account for these problems, we propose metagenomic Lognormal-Dirichlet-Multinomial (mLDM), a hierarchical Bayesian model with sparsity constraints, to estimate absolute microbial abundance and simultaneously infer both conditionally dependent associations among microbes and direct associations between microbes and environmental factors. We empirically show the effectiveness of the mLDM model using synthetic data, data from the TARA Oceans project, and a colorectal cancer dataset. Finally, we apply mLDM to 16S sequencing data from the western English Channel and report several associations. Our model can be used on both natural environmental and human metagenomic datasets, promoting the understanding of associations in the microbial community.

Alopecia areata (AA) is a chronic inflammatory disease mediated by an array of cells and cytokines. Immunohistochemistry (IHC) of histological sections with antibodies to mast cell tryptase, CD4, CD8, CD1a and semi-quantitative real-time PCR analysis of Th1- and Th2-type cytokines were performed in 55 patients to investigate the infiltration features of mast cells (MCs), T lymphocytes and Langerhans cells (LCs) in scalp lesions of patients with AA. In AA patients, increased MCs mainly infiltrated the peri-follicular and peri-vascular areas, and correlated positively with numbers of CD8(+) T lymphocytes in deep peri-follicular areas (P = 0.04), but negatively with CD4(+) T lymphocytes in deep peri-vascular areas (P = 0.031). In patients with active hair loss, LCs in epidermis, deep dermis and peri-vascular were elevated (Ps < 0.05). Infiltration of LCs in upper peri-vascular areas and CD8(+) T cell infiltration in deep peri-follicular areas were positively correlated (R = 0.618, P = 0.011), as well as LCs in deep peri-vascular areas with CD8(+) T cells in upper peri-follicular areas (R = 0.570, P = 0.017). In patients with active hair loss, Th1-type cytokine (IL-2, IL-8, TNF-α) mRNA expression in deep dermis were higher than in upper dermis (Ps < 0.05). However, in patients with non-active hair loss, Th2-type cytokine (IL-5, IL-10) mRNA expression in deep dermis was higher than that in the upper dermis (Ps < 0.05). Positive correlations were found existing between MCs and CD8(+) T cells, as well as between LCs and CD8(+) T cells. In conclusion, findings in this study allow us to propose a close relationship between mast cells and CD8(+) T cells, as well as between LCs and CD8(+) T cells in AA, as well as allergy may interfere with infiltrating T lymphocytes in AA lesional regions. Also, Th1-type cytokine are related to disease activity of alopecia areata, whereas Th2-type cytokines may be associated with persistence of AA.

Background Padua linear model is widely used for the risk assessment of venous thromboembolism (VTE), a common but preventable complication for inpatients. However, genetic and environmental differences between Western and Chinese population limit the validity of Padua model in Chinese patients. Medical records which contain rich information about disease progression, are useful in mining new risk factors related to Chinese VTE patients. Furthermore, machine learning (ML) methods provide new opportunities to build precise risk prediction model by automatic selection of risk factors based on original medical records. Methods Medical records of 3,106 inpatients including 224 VTE patients were collected and various types of ontologies were integrated to parse unstructured text. A workflow of ontology-based VTE risk prediction model, that combines natural language processing (NLP) and machine learning (ML) technologies, was proposed. Firstly ontology terms were extracted from medical records, then sorted according to their calculated weights. Next importance of each term in the unit of section was evaluated and finally a ML model was built based on a subset of terms. Four ML methods were tested, and the best model was decided by comparing area under the receiver operating characteristic curve (AUROC). Results Medical records were first split into different sections and subsequently, terms from each section were sorted by their weights calculated by multiple types of information. Greedy selection algorithm was used to obtain significant sections and terms. Top terms in each section were selected to construct patients’ distributed representations by word embedding technique. Using top 300 terms of two important sections, namely the ‘Progress Note’ section and ‘Admitting Diagnosis’ section, the model showed relatively better predictive performance. Then ML model which utilizes a subset of terms from two sections, about 110 terms, achieved the best AUC score, of 0.973 ± 0.006, which was significantly better compared to the Padua’s performance of 0.791 ± 0.022. Terms found by the model showed their potential to help clinicians explore new risk factors. Conclusions In this study, a new VTE risk assessment model based on ontologies extraction from raw medical records is developed and its performance is verified on real clinical dataset. Results of selected terms can help clinicians to discover meaningful risk factors.

Allergy to dust mites may have an effect on the immune response in AA, and may contribute to its early onset and severity in patients of Chinese origin.

Objective: Venous thromboembolism (VTE) is a fatal complication and the most common preventable cause of death in hospitals. The risk-to-benefit ratio of thromboprophylaxis depends on the performance of the risk assessment model. A linear model, the Padua model, is recommended for medical inpatients in the United States but is not suitable for Chinese inpatients due to differences in race and disease spectrum. Currently, machine learning (ML) methods show advantages in modeling complex data patterns and have been applied to clinical data analysis. This study aimed to build VTE risk assessment ML models among Chinese inpatients and compare the predictive validity of the ML models with that of the Padua model. Methods: We used 376 patients, including 188 patients with VTE, to build a model and then evaluate the predictive validity of the model in a consecutive clinical dataset from Peking Union Medical College Hospital. Nine widely used ML methods were trained on the model derivation set and then compared with the Padua model. Results: Among the nine ML methods, random forest (RF), boosting-based methods, and logistic regression achieved a higher specificity, Youden index, positive predictive value, and area under the receiver operating characteristic curve than the Padua model on both the test and clinical validation sets. However, their sensitivities were inferior to that of the Padua model. Combined with the receiver operating characteristic curve, RF, as the best performing model, maintained high specificity with relatively better sensitivity and captured VTE patients' patterns more precisely. Conclusions: Advances in ML technology provide powerful tools for medical data analysis, and choosing models conforming to the disease pattern would achieve good performance. Popular ML models do not surpass the Padua model on all indicators of validity, and the drawback of low sensitivity should be improved upon in the future.

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