IDPpub: Illuminating the Dark Phosphoproteome Through PubMed Mining.

Global phosphoproteomics experiments quantify tens of thousands of phosphorylation sites. However, data interpretation is hampered by our limited knowledge on functions, biological contexts, or precipitating enzymes of the phosphosites. This study establishes a repository of phosphosites with associated evidence in biomedical abstracts, using deep learning-based natural language processing techniques. Our model for illuminating the dark phosphoproteome through PubMed mining (IDPpub) was generated by fine-tuning BioBERT, a deep learning tool for biomedical text mining. Trained using sentences containing protein substrates and phosphorylation site positions from 3000 abstracts, the IDPpub model was then used to extract phosphorylation sites from all MEDLINE abstracts. The extracted proteins were normalized to gene symbols using the National Center for Biotechnology Information gene query, and sites were mapped to human UniProt sequences using ProtMapper and mouse UniProt sequences by direct match. Precision and recall were calculated using 150 curated abstracts, and utility was assessed by analyzing the CPTAC (Clinical Proteomics Tumor Analysis Consortium) pan-cancer phosphoproteomics datasets and the PhosphoSitePlus database. Using 10-fold cross validation, pairs of correct substrates and phosphosite positions were extracted with an average precision of 0.93 and recall of 0.94. After entity normalization and site mapping to human reference sequences, an independent validation achieved a precision of 0.91 and recall of 0.77. The IDPpub repository contains 18,458 unique human phosphorylation sites with evidence sentences from 58,227 abstracts and 5918 mouse sites in 14,610 abstracts. This included evidence sentences for 1803 sites identified in CPTAC studies that are not covered by manually curated functional information in PhosphoSitePlus. Evaluation results demonstrate the potential of IDPpub as an effective biomedical text mining tool for collecting phosphosites. Moreover, the repository (http://idppub.ptmax.org), which can be automatically updated, can serve as a powerful complement to existing resources.

Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study.

BACKGROUND: Asthma exacerbation is an acute or subacute episode of progressive worsening of asthma symptoms and can have a significant impact on patients' quality of life. However, efficient methods that can help identify personalized risk factors and make early predictions are lacking. OBJECTIVE: This study aims to use advanced deep learning models to better predict the risk of asthma exacerbations and to explore potential risk factors involved in progressive asthma. METHODS: We proposed a novel time-sensitive, attentive neural network to predict asthma exacerbation using clinical variables from large electronic health records. The clinical variables were collected from the Cerner Health Facts database between 1992 and 2015, including 31,433 adult patients with asthma. Interpretations on both patient and cohort levels were investigated based on the model parameters. RESULTS: The proposed model obtained an area under the curve value of 0.7003 through a five-fold cross-validation, which outperformed the baseline methods. The results also demonstrated that the addition of elapsed time embeddings considerably improved the prediction performance. Further analysis observed diverse distributions of contributing factors across patients as well as some possible cohort-level risk factors, which could be found supporting evidence from peer-reviewed literature such as respiratory diseases and esophageal reflux. CONCLUSIONS: The proposed neural network model performed better than previous methods for the prediction of asthma exacerbation. We believe that personalized risk scores and analyses of contributing factors can help clinicians better assess the individual's level of disease progression and afford the opportunity to adjust treatment, prevent exacerbation, and improve outcomes.

Parsing clinical text using the state-of-the-art deep learning based parsers: a systematic comparison.

BACKGROUND: A shareable repository of clinical notes is critical for advancing natural language processing (NLP) research, and therefore a goal of many NLP researchers is to create a shareable repository of clinical notes, that has breadth (from multiple institutions) as well as depth (as much individual data as possible). METHODS: We aimed to assess the degree to which individuals would be willing to contribute their health data to such a repository. A compact e-survey probed willingness to share demographic and clinical data categories. Participants were faculty, staff, and students in two geographically diverse major medical centers (Utah and New York). Such a sample could be expected to respond like a typical potential participant from the general public who is given complete and fully informed consent about the pros and cons of participating in a research study. RESULTS: 2140 respondents completed the surveys. 56% of respondents were "somewhat/definitely willing" to share clinical data with identifiers, while 89% of respondents were "somewhat (17%) /definitely willing (72%)" to share without identifiers. Results were consistent across gender, age, and education, but there were some differences by geographical region. Individuals were most reluctant (50-74%) sharing mental health, substance abuse, and domestic violence data. CONCLUSIONS: We conclude that a substantial fraction of potential patient participants, once educated about risks and benefits, would be willing to donate de-identified clinical data to a shared research repository. A slight majority even would be willing to share absent de-identification, suggesting that perceptions about data misuse are not a major concern. Such a repository of clinical notes should be invaluable for clinical NLP research and advancement.

The International Conference on Intelligent Biology and Medicine 2018: Medical Informatics Thematic Track (MedicalInfo2018).

In this editorial, we first summarize the 2018 International Conference on Intelligent Biology and Medicine (ICIBM 2018) that was held on June 10-12, 2018 in Los Angeles, California, USA, and then briefly introduce the six research articles included in this supplement issue. At ICIBM 2018, a special theme of Medical Informatics was dedicated to recent advances of data science in the medical domain. After peer review, six articles were selected in this thematic issue, covering topics such as clinical predictive modeling, clinical natural language processing (NLP), electroencephalogram (EEG) network analysis, and text mining in biomedical literature.

Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text.

BACKGROUND: Extracting relations between important clinical entities is critical but very challenging for natural language processing (NLP) in the medical domain. Researchers have applied deep learning-based approaches to clinical relation extraction; but most of them consider sentence sequence only, without modeling syntactic structures. The aim of this study was to utilize a deep neural network to capture the syntactic features and further improve the performances of relation extraction in clinical notes. METHODS: We propose a novel neural approach to model shortest dependency path (SDP) between target entities together with the sentence sequence for clinical relation extraction. Our neural network architecture consists of three modules: (1) sentence sequence representation module using bidirectional long short-term memory network (Bi-LSTM) to capture the features in the sentence sequence; (2) SDP representation module implementing the convolutional neural network (CNN) and Bi-LSTM network to capture the syntactic context for target entities using SDP information; and (3) classification module utilizing a fully-connected layer with Softmax function to classify the relation type between target entities. RESULTS: Using the 2010 i2b2/VA relation extraction dataset, we compared our approach with other baseline methods. Our experimental results show that the proposed approach achieved significant improvements over comparable existing methods, demonstrating the effectiveness of utilizing syntactic structures in deep learning-based relation extraction. The F-measure of our method reaches 74.34% which is 2.5% higher than the method without using syntactic features. CONCLUSIONS: We propose a new neural network architecture by modeling SDP along with sentence sequence to extract multi-relations from clinical text. Our experimental results show that the proposed approach significantly improve the performances on clinical notes, demonstrating the effectiveness of syntactic structures in deep learning-based relation extraction.

Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text.

BACKGROUND: To detect attributes of medical concepts in clinical text, a traditional method often consists of two steps: named entity recognition of attributes and then relation classification between medical concepts and attributes. Here we present a novel solution, in which attribute detection of given concepts is converted into a sequence labeling problem, thus attribute entity recognition and relation classification are done simultaneously within one step. METHODS: A neural architecture combining bidirectional Long Short-Term Memory networks and Conditional Random fields (Bi-LSTMs-CRF) was adopted to detect various medical concept-attribute pairs in an efficient way. We then compared our deep learning-based sequence labeling approach with traditional two-step systems for three different attribute detection tasks: disease-modifier, medication-signature, and lab test-value. RESULTS: Our results show that the proposed method achieved higher accuracy than the traditional methods for all three medical concept-attribute detection tasks. CONCLUSIONS: This study demonstrates the efficacy of our sequence labeling approach using Bi-LSTM-CRFs on the attribute detection task, indicating its potential to speed up practical clinical NLP applications.

Time-sensitive clinical concept embeddings learned from large electronic health records.

BACKGROUND: Learning distributional representation of clinical concepts (e.g., diseases, drugs, and labs) is an important research area of deep learning in the medical domain. However, many existing relevant methods do not consider temporal dependencies along the longitudinal sequence of a patient's records, which may lead to incorrect selection of contexts. METHODS: To address this issue, we extended three popular concept embedding learning methods: word2vec, positive pointwise mutual information (PPMI) and FastText, to consider time-sensitive information. We then trained them on a large electronic health records (EHR) database containing about 50 million patients to generate concept embeddings and evaluated them for both intrinsic evaluations focusing on concept similarity measure and an extrinsic evaluation to assess the use of generated concept embeddings in the task of predicting disease onset. RESULTS: Our experiments show that embeddings learned from information within one visit (time window zero) improve performance on the concept similarity measure and the FastText algorithm usually had better performance than the other two algorithms. For the predictive modeling task, the optimal result was achieved by word2vec embeddings with a 30-day sliding window. CONCLUSIONS: Considering time constraints are important in training clinical concept embeddings. We expect they can benefit a series of downstream applications.

Identifying direct temporal relations between time and events from clinical notes.

BACKGROUND: Most of the current work on clinical temporal relation identification follows the convention developed in the general domain, aiming to identify a comprehensive set of temporal relations from a document including both explicit and implicit relations. While such a comprehensive set can represent temporal information in a document in a complete manner, some of the temporal relations in the comprehensive set may not be essential depending on the clinical application of interest. Moreover, as the types of evidence that should be used to identify explicit and implicit relations are different, current clinical temporal relation identification systems that target both explicit and implicit relations still show low performances for practical use. METHODS: In this paper, we propose to focus on a sub-task of conventional temporal relation identification task in order to provide insight into building practical temporal relation identification modules for clinical text. We focus on identification of direct temporal relations, a subset of temporal relations that is chosen to minimize the amount of inference required to identify the relations. A corpus on direct temporal relations between time expressions and event mentions is constructed, and an automatic system tailored for direct temporal relations is developed. RESULTS: It is shown that the direct temporal relations constitute a major category of temporal relations that contain important information needed for clinical applications. The system optimized for direct temporal relations achieves better performance than the state-of-the-art system developed with comprehensive set of both explicit and implicit relations in mind. CONCLUSIONS: We expect direct temporal relations to facilitate the development of practical temporal information extraction tools in clinical domain.

Extracting psychiatric stressors for suicide from social media using deep learning.

BACKGROUND: Suicide has been one of the leading causes of deaths in the United States. One major cause of suicide is psychiatric stressors. The detection of psychiatric stressors in an at risk population will facilitate the early prevention of suicidal behaviors and suicide. In recent years, the widespread popularity and real-time information sharing flow of social media allow potential early intervention in a large-scale population. However, few automated approaches have been proposed to extract psychiatric stressors from Twitter. The goal of this study was to investigate techniques for recognizing suicide related psychiatric stressors from Twitter using deep learning based methods and transfer learning strategy which leverages an existing annotation dataset from clinical text. METHODS: First, a dataset of suicide-related tweets was collected from Twitter streaming data with a multiple-step pipeline including keyword-based retrieving, filtering and further refining using an automated binary classifier. Specifically, a convolutional neural networks (CNN) based algorithm was used to build the binary classifier. Next, psychiatric stressors were annotated in the suicide-related tweets. The stressor recognition problem is conceptualized as a typical named entity recognition (NER) task and tackled using recurrent neural networks (RNN) based methods. Moreover, to reduce the annotation cost and improve the performance, transfer learning strategy was adopted by leveraging existing annotation from clinical text. RESULTS & CONCLUSIONS: To our best knowledge, this is the first effort to extract psychiatric stressors from Twitter data using deep learning based approaches. Comparison to traditional machine learning algorithms shows the superiority of deep learning based approaches. CNN is leading the performance at identifying suicide-related tweets with a precision of 78% and an F-1 measure of 83%, outperforming Support Vector Machine (SVM), Extra Trees (ET), etc. RNN based psychiatric stressors recognition obtains the best F-1 measure of 53.25% by exact match and 67.94% by inexact match, outperforming Conditional Random Fields (CRF). Moreover, transfer learning from clinical notes for the Twitter corpus outperforms the training with Twitter corpus only with an F-1 measure of 54.9% by exact match. The results indicate the advantages of deep learning based methods for the automated stressors recognition from social media.

Mouse ß-Defensin 3, A Defensin Inhibitor of Both Its Endogenous and Exogenous Potassium Channels.

The human defensins are recently discovered to inhibit potassium channels, which are classical targets of the animal toxins. Whether other vertebrate defensins are potassium channel inhibitors remains unknown. In this work, we reported that the mouse ß-defensin 3 (mBD3) was a novel inhibitor of both endogenous and exogenous potassium channels. The structural analysis showed that mBD3 is the most identical to human Kv1.3 channel-sensitive human ß-defensin 2 (hBD2). However, the pharmacological profiles indicated that the recombinant mBD3 (rmBD3) weakly inhibited the mouse and human Kv1.3 channels. Different from the pharmacological features of human ß-defensins, mBD3 more selectively inhibited the mouse Kv1.6 and human KCNQ1/KCNE1 channels with IC50 values of 0.6 ± 0.4 µM and 1.2 ± 0.8 µM, respectively. The site directed mutagenesis experiments indicated that the extracellular pore region of mouse Kv1.6 channel was the interaction site of rmBD3. In addition, the minor effect on the channel conductance-voltage relationship curves implied that mBD3 might bind the extracellular transmembrane helices S1-S2 linker and/or S3-S4 linker of mouse Kv1.6 channel. Together, these findings not only revealed mBD3 as a novel inhibitor of both endogenous and exogenous potassium channels, but also provided a clue to investigate the role of mBD3-Kv1.6 channel interaction in the physiological and pathological field in the future.

A hybrid approach to automatic de-identification of psychiatric notes.

De-identification, or identifying and removing protected health information (PHI) from clinical data, is a critical step in making clinical data available for clinical applications and research. This paper presents a natural language processing system for automatic de-identification of psychiatric notes, which was designed to participate in the 2016 CEGS N-GRID shared task Track 1. The system has a hybrid structure that combines machine leaning techniques and rule-based approaches. The rule-based components exploit the structure of the psychiatric notes as well as characteristic surface patterns of PHI mentions. The machine learning components utilize supervised learning with rich features. In addition, the system performance was boosted with integration of additional data to the training set through domain adaptation. The hybrid system showed overall micro-averaged F-score 90.74 on the test set, second-best among all the participants of the CEGS N-GRID task.

Psychiatric symptom recognition without labeled data using distributional representations of phrases and on-line knowledge.

OBJECTIVE: Mental health is becoming an increasingly important topic in healthcare. Psychiatric symptoms, which consist of subjective descriptions of the patient's experience, as well as the nature and severity of mental disorders, are critical to support the phenotypic classification for personalized prevention, diagnosis, and intervention of mental disorders. However, few automated approaches have been proposed to extract psychiatric symptoms from clinical text, mainly due to (a) the lack of annotated corpora, which are time-consuming and costly to build, and (b) the inherent linguistic difficulties that symptoms present as they are not well-defined clinical concepts like diseases. The goal of this study is to investigate techniques for recognizing psychiatric symptoms in clinical text without labeled data. Instead, external knowledge in the form of publicly available "seed" lists of symptoms is leveraged using unsupervised distributional representations. MATERIALS AND METHODS: First, psychiatric symptoms are collected from three online repositories of healthcare knowledge for consumers-MedlinePlus, Mayo Clinic, and the American Psychiatric Association-for use as seed terms. Candidate symptoms in psychiatric notes are automatically extracted using phrasal syntax patterns. In particular, the 2016 CEGS N-GRID challenge data serves as the psychiatric note corpus. Second, three corpora-psychiatric notes, psychiatric forum data, and MIMIC II-are adopted to generate distributional representations with paragraph2vec. Finally, semantic similarity between the distributional representations of the seed symptoms and candidate symptoms is calculated to assess the relevance of a phrase. Experiments were performed on a set of psychiatric notes from the CEGS N-GRID 2016 Challenge. RESULTS & CONCLUSION: Our method demonstrates good performance at extracting symptoms from an unseen corpus, including symptoms with no word overlap with the provided seed terms. Semantic similarity based on the distributional representation outperformed baseline methods. Our experiment yielded two interesting results. First, distributional representations built from social media data outperformed those built from clinical data. And second, the distributional representation model built from sentences resulted in better representations of phrases than the model built from phrase alone.

Lightweight predicate extraction for patient-level cancer information and ontology development.

BACKGROUND: Knowledge engineering for ontological knowledgebases is resource and time intensive. To alleviate these issues, especially for novices, automated tools from the natural language domain can assist in the development process of ontologies. We focus towards the development of ontologies for the public health domain and use patient-centric sources from MedlinePlus related to HPV-causing cancers. METHODS: This paper demonstrates the use of a lightweight open information extraction (OIE) tool to derive accurate knowledge triples that can lead to the seeding of an ontological knowledgebase. We developed a custom application, which interfaced with an information extraction software library, to help facilitate the tasks towards producing knowledge triples from textual sources. RESULTS: The results of our efforts generated accurate extractions ranging from 80-89% precision. These triples can later be transformed to OWL/RDF representation for our planned ontological knowledgebase. CONCLUSIONS: OIE delivers an effective and accessible method towards the development ontologies.

Thermodynamic Analysis of Eplerenone in 13 Pure Solvents at Temperatures from 283.15 to 323.15 K.

The solubility of eplerenone (EP) in 13 pure solvents (acetonitrile, N,N-dimethylformamide (DMF), acetone, 2-butanone, 4-methyl-2-pentanone, ethyl formate, methyl acetate, ethyl acetate, propyl acetate, butyl acetate, methyl propionate, ethyl propionate, ethanol, and 1-propanol) was determined by the gravimetric method at atmospheric pressure and various temperatures (from 283.15 to 323.15 K). The results showed that the solubility of EP in the selected solvents was positively correlated with the thermodynamic temperature, and the order of solubility of EP at 298.15 K was acetonitrile > DMF > 2-butanone > methyl acetate > 4-methyl-2-pentanone > methyl propionate > ethyl acetate > propyl acetate > ethyl formate > acetone > butyl acetate > ethanol >1-propanol. The modified Apelblat model, van't Hoff model, λh model, and polynomial empirical model were used for fitting the solubility data, and then the λh model was found to have the highest fitting accuracy with a minimum ARD of 7.0 × 10-3 and a minimum RMSD of 6.1 × 10-6. The solvent effect between the solute and the solvent was analyzed using linear solvation energy relationship (LSER), and the enthalpy of solvation (ΔsolH°), entropy of solvation (ΔsolS°), and Gibbs free energy of solvation (ΔsolG°) of the dissolution process of EP were calculated by the van't Hoff model, which indicated that the dissolution process of EP in the selected solvents was endothermic, nonspontaneous, and entropy-increasing. In this work, the solubility, dissolution characteristics, and thermodynamic parameters of EP were studied, which will provide data support for the production, crystallization, and purification of EP and will provide important guidance for the crystallization optimization of EP in industry.

Computed tomography radiomics study of invasion and instability of lung adenocarcinoma manifesting as ground glass nodule.

Background: Ground-glass nodule (GGN) is the most common manifestation of lung adenocarcinoma on computed tomography (CT). Clinically, the success rate of preoperative diagnosis of GGN by puncture biopsy and other means is still low. The aim of this study is to investigate the clinical and radiomics characteristics of lung adenocarcinoma presenting as GGN on CT images using radiomics analysis methods, establish a radiomics model, and predict the classification of pathological tissue and instability of GGN type lung adenocarcinoma. Methods: This study retrospectively collected 249 patients with 298 GGN lesions who were pathologically confirmed of having lung adenocarcinoma. The images were imported into the Siemens scientific research prototype software to outline the region of interest and extract the radiomics features. Logistic model A (a radiomics model to identify the infiltration of lung adenocarcinoma manifesting as GGNs) was established using features after the dimensionality reduction process. The receiver operating characteristic (ROC) curve of the model on training set and the verification set was drawn, and the area under the curve (AUC) was calculated. Second, a total of 112 lesions were selected from 298 lesions originating from CT images of at least two occasions, and the time between the first CT and the preoperative CT was defined as not less than 90 days. The mass doubling time (MDT) of all lesions was calculated. According to the different MDT diagnostic thresholds instability was predicted. Finally, their AUCs were calculated and compared. Results: There were statistically significant differences in age and lesion location distribution between the "noninvasive" lesion group and the invasive lesion group (P<0.05), but there were no statistically significant differences in sex (P>0.05). Model A had an AUC of 0.89, sensitivity of 0.75, and specificity of 0.86 in the training set and an AUC of 0.87, sensitivity of 0.63, and specificity of 0.90 in the validation set. There was no significant difference statistically in MDT between "noninvasive" lesions and invasive lesions (P>0.05). The AUCs of radiomics models B1, B2 and B3 were 0.89, 0.80, and 0.81, respectively; the sensitivities were 0.71, 0.54, and 0.76, respectively; the specificities were 0.83, 0.77, and 0.60, respectively; and the accuracies were 0.78, 0.65, and 0.69, respectively. Conclusions: There were statistically significant differences in age and location of lesions between the "noninvasive" lesion group and the invasive lesion group. The radiomics model can predict the invasiveness of lung adenocarcinoma manifesting as GGNs. There was no significant difference in MDT between "noninvasive" lesions and invasive lesions. The radiomics model can predict the instability of lung adenocarcinoma manifesting as GGN. When the threshold of MDT was set at 813 days, the model had higher specificity, accuracy, and diagnostic efficiency.

Structure-activity relationship study of anti-wear additives in rapeseed oil based on machine learning and logistic regression.

Anti-wear performance is a crucial quality of lubricants, and it is important to conduct research into the structure-activity relationship of anti-wear additives in bio-based lubricants. These lubricants are eco-friendly and energy-efficient. A literature review resulted in the construction of a dataset comprising 779 anti-wear properties of 79 anti-wear additives in rapeseed oil, at various loadings and additive levels. The anti-wear additives were classified into six groups, including phosphoric acid, formate esters, borate esters, thiazoles, triazine derivatives, and thiophene. Logistic regression analysis revealed that the quantity and kind of anti-wear agents had significant effects on the anti-wear properties of rapeseed oil, with phosphoric acid being the most effective and thiophene being the least effective. To identify the specific structural data that affect the anti-wear capabilities of additives in bio-based lubricants of rapeseed oil, a random forest classification model was developed. The results showed a 0.964 accuracy (ACC) and a 0.931 Matthews Correlation Coefficient (MCC) on the test set. The ranking of importance and characterization of MACCS descriptors in the model confirms that anti-wear additives with chemical structures containing P, O, N, S and heterocyclic groups, along with more than two methyl groups, improve the anti-wear performance of rapeseed oil. The application of data analysis and machine learning to investigate the classifications and structural characteristics of anti-wear additives in rapeseed oil provides data references and guiding principles for designing anti-wear additives in bio-based lubricants.

Case-fatality ratio and effectiveness of ribavirin therapy among hospitalized patients in china who had severe fever with thrombocytopenia syndrome.

BACKGROUND: The wide distribution and high case-fatality ratio of severe fever with thrombocytopenia syndrome (SFTS) have made it a significant public health problem. This study was designed to identify the predictors of fatal outcomes and to evaluate the effectiveness of antiviral therapy in treating SFTS virus (SFTSV)-infected patients. METHODS: A cross-sectional study was performed in a general hospital located in Xinyang city, whereas the largest number of patients with SFTS in China were treated during 2011-2012. The primary outcome for the treatment effect analysis was death. Other outcomes included sequential platelet levels and viral loads observed throughout the hospitalization and the interval between the initiation of ribavirin therapy and the return of the platelet count to a normal level. RESULTS: A total of 311 SFTSV-infected patients were included in the study. The most frequent clinical presentations were fever, weakness, myalgia, and gastrointestinal symptoms. Each patient had thrombocytopenia, leukopenia, or both. The case-fatality ratio (CFR) was 17.4% (95% confidence interval [CI], 13.1%-21.6%). Older age (odds ratio [OR], 1.061; 95% CI, 1.023-1.099; P = .001), decreased level of consciousness (OR, 5.397; 95% CI, 2.660-10.948; P < .001), and elevated levels of lactate dehydrogenase (>1200 U/L; OR, 2.620; 95% CI, 1.073-6.399; P = .035) and creatine kinase (>800 U/L; OR, 2.328; 95% CI, 1.129-4.800; P = .022) were significantly associated with fatal outcome. The CFRs were similar between patients who received ribavirin and those who did not. Ribavirin treatment showed no significant effect on either platelet counts or viral loads during hospitalization of patients with fatal or nonfatal cases. CONCLUSIONS: These findings can improve knowledge about the characteristics of patients with fatal outcomes and the use of antiviral drug for SFTS.

Transmission and maintenance cycle of Bartonella quintana among rhesus macaques, China.

We detected Bartonella quintana in 48.6% of captive rhesus macaques from an animal facility in Beijing, China. Prevalence of infection increased over the period of observation. Our findings suggest that macaques may serve as reservoir hosts for B. quintana and that Pedicinus obtusus lice might act as efficient vectors.

Factors Affecting the Breastfeeding Duration of Infants and Young Children in China: A Cross-Sectional Study.

OBJECTIVE: To investigate the factors affecting the duration of continuous breastfeeding of infants within 2 years of age, and to explore intervention strategies that may promote breastfeeding duration in China. METHOD: A self-made electronic questionnaire was used to investigate the breastfeeding duration of infants, and the influencing factors were collected from three levels of individual, family, and social support. The Kruskal-Wallis rank sum test and the multivariable ordinal logistic regression model were used for data analysis. Subgroup analysis was carried out according to region and parity. RESULTS: A total of 1001 valid samples from 26 provinces across the country were obtained. Among them, 9.9% breastfed for less than 6 months, 38.6% for 6 to 12 months, 31.8% for 12 to 18 months, 6.7% for 18 to 24 months, and 13.1% for more than 24 months. Barriers to sustained breastfeeding included the mother's age at birth being over 31, education level below junior high, cesarean delivery, and the baby's first nipple sucking at 2 to 24 h after birth. Factors that promote continued breastfeeding included freelancer or full-time mother, high breastfeeding knowledge score, supporting breastfeeding, baby with low birth weight, first bottle feeding at 4 months and later, first supplementary food at over 6 months old, high family income, the mother's family and friends supporting breastfeeding, breastfeeding support conditions after returning to work, etc. Conclusion: The breastfeeding duration in China is generally short, and the proportion of mothers breastfeeding until the age of 2 years and above, recommended by WHO, is very low. Multiple factors at the individual, family, and social support levels influence the duration of breastfeeding. It is suggested to improve the current situation by strengthening health education, improving system security, and enhancing social support.