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Background: Clinical notes are unstructured text documents generated by clinicians during patient encounters, generally are annotated with International Classification of Diseases (ICD) codes, which give formatted information about the diagnosis and treatment. ICD code has shown its potentials in many fields, but manual coding is labor-intensive and error-prone, lead to researches of automatic coding. Two specific challenges of this task are (1) given an annotated clinical notes, the reasons behind specific diagnoses and treatments are  implicit; (2) explainability is important for practical automatic coding method, the method should not only explain its prediction output but also have explainable internal mechanics. This study aims to develop an explainable CNN approach to address these two challenges.Method: Our key idea is that for the automatic ICD coding task, the presence of informative snippets in the clinical text that correlated with each code plays an important role in the prediction of codes, and an informative snippet can be considered as a local and low-level feature. We infer that there exists a correspondence between a convolution filter and a local and low-level feature. Base on the inference, we come up with the Shallow and Wide Attention convolutional Mechanism (SWAM) to improve the CNN-based models' ability to learn local and low-level features for each label.Results: We evaluate our approach on MIMIC-III, an open-access dataset of ICU medical records. Our approach substantially outperforms previous results on top-50 medical code prediction on MIMIC-III dataset, the precision of the worst-performing 10% labels in previous works is increased from 0% to 53% on average. We attribute this improvement to SWAM, by which the wide architecture with attention mechanism gives the model ability to more extensively learn the unique features of different codes, and we prove it by an ablation experiment. Besides, we perform manual analysis of the performance imbalance between different codes, and preliminary conclude the characteristics that determine the difficulty of learning specific codes.Conclusions: Our main contributions can be summarized into the following three: (1) We present local and low-level features, a.k.a. informative snippets play an important role in the automatic ICD coding task, and the informative snippets extracted from the clinical text provide explanations for each code. (2) We propose that there exists a correspondence between a convolution filter and a local and low-level feature. A combination of wide and shallow convolutional layer and attention layer can help the CNN-based models better learn local and low-level features. (3) We improved the precision of the worst-performing 10% labels from 0 to 53% on average. [ABSTRACT FROM AUTHOR]

Background: The objective of this study was to detect the urinary levels of dimethoate, benzo(a) pyrene (BaP), and bisphenol A (BPA) in first-year Hohai University students with different geographic origins.Methods: First-morning urine samples were collected from 540 healthy freshmen aged 17 to 19 years. Chemical levels were measured using β-glucuronidase hydrolysis followed by a high-performance liquid chromatography-tandem mass spectrometry-based method. Geometric means (GMs) of these three chemicals are presented by body mass index (BMI) and location in a volume-based and creatinine-standardized way.Results: GM concentrations of omethoate, BPA and 3-OHBaP were 9.47 μg/L (10.80 μg/g creatinine), 3.54 μg/L (4.04 μg/g creatinine) and 0.34 ng/L (0.39 ng/g creatinine), respectively. The GM concentration of omethoate in males was significantly higher than that in females. The individuals with a BMI higher than 23.9 had higher GM concentrations of omethoate, BPA, and 3-OHBaP. The inhabitants of Southwest China had significantly lower GM concentrations of omethoate, BPA, and 3-OHBaP than those who lived in other locations in China.Conclusion: The average level of environmental chemical accumulation in freshmen is lower in Southwest China and differs in youth who live in different regions. In addition, obesity is correlated with higher toxin levels in youth. [ABSTRACT FROM AUTHOR]

Background: Lin28B and its paralog Lin28A are small RNA binding proteins that have similar inhibitory effects, although they target separate steps in the maturation of let-7 miRNAs in mammalian cells. Because Lin28B participates in the promotion and development of tumors mostly by blocking the let-7 tumor suppressor family members, we sought to explore the associated mechanisms to gain insights into how Lin28B might be decreased in human cancer cells to increase let-7 levels and reverse malignancy.Results: We demonstrated that the histone acetyltransferase PCAF, via its cold shock domain, directly interacts with and subsequently acetylates Lin28B in lung adenocarcinoma-derived H1299 cells. RT-qPCR assays showed that both let-7a-1 and let-7g were increased in PCAF-transfected H1299 cells. Lin28B is acetylated by ectopic PCAF and translocates from the nucleus to the cytoplasm in H1299 cells.Conclusions: The effects of acetylated Lin28B on let-7a-1 and let-7g are similar to that of stable knockdown of Lin28B in H1299 cells. The new role of PCAF in mediating Lin28B acetylation and the specific release of its target microRNAs in H1299 cells may shed light on the potential application of let-7 in the clinical treatment of lung cancer patients. [ABSTRACT FROM AUTHOR]

Background: Chronic rhinosinusitis (CRS) is defined as a condition of inflammation in the paranasal sinus mucosa persisting for more than 12 weeks. We previously reported that the prevalence of CRS was about 8 % in China. Here, we aim to investigate the occupational and environmental risk factors associated with CRS.Methods: Data were collected from seven Chinese cities: Urumqi, Changchun, Beijing, Wuhan, Chengdu, Huaian and Guangzhou. CRS was diagnosed according to the European Position Paper on Rhinosinusitis and Nasal Polyps (EP(3)OS) document. Participants were asked to complete a standardized questionnaire, which was developed by the Global Allergy and Asthma European Network (GA(2)LEN) project and covered sociodemographic characteristics, CRS-related symptoms and occupational and environmental exposures. We evaluated the association between CRS and various occupational and environmental factors using odds ratios (ORs) and 95 % confidence intervals (95 % CIs).Results: The total study population consisted of 10,633 subjects, 850 (7.99 %) of whom were defined as having CRS according to the EP(3)OS criteria. We found that there were significant associations between occupational and environmental factors and CRS. Specifically, having a clearance-related job, occupational exposure to dust, occupational exposure to poisonous gas, a pet at home or carpet at home or at the workplace were risk factors for CRS. Additionally, the method used to keep warm in winter, the duration of time spent using air conditioning in summer and the frequency of exposure to mouldy or damp environments were significantly different in subjects with and without CRS.Conclusions: Our data showed that some occupational and environmental exposures are strongly associated with CRS, which aids in understanding the epidemiology of CRS. [ABSTRACT FROM AUTHOR]

Background: Single-incision laparoscopic surgery (SILS) is an emerging minimally invasive surgery to reduce abdominal incisions. However, despite the increasing clinical application of SILS, no evidence from large-scale, randomized controlled trials is available for assessing the feasibility, short-term safety, oncological safety, and potential benefits of SILS compared with conventional laparoscopic surgery (CLS) for colorectal cancer.Methods/design: This is a single-center, open-label, noninferiority, randomized controlled trial. A total of 198 eligible patients will be randomly assigned to transumbilical single incision plus one port laparoscopic surgery (SILS plus one) group or to a CLS group at a 1:1 ratio. Patients ranging in age from 18 to 80 years with rectosigmoid cancer diagnosed as cT1-4aN0-2 M0 and a tumor size no larger than 5 cm are considered eligible. The primary endpoint is early morbidity, as evaluated by an independent investigator. Secondary outcomes include operative outcomes (operative time, estimated blood loss, and incision length), pathologic outcomes (tumor size, length of proximal and distal resection margins, and number of harvested lymph nodes), postoperative inflammatory and immune responses (white blood cells [WBC], neutrophil percentage [NE %], C-reactive protein [CRP], interleukin-6 [IL-6], and tumor necrosis factor-α [TNF-α]), postoperative recovery (time to first ambulation, flatus, liquid diet, soft diet, and duration of hospital stay), pain intensity, body image and cosmetic assessment, 3-year disease free survival (DFS), and 5-year overall survival (OS). Follow-up visits are scheduled for 1 and 3 months after surgery, then every 3 months for the first 2 years and every 6 months for the next 3 years.Discussion: This trial will provide valuable clinical evidence for the objective assessment of the feasibility, safety, and potential benefits of SILS plus one compared with CLS for the radical resection of rectosigmoid cancer. The hypothesis is that SILS plus one is feasible for the radical resection of rectosigmoid cancer and offers short-term safety and long-term oncological safety comparable to that of CLS, and that SILS plus one offers better cosmetic results and faster convalescence compared to CLS.Trial Registration: ClinicalTrials.gov: NCT02117557 (registered on 16 April 2014). [ABSTRACT FROM AUTHOR]

Background: Extracting relationships between chemicals and diseases from unstructured literature have attracted plenty of attention since the relationships are very useful for a large number of biomedical applications such as drug repositioning and pharmacovigilance. A number of machine learning methods have been proposed for chemical-induced disease (CID) extraction due to some publicly available annotated corpora. Most of them suffer from time-consuming feature engineering except deep learning methods. In this paper, we propose a novel document-level deep learning method, called recurrent piecewise convolutional neural networks (RPCNN), for CID extraction.Results: Experimental results on a benchmark dataset, the CDR (Chemical-induced Disease Relation) dataset of the BioCreative V challenge for CID extraction show that the highest precision, recall and F-score of our RPCNN-based CID extraction system are 65.24, 77.21 and 70.77%, which is competitive with other state-of-the-art systems.Conclusions: A novel deep learning method is proposed for document-level CID extraction, where domain knowledge, piecewise strategy, attention mechanism, and multi-instance learning are combined together. The effectiveness of the method is proved by experiments conducted on a benchmark dataset. [ABSTRACT FROM AUTHOR]