Anti-Psoriatic Activity of Black, Green and White Tea Extracts from Southeastern China.

Psoriasis is a common chronic inflammatory disease, but most of its current treatments come with a high risk of side effects. As one of the world's top three beverages, tea has a traditional history of being used as a treatment for skin conditions due to its high safety profile, anti-inflammatory and other properties. In this study, we investigated the anti-psoriasis effects of ethanol extracts of black tea, green tea and white tea from southeastern China. The compositions of the tea extracts (TEs) were first determined by UPLC-Q-Exactive-Orbitrap MS and then genetic analysis, antibacterial, anti-inflammatory, and immunocompetence assays were performed. Imiquimod was used to establish a mouse model of psoriasis-like dermatitis and treating with the extracts to examine their efficacy. A total of 88 chemical components, mainly phenols and organic acids, were identified from the TEs. These TEs ameliorated skin damage and they all reduced the expression of cytokines IL-17 and TNF-α. By analyzing the genes, TEs may affect the inflammatory signaling pathway by regulating the metabolic changes. In addition, TEs can significantly scavenge ROS, NO, and inhibit cellular inflammation. In conclusion, this study examined the inhibitory effects of three TEs on psoriasis and their potential as nutritional supplements for the treatment of skin inflammation.

Clinical effect of different concentrations of ropivacaine in the labor analgesia of dural puncture epidural technique for obese puerperae.

BACKGROUND: This study was performed to analyze the clinical effect of different concentrations of ropivacaine in the labor analgesia of the dural puncture epidural (DPE) technique for obese puerperae. METHODS: One hundred and fifty first-term obese women who received vaginal delivery and required labor analgesia in our hospital were selected prospectively for this study, and divided into groups A, B, and C. The three groups of puerpera were given epidurals with different concentrations of ropivacaine (0.075%, 0.10%, and 0.125%) with sufentanil (0.5 µg/ml) for the labor analgesia regimen. The visual analog scale (VAS), Ramsay scale, and Bromage scale of puerperae before analgesia and at different time points after anesthesia, and analgesic onset time, analgesia time, first PCEA time, PCEA pressing time, ropivacaine consumption, labor time, maternal blood pressure and heart rate, maternal adverse reactions, blood gas analysis in the neonatal umbilical artery, and Apgar score were observed. RESULTS: The analgesia onset time, PCEA pressing time, and ropivacaine consumption in group C were lower and the analgesia time and the first PCEA time were longer than those in groups A and B. At T1-T3 and T5, VAS scores of group A were higher than those in groups B and C, Ramsay score of group A was lower than that of groups B and C at T2-T3, and Bromage score of group C at any time point was higher than other two groups. The time of the second stage of labor in groups B and C was longer than that in group A, which in group C was longer than that in group B. Compared with groups A and C, the blood pressure and heart rate of puerperae in group B were closer to normal values. Three different concentrations of ropivacaine had no significant effect on the umbilical artery blood gas analysis indices and Apgar scores at 1st minute and 5th minute in neonates. The incidence of maternal adverse reactions in group C was lower than those in groups A and B. CONCLUSION: 0.1% ropivacaine combined with 0.5 µg/ml sufentanil through DPE technique has good analgesic efficacy and few adverse effects in obese puerperae.

The space cold atom interferometer for testing the equivalence principle in the China Space Station.

The precision of the weak equivalence principle (WEP) test using atom interferometers (AIs) is expected to be extremely high in microgravity environment. The microgravity scientific laboratory cabinet (MSLC) in the China Space Station (CSS) can provide a higher-level microgravity than the CSS itself, which provides a good experimental environment for scientific experiments that require high microgravity. We designed and realized a payload of a dual-species cold rubidium atom interferometer. The payload is highly integrated and has a size of [Formula: see text]. It will be installed in the MSLC to carry out high-precision WEP test experiment. In this article, we introduce the constraints and guidelines of the payload design, the compositions and functions of the scientific payload, the expected test precision in space, and some results of the ground test experiments.

The space cold atom interferometer for testing the equivalence principle in the China Space Station.

The precision of the weak equivalence principle (WEP) test using atom interferometers (AIs) is expected to be extremely high in microgravity environment. The microgravity scientific laboratory cabinet (MSLC) in the China Space Station (CSS) can provide a higher-level microgravity than the CSS itself, which provides a good experimental environment for scientific experiments that require high microgravity. We designed and realized a payload of a dual-species cold rubidium atom interferometer. The payload is highly integrated and has a size of 460 mm × 330 mm × 260 mm. It will be installed in the MSLC to carry out high-precision WEP test experiment. In this article, we introduce the constraints and guidelines of the payload design, the compositions and functions of the scientific payload, the expected test precision in space, and some results of the ground test experiments.

Environmental geochemical baseline determination and pollution assessment of heavy metals in farmland soil of typical coal-based cities: A case study of Suzhou City in Anhui Province, China.

Soil is the foundation of agricultural green development and human survival; the study of local environmental geochemical baselines can guide soil management and ensure the safe use of soil. In this study, a total of 100 shallow farmland soil samples were collected in each township of Yongqiao District, Suzhou City, Anhui Province, China. Herein, the contents of 10 heavy metal elements including As, Hg, Cd, Pb, Cr, Cu, Mn, Ni, Zn and Fe were determined. In addition, the geochemical baseline of heavy metals was calculated based on the relative cumulative frequency curve method, and the soil pollution status was evaluated. The results show that the average contents of As, Hg, Cd, Cu, Mn and Zn exceeded the soil background values of Anhui Province by 3.82%-64.74% (1.04-1.65 times), meanwhile, the average contents of Pb and Cr were lower than the soil background values of Anhui Province. The average contents of Cd, Cr, Cu, Mn and Ni exceeded the Chinese soil background values by 1.61%-64.74% (0.98-1.65 times). The geochemical baseline values of As, Hg, Cd, Pb, Cr, Cu, Mn, Ni, Zn and Fe were 9.585 mg/kg, 0.028 mg/kg, 0.112 mg/kg, 21.59 mg/kg, 53.66 mg/kg, 19.31 mg/kg, 543.8 mg/kg, 24.93 mg/kg, 50.57 mg/kg and 2.654%, respectively. The pollution assessment results also showed that most of the farmland soil samples in the study area were non-polluting or slightly polluted based on geochemical baselines. Hg and Cu in a few samples were moderately polluted, and Cd in only one sample was moderately intensely polluted. Combined with the distribution of pollution and field investigation, it is considered that Hg comes from atmospheric deposition and agricultural non-point source pollution of industrial pollution. Cu comes from animal husbandry and agricultural pollution. Meanwhile, Cd is related to natural sources, wood processing and agricultural fertiliser application. The study demonstrated that the calculation of soil geochemical background value should take full account of the differences between the various regions, combined with the current state, particularly the pre-consideration of the distribution of elements or pollutants. Then, reasonably select the evaluation standard value so that the evaluation results can truly reflect the state of soil pollution.

Evaluation and characterization of biochar on the biogeochemical behavior of polycyclic aromatic hydrocarbons in mangrove wetlands.

As the inter-tidal regions between land and ocean, mangrove ecosystems have high polycyclic aromatic hydrocarbons (PAHs) content, and the over accumulation of PAHs in mangrove wetland poses a serious ecological risk to the health of plant and living creatures. Comparison to the agricultural sources -biochar, biochar produced from wetland plant has lower O/C (molar ratio), larger N contents, higher stability and more benefits. However, whether the rhizosphere action occurs in biochar- amended sediment and how to influence the biogeochemical behavior of PAH have rarely been reported. In this context, a leaching procedure and pot experiment (60-d) were performed on migration and transformation of PAH at the sediment, and toxicity and their bioavailability in plant affected by the presence of Kandelia obovate-derived biochar in Southeast China. Root exudates amendments significantly increased the cumulative leaching-loss of pyrene by 36-51 % with or without biochar amendment via continuous diffusion and partition process, and biochar amendments decreased the bioavailability of pyrene (16.8-25.8 %) probably due to a faster pyrene sorption on inter-phase transport against desorption. The regression analysis indicated a significant relationship (p < 0.05) between leachate pH and pyrene concentrations. Notably, the bioaccumulation of pyrene on K. obovate parts had significant negative correlation (p < 0.05) to biochar. The activities of four key antioxidizes (phenylalanine ammonia-lyase, dismutases, peroxidases and catalases) were significantly decreased with the application of biochar. Moreover, biochar plays a positive role in cytochrome C release and phosphatidylserine secretion, and a combined biochar-rhizosphere approach can improve the stress tolerance and resistance of K. obovate with an enhanced synergetic effect, which could be a feasible remediation strategy for alleviating the mangrove sediment contaminated by PAH.

Seasonal changes of heavy metals and health risk assessment based on Monte Carlo simulation in alternate water sources of the Xinbian River in Suzhou City, Huaibei Plain, China.

The urban alternate water source (AWS) is of great significance to the sustainable development of the city, the pollution degree, and source of heavy metals (HMs) in AWS, and whether it will adversely affect human health has received widespread attention. In this study, the urban AWS of Xinbian River in Suzhou City, Huaibei Plain, China, was used as the research object to study the seasonal changes of HMs (As, Cr, Cu, Cd, Pb, and Zn), quantitative identification of pollution sources, and human health risks (HHR). Research results show that the contents of those HMs, except As, are less than the drinking standards limit set by the World Health Organization (WHO), and the contents of As, Cr, and Zn are the largest in summer. The multivariate statistical analysis combined with positive matrix factorization (PMF) model analysis revealed that industrial sources accounted for 44.83%, and agricultural sources accounted for 55.17%. HHR assessment based on Monte Carlo simulation shows that the noncarcinogenic risks of adults and children are in the acceptable range (hazardous ingestion (HI) < 1), and the probability of carcinogenic risk values of children and adults are 95.03% and 38.96%, respectively, which are exceed the acceptable range (1 × 10-4) recommended by the United States Environment Protection Agency (USEPA). Approximately 30.75% of the carcinogenic risk value of agricultural source HMs to children exceeds the acceptable range (1 × 10-4). The above research results indicate that the effect of agricultural non-point source pollution on AWS should be prevented.

Mining human preference via self-correction causal structure learning.

Spurred by causal structure learning (CSL) ability to reveal the cause-effect connection, significant research efforts have been made to enhance the scalability of CSL algorithms in various artificial intelligence applications. However, less effort has been made regarding the stability and the interpretability of CSL algorithms. Thus, this work proposes a self-correction mechanism that embeds domain knowledge for CSL, improving the stability and accuracy even in low-dimensional but high-noise environments by guaranteeing a meaningful output. The suggested algorithm is challenged against multiple classic and influential CSL algorithms in synthesized and field datasets. Our algorithm achieves a superior accuracy on the synthesized dataset, while on the field dataset, our method interprets the learned causal structure as a human preference for investment, coinciding with domain expert analysis.

Biomedical relation extraction via knowledge-enhanced reading comprehension.

BACKGROUND: In biomedical research, chemical and disease relation extraction from unstructured biomedical literature is an essential task. Effective context understanding and knowledge integration are two main research problems in this task. Most work of relation extraction focuses on classification for entity mention pairs. Inspired by the effectiveness of machine reading comprehension (RC) in the respect of context understanding, solving biomedical relation extraction with the RC framework at both intra-sentential and inter-sentential levels is a new topic worthy to be explored. Except for the unstructured biomedical text, many structured knowledge bases (KBs) provide valuable guidance for biomedical relation extraction. Utilizing knowledge in the RC framework is also worthy to be investigated. We propose a knowledge-enhanced reading comprehension (KRC) framework to leverage reading comprehension and prior knowledge for biomedical relation extraction. First, we generate questions for each relation, which reformulates the relation extraction task to a question answering task. Second, based on the RC framework, we integrate knowledge representation through an efficient knowledge-enhanced attention interaction mechanism to guide the biomedical relation extraction. RESULTS: The proposed model was evaluated on the BioCreative V CDR dataset and CHR dataset. Experiments show that our model achieved a competitive document-level F1 of 71.18% and 93.3%, respectively, compared with other methods. CONCLUSION: Result analysis reveals that open-domain reading comprehension data and knowledge representation can help improve biomedical relation extraction in our proposed KRC framework. Our work can encourage more research on bridging reading comprehension and biomedical relation extraction and promote the biomedical relation extraction.

Comprehensive copy number analysis of Y chromosome-linked loci for detection of structural variations and diagnosis of male infertility.

Infertility affects about 15% of heterosexual couples and male factors account for ~45-50% of clinical cases. Genetic factors play an important role in male infertility and thus we try to develop a cost-effective method for screening the genetic factors in male infertility. In our retrospective proof-of-concept study, we employed the high-throughput ligation-dependent probe amplification (HLPA) to examine the copy number by 115 genomic loci covering the Y chromosome, and 5 loci covering the X chromosome-specific region. We identified 8 sex chromosome aneuploid people from the low sperm concentration (LSC) group, and Y chromosome-specific microdeletion/duplications in 211 samples from the LSC group, and in 212 samples from the control group. 35 samples showed complete loss of AZFc (BPY2 to CDY1B deletion), which was not observed in controls. Nevertheless, a partial loss of AZFc (BPY2 to BPY2B deletion) was detected at comparable frequencies in both groups (68/211 vs. 108/212, respectively). And we further found structural variations in 28.6 and 26.9% samples from infertility and fertility groups. Moreover, we found that there were lower copy numbers for heterochromatic sequences in men with LSC. Especially, we reported that ultra-low relative copy number (RCN) (<0.5) type and low RCN (0.5 to <0.75) type in Yq12 were more often in the LSC group for the first time. Our results not only shed light on the potential role of low RCN in Yq12 in male infertility but also showed that HLPA can be a powerful and cost-effective tool for clinical screening in male infertility.

A Unified Machine Reading Comprehension Framework for Cohort Selection.

Cohort selection is an essential prerequisite for clinical research, determining whether an individual satisfies given selection criteria. Previous works for cohort selection usually treated each selection criterion independently and ignored not only the meaning of each selection criterion but the relations among cohort selection criteria. To solve the problems above, we propose a novel unified machine reading comprehension (MRC) framework. In this MRC framework, we design simple rules to generate questions for each criterion from cohort selection guidelines and treat clues extracted by trigger words from patients' medical records as passages. A series of state-of-the-art MRC models based on BiDAF, BIMPM, BERT, BioBERT, NCBI-BERT, and RoBERTa are deployed to determine which question and passage pairs match. We also introduce a cross-criterion attention mechanism on representations of question and passage pairs to model relations among cohort selection criteria. Results on two datasets, that is, the dataset of the 2018 National NLP Clinical Challenge (N2C2) for cohort selection and a dataset from the MIMIC-III dataset, show that our NCBI-BERT MRC model with cross-criterion attention mechanism achieves the highest micro-averaged F1-score of 0.9070 on the N2C2 dataset and 0.8353 on the MIMIC-III dataset. It is competitive to the best system that relies on a large number of rules defined by medical experts on the N2C2 dataset. Comparing these two models, we find that the NCBI-BERT MRC model mainly performs worse on mathematical logic criteria. When using rules instead of the NCBI-BERT MRC model on some criteria regarding mathematical logic on the N2C2 dataset, we obtain a new benchmark with an F1-score of 0.9163, indicating that it is easy to integrate rules into MRC models for improvement.

Source apportionment and natural background levels of major ions in shallow groundwater using multivariate statistical method: A case study in Huaibei Plain, China.

Understanding the sources, natural background levels (NBLs), and threshold values (TVs) of the major ions in groundwater is essential for the effective protection of water resources. In this study, a total of 70 shallow groundwater samples were collected in Suzhou, Huaibei Plain, China. A variety of statistical methods and cumulative probability distribution techniques were performed to identify the sources, NBLs, and TVs of the major ions. The major ion concentrations found in decreasing order as follows: HCO3- > SO42- > NO3- > Cl- and Na+ > Ca2+ > Mg2+. Piper diagram for hydrochemical types shows that groundwater types were Mg-HCO3 (36%), Ca-HCO3 (34%), and Na-HCO3 (30%). According to the factor and the Unmix model analysis, anthropogenic (agriculture-related) and geogenic source (water-rock interactions-related) were identified to be responsible for the chemical composition of the groundwater in the study area, and their mean contributions for the major ion concentrations are 47.9% and 52.1%, respectively. The NBLs for Na+, Ca2+, Mg2+, Cl-, SO42-, and NO3- were determined to be 29.5-44.2, 26.2-38.9, 18.9-39.5, 1.0-9.9, 12.9-19.4, and 2.1-16.5 mg/L, respectively, and the TVs were calculated as 122.1, 169.5, 39.5, 129.6, 134.7, and 18.3 mg/L, respectively. Moreover, this study shows the feasibility and reliability of using these multivariate statistical methods and natural background levels to evaluate the status of groundwater quality.

In situ preparation of a Bi2O2CO3/BiOI with 2D/2D p-n heterojunction photocatalyst for water purification under visible light.

Introduction: Semiconductors have similar crystal structures and matched energy levels could form a coupled heterojunction at an interface between them which may allow response to visible light, achieving efficient decomposition of organic compounds. Methods: The Bi2O2CO3/BiOI (BOC/BOI) with 2D/2D p-n heterojunction was prepared by one-pot room-temperature strategy. The prepared materials were tested by various technologies, and the three-dimensional structure, light absorption properties, electrochemical properties and other information were obtained. Photocatalytic tests have also been carried out. Results and discussion: BOC/BOI heterojunction with oxygen vacancies showed much higher photocatalytic activity than pure BOC and BOI. For example, the preferred BOC/BOI-0.5 heterojunction of the degradation rate for Rhodamine B (RhB) is 97.6 % within 2 h, which is 15.8 and 2.2 times faster than that of BiOI and BOC. In addition, the removal rates of tetracycline, ciprofloxacin and bisphenol A by BOC/ BOI-0.5 were 92.4, 80.3 and 68.6%, respectively. The 2D/2D structures of BOC/BOI-0.5 with rich in oxygen vacancies combined p-n junction can effectively inhibit the photoinduced electron-hole pair recombination and increase the production of active free radicals. The O2- and h+ are the main reactants, giving the composite catalyst potential for degrading a variety of pollutants.

Drug knowledge discovery via multi-task learning and pre-trained models.

BACKGROUND: Drug repurposing is to find new indications of approved drugs, which is essential for investigating new uses for approved or investigational drug efficiency. The active gene annotation corpus (named AGAC) is annotated by human experts, which was developed to support knowledge discovery for drug repurposing. The AGAC track of the BioNLP Open Shared Tasks using this corpus is organized by EMNLP-BioNLP 2019, where the "Selective annotation" attribution makes AGAC track more challenging than other traditional sequence labeling tasks. In this work, we show our methods for trigger word detection (Task 1) and its thematic role identification (Task 2) in the AGAC track. As a step forward to drug repurposing research, our work can also be applied to large-scale automatic extraction of medical text knowledge. METHODS: To meet the challenges of the two tasks, we consider Task 1 as the medical name entity recognition (NER), which cultivates molecular phenomena related to gene mutation. And we regard Task 2 as a relation extraction task, which captures the thematic roles between entities. In this work, we exploit pre-trained biomedical language representation models (e.g., BioBERT) in the information extraction pipeline for mutation-disease knowledge collection from PubMed. Moreover, we design the fine-tuning framework by using a multi-task learning technique and extra features. We further investigate different approaches to consolidate and transfer the knowledge from varying sources and illustrate the performance of our model on the AGAC corpus. Our approach is based on fine-tuned BERT, BioBERT, NCBI BERT, and ClinicalBERT using multi-task learning. Further experiments show the effectiveness of knowledge transformation and the ensemble integration of models of two tasks. We conduct a performance comparison of various algorithms. We also do an ablation study on the development set of Task 1 to examine the effectiveness of each component of our method. RESULTS: Compared with competitor methods, our model obtained the highest Precision (0.63), Recall (0.56), and F-score value (0.60) in Task 1, which ranks first place. It outperformed the baseline method provided by the organizers by 0.10 in F-score. The model shared the same encoding layers for the named entity recognition and relation extraction parts. And we obtained a second high F-score (0.25) in Task 2 with a simple but effective framework. CONCLUSIONS: Experimental results on the benchmark annotation of genes with active mutation-centric function changes corpus show that integrating pre-trained biomedical language representation models (i.e., BERT, NCBI BERT, ClinicalBERT, BioBERT) into a pipe of information extraction methods with multi-task learning can improve the ability to collect mutation-disease knowledge from PubMed.

Distantly supervised biomedical relation extraction using piecewise attentive convolutional neural network and reinforcement learning.

OBJECTIVE: There have been various methods to deal with the erroneous training data in distantly supervised relation extraction (RE), however, their performance is still far from satisfaction. We aimed to deal with the insufficient modeling problem on instance-label correlations for predicting biomedical relations using deep learning and reinforcement learning. MATERIALS AND METHODS: In this study, a new computational model called piecewise attentive convolutional neural network and reinforcement learning (PACNN+RL) was proposed to perform RE on distantly supervised data generated from Unified Medical Language System with MEDLINE abstracts and benchmark datasets. In PACNN+RL, PACNN was introduced to encode semantic information of biomedical text, and the RL method with memory backtracking mechanism was leveraged to alleviate the erroneous data issue. Extensive experiments were conducted on 4 biomedical RE tasks. RESULTS: The proposed PACNN+RL model achieved competitive performance on 8 biomedical corpora, outperforming most baseline systems. Specifically, PACNN+RL outperformed all baseline methods with the F1-score of 0.5592 on the may-prevent dataset, 0.6666 on the may-treat dataset, and 0.3838 on the DDI corpus, 2011. For the protein-protein interaction RE task, we obtained new state-of-the-art performance on 4 out of 5 benchmark datasets. CONCLUSIONS: The performance on many distantly supervised biomedical RE tasks was substantially improved, primarily owing to the denoising effect of the proposed model. It is anticipated that PACNN+RL will become a useful tool for large-scale RE and other downstream tasks to facilitate biomedical knowledge acquisition. We also made the demonstration program and source code publicly available at http://112.74.48.115:9000/.

Neurological Complications of Veno-Arterial Extracorporeal Membrane Oxygenation: A Retrospective Case-Control Study.

Background: To explore the epidemiology, clinical features, risk indicators, and long-term outcomes of neurological complications caused by veno-arterial extracorporeal membrane oxygenation (V-A ECMO). Methods: We retrospectively analyzed 60 adult patients who underwent V-A ECMO support in our unit from February 2012 to August 2020. These patients were separated into the neurological complications group (NC group) and the non-neurological complications group (nNC group). The differences in basic data and ECMO data between the two groups were compared. The data of long-term neurological prognosis were collected by telephone follow-up. Results: Thirty-nine patients (65.0%) had neurological complications. There were significant differences between the two groups in terms of median age, hypertension, median blood urea nitrogen, median troponin I (TNI), median lactic acid, pre-ECMO percutaneous coronary intervention, continuous renal replacement therapy (CRRT), median Sequential Organ Failure Assessment score, median Acute Physiology and Chronic Health Evaluation II score, median peak inspiratory pressure, median positive end expiratory pressure, and median fresh frozen plasma (P < 0.05). The median Intensive Care Unit length of stay (ICU LOS), 28-day mortality, median post-ECMO vasoactive inotropic score, non-pulsate perfusion (NP), and median ECMO duration of the NC group were significantly higher than those of the nNC group (P < 0.05). Furthermore, multiple logistic regression analysis revealed that TNI (P = 0.043), CRRT (P = 0.047), and continuous NP > 12 h (P = 0.043) were independent risk indicators for neurological complications in patients undergoing ECMO. Forty-four patients (73.3%) survived after discharge, and 38 patients (63.3%) had Cerebral Performance Category score of 1-2. And there were significant differences between the two groups in long-term neurological outcomes after discharge for 6 months (P < 0.05). Conclusion: The incidence of neurological complications was higher in patients undergoing V-A ECMO and was closely related to adverse outcomes (including ICU LOS and 28-day mortality). TNI, CRRT, and continuous NP > 12 h were independent risk indicators for predicting neurological complications in ECMO supporting patients. And the neurological complications of patients during ECMO support had significant adverse effect on long-term surviving and neurological outcomes of patients after discharge for 6 months.

Novel Graph-Based Model With Biaffine Attention for Family History Extraction From Clinical Text: Modeling Study.

BACKGROUND: Family history information, including information on family members, side of the family of family members, living status of family members, and observations of family members, plays an important role in disease diagnosis and treatment. Family member information extraction aims to extract family history information from semistructured/unstructured text in electronic health records (EHRs), which is a challenging task regarding named entity recognition (NER) and relation extraction (RE), where named entities refer to family members, living status, and observations, and relations refer to relations between family members and living status, and relations between family members and observations. OBJECTIVE: This study aimed to introduce the system we developed for the 2019 n2c2/OHNLP track on family history extraction, which can jointly extract entities and relations about family history information from clinical text. METHODS: We proposed a novel graph-based model with biaffine attention for family history extraction from clinical text. In this model, we first designed a graph to represent family history information, that is, representing NER and RE regarding family history in a unified way, and then introduced a biaffine attention mechanism to extract family history information in clinical text. Convolution neural network (CNN)-Bidirectional Long Short Term Memory network (BiLSTM) and Bidirectional Encoder Representation from Transformers (BERT) were used to encode the input sentence, and a biaffine classifier was used to extract family history information. In addition, we developed a postprocessing module to adjust the results. A system based on the proposed method was developed for the 2019 n2c2/OHNLP shared task track on family history information extraction. RESULTS: Our system ranked first in the challenge, and the F1 scores of the best system on the NER subtask and RE subtask were 0.8745 and 0.6810, respectively. After the challenge, we further fine tuned the parameters and improved the F1 scores of the two subtasks to 0.8823 and 0.7048, respectively. CONCLUSIONS: The experimental results showed that the system based on the proposed method can extract family history information from clinical text effectively.

Intestinal Epithelial Cell Exosome Launches IL-1ß-Mediated Neuron Injury in Sepsis-Associated Encephalopathy.

BACKGROUND: Gut-microbiota-brain axis links the relationship between intestinal microbiota and sepsis-associated encephalopathy (SAE). However, the key mediators between them remain unclear. METHODS: Memory test was determined by Water maze. Intestinal flora was measured by 16S RNA sequencing. Neurotransmitter was detected by high-performance liquid chromatography (HPLC). Histopathology was determined by H&E, immunofluorescence (IF), and terminal-deoxynucleoitidyl transferase mediated nick end labeling (TUNEL) staining. Flow cytometry was employed to determine the proportion of macrophages. RESULTS: Fecal microbiota transplantation (FMT) relieved hippocampus impairment of SAE rats by inhibiting inflammation cytokine secretion, the expression of IBA-1 and neurotransmitter disturbance, and cell apoptosis and autophagy, accompanied by the reduced M1 polarization and M1 pro-inflammation factors produced by macrophages in mesenteric lymph nodes (MLNs). Actually, M1 polarization in SAE rats depended on intestinal epithelial cell (IEC)-derived exosome. GW4869-initiated inhibition of exosome secretion notably abolished M1 polarization and the secretion of IL-1ß. However, GW4869-mediated improvement of hippocampus impairment was counteracted by the delivery of recombinant interleukin (IL)-1ß to hippocampus. Mechanistically, IEC-derived exosome induced the excessive circulating IL-1ß produced by CP-R048 macrophages, which subsequently induced damage and apoptosis of hippocampal neurons H19-7 in an autophagy-dependent manner. And reactivation of autophagy facilitates intestinal IL-1ß-mediated hippocampal neuron injury. CONCLUSION: Collectively, intestinal flora disturbance induced the exosome release of IECs, which subsequently caused M1 polarization in MLNs and the accumulation of circulating IL-1ß. Circulating IL-1ß promoted the damage and apoptosis of neurons in an autophagy-dependent manner. Possibly, targeting intestinal flora or IEC-derived exosome contributes to the treatment of SAE.

Clinical Characteristics of 10 Pregnant and Postpartum Women With Extracorporeal Membrane Oxygenation: A Retrospective Study.

Background: The aim of study was to summarize the clinical characteristics and experience of extracorporeal membrane oxygenation (ECMO) in pregnant and postpartum patients. Methods and Results: We retrospectively reviewed 131 consecutive ECMO patients at our center from May 2015 to May 2021. A total of 10 Chinese patients were pregnant or postpartum at the time of ECMO initiation. Patients ranged in age from 25 to 36 years (median age 30.5 years). The ECMO duration ranged from 3 to 31 days (median duration 8 days). There was a stabilizing trend of acid-base balance and decreasing lactic acid over the 3 days following ECMO initiation. Seven (70%) patients survived at least 48 h after weaning from ECMO. Four (40%) patients survived until discharge, and four (40%) fetuses survived until discharge. Conclusion: ECMO provides a suitable temporary cardiopulmonary support for pregnant and postpartum patients. ECMO shows a favorable effect on short-term stability in critical obstetric patients.

Integrated remediation of sulfate reducing bacteria and nano zero valent iron on cadmium contaminated sediments.

Integrated-remediation technologies on heavy metal polluted sediments have received much attention. In this study, Cd contaminated sediments were treated with various conditions: sulfate reducing bacteria (SRB) only and SRB combined with different dosages of nano zero valent iron (nZVI (0.5-10 mg/g)). The immobilization of Cd was found in all remediation treatments according to the decreases of mobile Cd and the increases of more stable Cd compared with control. Five typical SRBs (Desulfobulbaceae, Desulfobacteraceae, Syntrophobacteraceae, Desulfovibrionaceae and Desulfomicrobiaceae) were identified having significant influences on Cd speciation transformation and they could stabilize Cd into sulfide precipitation through dissimilatory sulfate reduction (DSR). The ANOVA results of mobilization index and Cd concentration in overlying water both demonstrated that integrated-remediation systems with 5 mg/g and 10 mg/g of nZVI (Fe5 and Fe10 systems, respectively) presented better immobilization performance than conventional SRB only system (P < 0.05). It is confirmed that nZVI could stimulate the SRB bio-immobilization possibily through providing electrons and enhancing enzyme activities during DSR. The XPS analyses and Pourbaix diagrams revealed that mackinawite may be produced in the Fe10, resulting in the possible formation of Cd-S-Fe. This study indicates that integrated-remediation of SRB and nZVI have great potential in Cd immobilization of sediments, especially with higher addition of nZVI.