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Background: Adrenocorticotropic hormone (ACTH)-independent Cushing's syndrome (CS) with right adrenal adenoma combined with HIV infection has rarely been reported. Case presentation: A 39-year-old Chinese male patient with HIV infection was admitted to our hospital due to increased blood pressure in the previous 2 years and weight gain in the previous 6 months. Endocrinological examinations showed that blood cortisol (8 a.m.) was 22.23 μg/dl, the level of ACTH (8 a.m.) was less than 1pg/ml and twenty-four-hour urinary cortisol was 1429 μg/24h. ACTH-independent CS was diagnosed based on low ACTH levels ( 200 cells/μl. Subsequent immunohistochemical staining confirmed right adrenal adenoma. Results: The postoperative recovery was good, and wound healing was possible. After surgical treatment, endocrinological examinations indicated that the level of ACTH increased and the levels of serum cortisol and twenty-four-hour urinary cortisol decreased, which indicated that CS was controlled. CD4/CD8 was 0.47 at reexamination, and the patient's immunity was improved. Conclusion: Due to the potential side effects of steroid drugs, clinicians should use these medications with caution and closely monitor the development of adrenal deficiency.

BACKGROUND Emphysema pyelonephritis (EPN) is a very dangerous type of urinary tract infection. It is a lethal disease that develops rapidly and causes the patient to deteriorate rapidly, and it can easily lead to systemic infections and even sepsis. The incidence is extremely low, and it is prevalent in patients with diabetes. We here report a case of EPN in a non-diabetic patient with autosomal dominant polycystic kidney disease (ADPKD). We share the diagnosis and treatment procedure for this extremely rare condition to make this disease easier to identify and address early. CASE SUMMARY A 47-year-old woman presented to the emergency department of our hospital with a high fever and left back pain lasting 4 d. She had a history of autosomal dominant polycystic kidney and polycystic liver. She was diagnosed with left type I EPN and her vital signs deteriorated so quickly that she underwent an emergency operation in which a D-J tube was inserted into her left ureter on the second day after admission. Two months later, she underwent a second-stage flexible ureteroscopy and lithotripsy. Despite postoperative sepsis, she finally recovered after active symptomatic support treatment and effective anti-infective treatment. CONCLUSION Although EPN is more likely to occur in diabetic patients, for non-diabetic patients with ADPKD and upper urinary tract obstruction, the disease also causes rapid deterioration. Early and accurate diagnosis and timely removal of the obstruction by invasive means may be able to save the damaged kidney and the patient’s life.

Background: The bone is the most common site of distant metastasis in prostate cancer. However, treatments for the bone metastasis of prostate cancer remain unsatisfactory. MicroRNAs (miRNAs) are small noncoding RNAs that play a variety of critical roles in tumor development and progression. Studies have confirmed that miRNA mimics could regulate the response to therapy in many cancers. Methods: In this study, a set of forty-four miRNAs were reduced in prostate cancer patients with bone metastases by high-throughput sequencing analysis. Wound healing, transwell assays and western blotting analysis were used to explore the role of miRNA mimic in prostate cancer bone metastasis. Results: Further gene ontology and pathway analysis showed that these miRNAs target genes are mainly involved in cellular metabolic process, intracellular membrane-bounded organelle, as well as proteoglycans in cancer and focal adhesion. Therefore, these down-regulated miRNAs may play a key role for prostate cancer bone metastasis treatment, including hsa-miR-221-3p, hsa-miR-222-3p, hsa-miR-133a-3p, hsa-miR-222-5p, hsa-miR-204-3p, hsa-miR-145-5p, hsa-miR-3681-5p, hsa-miR-184, hsa-miR-144-3p, hsa-miR-204-5p, and hsa-miR-221-5p. To further investigate the role of these miRNA mimics on prostate cancer bone metastasis, miR-145-5p was randomly selected for validation. Bioinformatics analysis showed that miR-145-5p target genes significantly affected TGF-beta and adherens junction signaling pathway. Wound healing and transwell assays and western blotting analysis revealed that miR-145-5p mimic inhibited proliferation, migration and invasion. Importantly, miR-145-5p mimic increased the expression of E-cadherin and reduced the expression of matrix metalloproteinase 2 and 9. These results revealed that miR-145-5p mimic mediated epithelial mesenchymal transition. Meanwhile, miR-145-5p mimic enhanced the level of caspase 9, which is an important promoter of apoptosis. Conclusions: These results indicate that miR-145-5p mimic could inhibit the progress of prostate cancer bone metastasis via regulation of epithelial mesenchymal transition. In addition, miR-145-5p mimic could induce the apoptosis of prostate cancer cells with bone metastases. In summary, the miR-145-5p mimic is expected to become a novel strategy for the treatment of tumor metastasis.

Link prediction is the task of predicting missing connections between entities in the knowledge graph (KG). While various forms of models are proposed for the link prediction task, most of them are designed based on a few known relation patterns in several well-known datasets. Due to the diversity and complexity nature of the real-world KGs, it is inherently difficult to design a model that fits all datasets well. To address this issue, previous work has tried to use Automated Machine Learning (AutoML) to search for the best model for a given dataset. However, their search space is limited only to bilinear model families. In this paper, we propose a novel Neural Architecture Search (NAS) framework for the link prediction task. First, the embeddings of the input triplet are refined by the Representation Search Module. Then, the prediction score is searched within the Score Function Search Module. This framework entails a more general search space, which enables us to take advantage of several mainstream model families, and thus it can potentially achieve better performance. We relax the search space to be continuous so that the architecture can be optimized efficiently using gradient-based search strategies. Experimental results on several benchmark datasets demonstrate the effectiveness of our method compared with several state-of-the-art approaches., Accepted by CIKM 2020, short paper

Neural network (NN) based data2text models achieve state-of-the-art (SOTA) performance in most metrics, but they sometimes drop or modify the information in the input, and it is hard to control the generation contents. Moreover, it requires paired training data that are usually expensive to collect. Template-based methods have good fidelity and controllability but require heavy human involvement. We propose a novel template-based data2text system powered by a text stitch model. It ensures fidelity and controllability by using templates to produce the main contents. In addition, it reduces human involvement in template design by using a text stitch model to automatically stitch adjacent template units, which is a step that usually requires careful template design and limits template reusability. The text stitch model can be trained in self-supervised fashion, which only requires free texts. The experiments on a benchmark dataset show that our system outperforms SOTA NN-based systems in fidelity and surpasses template-based systems in diversity and human involvement.

Aspect-based sentiment analysis (ABSA) aims to identify the opinion polarity towards a specific aspect. Traditional approaches formulate ABSA as a sentence classification task. However, it is observed that the single sentence classification paradigm cannot take full advantage of pre-trained language models. Previous work suggests it is better to cast ABSA as a question answering (QA) task for each aspect, which can be solved in the sentence-pair classification paradigm. Though QA-style ABSA achieves state-of-the-art (SOTA) results, it naturally separates the prediction process of multiple aspects belonging to the same sentence. It thus is unable to take full advantage of the correlation between different aspects. In this paper, we propose to use the global-perspective (GP) question to replace the original question in QA-style ABSA, which explicitly tells the model the existence of other relevant aspects using additional instructions. In this way, the model can distinguish relevant phrases for each aspect better and utilize the underlying relationship between different aspects. The experimental results on three benchmark ABSA datasets demonstrate the effectiveness of our method.

With studies indicative of altered renal excretion under high altitude-induced hypobaric hypoxia, the consideration of better therapeutic approaches has long been the aim of research on the management of high altitude related illness. The pharmacokinetics of drugs such as furosemide might be altered under hypoxic conditions, making it essential to establish different dose-regimens to maintain therapeutic efficacy or to avoid toxic side effects at high altitude. Simultaneously, drug-drug interactions (DDIs) mediated by OAT1 occur at high altitude, severely affecting furosemide pharmacokinetics. This study investigated the influence of acute exposure to high altitude at 4300 m on the renal excretion of furosemide in rats. Significant changes in physiological parameters and kidney histopathology were found after acute high altitude exposure. Compared with low altitude, the pharmacokinetics of furosemide and the expression level of OAT1 in kidney were significantly changed after rapid ascent to high altitude. Additionally, the down-regulated OAT1 expression further sustained the potential mechanism for the decreased renal excretion of furosemide, resulting in extended residence of the drug in the human body. The elevation of AUC, Cmax , MRT, t1/2 of furosemide, and decreased CL at high altitude further reinforced the current findings. Moreover, the absorption of furosemide was markedly increased and renal excretion significantly declined after co-administration of captopril, resulting in local drug interaction at high altitude. In conclusion, acute exposure to high altitude may significantly affect the renal excretion of furosemide and the pharmacokinetic parameters of furosemide were altered after co-administration of captopril, which may then impact the conventional therapeutic dosage.

Cushing's syndrome (CS) during pregnancy is rare. It causes the clinical disorder by overproduction of cortisol. Hypercortisolemia in pregnancy can lead to severe complications, both for the mother and the fetus, including spontaneous abortion, perinatal death, prematurity, maternal hypertension, heart failure, diabetes and opportunistic infections. The most common cause of hypercortisolemia in pregnancy is a cortisol-secreting adrenal tumor. Herein we present a 31 year-old female patient, at 20 weeks' gestation, with CS secondary to a left adrenal tumor. A brief review of reported similar cases is included.

Relation extraction is the task of identifying predefined relationship between entities, and plays an essential role in information extraction, knowledge base construction, question answering and so on. Most existing relation extractors make predictions for each entity pair locally and individually, while ignoring implicit global clues available across different entity pairs and in the knowledge base, which often leads to conflicts among local predictions from different entity pairs. This paper proposes a joint inference framework that employs such global clues to resolve disagreements among local predictions. We exploit two kinds of clues to generate constraints which can capture the implicit type and cardinality requirements of a relation. Those constraints can be examined in either hard style or soft style, both of which can be effectively explored in an integer linear program formulation. Experimental results on both English and Chinese datasets show that our proposed framework can effectively utilize those two categories of global clues and resolve the disagreements among local predictions, thus improve various relation extractors when such clues are applicable to the datasets. Our experiments also indicate that the clues learnt automatically from existing knowledge bases perform comparably to or better than those refined by human., to appear in Artificial Intelligence

The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answer, we develop novel methods to exploit the rich expressiveness of REs at different levels within a NN, showing that the combination significantly enhances the learning effectiveness when a small number of training examples are available. We evaluate our approach by applying it to spoken language understanding for intent detection and slot filling. Experimental results show that our approach is highly effective in exploiting the available training data, giving a clear boost to the RE-unaware NN., 11 Pages, 2 Figures, Accepted by ACL 2018

The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description. We argue that relevant law articles play an important role in this task, and therefore propose an attention-based neural network method to jointly model the charge prediction task and the relevant article extraction task in a unified framework. The experimental results show that, besides providing legal basis, the relevant articles can also clearly improve the charge prediction results, and our full model can effectively predict appropriate charges for cases with different expression styles., 10 pages, accepted by EMNLP 2017

Distant supervision significantly reduces human efforts in building training data for many classification tasks. While promising, this technique often introduces noise to the generated training data, which can severely affect the model performance. In this paper, we take a deep look at the application of distant supervision in relation extraction. We show that the dynamic transition matrix can effectively characterize the noise in the training data built by distant supervision. The transition matrix can be effectively trained using a novel curriculum learning based method without any direct supervision about the noise. We thoroughly evaluate our approach under a wide range of extraction scenarios. Experimental results show that our approach consistently improves the extraction results and outperforms the state-of-the-art in various evaluation scenarios., Comment: 10 pages, accepted by ACL 2017

In this paper, we deal with the task of extracting first order temporal facts from free text. This task is a subtask of relation extraction and it aims at extracting relations between entity and time. Currently, the field of relation extraction mainly focuses on extracting relations between entities. However, we observe that the multi-granular nature of time expressions can help us divide the dataset constructed by distant supervision into reliable and less reliable subsets, which can help to improve the extraction results on relations between entity and time. We accordingly contribute the first dataset focusing on the first order temporal fact extraction task using distant supervision. To fully utilize both the reliable and the less reliable data, we propose to use curriculum learning to rearrange the training procedure, label dropout to make the model be more conservative about less reliable data, and instance attention to help the model distinguish important instances from unimportant ones. Experiments show that these methods help the model outperform the model trained purely on the reliable dataset as well as the model trained on the dataset where all subsets are mixed together.

Discourse parsing aims to identify the relationship between different discourse units, where most previous works focus on recovering the constituency structure among discourse units with carefully designed features. In this paper, we propose to exploit Long Short Term Memory (LSTM) to properly represent discourse units, while using as few feature engineering as possible. Our transition based parsing model features a multilayer stack LSTM framework to discover the dependency structures among different units. Experiments on RST Discourse Treebank show that our model can outperform traditional feature based systems in terms of dependency structures, without complicated feature design. When evaluated in discourse constituency, our parser can also achieve promising performance compared to the state-of-the-art constituency discourse parsers.

Automatically constructed knowledge bases often suffer from quality issues such as the lack of attributes for existing entities. Manually finding and filling missing attributes is time consuming and expensive since the volume of knowledge base is growing in an unforeseen speed. We, therefore, propose an automatic approach to suggest missing attributes for entities via hierarchical clustering based on the intuition that similar entities may share a similar group of attributes. We evaluate our method on a randomly sampled set of 20,000 entities from DBPedia. The experimental results show that our method can achieve a high precision and outperform existing methods.