Prediction and analysis of risk factors for diabetic retinopathy based on machine learning and interpretable models.

Objective: Diabetic retinopathy is one of the major complications of diabetes. In this study, a diabetic retinopathy risk prediction model integrating machine learning models and SHAP was established to increase the accuracy of risk prediction for diabetic retinopathy, explain the rationality of the findings from model prediction and improve the reliability of prediction results. Methods: Data were preprocessed for missing values and outliers, features selected through information gain, a diabetic retinopathy risk prediction model established using the CatBoost and the outputs of the mode interpreted using the SHAP model. Results: One thousand early warning data of diabetes complications derived from diabetes complication early warning dataset from the National Clinical Medical Sciences Data Center were used in this study. The CatBoost-based model for diabetic retinopathy prediction performed the best in the comparative model test. ALB_CR, HbA1c, UPR_24, NEPHROPATHY and SCR were positively correlated with diabetic retinopathy, while CP, HB, ALB, DBILI and CRP were negatively correlated with diabetic retinopathy. The relationships between HEIGHT, WEIGHT and ESR characteristics and diabetic retinopathy were not significant. Conclusion: The risk factors for diabetic retinopathy include poor renal function, elevated blood glucose level, liver disease, hematonosis and dysarteriotony, among others. Diabetic retinopathy can be prevented by monitoring and effectively controlling relevant indices. In this study, the influence relationships between the features were also analyzed to further explore the potential factors of diabetic retinopathy, which can provide new methods and new ideas for the early prevention and clinical diagnosis of subsequent diabetic retinopathy.

Electroacupuncture reduces oxidative stress response and improves secondary injury of intracerebral hemorrhage in rats by activating the peroxisome proliferator-activated receptor-γ/nuclear factor erythroid2-related factor 2/γ-glutamylcysteine synthetase pathway.

Intracerebral hemorrhage (ICH) is a severe stroke subtype. Secondary injury is a key factor leading to neurological deficits after ICH. Electroacupuncture (EA) can improve the neurological function after ICH, however, its internal mechanism is still unclear. The aim of this study is to investigate whether EA could ameliorate secondary injury after ICH through antioxidative stress and its potential regulatory mechanism. A rat model of ICH was established by injecting autologous blood into striatum. After the intervention of EA and EA combined with peroxisome proliferator-activated receptor-γ (PPARγ) blocker, Zea-longa scores, modified neurological severity scores and open field tests were used to evaluate the neurological function of the rats. Flow cytometry detected tissue reactive oxygen species (ROS) levels. Tissue tumor necrosis factor-α (TNF-α) levels were analyzed by enzyme-linked immunosorbent assays. The protein expressions of PPAR γ, nuclear factor erythroid2-related factor 2 (Nrf2) and γ-glutamylcysteine synthetase (γ-GCS) were detected by Western blot. Immunohistochemistry was used to observe the activation of microglia. The demyelination degree of axon myelin was observed by transmission electron microscope. Compared with the model group, EA intervention improved neurological function, decreased ROS and TNF-α levels, increased the protein expression of PPARγ, Nrf2 and γ-GCS, and reduced the activation of microglia, it also alleviated axonal myelin sheath damage. In addition, the neuroprotective effect of EA was partially attenuated by PPARγ blocker. EA ameliorated the neurological function of secondary injury after ICH in rats, possibly by activating the PPARγ/Nrf2/γ-GCS signaling pathway, reducing microglia activation, and inhibiting oxidative stress, thus alleviating the extent of axonal demyelination plays a role.

Gut microbiota in chronic pain: Novel insights into mechanisms and promising therapeutic strategies.

Chronic pain remains one of the world's most persistent and unsolved clinical challenges that severely affect patients' quality of life. Presently, considering that the mechanisms underlying chronic pain are not fully understood, there is a lack of effective drugs and interventions to treat chronic pain in clinical practice. Therefore, exploring the pathogenic mechanism of chronic pain and establishing potential targets are the keys to treating chronic pain. Substantial evidence has indicated that gut microbiota plays a crucial role in modulating chronic pain, which has opened up a new frontier for investigating the pathogenesis of chronic pain. The gut microbiota is a pivotal junction point between the neuroimmune-endocrine and the microbiome-gut-brain axes that could directly or indirectly affect chronic pain. Different signaling molecules (such as metabolites, neuromodulators, neuropeptides, and neurotransmitters) from the gut microbiota regulate the progress of chronic pain by modulating the peripheral and central sensitization by targeting the corresponding receptors. Furthermore, gut microbiota dysbiosis is associated with the progress of different chronic pain disorders, such as visceral pain, neuropathic pain, inflammatory pain, migraine, and fibromyalgia. Therefore, the present review attempted to systematically summarize the action of the gut microbiota toward regulating the pathological mechanisms of chronic pain and discussed the beneficial effects of probiotics supplementation or fecal microbiota transplantation (FMT) to restore the gut microbiota in chronic pain patients so as to provide a new strategy for targeting the gut microbiota for alleviating chronic pain issues.

Chinese Clinical Named Entity Recognition From Electronic Medical Records Based on Multisemantic Features by Using Robustly Optimized Bidirectional Encoder Representation From Transformers Pretraining Approach Whole Word Masking and Convolutional Neural Networks: Model Development and Validation.

BACKGROUND: Clinical electronic medical records (EMRs) contain important information on patients' anatomy, symptoms, examinations, diagnoses, and medications. Large-scale mining of rich medical information from EMRs will provide notable reference value for medical research. With the complexity of Chinese grammar and blurred boundaries of Chinese words, Chinese clinical named entity recognition (CNER) remains a notable challenge. Follow-up tasks such as medical entity structuring, medical entity standardization, medical entity relationship extraction, and medical knowledge graph construction largely depend on medical named entity recognition effects. A promising CNER result would provide reliable support for building domain knowledge graphs, knowledge bases, and knowledge retrieval systems. Furthermore, it would provide research ideas for scientists and medical decision-making references for doctors and even guide patients on disease and health management. Therefore, obtaining excellent CNER results is essential. OBJECTIVE: We aimed to propose a Chinese CNER method to learn semantics-enriched representations for comprehensively enhancing machines to understand deep semantic information of EMRs by using multisemantic features, which makes medical information more readable and understandable. METHODS: First, we used Robustly Optimized Bidirectional Encoder Representation from Transformers Pretraining Approach Whole Word Masking (RoBERTa-wwm) with dynamic fusion and Chinese character features, including 5-stroke code, Zheng code, phonological code, and stroke code, extracted by 1-dimensional convolutional neural networks (CNNs) to obtain fine-grained semantic features of Chinese characters. Subsequently, we converted Chinese characters into square images to obtain Chinese character image features from another modality by using a 2-dimensional CNN. Finally, we input multisemantic features into Bidirectional Long Short-Term Memory with Conditional Random Fields to achieve Chinese CNER. The effectiveness of our model was compared with that of the baseline and existing research models, and the features involved in the model were ablated and analyzed to verify the model's effectiveness. RESULTS: We collected 1379 Yidu-S4K EMRs containing 23,655 entities in 6 categories and 2007 self-annotated EMRs containing 118,643 entities in 7 categories. The experiments showed that our model outperformed the comparison experiments, with F1-scores of 89.28% and 84.61% on the Yidu-S4K and self-annotated data sets, respectively. The results of the ablation analysis demonstrated that each feature and method we used could improve the entity recognition ability. CONCLUSIONS: Our proposed CNER method would mine the richer deep semantic information in EMRs by multisemantic embedding using RoBERTa-wwm and CNNs, enhancing the semantic recognition of characters at different granularity levels and improving the generalization capability of the method by achieving information complementarity among different semantic features, thus making the machine semantically understand EMRs and improving the CNER task accuracy.

Protective Effect of the Eluting Fraction of Da-Yuan-Yin Decoction on Acute Lung Injury.

Context: The Da-yuan-yin (DYY) decoction is a classical prescription of traditional Chinese medicine that has antipyretic and anti-inflammatory effects. Network Pharmacology (NP) is an emerging discipline based on system-biology theory and biosystem network analysis that researchers can use to predict drug-action targets and mechanisms. Objective: The study intended to use NP evaluate the protective effects of the fifth eluting fraction of the supernatant of the DYY decoction (DYY-5) for mice induced with acute lung injury (ALI) using lipopolysaccharide (LPS) and to explore DYY-5's mechanisms. Design: The research team performed an animal study. Setting: The study took place at the College of Pharmaceutical Science at Soochow University in Suzhou, China. Animals: The animals were 42 male Balb/c mice, about 20 to 25 g in weight. Intervention: The research team: instilled 2 mg/kg of LPS intratracheally (i.t.) to induce ALI. The team divided the mice into seven groups of six mice: (1) a control group; (2) a negative control group-the DYY-5 group with mice treated only with a high dosage, 60 mg/kg, of DYY-5 to investigate the effects of DYY-5 on normal mice; (3) the positive control group, the LPS group, with induced ALI but no treatments; (4) the LPS+60 mg/kg-DYY-5 group with induced ALI treated with a high dosage of DYY-5; (5) the LPS+30 mg/kg-DYY-5 group with induced ALI treated with a medium dosage of DYY-5; (6) the LPS+15 mg/kg-DYY-5 group with induced ALI treated with a low dosage of DYY-5; and (7) a reference drug control group, the LPS+DXM group, with induced ALI treated with 5 mg/kg of dexamethasone (DXM). Outcome Measures: The research team: (1) determined the chemical components of DYY; (2) identified the anticomplementary activities of DYY-5; (3) took lung specimens, serum, and bronchoalveolar lavage fluid (BALF) from the mice for histopathological examination, Western blot, and biochemical analysis; (4) measured total protein concentrations and lung W/D ratios; (5) measured the expressions of cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) messenger RNA (mRNA) using quantitative real-time polymerase chain reaction (PCR); (6) measured the levels of pro-inflammatory and anti-inflammatory factors, the activity of myeloperoxidase (MPO) and superoxide dismutase (SOD), and the levels of complements, including complements 3 (C3), C3c, C5a, C5aR1, and C5b-9, using kits; (7) analyzed the levels of nuclear factor-kappa B (NF-κB) and IkB kinase (IKK) using Western blot; and (8) used network pharmacology (NP) to predict DYY-5's mechanisms and potential targets. Results: The study's results were consistent with the NP analysis, which reflected the multitarget and multipathway characteristics of DYY-5 in alleviating ALI. The LPS+30 mg/kg-DYY-5 group had significantly lower lung wet-to-dry (W/D) ratios and total protein concentrations in BALF than the LPS group did, with P < .01 and P < .0001, respectively as did the LPS+60 mg/kg-DYY-5 group (both P < .0001). The 60 mg/kg of DYY-5 compared to the LPS group: (1) regulated the levels tumor necrosis factor-alpha (TNF-α), interleukin 6 (IL-6), and interleukin-1 beta (IL-1ß), with all P < .0001, anti-inflammatory factors-IL-4 (P < .05), IL-10 (P < .001), and IL-13 (P < .001); (2) increased the activity of SOD (P < .0001) and decreased the activity of MPO (P < .0001) and the expressions of iNOS and COX-2 mRNA (both P < .01); (3) blocked the activation of NF-κB and IKK; and (4) alleviated the pathological changes in the lung tissue, by reducing the depositions of C3c and decreasing the levels of C3, C5a and C5aR1 (all P < .0001), C5b-9 (P < .001) and C3c (P < .01) in serum. Conclusions: The protective effects of DYY-5 on ALI were related to antioxidation, anti-complementary activities, and regulation of inflammatory factors through the IKK/NF-κB signal pathway. DYY-5 may be useful as a potential therapeutic agent for treating ALI in clinics.

Intramedullary spinal cord abscess with brain abscess due to subacute infective endocarditis.

BACKGROUND: Intramedullary spinal cord abscesses (ISCA) are rare, even more so in association with brain abscesses. Infective endocarditis is an uncommon cause of ISCA. In this case study, we report a patient with intramedullary abscesses and multiple brain abscesses due to subacute infective endocarditis. CASE PRESENTATION: A 54-year-old man presented with a 7-day history of head and neck pain and numbness in both lower limbs. Intramedullary abscess combined with multiple brain abscesses was diagnosed based on blood culture, head and spinal magnetic resonance imaging (MRI), contrast-enhanced MRI, and magnetic resonance spectroscopy. Echocardiography revealed vegetations on the mitral valve and severe mitral regurgitation, which the authors believe was caused by subacute infective endocarditis. With ceftriaxone combined with linezolid anti-infective therapy, the patient's symptoms and imaging was improved during follow-up. CONCLUSIONS: This case hopes to raise the vigilance of clinicians for ISCA. When considering a patient with an ISCA, it is necessary to complete blood culture, MRI of the brain and spinal cord, and echocardiography to further identify whether the patient also has a brain abscess and whether the cause is infective endocarditis.

Da-Yuan-Yin decoction polyphenol fraction attenuates acute lung injury induced by lipopolysaccharide.

CONTEXT: Da-Yuan-Yin is a Chinese traditional prescription. OBJECTIVE: This study explores the therapeutic effects of the Da-Yuan-Yin decoction polyphenol fraction (DYY-4) on acute lung injury (ALI) in mice induced by lipopolysaccharide (LPS). MATERIALS AND METHODS: The mice (n = 10) were orally administrated with DYY-4 (15, 30, and 60 mg/kg) or DXM (5 mg/kg), half an hour after LPS (2 mg/kg) instilled intratracheally. The protein content and the levels of inflammatory factors, the levels of complements, the mRNA expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2), the level of myeloperoxidase (MPO) and superoxide dismutase (SOD), the expression of the IkB kinase (IKK) and nuclear factor-kappa B (NF-κB), the lung wet-to-dry weight (W/D) ratio and lung tissue were evaluated, 24 h after LPS challenge. Network pharmacology predicted potential targets. RESULTS: DYY-4 (30, 60 mg/kg, p < 0.01, p < 0.01) decreased the lung W/D ratio, total protein concentration, the levels of C3, C3c and C5a, the levels of TNF-α, IL-6, and IL-1ß, while increased the levels of IL-4 and IL-10. DYY-4 (60 mg/kg) decreased the levels of C5aR1, C5b-9 and COX-2 mRNA (p < 0.05), the levels of MPO and iNOS mRNA, the activation of the IKK/NF-κB pathway (p < 0.01), and increased the levels of IL-13 and SOD (p < 0.01). DYY-4 (60 mg/kg) relieved the lung tissue pathological changes and reduced the C3c deposition. DISCUSSION AND CONCLUSIONS: Network pharmacology combined with animal experiments revealed the targets of DYY-4 alleviating ALI.

A/(H1N1) pdm09 NS1 promotes viral replication by enhancing autophagy through hijacking the IAV negative regulatory factor LRPPRC.

The quadrilateral reassortant IAV A/(H1N1) pdm09 is the pathogen responsible for the first influenza pandemic of the 21st century. The virus spread rapidly among hosts causing high mortality within human population. Efficient accumulation of virions is known to be important for the rapid transmission of virus. However, the mechanism by which A/(H1N1) pdm09 promotes its rapid replication has not been fully studied. Here, we found the NS1 of A/(H1N1) pdm09 mediated complete macroautophagy/autophagy, and then facilitated self-replication, which may be associated with the more rapid spread of this virus compared with H1N1WSN and H3N8JL89. We found that the promotion of self-replication could be mainly attributed to NS1pdm09 strongly antagonizing the inhibitory effect of LRPPRC on autophagy. The interaction between NS1pdm09 and LRPPRC competitively blocked the interaction of LRPPRC with BECN1/Beclin1, resulting in increased recruitment of BECN1 for PIK3C3 (phosphatidylinositol 3-kinase catalytic subunit type 3) and induction of the initiation of autophagy. In conclusion, we uncover the unique molecular mechanism by which A/(H1N1) pdm09 utilizes autophagy to promote self-replication, and we provide theoretical basics for the analysis of the etiological characteristics of the A/(H1N1) pdm09 pandemic and the development of anti-influenza drugs and vaccines.Abbreviations: 293T: human embryonic kidney 293 cells; 293T_LRPPRC: stable LRPPRC expression 293T cells; 3-MA: 3-methyladenine; A549 cells: human non-small cell lung cancer cells; AA: amino acid; ACTB: actin beta; BECN1: beclin 1; BECN1 KO: BECN1 knockout 293T cells; Cal: calyculin A; Co-IP: co-immunoprecipitation; CQ: chloroquine; DC: dendritic cell; Eug: eugenol; GFP: green fluorescent protein; HA: hemagglutinin; HIV: human immunodeficiency virus; IAVs: Influenza A viruses; IFN: interferon; JL89: A/equine/Jilin/1/1989 (H3N8); LAMP2: lysosomal associated membrane protein 2; LRPPRC: leucine rich pentatriicopeptide repeat containing; LRPPRC KO: LRPPRC knockout 293T cells; M2: matrix 2; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; MDCK: Madin-Darby canine kidney cells; MOI: multiplicity of infection; MS: mass spectrometry; NP: nucleoprotein; NS1: non-structural protein 1; NS1JL89: non-structural protein 1 of A/equine/Jilin/1/1989 (H3N8); NS1pdm09: non-structural protein 1 of A/(H1N1) pdm09; NS1SC09: non-structural protein 1 of A/Sichuan/2009 (H1N1); NS1WSN: non-structural protein 1 of A/WSN/1933 (H1N1); PB1: polymerase basic protein 1; PB1-F2: alternate reading frame discovered in PB1 gene segment; PIK3C3: phosphatidylinositol 3-kinase catalytic subunit type 3; PR8: A/PR/8/34 (H1N1); Rapa: rapamycin; RFP: red fluorescent protein; SC09: A/Sichuan/2009 (H1N1); SQSTM1/p62: sequestosome 1; STK4/MST1: serine/threonine kinase 4; TEM: transmission electron microscopy; TOMM20: translocase of outer mitochondrial membrane 20; WHO: World Health Organization; WSN: A/WSN/1933 (H1N1); WSN-NS1JL89: WSN recombinant strain in which NS1 was replaced with that of JL89; WSN-NS1SC09: WSN recombinant strain in which NS1 was replaced with that of SC09.

Surgical procedure long terms recognition from Chinese literature incorporating structural feature.

With rapid development of technologies in medical diagnosis and treatment, the novel and complicated concepts and usages of clinical terms especially of surgical procedures have become common in daily routine. Expected to be performed in an operating room and accompanied by an incision based on expert discretion, surgical procedures imply clinical understanding of diagnosis, examination, testing, equipment, drugs and symptoms, etc., but terms expressing surgical procedures are difficult to recognize since the terms are highly distinctive due to long morphological length and complex linguistics phenomena. To achieve higher recognition performance and overcome the challenge of the absence of natural delimiters in Chinese sentences, we propose a Named Entity Recognition (NER) model named Structural-SoftLexicon-Bi-LSTM-CRF (SSBC) empowered by pre-trained model BERT. In particular, we pre-trained a lexicon embedding over large-scale medical corpus to better leverage domain-specific structural knowledge. With input additionally augmented by BERT, rich multigranular information and structural term information is transferred from Structural-SoftLexicon to downstream model Bi-LSTM-CRF. Therefore, we could get a global optimal prediction of input sequence. We evaluate our model on a self-built corpus and results show that SSBC with pre-trained model outperforms other state-of-the-art benchmarks, surpassing at most 3.77% in F1 score. This study hopefully would benefit Diagnostic Related Groups (DRGs) and Diagnosis Intervention Package (DIP) grouping system, medical records statistics and analysis, Medicare payment system, etc.

Membrane fouling mitigation by coagulation and electrostatic repulsion using an electro-AnMBR in kitchen wastewater treatment.

Anaerobic membrane bioreactor (AnMBR) is considered an efficient technique for kitchen wastewater treatment; however, membrane fouling restricts their applicability. In this study, a novel AnMBR with an Fe anode and Ti membrane cathode (electro-AnMBR) was constructed. The reactor exhibited good performance in pollutant removal and antifouling in kitchen wastewater treatment. Compared with the traditional AnMBR, the electro-AnMBR increased phosphate removal by approximately 55% and reduced transmembrane pressure (TMP) by 50%. Coagulation from the Fe2+/Fe3+ released by the sacrificial anode increased the sludge floc size and porosity, significantly reducing the membrane fouling potential. In addition, the lower amounts of extracellular polymeric substances (EPS) in the electro-AnMBR, due to an increased Methanosarcina abundance, facilitated membrane-fouling mitigation. Almost no TMP difference was observed between the AnMBRs with Ti, ceramic, and polyvinylidene fluoride (PVDF) membranes. Quantitative analysis using an electrochemical quartz crystal microbalance with dissipation monitoring indicated that the electrostatic repulsion between EPS and cathodic membrane was positively correlated with the applied voltage. In addition, proteins in EPS had a higher membrane fouling potential than polysaccharides, and Fe3+ coagulation reduced adhesion capacity and alleviated membrane fouling. This study provides a perspective viewpoint for AnMBR membrane fouling mitigation and reactor design.

Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records.

OBJECTIVE: Pituitary adenomas are the most common type of pituitary disorders, which usually occur in young adults and often affect the patient's physical development, labor capacity and fertility. Clinical free texts noted in electronic medical records (EMRs) of pituitary adenomas patients contain abundant diagnosis and treatment information. However, this information has not been well utilized because of the challenge to extract information from unstructured clinical texts. This study aims to enable machines to intelligently process clinical information, and automatically extract clinical named entity for pituitary adenomas from Chinese EMRs. METHODS: The clinical corpus used in this study was from one pituitary adenomas neurosurgery treatment center of a 3A hospital in China. Four types of fine-grained texts of clinical records were selected, which included notes from present illness, past medical history, case characteristics and family history of 500 pituitary adenoma inpatients. The dictionary-based matching, conditional random fields (CRF), bidirectional long short-term memory with CRF (BiLSTM-CRF), and bidirectional encoder representations from transformers with BiLSTM-CRF (BERT-BiLSTM-CRF) were used to extract clinical entities from a Chinese EMRs corpus. A comprehensive dictionary was constructed based on open source vocabularies and a domain dictionary for pituitary adenomas to conduct the dictionary-based matching method. We selected features such as part of speech, radical, document type, and the position of characters to train the CRF-based model. Random character embeddings and the character embeddings pretrained by BERT were used respectively as the input features for the BiLSTM-CRF model and the BERT-BiLSTM-CRF model. Both strict metric and relaxed metric were used to evaluate the performance of these methods. RESULTS: Experimental results demonstrated that the deep learning and other machine learning methods were able to automatically extract clinical named entities, including symptoms, body regions, diseases, family histories, surgeries, medications, and disease courses of pituitary adenomas from Chinese EMRs. With regard to overall performance, BERT-BiLSTM-CRF has the highest strict F1 value of 91.27% and the highest relaxed F1 value of 95.57% respectively. Additional evaluations showed that BERT-BiLSTM-CRF performed best in almost all entity recognition except surgery and disease course. BiLSTM-CRF performed best in disease course entity recognition, and performed as well as the CRF model for part of speech, radical and document type features, with both strict and relaxed F1 value reaching 96.48%. The CRF model with part of speech, radical and document type features performed best in surgery entity recognition with relaxed F1 value of 95.29%. CONCLUSIONS: In this study, we conducted four entity recognition methods for pituitary adenomas based on Chinese EMRs. It demonstrates that the deep learning methods can effectively extract various types of clinical entities with satisfying performance. This study contributed to the clinical named entity extraction from Chinese neurosurgical EMRs. The findings could also assist in information extraction in other Chinese medical texts.

Subcarrier-pairing entropy loading for digital subcarrier-multiplexing systems with colored-SNR distributions.

A subcarrier-pairing entropy loading (SubP-EL) scheme with fairly low complexity is proposed for digital subcarrier-multiplexing (SCM) systems with colored signal-to-noise ratio (SNR) distributions. With the constraint of the target entropy, SubP-EL iteratively optimizes the entropy of subcarriers in pair. After convergence, SubP-EL can approach the optimal performance which is evaluated in simulations and experiments by comparison with the brute-force search method. Meanwhile the complexity of SubP-EL is significantly reduced compared with the brute-force search. In particular, in an 8-subcarrier system with five different SNRs, the complexity of SubP-EL is reduced by approximately a factor of 764, 95 and 13 with the entropy granularities of 0.05 bits/2D-symbol, 0.1 bits/2D-symbol and 0.2 bits/2D-symbol, respectively. The performance of SubP-EL is evaluated in simulations and experiments. In the experiments with 345 km fiber transmissions, the average NGMI gain of SubP-EL over the system without entropy loading is 0.0286 for different optical filtering cases.

Frequency-offset-tolerant optical frequency comb-based coherent transmission for intra-datacenter interconnections.

In recent years, in order to increase the capacity and scalability of intra-datacenter (DC) transmission, the optical frequency comb (OFC) source has been considered promising to replace discrete lasers, aiming to reduce the cost of wavelength division multiplexing (WDM) transmission within DC. In this paper, an OFC based coherent architecture is proposed. An OFC, in the receiver side, is split by a splitter with a uniform power ratio and separately used as local oscillators (LOs) to detect the demultiplexed signals. The signal spectrum is copied onto every tone of the LO-OFC, and a large frequency offset (FO) tolerance is achieved. In addition, the required ADC sampling rate is the same as a system without FO. Extensive simulations are conducted. In the simulated coherent WDM transmission system, a 3-tone-OFC is used to provide 3 carriers, and an 11-tone-OFC is split and used to provide LO-OFCs. For a 64GBd polarization multiplexing 16 quadrature amplitude modulation (PM-16QAM) WDM transmission, the tolerances of FO are up to about ±0.3THz and ±0.374THz for the 1st/3rd signal, and the 2nd signal, respectively, below the pre-forward error correction (FEC) bit error rate (BER) level of 1.25×10-2. Moreover, the maximum tolerance of FO linearly increases with the number of effective tones in LO-OFC. Further, extensive experiments with back-to-back connection are conducted to verify the performance. The tolerance of FO is up to >36 GHz for 36GBd PM-16QAM transmission with a 3-tone-LO-OFC below the BER level of 1.25×10-2. The proposed OFC based coherent architecture is a promising solution for intra-DC interconnections with a large FO.

Head and Tail Entity Fusion Model in Medical Knowledge Graph Construction: Case Study for Pituitary Adenoma.

BACKGROUND: Pituitary adenoma is one of the most common central nervous system tumors. The diagnosis and treatment of pituitary adenoma remain very difficult. Misdiagnosis and recurrence often occur, and experienced neurosurgeons are in serious shortage. A knowledge graph can help interns quickly understand the medical knowledge related to pituitary tumor. OBJECTIVE: The aim of this study was to develop a data fusion method suitable for medical data using data of pituitary adenomas integrated from different sources. The overall goal was to construct a knowledge graph for pituitary adenoma (KGPA) to be used for knowledge discovery. METHODS: A complete framework suitable for the construction of a medical knowledge graph was developed, which was used to build the KGPA. The schema of the KGPA was manually constructed. Information of pituitary adenoma was automatically extracted from Chinese electronic medical records (CEMRs) and medical websites through a conditional random field model and newly designed web wrappers. An entity fusion method is proposed based on the head-and-tail entity fusion model to fuse the data from heterogeneous sources. RESULTS: Data were extracted from 300 CEMRs of pituitary adenoma and 4 health portals. Entity fusion was carried out using the proposed data fusion model. The F1 scores of the head and tail entity fusions were 97.32% and 98.57%, respectively. Triples from the constructed KGPA were selected for evaluation, demonstrating 95.4% accuracy. CONCLUSIONS: This paper introduces an approach to fuse triples extracted from heterogeneous data sources, which can be used to build a knowledge graph. The evaluation results showed that the data in the KGPA are of high quality. The constructed KGPA can help physicians in clinical practice.

Equine lentivirus counteracts SAMHD1 restriction by Rev-mediated degradation of SAMHD1 via the BECN1-dependent lysosomal pathway.

The innate immune restriction factor SAMHD1 can inhibit diverse viruses in myeloid cells. Mechanistically, SAMHD1 inhibits lentiviral replication including HIV-1 by depleting the nucleotide pool to interfere with their reverse transcription. Equine infectious anemia virus (EIAV) is an ancient lentivirus that preferentially attacks macrophages. However, the mechanism by which EIAV successfully establishes infection in macrophages with functional SAMHD1 remains unclear. Here, we demonstrate that while equine SAMDH1 can limit EIAV replication in equine macrophages at the reverse transcription stage, the antiviral effect is counteracted by the well-known transcriptional regulator Rev, which downregulates equine SAMHD1 through the lysosomal pathway. Remarkably, Rev hijacks BECN1 (beclin 1) and PIK3C3 to mediate SAMHD1 degradation in a canonical macroautophagy/autophagy-independent pathway. Our study illustrates that equine lentiviral Rev possesses important functions in evading cellular innate immunity in addition to its RNA regulatory function, and may provide new insights into the co-evolutionary arms race between SAMHD1 and lentiviruses.Abbreviations:3-MA: 3-methyladenine; AA: amino acid; ACTB: actin beta; AD: activation domain; ATG: autophagy related; Baf A1: bafilomycin A1; BD: binding domain; BECN1: beclin 1; BH3: BCL2-homology-3 domain; BiFC: bimolecular fluorescence complementation; CCD: coiled-coil domain; class III PtdIns3K: class III phosphatidylinositol 3-kinase; CQ: chloroquine; Co-IP: co-immunoprecipitation; dNTPase: dGTP-stimulated deoxynucleoside triphosphate triphosphohydrolase; ECD: evolutionarily conserved domain; EIAV: equine infectious anemia virus; eMDMs: equine monocyte-derived macrophages; GFP: green fluorescent protein; HD: histidine-aspartic; HIV-1: human immunodeficiency virus-1; hpi: hours post infection; hpt: hours post transfection; KO: knockout; LAMP2: lysosomal associated membrane protein 2; LMB: leptomycin B; PMA: phorbol 12-myristate 13-acetate; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; ND: unknown non-essential domain; NES: nuclear export signal; NLS: localization signal; NS: statistically non-significant; PIK3C3: phosphatidylinositol 3-kinase catalytic subunit type 3; RBD: RNA binding domain; RT: reverse transcriptase; siRNAs: small interfering RNAs; SAMHD1: SAM and HD domain containing deoxynucleoside triphosphate triphosphohydrolase 1; SIV: simian immunodeficiency virus; VN: C-terminal residues of Venus 174 to 238; VC: N-terminal residues 2 to 173 of Venus.

Ontological Organization and Bioinformatic Analysis of Adverse Drug Reactions From Package Inserts: Development and Usability Study.

BACKGROUND: Licensed drugs may cause unexpected adverse reactions in patients, resulting in morbidity, risk of mortality, therapy disruptions, and prolonged hospital stays. Officially approved drug package inserts list the adverse reactions identified from randomized controlled clinical trials with high evidence levels and worldwide postmarketing surveillance. Formal representation of the adverse drug reaction (ADR) enclosed in semistructured package inserts will enable deep recognition of side effects and rational drug use, substantially reduce morbidity, and decrease societal costs. OBJECTIVE: This paper aims to present an ontological organization of traceable ADR information extracted from licensed package inserts. In addition, it will provide machine-understandable knowledge for bioinformatics analysis, semantic retrieval, and intelligent clinical applications. METHODS: Based on the essential content of package inserts, a generic ADR ontology model is proposed from two dimensions (and nine subdimensions), covering the ADR information and medication instructions. This is followed by a customized natural language processing method programmed with Python to retrieve the relevant information enclosed in package inserts. After the biocuration and identification of retrieved data from the package insert, an ADR ontology is automatically built for further bioinformatic analysis. RESULTS: We collected 165 package inserts of quinolone drugs from the National Medical Products Administration and other drug databases in China, and built a specialized ADR ontology containing 2879 classes and 15,711 semantic relations. For each quinolone drug, the reported ADR information and medication instructions have been logically represented and formally organized in an ADR ontology. To demonstrate its usage, the source data were further bioinformatically analyzed. For example, the number of drug-ADR triples and major ADRs associated with each active ingredient were recorded. The 10 ADRs most frequently observed among quinolones were identified and categorized based on the 18 categories defined in the proposal. The occurrence frequency, severity, and ADR mitigation method explicitly stated in package inserts were also analyzed, as well as the top 5 specific populations with contraindications for quinolone drugs. CONCLUSIONS: Ontological representation and organization using officially approved information from drug package inserts enables the identification and bioinformatic analysis of adverse reactions caused by a specific drug with regard to predefined ADR ontology classes and semantic relations. The resulting ontology-based ADR knowledge source classifies drug-specific adverse reactions, and supports a better understanding of ADRs and safer prescription of medications.

Env diversity-dependent protection of the attenuated equine infectious anaemia virus vaccine.

Lentiviruses harbour high genetic variability for efficient evasion from host immunity. An attenuated equine infectious anaemia (EIA) vaccine was developed decades ago in China and presented remarkably robust protection against EIA. The vaccine was recently proven to have high genomic diversity, particular in env. However, how and to what extent the high env diversity relates to immune protection remains unclear. In this study, we compared immune protections and responses of three groups of horses stimulated by the high-diversity vaccine EIAV_HD, a single molecular clone of the vaccine EIAV_LD with low env diversity, as well as a constructed vaccine strain EIAV_MD with moderate env diversity. The disparity of virus-host interactions between three env diversity-varied groups (5 horses in each group) was evaluated using clinical manifestation, pathological scores, and env-specific antibody. We found the highest titres of env antibodies (Abs) or neutralizing Abs (nAbs) in the EIAV_HD group, followed by the EIAV_MD group, and the lowest titres in the EIAV_LD group (P<0.05). The occurrence of disease/death was different between EIAV_HD group (1/0), EIAV_MD (2/2), and EIAV_LD group (4/2). A similar env diversity-related linear relationship was observed in the clinical manifestations and pathological changes. This diversity-dependent disparity in changes between the three groups was more distinct after immunosuppression, suggesting that env diversity plays an important role in protection under low host immunocompetence. In summary, inoculation with vaccines with higher genetic diversity could present broader and more efficient protection. Our findings strongly suggest that an abundance of Env antigens are required for efficient protection against lentiviruses.

Microarray Analysis of Differential Gene Expression in Alzheimer's Disease Identifies Potential Biomarkers with Diagnostic Value.

BACKGROUND Alzheimer disease (AD) is a common and fatal subtype of dementia that remains a challenge to diagnose and treat. This study aimed to identify potential biomarkers that influence the prognosis of AD. MATERIAL AND METHODS A total of 6 gene expression profiles from the Gene Expression Omnibus (GEO) database were assessed for their potential as AD biomarkers. We identified differentially expressed genes (DEGs) using the prediction analysis for microarray (PAM) algorithm and obtained hub genes through the analysis of the protein-protein interaction (PPI) network and module analysis. RESULTS We identified 6 gene expression profiles from the GEO database and assessed their potential as AD biomarkers. Shared gene sets were extracted and integrated into large expression profile matrices. We identified 2514 DEGs including 68 upregulated- and 2446 downregulated genes through analysis of the limma package. We screened 379 significant DEGs including 68 upregulated and 307 downregulated genes for their ability to distinguish AD from control samples using PAM algorithm. Functional enrichment of the 379 target genes was produced from Database for Annotation, Visualization and Integrated Discovery.(DAVID) and included histone function, beta receptor signaling, cell growth, and angiogenesis. The downregulated genes were significantly enriched in MAPK signaling, synaptic signaling, neuronal apoptosis and AD associated pathways. Upon analysis of the PPI network, 32 hub genes including ENO2, CCT2, CALM2, ACACB, ATP5B, MDH1, and PP2CA were screened. Of these hub genes, NFKBIA and ACACB were upregulated and 29 genes were downregulated in AD patients. CONCLUSIONS We screened 379 significant DEGs as potential biomarkers of AD using PAM and obtained 32 hub genes through PPI network and module analysis. These findings reveal new potential AD biomarkers with prognostic and therapeutic value.

TBench: A Collaborative Work Platform for Multilingual Terminology Editing and Development.

Terminology facilitates consistent use and semantic integration of heterogeneous, multimodal data within and across domains. This paper presents TBench (Termilology Workbench) for multilingual terminology editing and development within a distributed environment. TBench is a web-service Java tool consisting of two main functionalities that are knowledge construction (i.e.extended model based on ISO25964, batch reusing and constructing multilingual concept hierarchy and relationships) and collaborative control in order to achieve custom extensions, reuse, multilingual alignment, integration and refactoring.

Top-Level Design of a Normalized Chinese Clinical Terminology: An Integrated Application of National and International Data Standards and Terminologies.

This work describes the design and building of a Chinese clinical terminology (called CCTS). The terminology is similar to an ontology, and will promote the use of Chinese clinical data, such as indexing, retrieval and exchange. The terminology is a TOPL concept framework, which integrates hierarchical structures of Chinese and interenational reference terminology standards for health. Our framework includes 14 subtrees, 2286 classes and 65 relationships.