The RNA helicase Dhx15 mediates Wnt-induced antimicrobial protein expression in Paneth cells.

RNA helicases play roles in various essential biological processes such as RNA splicing and editing. Recent in vitro studies show that RNA helicases are involved in immune responses toward viruses, serving as viral RNA sensors or immune signaling adaptors. However, there is still a lack of in vivo data to support the tissue- or cell-specific function of RNA helicases owing to the lethality of mice with complete knockout of RNA helicases; further, there is a lack of evidence about the antibacterial role of helicases. Here, we investigated the in vivo role of Dhx15 in intestinal antibacterial responses by generating mice that were intestinal epithelial cell (IEC)-specific deficient for Dhx15 (Dhx15 f/f Villin1-cre, Dhx15ΔIEC). These mice are susceptible to infection with enteric bacteria Citrobacter rodentium (C. rod), owing to impaired α-defensin production by Paneth cells. Moreover, mice with Paneth cell-specific depletion of Dhx15 (Dhx15 f/f Defensinα6-cre, Dhx15ΔPaneth) are more susceptible to DSS (dextran sodium sulfate)-induced colitis, which phenocopy Dhx15ΔIEC mice, due to the dysbiosis of the intestinal microbiota. In humans, reduced protein levels of Dhx15 are found in ulcerative colitis (UC) patients. Taken together, our findings identify a key regulator of Wnt-induced α-defensins in Paneth cells and offer insights into its role in the antimicrobial response as well as intestinal inflammation.

Prognostic value of systemic immune-inflammation index and monocyte-to-HDL-cholesterol ratio in early cardio-cerebral complications in elderly patients with acute severe carbon monoxide poisoning.

To investigate how effectively systemic immune-inflammation index (SII) and Monocyte-to-HDL-cholesterol ratio (MHR) predict the development of early cardio-cerebral complications in elderly patients who have experienced acute severe carbon monoxide poisoning (ASCMP). A retrospective analysis was conducted on 77 elderly patients with ASCMP admitted to the emergency department of Harrison International Peace Hospital from November 2020 to March 2022. The prevalence of early-onset complications among the 77 individuals was 38.96%. Binary Logistics regression analysis showed that SII and MHR were independent influencing factors of early cardio-cerebral complications in elderly patients with ASCMP. The complication group had a longer length of stay, a greater mortality rate, and a higher incidence of delayed encephalopathy after acute carbon monoxide poisoning (p < .05) than the non-complication group. The area under the curve (AUC) of SII and MHR in predicting early cardio-cerebral complications in elderly patients with ASCMP were 0.724 and 0.796, respectively, with 80.0% and 63.3% sensitivity, and 61.7% and 87.2% specificity. The incidence of early cardio-cerebral complications in elderly patients who had ASCMP is high and the prognosis is poor. SII and MHR can be utilized as independent predictors of early cardio-cerebral complications in elderly patients with ASCMP, allowing doctors to diagnose and treat cardio-cerebral complications earlier and improve prognosis.

NeuM: A Neuron-Selective Probe Incorporates into Live Neuronal Membranes via Enhanced Clathrin-Mediated Endocytosis in Primary Neurons.

The development of a small-molecule probe designed to selectively target neurons would enhance the exploration of intricate neuronal structures and functions. Among such probes, NeuO stands out as the pioneer and has gained significant traction in the field of research. Nevertheless, neither the mechanism behind neuron-selectivity nor the cellular localization has been determined. Here, we introduce NeuM, a derivative of NeuO, designed to target neuronal cell membranes. Furthermore, we elucidate the mechanism behind the selective neuronal membrane trafficking that distinguishes neurons. In an aqueous buffer, NeuM autonomously assembles into micellar structures, leading to the quenching of its fluorescence (Φ=0.001). Upon exposure to neurons, NeuM micelles were selectively internalized into neuronal endosomes via clathrin-mediated endocytosis. Through the endocytic recycling pathway, NeuM micelles integrate into neuronal membrane, dispersing fluorescent NeuM molecules in the membrane (Φ=0.61). Molecular dynamics simulations demonstrated that NeuM, in comparison to NeuO, possesses optimal lipophilicity and molecular length, facilitating its stable incorporation into phospholipid layers. The stable integration of NeuM within neuronal membrane allows the prolonged monitoring of neurons, as well as the visualization of intricate neuronal structures.

Measuring the worldwide spread of COVID-19 using a comprehensive modeling method.

BACKGROUND: With the global spread of COVID-19, detecting high-risk countries/regions timely and dynamically is essential; therefore, we sought to develop automatic, quantitative and scalable analysis methods to observe and estimate COVID-19 spread worldwide and further generate reliable and timely decision-making support for public health management using a comprehensive modeling method based on multiple mathematical models. METHODS: We collected global COVID-19 epidemic data reported from January 23 to September 30, 2020, to observe and estimate its possible spread trends. Countries were divided into three outbreak levels: high, middle, and low. Trends analysis was performed by calculating the growth rate, and then country grouping was implemented using group-based trajectory modeling on the three levels. Individual countries from each group were also chosen to further disclose the outbreak situations using two predicting models: the logistic growth model and the SEIR model. RESULTS: All 187 observed countries' trajectory subgroups were identified using two grouping strategies: with and without population consideration. By measuring epidemic trends and predicting the epidemic size and peak of individual countries, our study found that the logistic growth model generally estimated a smaller epidemic size than the SEIR model. According to SEIR modeling, confirmed cases in each country would take an average of 9-12 months to reach the outbreak peak from the day the first case occurred. Additionally, the average number of cases at the peak time will reach approximately 10-20% of the countries' populations, and the countries with high trends and a high predicted size must pay special attention and implement public health interventions in a timely manner. CONCLUSIONS: We demonstrated comprehensive observations and predictions of the COVID-19 outbreak in 187 countries using a comprehensive modeling method. The methods proposed in this study can measure COVID-19 development from multiple perspectives and are generalizable to other epidemic diseases. Furthermore, the methods also provide reliable and timely decision-making support for public health management.

Choline and trimethylamine N-oxide supplementation in normal chow diet and western diet promotes the development of atherosclerosis in Apoe -/- mice through different mechanisms.

Trimethylamine N-oxide (TMAO), a gut microbiota-dependent metabolite, has been shown to aggravate cardiovascular disease. However, the mechanisms of TMAO in the setting of cardiovascular disease progress remain unclear. Here, we aim to investigate the effects of TMAO on atherosclerosis (AS) development and the underlying mechanisms. Apoe -/- mice received choline or TMAO supplementation in a normal diet and a western diet for 12 weeks. Choline or TMAO supplementation in both normal diet and western diet significantly promoted plaque progression in Apoe-/- mice. Besides, serum lipids levels and inflammation response in the aortic root were enhanced by choline or TMAO supplementation. In particular, choline or TMAO supplementation in the western diet changed intestinal microbiota composition and bile acid metabolism. Therefore, choline or TMAO supplementation may promote AS by modulating gut microbiota in mice fed with a western diet and by other mechanisms in mice given a normal diet, even choline or TMAO supplementation in a normal diet can promote AS.

Macrophage migration inhibitory factor as a potential novel biomarker for cognitive function in patients with first-episode schizophrenia.

OBJECTIVE: Cognitive impairment is prevalent in schizophrenia. Macrophage migration inhibitory factor which is released into the circulation under stress or inflammation, is associated with cognition and also plays an important role in immunity. However, no study has investigated the relationship between macrophage migration inhibitory factor and cognitive function in first-episode schizophrenia patients at baseline or after treatment. This study investigated the pre- and post-risperidone treatment correlations between serum macrophage migration inhibitory factor levels and cognitive function in first-episode schizophrenia patients. METHODS: A total of 83 first-episode schizophrenia patients who received risperidone monotherapy and 57 healthy controls - matched for sex, age, smoking status, education (years), marital status and waist-to-hip ratio - were included. Macrophage migration inhibitory factor levels were measured before and 10 weeks after treatment in the patient group and at baseline in the controls. Pre- and post-treatment cognitive functions in patients were assessed using the MATRICS Consensus Cognitive Battery. RESULTS: At baseline, macrophage migration inhibitory factor levels were significantly higher in first-episode schizophrenia patients than those in healthy controls (p < 0.01) and decreased in patients after 10 weeks of risperidone treatment compared with baseline (p < 0.05). The MATRICS Consensus Cognitive Battery total score and the sub-scores for the Trail Making Test, Symbol Coding, Letter Number Sequence, Maze and Brief Visuospatial Memory Test-Revised improved significantly after risperidone treatment. After controlling for age, sex, education, waist-to-hip ratio and smoking status, partial correlation analysis showed a positive correlation between baseline macrophage migration inhibitory factor levels and patients' baseline MATRICS Consensus Cognitive Battery verbal memory scores (r = 0.29, p = 0.01). Macrophage migration inhibitory factor changes correlated negatively with verbal memory changes (r = -0.26, p = 0.04). Multiple linear regression analysis identified a definite correlation between the changes in word memory test score and macrophage migration inhibitory factor level (ß = -0.09, p = 0.04). CONCLUSION: Macrophage migration inhibitory factor may be involved in the process of cognitive impairment in first-episode schizophrenia and repair mechanisms following risperidone treatment.

[Comparison of therapeutic efficacy of Wuling Capsules prepared with different methods for rats with syndrome of liver Qi stagnation, spleen deficiency, and blood stasis].

Wuling Capsules is one of the commonly used drugs for the clinical treatment of chronic hepatitis B with the syndrome of liver Qi stagnation, spleen deficiency, and blood stasis. However, the present preparation method of Wuling Capsules ignores some macromolecules like polysaccharides. In this study, the influences of different ethanol concentrations in the preparation process on the extraction rates of macro-and micro-molecules were investigated. Further, the therapeutic efficacy of Wuling Capsules was evaluated with the reserpine-induced rat model of liver Qi stagnation, spleen deficiency, and blood stasis. When 50% ethanol was used for the last time of extraction, the concentrations of polysaccharides, salvianolic acid B, and schisandrin in the extract, as well as the dry extract yield, increased significantly compared with those of the original preparation method. However, the fingerprints of micro-molecules showed little difference between the two methods, with a similarity of 0.862. The study then set the 50% ethanol extraction as the new preparation method. The pharmacodynamics evaluation showed that the Wuling Capsules prepared with the original and new methods both significantly alleviated the emotional depression and metabolic disturbance in model rats, demonstrating good performance in protecting the rats against gastric mucosal injuries, modulating intestinal function, and activating blood circulation. The mechanism of action may be related to the regulation of gastrointestinal hormone secretion, reduction of inflammation, and promotion of dopamine synthesis in cortex and hippocampus. At the same dose, the Wuling Capsules prepared with the original and new methods showed roughly the same overall therapeutic efficacy. However, the Wuling Capsules prepared with the new method had stronger effect in activating blood circulation and modulating inflammation, but weaker effects in regulating gastrin and dopamine. The present study provides basis data for optimizing the preparation process of Wuling Capsules and deciphering the mechanism of its therapeutic effect on liver Qi stagnation, spleen deficiency, and blood stasis.

[Chemical constituents and anti-liver fibrosis mechanism of Meconopsis quintuplinervia based on UPLC-Q-Exactive-MS/MS and network pharmacology].

In this study, UPLC-Q-Exactive-MS/MS was used to rapidly analyze the chemical constituents of Meconopsis quintupli-nervia, and the anti-liver fibrosis mechanism of M. quintuplinervia was preliminarily analyzed by network pharmacology, molecular docking, and cell experiments. The chemical constituents of M. quintuplinervia were identified according to the information of MS~1 and MS~2, as well as the data in the literature and databases. SwissTargetPrediction and TargetNet were used to predict the potential targets. The targets related to liver fibrosis were collected from GeneCards and OMIM. The protein-protein interaction(PPI) network was constructed by STRING. Cytoscape 3.6.1 was used to construct and analyze the "constituent-target-disease" network to obtain key targets and their corresponding constituents in the network. DAVID 6.8 was used for GO analysis and KEGG signaling pathway enrichment analysis. Finally, the preliminary verification was carried out by molecular docking and cell experiments. As a result, 106 chemical constituents were identified from M. quintuplinervia, including 66 flavonoids, 16 alkaloids, 18 phenolic acids, 1 anthocyanin, and 5 other constituents. Among them, 3 constituents were identified as potential new compounds, and 59 constituents were reported in M. quintuplinervia for the first time. Network pharmacology analysis showed that M. quintuplinervia presumably acted on AKT1, SRC, JUN, EGFR, STAT3, HSP90 AA1, MAPK3, and other core targets through luteolin, isorhamnetin, quercetin, apigenin, kaempferide, amurine, 2-methylflavinantine, allocryptopine, the multi and other active compounds, thereby regulating the PI3 K/AKT signaling pathway, pathways in cancer, proteoglycans in cancer, FoxO signaling pathway, and other pathways to exert anti-liver fibrosis effects. M. quintuplinervia extract(MQE) could significantly down-regulate PI3 K and AKT protein levels in the HSC-T6 cell model induced by TGF-ß1, suggesting that MQE may have the ability to regulate the PI3 K/AKT signaling pathway. The findings of this study indicated that the anti-liver fibrosis effect of M. quintuplinervia had multi-constituent, multi-target, and multi-pathway characteristics, which may provide a scientific basis for the research on the pharmacodynamic materials, action mechanism, and quality markers of M. quintupli-nervia.

A Clinical Prediction Model to Predict Heparin Treatment Outcomes and Provide Dosage Recommendations: Development and Validation Study.

BACKGROUND: Unfractionated heparin is widely used in the intensive care unit as an anticoagulant. However, weight-based heparin dosing has been shown to be suboptimal and may place patients at unnecessary risk during their intensive care unit stay. OBJECTIVE: In this study, we intended to develop and validate a machine learning-based model to predict heparin treatment outcomes and to provide dosage recommendations to clinicians. METHODS: A shallow neural network model was adopted in a retrospective cohort of patients from the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC III) database and patients admitted to the Peking Union Medical College Hospital (PUMCH). We modeled the subtherapeutic, normal, and supratherapeutic activated partial thromboplastin time (aPTT) as the outcomes of heparin treatment and used a group of clinical features for modeling. Our model classifies patients into 3 different therapeutic states. We tested the prediction ability of our model and evaluated its performance by using accuracy, the kappa coefficient, precision, recall, and the F1 score. Furthermore, a dosage recommendation module was designed and evaluated for clinical decision support. RESULTS: A total of 3607 patients selected from MIMIC III and 1549 patients admitted to the PUMCH who met our criteria were included in this study. The shallow neural network model showed results of F1 scores 0.887 (MIMIC III) and 0.925 (PUMCH). When compared with the actual dosage prescribed, our model recommended increasing the dosage for 72.2% (MIMIC III, 1240/1718) and 64.7% (PUMCH, 281/434) of the subtherapeutic patients and decreasing the dosage for 80.9% (MIMIC III, 504/623) and 76.7% (PUMCH, 277/361) of the supratherapeutic patients, suggesting that the recommendations can contribute to clinical improvements and that they may effectively reduce the time to optimal dosage in the clinical setting. CONCLUSIONS: The evaluation of our model for predicting heparin treatment outcomes demonstrated that the developed model is potentially applicable for reducing the misdosage of heparin and for providing appropriate decision recommendations to clinicians.

Construction of the cervical cancer common terminology for promoting semantic interoperability and utilization of Chinese clinical data.

BACKGROUND: We aimed to build a common terminology in the domain of cervical cancer, named Cervical Cancer Common Terminology (CCCT), that will facilitate clinical data exchange, ensure quality of data and support large scale data analysis. METHODS: The standard concepts and relations of CCCT were collected from ICD-10-CM Chinese Version, ICD-9-PC Chinese Version, officially issued commonly used Chinese clinical terms, Chinese guidelines for diagnosis and treatment of cervical cancer and Chinese medical book Lin Qiaozhi Gynecologic Oncology. 2062 cervical cancer electronic medical records (EMRs) from 16 hospitals, belong to different regions and hospital tiers, were collected for terminology enrichment and building common terms and relations. Concepts hierarchies, terms and relationships were built using Protégé. The performance of natural language processing results was evaluated by average precision, recall, and F1-score. The usability of CCCT were evaluated by terminology coverage. RESULTS: A total of 880 standard concepts, 1182 common terms, 16 relations and 6 attributes were defined in CCCT, which organized in 6 levels and 11 classes. Initial evaluation of the natural language processing results demonstrated average precision, recall, and F1-score percentages of 96%, 72.6%, and 88.5%. The average terminology coverage for three classes of terms, clinical manifestation, treatment, and pathology, were 87.22%, 92.63%, and 89.85%, respectively. Flexible Chinese expressions exist between regions, traditions, cultures, and language habits within the country, linguistic variations in different settings and diverse translation of introduced western language terms are the main reasons of uncovered terms. CONCLUSIONS: Our study demonstrated the initial results of CCCT construction. This study is an ongoing work, with the update of medical knowledge, more standard clinical concepts will be added in, and with more EMRs to be collected and analyzed, the term coverage will be continuing improved. In the future, CCCT will effectively support clinical data analysis in large scale.

Early warning of citric acid overdose and timely adjustment of regional citrate anticoagulation based on machine learning methods.

BACKGROUND: Regional citrate anticoagulation (RCA) is an important local anticoagulation method during bedside continuous renal replacement therapy. To improve patient safety and achieve computer assisted dose monitoring and control, we took intensive care units patients into cohort and aiming at developing a data-driven machine learning model to give early warning of citric acid overdose and provide adjustment suggestions on citrate pumping rate and 10% calcium gluconate input rate for RCA treatment. METHODS: Patient age, gender, pumped citric acid dose value, 5% NaHCO3 solvent, replacement fluid solvent, body temperature value, and replacement fluid PH value as clinical features, models attempted to classify patients who received regional citrate anticoagulation into correct outcome category. Four models, Adaboost, XGBoost, support vector machine (SVM) and shallow neural network, were compared on the performance of predicting outcomes. Prediction results were evaluated using accuracy, precision, recall and F1-score. RESULTS: For classifying patients at the early stages of citric acid treatment, the accuracy of neutral networks model is higher than Adaboost, XGBoost and SVM, the F1-score of shallow neutral networks (90.77%) is overall outperformed than other models (88.40%, 82.17% and 88.96% for Adaboost, XGBoost and SVM). Extended experiment and validation were further conducted using the MIMIC-III database, the F1-scores for shallow neutral networks, Adaboost, XGBoost and SVM are 80.00%, 80.46%, 80.37% and 78.90%, the AUCs are 0.8638, 0.8086, 0.8466 and 0.7919 respectively. CONCLUSION: The results of this study demonstrated the feasibility and performance of machine learning methods for monitoring and adjusting local regional citrate anticoagulation, and further provide decision-making recommendations to clinicians point-of-care.

Selection strategy for sedation depth in critically ill patients on mechanical ventilation.

BACKGROUND: Analgesia and sedation therapy are commonly used for critically ill patients, especially mechanically ventilated patients. From the initial nonsedation programs to deep sedation and then to on-demand sedation, the understanding of sedation therapy continues to deepen. However, according to different patient's condition, understanding the individual patient's depth of sedation needs remains unclear. METHODS: The public open source critical illness database Medical Information Mart for Intensive Care III was used in this study. Latent profile analysis was used as a clustering method to classify mechanically ventilated patients based on 36 variables. Principal component analysis dimensionality reduction was used to select the most influential variables. The ROC curve was used to evaluate the classification accuracy of the model. RESULTS: Based on 36 characteristic variables, we divided patients undergoing mechanical ventilation and sedation and analgesia into two categories with different mortality rates, then further reduced the dimensionality of the data and obtained the 9 variables that had the greatest impact on classification, most of which were ventilator parameters. According to the Richmond-ASS scores, the two phenotypes of patients had different degrees of sedation and analgesia, and the corresponding ventilator parameters were also significantly different. We divided the validation cohort into three different levels of sedation, revealing that patients with high ventilator conditions needed a deeper level of sedation, while patients with low ventilator conditions required reduction in the depth of sedation as soon as possible to promote recovery and avoid reinjury. CONCLUSION: Through latent profile analysis and dimensionality reduction, we divided patients treated with mechanical ventilation and sedation and analgesia into two categories with different mortalities and obtained 9 variables that had the greatest impact on classification, which revealed that the depth of sedation was limited by the condition of the respiratory system.

Five novel clinical phenotypes for critically ill patients with mechanical ventilation in intensive care units: a retrospective and multi database study.

BACKGROUND: Although protective mechanical ventilation (MV) has been used in a variety of applications, lung injury may occur in both patients with and without acute respiratory distress syndrome (ARDS). The purpose of this study is to use machine learning to identify clinical phenotypes for critically ill patients with MV in intensive care units (ICUs). METHODS: A retrospective cohort study was conducted with 5013 patients who had undergone MV and treatment in the Department of Critical Care Medicine, Peking Union Medical College Hospital. Statistical and machine learning methods were used. All the data used in this study, including demographics, vital signs, circulation parameters and mechanical ventilator parameters, etc., were automatically extracted from the electronic health record (EHR) system. An external database, Medical Information Mart for Intensive Care III (MIMIC III), was used for validation. RESULTS: Phenotypes were derived from a total of 4009 patients who underwent MV using a latent profile analysis of 22 variables. The associations between the phenotypes and disease severity and clinical outcomes were assessed. Another 1004 patients in the database were enrolled for validation. Of the five derived phenotypes, phenotype I was the most common subgroup (n = 2174; 54.2%) and was mostly composed of the postoperative population. Phenotype II (n = 480; 12.0%) led to the most severe conditions. Phenotype III (n = 241; 6.01%) was associated with high positive end-expiratory pressure (PEEP) and low mean airway pressure. Phenotype IV (n = 368; 9.18%) was associated with high driving pressure, and younger patients comprised a large proportion of the phenotype V group (n = 746; 18.6%). In addition, we found that the mortality rate of Phenotype IV was significantly higher than that of the other phenotypes. In this subgroup, the number of patients in the sequential organ failure assessment (SOFA) score segment (9,22] was 198, the number of deaths was 88, and the mortality rate was higher than 44%. However, the cumulative 28-day mortality of Phenotypes IV and II, which were 101 of 368 (27.4%) and 87 of 480 (18.1%) unique patients, respectively, was significantly higher than those of the other phenotypes. There were consistent phenotype distributions and differences in biomarker patterns by phenotype in the validation cohort, and external verification with MIMIC III further generated supportive results. CONCLUSIONS: Five clinical phenotypes were correlated with different disease severities and clinical outcomes, which suggested that these phenotypes may help in understanding heterogeneity in MV treatment effects.

Geraniin inhibits proliferation and induces apoptosis through inhibition of phosphatidylinositol 3-kinase/Akt pathway in human colorectal cancer in vitro and in vivo.

Geraniin, a polyphenolic component isolated from Phyllanthus amarus, has been reported to possess diverse biological activities, including antitumor, antiinflammatory, antihyperglycemic, antihypertensive, and antioxidant. However, the role and underlying mechanisms of geraniin in colorectal cancer still remain unclear. In the present study, we found that geraniin notably inhibited cell proliferation and clonogenic formation of colorectal cancer cell SW480 and HT-29 in a dose-dependent manner by Cell Counting Kit 8, EdU, and colony formation assays, respectively. Additionally, geraniin remarkably induced apoptosis of SW480 and HT-29 cells in a dose-dependent way by Hoechst 33342 staining, flow cytometric analysis, and TdT-mediated dUTP nick-end labeling assays and increased the expressions of Bax, caspase-3, and caspase-9, while decreased the level of Bcl-2. Besides, wound healing, transwell migration, and invasion assays demonstrated that geraniin obviously inhibited the migration and invasion of SW480 and HT-29 cells. Moreover, it also inhibited the levels of phospho (p)-phosphatidylinositol 3-kinase and p-Akt. Furthermore, in-vivo animal study revealed that geraniin had the significant inhibitory effects on tumor growth and promoted cancer cell apoptosis remarkably, which further confirmed the antitumor effect of geraniin. Taken together, the present study exhibited the positive role of geraniin in inhibiting proliferation and inducing apoptosis through suppression of phosphatidylinositol 3-kinase/Akt pathway in colorectal cancer cells in vitro and in vivo, which might provide new insights in searching for new drug candidates of anticolorectal cancer.

Numerical Study on the Characteristics of Boger Type Viscoelastic Fluid Flow in a Micro Cross-Slot under Sinusoidal Stimulation.

The cross-slot geometry plays an important role in the study of nonlinear effects of viscoelastic fluids. The flow of viscoelastic fluid in a micro cross-slot with a high channel aspect ratio (AR, the ratio of channel depth to width) can be divided into three types, which are symmetric flow, steady-state asymmetric flow and time-dependent flow under the inlet condition with a constant velocity. However, the flow pattern of a viscoelastic fluid in the cross-slot when a stimulation is applied at inlets has been rarely reported. In this paper, the response of cross-slot flow under an external sinusoidal stimulation is studied by numerical simulations of a two-dimensional model representing the geometry with a maximum limit of AR. For the cases under constant inlet velocity conditions, three different flow patterns occur successively with the increase of Weissenberg number (Wi). For the cases under sinusoidal varying inlet velocity conditions, when the stimulation frequency is far away from the natural frequency of a viscoelastic fluid, the frequency spectrum of velocity fluctuation field shows the characteristics of a fundamental frequency and several harmonics. However, the harmonic frequency disappears when the stimulation frequency is close to the natural frequency of the viscoelastic fluid. Besides, the flow pattern shows spatial symmetry and changes with time. In conclusion, the external stimulation has an effect on the flow pattern of viscoelastic fluid in the 2D micro cross-slot channel, and a resonance occurs when the stimulation frequency is close to the natural frequency of the fluid.

[Study on rapid quality analysis method of Shengxuebao Mixture].

A rapid analysis method based on ultraviolet-visual(UV-Vis) spectroscopy, near infrared(NIR) spectroscopy and multivariable data analysis was established for quality evaluation of Shengxuebao Mixture. The contents of eight active ingredients of Shengxuebao Mixture including albiflorin, paeoniflorin, 2, 3, 5, 4'-tetra-hydroxy-stilbene-2-O-ß-D-glucopyranoside, specnuezhenide,ecliptasaponin D, emodin, calycosin-7-glucoside and astragaloside Ⅳ were simultaneously detected by using this method. HPLC-UV-MS was used as a reference method for determining the contents of these ingredients. Partial least squares(PLS) analysis was implemented as a linear method for multivariate models calibrated between UV spectrum/NIR spectrum and contents of 8 ingredients. Finally, the performance of the model was evaluated by 24 batches of test samples. The results showed that both UV-Vis and NIR models gave a good calibration ability with an R~2 value above 0.9, and the prediction ability was also satisfactory, with an R~2 value higher than 0.83 for UV-Vis model and higher than 0.79 for NIR model. The overall results demonstrate that the established method is accurate, robust and fast, therefore, it can be used for rapid quality evaluation of Shengxuebao Mixture.

Understanding the patient perspective of epilepsy treatment through text mining of online patient support groups.

OBJECTIVE: Epilepsy is among the most common chronic neurologic diseases. There is a need for more data on patient perspectives of treatment to guide patient-centered care initiatives. Patients with epilepsy share their experiences on social media anonymously, but little is known about those discussions. Our aim was to learn what patients with epilepsy discuss regarding their condition and identify treatment-related themes from online patient support groups. METHODS: A total of 355,838 posts were collected from three online support groups for patients with epilepsy through a crawling script, and an analytical pipeline was built to identify patient conversation content through leveraging of multiple text mining methods. Results were also displayed by network visualization methods. RESULTS: Patients with epilepsy sought information about medical treatments, shared their treatment experiences, and sought help from other posters. Key themes related to treatments included the search for optimal personalized treatment strategies as well as identifying and coping with adverse effects. SIGNIFICANCE: This study showed the feasibility of learning about concerns of patients with epilepsy, especially treatment issues, through text mining methods. However, some manual selection and filtering were necessary to ensure quality results for the treatment analysis. Providers should be aware of online discussions and use analyses of such discussions to help guide effective patient engagement during care.

ADEpedia-on-OHDSI: A next generation pharmacovigilance signal detection platform using the OHDSI common data model.

OBJECTIVE: Supplementing the Spontaneous Reporting System (SRS) with Electronic Health Record (EHR) data for adverse drug reaction detection could augment sample size, increase population heterogeneity and cross-validate results for pharmacovigilance research. The difference in the underlying data structures and terminologies between SRS and EHR data presents challenges when attempting to integrate the two into a single database. The Observational Health Data Sciences and Informatics (OHDSI) collaboration provides a Common Data Model (CDM) for organizing and standardizing EHR data to support large-scale observational studies. The objective of the study is to develop and evaluate an informatics platform known as ADEpedia-on-OHDSI, where spontaneous reporting data from FDA's Adverse Event Reporting System (FAERS) is converted into the OHDSI CDM format towards building a next generation pharmacovigilance signal detection platform. METHODS: An extraction, transformation and loading (ETL) tool was designed, developed, and implemented to convert FAERS data into the OHDSI CDM format. A comprehensive evaluation, including overall ETL evaluation, mapping quality evaluation of drug names to RxNorm, and an evaluation of transformation and imputation quality, was then performed to assess the mapping accuracy and information loss using the FAERS data collected between 2012 and 2017. Previously published findings related to vascular safety profile of triptans were validated using ADEpedia-on-OHDSI in pharmacovigilance research. For the triptan-related vascular event detection, signals were detected by Reporting Odds Ratio (ROR) in high-level group terms (HLGT) level, high-level terms (HLT) level and preferred term (PT) level using the original FAERS data and CDM-based FAERS respectively. In addition, six standardized MedDRA queries (SMQs) related to vascular events were applied. RESULTS: A total of 4,619,362 adverse event cases were loaded into 8 tables in the OHDSI CDM. For drug name mapping, 93.9% records and 47.0% unique names were matched with RxNorm codes. Mapping accuracy of drug names was 96% based on a manual verification of randomly sampled 500 unique mappings. Information loss evaluation showed that more than 93% of the data is loaded into the OHDSI CDM for most fields, with the exception of drug route data (66%). The replication study detected 5, 18, 47 and 6, 18, 50 triptan-related vascular event signals in MedDRA HLGT level, HLT level, and PT level for the original FAERS data and CDM-based FAERS respectively. The signal detection scores of six standardized MedDRA queries (SMQs) of vascular events in the raw data study were found to be lower than those scores in the CDM study. CONCLUSION: The outcome of this work would facilitate seamless integration and combined analyses of both SRS and EHR data for pharmacovigilance in ADEpedia-on-OHDSI, our platform for next generation pharmacovigilance.

Developing a FHIR-based EHR phenotyping framework: A case study for identification of patients with obesity and multiple comorbidities from discharge summaries.

BACKGROUND: Standards-based clinical data normalization has become a key component of effective data integration and accurate phenotyping for secondary use of electronic healthcare records (EHR) data. HL7 Fast Healthcare Interoperability Resources (FHIR) is an emerging clinical data standard for exchanging electronic healthcare data and has been used in modeling and integrating both structured and unstructured EHR data for a variety of clinical research applications. The overall objective of this study is to develop and evaluate a FHIR-based EHR phenotyping framework for identification of patients with obesity and its multiple comorbidities from semi-structured discharge summaries leveraging a FHIR-based clinical data normalization pipeline (known as NLP2FHIR). METHODS: We implemented a multi-class and multi-label classification system based on the i2b2 Obesity Challenge task to evaluate the FHIR-based EHR phenotyping framework. Two core parts of the framework are: (a) the conversion of discharge summaries into corresponding FHIR resources - Composition, Condition, MedicationStatement, Procedure and FamilyMemberHistory using the NLP2FHIR pipeline, and (b) the implementation of four machine learning algorithms (logistic regression, support vector machine, decision tree, and random forest) to train classifiers to predict disease state of obesity and 15 comorbidities using features extracted from standard FHIR resources and terminology expansions. We used the macro- and micro-averaged precision (P), recall (R), and F1 score (F1) measures to evaluate the classifier performance. We validated the framework using a second obesity dataset extracted from the MIMIC-III database. RESULTS: Using the NLP2FHIR pipeline, 1237 clinical discharge summaries from the 2008 i2b2 obesity challenge dataset were represented as the instances of the FHIR Composition resource consisting of 5677 records with 16 unique section types. After the NLP processing and FHIR modeling, a set of 244,438 FHIR clinical resource instances were generated. As the results of the four machine learning classifiers, the random forest algorithm performed the best with F1-micro(0.9466)/F1-macro(0.7887) and F1-micro(0.9536)/F1-macro(0.6524) for intuitive classification (reflecting medical professionals' judgments) and textual classification (reflecting the judgments based on explicitly reported information of diseases), respectively. The MIMIC-III obesity dataset was successfully integrated for prediction with minimal configuration of the NLP2FHIR pipeline and machine learning models. CONCLUSIONS: The study demonstrated that the FHIR-based EHR phenotyping approach could effectively identify the state of obesity and multiple comorbidities using semi-structured discharge summaries. Our FHIR-based phenotyping approach is a first concrete step towards improving the data aspect of phenotyping portability across EHR systems and enhancing interpretability of the machine learning-based phenotyping algorithms.

Monitoring Based on Narrow-Band Resonance Raman for "Phase-Shifting" π-Conjugated Polydiacetylene Vesicles upon Host-Guest Interaction and Thermal Stimuli.

The present study reports a quantified monitoring by means of in situ resonance Raman scattering that analyzes phase-shifting characteristics of π-systems upon interacting with target analytes. A chemo- and thermochromic polydiacetylene vesicular probe is evaluated with multiple-wavelength Raman scattering modes in resonance with its phases, respectively, and thus can trace the phase-shifts. This Raman scattering-based analytical quantification is also successful in monitoring host-guest recognition events by utilizing much narrower bands, compared to those in conventional absorption or photoluminescence (PL) methods. As one of the outcomes, the monitoring analysis overcomes the limitations based on widely used colorimetric response (%CR) or PL that failed in the case of interaction with a surfactant, CTAB.