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The small intestinal mucosa constitutes a physical barrier separating the gut lumen from sterile internal tissues. Junctional complexes between cells regulate transport across the barrier, preventing water loss and the entry of noxious molecules or pathogens. Inflammatory diseases in cattle disrupt this barrier; nonetheless, mechanisms of barrier disruption in cattle are poorly understood. We investigated the direct effects of three inflammatory cytokines, TNFα, IFNγ, and IL-18, on the bovine intestinal barrier utilizing intestinal organoids. Flux of fluorescein isothiocyanate (FITC)-labeled dextran was used to investigate barrier permeability. Immunocytochemistry and transmission electron microscopy were used to investigate junctional morphology, specifically tortuosity and length/width, respectively. Immunocytochemistry and flow cytometry was used to investigate cellular turnover via proliferation and apoptosis. Our study shows that 24-h cytokine treatment with TNFα or IFNγ significantly increased dextran permeability and tight junctional tortuosity, and reduced cellular proliferation. TNFα reduced the percentage of G2/M phase cells, and IFNγ treatment increased cell apoptotic rate. IL-18 did not directly induce significant changes to barrier permeability or cellular turnover. Our study concludes that the inflammatory cytokines, TNFα and IFNγ, directly induce intestinal epithelial barrier dysfunction and alter the tight junctional morphology and rate of cellular turnover in bovine intestinal epithelial cells.

Diabetes mellitus (DM) is a common chronic metabolic disease in humans and household cats that is characterized by persistent hyperglycemia. DM is associated with dysfunction of the intestinal barrier. This barrier is comprised of an epithelial monolayer that contains a network of tight junctions that adjoin cells and regulate paracellular movement of water and solutes. The mechanisms driving DM-associated barrier dysfunction are multifaceted, and the direct effects of hyperglycemia on the epithelium are poorly understood. Preliminary data suggest that fenofibrate, An FDA-approved peroxisome proliferator-activated receptor-alpha (PPARα) agonist drug attenuates intestinal barrier dysfunction in dogs with experimentally-induced DM. We investigated the effects of hyperglycemia-like conditions and fenofibrate treatment on epithelial barrier function using feline intestinal organoids. We hypothesized that glucose treatment directly increases barrier permeability and alters tight junction morphology, and that fenofibrate administration can ameliorate these deleterious effects. We show that hyperglycemia-like conditions directly increase intestinal epithelial permeability, which is mitigated by fenofibrate. Moreover, increased permeability is caused by disruption of tight junctions, as evident by increased junctional tortuosity. Finally, we found that increased junctional tortuosity and barrier permeability in hyperglycemic conditions were associated with increased protein kinase C-α (PKCα) activity, and that fenofibrate treatment restored PKCα activity to baseline levels. We conclude that hyperglycemia directly induces barrier dysfunction by disrupting tight junction structure, a process that is mitigated by fenofibrate. We further propose that counteracting modulation of PKCα activation by increased intracellular glucose levels and fenofibrate is a key candidate regulatory pathway of tight junction structure and epithelial permeability.

Abstract Background Drain insertion following chronic subdural hematoma (CSDH) evacuation improves patient outcomes. Objective To examine whether this is influenced by variation in drain location, positioning or duration of placement. Methods We performed a subgroup analysis of a previously reported multicenter, prospective cohort study of CSDH patients performed between May 2013 and January 2014. Data were analyzed relating drain location (subdural or subgaleal), position (through a frontal or parietal burr hole), and duration of insertion, to outcomes in patients aged >16 yr undergoing burr-hole drainage of primary CSDH. Primary outcomes comprised modified Rankin scale (mRS) at discharge and symptomatic recurrence requiring redrainage within 60 d. Results A total of 577 patients were analyzed. The recurrence rate of 6.7% (12/160) in the frontal subdural drain group was comparable to 8.8% (30/343) in the parietal subdural drain group. Only 44/577 (7.6%) patients underwent subgaleal drain insertion. Recurrence rates were comparable between subdural (7.7%; 41/533) and subgaleal (9.1%; 4/44) groups (P = .95). We found no significant differences in discharge mRS between these groups. Recurrence rates were comparable between patients with postoperative drainage for 1 or 2 d, 6.4% and 8.4%, respectively (P = .44). There was no significant difference in mRS scores between these 2 groups (P = .56). CONCLUSION Drain insertion after CSDH drainage is important, but position (subgaleal or subdural) and duration did not appear to influence recurrence rate or clinical outcomes. Similarly, drain location did not influence recurrence rate nor outcomes where both parietal and frontal burr holes were made. Further prospective cohort studies or randomized controlled trials could provide further clarification.

ObjectivesWe aim to present our experience of managing traumatic base of skull fractures and our outcomes.DesignRetrospective case note review.SubjectsAll patients in our trauma database with radiographic evidence of a skull base fracture from January 2010 to June 2017.MethodsSkull base fractures were classified according to their anatomical location. Evidence of vascular injury, CSF leak or cranial nerve injury were recorded. The mechanism of injury, length of follow up, interventions and outcomes were documented.Results872 cases had a skull base fracture diagnosed by head CT scan, of which 760 had sufficient radiological and clinic data to analyse. 79.4% of the cohort were male, with a mean age of 43 years. Median length of stay was 8 days and median length of follow up was 114 days. 40.1% were lost to follow up. The mortality rate was 14.9%. Injuries predominantly affected the middle cranial fossa. Vascular injury was the commonest complication (n=87), followed by CSF leak (n=38). 28 patients sustained injury to the facial nerve. Complications were managed conservatively in most cases.ConclusionsThere is little reported literature on the long term outcomes of patients who sustain CSF leak, vascular injury or cranial nerve deficit following a base of skull fracture. In our experience, many of these patients are lost to follow up and indeed, the nature of this study is limited by it’s retrospective nature. Further prospective work must be done in this patient group to better understand the history of these patients.

BackgroundChronic subdural hematoma (CSDH) is a common neurological condition; surgical evacuation is the mainstay of treatment for symptomatic patients. No clear evidence exists regarding the impact of timing of surgery on outcomes. We investigated factors influencing time to surgery and its impact on outcomes of interest.MethodsPatients with CSDH who underwent burr-hole craniostomy were included. This is a subset of data from a prospective observational study conducted in the UK. Logistic mixed modelling was performed to examine the factors influencing time to surgery. The impact of time to surgery on discharge modified Rankin Scale (mRS), complications, recurrence, length of stay and survival was investigated with multivariable logistic regression analysis.Results656 patients were included. Time to surgery ranged from 0 to 44 days (median 1, IQR 1–3). Older age, more favorable mRS on admission, high preoperative Glasgow Coma Scale score, use of antiplatelet medications, comorbidities and bilateral hematomas were associated with increased time to surgery. Time to surgery showed a significant positive association with length of stay; it was not associated with outcome, complication rate, reoperation rate, or survival on multivariable analysis. There was a trend for patients with time to surgery of ≥7 days to have lower odds of favorable outcome at discharge (p=0.061).ConclusionsThis study provides evidence that time to surgery does not substantially impact on outcomes following CSDH. However, increasing time to surgery is associated with increasing length of stay. These results should not encourage delaying operations for patients when they are clinically indicated.

Influenza A virus (IAV) is one of the most circulated human pathogens, and influenza disease, commonly known as the flu, remains one of the most recurring and prevalent infectious human diseases globally. IAV continues to challenge existing vaccines and antiviral drugs via its ability to evolve constantly. It is critical to identify the molecular determinants of IAV pathogenesis to understand the basis of flu severity in different populations and design improved antiviral strategies. In recent years, acetylation has been identified as one of the determinants of IAV pathogenesis. Acetylation was originally discovered as an epigenetic protein modification of histones. But, it is now known to be one of the ubiquitous protein modifications of both histones and non-histone proteins and a determinant of proteome complexity. Since our first observation in 2007, significant progress has been made in understanding the role of acetylation during IAV infection. Now, it is becoming clearer that acetylation plays a pro-IAV function via at least three mechanisms: (1) by reducing the host’s sensing of IAV infection, (2) by dampening the host’s innate antiviral response against IAV, and (3) by aiding the stability and function of viral and host proteins during IAV infection. In turn, IAV antagonizes the host deacetylases, which erase acetylation, to facilitate its replication. This review provides an overview of the research progress made on this subject so far and outlines research prospects for the significance of IAV-acetylation interplay.

Influenza virus has been one of the most prevalent and researched viruses globally. Consequently, there is ample information available about influenza virus lifecycle and pathogenesis. However, there is plenty yet to be known about the determinants of influenza virus pathogenesis and disease severity. Influenza virus exploits host factors to promote each step of its lifecycle. In turn, the host deploys antiviral or restriction factors that inhibit or restrict the influenza virus lifecycle at each of those steps. Two broad categories of host restriction factors can exist in virus-infected cells: (1) encoded by the interferon-stimulated genes (ISGs) and (2) encoded by the constitutively expressed genes that are not stimulated by interferons (non-ISGs). There are hundreds of ISGs known, and many, e.g., Mx, IFITMs, and TRIMs, have been characterized to restrict influenza virus infection at different stages of its lifecycle by (1) blocking viral entry or progeny release, (2) sequestering or degrading viral components and interfering with viral synthesis and assembly, or (3) bolstering host innate defenses. Also, many non-ISGs, e.g., cyclophilins, ncRNAs, and HDACs, have been identified and characterized to restrict influenza virus infection at different lifecycle stages by similar mechanisms. This review provides an overview of those ISGs and non-ISGs and how the influenza virus escapes the restriction imposed by them and aims to improve our understanding of the host restriction mechanisms of the influenza virus.

Predictions of stock and foreign exchange (Forex) have always been a hot and profitable area of study. Deep learning applications have been proven to yield better accuracy and return in the field of financial prediction and forecasting. In this survey, we selected papers from the Digital Bibliography & Library Project (DBLP) database for comparison and analysis. We classified papers according to different deep learning methods, which included Convolutional neural network (CNN); Long Short-Term Memory (LSTM); Deep neural network (DNN); Recurrent Neural Network (RNN); Reinforcement Learning; and other deep learning methods such as Hybrid Attention Networks (HAN), self-paced learning mechanism (NLP), and Wavenet. Furthermore, this paper reviews the dataset, variable, model, and results of each article. The survey used presents the results through the most used performance metrics: Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE), accuracy, Sharpe ratio, and return rate. We identified that recent models combining LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning methods yielded great returns and performances. We conclude that, in recent years, the trend of using deep-learning-based methods for financial modeling is rising exponentially.

Real-world datasets are heavily skewed where some classes are significantly outnumbered by the other classes. In these situations, machine learning algorithms fail to achieve substantial efficacy while predicting these underrepresented instances. To solve this problem, many variations of synthetic minority oversampling methods (SMOTE) have been proposed to balance datasets which deal with continuous features. However, for datasets with both nominal and continuous features, SMOTE-NC is the only SMOTE-based oversampling technique to balance the data. In this paper, we present a novel minority oversampling method, SMOTE-ENC (SMOTE—Encoded Nominal and Continuous), in which nominal features are encoded as numeric values and the difference between two such numeric values reflects the amount of change of association with the minority class. Our experiments show that classification models using the SMOTE-ENC method offer better prediction than models using SMOTE-NC when the dataset has a substantial number of nominal features and also when there is some association between the categorical features and the target class. Additionally, our proposed method addressed one of the major limitations of the SMOTE-NC algorithm. SMOTE-NC can be applied only on mixed datasets that have features consisting of both continuous and nominal features and cannot function if all the features of the dataset are nominal. Our novel method has been generalized to be applied to both mixed datasets and nominal-only datasets.

Recently there has been an exponential increase in the use of artificial intelligence for trading in financial markets such as stock and forex. Reinforcement learning has become of particular interest to financial traders ever since the program AlphaGo defeated the strongest human contemporary Go board game player Lee Sedol in 2016. We systematically reviewed all recent stock/forex prediction or trading articles that used reinforcement learning as their primary machine learning method. All reviewed articles had some unrealistic assumptions such as no transaction costs, no liquidity issues and no bid or ask spread issues. Transaction costs had significant impacts on the profitability of the reinforcement learning algorithms compared with the baseline algorithms tested. Despite showing statistically significant profitability when reinforcement learning was used in comparison with baseline models in many studies, some showed no meaningful level of profitability, in particular with large changes in the price pattern between the system training and testing data. Furthermore, few performance comparisons between reinforcement learning and other sophisticated machine/deep learning models were provided. The impact of transaction costs, including the bid/ask spread on profitability has also been assessed. In conclusion, reinforcement learning in stock/forex trading is still in its early development and further research is needed to make it a reliable method in this domain.