Prediction of treatment response in patients with neovascular age-related macular degeneration

Neovascular age-related macular degeneration (nAMD) is a common cause of visual impairment, and is currently treated with intravitreal anti-vascular endothelial growth factor agents such as aflibercept. While these treatments may improve visual acuity (VA) in some patients, clinicians cannot currently predict who is likely to benefit before treatment starts. The aim of this study is to explore the effectiveness of using Deep Learning approaches to train models for predicting whether a patient’s VA will respond favourably to three months of aflibercept therapy, using pre-treatment OCT images and clinical/demographic variables. We train a number of models using standard machine learning, Deep Learning transfer learning, and fully trained Deep Learning approaches in two experiments using outcomes based on the VA at 4- 10 weeks after the final dose. In experiment one, we trained models to predict whether the VA will be at least 54 Early Treatment Diabetic Retinopathy Study (ETDRS) letters, while in experiment two we trained them to predict whether the VA will have increased by 10 or more letters. Model prediction quality was assessed using the Area Under the Curve (AUC) of the Receiver-Operating-Characteristic (ROC) curves. We found that all models performed significantly better than chance in both experiments, except for the fully trained Deep Learning model using just images in experiment two. The best performing model for experiment one was the Deep Learning transfer model using images and clinical/demographic variables (AUC=0.901), while in experiment two, none of the Deep Learning approaches performed better than a random forest using only clinical/demographic variables (AUC=0.751). Our experiments suggest that different Deep Learning approaches are required for predicting the second outcome if we want the models to perform better than those that use clinical/demographic variables alone

Intrusion Detection and Anomaly Detection System Using Sequential Pattern Mining

Nowadays the security methods from password protected access up to firewalls which are used to secure the data as well as the networks from attackers. Several times these type of security methods are not enough to protect data. We can consider the use of Intrusion Detection Systems (IDS) is the one way to secure the data on critical systems. Most of the research work is going on the effectiveness and exactness of the intrusion detection, but these attempts are for the detection of the intrusions at the operating system and network level only. It is unable to detect the unexpected behavior of systems due to Malicious transactions in databases. The method used for spotting any interferes on the information in the form of database known as database intrusion detection. It relies on enlisting the execution of a transaction. After that, if the recognized pattern is aside from those regular patterns actual is considered as an intrusion. But the identified problem with this process is that the accuracy algorithm which is used may not identify entire patterns. This type of challenges can affect in two ways. 1) Missing of the database with regular patterns. 2) The detection process neglects some new patterns. Therefore we proposed sequential data mining method by using new Modified Apriori Algorithm. The algorithm upturns the accurateness and rate of pattern detection by the process. The Apriori algorithm with modifications is used in the proposed model

A study of drug prescription patterns, disease-therapy awareness and of quality of life among patients with migraine visiting a tertiary care hospital in Mumbai, India

Background: Migraine is one of the leading causes of disability globally. There is scarcity of data on disease -therapy awareness and its correlation with demographic factors. Hence its was of interest to assess those along with quality of life in these patients. Methods: A Cross-sectional observational study was conducted in108 patients attending the Neurology and headache OPD at a tertiary care hospital between March 2017 and August 2018. Disease and therapy awareness among patients were evaluated using validated questionnaires and correlation was done with demographic factors. The severity of the disease and its impact on the patients' quality of life were assessed using the migraine disability assessment scale. Results: The mean disease and therapy awareness scores were 9 and 7 respectively. Both had a positive correlation with education and socioeconomic factors. The quality of life was affected moderately in 48.1% of the patients followed by severely 32.4% of the patients. The average number of drugs prescribed per encounter was 3.05. NSAIDS were used more commonly as compared to Triptans for acute attacks. Conclusions: The disease and therapy awareness were fair and positively correlated with education/ socioeconomic status. However, a significant disability was found among patients even with treatment. This highlights the need for educating these patients for effectively controlling the disability

Quantification and expert evaluation of evidence for chemopredictive biomarkers to personalize cancer treatment.

Predictive biomarkers have the potential to facilitate cancer precision medicine by guiding the optimal choice of therapies for patients. However, clinicians are faced with an enormous volume of often-contradictory evidence regarding the therapeutic context of chemopredictive biomarkers.We extensively surveyed public literature to systematically review the predictive effect of 7 biomarkers claimed to predict response to various chemotherapy drugs: ERCC1-platinums, RRM1-gemcitabine, TYMS-5-fluorouracil/Capecitabine, TUBB3-taxanes, MGMT-temozolomide, TOP1-irinotecan/topotecan, and TOP2A-anthracyclines. We focused on studies that investigated changes in gene or protein expression as predictors of drug sensitivity or resistance. We considered an evidence framework that ranked studies from high level I evidence for randomized controlled trials to low level IV evidence for pre-clinical studies and patient case studies.We found that further in-depth analysis will be required to explore methodological issues, inconsistencies between studies, and tumor specific effects present even within high evidence level studies. Some of these nuances will lend themselves to automation, others will require manual curation. However, the comprehensive cataloging and analysis of dispersed public data utilizing an evidence framework provides a high level perspective on clinical actionability of these protein biomarkers. This framework and perspective will ultimately facilitate clinical trial design as well as therapeutic decision-making for individual patients

Kinetics and mechanism of the oxidation of phosphinic, phenylphosphinic and phosphorous acids by benzyltrimethylammonium tribromide

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