Anesthetics affect peripheral venous pressure waveforms and the cross-talk with arterial pressure

AbstractAnalysis of peripheral venous pressure (PVP) waveforms is a novel method of monitoring intravascular volume. Two pediatric cohorts were studied to test the effect of anesthetic agents on the PVP waveform and cross-talk between peripheral veins and arteries: (1) dehydration setting in a pyloromyotomy using the infused anesthetic propofol and (2) hemorrhage setting during elective surgery for craniosynostosis with the inhaled anesthetic isoflurane. PVP waveforms were collected from 39 patients that received propofol and 9 that received isoflurane. A multiple analysis of variance test determined if anesthetics influence the PVP waveform. A prediction system was built using k-nearest neighbor (k-NN) to distinguish between: (1) PVP waveforms with and without propofol and (2) different minimum alveolar concentration (MAC) groups of isoflurane. 52 porcine, 5 propofol, and 7 isoflurane subjects were used to determine the cross-talk between veins and arteries at the heart and respiratory rate frequency during: (a) during and after bleeding with constant anesthesia, (b) before and after propofol, and (c) at each MAC value. PVP waveforms are influenced by anesthetics, determined by MANOVA: p value < 0.01, η2 = 0.478 for hypovolemic, and η2 = 0.388 for euvolemic conditions. The k-NN prediction models had 82% and 77% accuracy for detecting propofol and MAC, respectively. The cross-talk relationship at each stage was: (a) ρ = 0.95, (b) ρ = 0.96, and (c) could not be evaluated using this cohort. Future research should consider anesthetic agents when analyzing PVP waveforms developing future clinical monitoring technology that uses PVP.

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Managing Illicit Online Pharmacies: Web Analytics and Predictive Models Study

Journal of Medical Internet Research ◽

10.2196/17239 ◽

2020 ◽

Vol 22 (8) ◽

pp. e17239

Author(s):

Hui Zhao ◽

Sowmyasri Muthupandi ◽

Soundar Kumara

Keyword(s):

Prescription Drugs ◽

Performance Metrics ◽

Prediction Models ◽

Future Research ◽

K Nearest Neighbor ◽

Data Set ◽

Specificity And Sensitivity ◽

The Status ◽

Online Pharmacies ◽

Google Search

Background Online pharmacies have grown significantly in recent years, from US $29.35 billion in 2014 to an expected US $128 billion in 2023 worldwide. Although legitimate online pharmacies (LOPs) provide a channel of convenience and potentially lower costs for patients, illicit online pharmacies (IOPs) open the doors to unfettered access to prescription drugs, controlled substances (eg, opioids), and potentially counterfeits, posing a dramatic risk to the drug supply chain and the health of the patient. Unfortunately, we know little about IOPs, and even identifying and monitoring IOPs is challenging because of the large number of online pharmacies (at least 30,000-35,000) and the dynamic nature of the online channel (online pharmacies open and shut down easily). Objective This study aims to increase our understanding of IOPs through web data traffic analysis and propose a novel framework using referral links to predict and identify IOPs, the first step in fighting IOPs. Methods We first collected web traffic and engagement data to study and compare how consumers access and engage with LOPs and IOPs. We then proposed a simple but novel framework for predicting the status of online pharmacies (legitimate or illicit) through the referral links between websites. Under this framework, we developed 2 prediction models, the reference rating prediction method (RRPM) and the reference-based K-nearest neighbor. Results We found that direct (typing URL), search, and referral are the 3 major traffic sources, representing more than 95% traffic to both LOPs and IOPs. It is alarming to see that direct represents the second-highest traffic source (34.32%) to IOPs. When tested on a data set with 763 online pharmacies, both RRPM and R2NN performed well, achieving an accuracy above 95% in their predictions of the status for the online pharmacies. R2NN outperformed RRPM in full performance metrics (accuracy, kappa, specificity, and sensitivity). On implementing the 2 models on Google search results for popular drugs (Xanax [alprazolam], OxyContin, and opioids), they produced an error rate of only 7.96% (R2NN) and 6.20% (RRPM). Conclusions Our prediction models use what we know (referral links) to tackle the many unknown aspects of IOPs. They have many potential applications for patients, search engines, social media, payment companies, policy makers or government agencies, and drug manufacturers to help fight IOPs. With scarce work in this area, we hope to help address the current opioid crisis from this perspective and inspire future research in the critical area of drug safety.

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Managing Illicit Online Pharmacies: Web Analytics and Predictive Models Study (Preprint)

10.2196/preprints.17239 ◽

2019 ◽

Author(s):

Hui Zhao ◽

Sowmyasri Muthupandi ◽

Soundar Kumara

Keyword(s):

Prescription Drugs ◽

Performance Metrics ◽

Prediction Models ◽

Future Research ◽

K Nearest Neighbor ◽

Data Set ◽

Specificity And Sensitivity ◽

The Status ◽

Online Pharmacies ◽

Google Search

BACKGROUND Online pharmacies have grown significantly in recent years, from US $29.35 billion in 2014 to an expected US $128 billion in 2023 worldwide. Although legitimate online pharmacies (LOPs) provide a channel of convenience and potentially lower costs for patients, illicit online pharmacies (IOPs) open the doors to unfettered access to prescription drugs, controlled substances (eg, opioids), and potentially counterfeits, posing a dramatic risk to the drug supply chain and the health of the patient. Unfortunately, we know little about IOPs, and even identifying and monitoring IOPs is challenging because of the large number of online pharmacies (at least 30,000-35,000) and the dynamic nature of the online channel (online pharmacies open and shut down easily). OBJECTIVE This study aims to increase our understanding of IOPs through web data traffic analysis and propose a novel framework using referral links to predict and identify IOPs, the first step in fighting IOPs. METHODS We first collected web traffic and engagement data to study and compare how consumers access and engage with LOPs and IOPs. We then proposed a simple but novel framework for predicting the status of online pharmacies (legitimate or illicit) through the referral links between websites. Under this framework, we developed 2 prediction models, the reference rating prediction method (RRPM) and the reference-based K-nearest neighbor. RESULTS We found that direct (typing URL), search, and referral are the 3 major traffic sources, representing more than 95% traffic to both LOPs and IOPs. It is alarming to see that direct represents the second-highest traffic source (34.32%) to IOPs. When tested on a data set with 763 online pharmacies, both RRPM and R2NN performed well, achieving an accuracy above 95% in their predictions of the status for the online pharmacies. R2NN outperformed RRPM in full performance metrics (accuracy, kappa, specificity, and sensitivity). On implementing the 2 models on Google search results for popular drugs (Xanax [alprazolam], OxyContin, and opioids), they produced an error rate of only 7.96% (R2NN) and 6.20% (RRPM). CONCLUSIONS Our prediction models use what we know (referral links) to tackle the many unknown aspects of IOPs. They have many potential applications for patients, search engines, social media, payment companies, policy makers or government agencies, and drug manufacturers to help fight IOPs. With scarce work in this area, we hope to help address the current opioid crisis from this perspective and inspire future research in the critical area of drug safety.

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The cross-talk between peripheral and central infl ammation in depression and the potential for anti-infl ammatory treatments

PNEI REVIEW ◽

10.3280/pnei2018-002005 ◽

2018 ◽

pp. 60-71

Author(s):

Maria Antonietta Nettis ◽

Valeria Mondelli

Keyword(s):

Cross Talk ◽

The Cross

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Metabolism Meets Function: Untangling the Cross-Talk Between Signalling and Metabolism

10.3389/978-2-88966-262-3 ◽

2020 ◽

Keyword(s):

Cross Talk ◽

The Cross

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COVID-19 Outbreak Prediction with Machine Learning

10.34055/osf.io/xr4js ◽

2020 ◽

Author(s):

Sina Faizollahzadeh Ardabili ◽

Amir Mosavi ◽

Pedram Ghamisi ◽

Filip Ferdinand ◽

Annamaria R. Varkonyi-Koczy ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Fuzzy Inference ◽

Control Measures ◽

Future Research ◽

Complex Nature ◽

Inference System ◽

Wide Range ◽

Standard Models ◽

High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

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Faculty Opinions recommendation of Wnt signalling mediates the cross-talk between bone marrow derived pre-adipocytic and pre-osteoblastic cell populations.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.10751956.11645054 ◽

2011 ◽

Author(s):

Robert AJ Oostendorp ◽

Franziska Bock

Keyword(s):

Bone Marrow ◽

Cross Talk ◽

Wnt Signalling ◽

Cell Populations ◽

Osteoblastic Cell ◽

The Cross

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Faculty Opinions recommendation of Peripheral Venous Pressure Measurements in Patients With Acute Decompensated Heart Failure (PVP-HF).

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727798869.793541126 ◽

2018 ◽

Author(s):

Nancy Sweitzer

Keyword(s):

Heart Failure ◽

Acute Decompensated Heart Failure ◽

Venous Pressure ◽

Decompensated Heart Failure ◽

Pressure Measurements ◽

Peripheral Venous Pressure

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Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review

Protein and Peptide Letters ◽

10.2174/0929866526666191025104043 ◽

2020 ◽

Vol 27 (5) ◽

pp. 385-391

Author(s):

Lin Zhong ◽

Zhong Ming ◽

Guobo Xie ◽

Chunlong Fan ◽

Xue Piao

Keyword(s):

Supervised Learning ◽

Protein Interactions ◽

Computational Models ◽

Prediction Models ◽

Chromatin Modification ◽

Computational Prediction ◽

Human Diseases ◽

Future Research ◽

Non Coding Rna ◽

Long Non Coding Rna

: In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

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