scholarly journals Nanostructured Chemiresistive Gas Sensors for Medical Applications

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 462 ◽  
Author(s):  
Noushin Nasiri ◽  
Christian Clarke
Keyword(s):  
Gas Sensors ◽  
Disease Diagnosis ◽  
Medical Diagnostics ◽  
Cost Of Treatment ◽  
Promising Candidate ◽  
Human Breath ◽  
Multi Scale ◽  
Chemiresistive Gas Sensors ◽  
The Cost ◽  
Nanostructured Sensors

Treating diseases at their earliest stages significantly increases the chance of survival while decreasing the cost of treatment. Therefore, compared to traditional blood testing methods it is the goal of medical diagnostics to deliver a technique that can rapidly predict and if required non-invasively monitor illnesses such as lung cancer, diabetes, melanoma and breast cancer at their very earliest stages, when the chance of recovery is significantly higher. To date human breath analysis is a promising candidate for fulfilling this need. Here, we highlight the latest key achievements on nanostructured chemiresistive sensors for disease diagnosis by human breath with focus on the multi-scale engineering of both composition and nano-micro scale morphology. We critically assess and compare state-of-the-art devices with the intention to provide direction for the next generation of chemiresistive nanostructured sensors.

scholarly journals Wireless E-Nose Sensors to Detect Volatile Organic Gases through Multivariate Analysis

Micromachines ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 597
Author(s):  
Saifur Rahman ◽  
Abdullah S. Alwadie ◽  
Muhammed Irfan ◽  
Rabia Nawaz ◽  
Mohsin Raza ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Gas Sensors ◽  
Chemical Engineering ◽  
Disease Diagnosis ◽  
Medical Diagnostics ◽  
Pollution Monitoring ◽  
Oxide Semiconductor ◽  
Food And Beverage ◽  
Critical Components ◽  
Mos Sensor

Gas sensors are critical components when adhering to health safety and environmental policies in various manufacturing industries, such as the petroleum and oil industry; scent and makeup production; food and beverage manufacturing; chemical engineering; pollution monitoring. In recent times, gas sensors have been introduced to medical diagnostics, bioprocesses, and plant disease diagnosis processes. There could be an adverse impact on human health due to the mixture of various gases (e.g., acetone (A), ethanol (E), propane (P)) that vent out from industrial areas. Therefore, it is important to accurately detect and differentiate such gases. Towards this goal, this paper presents a novel electronic nose (e-nose) detection method to classify various explosive gases. To detect explosive gases, metal oxide semiconductor (MOS) sensors are used as reliable tools to detect such volatile gases. The data received from MOS sensors are processed through a multivariate analysis technique to classify different categories of gases. Multivariate analysis was done using three variants—differential, relative, and fractional analyses—in principal components analysis (PCA). The MOS sensors also have three different designs: loading design, notch design, and Bi design. The proposed MOS sensor-based e-nose accurately detects and classifies three different gases, which indicates the reliability and practicality of the developed system. The developed system enables discrimination of these gases from the mixture. Based on the results from the proposed system, authorities can take preventive measures to deal with these gases to avoid their potential adverse impacts on employee health.

scholarly journals Nanostructured Gas Sensors for Medical and Health Applications: Low to High Dimensional Materials

Biosensors ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 43 ◽  
Author(s):  
Noushin Nasiri ◽  
Christian Clarke
Keyword(s):  
Patient Outcomes ◽  
Gas Sensors ◽  
State Of The Art ◽  
Promising Candidate ◽  
Human Breath ◽  
Non Invasive ◽  
Current State ◽  
Specificity And Sensitivity ◽  
Wide Range ◽  
Health Applications

Human breath has long been known as a system that can be used to diagnose diseases. With advancements in modern nanotechnology, gas sensors can now diagnose, predict, and monitor a wide range of diseases from human breath. From cancer to diabetes, the need to treat at the earliest stages of a disease to both increase patient outcomes and decrease treatment costs is vital. Therefore, it is the promising candidate of rapid and non-invasive human breath gas sensors over traditional methods that will fulfill this need. In this review, we focus on the nano-dimensional design of current state-of-the-art gas sensors, which have achieved records in selectivity, specificity, and sensitivity. We highlight the methods of fabrication for these devices and relate their nano-dimensional materials to their record performance to provide a pathway for the gas sensors that will supersede.

scholarly journals Augmenting Vascular Disease Diagnosis by Vasculature-aware Unsupervised Learning

2020 ◽  
Author(s):  
Yong Wang ◽  
Mengqi Ji ◽  
Shengwei Jiang ◽  
Xukang Wang ◽  
Jiamin Wu ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Causes Of Death ◽  
Learning Algorithm ◽  
Vascular Diseases ◽  
Disease Diagnosis ◽  
Vascular Pathology ◽  
Digital Subtraction ◽  
Blood Flows ◽  
Multi Scale ◽  
The Cost

AbstractVascular diseases are among the leading causes of death and threaten human health worldwide. Imaging examination of vascular pathology with reduced invasiveness is challenging due to the intrinsic vasculature complexity and the non-uniform scattering from bio-tissues. Here, we report VasNet, a vasculature-aware unsupervised learning algorithm that augments pathovascular recognition from small sets of unlabeled fluorescence and digital subtraction angiography (DSA) images. The VasNet adopts the multi-scale fusion strategy with a domain adversarial neural network (DANN) loss function that induces biased pattern reconstruction, by strengthening the features relevant to the retinal vasculature reference while weakening the irrelevant features. VasNet delivers outputs of “Structure + X”, where X refers to multi-dimensional features such as blood flows, the distinguishment of blood dilation and its suspicious counterparts, and the dependence of new pattern emergence on a disease progression, which may assist the discovery of novel diagnostics. Therefore, explainable imaging output from VasNet and other algorithm extensions hold the promise to revolutionize the practice of medical diagnosis, as it improves performance while reduces the cost on human expertise, equipment exquisite and time consumption.

scholarly journals Cost of Management of Diabetes Mellitus: A Pan India Study

2021 ◽  
pp. 097275312199849
Author(s):  
Raghuram Nagarathna ◽  
M Madhava ◽  
Suchitra S Patil ◽  
Amit Singh ◽  
K. Perumal ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Urban Areas ◽  
Noncommunicable Disease ◽  
Cost Of Treatment ◽  
Rural And Urban Areas ◽  
Cost Of Disease ◽  
Rural And Urban ◽  
Significant Difference ◽  
The Cost ◽  
Prevalence Of Diabetes Mellitus

Background: Diabetes mellitus is a major noncommunicable disease. While mortality rates are increasing, the costs of managing the disease are also increasing. The all-India average monthly expenditure per person (pppm) is reported to be ₹ 1,098.25, which translates to an annual expenditure of ₹13,179 per person. Purpose: While a number of studies have gone into the aspect of the cost of disease management, we do not find any study which has pan-India reach. We also do not find studies that focus on differences (if any) between rural and urban areas, age or on the basis of gender. We planned to report the cost of illness (COI) in diabetes individuals as compared to others from the data of a pan-India trial. Methods: Government of India commissioned the Indian Yoga Association to study the prevalence of diabetes mellitus in India in 2017. As part of the questionnaire, the cost of treatment was also captured. Data collected from 25 states and union territories were analyzed using the analysis of covriance (ANCOVA) test on SPSS version 21. Results: There was a significant difference ( P < .05) between the average expenses per person per month (pppm) of individuals with self-reported known diabetes (₹1,357.65 pppm) and others (unknown and/or nondiabetes individuals–₹ 999.91 pppm). Similarly, there was a significant difference between rural (₹2,893 pppm) and urban (₹4,162 pppm) participants and between those below (₹1,996 pppm) and above 40 years (₹5,059 pppm) of age. Conclusion: This preliminary report has shown that the COI because of diabetes is significantly higher than others pointing to an urgent need to promote disease-preventive measures.

Response Curve Modeling of Chemiresistive Gas Sensors by Modified Gompertz Functions

2021 ◽  
pp. 1-1
Author(s):  
Guanlin Tang ◽  
Sachin Navale ◽  
Pianpian Yang ◽  
Vikas Patil ◽  
Florian Stadler
Keyword(s):  
Gas Sensors ◽  
Response Curve ◽  
Curve Modeling ◽  
Chemiresistive Gas Sensors

scholarly journals Why Do Some Firms Spend So Much on Medical Care? Accounting for Variation

2000 ◽  
Vol 3 (1) ◽  
Author(s):  
Matthew Eichner ◽  
Mark McClellan ◽  
David A. Wise
Keyword(s):  
Health Care ◽  
Health Care Cost ◽  
Health Care Expenditures ◽  
Cost Of Treatment ◽  
Consistent Method ◽  
The Difference ◽  
The Cost ◽  
Cost Differences ◽  
Average Expenditure

We are engaged in a long-term project to analyze the determinants of health care cost differences across firms. An important first step is to summarize the nature of expenditure differences across plans. The goal of this article is to develop methods for identifying and quantifying those factors that account for the wide differences in health care expenditures observed across plans.We consider eight plans that vary in average expenditure for individuals filing claims, from a low of $1,645 to a high of $2,484. We present a statistically consistent method for decomposing the cost differences across plans into component parts based on demographic characteristics of plan participants, the mix of diagnoses for which participants are treated, and the cost of treatment for particular diagnoses. The goal is to quantify the contribution of each of these components to the difference between average cost and the cost in a given firm. The demographic mix of plan enrollees accounts for wide differnces in cost ($649). Perhaps the most noticeable feature of the results is that, after adjusting for demographic mix, the difference in expenditures accounted for by the treatment costs given diagnosis ($807) is almost as wide as the unadjusted range in expenditures ($838). Differences in cost due to the different illnesses that are treated, after adjusting for demographic mix, also accounts for large differences in cost ($626). These components of cost do not move together; for example, demographic mix may decrease expenditure under a particular plan while the diagnosis mix may increase costs.Our hope is that understanding the reasons for cost differences across plans will direct more focused attention to controlling costs. Indeed, this work is intended as an important first step toward that goal.

AEC Classifier: A Tree-Based Classifier with Error Control for Medical Disease Diagnosis and Other Applications

2021 ◽  
Vol 15 (02) ◽  
pp. 241-262
Author(s):  
Wasif Bokhari ◽  
Ajay Bansal
Keyword(s):  
Error Control ◽  
Binary Classification ◽  
False Negative ◽  
Disease Diagnosis ◽  
False Negatives ◽  
Medical Disease ◽  
Unique Nature ◽  
Comparable Accuracy ◽  
The Cost ◽  
Roc Score

In medical disease diagnosis, the cost of a false negative could greatly outweigh the cost of a false positive. This is because the former could cost a life, whereas the latter may only cause medical costs and stress to the patient. The unique nature of this problem highlights the need of asymmetric error control for binary classification applications. In this domain, traditional machine learning classifiers may not be ideal as they do not provide a way to control the number of false negatives below a certain threshold. This paper proposes a novel tree-based binary classification algorithm that can control the number of false negatives with a mathematical guarantee, based on Neyman–Pearson (NP) Lemma. This classifier is evaluated on the data obtained from different heart studies and it predicts the risk of cardiac disease, not only with comparable accuracy and AUC-ROC score but also with full control over the number of false negatives. The methodology used to construct this classifier can be expanded to many more use cases, not only in medical disease diagnosis but also beyond as shown from analysis on different diverse datasets.

scholarly journals Unstable Hemostasis and Specific Postoperative Complications (Literature Review)

2020 ◽  
Vol 5 (4) ◽  
pp. 98-102
Author(s):  
D. V. Lebedeva ◽  
E. A. Ilyicheva
Keyword(s):  
Postoperative Complications ◽  
Rapid Development ◽  
Point Of View ◽  
The Body ◽  
Cost Of Treatment ◽  
Duration Of Hospital Stay ◽  
Technical Errors ◽  
Perioperative Bleeding ◽  
The Cost

Perioperative bleeding occupies a leading place among all surgical complications and, despite the rapid development of surgery, remains relevant to this day. In addition to an increase in mortality, bleeding can cause the development of other postoperative complications, which lead to disability of patients and to a decrease in the quality of life in all age groups. Most perioperative bleeding are caused by technical errors. This article reviews the problem of perioperative bleeding from the point of view of impaired coagulation capabilities of the body. The main etiopathogenetic features of hemostasis during the development of this complication are considered. The analysis of postoperative complications, which were directly or indirectly caused by bleeding during or after surgery, is presented. The prevalence of these complications in various areas of surgery has been demonstrated. More detailed study of the hemostasis system and the identification of predictors of hemostasis difficulties before the surgery may cause an improvement in the results of surgical treatment and reduce the number of postoperative complications and the duration of hospital stay. Accordingly, this will lead to a decrease in the cost of treatment and an increase in patient satisfaction with the medical care. In connection with the above, there is a great interest among surgeons and anesthesiologists in preventing the development of perioperative bleeding.

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