scholarly journals Monitoring percent free PSA in serial specimens: Improvement of test specificity, early detection, and identification of occult tumors

1998 ◽  
Vol 12 (1) ◽  
pp. 26-31 ◽  
Author(s):  
James T. Wu ◽  
Grace H. Liu ◽  
Ping Zhang ◽  
Robert A. Stephenson
Keyword(s):  
Early Detection ◽  
Free Psa ◽  
Detection And Identification ◽  
Test Specificity

scholarly journals Early Detection of Exposure to Toxic Chemicals Using Continuously Recorded Multi-Sensor Physiology

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3616
Author(s):  
Jan Ubbo van Baardewijk ◽  
Sarthak Agarwal ◽  
Alex S. Cornelissen ◽  
Marloes J. A. Joosen ◽  
Jiska Kentrop ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Physiological Signals ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Chemical Agent ◽  
Counter Measures ◽  
Detection And Identification ◽  
Life Saving ◽  
Future Work

Early detection of exposure to a toxic chemical, e.g., in a military context, can be life-saving. We propose to use machine learning techniques and multiple continuously measured physiological signals to detect exposure, and to identify the chemical agent. Such detection and identification could be used to alert individuals to take appropriate medical counter measures in time. As a first step, we evaluated whether exposure to an opioid (fentanyl) or a nerve agent (VX) could be detected in freely moving guinea pigs using features from respiration, electrocardiography (ECG) and electroencephalography (EEG), where machine learning models were trained and tested on different sets (across subject classification). Results showed this to be possible with close to perfect accuracy, where respiratory features were most relevant. Exposure detection accuracy rose steeply to over 95% correct during the first five minutes after exposure. Additional models were trained to correctly classify an exposed state as being induced either by fentanyl or VX. This was possible with an accuracy of almost 95%, where EEG features proved to be most relevant. Exposure detection models that were trained on subsets of animals generalized to subsets of animals that were exposed to other dosages of different chemicals. While future work is required to validate the principle in other species and to assess the robustness of the approach under different, realistic circumstances, our results indicate that utilizing different continuously measured physiological signals for early detection and identification of toxic agents is promising.

scholarly journals A method for early detection and identification of fungal contamination of building materials using e-nose

PLoS ONE ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. e0215179 ◽  
Author(s):  
Zbigniew Suchorab ◽  
Magdalena Frąc ◽  
Łukasz Guz ◽  
Karolina Oszust ◽  
Grzegorz Łagód ◽  
...  
Keyword(s):  
Early Detection ◽  
Building Materials ◽  
Fungal Contamination ◽  
Detection And Identification

Discordant prostate specific antigen test results despite WHO assay standardization

2018 ◽  
Vol 33 (3) ◽  
pp. 275-282 ◽  
Author(s):  
Martin Boegemann ◽  
Christian Arsov ◽  
Boris Hadaschik ◽  
Kathleen Herkommer ◽  
Florian Imkamp ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Early Detection ◽  
Specific Antigen ◽  
Test Results ◽  
Serum Samples ◽  
Prostate Specific Antigen Test ◽  
Free Psa ◽  
Prostate Biopsies ◽  
Cancer Early Detection ◽  
Total Psa

Introduction: Total PSA (tPSA) and free PSA (fPSA) are the most commonly used biomarkers for early detection of prostate cancer. Despite standardization efforts, many available PSA assays may still produce discordant results. In the present study, we compared four PSA assays calibrated to the WHO standards 96/670 and 96/668 for tPSA and fPSA, respectively. Methods: Within the scope of the Prostate Cancer Early Detection Study Based on a ‘‘Baseline’’ PSA Value in Young Men (PROBASE), we tested tPSA and fPSA in serum samples from 50 patients in the four different PROBASE sites using four WHO-calibrated assays from Roche (Elecsys, Cobas), Beckman-Coulter (Access-II) and Siemens (ADVIA Centaur). The comparison was performed using the Passing–Bablok regression method. Results: Compared to Access, the median tPSA levels for Centaur, Elecsys, and Cobas were +3%, +11%–20%, and +17%–23%, respectively, while for median fPSA levels the differences for Centaur, Elecsys, and Cobas were +49%, +29%–31%, and +22%, respectively. Discussion: Despite all investigated assays being WHO-calibrated, the Elecsys and Cobas tPSA assays produced considerably higher results than the Access and Centaur assays. Differences in fPSA-recovery between all investigated assays were even more pronounced. When applying the tPSA cutoff of 3.1 μg/L recommended for WHO-calibrated assays, the use of higher calibrated assays may lead to unnecessary prostate biopsies. Conversely, if the historical threshold of 4 μg/L is applied when using WHO-calibrated assays, it could lead to falsely omitted prostate biopsies.

A Novel Approach for the Early Detection and Identification of Botnets

2011 ◽  
Vol 403-408 ◽  
pp. 4469-4475
Author(s):  
S. Benson Edwin Raj ◽  
V.S. Jayanthi ◽  
R. Shalini
Keyword(s):  
Early Detection ◽  
Optimization Technique ◽  
Machine Learning Algorithms ◽  
Ip Address ◽  
Detection Techniques ◽  
Detection And Identification ◽  
Novel Approach ◽  
Size Number ◽  
Ip Spoofing ◽  
The Right

Botnets are growing in size, number and impact. It continues to be one of the top three web threats that mankind has ever known. The botnets are the souped-up cyber engines driving nearly all criminal commerce on the Internet and are seen as relaying millions of pieces of junk e-mail, or spam. Thus, the need of the hour is the early detection and identification of the heart of network packet flooding or the C&C centre. Most of the botmasters perform DDos attacks on a target server by spoofing the source IP address. The existing botnet detection techniques rely on machine learning algorithms and do not expound the IP spoofing issue. These approaches are also found to be unsuccessful in the meticulous identification of the botmasters. Here we propose an architecture that depend on the PSO-based IP tracebacking. Our architecture also introduces the IP spoofing detector unit so as to ensure that the Traceback moves in the right direction. The approach also detects the zombies and utilizes the PSO optimization technique that aid in the identification of the C&C node. The experimental results show that our approach is successful in prompt detection of the bots.

scholarly journals Early Detection and Identification of Commonly Encountered Candida Species from Simulated Blood Cultures by Using a Real-Time PCR-Based Assay

2004 ◽  
Vol 6 (2) ◽  
pp. 108-114 ◽  
Author(s):  
Younes Maaroufi ◽  
Jean-Marc De Bruyne ◽  
Valérie Duchateau ◽  
Aspasia Georgala ◽  
Françoise Crokaert
Keyword(s):  
Early Detection ◽  
Real Time ◽  
Real Time Pcr ◽  
Candida Species ◽  
Blood Cultures ◽  
Detection And Identification

Novel Artificial Neural Network for Early Detection of Prostate Cancer

2002 ◽  
Vol 20 (4) ◽  
pp. 921-929 ◽  
Author(s):  
Bob Djavan ◽  
Mesut Remzi ◽  
Alexandre Zlotta ◽  
Christian Seitz ◽  
Peter Snow ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Early Detection ◽  
Roc Curve ◽  
Predictive Accuracy ◽  
Specific Antigen ◽  
Psa Density ◽  
Free Psa ◽  
Ann Models ◽  
Artificial Neural ◽  
Total Psa

PURPOSE: Two artificial neural networks (ANN) for the early detection of prostate cancer in men with total prostate-specific antigen (PSA) levels from 2.5 to 4 ng/mL and from 4 to 10 ng/mL were prospectively developed. The predictive accuracy of the ANN was compared with that obtained by use of conventional statistical analysis of standard PSA parameters. PATIENTS AND METHODS: Consecutive men with a serum total PSA level between 4 and 10 ng/mL (n = 974) and between 2.5 and 4 ng/mL (n = 272) were analyzed. A separate ANN model was developed for each group of patients. Analyses were performed to determine the presence of prostate cancer. RESULTS: The area under the receiver operator characteristic (ROC) curve (AUC) was 87.6% and 91.3% for the 2.5 to 4 ng/mL and 4 to 10 ng/mL ANN models, respectively. For the latter model, the AUC generated by the ANN was significantly higher than that produced by the single variables of total PSA, percentage of free PSA, PSA density of the transition zone (TZ), and TZ volume (P < .01), but not significantly higher compared with multivariate analysis. For the 2.5 to 4 ng/mL model, the AUC of the ANN ROC curve was significantly higher than the AUCs for percentage of free PSA (P = .0239), PSA-TZ (P = .0204), and PSA density and total prostate volume (P < .01 for both). CONCLUSION: The predictive accuracy of the ANN was superior to that of conventional PSA parameters. ANN models might change the way patients referred for early prostate cancer detection are counseled regarding the need for prostate biopsy.

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