scholarly journals Peer Review of “Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study”

JMIRx Med ◽  
10.2196/28919 ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. e28919
Author(s):  
W Benjamin Nowell
Keyword(s):  
Machine Learning ◽  
Herpes Simplex Virus ◽  
Herpes Simplex ◽  
Data Collection ◽  
Validation Study ◽  
Risk Group ◽  
Group Identification ◽  
User Data ◽  
Simplex Virus ◽  
Development And Validation

scholarly journals Machine Learning for Risk Group Identification and User Data Collection, a Case-Study of Herpes Simplex Virus Patient Registry: Algorithm Development and Validation (Preprint)

JMIRx Med ◽  
10.2196/25560 ◽  
2020 ◽  
Author(s):  
Svitlana Surodina ◽  
Ching Lam ◽  
Svetislav Grbich ◽  
Madison Milne-Ives ◽  
Michelle van Velthoven ◽  
...  
Keyword(s):  
Machine Learning ◽  
Herpes Simplex Virus ◽  
Herpes Simplex ◽  
Data Collection ◽  
Risk Group ◽  
Group Identification ◽  
User Data ◽  
Simplex Virus ◽  
Development And Validation

scholarly journals Authors’ Response to Peer Reviews of “Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study” (Preprint)

2021 ◽  
Author(s):  
Svitlana Surodina ◽  
Ching Lam ◽  
Svetislav Grbich ◽  
Madison Milne-Ives ◽  
Michelle van Velthoven ◽  
...  
Keyword(s):  
Machine Learning ◽  
Herpes Simplex Virus ◽  
Herpes Simplex ◽  
Data Collection ◽  
Risk Group ◽  
Group Identification ◽  
Peer Reviews ◽  
User Data ◽  
Simplex Virus ◽  
Development And Validation

UNSTRUCTURED This is author responses.

scholarly journals Authors’ Response to Peer Reviews of “Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study”

JMIRx Med ◽  
10.2196/28917 ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. e28917
Author(s):  
Svitlana Surodina ◽  
Ching Lam ◽  
Svetislav Grbich ◽  
Madison Milne-Ives ◽  
Michelle van Velthoven ◽  
...  
Keyword(s):  
Machine Learning ◽  
Herpes Simplex Virus ◽  
Herpes Simplex ◽  
Data Collection ◽  
Risk Group ◽  
Group Identification ◽  
Peer Reviews ◽  
User Data ◽  
Simplex Virus ◽  
Development And Validation

scholarly journals Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jung Su Lee ◽  
Jihye Yun ◽  
Sungwon Ham ◽  
Hyunjung Park ◽  
Hyunsu Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Differential Diagnosis ◽  
Herpes Simplex Virus ◽  
Herpes Simplex ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Previous History ◽  
Clinical Factor ◽  
Endoscopic Images ◽  
Simplex Virus

AbstractThe endoscopic features between herpes simplex virus (HSV) and cytomegalovirus (CMV) esophagitis overlap significantly, and hence the differential diagnosis between HSV and CMV esophagitis is sometimes difficult. Therefore, we developed a machine-learning-based classifier to discriminate between CMV and HSV esophagitis. We analyzed 87 patients with HSV esophagitis and 63 patients with CMV esophagitis and developed a machine-learning-based artificial intelligence (AI) system using a total of 666 endoscopic images with HSV esophagitis and 416 endoscopic images with CMV esophagitis. In the five repeated five-fold cross-validations based on the hue–saturation–brightness color model, logistic regression with a least absolute shrinkage and selection operation showed the best performance (sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating characteristic curve: 100%, 100%, 100%, 100%, 100%, and 1.0, respectively). Previous history of transplantation was included in classifiers as a clinical factor; the lower the performance of these classifiers, the greater the effect of including this clinical factor. Our machine-learning-based AI system for differential diagnosis between HSV and CMV esophagitis showed high accuracy, which could help clinicians with diagnoses.

scholarly journals Development and Validation of an LC–MS-Based Quantification Assay for New Therapeutic Antibodies: Application to a Novel Therapy against Herpes Simplex Virus

ACS Omega ◽  
2020 ◽  
Vol 5 (38) ◽  
pp. 24329-24339
Author(s):  
Margaux Fresnais ◽  
Rémi Longuespée ◽  
Max Sauter ◽  
Torsten Schaller ◽  
Michaela Arndt ◽  
...  
Keyword(s):  
Herpes Simplex Virus ◽  
Herpes Simplex ◽  
Therapeutic Antibodies ◽  
Novel Therapy ◽  
Simplex Virus ◽  
Development And Validation

scholarly journals GENITAL HERPES AND PREGNANCY

2017 ◽  
Vol 4 (3) ◽  
pp. 124-130
Author(s):  
Anastasiya V. Belova ◽  
O. R Astsaturova ◽  
N. S Naumenko ◽  
A. P Nikonov
Keyword(s):  
Herpes Simplex Virus ◽  
Herpes Simplex ◽  
High Risk ◽  
Genital Herpes ◽  
Vertical Transmission ◽  
Risk Group ◽  
Recurrent Infection ◽  
Laboratory Diagnosis ◽  
High Risk Group ◽  
Simplex Virus

Herpes simplex virus of two types - HSV-1 and HSV-2, which can manifest as a primary and recurrent infection is the etiological factor of genital herpes. Herpes simplex virus is one of the most common sexually transmitted infections, in connection with which there is an increase in physical and psychological morbidity, which often remains an underestimated medical problem. With the vertical transmission of HSV during labor, diseases that are dangerous to the life and health in newborns can occur. Pregnant women with a primary infection of genital herpes belong to the high-risk group for the transmission of HSV to the newborn. Prophylaxis and prevention of vertical transmission of HSV are implemented in three directions: prevention of the recurrence of maternal genital HSV infection; prevention of transmission of the virus during pregnancy and childbirth; puerperal prophylaxis of the disease in a neonate infant born of a mother belonging to a high-risk group. This review focuses on such important aspects as laboratory diagnosis of HSV, antiviral therapy and prevention of viral infection during pregnancy, delivery and in the early neonatal period.

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