Monkeypox Virus Infection in Humans across 16 Countries — April–June 2022

Thornhill, John; Barkati, Sapha; Walmsley, Sharon; Rockstroh, Juergen; Antinori, Andrea; Harrison, Luke B.; Palich, Romain; Nori, Achyuta; Reeves, Iain; Habibi, Maximillian S.; Apea, Vanessa; Boesecke, Christoph; Vandekerckhove, Linos; Yakubovsky, Michal; Sendagorta, Elena; Blanco, José Luis Santiago; Florence, Éric; Moschese, Davide; Maltêz, Fernando; Goorhuis, Abraham; Pourcher, Valérie; Migaud, Pascal; Noe, Sebastian; Pintado, Claire; Maggi, Fabrizio; Hansen, Ann-Brit Eg; Hoffmann, Christian; Lezama, Jezer I.; Mussini, Cristina; Cattelan, AnnaMaria; Makofane, Keletso; Tan, Darrell H. S.; Nozza, Silvia; Nemeth, Johannes; Klein, Marina B.; Orkin, Chloe

doi:10.1056/nejmoa2207323

Cited by 1,369 publications

(1,866 citation statements)

References 11 publications

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“…We performed 19 augmentation operations on the web-scrapped images using Python Imaging Library (PIL) version 9.2.0, and scikit-image library version 0.19.3. to increase the number of images and introduce variability in the data. Our augmentation operations include (1) brightness modification with a randomly generated factor (range [0.5, 2]), (2) color modification with a randomly generated factor (range [0.5, 1.5]), (3) sharpness modification with a randomly generated factor (range [0.5, 2]), (4) image translation along height and width with a randomly generated distance between -25 and 25 pixels, (5) image shearing along height and width with randomly generated parameters, (6) adding Gaussian noise of zero mean and randomly generated variance (range [0.005, 0.02]) to images, (7) adding zero-mean speckle noise and randomly generated variance (range [0.005, 0.02]) to images, (8) adding salt noise to randomly generated number of image pixels (range [2%, 8%]), (9) adding pepper noise to the randomly generated number of image pixels (range [2%, 8%]), (10) adding salt & pepper noise to randomly generated number of image pixels (range [2%, 8%]), (11) modifying the values of the image pixels based on the local variance, (12) blurring an image with a Gaussian kernel with a randomly generated radius (range [1, 3] pixels), (13) contrast modification with a randomly generated factor (range [1, 1.5]), (14) rotating all images by 90 ◦ , (15) rotating images at random angle (range [-45 ◦ , 45 ◦ ]), (16) zooming in an image by about 9%, (17) zooming in an image by about 18%, (18) flipping images along the height, and (19) flipping images along the width. In Table I, we show the number of original and augmented images per class in our database.…”

Section: Methodsmentioning

confidence: 99%

Can Artificial Intelligence Detect Monkeypox from Digital Skin Images?

Islam

Hussain²,

Chowdhury

et al. 2022

Preprint

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An outbreak of Monkeypox has been reported in 75 countries so far, and it is spreading in fast pace around the world. The clinical attributes of Monkeypox resemble those of Smallpox, while skin lesions and rashes of Monkeypox often resemble those of other poxes, for example, Chickenpox and Cowpox. These similarities make Monkeypox detection challenging for healthcare professionals by examining the visual appearance of lesions and rashes. Additionally, there is a knowledge gap among healthcare professionals due to the rarity of Monkeypox before the current outbreak. Motivated by the success of artificial intelligence (AI) in COVID-19 detection, the scientific community has shown an increasing interest in using AI in Monkeypox detection from digital skin images. However, the lack of Monkeypox skin image data has been the bottleneck of using AI in Monkeypox detection. Therefore, recently, we introduced the Monkeypox Skin Image Dataset 2022, the largest of its kind so far. In addition, in this paper, we utilize this dataset to study the feasibility of using state-of-the-art AI deep models on skin images for Monkeypox detection. Our study found that deep AI models have great potential in the detection of Monkeypox from digital skin images (precision of 85%). However, achieving a more robust detection power requires larger training samples to train those deep models.

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Section: Methodsmentioning

confidence: 99%

Can Artificial Intelligence Detect Monkeypox from Digital Skin Images?

Islam

Hussain²,

Chowdhury

et al. 2022

Preprint

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show abstract

“…By late July, the WHO declared a public health emergency of international concern. Unlike historical outbreaks, the current global outbreak features human-to-human transmission 2,3 , and cases with no association with each other have been identified, suggesting unrecognized community transmission.…”

Section: Introductionmentioning

confidence: 86%

Detection of monkeypox viral DNA in a routine wastewater monitoring program

Wolfe

Duong

Hughes

et al. 2022

Preprint

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Wastewater represents a composite biological sample from the entire contributing population. People infected with monkeypox excrete monkeypox virus DNA via skin lesions, saliva, feces and urine and these can enter the wastewater via toilets, sinks, and shower drains. To test whether monkeypox can be detected and monitored in wastewater during a period when publicly reported monkey cases in the region were increasing, we deployed digital PCR assays that target genomic DNA from the monkeypox virus in our routine, ongoing wastewater surveillance program in the Greater Bay Area of California, USA. We measured monkeypox virus DNA daily in settled solids samples from nine wastewater plants over the period of approximately 4 weeks. During that period, we detected monkeypox virus DNA in wastewater solids at nearly all the wastewater plants we routinely sample. Frequency of occurrence and concentrations were highest at plants serving San Francisco County. To confirm the presence of monkeypox DNA, we used two assays that target distinct sequences on the monkeypox genome on a subset of samples and results from both assays were in close agreement strongly suggesting true positives in the wastewater. Additionally, we show that concentrations of monkeypox DNA is 103 times higher in the solid fraction compared to the liquid fraction of wastewater on a mass-equivalent basis.

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“…In the current outbreak, there is a clear predominance of infections among men who have sex with men (MSM), and several large public events have been associated with rapid emergence of cases in different parts of the world. Currently, transmission via close skin and mucosal contact, possibly including sexual transmission, seems likely [7][8][9][10]. Even though the current outbreak is still in its early stages, a selflimiting course cannot be assumed; rather, it is a longer-term public-health problem that will hopefully bring diagnostic and therapeutic benefits to endemic African countries.…”

Section: Introductionmentioning

confidence: 99%

The human host response to monkeypox infection: a proteomic case series study

Wang

Tober‐Lau

Farztdinov

et al. 2022

Preprint

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Monkeypox (MPX) is caused by the homonymous orthopoxvirus (MPXV) known since the 1970s to occur at low frequency in West and Central Africa. Recently, the disease has been spreading quickly in Europe and the US. The rapid rise of MPX cases outside previously endemic areas and the different clinical presentation prompt for a better understanding of the disease, including the development of clinical tests for rapid diagnosis and monitoring. Here, using Zeno SWATH MS - a latest-generation proteomic technology - we studied the plasma proteome of a group of MPX patients with a similar infection history and clinical severity typical for the current outbreak. Moreover, we compared their proteomes to those of healthy volunteers and COVID-19 patients. We report that MPX is associated with a strong and characteristic plasma proteomic response and describe MPXV infection biomarkers among nutritional and acute phase response proteins. Moreover, we report a correlation between plasma protein markers and disease severity, approximated by the degree of skin manifestation. Contrasting the MPX host response with that of COVID-19, we find a range of similarities, but also important differences. For instance, Complement factor H-related protein 1 (CFHR1) is induced in COVID-19, but suppressed in MPX, reflecting the different role of the complement system in the two infectious diseases. However, the partial overlap between MPX and COVID-19 host response proteins allowed us to explore the repurposing of a clinically applicable COVID-19 biomarker panel assay, resulting in the successful classification of MPX patients. Hence, our results provide a first proteomic characterization of the MPX human host response based on a case series. The results obtained highlight that proteomics is a promising technology for the timely identification of disease biomarkers in studies with moderate cohorts, and we reveal a thus far untapped potential for accelerating the response to disease outbreaks through the repurposing of multiplex biomarker assays.

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Monkeypox Virus Infection in Humans across 16 Countries — April–June 2022

Abstract: The authors' full names, academic degrees, and affiliations are listed in the Appendix. Prof. Orkin can be contacted at c . m .

Cited by 1,369 publications

References 11 publications

Can Artificial Intelligence Detect Monkeypox from Digital Skin Images?

Can Artificial Intelligence Detect Monkeypox from Digital Skin Images?

Detection of monkeypox viral DNA in a routine wastewater monitoring program

The human host response to monkeypox infection: a proteomic case series study

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