A deep-learning algorithm to classify skin lesions from mpox virus infection

The Three-Dimensional Reference Interaction Site Model Approach as a Promising Tool for Studying Hydrated Viruses and Their Complexes with Ligands

Viruses are the most numerous biological form living in any ecosystem. Viral diseases affect not only people but also representatives of fauna and flora. The latest pandemic has shown how important it is for the scientific community to respond quickly to the challenge, including critically assessing the viral threat and developing appropriate measures to counter this threat. Scientists around the world are making enormous efforts to solve these problems. In silico methods, which allow quite rapid obtention of, in many cases, accurate information in this field, are effective tools for the description of various aspects of virus activity, including virus-host cell interactions, and, thus, can provide a molecular insight into the mechanism of virus functioning. The three-dimensional reference interaction site model (3D-RISM) seems to be one of the most effective and inexpensive methods to compute hydrated viruses, since the method allows us to provide efficient calculations of hydrated viruses, remaining all molecular details of the liquid environment and virus structure. The pandemic challenge has resulted in a fast increase in the number of 3D-RISM calculations devoted to hydrated viruses. To provide readers with a summary of this literature, we present a systematic overview of the 3D-RISM calculations, covering the period since 2010. We discuss various biophysical aspects of the 3D-RISM results and demonstrate capabilities, limitations, achievements, and prospects of the method using examples of viruses such as influenza, hepatitis, and SARS-CoV-2 viruses.

Automated Identification of Cutaneous Leishmaniasis Lesions Using Deep-Learning-Based Artificial Intelligence

The polymorphism of cutaneous leishmaniasis (CL) complicates diagnosis in health care services because lesions may be confused with other dermatoses such as sporotrichosis, paracocidiocomycosis, and venous insufficiency. Automated identification of skin diseases based on deep learning (DL) has been applied to assist diagnosis. In this study, we evaluated the performance of AlexNet, a DL algorithm, to identify pictures of CL lesions in patients from Midwest Brazil. We used a set of 2458 pictures (up to 10 of each lesion) obtained from patients treated between 2015 and 2022 in the Leishmaniasis Clinic at the University Hospital of Brasilia. We divided the picture database into training (80%), internal validation (10%), and testing sets (10%), and trained and tested AlexNet to identify pictures of CL lesions. We performed three simulations and trained AlexNet to differentiate CL from 26 other dermatoses (e.g., chromomycosis, ecthyma, venous insufficiency). We obtained an average accuracy of 95.04% (Confidence Interval 95%: 93.81-96.04), indicating an excellent performance of AlexNet in identifying pictures of CL lesions. We conclude that automated CL identification using AlexNet has the potential to assist clinicians in diagnosing skin lesions. These results contribute to the development of a mobile application to assist in the diagnosis of CL in health care services.

The diagnostic dilemma for atypical presentation of progressive human Mpox

BACKGROUND

Human mpox has increasingly been reported worldwide since May 2022, with higher incidence in men who have sex with men (MSM) and persons living with HIV (PLHIV) with presentation typical for generalized macules and papules.

CASE PRESENTATION

We are describing a case of human mpox, which presented as widespread, atypical round verrucous lesions that went undiagnosed in the community for six months and was treated with antibacterials and antifungals given the similarity to skin manifestations associated with endemic mycoses.

CONCLUSIONS

Suspicion for human mpox should be high in young MSM and PLHIV who present with rash and mpox should be ruled out earlier.

Predictors of laboratory-confirmed mpox in people with mpox-like illness

OBJECTIVES

We aimed to identify predictors of confirmed monkeypox (mpox) among people with mpox-like illness and to develop a multivariable model for confirmed mpox.

METHODS

We performed an observational study using national epidemiologic surveillance data in Mexico from May to November 2022. People with mpox-like illness were reported to the Mexican Ministry of Health and real-time polymerase chain reaction was performed in clinical samples to confirm mpox. Sociodemographic and clinical data were collected with a case report form. We performed univariable logistic regressions to estimate the predictive capability of individual characteristics, reported with ORs and 95% CIs. Variables of interest were included in multivariable logistic regression models and Akaike information criterion was used to retain variables for the final model. Discrimination and calibration of the model were estimated in bootstrap resamples.

RESULTS

A total of 5078 people were reported with mpox-like illness. Of 5078 people, 3291 (64.8%) had confirmed mpox. The strongest clinical predictors of confirmed mpox in univariable models were proctitis (OR 6.54, 5.93-7.21), inguinal adenopathy (OR 5.91, 5.36-6.52), and anogenital lesions (OR 5.45, 4.94-6.02). The final model included being a man who has sex with men (8.75, 7.37-10.38), HIV diagnosis (3.04, 2.51-3.69), inguinal adenopathy (2.24, 1.81-2.77), anogenital lesions (2.32, 1.97-2.74), and pustules (1.55, 1.32-1.81). Discrimination capability was excellent (c-statistic 0.88, 95% CI 0.87-0.89) and it was well calibrated (calibration slope 1, 95% CI 0.95-1.05).

DISCUSSION

A third of people with mpox-like illness do not have mpox. Factors such as being a man who has sex with men, HIV diagnosis, inguinal adenopathy, pustules, and anogenital lesions are associated with confirmed mpox.

AI diagnosis of Bethesda category IV thyroid nodules

Thyroid nodules are a common disease, and fine needle aspiration cytology (FNAC) is the primary method to assess their malignancy. For the diagnosis of follicular thyroid nodules, however, FNAC has limitations. FNAC can classify them only as Bethesda IV nodules, leaving their exact malignant status and pathological type undetermined. This imprecise diagnosis creates difficulties in selecting the follow-up treatment. In this retrospective study, we collected ultrasound (US) image data of Bethesda IV thyroid nodules from 2006 to 2022 from five hospitals. Then, US image-based artificial intelligence (AI) models were trained to identify the specific category of Bethesda IV thyroid nodules. We tested the models using two independent datasets, and the best AI model achieved an area under the curve (AUC) between 0.90 and 0.95, demonstrating its potential value for clinical application. Our research findings indicate that AI could change the diagnosis and management process of Bethesda IV thyroid nodules.

Clinical Predictors of Monkeypox Diagnosis: A Case-Control Study in a Nonendemic Region during the 2022 Outbreak

Monkeypox (Mpox) is an emerging zoonotic disease with the potential for severe complications. Early identification and diagnosis are essential to prompt treatment, control its spread, and reduce the risk of human-to-human transmission. This study aimed to develop a clinical diagnostic tool and describe the clinical and sociodemographic features of 19 PCR-confirmed Mpox cases during an outbreak in a nonendemic region of northwestern Mexico. The median age of patients was 35 years, and most were male. Mpox-positive patients commonly reported symptoms such as fever, lumbago, and asthenia, in addition to experiencing painful ulcers and a high frequency of HIV infection among people living with HIV (PLWH). Two diagnostic models using logistic regression were devised, with the best model exhibiting a prediction accuracy of 0.92 (95% CI: 0.8-1), a sensitivity of 0.86, and a specificity of 0.93. The high predictive values and accuracy of the top-performing model highlight its potential to significantly improve early Mpox diagnosis and treatment in clinical settings, aiding in the control of future outbreaks.

Monkeypox: a review of epidemiological modelling studies and how modelling has led to mechanistic insight

Human monkeypox (mpox) virus is a viral zoonosis that belongs to the Orthopoxvirus genus of the Poxviridae family, which presents with similar symptoms as those seen in human smallpox patients. Mpox is an increasing concern globally, with over 80,000 cases in non-endemic countries as of December 2022. In this review, we provide a brief history and ecology of mpox, its basic virology, and the key differences in mpox viral fitness traits before and after 2022. We summarize and critique current knowledge from epidemiological mathematical models, within-host models, and between-host transmission models using the One Health approach, where we distinguish between models that focus on immunity from vaccination, geography, climatic variables, as well as animal models. We report various epidemiological parameters, such as the reproduction number, R0, in a condensed format to facilitate comparison between studies. We focus on how mathematical modelling studies have led to novel mechanistic insight into mpox transmission and pathogenesis. As mpox is predicted to lead to further infection peaks in many historically non-endemic countries, mathematical modelling studies of mpox can provide rapid actionable insights into viral dynamics to guide public health measures and mitigation strategies.

Detection of Monkeypox Disease from Human Skin Images with a Hybrid Deep Learning Model

Monkeypox, a virus transmitted from animals to humans, is a DNA virus with two distinct genetic lineages in central and eastern Africa. In addition to zootonic transmission through direct contact with the body fluids and blood of infected animals, monkeypox can also be transmitted from person to person through skin lesions and respiratory secretions of an infected person. Various lesions occur on the skin of infected individuals. This study has developed a hybrid artificial intelligence system to detect monkeypox in skin images. An open source image dataset was used for skin images. This dataset has a multi-class structure consisting of chickenpox, measles, monkeypox and normal classes. The data distribution of the classes in the original dataset is unbalanced. Various data augmentation and data preprocessing operations were applied to overcome this imbalance. After these operations, CSPDarkNet, InceptionV4, MnasNet, MobileNetV3, RepVGG, SE-ResNet and Xception, which are state-of-the-art deep learning models, were used for monkeypox detection. In order to improve the classification results obtained in these models, a unique hybrid deep learning model specific to this study was created by using the two highest-performing deep learning models and the long short-term memory (LSTM) model together. In this hybrid artificial intelligence system developed and proposed for monkeypox detection, test accuracy was 87% and Cohen's kappa score was 0.8222.

Deep learning for Mpox: Advances, challenges, and opportunities

Although deep-learning algorithms in dermatology have shown promise in diagnosing skin cancers, less is known about potential applications for the diagnosis of infectious diseases. In a recent publication in Nature Medicine, Thieme et al. develop a deep-learning algorithm to classify skin lesions from Mpox virus (MPXV) infections.¹.