Microbiology laboratory and the management of mother-child varicella-zoster virus infection

CGO-ensemble: Chaos game optimization algorithm-based fusion of deep neural networks for accurate Mpox detection

The rising global incidence of human Mpox cases necessitates prompt and accurate identification for effective disease control. Previous studies have predominantly delved into traditional ensemble methods for detection, we introduce a novel approach by leveraging a metaheuristic-based ensemble framework. In this research, we present an innovative CGO-Ensemble framework designed to elevate the accuracy of detecting Mpox infection in patients. Initially, we employ five transfer learning base models that integrate feature integration layers and residual blocks. These components play a crucial role in capturing significant features from the skin images, thereby enhancing the models' efficacy. In the next step, we employ a weighted averaging scheme to consolidate predictions generated by distinct models. To achieve the optimal allocation of weights for each base model in the ensemble process, we leverage the Chaos Game Optimization (CGO) algorithm. This strategic weight assignment enhances classification outcomes considerably, surpassing the performance of randomly assigned weights. Implementing this approach yields notably enhanced prediction accuracy compared to using individual models. We evaluate the effectiveness of our proposed approach through comprehensive experiments conducted on two widely recognized benchmark datasets: the Mpox Skin Lesion Dataset (MSLD) and the Mpox Skin Image Dataset (MSID). To gain insights into the decision-making process of the base models, we have performed Gradient Class Activation Mapping (Grad-CAM) analysis. The experimental results showcase the outstanding performance of the CGO-ensemble, achieving an impressive accuracy of 100% on MSLD and 94.16% on MSID. Our approach significantly outperforms other state-of-the-art optimization algorithms, traditional ensemble methods, and existing techniques in the context of Mpox detection on these datasets. These findings underscore the effectiveness and superiority of the CGO-Ensemble in accurately identifying Mpox cases, highlighting its potential in disease detection and classification.

Metaheuristics optimization-based ensemble of deep neural networks for Mpox disease detection

The rising number of cases of human Mpox has emerged as a major global concern due to the daily increase of cases in several countries. The disease presents various skin symptoms in infected individuals, making it crucial to promptly identify and isolate them to prevent widespread community transmission. Rapid determination and isolation of infected individuals are therefore essential to curb the spread of the disease. Most research in the detection of Mpox disease has utilized convolutional neural network (CNN) models and ensemble methods. However, to the best of our knowledge, none have utilized a meta-heuristic-based ensemble approach. To address this gap, we propose a novel metaheuristics optimization-based weighted average ensemble model (MO-WAE) for detecting Mpox disease. We first train three transfer learning (TL)-based CNNs (DenseNet201, MobileNet, and DenseNet169) by adding additional layers to improve their classification strength. Next, we use a weighted average ensemble technique to fuse the predictions from each individual model, and the particle swarm optimization (PSO) algorithm is utilized to assign optimized weights to each model during the ensembling process. By using this approach, we obtain more accurate predictions than individual models. To gain a better understanding of the regions indicating the onset of Mpox, we performed a Gradient Class Activation Mapping (Grad-CAM) analysis to explain our model's predictions. Our proposed MO-WAE ensemble model was evaluated on a publicly available Mpox dataset and achieved an impressive accuracy of 97.78%. This outperforms state-of-the-art (SOTA) methods on the same dataset, thereby providing further evidence of the efficacy of our proposed model.

Computer-Aided Detection and Classification of Monkeypox and Chickenpox Lesion in Human Subjects Using Deep Learning Framework

Monkeypox is a zoonotic viral disease caused by the monkeypox virus. After its recent outbreak, it has become clear that a rapid, accurate, and reliable diagnosis may help reduce the risk of a future outbreak. The presence of skin lesions is one of the most prominent symptoms of the disease. However, this symptom is also peculiar to chickenpox. The resemblance in skin lesions in the human subject may disrupt effective diagnosis and, as a result, lead to misdiagnosis. Such misdiagnosis can lead to the further spread of the disease as it is a communicable disease and can eventually result in an outbreak. As deep learning (DL) algorithms have recently been regarded as a promising technique in medical fields, we have been attempting to integrate a well-trained DL algorithm to assist in the early detection and classification of skin lesions in human subjects. This study used two open-sourced digital skin images for monkeypox and chickenpox. A two-dimensional convolutional neural network (CNN) consisting of four convolutional layers was applied. Afterward, three MaxPooling layers were used after the second, third, and fourth convolutional layers. Finally, we evaluated the performance of our proposed model with state-of-the-art deep-learning models for skin lesions detection. Our proposed CNN model outperformed all DL models with a test accuracy of 99.60%. In addition, a weighted average precision, recall, F1 score of 99.00% was recorded. Subsequently, Alex Net outperformed other pre-trained models with an accuracy of 98.00%. The VGGNet consisting of VGG16 and VGG19 performed least well with an accuracy of 80.00%. Due to the uniqueness of the proposed model and image augmentation techniques applied, the proposed CNN model is generalized and avoids over-fitting. This model would be helpful for the rapid and accurate detection of monkeypox using digital skin images of patients with suspected monkeypox.

The global disease burden of varicella-zoster virus infection from 1990 to 2019

Data on the global epidemiology of varicella-zoster virus infection (VZVI) is limited. This study aimed to investigate the burden of VZVI based on the global burden of disease study 2019 data. The age-standardized rates, including the incidence, death, disability-adjusted life years (DALYs), and the estimated annual percentage changes (EAPC) of VZVI were calculated to evaluate the disease burden of VZVI. The global numbers of incident and death cases due to VZVI were 83 963 744 and 14 553, respectively. The age-standardized incidence rate of VZVI increased slightly all over the world, while the age-standardized death and DALYs rate decreased from 1990 to 2019 (EAPC = -2.31 and -1.61, respectively). The younger age (<5 years old) and older groups had the highest VZVI burden. The high sociodemographic index (SDI) region had the highest age-standardized incidence rates in 2019 (1236.28/100 000, 95% uncertainty interval [UI]: 1156.66-1335.50) and the low SDI region had the lowest incidence (1111.24/100 000, 95% UI: 1040.46-1209.55). The age-standardized death and DALYs rate of VZVI decreased with the increase of SDI. Amongst the 21 geographical regions, the high-income Asia-Pacific (1269.08/100 000) region had the highest age-standardized incidence rate in 2019, while Sub-Saharan Africa had the highest age-standardized death and DALYs rate. The global incidence of VZVI has continued to increase in the past 3 decades, while the age-standardized death and DALYs rates have decreased. More attention should be paid to the younger and older population, as well as low SDI regions.

Uptrend prevalence of varicella parallel with low serum antibodies and low second-dose rate among children 10-14 years old in Wenzhou, China

In recent years, the incidence of varicella cases is rising, and outbreaks of varicella are frequently being reported worldwide. Our study aims to analyze the association between the varicella incidence and serum antibody level in the post-vaccine era. We retrieved and analyzed the incidence and prevalence data for children age 1-14 years in Wenzhou, China during 2010-2018. A cross-sectional seroepidemiology analysis was carried out in a series of 168 general healthy children age 1-14 years as well as children at a varicella outbreak in Wenzhou. Our data showed a significant surge in the incidence and prevalence of varicella in children aged 10-14 years in 2017 and 2018 while they were kept relatively stable in 2010-2016. The seroepidemiological analysis revealed a 7.3-fold significantly higher level of serum varicella IgG in healthy control students who exposed at the outbreak than that in general healthy children (median 523.5 vs. 71.7 mIU/mL, p < .01). The children 10-14 years old had the lowest rate of second-dose vaccination among the three age classes (7%, 41%, and 65% in 10-14, 5-9, and 2-4 age class, respectively), and children 5-9 years old who received the second dose had a higher level of serum protective IgG than those who did not (254.7 vs 98 mIU/mL, p = .06). The findings from the present study warn a two-dose vaccine schedule to reduce the climbing incidence and prevalence observed in the older children and suggest a higher serum IgG threshold for effective protection of children from the varicella outbreak.

Prevalence of herpes -, measles morbillivirus-, parvovirus B19 - and rubella viruses immunoglobulin G among women with chronic hepatitis B of reproductive age in Denmark: A cross-sectional study

OBJECTIVES

The aim of this study was to investigate whether the seroprevalence of IgG antibodies against seven viruses (cytomegalovirus, herpes simplex virus 1&2, measles morbillivirus, parvovirus B19, rubella, and varicella-zoster virus), which can potentially compromise maternal and fetal wellbeing, differs based on country of origin among women with chronic hepatitis B (CHB).

METHOD

This study was a single-center, hospital-based cross-sectional study. The study included women with CHB 15-45 years of age, included in the Danish Database for Hepatitis B and C. Seroprevalence estimates were calculated with a 95% confidence interval and were compared between age groups, regions of origin, and to the general population.

RESULTS

177 women were included in the study. Overall, the seroprevalences of antibodies were similar among women with CHB with origin outside Denmark and compared to the general population in Denmark, but there was a notable difference in the seroprevalence of antibodies against herpes simplex 2 between women from Africa (37.1% CI 95% 22.0;55.1) and women from the Middle East (2.5% CI 95% 0.1;14.7).

CONCLUSION

Women with CHB whose origin is outside Denmark do not appear to differ, based on origin, or be at greater risk of acquiring these viruses during pregnancy than their Danish counterparts.

Infectious Lung Disease

Infectious diseases are one of the main causes of morbidity and mortality worldwide. With new pathogens continuously emerging, known infectious diseases reemerging, increasing microbial resistance to antimicrobial agents, global environmental change, ease of world travel, and an increasing immunosuppressed population, recognition of infectious diseases plays an ever-important role in surgical pathology. This becomes particularly significant in cases where infectious disease is not suspected clinically and the initial diagnostic workup fails to include samples for culture. As such, it is not uncommon that a lung biopsy becomes the only material available in the diagnostic process of an infectious disease. Once the infectious nature of the pathological process is established, careful search for the causative agent is advised. This can often be achieved by examination of the hematoxylin and eosin-stained sections alone as many organisms or their cytopathic effects are visible on routine staining. However, ancillary studies such as histochemical stains, immunohistochemistry, in situ hybridization, or molecular techniques may be needed to identify the organism in tissue sections or for further characterization, such as speciation.

Herpes Zoster and Immunogenicity and Safety of Zoster Vaccines in Transplant Patients: A Narrative Review of the Literature

This narrative review focuses on the herpes zoster (HZ) and its prevention in transplant patients. Varicella zoster virus (VZV) is highly contagious and distributed worldwide in humans. Primary VZV infection usually causes varicella and then establishes a lifelong latency in dorsal root ganglia. Reactivation of VZV leads to HZ and related complications such as postherpetic neuralgia. Age and decreased immunity against VZV are important risk factors for developing HZ. Transplant patients are at increased risk for developing HZ and related complications due to their immunocompromised status and the need for lifetime immunosuppression. Diagnosis of HZ in transplant patients is often clinically difficult, and VZV-specific antibodies should be determined by serologic testing to document prior exposure to VZV during their pre-transplant evaluation process. Although antiviral agents are available, vaccination should be recommended for preventing HZ in transplant patients considering their complicated condition and weak organ function. Currently, there are two licensed HZ vaccines, of which one is a live-attenuated vaccine and the other is a HZ subunit vaccine. Both vaccines have shown promising safety and efficacy in transplants patients and especially the subunit vaccine could be administered post-transplant since this vaccine does not contain any live virus. Larger studies are needed about safety and immunogenicity of HZ vaccines in transplant populations, and extra efforts are needed to increase vaccine usage according to guidelines.

Pregnancy and infection: using disease pathogenesis to inform vaccine strategy

Vaccination is the mainstay of preventative medicine for many infectious diseases. Pregnant women, unborn fetuses, and neonates represent three at-risk populations that can be simultaneously protected by strategic vaccination protocols. Because the pathogenesis of different infectious microbes varies based on tissue tropism, timing of infection, and host susceptibility, the goals of immunization are not uniform across all vaccines. Mechanistic understanding of infectious disease pathogenesis and immune responses is therefore essential to inform vaccine design and the implementation of appropriate immunization protocols that optimize protection of pregnant women, fetuses, and neonates.

Advances in pharmacotherapeutic management of common skin diseases in neonates and infants

INTRODUCTION

neonatal and infantile skin diseases are frequently encountered in the clinical practice and represent worldwide a socioeconomic issue. They encompass a wide range of acquired or congenital conditions, including infections, vascular lesions and inflammatory diseases and can present with different degrees of severity, leading in some cases to dramatic complications. Areas covered: In this paper we report the most recent evidences on the management of some common skin diseases in neonates and infants. Hemangiomas, viral, fungal and bacterial infections, omphalitis, atopic and seborrhoeic dermatitis, napkin disease will be treated and discussed. Expert opinion: The majority of the dermatologic alterations in neonates are physiological, transient and do not require any treatment, thus the parents can be reassured about the good prognosis. However, in some cases, serious conditions must be excluded. In particular neonatal and infantile infections should be promptly recognized and properly managed, to avoid severe complications. The therapeutic options include traditional and, although few, innovative medical treatments, which will be carefully taken into consideration by the expert Dermatologists and Paediatricians.