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Abstract Purpose and Background: To create awareness among dermatosurgeons about the versatility of keystone flaps in re-surfacing post-excisional small, medium and large skin defects. Aims and Objectives: Single-staged tension-free re-surfacing of various sized skin defects using keystone flap and to have least donor site morbidity, primary healing and maximum functional outcome. Material and Methods: This retrospective study was conducted, between October 2021 - January 2023 in Department of Plastic Surgery, PGIMS Rohtak, on 15 males and 1 female aged between 18 - 65 years. Sixteen Keystone flaps (type IIA n=13, Omega cum Sydney melanoma unit modification n=3) were utilized for resurfacing skin defects arising after excision of basal cell carcinoma (n=3) and benign hairy nevus (n=2) on cheek, basal cell carcinoma forehead (n=1), electric burns on deltoid (n=2), giant neurofibroma back (n=1), tattoo forearm (n=3), chronic non-specific granuloma thumb (n=2) and pilonidal sinus disease (n=2). Skin with maximum expansibility was chosen as donor site with utmost care of facial lines and its aesthetic subunits. Flaps were dissected all around preserving maximum number of hand-held Doppler-identified perforators in pedicular area. Sutures were removed on 10th to 15th post-operative day. Follow-ups ranged from 2 to 15 months. Results: None had wound dehiscence or loss of flap. Conclusion: Keystone flaps have defect-adaptive design, simple bio-geometry, technical simplicity, decreased downtime, cost effectivity and efficiency and can be designed to resurface post-excisional skin defect from head to toe with acceptable results, therefore should strongly be considered by dermatosurgeons while excising skin lesions. Keywords: Keystone flap, perforators, pedicular area, Author(s): Abhinav Singh (corresponding author) [1]; Rohini Singh [2]; Gaurav Chaturvedi [1]; Malika Singh [3]; RB Singh [4] Introduction Since many dermatologists have turned into dermatosurgeons, they need to know [...]

Introduction: In the aftermath of anthrax bioterrorism, the US military began its smallpox immunization program in 2002. Dryvax was superseded in 2008 by ACAM2000, a second-generation smallpox vaccine, after clinical trials demonstrated favorable outcomes. However, these trials focused on significant adverse effects and provided less specific classifications and descriptions of cutaneous eruptions. The purpose of this systematic review was to investigate the clinicopathological characteristics of skin lesions that emerged in US military personnel following the reinstatement of new smallpox immunizations. Methods: PubMed, ScienceDirect, and Google Scholar were searched. The search was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, using appropriate keywords. Results: Of the 467 studies initially identified, 5 (1%) were analyzed, with a sample size of 15. There were 10 men and 4 women. The mean age of presentation was 24.3 years. The interval between inoculation and eruption was 15 days. The length of the eruption until clearance was 36.26 days. Grossly, most skin lesions were described as having papules (n = 9). Histological examination revealed vesicles with spongiotic dermatitis and eosinophils (n = 5) and a dermal hypersensitivity reaction with lymphocytic vasculitis (capillaritis) (n = 2). Definitive diagnoses included acral and vesiculopustular dermatosis (n = 7), generalized vaccinia (GV) (n = 1), and progressive vaccinia (n = 1). Concurrent or near-concurrent vaccination was administered (n = 12). Conclusion: Although rare, clinically significant skin lesions can occur after ACAM2000 administration. A feared complication of progressive vaccinia has been reported; however, to determine its causal relationship, further clinical trials are required to provide universal guidelines. [ABSTRACT FROM AUTHOR]

Skin cancer diagnosis greatly benefits from advanced machine learning techniques, particularly fine-tuned deep learning models. In our research, we explored the impact of traditional machine learning and fine-tuned deep learning approaches on prediction accuracy. Our findings reveal significant improvements in predictability and accuracy with fine-tuning, particularly evident in deep learning models. The CNN, SVM, and Random Forest Classifier achieved high accuracy. However, fine-tuned deep learning models such as EfficientNetB0, ResNet34, VGG16, Inception _v3, and DenseNet121 demonstrated superior performance. To ensure comparability, we fine-tuned these models by incorporating additional layers, including one flatten layer and three densely interconnected layers. These layers play a crucial role in enhancing model efficiency and performance. The flatten layer preprocesses multidimensional feature maps, facilitating efficient information flow, while subsequent dense layers refine feature representations, capturing intricate patterns and relationships within the data. Leveraging LeakyReLU activation functions in the dense layers mitigates the vanishing gradient problem and promotes stable training. Finally, the output dense layer with a sigmoid activation function simplifies decision making for healthcare professionals by providing binary classification output. Our study underscores the significance of incorporating additional layers in fine-tuned neural network models for skin cancer classification, offering improved accuracy and reliability in diagnosis. [ABSTRACT FROM AUTHOR]

The phenomenon of child abuse/maltreatment is underestimated and often represents a difficult challenge for healthcare professionals and forensic pathologists who must proceed with the differential diagnosis with accidental or self-induced events, or with lesions due to pathologies that overlap with that of mistreatment, defined as "Mimics". This study presents a case series with the aim of discussing lesions that may mimic signs of physical abuse in children but are due to a different etiology to raise awareness and train healthcare professionals and forensic pathologists on possible confounding factors in order to avoid diagnostic errors. Six cases of "Mimics" out of 418 cases of suspected mistreatment (1.43% of cases) were identified, presenting skin lesions initially classified as injuries of abuse due to their location and type and, in particular, sexual abuse for three cases. Then, the lesions and the subjects, in particular the anamnestic history, were examined by a multidisciplinary team and the diagnosis of genital lichen sclerosus et atrophicus in three cases, and the results of popular healing techniques (i.e., "cupping") in the other three cases were ascertained. These situations require specific skills and a forensic background from healthcare professionals to conduct a correct differential diagnosis and the intervention of a multidisciplinary team to investigate every possible pathology or alternative therapeutic practice that could simulate child abuse. In particular, when "mimics" are due to alternative medicine, it should not strictly be considered child abuse, but professionals must be aware of the hypothesis of mistreatment in case of non-medical indication or potential personal injuries from other crimes, such as illegal practice of the medicine. This awareness is also crucial to direct the child toward appropriate medical care, and it is essential to recognize that these conditions can coexist within the same clinical presentation. [ABSTRACT FROM AUTHOR]

Abstract Background Two outbreaks of swinepox were investigated in free-range domestic pig farms located in the northeastern side of Sicily, Italy. The disease is generally self-limiting with a low mortality rate, but morbidity can reach high rates in case of poor sanitary conditions, improper husbandry practices and ectoparasitic infestation. The presented cases are the first ever reported on the island and part of the few cases reported in domestic pigs. Case presentation Carcasses condemned at the slaughterhouse and deceased pigs from Farm A and Farm B respectively, were referred for post-mortem examination and further investigations, with a strong suspect of SwinePox virus (SWPV) infection. Twelve deceased pigs were examined in total, showing poor body condition and pustular lesions scattered all over the cutaneous surfaces. Moreover, pigs from Farm B showed ocular lesions classified from Grade I to IV (from mild conjunctivitis to severe keratoconjunctivitis with corneal oedema, opacity, and ulcers). Final diagnosis was pursued by the microscopic assessment of skin lesions in both farms, which revealed the typical SWPV-lesion appearance, such as severe and disseminated ulcerative dermatitis and suspected inclusion bodies multifocally observed in the epidermis. Moreover, negative staining Electron Microscopy (nsEM) was performed on skin lesions and ocular swabs from Farm B, revealing in two samples the presence of brick-shaped viral particles, 220 nm long and 160 nm wide, with irregularly arranged surface tubules, identified as SWPV. The gene encoding the 482-bp fragment of the virus late transcription factor–3 was detected by PCR and sequencing revealed 99.79% identity and 100% query-cover with a strain previously isolated in Germany. Field clinical assessment was then performed in Farm B, revealing high overcrowding, poor sanitary conditions and improper husbandry practices, which are relevant risk factors for SWPV transmission. Conclusions The present is the first case report of SWPV in free-range pigs raised in Sicily, an island of the Southern coast of Italy, and wants to raise awareness on a neglected disease, and cause of animal health and welfare issues.

Background: Skin diseases are severe diseases. Identification of these severe diseases depends upon the abstraction of atypical skin regions. The segmentation of these skin diseases is essential to rheumatologists in risk impost and for valuable and vital decision‐making. Skin lesion segmentation from images is a crucial step toward achieving this goal—timely exposure of malignancy in psoriasis expressively intensifies the persistence ratio. Defies occur when people presume skin diseases they have without accurately and precisely incepted. However, analyzing malignancy at runtime is a big challenge due to the truncated distinction of the visual similarity between malignance and non‐malignance lesions. However, images' different shapes, contrast, and vibrations make skin lesion segmentation challenging. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. Materials and methods: This paper introduces a skin lesions segmentation model that integrates two intelligent methodologies: Bayesian inference and edge intelligence. In the segmentation model, we deal with edge intelligence to utilize the texture features for the segmentation of skin lesions. In contrast, Bayesian inference enhances skin lesion segmentation's accuracy and efficiency. Results: We analyze our work along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions from seminal works and a systematic viewpoint and examine how these dimensions have influenced current trends. Conclusion: We summarize our work with previously used techniques in a comprehensive table to facilitate comparisons. Our experimental results show that Bayesian‐Edge networks can boost the diagnostic performance of skin lesions by up to 87.80% without incurring additional parameters of heavy computation. [ABSTRACT FROM AUTHOR]

Persistent infections caused by Strongyloides stercoralis are probably underestimated in the elderly Italian population. This nematode is unique among helminths: it can last asymptomatically in the host for decades and may present with a broad range of clinical pictures upon reactivation. Misdiagnosis often occurs even when the clinical picture is suggestive. If undetected, this parasitosis can lead to serious consequences when hyperinfection occurs. Herein, we report two peculiar clinical cases of complicated strongyloidiasis with multiple skin lesions. The aim of our report is to lead clinicians to familiarize themselves with skin patterns and clinical features that can suggest a possible underlying strongyloidiasis. [ABSTRACT FROM AUTHOR]

Background: Two outbreaks of swinepox were investigated in free-range domestic pig farms located in the northeastern side of Sicily, Italy. The disease is generally self-limiting with a low mortality rate, but morbidity can reach high rates in case of poor sanitary conditions, improper husbandry practices and ectoparasitic infestation. The presented cases are the first ever reported on the island and part of the few cases reported in domestic pigs. Case presentation: Carcasses condemned at the slaughterhouse and deceased pigs from Farm A and Farm B respectively, were referred for post-mortem examination and further investigations, with a strong suspect of SwinePox virus (SWPV) infection. Twelve deceased pigs were examined in total, showing poor body condition and pustular lesions scattered all over the cutaneous surfaces. Moreover, pigs from Farm B showed ocular lesions classified from Grade I to IV (from mild conjunctivitis to severe keratoconjunctivitis with corneal oedema, opacity, and ulcers). Final diagnosis was pursued by the microscopic assessment of skin lesions in both farms, which revealed the typical SWPV-lesion appearance, such as severe and disseminated ulcerative dermatitis and suspected inclusion bodies multifocally observed in the epidermis. Moreover, negative staining Electron Microscopy (nsEM) was performed on skin lesions and ocular swabs from Farm B, revealing in two samples the presence of brick-shaped viral particles, 220 nm long and 160 nm wide, with irregularly arranged surface tubules, identified as SWPV. The gene encoding the 482-bp fragment of the virus late transcription factor–3 was detected by PCR and sequencing revealed 99.79% identity and 100% query-cover with a strain previously isolated in Germany. Field clinical assessment was then performed in Farm B, revealing high overcrowding, poor sanitary conditions and improper husbandry practices, which are relevant risk factors for SWPV transmission. Conclusions: The present is the first case report of SWPV in free-range pigs raised in Sicily, an island of the Southern coast of Italy, and wants to raise awareness on a neglected disease, and cause of animal health and welfare issues. [ABSTRACT FROM AUTHOR]

Deep learning utilizing convolutional neural networks (CNNs) stands out among the state-of-the-art procedures in PC-supported medical findings. The method proposed in this paper consists of two key stages. In the first stage, the proposed deep sequential CNN model preprocesses images to isolate regions of interest from skin lesions and extracts features, capturing the relevant patterns and detecting multiple lesions. The second stage incorporates a web tool to increase the visualization of the model by promising patient health diagnoses. The proposed model was thoroughly trained, validated, and tested utilizing a database related to the HAM 10,000 dataset. The model accomplished an accuracy of 96.25% in classifying skin lesions, exhibiting significant areas of strength. The results achieved with the proposed model validated by evaluation methods and user feedback indicate substantial improvement over the current state-of-the-art methods for skin lesion classification (malignant/benign). In comparison to other models, sequential CNN surpasses CNN transfer learning (87.9%), VGG 19 (86%), ResNet-50 + VGG-16 (94.14%), Inception v3 (90%), Vision Transformers (RGB images) (92.14%), and the Entropy-NDOELM method (95.7%). The findings demonstrate the potential of deep learning, convolutional neural networks, and sequential CNN in disease detection and classification, eventually revolutionizing melanoma detection and, thus, upgrading patient consideration. [ABSTRACT FROM AUTHOR]

Sporotrichosis is a fungal infection of worldwide importance caused by a saprophyte fungus of the genus Sporothrix. The objective of this study was to report the occurrence of feline sporotrichosis in the Metropolitan Region of the city of Recife and the anatomical distribution of lesions on the bodies of cats. Stray cats (n=100) with suggestive sporotrichosis lesions were examined. Material from the lesion was collected from all patients using an imprint and/or sterile swab for cytology. The material was placed on a glass slide and stained using rapid panoptic staining. Data on the localization of each skin lesion were recorded, and cigar-shaped yeast structures were searched at the microscopical examination. A heat map graphically represented the anatomical distribution of each lesion. In 70% (70/100) of samples were visualized fungal structure compatible with Sporothrix spp. There was a predominance of positive males (?2=6.691; p=0.0097). Skin lesions related to Sporothrix spp. infections were unevenly distributed throughout the animal's body. Based on the interpolation of data, the chance for detecting lesions was higher on the nose, anterior paw, and ears than on other body sites (H=40.0043; p=0.0000). Because of the high positivity in these cats, measures of health vigilance are imperative to reduce the risk of animal and human infection.

Introduction and Objective: Pemphigoid gestationis (PG) is a rare, self-limiting autoimmune disorder, typically appearing in the second or third trimester of pregnancy. It can lead to postpartum exacerbations, and studies suggest an increased risk of preterm birth and the development of Graves' disease in the mother. The aim of this article is to gather the latest information on pemphigoid gestationis. Review Methods: A review of studies available in the PubMed database was conducted using the keywords "pemphigoid gestationis" and "dermatoses of pregnancy" in order to find publications from the last 8 years. Brief Description of the State of Knowledge: Pemphigoid gestationis (PG) is characterized by the production of IgG1 antibodies against bullous pemphigoid antigen 180 (BP180). The primary symptom of PG is intense itching, which often occurs before skin lesions develop. Diagnosis is typically confirmed through direct immunofluorescence (DIF) of perilesional skin. Treatment focuses on relieving itching and preventing new blister formation, usually starting with topical corticosteroids for mild cases, while systemic corticosteroids are reserved for more severe presentations. Recent research indicates that biologic therapies may also offer effective management options for PG. Summary: The authors highlight the need for further research due to the limited sample sizes in existing studies, as well as the potential complications during pregnancy and long-term health effects.

Background: Inflammatory bowel disease (IBD)` may affect organs outside the intestines, it is called extraintestinal manifestations of IBD. Data on the prevalence of mu-cocutaneous manifestations in IBD patients are very limited, therefore, the aim of this study was to assess the prevalence of skin and mucosal lesions and to determine the relationship with demographic factors, clinical features, and systemic treatment. Materials and methods: Prospective study included 162 out-patients with IBD who were managed in the tertiary care center. Ulcerative colitis (UC) was diagnosed in 117 patients, Crohn‘s disease (CD) in 45. Patients completed the questionnaire containing demographic and IBD data, questions about mucocutaneous lesions (in past or present state). Results: Overall mucocutaneous lesions were reported by 48.1% of IBD patients. Skin lesions were reported by 40.7% of patients, oral mucosal lesions were reported by 16.7%, without significant differences between sexes or IBD types. In 47 (29%) of patients, skin lesions appeared together with IBD or during the course of the disease. The most common skin lesions were psoriasis (8.0%), erythema nodosum (5.6%), pyoderma gangrenosum and acne (3.7% each). UC patients mostly reported about psoriasis (9.4%), while CD patients about erythema nodosum (11.1%). There were more frequent skin lesions in patients with more extensive UC type (p = 0.01), while no difference was noticed between different types of CD. The average duration of IBD in patients with skin lesions was similar to those without lesions (9.3±6.7 vs. 9.4±6.7 years). Conclusions: Mucocutaneous lesions were reported by 48.1% of inflammatory bowel disease patients. The frequency of mucocutaneous lesions does not differ significantly between UC and CD, and a longer duration of illness is not a predictive factor for the appearance of lesions. More extensive UC is related to higher frequency of skin lesions. Erratum note The title, author's name, and abstract have been updated to reflect the correct information following a typographical error during typesetting. We apologize for any inconvenience caused by these errors. Corrections were made on 2024-09-01.

Abstract Occupational health must be strictly considered in industries particularly in nanoparticle factories where workers were exposed to different types of chemicals. We measured the serum levels of inflammatory cytokines in workers who developed skin lesions after exposure to silver and silica nanoparticles. Using a questionnaire in this cross-sectional study, we identified 110 workers in nanoparticle industries who were exposed to silver and silica nanoparticles. We also included 40 healthy subjects as controls from the administrative department of the same factories who were not exposed to nanoparticles. Peripheral blood samples used to measure the mRNA levels of inflammatory cytokines by qRT-PCR. In comparison with the control group, the workers who developed skin lesions had significantly higher levels of interleukin IL4, IL6, IL8, and TNF-α, particularly after two or three decades of exposure to silver and silica nanoparticles. Participants who were exposed to silver had higher levels of IL6 and IL8 compared with those who were exposed to silica. Necessary measures must be considered to protect workers in nanoparticle industries against the potential toxic effects of these compounds. Our network pharmacology study suggests corresponding biochemical pathways for these disorders.

Background: Skin cancer is the most common form of cancer worldwide. As artificial intelligence (AI) expands its scope within dermatology, leveraging technology may aid skin cancer detection. Objective: To assess the safety and effectiveness of an elastic-scattering spectroscopy (ESS) device in evaluating lesions suggestive of skin cancer. Methods: This prospective, multicenter clinical validation study was conducted at 4 US investigational sites. Patients with skin lesions suggestive of melanoma and nonmelanoma skin cancers were clinically assessed by expert dermatologists and evaluated by a device using AI algorithms comparing current ESS lesion readings with training data sets. Statistical analyses included sensitivity, specificity, AUROC, negative predictive value (NPV), and positive predictive value (PPV). Results: Overall device sensitivity was 97.04%, with subgroup sensitivity of 96.67% for melanoma, 97.22% for basal cell carcinoma, and 97.01% for squamous cell carcinoma. No statistically significant difference was found between the device and dermatologist performance (P = .8203). Overall specificity of the device was 26.22%. Overall NPV of the device was 89.58% and PPV was 57.54%. Conclusion: The ESS device demonstrated high sensitivity in detecting skin cancer. Use of this device may assist primary care clinicians in assessing suspicious lesions, potentially reducing skin cancer morbidity and mortality through expedited and enhanced detection and intervention.

In recent decades, skin cancer has emerged as a significant global health concern, demanding timely detection and effective therapeutic interventions. Automated image classification via computational algorithms holds substantial promise in significantly improving the efficacy of clinical diagnoses. This study is committed to mitigating the challenge of diagnostic accuracy in the classification of multiclass skin lesions. This endeavor is inherently formidable owing to the resemblances among various lesions and the constraints associated with extracting precise global and local image features within diverse dimensional spaces using conventional convolutional neural network methodologies. Consequently, this study introduces the SkinSwinViT methodology for skin lesion classification, a pioneering model grounded in the Swin Transformer framework featuring a global attention mechanism. Leveraging the inherent cross-window attention mechanism within the Swin Transformer architecture, the model adeptly captures local features and interdependencies within skin lesion images while additionally incorporating a global self-attention mechanism to discern overarching features and contextual information effectively. The evaluation of the model's performance involved the ISIC2018 challenge dataset. Furthermore, data augmentation techniques augmented training dataset size and enhanced model performance. Experimental results highlight the superiority of the SkinSwinViT method, achieving notable metrics of accuracy, recall, precision, specificity, and F1 score at 97.88%, 97.55%, 97.83%, 99.36%, and 97.79%, respectively. [ABSTRACT FROM AUTHOR]

Simple Summary: This study aimed to describe the epidemiological, clinicopathological, and therapeutic aspects of allergic dermatitis caused by Culicoides spp. bites in donkeys. Clinical signs included restlessness and severe itching. Skin lesions were observed across various parts of the body and included areas of alopecia with crusts and serosanguinous exudates. Overall, 378 Culicoides insects were collected and Culicoides ocumarensis Ortiz was identified as the most common species. These findings suggest an association between allergic dermatitis and Culicoides. Additionally, a combination of copaiba oil and a multivitamin emulsion showed therapeutic potential. To date, this is the first study on allergic dermatitis in donkeys in Brazil. An allergy to bites from Culicoides (Diptera: Ceratopogonidae) occurs because of a hypersensitivity reaction caused by the inoculation of insect salivary antigens during the bite, resulting in immune-mediated dermatitis. To the best of our knowledge, no previous studies have focused on allergic dermatitis in donkeys in Brazil. Therefore, this study aimed to describe the epidemiological, clinicopathological, and therapeutic aspects of allergic dermatitis in donkeys and to identify the insects involved in its epidemiology. This study reported the occurrence of dermatitis in 17 animals. The clinical signs were restlessness and severe itching. Skin lesions were found on the head, depigmented areas of the muzzle and cheeks, flanks, pelvic and thoracic limbs, and the scrotal sac. The lesions were characterized by areas of alopecia with crusts accompanied by serosanguineous exudates. Histologically, the lesions were characterized as moderate superficial dermatitis with irregular epidermal acanthosis and pronounced diffuse orthokeratotic hyperkeratosis. In total, 378 Culicoides specimens were collected, with Culicoides ocumarensis Ortiz being the most abundant species. The combined application of copaiba oil and a multivitamin emulsion exhibited potential for topical treatment of allergic dermatitis caused by insect bites in donkeys. Our study revealed an association between allergic dermatitis in donkeys and Culicoides. [ABSTRACT FROM AUTHOR]

In the field of medical image segmentation, achieving fast and accurate semantic segmentation of tumor cell nuclei and skin lesions is of significant importance. However, the considerable variations in skin lesion forms and cell types pose challenges to attaining high network accuracy and robustness. Additionally, as network depth increases, the growing parameter size and computational complexity make practical implementation difficult. To address these issues, this paper proposes MD-UNet, a fast cell nucleus segmentation network that integrates Tokenized Multi-Layer Perceptron modules, attention mechanisms, and Inception structures. Firstly, tokenized MLP modules are employed to label and project convolutional features, reducing computational complexity. Secondly, the paper introduces Depthwise Attention blocks and Multi-layer Feature Extraction modules. The Depthwise Attention blocks eliminate irrelevant and noisy responses from coarse-scale extracted information, serving as alternatives to skip connections in the UNet architecture. The Multilayer Feature Extraction modules capture a wider range of high-level and lowlevel semantic features during decoding and facilitate feature fusion. The proposed MD-UNet approach is evaluated on two datasets: the International Skin Imaging Collaboration (ISIC2018) dataset and the PanNuke dataset. The experimental results demonstrate that MD-UNet achieves the best performance on both datasets. [ABSTRACT FROM AUTHOR]

Simple Summary: This work describes a case study of pseudocowpoxvirus (PCPV) infection in Russia (Irkutsk region). The clinical manifestation, molecular identification, and molecular characterization of the disease were established. Epidemiological information were provided by the state veterinary service; additionally, the cows' owners were interviewed using a semi-structured questionnaire. The description of clinical symptoms was based on clinical examination and farmer interviewing. Samples (blood, serum, scab) were collected from the affected cattle for virological studies and molecular analyses. The identification of PCPV was accomplished through the B2L gene fragment amplification, sequencing, and phylogenetic analysis. Based on the nucleotide identities and phylogenetic analysis of the partial-length B2L gene, the Irkutsk 2019 isolate was classified as PCPV. Because of high rates of virus transmission amongst cattle-to-cattle and cattle-to-humans through direct contact, it is important to raise farmers' awareness of the clinical signs of the disease, highlight its preventive measures, and to develop a strategy to motivate them to seek qualified veterinary care. Parapoxviruses are worldwide epitheliotropic viruses that affect ruminants. Viruses of this genus have a narrow host range; however, the pseudocowpox virus (PCPV) also infects humans. Unfortunately, these cases are not well documented, and the epidemiology and the properties of the causative agents are not properly described. Here, we report the first case of PCPV in northern Russia (the Irkutsk region). The infection occurred in non-immune herds where no new arrivals of animals had been reported. Moreover, clinical signs of infection (skin lesions) were observed in humans. Based on the nucleotide identity and phylogenetic analysis of the partial-length B2L gene, the Irkutsk 2019 isolate was classified as PCPV. Phylogenetic analysis based on the nucleotide sequence of the B2L gene fragment of PCPV revealed a close phylogenetic relationship between the Irkutsk 2019 isolate and the PCPV strains isolated in Europe and the USA. The high degree of conservatism of the B2L gene does not allow for finding a correlation between their geographical origin and the results of phylogenetic analysis. [ABSTRACT FROM AUTHOR]

Occupational health must be strictly considered in industries particularly in nanoparticle factories where workers were exposed to different types of chemicals. We measured the serum levels of inflammatory cytokines in workers who developed skin lesions after exposure to silver and silica nanoparticles. Using a questionnaire in this cross-sectional study, we identified 110 workers in nanoparticle industries who were exposed to silver and silica nanoparticles. We also included 40 healthy subjects as controls from the administrative department of the same factories who were not exposed to nanoparticles. Peripheral blood samples used to measure the mRNA levels of inflammatory cytokines by qRT-PCR. In comparison with the control group, the workers who developed skin lesions had significantly higher levels of interleukin IL4, IL6, IL8, and TNF-α, particularly after two or three decades of exposure to silver and silica nanoparticles. Participants who were exposed to silver had higher levels of IL6 and IL8 compared with those who were exposed to silica. Necessary measures must be considered to protect workers in nanoparticle industries against the potential toxic effects of these compounds. Our network pharmacology study suggests corresponding biochemical pathways for these disorders. [ABSTRACT FROM AUTHOR]

Introduction: This research discusses the potential of Orthosiphon aristatus, a medicinal plant, in improving skin lesions in Atopic Dermatitis by regulating Th2 cytokines and showing anti-allergic activity. Methods: The study was conducted using BALB/C mice induced with DNCB for 21 days to create an atopic dermatitis model. Then, the mice were orally administered Orthosiphon aristatus extract for 14 days (after 7 days of induction). The doses given were divided into six groups: 17.5 mg/kgbw, 35 mg/kgbw, 70 mg/kgbw, and 140 mg/kgbw. Molecular levels such as IgE, IL4, IL22, and PGE2 were analyzed from blood samples. In addition, the severity of skin lesions was assessed morphologically, and histological examination was performed to confirm clinical improvement. Results: This study showed that the administration of Orthosiphon aristatus extract reduced the severity of skin lesions in all intervention groups. Histologically, this extract reduced epidermal thickness and mast cell infiltration. These findings were also supported by dose-dependent decreases in IgE, IL4, IL22, and PGE2 levels. So, the conclusion of this study is that Orthosiphon aristatus extract shows potential for improving skin lesions in atopic dermatitis by regulating Th2 cytokines and demonstrating anti-allergic activity in a mice model with DNCBinduced atopic dermatitis. Conclusion; The administration of Orthosiphon aristatus extract clinically and histologically exhibits anti-atopic effects that can be explained through the regulation of molecules such as IgE, IL4, IL22, PGE2, and NO. Orthosiphon aristatus has the potential to be a valuable herbal therapy for managing atopic dermatitis. [ABSTRACT FROM AUTHOR]

Every year, ulcerative dermal necrosis (UDN) affects salmonids that spend most of their lives in the sea during their migration to the rivers of northern Poland to spawn. The clinical form of the disease manifests itself in ulcerative skin lesions, which lead to significant weakening of the fish and, in most cases, result in their death. This study was carried out on samples taken from sea trout in the Słupia River in northern Poland. In order to identify the pathogen, experiments on the transmission of the disease were carried out, and additional histopathological, microbiological and electron microscopic examinations were performed. As a result of these studies, it was possible to experimentally transfer the disease from sick to healthy fish. The results indicate a complex etiology of the disease (lack of a clearly defined pathogen), in which the change in the environment from salty to freshwater triggers the related changes in skin physiology, which are the main causes of increased susceptibility to the development of the disease. [ABSTRACT FROM AUTHOR]

Background: Lupus patients frequently exhibit specific and nonspecific skin lesions and lesions associated with skin infections. This study aimed to determine the frequency of lupus-specific and non-specific skin lesions and the incidence of skin infections. Methods: This study was conducted in the Department of Rheumatology at Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh, from 2014 to 2016. After obtaining ethical clearance from the Institutional Review Board of BSMMU, 136 consecutive patients fulfilling the ACR criteria for SLE were enrolled and followed up for at least one year. A dermatologist confirmed lupus-specific, non-specific, and skin lesions related to infections and noted them in a datasheet. Relevant investigations were performed at baseline and during subsequent follow-up visits. Results: One hundred thirty-one patients completed their follow-up period. The mean (standard deviation) follow-up period was 13.3 (2.0) months. The patients' mean (standard deviation) age was 28.8 (8.2) years. Skin lesions and skin infections were present in 71.8% and 26.7%, respectively of patients. Common lupus-specific lesions were malar rash (75.4%) and DLE (12.3%). Photosensitivity (72.6%), non-scarring alopecia (67.9%), mucosal ulcers (47.6%), Raynaud’s phenomenon (23.8%), and hyperpigmentation (23.8%) were the prevalent lupus non-specific skin lesions. The common skin infections were tinea (42.9%), herpes infection (34.3%), paronychia (20%), and scabies (17%). Conclusions: Skin lesions related to infections were common, along with lupus-specific and nonspecific lesions skin lesions. Tinea and herpes infections were more common skin infections.

A 54-year-old woman presented to our emergency department two months after the removal by Video-Assisted Thoracoscopic Surgery (VATS) of her middle lung lobe affected by severe inflammation and fibrosis. She was experiencing pain from a large ulcer (diameter 13 cm) starting from the surgical scar. She suffered from Calcinosis, Raynaud phenomenon, Esophageal dysmotility, Sclerodactyly, and Telangiectasia (CREST) syndrome, and hypothyroidism. The laboratory results showed White Blood Cell count (WBC) 13,600/mm3 (N 76%), C-Reactive Protein (CRP) 12 mg/L (nv

Resumen: La atención a pacientes migrantes abarca el cribado inicial y su seguimiento a lo largo del proceso vital, de ahí la importancia de la prevención y del seguimiento de las enfermedades crónicas, transmisibles o no.La prevalencia de hipertensión arterial, diabetes mellitus, dislipemia y obesidad varían según la etnia, esto se atribuye a factores genéticos, además del estilo de vida y de la situación socioeconómica. Es fundamental la prevención y la promoción de la salud, y la detección de los factores de riesgo.Las enfermedades crónicas transmisibles pueden cronificarse y dar clínica años después de su transmisión, siendo fundamental el cribado desde atención primaria de poblaciones procedentes de zonas endémicas y en riesgo.Las lesiones cutáneas importadas son un motivo de consulta frecuente en los pacientes migrantes y los viajeros. La etiología es variada, desde algunas frecuentes en nuestro medio; como escabiosis, micosis, urticaria, etc., a dermatosis tropicales como la filariasis y la lepra. Abstract: The care of migrant patients includes initial screening and lifelong monitoring, highlighting the importance of preventing and tracking chronic, communicable and non-communicable diseases.The prevalence of hypertension, diabetes mellitus, dyslipidemia, and obesity varies by ethnicity, influenced by genetic factors, lifestyle, and socio-economic status. Preventive measures, health promotion, and risk factor identification are crucial.Chronic communicable diseases may manifest years after transmission, underscoring the necessity of primary care screening, especially for populations from endemic or high-risk areas.Imported skin lesions are a common reason for consultation among migrant and traveller patients. Their ethiology is varied, ranging from common conditions such as scabies, mycoses, and urticaria to tropical dermatoses like filariasis and leprosy.

The classification of skin lesions is crucial because it increases the likelihood that malignant skin lesions will be discovered early on, allowing for more effective treatment. Due to the abundance of lesion images and the possibility of human error, early detection can be difficult for dermatologists. This work aims to classify skin lesions using two pipelines that were designed using support vector machine (SVM) and AlexNet convolutional neural network (CNN) models. Pipeline-1 uses the AlexNet CNN, while pipeline-2 proposes a bisectional feature extraction approach with an SVM model. The skin lesion images are initially preprocessed and the lesion regions are segmented. The lesion regions are further subdivided into four regions based on the intensity mapping function. The bisectional features are then extracted from the subdivided regions and the extracted features are trained with the SVM model. The dataset used in the experiment is the HAM-10000 dataset and the PAD-UFES-20 dataset, which consists of dermatoscopic skin lesions images. Based on the models’ accuracy, sensitivity, DCI, specificity, and F1-score, the experiment’s findings will be assessed for five different skin lesion conditions. By accurately and effectively classifying skin lesions, the study’s findings will help in the diagnosis and treatment of skin disorders. The SVM pipeline performs better than the AlexNet CNN pipeline where the SVM pipeline and AlexNet CNN pipeline result in an accuracy of 98.66% and 97.68% respectively for the HAM-10000 dataset. The AlexNet CNN and SVM pipeline structure results in an accuracy of 96.87% and 98.10% respectively for the PAD-UFES-20 dataset.

Deep convolutional neural networks have made some progress in skin lesion classification and cancer diagnosis, but there are still some problems to be solved, such as the challenge of small inter-class feature differences and large intra-class feature differences, which might limit the classification performance of the model as high-level and low-level features are not properly utilized. This paper proposes a multi-scale skin cancer image identification network using self-interactive attention pyramid and cross-layer bilinear-trilinear pooling(SPCB-Net), which mainly consists of three proposed sub-modules that are the self-interacting attention pyramid (SAP), the across-layer bilinear-trilinear pooling operation and the global average algorithm(GAA). The SPCB-Net is applied to two representative datasets of medical images in dermatology and histopathology (HAM10000 and NCT-CRC-HE-100K) to demonstrate the effectiveness of in the skin lesion classification. SPCB-Net(ResNet101) achieves 97.10% and 99.87% accuracy on HAM10000 and NCT-CRC-HE-100K respectively, which are both achieved performance improvements of 0.4% compared to the state-of-the-art models. In addition, a large number of experiments on HAM10000 show that the interactive attention pyramid(SPA) proposed in this paper is superior to the common attention module, and the method with a cross-layer bilinear-trilinear pooling is superior to the cross-layer trilinear pooling method. SPCB-Net is configured on Vgg19 and ResNet101 to evaluate the effectiveness of our proposed module. The experimental results show that SPCB-Net has shown state-of-the-art performance in the two field of dermatology and histopathology. Therefore, it is not only well qualified for the task of identifying skin cancer image but also has the potential to identify skin cancer by identifying pathological tissue.

The study aimed to assess the prevalence of arsenic-induced skin lesions and associated factors among the population in the Adami Tulu Jido Kombolcha district, Ethiopia. A community-based cross-sectional study design was employed among 403 participants from June 02–20, 2022. A two-stage cluster sampling method was conducted to select study subjects. A pretested structured and semi-structured interview questionnaire and observation using a WHO flow chart were used for data collection. Data analysis was performed using SPSS version 24 statistical software for Windows. A multivariable binary logistic regression model was applied to examine the relationship between predictor variables and an outcome variable. The degrees of association between outcomes and predictor variables were assessed using ORs and 95% CIs, and P-values

The phenomenon of child abuse/maltreatment is underestimated and often represents a difficult challenge for healthcare professionals and forensic pathologists who must proceed with the differential diagnosis with accidental or self-induced events, or with lesions due to pathologies that overlap with that of mistreatment, defined as “Mimics”. This study presents a case series with the aim of discussing lesions that may mimic signs of physical abuse in children but are due to a different etiology to raise awareness and train healthcare professionals and forensic pathologists on possible confounding factors in order to avoid diagnostic errors. Six cases of “Mimics” out of 418 cases of suspected mistreatment (1.43% of cases) were identified, presenting skin lesions initially classified as injuries of abuse due to their location and type and, in particular, sexual abuse for three cases. Then, the lesions and the subjects, in particular the anamnestic history, were examined by a multidisciplinary team and the diagnosis of genital lichen sclerosus et atrophicus in three cases, and the results of popular healing techniques (i.e., “cupping”) in the other three cases were ascertained. These situations require specific skills and a forensic background from healthcare professionals to conduct a correct differential diagnosis and the intervention of a multidisciplinary team to investigate every possible pathology or alternative therapeutic practice that could simulate child abuse. In particular, when “mimics” are due to alternative medicine, it should not strictly be considered child abuse, but professionals must be aware of the hypothesis of mistreatment in case of non-medical indication or potential personal injuries from other crimes, such as illegal practice of the medicine. This awareness is also crucial to direct the child toward appropriate medical care, and it is essential to recognize that these conditions can coexist within the same clinical presentation.

In order to effectively treat skin diseases, an accurate and prompt diagnosis is required. In this article, a novel method for classifying skin disorders using a multimodal classifier is presented. The proposed classifier utilizes multiple information sources to enhance the accuracy of disease classification. It incorporates images of skin lesions and patient-specific data. The multimodal classifier simultaneously classifies diseases by combining image and structured data inputs. The effectiveness of the proposed classifier was evaluated using the ISIC 2018 dataset, which includes images and clinical data for seven categories of skin diseases. The results indicate that the proposed model outperforms conventional single-modal and single-task classifiers, achieving an accuracy of 98.66% for image classification and 94.40% for clinical data classification. In addition, we compare the performance of the proposed model with that of other methodologies, demonstrating its superiority. Despite yielding promising results, the proposed method has limitations in terms of data requirements and generalizability. Future research directions include incorporating additional information sources, investigating genetic data integration, and applying the method to various medical conditions. This study illustrates the potential of integrating multimodal techniques with transfer learning in deep neural networks to enhance the classification accuracy of cutaneous diseases. [ABSTRACT FROM AUTHOR]

Mueller polarimetry has proven to be a powerful optical technique to complement medical doctors in their conventional histology analysis. In this work, various degenerative and malignant human skin lesions were evaluated ex vivo using imaging Mueller polarimetry. The Mueller matrix images of thin sections of biopsies were recorded and the differential decomposition of Mueller matrices was applied pixel-wise to extract the polarization fingerprint of the specimens under study. To improve the classification accuracy, a deep learning model was created. The results indicate the sensitivity of polarimetry to different skin lesions and healthy skin zones and their differentiation, while using standard histological analysis as a ground truth. In particular, the deep learning model was found sufficiently accurate to detect and differentiate between all eight classes in the data set. Special attention was paid to the overfitting problem and the reduction of the loss function of the model. Our approach is an effort in establishing digital histology for clinical applications by complementing medical doctors in their diagnostic decisions. [ABSTRACT FROM AUTHOR]

BACKGROUND: The skin, being the greatest sensory organ of the body, serves as a defensive shield against a wide range of harmful environmental substances. Consequently, it is implicated in a broad range of illnesses, including both inflammatory and neoplastic conditions. A skin biopsy, together with histological analysis, is necessary to achieve a precise diagnosis, thus aiding clinicians in determining the most suitable treatment. AIMS: This study aims to provide a comprehensive description of the histopathological range of all skin lesions, establish their distribution by age and sex, and identify the most prevalent skin lesions in a tertiary care hospital. MATERIALS & METHODS: A retrospective, cross-sectional, non-interventional study was carried out on 255 skin biopsies received in the department of pathology at Government Medical College, Ananthapuram, Andhra Pradesh, for a period of four years, from January 2019 to June 2023. RESULTS: Among the 255 skin biopsies analysed, the demographic most significantly impacted were those aged 31 to 70 years, with a male preponderance in all the categories of skin lesions. 63 (24.7%) were non-neoplastic, and 192 (75.3%) were neoplastic. Among the neoplastic cases, 84.9% were classified as benign. CONCLUSION: Dermatological lesions exhibit a wide spectrum of clinical presentations, with a wide age distribution and histopathology. The significance of specific histomorphological characteristics lies in differentiating various skin lesions and is crucial in establishing the definitive diagnosis of these heterogeneous skin lesions. This underscores the significance of conducting a histopathological study to ensure proper patient management. [ABSTRACT FROM AUTHOR]

Background: Granuloma annulare (GA) is a rare, benign, inflammatory, self-limited, granulomatous dermatosis that affects children and young adults. The most frequent clinical form is localized GA. Deep GA generally presents as painless palpable subcutaneous nodules in the lower extremities, buttocks, hands and scalp. They may have a fast-growing firm subcutaneous mass presentation, mimicking a malignant lesion which requires an imaging evaluation. Diagnosis of deep GA can be more difficult and imaging evaluation is frequently performed, ultrasound being one of the techniques used. Objective: To describe the US characteristics of GA in a pediatric series. Materials and method: Descriptive, retrospective, 14-year study of all pediatrics GA cases. Results: Twelve pediatric cases with GA. 66% females. The lesions were mainly distributed in the extremities: 50% in the lower extremities and 42% in the upper extremities, mostly with multiple lesions. A total of 45 lesions were analyzed, 8 superficial lesions and 37 deep lesions. On ultrasound, the superficial GA corresponded to hypoechoic poorly defined solid plaque like or nodular lesions, located in the dermal-epidermal plane. The deep GA presented as solid nodular, poorly defined hypoechoic lesions that compromised the deep subcutaneous-aponeurotic plane. Conclusion: GA is an inflammatory lesion that presents as a superficial or deep palpable nodule that predominantly affects children. Superficial and deep GA present characteristic findings on US that can guide the diagnosis. The radiologist needs to know its US appearance to be able to suggest the diagnosis, especially in multiples lesions. [ABSTRACT FROM AUTHOR]

The prediction of skin cancer at the earlier stage is extremely essential for melanoma. There is a need for intellectual computer analysis for skin lesions. The segmentation of lesion boundaries is vital to accurately identify the lesions from the dermoscopic images where the diagnosis is complex for various skin lesion types. Thus, some pre-processing steps are required to attain higher sensitive lesion boundary segmentation and classification. Initially, pre-processing is done with median filter to offer reputation of boundary preservation and does not in-cooperate newer pixel values to the processed image. Next is the process of image segmentation for Region of Interest (ROI) and non-Region of Interest (n R O I) using the Jaccard Distance segmentation process. The novelty of the work relies on the inclusion of compression techniques to make the access easier without any loss. The extracted regions are encoded with Freeman Chain Coding and compressed with Lempel–Ziv–Welch (LZW) and Zero Tree Wavelet (EZW) for ROI and non-Region of Interest regions. Finally, image classification is done with Hybrid Particle Swarm Optimization and Convolutional Neural Networks (h PSO-CNN). The simulation is done with TensorFlow & Python environment and the proposed model outperforms the existing standard approaches. Some metrics like objective function, confusion matrix, accuracy, precision, F -measure, and recall are evaluated. The model attains 77.5% accuracy, 86.36% precision, 77.5% recall and 77.92% F -measure for proposed h PSO-CNN which is higher than the standard CNN model. [ABSTRACT FROM AUTHOR]

Skin disease recognition is one of the essential topics in the medical industry. Detecting skin disease from appearance can be difficult due to the similar appearance of skin lesions. In some cases, such as the monkeypox virus, the illness must be quickly determined, and the patients must be isolated to reduce the spreading of the disease. This study aims to create a deep learning‐based automated intelligent mobile application to detect skin disease. First, different small‐size pretrained networks are trained for skin lesion image classification. Then, the most suitable network from the viewpoint of both performance and mobile compatibility is transformed into the TensorFlow Lite format. Finally, a mobile application is created on the Android platform that utilizes the smartphone's camera to obtain images and uses TensorFlow Lite to make predictions. The proposed system produces 74.27% classification accuracy for seven classes on a combined dataset. It produces comparable/better results compared to the literature. Owing to the proposed system, the patients can make a preliminary diagnosis of their lesions using their smartphones. Thus, risky patients can be encouraged to visit the hospital for a definitive diagnosis. In addition, the mobile application can avoid undue stress and false alarms. [ABSTRACT FROM AUTHOR]

ABSTRACT: Subclinical mastitis can be common among freshly calved heifers (FCH), but the prevalence differs between herds, possibly due to variation in risk factors. The aims of this observational study were to identify differences in occurrence of intramammary infection (IMI) in FCH between herds with documented good or poorer first-parity udder health based on cow somatic cell count (CSCC) in early lactation, and to study herd differences in animal factors important for udder health, such as udder and hock skin lesions and animal cleanliness. Three groups of herds were included: those with high proportions of FCH with low CSCC (≤75,000 cells/mL) at the first 2 milk recordings after calving (LL), herds with high proportions of FCH with high CSCC (>100,000 cells/mL) at the first and low CSCC at the second recording (HL), and herds with high proportions of FCH with high CSCC at both recordings (HH). Thirty-nine herds (13 LL, 11 HL, 15 HH) were visited 3 times during a 12-mo period for observation of cleanliness and hock lesions, and sampling of udder and teat skin using swab cloths of milk-fed calves, early-pregnant heifers, and late-pregnant heifers. In 25 (9 LL, 9 HL, 7 HH) udder quarter samples from colostrum and milk on d 3 to 4 after calving were taken by the farmers from FCH during one year. The farmers also provided information on calving (individual or group), use of restraint and oxytocin at milking, and presence of teat and udder skin lesions. Bacterial growth in swab samples and quarter samples was investigated by culturing, and a selection of isolates was genotyped using whole-genome sequencing. Cleanliness, hock and udder skin lesions other than udder-thigh dermatitis, and growth of bacteria in swab samples did not differ between herd groups. It was more common that FCH from LL herds, compared with FCH in HH and HL herds, calved in a group of animals. Use of restraint at milking was more common in LL herds than in HH herds, whereas presence of udder-thigh dermatitis was lowest in LL herds. Specific infection was found in 14% of 5,593 quarter samples from 722 FCH. The most common IMI was Staphylococcus chromogenes. Growth of Staphylococcus simulans was more common in HH than in LL and HL herds. In colostrum samples, Staphylococcus haemolyticus was more common in HL and HH than in LL herds. The proportion of quarters with the same specific infection at both samplings was higher in HH than in LL herds and tended to be higher in HH than in HL herds. The proportion of quarters with Staph. chromogenes IMI at both samplings tended to differ between herd groups and was highest in HH herds. Whole-genome sequencing found the same sequence type of Staph. chromogenes and Staphylococcus aureus in both samples in almost all quarters with the same infection at both samplings. The differences in IMI between herd groups were in line with the higher somatic cell count in HH herds. The reasons for the predominance of Staph. chromogenes IMI in FCH need further studies.

Abstract Background The study aimed to evaluate the influence of wheat straw and different coffee husk (CHs) levels in pellet bedding on its quality, broiler chickens’ performance, meat quality, and welfare indicators. In total, 200 Ross 308 chickens were divided into 4 groups: C – control with wheat straw pellet; CH10 – pellet with 10% CHs, CH25 – pellet with 25% CHs, and CH50 – pellet with 50% CHs. During 42 days of rearing, each bedding's physicochemical features were analyzed. The production results were controlled, and the footpad dermatitis, hock burns, and feather quality were assessed. From chosen birds, carcass composition was analyzed, as well as the qualitative features (color, water-holding capacity, drip loss) and breaking bone strength. Results The bedding material and rearing days influenced the content of dry matter, crude fiber, nitrogen, phosphorus, potassium, NDF, ADF, and pH. The results were inconclusive. The increasing trends in nitrogen, phosphorus, and potassium content were noticed at the end of rearing. Strong coefficient determination in bedding features was found (0.580 – 0.986). The pellet with CHs had no adverse effect on the growth performance of broilers. In the CH50 group, a lower fat percentage was found. A beneficial effect on water-holding capacity was noticed in leg muscles from CH10 and pectoral muscles from CH25. A significant decrease was found in footpad dermatitis incidence in groups CH25 and CH50. Conclusions It can be concluded that CHs reuse in broilers as the pellet bedding material is possible due to the beneficial effect on some meat quality features and no adverse effect on the performance of broiler chickens. The positive impact on lower foot pad dermatitis incidence indicated the possibility of using CHs in pellet bedding.

Skin cancer diagnosis greatly benefits from advanced machine learning techniques, particularly fine-tuned deep learning models. In our research, we explored the impact of traditional machine learning and fine-tuned deep learning approaches on prediction accuracy. Our findings reveal significant improvements in predictability and accuracy with fine-tuning, particularly evident in deep learning models. The CNN, SVM, and Random Forest Classifier achieved high accuracy. However, fine-tuned deep learning models such as EfficientNetB0, ResNet34, VGG16, Inception _v3, and DenseNet121 demonstrated superior performance. To ensure comparability, we fine-tuned these models by incorporating additional layers, including one flatten layer and three densely interconnected layers. These layers play a crucial role in enhancing model efficiency and performance. The flatten layer preprocesses multidimensional feature maps, facilitating efficient information flow, while subsequent dense layers refine feature representations, capturing intricate patterns and relationships within the data. Leveraging LeakyReLU activation functions in the dense layers mitigates the vanishing gradient problem and promotes stable training. Finally, the output dense layer with a sigmoid activation function simplifies decision making for healthcare professionals by providing binary classification output. Our study underscores the significance of incorporating additional layers in fine-tuned neural network models for skin cancer classification, offering improved accuracy and reliability in diagnosis.

Persistent infections caused by Strongyloides stercoralis are probably underestimated in the elderly Italian population. This nematode is unique among helminths: it can last asymptomatically in the host for decades and may present with a broad range of clinical pictures upon reactivation. Misdiagnosis often occurs even when the clinical picture is suggestive. If undetected, this parasitosis can lead to serious consequences when hyperinfection occurs. Herein, we report two peculiar clinical cases of complicated strongyloidiasis with multiple skin lesions. The aim of our report is to lead clinicians to familiarize themselves with skin patterns and clinical features that can suggest a possible underlying strongyloidiasis.

Mpox (formerly called monkeypox) is a zoonosis caused by the monkeypox virus (MPXV). MPXV is an orthopoxvirus genetically and antigenically similar to the variola virus responsible for smallpox. Mpox is endemic to Central and West Africa, but in 2022 it spread rapidly leading to many cases in non-endemic countries. Human-to-human contact is currently the main mode of transmission. Patients usually present with a prodromal syndrome of fever, myalgia, headache, lymphadenopathy, and fatigue. The characteristic centrifugal rash with mucocutaneous lesions develops 1–4 days later. Lesion evolution typically follows a sequence of changes (macules, papules, vesicles, pustules, and crusts). The disease is mostly mild and self-limiting. Distinct clinical presentations have been observed during the ongoing outbreak. Prodromal symptoms may be less pronounced or absent. Lesions are located predominantly in the anogenital and perioral areas. Patients may present with few lesions clustered in the genital region or a solitary lesion. Mpox diagnosis is usually confirmed through viral DNA detection with polymerase chain reaction (PCR). There is no specific treatment for mpox. Therapy remains supportive in most cases. Antiviral drugs include tecovirimat, cidofovir, and brincidofovir. Antibody-based therapies are another treatment option. Smallpox vaccines should be considered for high-risk patients (pre-exposure prophylaxis) and following high-risk exposures (post-exposure prophylaxis). Comprehensive differential diagnosis and screening for co-infections with other sexually transmitted pathogens are needed.

Abstract Human Skin cancer is commonly detected visually through clinical screening followed by a dermoscopic examination. However, automated skin lesion classification remains challenging due to the visual similarities between benign and melanoma lesions. In this work, the authors proposed a new Artificial Intelligence-Based method to classify skin lesions. In this method, we used Residual Deep Convolution Neural Network. We implemented several convolution filters for multi-layer feature extraction and cross-channel correlation by sliding dot product filters instead of sliding filters along the horizontal axis. The proposed method overcomes the imbalanced dataset problem by converting the dataset from image and label to vector of image and weight. The proposed method is tested and evaluated using the challenging datasets ISIC-2019 & ISIC-2020. It outperformed the existing deep convolutional networks in the multiclass classification of skin lesions. Graphical Abstract

Donkey welfare is becoming a subject of much interest; for decades, people around the world have voiced rising worry about it. There are no studies on donkey welfare in Nigeria. This study aimed to assess the welfare state of donkeys in Amaru, Zaria Ancient City, Kaduna State, Nigeria. A cross-sectional study was done to purposefully select 38 pack donkeys for welfare assessment based on the first level of the Animal Welfare Indicators (AWIN) for donkeys, which covered behaviour, health, feeding, and environment and housing. Information regarding age and sex was collected. The analysed data were reported in terms of frequencies and percentages. The prevalence of donkeys with no social contact, avoidance behaviour, and walking downside was 2.6 %, 5.36 %, and 40.56 %, respectively. The most common health problems are integumentary alterations, which include an unhealthy hair coat (86.8 %), skin lesions (86.6 %), alopecia (60.5 %), and deep wounds (47.4 %). Only 5.3 % of the donkeys were lame. Around 42 % had a body condition score of 2 (moderate), while the remaining 57.9 % had a body condition score of 3 (ideal). The donkeys were poorly kept and housed, as evidenced by the high prevalence of unsatisfactory shelter dimensions (100 %), dirty bedding (89.5 %), and insufficient bedding (89.5 %). The current study found that welfare issues are serious challenges for donkeys in Amaru, Zaria Ancient City, Kaduna State, Nigeria. As a consequence of the current findings, it is recommended that donkey welfare, policies and legislative frameworks that support animal welfare and inspect animal facilities in Nigeria should to be reviewed and enforced.

Skin disease recognition is one of the essential topics in the medical industry. Detecting skin disease from appearance can be difficult due to the similar appearance of skin lesions. In some cases, such as the monkeypox virus, the illness must be quickly determined, and the patients must be isolated to reduce the spreading of the disease. This study aims to create a deep learning‐based automated intelligent mobile application to detect skin disease. First, different small‐size pretrained networks are trained for skin lesion image classification. Then, the most suitable network from the viewpoint of both performance and mobile compatibility is transformed into the TensorFlow Lite format. Finally, a mobile application is created on the Android platform that utilizes the smartphone's camera to obtain images and uses TensorFlow Lite to make predictions. The proposed system produces 74.27% classification accuracy for seven classes on a combined dataset. It produces comparable/better results compared to the literature. Owing to the proposed system, the patients can make a preliminary diagnosis of their lesions using their smartphones. Thus, risky patients can be encouraged to visit the hospital for a definitive diagnosis. In addition, the mobile application can avoid undue stress and false alarms.

A 90-year-old woman presented at our emergency department complaining of dyspnea (sO2 88% on room air, respiratory rate 24/min) and severe (NRS 8/10) burning chest pain. Ten years earlier, she had undergone a bilateral radical mastectomy and radio-chemotherapy for breast cancer. In September 2023, she developed some nodules on her chest skin treated with electrochemotherapy, multiple liver lesions, and a bilateral paraneoplastic pleural effusion.

Accurate segmentation of skin lesions is a challenging task because the task is highly influenced by factors such as location, shape and scale. In recent years, Convolutional Neural Networks (CNNs) have achieved advanced performance in automated medical image segmentation. However, existing CNNs have problems such as inability to highlight relevant features and preserve local features, which limit their application in clinical decision-making. This paper proposes a CNN with an added attention mechanism (EA-Net) for more accurate medical image segmentation.EA-Net is based on the U-Net network model framework. Specifically, we added a pixel-level attention module (PA) to the encoder section to preserve the local features of the image during downsampling, making the feature maps input to the decoder more relevant to the ground-truth. At the same time, we added a spatial multi-scale attention module (SA) after the decoding process to increase the spatial weight of the feature maps that are more relevant to the ground-truth, thereby reducing the gap between the output results and the ground-truth. We conducted extensive segmentation experiments on skin lesion images from the ISIC 2017 and ISIC 2018 datasets. The results demonstrate that, when compared to U-Net, our proposed EA-Net achieves an average Dice score improvement of 1.94% and 5.38% for skin lesion tissue segmentation on the ISIC 2017 and ISIC 2018 datasets, respectively. The IoU also increases by 2.69% and 8.31%, and the ASSD decreases by 0.3783 pix and 0.5432 pix, indicating superior segmentation performance. EA-Net can achieve better segmentation results when the original image of skin lesions has an obscure boundary and the segmentation area contains interference factors, which proves that the addition of attention mechanism in the encoder and the application of comprehensive attention mechanism can improve the performance of neural network in the field of skin lesions image segmentation.

Simple Summary: Leukemia cutis occurs in different types of leukemia, most commonly in chronic lymphocytic leukemia and acute myeloid leukemia. Its varied clinical appearance makes it difficult to differentiate from other skin lesions. The leukemic skin changes are localized or disseminated and can be located in any site of the body. The diagnosis is mainly based on the clinical characteristics and histopathologic, as well as immunophenotypic features of the skin lesions. The treatment of leukemia cutis depends on the specific diagnosis of hematologic malignancy and is aimed at eradicating the primary underlying disease. Local irradiation for skin lesions is sometimes useful for palliative treatment. Leukemia cutis (LC) is defined as the leukemic infiltration of the epidermis, the dermis, and the subcutaneous tissue. Leukemia cutis may follow or occur simultaneously with the diagnosis of systemic leukemia. However, cutaneous lesions are occasionally diagnosed as the primary manifestation of leukemia. Leukemic skin infiltrations demonstrate considerable variation regarding a number of changes, distribution, and morphology. The highest incidence of LC is observed in chronic lymphocytic leukemia, monocytic and myelomonocytic acute myeloid leukemia, and T-cell lineage leukemia. Although the pathogenic mechanism of the invasion of leukemic cells into the skin is not well understood, chemokine receptors and adhesion molecules as well as the genetic characteristics of leukemia are thought to play a role. Leukemic skin lesions may be localized or disseminated and may occur alone or in combination on any site of the skin, most frequently in the trunk and extremities. The most common clinical presentations of leukemia cutis are papules, nodules, macules, plaques, and ulcers. In most patients, the complete or partial resolution of cutaneous infiltrations occurs simultaneously with hematologic remission. However, in patients with resistant disease or recurrent skin infiltration, local radiotherapy can be used. This review presents recent data on the pathogenesis, diagnosis, and treatment of leukemic skin involvement in different types of leukemia. [ABSTRACT FROM AUTHOR]

Subclinical mastitis can be common among freshly calved heifers (FCH), but the prevalence differs between herds, possibly due to variation in risk factors. The aims of this observational study were to identify differences in occurrence of intramammary infection (IMI) in FCH between herds with documented good or poorer first-parity udder health based on cow somatic cell count (CSCC) in early lactation, and to study herd differences in animal factors important for udder health, such as udder and hock skin lesions and animal cleanliness. Three groups of herds were included: those with high proportions of FCH with low CSCC (≤75,000 cells/mL) at the first 2 milk recordings after calving (LL), herds with high proportions of FCH with high CSCC (>100,000 cells/mL) at the first and low CSCC at the second recording (HL), and herds with high proportions of FCH with high CSCC at both recordings (HH). Thirty-nine herds (13 LL, 11 HL, 15 HH) were visited 3 times during a 12-mo period for observation of cleanliness and hock lesions, and sampling of udder and teat skin using swab cloths of milk-fed calves, early-pregnant heifers, and late-pregnant heifers. In 25 (9 LL, 9 HL, 7 HH) udder quarter samples from colostrum and milk on d 3 to 4 after calving were taken by the farmers from FCH during one year. The farmers also provided information on calving (individual or group), use of restraint and oxytocin at milking, and presence of teat and udder skin lesions. Bacterial growth in swab samples and quarter samples was investigated by culturing, and a selection of isolates was genotyped using whole-genome sequencing. Cleanliness, hock and udder skin lesions other than udder-thigh dermatitis, and growth of bacteria in swab samples did not differ between herd groups. It was more common that FCH from LL herds, compared with FCH in HH and HL herds, calved in a group of animals. Use of restraint at milking was more common in LL herds than in HH herds, whereas presence of udder-thigh dermatitis was lowest in LL herds. Specific infection was found in 14% of 5,593 quarter samples from 722 FCH. The most common IMI was Staphylococcus chromogenes. Growth of Staphylococcus simulans was more common in HH than in LL and HL herds. In colostrum samples, Staphylococcus haemolyticus was more common in HL and HH than in LL herds. The proportion of quarters with the same specific infection at both samplings was higher in HH than in LL herds and tended to be higher in HH than in HL herds. The proportion of quarters with Staph. chromogenes IMI at both samplings tended to differ between herd groups and was highest in HH herds. Whole-genome sequencing found the same sequence type of Staph. chromogenes and Staphylococcus aureus in both samples in almost all quarters with the same infection at both samplings. The differences in IMI between herd groups were in line with the higher somatic cell count in HH herds. The reasons for the predominance of Staph. chromogenes IMI in FCH need further studies. [ABSTRACT FROM AUTHOR]

Featured Application: A method to develop a potential automated skin lesion classification. This manuscript proposes the possibility of concatenated signatures (instead of images) obtained from different integral transforms, such as Fourier, Mellin, and Hilbert, to classify skin lesions. Eight lesions were analyzed using some algorithms of artificial intelligence: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), melanoma (MEL), actinic keratosis (AK), benign keratosis (BKL), dermatofibromas (DF), melanocytic nevi (NV), and vascular lesions (VASCs). Eleven artificial intelligence models were applied so that eight skin lesions could be classified by analyzing the signatures of each lesion. The database was randomly divided into 80% and 20% for the training and test dataset images, respectively. The metrics that are reported are accuracy, sensitivity, specificity, and precision. Each process was repeated 30 times to avoid bias, according to the central limit theorem in this work, and the averages and ± standard deviations were reported for each metric. Although all the results were very satisfactory, the highest average score for the eight lesions analyzed was obtained using the subspace k-NN model, where the test metrics were 99.98% accuracy, 99.96% sensitivity, 99.99% specificity, and 99.95% precision. [ABSTRACT FROM AUTHOR]

Introduction: Cobalamin C (cblC) deficiency is a rare hereditary disorder affecting intracellular cobalamin metabolism, primarily caused by mutations in MMACHC. This condition is characterized by combined methylmalonic acidemia and hyperhomocysteinemia, displaying a wide range of clinical manifestations involving multiple organs. Owing to its uncommon occurrence and diverse clinical phenotypes, diagnosing cblC deficiency is challenging and often leads to delayed or missed diagnoses. Case description: In this report, we present a case of late-onset cblC deficiency with brown desquamating dermatitis on the buttocks. Magnetic resonance imaging (MRI) of the brain revealed bilateral cerebellar abnormalities. The suspicion of an inherited metabolic disorder was raised by abnormal serum amino acid and acylcarnitine levels, along with increased urine methylmalonic acid and serum homocysteine levels. Whole-exome sequencing helped identify a homozygous variant (c.482G>A) in MMACHC, confirming the diagnosis of cblC deficiency. However, despite receiving treatment with hydroxocobalamin and betaine, the patient did not experience clinical improvement, which may be attributed to the delayed diagnosis as indicated by the declining homocysteine and methylmalonic acid levels. Conclusion: Collectively, we emphasize the significance of recognizing the skin lesions and observing serial MRI changes in patients with cblC deficiency. Our case underscores the importance of early diagnosis and timely therapeutic intervention for this severe yet frequently manageable condition. [ABSTRACT FROM AUTHOR]