Effects of Intravenous Immunoglobulin and Acyclovir in Preventing Neonatal Varicella

Neonatal varicella mostly results from maternal varicella. The disease can cause presentation ranging from mild symptoms to varicella pneumonia, hepatitis, meningoencephalitis, or fatality. If the mother develops symptoms implying varicella 4–5 days antepartum to 2 days postpartum, the mortality rate of the baby may reach 20%. We report a case of neonatal varicella from maternal varicella. The patient’s mother initially developed maculopapular rash over her trunk 1 day after giving birth; she had a family member in the same household diagnosed with herpes zoster recently, and another member with diagnosed varicella, whose rash disappeared before the patient's birth. On the baby's third day of life, discrete vesicular rashes on erythematous background and discrete erythematous maculopapular rashes were found over his trunk, arms, and legs. The baby was subsequently diagnosed with neonatal varicella and was treated by intravenous immunoglobulin (IVIG) because there was no varicella zoster immunoglobulin (VZIG) available in the hospital, and also, intravenous acyclovir was given for 7 days. The rash completely resolved by the baby's fifth day of life, without any complications. The combination of IVIG and acyclovir might not effectively prevent neonatal varicella, but the medication could prevent the baby from developing serious complications and shorten the clinical course

DeepMetaForge: A Deep Vision-Transformer Metadata-Fusion Network for Automatic Skin Lesion Classification

Skin cancer is a dangerous form of cancer that develops slowly in skin cells. Delays in diagnosing and treating these malignant skin conditions may have serious repercussions. Likewise, early skin cancer detection has been shown to improve treatment outcomes. This paper proposes DeepMetaForge, a deep-learning framework for skin cancer detection from metadata-accompanied images. The proposed framework utilizes BEiT, a vision transformer pre-trained as a masked image modeling task, as the image-encoding backbone. We further propose merging the encoded metadata with the derived visual characteristics while compressing the aggregate information simultaneously, simulating how photos with metadata are interpreted. The experiment results on four public datasets of dermoscopic and smartphone skin lesion images reveal that the best configuration of our proposed framework yields 87.1% macro-average F1 on average. The empirical scalability analysis further shows that the proposed framework can be implemented in a variety of machine-learning paradigms, including applications on low-resource devices and as services. The findings shed light on not only the possibility of implementing telemedicine solutions for skin cancer on a nationwide scale that could benefit those in need of quality healthcare but also open doors to many intelligent applications in medicine where images and metadata are collected together, such as disease detection from CT-scan images and patients’ expression recognition from facial images