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Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

We smell different as we age. An odor-producing molecule 2-nonenal is only part of the story. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. There's a scientific explanation behind an aging body's distinct scent, but the chemistry only tells half the story. Breakthroughs, discoveries, and DIY tips sent six days a week.

Image caption, More than 2,000 images were taken of Ruth's skin I don't mind having my photo taken - triple checked and filtered for Instagram - but 70 cameras pointing at me while I'm down to my knickers is a bit daunting. A robotic voice tells me to stay still and close my eyes as I stand in a huge curved scanner while classical music plays in the background. With a flash of light, 2,000 photographs are taken of my body, in the hope of capturing every mark, freckle and mole to analyse for different skin cancers. This full-body scan is happening at a sci-fi-style clinic in Manchester city centre, with me wearing a dressing gown and hexagon-shaped rubber slippers. The millions of data points collected will create a 3D avatar of my body using AI.

Does summer actually make your hair grow faster? Probably not, but it might affect beard growth. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. All hair follicles go through growth and shedding cycles. Breakthroughs, discoveries, and DIY tips sent six days a week.

Things to Do in L.A. Tap to enable a layout that focuses on the article. I'm not sure I want to be around that long Mel Brooks, shown in January, celebrated his 100th birthday this week. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search.

Sun hoodies are the greatest new garment since the original hoodie. I grew up in the late 1900s, in a time when attitudes toward sunburns were extremely lax compared to today. Red, peeling skin was seen as a minor and temporary inconvenience and the natural outcome of an active childhood, not a reckless lifestyle choice with potentially deadly consequences. There were little brown bottles of coconut-scented low-SPF suntan oil around, but no one took it very seriously. We were kings--buttered up in Hawaiian Tropic, Jams billowing in the breeze, sipping ice-cold Crystal Pepsi with candy cigarettes dangling from our lips.

Recent advances in computational pathology have led to the emergence of numerous foundation models. These models typically rely on general-purpose encoders with multi-instance learning for whole slide image (WSI) classification or apply multimodal approaches to generate reports directly from images. However, these models cannot emulate the diagnostic approach of pathologists, who systematically examine slides at low magnification to obtain an overview before progressively zooming in on suspicious regions to formulate comprehensive diagnoses.

Artificial intelligence is poised to augment dermatological care by enabling scalable image-based diagnostics. Yet, the development of robust and equitable models remains hindered by datasets that fail to capture the clinical and demographic complexity of real-world practice. This complexity stems from region-specific disease distributions, wide variation in skin tones, and the underrepresentation of outpatient scenarios from non-Western populations. We introduce DermaCon-IN, a prospectively curated dermatology dataset comprising 5,450 clinical images from 3,002 patients across outpatient clinics in South India. Each image is annotated by board-certified dermatologists with 245 distinct diagnoses, structured under a hierarchical, aetiology-based taxonomy adapted from Rook's classification. The dataset captures a wide spectrum of dermatologic conditions and tonal variation commonly seen in Indian outpatient care. We benchmark a range of architectures, including convolutional models (ResNet, DenseNet, EfficientNet), transformerbased models (ViT, MaxViT, Swin), and Concept Bottleneck Models to establish baseline performance and explore how anatomical and concept-level cues may be integrated. These results are intended to guide future efforts toward interpretable and clinically realistic models. DermaCon-IN provides a scalable and representative foundation for advancing dermatology AI in real-world settings.