Objectives Early melanoma detection decreases morbidity and mortality. Early detection classically involves dermoscopy to identify suspicious lesions for which biopsy is indicated. Biopsy and histological examination then diagnose benign nevi, atypical nevi, or cancerous growths. With current methods, a considerable number of unnecessary biopsies are performed as only 11% of all biopsied, suspicious lesions are actually melanomas. Thus, there is a need for more advanced noninvasive diagnostics to guide the decision of whether or not to biopsy. Artificial intelligence can generate screening algorithms that transform a set of imaging biomarkers into a risk score that can be used to classify a lesion as a melanoma or a nevus by comparing the score to a classification threshold. Melanoma imaging biomarkers have been shown to be spectrally dependent in Red, Green, Blue (RGB) color channels, and hyperspectral imaging may further enhance diagnostic power. The purpose of this study was to use the same melanoma imaging biomarkers previously described, but over a wider range of wavelengths to determine if, in combination with machine learning algorithms, this could result in enhanced melanoma detection. Methods We used the melanoma advanced imaging dermatoscope (mAID) to image pigmented lesions assessed by dermatologists as requiring a biopsy. The mAID is a 21‐wavelength imaging device in the 350–950 nm range. We then generated imaging biomarkers from these hyperspectral dermoscopy images, and, with the help of artificial intelligence algorithms, generated a melanoma Q‐score for each lesion (0 = nevus, 1 = melanoma). The Q‐score was then compared to the histopathologic diagnosis. Results The overall sensitivity and specificity of hyperspectral dermoscopy in detecting melanoma when evaluated in a set of lesions selected by dermatologists as requiring biopsy was 100% and 36%, respectively. Conclusion With widespread application, and if validated in larger clinical trials, this non‐invasive methodology could decrease unnecessary biopsies and potentially increase life‐saving early detection events. Lasers Surg. Med. 51:214–222, 2019. © 2019 The Authors. Lasers in Surgery and Medicine Published by Wiley Periodicals, Inc.

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is a highly successful intracellular pathogen with the ability to withstand harsh conditions and reside long-term within its host. In the dormant and persistent states, the bacterium tunes its metabolism and is able to resist the actions of antibiotics. One of the main strategies Mtb adopts is through its metabolic versatility—it is able to cometabolize a variety of essential nutrients and direct these nutrients simultaneously to multiple metabolic pathways to facilitate the infection of the host. Mtb further undergo extensive remodeling of its metabolic pathways in response to stress and dormancy. In recent years, advancement in systems biology and its applications have contributed substantially to a more coherent view on the intricate metabolic networks of Mtb. With a more refined appreciation of the roles of metabolism in mycobacterial infection and drug resistance, and the success of drugs targeting metabolism, there is growing interest in further development of anti-TB therapies that target metabolism, including lipid metabolism and oxidative phosphorylation. Here, we will review current knowledge revolving around the versatility of Mtb in remodeling its metabolism during infection and dormancy, with a focus on central carbon metabolism and lipid metabolism.

Seven cultures referred to in our laboratories as unidentified gram-negative bacilli or Haemophilus species were identified as atypical strains of FranciseUa tularensis lacking a requirement for cysteine or enriched medium for growth. The use of cellular fatty acid composition analysis facilitated early recognition of this pathogen and prompt implementation of appropriate biosafety measures.

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