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The skin microbiome consists of bacteria, fungi, viruses, and mites, which play a crucial role in maintaining skin health and immune function. Imbalances in this microbial community, known as dysbiosis, are implicated in various dermatological conditions. While skincare products are known to influence the skin microbiome, the effects of dermatological procedures have not been extensively studied. Here, we perform a scoping review to outline the studies investigating the impacts of dermatological interventions on the skin microbiome. Phototherapy emerged as the most studied intervention, encompassing UV phototherapy, light therapy, laser therapy, and photodynamic therapy. Chemical interventions, such as chemical peels, micropigmentation, and debridement, have comparatively limited studies describing their impacts on the skin microbiome. To date, no studies have been done on a wide variety of common dermatological procedures such as cryotherapy, skin grafts, and dermabrasion, which may have stronger likelihoods of affecting the skin microbiome. This underscores the need for further research on the influences of dermatological procedures, especially chemical and physical interventions, on the skin microbiome. More comprehensive pre-clinical and clinical studies are essential not only for understanding the long-term consequences of these procedures, but also for optimizing patient outcomes in dermatological care. [ABSTRACT FROM AUTHOR]

Background and purpose: An essential step during endovascular thrombectomy is identifying the occluded arterial vessel on a cerebral digital subtraction angiogram (DSA). We developed an algorithm that can detect and localize the position of occlusions in cerebral DSA. Methods: We retrospectively collected cerebral DSAs from a single institution between 2018 and 2020 from 188 patients, 86 of whom suffered occlusions of the M1 and proximal M2 segments. We trained an ensemble of deep‐learning models on fewer than 60 large‐vessel occlusion (LVO)‐positive patients. We evaluated the model on an independent test set and evaluated the truth of its predicted localizations using Intersection over Union and expert review. Results: On an independent test set of 166 cerebral DSA frames with an LVO prevalence of 0.19, the model achieved a specificity of 0.95 (95% confidence interval [CI]: 0.90, 0.99), a precision of 0.7450 (95% CI: 0.64, 0.88), and a sensitivity of 0.76 (95% CI: 0.66, 0.91). The model correctly localized the LVO in at least one frame in 13 of the 14 LVO‐positive patients in the test set. The model achieved a precision of 0.67 (95% CI: 0.52, 0.79), recall of 0.69 (95% CI: 0.46, 0.81), and a mean average precision of 0.75 (95% CI: 0.56, 0.91). Conclusion: This work demonstrates that a deep learning strategy using a limited dataset can generate effective representations used to identify LVOs. Generating an expanded and more complete dataset of LVOs with obstructed LVOs is likely the best way to improve the model's ability to localize LVOs. [ABSTRACT FROM AUTHOR]

G protein–coupled receptors (GPCRs) are the largest family of cell surface receptors and signal through the proximal effectors, G proteins and β-arrestins, to influence nearly every biological process. The G protein and β-arrestin signaling pathways have largely been considered separable; however, direct interactions between Gα proteins and β-arrestins have been described that appear to be part of a distinct GPCR signaling pathway. Within these complexes, Gαi/o, but not other Gα protein subtypes, directly interacts with β-arrestin, regardless of the canonical Gα protein that is coupled to the GPCR. Here, we report that the endogenous biased chemokine agonists of CXCR3 (CXCL9, CXCL10, and CXCL11), together with two small-molecule biased agonists, differentially formed Gαi:β-arrestin complexes. Formation of the Gαi:β-arrestin complexes did not correlate well with either G protein activation or β-arrestin recruitment. β-arrestin biosensors demonstrated that ligands that promoted Gαi:β-arrestin complex formation generated similar β-arrestin conformations. We also found that Gαi:β-arrestin complexes did not couple to the mitogen-activated protein kinase ERK, as is observed with other receptors such as the V2 vasopressin receptor, but did couple with the clathrin adaptor protein AP-2, which suggests context-dependent signaling by these complexes. These findings reinforce the notion that Gαi:β-arrestin complex formation is a distinct GPCR signaling pathway and enhance our understanding of the spectrum of biased agonism. A complex to complicate chemokine signaling: G protein–coupled receptors (GPCRs) represent about 30% of the targets of FDA-approved drugs. GPCRs signal through two pathways that are considered separable: one dependent on direct activation of heterotrimeric G proteins and the other on the recruitment of β-arrestin proteins. Building on their previous work, Zheng et al. showed that some, but not all, of the endogenous agonists of the chemokine receptor CXCR3 induced the formation of a Gαi:β-arrestin complex, which was independent of the relative extent of activation of either G protein or β-arrestin signaling pathways. Signaling downstream of the Gαi:β-arrestin complex was different from that stimulated by β-arrestin alone. Together, these data add to the evidence of crosstalk between G proteins and β-arrestins and may have implications for the development of GPCR-targeting drugs. [ABSTRACT FROM AUTHOR]

Background: Tools to increase the turnaround speed and accuracy of imaging reports could positively influence ED logistics. The Caire ICH is an artificial intelligence (AI) software developed for ED physicians to recognise intracranial haemorrhages (ICHs) on non-contrast enhanced cranial CT scans to manage the clinical care of these patients in a timelier fashion., Methods: A dataset of 532 non-contrast cranial CT scans was reviewed by five board-certified emergency physicians (EPs) with an average of 14.8 years of practice experience. The scans were labelled in random order for the presence or absence of an ICH. If an ICH was detected, the reader further labelled all subtypes present (ie, epidural, subdural, subarachnoid, intraparenchymal and/or intraventricular haemorrhage). After a washout period, the five EPs reviewed again the scans individually with the assistance of Caire ICH. The mean accuracy of the EP readings with AI assistance was compared with the mean accuracy of three general radiologists reading the films individually. The final diagnosis (ie, ground truth) was adjudicated by a consensus of the radiologists after their individual readings., Results: Mean EP reader accuracy significantly increased by 6.20% (95% CI for the difference 5.10%-7.29%; p=0.0092) when using Caire ICH to detect an ICH. Mean accuracy of the EP cohort in detecting an ICH using Caire ICH was found to be more accurate than the radiologist cohort prior to discussion; this difference, however, was not statistically significant., Conclusion: The Caire ICH software significantly improved the accuracy and sensitivity of detecting an ICH by the EP to a level comparable to general radiologists. Further prospective research with larger numbers will be needed to understand the impact of Caire ICH on ED logistics and patient outcomes., Competing Interests: Competing interests: AD, JB, DS, PM, ZC, SM and VC received compensation for their involvement with this work. PW, AW and RW are employees of Caire Health., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)