Hair follicle stem cells are regulated by intrafollicular and extrafollicular niche signals. Appropriate hair follicle regeneration relies on the coordinated release and integration of these signals. How immune cells, particularly cutaneous macrophages, influence the hair follicle stem cell niche and regeneration is not well understood. We took advantage of wound-induced hair growth (WIHG) to explore the relationship between wound macrophages and hair follicle regeneration. First, we showed that WIHG is dependent on CD11bF4/80 macrophages at 7-11 days after injury. Next, using CXCR1:CCR2 mice to capture the dynamic spectrum of macrophage phenotypes during wound healing, we showed that wound macrophages transition from a CXCR1 to a CXCR1 phenotype at the onset of WIHG. Finally, WIHG is abolished in mice deficient for CXCR1, delayed with pharmacological inhibition of transforming growth factor-β receptor type 1, and rescued with exogenous transforming growth factor-β1. Overall, we propose a model in which transforming growth factor-β1 and CXCR1 are critical for recruiting and maintaining the CCR2CXCR1Ly6CTNFα macrophages critical for stimulating WIHG.

Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.

The ubiquitous and openly accessible information produced by the public on the Internet has sparked an increasing interest in developing digital public health surveillance (DPHS) systems. We conducted a systematic scoping review in accordance with the PRISMA extension for scoping reviews to consolidate and characterize the existing research on DPHS and identify areas for further research. We used Natural Language Processing and content analysis to define the search strings and searched Global Health, Web of Science, PubMed, and Google Scholar from 2005 to January 2020 for peer-reviewed articles on DPHS, with extensive hand searching. Seven hundred fifty-five articles were included in this review. The studies were from 54 countries and utilized 26 digital platforms to study 208 sub-categories of 49 categories associated with 16 public health surveillance (PHS) themes. Most studies were conducted by researchers from the United States (56%, 426) and dominated by communicable diseases-related topics (25%, 187), followed by behavioural risk factors (17%, 131). While this review discusses the potentials of using Internet-based data as an affordable and instantaneous resource for DPHS, it highlights the paucity of longitudinal studies and the methodological and inherent practical limitations underpinning the successful implementation of a DPHS system. Little work studied Internet users’ demographics when developing DPHS systems, and 39% (291) of studies did not stratify their results by geographic region. A clear methodology by which the results of DPHS can be linked to public health action has yet to be established, as only six (0.8%) studies deployed their system into a PHS context.

No abstract

No abstract

No abstract

No abstract

No abstract