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TOPIC: To provide an overview on the incidence of Acanthamoeba Keratitis (AK). CLINICAL RELEVANCE: Although being a major and sight-threatening cause of infectious keratitis in the population, a comprehensive assessment of the incidence of this condition is lacking. METHODS: Incidence of AK was computed as the number of AK eyes, per healthcare center, per year (annualized-center-incidence, or ACI). Two meta-analytical ratios were also calculated: a) the ratio of AK eyes to the count of non-viral microbial keratitis (MK) eyes; b) the ratio of AK eyes to the overall population (i.e., the total number of subjects of a nation or region, as indicated by the authors in each study). Center was defined as the healthcare facility (e.g., Hospital, Private Practice, Clinic) where the study took place. Actual and projected estimates of the number of AK eyes in years were calculated multiplying the ratio of AK to the total population and the corresponding present and projected population estimates (age range: 15 to 70), sourced from the United Nations (UN) Population Prospects. RESULTS: Overall, 105 articles were included, published between 1987 and 2022. The total number of eyes identified was 91,951, with 5,660 affected by AK and 86,291 by non-viral MK. The median ACI was 1.9 new AK eyes per healthcare center per year (95%CI of the median: 1.5 to 2.6), with no statistically significant differences observed among continents. The ratio of AK eyes to the total number of MK eyes was 1.52% (95%CI: 1.02% to 2.24%), while the ratio of AK in relation to the entire population was estimated at 0.0002% (95%CI: 0.0001 to 0.0006), or 2.34 eyes per 1,000,000 subjects (95%CI: 0.98 to 5.55 per 1.000.000 subjects). The projected increase in the numbers of AK eyes indicates a rise of +18.5% (15,356 AK eyes) in 2053 and +25.5% (16,253 AK eyes) in 2073, compared to the baseline of 2023 (12,954 AK eyes) CONCLUSION: AK emerged as a relatively low-incident disorder, and no significant differences in terms of its incidence were found among different continents.
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Optoelectronic devices, such as light-emitting diodes, have been demonstrated as one of the most demanded forthcoming display and lighting technologies because of their low cost, low power consumption, high brightness, and high contrast. The improvement of device performance relies on advances in precisely designing novelty functional materials, including light-emitting materials, hosts, hole/electron transport materials, and yet which is a time-consuming, laborious and resource-intensive task. Recently, machine learning (ML) has shown great prospects to accelerate material discovery and property enhancement. This review will summarize the workflow of ML in optoelectronic materials discovery, including data collection, feature engineering, model selection, model evaluation and model application. We highlight multiple recent applications of machine-learned potentials in various optoelectronic functional materials, ranging from semiconductor quantum dots (QDs) or perovskite QDs, organic molecules to carbon-based nanomaterials. We furthermore discuss the current challenges to fully realize the potential of ML-assisted materials design for optoelectronics applications. It is anticipated that this review will provide critical insights to inspire new exciting discoveries on ML-guided of high-performance optoelectronic devices with a combined effort from different disciplines.
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Glucocorticoid (GC) is essential for maintaining immune homeostasis. While GC is known to regulate the expression of genes related to inflammation in immune cells, the effects of GC, especially in the presence of inflammation, on non-immune cells remain largely unexplored. In particular, the impact of GC on inflammatory cytokine-induced immune modulatory responses of tissue stromal cells is unknown, though it has been widely used to modulate tissue injuries. Here we found that GC could enhance the expression of TSG6, a vital tissue repair effector molecule, in IFNγ and TNFα treated human umbilical cord (UC)-MSCs. NF-κB activation was found to be required for GC-augmented TSG6 upregulation. STAT3, but not STAT1, was also found to be required for the TSG6 upregulation in MSCs exposed to IFNγ, TNFα and GC. Moreover, the phosphorylation (activation) of STAT3 was attenuated when NF-κB was knocked down. Importantly, human UC-MSCs pretreated with a cocktail containing GC, IFNγ, and TNFα could significantly enhance the therapeutic effect of human UC-MSCs in an acute lung injury mouse model, as reflected by reduced infiltration of immune cells and down-regulation of iNOS in macrophages in the lung. Together, the findings reveal a novel link between GR, NF-κB and STAT3 in regulating the immunomodulatory and regenerative properties of MSCs, providing novel information for the understanding and treatment of inflammatory conditions.