Fucosylation of HLA-DRB1 regulates CD4+ T cell-mediated anti-melanoma immunity and enhances immunotherapy efficacy.

Immunotherapy efficacy is limited in melanoma, and combinations of immunotherapies with other modalities have yielded limited improvements but also adverse events requiring cessation of treatment. In addition to ineffective patient stratification, efficacy is impaired by paucity of intratumoral immune cells (itICs); thus, effective strategies to safely increase itICs are needed. We report that dietary administration of L-fucose induces fucosylation and cell surface enrichment of the major histocompatibility complex (MHC)-II protein HLA-DRB1 in melanoma cells, triggering CD4+ T cell-mediated increases in itICs and anti-tumor immunity, enhancing immune checkpoint blockade responses. Melanoma fucosylation and fucosylated HLA-DRB1 associate with intratumoral T cell abundance and anti-programmed cell death protein 1 (PD1) responder status in patient melanoma specimens, suggesting the potential use of melanoma fucosylation as a strategy for stratifying patients for immunotherapies. Our findings demonstrate that fucosylation is a key mediator of anti-tumor immunity and, importantly, suggest that L-fucose is a powerful agent for safely increasing itICs and immunotherapy efficacy in melanoma.

Synthetic tissue engineering with smart, cytomimetic protocells.

Synthetic protocells are rudimentary origin-of-life versions of natural cell counterparts. Protocells are widely engineered to advance efforts and useful accepted outcomes in synthetic biology, soft matter chemistry and bioinspired materials chemistry. Protocells in collective symbiosis generate synthetic proto-tissues that display unprecedented autonomy and yield advanced materials with desirable life-like features for smart multi-drug delivery, micro bioreactors, renewable fuel production, environmental clean-up, and medicine. Current levels of protocell and proto-tissue functionality and adaptivity are just sufficient to apply them in tissue engineering and regenerative medicine, where they animate biomaterials and increase therapeutic cell productivity. As of now, structural biomaterials for tissue engineering lack the properties of living biomaterials such as self-repair, stochasticity, cell synergy and the sequencing of molecular and cellular events. Future protocell-based biomaterials provide these core properties of living organisms, but excluding evolution. Most importantly, protocells are programmable for a broad array of cell functions and behaviors and collectively in consortia are tunable for multivariate functions. Inspired by upcoming designs of smart protocells, we review their developmental background and cover the most recently reported developments in this promising field of synthetic proto-biology. Our emphasis is on manufacturing proto-tissues for tissue engineering of organoids, stem cell niches and reprogramming and tissue formation through stages of embryonic development. We also highlight the exciting reported developments arising from fusing living cells and tissues, in a valuable hybrid symbiosis, with synthetic counterparts to bring about novel functions, and living tissue products for a new synthetic tissue engineering discipline.

Fucosylated Proteome Profiling Identifies a Fucosylated, Non-Ribosomal, Stress-Responsive Species of Ribosomal Protein S3.

Alterations in genes encoding for proteins that control fucosylation are known to play causative roles in several developmental disorders, such as Dowling-Degos disease 2 and congenital disorder of glycosylation type IIc (CDGIIc). Recent studies have provided evidence that changes in fucosylation can contribute to the development and progression of several different types of cancers. It is therefore important to gain a detailed understanding of how fucosylation is altered in disease states so that interventions may be developed for therapeutic purposes. In this report, we find that fucosylation occurs on many intracellular proteins. This is an interesting finding, as the fucosylation machinery is restricted to the secretory pathway and is thought to predominately affect cell-membrane-bound and secreted proteins. We find that Ribosomal protein S3 (RPS3) is fucosylated in normal tissues and in cancer cells, and that the extent of its fucosylation appears to respond to stress, including MAPK inhibitors, suggesting a new role in posttranslational protein function. Our data identify a new ribosome-independent species of fucosylated RPS3 that interacts with proteins involved in posttranscriptional regulation of RNA, such as Heterogeneous nuclear ribonucleoprotein U (HNRNPU), as well as with a predominance of non-coding RNAs. These data highlight a novel role for RPS3, which, given previously reported oncogenic roles for RPS3, might represent functions that are perturbed in pathologies such as cancer. Together, our findings suggest a previously unrecognized role for fucosylation in directly influencing intracellular protein functions.

TFEB links MYC signaling to epigenetic control of myeloid differentiation and acute myeloid leukemia.

MYC oncoproteins regulate transcription of genes directing cell proliferation, metabolism and tumorigenesis. A variety of alterations drive MYC expression in acute myeloid leukemia (AML) and enforced MYC expression in hematopoietic progenitors is sufficient to induce AML. Here we report that AML and myeloid progenitor cell growth and survival rely on MYC-directed suppression of Transcription Factor EB (TFEB), a master regulator of the autophagy-lysosome pathway. Notably, although originally identified as an oncogene, TFEB functions as a tumor suppressor in AML, where it provokes AML cell differentiation and death. These responses reflect TFEB control of myeloid epigenetic programs, by inducing expression of isocitrate dehydrogenase-1 (IDH1) and IDH2, resulting in global hydroxylation of 5-methycytosine. Finally, activating the TFEB-IDH1/IDH2-TET2 axis is revealed as a targetable vulnerability in AML. Thus, epigenetic control by a MYC-TFEB circuit dictates myeloid cell fate and is essential for maintenance of AML.

Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model.

Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.

An evidence review of face masks against COVID-19.

The science around the use of masks by the public to impede COVID-19 transmission is advancing rapidly. In this narrative review, we develop an analytical framework to examine mask usage, synthesizing the relevant literature to inform multiple areas: population impact, transmission characteristics, source control, wearer protection, sociological considerations, and implementation considerations. A primary route of transmission of COVID-19 is via respiratory particles, and it is known to be transmissible from presymptomatic, paucisymptomatic, and asymptomatic individuals. Reducing disease spread requires two things: limiting contacts of infected individuals via physical distancing and other measures and reducing the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces transmissibility per contact by reducing transmission of infected respiratory particles in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. Given the current shortages of medical masks, we recommend the adoption of public cloth mask wearing, as an effective form of source control, in conjunction with existing hygiene, distancing, and contact tracing strategies. Because many respiratory particles become smaller due to evaporation, we recommend increasing focus on a previously overlooked aspect of mask usage: mask wearing by infectious people ("source control") with benefits at the population level, rather than only mask wearing by susceptible people, such as health care workers, with focus on individual outcomes. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.

Impact of a Surgical Subspecialty Roundtable on Career Perception for Preclerkship Medical Students.

BACKGROUND: Limited exposure to surgical subspecialties during medical school may be responsible for decreasing medical student interest in surgery. Although most medical schools have surgery interest groups to increase exposure, our aim was to evaluate the impact of a focused surgical subspecialty roundtable on preclerkship students' perceptions of surgical careers. METHODS: Faculty members from each surgical subspecialty shared their experiences and led roundtable discussions with five to seven first- and second-year medical students at a time (total n = 59). Pre-event and post-event surveys were administered to assess students' interest in surgery, knowledge of training paths, values related to specialty selection, and perception of surgeons. RESULTS: Forty students completed pre-event and post-event surveys. The number of students who were extremely or very interested in surgery increased after this event (65% versus 72.5%, P < 0.001). The greatest number of students indicated an interest in orthopedic surgery, and the fewest indicated an interest in neurosurgery. After the event, thirteen (32.5%) students changed their preferences for the subspecialty in which they were most interested. Students demonstrated improved knowledge of training length and integrated residencies (83.8% versus 96.3%, P = 0.003). The perceived importance of intellectual challenge, research opportunities, and training length decreased, whereas the importance of compensation, work/life balance, long-term patient follow-up, and the job market increased. Students' perceptions of surgeons' work/life balance (10% versus 25%, P < 0.001) and ability to be team players (82.5% versus 85%, P = 0.01) improved significantly after the roundtable. CONCLUSIONS: The surgical specialty roundtable increased students' interest in surgery, improved knowledge of training paths, and altered perceptions related to career decision-making.

Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California.

In emerging epidemics, early estimates of key epidemiological characteristics of the disease are critical for guiding public policy. In particular, identifying high-risk population subgroups aids policymakers and health officials in combating the epidemic. This has been challenging during the coronavirus disease 2019 (COVID-19) pandemic because governmental agencies typically release aggregate COVID-19 data as summary statistics of patient demographics. These data may identify disparities in COVID-19 outcomes between broad population subgroups, but do not provide comparisons between more granular population subgroups defined by combinations of multiple demographics. We introduce a method that helps to overcome the limitations of aggregated summary statistics and yields estimates of COVID-19 infection and case fatality rates - key quantities for guiding public policy related to the control and prevention of COVID-19 - for population subgroups across combinations of demographic characteristics. Our approach uses pseudo-likelihood based logistic regression to combine aggregate COVID-19 case and fatality data with population-level demographic survey data to estimate infection and case fatality rates for population subgroups across combinations of demographic characteristics. We illustrate our method on California COVID-19 data to estimate test-based infection and case fatality rates for population subgroups defined by gender, age, and race/ethnicity. Our analysis indicates that in California, males have higher test-based infection rates and test-based case fatality rates across age and race/ethnicity groups, with the gender gap widening with increasing age. Although elderly infected with COVID-19 are at an elevated risk of mortality, the test-based infection rates do not increase monotonically with age. The workforce population, especially, has a higher test-based infection rate than children, adolescents, and other elderly people in their 60-80. LatinX and African Americans have higher test-based infection rates than other race/ethnicity groups. The subgroups with the highest 5 test-based case fatality rates are all-male groups with race as African American, Asian, Multi-race, LatinX, and White, followed by African American females, indicating that African Americans are an especially vulnerable California subpopulation.

Sequenced Somatic Cell Reprogramming and Differentiation Inside Nested Hydrogel Droplets.

The efficient genesis of pluripotent cells or therapeutic cells for regenerative medicine involves several external manipulations and conditioning protocols, which drives down clinical applicability. Automated programming of the genesis by microscale physical forces and chronological biochemistry can increase clinical success. The design and fabrication of nested polysaccharide droplets (millimeter-sized) with cell sustaining properties of natural tissues and intrinsic properties for time and space evolution of cell transformation signals between somatic cells, pluripotent cells and differentiated therapeutic cells in a swift and efficient manner without the need for laborious external manipulation are reported. Cells transform between phenotypic states by having single and double nested droplets constituted with extracellular matrix proteins and reprogramming, and differentiation factors infused chronologically across the droplet space. The cell transformation into germ layer cells and bone cells is successfully tested in vitro and in vivo and promotes the formation of new bone tissues. Thus, nested droplets with BMP-2 loaded guests synthesize mineralized bone tissue plates along the length of a cranial non-union bone defect at 4 weeks. The advantages of sequenced somatic cell reprogramming and differentiation inside an individual hydrogel module without external manipulation, promoted by formulating tissue mimetic physical, mechanical, and chemical microenvironments are shown.

Machine learning models accurately predict ozone exposure during wildfire events.

Epidemiologists use prediction models to downscale (i.e., interpolate) air pollution exposure where monitoring data is insufficient. This study compares machine learning prediction models for ground-level ozone during wildfires, evaluating the predictive accuracy of ten algorithms on the daily 8-hour maximum average ozone during a 2008 wildfire event in northern California. Models were evaluated using a leave-one-location-out cross-validation (LOLO CV) procedure to account for the spatial and temporal dependence of the data and produce more realistic estimates of prediction error. LOLO CV avoids both the well-known overly optimistic bias of k-fold cross-validation on dependent data and the conservative bias of evaluating prediction error over a coarser spatial resolution via leave-k-locations-out CV. Gradient boosting was the most accurate of the ten machine learning algorithms with the lowest LOLO CV estimated root mean square error (0.228) and the highest LOLO CV Rˆ2 (0.677). Random forest was the second best performing algorithm with an LOLO CV Rˆ2 of 0.661. The LOLO CV estimates of predictive accuracy were less optimistic than 10-fold CV estimates for all ten models. The difference in estimated accuracy between the 10-fold CV and LOLO CV was greater for more flexible models like gradient boosting and random forest. The order of estimated model accuracy depended on the choice of evaluation metric, indicating that 10-fold CV and LOLO CV may select different models or sets of covariates as optimal, which calls into question the reliability of 10-fold CV for model (or variable) selection. These prediction models are designed for interpolating ozone exposure, and are not suited to inferring the effect of wildfires on ozone or extrapolating to predict ozone in other spatial or temporal domains. This is demonstrated by the inability of the best performing models to accurately predict ozone during 2007 southern California wildfires.

Associations between respiratory health and ozone and fine particulate matter during a wildfire event.

Wildfires have been increasing in frequency in the western United States (US) with the 2017 and 2018 fire seasons experiencing some of the worst wildfires in terms of suppression costs and air pollution that the western US has seen. Although growing evidence suggests respiratory exacerbations from elevated fine particulate matter (PM2.5) during wildfires, significantly less is known about the impacts on human health of ozone (O3) that may also be increased due to wildfires. Using machine learning, we created daily surface concentration maps for PM2.5 and O3 during an intense wildfire in California in 2008. We then linked these daily exposures to counts of respiratory hospitalizations and emergency department visits at the ZIP code level. We calculated relative risks of respiratory health outcomes using Poisson generalized estimating equations models for each exposure in separate and mutually-adjusted models, additionally adjusted for pertinent covariates. During the active fire periods, PM2.5 was significantly associated with exacerbations of asthma and chronic obstructive pulmonary disease (COPD) and these effects remained after controlling for O3. Effect estimates of O3 during the fire period were non-significant for respiratory hospitalizations but were significant for ED visits for asthma (RR = 1.05 and 95% CI = (1.022, 1.078) for a 10 ppb increase in O3). In mutually-adjusted models, the significant findings for PM2.5 remained whereas the associations with O3 were confounded. Adjusted for O3, the RR for asthma ED visits associated with a 10 µg/m3 increase in PM2.5 was 1.112 and 95% CI = (1.087, 1.138). The significant findings for PM2.5 but not for O3 in mutually-adjusted models is likely due to the fact that PM2.5 levels during these fires exceeded the 24-hour National Ambient Air Quality Standard (NAAQS) of 35 µg/m3 for 4976 ZIP-code days and reached levels up to 6.073 times the NAAQS, whereas our estimated O3 levels during the fire period only occasionally exceeded the NAAQS of 70 ppb with low exceedance levels. Future studies should continue to investigate the combined role of O3 and PM2.5 during wildfires to get a more comprehensive assessment of the cumulative burden on health from wildfire smoke.

Natural History Collections as Inspiration for Technology.

Living organisms are the ultimate survivalists, having evolved phenotypes with unprecedented adaptability, ingenuity, resourcefulness, and versatility compared to human technology. To harness these properties, functional descriptions and design principles from all sources of biodiversity information must be collated - including the hundreds of thousands of possible survival features manifest in natural history museum collections, which represent 12% of total global biodiversity. This requires a consortium of expert biologists from a range of disciplines to convert the observations, data, and hypotheses into the language of engineering. We hope to unite multidisciplinary biologists and natural history museum scientists to maximize the coverage of observations, descriptions, and hypotheses relating to adaptation and function across biodiversity, to make it technologically useful. This is to be achieved by developments in meta- taxonomic classification, phylogenetics, systematics, biological materials research, structure and morphological characterizations, and ecological data gathering from the collections - the aim being to identify and catalogue features essential for good biomimetic design.

Blunt cerebrovascular injury in elderly fall patients: are we screening enough?

Background: Blunt cerebrovascular injuries (BCVI) are generally associated with high-energy injury mechanisms. Less is known regarding lower-energy injuries in elderly patients. We sought to determine the incidence of BCVI and characterize current BCVI screening practices and associated complications in elderly ground-level fall patients (EGLF, ≥ 65 years). We hypothesized that BCVI in EGLF patients would be clinically significant and screening would be less common. Methods: A retrospective study was performed utilizing the National Trauma Data Bank (NTDB, 2007-2014) and single institutional data. BCVI risk factors and diagnosis were determined by ICD-9 codes. Presenting patient characteristics and clinical course were obtained by chart review. The NTDB dataset was used to determine the incidence of BCVI, risk factors for BCVI, and outcomes in the EGLF cohort. Local chart review focused on screening rates and complications. Results: The incidence of BCVI in EGLF patients was 0.15% overall and 0.86% in those with at least one BCVI risk factor in the NTDB. Upper cervical spine fractures were the most common risk factor for BCVI in EGLF patients. In EGLF patients, the diagnosis of BCVI was an independent risk factor for mortality (OR1.8, 95% C.I. 1.5-2.1). The local institutional data (2007-2014) had a BCVI incidence of 0.37% (n = 6487) and 1.47% in those with at least one risk factor (n = 1429). EGLF patients with a risk factor for BCVI had a very low rate of screening (44%). Only 8% of EGLF patients not screened had documented contraindications. The incidence of renal injury was 9% irrespective of BCVI screening. Conclusions: The incidence of BCVI is clinically significant in EGLF patients and an independent predictor of mortality. Screening is less common in EGLF patients despite few contraindications. This data suggests that using age and injury mechanism to omit BCVI screening in EGLF patients may exclude an at-risk population. Trial registration: IRB approval number: PRO15020269. Retrospective trial not registered.

Differences in Nanostructure and Hydrophobicity of Cicada (Cryptotympana atrata) Forewing Surface with the Distribution of Precipitation.

Although the cicada wing has a variety of functions and the nanostructure and surface properties of many species have been extensively investigated, there are no reports investigating diversity of nanostructures and wetting properties within a single species collected at locations with different rainfall conditions. In this study, the hydrophobicity and nanostructure dimensions of the forewing surface of Cryptotympana atrata were measured, based on specimens collected from 12 distributions with varying precipitation averages in China and Japan. The relationships among hydrophobicity, nanostructures, and precipitation were analyzed, and the adaption of hydrophobic nanostructures under different wet environments is discussed. The precipitation of locations in the years the samples of C. atrata were collected only has an effect on the diameter and spacing of wing surface nanostructure, and the multiple years of precipitation may have an influence on the basic diameter and spacing, as well as the height of protrusions. The rougher the wing surface, the stronger the hydrophobicity which was observed from samples taken where the rainfall conditions of the collection years are high. To our knowledge, this is one special example providing evidence of hydrophobic nanostructures found on a biological surface of a single species which shows adaption for specific wet environments.

The phytochemical 3,3'-diindolylmethane decreases expression of AR-controlled DNA damage repair genes through repressive chromatin modifications and is associated with DNA damage in prostate cancer cells.

Androgen receptor (AR) is a transcription factor involved in normal prostate physiology and prostate cancer (PCa) development. 3,3'-Diindolylmethane (DIM) is a promising phytochemical agent against PCa that affects AR activity and epigenetic regulators in PCa cells. However, whether DIM suppresses PCa via epigenetic regulation of AR target genes is unknown. We assessed epigenetic regulation of AR target genes in LNCaP PCa cells and showed that DIM treatment led to epigenetic suppression of AR target genes involved in DNA repair (PARP1, MRE11, DNA-PK). Decreased expression of these genes was accompanied by an increase in repressive chromatin marks, loss of AR occupancy and EZH2 recruitment to their regulatory regions. Decreased DNA repair gene expression was associated with an increase in DNA damage (γH2Ax) and up-regulation of genomic repeat elements LINE1 and α-satellite. Our results suggest that DIM suppresses AR-dependent gene transcription through epigenetic modulation, leading to DNA damage and genome instability in PCa cells.

Insect Analogue to the Lotus Leaf: A Planthopper Wing Membrane Incorporating a Low-Adhesion, Nonwetting, Superhydrophobic, Bactericidal, and Biocompatible Surface.

Nature has produced many intriguing and spectacular surfaces at the micro- and nanoscales. These small surface decorations act for a singular or, in most cases, a range of functions. The minute landscape found on the lotus leaf is one such example, displaying antiwetting behavior and low adhesion with foreign particulate matter. Indeed the lotus leaf has often been considered the "benchmark" for such properties. One could expect that there are animal counterparts of this self-drying and self-cleaning surface system. In this study, we show that the planthopper insect wing (Desudaba danae) exhibits a remarkable architectural similarity to the lotus leaf surface. Not only does the wing demonstrate a topographical likeness, but some surface properties are also expressed, such as nonwetting behavior and low adhering forces with contaminants. In addition, the insect-wing cuticle exhibits an antibacterial property in which Gram-negative bacteria (Porphyromonas gingivalis) are killed over many consecutive waves of attacks over 7 days. In contrast, eukaryote cell associations, upon contact with the insect membrane, lead to a formation of integrated cell sheets (e.g., among human stem cells (SHED-MSC) and human dermal fibroblasts (HDF)). The multifunctional features of the insect membrane provide a potential natural template for man-made applications in which specific control of liquid, solid, and biological contacts is desired and required. Moreover, the planthopper wing cuticle provides a "new" natural surface with which numerous interfacial properties can be explored for a range of comparative studies with both natural and man-made materials.

Distinctive Nuclear Localization Signals in the Oomycete Phytophthora sojae.

To date, nuclear localization signals (NLSs) that target proteins to nuclei in oomycetes have not been defined, but have been assumed to be the same as in higher eukaryotes. Here, we use the soybean pathogen Phytophthora sojae as a model to investigate these sequences in oomycetes. By establishing a reliable in vivo NLS assay based on confocal microscopy, we found that many canonical monopartite and bipartite classical NLSs (cNLSs) mediated nuclear import poorly in P. sojae. We found that efficient localization of P. sojae nuclear proteins by cNLSs requires additional basic amino acids at distal sites or collaboration with other NLSs. We found that several representatives of another well-characterized NLS, proline-tyrosine NLS (PY-NLS) also functioned poorly in P. sojae. To characterize PY-NLSs in P. sojae, we experimentally defined the residues required by functional PY-NLSs in three P. sojae nuclear-localized proteins. These results showed that functional P. sojae PY-NLSs include an additional cluster of basic residues for efficient nuclear import. Finally, analysis of several highly conserved P. sojae nuclear proteins including ribosomal proteins and core histones revealed that these proteins exhibit a similar but stronger set of sequence requirements for nuclear targeting compared with their orthologs in mammals or yeast.

ATF2, a paradigm of the multifaceted regulation of transcription factors in biology and disease.

Stringent transcriptional regulation is crucial for normal cellular biology and organismal development. Perturbations in the proper regulation of transcription factors can result in numerous pathologies, including cancer. Thus, understanding how transcription factors are regulated and how they are dysregulated in disease states is key to the therapeutic targeting of these factors and/or the pathways that they regulate. Activating transcription factor 2 (ATF2) has been studied in a number of developmental and pathological conditions. Recent findings have shed light on the transcriptional, post-transcriptional, and post-translational regulatory mechanisms that influence ATF2 function, and thus, the transcriptional programs coordinated by ATF2. Given our current knowledge of its multiple levels of regulation and function, ATF2 represents a paradigm for the mechanistic complexity that can regulate transcription factor function. Thus, increasing our understanding of the regulation and function of ATF2 will provide insights into fundamental regulatory mechanisms that influence how cells integrate extracellular and intracellular signals into a genomic response through transcription factors. Characterization of ATF2 dysfunction in the context of pathological conditions, particularly in cancer biology and response to therapy, will be important in understanding how pathways controlled by ATF2 or other transcription factors might be therapeutically exploited. In this review, we provide an overview of the currently known upstream regulators and downstream targets of ATF2.

Diversity of Cuticular Micro- and Nanostructures on Insects: Properties, Functions, and Potential Applications.

Insects exhibit a fascinating and diverse range of micro- and nanoarchitectures on their cuticle. Beyond the spectacular beauty of such minute structures lie surfaces evolutionarily modified to act as multifunctional interfaces that must contend with a hostile, challenging environment, driving adaption so that these can then become favorable. Numerous cuticular structures have been discovered this century; and of equal importance are the properties, functions, and potential applications that have been a key focus in many recent studies. The vast range of insect structuring, from the most simplistic topographies to the most elegant and geometrically complex forms, affords us with an exhaustive library of natural templates and free technologies to borrow, replicate, and employ for a range of applications. Of particular importance are structures that imbue cuticle with antiwetting properties, self-cleaning abilities, antireflection, enhanced color, adhesion, and antimicrobial and specific cell-attachment properties.