Cluster Editing, also known as Correlation Clustering, is a well-studied graph modification problem. In this problem, one is given a graph and the task is to perform up to k edge additions or deletions to transform it into a cluster graph, i.e., a graph consisting of a disjoint union of cliques. However, in real-world networks, clusters are often overlapping. For example in social networks, a person might belong to several communities - e.g. those corresponding to work, school, or neighborhood. Other strong motivations come from biological network analysis and from language networks. Trying to cluster words with similar usage in the latter can be confounded by homonyms, that is, words with multiple meanings like "bat." In this paper, we introduce a new variant of Cluster Editing whereby a vertex can be split into two or more vertices. First used in the context of graph drawing, this operation allows a vertex v to be replaced by two vertices whose combined neighborhood is the neighborhood of v (and thus v can belong to more than one cluster). We call the new problem Cluster Editing with Vertex Splitting and we initiate the study of it. We show that it is NP-complete and fixed-parameter tractable when parameterized by the total number k of allowed vertex-splitting and edge-editing operations. In particular, we obtain an O(2^9k log k + n + m)-time algorithm and a 6k-vertex kernel.

The rapid emergence of anisotropic collagen fibers in the tissue microenvironment is a critical transition point in late-stage breast cancer. Specifically, the fiber orientation facilitates the likelihood of high-speed tumor cell invasion and metastasis, which pose lethal threats to patients. Thus, based on this transition point, one key issue is how to determine and evaluate efficient combination chemotherapy treatments in late-stage cancer. In this study, we designed a collagen microarray chip containing 241 high-throughput microchambers with embedded metastatic breast cancer cell MDA-MB-231-RFP. By utilizing collagen's unique structure and hydromechanical properties, the chip constructed three-dimensional isotropic and anisotropic collagen fiber structures to emulate the tumor cell microenvironment at early and late stages. We injected different chemotherapeutic drugs into its four channels and obtained composite biochemical concentration profiles. Our results demonstrate that anisotropic collagen fibers promote cell proliferation and migration more than isotropic collagen fibers, suggesting that the geometric arrangement of fibers plays an important role in regulating cell behavior. Moreover, the presence of anisotropic collagen fibers may be a potential factor leading to the poor efficacy of combined chemotherapy in late-stage breast cancer. We investigated the efficacy of various chemotherapy drugs using cell proliferation inhibitors paclitaxel and gemcitabine and tumor cell migration inhibitors 7rh and PP2. To ensure the validity of our findings, we followed a systematic approach that involved testing the inhibitory effects of these drugs. According to our results, the drug combinations' effectiveness could be ordered as follows: paclitaxel + gemcitabine > gemcitabine + 7rh > PP2 + paclitaxel > 7rh + PP2. This study shows that the biomimetic chip system not only facilitates the creation of a realistic in vitro model for examining the cell migration mechanism in late-stage breast cancer but also has the potential to function as an effective tool for future chemotherapy assessment and personalized medicine.

Angiopoietin-like proteins (ANGPTL) constitute a family of eight proteins (1–8) which play a pivotal role in the regulation of various pathophysiological processes. The current study sought to identify high-risk, “non-synonymous, single-nucleotide polymorphisms” (nsSNPs) in both ANGPTL3 and ANGPTL8 to evaluate the role that these nsSNPs play in various types of cancer. We retrieved a total of 301 nsSNPs from various databases; 79 of these candidates constitute high-risk nsSNPs. Moreover, we identified eleven high-risk nsSNPs that cause various types of cancer: seven candidates for ANGPTL3 (L57H, F295L, L309F, K329M, R332L, S348C, and G409R) and four candidates for ANGPTL8 (P23L, R85W, R138S, and E148D). Protein–protein interaction analysis revealed a strong association of ANGPTL proteins with several tumor-suppressor proteins such as ITGB3, ITGAV, and RASSF5. ‘Gene-expression profiling interactive analysis’ (GEPIA) showed that expression of ANGPTL3 is significantly downregulated in five cancers: sarcoma (SARC); cholangio carcinoma (CHOL); kidney chromophobe carcinoma (KICH); kidney renal clear cell carcinoma (KIRC); and kidney renal papillary cell carcinoma (KIRP). GEPIA also showed that expression of ANGPTL8 remains downregulated in three cancers: CHOL; glioblastoma (GBM); and breast invasive carcinoma (BRCA). Survival rate analysis indicated that both upregulation and downregulation of ANGPTL3 and ANGPTL8 leads to low survival rates in various types of cancer. Overall, the current study revealed that both ANGPTL3 and ANGPTL8 constitute potential prognostic biomarkers for cancer; moreover, nsSNPs in these proteins might lead to the progression of cancer. However, further in vivo investigation will be helpful to validate the role of these proteins in the biology of cancer.

In this chapter we invite the reader to consider the importance of context in the history and evolution of both artificial intelligence and the virtual university. There are histories in the terminology as much as what they refer to. Thus, the virtual universities that emerged late last century are very different in scope and vision compared to contemporary practice. There are likewise different theoretical perspectives with Gartner’s “Hype Cycle,” one among many methods that provides perspective on the stages of AI-driven advances in technological innovation. An imperative to foreground questioning of the domain is what frames our discussion. One key question among many concerns the affordances and challenges that AI presents for a virtual university in the coming decades. A university must prepare for AI-based auditing of the integrity of its student records. AI can help spot plagiarism and fake IDs and support academic integrity. Policy-makers might use AI to spot potentially impossible situations that could give rise to big penalties. But as AI merges into our environment, it is no longer just a tool with new ethical and social issues arising. There already exist forms of colonial biases embedded into AI and machine learning systems. What practical choices exist now? Every virtual university might consider collaborating on how to ensure trustworthiness of their operational systems that use AI. Emerging public policy such as the EU’s Artificial Intelligence Act provides a legal mechanism for building a reliable AI ecosystem. Research and innovation into both AI and the kinds of services virtual universities will need into the future are now proliferating. Many futures are possible. From an educational perspective developing human agency through this next era should be a priority. Students could be taught how to use AI-supported tools to solve increasingly challenging questions. Even more imperative will be the questions they ask.

Breast cancer metastasis involves complex mechanisms, particularly when patients are undergoing chemotherapy. In tissues, tumor cells encounter cell-cell interactions, cell-microenvironment interactions, complex nutrient, and drug gradients. Currently, two-dimensional cell culture systems and animal models are challenging to observe and analyze cell responses to microenvironments with various physical and bio-chemical conditions, and microfluidic technology has been systematically developed to address this dilemma. In this study, we have constructed a combined chemotherapy evaluation chip (CCEC) based on microfluidic technology. The chip possesses 192 diamond-shaped microchambers containing MDA-MB-231-RFP cells, and each microchamber is composed of collagen to mimic breast cancer and its surrounding microenvironment. In addition, by adding medium containing different drugs to the medium channels of CCEC, composite drug (paclitaxel+gemcitabine+7rh and paclitaxel+fluorouracil+PP2) concentration gradients, and single drug (paclitaxel, gemcitabine, 7rh, fluorouracil, PP2) concentration gradients have been established in the five collagen regions, respectively, so that each localized microchamber in the regions has a unique drug microenvironment. In this way, we evaluated the composite and single chemotherapy efficacy on the same chip by statistically analyzing their effects on the numbers and migration of the cell. The quantitative results in CCECs reveal that the inhibition effects on the numbers and migration of MDA-MB-231-RFP cell under the composite drug gradients are more optimal than those of the single drugs. Besides, the cancer cell inhibition effect between the groups composed of two drugs has also been compared, that is the paclitaxel+gemcitabine, paclitaxel+fluorouracil, and paclitaxel+PP2 have better cell numbers and migration inhibition effects than paclitaxel+7rh. The results indicate that the bio-mimetic and high-throughput combined chemotherapy evaluation platform can serve as a more efficient and accurate tool for preclinical drug development and screening.

The axiom that a good model strikes a good balance between bias and variance may be regarded as a corollary of the principle of parsimony or Occam's Razor. In this context, the bias is measured in training cost, while the variance of, say, a regression model is measured by the cost associated with a validation set. If reducing bias is the goal, one will strive to fetch as complex a model as necessary. Still, complexity is invariably coupled with an increase in variance: greater complexity implies greater variance. In practice, driving training cost to near zero does not pose a fundamental problem; in fact, a sufficiently complex decision tree can drive training cost to zero; however, the problem is controlling the model's variance. We investigate various regression model frameworks, including generalized linear models, Cox proportional hazard models, ARMA, and illustrate how misspecifying a model with ‘excessive’ complexity affects the variance.

Supplementary Methods, Figures 1-9 from Modulation of Gene Expression and Tumor Cell Growth by Redox Modification of STAT3

Here, we present the R package, minSNPs. This is a re-development of a previously described Java application named Minimum SNPs. MinSNPs assembles resolution-optimised sets of single nucleotide polymorphisms (SNPs) from sequence alignments such as genome-wide orthologous SNP matrices. MinSNPs can derive sets of SNPs optimised for discriminating any user-defined combination of sequences from all others. Alternatively, SNP sets may be optimised to determine all sequences from all other sequences, i.e. , to maximise diversity. MinSNPs encompasses functions that facilitate rapid and flexible SNP mining, and clear and comprehensive presentation of the results. The minSNPs’ running time scales in a linear fashion with input data volume and the numbers of SNPs and SNPs sets specified in the output. MinSNPs was tested using a previously reported orthologous SNP matrix of Staphylococcus aureus and an orthologous SNP matrix of 3,279 genomes with 164,335 SNPs assembled from four S. aureus short read genomic data sets. MinSNPs was shown to be effective for deriving discriminatory SNP sets for potential surveillance targets and in identifying SNP sets optimised to discriminate isolates from different clonal complexes. MinSNPs was also tested with a large Plasmodium vivax orthologous SNP matrix. A set of five SNPs was derived that reliably indicated the country of origin within three south-east Asian countries. In summary, we report the capacity to assemble comprehensive SNP matrices that effectively capture microbial genomic diversity, and to rapidly and flexibly mine these entities for optimised marker sets.

Coronavirus disease 2019 (COVID-19), the agent behind the worst global pandemic of the 21st century (COVID-19), is primarily a respiratory-disease-causing virus called SARS-CoV-2 that is responsible for millions of new cases (incidence) and deaths (mortalities) worldwide. Many factors have played a role in the differential morbidity and mortality experienced by nations and ethnicities against SARS-CoV-2, such as the quality of primary medical health facilities or enabling economies. At the same time, the most important variable, i.e., the subsequent ability of individuals to be immunologically sensitive or resistant to the infection, has not been properly discussed before. Despite having excellent medical facilities, an astounding issue arose when some developed countries experienced higher morbidity and mortality compared with their relatively underdeveloped counterparts. Hence, this investigative review attempts to analyze the issue from an angle of previously undiscussed genetic, epigenetic, and molecular immune resistance mechanisms in correlation with the pathophysiology of SARS-CoV-2 and varied ethnicity-based immunological responses against it. The biological factors discussed here include the overall landscape of human microbiota, endogenous retroviral genes spliced into the human genome, and copy number variation, and how they could modulate the innate and adaptive immune systems that put a certain ethnic genetic architecture at a higher risk of SARS-CoV-2 infection than others. Considering an array of these factors in their entirety may help explain the geographic disparity of disease incidence, severity, and subsequent mortality associated with the disease while at the same time encouraging scientists to design new experimental approaches to investigation.

The androgen receptor (AR) is a crucial coactivator of ELK1 for prostate cancer (PCa) growth, associating with ELK1 through two peptide segments (358-457 and 514-557) within the amino-terminal domain (NTD) of AR. The small-molecule antagonist 5-hydroxy-2-(3-hydroxyphenyl)chromen-4-one (KCI807) binds to AR, blocking ELK1 binding and inhibiting PCa growth. We investigated the mode of interaction of KCI807 with AR using systematic mutagenesis coupled with ELK1 coactivation assays, testing polypeptide binding and Raman spectroscopy. In full-length AR, deletion of neither ELK1 binding segment affected sensitivity of residual ELK1 coactivation to KCI807. Although the NTD is sufficient for association of AR with ELK1, interaction of the isolated NTD with ELK1 was insensitive to KCI807. In contrast, coactivation of ELK1 by the AR-V7 splice variant, comprising the NTD and the DNA binding domain (DBD), was sensitive to KCI807. Deletions and point mutations within DBD segment 558-595, adjacent to the NTD, interfered with coactivation of ELK1, and residual ELK1 coactivation by the mutants was insensitive to KCI807. In a glutathione S-transferase pull-down assay, KCI807 inhibited ELK1 binding to an AR polypeptide that included the two ELK1 binding segments and the DBD but did not affect ELK1 binding to a similar AR segment that lacked the sequence downstream of residue 566. Raman spectroscopy detected KCI807-induced conformational change in the DBD. The data point to a putative KCI807 binding pocket within the crystal structure of the DBD and indicate that either mutations or binding of KCI807 at this site will induce conformational changes that disrupt ELK1 binding to the NTD. SIGNIFICANCE STATEMENT The small-molecule antagonist KCI807 disrupts association of the androgen receptor (AR) with ELK1, serving as a prototype for the development of small molecules for a novel type of therapeutic intervention in drug-resistant prostate cancer. This study provides basic information needed for rational KCI807-based drug design by identifying a putative binding pocket in the DNA binding domain of AR through which KCI807 modulates the amino-terminal domain to inhibit ELK1 binding.

Feature or variable selection is a fundamental problem in data analysis and statistical modeling. Classic methods resulting in dimensionality reduction are diverse and include things like statistical hypotheses testing for zero coefficients, ‘stepwise’ methods minimizing, e.g., AIC, the spectra of a data matrix or principal component analysis, regularization methods especially the Lasso, various heuristic and ‘shrinkage’ methods all of which result in a subset of the feature space used as a basis for statistical modeling. Combinatorial variable selection has also been used in a manner that aids in the selection of a good subset of the feature space. A graph, or the ‘data graph’, is based on the pairwise correlations of features and may be used to extract the most distinguishing features. Partly due to high computational cost, combinatorial variable selection methods have not been well studied. We consider a variable selection procedure via the Minimum Independent Dominating Set problem. We explore the use of some exact and heuristic methods that proved to be effective for feature extracting and ranking.

BACKGROUND:The most often diagnosed malignancy in women worldwide is cancer of the cervix. It is also the most prevalent kind of gynecological cancer in women. This cancer originates in the opening of the cervix and spreads through sexual contact. Even though human papillomavirus (HPV) may not cause cancer immediately, it does develop over time as a result of the virus's lengthy persistence to cause dysplastic changes overtime, particularly in high-risk kinds. The primary objective of this research is to see if miRNAs are dysregulated as a result of treatment resistance in cervical cancer (CC). The aim is to see if these microRNAs may be utilized as biomarkers for detecting chemoresistance in CC, particularly for clinical applications.METHODS:The recommended protocol for comprehensive study and meta-analysis (PRISMA-P) standards will be utilized for the analysis and data interpretation. The bibliographic databases will be methodically searched using a combination of search keywords. Based on established inclusion and exclusion criteria, the acquired findings will be reviewed, and data retrieved from the selected scientific papers for systematic review. We will then construct a forest from the pooled Hazard ratio (HR) and 95% C.I. values, data obtained using the random-effects model.DISCUSSION:The focus of this study is to identify the function of miRNAs as a chemoresistance regulator and determine if they have the potential scope to be considered as biomarkers for cervical cancer. Through this systematic review and meta-analysis, the goal is to collect, compare, and analyze the data pertaining to the role of miRNAs in cervical cancer, thereby, enabling us to understand the role they play in chemosensitivity.

miRNAs biomarkers are emerging as an essential part of clinical oncology. Their oncogenic and tumour suppressor properties playing a role in malignancy has generated interest in their potential for use in disease prognosis. While several studies on miRNA have been carried out across the globe, evaluating the clinical implications of miRNAs in cancer diagnosis and prognosis research has currently not been attempted. A study delineating the area of miRNA research, including the topics presently being focused on, the seminal papers in this field, and the direction of research interest, does not exist. This study aims to conduct a large-scale, global data analysis and bibliometric profiling analysis of studies to evaluate the research output of clinical implications of miRNAs in cancer diagnosis and prognosis listed in the SCOPUS database. A systematic search strategy was followed to identify and extract all relevant studies, subsequently analysed to generate a bibliometric map. SPSS software (version 27) was used to calculate bibliometric indicators or parameters for analysis, such as year and country of affiliation with leading authors, journals, and institutions. It is also used to analyse annual research outputs, including total citations and the number of times it has been cited with productive nations and H-index. The number of global research articles retrieved for miRNA-Cancer research over the study period 2003 to 2019 was 18,636. Between 2012 and 2019, the growth rate of global publications is six times (n = 15,959; 90.71 percent articles) that of 2003 to 2011. (2704; 9.29 per cent articles). China published the most publications in the field of miRNA in cancer (n = 7782; 41%), while the United States had the most citations (n = 327,538; 48%) during the time span. Of these journals, Oncotarget has the highest percentage of article publications. The journal Cancer Research had the most citations (n = 41,876), with 6.20 per cent (n = 41,876). This study revealed a wide variety of journals in which miRNA-Cancer research are published; these bibliometric parameters exhibit crucial clinical information on performance assessment of research productivity and quality of research output. Therefore, this study provides a helpful reference for clinical oncologists, cancer scientists, policy decision-makers and clinical data researchers.

We present a significant improvement to a methodology which was described in several earlier articles by Barr, et al. where we demonstrated a workflow which classifies the source code of large open source projects for vulnerability. Whereas in the past, to deal with dearth of minority examples we’ve applied upsampling and simulation technique, this present approach demonstrates that a clever choice of cost function sans upsampling results in excellent performance surpassing previous results. In this iteration a feed-forward neural network classifier was trained on Area Under Min(FP, FN) (AUM) loss. The AUM method is described in Hillman & Hocking. Similar to earlier work, to overcome the out-of-vocabulary challenge, an intermediate step Byte-Pair Encoding which ‘compresses’ the data and subsequently, with the compressed data, long short-term memory (LSTM) network is used to embed the tokens from which we assemble an embedding of function labels. This results in 128D embedding which along with additional ‘interpretable’, heuristics-based features which are used to classify CVEs. The resulting labeled dataset is extremely sparse, with a minority class consisting of roughly 0.5% of total. Demonstratively, the AUM cost function is undeterred by sparsity of data; this is amply demonstrated by the performance of the classifier.

Post-translational modifications (PTMs) are important for protein folding and activity, and the ability to recreate physiologically relevant PTM profiles on recombinantly-expressed proteins is vital for meaningful functional analysis. The ETS transcription factor ELK-1 serves as a paradigm for cellular responses to mitogens and can synergise with androgen receptor to promote prostate cancer progression, although in vitro protein function analyses to date have largely overlooked its complex PTM landscapes. We expressed and purified human ELK-1 using mammalian (HEK293T), insect (Sf9) and bacterial (E. coli) systems in parallel and compared PTMs imparted upon purified proteins, along with their performance in DNA and protein interaction assays. Phosphorylation of ELK-1 within its transactivation domain, known to promote DNA binding, was most apparent in protein isolated from human cells and accordingly conferred the strongest DNA binding in vitro, while protein expressed in insect cells bound most efficiently to the androgen receptor. We observed lysine acetylation, a hitherto unreported PTM of ELK-1, which appeared highest in insect cell-derived ELK-1 but was also present in HEK293T-derived ELK-1. Acetylation of ELK-1 was enhanced in HEK293T cells following starvation and mitogen stimulation, and modified lysines showed overlap with previously identified regulatory SUMOylation and ubiquitination sites. Our data demonstrate that the choice of recombinant expression system can be tailored to suit biochemical application rather than to maximise soluble protein production and suggest the potential for crosstalk and antagonism between different PTMs of ELK-1.

Prostate cancer (PC) is generally dependent on the androgen signaling axis for growth. Advanced PC is managed by androgen deprivation therapy (ADT). However, the tumors frequently progress by restoring AR signaling that is either androgen-independent or hypersensitive to androgen, leading to castration resistant prostate cancer (CRPC). CRPC is also supported by hormone-independent actions of AR splice variants which lack the ligand binding domain. Additionally, ADT has many undesirable side effects on a variety of normal tissues that depend on androgen for non-growth related functions, including effects on the cardiovascular system, central nervous system (cognitive effects), bone and muscle. Therefore, a more strategic therapy approach would be to disrupt a functional arm of AR-signaling that is critical for PC/CRPC growth but not for the essential physiological roles of AR in normal tissues. Previous studies in this laboratory have identified ELK1 as an AR tethering protein essential for activation of a critical set of androgen/AR growth genes in various PC model cell line models, both in vitro and in vivo, including those resistant to castration and enzalutamide. It was also established that the N-terminal A/B domain of AR binds to ELK1 by co-opting the two ERK docking sites on ELK1 to constitutively activate genes enriched for cell growth functions in PC cells. A platform antagonist (KCI807) was discovered that bound to AR (Kd = 7x10-8 M), blocking its association with ELK1 and inhibiting PC/CRPC tumor growth. KCI807 has a limited target gene set (a subset of AR target genes) that is primarily and highly enriched for functions in cell cycle progression and mitosis. To further drug development, we must identify the ELK1 docking site recognition sites in AR and the KCI807 binding site. Using a variety of complimentary approaches, we have now mapped and validated the ELK1 docking site recognition sites in the AR A/B domain and also the binding site of KCI807 in AR. The two docking site recognition sites in the AR A/B domain for the two docking sites in ELK1 span amino acids 358-456 and amino acids 514-557. We have also developed a working model in which KCI807 binds in a cleft adjacent to the downstream ELK1-binding site, within the DBD, displacing an alpha helix which in turn disrupts ELK1 binding. Further structural and functional studies based on this knowledge will directly aid in design of next-generation antagonists to selectively target ELK1-dependent growth signaling by AR in PC/CRPC tumors. Citation Format: Claire L. Soave, Charles Ducker, Nathan Nicely, Yanfang Huang, Luke Pardy, Peter Shaw, Manohar Ratnam. New insights into interactions of the androgen receptor with ELK1 and the platform antagonist KCI807 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2923.

Introduction: Melanoma is a global disease that is predominant in Western countries. However, reliable data resources and comprehensive studies on the theragnostic efficiency of miRNAs in melanoma are scarce. Hence, a decisive study or comprehensive review is required to collate the evidence for profiling miRNAs as a theragnostic marker. This protocol details a comprehensive systematic review and meta-analysis on the impact of miRNAs on chemoresistance and their association with theragnosis in melanoma. Methods and analysis: The articles will be retrieved from online bibliographic databases, including Cochrane Review, EMBASE, MEDLINE, PubMed, Scopus, Science Direct, and Web of Science, with different permutations of 'keywords'. To obtain full-text papers of relevant research, a stated search method will be used, along with selection criteria. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Protocols 2015 (PRISMA-P) standards were used to create this study protocol. The hazard ratio (HR) with a 95% confidence interval will be analyzed using Comprehensive Meta-Analysis (CMA) software 3.0. (CI). The pooled effect size will be calculated using a random or fixed-effects meta-analysis model. Cochran's Q test and the I2 statistic will be used to determine heterogeneity. Egger's bias indicator test, Orwin's and the classic fail-safe N tests, the Begg and Mazumdar rank collection test, and Duval and Tweedie's trim and fill calculation will all be used to determine publication bias. The overall standard deviation will be evaluated using Z-statistics. Subgroup analyses will be performed according to the melanoma participants' clinicopathological and biological characteristics and methodological factors if sufficient studies and retrieved data are identified and available. The source of heterogeneity will be assessed using a meta-regression analysis. A pairwise matrix could be developed using either a pairwise correlation or expression associations of miRNA with patients' survival for the same studies.

Bacteria form biofilms on material surfaces within hours. Biofilms are often considered problematic substances in the fields such as biomedical devices and the food industry; however, they are beneficial in other fields such as fermentation, water remediation, and civil engineering. Biofilm properties depend on their genome and the extracellular environment, including pH, shear stress, and matrices topography, stiffness, wettability, and charges during biofilm formation. These surface properties have feedback effects on biofilm formation at different stages. Due to emerging technology such as synthetic biology and genome editing, many studies have focused on functionalizing biofilm for specific applications. Nevertheless, few studies combine these two approaches to produce or modify biofilms. This review summarizes up-to-date materials science and synthetic biology approaches to controlling biofilms. The review proposed a potential research direction in the future that can gain better control of bacteria and biofilms.

The diminishing of the polarization effect is important in the applications of dielectric multilayer reflectors in many optical systems, such as low-loss broadband waveguides, optical fibers, and LEDs. Low-polarizing broadband reflections were identified from birefringent-guanine-crystal-based multilayer reflectors in the skins of some fish. Previous models for these intriguing natural optical phenomena suggested the combined action of two populations of guanine crystals with an orthogonal low-refractive-index optic axis. Here we report a novel realization of polarization-insensitive broadband reflectivity in a spider, Phoroncidia rubroargentea, based solely on the type of guanine crystals with the low-refractive-index optic axis normal to the crystal plates. We examined the three-dimensional structure of the guanine assembly in the spider and performed finite-difference time-domain (FDTD) optical modeling of the guanine-based multilayer reflector. Comparative modeling studies reveal that the biological selection of the guanine crystal type and specific spatial arrangement work synergistically to optimize the polarization-insensitive broadband reflection. This study demonstrates the importance of both crystallographic characteristics and 3D arrangement of guanine crystals in understanding relevant natural optical effects and also provides new insights into similar broadband, low-polarizing reflections in biological optical systems. Learning from relevant biofunctional assembly of guanine crystals could promote the bioinspired design of nonpolarizing dielectric multilayer reflectors.

Background: Patients with cardiovascular disease and risk factors for cardiovascular illness are more likely to acquire severe 2019 novel coronavirus (2019-nCoV) infection (COVID-19). COVID-19 infection is more common in patients with cardiovascular illness, and they are more likely to develop severe symptoms. Nevertheless, whether COVID-19 patients are more likely to develop cardiovascular disorders such as acute myocardial infarction (AMI) is still up for debate. Methods: We will follow the preferred reporting items for systematic review and meta-analysis (PRISMA) to report our final study, including a systematic search of the bibliographic database using the appropriate combination of search terms or keywords. The choice of search terms is discussed in more detail later in this paper. The obtained results will be screened, and the data extracted from the studies selected for systematic review will be based on the predefined inclusion and exclusion criteria. Using the obtained data, we will then perform the associated Meta-analysis to generate the forest plot (pooled estimated effect size Hazard Ratio (HR) and 95% Confidence Intervals (CI) values) using the random-effects model. Any publication bias will be assessed using the funnel plot symmetry, Orwin and Classic Fail-Safe N Test and Begg and Mazumdar Rank Correlation Test and Egger’s Test of the intercept. In cases where insufficient data occur, we will also perform a qualitative review. Discussion: This systematic review will explore COVID-19 clinical outcomes, especially survival in patients hospitalised with Acute Myocardial Infarction, by utilising a collection of previously published data on hospitalised COVID-19 patients and Myocardial Infarction. Highlighting these prognostic survival analyses of COVID-19 patients with AMI will have significant clinical implications by allowing for better overall treatment strategies and patient survival estimates by offering clinicians a method of quantitatively analysing the pattern of COVID-19 cardiac complications.