Investigating Immune Profiles in Differentiated Thyroid Cancer by Multiplex Immunofluorescence

BACKGROUND: As the most common endocrine malignancy in the United States (U.S.), differentiated thyroid cancer (DTC) accounts for 3.8% of all cancers in the U.S., with roughly 10% of cases progressing to distant metastatic DTC, which is associated with a poor five year survival outcome despite conventional management, including surgery and radioactive iodine ablation. Recently, novel immunotherapies have garnered attention as a viable therapeutic resource for patients with advanced DTC. However, the response to therapy has been variable and unpredictable, which may be associated with an immune suppressive circulating phenotype. Nonetheless, the intra-tumoral immune infiltrate remains to be elucidated, demonstrating a critical need to address the gap in understanding in order to better prognosticate the disease. OBJECTIVE: To identify and compare tumor-infiltrating immune markers with those present in the adjacent normal thyroid tissue, and collate these immune infiltrates with tumor characteristics. METHODS: Twenty-nine adult tissue samples containing tumor and stromal regions were collected from patients with DTC. The samples were analyzed using multiplex immunofluorescence (MxIF) with antibodies against cell-surface molecules CD56, PD-1, PD-L1, FOXP3, CD3, CD8, CD4, CD45, CD68, CD163, INOS, HLA-DR, CD33, and CD19. 17 of the specimens were analyzed using HALO and a positive threshold was assigned based on review by a trained researcher. RESULTS: In evaluating the immune profiles, important differences in the immune infiltrates between different stages of the cancer were observed. Generally, PD-1 and PD-L1 were highly expressed within the tumor, despite variability in lymphocyte infiltration, indicating the importance of PD-1 and PD-L1 as potential predictive biomarkers for the aggressiveness of thyroid cancer. Tumor from patients with distant metastases demonstrated higher T cell infiltration, T regulatory cells, macrophages and PD-L1 positive cells as compared to localized tumor. CONCLUSION: Immune profiling demonstrated significant differences between tumor and adjacent healthy regions, particularly in terms of PD-1 and PD-L1 expression and lymphocyte infiltration, indicating that higher intratumor infiltration of T regulatory cells, macrophages and PD-1/PD-L1 positive cells may be associated with advanced thyroid cancer. Therefore, the data demonstrates the efficacy of MxIF in gathering valuable information regarding the tumor microenvironment, which will have major implications in guiding the selection of patients for immunotherapy.https://digitalcommons.unmc.edu/surp2021/1042/thumbnail.jp

Machine Learning Analyses of Highly-Multiplexed Immunofluorescence Identifies Distinct Tumor and Stromal Cell Populations in Primary Pancreatic Tumors

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge for patients and clinicians. OBJECTIVE: To analyze the distribution of 31 different markers in tumor and stromal portions of the tumor microenvironment (TME) and identify immune cell populations to better understand how neoplastic, non-malignant structural, and immune cells, diversify the TME and influence PDAC progression. METHODS: Whole slide imaging (WSI) and cyclic multiplexed-immunofluorescence (MxIF) was used to collect 31 different markers over the course of nine distinctive imaging series of human PDAC samples. Image registration and machine learning algorithms were developed to largely automate an imaging analysis pipeline identifying distinct cell types in the TME. RESULTS: A random forest algorithm accurately predicted tumor and stromal-rich areas with 87% accuracy using 31 markers and 77% accuracy using only five markers. Top tumor-predictive markers guided downstream analyses to identify immune populations effectively invading into the tumor, including dendritic cells, CD4+ T cells, and multiple immunoregulatory subtypes. CONCLUSIONS: Immunoprofiling of PDAC to identify differential distribution of immune cells in the TME is critical for understanding disease progression, response and/or resistance to treatment, and the development of new treatment strategies

Fingerprint Recognition with Identical Twin Fingerprints

Fingerprint recognition with identical twins is a challenging task due to the closest genetics-based relationship existing in the identical twins. Several pioneers have analyzed the similarity between twins' fingerprints. In this work we continue to investigate the topic of the similarity of identical twin fingerprints. Our study was tested based on a large identical twin fingerprint database that contains 83 twin pairs, 4 fingers per individual and six impressions per finger: 3984 (83*2*4*6) images. Compared to the previous work, our contributions are summarized as follows: (1) Two state-of-the-art fingerprint identification methods: P071 and VeriFinger 6.1 were used, rather than one fingerprint identification method in previous studies. (2) Six impressions per finger were captured, rather than just one impression, which makes the genuine distribution of matching scores more realistic. (3) A larger sample (83 pairs) was collected. (4) A novel statistical analysis, which aims at showing the probability distribution of the fingerprint types for the corresponding fingers of identical twins which have same fingerprint type, has been conducted. (5) A novel analysis, which aims at showing which finger from identical twins has higher probability of having same fingerprint type, has been conducted. Our results showed that: (a) A state-of-the-art automatic fingerprint verification system can distinguish identical twins without drastic degradation in performance. (b) The chance that the fingerprints have the same type from identical twins is 0.7440, comparing to 0.3215 from non-identical twins. (c) For the corresponding fingers of identical twins which have same fingerprint type, the probability distribution of five major fingerprint types is similar to the probability distribution for all the fingers' fingerprint type. (d) For each of four fingers of identical twins, the probability of having same fingerprint type is similar

The use of phylogeny to interpret cross-cultural patterns in plant use and guide medicinal plant discovery: an example from Pterocarpus (Leguminosae)

The study of traditional knowledge of medicinal plants has led to discoveries that have helped combat diseases and improve healthcare. However, the development of quantitative measures that can assist our quest for new medicinal plants has not greatly advanced in recent years. Phylogenetic tools have entered many scientific fields in the last two decades to provide explanatory power, but have been overlooked in ethnomedicinal studies. Several studies show that medicinal properties are not randomly distributed in plant phylogenies, suggesting that phylogeny shapes ethnobotanical use. Nevertheless, empirical studies that explicitly combine ethnobotanical and phylogenetic information are scarce.In this study, we borrowed tools from community ecology phylogenetics to quantify significance of phylogenetic signal in medicinal properties in plants and identify nodes on phylogenies with high bioscreening potential. To do this, we produced an ethnomedicinal review from extensive literature research and a multi-locus phylogenetic hypothesis for the pantropical genus Pterocarpus (Leguminosae: Papilionoideae). We demonstrate that species used to treat a certain conditions, such as malaria, are significantly phylogenetically clumped and we highlight nodes in the phylogeny that are significantly overabundant in species used to treat certain conditions. These cross-cultural patterns in ethnomedicinal usage in Pterocarpus are interpreted in the light of phylogenetic relationships.This study provides techniques that enable the application of phylogenies in bioscreening, but also sheds light on the processes that shape cross-cultural ethnomedicinal patterns. This community phylogenetic approach demonstrates that similar ethnobotanical uses can arise in parallel in different areas where related plants are available. With a vast amount of ethnomedicinal and phylogenetic information available, we predict that this field, after further refinement of the techniques, will expand into similar research areas, such as pest management or the search for bioactive plant-based compounds

Ecological implications of a flower size/number trade-off in tropical forest trees

Peer reviewedPublisher PD

Payer leverage and hospital compliance with a benchmark: a population-based observational study

<p>Abstract</p> <p>Background</p> <p>Since 1976, Medicare has linked reimbursement for hospitals performing organ transplants to the attainment of certain benchmarks, including transplant volume. While Medicare is a stakeholder in all transplant services, its role in renal transplantation is likely greater, given its coverage of end-stage renal disease. Thus, Medicare's transplant experience allows us to examine the role of payer leverage in motivating hospital benchmark compliance.</p> <p>Methods</p> <p>Nationally representative discharge data for kidney (<it>n </it>= 29,272), liver (<it>n </it>= 7,988), heart (<it>n </it>= 3,530), and lung (<it>n </it>= 1,880) transplants from the Nationwide Inpatient Sample (1993 – 2003) were employed. Logistic regression techniques with robust variance estimators were used to examine the relationship between hospital volume compliance and Medicare market share; generalized estimating equations were used to explore the association between patient-level operative mortality and hospital volume compliance.</p> <p>Results</p> <p>Medicare's transplant market share varied by organ [57%, 28%, 27%, and 18% for kidney, lung, heart, and liver transplants, respectively (<it>P </it>< 0.001)]. Volume-based benchmark compliance varied by transplant type [85%, 75%, 44%, and 39% for kidney, liver, heart, and lung transplants, respectively (<it>P </it>< 0.001)], despite a lower odds of operative mortality at compliant hospitals. Adjusting for organ supply, high market leverage was independently associated with compliance at hospitals transplanting kidneys (OR, 143.00; 95% CI, 18.53 – 1103.49), hearts (OR, 2.84; 95% CI, 1.51 – 5.34), and lungs (OR, 3.24; 95% CI, 1.57 – 6.67).</p> <p>Conclusion</p> <p>These data highlight the influence of payer leverage–an important contextual factor in value-based purchasing initiatives. For uncommon diagnoses, these data suggest that at least 30% of a provider's patients might need to be "at risk" for an incentive to motivate compliance.</p

Ctp1 and the MRN-Complex Are Required for Endonucleolytic Rec12 Removal with Release of a Single Class of Oligonucleotides in Fission Yeast

DNA double-strand breaks (DSBs) are formed during meiosis by the action of the topoisomerase-like Spo11/Rec12 protein, which remains covalently bound to the 5′ ends of the broken DNA. Spo11/Rec12 removal is required for resection and initiation of strand invasion for DSB repair. It was previously shown that budding yeast Spo11, the homolog of fission yeast Rec12, is removed from DNA by endonucleolytic cleavage. The release of two Spo11 bound oligonucleotide classes, heterogeneous in length, led to the conjecture of asymmetric cleavage. In fission yeast, we found only one class of oligonucleotides bound to Rec12 ranging in length from 17 to 27 nucleotides. Ctp1, Rad50, and the nuclease activity of Rad32, the fission yeast homolog of Mre11, are required for endonucleolytic Rec12 removal. Further, we detected no Rec12 removal in a rad50S mutant. However, strains with additional loss of components localizing to the linear elements, Hop1 or Mek1, showed some Rec12 removal, a restoration depending on Ctp1 and Rad32 nuclease activity. But, deletion of hop1 or mek1 did not suppress the phenotypes of ctp1Δ and the nuclease dead mutant (rad32-D65N). We discuss what consequences for subsequent repair a single class of Rec12-oligonucleotides may have during meiotic recombination in fission yeast in comparison to two classes of Spo11-oligonucleotides in budding yeast. Furthermore, we hypothesize on the participation of Hop1 and Mek1 in Rec12 removal

Pathobiological Implications of MUC16 Expression in Pancreatic Cancer

MUC16 (CA125) belongs to a family of high-molecular weight O-glycosylated proteins known as mucins. While MUC16 is well known as a biomarker in ovarian cancer, its expression pattern in pancreatic cancer (PC), the fourth leading cause of cancer related deaths in the United States, remains unknown. The aim of our study was to analyze the expression of MUC16 during the initiation, progression and metastasis of PC for possible implication in PC diagnosis, prognosis and therapy. In this study, a microarray containing tissues from healthy and PC patients was used to investigate the differential protein expression of MUC16 in PC. MUC16 mRNA levels were also measured by RT-PCR in the normal human pancreatic, pancreatitis, and PC tissues. To investigate its expression pattern during PC metastasis, tissue samples from the primary pancreatic tumor and metastases (from the same patient) in the lymph nodes, liver, lung and omentum from Stage IV PC patients were analyzed. To determine its association in the initiation of PC, tissues from PC patients containing pre-neoplastic lesions of varying grades were stained for MUC16. Finally, MUC16 expression was analyzed in 18 human PC cell lines. MUC16 is not expressed in the normal pancreatic ducts and is strongly upregulated in PC and detected in pancreatitis tissue. It is first detected in the high-grade pre-neoplastic lesions preceding invasive adenocarcinoma, suggesting that its upregulation is a late event during the initiation of this disease. MUC16 expression appears to be stronger in metastatic lesions when compared to the primary tumor, suggesting a role in PC metastasis. We have also identified PC cell lines that express MUC16, which can be used in future studies to elucidate its functional role in PC. Altogether, our results reveal that MUC16 expression is significantly increased in PC and could play a potential role in the progression of this disease

Does the Effectiveness of Control Measures Depend on the Influenza Pandemic Profile?

BACKGROUND: Although strategies to contain influenza pandemics are well studied, the characterization and the implications of different geographical and temporal diffusion patterns of the pandemic have been given less attention. METHODOLOGY/MAIN FINDINGS: Using a well-documented metapopulation model incorporating air travel between 52 major world cities, we identified potential influenza pandemic diffusion profiles and examined how the impact of interventions might be affected by this heterogeneity. Clustering methods applied to a set of pandemic simulations, characterized by seven parameters related to the conditions of emergence that were varied following Latin hypercube sampling, were used to identify six pandemic profiles exhibiting different characteristics notably in terms of global burden (from 415 to >160 million of cases) and duration (from 26 to 360 days). A multivariate sensitivity analysis showed that the transmission rate and proportion of susceptibles have a strong impact on the pandemic diffusion. The correlation between interventions and pandemic outcomes were analyzed for two specific profiles: a fast, massive pandemic and a slow building, long-lasting one. In both cases, the date of introduction for five control measures (masks, isolation, prophylactic or therapeutic use of antivirals, vaccination) correlated strongly with pandemic outcomes. Conversely, the coverage and efficacy of these interventions only moderately correlated with pandemic outcomes in the case of a massive pandemic. Pre-pandemic vaccination influenced pandemic outcomes in both profiles, while travel restriction was the only measure without any measurable effect in either. CONCLUSIONS: our study highlights: (i) the great heterogeneity in possible profiles of a future influenza pandemic; (ii) the value of being well prepared in every country since a pandemic may have heavy consequences wherever and whenever it starts; (iii) the need to quickly implement control measures and even to anticipate pandemic emergence through pre-pandemic vaccination; and (iv) the value of combining all available control measures except perhaps travel restrictions