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F1 materials of half-diallel crosses for nine characters in lentils were studied for correlation, path-coefficient, and selection index. The phenotypic component of variation (σ2p) was higher than the genotypic component of variation (σ2g). The highest σ2g and σ2pwere obtained for CAMF. Investigation showed that genotypic correlations (rg) were higher than the respective phenotypic correlations (rp) for most of the characters. SWPP showed a highly significant and positive correlation coefficient with other characters except for the NPBFF at the genotypic level and except NPBFF and DF at the phenotypic level. The highest significant and positive genotypic correlation coefficient was recorded for NSBFF with PdWPP at the genotypic level and PdWPP with SWPP at the phenotypic level. PdWPP had the highest positive direct effect on SWPP at both genotypic and phenotypic levels. The maximum expected genetic gain of 4603.196% was found when NPBFF and RW were included in the index. These two characters a had high correlation coefficient with most of the characters studied as well as a direct effect at the genotypic level may be considered as primary yield components.

Cervical erosion is caused by the replacement of the stratified squamous epithelium of the ectocervix with the endocervical columnar epithelium. In Ayurvedic classics, it can be co-related to garbhashayagrivagatavrana. Its management mainly includes destruction of overgrown epithelium by different techniques has its own limitations and side effects. Further observing its prevalence there is a requirement for a treatment modality which is more efficient and cost-effective. Kshara which possesses lekhana (scrapping), vrana-shodhana (wound cleansing), and ropana (wound healing) properties as described in Ayurveda, provides a scientific background of the therapeutic application in the management. Different review works revealed the potential use of kshara on wound healing due to its alkalinity which maintain the body pH within the normal range and thus maintains different cellular and extracellular function. Therefore, kshara is found to be an important preparation that has been investigated for its gynecological uses by different scholars.

Introduction: Vatajayonivyapada having signs and symptoms such as pain along with abnormalities in menstrual bleeding matches with many of the gynaecologicaldisorders in women of reproductive age group. It hampers their day-to-day chores and disturbing their interpersonal relationship and most importantly compromises quality of life. Therefore, an instant relief of symptoms is required along with an overall improvement in the general condition of those women.In such conditions, a better alterative non-hormonal Ayurvedic management is planned. Ksheerapaka(medicated milk preparations) of Rasna, Gokshuraand Vasa having vedanahara (analgesic effect) is used in the study. Objective: In this research work our objective was to evaluate the therapeutic efficacy of oral RasnadiKsheerapaka in Vatajayonivyapada. Materials and method: Total 30 patients within 20-40 years of ageirrespective of the marital status having classical symptoms of Vatajayonivyapada have been selected randomly from Prasuti Tantra OPD, S.S. Hospital, BHU, Varanasi. After detailed history, complete examination and investigations patients selected for the study on the basis of selection criteria.Patientswere treatedwith Rasnadiksheerapaka50ml orally once in a day for total duration of 45 days. Study was assessed after completion of follow-up period. Result: Among the 28 cases, 5 patients became cured and 6 remained without any improvement and all the remaining 17 patients have got mild to marked improvement in the signs and symptoms. Conclusion: It is concluded that Rasnadiksheerapakahas got the therapeutic efficacy in the Vatajayonivyapadamanagement. Also helps in improving the overall health status and quality of life in such women.

Huntington's disease is a progressive and lethal neurodegenerative disease caused by an increased CAG repeat mutation in exon 1 of the huntingtin gene (mutant huntingtin). Current drug treatments provide only limited symptomatic relief without impacting disease progression. Previous studies in our lab and others identified the abnormal binding of mutant huntingtin protein with calmodulin, a key regulator of calcium signaling. Disrupting the abnormal binding of mutant huntingtin to calmodulin reduces perturbations caused by mutant huntingtin in cell and mouse models of Huntington's disease and importantly normalizes receptor-stimulated calcium release. Using a series of high-throughput

Glycosaminoglycans (GAGs) are responsible for preserving bone tissue toughness as well as regulating collagen formation and mineralization in the extracellular matrix. However, current methods for characterization of GAGs in bone are destructive, thus unable to capture in situ changes or differences in GAGs between experimental groups. As an alternative, Raman spectroscopy is a non-destructive method and can detect concurrent changes in GAGs and other bone constituents. In this study, we hypothesized that the two most prominent Raman peaks of sulfated GAGs (at ~1066 cm(−1) and at ~1378 cm(−1)) could be used to detect differences in GAGs content of bone. To test this hypothesis, three experimental models were utilized: an in vitro model (enzymatic removal of GAGs from human cadaver bone), an in vivo mouse model (biglycan KO vs. WT), and an ex vivo aging model (comparing cadaveric bone samples from young and old donors). All Raman measurements were compared to Alcian blue measurements to confirm the validity of Raman spectroscopy in detecting GAGs changes in bone. Irrespective of different models, it was found that the ~1378cm(−1) peak in Raman spectra of bone was uniquely sensitive to changes of GAGs content in bone when normalized with respect to the phosphate phase (~960cm(−1)); i.e., 1378cm(−1)/960cm(−1) (peak intensity ratio) or 1370-1385cm(−1)/930-980cm(−1) (integrated peak area ratio). In contrast, the 1070cm(−1) peak, which includes another major peak of GAGs (1066cm(−1)), seemed to be compromised to detect changes of GAGs in bone due to concurrent changes of carbonate (CO(3)) in the similar peak range. This study validates the ability of Raman spectroscopy to detect in situ treatment-, genotype-, and age-related changes in GAG levels of bone matrix.

The problem of testing the intercept and slope parameters of doubly multivariate linear models with site-dependent covariates using Rao's score test (RST) is studied. The RST statistic is developed for a block exchangeable covariance structure on the error vector under the assumption of multivariate normality. We compare our developed RST statistic with the likelihood ratio test (LRT) statistic. Monte Carlo simulations indicate that the RST statistic is much more accurate than its counterpart LRT statistic and it takes significantly less computation time than the LRT statistic. The proposed method is illustrated with an example of multiple response variables measured on multiple trees in a single plot in an agricultural study.

Overexpression of interferon induced transmembrane protein-1 (IFITM1) enhances tumor progression in multiple cancers, but its role in triple-negative breast cancer (TNBC) is unknown. Here, we explore the functional significance and regulation of IFITM1 in TNBC and strategies to target its expression. Immunohistochemistry staining of a tissue microarray demonstrates that IFITM1 is overexpressed in TNBC samples which is confirmed by TCGA analysis. Targeting IFITM1 by siRNA or CRISPR/Cas9 in TNBC cell lines significantly inhibits proliferation, colony formation, and wound healing in vitro. Orthotopic mammary fat pad and mammary intraductal studies reveal that loss of IFITM1 reduces TNBC tumor growth and invasion in vivo. RNA-seq analysis of IFITM1/KO cells reveals significant downregulation of several genes involved in proliferation, migration, and invasion and functional studies identified NF-κB as an important downstream target of IFITM1. Notably, siRNA knockdown of p65 reduces IFITM1 expression and a drug-repurposing screen of FDA approved compounds identified parthenolide, an NFκB inhibitor, as a cytotoxic agent for TNBC and an inhibitor of IFITM1 in vitro and in vivo. Overall, our findings suggest that targeting IFITM1 by suppressing interferon-alpha/NFκB signaling represents a novel therapeutic strategy for TNBC treatment.

Most cases of epithelial ovarian cancer (EOC) exhibit extensive molecular heterogeneity, presenting challenges in developing targeted therapies. Despite extensive efforts and incremental successes in developing targeted drugs and immunotherapies for other cancers, chemotherapies continue to be the most used treatment of ovarian cancer. Although seemingly simplistic, identification of drugs targeting cellular machinery independent of genomic and genetic status continues to be a strong clinical need. Previously, we performed an RNAi-based screen of the druggable genome across a diverse histological panel of EOC cell line representing both platinum-sensitive and -resistant tumors (PMID: 23056589). This screen elucidated KIF11 as essential in maintaining EOC cell viability. KIF11, a mitotic spindle assembly motor protein, has been targeted clinically. Although drugs are well tolerated, potent, and specific, the objective response rates to KIF11 inhibitors in clinical trials were commonly less than 10%. The efficacy of KIF11 inhibitors is blunted via a compensatory motor kinesin, KIF15. The overexpression of KIF15 has been shown to compensate for absent KIF11 in the formation of the bipolar spindle apparatus during mitosis. Silencing KIF15 significantly sensitizes cells to KIF11 inhibitors and resensitizes resistant cells to KIF11 inhibitors. We developed a high throughput screening approach using Alpha technology to identify compounds that inhibit the protein-protein interaction (PPI) between KIF15 and TPX2, a unique approach to inhibiting KIF15 from previous efforts. Of the nearly 200,000 compounds screened, 177 compounds were selected to be further characterized based on assay performance and chemical properties. These compounds were screened for TPX2 or KIF15 binding by STD-NMR and waterLOGSY. Three compounds across two chemotypes were confirmed to bind KIF15. No compounds were found to bind TPX2. Additionally, 168 of the 177 compounds were screened for drug synergism in vitro. The synergism assay yielded 32 strongly and 8 weakly synergistic hits. The lead compound in each of the two chemotypes revealed by NMR were classified as strongly synergistic (max. bliss score of 8.0 and 29.9). To expand the potential lead compounds identified for further development, an antibody-free cellular thermal shift assay (CETSA) is being completed with 168 compounds. Preliminarily, CETSA has shown that the two lead compounds significantly stabilize KIF15 (ΔTm = 5.6 °C and 9.6 °C). These two lead compounds behaved in a dose-dependent manner in the AlphaScreen (IC50= 2 µM and 6 µM), a favorable characteristic for further development. To date, two chemotypes have been identified as KIF15 inhibitors uniquely targeting the KIF15-TPX2 PPI. The data indicates KIF15 inhibition in combination with KIF11 inhibition is synergistic, thus demonstrating a potential novel treatment approach for people with EOCs. Citation Format: Benjamin K. Gibbs, Justin T. Douglas, Rebecca J. Wates, Peter R. McDonald, Amy M. Whitaker, Cornelius N. Ndi, Harsh B. Pathak, Laurie A. Harned, Sarah A. Neuenswander, Melinda A. Broward, Bret D. Freudenthal, Anuradha Roy, Frank J. Schoenen, Andrew K. Godwin. Targeting the KIF15-TPX2 PPI to overcome KIF11 inhibitor resistance in epithelial ovarian cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5334.

The transcription factor NRF2 is a master regulator of cellular responses to oxidative stress, contributing to the pathogenesis of autoimmunity, metabolic disorders, and neurodegeneration. Somatic alterations in the NRF2 pathway also contribute to the growth and metastasis of many cancer types including ~30% of lung cancers. Still, the activation of NRF2 has frequently been observed in the absence of known genomic alterations, indicating that other pathways may drive its dysregulation. Further, approaches to target NRF2 pharmacologically have remained elusive. Here, we conducted a screen that identified a small molecule, ML329, exhibiting selective cytotoxicity in cells exhibiting NRF2 dependency and synthetic lethality to NRF2 pathway mutations across 489 cell lines. Surprisingly, we find that melanomas—which rarely have somatic mutations in the NRF2 pathway—were commonly sensitive to ML329. Melanomas were seen to exhibit NRF2-dependent metabolomic and transcriptional programs through the transcriptional activation of the adaptor protein p62/SQSTM1 by the melanocyte master regulator and oncoprotein MITF. This pathway was found to be conserved among all cancers characterized by genomic alterations of the MiT family (MITF, TFEB and TFE3) including subsets of renal cell carcinomas, pediatric sarcomas, and uveal and cutaneous melanomas. Our data identify a previously unrecognized, non-canonical mechanism of NRF2 activation by the MiT family, clarifying the regulation of NRF2 in pathologic and physiologic contexts. Pharmacologic inhibition of NRF2 could be valuable in the treatment of conditions with MITF family dysregulation. Citation Format: Xinbo Luo, Bart Lutterbach, Priya Pancholi, Yeon Sook Choi, Xiao Liu, Phillip Munson, Saqib Faisal, David A. Whipple, Robert A. Smith, Warren S. Weiner, David K. Johnson, Myriam Boukhali, Nicole S. Persky, Matthew G. Rees, Shunsuke Kitajima, David Barbie, Anuradha Roy, Michael Baltezor, Lian Rajewski, William McGuinness, John Haslam, Ananthan Sadagopan, Charles H. Yoon, Cory M. Johannessen, Christine G. Lian, Jason L. Hornick, Srinivas R. Viswanathan, David Liu, Vicki Nienaber, Wilhelm Haas, Frank J. Schoenen, David E. Fisher, Rizwan Haq. A non-canonical MiT/TFE-dependent NRF2 program is a druggable vulnerability in multiple cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1706.

The incidence of human papillomavirus-positive head and neck squamous cell carcinoma (HPV+-HNSCC) has increased dramatically over the past decades due to an increase in infection of the oral mucosa by HPV. The etiology of HPV+-HNSCC is linked to expression of the HPV oncoprotein, E6, which influences tumor formation, growth and survival. E6 effects this oncogenic phenotype in part through inhibitory protein-protein interactions (PPIs) and accelerated degradation of proteins with tumor suppressor properties, such as p53 and caspase 8. Interfering with the binding between E6 and its cellular partners may therefore represent a reasonable pharmacological intervention in HPV+ tumors. In this study, we probed a small-molecule library using AlphaScreen™ technology to discover novel E6 inhibitors. Following a cascade of screens we identified and prioritized one hit compound. Structure activity relationship (SAR) studies of this lead uncovered an analog, 30-hydroxygambogic acid (GA-OH), that displayed improved activity. Further testing of this analog in a panel of HPV+ and HPV- cell lines showed good potency and a large window of selectivity as demonstrated by apoptosis induction and significant inhibition of cell growth, cell survival in HPV+ cells. In summary, GA-OH may serve as a starting point for the development of potent E6-specific inhibitors.

The popularity of the classical general linear model (CGLM) is attributable mostly to its ease of fitting and validating; however, the CGLM is inappropriate for correlated observations. In this paper we explore linear models for correlated observations with an exchangeable structure (Arnold in J Am Stat Assoc 74:194–199, 1979). For the case of $$N>1$$ repeated measures observations having site-dependent or site-independent covariates, the maximum likelihood estimates (MLEs) of the model’s parameters are derived, likelihood ratio tests are obtained for relevant model building hypotheses, and some Monte Carlo simulation studies are performed to illuminate important aspects of the models and their tests of hypotheses. For the case of site-independent covariates, closed-form solutions exist for the MLEs and exact tests can be constructed for the model building hypotheses. Simulations revealed that these exact tests remain robust in the presence of moderate skewness or outliers. However, these fortuitous closed-form occurrences vanish for the case of site-dependent covariates. In order to ameliorate this deficiency, some Monte Carlo simulations are performed to estimate the bias of these MLEs, the probability of a multimodal likelihood, and the suitability of the limiting chi-squared approximation to the model building hypotheses. These simulations reveal that the estimated biases of the slope parameters are negligible for sample size combinations (n, N) as small as (2, 6). Likewise, this sample size combination resulted in only an approximate 1% estimated probability of a multimodal likelihood, which drastically decreased with the increase of either n or N. Moreover, the limiting $$\chi ^2$$ distributional assumption appears to hold reasonably well for a sample size of $$N=100$$ , regardless of the value of n. Finally, we provide examples of fitting our model and conducting tests of hypotheses using two medical datasets.

Behavioral pain treatments may improve postsurgical analgesia and recovery; however, effective and scalable options are not widely available. This study tested a digital perioperative behavioral medicine intervention in orthopedic trauma surgery patients for feasibility and efficacy for reducing pain intensity, pain catastrophizing, and opioid cessation up to 3 months after surgery.A randomized controlled clinical trial was conducted at an orthopedic trauma surgery unit at a major academic hospital to compare a digital behavioral pain management intervention ("My Surgical Success" [MSS]) to a digital general health education (HE) intervention (HE; no pain management skills). The enrolled sample included 133 patients; 84 patients were randomized (MSS, n = 37; HE, n = 47) and completed study procedures. Most patients received their assigned intervention within 3 days of surgery (85%). The sample was predominantly male (61.5%), White (61.9%), and partnered (65.5%), with at least a bachelor's degree (69.0%). Outcomes were collected at 1-3 months after intervention through self-report e-surveys and electronic medical record review; an intention-to-treat analytic framework was applied. Feasibility was dually determined by the proportion of patients engaging in their assigned treatment and an application of an 80% threshold for patient-reported acceptability. We hypothesized that MSS would result in greater reductions in pain intensity and pain catastrophizing after surgery and earlier opioid cessation compared to the digital HE control group.The engagement rate with assigned interventions was 63% and exceeded commonly reported rates for fully automated Internet-based e-health interventions. Feasibility was demonstrated for the MSS engagers, with80% reporting treatment acceptability. Overall, both groups improved in the postsurgical months across all study variables. A significant interaction effect was found for treatment group over time on pain intensity, such that the MSS group evidenced greater absolute reductions in pain intensity after surgery and up to 3 months later (treatment × time fixed effects; F [215] = 5.23; P = .024). No statistically significant between-group differences were observed for time to opioid cessation or for reductions in pain catastrophizing ( F [215] = 0.20; P = .653), although the study sample notably had subclinical baseline pain catastrophizing scores (M = 14.10; 95% confidence interval, 11.70-16.49).Study findings revealed that a fully automated behavioral pain management skills intervention (MSS) may be useful for motivated orthopedic trauma surgery patients and reduce postsurgical pain up to 3 months. MSS was not associated with reduced time to opioid cessation compared to the HE control intervention.

This article explores the parameter space of multivariate skew normal families having identical distributed marginal distributions under a few autoregressive-moving average correlation structures (...

Spherical gold nanoseed (∼5–6 nm)-induced (but not seed-mediated) silver nanorods (Hy-Au@AgNRs) of variable lengths have been synthesized by a new methodology that shows enhancement in catalytic ac...

Leukaemia stem cells (LSCs) underlie cancer therapy resistance but targeting these cells remains difficult. The Wnt-β-catenin and PI3K-Akt pathways cooperate to promote tumorigenesis and resistance to therapy. In a mouse model in which both pathways are activated in stem and progenitor cells, LSCs expanded under chemotherapy-induced stress. Since Akt can activate β-catenin, inhibiting this interaction might target therapy-resistant LSCs. High-throughput screening identified doxorubicin (DXR) as an inhibitor of the Akt-β-catenin interaction at low doses. Here we repurposed DXR as a targeted inhibitor rather than a broadly cytotoxic chemotherapy. Targeted DXR reduced Akt-activated β-catenin levels in chemoresistant LSCs and reduced LSC tumorigenic activity. Mechanistically, β-catenin binds multiple immune-checkpoint gene loci, and targeted DXR treatment inhibited expression of multiple immune checkpoints specifically in LSCs, including PD-L1, TIM3 and CD24. Overall, LSCs exhibit distinct properties of immune resistance that are reduced by inhibiting Akt-activated β-catenin. These findings suggest a strategy for overcoming cancer therapy resistance and immune escape.

A series of amino acid based7H-pyrrolo[2,3-d]pyrimidines were designed and synthesized to discern the structure activity relationships against the SARS-CoV-2 nsp3 macrodomain (Mac1), an ADP-ribosylhydrolase that is critical for coronavirus replication and pathogenesis. Structure activity studies identified compound15cas a low-micromolar inhibitor of Mac1 in two ADP-ribose binding assays. This compound also demonstrated inhibition in an enzymatic assay of Mac1 and displayed a thermal shift comparable to ADPr in the melting temperature of Mac1 supporting binding to the target protein. A structural model reproducibly predicted a binding mode where the pyrrolo pyrimidine forms a hydrogen bonding network with Asp22and the amide backbone NH of Ile23in the adenosine binding pocket and the carboxylate forms hydrogen bonds to the amide backbone of Phe157and Asp156, part of the oxyanion subsite of Mac1. Compound15calso demonstrated notable selectivity for coronavirus macrodomains when tested against a panel of ADP-ribose binding proteins. Together, this study identified several low MW, low μM Mac1 inhibitors to use as small molecule chemical probes for this potential anti-viral target and offers starting points for further optimization.Graphical Abstract

Malaria is caused by infection with protozoan parasites of the Plasmodium genus, which is part of the phylum Apicomplexa. Most organisms in this phylum contain a relic plastid called the apicoplast. The apicoplast genome is replicated by a single DNA polymerase (apPOL), which is an attractive target for anti-malarial drugs. We screened small-molecule libraries (206,504 compounds) using a fluorescence-based high-throughput DNA polymerase assay. Dose/response analysis and counter-screening identified 186 specific apPOL inhibitors. Toxicity screening against human HepaRG human cells removed 84 compounds and the remaining were subjected to parasite killing assays using chloroquine resistant P. falciparum parasites. Nine compounds were potent inhibitors of parasite growth and may serve as lead compounds in efforts to discover novel malaria drugs.

An extension of the D2 test statistic to test the equality of mean for high-dimensional and k-th order array-variate data using k-self similar compound symmetry (k-SSCS) covariance structure is derived. The k-th order data appear in many scientific fields including agriculture, medical, environmental and engineering applications. We discuss the property of this k-SSCS covariance structure, namely, the property of Jordan algebra. We formally show that our D2 test statistic for k-th order data is an extension or the generalization of the D2 test statistic for second-order data and for third-order data, respectively. We also derive the D2 test statistic for third-order data and illustrate its application using a medical dataset from a clinical trial study of the eye disease glaucoma. The new test statistic is very efficient for high-dimensional data where the estimation of unstructured variance-covariance matrix is not feasible due to small sample size.

Herpes simplex virus 1 (HSV-1) is a ubiquitous virus that results in lifelong infections due to it’s ability to cycle between lytic replication and latency. As an obligate intracellular pathogen, HSV-1 exploits host cellular factors to replicate and aid in its life cycle. HSV-1 expresses infected cell protein 0 (ICP0), an immediate-early regulator, to stimulate the transcription of all classes of viral genes via its E3 ubiquitin ligase activity. Mechanisms by which ICP0 activates viral gene expression and the cellular factors involved are largely unknown. Here we report an automated, inexpensive, and rapid high-throughput approach to examine the effects of small molecule compounds on ICP0 transactivator function in cells. Two HSV-1 reporter viruses, KOS6β (wt) and dlx3.1-6β (ICP0-null mutant), were used to monitor ICP0 transactivation activity through the HSV-1 ICP6 promoter::lacz expression cassette. A ≥10-fold difference in β-galactosidase activity was observed in cells infected with KOS6β compared to dlx3.1-6β, demonstrating that ICP0 potently transactivates the ICP6 promoter. We established the robustness and reproducibility with a Z′- factor score of ≥0.69, an important criterium for high-throughput analyses. Approximately 19,000 structurally diverse compounds were screened and 76 potential inhibitors of the HSV-1 transactivator ICP0 were identified. We expect this assay will aid in the discovery of novel inhibitors and tools against HSV-1 ICP0. Using well-annotated compounds could identify potential novel factors and pathways that interact with ICP0 to promote HSV-1 gene expression.

In this paper the authors study the problem of testing the hypothesis of a doubly exchangeable covariance matrix for three-level multivariate observations, taken on m variables over u sites and over v time/space points. Through the decomposition of the main hypothesis into a set of three sub-hypotheses, the likelihood ratio test statistic is defined, its exact moments are determined, and its exact distribution is studied. Because this distribution is very much intricate, a very precise near-exact distribution is developed. Numerical studies conducted to evaluate the closeness between this near-exact distribution and the exact distribution show the very good performance of this approximation even for very small sample sizes. A simulation study is also conducted and two real-data examples are presented.

A new parametric hypothesis test of mean interval for interval-valued data set, which can deal with massive information contained in nowadays massive data “Big data” sets, is proposed. An approach ...

Self similar compound symmetry (SSCS) covariance structure is introduced and studied. The $$k-$$ SSCS covariance structure (defined in Sect. 3) for array-variate kth order data incorporates the exchangeable feature of $$k-$$ dimensional arrays into the model. $$3-$$ SSCS covariance structure or double block compound symmetry covariance structure for array-variate 3rd order data is a generalization of $$2-$$ SSCS covariance structure or block compound symmetry covariance structure for the matrix-variate 2nd order data, which in turn is a generalization of compound symmetry covariance structure for traditional vector-variate (multivariate) 1st order data. This article generalizes this compound symmetry covariance structure for array-variate kth order data, and we name it as “k self similar compound symmetry” ( $$k-$$ SSCS) covariance structure. This is of critical importance to a variety of applied problems in agricultural, biomedical, medical, environmental, engineering and space missions among many other fields with $$k-$$ dimensional array-variate data. The proposed method is illustrated with a medical dataset.

The aberrant protein-protein interaction between calmodulin and mutant huntingtin protein in Huntington's disease patients has been found to contribute to Huntington's disease progression. A high-throughput screen for small molecules capable of disrupting this interaction revealed a sultam series as potent small-molecule disruptors. Diversification of the sultam scaffold afforded a set of 24 analogs or further evaluation. Several structure-activity trends within the analog set were found, most notably a negligible effect of absolute stereochemistry and a strong beneficial correlation with electron-withdrawing aromatic substituents. The most promising analogs were profiled for off-target effects at relevant kinases and, ultimately, one candidate molecule was evaluated for neuroprotection in a neuronal cell model of Huntington's disease.

We generalize the theory of linear models for doubly multivariate data from matrix-variate normally distributed errors to matrix-variate skew normally distributed errors. In addition, we assume that the covariance matrix $$\varvec{\varOmega }$$ defining the location-scale matrix-variate skew normal distribution has block compound symmetry structure. We derive the maximum likelihood estimators of the model’s parameters; the Fisher information matrix for the direct, working, and centered parametrizations; Rao’s score tests and likelihood ratio tests for model building tests of hypotheses; and a hypothesis test for the centered intercept. A profiling argument is used to reduce the dimensionality of the optimization method used to obtain the maximum likelihood estimators and a comprehensive discussion of initial values is provided. Finally, we provide a real-world example to illuminate these derivations and apply a goodness-of-fit test to validate the distributional assumption.

SARS-CoV-2 has recently emerged into the human population and has led to a worldwide pandemic of COVID-19 that has caused more than 1.2 million deaths worldwide. With no currently approved treatments, novel therapeutic strategies are desperately needed., Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other SARS-related CoVs encode 3 tandem macrodomains within nonstructural protein 3 (nsp3). The first macrodomain, Mac1, is conserved throughout CoVs and binds to and hydrolyzes mono-ADP-ribose (MAR) from target proteins. Mac1 likely counters host-mediated antiviral ADP-ribosylation, a posttranslational modification that is part of the host response to viral infections. Mac1 is essential for pathogenesis in multiple animal models of CoV infection, implicating it as a virulence factor and potential therapeutic target. Here, we report the crystal structure of SARS-CoV-2 Mac1 in complex with ADP-ribose. SARS-CoV-2, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV) Mac1 domains exhibit similar structural folds, and all 3 proteins bound to ADP-ribose with affinities in the low micromolar range. Importantly, using ADP-ribose-detecting binding reagents in both a gel-based assay and novel enzyme-linked immunosorbent assays (ELISAs), we demonstrated de-MARylating activity for all 3 CoV Mac1 proteins, with the SARS-CoV-2 Mac1 protein leading to a more rapid loss of substrate than the others. In addition, none of these enzymes could hydrolyze poly-ADP-ribose. We conclude that the SARS-CoV-2 and other CoV Mac1 proteins are MAR-hydrolases with similar functions, indicating that compounds targeting CoV Mac1 proteins may have broad anti-CoV activity. IMPORTANCE SARS-CoV-2 has recently emerged into the human population and has led to a worldwide pandemic of COVID-19 that has caused more than 1.2 million deaths worldwide. With no currently approved treatments, novel therapeutic strategies are desperately needed. All coronaviruses encode a highly conserved macrodomain (Mac1) that binds to and removes ADP-ribose adducts from proteins in a dynamic posttranslational process that is increasingly being recognized as an important factor that regulates viral infection. The macrodomain is essential for CoV pathogenesis and may be a novel therapeutic target. Thus, understanding its biochemistry and enzyme activity are critical first steps for these efforts. Here, we report the crystal structure of SARS-CoV-2 Mac1 in complex with ADP-ribose and describe its ADP-ribose binding and hydrolysis activities in direct comparison to those of SARS-CoV and MERS-CoV Mac1 proteins. These results are an important first step for the design and testing of potential therapies targeting this unique protein domain.

This article deals with the estimation and hypotheses testing problems for two-level and three-level multivariate data. The coordinate-free approach is used to prove that the quadratic estimation of covariance parameters is equivalent to linear estimation with a properly defined inner product in the space of symmetric matrices for both two-level and three-level multivariate data. The estimators are shown to be unbiased, sufficient, complete, and consistent. A competitor for the likelihood ratio test on covariance components under linear constraints and the mean vectors are proposed, based on the F distribution. Simulation studies are conducted to see the power of the proposed tests and the proposed methods are implemented with two medical datasets.

The incidence of human papillomavirus-positive head and neck squamous cell carcinoma (HPV+-HNSCC) has increased dramatically over the past decades due to an increase in infection of the oral mucosa by HPV. The etiology of HPV+-HNSCC is linked to expression of the HPV oncoprotein, E6, which influences tumor formation, growth and survival. E6 effects this oncogenic phenotype in part through inhibitory protein-protein interactions (PPIs) and accelerated degradation of proteins with tumor suppressor properties, such as p53 and caspase 8. Interfering with the binding between E6 and its cellular partners may therefore represent a reasonable pharmacological intervention in HPV+ tumors. In this study, we probed a small-molecule library using AlphaScreen™ technology to discover novel E6 inhibitors. Following a cascade of screens we identified and prioritized one hit compound. Structure activity relationship (SAR) studies of this lead uncovered an analog, 30-hydroxygambogic acid (GA-OH), that displayed improved activity. Further testing of this analog in a panel of HPV+ and HPV– cell lines showed good potency and a large window of selectivity as demonstrated by apoptosis induction and significant inhibition of cell growth, cell survival in HPV+ cells. In summary, GA-OH may serve as a starting point for the development of potent E6-specific inhibitors.

The area of material synthesis is a rapidly developing field of research which enables scientists not only to discover the high yield, cost effective or environment friendly methods , but also providing them some new opportunities to work in the world of nano science. The green approaches in one hand are the alternative way to synthesize the material by minimizing the wastes as well as toxic substances, on the other hand the synthesis of nanoparticles has gained tremendous attention owing to their many fold applications in various fields. This book is designed to give the readers an outline of some of the very special topics from current prospective of different approaches for material synthesis.

Our recent studies have demonstrated multiple health-promoting benefits from black walnut kernels. These biological functions of black walnuts are likely associated with their bioactive constituents. Characterization of phenolic compounds found in black walnut could point out underexplored bioactive activities of black walnut extracts and promote the development of novel applications of black walnut and its by-products. In the present study, we assessed bioactivity profiles of phenolic compounds identified in the kernels of black walnuts using a high-throughput screening (HTS) approach. Black walnut phenolic compounds were evaluated in terms of their total antioxidant capacity, antioxidant response element (ARE) induction, and anticancer activities. The anticancer activities were identified by evaluating the effects of the phenolic compounds on the growth of the tumorigenic alveolar epithelial cells (A549) and non-tumorigenic lung fibroblast cells (MRC-5). Out of 16 phenolic compounds tested, several compounds (penta-O-galloyl-β-d-glucose, epicatechin gallate, quercetin, (–)-epicatechin, rutin, quercetin 3-β-d-glucoside, gallic acid, (+)-catechin, ferulic acid, syringic acid) exerted antioxidant activities that were significantly higher compared to Trolox, which was used as a control. Two phenolic compounds, penta-O-galloyl-β-d-glucose and quercetin 3-β-d-glucoside, exhibited antiproliferative activities against both the tumorigenic alveolar epithelial cells (A549) and non-tumorigenic lung fibroblast cells (MRC-5). The antioxidant activity of black walnut is likely driven not only by penta-O-galloyl-β-d-glucose but also by a combination of multiple phenolic compounds. Our findings suggested that black walnut extracts possibly possess anticancer activities and supported that penta-O-galloyl-β-d-glucose could be a potential bioactive agent for the cosmetic and pharmaceutical industries.

The world of sensor is an important and fast growing industry. Backed by active groups of researchers and technologists round the globe, sensor technology has become indispensible for its applications in the different field especially in the segment of environmental monitoring, gas composition analysis, industrial development monitoring, national defence security and medicine etc. Naturally, it plays a crucial role in the environment monitoring sector as well. This book documents an overview of different chemi- and bio-sensors, their design aspects, prospect of computational molecular studies in sensor design, and some important applications including the recent SARS-CoV-2. We hope this book will be of benefit for the active as well as prospective researchers planning to work in the field of sensor technology.

RNA-binding protein Musashi-1 (MSI1) is a key regulator of several stem cell populations. MSI1 is involved in tumor proliferation and maintenance, and it regulates target mRNAs at the translational level. The known mRNA targets of MSI1 include Numb, APC, and P21WAF-1, key regulators of Notch/Wnt signaling and cell cycle progression, respectively. In this study, we aim to identify small molecule inhibitors of MSI1&ndash, mRNA interactions, which could block the growth of cancer cells with high levels of MSI1. Using a fluorescence polarization (FP) assay, we screened small molecules from several chemical libraries for those that disrupt the binding of MSI1 to its consensus RNA. One cluster of hit compounds is the derivatives of secondary metabolites from Aspergillus nidulans. One of the top hits, Aza-9, from this cluster was further validated by surface plasmon resonance and nuclear magnetic resonance spectroscopy, which demonstrated that Aza-9 binds directly to MSI1, and the binding is at the RNA binding pocket. We also show that Aza-9 binds to Musashi-2 (MSI2) as well. To test whether Aza-9 has anti-cancer potential, we used liposomes to facilitate Aza-9 cellular uptake. Aza-9-liposome inhibits proliferation, induces apoptosis and autophagy, and down-regulates Notch and Wnt signaling in colon cancer cell lines. In conclusion, we identified a series of potential lead compounds for inhibiting MSI1/2 function, while establishing a framework for identifying small molecule inhibitors of RNA binding proteins using FP-based screening methodology.

Four decades after the eradication of smallpox, poxviruses continue to threaten the health of humans and other animals. Vaccinia virus (VACV) was used as the vaccine that successfully eradicated smallpox and is a prototypic member of the poxvirus family. Many cellular pathways play critical roles in productive poxvirus replication. These pathways provide opportunities to expand the arsenal of poxvirus antiviral development by targeting the cellular functions required for efficient poxvirus replication. In this study, we developed and optimized a secreted Gaussia luciferase-based, simplified assay procedure suitable for high throughput screening. Using this procedure, we screened a customized compound library that contained over 3200 bioactives and FDA (Food and Drug Administration)-approved chemicals, most having known cellular targets, for their inhibitory effects on VACV replication. We identified over 140 compounds that suppressed VACV replication. Many of these hits target cellular pathways previously reported to be required for efficient VACV replication, validating the effectiveness of our screening. Importantly, we also identified hits that target cellular functions with previously unknown roles in the VACV replication cycle. Among those in the latter category, we verified the antiviral role of several compounds targeting the janus kinase/signal transducer and activator of transcription-3 (JAK/STAT3) signaling pathway by showing that STAT3 inhibitors reduced VACV replication. Our findings identify pathways that are candidates for use in the prevention and treatment of poxvirus infections and additionally provide a foundation to investigate diverse cellular pathways for their roles in poxvirus replications.

Extracellular vesicles (EVs), naturally occurring nanosized vesicles secreted from cells, are essential for intercellular communication. They carry unique biomolecules on the surface or interior that are of great interest as biomarkers for various pathological conditions such as cancer. In this work, we use high-resolution atomic force microscopy (AFM) and spectroscopy (AFS) techniques to demonstrate differences between EVs derived from colon cancer cells and colon epithelial cells at the single-vesicle level. We observe that EV populations are significantly increased in the cancer cell media compared to the normal cell EVs. We show that both EVs display an EV marker, CD9, while EVs derived from the cancer cells are slightly higher in density. Hyaluronan (HA) is a nonsulfated glycosaminoglycan linked to malignant tumor growth according to recent reports. Interestingly, at the single-vesicle level, colon cancer EVs exhibit significantly increased HA surface densities compared to the normal EVs. Spectroscopic measurements such as Fourier transform infrared (FT-IR), circular dichroism (CD), and Raman spectroscopy unequivocally support the AFM and AFS measurements. To our knowledge, it represents the first report of detecting HA-coated EVs as a potential colon cancer biomarker. Taken together, this sensitive approach will be useful in identifying biomarkers in the early stages of detection and evaluation of cancer.

RNA-binding protein Musashi-1 (MSI1) is a key regulator of several stem cell populations. MSI1 is involved in tumor proliferation and maintenance, and it regulates target mRNAs at the translational level. The known mRNA targets of MSI1 include Numb, APC, and P21WAF-1, key regulators of Notch/Wnt signaling and cell cycle progression, respectively. In this study, we aim to identify small molecule inhibitors of MSI1–mRNA interactions, which could block the growth of cancer cells with high levels of MSI1. Using a fluorescence polarization (FP) assay, we screened small molecules from several chemical libraries for those that disrupt the binding of MSI1 to its consensus RNA. One cluster of hit compounds is the derivatives of secondary metabolites from Aspergillus nidulans. One of the top hits, Aza-9, from this cluster was further validated by surface plasmon resonance and nuclear magnetic resonance spectroscopy, which demonstrated that Aza-9 binds directly to MSI1, and the binding is at the RNA binding pocket. We also show that Aza-9 binds to Musashi-2 (MSI2) as well. To test whether Aza-9 has anti-cancer potential, we used liposomes to facilitate Aza-9 cellular uptake. Aza-9-liposome inhibits proliferation, induces apoptosis and autophagy, and down-regulates Notch and Wnt signaling in colon cancer cell lines. In conclusion, we identified a series of potential lead compounds for inhibiting MSI1/2 function, while establishing a framework for identifying small molecule inhibitors of RNA binding proteins using FP-based screening methodology.

We report relative bioactivities of extracts prepared from a large collection of plants from three national parks in Panama. Over 181 plants were collected, taxonomically identified and their detannified dichloromethane (DCM)-methanolic extracts were used for profiling selected bioactivities. Assays were performed to evaluate the antioxidant activity of the extracts for Antioxidant Response Element (ARE) induction, total non-enzymatic antioxidant potential, anti-inflammatory and anticancer properties. The high throughput analysis of 280 extracts resulted in identification of 57.5% of the extracts that could induce ARE at one or more concentrations tested, 93.5% that harbored total antioxidant capacity, and 2.1% of the extracts that showed lung cancer cell line-specific cytotoxicity. Data from our profiling experiments indicate that a large number of extracts could be a source for further isolation and chemical identification of compounds that could serve as leads for discovery of antioxidant, anticancer and anti-inflammatory agents to prevent or treat complex diseases like cancer and neurodegenerative disorders.

We consider matrix-valued multivariate observation model with three-level doubly-exchangeable covariance structure. We derive estimators of unknown parameters and their distributions under multivariate normality assumption. Test statistic for testing a mean value is proposed, and its exact distribution is derived. Several methods of computing p-values and critical values of the distribution are compared in real data example.

We are interested in identifying and characterizing small molecule inhibitors of bacterial virulence factors for their potential use as anti-virulence inhibitors. We identified from high-throughput screening assays a potential activity for avasimibe, a previously characterized acyl-coenzyme A: cholesterol acyltransferase inhibitor, in inhibiting the NleB and SseK arginine glycosyltransferases from Escherichiacoli and Salmonella enterica, respectively. Avasimibe inhibited the activity of the Citrobacter rodentium NleB, E. coli NleB1, and S. enterica SseK1 enzymes, without affecting the activity of the human serine/threonine N-acetylglucosamine (O-GlcNAc) transferase. Avasimibe was not toxic to mammalian cells at up to 200 µM and was neither bacteriostatic nor bactericidal at concentrations of up to 125 µM. Doses of 10 µM avasimibe were sufficient to reduce S. enterica abundance in RAW264.7 macrophage-like cells, and intraperitoneal injection of avasimibe significantly reduced C. rodentium survival in mice, regardless of whether the avasimibe was administered pre- or post-infection. We propose that avasimibe or related derivates created using synthetic chemistry may have utility in preventing or treating bacterial infections by inhibiting arginine glycosyltransferases that are important to virulence.

The maximum diameter criterion is the most important factor in the clinical management of abdominal aortic aneurysms (AAA). Consequently, interventional repair is recommended when an aneurysm reaches a critical diameter, typically 5.0 cm in the United States. Nevertheless, biomechanical measures of the aneurysmal abdominal aorta have long been implicated in AAA risk of rupture. The purpose of this study is to assess whether other geometric characteristics, in addition to maximum diameter, may be highly correlated with the AAA peak wall stress (PWS). Using in-house segmentation and meshing algorithms, 30 patient-specific AAA models were generated for finite element analysis using an isotropic constitutive material for the AAA wall. PWS, evaluated as the spatial maximum of the first principal stress, was calculated at a systolic pressure of 120 mmHg. The models were also used to calculate 47 geometric indices characteristic of the aneurysm geometry. Statistical analyses were conducted using a feature reduction algorithm in which the 47 indices were reduced to 11 based on their statistical significance in differentiating the models in the population (p < 0.05). A subsequent discriminant analysis was performed and 7 of these indices were identified as having no error in discriminating the AAA models with a significant nonlinear regression correlation with PWS. These indices were: D(max) (maximum diameter), T (tortuosity), DDr (maximum diameter to neck diameter ratio), S (wall surface area), K(median) (median of the Gaussian surface curvature), C(max) (maximum lumen compactness), and M(mode) (mode of the Mean surface curvature). Therefore, these characteristics of an individual AAA geometry are the highest correlated with the most clinically relevant biomechanical parameter for rupture risk assessment. We conclude that the indices can serve as surrogates of PWS in lieu of a finite element modeling approach for AAA biomechanical evaluation.

The article addresses the best unbiased estimators of the block compound symmetric covariance structure for m −variate observations with equal mean vector over u sites under the assumption of multivariate quasi-normality. The free-coordinate approach is used to prove that the quadratic estimation of covariance parameters is equivalent to linear estimation with a properly defined inner product in the space of symmetric matrices. Without assumption of normality but quasi-normality, meaning that up to fourth moments are the same as in the normal case, the estimators are best linear and best quadratic for mean and covariance parameters, respectively. Finally, strong consistency is proven. The properties of the estimators in the proposed model are compared against a similar model available in the literature. An application of the proposed approach to a clinical study data is presented.

A new hypothesis testing of equality of mean vectors in two populations using $$D^2$$ statistic for multivariate repeated measures data on q response variables at p sites or time points in a block exchangeable covariance matrix setting is proposed. The minimum sample size needed for our new test is only $$q +1$$ , unlike $$pq +1$$ in Hotelling’s $$T^2$$ test. The new test is very efficient in small sample scenario, when the number of observations is not adequate to estimate the $$pq \times pq$$ dimensional unknown unstructured variance–covariance matrix. Some simulation studies are performed to compare the power of the new $$D^2$$ test and the existing $$BT^2$$ test. The performance of the proposed $$D^2$$ test is demonstrated with the two medical data sets.