Conceptualisation, Development, Fabrication and In Vivo Validation of a Novel Disintegration Tester for Orally Disintegrating Tablets

Disintegration time is the key critical quality attribute for a tablet classed as an Orally Disintegrating Tablet (ODT). The currently accepted in vitro testing regimen for ODTs is the standard United States Pharmacopeia (USP) test for disintegration of immediate release tablets, which requires a large volume along with repeated submergence of the dosage form within the disintegration medium. The aim of this study was to develop an in vivo relevant ODT disintegration test that mimicked the environment of the oral cavity, including lower volume of disintegration medium, with relevant temperature and humidity that represent the conditions of the mouth. The results showed that the newly developed Aston test was able to differentiate between different ODTs with small disintegration time windows, as well as between immediate release tablets and ODTs. The Aston test provided higher correlations between ODT properties and disintegration time compared to the USP test method and most significantly, resulted in a linear in vitro/in vivo correlation (IVIVC) (R 2 value of 0.98) compared with a "hockey stick" profile of the USP test. This study therefore concluded that the newly developed Aston test is an accurate, repeatable, relevant and robust test method for assessing ODT disintegration time which will provide the pharmaceutical industry and regulatory authorities across the world with a pragmatic ODT testing regime

Survival models with preclustered gene groups as covariates

<p>Abstract</p> <p>Background</p> <p>An important application of high dimensional gene expression measurements is the risk prediction and the interpretation of the variables in the resulting survival models. A major problem in this context is the typically large number of genes compared to the number of observations (individuals). Feature selection procedures can generate predictive models with high prediction accuracy and at the same time low model complexity. However, interpretability of the resulting models is still limited due to little knowledge on many of the remaining selected genes. Thus, we summarize genes as gene groups defined by the hierarchically structured Gene Ontology (GO) and include these gene groups as covariates in the hazard regression models. Since expression profiles within GO groups are often heterogeneous, we present a new method to obtain subgroups with coherent patterns. We apply preclustering to genes within GO groups according to the correlation of their gene expression measurements.</p> <p>Results</p> <p>We compare Cox models for modeling disease free survival times of breast cancer patients. Besides classical clinical covariates we consider genes, GO groups and preclustered GO groups as additional genomic covariates. Survival models with preclustered gene groups as covariates have similar prediction accuracy as models built only with single genes or GO groups.</p> <p>Conclusions</p> <p>The preclustering information enables a more detailed analysis of the biological meaning of covariates selected in the final models. Compared to models built only with single genes there is additional functional information contained in the GO annotation, and compared to models using GO groups as covariates the preclustering yields coherent representative gene expression profiles.</p

Laparoscopic versus open peritoneal dialysis catheter insertion, the LOCI-trial: a study protocol

Background: Peritoneal dialysis (PD) is an effective treatment for end-stage renal disease. It allows patients more freedom to perform daily activities compared to haemodialysis. Key to successful PD is the presence of a well-functioning dialysis catheter. Several complications, such as in- and outflow obstruction, peritonitis, exit-site infections, leakage and migration, can lead to catheter removal and loss of peritoneal access. Currently, different surgical techniques are in practice for PD-catheter placement. The type of insertion technique used may greatly influence the occurrence of complications. In the literature, up to 35% catheter failure has been described when using the open technique and only 13% for the laparoscopic technique. However, a

The impact of COVID-19 on kidney transplant recipients in pre-vaccination and delta strain era: a systematic review and meta-analysis

Herein, we performed a meta-analysis of published clinical outcomes of corona virus disease 2019 (COVID-19) in hospitalized kidney transplant recipients. A systematic database search was conducted between December 1, 2019 and April 20, 2020. We analyzed 48 studies comprising 3137 kidney transplant recipients with COVID-19. Fever (77%), cough (65%), dyspnea (48%), and gastrointestinal symptoms (28%) were predominant on hospital admission. The most common comorbidities were hypertension (83%), diabetes mellitus (34%), and cardiac disease (23%). The pooled prevalence of acute respiratory distress syndrome and acute kidney injury were 58% and 48%, respectively. Invasive ventilation and dialysis were required in 24% and 22% patients, respectively. In-hospital mortality rate was as high as 21%, and increased to over 50% for patients in intensive care unit (ICU) or requiring invasive ventilation. Risk of mortality in patients with acute respiratory distress syndrome (ARDS), on mechanical ventilation, and ICU admission was increased: OR = 19.59, OR = 3.80, and OR = 13.39, respectively. Mortality risk in the elderly was OR = 3.90; however, no such association was observed in terms of time since transplantation and gender. Fever, cough, dyspnea, and gastrointestinal symptoms were common on admission for COVID-19 in kidney transplant patients. Mortality was as high as 20% and increased to over 50% in patients in ICU and required invasive ventilation

Can Survival Prediction Be Improved By Merging Gene Expression Data Sets?

BACKGROUND:High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets. RESULTS:Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1. CONCLUSIONS:Merging did not deteriorate performance on average despite (a) The diversity of microarray platforms used. (b) The heterogeneity of patients cohorts. (c) The heterogeneity of breast cancer disease. (d) Substantial variation of time to death or relapse. (e) The reduced number of genes in the merged data sets. Predictors derived from the merged data sets were more robust, consistent and reproducible across microarray platforms. Moreover, merging data sets from different studies helps to better understand the biases of individual studies and can lead to the identification of strong survival factors like CYB5D1 expression

Serum VEGF levels are related to the presence of pulmonary arterial hypertension in systemic sclerosis

<p>Abstract</p> <p>Background</p> <p>The association between systemic sclerosis and pulmonary arterial hypertension (PAH) is well recognized. Vascular endothelial growth factor (VEGF) has been reported to play an important role in pulmonary hypertension. The aim of the present study was to examine the relationship between systolic pulmonary artery pressure, clinical and functional manifestations of the disease and serum VEGF levels in systemic sclerosis.</p> <p>Methods</p> <p>Serum VEGF levels were measured in 40 patients with systemic sclerosis and 13 control subjects. All patients underwent clinical examination, pulmonary function tests and echocardiography.</p> <p>Results</p> <p>Serum VEGF levels were higher in systemic sclerosis patients with sPAP ≥ 35 mmHg than in those with sPAP < 35 mmHg (352 (266, 462 pg/ml)) vs (240 (201, 275 pg/ml)) (p < 0.01), while they did not differ between systemic sclerosis patients with sPAP < 35 mmHg and controls. Serum VEGF levels correlated to systolic pulmonary artery pressure, to diffusing capacity for carbon monoxide and to MRC dyspnea score. In multiple linear regression analysis, serum VEGF levels, MRC dyspnea score, and D_LCOwere independent predictors of systolic pulmonary artery pressure.</p> <p>Conclusion</p> <p>Serum VEGF levels are increased in systemic sclerosis patients with sPAP ≥ 35 mmHg. The correlation between VEGF levels and systolic pulmonary artery pressure may suggest a possible role of VEGF in the pathogenesis of PAH in systemic sclerosis.</p

Expression analysis onto microarrays of randomly selected cDNA clones highlights HOXB13 as a marker of human prostate cancer

In a strategy aimed at identifying novel markers of human prostate cancer, we performed expression analysis using microarrays of clones randomly selected from a cDNA library prepared from the LNCaP prostate cancer cell line. Comparisons of expression profiles in primary human prostate cancer, adjacent normal prostate tissue, and a selection of other (nonprostate) normal human tissues, led to the identification of a set of clones that were judged as the best candidate markers of normal and/or malignant prostate tissue. DNA sequencing of the selected clones revealed that they included 10 genes that had previously been established as prostate markers: NKX3.1, KLK2, KLK3 (PSA), FOLH1 (PSMA), STEAP2, PSGR, PRAC, RDH11, Prostein and FASN. Following analysis of the expression patterns of all selected and sequenced genes through interrogation of SAGE databases, a further three genes from our clone set, HOXB13, SPON2 and NCAM2, emerged as additional candidate markers of human prostate cancer. Quantitative RT–PCR demonstrated the specificity of expression of HOXB13 in prostate tissue and revealed its ubiquitous expression in a series of 37 primary prostate cancers and 20 normal prostates. These results demonstrate the utility of this expression-microarray approach in hunting for new markers of individual human cancer types

Bayesian profiling of molecular signatures to predict event times

BACKGROUND: It is of particular interest to identify cancer-specific molecular signatures for early diagnosis, monitoring effects of treatment and predicting patient survival time. Molecular information about patients is usually generated from high throughput technologies such as microarray and mass spectrometry. Statistically, we are challenged by the large number of candidates but only a small number of patients in the study, and the right-censored clinical data further complicate the analysis. RESULTS: We present a two-stage procedure to profile molecular signatures for survival outcomes. Firstly, we group closely-related molecular features into linkage clusters, each portraying either similar or opposite functions and playing similar roles in prognosis; secondly, a Bayesian approach is developed to rank the centroids of these linkage clusters and provide a list of the main molecular features closely related to the outcome of interest. A simulation study showed the superior performance of our approach. When it was applied to data on diffuse large B-cell lymphoma (DLBCL), we were able to identify some new candidate signatures for disease prognosis. CONCLUSION: This multivariate approach provides researchers with a more reliable list of molecular features profiled in terms of their prognostic relationship to the event times, and generates dependable information for subsequent identification of prognostic molecular signatures through either biological procedures or further data analysis

Detection of recurrent rearrangement breakpoints from copy number data

<p>Abstract</p> <p>Background</p> <p>Copy number variants (CNVs), including deletions, amplifications, and other rearrangements, are common in human and cancer genomes. Copy number data from array comparative genome hybridization (aCGH) and next-generation DNA sequencing is widely used to measure copy number variants. Comparison of copy number data from multiple individuals reveals recurrent variants. Typically, the interior of a recurrent CNV is examined for genes or other loci associated with a phenotype. However, in some cases, such as gene truncations and fusion genes, the target of variant lies at the boundary of the variant.</p> <p>Results</p> <p>We introduce Neighborhood Breakpoint Conservation (NBC), an algorithm for identifying rearrangement breakpoints that are highly conserved at the same locus in multiple individuals. NBC detects recurrent breakpoints at varying levels of resolution, including breakpoints whose location is exactly conserved and breakpoints whose location varies within a gene. NBC also identifies pairs of recurrent breakpoints such as those that result from fusion genes. We apply NBC to aCGH data from 36 primary prostate tumors and identify 12 novel rearrangements, one of which is the well-known TMPRSS2-ERG fusion gene. We also apply NBC to 227 glioblastoma tumors and predict 93 novel rearrangements which we further classify as gene truncations, germline structural variants, and fusion genes. A number of these variants involve the protein phosphatase PTPN12 suggesting that deregulation of PTPN12, via a variety of rearrangements, is common in glioblastoma.</p> <p>Conclusions</p> <p>We demonstrate that NBC is useful for detection of recurrent breakpoints resulting from copy number variants or other structural variants, and in particular identifies recurrent breakpoints that result in gene truncations or fusion genes. Software is available at <url>http://http.//cs.brown.edu/people/braphael/software.html</url>.</p