Validation Batch Research Articles

Robust Fine-Grained Visual Recognition With Neighbor-Attention Label Correction.

Existing deep learning methods for fine-grained visual recognition often rely on large-scale, well-annotated training data. Obtaining fine-grained annotations in the wild typically requires concentration and expertise, such as fine category annotation for species recognition, instance annotation for person re-identification (re-id) and dense annotation for segmentation, which inevitably leads to label noise. This paper aims to tackle label noise in deep model training for fine-grained visual recognition. We propose a Neighbor-Attention Label Correction (NALC) model to correct labels during the training stage. NALC samples a training batch and a validation batch from the training set. It hence leverages a meta-learning framework to correct labels in the training batch based on the validation batch. To enhance the optimization efficiency, we introduce a novel nested optimization algorithm for the meta-learning framework. The proposed training procedure consistently improves label accuracy in the training batch, consequently enhancing the learned image representation. Experimental results demonstrate that our method significantly increases label accuracy from 70% to over 98% and outperforms recent approaches by up to 13.4% in mean Average Precision (mAP) on various fine-grained image retrieval (FGIR) tasks, including instance retrieval on CUB200 and person re-id on Market1501. We also demonstrate the efficacy of NALC on noisy semantic segmentation datasets generated from Cityscapes, where it achieves a significant 7.8% improvement in mIOU score. NALC also exhibits robustness to different types of noise, including simulated noise such as Asymmetric, Pair-Flip, and Pattern noise, as well as practical noisy labels generated by tracklets and clustering.

The influence of rolling pressure on the changes in non-volatile compounds and sensory quality of congou black tea: The combination of metabolomics, E-tongue, and chromatic differences analyses

Rolling represents an essential stage in congou black tea processing. However, the influence of rolling pressure on tea flavor and non-volatile compounds remains unclear. Herein, a combination of untargeted metabolomics, tea pigments quantification, E-tongue, colorimeter and sensory evaluation was used to evaluate the effect of rolling pressure on black tea quality. As the rolling pressure increased, theaflavins (TFs), thearubigins (TRs), and theabrownins (TBs) significantly elevated. The tea metabolic profiles fluctuated and 47 metabolites were identified as key differential metabolites including flavan-3-ols, flavonol/flavone glycosides, phenolic acids, amino acids. These substances altered possibly due to the variations in enzymatic oxidation of tea phenolics and amino acids. Overall, black tea with moderate rolling pressure presented higher sweetness, lower bitterness, and higher quality index (10 TFs + TRs)/TBs. The results were verified by a validation batch. This study provided new insights into the regulation of rolling pressure and a guidance for black tea processing.

Development of an image processing software for quantification of histological calcification staining images.

Quantification of the histological staining images gives important insights in biomedical research. In wet lab, it is common to have some stains off the target to become unwanted noisy stains during the generation of histological staining images. The current tools designed for quantification of histological staining images do not consider such situations; instead, the stained region is identified based on assumptions that the background is pure and clean. The goal of this study is to develop a light software named Staining Quantification (SQ) tool which could handle the image quantification job with features for removing a large amount of unwanted stains blended or overlaid with Region of Interest (ROI) in complex scenarios. The core algorithm was based on the method of higher order statistics transformation, and local density filtering. Compared with two state-of-art thresholding methods (i.e. Otsu's method and Triclass thresholding method), the SQ tool outperformed in situations such as (1) images with weak positive signals and experimental caused dirty stains; (2) images with experimental counterstaining by multiple colors; (3) complicated histological structure of target tissues. The algorithm was developed in R4.0.2 with over a thousand in-house histological images containing Alizarin Red (AR) and Von Kossa (VK) staining, and was validated using external images. For the measurements of area and intensity in total and stained region, the average mean of difference in percentage between SQ and ImageJ were all less than 0.05. Using this as a criterion of successful image recognition, the success rate for all measurements in AR, VK and external validation batch were above 0.8. The test of Pearson's coefficient, difference between SQ and ImageJ, and difference of proportions between SQ and ImageJ were all significant at level of 0.05. Our results indicated that the SQ tool is well established for automatic histological staining image quantification.

Quantification of OATP1B1 endogenous metabolites coproporphyrin I and III in human urine.

Coproporphyrin (CP)-I and CP-III are the markers of organic anion-transporting polypeptides' (OATPs) activities, and they are porphyrin metabolites that originate from heme synthesis. Furthermore, CP-I and CP-III, which are OATP1B endogenous metabolites, have gradually attracted the attention of scientists and researchers in recent years. Previous studies have also observed CP-I and CP-III levels as clinical biomarkers for predicting OATP1B inhibition in drug-drug interaction studies. To establish an accurate ultra-high performance liquid chromatography-mass spectrometry method for the quantitation of CP-I and CP-III, we reviewed previous methodological publications and applied them to a clinical pharmacology study using a human urine matrix. We used 13.25 M formic acid as a working solution for internal standards (CP-I 15N4 and CP-III d8) to avoid isobaric interference. The calibration curve showed good linearity in the range of 1-100 ng/mL, with a correlation coefficient (R2) higher than 0.996 in each validation batch. Both the between-run and within-run assays achieved good precision and accuracy, and we found that both CP-I and CP-III were stable in the pre-study validation. The method exhibited suitable dilution integrity, allowing for the re-analysis of samples with concentrations exceeding the upper limit of quantification through dilution. Overall, the application of the described method in a clinical study revealed that it can be utilized effectively to monitor drug-drug interactions mediated by OATP1B.

Non-targeted metabolomics and electronic tongue analysis reveal the effect of rolling time on the sensory quality and nonvolatile metabolites of congou black tea

Rolling is a crucial step in congou black tea manufacturing. Herein, the influence of rolling time (50, 75, 100, 125 min) on the sensory quality and chemical composition of black tea was investigated by human sensory evaluation, electronic tongue, colorimetric analysis, tea pigments quantification and liquid chromatography-mass spectrometry (LC-MS) based non-targeted metabolomics. With increasing rolling time, tea liquor color attributes a*, b*, C* and pigments theaflavins (TFs), thearubigins (TRs), and theabrownins (TBs) were significantly increased. Fifty-two key differential metabolites were identified among four treatments. Catechins, dimeric/trimeric catechins, amino acids were significantly declined. The changes of flavonol/flavone glycosides depended on the linkage pattern. Flavonol O-glycosides such as myricetin 3-O-glucoside were decreased, whereas flavone C-glycosides such as vitexin were increased. These results suggested that different rolling times influenced the catechins oxidative polymerization, flavonol O-glycosides hydrolysis and amino acids oxidation. Black tea by 100-min rolling rendered optimal sensory quality, with highest umami and low bitter intensities in electronic tongue, and higher quality index (10 TFs + TRs)/TBs. Similar result was obtained in a validation batch in a different year. This study provides novel insights into the effect of rolling on black tea quality, which may serve as guidance for the manufacturing of high-quality congou black tea.

Process Modelling, Scale-Up and Characterization of Acetaminophen Spray Dried Milk Powder as Novel Pediatric Dosage Form.

Successful drug therapy in children is contingent upon hassle-free administration of pediatric dosage forms. Pediatric patients suffer from difficulty in swallowing due to weak esophagus muscles in their early age. Considering this challenge liquid formulations are preferred over solid dosage form among pediatric patients to avoid the possibility of choking which can be a serious life-threatening condition in children. The main aim of the present research work was to develop a reconstitutable amorphous acetaminophen spray-dried milk powder (ASDM) as novel pediatric formulation. ASDM was prepared by spray drying process and the spray drying process was optimized using Box-Behnken design to study the effect of spray drying process parameters at X1 [inlet temperature], X2 [aspiration rate] and X3 [feed rate] to Y1 [% yield], Y2 [angle of repose], Y3 [Hausner's Ratio] and Y4 [Carr's Index] as dependent variables of ASDM. In addition, each batch was characterized for particle size by polarized light microscopy and drug entrapment. Predicted parameters from optimized spray drying process model were successfully employed to manufacture a scale up cum validation batch of ASDM, which showed notably improved yield and desirable flow properties. The scale-up validation batch was further characterized using thermal analysis, diffraction studies, spectroscopic analysis, dispersion studies, stability APAP in dispersion formulation and formulation stability studies to confirm the physico-chemical stability of ASDM. Thus, ASDM for oral use can serve as a promising pediatric formulation and the developed prototype formulation can be further extended to future newly discovered drugs with similar characteristics.

A Machine Learning Model for Post-Transplant Lymphoproliferative Disorder Using the UNOS Database

<h3>Purpose</h3> Post Transplant Lymphoproliferative Disorder (PTLD) is a complication noted in cardiac allograft recipients. This study was undertaken to generate a risk prediction model for PTLD at 1, 3 and 5 years post heart transplant using machine learning models. The United Network for Organ Sharing (UNOS) database was used for data generation <h3>Methods</h3> The UNOS database was probed from 1987 to 2015<b>.</b> After excluding retransplants and those with incomplete information regarding malignancy, 39696 adult patients were identified out of which 884 patients had PTLD. A Random Under-sampling Boosting ("RUSBoost") technique in combination with Decision Tree models ("ensemble of models") was used to create a PTLD prediction model. Random Under-sampling technique was combined with boosting (AdaBoost M2) to enhance the prediction model. Variables with higher predictive power were used for testing and training. A 5-fold cross validation was performed to train and test them on five different subsets of data. Model performance was evaluated using average Area Under the Curve (AUC) using Receiver Operating Curves (ROC) for the 5 test subsets <h3>Results</h3> The significant continuous variables for predicting PTLD were ischemic time, recipient serum creatinine, and presence of B2, DR1,DR2 antigens in recipient serum. Of the categorical variables investigated gender, diabetes in the recipient, ethnicity, CMV status of the donor and induction with azathioprine, OKT3,thymoglobulin or cyclosporine were significant. The AUCs ranged from 0.76 to 0.79 and 0.66 to 0.7 in the testing or validation batch respectively. <h3>Conclusion</h3> This is the first report of using machine learning algorithms to derive a risk prediction model for PTLD. This model has a robust AUC for predicting PTLD at years 1, 3 and 5 posttransplant. The limitations of this study are its retrospective nature and that it needs external validation.

PROCESS DEVELOPMENT, OPTIMIZATION AND VALIDATION OF MANUFACTURING PROCESS OF ALLOPURINOL

Objective: The objective of the present research work is to identify the critical parameters in the process that affect the quality of the finished product. The processs validation of the manufacturing process for Allopurinol tablet USP, 300 mg to be manufactured with change in manufacturing site and with increased batch size.&#x0D; Methods: Prior to Scaling up to production, pilot batches were manufactured. Three consecutive commercial batches are to be produced using similar test equipment and in full compliance with cGMP and existing SOPs. Critical process parameters of the machine was set within operating range and was not exceeded upper and lower control limits during process operation. Equipment were operated within its assigned control limits, which shows upon scale up into new site, the specification for hardness did not meet requirements that had been set during development. There was no significant effect observed on thickness, friability, disintegration, dissolution, uniformity of dosage and were found within specification. Pre process batch was challenged by manufacturing product at upper and lower processing limits and circumstances which pose the greatest chance of process or product failure compared to ideal condition.&#x0D; Results: Based on the data obtained from pre-process validation batch, operating ranges for compression force and machine speed had been established. Collected data shows robustness around these parameters and there is increased confidence that the process can be successfully scaled up under target conditions for these parameters.&#x0D; Conclusion: From this study, it was concluded that process was optimized and gave a reproducible product that meet predetermined quality requirements and do so consistently and reliably.

High soluble interleukin-2 receptor values in Indian paediatric &amp; adult controls – Need for population-specific threshold in the diagnosis of haemophagocytic lymphohistiocytosis

Background & objectives:Elevated soluble interleukin-2 receptor (sIL2R) is a diagnostic criterion for haemophagocytic lymphohistiocytosis (HLH). International guidelines propose a 2400 U/ml cut-off or individual laboratory-defined cut-off. However, sIL2R normal values are so far not known in Indians. So, this study was undertaken to measure sIL2R in healthy children and adults to establish age-related reference values.Methods:Healthy controls and cases (participants with persistent fever, organomegaly, cytopenias and biochemical markers of HLH) were prospectively enrolled. Serum sIL2R was measured by double-sandwich enzyme immunoassay in a standardization batch to determine the optimum cut-off value using receiver operator characteristic curve and was subsequently validated.Results:One hundred and forty six age- and sex-matched children (80 controls and 66 suspected HLH cases) and 55 adults (49 controls and 6 suspected HLH cases) were prospectively enrolled. The optimal sIL2R cut-off ≥23 ng/ml was defined as raised sIL2R in the standardization batch. No controls had sIL2R ≥23 ng/ml in the validation batch. In healthy controls, median sIL2R (interquartile range) decreased with increasing age from 9.0 ng/ml (6.6-13.4) below five years of age to 3.2 ng/ml (2.8-5.1) in adults. Proposed upper limit of normal value for sIL2R is 17.4 ng/ml in less than five year, 12.2 ng/ml in 5-9 yr, 6.7 ng/ml in 10-17 yr and 5.2 ng/ml in ≥18 yr. sIL2R accuracy to diagnose HLH marginally improved with age-appropriate cut-off.Interpretation & conclusions:Paediatric controls in India showed higher sIL2R levels than most studies conducted in other countries, except for some reports in Chinese and Russian populations. Age-appropriate reference values of sIL2R in a specific population may be considered to determine elevated sIL2R as a marker of HLH.

A validated simple LC-MS/MS method for quantifying trimethylamine N-oxide (TMAO) using a surrogate matrix and its clinical application.

Trimethylamine N-oxide (TMAO) is a small molecular amine oxide generated from dietary choline and carnitine through intestinal microbial metabolism. Recently, TMAO has attracted much public attention as its role in disease progression has been proven in many clinical studies. The plasma concentration of TMAO in humans was found to be positively associated with the increased risk of many diseases including cardiovascular diseases and chronic kidney diseases. To achieve accurate and sensitive quantitation of TMAO for clinical applications, we established and validated a simple quantitative method using a liquid chromatography tandem mass spectrometry (LC-MS/MS) system. We constructed an eight-point calibration curve in an artificial surrogate matrix instead of the commonly used biological matrices to avoid interference from the endogenous TMAO. The calibration curve showed excellent linearity in the range of 1 to 5,000 ng/mL, with a correlation coefficient (R2) higher than 0.996 in each validation batch. Moreover, both the intra-day and inter-day assays achieved satisfactory precision and accuracy results ranging from 1.65–7.15% and 96.36–111.43%, respectively. Further, this method was cross-validated using a human plasma matrix and applied to a clinical pharmacology study. Overall, these results demonstrate that the developed quantitation method is applicable in clinical research for monitoring disease progression and evaluating drug effects.

Process validation for antiviral famciclovir tablet

Validation is consequently one element of quality assurance programs related with individual procedure. The validation batch encountered the specification of tablets. Tablets having batch size of maximum no. of tablets was successfully completed and the manufacturing critical process parameters were validated of this transferred product to show that the process was under control. The study embraces the validation of critical steps of manufacturing such as blending, compression, coating and container packing. It shall also establish the suitability of equipment's and area used for the production. The process of manufacturing was carried as per the permitted batch manufacturing card. The all process validation batches had been manufactured and validated in full compliance with cGMP requirement.

Optimal Accuracy Zone Identification in Object Detection Technique – A Learning Rate Methodology

In the recent past, Deep Learning models [1] are predominantly being used in Object Detection algorithms due to their accurate Image Recognition capability. These models extract features from the input images and videos [2] for identification of objects present in them. Various applications of these models include Image Processing, Video analysis, Speech Recognition, Biomedical Image Analysis, Biometric Recognition, Iris Recognition, National Security applications, Cyber Security, Natural Language Processing [3], Weather Forecasting applications, Renewable Energy Generation Scheduling etc. These models utilize the concept of Convolutional Neural Network (CNN) [3], which constitutes several layers of artificial neurons. The accuracy of Deep Learning models [1] depends on various parameters such as ‘Learning-rate’, ‘Training batch size’, ‘Validation batch size’, ‘Activation Function’, ‘Drop-out rate’ etc. These parameters are known as Hyper-Parameters. Object detection accuracy depends on selection of Hyper-parameters and these in-turn decides the optimum accuracy. Hence, finding the best values for these parameters is a challenging task. Fine-Tuning is a process used for selection of a suitable Hyper-Parameter value for improvement of object detection accuracy. Selection of an inappropriate Hyper-Parameter value, leads to Over-Fitting or Under-Fitting of data. Over-Fitting is a case, when training data is larger than the required, which results in learning noise and inaccurate object detection. Under-fitting is a case, when the model is unable to capture the trend of the data and which leads to more erroneous results in testing or training data. In this paper, a balance between Over-fitting and Under-fitting is achieved by varying the ‘Learning rate’ of various Deep Learning models. Four Deep Learning Models such as VGG16, VGG19, InceptionV3 and Xception are considered in this paper for analysis purpose. The best zone of Learning-rate for each model, in respect of maximum Object Detection accuracy, is analyzed. In this paper a dataset of 70 object classes is taken and the prediction accuracy is analyzed by changing the ‘Learning-rate’ and keeping the rest of the Hyper-Parameters constant. This paper mainly concentrates on the impact of ‘Learning-rate’ on accuracy and identifies an optimum accuracy zone in Object Detection.

Determining Mandatory Nutritional Parameters for Iberian Meat Products Using a New Method Based on Near Infra-Red Reflectance Spectroscopy and Data Mining

The new regulation about mandatory labelling on nutrition requires the declaration of specific parameters: protein, lipid, salt and carbohydrate contents. This study reports a fast, accurate method to determine the values of these mandatory nutritional parameters based on near infra-red reflectance spectroscopy (NIRs) technology and data mining techniques, used in an automatic way. For that, two batches of different Iberian pork meat products (dry-cured ham, dry-cured loin, dry-cured shoulder, dry-fermented Salchichon sausage, and dry-fermented Chorizo sausage were used. One batch of each product was used to train the method and the remaining batch was used for validation. To develop the method, prediction equations were obtained from the NIRs, while nutritional data for the training batches were obtained by applying data mining techniques, and the prediction equations were evaluated against the NIRs data from the validation batch. The prediction equations achieved from very good to excellent degrees of relationship (R > 0.75) and accurate results (MAE 0.75). This new method is rapid, as it takes around 10 minutes in comparison with traditional methods that take around 6 days.

Abstract 3428: Development and validation of the plasma monitor test system

Abstract Introduction: Non-invasive monitoring of variants in a cancer patient for minimal residual disease, recurrence and/or resistance has tremendous clinical utility and warrants the development of a cell-free circulating tumor DNA panel, which involves the use of a simple blood draw. Towards this end, JAX has validated a new liquid biopsy assay called the Plasma MonitorTM that focuses on 84 clinically significant hotspots across 14 genes to complement our current clinical test menu, with a focus on comprehensive profiling of cancer. Methods: Post development and optimization of a custom amplicon panel, which included running multiple batches of samples through the assay to determine appropriate extraction methods, input DNA QC metrics, sequencing batch size, and sequencing loading concentrations, clinical validation of The Plasma MonitorTM was initiated. Validation included the evaluation of 20 uncharacterized plasma samples and 14 known controls and was executed in 5 phases: (1) Sample Processing for Validation Parameter determination; (2) LOD, sensitivity, specificity and accuracy (3) inter-assay concordance; (4) intra-assay concordance; (5) clinical validity in terms of interpretation and reporting of variants identified. Results: The final clinical protocol was developed using an input of 10ng of ctcfDNA quantified and qualified by a custom qPCR assay. Wet lab results of the first validation batch can be seen in Table 1. Using samples containing variants with known allele frequencies and a droplet digital PCR (ddPCR) based confirmation of novel variants identified by our assay, we established the assay's limit of detection (LOD) to be 0.9%. At this LOD, sensitivity, specificity, and accuracy were found to be 96.6%, 100%, and 98% respectively. Conclusion: Based on the results of this validation, the Plasma Monitor assay will be incorporated into the JAX clinical test menu. This new assay allows for comprehensive profiling and monitoring of cancer progression, response to therapy and minimal residual disease, and a significant benefit to both patients and clinicians. Citation Format: Kevin J. Kelly, Jasmina Uvalic, Daniel Bergeron, Shelbi Burns, Melissa Soucy, Guruprasad Ananda, Andrew Hesse, Pavalan Panneer Selvam, Honey V. Reddi. Development and validation of the plasma monitor test system [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3428.

Abstract 757: Development and validation of the ActionSeqTM test system

Abstract Introduction In the constantly changing field of oncology precision medicine, it is exceedingly important to keep diagnostic and therapeutic assays clinically relevant. Next generation sequencing (NGS) panels in oncology are greatly impacted by new findings in clinical actionability. In order to ensure that cancer panels continue to provide the most beneficial results to patients, they must be regularly updated. In keeping with this idea, JAX has launched a new 212 oncology gene panel which focuses on genes and variants with documented actionability, referred to as ActionSeq. Methods Development of ActionSeq included the optimization of a new targeted capture assay. This process included running multiple batches of samples through the assay to determine appropriate DNA input, ligation times, PCR cycles, and pooling conditions. The fully optimized assay was then validated using 24 uncharacterized FFPE samples. The validation was executed in 5 phases: (1) confirm that assay optimizations yielded sufficient wet lab results; (2) LOD &amp; sensitivity (3) inter-assay concordance; (4) intra-assay concordance; (5) specificity and accuracy. Results During development, the standard protocol was optimized using a 200ng input, 30 minute ligation period, 5 cycles of pre-PCR, and the pooling of 4 samples per hybridization reaction. Wet lab processing results of the first validation batch can be seen in Table 1. The inter- and intra-assay concordances were found to be ≥ 96% for variants and 100% for CNVs. The sensitivity was calculated to be 98.92% at a LOD of 3% for SNVs, 100% at a LOD of 8% for INDELs, 100% at a LOD of 6 copies for CNV amplifications, and 100% at a LOD of 0 copies for CNV deletions. The specificity and accuracy were found to be 100% for all mutation types. Conclusion Based on the success of this validation ActionSeq has been incorporated into the JAX clinical test menu. This addition accomplished the goal of providing a more clinically relevant (actionable) somatic tumor profiling assay to patients and clinicians. Table 1.This table displays the wet lab processing results for the 24 samples used to confirm the efficiency of the developmental optimizations made to the assay prior to beginning the clinical validation.SamplePost Prep ng/ulCapture Pool ng/ulAverage Base Pair SizeMean Target CoveragePercent Duplication1271.4130951123%2241.4130959423%3241.5230265922%4291.5230265221%5212.2229053516%6352.2229056013%7292.2329953017%8722.58304119614%9652.5830473214%10432.5830457113%1142230372218%12372.030361116%13272.030373518%14421.930973120%15401.930968319%16671.930967516%17481.930957320%18531.8930165519%19311.8930176322%20821.8930176117%21561.8930187719%22341.9932977118%23481.9932988616%24781.9932984115%Established Quality Control Cut Offs:Post Library Prep ng/ul:≥ 20Post Capture Pool ng/ul:≥ 0.5PC Average Base Pair Size:250-350Mean Target Coverage:≥ 500Percent Duplication:≤ 25% Citation Format: Samantha Helm, Vanessa Spotlow, Aleksandra Ras, Kevin Kelly, Guruprasad Ananda, Sara Patterson, Honey V. Reddi. Development and validation of the ActionSeqTM test system [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 757. doi:10.1158/1538-7445.AM2017-757

Abstract 4429: An exhaled microRNA panel interrogation and validation as risk biomarkers for lung cancer

Abstract Background: There is a need for non-invasive airway-based biomarkers in lung carcinogenesis for both risk assessment of the ex-smoker, and earlier diagnosis. Recent studies have revealed that microRNAs play important regulatory roles in carcinogenesis. Exhaled breath condensate (EBC) contains airway lining fluid molecules, including nucleic acids, presumably in part from epithelial cellular origins. Here we further develop and begin validation of the detection of microRNAs in EBC from lung cancer patients and controls. Methods: Exhaled breath condensate (EBC) was collected non-invasively, using a handheld device in ambulatory subjects. MicroRNA expression profiling using RNA-specific RT-qPCR was performed in EBC in an initial batch (E) of 88 individuals (41 lung cancer cases, 47 controls), and a second batch D of 88 individuals (41, cases and 47 controls). We generated a 39 miR panel based on literature-derived microRNAs, combined with a lung tissue-based discovery effort we have performed using microRNA-seq on lung tumors and surrounding non-tumor tissue. The qPCR primers were designed using our previously published RNA-specific realtime RT-PCR technique. All samples were run twice with positive and negative controls. Results: Qualitative analyses of the first donor batch E was performed on 39 microRNAs measured in 41 cases and 47 controls. Chi-square analysis (univariate) within the training set (batch E) revealed that exhaled miR-31, 33, 105, 200b, 944, 1269a were significantly different between the initial set of cases and controls; their absence marked the cases. In the validation batch (D) preliminary analysis, exhaled miRs 200b (this time present in cases, p=0.044) and 944 (again absent in cases, p=0.052) were case-control discriminant. In addition, test set batch D showed case-control discrimination for additional miRs 130b-3p (present in cases, p=0.035), 212 (absent in cases, p=0.009), 1910a (absent in cases, p=0.006) and other microRNAs that were not seen in the qualitative training set (batch E). Importantly, multi-miR discriminant signatures, quantitative analysis, and risk ROC/modelling analyses are pending as of this AACR abstract deadline. Conclusion: This new exhaled biomarker platform can yield case-control discriminant microRNA sets, including a subset of miRs that can validate in test sets. Once further distilled and validated, our goal is to apply this non-invasive biomarker approach to prospective cohorts for non-invasive lung cancer risk assessment, in order to better select higher risk individuals who should undergo CT screening. Note: This abstract was not presented at the meeting. Citation Format: Simon D. Spivack. An exhaled microRNA panel interrogation and validation as risk biomarkers for lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4429. doi:10.1158/1538-7445.AM2017-4429

The application of near-infrared spectroscopy in beer fermentation for online monitoring of critical process parameters and their integration into a novel feedforward control strategy

Traditional methods used in the analysis of fermentation media suffer from a number of limitations. The search for more rapid and efficient methods has led to the development and application of near-infrared spectroscopy. Near-infrared spectroscopy has been applied successfully in a variety of industrial processes: agricultural, food, chemical and pharmaceutical, generally in the areas of raw material quality control but also including intermediate and finished product testing. The present research explores its potential for online fermentation monitoring of total cell count (TCC), specific gravity (SG), free amino nitrogen (FAN) and percentage alcohol by volume (% v v−1) in a 300 L pilot-scale validation batch. Models that were generated from three calibration batches for each of these constituents exhibited overall favourable standard error of cross validation (SECV) and fit of predicted vs actual cross validated results (SECV, R2): SG (0.00072, 0.995), ethanol (0.17% v v−1, 0.990), FAN (16.5 mg L−1, 0.886) and TCC (1.24 × 106 cells mL−1, 0.640). The data that was most relevant to cell metabolism was determined to be sugar consumption rate, ethanol production rate, yield of ethanol and fermentation lag time. These ‘critical performance parameters’ were incorporated into a novel feed-forward control strategy where yeast pitching rate was modified based on values of the critical performance parameters from the previous batch. Use of this feed-forward strategy demonstrated how brewers can utilize near-infrared monitoring for quality assurance through early detection of shifts in fermentation performance. Copyright © 2017 The Institute of Brewing & Distilling

Pale Lager Clarification Using Novel Ceramic Hollow-Fiber Membranes and CO2 Backflush Program

In this work, the cross-flow microfiltration (CFMF) performance of a rough pale lager, produced in the industrial brewery Birra Peroni Srl (Rome, Italy), was assessed in a bench-top rig, equipped with a 0.8-μm ceramic hollow-fiber membrane module, to overcome the recognized inefficacy of back-flushing cleaning techniques in ceramic multi-channel monolithic modules. In total recycle CFMF trials, as the transmembrane pressure difference (TMP) was increased from 0.59 to 3.56 bar, the quasi-steady-state permeation flux (J*) tended to a limiting value increasing with the cross-flow velocity (v S). To minimize the overall membrane surface to be installed for a prefixed permeate recovery, it was found to be expedient to operate at the aforementioned high cross-flow velocity and TMP of 3.56 bar, thus obtaining quite a high quasi-steady-state permeation flux (mean, μ = 173 L m−2 h−1; standard deviation, sd = 7 L m−2 h−1; number of observations, N = 12). The energy consumption per liter of permeate collected was found to be practically independent of the operating variables v S and TMP selected (μ = 55 W h L−1; sd = 2 W h L−1; N = 21). Yet, a permeate flux greater than 100 L m−2 h−1 was achieved on condition that TMP was greater than 2 bar and v S varied from 4 to 6 m s−1. A few validation batch CFMF tests, carried out using pre-centrifuged, PVPP-stabilized, and cartridge-filtered rough pale lager at TMP = 3.56 bar, v S = 6 m s−1, and 10 °C under a predefined CO2 backwashing program, resulted in an average permeation flux (μ = 239 L m−2 h−1; sd = 24 L m−2 h−1; N = 2) by far greater than that (50–100 L m−2 h−1) claimed at 0–2 °C by the three CFMF processes commercially available. Finally, it was proved the easy transferability of the lager beer clarification and stabilization process, previously developed in a single-tube membrane module, to a ceramic hollow-fiber membrane module industrially upscalable.

Optimization, validation and application of an assay for the activity of HMG-CoA reductase in vitro by LC–MS/MS

A stable HMG-CoA reductase (HMGR) reaction in vitro was developed by a sensitive, selective and precise liquid chromatography–tandem mass spectrometry (LC–MS/MS) method. The optimized enzyme reaction condition contained 1.5μg of HMGR, 20nM of NADPH with 50min of reaction time. The method was validated by several intra- and inter-day assays. The production transitions of m/z 147.0/59.1 and m/z 154.0/59.1 were used to detect and quantify mevalonolactone (MVAL) and MVAL-D7, respectively. The accuracy and precision of the method were evaluated over the concentration range of 0.005–1.000μg/mL for MVAL and 0.010–0.500μg/mL for lovastatin acid in three validation batch runs. The lower limit of quantitation was found to be 0.005μg/mL for MVAL and 0.010μg/mL for lovastatin acid. Intra-day and inter-day precision ranged from 0.95% to 2.39% and 2.26% to 3.38% for MVAL, 1.46% to 3.89% and 0.57% to 5.10% for lovastatin acid, respectively. The results showed that the active ingredients in Xuezhikang capsules were 12.2 and 14.5mg/g, respectively. This assay method could be successfully applied to the quality control study of Xuezhikang capsule for the first time.

Modulated release of 5-fluorouracil from pH-sensitive and colon targeted pellets: An industrially feasible approach

Pellets, reliant on pH-sensitivity and time-dependency for drug delivery, provide one of the most versatile opportunities for targeting colon. 5-Fluorouracil (5-FU) loaded pellets were prepared by extrusion-spheronization using Avicel® PH101 as a spheronization aid and hydroxypropylmethylcellulose K4M (HPMC K4M) solution as a binder. A 32 full factorial design was employed to optimize spheronization speed and time. Obtained pellets were evaluated for flow properties, pellet size, roundness and aspect ratio. Optimized batch was coated in a bottom-spray fluidized bed processor (FBP) with an inner coat of sustained release polymer Eudragit NE30D and an outer coat of pH-sensitive polymer Eudragit FS30D. The coating levels were statistically optimized and in vitro drug release was monitored by changing pH media method. Optimized system with 15% inner and outer coating levels revealed t50% (time required for 50% drug release) to be about 9 h while almost complete drug was released in 24 h (98.71 ± 1.33%) with highest dissolution efficiency (DE24h) of 58.71%. The optimization model was validated; the predicted and experimental/actual values for validation batch (M1) were in close tolerance and the standard error (SE) was also small. Drug release was also studied at pH 7.4. Scanning electron microscopy (SEM) demonstrated average coating thickness to be 32.50 ± 3.0 µm. Hence, the present study provides constructive results for colon targeting of 5-FU pellets with industrially feasible processes.