Mouse Anti-Human Synaptophysin monoclonal antibody, clone JID780

Sorption isotherm is a quantitative approach to predict the shelf life of dried foods. Adsorption isotherms of spray dried sweetened yoghurt powder (SYP) were determined by static gravimetric technique at 20, 30, 40 and 50 A degrees C. The data obtained were fitted to eight different sorption models. A non-linear least square regression analysis was adopted to evaluate the model constants. The experimental sorption data were best fitted to four parameter Peleg model. The monolayer moisture contents found from GAB model were 4.88, 4.54, 3.86 and 3.52% at 20, 30, 40 and 50 A degrees C, respectively. The maximum net isosteric heat of sorption and sorption entropy of SYP were 9.399 kJ/mol and 20.28 J/mol K, respectively. The Gibb's free energy change for sorption was in the range 3436.19-303.91 J/mol. The storage stability in terms of moisture content, thiobarbituric acid, free fatty acid, hydroxymethyl furfural values and starter counts of SYP packed in aluminium laminated polyethylene (ALPE) and low density polyethylene (LDPE) were studied along with their change kinetics. The relationship between the water vapour permeability of packaging materials and adsorbed moisture (determined from GAB equation) in powder was used to predict the shelf life and was predicted as 28 and 44.44 days in LDPE and ALPE pouches, respectively.

Accurate histological classification and identification of fusion genes represent two cornerstones of clinical diagnostics in non-small cell lung cancer (NSCLC). Here, we present a NanoString gene expression platform and a novel platform-independent, single sample predictor (SSP) of NSCLC histology for combined, simultaneous, histological classification and fusion gene detection in minimal formalin fixed paraffin embedded (FFPE) tissue. The SSP was developed in 68 NSCLC tumors of adenocarcinoma (AC), squamous cell carcinoma (SqCC) and large-cell neuroendocrine carcinoma (LCNEC) histology, based on NanoString expression of 11 (CHGA, SYP, CD56, SFTPG, NAPSA, TTF-1, TP73L, KRT6A, KRT5, KRT40, KRT16) relevant genes for IHC-based NSCLC histology classification. The SSP was combined with a gene fusion detection module (analyzing ALK, RET, ROS1, MET, NRG1, and NTRK1) into a multicomponent NanoString assay. The histological SSP was validated in six cohorts varying in size (n = 11-199), tissue origin (early or advanced disease), histological composition (including undifferentiated cancer), and gene expression platform. Fusion gene detection revealed five EML4-ALK fusions, four KIF5B-RET fusions, two CD74-NRG1 fusion and three MET exon 14 skipping events among 131 tested cases. The histological SSP was successfully trained and tested in the development cohort (mean AUC = 0.96 in iterated test sets). The SSP proved successful in predicting histology of NSCLC tumors of well-defined subgroups and difficult undifferentiated morphology irrespective of gene expression data platform. Discrepancies between gene expression prediction and histologic diagnosis included cases with mixed histologies, true large cell carcinomas, or poorly differentiated adenocarcinomas with mucin expression. In summary, we present a proof-of-concept multicomponent assay for parallel histological classification and multiplexed fusion gene detection in archival tissue, including a novel platform-independent histological SSP classifier. The assay and SSP could serve as a promising complement in the routine evaluation of diagnostic lung cancer biopsies.