Counter-on-chip for bacterial cell quantification, growth, and live-dead estimations.

Quantifying bacterial cell numbers is crucial for experimental assessment and reproducibility, but the current technologies have limitations. The commonly used colony forming units (CFU) method causes a time delay in determining the actual numbers. Manual microscope counts are often error-prone for submicron bacteria. Automated systems are costly, require specialized knowledge, and are erroneous when counting smaller bacteria. In this study, we took a different approach by constructing three sequential generations (G1, G2, and G3) of counter-on-chip that accurately and timely count small particles and/or bacterial cells. We employed 2-photon polymerization (2PP) fabrication technology; and optimized the printing and molding process to produce high-quality, reproducible, accurate, and efficient counters. Our straightforward and refined methodology has shown itself to be highly effective in fabricating structures, allowing for the rapid construction of polydimethylsiloxane (PDMS)-based microfluidic devices. The G1 comprises three counting chambers with a depth of 20 µm, which showed accurate counting of 1 µm and 5 µm microbeads. G2 and G3 have eight counting chambers with depths of 20 µm and 5 µm, respectively, and can quickly and precisely count Escherichia coli cells. These systems are reusable, accurate, and easy to use (compared to CFU/ml). The G3 device can give (1) accurate bacterial counts, (2) serve as a growth chamber for bacteria, and (3) allow for live/dead bacterial cell estimates using staining kits or growth assay activities (live imaging, cell tracking, and counting). We made these devices out of necessity; we know no device on the market that encompasses all these features.

A High-Throughput Size Exclusion Chromatography Method to Determine the Molecular Size Distribution of Meningococcal Polysaccharide Vaccine.

Molecular size distribution of meningococcal polysaccharide vaccine is a readily identifiable parameter that directly correlates with the immunogenicity. In this paper, we report a size exclusion chromatography method to determine the molecular size distribution and distribution coefficient value of meningococcal polysaccharide serogroups A, C, W, and Y in meningococcal polysaccharide (ACWY) vaccines. The analyses were performed on a XK16/70 column packed with sepharose CL-4B with six different batches of Ingovax® ACWY, a meningococcal polysaccharide vaccine produced by Incepta Vaccine Ltd., Bangladesh. A quantitative rocket immunoelectrophoresis assay was employed to determine the polysaccharide contents of each serogroup. The calculated distribution coefficient values of serogroups A, C, W, and Y were found to be 0.26 ± 0.16, 0.21 ± 0.11, 0.21 ± 0.11, and 0.14 ± 0.12, respectively, and met the requirements of British Pharmacopeia. The method was proved to be robust for determining the distribution coefficient values which is an obligatory requirement for vaccine lot release.

Comparative analysis of differential network modularity in tissue specific normal and cancer protein interaction networks.

BACKGROUND: Large scale understanding of complex and dynamic alterations in cellular and subcellular levels during cancer in contrast to normal condition has facilitated the emergence of sophisticated systemic approaches like network biology in recent times. As most biological networks show modular properties, the analysis of differential modularity between normal and cancer protein interaction networks can be a good way to understand cancer more significantly. Two aspects of biological network modularity e.g. detection of molecular complexes (potential modules or clusters) and identification of crucial nodes forming the overlapping modules have been considered in this regard. METHODS: In the current study, the computational analysis of previously published protein interaction networks (PINs) has been conducted to identify the molecular complexes and crucial nodes of the networks. Protein molecules involved in ten major cancer signal transduction pathways were used to construct the networks based on expression data of five tissues e.g. bone, breast, colon, kidney and liver in both normal and cancer conditions. MCODE (molecular complex detection) and ModuLand methods have been used to identify the molecular complexes and crucial nodes of the networks respectively. RESULTS: In case of all tissues, cancer PINs show higher level of clustering (formation of molecular complexes) than the normal ones. In contrast, lower level modular overlapping is found in cancer PINs than the normal ones. Thus a proposition can be made regarding the formation of some giant nodes in the cancer networks with very high degree and resulting in reduced overlapping among the network modules though the predicted molecular complex numbers are higher in cancer conditions. CONCLUSION: The study predicts some major molecular complexes that might act as the important regulators in cancer progression. The crucial nodes identified in this study can be potential drug targets to combat cancer.