Mercury (Hg ²⁺ ) ion is one of the heavy metal ions present in water and highly poisonous to human consumption. Thus, developing an efficient sensor for detection of mercury ions at different concentration levels in the water down to nM is essential. Here, we report the development of fluorometric sensor using the fluidic MEMS device (i.e., microfluidic device) with high sensitivity and selective detection of Hg ²⁺ in water using tiny quantity of sensing fluids (~2.8 μL). L-Arginine-capped gold nanoparticles functionalized with Rhodamine 6G allowed the on-chip fluorescence detection of Hg ²⁺ ions concentration regime from 0 to 16 nM in water samples. The developed microfluidic device senses the presence of Hg ²⁺ ions through the change in intensity of fluid fluorescence. The sensor response is found to be linear with respect to the Hg ²⁺ ions concentration ranging from 2 to 12 nM.

This paper deals with design, simulation, fabrication, analysis of mixing efficiency and thin film bonding stability of the micromixer devices with different flow rates used for lab on chip applications. The objective of the present study is to achieve complete mixing with low flow rate and less pressure drop in low cost polymer microfluidic devices. This paper emphasis the design, simulation and fabrication of straight channel micromixer, serpentine channel micromixer with and without quadrant shaped grooves to study the mixing behavior by the effect of structural dimensions of the microfluidic channel at different flow rates. The designed micromixers were tested with varying rates of flow such as 1, 10, 25, 50, 75 and 100 µL/min.

Patient‐derived xenografts (PDX) involve transplanting patient cells or tissues into immunodeficient mice, offering superior disease models compared with cell line xenografts and genetically engineered mice. In contrast to traditional cell‐line xenografts and genetically engineered mice, PDX models harbor the molecular and biologic features from the original patient tumor and are generationally stable. This high fidelity makes PDX models particularly suitable for preclinical and coclinical drug testing, therefore better predicting therapeutic efficacy. Although PDX models are becoming more useful, the several factors influencing their reliability and predictive power are not well understood. Several existing studies have looked into the possibility that PDX models could be important in enhancing our knowledge with regard to tumor genetics, biomarker discovery, and personalized medicine; however, a number of problems still need to be addressed, such as the high cost and time‐consuming processes involved, together with the variability in tumor take rates. This review addresses these gaps by detailing the methodologies to generate PDX models, their application in cancer research, and their advantages over other models. Further, it elaborates on how artificial intelligence and machine learning were incorporated into PDX studies to fast‐track therapeutic evaluation. This review is an overview of the progress that has been done so far in using PDX models for cancer research and shows their potential to be further improved in improving our understanding of oncogenesis.

This paper deals with the design, analysis and optimization of micro-mixer for fluids having very low diffusivity (in the order of 10 ⁻¹² m ² /s) to be used in Lab on Chip (LOC) for medical diagnosis. As flow is laminar and the cross-sectional area is in microscale, the viscous forces are strong causing the fluids to be transported in streamline with minimum diffusion. The main objective in designing a micro mixer is to achieve complete mixing with minimum channel length and pressure drop. In this work a passive micro mixer with two inlets and one outlet (Y shaped passive micro mixer) with obstacles in various shapes and sizes is modelled, to study the effect of mixing. After a CFD analysis, Analysis of variance (ANOVA) of 3 ^K design with 3 parameters as well as a 2 ^K design with 4 parameters was performed to study the effect of parameters on mixing index (mixing length) and pressure loss. There is a negative correlation between the response obtained for mixing length and pressure loss while varying the parameters. This makes it difficult to predict the optimum configuration. Taguchi method is used to obtain an optimum configuration to overcome this negative correlatiozn.

The objective of this study is the design, simulation, and performance optimization of a micromixer device using the three input parameters of device structure, flow rate and diffusion coefficient of gold nanoparticles while the output parameters are concentration, velocity, pressure and time domain analysis. Each input parameter in the microfluidic chip influences the system output. The data were gathered through extensive study in order to optimize the diffusion control. The fuzzy logic approach is used to optimize the performance of the device with respect to the input parameters. In this study, we have chosen three different flow rates of 1, 5, and 10 μL min⁻¹, three different diffusion coefficient values of low, average and high diffusivity gold nanofluids (15.3 e⁻¹², 15.3 e⁻¹¹, 15.3 e⁻¹⁰ m² s⁻¹) which are used in three different shapes of micromixer device, Y-shaped straight channel micromixer, herringbone-shaped micromixer, and herringbone shape with obstacles micromixer, and we measured the output performance, such as mixing efficiency, pressure drop, concentration across the microchannel and time domain. The data were obtained by fuzzy logic analysis and it was found that the herringbone shape with obstacles micromixer shows 100% mixing efficiency within a short duration of 5000 μm, and complete mixing was achieved within 10 seconds with a low pressure drop of 128 Pa.