Design and fabrication of a microelectrode array for studying epileptiform discharges from rodents.

Local field potentials, the extracellular electrical activities from brain regions, provide clinically relevant information about the status of neurophysiological conditions, including epilepsy. In this study, a 13-channel silicon-based single-shank microelectrode array (MEA) was designed and fabricated to record local field potentials (LFPs) from the different depths of a rat's brain. A titanium/gold layer was patterned as electrodes on an oxidized silicon substrate, and silicon dioxide was deposited as a passivation layer. The fabricated array was implanted in the somatosensory cortex of the right hemisphere of an anesthetized rat. The developed MEA was interfaced with an OpenBCI Cyton Daisy Biosensing Board to acquire the local field potentials. The LFPs were acquired at three different neurophysiological conditions, including baseline signals, chemically-induced epileptiform discharges, and recovered baseline signals after anti-epileptic drug (AED) administration. Further, time-frequency analyses were performed on the acquired biopotentials to study the difference in spatiotemporal features. The processed signals and time-frequency analyses clearly distinguish between pre-convulsant and post-AED baselines and evoked epileptiform discharges.

RapidET: a MEMS-based platform for label-free and rapid demarcation of tumors from normal breast biopsy tissues.

The rapid and label-free diagnosis of malignancies in ex vivo breast biopsy tissues has significant utility in pathology laboratories and operating rooms. We report a MEMS-based platform integrated with microchips that performs phenotyping of breast biopsy tissues using electrothermal sensing. The microchip, fabricated on a silicon substrate, incorporates a platinum microheater, interdigitated electrodes (IDEs), and resistance temperature detectors (RTDs) as on-chip sensing elements. The microchips are integrated onto the platform using a slide-fit contact enabling quick replacement for biological measurements. The bulk resistivity (ρ B ), surface resistivity (ρ S ), and thermal conductivity (k) of deparaffinized and formalin-fixed paired tumor and adjacent normal breast biopsy samples from N = 8 patients were measured. For formalin-fixed samples, the mean ρ B for tumors showed a statistically significant fold change of 4.42 (P = 0.014) when the tissue was heated from 25 °C to 37 °C compared to the adjacent normal tissue, which showed a fold change of 3.47. The mean ρ S measurements also showed a similar trend. The mean k of the formalin-fixed tumor tissues was 0.309 ± 0.02 W m-1 K-1 compared to a significantly higher k of 0.563 ± 0.028 W m-1 K-1 for the adjacent normal tissues. A similar trend was observed in ρ B, ρ S, and k for the deparaffinized tissue samples. An analysis of a combination of ρ B , ρ S , and k using Fisher's combined probability test and linear regression suggests the advantage of using all three parameters simultaneously for distinguishing tumors from adjacent normal tissues with higher statistical significance.

Emerging technologies for antibiotic susceptibility testing.

Superbugs such as infectious bacteria pose a great threat to humanity due to an increase in bacterial mortality leading to clinical treatment failure, lengthy hospital stay, intravenous therapy and accretion of bacteraemia. These disease-causing bacteria gain resistance to drugs over time which further complicates the treatment. Monitoring of antibiotic resistance is therefore necessary so that bacterial infectious diseases can be diagnosed rapidly. Antimicrobial susceptibility testing (AST) provides valuable information on the efficacy of antibiotic agents and their dosages for treatment against bacterial infections. In clinical laboratories, most widely used AST methods are disk diffusion, gradient diffusion, broth dilution, or commercially available semi-automated systems. Though these methods are cost-effective and accurate, they are time-consuming, labour-intensive, and require skilled manpower. Recently much attention has been on developing rapid AST techniques to avoid misuse of antibiotics and provide effective treatment. In this review, we have discussed emerging engineering AST techniques with special emphasis on phenotypic AST. These techniques include fluorescence imaging along with computational image processing, surface plasmon resonance, Raman spectra, and laser tweezer as well as micro/nanotechnology-based device such as microfluidics, microdroplets, and microchamber. The mechanical and electrical behaviour of single bacterial cell and bacterial suspension for the study of AST is also discussed.

Electronic nose: a non-invasive technology for breath analysis of diabetes and lung cancer patients.

In human exhaled breath, more than 3000 volatile organic compounds (VOCs) are found, which are directly or indirectly related to internal biochemical processes in the body. Electronic noses (E-noses) could play a potential role in screening/analyzing various respiratory and systemic diseases by studying breath signatures. An E-nose integrates a sensor array and an artificial neural network that responds to specific patterns of VOCs, and thus can act as a non-invasive technology for disease monitoring. The gold standard blood glucose monitoring test for diabetes diagnostics is invasive and highly uncomfortable. This contributes to the massive need for technologies which are non-invasive and can be used as an alternative to blood measurements for glucose detection. While lung cancer is one of the deadliest cancers with the highest death rate and an extremely high yearly global burden, the conventional diagnosis means, such as sputum cytology, chest radiography, or computed tomography, do not support wide-range population screening. A few standard non-invasive techniques, such as mass spectrometry and gas chromatography, are expensive, non-portable, and require skilled personnel for operation and are again not suitable for large-scale screening. Breath contains markers for both diabetes and lung cancer along with markers for several diseases and thus, a non-invasive technique such as the E-nose would greatly improve analysis procedures over existing invasive methods. This review shows the state-of-the-art technologies for VOC detection and machine learning approaches for two clinical models: diabetes and lung cancer detection.