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n this research, we investigated the feasibility of using static analysis for IoT applications with Frama-C. We looked at different kinds of possible IoT vulnerabilities and how static analysis specifically could be used to identify them. With certain Frama-C plugins such as Eva, we were able to run static analysis on most IoT code without modifying the code itself and catch errors that could potentially be exploited in real-world applications that would have otherwise been missed. Additionally, we created a simple IoT device, by utilizing Raspberry Pi 4 hardware with a set of different SunFounder sensors, and ran our created code for it through Frama-C to find any errors. The static analysis done gave a significant amount of potential vulnerabilities in our code, mostly consisting of integer overflows. We learned how we could use static analysis tools, like Frama-C, as a powerful way to find potential vulnerabilities with minimal changes to code. 

Osteoarthritis (OA) is a degenerative disease associated with cartilage degradation, osteophyte formation, and fibrillation. Autologous Protein Solution (APS), a type of autologous anti-inflammatory orthobiologic, is used for pain management and treatment of OA. Various compositions of autologous PRP formulations are in clinical use for musculoskeletal pathologies, by nature of their minimal processing and source of bioactive molecules. Currently, there is no consensus on the optimal composition of the complex mixture. In this study, we focused on elucidating the immune cell subtypes and phenotypes in APS. We identified the immune cell types in APS from healthy donors and investigated phenotypic changes in the immune cells after APS processing. Based on flow cytometric analysis, we found that neutrophils and T cells are the most abundant immune cell types in APS, while monocytes experience the largest fold change in concentration compared to WBCs. Gene expression profiling revealed that APS processing results in differential gene expression changes dependent on immune cell type, with the most significantly differentially regulated genes occurring in the monocytes. Our results demonstrate that the mechanical processing of blood, whose main purpose is enrichment and separation, can alter its protein and cellular composition, as well as cellular phenotypes in the final product.

 

Rapid, simple, inexpensive, accurate, and sensitive point-of-care (POC) detection of viral pathogens in bodily fluids is a vital component of controlling the spread of infectious diseases. The predominant laboratory-based methods for sample processing and nucleic acid detection face limitations that prevent them from gaining wide adoption for POC applications in low-resource settings and self-testing scenarios. Here, we report the design and characterization of an integrated system for rapid sample-to-answer detection of a viral pathogen in a droplet of whole blood comprised of a 2-stage microfluidic cartridge for sample processing and nucleic acid amplification, and a clip-on detection instrument that interfaces with the image sensor of a smartphone. The cartridge is designed to release viral RNA from Zika virus in whole blood using chemical lysis, followed by mixing with the assay buffer for performing reverse-transcriptase loop-mediated isothermal amplification (RT-LAMP) reactions in six parallel microfluidic compartments. The battery-powered handheld detection instrument uniformly heats the compartments from below, and an array of LEDs illuminates from above, while the generation of fluorescent reporters in the compartments is kinetically monitored by collecting a series of smartphone images. We characterize the assay time and detection limits for detecting Zika RNA and gamma ray-deactivated Zika virus spiked into buffer and whole blood and compare the performance of the same assay when conducted in conventional PCR tubes. Our approach for kinetic monitoring of the fluorescence-generating process in the microfluidic compartments enables spatial analysis of early fluorescent “bloom” events for positive samples, in an approach called “Spatial LAMP” (S-LAMP). We show that S-LAMP image analysis reduces the time required to designate an assay as a positive test, compared to conventional analysis of the average fluorescent intensity of the entire compartment. S-LAMP enables the RT-LAMP process to be as short as 22 minutes, resulting in a total sample-to-answer time in the range of 17–32 minutes to distinguish positive from negative samples, while demonstrating a viral RNA detection as low as 2.70 × 10 2 copies per μl, and a gamma-irradiated virus of 10 3 virus particles in a single 12.5 μl droplet blood sample. 

The COVID-19 pandemic provides an urgent example where a gap exists between availability of state-of-the-art diagnostics and current needs. As assay protocols and primer sequences become widely known, many laboratories perform diagnostic tests using methods such as RT-PCR or reverse transcription loop mediated isothermal amplification (RT-LAMP). Here, we report an RT-LAMP isothermal assay for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and demonstrate the assay on clinical samples using a simple and accessible point-of-care (POC) instrument. We characterized the assay by dipping swabs into synthetic nasal fluid spiked with the virus, moving the swab to viral transport medium (VTM), and sampling a volume of the VTM to perform the RT-LAMP assay without an RNA extraction kit. The assay has a limit of detection (LOD) of 50 RNA copies per μL in the VTM solution within 30 min. We further demonstrate our assay by detecting SARS-CoV-2 viruses from 20 clinical samples. Finally, we demonstrate a portable and real-time POC device to detect SARS-CoV-2 from VTM samples using an additively manufactured three-dimensional cartridge and a smartphone-based reader. The POC system was tested using 10 clinical samples, and was able to detect SARS-CoV-2 from these clinical samples by distinguishing positive samples from negative samples after 30 min. The POC tests are in complete agreement with RT-PCR controls. This work demonstrates an alternative pathway for SARS-CoV-2 diagnostics that does not require conventional laboratory infrastructure, in settings where diagnosis is required at the point of sample collection.