Microfluidic fabrication of lipid nanoparticles for the delivery of nucleic acids.

Gene therapy has emerged as a potential platform for treating several dreaded and rare diseases that would not have been possible with traditional therapies. Viral vectors have been widely explored as a key platform for gene therapy due to their ability to efficiently transport nucleic acid-based therapeutics into the cells. However, the lack of precision in their delivery has led to several off-target toxicities. As such, various strategies in the form of non-viral gene delivery vehicles have been explored and are currenlty employed in several therapies including the SARS-CoV-2 vaccine. In this review, we discuss the opportunities lipid nanoparticles (LNPs) present for efficient gene delivery. We also discuss various synthesis strategies via microfluidics for high throughput fabrication of non-viral gene delivery vehicles. We conclude with the recent applications and clinical trials of these vehicles for the delivery of different genetic materials such as CRISPR editors and RNA for different medical conditions ranging from cancer to rare diseases.

SARS-CoV-2 RNA Detection by a Cellphone-Based Amplification-Free System with CRISPR/CAS-Dependent Enzymatic (CASCADE) Assay.

CRISPR (Clustered regularly interspaced short palindromic repeats)-based diagnostic technologies have emerged as a promising alternative to accelerate delivery of SARS-CoV-2 molecular detection at the point of need. However, efficient translation of CRISPR-diagnostic technologies to field application is still hampered by dependence on target amplification and by reliance on fluorescence-based results readout. Herein, an amplification-free CRISPR/Cas12a-based diagnostic technology for SARS-CoV-2 RNA detection is presented using a smartphone camera for results readout. This method, termed Cellphone-based amplification-free system with CRISPR/CAS-dependent enzymatic (CASCADE) assay, relies on mobile phone imaging of a catalase-generated gas bubble signal within a microfluidic channel and does not require any external hardware optical attachments. Upon specific detection of a SARS-CoV-2 reverse-transcribed DNA/RNA heteroduplex target (orf1ab) by the ribonucleoprotein complex, the transcleavage collateral activity of the Cas12a protein on a Catalase:ssDNA probe triggers the bubble signal on the system. High analytical sensitivity in signal detection without previous target amplification (down to 50 copies µL-1) is observed in spiked samples, in ≈71 min from sample input to results readout. With the aid of a smartphone vision tool, high accuracy (AUC = 1.0; CI: 0.715 - 1.00) is achieved when the CASCADE system is tested with nasopharyngeal swab samples of PCR-positive COVID-19 patients.

Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning.

Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for large amounts of specialist-annotated images for traditional DL model training may preclude generalizability of smartphone-based diagnostics. Here, we employed adversarial neural networks with conditioning to develop an easily reconfigurable virus diagnostic platform that leverages a dataset of smartphone-taken microfluidic chip photos to rapidly generate image classifiers for different target pathogens on-demand. Adversarial learning was also used to augment this real image dataset by generating 16,000 realistic synthetic microchip images, through style generative adversarial networks (StyleGAN). We used this platform, termed smartphone-based pathogen detection resource multiplier using adversarial networks (SPyDERMAN), to accurately detect different intact viruses in clinical samples and to detect viral nucleic acids through integration with CRISPR diagnostics. We evaluated the performance of the system in detecting five different virus targets using 179 patient samples. The generalizability of the system was confirmed by rapid reconfiguration to detect SARS-CoV-2 antigens in nasal swab samples (n = 62) with 100% accuracy. Overall, the SPyDERMAN system may contribute to epidemic preparedness strategies by providing a platform for smartphone-based diagnostics that can be adapted to a given emerging viral agent within days of work.

Profiling of plasma metabolomics in patients with hepatitis C-related liver cirrhosis and hepatocellular carcinoma.

AIM OF THE STUDY: The diagnosis of hepatocellular carcinoma (HCC) is usually late, due to the lack of early detection of biomarkers for HCC. Metabolomics analysis has emerged as a useful tool for studying human diseases. The objective of the study was to investigate the differences in plasma metabolites between hepatitis C virus (HCV)-induced cirrhosis and HCC. MATERIAL AND METHODS: 22 subjects with HCV-related liver cirrhosis and 22 subjects with HCC were enrolled. Clinical, routine laboratory and imaging studies were done. Gas chromatography/mass spectrometry (GC/MS) was used for metabolomics analysis of patients' plasma samples. RESULTS: 34 known metabolites were detected, of which five metabolites were identified to have the strongest discriminatory power for separation between HCC and cirrhosis groups: octanoic acid (caprylic acid), decanoic (capric acid), oleic acid, oxalic acid and glycine. These are 3 fatty acids (FA), a dicarboxylic acid and a glucogenic amino acid, respectively. No significant correlation was found between the relative intensities of the five metabolites and any of the patient or tumor characteristics (Child-Turcotte-Pugh (CTP) score, Barcelona Clinic Liver Cancer (BCLC) stage, number of focal lesions and size of largest focal lesion). ROC curve analysis was performed and area under the curve (AUC) was calculated, revealing that oleic acid, octanoic (caprylic) acid and glycine had higher positive predictive value than α-fetoprotein. CONCLUSIONS: The study of metabolomics (particularly involving FA) may help define distinct metabolic patterns to distinguish HCV-induced liver cirrhosis from HCC patients. Future research in this field is still needed, particularly concerning HCC treatment strategies which target fatty acid-related metabolic pathways.