A simple cost-effective microfluidic platform for rapid synthesis of diverse metal nanoparticles: A novel approach towards fighting SARS-CoV-2.

The latest addition to the family of Coronaviruses, SARS-CoV-2, unleashed its wrath across the globe. The outbreak has been so rapid and widespread that even the most developed countries are still struggling with ways to contain the spread of the virus. The virus began spreading from Wuhan in China in December 2019 and has currently affected more than200 countries worldwide. Nanotechnology has huge potential for killing viruses as severe as HIV, herpes, human papilloma virus, and viruses of the respiratory tract, both inside as well as outside the host. Metal-nanoparticles can be employed for biosensing methodology of viruses/bacteria, along with the development of novel drugs and vaccines for COVID-19 and future pandemics. It is thus required for the nanoparticles to be synthesized quickly along with precise control over their size distribution. In this study, we propose a simple microfluidic-reactor-platform for in-situ metal-nanoparticle synthesis to be used against the pandemic for the development of preventive, diagnostic, and antiviral drug therapies. The device has been fabricated using a customized standard photolithography process using a simple and cost-effective setup. The confirmation on standard silver and gold metal nanoparticle formation in the microfluidic reactor platform was analysed using optical fiber spectrophotometer. This novel microfluidic platform provides the advantage of in-situ synthesis, flow parameter control and reduced agglomeration of nanoparticles over the bulk synthesis due to segregation of nucleation and growth stages inside a microchannel. The results are highly reproducible and hence scaling up of the nanoparticle production is possible without involving complex instrumentation.

Model-based stationarity filtering of long-term memory data applied to resting-state blood-oxygen-level-dependent signal.

Resting-state blood-oxygen-level-dependent (BOLD) signal acquired through functional magnetic resonance imaging is a proxy of neural activity and a key mechanism for assessing neurological conditions. Therefore, practical tools to filter out artefacts that can compromise the assessment are required. On the one hand, a variety of tailored methods to preprocess the data to deal with identified sources of noise (e.g., head motion, heart beating, and breathing, just to mention a few) are in place. But, on the other hand, there might be unknown sources of unstructured noise present in the data. Therefore, to mitigate the effects of such unstructured noises, we propose a model-based filter that explores the statistical properties of the underlying signal (i.e., long-term memory). Specifically, we consider autoregressive fractional integrative process filters. Remarkably, we provide evidence that such processes can model the signals at different regions of interest to attain stationarity. Furthermore, we use a principled analysis where a ground-truth signal with statistical properties similar to the BOLD signal under the injection of noise is retrieved using the proposed filters. Next, we considered preprocessed (i.e., the identified sources of noise removed) resting-state BOLD data of 98 subjects from the Human Connectome Project. Our results demonstrate that the proposed filters decrease the power in the higher frequencies. However, unlike the low-pass filters, the proposed filters do not remove all high-frequency information, instead they preserve process-related higher frequency information. Additionally, we considered four different metrics (power spectrum, functional connectivity using the Pearson's correlation, coherence, and eigenbrains) to infer the impact of such filter. We provided evidence that whereas the first three keep most of the features of interest from a neuroscience perspective unchanged, the latter exhibits some variations that could be due to the sporadic activity filtered out.

COVAN Leading to ESKD Despite Minimal COVID Symptoms.

We report a case of dialysis dependence in a patient with COVID-19-associated nephropathy (COVAN) who had minimal respiratory manifestations. A 25-year-old man with a history of multiple sclerosis in remission presented with mild dyspnea due to COVID-19 pneumonia and was found to have rapidly worsening kidney function. He only required nasal cannula and was able to be weaned off within a few days. Despite having only mild respiratory disease, his kidney function worsened and urgent hemodialysis was started for hyperkalemia and uremic encephalopathy. Kidney biopsy demonstrated collapsing glomerulopathy due to COVID-19 with moderate interstitial fibrosis and tubular atrophy. His kidney function did not recover, and he unfortunately now has been dependent on hemodialysis for over 3 months. Multiple case reports have described COVAN causing dialysis dependence, but to our knowledge this is the first reported case of COVAN causing dialysis dependence in a patient with such mild respiratory disease. Currently the indications for intensive COVID-19 therapies are based on oxygen requirements. This case demonstrates that the oxygen requirement may not fully reflect the severity of COVID-19 and raises the question of whether these therapies should be considered in patients with COVAN.