Label-free live cell imaging was performed using a custom-built high-speed confocal Raman microscopy system. For various cell types, cell-intrinsic Raman bands were monitored. The high-resolution temporal Raman images clearly delineated the intracellular distribution of biologically important molecules such as protein, lipid, and DNA. Furthermore, optical phase delay measured using quantitative phase microscopy shows similarity with the image reconstructed from the protein Raman peak. This reported work demonstrates that Raman imaging is a powerful label-free technique for studying various biomedical problems in vitro with minimal sample preparation and external perturbation to the cellular system.
Vitamins such as riboflavin and ascorbic acid are frequently utilized in a range of biomedical applications as drug delivery targets, fluidic tracers, and pharmaceutical excipients. Sensing these biochemicals in the human body has the potential to significantly advance medical research and clinical applications. In this work, a nanosensor platform consisting of single-walled carbon nanotubes (SWCNTs) with nanoparticle corona phases engineered to allow for the selective molecular recognition of ascorbic acid and riboflavin, is developed. The study provides a methodological framework for the implementation of colloidal SWCNT nanosensors in an intraperitoneal SKH1-E murine model by addressing complications arising from tissue absorption and scattering, mechanical perturbations, as well as sensor diffusion and interactions with the biological environment. Nanosensors are encapsulated in a polyethylene glycol diacrylate hydrogel and a diffusion model is utilized to validate analyte transport and sensor responses to local concentrations at the boundary. Results are found to be reproducible and stable after exposure to 10% mouse serum even after three days of in vivo implantation. A geometrical encoding scheme is used to reference sensor pairs, correcting for in vivo optical and mechanical artifacts, resulting in an order of magnitude improvement of p-value from 0.084 to 0.003 during analyte sensing.
The COVID-19 pandemic has profoundly affected life worldwide. Governments have been faced with the formidable task of implementing public health measures, such as social distancing, quarantines, and lockdowns, while simultaneously supporting a sluggish economy and stimulating research and development (R&D) for the pandemic. Catalyzing bottom-up entrepreneurship is one method to achieve this. Home-grown efforts by citizens wishing to contribute their time and resources to help have sprouted organically, with ideas shared widely on the internet. We outline a framework for structured, crowdsourced innovation that facilitates collaboration to tackle real, contextualized problems. This is exemplified by a series of virtual hackathon events attracting over 9000 applicants from 142 countries and 49 states. A hackathon is an event that convenes diverse individuals to crowdsource solutions around a core set of predetermined challenges in a limited amount of time. A consortium of over 100 partners from across the healthcare spectrum and beyond defined challenges and supported teams after the event, resulting in the continuation of at least 25% of all teams post-event. Grassroots entrepreneurship can stimulate economic growth while contributing to broader R&D efforts to confront public health emergencies.
Background: Convalescent plasma (CP) for treatment of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has shown preliminary signs of effectiveness in moderate to severely ill patients in reducing mortality. While studies have demonstrated a low risk of serious adverse events, the comprehensive incidence and nature of the spectrum of transfusion reactions to CP is unknown. We retrospectively examined 427 adult inpatient CP transfusions to determine incidence and types of reactions, as well as clinical parameters and risk factors associated with transfusion reactions. Study Design and Methods: Retrospective analysis was performed for 427 transfusions to 215 adult patients with coronavirus 2019 (COVID‐19) within the Mount Sinai Health System, through the US Food and Drug Administration emergency investigational new drug and the Mayo Clinic Expanded Access Protocol to Convalescent Plasma approval pathways. Transfusions were blindly evaluated by two reviewers and adjudicated by a third reviewer in discordant cases. Patient demographics and clinical and laboratory parameters were compared and analyzed. Results: Fifty‐five reactions from 427 transfusions were identified (12.9% incidence), and 13 were attributed to transfusion (3.1% incidence). Reactions were classified as underlying COVID‐19 (76%), febrile nonhemolytic (10.9%), transfusion‐associated circulatory overload (9.1%), and allergic (1.8%) and hypotensive (1.8%) reactions. Statistical analysis identified increased transfusion reaction risk for ABO blood group B or Sequential Organ Failure Assessment scores of 12 to 13, and decreased risk within the age group of 80 to 89 years. Conclusion: Our findings support the use of CP as a safe, therapeutic option from a transfusion reaction perspective, in the setting of COVID‐19. Further studies are needed to confirm the clinical significance of ABO group B, age, and predisposing disease severity in the incidence of transfusion reaction events.
Passive transfer of antibodies from COVID-19 convalescent patients is being used as an experimental treatment for eligible patients with SARS-CoV-2 infections. The United States Food and Drug Administration’s (FDA) guidelines for convalescent plasma initially recommended target antibody titers of 160. We evaluated SARS-CoV-2 neutralizing antibodies in sera from recovered COVID-19 patients using plaque reduction neutralization tests (PRNT) at moderate (PRNT50) and high (PRNT90) stringency thresholds. We found that neutralizing activity significantly increased with time post symptom onset (PSO), reaching a peak at 31–35 days PSO. At this point, the number of sera having neutralizing titers of at least 160 was approximately 93% (PRNT50) and approximately 54% (PRNT90). Sera with high SARS-CoV-2 antibody levels (>960 enzyme-linked immunosorbent assay titers) showed maximal activity, but not all high-titer sera contained neutralizing antibody at FDA recommended levels, particularly at high stringency. These results underscore the value of serum characterization for neutralization activity.
New York City has been recognized as the world’s epicenter of the novel Coronavirus pandemic. To identify the key inherent factors that are highly correlated to the Increase Rate of COVID-19 new cases in NYC, we propose an unsupervised machine learning framework. Based on the assumption that ZIP code areas with similar demographic, socioeconomic, and mobility patterns are likely to experience similar outbreaks, we select the most relevant features to perform a clustering that can best reflect the spread, and map them down to 9 interpretable categories. We believe that our findings can guide policy makers to promptly anticipate and prevent the spread of the virus by taking the right measures.
Dynamic measurements of steroid hormones in vivo are critical, but steroid sensing is currently limited by the availability of specific molecular recognition elements due to the chemical similarity of these hormones. In this work, a new, self‐templating synthetic approach is applied using corona phase molecular recognition (CoPhMoRe) targeting the steroid family of molecules to produce near infrared fluorescent, implantable sensors. A key limitation of CoPhMoRe has been its reliance on library generation for sensor screening. This problem is addressed with a self‐templating strategy of polymer design, using the examples of progesterone and cortisol sensing based on a styrene and acrylic acid copolymer library augmented with an acrylated steroid. The pendant steroid attached to the corona backbone is shown to self‐template the phase, providing a unique CoPhMoRE design strategy with high efficacy. The resulting sensors exhibit excellent stability and reversibility upon repeated analyte cycling. It is shown that molecular recognition using such constructs is viable even in vivo after sensor implantation into a murine model by employing a poly (ethylene glycol) diacrylate (PEGDA) hydrogel and porous cellulose interface to limit nonspecific absorption. The results demonstrate that CoPhMoRe templating is sufficiently robust to enable a new class of continuous, in vivo biosensors.
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a new human disease with few effective treatments. Convalescent plasma, donated by persons who have recovered from COVID-19, is the acellular component of blood that contains antibodies, including those that specifically recognize SARS-CoV-2. These antibodies, when transfused into patients infected with SARS-CoV-2, are thought to exert an antiviral effect, suppressing virus replication before patients have mounted their own humoral immune responses. Virus-specific antibodies from recovered persons are often the first available therapy for an emerging infectious disease, a stopgap treatment while new antivirals and vaccines are being developed. This retrospective, propensity score–matched case–control study assessed the effectiveness of convalescent plasma therapy in 39 patients with severe or life-threatening COVID-19 at The Mount Sinai Hospital in New York City. Oxygen requirements on day 14 after transfusion worsened in 17.9% of plasma recipients versus 28.2% of propensity score–matched controls who were hospitalized with COVID-19 (adjusted odds ratio (OR), 0.86; 95% confidence interval (CI), 0.75–0.98; chi-square test P value = 0.025). Survival also improved in plasma recipients (adjusted hazard ratio (HR), 0.34; 95% CI, 0.13–0.89; chi-square test P = 0.027). Convalescent plasma is potentially effective against COVID-19, but adequately powered, randomized controlled trials are needed.
The COVID-19 virus is a formidable global threat, impacting all aspects of society and exacerbating the existing inequities of our current social systems. As we battle the virus across multiple fronts, data are critical for understanding this disease and for coordinating an effective global response. Given the current digitisation of so many aspects of life, we are amassing data that can be extrapolated and analysed for the effective forecasting, prevention and treatment of COVID-19. With responsible stewardship, the tools and data-driven solutions currently in development for the COVID-19 pandemic will serve in the present while providing a much-needed foundation for a data-based response to future outbreaks and disasters.
In response to COVID-19, and using data generated thus far, groups at the Massachusetts Institute of Technology (MIT) in partnership with the American Civil Liberties Union (ACLU) of Massachusetts, Google Cloud, Beth Israel Deaconess Medical Center (BIDMC) Innovations Group and Harvard Medical Faculty Physicians at BIDMC came together to host the MIT Challenge COVID-19 Datathon (COVID-19 Datathon) from 10–16 May 2020. A ‘datathon’ adopts the ‘hackathon’ model, with a focus on data and data science methodologies, which promotes collaboration, design thinking and problem solving. In a typical hackathon, participants with disparate but complementary backgrounds work together in small groups for a prescribed and intensive ‘sprint’, typically over the course of one weekend, to develop a new concept, product or business idea. Subject matter expert ‘mentors’’ oversee and advise the teams. At the conclusion of the event, the teams present to a panel of judges. Winners are selected and are typically awarded seed funding. Datathons differ from hackathons in that the output is data analysis. MIT Critical Data, one of the organising groups of the COVID-19 Datathon, has hosted 36 international healthcare datathons.