There is an urgent need for ultrarapid testing regimens to detect the severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] infections in real-time within seconds to stop its spread. Current testing approaches for this RNA virus focus primarily on diagnosis by RT-qPCR, which is time-consuming, costly, often inaccurate, and impractical for general population rollout due to the need for laboratory processing. The latency until the test result arrives with the patient has led to further virus spread. Furthermore, latest antigen rapid tests still require 15-30 min processing time and are challenging to handle. Despite increased polymerase chain reaction (PCR)-test and antigen-test efforts, the pandemic continues to evolve worldwide. Herein, we developed a superfast, reagent-free, and nondestructive approach of attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy with subsequent chemometric analysis toward the prescreening of virus-infected samples. Contrived saliva samples spiked with inactivated γ-irradiated COVID-19 virus particles at levels down to 1582 copies/mL generated infrared (IR) spectra with a good signal-to-noise ratio. Predominant virus spectral peaks are tentatively associated with nucleic acid bands, including RNA. At low copy numbers, the presence of a virus particle was found to be capable of modifying the IR spectral signature of saliva, again with discriminating wavenumbers primarily associated with RNA. Discrimination was also achievable following ATR-FTIR spectral analysis of swabs immersed in saliva variously spiked with virus. Next, we nested our test system in a clinical setting wherein participants were recruited to provide demographic details, symptoms, parallel RT-qPCR testing, and the acquisition of pharyngeal swabs for ATR-FTIR spectral analysis. Initial categorization of swab samples into negative versus positive COVID-19 infection was based on symptoms and PCR results (n = 111 negatives and 70 positives). Following training and validation (using n = 61 negatives and 20 positives) of a genetic algorithm-linear discriminant analysis (GA-LDA) algorithm, a blind sensitivity of 95% and specificity of 89% was achieved. This prompt approach generates results within 2 min and is applicable in areas with increased people traffic that require sudden test results such as airports, events, or gate controls.

Ultrarapid On-Site Detection of SARS-CoV-2 Infection Using Simple ATR-FTIR Spectroscopy and an Analysis Algorithm: High Sensitivity and Specificity.

Abstract Objective To evaluate the effectiveness of heterologous and homologous covid-19 vaccine regimens with and without boosting in preventing covid-19 related infection, hospital admission, and death. Design Living systematic review and network meta-analysis. Data sources World Health Organization covid-19 databases, including 38 sources of published studies and preprints. Study selection Randomised controlled trials, cohort studies, and case-control studies. Methods 38 WHO covid-19 databases were searched on a weekly basis from 8 March 2022. Studies that assessed the effectiveness of heterologous and homologous covid-19 vaccine regimens with or without a booster were identified. Studies were eligible when they reported the number of documented, symptomatic, severe covid-19 infections, covid-19 related hospital admissions, or covid-19 related deaths among populations that were vaccinated and unvaccinated. The primary measure was vaccine effectiveness calculated as 1−odds ratio. Secondary measures were surface under the cumulative ranking curve (SUCRA) scores and the relative effects for pairwise comparisons. The risk of bias was evaluated by using the risk of bias in non-randomised studies of interventions (ROBINS-I) tool for all cohort and case-control studies. The Cochrane risk of bias tool (version 2; ROB-2) was used to assess randomised controlled trials. Results The first round of the analysis comprised 53 studies. 24 combinations of covid-19 vaccine regimens were identified, of which a three dose mRNA regimen was found to be the most effective against asymptomatic and symptomatic covid-19 infections (vaccine effectiveness 96%, 95% credible interval 72% to 99%). Heterologous boosting using two dose adenovirus vector vaccines with one mRNA vaccine has a satisfactory vaccine effectiveness of 88% (59% to 97%). A homologous two dose mRNA regimen has a vaccine effectiveness of 99% (79% to 100%) in the prevention of severe covid-19 infections. Three dose mRNA is the most effective in reducing covid-19 related hospital admission (95%, 90% to 97%). The vaccine effectiveness against death in people who received three doses of mRNA vaccine remains uncertain owing to confounders. In the subgroup analyses, a three dose regimen is similarly effective in all age groups, even in the older population (≥65 years). A three dose mRNA regimen works comparably well in patients who are immunocompromised and those who are non-immunocompromised. Homologous and heterologous three dose regimens are effective in preventing infection by covid-19 variants (alpha, delta, and omicron). Conclusion An mRNA booster is recommended to supplement any primary vaccine course. Heterologous and homologous three dose regimens work comparably well in preventing covid-19 infections, even against different variants. The effectiveness of three dose vaccine regimens against covid-19 related death remains uncertain. Systematic review registration This review was not registered. The protocol is included in the supplementary document. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication.

https://www.bmj.com/content/bmj/377/bmj-2022-069989.full.pdf

Effectiveness of heterologous and homologous covid-19 vaccine regimens: living systematic review with network meta-analysis

Several SARS-CoV-2 variants have emerged around the world, and the appearance of other variants depends on many factors. These new variants might have different characteristics that can affect the transmissibility and death rate. The administration of vaccines against the coronavirus disease 2019 (COVID-19) started in early December of 2020 and in some countries the vaccines will not soon be widely available. For this article, we studied the impact of a new more transmissible SARS-CoV-2 strain on prevalence, hospitalizations, and deaths related to the SARS-CoV-2 virus. We studied different scenarios regarding the transmissibility in order to provide a scientific support for public health policies and bring awareness of potential future situations related to the COVID-19 pandemic. We constructed a compartmental mathematical model based on differential equations to study these different scenarios. In this way, we are able to understand how a new, more infectious strain of the virus can impact the dynamics of the COVID-19 pandemic. We studied several metrics related to the possible outcomes of the COVID-19 pandemic in order to assess the impact of a higher transmissibility of a new SARS-CoV-2 strain on these metrics. We found that, even if the new variant has the same death rate, its high transmissibility can increase the number of infected people, those hospitalized, and deaths. The simulation results show that health institutions need to focus on increasing non-pharmaceutical interventions and the pace of vaccine inoculation since a new variant with higher transmissibility, such as, for example, VOC-202012/01 of lineage B.1.1.7, may cause more devastating outcomes in the population.

https://www.mdpi.com/2297-8747/26/2/25/pdf

Impact of a New SARS-CoV-2 Variant on the Population: A Mathematical Modeling Approach

Abstract AlphaFold 2 (AF2) has placed Molecular Biology in a new era where we can visualize, analyze and interpret the structures and functions of all proteins solely from their primary sequences. We performed AF2 structure predictions for various protein systems, including globular proteins, a multi-domain protein, an intrinsically disordered protein (IDP), a randomized protein, two larger proteins (&gt; 1000 AA), a heterodimer and a homodimer protein complex. Our results show that along with the three dimensional (3D) structures, AF2 also decodes protein sequences into residue flexibilities via both the predicted local distance difference test (pLDDT) scores of the models, and the predicted aligned error (PAE) maps. We show that PAE maps from AF2 are correlated with the distance variation (DV) matrices from molecular dynamics (MD) simulations, which reveals that the PAE maps can predict the dynamical nature of protein residues. Here, we introduce the AF2-scores, which are simply derived from pLDDT scores and are in the range of [0, 1]. We found that for most protein models, including large proteins and protein complexes, the AF2-scores are highly correlated with the root mean square fluctuations (RMSF) calculated from MD simulations. However, for an IDP and a randomized protein, the AF2-scores do not correlate with the RMSF from MD, especially for the IDP. Our results indicate that the protein structures predicted by AF2 also convey information of the residue flexibility, i.e., protein dynamics. 

/pdf/alphafold2-models-indicate-that-protein-sequence-determines-1k4rmyon.pdf

AlphaFold2 models indicate that protein sequence determines both structure and dynamics

https://doi.org/10.1016/j.synbio.2022.02.005

Engineering and characterization of dehalogenase enzymes from Delftia acidovorans in bioremediation of perfluorinated compounds

The lack of widespread COVID-19 testing and the prevalence of asymptomatic infections have been major factors in the current pandemic. Despite the improvements in clinical testing, as we move toward reopening USA, widespread surveillance testing becomes critical. Academic (nonmedical) labs can help provide such testing; the CDC-approved guidelines for COVID-19 testing require routine equipment and protocols that are commonly used in academic research labs around the country. Faculty at the authors’ institution were successfully able to test asymptomatic students for COVID-19. By empowering nonmedical academic scientists with preexisting knowledge, expertise with the protocols, and access to the instruments, an additional 1.2–3.5 million COVID-19 tests could be processed each day at local universities and academic labs. SARS-CoV-2, the causative agent for COVID-19 continues to decimate high-risk populations, cripple economies and stress an overtaxed medical system. On the front lines, medical personnel plead for increased personal protective equipment, more test kits and faster turnaround times. With this highly communicable virus, rapid testing is essential to identifying and isolating infected individuals, slowing the spread and containing the disease. Some countries, such as South Korea and Iceland, implemented widespread testing of their populations, resulting in less cases, fewer fatalities and an intact economy [1]. This inverse relationship, whereby increased COVID-19 testing leads to decreased impacts on society, means businesses and schools can reopen safely and the public health concerns remain low because the presumptive infection status of each individual is known, regardless of disease severity. Recent studies of complete populations on cruise ships and isolated aircraft carriers have shown that up to 50% of cases are asymptomatic [2–4]. These individuals do not have symptoms and therefore may not be isolating, further spreading the virus to individuals who may not be so fortunate. Despite this important, poorly understood population of asymptomatic individuals, the scarcity of reagents and the testing backlog in overworked diagnostic labs currently limits testing to symptomatic individuals. According to CDC guidelines, only hospitalized patients (Priority 1) and healthcare workers (Priority 3) are tested if asymptomatic [5]. With calls for the economy to reopen, implementation of robust testing could reduce the potential risk for a resurgent outbreak [6]. Large-scale testing programs can be (and have been) instrumental in identifying asymptomatic and presymptomatic carriers, yet this approach comes down to capacity: who will perform these tests and how will they do it? In a time of social media, crowdsourcing, citizen scientists and resource pooling, one invaluable group remarkably remains overlooked: the nonmedical academic scientist. Early in the pandemic, efforts at universities in California [7] and Washington [8] proved the utility of research labs performing large, wide-scale testing in their communities. Academic labs have the expertise and capabilities to test for COVID-19; however, this vast resource remains untapped and left out of most public health conversations. Nonmedical academic scientists provide the scientific knowledge that allows companies to develop diagnostic kits, vaccines and therapeutics, yet a majority of them are not involved in COVID-19 testing. A recent survey of NIH-funded labs showed that nearly 40% have the capability for COVID-19 testing, but only 3% are actually involved in current testing [9]. The technique most commonly used to detect current infection is already routinely utilized across the country in labs of all sizes, and even taught to undergraduate students: quantitative reverse transcription PCR (qRT-PCR). The technique of qRT-PCR is simple, effective and in widespread use. It is the starting point for most viral clinical diagnostics and was the first screening technique developed for SARS-CoV-2, using a nasal swab sample to screen for viral RNA from the virus. Specific primers and probes complementarily bind to the viral genome and allow highly specific identification of SARS-CoV-2 RNA, indicating the presence of the virus in the infected sample. In addition to the CDC-approved qRT-PCR protocol for detecting SARS-CoV-2 in nasal swabs, there are over 60 other USFDA Emergency Use Authorized kits. For these kits, the primers, probes and reagents are publicly available for purchase, while the diversity of manufacturers ensures there is a protocol for most labs’ current setups, meaning that no additional instrumentation is required. The ease of these techniques and their widespread use in collegiate teaching and research labs means the capabilities to perform these tests exist in most college and university biology laboratories. Could this untapped capacity

Empowering academic labs and scientists to test for COVID-19.

A closed-form equation, the Fizzle Equation, was derived from a mathematical model predicting Severe Acute Respiratory Virus-2 dynamics, optimized for a 4000-student university cohort. This equation sought to determine the frequency and percentage of random surveillance testing required to prevent an outbreak, enabling an institution to develop scientifically sound public health policies to bring the effective reproduction number of the virus below one, halting virus progression. Model permutations evaluated the potential spread of the virus based on the level of random surveillance testing, increased viral infectivity and implementing additional safety measures. The model outcomes included: required level of surveillance testing, the number of infected individuals, and the number of quarantined individuals. Using the derived equations, this study illustrates expected infection load and how testing policy can prevent outbreaks in an institution. Furthermore, this process is iterative, making it possible to develop responsive policies scaling the amount of surveillance testing based on prior testing results, further conserving resources.

Fizzle Testing: An Equation Utilizing Random Surveillance to Help Reduce COVID-19 Risks

Robust surveillance testing is a key strategic plan to prevent COVID-19 outbreaks and slow the spread of the SARS-CoV-2 pandemic; however, limited resources, facilities and time often impair the implementation of a widespread surveillance effort. To mitigate these resource limitations, we employed a strategy of pooling samples, reducing reagent cost and processing time. Through utilizing academic faculty and labs, successful pooled surveillance testing was conducted throughout Fall 2020 semester to detect positive SARS-CoV-2 infections in a population of 4400 students. During the semester, over 25,000 individual COVID status evaluations were made by pooling eight individual samples into one quantitative reverse transcription polymerase chain reaction. This pooled surveillance strategy was highly effective at detecting infection and significantly reduced financial burden and cost by $3.6 million.

Efficiency of pooled surveillance testing in academic labs to detect and inhibit COVID-19 outbreaks.

Delftia acidovorans strain D4B is an aerobic bacterium within the Betaproteobacteria lineage that was isolated from soil. The genome size is 6.26 Mbp, with a G+C content of 67%. The genome encodes enzymes potentially involved in the degradation of fluorinated compounds.

Draft Genome Sequence of the Bacterium Delftia acidovorans Strain D4B, Isolated from Soil.

Beginning in early March 2021 and continuing through May 2021, the USAF Academy began vaccinating cadets for protection against the SARS-CoV-2 virus with the BNT162b2 (Pfizer-BioNTech) mRNA vaccine. During this period, vaccination of the almost 4200 cadet population increased from 3% to 85% and prevalence of COVID-19 in the cadet population was constant at approximately 0.4% as indicated by weekly surveillance testing. In this study, vaccine effectiveness at preventing infection is estimated by comparing infection risk as a function of time since vaccination. A statistically significant four-fold reduction in infection risk was observed 14 days after the first vaccine dose and an eleven-fold reduction in infection risk was observed in fully vaccinated cadets. Overall, the Pfizer-BioNTech vaccine was 91% (95% confidence interval = 55-99%) effective at preventing infection in healthy young adults (17-26 years of age) in a university setting and military training environment.

/pdf/estimates-of-single-dose-and-full-dose-bnt162b2-vaccine-3dfsthbc8y.pdf

John C Sitko

Papers

Empowering academic labs and scientists to test for COVID-19.

Fizzle Testing: An Equation Utilizing Random Surveillance to Help Reduce COVID-19 Risks

Efficiency of pooled surveillance testing in academic labs to detect and inhibit COVID-19 outbreaks.

Draft Genome Sequence of the Bacterium Delftia acidovorans Strain D4B, Isolated from Soil.

Estimates of Single Dose and Full Dose BNT162b2 Vaccine Effectiveness among USAF Academy cadets, 1 Mar - 1 May 2021