Assessment of the feasibility of pool testing for SARS-CoV-2 infection screening

Paganini, Irene; Sani, Cristina; Chilleri, Chiara; Baccini, Michela; Antonelli, Alberto; Bisanzi, Simonetta; Burroni, Elena; Cellai, Filippo; Coppi, Marco; Mealli, Fabrizia; Pompeo, Giampaolo; Viti, Jessica; Rossolini, Gian María; Carozzi, Francesca

doi:10.1080/23744235.2022.2044512

Cited by 2 publications

(4 citation statements)

References 37 publications

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“…Yelin and collaborators evaluated different pooling sizes and found 95% sensitivity in pooling specimens up to 32-in-one and an average increase cycle threshold (Ct) of 1.24 with every dilution by a factor of 2 [16]. Comparable studies were four-in-one specimen pools were constructed showed a correlation with individual testing of 91.4 to 100% [17][18][19][20]. Another study found that sensitivity is not impacted negatively with ten-in-one pooling with 1-2 positives with a Ct < 35.…”

Section: Discussionmentioning

confidence: 99%

Increasing SARS-CoV-2 testing capacity through specimen pooling: An acute care center experience

Cabrera,

Al Mutawah,

Kadour

et al. 2023

PLoS ONE

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Innovation in laboratory testing algorithms to address seemingly uncontrollable global supply chain shortages in plastics and other consumables during emergencies such as the current COVID-19 pandemic have been urgently needed. We report our experience with specimen pooling on SARS-CoV-2 testing in an acute care hospital microbiology laboratory during a high testing demand period that exceeded available processing capacity. A fully automated four-in-one pooling algorithm was designed and validated. Correlation and agreement were calculated. A custom Microsoft Excel tool was designed for use by the technologists to aid interpretation, verification and result entry. Cost-per-test impact for pooling was measured in reference to the consumable cost and was denoted as the percentage reduction of cost versus the baseline cost-per-test of testing specimens individually. Validation showed a strong correlation between the signals observed when testing specimens individually versus those that were pooled. Average crossing point difference was 1.352 cycles (95% confidence interval of -0.235 and 2.940). Overall agreement observed between individually and pooled tested specimens was 96.8%. Stratified agreement showed an expected decreased performance of pooling for weakly positive specimens dropping below 60% after a crossing point of 35. Post-implementation data showed the consumable cost-savings achieved through this algorithm was 85.5% after 8 months, creating both testing and resource capacity. Pooling is an effective method to be used for SARS-CoV-2 testing during the current pandemic to address resource shortages and provide quick turnaround times for high test volumes without compromising performance.

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Section: Discussionmentioning

confidence: 99%

Increasing SARS-CoV-2 testing capacity through specimen pooling: An acute care center experience

Cabrera,

Al Mutawah,

Kadour

et al. 2023

PLoS ONE

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“…The researchers aimed to explore the potential of these compounds in combating various microbial infections, including tuberculosis, while also assessing their ability to reduce inflammation. Gupta et al synthesized 4-amino-5-(substituted-phenyl)-4H- [1,2,4] triazole-3-thiol derivatives and antifungal activity. [46] Dandia et al synthesized 4-amino-3-alkyl-5-mercapto-1, 2, 4-triazole derivatives as green corrosion inhibitors for copper in hydrochloric acid.…”

Section: Significance In Enzymatic Reactionsmentioning

confidence: 99%

“…Gupta et al. synthesized 4‐amino‐5‐(substituted‐phenyl)‐4H‐ [ 1, 2, 4 ] triazole‐3‐thiol derivatives and antifungal activity. [ 46 ] Dandia et al.…”

Section: In‐silico Analysismentioning

confidence: 99%

“…Intensive efforts are underway to uncover effective pharmacological agents to combat the virus, with research and development operating at full capacity. [1,2] As per the CDC report 2019AR, the number of deaths has increased by more than 35 000 in the United States and almost 2.8 million cases of infection are being raised every year. In 2017, Clostridioides difficile become the reason for 223 900 cases of bacterial infection.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computational/In‐Silico Methods: An Influential Approach for Drug Designing and Development

Khatana,

Gupta,

Ghosal

et al. 2024

Macromolecular Symposia

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As the world is struggling with the pandemic. Various infectious diseases have become a threat to human health. Millions of deaths are caused by these microbial infections (bacterial, fungal, and viral infections). The process of designing and discovering new drugs is very expensive and it also consumes a good amount of time. As per available data, the process of discovery and designing takes 3–20 years to complete. There is a strong urge and demand to discover new methods to make the process of drug design and development more cost‐effective. Computational methods are one the novel methods for designing and developing a drug. In this respect, in‐silico parameters; ADME (Absorption, Distribution, Metabolism, and Excretion) models have been established with various levels of complication for the transmission of huge data of derivatives/ligands/drugs. Nowadays, in‐silico tools are more cost‐effective, faster, and simpler than transitional experimental trials. Currently, the pharmaceutical industry faces a huge erosion rate of preclinical and clinical applicants due to the unavailability of pharmacokinetics properties and huge toxicity. These can be minimized via structural modifications of drugs and can help medicinal chemists/pharmacists. In this report, various methodologies and steps have been explained via molecular docking that will lead to drug development in lesser time against various stains.

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Assessment of the feasibility of pool testing for SARS-CoV-2 infection screening

Cited by 2 publications

References 37 publications

Increasing SARS-CoV-2 testing capacity through specimen pooling: An acute care center experience

Increasing SARS-CoV-2 testing capacity through specimen pooling: An acute care center experience

Computational/In‐Silico Methods: An Influential Approach for Drug Designing and Development

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