A Protocol for Sensitive Large-Scale Screening of Blood Donors for IgA Deficiency

Glycophorin hybrids such as GP.Mur are common in Southeast Asians. In Taiwan, clinically significant alloantibodies to the GP.Mur phenotype are the most important issue in blood banks. A large-scale screening of glycophorin hybrids in the Taiwanese population is urgently needed to ensure transfusion safety. Four clones of human hybridomas that secrete anti-Mia, anti-MUT, and anti-Mur were established by fusing human B-lymphocytes and myeloma cells (JMS-3). The specificity of each monoclonal antibody (MoAb) was characterized. Three MoAbs were applied on an Automated Pretransfusion Blood Testing Analyzer (PK7300/PK7400) for donor screening. Genotyping was performed to determine the detailed subgrouping of glycophorin hybrids. Four MoAbs are IgM antibodies. Anti-Mia (377T) binds to 46DXHKRDTYA54, 48HKRDTYAAHT57 peptides, and anti-Mia (367T) binds to 43QTNDXHKRD51 peptides (X indicates T, M, or K). Anti-Mur is reactive with 49KRDTYPAHTA58 peptides. Anti-MUT is reactive with 47KHKRDTYA54. A total of 78,327 donors were screened using three MoAbs, and 3690 (4.71%) were GP.Mur, 20 (0.025%) were GP.Hut, and 18 (0.022%) were GP.Vw. When the Mia antigen was introduced as routine screening, the frequency of Mi(a+) among blood donors in Taiwan was 4.66% (67,348/1,444,541). Mia antigen was implemented as a routine blood testing, and the results were labeled on all red blood cell (RBC) units.

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Training Aides to Screen Children for Speech and Language Problems

Language Speech and Hearing Services in Schools ◽

10.1044/0161-1461.0704.236 ◽

1976 ◽

Vol 7 (4) ◽

pp. 236-241 ◽

Cited By ~ 5

Author(s):

Marisue Pickering ◽

William R. Dopheide

Keyword(s):

Rural Areas ◽

Large Scale ◽

School Personnel ◽

School Age Children ◽

Speech And Language ◽

Elementary School Age ◽

Evaluation Procedures ◽

Language Problems ◽

Competency Based ◽

Large Scale Screening

This report deals with an effort to begin the process of effectively identifying children in rural areas with speech and language problems using existing school personnel. A two-day competency-based workshop for the purpose of training aides to conduct a large-scale screening of speech and language problems in elementary-school-age children is described. Training strategies, implementation, and evaluation procedures are discussed.

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Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666190122151634 ◽

2019 ◽

Vol 19 (1) ◽

pp. 4-16 ◽

Cited By ~ 6

Author(s):

Qihui Wu ◽

Hanzhong Ke ◽

Dongli Li ◽

Qi Wang ◽

Jiansong Fang ◽

...

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Large Scale ◽

Recent Progress ◽

High Specificity ◽

Learning Approaches ◽

Anticancer Peptides ◽

The Past ◽

Traditional Approaches ◽

Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

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Analytical Validation and Clinical Application of Rapid Serological Tests for SARS-CoV-2 Suitable for Large-Scale Screening

Diagnostics ◽

10.3390/diagnostics11050869 ◽

2021 ◽

Vol 11 (5) ◽

pp. 869

Author(s):

Amedeo De Nicolò ◽

Valeria Avataneo ◽

Jessica Cusato ◽

Alice Palermiti ◽

Jacopo Mula ◽

...

Keyword(s):

Early Diagnosis ◽

Real Time Pcr ◽

Large Scale ◽

High Sensitivity ◽

Analytical Validation ◽

Pcr Assay ◽

Serological Tests ◽

Flow Test ◽

Lateral Flow Test ◽

Large Scale Screening

Recently, large-scale screening for COVID-19 has presented a major challenge, limiting timely countermeasures. Therefore, the application of suitable rapid serological tests could provide useful information, however, little evidence regarding their robustness is currently available. In this work, we evaluated and compared the analytical performance of a rapid lateral-flow test (LFA) and a fast semiquantitative fluorescent immunoassay (FIA) for anti-nucleocapsid (anti-NC) antibodies, with the reverse transcriptase real-time PCR assay as the reference. In 222 patients, LFA showed poor sensitivity (55.9%) within two weeks from PCR, while later testing was more reliable (sensitivity of 85.7% and specificity of 93.1%). Moreover, in a subset of 100 patients, FIA showed high sensitivity (89.1%) and specificity (94.1%) after two weeks from PCR. The coupled application for the screening of 183 patients showed satisfactory concordance (K = 0.858). In conclusion, rapid serological tests were largely not useful for early diagnosis, but they showed good performance in later stages of infection. These could be useful for back-tracing and/or to identify potentially immune subjects.

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BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space

International Journal of Molecular Sciences ◽

10.3390/ijms22157773 ◽

2021 ◽

Vol 22 (15) ◽

pp. 7773

Author(s):

Neann Mathai ◽

Conrad Stork ◽

Johannes Kirchmair

Keyword(s):

Large Scale ◽

Computational Approach ◽

Large Sets ◽

Compound Libraries ◽

Wide Range ◽

Protein Space ◽

High Chance ◽

Large Scale Screening ◽

Early Drug ◽

Selection Of

Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the “fitness” of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle (“BonMOLière”).

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Large-scale screening of natural genetic resource in the hydrocarbon-producing microalga Botrycoccus braunii identified novel fast-growing strains

Scientific Reports ◽

10.1038/s41598-021-86760-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Koji Kawamura ◽

Suzune Nishikawa ◽

Kotaro Hirano ◽

Ardianor Ardianor ◽

Rudy Agung Nugroho ◽

...

Keyword(s):

Large Scale ◽

Biomass Productivity ◽

Botryococcus Braunii ◽

Algal Biofuel ◽

Fast Growing ◽

Wild Strains ◽

Glass Tubes ◽

The Tropics ◽

Large Scale Screening ◽

Hydrocarbon Productivity

AbstractAlgal biofuel research aims to make a renewable, carbon–neutral biofuel by using oil-producing microalgae. The freshwater microalga Botryococcus braunii has received much attention due to its ability to accumulate large amounts of petroleum-like hydrocarbons but suffers from slow growth. We performed a large-scale screening of fast-growing strains with 180 strains isolated from 22 ponds located in a wide geographic range from the tropics to cool-temperate. A fast-growing strain, Showa, which recorded the highest productivities of algal hydrocarbons to date, was used as a benchmark. The initial screening was performed by monitoring optical densities in glass tubes and identified 9 wild strains with faster or equivalent growth rates to Showa. The biomass-based assessments showed that biomass and hydrocarbon productivities of these strains were 12–37% and 11–88% higher than that of Showa, respectively. One strain, OIT-678 established a new record of the fastest growth rate in the race B strains with a doubling time of 1.2 days. The OIT-678 had 36% higher biomass productivity, 34% higher hydrocarbon productivity, and 20% higher biomass density than Showa at the same cultivation conditions, suggesting the potential of the new strain to break the record for the highest productivities of hydrocarbons.

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