BackgroundOriginating in Africa, African swine fever (ASF) was introduced to China in 2018. This acute and highly virulent infectious disease affects domestic pigs. The World Organization for Animal Health has listed it as a statutory reportable disease, and China has listed it as a category A infectious disease.MethodsPrimers and probes were designed for four ASFV genes (B646L, EP402R, MGF505-3R, and A137R). The primers/probes were highly conserved compared with the gene sequences of 21 ASFV strains.ResultsAfter optimization, the calibration curve showed good linearity (R2 > 0.99), the minimum concentration of positive plasmids that could be detected was 50 copies/µL, and the minimum viral load detection limit was 102 HAD50/mL. Furthermore, quadruple quantitative polymerase chain reaction (qPCR) with nucleic acids from three porcine-derived DNA viruses and cDNAs from eight RNA viruses did not show amplification curves, indicating that the method was specific. In addition, 1 × 106, 1 × 105, and 1 × 104 copies/µL of mixed plasmids were used for the quadruple qPCR; the coefficient of variation for triplicate determination between groups was < 2%, indicating the method was reproducible.ConclusionsThe results obtained by testing clinical samples containing detectable EP402R, MGF505-3R, and A137R strains with different combinations of gene deletions were as expected. Therefore, the established quadruple qPCR method was validated for the molecular diagnosis of ASF using gene-deleted ASFV strains.

BackgroundMultiple myeloma (MM) is the second most common hematologic neoplasm which is characterized by proliferation and infiltration of plasmatic cells in the bone marrow. Currently, MM is considered incurable due to resistance to treatment. The CRISPR/Cas9 system has emerged as a powerful tool for understanding the role of different genetic alterations in the pathogenesis of hematologic malignancies in both cell lines and mouse models. Despite current advances of gene editing tools, the use of CRISPR/Cas9 technology for gene editing of MM have not so far been extended. In this work, we want to repress Rnd3 expression, an atypical Rho GTPase involved in several cellular processes, in MM cell lines using a CRISPR interference strategy.ResultsWe have designed different guide RNAs and cloning them into a lentiviral plasmid, which contains all the machinery necessary for developing the CRISPR interference strategy. We co-transfected the HEK 293T cells with this lentiviral plasmid and 3rd generation lentiviral envelope and packaging plasmids to produce lentiviral particles. The lentiviral particles were used to transduce two different multiple myeloma cell lines, RPMI 8226 and JJN3, and downregulate Rnd3 expression. Additionally, the impact of Rnd3 expression absence was analyzed by a transcriptomic analysis consisting of 3’ UTR RNA sequencing. The Rnd3 knock-down cells showed a different transcriptomic profile in comparison to control cells.ConclusionsWe have developed a CRISPR interference strategy to generate stable Rnd3 knockdown MM cell lines by lentiviral transduction. We have evaluated this strategy in two MM cell lines, and we have demonstrated that Rnd3 silencing works both at transcriptional and protein level. Therefore, we propose CRISPR interference strategy as an alternative tool to silence gene expression in MM cell lines. Furthermore, Rnd3 silencing produces changes in the cellular transcriptomic profile.

BackgroundCost-effectiveness analysis (CEA) is a standard tool for evaluating health programs and informing decisions about resource allocation and prioritization. Most CEAs evaluating health interventions in low- and middle-income countries adopt a health sector perspective, accounting for resources funded by international donors and country governments, while often excluding out-of-pocket expenditures and time costs borne by program beneficiaries. Even when patients’ costs are included, a companion analysis focused on the patient perspective is rarely performed. We view this as a missed opportunity.MethodsWe developed methods for assessing intervention affordability and evaluating whether optimal interventions from the health sector perspective also represent efficient and affordable options for patients. We mapped the five different patterns that a comparison of the perspective results can yield into a practical framework, and we provided guidance for researchers and decision-makers on how to use results from multiple perspectives. To illustrate the methodology, we conducted a CEA of six HIV treatment delivery models in Mozambique. We conducted a Monte Carlo microsimulation with probabilistic sensitivity analysis from both patient and health sector perspectives, generating incremental cost-effectiveness ratios for the treatment approaches. We also calculated annualized patient costs for the treatment approaches, comparing the costs with an affordability threshold. We then compared the cost-effectiveness and affordability results from the two perspectives using the framework we developed.ResultsIn this case, the two perspectives did not produce a shared optimal approach for HIV treatment at the willingness-to-pay threshold of 0.3 × Mozambique’s annual GDP per capita per DALY averted. However, the clinical 6-month antiretroviral drug distribution strategy, which is optimal from the health sector perspective, is efficient and affordable from the patient perspective. All treatment approaches, except clinical 1-month distributions of antiretroviral drugs which were standard before Covid-19, had an annual cost to patients less than the country’s annual average for out-of-pocket health expenditures.ConclusionIncluding a patient perspective in CEAs and explicitly considering affordability offers decision-makers additional insights either by confirming that the optimal strategy from the health sector perspective is also efficient and affordable from the patient perspective or by identifying incongruencies in value or affordability that could affect patient participation.

BackgroundEvidence-based medicine requires synthesis of research through rigorous and time-intensive systematic literature reviews (SLRs), with significant resource expenditure for data extraction from scientific publications. Machine learning may enable the timely completion of SLRs and reduce errors by automating data identification and extraction.MethodsWe evaluated the use of machine learning to extract data from publications related to SLRs in oncology (SLR 1) and Fabry disease (SLR 2). SLR 1 predominantly contained interventional studies and SLR 2 observational studies.Predefined key terms and data were manually annotated to train and test bidirectional encoder representations from transformers (BERT) and bidirectional long-short-term memory machine learning models. Using human annotation as a reference, we assessed the ability of the models to identify biomedical terms of interest (entities) and their relations. We also pretrained BERT on a corpus of 100,000 open access clinical publications and/or enhanced context-dependent entity classification with a conditional random field (CRF) model.Performance was measured using the F1 score, a metric that combines precision and recall. We defined successful matches as partial overlap of entities of the same type.ResultsFor entity recognition, the pretrained BERT+CRF model had the best performance, with an F1 score of 73% in SLR 1 and 70% in SLR 2. Entity types identified with the highest accuracy were metrics for progression-free survival (SLR 1, F1 score 88%) or for patient age (SLR 2, F1 score 82%). Treatment arm dosage was identified less successfully (F1 scores 60% [SLR 1] and 49% [SLR 2]). The best-performing model for relation extraction, pretrained BERT relation classification, exhibited F1 scores higher than 90% in cases with at least 80 relation examples for a pair of related entity types.ConclusionsThe performance of BERT is enhanced by pretraining with biomedical literature and by combining with a CRF model. With refinement, machine learning may assist with manual data extraction for SLRs.