作者: Aleixo, Alexandre ; Sánchez-Vendizú, Pamela Y. ; Pacheco, Víctor ; Leo, Mariela ; Batista, Romina ; Abreu, Edson F. ; Murta-Fonseca, Roberta A. ; Pradel, Renzo ; Peloso, Pedro ; Pavan, Silvia E. ; Percequillo, Alexandre R. ; Rengifo, Edgardo M.

2024-12-31·Human Vaccines & Immunotherapeutics

Factors influencing adverse events following COVID-19 vaccination

Article

作者: McDonald, Ellie ; Curtis, Nigel ; Lacerda, Marcus ; Dalcolmo, Margareth ; Croda, Mariana Garcia ; Val, Fernando ; Croda, Julio ; Messina, Nicole L. ; Pittet, Laure F. ; Oliveira, Roberto D. ; Sawka, Alice ; Marshall, Helen ; Villanueva, Paola ; Rocha, Jorge ; dos Santos, Glauce ; Lynn, David J. ; Jardim, Bruno

Various novel platform technologies have been used for the development of COVID-19 vaccines. In this nested cohort study among healthcare workers in Australia and Brazil who received three different COVID-19-specific vaccines, we (a) evaluated the incidence of adverse events following immunization (AEFI); (b) compared AEFI by vaccine type, dose and country; (c) identified factors influencing the incidence of AEFI; and (d) assessed the association between reactogenicity and vaccine anti-spike IgG antibody responses. Of 1302 participants who received homologous 2-dose regimens of ChAdOx1-S (Oxford-AstraZeneca), BNT162b2 (Pfizer-BioNTech) or CoronaVac (Sinovac), 1219 (94%) completed vaccine reaction questionnaires. Following the first vaccine dose, the incidence of any systemic reaction was higher in ChAdOx1-S recipients (374/806, 46%) compared with BNT162b2 (55/151, 36%; p = 0.02) or CoronaVac (26/262, 10%; p < 0.001) recipients. After the second vaccine dose, the incidence of any systemic reaction was higher in BNT162b2 recipients (66/151, 44%) compared with ChAdOx1-S (164/806, 20%; p < 0.001) or CoronaVac (23/262, 9%; p < 0.001) recipients. AEFI risk was higher in younger participants, females, participants in Australia, and varied by vaccine type and dose. Prior COVID-19 did not impact the risk of AEFI. Participants in Australia compared with Brazil reported a higher incidence of any local reaction (170/231, 74% vs 222/726, 31%, p < 0.001) and any systemic reaction (171/231, 74% vs 328/726, 45%, p < 0.001), regardless of vaccine type. Following a primary course of ChAdOx1-S or CoronaVac vaccination, participants who did not report AEFI seroconverted at a similar rate to those who reported local or systemic reactions. In conclusion, we found that the incidence of AEFI was influenced by participant age and COVID-19 vaccine type, and differed between participants in Australia and Brazil.

2024-11-01·Infrared Physics & Technology

Prediction of secondary metabolites in maize under different nitrogen inputs by hyperspectral sensing and machine learning

作者: Candido, Ana Carina da Silva ; Silva, Carlos Antonio da Junior ; Teodoro, Paulo Eduardo ; Campos, Cid Naudi Silva ; Baio, Fábio Henrique Rojo ; Silva Junior, Carlos Antonio da ; Santana, Dthenifer Cordeiro ; Prado, Renato de Mello ; Silva, Meessias Antônio da ; Oliveira, Izabela Cristina de ; Baio, Fabio Henrique Rojo ; Santos, Alessandra Rodrigues dos ; Teodoro, Larissa Pereira Ribeiro ; Silva, Meessias Antonio da

Flavonoids are compounds resulting from secondary plant metabolism that provide benefits to human health by food.This study aimed to accuracy of predicting flavonoids in maize plants subjected to different nitrogen rates using hyperspectral reflectance and machine learning (ML) algorithms.The experiment was carried out in randomized blocks in a 4 x 5 factorial design (four N inputs: 0; 30; 60 and 120 % of the recommended N input; and five readings of the reflectance spectra in maize leaves from different vegetative stages: V6, V8, V10, V12 and V14, in four replications, totaling 80 treatments. N rates were applied in the V4 and V8 phenol. stages, using urea as the N source. For hyperspectral anal., four leaves from each treatment were collected and analyzed using a spectroradiometer (FieldSpec 4 HRes, Anal. Spectral Devices), capturing the spectrum in the 350 to 2500 nm range.Subsequently, the leaf samples used in the reflectance readings were dried, ground and subjected to isoflavone quantification, analyzed by ultra-performance liquid chromatog. in three repetitions, quantifying daidzein 1 (D1), daidzein 2 (D2), genistein 1 (G1), genistein 2 (G2), and total isoflavones.Data obtained was subjected to machine learning anal., testing two data set input configurations: wavelengths (WL) and calculated spectral bands (B), and D1, D2, G1, G2 and total isoflavones as output variables.The ML algorithms tested were artificial neural networks (ANN), REPTree (DT), M5P decision tree (M5P), ZeroR (R), Random Forest (RF) and support vector machine (SVM), evaluated according to their performance by the correlation coefficient (r) and mean absolute error (MAE).The results show that the SVM algorithm had the highest accuracy in predicting the variables D1, D2, G1, G2 and total isoflavones, outperforming the other algorithms when WL was used as input in dataset.

项与 Universidade Federal de Mato Grosso do Sul 相关的新闻（医药）

2022-12-15

·PRNewswire

Lunit AI Can Help Tuberculosis Screening in Prisons, as Published in The Lancet Regional Health - Americas

Recent study suggests that AI-based x-ray interpretation algorithms may be an efficient means to improve TB case detection among incarcerated populations Lunit INSIGHT CXR showed the most superior performance among the tested cohort, with greater accuracy and generalizability among subgroups SEOUL, South Korea, Dec. 15, 2022 /PRNewswire/ -- Findings from a recent study published in the January 2023 issue of The Lancet Regional Health – Americas[1] demonstrate the effectiveness of Lunit INSIGHT CXR as a triage tool for tuberculosis (TB) screening in prisons. Continue Reading Lunit INSIGHT CXR, Lunit's AI solution for chest x-ray image analysis Globally, prisons are high-risk settings for TB. In South America, TB cases in prisons have more than doubled since 2000 amid rising incarceration rates. To address this growing burden, the World Health Organization (WHO) released updated guidelines[2] in 2021 to recommend Lunit INSIGHT CXR as one of the computer-aided detection (CAD) software that can be used in place of human readers for TB screening. Lunit INSIGHT CXR detects suspicious lesions in chest x-ray images, helping radiologists distinguish disease areas by providing the location of the lesion with an abnormality score that reflects the AI's confidence level. The AI solution can detect 10 of the most common chest abnormalities, including tuberculosis, with 97-99% accuracy.[3] A study was conducted by an international team led by Dr. Julio Croda, Ph.D., of the Federal University of Mato Grosso do Sul in Brazil to assess the accuracy of AI-based chest x-ray interpretation algorithms for TB screening in prisons. Researchers performed prospective TB screening in three male prisons in Brazil from October 2017 to December 2019. All individuals underwent portable chest x-ray exams, with sputum collected to confirm TB by lab tests. The x-ray images were evaluated using three commercially available x-ray algorithms, including Lunit INSIGHT CXR. Among 2,075 incarcerated individuals, 259 (12.5%) had confirmed TB. While all three algorithms performed similarly overall with areas under the receiver operating characteristic curve (AUROC) of 0.88 to 0.91, Lunit INSIGHT CXR was among the only two algorithms that met WHO requirements for a triage test. Researchers noted Lunit INSIGHT CXR to be the best-performing algorithm among the cohort, showing greater accuracy and generalizability among subgroups, with particularly superior robustness to age compared to the other two algorithms. Furthermore, Lunit INSIGHT CXR was the only algorithm that met the WHO's target product profile (TPP) criteria among individuals 45 years and older. Ultimately, TB screening by chest x-rays with AI-based interpretation algorithms, followed by confirmatory testing using molecular diagnostics, may be an efficient means to improve case detection among incarcerated populations, the authors suggested. "The use of computer-aided detection (CAD) software is practical and inexpensive in the prison environment. CAD is accurate for mass screening in populations with a high TB burden," explained Dr. Julio Croda. "We observed a decrease in CAD specificity only in people who had already had tuberculosis. In this specific group, it is important to be cautious and perform microbiological screening before any clinical decision." "This is a meaningful study that shows the value Lunit INSIGHT CXR can bring to environments like prisons which are highly vulnerable to tuberculosis yet lacking diagnostic specialists," said Brandon Suh, CEO of Lunit. "We hope that this study will provide the means to facilitate new cost-effective screening programs utilizing AI-based solutions." The Lancet Regional Health – Americas is an open-access journal launched in 2021 as part of The Lancet's global initiative to advocate for healthcare quality and access in all regions of the world. The Lancet is an international peer-reviewed general medical journal and the world's highest-impact academic journal (impact factor: 202.7). SOURCE Lunit