Assessing optimal frequency for image acquisition in computer vision systems developed to monitor feeding behavior of group-housed Holstein heifers.

Computer vision systems have emerged as a potential tool to monitor the behavior of livestock animals. Such high-throughput systems can generate massive redundant data sets for training and inference, which can lead to higher computational and economic costs. The objectives of this study were (1) to develop a computer vision system to individually monitor detailed feeding behaviors of group-housed dairy heifers, and (2) to determine the optimal frequency of image acquisition to perform inference with minimal effect on feeding behavior prediction quality. Eight Holstein heifers (96 ± 6 d old) were housed in a group and a total of 25,214 images (1 image every second) were acquired using 1 RGB camera. A total of 2,209 images were selected and each animal in the image was labeled with its respective identification (1-8). The label was annotated only on animals that were at the feed bunk (head through the feed rail). From the labeled images, 1,392 were randomly selected to train a deep learning algorithm for object detection with YOLOv3 ("You Only Look Once" version 3) and 154 images were used for validation. An independent data set (testing set = 663 out of the 2,209 images) was used to test the algorithm. The average accuracy for identifying individual animals in the testing set was 96.0%, and for each individual heifer from 1 to 8 the accuracy was 99.2, 99.6, 99.2, 99.6, 99.6, 99.2, 99.4, and 99.6%, respectively. After identifying the animals at the feed bunk, we computed the following feeding behavior parameters: number of visits (NV), mean visit duration (MVD), mean interval between visits (MIBV), and feeding time (FT) for each heifer using a data set composed by 8,883 sequential images (1 image every second) from 4 time points. The coefficient of determination (R2) was 0.39, 0.78, 0.48, and 0.99, and the root mean square error (RMSE) were 12.3 (count), 0.78, 0.63, and 0.31 min for NV, MVD, MIBV, and FT, respectively, considering 1 image every second. When we moved from 1 image per second to 1 image every 5 (MIBV) or 10 (NV, MDV, and FT) s, the R2 observed were 0.55 (NV), 0.74 (MVD), 0.70 (MIBV), and 0.99 (FT); and the RMSE were 2.27 (NV, count), 0.38 min (MVD), 0.22 min (MIBV), and 0.44 min (FT). Our results indicate that computer vision systems can be used to individually identify group-housed Holstein heifers (overall accuracy = 99.4%). Based on individual identification, feeding behavior such as MVD, MIBV, and FT can be monitored with reasonable accuracy and precision. Regardless of the frequency for optimal image acquisition, our results suggested that longer time intervals of image acquisition would reduce data collecting and model inference while maintaining adequate predictive performance. However, we did not find an optimal time interval for all feeding behavior; instead, the optimal frequency of image acquisition is phenotype-specific. Overall, the best R2 and RMSE for NV, MDV, and FT were achieved using 1 image every 10 s, and for MIBV it was achieved using 1 image every 5 s, and in both cases model inference and data storage could be drastically reduced.

Effect of binders on 500mg metformin hydrochloride tablets produced by wet granulation

Metformin hydrochloride (MH) is an oral hypoglycemic agent and a high-dose drug that has poor flow and compression properties. In this study, the feasibility of developing adequate, low cost 500mg tablets of metformin hydrochloride by wet granulation was tested with several binders (Starch / PVP K30®; Starch1500® /PVP K30®, PVP K30® and PVP K90®) in a simple tablet press of the type used in small pharmaceutical laboratories. The drug powder was tested for ability to flow, by determining Carr?s Index (CI) and the Hausner ratio (HR). Differential scanning calorimetry and thermogravimetric analysis were carried out on isolated MH and 1:1 (w/w) binary mixtures with the excipients. The size distribution, friability, flow properties and drug content of the granules were analyzed, as were the hardness, friability, disintegration, dissolution and uniformity of the dosage form. The drug powder showed CI > 22% and HR > 1.25, characteristic of a poor flow powder, and no significant incompatibilities with the excipients. All the granules showed adequate flow properties and were suitable for pressing into tablets, all of which complied with pharmacopeial specifications. The starch /PVP K30® and starch 1500®/PVP K30® mixtures were best for producing 500 mg MH tablets.

Cloridrato de metformina é um fármaco hipoglicemiante oral que apresenta propriedades pobres de fluxo e compressibilidade. Este trabalho teve como objetivo o desenvolvimento de comprimidos de baixo custo,após granulação por via úmida, contendo 500 mg de cloridrato de metformina e diferentes aglutinantes (F1-amido / PVP K30®; F2- Starch 1500® / PVP K30®, F3-PVP K30®, F4- PVP K90®) em máquinas de compressão de baixo desempenho usadas em laboratórios farmacêuticos de pequeno porte. As propriedades defluxo do fármaco foram analisadas através do índice de Carr (IC) e fator de Hausner (FH). Cloridrato de metformina e suas misturas binárias com os excipientes na relação 1:1 (m/m) foram analisadas por calorimetria diferencial por varredura e análise termogravimétrica. Os granulados foram analisados quanto a distribuição granulométrica, friabilidade, propriedades de fluxo e teor e os comprimidos em relação à dureza, friabilidade, desintegração, dissolução e uniformidade de conteúdo.O cloridrato de metformina apresentou IC > 22% e FH> 1,25, característicos de fluxo pobre e não apresentou incompatibilidades com os outros excipientes. Todos os granulados demonstraram adequadas propriedades de fluxo e facilidade no processo de compressão. Os comprimidos apresentaram conformidade com as especificações farmacopeicas. As misturas amido / PVPK30® e Starch 1500® / PVP K30® foram mais adequadas para produzir comprimidos de cloridrato de metformina 500 mg.

Pharmaceutical equivalence of metformin tablets with various binders

Metformin hydrochloride is a high-dose drug widely used as an oral anti-hyperglycemic agent. As it is highly crystalline and has poor compaction properties, it is difficult to form tablets by direct compression. The aim of this study was to develop adequate metformin tablets, pharmaceutically equivalent to the reference product, Glucophage® (marketed as Glifage® in Brazil). Metformin 500mg tablets were produced by wet granulation with various binders (A equal starch, B equal starch 1500®, C equal PVP K30®, D equal PVP K90®). The tablets were analyzed for their hardness, friability, disintegration, dissolution, content uniformity and dissolution profile (basket apparatus at 50 rpm, pH 6.8 phosphate buffer). The 4 formulations, F1 (5% A and 5% C), F2 (5% Band 5% C), F3 (10% C) and F4 (5% D), demonstratedadequate uniformity of content, hardness, friability, disintegration and total drug dissolution after 30minutes (F1, F2 and F4), and after 60 minutes (F3). The drug release time profiles fitted a Higuchi model (F1, F2and F3), similarly to the pharmaceutical reference, or a zero order model (F4). The dissolution efficiency for all the formulations was 75%, except for F3 (45%). F1 andF2 were thus equivalent to Glifage®.

Análise da cápsula de captopril manipuladas em farmácias / Analysis of compounded captopril capsules

Cápsulas de captropil 25mg elaboradas em quatro farmácias de manipulação localizadas no vale do Itajaí e na região norte do estado de Santa Catarina, foram analisadas em relação ao peso médio, teor de captropil e limite de dissulfeto de captopril, por HPLC em comparação com comprimidos de uma especialidade farmacêutica.Todas as amostras analisadas foram aprovadas quanto ao teor médio de captopril e limite de dissulfeto de captopril, indicando a qualidade da matéria-prima utilizada.Por outro lado, das quatro amostras adquiridas em farmácias de manipulação, duas foram reprovadas no ensaio de peso médio, provavelmente devido a problemas durante o encapsulamento.Estes dados denotam a necessidade da implementação do controle do processo a fim de garantir a qualidade dos produtos manipulados