PHARMACOKINETICS AND BIOEQUIVALENCE STUDIES OF WARFARIN SODIUM 5 MILLIGRAMS TABLET IN HEALTY THAI SUBJECTS

Objective:  The present study aimed to evaluate the bioequivalence between the generic warfarin sodium tablet and a reference product when gave as equal labeled doses in healthy Thai subjects under fasting condition.Methods:  A randomized, open-label, single dose, two treatments, two periods, two sequences, crossover design between 5 mg of warfarin administration under fasting condition was conducted in 22 male and female healthy Thai subjects. Each subject was assigned randomly to receive a single oral dose of the test formulation or the reference formulation of 5 mg warfarin tablets. Study periods were separated by a 14-day washout period. Blood samples were collected at 0.0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 2.0, 2.5, 3.0, 4.0, 8.0, 12.0, 24.0, 36.0, 48.0 and 72.0 h after drug administration. A simple, sensitive and specific HPLC method was used for quantification of warfarin in plasma. Pharmacokinetic parameters were analyzed including Cmax, Tmax, t1/2 and AUC0-72h.Results:  Twenty subjects, selected randomly from healthy adult Thai subjects were enrolled, age of 22.5 + 3.1 years, weight, 59 + 6 kg. Twenty-one subjects completed both periods of the study. The mean Cmax values were 759.63 and 778.20 ng/ml and the mean AUC0-72h were 20010.89 and 20418.55 ng. h./ml for test and reference formulations, respectively. The mean ratios for log-transformed data were 0.9955 and 0.9971 for Cmax, and AUC0-72h, respectively. The 90% confidence intervals of the ratios of Cmax and AUC0-72h between test and reference tablets were 88.23% – 105.70% and 94.40% – 99.61%.Conclusion:  It can be concluded that test and reference warfarin 5 mg products were bioequivalent in terms of rate and extent of absorption.Â

Streptococcus agalactiae in adults at chiang mai university hospital: a retrospective study

<p>Abstract</p> <p>Background</p> <p>Infection caused by <it>Streptococcus agalactiae</it>, a Group B streptococcus, is an emerging disease in non-pregnant adults. This study describes the epidemiological, clinical, and microbiological characteristics of <it>S. agalactiae </it>infection in adult patients in northern Thailand.</p> <p>Methods</p> <p>A retrospective study was conducted between January 1, 2006 and December 31, 2009 at Chiang Mai University Hospital among patients aged ≥15 years, whose clinical specimens obtained from normally sterile sites grew <it>S. agalactiae</it>.</p> <p>Results</p> <p>One-hundred and eighty-six patients and 197 specimens were identified during the 4-year period. Among 186 patients, 82 were documented as having invasive infection; 42 patients were male (51.2%) with the mean age of 48.5 ± 19.4 years (range 17, 83). Fifty-three patients (64.6%) had underlying medical conditions; 17 patients (20.7%), 10 (12.2%), 8 (9.7%) had diabetes, chronic renal diseases, and malignancy, respectively. Among 40 patients (48.8%) with bloodstream infection, no other site of infection was determined in 29 (35.4%) patients. In the remaining 11 patients, 5 patients (6.1%), 5 (6.1%), and 1 (1.2%) had meningitis, arthritis, and meningitis with arthritis, respectively. Forty-two patients (51.2%) presented with localized infection, i.e., subcutaneous abscess (19 patients, 23.2%), chorioamnionitis (10 patients, 12.2%), urinary tract infection (5 patients, 6.1%), arthritis (3 patients, 3.7%), meningitis (2 patients, 2.4%), and spontaneous bacterial peritonitis, uveitis, and tracheobronchitis (1 patient each, 1.2%). The overall mortality was 14.6% (12 patients).</p> <p>Conclusions</p> <p><it>S. agalactiae </it>infection is a growing problem in non-pregnant patients, particularly in those with underlying medical conditions. Physicians should add <it>S. agalactiae </it>infection in the list of differential diagnoses in patients with meningitis and/or septicemia.</p

Do We Need a Specific Corpus and Multiple High-Performance GPUs for Training the BERT Model? An Experiment on COVID-19 Dataset

The COVID-19 pandemic has impacted daily lives around the globe. Since 2019, the amount of literature focusing on COVID-19 has risen exponentially. However, it is almost impossible for humans to read all of the studies and classify them. This article proposes a method of making an unsupervised model called a zero-shot classification model, based on the pre-trained BERT model. We used the CORD-19 dataset in conjunction with the LitCovid database to construct new vocabulary and prepare the test dataset. For NLI downstream task, we used three corpora: SNLI, MultiNLI, and MedNLI. We significantly reduced the training time by 98.2639% to build a task-specific machine learning model, using only one Nvidia Tesla V100. The final model can run faster and use fewer resources than its comparators. It has an accuracy of 27.84%, which is lower than the best-achieved accuracy by 6.73%, but it is comparable. Finally, we identified that the tokenizer and vocabulary more specific to COVID-19 could not outperform the generalized ones. Additionally, it was found that BART architecture affects the classification results

Do We Need a Specific Corpus and Multiple High-Performance GPUs for Training the BERT Model? An Experiment on COVID-19 Dataset

The COVID-19 pandemic has impacted daily lives around the globe. Since 2019, the amount of literature focusing on COVID-19 has risen exponentially. However, it is almost impossible for humans to read all of the studies and classify them. This article proposes a method of making an unsupervised model called a zero-shot classification model, based on the pre-trained BERT model. We used the CORD-19 dataset in conjunction with the LitCovid database to construct new vocabulary and prepare the test dataset. For NLI downstream task, we used three corpora: SNLI, MultiNLI, and MedNLI. We significantly reduced the training time by 98.2639% to build a task-specific machine learning model, using only one Nvidia Tesla V100. The final model can run faster and use fewer resources than its comparators. It has an accuracy of 27.84%, which is lower than the best-achieved accuracy by 6.73%, but it is comparable. Finally, we identified that the tokenizer and vocabulary more specific to COVID-19 could not outperform the generalized ones. Additionally, it was found that BART architecture affects the classification results