Complete genome sequences of paired isogenic Burkholderia pseudomallei isolated from a Thai pediatric patient with a urinary tract infection.

Burkholderia pseudomallei is the causative agent of melioidosis, the disease endemic in Southeast Asia and northern Australia. We report complete genome sequences of paired isogenic B. pseudomallei isolated from a 12-year-old Thai male presenting with acute urinary tract infection before (SCBP001) and after (SCBP007) a decrease in susceptibility to ceftazidime.

Machine-learning-assisted high-throughput identification of potent and stable neutralizing antibodies against all four dengue virus serotypes.

Several computational methods have been developed to identify neutralizing antibodies (NAbs) covering four dengue virus serotypes (DENV-1 to DENV-4); however, limitations of the dataset and the resulting performance remain. Here, we developed a new computational framework to predict potent and stable NAbs against DENV-1 to DENV-4 using only antibody (CDR-H3) and epitope sequences as input. Specifically, our proposed computational framework employed sequence-based ML and molecular dynamic simulation (MD) methods to achieve more accurate identification. First, we built a novel dataset (n = 1108) by compiling the interactions of CDR-H3 and epitope sequences with the half maximum inhibitory concentration (IC50) values, which represent neutralizing activities. Second, we achieved an accurately predictive ML model that showed high AUC values of 0.879 and 0.885 by tenfold cross-validation and independent tests, respectively. Finally, our computational framework could be applied to filter approximately 2.5 million unseen antibodies into two final candidates that showed strong and stable binding to all four serotypes. In addition, the most potent and stable candidate (1B3B9_V21) was evaluated for its development potential as a therapeutic agent by molecular docking and MD simulations. This study provides an antibody computational approach to facilitate the high-throughput identification of NAbs and accelerate the development of therapeutic antibodies.

Co-resistance to isoniazid and second-line anti-tuberculosis drugs in isoniazid-resistant tuberculosis at a tertiary care hospital in Thailand.

Isoniazid-resistant tuberculosis (Hr-TB) is an important drug-resistant tuberculosis (TB). In addition to rifampicin, resistance to other medications for Hr-TB can impact the course of treatment; however, there are currently limited data in the literature. In this study, the drug susceptibility profiles of Hr-TB treatment and resistance-conferring mutations were investigated for Hr-TB clinical isolates from Thailand. Phenotypic drug susceptibility testing (pDST) and genotypic drug susceptibility testing (gDST) were retrospectively and prospectively investigated using the Mycobacterium Growth Indicator Tube (MGIT), the broth microdilution (BMD) method, and whole-genome sequencing (WGS)-based gDST. The prevalence of Hr-TB cases was 11.2% among patients with TB. Most Hr-TB cases (89.5%) were newly diagnosed patients with TB. In the pDST analysis, approximately 55.6% (60/108) of the tested Hr-TB clinical isolates exhibited high-level isoniazid resistance. In addition, the Hr-TB clinical isolates presented co-resistance to ethambutol (3/161, 1.9%), levofloxacin (2/96, 2.1%), and pyrazinamide (24/118, 20.3%). In 56 Hr-TB clinical isolates, WGS-based gDST predicted resistance to isoniazid [katG S315T (48.2%) and fabG1 c-15t (26.8%)], rifampicin [rpoB L430P and rpoB L452P (5.4%)], and fluoroquinolones [gyrA D94G (1.8%)], but no mutation for ethambutol was detected. The categorical agreement for the detection of resistance to isoniazid, rifampicin, ethambutol, and levofloxacin between WGS-based gDST and the MGIT or the BMD method ranged from 80.4% to 98.2% or 82.1% to 100%, respectively. pDST and gDST demonstrated a low co-resistance rate between isoniazid and second-line TB drugs in Hr-TB clinical isolates. IMPORTANCE: The prevalence of isoniazid-resistant tuberculosis (Hr-TB) is the highest among other types of drug-resistant tuberculosis. Currently, the World Health Organization (WHO) guidelines recommend the treatment of Hr-TB with rifampicin, ethambutol, pyrazinamide, and levofloxacin for 6 months. The susceptibility profiles of Hr-TB clinical isolates, especially when they are co-resistant to second-line drugs, are critical in the selection of the appropriate treatment regimen to prevent treatment failure. This study highlights the susceptibility profiles of the WHO-recommended treatment regimen in Hr-TB clinical isolates from a tertiary care hospital in Thailand and the concordance and importance of using the phenotypic drug susceptibility testing or genotypic drug susceptibility testing for accurate and comprehensive interpretation of results.

Antibodies against SARS-CoV-2 in unvaccinated children hospitalized with COVID-19: An observational cohort study of pre-Omicron and Omicron variants era.

PURPOSE: This study aimed to investigate the antibodies against SARS-CoV-2 in children hospitalized due to COVID-19 during the era of pre-Omicron and Omicron variants. METHODS: This was a retrospective observational study conducted at a tertiary academic medical center in Thailand between June 2021 and August 2022. We collected the data of children aged under 18-year who were hospitalized from SARS-CoV-2 infection. After hospital discharge, we scheduled clinical follow-up 60 to 90 days post-infection clinical follow-up. We measured antibodies against SARS-CoV-2 anti-spike protein receptor-binding domain in the serum during a follow-up visit and compared the mean difference of antibody levels between children infected with COVID-19 during the pre-Omicron and Omicron eras. RESULTS: A total of 119 children enrolled into the study. There were 58 and 61 children hospitalized due to COVID-19 during pre-Omicron and Omicron era, respectively. The median (interquartile range, IQR) of SARS-CoV-2 antibodies in all cases was 206.1 (87.9-424.1) U/mL at follow-up. Children infected during pre-Omicron had SARS-CoV-2 antibody levels at follow-up higher than children infected during Omicron era [mean difference 292.57 U/mL, 95% CI 53.85-531.28, p = 0.017). There was no difference in SARS-CoV-2 antibody levels between the children based on gender, age, co-morbidities, chest radiograph classification, or diagnosis. CONCLUSIONS: The antibodies response to SARS-CoV-2 infection was weaker during the Omicron era than previous variant of concern. Immunization strategies and policies should be implemented in children even if they had been previously infected.

Determination of latent tuberculosis infection from plasma samples via label-free SERS sensors and machine learning.

Effective diagnostic tools for screening of latent tuberculosis infection (LTBI) are lacking. We aim to investigate the performance of LTBI diagnostic approaches using label-free surface-enhanced Raman spectroscopy (SERS). We used 1000 plasma samples from Northeast Thailand. Fifty percent of the samples had tested positive in the interferon-gamma release assay (IGRA) and 50 % negative. The SERS investigations were performed on individually prepared protein specimens using the Raman-mapping technique over a 7 × 7 grid area under measurement conditions that took under 10 min to complete. The machine-learning analysis approaches were optimized for the best diagnostic performance. We found that the SERS sensors provide 81 % accuracy according to train-test split analysis and 75 % for LOOCV analysis from all samples, regardless of the batch-to-batch variation of the sample sets and SERS chip. The accuracy increased to 93 % when the logistic regression model was used to analyze the last three batches of samples, following optimization of the sample collection, SERS chips, and database. We demonstrated that SERS analysis with machine learning is a potential diagnostic tool for LTBI screening.