The spread of tuberculosis (TB), especially multidrug-resistant TB and extensively drug-resistant TB, has strongly motivated the research and development of new anti-TB drugs. New strategies to facilitate drug combinations, including pharmacokinetics-guided dose optimization and toxicology studies of first- and second-line anti-TB drugs have also been introduced and recommended. Liquid chromatography-mass spectrometry (LC-MS) has arguably become the gold standard in the analysis of both endo- and exo-genous compounds. This technique has been applied successfully not only for therapeutic drug monitoring (TDM) but also for pharmacometabolomics analysis. TDM improves the effectiveness of treatment, reduces adverse drug reactions, and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window. Based on TDM, the dose would be optimized individually to achieve favorable outcomes. Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs, aiding in the discovery of potential biomarkers for TB diagnostics, treatment monitoring, and outcome evaluation. This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades. Besides, we discussed the advantages and disadvantages of this technique in practical use. The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted. Lastly, we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies (pharmacometrics, drug and vaccine developments, machine learning/artificial intelligence, among others) to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.

Methods: Data analysis was carried out to identify the variables that had a significant impact on survival. For all patients with spinal metastasis from lung cancer who received non-surgical treatment, the Tomita score, revised Tokuhashi score, modified Bauer score, Van der Linden score, classic SORG algorithm, SORG nomogram, and NESMS were calculated. The performance of the scoring systems was assessed by using receiver operating characteristic (ROC) curves at 3 months, 6 months, and 12 months. The predictive accuracy of the scoring systems was quantified using the area under the ROC curve (AUC). Results: A total of 127 patients are included in the present study. The median survival of the population study was 5.3 months (95% confidence interval [CI], 3.7–9.6 months). Low hemoglobin was associated with shorter survival (hazard ratio [HR], 1.49; 95% CI, 1.00–2.23; p =0.049), while targeted therapy after spinal metastasis was associated with longer survival (HR, 0.34; 95% CI, 0.21–0.51; p <0.001). In the multivariate analysis, targeted therapy was independently associated with longer survival (HR, 0.3; 95% CI, 0.17–0.5; p <0.001). The AUC of the time-dependent ROC curves for the above prognostic scores revealed all of them performed poorly (AUC <0.7). Conclusions The seven scoring systems investigated are ineffective at predicting survival in patients with spinal metastasis from lung cancer who are treated non-surgically.

Introduction: Laboratory capacity is needed in central Viet Nam to provide early warning to public health authorities of respiratory outbreaks of importance to human health, for example the outbreak of influenza A(H1N1) pandemic in 2009. Polymerase chain reaction (PCR) procedures established as part of a capacity-building process were used to conduct prospective respiratory surveillance in a region where few previous studies have been undertaken. Methods: Between October 2008 and September 2010, nose and throat swabs from adults and children (approximately 20 per week) presenting with an acute respiratory illness to the Ninh Hoa General Hospital were collected. Same-day PCR testing and result reporting for 13 respiratory viruses were carried out by locally trained scientists. Results: Of 2144 surveillance samples tested, 1235 (57.6%) were positive for at least one virus. The most common were influenza A strains (17.9%), with pandemic influenza A(H1N1) 2009 and seasonal H3N2 strain accounting for 52% and 43% of these, respectively. Other virus detections included: rhinovirus (12.4%), enterovirus (8.9%), influenza B (8.3%), adenovirus (5.3%), parainfluenza (4.7%), respiratory syncytial virus (RSV) (3.9%), human coronavirus (3.0%) and human metapneumovirus (0.3%). The detection rate was greatest in the 0–5 year age group. Viral co-infections were identified in 148 (6.9%) cases. Discussion: The outbreak in 2009 of the influenza A(H1N1) pandemic strain provided a practical test of the laboratory’s pandemic plan. This study shows that the availability of appropriate equipment and molecular-based testing can contribute to important individual and public health outcomes in geographical locations susceptible to emerging infections.