Genomic adaptation to small population size and saltwater consumption in the critically endangered Cat Ba langur.

Many mammal species have declining populations, but the consequences of small population size on the genomic makeup of species remain largely unknown. We investigated the evolutionary history, genetic load and adaptive potential of the Cat Ba langur (Trachypithecus poliocephalus), a primate species endemic to Vietnam's famous Ha Long Bay and with less than 100 living individuals one of the most threatened primates in the world. Using high-coverage whole genome data of four wild individuals, we revealed the Cat Ba langur as sister species to its conspecifics of the northern limestone langur clade and found no evidence for extensive secondary gene flow after their initial separation. Compared to other primates and mammals, the Cat Ba langur showed low levels of genetic diversity, long runs of homozygosity, high levels of inbreeding and an excess of deleterious mutations in homozygous state. On the other hand, genetic diversity has been maintained in protein-coding genes and on the gene-rich human chromosome 19 ortholog, suggesting that the Cat Ba langur retained most of its adaptive potential. The Cat Ba langur also exhibits several unique non-synonymous variants that are related to calcium and sodium metabolism, which may have improved adaptation to high calcium intake and saltwater consumption.

Metabolic phenotyping combined with transcriptomics metadata fortifies the diagnosis of early-stage Hepatocellular carcinoma.

INTRODUCTION: The low sensitivity of alpha-fetoprotein (AFP) renders it unsuitable as a stand-alone marker for early hepatocellular carcinoma (eHCC) surveillance. Therefore, additional blood-based biomarkers with enhanced sensitivities are required. OBJECTIVES: In light of the metabolic changes that are distinctive to eHCC development, the current study presents a panel of serum metabolites that may serve as noninvasive diagnostic indicators for patients with eHCC. METHODS: Serum samples obtained from normal control (NC), cirrhosis, and eHCC patients were analyzed by four different metabolomic platforms. A meta-analysis of very early-stage HCC transcriptomic datasets retrieved from public sources supports the integrated interpretation with metabolic changes. RESULTS: A total of 94 metabolites were significantly correlated with a progressive disease status. Integrated analysis of the significant metabolites and differentially expressed genes from meta-analysis emphasized metabolic pathways including bile acid biosynthesis, phenylalanine and tyrosine metabolism, and butanoate metabolism. The 11 metabolites associated with these pathways were compiled into a metabolite panel for use as diagnostic signatures. With an accuracy of 81.8%, compared with 45.4% for a model trained solely on AFP, the model enhanced its ability to differentiate between the three groups by incorporating a metabolite panel and AFP. Upon examining the trained models using receiver operating characteristic curves, the AFP and metabolite panel combined model exhibited greater area under the curve values in comparisons between NC and eHCC (1.000 versus 0.810) and cirrhosis and eHCC (0.926 versus 0.556). The result was consistent in an independent validation cohort. CONCLUSION: This study emphasizes the role of circulating metabolite markers in the diagnosis of eHCC.

Correction: Shape-controlled synthesis of micro-/nanosized Cu particles with spherical and polyhedral shapes using the polyol process.

[This corrects the article DOI: 10.1039/D4RA03643C.].

Suicide risk among young people who use drugs in Hanoi, Vietnam: Prevalence and related factors.

AIM: Adolescents and young adults are vulnerable to suicidality, especially those at high risk such as young people who use drugs (YPUD). This study aimed to assess the prevalence and related factors of suicide risk among this population. METHODS: We conducted a descriptive, cross-sectional study on YPUD aged 16-24 in the community in Hanoi, Vietnam. Data on socio-demographic characteristics, drug use, and adverse childhood experiences were collected using face-to-face questionnaires by research assistants. YPUD were screened by psychiatrists for depression, psychotic symptoms, and suicide risk, using the MINI questionnaire. RESULTS: Three hundred-seven YPUD (250 males, 57 females) participated in the study; of those, 86 (28.0%) were at risk of suicide. Gender (female), adverse childhood experiences, depression, and psychosis were relevant factors. CONCLUSION: The prevalence of suicide risk among young people using drugs was high. Therefore, suicide risk should be screened and monitored in the clinical assessment of this population. In addition, the intervention efforts to detect and intervene in adverse events during childhood may be one way to prevent mental health and suicide in later life.

Suicidal Ideation was Associated with Quality of Life Impairment of Patients with Lung Cancer: A Cross-Sectional Study in Vietnam.

OBJECTIVE: This study aimed to measure the quality of life (QOL) of lung cancer patients and evaluate the relationship between QOL and suicidal ideation (SI) in a tertiary hospital in Vietnam. METHODS: A cross-sectional study was conducted at Bach Mai Hospital in Hanoi, Vietnam. A total of 256 patients with lung cancer were recruited. The QOL of cancer patients was evaluated using the European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire (EORTC QLQ-C30) version 3. Multivariate Tobit regression was employed to identify associations between SI and QOL. RESULT: Overall, the mean global health status score was 58.3 ± 15.8, and those with SI had significantly higher scores than those without SI. Regarding functional scales, patients with SI had significantly lower physical functioning (82.22 ± 11.42) compared to those without SI (90.58 ± 11.90) (p<0.05). Meanwhile, regarding symptom scales, scores for fatigue, nausea and vomiting, pain, insomnia, appetite loss, constipation, diarrhea, and financial difficulties among patients with SI were significantly higher than those without SI (p<0.05). In the multivariate analysis, having SI increased the role functioning score (Coef. = 9.41, 95% CI = 0.38-18.45) and diarrhea score (Coef. = 22.33, 95% CI = 2.05-42.60). CONCLUSION: This study indicated moderate QOL and perceived the lowest social functioning in lung cancer patients. SI was associated with role functioning scores and diarrhea scores. SI should be monitored and controlled regularly in this population during treatment to improve their QOL.

Regulation of host metabolism and defense strategies to survive neonatal infection.

Two distinct defense strategies, disease resistance (DR) and disease tolerance (DT), enable a host to survive infectious diseases. Newborns, constrained by limited energy reserves, predominantly rely on DT to cope with infection. However, this approach may fail when pathogen levels surpass a critical threshold, prompting a shift to DR that can lead to dysregulated immune responses and sepsis. The mechanisms governing the interplay between DR and DT in newborns remain poorly understood. Here, we compare metabolic traits and defense strategies between survivors and non-survivors in Staphylococcus epidermidis (S. epidermidis)-infected preterm piglets, mimicking infection in preterm infants. Compared to non-survivors, survivors displayed elevated DR during the initial phase of infection, followed by stronger DT in later stages. In contrast, non-survivors showed clear signs of respiratory and metabolic acidosis and hyperglycemia, together with exaggerated inflammation and organ dysfunctions. Hepatic transcriptomics revealed a strong association between the DT phenotype and heightened oxidative phosphorylation in survivors, coupled with suppressed glycolysis and immune signaling. Plasma metabolomics confirmed the findings of metabolic regulations associated with DT phenotype in survivors. Our study suggests a significant association between the initial DR and subsequent DT, which collectively contributes to improved infection survival. The regulation of metabolic processes that optimize the timing and balance between DR and DT holds significant potential for developing novel therapeutic strategies for neonatal infection.

Shape-controlled synthesis of micro-/nanosized Cu particles with spherical and polyhedral shapes using the polyol process.

This study reports the synthesis of Cu micro-/nanosized particles through the polyol process. Cu particles were synthesized by reducing copper(ii) chloride in ethylene glycol (EG), polyvinylpyrrolidone (PVP), and potassium bromide (KBr) at low temperatures with or without the use of sodium borohydride (NaBH4).

Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning.

Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehensive statistical modeling to discover potential biomarkers for the differential diagnosis of NTM infection versus TB. Urine samples from 19 NTM and 35 TB patients were collected, and untargeted metabolomics was performed using rapid liquid chromatography-mass spectrometry. The urine metabolome was analyzed using a combination of univariate and multivariate statistical approaches, incorporating machine learning. Univariate analysis revealed significant alterations in amino acids, especially tryptophan metabolism, in NTM infection compared to TB. Specifically, NTM infection was associated with upregulated levels of methionine but downregulated levels of glutarate, valine, 3-hydroxyanthranilate, and tryptophan. Five machine learning models were used to classify NTM and TB. Notably, the random forest model demonstrated excellent performance [area under the receiver operating characteristic (ROC) curve greater than 0.8] in distinguishing NTM from TB. Six potential biomarkers for NTM infection diagnosis, including methionine, valine, glutarate, 3-hydroxyanthranilate, corticosterone, and indole-3-carboxyaldehyde, were revealed from univariate ROC analysis and machine learning models. Altogether, our study suggested new noninvasive biomarkers and laid a foundation for applying machine learning to NTM differential diagnosis.

Efficient implementation of the linear layer of block ciphers with large MDS matrices based on a new lookup table technique.

Block cipher is a cryptographic field that is now widely applied in various domains. Besides its security, deployment issues, implementation costs, and flexibility across different platforms are also crucial in practice. From an efficiency perspective, the linear layer is often the slowest transformation and requires significant implementation costs in block ciphers. Many current works employ lookup table techniques for linear layers, but they are quite costly and do not save memory storage space for the lookup tables. In this paper, we propose a novel lookup table technique to reduce memory storage when executing software. This technique is applied to the linear layer of block ciphers with recursive Maximum Distance Separable (MDS) matrices, Hadamard MDS matrices, and circulant MDS matrices of considerable sizes (e.g. sizes of 16, 32, 64, and so on). The proposed lookup table technique leverages the recursive property of linear matrices and the similarity in elements of Hadamard or circulant MDS matrices, allowing the construction of a lookup table for a submatrix instead of the entire linear matrix. The proposed lookup table technique enables the execution of the diffusion layer with unchanged computational complexity (number of XOR operations and memory accesses) compared to conventional lookup table implementations but allows a substantial reduction in memory storage for the pre-computed tables, potentially reducing the storage needed by 4 or 8 times or more. The memory storage will be reduced even more as the size of the MDS matrix increases. For instance, analysis shows that when the matrix size is 64, the memory storage ratio with the proposed lookup table technique decreases by 87.5% compared to the conventional lookup table technique. This method also allows for more flexible software implementations of large-sized linear layers across different environments.

Utilizing the Short Mood and Feelings Questionnaire to measure symptoms of depression among Vietnamese adolescents in Hanoi, Vietnam, during the COVID-19 pandemic.

Objective: This study aimed to measure depression among children and adolescents during the COVID-19 pandemic in Hanoi, Vietnam and its associated factors by using the Short Mood and Feelings Questionnaire (SMFQ) instrument. Methods: We conducted a cross-sectional study among students from grades 6 to 9 within two secondary schools in Hanoi, the capital of Vietnam. A structured questionnaire was used, including information about personal characteristics, perception of COVID-19, and SMFQ. Factor analysis, Multivariate logistic and Tobit regression models were used. Results: Among 2378 students, 8.8% had depressive symptoms. The mean SMFQ score was 4.5 (SD=5.0). Being female, studying in higher grades, perceived low household income, higher perceived impacts of COVID-19 on health and higher perceived impacts of COVID-19-related quarantine on life were positively associated with factors' scores, SMFQ score and depressive symptoms. Meanwhile, having better academic performance, living with parents and having higher perceived knowledge about COVID-19 were negatively associated with factors scores, SMFQ score and depressive symptoms. Conclusions: Depressive symptoms were common among secondary school students in Hanoi, Vietnam, during the COVID-19 pandemic. Tailored interventions to improve pandemic-related knowledge and family and school support should be warranted for the students to enhance their mental well-being.

Evaluation of Dihydroartemisinin-Piperaquine Efficacy and Molecular Markers in Uncomplicated Falciparum Patients: A Study across Binh Phuoc and Dak Nong, Vietnam.

Background and Objectives: Malaria continues to be a significant global health challenge. The efficacy of artemisinin-based combination therapies (ACTs) has declined in many parts of the Greater Mekong Subregion, including Vietnam, due to the spread of resistant malaria strains. This study was conducted to assess the efficacy of the Dihydroartemisinin (DHA)-Piperaquine (PPQ) regimen in treating uncomplicated falciparum malaria and to conduct molecular surveillance of antimalarial drug resistance in Binh Phuoc and Dak Nong provinces. Materials and Methods: The study included 63 uncomplicated malaria falciparum patients from therapeutic efficacy studies (TES) treated following the WHO treatment guidelines (2009). Molecular marker analysis was performed on all 63 patients. Methods encompassed Sanger sequencing for pfK13 mutations and quantitative real-time PCR for the pfpm2 gene. Results: This study found a marked decrease in the efficacy of the DHA-PPQ regimen, with an increased rate of treatment failures at two study sites. Genetic analysis revealed a significant presence of pfK13 mutations and pfpm2 amplifications, indicating emerging resistance to artemisinin and its partner drug. Conclusions: The effectiveness of the standard DHA-PPQ regimen has sharply declined, with rising treatment failure rates. This decline necessitates a review and possible revision of national malaria treatment guidelines. Importantly, molecular monitoring and clinical efficacy assessments together provide a robust framework for understanding and addressing detection drug resistance in malaria.

Time-course cross-species transcriptomics reveals conserved hepatotoxicity pathways induced by repeated administration of cyclosporine A.

Cyclosporine A (CsA) has shown efficacy against immunity-related diseases despite its toxicity in various organs, including the liver, emphasizing the need to elucidate its underlying hepatotoxicity mechanism. This study aimed to capture the alterations in genome-wide expression over time and the subsequent perturbations of corresponding pathways across species. Six data from humans, mice, and rats, including animal liver tissue, human liver microtissues, and two liver cell lines exposed to CsA toxic dose, were used. The microtissue exposed to CsA for 10 d was analyzed to obtain dynamically differentially expressed genes (DEGs). Single-time points data at 1, 3, 5, 7, and 28 d of different species were used to provide additional evidence. Using liver microtissue-based longitudinal design, DEGs that were consistently up- or down-regulated over time were captured, and the well-known mechanism involved in CsA toxicity was elucidated. Thirty DEGs that consistently changed in longitudinal data were also altered in 28-d rat in-house data with concordant expression. Some genes (e.g. TUBB2A, PLIN2, APOB) showed good concordance with identified DEGs in 1-d and 7-d mouse data. Pathway analysis revealed up-regulations of protein processing, asparagine N-linked glycosylation, and cargo concentration in the endoplasmic reticulum. Furthermore, the down-regulations of pathways related to biological oxidations and metabolite and lipid metabolism were elucidated. These pathways were also enriched in single-time-point data and conserved across species, implying their biological significance and generalizability. Overall, the human organoids-based longitudinal design coupled with cross-species validation provides temporal molecular change tracking, aiding mechanistic elucidation and biologically relevant biomarker discovery.

Digital Twins in Agriculture: Orchestration and Applications.

Digital Twins have emerged as an outstanding opportunity for precision farming, digitally replicating in real-time the functionalities of objects and plants. A virtual replica of the crop, including key agronomic development aspects such as irrigation, optimal fertilization strategies, and pest management, can support decision-making and a step change in farm management, increasing overall sustainability and direct water, fertilizer, and pesticide savings. In this review, Digital Twin technology is critically reviewed and framed in the context of recent advances in precision agriculture and Agriculture 4.0. The review is organized for each step of agricultural lifecycle, edaphic, phytotechnologic, postharvest, and farm infrastructure, with supporting case studies demonstrating direct benefits for agriculture production and supply chain considering both benefits and limitations of such an approach. Challenges and limitations are disclosed regarding the complexity of managing such an amount of data and a multitude of (often) simultaneous operations and supports.

Omics analysis unveils changes in the metabolome and lipidome of Caenorhabditis elegans upon polydopamine exposure.

Polydopamine (PDA) is an insoluble biopolymer with a dark brown-black color that forms through the autoxidation of dopamine. Because of its outstanding biocompatibility and durability, PDA holds enormous promise for various applications, both in the biomedical and non-medical domains. To ensure human safety, protect health, and minimize environmental impacts, the assessment of PDA toxicity is important. In this study, metabolomics and lipidomics assessed the impact of acute PDA exposure on Caenorhabditis elegans (C. elegans). The findings revealed a pronounced perturbation in the metabolome and lipidome of C. elegans at the L4 stage following 24 hours of exposure to 100 µg/mL PDA. The changes in lipid composition varied based on lipid classes. Increased lipid classes included lysophosphatidylethanolamine, triacylglycerides, and fatty acids, while decreased species involved in several sub-classes of glycerophospholipids and sphingolipids. Besides, we detected 37 significantly affected metabolites in the positive and 8 in the negative ion modes due to exposure to PDA in C. elegans. The metabolites most impacted by PDA exposure were associated with purine metabolism, biosynthesis of valine, leucine, and isoleucine; aminoacyl-tRNA biosynthesis; and cysteine and methionine metabolism, along with pantothenate and CoA biosynthesis; the citrate cycle (TCA cycle); and beta-alanine metabolism. In conclusion, PDA exposure may intricately influence the metabolome and lipidome of C. elegans. The combined application of metabolomics and lipidomics offers additional insights into the metabolic perturbations involved in PDA-induced biological effects and presents potential biomarkers for the assessment of PDA safety.

Cyclosporine A-induced systemic metabolic perturbations in rats: A comprehensive metabolome analysis.

A better understanding of cyclosporine A (CsA)-induced nephro- and hepatotoxicity at the molecular level is necessary for safe and effective use. Utilizing a sophisticated study design, this study explored metabolic alterations after long-term CsA treatment in vivo. Rats were exposed to CsA with 4, 10, and 25 mg/kg for 4 weeks and then sacrificed to obtain liver, kidney, urine, and serum for untargeted metabolomics analysis. Differential network analysis was conducted to explore the biological relevance of metabolites significantly altered by toxicity-induced disturbance. Dose-dependent toxicity was observed in all biospecimens. The toxic effects were characterized by alterations of metabolites related to energy metabolism and cellular membrane composition, which could lead to the cholestasis-induced accumulation of bile acids in the tissues. The unfavorable impacts were also demonstrated in the serum and urine. Intriguingly, phenylacetylglycine was increased in the kidney, urine, and serum treated with high doses versus controls. Differential correlation network analysis revealed the strong correlations of deoxycytidine and guanosine with other metabolites in the network, which highlighted the influence of repeated CsA exposure on DNA synthesis. Overall, prolonged CsA administration had system-level dose-dependent effects on the metabolome in treated rats, suggesting the need for careful usage and dose adjustment.

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management.

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.

Novel chitosan-zeolite X composite beads prepared by phase-inversion method for CO2 adsorptive capture.

The urgent need to mitigate carbon emissions from industrial heat production has led to the exploration of novel carbon-based materials for carbon capture. Chitosan, a versatile framework, has been widely utilized for embedding many materials such as grafted graphene oxide, zeolite, and activated carbon to enhance the carbon capture capacity of diverse carbon-based materials. Remarkably, the combination of chitosan and zeolite overcomes the inherent drawbacks of powdery zeolite, resulting in improved stability and efficiency in carbon capture applications. In this study, zeolite X-chitosan composite was successfully synthesized using phase inversion method followed by solvent exchange and air drying. The synthesis procedure described in this study presents a notable advantage in terms of simplicity and ease of fabrication. The combination of SEM and XRD analyses provided evidence that the composite exhibited a uniform arrangement of zeolite within the chitosan framework and maintained the original properties of the powdered zeolite. The adsorption capacity of the composite was first investigated by varying mass ratio of zeolite per chitosan. The composite with mass ratio that gave the best adsorption capacity were then tested under various temperatures (-20 °C, 0 °C, 30 °C, and 50 °C) and pressures (1 kPa, 3 kPa, 5 kPa, and 30 kPa). The application of different adsorption models was also employed to simulate the breakthrough curves. Furthermore, the material also underwent multiple continuous adsorption-desorption cycles showing its potential for repeated rechargeability. In contrast, the synthesis and characterization of copper ion-exchanged composite yielded significant drop in adsorption capacity, likely due to the formation of ligands or the inherent reactivity of Cu2+ ions towards hydroxide.

Metabolic phenotyping and global functional analysis facilitate metabolic signature discovery for tuberculosis treatment monitoring.

Tracking alterations in polar metabolite and lipid levels during anti-tuberculosis (TB) interventions is an emerging biomarker discovery and validation approach due to its sensitivity in capturing changes and reflecting on the host status. Here, we employed deep plasma metabolic phenotyping to explore the TB patient metabolome during three phases of treatment: at baseline, during intensive phase treatment, and upon treatment completion. Differential metabolites (DMs) in each period were determined, and the pathway-level biological alterations were explored by untargeted metabolomics-guided functional interpretations that bypassed identification. We identified 41 DMs and 39 pathways that changed during intensive phase completion. Notably, levels of certain amino acids including histidine, bile acids, and metabolites of purine metabolism were dramatically increased. The altered pathways included those involved in the metabolism of amino acids, glycerophospholipids, and purine. At the end of treatment, 44 DMs were discovered. The levels of glutamine, bile acids, and lysophosphatidylinositol significantly increased compared to baseline; the levels of carboxylates and hypotaurine declined. In addition, 37 pathways principally associated with the metabolism of amino acids, carbohydrates, and glycan altered at treatment completion. The potential of each DM for diagnosing TB was examined using a cohort consisting of TB patients, those with latent infections, and controls. Logistic regression revealed four biomarkers (taurine, methionine, glutamine, and acetyl-carnitine) that exhibited excellent performance in differential diagnosis. In conclusion, we identified metabolites that could serve as useful metabolic signatures for TB management and elucidated underlying biological processes affected by the crosstalk between host and TB pathogen during treatment.

Towards safer and efficient formulations: Machine learning approaches to predict drug-excipient compatibility.

Predicting drug-excipient compatibility is a critical aspect of pharmaceutical formulation design. In this study, we introduced an innovative approach that leverages machine learning techniques to improve the accuracy of drug-excipient compatibility predictions. Mol2vec and 2D molecular descriptors combined with the stacking technique were used to improve the performance of the model. This approach achieved a significant advancement in the predictive capacity as demonstrated by the accuracy, precision, recall, AUC, and MCC of 0.98, 0.87, 0.88, 0.93 and 0.86, respectively. Using the DE-INTERACT model as the benchmark, our stacking model could remarkably detect drug-excipient incompatibility in 10/12 tested cases, while DE-INTERACT managed to recognize only 3 out of 12 incompatibility cases in the validation experiments. To ensure user accessibility, the trained model was deployed to a user-friendly web platform (URL: https://decompatibility.streamlit.app/). This interactive interface accommodated inputs through various types, including names, PubChem CID, or SMILES strings. It promptly generated compatibility predictions alongside corresponding probability scores. However, the continual refinement of model performance is crucial before applying this model in practice.

Population pharmacokinetic model of rifampicin for personalized tuberculosis pharmacotherapy: Effects of SLCO1B1 polymorphisms on drug exposure.

BACKGROUND: Rifampicin (RIF) exhibits high pharmacokinetic (PK) variability among individuals; a low plasma concentration might result in unfavorable treatment outcomes and drug resistance. This study evaluated the contributions of non- and genetic factors to the interindividual variability of RIF exposure, then suggested initial doses for patients with different weight bands. METHODS: This multicenter prospective cohort study in Korea analyzed demographic and clinical data, the solute carrier organic anion transporter family member 1B1 (SLCO1B1) genotypes, and RIF concentrations. Population PK modeling and simulations were conducted using nonlinear mixed-effect modeling. RESULTS: In total, 879 tuberculosis (TB) patients were divided into a training dataset (510 patients) and a test dataset (359 patients). A one-compartment model with allometric scaling for effect of body size best described the RIF PKs. The apparent clearance (CL/F) was 16.6% higher among patients in the SLCO1B1 rs4149056 wild-type group than among patients in variant group, significantly decreasing RIF exposure in the wild-type group. The developed model showed better predictive performance compared with previously reported models. We also suggested that patients with body weights of <40 kg, 40-55 kg, 55-70 kg, and >70 kg patients receive RIF doses of 450, 600, 750, and 1050 mg/day, respectively. CONCLUSIONS: Total body weight and SLCO1B1 rs4149056 genotypes were the most significant covariates that affected RIF CL/F variability in Korean TB patients. We suggest initial doses of RIF based on World Health Organization weight-band classifications. The model may be implemented in treatment monitoring for TB patients.