Targeted sputum sequencing for rapid and broad drug resistance of MTB [TBN 066]

Who
The study analyzed 55 clinical sputum samples from Kaohsiung Veterans General Hospital (KSVGH): 51 Mtb-positive samples and 4 Mtb-negative controls. Positive samples included 12 samples sequenced on a Flongle flow cell and 40 samples sequenced on a MinION flow cell, with overlap in the described cohorts as labeled F01–F12, M01–M36, and T01–T03. Samples were categorized by acid-fast staining (AFS) grades, including trace, 1+, 2+, 3+, and 4+. The study also used spike-in sputum experiments with Mtb H37Ra and analyzed 442 publicly available nanopore whole-genome datasets (291 from PRJNA650381 and 151 from PRJEB49093).

What
The study developed a streamlined thermo-protection DNA preparation method, a single-tube 17-plex PCR assay targeting 16 resistance-associated genes, and a Python-based resistance prediction pipeline called ARapidTb for targeted nanopore sequencing directly from clinical sputum. The assay was designed around resistance genes and mutations identified from the WHO mutation catalogue and tbdb/TBProfiler database.

Key findings reported in the text:

• The thermo-protection approach increased mapped Mtb read counts from 6,183 to 32,301 and increased the overall mapped-read percentage from 5.73% to 61.08% in the pilot comparison.
• Average read length increased from 844.2 bp in control samples to 942.6 bp in thermo-protected samples.
• In spike-in experiments, the thermo-protection buffer improved enrichment, and the apparent limit of detection was around 873 CFU/ml for sequencing 12 spike-in samples on a MinION flow cell.
• On the Flongle, 6 clinical samples were correctly predicted as susceptible; the workflow was presented as enabling next-morning AMR prediction, with a reported total run cost of about $200 USD, or less than $35 per sample.
• On the MinION, Mtb was detected in 28 of 36 Mtb-positive samples, and correct AMR predictions were made for 21 of those 28 detected samples using 15-hour reads.
• Among 17 clinical samples with AFS 2+ or above, predictions were described as accurate except for one INH-resistant sample (M03) that was falsely predicted susceptible.
• Using 4-hour MinION reads, correct AMR predictions were still obtained for 13 AFS 2+ or above samples plus one trace sample, supporting same-day reporting in some cases.
• In PRJNA650381, ARapidTb showed 95.8% agreement with phenotypic drug susceptibility testing across 1,417 AMR predictions, outperforming Mykrobe and TBProfiler according to the authors.
• In PRJEB49093, ARapidTb results were described as concordant with prior findings that nanopore-based genotyping aligns well with Illumina-based resistance calls. Across 442 isolates, ARapidTb was reported as comparable to Mykrobe and better than TBProfiler overall.

The authors conclude that this workflow can provide rapid, targeted, sputum-based AMR prediction for tuberculosis, especially in higher-smear samples, while expanding mutation coverage beyond some prior targeted assays.

When
The study duration, enrollment period, and dates of sample collection were not specified. Sequencing analyses were evaluated using 4-hour, 15-hour, 24-hour, and 72-hour run scenarios depending on platform and simulation of laboratory turnaround. Spike-in cultures for CFU validation were incubated for 3–4 weeks.

Where
Clinical samples came from Kaohsiung Veterans General Hospital (KSVGH). The study used NaOH-treated sputum samples from that clinical setting. Public external validation datasets were downloaded from PRJNA650381 and PRJEB49093. The reference genome used for primer design was NC_000962.3.

Why
The rationale was to address the need for faster and broader antimicrobial resistance detection for tuberculosis directly from sputum, without relying on slower culture-based workflows or more limited targeted assays. The authors sought to:

• reduce human DNA contamination during DNA preparation,
• expand coverage of resistance-associated mutations beyond existing assays,
• exploit long-read nanopore sequencing more effectively,
• and enable same-day or next-day AMR prediction from clinical samples.

The study also aimed to improve on earlier assays such as Deeplex-MycTB, “nanopore 2021,” and “nanopore 2023” by covering additional genes and larger targeted regions.

How
This was a diagnostic assay development and evaluation study combining laboratory protocol optimization, clinical sample testing, spike-in analytical validation, and comparative bioinformatics benchmarking.

Methods included:

• Primer design: A panel of 10 full-length genes and 6 partial genes was designed using oli2go with target amplicons of about 1000–5000 bp. The final assay became a 17-plex PCR covering 16 resistance-associated genes.
• Sample preparation: Sputum pellets were treated with a thermo-protection buffer (2 M KCl, 0.025 M HEPES, pH 7.5), heat-inactivated at 99 °C for 30 min, bead-cleaned, and eluted for PCR.
• Pilot comparison: One clinical sample was split into control and thermo-protection workflows to assess enrichment.
• Spike-in testing: Mtb H37Ra was serially diluted into Mtb-negative sputum to assess enrichment and detection limit.
• PCR: Multiplex PCR used Platinum SuperFi II Green PCR Master Mix with 35 cycles.
• Sequencing: Libraries were prepared with the Oxford Nanopore Rapid Barcoding Kit 96, then sequenced on Flongle and MinION R9.4.1 flow cells. Basecalling/demultiplexing used MinKNOW v4.3.4 and Guppy v5.0.11.
• Bioinformatics: Reads were mapped to expected amplicons with Minimap2 v2.26; consensus and variants were generated with Medaka v1.5.0; resistance-associated variants were compiled into Tbresdb from tbdb and the WHO catalogue; AMR prediction was performed with ARapidTb.
• Comparators: ARapidTb was compared against Mykrobe v0.12.1 and TBProfiler v2.3.0 using public nanopore datasets.

Limitations noted in the text

• Clinical drug-resistant samples were limited in number.
• Sensitivity was low for some drugs, particularly PZA and ETH.
• ethA deletions were incompletely represented in the resistance database, contributing to false negatives.
• Nanopore indel characterization issues contributed to false positives in other tools.
• The omission of the gid gene from primer design likely reduced streptomycin resistance detection.
• Performance was best in samples with higher smear grades, indicating limited sensitivity in lower-burden samples.

Level of evidence
This is a diagnostic development and validation study, not a randomized or interventional trial. Its evidence is strongest for technical feasibility and comparative diagnostic performance, but more limited for broad clinical effectiveness because the clinical evaluation sample was relatively small and resistant cases were uncommon.

Source: Dou, H. Y., Huang, T. S., Wu, H. C., Hsu, C. H., Chen, F. J., & Liao, Y. C. (2025). Targeted sputum sequencing for rapid and broad drug resistance of Mycobacterium tuberculosis. Infection, 53(4), 1413-1423. https://benangmerah.net/record/31/targeted-sputum-sequencing

Geospatial codistribution of TB and DM in Indonesia [TBN 065]

Who

• Population: 514 districts in Indonesia

• Data Source Population: ~345,000 households from 34,500 census blocks

• Subgroups Identified:

  • Age ≥40 years (higher TB and DM prevalence)

  • Gender differences:

    • TB higher in males (0.38%) vs females (0.22%)

    • DM higher in females (2.85%) vs males (1.90%)

  • Higher-risk groups:

    • Urban residents

    • Lower education levels

    • Informal or non-working populations

• Data Sources:

  • 2023 Indonesian Health Survey (SKI)

  • Badan Pusat Statistik (BPS)

  • Ministry of Health databases

  • Ministry of Home Affairs data

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What

Study Focus

• Mapping spatial distribution of:

  • Tuberculosis (TB)

  • Diabetes Mellitus (DM)

• Identifying:

  • High-risk districts

  • Socio-demographic risk factors

  • Co-occurring TB-DM burden

Key Findings

• National prevalence

  • TB: 0.30%

  • DM: 2.37%

• Age ≥40 years

  • TB: 0.42%

  • DM: 4.37%

Socio-demographic Associations

TB

• Poverty positively associated

  • β = 0.015 (95% CrI: 0.005–0.024)

• Population density: positive but not significant

DM

• Population density positively associated

  • β = 0.059 (95% CrI: 0.039–0.080)

• Poverty negatively associated

  • β = −0.007 (95% CrI: −0.013 to −0.001)

Geographic Patterns

High TB prevalence

• Papua (highest)

• West Java

• Banten

High DM prevalence

• Central Java

• East Java

• Riau

• Sumatra regions

High TB-DM overlap (62 districts >50% probability)

• West Java

• Banten

• Aceh

• East Kalimantan

• Central Kalimantan

• North Sulawesi

Authors' Conclusions

• TB and DM co-burden shows distinct but overlapping spatial patterns

• High-risk areas often:

  • Urban

  • High population density

  • Low-income settings

Policy Implications

• Targeted geographic interventions recommended:

  • Integrated TB-DM screening

  • Strengthening primary care

  • Resource prioritization for high-burden districts

  • Improved referral pathways

  • Community health worker engagement
    

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When

• Data year: 2023 Indonesian Health Survey (SKI)

• Study type: Cross-sectional spatial analysis

• Follow-up: Not applicable (ecological cross-sectional)
  

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Where

• Country: Indonesia

• Geographic unit: 514 districts

• Settings:

  • National survey data

  • District-level socio-demographic indicators

  • Spatial modelling using district shapefiles
    

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Why

• TB and diabetes increasingly co-occur

• Limited understanding of:

  • Spatial overlap

  • Shared risk factors

  • Geographic clustering

• Objective:

  • Identify districts with dual burden

  • Inform targeted policy and healthcare planning
    

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How

Study Design

• Ecological spatial study

• Cross-sectional national survey data

• Level of Evidence:

  • Observational ecological modelling study

Methods

• Bayesian Model-Based Geostatistics (MBG)

• Binomial logistic regression

• Fixed + random effects (BYM2 model)

Model Evaluation

• Deviance Information Criterion (DIC)

• WAIC

• RMSE

• Probability Integral Transform (PIT)

• Fivefold cross-validation

Spatial Analysis

• Quintile classification

• Joint exceedance probability mapping

• Residual spatial random effect mapping

Covariates Included

• Population density

• Poverty proportion

• Hospital service ratio

• Primary healthcare availability

Limitations (Implied / Reported)

• Ecological design (district-level aggregation)

• Non-public dataset access

• Potential under-diagnosis in remote areas

• Cross-sectional data limits causal inference
  

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Strength of Evidence

• Moderate (Ecological spatial modelling study)

• Strong national dataset

• No causal inference possible

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Narrative Summary

This ecological spatial study analyzed data from 514 districts in Indonesia using the 2023 Indonesian Health Survey to examine the geographic distribution and co-occurrence of tuberculosis (0.30%) and diabetes mellitus (2.37%). Using Bayesian geostatistical modelling, the study identified significant associations between TB and poverty, and between DM and population density, with overlapping high-burden districts concentrated in West Java, Banten, Aceh, Kalimantan, and North Sulawesi. Approximately 62 districts showed high joint probability of TB-DM co-occurrence. Urban residence, lower education, and informal employment were associated with higher prevalence of both diseases. The findings highlight geographic clustering and emphasize the need for integrated TB-DM screening, targeted resource allocation, and strengthened primary healthcare in high-burden districts within Indonesia's decentralized health system.

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Source: Dwinata I, Tsheten T, Ansariadi A, Wagnew F, Alene KA, Sutarsa IN, Moraga P, Putra IW, Kelly M. Geospatial codistribution of tuberculosis and diabetes mellitus in Indonesia. Infectious Diseases of Poverty. 2026 Mar 30;15(1):37.

Structural Interventions at the Community Level

(Yoseph Samodra)

Host Vulnerability and Clinical Risk Stratification Across the TB Spectrum

This theme integrates evidence on how biological vulnerability, comorbid disease, age, and prior TB exposure shape TB risk, recurrence, diagnosis, and outcomes. See also: Lin TB Lab

1. TB risk is amplified well before “end-stage” disease: Predialysis CKD increased active TB risk even at stage 1, suggesting immune dysfunction occurs earlier than traditionally assumed and warrants earlier TB surveillance.
2. Recurrence is strongly cumulative and non-linear: In treated TB patients, recurrence risk rose dramatically when low BMI, prior TB, and delayed culture conversion coexisted, reaching nearly 30%, far exceeding average recurrence estimates.
3. Extreme age alters TB biology and presentation: In adults ≥85 years, atypical pulmonary TB was more strongly predicted by age itself than by frailty markers, highlighting aging as an independent modifier of TB expression.
4. Malnutrition is both a cause and consequence of TB vulnerability: Low BMI predicted TB recurrence in Taiwan and undernutrition affected one-third of Bangladeshi TB patients, reinforcing malnutrition as a bidirectional driver of poor TB outcomes.
5. Chronic lung disease creates a convergence of risks: Older COPD patients had high LTBI prevalence, with smoking, steroid exposure, and disease duration synergistically increasing infection risk, rather than any single factor dominating.
6. Diabetes consistently worsens TB risk and detection: Diabetes emerged across studies as a predictor of recurrence, delayed diagnosis (aPTB), and undernutrition, reinforcing its role as a central TB amplifier.
7. Short-term TB mortality reflects baseline host reserve: CKD patients who developed TB had markedly higher 1-year mortality, indicating that TB outcomes depend heavily on pre-existing physiological resilience.
8. Traditional symptom-based TB screening fails in high-risk hosts: Nearly 70% of late-elderly TB patients lacked classic symptoms, yet were identifiable through simple clinical–radiologic risk scores, underscoring the need for host-adapted diagnostic strategies.

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Environment, Living Conditions, and Structural Determinants of TB Transmission

This theme synthesizes evidence showing that where and how people live remains a decisive determinant of TB risk, especially for children and communities.

1. Indoor micro-environments outweigh macro-climate factors: Ventilation, lighting, and housing density predicted pediatric TB, while rainfall, humidity, and population density did not—challenging assumptions about climate-driven TB risk.
2. Household exposure is more decisive than ambient exposure: A family history of TB increased pediatric TB risk nearly tenfold, far exceeding the effect size of environmental or behavioral variables.
3. Housing quality operates as a population-level protective factor: Ecological data showed that coverage of healthy housing correlated inversely with TB incidence, supporting structural interventions beyond individual case management.
4. Orientation and airflow are under-recognized risk modifiers: Non–east-facing bedrooms and inadequate air circulation were significantly associated with childhood TB, highlighting design-level prevention opportunities.
5. Behavioral risk is context-dependent: Cigarette smoke exposure did not independently predict pediatric TB once household and environmental factors were considered, suggesting indirect rather than primary effects in children.
6. Urban TB risk is driven by deprivation, not density alone: In Bangladesh and Indonesia, poor housing and sanitation—not simple crowding—were linked to TB and undernutrition.
7. Environmental interventions are scalable and preventive: Improving ventilation, lighting, and housing standards offers population-wide risk reduction without relying on individual adherence.
8. Community-level indicators can guide TB prevention: Healthy-house coverage and healthy-lifestyle adoption function as actionable surveillance metrics for targeting TB control resources.

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Health Systems, Treatment Strategies, and Policy-Level Leverage Points

This theme highlights how program design, treatment choices, and funding decisions profoundly influence TB outcomes across populations.

1. Early microbiological response is a critical prognostic marker: Failure of sputum culture conversion at 2 months strongly predicted TB recurrence, supporting intensified monitoring or prolonged therapy in slow responders.
2. Shorter preventive regimens improve adherence—but not without trade-offs: Rifapentine- and rifampin-based LTBI regimens achieved >90% completion in COPD patients but carried higher risks of systemic drug reactions.
3. TB control gains are fragile and funding-dependent: Modelling showed that abrupt withdrawal of US and Global Fund support could more than double paediatric TB deaths, reversing decades of progress.
4. Speed of funding restoration matters more than duration of cuts: Even a one-year interruption in global TB financing caused large mortality surges, but rapid restoration could avert over 90% of excess deaths.
5. Risk-stratified follow-up is more efficient than universal surveillance: Identifying patients with high recurrence risk (e.g., low BMI, prior TB) allows focused use of limited public health resources.
6. Diagnostic tools must be adapted to frontline realities: A simple clinical score enabled non-pulmonologists to detect atypical TB with >95% accuracy, addressing diagnostic delays where specialists are unavailable.
7. Nutrition support is a missing pillar of TB care: Despite strong links between nutrition and TB outcomes, nutritional counseling and food security interventions remain inconsistently integrated into TB programs.
8. TB elimination depends on systems, not just drugs: The studies collectively show that TB outcomes hinge on coordinated action across clinical care, housing policy, nutrition, and global financing, rather than biomedical advances alone.

References:

1. Hsu, C.M., Wu, C.J., Chang, C.J., Pan, S.W., Tseng, Y.H., Huang, J.R., Su, W.J., Feng, J.Y. and Chen, Y.M., 2025. Recurrence of tuberculosis and associated risk factors among Non-HIV patients in Taiwan: A retrospective cohort study. Journal of Infection and Public Health, p.102912.
2. Huang, H.L., Cheng, M.H., Lee, M.R., Chien, J.Y., Lu, P.L., Sheu, C.C., Wang, J.Y., Chong, I.W., Yang, J.M. and Huang, W.C., 2025. Prevalence and treatment outcomes of latent tuberculosis infection among older patients with chronic obstructive pulmonary disease in an area with intermediate tuberculosis burden. Emerging Microbes & Infections, 14(1), p.2497302.
3. Syukur, A., Yulia, Y. and Istikomah, N.R., 2024. Hubungan Kondisi Lingkungan Rumah Dengan Kejadian Tb. Paru Pada Anak Di Kabupaten Sambas. Journal of Innovation Research and Knowledge, 4(6), pp.3795-3806.
4. Menzies, N.A., Brown, T.S., Imai-Eaton, J.W., Dodd, P.J., Cohen, T. and Martinez, L., 2025. Potential paediatric tuberculosis incidence and deaths resulting from interruption in programmes supported by international health aid, 2025–34: a mathematical modelling study. The Lancet Child & Adolescent Health, 9(11), pp.787-795.
5. Park, S., Lee, S., Kim, Y., Lee, Y., Kang, M.W., Cho, S., Han, K., Han, S.S., Lee, H., Lee, J.P. and Joo, K.W., 2019. Association of CKD with incident tuberculosis. Clinical Journal of the American Society of Nephrology, 14(7), pp.1002-1010.
6. Nabi, S.G., Aziz, M.M., Uddin, M.R., Tuhin, R.A., Shuchi, R.R., Nusreen, N., Jahan, R., Afroz, F. and Islam, M.S., 2024. Nutritional status and other associated factors of patients with tuberculosis in selected urban areas of Bangladesh. Well Testing Journal, 33(S2), pp.571-590.
7. Alma, L.R., Olivionita, V. and Wardani, H.E., 2024. An Ecological Study of Determinants of The Incidence of Tuberculosis in Malang Raya Area. Preventia: The Indonesian Journal of Public Health, 9(1), 112–120.
8. Yeh, J.J., Chen, J.H., Kuo, Y.L., Tsai, C.H. and Ko, Y.E., 2025. A Clinical Prediction Model for Atypical Tuberculosis Manifestations Among Older Adults. Medicina, 61(10), p.1888.

TBN 003

Strengthening Tuberculosis Control

Yoseph Leonardo Samodra

Recent evidence from diverse settings, including Taiwan, multiple regions in Indonesia (Palembang, Medan, Lampung), and historical lessons from Europe and the United States, demonstrates that tuberculosis (TB) control requires an integrated approach across behavioral, biological, and system-level domains. Key patterns highlight persistent social drivers of risk, diagnostic gaps, genetically influenced susceptibility, and the critical role of patient-centered health systems.

See also: Lin TB Lab

Social and Behavioral Determinants Remain Central to TB Outcomes

Key Insights

• In several Southeast Asian urban communities, caregiver characteristics strongly influence children’s receipt of preventive therapy.
• Behavioral risk factors such as smoking and undernutrition continue to heighten adult disease susceptibility in East Asian clinical cohorts.
• In northern Indonesia, limited social support significantly increases loss-to-follow-up among drug-resistant TB patients.

Policy Recommendations

• Implement family-centered TB support packages: Provide bundled services such as counseling, peer navigators, household education, and regular caregiver engagement. Prioritize households with young caregivers or limited social support.
• Fund social protection programs linked to TB care: Use transport vouchers, food baskets, or conditional cash transfers to reduce dropouts among vulnerable adults and families. Align TB social support with broader poverty-alleviation initiatives.

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Persistent Gaps in Diagnostic Access and Preventive Therapy

Key Insights

• In community health centers in southern Indonesia, preventive therapy for child household contacts remains extremely low despite adequate facility readiness.
• Underweight adults in clinical settings in Sumatra show improved TB detection using urine-based antigen tests when sputum testing is challenging.
• Historical global experience shows that diagnostic innovation, such as staining and culture methods, has repeatedly transformed TB care.

Policy Recommendations

• Expand decentralized diagnostic capacity: Integrate point-of-care tools such as rapid molecular testing and simple urine-based assays in primary clinics. Target facilities serving undernourished populations or areas with low access to sputum testing.
• Accelerate preventive therapy scale-up for children: Implement simplified screening and “fast-track” TPT initiation models in community clinics. Engage caregivers through mobile reminders, home visits, and outreach workers to ensure completion.

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Biological and Genetic Factors Influence TB Susceptibility and Detection

Key Insights

• In population-level clinical genomics data from Taiwan, specific HLA variants significantly increase TB risk independent of traditional factors.
• Among underweight adults in Indonesian hospital settings, nutritional status alters performance of certain diagnostic tests.

Policy Recommendations

• Support the development of risk-stratified screening models: Incorporate host genetic markers, nutritional assessments, and comorbidity profiles to identify high-risk groups. Pilot genomic-supported TB risk prediction in large health systems with existing biobank infrastructure.
• Develop ethical and regulatory frameworks for precision TB prevention: Ensure privacy protections, informed consent standards, and equitable access for genetic-informed screening tools.

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Health-System Responsiveness Determines Treatment Success

Key Insights

• In tertiary hospitals in northern Indonesia, perceived support from healthcare providers strongly influences whether drug-resistant TB patients remain in care.
• Historical experience worldwide demonstrates that adherence strategies such as directly observed therapy (DOT) have been essential in bridging the gap between drug efficacy and real-world outcomes.

Policy Recommendations

• Invest in patient-centered care and provider communication training: Equip frontline workers with skills in motivational interviewing, empathetic counseling, and stigma-free communication. Introduce structured follow-up systems and adherence monitoring.
• Scale digital adherence technologies (DATs): Deploy SMS reminders, video-observed therapy, and smart pillbox technologies to reduce treatment interruption. Integrate DATs with national TB databases for real-time monitoring.

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Conclusion

TB control demands a coordinated response that integrates social support, modern diagnostics, precision health approaches, and robust patient-centered systems. Governments and donors should prioritize investments in social protection, decentralized care, genomics-informed risk assessment, and innovative adherence strategies. Evidence from Taiwan, multiple Indonesian provinces, and long-standing global TB history underscores that multifaceted interventions offer the highest potential for reducing disease burden and achieving sustainable TB elimination goals.

References:

1. Lin, S.P., Chen, I.C., Lin, C.H., Hsiao, T.H., Liu, P.Y. and Chen, Y.M., 2025. Host Genetic Factors and Clinical Comorbidities Associated With Tuberculosis Risk. HLA, 106(3), p.e70384.
2. Karakousis, P.C. and Mooney, G., 2025. Respiratory isolation for tuberculosis: a historical perspective. The Journal of Infectious Diseases, 231(1), pp.3-9.
3. Ridwan, I., Sofiah, F. and Rismarini, R., 2025. Rate of administration of tuberculosis preventive treatment to pediatric household contacts and influencing factors. Paediatrica Indonesiana, 65(5):422-430.
4. Eksa, D.R., Hendarto, G.S., Sinaga, F.T., Dilangga, P., Herdato, M.J.D., Infianto, A., Ekawati, D., Gozali, A. and Ajipurnomo, A., 2025. Comparative Diagnostic Accuracy of LF-LAM TB Antigen and Xpert MTB/RIF in Pulmonary Tuberculosis among Underweight Patients. Jurnal Respirologi Indonesia, 45(4), pp.272-279.
5. Dalimunthe, A., Sinaga, B.Y.M., Siagian, P. and Amelia, R., 2025. Social Support and Healthcare Service Quality as Determinants of Treatment Interruption Among Drug-Resistant Tuberculosis Patients in Medan, Indonesia. Jurnal Impresi Indonesia, 4(11), pp.5176-5183.

See also: Yoseph Samodra

TBN 002