Evidence map›Paper›PMID 40849745›Full record

ReviewCurrent molecular medicine2026

Integrating Traditional Medicine with Conventional Therapies to Combat Tuberculosis: A Comprehensive Review.

Akriti Gaurav, Zeeshan Fatima, Ahmed Yaqinuddin, Saif Hameed

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current molecular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Akriti GauravAmity Institute of Biotechnology, Amity University Haryana, Manesar, Gurugram, 122413, India.
Zeeshan FatimaAmity Institute of Biotechnology, Amity University Haryana, Manesar, Gurugram, 122413, India.
Ahmed YaqinuddinCollege of Medicine, Alfaisal University, Riyadh, 11533, Saudi Arabia.
Saif HameedAmity Institute of Biotechnology, Amity University Haryana, Manesar, Gurugram, 122413, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculosis (TB) poses a serious public health risk and is a hot topic in the international health forums. Global health organizations emphasize the importance of effectively managing and eradicating TB. The emergence of drug-resistant TB and the elevated risk of hepatotoxicity associated with anti-TB medications have highlighted the need for reevaluation of existing TB drugs. These challenges have led to prolonged dosing schedules and increased dosages to combat resistance and effectively eliminate the disease. In India, the government revised the National Tuberculosis Control Program to address this growing concern. India is home to six well-established traditional medical systems: Ayurveda, Siddha, Unani, Yoga, Naturopathy, and Homoeopathy (collectively known as AYUSH). This review compares the effectiveness of traditional medicinal regimens with conventional TB treatment. Herbal extracts used in Ayurveda, Siddha, and Unani offer promising alternatives for TB treatment, potentially reducing hepatotoxicity and liver damage while combating antibiotic resistance. These natural remedies are generally safe for consumption in larger quantities, cost-effective to produce, and free from harmful toxins. The findings in this article provide scientific support for the anti-TB potential of the diverse medical systems recognized by India's Ministry of AYUSH.

Indexed as

Antitubercular AgentsMedicine, TraditionalTuberculosisAnimalsHumansIndiaAntitubercular Agentsantituberculosis therapyAYUSHhepatotoxicitynaturopathytraditional medicineTuberculosis

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.