Objective: This study aimed at recording therapeutic plant species used by inhabitants to treat dental disorders in the district of North Waziristan, Pakistan. The indigenous people of the studied area are dependent on medicinal plants for their basic health care needs including dental care.

Methods: Ethnomedicinal data were collected using a semi-structured questionnaires, and in addition 130 local informants were interviewed. The collected data were evaluated using various quantitative indices, including use value (UV), relative frequency of citation (RFC), fidelity level (FL%), and Jaccard Index (JI).

Results: A total of 69 plants belonging to 48 plant families used in dental disorders were identified. The Lamiaceae was the leading family that shared 7 species, followed by Solanaceae (4 spp).The dominant life form used was herbs (47.83%), folowed by leaves (43.90%) in preparing remedies for different dental disorders. Decoction was the most common mode of preparation (34.21%), followed by pastes (21.05%). The highest RFC (0.36) was reported for Bergenia ciliata, followed by Salvadora oleoides (0.35). The majority of the plants (36 spp) were utilised as herbal medicine to treat toothache, followed by 13 species for periodontal (gum) infections, 11 species used for teeth cleaning, and 9 species for halitosis (bad breath).

Conclusions: This study is the first-ever record of ethnomedicinal applications for the treatment of dental diseases from Pakistan. Some of the forgeoing hebal medications should be further evalauted for the development of pahrmaceutical bio-products for the treatment of dental disorders.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10988256PMC
http://dx.doi.org/10.1016/j.identj.2023.10.001DOI Listing

Publication Analysis

Top Keywords

dental disorders
16
medicinal plants
8
north waziristan
8
waziristan pakistan
8
treatment dental
8
dental
6
species
5
ethno-dentistry medicinal
4
plants
4
plants north
4

Similar Publications

NEAT1 regulates BMSCs aging through disruption of FGF2 nuclear transport.

Stem Cell Res Ther

January 2025

College & Hospital of Stomatology, Key Laboratory of Oral Diseases Research of Anhui Province, Anhui Medical University, Hefei, 230032, China.

Background: The aging of bone marrow mesenchymal stem cells (BMSCs) impairs bone tissue regeneration, contributing to skeletal disorders. LncRNA NEAT1 is considered as a proliferative inhibitory role during cellular senescence, but the relevant mechanisms remain insufficient. This study aims to elucidate how NEAT1 regulates mitotic proteins during BMSCs aging.

View Article and Find Full Text PDF

Dental and oral health assessments in the German National Cohort (NAKO).

BMC Oral Health

January 2025

Department of Restorative Dentistry, Periodontology and Endodontology, University Medicine Greifswald, Greifswald, Germany.

Background: Despite considerable improvements in oral health in recent decades, caries and periodontitis are still widespread, ranking among the most prevalent diseases worldwide and requiring future research. The German National Cohort (NAKO Gesundheitsstudie, NAKO) is a large-scaled, multidisciplinary, nationwide, multi-centre, population-based, prospective cohort study with oral examinations that aims to provide a resource to study risk factors for major diseases. The aim of the present article is to provide the methodological background, to report on the data quality, and to present initial results of the oral examinations.

View Article and Find Full Text PDF

Purpose: This epidemiological study leverages data from the Global Burden of Disease (GBD) database spanning from 1990 to 2021 to analyze the global burden of oral cancer. The research aims to provide a comprehensive assessment of the age-standardized incidence rate (ASIR), age-standardized mortality rate (ASDR), and disability-adjusted life years (DALYs) for oral cancer, examining trends over three decades.

Methods: The study used age standardized rate (ASRs) as an indicator of oral cancer epidemiological data.

View Article and Find Full Text PDF

Clinical performance of minimally invasive full-mouth rehabilitation using different materials and techniques for patients with moderate to severe tooth wear: a systematic review and meta-analysis.

Clin Oral Investig

January 2025

Department of Prosthodontics, Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou, 310000, Zhejiang, China.

Objective: To evaluate short, mid and long-term clinical outcomes and patients' satisfaction of minimally invasive full-mouth rehabilitation using different materials and techniques for patients with moderate to severe tooth wear. Furthermore, materials were analyzed to identify their influences on clinical results.

Materials And Methods: Search was conducted in PubMed, Cochrane Central Register of Controlled Trial, Embase, Web of science and Scopus until December 19, 2024.

View Article and Find Full Text PDF

Adapting a style based generative adversarial network to create images depicting cleft lip deformity.

Sci Rep

January 2025

Division of Plastic, Craniofacial and Hand Surgery, Sidra Medicine, and Weill Cornell Medical College, C1-121, Al Gharrafa St, Ar Rayyan, Doha, Qatar.

Training a machine learning system to evaluate any type of facial deformity is impeded by the scarcity of large datasets of high-quality, ethics board-approved patient images. We have built a deep learning-based cleft lip generator called CleftGAN designed to produce an almost unlimited number of high-fidelity facsimiles of cleft lip facial images with wide variation. A transfer learning protocol testing different versions of StyleGAN as the base model was undertaken.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!