Unraveling the Mysteries of Ameloblastoma in African Population: A Comprehensive Analysis of 371 Cases from Clinical, Radiological, and Histopathological Perspectives.

Objective: To analyze the frequency, clinical, histopathological, and radiological characteristics of ameloblastoma in Nigeria over the course of two decades.

Study Design: A retrospective analysis was conducted on 371 cases at a Nigerian university hospital between 2000 and 2023. Age, gender, site, histological variants, tumor size and duration were analyzed.

A state-of-the-art review of metal oxide nanoflowers for wastewater treatment: Dye removal.

Dye wastewater consists of high solids concentrations, heavy metals, minor contaminants, dissolved chemical oxygen demand, and microorganisms. Nanoflowers are nanoparticles that resemble flowers when viewed at a microscopic level. Inorganic metal oxide nanoflowers have been discovered to be a potential source for overcoming this situation.

Advanced oxidation and biological integrated processes for pharmaceutical wastewater treatment: A review.

The stress of pharmaceutical and personal care products (PPCPs) discharging to water bodies and the environment due to increased industrialization has reduced the availability of clean water. This poses a potential health hazard to animals and human life because water contamination is a great issue to the climate, plants, humans, and aquatic habitats. Pharmaceutical compounds are quantified in concentrations ranging from ng/Lto μg/L in aquatic environments worldwide.

Leveraging artificial intelligence for perioperative cancer risk assessment of oral potentially malignant disorders.

Oral potentially malignant disorders (OPMDs) are mucosal conditions with an inherent disposition to develop oral squamous cell carcinoma. Surgical management is the most preferred strategy to prevent malignant transformation in OPMDs, and surgical approaches to treatment include conventional scalpel excision, laser surgery, cryotherapy, and photodynamic therapy. However, in reality, since all patients with OPMDs will not develop oral squamous cell carcinoma in their lifetime, there is a need to stratify patients according to their risk of malignant transformation to streamline surgical intervention for patients with the highest risks.

Validity of nomograms for predicting cancer risk in oral leukoplakia and oral lichen planus.

Objectives: This study assessed the validity of nomograms for predicting malignant transformation (MT) among patients with oral leukoplakia (OL) and oral lichen planus (OLP).

Materials And Methods: Two nomograms were identified following a systematic search. Variables to interrogate both nomograms were obtained for a retrospective OL/OLP cohort.

Bayesian Disease Mapping to Identify High-Risk Population for Oral Cancer: A Retrospective Spatiotemporal Analysis.

Objectives: Bayesian mapping is an effective spatiotemporal approach to identify high-risk geographic areas for diseases and has not been used to identify oral cancer hotspots in Australia previously. This retrospective disease mapping study was undertaken to identify the oral cancer trends and patterns within the Queensland state in Australia.

Methods: This study included data obtained from Queensland state Cancer Registry from 1982 to 2018.

The quorum-sensing peptidic inhibitor rescues host immune system eradication: A novel infectivity mechanism.

Subverting the host immune system is a major task for any given pathogen to assure its survival and proliferation. For the opportunistic human pathogen (Bc), immune evasion enables the establishment of potent infections. In various species of the Bc group, the pleiotropic regulator PlcR and its cognate cell-cell signaling peptide PapR regulate virulence gene expression in response to fluctuations in population density, i.

Artificial intelligence in salivary biomarker discovery and validation for oral diseases.

Salivary biomarkers can improve the efficacy, efficiency, and timeliness of oral and maxillofacial disease diagnosis and monitoring. Oral and maxillofacial conditions in which salivary biomarkers have been utilized for disease-related outcomes include periodontal diseases, dental caries, oral cancer, temporomandibular joint dysfunction, and salivary gland diseases. However, given the equivocal accuracy of salivary biomarkers during validation, incorporating contemporary analytical techniques for biomarker selection and operationalization from the abundant multi-omics data available may help improve biomarker performance.

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review.

Background: The impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs.

Explainable ensemble learning model improves identification of candidates for oral cancer screening.

Objectives: Artificial intelligence could enhance the use of disparate risk factors (crude method) for better stratification of patients to be screened for oral cancer. This study aims to construct a meta-classifier that considers diverse risk factors to identify patients at risk of oral cancer and other suspicious oral diseases for targeted screening.

Materials And Methods: A retrospective dataset from a community oral cancer screening program was used to construct and train the novel voting meta-classifier.

Predicting Overall Survival Using Machine Learning Algorithms in Oral Cavity Squamous Cell Carcinoma.

Background/aim: Machine learning (ML) models are often modelled to predict cancer prognosis but rarely consider spatial factors in a region. Hence this study explored machine learning algorithms utilising Local Government Areas (LGAs) in Queensland, Australia to spatially predict 3- and 5-year prognosis of oral cancer patients and provide clinical interpretability of the predicted outcome made by the ML model.

Patients And Methods: Data from a total of 3,841 oral cancer patients were retrieved from the Queensland Cancer Registry (QCR).

Machine Learning-Based Genome-Wide Salivary DNA Methylation Analysis for Identification of Noninvasive Biomarkers in Oral Cancer Diagnosis.

This study aims to examine the feasibility of ML-assisted salivary-liquid-biopsy platforms using genome-wide methylation analysis at the base-pair and regional resolution for delineating oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMDs). A nested cohort of patients with OSCC and OPMDs was randomly selected from among patients with oral mucosal diseases. Saliva samples were collected, and DNA extracted from cell pellets was processed for reduced-representation bisulfite sequencing.

Performance of a simplified scoring system for risk stratification in oral cancer and oral potentially malignant disorders screening.

Background: Impact and efficiency of oral cancer and oral potentially malignant disorders screening are most realized in "at-risk" individuals. However, tools that can provide essential knowledge on individuals' risks are not applied in risk-based screening. This study aims to optimize a simplified risk scoring system for risk stratification in organized oral cancer and oral potentially malignant disorders screening.

Comparison of time-to-event machine learning models in predicting oral cavity cancer prognosis.

Background: Applying machine learning to predicting oral cavity cancer prognosis is important in selecting candidates for aggressive treatment following diagnosis. However, models proposed so far have only considered cancer survival as discrete rather than dynamic outcomes.

Objectives: To compare the model performance of different machine learning-based algorithms that incorporate time-to-event data.

Prediction models applying machine learning to oral cavity cancer outcomes: A systematic review.

Objectives: Machine learning platforms are now being introduced into modern oncological practice for classification and prediction of patient outcomes. To determine the current status of the application of these learning models as adjunctive decision-making tools in oral cavity cancer management, this systematic review aims to summarize the accuracy of machine-learning based models for disease outcomes.

Methods: Electronic databases including PubMed, Scopus, EMBASE, Cochrane Library, LILACS, SciELO, PsychINFO, and Web of Science were searched up until December 21, 2020.

"Fact or fiction?": Oral cavity cancer in nonsmoking, nonalcohol drinking patients as a distinct entity-Scoping review.

Oral cavity cancer is often described as a lifestyle-related malignancy due to its strong associations with habitual factors, including tobacco use, heavy alcohol consumption, and betel nut chewing. However, patients with no genetically predisposing conditions who do not indulge in these risk habits are still being encountered, albeit less commonly. The aim of this review is to summarize contemporaneous reports on these nonsmoking, nonalcohol drinking (NSND) patients.

The Tale of the Unrepaired Cleft-Retrospective Evaluation of Cases Encountered by an Indigenous Mission in a Northern Nigerian Community.

Objective: This study aims to document the experience of an indigenous surgical mission on the occurrence of unrepaired cleft in 2 visits to Minna, North-Central Nigeria.

Design: This retrospective study involved participants with orofacial cleft anomaly at 2 surgical outreaches held in Minna in 2011 and 2017. Baseline data were initially obtained from case files of patients at both programs.

Prognostic value of non-smoking, non-alcohol drinking status in oral cavity cancer.

Objectives: To compare the treatment response and prognosis of oral cavity cancer between non-smoking and non-alcohol-drinking (NSND) patients and smoking and alcohol-drinking (SD) patients.

Methods: A total of 313 consecutively treated patients from 2000 to 2019 were included. Demographic, clinicopathologic, treatment, and prognosis information were obtained.

Multiple tumour recurrence in oral, head and neck cancer: Characterising the patient journey.

Background: Oral squamous cell carcinoma (OSCC) is the 15th most common cause of cancer-related mortality worldwide and approximately one oral cancer-related death occurs for every two new diagnoses. Death-due-to-disease is usually ascribed to inoperable primary tumours, treatment complications, second primary tumours arising due to field cancerization, or locoregional recurrence and distant metastases.

Methods: A retrospective review of OSCC patients treated over a 19-year period, betweenOctober 1 , 2000 and October 1 , 2019.

Efficacy of hypermethylated DNA biomarkers in saliva and oral swabs for oral cancer diagnosis: Systematic review and meta-analysis.

Objectives: This study aims to determine the diagnostic test accuracy (DTA) of hypermethylated DNA biomarkers in saliva and oral swabs for oral squamous cell carcinoma (OSCC) detection from the prevalidation studies available.

Materials And Methods: Electronic database searching of PubMed, EMBASE, Cochrane Library, Scopus, Web of Science, and LILACS was conducted to identify relevant articles that were published between January 1, 2000, and August 1, 2020.

Results: Meta-analysis was conducted based on 11 of 20 studies selected for review.

Mock clinical testing in the validation of fluid-phase biomarkers for head and neck carcinoma diagnosis: Scoping review.

This review sought to determine the range and nature of prospective-sampling and blinding methods for validating nonviral biofluid markers diagnostic of head and neck carcinomas. Electronic database searching was conducted to identify studies published in English from January 1, 2009 to August 1, 2020. Sixteen studies from 17 articles published between 2011 and 2020 were included in this review.