Oral potentially malignant disorders can be defined as mucosal lesions and conditions with an increased risk of malignant transformation. Oral potentially malignant disorders are a significant health burden, and they are often diagnosed late due to scant attention to routine dental practice and the low number of specialized oral medicine centres. This report summarizes the DoctOral experience, a research initiative, providing a free smartphone-based decision support tool for the general medical/dental practitioner; the tool is based on the clinical appearance of oral lesions. Captured, oral pictures can be immediately examined via interactive decision trees and constructed on the smartphone. Such decision trees are expressed in standard formats, and they are readily accessible for facilitating the completion of a hypothetical diagnostic path. Since October 2017 the DoctOral mobile app has been downloaded by 10K + users, achieving a score of 4.8 out of 5. DoctOral also supports an unfolding joint initiative, called DoctOralAI: this involves selecting reference images, with which to create an open-source model, and perform a Case-Based Reasoning method, both of which are combined with machine learning. The DoctOral mobile app has revolutionized oral pathology by providing dental students and professionals with an interactive platform for recognizing and diagnosing oral lesions.
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http://dx.doi.org/10.1177/20552076231177141 | DOI Listing |
Naunyn Schmiedebergs Arch Pharmacol
January 2025
Department of Pharmacognosy, Faculty of Pharmacy, Alexandria University, Alexandria, Egypt.
Non-small cell lung cancer (NSCLC) is a widespread highly malignant type of lung cancer. Conventional chemotherapeutic drugs may be accompanied by both drug resistance and serious side effects in patients. Therefore, safer and more effective medications are urgently needed for the treatment of NSCLC.
View Article and Find Full Text PDFClin Exp Dent Res
February 2025
School of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran.
Background And Objective: Tongue squamous cell carcinoma (TSCC) is the most prevalent oral cancer. Despite considerable advancements in treatment, the 5-year survival rate remains relatively unchanged. Langerhans cells (LCs) play an important role in antitumor immunity.
View Article and Find Full Text PDFCancer Med
February 2025
Department of Pathology and Oncology, Juntendo University Faculty of Medicine, Tokyo, Japan.
Background: Cancer-associated fibroblasts (CAFs) play a significant role in human breast cancer as a major stromal component. While their role in promoting cancer proliferation and malignancy through interaction with cancer cells in the tumor microenvironment is known, the exact mechanisms behind this interaction are not fully understood.
Results: Our study reveals that lymphoid enhancer-binding factor 1 (LEF1), a central transcription factor for Wnt/β-catenin signaling, is expressed in experimentally generated tumor-promoting CAFs (exp-CAFs) as well as in CAFs from breast cancer patients, particularly those with a poor prognosis.
Cancer Rep (Hoboken)
January 2025
Histopathology Department, Teaching Hospital Anuradhapura, Anuradhapura, Sri Lanka.
Background: Basocellular carcinoma (BCC) is the most prevalent skin malignancy, often localizing to the UV-exposed skin of the face. While most BCC is relatively indolent, aggressive subtypes, including infiltrative BCC, pose the treatment challenges of ensuring functional and aesthetic preservation with a high risk of recurrence.
Case: A 78-year-old female patient complained of recurrent left chin BCC of infiltrative subtype, which was first treated in 2013 by wide local excision and adjuvant radiotherapy.
BMC Oral Health
January 2025
Department of Anaesthesiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Background: Postoperative fever (POF) is a common occurrence in patients undergoing major surgery, presenting challenges and burdens for both patients and surgeons yet. This study endeavors to examine the incidence, identify risk factors, and establish a machine learning-based predictive model for POF following surgery of oral cancer.
Methods: A total of seven hundred and twenty-seven consecutive patients undergoing radical resection of oral cancer were retrospectively investigated.
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