Exploring the Bioactive Potential of Exotic Fruits from Sri Lanka: A Treasure Trove of Medicinal Properties.

Tropical fruits are often studied to determine their content of bioactive compounds that contain health-enhancing properties and are often identified to hold a rich nutritional composition. Their bioactive compounds are classified through their phenolic, flavonoid, and antioxidant properties, while some tropical fruits are known to have other properties such as anticancer and anti-inflammatory activity. Sri Lanka is an island with abundant resources. One such resource is exotic fruits. Exotic fruits are known as edible fruits, which are not necessarily native but consist of a unique flavor profile, fragrance, shape, or appearance. Exotic fruits are usually consumed on their own or consumed as beverages, pickles, jams, salads, and desserts. The market-friendly tropical fruits in Sri Lanka include a vast number, and some of them are mango, Ceylon olives, durian, jackfruit, rambutan, soursop, passion fruit, and star fruit. These fruits contribute to the rice culinary heritage of Sri Lanka, and most of them are exported worldwide. At present, the traditional medicine system is quite popular among the public due to its less toxic nature and easy access. This review is aimed at evaluating the antioxidant, cytotoxic, anticancer, and anti-inflammatory properties of eight selected exotic fruits mentioned above and their traditional usage, which is based on the literature of various scientific studies conducted on these tropical fruits.

A comprehensive dataset of annotated oral cavity images for diagnosis of oral cancer and oral potentially malignant disorders.

OBJECTIVES: This study aims to address the critical gap of unavailability of publicly accessible oral cavity image datasets for developing machine learning (ML) and artificial intelligence (AI) technologies for the diagnosis and prognosis of oral cancer (OCA) and oral potentially malignant disorders (OPMD), with a particular focus on the high prevalence and delayed diagnosis in Asia. MATERIALS AND METHODS: Following ethical approval and informed written consent, images of the oral cavity were obtained from mobile phone cameras and clinical data was extracted from hospital records from patients attending to the Dental Teaching Hospital, Peradeniya, Sri Lanka. After data management and hosting, image categorization and annotations were done by clinicians using a custom-made software tool developed by the research team. RESULTS: A dataset comprising 3000 high-quality, anonymized images obtained from 714 patients were classified into four distinct categories: healthy, benign, OPMD, and OCA. Images were annotated with polygonal shaped oral cavity and lesion boundaries. Each image is accompanied by patient metadata, including age, sex, diagnosis, and risk factor profiles such as smoking, alcohol, and betel chewing habits. CONCLUSION: Researchers can utilize the annotated images in the COCO format, along with the patients' metadata, to enhance ML and AI algorithm development.

Estimation of sex from the osteometric measurements of the femur in a contemporary Sri Lankan population / Estimación del sexo a partir de las medidas osteométricas del fémur en una población contemporánea de Sri Lanka

SUMMARY: Sex estimation from human skeletal remains is of vital importance in the buildup of a biological profile of an individual in medico-legal and bioarchaeological studies. The present study is focused on the estimation of sex from osteometric measurements of the complete femur and its fragmentary parts, and the development of a web based application related to this. Fifteen osteometric measurements were taken from 78 dry cadaveric femurs from the Faculty of Medicine, University of Kelaniya. Using R software, linear discriminant analysis and logistic regression methods were applied to build classification models with the help of the application of a stepwise procedure, to identify the best combination of measurements to estimate the sex of the femur. A cross-validation method was applied to estimate the predictive accuracy of each model. Since the linear discriminant analysis model gave more predictive accuracy than the regression model, we suggest using linear discriminant analysis to estimate the sex using osteometric measurements of the femur. From the whole femur measurements, a formula to determine sex was developed with highest total accuracy of 83 % using four parameters; epicondylar breadth, anteroposterior mid-shaft diameter, bi-trochanter length, and maximum shaft diameter. Similarly, measurements of transverse head diameter and bi-trochanter length with a total accuracy of 76 % for the proximal part of the femur, measurements of anteroposterior mid-shaft diameter with a total accuracy of 77 % for the mid-shaft, and measurements of epicondylar breadth and maximum length of the lateral condyle with a total accuracy of 70 % for the distal part of the femur were identified as significant discriminants to determine sex, and formulae were written accordingly. Average accuracy ranged from 83 % to 70 %, with male accuracy slightly higher than that of females. A web application to estimate the sex of femur using these formulae was developed and this will be of great importance for forensic medicine and bio-archaeological research in Sri Lanka.

RESUMEN: La estimación del sexo a partir de restos óseos humanos en los estudios médico-legales y bioarqueológicos es de vital importancia en la construcción de un perfil biológico de un individuo. El objetivo de este estudio fue la estimación del sexo a partir de medidas osteométricas del fémur completo y sus partes fragmentarias, y el desarrollo de una aplicación web relacionada con esto. Se tomaron quince medidas osteométricas de 78 fémures cadavéricos secos de la Facultad de Medicina de la Universidad de Kelaniya. Utilizando el software R, se aplicaron métodos de análisis discriminante lineal y regresión logística para construir modelos de clasificación con la aplicación de un procedimiento por pasos, para identificar la mejor combinación de medidas y estimar el sexo a partir del fémur. Se aplicó un método de validación cruzada para estimar la precisión predictiva de cada modelo. Dado que el modelo de análisis discriminante lineal proporcionó una mayor precisión predictiva que el modelo de regresión, sugerimos su utilización para estimar el sexo mediante mediciones osteométricas del fémur. A partir de las mediciones del fémur completo, se desarrolló una fórmula para determinar el sexo con la mayor precisión total del 83 % utilizando cuatro parámetros; anchura del epicóndilo, diámetro anteroposterior del tercio medio de la diáfisis, longitud bitrocánter y diámetro máximo de la diáfisis. De manera similar, utilizamos las mediciones del diámetro transversal de la cabeza del fémur y la longitud del bitrocánter con una precisión del 76 % para la parte proximal del hueso, las mediciones del diámetro anteroposterior del tercio medio de la diáfisis se obtuvo con una precisión del 77 %. El ancho del epicóndilo y la longitud máxima del cóndilo lateral con una precisión del 70 % para la parte distal del fémur se identificaron como discriminantes significativos para determinar el sexo y se escribieron fórmulas. La precisión promedio osciló entre el 83 % y el 70 %, siendo la precisión en los hombres ligeramente superior al de las mujeres. Se desarrolló una aplicación web para estimar el sexo del fémur utilizando estas fórmulas y creemos será importante para la medicina forense y la investigación bioarqueológica en Sri Lanka.