Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives.

A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range of health issues. As malignant brain tumors grow rapidly, the mortality rate of individuals with this cancer can increase substantially with each passing week. Hence it is vital to detect these tumors early so that preventive measures can be taken at the initial stages. Computer-aided diagnostic (CAD) systems, in coordination with artificial intelligence (AI) techniques, have a vital role in the early detection of this disorder. In this review, we studied 124 research articles published from 2000 to 2022. Here, the challenges faced by CAD systems based on different modalities are highlighted along with the current requirements of this domain and future prospects in this area of research.

Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach.

BACKGROUND AND OBJECTIVES: Celiac disease is an autoimmune disease occurring in about 1 in 100 people worldwide. Early diagnosis and efficient treatment are crucial in mitigating the complications that are associated with untreated celiac disease, such as intestinal lymphoma and malignancy, and the subsequent high morbidity. The current diagnostic methods using small intestinal biopsy histopathology, endoscopy, and video capsule endoscopy (VCE) involve manual interpretation of photomicrographs or images, which can be time-consuming and difficult, with inter-observer variability. In this paper, a machine learning technique was developed for the automation of biopsy image analysis to detect and classify villous atrophy based on modified Marsh scores. This is one of the first studies to employ conventional machine learning to automate the use of biopsy images for celiac disease detection and classification. METHODS: The Steerable Pyramid Transform (SPT) method was used to obtain sub bands from which various types of entropy and nonlinear features were computed. All extracted features were automatically classified into two-class and multi-class, using six classifiers. RESULTS: An accuracy of 88.89%, was achieved for the classification of two-class villous abnormalities based on analysis of Hematoxylin and Eosin (H&E) stained biopsy images. Similarly, an accuracy of 82.92% was achieved for the two-class classification of red-green-blue (RGB) biopsy images. Also, an accuracy of 72% was achieved in the classification of multi-class biopsy images. CONCLUSION: The results obtained are promising, and demonstrate the possibility of automating biopsy image interpretation using machine learning. This can assist pathologists in accelerating the diagnostic process without bias, resulting in greater accuracy, and ultimately, earlier access to treatment.

5-Fluorouracil encapsulated HA/PLGA composite microspheres for cancer therapy.

5-Fluorouracil (5FU) was successfully entrapped within poly(lactide-co-glycolide) (PLGA) and hydroyapatite (HA) composite microspheres using the emulsification/solvent extraction technique. The effects of HA to PLGA ratio, solvent ratio as well as polymer inherent viscosity (IV) on encapsulation efficiency were investigated. The degradation and drug release rates of the microspheres were studied for 5 weeks in vitro in phosphate buffered solution of pH 7.4 at 37 °C. The drug release profile followed a biphasic pattern with a small initial burst followed by a zero-order release for up to 35 days. The initial burst release decreased with increasing HA content. The potential of HA in limiting the initial burst release makes the incorporation of HA into PLGA microspheres advantageous since it reduces the risk of drug overdose from high initial bursts. The linear sustained drug release profile over the course of 5 weeks makes these 5-FU-loaded HA/PLGA composite microparticles a promising delivery system for the controlled release of chemotherapy drugs in the treatment of cancer.

Fabrication of cisplatin-loaded poly(lactide-co-glycolide) composite microspheres for osteosarcoma treatment.

PURPOSE: To reduce the toxicity and achieve a sustainable and controllable release of cisplatin (CDDP). METHODS: CDDP was loaded onto Fe5 (Fe(3+) doped hydroxyapatite at atomic ratio of Fe(added)/Ca(added) = 5%) nanoparticles through surface adsorption. Subsequently, CDDP-loaded Fe5 nanoparticles (CDDP-Fe5) and/or CDDP were encapsulated into poly(lactide-co-glycolide) (PLGA) microspheres using oil-in-water single emulsion. Drug release profiles and degradation behaviors were monitored. RESULTS: CDDP-Fe5 demonstrated a high initial burst (42% on day 1) and short release time (25 days) as CDDP was directly released from Fe5 nanoparticles. CDDP-Fe5 encapsulated within the PLGA microspheres revealed a lower initial burst (23% on day 1) and longer release time (55 days) than CDDP-Fe5. Compared with PLGA microspheres containing only CDDP, which showed typical biphasic release manner, microspheres with CDDP-Fe5 and CDDP demonstrated a nearly linear release after the initial burst. Fe5 and CDDP delayed microsphere degradation. All samples became porous, disintegrated, fused, and formed pellets at the end of the study. CONCLUSION: Fe5/PLGA composite microspheres showed favorable CDDP release behavior compared to microspheres composed of polymer alone, suggesting its potential as a new CDDP formulation.

Influence of electron-beam radiation on the hydrolytic degradation behaviour of poly(lactide-co-glycolide) (PLGA).

The purpose of this study is to examine the effect of electron-beam (e-beam) radiation on the hydrolytic degradation of poly(lactide-co-glycolide) (PLGA) films. PLGA films were irradiated and observed to undergo radiation-induced degradation through chain scission, as observed from a drop in its average molecular weight with radiation dose. Irradiated (5, 10 and 20 Mrad) and non-irradiated (0 Mrad) samples of PLGA were subsequently hydrolytically degraded in phosphate-buffered saline solution at 37.0 degrees C over a span of 12 weeks. It was observed that the natural logarithmic molecular weight (lnMn) of PLGA decreases linearly with hydrolytic degradation time. The rate of water uptake is higher for samples irradiated at higher radiation dose (e.g. 20 Mrad) and subsequently causing an earlier onset of mass loss. It is postulated that the increase in water uptake is due to the presence of more hydrophilic end groups, which results in the formation of microcavities because of an increase in osmotic pressure. A relationship between radiation dose and the rate of hydrolytic degradation of PLGA films, through its molecular weight was also established. This relationship allows a more accurate and precise control of the life span of PLGA through the use of e-beam radiation.