Predicting Prognosis of Early-Stage Mycosis Fungoides with Utilization of Machine Learning.

Mycosis fungoides (MF) is the most prevalent type of cutaneous T cell lymphomas. Studies on the prognosis of MF are limited, and no research exists on the potential of artificial intelligence to predict MF prognosis. This study aimed to compare the predictive capabilities of various machine learning (ML) algorithms in predicting progression, treatment response, and relapse and to assess their predictive power against that of the Cox proportional hazards (CPH) model in patients with early-stage MF.

Development of Graphene Oxide-Based Anticancer Drug Combination Functionalized with Folic Acid as Nanocarrier for Targeted Delivery of Methotrexate.

Graphene has become a prominent material in cancer research in recent years. Graphene and its derivatives also attract attention as carriers in drug delivery systems. In this study, we designed a graphene oxide (GO)-based methotrexate (MTX)-loaded and folic acid (FA)-linked drug delivery system.

Higher heating value estimation of wastes and fuels from ultimate and proximate analysis by using artificial neural networks.

Higher heating value (HHV) is one of the most important parameters in determining the quality of the fuels. In this study, comparatively large datasets of ultimate and proximate analysis are constructed to be used in HHV estimation of several classes of fuels, including char & fossil fuels, agricultural wastes, manure (chicken, cow, horse, sheep, llama, and pig), sludge (like paper, paper-mil, sewage, and pulp), micro/macro-algae's, wastes (RDF and MSW), treated woods, untreated woods, and others (non-fossil pyrolysis oils) between the HHV range of 4.22-55.

Three-Dimensional-Printed GelMA-KerMA Composite Patches as an Innovative Platform for Potential Tissue Engineering of Tympanic Membrane Perforations.

Tympanic membrane (TM) perforations, primarily induced by middle ear infections, the introduction of foreign objects into the ear, and acoustic trauma, lead to hearing abnormalities and ear infections. We describe the design and fabrication of a novel composite patch containing photocrosslinkable gelatin methacryloyl (GelMA) and keratin methacryloyl (KerMA) hydrogels. GelMA-KerMA patches containing conical microneedles in their design were developed using the digital light processing (DLP) 3D printing approach.

Serial progesterone levels more accurately predict the time of ovulation in subfertile women: a prospective cohort study.

Purpose: To predict ovulation in subfertile women using serial follicular growth (FG) and serum hormone measures (estradiol (E2), luteinizing hormone (LH), and progesterone (P) levels) in mathematical models.

Methods: This was a prospective observational study of 116 subfertile women aged between 18 and 40 years. FG was assessed by serial transvaginal ultrasonography starting from cycle days 8-12, depending on cycle length.

Characterization, optimization, and evaluation of preservative efficacy of carboxymethyl cellulose/hydromagnesite stromatolite bio-nanocomposite.

Currently, researchers are focusing on the development of nano-additive preservatives during the worldwide COVID-19 pandemic. This research aimed to constitute a small sized preservative nano-formulation which emerges from the biopolymer carboxymethyl cellulose (a green stabilizing agent) and hydromagnesite stromatolite (a fossilized natural additive). In this study, we investigated the optimization of the experimental design of carboxymethyl cellulose/hydromagnesite stromatolite (CMC/HS) bio-nanocomposites using a green and one-step sonochemical method at room temperature.