Influence of augmentation on the performance of the double ResNet-based model for chest X-ray classification

Purpose: A pandemic disease elicited by the SARS-CoV-2 virus has become a serious health issue due to infecting millions of people all over the world. Recent publications prove that artificial intelligence (AI) can be used for medical diagnosis purposes, including interpretation of X-ray images. X-ray scanning is relatively cheap, and scan processing is not computationally demanding. Material and methods: In our experiment a baseline transfer learning schema of processing of lung X-ray images, including augmentation, in order to detect COVID-19 symptoms was implemented. Seven different scenarios of augmentation were proposed. The model was trained on a dataset consisting of more than 30,000 X-ray images. Results: The obtained model was evaluated using real images from a Polish hospital, with the use of standard metrics, and it achieved accuracy = 0.9839, precision = 0.9697, recall = 1.0000, and F1-score = 0.9846. Conclusions: Our experiment proved that augmentations and masking could be important steps of data pre-processing and could contribute to improvement of the evaluation metrics. Because medical professionals often tend to lack confidence in AI-based tools, we have designed the proposed model so that its results would be explainable and could play a supporting role for radiology specialists in their work

Modulation of expression of genes involved in glycosaminoglycan metabolism and lysosome biogenesis by flavonoids

Flavonoids were found previously to modulate efficiency of synthesis of glycosaminoglycans (GAGs), compounds which are accumulated in cells of patients suffering from mucopolysaccharidoses (MPSs). The aim of this work was to determine effects of different flavonoids (genistein, kaempferol, daidzein) used alone or in combinations, on expression of genes coding for proteins involved in GAG metabolism. Analyses with DNA microarray, followed by real-time qRT-PCR revealed that genistein, kaempferol and combination of these two compounds induced dose- and time-dependent remarkable alterations in transcript profiles of GAG metabolism genes in cultures of wild-type human dermal fibroblasts (HDFa). Interestingly, effects of the mixture of genistein and kaempferol were stronger than those revealed by any of these compounds used alone. Similarly, the most effective reduction in levels of GAG production, in both HDFa and MPS II cells, was observed in the presence of genistein, keampferol and combination of these compounds. Forty five genes were chosen for further verification not only in HDFa, but also inMPS II fibroblasts by using real-time qRT-PCR. Despite effects on GAG metabolism-related genes, we found that genistein, kaempferol and mixture of these compounds significantly stimulated expression of TFEB. Additionally, a decrease inMTOR transcript level was observed at these conditions

Circulating Visfatin in Hypothyroidism Is Associated with Free Thyroid Hormones and Antithyroperoxidase Antibodies

We hypothesized that regulation of visfatin in hypothyroidism might be altered by coexisting chronic autoimmune thyroiditis. This is a prospective case-control study of 118 subjects. The autoimmune study group (AIT) consisted of 39 patients newly diagnosed with hypothyroidism in a course of chronic autoimmune thyroiditis. The nonautoimmune study group (TT) consisted of 40 patients thyroidectomized due to the differentiated thyroid cancer staged pT1. The control group comprised 39 healthy volunteers adjusted for age, sex, and BMI with normal thyroid function and negative thyroid antibodies. Exclusion criteria consisted of other autoimmune diseases, active neoplastic disease, diabetes mellitus, and infection, which were reported to alter visfatin level. Fasting blood samples were taken for visfatin, TSH, free thyroxine (FT4), free triiodothyronine (FT3), antithyroperoxidase antibodies (TPOAb), antithyroglobulin antibodies (TgAb), glucose, and insulin levels. The highest visfatin serum concentration was in AIT group, and healthy controls had visfatin level higher than TT (p=0.0001). Simple linear regression analysis revealed that visfatin serum concentration was significantly associated with autoimmunity (β=0.1014; p=0.003), FT4 (β=0.05412; p=0.048), FT3 (β=0.05242; p=0.038), and TPOAb (β=0.0002; p=0.0025), and the relationships were further confirmed in the multivariate regression analysis

Analiza danych GWAS przy użyciu algorytmów uczenia maszynowego – przegląd literatury

Machine learning is a part of field concerned with AI. The main goal of machine learning algorithms is to create automatic system that improves itself with the use of its experience (given data) to gain new knowledge. Genome-Wide Association Studies compare whole genomes of different individuals in order to see if any of genetic variants are correlated with a trait. Using ML for GWAS analysis can be beneficial for scientists. It has been proved several times in various ways.Uczenie maszynowe jest dziedziną nauki związaną ze sztuczną inteligencją. Głównym celem algorytmów uczenia maszynowego jest stworzenie automatycznego systemu, który poprawia się dzięki wykorzystaniu swojego doświadczenia (danych) w celu zdobycia nowej wiedzy. Badania asocjacyjne całego genomu (GWAS) porównują całe genomy różnych osobników, aby sprawdzić, czy którykolwiek z wariantów genetycznych jest skorelowany z cechą. Wykorzystanie ML do analizy GWAS może być korzystne dla naukowców. Zostało to udowodnione na różne sposoby

Evaluating the risk of endometriosis based on patients’ self-assessment questionnaires

Abstract Background Endometriosis is a condition that significantly affects the quality of life of about 10 % of reproductive-aged women. It is characterized by the presence of tissue similar to the uterine lining (endometrium) outside the uterus, which can lead lead scarring, adhesions, pain, and fertility issues. While numerous factors associated with endometriosis are documented, a wide range of symptoms may still be undiscovered. Methods In this study, we employed machine learning algorithms to predict endometriosis based on the patient symptoms extracted from 13,933 questionnaires. We compared the results of feature selection obtained from various algorithms (i.e., Boruta algorithm, Recursive Feature Selection) with experts’ decisions. As a benchmark model architecture, we utilized a LightGBM algorithm, along with Multivariate Imputation by Chained Equations (MICE) and k-nearest neighbors (KNN), for missing data imputation. Our primary objective was to assess the model’s performance and feature importance compared to existing studies. Results We identified the top 20 predictors of endometriosis, uncovering previously overlooked features such as Cesarean section, ovarian cysts, and hernia. Notably, the model’s performance metrics were maximized when utilizing a combination of multiple feature selection methods. Specifically, the final model achieved an area under the receiver operator characteristic curve (AUC) of 0.85 on the training dataset and an AUC of 0.82 on the testing dataset. Conclusions The application of machine learning in diagnosing endometriosis has the potential to significantly impact clinical practice, streamlining the diagnostic process and enhancing efficiency. Our questionnaire-based prediction approach empowers individuals with endometriosis to proactively identify potential symptoms, facilitating informed discussions with healthcare professionals about diagnosis and treatment options