Differentiation of glioblastoma G4 and two types of meningiomas using FTIR spectra and machine learning.

Brain tumors are among the most dangerous, due to their location in the organ that governs all life processes. Moreover, the high differentiation of these poses a challenge in diagnostics. Therefore, this study focused on the chemical differentiation of glioblastoma G4 (GBM) and two types of meningiomas (atypical - MAtyp and angiomatous - MAng) were done using Fourier Transform InfraRed (FTIR) spectroscopy, combined with statistical, multivariate, machine learning and rate of spectrum changes methods.

Urine Analysed by FTIR, Chemometrics and Machine Learning Methods in Determination Spectroscopy Marker of Prostate Cancer in Urine.

Prostate-specific antigen (PSA) is the most commonly used marker of prostate cancer. However, nearly 25% of men with elevated PSA levels do not have cancer and nearly 20% of patients with prostate cancer have normal serum PSA levels. Therefore, in this study, Fourier transform infrared (FTIR) spectroscopy was investigated as a new tool for detection of prostate cancer from urine.

Fourier transform InfraRed spectra analyzed by multivariate and machine learning methods in determination spectroscopy marker of prostate cancer in dried serum.

Prostate cancer represents the second most prevalent form of cancer in males globally. In the diagnosis of prostate cancer, the most commonly utilised biomarker is prostate-specific antigen (PSA). It is unfortunate that approximately 25 % of men with elevated PSA levels do not have cancer, and that approximately 20 % of patients with prostate cancer have normal serum PSA levels.

Determination of platinum-resistance of women with ovarian cancer by FTIR spectroscopy combined with multivariate analyses and machine learning methods.

Patients with high-grade ovarian cancer have a poor prognosis, thus effective treatment remains an unmet medical issue of high importance. Moreover, finding the reason for resistance to cisplatin is a crucial task for the improvement of anti-cancer drugs. In this study, we showed for the first time a chemical difference in a serum collected from platinum-resistance and platinum-sensitive women suffering from ovarian cancer using Fourier Transform InfraRed (FTIR) spectroscopy followed by a data analysis by Principal Component Analysis (PCA), Hierarchical Component Analysis (HCA) and 4 different machine learning algorithms.

Determination of spectroscopy marker of atherosclerotic carotid stenosis using FTIR-ATR combined with machine learning and chemometrics analyses.

Atherosclerotic carotid stenosis (ACS) is a recognized risk factor for ischemic stroke. Currently, the gold diagnostic standard is Doppler ultrasound, the results of which do not provide certainty whether a given person should be qualified for surgery or not, because in some patients, carotid artery stenosis, for example at the level of 70 %, does not cause ischemic stroke in others yes. Therefore, there is a need for new methods that will clearly indicate the marker qualifying the patient for surgery.

Raman spectroscopy combined with machine learning and chemometrics analyses as a tool for identification atherosclerotic carotid stenosis from serum.

Atherosclerosis carotid stenosis (ACS) is one of the main causes of stroke. Unfortunately, the highest number of people go to the doctor with an advanced disease or as a result of a stroke, because carotid atherosclerosis does not cause obvious symptoms. Therefore, it is important to find a diagnostic method to detect the disease during routine tests (using blood or serum).

Lipids balance as a spectroscopy marker of diabetes. Analysis of FTIR spectra by 2D correlation and machine learning analyses.

The number of people suffering from type 2 diabetes has rapidly increased. Taking into account, that elevated intracellular lipid concentrations, as well as their metabolism, are correlated with diminished insulin sensitivity, in this study we would like to show lipids spectroscopy markers of diabetes. For this purpose, serum collected from rats (animal model of diabetes) was analyzed using Fourier Transformed Infrared-Attenuated Total Reflection (FTIR-ATR) spectroscopy.

FT-Raman and FTIR spectroscopy as a tools showing marker of platinum-resistant phenomena in women suffering from ovarian cancer.

Platinum-resistant phenomena in ovarian cancer is very dangerous for women suffering from this disease, because reduces the chances of complete recovery. Unfortunately, until now there are no methods to verify whether a woman with ovarian cancer is platinum-resistant. Importantly, histopathology images also were not shown differences in the ovarian cancer between platinum-resistant and platinum-sensitive tissues.

FT-Raman spectra in combination with machine learning and multivariate analyses as a diagnostic tool in brain tumors.

Brain tumors are one of the most dangerous, because the position of these are in the organ that governs all life processes. Moreover, a lot of brain tumor types were observed, but only one main diagnostic method was used - histopathology, for which preparation of sample was long. Consequently, a new, quicker diagnostic method is needed.

FT-Raman data analyzed by multivariate and machine learning as a new methods for detection spectroscopy marker of platinum-resistant women suffering from ovarian cancer.

The phenomenon of platinum resistance is a very serious problem in the treatment of ovarian cancer. Unfortunately, no molecular, genetic marker that could be used in assigning women suffering from ovarian cancer to the platinum-resistant or platinum-sensitive group has been discovered so far. Therefore, in this study, for the first time, we used FT-Raman spectroscopy to determine chemical differences and chemical markers presented in serum, which could be used to differentiate platinum-resistant and platinum-sensitive women.

Application of the Fuzzy Approach for Evaluating and Selecting Relevant Objects, Features, and Their Ranges.

Relevant attribute selection in machine learning is a key aspect aimed at simplifying the problem, reducing its dimensionality, and consequently accelerating computation. This paper proposes new algorithms for selecting relevant features and evaluating and selecting a subset of relevant objects in a dataset. Both algorithms are mainly based on the use of a fuzzy approach.

Fourier transform infrared spectroscopic marker of glioblastoma obtained from machine learning and changes in the spectra.

Background: Glioblastoma is among the most malignant brain cancer with an average survival rate measured in months. In neurosurgical practice, it is considered impossible to completely remove a glioblastoma because of difficulties in the intraoperative assessment of the boundaries between healthy brain tissue and glioblastoma cells. Therefore, it is important to find a new, quick, cost-effective and useful neurosurgical practice method for the intraoperative differentiation of glioblastoma from healthy brain tissue.

Increased levels of nerve growth factor accompany oxidative load in recurrent pregnancy loss. Machine learning applied to FT-Raman spectra study.

The presented article is focused on developing and validating an efficient, credible, minimally invasive technique based on spectral signatures of blood serum samples in patients with diagnosed recurrent pregnancy loss (RPL) versus healthy individuals who were followed at the Gynecology department. A total of 120 participants, RPL disease (n = 60) and healthy individuals (n = 60), participated in the study. First, we investigated the effect of circulating nerve growth factor (NGF) in RPL and healthy groups.

Correlation between human colon cancer specific antigens and Raman spectra. Attempting to use Raman spectroscopy in the determination of tumor markers for colon cancer.

Colorectal cancer is the second most common cause of cancer-related deaths worldwide. To follow up on the progression of the disease, tumor markers are commonly used. Here, we report serum analysis based on Raman spectroscopy to provide a rapid cancer diagnosis with tumor markers and two new cell adhesion molecules measured using the ELİSA method.

COVID-19 antibody level analysis with feature selection approach.

The study presented here considers the analysis of a medical dataset for the identification of the stage of onset of COVID-19 coronavirus. These data, presented in previous work by the authors, have been subjected to extensive analysis and additional calculations. The data were obtained by analyzing blood samples of infected individuals at 1, 3, and 6 months after COVID-19 infection.

Blood serum lipid profiling may improve the management of recurrent miscarriage: a combination of machine learning of mid-infrared spectra and biochemical assays.

The present article is focused on developing and validating an efficient, credible, minimally invasive technique based on spectral signatures of blood samples of women with recurrent miscarriage vs. those of healthy individuals who were followed in the Department of Obstetrics and Gynecology for 2 years. For this purpose, blood samples from a total of 120 participants, including healthy women (n=60) and women with diagnosed recurrent miscarriage (n=60), were obtained.

Apocynin reduces cytotoxic effects of monosodium glutamate in the brain: A spectroscopic, oxidative load, and machine learning study.

Herein, we examined the modulatory effects ofApocynum (APO) on Monosodium Glutamate (MSG)-induced oxidative damage on the brain tissue of rats after long-term consumption of blood serum components by biochemical assays, Fourier transform infrared spectroscopy(FTIR), and machine learning methods. Sprague-Dawley male rats were randomly divided into the Control, Control + APO, MSG, and MSG + APO groups (n = 8 per group). All administrations were made by oral gavage saline, MSG, or APO and they were repeated for 28 days of the experiments.

Development of novel spectroscopic and machine learning methods for the measurement of periodic changes in COVID-19 antibody level.

In this research, blood samples of 47 patients infected by COVID were analyzed. The samples were taken on the 1st, 3rd and 6th month after the detection of COVID infection. Total antibody levels were measured against the SARS-CoV-2 N antigen and surrogate virus neutralization by serological methods.

Determination of idiopathic female infertility from infrared spectra of follicle fluid combined with gonadotrophin levels, multivariate analysis and machine learning methods.

By in vitro fertilization, oocytes can be removed and the embryo can be cultured, and then trans cervically replaced when they reach cleavage or at the blastocyst stage. The characterization of the follicular fluid is important for the treatment process. Women who applied to the Academic Hospital in vitro fertilization (IVF) Center diagnosed with idiopathic female infertility (IFI) were sought in the patient group.

Correlation between endometriomas volume and Raman spectra. Attempting to use Raman spectroscopy in the diagnosis of endometrioma.

The formation of the uterus lining, i.e. the endometrium, outside the uterus (ex.

Identification of polycystic ovary syndrome from blood serum using hormone levels via Raman spectroscopy and multivariate analysis.

Polycystic ovarian syndrome (PCOS) is a disease, which causes infertility in women. The factors for the development of the disease are still not well understood and diagnostic methods need to be improved. Therefore, in this study, Raman spectroscopy as a potential diagnostic tool, was investigated and spectra of blood serum were collected from PCOS and healthy women.

Characterization of Covid-19 infected pregnant women sera using laboratory indexes, vibrational spectroscopy, and machine learning classifications.

Herein, we show differences in blood serum of asymptomatic and symptomatic pregnant women infected with COVID-19 and correlate them with laboratory indexes, ATR FTIR and multivariate machine learning methods. We collected the sera of COVID-19 diagnosed pregnant women, in the second trimester (n = 12), third-trimester (n = 7), and second-trimester with severe symptoms (n = 7) compared to the healthy pregnant (n = 11) women, which makes a total of 37 participants. To assign the accuracy of FTIR spectra regions where peak shifts occurred, the Random Forest algorithm, traditional C5.

Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations.

Stock price prediction is a popular yet challenging task and deep learning provides the means to conduct the mining for the different patterns that trigger its dynamic movement. In this paper, the task is to predict the close price for 25 companies enlisted at the Bucharest Stock Exchange, from a novel data set introduced herein. Towards this scope, two traditional deep learning architectures are designed in comparison: a long short-memory network and a temporal convolutional neural model.

Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data.

Background: Transcriptional regulation in multi-cellular organisms is a complex process involving multiple modular regulatory elements for each gene. Building whole-genome models of transcriptional networks requires mapping all relevant enhancers and then linking them to target genes. Previous methods of enhancer identification based either on sequence information or on epigenetic marks have different limitations stemming from incompleteness of each of these datasets taken separately.