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Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.

In the 2022 Qatar World Cup, FIFA increased additional time by adding every moment when the ball isn’t played to the play time as an allowance for time lost. This also increased the number of goals scored during additional time. This research studies the impact of goals scored during additional time on match results caused by this extension in the second half of matches in Turkish Male Football Super League. Quantitative and qualitative methods were used. 250 matches played on weeks 1st–30th of 2022–2023 season of Turkish Spor Toto Male Football Super League were analyzed with document analysis, a qualitative research method. The matches, results, additional time, and goals scored during additional time were recorded. Then matches were separated into two equal-numbered groups including before and after the 2022 Qatar World Cup. The 125 matches on weeks 1st–14th and the 125 matches on weeks 14th–30th were analyzed for the number of total goals, total additional time, and the number of total goals scored during additional time with causal-comparative research model as a quantitative research method. In this research, Frequencies Analysis chosen from statistical analyses, Paired Sample t-Test and Pearson Correlation Analysis were used. According to the results, especially after the 2022 Qatar World Cup, the matches aren’t only ninety minutes for footballers any more. The matches may approach two hours with extended additional time. As a conclusion of this research, teams can be suggested to consider this situation and properly organize training plans and programs.

There is an intense competition across many sectors in the maritime industry. Coaster owners being in a market where perfect competition rules apply has to be competitive in order to survive. This study aims to analyze the competitiveness of Turkish coaster merchant fleet that consists of the vessels whose cargo carrying capacity differs from 1.000 dwt to 12.000 dwt and that carries dry bulk goods and general cargo based on short sea shipping concept covering Black Sea, Mediterranean and Continental Europe. Based on a qualitative research methodology, interviews were held and data were collected by using semi-structured questionnaire, and the data collected from the interviews was categorized in order to describe and explain patterns and themes. The findings of the interviews were analyzed within the context of sector-specific external factors and company-specific internal factors, and the competitiveness of shipowners operating in the Turkish coaster market was analyzed in detail. It is expected that the findings and implications will especially shed light on the decision makers and policy makers in the sector and contribute to the current literature.

In this study, analyses were carried out in a fused-silica capillary (i.d. 50.0 µm, total length 48.5 cm and effective length 40.0 cm), in normal mode, applying a voltage of 20 kV. Sample injections were made in a hydrodynamic mode over 7 seconds under a pressure of 50 mbar. Capillary temperature was set at 35 °C and the detection was performed at 205 nm wavelenght. Background electrolyte was 40 mM citrate buffer at pH 6.0 and the internal standard was labetalol HCl. Total analysis time was shorter than 5 minutes. The method was validated according to the ICH guidelines and it was found to be linear, precise, accurate, specific, robust and rugged. Linearity range was found to be 1.0 – 60.0 µg mL-1 and the limit of detection and quantitation were found as 0.5 and 1.0 µg mL-1, respectively.

Multiple sclerosis (MS) manifesting before age 18 years is defined as pediatric MS (pMS). We analysed plasma proteins in pMS by an untargeted proteomic approach. Patients with pMS (Group pMS, n = 33), patients with demyelinating disease not meeting pMS diagnostic criteria (unclassified demyelinating disease, Group U, n = 4) and age-matched healthy subjects (Group C, n = 40) were included. Plasma proteomic analysis was performed using Q-TOF LC/MS. Proteins having fold change >1.2 and found to be statistically different (p < 0.05) between the groups were identified and discussed with a clinical perspective. Group pMS had higher alpha 1B glycoprotein (A1BG), complement factor B (CFB), plasminogen (PLG), alpha-2-antiplasmin (α 2 -AP, SERPINF2), inter alpha trypsin inhibitor heavy chain H2 (ITIH2), and lower centrosomal protein of 290 (CEP290) and F-box/LRR-repeat protein 17 (FBXL17) concentrations than Group C. Measurements from Group U, whose definite diagnoses were established as pMS (n = 3) and myelin oligodendrocyte glycoprotein antibody-associated disease (n = 1) on follow-up after the study, were statistically close to the results of Group pMS. Plasma protein changes observed in our study were related to the inflammation, coagulation and oxidative stress pathways. If confirmed and validated in larger groups, these results may indicate potential biomarker(s) for demyelinating diseases at proteome level and could encourage studies for the development of novel diagnostic kits. Unlabelled Image • Significant proteomic differences were found between pediatric MS and healthy control groups. • The detected proteins were mostly in coagulation and inflammation pathways. • Three/four undiagnosed patients whose proteomic analysis clustered with MS received this diagnosis on follow-up. • Results need to be confirmed in larger studies and by targeted analysis of these proteins. [ABSTRACT FROM AUTHOR]

Cancer is one of the major causes of death globally, which involves uncontrollable growth and spread of abnormal cells. Cytotoxic plant extracts may act on various cancer cells. In this study cytotoxicity of five cultivated Asteraceae plant extracts (Echinacea purpurea L., Achillea filipendulina L., Achillea millefolium L., Tanacetum parthenium L., Silybum marianum L.) has been investigated on different cell lines. Metabolomic profiling of the plants was performed using the LC-qTOF-MS system. In vitro MTT assay applied for determine cytotoxic activity of samples on HT29 (Human colon cancer cells) and L929 (mouse fibroblast) cells. Correlation has been examined between cytotoxicity and metabolomic profile to find active seconder metabolites (r ≥ 0.60, r ≤ −0.60). Total 585 seconder metabolites have been detected for five Asteraceae plants. 118 metabolite showed positive correlation with the cell viability on L929 cell lines while 79 metabolites showed negative correlation. 112 metabolites showed positive correlation with cytotoxic activity on HT29 cell lines while 70 metabolites showed negative. 18 uncommon metabolites were detected in S. marianum extract which has selective cytotoxic activity on cell lines. 16 common metabolites were highly correlated positively as selective cytotoxic activity. These results showed us correlation analyzes between activity and metabolomic profile can be an easy and appropriate method to determine active seconder metabolites in plant extracts.

Rivaroxaban, an anti-clotting medication, acts at a crucial point in the blood-clotting process and stops the formation of blood clots. In this study, RP-HPLC method was developed for the determination of rivaroxaban in tablets (Xarelto® (10 mg)). Phenomenex Luna 5 µm C18 100 Å LC Column (250 x 4.6 mm) was used at 40 ºC. Isocratic elution was performed with ACN:Water (55:45 v/v) mixture. The flow rate was 1.2 mL min-1 and UV detection was at 249 nm. Internal standard (Caffeine) and rivaroxaban were eluted within 2.21 and 3.37 minutes, respectively. The developed method was validated according to the ICH guidelines and found to be linear within the range 0.005 - 40.0 µg mL-1. The method was accurate, precise, robust and rapid. Thus, it was applied successfully for the quality control assay of rivaroxaban in tablet dosage form.