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Objectives: Regarding the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors, the isocitrate dehydrogenase (IDH) mutation status is one of the most important factors for CNS tumor classification. The aim of our study is to analyze which of the commonly used magnetic resonance imaging (MRI) sequences is best suited to obtain this information non-invasively using radiomics-based machine learning models. We developed machine learning models based on different MRI sequences and determined which of the MRI sequences analyzed yields the highest discriminatory power in predicting the IDH mutation status. Material and Methods: In our retrospective IRB-approved study, we used the MRI images of 106 patients with histologically confirmed gliomas. The MRI images were acquired using the T1 sequence with and without administration of a contrast agent, the T2 sequence, and the Fluid-Attenuated Inversion Recovery (FLAIR) sequence. To objectively compare performance in predicting the IDH mutation status as a function of the MRI sequence used, we included only patients in our study cohort for whom MRI images of all four sequences were available. Seventy-one of the patients had an IDH mutation, and the remaining 35 patients did not have an IDH mutation (IDH wild-type). For each of the four MRI sequences used, 107 radiomic features were extracted from the corresponding MRI images by hand-delineated regions of interest. Data partitioning into training data and independent test data was repeated 100 times to avoid random effects associated with the data partitioning. Feature preselection and subsequent model development were performed using Random Forest, Lasso regression, LDA, and Naïve Bayes. The performance of all models was determined with independent test data. Results: Among the different approaches we examined, the T1-weighted contrast-enhanced sequence was found to be the most suitable for predicting IDH mutations status using radiomics-based machine learning models. Using contrast-enhanced T1-weighted MRI images, our seven-feature model developed with Lasso regression achieved a mean area under the curve (AUC) of 0.846, a mean accuracy of 0.792, a mean sensitivity of 0.847, and a mean specificity of 0.681. The administration of contrast agents resulted in a significant increase in the achieved discriminatory power. Conclusions: Our analyses show that for the prediction of the IDH mutation status using radiomics-based machine learning models, among the MRI images acquired with the commonly used MRI sequences, the contrast-enhanced T1-weighted images are the most suitable.

Abstract Our aim is to define the capabilities of radiomics and machine learning in predicting pseudoprogression development from pre-treatment MR images in a patient cohort diagnosed with high grade gliomas. In this retrospective analysis, we analysed 131 patients with high grade gliomas. Segmentation of the contrast enhancing parts of the tumor before administration of radio-chemotherapy was semi-automatically performed using the 3D Slicer open-source software platform (version 4.10) on T1 post contrast MR images. Imaging data was split into training data, test data and an independent validation sample at random. We extracted a total of 107 radiomic features by hand-delineated regions of interest (ROI). Feature selection and model construction were performed using Generalized Boosted Regression Models (GBM). 131 patients were included, of which 64 patients had a histopathologically proven progressive disease and 67 were diagnosed with mixed or pure pseudoprogression after initial treatment. Our Radiomics approach is able to predict the occurrence of pseudoprogression with an AUC, mean sensitivity, mean specificity and mean accuracy of 91.49% [86.27%, 95.89%], 79.92% [73.08%, 87.55%], 88.61% [85.19%, 94.44%] and 84.35% [80.19%, 90.57%] in the full development group, 78.51% [75.27%, 82.46%], 66.26% [57.95%, 73.02%], 78.31% [70.48%, 84.19%] and 72.40% [68.06%, 76.85%] in the testing group and finally 72.87% [70.18%, 76.28%], 71.75% [62.29%, 75.00%], 80.00% [69.23%, 84.62%] and 76.04% [69.90%, 80.00%] in the independent validation sample, respectively. Our results indicate that radiomics is a promising tool to predict pseudo-progression, thus potentially allowing to reduce the use of biopsies and invasive histopathology.

Our aim is to investigate the added value of automated machine learning (AutoML) for potential future applications in cancer diagnostics. Using two important diagnostic questions, the non-invasive determination of IDH mutation status and ATRX status, we analyze whether it is possible to use AutoML to develop models that are comparable in performance to conventional machine learning models (ML) developed by experts. For this purpose, we develop AutoML models using different feature preselection methods and compare the results with previously developed conventional ML models. The cohort used for our study comprises T2-weighted MRI images of 124 patients with histologically confirmed gliomas. Using AutoML, we were able to develop sophisticated models in a very short time with only a few lines of computer code. In predicting IDH mutation status, we obtained a mean AUC of 0.7400 and a mean AUPRC of 0.8582. ATRX mutation status was predicted with very similar discriminatory power, with a mean AUC of 0.7810 and a mean AUPRC of 0.8511. In both cases, AutoML was even able to achieve a discriminatory power slightly above that of the respective conventionally developed models in a very short computing time, thus making such methods accessible to non-experts in the near future.

Ahmet Metin,1 Nursel Dilek,2 Serap Gunes Bilgili3 1Department of Dermatology and Venereology, Medical School of Ankara, Yildirim Beyazit University, Ankara, Turkey; 2Department of Dermatology and Venereology, Medical School of Recep, Tayyip Erdoğan University, Rize, Turkey; 3Department of Dermatology and Venereology, Medical School of Yuzuncu, Yil University, Van, Turkey Abstract: Intertrigo is a common inflammatory dermatosis of opposing skin surfaces that can be caused by a variety of infectious agents, most notably candida, under the effect of mechanical and environmental factors. Symptoms such as pain and itching significantly decrease quality of life, leading to high morbidity. A multitude of predisposing factors, particularly obesity, diabetes mellitus, and immunosuppressive conditions facilitate both the occurrence and recurrence of the disease. The diagnosis of candidal intertrigo is usually based on clinical appearance. However, a range of laboratory studies from simple tests to advanced methods can be carried out to confirm the diagnosis. Such tests are especially useful in treatment-resistant or recurrent cases for establishing a differential diagnosis. The first and key step of management is identification and correction of predisposing factors. Patients should be encouraged to lose weight, followed up properly after endocrinologic treatment and intestinal colonization or peri-orificial infections should be medically managed, especially in recurrent and resistant cases. Medical treatment of candidal intertrigo usually requires topical administration of nystatin and azole group antifungals. In this context, it is also possible to use magistral remedies safely and effectively. In case of predisposing immunosuppressive conditions or generalized infections, novel systemic agents with higher potency may be required. Keywords: Candida, intertrigo, recurrent candidal intertrigo, candidiasis, candidosis, candidal predisposals

Background and Aims: The Charcot–Marie–Tooth Disease Health Index (CMT‐HI) is a disease‐specific, patient‐reported disease burden measure. As part of an international clinical trial readiness study, individuals with CMT1A (ages 18–75 years) underwent clinical outcome assessments (COAs), including the CMT‐HI, to capture their longitudinal perspective on the disease burden. Methods: Two hundred and fifteen participants underwent serial COAs including the CMT‐HI, CMT Functional Outcome Measure (CMT‐FOM), CMT Neuropathy Score (CMTNSv2R), and CMT Exam Score (CMTES/CMTES‐R). Correlations between the total and subscale scores for the CMT‐HI and other COAs were determined. Changes in the CMT‐HI scores over 12 months were assessed using paired t‐tests. The minimum clinically important difference (MCID) for the CMT‐HI and its subscales were calculated by anchoring to a participant global impression of change scale. Results: At baseline, CMT1A participants were 44.5 ± 15 years old (range: 18–75) and 58% were women. The mean CMT‐HI was 25.7 ± 18.8 (range: 0–91.9; 100 reflecting maximal disease burden). The CMT‐HI correlated with the CMT‐FOM (r =.54, p <.0001), CMTNSv2R (r =.48, p <.0001), and CMTES/CMTES‐R (r =.52/r =.54, p <.0001). Disease burden was greater in women than in men (CMT‐HI 29.1 ± 19.1 vs. 21.2 ± 17.3, p =.001). Over 12 months, there was a nonsignificant mean increase in CMT‐HI of 0.40 ± 10.0 (n = 189, p =.89). The MCID for the CMT‐HI total score was 3.8 points (95% CI: 1.7–5.9). Discussion: Patient‐reported disease burden in CMT1A as measured by the CMT‐HI is associated with measures of neurologic impairment and physical functioning. Women reported a higher disease burden than men. These data will inform the design of clinical trials in CMT1A. [ABSTRACT FROM AUTHOR]

Our aim is to investigate the added value of automated machine learning (AutoML) for potential future applications in cancer diagnostics. Using two important diagnostic questions, the non-invasive determination of IDH mutation status and ATRX status, we analyze whether it is possible to use AutoML to develop models that are comparable in performance to conventional machine learning models (ML) developed by experts. For this purpose, we develop AutoML models using different feature preselection methods and compare the results with previously developed conventional ML models. The cohort used for our study comprises T2-weighted MRI images of 124 patients with histologically confirmed gliomas. Using AutoML, we were able to develop sophisticated models in a very short time with only a few lines of computer code. In predicting IDH mutation status, we obtained a mean AUC of 0.7400 and a mean AUPRC of 0.8582. ATRX mutation status was predicted with very similar discriminatory power, with a mean AUC of 0.7810 and a mean AUPRC of 0.8511. In both cases, AutoML was even able to achieve a discriminatory power slightly above that of the respective conventionally developed models in a very short computing time, thus making such methods accessible to non-experts in the near future. [ABSTRACT FROM AUTHOR]

1. The purpose of the present research was to detect the efficacies of various vinegars (aronia, grape and hawthorn) used as marination solutions on the physicochemical, technological, textural and sensory properties of spent chicken meat. The pH, colour parameters, cooking loss, marinade absorption, yield, texture (TPA and MORS), characteristics via scanning electron microscopy (SEM) and sensory properties of marinated spent chicken samples were determined. 2. Marination solutions used in the treatment groups were prepared with aronia, grape and hawthorn vinegars. The samples were agitated by hand to provide even dispersion of the solid ingredients in the marinades and were kept at 4°C for 24 h. 3. The pH values of the samples were between 4.70 and 6.04. Marination with various marination solutions caused significant differences in terms of the a* and b* values of samples (P < 0.05). While marination with aronia vinegar decreased the b value of the samples, the use of grape and hawthorn vinegars in the marination solutions increased this value. 4. Hardness and chewiness were the lowest in samples marinated with grape vinegar (P < 0.05) and were 135.14 and 48.79 N, respectively. As a result of marination with various vinegars, there was a significant (P < 0.05) decrease in the MORSF and MORSE values. 5. Marinade absorption values decreased by marination with various vinegars. The highest yield values were found in the samples marinated with hawthorn vinegar (P < 0.05). The SEM indicated that marination with aronia, grape and hawthorn vinegar caused larger gaps between muscle fibres compared to the control samples. 6. Samples marinated with vinegars had higher texture scores compared with the control. Consequently, the marination with vinegars such as aronia, grape and hawthorn has the potential to improve the technical and textural properties of spent chicken meat. [ABSTRACT FROM AUTHOR]

Abstract Background Breast cancer has been a disease in which treatment strategy has changed over time under the influence of different hypotheses and evidence for more than a century. We analyzed the contribution of radiotherapy to disease-free survival and overall survival by classifying according to stage, 1–3 lymph node involvement, and molecular subgroups. Methods Following the approval of the Institutional Review Board, records of patients with breast cancer who were admitted to University School of Medicine Departments of Radiation Oncology and Medical Oncology between July 1999 and December 2020 were reviewed. Using data propensity score matching was performed between the groups that did and did not receive radiotherapy using an optimal matching algorithm (optimum, 1:1). Disease-free survival and overall survival after propensity score matching were calculated using the Kaplan-Meier method. Univariate and multivariate Cox regression analysis was used to estimate hazard ratios. Results In the radiotherapy and non-radiotherapy groups, disease-free survival was 257.42 ± 5.46 (246.72- 268.13), 208,96 ± 8,15 (192,97–224,94) months respectively, (p =

Aim: Family is the most natural environment where people can meet their needs of love, compassion, affection and care for mental and physical health. In this study, we aim to investigate the relation between smoking and family functions. Material and Method: Ninety seven patients between 18-60 years age who were admitted to the Ankara Training and Research Hospital, live with at least one family member and agreed to participate in the study were included in this study. Cross-sectional, observational and analytic methods were applied. The recorded data of the participants were as follows: age, sex, occupation, marital status, education level, the family members whom living with, the status of smoking at home, chronic diseases and current medication. McMaster Model of Family Functioning (MMFF) and Fagerström Test for Nicotine Dependence (FTND) were used. Results: Statistically significant differences between MMFF scores of smoking patients in the subscales of “Roles” and “Affective Involvement” were found (p=0.004, p= 0.002, respectively). We have seen in the subscale of “Problem Solving” that single members were negatively affected (p=0.033). The negative effects of smoking were found to be decreasing by age in the “Communication” subscale (p=0.002). The “Roles” subscale was observed to be negatively disturbed in the group of smokers with chronic diseases(p=0,050). We also found that being single and having a chronic disease negatively affected “Affective Responsiveness” subscale (p=0.050, p=0.020, respectively). Conclusion: Smoking affects the family functions negatively. Thus, the fight against smoking might be thought to make a favorable effect on the family functions.

Objective: Our aim is to define the capabilities of radiomics in predicting pseudoprogression from pre-treatment MR images in patients diagnosed with high-grade gliomas using T1 non-contrast-enhanced and contrast-enhanced images. Material & methods: In this retrospective IRB-approved study, image segmentation of high-grade gliomas was semi-automatically performed using 3D Slicer. Non-contrast-enhanced T1-weighted images and contrast-enhanced T1-weighted images were used prior to surgical therapy or radio-chemotherapy. Imaging data was split into a training sample and an independent test sample at random. We extracted 107 radiomic features by use of PyRadiomics. Feature selection and model construction were performed using Generalized Boosted Regression Models (GBM). Results: Our cohort included 124 patients (female: n = 53), diagnosed with progressive (n = 61) and pseudoprogressive disease (n = 63) of primary high-grade gliomas. Based on non-contrast-enhanced T1-weighted images of the independent test sample, the mean area under the curve (AUC), mean sensitivity, mean specificity and mean accuracy of our model were 0.651 [0.576, 0.761], 0.616 [0.417, 0.833], 0.578 [0.417, 0.750] and 0.597 [0.500, 0.708] to predict the development of pseudoprogression. In comparison, the independent test data of contrast-enhanced T1-weighted images yielded significantly higher values of AUC = 0.819 [0.760, 0.872], sensitivity = 0.817 [0.750, 0.833], specificity = 0.723 [0.583, 0.833] and accuracy = 0.770 [0.687, 0.833]. Conclusion: Our findings show that it is possible to predict pseudoprogression of high-grade gliomas with a Radiomics model using contrast-enhanced T1-weighted images with comparatively good discriminatory power. The use of a contrast agent results in a clear added value.

In this work, Ni(II) complexes with unsymmetric Schiff bases (NiL1, NiL2, NiL3, NiL4) were prepared by a two-stage method reported by one of us recently for investigate antimutagenic properties. Sodium azide-induced antimutagenic effect in lymphocytes was determined by sister chromatid exchange (SCE) and micronucleus (MN) methods. It has been determined that the synthesized compounds have antimutagenic properties and reduce the mutagenicity caused by sodium azide (NaN3) which is used as a positive control.