The Role of 68 Ga PSMA Imaging in Evaluating Adrenal Lesions in Prostate Cancer Patients.

Objectives Gallium-68 prostate-specific membrane antigen ( 68 Ga-PSMA) imaging is valuable for staging because an accurate diagnosis, metastatic or nonmetastatic for prostate cancer patients, is required for deciding to treatment approaches and prognostic assessment. The aim of this study was primarily to distinguish between benign and metastatic adrenal gland lesions detected during 68 Ga-PSMA positron emission tomography (PET)/CT imaging, to evaluate the presence of factors predicting its development, and then to determine the life expectancy of patients with metastatic adrenal lesions. Materials and Methods We performed a database search for PET/CT records generated from June 2016 to February 2021 for "adrenal gland" in report for patients who underwent 68 Ga-PSMA examination with prostate cancer patients. Results Twenty-three patients (10 benign and 13 metastatic) were included in this study. The total prostate-specific antigen, adrenal gland size, adrenal gland density, and maximum standardized uptake (SUVmax) values are significantly different between groups ( p < 0.05). On receiver operating characteristic curve analysis, the SUVmax cutoff value > 6.8 provided both sensitivity and specificity of 100%. However, with 29 mm as the adrenal gland size cutoff and 21.2 as Hounsfield unit, the sensitivity and specificity were 56.2 and 92.3%, and 93.8 and 92.3%, respectively. The survival of the benign and metastatic groups was compared and a statistically significant difference was found ( p = 0.006). The presence of pelvic lymph nodes was statistically negatively affected the surveillance between the groups. Conclusion The presence of atypical metastases such as adrenal gland is not insignificant in prostate cancer patients. Because of this degree of impact on patient management, accurate staging by imaging with 68 Ga-PSMA should be an integral part of prostate cancer management.

Binding Activity Classification of Anti-SARS-CoV-2 Molecules using Deep Learning Across Multiple Assays

Background: The coronavirus disease-2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has urgently necessitated effective therapeutic solutions, with a focus on rapidly identifying and classifying potential small-molecule drugs. Given traditional methods' labor-intensive and time-consuming nature, deep learning has emerged as an essential tool for efficiently processing and extracting insights from complex biological data. Aims: To utilize deep learning techniques, particularly deep neural networks (DNN) enhanced with the synthetic minority oversampling technique (SMOTE), to enhance the classification of binding activities in anti-SARS-CoV-2 molecules across various bioassays. Methods: We used 11 bioassay datasets covering various SARS-CoV-2 interactions and inhibitory mechanisms. These assays ranged from spike-ACE2 protein-protein interaction to ACE2 enzymatic activity and 3CL enzymatic activity. To address the prevalent class imbalance in these datasets, the SMOTE technique was employed to generate new samples for the minority class. In our model-building approach, we divided the dataset into 80% training and 20% test sets, reserving 10% of the training set for validation. Our approach involved employing a DNN that integrates ReLU and sigmoid activation functions, incorporates batch normalization, and uses Adam optimization. The hyperparameters and architecture of the DNN were optimized through various tests on layers, minibatch sizes, epoch sizes, and learning rates. A 40% dropout rate was incorporated to mitigate overfitting. For model evaluation, we computed performance metrics, such as balanced accuracy (BACC), precision, recall, F1 score, Matthews' correlation coefficient (MCC), and area under the curve (AUC). Results: The performance of the DNN across 11 bioassay test sets revealed varying outcomes, significantly influenced by the ratios of active-to-inactive compounds. Assays, such as AlphaLISA and CoV-PPE, demonstrated robust performance across various metrics, including BACC, precision, recall, and AUC, when configured with more balanced ratios (1:3 and 1:1, respectively). This suggests the effective identification of active compounds in both cases. In contrast, assays with higher imbalance ratios, such as 3CL (1:38) and cytopathic effect (1:15), demonstrated higher recall but lower precision, highlighting challenges in accurately identifying active compounds among numerous inactive compounds. However, even in these challenging settings, the model achieved favorable BACC and recall scores. Overall, the DNN model generally performed well, as indicated by the BACC, MCC, and AUC values, especially when considering the degree of dataset imbalance in each assay. Conclusion: This study demonstrates the significant impact of deep learning, particularly DNN models enhanced with SMOTE, in improving the identification of active compounds in bioassay datasets for COVID-19 drug discovery, outperforming traditional machine learning models. Furthermore, this study highlights the efficacy of advanced computational techniques in addressing high-throughput screening data imbalances.

The Role of Superb Microvascular Imaging and Shear Wave Elastography in the Prediction of Hemorrhage Complications After Renal Parenchyma Biopsy.

OBJECTIVES: The aim of study was to evaluate the diagnostic utility of the renal parenchyma elasticity with the shear wave elastography (SWE) and microvascularization with the superb microvascular imaging (SMI) technique before kidney biopsy and to predict the complication of hemorrhage before kidney biopsy. METHODS: A total of 75 patients were included in the prospective study. Before the biopsy, vascularity features of the kidney parenchyma in the area to be biopsied were assessed by SMI and parenchymal stiffness by SWE and were examined by 2 independent radiologists. RESULTS: A statistically significant difference was found in the SMI and SWE values between the groups with and without hematoma and hematuria when compared with the Student t test and Mann-Whitney U test ( P < 0.05). The SWE hardness cutoff value, which maximizes the prediction of the development of hematuria, was found to be 18.40 kPa, and the sensitivity and specificity values were 84.4% and 62.8%, respectively. In SMI vascularity index values, the cutoff value was found to be 0.247410800 kPa, and sensitivity and specificity values were 81.3% and 83.7%, respectively. The cutoff value of the SMI vascularity index values that maximized the prediction of hematoma development was 0.297009650, and the sensitivity and specificity values were 87% and 87%, respectively. CONCLUSIONS: We believe that evaluating and standardizing the microvascularization and elasticity of the kidney parenchyma before a percutaneous kidney biopsy will be potentially useful as a guiding method in the prediction of postbiopsy hemorrhage development.

An experimental study: the effect of S. boulardii on abemaciclibinduced diarrhea.

BACKGROUND: In our study, we aimed to investigate the protective effects of Saccharomyces boulardii on abemaciclib-induced diarrhea model, which is a commonly used drug in breast cancer. METHODS: Thirty rats were divided into 3 groups as control (Group 1), abemaciclib (Group 2), and abemaciclib + Saccharomyces boulardii (Group 3) groups. The clinical status, body weight, and defecation status were monitored daily. At the end of the 15-day experiment period, the rats were killed with high-dose anesthesia and the resected small intestine segments were evaluated histopathologically. Lesions were classified according to thickening of the villus, inflammation and edema of mucosa and intraepithelial leukocyte accumulation. Then, mean values of both crypt depths and villi thicknesses were calculated for each rat. Normal distribution assumption was controlled with the Shapiro-Wilk test. One-way analysis of variance for normally distributed variables in the comparisons of more than two independent groups and Kruskal-Wallis test for nonnormally distributed variables were used. The significance value was accepted as 0.05. RESULTS: : There was one death in Group 3, but none in the others. There were no findings of mucositis in Group I. There was mild diarrhea and weight loss in only one rat in Group 1. For the comparison of the severity of diarrhea (72.5%/39%) and weight loss (72.5%/45%), a decrease was found in Group 3 according to Group 2 (p < 0.01). Histopathological findings such as edema, inflammation, and intraepithelial leukocyte accumulation also showed a decrease in Group 3 compared to Group 2 (p < 0.01). DISCUSSION: Saccharomyces boulardii should be considered as a treatment option in abaemaciclib (chemotherapy)-induced diarrhea. Further comparative studies and in vivo human randomized controlled studies can be conducted in the future.

Prediction of a 10-year risk of type 2 diabetes mellitus in the Turkish population: A cross-sectional study.

ABSTRACT: According to the International Diabetes Federation, Turkey will be among the top 10 countries in the world with the highest prevalence of diabetes mellitus (DM) by 2045, with a speculated number of cases of 10.4 million.This study aimed to predict the 10-year risk of type 2 DM in a Turkish population, assess potential factors of the 10-year risk of DM, and assess the outcomes of Turkey's 2015 to 2020 program for DM.Individuals aged 20-64 years were categorized and stratified according to age (in ranges of 5 years), sex, and populations of family medicine centers to reflect the whole population. The Finnish Diabetes Risk Score, sociodemographic characteristics, body fat, muscle, bone ratio, blood pressure, and waist-to-height ratio were evaluated.We found that 9.5% (n = 71) of the population aged 20 to 64 years will have DM within the next 10 years. Low levels of education (odds ratio [OR]: 2.054; 95% confidence interval [CI]: 1.011-4.174), smoking cessation (OR: 2.636; 95% CI: 1.260-5.513), a waist-to-height ratio >0.5 (OR: 6.885; 95% CI: 2.301-20.602), body fat percentage (OR: 1.187; 95% CI: 1.130-1.247), high systolic blood pressure (OR: 1.025; 95% CI: 1.009-1.041), and alcohol consumption (beta-estimation: -0.690; OR: 0.501; 95% CI: 0.275-0.914) affect the 10-year risk of type 2 DM.Individuals at risk for DM can be easily identified using risk assessment tools in primary care; however, there is no active screening program in the healthcare system, and only proposals exist. In addition to screening, preventive measures should focus on raising awareness of DM, reducing body fat percentage and systolic blood pressure, and decreasing the waist-to-height ratio to <0.5.

Agreement of Anterior Segment Parameters Between Schiempflug Topography and Swept-Source Optic Coherence Based Optic Biometry in Keratoconus and Healthy Subjects.

PURPOSE: The aim of this study is to compare anterior segment parameters, including corneal thickness (CCT), keratometry and anterior chamber depth (ACD), and white to white corneal diameter (WTW), obtained by Pentacam Schiempflug imaging and intraocular lens (IOL) Master 700 swept-source optic coherence tomography biometry in keratoconus patients and healthy subjects. METHODS: This prospective cross-sectional instrument agreement analysis includes 88 eyes of 50 keratoconus patients and 87 eyes of 50 healthy subjects. Biometry was performed using IOL Master 700, and topography was performed using Pentacam. The keratometry values (Kf, Ks, Km, and Kmax), ACD, WTW, CCT, axial length (AL), anterior chamber angle (ACA), and lens thickness (LT) were evaluated. Levels of agreement between devices were evaluated by Bland-Altman plots with 95% limits of agreement. RESULTS: Intraocular lens Master 700 showed higher WTW, ACD, pupil diameter, and CCT values than Pentacam in both the keratoconus and control groups. However, there were no statistically significant differences in flat keratometry (Kf) and steep keratometry (Ks) values between the groups. CONCLUSION: Pentacam and IOL Master 700 may be used interchangeably in normal eyes and keratoconus eyes for the measurement of keratometry values and axis; however, these two devices should not be considered interchangeable for WTW, ACD, pupil diameter, and CCT measurements in both keratoconus patients and healthy subjects.

Deep Learning-Based Imbalanced Data Classification for Drug Discovery.

Drug discovery studies have become increasingly expensive and time-consuming processes. In the early phase of drug discovery studies, an extensive search has been performed to find drug-like compounds, which then can be optimized over time to become a marketed drug. One of the conventional ways of detecting active compounds is to perform an HTS (high-throughput screening) experiment. As of July 2019, the PubChem repository contains 1.3 million bioassays that are generated through HTS experiments. This feature of PubChem makes it a great resource for performing machine learning algorithms to develop classification models to detect active compounds for drug discovery studies. However, data sets obtained from PubChem are highly imbalanced. This imbalanced nature of the data sets has a negative impact on the classification performance of machine learning algorithms. Here, we explored the classification performance of deep neural networks (DNN) on imbalance compound data sets after applying various data balancing methods. We used five confirmatory HTS bioassays from the PubChem repository and applied one undersampling and three oversampling methods as data balancing methods. We used a fully connected, two-hidden-layer DNN model for the classification of active and inactive molecules. To evaluate the performance of the network, we calculated six performance metrics, including balanced accuracy, precision, recall, F1 score, Matthews correlation coefficient, and area under the ROC curve. The study results showed that the effect of imbalanced data on network performance could be mitigated to a degree by applying the data balancing methods. The level of imbalance, however, has a negative effect on the performance of the network.

Quantitative Assessment of Salivary Gland Parenchymal Vascularization Using Power Doppler Ultrasound and Superb Microvascular Imaging: A Potential Tool in the Diagnosis of Sjögren's Syndrome

Background: Primary Sjögren's syndrome is a chronic inflammatory autoimmune disease. Minor salivary gland biopsy is the gold standard for the diagnosis of primary Sjögren's syndrome. Superb microvascular imaging, power Doppler ultrasound, and color Doppler of the salivary glands represent non-invasive, non-irradiating modality for evaluating the vascularity of the salivary glands in the diagnosis and follow-up of primary Sjögren's syndrome. Aims: To evaluate the efficacy of superb microvascular imaging and vascularity index in salivary glands for the sonographic diagnosis of primary Sjögren's syndrome. Study Design: Prospective case-control study. Methods: Twenty participants with primary Sjögren's syndrome and 20 healthy subjects were included in the study. Both parotid glands and submandibular glands were evaluated by superb microvascular imaging, power Doppler ultrasound, and color Doppler. The diagnostic accuracy of superb microvascular imaging was compared using these techniques. Results: In the patient group, the vascularity index values of superb microvascular imaging in parotid glands and submandibular glands were 3.5±1.66, 5.06±1.94, respectively. While the same values were 1.0±0.98 and 2.44±1.34 in the control group (p≤0.001). In the patient group, the vascularity index values of power Doppler ultrasound in parotid glands and submandibular glands were 1.3±1.20 and 2.59±1.82, respectively. While the same values were 0.3±0.32 and 0.85±0.68 in the control group (p≤0.001). The superb microvascular imaging vascularity index cut-off value for the diagnosis of primary Sjögren's syndrome in parotid glands that maximizes the accuracy was 1.85 (area under the curve: 0.906; 95% confidence interval: 0.844, 0.968), and its sensitivity and specificity were 87.5% and 72.5%, respectively. While the superb microvascular imaging vascularity index cut-off value for the diagnosis of primary Sjögren's syndrome in submandibular gland that maximizes the accuracy was 3.35 (area under the curve: 0.873; 95% confidence interval: 0.800, 0.946), its sensitivity and specificity were 82.5% and 70%, respectively. Conclusion: Superb microvascular imaging with high reproducibility of the vascularity index has a higher sensitivity and specificity than the power Doppler ultrasound in the diagnosis of primary Sjögren's syndrome. It can be a noninvasive technique in the diagnosis of primary Sjögren's syndrome when used with clinical, laboratory and other imaging methods.

The evaluation of the distribution of CD133, CXCR1 and the tumor associated macrophages in different molecular subtypes of breast cancer.

Breast cancer has different molecular subtypes, which determine the prognosis and response to the treatment. CD133 is a marker for cancer stem cells in tumor microenvironment with diagnostic/therapeutic importance. The tumor associated macrophages (TAMs) interact with the cancer stem cells through the CXCR1 receptor. In this study, we wanted to investigate the expression of these markers in patients with different molecular subtypes, in order to detect pathophysiological mechanisms and new molecular targets for the prospective targeted therapies. In this study we hypothesized a difference in expression of these antigens among different subtypes. We investigated expression of antigens in breast cancer patients with luminal A (LA), luminal B (LB), HER2 overexpressing (HER2OE), triple negative (TN) subtypes (n=70) and control patients (n=10) without cancer diagnosis. We applied indirect immunohistochemistry and evaluated immunostaining. CD133 expression was at the periphery and CXCR1 expression was at the central area of the tumor. The cytoplasmic CXCR1, CD133 expressions and nuclear CD133 expression, which is prominent in the TN subtype, were observed in patients. There was a statistically significant difference between the groups for CD133 (p=0.004), CXCR1 (p=0.002) H-Score values and M2 macrophages/whole TAM ratios (p=0.022). Between the CD133 and CXCR1 H-scores, there was a weak positive correlation (r=0.249, p=0.035). This study showed the compartment specific expression of the CD133 and CXCR1 antigens in neoplastic cells. The use of CD133 as a stem cell marker may be limited to TN subtype, due to its heterogeneous expression.

Cardiac Magnetic Resonance Imaging and Transthoracic Echocardiography: Investigation of Concordance between the Two Methods for Measurement of the Cardiac Chamber.

Background and objectives: Cardiac magnetic resonance (CMR) imaging is the gold standard method for the detection of ventricular volumes and myocardial edema/scar. Transthoracic echocardiography (TTE) imaging is primarily used in the evaluation of cardiac functions and chamber dimensions. This study aims to investigate whether the chamber diameter measurements are concordant with each other in the same patient group who underwent TTE and CMR. Materials and Methods: The study included 41 patients who underwent TTE and CMR imaging. Ventricular and atrial diameter measurements from TTE-derived standard parasternal long axis and apical four-chamber views and CMR-derived three- and four-chamber views were recorded. The concordance between the two methods was compared using intra-class correlation coefficients (ICC) and Bland-Altman plots. Results: Of the patients, 25 (61%) were male and the mean age was 48.12 ± 16.79. The mean ICC for LVDD between CMR observers was 0.957 (95% CI: 0.918-0.978), while the mean ICC between CMR and TTE measurements were 0.849 (95% CI: 0.709-0.922) and 0.836 (95% CI: 0.684-0.915), respectively. The mean ICC for the right ventricle between CMR observers was 0.985 (95% CI: 0.971-0.992), while the mean ICC between CMR and TTE measurements were 0.869 (95% CI: 0.755-0.930) and 0.892 (95% CI: 0.799-0.942), respectively. Passing-Bablok Regression and Bland-Altman plots indicated high concordance between the two methods. Conclusions: TTE and CMR indicated high concordance in chamber diameter measurements for which the CMR should be considered in patients for whom optimal evaluation with TTE could not be performed due to their limitations.

MLSeq: Machine learning interface for RNA-sequencing data.

BACKGROUND AND OBJECTIVE: In the last decade, RNA-sequencing technology has become method-of-choice and prefered to microarray technology for gene expression based classification and differential expression analysis since it produces less noisy data. Although there are many algorithms proposed for microarray data, the number of available algorithms and programs are limited for classification of RNA-sequencing data. For this reason, we developed MLSeq, to bring not only frequently used classification algorithms but also novel approaches together and make them available to be used for classification of RNA sequencing data. This package is developed using R language environment and distributed through BIOCONDUCTOR network. METHODS: Classification of RNA-sequencing data is not straightforward since raw data should be preprocessed before downstream analysis. With MLSeq package, researchers can easily preprocess (normalization, filtering, transformation etc.) and classify raw RNA-sequencing data using two strategies: (i) to perform algorithms which are directly proposed for RNA-sequencing data structure or (ii) to transform RNA-sequencing data in order to bring it distributionally closer to microarray data structure, and perform algorithms which are developed for microarray data. Moreover, we proposed novel algorithms such as voom (an acronym for variance modelling at observational level) based nearest shrunken centroids (voomNSC), diagonal linear discriminant analysis (voomDLDA), etc. through MLSeq. MATERIALS: Three real RNA-sequencing datasets (i.e cervical cancer, lung cancer and aging datasets) were used to evalute model performances. Poisson linear discriminant analysis (PLDA) and negative binomial linear discriminant analysis (NBLDA) were selected as algorithms based on dicrete distributions, and voomNSC, nearest shrunken centroids (NSC) and support vector machines (SVM) were selected as algorithms based on continuous distributions for model comparisons. Each algorithm is compared using classification accuracies and sparsities on an independent test set. RESULTS: The algorithms which are based on discrete distributions performed better in cervical cancer and aging data with accuracies above 0.92. In lung cancer data, the most of algorithms performed similar with accuracies of 0.88 except that SVM achieved 0.94 of accuracy. Our voomNSC algorithm was the most sparse algorithm, and able to select 2.2% and 6.6% of all features for cervical cancer and lung cancer datasets respectively. However, in aging data, sparse classifiers were not able to select an optimal subset of all features. CONCLUSION: MLSeq is comprehensive and easy-to-use interface for classification of gene expression data. It allows researchers perform both preprocessing and classification tasks through single platform. With this property, MLSeq can be considered as a pipeline for the classification of RNA-sequencing data.

The Effect of Thyroid Stimulating Hormone Level Within the Reference Range on In-Hospital and Short-Term Prognosis in Acute Coronary Syndrome Patients.

Background and objectives: Despite being within the normal reference range, changes in thyroid stimulating hormone (TSH) levels have negative effects on the cardiovascular system. The majority of patients admitted to hospital with acute coronary syndrome (ACS) are euthyroid. The aim of this study was to investigate the effect of TSH level on the prognosis of in-hospital and follow-up periods of euthyroid ACS patients. Materials and Methods: A total of 629 patients with acute coronary syndrome without thyroid dysfunction were included in the study. TSH levels of patients were 0.3-5.33 uIU/mL. Patients were divided into three TSH tertiles: TSH level between (1) 0.3 uIU/mL and <0.90 uIU/mL (n = 209), (2) 0.90 uIU/mL and <1.60 uIU/mL (n = 210), and (3) 1.60 uIU/mL and 5.33 uIU/mL (n = 210). Demographic, clinical laboratory, and angiographic characteristics were compared between groups in terms of in-hospital and follow-up prognosis. Results: Mean age was 63.42 ± 12.5, and 73.9% were male. There was significant difference between tertiles in terms of TSH level at admission (p < 0.001), the severity of coronary artery disease (p = 0.024), in-hospital mortality (p < 0.001), in-hospital major hemorrhage (p = 0.005), total adverse clinical event (p = 0.03), follow-up mortality (p = 0.022), and total mortality (p < 0.001). In multivariate logistic regression analysis, the high-normal TSH tertile was found to be cumulative mortality increasing factor (OR = 6.307, 95%; CI: 1.769-22.480; p = 0.005) during the 6-month follow-up period after hospitalization and discharge. Conclusions: High-normal TSH tertile during hospital admission in euthyroid ACS patients is an independent predictor of total mortality during the 6-month follow-up period after hospitalization and discharge.

Biological variation of peripheral blood T-lymphocytes.

BACKGROUND: Flow cytometric analysis of the lymphocyte subsets has become one of the most commonly used techniques in the routine clinical laboratory. It is frequently used in monitoring lymphocyte recovery after hematopoietic stem cell transplantation (HSCT), as well as diagnosis and treatment of acquired immunodeficiency syndrome (AIDS). Reliable biological variation (BV) data is needed for safe clinical application of these tests. In this study, similar preanalytical and analytical protocols to the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) checklist were followed and a stringent statistical approach was applied to define BV of T-lymphocytes. METHODS: During the 10 weeks study period, weekly blood samples were obtained from 30 healthy individuals (20 females, 10 males) and analyzed with Facs Canto (BD Biosciences, San Jose, CA, USA) analyzer using 4-colour BD Multitest CD3/CD8/CD45/CD4 reagents. Data were assessed in terms of normality, tendencies, outliers and variance homogeneity prior to applying coefficient of variance (CV)- analysis of variance (ANOVA) test. Sex-stratified within-individual (CVI) and between-individual (CVG) BV estimates of CD3+, CD3 + CD4+, CD3 + CD8+, and CD3 + CD4 + CD8+ T lymphocytes were calculated. RESULTS: No difference was found between males and females. Except for the CD3 + CD4 + CD8+ subset, stable BV was found for CD3+, CD3 + CD4+, and CD3 + CD8+ subsets. CONCLUSSION: Instead of using the conventional reference ranges of CD3+, CD3 + CD4+ and CD3 + CD8+ counts for monitoring HIV positive or post-HSCT patients, RCV should be used. Because individualityis characteristic of lymphocytes subsets RCVs should be used instead of RIs for patient monitoring.

Can myometrial thickness/cervical length ratio predict preterm delivery in singleton pregnancies with threatened preterm labor? A prospective study.

OBJECTIVE: To investigate whether myometrial thickness (MT) to cervical length (CL) ratio could be used in the prediction of preterm birth (PTB) in singleton pregnancies presented with threatened preterm labor (TPL). METHODS: After 48 h of successful tocolysis, MT was measured transabdominally from the fundal, mid-anterior walls and the lower uterine segment (LUS) in 46 pregnancies presented with TPL. MT measurements were divided into CL, individually. The main outcome was PTB before 37 weeks of gestation. RESULTS: The patients were divided into two groups as women delivered ≥ 37 weeks (38.68 ± 1.01 weeks) (n = 25) and those delivered < 37 weeks (34.28 ± 2.53 weeks) (n = 21). The mean ± SD CL in the preterm delivery group was significantly shorter than the term delivery group (23.77 ± 9.23 vs 29.91 ± 7.03 mm, p < 0.05). Fundal, mid-anterior or LUS MT values were similar in both groups. However, in those who delivered preterm, the ratios of fundal MT-to-CL (p = 0.026) and mid-anterior MT-to-CL (p = 0.0085) were significantly different compared to those delivered at term. The optimal cutoff values for CL, fundal MT-to-CL and mid-anterior MT-to-CL ratios in predicting PTB were calculated as 31.1 mm, 0.19 and 0.20, respectively. Fundal MT-to-CL ratio predicted preterm delivery with 71% sensitivity, 72% specificity, 68% positive and 75% negative predictive values. For mid-anterior MT-to-CL ratio, respective values were 76, 76, 73 and 79%. CONCLUSION: Measurement of MT along with CL may offer a promising method in the management of women presented with TPL.

The Relationship between Diffusion-Weighted Magnetic Resonance Imaging Lesions and 24-Hour Rhythm Holter Findings in Patients with Cryptogenic Stroke.

BACKGROUND AND OBJECTIVES: Cranial magnetic resonance imaging findings of patients considered to be cryptogenic stroke may be useful in determining the clinical and prognostic significance of arrhythmias, such as atrial premature beats and atrial run attacks, that are frequently encountered in rhythm Holter analysis. This study was conducted to investigate the relationship between short atrial runs and frequent premature atrial contractions detected in Holter monitors and infarct distributions in cranial magnetic resonance imaging of patients diagnosed with cryptogenic stroke. MATERIALS AND METHODS: We enrolled the patients with acute ischemic stroke whose etiology were undetermined. We divided the patients in two groups according to diffusion-weighted magnetic resonance imaging as single or multiple vascular territory acute infarcts. The demographic, clinical, laboratory, echocardiographic, and rhythm Holter analyses were compared. RESULTS: The study investigated 106 patients diagnosed with cryptogenic stroke. Acute cerebral infarctions were detected in 31% of the investigated patients in multiple territories and in 69% in a single territory. In multivariate logistic regression analysis, the total premature atrial contraction count (OR = 1.002, 95% CI: 1.001⁻1.004, p = 0.001) and short atrial run count (OR = 1.086, 95% CI: 1.021⁻1.155, p = 0.008) were found as independent variables that could distinguish between infarctions in a single or in multiple vascular territories. CONCLUSIONS: Rhythm Holter monitoring of patients with infarcts detected in multiple vascular territories showed significantly higher premature atrial contractions and short atrial run attacks. More effort should be devoted to the identification of cardioembolic etiology in cryptogenic stroke patients with concurrent acute infarcts in the multiple vascular territories of the brain.

Retinal ganglion cell complex and peripapillary retinal nerve fiber layer thicknesses following carotid endarterectomy.

PURPOSE: To examine changes in retinal ganglion cell complex (GCC) and peripapillary retinal nerve fiber layer (RNFL) thicknesses by optical coherence tomography (OCT) in contralateral and ipsilatateral eyes of carotid artery stenosis (CAS) patients before and after carotid endarterectomy (CEA). METHODS: Forty-two consecutive patients diagnosed with CAS (70-99% stenosis rate) who underwent CEA were included in this prospective cross-sectional study. The indication for CEA was based on the Asymptomatic Carotid Atherosclerosis Study. Doppler ultrasonography and computed tomography angiography were performed to calculate CAS. All the subjects underwent an ophthalmological examination, including best corrected visual acuity (BCVA), intraocular pressure (IOP) measurements, biomicroscopy, fundoscopy, and OCT before and after the surgery. RESULTS: The mean preoperative intraocular pressure was 15.2 ± 2.1 mmHg in the ipsilateral eye and 15.8 ± 2.7 in the contralateral eye. The mean postoperative intraocular pressure in the ipsilateral and contralateral eye was 18.6 ± 3.0 and 19.3 ± 3.8, respectively. The intraocular pressure was significantly higher in postoperative eyes (p = 0.0001). There was a statistically significant decrease in peripapillary RNFL thickness in superior quadrants postoperatively in ipsilateral eyes. The retinal GCC layer thickness was not significantly different before and after CEA in ipsilateral and contralateral eyes. CONCLUSIONS: Carotid endarterectomy results in thinning of the superior peripapillary RNFL thickness. To the best of our knowledge, this is the first study to examine peripapillary RNFL and GCC thicknesses before and after CEA.

Machine learning based classification of cells into chronological stages using single-cell transcriptomics.

Age-associated deterioration of cellular physiology leads to pathological conditions. The ability to detect premature aging could provide a window for preventive therapies against age-related diseases. However, the techniques for determining cellular age are limited, as they rely on a limited set of histological markers and lack predictive power. Here, we implement GERAS (GEnetic Reference for Age of Single-cell), a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes. GERAS displays greater than 90% accuracy in classifying the chronological stage of zebrafish and human pancreatic cells. The framework demonstrates robustness against biological and technical noise, as evaluated by its performance on independent samplings of single-cells. Additionally, GERAS determines the impact of differences in calorie intake and BMI on the aging of zebrafish and human pancreatic cells, respectively. We further harness the classification ability of GERAS to identify molecular factors that are potentially associated with the aging of beta-cells. We show that one of these factors, junba, is necessary to maintain the proliferative state of juvenile beta-cells. Our results showcase the applicability of a machine learning framework to classify the chronological stage of heterogeneous cell populations, while enabling detection of candidate genes associated with aging.

Biological variation estimates of prothrombin time, activated partial thromboplastin time, and fibrinogen in 28 healthy individuals.

BACKGROUND: Although tests of global hemostasis prothrombin time (PT) and activated partial thromboplastin time (aPTT) should not be used for prediction of bleeding risk, these tests are often used by many clinicians in daily practice particularly as a preoperative screening test. Robust biological variation (BV) data are needed for safe clinical applications of these tests. In this study, a stringent protocol was followed to estimate the BV's for PT, aPTT, and fibrinogen levels. METHODS: Weekly blood samples were obtained from 28 healthy individuals (18 females, 10 males) during 10 weeks study period. All measurements were performed with Stago STA-R coagulation analyzer. Prior to coefficient of variation (CV)-analysis of variance (ANOVA), the data were assessed for normality, trends, outliers, and variance homogeneity. Sex-stratified within-individual (CVI ) and between-individual (CVG ) BV estimates were determined for PT, aPTT, and fibrinogen tests. RESULTS: No difference was found between male and female estimates of BV. The observed CVI and CVG estimates were found to be lower than those previously published. Only for fibrinogen, CVI was higher than CVG . CONCLUSION: Following a meticulous protocol, our study results provide up-to-date and more stringent BV estimates of global hemostasis tests.

Investigation of protein quaternary structure via stoichiometry and symmetry information.

The Protein Data Bank (PDB) is the single worldwide archive of experimentally-determined three-dimensional (3D) structures of proteins and nucleic acids. As of January 2017, the PDB housed more than 125,000 structures and was growing by more than 11,000 structures annually. Since the 3D structure of a protein is vital to understand the mechanisms of biological processes, diseases, and drug design, correct oligomeric assembly information is of critical importance. Unfortunately, the biologically relevant oligomeric form of a 3D structure is not directly obtainable by X-ray crystallography, whilst in solution methods (NMR or single particle EM) it is known from the experiment. Instead, this information may be provided by the PDB Depositor as metadata coming from additional experiments, be inferred by sequence-sequence comparisons with similar proteins of known oligomeric state, or predicted using software, such as PISA (Proteins, Interfaces, Structures and Assemblies) or EPPIC (Evolutionary Protein Protein Interface Classifier). Despite significant efforts by professional PDB Biocurators during data deposition, there remain a number of structures in the archive with incorrect quaternary structure descriptions (or annotations). Further investigation is, therefore, needed to evaluate the correctness of quaternary structure annotations. In this study, we aim to identify the most probable oligomeric states for proteins represented in the PDB. Our approach evaluated the performance of four independent prediction methods, including text mining of primary publications, inference from homologous protein structures, and two computational methods (PISA and EPPIC). Aggregating predictions to give consensus results outperformed all four of the independent prediction methods, yielding 83% correct, 9% wrong, and 8% inconclusive predictions, when tested with a well-curated benchmark dataset. We have developed a freely-available web-based tool to make this approach accessible to researchers and PDB Biocurators (http://quatstruct.rcsb.org/).