The association of reproductive hormones, thyroid function, and vitamin levels with premature ejaculation: A prospective case-control study.

PURPOSE: To investigate whether serum hormone (testosterone, prolactin, gonadotropins, and thyroid hormones) and vitamin (vitamin B12, folic acid, and vitamin D) levels are associated with premature ejaculation (PE). MATERIALS AND METHODS: This prospective case-control study included 126 patients with PE (lifelong PE [LPE] in 94 and acquired PE [APE] in 32) who presented to the urology outpatient clinic between April 2016 and January 2023 and 92 healthy men as a control group. The diagnosis of PE was based on the criteria defined by the International Society for Sexual Medicine. Serum total testosterone (TT), free and bioavailable testosterone, follicle-stimulating hormone, luteinizing hormone, prolactin, thyroid-stimulating hormone, free triiodothyronine, thyroxine (fT4), vitamin B12, folic acid, and vitamin D levels were measured. RESULTS: Serum TT, fT4, and vitamin D levels were significantly higher in patients with PE than in the control group (p=0.022, p=0.002, and p=0.044, respectively). However, the serum vitamin B12 level was significantly lower in the PE group (p=0.021). In the multivariate logistic regression analysis, only vitamin B12 was found to be an independent risk factor for PE, with an estimated odds ratio of 0.997 (95% confidence interval 0.994-0.999, p=0.036). CONCLUSIONS: This study demonstrated that lower vitamin B12 levels are associated with the presence of PE. Therefore, we believe that it would be beneficial to consider vitamin B12 levels in the evaluation of patients with PE.

Predictive value of SIRI and SII for metastases in RCC: a prospective clinical study.

OBJECTIVES: In this prospective cross-sectional clinical study, we aimed to determine the efficiency of preoperative hematological markers namely SIRI (systemic inflammatory response index) and SII (systemic inflammatory index) for renal cell cancer to predict the possibility of postoperative metastases. METHODS: Istanbul Education and Research Hospital, Clinic of Urology and Medical Oncology in the clinic between the dates of June 2022 to 2023 February, a diagnosis of renal cell cancer by surgical or medical oncology units imported into the treatment planning of 72 patients were included in the study. All cases with diagnoses of renal cell carcinoma were searched from hospital records. Patients with secondary malignancy, hematological or rheumatological disorders or ones with recent blood product transfusion or diagnoses of infection within the 1-month-time of diagnoses were excluded for data analyses. The data within complete blood counts (CBC) analyzed just before the time of renal biopsy or surgery were studied for SIRI and SII calculations. Twenty-two metastatic and 50 non-metastatic RCC patients were included. SIRI and SII values were compared among groups to seek change of values in case of metastasis and in non-metastatic patients a cut-off value were sought to indicate malignancy before pathological diagnosis. RESULTS: Mean age of non-metastatic RCC patients were 60.12+/-11.55 years and metastatic RCC patients were 60.25+/-11.72. Histological sub-types of the RCC specimens were clear cell (72%), chromophobe cell (17%), papillary cell (7%) and others (4%). Median SIRI values for non-metastatic and metastatic groups were 1.26 and 2.1 (mean+/-S.D. 1.76 +/-1.9 and 3.12+/-4.22 respectively (p < 0.05). Median SII values for non-metastatic and metastatic groups were 566 and 1434 (mean+/-S.D. 870 +/-1019 and 1537+/-917) respectively (p < 0.001). AUC for detection of metastasis were 0.809 for SII and 0.737 for SIRI. CONCLUSIONS: SIRI and SII indexes seem to show a moderate efficiency to show metastases in RCC.

Input Shape Effect on Classification Performance of Raw EEG Motor Imagery Signals with Convolutional Neural Networks for Use in Brain-Computer Interfaces.

EEG signals are interpreted, analyzed and classified by many researchers for use in brain-computer interfaces. Although there are many different EEG signal acquisition methods, one of the most interesting is motor imagery signals. Many different signal processing methods, machine learning and deep learning models have been developed for the classification of motor imagery signals. Among these, Convolutional Neural Network models generally achieve better results than other models. Because the size and shape of the data is important for training Convolutional Neural Network models and discovering the right relationships, researchers have designed and experimented with many different input shape structures. However, no study has been found in the literature evaluating the effect of different input shapes on model performance and accuracy. In this study, the effects of different input shapes on model performance and accuracy in the classification of EEG motor imagery signals were investigated, which had not been specifically studied before. In addition, signal preprocessing methods, which take a long time before classification, were not used; rather, two CNN models were developed for training and classification using raw data. Two different datasets, BCI Competition IV 2A and 2B, were used in classification processes. For different input shapes, 53.03-89.29% classification accuracy and 2-23 s epoch time were obtained for 2A dataset, 64.84-84.94% classification accuracy and 4-10 s epoch time were obtained for 2B dataset. This study showed that the input shape has a significant effect on the classification performance, and when the correct input shape is selected and the correct CNN architecture is developed, feature extraction and classification can be done well by the CNN architecture without any signal preprocessing.

Three-Dimensional Finite Element Analysis of the Effect of Endosteal Implants with Different Macro Designs on Stress Distribution in Different Bone Qualities.

PURPOSE: The aim of this study was to analyze stress distribution on four different implant designs and four different qualities of cortical bone surrounding the implant crest modules using three-dimensional finite element analysis. MATERIALS AND METHODS: Four different implant designs of the same diameter and length (3.75 ± 11.5 mm) were used. Rhinoceros 4.0 3D modeling software was used for preparing the four types of bone quality (D1, D2, D3, and D4). Mandibular second premolar crowns were constructed as prosthetic superstructures of the implants, and a total force of 300 N was vertically applied to the crowns. Stress distribution on dental implants and cortical bone of D1, D2, D3, and D4 bone quality was evaluated. RESULTS: Among all of the implants, the maximum von Mises stress value on cortical bone surrounding the neck region of the implants was found in D4 bone quality, while the minimum stress was detected in D1 bone quality. The stress distribution on the threads of dental implants exhibited the most homogeneous distribution in D1 bone quality, while higher von Mises stress values were observed in the apical blade segment of the implants in D3 and D4 bone quality. CONCLUSION: It was found that the maximum stress of cortical bone surrounding the implant crest module decreased as the bone density increased. The microthreads on the implant crest module might cause an increase in stress to cortical bone surrounding the neck region of implants.