Learning from Imbalanced Data: Integration of Advanced Resampling Techniques and Machine Learning Models for Enhanced Cancer Diagnosis and Prognosis.

BACKGROUND/OBJECTIVES: This study aims to evaluate the performance of various classification algorithms and resampling methods across multiple diagnostic and prognostic cancer datasets, addressing the challenges of class imbalance. METHODS: A total of five datasets were analyzed, including three diagnostic datasets (Wisconsin Breast Cancer Database, Cancer Prediction Dataset, Lung Cancer Detection Dataset) and two prognostic datasets (Seer Breast Cancer Dataset, Differentiated Thyroid Cancer Recurrence Dataset). Nineteen resampling methods from three categories were employed, and ten classifiers from four distinct categories were utilized for comparison. RESULTS: The results demonstrated that hybrid sampling methods, particularly SMOTEENN, achieved the highest mean performance at 98.19%, followed by IHT (97.20%) and RENN (96.48%). In terms of classifiers, Random Forest showed the best performance with a mean value of 94.69%, with Balanced Random Forest and XGBoost following closely. The baseline method (no resampling) yielded a significantly lower performance of 91.33%, highlighting the effectiveness of resampling techniques in improving model outcomes. CONCLUSIONS: This research underscores the importance of resampling methods in enhancing classification performance on imbalanced datasets, providing valuable insights for researchers and healthcare professionals. The findings serve as a foundation for future studies aimed at integrating machine learning techniques in cancer diagnosis and prognosis, with recommendations for further research on hybrid models and clinical applications.

Effects of Sandbag-Free Follow-up After Manual Compression in Patients Who Underwent Transfemoral Access for Percutaneous Intervention.

OBJECTIVE: Femoral access site complications (ASC) are frequent yet significant conditions associated with percutaneous intervention procedures that affect patient-physician comfort. In this study, we compared ASC rates between patients who received compression with a sandbag, the standard practice in many clinics, and patients monitored solely with bedrest without sandbag compression. METHODS: This study included patients undergoing any transfemoral percutaneous intervention (mostly coronary interventions) between April 2019 and May 2023 at our clinic. Patients were classified into two groups: those monitored without a sandbag (n = 160) and those with a sandbag (n = 158). ASC rates (ecchymosis, pseudoaneurysm, hematoma, bleeding) were compared between the two groups. RESULTS: No differences were observed between the two groups in gender, age, sheath size, and bed rest times. Complications were observed in 16.9% (n = 27) of patients without sandbags and 25.3% (n = 40) of patients with sandbags. The most common complication was ecchymosis, seen in 10.6% (n = 17) of the no-sandbag group and 13.9% (n = 22) of the sandbag group. CONCLUSION: Following manual compression after femoral sheath removal, patients receiving bedrest without sandbag use are less likely to develop ASC. Additionally, dismissing sandbag use leads to a significant increase in patient comfort.

The relationship of systemic and pulmonary arterial parameters with HFpEF scores (H2 FPEF, HFA-PEFF) and diastolic dysfunction parameters in heart failure patients with preserved ejection fraction.

OBJECTIVE: We aimed to show the relationship between pulmonary pulse wave transit time (pPTT), pulmonary artery stiffness (PAS), and aortic stiffness parameters measured by non-invasive methods, HFpEF patients, and HFpEF scores (H2 FPEF, HFA-PEFF). METHOD: A total of 101 patients were included in our study, 52 of whom were HFpEF patients and 49 were control groups without heart failure. Echocardiographic parameters for PAS and pPTT were calculated, along with diastolic parameters that support the diagnosis of HFpEF. Aortic stiffness was assessed using a PWA monitor. Demographic features, laboratory findings, aortic stiffness parameters, and echocardiographic findings including pulmonary artery parameters were compared with the control group. RESULTS: PAS, pPTT, PWV were significantly higher in the HFpEF group than in the control group (p < 0.001). PAS and pPTT correlated positively with HFpEF scores. In linear regression analysis for PAS, a directly positive correlation was found between E/e' and PAS, independent of aortic stiffness. CONCLUSION: These parameters can be used as a predictive value in the diagnosis process of patients with suspected HFpEF. A significant relationship between PAS and ventricular stiffness (E/e') was shown independently of aortic stiffness.

Machine learning based assessment of preclinical health questionnaires.

BACKGROUND: Within modern health systems, the possibility of accessing a large amount and a variety of data related to patients' health has increased significantly over the years. The source of this data could be mobile and wearable electronic systems used in everyday life, and specialized medical devices. In this study we aim to investigate the use of modern Machine Learning (ML) techniques for preclinical health assessment based on data collected from questionnaires filled out by patients. METHOD: To identify the health conditions of pregnant women, we developed a questionnaire that was distributed in three maternity hospitals in the Mureș County, Romania. In this work we proposed and developed an ML model for pattern detection in common risk assessment based on data extracted from questionnaires. RESULTS: Out of the 1278 women who answered the questionnaire, 381 smoked before pregnancy and only 216 quit smoking during the period in which they became pregnant. The performance of the model indicates the feasibility of the solution, with an accuracy of 98 % confirmed for the considered case study. CONCLUSION: The proposed solution offers a simple and efficient way to digitize questionnaire data and to analyze the data through a reduced computational effort, both in terms of memory and computing power used.

Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images.

Brain tumors have been the subject of research for many years. Brain tumors are typically classified into two main groups: benign and malignant tumors. The most common tumor type among malignant brain tumors is known as glioma. In the diagnosis of glioma, different imaging technologies could be used. Among these techniques, MRI is the most preferred imaging technology due to its high-resolution image data. However, the detection of gliomas from a huge set of MRI data could be challenging for the practitioners. In order to solve this concern, many Deep Learning (DL) models based on Convolutional Neural Networks (CNNs) have been proposed to be used in detecting glioma. However, understanding which CNN architecture would work efficiently under various conditions including development environment or programming aspects as well as performance analysis has not been studied so far. In this research work, therefore, the purpose is to investigate the impact of two major programming environments (namely, MATLAB and Python) on the accuracy of CNN-based glioma detection from Magnetic Resonance Imaging (MRI) images. To this end, experiments on the Brain Tumor Segmentation (BraTS) dataset (2016 and 2017) consisting of multiparametric magnetic MRI images are performed by implementing two popular CNN architectures, the three-dimensional (3D) U-Net and the V-Net in the programming environments. From the results, it is concluded that the use of Python with Google Colaboratory (Colab) might be highly useful in the implementation of CNN-based models for glioma detection. Moreover, the 3D U-Net model is found to perform better, attaining a high accuracy on the dataset. The authors believe that the results achieved from this study would provide useful information to the research community in their appropriate implementation of DL approaches for brain tumor detection.

Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines.

Big data pipelines are developed to process data characterized by one or more of the three big data features, commonly known as the three Vs (volume, velocity, and variety), through a series of steps (e.g., extract, transform, and move), making the ground work for the use of advanced analytics and ML/AI techniques. Computing continuum (i.e., cloud/fog/edge) allows access to virtually infinite amount of resources, where data pipelines could be executed at scale; however, the implementation of data pipelines on the continuum is a complex task that needs to take computing resources, data transmission channels, triggers, data transfer methods, integration of message queues, etc., into account. The task becomes even more challenging when data storage is considered as part of the data pipelines. Local storage is expensive, hard to maintain, and comes with several challenges (e.g., data availability, data security, and backup). The use of cloud storage, i.e., storage-as-a-service (StaaS), instead of local storage has the potential of providing more flexibility in terms of scalability, fault tolerance, and availability. In this article, we propose a generic approach to integrate StaaS with data pipelines, i.e., computation on an on-premise server or on a specific cloud, but integration with StaaS, and develop a ranking method for available storage options based on five key parameters: cost, proximity, network performance, server-side encryption, and user weights/preferences. The evaluation carried out demonstrates the effectiveness of the proposed approach in terms of data transfer performance, utility of the individual parameters, and feasibility of dynamic selection of a storage option based on four primary user scenarios.

Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography.

Renal failure, a public health concern, and the scarcity of nephrologists around the globe have necessitated the development of an AI-based system to auto-diagnose kidney diseases. This research deals with the three major renal diseases categories: kidney stones, cysts, and tumors, and gathered and annotated a total of 12,446 CT whole abdomen and urogram images in order to construct an AI-based kidney diseases diagnostic system and contribute to the AI community's research scope e.g., modeling digital-twin of renal functions. The collected images were exposed to exploratory data analysis, which revealed that the images from all of the classes had the same type of mean color distribution. Furthermore, six machine learning models were built, three of which are based on the state-of-the-art variants of the Vision transformers EANet, CCT, and Swin transformers, while the other three are based on well-known deep learning models Resnet, VGG16, and Inception v3, which were adjusted in the last layers. While the VGG16 and CCT models performed admirably, the swin transformer outperformed all of them in terms of accuracy, with an accuracy of 99.30 percent. The F1 score and precision and recall comparison reveal that the Swin transformer outperforms all other models and that it is the quickest to train. The study also revealed the blackbox of the VGG16, Resnet50, and Inception models, demonstrating that VGG16 is superior than Resnet50 and Inceptionv3 in terms of monitoring the necessary anatomy abnormalities. We believe that the superior accuracy of our Swin transformer-based model and the VGG16-based model can both be useful in diagnosing kidney tumors, cysts, and stones.

Comparison of spermiograms of infertile men before and during the COVID-19 pandemic.

OBJECTIVE: Since the start of the COVID-19 pandemic, there has been interest in the impact of both SARS-CoV-2 infection and pandemic-induced social restrictions on male reproductive health. This study aimed to evaluate the spermiogram values of men who presented for infertility during the pandemic compared with the previous 2 years. METHODS: Patients who presented to a urology outpatient clinic for the first time due to infertility were included. The patients' age, semen volume, and spermiogram results were recorded. Based on the presentation date, the patients were divided into prepandemic group 1 (March 2018-February 2019), prepandemic group 2 (March 2019-February 2020), and pandemic group (March 2020-February 2021) for comparison. RESULTS: A total of 594 patients were included. There was no significant difference between the three groups in terms of the number of patients who presented for infertility (207, 190, and 197 patients, respectively; p=0.691). The mean age was 36.6±7.2 in the prepandemic group 1, 35.5±7.1 in the prepandemic group 2, and 33.1±6.3 in the pandemic group. Patients who presented during the pandemic were significantly younger (p<0.001). There were no differences between the groups in terms of semen volume (p=0.910) or rates of normospermia and pathological spermiogram findings (p=0.222). CONCLUSIONS: In the first year of the COVID-19 pandemic, there was no significant difference in the number of patients who presented for infertility or in their spermiogram results compared with 2018 and 2019. However, it is noteworthy that the patients were significantly younger during the pandemic than in the previous 2 years.

Comparison of spermiograms of infertile men before and during the COVID-19 pandemic

SUMMARY OBJECTIVE: Since the start of the COVID-19 pandemic, there has been interest in the impact of both SARS-CoV-2 infection and pandemic-induced social restrictions on male reproductive health. This study aimed to evaluate the spermiogram values of men who presented for infertility during the pandemic compared with the previous 2 years. METHODS: Patients who presented to a urology outpatient clinic for the first time due to infertility were included. The patients' age, semen volume, and spermiogram results were recorded. Based on the presentation date, the patients were divided into prepandemic group 1 (March 2018-February 2019), prepandemic group 2 (March 2019-February 2020), and pandemic group (March 2020-February 2021) for comparison. RESULTS: A total of 594 patients were included. There was no significant difference between the three groups in terms of the number of patients who presented for infertility (207, 190, and 197 patients, respectively; p=0.691). The mean age was 36.6±7.2 in the prepandemic group 1, 35.5±7.1 in the prepandemic group 2, and 33.1±6.3 in the pandemic group. Patients who presented during the pandemic were significantly younger (p<0.001). There were no differences between the groups in terms of semen volume (p=0.910) or rates of normospermia and pathological spermiogram findings (p=0.222). CONCLUSIONS: In the first year of the COVID-19 pandemic, there was no significant difference in the number of patients who presented for infertility or in their spermiogram results compared with 2018 and 2019. However, it is noteworthy that the patients were significantly younger during the pandemic than in the previous 2 years.

Big Data Workflows: Locality-Aware Orchestration Using Software Containers.

The emergence of the edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the performance penalties from data transfers among remote data centres. Existing big data processing solutions provide limited support for handling data locality and are inefficient in processing small and frequent events specific to the edge environments. This article proposes a novel architecture and a proof-of-concept implementation for software container-centric big data workflow orchestration that puts data locality at the forefront. The proposed solution considers the available data locality information, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare the proposed solution with Argo workflows and demonstrate a significant performance improvement in the execution speed for processing the same data units. Finally, we carry out experiments with the proposed solution under different configurations and analyze individual aspects affecting the performance of the overall solution.

Human activity recognition using wearable sensors, discriminant analysis, and long short-term memory-based neural structured learning.

Healthcare using body sensor data has been getting huge research attentions by a wide range of researchers because of its good practical applications such as smart health care systems. For instance, smart wearable sensor-based behavior recognition system can observe elderly people in a smart eldercare environment to improve their lifestyle and can also help them by warning about forthcoming unprecedented events such as falls or other health risk, to prolong their independent life. Although there are many ways of using distinguished sensors to observe behavior of people, wearable sensors mostly provide reliable data in this regard to monitor the individual's functionality and lifestyle. In this paper, we propose a body sensor-based activity modeling and recognition system using time-sequential information-based deep Neural Structured Learning (NSL), a promising deep learning algorithm. First, we obtain data from multiple wearable sensors while the subjects conduct several daily activities. Once the data is collected, the time-sequential information then go through some statistical feature processing. Furthermore, kernel-based discriminant analysis (KDA) is applied to see the better clustering of the features from different activity classes by minimizing inner-class scatterings while maximizing inter-class scatterings of the samples. The robust time-sequential features are then applied with Neural Structured Learning (NSL) based on Long Short-Term Memory (LSTM), for activity modeling. The proposed approach achieved around 99% recall rate on a public dataset. It is also compared to existing different conventional machine learning methods such as typical Deep Belief Network (DBN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) where they yielded the maximum recall rate of 94%. Furthermore, a fast and efficient explainable Artificial Intelligence (XAI) algorithm, Local Interpretable Model-Agnostic Explanations (LIME) is used to explain and check the machine learning decisions. The robust activity recognition system can be adopted for understanding peoples' behavior in their daily life in different environments such as homes, clinics, and offices.

Prognostic value of HPV DNA in Urothelial Carcinoma of the Bladder: A Preliminary Report of 2-Year Follow-up Results.

PURPOSE: The association between the human papillomavirus (HPV) and anogenital carcinomas is well established. However, despite its anatomic adjacency, the relationship between HPV and urothelial carcinoma of the bladder (UCB) is less clear. Recent meta-analysis and case-control studies demonstrated a significant relationship between the presence of HPV DNA and UCB. The aim of this clinical study was to compare the 2-year follow-up results of HPV-positive and HPV-negative UCB patients to evaluate the prognostic value of HPV DNA positivity in UCB. METHODS: The study included patients with stage pTa and pT1 UCB who underwent polymerase chain reaction (PCR) analysis of HPV DNA between January 1 and November 30, 2018. Based on their PCR results, 19 HPV-positive and 38 HPV-negative UCB patients who had regular follow-up in our clinic were evaluated in terms of tumor recurrence and disease progression over a 2-year follow-up period. RESULTS: There was no significant difference between the groups in terms of age, follow-up time, smoking, or tumor grade (P= .576, P= .368, P= .080, and P= .454). Tumor recurrence was observed at least once in 47.3% (n=9) of the 19 HPV-positive patients and 36.8% (n=14) of the 38 HPV-negative patients (P= .445). There was no difference in disease progression between the groups during follow-up. CONCLUSION: In our sample of UCB patients, the presence of HPV DNA was associated with a trend toward higher recurrence rate during the 2-year follow-up, though the difference was not statistically significant. No difference in disease progression was observed based on HPV DNA positivity.

Is There any Association between Urothelial Carcinoma of the Bladder and Human Papillomavirus? A Case-Control Study.

OBJECTIVES: Human papillomavirus (HPV) is a well-known oncogenic virus associated with anogenital carcinomas. Despite the anatomical proximity of the bladder and the anogenital region, the relationship between HPV and urothelial carcinoma of the bladder (UCB) is still a controversial issue. This study aimed to test the urethral swabs and first-void urine samples of patients with UCB for HPV-Deoxyribonucleic acid (DNA) using polymerase chain reaction (PCR) assay and to compare the results with a control group. MATERIALS AND METHODS: Sixty-nine patients who were diagnosed with UCB between January and December 2018 were included in this case-control study. Sixty-nine patients who visited the urology outpatient clinic for non-oncological reasons within the study period were designated as the control group. Urethral swab and first-void morning urine samples were collected from each patient. HPV-DNA presence was investigated using a PCR kit that can detect a total of 22 HPV genotypes, of which 18 are high-risk and 3 are low-risk genotypes. RESULTS: The mean age of the patients included in the study was 63.2 ± 12.6 years and the male to female ratio was 5.3. HPV-DNA was detected in 28.9% (20/69) of the patients in the case group and in 8.7% (6/69) of the patients in the control group. HPV-DNA positivity was significantly higher in the case group (OR 4.24; 95% CI 1.63-12.34). No statistically significant relationship was found between HPV-DNA positivity and tumor grade (p = 0.36). CONCLUSION: A statistically significant relationship exists between HPV infection and UCB, regardless of the tumor grade.

HPV infection in urology practice.

Human papillomavirus (HPV) is the most common pathogen of sexually transmitted disease worldwide. While HPV is responsible for low-grade benign lesions in the anogenital area such as condyloma acuminatum, it is also strongly associated with cervical, anal, vulvar/vaginal, and penile carcinomas. In addition to being an oncogenic virus, HPV causes a substantial socioeconomic burden due to the recurrence of benign lesions, the lack of a definitive treatment option that provides a complete cure, and the high cost of treatment. The global incidence of HPV infection is rising, especially among young and sexually active individuals; as a result, in recent years these infections have also become increasingly conspicuous in urology practice, both as incidental findings and primary complaints. The aim of this review is to evaluate the pathogenesis, diagnosis, and treatment modalities of HPV infections in light of the current literature from the urologist's perspective.

Comparison of the Efficacy of Intravenous and Intramuscular Lornoxicam for the Initial Treatment of Acute Renal Colic: A Randomized Clinical Trial.

PURPOSE: We aimed to find out if there was any difference between intramuscular and intravenous administration of lornoxicam in terms of efficacy and side effects. MATERIALS AND METHODS: This study was a single-blind parallel-group randomized clinical trial. A total of 51 patients who were diagnosed with acute renal colic at our clinic were included in the study. Pain severity prior to treatment was rated using the Visual Analogue Scale (VAS). Patients were randomized into 2 groups: Group 1 (n = 27) received intramuscular 8mg lornoxicam and Group 2 (n=24) received intravenous 8mg lornoxicam. Pain severity was reassessed 30 minutes after the treatment. Pre- and post-treatment VAS scores and the mean changein the VAS scores of the 2 groups were statistically compared. RESULTS: The mean VAS scores decreased significantly from 7.65 to 2.07 in Group 1, from 7.96 to 1.38 in Group 2, and from 7.79 to 1.75 in total (P < 0.001). No statistically significant difference was observed between Groups 1 and 2 in terms of VAS score reduction (P = 0.128). None of the patients suffered any side effects except for 1 (2%) patient who had dyspepsia. CONCLUSION: Parenteral lornoxicam provides significant pain relief in patients with acute renal colic. However, no significant difference was found between intramuscular and intravenous administration in terms of analgesic efficacy.

Microscopy of Gram-stained urethral smear in the diagnosis of urethritis: Which threshold value should be selected?

The aim of this study was to investigate the sensitivity of GSS in the diagnosis of urethritis in patients who present to the outpatient clinic with symptoms of urethritis. Sixty-three male patients who presented to our outpatient clinic with symptoms of urethritis between January and March 2018 were evaluated. Urethral smear samples obtained from patients were evaluated both by GSS examination and by Real-time Multiplex Polymerase Chain Reaction (rt-MPCR) assay. The sensitivity and specificity of GSS in detecting gonococcal urethritis (GU) and nongonococcal urethritis (NGU) were calculated for threshold values of ≥5 and ≥2 PMNL/HPF. The mean age was 33.2 ± 7.1 years. According to the rt-MPCR results, 14 patients had GU and 27 patients had NGU. A threshold value of ≥5 PMNL/HPF in the GSS demonstrated 92.9% sensitivity in the diagnosis of GU and 55.6% sensitivity in the diagnosis of NGU. A threshold value of ≥2 PMNL/HPF reached 100% sensitivity for GU and 92.6% sensitivity for NGU. A cut-off value of ≥5 PMNL/HPF in the GSS has low sensitivity in the diagnosis of NGU. On the other hand, a threshold value of ≥2 PMNL/HPF seems to have higher sensitivity in the diagnosis of both GU and NGU.

Outcomes of Conservative Management of Asymptomatic Live Donor Kidney Stones.

OBJECTIVE: To evaluate the long-term outcomes of asymptomatic stones ≤4 mm that are left in situ during renal transplantation (RT). MATERIALS AND METHODS: Between 2009 and 2017, 31 patients who received stone-bearing (≤4 mm) kidneys were analyzed. At their last follow-up, the patients were evaluated with computerized tomography (CT) imaging and the results were compared to the initial CT findings obtained during donor evaluation. RESULTS: The mean stone size was 2.9 mm (range 1-4.3). The mean follow-up period after RT was 43.1 months (range 12-97). According to the CT findings of the last follow-up, the stone had passed spontaneously in 26 patients (83.9%). Three patients (9.6%) with a stone size of 4 mm required surgical intervention because the stone became symptomatic within the first year after transplantation. In the remaining 2 patients (6.4%), the stone remained in situ, without a change in its size. In terms of spontaneous passage rate, there was no significant difference between lower and midupper pole stones (P = .948). De novo stone formation was observed in 2 patients (6.4%). CONCLUSION: Asymptomatic stones <4 mm that are detected on donor evaluation may safely be left in situ during RT. Regardless of their localization in the kidney, these stones have high spontaneous passage rates after RT, and the long-term recurrence rates are also considerably low.

PepeSearch: Semantic Data for the Masses.

With the emergence of the Web of Data, there is a need of tools for searching and exploring the growing amount of semantic data. Unfortunately, such tools are scarce and typically require knowledge of SPARQL/RDF. We propose here PepeSearch, a portable tool for searching semantic datasets devised for mainstream users. PepeSearch offers a multi-class search form automatically constructed from a SPARQL endpoint. We have tested PepeSearch with 15 participants searching a Linked Open Data version of the Norwegian Register of Business Enterprises for non-trivial challenges. Retrieval performance was encouragingly high and usability ratings were also very positive, thus suggesting that PepeSearch is effective for searching semantic datasets by mainstream users. We also assessed its portability by configuring PepeSearch to query other SPARQL endpoints.

Glutathione S-transferase M1 and T1 genotypes and myocardial infarction.

Myocardial infarction (MI), which is the most important manifestation of coronary artery disease, is the leading cause of morbidity and mortality in the world. Glutathione S transferases (GSTs) are enzymes responsible for the metabolism of numerous xenobiotics and are known to be polymorphic in humans. We investigated the association between the GSTM1 and GSTT1 gene polymorphisms and MI. The study consists of 296 healthy controls and 324 consecutive patients who had undergone coronary angiography for suspicion of coronary artery disease and with a past history of myocardial infarction. DNA was extracted from whole blood of patient and control. GSTM1 and GSTT1 gene polymorphisms were examined using multiplex PCR. We found that the null GSTM1 was associated with protective effect on MI, although this increase was not significant for GSTM1 (p < 0.054). However, GSTT1 genotype was associated with an increase in the risk of developing MI. In addition to after adjusting other all coronary risk factors, the interactive effect of GSTT1 null genotype remained statistically significant (p < 0.001) for MI disease but GSTM1 null genotype was not statistically significant. Patients, who smoke having the null genotypes of GSTM1, were at a higher risk for developing MI (p < 0.001, OR = 0.41, 95 % CI = 0.240-0.207). There was an effect of interaction of GSTM1 null genotype and smoking on MI development between patient and control groups (p < 0.001). Our results showed that individuals with the null genotypes for GSTM1 had protective effect, while GSTT1 was at a higher risk for MI disease. In addition, there was additional effects of smoking when smoking and non-smoking groups were compared.

Effects of acute exercise on the diameter of the spermatic vein, and duration of reflux in patients with varicocele.

OBJECTIVE: The aim of this study was to investigate the effects of acute exercise on the diameter of the spermatic vein, and on the duration of reflux in patients with varicocele. MATERIAL AND METHODS: The study included 38 patients with complaints of infertility and scrotal pain between 2009 and 2010. The diagnoses were made by physical examination and colour Doppler ultrasound, with both performed before and after exercise tests. RESULTS: The mean age of the participants was 25.7 ± 4.9 years. During the first examination, the grades of the varicoceles detected were as follows: grade I, n = 7; II, n = 10; and III, n = 21. The diameters of veins in patients with grades I, II and III varicocele were 2.1 mm, 2.9 mm and 4.2 mm, respectively, before exercise, whereas they were 2.6 mm, 3.2 mm and 4.3 mm, respectively, after exercise. In patients with grade I varicoceles, compared with pre-exercise values, the diameter of the left spermatic vein and duration of reflux measured during Valsalva manoeuvres were increased significantly after exercise (p = 0.042 and p = 0.034, respectively); similar results were obtained for the patients with grade II varicoceles (p = 0.007 and p = 0.008, respectively). However, the minimal relative increase in cases with grade III varicoceles was not statistically significant (p > 0.05). CONCLUSIONS: This study demonstrates that acute exercise increases the spermatic vein diameter and reflux time in patients with varicoceles. These outcomes demonstrate that acute exercise may be an aggravating factor for varicocele, as seen in chronic exercise.