Serum cytokine profiles in healthy young and elderly population assessed using multiplexed bead-based immunoassays

<p>Abstract</p> <p>Background</p> <p>Lipid metabolites and cytokines, including chemokines and growth factors, are the key regulators of immune cell function and differentiation, and thus, dysregulation of these regulators is associated with various human diseases. However, previous studies demonstrating a positive correlation of cytokine levels with aging may have been influenced by various environmental factors and underlying diseases. Also, data regarding cytokine profiling in the elderly are limited to a small subset of cytokines.</p> <p>Methods</p> <p>We compared the profiles of 22 cytokines, including chemokines and growth factors, in a case-controlled study group of a gender-matched, healthy cohort of 55 patients over the age of 65 and 55 patients under the age of 45. Assessment of serum cytokine concentrations was performed using commercially-available multiplex bead-based sandwich immunoassays.</p> <p>Results</p> <p>Soluble CD40 ligand (sCD40L) and transforming growth factor alpha (TGF-α) levels were significantly higher in the elderly patients, whereas granulocyte colony-stimulating factor (G-CSF), granulocyte-monocyte colony-stimulating factor (GM-CSF), and monocyte chemoattractant protein-1 (MCP-1) levels were significantly lower in the elderly patients. The partial correlation analysis demonstrating the correlation between cytokine levels when controlled for gender, systolic blood pressure, total cholesterol, HDL cholesterol, triglyceride, and serum creatinine levels further demonstrated that G-CSF, GM-CSF, and MCP-1 had significant negative correlations with age, whereas sCD40L and TGF-α had significant positive correlations.</p> <p>Conclusions</p> <p>Future studies will focus on examining the significance of these age-related changes in circulating cytokines and other biological markers and their potential contribution to the development of different age-associated diseases.</p

The Current State of Artificial Intelligence Application in Urology

Artificial intelligence (AI) is being used in many areas of healthcare, including disease diagnosis and personalized treatment and rehabilitation management. Medical AI research and development has primarily focused on diagnosis, prediction, treatment, and management as an aid to patient care. AI is being utilized primarily in the areas of personal healthcare and diagnostic imaging. In the field of urology, significant investments are being made in the development of urination monitoring systems in the field of personal healthcare and ureteral stricture and urinary stone diagnosis solutions in the field of diagnostic imaging. In addition, AI technology is also being applied in the field of neurogenic bladder to develop risk monitoring systems based on video and audio data. This paper examines the application of AI to urological diseases and discusses the current trends and future prospects of AI research

Artificial Intelligence-Based Patient Monitoring System for Medical Support

Purpose In this paper, we present the development of a monitoring system designed to aid in the management and prevention of conditions related to urination. The system features an artificial intelligence (AI)-based recognition technology that automatically records a user’s urination activity. Additionally, we developed a technology that analyzes movements to prevent neurogenic bladder. Methods Our approach included the creation of AI-based recognition technology that automatically logs users’ urination activities, as well as the development of technology that analyzes movements to prevent neurogenic bladder. Initially, we employed a recurrent neural network model for the urination activity recognition technology. For predicting the risk of neurogenic bladder, we utilized convolutional neural network (CNN)-based AI technology. Results The performance of the proposed system was evaluated using a study population of 30 patients with urinary tract dysfunction, who collected data over a 60-day period. The results demonstrated an average accuracy of 94.2% in recognizing urinary tract activity, thereby confirming the effectiveness of the recognition technology. Furthermore, the motion analysis technology for preventing neurogenic bladder, which also employed CNN-based AI, showed promising results with an average accuracy of 83%. Conclusions In this study, we developed a urination disease monitoring system aimed at predicting and managing risks for patients with urination issues. The system is designed to support the entire care cycle of a patient by leveraging AI technology that processes various image and signal data. We anticipate that this system will evolve into digital treatment products, ultimately providing therapeutic benefits to patients

Cases of Hemolytic Anemia with Periprosthetic Leaks Evaluated by Real-Time 3-Dimensional Transesophageal Echocardiography

Hemolytic anemia is recognized as a rare complication of mitral valve replacement or repair. We report on a 44-year-old man with shortness of breath and hemolytic anemia, 23 years after mitral valve replacement (Hall-Kaster), and a 63-year-old woman diagnosed of hemolytic anemia, 4 years after mitral and tricuspid annuloplasty (Tailor ring, An-core ring). Routine 2-dimensional transthoracic echocardiography revealed paravalvular leakage around the prosthesis. Subsequent real-time 3-dimensional (3D)transesophageal echocardiography helped the perceptional appreciation of the leakage and the measuring of the regurgitant orifice area using the anatomically correct plane. Surgical findings of each case fit those of 3D volumetric images

Lack of Mitochondrial DNA Sequence Divergence between Two Subspecies of the Siberian Weasel from Korea: Mustela sibirica coreanus from the Korean Peninsula and M. s. quelpartis from Jeju Island

The objective of this study was to determine the degree of mitochondrial DNA (mtDNA) divergence between two subspecies of Mustela sibirica from Korea (M. s. coreanus on the Korean Peninsula and M. s. quelpartis on Jeju Island) and to examine the taxonomic status of M. s. quelpartis. Thus, we obtained complete sequences of mtDNA cytochrome b gene (1,140 bp) from the two subspecies, and these sequences were compared to a corresponding haplotype of M. s. coreanus, downloaded from GenBank. From this analysis, it was observed that the sequences from monogenic M. s. quelpartis on Jeju Island were identical to the sequences of four M. s. coreanus from four locations across the Korean Peninsula, and that the two subspecies formed a single clade; the average nucleotide distance between the two subspecies was 0.26% (range, 0.00 to 0.53%). We found that the subspecies quelpartis is not genetically distinct from the subspecies coreanus, and that this cytochrome b sequencing result does not support the current classification, distinguishing these two subspecies by pelage color. Further systematic analyses using morphometric characters and other DNA markers are necessary to confirm the taxonomic status of M. s. quelpartis

Throughput Fairness Enhancement Using Differentiated Channel Access in Heterogeneous Sensor Networks

Nowadays, with wireless sensor networks (WSNs) being widely applied to diverse applications, heterogeneous sensor networks (HSNs), which can simultaneously support multiple sensing tasks in a common sensor field, are being considered as the general form of WSN system deployment. In HSNs, each application generates data packets with a different size, thereby resulting in fairness issues in terms of the network performance. In this paper, we present the design and performance evaluation of a differentiated channel access scheme (abbreviated to DiffCA) to resolve the fairness problem in HSNs. DiffCA achieves fair performance among the application groups by providing each node with an additional backoff counter, whose value varies according to the size of the packets. A mathematical model based on the discrete time Markov chain is presented and is analyzed to measure the performance of DiffCA. The numerical results show that the performance degradation of disadvantaged application groups can be effectively compensated for by DiffCA. Simulation results are given to verify the accuracy of the numerical model

Lumbar plexopathy after radical nephrectomy -A case report-

Lumbar plexopathy is characterized by an abrupt onset of sensory disturbances, weakness, and loss of deep tendon reflexes of lower extremities. The various causes of lumbar plexopathy include trauma, infections, space-occupying lesion, vascular diseases, metabolic diseases, and the use of drugs such as heroin. Postoperative rhabdomyolysis occurs secondary to prolonged muscle compression due to surgical positioning. Herein, we report a case of lumbar plexopathy, complicating an injury to the paraspinal muscle and iliopsoas muscle that occurred in the flexed lateral decubitus position following radical nephrectomy