The antigenic domain of the major surface protein (Nc-p43) of Neospora caninum was examined by polymerase chain reaction of its gene fragments and recombinant expression as GST fusion proteins. The fragments of Nc-p43 were as follow: a total open reading frame (OFR), T; OFR without signal sequence and C-terminal hydrophobic sequence, S; N-terminal 2/3 parts of S, A; C-terminal 2/3 parts, P; N-terminal 1/3 part, X; middle 1/3 part, Y; and C-terminal 1/3 part, Z, respectively. The DNA fragments were cloned into pGEX-4T vector. Recombinant plasmids transformed into Escherichia coli of BL21 pLysS (DE3) strain were induced to express GST or GST fused fragments of Nc-p43 such as 69 kDa protein for T, 66 kDa for S, 52 kDa for A, 53 kDa for P, and 40 kDa proteins for X, Y, and Z, respectively in SDS-PAGE. The Nc-p43 fragments of T, S, and P reacted with a bovine serum of neosporosis while those of A, X, Y, and Z together with GST did not in the western blot. These findings suggest that the antigenic domain of Nc-p43 of N. caninum may be localized in the C-terminal 2/3 parts. Together with A19 clone in SAG1 of Toxoplasma gondii (Nam et al., 1996), the P fragment of Nc-p43 could be used as efficient antigens to diagnose and differentiate those infections with both species.
Amino Acid Sequence ; Animals ; Base Sequence ; Biological Markers/analysis ; Blotting, Western ; Cells, Cultured ; Cercopithecus aethiops ; Coccidiosis/diagnosis ; Neospora/*immunology ; Protozoan Proteins/*analysis/genetics/immunology ; Reverse Transcriptase Polymerase Chain Reaction ; Support, Non-U.S. Gov't ; Vero Cells/parasitology

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.

We evaluated MR imaging of spondylolytic spondylolisthesis degenerative spondylolisthesis and retrolisthesis in 14, 9 and 20 patients respectively. Sagittal and axial spin echo and gradient echo images were obtained with 25-30cm FOV and 5mm/0.5mm thickness/gap by using spine surface coil. Sagittal images showed defects of pars interarticularis just inside of the pedicles of spines in all the cases of spondylolytic spondylolisthesis with relatively variable signal intensity. Displaced vertebrae were commonly observed at L5 (8/14) in spondylolytic spondylolisthesis, at L4 (5/9) in degenerative spondylolisthesis and at variable locations in retrolisthesis. The mean length of displacement of vertebrae in spondylolytic spondylolisthesis was about 7mm and less displacement was onserved in degenerative spondylolisthesis and retrolisthesis. Seven, four and six cases of pseudobulging of disk at displaced level were observed in cases of spondylolytic spondylolisthesis, degenerative spondylolisthesis and retrolisthesis respectively. Seven, five and 14 cases of true disk lesions were onserved in cases of spondylolytic spondylolisthesis, degenertive spondylolisthesis and retrolisthesis respectively. Grade II neural foraminal stenoses (obliteration of one half epidural fat of neural foramen) were commonly (8/14) seen in spondylolytic spondylolisthesis, however the other two types showed less severe neural foraminal stenosis. In conclusion, MR imaging is a highly accurate method for the diagnosis and evaluation of spondylolisthesis and associated lesions of spine and disks.
Constriction, Pathologic ; Diagnosis ; Humans ; Magnetic Resonance Imaging* ; Methods ; Spine ; Spondylolisthesis*

Vestibular stimulation by air-conducted sound (ACS), bone-conducted vibration (BCV), or electrical currents (galvanic stimulation) elicits vestibular evoked myogenic potentials (VEMPs) that are now widely used to assess otolith function. Conventional cervical VEMPs, which are a manifestation of the vestibulocollic reflex, measure electromyographic activity from surface electrodes placed over the tonically activated sternocleidomastoid muscles. VEMPs can also be recorded from the extraocular muscles using surface electrodes placed over the inferior oblique and inferior rectus muscles, known as ocular VEMPs (oVEMPs). oVEMPs are a manifestation of the vestibulo-ocular pathways. We describe herein the neurophysiological properties of VEMPs obtained in response to ACS and BCV stimulation, and outline the known characteristics of VEMPs in central and peripheral disorders commonly encountered in neuro-otology clinics.
Electrodes ; Muscles ; Neurotology ; Otolithic Membrane ; Reflex ; Saccule and Utricle ; Vestibular Evoked Myogenic Potentials ; Vibration

No abstract available.
Female ; Pregnancy ; Pregnancy, Ectopic* ; Salpingectomy*

No abstract available.
Apgar Score* ; Depression* ; Humans ; Psychology*

No abstract available.
Apgar Score* ; Depression* ; Humans ; Psychology*

PURPOSE: The aim of this study was to develop and test an explanatory model for sleep disorders in people with cancer. A hypothetical model was constructed on the basis of a review of previous studies, literature, and sleep models, and 10 latent variables were used to construct a hypothetical model. METHODS: Data were collected from April 19 to June 25, 2010, using self-report questionnaires. The sample was 291 outpatients with cancer who visited the oncology cancer center at a university hospital. Collected data were analyzed using SPSS Win 15.0 program for descriptive statistics and correlation analysis and AMOS 7.0 program for covariance structural analysis. RESULTS: It appeared that overall fit index was good as chi2/df=1.162, GFI=.969, AGFI=.944, SRMR=.052, NFI=.881, NNFI=.969, CFI=.980, RMSEA=.024, CN=337 in the modified model. The explanatory power of this model for sleep disorders in people with cancer was 62%. Further, sleep disorders were influenced directly by cancer symptom experience, dysfunctional beliefs and attitudes about sleep, and past sleep pattern. CONCLUSION: Findings suggest that nurses should assess past sleep pattern and consider the development of a comprehensive nursing intervention program to minimize the cancer symptom experience, dysfunctional beliefs and attitudes about sleep, and thus, reduce sleep disorders in people with cancer.
Anxiety ; Depression ; Female ; Hospitals, University ; Humans ; Male ; Middle Aged ; *Models, Theoretical ; Neoplasms/*complications/*diagnosis/psychology ; Questionnaires ; Self Concept ; Sleep Disorders/epidemiology/*etiology/*psychology ; Social Support

Two major salivary glands, submandibular duct, lingual nerve, and vessels are situated beneath the mouth floor. Among these, passing through the pterygomandibular space, lingual nerve is innervated to the lingual gingiva and the mucosa of mouth floor, and is responsible for the general sensation of the anterior two thirds of the tongue. So, the injury of the lingual nerve during an anesthesia or surgery in the retromolar area may cause complications such as a numbness, a loss of taste of the tongue and the other dysfunctions. Therefore, to find out the morphology and the course of lingual nerve and to clarify the topographical relationships of lingual nerve at the infratemporal fossa and paralingual space area, 32 Korean hemi-sectioned heads were dissected macroscopically and microscopically with a viewpoint of clinical aspect in this study. This study demonstrated various anatomical characteristics with relation to the course and topography of the lingual nerve in Koreans. And clinical significances based on the anatomical variations through the topography of the courses and communications between the mandibular nerve branches were described in details.
Anesthesia ; Chorda Tympani Nerve ; Gingiva ; Head ; Hypesthesia ; Lingual Nerve* ; Mandibular Nerve ; Mouth Floor ; Mucous Membrane ; Salivary Glands ; Sensation ; Tongue

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.