No abstract available.
Schizophrenia*

No abstract available.
Angiotensin II* ; Angiotensins* ; Animals ; Epidermal Growth Factor* ; Rats* ; Ureter* ; Ureteral Obstruction*

PURPOSE: The purpose of this study was to develop an instrument of task performance evaluation for clinical nurses, thus testing the validity and the reliability of the scale. METHOD: Data was collected from 84 Head Nurses and 255 General Nurses. A conceptual framework, composed of 4 factors of meaning in task performance evaluation, was identified through review of the relevant literature. A total of 78 items were developed and were used on a five-point likert scale. Through factor analysis, items whose factor loading was below 0.50 were deleted, thus 35 items remained. To test the validity and reliability of the instrument, the SPSS 11.0 windows program was used. RESULT: The results of the factor analysis indicated that 4 factors were classified and the cumulative percent of variance was 67.54%. The results of the reliability test indicated that Cronbach's coefficient of the total 35 items was over 0.9176. The results of the factor analysis indicated that factor loadings of all items was over 0.50. Conclusively, the validity and the reliability of the scale were proven. CONCLUSION: This study was identified as a tool with a high degree of reliability and validity.
*Task Performance and Analysis ; *Nursing Evaluation Research ; Nursing/*standards ; Middle Aged ; Korea ; Humans ; Female ; Adult

Asthma is the most common disease of the lungs, and one that poses specific challenges for the physicians including radiologist. This article reviews for the clinical diagnosis, Radiologic features, and differential diagnosis of asthma, and outlines the radiologic features of the complications of asthma. Bronchial wall thickening and hyperinflation characterize the chest radiograph of the patients with asthma. On CT scan one may see airway wall thickening, thickened centrilobular structures, and focal or diffuse hyperlucency. Apparent bronchial dilatation may be seen, but the diagnosis of bronchiectasis should be made with caution. Quantification of changes in the airway wall and lung parenchyma may be valuable in understanding the mechanisms of asthma and in evaluating the effects of treatment. The challenge for the physician evaluating the images of a patient with asthma is to find complications.
Asthma* ; Bronchiectasis ; Diagnosis ; Diagnosis, Differential ; Dilatation ; Humans ; Lung ; Radiography, Thoracic ; Tomography, X-Ray Computed

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%–80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors’ experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%–80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors’ experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%–80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors’ experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.

An unusual tracheal tumor was found in a 50 year old male who was admitted due to mild dyspnea on exertion. Simple chest X-ray showed an abnorma mass shadow in the trachea and computerized chest tomogram revealed a tumor in the mid 1/3 of the trachea obstructing 80% of the lumen. Through a right thoracotomy incision, resection of a 2.5 cm segment of the trachea with end-to-end anastomosis was done and microscopic findings showed many cystic spaces with myxomatous hyalinous stroma. It was diagnosed as a pleomorphic adenoma of the trachea.
Adenoma, Pleomorphic/*pathology/*radiography/surgery ; Case Report ; Human ; Male ; Middle Age ; Radiography, Thoracic ; Tomography, X-Ray Computed ; Tracheal Neoplasms/*pathology/*radiography/surgery

BACKGROUND/AIMS: During the past several decades pancreaticoduodenectomy has carried a tremendous operative risk. Recently, however, mortality has decreased markedly to around 5%. The aim of this study was to determine the indication, clinical course and complications for the procedure. We also assessed the risk factors of operative morbidity and mortality. METHODS: During the period from June 1979 to July 1998, 155 patients underwent pancreaticoduodenectomy at our institution. The clinical records and pathologic reports were reviewed retrospectively. RESULTS: There were 102 men and 53 women. Mean age was 55.5 years. Indications for the procedure were periampullary carcinoma in 121 patients(78.1%), other malignant tumors in 13 patients(8.2%), and benign diseases including trauma in 21 patients(13.5%). A standard pancreaticoduodenectomy was performed in 113 patients. The remaining 42 patients underwent pylorus preserving pancreaticoduodenectomy. Operative morbidity occurred in 46 patients (29.7%). Pancreatic fistula was the most common complication (11.0%). There were nine operative deaths(5.8%). For the patients with pancreatic fistula, the mortality rate was 41.1%. Patient's age, serum albumin, serum bilirubin, hemoglobin, and leucocyte count had no significant influences on the result of operation. Type of procedure, operation time, amount of transfusion, or use of octreotide were not determining factors for operative mortality. CONCLUSIONS: Pancreaticoduodenectomy was performed in 155 patients with a mortality of 5.8% and a morbidity of 29.7%. The main cause of operative death was pancreatic fistula. There were no specific contributing factors leading to operative morbidity and mortality. It is suggested that technical precision and gentleness are of great importance for patients undergoing pancreaticoduodenectomy
Bilirubin ; Female ; Humans ; Male ; Mortality ; Octreotide ; Pancreatic Fistula ; Pancreaticoduodenectomy* ; Pylorus ; Retrospective Studies ; Risk Factors ; Serum Albumin

The role of neoadjuvant chemoradiation therapy in locally advanced pancreatic cancer (LAPC) is still controversial. The aim of this study was to evaluate surgical downstaging after concurrent chemoradiation therapy (CCRT) for LAPC by measuring the objective changes after treatment. From January 2003 through July 2011, 54 patients with LAPC underwent neoadjuvant CCRT. Computed tomography findings of the tumor size, including major vessel invasion, were analyzed before and after CCRT. Among the total recruited patients, 14 had borderline resectable malignancy and another 40 were unresectable before CCRT. After CCRT, a partial response was achieved in four patients. Stable disease and further disease progression were achieved in 36 and 14 patients, respectively. Tumor size showed no significant difference before and after CCRT (3.6 +/- 1.1 vs. 3.6 +/- 1.0 cm, P = 0.61). Vessel invasion showed improvement in two patients, while 13 other patients showed further tumor progression. Thirty-nine patients with unresectable malignancy and 11 patients with borderline resectable malignancy at time of initial diagnosis remained unchanged after CCRT. Four patients with borderline pancreatic malignancy progressed to an unresectable stage, whereas one unresectable pancreatic malignancy improved to a borderline resectable stage. Only one patient with borderline resectable disease underwent operation after CCRT; however, curative resection failed due to celiac artery invasion and peritoneal seeding. The adverse events associated with CCRT were tolerable. In conclusion, preoperative CCRT in LAPC rarely leads to surgical downstaging, and it could lower resectability rates.
Adenocarcinoma/radiography/therapy ; Adult ; Aged ; Aged, 80 and over ; Antimetabolites, Antineoplastic/therapeutic use ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Capecitabine/therapeutic use ; Carcinoma, Pancreatic Ductal/*radiography/*therapy ; Chemoradiotherapy/adverse effects/*methods ; Combined Modality Therapy ; Deoxycytidine/analogs & derivatives/therapeutic use ; Disease Progression ; Female ; Fluorouracil/therapeutic use ; Humans ; Male ; Middle Aged ; Neoadjuvant Therapy ; Neoplasm Staging ; Pancreas/blood supply/pathology ; Pancreatic Neoplasms/*radiography/*therapy ; Retrospective Studies ; Treatment Outcome