BACKGROUND: The possibility that anesthesia may alter the course of an infection has been under consideration for more a century. Alterations have been found in every component of the immune response during anesthesia and surgery. In this work, we have investigated the effect of lidocaine and propofol on interleukin-2 (IL-2), interleukin-4 (IL-4), and nitric oxide (NO) production in mice. METHODS: The culture supernatants of splenocytes exposed with anesthetics and lipopolysaccharide (LPS), or sera from mice injected with these agents were harvested to assay IL-2, IL-4, and NO. RESULTS: We detected that IL-2 productions of splenocytes culture supernatants and mice sera after exposure with lidocaine or propofol were decreased while IL-4 productions were increased. In addition, NO of mice sera was increased after lidocaine or propofol exposures. CONCLUSIONS: These findings suggest that lidocaine and propofol interfere with IL-2, IL-4, and NO production. This may explain the clinically well-recognized disturbance of human immunity after surgery and anesthesia.
Anesthesia ; Anesthetics ; Animals ; Humans ; Interleukin-2 ; Interleukin-4* ; Interleukins* ; Lidocaine* ; Mice* ; Nitric Oxide* ; Propofol*

The incidence of abdominal trauma has increased in recent decades as the frequency of traffic accidents increased. Early symptoms and signs of blunt abdominal trauma may be absent and associated injuries frequently detract physicians from early diagnosis of abdominal trauma. Delayed diagnosis has been shown to be associated with higher morbidity and mortality. Gastrointestinal tract is the third most commonly injured organ from blunt abdominal trauma. Gastric ruptures after blunt abdominal trauma were reported occasionally, but reports of upper gastrointestinal bleeding by gastric mucosal tear were very rare. Four cases of upper gastrointestinal bleeding due to gastric mucosal tear after blunt abdominal trauma are herein reported with a review of related literatures.
Accidents, Traffic ; Delayed Diagnosis ; Early Diagnosis ; Gastrointestinal Tract ; Hemorrhage ; Incidence ; Mortality ; Stomach Rupture

BACKGROUND: Diabetes mellitus is independent risk factor for the development and the extent of coronary artery disease, and increase the morbidity and the mortality of rdiovascular disease. But there is a debate concerning about the severity, distribution, length, number of coronary artery stenosis in diabetes. METHODS: We retrospectively reviewed clinical and coronary angiographic findings to access the prevalence and length of proximal, middle, distal coronary atherosclerotic lesions, and the relationship between coronary artery stenosis and coronary risk factors of coronany artery disease in 39 diabetics and 39 non-diabetics diagnosed at the Paik Hospital from January, 2002 to June, 2002. RESULTS: The mean age of the patients, gender ratio, smoking history, history of hypertension, family history of coronary artery disease were not different between diabetics and non-diabetics, and the level of total cholesterol, triglyceride, LDL-Cholesterol showed no significant difference between diabetics and non-diabetics. However, HDL-Cholesterol levels were significantly higher in diabetics (p<0.0.5). Diabetic patients had a significantly greater frequency of severe stenosis (>50% narrowing) in the distal portion of left anterior descending artery and left circumflex artery (p<0.05), but there were no significant differences in proximal, middle portion of left anterior descending artery and left circumflex artery, and all portion of right coronary artery. Also, they had a significantly greater frequency of severe diffuse stenosis (>50% narrowing, >2cm in length) in the middle portion of left anterior descending artery, and distal portion of left circumflex artery (p<0.05). However, no significant differences exist in proximal and distal portion of left anterior descending artery, proximal and middle portion of left circumflex artery, and all portion of right coronary artery. Three vessel disease was more common in diabetics compared to non-diabetics (p<0.05). CONCLUSION: Diabetes mellitus is one of risk factors affecting the severity, distribution, length, number of coronary artery stenosis.
Arteries ; Cholesterol ; Constriction, Pathologic ; Coronary Artery Disease* ; Coronary Stenosis ; Coronary Vessels* ; Diabetes Mellitus ; Humans ; Hypertension ; Mortality ; Prevalence ; Retrospective Studies ; Risk Factors ; Smoke ; Smoking ; Triglycerides

Fenoverine is a non-atropine like spasmolytic drug that inhibits calcium channel currents in the smooth muscle. It has been occassionally reported that fenoverine can cause rhabdomyolysis under the certain conditions such as hepatic dysfunction, concomitant use of HMG-CoA reductase, mitochondrial myopathy, lipid storage myopathy or malignant hyperthermia. However, there is no report of fenoverine-induced rhabdomyolysis in type 2 diabetic nephropathy patient. So we describe here a case of fenoverine-induced rhabdomyolysis in type 2 diabetic patient. A 70-year-old man had both lower legs and shoulder pain for 5 days prior to hospital admission. He was a type 2 diabetic patient and had been managed for diabetic nephropathy. He had been consumed common doses of fenoverine for 20 days due to abdominal pain and diarrhea. Results of investigations showed evidence of rhabdomyolysis. Fenoverine therapy was stopped after admission and he was treated supportive care, his condition was recovered. In this case, renal function impairment may have been a predisposing factor for fenoverine-induced rhabdomyolysis. The incidence of muscular complications of fenoverine therapy could be reduced by avoidance of prescription of the drug in patients with diabetic nephropathy.
Abdominal Pain ; Aged ; Calcium Channels ; Causality ; Diabetic Nephropathies* ; Diarrhea ; Humans ; Incidence ; Leg ; Malignant Hyperthermia ; Mitochondrial Myopathies ; Muscle, Smooth ; Muscular Diseases ; Oxidoreductases ; Prescriptions ; Rhabdomyolysis* ; Shoulder Pain

BACKGROUND: Thiopental and propofol are the most widely used intravenous anesthetics as induction agents in general anesthesia. Thiopental is a very strong alkaline drug, and when it is extravasated, it can cause pain and skin necrosis. Propofol also can cause pain on injection in many populations. Therefore, we planed this study to compare inflammatory reactions of skin tissues after subdermal injections of thiopental and propofol in rabbits. METHODS: Four rabbits were divided into 2 groups: Standard dose (S) group and double dose (D) group. In the S group, thiopental 0.4 ml and propofol 0.4 ml were injected subcutaneously on each side of the posterior proximal ear. In the D group, the dose was doubled to 0.8 ml of each drug and injection was done in the same manner. Skin tissue at the injection sites were excised after 1 day, 3 days, and 7 days. Then each skin tissue slide was examined under an optical microsccpe. RESULTS: In the S group, the inflammatory reaction after the subdermal injection of 2.5% thiopental revealed a more progressed and more severe pattern than 1% propofol. In the D group, the inflammatory reaction after a subdermal injection of 2.5% thiopental revealed a more progressed and more severe pattern than 1% propofol at 3 days, but there was no significant difference in the degree of progression and severity between the 2 drugs at 7 days. CONCLUSIONS: When propofol is extravasated during continuous infusion for maintenance of anesthesia, it can cause distinct inflammatory reaction; though the inflammatory reaction is milder and the possibility of complications is lower than with thiopental.
Anesthesia ; Anesthesia, General ; Anesthetics, Intravenous ; Ear ; Inflammation ; Necrosis ; Propofol* ; Rabbits* ; Skin ; Thiopental*

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70–0.83 for GPT-4, 0.51–0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51–0.59 for GPT-4, 0.47–0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. Conclusions GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70–0.83 for GPT-4, 0.51–0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51–0.59 for GPT-4, 0.47–0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. Conclusions GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70–0.83 for GPT-4, 0.51–0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51–0.59 for GPT-4, 0.47–0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. Conclusions GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70–0.83 for GPT-4, 0.51–0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51–0.59 for GPT-4, 0.47–0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. Conclusions GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.

Ultrasound (US) elastography is a valuable imaging technique for tissue characterization. Two main types of elastography, strain and shear-wave, are commonly used to image breast tissue. The use of elastography is expected to increase, particularly with the increased use of US for breast screening. Recently, the US elastographic features of breast masses have been incorporated into the 2nd edition of the Breast Imaging Reporting and Data System (BI-RADS) US lexicon as associated findings. This review suggests practical guidelines for breast US elastography in consensus with the Korean Breast Elastography Study Group, which was formed in August 2013 to perform a multicenter prospective study on the use of elastography for US breast screening. This article is focused on the role of elastography in combination with B-mode US for the evaluation of breast masses. Practical tips for adequate data acquisition and the interpretation of elastography results are also presented.
Breast* ; Consensus ; Elasticity Imaging Techniques* ; Information Systems ; Mass Screening ; Ultrasonography*