ABSTRACT
A 74-year-old man having a right refractory foot ulcer was referred to our hospital with a diagnosis of arteriosclerosis obliterans. Angiography of the lower extremities showed occlusive lesions in the middle popliteal artery and lower-leg arteries. Preoperative examination revealed decreased cardiac function and severe stenosis of the left and right coronary arteries. Therefore, we first performed coronary artery bypass grafting, followed by revascularization of the lower limbs at a later date. Owing to the lack of suitable autologous vein grafts, our procedure of choice was popliteal endarterectomy via a posterior approach with short saphenous vein angioplasty. The patient's foot ulcer healed completely following surgery. His postoperative course was uneventful, and he remained symptom-free during a 1-year follow-up.
ABSTRACT
Objective@#Most women with early stage endometrial cancer have a favorable prognosis. However, there is a subset of patients who develop recurrence. In addition to the pathological stage, clinical and therapeutic factors affect the probability of recurrence. Machine learning is a subtype of artificial intelligence that is considered effective for predictive tasks. We tried to predict recurrence in early stage endometrial cancer using machine learning methods based on clinical data. @*Methods@#We enrolled 75 patients with early stage endometrial cancer (International Federation of Gynecology and Obstetrics stage I or II) who had received surgical treatment at our institute. A total of 5 machine learning classifiers were used, including support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and boosted tree, to predict the recurrence based on 16 parameters (age, body mass index, gravity/parity, hypertension/diabetic, stage, histological type, grade, surgical content and adjuvant chemotherapy). We analyzed the classification accuracy and the area under the curve (AUC). @*Results@#The highest accuracy was 0.82 for SVM, followed by 0.77 for RF, 0.74 for LR, 0.66 for DT, and 0.66 for boosted trees. The highest AUC was 0.53 for LR, followed by 0.52 for boosted trees, 0.48 for DT, and 0.47 for RF. Therefore, the best predictive model for this analysis was LR. @*Conclusion@#The performance of the machine learning classifiers was not optimal owing to the small size of the dataset. The use of a machine learning model made it possible to predict recurrence in early stage endometrial cancer.
ABSTRACT
Objective@#Most women with early stage endometrial cancer have a favorable prognosis. However, there is a subset of patients who develop recurrence. In addition to the pathological stage, clinical and therapeutic factors affect the probability of recurrence. Machine learning is a subtype of artificial intelligence that is considered effective for predictive tasks. We tried to predict recurrence in early stage endometrial cancer using machine learning methods based on clinical data. @*Methods@#We enrolled 75 patients with early stage endometrial cancer (International Federation of Gynecology and Obstetrics stage I or II) who had received surgical treatment at our institute. A total of 5 machine learning classifiers were used, including support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and boosted tree, to predict the recurrence based on 16 parameters (age, body mass index, gravity/parity, hypertension/diabetic, stage, histological type, grade, surgical content and adjuvant chemotherapy). We analyzed the classification accuracy and the area under the curve (AUC). @*Results@#The highest accuracy was 0.82 for SVM, followed by 0.77 for RF, 0.74 for LR, 0.66 for DT, and 0.66 for boosted trees. The highest AUC was 0.53 for LR, followed by 0.52 for boosted trees, 0.48 for DT, and 0.47 for RF. Therefore, the best predictive model for this analysis was LR. @*Conclusion@#The performance of the machine learning classifiers was not optimal owing to the small size of the dataset. The use of a machine learning model made it possible to predict recurrence in early stage endometrial cancer.
ABSTRACT
Well water could be a stable source of drinking water. Recently, the use of well water as drinking water has been encouraged in developing countries. However, many kinds of disorders caused by toxic elements in well drinking water have been reported. It is our urgent task to resolve the global issue of element-originating diseases. In this review article, our multidisciplinary approaches focusing on oncogenic toxicities and disturbances of sensory organs (skin and ear) induced by arsenic and barium are introduced. First, our environmental monitoring in developing countries in Asia showed elevated concentrations of arsenic and barium in well drinking water. Then our experimental studies in mice and our epidemiological studies in humans showed arsenic-mediated increased risks of hyperpigmented skin and hearing loss with partial elucidation of their mechanisms. Our experimental studies using cultured cells with focus on the expression and activity levels of intracellular signal transduction molecules such as c-SRC, c-RET, and oncogenic RET showed risks for malignant transformation and/or progression arose from arsenic and barium. Finally, our original hydrotalcite-like compound was proposed as a novel remediation system to effectively remove arsenic and barium from well drinking water. Hopefully, comprehensive studies consisting of (1) environmental monitoring, (2) health risk assessments, and (3) remediation will be expanded in the field of environmental health to prevent various disorders caused by environmental factors including toxic elements in drinking water.
Subject(s)
Animals , Humans , Mice , Arsenic , Toxicity , Barium , Toxicity , Drinking Water , Environmental Exposure , Environmental Health , Environmental Monitoring , Water Pollutants, Chemical , Toxicity , Water WellsABSTRACT
BACKGROUND@#Melanin is detectable in various sense organs including the skin in animals. It has been reported that melanin adsorbs toxic elements such as mercury, cadmium, and lead. In this study, we investigated the adsorption of molybdenum, which is widely recognized as a toxic element, by melanin.@*METHODS@#Molybdenum level of the mouse skin was measured by inductively coupled plasma mass spectrometry. The pigmentation level of murine skin was digitalized as the L* value by using a reflectance spectrophotometer. An in vitro adsorption assay was performed to confirm the interaction between molybdenum and melanin.@*RESULTS@#Our analysis of hairless mice with different levels of skin pigmentation showed that the level of molybdenum increased with an increase in the level of skin pigmentation (L* value). Moreover, our analysis by Spearman's correlation coefficient test showed a strong correlation (r = - 0.9441, p < 0.0001) between L* value and molybdenum level. Our cell-free experiment using the Langmuir isotherm provided evidence for the adsorption of molybdenum by melanin. The maximum adsorption capacity of 1 mg of synthetic melanin for molybdenum was 131 μg in theory.@*CONCLUSION@#Our in vivo and in vitro results showed a new aspect of melanin as an adsorbent of molybdenum.
Subject(s)
Animals , Mice , Adsorption , Melanins , Chemistry , Metabolism , Mice, Hairless , Mice, Transgenic , Molybdenum , Chemistry , Metabolism , Pharmacology , Skin , Chemistry , Skin Pigmentation , Water Pollutants, Chemical , Chemistry , Metabolism , PharmacologyABSTRACT
A 64-year-old man under dialysis was referred for surgical treatment of Crawford type I thoracoabdominal aortic aneurysm. He had a history of idiopathic portal hypertension and chronic total occulusion of supra-renal abdominal aorta and appeared to have massive development of collateral arteries and veins in the abdomen. We chose endovascular repair with debranching of visceral arteries and bypass grafting to bilateral superficial femoral artery considering bleeding from collateral arteries and veins by conventional open surgery. Postoperative CT scan revealed no endoleak and all debranched and bypass grafts were patent. He was discharged with no postoperative complications including paraplegia.