Septic embolism and stroke are serious complications in patients with sepsis and often necessitate urgent surgical intervention to control the source of infection. A 69-year-old man presented with severe pain in his back and left thigh. MRI revealed extensive posterior epidural or subdural abscesses extending from the cervical to the lumbar level, as well as an abscess in the iliopsoas muscle. The patient underwent urgent drainage of the abscesses and decompression of the lumbar spine.Postoperatively, he developed sudden-onset atrial fibrillation and altered mental status. Brain CT showed multiple embolic infarctions. His condition deteriorated due to persistent infection, leading to disseminated intravascular coagulation, acute kidney injury, and septic shock. This case highlights the risk of cerebral embolism and hemorrhagic complications in patients with sepsis who undergo surgery.Early recognition of individuals at high risk and comprehensive perioperative management are critical to reducing the likelihood of such complications.

Septic embolism and stroke are serious complications in patients with sepsis and often necessitate urgent surgical intervention to control the source of infection. A 69-year-old man presented with severe pain in his back and left thigh. MRI revealed extensive posterior epidural or subdural abscesses extending from the cervical to the lumbar level, as well as an abscess in the iliopsoas muscle. The patient underwent urgent drainage of the abscesses and decompression of the lumbar spine.Postoperatively, he developed sudden-onset atrial fibrillation and altered mental status. Brain CT showed multiple embolic infarctions. His condition deteriorated due to persistent infection, leading to disseminated intravascular coagulation, acute kidney injury, and septic shock. This case highlights the risk of cerebral embolism and hemorrhagic complications in patients with sepsis who undergo surgery.Early recognition of individuals at high risk and comprehensive perioperative management are critical to reducing the likelihood of such complications.

Septic embolism and stroke are serious complications in patients with sepsis and often necessitate urgent surgical intervention to control the source of infection. A 69-year-old man presented with severe pain in his back and left thigh. MRI revealed extensive posterior epidural or subdural abscesses extending from the cervical to the lumbar level, as well as an abscess in the iliopsoas muscle. The patient underwent urgent drainage of the abscesses and decompression of the lumbar spine.Postoperatively, he developed sudden-onset atrial fibrillation and altered mental status. Brain CT showed multiple embolic infarctions. His condition deteriorated due to persistent infection, leading to disseminated intravascular coagulation, acute kidney injury, and septic shock. This case highlights the risk of cerebral embolism and hemorrhagic complications in patients with sepsis who undergo surgery.Early recognition of individuals at high risk and comprehensive perioperative management are critical to reducing the likelihood of such complications.

Purpose: Recently, deep learning techniques have been introduced for age estimation, with automated methods based on radiographic analysis demonstrating high accuracy. In this study, we applied convolutional neural network (CNN) techniques to the lower dentition area on orthopantomograms (OPGs) of children to develop an automated age estimation model and evaluate its accuracy for use in forensic dentistry. Methods: In this study, OPGs of 2,856 subjects aged 3-14 years were analyzed. The You Only Look Once (YOLO) V8 object detection technique was applied to extract the mandibular dentition area on OPGs, designating it as the region of interest (ROI). First, 200 radiographs were randomly selected, and were used to train a model for extracting the ROI. The trained model was then applied to the entire dataset. For the CNN image classification task, 80% of OPGs were allocated to the training set, while the remaining 20% were used as the test set. A transfer learning approach was employed using the ResNet50 and VGG19 backbone models, with an ensemble technique combining these models to improve performance. The mean absolute error (MAE) on the test set was used as the validation metric, and the model with the lowest MAE was selected. Results: In this study, the age estimation model developed using mandibular dentition region from OPGs achieved MAE and root mean squared error (RMSE) values of 0.501 and 0.742, respectively, on the test set, and MAE and RMSE values of 0.273 and 0.354, respectively, on the training set. Conclusions The automated age estimation model developed in this study demonstrated accuracy comparable to that of previous research and shows potential for applications in forensic investigations. Increasing the sample size and incorporating diverse deep learning techniques are expected to further enhance the accuracy of future age estimation models.

Septic embolism and stroke are serious complications in patients with sepsis and often necessitate urgent surgical intervention to control the source of infection. A 69-year-old man presented with severe pain in his back and left thigh. MRI revealed extensive posterior epidural or subdural abscesses extending from the cervical to the lumbar level, as well as an abscess in the iliopsoas muscle. The patient underwent urgent drainage of the abscesses and decompression of the lumbar spine.Postoperatively, he developed sudden-onset atrial fibrillation and altered mental status. Brain CT showed multiple embolic infarctions. His condition deteriorated due to persistent infection, leading to disseminated intravascular coagulation, acute kidney injury, and septic shock. This case highlights the risk of cerebral embolism and hemorrhagic complications in patients with sepsis who undergo surgery.Early recognition of individuals at high risk and comprehensive perioperative management are critical to reducing the likelihood of such complications.

Septic embolism and stroke are serious complications in patients with sepsis and often necessitate urgent surgical intervention to control the source of infection. A 69-year-old man presented with severe pain in his back and left thigh. MRI revealed extensive posterior epidural or subdural abscesses extending from the cervical to the lumbar level, as well as an abscess in the iliopsoas muscle. The patient underwent urgent drainage of the abscesses and decompression of the lumbar spine.Postoperatively, he developed sudden-onset atrial fibrillation and altered mental status. Brain CT showed multiple embolic infarctions. His condition deteriorated due to persistent infection, leading to disseminated intravascular coagulation, acute kidney injury, and septic shock. This case highlights the risk of cerebral embolism and hemorrhagic complications in patients with sepsis who undergo surgery.Early recognition of individuals at high risk and comprehensive perioperative management are critical to reducing the likelihood of such complications.

In children and adolescents, dental age estimation is performed with the development of the teeth. Various statistical analysis methods have been used to determine the relationship between age and dental maturity and develop an accurate method of age calculation. This study attempted to apply a neural network model for the statistical analysis of dental age estimation in children and evaluated its applicability. This study used 1196 panoramic radiographs of patients aged 3–16 years, and 996 and 200 were randomly classified into training and test sets, respectively. The dental maturity of the mandibular left teeth was evaluated using Demirjian's method, the neural network model using the backpropagation algorithm was derived using training sets, and the errors were evaluated using 100 radiographs of each male and female as test sets. In addition, multiple linear regression analysis was conducted on the same training set, and the error was calculated by applying it to the test set and comparing it with the error of the neural network model. In the neural network model, the mean absolute error (MAE) and root mean squared error (RMSE) were 0.589 and 0.783 in male subjects and 0.529 and 0.760 in female subjects, respectively. In the multiple linear regression model, the MAE and RMSE were 0.600 and 0.748 in male subjects and 0.566 and 0.789 in female subjects, respectively. When applying the neural network model to the statistical analysis of the dental developmental stage, the results were as accurate as those of conventional statistical analysis methods. This study’s approach is expected to be useful for estimating the ages of children.

Given that tooth loss is a degenerative change, the number of residual teeth may be used to specify a particular age range as a marker for age estimation. This study examined changes in the number of teeth with age using a nationwide oral survey database and derived the age distribution of the Korean population according to the number of teeth. Data on the number of teeth and age were extracted from the oral examination data of the Korean National Health and Nutrition Examination Survey (KNHANES) from 2016 to 2018. Statistical analyses of a complex sample survey were performed using weighted values. The distribution range of the number of teeth by age was broad. The proportion of young people decreased progressively as the number of remaining teeth decreased. In contrast, the proportion of those from the older age group decreased slightly as the number of teeth increased. The number of teeth was subdivided into groups of four, age was categorized into 5-year intervals, and the distribution of age groups by the number of teeth was analyzed. We attempted to determine the age group threshold at approximately 95th percentile for age. In summary, we found that if there were ≤4, 5-12, and 13-20 residual teeth, the estimated age was ≥60, ≥55, and ≥50 years, respectively, with an approximately 95% probability. When many teeth are lost and it is difficult to apply conventional dental age estimation methods, our method may assist in narrowing the age range, although it is not an accurate age determination method.

The aim of the present study was to investigate the pathophysiological etiology of osteoarthritis that is mediated by the apoptosis of chondrocytes exposed to 25-hydroxycholesterol (25-HC), an oxysterol synthesized by the expression of cholesterol-25-hydroxylase (CH25H) under inflammatory conditions. Interleukin-1β induced the apoptosis of chondrocytes in a dose- dependent manner. Furthermore, the production of 25-HC increased in the chondrocytes treated with interleukin-1β through the expression of CH25H. 25-HC decreased the viability of chondrocytes. Chondrocytes with condensed nucleus and apoptotic populations increased by 25- HC. Moreover, the activity and expression of caspase-3 were increased by the death ligand-mediated extrinsic and mitochondria-dependent intrinsic apoptotic pathways in the chondrocytes treated with 25-HC. Finally, 25-HC induced not only caspasedependent apoptosis, but also induced proteoglycan loss in articular cartilage ex vivo cultured rat knee joints. These data indicate that 25-HC may act as a metabolic pathophysiological factor in osteoarthritis that is mediated by progressive chondrocyte death in the articular cartilage with inflammatory condition.

The aim of the present study was to investigate the pathophysiological etiology of osteoarthritis that is mediated by the apoptosis of chondrocytes exposed to 25-hydroxycholesterol (25-HC), an oxysterol synthesized by the expression of cholesterol-25-hydroxylase (CH25H) under inflammatory conditions. Interleukin-1β induced the apoptosis of chondrocytes in a dose- dependent manner. Furthermore, the production of 25-HC increased in the chondrocytes treated with interleukin-1β through the expression of CH25H. 25-HC decreased the viability of chondrocytes. Chondrocytes with condensed nucleus and apoptotic populations increased by 25- HC. Moreover, the activity and expression of caspase-3 were increased by the death ligand-mediated extrinsic and mitochondria-dependent intrinsic apoptotic pathways in the chondrocytes treated with 25-HC. Finally, 25-HC induced not only caspasedependent apoptosis, but also induced proteoglycan loss in articular cartilage ex vivo cultured rat knee joints. These data indicate that 25-HC may act as a metabolic pathophysiological factor in osteoarthritis that is mediated by progressive chondrocyte death in the articular cartilage with inflammatory condition.