In forensic medicine, age estimation commonly involves assessing age-related changes in teeth and skeletal structures. Vertebral morphological alterations, such as osteophyte formation, serve as age indicators. Recent studies using deep-learning techniques, such as neural networks, for age estimation from radiographic images have been conducted, reporting significantly higher accuracy than previous studies. This study aimed to estimate age using neural network-based deep-learning techniques applied to computed tomography (CT) cross-sectional images of the spine and evaluate its feasibility. Postmortem CT scans of 214 cadavers with varying decomposition levels were used. Coronal and sagittal cross-sectional images penetrating the center of each vertebral body were extracted for the 11th and 12th thoracic vertebrae and the first to fifth lumbar vertebrae. Using these images, along with the chronological ages of deceased individuals, an age estimation model was developed through regression analysis in PyTorch, employing a convolutional neural networks architecture with five-fold cross-validation. The model achieved a mean absolute error of 5.385 years, root mean squared error of 7.029 years, and coefficient of determination of 0.793. Although the sample size was relatively small, the results suggested the potential applicability of vertebral imagingbased age estimation in the Korean population. Further research using a larger dataset may improve the accuracy and reliability of the model.

Background: Epidermal growth factor (EGF) is widely applied in dermatology for its regenerative effects; however, its clinical utility is limited by low stability and high production costs.To overcome these limitations, we developed a novel probiotic-derived epidermal growth factor (pEGF) by engineering Pediococcus pentosaceus CBT SL4 to express and secrete EGF. Objective: To investigate the anti-photoaging effects of topical pEGF in a ultraviolet B (UVB)-irradiated mice by assessing histological and molecular changes related to collagen synthesis, extracellular matrix (ECM) remodeling, oxidative stress, and inflammation. Methods: SKH1 hairless mice were exposed to UVB for twelve weeks to induce photoaging and treated with topical 10% pEGF cream from week 9 to 12. Skin samples were analyzed using dermoscopy, histology, immunohistochemistry, quantitative real-time polymerase chain reaction, western blotting, and proteomics to evaluate morphological and molecular alterations in ECM components and inflammatory markers. Results: Topical pEGF treatment improved skin texture and reduced wrinkles. Histological evaluation revealed that EGF significantly attenuated UVB-induced epidermal thickening and restored collagen density, with the strongest effects observed in the pEGF group. Molecular analysis showed downregulation of matrix metalloproteinases (MMPs; MMP-1, MMP-3), upregulation of collagen (type I collagen, type III collagen) and tissue inhibitors of metalloproteinases (TIMPs; TIMP-1, TIMP-2). Additionally, pEGF reduced pro-inflammatory cytokines (tumor necrosis factor-α, interleukin-1β) and oxidative stress markers. Conclusion pEGF demonstrated potent anti-photoaging effects through ECM restoration, inflammation modulation, and barrier reinforcement. These findings support the therapeutic potential of pEGF as a stable, cost-effective alternative to conventional EGF in dermatological applications.

Background: Posterior spinal fusion is performed for severe adolescent idiopathic scoliosis (AIS), with bone graft playing a critical role in achieving solid fusion. Iliac crest bone graft (ICBG) is the gold standard but has donor site morbidity, leading to increased use of non-ICBG. This study compared the effectiveness and safety of ICBG and non-ICBG bone grafts in AIS. Methods: This meta-analysis included 23 studies with 3,350 patients, categorized into ICBG and non-ICBG groups. The primary outcome was fusion rate; secondary outcomes were implant-related complication rate, infection rate, donor site pain, blood loss, and operation time. A proportion meta-analysis was conducted to estimate binary outcomes, and a weighted mean model was used to estimate continuous variables. Results: The fusion rate was 97% (95% confidence interval [CI], 93-99) in ICBG group and 97% (95% CI, 95-98) in non-ICBG group. Implant-related complications occurred in 5% (95% CI, 3-7) and 4% (95% CI, 2-6) of each group, while surgical site infections occurred in 3% (95% CI, 2-7) and 2% (95% CI, 1-3). Donor site pain was observed exclusively in ICBG group, with a prevalence of 49% (95% CI, 3–97). Estimated pooled blood loss was 1,461.10 mL (95% CI, 545.30-2,376.90) in ICBG group and 1,047.45 mL (95% CI, 666.92-1,427.99) in non-ICBG group. The estimated pooled operation time was 246.77 (95% CI, 221.74-271.80) and 232.37 (95% CI, 194.88-269.86) for each group. Conclusions Considering both ICBG and non-ICBG demonstrated high fusion rates and low complication rates, non-ICBG grafts may serve as an effective and safe alternative to ICBG, minimizing morbidity, donor site pain, and blood loss.

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.

Background: The nonunion rate for atypical femoral fractures (AFF) is known to be higher than that for typical fractures of the femur. We performed a meta-analysis to determine the incidence of nonunion necessitating reoperation following fixation for AFF and compare the rates according to the fracture site (subtrochanter or midshaft). Methods: A total of 742 AFFs from 29 studies were included. A proportion meta-analysis utilizing a random-effects model was conducted to estimate the prevalence of nonunion. The outcomes were the incidence of reoperations that included osteosynthesis. To determine the association of nonunion with patient mean age or average duration of bisphosphonate use, meta-regression analysis was done. Results: In proportion meta-analysis, the estimated pooled prevalence of nonunion was 7% (95% confidence interval [CI], 5%–10%) from all studies. There was a significant difference in nonunion rate between the 2 groups (I2 = 34.4%, p = 0.02); the estimated prevalence of nonunion was 15% (95% CI, 10%–20%) in subtrochanteric AFFs and 4% (95% CI, 2%–6%) in midshaft AFFs. From meta-regression analysis, significant correlations were identified between nonunion rate and patient mean age (coefficient:–0.0071, p = 0.010), but not in the average duration of bisphosphonate use (coefficient: –0.0024, p = 0.744). Conclusions A notable disparity existed in the nonunion rate among subtrochanteric AFFs and midshaft AFFs group. Therefore, it is critical for orthopedic surgeons to consider the complexity and challenges associated with AFF and to estimate the proper possibility of nonunion according to the fracture site.

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.

Background and Objectives: The prognostic value of left atrial (LA) function in terms of long-term clinical outcomes after mitral regurgitation (MR) surgery remains unclear.Therefore, we investigated the impact of preoperative LA global longitudinal strain (LAGLS) on the long-term postoperative clinical outcomes in chronic severe MR patients who underwent mitral valve (MV) repair surgery. Methods: From January 2012 to December 2017, we analyzed 338 patients (mean age, 51.9±12.5 years; 218 males [64.5%]) treated with MV repair surgery for severe MR. The primary outcome was cardiovascular events, defined as the composite of all-cause death, newly developed atrial fibrillation (AF), and re-hospitalization for cardiovascular causes. Results: During a median follow-up of 45 months (interquartile range, 26–65), 30 (8.9%) cardiovascular events, 5 (1.5%) all-cause death, 8 (2.4%) newly developed AF, and 26 (7.7%) re-hospitalizations occurred. On multivariable analysis, baseline LAGLS was an independent predictor of cardiovascular events (adjusted hazard ratio [HR], 0.91; 95% confidential interval [CI], 0.85–0.97; p=0.004) and re-hospitalization (adjusted HR, 0.93; 95% CI,0.86–1.00; p=0.037). According to the optimal cutoff value of LAGLS, patients with low LAGLS (<23.6%) had a significantly higher risk of cardiovascular events (adjusted HR, 2.70;95% CI, 1.04–7.00; p=0.041) than those with high LAGLS (≥23.6%). In a subgroup analysis, patients with high LAGLS had better clinical outcomes regardless of whether the patient had a LA volume index <60 mL/m2 . Conclusions In patients with chronic severe MR who received successful MV repair surgery, preoperative LAGLS is an independent predictor of long-term postoperative outcomes.

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.

Chronic spontaneous urticaria (CSU) is defined as the occurrence of spontaneous wheals, angioedema, or both for >6 weeks in the absence of specific causes. It is a common condition associated with substantial disease burden both for affected individuals and societies in many countries, including Korea. CSU frequently persists for several years and requires high-intensity treatment; therefore, patients experience deteriorations in quality of life and medication-associated complications. During the last decade, there have been major advances in the pharmacological treatment of CSU and there is an outstanding need for evidence-based guidelines that reflect clinical practice in Korea. The guidelines reported here represent a joint initiative of the Korean Academy of Asthma, Allergy and Clinical Immunology and the Korean Dermatological Association, and aim to provide evidence-based guidance for the management of CSU in Korean adults and children. In Part 1, disease definition, guideline scope and development methodology as well as evidence-based recommendations on the use of antihistamines and corticosteroids are summarized.

Chronic spontaneous urticaria (CSU) is defined as the occurrence of spontaneous wheals, angioedema, or both for >6 weeks in the absence of specific causes. It is a common condition associated with substantial disease burden both for affected individuals and societies in many countries, including Korea. CSU frequently persists for several years and requires high-intensity treatment; therefore, patients experience deteriorations in quality of life and medication-associated complications. During the last decade, there have been major advances in the pharmacological treatment of CSU and there is an outstanding need for evidence-based guidelines that reflect clinical practice in Korea. The guidelines reported here represent a joint initiative of the Korean Academy of Asthma, Allergy and Clinical Immunology and the Korean Dermatological Association, and aim to provide evidence-based guidance for the management of CSU in Korean adults and children. In Part 1, disease definition, guideline scope and development methodology as well as evidence-based recommendations on the use of antihistamines and corticosteroids are summarized.