Purpose: The male predominance in the incidence of gastric cancer (GC) is established; however, sex differences in the prognosis of GC remain controversial. As such, this study analyzed the prognosis of patients with GC based on age and sex. Materials and Methods: Data from 14,739 patients diagnosed with GC at Seoul National University Bundang Hospital between 2003 and 2023 were analyzed. Baseline characteristics, histological types of GC, overall and GC-specific survival rates (age and stage stratification), and associated risk factors were analyzed. Results: Females were significantly younger (p < 0.001) and exhibited more gastric body cancers (p < 0.001) and tumors with diffuse-type or poorly differentiated histology (p < 0.001) than males. Females exhibited an advantage over males in terms of overall survival (p=0.004), but not in GC-specific survival. However, age stratification revealed significant sex differences, that females < 50 years of age exhibited survival disadvantages (p < 0.001); however, this trend was reversed with age, and females > 60 years exhibited survival advantages (p < 0.001) for both overall and GC-specific survival. This may be explained by the lower ratio of diffuse-type GC as females age. Furthermore, in the analysis according to stage, females with stage IV disease exhibited significant survival disadvantages, with significantly younger age and a higher proportion of diffuse-type GC which exhibits aggressive features, resulting in poorer survival than in males. Conclusion Age and stage stratification revealed significant differences in survival between the sexes, which can be helpful for public health strategies.

Objectives: Clinical situations that make it challenging to differentiate odontogenic cysts from non-odontogenic cysts and benign tumors of the jaw include cases with cystic conditions accompanied by secondary infection, impacted teeth, cortical thinning and expansion, or external root resorption.This study aimed to identify risk factors for complications in patients undergoing cyst enucleation of the jaw, propose a clinical model, and determine the necessary indications for preoperative root canal of adjacent teeth. Materials and Methods: A review of surgical, pathological, and radiological reports, as well as medical records, was conducted. Pathological diagnosis, lesion size, history of preoperative endodontic treatment of the adjacent tooth, operator details, surgical procedures, age, gender, and complications (with severity) were analyzed. Results: This study involved 77 patients (55 men, 22 women) and found 10 complications. Procedure type (cyst enucleation only, apicoectomy and bone graft, or bone graft only) and lesion size were significant risk factors. Preoperative root canal therapy and men gender also tended to positively correlate with complications, while age was not a factor. Conclusion Within the limitations of our study, additional procedures contributed to lower risk of complications with the exception of bone graft.A large cyst size was also associated with a higher risk of complications. It is important to consider the possibility of a second procedure and take thorough precautions to prevent infection when performing bone grafts. Patients should be informed of these risks in advance, scheduled for regular follow-up, and provided additional treatment when necessary.

Background and Objectives: Voice disorders can significantly impact quality of life. This study evaluates the feasibility of using deep learning models to detect voice disorders using an opensource dataset.Materials and Method We utilized the Saarbrücken Voice Database, which contains 1231 voice recordings of various pathologies. Datasets were used for training (n=1036) and validation (n=195). Key vocal parameters, including fundamental frequency (F0), formants (F1, F2), harmonics-to-noise ratio, jitter, and shimmer, were analyzed. A convolutional neural network (CNN) was designed to classify voice recordings into normal, vox senilis, and laryngocele. Performance was assessed using precision, recall, F1-score, and accuracy. Results: The CNN model demonstrated high classification performance, with precision, recall, and F1-scores of 1.00 for normal and 0.99 for vox senilis and laryngocele. Accuracy reached 1.00 after 50 epochs and remained stable through 100 epochs. Time-frequency analysis supported the model’s ability to differentiate between classes. Conclusion This study highlights the potential of deep learning for voice disorder detection, achieving high accuracy and precision. Future research should address dataset diversity and realworld integration for broader clinical adoption.

Objectives: Clinical situations that make it challenging to differentiate odontogenic cysts from non-odontogenic cysts and benign tumors of the jaw include cases with cystic conditions accompanied by secondary infection, impacted teeth, cortical thinning and expansion, or external root resorption.This study aimed to identify risk factors for complications in patients undergoing cyst enucleation of the jaw, propose a clinical model, and determine the necessary indications for preoperative root canal of adjacent teeth. Materials and Methods: A review of surgical, pathological, and radiological reports, as well as medical records, was conducted. Pathological diagnosis, lesion size, history of preoperative endodontic treatment of the adjacent tooth, operator details, surgical procedures, age, gender, and complications (with severity) were analyzed. Results: This study involved 77 patients (55 men, 22 women) and found 10 complications. Procedure type (cyst enucleation only, apicoectomy and bone graft, or bone graft only) and lesion size were significant risk factors. Preoperative root canal therapy and men gender also tended to positively correlate with complications, while age was not a factor. Conclusion Within the limitations of our study, additional procedures contributed to lower risk of complications with the exception of bone graft.A large cyst size was also associated with a higher risk of complications. It is important to consider the possibility of a second procedure and take thorough precautions to prevent infection when performing bone grafts. Patients should be informed of these risks in advance, scheduled for regular follow-up, and provided additional treatment when necessary.

Background and Objectives: Voice disorders can significantly impact quality of life. This study evaluates the feasibility of using deep learning models to detect voice disorders using an opensource dataset.Materials and Method We utilized the Saarbrücken Voice Database, which contains 1231 voice recordings of various pathologies. Datasets were used for training (n=1036) and validation (n=195). Key vocal parameters, including fundamental frequency (F0), formants (F1, F2), harmonics-to-noise ratio, jitter, and shimmer, were analyzed. A convolutional neural network (CNN) was designed to classify voice recordings into normal, vox senilis, and laryngocele. Performance was assessed using precision, recall, F1-score, and accuracy. Results: The CNN model demonstrated high classification performance, with precision, recall, and F1-scores of 1.00 for normal and 0.99 for vox senilis and laryngocele. Accuracy reached 1.00 after 50 epochs and remained stable through 100 epochs. Time-frequency analysis supported the model’s ability to differentiate between classes. Conclusion This study highlights the potential of deep learning for voice disorder detection, achieving high accuracy and precision. Future research should address dataset diversity and realworld integration for broader clinical adoption.

Objectives: Clinical situations that make it challenging to differentiate odontogenic cysts from non-odontogenic cysts and benign tumors of the jaw include cases with cystic conditions accompanied by secondary infection, impacted teeth, cortical thinning and expansion, or external root resorption.This study aimed to identify risk factors for complications in patients undergoing cyst enucleation of the jaw, propose a clinical model, and determine the necessary indications for preoperative root canal of adjacent teeth. Materials and Methods: A review of surgical, pathological, and radiological reports, as well as medical records, was conducted. Pathological diagnosis, lesion size, history of preoperative endodontic treatment of the adjacent tooth, operator details, surgical procedures, age, gender, and complications (with severity) were analyzed. Results: This study involved 77 patients (55 men, 22 women) and found 10 complications. Procedure type (cyst enucleation only, apicoectomy and bone graft, or bone graft only) and lesion size were significant risk factors. Preoperative root canal therapy and men gender also tended to positively correlate with complications, while age was not a factor. Conclusion Within the limitations of our study, additional procedures contributed to lower risk of complications with the exception of bone graft.A large cyst size was also associated with a higher risk of complications. It is important to consider the possibility of a second procedure and take thorough precautions to prevent infection when performing bone grafts. Patients should be informed of these risks in advance, scheduled for regular follow-up, and provided additional treatment when necessary.

Background and Objectives: Voice disorders can significantly impact quality of life. This study evaluates the feasibility of using deep learning models to detect voice disorders using an opensource dataset.Materials and Method We utilized the Saarbrücken Voice Database, which contains 1231 voice recordings of various pathologies. Datasets were used for training (n=1036) and validation (n=195). Key vocal parameters, including fundamental frequency (F0), formants (F1, F2), harmonics-to-noise ratio, jitter, and shimmer, were analyzed. A convolutional neural network (CNN) was designed to classify voice recordings into normal, vox senilis, and laryngocele. Performance was assessed using precision, recall, F1-score, and accuracy. Results: The CNN model demonstrated high classification performance, with precision, recall, and F1-scores of 1.00 for normal and 0.99 for vox senilis and laryngocele. Accuracy reached 1.00 after 50 epochs and remained stable through 100 epochs. Time-frequency analysis supported the model’s ability to differentiate between classes. Conclusion This study highlights the potential of deep learning for voice disorder detection, achieving high accuracy and precision. Future research should address dataset diversity and realworld integration for broader clinical adoption.

Background: Psoriasis is a persistent systemic inflammatory condition mediated by the interleukin (IL)-23/IL-17 signaling pathway. Existing murine models, including imiquimod (IMQ)-applied wild-type (WT) mice, may not reflect chronicity and systemic comorbidities of psoriasis, particularly gut-related manifestations linked to the gut–skin axis. Objective: To establish a murine model that more accurately reflects chronic psoriasis, its systemic comorbidities, and associated gut environment alterations. Methods: C57BL/6 IL-10-deficient (IL-10 knockout [KO]) and WT mice received topical IMQ or vehicle for 6 weeks. Subsequently, tissue samples from skin, colon, joints, kidneys, liver, abdominal aortas, lymph nodes, and spleens, as well as fecal and blood samples, were collected for histopathologic, immunologic, gut environment analysis. Results: IMQ-treated IL-10 KO mice developed prolonged psoriatic inflammatory responses with increased epidermal thickness and higher infiltration of CD45+, myeloperoxidase+, and IL-17+ cells. They also exhibited early-onset, severe colitis with marked weight loss, shortened colon length, and elevated colitis severity scores. While IMQ induced systemic inflammation in multiple organs, IL-10 KO mice did not show more severe joint, liver, or kidney involvement than WT mice. Elevated serum tumor necrosis factor alpha and plasminogen activator inhibitor-1 levels, increased heart/body weight ratio, enhanced gut permeability, and distinct gut microbiota profiles were observed in IL-10 KO mice. Conclusion The 6-week IMQ-applied IL-10 KO model may better reflect chronic and severe psoriasis with gut-related comorbidities, offering a valuable platform to investigate the gut–skin axis.

Objective: As the coronavirus disease 2019 (COVID-19) pandemic has been prolonged, the pre-hospital screening test performed in hospitals has changed from polymerase chain reaction (PCR) to rapid antigen test (RAT). RAT has lower sensitivity than PCR, but a lower cost and shorter test time. In the hospital where this study was conducted, the preadmission COVID-19 test was changed from PCR to RAT on February 24, 2023. This study compared the efficacy of PCR and RAT as the pre-admission COVID-19 test. Methods: This was a retrospective observational study comparing two periods, 90 days from November 25, 2022 to February 23, 2023, and 90 days from February 28, 2023 to May 29, 2023. Patients hospitalized from the emergency medical center during these two periods were included. Binomial logistic regression was used to compare the emergency department length of stay (ED LOS) between the two groups (PCR vs. RAT). Logistic regression was used to compare the mortality. Results: Four thousand seven hundred and thirty-eight patients were admitted through the emergency room: 2,202 patients in the PCR group and 2,443 patients in the RAT group. No statistically significant differences in the characteristics and severity were observed between the two groups. The mean ED LOS of PCR 5.07 hours (interquartile range, 3.80-6.75) was longer than RAT 3.65 hours (interquartile range, 2.83-4.68) (P<0.001). Mortality was higher in PCR 8.9% than RAT 6.6% (P=0.005). Conclusion The study showed that ED LOS and mortality were reduced when using RAT rather than PCR as a preadmission COVID-19 test.

Background: Catheter-associated urinary tract infections (CAUTIs) account for a large proportion of healthcare-associated infections and have a significant impact on morbidity, length of hospital stay, and mortality. Adherence to the recommended infection prevention practices can effectively reduce the incidence of CAUTIs. This study aimed to assess the characteristics of CAUTIs and the efficacy of prevention programs across hospitals of various sizes. Methods: Intervention programs, including training, surveillance, and monitoring, were implemented. Data on the microorganisms responsible for CAUTIs, urinary catheter utilization ratio, rate of CAUTIs per 1,000 device days, and factors associated with the use of indwelling catheters were collected from 2017 to 2019. The incidence of CAUTIs and associated data were compared between university hospitals and small- and medium-sized hospitals. Results: Thirty-two hospitals participated in the study, including 21 university hospitals and 11 small- and medium-sized hospitals. The microorganisms responsible for CAUTIs and their resistance rates did not differ between the two groups. In the first quarter of 2018, the incidence rate was 2.05 infections/1,000 device-days in university hospitals and 1.44 infections/1,000 device-days in small- and medium-sized hospitals. After implementing interventions, the rate gradually decreased in the first quarter of 2019, with 1.18 infections/1,000 device-days in university hospitals and 0.79 infections/1,000 device-days in small- and medium-sized hospitals. However, by the end of the study, the infection rate increased to 1.74 infections/1,000 device-days in university hospitals and 1.80 infections/1,000 device-days in small- and medium-sized hospitals. Conclusion We implemented interventions to prevent CAUTIs and evaluated their outcomes. The incidence of these infections decreased in the initial phases of the intervention when adequate support and personnel were present. The rate of these infections may be reduced by implementing active interventions such as consistent monitoring and adherence to guidelines for preventing infections.