Objective: To analyze the microbiological and clinical characteristics of vulvovaginitis in girls, distinguishing between the premenarcheal and postmenarcheal groups in a tertiary center in South Korea. Methods: This retrospective cohort study included 195 patients under 20 years of age diagnosed with vulvovaginitis at a tertiary hospital between 2014 and 2023. The patients were categorized into premenarcheal (n=95) and postmenarcheal (n=100) groups. Data on initial symptoms, microbial cultures, and treatment methods were analyzed. Results: The most common initial symptom was vaginal discharge, reported in 63.1% of cases. Culture results showed a 51.3% positivity rate for any microorganism, with a prevalence of gram-negative rods (32.8%) and gram-positive cocci (14.4%). The most frequently isolated microorganisms were Escherichia coli (17.9%), Candida albicans (7.7%), and Enterococcus faecalis (6.7%). Gram-negative rods were more common in the premenarcheal group (37.1% vs. 25.0%; p=0.01). No significant differences were observed in the prevalence of gram-positive cocci and Candida species between the two groups (16.8% vs. 12.0%, p=0.22; 6.3% vs. 13.0%, p=0.09; respectively). The susceptibilities of grampositive microorganisms to penicillin, oxacillin, clindamycin, vancomycin, and tetracycline were 58.8%, 58.3%, 94.7%, 100.0%, and 73.7%, respectively. The susceptibilities of gram-negative microorganisms to amoxicillin/clavulanic acid, ciprofloxacin, ceftriaxone, and nitrofurantoin were 89.3%, 85.3%, 76.0%, and 100.0%, respectively. Conclusion This study identified differences in the microbial profiles associated with vulvovaginitis between premenarcheal and postmenarcheal girls. Age-specific and history-based clinical approaches tailored to menarcheal status are warranted to improve the management and outcomes of pediatric and adolescent vulvovaginitis.

Background: Skipping breakfast is associated with an increased risk of chronic inflammatory diseases. This study aimed to examine the association between breakfast-eating habits and inflammation, using high-sensitivity C-reactive protein (hs-CRP) as a marker. Methods: A total of 4,000 Korean adult males with no history of myocardial infarction, angina, stroke, diabetes, rheumatoid arthritis, cancer, or current smoking were included. Data from the 2016–2018 Korea National Health and Nutrition Examination Survey were used for analysis. The frequency of breakfast consumption was assessed through a questionnaire item in the dietary survey section asking participants about their weekly breakfast consumption routines over the past year. Participants were categorized into two groups, namely “0–2 breakfasts per week” and “3–7 breakfasts per week”; hs-CRP concentrations were measured through blood tests. Results: Comparing between the “infrequent breakfast consumption (0–2 breakfasts per week)” and “frequent breakfast consumption (3–7 breakfasts per week)” groups, the mean hs-CRP was found to be significantly higher in the “infrequent breakfast consumption” group, even after adjusting for age, body mass index, physical activity, alcohol consumption, systolic blood pressure, blood pressure medication, fasting blood glucose, and triglycerides (mean hs-CRP: frequent breakfast consumption, 1.36±0.09 mg/L; infrequent breakfast consumption, 1.17±0.05 mg/L; P-value=0.036). Conclusion Less frequent breakfast consumption was associated with elevated hs-CRP levels. Further large-scale studies incorporating adjusted measures of daily eating patterns as well as food quality and quantity are required for a deeper understanding of the role of breakfast in the primary prevention of chronic inflammatory diseases.

Early-life stress (ES) leads to cognitive dysfunction in female adolescents, but the underlying neural mechanisms remain elusive. Recent evidence suggests that the cell adhesion molecules NECTIN1 and NECTIN3 play a role in cognition and ES-related cognitive deficits in male rodents. In this study, we aimed to investigate whether and how nectins contribute to ES-induced cognitive dysfunction in female adolescents. Applying the well-established limited bedding and nesting material paradigm, we found that ES impairs recognition memory, suppresses prefrontal NECTIN1 and hippocampal NECTIN3 expression, and upregulates corticotropin-releasing hormone (Crh) and its receptor 1 (Crhr1) mRNA levels in the hippocampus of adolescent female mice. Genetic experiments revealed that the reduction of dorsal CA1 (dCA1) NECTIN3 mediates ES-induced object recognition memory deficits, as knocking down dCA1 NECTIN3 impaired animals' performance in the novel object recognition task, while overexpression of dCA1 NECTIN3 successfully reversed the ES-induced deficits. Notably, prefrontal NECTIN1 knockdown did not result in significant cognitive impairments. Furthermore, acute systemic administration of antalarmin, a CRHR1 antagonist, upregulated hippocampal NECTIN3 levels and rescued object and spatial memory deficits in stressed mice. Our findings underscore the critical role of dCA1 NECTIN3 in mediating ES-induced object recognition memory deficits in adolescent female mice, highlighting it as a potential therapeutic target for stress-related psychiatric disorders in women.
Animals ; Female ; Mice ; CA1 Region, Hippocampal/metabolism* ; Cell Adhesion Molecules/metabolism* ; CRF Receptor, Type 1/metabolism* ; Memory Disorders/etiology* ; Mice, Inbred C57BL ; Nectins/genetics* ; Receptors, Corticotropin-Releasing Hormone/antagonists & inhibitors* ; Recognition, Psychology/physiology* ; Stress, Psychological/complications*

Objective To investigate the impact of different lymph node dissection(RLNLN)around the recurrent laryngeal nerve(RLN)on the clinical efficacy of esophagectomy(EC)with thoracoscopic radical surgery.Methods Ninety-eight EC patients were selected from 2022-01 to 2022-10 in our hospital and divided into the control group and the study group,each with 49 cases,using simple randomization method.Both groups underwent EC thoracoscopic radical surgery,with conventional RLNLN clearance in the control group and modified RLNLN clearance in the study group.The operation and postoperative recovery of the two groups were compared,as well as myeloid triggered receptor-1(TREM-1),tumor necrosis factor receptor-associated protein-1(TRAP1),and complications before and after the operation,and the recurrence rate and survival rate of the two groups were counted at 1 year after the operation.Results The RLNLN dissection time in the study group was(11.93±3.57)minutes,which was shorter than that in the control group(17.15±4.28)minutes.The number of RLNLN dissections on both sides was(7.19±1.24),which was higher than that in the control group(5.56±1.10),and the differences were statistically significant(P＜0.05).Three and seven days after surgery,the CD3+,CD4+/CD8+of the study group were higher than those of the control group,while CRP,PCT,TREM-1,and TRAP1 were lower than those of the control group,and the differences were statistically significant(P＜0.05).The postoperative complication and recurrence rates in the study group were 4.08％(2/49)and 10.87％(5/46),respectively,which were lower than the control group[18.37％(9/44),29.55％(13/44)].Conclusion Thoracoscopic radical resection of esophageal cancer with modified RLNLN dissection can can enhance the effect of lymph node clearance,down-regulate the expression of TREM-1 and TRAP1,reduce the inflammatory response of the body,regulate the immune function,reduce the risk of complications and recurrence,and improve the quality of life.

Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Objective: To analyze the microbiological and clinical characteristics of vulvovaginitis in girls, distinguishing between the premenarcheal and postmenarcheal groups in a tertiary center in South Korea. Methods: This retrospective cohort study included 195 patients under 20 years of age diagnosed with vulvovaginitis at a tertiary hospital between 2014 and 2023. The patients were categorized into premenarcheal (n=95) and postmenarcheal (n=100) groups. Data on initial symptoms, microbial cultures, and treatment methods were analyzed. Results: The most common initial symptom was vaginal discharge, reported in 63.1% of cases. Culture results showed a 51.3% positivity rate for any microorganism, with a prevalence of gram-negative rods (32.8%) and gram-positive cocci (14.4%). The most frequently isolated microorganisms were Escherichia coli (17.9%), Candida albicans (7.7%), and Enterococcus faecalis (6.7%). Gram-negative rods were more common in the premenarcheal group (37.1% vs. 25.0%; p=0.01). No significant differences were observed in the prevalence of gram-positive cocci and Candida species between the two groups (16.8% vs. 12.0%, p=0.22; 6.3% vs. 13.0%, p=0.09; respectively). The susceptibilities of grampositive microorganisms to penicillin, oxacillin, clindamycin, vancomycin, and tetracycline were 58.8%, 58.3%, 94.7%, 100.0%, and 73.7%, respectively. The susceptibilities of gram-negative microorganisms to amoxicillin/clavulanic acid, ciprofloxacin, ceftriaxone, and nitrofurantoin were 89.3%, 85.3%, 76.0%, and 100.0%, respectively. Conclusion This study identified differences in the microbial profiles associated with vulvovaginitis between premenarcheal and postmenarcheal girls. Age-specific and history-based clinical approaches tailored to menarcheal status are warranted to improve the management and outcomes of pediatric and adolescent vulvovaginitis.

Purpose: To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). Materials and Methods: This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CACscoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients’ medical records were monitored until November 2023. Results: A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers’ sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD. Conclusion DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CACscoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.