Acne is a chronic inflammatory condition affecting the sebaceous glands, with approximately 80% of individuals experiencing it at some point in their lives. Among adolescents, the incidence is reported to exceed 85%. The disease can significantly impact both physical and emotional aspects of a person’s quality of life, leading to permanent scarring, poor self-image, depression, and anxiety. The standard first-line treatment for acne vulgaris includes conventional pharmacological approaches such as keratolytics, topical or oral antibiotics, retinoids, and hormonal agents. However, these treatments are not universally effective due to patient noncompliance, adverse drug effects, and the emergence of antibiotic resistance in Cutibacterium acnes, often resulting in high rates of recurrence. Consequently, non-pharmacological therapies have been developed as safe and effective alternatives or supplements to pharmacological treatment. These non-pharmacological approaches can serve as standalone treatment modalities, adjuncts to pharmacological therapy, or maintenance treatments. Current literature lacks comprehensive data on the classification of these non-pharmacological treatment options. This paper aims to provide a brief overview of recent research on the practical applications and potential mechanisms of nonpharmacological therapies for both acne and acne scars. Through elucidating the distinct mechanisms and therapeutic roles of these treatments, we aim to assist dermatologists and other healthcare providers in formulating more effective disease management strategies, thereby encouraging further research in this area.

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.

Background: The positive relationship between triiodothyronine (T3) and steatotic liver disease (SLD) demonstrated only in crosssectional study. We aimed to evaluated whether total T3 (TT3) is associated with the development/resolution of SLD in longitudinal design. Methods: This retrospective, longitudinal, population-based cohort study included 1,665 South Korean euthyroid adults with ≥4 thyroid function test. We explored the impact of mean TT3 during follow-up on development/resolution of either SLD (diagnosed by ultrasound) or modified metabolic dysfunction-associated steatotic liver disease (MASLD) using Cox proportional hazards regression models. Results: During about median 5 years follow-up, 807/1,216 (66.3%) participants among participants without SLD at baseline developed SLD, and 253/318 (79.5%) participants among participants with SLD at baseline SLD resolved fatty liver. Mean TT3 rather than thyroid stimulating hormone or mean free thyroxine was significantly related with development (adjusted hazard ratio [HR], 1.01; 95% confidence interval [CI], 1.00 to 1.02; P=0.002) and resolution (adjusted HR, 0.97; 95% CI, 0.96 to 0.99; P=0.005) of SLD. Compared with low mean TT3 group, high mean TT3 group was positively associated with development of SLD (adjusted HR, 1.20; 95% CI, 1.05 to 1.38; P=0.008) and inversely associated with resolution of SLD (adjusted HR, 0.66; 95% CI, 0.51 to 0.85; P=0.001). The statistical significance remained for development (adjusted HR, 1.29; 95% CI, 1.10 to 1.51; P=0.001) and resolution (adjusted HR, 0.71; 95% CI, 0.54 to 0.94; P=0.018) of modified MASLD. Conclusion In Korean euthyroid adults, TT3 level was associated with development and resolution of either SLD or modified MASLD.

Background: Intellectual disability (ID) may be associated with an increased risk of diabetes mellitus (DM). However, evidence from longitudinal studies is scarce, particularly in Asian populations. Methods: This retrospective cohort study used representative linked data from the Korea National Disability Registration System and the National Health Insurance Service database. Adults (≥20 years) who received a national health examination in 2009 (3,385 individuals with ID and 3,463,604 individuals without ID) were included and followed until 2020. ID was identified using legal registration information. Incident DM was defined by prescription records with relevant diagnostic codes. Multivariable-adjusted Cox proportional hazards regression models were used to estimate the adjusted hazard ratio (aHR) and 95% confidence interval (CI) for DM risks in individuals with ID compared to those without ID. Results: Over a mean follow-up of 9.8 years, incident DM occurred in 302 (8.9%) individuals with ID and 299,156 (8.4%) individuals without ID. Having ID was associated with increased DM risk (aHR, 1.38; 95% CI, 1.23 to 1.55). Sensitivity analysis confirmed a higher DM risk in individuals with ID (aHR, 1.39; 95% CI, 1.24 to 1.56) than those with other disabilities (aHR, 1.11; 95% CI, 1.10 to 1.13) or no disability (reference). Stratified analysis showed higher DM risk in non-hypertensive subjects (aHR, 1.63; 95% CI, 1.43 to 1.86) compared to hypertensive subjects (aHR, 1.00; 95% CI, 0.80 to 1.26; P for interaction <0.001). Conclusion Adults with ID have an increased risk of developing DM, highlighting the need for targeted public health strategies to promote DM prevention in this population.

Purpose: The long-term outcomes and efficacy of partial stapled hemorrhoidopexy (PSH) compared with those of conventional hemorrhoidectomy (CH) are not fully understood. This study aimed to introduce a modified PSH (mPSH) and compare its clinical efficacy and safety with those of CH. Methods: A prospective randomized controlled trial was conducted. This study was performed at a single hospital and involved 6 colorectal surgeons. In total, 110 patients were enrolled between July 2019 and September 2020. Patients were randomly assigned to undergo either mPSH group (n=55) or CH group (n=55). The primary outcome was to compare postoperative average pain and postoperative peak pain using visual analog scale score between the 2 groups. Results: The required duration of analgesia was shorter in the mPSH group than in the CH group, although the difference was not statistically significant (P=0.096). However, the laxative requirement duration (P<0.010), return to work (P<0.010), satisfaction score (P<0.010), and Vaizey score (P=0.014) were significantly better in the mPSH group. The average and peak postoperative pain scores were significantly lower in the mPSH group during the 15 days after surgery (P<0.001). The overall complication rate in both groups was 9.1%, with no significant difference between the groups (P=0.867). Conclusion The mPSH group demonstrated better improvement in symptoms, lower pain scores, and greater patient early satisfaction after surgery than the CH group. Therefore, this surgical technique appears to be a safe and effective alternative for CH.

Hangovers from alcohol consumption cause symptoms like headaches, nausea, and fatigue, disrupting daily activities and overall well-being. Over time, they can also lead to inflammation and oxidative stress. Effective hangover relief alleviates symptoms, prevents dehydration, and replenishes energy needed for daily tasks. Natural foods considered high in antioxidants and antiinflammatory properties may aid in the hepatic breakdown of alcohol. The study aims to investigate the impact of glutathione or its enriched yeast extract, which is recognized for its antioxidant characteristics, on alcohol metabolism and alleviating hangovers in a rat model exposed to binge drinking. In this study, glutathione and its enriched yeast extract controlled hangover behaviour patterns, including locomotor activity. Additionally, it enhanced the activities of alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) following ethanol ingestion (3 g/kg). Further, the incorporation of glutathione led to an increase in the expression of antioxidant enzymes, such as SOD and catalase, by activating the nuclear erythroid 2-related factor 2 (Nrf2) signaling pathway.This activation reduced the excessive production of reactive oxygen species (ROS) and malondialdehyde. Next, glutathione modulated the activity of cytochrome P450 2E1 (CYP2E1) and the protein expressions of Bax and Bcl2. Besides, in vitro and in vivo investigations with glutathione demonstrated a regulating effect on the pan-s-glutathionylation and its associated protein expression, glutaredoxin 1 (Grx1), glutathione-S-transferase Pi (GST-π), and glutathione reductase (GR). Together, these findings suggest that glutathione or its enriched yeast extract as a beneficial dietary supplement for alleviating hangover symptoms by enhancing alcohol metabolism and its associated Nrf2/Keap1 signalings.

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.

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.

Purpose: To present a case of recurrent Nocardia keratitis following the use of topical steroids.Case summary: A 57-year-old man presented with decreased visual acuity and conjunctival injection in the right eye which began 15 days prior. Slit-lamp examination revealed epithelial defects smaller than the circular infiltrate and empirical topical treatment was initiated. Since the corneal lesion improved with a therapeutic contact lens and topical antibiotics, a steroid eye drop was added. After 7 days, the corneal infiltrate worsened in a wreath-like pattern with a positive result on a KOH (potassium hydroxide) smear, and antifungal eye drops were started. However, Nocardia species was confirmed on the 9th day of culture. While complete epithelial regeneration was achieved after 10 days using amikacin eye drops, steroid eye drops were reused to reduce the corneal haze. Twenty days later, the corneal infiltrate and epithelial defects reappeared adjacent to the initial opacity, and a culture confirmed Nocardia. Gradual improvement was achieved with amikacin eye drops and oral Septrin. Ultimately, a combination of moxifloxacin, tobramycin, and bromfenac eye drops was tapered over several months, resulting in healing with mild opacity. Conclusions Nocardia keratitis commonly arises from trauma involving soil, progresses slowly, and is often misdiagnosed as a fungal infection. While amikacin eye drops proved effective, prolonged topical treatment is essential. Early steroid use should be considered cautiously, as it may lead to recurrence and worsening of the corneal lesion.

Acne is a chronic inflammatory condition affecting the sebaceous glands, with approximately 80% of individuals experiencing it at some point in their lives. Among adolescents, the incidence is reported to exceed 85%. The disease can significantly impact both physical and emotional aspects of a person’s quality of life, leading to permanent scarring, poor self-image, depression, and anxiety. The standard first-line treatment for acne vulgaris includes conventional pharmacological approaches such as keratolytics, topical or oral antibiotics, retinoids, and hormonal agents. However, these treatments are not universally effective due to patient noncompliance, adverse drug effects, and the emergence of antibiotic resistance in Cutibacterium acnes, often resulting in high rates of recurrence. Consequently, non-pharmacological therapies have been developed as safe and effective alternatives or supplements to pharmacological treatment. These non-pharmacological approaches can serve as standalone treatment modalities, adjuncts to pharmacological therapy, or maintenance treatments. Current literature lacks comprehensive data on the classification of these non-pharmacological treatment options. This paper aims to provide a brief overview of recent research on the practical applications and potential mechanisms of nonpharmacological therapies for both acne and acne scars. Through elucidating the distinct mechanisms and therapeutic roles of these treatments, we aim to assist dermatologists and other healthcare providers in formulating more effective disease management strategies, thereby encouraging further research in this area.