Purpose: To determine the corneal stabilization timeline after pterygium excision adjusted for horizontal invasion length (HIL). Methods: In this single-center retrospective cohort study, we analyzed 88 eyes of 80 patients with nasal or temporal primary pterygium who underwent excision and perioperative evaluation with anterior segment (AS) swept-source optical coherence tomography (SS-OCT; Anterion). Anterior simulated mean keratometry (SimKmean; 3-mm ring) and HIL were measured on AS SS-OCT. Keratometric stabilization was assessed at postoperative cutoffs of 8, 12, 16, 20, and 24 weeks using analysis of covariance (ANCOVA) with HIL as a covariate. Subgroup analysis was performed after stratifying by the median HIL (3.82 mm). Results: Mean anterior SimKmean increased from 42.95 ± 1.79 diopters (D) preoperatively to 43.73 ± 1.53 D postoperatively (p < 0.001). Preoperative HIL correlated positively with the perioperative SimKmean change (r = 0.728, p < 0.001). While postoperative duration showed no significant unadjusted correlation with keratometric change (r = −0.133, p = 0.218), partial correlation controlling for HIL was negative (r = −0.248, p = 0.021), indicating diminishing change over time after severity adjustment. ANCOVA demonstrated significant stabilization at 16 weeks (p = 0.036). In the HIL ≥3.82 mm subgroup, SimKmean change was greater before 20 weeks than after 20 weeks (1.73 ± 1.59 D vs. 0.59 ± 0.74 D, p = 0.030), whereas no significant time-point differences were found for HIL <3.82 mm. Conclusions Corneal keratometric stabilization after pterygium excision depends on preoperative HIL. A minimum of 16 weeks is recommended before cataract surgery planning, with at least 20 weeks for eyes with HIL ≥3.82 mm.

Background: Identifying the risks related to the complications of electrical cardioversion (ECV) can alert the determinaton of rhythm control in patients with atrial fibrillation (AF). Methods: We retrospectively reviewed 1,058 patients who underwent ECV for persistent or long-standing persistent AF/atrial flutter (AFL) from multiple centers. Patients were classified into the no-complication (1,023 patients) and complication (35 patients) groups based on the following major complications: stroke and/or systemic embolism (SSE), myocardial infarction, major bleeding, implantation of cardiac implantable electronic devices, ventricular tachycardia/fibrillation, and death at 1 year follow-up after ECV. Results: Compared with the no-complication group, the complication group exhibited a higher proportion of female patients (37% vs. 22%), as well as a higher proportion of patients with older age (67 ± 11 vs. 61 ± 10 years), diabetes mellitus (DM) (49% vs. 24%), heart failure (HF) (49% vs. 30%), SSE (23% vs. 9%), high CHA 2 DS 2 -VASc (CV) score (3.6 ± 1.8 vs.2.2 ± 1.4), low left ventricular ejection fraction (LVEF) (50 ± 16% vs. 58 ± 21%), and high left atrial volume index (LAVI) (51 ± 26 vs. 40 ± 20 mL/m 2 ). Class I and III antiarrhythmics were less prescribed in the complication group than in the no-complication group (57% vs.76%). Univariate analysis for complications revealed age (≥ 65 years), female sex, DM, HF, SSE, LVEF (< 50%), LAVI (≥ 40 mL/m 2 ), CV score (≥ 3), bradycardia on Holter (< 60/min), and no antiarrhythmics as risk factors. Among these, multivariate analysis revealed clinical significance of female sex and SSE. Conclusion Female sex and a history of SSE were the most important risk factors of complications in patients with persistent or long-standing persistent AF/AFL who underwent ECV.

Auricular vagus nerve stimulation (aVNS) is one of the promising neuromodulation techniques due to its non-invasiveness, convenience, and effectiveness. aVNS has been suggested as a potential treatment for neurodegenerative diseases showing impaired cerebrospinal fluid (CSF) dynamics. Improving CSF flow has been proposed as a key mechanism of the therapeutic effect on neurodegenerative diseases. However, aVNS parameters have been set empirically and the effective parameter that maximize the effect remains elusive. Here we show that 30 minutes of low-frequency aVNS increased arterial vasomotion events and enhanced cortical CSF influx along the branches of middle cerebral arteries. By using in vivo two photon imaging or widefield fluorescence microscopy with plasma and CSF tracers for visualizing blood vessels and perivascular spaces, arterial vasomotion and cortical CSF influx dynamics were acquired. The low-frequency (2 Hz) aVNS, but not middleand high-frequency (40 and 100 Hz) aVNS, significantly increased the number of vasomotion events compared to the sham group. Accordingly, in the CSF imaging, 2 Hz of aVNS markedly enhanced the CSF influx. Our findings demonstrate that lowfrequency aVNS is the effective parameter in respect to modulating vasomotion and CSF influx, resulting in brain clearance effect.

Auricular vagus nerve stimulation (aVNS) is one of the promising neuromodulation techniques due to its non-invasiveness, convenience, and effectiveness. aVNS has been suggested as a potential treatment for neurodegenerative diseases showing impaired cerebrospinal fluid (CSF) dynamics. Improving CSF flow has been proposed as a key mechanism of the therapeutic effect on neurodegenerative diseases. However, aVNS parameters have been set empirically and the effective parameter that maximize the effect remains elusive. Here we show that 30 minutes of low-frequency aVNS increased arterial vasomotion events and enhanced cortical CSF influx along the branches of middle cerebral arteries. By using in vivo two photon imaging or widefield fluorescence microscopy with plasma and CSF tracers for visualizing blood vessels and perivascular spaces, arterial vasomotion and cortical CSF influx dynamics were acquired. The low-frequency (2 Hz) aVNS, but not middleand high-frequency (40 and 100 Hz) aVNS, significantly increased the number of vasomotion events compared to the sham group. Accordingly, in the CSF imaging, 2 Hz of aVNS markedly enhanced the CSF influx. Our findings demonstrate that lowfrequency aVNS is the effective parameter in respect to modulating vasomotion and CSF influx, resulting in brain clearance effect.

Auricular vagus nerve stimulation (aVNS) is one of the promising neuromodulation techniques due to its non-invasiveness, convenience, and effectiveness. aVNS has been suggested as a potential treatment for neurodegenerative diseases showing impaired cerebrospinal fluid (CSF) dynamics. Improving CSF flow has been proposed as a key mechanism of the therapeutic effect on neurodegenerative diseases. However, aVNS parameters have been set empirically and the effective parameter that maximize the effect remains elusive. Here we show that 30 minutes of low-frequency aVNS increased arterial vasomotion events and enhanced cortical CSF influx along the branches of middle cerebral arteries. By using in vivo two photon imaging or widefield fluorescence microscopy with plasma and CSF tracers for visualizing blood vessels and perivascular spaces, arterial vasomotion and cortical CSF influx dynamics were acquired. The low-frequency (2 Hz) aVNS, but not middleand high-frequency (40 and 100 Hz) aVNS, significantly increased the number of vasomotion events compared to the sham group. Accordingly, in the CSF imaging, 2 Hz of aVNS markedly enhanced the CSF influx. Our findings demonstrate that lowfrequency aVNS is the effective parameter in respect to modulating vasomotion and CSF influx, resulting in brain clearance effect.

Auricular vagus nerve stimulation (aVNS) is one of the promising neuromodulation techniques due to its non-invasiveness, convenience, and effectiveness. aVNS has been suggested as a potential treatment for neurodegenerative diseases showing impaired cerebrospinal fluid (CSF) dynamics. Improving CSF flow has been proposed as a key mechanism of the therapeutic effect on neurodegenerative diseases. However, aVNS parameters have been set empirically and the effective parameter that maximize the effect remains elusive. Here we show that 30 minutes of low-frequency aVNS increased arterial vasomotion events and enhanced cortical CSF influx along the branches of middle cerebral arteries. By using in vivo two photon imaging or widefield fluorescence microscopy with plasma and CSF tracers for visualizing blood vessels and perivascular spaces, arterial vasomotion and cortical CSF influx dynamics were acquired. The low-frequency (2 Hz) aVNS, but not middleand high-frequency (40 and 100 Hz) aVNS, significantly increased the number of vasomotion events compared to the sham group. Accordingly, in the CSF imaging, 2 Hz of aVNS markedly enhanced the CSF influx. Our findings demonstrate that lowfrequency aVNS is the effective parameter in respect to modulating vasomotion and CSF influx, resulting in brain clearance effect.

Auricular vagus nerve stimulation (aVNS) is one of the promising neuromodulation techniques due to its non-invasiveness, convenience, and effectiveness. aVNS has been suggested as a potential treatment for neurodegenerative diseases showing impaired cerebrospinal fluid (CSF) dynamics. Improving CSF flow has been proposed as a key mechanism of the therapeutic effect on neurodegenerative diseases. However, aVNS parameters have been set empirically and the effective parameter that maximize the effect remains elusive. Here we show that 30 minutes of low-frequency aVNS increased arterial vasomotion events and enhanced cortical CSF influx along the branches of middle cerebral arteries. By using in vivo two photon imaging or widefield fluorescence microscopy with plasma and CSF tracers for visualizing blood vessels and perivascular spaces, arterial vasomotion and cortical CSF influx dynamics were acquired. The low-frequency (2 Hz) aVNS, but not middleand high-frequency (40 and 100 Hz) aVNS, significantly increased the number of vasomotion events compared to the sham group. Accordingly, in the CSF imaging, 2 Hz of aVNS markedly enhanced the CSF influx. Our findings demonstrate that lowfrequency aVNS is the effective parameter in respect to modulating vasomotion and CSF influx, resulting in brain clearance effect.

This study aimed to identify metabolic biomarkers and investigate changes in intestinal microbiota in the feces of healthy participants following administration of Lactococcus lactis GEN-001. GEN-001 is a single-strain L. lactis strain isolated from the gut of a healthy human volunteer. The study was conducted as a parallel, randomized, phase 1, open design trial. Twenty healthy Korean males were divided into five groups according to the GEN-001 dosage and dietary control.Groups A, B, C, and D1 received 1, 3, 6, and 9 GEN-001 capsules (1 × 1011 colony forming units), respectively, without dietary adjustment, whereas group D2 received 9 GEN-001 capsules with dietary adjustment. All groups received a single dose. Fecal samples were collected 2 days before GEN-001 administration to 7 days after for untargeted metabolomics and gut microbial metagenomic analyses; blood samples were collected simultaneously for immunogenicity analysis. Levels of phenylalanine, tyrosine, cholic acid, deoxycholic acid, and tryptophan were significantly increased at 5–6 days after GEN-001 administration when compared with predose levels. Compared with predose, the relative abundance (%) of Parabacteroides and Alistipes significantly decreased, whereas that of Lactobacillus and Lactococcus increased; Lactobacillus and tryptophan levels were negatively correlated. A single administration of GEN-001 shifted the gut microbiota in healthy volunteers to a more balanced state as evidenced by an increased abundance of beneficial bacteria, including Lactobacillus, and higher levels of the metabolites that have immunogenic properties.

Objective: Diagnosis and assessment of depression rely on scoring systems based on questionnaires, either self-reported by patients or administered by clinicians, and observation of patient facial expressions during the interviews plays a crucial role in making impressions in clinical settings. Deep learning driven approaches can assist clinicians in the course of diagnosis of depression by recognizing subtle facial expressions and emotions in depression patients. Methods: Seventeen simulated patients who acted as depressed patients participated in this study. A trained psychiatrist structurally interviewed each participant with moderate depression in accordance with a prepared scenario and without depressive features. Interviews were video-recorded, and a facial emotion recognition algorithm was used to classify emotions of each frame. Results: Among seven emotions (anger, disgust, fear, happiness, neutral, sadness, and surprise), sadness was expressed in a higher proportion on average in the depression-simulated group compared to the normal group. Neutral and fear were expressed in higher proportions on average in the normal group compared to the normal group. The overall distribution of emotions between the two groups was significantly different (p ＜ 0.001). Variance in emotion was significantly less in the depression-simulated group (p ＜ 0.05). Conclusion This study suggests a novel and practical approach to understand the emotional expression of depression patients based on deep learning techniques. Further research would allow us to obtain more perspectives on the emotional profiles of clinical patients, potentially providing helpful insights in making diagnosis of depression patients.