Monitored anesthesia care is a feasible option for anesthetic management during awake craniotomy. Patients selected for surgery are thoroughly evaluated by anesthesiologists, primarily focusing on their risk for airway emergencies, such as respiratory depression and obstruction, throughout the procedure. For patients with relative contraindications, a tailored approach is used to assess their suitability. Neuropsychiatric counseling is also helpful for enhancing the patient’s ability to participate in and perform the necessary tasks during brain mapping. Building good rapport with the patient is essential for the success of awake craniotomy, as it helps foster trust and cooperation. Analgesia during awake craniotomy is primarily achieved through scalp nerve blocks or infiltration. Among the six scalp nerve blocks, I have described the zygotemporal nerve block in detail. Proper positioning is crucial for both the surgical approach and the safety and comfort of the patient. Even when local anesthetics are effectively administered, many patients may still experience mild to moderate pain during the procedure. This pain is common and transient, typically occurring around the temporal region. In some cases, sedatives or additional analgesics may be necessary. Serious adverse events can arise, including those that require urgent life-saving interventions or those that interfere with brain mapping and the patient's ability to perform tasks. However, MAC in neurosurgery offers the potential for an improved quality of life for individuals with brain tumors or epileptic seizures, as well as for those with disabilities, such as the deaf or visually impaired, in the future.

Purpose: The classification of asthma phenotypes frequently depends on the age of onset. However, the rationale for specific age cutoffs remains unclear. This study aimed to explore the distribution of asthma onset age, to define subgroups based on onset age, and to examine their characteristics within a broad Korean population. Methods: An analysis of cross-sectional data involving 56,632 participants from the Korean National Health and Nutrition Examination Survey (2010–2016) was conducted. Data on asthma history, including diagnosis, self-reported age of asthma onset, and current disease status, were collected using structured questionnaires. Results: The distribution of asthma onset age showed a distinct peak in early childhood, with a decline between the ages 15 and 20.Based on this distribution, asthma was categorized into childhood-onset ( ≤ 18 years) and adult-onset ( > 18 years) for further analysis.Multivariate analyses indicated that adult-onset asthma was associated with older age, female sex, obesity, and a history of smoking, whereas childhood-onset asthma was linked to younger age, male sex, allergic rhinitis, and atopic dermatitis. Among the adultonset group, current asthma had a later onset age, increased history of smoking history, and atopic dermatitis compared to past asthma. Conclusion This analysis of nationwide general population data suggests that an age threshold around 18 years may be relevant for defining adult-onset asthma.

Purpose: The classification of asthma phenotypes frequently depends on the age of onset. However, the rationale for specific age cutoffs remains unclear. This study aimed to explore the distribution of asthma onset age, to define subgroups based on onset age, and to examine their characteristics within a broad Korean population. Methods: An analysis of cross-sectional data involving 56,632 participants from the Korean National Health and Nutrition Examination Survey (2010–2016) was conducted. Data on asthma history, including diagnosis, self-reported age of asthma onset, and current disease status, were collected using structured questionnaires. Results: The distribution of asthma onset age showed a distinct peak in early childhood, with a decline between the ages 15 and 20.Based on this distribution, asthma was categorized into childhood-onset ( ≤ 18 years) and adult-onset ( > 18 years) for further analysis.Multivariate analyses indicated that adult-onset asthma was associated with older age, female sex, obesity, and a history of smoking, whereas childhood-onset asthma was linked to younger age, male sex, allergic rhinitis, and atopic dermatitis. Among the adultonset group, current asthma had a later onset age, increased history of smoking history, and atopic dermatitis compared to past asthma. Conclusion This analysis of nationwide general population data suggests that an age threshold around 18 years may be relevant for defining adult-onset asthma.

Purpose: The classification of asthma phenotypes frequently depends on the age of onset. However, the rationale for specific age cutoffs remains unclear. This study aimed to explore the distribution of asthma onset age, to define subgroups based on onset age, and to examine their characteristics within a broad Korean population. Methods: An analysis of cross-sectional data involving 56,632 participants from the Korean National Health and Nutrition Examination Survey (2010–2016) was conducted. Data on asthma history, including diagnosis, self-reported age of asthma onset, and current disease status, were collected using structured questionnaires. Results: The distribution of asthma onset age showed a distinct peak in early childhood, with a decline between the ages 15 and 20.Based on this distribution, asthma was categorized into childhood-onset ( ≤ 18 years) and adult-onset ( > 18 years) for further analysis.Multivariate analyses indicated that adult-onset asthma was associated with older age, female sex, obesity, and a history of smoking, whereas childhood-onset asthma was linked to younger age, male sex, allergic rhinitis, and atopic dermatitis. Among the adultonset group, current asthma had a later onset age, increased history of smoking history, and atopic dermatitis compared to past asthma. Conclusion This analysis of nationwide general population data suggests that an age threshold around 18 years may be relevant for defining adult-onset asthma.

Purpose: The classification of asthma phenotypes frequently depends on the age of onset. However, the rationale for specific age cutoffs remains unclear. This study aimed to explore the distribution of asthma onset age, to define subgroups based on onset age, and to examine their characteristics within a broad Korean population. Methods: An analysis of cross-sectional data involving 56,632 participants from the Korean National Health and Nutrition Examination Survey (2010–2016) was conducted. Data on asthma history, including diagnosis, self-reported age of asthma onset, and current disease status, were collected using structured questionnaires. Results: The distribution of asthma onset age showed a distinct peak in early childhood, with a decline between the ages 15 and 20.Based on this distribution, asthma was categorized into childhood-onset ( ≤ 18 years) and adult-onset ( > 18 years) for further analysis.Multivariate analyses indicated that adult-onset asthma was associated with older age, female sex, obesity, and a history of smoking, whereas childhood-onset asthma was linked to younger age, male sex, allergic rhinitis, and atopic dermatitis. Among the adultonset group, current asthma had a later onset age, increased history of smoking history, and atopic dermatitis compared to past asthma. Conclusion This analysis of nationwide general population data suggests that an age threshold around 18 years may be relevant for defining adult-onset asthma.

Purpose: The classification of asthma phenotypes frequently depends on the age of onset. However, the rationale for specific age cutoffs remains unclear. This study aimed to explore the distribution of asthma onset age, to define subgroups based on onset age, and to examine their characteristics within a broad Korean population. Methods: An analysis of cross-sectional data involving 56,632 participants from the Korean National Health and Nutrition Examination Survey (2010–2016) was conducted. Data on asthma history, including diagnosis, self-reported age of asthma onset, and current disease status, were collected using structured questionnaires. Results: The distribution of asthma onset age showed a distinct peak in early childhood, with a decline between the ages 15 and 20.Based on this distribution, asthma was categorized into childhood-onset ( ≤ 18 years) and adult-onset ( > 18 years) for further analysis.Multivariate analyses indicated that adult-onset asthma was associated with older age, female sex, obesity, and a history of smoking, whereas childhood-onset asthma was linked to younger age, male sex, allergic rhinitis, and atopic dermatitis. Among the adultonset group, current asthma had a later onset age, increased history of smoking history, and atopic dermatitis compared to past asthma. Conclusion This analysis of nationwide general population data suggests that an age threshold around 18 years may be relevant for defining adult-onset asthma.

The impact of age on the relationship between body mass index (BMI) and all-cause mortality in hemodialysis (HD) patients is not clearly understood. Using data from the Korean Renal Data System (2001–2022), we analyzed 66,129 HD patients, stratified into young (<65 years, n = 24,589), younger-old (65–74 years, n = 17,732), and older-old (≥75 years, n = 23,808) groups. Patients were categorized into BMI quartiles, and survival outcomes were evaluated using Kaplan-Meier curves and event time ratios for the relative change in the survival time. During the follow-up period, 14,360 of the patients (21.7%) died, with a median follow-up of 3.4 years. Kaplan-Meier curves revealed poorer outcomes in lower BMI quartiles across all age groups. The lowest BMI quartile was significantly associated with a shorter survival time compared to the highest BMI quartile, with a 15% reduction in the young group (p = 0.001) and a 12% reduction in the older-old group (p = 0.002). Predicted survival time increases with rising BMI in the young group, but the rate of increase slows in the younger-old group and plateaus in the older-old group after a BMI of 25 kg/m2. Lower BMI correlates with higher mortality, particularly in younger HD patients.

Prosthodontic treatment is being performed for morphology and functional restoration due to damage and loss of teeth. As the aesthetic demands of patients increase, interest in ceramic materials with shades and translucency similar to natural teeth has increased.Recently, the manufacturing and processing technology of ceramic materials has greatly improved, and the market for dental ceramic materials is growing rapidly. The purpose of this literature review and evaluation is to provide information on the classification and properties of dental ceramic materials with excellent aesthetics and fracture resistance. In this article, it is classified as follows: I) Dental porcelain; II) Sinterable all-ceramic; III) Glass-ceramic for casting; IV) Glass-infiltrated alumina ceramic; V) Glass-ceramic ingots for heat-pressing technique; Vl) Blocks for CAD/CAM; Vll) Ceramic for CAD/3D printing. Dental ceramic materials and their restoration manufacturing methods have evolved significantly over the past decade. As a result, the manufacturing method of restorations has progressed from the layered firing technique of powdered materials or heat-pressing technique to the cutting and processing of single and multi-layer blocks using CAD/CAM technology, leading to the introduction of CAD/3D printing technology. In this manuscript, we will review the types of ceramic materials used in the fabrication of dental restorations and their advantages and disadvantages.

Purpose: This study aims to analyze the learning curve of hand-assisted laparoscopic living donor nephrectomy (HLDN) conducted by a trained gastrointestinal surgeon. Methods: A retrospective analysis was performed on the perioperative clinical data of 96 consecutive patients who underwent HLDN from May 2013 to March 2023. The learning curve was evaluated using the cumulative sum (CUSUM) test based on operation time and risk-adjusted CUSUM for postoperative complications. Patients were divided into three groups (novice, development, and competency phases) based on changes in operation time. Patient demographics and perioperative outcomes were compared between each group. Results: Among the patients, 35 were male, with a mean age of 48.9 ± 11.3 years and a mean body mass index (BMI) of 24.5 ± 3.2 kg/m 2 . The novice phase (phase 1) included the first 30 cases, with the development phase (phase 2) up to the 65th case. Operation times were significantly different across phases, averaging 263.2 ± 33.4, 211.1 ± 34.4, and 161.1 ± 31.3 minutes for phases 1, 2, and 3, respectively (P < 0.001). Blood loss decreased gradually across phases (phase 1, 264.7 ± 144.4 mL; phase 2, 239.7 ± 166.3 mL; phase 3, 198.8 ± 103.5 mL), though not statistically significant. BMI impacted operation time only in phase 1. Overall postoperative complications occurred in 13 cases (Clavien-Dindo grade I, 4 cases;grade II, 9 cases), with no significant differences across phases. Conclusion HLDN can be safely performed by a trained gastrointestinal surgeon, with approximately 30 cases needed to achieve proficiency.

Background/Aims: Shifts in the gut microbiota and metabolites are interrelated with liver cirrhosis progression and complications. However, causal relationships have not been evaluated comprehensively. Here, we identified complication-dependent gut microbiota and metabolic signatures in patients with liver cirrhosis. Methods: Microbiome taxonomic profiling was performed on 194 stool samples (52 controls and 142 cirrhosis patients) via V3-V4 16S rRNA sequencing. Next, 51 samples (17 controls and 34 cirrhosis patients) were selected for fecal metabolite profiling via gas chromatography mass spectrometry and liquid chromatography coupled to timeof-flight mass spectrometry. Correlation analyses were performed targeting the gut-microbiota, metabolites, clinical parameters, and presence of complications (varices, ascites, peritonitis, encephalopathy, hepatorenal syndrome, hepatocellular carcinoma, and deceased). Results: Veillonella bacteria, Ruminococcus gnavus, and Streptococcus pneumoniae are cirrhosis-related microbiotas compared with control group. Bacteroides ovatus, Clostridium symbiosum, Emergencia timonensis, Fusobacterium varium, and Hungatella_uc were associated with complications in the cirrhosis group. The areas under the receiver operating characteristic curve (AUROCs) for the diagnosis of cirrhosis, encephalopathy, hepatorenal syndrome, and deceased were 0.863, 0.733, 0.71, and 0.69, respectively. The AUROCs of mixed microbial species for the diagnosis of cirrhosis and complication were 0.808 and 0.847, respectively. According to the metabolic profile, 5 increased fecal metabolites in patients with cirrhosis were biomarkers (AUROC >0.880) for the diagnosis of cirrhosis and complications. Clinical markers were significantly correlated with the gut microbiota and metabolites. Conclusions Cirrhosis-dependent gut microbiota and metabolites present unique signatures that can be used as noninvasive biomarkers for the diagnosis of cirrhosis and its complications.