BACKGROUND/OBJECTIVES: Supplementation of nicotinamide adenine dinucleotide (NAD) precursors has been investigated for its potential to counteract age-related physiological decline. While decreased NAD + level is associated with conditions such as obesity and dyslipidemia, few studies have explored its metabolic impact in relatively young animals.Here, we hypothesized that nicotinamide mononucleotide (NMN) could alleviate highfat diet-induced metabolic stress in young mice and sought to elucidate the underlying mechanisms.MATERIALS/METHODS: We first conducted a secondary analysis of hepatic transcriptome datasets from multiple NMN intervention studies to identify target pathways. Next, 11-weekold lean and obese C57BL/6N mice administrated NMN (500 mg/kg/day) for 1 mon were analyzed for metabolic parameters and gene expression for the target pathways. In vitro study using AML12 cell line was then conducted to confirm the effects of NMN. Additionally, transcriptomic data from liver-specific nicotinamide phosphoribosyltransferase (NAMPT)-deficient mice were analyzed to examine the relationship between NAD + metabolism, endoplasmic reticulum (ER) stress, and circadian rhythm. RESULTS: The secondary analysis of hepatic transcriptome datasets revealed potential target pathways related to circadian regulation and hepatic ER stress. In mouse study, short-term NMN administration significantly decreased body weight and fat mass. NMN also alleviated hepatic ER stress response in vivo and in vitro. Additional analysis revealed that the mice with diet-induced metabolic stress or NAMPT deficiency led to phase shifts of the hepatic circadian core clock oscillation by advancing the peak time, while time-restricted feeding shifted the opposite, indicating the metabolic regulation of the circadian phase. CONCLUSION Short-term NMN administration effectively attenuated obese condition in young mice and mitigated hepatic ER stress. The altered ER stress was regulated by NAMPT, potentially through the phase shift of the hepatic circadian rhythm.

Circadian rhythms orchestrate daily fluctuations in feeding behavior, hormonal activity, and metabolic function through coordinated interactions between the central clock and peripheral oscillators. Dietary patterns, defined by both macronutrient composition and the timing of food intake, function as potent cues that reshape these rhythms. This narrative review summarizes experimental evidence describing how diet influences circadian organization and how circadian disruption alters metabolic outcomes primarily in murine models. Genetic disruption of core clock components such as CLOCK, BMAL1, CRY, or PER abolishes feeding rhythmicity and desynchronizes metabolic pathways, resulting in obesity, impaired glucose regulation, and hepatic lipid accumulation. Dietary composition also influences circadian organization, as high-fat, ketogenic, or modified-protein diets shift the phase or amplitude of peripheral clock gene rhythms and alter metabolic outputs across tissues. Feeding-time misalignment produces substantial phase shifts in hepatic and adipose oscillations and aggravates diet-induced metabolic dysfunction. In contrast, restricting food intake to consistent daily windows restores rhythmic metabolic states in the liver, adipose tissue, and intestine and improves systemic homeostasis even without reducing caloric intake. Together, these findings show that both macronutrient composition and feeding-time alignment influence the phase and synchrony of peripheral circadian rhythms and the metabolic processes governed by these rhythms. Understanding these relationships provides mechanistic insight into diet–circadian interactions and supports the development of circadian-aligned dietary strategies aimed at promoting metabolic stability.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.

Background and Objectives: Recently, the improvement of music perception abilities for emotional stability and high quality of life has become important for the hearing loss group. This study aimed to examine and compare the music perception abilities of the normal hearing (NH) and hearing amplification system (HAS) groups to find the needs and methods of music rehabilitation. Subjects and Methods: The data were collected from 15 NH adults (33.1±11.4 years) and 15 HAS adults (38.7±13.4 years), of whom eight wore cochlear implant [CI] systems and seven wore CI and hearing aid systems depending on pitch, melody, rhythm, timbre, emotional reaction, and harmony perception tests. A mismatch negativity test was also conducted, and attitudes toward and satisfaction with listening to music were measured. Results: The correction percentages for the NH and HAS groups were 94.0%±6.1% and 75.3%±23.2% in the pitch test; 94.0%±7.1% and 30.3%±25.9% in the melody test; 99.3%±1.8% and 94.0%± 7.6% in the rhythm test; 78.9%±41.8% and 64.4%±48.9% in the timbre test; 96.7%±10.4% and 81.7%±16.3% in the emotional reaction test; and 85.7%±14.1% and 58.4%±13.9% in the harmony test, respectively, showing statistical significance (p<0.05). For the mismatch negativity test, the area of the waveform was smaller in the HAS groups than in the NH groups, with 70 dB of stimulation showing no statistical significance. The response rates for satisfaction with listening to music were 80% and 93.3% for the NH and HAS groups, showing no statistical significance. Conclusions Although the HAS group showed lower music perception ability than the NH group overall, they showed a strong desire for music listening. Also, the HAS group revealed a higher degree of satisfaction even when listening to unfamiliar music played with unusual instruments. It is suggested that systematic and constant musical rehabilitation based on musical elements and different listening experiences will improve music perception qualities and abilities for HAS users.

Neurocritical patients who can self-report pain use the 0-10 numerical rating scale (NRS, verbal or visual form). However, critically ill patients whose nervous systems cannot express pain use the behavioral pain scale (BPS) and the critical care pain observation tool (CPOT) behavioral pain assessment tools. These tools reveal pain-related changes in movement, facial expression, posture, and physiological indicators such as heart rate, blood pressure, and respiratory rate. In pain control, it is first essential to reduce unnecessary painkillers through non-drug therapy and maximize the effect of the administered analgesics. For nonneuropathic pain, narcotic analgesics such as fentanyl, hydromorphone, morphine, and remifentanil are administered intravenously. Gabapentin, pregabalin, and carbamazepine are recommended along with narcotic analgesics for neuropathic pain control. In addition, nonnarcotic analgesics for multi-modal analgesia are used to reduce the use of narcotic analgesics or the side effects of narcotic analgesics. In the intensive care unit (ICU), the sedation-agitation scale (SAS) and the Richmond agitation-sedation scale (RASS) are used to determine the depth of sedation to be maintained during shallow or deep sedation, considering the condition of the critically ill patient. When selecting sedatives for critically ill patients, preferentially consider nonbenzodiazepines such as propofol or dexmedetomidine rather than benzodiazepines such as midazolam or lorazepam. In addition, patients use painkillers or sedatives for over a week, and neurological changes or physiological dependence may occur. Therefore, clinicians should evaluate the critically ill patient’s condition, and sedatives and painkillers should be reduced or discontinued.