Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: We intended to investigate the relationships between visual sensory impairment (VSI) or auditory sensory impairment (ASI) and brain pathological changes associated with cognitive decline in older adults. Methods: We primarily tried to examine whether each sensory impairment is related to Alzheimer’s disease (AD) pathology, specifically beta-amyloid (Aβ) deposition, through both cross-sectional and longitudinal approaches in cognitively unimpaired older adults. Self-report questionnaires on vision and hearing status were administered at the baseline.Neuroimaging scans including brain [ 11 C] Pittsburgh Compound B PET and MRI, as well as clinical assessments, were performed at baseline and 2-year follow-up. Results: Cross-sectional analyses showed that the VSI-positive group had significantly higher Aβ deposition than the VSI-negative group, whereas there was no significant association between ASI positivity and Aβ deposition. Longitudinal analyses revealed that VSI positivity at baseline was significantly associated with increased Aβ deposition over 2 years (β = 0.153, p = 0.025), although ASI positivity was not (β = 0.045, p = 0.518). VSI positivity at baseline was also significantly associated with greater atrophic changes in AD-related brain regions over the 2-year follow-up period (β = −0.207, p = 0.005), whereas ASI positivity was not (β = 0.024, p = 0.753). Neither VSI nor ASI positivity was related to cerebrovascular injury, as measured based on the white matter hyperintensity volume. Conclusion The findings suggest that VSI is probably related to AD-specific pathological changes, which possibly mediate the reported relationship between VSI and cognitive decline. In contrast, ASI appears not associated with AD pathologies but may contribute to cognitive decline via other mechanisms.

Objective: Previous studies have reported that vitamin D deficiency increased the risk of Alzheimer’s disease (AD) dementia in older adults. However, little is known about how vitamin D is involved in the pathophysiology of AD. Thus, this study aimed to examine the association and interaction of serum vitamin D levels with in vivo AD pathologies including cerebral beta-amyloid (Aβ) deposition and neurodegeneration in nondemented older adults. Methods: 428 Nondemented older adults were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease, a prospective cohort that began in 2014. All participants underwent comprehensive clinical assessments, measurement of serum 25-hydroxyvitamin D (25[OH]D), and multimodal brain imaging including Pittsburgh compound B (PiB) positron emission tomography and magnetic resonance imaging. Global PiB deposition was measured for the Aβ biomarker. Intracranial volume-adjusted hippocampal volume (HVa) was used as a neurodegeneration biomarker. Results: Overall, serum 25(OH)D level was not associated with either Aβ deposition or HVa after controlling for age, sex, apolipoprotein E ε4 positivity, and vascular risk factors. However, serum 25(OH)D level had a significant moderating effect on the association between Aβ and neurodegeneration, with lower serum 25(OH)D level significantly exacerbating cerebral Aβ-associated hippocampal volume loss (B = 34.612, p = 0.008). Conclusion Our findings indicate that lower serum vitamin D levels may contribute to AD by exacerbating Aβ-associated neurodegeneration in nondemented older adults. Further studies to explore the potential therapeutic effect of vitamin D supplementation on the progression of AD pathology will be necessary.

Objectives: . The presence of extrathyroidal extension (ETE) in patients with differentiated thyroid cancer (DTC) serves as a significant prognostic indicator. Consequently, the staging of DTC is categorized into extensive ETE and gross ETE that solely impacts the strap muscles (gross strap muscle invasion [gSMI]). However, there is a lack of sufficient evidence concerning the relationship between gSMI and prognosis, particularly in terms of tumor size. Methods: . Relevant literature was searched in Medline, Embase, Cochrane Library, and KoreaMed. All procedures were conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and carried out by two independent reviewers. The meta-analysis utilized a random-effects model to account for the diversity of the studies. Risk of Bias for Nonrandomized Studies (RoBANS) version 2.0, an evaluation tool for non-randomized studies, was employed to assess the quality of the selected research. Clinical data from observational studies that examined the relationship between the degree of ETE and prognosis were gathered, and a meta-analysis was conducted. Results: . Eighteen observational studies were included in this analysis. Subgroup analyses were conducted for each outcome. The findings revealed that the recurrence rate (odds ratio [OR], 2.498), disease-specific mortality (risk ratio [RR], 2.984), overall mortality (RR, 1.361), and lymph node (LN) metastasis (OR, 5.355) were significantly higher in patients with gSMI than in those without ETE. However, when the analysis was restricted to tumors measuring 4 cm or smaller, no significant differences in prognostic outcomes were observed, with the exception of LN metastasis. Conclusion . gSMI negatively impacts prognosis; however, this correlation diminishes with smaller tumor sizes. Thus, a more cautious approach is warranted during the treatment process.

Background: A clear and precise definition of the “intended use” in developing new medical devices can determine the success of entering the healthcare market. For this, practical collaboration between the clinical and engineering experts is necessary, and an appropriate tool is required for effective information collection and decision-making in the process. Methods: The Korean Academy of Medical Sciences, in cooperation with the Korean Medical Device Development Fund, implemented the Healthcare Experts’ Advisory Unit and Support (HAUS) program to match advisory clinical experts in medical device development projects. Three and five collaborative academic conferences were held in 2022 and 2023 to raise awareness of the HAUS program. In the consultation meeting, checklists were used to facilitate communications and satisfaction surveys were conducted afterward. Then, the results of the consultation meetings were compiled to build an integrated document. Results: The HAUS program was conducted with a gradually increasing number of consultation sessions from 31 in 2021 to 128 in 2023. The medical device development teams (development teams) expressed a higher level of satisfaction (91.4% to 100%) compared to the advisors (clinical experts) (78.6% to 100%) across the survey items. Based on the experiences and observations of the HAUS consultation meetings, the “Clinical Unmet Needs-based Intended Use Establishment (CLUE) templates” were developed, which were purposes to improve communication efficiency and to support a systematic approach in establishing the intended use. The CLUE process comprises four main stages for processing:Stage 1, Initial Concept; Stage 2, Expert Consultation; Stage 3, Decision-making; and Stage 4, Intended Use. Conclusion The HAUS program seemed to be helpful for the development teams by providing opinions of clinical experts. And the resultant product, the CLUE templates have been proposed to facilitate collaboration between the development teams and the advisors and to define robust clinical intended use.

Background: A clear and precise definition of the “intended use” in developing new medical devices can determine the success of entering the healthcare market. For this, practical collaboration between the clinical and engineering experts is necessary, and an appropriate tool is required for effective information collection and decision-making in the process. Methods: The Korean Academy of Medical Sciences, in cooperation with the Korean Medical Device Development Fund, implemented the Healthcare Experts’ Advisory Unit and Support (HAUS) program to match advisory clinical experts in medical device development projects. Three and five collaborative academic conferences were held in 2022 and 2023 to raise awareness of the HAUS program. In the consultation meeting, checklists were used to facilitate communications and satisfaction surveys were conducted afterward. Then, the results of the consultation meetings were compiled to build an integrated document. Results: The HAUS program was conducted with a gradually increasing number of consultation sessions from 31 in 2021 to 128 in 2023. The medical device development teams (development teams) expressed a higher level of satisfaction (91.4% to 100%) compared to the advisors (clinical experts) (78.6% to 100%) across the survey items. Based on the experiences and observations of the HAUS consultation meetings, the “Clinical Unmet Needs-based Intended Use Establishment (CLUE) templates” were developed, which were purposes to improve communication efficiency and to support a systematic approach in establishing the intended use. The CLUE process comprises four main stages for processing:Stage 1, Initial Concept; Stage 2, Expert Consultation; Stage 3, Decision-making; and Stage 4, Intended Use. Conclusion The HAUS program seemed to be helpful for the development teams by providing opinions of clinical experts. And the resultant product, the CLUE templates have been proposed to facilitate collaboration between the development teams and the advisors and to define robust clinical intended use.

Objective: We intended to investigate the relationships between visual sensory impairment (VSI) or auditory sensory impairment (ASI) and brain pathological changes associated with cognitive decline in older adults. Methods: We primarily tried to examine whether each sensory impairment is related to Alzheimer’s disease (AD) pathology, specifically beta-amyloid (Aβ) deposition, through both cross-sectional and longitudinal approaches in cognitively unimpaired older adults. Self-report questionnaires on vision and hearing status were administered at the baseline.Neuroimaging scans including brain [ 11 C] Pittsburgh Compound B PET and MRI, as well as clinical assessments, were performed at baseline and 2-year follow-up. Results: Cross-sectional analyses showed that the VSI-positive group had significantly higher Aβ deposition than the VSI-negative group, whereas there was no significant association between ASI positivity and Aβ deposition. Longitudinal analyses revealed that VSI positivity at baseline was significantly associated with increased Aβ deposition over 2 years (β = 0.153, p = 0.025), although ASI positivity was not (β = 0.045, p = 0.518). VSI positivity at baseline was also significantly associated with greater atrophic changes in AD-related brain regions over the 2-year follow-up period (β = −0.207, p = 0.005), whereas ASI positivity was not (β = 0.024, p = 0.753). Neither VSI nor ASI positivity was related to cerebrovascular injury, as measured based on the white matter hyperintensity volume. Conclusion The findings suggest that VSI is probably related to AD-specific pathological changes, which possibly mediate the reported relationship between VSI and cognitive decline. In contrast, ASI appears not associated with AD pathologies but may contribute to cognitive decline via other mechanisms.