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.

Background/Aims: Serum hepatitis B virus (HBV) DNA levels and non-invasive liver fibrosis scores are significantly associated with hepatocellular carcinoma (HCC) risk in chronic hepatitis B (CHB) patients. Nonetheless, the relationship between HBV DNA levels and liver fibrosis scores is unclear. Methods: A historical cohort comprising 6,949 non-cirrhotic Korean CHB patients without significant alanine aminotransferase elevation was investigated. The association of HBV DNA levels with the aspartate aminotransferase to platelet ratio index (APRI) and fibrosis (FIB)-4 score at baseline was analyzed using general linear models. Results: In HBeAg-negative patients (n=4,868), HBV DNA levels correlated linearly with both APRI and FIB-4 scores. In contrast, in HBeAg-positive patients (n=2,081), HBV DNA levels correlated inversely with both APRI and FIB-4 scores. Across the entire cohort, a significant non-linear parabolic relationship was identified between HBV DNA levels and fibrosis scores, independent of age and other covariates. Notably, moderate viral loads (6–7 log10 IU/mL) corresponded to the highest APRI and FIB-4 scores (p<0.001). Over a median 10-year follow-up, 435 patients (6.3%) developed HCC. Higher APRI scores ≥0.5 and FIB-4 scores ≥1.45 were significantly associated with elevated HCC risk (p<0.001 for both). HBV DNA level remained a significant predictive factor for HCC development, even after adjusting for APRI or FIB-4 scores. Conclusions HBV viral load is significantly correlated with APRI and FIB-4 scores, and is also associated with HCC risk independent of those scores in CHB patients. These findings suggest that HBV DNA level is associated with hepatocarcinogenesis through both direct and indirect pathways.

Purpose: Extremely preterm infants are prone to brain injury and underdevelopment. Intraventricular hemorrhage (IVH) is the most common cause of brain injury and a significant risk factor for neurodevelopmental delay in preterm infants. Severe IVH is known to have a poor outcome; however, the outcomes of low-grade IVH remain controversial. This study aimed to evaluate neurodevelopmental outcomes and brain segmental volumes of preterm infants with low-grade IVH. Methods: This retrospective cohort study included 109 extremely preterm infants who underwent term equivalent age-magnetic resonance imaging and neurodevelopmental evaluation at a corrected age of 18 to 24 months. We compared infants with and without low-grade IVH. Results: Among the 109 extremely preterm infants, 25 had low-grade IVH and 84 had no IVH. There were no significant differences in the neurodevelopmental outcomes between the low-grade and no IVH groups. In multivariate analysis, low-grade IVH was associated with a smaller medullary volume (adjusted odds ratio, 0.575; 95% confidence interval, 0.346 to 0.957; P=0.034). Conclusion We found no significant differences in the neurodevelopmental outcomes of extremely preterm infants at a corrected age of 18 to 24 months between those with low-grade IVH and those without IVH. Low-grade IVH was associated with a smaller medullary volume.

Background: To date, few studies have investigated the feasibility of the loop-mediated isothermal amplification (LAMP) assay for identifying pathogens in tissue samples. This study aimed to investigate the feasibility of LAMP for the rapid detection of methicillin-susceptible or methicillin-resistant Staphylococcus aureus (MSSA or MRSA) in tissue samples, using a bead-beating DNA extraction method. Methods: Twenty tissue samples infected with either MSSA (n = 10) or MRSA (n = 10) were obtained from patients who underwent orthopedic surgery for suspected musculoskeletal infection between December 2019 and September 2020. DNA was extracted from the infected tissue samples using the bead-beating method. A multiplex LAMP assay was conducted to identify MSSA and MRSA infections. To recognize the Staphylococcus genus, S. aureus, and methicillin resistance, 3 sets of 6 primers for the 16S ribosomal ribonucleic acid (rRNA) and the femA and mecA genes were used, respectively. The limit of detection and sensitivity (detection rate) of the LAMP assay for diagnosing MSSA and MRSA infection were analyzed. Results: The LAMP result was positive for samples containing 10 3 colony-forming unit (CFU)/mL for 16S rRNA, 10 4 CFU/mL for femA, and 10 5 CFU/mL formecA. The limits of detection for 16S rRNA and femA were not different between MSSA and MRSA. For the 10 MSSA-positive samples, the LAMP assay showed 100% positive reactions for 16S rRNA and femA and a 100% negative reaction for mecA. For the 10 MRSA-positive samples, the LAMP assay showed 100% positive reactions for 16S rRNA and mecA but only 90% positive reactions for femA. The sensitivity (detection rate) of the LAMP assay for identifying MSSA and MRSA in infected tissue samples was 100% and 90%, respectively. Conclusions The results of this study suggest that the LAMP assay performed with tissue DNA samples can be a useful diagnostic method for the rapid detection of musculoskeletal infections caused by MSSA and MRSA.