Microsurgical anatomy of the facial nerve

The facial nerve connections and pathways from the cortex to the brainstem are intricate and complicated. The extra‐axial part of the facial nerve leaves the lateral part of the pontomedullary sulcus and enters the temporal bone through the internal acoustic meatus. In the temporal bone, the facial nerve branches into fibers innervating the glands and tongue. After it emerges from the temporal bone it supplies various facial muscles. It contains a motor, general sensory, special sensory, and autonomic components. The physician needs comprehensive knowledge of the anatomy and courses of the facial nerve to diagnose and treat lesions and diseases of it so that surgical complications due to facial nerve injury can be avoided. This review describes the microsurgical anatomy of the facial nerve and illustrates its anatomy in relation to the surrounding bone, connective, and neurovascular structures.     

Risk Factors of Outcomes of COVID-19 Patients in Korea: Focus on Early Symptoms

BackgroundCoronavirus disease 2019 (COVID-19) has spread around the globe, and it is important to determine the risk factors of death in the general population. Our study aimed to determine the risk factors of death and severe illness requiring supplemental oxygen therapy based on the demographic and clinical characteristics of COVID-19 patients in Korea.MethodsIn this study, we used data provided by the Korea Disease Control and Prevention Agency (KDCA) and analyzed a total of 5,068 patients with COVID-19, excluding 19 pregnant women and 544 individuals with missing data. We performed logistic regression analysis to determine the impact of early symptoms on survival and severe disease. Logistic regression models included sex, age, number of comorbidities, symptoms on admission, blood pressure, heart rate, and body temperature as explanatory variables, and death and oxygen therapy as outcome variables.ResultsLogistic regression analyses revealed that the male sex, older age (≥ 60 years), higher number of comorbidities, presence of symptoms on admission, heart rate ≥ 120 bpm, and body temperature ≥ 37.5°C presented with higher risk of in-hospital death and oxygen therapy requirement. Conversely, rhinorrhea and headache were associated with a low risk of death and oxygen therapy requirement. The findings showed that cough, sputum, and fever were the most common symptoms on admission, while 25.3% of patients with COVID-19 were asymptomatic.ConclusionCOVID-19 patients with high-risk early symptoms on admission, such as dyspnea and altered mental status, and those without low-risk symptoms of rhinorrhea and headache should be included in priority treatment groups.     

Association between Serum Osteocalcin Levels and Metabolic Syndrome according to the Menopausal Status of Korean Women

BackgroundOsteocalcin is known to regulate energy metabolism. Recently, metabolic syndrome (MetS) has been found to be associated with reduced levels of osteocalcin in men, as well as in postmenopausal women. The aim of this study was to investigate the association between serum osteocalcin and MetS in premenopausal women, compared with that in postmenopausal women.MethodsThis cross-sectional study was based on 5,896 participants who completed a health screening examination. They were classified according to their menopausal status. Each group was subdivided into non-MetS and MetS groups according to the modified National Cholesterol Education Program-Adult Treatment Panel III criteria. Serum osteocalcin levels were measured using the electrochemiluminescence immunoassay.ResultsSerum osteocalcin level was significantly lower in women with MetS than in those without MetS, after adjusting for confounders (14.12 ± 0.04 vs. 13.17 ± 0.13 [P = 0.004] in premenopausal women, and 20.34 ± 0.09 vs. 19.62 ± 0.21 [P < 0.001] in postmenopausal women), regardless of their menopausal status. Serum osteocalcin levels decreased correspondingly with an increasing number of MetS elements (P for trend < 0.001). Multiple regression analysis demonstrated that waist circumference (β = −0.085 [P < 0.001] and β = −0.137 [P < 0.001]) and hemoglobin A1c (β = −0.09 [P < 0.001] and β = −0.145 [P < 0.001]) were independent predictors of osteocalcin in premenopausal and postmenopausal women. Triglyceride levels were also independently associated with osteocalcin levels in premenopausal women (β = −0.004 [P < 0.013]). The odds ratio (OR) for MetS was significantly higher in the lowest quartile than in the highest quartile of serum osteocalcin levels after adjusting for age, alkaline phosphatase, uric acid, high sensitivity C-reactive protein, and body mass index in all women (OR, 2.00; 95% confidence interval [CI], 1.49–2.68) as well as in premenopausal (OR, 2.23; 95% CI, 1.39–3.58) and postmenopausal (OR, 2.01; 95% CI, 1.26–3.23) subgroups.ConclusionLower serum osteocalcin concentrations were significantly associated with MetS in both premenopausal and postmenopausal women and were therefore independent of menopausal status.     

β-blocker therapy for infantile hemangioma

ABSTRACT

Introduction

Fifteen percent of proliferating infantile hemangioma (IH) require intervention because of the threat to function or life, ulceration, or tissue distortion. Propranolol is the mainstay treatment for problematic proliferating IH. Other β-blockers and angiotensin-converting enzyme (ACE) inhibitors have been explored as alternative treatments.     

Opportunistic Osteoporosis Screening Using Chest Radiographs With Deep Learning: Development and External Validation With a Cohort Dataset

Osteoporosis is a common, but silent disease until it is complicated by fractures that are associated with morbidity and mortality. Over the past few years, although deep learning-based disease diagnosis on chest radiographs has yielded promising results, osteoporosis screening remains unexplored. Paired data with 13,026 chest radiographs and dual-energy X-ray absorptiometry (DXA) results from the Health Screening and Promotion Center of Asan Medical Center, between 2012 and 2019, were used as the primary dataset in this study. For the external test, we additionally used the Asan osteoporosis cohort dataset (1089 chest radiographs, 2010 and 2017). Using a well-performed deep learning model, we trained the OsPor-screen model with labels defined by DXA based diagnosis of osteoporosis (lumbar spine, femoral neck, or total hip T-score ≤ −2.5) in a supervised learning manner. The OsPor-screen model was assessed in the internal and external test sets. We performed substudies for evaluating the effect of various anatomical subregions and image sizes of input images. OsPor-screen model performances including sensitivity, specificity, and area under the curve (AUC) were measured in the internal and external test sets. In addition, visual explanations of the model to predict each class were expressed in gradient-weighted class activation maps (Grad-CAMs). The OsPor-screen model showed promising performances. Osteoporosis screening with the OsPor-screen model achieved an AUC of 0.91 (95% confidence interval [CI], 0.90–0.92) and an AUC of 0.88 (95% CI, 0.85–0.90) in the internal and external test set, respectively. Even though the medical relevance of these average Grad-CAMs is unclear, these results suggest that a deep learning-based model using chest radiographs could have the potential to be used for opportunistic automated screening of patients with osteoporosis in clinical settings. © 2021 American Society for Bone and Mineral Research (ASBMR).     

Explained and unexplained racial and regional inequality in obesity prevalence in the United States

ABSTRACT

Objective: There are substantial racial and regional disparities in obesity prevalence in the United States. This study partitioned the mean Body Mass Index (BMI) and obesity prevalence rate gaps between non-Hispanic blacks and non-Hispanic whites into the portion attributable to observable obesity risk factors and the remaining portion attributable to unobservable factors at the national and the state levels in the United States (U.S.) in 2010.

Design: This study used a simulated micro-population dataset combining common information from the Behavioral Risk Factor Surveillance System and the U.S. Census data to obtain a reliable, large sample representing the adult populations at the national and state levels. It then applied a reweighting decomposition method to decompose the black-white mean BMI and obesity prevalence disparities at the national and state levels into the portion attributable to the differences in distribution of observable obesity risk factors and the remaining portion unexplainable with risk factors.

Results: We found that the observable differences in distribution of known obesity risk factors explain 18.5% of the mean BMI difference and 20.6% of obesity prevalence disparities between non-Hispanic blacks and non-Hispanic whites. There were substantial variations in how much the differences in distribution of known obesity risk factors can explain black-white gaps in mean BMI (?67.7% to 833.6%) and obesity prevalence (?278.5% to 340.3%) at the state level.

Conclusion: The results from this study demonstrate that known obesity risk factors explain a small proportion of the racial, ethnic and between-state disparities in obesity prevalence in the United States. Future etiologic studies are required to further understand the causal factors underlying obesity and racial, ethnic and geographic disparities.