Previous reports have shown that cigarette smoking is associated with white matter hyperintensities (WMHs). However, it remains unclear whether this is true for all ages. We investigated the association between cigarette smoking, WMHs, and age. We retrospectively reviewed charts from 595 patients, who presented as outpatients from January 2007 to March 2010. Grading of periventricular WMHs (PVWMHs) and the scores of deep WMHs (DWMHs) was determined based on criteria established by the Rotterdam Scan Study. We compared the degree of WMHs between smokers and non-smokers, and those younger than the age of 65 years versus those above. In younger age group, smokers had higher grades of PVWMHs and more microbleeds than non-smokers. In the older age group, total burden of DWMHs was much greater in smokers than nonsmokers. Multivariate regression analysis showed that cigarette smoking was an independent risk factor for PVWMHs in the younger age group and for DWMHs in the older age group. The location of WMHs in association with smoking seems to differ among age groups. Age should be considered when interpreting the effects of smoking on the brain.

We report the first Korean patient with familial hemiplegic migraine type 1, with clinical and multimodal imaging findings. A 43-yr-old man was admitted for right hemianopia and aphasia, followed by coma. MRI showed only cerebellar atrophy. CT angiography showed mild vasodilation of intracranial blood vessels and increased vascularity in the left hemisphere and perfusion-weighted imaging showed elevated cerebral blood flow. Gene analysis of the patient and his mother led to the identification of a heterozygous point mutation (1997C→T, T666M) in exon 16 of the CACNA1A gene. Familial hemiplegic migraine should be considered in patients with episodic neurological dysfunction with cerebellar atrophy.

Purpose: To compare the efficacy of inflammatory markers, the Laboratory-score, and a new laboratory combined model for predicting serious bacterial infection (SBI) in young febrile children. Methods: The presence of SBI was reviewed in previously healthy children aged 3 years or younger with fever (> 38。 C) who visited the emergency department from 2017 through 2018. Areas under the curves (AUCs) of the receiver operating characteristic curve for SBI were compared with individual inflammatory markers (white blood cells [WBC] count, erythrocyte sedimentation rate [ESR], C-reactive protein [CRP], procalcitonin [PCT], and urine WBC count), the Laboratory-score, and a laboratory combined model. The latter model was developed using logistic regression analysis including ESR, CRP, and PCT. Results: Of the 203 enrolled children, SBI was diagnosed in 58 (28.6%). For SBI prediction, the Laboratory-score showed 51.7% sensitivity (95% confidence interval [CI], 38.2%-65.0%) and 83.5% specificity (95% CI, 76.4%-89.1%). The AUC of the Laboratory-score (0.76) was significantly superior to the values of all individual inflammatory markers (WBC, 0.59 [P = 0.032]; ESR, 0.69; and CRP, 0.74 [P < 0.001]) except that of PCT (0.77, [P < 0.001]). The AUC of the laboratory combined model (0.80) was superior to that of the Laboratory-score (0.76) (P < 0.001). Conclusion: In this study, the new laboratory combined model showed good predictability for SBI. This finding suggests the usefulness of combining ESR, CRP, and PCT in predicting SBI.

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