Background: Blood pressure variability (BPV) is associated with target organ damage progression and increased cardiovascular events, including stroke. The aim of this study was to evaluate the associations between short-term BPV during acute periods and recanalization degree, early neurological deterioration (END) occurrence, and functional outcomes in acute ischemic stroke patients who had undergone intra-arterial thrombectomy (IAT). Methods: We retrospectively analyzed 303 patients with large vessel occlusive stroke who underwent IAT. The following BPV parameters, measured over 24 and 48 h after IAT, were compared: the mean, SD, coefficient of variation (CV), variation independent of the mean (VIM) for both the systolic BP (SBP) and diastolic BP, and the proportion of nocturnal SBP risers. Results: BPV parameters decreased with higher recanalization degree. The mean SBP (SBP_mean) over 24 and 48 h after IAT, and the SD of SBP (SBP_SD), CV of SBP (SBP_CV), and VIM of SBP (SBP_VIM) during the 48 h following the procedure had significant associations with recanalization degree. Patients with END had higher BPV than that of those without END, and the difference was more evident for incomplete recanalization. Increased BPV was associated with a shift toward poor functional outcome at 3 months after adjustment, including recanalization degree (OR range for significant parameters, 1.26–1.64, p = 0.006 for 48 h SBP_mean, p = 0.003 for 48 h SBP_CV, otherwise p < 0.002). Conclusions: Short-term BPV over 24 and 48 h after IAT in acute ischemic stroke patients was related to recanalization degree, and END occurrence, and may be an independent predictor of clinical outcome.

Charcot-Marie-Tooth disease (CMT) is the most common form of inherited motor and sensory neuropathy. Moreover, CMT is a genetically heterogeneous disorder of the peripheral nervous system, with many genes identified as CMT-causative. CMT has two usual classifications: type 1, the demyelinating form (CMT1); and type 2, the axonal form (CMT2). In addition, patients are classified as CMTX if they have an X-linked inheritance pattern and CMT4 if the inheritance pattern is autosomal recessive. A large amount of new information on the genetic causes of CMT has become available, and mutations causing it have been associated with more than 17 different genes and 25 chromosomal loci. Advances in our understanding of the molecular basis of CMT have revealed an enormous diversity in genetic mechanisms, despite a clinical entity that is relatively uniform in presentation. In addition, recent encouraging studies - shown in CMT1A animal models - concerning the therapeutic effects of certain chemicals have been published; these suggest potential therapies for the most common form of CMT, CMT1A. This review focuses on the inherited motor and sensory neuropathy subgroup for which there has been an explosion of new molecular genetic information over the past decade.

Charcot-Marie-Tooth disease type 1A (CMT1A) is associated with duplication of chromosome 17p11.2-p12, whereas hereditary neuropathy with liability to pressure palsies (HNPP), which is an autosomal dominant neuropathy showing characteristics of recurrent pressure palsies, is associated with 17p11.2-p12 deletion. An altered gene dosage of PMP22 is believed to the main cause underlying the CMT1A and HNPP phenotypes. Although CMT1A and HNPP are associated with the same locus, there has been no report of these two mutations within a single family. We report a rare family harboring CMT1A duplication and HNPP deletion.

To investigate whether high-sensitivity troponin I (hs-TnI) elevation is associated with in-hospital mortality and major adverse cardiac events (MACEs) in neurosurgical and neurocritically ill patients. Among neurosurgical patients admitted to the intensive care unit (ICU) from January 2013 to December 2019, those whose serum hs-TnI levels were obtained within 7 days after ICU admission were included. Propensity score matching was used. Each patient with hs-TnI elevation was matched to a control patient. The primary endpoint was in-hospital mortality and the secondary outcome was MACEs. The hs-TnI elevation was shown in 848 (14.1%) of 6004 patients. After propensity score matching, 706 pairs of data were generated by 1:1 individual matching without replacement. In multivariable analysis of overall and propensity score-matched population, hs-TnI elevation was associated with in-hospital mortality (adjusted odds ratio (OR): 2.37, 95% confidence interval (CI): 1.68–3.33 and adjusted OR: 1.89, 95% CI: 1.28–2.81, respectively). In addition, hs-TnI elevation was associated with MACEs (adjusted OR: 2.73, 95% CI: 1.74–4.29 and adjusted OR: 2.64, 95% CI: 1.60–4.51, respectively). In this study, hs-TnI elevation was associated with in-hospital mortality and MACEs in neurosurgical and neurocritically ill patients.

Background Paralysis of medical systems has emerged as a major problem not only in Korea but also globally because of the COVID-19 pandemic. Therefore, early identification and treatment of COVID-19 are crucial. This study aims to develop a machine-learning algorithm based on bio-signals that predicts the infection three days in advance before it progresses from mild to severe, which may necessitate high-flow oxygen therapy or mechanical ventilation. Methods The study included 2758 hospitalized patients with mild severity COVID-19 between July 2020 and October 2021. Bio-signals, clinical information, and laboratory findings were retrospectively collected from the electronic medical records of patients. Machine learning methods included random forest, random forest ranger, gradient boosting machine, and support vector machine (SVM). Results SVM showed the best performance in terms of accuracy, kappa, sensitivity, detection rate, balanced accuracy, and run-time; the area under the receiver operating characteristic curve was also quite high at 0.96. Body temperature and SpO2 three and four days before discharge or exacerbation were ranked high among SVM features. Conclusions The proposed algorithm can predict the exacerbation of severity three days in advance in patients with mild COVID-19. This prediction can help effectively manage the reallocation of appropriate medical resources in clinical settings. Therefore, this algorithm can facilitate adequate oxygen therapy and mechanical ventilator preparation, thereby improving patient prognosis, increasing the efficiency of medical systems, and mitigating the damage caused by a global pandemic.