Background: Despite widespread vaccination efforts against severe acute respiratory syndrome coronavirus 2, variants with increased transmissibility or immune evasion continue to emerge, posing a considerable challenge. Understanding the immunological factors associated with coronavirus disease (COVID-19) progression is essential for improving patient management and treatment strategies. We explored the dynamic changes in the peripheral white blood cell (WBC) profile, including T lymphocyte subsets, to assess their potential as predictors of disease severity and progression. Methods: Two hundred fifty-eight patients hospitalized for confirmed COVID-19 were classified into four sub-cohorts based on changes in disease severity over 7 days. WBC parameters, including absolute neutrophil, total lymphocyte, and T cell subset counts, and the neutrophil-to-lymphocyte ratio (NLR) were assessed at admission and after 7 days. Results: Patients with persistent mild-to-moderate illness exhibited a marked increase in the lymphocyte count and a decrease in the NLR over time. In contrast, patients with sustained severe-to-critical illness showed an increasing WBC count without a corresponding increase in the lymphocyte count, in addition to a marked elevation in the NLR. Patients whose condition improved from severe-to-critical to mild-to-moderate illness showed increased cluster of differentiation (CD)3+ and CD4+ T cell counts and an elevated CD4/CD8 ratio, whereas the NLR did not significantly change. Conclusions The early-phase dynamics of T cell subsets may serve as a useful biomarker of disease severity and recovery in patients with COVID-19. Monitoring these immunological changes may help support clinical decision-making and inform the timing of therapeutic interventions.

Background: Atherogenic dyslipidemia, which is frequently associated with type 2 diabetes (T2D) and insulin resistance, contributes to the development of vascular complications. Statin therapy is the primary approach to dyslipidemia management in T2D, however, the role of non-statin therapy remains unclear. Ezetimibe reduces cholesterol burden by inhibiting intestinal cholesterol absorption. Fibrates lower triglyceride levels and increase high-density lipoprotein cholesterol (HDL-C) levels via peroxisome proliferator- activated receptor alpha agonism. Therefore, when combined, these drugs effectively lower non-HDL-C levels. Despite this, few clinical trials have specifically targeted non-HDL-C, and the efficacy of triple combination therapies, including statins, ezetimibe, and fibrates, has yet to be determined. Methods: This is a multicenter, prospective, randomized, open-label, active-comparator controlled trial involving 3,958 eligible participants with T2D, cardiovascular risk factors, and elevated non-HDL-C (≥100 mg/dL). Participants, already on moderate-intensity statins, will be randomly assigned to either Ezefeno (ezetimibe/fenofibrate) addition or statin dose-escalation. The primary end point is the development of a composite of major adverse cardiovascular and diabetic microvascular events over 48 months. Conclusion This trial aims to assess whether combining statins, ezetimibe, and fenofibrate is as effective as, or possibly superior to, statin monotherapy intensification in lowering cardiovascular and microvascular disease risk for patients with T2D. This could propose a novel therapeutic approach for managing dyslipidemia in T2D.

Background: Before the omicron era, health care workers were usually vaccinated with either the primary 2-dose ChAdOx1 nCoV-19 (Oxford-AstraZeneca) series plus a booster dose of BNT162b2 (Pfizer-BioNTech) (CCB group) or the primary 2-dose BNT162b2 series plus a booster dose of BNT162b2 (BBB group) in Korea. Methods: The two groups were compared using quantification of the surrogate virus neutralization test for wild type severe acute respiratory syndrome coronavirus 2 (SVNT-WT), the omicron variant (SVNT-O), spike-specific IgG, and interferon-gamma (IFN-γ), as well as the omicron breakthrough infection cases. Results: There were 113 participants enrolled in the CCB group and 51 enrolled in the BBB group. Before and after booster vaccination, the median SVNT-WT and SVNT-O values were lower in the CCB (SVNT-WT [before-after]: 72.02–97.61%, SVNT-O: 15.18–42.29%) group than in the BBB group (SVNT-WT: 89.19–98.11%, SVNT-O: 23.58–68.56%; all P < 0.001). Although the median IgG concentrations were different between the CCB and BBB groups after the primary series (2.677 vs. 4.700 AU/mL, respectively, P < 0.001), they were not different between the two groups after the booster vaccination (7.246 vs. 7.979 AU/mL, respectively, P = 0.108). In addition, the median IFN-γ concentration was higher in the BBB group than in the CCB group (550.5 and 387.5 mIU/mL, respectively, P = 0.014). There was also a difference in the cumulative incidence curves over time (CCB group 50.0% vs. BBB group 41.8%; P = 0.045), indicating that breakthrough infection occurred faster in the CCB group. Conclusion The cellular and humoral immune responses were low in the CCB group so that the breakthrough infection occurred faster in the CCB group than in the BBB group.

Background: Korea recently established 70 emergency medical service areas. However, there are many concerns that medical resources for stroke could not be evenly distributed through the country. We aimed to compare the treatment quality and outcomes of acute stroke among the emergency medical service areas. Methods: This study analyzed the data of 28,800 patients admitted in 248 hospitals which participated in the 8th acute stroke quality assessment by Health Insurance Review and Assessment Service. Individual hospitals were regrouped into emergency service areas according to the address of the location. Assessment indicators and fatality were compared by the service areas. We defined the appropriate hospital by the performance of intravenous thrombolysis. Results: In seven service areas, there were no hospitals which received more than 10 stroke patients for 6 months. In nine service areas, there were no patients who underwent intravenous thrombolysis (IVT). Among 167 designated emergency medical centers, 50 hospitals (29.9%) responded that IVT was impossible 24 hours a day. There are 97 (39.1%) hospitals that meet the definitions of appropriate hospital. In 23 service areas (32.9%) had no appropriate or feasible hospitals. The fatality of service areas with stroke centers were 6.9% within 30 days and 15.6% within 1 year from stroke onset than those without stroke centers (7.7%, 16.9%, respectively). Conclusions There was a wide regional gap in the medical resource and the quality of treatments for acute stroke among emergency medical service areas in Korea. The poststroke fatality rate of the service areas which have stroke centers or appropriate hospitals were significantly low.

Background: During the coronavirus disease 2019 (COVID-19) pandemic, the incidence of rhinovirus (RV) is inversely related to the intensity of non-pharmacological interventions (NPIs), such as universal mask wearing and physical distancing. Methods: Using RV surveillance data, changes in the effect of NPIs were investigated in South Korea during the pandemic. The time to the first visible effect of NPIs after the onset of NPIs (T1), time to the maximum effect (T2), and duration of the maximum effect (T3) were measured for each surge. For each week, the RVdiff [(RV incidence during the pandemic) − (RV incidence within 5 years before the pandemic)] was calculated, and number of weeks for RVdiff to be below zero after NPIs (time to RVdiff ≤ 0) and number of weeks RVdiff remains below zero after NPIs (duration of RVdiff ≤ 0) were measured for each surge. Results: During the study period, four surges of COVID-19 were reported. As the pandemic progressed, T1 and T2 increased, but T3 decreased. Additionally, the “time to RVdiff of ≤ 0” increased and “duration of RVdiff of ≤ 0” decreased. These changes became more pronounced during the third surge (mid-November 2020), before the introduction of the COVID-19 vaccine, and from the emergence of the delta variant. Conclusion The effect of NPIs appears slower, the duration of the effect becomes shorter, and the intensity also decreases less than a year after the onset of the pandemic owing to people’s exhaustion in implementing NPIs. These findings suggest that the COVID-19 response strategy must be completely overhauled

Antithrombotic therapy is a cornerstone of acute ischemic stroke (AIS) management and secondary stroke prevention. Since the first version of the Korean Clinical Practice Guideline (CPG) for stroke was issued in 2009, significant progress has been made in antithrombotic therapy for patients with AIS, including dual antiplatelet therapy in acute minor ischemic stroke or high-risk transient ischemic stroke and early oral anticoagulation in AIS with atrial fibrillation. The evidence is widely accepted by stroke experts and has changed clinical practice. Accordingly, the CPG Committee of the Korean Stroke Society (KSS) decided to update the Korean Stroke CPG for antithrombotic therapy for AIS. The writing members of the CPG committee of the KSS reviewed recent evidence, including clinical trials and relevant literature, and revised recommendations. A total of 35 experts were invited from the KSS to reach a consensus on the revised recommendations. The current guideline update aims to assist healthcare providers in making well-informed decisions and improving the quality of acute stroke care. However, the ultimate treatment decision should be made using a holistic approach, considering the specific medical conditions of individual patients.

Background: Tuberculous meningitis (TBM) is the most severe form of tuberculosis, but differentiating between the diagnosis of TBM and viral meningitis (VM) is difficult. Thus, we have developed machine-learning modules for differentiating TBM from VM.Material and Methods: For the training data, confirmed or probable TBM and confirmed VM cases were retrospectively collected from five teaching hospitals in Korea between January 2000 - July 2018. Various machine-learning algorithms were used for training. The machinelearning algorithms were tested by the leave-one-out cross-validation. Four residents and two infectious disease specialists were tested using the summarized medical information. Results: The training study comprised data from 60 patients with confirmed or probable TBM and 143 patients with confirmed VM. Older age, longer symptom duration before the visit, lower serum sodium, lower cerebrospinal fluid (CSF) glucose, higher CSF protein, and CSF adenosine deaminase were found in the TBM patients. Among the various machinelearning algorithms, the area under the curve (AUC) of the receiver operating characteristics of artificial neural network (ANN) with ImperativeImputer for matrix completion (0.85; 95% confidence interval 0.79 - 0.89) was found to be the highest. The AUC of the ANN model was statistically higher than those of all the residents (range 0.67 - 0.72, P <0.001) and an infectious disease specialist (AUC 0.76; P = 0.03). Conclusion The machine-learning techniques may play a role in differentiating between TBM and VM. Specifically, the ANN model seems to have better diagnostic performance than the non-expert clinician.

Background: Tuberculous meningitis (TBM) is the most severe form of tuberculosis, but differentiating between the diagnosis of TBM and viral meningitis (VM) is difficult. Thus, we have developed machine-learning modules for differentiating TBM from VM.Material and Methods: For the training data, confirmed or probable TBM and confirmed VM cases were retrospectively collected from five teaching hospitals in Korea between January 2000 - July 2018. Various machine-learning algorithms were used for training. The machinelearning algorithms were tested by the leave-one-out cross-validation. Four residents and two infectious disease specialists were tested using the summarized medical information. Results: The training study comprised data from 60 patients with confirmed or probable TBM and 143 patients with confirmed VM. Older age, longer symptom duration before the visit, lower serum sodium, lower cerebrospinal fluid (CSF) glucose, higher CSF protein, and CSF adenosine deaminase were found in the TBM patients. Among the various machinelearning algorithms, the area under the curve (AUC) of the receiver operating characteristics of artificial neural network (ANN) with ImperativeImputer for matrix completion (0.85; 95% confidence interval 0.79 - 0.89) was found to be the highest. The AUC of the ANN model was statistically higher than those of all the residents (range 0.67 - 0.72, P <0.001) and an infectious disease specialist (AUC 0.76; P = 0.03). Conclusion The machine-learning techniques may play a role in differentiating between TBM and VM. Specifically, the ANN model seems to have better diagnostic performance than the non-expert clinician.