Objectives: This study evaluated the effectiveness of government epidemic control policies centered on diagnostic testing and examined their impact on the in vitro diagnostics (IVD) industry. It also analyzed the complex interplay among policy interventions, epidemic dynamics, and the IVD industry’s value chain to identify key leverage points for managing future public health crises. Methods: A system dynamics (SD) model calibrated using national data from the Republic of Korea simulated the interactions between epidemic progression and the IVD value chain.We conducted a scenario analysis encompassing 6 policy interventions: research and development (R&D) investment, public–private collaboration, regulatory easing, diagnostic test performance, testing intensity, and social distancing. Results: Policies promoting investment, public–private collaboration, and regulatory easing accelerated the market entry of diagnostics, thereby reducing infections and deaths. However, these interventions were associated with lower overall industry revenue, attributable toincreased market competition and a reduced patient population. A critical trade-off was noted:although regulatory speed is advantageous, using low-sensitivity diagnostics substantially worsened public health outcomes. Aggressive testing strategies and stringent social distancingwere also confirmed to be effective in reducing both infections and mortality. Conclusion This study provides a strategic framework for understanding interactions between pandemic control policies and the IVD industry. Sustained pre-crisis investment in R&D,public–private networks, and public health infrastructure is essential for effective pandemicpreparedness. During a crisis, policymakers must carefully manage the critical trade-off between regulatory speed and diagnostic quality to ensure that rapid responses do not compromise public health outcomes.

This retrospective cohort study aimed to compare coronavirus disease 2019 (COVID-19)-related clinical outcomes between patients with and without gout. Electronic health recordbased data from two centers (Seoul National University Hospital [SNUH] and Boramae Medical Center [BMC]), from January 2021 to April 2022, were mapped to a common data model. Patients with and without gout were matched using a large-scale propensityscore algorithm based on population-level estimation methods. At the SNUH, the risk for COVID-19 diagnosis was not significantly different between patients with and without gout (hazard ratio [HR], 1.07; 95% confidence interval [CI], 0.59–1.84). Within 30 days after COVID-19 diagnosis, no significant difference was observed in terms of hospitalization (HR, 0.57; 95% CI, 0.03–3.90), severe outcomes (HR, 2.90; 95% CI, 0.54–13.71), or mortality (HR, 1.35; 95% CI, 0.06–16.24). Similar results were obtained from the BMC database, suggesting that gout does not increase the risk for COVID-19 diagnosis or severe outcomes.

Objectives: The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea. Methods: A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI among patients taking ARBs and compared it to a control group. Temporal patterns of important variables were analyzed using an interpretable timeseries model. Results: The overall incidence rate of DILI among patients taking ARBs was found to be 1.09%. The incidence rates varied for each specific ARB drug and institution, with valsartan having the highest rate (1.24%) and olmesartan having the lowest rate (0.83%). The DILI prediction models showed varying performance, measured by the average area under the receiver operating characteristic curve, with telmisartan (0.93), losartan (0.92), and irbesartan (0.90) exhibiting higher classification performance. The aggregated attention scores from the models highlighted the importance of variables such as hematocrit, albumin, prothrombin time, and lymphocytes in predicting DILI. Conclusions Implementing a multicenter-based timeseries classification model provided evidence that could be valuable to clinicians regarding temporal patterns associated with DILI in ARB users. This information supports informed decisions regarding appropriate drug use and treatment strategies.

Purpose: To elucidate the brain’s intrinsic response to injury, we tracked the response of neural stem/progenitor cells (NSPCs) located in ventricular-subventricular zone (V-SVZ) to hypoxic-ischemic brain injury (HI). We also evaluated whether transduction of V-SVZ NSPCs with neurogenic factor NeuroD1 could enhance their neurogenesis in HI. Materials and Methods: Unilateral HI was induced in ICR neonatal mice. To label proliferative V-SVZ NSPCs in response to HI, bromodeoxyuridine (BrdU) and retroviral particles encoding LacZ or NeuroD1/GFP were injected. The cellular responses of NSPCs were analyzed by immunohistochemistry. Results: Unilateral HI increased the number of BrdU+ newly-born cells in the V-SVZ ipsilateral to the lesion while injury reduced the number of newly-born cells reaching the ipsilateral olfactory bulb, which is the programmed destination of migratory V-SVZ NSPCs in the intact brain. These newly-born cells were directed from this pathway towards the lesions. HI significantly increased the number of newly-born cells in the cortex and striatum by the altered migration of V-SVZ cells. Many of these newly-born cells differentiated into active neurons and glia. LacZ-expressing V-SVZ NSPCs also showed extensive migration towards the non-neurogenic regions ipsilateral to the lesion, and expressed the neuronal marker NeuN. NeuroD1+/GFP+ V-SVZ NSPCs almost differentiated into neurons in the peri-infarct regions. Conclusion HI promotes the establishment of a substantial number of new neurons in non-neurogenic regions, suggesting intrinsic repair mechanisms of the brain, by controlling the behavior of endogenous NSPCs. The activation of NeuroD1 expression may improve the therapeutic potential of endogenous NSPCs by increasing their neuronal differentiation in HI.

Background: We investigated RNA sequencing-based transcriptome profiling and the transformation of mature osteoblasts into bone lining cells (BLCs) through a lineage tracing study to better understand the effect of mechanical unloading on bone loss. Methods: Dmp1-CreERt2(+):Rosa26R mice were injected with 1 mg of 4-hydroxy-tamoxifen three times a week starting at postnatal week 7, and subjected to a combination of botulinum toxin injection with left hindlimb tenotomy starting at postnatal week 8 to 10. The animals were euthanized at postnatal weeks 8, 9, 10, and 12. We quantified the number and thickness of X-gal(+) cells on the periosteum of the right and left femoral bones at each time point. Results: Two weeks after unloading, a significant decrease in the number and a subtle change in the thickness of X-gal(+) cells were observed in the left hindlimbs compared with the right hindlimbs. At 4 weeks after unloading, the decrease in the thickness was accelerated in the left hindlimbs, although the number of labeled cells was comparable. RNA sequencing analysis showed downregulation of 315 genes in the left hindlimbs at 2 and 4 weeks after unloading. Of these, Xirp2, AMPD1, Mettl11b, NEXN, CYP2E1, Bche, Ppp1r3c, Tceal7, and Gadl1 were upregulated during osteoblastogenic/osteocytic and myogenic differentiation in vitro. Conclusion These findings demonstrate that mechanical unloading can accelerate the transformation of mature osteoblasts into BLCs in the early stages of bone loss in vivo. Furthermore, some of the genes involved in this process may have a pleiotropic effect on both bone and muscle.