Fatal outcome of severe fever with thrombocytopenia syndrome (SFTS) and severe and critical COVID-19 is associated with the hyperproduction of IL-10 and IL-6 and the low production of TGF-ß.

Severe fever with thrombocytopenia syndrome virus (SFTSV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can cause the hyperproduction of inflammatory cytokines, which have pathological effects in patient including severe or fatal cytokine storms. To characterize the effect of SFTSV and SARS-CoV-2 infection on the production of cytokines in severe fever with thrombocytopenia syndrome (SFTS) and COVID-19 patients, we performed an analysis of cytokines in SFTS and COVID-19 patients and also investigated the role of interleukin-10 (IL-10) in vitro studies: lipopolysaccharide-induced THP-1-derived macrophages, SFTSV infection of THP-1 cells, and SARS-CoV-2 infection of THP-1 cells. In this study, we found that levels of both IL-10 and IL-6 were significantly elevated, the level of transforming growth factor-ß (TGF-ß) was significantly decreased and IL-10 was elevated earlier than IL-6 in severe and critical COVID-19 and fatal SFTS patients, and inhibition of IL-10 signaling decreased the production of IL-6 and elevated that of TGF-ß. Therefore, the hyperproduction of IL-10 and IL-6 and the low production of TGF-ß have been linked to cytokine storm-induced mortality in fatal SFTS and severe and critically ill COVID-19 patients and that IL-10 can play an important role in the host immune response to severe and critical SARS-CoV-2 and fatal SFTSV infection.

Hierarchically Designed Electron Paths in 3D Printed Energy Storage Devices.

Three-dimensional (3D) microsupercapacitors (MSC) have been spotlighted, because they overcome limited areal capacitance of two-dimensional planar MSCs. Specially, 3D printing technology offers numerous advantages to generate 3D electrodes for MSCs, which includes time-saving, cost-effective manufacturing, and realization of tailorable complex electrode designs. In this paper, we report novel hierarchical 3D designs of conductive 3D electrodes for MSC by digital light processing (DLP)-based 3D printing. Photocurable composite resin with silver nanowires was optimized for DLP printing for the hierarchical design of high aspect ratio in 3D electrodes. The hierarchical 3D electrodes showed unique patterns on the structure corresponding to stacking of layers in the direction of 3D printing. The fabricated 3D MSCs demonstrated low electrical resistance to be used as feasible MSC electrodes. Energy storage from silver redox reactions was demonstrated in hierarchical 3D electrodes designed with mechanically durable 3D octet trusses.

Enhanced Mechanical Properties of PUMA/SiO2 Ceramic Composites via Digital Light Processing.

This study aims to enhance the mechanical properties of additively manufactured polymer parts by incorporating ceramic particles (SiO2) into diluted urethane methacrylate (UDMA) photopolymer resin using digital light processing (DLP) technology. The resulting PUMA/SiO2 composites, featuring varying SiO2 contents (16.7, 28.5, and 37.5 wt%) and processed under different conditions, underwent a comprehensive series of mechanical, thermal, and chemical tests. Hardness tests showed that composites with 37.5 wt% SiO2 demonstrated superior hardness with low sensitivity to processing conditions. Bending tests indicated that elevated vat temperatures tended to degrade flexural properties, yet this degradation was mitigated in the case of the 37.5 wt% SiO2 composition. Tensile tests revealed a transition from viscoelastic to linear elastic behaviors with increasing SiO2 content, with high tensile strength sustained at low vat temperatures (<35 °C) when the SiO2 content exceeded 28.5 wt%. Thermogravimetric analysis supported these findings, indicating that increased SiO2 content ensured a more uniform dispersion, enhancing mechanical properties consequently. Thermal tests showed augmented thermal conductivity and diffusivity with reduced specific heat in SiO2-inclusive composites. This study provides guidelines for optimal PUMA/SiO2 composite utilization that emphasizes high SiO2 content and low vat temperature, offering comprehensive insights for high-performance ceramic composite fabrication in functional applications.

Deep learning algorithm for the automated detection and classification of nasal cavity mass in nasal endoscopic images.

Nasal endoscopy is routinely performed to distinguish the pathological types of masses. There is a lack of studies on deep learning algorithms for discriminating a wide range of endoscopic nasal cavity mass lesions. Therefore, we aimed to develop an endoscopic-examination-based deep learning model to detect and classify nasal cavity mass lesions, including nasal polyps (NPs), benign tumors, and malignant tumors. The clinical feasibility of the model was evaluated by comparing the results to those of manual assessment. Biopsy-confirmed nasal endoscopic images were obtained from 17 hospitals in South Korea. Here, 400 images were used for the test set. The training and validation datasets consisted of 149,043 normal nasal cavity, 311,043 NP, 9,271 benign tumor, and 5,323 malignant tumor lesion images. The proposed Xception architecture achieved an overall accuracy of 0.792 with the following class accuracies on the test set: normal = 0.978 ± 0.016, NP = 0.790 ± 0.016, benign = 0.708 ± 0.100, and malignant = 0.698 ± 0.116. With an average area under the receiver operating characteristic curve (AUC) of 0.947, the AUC values and F1 score were highest in the order of normal, NP, malignant tumor, and benign tumor classes. The classification performances of the proposed model were comparable with those of manual assessment in the normal and NP classes. The proposed model outperformed manual assessment in the benign and malignant tumor classes (sensitivities of 0.708 ± 0.100 vs. 0.549 ± 0.172, 0.698 ± 0.116 vs. 0.518 ± 0.153, respectively). In urgent (malignant) versus nonurgent binary predictions, the deep learning model achieved superior diagnostic accuracy. The developed model based on endoscopic images achieved satisfactory performance in classifying four classes of nasal cavity mass lesions, namely normal, NP, benign tumor, and malignant tumor. The developed model can therefore be used to screen nasal cavity lesions accurately and rapidly.

Heterogenization of Molecular Electrocatalytic Active Sites through Reticular Chemistry.

The electrochemical conversion of small molecules, such as CO2 , O2 , and H2 O, has received significant attention as a potential engine for sustainable life. Metal-organic frameworks (MOFs) are a promising class of electrocatalytic materials for such processes. An attractive aspect of utilizing this class of materials as electrocatalysts is that well-known molecular active sites can be introduced to well-defined crystalline heterogeneous catalytic systems with high tunability. This review offers strategic insights into recent studies on MOF-based electrocatalysts by discussing the notable active sites that have been utilized in both homogeneous and heterogeneous catalysts, while highlighting instances where such active sites have been introduced into MOFs. In addition, material design principles enabling the integration of electrochemically active components with the MOF platform are outlined. Viewpoints on the viability of MOFs as an alternative to currently used electrocatalysts are also discussed. Finally, the future direction of MOF-based electrocatalysis research is established.

Centralized Ambulance Diversion Policy Using Rolling-Horizon Optimization Framework to Minimize Patient Tardiness.

Ambulance diversion (AD) is a common method for reducing crowdedness of emergency departments by diverting ambulance-transported patients to a neighboring hospital. In a multi-hospital system, the AD of one hospital increases the neighboring hospital's congestion. This should be carefully considered for minimizing patients' tardiness in the entire multi-hospital system. Therefore, this paper proposes a centralized AD policy based on a rolling-horizon optimization framework. It is an iterative methodology for coping with uncertainty, which first solves the centralized optimization model formulated as a mixed-integer linear programming model at each discretized time, and then moves forward for the time interval reflecting the realized uncertainty. Furthermore, the decentralized optimization, decentralized priority, and No-AD models are presented for practical application, which can also show the impact of using the following three factors: centralization, mathematical model, and AD strategy. The numerical experiments conducted based on the historical data of Seoul, South Korea, for 2017, show that the centralized AD policy outperforms the other three policies by 30%, 37%, and 44%, respectively, and that all three factors contribute to reducing patients' tardiness. The proposed policy yields an efficient centralized AD management strategy, which can improve the local healthcare system with active coordination between hospitals.

Two-Tiered Ambulance Dispatch and Redeployment considering Patient Severity Classification Errors.

A two-tiered ambulance system, consisting of advanced and basic life support for emergency and nonemergency patient care, respectively, can provide a cost-efficient emergency medical service. However, such a system requires accurate classification of patient severity to avoid complications. Thus, this study considers a two-tiered ambulance dispatch and redeployment problem in which the average patient severity classification errors are known. This study builds on previous research into the ambulance dispatch and redeployment problem by additionally considering multiple types of patients and ambulances, and patient classification errors. We formulate this dynamic decision-making problem as a semi-Markov decision process and propose a mini-batch monotone-approximate dynamic programming (ADP) algorithm to solve the problem within a reasonable computation time. Computational experiments using realistic system dynamics based on historical data from Seoul reveal that the proposed approach and algorithm reduce the risk level index (RLI) for all patients by an average of 11.2% compared to the greedy policy. In this numerical study, we identify the influence of certain system parameters such as the percentage of advanced-life support units among all ambulances and patient classification errors. A key finding is that an increase in undertriage rates has a greater negative effect on patient RLI than an increase in overtriage rates. The proposed algorithm delivers an efficient two-tiered ambulance management strategy. Furthermore, our findings could provide useful guidelines for practitioners, enabling them to classify patient severity in order to minimize undertriage rates.

Clinical and Radiologic Features of Osteoporotic Spine Fracture with Delayed Neurologic Compromises.

OBJECTIVES: Osteoporotic spine fractures (OSFs) with delayed neurologic compromises (NCs) have been increasingly reported. Although several studies have addressed that the pathologic mechanism of NC involves nonunion and segmental instability, the risk factors remain unclear. Therefore, the purpose of this study was to assess the radiologic and clinical features of OSFs with delayed NC. METHODS: Thirty patients with delayed NC (group 1) were matched in a 1-to-1 format with 30 patients without delayed NC (group 2) by age, bone mineral density, body mass index, and medical treatment for osteoporosis. Clinical and radiologic parameters were assessed to determine the risk factors related to delayed NC. Clinical outcomes were also compared between the 2 groups. RESULTS: Neurologic compromises were presented as myelopathy in 20 patients and radiculopathy in 10 patients. Initial kyphotic angle (KA) and height loss (HL) were significantly greater in group 1 (21.6 ± 12.9 degrees vs. 10.5 ± 8.6 degrees, P = 0.0001 for KA; 55.8% ± 15.2% vs. 19.9% ± 10.9%, P = 0.0001 for HL). Fracture instability with intravertebral cleft, posterior wall involvement, midportion type of magnetic resonance classification, thoracolumbar level, and aortic calcification were significantly correlated with delayed NC. In multivariate analysis, initial HL (hazard ratio = 1.24; P = 0.012) and midportion-type fracture (hazard ratio = 14.9: P = 0.03) were the independent risk factors related to delayed NC. In addition, clinical outcomes at the last follow-up were significantly better in group 2. CONCLUSIONS: Initial HL and midportion-type fracture were correlated with delayed NC following OSFs. Moreover, pre-existing stenotic lesions might be associated with delayed NC.