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Background Continuous renal replacement therapy (CRRT) has become the standard modality of renal replacement therapy (RRT) in critically ill patients. However, consensus is lacking regarding the criteria for discontinuing CRRT. Here we validated the usefulness of the prediction model for successful discontinuation of CRRT in a multicenter retrospective cohort. Methods One temporal cohort and four external cohorts included 1,517 patients with acute kidney injury who underwent CRRT for >2 days from 2018 to 2020. The model was composed of four variables: urine output, blood urea nitrogen, serum potassium, and mean arterial pressure. Successful discontinuation of CRRT was defined as the absence of an RRT requirement for 7 days thereafter. Results The area under the receiver operating characteristic curve (AUROC) was 0.74 (95% confidence interval, 0.71–0.76). The probabilities of successful discontinuation were approximately 17%, 35%, and 70% in the low-score, intermediate-score, and high-score groups, respectively. The model performance was good in four cohorts (AUROC, 0.73–0.75) but poor in one cohort (AUROC, 0.56). In one cohort with poor performance, attending physicians primarily controlled CRRT prescription and discontinuation, while in the other four cohorts, nephrologists determined all important steps in CRRT operation, including screening for CRRT discontinuation. Conclusion The overall performance of our prediction model using four simple variables for successful discontinuation of CRRT was good, except for one cohort where nephrologists did not actively engage in CRRT operation. These results suggest the need for active engagement of nephrologists and protocolized management for CRRT discontinuation.

Abstract Anemia is common in critically ill patients undergoing continuous renal replacement therapy (CRRT). We investigated the impact of anemia requiring red blood cell (RBC) transfusion or erythropoiesis-stimulating agents (ESAs) on patient outcomes after hospital discharge in critically ill patients with acute kidney injury (AKI) requiring CRRT. In this retrospective cohort study using the Health Insurance Review and Assessment database of South Korea, 10,923 adult patients who received CRRT for 3 days or more between 2010 and 2019 and discharged alive were included. Anemia was defined as the need for RBC transfusion or ESAs. Outcomes included cardiovascular events (CVEs) and all-cause mortality after discharge. The anemia group showed a tendency to be older with more females and had more comorbidities compared to the control group. Anemia was not associated with an increased risk of CVEs (adjusted hazard ratio [aHR]: 1.05; 95% confidence interval [CI]: 0.85–1.29), but was associated with an increased risk of all-cause mortality (aHR: 1.41; 95% CI 1.30–1.53). For critically ill patients with AKI requiring CRRT, anemia, defined as requirement for RBC transfusion or ESAs, may increase the long-term risk of all-cause mortality.

Abstract Multiple types of T cells have been described and assigned pathophysiologic functions in the kidneys. However, the existence and functions of TCR+CD4+CD8+ (double positive; DP) T cells are understudied in normal and diseased murine and human kidneys. We studied kidney DPT cells in mice at baseline and after ischemia reperfusion (IR) and cisplatin injury. Additionally, effects of viral infection and gut microbiota were studied. Human kidneys from patients with renal cell carcinoma were evaluated. Our results demonstrate that DPT cells expressing CD4 and CD8 co-receptors constitute a minor T cell population in mouse kidneys. DPT cells had significant Ki67 and PD1 expression, effector/central memory phenotype, proinflammatory cytokine (IFNγ, TNFα and IL-17) and metabolic marker (GLUT1, HKII, CPT1a and pS6) expression at baseline. IR, cisplatin and viral infection elevated DPT cell proportions, and induced distinct functional and metabolic changes. scRNA-seq analysis showed increased expression of Klf2 and Ccr7 and enrichment of TNFα and oxidative phosphorylation related genes in DPT cells. DPT cells constituted a minor population in both normal and cancer portion of human kidneys. In conclusion, DPT cells constitute a small population of mouse and human kidney T cells with distinct inflammatory and metabolic profile at baseline and following kidney injury.

Abstract Excessive activation of poly (ADP-ribose) polymerase (PARP) contributes to ischemic acute kidney injury (AKI). PARP inhibition has been shown to be beneficial in renal ischemia–reperfusion injury (IRI) in the early phase, but its role in the repair process remains unclear. The effects of JPI-289, a novel PARP inhibitor, during the healing phase after renal IRI were investigated. IRI was performed on 9-week-old male C57BL/6 mice. Saline or JPI-289 100 mg/kg was intraperitoneally administered once at 24 h or additionally at 48 h after IRI. Hypoxic HK-2 cells were treated with JPI-289. Renal function and fibrosis extent were comparable between groups. JPI-289 treatment caused more prominent tubular atrophy and proinflammatory intrarenal leukocyte phenotypes and cytokines/chemokines changes at 12 weeks after unilateral IRI. JPI-289 treatment enhanced gene expressions associated with collagen formation, toll-like receptors, and the immune system in proximal tubules and endothelial cells after IRI. JPI-289 treatment at 3 or 6 h after hypoxia facilitated proliferation of hypoxic HK-2 cells, whereas further treatment after 24 h suppressed proliferation. Delayed inhibition of PARP after renal IRI did not facilitate the repair process during the early healing phase but rather may aggravate renal tubular atrophy during the late healing phase in ischemic AKI.

To address continuously increasing cyber threats, security professionals within organizations are fortifying internal security by implementing security policies such as network segregation and emerging concepts such as Zero Trust. However, despite these changes in the cybersecurity landscape, the ultimate goal of cyber attackers, which is to exfiltrate critical information stored within an organization’s intranet, remains unchanged. Consequently, attackers with motives such as hacktivists persistently and repeatedly target key systems within an organization’s intranet to achieve their ultimate objectives. Considering the tendencies of intranet attackers, this study proposes the inclusion of the number of connection attempts for attack detection as an additional attribute alongside commonly used attributes such as source IP, destination IP, protocol, and attack signatures in intrusion detection rules. This proposal is supported by establishing an experimental environment for conducting intranet attacks and collecting raw data. Using feature engineering techniques, the raw data were transformed into analyzable datasets, and the performance was measured using six supervised machine learning algorithms. Through this research, we aim to contribute to the field of cybersecurity by going beyond the conventional focus on Internet-based attacks and providing a methodology for analyzing various intranet-based attacks in a post-stage environment. In addition, we share the method of feature engineering Zeek IDS raw data and release the resulting dataset to further advance the field. We hope that these contributions will foster future developments in this domain.

Abstract T cells are important in the pathogenesis of acute kidney injury (AKI), and TCR+CD4-CD8- (double negative-DN) are T cells that have regulatory properties. However, there is limited information on DN T cells compared to traditional CD4+ and CD8+ cells. To elucidate the molecular signature and spatial dynamics of DN T cells during AKI, we performed single-cell RNA sequencing (scRNA-seq) on sorted murine DN, CD4+, and CD8+ cells combined with spatial transcriptomic profiling of normal and post AKI mouse kidneys. scRNA-seq revealed distinct transcriptional profiles for DN, CD4+, and CD8+ T cells of mouse kidneys with enrichment of Kcnq5, Klrb1c, Fcer1g, and Klre1 expression in DN T cells compared to CD4+ and CD8+ T cells in normal kidney tissue. We validated the expression of these four genes in mouse kidney DN, CD4+ and CD8+ T cells using RT-PCR and Kcnq5, Klrb1, and Fcer1g genes with the NIH human kidney precision medicine project (KPMP). Spatial transcriptomics in normal and ischemic mouse kidney tissue showed a localized cluster of T cells in the outer medulla expressing DN T cell genes including Fcer1g. These results provide a template for future studies in DN T as well as CD4+ and CD8+ cells in normal and diseased kidneys.

Ships are constructed using the shipbuilding method, which involves several blocks. Every block produced in each unit process features a different production speed depending on the block shape, number of members used, and process complexity. Consequently, the blocks produced in one process is stored in the stockyard for the next process operation. As the number of blocks stacked in the stockyard increases, the movement of the blocks and that of their transporters increase, thus resulting in an increase in operating costs. Hence, we propose a method for monitoring the block storage status based on drone images and shipyard Geographic Information System (GIS) information to support the efficient use of block stockyards. Shipyard GIS information is mapped onto the image acquired from the drone, and the area of the stacked block is detected in the image. The block storage area and storage load factor of the stockyard are calculated using the saved block areas and shipyard GIS information. By evaluating shipyard stockyards, we confirmed the possibility of monitoring the status of shipyard block storage based on drone images and GIS information.

Abstract It remains uncertain whether albuminuria can identify elderly patients with diabetes at a high risk of incident end-stage kidney disease (ESKD) or mortality. 3065 patients (aged ≥ 65 years) with type 2 diabetes were included. We examined the association between albuminuria stages (normoalbuminuria, A1; microalbuminuria, A2; and macroalbuminuria, A3) and the risk of incident ESKD and all-cause mortality for each age group (65–69, 70–74, and ≥ 75 years). A2 and A3 were observed in 25.5% and 9.4% of the subjects, respectively. For A1, A2, and A3, the probabilities of ESKD at 8 years were 1.0%, 6.3%, and 29.7% (P

AbstractLeft ventricular assist device (LVAD) has been highlighted as a new treatment option in the end-stage heart failure (HF). Kidney outcome after LVAD in severe cardiorenal syndrome (CRS) patients requiring kidney replacement therapy (KRT) is unclear. We investigated the impact of preoperative KRT on kidney function and survival in LVAD patients with severe CRS. A total of 50 patients followed up for at least 1 year after LVAD implantation was analyzed. The primary outcomes were estimated glomerular filtration rate and survival rate. Patients were divided into two groups depending on in-hospital KRT before LVAD implantation: the control group (n = 33) and the KRT group (n = 17). Postoperative KRT was performed for 76.5% of patients in the KRT group, and all of them discontinued KRT before discharge. There were no statistically significant differences in the degree of eGFR decline in survivors according to preoperative KRT. Although there were no statistically significant differences in the degree of eGFR decline in survivors regardless of preoperative KRT, old age (β −0.94, p

Boosting activity of oxide catalysts is a long-lasting challenge to developing efficient catalysts for industrially important reactions such as CO2-to-methanol. Here, the authors report a strategy for enhancing the activity of a ZnZrO x methanol synthesis catalyst via engineered nanoscale H supply.

Ransomware is a growing threat and is building ecosystems in the form of ransomware as a service (RaaS). While there have been diverse efforts to detect and mitigate such threats, techniques to bypass such countermeasures have advanced considerably. Since detecting all evolving threats has become challenging, there is a growing interest in developing proactive countermeasures that can minimize the damage even in environments where ransomware has already been executed. In this study, we gained insights from an attacker’s perspective by analyzing ransomware such as LockBit and derived a generic counterstrategy against features that are common in ransomware attacks. Our proposed method protects critical files from existing ransomware by applying a hiding strategy that poses a challenge to attackers in finding the target files. We also present best practices for implementing the strategy while considering both in terms of security and usability using the link file and improving the method through the addition of a linker and encrypted database to reduce the attack surface. By using real-world ransomware samples, our experiments show that the proposed method successfully protects valuable files against ransomware in a cost-effective manner.

IntroductionPost-donation renal outcomes are a crucial issue for living kidney donors considering young donors’ high life expectancy and elderly donors’ comorbidities that affect kidney function. We developed a prediction model for renal adaptation after living kidney donation using interpretable machine learning.MethodsThe study included 823 living kidney donors who underwent nephrectomy in 2009–2020. AutoScore, a machine learning-based score generator, was used to develop a prediction model. Fair and good renal adaptation were defined as post-donation estimated glomerular filtration rate (eGFR) of ≥ 60 mL/min/1.73 m2 and ≥ 65% of the pre-donation values, respectively.ResultsThe mean age was 45.2 years; 51.6% were female. The model included pre-donation demographic and laboratory variables, GFR measured by diethylenetriamine pentaacetate scan, and computed tomography kidney volume/body weight of both kidneys and the remaining kidney. The areas under the receiver operating characteristic curve were 0.846 (95% confidence interval, 0.762–0.930) and 0.626 (0.541–0.712), while the areas under the precision-recall curve were 0.965 (0.944–0.978) and 0.709 (0.647–0.788) for fair and good renal adaptation, respectively. An interactive clinical decision support system was developed.1ConclusionThe prediction tool for post-donation renal adaptation showed good predictive capability and may help clinical decisions through an easy-to-use web-based application.

T cells play an important role in acute kidney injury (AKI). Metabolic programming of T cells regulates their function, is a rapidly emerging field, and is unknown in AKI. We induced ischemic AKI in C57BL/6J mice and collected kidneys and spleens at multiple time points. T cells were isolated and analyzed by an immune-metabolic assay. Unbiased machine learning analyses identified a distinct T cell subset with reduced voltage-dependent anion channel 1 and mTOR expression in post-AKI kidneys. Ischemic kidneys showed higher expression of trimethylation of histone H3 lysine 27 and glutaminase. Splenic T cells from post-AKI mice had higher expression of glucose transporter 1, hexokinase II, and carnitine palmitoyltransferase 1a. Human nonischemic and ischemic kidney tissue displayed similar findings to mouse kidneys. Given a convergent role for glutamine in T cell metabolic pathways and the availability of a relatively safe glutamine antagonist, JHU083, effects on AKI were evaluated. JHU083 attenuated renal injury and reduced T cell activation and proliferation in ischemic and nephrotoxic AKI, whereas T cell–deficient mice were not protected by glutamine blockade. In vitro hypoxia demonstrated upregulation of glycolysis-related enzymes. T cells undergo metabolic reprogramming during AKI, and reconstitution of metabolism by targeting T cell glutamine pathway could be a promising novel therapeutic approach.

Ischemic acute kidney injury (AKI) is a common medical problem with significant mortality and morbidity, affecting a large number of patients globally. Ischemic AKI is associated with intrarenal inflammation as well as systemic inflammation; thus, the innate and adaptive immune systems are implicated in the pathogenesis of ischemic AKI. Among various intrarenal immune cells, T cells play major roles in the injury process and in the repair mechanism affecting AKI to chronic kidney disease transition. Importantly, T cells also participate in distant organ crosstalk during AKI, which affects the overall outcomes. Therefore, targeting T cell-mediated pathways and T cell-based therapies have therapeutic promise for ischemic AKI. Here, we review the major populations of kidney T cells and their roles in ischemic AKI.

Efficiently and accurately identifying fraudulent credit card transactions has emerged as a significant global concern along with the growth of electronic commerce and the proliferation of Internet of Things (IoT) devices. In this regard, this paper proposes an improved algorithm for highly sensitive credit card fraud detection. Our approach leverages three machine learning models: K-nearest neighbor, linear discriminant analysis, and linear regression. Subsequently, we apply additional conditional statements, such as “IF” and “THEN”, and operators, such as “>“ and “

Abstract We investigated the clinical relevance of urinary cytokines/chemokines reflecting intrarenal immunologic micromilieu as prognostic markers and the optimal measurement timing after living donor kidney transplantation (LDKT). This prospective cohort study included 77 LDKT patients who were followed for ≥ 5 years. Patients were divided into control (n = 42) or acute rejection (AR, n = 35) group. Early AR was defined as AR occurring within 3 months. Serum and urine cytokines/chemokines were measured serially as follows: intraoperative, 8/24/72 h, 1 week, 3 months, and 1 year after LDKT. Intrarenal total leukocytes, T cells, and B cells were analyzed with immunohistochemistry followed by tissueFAXS. Urinary MCP-1 and fractalkine were also analyzed in a validation cohort. Urinary MCP-1 after one week was higher in the AR group. Urinary MCP-1, fractalkine, TNF-α, RANTES, and IL-6 after one week were significantly higher in the early AR group. Intrarenal total leukocytes and T cells were elevated in the AR group compared with the control group. Urinary fractalkine, MCP-1, and IL-10 showed positive correlation with intrarenal leukocyte infiltration. Post-KT 1 week urinary MCP-1 showed predictive value in the validation cohort. One-week post-KT urinary MCP-1 may be used as a noninvasive diagnostic marker for predicting AR after LDKT.

Since the GDPR was implemented in 2018, organizations that collect data from the EU residents are required to receive the user’s consent. Organizational measures to ensure that the organizations are compliant to the recently enacted GDPR are still abstract and ambiguous. Moreover, data subjects and controllers have demanded the practice of obtaining consent from organizations. By observing the case law and guidelines related to the GDPR provisions, we deduced four consent conditions. Then, we examined how online service provider’s websites are making efforts to implement the GDPR framework. For this, we identified key characteristics of these websites, such as the existence of consent buttons. In order to help the data subjects obtain consent, we proposed an automatic tool that can check the consent conditions by checking the websites. Our study examined 10,000 websites for 26 days using the Python libraries with the tool automatically crawling the website information and analyzes the HTML structure according to the specified conditions. In addition, this tool crawls the privacy policy of each website. Moreover, it automatically determines whether it meets the four consent conditions by calculating it according to the formula defined in the consent condition. To evaluate the tool’s accuracy, the researchers manually analyzed 500 websites and compared the manual analysis with the results of the tool’s automatic analysis. We found that this tool differentiates itself through qualitative comparisons with other GDPR meters.

Rheumatoid arthritis (RA) is an inflammatory disease marked by a massive proliferation of synovial cells in the joints. In this study, we investigated the pro-apoptotic effects of docosahexaenoic acid (DHA) in human fibroblast-like synovial cells from RA patients (RA-FLS). An in vitro study using MH7A cells showed that DHA treatment induced caspase-8-dependent apoptosis in a dose-dependent manner and reduced the TNF-α-mediated induction of MMP-9 and IL-1β. DHA also induced the phosphorylation of eIF2α, the expression of the ER stress markers ATF4 and C/EBP homologous protein (CHOP), and death receptor 5 (DR5). The knockdown of CHOP or DR5 increased cell viability and reduced apoptosis in DHA-treated cells. Furthermore, the knockdown of CHOP reduced DHA-mediated DR5 expression, while the overexpression of CHOP increased DR5 expression. We also found that DHA treatment induced the accumulation of reactive oxygen species (ROS), and pretreatment with the anti-oxidant Tiron effectively abrogated not only the expression of CHOP and DR5, but also DHA-induced apoptosis. Under this condition, cell viability was increased, while PARP-1 cleavage and caspase-8 activation were reduced. All the findings were reproduced in human primary synovial cells obtained from RA patients. These results suggest that the DHA-mediated induction of ROS and CHOP induced apoptosis through the upregulation of DR5 in RA-FLSs, and that CHOP could be used as a therapy for RA.

Security professionals, government agencies, and corporate organizations have found an inherent need to prevent or mitigate attacks from insider threats. Accordingly, active research on insider threat detection has been conducted to prevent and mitigate adverse effects such as leakage of valuable information that may be caused by insiders. Along with the growth of Internet-of-Things (IoT), new security challenges arise in the existing security frameworks. Attack surfaces are significantly enlarged which could cause a severe risk in terms of company insider threat management. In this work, we provide a generalization of aspects of insider threats with IoT and analyze the surveyed literature based on both private and public sources. We then examine data sources considering IoT environments based on the characteristics and the structure of IoT (perceptual, network, and application layers). The result of reviewing the study shows that using the data source of the network and application layer is more suitable than the perceptual layer in the IoT environment. We also categorized each layer's data sources according to their features, and we investigated research objectives and methods for each category. Finally, the potential for utilization and limitations under the IoT environment are presented at the end of each layer examination.

Abstract Background Reactive oxygen species (ROS) produce different lesions in DNA by ROS-induced DNA damage. Detection and quantification of 8-oxo-7,8-dihydroguanine (8-oxoG) within cells are important for study. Human ribosomal protein S3 (hRpS3) has a high binding affinity to 8-oxoG. In this study, we developed an imaging probe to detect 8-oxoG using a specific peptide from hRpS3. Transactivator (TAT) proteins are known to have cell-penetrating properties. Therefore, we developed a TAT-S3 probe by attaching a TAT peptide to our imaging probe. Results A DNA binding assay was conducted to confirm that our probe bound to 8-oxoG and apurinic/apyrimidinic (AP) sites. We confirmed that the TAT-S3 probe localized in the mitochondria, without permeabilization, and fluoresced in H2O2-treated HeLa cells and zebrafish embryos. Treatment with Mitoquinone (MitoQ), a mitochondria-targeted antioxidant, reduced TAT-S3 probe fluorescence. Additionally, treatment with O8, an inhibitor of OGG1, increased probe fluorescence. A competition assay was conducted with an aldehyde reaction probe (ARP) and methoxyamine (MX) to confirm binding of TAT-S3 to the AP sites. The TAT-S3 probe showed competitive binding to AP sites with ARP and MX. Conclusions These results revealed that the TAT-S3 probe successfully detected the presence of 8-oxoG and AP sites in damaged cells. The TAT-S3 probe may have applications for the detection of diseases caused by reactive oxygen species.

Triple-negative breast cancer is more aggressive than other types of breast cancer. Protein kinase R (PKR), which is activated by dsRNA, is known to play a role in doxorubicin-mediated apoptosis; however, its role in DNA damage-mediated apoptosis is not well understood. In this study, we investigated the roles of PKR and its downstream players in doxorubicin-treated HCC1143 triple-negative breast cancer cells. Doxorubicin treatment induces DNA damage and apoptosis. Interestingly, doxorubicin treatment induced the phosphorylation of eukaryotic initiation factor 2 alpha (eIF2α) via PKR, whereas the inhibition of PKR with inhibitor C16 reduced eIF2α phosphorylation. Under these conditions, doxorubicin-mediated DNA fragmentation, cell death, and poly(ADP ribose) polymerase and caspase 7 levels were recovered. In addition, phosphorylation of checkpoint kinase 1 (CHK1), which is known to be involved in doxorubicin-mediated DNA damage, was increased by doxorubicin treatment, but blocked by PKR inhibition. Protein translation was downregulated by doxorubicin treatment and upregulated by blocking PKR phosphorylation. These results suggest that PKR activation induces apoptosis by increasing the phosphorylation of eIF2α and CHK1 and decreasing the global protein translation in doxorubicin-treated HCC1143 triple-negative breast cancer cells.

The versatility of the intrarenal immunologic micromilieu through dietary modification and the subsequent effects on susceptibility to ischemic acute kidney injury (AKI) are unclear. We investigated the effects of high-salt (HS) or high-fat (HF) diet on intrarenal immunologic micromilieu and development of ischemic AKI using murine ischemic AKI and human kidney-2 (HK-2) cell hypoxia models. Four different diet regimens [control, HF, HS, and high-fat diet with high-salt (HF+HS)] were provided individually to groups of 9-week-old male C57BL/6 mice for 1 or 6 weeks. After a bilateral ischemia-reperfusion injury (BIRI) operation, mice were sacrificed on day 2 and renal injury was assessed with intrarenal leukocyte infiltration. Human kidney-2 cells were treated with NaCl or lipids. The HF diet increased body weight and total cholesterol, whereas the HF+HS did not. Although the HF or HS diet did not change total leukocyte infiltration at 6 weeks, the HF diet and HF+HS diet increased intrarenal CD8 T cells. Plasma cells increased in the HF and HS diet groups. The expression of proinflammatory cytokines including TNF-α, IFN-γ, MCP-1, and RANTES was increased by the HF or HS diet, and intrarenal VEGF decreased in the HS and HF+HS diet groups at 6 weeks. Deterioration of renal function following BIRI tended to be aggravated by the HF or HS diet. High NaCl concentration suppressed proliferation and enhanced expression of TLR-2 in hypoxic HK-2 cells. The HF or HS diet can enhance susceptibility to ischemic AKI by inducing proinflammatory changes to the intrarenal immunologic micromilieu.

Background: Cardiovascular complications are the leading cause of mortality in patients with chronic kidney disease (CKD). Uremic vasculopathy plays a crucial role in facilitating the progression of cardiovascular complications in advanced CKD. However, the improvement of conventional research methods could provide further insights into CKD.Objectives: In this study, we aimed to develop a novel model of uremic vasculopathy as a potential drug screening system.Methods and Results: The effects of uremic serum and different combinations of uremic toxins on induced pluripotent stem cell (iPSC)-derived endothelial cells (ECs) of a normal control and a CKD patient were investigated using several functional assays. We found that a mixture of uremic toxins composed of high urea, creatinine, uric acid, and indoxyl sulfate exerted deleterious effects on normal control iPSC-ECs that were comparable to uremic serum by increasing reactive oxygen species and apoptosis, as well as suppression of tube formation. Additional characterization revealed a potential involvement of dysregulated TGF-β signaling as treatment with either losartan or TGF-β inhibitors led to the attenuation of adverse effects induced by uremic toxins. Importantly, impaired wound healing potential seen in CKD patient-specific iPSC-ECs was rescued by treatment with losartan and TGF-β inhibitors.Conclusion: Our study demonstrated that simplified uremic toxin mixtures can simulate the uremic micromilieu reproducibly and CKD patient-specific iPSC-ECs can potentially recapitulate susceptibility to uremic vasculopathy. This novel model of uremic vasculopathy may provide a new research tool as a drug screening system.

Intrarenal robust inflammatory response following ischemia-reperfusion injury (IRI) is a major factor in the pathogenesis of renal injury in ischemic acute kidney injury (AKI). Although numerous studies have investigated various agents of immune modulation or suppression for ischemic AKI, few showed reproducible effects. We hypothesized that poly (ADP-ribose) polymerase (PARP) inhibitor may favorably change post-ischemic intrarenal immunologic micromilieu by reducing damage-associated molecular pattern (DAMP) signals and improve renal outcome in ischemic AKI. The effects of JPI-289 (a PARP inhibitor) on early renal injury in a murine IRI model and hypoxic HK-2 cell model were investigated. Bilateral IRI surgery was performed in three groups of 9-week-old male C57BL/6 mice (control, JPI-289 50 mg/kg, and JPI-289 100 mg/kg; n = 9–10 in each group). Saline or JPI-289 was intraperitoneally injected. Renal function deterioration was significantly attenuated in the JPI-289 treatment groups in a dose-dependent manner. Inflammatory cell infiltration and proinflammatory cytokine/chemokine expressions in the post-ischemic kidneys were also attenuated by JPI-289 treatment. JPI-289 treatment at 0.5 and 0.75 μg/ml facilitated the proliferation of hypoxic HK-2 cells. PARP inhibition with JPI-289 treatment showed favorable effects in ischemic AKI by attenuating intrarenal inflammatory cascade in a murine model and facilitating proliferation of hypoxic HK-2 cells.

Protein synthesis is important for maintaining cellular homeostasis under various stress responses. In this study, we screened an anticancer drug library to select compounds with translational repression functions. AZD8055, an ATP-competitive mechanistic target of rapamycin complex 1/2 (mTORC1/2) inhibitor, was selected as a translational suppressor. AZD8055 inhibited protein synthesis in mouse embryonic fibroblasts and hepatocellular carcinoma HepG2 cells. Extracellular signal-regulated kinase (ERK) and p38 mitogen-activated protein kinase (MAPK) were activated during the early phase of mTORC1/2 inhibition by AZD8055 treatment. Combined treatment of AZD8055 with the MAPK kinase1/2 (MEK1/2) inhibitor refametinib or the p38 inhibitor SB203580 markedly decreased translation in HepG2 cells. Thus, the inhibition of ERK1/2 or p38 may enhance the efficacy of AZD8055-mediated inhibition of protein synthesis. In addition, AZD8055 down-regulated the phosphorylation of eukaryotic initiation factor 4E-binding protein 1 (4E-BP1), and AZD8055-induced phosphorylation of ERK1/2 and p38 had no effect on phosphorylation status of 4E-BP1. Interestingly, AZD8055 modulated the 4E-BP1 mRNA pool by up-regulating ERK1/2 and p38 pathways. Together, these results suggest that AZD8055-induced activation of MAPKs interferes with inhibition of protein synthesis at an early stage of mTORC1/2 inhibition, and that it may contribute to the development of resistance to mTORC1/2 inhibitors.

During the past decade, mobile attacks have been established as an indispensable attack vector adopted by Advanced Persistent Threat (APT) groups. The ubiquitous nature of the smartphone has allowed users to use mobile payments and store private or sensitive data (i.e., login credentials). Consequently, various APT groups have focused on exploiting these vulnerabilities. Past studies have proposed automated classification and detection methods, while few studies have covered the cyber attribution. Our study introduces an automated system that focuses on cyber attribution. Adopting MITRE’s ATT&CK for mobile, we performed our study using the tactic, technique, and procedures (TTPs). By comparing the indicator of compromise (IoC), we were able to help reduce the false flags during our experiment. Moreover, we examined 12 threat actors and 120 malware using the automated method for detecting cyber attribution.

Background: Patients undergoing kidney transplantation (KT) often receive red blood cell (RBC) transfusion during admission for KT which may increase the risk of allosensitization. The association between peri-transplant RBC transfusion and graft survival was evaluated using a nationwide cohort. Methods: This retrospective study analyzed 13,871 patients who underwent KT in Korea between 2007 and 2015. The outcomes were graft failure rate and overall patient survival depending on the amount of RBC transfusion. Results: The overall graft failure rate was 15.5%. Compared to the graft failure rate of 13.5% in the no transfusion group, the graft failure rate was 15.4% in the 1–2 units group (sHR 1.06 (95% CI 0.97–1.17), p = 0.216), 21.4% in the 3–5 units group (sHR 1.39 (1.21–1.61), p < 0.001), and 35.3% in the 6 or more units group (sHR 2.20 (1.70–2.85), p < 0.001). The overall survival rate was 97.5% in the no transfusion group, compared to 95.9% in the 1–2 units group (HR 1.50 (1.22–1.83), p < 0.001), 92.0% in the 3–5 units group (HR 2.43 (1.87–3.15), p < 0.001), and 67.5% in the 6 or more units group (HR 6.81 (5.03–9.22), p < 0.001). Conclusions: Peri-transplant RBC transfusion was independently associated with the increased risk of renal allograft failure and death in KT patients.

During the past decade, the technological advancement have allowed the gambling industry worldwide to deploy various platforms such as the web and mobile applications. Government agencies and local authorities have placed strict regulations regarding the location and amount allowed for gambling. These efforts are made to prevent gambling addictions and monitor fraudulent activities. The revenue earned from gambling provides a considerable amount of tax revenue. The inception of internet gambling have allowed professional gamblers to par take in unlawful acts. However, the lack of studies on the technical inspections and systems to prohibit unlawful internet gambling has caused incidents such as the Walkerhill Hotel incident in 2016, where fraudsters placed bets abnormally by modifying an Internet of Things (IoT)-based application called “MyCard”. This paper investigates the logic used by smartphone IoT applications to validate the location of users and then confirm continuous threats. Hence, our research analyzed transactions made on applications that operated using location authentication through IoT devices. Drawing on gambling transaction data from the Korea Racing Authority, this research used time series machine learning algorithms to identify anomalous activities and transactions. In our research, we propose a method to detect and prevent these anomalies by conducting a comparative analysis of the results of existing anomaly detection techniques and novel techniques.

Cybersecurity in Industrial Internet of Things (IIoT) has become critical as smart cities are becoming increasingly linked to industrial control systems (ICSs) used in critical infrastructure. Consequently, data-driven security systems for analyzing massive amounts of data generated by smart cities have become essential. A representative method for analyzing large-scale data is the game bot detection approach used in massively multiplayer online role-playing games. We reviewed the literature on bot detection methods to extend the anomaly detection approaches used in bot detection schemes to IIoT fields. Finally, we proposed a process wherein the data envelopment analysis (DEA) model was applied to identify features for efficiently detecting anomalous behavior in smart cities. Experimental results using random forest show that our extracted features based on a game bot can achieve an average F1-score of 0.99903 using 10-fold validation. We confirmed the applicability of the analyzed game-industry methodology to other fields and trained a random forest on the high-efficiency features identified by applying a DEA, obtaining an F1-score of 0.997 using the validation set approach. In this study, an anomaly detection method for analyzing massive smart city data based on a game industry methodology was presented and applied to the ICS dataset.

The visual fidelity of a virtual environment lacks the exceedingly complex layers from the physical world, but the continuous improvements of image rendering technology and computation powers have led to greater demands for virtual simulations. Our study employs Crime Prevention through Environmental Design (CPTED) as a risk control measure and utilizes two principles: Access Control and Natural Surveillance. We conducted an experiment with (n-sample: 100) graduate students. For the experiment, we utilized the Factor Analysis of Information Risk (FAIR) to quantitatively analyze the risk. Furthermore, we adopted the lme4 package for R to estimate the mixed effect of the 6,242,880 observations retrieved from Kaggle. Based on the two experiments, we were able to critically evaluate the contributions of CPTED through a multi-component analysis. Our study investigates how spatial syntax and territorial demarcation may translate in the cyberspace realm. We found that the corollaries of the mophology in the virtual environment effects the distribution of crime. The results of our study discusses how to determine the criminogenic designs and capacity in the cyberspace realm.

Previous studies have proposed various frameworks and algorithms to optimize routes to reduce total transportation cost, which accounts for over 29.4% of overall logistics costs. However, it is very hard to find cases in which mathematical models or algorithms are applied to practical business environment cases which require reusable packaging, especially daily operating logistics services like convenience store support systems. Most previous studies have considered developing an optimal algorithm which can solve the mathematical problem within a practical amount of time while satisfying all constraints, such as the capacity of delivery and pick-up, and hard or soft time windows. For daily delivery and pick-up services, like those supporting several convenience stores, it is required to consider the unit transporting the container, as well as the demand, capacity of trucks, travel distance, and traffic congestion. In particular, reusable transport containers and trays should be regarded as important assets of logistics centers. However, if the mathematical model focuses on only satisfying constraints related to delivery and not considering the cost of trays, it is often to leave the empty trays on the pick-up points when there is not enough space in the track. In this study, we propose a mathematical model for optimizing delivery and pick-up plans by extending the general vehicle routing problem of simultaneous delivery and pick-up with time windows, while considering left-over cost. With numerical experiments it has been proved that the proposed model may reduce the total delivery cost. Also, it seems possible to apply the proposed approach to the various logistics businesses which require reusable transport containers like shipping containers, refrigerating containers, trays, and pallets.

Internet-of-Things (IoT) is a technology that is extensively being used in various fields. Companies like Samsung, LG, and Apple are launching home appliances that use IoT as a part of their smart home business. Currently, Intelligent Things which combine artificial intelligence (AI) and IoT are being developed. Most of these devices are configured to collect and respond to human behavior (motion, voice, etc.) through built-in sensors. If IoT devices do not ensure high security, personal information could be leaked. This paper describes the IoT security threats that can cause information leakage from a hierarchical viewpoint of cyberspace. In addition, because these smart home-based IoT devices are closely related to human life, considering social damage is a problem. To overcome this, we propose a framework to measure the risk of IoT devices based on security scenarios that can occur in a smart home.

Although Internet of Things (IoT) technologies and services are being developed rapidly worldwide, concerns of potential security threats such as privacy violation, information leak, and hacking are increasing as more various sensors are connected to the Internet. There is a need for the study of introducing risk management and existing security management standard (e.g., ISO27001) to ensure the stability and reliability of IoT services. K-ISMS is a representative certification system that evaluates the security management level of the enterprise in Korea and is possible to apply as a standardized process to enhance the security management of IoT services. However, there are growing concerns about the quality deterioration of the K-ISMS certification assessment these days because of internet security incidents occurring frequently in K-ISMS certified enterprises. Therefore, various researches are required to improve the accuracy and objectivity of the certification assessment. Since existing studies mainly focus on simple statistical analysis of the K-ISMS assessment results, analysis on the cause of certification assessment fault based on past data analysis is insufficient. As a method of managing the certification inspection quality, in this paper, we analyze the association among the fault items of the K-ISMS certification assessment results using association rule mining which involves identifying an association rule among items in the database.

Organizations make use of important information in day-to-day business. Protecting sensitive information is imperative and must be managed. Companies in many parts of the world protect sensitive information using the international standard known as the information security management system (ISMS). ISO 27000 series is the international standard ISMS used to protect confidentiality, integrity, and availability of sensitive information. While an ISMS based on ISO 27000 series has no particular flaws for general information systems, it is unfit to manage sensitive information for industrial control systems (ICSs) because the first priority of industrial control is safety of the system. Therefore, a new information security management system based on confidentiality, integrity, and availability as well as safety is required for ICSs. This new ISMS must be mutually exclusive of an ICS. This paper provides a new paradigm of ISMS for ICSs, which will be shown to be more suitable than the existing ISMS.

In spite of the fact that hacking is a widely used term, it is still not legally established. Moreover, the definition of the concept of hacking has been deployed in a wide variety of ways in national literature. This ambiguity has led to various side effects. Recently in the United States, reforms collectively known as Aaron's Law were proposed as intended amendments to the Computer Fraud and Abuse Act (CFAA). Most experts expect that this change will put the brakes on the CFAA as a severe punishment policy, and result in a drop in controversial court decisions. In this study, we analyze the definitions and the penalties for hacking for each country and compare them with the national law and then make suggestions through more specific legislation. We expect it will reduce legal controversy and prevent excessive punishment.

At AES’00, a collision attack on 7-round reduced AES was proposed. In this paper, we apply this idea to seven SPN block ciphers, AES-192/256, Crypton-192/256, mCrypton-96/128, and Anubis. Applying our attacks on AES-192/256, we improve the attack result based on meet-in-the-middle attack (AES-192) and the attack result proposed in AES’00 (AES-256), respectively. Our attack result on Anubis is superior to known cryptanalytic result on it. In the cases of Crypton-192/256 and mCrypton-96/128, our attacks are applicable to 8-round reduced versions. The attack results on mCrypton-96/128 are more practical than known cryptanalytic results on them.

According to BBC News, online hate speech increased by 20% during the COVID-19 pandemic. Hate speech from anonymous users can result in psychological harm, including depression and trauma, and can even lead to suicide. Malicious online comments are increasingly becoming a social and cultural problem. It is therefore critical to detect such comments at the national level and detect malicious users at the corporate level. To achieve a healthy and safe Internet environment, studies should focus on institutional and technical topics. The detection of toxic comments can create a safe online environment. In this study, to detect malicious comments, we used approximately 9,400 examples of hate speech from a Korean corpus of entertainment news comments. We developed toxic comment classification models using supervised learning algorithms, including decision trees, random forest, a support vector machine, and K-nearest neighbors. The proposed model uses random forests to classify toxic words, achieving an F1-score of 0.94. We analyzed the trained model using the permutation feature importance, which is an explanatory machine learning method. Our experimental results confirmed that the toxic comment classifier properly classified hate words used in Korea. Using this research methodology, the proposed method can create a healthy Internet environment by detecting malicious comments written in Korean. [ABSTRACT FROM AUTHOR]

As technology develops, the amount of information being used has increased a lot. Every company learns big data to provide customized services with its customers. Accordingly, collecting and analyzing data of the data subject has become one of the core competencies of the companies. However, when collecting and using it, the authority of the data subject may be violated. The data often identifies its subject by itself, and even if it is not a personal information that infringes on an individual's authority, the moment it is connected, it becomes important and sensitive personal information that we have never thought of. Therefore, recent privacy regulations such as GDPR(GeneralData ProtectionRegulation) are changing to guarantee more rights of the data subjects. To use data effectively without infringing on the rights of the data subject, the concept of de-identification has been created. Researchers and companies can make personal information less identifiable through appropriate de-identification/pseudonymization and use the data for the purpose of statistical research. De-identification/pseudonymization techniques have been studied a lot, but it is difficult for companies and researchers to know how to de-identify/pseudonymize data. It is difficult to clearly understand how and to what extent each organization should take deidentification measures. Currently, each organization does not systematically analyze and conduct the situation but only takes minimal action while looking at the guidelines distributed by each country. We solved this problem from the perspective of risk management. Several steps are required to secure the dataset starting from pre-processing to releasing the dataset. We can analyze the dataset, analyze the risk, evaluate the risk, and treat the risk appropriately. The outcomes of each step can then be used to take appropriate action on the dataset to eliminate or reduce its risk. Then, we can release the dataset under its own purpose. These series of processes were reconstructed to fit the current situation by analyzing various standards such as ISO/IEC (International Organization for Standardization/International Electrotechnical Commission) 20889, NIST IR (National Institute of Standards and Technology Interagency Reports) 8053, NIST SP (National Institute of Standards and Technology Special Publications) 800-188, and ITU-T (International Telecommunications Union-Telecommunication) X.1148. We propose an integrated framework based on situational awareness model and risk management model. We found that this framework can be specialized for multiple domains, and it is useful because it is based on a variety of case and utility-based ROI calculations. [ABSTRACT FROM AUTHOR]

The damage caused by malicious software is increasing owing to the COVID-19 pandemic, such as ransomware attacks on information technology and operational technology systems based on corporate networks and social infrastructures and spear-phishing attacks on business or research institutes. Recently, several studies have been conducted to prevent further phishing emails in the workplace because malware attacks employ emails as the primary means of penetration. However, according to the latest research, there appears to be a limitation in blocking email spoofing through advanced blocking systems such as spam email filtering solutions and advanced persistent threat systems. Therefore, experts believe that it is more critical to restore services immediately through resilience than the advanced prevention program in the event of damage caused by malicious software. In accordance with this trend, we conducted a survey among 100 employees engaging in information security regarding the effective factors for countering malware attacks through email. Furthermore, we confirmed that resilience, backup, and restoration were effective factors in responding to phishing emails. In contrast, practical exercise and attack visualization were recognized as having little effect on malware attacks. In conclusion, our study reminds business and supervisory institutions to carefully examine their regular voluntary exercises or mandatory training programs and assists private corporations and public institutions to establish counter-strategies for dealing with malware attacks. [ABSTRACT FROM AUTHOR]

An improved direct parameter extraction method of SiGe heterojunction bipolar transistors (HBTs) for the vertical bipolar intercompany (VBIC)-type hybrid-pi model is developed. This extraction method is a simple and reliable routine applicable to the optimization of transistor design, process control and the improvement of VBIC compact model, especially for SiGe HBTs.