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Abstract Background This study aimed to explore the prevalence and related risk factors of sarcopenia in patients on maintenance hemodialysis (MHD). Methods This cohort study enrolled 165 patients on MHD. The patients were divided into sarcopenia and non-sarcopenia groups based on the presence of sarcopenia or not. Sarcopenia was diagnosed according to the consensus of the Asian Sarcopenia Working Group that considers reduced muscle mass and decreased muscle strength (19). The muscle mass was measured using the multi-frequency bioelectrical impedance (Inbody260) and skeletal muscle index (SMI) was used:

Abstract Background Sodium-glucose cotransporter 2 (SGLT2) inhibition is recognized for its evident renoprotective benefits in diabetic renal disease. Recent data suggest that SGLT2 inhibition also slows down kidney disease progression and reduces the risk of acute kidney injury, regardless of whether the patient has diabetes or not, but the mechanism behind these observed effects remains elusive. The objective of this study is to utilize a mendelian randomization (MR) methodology to comprehensively examine the influence of metabolites in circulation regarding the impact of SGLT2 inhibition on kidney function. Methods We used a MR study to obtain associations between genetic proxies for SGLT2 inhibition and kidney function. We retrieved the most recent and comprehensive summary statistics from genome-wide association studies (GWAS) that have been previously published and involved kidney function parameters such as estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (UACR), and albuminuria. Additionally, we included blood metabolite data from 249 biomarkers in the UK Biobank for a more comprehensive analysis. We performed MR analyses to explore the causal relationships between SGLT2 inhibition and kidney function and two-step MR to discover potential mediating metabolites. Results The study found that a decrease in HbA1c levels by one standard deviation, which is genetically expected to result in SGLT2 inhibition, was linked to a decreased likelihood of developing type 2 diabetes mellitus (T2DM) (odds ratio [OR] = 0.55 [95% CI 0.35, 0.85], P = 0.007). Meanwhile, SGLT2 inhibition also protects eGFR (β = 0.05 [95% CI 0.03, 0.08], P = 2.45 × 10− 5) and decreased UACR (-0.18 [95% CI -0.33, -0.02], P = 0.025) and albuminuria (-1.07 [95% CI -1.58, -0.57], P = 3.60 × 10− 5). Furthermore, the study found that of the 249 metabolites present in the blood, only one metabolite, specifically the concentration of small high-density lipoprotein (HDL) particles, was significantly correlated with both SGLT2 inhibition and kidney function. This metabolite was found to play a crucial role in mediating the improvement of renal function through the use of SGLT2 inhibition (β = 0.01 [95% CI 0.005, 0.018], P = 0.001), with a mediated proportion of 13.33% (95% CI [5.71%, 26.67%], P = 0.020). Conclusions The findings of this investigation provide evidence in favor of a genetically anticipated biological linkage between the inhibition of SGLT2, the presence of circulating metabolites, and renal function. The findings demonstrate that the protective effect of SGLT2 inhibition on renal function is mostly mediated by HDL particle concentrations in circulating metabolites. These results offer significant theoretical support for both the preservation of renal function and a better comprehension of the mechanisms underlying SGLT2 inhibition.

Abstract Background Renal fibrosis is considered an irreversible pathological process and the ultimate common pathway for the development of all types of chronic kidney diseases and renal failure. Diosmin is a natural flavonoid glycoside that has antioxidant, anti-inflammatory, and antifibrotic activities. However, whether Diosmin protects kidneys by inhibiting renal fibrosis is unknown. We aimed to investigate the role of Diosmin in renal interstitial fibrosis and to explore the underlying mechanisms. Methods The UUO mouse model was established and gavaged with Diosmin (50 mg/kg·d and 100 mg/kg·d) for 14 days. HE staining, Masson staining, immunohistochemistry, western blotting and PCR were used to assess renal tissue injury and fibrosis. Elisa kits were used to detect the expression levels of IL-1β, IL-6, and TNF-α and the activity of SIRT3 in renal tissues. In addition, enrichment maps of RNA sequencing analyzed changes in signaling pathways. In vitro, human renal tubular epithelial cells (HK-2) were stimulated with TGF-β1 and then treated with diosmin (75 μM). The protein and mRNA expression levels of SIRT3 were detected in the cells. In addition, 3-TYP (selective inhibitor of SIRT3) and SIRT3 small interfering RNA (siRNA) were used to reduce SIRT3 levels in HK-2. Results Diosmin attenuated UUO-induced renal fibrosis and TGF-β1-induced HK-2 fibrosis. In addition, Diosmin reduced IL-1β, IL-6, and TNF-α levels in kidney tissues and supernatants of HK-2 medium. Interestingly, Diosmin administration increased the enzymatic activity of SIRT3 in UUO kidneys. In addition, Diosmin significantly increased mRNA and protein expression of SIRT3 in vitro and in vivo. Inhibition of SIRT3 expression using 3-TYP or SIRT3 siRNA abolished the anti-inflammatory effects of diosmin in HK-2 cells. Enrichment map analysis by RNA sequencing indicates that the nuclear factor-kappa B (NF-κB) signaling pathway was inhibited in the Diosmin intervention group. Furthermore, we found that TGF-β1 increased the nuclear expression of nuclear NF-κB p65 but had little significant effect on the total intracellular expression of NF-κB p65. Additionally, Diosmin reduced TGF-β1-caused NF-κB p65 nuclear translocation. Knockdown of SIRT3 expression by SIRT3 siRNA increased the nuclear expression of NF-κB p65 and abolished the inhibition effect of Diosmin in NF-κB p65 expression. Conclusions Diosmin reduces renal inflammation and fibrosis, which is contributed by inhibiting nuclear translocation of NF-κB P65 through activating SIRT3.

Introduction: Observational studies have shown that obesity is a risk factor for various autoimmune diseases. However, the causal relationship between obesity and autoimmune diseases is unclear. Mendelian randomization (MR) was used to investigate the causal effects of obesity on 15 autoimmune diseases. Methods: MR analysis employed instrumental variables, specifically single-nucleotide polymorphisms associated with obesity measures such as body mass index (BMI), waist circumference, hip circumference, and waist-to-hip ratio. The study utilized UK Biobank and FinnGen data to estimate the causal relationship between obesity and autoimmune diseases. Results: Genetically predicted BMI was associated with risk for five autoimmune diseases. The odds ratio per 1-SD increase in genetically predicted BMI, the OR was 1.28 (95% CI, 1.18–1.09; p < 0.001) for asthma, 1.37 (95% CI, 1.24–1.51; p < 0.001) for hypothyroidism, 1.52 (95% CI, 1.27–1.83; p < 0.001) for psoriasis, 1.22 (95% CI, 1.06–1.40; p = 0.005) for rheumatoid arthritis, and 1.55 (95% CI, 1.32–1.83; p < 0.001) for type 1 diabetes. However, after adjusting for genetic susceptibility to drinking and smoking, the correlation between BMI and rheumatoid arthritis was not statistically significant. Genetically predicted waist circumference, hip circumference, and waist and hip circumference were associated with 6, 6, and 1 autoimmune disease, respectively. Conclusion: This study suggests that obesity may be associated with an increased risk of several autoimmune diseases, such as asthma, hypothyroidism, psoriasis, rheumatoid arthritis, and type 1 diabetes.

AbstractBackground This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD).Methods This study collected data on CKD patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Six ML approaches were used to build the model. Accuracy and area under the curve (AUC) were used to choose the best model. In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values.Results There were 8527 CKD patients eligible for participation; the median age was 75.1 (interquartile range: 65.0–83.5) years, and 61.7% (5259/8527) were male. We developed six ML models with clinical variables as input factors. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.860. According to the SHAP values, the sequential organ failure assessment score, urine output, respiratory rate, and simplified acute physiology score II were the four most influential variables in the XGBoost model.Conclusions In conclusion, we successfully developed and validated ML models for predicting mortality in critically ill patients with CKD. Among all ML models, the XGBoost model is the most effective ML model that can help clinicians accurately manage and implement early interventions, which may reduce mortality in critically ill CKD patients with a high risk of death.

The importance of organizational citizenship behavior for the environment (OCBE) has received increasing attention in recent years because organizations face increasing pressure from environmental deterioration. The purpose of this study is to use social identity theory to construct a cross-level theoretical model of ethical leadership on OCBE, and to explore the cross-level influential mechanisms of ethical leadership on OCBE. Data collection was conducted via a two-wave distribution of leader-employee paired questionnaires in 20 manufacturing companies in China. In the first wave, data about OCBE and team environmental atmosphere were collected from leaders. Subsequently, 2 months later, we conducted the second wave of data collection when data about ethical leadership and leader identity were obtained from their employees. The results showed that at the individual level, ethical leadership has a significant positive impact on employees’ OCBE, and such relationship is partially mediated by employees’ leader identity and positively moderated by team environmental atmosphere across levels. At the team level, ethical leadership has a significant positive impact on employees’ OCBE, and such relationship is completely mediated by team environmental atmosphere. This study investigates the cross-level influential mechanism of ethical leadership on OCBE in China and provides theoretical guidance for enterprises to promote OCBE effectively.

Abstract Background Interstitial fibrosis is involved in the progression of various chronic kidney diseases and renal failure. Diosmin is a naturally occurring flavonoid glycoside that has antioxidant, anti-inflammatory, and antifibrotic activities. However, whether diosmin protects kidneys by inhibiting renal fibrosis is unknown. Methods The molecular formula of diosmin was obtained, targets related to diosmin and renal fibrosis were screened, and interactions among overlapping genes were analyzed. Overlapping genes were used for gene function and KEGG pathway enrichment analysis. TGF-β1 was used to induce fibrosis in HK-2 cells, and diosmin treatment was administered. The expression levels of relevant mRNA were then detected. Results Network analysis identified 295 potential target genes for diosmin, 6828 for renal fibrosis, and 150 hub genes. Protein–protein interaction network results showed that CASP3, SRC, ANXA5, MMP9, HSP90AA1, IGF1, RHOA, ESR1, EGFR, and CDC42 were identified as key therapeutic targets. GO analysis revealed that these key targets may be involved in the negative regulation of apoptosis and protein phosphorylation. KEGG indicated that pathways in cancer, MAPK signaling pathway, Ras signaling pathway, PI3K-Akt signaling pathway, and HIF-1 signaling pathway were key pathways for renal fibrosis treatment. Molecular docking results showed that CASP3, ANXA5, MMP9, and HSP90AA1 stably bind to diosmin. Diosmin treatment inhibited the protein and mRNA levels of CASP3, MMP9, ANXA5, and HSP90AA1. Network pharmacology analysis and experimental results suggest that diosmin ameliorates renal fibrosis by decreasing the expression of CASP3, ANXA5, MMP9, and HSP90AA1. Conclusions Diosmin has a potential multi-component, multi-target, and multi-pathway molecular mechanism of action in the treatment of renal fibrosis. CASP3, MMP9, ANXA5, and HSP90AA1 might be the most important direct targets of diosmin.

Abstract This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in patients with sepsis-associated acute kidney injury (SA-AKI). This study collected data on SA-AKI patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. After employing Lasso regression for feature selection, six ML approaches were used to build the model. The optimal model was chosen based on precision and area under curve (AUC). In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values and Local Interpretable Model-Agnostic Explanations (LIME) algorithms. There were 8129 sepsis patients eligible for participation; the median age was 68.7 (interquartile range: 57.2–79.6) years, and 57.9% (4708/8129) were male. After selection, 24 of the 44 clinical characteristics gathered after intensive care unit admission remained linked with prognosis and were utilized developing ML models. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.794. According to the SHAP values, the sequential organ failure assessment score, respiration, simplified acute physiology score II, and age were the four most influential variables in the XGBoost model. Individualized forecasts were clarified using the LIME algorithm. We built and verified ML models that excel in early mortality risk prediction in SA-AKI and the XGBoost model performed best.

Background: This study aimed to develop a nomogram for predicting gram-negative bacterial (GNB) infections in patients with peritoneal dialysis-associated peritonitis (PDAP) to identify patients at high risk for GNB infections. Methods: In this investigation, hospitalization information was gathered retrospectively for patients with PDAP from January 2016 to December 2021. The concatenation of potential biomarkers obtained by univariate logistic regression, LASSO analysis, and RF algorithms into multivariate logistic regression was used to identify confounding factors related to GNB infections, which were then integrated into the nomogram. The concordance index (C-Index) was utilized to assess the precision of the model's predictions. The area under the curve (AUC) and decision curve analysis (DCA) was used to assess the predictive performance and clinical utility of the nomogram. Results: The final study population included 217 patients with PDAP, and 37 (17.1%) patients had gram-negative bacteria due to dialysate effluent culture. After multivariate logistic regression, age, procalcitonin, and hemoglobin were predictive factors of GNB infections. The C-index and bootstrap-corrected index of the nomogram for estimating GNB infections in patients were 0.821 and 0.814, respectively. The calibration plots showed good agreement between the predictions of the nomogram and the actual observation of GNB infections. The AUC of the receiver operating characteristic curve was 0.821, 95% CI: 0.747–0.896, which indicates that the model has good predictive accuracy. In addition, the DCA curve showed that the nomogram had a high clinical value in the range of 1%–94%, which further demonstrated that the nomogram could accurately predict GNB infection in patients with PDAP. Conclusions: We have created a new nomogram for predicting GNB infections in patients with PDAP. The nomogram model may improve the identification of GNB infections in patients with PDAP and contribute to timely intervention to improve patient prognosis.

Abstract In order to solve the problem of traffic congestion in a certain area, this paper develops a set of traffic optimization decision system. For analyzing the actual traffic conditions and calculating the traffic volume, density and traffic speed, a traffic prediction model is established and updated iteratively to modify the prediction model parameters. Based on this model, the congestion degree is estimated at the current road section, thus, an intelligent decision-making and the coordinated optimization methods are proposed. Moreover, this paper implements some application experiments on the isometric road of a three-intersection and obtains better prediction results of traffic density and traffic speed on the three-section highway. At the same time, compared with other existing prediction methods, the prediction model presented in this paper not only has higher accuracy, shorter prediction time and stronger anti-interference ability, but also has better effect on vehicle diversion. In addition, it also greatly relieves the traffic pressure on the road, maximizes the complementary advantages between intersections, and balances the good cooperation between each intersection.

Natural products, and especially the active ingredients found in traditional Chinese medicine (TCM), have a thousand-year-long history of clinical use and a strong theoretical basis in TCM. As such, traditional remedies provide shortcuts for the development of original new drugs in China, and increasing numbers of natural products are showing great therapeutic potential in various diseases. This paper reviews the molecular mechanisms of action of natural products from different sources used in the treatment of inflammatory diseases and cancer, introduces the methods and newly emerging technologies used to identify and validate the targets of natural active ingredients, enumerates the expansive list of TCM used to treat inflammatory diseases and cancer, and summarizes the patterns of action of emerging technologies such as single-cell multiomics, network pharmacology, and artificial intelligence in the pharmacological studies of natural products to provide insights for the development of innovative natural product-based drugs. Our hope is that we can make use of advances in target identification and single-cell multiomics to obtain a deeper understanding of actions of mechanisms of natural products that will allow innovation and revitalization of TCM and its swift industrialization and internationalization.

Background The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD).Methods After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm.Results This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions.Conclusions In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed.

Current treatments cannot significantly alleviate the radiographic progression in ankylosing spondylitis (AS), which results in joints stiffness and bony fusion of AS. Smo inhibitor sonidegib retards the pathological new bone formation in AS through targeting dysfunctional chondrogenesis.

Bufotenines, a natural component from toad venom, showed great potential for development as a novel anti-inflammation and analgesia agent, but the potential toxicity limited its clinic use. In this paper, bufotenines-loaded liposome was prepared and optimized. Then, the therapeutic effects and drug safety of bufotenines-liposome were investigated against inflammation and pain on animal models, with a focus on gastrointestinal toxicity. Bufotenines and its liposome significantly increased paw withdrawal mechanical threshold (PWMT) in Von Frey test and hot paw withdrawal latency (HPWL) in hot-plate test. Moreover, intestinal absorption in vitro and pathological analysis in vivo showed that total bufotenines-loaded liposome significantly reduced the gastrointestinal irritation through reducing exposure of total bufotenines on intestinal tissue. High-sensitivity lipidomics analysis revealed the effect of total bufotenines-loaded liposome were be related to the down-regulation of inflammatory mediators from cyclooxygenase (COX) and lipoxygenase (LOX), the up-regulation of cytochrome P450 (CYP450), and other pathways, thus regulating lipid metabolism pathway and ultimately reducing gastrointestinal irritation. This study shows that liposome-loaded bufotenines has anti-inflammatory, analgesic effects and achieves toxicity reduction. These results provide systematic evidences for efficacy and safety of toad venom active ingredients.

Metabolic syndrome is a clustering of metabolic disorder with unclear molecular mechanism. Increasing studies have found that the pathogenesis and progression of metabolic syndrome are closely related to inflammation. Here, we report celastrol, a traditional Chinese medicine, can improve high fat diet-induced metabolic syndrome through suppressing resistin-induced inflammation. Mechanistically, celastrol binds to adenylyl cyclase associated protein 1 (CAP1) and inhibits the interaction between CAP1 and resistin, which restrains the cyclic adenylate monophosphate (cAMP)–protein kinase A (PKA)–nuclear factor kappa-B (NF-κB) signaling pathway and ameliorates high fat diet-induced murine metabolic syndrome. Knockdown of CAP1 in macrophages abrogated the resistin-mediated inflammatory activity. In contrast, overexpression of CAP1 in macrophages aggravated inflammation. Taken together, our study identifies celastrol, which directly targets CAP1 in macrophages, might be a promising drug candidate for the treatment of inflammatory metabolic diseases, such as metabolic syndrome.

Aim Previous studies have shown that the fibrinogen to albumin ratio (FAR) is closely related to the severity and prognosis of coronary atherosclerosis. In this study, we sought to evaluate the association between FAR and the degree of coronary artery calcification (CAC) in patients with chronic kidney disease (CKD). Methods In this retrospective study, 218 patients with CKD were stratified into low, medium and high FAR groups according to the tertiles of the FAR values. The CAC scores, clinical information and laboratory test results of the three FAR groups were compared. To explore the relationship between FAR and CAC we conducted binary logistic regression and correlation analyses. Results In the low FAR group, the CAC scores were significantly lower than those in the medium and high FAR groups (P

As the hydrocarbon vertical migration pathway, fault and its relationship with reservoir extremely affect the distribution and accumulation of oil and gas in the non-hydrocarbon generating layers. On both sides of the fault, there are frequently various sand bodies with different oil and gas enrichment. However, previous researches on this differential charging are mostly based on empirical formulas or physical simulation experiments instead of fluid seepage mechanism. This paper proposed a quantitative evaluation method on the basis of Darcy's law to analyze the difference in hydrocarbon lateral diversion capacity through the oil-source fault. It can be seen that in the process of hydrocarbon migrate along the oil-source fault, hydrocarbons could divert laterally into sand bodies through the fault when there is a downward pressure gradient. Conversely, hydrocarbons can only migrate upward along the fault. Furthermore, among the four normal fault-sand body configuration types, the opposite fastigium type is most favorable for hydrocarbons laterally charging whereas the fastigium type is the least beneficial. The ratio of resistance in the sand body to in the fault was proposed as the evaluation index ( F s ) to quantitatively evaluate the hydrocarbon lateral diversion migration capacity, on the assumption that hydrocarbons in different sand bodies are sourced from the same source rocks. The index ( F s ) is directly proportional to the lateral diversion capacity of hydrocarbon, and is complex controlled by many geological factors including the reservoir permeability, fault-sand body contact length and dip angles of sand body and fault. And the higher the value of F s, the stronger the hydrocarbons laterally divert ability of the sand body. In addition, the calculation result of well GG16102 in the Beidagang buried hill coincide well with the oil and gas interpretation result indicating that the method is practical to quantitatively evaluate the laterally diverting hydrocarbons of sand bodies under the condition of fault-sandstone configuration.

Summary: SHP2 is the first oncogenic tyrosine phosphatase encoded by PTPN11, which plays a significant regulatory role in cancer and inflammation-related diseases. Although SHP2 allosteric inhibitors have been used in phase I/II clinical trials for solid tumors, whether SHP2 inhibition alleviates psoriasis remains unclear. Here we expressed and purified SHP2 related proteins, and established an enzyme activity screening system for different conformations of SHP2. We launched an iterative medicinal chemistry program and identified the lead compound, TK-453. Importantly, TK-453 possessed stronger affinity with SHP2 than SHP099, evidenced by the cocrystal structure of SHP2/TK-453, revealing that the additional aryl-S-aryl bridge in TK-453 induces a 1.8 Å shift of the dichlorophenyl ring and an approximate 20° deviation of the pyrazine ring plane relative to SHP099. Furthermore, TK-453 significantly ameliorated imiquimod-triggered skin inflammation in mice via inhibition of the IL-23/Th17 axis, proving that SHP2 is a potential therapeutic target for psoriasis.

Abstract Psoriasis is a complex chronic inflammatory skin disease with unclear molecular mechanisms. We found that the Src homology‐2 domain‐containing protein tyrosine phosphatase‐2 (SHP2) was highly expressed in both psoriatic patients and imiquimod (IMQ)‐induced psoriasis‐like mice. Also, the SHP2 allosteric inhibitor SHP099 reduced pro‐inflammatory cytokine expression in PBMCs taken from psoriatic patients. Consistently, SHP099 significantly ameliorated IMQ‐triggered skin inflammation in mice. Single‐cell RNA sequencing of murine skin demonstrated that SHP2 inhibition impaired skin inflammation in myeloid cells, especially macrophages. Furthermore, IMQ‐induced psoriasis‐like skin inflammation was significantly alleviated in myeloid cells (monocytes, mature macrophages, and granulocytes)—but not dendritic cells conditional SHP2 knockout mice. Mechanistically, SHP2 promoted the trafficking of toll‐like receptor 7 (TLR7) from the Golgi to the endosome in macrophages by dephosphorylating TLR7 at Tyr1024, boosting the ubiquitination of TLR7 and NF‐κB‐mediated skin inflammation. Importantly, Tlr7 point‐mutant knock‐in mice showed an attenuated psoriasis‐like phenotype compared to wild‐type littermates following IMQ treatment. Collectively, our findings identify SHP2 as a novel regulator of psoriasis and suggest that SHP2 inhibition may be a promising therapeutic approach for psoriatic patients.

Abstract Psoriasis is a chronic inflammatory skin disease, often accompanied by increased infiltration of immune cells, especially neutrophils. However, the detailed mechanism of the neutrophil function in psoriasis progression remains unclear. Here, we found that both Src homology‐2 domain‐containing protein tyrosine phosphatase‐2 (SHP2) and neutrophils were highly correlated to developing psoriasis by single‐cell ribonucleic acid (RNA) sequencing and experiment verification. The deficiency of SHP2 in neutrophils significantly alleviated psoriasis‐like phenotype in an imiquimod‐induced murine model. Interestingly, high levels of neutrophil extracellular traps (NETs) were produced in the inflamed lesions of psoriatic patients. In addition, imiquimod‐induced psoriasis‐like symptoms were remarkably ameliorated in peptidyl arginine deiminase 4 (PAD4) knockout mice, which cannot form NETs. Mechanistically, RNA‐seq analysis revealed that SHP2 promoted the formation of NETs in neutrophils via the ERK5 pathway. Functionally, this mechanism resulted in the infiltration of pro‐inflammatory cytokines such as TNF‐α, IL‐1β, IL‐6, IL‐17A, and CXCL‐15, which enhances the inflammatory response in skin lesions and reinforces the cross‐talk between neutrophils and keratinocytes, ultimately aggravating psoriasis. Our findings uncover a role for SHP2 in NET release and subsequent cell death known as NETosis in the progression of psoriasis and suggest that SHP2 may be a promising therapeutic target for psoriasis.

In order to meet the informatization requirements of coal mine safety monitoring, the author proposes a method for the application of smart sensors in the acquisition of mine electrical equipment. The system uses a variety of sensor fusion methods, with the help of Zigbee wireless network nodes, and passes the data collected by the sensor to the MCU core processor; thus, the collected data are processed, and then, the RS-485 communication protocol is used to upload the data to the upper station; finally, the monitoring of coal mine safety is realized through the background monitoring interface. Experimental results show that, among the five randomly selected nodes, most of the errors between the actual measured results and the collected results are concentrated within the 2% error range. Conclusion. The effect of the abovementioned acquisition scheme in coal mine application is verified, so as to realize the scientific monitoring of coal mine safety.

This paper studies the electromagnetic properties of defected ground structure (DGS) with von Koch snowflake fractals. First, the snowflake fractal is employed in the dumbbell shaped DGS, and we have discovered that it has the characteristics of a low-pass filter and the out-of-band rejection increases with fractal iterations. In consideration of fabrication accuracy and performance, the third iteration has been adopted. Based on this structure, we have designed a narrow-band filter and a wide-band filter to verify our theory. From the simulated and measured results, we can see that the out-of-band rejection level has been greatly improved. The proposed structure can be applied in communication planar circuits.

Tyrosine phosphatase SHP2 is a promising drug target in cancer immunotherapy due to its bidirectional role in both tumor growth promotion and T-cell inactivation. Its allosteric inhibitor SHP099 is known to inhibit cancer cell growth both in vitro and in vivo. However, whether SHP099-mediated SHP2 inhibition retards tumor growth in vivo via anti-tumor immunity remains elusive. To address this, a CT-26 colon cancer xenograft model was established in mice since this cell line is insensitive to SHP099. Consequently, SHP099 minimally affected CT-26 tumor growth in immuno-deficient nude mice, but significantly decreased the tumor burden in CT-26 tumor-bearing mice with intact immune system. SHP099 augmented anti-tumor immunity, as shown by the elevated proportion of CD8+IFN-γ+ T cells and the upregulation of cytotoxic T-cell related genes including Granzyme B andPerforin, which decreased the tumor load. In addition, tumor growth in mice with SHP2-deficient T-cells was markedly slowed down because of enhanced anti-tumor responses. Finally, the combination of SHP099 and anti-PD-1 antibody showed a higher therapeutic efficacy than either monotherapy in controlling tumor growth in two colon cancer xenograft models, indicating that these agents complement each other. Our study suggests that SHP2 inhibitor SHP099 is a promising candidate drug for cancer immunotherapy. KEY WORDS: Cancer immunotherapy, SHP2, SHP099, PD-1, Colon cancer