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Abstract Background This study aimed to create a method for promptly predicting acute kidney injury (AKI) in intensive care patients by applying interpretable, explainable artificial intelligence techniques. Methods Population data regarding intensive care patients were derived from the Medical Information Mart for Intensive Care IV database from 2008 to 2019. Machine learning (ML) techniques with six methods were created to construct the predicted models for AKI. The performance of each ML model was evaluated by comparing the areas under the curve (AUC). Local Interpretable Model-Agnostic Explanations (LIME) method and Shapley Additive exPlanation values were used to decipher the best model. Results According to inclusion and exclusion criteria, 53,150 severely sick individuals were included in the present study, of which 42,520 (80%) were assigned to the training group, and 10,630 (20%) were allocated to the validation group. Compared to the other five ML models, the eXtreme Gradient Boosting (XGBoost) model greatly predicted AKI following ICU admission, with an AUC of 0.816. The top four contributing variables of the XGBoost model were SOFA score, weight, mechanical ventilation, and the Simplified Acute Physiology Score II. An AKI and Non-AKI cases were predicted separately using the LIME algorithm. Conclusion Overall, the constructed clinical feature-based ML models are excellent in predicting AKI in intensive care patients. It would be constructive for physicians to provide early support and timely intervention measures to intensive care patients at risk of AKI.

Abstract Background The purpose of this study was to develop a nomogram for predicting in-hospital mortality in cirrhotic patients with acute kidney injury (AKI) in order to identify patients with a high risk of in-hospital death early. Methods This study collected data on cirrhotic patients with AKI from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Multivariate logistic regression was used to identify confounding factors related to in-hospital mortality, which were then integrated into the nomogram. The concordance index (C-Index) was used to evaluate the accuracy of the model 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 886 cirrhotic patients with AKI, and 264 (29.8%) died in the hospital. After multivariate logistic regression, age, gender, cerebrovascular disease, heart rate, respiration rate, temperature, oxygen saturation, hemoglobin, blood urea nitrogen, serum creatinine, international normalized ratio, bilirubin, urine volume, and sequential organ failure assessment score were predictive factors of in-hospital mortality. In addition, the nomogram showed good accuracy in estimating the in-hospital mortality of patients. The calibration plots showed the best agreement with the actual presence of in-hospital mortality in patients. In addition, the AUC and DCA curves showed that the nomogram has good prediction accuracy and clinical value. Conclusions We have created a prognostic nomogram for predicting in-hospital death in cirrhotic patients with AKI, which may facilitate timely intervention to improve prognosis in these patients.

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.

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.

This study conducted data on 15,446 adults to explore the impact of flavonoids on weight-adjusted waist index (WWI). This was a nationwide cross-sectional study among US adults aged 20 years or older. Dietary intake of flavonoids was assessed through 24-h recall questionnaire. WWI was calculated by dividing waist circumference (WC) by the square root of weight. We utilized weighted generalized linear regression to evaluate the association between flavonoids intake and WWI, and restricted cubic splines (RCS) to explore potential non-linear relationships. Our findings indicated that individuals with lower WWI experienced a notable increase in their consumption of total flavonoids, flavanones, flavones, flavan-3-ols, and anthocyanidins intake (β (95% CI); −0.05(−0.09, −0.01); −0.07(−0.13, 0.00); −0.07(−0.11, −0.02); −0.06(−0.11, 0.00); −0.13(−0.18, −0.08), respectively), with the exception of flavonols and isoflavones. Additionally, consumption of total flavonoids, flavonols, flavanones, isoflavones, and flavan-3-ols had a non-linear relationship with WWI (all P for non-linearity

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.

AbstractBackground The effects of serum uric acid (SUA) on clinical outcomes in patients with acute kidney injury (AKI) are unclear. The aim of this study was to investigate the association of SUA levels with clinical outcomes of AKI patients.Methods The data of AKI patients hospitalized in the Affiliated Hospital of Qingdao University were retrospectively reviewed. Multivariable logistic regression was utilized to assess the association between SUA levels and the clinical outcomes of AKI patients. Receiver operating characteristic (ROC) analysis was applied to assess the predictive ability of SUA levels for in-hospital mortality in patients with AKI.Results A total of 4,646 AKI patients were eligible for study inclusion. In multivariable analysis, after adjustment for various confounding factors in the fully adjusted model, a higher SUA level was found to be associated with increased in-hospital mortality of AKI patients with an odds ratio (OR) of 1.72 (95% CI, 1.21–2.33, p = 0.005) for the SUA level >5.1–6.9 mg/dl group and 2.75 (95% CI, 1.78–4.26, p 6.9 mg/dl group compared with the reference group (SUA ≤3.6 mg/dl). In the ROC analysis, the area under the curve (AUC) of SUA was 0.65 with a sensitivity of 51% and a specificity of 73%.Conclusions An elevated SUA level is associated with an increased risk of in-hospital mortality in patients with AKI, and it appears to be an independent prognostic marker for these patients.

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.

Transmissible gastroenteritis (TGE), cause by transmissible gastroenteritis virus (TGEV), is an acute digestive and highly infectious disease in piglets. In this study, porcine interleukin-12 immunological adjuvant combined with a DNA vaccine bearing the TGEV-S1 gene was used to immunize piglets, and assessed the immune response of piglets. The results showed that CD4+ and CD8+CD28+ T lymphocytes were significantly increased in immunized piglets compared to control groups. In addition, the observed increase of IFN-γ suggested that T helper cells tended to convert to Th1 cells during immune response. HE stain and indirect immunofluorescence assays indicated obvious differences between the immunized piglets and the controls, suggesting that immunized piglets suffered less pathological changes relative to piglets in the control groups. Moreover, the levels of anti-TGEV and neutralizing antibodies in serum also indicated an effective immune response in the immunized piglets by which the pathogen was rapidly controlled. Altogether, our results suggest that the combination of pVAX1-(pIL-12) and pVAX1-(TGEV-S1) in a vaccine is capable of producing a significantly enhanced immune response in piglets.

Abstract Background Lipid rafts are major structural components in plasma membranes that play critical roles in many biological processes including virus infection. However, few reports have described the relationship between lipid rafts and porcine rotavirus (PRV) infection. In this study, we investigated whether or not the locally high concentrations (3–5 fold) of cholesterol present in lipid rafts are required for PRV infection, and further examined which stages of the infection process are most affected. Results When cellular cholesterol was depleted by methyl-β-cyclodextrin (MβCD), PRV infectivity significantly declined in a dose-dependent manner. This inhibition was partially reversed upon reintroduction of cholesterol into the system. This was corroborated by the co-localization of PRV with a recombinant, GPI-anchored green fluorescent protein, which functioned as a marker for membranous regions high in cholesterol and indicative of lipid rafts. Changes in virus titer and western blot analyses indicated that depletion of cellular cholesterol with MβCD had no apparent effect on PRV adsorption; however, depletion of cholesterol significantly restricted entry and post-entry of PRV into the cell. Both points of inhibition were restored to near normal levels by the addition of exogenous cholesterol. Conclusions We conclude from these studies that membrane-based cholesterol and in particular that localized to lipid rafts, is an indispensable biomolecule for PRV infection, and that cholesterol-based control of the infection process takes place during entry and immediately post-entry into the cell.

Hypericum perforatum L., also known as Saint John’s Wort, has been well studied for its chemical composition and pharmacological activity. In this study, the antiviral activities of H. perforatum on infectious bronchitis virus (IBV) were evaluated in vitro and in vivo for the first time. The results of in vitro experiments confirmed that the antiviral component of H. perforatum was ethyl acetate extraction section (HPE), and results showed that treatment with HPE significantly reduced the relative messenger ribonucleic acid (mRNA) expression and virus titer of IBV, and reduced positive green immunofluorescence signal of IBV in chicken embryo kidney (CEK) cells. HPE treatment at doses of 480–120 mg/kg for 5 days, reduced IBV induced injury in the trachea and kidney, moreover, reduced the mRNA expression level of IBV in the trachea and kidney in vivo. The mRNA expression levels of IL-6, tumor necrosis factor alpha (TNF-α), and nuclear factor kappa beta (NF-κB) significantly decreased, but melanoma differentiation-associated protein 5 (MDA5), mitochondrial antiviral signaling gene, interferon alpha (IFN-α), and interferon beta (IFN-β) mRNA levels significantly increased in vitro and in vivo. Our findings demonstrated that HPE had significant anti-IBV effects in vitro and in vivo, respectively. In addition, it is possible owing to up-regulate mRNA expression of type I interferon through the MDA5 signaling pathway and down-regulate mRNA expression of IL-6 and TNF-α via the NF-κB signaling pathway. Moreover, the mainly active compositions of HPE analyzed by high-performance liquid chromatography/electrospray ionization–mass spectroscopy (ESI-MS) are hyperoside, quercitrin, quercetin, pseudohypericin, and hypericin, and a combination of these compounds could mediate the antiviral activities. This might accelerate our understanding of the antiviral effect of H. perforatum and provide new insights into the development of effective therapeutic strategies.

Porcine transmissible gastroenteritis virus (TGEV) and porcine epidemic diarrhea virus (PDEV) can cause severe diarrhea in pigs. Development of effective vaccines against TGEV and PEDV is one of important prevention measures. The spike (S) protein is the surface glycoprotein of TGEV and PEDV, which can induce specific neutralization antibodies and is a candidate antigen for vaccination attempts. In this study, the open reading frames of the TGEV S1 protein and in addition of the S or S1 proteins of PEDV were inserted into the eukaryotic expression vector, pIRES, resulting in recombinant plasmids, pIRES-(TGEV-S1-PEDV-S1) and pIRES-(TGEV-S1-PEDV-S). Subsequently, 6-8 weeks old Kunming mice were inoculated with both DNA plasmids. Lymphocyte proliferation assay, virus neutralization assay, IFN-γ assay and CTL activity assay were performed. TGEV/PEDV specific antibody responses as well as kinetic changes of T lymphocyte subgroups of the immunized mice were analyzed. The results showed that the recombinant DNA plasmids increased the proliferation of T lymphocytes and the number of CD4+ and CD8+ T lymphocyte subgroups. In addition, the DNA vaccines induced a high level of IFN-γ in the immunized mice. The specific CTL activity in the pIRES-(TGEV-S1-PEDV-S) group became significant at 42 days post-immunization. At 35 days post-immunization, the recombinant DNA plasmids bearing full-length S genes of TGEV and PEDV stimulated higher levels of specific antibodies and neutralizing antibodies in immunized mice.

Transmissible gastroenteritis virus (TGEV) is a porcine coronavirus. Lithium chloride (LiCl) has been found to be effective against several DNA viruses, such as Herpes simplex virus and vaccinia virus. Recently, we and others have reported the inhibitory effect of LiCl on avian infectious bronchitis coronavirus (IBV) infection, an RNA virus. In the current study, the action mechanism of LiCl on cell infection by TGEV was investigated. Plaque assays and 3-(4,5)-dimethylthiahiazo(-z-y1)-3,5-di-phenyl tetrazoliumbromide (MTT) assays showed that the cell infection by TGEV was inhibited in a dose-dependent manner, when LiCl was added to virus-infected cells; the cell infection was not affected when either cells or viruses were pretreated with the drug. The inhibition of TGEV infection in vitro by LiCl was observed at different virus doses and with different cell lines. The inhibitory effect of LiCl against TGEV infection and transcription was confirmed by RT-PCR and real-time PCR targeting viral S and 3CL-protease genes. The time-of-addition effect of the drug on TGEV infection indicated that LiCl acted on the initial and late stage of TGEV infection. The production of virus was not detected at 36 h post-infection due to the drug treatment. Moreover, immunofluorescence (IF) and flow cytometry analyses based on staining of Annexin V and propidium iodide staining of nuclei indicated that early and late cell apoptosis induced by TGEV was inhibited efficiently. The ability of LiCl to inhibit apoptosis was investigated by IF analysis of caspase-3 expression. Our data indicate that LiCl inhibits TGEV infection by exerting an anti-apoptotic effect. The inhibitory effect of LiCl was also observed with porcine epidemic diarrhea coronavirus. Together with other reports concerning the inhibitory effect of lithium salts on IBV in cell culture, our results indicate that LiCl may be a potent agent against porcine and avian coronaviruses.

Cyst nematodes co-opt plant developmental programs for the establishment of a permanent feeding site called a syncytium in plant roots. In recent years, the role of plant developmental genes in syncytium formation has gained much attention. One main obstacle in studying the function of development-related genes in syncytium formation is that mutation or ectopic expression of such genes can cause pleiotropic phenotypes, making it difficult to interpret nematode-related phenotypes or, in some cases, impossible to carry out infection assays due to aberrant root development. Here, we tested three commonly used inducible gene expression systems for their application in beet cyst nematode infection assays of the model plant Arabidopsis thaliana. We found that even a low amount of ethanol diminished nematode development, deeming the ethanol-based system unsuitable for use in cyst nematode infection assays, whereas treatment with estradiol or dexamethasone did not negatively affect cyst nematode viability. Dose and time course responses showed that in both systems, a relatively low dose of inducer (1 μM) is sufficient to induce high transgene expression within 24 h of treatment. Transgene expression peaked at 3 to 5 days post-induction and began to decline thereafter, providing a perfect window for inducible transgenes to interfere with syncytium establishment while minimizing any adverse effects on root development. These results indicate that both estradiol- and dexamethasone-based inducible gene expression systems are suitable for cyst nematode infection assays. The employment of such systems provides a powerful tool to investigate the function of essential plant developmental genes in syncytium formation. [Figure: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Cyst nematodes use a stylet to secrete CLE-like peptide effector mimics into selected root cells of their host plants to hijack endogenous plant CLE signaling pathways for feeding site (syncytium) formation. Here, we identified ATHB8, encoding a HD-ZIP III family transcription factor, as a downstream component of the CLE signaling pathway in syncytium formation. ATHB8 is expressed in the early stages of syncytium initiation, and then transitions to neighboring cells of the syncytium as it expands; an expression pattern coincident with auxin response at the infection site. Conversely, MIR165a, which expresses in endodermal cells and moves into the vasculature to suppress HD-ZIP III TFs, is down-regulated near the infection site. Knocking down HD-ZIP III TFs by inducible over-expression of MIR165a in Arabidopsis dramatically reduced female development of the sugar beet cyst nematode (Heterodera schachtii). HD-ZIP III TFs are known to function downstream of auxin to promote cellular quiescence and define stem cell organizer cells in vascular patterning. Taken together, our results suggest that HD-ZIP III TFs function together with a CLE and auxin signaling network to promote syncytium formation, possibly by inducing root cells into a quiescent status and priming them for initial syncytial cell establishment and/or subsequent cellular incorporation.

This paper establishes a finite element model that includes the interface roughness characteristics to evaluate the stress concentration in the atmospheric plasma sprayed (APS) thermal barrier coatings (TBCs) with an uneven temperature field. We further scrutinize the effects of crack initiation at the interface between the thermally grown oxide (TGO) and the bond coat (BC) and in the ceramic top-coat (TC) on stress redistribution by introducing the debonding model for crack analysis. Results indicate that the interfacial residual stress σ22 achieves the critical value at the end of the cooling stage. As the temperature drop intensifies, the σ22 declines at the interface while it escalates within the TC. However, the interfacial crack propagation results in the redistribution of stress in the TBCs, and the σ22 changes from tensile stress to compressive stress in the peak of the TC. The propagation rate of the interfacial crack accelerates with a smaller temperature difference and a thicker initial TGO. When the TGO/BC interfacial morphology is uniform, the crack growth rate is the most rapid. The stress redistribution leads to an off-peak tensile stress concentration area in the TC and increased maximum tensile stress near the horizontal micro-crack tip. The concentration area of σ11 occurs in the TC above the peak of the interface, indicating that the vertical crack in the TC is likely to initiate above the peak.

Dissolved organic carbon (DOC) is indicative of water quality in drinking source water and has an impact on drinking water treatment. Exploring occurrence of DOC in drinking source water is of great significance for the drinking water safety in Beijing. This study elucidated spatial-temporal DOC dynamics in the Miyun section of Chaobai River in Beijing of China and identified sources of DOC based on DOC fluorescent spectral characteristics. Results showed that the average riverine DOC concentration was 30.60 mg/L. DOC concentrations in the Miyun section of the Chaobai River were 30.40, 30.24, and 31.09 mg/L in spring, summer and autumn, respectively. DOC concentrations at the three segments of the Miyun section of the Chaobai River (the Chaohe River, the Baihe River and the Chaobai River) were 29.93, 30.29 and 32.57 mg/L, respectively. Land use contributed to DOC variations rather than season and river segment. Significant increases in DOC concentration were observed in river water flowing through farmland and urban areas, and DOC concentration presented the highest after flowing through the farmland area. The results of FI, HIX and BIX showed that DOC mainly came from endogenous sources such as aquatic biological activities, and was less affected by terrestrial inputs. Special attentions ought to be paid on prevention and control of endogenous DOC inputs.

The cellular automata-lattice Boltzmann method is used to simulate the dendritic growth process of aluminum alloys under the action of hypergravity by performing coupling heat and mass transfer, solidification and flow. The dendrite arm spacing, growth rate, and dendrite morphology vary greatly with the size and direction of hypergravity, and solute segregation occurs. Compared with the gravity of the earth (1 g), hypergravity strongly strengthens the buoyancy-driven flow and considerably affects the morphology of the solidified grain. The dendritic growth rate is also accelerating. According to the direction of hypergravity in relation to the dendritic growth direction, there exist different flow states that show stable or unstable dendritic growth dynamics. For columnar crystal growth, when the hypergravity and growth direction are identical, the dendrite tip undergoes downward melt flow, and the dendrite grows in a stable manner. When the hypergravity and the growth direction are opposite, the dendrite tip undergoes upward melt flow, the dendrite grows in an unstable manner, and the primary dendrite spacing decreases. For the growth of equiaxed crystals, the convection induced by hypergravity causes the equiaxed crystals to be asymmetric, and the solute segregates in the direction of gravity. Channel segregation occurs in the mushy zone in the presence of equiaxed crystal chains.

Ternary cathode materials hold great promise in the lithium battery market, yet analyses regarding their calcination process in simulation and on-site experimentation, are few. In this paper, a multiphysics-coupled computational fluid dynamics (CFD) model was developed to simulate the primary calcination process (The actual production process is divided into two calcinations, because the production conditions affect the measurement, this paper focuses on the analysis of the primary calcination process) of lithium battery raw materials (namely, Ni0.8Co0.1Mn0.1(OH)2 and LiOH∙H2O mixture, hereinafter referred to as raw materials) under oxidizing atmosphere conditions. The process involves fluid flow, heat and mass transfer, and chemical reactions. A new method which combined steady-state calculation with transient calculation was adopted. Firstly, the steady-state simulation of the whole furnace was carried out, and then the transient simulation of each furnace area is carried out consequently, the heat and mass transfer processes of raw materials in different furnace zones were solved, ultimately achieving the simulation of the entire furnace calcination process. On-site black box experiments were performed to obtain the temperature evolution during the calcination process. Meanwhile, compared with the calculated results of the model, it was found that the average hit rate with an absolute error of less than ±20 °C above 300 °C can reach 86.9%, confirming the applicability of the model. The multiphysics-coupled CFD model simultaneously solves the oxygen concentration. The process parameters were analyzed based on the model, providing a fundamental method for the improvement of the performance of lithium battery cathode materials calcination process.

Annealing is necessary to reduce the residual stress and no-load loss of 3D coil core. In production, because monitoring the changes in temperature and stress of 3D coil core in real time is impossible, the trial and error method is usually used to formulate the annealing process parameters. The annealing simulation experiment of grain-oriented electrical steel (GOES) is carried out to explore the influence of soaking time on iron loss and magnetic flux density. The mechanical properties of GOES are tested, and the true stress–strain curves at different temperatures are obtained. Based on the existing research and experimental results, an anisotropic thermal mechanical coupling model of 3D coil core is established. An optimization scheme of 3D coil core annealing process is obtained, considering the temperature difference, stress and strain. According to the onsite measured parameters, the annealing process is simulated and optimized by the coupling model. Results show that the total annealing time is shortened by about 18.7% without increasing the stress and strain.

The influence of the thermal conductivity of silica bricks on the coking process as an important component of the coking and combustion chambers is still unclear. A multi-chamber coupling mathematical model of a coke oven is developed to investigate the energy-saving effect of using high thermal conductivity silica bricks in the coke oven. The model is validated using measured temperatures of coal and flue gas bed in a coke oven in operation for a coking cycle. Temperature variation and its distribution in the coking chamber, combustion chamber, and heating wall are simulated for the coke oven using silica bricks with different thermal conductivities. The obtained results showed that compared with the use of common silica bricks, coking time of one cycle can be reduced by ∼60 min, with a decreased consumption of fuel gas by ∼4.9%. The enhancement of heat transfer from the flue gas to the heating wall and the heating wall to coal using silicon bricks with high thermal conductivity can effectively shorten not only the temperature difference between the two sides of the heating wall but also reduce the mean flue temperature of the flue gas by ∼24.5 °C.

Transmissible gastroenteritis (TGE), caused by transmissible gastroenteritis virus (TGEV), is a highly infectious disease in pigs. Vaccination is an effective approach to prevent TGEV infection. Here, we evaluated the potential of TGEV S1 as a DNA vaccine and porcine interleukin (pIL)-12 as an adjuvant in a mouse model. A DNA vaccine was constructed with the TGEV S1 gene to induce immune response in an experimental mouse model; pIL-12 was chosen as the immunological adjuvant within this DNA vaccine. The pVAX1-(TGEV-S1) and pVAX1-(pIL-12) vectors were transfected into BHK-21 cells and expressed in vitro. Experimental mice were separately immunized with each of the recombinant plasmids and controls through the intramuscular route. The lymphocytes isolated from the blood and spleen were analyzed for proliferation, cytotoxic activities, and populations of CD4+ and CD8+ cells. The titers of TGEV S1 in an enzyme-linked immunosorbent assay (ELISA) and TGEV neutralizing antibodies and the concentrations of interferon (IFN)-γ and IL-4 were also analyzed in the serum. The plasmids pVAX1-(TGEV-S1) and pVAX1-(pIL-12) could be expressed in BHK-21 cells, and the combination of pVAX1-(TGEV-S1) and pVAX1-(pIL-12) could induce a significant increase in all markers. pIL-12 could act as an immunological adjuvant in the DNA vaccine for TGEV-S1. Furthermore, the DNA vaccine prepared using TGEV-S1 and porcine IL-12 could induce excellent humoral and cellular immune responses. [ABSTRACT FROM AUTHOR]

Slag mixing technique is a process that eliminates the lumping phenomenon of slag and iron in desulfurization slag. This process is conducted by mixing the high-temperature converter slag and iron desulfurization slag. The slag crushing difficulty is remarkably reduced after mixing. This technique eliminates the lumping phenomenon in the iron desulfurization slag. A numerical model based on Eulerian multiphase flow was established for the mixing process. The model obtained the characteristics of the phase field distribution inside the slag tank. The slag expansion phenomenon was identical with experimental results. A simplified temperature dropping model of slag mixing process was established based on the principle of energy conservation. According to this simplified model, the time dependence of the chemical reaction rate and temperature was investigated. The simplified model built by multifactor regression method can effectively predict the chemical reaction rate and temperature change under the following conditions: converter slag with an initial mass of 4–12 t, an initial iron oxide content of 20%–40%, an iron desulfurization slag mixing volume of 1–5 t, and an iron desulfurization slag mass flow rate of 0.1–1 t/s. The simplified model provides basic physical parameters for post-processing while ensuring safe production.

The vertical waste heat recovery technology of the sinter in the iron and steel industry will be a great driving force for China to realize the “double Carbon” in the near future. For promoting the application of the new technology, the influence of the confined wall on the pressure distribution and pressure drops of the gas flow in the sinter bed was experimentally studied. For the irregular sinter with the rough surface, the gas pressure near the wall is higher than that at the center. Moreover, the radial distribution of the dimensionless pressure is nearly the same at different gas velocities. Therefore, whatever flow state the gas is in, the wall effect on irregular sinters only reduces the pressure drop of the gas flow, which is different from that on spherical particles. The vertical wall limits the randomness and uniformity of the particle accumulation, which is further intensified with the increase of the particle irregularity and particle size. Therefore, the confined wall causes a greater difference in the gas pressure between the wall and the center. With the particle size increasing from 5~10 mm to 55~60 mm, the ratio of the gas pressure between the wall and the center increases from 1.03 to 1.26. If the wall effect is ignored, the pressure drop of the gas flow would be overestimated by 16.01% on average, whereas the correlation of the wall correction can well predict the pressure drop with the mean error and maximum error of 2.74% and 9.48%, respectively.

To establish an accurate model to optimize the vertical cooling process of the sinter, the inverse problem method is used to calculate the gas-solid heat transfer coefficient based on the gas outlet temperature, which is fitted into the correlation. The research indicates that the increase in the gas velocity is beneficial to the enhancement of the gas-solid heat transfer. With the gas velocity ug increasing from 0.8 m·s-1 to 1.6 m·s-1, the heat transfer coefficient hv increases by about twice. But this effect will weaken with the increase in the particle size. Besides, the reduction of the particle size is conducive to improving the convective heat transfer intensity between the gas and solid. With the particle size decreasing, this enhancement effect is progressively evident. At ug of 0.8 m·s-1, the increasing extent of hv is 1142.25 W·m-3·K-1 with the particle size decreasing from 20~25 mm to 15~20 mm, while that is 3152.65 W·m-3·K-1 with the particle size decreasing from 15~20 mm to 10~15 mm. In addition, the variation of the measured value of the Nusselt number with the Reynolds number has the same trend as predicted values obtained by other works. However, there is a considerable deviation in the value. Among them, the minimum value of the mean relative error is 26.81%. It is proved that the previous empirical correlations are no longer applicable, while the predicted value of this work is in good agreement with the measured value with the mean deviation of only 7.61%. Therefore, the modified correlation can accurately predict the gas-solid heat transfer characteristics in the sinter bed, which lays a foundation for the numerical design and optimization of the new process.

Solid-state batteries (SSBs) have proven to have the potential to be a proper substitute for conventional lithium-ion batteries due to their promising features. In order for the SSBs to be market-ready, the prognostics and health management (PHM) of battery systems plays a critical role in achieving such a goal. PHM ensures the reliability and availability of batteries during their operational time with acceptable safety margin. In the past two decades, much of the focus has been directed towards the PHM of lithium-ion batteries, while little attention has been given to PHM of solid-state batteries. Hence, this report presents a holistic review of the recent advances and current trends in PHM techniques of solid-state batteries and the associated challenges. For this purpose, notable commonly employed physics-based, data-driven, and hybrid methods are discussed in this report. The goal of this study is to bridge the gap between liquid state and SSBs and present the crucial aspects of SSBs that should be considered in order to have an accurate PHM model. The primary focus is given to the ML-based data-driven methods and the requirements that are needed to be included in the models, including anode, cathode, and electrolyte materials.

A horizontal centrifugal casting experiment was designed to determine the change in the solidification structure of Al-Cu alloy casting and the underlying variation in temperature, then obtained interface heat transfer coefficient by inverse calculation. The continuous simulation of metal melt filling from the gate to copper mold cavity is realized. The influence of centrifugal speed, pouring temperature, and mold preheating temperature on the solidified structure was analyzed. Simulation results showed that increasing the centrifugal speed mainly enhance the solidification rate of the molten metal and then refined the solidified structure of the Al-Cu alloy. Increasing the pouring temperature and mold preheating temperatures coarsen the grain size of the casting, but the range of change is small.

The new process of flue gas recirculation, which reduces coke consumption and reducing NOx emissions, is now extensively used. Compared with traditional sintering, the characteristics of circulating flue gas and coke parameters significantly affect the combustion atmosphere and coke combustion efficiency. Based on the actual complex process of sintering machine, this study proposes a relatively comprehensive one-dimensional, unsteady mathematical model for flue gas recirculation research. The model encompasses NOx pollutant generation and reduction, as well as SO2 generation and adsorption. We focus on the effects of cyclic flue gas characteristics on the sintering-bed temperature and NOx emissions, which are rarely studied, and provide a theoretical basis for NOx emission reduction. Simulation results show that during sintering, the fuel NOx is reduced by 50% and 10% when passing through the surface of coke particles and CO, respectively. During flue gas recirculation sintering, the increase in circulating gas O2 content, temperature, and supply-gas volume cause increased combustion efficiency of coke, reducing atmosphere, and NOx content in the circulating area; the temperature of the material layer also increases significantly and the sintering endpoint advances. During cyclic sintering, the small coke size and increased coke content increase the char-N release rate while promoting sufficient contact of NOx with the coke surface. Consequently, the NOx reduction rate increases. Compared with the conventional sintering, the designed flue gas recirculation condition saves 3.75% of coke consumption, i.e., for 1.2 kg of solid fuel per ton of sinter, the amount of flue gas treatment is reduced by 21.64% and NOx emissions is reduced by 23.59%. Moreover, without changing the existing sintering equipment, sintering capacity increases by about 5.56%.

In this work, an improved comprehensive model was developed for large coal particles to predict temperature variation and volatile component yields. The kinetics model of volatile component yields, where the volatile matters were assumed to comprise nine species, was combined with heat transfer model. The interaction between volatile yield and heat transfer during pyrolysis of large Maltby coal particles was investigated. An apparent temperature difference has been observed between the surface and core of particles at the initial heating stage. The non-uniform temperature distribution inside coal particles causes non-simultaneous volatile yields release from the surface and core area. The volatile release occurs after the coal temperature rises higher than 350 °C, and its yield steeply increases within the temperature range of 450–520 °C. The peak of volatile release rate corresponds to about 485 °C due to the rapid release of tar and H2O. The tar is almost completely released at around 550 °C. With the increasing particle size, the difference in temperature and volatile yield between the surface and core increases at the end of heating. The results are expected to provide insights into the interaction between heat transfer and volatile yields during pyrolysis of large coal particles.