Integrative analysis and validation of necroptosis-related molecular signature for evaluating diagnosis and immune features in Rheumatoid arthritis.

OBJECTIVES: Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that is characterized by persistent morning stiffness, joint pain, and swelling. However, there is a lack of reliable diagnostic markers and therapeutic targets that are both effective and trustworthy. METHODS: In this study, gene expression profiles (GSE89408, GSE55235, GSE55457, and GSE77298) were obtained from the Gene Expression Omnibus database. Differentially expressed necroptosis-related genes were attained from intersection of necroptosis-related gene set, differentially expressed genes, and weighted gene co-expression network analysis. The LASSO, random forest, and SVM-RFE machine learning algorithms were utilized to further screen potential diagnostic genes for RA. Immune cell infiltration was analyzed using the CIBERSORT method. The expressions of diagnostic genes were validated through quantitative real-time PCR, western blotting, immunohistochemistry, and immunofluorescence staining in synovial tissues collected from three trauma controls and three RA patients. RESULTS: Five core necroptosis-related genes (FAS, CYBB, TNFSF10, EIF2AK2, and BIRC2) were identified as potential biomarkers for RA. Two different necroptosis patterns based on these five genes were confirmed to significantly correlated with immune cells (especially macrophages). In vitro experiments showed significantly higher mRNA and protein expression levels of CYBB and EIF2AK2 in RA patients compared to normal controls, consistent with the bioinformatics analysis results. CONCLUSION: Our study identified a novel necroptosis-related subtype and five diagnostic biomarkers of RA, revealed vital roles in the development and occurrence of RA, and offered potential targets for clinical diagnosis and immunotherapy.

Urbanization drives biotic homogenization of the avian community in China.

Urbanization-driven biotic homogenization has been recorded in various ecosystems on local and global scales; however, it is largely unexplored in developing countries. Empirical studies on different taxa and bioregions show conflicting results (i.e. biotic homogenization vs. biotic differentiation); the extent to which the community composition changes in response to anthropogenic disturbances and the factors governing this process, therefore, require elucidation. Here, we used a compiled database of 760 bird species in China to quantify the multiple-site ß-diversity and fitted distance decay in pairwise ß-diversities between natural and urban assemblages to assess whether urbanization had driven biotic homogenization. We used generalized dissimilarity models (GDM) to elucidate the roles of spatial and environmental factors in avian community dissimilarities before and after urbanization. The multiple-site ß-diversities among urban assemblages were markedly lower than those among natural assemblages, and the distance decays in pairwise similarities in natural assemblages were more rapid. These results were consistent among taxonomic, phylogenetic, and functional aspects, supporting a general biotic homogenization driven by urbanization. The GDM results indicated that geographical distance and temperature were the dominant predictors of avian community dissimilarity. However, the contribution of geographical distance and climatic factors decreased in explaining compositional dissimilarities in urban assemblages. Geographical and environmental distances accounted for much lower variations in compositional dissimilarities in urban than in natural assemblages, implying a potential risk of uncertainty in model predictions under further climate change and anthropogenic disturbances. Our study concludes that taxonomic, phylogenetic, and functional dimensions elucidate urbanization-driven biotic homogenization in China.

Geographic variations in eco-evolutionary factors governing urban birds: The case of university campuses in China.

Urbanization alters natural habitats, restructures biotic communities and serves as a filter for selecting species from regional species pools. However, empirical evidence of the specific traits that allow species to persist in urban areas yields mixed results. More importantly, it remains unclear which traits are widespread for species utilizing urban spaces (urban utilizers) and which are environment-dependent traits. Using 745 bird species from 287 university/institute campuses in 74 cities and their species pools across China, we tested whether species that occur in urban areas are correlated with regards to their biological (body mass, beak shape, flight capacity and clutch size), ecological (diet diversity, niche width and habitat breadth), behavioural (foraging innovation) and evolutionary (diversification rate) attributes. We used Bayesian phylogenetic generalized linear mixed models to disentangle the relative roles of these predictors further, and to determine the extent to which the effects of these predictors varied among different cities. We found that urban birds were more phylogenetically clustered than expected by chance, and were generally characterized by a larger habitat breadth, faster diversification rate, more behavioural innovation and smaller body size. Notably, the relative effects of the attributes in explaining urban bird communities varied with city temperature and elevation, indicating that the filters used to determine urban species were environment dependent. We conclude that, while urban birds are typically small-sized, generalists, innovative and rapidly diversifying, the key traits that allow them to thrive vary spatially, depending on the climatic and topographic conditions of the city. These findings emphasize the importance of studying species communities within specific cities to better understand the contextual dependencies of key traits that are filtered by urban environments.

Interpretable machine learning for predicting 28-day all-cause in-hospital mortality for hypertensive ischemic or hemorrhagic stroke patients in the ICU: a multi-center retrospective cohort study with internal and external cross-validation.

Background: Timely and accurate outcome prediction plays a critical role in guiding clinical decisions for hypertensive ischemic or hemorrhagic stroke patients admitted to the ICU. However, interpreting and translating the predictive models into clinical applications are as important as the prediction itself. This study aimed to develop an interpretable machine learning (IML) model that accurately predicts 28-day all-cause mortality in hypertensive ischemic or hemorrhagic stroke patients. Methods: A total of 4,274 hypertensive ischemic or hemorrhagic stroke patients admitted to the ICU in the USA from multicenter cohorts were included in this study to develop and validate the IML model. Five machine learning (ML) models were developed, including artificial neural network (ANN), gradient boosting machine (GBM), eXtreme Gradient Boosting (XGBoost), logistic regression (LR), and support vector machine (SVM), to predict mortality using the MIMIC-IV and eICU-CRD database in the USA. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Model performance was evaluated based on the area under the curve (AUC), accuracy, positive predictive value (PPV), and negative predictive value (NPV). The ML model with the best predictive performance was selected for interpretability analysis. Finally, the SHapley Additive exPlanations (SHAP) method was employed to evaluate the risk of all-cause in-hospital mortality among hypertensive ischemic or hemorrhagic stroke patients admitted to the ICU. Results: The XGBoost model demonstrated the best predictive performance, with the AUC values of 0.822, 0.739, and 0.700 in the training, test, and external cohorts, respectively. The analysis of feature importance revealed that age, ethnicity, white blood cell (WBC), hyperlipidemia, mean corpuscular volume (MCV), glucose, pulse oximeter oxygen saturation (SpO2), serum calcium, red blood cell distribution width (RDW), blood urea nitrogen (BUN), and bicarbonate were the 11 most important features. The SHAP plots were employed to interpret the XGBoost model. Conclusions: The XGBoost model accurately predicted 28-day all-cause in-hospital mortality among hypertensive ischemic or hemorrhagic stroke patients admitted to the ICU. The SHAP method can provide explicit explanations of personalized risk prediction, which can aid physicians in understanding the model.

Heat induced superfast diclofenac removal in Cu(II)-activated peracetic acid system: Mediation from non-radical to radical pathway.

A Cu(II)/heat coactivated peracetic acid (PAA) system for enhancing diclofenac (DCF) degradation was proposed in this work. The superiority of this synergetic activation strategy for PAA, working reactive species, catalytic mechanism and effects of reaction parameters on DCF elimination in this system were simultaneously investigated. Based on our results, the DCF loss rate in Cu(II)-heat/PAA process at pH 8.0 was about 49.3 and 4.2 times of that in Cu(II)/PAA and heat/PAA processes, respectively. Increasing the reaction temperature to 60 оC not only motivated the conversion of Cu(II) to Cu(I) but also facilitated the one-electron transfer between Cu(I) and PAA, boosting the generation of radicals. Organic radicals (mainly CH3C(O)O• and CH3C(O)OO•) were evidenced to be the core oxidizing substances dominating in the destruction of DCF while hydroxyl radical (•OH) made a minor contribution in this system by electron paramagnetic resonance (EPR) method together with scavenging experiments. This study broads the eyes into enhanced PAA activation initiated by homogenous Cu(II), providing a simple but efficient tool to degrade micropollutants.

Phosphate enhanced Cu(II)/peracetic acid process for diclofenac removal: Performance and mechanism.

Since limitedly existing researches suggested Cu(II) had deficiently catalytic ability to PAA, in this work, we tested the oxidation performance of Cu(II)/PAA system on diclofenac (DCF) degradation under neutral conditions. It was found that overwhelming DCF removal could be obtained in Cu(II)/PAA system at pH 7.4 using phosphate buffer solution (PBS) compared to poor loss of DCF without PBS, and the apparent rate constant of DCF removal in PBS/Cu(II)/PAA system was 0.0359 min-1, 6.53 times of that in Cu(II)/PAA system. Organic radicals (i.e., CH3C(O)O• and CH3C(O)OO•) were evidenced as the dominant contributors to DCF destruction in PBS/Cu(II)/PAA system. PBS motivated the reduction of Cu(II) to Cu(I) through chelation effect, and then the activation of PAA by Cu(I) was facilitated. Besides, due to the steric hindrance of Cu(II)-PBS complex (CuHPO4), PAA activation was mediated from non-radical-generating pathway to radical-generating pathway, leading to desirably effective DCF removal by radicals. The transformation of DCF mainly experienced hydroxylation, decarboxylation, formylation and dehydrogenation in PBS/Cu(II)/PAA system. This work proposes the potential of coupling of phosphate and Cu(II) in optimizing PAA activation for organic pollutants elimination.

Intestinal Behcet's syndrome with an unusual complication bladder-intestines fistula and urinary tract infections-A case report.

A 33-year-old male patient with a 17-year Behcet's syndrome history showed abdominal pain and fever symptoms. The abdominal CT was suggestive of an acute ileocecal intestinal perforation. In addition, the symptoms disappeared after the conservative treatment. Some related examinations, including capsule endoscopy, were performed to explain the phenomenon of the food residue urine. These results indicated the intestine-urinary tract fistula formation, supposed to be the outcome of intestinal Behcet's syndrome perforation. This is a rare case of intestinal Behcet's syndrome with abdominal symptoms as the main manifestation. It was complicated by entero-urinary fistula formation and urinary tract infections. Now, we report this story to emphasize that capsule endoscopy is conducive to the diagnosis and assessment of the intestinal Behcet's syndrome; moreover, anti-inflammatory treatment including biological agents is effective to relieve the disease at the acute stage in addition to surgical methods.

miR-197-3p Promotes Osteosarcoma Stemness and Chemoresistance by Inhibiting SPOPL.

First-line treatment for osteosarcoma includes chemotherapy and surgery. However, the five-year survival rate of refractory osteosarcoma remains unsatisfactory. Osteosarcoma cancer stem cells, possessing stemness and chemoresistance, are one of the critical causes of poor response to chemotherapy. Elucidating regulatory signaling pathways of osteosarcoma cancer stem cells may provide a rationale for improving regimens against chemoresistant osteosarcoma. Methotrexate (MTX)-resistant osteosarcoma cells were established. microRNA expression profiles were used for detecting differentially expressed microRNA in resistant clones and the parental cells. microRNA target databases were employed to predict potential microRNA and mRNA interactions. Flow cytometry was performed to measure stem cell marker Prominin-1 (CD133)-positive cells. Immunofluorescence staining was applied to detect CD133 expression. miR-197-3p mimic or anti-miR-197-3p stably transfected cells were used to generate xenograft models. In the study, we found that miR-197-3p was increased in MTX-resistant cell lines. Overexpression of miR-197-3p enhanced the expression of cancer stem cell markers CD133, Octamer-binding protein 4 (OCT4), Transcription factor SOX-2 (SOX2), and Homeobox protein NANOG (NANOG), as well as chemoresistance-associated genes ATP-dependent translocase ABCB1 (ABCB1) and Broad substrate specificity ATP-binding cassette transporter ABCG2 (ABCG2), whereas miR-197-3p knockdown inhibited stemness and recovered sensitivity to MTX. We also classified the tumor suppressor Speckle-type POZ protein-like (SPOPL) as a target of miR-197-3p. The miR-197-3p mutation that could not combine SPOPL promoter regions was unable to sustain stemness or chemoresistance. Collectively, we discovered miR-197-3p conferred osteosarcoma stemness and chemotherapy resistance by targeting SPOPL, prompting promising therapeutic candidates for refractory osteosarcoma treatment.

Development and assessment of scoring model for ICU stay and mortality prediction after emergency admissions in ischemic heart disease: a retrospective study of MIMIC-IV databases.

Ischemic heart disease (IHD) is the leading cause of death and emergency department (ED) admission. We aimed to develop more accurate and straightforward scoring models to optimize the triaging of IHD patients in ED. This was a retrospective study based on the MIMIC-IV database. Scoring models were established by AutoScore formwork based on machine learning algorithm. The predictive power was measured by the area under the curve in the receiver operating characteristic analysis, with the prediction of intensive care unit (ICU) stay, 3d-death, 7d-death, and 30d-death after emergency admission. A total of 8381 IHD patients were included (median patient age, 71 years, 95% CI 62-81; 3035 [36%] female), in which 5867 episodes were randomly assigned to the training set, 838 to validation set, and 1676 to testing set. In total cohort, there were 2551 (30%) patients transferred into ICU; the mortality rates were 1% at 3 days, 3% at 7 days, and 7% at 30 days. In the testing cohort, the areas under the curve of scoring models for shorter and longer term outcomes prediction were 0.7551 (95% CI 0.7297-0.7805) for ICU stay, 0.7856 (95% CI 0.7166-0.8545) for 3d-death, 0.7371 (95% CI 0.6665-0.8077) for 7d-death, and 0.7407 (95% CI 0.6972-0.7842) for 30d-death. This newly accurate and parsimonious scoring models present good discriminative performance for predicting the possibility of transferring to ICU, 3d-death, 7d-death, and 30d-death in IHD patients visiting ED.

Heat-activated peracetic acid for degradation of diclofenac: kinetics, influencing factors and mechanism.

ABSTRACTHeat-activated peracetic acid (PAA) was used to degrade diclofenac (DCF) in this study. Electron paramagnetic resonance and radical scavenging experiments proved that organic radicals (i.e. CH3C(=O)O• and CH3C(=O)OO•) were the primary active species for DCF removal in the heat/PAA process. The degradation efficiency of DCF increased with the increase of temperature or initial PAA concentration in the heat/PAA process, and the optimal reaction pH for DCF removal was neutral. The presence of NO3- or SO42- insignificantly affected DCF degradation, while Cl- was favourable for DCF removal in this process. In contrast, an obvious inhibition on the removal of DCF was observed with the addition of natural organic matter, which might be responsible for the lower DCF removal in real waters. Finally, dechlorination, formylation, dehydrogenation and hydroxylation were proposed to be four degradation pathways of DCF in the heat/PAA system based on the five detected transformation products.

Mending a broken heart-targeting cardiomyocyte regeneration: a literature review.

Background and Objectives: Cardiovascular diseases have been the leading cause of death globally for decades. Pharmacological advances targeting the sympathetic nervous system, renin-angiotensin-aldosterone system, and fibrosis slow the progression of diverse cardiovascular diseases. However, ongoing cardiomyocyte loss is inevitable in divergent cardiovascular diseases, eventually leading to heart failure as the end stage. In this review, we focused on the key biomedical findings and underlying principles of cardiomyocyte regeneration. Methods: Literature regarding the key findings in cardiomyocyte regeneration research, including controversies on the origins of newly formed cardiomyocytes, potential barriers and strategies to heart regeneration, and the key animals, models, and methods applied in the study of heart regeneration, were broadly researched using the PubMed and Web of Science databases. Key Content and Findings: In the mammalian heart, cardiomyocytes proliferate during the embryonic and early postnatal stages, while the capability of proliferation disappears in the adult stage. An increasing amount of evidence suggests that cardiomyocytes self-renew at a very limited level and that most newly formed cardiomyocytes originate from pre-existing cardiomyocytes and not cardiac progenitor cells (CPCs). Several potential barriers to heart regeneration have been addressed, including metabolic switch, a large increase in multinucleated and polyploid cardiomyocytes, and alteration in the epigenome and transcriptome. In addition, immune system evolution is also associated with the loss of regenerative capacity. However, the activation of resident cardiomyocytes, somatic cell reprogramming, and direct reprogramming, in addition to the promotion of neovascularization and immune modulation, constitute the new insights into those strategies that can boost cardiac regeneration. Conclusions: Heart regeneration is one of the most popular fields in cardiovascular research and represents a promising avenue of therapeutics for mending a broken heart. Despite the controversies and challenges, a clearer picture of adult mammalian cardiac regeneration is emerging.

Influence of Marangoni Effect on Heat and Mass Transfer during Evaporation of Sessile Microdroplets.

Evaporative cooling is an important method for controlling the temperature of micro devices, and heat and mass transfer from the microdroplets in the evaporation process directly affect the cooling performance. In order to study the droplet heat and mass transfer law in the droplet evaporation process, this paper builds a coupled thermal mass model of droplet evaporation and tests the accuracy of the numerical model through theoretical results. In order to study the influence of the Marangoni effect on the droplet evaporation process and the effects of different initial droplet radius and ambient temperature on the temperature and flow, fields within the droplet are compared. From this result, it can be seen that the droplet volume is 20 µL, and the maximum flow velocity in the droplet is 0.34 mm/s, without taking into account the Marangoni effect. When the Marangoni effect is taken into account, the maximum flow velocity increases by almost 100 times. The Marangoni effect can cause the convection in the droplet to change direction, and the formation of the Marangoni flow may affect the temperature distribution within the droplet, thereby increasing the evaporation efficiency by 2.5%. The evaporation process will increase the velocity of the air close to the surface of the liquid, but the increase in air velocity close to the liquid surface is not sufficient to reinforce evaporation. There is a non-linear relationship between increasing ambient temperature and increasing evaporation efficiency. For every 5 °C increase in ambient temperature, the maximum increase in the rate of evaporation is approximately 22.7%.

Dexras1 Induces Dysdifferentiation of Oligodendrocytes and Myelin Injury by Inhibiting the cAMP-CREB Pathway after Subarachnoid Hemorrhage.

White matter damage (WMD), one of the research hotspots of subarachnoid hemorrhage (SAH), mainly manifests itself as myelin injury and oligodendrocyte differentiation disorder after SAH, although the specific mechanism remains unclear. Dexamethasone-induced Ras-related protein 1(Dexras1) has been reported to be involved in nervous system damage in autoimmune encephalitis and multiple sclerosis. However, whether Dexras1 participates in dysdifferentiation of oligodendrocytes and myelin injury after SAH has yet to be examined, which is the reason for creating the research content of this article. Here, intracerebroventricular lentiviral administration was used to modulate Dexras1 levels in order to determine its functional influence on neurological injury after SAH. Immunofluorescence, transmission electron microscopy, and Western blotting methods, were used to investigate the effects of Dexras1 on demyelination, glial cell activation, and differentiation of oligodendrocyte progenitor cells (OPCs) after SAH. Primary rat brain neurons were treated with oxyhemoglobin to verify the association between Dexras1 and cAMP-CREB. The results showed that Dexras1 levels were significantly increased upon in vivo SAH model, accompanied by OPC differentiation disturbances and myelin injury. Dexras1 overexpression significantly worsened OPC dysdifferentiation and myelin injury after SAH. In contrast, Dexras1 knockdown ameliorated myelin injury, OPC dysdifferentiation, and glial cell activation. Further research of the underlying mechanism discovered that the cAMP-CREB pathway was inhibited after Dexras1 overexpression in the in vitro model of SAH. This study is the first to confirm that Dexras1 induced oligodendrocyte dysdifferentiation and myelin injury after SAH by inhibiting the cAMP-CREB pathway. This present research may reveal novel therapeutic targets for the amelioration of brain injury and neurological dysfunction after SAH.

Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases.

Background: Heart failure (HF) combined with hypertension is an extremely important cause of in-hospital mortality, especially for the intensive care unit (ICU) patients. However, under intense working pressure, the medical staff are easily overwhelmed by the large number of clinical signals generated in the ICU, which may lead to treatment delay, sub-optimal care, or even wrong clinical decisions. Individual risk stratification is an essential strategy for managing ICU patients with HF combined with hypertension. Artificial intelligence, especially machine learning (ML), can develop superior models to predict the prognosis of these patients. This study aimed to develop a machine learning method to predict the 28-day mortality for ICU patients with HF combined with hypertension. Methods: We enrolled all critically ill patients with HF combined with hypertension in the Medical Information Mart for IntensiveCare Database-IV (MIMIC-IV, v.1.4) and the eICU Collaborative Research Database (eICU-CRD) from 2008 to 2019. Subsequently, MIMIC-IV was divided into training cohort and testing cohort in an 8:2 ratio, and eICU-CRD was designated as the external validation cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression with internal tenfold cross-validation was used for data dimension reduction and identifying the most valuable predictive features for 28-day mortality. Based on its accuracy and area under the curve (AUC), the best model in the validation cohort was selected. In addition, we utilized the Shapley Additive Explanations (SHAP) method to highlight the importance of model features, analyze the impact of individual features on model output, and visualize an individual's Shapley values. Results: A total of 3,458 and 6582 patients with HF combined with hypertension in MIMIC-IV and eICU-CRD were included. The patients, including 1,756 males, had a median (Q1, Q3) age of 75 (65, 84) years. After selection, 22 out of a total of 58 clinical parameters were extracted to develop the machine-learning models. Among four constructed models, the Neural Networks (NN) model performed the best predictive performance with an AUC of 0.764 and 0.674 in the test cohort and external validation cohort, respectively. In addition, a simplified model including seven variables was built based on NN, which also had good predictive performance (AUC: 0.741). Feature importance analysis showed that age, mechanical ventilation (MECHVENT), chloride, bun, anion gap, paraplegia, rdw (RDW), hyperlipidemia, peripheral capillary oxygen saturation (SpO2), respiratory rate, cerebrovascular disease, heart rate, white blood cell (WBC), international normalized ratio (INR), mean corpuscular hemoglobin concentration (MCHC), glucose, AIDS, mean corpuscular volume (MCV), N-terminal pro-brain natriuretic peptide (Npro. BNP), calcium, renal replacement therapy (RRT), and partial thromboplastin time (PTT) were the top 22 features of the NN model with the greatest impact. Finally, after hyperparameter optimization, SHAP plots were employed to make the NN-based model interpretable with an analytical description of how the constructed model visualizes the prediction of death. Conclusion: We developed a predictive model to predict the 28-day mortality for ICU patients with HF combined with hypertension, which proved superior to the traditional logistic regression analysis. The SHAP method enables machine learning models to be more interpretable, thereby helping clinicians to better understand the reasoning behind the outcome and assess in-hospital outcomes for critically ill patients.

Rapid degradation of sulfamethoxazole by permanganate combined with bisulfite: efficiency, influence factors and mechanism.

In this study, permanganate combined with bisulfite (PM/BS), a novel advanced oxidation process, was used for rapidly removing sulfamethoxazole (SMX) from contaminated water. The results showed that 80% SMX was removed within 10 s in the PM/BS system, while no obvious SMX degradation was observed in the PM or BS alone system within 300 s. Reactive manganese species (RMnS, Mn(III), Mn(V) and Mn(VI)), sulfate radical (SO4•-) and hydroxyl radical (HO•) formed in the PM/BS system all played a role in accelerated degradation of SMX. Due to the loss of RMnS, SMX degradation was significantly inhibited with the increase in pH. The best [BS]:[PM] ratio for SMX removal was 7.5:1-10:1. The presence of Cl-, HCO3- or natural organic matter (NOM) significantly inhibited the degradation of SMX, while SO42- and NO3- had little impact on SMX removal. Based on the detected transformation products, two degradation pathways of SMX by PM/BS, namely N-S bond cleavage and amino oxidation, were proposed.

Characterizing Risk of In-Hospital Mortality Following Subarachnoid Hemorrhage Using Machine Learning: A Retrospective Study.

Background: Subarachnoid hemorrhage has a high rate of disability and mortality, and the ability to use existing disease severity scores to estimate the risk of adverse outcomes is limited. Collect relevant information of patients during hospitalization to develop more accurate risk prediction models, using logistic regression (LR) and machine learning (ML) technologies, combined with biochemical information. Methods: Patient-level data were extracted from MIMIC-IV data. The primary outcome was in-hospital mortality. The models were trained and tested on a data set (ratio 70:30) including age and key past medical history. The recursive feature elimination (RFE) algorithm was used to screen the characteristic variables; then, the ML algorithm was used to analyze and establish the prediction model, and the validation set was used to further verify the effectiveness of the model. Result: Of the 1,787 patients included in the mimic database, a total of 379 died during hospitalization. Recursive feature abstraction (RFE) selected 20 variables. After simplification, we determined 10 features, including the Glasgow coma score (GCS), glucose, sodium, chloride, SPO2, bicarbonate, temperature, white blood cell (WBC), heparin use, and sepsis-related organ failure assessment (SOFA) score. The validation set and Delong test showed that the simplified RF model has a high AUC of 0.949, which is not significantly different from the best model. Furthermore, in the DCA curve, the simplified GBM model has relatively higher net benefits. In the subgroup analysis of non-traumatic subarachnoid hemorrhage, the simplified GBM model has a high AUC of 0.955 and relatively higher net benefits. Conclusions: ML approaches significantly enhance predictive discrimination for mortality following subarachnoid hemorrhage compared to existing illness severity scores and LR. The discriminative ability of these ML models requires validation in external cohorts to establish generalizability.

Dectin-2 promotes house dust mite-skewed Th2 response through the activation of cDC2s.

The role of Dectin-2 (gene symbol, Clec4n) in house dust mite (HDM) induced Th2 immune response and the exact mechanism remains controversial. In this study, we illustrated that, Clec4n-/- mice had decreased Th2 immune response following HDM challenge, which may ascribe to dramatically reduced type 2 conventional dendritic cells (cDC2s) in lung of Clec4n-/- mice, as cDC2s from lung of Clec4n-/- mice after challenging had less ability to induce Th2 response with decreased production of IL-4/IL-13. Further in vitro experiments showed the activation of Clec4n-/--BMDCs significantly decreased after HDM stimulation accompanied with decreased activation of Syk-NF-κB and Syk-JNK signal pathway. Importantly, Dectin-2 expression in PBMCs from asthmatic patients was significantly higher than that in healthy controls. Taken together, these results demonstrated that Dectin-2 could promote cDC2s activation in lung, which polarizes Th2 immune response outlining a novel mechanism of asthma development.

Photocurable Hydrogel Substrate-Better Potential Substitute on Bone-Marrow-Derived Dendritic Cells Culturing.

Dendritic cells (DCs) are recognized as the most effective antigen-presenting cells at present. DCs have corresponding therapeutic effects in tumor immunity, transplantation immunity, infection inflammation and cardiovascular diseases, and the activation of T cells is dependent on DCs. However, normal bone-marrow-derived Dendritic cells (BMDCs) cultured on conventional culture plates are easy to be activated during culturing, and it is difficult to imitate the internal immune function. Here, we reported a novel BMDCs culturing with hydrogel substrate (CCHS), where we synthesized low substituted Gelatin Methacrylate-30 (GelMA-30) hydrogels and used them as a substitute for conventional culture plates in the culture and induction of BMDCs in vitro. The results showed that 5% GelMA-30 substrate was the best culture condition for BMDCs culturing. The low level of costimulatory molecules and the level of development-related transcription factors of BMDCs by CCHS were closer to that of spleen DCs and were capable of better promoting T cell activation and exerting an immune effect. CCHS was helpful to study the transformation of DCs from initial state to activated state, which contributes to the development of DC-T cell immunotherapy.

Phosphate-induced activation of peracetic acid for diclofenac degradation: Kinetics, influence factors and mechanism.

Activating peroxides to produce active substances is the key to advanced oxidation processes (AOPs), but this usually requires energy or is accompanied by additional contaminants. In this study, diclofenac (DCF) was effectively removed by peracetic acid (PAA) in phosphate buffer (PBS). According to the results of radical scavenging experiments and electron paramagnetic resonance (EPR), hydroxyl radical (•OH) and organic radicals (i.e., CH3C(=O)OO• and CH3C(=O)O•) generated from PBS-activated PAA might be the dominant reactive species responsible for DCF degradation. At neutral pH, PBS/PAA system exhibited the best degradation efficiency on DCF. Presence of NO3-, SO42- and Cl- had little effect on the removal of DCF, while HCO3- and natural organic matter (NOM) significantly inhibited DCF degradation in PBS/PAA system, resulting in the lower degradation efficiency of DCF in natural waters than that in ultrapure water. Finally, four possible degradation pathways, including hydroxylation, formylation, dehydrogenation and dechlorination, were proposed based on the detected reaction products. This study suggests that PBS used to control solution pH should be applied cautiously in PAA-based AOPs.

Twenty-eight-day in-hospital mortality prediction for elderly patients with ischemic stroke in the intensive care unit: Interpretable machine learning models.

Background: Risk stratification of elderly patients with ischemic stroke (IS) who are admitted to the intensive care unit (ICU) remains a challenging task. This study aims to establish and validate predictive models that are based on novel machine learning (ML) algorithms for 28-day in-hospital mortality in elderly patients with IS who were admitted to the ICU. Methods: Data of elderly patients with IS were extracted from the electronic intensive care unit (eICU) Collaborative Research Database (eICU-CRD) records of those elderly patients admitted between 2014 and 2015. All selected participants were randomly divided into two sets: a training set and a validation set in the ratio of 8:2. ML algorithms, such as Naïve Bayes (NB), eXtreme Gradient Boosting (xgboost), and logistic regression (LR), were applied for model construction utilizing 10-fold cross-validation. The performance of models was measured by the area under the receiver operating characteristic curve (AUC) analysis and accuracy. The present study uses interpretable ML methods to provide insight into the model's prediction and outcome using the SHapley Additive exPlanations (SHAP) method. Results: As regards the population demographics and clinical characteristics, the analysis in the present study included 1,236 elderly patients with IS in the ICU, of whom 164 (13.3%) died during hospitalization. As regards feature selection, a total of eight features were selected for model construction. In the training set, both the xgboost and NB models showed specificity values of 0.989 and 0.767, respectively. In the internal validation set, the xgboost model identified patients who died with an AUC value of 0.733 better than the LR model which identified patients who died with an AUC value of 0.627 or the NB model 0.672. Conclusion: The xgboost model shows the best predictive performance that predicts mortality in elderly patients with IS in the ICU. By making the ML model explainable, physicians would be able to understand better the reasoning behind the outcome.