Table_5_Machine learning for the prediction of acute kidney injury in patients after cardiac surgery.docx

Cardiac surgery-associated acute kidney injury (CSA-AKI) is the most prevalent major complication of cardiac surgery and exerts a negative effect on a patient's prognosis, thereby leading to mortality. Although several risk assessment models have been developed for patients undergoing cardiac surgery, their performances are unsatisfactory. In this study, a machine learning algorithm was employed to obtain better predictive power for CSA-AKI outcomes relative to statistical analysis. In addition, random forest (RF), logistic regression with LASSO regularization, extreme gradient boosting (Xgboost), and support vector machine (SVM) methods were employed for feature selection and model training. Moreover, the calibration capacity and differentiation ability of the model was assessed using net reclassification improvement (NRI) along with Brier scores and receiver operating characteristic (ROC) curves, respectively. A total of 44 patients suffered AKI after surgery. Fatty acid-binding protein (FABP), hemojuvelin (HJV), neutrophil gelatinase-associated lipocalin (NGAL), mechanical ventilation time, and troponin I (TnI) were correlated significantly with the incidence of AKI. RF was the best model for predicting AKI (Brier score: 0.137, NRI: 0.221), evidenced by an AUC value of 0.858 [95% confidence interval (CI): 0.792–0.923]. Overall, RF exhibited the best performance as compared to other machine learning algorithms. These results thus provide new insights into the early identification of CSA-AKI.</p

Table_4_Machine learning for the prediction of acute kidney injury in patients after cardiac surgery.docx

Cardiac surgery-associated acute kidney injury (CSA-AKI) is the most prevalent major complication of cardiac surgery and exerts a negative effect on a patient's prognosis, thereby leading to mortality. Although several risk assessment models have been developed for patients undergoing cardiac surgery, their performances are unsatisfactory. In this study, a machine learning algorithm was employed to obtain better predictive power for CSA-AKI outcomes relative to statistical analysis. In addition, random forest (RF), logistic regression with LASSO regularization, extreme gradient boosting (Xgboost), and support vector machine (SVM) methods were employed for feature selection and model training. Moreover, the calibration capacity and differentiation ability of the model was assessed using net reclassification improvement (NRI) along with Brier scores and receiver operating characteristic (ROC) curves, respectively. A total of 44 patients suffered AKI after surgery. Fatty acid-binding protein (FABP), hemojuvelin (HJV), neutrophil gelatinase-associated lipocalin (NGAL), mechanical ventilation time, and troponin I (TnI) were correlated significantly with the incidence of AKI. RF was the best model for predicting AKI (Brier score: 0.137, NRI: 0.221), evidenced by an AUC value of 0.858 [95% confidence interval (CI): 0.792–0.923]. Overall, RF exhibited the best performance as compared to other machine learning algorithms. These results thus provide new insights into the early identification of CSA-AKI.</p

Sensitivity-Photo-Patternable Ionic Pressure Sensor Array with a Wearable Measurement Unit

A flexible pressure sensor array provides more information than a single pressure sensor as electronic skin, and independently definable sensitivities of sensing pixels enable more accurate pressure measurements. However, the reported approaches, either changing the mold for the dielectric layer or tuning the dielectric properties, overcomplicate the manufacturing process for the devices. Here, we present a pressure sensor array with photo-patterned sensitivity, which is realized through the synergistic creation of the photo-defined mechanical properties of the dielectric layer and the interfacial capacitive sensing mechanism. Via this design, the sensitivity of each sensing pixel can be photo-defined over a range of ∼70 times of magnitude. Additionally, we created the first wearable measurement unit for the ionic pressure sensor array. The sensitivity-photo-patternable pressure sensor array and the wearable measurement unit fulfill the open need of mapping the pressure distribution over a broad range of magnitude, such as the plantar pressure

Sensitivity-Photo-Patternable Ionic Pressure Sensor Array with a Wearable Measurement Unit

A flexible pressure sensor array provides more information than a single pressure sensor as electronic skin, and independently definable sensitivities of sensing pixels enable more accurate pressure measurements. However, the reported approaches, either changing the mold for the dielectric layer or tuning the dielectric properties, overcomplicate the manufacturing process for the devices. Here, we present a pressure sensor array with photo-patterned sensitivity, which is realized through the synergistic creation of the photo-defined mechanical properties of the dielectric layer and the interfacial capacitive sensing mechanism. Via this design, the sensitivity of each sensing pixel can be photo-defined over a range of ∼70 times of magnitude. Additionally, we created the first wearable measurement unit for the ionic pressure sensor array. The sensitivity-photo-patternable pressure sensor array and the wearable measurement unit fulfill the open need of mapping the pressure distribution over a broad range of magnitude, such as the plantar pressure

One-step preparation of durable pH-responsive polyurethane foam for oil/water separation

The smart responsive materials have become one of the hottest areas of research in oil/water separation field and there are various studies on preparation technologies. However, most of the materials reported were prepared by coating or coupling to attach functional polymer to substrates, which generally had unstable physical and chemical properties and were difficult to be applied in harsh environments. In this work, we fabricated a smart and durable oil/water separation material by one-step foaming method to graft pH-responsive copolymer onto a polyurethane foam (PUF) substrate. The foam exhibited reversible wettability under different pH environments, had excellent oil adsorption capacity (24.78–64.27g/g) and high oil/water separation efficiency (above 98%). Besides, the foam also showed stable performance and excellent recyclability in a series of durability experiments, including mechanical abrasion, light radiation and chemical immersion. The foam with simple preparation process and excellent stability will have promising application prospects in the field of oil/water separation.</p

Representative results of immunohistological staining for type II collagen of repaired tissues after transplantations in treatment group.

<p>Immunohistological staining of regenerative tissues 4 weeks after transplantation of non-transfected chontrocytes (A), and Shh transduced chondrocytes (C). Regenerative tissues 8 weeks after transplantation with immunohistological staining: non-transfected chondrocytes (B), and Shh transfected chondrocytes (D). Original magnification 20×. 4w: 4weeks, 8w: 8weeks.</p

Construction and Regulation of a Superhydrophobic Sponge via In Situ Anchoring of a Hyper-Cross-Linked Polymer for Efficient Oil/Water Separation

In the ever-growing environmental concerns caused by crude oil spills and solvent discharges, our study pioneered an ingenious approach to fabricate superhydrophobic melamine formaldehyde (HMF) materials through in situ anchoring of a porous hyper-cross-linked polymer (HCP) and achieved stable integration of HCP on the MF surface by covalent bonds and hydrogen bonds instead of traditional adhesives. The resulting composite material exhibits exceptional performance with an oil adsorption capacity of 130 mL/g, a filtration/separation efficiency exceeding 99%, and remarkable environmental resistance and recyclability. The robust interfacial strength and high degree of cross-linking porous HCP facilitate tailorable design and easy adjustment of pore structures and ensure repeated use through simple squeezing. Notably, the hydrophobicity and porous structure of the sponge can be conveniently regulated by controlling the deposition amount of HCP, realizing a high adsorption capacity and/or efficient emulsion separation on demand. This study not only contributes to the advancement of wettability materials but also presents an efficient, versatile, and convenient method and toolbox to address diverse oil/water separation challenges, paving the way for sustainable environmental solutions and marking a significant stride toward a cleaner future

Shh expression in vivo. In non-transfected group, Shh expression was not detected (A).

<p>In the defects in Shh group, the repair tissues contained Shh protein proved by immunohistochemical staining 2 weeks after transplantation (B). In the negative control without primary antibody, Shh expression was not detected (data not shown). Original magnification 20×.</p

Redifferentiation of dedifferentiated chondrocytes stimulated by Shh in monolayer and pellet culture.

<p>RT-PCR revealed that the expression of type II collagen and aggrecan in Shh transfected cells after 4 days in monolayer culture in differentiation medium (A). The matrix of Shh transfected chondrocytes was toluidine blue positive after 14 days in the pellet cultured in differentiation medium, and chondrocytes-like cells were found in the pellet. Type II collagen expression was proved by immunological staining (B). Original magnification 10×. *p<0.05</p

Additional file 1: of Hydrogel coils versus bare platinum coils for the endovascular treatment of intracranial aneurysms: a meta-analysis of randomized controlled trials

The sensitivity analysis showed that all of the consolidated results were stable. Figure S1. Fig. 3 C Sensitivity analysis of Periprocedural mortality from 4 RCTs. Figure S2. Fig. 4 A Sensitivity analysis of Mid-term complete occlusion from 4 RCTs. Figure S3. Fig. 5 G Sensitivity analysis of Mid-term mortality from 4 RCTs. (DOCX 281 kb