Data_Sheet_2_Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost.ZIP

ObjectiveThe purpose of this study was to develop and validate a predictive model based on a machine learning (ML) approach to identify patients with DKA at increased risk of AKI within 1 week of hospitalization in the intensive care unit (ICU).MethodsPatients diagnosed with DKA from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database according to the International Classification of Diseases (ICD)-9/10 code were included. The patient’s medical history is extracted, along with data on their demographics, vital signs, clinical characteristics, laboratory results, and therapeutic measures. The best-performing model is chosen by contrasting the 8 Ml models. The area under the receiver operating characteristic curve (AUC), sensitivity, accuracy, and specificity were calculated to select the best-performing ML model.ResultsThe final study enrolled 1,322 patients with DKA in total, randomly split into training (1,124, 85%) and validation sets (198, 15%). 497 (37.5%) of them experienced AKI within a week of being admitted to the ICU. The eXtreme Gradient Boosting (XGBoost) model performed best of the 8 Ml models, and the AUC of the training and validation sets were 0.835 and 0.800, respectively. According to the result of feature importance, the top 5 main features contributing to the XGBoost model were blood urea nitrogen (BUN), urine output, weight, age, and platelet count (PLT).ConclusionAn ML-based individual prediction model for DKA-associated AKI (DKA-AKI) was developed and validated. The model performs robustly, identifies high-risk patients early, can assist in clinical decision-making, and can improve the prognosis of DKA patients to some extent.</p

Data_Sheet_1_Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost.ZIP

ObjectiveThe purpose of this study was to develop and validate a predictive model based on a machine learning (ML) approach to identify patients with DKA at increased risk of AKI within 1 week of hospitalization in the intensive care unit (ICU).MethodsPatients diagnosed with DKA from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database according to the International Classification of Diseases (ICD)-9/10 code were included. The patient’s medical history is extracted, along with data on their demographics, vital signs, clinical characteristics, laboratory results, and therapeutic measures. The best-performing model is chosen by contrasting the 8 Ml models. The area under the receiver operating characteristic curve (AUC), sensitivity, accuracy, and specificity were calculated to select the best-performing ML model.ResultsThe final study enrolled 1,322 patients with DKA in total, randomly split into training (1,124, 85%) and validation sets (198, 15%). 497 (37.5%) of them experienced AKI within a week of being admitted to the ICU. The eXtreme Gradient Boosting (XGBoost) model performed best of the 8 Ml models, and the AUC of the training and validation sets were 0.835 and 0.800, respectively. According to the result of feature importance, the top 5 main features contributing to the XGBoost model were blood urea nitrogen (BUN), urine output, weight, age, and platelet count (PLT).ConclusionAn ML-based individual prediction model for DKA-associated AKI (DKA-AKI) was developed and validated. The model performs robustly, identifies high-risk patients early, can assist in clinical decision-making, and can improve the prognosis of DKA patients to some extent.</p

Self-Tandem Electrocatalytic NO Reduction to NH₃ on a W Single-Atom Catalyst

We design single-atom W confined in MoO3–x amorphous nanosheets (W1/MoO3–x) comprising W1–O5 motifs as a highly active and durable NORR catalyst. Theoretical and operando spectroscopic investigations reveal the dual functions of W1–O5 motifs to (1) facilitate the activation and protonation of NO molecules and (2) promote H2O dissociation while suppressing *H dimerization to increase the proton supply, eventually resulting in a self-tandem NORR mechanism of W1/MoO3–x to greatly accelerate the protonation energetics of the NO-to-NH3 pathway. As a result, W1/MoO3–x exhibits the highest NH3–Faradaic efficiency of 91.2% and NH3 yield rate of 308.6 μmol h–1 cm–2, surpassing that of most previously reported NORR catalysts

Sensitive Fluorescent Determination of Silver Ion and Glutathione in Human Serum Using Polydopamine Nanodots as the Probe

Glutathione (GSH)-switched fluorescent assays have received attention due to their rapid signal changes of fluorescent nanoprobes. Small polydopamine nanodots (PDs) exhibit remarkable photoluminescent properties. In this work, fluorescent PDs were synthesized through oxidation using dopamine as precursor. The prepared PDs had rich surface functionalities and exhibited good optical properties with maximum emission using 330 nm excitation. The fluorescence was quenched by Ag+ through the interaction with catechols in the PDs. After the addition of GSH, Ag was bound with sulfhydryl groups in GSH and the fluorescence of the PDs was restored. Therefore, a fluorescence turn on-off-on strategy was constructed for Ag+ and GSH using the synthesized PDs as the probe. The PDs showed good performance for Ag+ from 0.01 to 1 μM, with a limit of detection (LOD) equal to 2.82 nM. The system also exhibited a linear relationship for GSH from 0.01 to 0.5 μM with a LOD equal to 1.93 nM. GSH was determined in human serum to demonstrate a practical application of the assay. This work expands the application of PDs in biological analysis.</p

Tolerated outlier prediction method of excavation damaged zone thickness of drift based on interpretable SOA-QRF ensemble learning

Drift excavation induces excavation damaged zones (EDZ) due to stress redistribution, impacting drift stability and rock deformation support. Predicting EDZ thickness is crucial, but traditional machine learning models are susceptible to potential outliers in dataset. Directly eliminating outliers, however, impacts training effectiveness. This study introduces an EDZ thickness prediction model utilising quantile loss and random forest (RF) optimised by the seagull optimisation algorithm (SOA), enabling median regression with tolerated outlier performance. 209 sets of data sets containing 34 mine borehole data were used to establish the prediction model. Evaluation using R2, explained variance score (EVS), mean absolute error (MAE), and mean square error (MSE) demonstrates the superior accuracy of the proposed SOA-QRF model compared to traditional models. Based on the discussion on the treatment of outliers, the outcomes indicate that the SOA-QRF model is more suitable for the dataset with outliers as well as being able to effectuate tolerated outlier prediction. Additionally, three interpretation methods were utilised to explain the SOA-QRF model and enhance the transparency of the model’s prediction process and facilitating the analysis of dispatcher regulation.</p

Table_1_The Interaction of Influenza A NS1 and Cellular TRBP Protein Modulates the Function of RNA Interference Machinery.DOCX

Influenza A virus (IAV), one of the most prevalent respiratory diseases, causes pandemics around the world. The multifunctional non-structural protein 1 (NS1) of IAV is a viral antagonist that suppresses host antiviral response. However, the mechanism by which NS1 modulates the RNA interference (RNAi) pathway remains unclear. Here, we identified interactions between NS1 proteins of Influenza A/PR8/34 (H1N1; IAV-PR8) and Influenza A/WSN/1/33 (H1N1; IAV-WSN) and Dicer’s cofactor TAR-RNA binding protein (TRBP). We found that the N-terminal RNA binding domain (RBD) of NS1 and the first two domains of TRBP protein mediated this interaction. Furthermore, two amino acid residues (Arg at position 38 and Lys at position 41) in NS1 were essential for the interaction. We generated TRBP knockout cells and found that NS1 instead of NS1 mutants (two-point mutations within NS1, R38A/K41A) inhibited the process of microRNA (miRNA) maturation by binding with TRBP. PR8-infected cells showed masking of short hairpin RNA (shRNA)-mediated RNAi, which was not observed after mutant virus-containing NS1 mutation (R38A/K41A, termed PR8/3841) infection. Moreover, abundant viral small interfering RNAs (vsiRNAs) were detected in vitro and in vivo upon PR8/3841 infection. We identify, for the first time, the interaction between NS1 and TRBP that affects host RNAi machinery.</p

Freestanding TiO₂ Nanoparticle-Embedded High Directional Carbon Composite Host for High-Loading Low-Temperature Lithium–Sulfur Batteries

Improving the low-temperature performance of lithium–sulfur batteries is significant for future applications. Meanwhile, a low temperature often leads to sluggish charge transfer kinetics and low energy output. Herein, we designed a thick freestanding TiO2 nanoparticle-embedded three-dimensional carbon composite (TiO2@C@CSC) host with high directional channels, aiming at achieving a high-performance low-temperature lithium–sulfur battery. The carbon-coated TiO2 nanoparticles (TiO2@C) are derived from polyimide-coated TiO2 nanoparticles and embedded in the channels. The chitosan-foam-derived carbon framework (CSC) has vertically aligned channels, which kinetically accelerates ion/electron transport and precisely confines sulfur/polysulfides within its channels. TiO2@C nanoparticles could facilitate the adsorption and conversion of polysulfides. The combination of vertically aligned channels and TiO2@C nanoparticles further provides a chemical gradient that prevents the diffusion of polysulfides and enhances the reaction kinetics at low temperatures. The designed host also has enough accommodation space, which is beneficial to improving the mass loading and utilization efficiency of active sulfur. Finally, the energy storage performance of TiO2@C@CSC as a sulfur host was investigated under 30, −20, and −40 °C. Under 30 °C, the initial discharge capacity of TiO2@C@CSC is 679 mAh g–1 at 1C with 4.0 mg cm–2 sulfur, and an initial capacity of 969 mAh g–1 could be obtained at 0.1C with the sulfur loading mass of 10.0 mg cm–2. Under −20 °C, the performance of TiO2@C@CSC with loading masses of 2.5, 5.0, 10.0, and 20.0 mg cm–2 was investigated, and the corresponding initial capacities at 0.05C were 1573, 924, 242, and 13 mAh g–1, respectively. As the temperature drops to −40 °C, the efficiency becomes lower but the charge–discharge process can still be complete. This work presents a promising direction for developing high-energy low-temperature lithium–sulfur batteries

Freestanding TiO₂ Nanoparticle-Embedded High Directional Carbon Composite Host for High-Loading Low-Temperature Lithium–Sulfur Batteries

Improving the low-temperature performance of lithium–sulfur batteries is significant for future applications. Meanwhile, a low temperature often leads to sluggish charge transfer kinetics and low energy output. Herein, we designed a thick freestanding TiO2 nanoparticle-embedded three-dimensional carbon composite (TiO2@C@CSC) host with high directional channels, aiming at achieving a high-performance low-temperature lithium–sulfur battery. The carbon-coated TiO2 nanoparticles (TiO2@C) are derived from polyimide-coated TiO2 nanoparticles and embedded in the channels. The chitosan-foam-derived carbon framework (CSC) has vertically aligned channels, which kinetically accelerates ion/electron transport and precisely confines sulfur/polysulfides within its channels. TiO2@C nanoparticles could facilitate the adsorption and conversion of polysulfides. The combination of vertically aligned channels and TiO2@C nanoparticles further provides a chemical gradient that prevents the diffusion of polysulfides and enhances the reaction kinetics at low temperatures. The designed host also has enough accommodation space, which is beneficial to improving the mass loading and utilization efficiency of active sulfur. Finally, the energy storage performance of TiO2@C@CSC as a sulfur host was investigated under 30, −20, and −40 °C. Under 30 °C, the initial discharge capacity of TiO2@C@CSC is 679 mAh g–1 at 1C with 4.0 mg cm–2 sulfur, and an initial capacity of 969 mAh g–1 could be obtained at 0.1C with the sulfur loading mass of 10.0 mg cm–2. Under −20 °C, the performance of TiO2@C@CSC with loading masses of 2.5, 5.0, 10.0, and 20.0 mg cm–2 was investigated, and the corresponding initial capacities at 0.05C were 1573, 924, 242, and 13 mAh g–1, respectively. As the temperature drops to −40 °C, the efficiency becomes lower but the charge–discharge process can still be complete. This work presents a promising direction for developing high-energy low-temperature lithium–sulfur batteries

Image_1_Identification of C-PLAN index as a novel prognostic predictor for advanced lung cancer patients receiving immune checkpoint inhibitors.tif

ObjectiveIncreasing studies have highlighted the potential utility of non-invasive prognostic biomarkers in advanced lung cancer patients receiving immune checkpoint inhibitor (ICI) based anti-cancer therapies. Here, a novel prognostic predictor named as C-PLAN integrating C-reactive protein (CRP), Performance status (PS), Lactate dehydrogenase (LDH), Albumin (ALB), and derived Neutrophil-to-lymphocyte ratio (dNLR) was identified and validated in a single-center retrospective cohort.MethodsThe clinical data of 192 ICI-treated lung cancer patients was retrospectively analyzed. The pretreatment levels of CRP, PS, LDH, ALB and dNLR were scored respectively and then their scores were added up to form C-PLAN index. The correlation of C-PLAN index with the progression-free survival (PFS) or overall survival (OS) was analyzed by a Kaplan–Meier model. The multivariate analysis was used to identify whether C-PLAN index was an independent prognostic predictor.ResultsA total of 88 and 104 patients were included in the low and high C-PLAN index group respectively. High C-PLAN index was significantly correlated with worse PFS and OS in ICI-treated lung cancer patients (both pConclusionThe C-PLAN index has great potential to be utilized as a non-invasive, inexpensive and reliable prognostic predictor for advanced lung cancer patients receiving ICI-based anti-cancer therapies.</p

Image_3_Identification of C-PLAN index as a novel prognostic predictor for advanced lung cancer patients receiving immune checkpoint inhibitors.tif

ObjectiveIncreasing studies have highlighted the potential utility of non-invasive prognostic biomarkers in advanced lung cancer patients receiving immune checkpoint inhibitor (ICI) based anti-cancer therapies. Here, a novel prognostic predictor named as C-PLAN integrating C-reactive protein (CRP), Performance status (PS), Lactate dehydrogenase (LDH), Albumin (ALB), and derived Neutrophil-to-lymphocyte ratio (dNLR) was identified and validated in a single-center retrospective cohort.MethodsThe clinical data of 192 ICI-treated lung cancer patients was retrospectively analyzed. The pretreatment levels of CRP, PS, LDH, ALB and dNLR were scored respectively and then their scores were added up to form C-PLAN index. The correlation of C-PLAN index with the progression-free survival (PFS) or overall survival (OS) was analyzed by a Kaplan–Meier model. The multivariate analysis was used to identify whether C-PLAN index was an independent prognostic predictor.ResultsA total of 88 and 104 patients were included in the low and high C-PLAN index group respectively. High C-PLAN index was significantly correlated with worse PFS and OS in ICI-treated lung cancer patients (both pConclusionThe C-PLAN index has great potential to be utilized as a non-invasive, inexpensive and reliable prognostic predictor for advanced lung cancer patients receiving ICI-based anti-cancer therapies.</p