Abstract Objective The present study aims to evaluate the predictive ability of estimated maximum oxygen consumption (e $$\dot{V}$$ V˙ O 2max ) and 6-min walk distance (6MWD) for postoperative pulmonary complications (PPCs) in adult surgical patients undergoing major upper abdominal surgery. Method This study was conducted by collecting data prospectively from a single center. The two predictive variables in the study were defined as 6MWD and e $$\dot{V}$$ V˙ O 2max . Patients scheduled for elective major upper abdominal surgery from March 2019 to May 2021 were included. The 6MWD was measured for all patients before surgery. e $$\dot{V}$$ V˙ O 2max was calculated using the regression model of Burr, which uses 6MWD, age, gender, weight, and resting heart rate (HR) to predict aerobic fitness. The patients were categorized into PPC and non-PPC group. The sensitivity, specificity, and optimum cutoff values for 6MWD and e $$\dot{V}$$ V˙ O 2max were calculated to predict PPCs. The area under the receiver operating characteristic curve (AUC) of 6MWD or e $$\dot{V}$$ V˙ O 2max was constructed and compared using the Z test. The primary outcome measure was the AUC of 6MWD and e $$\dot{V}$$ V˙ O 2max in predicting PPCs. In addition, the net reclassification index (NRI) was calculated to assess ability of e $$\dot{V}$$ V˙ O 2max compared with 6MWT in predicting PPCs. Results A total of 308 patients were included 71/308 developed PPCs. Patients unable to complete the 6-min walk test (6MWT) due to contraindications or restrictions, or those taking beta-blockers, were excluded. The optimum cutoff point for 6MWD in predicting PPCs was 372.5 m with a sensitivity of 63.4% and specificity of 79.3%. The optimum cutoff point for e $$\dot{V}$$ V˙ O 2max was 30.8 ml/kg/min with a sensitivity of 91.6% and specificity of 79.3%. The AUC for 6MWD in predicting PPCs was 0.758 (95% confidence interval (CI): 0.694–0.822), and the AUC for e $$\dot{V}$$ V˙ O 2max was 0.912 (95%CI: 0.875–0.949). A significantly increased AUC was observed in e $$\dot{V}$$ V˙ O 2max compared to 6MWD in predicting PPCs ( P < 0.001, Z = 4.713). And compared with 6MWT, the NRI of e $$\dot{V}$$ V˙ O 2max was 0.272 (95%CI: 0.130, 0.406). Conclusion The results suggested that e $$\dot{V}$$ V˙ O 2max calculated from the 6MWT is a better predictor of PPCs than 6MWD in patients undergoing upper abdominal surgery and can be used as a tool to screen patients at risk of PPCs.
Abstract High‐capacity electrodes face a great challenge of cycling stability due to particle fragmentation induced conductive network failure and accompanied by sustained electrolyte decomposition for repeatedly build solid electrolyte interphase (SEI). Herein, Se‐solubility induced Se x 2− as self‐adjustment electrolyte additive to regulate electric double layer (EDL) for constructing novel triple‐layer SEI (inner layer: Se; mediate layer: inorganic; outer layer: organic) on high‐capacity FeS 2 anode as an example for achieving stable and fast sodium storage. In detail, Se x 2− in situ generated at 1.30 V (vs. Na + /Na) and was preferentially adsorbed onto EDL of anode, then converted to Se 0 as inner layer of SEI. In addition, the Se x 2− causes anion‐enhanced Na + solvation structure could produce more inorganic (Se 0 , NaF) and less organic SEI components. The unique triple‐layer SEI with layer‐by‐layer dense structure alleviate the excessive electrolyte consumption with less gas evolution. As a result, the anode delivered long‐lifespan at 10 A g −1 (383.7 mAh g −1 , 6000 cycles, 93.1 %, 5 min/cycle). The Se‐induced triple‐layer SEI could be also be formed on high‐capacity SnS 2 anode. This work provides a novel SEI model by anion‐tailored EDL towards stable sodium‐storage of high‐capacity anode for fast‐charging.
Objectives . Recently, it has been demonstrated that patients with subtle preexisting cognitive impairment were susceptible to delayed neurocognitive recovery (DNR). This present study investigated whether preoperative alterations in gray matter volume, spontaneous activity, or functional connectivity (FC) were associated with DNR. Methods . This was a nested case-control study of older adults (≥60 years) undergoing noncardiac surgery. All patients received MRI scan at least 1 day prior to surgery. Cognitive function was assessed prior to surgery and at 7-14 days postsurgery. Preoperative gray matter volume, amplitude of low-frequency fluctuation (ALFF), and FC were compared between the DNR patients and non-DNR patients. The independent risk factors associated with DNR were identified using a multivariate logistic regression model. Results . Of the 74 patients who completed assessments, 16/74 (21.6%) had DNR following surgery. There were no differences in gray matter volume between the two groups. However, the DNR patients exhibited higher preoperative ALFF in the bilateral middle cingulate cortex (MCC) and left fusiform gyrus and lower preoperative FC between the bilateral MCC and left calcarine than the non-DNR patients. The multivariate logistic regression analysis showed that higher preoperative spontaneous activity in the bilateral MCC was independently associated with a higher risk of DNR (OR=3.11, 95% CI, 1.30-7.45; P=0.011). A longer education duration (OR=0.57, 95% CI, 0.41-0.81; P=0.001) and higher preoperative FC between the bilateral MCC and left calcarine (OR=0.40, 95% CI, 0.18-0.92; P=0.031) were independently correlated with a lower risk of DNR. Conclusions . Preoperative higher ALFF in the bilateral MCC and lower FC between the bilateral MCC and left calcarine were independently associated with the occurrence of DNR. The present fMRI study identified possible preoperative neuroimaging risk factors for DNR. This trial is registered with Chinese Clinical Trial Registry ChiCTR-DCD-15006096 .
The development of high-efficiency lithium-ion battery electrodes composed of recycled materials is crucial for the commercialization of retired batteries, but it remains a significant barrier. The usage and recycling of spent graphite are encouraged by the huge number of batteries that are going to be dismantled. Here, an anode made of phosphorus-doped Ni/NiO yolk-shell nanospheres embedded on wasted graphite is developed. Electroless deposition and a subsequent heat-treatment procedure are used to make it in a methodical manner. The internal vacuum space of the nanospheres mitigates volume expansion and facilitates Li+ diffusion, whereas the embedded metallic Ni and conductive graphite layer expedite charge transfer. The optimal reusable composite electrode is ecologically benign and has high specific capacities (724 mAh g-1 at 0.1 A g-1 ) as well as outstanding cycle stability (500 cycles). The unusual 3D sandwich-like arrangement with strong spent graphite, the yolk-shell hetero-structure, continuous electron/ion transport routes, and attractive structure stability all contribute to this degree of performance. Such a nanoscale design and engineering strategy not only provides a green recovery method for anode graphite, but also enlightens other nanocomposites to boost their lithium storage performance.
High-capacity electrodes face a great challenge of cycling stability due to particle fragmentation induced conductive network failure and accompanied by sustained electrolyte decomposition for repeatedly build solid electrolyte interphase (SEI). Herein, Se-solubility induced Se
A acquisition module with ARM and Linux as a core was developed. This paper presents the hardware configuration and the software design. It is shown that the module can extract human lung sound reliably and effectively.
Delayed neurocognitive recovery (DNR) is a common subtype of postoperative neurocognitive disorders. An objective approach for identifying subjects at high risk of DNR is yet lacking. The present study aimed to predict DNR using the machine learning method based on multiple cognitive-related brain network features. A total of 74 elderly patients (≥ 60-years-old) undergoing non-cardiac surgery were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) before the surgery. Seed-based whole-brain functional connectivity (FC) was analyzed with 18 regions of interest (ROIs) located in the default mode network (DMN), limbic network, salience network (SN), and central executive network (CEN). Multiple machine learning models (support vector machine, decision tree, and random forest) were constructed to recognize the DNR based on FC network features. The experiment has three parts, including performance comparison, feature screening, and parameter adjustment. Then, the model with the best predictive efficacy for DNR was identified. Finally, independent testing was conducted to validate the established predictive model. Compared to the non-DNR group, the DNR group exhibited aberrant whole-brain FC in seven ROIs, including the right posterior cingulate cortex, right medial prefrontal cortex, and left lateral parietal cortex in the DMN, the right insula in the SN, the left anterior prefrontal cortex in the CEN, and the left ventral hippocampus and left amygdala in the limbic network. The machine learning experimental results identified a random forest model combined with FC features of DMN and CEN as the best prediction model. The area under the curve was 0.958 (accuracy = 0.935, precision = 0.899, recall = 0.900, F1 = 0.890) on the test set. Thus, the current study indicated that the random forest machine learning model based on rs-FC features of DMN and CEN predicts the DNR following non-cardiac surgery, which could be beneficial to the early prevention of DNR. Clinical Trial Registration: The study was registered at the Chinese Clinical Trial Registry (Identification number: ChiCTR-DCD-15006096).
Abstract High initial coulombic efficiency is highly desired because it implies effective interface construction and few electrolyte consumption, indicating enhanced batteries’ life and power output. In this work, a high‐capacity sodium storage material with FeS 2 nanoclusters (≈1–2 nm) embedded in N, S‐doped carbon matrix (FeS 2 /N,S‐C) was synthesized, the surface of which displays defects‐repaired characteristic and detectable dot‐matrix distributed Fe‐N‐C/Fe‐S‐C bonds. After the initial discharging process, the uniform ultra‐thin NaF‐rich (≈6.0 nm) solid electrolyte interphase was obtained, thereby achieving verifiable ultra‐high initial coulombic efficiency (≈92 %). The defects‐repaired surface provides perfect platform, and the catalysis of dot‐matrix distributed Fe‐N‐C/Fe‐S‐C bonds to the rapid decomposing of NaSO 3 CF 3 and diethylene glycol dimethyl ether successfully accelerate the building of two‐dimensional ultra‐thin solid electrolyte interphase. DFT calculations further confirmed the catalysis mechanism. As a result, the constructed FeS 2 /N,S‐C provides high reversible capacity (749.6 mAh g −1 at 0.1 A g −1 ) and outstanding cycle stability (92.7 %, 10 000 cycles, 10.0 A g −1 ). Especially, at −15 °C, it also obtains a reversible capacity of 211.7 mAh g −1 at 10.0 A g −1 . Assembled pouch‐type cell performs potential application. The insight in this work provides a bright way to interface design for performance improvement in batteries.
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