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This paper summarized the nursing management of resection for giant abdominal tumor in a patient with lipomatous tumor. Based on the nursing assessment and illness condition monitoring, comprehensive nursing interventions including pain control, nutrition support, health education and psychological care were carried out to relieve the patient’s negative emotion, prevention postoperative complications and improve the recovery. (本文总结1例非典型脂肪瘤样肿瘤患者行腹部巨大肿瘤切除术后的护理经验。患者入院后完善常规检查, 做好术前准备和术中配合, 术后加强病情观察, 针对性止痛, 给予营养支持, 开展健康教育和心理干预, 缓解患者负性情绪, 预防并发症, 促进患者早日康复。)

Sodium transition metal oxides with layered structures are attractive cathode materials for sodium-ion batteries due to their large theoretical specific capacities. However, these layered oxides suffer from poor cyclability and low rate performance because of structural instability and sluggish electrode kinetics. In the present work, we show the sodiation reaction of Mn3O4 to yield crystal water free NaMnO2-y-δ(OH)2y, a monoclinic polymorph of sodium birnessite bearing Na/Mn(OH)8 hexahedra and Na/MnO6 octahedra. With the new polymorph, NaMnO2-y-δ(OH)2y exhibits an enlarged interlayer distance of about 7 Å, which is in favor of fast sodium ion migration and good structural stability. In combination of the favorable nanosheet morphology, NaMn2-y-δ(OH)2y cathode delivers large specific capacity up to 211.9 mAh g-1, excellent cycle performance (94.6% capacity retention after 1000 cycles), and outstanding rate capability (156.0 mAh g-1 at 50 C). This study demonstrates an effective approach in tailoring the structural and electrochemical properties of birnessite towards superior cathode performance in sodium-ion batteries.

Functional porous materials are of increasing importance in energy conversion and catalysis. Improved analysis is needed to guide optimization. Four-dimensional (4D) imaging [chemical mapping in three-dimensional (3D) by spectro-tomography] of an Al2O3 aerogel coated with ZnO by atomic layer deposition was performed using soft X-ray ptychography at the Zn L-edge. A two-dimensional spatial resolution of 14(2) nm was achieved. Visualizations of the 3D chemical structure are provided. The degree of ZnO coverage of the surface of the Al2O3 aerogel framework in two different samples was estimated and found to be both thicker and less homogeneous than expected. Other analyses of the 4D results, including the degree of contact between Al2O3 and ZnO, were extracted from the reconstructed 3D data. This pioneering soft X-ray spectro-ptycho-tomography study will anchor further studies of functional porous materials.

Preimplantation genetic testing (PGT) can minimize the risk of birth defects. However, the accuracy and applicability of routine PGT is confounded by uneven genome coverage and high allele drop‐out rate from existing single‐cell whole genome amplification methods. Here, a method to diagnose genetic mutations and concurrently evaluate embryo competence by leveraging the abundant mRNA transcript copies present in trophectoderm cells is developed. The feasibility of the method is confirmed with 19 donated blastocysts. Next, the method is applied to 82 embryos from 26 families with monogenic defects for simultaneous mutation detection and competence assessment. The accuracy rate of direct mutation detection is up to 95%, which is significantly higher than DNA‐based method. Meanwhile, this approach correctly predicted seven out of eight (87.5%) embryos that failed to implant. Of six embryos that are predicted to implant successfully, four met such expectations (66.7%). Notably, this method is superior at conditions for mutation detection that are challenging when using DNA‐based PGT, such as when detecting pathogenic genes with a high de novo rate, multiple pseudogenes, or an abnormal expansion of CAG trinucleotide repeats. Taken together, this study establishes the feasibility of an RNA‐based PGT that is also informative for assessing implantation competence. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a highly heritable disease. The morphological changes of cortical cortex (such as, cortical thickness and surface area) in AD always accompany by the change of the functional connectivity to other brain regions and influence the short‐ and long‐range brain network connections, causing functional deficits of AD. In this study, the first hypothesis is that genetic variations might affect morphology‐based brain networks, leading to functional deficits; the second hypothesis is that protein–protein interaction (PPI) between the candidate proteins and known interacting proteins to AD might exist and influence AD. 600 470 variants and structural magnetic resonance imaging scans from 175 AD patients and 214 healthy controls were obtained from the Alzheimer's Disease Neuroimaging Initiative‐1 database. A co‐sparse reduced‐rank regression model was fit to study the relationship between non‐synonymous mutations and morphology‐based brain networks. After that, PPIs between selected genes and BACE1, an enzyme that was known to be related to AD, are explored by using molecular dynamics (MD) simulation and co‐immunoprecipitation (Co‐IP) experiments. Eight genes affecting morphology‐based brain networks were identified. The results of MD simulation showed that the PPI between TGM4 and BACE1 was the strongest among them and its interaction was verified by Co‐IP. Hence, gene variations influence morphology‐based brain networks in AD, leading to functional deficits. This finding, validated by MD simulation and Co‐IP, suggests that the effect is robust. [ABSTRACT FROM AUTHOR]

Rare earth (RE)-doped CaS phosphors have been widely used as light-emitting components in various fields. Nevertheless, the application of nanosized CaS particles is still significantly limited by their poor water resistance and weak luminescence. Herein, a lattice-matching strategy is developed by growing an inert shell of cubic NaYF4 phase on the CaS luminescent core. Due to their similarity in crystal structure, a uniform core–shell heterostructure (CaS:Ce3+@NaYF4) can be obtained, which effectively protects the CaS:Ce3+ core from degradation in aqueous environment and enhances its luminescence intensity. As a proof of concept, a label-free aptasensor is further constructed by combining core–shell CaS:Ce3+@NaYF4 and Au nanoparticles (AuNPs) for the ultrasensitive detection of kanamycin antibiotics. Based on the efficient FRET process, the detection linear range of kanamycin spans from 100 to 1000 nM with a detection limit of 7.8 nM. Besides, the aptasensor shows excellent selectivity towards kanamycin antibiotics, and has been successfully applied to the detection of kanamycin spiked in tap water and milk samples, demonstrating its high potential for sensing applications. [ABSTRACT FROM AUTHOR]

Objective: Motivated by Health Care 4.0, this study aims to reducing the dimensionality of traditional EEG features based on manual extracted features, including statistical features in the time and frequency domains. Methods: A total of 22 multi-scale features were extracted from the UNM and Iowa datasets using a 4th order Butterworth filter and wavelet packet transform. Based on single-channel validation, 29 channels with the highest R2 scores were selected from a pool of 59 common channels. The proposed channel selection scheme was validated on the UNM dataset and tested on the Iowa dataset to compare its generalizability against models trained without channel selection. Results: The experimental results demonstrate that the proposed model achieves an optimal classification accuracy of 100%. Additionally, the generalization capability of the channel selection method is validated through out-of-sample testing based on the Iowa dataset Conclusions: Using single-channel validation, we proposed a channel selection scheme based on traditional statistical features, resulting in a selection of 29 channels. This scheme significantly reduced the dimensionality of EEG feature vectors related to Parkinson's disease by 50%. Remarkably, this approach demonstrated considerable classification performance on both the UNM and Iowa datasets. For the closed-eye state, the highest classification accuracy achieved was 100%, while for the open-eye state, the highest accuracy reached 93.75%. [ABSTRACT FROM AUTHOR]

The welding quality during welding body-in-white (BIW) determines the safety of automobiles. Due to the limitations of testing cost and cycle time, the prediction of welding quality has become an essential safety issue in the process of automobile production. Conventional prediction methods mainly consider the welding process parameters and ignore the material parameters, causing their results to be unrealistic. Upon identifying significant correlations between vehicle body materials, we utilize principal component analysis (PCA) to perform dimensionality reduction and extract the underlying principal components. Thereafter, we employ a greedy feature selection strategy to identify the most salient features. In this study, a welding quality prediction model integrating process parameters and material characteristics is proposed, following which the influence of material properties is analyzed. The model is verified based on actual production data, and the results show that the accuracy of the model is improved through integrating the production process characteristics and material characteristics. Moreover, the overfitting phenomenon can be effectively avoided in the prediction process. [ABSTRACT FROM AUTHOR]