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Mixed-cation perovskite solar cells (PSCs) have attracted much attention because of the advantages of suitable bandgap and stability. It is still a challenge to rationally design and modify the perovskite/tin oxide (SnO2) heterogeneous interface for achieving highly efficient and stable PSCs. Herein, a strategy of one-stone-for-three-birds is proposed to achieve multi-functional interface regulation via introducing N-Chlorosuccinimide (NCS) into the solution of SnO2: i) CâO functional group in NCS can induces strong binding affinity to uncoordinated defects (oxygen vacancies, free lead ions, etc) at the buried interface and passivate them; ii) incomplete in situ hydrolysis reactions can occur spontaneously and adjust the pH value of the SnO2 solution to achieve a more matchable energy level; iii) effectively releasing the residual stress of the underlying perovskite. As a result, a champion power conversion efficiency (PCE) of 24.74% is achieved with a device structure of ITO/SnO2/Perovskite/Spiro-OMeTAD/Ag, which is one of the highest values for cesium-formamidinium-methylammonium (CsFAMA) triple cation PSCs. Furthermore, the device without encapsulation can sustain 94.6% of its initial PCE after the storage at room temperature and relative humidity (RH) of 20% for 40 days. The research provides a versatile way to manipulate buried interface for achieving efficient and stable PSCs.
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As the long non-coding RNAs (lncRNAs) play important roles during the incurrence and development of various human diseases, identifying disease-related lncRNAs can contribute to clarifying the pathogenesis of diseases. Most of the recent lncRNA-disease association prediction methods utilized the multi-source data about the lncRNAs and diseases. A single lncRNA may participate in multiple disease processes, and multiple lncRNAs usually are involved in the same disease process synergistically. However, the previous methods did not completely exploit the biological characteristics to construct the informative prediction models. We construct a prediction model based on adaptive hypergraph and gated convolution for lncRNA-disease association prediction (AGLDA), to embed and encode the biological characteristics about lncRNA-disease associations, the topological features from the entire heterogeneous graph perspective, and the gated enhanced pairwise features. First, the strategy for constructing hyperedges is designed to reflect the biological characteristic that multiple lncRNAs are involved in multiple disease processes. Furthermore, each hyperedge has its own biological perspective, and multiple hyperedges are beneficial for revealing the diverse relationships among multiple lncRNAs and diseases. Second, we encode the biological features of each lncRNA (disease) node using a strategy based on dynamic hypergraph convolutional networks. The strategy may adaptively learn the features of the hyperedges and formulate the dynamically evolved hypergraph topological structure. Third, a group convolutional network is established to integrate the entire heterogeneous topological structure and multiple types of node attributes within an lncRNA-disease-miRNA graph. Finally, a gated convolutional strategy is proposed to enhance the informative features of the lncRNA-disease node pairs. The comparison experiments indicate that AGLDA outperforms seven advanced prediction methods. The ablation studies confirm the effectiveness of major innovations, and the case studies validate AGLDA's ability in application for discovering potential disease-related lncRNA candidates.
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ARN Largo no Codificante , ARN Largo no Codificante/genética , Humanos , Biología Computacional/métodos , Predisposición Genética a la Enfermedad , Enfermedad/genética , Aprendizaje AutomáticoRESUMEN
Perovskite solar cells (PSCs) have emerged as one of the most promising and competitive photovoltaic technologies, and doctor-blading is a facile and robust deposition technique to efficiently fabricate PSCs in large scale, especially matching with roll-to-roll process. Herein, it demonstrates the encouraging results of one-step, antisolvent-free doctor-bladed methylammonium lead iodide (CH3 NH3 PbI3, MAPbI3 ) PSCs under a wide range of humidity from 45% to 82%. A synergy strategy of ionic-liquid methylammonium acetate (MAAc) and molecular phenylurea additives is developed to modulate the morphology and crystallization process of MAPbI3 perovskite film, leading to high-quality MAPbI3 perovskite film with large-size crystal, low defect density, and ultrasmooth surface. Impressive power conversion efficiency (PCE) of 20.34% is achieved for doctor-bladed PSCs under the humidity over 80% with a device structure of ITO/SnO2 /MAPbI3 /Spiro-OMeTAD/Ag. It is the highest PCEs for one-step solution-processed MAPbI3 PSCs without antisolvent assistance. The research provides a facile and robust large-scale deposition technique to fabricate highly efficient and stable PSCs under a wide range of humidity, even with the humidity over 80%.
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The absorption and accumulation of nanoplastics (NPs) by plants is currently attracting considerable attention. NPs also tend to adsorb surrounding organic pollutants, such as pesticides, which can damage plants. However, molecular mechanisms underlying the phytotoxicity of NPs are not sufficiently researched. Therefore, we analyzed the toxicological effects of 50 mg/L polystyrene NPs (PS 50 nm) and 5 mg/L the herbicide quinolinic (QNC) on rice (Oryza sativa L.) using 7-day hydroponic experiments, explaining the corresponding mechanisms by transcriptome analysis. The main conclusion is that all treatments inhibit rice growth and activate the antioxidant level. Compared with CK, the inhibition rates of PS, QNC, and PS+QNC on rice shoot length were 3.95%, 6.68%, and 11.43%, respectively. The gene ontology (GO) term photosynthesis was significantly enriched by QNC, and the combination PS+QNC significantly enriched the GO terms of amino sugar and nucleotide sugar metabolisms. The chemicals QNC and PS+QNC significantly affected the Kyoto Encyclopedia of Genes and Genomes (KEGG) of the MAPK signaling pathway, plant hormone signal transduction, and plant-pathogen interaction. Our findings provide a new understanding of the phytotoxic mechanisms and environmental impacts of the interactions between NPs and pesticides. It also provides insights into the impact of NPs and pesticides on plants in the agricultural system.
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Oryza , Plaguicidas , Transcriptoma , Oryza/metabolismo , Poliestirenos/metabolismo , Microplásticos/metabolismo , Plaguicidas/metabolismoRESUMEN
BACKGROUND: Preoperative prediction of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) is significant for surgical decision-making. OBJECTIVE: This study aims to develop a dual-modal radiomics (DMR) model based on grayscale ultrasound (GSUS) and dual-energy computed tomography (DECT) for non-invasive CLNM in PTC. METHODS: In this study, 348 patients with pathologically confirmed PTC at Jiangsu University Affiliated People's Hospital who completed preoperative ultrasound (US) and DECT examinations were enrolled and randomly assigned to training (nâ=â261) and test (nâ=â87) cohorts. The enrolled patients were divided into two groups based on pathology findings namely, CLNM (nâ=â179) and CLNM-Free (nâ=â169). Radiomics features were extracted from GSUS images (464 features) and DECT images (960 features), respectively. Pearson correlation coefficient (PCC) and the least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation were then used to select CLNM-related features. Based on the selected features, GSUS, DECT, and GSUS combined DECT radiomics models were constructed by using a Support Vector Machine (SVM) classifier. RESULTS: Three predictive models based on GSUS, DECT, and a combination of GSUS and DECT, yielded performance of areas under the curve (AUC)â=â0.700 [95% confidence interval (CI), 0.662-0.706], 0.721 [95% CI, 0.683-0.727], and 0.760 [95% CI, 0.728-0.762] in the training dataset, and AUCâ=â0.643 [95% CI, 0.582-0.734], 0.680 [95% CI, 0.623-0.772], and 0.744 [95% CI, 0.686-0.784] in the test dataset, respectively. It shows that the predictive model combined GSUS and DECT outperforms both models using GSUS and DECT only. CONCLUSIONS: The newly developed combined radiomics model could more accurately predict CLNM in PTC patients and aid in better surgical planning.
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Cuello , Neoplasias de la Tiroides , Humanos , Cáncer Papilar Tiroideo/diagnóstico por imagen , Metástasis Linfática/diagnóstico por imagen , Cuello/diagnóstico por imagen , Área Bajo la Curva , Neoplasias de la Tiroides/diagnóstico por imagenRESUMEN
BACKGROUND: RNA methylation refers to a form of methyl modification in RNA that modulates various epigenetic alterations. Mounting studies have focused on its potential mechanisms in cancer initiation and progression. However, the prognostic value and potential role of RNA methylation in the immune microenvironment of pancreatic cancer remain unclear. METHODS: Comprehensive bioinformatics analysis was performed to illuminate the expression profiles of RNA methylation modulators. In addition, the ConsensusClusterPlus algorithm was utilized to identify two remarkably different subtypes, and a feasible risk stratification method was established to accurately estimate prognosis. In addition, we validated our signature at the cytology and histology levels and conducted functional experiments to explore the biological functions of our key genes. RESULTS: Two subtypes with remarkable survival differences were identified by the consensus clustering algorithm. Cluster 2 tended to have higher expression levels of RNA methylation regulators and to be the high RNA methylation group. In addition, cluster 1 exhibited a significantly higher abundance of almost all immune cells and increased immune checkpoint expression compared to cluster 2. Chemotherapeutic sensitivity analysis indicated that there were significant differences in the sensitivity of four of the six drugs between different subgroups. Mutation investigation revealed a higher mutation burden and a higher number of mutations in cluster 2. An accurate and feasible risk stratification method was established based on the expression of key genes of each subtype. Patients with low risk scores exhibited longer survival times in one training (TCGA) and two validation cohorts (ICGC, GSE57495), with p values of 0.001, 0.0081, and 0.0042, respectively. In addition, our signature was further validated in a cohort from Fudan University Shanghai Cancer Center. The low-risk group exhibited higher immune cell abundance and immune checkpoint levels than the high-risk group. The characteristics of the low-risk group were consistent with those of cluster 1: higher stromal score, estimate score, and immune score and lower tumor purity. Additionally, cell function investigations suggested that knockdown of CDKN3 remarkably inhibited the proliferation and migration of pancreatic cancer cells. CONCLUSIONS: RNA methylation has a close correlation with prognosis, immune infiltration and therapy in pancreatic cancer. Our subtypes and risk stratification method can accurately predict prognosis and the efficacy of immune therapy and chemotherapy.
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Biomarcadores de Tumor , Neoplasias Pancreáticas , Biomarcadores de Tumor/genética , China , Humanos , Metilación , Pronóstico , ARN , Microambiente Tumoral/genética , Neoplasias PancreáticasRESUMEN
COVID-19 pneumonia started in December 2019 and caused large casualties and huge economic losses. In this study, we intended to develop a computer-aided diagnosis system based on artificial intelligence to automatically identify the COVID-19 in chest computed tomography images. We utilized transfer learning to obtain the image-level representation (ILR) based on the backbone deep convolutional neural network. Then, a novel neighboring aware representation (NAR) was proposed to exploit the neighboring relationships between the ILR vectors. To obtain the neighboring information in the feature space of the ILRs, an ILR graph was generated based on the k-nearest neighbors algorithm, in which the ILRs were linked with their k-nearest neighboring ILRs. Afterward, the NARs were computed by the fusion of the ILRs and the graph. On the basis of this representation, a novel end-to-end COVID-19 classification architecture called neighboring aware graph neural network (NAGNN) was proposed. The private and public data sets were used for evaluation in the experiments. Results revealed that our NAGNN outperformed all the 10 state-of-the-art methods in terms of generalization ability. Therefore, the proposed NAGNN is effective in detecting COVID-19, which can be used in clinical diagnosis.
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COVID-19 is a contagious infection that has severe effects on the global economy and our daily life. Accurate diagnosis of COVID-19 is of importance for consultants, patients, and radiologists. In this study, we use the deep learning network AlexNet as the backbone, and enhance it with the following two aspects: 1) adding batch normalization to help accelerate the training, reducing the internal covariance shift; 2) replacing the fully connected layer in AlexNet with three classifiers: SNN, ELM, and RVFL. Therefore, we have three novel models from the deep COVID network (DC-Net) framework, which are named DC-Net-S, DC-Net-E, and DC-Net-R, respectively. After comparison, we find the proposed DC-Net-R achieves an average accuracy of 90.91% on a private dataset (available upon email request) comprising of 296 images while the specificity reaches 96.13%, and has the best performance among all three proposed classifiers. In addition, we show that our DC-Net-R also performs much better than other existing algorithms in the literature. Supplementary Information: The online version contains supplementary material available at 10.1007/s11390-020-0679-8.
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The widely spreading COVID-19 has caused thousands of hundreds of mortalities over the world in the past few months. Early diagnosis of the virus is of great significance for both of infected patients and doctors providing treatments. Chest Computerized tomography (CT) screening is one of the most straightforward techniques to detect pneumonia which was caused by the virus and thus to make the diagnosis. To facilitate the process of diagnosing COVID-19, we therefore developed a graph convolutional neural network ResGNet-C under ResGNet framework to automatically classify lung CT images into normal and confirmed pneumonia caused by COVID-19. In ResGNet-C, two by-products named NNet-C, ResNet101-C that showed high performance on detection of COVID-19 are simultaneously generated as well. Our best model ResGNet-C achieved an averaged accuracy at 0.9662 with an averaged sensitivity at 0.9733 and an averaged specificity at 0.9591 using five cross-validations on the dataset, which is comprised of 296 CT images. To our best knowledge, this is the first attempt at integrating graph knowledge into the COVID-19 classification task. Graphs are constructed according to the Euclidean distance between features extracted by our proposed ResNet101-C and then are encoded with the features to give the prediction results of CT images. Besides the high-performance system, which surpassed all state-of-the-art methods, our proposed graph construction method is simple, transferrable yet quite helpful for improving the performance of classifiers, as can be justified by the experimental results.
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Prussian blue (PB) modified nanoporous gold (NPG) electrodes exhibit great potential for improving the detection sensitivity and stability for hydrogen peroxide monitoring. The NPG provides large surface-to-volume ratio as well as diffusion 'highways' to assist the transfer of the ions. In the present work, we optimized the deposition time for NPG fabrication and examine the electrochemical performance of the electrodes. A critical deposition time on the electrochemical performances including linear range, operational stability and sensitivity was experimentally determined. Below and above such a deposition time, two different growth patterns of the microstructures were observed. This transition of deposited structures corresponding to the critical time results in different pathways for electron transfer and ion diffusivity through PB lattice.
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BACKGROUND: Rice (Oryza sativa L.) is a staple food crop worldwide. Its yield and quality are affected by its tillering pattern and spikelet development. Although many genes involved in the vegetative and reproductive development of rice have been characterized in previous studies, the genetic mechanisms that control axillary tillering, spikelet development, and panicle exsertion remain incompletely understood. RESULTS: Here, we characterized a novel rice recombinant inbred line (RIL), panicle exsertion defect and aberrant spikelet (pds). It was derived from a cross between two indica varieties, S142 and 430. Intriguingly, no abnormal phenotypes were observed in the parents of pds. This RIL exhibited sheathed panicles at heading stage. Still, a small number of tillers in pds plants were fully exserted from the flag leaves. Elongated sterile lemmas and rudimentary glumes (occurred occasionally) were observed in the spikelets of the exserted panicles and were transformed into palea/lemma-like structures. Furthermore, more interestingly, tillers occasionally grew from the axils of the elongated rudimentary glumes. Via genetic linkage analysis, we found that the abnormal phenotype of pds manifesting as genetic incompatibility or hybrid weakness was caused by genetic interaction between a recessive locus, pds1, which was derived from S142 and mapped to chromosome 8, and a locus pds2, which not yet mapped from 430. We fine-mapped pds1 to an approximately 55-kb interval delimited by the markers pds-4 and 8 M3.51. Six RGAP-annotated ORFs were included in this genomic region. qPCR analysis revealed that Loc_Os080595 might be the target of pds1 locus, and G1 gene might be involved in the genetic mechanism underlying the pds phenotype. CONCLUSIONS: In this study, histological and genetic analyses revealed that the pyramided pds loci resulted in genetic incompatibility or hybrid weakness in rice might be caused by a genetic interaction between pds loci derived from different rice varieties. Further isolation of pds1 and its interactor pds2, would provide new insight into the molecular regulation of grass inflorescence development and exsertion, and the evolution history of the extant rice.
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Oryza/genética , Mapeo Cromosómico , Flores/crecimiento & desarrollo , Flores/ultraestructura , Regulación de la Expresión Génica de las Plantas , Genes de Plantas/genética , Genes de Plantas/fisiología , Estudios de Asociación Genética , Sitios Genéticos , Microscopía Electrónica de Rastreo , Oryza/crecimiento & desarrollo , Oryza/ultraestructura , Reacción en Cadena en Tiempo Real de la PolimerasaRESUMEN
BACKGROUND: Rice (Oryza sativa L.) is a thermophilic crop vulnerable to chilling stress. However, common wild rice (Oryza rufipogon Griff.) in Guangxi (China) has the ability to tolerate chilling stress. To better understand the molecular mechanisms underlying chilling tolerance in wild rice, iTRAQ-based proteomic analysis was performed to examine CTS-12, a major chilling tolerance QTL derived from common wild rice, mediated chilling and recovery-induced differentially expressed proteins (DEPs) between the chilling-tolerant rice line DC90 and the chilling-sensitive 9311. RESULTS: Comparative analysis identified 206 and 155 DEPs in 9311 and DC90, respectively, in response to the whole period of chilling and recovery. These DEPs were clustered into 6 functional groups in 9311 and 4 in DC90. The majority were enriched in the 'structural constituent of ribosome', 'protein-chromophore linkage', and 'photosynthesis and light harvesting' categories. Short Time-series Expression Miner (STEM) analysis revealed distinct dynamic responses of both chloroplast photosynthetic and ribosomal proteins between 9311 and DC90. CONCLUSION: CTS-12 might mediate the dynamic response of chloroplast photosynthetic and ribosomal proteins in DC90 under chilling (cold acclimation) and recovery (de-acclimation) and thereby enhancing the chilling stress tolerance of this rice line. The identified DEPs and the involvement of CTS-12 in mediating the dynamic response of DC90 at the proteomic level illuminate and deepen the understanding of the mechanisms that underlie chilling stress tolerance in wild rice.
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Genes de Plantas/genética , Oryza/genética , Sitios de Carácter Cuantitativo/genética , Cloroplastos/metabolismo , Frío , Respuesta al Choque por Frío , Cromatografía de Gases y Espectrometría de Masas , Genes de Plantas/fisiología , Oryza/metabolismo , Hojas de la Planta/metabolismo , Hojas de la Planta/fisiología , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo , Proteínas de Plantas/fisiología , Proteómica , TranscriptomaRESUMEN
We demonstrate triggered single photon emission up to 77K from an ordered 5x8 array of InGaAs single quantum dots (SQDs). The SQDs are grown selectively on patterned mesa tops utilizing substrate-encoded size-reducing epitaxy (SESRE). It exploits designed surface-curvature stress gradients to preferentially direct atom migration from mesa sidewalls to the top during growth. The emission from the SQDs exhibits a g(2)(0) of 0.19 ± 0.03 at 8K and decent emission spectral uniformity (standard deviation <1% of emission wavelength). The SESRE QDs are inherently compatible with on-chip integrated light manipulation elements, thereby enabling a path towards integrated nanophotonic systems for quantum information processing.
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PRCIS: There are significant mental, physical, and perceptual challenges in patients undergoing visual field testing, particularly in the elderly population with glaucoma. PURPOSE: To quantitatively and qualitatively investigate patient experiences of undergoing visual field testing. METHODS: This cross-sectional, mixed-methods study involves adult patients attending a private outpatient ophthalmology clinic in Brisbane, Australia. Participant experiences of visual field testing were assessed using a 100mm visual analog scale (VAS), followed by face-to-face semi-structured interviews. Outcome data comprised of VAS scores (1-100) and thematically analyzed verbatim notes. RESULTS: Out of the 152 participants (M:F 79 (52.0%):73 (48.0%)) the age group with the highest proportion of participants was 71-80 years 56 (36.8%), and most had a primary ocular diagnosis of glaucoma 107 (70.4%). The mean VAS score for visual field testing experience was (60.45 [SD=30.38]). The mean VAS score of participants with glaucoma was significantly lower than that of participants without glaucoma (55.34 [31.13] vs 72.67 [25.04]; P<0.05). Prevalent themes regarding participant experiences of visual field testing consisted of concentration difficulties, intra-test and peri-test anxiety, skepticism in the testing process, and physical discomfort. Age, gender and mean visual field index did not influence the participant's experience of visual field testing. CONCLUSIONS: Mental and physical barriers to visual field testing tolerability were identified in a predominantly elderly population with glaucoma. Our findings highlight the importance of addressing patient concerns and apprehensions regarding visual field testing and emphasize the value of patient-initiated breaks, particularly for individuals experiencing concentration difficulties and physical discomfort.
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Identifying the side effects related to drugs is beneficial for reducing the risk of drug development failure and saving the drug development cost. We proposed a graph reasoning method, RKDSP, to fuse the semantics of multiple connection relationships, the local knowledge within each meta-path, the global knowledge among multiple meta-paths, and the attributes of the drug and side effect node pairs. We constructed drug-side effect heterogeneous graphs consisting of the drugs, side effects, and their similarity and association connections. Multiple relational transformers were established to learn node features from diverse meta-path semantic perspectives. A knowledge distillation module was constructed to learn local and global knowledge of multiple meta-paths. Finally, an adaptive convolutional neural network-based strategy was presented to adaptively encode the attributes of each drug-side effect node pair. The experimental results demonstrated that RKDSP outperforms the compared state-of-the-art prediction approaches.
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Microplastic (MP) pollution can exert significant pressure on soil ecosystems, however, the interactive effects of MPs on soil bacterial, fungal and protist communities remains poorly understood. Soil macrofauna, such as earthworms, can be directly affected by MPs, potentially leading to a range of feedbacks on the soil microbial community. To address this, we conducted a microcosm experiment to examine the effects of conventional (i.e., polyethylene, polystyrene) and biodegradable MPs (i.e. PBAT, polylactic acid) on the structure of the soil bacterial, fungal, and protist communities in the presence or absence of earthworms. We found that MP contamination negatively affected the diversity and composition of soil microbial and protist communities, with smaller-sized conventional MPs having the most pronounced effects. For example, compared with the unamended control, small-sized polyethylene MPs both significantly reduced the Shannon diversity of soil bacteria, fungi, and protist by 4.3 %, 37.0 %, and 9.1 %, respectively. Biodegradable MPs increased negative correlations among bacteria, fungi, and protists. However, earthworms mitigated these effects, enhancing the diversity and altering the composition of these communities. They also increased the niche width and stability of the soil microbial food web network. Our study indicated that earthworms help attenuate the response of soil microorganisms to MPs stress by influencing the diversity and composition of soil microorganisms and soil physicochemical properties and underscores the importance of considering macrofauna in MPs research.
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Microplásticos , Oligoquetos , Microbiología del Suelo , Contaminantes del Suelo , Oligoquetos/fisiología , Animales , Microbiota/efectos de los fármacos , Hongos , Suelo/química , Bacterias/efectos de los fármacos , Ecosistema , Eucariontes/efectos de los fármacosRESUMEN
Ecosystem engineers influence the structure and function of soil food webs through non-trophic interactions. The activity of large soil animals, such as earthworms, has a significant impact on the soil microarthropod community. However, the influence of millipedes on soil microarthropod communities remains largely unknown. In this microcosm experiment, we examined the effects of adding, removing, and restricting millipede activity on Acari and Collembola communities in litter and soil by conducting two destructive sampling sessions on days 10 and 30, respectively. At the time of the first sampling event (10 d), Acari and Collembola abundance was shown to increase and the alpha diversity went higher in the treatments with millipedes. At the time of the second sampling event (30 d), millipedes significantly reduced the Collembola abundance and alpha diversity. The results were even more pronounced as the millipedes moved through the soil, which caused the collembolans to be more inclined to inhabit the litter, which in turn resulted in the increase in the abundance and diversity of Acari in the soil. The rapid growth of Collembola in the absence of millipedes significantly inhibited the abundance of Acari. The presence of millipedes altered the community structure of Acari and Collembola, leading to a stronger correlation between the two communities. Changes in these communities were driven by the dominant taxa of Acari and Collembola. These findings suggest that millipedes, as key ecosystem engineers, have varying impacts on different soil microarthropods. This study enhances our understanding of biological interactions and offers a theoretical foundation for soil biodiversity conservation.
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Objective: This retrospective study was aimed to develop a predictive model for assessing the necessity of tracheostomy (TT) in patients admitted to the neurosurgery intensive care unit (NSICU). Method: We analyzed data from 1626 NSICU patients with severe acute brain injury (SABI) who were admitted to the Department of NSICU at the Affiliated People's Hospital of Jiangsu University between January 2021 and December 2022. Data of the patients were retrospectively obtained from the clinical research data platform. The patients were randomly divided into training (70%) and testing (30%) cohorts. The least absolute shrinkage and selection operator (LASSO) regression identified the optimal predictive features. A multivariate logistic regression model was then constructed and represented by a nomogram. The efficacy of the model was evaluated based on discrimination, calibration, and clinical utility. Results: The model highlighted six predictive variables, including the duration of NSICU stay, neurosurgery, orotracheal intubation time, Glasgow Coma Scale (GCS) score, systolic pressure, and respiration rate. Receiver operating characteristic (ROC) analysis of the nomogram yielded area under the curve (AUC) values of 0.854 (95% confidence interval [CI]: 0.822-0.886) for the training cohort and 0.865 (95% CI: 0.817-0.913) for the testing cohort, suggesting commendable differential performance. The predictions closely aligned with actual observations in both cohorts. Decision curve analysis demonstrated that the numerical model offered a favorable net clinical benefit. Conclusion: We developed a novel predictive model to identify risk factors for TT in SABI patients within the NSICU. This model holds the potential to assist clinicians in making timely surgical decisions concerning TT.
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Disruption of corticospinal tracts (CST) is a leading factor for motor impairments following intracerebral hemorrhage (ICH) in the striatum. Previous studies have shown that therapeutic hypothermia (HT) improves outcomes of ICH patients. However, whether HT has a direct protection effect on the CST integrity and the underlying mechanisms remain largely unknown. In this study, we employed a chemogenetics approach to selectively activate bilateral warm-sensitive neurons in the preoptic areas to induce a hypothermia-like state. We then assessed effects of HT treatment on the integrity of CST and motor functional recovery after ICH. Our results showed that HT treatment significantly alleviated axonal degeneration around the hematoma and the CST axons at remote midbrain region, ultimately promoted skilled motor function recovery. Anterograde and retrograde tracing revealed that HT treatment protected the integrity of the CST over an extended period. Mechanistically, HT treatment prevented mitochondrial swelling in degenerated axons around the hematoma, alleviated mitochondrial impairment by reducing mitochondrial ROS accumulation and improving mitochondrial membrane potential in primarily cultured cortical neurons with oxyhemoglobin treatment. Serving as a proof of principle, our study provided novel insights into the application of HT to improve functional recovery after ICH.
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Hemorragia Cerebral , Hipotermia Inducida , Mitocondrias , Tractos Piramidales , Animales , Tractos Piramidales/patología , Hemorragia Cerebral/patología , Hemorragia Cerebral/complicaciones , Hemorragia Cerebral/metabolismo , Ratones , Mitocondrias/metabolismo , Mitocondrias/patología , Masculino , Hipotermia Inducida/métodos , Ratones Endogámicos C57BL , Recuperación de la Función/fisiología , Células CultivadasRESUMEN
Recently we reported an analysis that examined the potential of synthesized photovoltaic functional abiotic nanosystems (PVFANs) to modulate membrane potential and activate action potential firing in neurons. Here we extend the analysis to delineate the requirements on the electronic energy levels and the attendant photophysical properties of the PVFANs to induce repetitive action potential under continuous light, a capability essential for the proposed potential application of PVFANs as retinal cellular prostheses to compensate for loss of photoreceptors. We find that repetitive action potential firing demands two basic characteristics in the electronic response of the PVFANs: an exponential dependence of the PVFAN excited state decay rate on the membrane potential and a three-state system such that, following photon absorption, the electron decay from the excited state to the ground state is via intermediate state(s) whose lifetime is comparable to the refractory time following an action potential. FROM THE CLINICAL EDITOR: In this study, the potential of synthetic photovoltaic functional abiotic nanosystems (PVFANs) is examined under continuous light to modulate membrane potential and activate action potential firing in neurons with the proposed potential application of PVFANs as retinal cellular prostheses.