Multi-Functional Regulation on Buried Interface for Achieving Efficient Triple-Cation Perovskite Solar Cells.

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) C═O 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.

Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs.

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.

NAGNN: Classification of COVID-19 based on neighboring aware representation from deep graph neural network.

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.

Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction.

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.

Patient Experience and Barriers to the Visual Field Test for Glaucoma.

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.

A nomogram for predicting the necessity of tracheostomy after severe acute brain injury in patients within the neurosurgery intensive care unit: A retrospective cohort study.

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.

Earthworms alleviate microplastics stress on soil microbial and protist communities.

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.

Impacts of Millipedes on Acari and Collembola Communities-A Microcosm Experiment.

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.

Hypothermia protects the integrity of corticospinal tracts and alleviates mitochondria injury after intracerebral hemorrhage in mice.

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.

An endoscopic-assisted contralateral paramedian supracerebellar infratentorial approach in the treatment of thalamic hemorrhage with hematoma extension into the brainstem: a case report.

Objective: Thalamic hemorrhage (TH) with hematoma extension into the brainstem can lead to poor outcomes. In this study, we discuss the feasibility of the endoscopic-assisted contralateral paramedian supracerebellar infratentorial (SCIT) approach as a therapeutic method for treating such patients. Case presentation: A patient suffered from a sudden loss of consciousness and right limb weakness, and a CT scan indicated TH with hematoma extension into the brainstem. She consented to undergo surgery by the endoscopic-assisted contralateral paramedian SCIT approach. Results: Now, the patient can open her eyes on her own and move her left arm in response to commands. Conclusion: The endoscopic-assisted contralateral paramedian SCIT approach may be a viable therapeutic method for treating TH patients with hematoma extension into the brainstem.

DLBCNet: A Deep Learning Network for Classifying Blood Cells.

Background: Blood is responsible for delivering nutrients to various organs, which store important health information about the human body. Therefore, the diagnosis of blood can indirectly help doctors judge a person's physical state. Recently, researchers have applied deep learning (DL) to the automatic analysis of blood cells. However, there are still some deficiencies in these models. Methods: To cope with these issues, we propose a novel network for the multi-classification of blood cells, which is called DLBCNet. A new specifical model for blood cells (BCGAN) is designed to generate synthetic images. The pre-trained ResNet50 is implemented as the backbone model, which serves as the feature extractor. The extracted features are fed to the proposed ETRN to improve the multi-classification performance of blood cells. Results: The average accuracy, average sensitivity, average precision, average specificity, and average f1-score of the proposed model are 95.05%, 93.25%, 97.75%, 93.72%, and 95.38%, accordingly. Conclusions: The performance of the proposed model surpasses other state-of-the-art methods in reported classification results.