Micro-Nano Fractal Metal Grids as Transparent Conductive Electrodes for Highly Efficient and Stable Perovskite Solar Cells.

Metal mesh-based transparent electrodes possess several notable advantages, including exceptional flexibility, high electrical conductivity, and excellent light transmittance, making them the focus of increasing research interest. However, their widespread application in perovskite solar cells (PSCs) remains a significant challenge due to the susceptibility of the metal grids to corrosion and their inherently rough surface. In this study, we report environmentally stable, low surface roughness transparent conductive electrodes by integrated micro-nano fractal metal grid transparent conductive electrodes (MNF-TCEs) and PH1000.

Comprehensive clinical scale-based machine learning model for predicting subthalamic nucleus deep brain stimulation outcomes in Parkinson's disease.

Parkinson's Disease (PD) is a growing burden with varied clinical manifestations and responses to Subthalamic Nucleus Deep Brain Stimulation (STN-DBS). At present, there is no effective and simple machine learning model based on comprehensive clinical scales to predict the improvement in motor symptoms of PD treated with DBS. A total of 647 PD patients from the First Affiliated Hospital of University of Science and Technology of China were enrolled retrospectively.

Dynamic Reconstruction of Fluid Interface Manipulated by Fluid Balancing Agent for Scalable Efficient Perovskite Solar Cells.

Laboratory-scale spin-coating techniques are widely employed for fabricating small-size, high-efficiency perovskite solar cells. However, achieving large-area, high-uniformity perovskite films and thus high-efficiency solar cell devices remain challenging due to the complex fluid dynamics and drying behaviors of perovskite precursor solutions during large-area fabrication processes. In this work, a high-quality, pinhole-free, large-area FAPbI perovskite film is successfully obtained via scalable blade-coating technology, assisted by a novel bidirectional Marangoni convection strategy.

Real-World Effectiveness of Long-Acting Injectable and Oral Antipsychotic Agents in US Medicare Patients with Schizophrenia.

Introduction: Daily oral antipsychotics (OAPs) are the mainstay of schizophrenia treatment; however, long-acting injectable antipsychotics (LAIs) are associated with better treatment adherence and improved outcomes.

Methods: This study assessed the real-world comparative effectiveness of LAIs and daily OAPs using claims data from a nationally representative sample of fee-for-service Medicare beneficiaries with schizophrenia. Antipsychotic discontinuation, psychiatric hospitalization, and treatment failure were compared relative to different reference groups using within-individual Cox regression models.

A Graph Neural Network-Based Approach to XANES Data Analysis.

The determination of three-dimensional structures (3D structures) is crucial for understanding the correlation between the structural attributes of materials and their functional performance. X-ray absorption near edge structure (XANES) is an indispensable tool to characterize the atomic-scale local 3D structure of the system. Here, we present an approach to simulate XANES based on a customized 3D graph neural network (3DGNN) model, XAS3Dabs, which takes directly the 3D structure of the system as input, and the inherent relation between the fine structure of spectrum and local geometry is considered during the model construction.

Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning.

The aim of this study was to develop and validate a machine learning-based mortality risk prediction model for patients with severe community-acquired pneumonia (SCAP) in the intensive care unit (ICU). We collected data from two centers as the development and external validation cohorts. Variables were screened using the Recursive Feature Elimination method.

Predicting the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms.

Background: This study aimed to develop predictive models with robust generalization capabilities for assessing the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms.

Methods: Data were collected from two centers and categorized into development and validation cohorts. Using the development cohort, candidate variables were selected via the Recursive Feature Elimination (RFE) method.

Mining and Engineering the Di--glycosylation Pattern of UGT72B1 for the Highly Efficient -Glycosylation of Endogenous Quercetin.

Compared with mono--glycosylation, di--glycosylation endows the precursor with better performance. However, the mining and engineering of di--glycosylation patterns of glycosyltransferases are limited, hindering their synthetic applications. Here, an xenobiotic-transforming glycosyltransferase, UGT72B1, was found to catalyze the glycosylation of endogenous quercetin and its monoglycosides, generating di-glucosides.

Dynamic changes of media prefrontal cortex astrocytic activity in response to negative stimuli in male mice.

Astrocytes play significant roles in regulating the central stress response. Chronic stress impairs the structure and function of astrocytes in many brain regions such as media prefrontal cortex (mPFC) in multiple neuropsychiatric conditions, but the astrocytic dynamics on the timescale of behavior remains unclear. Here, we recorded mPFC astrocytic activity in freely behaving mice and found that astrocytes are activated immediately by different aversive stimuli.

Application of multimodal deep learning and multi-instance learning fusion techniques in predicting STN-DBS outcomes for Parkinson's disease patients.

Parkinson's Disease (PD) is a progressive neurodegenerative disorder with substantial impact on patients' quality of life. Subthalamic nucleus deep brain stimulation (STN-DBS) is an effective treatment for advanced PD, but patient responses vary, necessitating predictive models for personalized care. Recent advancements in medical imaging and machine learning offer opportunities to enhance predictive accuracy, particularly through deep learning and multi-instance learning (MIL) techniques.

A cardioviral 2C-ATP complex structure reveals the essential role of a conserved arginine in regulation of cardioviral 2C activity.

Unlabelled: 2C is a highly conserved picornaviral non-structural protein with ATPase activity and plays a multifunctional role in the viral life cycle as a promising target for anti-picornavirus drug development. While the structure-function of enteroviral 2Cs have been well studied, cardioviral 2Cs remain largely uncharacterized. Here, an endogenous ATP molecule was identified in the crystal structure of 2C from encephalomyocarditis virus (EMCV, Cardiovirus A).

High expression of COPZ2 is associated with poor prognosis and cancer progression in glioma.

Background: Coatomer protein complex zeta 2 (COPZ2) is a member of heptameric coatomer protein complex I and has been reported to be involved in various tumors. However, COPZ2's potential involvement in glioma remains to be explored.

Methods: The COPZ2 expression and related clinical data were obtained from The Cancer Genome Atlas (TCGA).

Enhanced MALDI-2 Sensitivity with Reflecting Post-Ionization Laser for High-Resolution MS Imaging Combined with Real-Time Microscope Imaging.

Laser-induced matrix-assisted laser desorption/ionization post-ionization (MALDI-2) could improve the MALDI sensitivity of biological metabolites by over 1 order of magnitude. Herein, we demonstrate that MALDI-2 sensitivity can be further enhanced with reflecting post-ionization laser that multiplies the intersection times between laser and MALDI plume. This method, which we named MALDI-2+, typically brought over 2 times sensitivity improvement from conventional MALDI-2.

Real-time performance improvement optimizes damage control resuscitation best practice adherence: Results of a pilot prospective observational study.

Background: Maintaining balanced blood product ratios during damage control resuscitation (DCR) is independently associated with improved survival. We hypothesized that real-time performance improvement (RT-PI) would increase adherence to DCR best practice.

Study Design And Methods: From December 2020-August 2021, we prospectively used a bedside RT-PI tool to guide DCR in severely injured patients surviving at least 30 min.

Admission Left-Arm Systolic Blood Pressure and In-Hospital Mortality After Acute Type A Aortic Dissection Repair.

Aim: Admission systolic blood pressure is a significant predictor of in-hospital mortality in patients with acute type A aortic dissection (ATAAD). While previous studies have focussed on recording the highest blood pressure value from both arms, this study aimed to evaluate the associations between blood pressure in bilateral arms and in-hospital mortality.

Methods: Data were analysed from 262 patients with ATAAD treated at a single centre.

A predicted model-aided reconstruction algorithm for X-ray free-electron laser single-particle imaging.

Ultra-intense, ultra-fast X-ray free-electron lasers (XFELs) enable the imaging of single protein molecules under ambient temperature and pressure. A crucial aspect of structure reconstruction involves determining the relative orientations of each diffraction pattern and recovering the missing phase information. In this paper, we introduce a predicted model-aided algorithm for orientation determination and phase retrieval, which has been tested on various simulated datasets and has shown significant improvements in the success rate, accuracy and efficiency of XFEL data reconstruction.

A modified phase-retrieval algorithm to facilitate automatic de novo macromolecular structure determination in single-wavelength anomalous diffraction.

The success of experimental phasing in macromolecular crystallography relies primarily on the accurate locations of heavy atoms bound to the target crystal. To improve the process of substructure determination, a modified phase-retrieval algorithm built on the framework of the relaxed alternating averaged reflection (RAAR) algorithm has been developed. Importantly, the proposed algorithm features a combination of the π-half phase perturbation for weak reflections and enforces the direct-method-based tangent formula for strong reflections in reciprocal space.

Brain functional specialization and cooperation in Alzheimer's disease.

Background: Cerebral specialization and interhemispheric cooperation are two vital features of the human brain. Their dysfunction may be associated with disease progression in patients with Alzheimer's disease (AD), which is featured as progressive cognitive degeneration and asymmetric neuropathology.

Objective: This study aimed to examine and define two inherent properties of hemispheric function in patients with AD by utilizing resting-state functional magnetic resonance imaging (rs-fMRI).

Single-Cell Spatial Transcriptomics Unveils Platelet-Fueled Cycling Macrophages for Kidney Fibrosis.

With the increasing incidence of kidney diseases, there is an urgent need to develop therapeutic strategies to combat post-injury fibrosis. Immune cells, including platelets, play a pivotal role in this repair process, primarily through their released cytokines. However, the specific role of platelets in kidney injury and subsequent repair remains underexplored.

A Study on the Effect of Executive Control Network Functional Connection on the Therapeutic Efficacy of Repetitive Transcranial Magnetic Stimulation in Alzheimer's Disease.

Background: Alzheimer's disease (AD) is a neurodegenerative disease characterized by brain network dysfunction. Few studies have investigated whether the functional connections between executive control networks (ECN) and other brain regions can predict the therapeutic effect of repetitive transcranial magnetic stimulation (rTMS).

Objective: The purpose of this study is to examine the relationship between the functional connectivity (FC) within ECN networks and the efficacy of rTMS.

Repetitive transcranial magnetic stimulation in Alzheimer's disease: effects on neural and synaptic rehabilitation.

Alzheimer's disease is a neurodegenerative disease resulting from deficits in synaptic transmission and homeostasis. The Alzheimer's disease brain tends to be hyperexcitable and hypersynchronized, thereby causing neurodegeneration and ultimately disrupting the operational abilities in daily life, leaving patients incapacitated. Repetitive transcranial magnetic stimulation is a cost-effective, neuro-modulatory technique used for multiple neurological conditions.