One-Dimensional Crystal-Structure Te-Se Alloy for Flexible Shortwave Infrared Photodetector and Imaging.

Flexible shortwave infrared detectors play a crucial role in wearable devices, bioimaging, automatic control, etc. Commercial shortwave infrared detectors face challenges in achieving flexibility due to the high fabrication temperature and rigid material properties. Herein, we develop a high-performance flexible Te0.7Se0.3 photodetector, resulting from the unique 1D crystal structure and small elastic modulus of Te-Se alloying. The flexible photodetector exhibits a broad-spectrum response ranging from 365 to 1650 nm, a fast response time of 6 µs, a broad linear dynamic range of 76 dB, and a specific detectivity of 4.8 × 1010 Jones at room temperature. The responsivity of the flexible detector remains at 93% of its initial value after bending with a small curvature of 3 mm. Based on the optimized flexible detector, we demonstrate its application in shortwave infrared imaging. These results showcase the great potential of Te0.7Se0.3 photodetectors for flexible electronics.

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

Tab-Cox: An Interpretable Deep Survival Analysis Model for Patients with Nasopharyngeal Carcinoma based on TabNet.

The nutritional status of cancer patients is closely associated with the clinical progression of the disease. A survival analysis model combined with a neural network can predict future disease trends in patients, facilitating early prevention and assisting physicians in making diagnoses. However, the complexity of neural networks and their incompatibility with medical tabular data can reduce the interpretability of the model. To address this issue, thr paper propose a novel survival analysis model called Tab-Cox, which combines TabNet and Cox models. This model is specifically designed to predict the survival outcomes of patients with nasopharyngeal carcinoma. The model utilizes TabNet's sequential attention mechanism to extract more interpretable features, providing an interpretable method for identifying disease risk factors. Consequently, the model ensures accurate survival prediction while also making the results more comprehensible for both patients and doctors. The paper tested the efficacy of the model by conducting experiments on various diverse datasets in comparison with other commonly used survival models. The results showed that the proposed model delivered the highest or second-highest accuracy across all datasets. Furthermore, the paper conducted a comparative interpretability analysis against the classical Cox model. In addition and compare the interpretability of the Tab-Cox model with the classical Cox model and discuss the advantages and disadvantages of its interpretability. This demonstrates that Tab-Cox can assist doctors in identifying risk factors that are challenging to capture using artificial methods.

Bienzyme-Locked Activatable Fluorescent Probes for Specific Imaging of Tumor-Associated Mast Cells.

Tumor-associated mast cells (TAMCs) have been recently revealed to play a multifaceted role in the tumor microenvironment. Noninvasive optical imaging of TAMCs is thus highly desired to gain insights into their functions in cancer immunotherapy. However, due to the lack of a single enzyme that is specific to mast cells, a common probe design approach based on single-enzyme activation is not applicable. Herein, we reported a bienzyme-locked molecular probe (THCMC) based on a photoinduced electron transfer-intramolecular charge-transfer hybrid strategy for in vivo imaging of TAMCs. The bienzyme-locked activation mechanism ensures that THCMC exclusively turns on near-infrared (NIR) fluorescence only in the presence of both tryptase and chymase specifically coexpressed by mast cells. Thus, THCMC effectively distinguishes mast cells from other leukocytes, including T cells, neutrophils, and macrophages, a capability lacking in single-locked probes. Such a high specificity of THCMC allows noninvasive tracking of the fluctuation of TAMCs in the tumor of living mice during cancer immunotherapy. The results reveal that the decreased intratumoral signal of THCMC after combination immunotherapy correlates well with the reduced population of TAMCs, accurately predicting the inhibition of tumor growth. Thus, this study not only presents the first NIR fluorescent probe specific for TAMCs but also proposes a generic bienzyme-locked probe design approach for in vivo cell imaging.

Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning.

BACKGROUND: Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the US with the morbidity and mortality being highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock allows prompt implementation of treatment measures. Our objective is to develop a new dynamic risk score, called CShock, to improve early detection of cardiogenic shock in cardiac intensive care unit (ICU). METHODS: We developed and externally validated a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict onset of cardiogenic shock. We prepared a cardiac ICU dataset using MIMIC-III database by annotating with physician adjudicated outcomes. This dataset that consisted of 1500 patients with 204 having cardiogenic/mixed shock was then used to train CShock. The features used to train the model for CShock included patient demographics, cardiac ICU admission diagnoses, routinely measured laboratory values and vital signs, and relevant features manually extracted from echocardiogram and left heart catheterization reports. We externally validated the risk model on the New York University (NYU) Langone Health cardiac ICU database that was also annotated with physician adjudicated outcomes. The external validation cohort consisted of 131 patients with 25 patients experiencing cardiogenic/mixed shock. RESULTS: CShock achieved an area under the receiver operator characteristic curve (AUROC) of 0.821 (95% CI 0.792-0.850). CShock was externally validated in the more contemporary NYU cohort and achieved an AUROC of 0.800 (95% CI 0.717-0.884), demonstrating its generalizability in other cardiac ICUs. Having an elevated heart rate is most predictive of cardiogenic shock development based on Shapley values. The other top ten predictors are having an admission diagnosis of myocardial infarction with ST-segment elevation, having an admission diagnosis of acute decompensated heart failure, Braden Scale, Glasgow Coma Scale, Blood urea nitrogen, Systolic blood pressure, Serum chloride, Serum sodium, and Arterial blood pH. CONCLUSIONS: The novel CShock score has the potential to provide automated detection and early warning for cardiogenic shock and improve the outcomes for the millions of patients who suffer from myocardial infarction and heart failure.

Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics.

Most sequencing-based spatial transcriptomics (ST) technologies do not achieve single-cell resolution where each captured location (spot) may contain a mixture of cells from heterogeneous cell types, and several cell-type decomposition methods have been proposed to estimate cell type proportions of each spot by integrating with single-cell RNA sequencing (scRNA-seq) data. However, these existing methods did not fully consider the effect of distribution difference between scRNA-seq and ST data for decomposition, leading to biased cell-type-specific genes derived from scRNA-seq for ST data. To address this issue, we develop an instance-based transfer learning framework to adjust scRNA-seq data by ST data to correctly match cell-type-specific gene expression. We evaluate the effect of raw and adjusted scRNA-seq data on cell-type decomposition by eight leading decomposition methods using both simulated and real datasets. Experimental results show that data adjustment can effectively reduce distribution difference and improve decomposition, thus enabling for a more precise depiction on spatial organization of cell types. We highlight the importance of data adjustment in integrative analysis of scRNA-seq with ST data and provide guidance for improved cell-type decomposition.

Ingestible Artificial Urinary Biomarker Probes for Urine Test of Gastrointestinal Cancer.

Although colorectal cancer diagnosed at an early stage shows high curability, methods simultaneously possessing point-of-care testing ability and high sensitivity are limited. Here, an orally deliverable biomarker-activatable probe (termed as HATS) for early detection of orthotopic tumors via remote urinalysis is presented. To enable its oral delivery to the colon, HATS is designed to have remarkable resistance to acidity and digestive enzymes in the stomach and small intestine and negligible intestinal absorption. Upon reaction with a cancer biomarker in the colon segment, HATS releases a small fragment of tetrazine that can transverse the intestinal barrier, enter blood circulation, and ultimately undergo renal clearance to urine. Subsequently, the urinary tetrazine fragment is detected by bioorthogonal reaction with trans-cyclooctene-caged resorufin (TCO-Reso) to afford a rapid and specific fluorescence enhancement of TCO-Reso. Such signal readout is correlated with the urinary tetrazine concentration and thus measures the level of cancer biomarkers in the colon. HATS-based optical urinalysis detects orthotopic colon tumors two weeks earlier than clinical serological tests and can be developed to a point-of-care paper test. Thereby, HATS-based urinalysis provides a non-invasive and sensitive approach to cancer screening at low-resource settings.

Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes.

It is poorly understood how different cells in a tissue organize themselves to support tissue functions. We describe the CytoCommunity algorithm for the identification of tissue cellular neighborhoods (TCNs) based on cell phenotypes and their spatial distributions. CytoCommunity learns a mapping directly from the cell phenotype space to the TCN space using a graph neural network model without intermediate clustering of cell embeddings. By leveraging graph pooling, CytoCommunity enables de novo identification of condition-specific and predictive TCNs under the supervision of sample labels. Using several types of spatial omics data, we demonstrate that CytoCommunity can identify TCNs of variable sizes with substantial improvement over existing methods. By analyzing risk-stratified colorectal and breast cancer data, CytoCommunity revealed new granulocyte-enriched and cancer-associated fibroblast-enriched TCNs specific to high-risk tumors and altered interactions between neoplastic and immune or stromal cells within and between TCNs. CytoCommunity can perform unsupervised and supervised analyses of spatial omics maps and enable the discovery of condition-specific cell-cell communication patterns across spatial scales.

Narrowband Fluorescent Emitters Based on BN-Doped Polycyclic Aromatic Hydrocarbons for Efficient and Stable Organic Light-Emitting Diodes.

Organic light-emitting diodes (OLEDs) using conventional fluorescent emitters are currently attracting considerable interests due to outstanding stability and abundant raw materials. To construct high-performance narrowband fluorophores to satisfy requirements of ultra-high-definition displays, a strategy fusing multi-resonance BN-doped moieties to naphthalene is proposed to construct two novel narrowband fluorophores. Green Na-sBN and red Na-dBN, manifest narrow full-width at half-maxima of 31 nm, near-unity photoluminescence quantum yields and molecular horizontal dipole ratios above 90 %. Their OLEDs exhibit the state-of-the-art performances including high external quantum efficiencies (EQE), ultra-low efficiency roll-off and long operational lifetimes. The Na-sBN-based device achieves EQE as high as 28.8 % and remains 19.8 % even at luminance of 100,000 cd m-2 , and Na-dBN-based device acquires a record-high EQE of 25.2 % among all red OLEDs using pure fluorescent emitters.

Pharmacological Effects of Botanical Drugs on Myocardial Metabolism in Chronic Heart Failure.

Although there have been significant advances in the treatment of heart failure in recent years, chronic heart failure remains a leading cause of cardiovascular disease-related death. Many studies have found that targeted cardiac metabolic remodeling has good potential for the treatment of heart failure. However, most of the drugs that increase cardiac energy are still in the theoretical or testing stage. Some research has found that botanical drugs not only increase myocardial energy metabolism through multiple targets but also have the potential to restore the balance of myocardial substrate metabolism. In this review, we summarized the mechanisms by which botanical drugs (the active ingredients/formulas/Chinese patent medicines) improve substrate utilization and promote myocardial energy metabolism by activating AMP-activated protein kinase (AMPK), peroxisome proliferator-activated receptors (PPARs) and other related targets. At the same time, some potential protective effects of botanical drugs on myocardium, such as alleviating oxidative stress and dysbiosis signaling, caused by metabolic disorders, were briefly discussed.

Defining and identifying cell sub-crosstalk pairs for characterizing cell-cell communication patterns.

Current cell-cell communication analysis focuses on quantifying intercellular interactions at cell type level. In the tissue microenvironment, one type of cells could be divided into multiple cell subgroups that function differently and communicate with other cell types or subgroups via different ligand-receptor-mediated signaling pathways. Given two cell types, we define a cell sub-crosstalk pair (CSCP) as a combination of two cell subgroups with strong and similar intercellular crosstalk signals and identify CSCPs based on coupled non-negative matrix factorization. Using single-cell spatial transcriptomics data of mouse olfactory bulb and visual cortex, we find that cells of different types within CSCPs are significantly spatially closer with each other than those in the whole single-cell spatial map. To demonstrate the utility of CSCPs, we apply 13 cell-cell communication analysis methods to sampled single-cell transcriptomics datasets at CSCP level and reveal ligand-receptor interactions masked at cell type level. Furthermore, by analyzing single-cell transcriptomics data from 29 breast cancer patients with different immunotherapy responses, we find that CSCPs are useful predictive features to discriminate patients responding to anti-PD-1 therapy from non-responders. Taken together, partitioning a cell type pair into CSCPs enables fine-grained characterization of cell-cell communication in tissue and tumor microenvironments.

RobustCCC: a robustness evaluation tool for cell-cell communication methods.

Cell-cell communication (CCC) inference has become a routine task in single-cell data analysis. Many computational tools are developed for this purpose. However, the robustness of existing CCC methods remains underexplored. We develop a user-friendly tool, RobustCCC, to facilitate the robustness evaluation of CCC methods with respect to three perspectives, including replicated data, transcriptomic data noise and prior knowledge noise. RobustCCC currently integrates 14 state-of-the-art CCC methods and 6 simulated single-cell transcriptomics datasets to generate robustness evaluation reports in tabular form for easy interpretation. We find that these methods exhibit substantially different robustness performances using different simulation datasets, implying a strong impact of the input data on resulting CCC patterns. In summary, RobustCCC represents a scalable tool that can easily integrate more CCC methods, more single-cell datasets from different species (e.g., mouse and human) to provide guidance in selecting methods for identification of consistent and stable CCC patterns in tissue microenvironments. RobustCCC is freely available at https://github.com/GaoLabXDU/RobustCCC.

Nitrogen vacancy-induced spin polarization of ultrathin zinc porphyrin nanosheets for efficient photocatalytic CO2 reduction.

Metalloporphyrin compounds have excellent electron transfer and visible light absorption ability, demonstrating broad application prospects in the field of photocatalysis. In this work, the nitrogen vacancies (NVs) were successfully introduced into zinc porphyrin (ZnTCPP) ultrathin nanosheets through surface N2 plasma treatment, which is environmentally friendly and can react in low temperatures. Furthermore, the prepared nitrogen vacancies-zinc porphyrin (NVs-ZnTCPP) materials exhibited excellent photocatalytic CO2 reduction activity and selectivity, specifically, the CO production rate of ZnTCPP-1 (N2 plasma treatment, 1 min) achieved as high as 12.5 µmol g-1h-1, which is about 2.7 times greater than that of untreated ZnTCPP. Based on the experimental and density functional theory calculation (DFT) results, it is found that the promoted photocatalytic performance of NVs-ZnTCPP could be mainly attributed to nitrogen vacancy-induced spin polarization by reducing the reaction barriers and inhibiting the recombination of photoexcited carriers. This work provides a new perspective for the construction of vacancy-based metalloporphyrin, and further explores the intrinsic mechanism between the electron spin property and the performance of the photocatalyst.

Precisely regulating the double-boron-based multi-resonance framework towards pure-red emitters: high-performance OLEDs with CIE coordinates fully satisfying the BT. 2020 standard.

Here, we propose a new simple and effective strategy for designing pure-red multi-resonance (MR) emitters through precisely regulating the double-boron-based MR framework. The two designed emitters exhibit ultrapure red emission together with superb photophysical properties, and further enable high-performance, high color-purity red OLEDs.

Precise modulation of multiple resonance emitters toward efficient electroluminescence with pure-red gamut for high-definition displays.

Multiple resonance (MR) compounds have garnered substantial attention for their prospective utility in wide color gamut displays. Nevertheless, developing red MR emitters with both high efficiency and saturated emission color remains demanding. We herein introduce a comprehensive strategy for spectral tuning in the red region by simultaneously regulating the π-conjugation and electron-donating strengths of a double boron-embedded MR skeleton while preserving narrowband characteristics. The proof-of-concept materials manifested emissions from orange-red to deep red, with bandwidths below 0.12 eV. The pure-red device based on CzIDBNO displayed superior color purity with CIE coordinates of (0.701, 0.298), approaching the Broadcast Television 2020 standard. In concert with high photoluminescence quantum yield and strong horizontal dipole orientation, CzIDBNO also achieved a maximum external quantum efficiency of 32.5% and a current efficiency of 20.2 cd A-1, outstripping prior reported organic light-emitting diodes (OLEDs) with CIEx exceeding 0.68. These findings offer a roadmap for designing high-performance emitters with exceptional color purity for future OLED material research advancements.

Carbon dots-based fluorescence enhanced probe for the determination of glucose.

In this work, a novel "turn-on" fluorescence sensor for the detection of H2O2 and glucose was developed based on green fluorescent carbon dots (CDs). The CDs was newly prepared by a facile one-pot hydrothermal method with Eosin Y and branched polyethylenimine as precursors. Interestingly, in the presence of H2O2 and HRP, the fluorescence of the CDs enhanced significantly with a red-shift emission due to their "aggregation". Meanwhile, the oxidation of glucose catalyzed by glucose oxidase could generate H2O2. Thus, a simple sensing system based on the CDs as fluorescent probes was constructed for H2O2 and glucose determination, avoiding the fluorescence quenching and subsequent recovery process in conventional turn-on strategy. The method showed good selectivity and sensitivity for glucose sensing with the detection limit of 0.12 µM. The method was further applied to glucose detection in real samples. The obtained results demonstrated the simplicity, selectivity and practicality of the method. This work expands the carbon nanomaterials with fluorescence emission enhancement properties. It provides a new and direct "turn-on" strategy for H2O2 and glucose detection, which could be a simple and effective tool for screening biological substances involved in H2O2-generation reaction.

In Silico Prediction of Human Organ Toxicity via Artificial Intelligence Methods.

Unpredicted human organ level toxicity remains one of the major reasons for drug clinical failure. There is a critical need for cost-efficient strategies in the early stages of drug development for human toxicity assessment. At present, artificial intelligence methods are popularly regarded as a promising solution in chemical toxicology. Thus, we provided comprehensive in silico prediction models for eight significant human organ level toxicity end points using machine learning, deep learning, and transfer learning algorithms. In this work, our results showed that the graph-based deep learning approach was generally better than the conventional machine learning models, and good performances were observed for most of the human organ level toxicity end points in this study. In addition, we found that the transfer learning algorithm could improve model performance for skin sensitization end point using source domain of in vivo acute toxicity data and in vitro data of the Tox21 project. It can be concluded that our models can provide useful guidance for the rapid identification of the compounds with human organ level toxicity for drug discovery.

Safety and immunogenicity of heterologous ChAdOx1-nCoV19 and BNT162b2 vaccination: A meta-analysis of the heterologous COVID-19 vaccination outcomes.

INTRODUCTION: Here, we systematically assessed the safety and immunogenicity of the heterologous ChAd/BNT vaccination regimens. MATERIALS AND METHODS: We evaluated the immunogenicity by the geometric mean titers ratio (GMTR) of the neutralizing antibody and anti-spike IgG. The safety of heterologous ChAd/BNT vaccination was evaluated using the pooled risk ratios (RRs) calculated by the random-effects model about the adverse events. Our study was registered with PROSPERO, CRD42021265165. RESULTS: Eleven studies were included in the analyses. Compared to the homologous ChAd/ChAd vaccination, the heterologous ChAd/BNT vaccination showed significantly higher immunogenicity in terms of the neutralizing antibody and GMTR of anti-spike IgG, but at the same time displayed higher incidence of total adverse reactions, especially for the local adverse reactions. Moreover, heterologous ChAd/BNT vaccination showed similar immunogenicity to the homologous BNT/BNT vaccination (GMTR of neutralizing antibody and anti-spike IgG) and similar safety. DISCUSSION: Heterologous ChAd/BNT vaccination showed robust immunogenicity and tolerable safety.

Peripherally Heavy-Atom-Decorated Strategy Towards High-Performance Pure Green Electroluminescence with External Quantum Efficiency over 40 .

Heavy-atom integration into thermally activated delayed fluorescence (TADF) molecule could significantly promote the reverse intersystem crossing (RISC) process. However, simultaneously achieving high efficiency, small roll-off, narrowband emission and good operational lifetime remains a big challenge for the corresponding organic light-emitting diodes (OLEDs). Herein, we report a pure green multi-resonance TADF molecule BN-STO by introducing a peripheral heavy atom selenium onto the parent BN-Cz molecule. The organic light-emitting diode device based on BN-STO exhibited state-of-the-art performance with a maximum external quantum efficiency (EQE) of 40.1 %, power efficiency (PE) of 176.9 lm W-1 , well-suppressed efficiency roll-off and pure green gamut. This work reveals a feasible strategy to reach a balance between fast RISC process and narrow full width at half maximum (FWHM) of MR-TADF by heavy atom effect.

Tex Se1-x Photodiode Shortwave Infrared Detection and Imaging.

Short-wave infrared detectors are increasingly important in the fields of autonomous driving, food safety, disease diagnosis, and scientific research. However, mature short-wave infrared cameras such as InGaAs have the disadvantage of complex heterogeneous integration with complementary metal-oxide-semiconductor (CMOS) readout circuits, leading to high cost and low imaging resolution. Herein, a low-cost, high-performance, and high-stability Tex Se1- x short-wave infrared photodiode detector is reported. The Tex Se1- x thin film is fabricated through CMOS-compatible low-temperature evaporation and post-annealing process, showcasing the potential of direct integration on the readout circuit. The device demonstrates a broad-spectrum response of 300-1600 nm, a room-temperature specific detectivity of 1.0 × 1010 Jones, a -3 dB bandwidth up to 116 kHz, and a linear dynamic range of over 55 dB, achieving the fastest response among Te-based photodiode devices and a dark current density 7 orders of magnitude smaller than Te-based photoconductive and field-effect transistor devices. With a simple Si3 N4 packaging, the detector shows high electric stability and thermal stability, meeting the requirements for vehicular applications. Based on the optimized Tex Se1- x photodiode detector, the applications in material identification and masking imaging is demonstrated. This work paves a new way for CMOS-compatible infrared imaging chips.