A fluorescence probe with targeted mitochondria for detecting hydrogen peroxide in vitro and in diabetic mice.

We designed and prepared probe W-1 for the detection of H2O2. W-1 showed excellent selectivity for H2O2 and was accompanied by colorimetric signal changes. The excellent linear relationship between fluorescence intensity and H2O2 concentration (0-100 µM) provided favorable conditions for its quantitative detection. In addition, the combination of portable test strips with a smartphone platform provided great convenience for on-site visual detection of H2O2. Moreover, W-1 possessed targeting mitochondria property and could be applied to image the exogenous and endogenous H2O2 in cells to distinguish normal cells and cancer cells. Lastly, W-1 was used for monitoring the H2O2 fluctuation of the diabetic process in mice, and the results showed an increase in H2O2 levels in diabetes. Therefore, the probe provided a tool for understanding the pathological and physiological mechanisms of diabetes by imaging H2O2.

Blood Urine Nitrogen Trajectories of Acute Pancreatitis Patients in Intensive Care Units.

Objective: To identify subclasses of acute pancreatitis (AP) patients in the intensive care unit (ICU) by analyzing blood urea nitrogen (BUN) trajectories. Methods: AP patients in West China Hospital System (development cohort) and three public databases in the United States (validation cohort) were included. Latent class trajectory modelling was used to identify subclasses based on BUN trajectories within the first 21 days after ICU admission. Clinical characteristics and outcomes were compared, and results were externally validated. Results: The study comprised 2971 and 930 patients in the development and validation cohorts, respectively, with five subclasses: Class 1 ("Moderate-azotemia, slow decreasing"), Class 2 ("Non-azotemia"), Class 3 ("Severe-azotemia, slow decreasing"), Class 4 ("Moderate-azotemia, rapid increasing"), and Class 5 ('Moderate-azotemia, slow increasing) identified. Azotemia patients showed significantly higher 30-day mortality risk in development and validation cohorts. Specifically, Class 4 patients exhibited notably highest mortality risk in both the development cohort (HR 5.32, 95% CI 2.62-10.82) and validation cohort (HR 6.23, 95% CI 2.93-13.22). Regarding clinical characteristics, AP patients in Class 4 showed lower mean arterial pressure and a higher proportion of renal disease. We also created an online early classification model to further identify Class 4 patients among all patients with moderate azotemia at baseline. Conclusion: This multinational study uncovers heterogeneity in BUN trajectories among AP patients. Patients with "Moderate-azotemia, rapid increasing" trajectory, had a higher mortality risk than patients with severe azotemia at baseline. This finding complements studies that solely rely on baseline BUN for risk stratification and enhanced our understanding of longitudinal progression of AP.

Customized m-RCNN and hybrid deep classifier for liver cancer segmentation and classification.

Diagnosing liver disease presents a significant medical challenge in impoverished countries, with over 30 billion individuals succumbing to it each year. Existing models for detecting liver abnormalities suffer from lower accuracy and higher constraint metrics. As a result, there is a pressing need for improved, efficient, and effective liver disease detection methods. To address the limitations of current models, this method introduces a deep liver segmentation and classification system based on a Customized Mask-Region Convolutional Neural Network (cm-RCNN). The process begins with preprocessing the input liver image, which includes Adaptive Histogram Equalization (AHE). AHE helps dehaze the input image, remove color distortion, and apply linear transformations to obtain the preprocessed image. Next, a precise region of interest is segmented from the preprocessed image using a novel deep strategy called cm-RCNN. To enhance segmentation accuracy, the architecture incorporates the ReLU activation function and the modified sigmoid activation function. Subsequently, a variety of features are extracted from the segmented image, including ResNet features, shape features (area, perimeter, approximation, and convex hull), and enhanced median binary pattern. These extracted features are then used to train a hybrid classification model, which incorporates classifiers like SqueezeNet and DeepMaxout models. The final classification outcome is determined by averaging the scores obtained from both classifiers.

When can we start early enteral nutrition safely in patients with shock on vasopressors?

Shock is a common critical illness characterized by microcirculatory disorders and insufficient tissue perfusion. Patients with shock and hemodynamic instability generally require vasopressors to maintain the target mean arterial pressure. Enteral nutrition (EN) is an important therapeutic intervention in critically ill patients and has unique benefits for intestinal recovery. However, the initiation of early EN in patients with shock receiving vasopressors remains controversial. Current guidelines make conservative and vague recommendations regarding early EN support in patients with shock. Increasing studies demonstrates that early EN delivery is safe and feasible in patients with shock receiving vasopressors; however, this evidence is based on observational studies. Changes in gastrointestinal blood flow vary by vasopressor and inotrope and are complex. The risk of gastrointestinal complications, especially the life-threatening complications of non-occlusive mesenteric ischemia and non-occlusive bowel necrosis, cannot be ignored in patients with shock during early EN support. It remains a therapeutic challenge in critical care nutrition therapy to determine the initiation time of EN in patients with shock receiving vasopressors and the safe threshold region for initiating EN with vasopressors. Therefore, the current review aimed to summarize the evidence on the optimal and safe timing of early EN initiation in patients with shock receiving vasopressors to improve clinical practice.

Early pathophysiology-driven airway pressure release ventilation versus low tidal volume ventilation strategy for patients with moderate-severe ARDS: study protocol for a randomized, multicenter, controlled trial.

BACKGROUND: Conventional Mechanical ventilation modes used for individuals suffering from acute respiratory distress syndrome have the potential to exacerbate lung injury through regional alveolar overinflation and/or repetitive alveolar collapse with shearing, known as atelectrauma. Animal studies have demonstrated that airway pressure release ventilation (APRV) offers distinct advantages over conventional mechanical ventilation modes. However, the methodologies for implementing APRV vary widely, and the findings from clinical studies remain controversial. This study (APRVplus trial), aims to assess the impact of an early pathophysiology-driven APRV ventilation approach compared to a low tidal volume ventilation (LTV) strategy on the prognosis of patients with moderate to severe ARDS. METHODS: The APRVplus trial is a prospective, multicenter, randomized clinical trial, building upon our prior single-center study, to enroll 840 patients from at least 35 hospitals in China. This investigation plans to compare the early pathophysiology-driven APRV ventilation approach with the control intervention of LTV lung-protective ventilation. The primary outcome measure will be all-cause mortality at 28 days after randomization in the intensive care units (ICU). Secondary outcome measures will include assessments of oxygenation, and physiology parameters at baseline, as well as on days 1, 2, and 3. Additionally, clinical outcomes such as ventilator-free days at 28 days, duration of ICU and hospital stay, ICU and hospital mortality, and the occurrence of adverse events will be evaluated. TRIAL ETHICS AND DISSEMINATION: The research project has obtained approval from the Ethics Committee of West China Hospital of Sichuan University (2019-337). Informed consent is required. The results will be submitted for publication in a peer-reviewed journal and presented at one or more scientific conferences. TRIAL REGISTRATION: The study was registered at Clinical Trials.gov (NCT03549910) on June 8, 2018.

A 3D pseudo-continuous arterial spin labeling study of altered cerebral blood flow correlation networks in mild cognitive impairment and Alzheimer's disease.

Objective: To investigate the abnormalities of the three-dimensional pseudo-continuous arterial spin labeling (3D PCASL) based cerebral blood flow (CBF) correlation networks in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Methods: 3D PCASL images of 53 cognitive normal (CN) subjects, 43 subjects with MCI, and 30 subjects with AD were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Whole-brain CBF maps were calculated using PCASL and proton density-weighted images (PDWI). The 246 regional CBF values, including the cortex and subcortex, were obtained after registering the Brainnetome Atlas to the individual CBF maps. The Pearson correlation coefficient between every two regions across subjects was calculated to construct the CBF correlation network. Then the topologies of CBF networks with regard to global properties (global network efficiency, clustering coefficient, characteristic path length, and small-world properties), hub regions, nodal properties (betweenness centrality, BC), and connections were compared among CN, MCI, and AD. Significant changes in the global and nodal properties were observed in the permutation tests, and connections with significant differences survived after the z-statistic and false discovery rate (FDR) correction. Results: The CBF correlation networks of CN, MCI, and AD all showed small-world properties. Compared with CN, global efficiency decreased significantly in AD. Significant differences in nodal properties and a loss of hub regions are noted in the middle temporal lobe in both MCI and AD. In the frontal lobe, BC is reduced in MCI while it is increased in the occipital lobe in AD. The identified altered hub regions with significant differences in MCI and AD were mainly distributed in the hippocampus and entorhinal cortex. In addition, disrupted hub regions in AD with significantly decreased connections were mainly found in the precuneus/posterior cingulate cortex (PCC) and hippocampus-cortical cortex. Conclusions: Noninvasive 3D PCASL-based CBF correlation networks are capable of showing changes in topological organization in subjects with MCI and AD, and the observed disruption in the topological organization may underlie cognitive decline in MCI and AD.

LINC00152: Potential driver oncogene in pan-cancer.

Long noncoding RNAs (lncRNA) are a class of non-coding RNAs greater than 200 bp in length with limited peptide-coding function. The transcription of LINC00152 is derived from chromosome 2p11.2. Many studies prove that LINC00152 influences the progression of various tumors via promoting the tumor cells malignant phenotype, chemoresistance, and immune escape. LINC00152 is regulated by multiple transcription factors and DNA hypomethylation. In addition, LINC00152 participates in the regulation of complex molecular signaling networks through epigenetic regulation, protein interactions, and competitive endogenous RNA (ceRNA). Here, we provide a systematic review of the upstream regulatory factors of LINC00152 expression level in different types of tumors. In addition, we revisit the main functions and mechanisms of LINC00152 as driver oncogene and biomarker in pan-cancer. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Methods > RNA Analyses in Cells RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes.

Regulation of VEGF-A expression and VEGF-A-targeted therapy in malignant tumors.

Vascular endothelial growth factor A (VEGF-A), a highly conserved dimeric glycoprotein, is a key regulatory gene and a marker molecule of angiogenesis. The upregulation of VEGF-A facilitates the process of tumor vascularization, thereby fostering the initiation and progression of malignant neoplasms. Many genes can adjust the angiogenesis of tumors by changing the expression of VEGF-A. In addition, VEGF-A also exhibits immune regulatory properties, which directly or indirectly suppresses the antitumor activity of immune cells. The emergence of VEGF-A-targeted therapy alone or in rational combinations has revolutionized the treatment of various cancers. This review discusses how diverse mechanisms in various tumors regulate VEGF-A expression to promote tumor angiogenesis and the role of VEGF-A in tumor immune microenvironment. The application of drugs targeting VEGF-A in tumor therapy is also summarized including antibody molecule drugs and traditional Chinese medicine.

Cardiac index and heart rate as prognostic indicators for mortality in septic shock: A retrospective cohort study from the MIMIC-IV database.

Background: Septic shock is a life-threatening condition that can lead to organ dysfunction and death. In the ICU, monitoring of cardiac index (CI) and heart rate (HR) is commonly used to guide management and predict outcomes in septic shock patients. However, there is a lack of research on the association between CI and HR and the risk of mortality in this patient population. Therefore, the aim of this study was to investigate the relationship between different levels of CI and HR and mortality in septic shock patients. Methods: Data analysis was obtained from the MIMIC-IV version 2.0 database. Sepsis and septic shock were primarily defined by sepsis-3, the third international consensus on sepsis and septic shock. CI was computed using cardiac output (CO) and body surface area (BSA). To evaluate the incidence of CI with respect to each endpoint (7-, 14-, 21-, and 28-day mortality), a restricted cubic spline curve function (RCS) was used. The optimal cutoff value for predicted mortality was determined using the Youden index. Analyses of KM curves, cox regression, and logistic regression were conducted separately to determine the relationship between various CI and HR and 28-day mortality. Results: This study included 1498 patients with septic shock. A U-shaped relationship between CI levels and risk of mortality in septic shock was found by RCS analysis (p < 0.001). CI levels within the intermediate range of 1.85-2.8 L/min/m2 were associated with a mortality hazard ratio (HR) < 1. In contrast, low CI (HR = 1.87 95% CI: 1.01-3.49) and high CI (HR = 1.93 95% CI: 1.26-2.97) had a significantly increased risk of mortality. The AUC for heart rate prediction of mortality by Youden index analysis was 0.70 95%CI:0.64-0.76 with a cut-off value of 93.63 bpm. According to the characteristics of HR and CI, patients were divided into six subgroups HR↓+CI intermediate group (n = 772), HR↓+CI↓ group (n = 126), HR↓+CI↑ group (n = 294), HR↑+CI intermediate group (n = 132), HR↑+CI↓ group (n = 24), and HR↑+CI↑ group (n = 150). The KM curves, COX regression, and logistic regression analysis showed that the survival rates the of HR↓+CI intermediate group, HR↓+CI↓ group, and HR↓+CI↑ were higher than the other groups. The risk factors of HR↑+CI intermediate group, HR↑+CI↓, and HR↑+CI↑ with ICU 28-day mortality were HR = 2.91 (95% CI: 1.39-5.97), HR = 3.67 (95% CI: 1.39-11.63), and HR = 5.77 (95% CI: 2.98-11.28), respectively. Conclusion: Our retrospective study shows that monitoring cardiac index and heart rate in patients with septic shock may help predict the organismal response and hemodynamic consequences, as well as the prognosis. Thus, healthcare providers should carefully monitor changes in these parameters in septic shock patients transferred to the ICU for treatment.

Effectiveness of nirmatrelvir-ritonavir versus azvudine for adult inpatients with severe or critical COVID-19.

BACKGROUND: In China, both nirmatrelvir-ritonavir (Paxlovid) and azvudine have been granted approval to treat adult SARS-CoV-2-infected patients with moderate symptoms. Information about the clinical effect of the two available agents among inpatients with severe or critical COVID-19 is scarce. PURPOSE: To compare the clinical outcomes of Paxlovid and azvudine among adult inpatients with severe or critical COVID-19. METHOD: We conducted a retrospective cohort study in two large medical centres after the epidemic control measures were lifted in China. A new propensity score matched-inverse probability of treatment weighting cohort was constructed to evaluate the in-hospital all-cause mortality, hospital length of stay, Sequential Organ Failure Assessment (SOFA) score and safety. RESULTS: A total of 955 individuals were in the cohort. The antiviral therapy strategies were decided by the senior physician and the supplies of the pharmacy. A total of 451 patients were in the Paxlovid group, and 504 patients were in the azvudine group. Compared with Paxlovid, the effects of azvudine on in-hospital all-cause mortality were not significantly different, and the OR (95% CI) was 1.084 (0.822 to 1.430), and the average hospital length of stay of patients discharged alive was also similar in the azvudine group, and the difference (day) and (95% CI) was 0.530 (-0.334 to 1.393). After 7 days of therapy, the degree of decline in the SOFA score was greater in the Paxlovid group than in the azvudine group (p<0.001). The change in glomerular filtration rate was not significantly different (p=0.824). CONCLUSION: Paxlovid and azvudine had similar effectiveness on in-hospital all-cause mortality and hospital length of stay. Compared with the azvudine group, after 7 days of therapy, the degree of decline in SOFA score was significantly higher in the Paxlovid group. These findings need to be verified in larger prospective studies or randomised controlled trials.

Acute exacerbation prediction of COPD based on Auto-metric graph neural network with inspiratory and expiratory chest CT images.

Chronic obstructive pulmonary disease (COPD) is a widely prevalent disease with significant mortality and disability rates and has become the third leading cause of death globally. Patients with acute exacerbation of COPD (AECOPD) often substantially suffer deterioration and death. Therefore, COPD patients deserve special consideration regarding treatment in this fragile population for pre-clinical health management. Based on the above, this paper proposes an AECOPD prediction model based on the Auto-Metric Graph Neural Network (AMGNN) using inspiratory and expiratory chest low-dose CT images. This study was approved by the ethics committee in the First Affiliated Hospital of Guangzhou Medical University. Subsequently, 202 COPD patients with inspiratory and expiratory chest CT Images and their annual number of AECOPD were collected after the exclusion. First, the inspiratory and expiratory lung parenchyma images of the 202 COPD patients are extracted using a trained ResU-Net. Then, inspiratory and expiratory lung Radiomics and CNN features are extracted from the 202 inspiratory and expiratory lung parenchyma images by Pyradiomics and pre-trained Med3D (a heterogeneous 3D network), respectively. Last, Radiomics and CNN features are combined and then further selected by the Lasso algorithm and generalized linear model for determining node features and risk factors of AMGNN, and then the AECOPD prediction model is established. Compared to related models, the proposed model performs best, achieving an accuracy of 0.944, precision of 0.950, F1-score of 0.944, ad area under the curve of 0.965. Therefore, it is concluded that our model may become an effective tool for AECOPD prediction.

Adaptive Feature Medical Segmentation Network: an adaptable deep learning paradigm for high-performance 3D brain lesion segmentation in medical imaging.

Introduction: In neurological diagnostics, accurate detection and segmentation of brain lesions is crucial. Identifying these lesions is challenging due to its complex morphology, especially when using traditional methods. Conventional methods are either computationally demanding with a marginal impact/enhancement or sacrifice fine details for computational efficiency. Therefore, balancing performance and precision in compute-intensive medical imaging remains a hot research topic. Methods: We introduce a novel encoder-decoder network architecture named the Adaptive Feature Medical Segmentation Network (AFMS-Net) with two encoder variants: the Single Adaptive Encoder Block (SAEB) and the Dual Adaptive Encoder Block (DAEB). A squeeze-and-excite mechanism is employed in SAEB to identify significant data while disregarding peripheral details. This approach is best suited for scenarios requiring quick and efficient segmentation, with an emphasis on identifying key lesion areas. In contrast, the DAEB utilizes an advanced channel spatial attention strategy for fine-grained delineation and multiple-class classifications. Additionally, both architectures incorporate a Segmentation Path (SegPath) module between the encoder and decoder, refining segmentation, enhancing feature extraction, and improving model performance and stability. Results: AFMS-Net demonstrates exceptional performance across several notable datasets, including BRATs 2021, ATLAS 2021, and ISLES 2022. Its design aims to construct a lightweight architecture capable of handling complex segmentation challenges with high precision. Discussion: The proposed AFMS-Net addresses the critical balance issue between performance and computational efficiency in the segmentation of brain lesions. By introducing two tailored encoder variants, the network adapts to varying requirements of speed and feature. This approach not only advances the state-of-the-art in lesion segmentation but also provides a scalable framework for future research in medical image processing.

The highly selective and sensitive fluorescence probe for detection of copper (II) ions and its bioimaging in vitro and vivo.

We designed and developed the probe W-3 for detection of Cu2+. The results showed probe can selectively detect Cu2+, accompanied by noticeable color change. The probe can detect the Cu2+ in water samples and drinks based on absorption detection. In addition, the combination of portable test paper and the smartphone platform obtained great convenience for on-site and visual detection of Cu2+, with satisfactory sensitivity and reliability. More importantly, the fluorescence probe W-3 can be used for the detection of Cu2+ in cells and mice. Therefore, the W-3 provided potential chemical tools for detecting Cu2+ in vitro and vivo.

Single-photon ranging lidar based on multi-repetition-rate pulse train correlation and accumulation.

A single-photon lidar based on multi-repetition-rate pulse train correlation and accumulation is proposed, and a ranging experiment is conducted on a 32 m target. By accumulating the correlation ranging results of pulse trains with internal spacings of 80, 100, and 125 ns, the signal-to-noise ratio of the cross correlation function is improved by about three-fold, which enables our method to improve the ranging precisions by more than 20% compared with the single repetition-rate method, and the shorter the acquisition time, the more obvious the advantage will be. Experimental results show that at an acquisition time of 0.01 s, our method can still achieve a ranging precision of 2.59 cm, while the single repetition-rate method can no longer obtain effective ranging results at this time. This method will be of great significance for realizing high-speed, large-scale unambiguous single-photon lidar ranging.

Dual-targeting tigecycline nanoparticles for treating intracranial infections caused by multidrug-resistant Acinetobacter baumannii.

Multidrug-resistant (MDR) Acinetobacter baumannii (A. baumannii) is a formidable pathogen responsible for severe intracranial infections post-craniotomy, exhibiting a mortality rate as high as 71%. Tigecycline (TGC), a broad-spectrum antibiotic, emerged as a potential therapeutic agent for MDR A. baumannii infections. Nonetheless, its clinical application was hindered by a short in vivo half-life and limited permeability through the blood-brain barrier (BBB). In this study, we prepared a novel core-shell nanoparticle encapsulating water-soluble tigecycline using a blend of mPEG-PLGA and PLGA materials. This nanoparticle, modified with a dual-targeting peptide Aß11 and Tween 80 (Aß11/T80@CSs), was specifically designed to enhance the delivery of tigecycline to the brain for treating A. baumannii-induced intracranial infections. Our findings demonstrated that Aß11/T80@CSs nanocarriers successfully traversed the BBB and effectively delivered TGC into the cerebrospinal fluid (CSF), leading to a significant therapeutic response in a model of MDR A. baumannii intracranial infection. This study offers initial evidence and a platform for the application of brain-targeted nanocarrier delivery systems, showcasing their potential in administering water-soluble anti-infection drugs for intracranial infection treatments, and suggesting promising avenues for clinical translation.

MMDB: Multimodal dual-branch model for multi-functional bioactive peptide prediction.

Bioactive peptides can hinder oxidative processes and microbial spoilage in foodstuffs and play important roles in treating diverse diseases and disorders. While most of the methods focus on single-functional bioactive peptides and have obtained promising prediction performance, it is still a significant challenge to accurately detect complex and diverse functions simultaneously with the quick increase of multi-functional bioactive peptides. In contrast to previous research on multi-functional bioactive peptide prediction based solely on sequence, we propose a novel multimodal dual-branch (MMDB) lightweight deep learning model that designs two different branches to effectively capture the complementary information of peptide sequence and structural properties. Specifically, a multi-scale dilated convolution with Bi-LSTM branch is presented to effectively model the different scales sequence properties of peptides while a multi-layer convolution branch is proposed to capture structural information. To the best of our knowledge, this is the first effective extraction of peptide sequence features using multi-scale dilated convolution without parameter increase. Multimodal features from both branches are integrated via a fully connected layer for multi-label classification. Compared to state-of-the-art methods, our MMDB model exhibits competitive results across metrics, with a 9.1% Coverage increase and 5.3% and 3.5% improvements in Precision and Accuracy, respectively.

Systematic review on antibacterial photodynamic therapeutic effects of transition metals ruthenium and iridium complexes.

The prevalence of antibiotic-resistant pathogenic bacteria poses a significant threat to public health and ranks among the principal causes of morbidity and mortality worldwide. Antimicrobial photodynamic therapy is an emerging therapeutic technique that has excellent potential to embark upon antibiotic resistance problems. The efficacy of this therapy hinges on the careful selection of suitable photosensitizers (PSs). Transition metal complexes, such as Ruthenium (Ru) and Iridium (Ir), are highly suitable for use as PSs because of their surface plasmonic resonance, crystal structure, optical characteristics, and photonics. These metals belong to the platinum family and exhibit similar chemical behavior due to their partially filled d-shells. Ruthenium and Iridium-based complexes generate reactive oxygen species (ROS), which interact with proteins and DNA to induce cell death. As photodynamic therapeutic agents, these complexes have been widely studied for their efficacy against cancer cells, but their potential for antibacterial activity remains largely unexplored. Our study focuses on exploring the antibacterial photodynamic effect of Ruthenium and Iridium-based complexes against both Gram-positive and Gram-negative bacteria. We aim to provide a comprehensive overview of various types of research in this area, including the structures, synthesis methods, and antibacterial photodynamic applications of these complexes. Our findings will provide valuable insights into the design, development, and modification of PSs to enhance their photodynamic therapeutic effect on bacteria, along with a clear understanding of their mechanism of action.

Multifunctional fluorescence probe for simultaneous detection of viscosity, polarity, and ONOO- and its bioimaging in vitro and vivo.

Intracellular microenvironment (viscosity and polarity) and peroxynitrite ions (ONOO-) are involved in maintaining cell morphology, cell function, and signaling so that it is crucial to explore their level changes in vitro and vivo. In this work, we designed and synthesized a mitochondria-targeted fluorescence probe XBL for monitoring the dynamic changes of viscosity, polarity, and ONOO- based on TICT and ICT mechanism. The fluorescence spectra showed obvious changes for polarity at 500 nm as well as ONOO- and viscosity at 660 nm, respectively. The XBL can image simultaneously viscosity, polarity, and ONOO- in cells, and the results showed excess ONOO- leaded to the increase of viscosity in mitochondrial. The ferroptosis process was accompanied by increase of intracellular viscosity and ONOO- levels (or decrease of polarity), which allowed us to better understand the relevant physiological and pathological processes. The XBL can distinguish normal cells and cancerous cells by the fluorescence intensity changes in green and red channels, and image viscosity in inflamed mice. Thus, XBL can provided the chemical tool to understand the physiological and pathological mechanisms of disease by simultaneous detection of viscosity, polarity and ONOO-.

Metal-organic cages for gas adsorption and separation.

The unique high surface area and tunable cavity size endow metal-organic cages (MOCs) with superior performance and broad application in gas adsorption and separation. Over the past three decades, for instance, numerous MOCs have been widely explored in adsorbing diverse types of gas including energy gases, greenhouse gases, toxic gases, noble gases, etc. To gain a better understanding of the structure-performance relationships, great endeavors have been devoted to ligand design, metal node regulation, active metal site construction, cavity size adjustment, and function-oriented ligand modification, thus opening up routes toward rationally designed MOCs with enhanced capabilities. Focusing on the unveiled structure-performance relationships of MOCs towards target gas molecules, this review consists of two parts, gas adsorption and gas separation, which are discussed separately. Each part discusses the cage assembly process, gas adsorption strategies, host-guest chemistry, and adsorption properties. Finally, we briefly overviewed the challenges and future directions in the rational development of MOC-based sorbents for application in challenging gas adsorption and separation, including the development of high adsorption capacity MOCs oriented by adsorbability and the development of highly selective adsorption MOCs oriented by separation performance.