ICG-labeled PD-L1-antagonistic affibody dimer for tumor imaging and enhancement of tumor photothermal-immunotherapy.

In clinical practice, tumor-targeting diagnosis and immunotherapy against programmed death ligand 1 (PD-L1) have a significant impact. In this research, a PD-L1-antagonistic affibody dimer (ZPD-L1) was successfully prepared through Escherichia coli expression system, and conjugated with the photosensitizer of ICG via N-hydroxysuccinimide (NHS) ester to develop a novel tumor-targeting agent (ICG-ZPD-L1) for both tumor imaging diagnosis and photothermal-immunotherapy simultaneously. In vitro, ZPD-L1 could specifically bind to PD-L1-positive LLC and MC38 tumor cells, and ICG-ZPD-L1-mediated photothermal therapy (PTT) also showed excellent phototoxicity to these tumor cells. In vivo, ICG-ZPD-L1 selectively enriched into the PD-L1-positive MC38 tumor tissues, and the high-contrast optical imaging of tumors was obtained. ICG-ZPD-L1-mediated PTT exhibited a potent anti-tumor effect in vivo due to its remarkable photothermal properties. Furthermore, ICG-ZPD-L1-mediated PTT significantly induced the immunogenic cell death (ICD) of primary tumors, promoted maturation of dendritic cells (DCs), up-regulated anti-tumor immune response, enhanced immunotherapy, and superiorly inhibited the growth of metastatic tumors. In addition, ICG-ZPD-L1 showed favorable biosafety throughout the brief duration of treatment. In summary, these results suggest that ICG-ZPD-L1 is a multifunctional tumor-targeting drug integrating tumor imaging diagnosis and photothermal-immunotherapy, and has great guiding significance for the diagnosis and treatment of clinical PD-L1-positive tumor patients.

HybMED: A Hybrid Neural Network Training Processor with Multi-Sparsity Exploitation for Internet of Medical Things.

Cloud-based training and edge-based inference modes for Artificial Intelligence of Medical Things (AIoMT) applications suffer from accuracy degradation due to physiological signal variations among patients. On-chip learning can overcome this issue by online adaptation of neural network parameters for user-specific tasks. However, existing on-chip learning processors have limitations in terms of versatility, resource utilization, and energy efficiency. We propose HybMED, which is a novel neural signal processor that supports on-chip hybrid neural network training using a composite direct feedback alignment-based paradigm. HybMED is suitable for general-purpose health monitoring AIoMT devices. It improves resource utilization and area efficiency by the reconfigurable homogeneous core with heterogeneous data flow and enhances energy efficiency by exploiting sparsity at different granularities. The chip was fabricated by TSMC 40nm process and tested in multiple physiological signal processing tasks, demonstrating an average improvement in accuracy of 41.16% following online few-shot learning. The chip demonstrates an area efficiency of 1.17 GOPS/mm2 and an energy efficiency of 1.58 TOPS/W. Compared to the previous state-of-the-art physiological signal processors with on-chip learning, the chip achieves a 65× improvement in area efficiency and 1.48× improvement in energy efficiency, respectively.

Genome-wide identification, evolution of DNA methyltransferases and their expression under salinity stress in Larimichthys crocea.

DNA methyltransferases (Dnmts) are responsible for DNA methylation which influences patterns of gene expression and plays a crucial role in response to environmental changes. In this study, 7 LcDnmt genes were identified in the genome of large yellow croaker (Larimichthys crocea). The comprehensive analysis was conducted on gene structure, protein and location site of LcDnmts. LcDnmt proteins belonged to three groups (Dnmt1, Dnmt2, and Dnmt3) according to their conserved domains and phylogenetic analysis. Although Dnmt3 can be further divided into three sub groups (Dnmt3a, Dnmt3b, and Dnmt3l), there is no Dnmnt3l member in the large yellow croaker. Phylogenetic analysis revealed that the Dnmt family was highly conserved in teleosts. Expression patterns derived from the RNA-seq, qRT-PCR and Western blot analysis revealed that 2 LcDnmt genes (LcDnmt1 and LcDnmt3a2) significantly regulated under salinity stress in the liver, which was found to be dominantly expressed in the intestine and brain, respectively. These two genes may play an important role in the salinity stress of large yellow croaker and represent candidates for future functional analysis. Our results revealed the conservation of Dnmts during evolution and indicated a potential role of Dnmts in epigenetic regulation of response to salinity stress.

Precise and low-power closed-loop neuromodulation through algorithm-integrated circuit co-design.

Implantable neuromodulation devices have significantly advanced treatments for neurological disorders such as Parkinson's disease, epilepsy, and depression. Traditional open-loop devices like deep brain stimulation (DBS) and spinal cord stimulators (SCS) often lead to overstimulation and lack adaptive precision, raising safety and side-effect concerns. Next-generation closed-loop systems offer real-time monitoring and on-device diagnostics for responsive stimulation, presenting a significant advancement for treating a range of brain diseases. However, the high false alarm rates of current closed-loop technologies limit their efficacy and increase energy consumption due to unnecessary stimulations. In this study, we introduce an artificial intelligence-integrated circuit co-design that targets these issues and using an online demonstration system for closed-loop seizure prediction to showcase its effectiveness. Firstly, two neural network models are obtained with neural-network search and quantization strategies. A binary neural network is optimized for minimal computation with high sensitivity and a convolutional neural network with a false alarm rate as low as 0.1/h for false alarm rejection. Then, a dedicated low-power processor is fabricated in 55 nm technology to implement the two models. With reconfigurable design and event-driven processing feature the resulting application-specific integrated circuit (ASIC) occupies only 5mm2 silicon area and the average power consumption is 142 µW. The proposed solution achieves a significant reduction in both false alarm rates and power consumption when benchmarked against state-of-the-art counterparts.

ILD-GAP combined with the monocyte ratio could be a better prognostic prediction model than ILD-GAP in patients with interstitial lung diseases.

BACKGROUND: The ILD-GAP scoring system is known to be useful in predicting prognosis in patients with interstitial lung disease (ILD). An elevated monocyte count was associated with increased risks of IPF poor prognosis. We examined whether the ILD-GAP scoring system combined with the monocyte ratio (ILD-GAPM) is superior to the conventional ILD-GAP model in predicting ILD prognosis. METHODS: In patients with ILD treated between April 2013 and April 2017, we were retrospectively assessed the relationships between baseline clinical parameters, including age, sex, Charlson Comorbidity Index score (CCIS), ILD diagnosis, blood biomarkers, pulmonary function test results, and disease outcomes. In ILD patients were included idiopathic pulmonary fibrosis (IPF), idiopathic nonspecific interstitial pneumonia (iNSIP), collagen vascular disease-related interstitial pneumonia (CVD-IP), chronic hypersensitivity pneumonitis (CHP), and unclassifiable ILD (UC-ILD). We also assessed the ability to predict prognosis was compared between the ILD-GAP and ILD-GAPM models. RESULTS: A total of 179 patients (mean age, 73 years) were assessed. All of them were taken pulmonary function test, including percentage predicted diffusion capacity for carbon monoxide. ILD patients included 56 IPF cases, 112 iNSIP and CVD-IP cases, 6 CHP cases and 5 UC-ILD cases. ILD-GAPM provided a greater area under the receiver-operating characteristic curve (0.747) than ILD-GAP (0.710) for predicting 3-year ILD-related events. Furthermore, the log-rank test showed that the Kaplan-Meier curves in ILD-GAPM were significantly different by stage (P = 0.015), but not by stage in ILD-GAP (P = 0.074). CONCLUSIONS: The ILD-GAPM model may be a more accurate predictor of prognosis for ILD patients than the ILD-GAP model.

Individual-Level Health Care Costs Attributable to Noncommunicable Diseases: A Longitudinal Study Based on the Older Adults in China.

Noncommunicable chronic diseases among the elderly population represent a significant economic burden in China. However, previous disease-related health cost studies lacked representation of older adults and comparability of the burden of multiple chronic diseases. The objective of this study was to determine the fraction of health care costs attributable to the 6 most prevalent chronic diseases and comorbidities in the sample of older adults. This study employed data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), with 3 waves in 2011, 2014, and 2018, and included 18 349 observations in total. Outpatient costs, inpatient costs, and total health care costs were included in this study. Based on a 2-part random effects model, the effect of chronic disease on health service utilization was first explored by constructing a dummy variable for whether or not to utilize health care, followed by estimation of attributable costs in the population with health care utilization. Among the older adults in the sample, hypertension, heart disease, cataracts, arthritis, stroke or Cerebrovascular disease (CVD) and chronic lung disease are the 6 most prevalent chronic conditions. The costs attributable to the 6 chronic diseases mentioned above were 36.00% of outpatient costs, 55.92% of inpatient costs, and 45.05% of total health care costs for older adults. Of these, heart disease, stroke or CVD, and chronic lung disease accounted for 22.11%, 13.24%, and 10.56% of total health care costs, respectively. Moreover, the proportion of health care costs attributable to chronic diseases was higher for older adults who were male, lived in urban areas, and had a lower level of education. The proportion of health care costs attributable to chronic diseases is substantial among older adults in China. Health care costs associated with chronic diseases can be decreased with well-targeted interventions and comprehensive access to health services.

Integrated analysis of bulk RNA-seq and single-cell RNA-seq reveals the function of pyrocytosis in the pathogenesis of abdominal aortic aneurysm.

Pyrocytosis is involved in the development of abdominal aortic aneurysm (AAA), we explored the pyrocytosis-related hub genes in AAA and conducted a diagnostic model based on the pyrocytosis-related genes score (PRGs). A total of 2 bulk RNA-seq (GSE57691 and GSE47472) datasets and pyrocytosis-related genes were integrated to obtain 24 pyrocytosis-related different expression genes (DEGs). The LASSO Cox regression analysis was conducted to filter out 7 genes and further establish the nomogram signature based on the PRGs that exhibited a good diagnosis value. Weighted gene co-expression network analysis (WGCNA) established 14 gene modules and further identified 6 hub genes which were involved in the regulatory process of pyrocytosis in AAA. At the single cell level, we further identified 3 immune cells were highly associated with the pyrocytosis process in AAA. Finally, the cell-cell communication demonstrated that fibroblasts and endothelial cells and myeloid cells maintained close communications. Here, we identified the dysfunctional expressed pyrocytosis-related genes and immune cells in AAA, which provide a comprehensive understanding of the pathogenesis of AAA.

Healthcare costs attributable to abnormal weight in China: evidence based on a longitudinal study.

BACKGROUND: The prevalence of abnormal weight is on the rise, presenting serious health risks and socioeconomic problems. Nonetheless, there is a lack of studies on the medical cost savings that can be attained through the mitigation of abnormal weight. The aim of this study was to estimate the impact of abnormal weight on healthcare costs in China. METHODS: The study employed a 4-wave panel data from China Family Panel Studies (CFPS) between 2012 and 2018 (11,209 participants in each wave). Inpatient, non-inpatient and total healthcare costs were outcome variables. Abnormal weight is categorized based on body mass index (BMI). Initially, the two-part model was employed to investigate the impact of overweight/obesity and underweight on healthcare utilisation and costs, respectively. Subsequently, the estimated results were utilised to calculate the overweight/obesity attributable fraction (OAF) and the underweight attributable fraction (UAF). RESULTS: In 2018, healthcare costs per person for overweight and obese population were estimated to be $607.51 and $639.28, respectively, and the underweight population was $755.55. In comparison to people of normal weight, individuals who were overweight/obese (OR = 1.067, p < 0.05) was more likely to utilise healthcare services. Overweight/obesity attributable fraction (OAF) was 3.90% of total healthcare costs and 4.31% of non-inpatient costs. Overweight/obesity does not result in additional healthcare expenditures for young people but increases healthcare costs for middle-aged adults (OAF = 7.28%) and older adults (OAF = 6.48%). The non-inpatient cost of underweight population was significantly higher than that of normal weight population (ß = 0.060,p < 0.1), but the non-inpatient health service utilisation was not significantly affected. CONCLUSIONS: Abnormal weight imposes a huge economic burden on individuals, households and the society. Abnormal weight in Chinese adults significantly increased healthcare utilisation and costs, particular in non-inpatient care. It is recommended that government and relevant social agencies provide a better social environment to enhance individual self-perception and promote healthy weight.

A 510 µW 0.738-mm 2 6.2-pJ/SOP Online Learning Multi-Topology SNN Processor With Unified Computation Engine in 40-nm CMOS.

Implementing neural networks (NN) on edge devices enables AI to be applied in many daily scenarios. The stringent area and power budget on edge devices impose challenges on conventional NNs with massive energy-consuming Multiply Accumulation (MAC) operations and offer an opportunity for Spiking Neural Networks (SNN), which can be implemented within sub-mW power budget. However, mainstream SNN topologies varies from Spiking Feedforward Neural Network (SFNN), Spiking Recurrent Neural Network (SRNN), to Spiking Convolutional Neural Network (SCNN), and it is challenging for the edge SNN processor to adapt to different topologies. Besides, online learning ability is critical for edge devices to adapt to local environments but comes with dedicated learning modules, further increasing area and power consumption burdens. To alleviate these problems, this work proposed RAINE, a reconfigurable neuromorphic engine supporting multiple SNN topologies and a dedicated trace-based rewarded spike-timing-dependent plasticity (TR-STDP) learning algorithm. Sixteen Unified-Dynamics Learning-Engines (UDLEs) are implemented in RAINE to realize a compact and reconfigurable implementation of different SNN operations. Three topology-aware data reuse strategies are proposed and analyzed to optimize the mapping of different SNNs on RAINE. A 40-nm prototype chip is fabricated, achieving energy-per-synaptic-operation (SOP) of 6.2 pJ/SOP at 0.51 V, and power consumption of 510 µW at 0.45 V. Finally, three examples with different SNN topologies, including SRNN-based ECG arrhythmia detection, SCNN-based 2D image classification, and end-to-end on-chip learning for MNIST digit recognition, are demonstrated on RAINE with ultra-low energy consumption of 97.7nJ/step, 6.28 µJ/sample, and 42.98 µJ/sample respectively. These results show the feasibility of obtaining high reconfigurability and low power consumption simultaneously on a SNN processor.

Software-Hardware Co-Design for Energy-Efficient Continuous Health Monitoring via Task-Aware Compression.

Low power consumption associated with data transmission and processing of wearable/implantable devices is crucial to ensure the usability of continuous health monitoring systems. In this paper, we propose a novel health monitoring framework where the signal acquired is compressed in a task-aware manner to preserve task-relevant information at the sensor end with a low computation cost. The resulting compressed signals can be transmitted with significantly lower bandwidth, analyzed directly without a dedicated reconstruction process, or reconstructed with high fidelity. Also, we propose a dedicated hardware architecture with sparse Booth encoding multiplication and the 1-D convolution pipeline for the task-aware compression and the analysis modules, respectively. Extensive experiments show that the proposed framework is accurate, with a seizure prediction accuracy of 89.70 % under a signal compression ratio of 1/16. The hardware architecture is implemented on an Alveo U250 FPGA board, achieving a power of 0.207 W at a clock frequency of 100 MHz.

A 0.99-to-4.38 uJ/class Event-Driven Hybrid Neural Network Processor for Full-Spectrum Neural Signal Analyses.

Versatile and energy-efficient neural signal processors are in high demand in brain-machine interfaces and closed-loop neuromodulation applications. In this paper, we propose an energy-efficient processor for neural signal analyses. The proposed processor utilizes three key techniques to efficiently improve versatility and energy efficiency. 1) Hybrid neural network design: The processor supports artificial neural network (ANN)- and spiking neural network (SNN)-based neuromorphic processing where ANN is used to support the processing of ExG signals and SNN is used for handling neural spike signals. 2) Event-driven processing: The processor can perform always-on binary neural network (BNN)-based event detection with low-energy consumption, and it only switches to the high-accuracy convolutional neural network (CNN)-based recognition mode when events are detected. 3) Reconfigurable architecture: By exploiting the computational similarity of different neural networks, the processor supports critical BNN, CNN, and SNN operations with the same processing elements, achieving significant area reduction and energy efficiency improvement over those of a naive implementation. It achieves 90.05% accuracy and 4.38 uJ/class in a center-out reaching task with an SNN and 99.4% sensitivity, 98.6% specificity, and 1.93 uJ/class in an EEG-based seizure prediction task with dual neural network-based event-driven processing. Moreover, it achieves a classification accuracy of 99.92%, 99.38%, and 86.39% and energy consumption of 1.73, 0.99, and 1.31 uJ/class for EEG-based epileptic seizure detection, ECG-based arrhythmia detection, and EMG-based gesture recognition, respectively.

NeuroCARE: A generic neuromorphic edge computing framework for healthcare applications.

Highly accurate classification methods for multi-task biomedical signal processing are reported, including neural networks. However, reported works are computationally expensive and power-hungry. Such bottlenecks make it hard to deploy existing approaches on edge platforms such as mobile and wearable devices. Gaining motivation from the good performance and high energy-efficiency of spiking neural networks (SNNs), a generic neuromorphic framework for edge healthcare and biomedical applications are proposed and evaluated on various tasks, including electroencephalography (EEG) based epileptic seizure prediction, electrocardiography (ECG) based arrhythmia detection, and electromyography (EMG) based hand gesture recognition. This approach, NeuroCARE, uses a unique sparse spike encoder to generate spike sequences from raw biomedical signals and makes classifications using the spike-based computing engine that combines the advantages of both CNN and SNN. An adaptive weight mapping method specifically co-designed with the spike encoder can efficiently convert CNN to SNN without performance deterioration. The evaluation results show that the overall performance, including the classification accuracy, sensitivity and F1 score, achieve 92.7, 96.7, and 85.7% for seizure prediction, arrhythmia detection and hand gesture recognition, respectively. In comparison with CNN topologies, the computation complexity is reduced by over 80.7% while the energy consumption and area occupation are reduced by over 80% and over 64.8%, respectively, indicating that the proposed neuromorphic computing approach is energy and area efficient and of high precision, which paves the way for deployment at edge platforms.

Empirical validation of the information-motivation-behavioral skills model of gestational weight management behavior: a framework for intervention.

BACKGROUND: Unhealthy gestational weight gain is a modifiable risk factor for adverse maternal and child health. Appropriate and effective intervention strategies that focus on behavioral change or maintenance are critical in weight management during pregnancy. Our aim was to uncover the influencing factors and psychosocial mechanisms of gestational weight control behavior, and to construct a behavioral model suitable for intervention based on Information-Motivation-Behavioral skills (IMB) model. METHODS: A sample of 559 pregnant women from a municipal maternal and child healthcare facility in Jiangsu Province, China was enrolled in this cross-sectional empirical study. Partial least square structural equation modelling was used to verify the hypothesized model, and post hoc analyses was used to test the effect of parity and pre-pregnancy BMI on the model. RESULTS: The IMB model elements can predict gestational weight management (GWM) behavior well, with information being the most influential factor. As predicted, information affects GWM directly (ß = 0.325, p < 0.05) and indirectly (ß = 0.054, p < 0.05) through behavioral skills. Likewise, motivation has direct (ß = 0.461, p < 0.05) effects on GWM, and has indirect (ß = 0.071, p < 0.05) effects through behavioral skills. Behavioral skills have a direct impact (ß = 0.154, p < 0.05). The model had a goodness of fit (GOF = 0.421) and was robust when tested in subgroups of different parity or pre-pregnancy BMI. CONCLUSION: Findings from this study supported the predictions of the IMB model for GWM behavior, and identified its modifiable determinants. The tested behavior model for GWM can serve as a new validated intervention strategy in weight management among pregnant women.

Regulation of biosynthesis of the main flavor-contributing metabolites in tea plant (Camellia sinensis): A review.

In the process of adapting to the environment, tea plants (Camellia sinensis) endow tea with unique flavor and health functions, which should be attributed to secondary metabolites, including catechins, L-theanine, caffeine and terpene volatiles. Since the content of these flavor-contributing metabolites are mainly determined by the growth of tea plant, it is very important to understand their alteration and regulation mechanisms. In the present work, we first summarize the distribution, change characteristics of the main flavor-contributing metabolites in different cultivars, organs and under environmental stresses of tea plant. Subsequently, we discuss the regulating mechanisms involved in the biosynthesis of these metabolites based on the existing evidence. Finally, we propose the remarks and perspectives on the future study relating flavor-contributing metabolites. This review would contribute to the acceleration of research on the characteristic secondary metabolites and the breeding programs in tea plants.

The impact of health related quality of life for children and adolescents aged 8-15 years in Nanjing / 中国学校卫生

Objective@#To investigate the impact of COVID-19 infection and health related behaviors on the health related quality of life of children and adolescents aged 8-15 years in Nanjing, so as to provide a theoretical basis for improving HRQoL in children and adolescents.@*Methods@#From December 2022 to January 2023, a total of 2 398 students aged 8-15 years from the third grade of primary school to junior middle school in Nanjing were selected by multistage random cluster sampling. The 3 level EuroQol 5 dimension Questionnaire Youth Vension (EQ-5D-Y-3L) was completed by the respondents on their own, and the parents assisted in completing the rest of the questionnaire.@*Results@#The EuroQol-index(EQ-index) and Visual Analogue Scale (VAS) scores of being infected individuals were lower than those of uninfected and previously infected ( P <0.05). The proportion of being infected individuals reporting difficulty on "Pain/Discomfort" was higher than that of uninfected and previously infected individuals, and the proportion of reporting difficulty on "Mobility" was also higher than that of uninfected individuals ( P <0.05). Lack of parental companionship( OR=10.19, 95%CI =3.12-33.22), irregular breakfast consumption ( OR=10.63, 95%CI =3.20-35.25), and excessive screen time ( OR=8.24, 95%CI =3.02-22.51) increased the risk of difficulty on "Mobility" in being infected individuals ( P <0.05). Irregular breakfast consumption ( OR=1.93, 95%CI =1.31-2.84) and consumption of sweetened beverages and snacks (OR=1.56, 95%CI =1.17-2.10) increased the risk of having lower EQ index in previously infected individuals compared to uninfected individuals. Furthermore, consumption of sweetened beverages and snacks ( OR=1.57, 95%CI =1.21-2.05) and excessive screen time ( OR=1.49, 95%CI =1.12-1.98) also increased the risk of VAS scores being lower in previously infected individuals compared to uninfected individuals ( P <0.05).@*Conclusion@#The COVID-19 infection impairs HRQoL, and unhealthy behaviors deteriorate its negative impact. Healthy behaviors and lifestyles should be advocated to reduce the impact of COVID-19 infection on HRQoL.

Tiny CNN for Seizure Prediction in Wearable Biomedical Devices.

Epilepsy is a life-threatening disease affecting millions of people all over the world. Artificial intelligence epileptic predictors offer excellent potential to improve epilepsy therapy. Particularly, deep learning models such as convolutional neural networks (CNN) can be used to accurately detect ictogenesis through deep structured learning representations. In this work, a tiny one-dimensional stacked convolutional neural network (1DSCNN) is proposed based on short-time Fourier transform (STFT) to predict epileptic seizure. The results demonstrate that the proposed method obtains better performance compared to recent state-of-the-art methods, achieving an average sensitivity of 94.44%, average false prediction rate (FPR) of 0.011/h and average area under the curve (AUC) of 0.979 on the test set of the American Epilepsy Society Seizure Prediction Challenge dataset, while featuring a model size of only 21.32kB. Furthermore, after adapting the model to 4-bit quantization, its size is significantly decreased by 7.08x with only 0.51% AUC score precision loss, which shows excellent potential for hardware-friendly wearable implementation.

Association between gestational depression and weight management behaviors during pregnancy: A cross-sectional study in Eastern China.

An undesirable psychological state may deteriorate individual's weight management-related behaviors. This study aims to see if ineffective weight control measures were linked to depressive symptoms during pregnancy. We conducted a cross-sectional questionnaire survey of 784 pregnant women and collected information on sociodemographic factors, maternal characteristics, depression, and weight management activities throughout pregnancy (exercise management, dietary management, self-monitoring regulation, and management objectives). About 17.5% of pregnant women exhibited depressive symptoms. The mean score on dietary management was upper-middle, exercise management and self-monitoring regulation were medium, and management objectives were lower-middle. Multivariable linear regression analysis revealed that pregnant women with depressive symptoms had lower levels of exercise management (ß = -1.585, p = 0.005), dietary management (adjusted ß = -0.984, p = 0.002), and management objectives (adjusted ß = -0.726, p = 0.009). However, there was no significant relationship between depressive symptoms and pregnant women's self-monitoring regulating behavior (p > 0.05). The findings indicated the inverse association between depressive symptoms and gestational weight management behaviors. These results offer important indications for pregnancy weight management professionals by highlighting the need for mental health interventions for pregnant women experiencing depressive symptoms.

Akkermansia muciniphila prevents cold-related atrial fibrillation in rats by modulation of TMAO induced cardiac pyroptosis.

BACKGROUND: Cold exposure is one of the most important risk factors for atrial fibrillation (AF), and closely related to the poor prognosis of AF patients. However, the mechanisms underlying cold-related AF are poorly understood. METHODS: Various techniques including 16S rRNA gene sequencing, fecal microbiota transplantation, and electrophysiological examination were used to determine whether gut microbiota dysbiosis promotes cold-related AF. Metabonomics were performed to investigate changes in fecal trimethylamine (TMA) and plasma trimethylamine N-oxide (TMAO) during cold exposure. The detailed mechanism underlying cold-related AF were examined in vitro. Transgenic mice were constructed to explore the role of pyroptosis in cold-related AF. The human cohort was used to evaluate the correlation between A. muciniphila and cold-related AF. FINDINGS: We found that cold exposure caused elevated susceptibility to AF and reduced abundance of Akkermansia muciniphila (A. muciniphila) in rats. Intriguingly, oral supplementation of A. muciniphila ameliorated the pro-AF property induced by cold exposure. Mechanistically, cold exposure disrupted the A. muciniphila, by which elevated the level of trimethylamine N-oxide (TMAO) through modulation of the microbial enzymes involved in trimethylamine (TMA) synthesis. Correspondingly, progressively increased plasma TMAO levels were validated in human subjects during cold weather. Raised TMAO enhanced the infiltration of M1 macrophages in atria and increased the expression of Casp1-p20 and cleaved-GSDMD, ultimately causing atrial structural remodeling. Furthermore, the mice with conditional deletion of caspase1 exhibited resistance to cold-related AF. More importantly, a cross-sectional clinical study revealed that the reduction of A. muciniphila abundance was an independent risk factor for cold-related AF in human subjects. INTERPRETATION: Our findings revealed a novel causal role of aberrant gut microbiota and metabolites in pathogenesis of cold-related AF, which raises the possibility of selectively targeting microbiota and microbial metabolites as a potential therapeutic strategy for cold-related AF. FUNDING: This work was supported by grants from the State Key Program of National Natural Science Foundation of China (No.81830012), and National Natural Science Foundation of China (No.82070336, No.81974024), Youth Program of the National Natural Science Foundation of China (No.81900374, No.81900302), and Excellent Young Medical Talents supporting project in the First Affiliated Hospital of Harbin Medical University (No. HYD2020YQ0001).

Large-area large-grain CsPbCl3perovskite films by confined re-growth for violet photodetectors.

CsPbCl3perovskite is an attractive semiconductor material with characteristics such as a wide bandgap, high chemical stability, and excellent optoelectronic properties, which broaden its application prospects for ultraviolet (UV) and violet photodetectors (PDs). However, large-area CsPbCl3films with high coverage, large grains, and controllable thickness are still difficult to prepare by using the solution method due to the extremely low solubility of their precursors in conventional solvents. Herein, a water-assisted confined re-growth method is developed, and a CsPbCl3microcrystalline film with an area of 3 cm × 3 cm is grown, the thickness of which is controllable within a range of several microns. The as-prepared thin film exhibits a flat and smooth surface, large grains, and enhanced photoluminescence. Furthermore, the fabricated violet PDs based on the prepared CsPbCl3film show a high responsivity of 2.17 A W-1, external quantum efficiency of 664%, on/off ratio of 2.58 × 103, and good stability. This study provides a prospective solution for the growth of large-area, large-grain, and surface-smooth CsPbCl3films for high-performance UV and violet PDs.

The GHG mitigation opportunity of sludge management in China.

Greenhouse gas (GHG) mitigation in wastewater treatment sector is indispensable in China's carbon neutral target. As an important component of wastewater system, sludge generation is rapidly increased with the acceleration of urbanization in China. It is crucial to investigate the carbon footprint of various sludge management strategies and quantify the potential optimization of GHG reduction effect at national scale. Therefore, this study conducted a comprehensive analysis of sludge distribution and GHG profiles of various sludge systems. The overall dry sludge generation in China is 12.15 Mt, with spatial resolution at city level. Different sludge treatment options were categorized into four types: energy recovery, nutrient recovery (e.g. phosphorus and nitrogen), material valorisation (e.g. brick, biochar) and conventional disposal. With various sludge treatment options, the GHG profile of annual sludge management in China ranges from -35.86 Mt/year to 57.11 Mt/year. The best GHG mitigation can be achieved through energy recovery by co-incineration system and the greatest reduction opportunity is concentrated in highly urbanized regions, such as Yangtze River Delta, Pearl River Delta, and Beijing-Tianjin-Hebei urban agglomerations.