Activated Clec4nhi Neutrophils Aggravate Lung Injury in an Endothelial IGFBP7-Dependent Manner.

The role of endothelial cells in acute lung injury (ALI) has been widely elaborated, but little is known about the role of different subtypes of endothelial cells in ALI. ALI models were established by lipopolysaccharide. Single-cell RNA sequencing was used to identify differential molecules in endothelial subtypes and the heterogeneity of lung immune cells. Specific antibodies were used to block insulin-like growth factor binding protein 7 (IGFBP7), and AAVshIGP7 was used to specifically knock down IGFBP7. Here, we found that IGFBP7 was the most differentially expressed molecule in diverse subsets of endothelial cells and that IGFBP7 was strongly associated with inflammatory responses. Elevated IGFBP7 significantly exacerbated barrier dysfunction in ALI, whereas blockade of IGFBP7 partially reversed barrier damage. General capillary cells are the primary source of elevated serum IGFBP7 after ALI. Using single-cell RNA sequencing, we identified significantly increased Clec4nhi neutrophils in mice with ALI, whereas IGFBP7 knockdown significantly reduced infiltration of Clec4nhi cells and mitigated barrier dysfunction in ALI. In addition, we found that IGFBP7 activated the NF-κB signaling axis by promoting phosphorylation and ubiquitination degradation of F-box/WD repeat-containing protein 2 (FBXW2), thereby exacerbating barrier dysfunction in ALI. Taken together, our data indicate that blockade of serum IGFBP7 or IGFBP7 depletion in general capillary cells reversed barrier damage in ALI. Therefore, targeting IGFBP7 depletion could be a novel strategy for treating ALI.

S1PR3 inhibition protects against LPS-induced ARDS by inhibiting NF-κB and improving mitochondrial oxidative phosphorylation.

BACKGROUND: Inflammation and endothelial barrier dysfunction are the major pathophysiological changes in acute respiratory distress syndrome (ARDS). Sphingosine-1-phosphate receptor 3 (S1PR3), a G protein-coupled receptor, has been found to mediate inflammation and endothelial cell (EC) integrity. However, the function of S1PR3 in ARDS has not been fully elucidated. METHODS: We used a murine lipopolysaccharide (LPS)-induced ARDS model and an LPS- stimulated ECs model to investigate the role of S1PR3 in anti-inflammatory effects and endothelial barrier protection during ARDS. RESULTS: We found that S1PR3 expression was increased in the lung tissues of mice with LPS-induced ARDS. TY-52156, a selective S1PR3 inhibitor, effectively attenuated LPS-induced inflammation by suppressing the expression of proinflammatory cytokines and restored the endothelial barrier by repairing adherens junctions and reducing vascular leakage. S1PR3 inhibition was achieved by an adeno-associated virus in vivo and a small interfering RNA in vitro. Both the in vivo and in vitro studies demonstrated that pharmacological or genetic inhibition of S1PR3 protected against ARDS by inhibiting the NF-κB pathway and improving mitochondrial oxidative phosphorylation. CONCLUSIONS: S1PR3 inhibition protects against LPS-induced ARDS via suppression of pulmonary inflammation and promotion of the endothelial barrier by inhibiting NF-κB and improving mitochondrial oxidative phosphorylation, indicating that S1PR3 is a potential therapeutic target for ARDS.

Chronic sleep deprivation impairs retinal circadian transcriptome and visual function.

Sleep loss is common in modern society and is increasingly associated with eye diseases. However, the precise effects of sleep loss on retinal structure and function, particularly on the retinal circadian system, remain largely unexplored. This study investigates these effects using a chronic sleep deprivation (CSD) model in mice. Our investigation reveals that CSD significantly alters the retinal circadian transcriptome, leading to remarkable changes in the temporal patterns of enriched pathways. This perturbation extends to metabolic and immune-related transcriptomes, coupled with an accumulation of reactive oxygen species in the retina. Notably, CSD rhythmically affects the thickness of the ganglion cell complex, along with diurnal shifts in microglial migration and morphology within the retina. Most critically, we observe a marked decrease in both scotopic and photopic retinal function under CSD conditions. These findings underscore the broad impact of sleep deprivation on retinal health, highlighting its role in altering circadian gene expression, metabolism, immune response, and structural integrity. Our study provides new insights into the broader impact of sleep loss on retinal health.

Long-term high fructose intake reprograms the circadian transcriptome and disrupts homeostasis in mouse extra-orbital lacrimal glands.

This study aims to explore the effects of long-term high fructose intake (LHFI) on the structure, functionality, and physiological homeostasis of mouse extra-orbital lacrimal glands (ELGs), a critical component of ocular health. Our findings reveal significant reprogramming of the circadian transcriptome in ELGs following LHFI, alongside the activation of specific inflammatory pathways, as well as metabolic and neural pathways. Notably, LHFI resulted in increased inflammatory infiltration, enhanced lipid deposition, and reduced nerve fiber density in ELGs compared to controls. Functional assessments indicated a marked reduction in lacrimal secretion following cholinergic stimulation in LHFI-treated mice, suggesting impaired gland function. Overall, our results suggest that LHFI disrupts lacrimal gland homeostasis, potentially leading to dry eye disease by altering its structure and secretory function. These insights underscore the profound impact of dietary choices on ocular health and highlight the need for strategies to mitigate these risks.

Unambiguous Models and Machine Learning Strategies for Anomalous Extreme Events in Turbulent Dynamical System.

Data-driven modeling methods are studied for turbulent dynamical systems with extreme events under an unambiguous model framework. New neural network architectures are proposed to effectively learn the key dynamical mechanisms including the multiscale coupling and strong instability, and gain robust skill for long-time prediction resistive to the accumulated model errors from the data-driven approximation. The machine learning model overcomes the inherent limitations in traditional long short-time memory networks by exploiting a conditional Gaussian structure informed of the essential physical dynamics. The model performance is demonstrated under a prototype model from idealized geophysical flow and passive tracers, which exhibits analytical solutions with representative statistical features. Many attractive properties are found in the trained model in recovering the hidden dynamics using a limited dataset and sparse observation time, showing uniformly high skill with persistent numerical stability in predicting both the trajectory and statistical solutions among different statistical regimes away from the training regime. The model framework is promising to be applied to a wider class of turbulent systems with complex structures.

Peak-tracking BOTDA with dynamic ternary search.

We propose a peak-tracking BOTDA (PT-BOTDA) equipped with an efficient dynamic Brillouin frequency shift (BFS) searching scheme based on ternary search. The proposed scheme establishes a feedback loop between the selected frequency and the corresponding Brillouin gain to reduce the required number of scanning frequencies in one measurement. We also demonstrate the performance evaluation of the proposed scheme under scenarios with different searching granularities and dynamic sensing ranges. Experimental results indicate that in all situations, the proposed PT-BOTDA can achieve at least 85% and 97% reduction in the number of scanning frequencies for 1-MHz and 0.1-MHz frequency steps, respectively, with a 3-meter spatial resolution, while maintaining a convincing BFS searching accuracy under sufficient SNR condition using a smaller searching interval.

Long-term high fructose intake promotes lacrimal gland dysfunction by inducing gut dysbiosis in mice.

The lacrimal gland is essential for maintaining ocular surface health through the secretion of the aqueous layer of the tear film. It is therefore important to explore the intrinsic and extrinsic factors that affect the structure and function of the lacrimal gland and the mechanisms underlying them. With the prevalence of Westernized diets characterized by high sugar and fat content, the susceptibility to many diseases, including ocular diseases, is increased by inducing dysbiosis of the gut microbiome. Here, we found that the composition, abundance, and diversity of the gut microbiome was significantly altered in mice by drinking 15% high fructose water for one month, as determined by 16S rRNA sequencing. This was accompanied by a significant increase in lipid deposition and inflammatory cell infiltration in the extraorbital lacrimal glands (ELGs) of mice. Transcriptome analysis based on bulk RNA-sequencing revealed abnormal activation of some of several metabolic and immune-related pathways. In addition, the secretory response to stimulation with the cholinergic receptor agonist pilocarpine was significantly reduced. However, when the composition and diversity of the gut microbiome of high fructose intake (HFI)-treated mice were improved by transplanting feces from normal young healthy mice, the pathological alterations in ELG structure, inflammatory cell infiltration, secretory function and transcriptome analysis described above were significantly reversed compared to age-matched control mice. In conclusion, our data suggest that prolonged HFI may cause pathological damage to the structure and function of the ELG through the induction of gut dysbiosis. Restoration of intestinal dysbiosis in HFI-treated mice by fecal transplantation has a potential role in ameliorating these pathological impairments.

Interferon regulatory factor 1 (IRF1) inhibits lung endothelial regeneration following inflammation-induced acute lung injury.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a respiratory condition caused by severe endothelial barrier dysfunction within the lung. In ARDS, excessive inflammation, tissue edema, and immune cell influx prevents endothelial cell regeneration that is crucial in repairing the endothelial barrier. However, little is known about the molecular mechanism that underpin endothelial cell regeneration in ARDS. METHODS: R-based bioinformatics tools were used to analyze microarray-derived transcription profiles in human lung microvascular endothelial cells (HLMVECs) subjected to non-treatment or lipopolysaccharide (LPS) exposure. We generated endothelial cell-specific interferon regulatory factor 1 (Irf1) knockout (Irf1EC-/-) and Irf1fl/fl control mice for use in an endotoxemic murine model of acute lung injury (ALI). In vitro studies (qPCR, immunoblotting, and ChIP-qPCR) were conducted in mouse lung endothelial cells (MLECs) and HLMVECs. Dual-luciferase promoter reporter assays were performed in HLMVECs. RESULTS: Bioinformatics analyses identified IRF1 as a key up-regulated gene in HLMVECs post-LPS exposure. Endothelial-specific knockout of Irf1 in ALI mice resulted in enhanced regeneration of lung endothelium, while liposomal delivery of endothelial-specific Irf1 to wild-type ALI mice inhibited lung endothelial regeneration in a leukemia inhibitory factor (Lif)-dependent manner. Mechanistically, we demonstrated that LPS-induced Stat1Ser727 phosphorylation promotes Irf1 transactivation, resulting in downstream up-regulation of Lif that inhibits endothelial cell proliferation. CONCLUSIONS: These results demonstrate the existence of a p-Stat1Ser727-Irf1-Lif axis that inhibits lung endothelial cell regeneration post-LPS injury. Thus, direct inhibition of IRF1 or LIF may be a promising strategy for enhancing endothelial cell regeneration and improving clinical outcomes in ARDS patients.

Pramipexole inhibits astrocytic NLRP3 inflammasome activation via Drd3-dependent autophagy in a mouse model of Parkinson's disease.

Inflammation is one of the pathogenic processes in Parkinson's disease (PD). Dopamine receptor agonist pramipexole (PPX) is extensively used for PD treatment in clinics. A number of studies show that PPX exerts neuroprotection on dopaminergic (DA) neurons, but the molecular mechanisms underlying the protective effects of PPX on DA neurons are not fully elucidated. In the present study, we investigated whether PPX modulated PD-related neuroinflammation and underlying mechanisms. PD model was established in mice by bilateral striatum injection of lipopolyssaccharide (LPS). The mice were administered PPX (0.5 mg·kg-1·d-1, i.p.) 3 days before LPS injection, and for 3 or 21 days after surgery, respectively, for biochemical and histological analyses. We showed that PPX administration significantly alleviated the loss of DA neurons, and suppressed the astrocyte activation and levels of proinflammatory cytokine IL-1ß in the substantia nigra of LPS-injected mice. Furthermore, PPX administration significantly decreased the expression of NLRP3 inflammasome-associated proteins, i.e., cleaved forms of caspase-1, IL-1ß, and apoptosis-associated speck-like protein containing a caspase recruit domain (ASC) in the striatum. These results were validated in LPS+ATP-stimulated primary mouse astrocytes in vitro. Remarkably, we showed that PPX (100-400 µM) dose-dependently enhanced the autophagy activity in the astrocytes evidenced by the elevations in LC3-II and BECN1 protein expression, as well as the increase of GFP-LC3 puncta formation. The opposite effects of PPX on astrocytic NLRP3 inflammasome and autophagy were eliminated by Drd3 depletion. Moreover, we demonstrated that both pretreatment of astrocytes with autophagy inhibitor chloroquine (40 µM) in vitro and astrocyte-specific Atg5 knockdown in vivo blocked PPX-caused inhibition on NLRP3 inflammasome and protection against DA neuron damage. Altogether, this study demonstrates an anti-neuroinflammatory activity of PPX via a Drd3-dependent enhancement of autophagy activity in astrocytes, and reveals a new mechanism for the beneficial effect of PPX in PD therapy.

Using machine learning to predict extreme events in complex systems.

Extreme events and the related anomalous statistics are ubiquitously observed in many natural systems, and the development of efficient methods to understand and accurately predict such representative features remains a grand challenge. Here, we investigate the skill of deep learning strategies in the prediction of extreme events in complex turbulent dynamical systems. Deep neural networks have been successfully applied to many imaging processing problems involving big data, and have recently shown potential for the study of dynamical systems. We propose to use a densely connected mixed-scale network model to capture the extreme events appearing in a truncated Korteweg-de Vries (tKdV) statistical framework, which creates anomalous skewed distributions consistent with recent laboratory experiments for shallow water waves across an abrupt depth change, where a remarkable statistical phase transition is generated by varying the inverse temperature parameter in the corresponding Gibbs invariant measures. The neural network is trained using data without knowing the explicit model dynamics, and the training data are only drawn from the near-Gaussian regime of the tKdV model solutions without the occurrence of large extreme values. A relative entropy loss function, together with empirical partition functions, is proposed for measuring the accuracy of the network output where the dominant structures in the turbulent field are emphasized. The optimized network is shown to gain uniformly high skill in accurately predicting the solutions in a wide variety of statistical regimes, including highly skewed extreme events. The technique is promising to be further applied to other complicated high-dimensional systems.

High-order moment closure models with random batch method for efficient computation of multiscale turbulent systems.

We propose a high-order stochastic-statistical moment closure model for efficient ensemble prediction of leading-order statistical moments and probability density functions in multiscale complex turbulent systems. The statistical moment equations are closed by a precise calibration of the high-order feedbacks using ensemble solutions of the consistent stochastic equations, suitable for modeling complex phenomena including non-Gaussian statistics and extreme events. To address challenges associated with closely coupled spatiotemporal scales in turbulent states and expensive large ensemble simulation for high-dimensional systems, we introduce efficient computational strategies using the random batch method (RBM). This approach significantly reduces the required ensemble size while accurately capturing essential high-order structures. Only a small batch of small-scale fluctuation modes is used for each time update of the samples, and exact convergence to the full model statistics is ensured through frequent resampling of the batches during time evolution. Furthermore, we develop a reduced-order model to handle systems with really high dimensions by linking the large number of small-scale fluctuation modes to ensemble samples of dominant leading modes. The effectiveness of the proposed models is validated by numerical experiments on the one-layer and two-layer Lorenz '96 systems, which exhibit representative chaotic features and various statistical regimes. The full and reduced-order RBM models demonstrate uniformly high skill in capturing the time evolution of crucial leading-order statistics, non-Gaussian probability distributions, while achieving significantly lower computational cost compared to direct Monte-Carlo approaches. The models provide effective tools for a wide range of real-world applications in prediction, uncertainty quantification, and data assimilation.

A random batch method for efficient ensemble forecasts of multiscale turbulent systems.

A new efficient ensemble prediction strategy is developed for a multiscale turbulent model framework with emphasis on the nonlinear interactions between large and small-scale variables. The high computational cost in running large ensemble simulations of high-dimensional equations is effectively avoided by adopting a random batch decomposition of the wide spectrum of the fluctuation states, which is a characteristic feature of the multiscale turbulent systems. The time update of each ensemble sample is then only subject to a small portion of the small-scale fluctuation modes in one batch, while the true model dynamics with multiscale coupling is respected by frequent random resampling of the batches at each time updating step. We investigate both theoretical and numerical properties of the proposed method. First, the convergence of statistical errors in the random batch model approximation is shown rigorously independent of the sample size and full dimension of the system. Next, the forecast skill of the computational algorithm is tested on two representative models of turbulent flows exhibiting many key statistical phenomena with a direct link to realistic turbulent systems. The random batch method displays robust performance in capturing a series of crucial statistical features with general interests, including highly non-Gaussian fat-tailed probability distributions and intermittent bursts of instability, while requires a much lower computational cost than the direct ensemble approach. The efficient random batch method also facilitates the development of new strategies in uncertainty quantification and data assimilation for a wide variety of general complex turbulent systems in science and engineering.

Mechanisms of dihydromyricetin against hepatocellular carcinoma elucidated by network pharmacology combined with experimental validation.

CONTEXT: Dihydromyricetin (DMY) is extracted from vine tea, a traditional Chinese herbal medicine with anti-cancer, liver protection, and cholesterol-lowering effects. OBJECTIVE: This study investigated the mechanism of DMY against hepatocellular carcinoma (HCC). MATERIALS AND METHODS: Potential DMY, HCC, and cholesterol targets were collected from relevant databases. PPI networks were created by STRING. Then, the hub genes of co-targets, screened using CytoHubba. GO and KEGG pathway enrichment, were performed by Metascape. Based on the above results, a series of in vitro experiments were conducted by using 40-160 µM DMY for 24 h, including transwell migration/invasion assay, western blotting, and Bodipy stain assay. RESULTS: Network pharmacology identified 98 common targets and 10 hub genes of DMY, HCC, and cholesterol, and revealed that the anti-HCC effect of DMY may be related to the positive regulation of lipid rafts. Further experiments confirmed that DMY inhibits the proliferation, migration, and invasion of HCC cells and reduces their cholesterol levels in vitro. The IC50 is 894.4, 814.4, 467.8, 1,878.8, 151.8, and 156.9 µM for 97H, Hep3B, Sk-Hep1, SMMC-7721, HepG2, and Huh7 cells, respectively. In addition, DMY downregulates the expression of lipid raft markers (CAV1, FLOT1), as well as EGFR, PI3K, Akt, STAT3, and Erk. DISCUSSION AND CONCLUSION: The present study reveals that DMY suppresses EGFR and its downstream pathways by reducing cholesterol to disrupt lipid rafts, thereby inhibiting HCC, which provides a promising candidate drug with low toxicity for the treatment of HCC.

Split & Join: An Efficient Approach for Simulating Stapled Intestinal Anastomosis in Virtual Reality.

Colorectal cancer is a life-threatening disease. It is the second leading cause of cancer-related deaths in the United States. Stapled anastomosis is a rapid treatment for colorectal cancer and other intestinal diseases and has become an integral part of routine surgical practice. However, to the best of our knowledge, there is no existing work simulating intestinal anastomosis that often involves sophisticated soft tissue manipulations such as cutting and stitching. In this paper, for the first time, we propose a novel split and join approach to simulate a side-to-side stapled intestinal anastomosis in virtual reality. We mimic the intestine model using a new hybrid representation - a grid-linked particles model for physics simulation and a surface mesh for rendering. The proposed split and join operations handle the updates of both the grid-linked particles model and the surface mesh during the anastomosis procedure. The simulation results demonstrate the feasibility of the proposed approach in simulating intestine models and the side-to-side anastomosis operation.

Microbial Reconstitution Improves Aging-Driven Lacrimal Gland Circadian Dysfunction.

Lacrimal glands are highly susceptible to aging and exhibit age-related structural and functional alterations. However, the mechanisms by which aging affects the lacrimal glands are not well-established. The current study explores the crosstalk between the aging process, gut microbiota, and circadian rhythm in age-associated lacrimal gland dysfunction. C57BL/6J mice were divided into young, old, and fecal microbiota transplant (FMT)-treated old groups. The gut bacterial community diversity was analyzed by 16S rRNA sequencing. Exorbital lacrimal glands (ELGs) were collected at 3-hour intervals over a 24-hour circadian cycle, and total RNA was subjected to high-throughput sequencing. Rhythmic transcriptional data were analyzed using the Jonckheere-Terpstra-Kendall algorithm and bioinformatics analysis technology. Immunostaining was used to identify lymphocytic infiltration, lipid deposition, and nerve innervation in the ELGs. Compared with young mice, old mice underwent a significant gut microbial community shift. The rhythmically transcriptomic profile was significantly reprogrammed over a 24-hour cycle in the old ELG group. Intervention with serial FMT from young donors for 1 month rejuvinated the gut microbial community of the old mice. Most alterations in rhythmic transcriptomic profiling were improved. Furthermore, chronic inflammation, lipid deposition, and aberrant neural response of the aging lacrimal glands were significantly reduced. Thus, the study shows that reconstitution of age-associated gut dysbiosis with FMTs from young donors improves aging-driven lacrimal gland circadian dysfunction.

Abscisic acid-induced cytoplasmic translocation of constitutive photomorphogenic 1 enhances reactive oxygen species accumulation through the HY5-ABI5 pathway to modulate seed germination.

Seed germination is a physiological process regulated by multiple factors. Abscisic acid (ABA) can inhibit seed germination to improve seedling survival under conditions of abiotic stress, and this process is often regulated by light signals. Constitutive photomorphogenic 1 (COP1) is an upstream core repressor of light signals and is involved in several ABA responses. Here, we demonstrate that COP1 is a negative regulator of the ABA-mediated inhibition of seed germination. Disruption of COP1 enhanced Arabidopsis seed sensitivity to ABA and increased reactive oxygen species (ROS) levels. In seeds, ABA induced the translocation of COP1 to the cytoplasm, resulting in enhanced ABA-induced ROS levels. Genetic evidence indicated that HY5 and ABI5 act downstream of COP1 in the ABA-mediated inhibition of seed germination. ABA-induced COP1 cytoplasmic localization increased HY5 and ABI5 protein levels in the nucleus, leading to increased expression of ABI5 target genes and ROS levels in seeds. Together, our results reveal that ABA-induced cytoplasmic translocation of COP1 activates the HY5-ABI5 pathway to promote the expression of ABA-responsive genes and the accumulation of ROS during ABA-mediated inhibition of seed germination. These findings enhance the role of COP1 in the ABA signal transduction pathway.

Dynamic polarization-insensitive BOTDA in direct-detection OFDM with CNN-based BFS extraction.

We propose a dynamic polarization-insensitive Brillouin optical time domain analyzer (D/PI-BOTDA) with orthogonal frequency division multiplexing (OFDM) based on intensity-modulated direct-detection (IM-DD). A polarization-division-multiplexed (PDM) pump signal enables polarization diversity of the stimulated Brillouin scattering while a multi-frequency OFDM probe signal realizes dynamic sensing with single-shot transmission. We experimentally demonstrated distributed temperature sensing along a total 940-meter fiber with a temperature sensing coefficient of 1.2°C/MHz. The experimental results indicated a remarkable suppression of Brillouin gain fluctuation up to 4.38 times compared to the case without polarization diversity. To facilitate the Brillouin frequency shift (BFS) extraction process, we also implement a CNN-based BFS extraction method with SE-Res2Net block. The adopted algorithm achieves a higher accuracy than conventional curve fitting method, with a 10-time enhancement in the time efficiency.

Protective effects of adipose-derived biogenic nanoparticles on the pulmonary microvascular endothelial barrier in mice with ventilator-induced lung injury via the TRPV4/ROCK1 signalling pathway.

PURPOSE: The "obesity paradox" phenomenon occurs in critically ill patients who receive mechanical ventilation. Our previous studies found that the adipose-derived exosomes secreted by obese mice have a protective effect on the pulmonary microvascular endothelial barrier. However, the extraction of exosomes is cumbersome, their yield is low, and their storage is difficult. After further research, we discovered a new type of adipose-derived bioactive material called: lipoaspirate nanoparticles (Lipo-NPs). METHODS: Lipo-NPs were extracted and identified using a tangential flow filtration system. The Lipo-NPs were used as an intervention in ventilator-induced lung injury (VILI) models in vivo and in vitro to investigate whether they have a protective effect on lung tissue damage (haematoxylin and eosin staining), lung barrier function (lung wet/dry [W/D] weight ratio, protein concentration in bronchoalveolar lavage fluid (BALF), and Vascular endothelial (VE)-expression), as well as their related mechanisms. RESULTS: In both in vivo and in vitro studies, Lipo-NPs can attenuate lung injury, reduce lung W/D ratio and protein concentration in BALF, and augment the expression of the adhesion link-protein VE-cadherin, thus playing a protective role in lung barrier function. This protective effect involves the activation of the transient receptor potential vanilloid 4 (TRPV4)/Rho-associated kinase1 (ROCK1) signalling pathway. We further verified the role of this signalling pathway via activation and inhibition of TRPV4 and ROCK1. Moreover, phosphorylation of myosin light chain 2 (MLC2) regulates F-actin and is a target of the ROCK pathway. CONCLUSION: Lipo-NPs can enhance the expression of VE-cadherin by inhibiting the TRPV4/ROCK1/pMLC2 signalling pathway in the mechanical ventilation model, thereby exerting a protective effect on the VILI pulmonary microvascular endothelial barrier.

Clinical characteristics and risk factors for a prolonged length of stay of patients with asymptomatic and mild COVID-19 during the wave of Omicron from Shanghai, China.

BACKGROUND: This study aims to investigate the clinical characteristics and the length of hospital stay (LOS), as well as risk factors for prolonged LOS in a cohort of asymptomatic and mild COVID-19 patients infected with the Omicron variant. METHODS: A total of 1166 COVID-19 patients discharged from the inpatient ward of the largest makeshift hospital (May 8-10, 2022) in Shanghai, China, were included. The demographics, medical history, and the lowest and admission cycle threshold (Ct) values of the RT-PCR tests for SARS-CoV-2 genes of the open reading frame 1ab (Ct-ORF) and the nucleocapsid protein (Ct-N) during hospitalization were recorded. Patients with LOS > 7 days, or LOS ≤ 7 days were included in the Prolonged group or the Control group, separately. The clinical characteristics and LOS of the participants in the two groups were described and compared. Multivariate Logistic and linear regression analyses were applied to explore the risk factors for prolonged LOS. The diagnostic efficacy of the lowest and admission Ct values for the Prolonged group was tested via the receiver operating characteristic (ROC) curve analysis. RESULTS: The median LOS was 6 days in the total study population. The age was older (45.52 ± 14.78 vs. 42.54 ± 15.30, P = 0.001), while both the lowest and admission Ct-ORF (27.68 ± 3.88 vs. 37.00 ± 4.62, P < 0.001; 30.48 ± 5.03 vs. 37.79 ± 3.81, P < 0.001) and Ct-N (25.79 ± 3.60 vs. 36.06 ± 5.39, P < 0.001; 28.71 ± 4.95 vs. 36.95 ± 4.59, P < 0.001) values were significantly lower in the Prolonged group. There were more mild cases in the Prolonged group (23.8% vs. 11.5%, P < 0.001). The symptom spectrum differed between the two groups. In multivariate analyses, age, disease category, and the lowest Ct-N values were shown to be associated with prolonged LOS. Besides, both the lowest and admission Ct-ORF (AUC = 0.911 and 0.873) and Ct-N (AUC = 0.912 and 0.874) showed robust diagnostic efficacy for prolonged LOS. CONCLUSIONS: Our study firstly reports the clinical characteristics and risk factors for prolonged LOS during the wave of the Omicron epidemic in Shanghai, China. These findings provide evidence for the early identification of asymptomatic and mild COVID-19 patients at a high risk of prolonged hospitalization who may require early intervention, and long-term monitoring and management.

Machine learning-based statistical closure models for turbulent dynamical systems.

We propose a machine learning (ML) non-Markovian closure modelling framework for accurate predictions of statistical responses of turbulent dynamical systems subjected to external forcings. One of the difficulties in this statistical closure problem is the lack of training data, which is a configuration that is not desirable in supervised learning with neural network models. In this study with the 40-dimensional Lorenz-96 model, the shortage of data is due to the stationarity of the statistics beyond the decorrelation time. Thus, the only informative content in the training data is from the short-time transient statistics. We adopt a unified closure framework on various truncation regimes, including and excluding the detailed dynamical equations for the variances. The closure framework employs a Long-Short-Term-Memory architecture to represent the higher-order unresolved statistical feedbacks with a choice of ansatz that accounts for the intrinsic instability yet produces stable long-time predictions. We found that this unified agnostic ML approach performs well under various truncation scenarios. Numerically, it is shown that the ML closure model can accurately predict the long-time statistical responses subjected to various time-dependent external forces that have larger maximum forcing amplitudes and are not in the training dataset. This article is part of the theme issue 'Data-driven prediction in dynamical systems'.