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Polaron, a typical quasi-particle that describes a single impurity dressed with surrounding environment, serves as an ideal platform for bridging few- and many-body physics. In particular, different few-body correlations can compete with each other and lead to many intriguing phenomena. In this work, we review the recent progresses made in understanding few-body correlation effects in attractive Fermi polarons of ultracold gases. By adopting a unified variational ansatz that incorporates different few-body correlations in a single framework, we will discuss their competing effects in Fermi polarons when the impurity and majority fermions have the same or different masses. For the equal-mass case, we review the nature of polaron-molecule transition that is driven by two-body correlations, and especially highlight the finite momentum character and huge degeneracy of molecule states. For the mass-imbalanced case, we focus on the smooth crossover between polaron and various dressed clusters that originate from high-order correlations. These competing few-body correlations reviewed in Fermi polarons suggest a variety of exotic new phases in the corresponding many-body system of Fermi-Fermi mixtures.

The study of ground-state properties of the Fermi-Hubbard model is a long-lasting task in the research of strongly correlated systems, and is crucial for the understanding of notable quantum phenomena including superconductivity and magnetism. Owing to the exponentially growing complexity of the system, a quantitative analysis usually demands high computational cost and is restricted to small samples, especially in two or higher dimensions. Here, we introduce a variational method in the frame of fermionic Gaussian states, and obtain the ground states of one- and two-dimensional attractive Hubbard models via imaginary-time evolution. We calculate the total energy and benchmark the results in a wide range of interaction strength and filling factor with those obtained via exact two-body results, the density matrix renormalization group based on matrix product states (MPS), and projector Quantum Monte Carlo (QMC) method. For both 1D and 2D cases, the Gaussian variational method presents accurate results for total energy with a maximum systematic error ∼ 4 % in the intermediate interaction region. The accuracy of these results has negligible dependence on the system size. We further calculate the double occupancy and find excellent agreement with MPS and QMC, as well as the experimental results of cold quantum gases in optical lattices. The results suggest that the Gaussian pairing state is a good approximation to the ground states of attractive Hubbard model, in particular in the strong and weak coupling limits. Moreover, we generalize the method to the attractive Hubbard model with a finite spin-polarization, which can be mapped to the repulsive interaction case via particle-hole transformation, and obtain accurate results of ground state energy and double occupancy. Our work demonstrates the ability of the Gaussian variational method to extract ground state properties of strongly correlated many-body systems with negligible computational cost, especially of large size and in higher dimensions.

According to the 2013 KDIGO standard, patients were divided into one of the cohorts and staged based on the worst serum creatinine and/or the lowest urine output [5]. The receiver operating characteristic analysis for the assessment of the diagnostic accuracy of pro-enkephalin and adrenomedullin in the prediction of AKI in septic patients showed significant predictive value for both biomarkers, with area under curve (AUC) of 0.884 (95% CI, 0.738–0.965) and 0.731 (95% CI, 0.560–0.863), respectively (Fig. 1). The limitations of our study include the limited sample size as well as the certain biomarkers, such as NGAL, KIM-1, and IL-18, lacking the detection data of urine specimens which may differ from the diagnostic accuracy of blood specimens.

Sepsis is a life-threatening condition that causes millions of deaths globally each year. The need for biomarkers to predict the progression of sepsis to septic shock remains critical, with rapid, reliable methods still lacking. Transcriptomics data has recently emerged as a valuable resource for disease phenotyping and endotyping, making it a promising tool for predicting disease stages. Therefore, we aimed to establish an advanced machine learning framework to predict sepsis and septic shock using transcriptomics datasets with rapid turnaround methods. We retrieved four NCBI GEO transcriptomics datasets previously generated from peripheral blood samples of healthy individuals and patients with sepsis and septic shock. The datasets were processed for bioinformatic analysis and supplemented with a series of bench experiments, leading to the identification of a hub gene panel relevant to sepsis and septic shock. The hub gene panel was used to establish a novel prediction model to distinguish sepsis from septic shock through a multistage machine learning pipeline, incorporating linear discriminant analysis, risk score analysis, and ensemble method combined with Least Absolute Shrinkage and Selection Operator analysis. Finally, we validated the prediction model with the hub gene dataset generated by RT-qPCR using peripheral blood samples from newly recruited patients. Our analysis led to identify six hub genes ( , and ) which are related to NK cell cytotoxicity and septic shock, collectively termed 6-HubG . Using this panel, we created SepxFindeR, a machine learning model that demonstrated high accuracy in predicting sepsis and septic shock and distinguishing septic shock from sepsis in a cross-database context. Remarkably, the SepxFindeR model proved compatible with RT-qPCR datasets based on the 6-HubG panel, facilitating the identification of newly recruited patients with sepsis and septic shock. Our bioinformatic approach led to the discovery of the 6-HubGss biomarker panel and the development of the SepxFindeR machine learning model, enabling accurate prediction of septic shock and distinction from sepsis with rapid processing capabilities.

Atmospheric chemistry transport models have been extensively applied in aerosol forecasts over recent decades, whereas they are facing challenges from uncertainties in emission rates, meteorological data, and over-simplified chemical parameterizations. Here, we developed a spatial-temporal deep learning framework, named PPN (Pollution-Predicting Net for PM 2.5 ), to accurately and efficiently predict regional PM 2.5 concentrations. It has an encoder-decoder architecture and combines the preceding PM 2.5 observations and numerical weather prediction. Besides, the model proposes a weighted loss function to promote the forecasting performance in extreme events. We applied the proposed model to forecast 3-day PM 2.5 concentrations over the Beijing-Tianjin-Hebei region in China on a three-hour-by-three-hour basis. Overall, the model showed good performance with R 2 and RMSE values of 0.7 and 17.7 μg m −3 , respectively. It could capture the high PM 2.5 concentration in the south and relatively low concentration in the north and exhibit better performance within the next 24 h. The use of the weighted loss function decreased the level of “high values underestimation, low values overestimation”, while incorporating the preceding PM 2.5 observations into the encoder phase improved the predictive accuracy within 24 h. We also compared the model result with that from a state-of-the-art numerical model (WRF-Chem with pollutant data assimilation). The temporal R 2 and RMSE from the WRF-Chem were 0.30−0.77 and 19−45 μg m −3 while those from the PPN model were 0.42−0.84 and 15−42 μg m −3 . The proposed model shows powerful capacity in aerosol forecasts and provides an efficient and accurate tool for early warning and management of regional pollution events.

Hypoxia is an important factor in the development of synovitis in rheumatoid arthritis (RA). The previous study of the research group found that monomeric derivatives of paeoniflorin (MDP) can alleviate joint inflammation in adjuvant-induced arthritis (AA) rats by inhibiting macrophage pyroptosis. This study revealed increased levels of hypoxia-inducible factor- (HIF-) 1α and N-terminal p30 fragment of GSDMD (GSDMD-N) in fibroblast-like synoviocytes (FLS) of RA patients and AA rats, while MDP significantly inhibited their expression. Subsequently, FLS were exposed to a hypoxic environment or treated with cobalt ion in vitro. Western blot and immunofluorescence analysis showed increased expression of G protein-coupled receptor kinase 2 (GRK2), HIF-1α, nucleotide-binding oligomerization segment-like receptor family 3 (NLRP3), ASC, caspase-1, cleaved-caspase-1, and GSDMD-N. Electron microscopy revealed FLS pyroptosis after exposure in hypoxia. Next, corresponding shRNAs were transferred into FLS to knock down hypoxia-inducible factor- (HIF-) 1α, and in turn, NLRP3 and western blot results confirmed the same. The enhanced level of GSDMD was reversed under hypoxia by inhibiting NLRP3 expression. Knockdown and overexpression of GRK2 in FLS revealed GRK2 to be a positive regulator of HIF-1α. Levels of GRK2 and HIF-1α were inhibited by eliminating excess reactive oxygen species (ROS). Furthermore, MDP reduced FLS pyroptosis through targeted inhibition of GRK2 phosphorylation. According to these findings, hypoxia induces FLS pyroptosis through the ROS/GRK2/HIF-1α/NLRP3 pathway, while MDP regulates this pathway to reduce FLS pyroptosis.

Mitochondria play an essential role in energy metabolism. Oxygen deprivation can poison cells and generate a chain reaction due to the free radical release. In patients with sepsis, the kidneys tend to be the organ primarily affected and the proximal renal tubules are highly susceptible to energy metabolism imbalances. Dynamin-related protein 1 (DRP1) is an essential regulator of mitochondrial fission. Few studies have confirmed the role and mechanism of DRP1 in acute kidney injury (AKI) caused by sepsis. We established animal and cell sepsis-induced AKI (S-AKI) models to keep DRP1 expression high. We found that Mdivi-1, a DRP1 inhibitor, can reduce the activation of the NOD-like receptor pyrin domain-3 (NLRP3) inflammasome-mediated pyroptosis pathway and improve mitochondrial function. Both S-AKI models showed that Mdivi-1 was able to prevent the mitochondrial content release and decrease the expression of NLRP3 inflammasome-related proteins. In addition, silencing NLRP3 gene expression further emphasized the pyroptosis importance in S-AKI occurrence. Our results indicate that the possible mechanism of action of Mdivi-1 is to inhibit mitochondrial fission and protect mitochondrial function, thereby reducing pyroptosis. These data can provide a potential theoretical basis for Mdivi-1 potential use in the S-AKI prevention.

Acute kidney injury (AKI), commonly occurring as complications of sepsis, cardiac surgery, and liver or kidney transplantation, is a critical care syndrome. It is well known that lipopolysaccharide (LPS) shock is a common triggering factor for AKI. This study is aimed to examine the effect of flavonoid compound hispidulin on LPS-induced AKI. For this, renal tubular epithelial cell HK-2 was treated with LPS to establish an in vitro model of AKI. The effect of hispidulin on HK-2 cell viability was examined using CCK-8 assay. Cell apoptosis was determined by TUNEL and flow cytometry. Apoptosis marker proteins were determined by using western blot. The levels of pro-inflammatory cytokines were determined by ELISA assay and qRT-PCR. The translocation of NF-κB was determined by western blot. The effect of MyD88 on the cytoprotective activities of hispidulin was examined by overexpressing MyD88 in HK-2 cells. Our results showed that hispidulin was not able to produce a cytotoxic effect on HK-2 cells at tested concentrations. However, hispidulin could protect HK-2 cells from LPS-induced cell injury. Our results also showed that hispidulin was able to attenuate LPS-induced HK-2 cell apoptosis. In addition, LPS led to an inflammatory response in HK-2 cells, evidenced by NF-κB p65 activation as well as increased expression and release of inflammatory cytokine IL-6 and TNF- α, which could be reversed by pretreatment with hispidulin. Overexpression MyD88 was found to significantly dampen the cytoprotective activities of hispidulin against LPS insult. More importantly, MyD88 was identified as a direct target of hsa-miR-203, and hispidulin was found to regulate the expression of MyD88 via upregulating hsa-miR-203. Our results showed that hispidulin attenuates LPS-induced HK-2 damage via regulating hsa-miR-203/MyD88 axis.

Objective: Neutrophil elastase (NE) plays an important role in the development of acute respiratory distress syndrome (ARDS). Sivelestat sodium, as a selective NE inhibitor, may improve the outcomes of patients with sepsis-induced ARDS in previous studies, but there is a lack of solid evidence. This trial aimed to evaluate the effect of sivelestat sodium on oxygenation in patients with sepsis-induced ARDS. Methods: We conducted a multicenter, double-blind, randomized, placebo-controlled trial enrolling patients diagnosed with sepsis-induced ARDS admitted within 48 hours of the advent of symptoms. Patients were randomized in a 1:1 fashion to sivelestat or placebo. Trial drugs were administered as a 24-hour continuous intravenous infusion, for a minimum duration of 5 days and a maximum duration of 14 days. The primary outcome was the proportion of [PaO.sub.2]/[FiO.sub.2] ratio improvement on Day 5 after randomization, defined by a greater than 50% improvement in [PaO.sub.2]/[FiO.sub.2] compared with that on ICU admission or [PaO.sub.2]/[FiO.sub.2] reached over 300 mmHg on Day 5. Results: The study was stopped midway due to a potential between-group difference in mortality observed during the interim analysis. Overall, a total of 70 patients were randomized, of whom 34 were assigned to receive sivelestat sodium and 36 placebo. On day 5, 19/34 (55.9%) patients in the sivelestat group had [PaO.sub.2]/[FiO.sub.2] ratio improvement compared with 7/36 (19.4%) patients in the placebo group (risk difference, 0.36; 95% CI, 0.14 to 0.56, p<0.001). The Kaplan-Meier curves showed a significantly improved 28-day survival rate in patients receiving sivelestat than those not (hazard ratio, 0.32; 95% CI, 0.11 to 0.95; p=0.041). Conclusion: In patients with sepsis-induced ARDS, sivelestat sodium could improve oxygenation within the first five days and may be associated with decreased 28-day mortality. Keywords: sepsis, acute respiratory distress syndrome, neutrophil elastase, sivelestat, oxygenation