Vascular smooth muscle cell-specific miRNA-214 deficiency alleviates simulated microgravity-induced vascular remodeling.

The human cardiovascular system has evolved to accommodate the gravity of Earth. Microgravity during spaceflight has been shown to induce vascular remodeling, leading to a decline in vascular function. The underlying mechanisms are not yet fully understood. Our previous study demonstrated that miR-214 plays a critical role in angiotensin II-induced vascular remodeling by reducing the levels of Smad7 and increasing the phosphorylation of Smad3. However, its role in vascular remodeling evoked by microgravity is not yet known. This study aimed to determine the contribution of miR-214 to the regulation of microgravity-induced vascular remodeling. The results of our study revealed that miR-214 expression was increased in the forebody arteries of both mice and monkeys after simulated microgravity treatment. In vitro, rotation-simulated microgravity-induced VSMC migration, hypertrophy, fibrosis, and inflammation were repressed by miR-214 knockout (KO) in VSMCs. Additionally, miR-214 KO increased the level of Smad7 and decreased the phosphorylation of Smad3, leading to a decrease in downstream gene expression. Furthermore, miR-214 cKO protected against simulated microgravity induced the decline in aorta function and the increase in stiffness. Histological analysis showed that miR-214 cKO inhibited the increases in vascular medial thickness that occurred after simulated microgravity treatment. Altogether, these results demonstrate that miR-214 has potential as a therapeutic target for the treatment of vascular remodeling caused by simulated microgravity.

A Systematic Review of Technique, Satisfaction, and Complications of Acellular Dermal Matrix in Rhinoplasty.

OBJECTIVE: Acellular dermal matrix (ADM) is a new dermal transplant replacement material prepared from allogeneic or xenograft skin through bioengineering technology. This is provided for patients who are unwilling to kill part of their autologous cartilage or autologous dermal tissue and require rhinoplasty and improvement in the appearance of the nasal tip. This systematic review aims to introduce the main techniques of ADM for rhinoplasty and related patient satisfaction and complications to further guide doctors. METHODS: Systematic reviews were conducted in accordance with the preferred reporting items for systematic reviews and meta-analyses guidelines. The authors searched PubMed, Web of Science, Embase, and Cochrane Library using appropriate keywords. Data collected for each study included patient satisfaction and complications in addition to relevant technology. RESULTS: After full-text screening of inclusion and exclusion criteria, 10 studies were included, with a total of 324 patients receiving ADM with different transplantation methods. Primary rhinoplasty or secondary rhinoplasty study for dorsal ridge augmentation, smooth contour irregularities, autograft camouflage including tip grafts. The incidence of dorsal implant distortion was 0.72% in patients. The incidence of deviation was 2.17% in patients. The incidence of mild edema was 5.17% in patients. The incidence of partial resorption was 10.87% in patients. The incidence of significant resorption was 13.04% in patients. The incidence of seroma was 0.72% in patients. The incidence of partial prolapse was 0.72% in patients. The incidence of overcorrection and reoperation was 0.72% in patients. The incidence of erythema was 0.72% in patients. The incidence of undercorrection was 0.72% in patients. The incidence of infection was 0.72% in patients. The incidence of high-lying implants was 1.45% in patients. CONCLUSION: The current research results show that ADM is long-term effective in improving nasal dorsum enhancement, nasal contour deformity, and nasal tip appearance, with high patient satisfaction and low overall complication rate. Overcorrection should be considered during surgery to deal with postoperative partial absorption.

Constituents of Chimaphila japonica and Their Diuretic Activity.

Three new phenols (1-3), one new cyclohexanol (4), two known phenols (5-6), and six known flavonoids (7-12) were isolated from the n-butanol of the 75% ethanol extract of all plants of Chimaphila japonica Miq. Among them, compound 5 was named and described in its entirety for the first time, and compounds 9 and 10 were reported in C. japonica for the first time. The structures of all compounds were confirmed using a comprehensive analysis of 1D and 2D NMR and HRESIMS data. Biological results show that compounds 4, 7, and 11 exhibited potent diuretic activity. The modes of interaction between the selected compounds and the target diuretic-related WNK1 kinase were investigated in a preliminary molecular docking study. These results provided insight into the chemodiversity and potential diuretic activities of metabolites in C. japonica.

Metal-Oxo Electronic Tuning via In Situ CO Decoration for Promoting Methane Conversion to Oxygenates over Single-Atom Catalysts.

Direct methane conversion (DMC) to oxygenates at low temperature is of great value but remains challenging due to the high energy barrier for C-H bond activation. Here, we report that in situ decoration of Pd1-ZSM-5 single atom catalyst (SAC) by CO molecules significantly promoted the DMC reaction, giving the highest turnover frequency of 207 h-1 ever reported at room temperature and ~100 % oxygenates selectivity with H2O2 as oxidant. Combined characterizations and DFT calculations illustrate that the C-atom of CO prefers to coordinate with Pd1, which donates electrons to the Pd1-O active center (L-Pd1-O, L=CO) generated by H2O2 oxidation. The correspondingly improved electron density over Pd-O pair renders a favorable heterolytic dissociation of C-H bond with low energy barrier of 0.48 eV. Applying CO decoration strategy to M1-ZSM-5 (M=Pd, Rh, Ru, Fe) enables improvement of oxygenates productivity by 3.2-11.3 times, highlighting the generalizability of this method in tuning metal-oxo electronic structure of SACs for efficient DMC process.

Exosomal ACADM sensitizes gemcitabine-resistance through modulating fatty acid metabolism and ferroptosis in pancreatic cancer.

This study aimed to evaluate the potential of exosomes from cancer cells to predict chemoresistance in pancreatic cancer (PC) and explore the molecular mechanisms through RNA-sequencing and mass spectrometry. We sought to understand the connection between the exosomal Medium-chain acyl-CoA dehydrogenase (ACADM) level and the reaction to gemcitabine in vivo and in patients with PC. We employed loss-of-function, gain-of-function, metabolome mass spectrometry, and xenograft models to investigate the effect of exosomal ACADM in chemoresistance in PC. Our results showed that the molecules involved in lipid metabolism in exosomes vary between PC cells with different gemcitabine sensitivity. Exosomal ACADM (Exo-ACADM) was strongly correlated with gemcitabine sensitivity in vivo, which can be used as a predictor for postoperative gemcitabine chemosensitivity in pancreatic patients. Moreover, ACADM was found to regulate the gemcitabine response by affecting ferroptosis through Glutathione peroxidase 4 (GPX4) and mevalonate pathways. It was also observed that ACADM increased the consumption of unsaturated fatty acids and decreased intracellular lipid peroxides and reactive oxygen species (ROS) levels. In conclusion, this research suggests that Exo-ACADM may be a viable biomarker for predicting the responsiveness of patients to chemotherapy.

Design, synthesis and anti-Chikungunya virus activity of lomerizine derivatives.

Chikungunya fever is an acute infectious disease caused by Chikungunya virus (CHIKV) and transmitted by Aedes mosquito. It is characterized by fever, rash and arthralgia with no effective drugs. Lomerizine (Lom) is a new generation calcium antagonist, which is mainly used in the treatment of migraine. Certain antiviral function of Lom was shown by some research. In our study, a series of new derivatives of Lom were designed and synthesized, and their in-vitro anti-CHIKV activity was tested. The results showed that Lom and its derivatives had potent anti-CHIKV activity and low cytotoxicity. Among them, compounds B1 and B7 showed most potent antiviral activity. Besides, structure-activity relationships, in-silico ADMET properties were also analyzed. Molecular docking study was performed to rationalize the SAR and analyze the possible binding modes between B1 and amino acid residues in the active site of nsP3 protein to enhance the understanding of their action as antiviral agents. These finding provides research basis for the design and synthesis of effective anti-CHIKV drugs with Lom as the lead compound.

Association between ozone and influenza transmissibility in China.

BACKGROUND: Common air pollutants such as ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter play significant roles as influential factors in influenza-like illness (ILI). However, evidence regarding the impact of O3 on influenza transmissibility in multi-subtropical regions is limited, and our understanding of the effects of O3 on influenza transmissibility in temperate regions remain unknown. METHODS: We studied the transmissibility of influenza in eight provinces across both temperate and subtropical regions in China based on 2013 to 2018 provincial-level surveillance data on influenza-like illness (ILI) incidence and viral activity. We estimated influenza transmissibility by using the instantaneous reproduction number ([Formula: see text]) and examined the relationships between transmissibility and daily O3 concentrations, air temperature, humidity, and school holidays. We developed a multivariable regression model for [Formula: see text] to quantify the contribution of O3 to variations in transmissibility. RESULTS: Our findings revealed a significant association between O3 and influenza transmissibility. In Beijing, Tianjin, Shanghai and Jiangsu, the association exhibited a U-shaped trend. In Liaoning, Gansu, Hunan, and Guangdong, the association was L-shaped. When aggregating data across all eight provinces, a U-shaped association was emerged. O3 was able to accounted for up to 13% of the variance in [Formula: see text]. O3 plus other environmental drivers including mean daily temperature, relative humidity, absolute humidity, and school holidays explained up to 20% of the variance in [Formula: see text]. CONCLUSIONS: O3 was a significant driver of influenza transmissibility, and the association between O3 and influenza transmissibility tended to display a U-shaped pattern.

Ground-to-satellite quantum teleportation.

An arbitrary unknown quantum state cannot be measured precisely or replicated perfectly. However, quantum teleportation enables unknown quantum states to be transferred reliably from one object to another over long distances, without physical travelling of the object itself. Long-distance teleportation is a fundamental element of protocols such as large-scale quantum networks and distributed quantum computation. But the distances over which transmission was achieved in previous teleportation experiments, which used optical fibres and terrestrial free-space channels, were limited to about 100 kilometres, owing to the photon loss of these channels. To realize a global-scale 'quantum internet' the range of quantum teleportation needs to be greatly extended. A promising way of doing so involves using satellite platforms and space-based links, which can connect two remote points on Earth with greatly reduced channel loss because most of the propagation path of the photons is in empty space. Here we report quantum teleportation of independent single-photon qubits from a ground observatory to a low-Earth-orbit satellite, through an uplink channel, over distances of up to 1,400 kilometres. To optimize the efficiency of the link and to counter the atmospheric turbulence in the uplink, we use a compact ultra-bright source of entangled photons, a narrow beam divergence and high-bandwidth and high-accuracy acquiring, pointing and tracking. We demonstrate successful quantum teleportation of six input states in mutually unbiased bases with an average fidelity of 0.80 ± 0.01, well above the optimal state-estimation fidelity on a single copy of a qubit (the classical limit). Our demonstration of a ground-to-satellite uplink for reliable and ultra-long-distance quantum teleportation is an essential step towards a global-scale quantum internet.

Ginseng-derived nanoparticles potentiate immune checkpoint antibody efficacy by reprogramming the cold tumor microenvironment.

Cold tumor microenvironment (TME) marked with low effector T cell infiltration leads to weak response to immune checkpoint inhibitor (ICI) treatment. Thus, switching cold to hot TME is critical to improve potent ICI therapy. Previously, we reported extracellular vesicle (EV)-like ginseng-derived nanoparticles (GDNPs) that were isolated from Panax ginseng C.A. Mey and can alter M2 polarization to delay the hot tumor B16F10 progression. However, the cold tumor is more common and challenging in the real world. Here, we explored a combinatorial strategy with both GDNPs and PD-1 (programmed cell death protein-1) monoclonal antibody (mAb), which exhibited the ability to alter cold TME and subsequently induce a durable systemic anti-tumor immunity in multiple murine tumor models. GDNPs enhanced PD-1 mAb anti-tumor efficacy in activating tumor-infiltrated T lymphocytes. Our results demonstrated that GDNPs could reprogram tumor-associated macrophages (TAMs) to increase CCL5 and CXCL9 secretion for recruiting CD8+ T cells into the tumor bed, which have the synergism to PD-1 mAb therapy with no detected systemic toxicity. In situ activation of TAMs by GDNPs may broadly serve as a facile platform to modulate the suppressive cold TME and optimize the PD-1 mAb immunotherapy in future clinical application.

Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and Evaluation.

BACKGROUND: In megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases. Existing prediction and early warning models for influenza and other acute respiratory infectious diseases have limitations and therefore there is room for improvement. OBJECTIVE: The aim of this study was to explore a new and better-performing deep-learning model to predict influenza trends from multisource heterogeneous data in a megacity. METHODS: We collected multisource heterogeneous data from the 26th week of 2012 to the 25th week of 2019, including influenza-like illness (ILI) cases and virological surveillance, data of climate and demography, and search engines data. To avoid collinearity, we selected the best predictor according to the weight and correlation of each factor. We established a new multiattention-long short-term memory (LSTM) deep-learning model (MAL model), which was used to predict the percentage of ILI (ILI%) cases and the product of ILI% and the influenza-positive rate (ILI%×positive%), respectively. We also combined the data in different forms and added several machine-learning and deep-learning models commonly used in the past to predict influenza trends for comparison. The R2 value, explained variance scores, mean absolute error, and mean square error were used to evaluate the quality of the models. RESULTS: The highest correlation coefficients were found for the Baidu search data for ILI% and for air quality for ILI%×positive%. We first used the MAL model to calculate the ILI%, and then combined ILI% with climate, demographic, and Baidu data in different forms. The ILI%+climate+demography+Baidu model had the best prediction effect, with the explained variance score reaching 0.78, R2 reaching 0.76, mean absolute error of 0.08, and mean squared error of 0.01. Similarly, we used the MAL model to calculate the ILI%×positive% and combined this prediction with different data forms. The ILI%×positive%+climate+demography+Baidu model had the best prediction effect, with an explained variance score reaching 0.74, R2 reaching 0.70, mean absolute error of 0.02, and mean squared error of 0.02. Comparisons with random forest, extreme gradient boosting, LSTM, and gated current unit models showed that the MAL model had the best prediction effect. CONCLUSIONS: The newly established MAL model outperformed existing models. Natural factors and search engine query data were more helpful in forecasting ILI patterns in megacities. With more timely and effective prediction of influenza and other respiratory infectious diseases and the epidemic intensity, early and better preparedness can be achieved to reduce the health damage to the population.

Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study.

BACKGROUND: Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. OBJECTIVE: This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. METHODS: We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. RESULTS: This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. CONCLUSIONS: Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models.

Network Anomaly Intrusion Detection Based on Deep Learning Approach.

The prevalence of internet usage leads to diverse internet traffic, which may contain information about various types of internet attacks. In recent years, many researchers have applied deep learning technology to intrusion detection systems and obtained fairly strong recognition results. However, most experiments have used old datasets, so they could not reflect the latest attack information. In this paper, a current state of the CSE-CIC-IDS2018 dataset and standard evaluation metrics has been employed to evaluate the proposed mechanism. After preprocessing the dataset, six models-deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), CNN + RNN and CNN + LSTM-were constructed to judge whether network traffic comprised a malicious attack. In addition, multi-classification experiments were conducted to sort traffic into benign traffic and six categories of malicious attacks: BruteForce, Denial-of-service (DoS), Web Attacks, Infiltration, Botnet, and Distributed denial-of-service (DDoS). Each model showed a high accuracy in various experiments, and their multi-class classification accuracy were above 98%. Compared with the intrusion detection system (IDS) of other papers, the proposed model effectively improves the detection performance. Moreover, the inference time for the combinations of CNN + RNN and CNN + LSTM is longer than that of the individual DNN, RNN and CNN. Therefore, the DNN, RNN and CNN are better than CNN + RNN and CNN + LSTM for considering the implementation of the algorithm in the IDS device.

Inverse In Vitro Selection Enables Comprehensive Analysis of Cross-Chiral L-Aptamer Interactions.

Aptamers composed of mirror-image L-(deoxy)ribose nucleic acids, referred to as L-aptamers, are a promising class of RNA-binding reagents. Yet, the selectivity of cross-chiral interactions between L-aptamers and their RNA targets remain poorly characterized, limiting the potential utility of this approach for applications in biological systems. Herein, we carried out the first comprehensive analysis of cross-chiral L-aptamer selectivity using a newly developed "inverse" in vitro selection approach that exploits the genetic nature of the D-RNA ligand. By employing a library of more than a million target-derived sequences, we determined the RNA sequence and structural preference of a model L-aptamer and revealed previously unidentified and potentially broad off-target RNA binding behaviors. These results provide valuable information for assessing the likelihood and consequences of potential off-target interactions and reveal strategies to mitigate these effects. Thus, inverse in vitro selection provides several opportunities to advance L-aptamer technology.

Numerical analysis and animal study of noninvasive handheld electroporation delivery device for skin superficial lesion treatment.

INTRODUCTION: This study aims to investigate the feasibility of a noninvasive handheld electroporation pulses delivery device (EPDD) for electroporation-based treatment (EBT) of skin superficial lesions through numerical analysis and animal study. METHODS: Finite element analysis was performed to investigate the performance of the EPDD. The electric field, temperature, EI and TI were calculated under pulse voltages of 600, 800, and 1000 V. A mouse subcutaneous tumor model was established to evaluate the performance of the EPDD through histopathology and survival analyses. RESULTS: The electrical field strength increased from 151 (600 V) to 252 V/cm (1000 V) in the skin and from 1302 (600 V) to 2171 V/cm (1000 V) in the tumor. The volume of EI grew and reached a plateau at the 165th pulse, whereas the maximum volume of EI increased with higher voltage. The growth tendency of TI differed between groups, and it was higher in the high-voltage group (HVG) than in the low-voltage group. Histopathological analysis showed that the depth and range of the ablation area could be controlled by adjusting pulse voltage. Survival analysis showed that the survival of the HVG was better than that of the low-voltage and the control group (p < 0.01). CONCLUSIONS: The results demonstrate that the EPDD is feasible, safe, and effective for skin EBT. The volume of EP tissue injury can be controlled by adjusting the pulse voltage, pulse number, and other parameters. The proposed noninvasive handheld EPDD can be a potential therapeutic tool for EBT of superficial skin lesions in the future.

M2 Macrophage-Derived Exosomes Promote Angiogenesis and Growth of Pancreatic Ductal Adenocarcinoma by Targeting E2F2.

Pancreatic ductal adenocarcinoma (PDAC), one of the most aggressive tumors all over the world, has a generally poor prognosis, and its progression is positively correlated with the density of blood vessels. Recently, tumor-associated macrophages (TAMs) were proven to be beneficial for angiogenesis, but their mechanism of action remains unclear. Our study indicated that M2 macrophages were positively correlated with the microvessel density (MVD) of PDAC tissues, and M2 macrophage-derived exosomes (MDEs) could promote the angiogenesis of mouse aortic endothelial cells (MAECs) in vitro. At the same time, the M2 MDEs could also promote the growth of subcutaneous tumors and increase the vascular density of mice. Moreover, we also found that miR-155-5p and miR-221-5p levels in the M2 MDEs were higher than those in M0 MDEs, and they could be transferred into MAECs, as demonstrated by RNA sequencing (RNA-seq) and qPCR analysis. Our data confirmed the interaction between TAMs and the angiogenesis of PDAC by exosomes. Additionally, targeting the exosomal miRNAs derived from TAMs might provide diagnostic and therapeutic strategies for PDAC.

Evaluation of the revised ISTH overt DIC score (2018) for predicting 90-day mortality in critically ill adult patients undergoing extracorporeal membrane oxygenation.

BACKGROUND AND OBJECTIVE: Coagulopathy is a common and serious problem in patients who received extracorporeal membrane oxygenation (ECMO), and this study evaluated whether the 2018 diffuse intravascular coagulation (DIC) score established by the International Society on Thrombosis and Hemostasis (ISTH) is associated with 90-day mortality in adult ECMO patients. METHODS: A retrospective study analyzed data from adult patients receiving ECMO in our hospital from September 2018 to April 2021. Pre-ECMO DIC score and other variables were assessed and compared to predict 90-day mortality. RESULTS: Among 103 eligible patients, 55.3% received V-V ECMO and 44.7% received V-A ECMO. The overall 90-day mortality for study patients was 54.4%, including 45.6% in the V-V group and 65.2% in the V-A group. Multiple logistic regression analysis showed that after adjusting for sex, sepsis, and APACHE II score, pre-ECMO DIC scores in the total and V-V group predicted 90-day mortality (odds ratio(OR): 1.419, 95% confidence interval (CI): 1.101-1.828; OR: 2.562; 95% CI: 1.452-4.520). Receiver operating characteristic (ROC) curves displayed that pre-ECMO DIC score of 4 in the total and V-V group was a good predictor of 90-day mortality (area under the curve [AUC] = 0.706, 95% CI: 0.606-0.806; AUC = 0.737, 95% CI: 0.604-0.870). Kaplan-Meier curves demonstrated the 90-day mortality of patients with pre-ECMO DIC score ≥ 4 in the total and V-V group was higher than that of patients with DIC score < 4 (hazard ratio [HR]: 2.821, 95% CI: 1.632-4.879; HR: 3.864, 95% CI: 1.660-8.992). CONCLUSION: The pre-ECMO ISTH DIC score was associated with 90-day mortality in adult patients undergoing ECMO, particularly in the V-V ECMO group.

Neoadjuvant chemotherapy endows CD9 with prognostic value that differs between tumor and stromal areas in patients with pancreatic cancer.

BACKGROUND: The selective pressure imposed by chemotherapy creates a barrier to tumor eradication and an opportunity for metastasis and recurrence. As a newly discovered stemness marker of pancreatic ductal adenocarcinoma (PDAC), the impact of CD9 on tumor progression and patient's prognosis remain controversial. METHODS: A total of 179 and 211 PDAC patients who underwent surgical resection with or without neoadjuvant chemotherapy, respectively, were recruited for immunohistochemical analyses of CD9 expression in both tumor and stromal areas prior to statistical analyses to determine the prognostic impact and predictive accuracy of CD9. RESULTS: The relationship between CD9 and prognostic indicators was not significant in the non-neoadjuvant group. Nevertheless, CD9 expression in both tumor (T-CD9) and stromal areas (S-CD9) was significantly correlated with the clinicopathological features in the neoadjuvant group. High levels of T-CD9 were significantly associated with worse OS (p = 0.005) and RFS (p = 0.007), while positive S-CD9 showed the opposite results (OS: p = 0.024; RFS: p = 0.008). Cox regression analyses identified CD9 in both areas as an independent prognostic factor. The T&S-CD9 risk-level system was used to stratify patients with different survival levels. The combination of T&S-CD9 risk level and TNM stage were accurate predictors of OS (C-index: 0.676; AIC: 512.51) and RFS (C-index: 0.680; AIC: 519.53). The calibration curve of the nomogram composed of the combined parameters showed excellent predictive consistency for 1-year RFS. These results were verified using a validation cohort. CONCLUSION: Neoadjuvant chemotherapy endows CD9 with a significant prognostic value that differs between tumor and stromal areas in patients with pancreatic cancer.

Human splenic TER cells: A relevant prognostic factor acting via the artemin-GFRα3-ERK pathway in pancreatic ductal adenocarcinoma.

Splenectomy is routinely performed during distal or total pancreatectomy (DP or TP) for pancreatic ductal adenocarcinoma (PDAC), but information about its oncological value is limited. TER cells, nonimmune cells discovered in the spleens of tumour-bearing mice, are elicited by tumours and promote tumour progression, while their role in the clinical outcomes of patients with PDAC remains unclear. In our study, postoperative specimens from 622 patients who underwent DP or TP with splenectomy were analysed by flow cytometry or immunofluorescence, and the relationship between splenic TER cell count and clinical parameters was calculated. We also purified human TER cells for functional experiments and mechanistic studies. We found that TER cell numbers were increased only in the spleens of patients with PDAC but not in PDAC tissue and adjacent pancreatic tissue. High splenic TER cell counts independently predicted poor prognosis (P < .001) and indicated large tumour size, lymph node metastasis, advanced 8th AJCC/mAJCC stage and high CA19-9 classification (all P < .050) in patients with PDAC. Mechanistic analysis showed that TER cells express artemin, which facilitates the proliferation and invasion of PDAC cells by activating GFRα3-ERK signalling. Our study reveals that TER cell count is an indicator of poor prognosis of PDAC, while splenectomy during pancreatic surgery might provide oncological benefits in addition to ensuring the radical resection of PDAC.

Animal model of coronary microembolization under transthoracic echocardiographic guidance in rats.

The aim of the study was to develop a model of coronary microembolization (CME) in rats at a lower cost. We developed a novel rat model without thoracotomy and ventilation under the guidance of echocardiography. Rats were sacrificed at 3 h, 24 h and 1 month postoperatively in both the Echo-CME and Open-chest CME groups for the comparison of the modeling accuracy, mortality, cardiopulmonary circulation, pleural adhesion and ventilation-induced lung injury (VILI). Results showed that the coronary microthrombus formed at 3 h and reached its peak at 24 h postoperatively, which included platelet aggregation and fibrin web. The Echo-group increases success rates, decreased mortality, postoperative complications including pleural adhesion, cardiopulmonary dysfunction and VILI postoperatively than the Open-chest group at 1month postoperatively. The ejection fraction of the CME group decreased to 50% and obvious cardiac fibrosis formed at 3 months postoperatively. Our unique surgical method provided a platform to study molecular mechanisms and potential new pathways for CME treatment.

Identifying High-Risk Subphenotypes and Associated Harms From Delayed Antibiotic Orders and Delivery.

OBJECTIVES: Early antibiotic administration is a central component of sepsis guidelines, and delays may increase mortality. However, prior studies have examined the delay to first antibiotic administration as a single time period even though it contains two distinct processes: antibiotic ordering and antibiotic delivery, which can each be targeted for improvement through different interventions. The objective of this study was to characterize and compare patients who experienced order or delivery delays, investigate the association of each delay type with mortality, and identify novel patient subphenotypes with elevated risk of harm from delays. DESIGN: Retrospective analysis of multicenter inpatient data. SETTING: Two tertiary care medical centers (2008-2018, 2006-2017) and four community-based hospitals (2008-2017). PATIENTS: All patients admitted through the emergency department who met clinical criteria for infection. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Patient demographics, vitals, laboratory values, medication order and administration times, and in-hospital survival data were obtained from the electronic health record. Order and delivery delays were calculated for each admission. Adjusted logistic regression models were used to examine the relationship between each delay and in-hospital mortality. Causal forests, a machine learning method, was used to identify a high-risk subgroup. A total of 60,817 admissions were included, and delays occurred in 58% of patients. Each additional hour of order delay (odds ratio, 1.04; 95% CI, 1.03-1.05) and delivery delay (odds ratio, 1.05; 95% CI, 1.02-1.08) was associated with increased mortality. A patient subgroup identified by causal forests with higher comorbidity burden, greater organ dysfunction, and abnormal initial lactate measurements had a higher risk of death associated with delays (odds ratio, 1.07; 95% CI, 1.06-1.09 vs odds ratio, 1.02; 95% CI, 1.01-1.03). CONCLUSIONS: Delays in antibiotic ordering and drug delivery are both associated with a similar increase in mortality. A distinct subgroup of high-risk patients exist who could be targeted for more timely therapy.