Soap Film Transfer Printing for Ultrathin Electronics.

Flexible and stretchable electronics have attractive applications inaccessible to conventional rigid electronics. However, the mainstream transfer printing techniques have challenges for electronic films in terms of thickness and size and limitations for target substrates in terms of curvature, depth, and interfacial adhesion. Here a facile, damage-free, and contamination-free soap film transfer printing technique is reported that enables the wrinkle-free transfer of ultrathin electronic films, precise alignment in a transparent manner, and conformal and adhesion-independent printing onto various substrates, including those too topographically and adhesively challenging by existing methods. In principle, not only the pattern, resolution, and thickness of transferred films, but also the curvature, depth, and adhesion of target substrates are unlimited, while the size of transferred films can be as high as meter-scale. To demonstrate the capabilities of soap film transfer printing, pre-fabricated ultrathin electronics with multiple patterns, single micron resolution, sub-micron thickness, and centimeter size are conformably integrated onto the ultrathin web, ultra-soft cotton, DVD-R disk with the minimum radius of curvature of 131 nm, interior cavity of Klein bottle and dandelion with ultralow adhesion. The printed ultrathin sensors show superior conformabilities and robust adhesion, leading to engineering opportunities including electrocardiogram (ECG) signal acquisition and temperature measurement in aqueous environments.

MEMS-based two-photon microscopy with Lissajous scanning and image reconstruction under a feed-forward control strategy.

Two-photon microscopy (TPM) based on two-dimensional micro-electro-mechanical (MEMS) system mirrors shows promising applications in biomedicine and the life sciences. To improve the imaging quality and real-time performance of TPM, this paper proposes Lissajous scanning control and image reconstruction under a feed-forward control strategy, a dual-parameter alternating drive control algorithm and segmented phase synchronization mechanism, and pipe-lined fusion-mean filtering and median filtering to suppress image noise. A 10 fps frame rate (512 × 512 pixels), a 140 µm × 140 µm field of view, and a 0.62 µm lateral resolution were achieved. The imaging capability of MEMS-based Lissajous scanning TPM was verified by ex vivo and in vivo biological tissue imaging.

Efficient Fabrication of Bioinspired Flexible Pressure Sensors via Electrohydrodynamic Jet Printing Method.

Bioinspired microdevices have made significant strides in various applications including human motion and health detection. However, facile and highly efficient fabrication approach of flexible pressure sensors remains a great challenge. Herein, inspired by the gecko's foot structure, a flexible pressure sensor with microdomes structure is fabricated by tip-assisted on-demand electrohydrodynamic jet (EHD-jet) printing method. Ascribed to the interlocking electrodes with microdome structure, 3D deformation rates are substantially enlarged. When the microdromes structure is under pressure, the resistivity of carbon nanotubes film coated on the surface of microdomes structure will change remarkably. By using the combined effect of assisted tip and ring focusing electrode, the influence and constraints on microstructure fabrication caused by substrate material and morphology are minimized. The desired uniform structures can be adjusted rapidly by changing the printing parameters and liquid properties. High length-height ratio (0.64) of microdomes enhances sensitivity, with minimum detection limit is 2 Pa and response time is 40 ms. Finally, the bionic flexible sensor indicated excellent performance in capable of detecting pressure, sound vibrations and human motion. This work presents a new method for high-efficiency fabrication micro-nano patterns for flexible sensors inspired, which could be used in wearable tech and health monitoring.

A randomized-controlled trial of ischemia-free liver transplantation for end-stage liver disease.

BACKGROUND & AIMS: Ischemia-reperfusion injury (IRI) has thus far been considered as an inevitable component of organ transplantation, compromising outcomes, and limiting organ availability. Ischemia-free organ transplantation is a novel approach designed to avoid IRI, with the potential to improve outcomes. METHODS: In this randomized-controlled clinical trial, recipients of livers from donors after brain death were randomly assigned to receive either an ischemia-free or a 'conventional' transplant. The primary endpoint was the incidence of early allograft dysfunction. Secondary endpoints included complications related to graft IRI. RESULTS: Out of 68 randomized patients, 65 underwent transplants and were included in the analysis. 32 patients received ischemia-free liver transplantation (IFLT), and 33 received conventional liver transplantation (CLT). Early allograft dysfunction occurred in two recipients (6%) randomized to IFLT and in eight (24%) randomized to CLT (difference -18%; 95% CI -35% to -1%; p = 0.044). Post-reperfusion syndrome occurred in three recipients (9%) randomized to IFLT and in 21 (64%) randomized to CLT (difference -54%; 95% CI -74% to -35%; p <0.001). Non-anastomotic biliary strictures diagnosed with protocol magnetic resonance cholangiopancreatography at 12 months were observed in two recipients (8%) randomized to IFLT and in nine (36%) randomized to CLT (difference, -28%; 95% CI -50% to -7%; p = 0.014). The comprehensive complication index at 1 year after transplantation was 30.48 (95% CI 23.25-37.71) in the IFLT group vs. 42.14 (95% CI 35.01-49.26) in the CLT group (difference -11.66; 95% CI -21.81 to -1.51; p = 0.025). CONCLUSIONS: Among patients with end-stage liver disease, IFLT significantly reduced complications related to IRI compared to a conventional approach. CLINICAL TRIAL REGISTRATION: chictr.org. ChiCTR1900021158. IMPACT AND IMPLICATIONS: Ischemia-reperfusion injury has thus far been considered as an inevitable event in organ transplantation, compromising outcomes and limiting organ availability. Ischemia-free liver transplantation is a novel approach of transplanting donor livers without interruption of blood supply. We showed that in patients with end-stage liver disease, ischemia-free liver transplantation, compared with a conventional approach, led to reduced complications related to ischemia-reperfusion injury in this randomized trial. This new approach is expected to change the current practice in organ transplantation, improving transplant outcomes, increasing organ utilization, while providing a clinical model to delineate the impact of organ injury on alloimmunity.

Pharmacokinetics of Voriconazole in Peritoneal Fluid of Critically Ill Patients.

Data on the distribution of voriconazole (VRC) in the human peritoneal cavity are sparse. This prospective study aimed to describe the pharmacokinetics of intravenous VRC in the peritoneal fluid of critically ill patients. A total of 19 patients were included. Individual pharmacokinetic curves, drawn after single (first dose on day 1) and multiple (steady-state) doses, displayed a slower rise and lower fluctuation of VRC concentrations in peritoneal fluid than in plasma. Good but variable penetration of VRC into the peritoneal cavity was observed, and the median (range) peritoneal fluid/plasma ratios of the area under the concentration-time curve (AUC) were 0.54 (0.34 to 0.73) and 0.67 (0.63 to 0.94) for single and multiple doses, respectively. Approximately 81% (13/16) of the VRC steady-state trough concentrations (Cmin,ss) in plasma were within the therapeutic range (1 to 5.5 µg/mL), and the corresponding Cmin,ss (median [range]) in peritoneal fluid was 2.12 (1.39 to 3.72) µg/mL. Based on the recent 3-year (2019 to 2021) surveillance of the antifungal susceptibilities for Candida species isolated from peritoneal fluid in our center, the aforementioned 13 Cmin,ss in peritoneal fluid exceeded the MIC90 of C. albicans, C. glabrata, and C. parapsilosis (0.06, 1.00, and 0.25 µg/mL, respectively), which supported VRC as a reasonable choice for initial empirical therapies against intraabdominal candidiasis caused by these three Candida species, prior to the receipt of susceptibility testing results.

Nanoengineering Ultrathin Flexible Pressure Sensor with Superior Sensitivity and Perfect Conformability.

Flexible pressure sensors play an increasingly important role in a wide range of applications such as human health monitoring, soft robotics, and human-machine interfaces. To achieve a high sensitivity, a conventional approach is introducing microstructures to engineer the internal geometry of the sensor. However, this microengineering strategy requires the sensor's thickness to be typically at hundreds to thousands of microns level, impairing the sensor's conformability on surfaces with microscale roughness like human skin. In this manuscript, a nanoengineering strategy is pioneered that paves a path to resolve the conflicts between sensitivity and conformability. A dual-sacrificial-layer method is initiated that facilitates ease of fabrication and precise assembly of two functional nanomembranes to manufacture the thinnest resistive pressure sensor with a total thickness of ≈850 nm that achieves perfectly conformable contact to human skin. For the first time, the superior deformability of the nanothin electrode layer on a carbon nanotube conductive layer is utilized by the authors to achieve a superior sensitivity (92.11 kPa-1 ) and an ultralow detection limit (<0.8 Pa). This work offers a new strategy that is able to overcome a key bottleneck for current pressure sensors, therefore is of potential to inspire the research community for a new wave of breakthroughs.

Deep Neural Network-Based Electron Microscopy Image Recognition for Source Distinguishing of Anthropogenic and Natural Magnetic Particles.

Deep learning models excel at image recognition of macroscopic objects, but their applications to nanoscale particles are limited. Here, we explored their potential for source-distinguishing environmental particles. Transmission electron microscopy (TEM) images can reveal distinguishable features in particle morphology from various sources, but cluttered foreground objects and scale variations pose challenges to visual recognition models. In this proof-of-concept work, we proposed a novel instance segmentation model named CoMask to tackle these issues with atmospheric magnetic particles, a key species of PM2.5. CoMask features a densely connected feature extraction module to excavate multiscale spatial cues at the single-particle level and enlarges the receptive field size for improved representation capability. We also employed a collaborative learning strategy to further improve performance. Compared with other state-of-the-art models, CoMask was competitive on benchmark and TEM data sets. The application of CoMask not only enables the source-distinguishing of magnetic particles but also opens up a new vista for machine learning applications.

Clinical Impacts and Outcomes With Possible Donor-Derived Infection in Infected Donor Liver Transplantation: A Single-Center Retrospective Study in China.

BACKGROUND: Information on possible donor-derived transmission events in China is limited. We evaluated the impacts of liver transplantation from infected deceased-donors, analyzed possible donor-derived bacterial or fungal infection events in recipients, and evaluated the etiologic agents' characteristics and cases outcomes. METHODS: A single-center observational study was performed from January 2015 to March 2017 to retrospectively collect data from deceased-donors diagnosed with infection. Clinical data were recorded for each culture-positive donor and the matched liver recipient. The microorganisms were isolated and identified, and antibiotic sensitivity testing was performed. The pathogens distribution and incidence of possible donor-derived infection (P-DDI) events were analyzed and evaluated. RESULTS: Information from 211 donors was collected. Of these, 82 donors were infected and classified as the donation after brain death category. Overall, 149 and 138 pathogens were isolated from 82 infected donors and 82 matched liver recipients, respectively. Gram-positive bacteria, Gram-negative bacteria, and fungi accounted for 42.3% (63 of 149), 46.3% (69 of 149), and 11.4% (17 of 149) of pathogens in infected donors. The incidence of multidrug-resistant bacteria was high and Acinetobacter baumannii was the most concerning species. Infections occurred within the first 2 weeks after liver transplantation with an organ from an infected donor. Compared with the noninfection recipient group, the infection recipient group experienced a longer mechanical ventilation time (P = .004) and intensive care unit stay (P = .003), a higher incidence of renal dysfunction (P = .026) and renal replacement therapy (P = .001), and higher hospital mortality (P = .015). Possible donor-derived infection was observed in 14.6% of cases. Recipients with acute-on-chronic liver failure were more prone to have P-DDI than recipients with other diseases (P = .007; odds ratio = 0.114; 95% confidence interval, .025-.529). CONCLUSIONS: When a liver recipient receives a graft from an infected deceased-donor, the postoperative incidence of infection is high and the infection interval is short. In addition, when a possible donor-derived, drug-resistant bacterial infection occurs, recipients may have serious complications and poor outcomes.

Time-resolved mRNA and miRNA expression profiling reveals crucial coregulation of molecular pathways involved in epithelial-pneumococcal interactions.

Streptococcus pneumoniae is a major causative agent of pneumonia worldwide and its complex interaction with the lung epithelium has not been thoroughly characterized. In this study, we exploited both RNA-sequencing and microRNA (miRNA)-sequencing approaches to monitor the transcriptional changes in human lung alveolar epithelial cells infected by S. pneumoniae in a time-resolved manner. A total of 1330 differentially expressed (DE) genes and 45 DE miRNAs were identified in all comparisons during the infection process. Clustering analysis showed that all DE genes were grouped into six clusters, several of which were primarily involved in inflammatory or immune responses. In addition, target gene enrichment analyses identified 11 transcription factors that were predicted to link at least one of four clusters, revealing transcriptional coregulation of multiple processes or pathways by common transcription factors. Notably, pharmacological treatment suggested that phosphorylation of p65 is important for optimal transcriptional regulation of target genes in epithelial cells exposed to pathogens. Furthermore, network-based clustering analysis separated the DE genes negatively regulated by DE miRNAs into two functional modules (M1 and M2), with an enrichment in immune responses and apoptotic signaling pathways for M1. Integrated network analyses of potential regulatory interactions in M1 revealed that multiple DE genes related to immunity and apoptosis were regulated by multiple miRNAs, indicating the coordinated regulation of multiple genes by multiple miRNAs. In conclusion, time-series expression profiling of messenger RNA and miRNA provides a wealth of information for global transcriptional changes, and offers comprehensive insight into the molecular mechanisms underlying host-pathogen interactions.

A Machine Learning Method for Vision-Based Unmanned Aerial Vehicle Systems to Understand Unknown Environments.

What makes unmanned aerial vehicles (UAVs) intelligent is their capability of sensing and understanding new unknown environments. Some studies utilize computer vision algorithms like Visual Simultaneous Localization and Mapping (VSLAM) and Visual Odometry (VO) to sense the environment for pose estimation, obstacles avoidance and visual servoing. However, understanding the new environment (i.e., make the UAV recognize generic objects) is still an essential scientific problem that lacks a solution. Therefore, this paper takes a step to understand the items in an unknown environment. The aim of this research is to enable the UAV with basic understanding capability for a high-level UAV flock application in the future. Specially, firstly, the proposed understanding method combines machine learning and traditional algorithm to understand the unknown environment through RGB images; secondly, the You Only Look Once (YOLO) object detection system is integrated (based on TensorFlow) in a smartphone to perceive the position and category of 80 classes of objects in the images; thirdly, the method makes the UAV more intelligent and liberates the operator from labor; fourthly, detection accuracy and latency in working condition are quantitatively evaluated, and properties of generality (can be used in various platforms), transportability (easily deployed from one platform to another) and scalability (easily updated and maintained) for UAV flocks are qualitatively discussed. The experiments suggest that the method has enough accuracy to recognize various objects with high computational speed, and excellent properties of generality, transportability and scalability.

Improved ORB Algorithm Using Three-Patch Method and Local Gray Difference.

This paper presents an improved Oriented Features from Accelerated Segment Test (FAST) and Rotated BRIEF (ORB) algorithm named ORB using three-patch and local gray difference (ORB-TPLGD). ORB takes a breakthrough in real-time aspect. However, subtle changes of the image may greatly affect its final binary description. In this paper, the feature description generation is focused. On one hand, instead of pixel patch pairs comparison method used in present ORB algorithm, a three-pixel patch group comparison method is adopted to generate the binary string. In each group, the gray value of the main patch is compared with that of the other two companion patches to determine the corresponding bit of the binary description. On the other hand, the present ORB algorithm simply uses the gray size comparison between pixel patch pairs, while ignoring the information of the gray difference value. In this paper, another binary string based on the gray difference information mentioned above is generated. Finally, the feature fusion method is adopted to combine the binary strings generated in the above two steps to generate a new feature description. Experiment results indicate that our improved ORB algorithm can achieve greater performance than ORB and some other related algorithms.

Obeticholic Acid Protects against Lipopolysaccharide-Induced Fetal Death and Intrauterine Growth Restriction through Its Anti-Inflammatory Activity.

Farnesoid X receptor (FXR) is expressed in human and rodent placentas. Nevertheless, its function remains obscure. This study investigated the effects of obeticholic acid (OCA), a novel synthetic FXR agonist, on LPS-induced fetal death and intrauterine growth restriction. All pregnant mice except controls were i.p. injected with LPS (100 µg/kg) daily from gestational day (GD) 15 to GD17. Some pregnant mice were orally administered with OCA (5 mg/kg) daily from GD13 to GD17. As expected, placental FXR signaling was activated by OCA. OCA pretreatment protected against LPS-induced fetal death. In addition, OCA pretreatment alleviated LPS-induced reduction of fetal weight and crown-rump length. Additional experiments showed that OCA inhibited LPS-evoked TNF-α in maternal serum and amniotic fluid. Moreover, OCA significantly attenuated LPS-induced upregulation of placental proinflammatory genes including Tnf-α, Il-1ß, IL-6, Il-12, Mip-2, Kc, and Mcp-1 By contrast, OCA elevated anti-inflammatory cytokine IL-10 in maternal serum, amniotic fluid, and placenta. Further analysis showed that OCA blocked nuclear translocation of NF-κB p65 and p50 subunits in trophoblast giant cells of the labyrinth zone. These results provide a mechanistic explanation for placental FXR-mediated anti-inflammatory activity. Overall, this study provides evidence for roles of FXR as an important regulator of placental inflammation.

New Keypoint Matching Method Using Local Convolutional Features for Power Transmission Line Icing Monitoring.

Power transmission line icing (PTLI) problems, which cause tremendous damage to the power grids, has drawn much attention. Existing three-dimensional measurement methods based on binocular stereo vision was recently introduced to measure the ice thickness in PTLI, but failed to meet requirements of practical applications due to inefficient keypoint matching in the complex PTLI scene. In this paper, a new keypoint matching method is proposed based on the local multi-layer convolutional neural network (CNN) features, termed Local Convolutional Features (LCFs). LCFs are deployed to extract more discriminative features than the conventional CNNs. Particularly in LCFs, a multi-layer features fusion scheme is exploited to boost the matching performance. Together with a location constraint method, the correspondence of neighboring keypoints is further refined. Our approach achieves 1.5%, 5.3%, 13.1%, 27.3% improvement in the average matching precision compared with SIFT, SURF, ORB and MatchNet on the public Middlebury dataset, and the measurement accuracy of ice thickness can reach 90.9% compared with manual measurement on the collected PTLI dataset.

Supervised methods for symptom name recognition in free-text clinical records of traditional Chinese medicine: an empirical study.

Clinical records of traditional Chinese medicine (TCM) are documented by TCM doctors during their routine diagnostic work. These records contain abundant knowledge and reflect the clinical experience of TCM doctors. In recent years, with the modernization of TCM clinical practice, these clinical records have begun to be digitized. Data mining (DM) and machine learning (ML) methods provide an opportunity for researchers to discover TCM regularities buried in the large volume of clinical records. There has been some work on this problem. Existing methods have been validated on a limited amount of manually well-structured data. However, the contents of most fields in the clinical records are unstructured. As a result, the previous methods verified on the well-structured data will not work effectively on the free-text clinical records (FCRs), and the FCRs are, consequently, required to be structured in advance. Manually structuring the large volume of TCM FCRs is time-consuming and labor-intensive, but the development of automatic methods for the structuring task is at an early stage. Therefore, in this paper, symptom name recognition (SNR) in the chief complaints, which is one of the important tasks to structure the FCRs of TCM, is carefully studied. The SNR task is reasonably treated as a sequence labeling problem, and several fundamental and practical problems in the SNR task are studied, such as how to adapt a general sequence labeling strategy for the SNR task according to the domain-specific characteristics of the chief complaints and which sequence classifier is more appropriate to solve the SNR task. To answer these questions, a series of elaborate experiments were performed, and the results are explained in detail.

Age-based treatment of aggressive fibromatosis in the head and neck region.

PURPOSE: To review our experience regarding the difference in management and treatment outcomes of aggressive fibromatosis of the head and neck region in children and adults, emphasizing, in particular, the role of conservative surgery in comprehensive treatment strategies. PATIENTS AND METHODS: A retrospective analysis of patients with aggressive fibromatosis was performed during a 5-year period (2008 to 2012). Nine patients were enrolled in the present study, including 5 children (age, <18 years) and 4 adults (age, >18 years). All patients underwent surgical intervention and were treated by surgical resection with different surgical margins. Adjuvant low-dose chemotherapy and radiotherapy were given to pediatric and adult patients, respectively, with macroscopically or microscopically positive surgical margins. RESULTS: All 5 pediatric patients (3 females and 2 males) received low-dose chemotherapy after conservative surgical resection (in 4 patients, microscopically incomplete resection; and in 1 patient, macroscopic residual tumor). Of the 4 adults (3 females and 1 male), 2 underwent complete surgical resection and 2 underwent surgery and postoperative radiotherapy (1 patient had microscopically suspected residual tumor and 1 had macroscopic residual tumor). The patients were followed up for a period of 7 to 51 months. Two pediatric patients and one adult patient had disease progression after resection and became stable after continued adjuvant therapy. None of the patients had functional or cosmetic defects. All patients had good long-term outcomes, with no disease progression. CONCLUSIONS: For the treatment of aggressive fibromatosis, conservative resection with preservation of form and function should be given greater priority in all age groups. Also, postoperative adjuvant therapy is vital for patients with gross or microscopic residual tumor to obtain progression-free survival.

Nanoengineering Ultrathin Flexible Pressure Sensors with Superior Sensitivity and Wide Range via Nanocomposite Structures.

Flexible pressure sensors have attracted great interest due to their bendable, stretchable, and lightweight characteristics compared to rigid pressure sensors. However, the contradictions among sensitivity, detection limit, thickness, and detection range restrict the performance of flexible pressure sensors and the scope of their applications, especially for scenarios requiring conformal fitting, such as rough surfaces such as the human skin. This paper proposes a novel flexible pressure sensor by combining the nanoengineering strategy and nanocomposite structures. The nanoengineering strategy utilizes the bending deformation of nanofilm instead of the compression of the active layer to achieve super high sensitivity and low detection limit; meanwhile, the nanocomposite structures introduce distributed microbumps that delay the adhesion of nanofilm to enlarge the detection range. As a result, this device not only ensures an ultrathin thickness of 1.6 µm and a high sensitivity of 84.29 kPa-1 but also offers a large detection range of 20 kPa and an ultralow detection limit of 0.07 Pa. Owing to the ultrathin thickness as well as high performance, this device promotes applications in detecting fingertip pressure, flexible mechanical gripping, and so on, and demonstrates significant potential in wearable electronics, human-machine interaction, health monitoring, and tactile perception. This device offers a strategy to resolve the conflicts among thickness, sensitivity, detection limit, and detection range; therefore, it will advance the development of flexible pressure sensors and contribute to the community and other related research fields.

4D Printable liquid crystal elastomers with restricted nanointerfacial slippage for long-term-cyclic-stability photothermal actuation.

Liquid crystal elastomers (LCEs) blended with photothermal nanofillers can reversibly and rapidly deform their shapes under external optical stimuli. However, nanointerfacial slipping inevitably occurs between the LCE molecules and the nanofillers due to their weak physical interactions, eventually resulting in cyclic instability. This work presents a versatile strategy to fabricate nanointerfacial-slipping-restricted photoactuation elastomers by chemically bonding the nanofillers into a thermally actuatable liquid crystal network. We experimentally and theoretically investigated three types of metal-based nanofillers, including zero-dimensional (0D) nanoparticles, one-dimensional (1D) nanowires, and two-dimensional (2D) nanosheets. The toughly crosslinked nanointerface allows for remarkably promoted interfacial thermal conductivity and stress transfer. Therefore, the resultant actuators enable the realization of long-term-cyclic-stability 4D-printed flexible intelligent systems such as the optical gripper, crawling robot, light-powered self-sustained windmill, butterflies with fluttering wings, and intelligent solar energy collection system.

Improving Intestinal Barrier Function in Sepsis by Partially Hydrolysed Guar Gum via the Suppression of the NF-κB/MLCK Pathway.

Partially hydrolyzed guar gum (PHGG) protects against intestinal barrier dysfunction and can ameliorate some intestinal diseases. However, whether PHGG has a role in protecting intestinal barrier function (IBF) during sepsis remains unclear. This study aimed to investigate the role and probable mechanism of PHGG in the intestinal mucosa in sepsis. A rat sepsis model was constructed using cecal ligation and puncture (CLP). FITC-dextran 4 (FD-4) flux, serum inflammatory mediator levels, tight junction (TJ) levels, jejunum mucosa pathology, and epithelial intercellular junction ultrastructure were monitored to evaluate the effect of PHGG on IBF. Caco-2 monolayers were used to study the impact and mechanism of PHGG on lipopolysaccharide (LPS)-induced barrier dysfunction in vitro. The expression of zonula occludens protein-1 and occludin and the location of P65 were studied by immunofluorescence. Nuclear factor kappa B (NF-κB) and myosin light chain kinase 3 (MLCK) pathway-related protein expression was verified by quantitative reverse transcriptase polymerase chain reaction or western blotting. The results indicated that the jejunal mucosa structure was destroyed, the villi were disrupted and shortened, and neutrophil infiltration was evident in the septic rats. Compared to Sham group, spetic rats had increased Chiu's score, serum inflammatory mediator levels, and FD-4 flux but decreased TJ and gap junction density. In addition, the expression of MLCK, p-MLC, and TJ proteins and the expression of P65 in the nucleus were increased in septic rats. Furthermore, compared to those in the Control group, LPS-treated Caco-2 cells showed lower cell viability and transepithelial electrical resistance, while had higher FD-4 flux and the expression of MLCK, p-MLC, TJ proteins and P65 in the nucleus. PHGG pretreatment reversed the above effects induced by CLP or LPS treatment. Moreover, SN50, an NF-κB inhibitor, attenuated the above effects of LPS on Caco-2 cells. Overall, PHGG reduced inflammation, increased TJ protein expression and localization, and relieved damage to the TJ structure and intestinal permeability through suppression of the NF-κB/MLCK pathway. This study provides new insights into the role of PHGG in sepsis therapy.

Exploring the optimal range of central venous pressure in sepsis and septic shock patients: A retrospective study in 208 hospitals.

BACKGROUND: The aim of this study was to investigate the optimal CVP range in sepsis and septic shock patients admitted to intensive care unit. METHODS: We performed a retrospective study with adult sepsis patients with CVP records based on the eICU Collaborative Research Database. Multivariable logistic regression was performed to explore the associations between CVP level and hospital mortality. Non-linear correlations and optimal CVP range were explored using restricted cubic splines (RCS). RESULTS: A total of 5302 sepsis patients were included in this study. Patients in 4-8 mmHg group owned the lowest odds ratio for raw hospital mortality (19.7%). The logistic regression analyses revealed that hospital death risk increased significantly when mean CVP level exceeds 12 mmHg compared to 4-8 mmHg level. U-shaped association of CVP with hospital mortality was revealed by RCS model in septic shock patients and the optimal range was 5.6-12 mmHg. While, there was a J-shaped trend for non-septic shock patients. For non-septic shock patients, patients had an increased risk of hospital death only if CVP exceeded 11 mmHg. CONCLUSIONS: We observed U-shaped association between mean CVP level and hospital mortality in septic shock patients and J-shaped association in non-septic shock patients. This may imply that patients with different severity of sepsis have different CVP requirements. We need to monitor and manage CVP according to the circulatory status of the sepsis patient.

Developing an Interpretable Machine Learning Model to Predict in-Hospital Mortality in Sepsis Patients: A Retrospective Temporal Validation Study.

BACKGROUND: Risk stratification plays an essential role in the decision making for sepsis management, as existing approaches can hardly satisfy the need to assess this heterogeneous population. We aimed to develop and validate a machine learning model to predict in-hospital mortality in critically ill patients with sepsis. METHODS: Adult patients fulfilling the definition of Sepsis-3 were included at a large tertiary medical center. Relevant clinical features were extracted within the first 24 h in ICU, re-classified into different genres, and utilized for model development under three strategies: "Basic + Lab", "Basic + Intervention", and "Whole" feature sets. Extreme gradient boosting (XGBoost) was compared with logistic regression (LR) and established severity scores. Temporal validation was conducted using admissions from 2017 to 2019. RESULTS: The final cohort included 24,272 patients, of which 4013 patients formed the test cohort for temporal validation. The trained and fine-tuned XGBoost model with the whole feature set showed the best discriminatory ability in the test cohort with AUROC as 0.85, significantly higher than the XGBoost "Basic + Lab" model (0.83), the LR "Whole" model (0.82), SOFA (0.63), SAPS-II (0.73), and LODS score (0.74). The performance in varying subgroups remained robust, and predictors, such as increased urine output and supplemental oxygen therapy, were crucially correlated with improved survival when interpretability was explored. CONCLUSIONS: We developed and validated a novel XGBoost-based model and demonstrated significantly improved performance to LR and other scores in predicting the mortality risks of sepsis patients in the hospital using features in the first 24 h.