Cervical extracellular matrix hydrogel optimizes tumor heterogeneity of cervical squamous cell carcinoma organoids.

Cervical cancer, primarily squamous cell carcinoma, is the most prevalent gynecologic malignancy. Organoids can mimic tumor development in vitro, but current Matrigel inaccurately replicates the tissue-specific microenvironment. This limitation compromises the accurate representation of tumor heterogeneity. We collected para-cancerous cervical tissues from patients diagnosed with cervical squamous cell carcinoma (CSCC) and prepared uterine cervix extracellular matrix (UCEM) hydrogels. Proteomic analysis of UCEM identified several tissue-specific signaling pathways including human papillomavirus, phosphatidylinositol 3-kinase-AKT, and extracellular matrix receptor. Secreted proteins like FLNA, MYH9, HSPA8, and EEF1A1 were present, indicating UCEM successfully maintained cervical proteins. UCEM provided a tailored microenvironment for CSCC organoids, enabling formation and growth while preserving tumorigenic potential. RNA sequencing showed UCEM-organoids exhibited greater similarity to native CSCC and reflected tumor heterogeneity by exhibiting CSCC-associated signaling pathways including virus protein-cytokine, nuclear factor κB, tumor necrosis factor, and oncogenes EGR1, FPR1, and IFI6. Moreover, UCEM-organoids developed chemotherapy resistance. Our research provides insights into advanced organoid technology through native matrix hydrogels.

An Injectable Puerarin Depot Can Potentiate Chimeric Antigen Receptor Natural Killer Cell Immunotherapy Against Targeted Solid Tumors by Reversing Tumor Immunosuppression.

Chimeric antigen receptor natural killer (CAR-NK) cell therapy represents a potent approach to suppressing tumor growth because it has simultaneously inherited the specificity of CAR and the intrinsic generality of NK cells in recognizing cancer cells. However, its therapeutic potency against solid tumors is still restricted by insufficient tumor infiltration, immunosuppressive tumor microenvironments, and many other biological barriers. Motivated by the high potency of puerarin, a traditional Chinese medicine extract, in dilating tumor blood vessels, an injectable puerarin depot based on a hydrogen peroxide-responsive hydrogel comprising poly(ethylene glycol) dimethacrylate and ferrous chloride is concisely developed. Upon intratumoral fixation, the as-prepared puerarin depot (abbreviated as puerarin@PEGel) can activate nitrogen oxide production inside endothelial cells and thus dilate tumor blood vessels to relieve tumor hypoxia and reverse tumor immunosuppression. Such treatment can thus promote tumor infiltration, survival, and effector functions of customized epidermal growth factor receptor (HER1)-targeted HER1-CAR-NK cells after intravenous administration. Consequently, such puerarin@PEGel-assisted HER1-CAR-NK cell treatment exhibits superior tumor suppression efficacy toward both HER1-overexpressing MDA-MB-468 and NCI-H23 human tumor xenografts in mice without inducing obvious side effects. This study highlights a potent strategy to activate CAR-NK cells for augmented treatment of targeted solid tumors through reprogramming tumor immunosuppression.

Hybrid-FHR: a multi-modal AI approach for automated fetal acidosis diagnosis.

BACKGROUND: In clinical medicine, fetal heart rate (FHR) monitoring using cardiotocography (CTG) is one of the most commonly used methods for assessing fetal acidosis. However, as the visual interpretation of CTG depends on the subjective judgment of the clinician, this has led to high inter-observer and intra-observer variability, making it necessary to introduce automated diagnostic techniques. METHODS: In this study, we propose a computer-aided diagnostic algorithm (Hybrid-FHR) for fetal acidosis to assist physicians in making objective decisions and taking timely interventions. Hybrid-FHR uses multi-modal features, including one-dimensional FHR signals and three types of expert features designed based on prior knowledge (morphological time domain, frequency domain, and nonlinear). To extract the spatiotemporal feature representation of one-dimensional FHR signals, we designed a multi-scale squeeze and excitation temporal convolutional network (SE-TCN) backbone model based on dilated causal convolution, which can effectively capture the long-term dependence of FHR signals by expanding the receptive field of each layer's convolution kernel while maintaining a relatively small parameter size. In addition, we proposed a cross-modal feature fusion (CMFF) method that uses multi-head attention mechanisms to explore the relationships between different modalities, obtaining more informative feature representations and improving diagnostic accuracy. RESULTS: Our ablation experiments show that the Hybrid-FHR outperforms traditional previous methods, with average accuracy, specificity, sensitivity, precision, and F1 score of 96.8, 97.5, 96, 97.5, and 96.7%, respectively. CONCLUSIONS: Our algorithm enables automated CTG analysis, assisting healthcare professionals in the early identification of fetal acidosis and the prompt implementation of interventions.

Role and function of plakophilin 3 in cancer progression and skin disease.

Plakophilin 3 (PKP3), a component of desmosome, is aberrantly expressed in many kinds of human diseases, especially in cancers. Through direct interaction, PKP3 binds with a series of desmosomal proteins, such as desmoglein, desmocollin, plakoglobin, and desmoplakin, to initiate desmosome aggregation, then promotes its stability. As PKP3 is mostly expressed in the skin, loss of PKP3 promotes the development of several skin diseases, such as paraneoplastic pemphigus, pemphigus vulgaris, and hypertrophic scar. Moreover, accumulated clinical data indicate that PKP3 dysregulates in diverse cancers, including breast, ovarian, colon, and lung cancers. Numerous lines of evidence have shown that PKP3 plays important roles in multiple cellular processes during cancer progression, including metastasis, invasion, tumor formation, autophagy, and proliferation. This review examines the diverse functions of PKP3 in regulating tumor formation and development in various types of cancers and summarizes its detailed mechanisms in the occurrence of skin diseases.

A lightweight fetal distress-assisted diagnosis model based on a cross-channel interactive attention mechanism.

Fetal distress is a symptom of fetal intrauterine hypoxia, which is seriously harmful to both the fetus and the pregnant woman. The current primary clinical tool for the assessment of fetal distress is Cardiotocography (CTG). Due to subjective variability, physicians often interpret CTG results inconsistently, hence the need to develop an auxiliary diagnostic system for fetal distress. Although the deep learning-based fetal distress-assisted diagnosis model has a high classification accuracy, the model not only has a large number of parameters but also requires a large number of computational resources, which is difficult to deploy to practical end-use scenarios. Therefore, this paper proposes a lightweight fetal distress-assisted diagnosis network, LW-FHRNet, based on a cross-channel interactive attention mechanism. The wavelet packet decomposition technique is used to convert the one-dimensional fetal heart rate (FHR) signal into a two-dimensional wavelet packet coefficient matrix map as the network input layer to fully obtain the feature information of the FHR signal. With ShuffleNet-v2 as the core, a local cross-channel interactive attention mechanism is introduced to enhance the model's ability to extract features and achieve effective fusion of multichannel features without dimensionality reduction. In this paper, the publicly available database CTU-UHB is used for the network performance evaluation. LW-FHRNet achieves 95.24% accuracy, which meets or exceeds the classification results of deep learning-based models. Additionally, the number of model parameters is reduced many times compared with the deep learning model, and the size of the model parameters is only 0.33 M. The results show that the lightweight model proposed in this paper can effectively aid in fetal distress diagnosis.

Protein regulation mechanism of cold tolerance in Haemaphysalis longicornis.

Ticks are external parasitic arthropods that can transmit a variety of pathogens by sucking blood. Low-temperature tolerance is essential for ticks to survive during the cold winter. Exploring the protein regulation mechanism of low-temperature tolerance of Haemaphysalis longicornis could help to explain how ticks survive in winter. In this study, the quantitative proteomics of several tissues of H. longicornis exposed to low temperature were studied by data independent acquisition technology. Totals of 3 699, 3 422, and 1 958 proteins were identified in the salivary gland, midgut, and ovary, respectively. The proteins involved in energy metabolism, cell signal transduction, protein synthesis and repair, and cytoskeleton synthesis changed under low-temperature stress. The comprehensive analysis of the protein regulation of multiple tissues of female ticks exposed to low temperature showed that maintaining cell homeostasis, maintaining cell viability, and enhancing cell tolerance were the most important means for ticks to maintain vital signs under low temperature. The expression of proteins involved in and regulating the above cell activities was the key to the survival of ticks under low temperatures. Through the analysis of a large amount of data, we found that the expression levels of arylamine N-acetyltransferase, inositol polyphosphate multikinase, and dual-specificity phosphatase were up-regulated under low temperature. We speculated that they might have important significance in low-temperature tolerance. Then, we performed RNA interference on the mRNA of these 3 proteins, and the results showed that the ability of female ticks to tolerate low temperatures decreased significantly.

The role and function of CLU in cancer biology and therapy.

Clusterin (CLU) is a highly evolutionary conserved glycoprotein with multiple isoform-specific functions and is widely distributed in different species. Accumulated evidence has shown the prominent role of CLU in regulating several essential physiological processes, including programmed cell death, metastasis, invasion, proliferation and cell growth via regulating diverse signaling pathways to mediate cancer progression in various cancers, such as prostate, breast, lung, liver, colon, bladder and pancreatic cancer. Several studies have revealed the potential benefit of inhibiting CLU in CLU inhibition-based targeted cancer therapies in vitro, in vivo or in human, suggesting CLU is a promising therapeutic target. This review discusses the multiple functions and mechanisms of CLU in regulating tumor progression of various cancers and summarizes the inhibitors of CLU used in CLU inhibition-based targeted cancer therapies.

Multimodal learning for fetal distress diagnosis using a multimodal medical information fusion framework.

Cardiotocography (CTG) monitoring is an important medical diagnostic tool for fetal well-being evaluation in late pregnancy. In this regard, intelligent CTG classification based on Fetal Heart Rate (FHR) signals is a challenging research area that can assist obstetricians in making clinical decisions, thereby improving the efficiency and accuracy of pregnancy management. Most existing methods focus on one specific modality, that is, they only detect one type of modality and inevitably have limitations such as incomplete or redundant source domain feature extraction, and poor repeatability. This study focuses on modeling multimodal learning for Fetal Distress Diagnosis (FDD); however, exists three major challenges: unaligned multimodalities; failure to learn and fuse the causality and inclusion between multimodal biomedical data; modality sensitivity, that is, difficulty in implementing a task in the absence of modalities. To address these three issues, we propose a Multimodal Medical Information Fusion framework named MMIF, where the Category Constrained-Parallel ViT model (CCPViT) was first proposed to explore multimodal learning tasks and address the misalignment between multimodalities. Based on CCPViT, a cross-attention-based image-text joint component is introduced to establish a Multimodal Representation Alignment Network model (MRAN), explore the deep-level interactive representation between cross-modal data, and assist multimodal learning. Furthermore, we designed a simple-structured FDD test model based on the highly modal alignment MMIF, realizing task delegation from multimodal model training (image and text) to unimodal pathological diagnosis (image). Extensive experiments, including model parameter sensitivity analysis, cross-modal alignment assessment, and pathological diagnostic accuracy evaluation, were conducted to show our models' superior performance and effectiveness.

Multi-center verification of the influence of data ratio of training sets on test results of an AI system for detecting early gastric cancer based on the YOLO-v4 algorithm.

Objective: Convolutional Neural Network(CNN) is increasingly being applied in the diagnosis of gastric cancer. However, the impact of proportion of internal data in the training set on test results has not been sufficiently studied. Here, we constructed an artificial intelligence (AI) system called EGC-YOLOV4 using the YOLO-v4 algorithm to explore the optimal ratio of training set with the power to diagnose early gastric cancer. Design: A total of 22,0918 gastroscopic images from Yixing People's Hospital were collected. 7 training set models were established to identify 4 test sets. Respective sensitivity, specificity, Youden index, accuracy, and corresponding thresholds were tested, and ROC curves were plotted. Results: 1. The EGC-YOLOV4 system completes all tests at an average reading speed of about 15 ms/sheet; 2. The AUC values in training set 1 model were 0.8325, 0.8307, 0.8706, and 0.8279, in training set 2 model were 0.8674, 0.8635, 0.9056, and 0.9249, in training set 3 model were 0.8544, 0.8881, 0.9072, and 0.9237, in training set 4 model were 0.8271, 0.9020, 0.9102, and 0.9316, in training set 5 model were 0.8249, 0.8484, 0.8796, and 0.8931, in training set 6 model were 0.8235, 0.8539, 0.9002, and 0.9051, in training set 7 model were 0.7581, 0.8082, 0.8803, and 0.8763. Conclusion: EGC-YOLOV4 can quickly and accurately identify the early gastric cancer lesions in gastroscopic images, and has good generalization.The proportion of positive and negative samples in the training set will affect the overall diagnostic performance of AI.In this study, the optimal ratio of positive samples to negative samples in the training set is 1:1~ 1:2.

Comparison of Efficacy and Safety Between Immunotherapy and Docetaxel Monotherapy in NSCLC Patients.

Objective: Meta analysis was used to compare the efficacy and safety of immune checkpoint inhibitor and docetaxel in the treatment of non-small cell lung cancer. Methods: CNKI, CBM, PubMed, EMBASE, Cochrane Library, web of science and other databases were searched by computer, and the randomized controlled trials of immune checkpoint inhibitors and docetaxel in the treatment of NSCLC published as of February 2022 were collected. Two researchers searched independently, screened the literature and extracted the data according to the nanodischarge criteria, and used Revman5.4. The included studies were statistically analyzed, and publication bias was analyzed with Egger test in Stata12. Results: A total of 8 RCTs were included, including 2444 cases treated with immune checkpoint inhibitors and 2097 cases treated with docetaxel. Compared with docetaxel, the overall survival (HR = 1.40, 95%CI: 1.30-1.50, P < 0.00001) and progression free survival (HR = 1.22, 95%CI: 1.13-1.32, P < 0.00001) of NSCLC treated with ICIs were longer. The risk ratio of any grade of adverse reactions (HR = 0.41, 95%CI: 0.32-0.52, P < 0.00001) and above grade III adverse reactions (HR = 0.27, 95%CI: 0.18-0.41, P < 0.00001) in the treatment of NSCLC with ICIs was lower. There was no publication bias in Egger test. Conclusion: Compared with docetaxel, immune checkpoint inhibitor treatment can improve the clinical efficacy of NSCLC patients and has a lower incidence of adverse reactions. This treatment may be a promising treatment for NSCLC patients.

[An EHG-based Preterm Delivery Prediction Algorithm via Convolution Neural Network].

Premature delivery is one of the direct factors that affect the early development and safety of infants. Its direct clinical manifestation is the change of uterine contraction intensity and frequency. Uterine Electrohysterography(EHG) signal collected from the abdomen of pregnant women can accurately and effectively reflect the uterine contraction, which has higher clinical application value than invasive monitoring technology such as intrauterine pressure catheter. Therefore, the research of fetal preterm birth recognition algorithm based on EHG is particularly important for perinatal fetal monitoring. We proposed a convolution neural network(CNN) based on EHG fetal preterm birth recognition algorithm, and a deep CNN model was constructed by combining the Gramian angular difference field(GADF) with the transfer learning technology. The structure of the model was optimized using the clinical measured term-preterm EHG database. The classification accuracy of 94.38% and F1 value of 97.11% were achieved. The experimental results showed that the model constructed in this paper has a certain auxiliary diagnostic value for clinical prediction of premature delivery.

Construction and Multicenter Diagnostic Verification of Intelligent Recognition System for Endoscopic Images From Early Gastric Cancer Based on YOLO-V3 Algorithm.

INTRODUCTION: Endoscopy is an important tool for the diagnosis of early gastric cancer. Therefore, a combination of artificial intelligence and endoscopy has the ability to increase the speed and efficiency of early gastric cancer diagnosis. YOU ONLY LOOK ONCE (YOLO) is an advanced object detection depth neural network algorithm that has not been widely used in gastrointestinal image recognition. OBJECTIVE: We developed an artificial intelligence system herein referred to as "EGC-YOLO" for the rapid and accurate diagnosis of endoscopic images from early gastric cancer. METHODS: More than 40000 gastroscopic images from 1653 patients in Yixing people's Hospital were used as the training set for the system, while endoscopic images from the other two hospitals were used as external validation test sets. The sensitivity, specificity, positive predictive value, Youden index and ROC curve were analyzed to evaluate detection efficiencies for EGC-YOLO. RESULTS: EGC-YOLO was able to diagnose early gastric cancer in the two test sets with a high superiority and efficiency. The accuracy, sensitivity, specificity and positive predictive value for Test Sets 1 and 2 were 85.15% and 86.02%, 85.36% and 83.02%, 84.41% and 92.21%, and 95.22% and 95.65%, respectively. In Test Sets 1 and 2, the corresponding Threshold-values were 0.02, 0.16 and 0.17 at the maximum of the Youden index. An increase in Threshold-values was associated with a downward trend in sensitivity and accuracy, while specificity remained relatively stable at more than 80%. CONCLUSIONS: The EGC-YOLO system is superior for the efficient, accurate and rapid detection of early gastric cancer lesions. For different data sets, it is important to select the appropriate threshold-value in advance to achieve the best performance of the EGC-YOLO system.

Reconstruction of Missing Samples in Antepartum and Intrapartum FHR Measurements Via Mini-Batch-Based Minimized Sparse Dictionary Learning.

Fetal Heart Rate (FHR), an important recording in Cardiotocography (CTG)-based fetal health status monitoring, is the only information that clinical obstetricians can directly obtain and use. A challenge, however, is that missing samples are very common in FHR due to various causes such as fetal movements and sensor malfunctions. The aim is the development of an inpainting tool which is suitable for different missing lengths q and various total missing percentages Q, as well as for use in online mode. This study focused on two major impediments to existing inpainting methods: the longer the missing length, the more difficult it is to recover with mathematical methods; the reliance on tens of thousands of training samples, and the computational burden caused by full batch-based dictionary learning algorithms. We present a regularized minimization approach to signal recovery, which combines a L0.6 - norm minimized sparse dictionary learning algorithm (MSDL) and a model optimization strategy for using a mini-batch version for signal recovery. Using 100 FHR recordings with 2 protocols designed to simulate missing clinical data scenarios, the combined method performed favorably in terms of 5 data analysis metrics and 3 clinical indicators. Comparing 4 inpainting methods, we were able to prove the superiority of the proposed algorithm for both large q and large Q. The experimental results showed the lowest values (2.64 (MAE), 4.68 (RMSE)) when Q = 5% with short interval lengths. The developed architecture provides a reference value for the practical application of recovering missing samples online.

In Vitro and in Vivo Analyses of the Effects of Source, Length, and Charge on the Cytotoxicity and Immunocompatibility of Cellulose Nanocrystals.

Cellulose nanocrystals (CNCs) are an emergent, sustainable nanomaterial that are biosourced, abundant, and biodegradable. On account of their high aspect ratio, low density, and mechanical rigidity, they have been employed in numerous areas of biomedical research including as reinforcing materials for bone or tissue scaffolds or as carriers in drug delivery systems. Given the promise of these materials for such use, characterizing and understanding their interactions with biological systems is an important step to prevent toxicity or inflammation. Reported herein are studies aimed at exploring the in vitro and in vivo effects that the source, length, and charge of the CNCs have on cytotoxicity and immune response. CNCs from four different biosources (cotton, wood, Miscanthus x Giganteus, and sea tunicate) were prepared and functionalized with positive or negative charges to obtain a small library of CNCs with a range of dimensions and surface charge. A method to remove endotoxic or other impurities on the CNC surface leftover from the isolation process was developed, and the biocompatibility of the CNCs was subsequently assayed in vitro and in vivo. After subcutaneous injection, it was found that unfunctionalized (uncharged) CNCs form aggregates at the site of injection, inducing splenomegaly and neutrophil infiltration, while charged CNCs having surface carboxylates, sulfate half-esters, or primary amines were biologically inert. No effect of the particle source or length was observed in the in vitro and in vivo studies conducted. The lack of an in vitro or in vivo immune response toward charged CNCs in these experiments supports their use in future biological studies.

Phosphoproteomic Analysis of Haemaphysalis longicornis Saliva Reveals the Influential Contributions of Phosphoproteins to Blood-Feeding Success.

Tick saliva, an essential chemical secretion of the tick salivary gland, is indispensable for tick survival owing to the physiological influence it exerts on the host defence mechanisms via the instrumentality of its cocktail of pharmacologically active molecules (proteins and peptides). Much research about tick salivary proteome has been performed, but how most of the individual salivary proteins are utilized by ticks to facilitate blood acquisition and pathogen transmission is not yet fully understood. In addition, the phosphorylation of some proteins plays a decisive role in their function. However, due to the low phosphorylation level of protein, especially for a small amount of protein, it is more difficult to study phosphorylation. Maybe, for this reason, the scarcity of works on the phosphorylated tick salivary proteomes still abound. Here, we performed a phosphoproteomic analysis of Haemaphysalis longicornis tick saliva via TiO2 enrichment and the most advanced Thermo Fisher Orbitrap Exploris 480 mass spectrometer for identification. A total of 262 phosphorylated tick saliva proteins were identified and were subjected to functional annotation/enrichment analysis. Cellular and metabolic process terms accounted for the largest proportion of the saliva proteins, with the participation of these proteins in vital intracellular and extracellular transport-oriented processes such as vesicle-mediated transport, exocytic process, cell adhesion, and movement of cell/subcellular component. "Endocytosis", "Protein processing in endoplasmic reticulum", and "Purine metabolism" were the most significantly enriched pathways. The knockdown (RNAi) of Tudor domain-containing protein (TCP), actin-depolymerizing factors (ADF), programmed cell death protein (PD), and serine/threonine-protein kinase (SPK) resulted in the dissociation of collagen fibers and the pilosebaceous unit, increased inflammatory infiltrates/granulocytes (possibly heterophiles), and the depletion of the epithelium. Ticks injected with SPK dsRNA engorged normally but with a change in skin colour (possibly an autoimmune reaction) and the failure to produce eggs pointing to a possible role of SPK in reproduction and host immune modulation. Ticks injected with ADF dsRNA failed to acquire blood, underscoring the role of ADF in facilitating tick feeding. The results of this study showed the presence of phosphorylation in tick saliva and highlight the roles of salivary phosphoproteins in facilitating tick feeding.

Squid Beak Inspired Cross-Linked Cellulose Nanocrystal Composites.

Bioinspired cross-linked polymer nanocomposites that mimic the water-enhanced mechanical gradient properties of the squid beak have been prepared by embedding either carboxylic acid- or allyl-functionalized cellulose nanocrystals (CNC) into an alkene-containing polymer matrix (poly(vinyl acetate-co-vinyl pentenoate), P(VAc-co-VP)). Cross-linking is achieved by imbibing the composite with a tetrathiol cross-linker and carrying out a photoinduced thiol-ene reaction. Central to this study was an investigation on how the placement of cross-links (i.e., within matrix only or between the matrix and filler) impacts the wet mechanical properties of these materials. Through cross-linking both the CNCs and matrix, it is possible to access larger wet mechanical contrasts (E'stiff/E'soft = ca. 20) than can be obtained by just cross-linking the matrix alone (where contrast E'stiff/E'soft of up 11 are observed). For example, in nanocomposites fabricated with 15 wt % of allyl-functionalized tunicate CNCs and P(VAc-co-VP) with about 30 mol % of the alkene-containing VP units, an increase in the modulus of the wet composite from about 14 MPa to about 289 MPa at physiological temperature (37 °C) can be observed after UV irradiation. The water swelling of the nanocomposites is greatly reduced in the cross-linked materials as a result of the thiol-ene cross-linking network, which also contributes to the wet modulus increase. Given the mechanical turnability and the relatively simple approach that also allows photopatterning the material properties, these water-activated bioinspired nanocomposites have potential uses in a broad range of biomedical applications, such as mechanically compliant intracortical microelectrodes.

Airway insertion first pass success and patient outcomes in adult out-of-hospital cardiac arrest: The Pragmatic Airway Resuscitation Trial.

OBJECTIVE: While emphasized in clinical practice, the association between advanced airway insertion first-pass success (FPS) and patient outcomes is incompletely understood. We sought to determine the association of airway insertion FPS with adult out-of-hospital cardiac arrest (OHCA) outcomes in the Pragmatic Airway Resuscitation Trial (PART). METHODS: We performed a secondary analysis of PART, a multicenter clinical trial comparing LT and ETI upon adult OHCA outcomes. We defined FPS as successful LT insertion or ETI on the first attempt as reported by EMS personnel. We examined the outcomes return of spontaneous circulation (ROSC), 72-h survival, hospital survival, and hospital survival with favorable neurologic status (Modified Rankin Scale ≤3). Using multivariable GEE (generalized estimating equations), we determined the association between FPS and OHCA outcomes, adjusting for age, sex, witnessed arrest, bystander CPR, initial rhythm, and initial airway type. RESULTS: Of 3004 patients enrolled in the trial, 1423 received LT, 1227 received ETI, 354 received bag-valve-mask ventilation only. FPS was: LT 86.2% and ETI 46.7%. FPS was associated with increased ROSC (aOR 1.23; 95%CI: 1.07-1.41)), but not 72-h survival (1.22; 0.94-1.58), hospital survival (0.90; 0.68-1.19) or hospital survival with favorable neurologic status (0.66; 0.37-1.19). CONCLUSION: In adult OHCA, airway insertion FPS was associated with increased ROSC but not other OHCA outcomes. The influence of airway insertion FPS upon OHCA outcomes is unclear.

Resveratrol prevents TNF-α-induced VCAM-1 and ICAM-1 upregulation in endothelial progenitor cells via reduction of NF-κB activation.

OBJECTIVE: To assess the effects of resveratrol (RSV) on expression of adhesion molecules in endothelial progenitor cells (EPCs) following tumor necrosis factor-α (TNF-α) stimulation. METHODS: EPCs were treated with RSV and stimulated with TNF-α. A mononuclear cell (MNC) adhesion assay was used to assess the effects of RSV on TNF-α-induced MNC adhesion. Vascular cell adhesion molecule-1 (VCAM-1), intercellular adhesion molecule-1 (ICAM-1) and E-selectin expression levels and nuclear factor κB (NF-κB) activation were assessed by immunoblotting. RESULTS: MNC adhesion to TNF-α-treated EPCs and VCAM-1/ICAM-1/E-selectin levels in EPCs were increased following TNF-α stimulation and decreased following RSV treatment. TNF-α enhanced NF-κB inhibitor α (IκB-α) phosphorylation in the cytosol as well as nuclear NF-κB p65 levels, both of which were decreased by RSV. CONCLUSIONS: These findings provide new insights into RSV's anti-inflammatory and anti-atherosclerotic effects. RSV's mechanism of action might involve downregulation of VCAM-1, ICAM-1 and E-selectin by partial blockade of TNF-α-induced NF-κB activation and IκB-α phosphorylation in EPCs.

Intent-to-treat analysis of cluster randomized trials when clusters report unidentifiable outcome proportions.

BACKGROUND: Cluster randomized trials are designed to evaluate interventions at the cluster or group level. When clusters are randomized but some clusters report no or non-analyzable data, intent-to-treat analysis, the gold standard for the analysis of randomized controlled trials, can be compromised. This article presents a very flexible statistical methodology for cluster randomized trials whose outcome is a cluster-level proportion (e.g. proportion from a cluster reporting an event) in the setting where clusters report non-analyzable data (which in general could be due to nonadherence, dropout, missingness, etc.). The approach is motivated by a previously published stratified randomized controlled trial called, "The Randomized Recruitment Intervention Trial (RECRUIT)," designed to examine the effectiveness of a trust-based continuous quality improvement intervention on increasing minority recruitment into clinical trials (ClinicalTrials.gov Identifier: NCT01911208). METHODS: The novel approach exploits the use of generalized estimating equations for cluster-level reports, such that all clusters randomized at baseline are able to be analyzed, and intervention effects are presented as risk ratios. Simulation studies under different outcome missingness scenarios and a variety of intra-cluster correlations are conducted. A comparative analysis of the method with imputation and per protocol approaches for RECRUIT is presented. RESULTS: Simulation results show the novel approach produces unbiased and efficient estimates of the intervention effect that maintain the nominal type I error rate. Application to RECRUIT shows similar effect sizes when compared to the imputation and per protocol approach. CONCLUSION: The article demonstrates that an innovative bivariate generalized estimating equations framework allows one to implement an intent-to-treat analysis to obtain risk ratios or odds ratios, for a variety of cluster randomized designs.

Diagnostic Values of Neutrophil and Neutrophil to Lymphocyte Ratio in Distinguishing between Acute Cerebral Infarction and Vertigo.

BACKGROUND: Vertigo and acute cerebral infarction (ACI) patients show similar symptoms such as dizziness and imbalance. This study was to investigate the diagnostic values of neutrophil counts and neutrophil to lymphocyte ratio (NLR) in distinguishing patients with ACI from those with vertigo. METHODS: This retrospective study was performed and data were gathered from medical records of patients with vertigo symptoms from the Department of Emergency and Neurology Clinics who were admitted to the Fourth Affiliated Hospital Zhejiang University School of Medicine between August 2017 and January 2019. Of the 173 patients with vertigo symptoms, 111 non-ACI vertigo patients (vertigo group) and 62 cases diagnosed with ACI (ACI group) were enrolled in this study. The neutrophil counts, lymphocyte counts, platelet counts, NLR, and PLT to lymphocyte ratio (PLR) within 24 hours after admission were compared between the two groups. Student's t-test of independent samples was adopted for the comparison of the mean between two groups. The neutrophil counts and NLR were evaluated by comparing the areas under the receiver operating characteristic curve (AUC) in distinguishing patients with ACI from those with vertigo. Comparison of AUC was performed using the Z-test. RESULTS: The neutrophil counts and NLR were significantly increased in the ACI group compared with the vertigo group (all p < 0.05), while there were no significant statistical differences of the lymphocyte counts, platelet counts, and PLR (all p > 0.05); moreover, AUC in distinguishing patients with ACI from those with vertigo was 0.647 (95% confidence interval (CI), 0.570 to 0.718) for neutrophil counts and 0.639 (95% CI, 0.562 to 0.710) for NLR, but there was no significant statistical difference (p > 0.05); finally, the cutoff values were 3.1 x 109/L in distinguishing patients with ACI from those with vertigo (specificity 41.44% and sensitivity 83.87%) for neutrophil counts and 2 (specificity 55.86% and sensitivity 67.74%) for NLR. CONCLUSIONS: As easy-to-obtain inflammatory biomarkers, both neutrophil counts and NLR could demonstrate diagnostic values in distinguishing between ACI and vertigo.