Intensive Ambulance-Delivered Blood-Pressure Reduction in Hyperacute Stroke.

BACKGROUND: Treatment of acute stroke, before a distinction can be made between ischemic and hemorrhagic types, is challenging. Whether very early blood-pressure control in the ambulance improves outcomes among patients with undifferentiated acute stroke is uncertain. METHODS: We randomly assigned patients with suspected acute stroke that caused a motor deficit and with elevated systolic blood pressure (≥150 mm Hg), who were assessed in the ambulance within 2 hours after the onset of symptoms, to receive immediate treatment to lower the systolic blood pressure (target range, 130 to 140 mm Hg) (intervention group) or usual blood-pressure management (usual-care group). The primary efficacy outcome was functional status as assessed by the score on the modified Rankin scale (range, 0 [no symptoms] to 6 [death]) at 90 days after randomization. The primary safety outcome was any serious adverse event. RESULTS: A total of 2404 patients (mean age, 70 years) in China underwent randomization and provided consent for the trial: 1205 in the intervention group and 1199 in the usual-care group. The median time between symptom onset and randomization was 61 minutes (interquartile range, 41 to 93), and the mean blood pressure at randomization was 178/98 mm Hg. Stroke was subsequently confirmed by imaging in 2240 patients, of whom 1041 (46.5%) had a hemorrhagic stroke. At the time of patients' arrival at the hospital, the mean systolic blood pressure in the intervention group was 159 mm Hg, as compared with 170 mm Hg in the usual-care group. Overall, there was no difference in functional outcome between the two groups (common odds ratio, 1.00; 95% confidence interval [CI], 0.87 to 1.15), and the incidence of serious adverse events was similar in the two groups. Prehospital reduction of blood pressure was associated with a decrease in the odds of a poor functional outcome among patients with hemorrhagic stroke (common odds ratio, 0.75; 95% CI, 0.60 to 0.92) but an increase among patients with cerebral ischemia (common odds ratio, 1.30; 95% CI, 1.06 to 1.60). CONCLUSIONS: In this trial, prehospital blood-pressure reduction did not improve functional outcomes in a cohort of patients with undifferentiated acute stroke, of whom 46.5% subsequently received a diagnosis of hemorrhagic stroke. (Funded by the National Health and Medical Research Council of Australia and others; INTERACT4 ClinicalTrials.gov number, NCT03790800; Chinese Trial Registry number, ChiCTR1900020534.).

Capsaicin Enhanced the Efficacy of Photodynamic Therapy Against Osteosarcoma via a Pro-Death Strategy by Inducing Ferroptosis and Alleviating Hypoxia.

Ferroptosis, a novel form of nonapoptotic cell death, can effectively enhance photodynamic therapy (PDT) performance by disrupting intracellular redox homeostasis and promoting apoptosis. However, the extremely hypoxic tumor microenvironment (TME) together with highly expressed hypoxia-inducible factor-1α (HIF-1α) presents a considerable challenge for clinical PDT against osteosarcoma (OS). Hence, an innovative nanoplatform that enhances antitumor PDT by inducing ferroptosis and alleviating hypoxia is fabricated. Capsaicin (CAP) is widely reported to specifically activate transient receptor potential vanilloid 1 (TRPV1) channel, trigger an increase in intracellular Ca2+ concentration, which is closely linked with ferroptosis, and participate in decreased oxygen consumption by inhibiting HIF-1α in tumor cells, potentiating PDT antitumor efficiency. Thus, CAP and the photosensitizer IR780 are coencapsulated into highly biocompatible human serum albumin (HSA) to construct a nanoplatform (CI@HSA NPs) for synergistic tumor treatment under near-infrared (NIR) irradiation. Furthermore, the potential underlying signaling pathways of the combination therapy are investigated. CI@HSA NPs achieve real-time dynamic distribution monitoring and exhibit excellent antitumor efficacy with superior biosafety in vivo. Overall, this work highlights a promising NIR imaging-guided "pro-death" strategy to overcome the limitations of PDT for OS by promoting ferroptosis and alleviating hypoxia, providing inspiration and support for future innovative tumor therapy approaches.

Ultrafast evanescently coupled waveguide MUTC-PDs with high responsivity.

Novel evanescently coupled waveguide modified uni-traveling carrier photodiodes (MUTC-PDs) employing a thick multi-layer coupling waveguide are reported. To improve the optical-to-electrical (O/E) conversion efficiency, a thick multi-layer coupling waveguide with a gradually increased refractive index from the bottom layer to the absorption layer is utilized. The refractive index profile facilitates the upward transmission of incident light into the absorption region, thereby enhancing the evanescent coupling efficiency. Meanwhile, the coupling waveguide, with a total thickness of 1.75 µm, expands the mode field diameter, thereby reducing the input coupling loss. Additionally, the top layer of the coupling waveguide also serves as the drift layer. This configuration facilitates efficient light absorption within a short PD length, thus ensuring ultrawide bandwidth and high O/E conversion efficiency simultaneously. Without an additional spot size coupler or anti-reflection coating, the measured responsivity is as high as 0.38 A/W for the PD with an active area of 5 × 6 µm2. Meanwhile, an ultrawide 3-dB bandwidth of 153 GHz has been demonstrated.

Targeting tumour surface collage with hydrogel probe: a new strategy to enhance intraoperative imaging sensitivity and stability of bladder cancer.

PURPOSE: The incomplete resection of non-muscle invasive bladder cancer (NMIBC) augments the risk of disease recurrence. Imaging-guided surgery by molecular probes represents a pivotal strategy for mitigating postoperative recurrence. Traditional optical molecular probes, primarily composed of antibodies/peptides targeting tumour cells and fluorescent groups, are challenged by the high heterogeneity of NMIBC cells, leading to inadequate probe sensitivity. We have developed a collagen-adhesive probe (CA-P) to target the collagen within the tumour microenvironment, aiming to address the issue of insufficient imaging sensitivity. METHODS: The distribution characteristics of collagen in animal bladder cancer models and human bladder cancer tissues were explored. The synthesis and properties of CA-P were validated. In animal models, the imaging performance of CA-P was tested and compared with our previously reported near-infrared probe PLSWT7-DMI. The clinical translational potential of CA-P was assessed using human ex vivo bladder tissues. RESULTS: The distribution of collagen on the surface of tumour cells is distinct from its expression in normal urothelium. In vitro studies have demonstrated the ability of the CA-P to undergo a "sol-gel" transition upon interaction with collagen. In animal models and human ex vivo bladder specimens, CA-P exhibits superior imaging performance compared to PLSWT7-DMI. The sensitivity of this probe is 94.1%, with a specificity of 81%. CONCLUSION: CA-P demonstrates the capability to overcome tumour cell heterogeneity and enhance imaging sensitivity, exhibiting favorable imaging outcomes in preclinical models. These findings provide a theoretical basis for the application of CA-P in intraoperative navigation for NMIBC.

sTREM2 in the prognostic evaluation of acute lung injury after cardiac surgery in infants.

BACKGROUND: Previous studies have shown that TREM2 plays a protective role in acute lung injury (ALI). This prospective study aimed to investigate the role of sTREM2 as a forecasting factor for ALI in infants after pediatric cardiac surgery undergoing cardiopulmonary bypass (CPB). METHODS: Seventy-five consecutive patients younger than 1 year who underwent cardiac surgery were enrolled in this study. Sixty-one fulfilled the inclusion criteria and had been divided into ALI and non-ALI groups. Children's demographic characteristics and clinical data were collected. Perioperative sTREM2 levels were analyzed at five timepoints. RESULTS: In this study, children in the ALI group were younger, lighter, with higher RACHS-1 scores and underwent significantly longer CPB time. Post-CPB ALI had an impact on clinical outcomes, which contributed to a longer duration of mechanical ventilation, ICU and hospital stay than non-ALI group. Significant differences were manifested off-CPB, 1 h/6 h after CPB, and day 1 after surgery between the two groups. Binary logistic models revealed that off-CPB sTREM2 was significantly associated with the incidence of post-CPB ALI after adjustment. ROC analysis showed that the AUC of off-CPB sTREM2 level was 0.791, and the optimal cutoff value was 788.6 pg/ml. CONCLUSIONS: The off-CPB sTREM2 level was an independent prognostic factor for post-CPB ALI in infants. IMPACT: Plasma sTREM2 works together with downstream TREM2 to regulate inflammation response by binding the receptor to other cells. Previous studies have shown that TREM2 plays a protective role in ischemia-reperfusion and has anti-inflammatory effects on acute lung injury (ALI). This study analyzed the risk factors of post-cardiopulmonary bypass (CPB) ALI. We found that weight and off-CPB sTREM2 level were independent prognostic factors for post-CPB ALI. Plasma sTREM2 may serve as an early biomarker in the prognostic evaluation of acute lung injury after cardiac surgery in infants.

A deep learning-powered diagnostic model for acute pancreatitis.

BACKGROUND: Acute pancreatitis is one of the most common diseases requiring emergency surgery. Rapid and accurate recognition of acute pancreatitis can help improve clinical outcomes. This study aimed to develop a deep learning-powered diagnostic model for acute pancreatitis. MATERIALS AND METHODS: In this investigation, we enrolled a cohort of 190 patients with acute pancreatitis who were admitted to Sichuan Provincial People's Hospital between January 2020 and December 2021. Abdominal computed tomography (CT) scans were obtained from both patients with acute pancreatitis and healthy individuals. Our model was constructed using two modules: (1) the acute pancreatitis classifier module; (2) the pancreatitis lesion segmentation module. Each model's performance was assessed based on precision, recall rate, F1-score, Area Under the Curve (AUC), loss rate, frequency-weighted accuracy (fwavacc), and Mean Intersection over Union (MIOU). RESULTS: Upon admission, significant variations were observed between patients with mild and severe acute pancreatitis in inflammatory indexes, liver, and kidney function indicators, as well as coagulation parameters. The acute pancreatitis classifier module exhibited commendable diagnostic efficacy, showing an impressive AUC of 0.993 (95%CI: 0.978-0.999) in the test set (comprising healthy examination patients vs. those with acute pancreatitis, P < 0.001) and an AUC of 0.850 (95%CI: 0.790-0.898) in the external validation set (healthy examination patients vs. patients with acute pancreatitis, P < 0.001). Furthermore, the acute pancreatitis lesion segmentation module demonstrated exceptional performance in the validation set. For pancreas segmentation, peripancreatic inflammatory exudation, peripancreatic effusion, and peripancreatic abscess necrosis, the MIOU values were 86.02 (84.52, 87.20), 61.81 (56.25, 64.83), 57.73 (49.90, 68.23), and 66.36 (55.08, 72.12), respectively. These findings underscore the robustness and reliability of the developed models in accurately characterizing and assessing acute pancreatitis. CONCLUSION: The diagnostic model for acute pancreatitis, driven by deep learning, exhibits excellent efficacy in accurately evaluating the severity of the condition. TRIAL REGISTRATION: This is a retrospective study.

Optimize individualized energy delivery for septic patients using predictive deep learning models.

BACKGROUND AND OBJECTIVES: We aim to establish deep learning models to optimize the individualized energy delivery for septic patients. METHODS AND STUDY DESIGN: We conducted a study of adult septic patients in ICU, collecting 47 indicators for 14 days. We filtered out nutrition-related features and divided the data into datasets according to the three metabolic phases proposed by ESPEN: acute early, acute late, and rehabilitation. We then established optimal energy target models for each phase using deep learning and conducted external validation. RESULTS: A total of 179 patients in training dataset and 98 patients in external validation dataset were included in this study, and total data size was 3115 elements. The age, weight and BMI of the patients were 63.05 (95%CI 60.42-65.68), 61.31(95%CI 59.62-63.00) and 22.70 (95%CI 22.21-23.19), respectively. And 26.0% (72) of the patients were female. The models indicated that the optimal energy targets in the three phases were 900kcal/d, 2300kcal/d, and 2000kcal/d, respectively. Excessive energy intake increased mortality rapidly in the early period of the acute phase. Insufficient energy in the late period of the acute phase significantly raised the mortality as well. For the rehabilitation phase, too much or too little energy delivery were both associated with elevated death risk. CONCLUSIONS: Our study established time-series prediction models for septic patients to optimize energy delivery in the ICU. We recommended permissive underfeeding only in the early acute phase. Later, increased energy intake may improve survival and settle energy debts caused by underfeeding.

EMDL_m6Am: identifying N6,2'-O-dimethyladenosine sites based on stacking ensemble deep learning.

BACKGROUND: N6, 2'-O-dimethyladenosine (m6Am) is an abundant RNA methylation modification on vertebrate mRNAs and is present in the transcription initiation region of mRNAs. It has recently been experimentally shown to be associated with several human disorders, including obesity genes, and stomach cancer, among others. As a result, N6,2'-O-dimethyladenosine (m6Am) site will play a crucial part in the regulation of RNA if it can be correctly identified. RESULTS: This study proposes a novel deep learning-based m6Am prediction model, EMDL_m6Am, which employs one-hot encoding to expressthe feature map of the RNA sequence and recognizes m6Am sites by integrating different CNN models via stacking. Including DenseNet, Inflated Convolutional Network (DCNN) and Deep Multiscale Residual Network (MSRN), the sensitivity (Sn), specificity (Sp), accuracy (ACC), Mathews correlation coefficient (MCC) and area under the curve (AUC) of our model on the training data set reach 86.62%, 88.94%, 87.78%, 0.7590 and 0.8778, respectively, and the prediction results on the independent test set are as high as 82.25%, 79.72%, 80.98%, 0.6199, and 0.8211. CONCLUSIONS: In conclusion, the experimental results demonstrated that EMDL_m6Am greatly improved the predictive performance of the m6Am sites and could provide a valuable reference for the next part of the study. The source code and experimental data are available at: https://github.com/13133989982/EMDL-m6Am .

Multicenter Analysis of Attrition from the Pediatric Tuberculosis Infection Care Cascade in Boston.

OBJECTIVE: To characterize losses from the pediatric tuberculosis (TB) infection care cascade to identify ways to improve TB infection care delivery. STUDY DESIGN: We conducted a retrospective cohort study of children (age <18 years) screened for TB within 2 Boston-area health systems between January 2017 and May 2019. Patients who received a tuberculin skin test (TST) and/or an interferon gamma release assay (IGRA) were included. RESULTS: We included 13 353 tests among 11 622 patients; 93.9% of the tests were completed. Of 199 patients with positive tests for whom TB infection evaluation was clinically appropriate, 59.3% completed treatment or were recommended to not start treatment. Age 12-17 years (vs < 5 years; aOR 1.59; 95% CI, 1.32-1.92), non-English/non-Spanish language preference (vs English; aOR, 1.34; 95% CI, 1.02-1.76), and receipt of an IGRA (vs TST, aOR, 30.82; 95% CI, 21.92-43.34) were associated with increased odds of testing completion. Odds of testing completion decreased as census tract social vulnerability index quartile increased (ie, social vulnerability worsened; most vulnerable quartile vs least vulnerable quartile, aOR, 0.77; 95% CI, 0.60-0.99). Odds of completing treatment after starting treatment were higher in females (vs males; aOR, 2.35; 95% CI, 1.14-4.85) and were lower in patients starting treatment in a primary care clinic (vs TB/infectious diseases clinic; aOR, 0.44; 95% CI, 0.27-0.71). CONCLUSIONS: Among children with a high proportion of negative TB infection tests, completion of testing was high, but completion of evaluation and treatment was moderate. Transitions toward IGRA testing will improve testing completion; interventions addressing social determinants of health are important to improve treatment completion.

JNK1 activated pRb/E2F1 and inhibited p53/p21 signaling pathway is involved in hydroquinone-induced pathway malignant transformation of TK6 cells by accelerating the cell cycle progression.

Hydroquinone (HQ) is an important metabolites of benzene in the body, and it has been found to result in cellular DNA damage, mutation, cell cycle imbalance, and malignant transformation. The JNK1 signaling pathway plays an important role in DNA damage repair. In this study, we focused on whether the JNK1 signaling pathway is involved in the HQ-induced cell cycle abnormalities and the underlying mechanism. The results showed that HQ induced abnormal progression of the cell cycle and initiated the JNK1 signaling pathway. We further confirmed that JNK1 suppression decelerated the cell cycle progression through inhibiting pRb/E2F1 signaling pathway and triggering p53/p21 pathway. Therefore, we concluded that JNK1 might be involved in HQ-induced malignant transformation associated with activating pRb/E2F1 and inhibiting p53/p21 signaling pathway which resulting in accelerating the cell cycle progression.

Nucleated Red Blood Cells Are Predictive of In-Hospital Mortality for Pediatric Patients.

PURPOSE: We sought to establish whether nucleated red blood cells (NRBCs) are predictive of disposition, morbidity, and mortality for pediatric patients presenting to the emergency department (ED). METHODS: A single-center retrospective cohort study examining all ED encounters from patients aged younger than 19 years between January 2016 and March 2020, during which a complete blood count was obtained. Univariate analysis and multivariable logistic regression were used to test the presence of NRBCs as an independent predictor of patient-related outcomes. RESULTS: The prevalence of NRBCs was 8.9% (4195/46,991 patient encounters). Patient with NRBCs were younger (median age 4.58 vs 8.23 years; P < 0.001). Those with NRBCs had higher rates of in-hospital mortality (30/2465 [1.22%] vs 65/21,741 [0.30%]; P < 0.001), sepsis (19% vs 12%; P < 0.001), shock (7% vs 4%; P < 0.001), and cardiopulmonary resuscitation (CPR) (0.62% vs 0.09%; P < 0.001). They were more likely to be admitted (59% vs 51%; P < 0.001), have longer median hospital length of stay {1.3 (interquartile range [IQR], 0.22-4.14) vs 0.8 days (IQR, 0.23-2.64); P < 0.001}, and median intensive care unit (ICU) length of stay (3.9 [IQR, 1.87-8.72] vs 2.6 days [IQR, 1.27-5.83]; P < 0.001). Multivariable regression revealed presence of NRBCs as an independent predictor for in-hospital mortality (adjusted odds ratio [aOR], 2.21; 95% confidence interval [CI], 1.38-3.53; P < 0.001), ICU admission (aOR, 1.30; 95% CI, 1.11-1.51; P < 0.001), CPR (aOR, 3.83; 95% CI, 2.33-6.30; P < 0.001), and 30-day return to the ED (aOR, 1.15; 95% CI, 1.15-1.26; P < 0.001). CONCLUSIONS: The presence of NRBCs is an independent predictor for mortality, including in-hospital mortality, ICU admission, CPR, and readmission within 30 days for children presenting to the ED.

ACP-BC: A Model for Accurate Identification of Anticancer Peptides Based on Fusion Features of Bidirectional Long Short-Term Memory and Chemically Derived Information.

Anticancer peptides (ACPs) have been proven to possess potent anticancer activities. Although computational methods have emerged for rapid ACPs identification, their accuracy still needs improvement. In this study, we propose a model called ACP-BC, a three-channel end-to-end model that utilizes various combinations of data augmentation techniques. In the first channel, features are extracted from the raw sequence using a bidirectional long short-term memory network. In the second channel, the entire sequence is converted into a chemical molecular formula, which is further simplified using Simplified Molecular Input Line Entry System notation to obtain deep abstract features through a bidirectional encoder representation transformer (BERT). In the third channel, we manually selected four effective features according to dipeptide composition, binary profile feature, k-mer sparse matrix, and pseudo amino acid composition. Notably, the application of chemical BERT in predicting ACPs is novel and successfully integrated into our model. To validate the performance of our model, we selected two benchmark datasets, ACPs740 and ACPs240. ACP-BC achieved prediction accuracy with 87% and 90% on these two datasets, respectively, representing improvements of 1.3% and 7% compared to existing state-of-the-art methods on these datasets. Therefore, systematic comparative experiments have shown that the ACP-BC can effectively identify anticancer peptides.

ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information.

Cancer is one of the most dangerous threats to human health. One of the issues is drug resistance action, which leads to side effects after drug treatment. Numerous therapies have endeavored to relieve the drug resistance action. Recently, anticancer peptides could be a novel and promising anticancer candidate, which can inhibit tumor cell proliferation, migration, and suppress the formation of tumor blood vessels, with fewer side effects. However, it is costly, laborious and time consuming to identify anticancer peptides by biological experiments with a high throughput. Therefore, accurately identifying anti-cancer peptides becomes a key and indispensable step for anticancer peptides therapy. Although some existing computer methods have been developed to predict anticancer peptides, the accuracy still needs to be improved. Thus, in this study, we propose a deep learning-based model, called ACPNet, to distinguish anticancer peptides from non-anticancer peptides (non-ACPs). ACPNet employs three different types of peptide sequence information, peptide physicochemical properties and auto-encoding features linking the training process. ACPNet is a hybrid deep learning network, which fuses fully connected networks and recurrent neural networks. The comparison with other existing methods on ACPs82 datasets shows that ACPNet not only achieves the improvement of 1.2% Accuracy, 2.0% F1-score, and 7.2% Recall, but also gets balanced performance on the Matthews correlation coefficient. Meanwhile, ACPNet is verified on an independent dataset, with 20 proven anticancer peptides, and only one anticancer peptide is predicted as non-ACPs. The comparison and independent validation experiment indicate that ACPNet can accurately distinguish anticancer peptides from non-ACPs.

Nitric Oxide-Dependent Electron Transport Chain Inhibition by the Cytochrome bc1 Inhibitor and Pretomanid Combination Kills Mycobacterium tuberculosis.

Mycobacterium tuberculosis, the causative agent of human tuberculosis, harbors a branched electron transport chain, preventing the bactericidal action of cytochrome bc1 inhibitors (e.g., TB47). Here, we investigated, using luminescent mycobacterial strains, the in vitro combination activity of cytochrome bc1 inhibitors and nitric oxide (NO) donors including pretomanid (PMD) and explored the mechanisms of combination activity. The TB47 and PMD combination quickly abolished the light emission of luminescent bacilli, as was the case for the combination of TB47 and aurachin D, a putative cytochrome bd inhibitor. The TB47 and PMD combination inhibited M. tuberculosis oxygen consumption, decreased ATP levels, and had a delayed bactericidal effect. The NO scavenger carboxy-PTIO prevented the bactericidal activity of the drug combination, suggesting the requirement for NO. In addition, cytochrome bc1 inhibitors were largely bactericidal when administered with DETA NONOate, another NO donor. Proteomic analysis revealed that the cotreated bacilli had a compromised expression of the dormancy regulon proteins, PE/PPE proteins, and proteins required for the biosynthesis of several cofactors, including mycofactocin. Some of these proteomic changes, e.g., the impaired dormancy regulon induction, were attributed to PMD. In conclusion, combination of cytochrome bc1 inhibitors with PMD inhibited M. tuberculosis respiration and killed the bacilli. The activity of cytochrome bc1 inhibitors can be greatly enhanced by NO donors. Monitoring of luminescence may be further exploited to screen cytochrome bd inhibitors.

Current status of official WeChat accounts for public health education.

BACKGROUND: To investigate the current use status of official WeChat accounts for the Centers for Disease Control and Prevention in public health education and relevant factors that can impact the effectiveness of message delivery. METHODS: A retrospective survey was conducted to evaluate the effectiveness of official WeChat accounts. About 531 official WeChat accounts and 50 939 articles were analyzed using a cluster sampling survey design. The Kruskal-Wallis test and multivariate logistic regression were used to explore factors associated with the usefulness of the number of views and "Likes" of the articles. RESULTS: The study identified a total of 531 public WeChat accounts, including 19 province-level accounts, 179 municipal-level accounts and 333 county-level accounts. In the univariable analysis, the administrative level of the account, article order, time segment, article originality and thematic category were associated with the number of views and "Likes." Province-level accounts, first articles, the 5:00-6:00 time segment, original articles and theme 3 (emergencies) had higher numbers of views and "Likes" than the others (P < 0.05). CONCLUSIONS: Promoting health education through Official WeChat account is an effective, sustainable and feasible strategy. Potential indicators of the impact of public health education suggest that administrators should effectively use official WeChat accounts for public health education.

Comprehensive Analysis of Protein N-Terminome by Guanidination of Terminal Amines.

Protein N-termini and their modifications not only represent different protein isoforms but also relate to the functional annotation and proteolytic activities. Currently, negative selection methods, such as terminal amine isotopic labeling of substrates (TAILS), are the most popular strategy to analyze the protein N-terminome, in which dimethylation or acetylation modification is commonly used to block the free amines of proteome samples. However, after tryptic digestion, the generated long peptides, caused by the missing cleavage of blocked lysine, could hardly be identified by MS, which hindered the deep-coverage analysis of N-terminome. Herein, to solve this problem, we developed an approach, named terminal amine guanidination of substrates (TAGS). 1H-Pyrazole-1-carboxamidine was used to effectively guanidinate lysine ε-amines and N-terminal α-amines, followed by tryptic digestion to generate N-terminal peptides without free amines and internal peptides with free amines. Then, the internal peptides with free amines were removed by hyperbranched polyglycerol-aldehyde polymers (HPG-ALDs) to achieve the negative enrichment of N-terminome. By TAGS, not only the cleavage rate of blocked lysine could be improved, but also the ionization efficiency of tryptic peptides was increased. In comparison, 1814 and 1620 protein N-termini were, respectively, identified by TAGS and TAILS in Saccharomyces cerevisiae (S. cerevisiae). Among them, 1012 N-termini were uniquely identified in TAGS. Furthermore, by the combination of TAGS and the stable isotope labeling with amino acids in cell culture (SILAC)/label-free quantitative method, we not only identified the known N-terminal cleavage fragment of gasdermin D but also identified some new cleavage sites during Val-boroPro-induced pyroptosis. All these results demonstrated that our developed approach, TAGS, might be of great promise for the comprehensive analysis of N-terminome and beneficial for promoting the identification of protein isoforms and studying in-depth the proteolytic activity of proteins.

Proteomic analysis of bovine mammary epithelial cells after in vitro incubation with S. agalactiae: potential biomarkers.

Streptococcus agalactiae is one of the causative agents of subclinical mastitis, a common disease of dairy cows that causes great economic losses in the industry worldwide. It is thought that pathology is mainly due to inflammatory damage of bovine mammary epithelial cells (bMECs); however, the mechanism by which S. agalactiae damages the bMECs is not clear. The aim of this study was to evaluate the inflammatory effects of S. agalactiae on bMECs and the resulting changes in protein profiles. The bMECs were incubated with S. agalactiae for different times and assayed for cell viability by MTT assay, apoptosis by annexin V and propidium iodide dual staining, and morphological and ultrastructural changes by scanning and transmission electron microscopy. Quantitative real-time PCR was used to determine the effect of S. agalactiae on expression of mRNA of inflammatory factors in bMECs and protein levels were quantitated by liquid chromatography/mass spectrometry. Exposure to S. agalactiae significantly decreased the cell viability and triggered apoptosis, as well as up-regulating TNF-α, IL-1ß and IL-6 mRNA, and inhibiting IL-8 expression. S. agalactiae also induced morphological and ultrastructural changes. Furthermore, we identified 325 up-regulated and 704 down-regulated proteins in the treated vs control group. All significant differentially expressed proteins (DSEPs) were classified into three major areas by function: biological processes, cellular components and molecular functions. These differentially expressed proteins included enzymes and proteins associated with various metabolic processes and cellular immunity. Pathway enrichment analysis showed that eight down-regulated signaling pathways were significantly enriched. Exposure to even subclinical levels of S. agalactiae can lead to inflammation and bMEC damage. Our data suggest some possible molecular mechanisms for the harmful effects of subclinical mastitis in dairy cows.

Cluster analysis of differences in medical economic burden among residents of different economic levels in Guangdong Province, China.

BACKGROUND: This study compares and analyzes the differences of residents' medical economic burden in different economic levels, explores the factors for improving the equity of health services in Guangdong, China. METHODS: Cluster analysis was carried out in 20 cities of Guangdong Province by taking 7 key factors on the equity of health services as indicators. Seven key factors were collected from Guangdong Statistical Yearbook 2017 and the Sixth National Population Census. R-type clustering was used to reduce the dimensionality of 7 candidate variables through similarity index. Q-type clustering was used to classify 20 cities in Guangdong Province. RESULTS: The cluster analysis divided Guangdong Province into three regions with different medical economic burden. The greater the proportion of the elderly over 65 years old, the greater the proportion of health care expenditure to per capita consumer expenditure of residents, and the heavier the medical economic burden. On average, 10.75% of the general budget expenditure of each city in Guangdong Province is spent on health care. CONCLUSIONS: The lower per capita GDP, the higher proportion of the elderly over 65 years old and the lack of medical technicians are risk factors for the heavier medical burden of the residents and the fairness of health services. While increasing the health expenditure, the government needs to further complete the reform of the medical and health system, improve the efficiency of the medical system and curb the rapid rise of absolute health expenditures of individuals, which can reduce the economic burden of residents' medical care.

Xanthatin inhibits STAT3 and NF-κB signalling by covalently binding to JAK and IKK kinases.

Aberrant activation of the signal transducer and activator of transcription 3 (STAT3) and the nuclear factor-κB (NF-κB) signalling pathways is associated with the development of cancer and inflammatory diseases. JAKs and IKKs are the key regulators in the STAT3 and NF-κB signalling respectively. Therefore, the two families of kinases have been the major targets for developing drugs to regulate the two signalling pathways. Here, we report a natural compound xanthatin from the traditional Chinese medicinal herb Xanthium L. as a potent inhibitor of both STAT3 and NF-κB signalling pathways. Our data demonstrated that xanthatin was a covalent inhibitor and its activities depended on its α-methylene-γ-butyrolactone group. It preferentially interacted with the Cys243 of JAK2 and the Cys412 and Cys464 of IKKß to inactivate their activities. In doing so, xanthatin preferentially inhibited the growth of cancer cell lines that have constitutively activated STAT3 and p65. These data suggest that xanthatin may be a promising anticancer and anti-inflammation drug candidate.

Metabolic dynamics in critically injured patients: a prospective cohort study integrated with 1H NMR metabolomics.

BACKGROUND AND OBJECTIVES: By combining the techniques of metabolomics and computational biology, this research aims to explore the mechanism of metabolic dynamics in critically injured patients and develop a new early warning method for mortality. METHODS AND STUDY DESIGN: A prospective cohort study was conducted, group plasma samples of critically injured patients were collected for 1H-NMR metabolomics analysis. The data was processed with partial least squares regression, to explore the role of enzyme-gene network regulatory mechanism in critically injured metabolic network regulation and to build a quantitative prediction model for early warning of fast death. RESULTS: In total, 60 patients were enrolled. There were significant differences in plasma metabolome between the surviving patients and the deceased ones. Compared to the surviving patients, 112 enzymes and genes regulating the 6 key metabolic marker disturbances of neopterin, corticosterone, 3-methylhistidine, homocysteine, Serine, tyrosine, prostaglandin E2, tryptophan, testosterone and estriol, were observed in the plasmas of deceased ones. Among patients of different injury stages, there were significant differences in plasma metabolome. Progressing from T0 to T50 stages of injury, increased levels of neopterin, corticosterone, prostaglandin E2, tryptophan and testosterone, together with decreased levels of homocysteine, and estriol, were observed. Eventually, the quantitative prediction model of death warning was established. Cross-validation results showed that the predictive effect was good (RMSE=0.18408, R2=0.87 p=0.036). CONCLUSIONS: Metabolomics approaches can be used to quantify the metabolic dynamics of patients with critically injuries and to predict death of critically injured patients by plasma 1H-NMR metabolomics.