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Abstract Background The effectiveness of the quadratus lumborum block (QLB) for postoperative pain management depends on the injection pathway used. There is limited research on the block area produced by intramuscular injection of local anesthesia in the quadratus lumborum muscle. This study aimed to determine the cutaneous sensory blockade area produced by an intramuscular quadratus lumborum block (QLBi) at the L2 level. Methods Twenty patients aged 18–60 years with ASA grade I-II and a BMI of 18–30 kg/m2 who were scheduled for open inguinal hernia repair with mesh underwent ultrasound-guided QLBi injection of 20 ml of 0.5% ropivacaine. The cutaneous sensory blockade area was measured by applying a cold stimulus 1 h after the block and then measured every hour after surgery until the sensation returned to normal. The duration of a blockade is defined as the time it takes for all affected areas to fully regain normal sensation following a blockade. Pain scores (numeric rating scale, NRS) were recorded at 2, 4, 8, 12, and 24 h after surgery. Adverse reactions to QLBi were recorded 24 h after surgery. Results All 20 patients had reduced or lost cold sensation areas. The greatest extent of cold sensation reduction occurred at T7 (10%), and the least amount of cold sensation reduction occurred at L3 (10%). The block level covered T8 (20%), T9 (30%), T10 (45%), T11 (90%), T12 (95%), L1 (100%), and L2 (15%). Eighteen patients experienced areas of sensory loss, with the highest range at T11 and the lowest at L2. The duration of the blockade was 8.9 ± 3.8 h, with a maximum of 24 h and a minimum of 5 h. One patient experienced quadriceps weakness after surgery. Conclusion Quadratus lumborum block of intramuscular pathway can produce effective cutaneous sensory blockade, which can be used for postoperative analgesia of indirect inguinal hernia operation, and may also be beneficial to analgesia of other lower abdominal operations. However, the best method needs further confirmation to determine specific anesthesia methods for various operations. Chinese clinical trial registry June 2, 2018; ChiCTR1800016457.

Summary: Background: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. Methods: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). Findings: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. Interpretation: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. Funding: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438.

Two‐dimensional (2D) intrinsic vacancy semiconductors possess great application prospects in optoelectronics fields, originating from abundant intrinsic vacancy structures and exceptional physical properties. Understanding the structure‐activity relationship between vacancies and physical/photoelectric properties is significant for building advanced photoelectric devices. However, limited by the increasing instability of 2D structure induced by intrinsic vacancies, the precise synthesis of 2D intrinsic vacancy semiconductors faces great challenges. Here, high‐quality 2D intrinsic vacancy semiconductor α‐Ga2Se3 is synthesized via low‐pressure and space‐confined physical vapor deposition. The vacancy structures induce an intermediate energy level and a super‐bandgap photoluminescence (PL) peak, which are verified by temperature‐dependent PL spectra. Furthermore, the vacancy energy level favorably endows 2D α‐Ga2Se3‐based photodetector with a UV‐vis‐NIR super‐bandgap photoresponse, excellent ultraviolet detection ability (photoresponsivity (41.86 A W−1), epitaxial quantum efficiency (1.42 × 104%), and detectivity (6.69 × 1011 Jones) @365 nm), fast photoresponse (20 ms), and superior photocurrent‐light power‐fitting factors. In addition, 2D α‐Ga2Se3‐based phototransistor displays p‐type transport behavior with a responsivity of 105 A W−1@365 nm at a gate voltage of −40 V. This work suggests a bright future of 2D intrinsic vacancy semiconductors in tailoring physical properties and enabling sophisticated device functionality.

Introduction: Cardiac arrest or arrhythmia caused by bupivacaine may be refractory to treatment. Apelin has been reported to directly increase the frequency of spontaneous activation and the propagation of action potentials, ultimately promoting cardiac contractility. This study aimed to investigate the effects of apelin-13 in reversing cardiac suppression induced by bupivacaine in rats. Methods: A rat model of cardiac suppression was established by a 3-min continuous intravenous infusion of bupivacaine at the rate of 5 mg.kg−1.min−1, and serial doses of apelin-13 (50, 150 and 450 μg.kg−1) were administered to rescue cardiac suppression to identify its dose-response relationship. We used F13A, an inhibitor of Angiotensin Receptor-Like 1 (APJ), and Protein Kinase C (PKC) inhibitor chelerythrine to reverse the effects of apelin-13. Moreover, the protein expressions of PKC, Nav1.5, and APJ in ventricular tissues were measured using Western blotting and immunofluorescence assay. Results: Compared to the control rats, the rats subjected to continuous intravenous administration of bupivacaine had impaired hemodynamic stability. Administration of apelin-13, in a dose-dependent manner, significantly improved hemodynamic parameters in rats with bupivacaine-induced cardiac suppression (p < 0.05), and apelin-13 treatment also significantly upregulated the protein expressions of p-PKC and Nav1.5 (p < 0.05), these effects were abrogated by F13A or chelerythrine (p < 0.05). Conclusion: Exogenous apelin-13, at least in part, activates the PKC signaling pathway through the apelin/APJ system to improve cardiac function in a rat model of bupivacaine-induced cardiac suppression.

With the construction and development of the “the Belt and Road”, the international community has an increasing demand for high-quality technical and skilled talents who both understand Chinese and have an international perspective. Exploring a Chinese curriculum system suitable for the development of international students is conducive to promoting the international development of vocational education, improving the quality of international student training, providing talent support for Chinese and foreign enterprises, and promoting the “going out” of Chinese enterprises and international production capacity cooperation. The training of international students in higher vocational colleges starts late, has little experience, lacks theoretical innovation, and lacks the development characteristics of vocational education. The teaching quality evaluation of international Chinese course in higher vocational colleges is multiple-attributes group decision-making (MAGDM). Recently, the TODIM and EDAS technique has been employed to manage MAGDM. The probabilistic hesitant fuzzy sets (PHFSs) are employed as a useful tool for depicting uncertain information during the teaching quality evaluation of international Chinese course in higher vocational colleges. In this paper, the probabilistic hesitant fuzzy TODIM-EDAS (PHF-TODIM-EDAS) technique is built to manage the MAGDM under PHFSs. Finally, the numerical example for teaching quality evaluation of international Chinese course in higher vocational colleges is employed to show the PHF-TODIM-EDAS technique. The main contribution of this paper is outlined: (1) the TODIM technique based on EDAS technique has been extended to PHFSs based on CRITIC technique; (2) the CRITIC technique is employed to derive weight numbers under PHFSs. (3) the PHF-TODIM-EDAS technique is founded to manage the MAGDM under PHFSs; (4) a numerical case study for teaching quality evaluation of international Chinese course in higher vocational colleges and some comparative analysis is supplied to validate the proposed PHF-TODIM-EDAS technique.

Aiming at the problem that surface EMG signals (sEMG) are inaccurate in estimating muscle fatigue due to their non-stationary, nonlinear, self-similarity and other complex characteristics, a method for analyzing muscle fatigue characteristics based on sEMG signal multifractal downtrend moving average method (MFDMA) was proposed. Firstly, the MFDMA method was used to analyze the nonlinear dynamics of the collected sEMG signal, shuffle signal and Gaussian white noise signal; secondly, MFDMA method was used to calculate the multifractal spectrum width, Hurst exponent variation difference, probability measure value and peak singularity exponent of sEMG signal; finally, the significant difference in multifractal characteristics between muscle fatigue and non-fatigue state was analyzed by t-test. The results show that MFDMA method can describe the multifractal behavior of sEMG signal, and the multifractal characteristics such as spectral width have significant differences between muscle fatigue and non-fatigue state. The proposed method can reliably characterize exercise-induced muscle fatigue, and provide some feature reference for muscle fatigue recognition model and rehabilitation medicine research.

Degenerative rotator cuff tendinopathy (RCT) is a chronic tendon disease caused by degeneration and inflammation, which often affects the elderly population. Mesenchymal stem cell senescence is generally recognized as an important pathophysiological mechanism in many age-related skeletal diseases. Herein, we collected human tendon-derived stem/progenitor cells (TSPCs) from degenerative supraspinatus tendons and found that TSPC senescence is closely related to RCT. We further identified that nuclear factor κB (NF-κB) pathway activation is involved in age-related inflammation (inflamm-aging) of degenerative RCT. Moreover, whole genome RNA sequencing revealed that in vitro inhibition of the I kappa B kinase β (IKKβ)/NF-κB signaling pathway could reverse the aged TSPC phenotype with decreased TSPC senescence and increased tenogenic potential. To achieve effective in vivo inhibition of IKKβ/NF-κB signaling, we fabricated IKKβ small interfering RNA (siRNA)-loaded gold nanoclusters (AuNC-siRNA) for efficient and convenient intra-articular delivery of IKKβ siRNA. We found that AuNC-siRNA prevented inflamm-aging-induced TSPC senescence and dysfunction in a degenerative RCT aged rat model. Together, these data show that inflamm-aging causes degenerative RCT through inducing TSPC senescence, which can be reversed by blocking the IKKβ/NF-κB pathway in vivo. Thus, our study provides a promising therapeutic strategy for degenerative RCT via intra-articular delivery of IKKβ siRNA using AuNCs.

Background: With cancer as one of the leading causes of death worldwide, accurate primary tumor type prediction is critical in identifying genetic factors that can inhibit or slow tumor progression. There have been efforts to categorize primary tumor types with gene expression data using machine learning, and more recently with deep learning, in the last several years. Methods In this paper, we developed four 1-dimensional (1D) Convolutional Neural Network (CNN) models to classify RNA-seq count data as one of 17 highly represented primary tumor types or 32 primary tumor types regardless of imbalanced representation. Additionally, we adapted the models to take as input either all Ensembl genes (60,483) or protein coding genes only (19,758). Unlike previous work, we avoided selection bias by not filtering genes based on expression values. RNA-seq count data expressed as FPKM-UQ of 9,025 and 10,940 samples from The Cancer Genome Atlas (TCGA) were downloaded from the Genomic Data Commons (GDC) corresponding to 17 and 32 primary tumor types respectively for training and validating the models. Results: All 4 1D-CNN models had an overall accuracy of 94.7% to 97.6% on the test dataset. Further evaluation indicates that the models with protein coding genes only as features performed with better accuracy compared to the models with all Ensembl genes for both 17 and 32 primary tumor types. For all models, the accuracy by primary tumor type was above 80% for most primary tumor types. Conclusions: We packaged all 4 models as a Python-based deep learning classification tool called TULIP ( TU mor C L ass I fication P redictor) for performing quality control on primary tumor samples and characterizing cancer samples of unknown tumor type. Further optimization of the models is needed to improve the accuracy of certain primary tumor types.

Abstract Background Horizontal gene transfer is the main source of adaptability for bacteria, through which genes are obtained from different sources including bacteria, archaea, viruses, and eukaryotes. This process promotes the rapid spread of genetic information across lineages, typically in the form of clusters of genes referred to as genomic islands (GIs). Different types of GIs exist, and are often classified by the content of their cargo genes or their means of integration and mobility. While various computational methods have been devised to detect different types of GIs, no single method is capable of detecting all types. Results We propose a method, which we call Shutter Island, that uses a deep learning model (Inception V3, widely used in computer vision) to detect genomic islands. The intrinsic value of deep learning methods lies in their ability to generalize. Via a technique called transfer learning, the model is pre-trained on a large generic dataset and then re-trained on images that we generate to represent genomic fragments. We demonstrate that this image-based approach generalizes better than the existing tools. Conclusions We used a deep neural network and an image-based approach to detect the most out of the correct GI predictions made by other tools, in addition to making novel GI predictions. The fact that the deep neural network was re-trained on only a limited number of GI datasets and then successfully generalized indicates that this approach could be applied to other problems in the field where data is still lacking or hard to curate.

Abstract Background Motivated by the size and availability of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating drug response data, a common question is whether the generalization performance of existing prediction models can be further improved with more training data. Methods We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four cell line drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these models. Results The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, thus suggesting that the actual shape of these curves depends on the unique pair of an ML model and a dataset. The multi-input NN (mNN), in which gene expressions of cancer cells and molecular drug descriptors are input into separate subnetworks, outperforms a single-input NN (sNN), where the cell and drug features are concatenated for the input layer. In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training set sizes for two of the tested datasets, whereas the mNN consistently performs better at the higher range of training sizes. Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs. These observations demonstrate the benefit of using learning curves to evaluate prediction models, providing a broader perspective on the overall data scaling characteristics. Conclusions A fitted power law learning curve provides a forward-looking metric for analyzing prediction performance and can serve as a co-design tool to guide experimental biologists and computational scientists in the design of future experiments in prospective research studies.

Abstract Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image. The algorithm searches for an optimized assignment by minimizing the difference between the ranking of distances between features and the ranking of distances between their assigned pixels in the image. We apply IGTD to transform gene expression profiles of cancer cell lines (CCLs) and molecular descriptors of drugs into their respective image representations. Compared with existing transformation methods, IGTD generates compact image representations with better preservation of feature neighborhood structure. Evaluated on benchmark drug screening datasets, CNNs trained on IGTD image representations of CCLs and drugs exhibit a better performance of predicting anti-cancer drug response than both CNNs trained on alternative image representations and prediction models trained on the original tabular data.

Abstract Background Bupivacaine blocks many ion channels in the heart muscle, causing severe cardiotoxicity. Small-conductance calcium-activated potassium type 2 channels (SK2 channels) are widely distributed in the heart cells and are involved in relevant physiological functions. However, whether bupivacaine can inhibit SK2 channels is still unclear. This study investigated the effect of bupivacaine on SK2 channels. Methods The SK2 channel gene was transfected into human embryonic kidney 293 cells (HEK-293 cells) with Lipofectamine 2000. The whole-cell patch-clamp technique was used to examine the effect of bupivacaine on SK2 channels. The concentration–response relationship of bupivacaine for inhibiting SK2 currents (0 mV) was fitted to a Hill equation, and the half-maximal inhibitory concentration (IC50) value was determined. Results Bupivacaine inhibited the SK2 channels reversibly in a dose-dependent manner. The IC50 value of bupivacaine, ropivacaine, and lidocaine on SK2 currents was 16.5, 46.5, and 77.8µM, respectively. The degree of SK2 current inhibition by bupivacaine depended on the intracellular concentration of free calcium. Conclusions The results of this study suggested the inhibitory effect of bupivacaine on SK2 channels. Future studies should explore the effects of SK2 on bupivacaine cardiotoxicity.

Abstract Contiguous genes in prokaryotes are often arranged into operons. Detecting operons plays a critical role in inferring gene functionality and regulatory networks. Human experts annotate operons by visually inspecting gene neighborhoods across pileups of related genomes. These visual representations capture the inter-genic distance, strand direction, gene size, functional relatedness, and gene neighborhood conservation, which are the most prominent operon features mentioned in the literature. By studying these features, an expert can then decide whether a genomic region is part of an operon. We propose a deep learning based method named Operon Hunter that uses visual representations of genomic fragments to make operon predictions. Using transfer learning and data augmentation techniques facilitates leveraging the powerful neural networks trained on image datasets by re-training them on a more limited dataset of extensively validated operons. Our method outperforms the previously reported state-of-the-art tools, especially when it comes to predicting full operons and their boundaries accurately. Furthermore, our approach makes it possible to visually identify the features influencing the network’s decisions to be subsequently cross-checked by human experts.

PurposeThis study aimed to compare the efficacy and safety of corneal refractive therapy (CRT) lenses and vision shaping treatment (VST) lenses for myopia control in children.MethodsMedical records of 1,001 children (2,002 eyes) who had been fitted with orthokeratology lenses for over 1.5 years were retrospectively reviewed. We collected the clinical data of four types of orthokeratology (OK) lenses available: one CRT lens (brand: CRT) and three VST lenses (brands: Euclid, Alpha, and Hiline) over 1.5 years. Results were compared and analyzed using a one-way ANOVA and Pearson's chi-square test.ResultsAxial length elongation in the CRT lens group was 0.13 ± 0.02 mm faster than that in the Euclid lens, 0.1 ± 0.02 mm faster in the Alpha lens, and 0.08 ± 0.02 mm faster in the Hiline lens over the 1.5-year period (all P < 0.05). Among the subjects, 37.3% of them using the CRT lens experienced more than 1 D of refractive growth, compared with 20.2–30.8% of subjects wearing the three groups of VST lenses (all P < 0.05). A lower incidence of total adverse events was found with the CRT lenses compared with the VST lenses (P < 0.05), especially corneal staining. No difference was found in axial length elongation, refraction growth, and incidence of adverse events among the three types of VST lenses (all P > 0.05).ConclusionsCompared with the VST lenses, CRT lenses demonstrated a weaker effect on myopia control but with a better safety profile. Different types of VST lenses had similar efficacy and safety in the context of controlling myopia progression.

Abstract Background Dexmedetomidine was proved to mitigate bupivacaine-induced cardiotoxicity but mechanism of this ability is still unclear. This study was designed to investigate the direct effects of dexmedetomidine on cardiotoxicity induced by bupivacaine on Langendorff rat heart preparation and the role of alpha 2 adrenoceptors in this process was explored. Methods Hearts of rat were isolated, mounted on a Langendorff system. Five experimental groups were assessed after 10 min Krebs-Henseleit buffer (KHB) infusions as follow: (1) Group Con, only KHB was perfused; (2) Group Dex, KHB was perfused for 5 min, then dexmedetomidine (10 nmol/L) was added; (3) Group Bupi, KHB was perfused for 25 min, then bupivacaine (50 μmol/L) was added; (4) Group Bupi + Dex, KHB was perfused for 5 min, then the dexmedetomidine (10 nmol/L) was added for 20 min, at last a mixture of KHB + dexmedetomidine + bupivacaine were perfused; (5) Group Bupi + Dex + Yoh, a combination of KHB + yohimbine (alpha 2 adrenoceptor antagonists, 1 μmol/L) was perfusion for 5 min, then dexmedetomidine (10 nmol/L) was added for 20 min, at last a mixture of KHB + yohimbine + dexmedetomidine + bupivacaine was perfused. The experimental perfusion was maintained for 35 min in group Con and group Dex, and the experimental perfusion was sustained until asystole in the other three groups. Results Compared with group Bupi, dexmedetomidine significantly increased the time to first arrhythmia (P

Abstract Background Podocyte plays an important role in maintaining the integrity and function of the glomerular filtration barrier. Various studies reported that forkhead transcription factor (Fox) O1 played a key role in anti-oxidative signaling. This study aimed to investigate the role of Stat1 in high glucose (HG) -induced podocyte injury. Methods Under normal glucose, hypertonic and HG stimulated podocyte conditions, cell counting kit-8 (CCK-8) assay, flow cytometry and western blot and quantitative real-time polymerase chain reaction (qRT-PCR) were respectively carried out to determine cell viability, apoptosis, reactive oxygen species (ROS) production and related genes expressions. We then respectively used silent Stat1, simultaneous silencing Stat1 and FoxO1 and over-expression of FoxO1, to observe whether they/it could reverse the damage of podocytes induced by HG. Results High glucose attenuated cell survival and promoted cell apoptosis in MPC-5 cells at the same time, and it was also observed to promote the protein expression of Stat1 and the FoxO1 expression inhibition. Silencing Stat1 could reverse HG-induced podocytes injury. Specifically, siStat1 increased cell viability, inhibited cell apoptosis and attenuated ROS level in a high-glucose environment. Cleaved caspase-3 and pro-apoptosis protein Bax was significantly down-regulated, and anti-apoptosis protein Bcl-2 was up-regulated by siStat1. The antioxidant genes Catalase, MnSOD, NQO1 and HO1 were up-regulated by siStat1. We found that silencing FoxO1 reversed the protective effect of siStat1 on the HG-induced podocytes injury. Conclusions Silencing Stat1 could reverse the effects of high glucose-triggered low cell viability, cell apoptosis and ROS release and the functions of Stat1 might be involved in FoxO1 mediated-oxidative stress in nucleus.

Abstract Background The National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity. Results We present a computational model for predicting cell line response to a subset of drug pairs in the NCI-ALMANAC database. Based on residual neural networks for encoding features as well as predicting tumor growth, our model explains 94% of the response variance. While our best result is achieved with a combination of molecular feature types (gene expression, microRNA and proteome), we show that most of the predictive power comes from drug descriptors. To further demonstrate value in detecting anticancer therapy, we rank the drug pairs for each cell line based on model predicted combination effect and recover 80% of the top pairs with enhanced activity. Conclusions We present promising results in applying deep learning to predicting combinational drug response. Our feature analysis indicates screening data involving more cell lines are needed for the models to make better use of molecular features.

Abstract Background Current multi-petaflop supercomputers are powerful systems, but present challenges when faced with problems requiring large machine learning workflows. Complex algorithms running at system scale, often with different patterns that require disparate software packages and complex data flows cause difficulties in assembling and managing large experiments on these machines. Results This paper presents a workflow system that makes progress on scaling machine learning ensembles, specifically in this first release, ensembles of deep neural networks that address problems in cancer research across the atomistic, molecular and population scales. The initial release of the application framework that we call CANDLE/Supervisor addresses the problem of hyper-parameter exploration of deep neural networks. Conclusions Initial results demonstrating CANDLE on DOE systems at ORNL, ANL and NERSC (Titan, Theta and Cori, respectively) demonstrate both scaling and multi-platform execution.

Abstract Background Successful resuscitation from asystole induced by bupivacaine requires the reestablishment of a sufficient coronary flow (CF) quickly. This study was designed to test whether levosimendan was superior to epinephrine in the reestablishment of crucial coronary flows after bupivacaine-induced asystole. Methods The isolated, perfused, nonrecirculating, Langendorff rat heart preparation was used. Bupivacaine 100 μmol/L was perfused into rat hearts to induce asystole, and then for 3 min thereafter. Three experimental groups were assessed after asystole with infusions as follow: (1) a mixture of 2% lipid emulsion and 40 μmol/L bupivacaine (control group), (2) a mixture of 0.15 μg/mL epinephrine combined with 2% lipid emulsion and 40 μmol/L bupivacaine (epinephrine group), and (3) a mixture of 5 μmol/L levosimendan combined with a 2% lipid emulsion and 40 μmol/L bupivacaine mixture (levosimendan group). Coronary flow (CF), the time to recovery (Trecovery), the number of ventricular arrhythmias, and cardiac function parameters were recorded for 40 min after heartbeat recovery. Results All hearts in the control, epinephrine and levosimendan groups had heartbeat recovery. The rank order of the mean CF from highest to lowest was the levosimendan group > the epinepgrine group > the control group (P

Urinary exosomes containing specific biomarkers have recently been considered as novel potential non-invasive candidates for renal disease diagnosis. However, the development of urinary exosomes in basic research and their subsequent diagnostic application are impeded by the lack of an efficient isolation method. One of the main challenges during urinary exosomes isolation is how to remove a large number of Tamm Horsfall proteins (around 92 kDa) and other biological components from exosome enrichment mixture. Herein, we report a facile and low-cost isolation method for highlypurified human urinary exosomes based on dialysis. The key protocol for exosome isolation includes only two steps: (1) Healthy person urines were collected. 10 mL urine in 300 kDa dialysis tubes was firstly dialyzed in phosphate-buffered saline solution three times for sequential nine hours; (2) The dialysis suspension was concentrated to 200 μL by using 100 kDa ultracentrifuge tubes to achieve urinary exosome isolation. For verification, the concentrated solution was examined by western blot, transmission electronic microscopy, atomic force microscopy and qNano, which demonstrated the highly-purified urinary exosomes were present. Furthermore, a total of 359 proteins were identified by the proteomic analysis of purified urinary exosomes from healthy persons. Those results demonstrated that highly-purified urinary exosomes could be achieved by our isolation method; 359 proteins were identified from healthy persons. Further works will focus on screening and identifying disease-related biomarkers from human urine exosomes for clinical diagnosis.

Abstract Background Limb ischemia/reperfusion causes inflammation and elicits oxidative stress that may lead to local tissue damage and remote organ such as lung injury. This study investigates pulmonary function after limb ischemia/reperfusion and the protective effect of a lipid emulsion (Intralipid). Methods Twenty-four rats were divided into three groups: sham operation group (group S), ischemia/reperfusion group (group IR), and lipid emulsion treatment group (group LE). limb ischemia/reperfusion was induced through occlusion of the infrarenal abdominal aorta for 3 h. The microvascular clamp was removed carefully and reperfusion was provided for 3 h. Results The mean arterial pressure in group LE was higher than group IR during the reperfusion period (P = 0.024). The heart rate of both group LE and IR are significantly higher than group S during the ischemia period(P

Gold nanorod-based gene delivery system potentially represents a powerful nanotechnology for cancer therapy. Here we for the first time reported the use of polyamidoamine (PAMAM) dendrimer modified-gold nanorods (dGNR) as a high efficient gene delivery system for the targeted silencing of survivin via RNA interference for breast cancer therapy. Gold nanorods were functionalized with thiol-terminated polyamidoamine dendrimer; survivin shRNA plasmid was constructed and conjugated with dGNR. The resultant survivin-shRNA-GNR nanocomposites were incubated with human breast cancer MCF-7 cells, irradiated with 140 mJ/pulse of laser light of 1,064 nm for 15 s, and then continued to culture for 1 to 3 days. These cells were collected and analyzed by MTT, quantitative reverse transcription-PCR, Western blotting, fluorescent microscopy, and high-resolution transmission electron microscopy. 25 nude mice models with breast cancer were established; the nanocomposites of survivin-shRNA-dGNR were injected into the tumor tissues at gradually increased dose, and then were irradiated with 140 mJ/pulse of laser light of 1,064 nm for 15 s one time per week. The nude mice were raised for two months, and then were sacrificed. The tumor tissues were picked out, and their sizes were measured. Results showed that PAMAM dendrimer-functionalized gold nanorods were successfully synthesized; they could enter into MCF-7 cells within 30 min, release survivin shRNA plasmids with high efficiency, enhance the expression of transferred survivin genes in tumor cells under near-IR laser irradiation, and cause remarkable down-regulation of survivin gene and protein, inhibite cell growth in dose-and time-dependent means, and induce cell apoptosis. EGFP fluorescent signals in the tumor localization of nude mice. The tumor sizes in nude mice became smaller and smaller as the dose of the injected nanocomposites increased. In conclusion, PAMAM dendrimer-functionalized gold nanorods may be a high efficient gene delivery release system for survivin-shRNA vector under near-infrared light irradiation; the constructed survivin-shRNA-dGNR composites can effectively inhibit the growth of breast cancer cells, and have potential applications in breast cancer therapy and molecular imaging.

The application of data science in cancer research has been boosted by major advances in three primary areas: (1) Data: diversity, amount, and availability of biomedical data; (2) Advances in Artificial Intelligence (AI) and Machine Learning (ML) algorithms that enable learning from complex, large-scale data; and (3) Advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build in silico ML models that can provide transformative insights from data including: molecular dynamics simulations, next-generation sequencing, omics, imaging, and unstructured clinical text documents. Unique challenges persist, however, in building ML models related to cancer, including: (1) access, sharing, labeling, and integration of multimodal and multi-institutional data across different cancer types; (2) developing AI models for cancer research capable of scaling on next generation high performance computers; and (3) assessing robustness and reliability in the AI models. In this paper, we review the National Cancer Institute (NCI) -Department of Energy (DOE) collaboration, Joint Design of Advanced Computing Solutions for Cancer (JDACS4C), a multi-institution collaborative effort focused on advancing computing and data technologies to accelerate cancer research on three levels: molecular, cellular, and population. This collaboration integrates various types of generated data, pre-exascale compute resources, and advances in ML models to increase understanding of basic cancer biology, identify promising new treatment options, predict outcomes, and eventually prescribe specialized treatments for patients with cancer.

Pneumonic tularemia is a highly debilitating and potentially fatal disease caused by inhalation of Francisella tularensis. Most of our current understanding of its pathogenesis is based on the highly virulent F. tularensis subsp. tularensis strain SCHU S4. However, multiple sources of SCHU S4 have been maintained and propagated independently over the years, potentially generating genetic variants with altered virulence. In this study, the virulence of four SCHU S4 stocks (NR-10492, NR-28534, NR-643 from BEI Resources and FTS-635 from Battelle Memorial Institute) along with another virulent subsp. tularensis strain, MA00-2987, were assessed in parallel. In the Fischer 344 rat model of pneumonic tularemia, NR-643 and FTS-635 were found to be highly attenuated compared to NR-10492, NR-28534, and MA00-2987. In the NZW rabbit model of pneumonic tularemia, NR-643 caused morbidity but not mortality even at a dose equivalent to 500x the LD50 for NR-10492. Genetic analyses revealed that NR-10492 and NR-28534 were identical to each other, and nearly identical to the reference SCHU S4 sequence. NR-643 and FTS-635 were identical to each other but were found to have nine regions of difference in the genomic sequence when compared to the published reference SCHU S4 sequence. Given the genetic differences and decreased virulence, NR-643/FTS-635 should be clearly designated as a separate SCHU S4 substrain and no longer utilized in efficacy studies to evaluate potential vaccines and therapeutics against tularemia.

How to enhance the therapeutic efficacy of human cytokine induced killers cell (CIK) has become a great challenge. Herein, we report for the first time that human CIK cells loaded with gold nanoprisms were successfully used for targeted photoacoustic imaging, enhanced immunotherapy and photothermal therapy of gastric cancer in vivo. Gold nanoprisms were synthesized and modified with PEG; human CIK cells were prepared and incubated with PEGylated gold nanoprisms (Au GNPrs), and then the effects of human CIK cells labeled with Au NPrs on gastric cancer MGC 803 cells were evaluated and further used for targeted photoacoustic imaging, immunotherapy and photothermal therapy of gastric cancer in vivo in mice models. Results showed that PEGylated Au NPrs could be uptaken high-efficiently by human CIK cells, resultant human CIK cells labeled with AuNPrs could inhibit the growth of gastric cancer MGC 803 cells actively by induced apoptosis and G1 phase arrest, and actively target and accumulate the tumor sites in gastric cancer-bearing nude mice. Enhanced synergistic therapeutic efficacy was demonstrated with the maximal inhibition of tumor through a combination of CIK cells-based immunotherapy for three days and then a continuous gold nanoprisms-based photothermal therapy. In conclusion, human CIK cells labeled with PEGylated Au NPrs can target gastric cancer cells in vivo, enhance immunotherapy and photothermal therapy efficacy, and have a great potential in applications such as targeted imaging, immunotherapy and photothermal therapy of gastric cancer in the near future.

Cellular immunotherapy has become a potential therapeutic method for different diseases. Herein, we reported clinical trial results of Cytokine-induced killer (CIK) cells used for patients with hepatitis B, cirrhosis and liver cancers from 2000 to 2015. Results showed CIK cell therapeutic effects were closely positively associated with CIK cell numbers, treated times and HBV genotypes. Different stages of HBV patients treated with > 1010 CIK cells per time for more than ten times exhibited remarkable decrease of HBV DNA numbers (P < 0.01), ALT and AST gradually recovered to normal scope, cytokine factors such as IFN-γ, IL-1b, IL-2, IL-4, IL-6, IL-10, IL-22 and IL-27 exhibited obvious increase, lifespan of patients with cirrhosis and hepatocellular carcinoma were extended, and that all the patients felt better in sleep, diet and pain during the period of CIK therapy. In conclusion, CIK cell therapy is a good alternative therapeutic method and can be effectively used for treatment of different stages of HBV patients.

ABSTRACT Klebsiella pneumoniae is a major human pathogen responsible for high morbidity and mortality rates. The emergence and spread of strains resistant to multiple antimicrobial agents and documented large nosocomial outbreaks are especially concerning. To develop new therapeutic strategies for K. pneumoniae, it is imperative to understand the population genomic structure of strains causing human infections. To address this knowledge gap, we sequenced the genomes of 1,777 extended-spectrum beta-lactamase-producing K. pneumoniae strains cultured from patients in the 2,000-bed Houston Methodist Hospital system between September 2011 and May 2015, representing a comprehensive, population-based strain sample. Strains of largely uncharacterized clonal group 307 (CG307) caused more infections than those of well-studied epidemic CG258. Strains varied markedly in gene content and had an extensive array of small and very large plasmids, often containing antimicrobial resistance genes. Some patients with multiple strains cultured over time were infected with genetically distinct clones. We identified 15 strains expressing the New Delhi metallo-beta-lactamase 1 (NDM-1) enzyme that confers broad resistance to nearly all beta-lactam antibiotics. Transcriptome sequencing analysis of 10 phylogenetically diverse strains showed that the global transcriptome of each strain was unique and highly variable. Experimental mouse infection provided new information about immunological parameters of host-pathogen interaction. We exploited the large data set to develop whole-genome sequence-based classifiers that accurately predict clinical antimicrobial resistance for 12 of the 16 antibiotics tested. We conclude that analysis of large, comprehensive, population-based strain samples can assist understanding of the molecular diversity of these organisms and contribute to enhanced translational research. IMPORTANCE Klebsiella pneumoniae causes human infections that are increasingly difficult to treat because many strains are resistant to multiple antibiotics. Clonal group 258 (CG258) organisms have caused outbreaks in health care settings worldwide. Using a comprehensive population-based sample of extended-spectrum beta-lactamase (ESBL)-producing K. pneumoniae strains, we show that a relatively uncommon clonal type, CG307, caused the plurality of ESBL-producing K. pneumoniae infections in our patients. We discovered that CG307 strains have been abundant in Houston for many years. As assessed by experimental mouse infection, CG307 strains were as virulent as pandemic CG258 strains. Our results may portend the emergence of an especially successful clonal group of antibiotic-resistant K. pneumoniae.

The objectives of this study are to determine the occurrence of antimicrobial resistance (AMR) genes using whole-genome sequence (WGS) of Streptococcus uberis (S. uberis) and Streptococcus dysgalactiae (S. dysgalactiae) isolates, recovered from dairy cows in the Canadian Maritime Provinces. A secondary objective included the exploration of the association between phenotypic AMR and the genomic characteristics (genome size, guanine–cytosine content, and occurrence of unique gene sequences). Initially, 91 isolates were sequenced, and of these isolates, 89 were assembled. Furthermore, 16 isolates were excluded due to larger than expected genomic sizes (>2.3 bp × 1,000 bp). In the final analysis, 73 were used with complete WGS and minimum inhibitory concentration records, which were part of the previous phenotypic AMR study, representing 18 dairy herds from the Maritime region of Canada (1). A total of 23 unique AMR gene sequences were found in the bacterial genomes, with a mean number of 8.1 (minimum: 5; maximum: 13) per genome. Overall, there were 10 AMR genes [ANT(6), TEM-127, TEM-163, TEM-89, TEM-95, Linb, Lnub, Ermb, Ermc, and TetS] present only in S. uberis genomes and 2 genes unique (EF-TU and TEM-71) to the S. dysgalactiae genomes; 11 AMR genes [APH(3′), TEM-1, TEM-136, TEM-157, TEM-47, TetM, bl2b, gyrA, parE, phoP, and rpoB] were found in both bacterial species. Two-way tabulations showed association between the phenotypic susceptibility to lincosamides and the presence of linB (P = 0.002) and lnuB (P 11 AMR genes present in the genome, compared with 250,000 cells/mL, a trend toward higher odds of resistance compared with the baseline category of

Recent insights suggest that non-specific and/or promiscuous enzymes are common and active across life. Understanding the role of such enzymes is an important open question in biology. Here we develop a genome-wide method, PROPER, that uses a permissive PSI-BLAST approach to predict promiscuous activities of metabolic genes. Enzyme promiscuity is typically studied experimentally using multicopy suppression, in which over-expression of a promiscuous 'replacer' gene rescues lethality caused by inactivation of a 'target' gene. We use PROPER to predict multicopy suppression in Escherichia coli, achieving highly significant overlap with published cases (hypergeometric p = 4.4e-13). We then validate three novel predicted target-replacer gene pairs in new multicopy suppression experiments. We next go beyond PROPER and develop a network-based approach, GEM-PROPER, that integrates PROPER with genome-scale metabolic modeling to predict promiscuous replacements via alternative metabolic pathways. GEM-PROPER predicts a new indirect replacer (thiG) for an essential enzyme (pdxB) in production of pyridoxal 5'-phosphate (the active form of Vitamin B6), which we validate experimentally via multicopy suppression. We perform a structural analysis of thiG to determine its potential promiscuous active site, which we validate experimentally by inactivating the pertaining residues and showing a loss of replacer activity. Thus, this study is a successful example where a computational investigation leads to a network-based identification of an indirect promiscuous replacement of a key metabolic enzyme, which would have been extremely difficult to identify directly.

The remarkable advance in sequencing technology and the rising interest in medical and environmental microbiology, biotechnology, and synthetic biology resulted in a deluge of published microbial genomes. Yet, genome annotation, comparison, and modeling remain a major bottleneck to the translation of sequence information into biological knowledge, hence computational analysis tools are continuously being developed for rapid genome annotation and interpretation. Among the earliest, most comprehensive resources for prokaryotic genome analysis, the SEED project, initiated in 2003 as an integration of genomic data and analysis tools, now contains >5,000 complete genomes, a constantly updated set of curated annotations embodied in a large and growing collection of encoded subsystems, a derived set of protein families, and hundreds of genome-scale metabolic models. Until recently, however, maintaining current copies of the SEED code and data at remote locations has been a pressing issue. To allow high-performance remote access to the SEED database, we developed the SEED Servers (http://www.theseed.org/servers): four network-based servers intended to expose the data in the underlying relational database, support basic annotation services, offer programmatic access to the capabilities of the RAST annotation server, and provide access to a growing collection of metabolic models that support flux balance analysis. The SEED servers offer open access to regularly updated data, the ability to annotate prokaryotic genomes, the ability to create metabolic reconstructions and detailed models of metabolism, and access to hundreds of existing metabolic models. This work offers and supports a framework upon which other groups can build independent research efforts. Large integrations of genomic data represent one of the major intellectual resources driving research in biology, and programmatic access to the SEED data will provide significant utility to a broad collection of potential users.