Machine Learning-Based Prediction of Complications and Prolonged Hospitalization with the GLIM Criteria Combinations Containing Calf Circumference in Elderly Asian Patients.

BACKGROUND AND AIMS: Malnutrition is widely present and influences the prognosis of elderly inpatients, so it is helpful to be able to identify it with a convenient method. However, in the widely accepted criteria for malnutrition, the Global Leadership Initiative on Malnutrition (GLIM), a lot of metrics can be used to define the phenotypic and etiological criteria. To identify muscle mass reduction, anthropometric parameters such as calf circumference (CC) and hand grip strength (HGS) are preferable to other expensive methods in many situations because they are easy and inexpensive to measure, but their applicability needs to be verified in specific clinical scenarios. This study aims to verify the value of CC- and HGS-identified muscle loss in diagnosing malnutrition and predicting in-hospital complications (IHC) and prolonged length of hospital stay (PLOS) in elderly inpatients using machine learning methods. METHODS: A sample of 7122 elderly inpatients who were enrolled in a previous multicenter cohort study in China were screened for eligibility for the current study and were then retrospectively diagnosed for malnutrition using 33 GLIM criteria that differ in their combinations of phenotypic and etiological criteria, in which CC or CC+HGS were used to identify muscle mass reduction. The diagnostic consistency with the subjective global assessment (SGA) criteria at admission was evaluated according to Kappa coefficients. The association and the predictive value of the GLIM-defined malnutrition with 30-day IHC and PLOS were evaluated with logistic regression and randomized forest models. RESULTS: In total, 2526 inpatients (average age 74.63 ± 7.12 years) were enrolled in the current study. The prevalence of malnutrition identified by the 33 criteria combinations ranged from 3.3% to 27.2%. The main IHCs was infectious complications (2.5%). The Kappa coefficients ranged from 0.130 to 0.866. Logistic regression revealed that malnutrition was identified by 31 GLIM criteria combinations that were significantly associated with 30-day IHC, and 22 were significantly associated with PLOS. Random forest prediction revealed that GLIM 15 (unconscious weight loss + muscle mass reduction, combined with disease burden/inflammation) performs best in predicting IHC; GLIM 30 (unconscious weight loss + muscle mass reduction + BMI reduction, combined with disease burden/inflammation) performs best in predicting PLOS. Importantly, CC alone performs better than CC+HGS in the criteria combinations for predicting adverse clinical outcomes. CONCLUSION: Muscle mass reduction defined by a reduced CC performs well in the GLIM criteria combinations for diagnosing malnutrition and predicting IHC and PLOS in elderly Asian inpatients. The applicability of other anthropometric parameters in these applications needs to be further explored.

The proliferation and angiogenesis in hemangioma-derived endothelial cells is affected by STC2 medicated VEGFR2/Akt/eNOS pathway.

Objective: Stanniocalcin-2 (STC2), a secreted glycoprotein that is involved in the regulation of angiogenesis, was proposed as one of the mechanisms of neovascularization in hemangioma (HA). We aimed to investigate the effect of STC2 on proliferation and angiogenesis in hemangioma-derived endothelial cells. Methods: The hemangioma samples from HA patients with the median age of six months were surgically collected in the Affiliated Hospital of Weifang Medical University from October 2019 to June 2021, and divided into normal skin tissues (n=10), involuting-phase HAs (n=10) and proliferating-phase HAs (n=10) according to the Mulliken classification. The expression of STC2 was detected in involuting-phase HAs and proliferating-phase HAs. Hemangioma endothelial cells (HemEC) were transfected with small interfering RNA (siRNA) specific for STC2, and cell survival and tube formation were analyzed. Results: STC2 expression in proliferating-phase HAs was markedly higher than in the normal skin tissues and involving-phase HAs. Similarly, STC2 expression was higher in HemEC compared to the control human umbilical vein endothelial cells (HUVEC). Knockdown of STC2 slowed the proliferation of HemEC and decreased the expression of proliferating cell nuclear antigen (PCNA) in HemEC. Moreover, knockdown of STC2 in HemEC inhibited vascular endothelial cell angiogenesis and regulated the expression and phosphorylation of vascular endothelial growth factor receptor 2 (VEGFR2). Mechanistically, STC2 knockdown attenuated the activation of Akt/eNOS signaling, which was abolished by insulin growth factor-1 (IGF-1), the activator of Akt signaling, accompanying by increased proliferation and tube formation of HemEC. Conclusion: Inhibition of STC2 suppresses HemEC proliferation and angiogenesis by VEGFR2/Akt/eNOS pathway, which warrants further development of STC2-based strategies for HA treatment.

The prognostic and biological importance of chromatin regulation-related genes for lung cancer is examined using bioinformatics and experimentally confirmed.

BACKGROUND: The pathogenesis and clinical diagnosis of lung adenocarcinoma (LUAD), a malignant illness with substantial morbidity and mortality, are still being investigated. Genes involved in chromatin regulation are crucial in the biological function of LUAD. METHODS: The prognostic prediction model for LUAD was developed using multivariables and least absolute shrinkage and selection operator (LASSO) regression. It consisted of 10 chromatin regulators. The LUAD has been divided into two groups, high- and low-risk, using a predictive model. The model was shown to be accurate in predicting survival by the nomogram, receiver operating characteristic (ROC) curves, and principal component analysis (PCA). An analysis of differences in immune-cell infiltration, immunologicalfunction, and clinical traits between low- and high-risk populations was conducted. Protein-protein interaction (PPI) networks and Gene Ontology (GO) pathways of differentially expressed genes (DEGs) in the high versus low risk group were also examined to investigate the association between genes and biological pathways. The biological roles of chromatin regulators (CRs) in LUAD were finally estimated using colony formation and cell movement. The important genes' mRNA expression has been measured using real-time polymerase chain reaction (RT-PCR). RESULTS AND CONCLUSION: Risk score and stage based on the model could be seen as separate prognostic indicators for patients with LUAD. The main signaling pathway difference across various risk groups was in cell cycle. The immunoinfiltration profile of the tumor microenvironment (TME) and individuals with different risk levels were correlated, suggesting that the interaction of immune cells with the tumor led to the creation of a favorable immunosuppressive microenvironment. These discoveries aid in the creation of individualized therapies for LUAD patients.

The Enhancement of CO Oxidation Performance and Stability in SO2 and H2S Environment on Pd-Au/FeOX/Al2O3 Catalysts.

Carbon monoxide (CO) is a colourless, odourless, and toxic gas. Long-term exposure to high concentrations of CO causes poisoning and even death; therefore, CO removal is particularly important. Current research has focused on the efficient and rapid removal of CO via low-temperature (ambient) catalytic oxidation. Gold nanoparticles are widely used catalysts for the high-efficiency removal of high concentrations of CO at ambient temperature. However, easy poisoning and inactivation due to the presence of SO2 and H2S affect its activity and practical application. In this study, a bimetallic catalyst, Pd-Au/FeOx/Al2O3, with a Au:Pd ratio of 2:1 (wt%) was formed by adding Pd nanoparticles to a highly active Au/FeOx/Al2O3 catalyst. Its analysis and characterisation proved that it has improved catalytic activity for CO oxidation and excellent stability. A total conversion of 2500 ppm of CO at -30 °C was achieved. Furthermore, at ambient temperature and a volume space velocity of 13,000 h-1, 20,000 ppm CO was fully converted and maintained for 132 min. Density functional theory (DFT) calculations and in situ FTIR analysis revealed that Pd-Au/FeOx/Al2O3 exhibited stronger resistance to SO2 and H2S adsorption than the Au/FeOx/Al2O3 catalyst. This study provides a reference for the practical application of a CO catalyst with high performance and high environmental stability.

Noninvasive Prediction of Ki-67 Expression in Hepatocellular Carcinoma Using Machine Learning-Based Ultrasomics: A Multicenter Study.

OBJECTIVES: To investigate the ability of ultrasomics to predict Ki-67 expression in hepatocellular carcinoma (HCC). METHODS: A total of 244 patients from three hospitals were retrospectively recruited (training dataset, n = 168; test dataset, n = 43; and validation dataset, n = 33). Lesion segmentation of the ultrasound images was performed manually by two radiologists. In total, 1409 ultrasomics features were extracted. Feature selection was conducted using the intra-class correlation coefficient, variance threshold, mutual information, and recursive feature elimination plus eXtreme Gradient Boosting. The support vector machine was combined with the learning curve and grid search parameter tuning to construct the clinical, ultrasomics, and combined models. The predictive performance of the models was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity and accuracy. RESULTS: The ultrasomics model performed well on the training, test, and validation datasets. The AUC (95% confidence interval [CI]) for these datasets were 0.955 (0.912-0.981), 0.861 (0.721-0.947), and 0.665 (0.480-0.819), respectively. The combination of ultrasomics and clinical features significantly improved model performance on all three datasets. The AUC (95% CI), sensitivity, specificity, and accuracy were 0.986 (0.955-0.998), 0.973, 0.840, and 0.869 on the training dataset; 0.871 (0.734-0.954), 0.750, 0.829, and 0.814 on the test dataset; and 0.742 (0.560-0.878), 0.714, 0.808, and 0.788 on the validation dataset, respectively. CONCLUSIONS: Ultrasomics was proved to be a potential noninvasive method to predict Ki-67 expression in HCC.

Ultrasomics prediction for cytokeratin 19 expression in hepatocellular carcinoma: A multicenter study.

Objective: The purpose of this study was to investigate the preoperative prediction of Cytokeratin (CK) 19 expression in patients with hepatocellular carcinoma (HCC) by machine learning-based ultrasomics. Methods: We retrospectively analyzed 214 patients with pathologically confirmed HCC who received CK19 immunohistochemical staining. Through random stratified sampling (ratio, 8:2), patients from institutions I and II were divided into training dataset (n = 143) and test dataset (n = 36), and patients from institution III served as external validation dataset (n = 35). All gray-scale ultrasound images were preprocessed, and then the regions of interest were then manually segmented by two sonographers. A total of 1409 ultrasomics features were extracted from the original and derived images. Next, the intraclass correlation coefficient, variance threshold, mutual information, and embedded method were applied to feature dimension reduction. Finally, the clinical model, ultrasonics model, and combined model were constructed by eXtreme Gradient Boosting algorithm. Model performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results: A total of 12 ultrasomics signatures were used to construct the ultrasomics models. In addition, 21 clinical features were used to construct the clinical model, including gender, age, Child-Pugh classification, hepatitis B surface antigen/hepatitis C virus antibody (positive/negative), cirrhosis (yes/no), splenomegaly (yes/no), tumor location, tumor maximum diameter, tumor number, alpha-fetoprotein, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, glutamyl-transpeptidase, albumin, total bilirubin, conjugated bilirubin, creatinine, prothrombin time, fibrinogen, and international normalized ratio. The AUC of the ultrasomics model was 0.789 (0.621 - 0.907) and 0.787 (0.616 - 0.907) in the test and validation datasets, respectively. However, the performance of the combined model covering clinical features and ultrasomics signatures improved significantly. Additionally, the AUC (95% CI), sensitivity, specificity, and accuracy were 0.867 (0.712 - 0.957), 0.750, 0.875, 0.861, and 0.862 (0.703 - 0.955), 0.833, 0.862, and 0.857 in the test dataset and external validation dataset, respectively. Conclusion: Ultrasomics signatures could be used to predict the expression of CK19 in HCC patients. The combination of clinical features and ultrasomics signatures showed excellent effects, which significantly improved prediction accuracy and robustness.

Machine Learning-Based Prediction of In-Hospital Complications in Elderly Patients Using GLIM-, SGA-, and ESPEN 2015-Diagnosed Malnutrition as a Factor.

BACKGROUND: Malnutrition is prevalent in elderly inpatients and is associated with various adverse outcomes during their hospital stay, but the diagnosis of malnutrition still lacks widely applicable criteria. This study aimed to investigate the association of malnutrition diagnosed with the SGA, ESPEN 2015, and GLIM criteria, respectively, with in-hospital complications in elderly patients. METHOD: Hospitalized patients over 65 years old who had been assessed with the SGA guideline for malnutrition at admission were retrospectively recruited from a large observational cohort study conducted in 34 level-A tertiary hospitals in 18 cities in China from June to September 2014. Malnutrition was then retrospectively diagnosed using the GLIM and ESPEN 2015 criteria, respectively, for comparison with the results of the SGA scale. The risk factors for malnutrition were analyzed using logistic regression, and the value of the three diagnostic criteria in predicting the in-hospital complications was subsequently explored using multivariate regression and the random forest machine learning algorithm. RESULTS: A total of 2526 subjects who met the inclusion and exclusion criteria of the study were selected from the 7122 patients in the dataset, with an average age of 74.63 ± 7.12 years, 59.2% male, and 94.2% married. According to the GLIM, SGA, and ESPEN 2015 criteria, the detection rates of malnutrition were 37.8% (956 subjects), 32.8% (829 subjects), and 17.0% (429 subjects), respectively. The diagnostic consistency between the GLIM and the SGA criteria is better than that between the ESPEN 2015 and the SGA criteria (Kappa statistics, 0.890 vs. 0.590). Logistic regression showed that the risk of developing complications in the GLIM-defined malnutrition patients is 2.414 times higher than that of normal patients, higher than those of the ESPEN 2015 and SGA criteria (1.786 and 1.745 times, respectively). The random forest classifications show that the GLIM criteria have a higher ability to predict complications in these elderly patients than the SGA and ESPEN 2015 criteria with a mean decrease in accuracy of 12.929, 10.251, and 5.819, respectively, and a mean decrease in Gini of 2.055, 1.817, and 1.614, respectively. CONCLUSION: The prevalence of malnutrition diagnosed with the GLIM criteria is higher than that of the SGA and the ESPEN 2015 criteria. The GLIM criteria are better than the SGA and the ESPEN 2015 criteria for predicting in-hospital complications in elderly patients.

Clinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study.

OBJECTIVE: This study aims to explore the clinical value of machine learning-based ultrasomics in the preoperative noninvasive differentiation between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). METHODS: The clinical data and ultrasonic images of 226 patients from three hospitals were retrospectively collected and divided into training set (n = 149), test set (n = 38), and independent validation set (n = 39). Manual segmentation of tumor lesion was performed with ITK-SNAP, the ultrasomics features were extracted by the pyradiomics, and ultrasomics signatures were generated using variance filtering and lasso regression. The prediction models for preoperative differentiation between HCC and ICC were established by using support vector machine (SVM). The performance of the three models was evaluated by the area under curve (AUC), sensitivity, specificity, and accuracy. RESULTS: The ultrasomics signatures extracted from the grayscale ultrasound images could successfully differentiate between HCC and ICC (p < 0.05). The combined model had a better performance than either the clinical model or the ultrasomics model. In addition to stability, the combined model also had a stronger generalization ability (p < 0.05). The AUC (along with 95% CI), sensitivity, specificity, and accuracy of the combined model on the test set and the independent validation set were 0.936 (0.806-0.989), 0.900, 0.857, 0.868, and 0.874 (0.733-0.961), 0.889, 0.867, and 0.872, respectively. CONCLUSION: The ultrasomics signatures could facilitate the preoperative noninvasive differentiation between HCC and ICC. The combined model integrating ultrasomics signatures and clinical features had a higher clinical value and a stronger generalization ability.

Preoperative prediction of pathological grading of hepatocellular carcinoma using machine learning-based ultrasomics: A multicenter study.

PURPOSE: The present study investigated the value of ultrasomics signatures in the preoperative prediction of the pathological grading of hepatocellular carcinoma (HCC) via machine learning. METHODS: A total of 193 patients were collected from three hospitals. The patients from two hospitals (n = 160) were randomly divided into training set (n = 128) and test set (n = 32) at a 8:2 ratio. The patients from a third hospital were used as an independent validation set (n = 33). The ultrasomics features were extracted from the tumor lesions on the ultrasound images. Support vector machine (SVM) was used to construct three preoperative pathological grading models for HCC on each dataset. The performance of the three models was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. RESULTS: The ultrasomics signatures extracted from the grayscale ultrasound images could successfully differentiate between high- and low-grade HCC lesions on the training set, test set, and the independent validation set (p < 0.05). On the test set and the validation set, the combined model's performance was the highest, followed by the ultrasomics model and the clinical model successively (p < 0.05). Their AUC (along with 95 %CI) of these models was 0.874(0.709-0.964), 0.789(0.608-0.912), 0.720(0.534-0.863) and 0.849(0.682-0.949), 0.825(0.654-0.935), 0.770(0.591-0.898), respectively. CONCLUSION: Machine learning-based ultrasomics signatures could be used for noninvasive preoperative prediction of pathological grading of HCC. The combined model displayed a better predictive performance for pathological grading of HCC and had a stronger generalization ability.

Identification, evolution and expression analysis of WRKY gene family in Eucommia ulmoides.

The WRKY transcription factors is one of the largest families of transcription factors (TFs) in plants and involved in multiple biological processes. However, the role of the WRKY family had not been reported in Eucommia ulmoides. In this study, 45 WRKY genes (EuWRKY1-45) with conserved WRKY domain were identified in E. ulmoides and classified into three groups. The group II was further divided into five subgroups based on phylogenetic analysis, and each clade was well supported by the conserved motifs. All the genes were located on 34 different scaffolds respectively. A number of development-, light-, hormone-, and stress-related elements were randomly distributed in the promoter sequences of EuWRKYs. Expression profiles indicated that EuWRKY genes were involved in leaf development, and majority of EuWRKYs genes were highly expressed in leaf buds. Co-expression analysis of WRKYs suggested an intricate interplay of growth-related responses. EuWRKY4 was involved in a complex proteins interaction network. Collectively, our results provide extensive insights into the WRKY gene family, thereby contributing to the screening of additional candidate genes in E. ulmoides.

Correction to: GASAL2: a GPU accelerated sequence alignment library for high-throughput NGS data.

Following publication of the original article [1], the author requested changes to the figures 4, 7, 8, 9, 12 and 14 to align these with the text. The corrected figures are supplied below.

GASAL2: a GPU accelerated sequence alignment library for high-throughput NGS data.

BACKGROUND: Due the computational complexity of sequence alignment algorithms, various accelerated solutions have been proposed to speedup this analysis. NVBIO is the only available GPU library that accelerates sequence alignment of high-throughput NGS data, but has limited performance. In this article we present GASAL2, a GPU library for aligning DNA and RNA sequences that outperforms existing CPU and GPU libraries. RESULTS: The GASAL2 library provides specialized, accelerated kernels for local, global and all types of semi-global alignment. Pairwise sequence alignment can be performed with and without traceback. GASAL2 outperforms the fastest CPU-optimized SIMD implementations such as SeqAn and Parasail, as well as NVIDIA's own GPU-based library known as NVBIO. GASAL2 is unique in performing sequence packing on GPU, which is up to 750x faster than NVBIO. Overall on Geforce GTX 1080 Ti GPU, GASAL2 is up to 21x faster than Parasail on a dual socket hyper-threaded Intel Xeon system with 28 cores and up to 13x faster than NVBIO with a query length of up to 300 bases and 100 bases, respectively. GASAL2 alignment functions are asynchronous/non-blocking and allow full overlap of CPU and GPU execution. The paper shows how to use GASAL2 to accelerate BWA-MEM, speeding up the local alignment by 20x, which gives an overall application speedup of 1.3x vs. CPU with up to 12 threads. CONCLUSIONS: The library provides high performance APIs for local, global and semi-global alignment that can be easily integrated into various bioinformatics tools.

GPU accelerated sequence alignment with traceback for GATK HaplotypeCaller.

BACKGROUND: Pairwise sequence alignment is widely used in many biological tools and applications. Existing GPU accelerated implementations mainly focus on calculating optimal alignment score and omit identifying the optimal alignment itself. In GATK HaplotypeCaller (HC), the semi-global pairwise sequence alignment with traceback has so far been difficult to accelerate effectively on GPUs. RESULTS: We first analyze the characteristics of the semi-global alignment with traceback in GATK HC and then propose a new algorithm that allows for retrieving the optimal alignment efficiently on GPUs. For the first stage, we choose intra-task parallelization model to calculate the position of the optimal alignment score and the backtracking matrix. Moreover, in the first stage, our GPU implementation also records the length of consecutive matches/mismatches in addition to lengths of consecutive insertions and deletions as in the CPU-based implementation. This helps efficiently retrieve the backtracking matrix to obtain the optimal alignment in the second stage. CONCLUSIONS: Experimental results show that our alignment kernel with traceback is up to 80x and 14.14x faster than its CPU counterpart with synthetic datasets and real datasets, respectively. When integrated into GATK HC (alongside a GPU accelerated pair-HMMs forward kernel), the overall acceleration is 2.3x faster than the baseline GATK HC implementation, and 1.34x faster than the GATK HC implementation with the integrated GPU-based pair-HMMs forward algorithm. Although the methods proposed in this paper is to improve the performance of GATK HC, they can also be used in other pairwise alignments and applications.

Slow-release lubrication effect of graphene oxide/poly(ethylene glycol) wrapped in chitosan/sodium glycerophosphate hydrogel applied on artificial joints.

The artificial joints would go through serious wear after implantation surgery due to the poor lubrication of the body fluid, and the biomimetic lubricants directly injected in vitro is easy to be absorbed by human tissues, and after a period of time, it will lose its lubrication effect. However, the composite hydrogel with slow-release lubrication effect provides a new way for the lubrication of artificial joints. In this study, Graphene oxide/Poly(ethylene glycol) (GO/PEG) composites were prepared to improve the artificial joint lubrication, and through wrapped in the Chitosan/Sodium glycerophosphate (CS/GP) hydrogel, the GO/PEG lubricant will be released under the squeezing action, thus to prolong the service time of biomimetic lubricants. The friction experimental results showed that GO/PEG had better lubrication effect, and the average friction coefficient of the slow-release solution was below 0.03, especially with the pressure increasing. GO, PEG and small molecule GP in the slow-release solution through hydrogen-bond interaction might form a particular structure, which led to the good lubricating effect. The experiments of cell and acute toxicity in vivo showed that GO and its composite hydrogel had good biocompatibility.

Synthesis, structures and properties of six lanthanide complexes based on a 2-(2-carboxyphenyl)imidazo(4,5-f)-(1,10)phenanthroline ligand.

Six lanthanide complexes [Tb(2-NCP)2(NO3)] n (1), [Eu(2-NCP)2(3-PYC)] n (2), [Sm(2-NCP)2(3-PYC)] n (3), [Eu(2-NCP)(SA)] n (4), [Tb(2-NCP)(SA)] n (5) and [Tb(2-NCP)(AA)] n (6) (2-HNCP = 2-(2-carboxyphenyl)imidazo(4,5-f)-(1,10)phenanthroline, 3-HPYC = pyridine-3-carboxylic acid, H2SA = succinic acid, H2AA = adipic acid) have been synthesized by hydrothermal route, and the crystal structures were analyzed through elemental analysis, infrared spectroscopy, single crystal X-ray diffraction. Complexes 1 and 6 present two-dimensional (2D) layers, which are further connected to three-dimensional (3D) supramolecular architectures through C-H⋯π interactions. Complexes 2 and 3 exhibit infinite one-dimensional chains. Finally, the neighboring chains are packed by C-H⋯π interactions, giving rise to 3D supramolecular network. Complexes 4 and 5 display 2D layers, which are further extend to 3D supramolecular structures via C-H⋯O intermolecular hydrogen bonding. Six complexes possess good thermal stabilities, characteristic photoluminescence properties, and photocatalytic activities for the degradation of organic dyes under visible light irradiation. In addition, the complex 6 exhibits significantly enhanced photocatalytic activity for methylene blue, and the degradation rate could reach 81.2% in 370 min. Meanwhile trapping experiments indicated that the hole, ·O2 - and ·OH are the main activated species. Furthermore, by comparing the photoluminescent and photocatalytic mutation results of same metal complexes induced by interconversion of coligands, we confirm that the properties mutation induced by coligands is much obvious and controllable.

Carbon dots intensified poly (ethylene glycol)/chitosan/sodium glycerophosphate hydrogel as artificial synovium tissue with slow-release lubricant.

The ultra-high molecular weight polyethylene (UHMWPE) and metal artificial joint pair is limited by wear debris and short service life. Here we report the development of a hydrogel which exhibits lubricant release to intensify the lubrication effect of artificial joints.This study adopted an injectable method to prepare carbon dots/poly (ethylene glycol)/chitosan/sodium glycerophosphate (CDs/PEG/CS/GP) composite hydrogel, and the carbon dots were used to intensify the rheological and mechanical properties. In addition, the composite hydrogel had slow-release properties, and the release solution contained CDs, PEG and GP has excellent lubrication effect. At last, the MTT assay, LIVE/DEAD staining, H&E staining results and safety evaluation in BALC/c mice proved that the hydrogels had good biocompatibilility and were safety for application in vivo.

Poly(ethylene glycol)/chitosan/sodium glycerophosphate gel replaced the joint capsule with slow-release lubricant after joint surgery.

Body fluid is normally the only lubricant after joint replacement surgery, but wear problems have occurred because body fluid has poor lubrication ability. However, traditional lubricant would be diluted by body fluids and then absorbed by the human body. Therefore, an injectable gel with the ability to slow-release lubricant was designed to replace the joint capsule. The proposed gel, poly(ethylene glycol)/chitosan/sodium glycerophosphate (PEG/CS/GP) composite gel was then tested. The tribology results showed that the PEG/CS/GP gel had excellent slow-release properties, especially under pressure, and the PEG played an important role in improving the gel's rheological and mechanical properties. Moreover, this study revealed that the release solution had a good lubrication effect because the PEG and GP could crosslink via the hydrogen bond effect.

Efficient Acceleration of the Pair-HMMs Forward Algorithm for GATK HaplotypeCaller on Graphics Processing Units.

GATK HaplotypeCaller (HC) is a popular variant caller, which is widely used to identify variants in complex genomes. However, due to its high variants detection accuracy, it suffers from long execution time. In GATK HC, the pair-HMMs forward algorithm accounts for a large percentage of the total execution time. This article proposes to accelerate the pair-HMMs forward algorithm on graphics processing units (GPUs) to improve the performance of GATK HC. This article presents several GPU-based implementations of the pair-HMMs forward algorithm. It also analyzes the performance bottlenecks of the implementations on an NVIDIA Tesla K40 card with various data sets. Based on these results and the characteristics of GATK HC, we are able to identify the GPU-based implementations with the highest performance for the various analyzed data sets. Experimental results show that the GPU-based implementations of the pair-HMMs forward algorithm achieve a speedup of up to 5.47× over existing GPU-based implementations.

Laser textured Co-Cr-Mo alloy stored chitosan/poly(ethylene glycol) composite applied on artificial joints lubrication.

Arthroplasty brings the wear problems because of body fluid has poor performance as lubricant. Lubricant which is used in artificial joints will rapidly degrade and be absorbed by human body after injecting. To prolong the lubricant's effectiveness, this study prepared chitosan/poly(ethylene glycol) (CS/PEG) and textures to play a role in joint lubrication and wear protection. Chitosan (CS) and poly(ethylene glycol) which have biocompatibility and biodegradability properties can be used in human body. The tribological results shown that CS/PEG sol has excellent performance when this sol was composed by 2wt% CS and 30wt% PEG, the average friction coefficient below 0.016 under the condition of 30-90N load (pressure 4.2-12.6MPa). In this study, CS/PEG was added in the texture of artificial joints, then the surfaces of the CS/PEG formed gel via NaOH solidification effect. The CS/PEG gel film could prevent the CS/PEG sol from diluting in body fluid. Meanwhile, FT-IR, XRD, UV/vis and Raman spectra revealed that CS associated with PEG via hydrogen bond effect may form a particular structure, which leaded the good tribological performance. This study provides a new, simple and green approach to enhance tribological performances of artificial joints.