Construction of Innovation Platform for CEEUSRO

On the basis of differentiating the meaning and function of platform of CEEUSRO, the contracture and the factors are discussed, and the approaches of organization and the administrative mechanism of the platform are put forward to supply specific thoughts to construct the platform of CEEUSRO

Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest

ObjectiveIn this study, we aim to identify the predictive variables for hemiplegic shoulder pain (HSP) through machine learning algorithms, select the optimal model and predict the occurrence of HSP.MethodsData of 332 stroke patients admitted to a tertiary hospital in Zhejiang Province from January 2022 to January 2023 were collected. After screening predictive variables by LASSO regression, three predictive models selected using the LazyPredict package, namely logistic regression (LR), support vector machine (SVM) and random forest (RF), were established respectively. The performance parameters (accuracy, precision, recall, and F1 score) of the models were calculated, the receiver operating characteristic curve (ROC) and the decision curve analysis (DCA) were plotted to compare the performance of the three models. An explainability analysis (SHAP) was conducted on the optimal model.ResultsThe RF model performed the best, with accuracy: 0.90, precision: 0.89, recall: 0.88, F1 score: 0.86, AUC-ROC: 0.94, and the range of the threshold probability in DCA: 7%−99%. Based on the SHAP analysis of the explainability of the RF model, the contribution degrees of the early HSP predictive variables from high to low are as follows: multiple injuries, shoulder joint flexion (p), biceps tendon effusion, sensory disorder, supraspinatus tendinopathy, subluxation, diabetes, and age.ConclusionThe RF prediction model has a good predictive effect on HSP and has good clinical explainability. It can provide objective references for the early warning and stratified management of HSP

Antibacterial, injectable, and adhesive hydrogel promotes skin healing

With the development of material science, hydrogels with antibacterial and wound healing properties are becoming common. However, injectable hydrogels with simple synthetic methods, low cost, inherent antibacterial properties, and inherent promoting fibroblast growth are rare. In this paper, a novel injectable hydrogel wound dressing based on carboxymethyl chitosan (CMCS) and polyethylenimine (PEI) was discovered and constructed. Since CMCS is rich in -OH and -COOH and PEI is rich in -NH2, the two can interact through strong hydrogen bonds, and it is theoretically feasible to form a gel. By changing their ratio, a series of hydrogels can be obtained by stirring and mixing with 5 wt% CMCS aqueous solution and 5 wt% PEI aqueous solution at volume ratios of 7:3, 5:5, and 3:7. Characterized by morphology, swelling rate, adhesion, rheological properties, antibacterial properties, in vitro biocompatibility, and in vivo animal experiments, the hydrogel has good injectability, biocompatibility, antibacterial (Staphylococcus aureus: 56.7 × 107 CFU/mL in the blank group and 2.5 × 107 CFU/mL in the 5/5 CPH group; Escherichia coli: 66.0 × 107 CFU/mL in the blank group and 8.5 × 107 CFU/mL in the 5/5 CPH group), and certain adhesion (0.71 kPa in the 5/5 CPH group) properties which can promote wound healing (wound healing reached 98.02% within 14 days in the 5/5 CPH group) and repair of cells with broad application prospects

Assessing the predictive value of smoking history for immunotherapy outcomes in bladder cancer patients

BackgroundThe therapeutic effectiveness of immune checkpoint inhibitors (ICIs) in bladder cancer varies among individuals. Identifying reliable predictors of response to these therapies is crucial for optimizing patient outcomes.MethodsThis retrospective study analyzed 348 bladder cancer patients treated with ICIs, with additional validation using data from 248 patients at our institution who underwent PD-L1 immunohistochemical staining. We examined patient smoking history, clinicopathological characteristics, and immune phenotypes. The main focus was the correlation between smoking history and immunotherapy outcomes. Multivariate logistic and Cox proportional hazard regressions were used to adjust for confounders.ResultsThe study cohort comprised 348 bladder cancer patients receiving ICIs. Among them, 116 (33.3%) were never smokers, 197 (56.6%) were former smokers (median pack-years = 28), and 35 (10.1%) were current smokers (median pack-years = 40). Analysis revealed no statistically significant difference in overall survival across different smoking statuses (objective response rates were 11.4% for current smokers, 17.2% for never smokers, and 22.3% for former smokers; P = 0.142, 0.410, and 0.281, respectively). However, a notable trend indicated a potentially better response to immunotherapy in former smokers compared to current and never smokers. In the validation cohort of 248 patients from our institution, immunohistochemical analysis showed that PD-L1 expression was significantly higher in former smokers (55%) compared to current smokers (37%) and never smokers (47%). This observation underscores the potential influence of smoking history on the tumor microenvironment and its responsiveness to ICIs.ConclusionIn conclusion, our study demonstrates the importance of incorporating smoking history in predicting the response to immunotherapy in bladder cancer patients, highlighting its role in personalized cancer treatment approaches. Further research is suggested to explore the comprehensive impact of lifestyle factors on treatment outcomes

The Calcineurin-TFEB-p62 Pathway Mediates the Activation of Cardiac Macroautophagy by Proteasomal Malfunction

Rationale: The ubiquitin-proteasome system (UPS) and the autophagic-lysosomal pathway (ALP) are pivotal to proteostasis. Targeting these pathways is emerging as an attractive strategy for treating cancer. However, a significant proportion of patients who receive a proteasome inhibitor-containing regime show cardiotoxicity. Moreover, UPS and ALP defects are implicated in cardiac pathogenesis. Hence, a better understanding of the cross-talk between the two catabolic pathways will help advance cardiac pathophysiology and medicine.Objective: Systemic proteasome inhibition (PSMI) was shown to increase p62/SQSTM1 expression and induce myocardial macroautophagy. Here we investigate how proteasome malfunction activates cardiac ALP.Methods and Results: Myocardial macroautophagy, transcription factor EB (TFEB) expression and activity, and p62 expression were markedly increased in mice with either cardiomyocyte-restricted ablation of Psmc1 (an essential proteasome subunit gene) or pharmacological PSMI. In cultured cardiomyocytes, PSMI-induced increases in TFEB activation and p62 expression were blunted by pharmacological and genetic calcineurin inhibition and by siRNA-mediated Molcn1 silencing. PSMI induced remarkable increases in myocardial autophagic flux in wild type (WT) mice but not p62 null (p62-KO) mice. Bortezomib-induced left ventricular wall thickening and diastolic malfunction was exacerbated by p62 deficiency. In cultured cardiomyocytes from WT mice but not p62-KO mice, PSMI induced increases in LC3-II flux and the lysosomal removal of ubiquitinated proteins. Myocardial TFEB activation by PSMI as reflected by TFEB nuclear localization and target gene expression was strikingly less in p62-KO mice compared with WT mice.Conclusions: (1) The activation of cardiac macroautophagy by proteasomal malfunction is mediated by the Mocln1-calcineurin-TFEB-p62 pathway; (2) p62 unexpectedly exerts a feed-forward effect on TFEB activation by proteasome malfunction; and (3) targeting the Mcoln1-calcineurin-TFEB-p62 pathway may provide new means to intervene cardiac ALP activation during proteasome malfunction

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Research and Design of an Improved ElGamal Digital Signature Algorithm

Abstract Firstly, the digital signature technology and its basic principle are introduced. Then, an ElGamal digital signature algorithm based on discrete logarithm is described, and two aspects of security and signature efficiency are improved on the basis of this algorithm, and the correctness, security and complexity of the improved scheme are analyzed. The results show that the improved ElGamal algorithm achieves the purpose of improvement.</jats:p

GPSClean: A Framework for Cleaning and Repairing GPS Data

The rise of GPS-equipped mobile devices has led to the emergence of big trajectory data. The collected raw data usually contain errors and anomalies information caused by device failure, sensor error, and environment influence. Low-quality data fails to support application requirements and therefore raw data will be comprehensively cleaned before usage. Existing methods are suboptimal to detect GPS data errors and do the repairing. To solve the problem, we propose a framework called GPSClean to analyze the anomalies data and develop effective methods to repair the data. There are primarily four modules in GPSClean : (i) data preprocessing, (ii) data filling, (iii) data repairing, and (iv) data conversion. For (i), we propose an approach named MDSort (Maximum Disorder Sorting) to efficiently solve the issue of data disorder. For (ii), we propose a method named NNF (Nearest Neighbor Filling) to fill missing data. For (iii), we design an approach named RCSWS (Range Constraints and Sliding Window Statistics) to repair anomalies and also improve the accuracy of data repairing by mak7ing use of driving direction. We use 45 million real trajectory data to evaluate our proposal in a prototype database system SECONDO. Experimental results show that the accuracy of RCSWS is three times higher than an alternative method SCREEN and nearly an order of magnitude higher than an alternative method EWMA. </jats:p