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"Ying, Zheng"
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COVID-19 and the cardiovascular system
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects host cells through ACE2 receptors, leading to coronavirus disease (COVID-19)-related pneumonia, while also causing acute myocardial injury and chronic damage to the cardiovascular system. Therefore, particular attention should be given to cardiovascular protection during treatment for COVID-19.
Journal Article
Atherogenic index of plasma (AIP): a novel predictive indicator for the coronary artery disease in postmenopausal women
Background
Dyslipidemia is one of the most important factors for coronary artery disease (CAD). Atherogenic index of plasma (AIP) is a novel indicator involved in dyslipidemia. However, the relation between AIP and CAD in postmenopausal women remains unclear. We hypotheses that AIP is a strong predictive indicator of CAD in postmenopausal women.
Methods
A propensity score matching case–control study including 348 postmenopausal CAD cases and 348 controls was conducted in the present study.
Results
Compared with controls, CAD patients had higher levels of total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (APOB), but lower high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A-1 (APOA-1). The values of nontraditional lipid profiles, including non-HDL-C, TC/HDL-C, LDL-C/HDL-C, non-HDL-C/HDL-C (atherogenic index, AI), TC∗TG∗LDL/HDL-C (lipoprotein combine index, LCI), log(TG/HDL-C) (atherogenic index of plasma, AIP) and APOB/APOA-1 were all significantly higher in the CAD patients. The results of Pearson correlation analyses showed AIP was positively and significantly correlated with TC (
r
= 0.092,
P
< 0.001), TG (
r
= 0.775,
P
= 0.015), APOB (
r
= 0.140,
P
< 0.001), non-HDL-C (
r
= 0.295,
P
< 0.001), TC/HDL-C (
r
= 0.626,
P
< 0.001), LDL-C/HDL-C (
r
= 0.469,
P
< 0.001), AI (
r
= 0.626,
P
< 0.001), LCI (
r
= 0.665,
P
< 0.001), APOB/APOA-1(
r
= 0.290,
P
< 0.001) and was negatively correlated with APOA-1 (
r
= − 0.278,
P
< 0.001) and HDL-C (
r
= − 0.665,
P
< 0.001). In the multivariate logistic regression analysis, AIP was an independent predictor of CAD. After adjusting for the traditional clinical prognostic factors including diabetes and hypertension, we found AIP could be an independent risk factor for CAD (odds ratio [OR], 3.290; 95% confidence interval [CI], 1.842–5.877,
P
< 0.001). After adjusting for multiple clinical factors include diabetes, hypertension, smoking, heart ratio, fasting blood glucose, we found AIP also could a powerful risk factor, OR = 3.619, 95%CI (2.003–6.538),
P
< 0.001.
Conclusion
The present study indicated that AIP might be a strong marker for predicting the risk of CAD in postmenopausal women.
Journal Article
Synthesis and bioactivities evaluation of oleanolic acid oxime ester derivatives as α-glucosidase and α-amylase inhibitors
Different oleanolic acid (OA) oxime ester derivatives (3a-3t) were designed and synthesised to develop inhibitors against α-glucosidase and α-amylase. All the synthesised OA derivatives were evaluated against α-glucosidase and α-amylase in vitro. Among them, compound 3a showed the highest α-glucosidase inhibition with an IC
50
of 0.35 µM, which was ∼1900 times stronger than that of acarbose, meanwhile compound 3f exhibited the highest α-amylase inhibitory with an IC
50
of 3.80 µM that was ∼26 times higher than that of acarbose. The inhibition kinetic studies showed that the inhibitory mechanism of compounds 3a and 3f were reversible and mixed types towards α-glucosidase and α-amylase, respectively. Molecular docking studies analysed the interaction between compound and two enzymes, respectively. Furthermore, cytotoxicity evaluation assay demonstrated a high level of safety profile of compounds 3a and 3f against 3T3-L1 and HepG2 cells.
Highlights
Oleanolic acid oxime ester derivatives (3a-3t) were synthesised and screened against α-glucosidase and α-amylase.
Compound 3a showed the highest α-glucosidase inhibitory with IC50 of 0.35 µM.
Compound 3f presented the highest α-amylase inhibitory with IC50 of 3.80 µM.
Kinetic studies and in silico studies analysed the binding between compounds and α-glucosidase or α-amylase.
Journal Article
Few-shot learning based on deep learning: A survey
In recent years, with the development of science and technology, powerful computing devices have been constantly developing. As an important foundation, deep learning (DL) technology has achieved many successes in multiple fields. In addition, the success of deep learning also relies on the support of large-scale datasets, which can provide models with a variety of images. The rich information in these images can help the model learn more about various categories of images, thereby improving the classification performance and generalization ability of the model. However, in real application scenarios, it may be difficult for most tasks to collect a large number of images or enough images for model training, which also restricts the performance of the trained model to a certain extent. Therefore, how to use limited samples to train the model with high performance becomes key. In order to improve this problem, the few-shot learning (FSL) strategy is proposed, which aims to obtain a model with strong performance through a small amount of data. Therefore, FSL can play its advantages in some real scene tasks where a large number of training data cannot be obtained. In this review, we will mainly introduce the FSL methods for image classification based on DL, which are mainly divided into four categories: methods based on data enhancement, metric learning, meta-learning and adding other tasks. First, we introduce some classic and advanced FSL methods in the order of categories. Second, we introduce some datasets that are often used to test the performance of FSL methods and the performance of some classical and advanced FSL methods on two common datasets. Finally, we discuss the current challenges and future prospects in this field.
Journal Article
Efficient assembly of nanopore reads via highly accurate and intact error correction
Long nanopore reads are advantageous in de novo genome assembly. However, nanopore reads usually have broad error distribution and high-error-rate subsequences. Existing error correction tools cannot correct nanopore reads efficiently and effectively. Most methods trim high-error-rate subsequences during error correction, which reduces both the length of the reads and contiguity of the final assembly. Here, we develop an error correction, and de novo assembly tool designed to overcome complex errors in nanopore reads. We propose an adaptive read selection and two-step progressive method to quickly correct nanopore reads to high accuracy. We introduce a two-stage assembler to utilize the full length of nanopore reads. Our tool achieves superior performance in both error correction and de novo assembling nanopore reads. It requires only 8122 hours to assemble a 35X coverage human genome and achieves a 2.47-fold improvement in NG50. Furthermore, our assembly of the human WERI cell line shows an NG50 of 22 Mbp. The high-quality assembly of nanopore reads can significantly reduce false positives in structure variation detection.
Nanopore reads have been advantageous for de novo genome assembly; however these reads have high error rates. Here, the authors develop an error correction and de novo assembly tool, NECAT, which produces efficient, high quality assemblies of nanopore reads.
Journal Article
A novel displacement prediction method using gated recurrent unit model with time series analysis in the Erdaohe landslide
Landslides are natural phenomena, causing serious fatalities and negative impacts on socioeconomic. The Three Gorges Reservoir (TGR) area of China is characterized by more prone to landslides for the rainfall and variation of reservoir level. Prediction of landslide displacement is favorable for the establishment of early geohazard warning system. Conventional machine learning methods as forecasting models often suffer gradient disappearance and explosion, or training is slow. Hence, a dynamic method for displacement prediction of the step-wise landslide is provided, which is based on gated recurrent unit (GRU) model with time series analysis. The establishment process of this method is interpreted and applied to Erdaohe landslide induced by multi-factors in TGR area: the accumulative displacements of landslide are obtained by the global positioning system; the measured accumulative displacements is decomposed into the trend and periodic displacements by moving average method; the predictive trend displacement is fitted by a cubic polynomial; and the periodic displacement is obtained by the GRU model training. And the support vector machine (SVM) model and GRU model are used as comparisons. It is verified that the proposed method can quite accurately predict the displacement of the landslide, which benefits for effective early geological hazards warning system. Moreover, the proposed method has higher prediction accuracy than the SVM model.
Journal Article
Explainable machine learning for early detection of Escherichia coli urinary tract infections: integrating SHAP interpretation and bacterial epidemiology
is the predominant uropathogen in urinary tract infections (UTIs), but culture-based identification is time-consuming. This study aimed to develop an explainable, culture-independent model to distinguish
from other uropathogens using routinely collected clinical data.
We retrospectively analyzed 308 hospitalized patients with culture-confirmed UTIs at Fuding Hospital, Fujian University of Traditional Chinese Medicine (January-December 2023), classified as
(n = 158) or non-
i (n = 150). Species identification was performed using an automated microbiology system. Nineteen predictors (sex, urinary leukocyte grade, and 17 routine laboratory variables) were used. Associations with
UTI were examined using univariate and multivariable logistic regression. A Random Forest (RF) classifier was developed with SHapley Additive exPlanations (SHAP) for interpretability. Data were split using a stratified 70/30 train-test split; 5-fold stratified cross-validation within the training set was used for hyperparameter tuning, and final performance (discrimination and calibration) was reported on the held-out test set. RF was additionally benchmarked against regularized logistic regression, calibrated linear SVM, and gradient boosting using the same protocol.
accounted for 51.3% of isolates, followed by
spp. (18.5%) and
spp. (7.8%). Compared with non-
cases,
infections were more common in females and showed higher lymphocyte counts (LYM), alanine aminotransferase (ALT), and albumin (ALB) (all P < 0.05). Multivariable logistic regression identified sex, LYM, and urinary leukocyte grade as independent predictors. On the held-out test set, RF achieved moderate discrimination (ROC-AUC = 0.66; average precision = 0.66) with calibration assessed by Brier score and calibration slope. SHAP highlighted Sex, LYM, and ALT as the most influential predictors and revealed patient-level heterogeneity in feature effects.
remains the predominant pathogen among hospitalized UTIs. An explainable RF model using routine laboratory variables provided moderate, reproducible discrimination of
vs non-
UTIs and may support earlier decision-making while awaiting culture results.
Journal Article
New evidence of trends in cognitive function among middle-aged and older adults in China, 2011-2018: an age-period-cohort analysis
Background
Increasing evidence from high-income countries suggests the risk of cognitive impairment has been declining recently. However, related studies in China have rarely been done, and the results are inconsistent. We analyze the trends in cognitive function among middle-aged and older adults in China between 2011 and 2018.
Methods
We used data from four waves of the China Health and Retirement Longitudinal Study (CHARLS), including 48918 individuals aged 45 years and older. Cognitive function was assessed using the CHARLS cognitive measures containing episodic memory, orientation, attention, and visuospatial abilities. The hierarchical age-period-cohort (APC) model was used to quantify the separate age, period, and cohort effects on trends in cognitive function.
Results
The study sample’s ages ranged from 45 to 105 years (Mean = 59.2, SD = 9.4). Cognitive function declined with age net of period and cohort effects, an apparent acceleration in the rate of cognitive decline after age 65 was found adjusting for individual characteristics. Although period effects on trends in cognitive function remained stable during the study period, hierarchical APC models demonstrated significant cohort variations. Independent of age and period effects, there was a fluctuating trend across cohorts before 1960 and an overall decline across successive cohorts.
Conclusions
Our study indicates that the age effect remains the most crucial factor regarding cognitive decline. Moreover, results demonstrate that cohorts living in social upheaval leading to educational deprivation and/or nutritional deficiency in early life may face a higher risk for cognitive deterioration later in life. Such findings indicate that dementia prevention from a life course perspective and cohort-specific strategies are critical to alleviating the future public-health burdens related to cognitive aging. Ongoing attention should be paid to the role of cross-cohort differences in education on cohort trends in cognition in countries like China that are aging rapidly and have a late start in educational expansion compared to other countries. Other factors, such as environmental stimulation, need to be noticed in younger cohorts.
Journal Article
Efficiency of Multifunctional Antibacterial Hydrogels for Chronic Wound Healing in Diabetes: A Comprehensive Review
Diabetic chronic wounds or amputation, which are complications of diabetes mellitus (DM), are a cause of great suffering for diabetics. In addition to the lack of oxygen, elevated reactive oxygen species (ROS) and reduced vascularization, microbial invasion is also a critical factor that induces non-healing chronic diabetic wounds, ie, wounds still remaining in the stage of inflammation, after which the wound tissue begins to age and becomes necrotic. To clear up the infection, alleviate the inflammation in the wound and prevent necrosis, many kinds of hydrogel have been fabricated to eliminate infections with pathogens. The unique properties of hydrogels make them ideally suited to wound dressings because they provide a moist environment for wound healing and act as a barrier against bacteria. This review article will mainly cover the recent developments and innovations of antibacterial hydrogels for diabetic chronic wound healing.
Journal Article