Change in coronary heart disease hospitalization after chronic disease management: a programme policy in China

This study aims at examining changes in coronary heart disease (CHD) hospitalisation associated with a novel county-scale chronic disease management (CDM) program policy implemented in March 2019 in China during the Thirteenth Five-Year period (Years 2016-2020). The CDM program was designed to improve the health of populations with chronic diseases by means of an integrated way involving both county-level public hospitals and primary care institutes. Data originated from the medical files of CHD inpatients discharged from a secondary hospital from January 2017 to December 2020. A total of 6,111 CHD patient records was collected. Univariate and multivariate regression analyses were performed to assess changes in hospitalisation direct medical costs and length of stay of CHD patients. The mean direct medical cost of CHD hospitalisation was 8,419.73 Yuan and mean length of stay was 7.57 days. Results suggested that the implementation of CDM reduced hospitalisation direct medical cost and bed days by about 23% (1,956.12 Yuan at means) and 11.5% (almost 1 day at means) respectively. In addition, a further decreasing trend in medical cost over time was associated with chronic disease management. It is implied that chronic disease management is an effective way of relieving medical and financial burden of hospitalisation

GDN: A Stacking Network Used for Skin Cancer Diagnosis

Skin cancer, the primary type of cancer that can be identified by visual recognition, requires an automatic identification system that can accurately classify different types of lesions. This paper presents GoogLe-Dense Network (GDN), which is an image-classification model to identify two types of skin cancer, Basal Cell Carcinoma, and Melanoma. GDN uses stacking of different networks to enhance the model performance. Specifically, GDN consists of two sequential levels in its structure. The first level performs basic classification tasks accomplished by GoogLeNet and DenseNet, which are trained in parallel to enhance efficiency. To avoid low accuracy and long training time, the second level takes the output of the GoogLeNet and DenseNet as the input for a logistic regression model. We compare our method with four baseline networks including ResNet, VGGNet, DenseNet, and GoogLeNet on the dataset, in which GoogLeNet and DenseNet significantly outperform ResNet and VGGNet. In the second level, different stacking methods such as perceptron, logistic regression, SVM, decision trees and K-neighbor are studied in which Logistic Regression shows the best prediction result among all. The results prove that GDN, compared to a single network structure, has higher accuracy in optimizing skin cancer detection.Comment: Published at ICSPS 202

Large Language Models are Good Prompt Learners for Low-Shot Image Classification

Low-shot image classification, where training images are limited or inaccessible, has benefited from recent progress on pre-trained vision-language (VL) models with strong generalizability, e.g. CLIP. Prompt learning methods built with VL models generate text features from the class names that only have confined class-specific information. Large Language Models (LLMs), with their vast encyclopedic knowledge, emerge as the complement. Thus, in this paper, we discuss the integration of LLMs to enhance pre-trained VL models, specifically on low-shot classification. However, the domain gap between language and vision blocks the direct application of LLMs. Thus, we propose LLaMP, Large Language Models as Prompt learners, that produces adaptive prompts for the CLIP text encoder, establishing it as the connecting bridge. Experiments show that, compared with other state-of-the-art prompt learning methods, LLaMP yields better performance on both zero-shot generalization and few-shot image classification, over a spectrum of 11 datasets. Code will be made available at: https://github.com/zhaohengz/LLaMP.Comment: CVPR 202

Study on the effect of hydrothermal charcoal source modifier on saline-alkaline soil improvement

Land salinization has become a global issue. To mitigate this issue, the modifiers and water-soluble fertilizers have emerged as a promising strategy to enhance soil nutrient content and promote crop production in saline soils. In this study, based on the hydrothermal method for preparing corn stalks derived biochar prepared modifiers and water-soluble fertilizers for using in soda saline-alkali soil. Through field microplot experiments, the separate and interactive effects of different concentrations of them were studied, as well as their influence on the growth of alfalfa and Leymus chinensis on saline-alkali soil. The combined application of modifiers and water-soluble fertilizers can effectively increase soil nutrient content and enzyme activity, significantly reducing soil pH and alkalinity. It was found that the optimal application rate of 20 g/kg of improver resulted in a 4.99% decrease in pH of soil and 11.23% decrease in alkalinity. Additionally, organic matter, available P2O5, NH4+-N, and NO3--N contents increased by 25.74%, 28.48%, 19.87%, and 32.90%, respectively. Soil enzyme activities generally peaked at 20 g/kg of modifiers and water-soluble fertilizers, with sucrase showing the most significant increases, with 31.14% for alfalfa and 25.52% for L. chinensis. Two-way ANOVA results demonstrated significant interaction effects between the modifiers and water-soluble fertilizer on pH, Ca2+ and Mg2+ content, quick-acting potassium content, soil sucrase and urease activity, biomass of soil and alfalfa leaf width. Moreover, planting alfalfa and L. chinensis indicated that combined use of modifies and fertilizer had a significant effect on promoting crop cultivation in saline-alkali soil. Our findings provided a robust theoretical groundwork for improving the management of saline soils and optimizing crop production in such challenging environments

Growth, Nutrient Uptake, and Foliar Gas Exchange in Pepper Cultured with Un-composted Fresh Spent Mushroom Residue

Spent mushroom substrate (SMS) can be used as the component of growing medium for the culture of crop plants. Fresh SMS may have the potential as an alternative to peat to raise horticultural plants. In this study, five container media characterized by the proportions of SMS to commercial peat in 0% (control), 25%, 50%, 75%, and 100% were used to raise pepper (Capsicum annum L.) plants. Initial SMS was found to have low available nitrogen (N) content (<20 mg kg-1) but moderate extractable phosphorus (P) content (900 mg kg-1). In the second month photosynthetic rate was found to decline in the 75% treatment. At harvest in the third month, plants in the 100% treatment nearly died out. The 25% treatment resulted in the highest height (19 cm) and diameter growth (0.3 cm), shoot (0.6 g) and root biomass accumulation (0.13 g), fruit weight (3 g), and shoot carbohydrate content (98 mg g-1), but lowest foliar acid phosphatase activity (30 µg NPP g-1 FW min-1). With the increase of SMS proportion in the substrate, the medium pH and electrical conductance (EC) increased with the decrease of foliar size. The available N and P contents in the substrates showed contrasting relationship with N and P contents in pepper plants. Therefore, fresh SMS cannot be directly used as the substrate for the culture of pepper plants. According to our findings fresh SMS was recommended to be mixed in the proportion of 25% with commercial peat for the culture of horticultural plants

Potentially Functional Variants of PLCE1 Identified by GWASs Contribute to Gastric Adenocarcinoma Susceptibility in an Eastern Chinese Population

BACKGROUND: Recent genome-wide association studies (GWAS) have found a single nucleotide polymorphism (SNP, rs2274223 A>G) in PLCE1 to be associated with risk of gastric adenocarcinoma. In the present study, we validated this finding and also explored the risk associated with another unreported potentially functional SNP (rs11187870 G>C) of PLCE1 in a hospital-based case-control study of 1059 patients with pathologically confirmed gastric adenocarcinoma and 1240 frequency-matched healthy controls. METHODOLOGY/PRINCIPAL FINDINGS: We determined genotypes of these two SNPs by the Taqman assay and used logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (95% CI). We found that a significant higher gastric adenocarcinoma risk was associated with rs2274223 variant G allele (adjusted OR = 1.35, 95% CI = 1.14-1.60 for AG+GG vs. AA) and rs11187870 variant C allele (adjusted OR = 1.26, 95% CI = 1.05-1.50 for CG+CC vs. GG). We also found that the number of combined risk alleles (i.e., rs2274223G and rs11187870C) was associated with risk of gastric adenocarcinoma in an allele-dose effect manner (P(trend) = 0.0002). Stratification analysis indicated that the combined effect of rs2274223G and rs11187870C variant alleles was more evident in subgroups of males, non-smokers, non-drinkers and patients with gastric cardia adenocarcinoma. Further real-time PCR results showed that expression levels of PLCE1 mRNA were significantly lower in tumors than in adjacent noncancerous tissues (0.019±0.002 vs. 0.008±0.001, P<0.05). CONCLUSIONS/SIGNIFICANCES: Our results further confirmed that genetic variations in PLCE1 may contribute to gastric adenocarcinoma risk in an eastern Chinese population

Insight of novel biomarkers for papillary thyroid carcinoma through multiomics

IntroductionThe overdiagnosing of papillary thyroid carcinoma (PTC) in China necessitates the development of an evidence-based diagnosis and prognosis strategy in line with precision medicine. A landscape of PTC in Chinese cohorts is needed to provide comprehensiveness.Methods6 paired PTC samples were employed for whole-exome sequencing, RNA sequencing, and data-dependent acquisition mass spectrum analysis. Weighted gene co-expression network analysis and protein-protein interactions networks were used to screen for hub genes. Moreover, we verified the hub genes' diagnostic and prognostic potential using online databases. Logistic regression was employed to construct a diagnostic model, and we evaluated its efficacy and specificity based on TCGA-THCA and GEO datasets.ResultsThe basic multiomics landscape of PTC among local patients were drawn. The similarities and differences were compared between the Chinese cohort and TCGA-THCA cohorts, including the identification of PNPLA5 as a driver gene in addition to BRAF mutation. Besides, we found 572 differentially expressed genes and 79 differentially expressed proteins. Through integrative analysis, we identified 17 hub genes for prognosis and diagnosis of PTC. Four of these genes, ABR, AHNAK2, GPX1, and TPO, were used to construct a diagnostic model with high accuracy, explicitly targeting PTC (AUC=0.969/0.959 in training/test sets).DiscussionMultiomics analysis of the Chinese cohort demonstrated significant distinctions compared to TCGA-THCA cohorts, highlighting the unique genetic characteristics of Chinese individuals with PTC. The novel biomarkers, holding potential for diagnosis and prognosis of PTC, were identified. Furthermore, these biomarkers provide a valuable tool for precise medicine, especially for immunotherapeutic or nanomedicine based cancer therapy

Hierarchical structure LiFePO4@C synthesized by oleylamine-mediated method for low temperature applications

NSFC [U1305246, 21321062]; Major Project; Xiamen city [3502Z20121002]In this paper, a hierarchical nanostructure LiFePO4@C composite was firstly fabricated by an oleylamine mediated method. The oleylamine played a multifunctional role in restricting the particle size and forming the porous nano-structure of LiFePO4@C composite. Benefiting from its hierarchical structure, LiFePO4@C exhibited superior electrochemical performance, especially at low temperature. It can deliver a capacity of 117 mA h g(-1) at a current density of up to 700 mA g(-1) (about 5 C) at -20 degrees C

Multi-ancestry genome-wide gene–smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids

The concentrations of high- and low-density-lipoprotein cholesterol and triglycerides are influenced by smoking, but it is unknown whether genetic associations with lipids may be modified by smoking. We conducted a multi-ancestry genome-wide gene–smoking interaction study in 133,805 individuals with follow-up in an additional 253,467 individuals. Combined meta-analyses identified 13 new loci associated with lipids, some of which were detected only because association differed by smoking status. Additionally, we demonstrate the importance of including diverse populations, particularly in studies of interactions with lifestyle factors, where genomic and lifestyle differences by ancestry may contribute to novel findings