Predictors of quitting support from nonsmoking mothers for smoking fathers: a cross-sectional study from Chinese pupils' families.

BACKGROUND: Quitting support from smokers' partners can predict quit attempts and smoking abstinence but research on factors that predict such support has been limited. To add more evidence for partner support and the improved interventions for smoking cessation, we analyzed some new potential predictors of quitting support from smokers' spouses. METHOD: This cross-sectional study was conducted in in 2022 and 2023, selecting the students' families in which fathers smoked and mothers didn't smoke from grade 1-5 of 13 primary schools in Qingdao, China. Parents who met the criteria completed the online questionnaires and 1018 families were included in the analysis. We measured personal information related to smokers and their spouses such as age, education and nicotine dependence, and variables related to family and marital relationship such as family functioning, perceived responsiveness and power in decision-making of quitting smoking. Quitting support from smokers' spouses was measured by Partner Interaction Questionnaire and generalized linear model was used to explore the potential predictors of partner support. RESULTS: In this study, the mean age of smokers was 39.97(SD = 5.57) and the mean age of smokers' spouses was 38.24(SD = 4.59). The regression analysis showed that for smokers and their spouses, the older age groups showed the lower ratio of positive/negative support(P < 0.05) and smokers with high education showed the less positive and negative partner support(P < 0.05). Nicotine dependence was positively associated with negative support (ß = 0.120, P < 0.01), and perceived responsiveness (ß = 0.124, P < 0.05) as well as family functioning (ß = 0.059, P < 0.05) was positively associated with positive support. These three factors were associated with ratio of positive/negative support(P < 0.05). In addition, power of smoker's spouse in decision-making of quitting smoking was positively associated with the positive (ß = 0.087, P < 0.001) and negative support (ß = 0.084, P < 0.001). CONCLUSIONS: Nicotine dependence, family functioning, power in decision-making of quitting smoking and perceived responsiveness were found to be the predictors of quitting support from smokers' spouses. By incorporating predictors of partner support and integrating some established theories that can improve family functioning and marital relationships, smoking cessation interventions can be further improved.

Food Waste and Lunar Phases: Evidence from a Randomized Controlled Trial.

To examine a potential correlation between food waste and lunar phases, we have devised a randomized controlled trial. The experiment spanned from 31 March to 10 July 2022, during which we employed the direct weighing method to collect 1903 valid data points on food waste. Utilizing propensity score matching, we meticulously controlled for various factors, including dining dates, the number of diners, dining times, spending levels, and store activities. The study revealed a close relationship between lunar phases and food waste. During the new moon phase, there was an increase in both orders and waste generated by consumers. Specifically, individuals, on average, squandered an additional 6.27% of animal protein (0.79 g), 24.5% of plant protein (1.26 g), 60.95% of starchy foods (3.86 g), and 61.09% of vegetables (5.12 g), resulting in an aggregate food waste of 32.14% (10.79 g). Conversely, during the full moon phase, consumers decreased their orders and subsequently decreased food waste. On average, individuals wasted 44.65% less animal protein (5.76 g), 43.36% less plant protein (2.5 g), 85.39% less seafood (0.73 g), and 8.43% less vegetables (0.93 g), resulting in a 20.52% (7.81 g) reduction in food waste. Furthermore, we validated our conclusions through various validation methods, including model replacement, to ensure robustness and reliability.

Anti-diabetic effect of anthocyanin cyanidin-3-O-glucoside: data from insulin resistant hepatocyte and diabetic mouse.

BACKGROUND: Anthocyanins are a group of natural products widely found in plants. They have been found to alleviate the disorders of glucose metabolism in type 2 diabetes mellitus (T2DM), while the underlying mechanisms remain unclear. METHODS: HepG2 and L02 cells were incubated with 0.2 mM PA and 30 mM glucose for 24 h to induce IR, and cells treated with 5 mM glucose were used as the control. C57BL/6 J male mice and db/db male mice were fed with a chow diet and gavaged with pure water or cyanidin-3-O-glucoside (C3G) solution (150 mg/kg/day) for 6 weeks. RESULTS: In this study, the anthocyanin C3G, extracted from red bayberry, was found to alleviate disorders of glucose metabolism, which resulted in increased insulin sensitivity in hepatocytes, and achieved by enhancing the glucose consumption as well as glycogen synthesis in insulin resistance (IR) hepatpcytes. Subsequently, the expression of key proteins involved in IR was detected by western blotting analysis. Protein tyrosine phosphatase-1B (PTP1B), a negative regulator of insulin signaling, could reduce cellular sensitivity to insulin by inhibiting the phosphorylation of insulin receptor substrate-2 (IRS-2). Results of this study showed that C3G inhibited the increase in PTP1B after high glucose and palmitic acid treatment. And this inhibition was accompanied by increased phosphorylation of IRS proteins. Furthermore, the effect of C3G on improving IR in vivo was validated by using a diabetic db/db mouse model. CONCLUSION: These findings demonstrated that C3G could alleviate IR in vitro and in vivo to increase insulin sensitivity, which may offer a new insight for regulating glucose metabolism during T2DM by using the natural dietary bioactive components. C3G promotes the phosphorylation of IRS-2 proteins by suppressing the expression of PTP1B, and then enhances the sensitivity of hepatocyte to insulin.

Gelatin-based multifunctional composite films integrated with dialdehyde carboxymethyl cellulose and coffee leaf extract for active food packaging.

In this study, dialdehyde carboxymethyl cellulose (DCMC, 10 wt% based on gelatin) and varying contents of coffee leaf extract (CLE, 1, 3, 5 and 7 wt% based on gelatin) were incorporated into gelatin (GEL) matrix to develop multifunctional food packaging films. DCMC acted as a physical reinforcing filler through crosslinking with GEL matrix by Schiff-base reaction, CLE served as an active filler to confer film functional properties. The micro-morphology, micro-structure, physicochemical and functional properties of the GEL/DCMC/CLE composite film were investigated. The results demonstrated that mechanical, barrier properties and thermal stability of films were significantly improved by incorporation of CLE. Compared with pure GEL film, the GEL/DCMC/5%CLE film exhibited excellent UV light blocking while kept enough transparency, the best mechanical property, water resistance, water vapor and oxygen barrier, as well as thermal stability. GEL/DCMC/5%CLE film also possessed strong antioxidant activity and some antibacterial activity against E. coli and S. aureus. Packaging application testing demonstrated that the resultant GEL/DCMC/5%CLE film effectively delayed the lipid oxidation of walnut oil and preserved the postharvest freshness of fresh walnut kernels under ambient conditions.

Disulfidptosis and its Role in Peripheral Blood Immune Cells after a Stroke: A New Frontier in Stroke Pathogenesis.

BACKGROUND: Stroke-Induced Immunodepression (SIID) is characterized by apoptosis in blood immune populations, such as T cells, B cells, NK cells, and monocytes, leading to the clinical presentation of lymphopenia. Disulfidptosis is a novel form of programmed cell death characterized by accumulating disulfide bonds in the cytoplasm, resulting in cellular dysfunction and eventual cell death. OBJECTIVE: In this study, we investigated the association between disulfidptosis and stroke by analyzing gene sequencing data from peripheral blood samples of stroke patients. METHODS: Differential gene expression analysis identified a set of disulfidptosis-related genes (DRGs) significantly associated with stroke. Initial exploration identified 32 DRGs and their interactions. Our study encompassed several analyses to understand the molecular mechanisms of DRGs in stroke. Weighted Gene Co-Expression Network Analysis (WGCNA) uncovered modules of co-expressed genes in stroke samples, and differentially expressed gene (DEG) analysis highlighted 1643 key genes. RESULTS: These analyses converged on four hub genes of DRGs (SLC2A3, SLC2A14, SLC7A11, NCKAP1) associated with stroke. Immune cell composition analysis indicated positive correlations between hub genes and macrophages M1, M2, and neutrophils and negative associations with CD4+ and CD8+ T cells, B cells, and NK cells. Sub-cluster analysis revealed two distinct clusters with different immune cell expression profiles. Gene Set Enrichment Analysis (GSEA) demonstrated enrichment of apoptosis-related pathways, neurotrophin signaling, and actin cytoskeleton regulation. Associations between hub genes and apoptosis, necroptosis, ferroptosis, and cuproptosis, were also identified. CONCLUSION: These results suggest that the DRG hub genes are interconnected with various cell death pathways and immune processes, potentially contributing to stroke pathological development.

Humidity-adjustable functional gelatin hydrogel/ethyl cellulose bilayer films for active food packaging application.

A sustainable functional bilayer film composed of gelatin hydrogel/ethyl cellulose was fabricated using a simple LBL casting method. The outer layer was hydrophobic ethyl cellulose (EC), while the inner layer was hydrophilic gelatin (GEL) hydrogel. Tannic acid (TA) served as a green cross-linker for GEL hydrogel preparation and as a reductant for AgNPs synthesis in-situ within the hydrogel network. Physicochemical and functional properties of the bilayer films containing different TA content were studied. When 3 wt% TA was added, the AgNPs@GT-3/EC bilayer film exhibited superior UV-light barrier, possessed desirable humidity-adjustable capability and oxygen barrier due to denser hydrogel network structure, and effectively inactivated foodborne pathogens S. aureus and E. coli with bacteriostatic rates of 99 %. The application results indicated that this bilayer film effectively maintained the postharvest quality of white button mushrooms and prolonged their shelf-life to 7 days under ambient storage, demonstrating its promising potential for fresh food packaging.

Exploring the Effects of Cyanidin-3-O-Glucoside on Type 2 Diabetes Mellitus: Insights into Gut Microbiome Modulation and Potential Antidiabetic Benefits.

Berries and their functional components have been put forward as an alternative to pharmacological treatments of type 2 diabetes mellitus (T2DM), and more attention has been paid to the gut microbiome in the pathophysiology of T2DM. Thus, we tried to examine the metabolic impact of red bayberry-derived cyanidin-3-O-glucoside (C3G) and investigate whether the antidiabetic effects of C3G were associated with the gut microbiome. As a result, C3G administration was found to reduce blood glucose levels of diabetic db/db mice, accompanied by increased levels of glucagon-like peptide (GLP-1) and insulin. Moreover, 16S rRNA analysis showed that the dominant microbiota modulated by C3G were pivotal in the glucose metabolism. Furthermore, the modulation of C3G on metabolic activities of gut bacteria leads to an increase in intestinal levels of key metabolites, particularly short-chain fatty acids. This contribution helps in promoting the secretion of GLP-1, which in turn increases insulin release with the purpose of reducing blood glucose levels. Overall, these findings may offer new thoughts concerning C3G against metabolic disorders in T2DM.

The burden of fracture in China from 1990 to 2019.

PURPOSE: Osteoporosis is one of the most common clinical problems among the elderly population. China is one of the countries most threatened by osteoporosis and fragility fracture, because of its large population and aging population trends during recent decades. We aimed to estimate the disease burden of fracture from 1990 to 2019 in China. METHODS: We performed a secondary analysis of fractures using detailed information for China from the Global Burden of Disease Study 2019. Fracture incidence and prevalence, rate of years lost to disability from fractures, and term secular trends in China from 1990 to 2019 were compared by sex, age, cause, and nature of fracture. RESULTS: The numbers for incidence and prevalence of fracture and years lived with disability (YLDs) from fractures in China increased from 12.54 million, 28.35 million, and 1.71 million in 1990 to 21.27 million, 67.85 million, and 3.79 million in 2019, respectively, increases of 70%, 139%, and 122%, respectively. In 2019, falls was the leading cause of fractures, with an age-standardized incidence rate (ASIR) of 762 per 100 000 (95% uncertainty interval [UI] 629-906), an age-standardized prevalence rate (ASPR) of 1863 per 100 000 (95% UI 1663-2094), and an age-standardized YLD rate (ASYR) of 103 per 100 000 (95% UI 69-147). Fall-associated deaths and disability-adjusted life-years (DALYs) from low bone mineral density increased greatly during the most recent three decades. Fracture of patella, tibia or fibula, and ankle were the most frequent fracture types, with an ASYR of 116 per 100 000 (95% UI 75-169). Hip fracture had more incident cases in adults ≥ 60 years old, and was more frequent for females. CONCLUSIONS: The burden from fractures has increased significantly since 1990 in China. Falls and road injuries are the main causes of the increase. The fall-associated health burden from osteoporosis needs to be prioritized, with longer-term commitment to its reduction required.

Identification of disulfidptosis-related genes and immune infiltration in lower-grade glioma.

Lower-grade glioma (LGG), a prevalent malignant tumor in the central nervous system, requires accurate prediction and treatment to prevent aggressive progression. We aimed to explore the role of disulfidptosis-related genes (DRGs) in LGG, a recently discovered form of programmed cell death characterized by abnormal disulfide accumulation. Leveraging public databases, we analyzed 532 LGG tumor tissues (The Cancer Genome Atlas), 1,157 normal samples (Genotype-Tissue Expression), and 21 LGG tumor samples with 8 paired normal samples (GSE16011). Our research uncovered intricate relationships between DRGs and crucial aspects of LGG, including gene expression, immune response, mutation, drug sensitivity, and functional enrichment. Notably, we identified significant heterogeneity among disulfidptosis sub-clusters and elucidated specific differential gene expression in LGG, with myeloid cell leukemia-1 (MCL1) as a key candidate. Machine learning techniques validated the relevance of MCL1, considering its expression patterns, prognostic value, diagnostic potential, and impact on immune infiltration. Our study offers opportunities and challenges to unravel potential mechanisms underlying LGG prognosis, paving the way for personalized cancer care and innovative immunotherapeutic strategies. By shedding light on DRGs, particularly MCL1, we enhance understanding and management of LGG.

Toward Reagent-Free Discrimination of Alzheimer's Disease Using Blood Plasma Spectral Digital Biomarkers and Machine Learning.

BACKGROUND: Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. The detection of early-stage AD is particularly desirable because it would allow early intervention. However, a minimally invasive, low-cost, and accurate discrimination or diagnostic method for AD is especially difficult in the earliest stage of AD. OBJECTIVE: The aim of this research is to discover blood plasma spectral digital biomarkers of AD, develop a novel intelligent method for the discrimination of AD and accelerate the translation of Fourier transform infrared (FTIR) spectral-based disease discrimination methods from the laboratory to clinical practice. METHODS: Since vibration spectroscopy can provide the structure and chemical composition information of biological samples at the molecular level, we investigated the potential of FTIR spectral biomarkers of blood plasma to differentiate between AD patients and healthy controls. Combined with machine learning technology, we designed a hierarchical discrimination system that provides reagent-free and accurate AD discrimination based on blood plasma spectral digital biomarkers of AD. RESULTS: Accurate segregation between AD patients and healthy controls was achieved with 89.3% sensitivity and 85.7% specificity for early-stage AD patients, 92.8% sensitivity and 87.5% specificity for middle-stage AD patients, and 100% sensitivity and 100% specificity for late-stage AD patients. CONCLUSIONS: Our results show that blood plasma spectral digital biomarkers hold great promise as discrimination markers of AD, indicating the potential for the development of an inexpensive, reagent-free, and less laborious clinical test. As a result, our research outcome will accelerate the clinical application of spectral digital biomarkers and machine learning.

Acute Basophilic Leukemia Arising from Chronic Myeloid Leukemia with +8, I(17q)(q10) and der(22)t(9;22) After Imatinib Therapy.

Acute basophilic leukemia (ABL) arising from chronic myeloid leukemia (CML) with abundant mast cells (MCs), coexisting with a complex karyotype is rare. Here, we report an 81-year-old man admitted to our hospital with a history of ABL. He was diagnosed with CML in the chronic phase in January 2018, and Imatinib was used at a daily dose of 400mg. Then, transformation to ABL with abundant MCs in the bone marrow and complex karyotypes including 48,XY, trisomy 8 (+8), isochromosome 17(q10) [i(17)(q10)], and derivative chromosome 22 t(9;22) [der(22)t(9;22)] were discovered simultaneously in January 2022. In conclusion, the increased number of MCs in our case is a reminder that they might play an important role in the prognosis of CML and trigger the development of complex karyotypes. Moreover, this is the first case report of ABL arising from CML with abundant MCs, coexisting with 48,XY, +8, i(17)(q10), and der(22)t(9;22), during Imatinib treatment. Further studies are needed to better characterize this rare condition.

Artificial intelligence-driven electrochemical immunosensing biochips in multi-component detection.

Electrochemical Immunosensing (EI) combines electrochemical analysis and immunology principles and is characterized by its simplicity, rapid detection, high sensitivity, and specificity. EI has become an important approach in various fields, such as clinical diagnosis, disease prevention and treatment, environmental monitoring, and food safety. However, EI multi-component detection still faces two major bottlenecks: first, the lack of cost-effective and portable detection platforms; second, the difficulty in eliminating batch differences and accurately decoupling signals from multiple analytes. With the gradual maturation of biochip technology, high-throughput analysis and portable detection utilizing the advantages of miniaturized chips, high sensitivity, and low cost have become possible. Meanwhile, Artificial Intelligence (AI) enables accurate decoupling of signals and enhances the sensitivity and specificity of multi-component detection. We believe that by evaluating and analyzing the characteristics, benefits, and linkages of EI, biochip, and AI technologies, we may considerably accelerate the development of EI multi-component detection. Therefore, we propose three specific prospects: first, AI can enhance and optimize the performance of the EI biochips, addressing the issue of multi-component detection for portable platforms. Second, the AI-enhanced EI biochips can be widely applied in home care, medical healthcare, and other areas. Third, the cross-fusion and innovation of EI, biochip, and AI technologies will effectively solve key bottlenecks in biochip detection, promoting interdisciplinary development. However, challenges may arise from AI algorithms that are difficult to explain and limited data access. Nevertheless, we believe that with technological advances and further research, there will be more methods and technologies to overcome these challenges.

Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome.

Metabolic syndrome (MetS) is a complex metabolic disorder with a global prevalence of 20%-25%. Early identification and intervention would help minimize the global burden on healthcare systems. Here, we measured over 400 proteins from ∼20,000 proteomes using data-independent acquisition mass spectrometry for 7,890 serum samples from a longitudinal cohort of 3,840 participants with two follow-up time points over 10 years. We then built a machine-learning model for predicting the risk of developing MetS within 10 years. Our model, composed of 11 proteins and the age of the individuals, achieved an area under the curve of 0.774 in the validation cohort (n = 242). Using linear mixed models, we found that apolipoproteins, immune-related proteins, and coagulation-related proteins best correlated with MetS development. This population-scale proteomics study broadens our understanding of MetS and may guide the development of prevention and targeted therapies for MetS.

ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks.

Predicting protein stability change upon variation through a computational approach is a valuable tool to unveil the mechanisms of mutation-induced drug failure and develop immunotherapy strategies. Some previous machine learning-based techniques exhibit anti-symmetric bias toward destabilizing situations, whereas others struggle with generalization to unseen examples. To address these issues, we propose a gated graph neural network-based approach to predict changes in protein stability upon mutation. The model uses message passing to encode the links between the molecular structure and property after eliminating the non-mutant structure and creating input feature vectors. While doing so, it also incorporates the coordinates of the raw atoms to provide spatial insights into the chemical systems. We test the model on the Ssym, Myoglobin, Broom, and p53 datasets to demonstrate the generalization performance. Compared to existing approaches, our proposed method achieves improved linearity with symmetry in less time. The code for this study is available at: https://github.com/HongzhouTang/Pros-GNN.

Identification of disulfidptosis-related genes with immune infiltration in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common malignant primary tumor that is usually diagnosed at an advanced stage; thus, there is an urgent need for efficient and sensitive novel diagnostic markers to determine the prognosis and halt disease progression in patients with HCC. Disulfidptosis is a recently discovered form of programmed cell death, essentially an abnormal accumulation of intracellular bisulfides. Therefore, our study aimed to investigate the role of disulfidptosis-related genes (DRGs) in the pathogenesis of HCC. Based on public databases, our work demonstrates the relationship between DRG and expression, immunity, mutation/drug sensitivity, and functional enrichment in HCC. We also revealed the significant heterogeneity of HCC in different DRGs sub-clusters and in differentially expressed genes (DEGs), respectively. Subsequently, the most relevant candidate gene, SLC7A11, was screened by machine learning to further validate the significance of SLC7A11 in the clinical features, prognosis, nomogram pattern, and immune infiltration of HCC. Our study, which elucidates the potential mechanisms of DRGs and HCC, reveals that SLC7A11 can serve as a novel prognostic biomarker and provides opportunities and challenges for individualized cancer immunotherapy strategies.

[Pharmacokinetics and tissue distribution of four alkaloids in Ermiao Pills and Sanmiao Pills in normal and arthritic model rats].

This work aimed to investigate the differences of pharmacokinetics and tissue distribution of four alkaloids in Ermiao Pills and Sanmiao Pills in normal and arthritic model rats. The rat model of arthritis was established by injecting Freund's complete adjuvant, and ultra-high performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS) in the positive ion multiple reaction monitoring(MRM) mode was used for the determination of four alkaloids in plasma and tissues of normal and arthritic rats after administration of Ermiao Pills and Sanmiao Pills, respectively. The differences in pharmacokinetics and tissue distribution of the four active components were compared, and the effect of Achyranthis Bidentatae Radix on the major components of Sanmiao Pills was explored. This study established an UPLC-MS/MS for simultaneous determination of four alkaloids, and the specificity, linearity, accuracy, precision, and stability of this method all met the requirements. Pharmacokinetics study found that as compared with normal rats, the AUC and C_(max) of phellodendrine, magnoflorine, berberine and palmatine in model rats were significantly decreased after administration of Ermiao Pills, the clearance rate CL/F was significantly increased, and the distribution and tissue/plasma concentration ratio of the four alkaloids in the liver, kidney, and joint were significantly reduced. Achyranthis Bidentatae Radix increased the AUC of phellodendrine, berberine, and palmatine, reduced the clearance rate, and significantly increased the distribution of the four alkaloids in the liver, kidney, and joints in arthritic rats. However, it had no significant effect on the pharmacokinetics and tissue distribution of the four alkaloids in normal rats. These results suggest that Achyranthis Bidentatae Radix may play a guiding role in meridian through increasing the tissue distribution of effective components in Sanmiao Pills under arthritis states.

Global, regional and national burden of endocrine, metabolic, blood and immune disorders 1990-2019: a systematic analysis of the Global Burden of Disease study 2019.

Background: Endocrine, metabolic, blood and immune disorders (EMBID) is a vital public health problem globally, but the study on its burden and global trends was scarce. We aimed to evaluate the global burden of disease and trends in EMBID from 1990 to 2019. Methods: We extracted the data of EMBID-related on death cases, Age-standardized death rates (ASDRs), disability-adjusted life-years (DALYs), Age-standardized DALY rates, years of life lost (YLLs), Age-standardized YLL rates, years lived with disability (YLDs) and Age-standardized YLD rates between 1990 and 2019 from the Global Burden of Disease 2019, by sex, age, and year at the global and geographical region levels. The Annual rate of change was directly extracted from Global Health Data Exchange (GHDx) and we also calculated the age-related age-standardized rate (ASR) to quantify trends in EMBID-related deaths, DALYs, YLLs and YLDs. Result: Globally, the EMBID-related ASDRs showed an increasing trend, whereas the DALYs ASR, YLLs ASR and YLDs ASR were decreased between 1990 to 2019. Furthermore, High-income North America and Southern Sub-Saharan Africa had the highest both ASDRs and DALYs ASR, and Southern Sub-Saharan Africa and Caribbean had the highest both YLDs ASR and YLLs ASR in 2019. Males had a higher EMBID-related ASDRs than females, but the DALYs ASR in females were higher than males. The burden of EMBID was higher in older-aged compared to other age groups, especially in developed regions. Conclusion: Although EMBID-related ASRs for DALYs-, YLLs- and YLDs declined at the global level from 1990 to 2019, but the ASDRs was increasing. This implied high healthcare costs and more burden of ASDRs due to EMBID in the future. Therefore, there was an urgent need to adopt geographic targets, age-specific targets, prevention strategies and treatments for EMBID to reduce negative health outcomes globally.

An extreme learning machine optimized by differential evolution and artificial bee colony for predicting the concentration of whole blood with Fourier Transform Raman spectroscopy.

Raman spectroscopy, with its advantages of non-contact nature, rapid detection, and minimum water interference, is promising for non-invasive blood detection or diagnosis in clinic applications. However, there is a critical issue that how to accurately analyze blood composition by Raman spectroscopy. In this study, we apply extreme learning machine (ELM) algorithm and a multivariate calibration regression model to analyze the results from Raman spectroscopy and determine the component's concentrations in blood samples, including glucose, cholesterol, and triglyceride. Self-adaption differential evolution artificial bee colony (SADEABC) algorithm was further applied to increase the data's accuracy and robustness. The obtained data for coefficient of determination, root mean square error of calibration, root mean square error of prediction, and relative percent deviation, were 0.9822, 0.3993, 0.3827, and 6.6679 for glucose, 0.9786, 0.2104, 0.2088 and 5.9533 for cholesterol, and 0.9921, 0.2744, 0.3433 and 10.5075 for triglyceride, respectively. Results demonstrated that the model based on SADEABC-ELM show much better prediction data than those models based on the ELM and ABC-ELM.

Unsupervised model adaptation for multivariate calibration by domain adaptation-regularization based kernel partial least square.

In chemometrics, calibration model adaptation is desired when training- and test-samples come from different distributions. Domain-invariant feature representation is currently a successful strategy to realize model adaptation and has received wide attention. The paper presents a nonlinear unsupervised model adaptation method termed as domain adaption regularization-based kernel partial least squares regression (DarKPLS). DarKPLS aims to minimize the source and target distributions in a low-dimensional latent space projected from the reproducing kernel Hilbert space (RKHS) generated with the labeled source data and unlabeled target data. Specially, the distributional means and variances between source and target latent variables are aligned in the RKHS. By extending existing domain invariant partial least square regression (di-PLS) with the projected maximum mean discrepancy (PMMD) to reduce the mean discrepancy in the RKHS further, DarKPLS could realize fine-grained domain alignment that further improves the adaptation performance. DarKPLS is applied to the γ-polyglutamic acid fermentation dataset, tobacco dataset and corn dataset, and it demonstrates improved prediction results in comparison with No adaptation partial least squares (PLS), null augmented regression (NAR), extended linear joint trained framework (ExtJT), scatter component analysis (SCA) and domain-invariant iterative partial least squares (DIPALS).

Fabrication and characterization of waste fish scale-derived gelatin/sodium alginate/carvacrol loaded ZIF-8 nanoparticles composite films with sustained antibacterial activity for active food packaging.

An environmental-friendly composite films containing waste fish scale-derived gelatin (FSG), sodium alginate (SA) and carvacrol loaded ZIF-8 (CV@ZIF-8) nanoparticles were designed and fabricated to develop active food packaging materials capable of sustained antibacterial activity. The microstructure and physicochemical properties of the FSG/SA/CV@ZIF-8 composite films were investigated. The incorporation of CV@ZIF-8 into FSG/SA matrix significantly enhanced the UV-light blocking and the elongation at break, improved water resistance and reduced water vapor permeability, and improved the thermal stability of composite film. The FSG/SA/CV@ZIF-8 film not only exhibited strong antioxidant activity with DPPH radical scavenging rate of 92.35 %, but also showed the satisfactory and long-acting antibacterial ability against E. coli and S. aureus due to slow release of CV from composite film. Strawberry preservation experiment revealed that FSG/SA/CV@ZIF-8 film decelerated the texture deterioration and retarded the growth of spoilage microorganism, resulting in the prolonged shelf-life of 8 days under ambient condition, indicating its promising application prospect in food preservation packaging.