Causal relationships between psychological disorders and functional gastrointestinal disorders: a bidirectional two-sample Mendelian randomization study.

BACKGROUND AND AIMS: Observational studies have shown bidirectional associations between psychological disorders (e.g. depression and anxiety) and functional gastrointestinal disorders. However, whether the relationships are causal is uncertain. Here, we used a bidirectional two-sample Mendelian randomization method to investigate the association between psychological disorders and functional gastrointestinal disorders (FGIDs). METHODS: We obtained genome-wide association study summary statistics for two common psychological disorders: depression (170 756 cases) and anxiety (31 977 cases), as well as for three common FGIDs: functional dyspepsia with 6666 cases, constipation with 26 919 cases, and irritable bowel syndrome (IBS) with 7053 cases. These summary statistics were retrieved from several publicly available genome-wide association study databases. The inverse variance weighted method was used as the main Mendelian randomization method. RESULTS: Inverse variance weighted Mendelian randomization analyses showed statistically significant associations between genetically predicted depression and risk of functional dyspepsia [odds ratio (OR): 1.40, 95% confidence interval (CI): 1.08-1.82], constipation (OR: 1.28, 95% CI: 1.13-1.44), and IBS (OR: 1.51, 95% CI: 1.37-1.67). Genetically predicted anxiety was associated with a higher risk of IBS (OR: 1.13, 95% CI: 1.10-1.17) instead of functional dyspepsia and constipation. In addition, genetically predicted IBS instead of functional dyspepsia and constipation was associated with a higher risk of depression (OR: 1.33, 95% CI: 1.12-1.57) and anxiety (OR: 2.05, 95% CI: 1.05-4.03). CONCLUSION: Depression is a causal risk factor for three common FGIDs. A bidirectional causal relationship between IBS and anxiety or depression was also identified.

Microbe-material hybrids for therapeutic applications.

As natural living substances, microorganisms have emerged as useful resources in medicine for creating microbe-material hybrids ranging from nano to macro dimensions. The engineering of microbe-involved nanomedicine capitalizes on the distinctive physiological attributes of microbes, particularly their intrinsic "living" properties such as hypoxia tendency and oxygen production capabilities. Exploiting these remarkable characteristics in combination with other functional materials or molecules enables synergistic enhancements that hold tremendous promise for improved drug delivery, site-specific therapy, and enhanced monitoring of treatment outcomes, presenting substantial opportunities for amplifying the efficacy of disease treatments. This comprehensive review outlines the microorganisms and microbial derivatives used in biomedicine and their specific advantages for therapeutic application. In addition, we delineate the fundamental strategies and mechanisms employed for constructing microbe-material hybrids. The diverse biomedical applications of the constructed microbe-material hybrids, encompassing bioimaging, anti-tumor, anti-bacteria, anti-inflammation and other diseases therapy are exhaustively illustrated. We also discuss the current challenges and prospects associated with the clinical translation of microbe-material hybrid platforms. Therefore, the unique versatility and potential exhibited by microbe-material hybrids position them as promising candidates for the development of next-generation nanomedicine and biomaterials with unique theranostic properties and functionalities.

Rational design of lipid nanoparticles: overcoming physiological barriers for selective intracellular mRNA delivery.

This review introduces the typical delivery process of messenger RNA (mRNA) nanomedicines and concludes that the delivery involves a at least four-step SCER cascade and that high efficiency at every step is critical to guarantee high overall therapeutic outcomes. This SCER cascade process includes selective organ-targeting delivery, cellular uptake, endosomal escape, and cytosolic mRNA release. Lipid nanoparticles (LNPs) have emerged as a state-of-the-art vehicle for in vivo mRNA delivery. The review emphasizes the importance of LNPs in achieving selective, efficient, and safe mRNA delivery. The discussion then extends to the technical and clinical considerations of LNPs, detailing the roles of individual components in the SCER cascade process, especially ionizable lipids and helper phospholipids. The review aims to provide an updated overview of LNP-based mRNA delivery, outlining recent innovations and addressing challenges while exploring future developments for clinical translation over the next decade.

Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model.

Introduction: To enhance the precision of evaluating the impact of urban environments on resident health, this study introduces a novel fuzzy intelligent computing model designed to address health risk concerns using multi-media environmental monitoring data. Methods: Three cities were selected for the study: Beijing (B City), Kunming (K City), and Wuxi (W City), representing high, low, and moderate pollution levels, respectively. The study employs a Fuzzy Inference System (FIS) as the chosen fuzzy intelligent computing model, synthesizing multi-media environmental monitoring data for the purpose of urban health risk assessment. Results: (1) The model reliably estimates health risks across diverse cities and environmental conditions. (2) There is a positive correlation between PM2.5 concentrations and health risks, though the impact of noise levels varies by city. In cities B, K, and W, the respective correlation coefficients are 0.65, 0.55, and 0.7. (3) The Root Mean Square Error (RMSE) values for cities B, K, and W, are 0.0132, 0.0125, and 0.0118, respectively, indicating that the model has high accuracy. The R2 values for the three cities are 0.8963, 0.9127, and 0.9254, respectively, demonstrating the model's high explanatory power. The residual values for the three cities are 0.0087, 0.0075, and 0.0069, respectively, indicating small residuals and demonstrating robustness and adaptability. (4) The model's p-values for the Indoor Air Quality Index (IAQI), Thermal Comfort Index (TCI), and Noise Pollution Index (NPI) all satisfy p < 0.05 for the three cities, affirming the model's credibility in estimating health risks under varied urban environments. Discussion: These results showcase the model's ability to adapt to diverse geographical conditions and aid in the accurate assessment of existing risks in urban settings. This study significantly advances environmental health risk assessment by integrating multidimensional data, enhancing the formulation of comprehensive environmental protection and health management strategies, and providing scientific support for sustainable urban planning.

Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis.

Conventional approaches to dietary assessment are primarily grounded in self-reporting methods or structured interviews conducted under the supervision of dietitians. These methods, however, are often subjective, potentially inaccurate, and time-intensive. Although artificial intelligence (AI)-based solutions have been devised to automate the dietary assessment process, prior AI methodologies tackle dietary assessment in a fragmented landscape (e.g., merely recognizing food types or estimating portion size), and encounter challenges in their ability to generalize across a diverse range of food categories, dietary behaviors, and cultural contexts. Recently, the emergence of multimodal foundation models, such as GPT-4V, has exhibited transformative potential across a wide range of tasks (e.g., scene understanding and image captioning) in various research domains. These models have demonstrated remarkable generalist intelligence and accuracy, owing to their large-scale pre-training on broad datasets and substantially scaled model size. In this study, we explore the application of GPT-4V powering multimodal ChatGPT for dietary assessment, along with prompt engineering and passive monitoring techniques. We evaluated the proposed pipeline using a self-collected, semi free-living dietary intake dataset comprising 16 real-life eating episodes, captured through wearable cameras. Our findings reveal that GPT-4V excels in food detection under challenging conditions without any fine-tuning or adaptation using food-specific datasets. By guiding the model with specific language prompts (e.g., African cuisine), it shifts from recognizing common staples like rice and bread to accurately identifying regional dishes like banku and ugali. Another GPT-4V's standout feature is its contextual awareness. GPT-4V can leverage surrounding objects as scale references to deduce the portion sizes of food items, further facilitating the process of dietary assessment.

Identification of a biomarker for Bacillus thuringiensis strains with high toxicity against Spodoptera frugiperda based on insecticidal gene linkage analysis.

BACKGROUND: Bacillus thuringiensis (Bt) is a Gram-positive bacterium that produces various insecticidal proteins used to control insect pests. Spodoptera frugiperda is a global insect pest which causes serious damage to crops, but bio-insecticides currently available to control this pest have limited activity and so new ones are always being sought. In this study we have tested the hypothesis that a biomarker for strain toxicity could be found that would greatly facilitate the identification of new potential products. RESULTS: Using genomic sequencing data we constructed a linkage network of insecticidal genes from 1957 Bt genomes and found that four gene families, namely cry1A, cry1I, cry2A and vip3A, showed strong linkage. For 95 strains isolated from soil samples we assayed them for toxicity towards S. frugiperda and for the presence of the above gene families. All of the strains that showed high toxicity also contained a member of the vip3A gene family. Two of them were more toxic than a commercially available strain and genomic sequencing identified a number of potentially novel toxin-encoding genes. CONCLUSIONS: The presence of a vip3A gene in the genome of a Bt strain proved to be a strong indicator of toxicity towards S. frugiperda validating this biomarker approach as a strategy for future discovery programs. © 2024 Society of Chemical Industry.

Partial class activation mapping guided graph convolution cascaded U-Net for retinal vessel segmentation.

Accurate segmentation of retinal vessels in fundus images is of great importance for the diagnosis of numerous ocular diseases. However, due to the complex characteristics of fundus images, such as various lesions, image noise and complex background, the pixel features of some vessels have significant differences, which makes it easy for the segmentation networks to misjudge these vessels as noise, thus affecting the accuracy of the overall segmentation. Therefore, accurately segment retinal vessels in complex situations is still a great challenge. To address the problem, a partial class activation mapping guided graph convolution cascaded U-Net for retinal vessel segmentation is proposed. The core idea of the proposed network is first to use the partial class activation mapping guided graph convolutional network to eliminate the differences of local vessels and generate feature maps with global consistency, and subsequently these feature maps are further refined by segmentation network U-Net to achieve better segmentation results. Specifically, a new neural network block, called EdgeConv, is stacked multiple layers to form a graph convolutional network to realize the transfer an update of information from local to global, so as gradually enhance the feature consistency of graph nodes. Simultaneously, in an effort to suppress the noise information that may be transferred in graph convolution and thus reduce adverse effects of noise on segmentation results, the partial class activation mapping is introduced. The partial class activation mapping can guide the information transmission between graph nodes and effectively activate vessel feature through classification labels, thereby improving the accuracy of segmentation. The performance of the proposed method is validated on four different fundus image datasets. Compared with existing state-of-the-art methods, the proposed method can improve the integrity of vessel to a certain extent when the pixel features of local vessels are significantly different, caused by objective factors such as inappropriate illumination and exudates. Moreover, the proposed method shows robustness when segmenting complex retinal vessels.

Versatile Hyper-Cross-Linked Polymers Derived from Waste Polystyrene: Synthesis, Properties, and Intentional Recycling.

Waste polystyrene contributes considerably to environmental pollution due to its persistent nature, prompting a widespread consensus on the urgent need for viable recycling solutions. Owing to the aromatic groups structure of polystyrene, hyper-cross-linked polymers can be synthesized through the Friedel-Crafts cross-linking reaction using Lewis acids as catalysts. In addition, hyper-cross-linked polystyrene and its carbonaceous counterparts can be used in several important applications, which helps in their efficient recycling. This review systematically explores methods for preparing multifunctional hyper-cross-linked polymers from waste polystyrene and their applications in sustainable recycling. We have comprehensively outlined various synthetic approaches for these polymers and investigated their physical and chemical properties. These multifunctional polymers not only exhibit structural flexibility but also demonstrate diversity in performance, making them suitable for various applications. Through a systematic examination of synthetic methods, we showcase the cutting-edge positions of these materials in the field of hyper-cross-linked polymers. Additionally, we provide in-depth insights into the potential applications of these hyper-cross-linked polymers in intentional recycling, highlighting their important contributions to environmental protection and sustainable development. This research provides valuable references to the fields of sustainable materials science and waste management, encouraging further exploration of innovative approaches for the utilization of discarded polystyrene.

Highly Regular Layered Structure via Dual-Spatially-Confined Alignment of Nanosheets Enables High-Performance Nanocomposites.

Assembling ultrathin nanosheets into layered structure represents one promising way to fabricate high-performance nanocomposites. However, how to minimize the internal defects of the layered assemblies to fully exploit the intrinsic mechanical superiority of nanosheets remains challenging. Here, a dual-scale spatially confined strategy for the co-assembly of ultrathin nanosheets with different aspect ratios into a near-perfect layered structure is developed. Large-aspect-ratio (LAR) nanosheets are aligned due to the microscale confined space of a flat microfluidic channel, small-aspect-ratio (SAR) nanosheets are aligned due to the nanoscale confined space between adjacent LAR nanosheets. During this co-assembly process, SAR nanosheets can flatten LAR nanosheets, thus reducing wrinkles and pores of the assemblies. Benefiting from the precise alignment (orientation degree of 90.74%) of different-sized nanosheets, efficient stress transfer between nanosheets and interlayer matrix is achieved, resulting in layered nanocomposites with multiscale mechanical enhancement and superior fatigue durability (100 000 bending cycles). The proposed co-assembly strategy can be used to orderly integrate high-quality nanosheets with different sizes or diverse functions toward high-performance or multifunctional nanocomposites.

The Rehabilitation Efficacy of the Novel Metronomic Breathing Technique for Gerontic Patients After Percutaneous Coronary Intervention for Acute Myocardial Infarction-A Pilot Study.

Background: The respiratory rehabilitation technique is a crucial component of early cardiac recovery in geriatric patients with acute myocardial infarction (AMI). This study primarily investigated the effectiveness of a novel respiratory rehabilitation technique, metronomic breathing (MB), on geriatric patients after percutaneous coronary intervention for AMI and compliance with home-based rehabilitation compared to traditional respiratory rehabilitation. Methods: From June 2022 to March 2023, 75 acute myocardial infarction (AMI) patients admitted to the Shanghai Tenth People's Hospital Cardiovascular Department were consecutively enrolled. Ultimately, 46 patients completed the follow-up in this study-26 in the MB group and 20 in the control group-who underwent the novel MB technique and conventional abdominal breathing training. The primary endpoint of the study was left ventricular function measured by noninvasive hemodynamics three months after discharge. The secondary endpoints were compliance and quality of life after three months of home rehabilitation. Results: After the intervention, several cardiac functional parameters (SV, SVI, CO, CI, LCW, and LCWI), myocardial contractility parameters (VI), and systemic vascular resistance parameters (SVR and SVRI) were significantly greater in the MB group than in the preintervention group (P < 0.05). Furthermore, post-treatment, the MB group exhibited greater SV, SVI, CO, CI, and VI; lower SVR, SVRI, and SBP; and a lower readmission rate three months later than did the control group. The SF-36 scores after three months of MB intervention, PE, BP, GH, VT, SF, RE, and MH, were all significantly greater than those before treatment (P < 0.05). Moreover, the MB group displayed greater compliance with home-based cardiac rehabilitation (P < 0.05). Conclusion: Compared to conventional respiratory rehabilitation training methods, short-term metronomic respiratory therapy is more effective for reducing systemic vascular resistance, enhancing left ventricular ejection function, enhancing quality of life, and increasing home-based rehabilitation compliance in geriatric patients following AMI with PCI.

Gene cloning, expression and performance validation of nitric oxide dismutase.

Nitrous oxide (N2O) is a significant contributor to global warming and possesses an ozone-depleting impact nearly 298 times that of CO2. To reduce N2O emissions, the newly-discovered nod gene which can directly convert NO into N2 and O2 was successfully cloned from the anaerobic denitrification sludge. The recombinant plasmid containing the nod gene was built, and the expression of nod gene in Escherichia coli was determined, leading to the construction of recombinant engineering bacteria. Results showed that the recombinant engineering bacteria E. coli BL21 (DE3)-pET28a-nod could autonomously degrade NO, with a degradation rate of 72 % within 48 h, and could produce 2479.72 ppm of N2 and 75.12 mL of O2. The cumulative O2 production of the sludge sample and recombinant E. coli within 8 h was 1.75 mL and 8.45 mL, respectively. The cumulative O2 production of recombinant E. coli was at least 4.82 times higher than that of the sludge sample. The investigation proposed a new biodegradation pathway for nitrogen pollution.

Metal oxide hybridization enhances room temperature phosphorescence of carbon dots-SiO2 matrix for information encryption and anti-counterfeiting.

Room temperature phosphorescent (RTP) carbon dot (CD) materials have been widely used in various fields, but it is difficult to achieve a long lifetime, high stability and easy synthesis. In particular, realizing the phosphorescence emission of CDs using a metal oxide (MO) matrix is a challenge. Here, solid gels are synthesized via in situ hydrolysis, and then RTP CDs are synthesized based on a SiO2 matrix (CDs@SiO2) and hybridized with a MO matrix (CDs@SiO2-MO) by high-temperature calcination. Among the materials synthesized, Al2O3 matrix RTP CDs (CDs@SiO2-Al2O3) have a long phosphorescence lifetime of 689 ms and can exhibit yellow-green light visible to the naked eye for 9 s after the UV light (365 nm) is turned off. Compared with the green phosphorescence of CDs@SiO2, the yellow-green phosphorescence lifetime of CDs@SiO2-Al2O3 is enhanced by 420 ms. In addition, CDs@SiO2-Al2O3 maintains good stability of phosphorescence emission in water, strongly oxidizing solutions and organic solvents. As a result, CDs@SiO2-Al2O3 can be applied to the field of information encryption and security anti-counterfeiting, and this work provides a new, easy and efficient synthesis method for MO as an RTP CD matrix.

Deconstruction of Polymers through Olefin Metathesis.

The consumption of synthetic polymers has ballooned; so has the amount of post-consumer waste generated. The current polymer economy, however, is largely linear with most of the post-consumer waste being either landfilled or incinerated. The lack of recycling, together with the sizable carbon footprint of the polymer industry, has led to major negative environmental impacts. Over the past few years, chemical recycling technologies have gained significant traction as a possible technological route to tackle these challenges. In this regard, olefin metathesis, with its versatility and ease of operation, has emerged as an attractive tool. Here, we discuss the developments in olefin-metathesis-based chemical recycling technologies, including the development of new materials and the application of olefin metathesis to the recycling of commercial materials. We delve into structure-reactivity relationships in the context of polymerization-depolymerization behavior, how experimental conditions influence deconstruction outcomes, and the reaction pathways underlying these approaches. We also look at the current hurdles in adopting these technologies and relevant future directions for the field.

Utilizing support vector machines to foster sustainable development and innovation in the clean energy sector via green finance.

As the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. in order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. through the use of Support Vector Machine (SVM) Multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. with wind power base a as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terAs the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. In order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. Through the use of Support Vector Machine (SVM) multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. With Wind Power Base A as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terms of the number of units, internal rate of return within the project, and annual average equivalent utilization hours at Wind Power Base A. The number of units decreases to 5860, with total purchase and transportation costs remaining basically unchanged, the internal rate of return increases to 9.3%, and annual equivalent utilization hours increase to 2044 h. Energy consumption and CO2 emissions are significantly reduced, with energy consumption decreasing by 0.68 × 109 kgce and CO2 emissions decreasing by 1.29 × 109 kg. The optimization effects are mainly concentrated in the production and installation stages, with emission reductions achieved through the recycling and disposal of materials consumed in the early stages. In terms of investment benefits, environmental benefits are enhanced, with a 13.93% reduction in CO2 emissions. Moreover, there is improved energy efficiency, with the energy input-output ratio increasing from 7.73 to 9.31. This indicates that the Wind Power Base A project has significant environmental and energy efficiency advantages in the clean energy industry. This work innovatively provides a comprehensive assessment and optimization scheme for clean energy projects and predicts the profitability of Wind Power Base A using SVM multi-factor models. Besides, this work optimizes key parameters of the project using a bi-level multi-objective approach, thus comprehensively improving the investment return and utilization efficiency of the wind power base. This work provides innovative methods and strong data support for the development of the clean energy industry, which is of great significance for promoting sustainable development under the backdrop of green finance.

The associations between functional dyspepsia and potential risk factors: A comprehensive Mendelian randomization study.

BACKGROUND: Previous cross-sectional studies have identified multiple potential risk factors for functional dyspepsia (FD). However, the causal associations between these factors and FD remain elusive. Here we aimed to fully examine the causal relationships between these factors and FD utilizing a two-sample MR framework. METHODS: A total of 53 potential FD-related modifiable factors, including those associated with hormones, metabolism, disease, medication, sociology, psychology, lifestyle and others were obtained through a comprehensive literature review. Independent genetic variants closely linked to these factors were screened as instrumental variables from genome-wide association studies (GWASs). A total of 8875 FD cases and 320387 controls were available for the analysis. The inverse variance weighted (IVW) method was employed as the primary analytical approach to assess the relationship between genetic variants of risk factors and the FD risk. Sensitivity analyses were performed to evaluate the consistency of the findings using the weighted median model, MR-Egger and MR-PRESSO methods. RESULTS: Genetically predicted depression (OR 1.515, 95% confidence interval (CI) 1.231 to 1.865, p = 0.000088), gastroesophageal reflux disease (OR 1.320, 95%CI 1.153 to 1.511, p = 0.000057) and years of education (OR 0.926, 95%CI 0.894 to 0.958, p = 0.00001) were associated with risk for FD in univariate MR analyses. Multiple medications, alcohol consumption, poultry intake, bipolar disorder, mood swings, type 1 diabetes, elevated systolic blood pressure and lower overall health rating showed to be suggestive risk factors for FD (all p<0.05 while ≥0.00167). The positive causal relationship between depression, years of education and FD was still significant in multivariate MR analyses. CONCLUSIONS: Our comprehensive MR study demonstrated that depression and lower educational attainment were causal factors for FD at the genetic level.

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology.

This paper explores methodologies to enhance the integration of a green supply chain circular economy within smart cities by incorporating machine learning technology. To refine the precision and effectiveness of the prediction model, the gravitational algorithm is introduced to optimize parameter selection in the support vector machine model. A nationwide prediction model for green supply chain economic development efficiency is meticulously constructed by leveraging public economic, environmental, and demographic data. A comprehensive empirical analysis follows, revealing a noteworthy reduction in mean squared error and root mean squared error with increasing iterations, reaching a minimum of 0.007 and 0.103, respectively-figures that are the lowest among all considered machine learning models. Moreover, the mean absolute percentage error value is remarkably low at 0.0923. The data illustrate a gradual decline in average prediction error and standard deviation throughout the model optimization process, indicative of both model convergence and heightened prediction accuracy. These results underscore the significant potential of machine learning technology in optimizing supply chain and circular economy management. The paper provides valuable insights for decision-makers and researchers navigating the landscape of sustainable development.

Clinical values of serum C5a in Alzheimer's disease patients with different dementia stages.

Alzheimer's disease (AD) is characterized by abnormal inflammatory responses, and complement C5a (C5a) is known to initiate inflammation. This study aimed to investigate the associations between serum C5a, inflammatory responses, and cognitive function in AD patients. A total of 242 AD patients and 132 age-matched controls were included. Enzyme-linked immunosorbent assay revealed increased levels of C5a, interleukin (IL)-4, IL-6, IL-10, IL-1ß, and tumor necrosis factor (TNF)-α with advancing stages of AD. Pearson correlation coefficient and receiver operating characteristic curve revealed positive correlations between serum C5a levels, inflammatory cytokine levels, Neuropsychiatric Inventory (NPI) and Activities of Daily Living (ADL) scores, and negative correlations with Mini-mental State Examination (MMSE) and Montreal cognitive assessment (MoCA) scores. Serum C5a above 68.68 pg/mL could aid in the diagnosis of AD. Multivariable logistic analysis revealed that serum C5a was an independent risk factor for IL-1ß/IL-6/IL-10/TNF-α and an independent protective factor for IL-4. Higher serum C5a levels were associated with lower MMSE and MoCA scores. In conclusion, elevated serum C5a levels were beneficial for AD diagnosis and predictive of inflammation and cognitive dysfunction.

Alcohol inducing macrophage M2b polarization in colitis by modulating the TRPV1-MAPK/NF-κB pathways.

BACKGROUND: Macrophages exhibit different phenotypes in inflammatory bowel disease (IBD) and promote inflammation or tissue repair depending on their polarization state. Alcohol is a widely used solvent in pharmaceutical formulations, and its consumption is associated with an increased risk of colitis; however, its effects on macrophages in IBD remain poorly understood. PURPOSE: This study aimed to investigate the effect of alcohol on macrophages in dextran sodium sulfate (DSS)-induced colitis and understand the underlying mechanisms. METHODS: DSS-treated C57BL/6 mice were exposed to varying concentrations of alcohol, transient receptor potential vanilloid 1 (TRPV1) antagonist, and 5-aminosalicylic acid. The distal colon was resected, fixed, stained, and histologically analyzed, through hematoxylin and eosin (H&E) staining and immunofluorescence staining. Ratio [Ca2+]i measurements, western blotting, quantitative polymerase chain reaction, cytokine measurements, and RNA sequencing analyses were also performed. Peritoneal macrophages and RAW264.7 cells were used for in vitro experiments, and various assays were performed to evaluate cellular responses, gene expression, and signaling pathways. RESULTS: Alcohol exacerbated DSS-treated mice colitis and promoted the secretion of various inflammatory cytokines from colonic macrophages. Alcohol enhances the calcium ion influx induced by lipopolysaccharide (LPS) in peritoneal macrophages, while the TRPV1 antagonist capsazepine (CPZ) inhibits LPS- and/or alcohol- induced calcium influx in macrophages. Alcohol and LPS activate the MAPK/P38, MAPK/ERK, and NF-κB signaling pathways and induce the macrophage M2b polarization, resulting in the increased expression level of inflammatory cytokines such as Tnf, Il1b, and Il10. Additionally, CPZ can inhibit the facilitatory effects of alcohol or LPS on the abovementioned pathways and inflammatory factors, reversing macrophage M2b polarization and promoting alcohol-induced colitis. The inhibition of nucleotide binding oligomerization domain containing 2 (NOD2) partially suppressed the alcohol and LPS effects on macrophages. CONCLUSION: Alcohol exacerbates experimental colitis and induces M2b polarization of macrophage via TRPV1-MAPK/NF-κB. Our study provides new insights into the potential therapeutic targets for IBD treatment by elucidating the role of TRPV1 in alcohol-exacerbated colitis, using CPZ as a potential therapeutic option. The identification of transient receptor potential ankyrin subtype 1 (TRPA1) as a therapeutic target expands the scope of future research.

TYG Index as a Novel Predictor of Clinical Outcomes in Advanced Chronic Heart Failure with Renal Dysfunction Patients.

Background: The triglyceride-glucose (TYG) index is a novel and reliable marker reflecting insulin resistance. Its predictive ability for cardiovascular disease onset and prognosis has been confirmed. However, for advanced chronic heart failure (acHF) patients, the prognostic value of TYG is challenged due to the often accompanying renal dysfunction (RD). Therefore, this study focuses on patients with aHF accompanied by RD to investigate the predictive value of the TYG index for their prognosis. Methods and Results: 717 acHF with RD patients were included. The acHF diagnosis was based on the 2021 ESC criteria for acHF. RD was defined as the eGFR < 90 mL/(min/1.73 m2). Patients were divided into two groups based on their TYG index values. The primary endpoint was major adverse cardiovascular events (MACEs), and the secondary endpoints is all-cause mortality (ACM). The follow-up duration was 21.58 (17.98-25.39) months. The optimal cutoff values for predicting MACEs and ACM were determined using ROC curves. Hazard factors for MACEs and ACM were revealed through univariate and multivariate COX regression analyses. According to the univariate COX regression analysis, high TyG index was identified as a risk factor for MACEs (hazard ratio = 5.198; 95% confidence interval [CI], 3.702-7.298; P < 0.001) and ACM (hazard ratio = 4.461; 95% CI, 2.962-6.718; P < 0.001). The multivariate COX regression analysis showed that patients in the high TyG group experienced 440.2% MACEs risk increase (95% CI, 3.771-7.739; P < 0.001) and 406.2% ACM risk increase (95% CI, 3.268-7.839; P < 0.001). Kaplan-Meier survival analysis revealed that patients with high TyG index levels had an elevated risk of experiencing MACEs and ACM within 30 months. Conclusion: This study found that patients with high TYG index had an increased risk of MACEs and ACM, and the TYG index can serve as an independent predictor for prognosis.

Mix-Key: graph mixup with key structures for molecular property prediction.

Molecular property prediction faces the challenge of limited labeled data as it necessitates a series of specialized experiments to annotate target molecules. Data augmentation techniques can effectively address the issue of data scarcity. In recent years, Mixup has achieved significant success in traditional domains such as image processing. However, its application in molecular property prediction is relatively limited due to the irregular, non-Euclidean nature of graphs and the fact that minor variations in molecular structures can lead to alterations in their properties. To address these challenges, we propose a novel data augmentation method called Mix-Key tailored for molecular property prediction. Mix-Key aims to capture crucial features of molecular graphs, focusing separately on the molecular scaffolds and functional groups. By generating isomers that are relatively invariant to the scaffolds or functional groups, we effectively preserve the core information of molecules. Additionally, to capture interactive information between the scaffolds and functional groups while ensuring correlation between the original and augmented graphs, we introduce molecular fingerprint similarity and node similarity. Through these steps, Mix-Key determines the mixup ratio between the original graph and two isomers, thus generating more informative augmented molecular graphs. We extensively validate our approach on molecular datasets of different scales with several Graph Neural Network architectures. The results demonstrate that Mix-Key consistently outperforms other data augmentation methods in enhancing molecular property prediction on several datasets.