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Objective: To elucidate the biological basis of the heart qi deficiency (HQD) pattern, an in-depth understanding of which is essential for improving clinical herbal therapy. Methods: We predicted and characterized HQD pattern genes using the new strategy, TCM-HIN2Vec, which involves heterogeneous network embedding and transcriptomic experiments. First, a heterogeneous network of traditional Chinese medicine (TCM) patterns was constructed using public databases. Next, we predicted HQD pattern genes using a heterogeneous network-embedding algorithm. We then analyzed the functional characteristics of HQD pattern genes using gene enrichment analysis and examined gene expression levels using RNA-seq. Finally, we identified TCM herbs that demonstrated enriched interactions with HQD pattern genes via herbal enrichment analysis. Results: Our TCM-HIN2Vec strategy revealed that candidate genes associated with HQD pattern were significantly enriched in energy metabolism, signal transduction pathways, and immune processes. Moreover, we found that these candidate genes were significantly differentially expressed in the transcriptional profile of mice model with heart failure with a qi deficiency pattern. Furthermore, herbal enrichment analysis identified TCM herbs that demonstrated enriched interactions with the top 10 candidate genes and could potentially serve as drug candidates for treating HQD. Conclusion: Our results suggested that TCM-HIN2Vec is capable of not only accurately identifying HQD pattern genes, but also deciphering the basis of HQD pattern. Furthermore our finding indicated that TCM-HIN2Vec may be further expanded to develop other patterns, leading to a new approach aimed at elucidating general TCM patterns and developing precision medicine.

Abstract Background Urban ecological health is crucial for the long-term sustainable development of watershed. Accurately evaluating the health level of the ecological environment helps to develop reasonable strategies for ecological environment restoration and resource management. This paper constructed a comprehensive evaluation index system based on the Pressure-State-Response (PSR) framework and evaluated the ecological health of eleven administrative regions in the Wei River Basin (WRB), northwest China in 1980, 2000, and 2020 using an evaluation model established by fuzzy mathematics. Further, obstacle degrees were used to quantify the contribution of pressure, state, and response modules, as well as individual indicators to ecological health. Results The comprehensive evaluation system constructed based on the PSR framework could effectively reflect the ecological health conditions of different regions in the WRB. During the study period, the ecological health went through a process of first deterioration and then improvement. By 2020, the ecological health of seven administrative regions reached healthy levels. The state module was the main obstacle module of the PSR framework to the ecological health of the most regions. The population density (P1), patch density of construction land (S5), comprehensive elasticity index (S8), soil erosion index (R1), and per capital GDP (R3) were the most crucial individual indicators affecting the ecological health. For different cities, the main obstacle factors varied. In economically developed cities, the limiting effect of P1 was more significant, while in economically underdeveloped cities, the limiting effect of R3 was stronger. Conclusions In response to the special natural environment and socio-economic conditions of arid and semi-arid areas in the WRB, an ecological health evaluation index system suitable for the characteristics of the basin was constructed. The results indicated that, to improve the levels of urban ecological health, it is necessary to restore the natural ecological environment and control population size while accelerating economic construction. Our results can provide scientific support for the ecological health evaluation and protection of the WRB and even the arid and semi-arid areas in northwest China.

Abstract Experimental realization of strong coupling between a single exciton and plasmons remains challenging as it requires deterministic positioning of the single exciton and alignment of its dipole moment with the plasmonic fields. This study aims to combine the host–guest chemistry approach with the cucurbit[7]uril-mediated active self-assembly to precisely integrate a single methylene blue molecule in an Au nanodimer at the deterministic position (gap center of the nanodimer) with the maximum electric field (EFmax) and perfectly align its transition dipole moment with the EFmax, yielding a large spectral Rabi splitting of 116 meV for a single-molecule exciton—matching the analytical model and numerical simulations. Statistical analysis of vibrational spectroscopy and dark-field scattering spectra confirm the realization of the single exciton strong coupling at room temperature. Our work may suggest an approach for achieving the strong coupling between a deterministic single exciton and plasmons, contributing to the development of room-temperature single-qubit quantum devices.

Abstract The development of neuromorphic visual systems has recently gained momentum due to their potential in areas such as autonomous vehicles and robotics. However, current machine visual systems based on silicon technology usually contain photosensor arrays, format conversion, memory and processing modules. As a result, the redundant data shuttling between each unit, resulting in large latency and high-power consumption, seriously limits the performance of neuromorphic vision chips. Here, we demonstrate an artificial neural network (ANN) architecture based on an integrated 2D MoS2/Ag nanograting phototransistor array, which can simultaneously sense, pre-process and recognize optical images without latency. The pre-processing function of the device under photoelectric synergy ensures considerable improvement of efficiency and accuracy of subsequent image recognition. The comprehensive performance of the proof-of-concept device demonstrates great potential for machine vision applications in terms of large dynamic range (180 dB), high speed (500 ns) and low energy consumption per spike (2.4 × 10−17 J).

IntroductionAlthough rural industrial integration is a crucial pathway for advancing the revitalization of rural economies, it continues to grapple with financial challenges. This paper delves into the theoretical underpinnings of how capital marketization influences rural industrial integration.MethodsUsing panel data from China’s provinces spanning the years 2010 to 2020, a comprehensive index of rural industrial integration is constructed from the vantage point of a new development paradigm. The paper employs the system GMM method to empirically investigate the impact of capital marketization on rural industrial integration and to dissect its transmission mechanisms. Additionally, a threshold regression model is applied to explore the specific patterns of the nonlinear relationship between the two variables.Results and discussionThe study’s findings reveal that the degree of rural industrial integration is significantly and positively influenced by its previous level, demonstrating an accumulative effect wherein the prior level of integration lays the groundwork for future advancements. The influence of capital marketization on the degree of rural industrial integration is characterized by a non-linear relationship, adhering to a “U-shaped” curve. Below the inflection point, the development of capital marketization is detrimental to rural industrial integration, whereas above this point, it exerts a positive influence. Currently, China’s overall level of capital marketization is positioned beyond the inflection point, indicating substantial potential for enhancing industry integration in rural China. In addition, the study indicates that at very low levels of economic development, capital marketization does not affect the development of rural industries. As the economic development level rises, so does the impact of capital marketization on rural industrial integration.

Selective and efficient electrochemical conversion of cheap and abundant biomass derivatives into high‐value‐added products can provide a way to store large‐scale renewable intermittent energy such as wind energy, solar energy, etc. Although a large amount of research is devoted to developing highly efficient catalysts or disclosing the reaction processes, there are always challenges of conversion rate and selectivity, and the relevant comprehensive and in‐depth discussion is relatively scarce. This review introduces the basic principles and reaction processes from the charge transfer pathway of biomass electrochemical conversion. Then, for the design of the anode, the representative work is summarized and discussed in detail from the point of view of material adjustment, theoretical calculations, and in situ characterizations. Finally, insights for further optimization of the reaction systems are put forward. It is believed that this review provides guidance for the selection, design, and application of materials for high‐value‐added organic electrochemical synthesis.

Abstract The general strategy for n‐type organic thermoelectric is to blend n‐type conjugated polymer hosts with small molecule dopants. In this work, all‐polymer n‐type thermoelectric is reported by dissolving a novel n‐type conjugated polymer and a polymer dopant, poly(ethyleneimine) (PEI), in alcohol solution, followed by spin‐coating to give polymer host/polymer dopant blend film. To this end, an alcohol‐soluble n‐type conjugated polymer is developed by attaching polar and branched oligo (ethylene glycol) (OEG) side chains to a cyano‐substituted poly(thiophene‐alt‐co‐thiazole) main chain. The main chain results in the n‐type property and the OEG side chain leads to the solubility in hexafluorineisopropanol (HFIP). In the polymer host/polymer dopant blend film, the Coulombic interaction between the dopant counterions and the negatively charged polymer chains is reduced and the ordered stacking of the polymer host is preserved. As a result, the polymer host/polymer dopant blend exhibits the power factor of 36.9 µW m−1 K−1, which is one time higher than that of the control polymer host/small molecule dopant blend. Moreover, the polymer host/polymer dopant blend shows much better thermal stability than the control polymer host/small molecule dopant blend. This research demonstrates the high performance and excellent stability of all‐polymer n‐type thermoelectric.

Crotonylation is a recently discovered protein acyl modification that shares many enzymes with acetylation. However, it possesses a distinct regulatory mechanism and biological function due to its unique crotonyl structure. Since the discovery of crotonylation in 2011, numerous crotonylation sites have been identified in both histones and other proteins. In recent studies, crotonylation was found to play a role in various diseases and biological processes. This paper reviews the initial discovery and regulatory mechanisms of crotonylation, including various writer, reader, and eraser proteins. Finally, we emphasize the relationship of dysregulated protein crotonylation with eight common malignancies, including cervical, prostate, liver, and lung cancer, providing new potential therapeutic targets.

Polygonatum sibiricum (P. sibiricum), recognized as a precious nourishing Chinese traditional medicine, exhibits the pharmacological effect of anti-aging. In this work, we proposed a novel mechanism underlying this effect related to the less studied bioactive compounds fructooligosaccharides in P. sibiricum (PFOS) to identify the inhibition effect of the small glycosyl molecules on the age-related zinc metalloprotease carbonic anhydrase II (CA II). Molecular docking and molecular dynamics simulation were used to investigate the structural and energetic properties of the complex systems consisting of the CA II enzyme and two possible structures of PFOS molecules (PFOS-A and PFOS-B). The binding affinity of PFOS-A (−7.27 ± 1.02 kcal/mol) and PFOS-B (−8.09 ± 1.75 kcal/mol) shows the spontaneity of the binding process and the stability of the combination in the solvent. Based on the residue energy decomposition and nonbonded interactions analysis, the C-, D- and G-sheet fragments of the CA II were found to be crucial in binding process. Van der Waals interactions form on the hydrophobic surface of CAII mainly with 131PHE and 135VAL, while hydrogen bonds form on the hydrophilic surface mainly with 67ASN and 92GLN. The binding of PFOS results in the blocking of the zinc ions pocket and then inhibiting its catalytic activity, the stability of which has been further demonstrated by free energy landscape. These findings provide evidence of the effective inhibition of PFOS to CA II enzyme, which leads to a novel direction for exploring the mechanism of traditional Chinese medicine focused on small molecule fructooligosaccharides.

Microfinance has gained significant importance in supporting the livelihoods of individuals in developing economies. This paper examines the influence of self-employment on preferences for microfinance, based on the hypothesis that self-employment choices and participation in microfinance share the same decision-making ability. The analysis utilizes data from a recent survey conducted in rural China, employing a generalized multinomial logit model (GMNL) to account for both preference heterogeneity and scale heterogeneity. This model allows us to investigate the choice probabilities and the impact of different types of self-employment on preferences for microfinance attributes. The empirical findings indicate that engaging in self-employment contributes to a sense of reassurance regarding preferences for microfinance among rural respondents. Specifically, non-agricultural self-employment has a positive effect on this preference, whereas agricultural self-employment has a negative effect.

The fish louse Argulus japonicus, a branchiuran crustacean of the Argulidae family, is attracting increasing attention because of its parasitic tendencies and significant health threats to global fish farming. The mitogenomes can yield a foundation for studying epidemiology, genetic diversity, and molecular ecology and therefore may be used to assist in the surveillance and control of A. japonicus. In this study, we sequenced and assembled the complete mitogenome of A. japonicus to shed light on its genetic and evolutionary blueprint. Our investigation indicated that the 15,045-bp circular genome of A. japonicus encodes 13 protein-coding genes (PCGs), 22 transfer RNAs (tRNAs), and 2 ribosomal RNAs (rRNAs) with significant AT and GC skews. Comparative genomics provided an evolutionary scenario for the genetic diversity of 13 PCGs: all were under purifying selection, with cox1 and nad6 having the lowest and highest evolutionary rates, respectively. Genome-wide phylogenetic trees established a close relationship between species of the families Argulidae (Arguloida) and Armilliferidae (Porocephalida) within Crustacea, and further, A. japonicus and Argulus americanus were determined to be more closely related to each other than to others within the family Argulidae. Single PCG-based phylogenies supported nad1 and nad6 as the best genetic markers for evolutionary and phylogenetic studies for branchiuran crustaceans due to their similar phylogenetic topologies with those of genome-based phylogenetic analyses. To sum up, these comprehensive mitogenomic data of A. japonicus and related species refine valuable marker resources and should contribute to molecular diagnostic methods, epidemiological investigations, and ecological studies of the fish ectoparasites in Crustacea.

Introduction: High concentrations of nonesterified fatty acids (NEFA) is the key of characteristic of fatty liver in dairy cows. Therefore, the aim of this study was to investigate the effect of high concentration of NEFA on lipid metabolism in hepatocytes through the lipidomic approach and molecular biology techniques.Methods: Stimulate AML-12 cells with different concentrations of NEFA, observe the cellular lipid accumulation, and select 0.6 mM NEFA stimulation concentration for subsequent experiments. Collect cells for lipidomics analysis.Results: High concentration of NEFA (0.6–2.4 mM) significantly reduced the cell viability in a concentration-dependent manner, indicating that high concentrations of NEFA have lipotoxicity on hepatocytes. In addition, NEFA promoted triglycerides (TAG) accumulation, increased the mRNA expression of the lipogenic molecules SREBP1c and FASN, and decreased the mRNA expression of lipolytic molecules CPT1A and HSL in hepatocytes. Mechanistically, high concentration of NEFA induced lipid metabolism disorders in hepatocytes by regulating metabolic pathways such as glycerol phospholipid metabolism, glycosyl phosphatidylinositol anchored biosynthesis, triglyceride metabolism, sphingolipid metabolism, and inositol phosphate metabolism.Discussion: High concentration of NEFA is lipotoxic to cells, promoting lipid accumulation. LPE (18:2), LPE (18:3), LPE (18:1) via glycerophospholipid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, glycerolipid metabolism, sphingolipid metabolism, and inositol phosphate metabolism, indicating their potential regulation role in the pathogenesis of fatty liver.

Background: The prognostic significance of the ratio of activated CD4 T cells to Tregs infiltrating tumor tissues in gastric cancer (GC) remains unknown. Materials and methods: For the quantification of infiltration of immune cells, the ssGSEA algorithm, which is a single sample gene set enrichment analysis, was utilized. Group A was defined as having activated CD4 T cells/Tregs >1, while group B was defined as having activated CD4 T cells/Tregs

The peroxidase-like behaviors of gold nanoparticles (AuNPs) have the potential to the development of rapid and sensitive colorimetric assays for specific food ingredients and contaminants. Here, using NaBH4 as a reducing agent, AuNPs with a supramolecular macrocyclic compound β-cyclodextrin (β-CD) capped were synthesized under alkaline conditions. Monodispersal of β-CD@AuNPs possessed a reduction in diameter size and performed great peroxidase-like activities toward both substrates, H2O2 and TMB. In the presence of H2O2, the color change of TMB oxidization to oxTMB was well-achieved using β-CD@AuNPs as the catalyst, which was further employed to develop colorimetric assays for ascorbic acid, with a limit of detection as low as 0.2 μM in ddH2O. With the help of the host-guest interaction between β-CD and adamantane, AuNPs conjugated with nanobodies to exhibit peroxidase-like activities and specific recognition against Salmonella Typhimurium simultaneously. Based on this bifunctional bioprobe, a selective and sensitive one-step colorimetric assay for S. Typhimurium was developed with a linear detection from 8.3 × 104 to 2.6 × 108 CFU/mL and can be provided to spiked lettuce with acceptable recoveries of 97.31% to 103.29%. The results demonstrated that the excellent peroxidase-like behaviors of β-CD@AuNPs can be applied to develop a colorimetric sensing platform in the food industry.

Abstract Excessive oxidative response, unbalanced immunomodulation, and impaired mesenchymal stem cell function in periodontitis in diabetes makes it a great challenge to achieve integrated periodontal tissue regeneration. Here, a polyphenol-mediated redox-active algin/gelatin hydrogel encapsulating a conductive poly(3,4-ethylenedioxythiopene)-assembled polydopamine-mediated silk microfiber network and a hydrogen sulfide sustained-release system utilizing bovine serum albumin nanoparticles is developed. This hydrogel is found to reverse the hyperglycemic inflammatory microenvironment and enhance functional tissue regeneration in diabetic periodontitis. Polydopamine confers the hydrogel with anti-oxidative and anti-inflammatory activity. The slow, sustained release of hydrogen sulfide from the bovine serum albumin nanoparticles recruits mesenchymal stem cells and promotes subsequent angiogenesis and osteogenesis. Moreover, poly(3,4-ethylenedioxythiopene)-assembled polydopamine-mediated silk microfiber confers the hydrogel with good conductivity, which enables it to transmit endogenous bioelectricity, promote cell arrangement, and increase the inflow of calcium ion. In addition, the synergistic effects of hydrogen sulfide gaseous-bioelectric coupling promotes bone formation by amplifying autophagy in periodontal ligament stem cells and modulating macrophage polarization via lipid metabolism regulation. This study provides innovative insights into the synergistic effects of conductivity, reactive oxygen species scavenging, and hydrogen sulfide on the periodontium in a hyperglycemic inflammatory microenvironment, offering a strategy for the design of gaseous-bioelectric biomaterials to promote functional tissue regeneration in immune-related diseases.

Abstract Diabetic vascular complications attract increased attention due to their high morbidity, mortality and disability rate. Comprehensive and in-depth exploration of the etiology and pathogenesis of diabetic vascular complications is important for diagnosis and treatment. Endothelial extracellular vesicles (EVs) serve as potential intercellular communicators, transmitting biological information from the donor cell to the recipient cell, exerting both harmful and beneficial effects on vascular function. Endothelial EVs are new diagnostic and therapeutic targets and biomarkers in diabetic vascular complications. This review summarizes the biogenesis and release of endothelial EVs, as well as isolation and characterization methods, and discusses the role of endothelial EVs in the maintenance of vascular homeostasis along with their contributions to vascular dysfunction. Finally, the article illustrates the impact of endothelial EVs on the pathogenesis of diabetic vascular complications and evaluates their potential as therapeutic tools and diagnostic markers in diabetic vascular complications.

This review examines the complexities of Type 2 Diabetes Mellitus (T2DM), focusing on the critical role of integrating omics technologies with traditional experimental methods. It underscores the advancements in understanding the genetic diversity of T2DM and emphasizes the evolution towards personalized treatment modalities. The paper analyzes a variety of omics approaches, including genomics, methylation, transcriptomics, proteomics, metabolomics, and intestinal microbiomics, delineating their substantial contributions to deciphering the multifaceted mechanisms underlying T2DM. Furthermore, the review highlights the indispensable role of non-omics experimental techniques in comprehending and managing T2DM, advocating for their integration in the development of tailored medicine and precision treatment strategies. By identifying existing research gaps and suggesting future research trajectories, the review underscores the necessity for a comprehensive, multidisciplinary approach. This approach synergistically combines clinical insights with cutting-edge biotechnologies, aiming to refine the management and therapeutic interventions of T2DM, and ultimately enhancing patient outcomes. This synthesis of knowledge and methodologies paves the way for innovative advancements in T2DM research, fostering a deeper understanding and more effective treatment of this complex condition.

Abstract Type 2 diabetes mellitus (T2DM) poses a significant global health burden. This is particularly due to its macrovascular complications, such as coronary artery disease, peripheral vascular disease, and cerebrovascular disease, which have emerged as leading contributors to morbidity and mortality. This review comprehensively explores the pathophysiological mechanisms underlying these complications, protective strategies, and both existing and emerging secondary preventive measures. Furthermore, we delve into the applications of experimental models and methodologies in foundational research while also highlighting current research limitations and future directions. Specifically, we focus on the literature published post-2020 concerning the secondary prevention of macrovascular complications in patients with T2DM by conducting a targeted review of studies supported by robust evidence to offer a holistic perspective.

ObjectiveGastrointestinal dysfunction are often associated with type 2 diabetes mellitus (T2DM), a complicated metabolic illness. Contributing factors have been proposed, including genetic predisposition, gene environmental, and lifestyle interactions, but the pathophysiology remains unknown.MethodsWe aim to explore the possible causes behind gastrointestinal dysfunction caused by type 2 diabetes in this study. A comprehensive analysis of the gastric sinus metabolome, transcriptome, and proteome in db/db mice with gastrointestinal dysfunction was conducted.ResultsThe model group of mice had considerably lower small intestine propulsion and gastric emptying rates, higher blood glucose levels, and were significantly obese compared to the control group. We identified 297 genes, 350 proteins, and 1,001 metabolites exhibiting significant differences between db/db and control mice (p < 0.05). Moreover, multi-omics analysis revealed that the genes, proteins, and metabolites in the T2DM-induced gastroenteropathy mice group were involved in arachidonic acid metabolism, glycerophospholipid metabolism and vitamin digestion and absorption. Specifically, Cbr3, Etnppl, and Apob were the major mRNAs associated with T2DM-induced gastrointestinal dysfunction, while Cyp2b10, Cyp2b19, Pgs1, Gpat3, Apoa4, and Tcn2 were the major proteins associated with T2DM-induced gastrointestinal injury, and 16(R)-HET, 5-HETE, LysoPC (22:0), and Pantothenic acid were the major metabolites associated with T2DM-induced gastrointestinal disorders.ConclusionThe mechanism of action of diabetic gastroenteropathy may be related to vitamin digestion and absorption, glycerophospholipid metabolism, and arachidonic acid metabolism.

BackgroundDiabetic foot complications impose a significant strain on healthcare systems worldwide, acting as a principal cause of morbidity and mortality in individuals with diabetes mellitus. While traditional methods in diagnosing and treating these conditions have faced limitations, the emergence of Machine Learning (ML) technologies heralds a new era, offering the promise of revolutionizing diabetic foot care through enhanced precision and tailored treatment strategies.ObjectiveThis review aims to explore the transformative impact of ML on managing diabetic foot complications, highlighting its potential to advance diagnostic accuracy and therapeutic approaches by leveraging developments in medical imaging, biomarker detection, and clinical biomechanics.MethodsA meticulous literature search was executed across PubMed, Scopus, and Google Scholar databases to identify pertinent articles published up to March 2024. The search strategy was carefully crafted, employing a combination of keywords such as “Machine Learning,” “Diabetic Foot,” “Diabetic Foot Ulcers,” “Diabetic Foot Care,” “Artificial Intelligence,” and “Predictive Modeling.” This review offers an in-depth analysis of the foundational principles and algorithms that constitute ML, placing a special emphasis on their relevance to the medical sciences, particularly within the specialized domain of diabetic foot pathology. Through the incorporation of illustrative case studies and schematic diagrams, the review endeavors to elucidate the intricate computational methodologies involved.ResultsML has proven to be invaluable in deriving critical insights from complex datasets, enhancing both the diagnostic precision and therapeutic planning for diabetic foot management. This review highlights the efficacy of ML in clinical decision-making, underscored by comparative analyses of ML algorithms in prognostic assessments and diagnostic applications within diabetic foot care.ConclusionThe review culminates in a prospective assessment of the trajectory of ML applications in the realm of diabetic foot care. We believe that despite challenges such as computational limitations and ethical considerations, ML remains at the forefront of revolutionizing treatment paradigms for the management of diabetic foot complications that are globally applicable and precision-oriented. This technological evolution heralds unprecedented possibilities for treatment and opportunities for enhancing patient care.

Single-cell sequencing is a novel and rapidly advancing high-throughput technique that can be used to investigating genomics, transcriptomics, and epigenetics at a single-cell level. Currently, single-cell sequencing can not only be used to draw the pancreatic islet cells map and uncover the characteristics of cellular heterogeneity in type 2 diabetes, but can also be used to label and purify functional beta cells in pancreatic stem cells, improving stem cells and islet organoids therapies. In addition, this technology helps to analyze islet cell dedifferentiation and can be applied to the treatment of type 2 diabetes. In this review, we summarize the development and process of single-cell sequencing, describe the potential applications of single-cell sequencing in the field of type 2 diabetes, and discuss the prospects and limitations of single-cell sequencing to provide a new direction for exploring the pathogenesis of type 2 diabetes and finding therapeutic targets.