The epithelial barrier: The gateway to allergic, autoimmune, and metabolic diseases and chronic neuropsychiatric conditions.

Since the 1960 s, our health has been compromised by exposure to over 350,000 newly introduced toxic substances, contributing to the current pandemic in allergic, autoimmune and metabolic diseases. The "Epithelial Barrier Theory" postulates that these diseases are exacerbated by persistent periepithelial inflammation (epithelitis) triggered by exposure to a wide range of epithelial barrier-damaging substances as well as genetic susceptibility. The epithelial barrier serves as the body's primary physical, chemical, and immunological barrier against external stimuli. A leaky epithelial barrier facilitates the translocation of the microbiome from the surface of the afflicted tissues to interepithelial and even deeper subepithelial locations. In turn, opportunistic bacterial colonization, microbiota dysbiosis, local inflammation and impaired tissue regeneration and remodelling follow. Migration of inflammatory cells to susceptible tissues contributes to damage and inflammation, initiating and aggravating many chronic inflammatory diseases. The objective of this review is to highlight and evaluate recent studies on epithelial physiology and its role in the pathogenesis of chronic diseases in light of the epithelial barrier theory.

MetaPredictor: in silico prediction of drug metabolites based on deep language models with prompt engineering.

Metabolic processes can transform a drug into metabolites with different properties that may affect its efficacy and safety. Therefore, investigation of the metabolic fate of a drug candidate is of great significance for drug discovery. Computational methods have been developed to predict drug metabolites, but most of them suffer from two main obstacles: the lack of model generalization due to restrictions on metabolic transformation rules or specific enzyme families, and high rate of false-positive predictions. Here, we presented MetaPredictor, a rule-free, end-to-end and prompt-based method to predict possible human metabolites of small molecules including drugs as a sequence translation problem. We innovatively introduced prompt engineering into deep language models to enrich domain knowledge and guide decision-making. The results showed that using prompts that specify the sites of metabolism (SoMs) can steer the model to propose more accurate metabolite predictions, achieving a 30.4% increase in recall and a 16.8% reduction in false positives over the baseline model. The transfer learning strategy was also utilized to tackle the limited availability of metabolic data. For the adaptation to automatic or non-expert prediction, MetaPredictor was designed as a two-stage schema consisting of automatic identification of SoMs followed by metabolite prediction. Compared to four available drug metabolite prediction tools, our method showed comparable performance on the major enzyme families and better generalization that could additionally identify metabolites catalyzed by less common enzymes. The results indicated that MetaPredictor could provide a more comprehensive and accurate prediction of drug metabolism through the effective combination of transfer learning and prompt-based learning strategies.

Identification of vital chemical information via visualization of graph neural networks.

Qualitative or quantitative prediction models of structure-activity relationships based on graph neural networks (GNNs) are prevalent in drug discovery applications and commonly have excellently predictive power. However, the network information flows of GNNs are highly complex and accompanied by poor interpretability. Unfortunately, there are relatively less studies on GNN attributions, and their developments in drug research are still at the early stages. In this work, we adopted several advanced attribution techniques for different GNN frameworks and applied them to explain multiple drug molecule property prediction tasks, enabling the identification and visualization of vital chemical information in the networks. Additionally, we evaluated them quantitatively with attribution metrics such as accuracy, sparsity, fidelity and infidelity, stability and sensitivity; discussed their applicability and limitations; and provided an open-source benchmark platform for researchers. The results showed that all attribution techniques were effective, while those directly related to the predicted labels, such as integrated gradient, preferred to have better attribution performance. These attribution techniques we have implemented could be directly used for the vast majority of chemical GNN interpretation tasks.

Manipulation of Moiré Superlattice in Twisted Monolayer-multilayer Graphene by Moving Nanobubbles.

In the heterostructure of two-dimensional (2D) materials, many novel physics phenomena are strongly dependent on the Moiré superlattice. How to achieve the continuous manipulation of the Moiré superlattice in the same sample is very important to study the evolution of various physical properties. Here, in minimally twisted monolayer-multilayer graphene, we found that bubble-induced strain has a huge impact on the Moiré superlattice. By employing the AFM tip to dynamically and continuously move the nanobubble, we realized the modulation of the Moiré superlattice, like the evolution of regular triangular domains into long strip domain structures with single or double domain walls. We also achieved controllable modulation of the Moiré superlattice by moving multiple nanobubbles and establishing the coupling of nanobubbles. Our work presents a flexible method for continuous and controllable manipulation of Moiré superlattices, which will be widely used to study novel physical properties in 2D heterostructures.

Highly Curved Defect Sites: How Curvature Effect Influences Metal-Free Defective Carbon Electrocatalysts.

Topological defects are widely recognized as effective active sites toward a variety of electrochemical reactions. However, the role of defect curvature is still not fully understood. Herein, carbon nanomaterials with rich topological defect sites of tunable curvature is reported. The curved defective surface is realized by controlling the high-temperature pyrolytic shrinkage process of precursors. Theoretical calculations demonstrate bending the defect sites can change the local electronic structure, promote the charge transfer to key intermediates, and lower the energy barrier for oxygen reduction reaction (ORR). Experimental results convince structural superiority of highly-curved defective sites, with a high kinetic current density of 22.5 mA cm-2 at 0.8 V versus RHE for high-curvature defective carbon (HCDC), ≈18 times that of low-curvature defective carbon (LCDC). Further raising the defect densities in HCDC leads to the dual-regulated products (HCHDC), which exhibit exceptionally outstanding ORR activity in both alkaline and acidic media (half-wave potentials: 0.88 and 0.74 V), outperforming most of the reported metal-free carbon catalysts. This work uncovers the curvature-activity relationship in carbon defect for ORR and provides new guidance to design advanced catalysts via curvature-engineering.

Cardiovascular MRI Reference Ranges for Heart, Aorta, and Pulmonary Artery in Healthy Chinese Children.

BACKGROUND: Cardiovascular magnetic resonance (cardiac MR) reference ranges in Chinese children are lacking. PURPOSE: To establish age- and sex-specific reference ranges for cardiac MR parameters in a cohort of healthy Chinese children. STUDY TYPE: Retrospective. SUBJECTS: One hundred ninety-six healthy children (mean age 9.5 ± 3.6 years, 111 boys). FIELD STRENGTH/SEQUENCE: 1.5 T; balanced steady-state free precession. ASSESSMENT: Biventricular volume and ejection fractions (EF), left atrial (LA) volume, right atrial (RA) area, left ventricular (LV) mass and thickness, aortic root (AR), and main pulmonary artery (MPA) dimensions were measured. Parameters were compared between age groups and sex. The relationships between parameters and age, body mass index (BMI) and body surface area (BSA) were investigated. STATISTICAL TESTS: Independent-samples t tests; Pearson's correlation. A P value <0.05 was considered statistically significant. RESULTS: Generally, boys exhibited greater absolute measurements of LV volume (end-diastolic: 94.4 ± 29.5 vs. 81.3 ± 31.0 mL), LA volume (end-diastolic: 42.6 ± 13.4 vs. 38.0 ± 13.3 mL), RA area (end-diastolic: 11.6 ± 2.5 vs. 10.8 ± 2.6 cm2), LV thickness (base: 4.4 ± 1.1 vs. 3.8 ± 0.9 mm), AR dimensions (annuls: 16.3 ± 2.7 vs. 15.0 ± 2.8 mm), and MPA dimensions (14.3 ± 2.3 vs. 13.1 ± 2.4 mm) than girls did. However, these differences were not observed when the measurements were normalized to BSA (LV volume: 75.3 ± 11.7 vs. 71.9 ± 12.3 mL/m2, P = 0.052; LA volume: 34.8 ± 8.9 vs. 34.5 ± 7.6 mL/m2, P = 0.783; RA area: 9.7 ± 2.3 vs. 10.2 ± 2.3 cm2/m2, P = 0.107; LV thickness: 3.6 ± 0.7 vs. 3.6 ± 0.9 mm/m2, P = 0.990; AR: 13.6 ± 2.7 vs. 14.3 ± 3.4 mm/m2, P = 0.108; MPA: 11.9 ± 2.3 vs. 12.4 ± 2.4 mm/m2, P = 0.118). Boys had greater RV volume (end-diastolic: 98.7 ± 33.5 vs. 82.7 ± 33.1 mL) and LV mass (52.6 ± 20.2 vs. 41.4 ± 16.0 g) compared to girls, irrespective of whether the values were indexed or not for BSA. Additionally, there were significant associations between age, BMI, and BSA with biventricular volume, LA volume, RA area, LV mass and thickness, AR and MPA dimensions in both boys and girls. DATA CONCLUSION: This study suggests reference ranges at 1.5 T for Chinese children. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Toxic effects of nanopolystyrene and cadmium on the intestinal tract of the Chinese mitten crab (Eriocheir sinensis).

Nanopolystyrene (NP) and cadmium (Cd) are ubiquitous contaminants in aquatic systems. The present study aimed to investigate the toxic effects of exposure to ambient concentrations of NP and/or Cd on the intestinal tract of the Chinese mitten crab (Eriocheir sinensis). Exposure to NP and/or Cd induced oxidative stress, as evidenced by a significant increase in lipid peroxide content (LPO), total antioxidant capacity (T-AOC), and peroxidase activity (POD), and significant decreases in superoxide dismutase (SOD) and glutathione peroxidase (GSH-PX) activities in E. sinensis. In addition, exposure to NP and/or Cd imbalanced the homeostasis of the intestinal microbiota, as demonstrated by the significantly increased abundance of Spiroplasma. Transcriptomic and metabolomic analyses were performed to investigate the mechanisms underlying intestinal toxicity. Our results showed that ferroptosis, ABC transporters, phosphotransferase system, apoptosis, and leukocyte transendothelial migration were disturbed after exposure to NP and/or Cd. In particular, Cd exposure affected mucin type O-glycan biosynthesis, purine metabolism, and neuroactive ligand-receptor interaction. Intriguingly, co-exposure to NP and Cd might mitigate intestinal toxicity by decreasing oxidative stress and affecting these pathways. Taken together, our study clearly demonstrates that exposure to NP and/or Cd at environmentally relevant concentrations causes intestinal toxicity in E. sinensis.

Exposure to nanopolystyrene and phoxim at ambient concentrations causes oxidative stress and inflammation in the intestines of the Chinese mitten crab (Eriocheir sinensis).

Nanopolystyrene (NP) and phoxim (PHO) are common environmental pollutants in aquatic systems. We evaluated the toxic effects of exposure to ambient concentrations of NP and/or PHO in the intestines of the Chinese mitten crab (Eriocheir sinensis). Our study showed that histopathological changes were observed in the intestines. Specifically, NP and/or PHO exposure increased intraepithelial lymphocytes. Furthermore, NP and/or PHO exposure induced oxidative stress, as evidenced by a significant decrease in superoxide dismutase activity (SOD), peroxidase activity (POD), and total antioxidant capacity (T-AOC). Pro-inflammatory gene expression and transcriptome analysis demonstrated that NP and/or PHO exposure induced the intestinal inflammatory response. Transcriptome results showed that NP and/or PHO exposure upregulated the NF-κB signaling pathway, which is considered a key pathway in the inflammatory response. Additionally, the expression of pro-inflammatory genes significantly increased after a single exposure to NP or PHO, but it exhibited a significant decrease after the co-exposure. The downregulation of these genes in the co-exposure group likely suggested that the co-exposure mitigated intestinal inflammation response in E. sinensis. Collectively, our findings mainly showed that NP and/or PHO exposure at ambient concentrations induces oxidative stress and inflammatory response in the intestines of E. sinensis.

Regulatory circular RNAs in viral diseases: applications in diagnosis and therapy.

Circular RNA (circRNA) forms closed loops via back-splicing in precursor mRNA, resisting exonuclease degradation. In higher eukaryotes, protein-coding genes create circRNAs through exon back-splicing. Unlike mRNAs, circRNAs possess unique production and structural traits, bestowing distinct cellular functions and biomedical potential. In this review, we explore the pivotal roles of viral circRNAs and associated RNA in various biological processes. Analysing the interactions between viral circRNA and host cellular machinery yields fresh insights into antiviral immunity, catalysing the development of potential therapeutics. Furthermore, circRNAs serve as enduring biomarkers in viral diseases due to their stable translation within specific tissues. Additionally, a deeper understanding of translational circRNA could expedite the establishment of circRNA-based expression platforms, meeting the rising demand for broad-spectrum viral vaccines. We also highlight the applications of circular RNA in biomarker studies as well as circRNA-based therapeutics. Prospectively, we expect a technological revolution in combating viral infections using circRNA.

Inferring single cell expression profiles from overlapped pooling sequencing data with compressed sensing strategy.

Though single cell RNA sequencing (scRNA-seq) technologies have been well developed, the acquisition of large-scale single cell expression data may still lead to high costs. Single cell expression profile has its inherent sparse properties, which makes it compressible, thus providing opportunities for solutions. Here, by computational simulation as well as experiment of 54 single cells, we propose that expression profiles can be compressed from the dimension of samples by overlapped assigning each cell into plenty of pools. And we prove that expression profiles can be inferred from these pool expression data with overlapped pooling design and compressed sensing strategy. We also show that by combining this approach with plate-based scRNA-seq measurement, it can maintain its superiorities in gene detection sensitivity and individual identity and recover the expression profile with high precision, while saving about half of the library cost. This method can inspire novel conceptions on the measurement, storage or computation improvements for other compressible signals in many biological areas.

Effects of ginseng consumption on the biomarkers of oxidative stress: A systematic review and meta-analysis.

Oxidative stress (OS) is a key factor involved in the initiation and development of chronic diseases. Despite its widespread acceptance as an antioxidant, the effects of ginseng on OS in human clinical trials have not been comprehensively analyzed. Therefore, this study aimed to synthesize the results of previous randomized clinical trials (RCTs) examining the impact of ginseng consumption on OS indicators. PubMed, Web of Science, Scopus, and Cochrane databases were searched for articles on the effects of ginseng consumption on oxidative stress markers up to March 20, 2023. Standardized mean difference (SMD) and 95% confidence intervals (CIs) were used to assess effect sizes. Twelve RCTs with 15 effect sizes revealed that the effects of ginseng lowered serum malondialdehyde (MDA) levels (SMD = 0.45, 95% CI: -0.87, -0.08; p = 0.03) and significantly increased the serum total antioxidant capacity (TAC) (SMD = 0.23, 95% CI: 0.01, 0.45; p = 0.04), oxidative dismutase (SOD) (SMD = 0.39, 95% CI: 0.21, 0.57; p < 0.0001), glutathione (GSH) (SMD = 0.36; 95% CI: 0.11, 0.61; p = 0.005), and glutathione reductase (GR) (SMD = 0.56; 95% CI: 0.31, 0.81; p < 0.0001) levels compared to the effects of placebo. However, the effects on serum glutathione peroxidase (GPx) and catalase (CAT) were not significant. Moreover, subgroup analysis based on intervention duration showed that ginseng consumption increased GPx (SMD = 0.91, 95% CI: 0.05, 1.78; p = 0.039) and CAT (SMD = 0.74, 95% CI: 0.27, 1.21; p = 0.002) levels after more than 4 weeks of intervention. According to the results of this meta-analysis, ginseng supplementation dramatically reduced MDA levels and increased TAC, SOD, GSH, and GR levels. Our results open up a new line of defense against oxidative stress-induced diseases.

IDL-PPBopt: A Strategy for Prediction and Optimization of Human Plasma Protein Binding of Compounds via an Interpretable Deep Learning Method.

The prediction and optimization of pharmacokinetic properties are essential in lead optimization. Traditional strategies mainly depend on the empirical chemical rules from medicinal chemists. However, with the rising amount of data, it is getting more difficult to manually extract useful medicinal chemistry knowledge. To this end, we introduced IDL-PPBopt, a computational strategy for predicting and optimizing the plasma protein binding (PPB) property based on an interpretable deep learning method. At first, a curated PPB data set was used to construct an interpretable deep learning model, which showed excellent predictive performance with a root mean squared error of 0.112 for the entire test set. Then, we designed a detection protocol based on the model and Wilcoxon test to identify the PPB-related substructures (named privileged substructures, PSubs) for each molecule. In total, 22 general privileged substructures (GPSubs) were identified, which shared some common features such as nitrogen-containing groups, diamines with two carbon units, and azetidine. Furthermore, a series of second-level chemical rules for each GPSub were derived through a statistical test and then summarized into substructure pairs. We demonstrated that these substructure pairs were equally applicable outside the training set and accordingly customized the structural modification schemes for each GPSub, which provided alternatives for the optimization of the PPB property. Therefore, IDL-PPBopt provides a promising scheme for the prediction and optimization of the PPB property and would be helpful for lead optimization of other pharmacokinetic properties.

Machine Learning Methods for Predicting Human-Adaptive Influenza A Viruses Based on Viral Nucleotide Compositions.

Each influenza pandemic was caused at least partly by avian- and/or swine-origin influenza A viruses (IAVs). The timing of and the potential IAVs involved in the next pandemic are currently unpredictable. We aim to build machine learning (ML) models to predict human-adaptive IAV nucleotide composition. A total of 217,549 IAV full-length coding sequences of the PB2 (polymerase basic protein-2), PB1, PA (polymerase acidic protein), HA (hemagglutinin), NP (nucleoprotein), and NA (neuraminidase) segments were decomposed for their codon position-based mononucleotides (12 nts) and dinucleotides (48 dnts). A total of 68,742 human sequences and 68,739 avian sequences (1:1) were resampled to characterize the human adaptation-associated (d)nts with principal component analysis (PCA) and other ML models. Then, the human adaptation of IAV sequences was predicted based on the characterized (d)nts. Respectively, 9, 12, 11, 13, 10 and 9 human-adaptive (d)nts were optimized for the six segments. PCA and hierarchical clustering analysis revealed the linear separability of the optimized (d)nts between the human-adaptive and avian-adaptive sets. The results of the confusion matrix and the area under the receiver operating characteristic curve indicated a high performance of the ML models to predict human adaptation of IAVs. Our model performed well in predicting the human adaptation of the swine/avian IAVs before and after the 2009 H1N1 pandemic. In conclusion, we identified the human adaptation-associated genomic composition of IAV segments. ML models for IAV human adaptation prediction using large IAV genomic data sets can facilitate the identification of key viral factors that affect virus transmission/pathogenicity. Most importantly, it allows the prediction of pandemic influenza.

Advances and highlights in biomarkers of allergic diseases.

During the past years, there has been a global outbreak of allergic diseases, presenting a considerable medical and socioeconomical burden. A large fraction of allergic diseases is characterized by a type 2 immune response involving Th2 cells, type 2 innate lymphoid cells, eosinophils, mast cells, and M2 macrophages. Biomarkers are valuable parameters for precision medicine as they provide information on the disease endotypes, clusters, precision diagnoses, identification of therapeutic targets, and monitoring of treatment efficacies. The availability of powerful omics technologies, together with integrated data analysis and network-based approaches can help the identification of clinically useful biomarkers. These biomarkers need to be accurately quantified using robust and reproducible methods, such as reliable and point-of-care systems. Ideally, samples should be collected using quick, cost-efficient and noninvasive methods. In recent years, a plethora of research has been directed toward finding novel biomarkers of allergic diseases. Promising biomarkers of type 2 allergic diseases include sputum eosinophils, serum periostin and exhaled nitric oxide. Several other biomarkers, such as pro-inflammatory mediators, miRNAs, eicosanoid molecules, epithelial barrier integrity, and microbiota changes are useful for diagnosis and monitoring of allergic diseases and can be quantified in serum, body fluids and exhaled air. Herein, we review recent studies on biomarkers for the diagnosis and treatment of asthma, chronic urticaria, atopic dermatitis, allergic rhinitis, chronic rhinosinusitis, food allergies, anaphylaxis, drug hypersensitivity and allergen immunotherapy. In addition, we discuss COVID-19 and allergic diseases within the perspective of biomarkers and recommendations on the management of allergic and asthmatic patients during the COVID-19 pandemic.

Recent Advances in Experimental Whole Genome Haplotyping Methods.

Haplotype plays a vital role in diverse fields; however, the sequencing technologies cannot resolve haplotype directly. Pioneers demonstrated several approaches to resolve haplotype in the early years, which was extensively reviewed. Since then, numerous methods have been developed recently that have significantly improved phasing performance. Here, we review experimental methods that have emerged mainly over the past five years, and categorize them into five classes according to their maximum scale of contiguity: (i) encapsulation, (ii) 3D structure capture and construction, (iii) compartmentalization, (iv) fluorography, (v) long-read sequencing. Several subsections of certain methods are attached to each class as instances. We also discuss the relative advantages and disadvantages of different classes and make comparisons among representative methods of each class.

Hotspot Selective Preference of the Chimeric Sequences Formed in Multiple Displacement Amplification.

Multiple displacement amplification (MDA) is considered to be a conventional approach to comprehensive amplification from low input DNA. The chimeric reads generated in MDA lead to severe disruption in some studies, including those focusing on heterogeneity, structural variation, and genetic recombination. Meanwhile, the generation of by-products gives a new approach to gain insights into the reaction process of φ29 polymerase. Here, we analyzed 36.7 million chimeras and screened 196 billion chimeric hotspots in the human genome, as well as evaluating the hotspot selective preference of chimeras. No significant preference was captured in the distributions of chimeras and hotspots among chromosomes. Hotspots with overlaps for 12-13 nucleotides (nt) were most likely to be selected as templates in chimera generation. Meanwhile, a regularly selective preference was noticed in overlap GC content. The preferences in overlap length and GC content was shown to be pertinent to the sequence denaturation temperature, which pointed out the optimization direction for reducing chimeras. Distance preference between two segments of chimeras was 80-280 nt. The analysis is beneficial for reducing the chimeras in MDA, and the characterization of MDA chimeras is helpful in distinguishing MDA chimeras from chimeric sequences caused by disease.

Functional Integration between Salience and Central Executive Networks: A Role for Action Video Game Experience.

Action video games (AVGs) have attracted increasing research attention as they offer a unique perspective into the relation between active learning and neural plasticity. However, little research has examined the relation between AVG experience and the plasticity of neural network mechanisms. It has been proposed that AVG experience is related to the integration between Salience Network (SN) and Central Executive Network (CEN), which are responsible for attention and working memory, respectively, two cognitive functions essential for AVG playing. This study initiated a systematic investigation of this proposition by analyzing AVG experts' and amateurs' resting-state brain functions through graph theoretical analyses and functional connectivity. Results reveal enhanced intra- and internetwork functional integrations in AVG experts compared to amateurs. The findings support the possible relation between AVG experience and the neural network plasticity.

LGBTQIA+ healthcare content in pre-registration nursing and midwifery curricula: A cross-sectional study.

BACKGROUND: Nurses and midwives are expected to provide inclusive care for LGBTQIA+ populations. However, there is a paucity of knowledge on how well-prepared nursing and midwifery graduates are for this aspect of their role. AIM: To explore LGBTQIA+ content in pre-registration nursing and midwifery curricula in Australia. DESIGN: Cross-sectional study. SETTINGS: Australian higher education institutions offering 86 pre-registration nursing and midwifery programs. SAMPLE: Publicly available pre-registration nursing and midwifery course programs in Australia as presented in the online course handbooks of 25 randomly selected educational institutions. METHODS: A LGBTQIA+ terminology matrix was developed to guide data extraction from subject titles, synopses, and learning outcomes. Explicit terms were those strongly associated with LGBTQIA+. Implicit terms were those indirectly associated with LGBTQIA+. Explicit and implicit LGBTQIA+ terms were analyzed for frequencies. Poisson regression was used to compare counts of LGBTQIA+ terms by course, subject, and university. RESULTS: Among the 1093 subjects, no subject titles included explicit terms and 6 (0.55 %) titles included implicit LGBTQIA+ terminology. Explicit LGBTQIA+ terminology was identified in 54 (4.9 %) subject synopses and implicit terminology was found in 323 (29.6 %) subject synopses. Explicit LGBTQIA+ terms were identified in 14 (1.3 %) learning outcomes while implicit terms were found in 323 (29.6 %) learning outcomes. Group of Eight universities were more likely to use explicit and implicit LGBTQIA+ terminology than non-Group of Eight universities (p < 0.001; p = 0.035). Undergraduate programs included more LGBTQIA+ terminology than postgraduate programs (p = 0.019; p < 0.001). Core subjects were more likely to include LGBTQIA+ content than elective subjects (p < 0.001). CONCLUSION: Explicit LGBTQIA+ terminology was minimal indicating limited overall content. The study results alert nursing and midwifery educators to take a targeted approach to teaching and learning with a LGBTQIA+ focus to support future nurses and midwives with quality knowledge and skills and ensure equitable and safe care for LGBTQIA+ people.

Comparative Morphology of the Wing Base Structure Illuminates Higher-Level Phylogeny of Holometabola.

Phylogenetic relationships among Holometabola have been the subject of controversy. The value of the wing base structure in phylogenetic analysis has been demonstrated but remains largely underexplored and scarce in studies of Holometabola. We studied the phylogenetic relationships among Holometabola (excluding Siphonaptera), focusing exclusively on wing base structure. Cladistic assessments were conducted using 53 morphological data points derived from the bases of both the forewing and hindwing. The results of wing base data revealed a sister relationship between Hymenoptera and remaining orders. The sister-group relationships between Strepsiptera and Coleoptera, Mecoptera and Diptera, Trichoptera and Lepidoptera, and Neuropterida and Coleopterida were corroborated. In Neuropterida, our results recovered the sister relationship between Megaloptera and Neuroptera, as well as the monophyly of Megaloptera.