Lasing from Doubly Degenerate Bound States in the Continuum.

This paper reports dual-mode vortex lasing with a topological charge of -2 via doubly degenerate bound states in the continuum. We patterned a photonic crystal slab with hexagonal TiO2 nanoparticles arranged in a hexagonal lattice to support doubly degenerate bound states in the continuum at the Γ point, and the finite array was truncated to have a square boundary. Because emission from bound states in the continuum at the Γ point is forbidden, lasing emerges at off-Γ points, where the double degeneracy is lifted, resulting in two lasing peaks close in wavelength. The coherence time of vortex lasing is about 1.4 ps under pulsed pumping with a width of 75 fs. Polarization- and angle-resolved spectral measurements reveal that the two lasing peaks show orthogonal polarization states.

HHIP's Dynamic Role in Epithelial Wound Healing Reveals a Potential Mechanism of COPD Susceptibility.

A genetic variant near HHIP has been consistently identified as associated with increased risk for Chronic Obstructive Pulmonary Disease (COPD), the third leading cause of death worldwide. However HHIP's role in COPD pathogenesis remains elusive. Canonically, HHIP is a negative regulator of the hedgehog pathway and downstream GLI1 and GLI2 activation. The hedgehog pathway plays an important role in wound healing, specifically in activating transcription factors that drive the epithelial mesenchymal transition (EMT), which in its intermediate state (partial EMT) is necessary for the collective movement of cells closing the wound. Herein, we propose a mechanism to explain HHIP's role in faulty epithelial wound healing, which could contribute to the development of emphysema, a key feature of COPD. Using two different Boolean models compiled from the literature, we show dysfunctional HHIP results in a lack of negative feedback on GLI, triggering a full EMT, where cells become mesenchymal and do not properly close the wound. We validate these Boolean models with experimental evidence gathered from published scientific literature. We also experimentally test if low HHIP expression is associated with EMT at the edge of wounds by using a scratch assay in a human lung epithelial cell line. Finally, we show evidence supporting our hypothesis in bulk and single cell RNA-Seq data from different COPD cohorts. Overall, our analyses suggest that aberrant wound healing due to dysfunctional HHIP, combined with chronic epithelial damage through cigarette smoke exposure, may be a primary cause of COPD-associated emphysema.

TCellSI: A novel method for T cell state assessment and its applications in immune environment prediction.

T cell is an indispensable component of the immune system and its multifaceted functions are shaped by the distinct T cell types and their various states. Although multiple computational models exist for predicting the abundance of diverse T cell types, tools for assessing their states to characterize their degree of resting, activation, and suppression are lacking. To address this gap, a robust and nuanced scoring tool called T cell state identifier (TCellSI) leveraging Mann-Whitney U statistics is established. The TCellSI methodology enables the evaluation of eight distinct T cell states-Quiescence, Regulating, Proliferation, Helper, Cytotoxicity, Progenitor exhaustion, Terminal exhaustion, and Senescence-from transcriptome data, providing T cell state scores (TCSS) for samples through specific marker gene sets and a compiled reference spectrum. Validated against sizeable pseudo-bulk and actual bulk RNA-seq data across a range of T cell types, TCellSI not only accurately characterizes T cell states but also surpasses existing well-discovered signatures in reflecting the nature of T cells. Significantly, the tool demonstrates predictive value in the immune environment, correlating T cell states with patient prognosis and responses to immunotherapy. For better utilization, the TCellSI is readily accessible through user-friendly R package and web server (https://guolab.wchscu.cn/TCellSI/). By offering insights into personalized cancer therapies, TCellSI has the potential to improve treatment outcomes and efficacy.

Ultra-pH-sensitive nanoplatform for precise tumor therapy.

The emergence of precision cancer treatment has triggered a paradigm shift in the field of oncology, facilitating the implementation of more effective and personalized therapeutic approaches that enhance patient outcomes. The pH of the tumor microenvironment (TME) plays a pivotal role in both the initiation and progression of cancer, thus emerging as a promising focal point for precision cancer treatment. By specifically targeting the acidic conditions inherent to the tumor microenvironment, innovative therapeutic interventions have been proposed, exhibiting significant potential in augmenting treatment efficacy and ameliorating patient prognosis. The concept of ultra-pH-sensitive (UPS) nanoplatform was proposed several years ago, demonstrating exceptional pH sensitivity and an adjustable pH transition point. Subsequently, diverse UPS nanoplatforms have been actively explored for biomedical applications, enabling the loading of fluorophores, therapeutic drugs, and photosensitizers. This review aims to elucidate the design strategy and response mechanism of the UPS nanoplatform, with a specific emphasis on its applications in surgical therapy, immunotherapy, drug delivery, photodynamic therapy, and photothermal therapy. The potential and challenges of translating in the clinic on UPS nanoplatforms are finally explored. Thanks to its responsive and easily modifiable nature, the integration of multiple functional units within a UPS nanoplatform holds great promise for future advancements in tumor precision theranositcs.

metsDB: a knowledgebase of cancer metastasis at bulk, single-cell and spatial levels.

Cancer metastasis, the process by which tumour cells migrate and colonize distant organs from a primary site, is responsible for the majority of cancer-related deaths. Understanding the cellular and molecular mechanisms underlying this complex process is essential for developing effective metastasis prevention and therapy strategies. To this end, we systematically analysed 1786 bulk tissue samples from 13 cancer types, 988 463 single cells from 17 cancer types, and 40 252 spots from 45 spatial slides across 10 cancer types. The results of these analyses are compiled in the metsDB database, accessible at https://relab.xidian.edu.cn/metsDB/. This database provides insights into alterations in cell constitutions, cell relationships, biological pathways, molecular biomarkers, and drug responses during cancer metastasis at bulk, single-cell, and spatial levels. Users can perform cell or gene searches to obtain multi-view and multi-scale metastasis-related data. This comprehensive resource is invaluable for understanding the metastasis process and for designing molecular therapies.

The Causal Role of Uterine Fibroid in Keloid and Hypertrophic Scar: A Bidirectional Mendelian Randomization Study on European Populations.

BACKGROUND: The relationship between uterine fibroids and keloid/hypertrophic scars has been contradictory. Our research employs a bidirectional Mendelian Randomization (MR) approach to establish a clearer understanding of this potential causal link. OBJECTIVE: This study aimed to determine the effect of uterine fibroids on keloid/hypertrophic scars and the effect of keloid/hypertrophic scars on uterine fibroids. PURPOSE: We aimed to demonstrate the relationship between uterine fibroids and keloid/ hypertrophic scars. METHOD: Our bidirectional MR study utilized summarized data from genome-wide association studies (GWAS) focused on European populations. Our primary tool for establishing causality was the Inverse-Variance Weighted (IVW) method. To reinforce the IVW findings, we also applied four alternative MR methods: MR-Egger, Maximum Likelihood, Weighted Mode, and Weighted Median. RESULT: The IVW method indicated a significant causal link, with uterine fibroids greatly raising the likelihood of developing keloids (Odds Ratio [OR] = 1.202, 95% Confidence Interval [CI]: 1.045-1.381; P=0.010) and hypertrophic scars (OR = 1.256, 95% CI: 1.039-1.519; P=0.018). Parallel results were observed with the MR-Egger, Maximum Likelihood, Weighted Mode, and Weighted Median methods. Sensitivity analyses indicated robustness in these findings, with no evidence of heterogeneity or horizontal pleiotropy. Conversely, the reverse MR analysis did not demonstrate an increased risk of uterine fibroids due to keloids or hypertrophic scars. CONCLUSION: This study elucidates a significant causal effect of uterine fibroids on the development of keloid and hypertrophic scars, offering valuable insights into their pathogenesis and potential therapeutic targets.

Multiplexed bacterial recognition based on "All-in-One" semiconducting polymer dots sensor and machine learning.

The accurate discrimination of bacterial infection is imperative for precise clinical diagnosis and treatment. Here, this work presents a simplified sensor array utilizing "All-in-One" Pdots for efficient discrimination of diverse bacterial samples. The "All-in-One" Pdots sensor (AOPS) were synthesized using three components that exhibit fluorescence resonance energy transfer (FRET) effect, facilitating the efficient integration of multiple discrimination channels to generate specific fluorescence response patterns through a single detection under single-wavelength excitation. Additionally, machine learning techniques were employed to visually represent the fluorescence response patterns of AOPS upon exposure to bacterial metabolites derived from diverse bacterial species. The as-prepared sensor platform demonstrated excellent performance in analyzing eight common bacteria, drug-resistant strains, mixed bacterial samples, bacterial biofilms and real samples, presenting significant potential in the identification of complex samples for bacterial analysis.

Applying Deep Learning with Convolutional Neural Networks to Laryngoscopic Imaging for Automated Segmentation and Classification of Vocal Cord Leukoplakia.

Objectives: Vocal cord leukoplakia is clinically described as a white plaque or patch on the vocal cords observed during macroscopic examination, which does not take into account histological features or prognosis. A clinical challenge in managing vocal cord leukoplakia is to assess the potential malignant transformation of the lesion. This study aims to investigate the potential of deep learning (DL) for the simultaneous segmentation and classification of vocal cord leukoplakia using narrow band imaging (NBI) and white light imaging (WLI). The primary objective is to assess the model's accuracy in detecting and classifying lesions, comparing its performance in WLI and NBI. Methods: We applied DL to segment and classify NBI and WLI of vocal cord leukoplakia, and used pathological diagnosis as the gold standard. Results: The DL model autonomously detected lesions with an average intersection-over-union (IoU) >70%. In classification tasks, the model differentiated between lesions in the surgical group with a sensitivity of 93% and a specificity of 94% for WLI, and a sensitivity of 99% and a specificity of 97% for NBI. In addition, the model achieved a mean average precision of 81% in WLI and 92% in NBI, with an IoU threshold >0.5. Conclusions: The model proposed by us is helpful in assisting in accurate diagnosis of vocal cord leukoplakia from NBI and WLI.

CircaKB: a comprehensive knowledgebase of circadian genes across multiple species.

Circadian rhythms, which are the natural cycles that dictate various physiological processes over a 24-h period, have been increasingly recognized as important in the management and treatment of various human diseases. However, the lack of sufficient data and reliable analysis methods have been a major obstacle to understanding the bidirectional interaction between circadian variation and human health. We have developed CircaKB, a comprehensive knowledgebase of circadian genes across multiple species. CircaKB is the first knowledgebase that provides systematic annotations of the oscillatory patterns of gene expression at a genome-wide level for 15 representative species. Currently, CircaKB contains 226 time-course transcriptome datasets, covering a wide variety of tissues, organs, and cell lines. In addition, CircaKB integrates 12 computational models to facilitate reliable data analysis and identify oscillatory patterns and their variations in gene expression. CircaKB also offers powerful functionalities to its users, including easy search, fast browsing, strong visualization, and custom upload. We believe that CircaKB will be a valuable tool and resource for the circadian research community, contributing to the identification of new targets for disease prevention and treatment. We have made CircaKB freely accessible at https://cdsic.njau.edu.cn/CircaKB.

DrugFormer: Graph-Enhanced Language Model to Predict Drug Sensitivity.

Drug resistance poses a crucial challenge in healthcare, with response rates to chemotherapy and targeted therapy remaining low. Individual patient's resistance is exacerbated by the intricate heterogeneity of tumor cells, presenting significant obstacles to effective treatment. To address this challenge, DrugFormer, a novel graph-augmented large language model designed to predict drug resistance at single-cell level is proposed. DrugFormer integrates both serialized gene tokens and gene-based knowledge graphs for the accurate predictions of drug response. After training on comprehensive single-cell data with drug response information, DrugFormer model presents outperformance, with higher F1, precision, and recall in predicting drug response. Based on the scRNA-seq data from refractory multiple myeloma (MM) and acute myeloid leukemia (AML) patients, DrugFormer demonstrates high efficacy in identifying resistant cells and uncovering underlying molecular mechanisms. Through pseudotime trajectory analysisunique drug-resistant cellular states associated with poor patient outcomes are revealed. Furthermore, DrugFormer identifies potential therapeutic targets, such as COX8A, for overcoming drug resistance across different cancer types. In conclusion, DrugFormer represents a significant advancement in the field of drug resistance prediction, offering a powerful tool for unraveling the heterogeneity of cellular response to drugs and guiding personalized treatment strategies.

Roles and Mechanisms of Dopamine Receptor Signaling in Catecholamine Excess Induced Endothelial Dysfunctions.

Endothelial dysfunction may contribute to pathogenesis of Takotsubo cardiomyopathy, but mechanism underlying endothelial dysfunction in the setting of catecholamine excess has not been clarified. The study reports that D1/D5 dopamine receptor signaling and small conductance calcium-activated potassium channels contribute to high concentration catecholamine induced endothelial cell dysfunction. For mimicking catecholamine excess, 100 µM epinephrine (Epi) was used to treat human cardiac microvascular endothelial cells. Patch clamp, FACS, ELISA, PCR, western blot and immunostaining analyses were performed in the study. Epi enhanced small conductance calcium-activated potassium channel current (ISK1-3) without influencing the channel expression and the effect was attenuated by D1/D5 receptor blocker. D1/D5 agonists mimicked the Epi effect, suggesting involvement of D1/D5 receptors in Epi effects. The enhancement of ISK1-3 caused by D1/D5 activation involved roles of PKA, ROS and NADPH oxidases. Activation of D1/D5 and SK1-3 channels caused a hyperpolarization, reduced NO production and increased ROS production. The NO reduction was membrane potential independent, while ROS production was increased by the hyperpolarization. ROS (H2O2) suppressed NO production. The study demonstrates that high concentration catecholamine can activate D1/D5 and SK1-3 channels through NADPH-ROS and PKA signaling and reduce NO production, which may facilitate vasoconstriction in the setting of catecholamine excess.

Challenges in LncRNA Biology: Views and Opinions.

This is a mini-review capturing the views and opinions of selected participants at the 2021 IEEE BIBM 3rd Annual LncRNA Workshop, held in Dubai, UAE. The views and opinions are expressed on five broad themes related to problems in lncRNA, namely, challenges in the computational analysis of lncRNAs, lncRNAs and cancer, lncRNAs in sports, lncRNAs and COVID-19, and lncRNAs in human brain activity.

Hypoxia-Induced Ephrin-B2 Facilitates Proliferation and Induces Glycolytic Metabolism in Cutaneous Squamous Cell Carcinoma by Modulating PKM2/HIF-1α Axis.

BACKGROUND: Cutaneous squamous cell carcinoma (cSCC) is a fatal disease characterized by metabolic dysregulation. The role of ephrin type-B receptor 2 (ephrin-B2), a crucial molecule in cancer cell biology, in regulating glycolysis and cell proliferation of cSCC is not well understood. This study aimed to investigate the biological pathways by which ephrin-B2 impacts the glycolysis and cell proliferation of cSCC. METHODS: Ephrin-B2 expression levels in cSCC were determined using quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and Western blotting. Ephrin-B2 expression in cSCC cells was manipulated using overexpression and knockdown approaches. A series of in vitro assays, such as cell counting kit-8 (CCK-8), Transwell assay, immunofluorescence assay, enzyme-linked immunosorbent assay (ELISA), qRT-PCR, and Western blotting, were employed to delineate the biological roles of ephrin-B2/pyruvate kinase muscle isoenzyme 2 (PKM2)/hypoxia-inducible factor 1 alpha (HIF-1α) in proliferation, migration, invasion, and glucose metabolism of cSCC. RESULTS: This study highlights an upregulation of ephrin-B2 expression in cSCC. Knockdown of ephrin-B2 significantly suppressed the proliferation, migration, invasion, and glucose metabolism of cSCC cells. Moreover, ephrin-B2 expression was upregulated under hypoxic conditions. At the molecular level, ephrin-B2 knockdown resulted in the downregulation of PKM2 and HIF-1α expression. Additionally, the overexpression of PKM2 or HIF-1α successfully rescued the diminished proliferation, migration, invasion and glucose metabolism induced by ephrin-B2 knockdown in cSCC cells. CONCLUSION: These findings suggest that ephrin-B2 suppression may hinder cSCC cell proliferation and glycolytic metabolism, potentially via the PKM2/HIF-1α axis modulation.

Aggregation-induced electrochemiluminescence based on intramolecular charge transfer and twisted molecular conformation for label-free Immunoassay.

Organic emitters with exceptional properties exhibit significant potential in the field of aggregation-induced electrochemiluminescence (AIECL); however, their practicality is impeded by limited ECL efficiency (ΦECL). This paper investigates a novel type of AIECL emitter (BDPPA NPs), where an efficient intramolecular charge transfer (ICT) effect and highly twisted conformation contribute to a remarkable enhancement of ECL. The ICT effect reduces the electron transfer path, while the twisted conformation effectively restricts π-π stacking and intramolecular motions. Intriguingly, compared to the standard system of [Ru(bpy)32+]/TPrA, bright emissions with up to 54 % ΦECL were achieved, enabling direct visual observation of ECL through the co-reactant route. The label-free immunosensor exhibited distinguished performance in detecting SARS-CoV-2 N protein across an exceptionally wide linear range of 0.001-500 ng mL-1, with a remarkably low detection limit of 0.28 pg mL-1. Furthermore, this developed ECL platform exhibited excellent sensitivity, specificity, and stability characteristics, providing an efficient avenue for constructing platforms for bioanalysis and clinical diagnosis analysis.

SexAnnoDB, a knowledgebase of sex-specific regulations from multi-omics data of human cancers.

BACKGROUND: Sexual differences across molecular levels profoundly impact cancer biology and outcomes. Patient gender significantly influences drug responses, with divergent reactions between men and women to the same drugs. Despite databases on sex differences in human tissues, understanding regulations of sex disparities in cancer is limited. These resources lack detailed mechanistic studies on sex-biased molecules. METHODS: In this study, we conducted a comprehensive examination of molecular distinctions and regulatory networks across 27 cancer types, delving into sex-biased effects. Our analyses encompassed sex-biased competitive endogenous RNA networks, regulatory networks involving sex-biased RNA binding protein-exon skipping events, sex-biased transcription factor-gene regulatory networks, as well as sex-biased expression quantitative trait loci, sex-biased expression quantitative trait methylation, sex-biased splicing quantitative trait loci, and the identification of sex-biased cancer therapeutic drug target genes. All findings from these analyses are accessible on SexAnnoDB ( https://ccsm.uth.edu/SexAnnoDB/ ). RESULTS: From these analyses, we defined 126 cancer therapeutic target sex-associated genes. Among them, 9 genes showed sex-biased at both the mRNA and protein levels. Specifically, S100A9 was the target of five drugs, of which calcium has been approved by the FDA for the treatment of colon and rectal cancers. Transcription factor (TF)-gene regulatory network analysis suggested that four TFs in the SARC male group targeted S100A9 and upregulated the expression of S100A9 in these patients. Promoter region methylation status was only associated with S100A9 expression in KIRP female patients. Hypermethylation inhibited S100A9 expression and was responsible for the downregulation of S100A9 in these female patients. CONCLUSIONS: Comprehensive network and association analyses indicated that the sex differences at the transcriptome level were partially the result of corresponding sex-biased epigenetic and genetic molecules. Overall, SexAnnoDB offers a discipline-specific search platform that could potentially assist basic experimental researchers or physicians in developing personalized treatment plans.

Sexual variations at the molecular level have a profound impact on cancer biology and outcomes, influencing drug responses that diverge between men and women exposed to the same drugs. Despite existing databases on sex differences in human tissues, our understanding of the regulations governing sex disparities in cancer is limited, lacking detailed mechanistic studies on sex-biased molecules. This study addresses this gap by conducting a comprehensive examination of molecular distinctions and regulatory networks across 27 cancer types, specifically focusing on sex-biased effects. The analyses led to the identification of 126 cancer therapeutic target sex-associated genes and shed light on the intricate relationship between sexual differences and cancer. Furthermore, the findings from these analyses are made accessible through SexAnnoDB, providing a specialized search platform. This platform has the potential to assist basic experimental researchers or physicians in developing personalized treatment plans based on a deeper understanding of sex-specific factors in cancer.

Circulating extracellular vesicle-derived miR-1299 disrupts hepatic glucose homeostasis by targeting the STAT3/FAM3A axis in gestational diabetes mellitus.

BACKGROUND: Extracellular vesicles (EVs) are membrane-enclosed structures containing lipids, proteins, and RNAs that play a crucial role in cell-to-cell communication. However, the precise mechanism through which circulating EVs disrupt hepatic glucose homeostasis in gestational diabetes mellitus (GDM) remains unclear. RESULTS: Circulating EVs isolated from human plasma were co-cultured with mammalian liver cells to investigate the potential induction of hepatic insulin resistance by GDM-EVs using glucose output assays, Seahorse assays, metabolomics, fluxomics, qRT-PCR, bioinformatics analyses, and luciferase assays. Our findings demonstrated that hepatocytes exposed to GDM-EVs exhibited increased gluconeogenesis, attenuated energy metabolism, and upregulated oxidative stress. Particularly noteworthy was the discovery of miR-1299 as the predominant miRNA in GDM-EVs, which directly targeting the 3'-untranslated regions (UTR) of STAT3. Our experiments involving loss- and gain-of-function revealed that miR-1299 inhibits the insulin signaling pathway by regulating the STAT3/FAM3A axis, resulting in increased insulin resistance through the modulation of mitochondrial function and oxidative stress in hepatocytes. Moreover, experiments conducted in vivo on mice inoculated with GDM-EVs confirmed the development of glucose intolerance, insulin resistance, and downregulation of STAT3 and FAM3A. CONCLUSIONS: These results provide insights into the role of miR-1299 derived from circulating GDM-EVs in the progression of insulin resistance in hepatic cells via the STAT3/FAM3A axis and downstream metabolic reprogramming.

Underdetermined Blind Source Separation of Audio Signals for Group Reared Pigs Based on Sparse Component Analysis.

In order to solve the problem of difficult separation of audio signals collected in pig environments, this study proposes an underdetermined blind source separation (UBSS) method based on sparsification theory. The audio signals obtained by mixing the audio signals of pigs in different states with different coefficients are taken as observation signals, and the mixing matrix is first estimated from the observation signals using the improved AP clustering method based on the "two-step method" of sparse component analysis (SCA), and then the audio signals of pigs are reconstructed by L1-paradigm separation. Five different types of pig audio are selected for experiments to explore the effects of duration and mixing matrix on the blind source separation algorithm by controlling the audio duration and mixing matrix, respectively. With three source signals and two observed signals, the reconstructed signal metrics corresponding to different durations and different mixing matrices perform well. The similarity coefficient is above 0.8, the average recovered signal-to-noise ratio is above 8 dB, and the normalized mean square error is below 0.02. The experimental results show that different audio durations and different mixing matrices have certain effects on the UBSS algorithm, so the recording duration and the spatial location of the recording device need to be considered in practical applications. Compared with the classical UBSS algorithm, the proposed algorithm outperforms the classical blind source separation algorithm in estimating the mixing matrix and separating the mixed audio, which improves the reconstruction quality.

AntiFormer: graph enhanced large language model for binding affinity prediction.

Antibodies play a pivotal role in immune defense and serve as key therapeutic agents. The process of affinity maturation, wherein antibodies evolve through somatic mutations to achieve heightened specificity and affinity to target antigens, is crucial for effective immune response. Despite their significance, assessing antibody-antigen binding affinity remains challenging due to limitations in conventional wet lab techniques. To address this, we introduce AntiFormer, a graph-based large language model designed to predict antibody binding affinity. AntiFormer incorporates sequence information into a graph-based framework, allowing for precise prediction of binding affinity. Through extensive evaluations, AntiFormer demonstrates superior performance compared with existing methods, offering accurate predictions with reduced computational time. Application of AntiFormer to severe acute respiratory syndrome coronavirus 2 patient samples reveals antibodies with strong neutralizing capabilities, providing insights for therapeutic development and vaccination strategies. Furthermore, analysis of individual samples following influenza vaccination elucidates differences in antibody response between young and older adults. AntiFormer identifies specific clonotypes with enhanced binding affinity post-vaccination, particularly in young individuals, suggesting age-related variations in immune response dynamics. Moreover, our findings underscore the importance of large clonotype category in driving affinity maturation and immune modulation. Overall, AntiFormer is a promising approach to accelerate antibody-based diagnostics and therapeutics, bridging the gap between traditional methods and complex antibody maturation processes.

The attenuation of gut microbiota-derived short-chain fatty acids elevates lipid transportation through suppression of the intestinal HDAC3-H3K27ac-PPAR-γ axis in gestational diabetes mellitus.

Gut flora is considered to modulate lipid transport from the intestine into the bloodstream, and thus may potentially participate in the development of GDM. Although previous studies have shown that the intestinal microbiota influences lipid transport and metabolism in GDM, the precise mechanisms remain elusive. To address this, we used a high-fat diet (HFD)-induced GDM mouse model and conducted 16s rRNA sequencing and fecal metabolomics to assess gut microbial community shifts and associated metabolite changes. Western blot, ELISA, and chromatin immunoprecipitation (ChIP) were utilized to elucidate how gut microbiota affect intestinal lipid transport and the insulin sensitivity of hepatic, adipose, and skeletal muscle tissues. We found that HFD impaired the oral glucose tolerance test (OGTT) and insulin tolerance test (ITT) in pregnant mice. 16s rRNA sequencing demonstrated profound compositional changes, especially in the relative abundances of Firmicutes and Bacteroidetes. Metabolomics analysis presented a decline in the concentration of short-chain fatty acids (SCFAs) in the GDM group. Western blot analyses showed an upregulation of HDAC3 and a concurrent reduction in H3K27 acetylation in the intestine. ChIP-qPCR showed that PPAR-γ was inhibited, which in turn activated lipid-transporter CD36. ELISA and insulin signaling pathway detection in insulin-target organs showed high concentrations of circulating fatty acids and triglycerides and insulin resistance in insulin-target organs. Our results suggest that gut microbiota is closely associated with the development of GDM, partly because decreased gut flora-associated SCFAs activate CD36 by suppressing the HDAC3-H3K27ac-PPAR-γ axis to transport excessive fatty acids and triglycerides into blood circulation, thereby dysregulating the insulin sensitivity of insulin target organs.

Low skeletal muscle mass is associated with inferior preoperative and postoperative shoulder function in elderly rotator cuff tear patients.

BACKGROUND: The age-related loss of skeletal muscle mass is an important characteristic of sarcopenia, an increasingly recognized condition with systemic implications. However, its association with shoulder function in elderly patients with rotator cuff tears (RCT) remains unknown. This study aimed to investigate the relationship between low skeletal muscle mass and shoulder function in elderly RCT patients. METHODS: A retrospective analysis was conducted on RCT patients who underwent chest computed tomography (CT) scans for clinical evaluation. Preoperative CT scan images of the chest were used to calculate the cross-sectional area (CSA) of thoracic muscle at the T4 level. The medical records were reviewed. Shoulder function was assessed using the ASES score and CMS score both preoperatively and at the final follow-up. Data on the preoperative range of motion (ROM) for the affected shoulder, were collected for analysis. Subgroup analyses by sex were also performed. RESULTS: A total of 283 RCT patients, consisting of 95 males and 188 females, with a mean age of 66.22 ± 4.89(range, 60-95 years) years were included in this retrospective study. The low muscle mass group showed significantly higher level of c-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) compared to the normal group(3.75 ± 6.64 mg/L vs. 2.17 ± 2.30 mg/L, p = 0.021; 19.08 ± 12.86 mm/H vs.15.95 ± 10.76 mm/H, p = 0.038; respectively). In the normal group, pre-operative passive ROM, including forward elevation, abduction, lateral rotation, and abductive external rotation, was significantly better than that in the low muscle mass group (127.18 ± 34.87° vs. 89.76 ± 50.61°; 119.83 ± 45.76° vs. 87.16 ± 53.32°; 37.96 ± 28.33° vs. 25.82 ± 27.82°; 47.71 ± 23.56° vs. 30.87 ± 27.76°, all p < 0.01, respectively). Similar results were found in the active ROM of the shoulder. The female low muscle mass group exhibited significantly poorer passive and active ROM (p < 0.05). The post-operative ASES scores and CMS scores of the female low muscle mass group were also statistically worse than those of the female normal group (p < 0.05). CONCLUSIONS: The results of present study revealed that the low skeletal muscle mass is associated with inferior ROM of the shoulder and per- and post-operative shoulder function, especially for elderly female patients.