Targeting KLF2 as a Novel Therapy for Glomerular Endothelial Cell Injury in Diabetic Kidney Disease.

BACKGROUND: DKD is a microvascular disease, and glomerular endothelial cell injury is a key pathological event in DKD development. Through unbiased screening of glomerular transcriptomes, we previously identified KLF2 as a highly regulated gene in diabetic kidneys. KLF2 exhibits protective effects in endothelial cells by inhibiting inflammation, thrombotic activation, and angiogenesis, all of which are protective for cardiovascular disease. We previously demonstrated that endothelial cell-specific ablation of Klf2 exacerbated diabetes-induced glomerular endothelial cell injury and DKD in mice. Therefore in this study, we sought to assess the therapeutic potential of KLF2 activation in murine models of DKD. METHODS: We first examined the effects of endothelial cell-specific inducible overexpression of KLF2 (KLF2ov) in streptozotocin-induced diabetic mice. We developed small molecule KLF2 activators and tested whether increased KLF2 activity could impede DKD progression in type 2 diabetic db/db and BTBR ob/ob mice. RESULTS: Diabetic KLF2ov mice had attenuated albuminuria, glomerular endothelial cell injury, and diabetic glomerulopathy compared to control diabetic mice. Novel KLF2 activator, compound 6 (C-6) effectively induced downstream Nos3 expression and suppressed NF-kB activation in glomerular endothelial cells. The administration of C-6 improved albuminuria and glomerulopathy in db/db and BTBR ob/ob mice, which was associated with improved glomerular endothelial cell and podocyte injury. CONCLUSIONS: These results validate KLF2 as a potential drug target and KLF2 activators such as C-6 as a novel therapy for DKD.

mosGraphFlow: a novel integrative graph AI model mining disease targets from multi-omic data.

Multi-omic data can better characterize complex cellular signaling pathways from multiple views compared to individual omic data. However, integrative multi-omic data analysis to rank key disease biomarkers and infer core signaling pathways remains an open problem. In this study, our novel contributions are that we developed a novel graph AI model, mosGraphFlow, for analyzing multi-omic signaling graphs (mosGraphs), 2) analyzed multi-omic mosGraph datasets of AD, and 3) identified, visualized and evaluated a set of AD associated signaling biomarkers and network. The comparison results show that the proposed model not only achieves the best classification accuracy but also identifies important AD disease biomarkers and signaling interactions. Moreover, the signaling sources are highlighted at specific omic levels to facilitate the understanding of the pathogenesis of AD. The proposed model can also be applied and expanded for other studies using multi-omic data. Model code is accessible via GitHub: https://github.com/FuhaiLiAiLab/mosGraphFlow.

Large-language-model empowered 3D dose prediction for intensity-modulated radiotherapy.

BACKGROUND: Treatment planning is currently a patient specific, time-consuming, and resource demanding task in radiotherapy. Dose-volume histogram (DVH) prediction plays a critical role in automating this process. The geometric relationship between DVHs in radiotherapy plans and organs-at-risk (OAR) and planning target volume (PTV) has been well established. This study explores the potential of deep learning models for predicting DVHs using images and subsequent human intervention facilitated by a large-language model (LLM) to enhance the planning quality. METHOD: We propose a pipeline to convert unstructured images to a structured graph consisting of image-patch nodes and dose nodes. A novel Dose Graph Neural Network (DoseGNN) model is developed for predicting DVHs from the structured graph. The proposed DoseGNN is enhanced with the LLM to encode massive knowledge from prescriptions and interactive instructions from clinicians. In this study, we introduced an online human-AI collaboration (OHAC) system as a practical implementation of the concept proposed for the automation of intensity-modulated radiotherapy (IMRT) planning. RESULTS: The proposed DoseGNN model was compared to widely employed DL models used in radiotherapy, including Swin Transformer, 3D U-Net CNN, and vanilla MLP. For PTV, DoseGNN achieved the mean absolute error (MAE) of D m a x ${D}_{max}$ , D m e a n ${D}_{mean}$ , D 95 ${D}_{95}$ , and D 1 ${D}_1$ between true plans and predicted plans that were 64%, 53%, 64%, 61% of the best baseline model. For the worst case among OARs (left lung, right lung, chest wall, heart, spinal cord), DoseGNN achieved the mean absolute error of D m a x ${D}_{max}$ , D m e a n ${D}_{mean}$ , D 50 ${D}_{50}$ that were 85%, 91%, 80% of the best baseline model. Moreover, the LLM-empowered DoseGNN model facilitates seamless adjustment to treatment plans through interaction with clinicians using natural language. CONCLUSION: We developed DoseGNN, a novel deep learning model for predicting delivered radiation doses from medical images, enhanced by LLM to allow adjustment through seamless interaction with clinicians. The preliminary results confirm DoseGNN's superior accuracy in DVH prediction relative to typical DL methods, highlighting its potential to facilitate an online clinician-AI collaboration system for streamlined treatment planning automation.

Antibody-drug conjugates and immune checkpoint inhibitors in cancer treatment: a systematic review and meta-analysis.

Although antibody-drug conjugate (ADC) or immune checkpoint inhibitors (ICIs) alone fosters hope for the treatment of cancer, the effect of single drug treatment is limited and the safety profile of ADC and ICI therapy remains unclear. This meta-analysis aimed to examine the efficacy and safety of the combination of ADC and ICI therapy. This study type is a systematic review and meta-analysis. Literature retrieval was carried out through PubMed, Embase, Cochrane from inception to Jun. 5, 2024. Then, after data extraction, overall response rate (ORR) and adverse effects (AEs) were used to study its efficiency and safety. Publication bias was also calculated through Funnel plot, Begg's Test and Egger's test. Heterogeneity was investigated through subgroup and sensitivity analysis. The research protocol was registered with the PROSPERO (CRD42023375601). A total of 12 eligible clinical studies with 584 patients were included. The pooled ORR was 58% (95%CI 46%, 70%). Subgroup analysis showed an ORR of 77% (95%CI 63%, 91%) in classical Hodgkin lymphoma (cHL) and an ORR of 73% (95%CI 56%, 90%) in non-Hodgkin lymphoma (NHL). The most common AEs was peripheral neuropathy (38.0%). Meanwhile, AEs on skin (13.1-20.0%) and digestive system (9.0-36.0%) was hard be overlooked. ADC + ICI therapy may be recommended in cancer treatment, especially in cHL and NHL. However, strategies to manage toxicities warranted further exploration.

M3NetFlow: A novel multi-scale multi-hop graph AI model for integrative multi-omic data analysis.

Multi-omic data-driven studies, characterizing complex disease signaling system from multiple levels, are at the forefront of precision medicine and healthcare. The integration and interpretation of multi-omic data are essential for identifying molecular targets and deciphering core signaling pathways of complex diseases. However, it remains an open problem due the large number of biomarkers and complex interactions among them. In this study, we propose a novel Multi-scale Multi-hop Multi-omic graph model, M3NetFlow, to facilitate generic multi-omic data analysis to rank targets and infer core signaling flows/pathways. To evaluate M3NetFlow, we applied it in two independent multi-omic case studies: 1) uncovering mechanisms of synergistic drug combination response (defined as anchor-target guided learning), and 2) identifying biomarkers and pathways of Alzheimer 's disease (AD). The evaluation and comparison results showed M3NetFlow achieves the best prediction accuracy (accurate), and identifies a set of essential targets and core signaling pathways (interpretable). The model can be directly applied to other multi-omic data-driven studies. The code is publicly accessible at: https://github.com/FuhaiLiAiLab/M3NetFlow.

Comprehensive assessment of distinct abdominal fat compartments beyond liver content in overweight/obese patients using MRI and ultrasound imaging.

BACKGROUND: Ectopic fat deposition, involving lipid infiltration within organs and fat accumulating surrounding organs, plays a crucial role in the development of metabolic abnormalities in obesity. Current imaging measurements of obesity primarily focus on lipid infiltration within liver, neglecting fat deposition in other areas. This study aims to explore the methods of measuring and correlating different types of abdominal ectopic fat deposition in obese patients using magnetic resonance imaging (MRI) and ultrasound techniques, and to investigate the relationship between these fat parameters and obesity-related metabolic markers. METHODS: Abdominal ectopic fat deposition including liver fat content, mesenteric fat thickness (MFT), perirenal fat thickness (PrFT) and preperitoneal fat thickness (PFT) were measured in 220 overweight/obese patients using both MRI and ultrasound techniques. Correlation analysis validated the concordance of fat parameters at specific sites between the two imaging methods and identified the cutoff values of hepatic attenuation coefficient (AC) for diagnosis of liver steatosis. Additionally, we investigated the correlation between fat parameters by both methods and obesity-related metabolic markers. RESULTS: Ultrasonic measurement of PrFT and hepatic AC both had high correlation with PrFT (r = 0.829, p < 0.001) and hepatic Proton-density fat fraction (PDFF, r = 0.822, p < 0.001) measured via MR. Hepatic AC cutoff values for diagnosing mild, moderate, and severe fatty liver were 0.705 dB/cm/MHz (AUC = 0.922), 0.755 dB/cm/MHz (AUC = 0.923), and 0.875 dB/cm/MHz (AUC = 0.890) respectively. Hepatic AC correlated significantly with AST and ALT (r = 0.477 ~ 0.533, p < 0.001). MFT measured by ultrasound were positively associated with glycated hemoglobin (r = 0.324 ~ 0.371, p < 0.001) and serum triglyceride levels (r = 0.303 ~ 0.353, p < 0.001). PrFT measured by both methods showed significant positive correlations with serum creatinine levels (r = 0.305 ~ 0.308, p < 0.001). CONCLUSIONS: Both MRI and ultrasound demonstrate metabolic correlations in quantifying mesenteric, hepatic, and perirenal fat. In addition to assessment of liver fat content, the measurements of ectopic fat deposition by MRI or ultrasound are a simple and crucial way for comprehensive fat evaluation in individuals with overweight/obesity.

Two C23-Steroids and a New Isocoumarin Metabolite from Mangrove Sediment-Derived Fungus Penicillium sp. SCSIO 41429.

Two new C23-steroids derivatives, cyclocitrinoic acid A (1) and cyclocitrinoic acid B (2), and a new isocoumarin metabolite, (3R,4S)-6,8-dihydroxy-3,4,5-trimethyl-7-carboxamidelisocoumarin (10), together with 12 known compounds (3-9, 11-15) were isolated from the mangrove-sediment fungus Penicillium sp. SCSIO 41429. The structures of the new compounds were comprehensively characterized by 1D and 2D NMR, HRESIMS and ECD calculation. All isolates were evaluated for pancreatic lipase (PL) inhibitory and antioxidant activities. The biological evaluation results revealed that compounds 2, 14 and 15 displayed weak or moderate inhibition against PL, with IC50 values of 32.77, 5.15 and 2.42 µM, respectively. In addition, compounds 7, 12 and 13 showed radical scavenging activities against DPPH, with IC50 values of 64.70, 48.13, and 75.54 µM, respectively. In addition, molecular docking results indicated that these compounds had potential for PL inhibitory and antioxidant activities, which provided screening candidates for antioxidants and a reduction in obesity.

Effect of machine learning models on clinician prediction of postoperative complications: the Perioperative ORACLE randomised clinical trial.

BACKGROUND: Anaesthesiologists might be able to mitigate risk if they know which patients are at greatest risk for postoperative complications. This trial examined the impact of machine learning models on clinician risk assessment. METHODS: This single-centre, prospective, randomised clinical trial enrolled surgical patients aged ≥18 yr. Anaesthesiologists and nurse anaesthetists providing remote telemedicine support reviewed electronic health records with (assisted group) or without (unassisted group) reviewing machine learning predictions. Clinicians predicted the likelihood of postoperative 30-day all-cause mortality and postoperative acute kidney injury (AKI) within 7 days. The primary outcome was area under the receiver operating characteristic curve (AUROC) for clinician predictions of mortality and AKI, comparing AUROCs between assisted and unassisted assessments. RESULTS: We analysed 5071 patients (mean [range] age: 58 [18-100] yr; 52% female) assessed by 89 clinicians. Of these, 98 (2.2%) patients died within 30 days of surgery and 450 (11.1%) patients sustained AKI. Clinician predictions agreed with the models more strongly in the assisted vs unassisted group (weighted kappa 0.75 vs 0.62 for death, mean difference: 0.13 [95% CI 0.10-0.17]; and 0.79 vs 0.54 for AKI, mean difference: 0.25 [95% CI 0.21-0.29]). Clinical prediction of death was similar between the assisted (AUROC 0.793) and unassisted (AUROC 0.780) groups (mean difference: 0.013 [95% CI -0.070 to 0.097]; P=0.76). Prediction of AKI had an AUROC of 0.734 in the assisted group vs 0.688 in the unassisted group (difference 0.046 [95% CI -0.003 to 0.091]; P=0.06). CONCLUSIONS: Clinician performance was not improved by machine learning assistance. Further work is needed to clarify the role of machine learning in real-time perioperative risk stratification. CLINICAL TRIAL REGISTRATION: NCT05042804.

Application of random survival forest to establish a nomogram combining clinlabomics-score and clinical data for predicting brain metastasis in primary lung cancer.

PURPOSE: To establish a nomogram for predicting brain metastasis (BM) in primary lung cancer at 12, 18, and 24 months after initial diagnosis. METHODS: In this study, we included 428 patients who were diagnosed with primary lung cancer at Harbin Medical University Cancer Hospital between January 2020 and January 2022. The endpoint event was BM. The patients were randomly categorized into two groups in a 7:3 ratio: training (n = 299) and validation (n = 129) sets. Least absolute shrinkage and selection operator was utilized to analyze the laboratory test results in the training set. Furthermore, clinlabomics-score was determined using regression coefficients. Then, clinlabomics-score was combined with clinical data to construct a nomogram using random survival forest (RSF) and Cox multivariate regression. Then, various methods were used to evaluate the performance of the nomogram. RESULTS: Five independent predictive factors (pathological type, diameter, lymph node metastasis, non-lymph node metastasis and clinlabomics-score) were used to construct the nomogram. In the validation set, the bootstrap C-index was 0.7672 (95% CI 0.7092-0.8037), 12-month AUC was 0.787 (95% CI 0.708-0.865), 18-month AUC was 0.809 (95% CI 0.735-0.884), and 24-month AUC was 0.858 (95% CI 0.792-0.924). In addition, the calibration curve, decision curve analysis and Kaplan-Meier curves revealed a good performance of the nomogram. CONCLUSIONS: Finally, we constructed and validated a nomogram to predict BM risk in primary lung cancer. Our nomogram can identify patients at high risk of BM and provide a reference for clinical decision-making at different disease time points.

The relationship between psychological capital and self-directed learning ability among undergraduate nursing students-a cross-sectional study.

Background: Psychological capital has become a prominent focus in positive psychology, highlighting the positive influence of higher psychological capital on individuals. Self-directed learning ability is a fundamental skill for students, vital for enhancing academic performance and professional development, and is integral to the continuous learning process of nursing students. Recognizing the relationship between psychological capital and self-directed learning ability is crucial for the progress and development of undergraduate nursing students. Objective: This study aims to investigate the correlation between psychological capital and self-directed learning ability in undergraduate nursing students, as well as to explore the factors that influence these variables. Methods: A cross-sectional survey was conducted with 667 full-time undergraduate nursing students from a nursing school in Taizhou, China. Psychological capital and self-directed learning ability were assessed using the Psychological Capital Questionnaire and Self-Directed Learning Scale, respectively. Correlation and stepwise multiple regression analyses were then carried out to evaluate the relationship between psychological capital and self-directed learning ability among the participants. Results: The study revealed that the psychological capital score averaged at 103.24 ± 15.51, while the self-directed learning scale score averaged at 230.67 ± 27.66. Variations in psychological capital scores were noted based on factors including grade level, being an only child, growth environment, monthly living expenses, parental education level, voluntary selection of nursing major, and club experience. Similarly, differences in self-directed learning scores were associated with factors such as grade level, gender, parental education level, and voluntary selection of nursing major. Moreover, a positive correlation was identified between the overall psychological capital scores and the total self-directed learning ability scores among nursing students. Notably, the multiple regression analysis highlighted that optimism and resilience played significant roles as predictors of self-directed learning ability. Conclusion: Psychological capital is positively correlated with the self-directed learning ability of nursing students, with optimism and resilience identified as crucial predictors. Nursing educators can utilize strategies rooted in positive psychology and perseverance to improve the self-directed learning ability of nursing students.

Near-Infrared Laser-Switching DNA Phase Separation Nanoinducer for Glioma Therapy.

DNA phase separation participates in chromatin packing for the modulation of gene transcription, but the induction of DNA phase separation in living cells for disease treatment faces huge challenges. Herein, we construct a Ru(II)-polypyridyl-loaded upconversion nanoplatform (denoted as UCSNs-R) to achieve the manipulation of DNA phase separation and production of abundant singlet oxygen (1O2) for efficient treatment of gliomas. The utilization of the UCSN not only facilitates high loading of Ru(II)-polypyridyl complexes (RuC) but also promotes the conversion of near-infrared (NIR) laser to ultraviolet light for efficient 1O2 generation. The released RuC exhibit DNA "light-switch" behavior and high DNA binding affinity that induce phase separation of DNA in living cells, thus resulting in DNA damage and suppressing tumor-cell growth. In vivo investigation demonstrates the high capability of UCSNs-R in inhibiting tumor proliferation under NIR laser illumination. This work represents a paradigm for designing a DNA phase separation nanoinducer through integration of the UCSN with Ru(II)-polypyridyl-based complexes for efficient therapy of gliomas.

Characteristic of epicotyl dormancy and its hormonal regulation in Chinese cork oak (Quercus variabilis).

Emergence heterogeneity caused by epicotyl dormancy contributes to variations in seedling quality during large-scale breeding. However, the mechanism of epicotyl dormancy release remains obscure. We first categorized the emergence stages of Chinese cork oak (Quercus variabilis) using the BBCH-scale. Subsequently, we identified the key stage of the epicotyl dormancy process. Our findings indicated that cold stratification significantly released epicotyl dormancy by increasing the levels of gibberellic acid 3 (GA3) and GA4. Genes associated with GA biosynthesis and signaling also exhibited altered expression patterns. Inhibition of GA biosynthesis by paclobutrazol (PAC) treatment severely inhibited emergence, with no effect on seed germination. Different concentrations (50 µM, 100 µM, and 200 µM) of GA3 and GA4+7 treatments of germinated seeds demonstrated that both can promote the emergence, with GA4 exhibiting a more pronounced effect. In conclusion, this study provides valuable insights into the characterization of epicotyl dormancy in Chinese cork oak and highlights the critical role of GA biosynthesis in seedling emergence. These findings serve as a basis for further investigations on epicotyl dormancy and advancing large-scale breeding techniques.

ZG16 impacts gut microbiota-associated intestinal inflammation and pulmonary mucosal function through bacterial metabolites.

Zymogen granule 16 (ZG16) is a secretory glycoprotein found in zymogen granules, which also plays an important role in colorectal inflammation and cancer. Herein, a ZG16 gene knock-out (ZG16-/-) mouse line was established and we found that ZG16 deletion damaged the intestinal mucosal barrier and gut microbiota, which resulted in low-level inflammation and further promoted the development of ulcerative colitis and inflammation-related colorectal cancer. Meanwhile, a metabolomics analysis on mouse feces showed that the metabolites significantly differed between ZG16-/- and WT mice, which were important mediators of host-microbiota communication and may impact the pulmonary inflammation of mice. Indeed, ZG16-/- mice showed more severe inflammation in a bronchial asthma model. Taken together, the results demonstrate that ZG16 plays a pivotal role in inhibiting inflammation and regulating immune responses in colorectum and lung of experimental animals, which may provide a better understanding of the underlying mechanism of human inflammatory diseases associated with ZG16.

CD4+ T cells reactive to a hybrid peptide from insulin-chromogranin A adopt a distinct effector fate and are pathogenic in autoimmune diabetes.

T cell-mediated islet destruction is a hallmark of autoimmune diabetes. Here, we examined the dynamics and pathogenicity of CD4+ T cell responses to four different insulin-derived epitopes during diabetes initiation in non-obese diabetic (NOD) mice. Single-cell RNA sequencing of tetramer-sorted CD4+ T cells from the pancreas revealed that islet-antigen-specific T cells adopted a wide variety of fates and required XCR1+ dendritic cells for their activation. Hybrid-insulin C-chromogranin A (InsC-ChgA)-specific CD4+ T cells skewed toward a distinct T helper type 1 (Th1) effector phenotype, whereas the majority of insulin B chain and hybrid-insulin C-islet amyloid polypeptide-specific CD4+ T cells exhibited a regulatory phenotype and early or weak Th1 phenotype, respectively. InsC-ChgA-specific CD4+ T cells were uniquely pathogenic upon transfer, and an anti-InsC-ChgA:IAg7 antibody prevented spontaneous diabetes. Our findings highlight the heterogeneity of T cell responses to insulin-derived epitopes in diabetes and argue for the feasibility of antigen-specific therapies that blunts the response of pathogenic CD4+ T cells causing autoimmunity.

mosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI.

Generative pretrained models represent a significant advancement in natural language processing and computer vision, which can generate coherent and contextually relevant content based on the pre-training on large general datasets and fine-tune for specific tasks. Building foundation models using large scale omic data is promising to decode and understand the complex signaling language patterns within cells. Different from existing foundation models of omic data, we build a foundation model, mosGraphGPT, for multi-omic signaling (mos) graphs, in which the multi-omic data was integrated and interpreted using a multi-level signaling graph. The model was pretrained using multi-omic data of cancers in The Cancer Genome Atlas (TCGA), and fine-turned for multi-omic data of Alzheimer's Disease (AD). The experimental evaluation results showed that the model can not only improve the disease classification accuracy, but also is interpretable by uncovering disease targets and signaling interactions. And the model code are uploaded via GitHub with link: https://github.com/mosGraph/mosGraphGPT.

Using DeepSignalingFlow to mine signaling flows interpreting mechanism of synergy of cocktails.

Complex signaling pathways are believed to be responsible for drug resistance. Drug combinations perturbing multiple signaling targets have the potential to reduce drug resistance. The large-scale multi-omic datasets and experimental drug combination synergistic score data are valuable resources to study mechanisms of synergy (MoS) to guide the development of precision drug combinations. However, signaling patterns of MoS are complex and remain unclear, and thus it is challenging to identify synergistic drug combinations in clinical. Herein, we proposed a novel integrative and interpretable graph AI model, DeepSignalingFlow, to uncover the MoS by integrating and mining multi-omic data. The major innovation is that we uncover MoS by modeling the signaling flow from multi-omic features of essential disease proteins to the drug targets, which has not been introduced by the existing models. The model performance was assessed utilizing four distinct drug combination synergy evaluation datasets, i.e., NCI ALMANAC, O'Neil, DrugComb, and DrugCombDB. The comparison results showed that the proposed model outperformed existing graph AI models in terms of synergy score prediction, and can interpret MoS using the core signaling flows. The code is publicly accessible via Github: https://github.com/FuhaiLiAiLab/DeepSignalingFlow.

Genome-wide identification of TCP transcription factors and their potential roles in hydrolyzable tannin production in Quercus variabilis cupule.

Hydrolyzable tannins (HTs) have garnered significant attention due to their proven beneficial effects in the clinical treatment of various diseases. The cupule of Chinese cork oak (Quercus variabilis Blume) has been used as raw material of traditional medicine for centuries for its high content of HTs. Previous studies have identified UGT84A13 as a key enzyme in the HT biosynthesis pathway in Q. variabilis, but the transcriptional regulation network of UGT84A13 remains obscure. Here, we performed a comprehensive genome-wide identification of the TCP transcription factors in Q. variabilis, elucidating their molecular evolution and gene structure. Gene expression analysis showed that TCP3 from the CIN subfamily and TCP6 from the PCF subfamily were co-expressed with UGT84A13 in cupule. Further functional characterization using dual-luciferase assays confirmed that TCP3, rather than TCP6, played a role in the transcriptional regulation of UGT84A13, thus promoting HT biosynthesis in the cupule of Q. variabilis. Our work identified TCP family members in Q. variabilis for the first time, and provided novel insights into the transcriptional regulatory network of UGT84A13 and HT biosynthesis in Q. variabilis, explaining the reason why the cupule enriches HTs that could be used for traditional medicine.

Identification of the Shared Gene Signatures Between Alzheimer's Disease and Diabetes-Associated Cognitive Dysfunction by Bioinformatics Analysis Combined with Biological Experiment.

Background: The connection between diabetes-associated cognitive dysfunction (DACD) and Alzheimer's disease (AD) has been shown in several observational studies. However, it remains controversial as to how the two related. Objective: To explore shared genes and pathways between DACD and AD using bioinformatics analysis combined with biological experiment. Methods: We analyzed GEO microarray data to identify DEGs in AD and type 2 diabetes mellitus (T2DM) induced-DACD datasets. Weighted gene co-expression network analysis was used to find modules, while R packages identified overlapping genes. A robust protein-protein interaction network was constructed, and hub genes were identified with Gene ontology enrichment and Kyoto Encyclopedia of Genome and Genome pathway analyses. HT22 cells were cultured under high glucose and amyloid-ß 25-35 (Aß25-35) conditions to establish DACD and AD models. Quantitative polymerase chain reaction with reverse transcription verification analysis was then performed on intersection genes. Results: Three modules each in AD and T2DM induced-DACD were identified as the most relevant and 10 hub genes were screened, with analysis revealing enrichment in pathways such as synaptic vesicle cycle and GABAergic synapse. Through biological experimentation verification, 6 key genes were identified. Conclusions: This study is the first to use bioinformatics tools to uncover the genetic link between AD and DACD. GAD1, UCHL1, GAP43, CARNS1, TAGLN3, and SH3GL2 were identified as key genes connecting AD and DACD. These findings offer new insights into the diseases' pathogenesis and potential diagnostic and therapeutic targets.

Fabrication of TiN Coatings Deposited on Laser Shock Micro-Textured Substrates for Improving the Interface Adhesion Properties of Coatings.

This paper aims to investigate the strengthening mechanism of laser shock peening on the interfacial bonding properties between TiN coatings and TC4 titanium alloy substrates. The different surface textures were induced by LSP on a TC4 titanium alloy substrate. Subsequently, titanium nitride (TiN) coatings were deposited on the surface texture. A scratch test and reciprocating sliding wear assessment were conducted to evaluate the impact of LSP on the interfacial bonding properties and wear performance of the coatings. The experimental results demonstrated that the adhesion of TiN coatings deposited on the surface texture formed by laser shock peening was significantly enhanced. The efficacy of laser shock treatment in reducing wear rates was found to be significantly enhanced in cases of both increased spot overlapping rate and increased laser power density. The surface texture created using laser parameters of 6.43 GW/cm2 and a 50% overlapping rate was found to have the most significant effect on improving the adhesion and anti-wear properties of the coating. The laser shock texture was identified as the main contributor to this improvement, providing a large interfacial contact area and a mechanical bond between the coating and the substrate. This bond inhibited the initiation and propagation of micro-cracks caused by the concentration of internal stress and interfacial stress of the coating.