A wirelessly programmable, skin-integrated thermo-haptic stimulator system for virtual reality.

Sensations of heat and touch produced by receptors in the skin are of essential importance for perceptions of the physical environment, with a particularly powerful role in interpersonal interactions. Advances in technologies for replicating these sensations in a programmable manner have the potential not only to enhance virtual/augmented reality environments but they also hold promise in medical applications for individuals with amputations or impaired sensory function. Engineering challenges are in achieving interfaces with precise spatial resolution, power-efficient operation, wide dynamic range, and fast temporal responses in both thermal and in physical modulation, with forms that can extend over large regions of the body. This paper introduces a wireless, skin-compatible interface for thermo-haptic modulation designed to address some of these challenges, with the ability to deliver programmable patterns of enhanced vibrational displacement and high-speed thermal stimulation. Experimental and computational investigations quantify the thermal and mechanical efficiency of a vertically stacked design layout in the thermo-haptic stimulators that also supports real-time, closed-loop control mechanisms. The platform is effective in conveying thermal and physical information through the skin, as demonstrated in the control of robotic prosthetics and in interactions with pressure/temperature-sensitive touch displays.

LSTM-Autoencoder Based Anomaly Detection Using Vibration Data of Wind Turbines.

The problem of energy depletion has brought wind energy under consideration to replace oil- or chemical-based energy. However, the breakdown of wind turbines is a major concern. Accordingly, unsupervised learning was performed using the vibration signal of a wind power generator to achieve an outlier detection performance of 97%. We analyzed the vibration data through wavelet packet conversion and identified a specific frequency band that showed a large difference between the normal and abnormal data. To emphasize these specific frequency bands, high-pass filters were applied to maximize the difference. Subsequently, the dimensions of the data were reduced through principal component analysis, giving unique characteristics to the data preprocessing process. Normal data collected from a wind farm located in northern Sweden was first preprocessed and trained using a long short-term memory (LSTM) autoencoder to perform outlier detection. The LSTM Autoencoder is a model specialized for time-series data that learns the patterns of normal data and detects other data as outliers. Therefore, we propose a method for outlier detection through data preprocessing and unsupervised learning, utilizing the vibration signals from wind generators. This will facilitate the quick and accurate detection of wind power generator failures and provide alternatives to the problem of energy depletion.

Complete mouth rehabilitation in a patient with condylar fracture malunion: A clinical report.

Mandibular condyle fracture malunion and tooth loss can cause functional and esthetic problems. A patient with restricted mouth opening associated with muscle atrophy required prosthetic rehabilitation. Since the remaining teeth had a poor prognosis and the patient had difficulty adapting to the interim denture, complete mouth rehabilitation with implants was chosen. The implants were placed by using nerve lateralization and an autogenous bone graft. Prosthetic rehabilitation combines digital diagnosis and conventional prosthetic restorations. The definitive prosthesis was fabricated to ensure adequate oral hygiene and functional adaptation of the orofacial structures. Treatment resulted in stable masticatory function, occlusion, and esthetics and restored the function of the atrophied lips and restricted mouth opening.

Advances in single-cell omics and multiomics for high-resolution molecular profiling.

Single-cell omics technologies have revolutionized molecular profiling by providing high-resolution insights into cellular heterogeneity and complexity. Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine.

FAM3C in Cancer-Associated Adipocytes Promotes Breast Cancer Cell Survival and Metastasis.

Adipose tissue within the tumor microenvironment (TME) plays a critical role in supporting breast cancer progression. In this study, we identified FAM3 metabolism-regulating signaling molecule C (FAM3C) produced by cancer-associated adipocytes (CAA) as a key regulator of tumor progression. FAM3C overexpression in cultured adipocytes significantly reduced cell death in both adipocytes and cocultured breast cancer cells while suppressing markers of fibrosis. Conversely, FAM3C depletion in CAAs resulted in adipocyte-mesenchymal transition (AMT) and increased fibrosis within the TME. Adipocyte FAM3C expression was driven by TGFß signaling from breast cancer cells and was reduced upon treatment with a TGFß-neutralizing antibody. FAM3C knockdown in CAAs early in tumorigenesis in a genetically engineered mouse model of breast cancer significantly inhibited primary and metastatic tumor growth. Circulating FAM3C levels were elevated in patients with metastatic breast cancer compared with those with nonmetastatic breast cancer. These results suggest that therapeutic inhibition of FAM3C expression levels in CAAs during early tumor development could be a promising approach in the treatment of patients with breast cancer. SIGNIFICANCE: High FAM3C levels in cancer-associated adipocytes contribute to tumor-supportive niches and are tightly associated with metastatic growth, indicating that FAM3C inhibition could be beneficial for treating patients with breast cancer.

Tensile bond strength of CAD-CAM all ceramic crowns before and after thermomechanical aging.

PURPOSE: The purpose of this in vitro study was to compare the tensile bond strength (TBS) of resin nanoceramics (RNC), zirconia, and lithium disilicate (LS2) restorations cemented to titanium abutments before and after thermomechanical aging. MATERIALS AND METHODS: Twelve specimens per group were fabricated to determine the TBS between a titanium abutment and four types of crown materials (2 RNCs, LS2, and translucent zirconia crowns for the maxillary molar). After milling, the abutments and crowns were cemented with resin cement after air-particle abrasion. In addition, thermomechanical aging (200,000 cycles, 50 N, 2 Hz) was applied to half of the specimens by using a mastication simulator. TBS was measured by using a universal testing machine. The interface between the crown and the cement was observed by using scanning electron microscopy (SEM). Two-way ANOVA was performed to analyze the effects of crown materials and thermomechanical aging. Failure-mode and interface analyses were also conducted. RESULTS: After thermomechanical aging, the TBS decreased in the LS2 specimens and increased in RNCs (p < 0.001). The ratio of mixed failure and debonding with the hole-sealing resin increased in the RNC group. SEM images showed the reduced gap between the crown and the resin cement after thermomechanical aging in the RNC group. CONCLUSIONS: Differences in TBS were affected by the crown materials after thermomechanical aging. After thermomechanical aging, the RNC crowns showed increased TBS, whereas LS2 and zirconia crowns exhibited decreased or similar TBS.

Nanoparticle Exsolution on Perovskite Oxides: Insights into Mechanism, Characteristics and Novel Strategies.

Supported nanoparticles have attracted considerable attention as a promising catalyst for achieving unique properties in numerous applications, including fuel cells, chemical conversion, and batteries. Nanocatalysts demonstrate high activity by expanding the number of active sites, but they also intensify deactivation issues, such as agglomeration and poisoning, simultaneously. Exsolution for bottom-up synthesis of supported nanoparticles has emerged as a breakthrough technique to overcome limitations associated with conventional nanomaterials. Nanoparticles are uniformly exsolved from perovskite oxide supports and socketed into the oxide support by a one-step reduction process. Their uniformity and stability, resulting from the socketed structure, play a crucial role in the development of novel nanocatalysts. Recently, tremendous research efforts have been dedicated to further controlling exsolution particles. To effectively address exsolution at a more precise level, understanding the underlying mechanism is essential. This review presents a comprehensive overview of the exsolution mechanism, with a focus on its driving force, processes, properties, and synergetic strategies, as well as new pathways for optimizing nanocatalysts in diverse applications.

Decomposition of Thermally Stable Fuel Using a Cerium-Modified Zeolite Catalyst and Endothermic Characteristics.

The increase in the speed of aircraft causes a thermal load in the system. A cooling system is needed to solve this. Proactively, research is being conducted to cool the thermal load through the endothermic reaction of aircraft fuel. The decomposition of thermally stable fuel was performed using a lanthanide-modified zeolite as a catalyst, which was coated onto metal foam and the inner wall of a tube reactor to confirm the endothermic characteristics of the catalytic decomposition reaction. When a cerium-modified zeolite was used, the heat sink was increased to 1100Btu/lb and showed excellent performance for the cracking reaction.

Soy Sauce Lowers Body Weight and Fat Mass in High-Fat Diet-Induced Obese Rats.

Soy sauce (SS) is a traditional fermented seasoning. Although fermented foods have diverse health beneficial effects, SS intake has been discouraged because of its high salt level. This study was designed to evaluate the antiobesity outcomes of SS and the potential involvement of salt content in SS by adding a high-salt group. Sprague-Dawley rats were randomly assigned into four groups: normal diet (ND, 10% fat of total kcal), high-fat diet (HD, 60% fat of total kcal), HD with salt water (HDSW, NaCl = 8%), and HD with SS (HDSS, NaCl = 8%). SS significantly decreased HD-induced body weight gain and lipogenic gene expression without affecting food consumption. Moreover, SS also reduced hepatic injury and lipid accumulation, and also improved hyperlipidemia. Furthermore, SS decreased the mRNA levels related to obesity-derived inflammatory responses, while HDSW did not change the levels of those markers. These observations indicate that SS ameliorates obesity in HD-fed obese rats by attenuating dyslipidemia. Moreover, SS might also have an anti-inflammatory effect in HD-induced obesity, which requires further investigation. Most importantly, SS offers these beneficial effects regardless of its high salt content, implying that different dietary salt sources lead to the distinct health outcomes. In conclusion, the findings of this study improve the understanding of the functional effect of SS.

Development and application of novel peptide-formulated nanoparticles for treatment of atopic dermatitis.

Atopic dermatitis is a chronic inflammatory skin condition that is characterized by skin inflammation, itching, and redness. Although various treatments can alleviate symptoms, they often come with side effects, highlighting the need for new treatments. Here, we discovered a new peptide-based therapy using the intra-dermal delivery technology (IDDT) platform developed by Remedi Co., Ltd (REMEDI). The platform screens and identifies peptides derived from proteins in the human body that possess cell-penetrating peptide (CPP) properties. We screened over 1000-peptides and identified several derived from the Speckled protein (SP) family that have excellent CPP properties and have anti-inflammatory effects. We assessed these peptides for their potential as a treatment for atopic dermatitis. Among them, the RMSP1 peptide showed the most potent anti-inflammatory effects by inhibiting the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and signal transducer and activator of transcription 3 (STAT3) signaling pathways while possessing CPP properties. To further improve efficacy and stability, we developed a palmitoylated version called Pal-RMSP1. Formulation studies using liposomes (Pal-RMSP1 LP) and micelles (Pal-RMSP1 DP) demonstrated improved anti-inflammatory effects in vitro and enhanced therapeutic effects in vivo. Our study indicates that nano-formulated Pal-RMSP1 could have the potential to become a new treatment option for atopic dermatitis.

Federated Learning for Predictive Maintenance and Anomaly Detection Using Time Series Data Distribution Shifts in Manufacturing Processes.

In the manufacturing process, equipment failure is directly related to productivity, so predictive maintenance plays a very important role. Industrial parks are distributed, and data heterogeneity exists among heterogeneous equipment, which makes predictive maintenance of equipment challenging. In this paper, we propose two main techniques to enable effective predictive maintenance in this environment. We propose a 1DCNN-Bilstm model for time series anomaly detection and predictive maintenance of manufacturing processes. The model combines a 1D convolutional neural network (1DCNN) and a bidirectional LSTM (Bilstm), which is effective in extracting features from time series data and detecting anomalies. In this paper, we combine a federated learning framework with these models to consider the distributional shifts of time series data and perform anomaly detection and predictive maintenance based on them. In this paper, we utilize the pump dataset to evaluate the performance of the combination of several federated learning frameworks and time series anomaly detection models. Experimental results show that the proposed framework achieves a test accuracy of 97.2%, which shows its potential to be utilized for real-world predictive maintenance in the future.

A Pilot Study Evaluating LV Diastolic Function with M-Mode Measurement of Mitral Valve Movement in the Parasternal Long Axis View.

This pilot study aimed to develop a new, reliable, and easy-to-use method for the evaluation of diastolic function through the M-mode measurement of mitral valve (MV) movement in the parasternal long axis (PSLA), similar to E-point septal separation (EPSS) used for systolic function estimation. Thirty healthy volunteers from a tertiary emergency department (ED) underwent M-mode measurements of the MV anterior leaflet in the PSLA view. EPSS, A-point septal separation (APSS), A-point opening length (APOL), and E-point opening length (EPOL) were measured in the PSLA view, along with the E and A velocities and e' velocity in the apical four-chamber view. Correlation analyses were performed to assess the relationship between M-mode and Doppler measurements, and the measurement time was evaluated. No significant correlations were found between M-mode and Doppler measurements in the study. However, M-mode measurements exhibited high reproducibility and faster acquisition, and the EPOL value consistently exceeded the APOL value, resembling the E and A pattern. These findings suggest that visually assessing the M-mode pattern on the MV anterior leaflet in the PSLA view may be a practical approach to estimating diastolic function in the ED. Further investigations with a larger and more diverse patient population are needed to validate these findings.

High levels of intracellular endotrophin in adipocytes mediate COPII vesicle supplies to autophagosome to impair autophagic flux and contribute to systemic insulin resistance in obesity.

BACKGROUND AND AIMS: Extracellular matrix (ECM) homeostasis plays a crucial role in metabolic plasticity and endocrine function of adipose tissue. High levels of intracellular endotrophin, a cleavage peptide of type VI collagen alpha 3 chain (Col6a3), have been frequently observed in adipocyte in obesity and diabetes. However, how endotrophin intracellularly traffics and influences metabolic homeostasis in adipocyte remains unknown. Therefore, we aimed to investigate the trafficking of endotrophin and its metabolic effects in adipocytes depending on lean or obese condition. METHODS: We used doxycycline-inducible adipocyte-specific endotrophin overexpressed mice for a gain-of-function study and CRISPR-Cas9 system-based Col6a3-deficient mice for a loss-of-function study. Various molecular and biochemical techniques were employed to examine the effects of endotrophin on metabolic parameters. RESULTS: In adipocytes during obesity, the majority of endosomal endotrophin escapes lysosomal degradation and is released into the cytosol to mediate direct interactions between SEC13, a major component of coat protein complex II (COPII) vesicles, and autophagy-related 7 (ATG7), leading to the increased formation of autophagosomes. Autophagosome accumulation disrupts the balance of autophagic flux, resulting in adipocyte death, inflammation, and insulin resistance. These adverse metabolic effects were ameliorated by either suppressing ATG7 with siRNA ex vivo or neutralizing endotrophin with monoclonal antibodies in vivo. CONCLUSIONS: High levels of intracellular endotrophin-mediated autophagic flux impairment in adipocyte contribute to metabolic dysfunction such as apoptosis, inflammation, and insulin resistance in obesity.

Water-stable, biocompatible, and highly luminescent perovskite nanocrystals-embedded fiber-based paper for anti-counterfeiting applications.

In this study, we present a promising and facile approach toward the fabrication of non-toxic, water-stable, and eco-friendly luminescent fiber paper composed of polycaprolactone (PCL) polymer and CsPbBr3@SiO2 core-shell perovskite nanocrystals. PCL-perovskite fiber paper was fabricated using a conventional electrospinning process. Transmission electron microscopy (TEM) clearly revealed incorporation of CsPbBr3@SiO2 nanocrystals in the fibers, while scanning electron microscopy (SEM) demonstrated that incorporation of CsPbBr3@SiO2 nanocrystals did not affect the surface and diameter of the PCL-perovskite fibers. In addition, thermogravimetric analysis (TGA) and contact angle measurements have demonstrated that the PCL-perovskite fibers exhibit excellent thermal and water stability. The fabricated PCL-perovskite fiber paper exhibited a bright green emission centered at 520 nm upon excitation by ultra-violet (UV) light (374 nm). We have demonstrated that fluorescent PCL-perovskite fiber paper is a promising candidate for anti-counterfeiting applications because various patterns can be printed on the paper, which only become visible after exposure to UV light at 365 nm. Cell proliferation tests revealed that the PCL-perovskite fibers are cytocompatibility. Consequently, they may be suitable for biocompatible anti-counterfeiting. The present study reveals that PCL-perovskite fibers may pave way toward next generation biomedical probe and anti-counterfeiting applications.

OView-AI Supporter for Classifying Pneumonia, Pneumothorax, Tuberculosis, Lung Cancer Chest X-ray Images Using Multi-Stage Superpixels Classification.

The deep learning approach has recently attracted much attention for its outstanding performance to assist in clinical diagnostic tasks, notably in computer-aided solutions. Computer-aided solutions are being developed using chest radiography to identify lung diseases. A chest X-ray image is one of the most often utilized diagnostic imaging modalities in computer-aided solutions since it produces non-invasive standard-of-care data. However, the accurate identification of a specific illness in chest X-ray images still poses a challenge due to their high inter-class similarities and low intra-class variant abnormalities, especially given the complex nature of radiographs and the complex anatomy of the chest. In this paper, we proposed a deep-learning-based solution to classify four lung diseases (pneumonia, pneumothorax, tuberculosis, and lung cancer) and healthy lungs using chest X-ray images. In order to achieve a high performance, the EfficientNet B7 model with the pre-trained weights of ImageNet trained by Noisy Student was used as a backbone model, followed by our proposed fine-tuned layers and hyperparameters. Our study achieved an average test accuracy of 97.42%, sensitivity of 95.93%, and specificity of 99.05%. Additionally, our findings were utilized as diagnostic supporting software in OView-AI system (computer-aided application). We conducted 910 clinical trials and achieved an AUC confidence interval (95% CI) of the diagnostic results in the OView-AI system of 97.01%, sensitivity of 95.68%, and specificity of 99.34%.

In Vivo Fluorescence Molecular Imaging Using Covalent Organic Nanosheets Without Labeling.

Organic nanomaterials, as nanocarrier platforms, have tremendous potential for biomedical applications. The authors successfully prepared novel two-dimensional covalent organic nanosheets (CONs) that can be used as efficient in vivo bioimaging probes by condensing 1,3,5-triformylglucinol (Tp) and 2,7-diaminopyrene (Py) to produce TpPy covalent organic frameworks (COFs). TpPy COFs are then subjected to a liquid exfoliation process to obtain TpPy CONs (< 200 nm in size and < 1.7 nm in thickness). TpPy CONs disperse well in water to provide a stable, homogeneous colloidal suspension, which shows favorable photoluminescence properties. Cell viability tests using MDA-MB-231 and RAW 264.7 cells reveal that TpPy CONs are low in cytotoxicity. Confocal microscopy reveals clear fluorescent cell images after incubation with TpPy CONs for 24 h, without reduction in cell activity or cytosolic aggregation. To investigate the biological behavior of the TpPy CONs, the authors perform an in vivo fluorescence imaging study using MDA-MB-231 tumor-bearing mice. After intravenous injection of TpPy CONs disperse in phosphate-buffered saline (PBS), persistent and strong fluorescence signals are observed in the tumor region, with low background signals from normal tissues at 1, 3, 12, and 24 h after injection. Furthermore, these in vivo imaging results concurred with ex vivo biodistribution and histological results.

Transparent and Flexible Graphene Pressure Sensor with Self-Assembled Topological Crystalline Ionic Gel.

This study demonstrates transparent and flexible capacitive pressure sensors using a high-k ionic gel composed of an insulating polymer (poly(vinylidene fluoride-co-trifluoroethylene-co-chlorofluoroethylene), P(VDF-TrFE-CFE)) blended with an ionic liquid (IL; 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl) amide, [EMI][TFSA]). The thermal melt recrystallization of the P(VDF-TrFE-CFE):[EMI][TFSA] blend films develops the characteristic topological semicrystalline surface of the films, making them highly sensitive to pressure. Using optically transparent and mechanically flexible graphene electrodes, a novel pressure sensor is realized with the topological ionic gel. The sensor exhibits a sufficiently large air dielectric gap between graphene and the topological ionic gel, resulting in a large variation in capacitance before and after the application of various pressures owing to the pressure-sensitive reduction of the air gap. The developed graphene pressure sensor exhibits a high sensitivity of 10.14 kPa-1 at 20 kPa, rapid response times of <30 ms, and durable device operation with 4000 repeated ON/OFF cycles. Furthermore, broad-range detections from lightweight objects to human motion are successfully achieved, demonstrating that the developed pressure sensor with a self-assembled crystalline topology is potentially suitable for a variety of cost-effective wearable applications.

Neurog1-Derived Peptides RMNE1 and DualPep-Shine Penetrate the Skin and Inhibit Melanin Synthesis by Regulating MITF Transcription.

Anti-pigmentation peptides have been developed as alternative skin-lightening agents to replace conventional chemicals that have adverse effects on the skin. However, the maximum size of these peptides is often limited by their low skin and cell penetration. To address this issue, we used our intra-dermal delivery technology (IDDT) platform to identify peptides with hypo-pigmenting and high cell-penetrating activity. Using our cell-penetrating peptides (CPPs) from the IDDT platform, we identified RMNE1 and its derivative RMNE3, "DualPep-Shine", which showed levels of α-Melanocyte stimulating hormone (α-MSH)-induced melanin inhibition comparable to the conventional tyrosinase inhibitor, Kojic acid. In addition, DualPep-Shine was delivered into the nucleus and regulated the gene expression levels of melanogenic enzymes by inhibiting the promoter activity of microphthalmia-associated transcription factor-M (MITF-M). Using a 3D human skin model, we found that DualPep-Shine penetrated the lower region of the epidermis and reduced the melanin content in a dose-dependent manner. Furthermore, DualPep-Shine showed high safety with little immunogenicity, indicating its potential as a novel cosmeceutical ingredient and anti-pigmentation therapeutic agent.

Fibronectin and JMJD6 Signature in Circulating Placental Extracellular Vesicles for the Detection of Preeclampsia.

Preeclampsia (PE) is a major obstetric complication that is challenging to predict. Currently, there are limited tools to assess placental health/function in crucial gestational periods for diagnosis and early prediction. The glycoprotein fibronectin (FN) is augmented in PE placentae, and associated with reduced activity of JMJD6, an oxygen sensor that regulates placental FN processing. Evidence implicates placenta-derived small extracellular vesicles (sEVs) in the pathogenesis of pregnancy-associated disorders. Here, we examined the utility of FN and JMJD6 in placental sEVs as putative markers for early- and late-onset PE (E-PE and L-PE). Maternal plasma was obtained from venous blood collected longitudinally during pregnancy (10-14, 16-22, and 26-32 weeks of gestation and at delivery) in normotensive term control, preterm control, L-PE, E-PE, and gestational hypertensive individuals. Placenta-derived sEVs were isolated and their FN and JMJD6 content and JMJD6 activity were measured. In women that went on to develop preeclampsia, FN content of circulating placental sEVs was significantly elevated as early as 10 to 14 weeks of gestation and remained augmented until the time of delivery. This was accompanied by a depletion in JMJD6 content. Multivariate receiver operating characteristic analysis revealed high predictive power for FN and JMJD6 as early markers of E-PE and L-PE. In vitro, hypoxia or JMJD6 loss promoted FN accumulation in sEVs that was reverted on restoring cellular iron balance with the natural compound, Hinokitiol. Elevated FN, along with diminished JMJD6 in circulating placental sEVs, serves as an early molecular signature for the detection of different hypertensive disorders of pregnancy and their severity.

Epigenetic regulation of Neuregulin 1 promotes breast cancer progression associated to hyperglycemia.

Hyperglycemia is a risk factor for breast cancer-related morbidity and mortality. Hyperglycemia induces Neuregulin 1 (Nrg1) overexpression in breast cancer, which subsequently promotes tumor progression. However, molecular mechanisms underlying hyperglycemia-induced Nrg1 overexpression remain poorly understood. Here, we show that hyperglycemia causes active histone modifications at the Nrg1 enhancer, forming enhanceosome complexes where recombination signal binding protein for immunoglobulin kappa J region (RBPJ), E1A binding protein p300 (P300), and SET domain containing 1 A (SETD1A) are recruited to upregulate Nrg1 expression. Deletions in RBPJ-binding sites causes hyperglycemia-controlled Nrg1 levels to be downregulated, resulting in decreased tumor growth in vitro and in vivo. Mice with modest-temporary hyperglycemia, induced by low-dose short-exposure streptozotocin, display accelerated tumor growth and lapatinib resistance, whereas combining lapatinib with N-[N-(3,5-difluorophenacetyl)-l-alanyl]-S42 phenylglycine t-butyl ester (DAPT) ameliorates tumor growth under these modest hyperglycemic conditions by inhibiting NOTCH and EGFR superfamilies. NOTCH activity is correlated with NRG1 levels, and high NRG1 levels predicts poor outcomes, particularly in HER2-positive breast cancer patients. Our findings highlight the hyperglycemia-linked epigenetic modulation of NRG1 as a potential therapeutic strategy for treating breast cancer patients with diabetes.