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Tamoxifen triggers a transcriptional switch from proliferation to differentiation in the circumvallate taste epithelium in mice
Abstract The tamoxifen-inducible Cre-loxP system is an indispensable experimental tool in life sciences for inducing spatiotemporally controlled genetic recombination in the target tissues of living animals. The use of this technology is expected to increase in taste research. However, the direct effects of tamoxifen on taste buds remain largely unexplored. Here, we demonstrate that tamoxifen reduces cell supply to the taste buds in a dose-dependent manner. RNA sequencing of the circumvallate epithelium revealed that tamoxifen induced a transcriptional shift from proliferation to differentiation. The genes regulating the cell cycle were downregulated, whereas genes promoting the differentiation of epithelial cells and keratinocytes were upregulated. Within taste buds, Shh was downregulated in immature precursor cells, whereas cell type-specific genes were broadly upregulated in mature taste bud cells. Notably, transcription factors driving taste cell type differentiation, such as Pou2f3 , Ascl1 , and Nkx2-2 , were induced, suggesting that tamoxifen activates transcription to promote the differentiation of all cell types in taste buds, rather than activating particular signaling pathways in specific cell types. These findings indicate that tamoxifen rapidly triggers a transcriptional switch from proliferation to differentiation in the circumvallate taste epithelium, highlighting a potential confounding effect in taste research that employs tamoxifen administration.
The Nature Podcast festive spectacular 2025
Association between red cell distribution width to albumin ratio and short-term mortality in patients with septic myocardial injury: a multicenter analysis
Genotype × environment interactions and genetic variation reveal stable, high-yielding, and organosulfur-rich garlic cultivars adapted to tropical conditions
The quest to hatch a bird-flu vaccine
Validation of an in vitro muscle platform to evaluate myogenesis and calcium handling in control and dystrophic human myotubes
Abstract Electrical impedance has emerged as a powerful tool for real-time, label-free, and non-invasive monitoring of cellular processes. Here, we employed an impedance-based assay to characterize the myogenic process of control and dystrophic human myoblasts. First, we conducted a comprehensive analysis of control myoblast differentiation, assessing the effects of initial seeding density and various extracellular matrix coatings. We also evaluated the influence of electrode presence and current application, both of which improved myoblast alignment. Immortalized myoblasts from Duchenne muscular dystrophy patients exhibited marked alterations in early differentiation and maturation, which were readily detected via impedance measurements. We further compared two differentiation protocols using one control and one dystrophic representative cell line. While both protocols supported the formation of mature myotubes, impedance profiles differed depending on the culture medium. Notably, we identified the protocol with superior impedance profile reproducibility over the culture lifespan. Finally, we successfully assessed calcium homeostasis in control and dystrophic myotubes differentiated on 96-well impedance plates. Our findings underscore the potential of impedance-based assays for monitoring myogenesis and identifying disease-associated phenotypes. Moreover, 96-well impedance plates represent a robust tool for high-throughput and high-content functional analysis in muscle disease modeling and therapeutic screening.
Evaluation of synergistic and regulatory effects of carbon-reduction and water-saving in the Yangtze River Delta Urban Agglomeration
Protease profiling in fecal samples: a novel non-invasive diagnostic tool for gastrointestinal disorders
Abstract Fecal protease profiling represents a promising frontier in the non-invasive diagnosis of gastrointestinal disorders, particularly inflammatory bowel diseases (IBD), such as Crohn’s disease and ulcerative colitis, and irritable bowel syndrome (IBS). These conditions share overlapping symptoms but differ significantly in etiology and pathology, making accurate differentiation essential for appropriate management. This pilot study investigated protease activity in stool samples using a custom panel of fluorogenic peptide substrates across varying pH conditions to uncover disease-specific enzymatic signatures. Samples from IBD patients revealed broad protease activation involving both serine and cysteine classes, while the small IBS cohort showed a tendency toward a pattern enriched in furin-like serine proteases pattern dominated by furin-like serine proteases, especially at alkaline pH. Notably, one substrate, Ac-RSVL-AMC, showed higher activity in UC than in CD at acidic pH and moderate discriminatory ability in this pilot cohort. Inhibition assays confirmed the enzymatic contribution of furin-like proteases, and follow-up analysis in remission-phase IBD patients indicated persistent dysregulation, suggesting potential biomarker utility beyond active inflammation. The observed substrate-specific activity profiles highlight the importance of sequence context in proteolytic cleavage and underscore the complexity of protease involvement in gastrointestinal pathology. These findings support fecal protease profiling as a promising, rapid and low-cost approach that could contribute to distinguishing IBD from IBS and differentiating IBD subtypes, providing a foundation for future minimally invasive diagnostic strategies that require validation in larger cohorts.
Mechanistic analysis of flow functions in tertiary enriched gas injection following secondary lean gas injection
High-efficiency removal of methyl orange from wastewaters using polyimide/chitosan-MoS2-UiO-66 nanofiber adsorbents
Abstract Conventional adsorbents such as chitosan and layered MoS 2 show promising functional groups or surface activity but suffer from intrinsic drawbacks such as limited surface area, and nanosheet aggregation, which restrict their efficiency in rapid dye removal. To overcome these limitations, we designed polyimide/chitosan-MoS 2 -UiO-66 (PI-CS-MoS 2 -UiO-66) nanofibrous adsorbents that integrate structural stability, abundant functional groups, and high porosity within a single hybrid system. The nanofibers were prepared by electrospinning PI as a robust support, followed by dip-coating with CS-MoS 2 -UiO-66 and crosslinking. XRD, BET, FT-IR, TGA, and SEM confirmed the successful incorporation of the composite. Adsorption tests on methyl orange (MO) showed strong dependence on pH, dosage, initial concentration, and temperature. The maximum adsorption capacity reached 195.61 $$\:\frac{\text{m}\text{g}}{\text{g}}$$ with excellent correlation to the Langmuir isotherm (R 2 = 0.9841). Thermodynamic parameters (ΔG < 0, ΔH < 0) indicated a spontaneous and exothermic process. Moreover, the adsorbent maintained good performance over three adsorption-desorption cycles, demonstrating reusability. These results demonstrate that combining PI, CS, UiO-66, and MoS 2 offers a synergistic platform that addresses the shortcomings of the individual components and enables high-efficiency dye removal from wastewater.
Unraveling the spatiotemporal clustering of malaria incidence and modeling a decade of epidemiological data in Jimma Zone, Oromia, Ethiopia
Multi-sensor observer-based residual learning with Auto-Permutation Feature Importance for fault diagnosis of multistage centrifugal pumps under variable pressures
Evaluating the impact of housing modifications on milk infrared spectra as indicators of dairy cow welfare status
LBNet: an optimized lightweight CNN for mammographic breast cancer classification with XAI-based interpretability
Abstract Breast cancer represents a major worldwide health burden, marked by high incidence and mortality rates across diverse socioeconomic populations. While deep learning has enabled advances in automated mammographic analysis, existing models often suffer from high computational complexity. They also face limited generalizability and a lack of interpretability. To overcome these challenges, this research introduces LBNet, a lightweight and interpretable convolutional neural network (CNN) built for accurate and efficient breast cancer detection, particularly in resource-constrained settings. With only 2.4 million trainable parameters, LBNet consists of five convolutional layers, leveraging ReLU activation, batch normalization, and max-pooling to optimize feature extraction while maintaining computational efficiency. LBNet was trained on the RSNA dataset using the Adam optimizer and five-fold cross-validation. It achieved 97.28% accuracy. For cancer cases, precision was 99% and recall was 96%. For non-cancer cases, precision was 96% and recall was 99%. In comparison, baseline models such as VGG19, SE-ResNet152, and ResNet152V2 yielded lower accuracies of 87.54%, 87.50%, and 85.24%, respectively, while transfer learning approaches peaked at 87.37% accuracy. LBNet’s generalizability was validated in external datasets, achieving 99.54% accuracy on CBIS-DDSM and 98.50% on MIAS. To enhance clinical trust, this work integrated SHAP (SHapley Additive exPlanations) and Grad-CAM (Gradient-weighted Class Activation Mapping). These methods effectively highlighted diagnostically relevant regions in mammograms. This improved prediction transparency. LBNet demonstrates strong potential as an accurate, efficient, and interpretable solution for breast cancer screening, and future studies could explore its extension to multi-view mammography and real-time clinical deployment.
Green waste biochar and plant growth-promoting bacteria enhance tomato growth under combined nutrient deficiency and salinity stress
Ultrasound-assisted oxidative desulfurization of model fuel oil using HPW/Al2O3/PANI composite: experimental and kinetic modeling
Reliable wrist PPG monitoring by mitigating poor skin sensor contact
Abstract Photoplethysmography (PPG) is a widely used non-invasive technique for monitoring cardiovascular health and various physiological parameters on consumer and medical devices. While motion artifacts are well-known challenges in dynamic settings, suboptimal skin-sensor contact in sedentary conditions - an important issue often overlooked in existing literature - can distort PPG signal morphology, leading to the loss or shift of essential waveform features and therefore degrading sensing performance. In this work, we propose a deep learning-based framework that transforms C ontact P ressure-distorted PPG signals into ones with the ideal morphology, known as CP-PPG. CP-PPG incorporates a well-crafted data processing pipeline and an adversarially trained deep generative model, together with a custom PPG-aware loss function. We validated CP-PPG through comprehensive evaluations, including 1) morphology transformation performance, 2) downstream physiological monitoring performance on public datasets, and 3) in-the-wild performance. Extensive experiments demonstrate substantial and consistent improvements in signal fidelity (Mean Absolute Error: 0.09, 40% improvement over the original signal) as well as downstream performance across all evaluations in Heart Rate (HR), Heart Rate Variability (HRV), Respiration Rate (RR), and Blood Pressure (BP) estimation (on average, 21% improvement in HR; 41-46% in HRV; 6% in RR; and 4-5% in BP). These findings highlight the critical importance of addressing skin-sensor contact issues for accurate and reliable PPG-based physiological monitoring. Our implementation is publicly available at: https://github.com/manhph2211/CP-PPG .