Browse Articles
Discover research articles across all indexed journals
Single-cell analysis reveals corticosteroid-associated impairment of tumor-infiltrating NK cells in glioblastoma patients
Abstract Glioblastoma is the most common and aggressive malignant primary brain tumor in adults, characterized by poor prognosis and limited response to current therapeutic strategies. Corticosteroids, particularly dexamethasone, are widely used in clinical practice to control symptomatic peritumoral edema, yet they exert profound immunosuppressive effects whose impact on tumor-infiltrating NK cells remains poorly defined. To address this issue, we analyzed a publicly available single-cell RNA sequencing dataset of CD45⁺ cells isolated from five IDH-wildtype glioblastomas, including two patients undergoing perioperative dexamethasone treatment and three untreated patients, alongside two non-tumor brain samples. Our results suggested that perioperative dexamethasone exposure was associated with a reshaped intratumoral NK cell landscape, characterized by a relative enrichment of CD56 bright subsets. Perioperative corticosteroid treatment was also associated with transcriptional features consistent with impaired NK cell effector programs, as evidenced by reduced cytotoxicity and inflammation scores, down-regulation of granzymes, perforin, pro-inflammatory cytokines, and activating receptors, and concomitant upregulation of inhibitory molecules. Gene set enrichment analysis further demonstrated strong downregulation of NK cell-mediated cytotoxicity and cell-killing pathways. Together with previous evidence of corticosteroid-associated impairment of glioma-infiltrating dendritic cells, these findings suggest that perioperative steroid therapy may affect both cytotoxic and antigen-presenting innate compartments. If confirmed in larger patient cohorts, these observations may support the importance of minimizing steroid exposure to optimize immunotherapeutic efficacy.
Author Correction: Titration of RAS alters senescent state and influences tumour initiation
Identifying sources of mixed groundwater contamination using PMF and stable isotopes: a case study of an industrial park
DFT and experimental evaluation of NiSnO₃ nanocomposites in photocatalytic applications
A data-driven framework for structural health monitoring using reinforcement learning and deep autoencoders
A robust pH-based second-order spectrophotometric method for the analysis of four anti-HIV drugs in pharmaceutical formulations
Three scientists pushing chemistry in new directions
Linking compression mechanics and pressure redistribution in hybrid support surfaces for pressure ulcer prevention
m⁶A-associated GAS6 expression is associated with pathogenic activation of fibroblast-like synoviocytes in rheumatoid arthritis
Numerical research of the influence mechanism of fan diameter and jet velocity on tunnel ventilation efficiency
Abstract Full-jet longitudinal ventilation is widely used in urban tunnels for its structural simplicity and high cost-effectiveness, yet its overall efficiency is constrained by high energy consumption. This study systematically investigates the influence of jet fan diameter and outlet velocity on ventilation performance. A three-dimensional numerical model of a typical urban tunnel was developed using ANSYS Fluent. Parametric simulations were conducted by varying jet velocity from 20 m/s to 38 m/s and fan diameter from 500 mm to 1600 mm. Jet flow evolution and the underlying mechanisms governing ventilation efficiency, including pressure development, air entrainment, and cross-sectional flow uniformity were rigorously analyzed. Results indicate that for a 500 mm diameter fan, increasing jet velocity from 20 m/s to 38 m/s elevates the ventilation pressure coefficient from 16% to 66%. At a fixed velocity of 38 m/s, enlarging the diameter from 500 mm to 1600 mm further improves the pressure enhancement coefficient from 66% to 73%, with fan pressure gain exhibiting a quadratic relationship with diameter. The analysis demonstrates that higher jet velocities enhance both momentum thrust and ambient air entrainment, thereby strengthening the longitudinal airflow driving force. Concurrently, larger fan diameters widen the jet diffusion angle, producing a more uniform cross-sectional airflow with reduced stratification. These findings clarify the distinct yet interdependent roles of jet velocity and fan diameter in jet behavior and ventilation efficacy, offering quantitative guidance for fan selection and energy-efficient longitudinal ventilation design.
Cusp-singularity-enhanced Coriolis effect for sensitive chip-scale gyroscopes
Abstract Gyroscopes, as fundamental inertial sensors, are crucial for rotation measurements in the consumer electronics, automotive and aerospace industries, with the most widely used kind relying on the Coriolis effect 1–6 . The chip-scale Coriolis vibratory gyroscopes (CVGs) show reduced size, weight and cost 1,2 but have far lower performance than traditional macroscale CVGs 3–6 , as the weak intrinsic Coriolis factor sets a fundamental limit on scaling the sensitivity against the inherently louder Brownian noise in microchips compared with the macroscale ones. Here, to overcome this physical limit, we propose and experimentally demonstrate the use of third-order singularities lying within cusp catastrophes in the phase-tracked oscillations of an on-chip CVG to facilitate a cubic-root scaling of the Coriolis-effect-induced frequency modulation. Using this effect, we achieve a three-orders-of-magnitude enhancement in the Coriolis factor, yielding a 253-fold improvement in the signal-to-noise ratio and a 297-fold increase in precision. Moreover, the cusp singularity enables a previously unattainable ultrasensitive phase-modulated sublinear measurement, achieving record signal-to-noise ratio performance for silicon-chip gyroscopes. These findings not only provide revolutionary advancements in gyroscope technologies, by filling the gap in observing and controlling the singularity-enhanced Coriolis effect, but also shed new light on other ultrasensitive sensing applications.
A multi-level attention CNN-transformer based framework for the detection of brain tumor using regional dual-score explainability
Abstract Deep learning models for brain tumor diagnosis often lack interpretability beyond qualitative visual heatmaps. Clinicians require not only tumor localization but also quantitative assessment of explanation quality and diagnostic relevance, capabilities absent in conventional explainability methods. This paper introduces a classification-explainability framework addressing these limitations. The Multi-Level Hybrid Network (MLHnet) integrates CNN-Transformer components with multi-level attention for robust feature learning. The key novelty lies in the Dual-Score Regional XAI framework, which: (1) identifies tumor regions using hybrid saliency maps combining gradient-based and activation-based information; (2) quantifies geometric tumor characteristics via a Shape Score capturing size, circularity, and saliency concentration; and (3) evaluates explanation faithfulness using regional perturbation analysis, measuring prediction sensitivity to tumor-specific versus non-tumor regions. The framework is evaluated on a publicly available brain Magnetic Resonance Imaging (MRI) dataset containing 7,023 images across four classes. Using five‑fold cross‑validation, the proposed model achieves an average test accuracy of 99.30% (best fold: 99.47%). Expert radiologist evaluation confirms 87.64% explanation correctness, with the Dual-Score metrics successfully differentiating tumor classes based on morphological and saliency patterns. Overall, the proposed framework offers a lightweight, high-performing, and interpretable solution for reliable brain tumor diagnosis from MRI scans on the evaluated public dataset.
A robust direction of arrival estimation method based on the chaotic MUSIC algorithm
Smartphone movement data can reliably predict smoking lapses and cravings to enable timely smoking cessation support
Abstract Decades of research aiming to develop effective smoking interventions have identified triggers that contribute to failed quitting attempts including environmental (e.g. location), social (presence of other smokers), or internal (e.g. stress). Here, it is shown for the first time that passively collected movement data from smokers’ smartphones’ sensors (accelerometer, gyroscope and magnetometer) can be used to predict smoking-behaviour. Feeding the movement data into a Deep Learning (DL) model (1D-CNN-BiLSTM), smoking-behaviour was predicted with 85% accuracy within the subsequent 5-minute window. This compares to 63% accuracy when using traditional triggers (e.g. time of the day). Crucially, movement data can be used to predict high-craving incidents and lapses in the 3 months period following quitting smoking with similarly high accuracy, even when predictions are made without any personal data (i.e. when the model is trained using only data from other smokers). These findings can transform smoking-cessation apps, enabling the provision of just-in-time personalised support to those wishing to quit smoking. Importantly, the findings have implications beyond smoking-cessation applications, by revealing that human movements, largely overlooked to date, can be used for early detection of, and intervention for, health (and other) behaviours, including those that are not genetic or typically characterised by changes in movement.
Loop-mediated isothermal amplification (LAMP) assay for identification of Australian Plague Locust (APL), Chortoicetes terminifera (Walker, 1870)
Abstract The Australian Plague Locust (APL), Chortoicetes terminifera , the most important pest locust in Australia, can cause significant damage to crops and grasslands when present in high numbers. We have developed a new LAMP (loop-mediated isothermal amplification) assay specifically targeting APL for rapid diagnostics to support locust management. We tested five DNA extraction methods for potential near-field use and designed an APL gBlock as a synthetic positive control. The performance of new LAMP assay was assessed against a panel of eleven closely related Australian grasshopper species (family Acrididae). The optimised LAMP assay produced amplification only from APL DNA, on average in 18.0 ± 2.9 min, with an anneal derivative of 79.9 ± 0.5 °C, and gBlock produced a different anneal derivative of 83.5 ± 0.5 °C. The LAMP assay was sensitive to very low levels of DNA, with detection down to 0.2 picogram of APL DNA. The speed of application and accurate identification results generated, enables this scalable assay to be easily used in the field as a new tool for APL control, providing positive/negative results within an hour. This capability will prove an essential aid during sporadic APL population growth events, when accurate species-level identification of substantial numbers of nymphs, prior to adult locusts swarming, is a critical aspect of pest management.
Gender disparities in random blood glucose levels among Pakistani adults with type 2 diabetes: a cross-sectional analysis
Deciphering the role of tubulin’s C-terminal tail in kinesin binding using computational and clustering approaches
High-fidelity identification of guest species in porous materials
Assessment of the dietary and nutritional status of Iranian pregnant women: a study employing both a priori and a posteriori analytical approaches
Abstract Dietary pattern analysis provides a holistic assessment of diet quality critical for maternal-fetal health, offering insights superior to single-nutrient evaluation. Hereby, we aimed to concurrently evaluate established a priori and data-driven a posteriori dietary patterns, alongside nutrient-mineral adequacy, among pregnant women. This cross-sectional study recruited 233 women in their first trimester from “Mazandaran, Iran” province between 2020 and 2023. Dietary intake was measured via a validated 168-item Food Frequency Questionnaire. Adherence to predefined guidelines was assessed using a modified 8-point Mediterranean Diet Score (MDS) and three Nutrient Rich Food (NRF) indices (NRF6.3, NRF9.3, NRF15.3). Empirical patterns were derived using Principal Component Analysis (PCA) on 26 food groups via R Studio. Nutritional status was quantified using the Mean Adequacy Ratio (MAR), Protein Adequacy Ratio (PAR), and adequacy metrics for other nutrients/minerals. The study exhibited high metabolic risk factors in pregnant women, with 61.0% overweight/obese and 59.2% vitamin D deficient. Overall diet quality was suboptimal, evidenced by low MDS (Mean = 3.35) and poor micronutrient density (Mean MAR = 0.624), signifying that diets met only approximately 62% of recommended intakes. PCA identified a healthy “Prudent” pattern (PA2), a mixed “Traditional” pattern (PA1), and an unhealthy “Western” pattern (PA3). Increased adherence to the Prudent pattern significantly correlated with improved MAR, PAR, and a desirable decrease in Na/K ratio. Conversely, adherence to the Beverage & Fast-food pattern was associated with pervasive declines in all adequacy markers, including a severely decreased MDS (β = -0.26, p-value = < 0.001). Pregnant women in this regional study exhibit critically suboptimal dietary quality and significant micronutrient shortfalls, largely driven by low adherence to prudent, nutrient-dense patterns, and the majority depend on unhealthy food choices.