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Epithelial wound healing is an essential process in multicellular organisms, primarily driven by lamellipodia-based crawling, purse-string contraction, and collective cell migration. One or more of these mechanisms participate in healing a wound, yet the choice, sequence, and coordination of these processes are poorly understood. Moreover, different mechanisms dominate in different tissues, organisms, wound types and wound sizes, further complicating our understanding of how cells select among healing mechanisms. In this study we analyzed wound healing across wound types and spatial scales in the basal eukaryote Clytia hemisphaerica (Clytia) to establish a unified model for mechanism selection within a single organism. We demonstrate that lamellipodial crawling and actomyosin cable contractions are sequential, partially redundant processes involved in healing all wounds. Furthermore, the exposure of the basement membrane acts as a central regulatory cue, orchestrating lamellipodia formation, actomyosin contraction and collective cell migration responses. Remarkably, we discovered that these same mechanisms operate in healing micro-wounds internal to a single cell. This work fundamentally advances our understanding of how diverse healing mechanisms are coordinated to respond to all types of wounds, while the use of a basal metazoan model expands our knowledge of fundamental strategies for maintaining epithelial integrity. [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text].

Coal and gas outburst represents a highly destructive dynamic phenomenon inherent in deep coal mining operations. Currently, outburst prediction frameworks rely heavily on a uniform critical threshold system recommended by national regulations. However, within heterogeneous coal seams characterized by complex geological conditions, this universal approach frequently leads to "low-index outburst" incidents or excessive engineering redundancy, significantly undermining the intrinsic safety of mine operations. To address this core scientific bottleneck, the present study establishes a theoretical methodology for the quantitative determination of sensitive prediction indicators and proposes a hierarchical optimization framework for both regional and local critical thresholds. By integrating long-term historical statistics, laboratory kinetic tests of gas desorption, and in-situ multi-point tracking and verification, the critical thresholds undergo scientific calibration and site-specific alignment. Empirical research conducted on the No. 1 coal seam of the Miluo Coal Mine in Guizhou demonstrates that, at the regional prediction level, gas content and gas pressure exhibit equivalent sensitivity, with established critical values of 8.0 m3/t and 0.74 MPa, respectively. Furthermore, the sensitivity hierarchy for local prediction indicators was determined as [Formula: see text]. Significantly, the finalized local thresholds ([Formula: see text]= 0.47 mL/(g·min0.5), [Formula: see text]= 184 Pa, and S= 6.0 kg/m) are more stringent than the recommendations set forth in the Detailed Rules for Prevention and Control of Coal and Gas Outburst. The proposed prediction system effectively standardizes disaster characterization in complex coal seams and provides strategic guidance for coal mining enterprises to establish precision-based, site-specific outburst prevention standards. Coal and gas outbursts constitute a highly destructive dynamic phenomenon inherent in deep coal mining operations. Current outburst prediction frameworks largely depend on a uniform critical threshold system mandated by national regulations. However, in heterogeneous coal seams characterized by complex geological conditions, this universal approach frequently leads to "low-index outburst" incidents or excessive engineering redundancy, significantly undermining the intrinsic safety of mining operations. To resolve this fundamental scientific bottleneck, the present study establishes a theoretical methodology for the quantitative determination of sensitive prediction indicators and proposes a hierarchical optimization framework for both regional and local critical thresholds. By integrating long-term historical statistics, laboratory kinetic tests of gas desorption, and in-situ multi-point tracking and verification, the critical thresholds undergo rigorous scientific calibration and site-specific alignment. Empirical research conducted on the No. 1 coal seam of the Miluo Coal Mine in Guizhou demonstrates that, at the regional prediction level, gas content (w) and gas pressure (p) exhibit equivalent sensitivity, with established critical values of 8.0 m3/t and 0.74 MPa, respectively. Furthermore, the sensitivity hierarchy for local prediction indicators was established as [Formula: see text]. Significantly, the finalized local thresholds ([Formula: see text]= 0.47 mL/(g·min0.5), [Formula: see text]= 184 Pa, and S= 6.0 kg/m) are more stringent than the standards set forth in the Detailed Rules for Prevention and Control of Coal and Gas Outburst. The proposed prediction system effectively standardizes hazard characterization in complex coal seams and provides strategic guidance for coal mining enterprises to establish precision-based, site-specific outburst prevention standards.

Predicting clinically significant drug-drug interactions (DDIs) continues to be an unresolved challenge in contemporary pharmacovigilance, primarily due to the inadequacy of current computational frameworks in addressing the nonlinear, multi-scale characteristics of simultaneous drug metabolism. This paper presents the Quantum Graph-Differential (QGD) model an exact mathematical framework that combines quantum-inspired graph theory with a set of interconnected fractional differential equations to describe and forecast pairwise drug-drug interactions (DDIs). The principal component of our construction is the quantum interaction graph [Formula: see text], wherein the vertex set represents distinct drug molecules as quantum states within a finite-dimensional Hilbert space, and the complex-valued edge weights are obtained from the overlap of shared metabolic pathways and transporter affinity profiles.A Schrödinger-type equation on [Formula: see text] governs drug-drug coupling, and the graph Hamiltonian [Formula: see text] is constructed from a novel fractional quantum graph Laplacian [Formula: see text], [Formula: see text]. A hybrid quantum-classical dynamical model is created by coupling the time evolution of the interaction wavefunction [Formula: see text] to a compartmental pharmacokinetic/pharmacodynamic (PK/PD) ordinary differential equation system. Using Banach fixed-point and semigroup theory, we prove existence, uniqueness, and long-time asymptotic stability of solutions. Using the QGD framework on a selected dataset of 7,428 clinically confirmed DDI pairs from DrugBank v5.1, TWOSIDES, and FAERS, our model outperforms five established baselines by 1.5-13.9 percentage points in AUC, with an average precision of 0.948 and an AUC of 0.962. Quantum edge weighting alone explains a 3.7% relative F1 gain over unweighted graph methods, according to ablation experiments. These results show that quantifiable, interpretable improvements in DDI prediction can be obtained by incorporating quantum mechanical concepts into graph-differential frameworks.

While multimodal fake news detection methods have made progress in aligning multimodal semantics, they still face significant challenges in analyzing background context, emotional tone, and the overall plausibility of news content. To address these limitations, we propose a novel human-like collaborative framework for multimodal fake news detection, which integrates large and small models. Specifically, we exploit large vision-language models (LVLMs) to perform deep semantic analysis and reflective summarization of news cues. By leveraging the contextual understanding, knowledge recall, and logical reasoning capabilities of large models, the proposed approach improves the accuracy and reliability of fake news detection. It comprises three key components: 1) designing a chain-of-thought (CoT) prompting strategy for the LVLM to analyze news content, including evaluating image credibility, identifying potential tampering, extracting linguistic styles, detecting emotional tones, uncovering logical connections within the text, and verifying factual accuracy; 2) independently reflecting on and summarizing the lengthy analytical outputs from both image and text modalities to reduce redundancy. The resulting summary is then encoded into compact representations using pretrained text encoders and integrated with the original multimodal features; and 3) proposing a progressive fusion mechanism that enables collaboration between large and small models, allowing effective utilization of deeply fused features at the surface level. Extensive experiments conducted on three benchmark multimodal fake news datasets demonstrate the effectiveness and robustness of the proposed method, consistently outperforming state-of-the-art baselines in multimodal fake news detection tasks. The code is available at https://github.com/xxx.

Rumor spreading has become a critical social issue with the widespread use of social media platforms. This study develops a stochastic fractional delay differential equation (SFDDE) model to describe rumor propagation in a population divided into four compartments: susceptible [Formula: see text], spreaders [Formula: see text], counter-rumor spreaders [Formula: see text], and stiflers [Formula: see text]. The proposed model ensures nonnegativity and boundedness of solutions for nonnegative initial conditions. Rigorous analytical investigations establish the local and global stability of both the Rumor-Free Equilibrium (RFE) and the Rumor-Present Equilibrium (RPE), with the reproduction number identified as a key threshold parameter. Supported by classical stability theorems, the model's positivity, boundedness, local and global dynamics, and sensitivity around the reproduction number are systematically examined. Furthermore, the Generalized Nonstandard Finite Difference (GL-NSFD) method is employed to obtain accurate and dynamically consistent numerical approximations, demonstrating the model's reliability and efficiency through simulations and graphical validation.

Military occupational blast and impulse exposure (MOBE) is a potential risk factor for increased Anger, Aggression, or Violence (AAV). The objective of this study was to assess the association between MOBE and AAV-related content in clinical text notes in Veterans Health Administration (VHA) data. This matched cohort study investigated AAV-related content in clinical text data from Veterans across high and low-risk MOBE occupations. Veterans with documentation of high-risk MOBE occupations were sampled from a VHA population database and matched 1:1 with low-risk MOBE controls on age, sex, and race/ethnicity. An algorithm leveraging semantic similarity and large language models (LLMs) identified AAV content in millions of VHA clinical text notes. Model performance was assessed by manual review. Veteran outcomes were classified as AAV-positive or AAV-negative based on the content of their medical records. Logistic regression was used to estimate the association between MOBE and AAV. Among the MOBE cohort (n = 5,000) and matched controls (n = 5,000), 3.64 million clinical notes (Mean: 364 notes/person) were classified using an LLM pipeline that achieved 96% classification accuracy in manual review. Raw group differences were significant, with 17.2% of the MOBE cohort meeting AAV criteria, compared to 12.0% of matched controls (unadjusted Odds Ratio [OR]: 1.53 [1.37-1.71]). In adjusted models, the association between MOBE and AAV remained significant (OR: 1.22 [1.08-1.38]). Combat exposure (OR: 1.32 [1.11-1.58]) and traumatic brain injury (TBI) (OR: 1.47 [1.29-1.67]) were associated with increased AAV, while female sex was protective (OR: 0.33 [0.24-0.45]). In nested models, the OR for AAV ranged from 1.53 to 1.16 depending on the covariates considered, and posttraumatic stress disorder (PTSD) was found to be a significant confounder of the MOBE-AAV association. This matched cohort study found that individuals who served in occupations at high risk for MOBE were significantly more likely to have evidence of AAV in clinical text data. Neurological and affective changes potentially linked to MOBE may be interconnected with other military health factors, such as combat exposure, TBI, and PTSD.

Intellectual disability often impairs an individual's ability to communicate and engage in social interactions. Augmentative and Alternative Communication (AAC) systems, as a subset of Assistive Technology (AT), provide essential support for individuals facing such challenges. Despite their potential, issues related to accessibility, usability, and social acceptability continue to hinder the widespread adoption of AAC solutions. This study presents AAC Vest, a novel wearable AAC solution, grounded in user need-driven scenarios and developed through iterative prototyping and interdisciplinary expert workshops. The developed AAC vest integrates touch and pressure sensors into a vest, enabling users to trigger pre-recorded voice messages and send text alerts via a Bluetooth-connected smartphone application. It offers a discreet and easy-to-use communication aid that supports both users and caregivers in everyday interactions. Findings highlight the potential of wearable AAC technologies to bridge gaps between user needs and technical solutions. Furthermore, the study underscores both the importance and the complexity of interdisciplinary collaboration in the development of effective assistive technologies. The AAC Vest demonstrates the potential of wearable assistive technology to support communication for individuals with intellectual disabilities. Using discreet textile-based sensors and a smartphone application, it enables users to express needs through touch, triggering audio and text alerts to caregivers. This supports independence and safety across contexts. Its comfortable, customizable design and interdisciplinary, user-informed development highlight the importance of collaborative approaches in creating effective AAC solutions.

This study introduces Kolmogorov-Arnold Networks (KANs) as an innovative framework for variational Monte Carlo (VMC) calculations of the deuteron ground state, serving as a proof of concept toward computationally demanding larger nuclear systems, using a leading-order Chiral Effective Field Theory (EFT) potential. KANs leverage trainable spline activations to provide superior flexibility in approximating short-range cusps and enhanced smoothness in high-order derivatives, directly addressing key challenges in quantum wave function representation. We employ VMC with the Adam optimizer to sample the KAN-parameterized wave function and compute energy and spatial observables. The optimized results yield a binding energy of [Formula: see text]MeV, a mean radius of [Formula: see text]fm, and a root-mean-square radius of [Formula: see text]fm, showing excellent agreement with reference Hulthen and GFMC calculations (relative energy deviation < 0.2%). Crucially, a direct performance comparison reveals that the KAN-based model converges ~&#x2009;10x faster in wall-clock time and captures the short-range cusp behavior more accurately and stably than a comparable multilayer perceptron (MLP), eliminating the need for ad hoc cusp-correction terms. These results confirm KAN's capability to accurately model the non-trivial short-range dynamics of nuclear interactions. As a proof of concept for systems where computational cost becomes a genuine bottleneck, this work establishes KAN-VMC as a highly promising, scalable approach for future ab initio studies of larger nuclear systems, such as 4He, and for extensions to higher chiral orders (NLO/NNLO).

Manganese (oxyhydr)oxides are abundant redox-active minerals that influence diverse biogeochemical processes, yet their redox reactivity remains poorly understood due to variations in mineral structure and manganese oxidation state. We quantified the reduction kinetics of three geochemically relevant manganese oxides-birnessite, manganite, and hausmannite-using extracellular electron shuttles with varying redox potentials to systematically modulate the driving force for electron transfer. While the Gibbs free energy ([Formula: see text]) described the kinetics of individual oxides well, the Pourbaix free-energy difference ([Formula: see text]) offered a distinct advantage by predicting reactivity without requiring detailed knowledge on reaction pathways, making it especially valuable for systems where exact redox reactions are undefined. We further developed a coupled kinetic-mass transport model, which showed that electron-transfer rate constants varied among oxides, whereas mass-transfer coefficients were similar. Classical nucleation theory was applied to contextualize these differences, indicating that the balance between surface and bulk energies controls the dissolution barrier. Our findings not only demonstrate how reaction thermodynamics and phase differences jointly control manganese oxide reduction kinetics but also support a generalizable predictive framework for the reactivity of redox-active minerals across diverse environmental conditions.

Alzheimer's disease and related dementias (ADRD) are progressive neurodegenerative conditions where early detection is critical for timely intervention and care planning. However, current diagnostic methods are often inaccessible, costly, and delayed, especially for underserved populations. There is a growing need for scalable, noninvasive tools that can support timely diagnosis. Spontaneous speech contains rich acoustic and linguistic markers that can serve as noninvasive behavioral markers for cognitive decline. Foundation models, pretrained on large-scale audio or text data, generate high-dimensional embeddings that encode rich contextual and acoustic information. This study benchmarks open-source foundation language and speech models to evaluate their effectiveness in detecting ADRD from spontaneous speech as a potential solution for early, noninvasive, and scalable ADRD detection. In this study, we used the Pioneering Research for Early Prediction of Alzheimer's and Related Dementias EUREKA (PREPARE) Challenge dataset, which consists of audio recordings from over 1600 participants with 3 distinct categories of cognitive decline: healthy control (HC), mild cognitive impairment (MCI), and Alzheimer's disease (AD). We further excluded samples that are non-English, nonspontaneous speech, or of poor quality. Our final samples included 703 (59.13%) HC, 81 (6.81%) MCI, and 405 (34.06%) AD cases. We systematically benchmarked 18 open-source foundation speech and language models to classify cognitive status into 3 categories (HC, MCI, or AD). Post hoc interpretability analysis was performed for the best-performing model using Shapley additive explanations linking high-dimensional embeddings with explainable acoustic and linguistic markers. Whisper-medium model achieved the highest performance among speech models at 0.731 accuracy and 0.802 area under the curve, while Bidirectional Encoder Representations from Transformers with pause annotation achieved the top accuracy of 0.662 and 0.744 area under the curve among language models. Overall, ADRD detection based on state-of-the-art automatic speech recognition model-generated audio-embeddings outperformed other models, and the inclusion of nonsemantic information, such as pause patterns, consistently improved the classification performance of text-embedding-based models. Our work presents a comprehensive comparative evaluation of state-of-the-art speech and language models for AD and MCI detection on a large, clinically relevant dataset. Embeddings derived from acoustic models, which capture both semantic and acoustic information, show promising performance and highlight the potential for developing a more scalable, noninvasive, and cost-effective early detection tool for ADRD.

In previous studies, identifying a position at high collision risk outside a range of CT imaging requires a cost, such as an infrared camera. In this study, we propose a simple method to identify the patient's body surface using localization radiographs in a planning CT that are available at any facility. Five metallic markers were attached to the mannequin's right elbow with the arm up. Twelve localization radiographs were acquired at 30&#xb0; intervals from 0&#xb0; to 330&#xb0; of the tube angle. The images acquired at 60&#xb0;, 90&#xb0;, 120&#xb0;, 150&#xb0;, 180&#xb0;, 270&#xb0;, 300&#xb0;, and 330&#xb0; of the tube angle were used to estimate the metallic markers' position. The three-dimensional (3D) estimated coordinate (EC) of the metallic markers (X, Y, Z) was calculated from the two selected localization radiographs and compared to the reference coordinate (RC). The average of the difference between the EC and the RC was [Formula: see text] -&#xa0; 1.4 &#xb1; 3.5 mm, [Formula: see text] = -&#xa0;3.1 &#xb1; 6.2 mm and [Formula: see text] = 0.8 &#xb1; 0.5 mm. When the set of combined tube angles (&#x3b1;, &#x3b2;) was (60&#xb0;,270&#xb0;), (120&#xb0;,300&#xb0;), and (180&#xb0;,330&#xb0;), the average distance between RC and EC exceeded 10.0 mm. The maximum distance between RC and EC was 46.7 mm. The distance between the mannequin's elbow and the gantry surface estimated by the TPS differed by 0.2 cm compared with the direct measurement in the treatment room. This study proposed a novel method for estimating patient body surface coordinates using localization radiographs, with sufficient accuracy to prevent collisions between the patient's body surface and the gantry.

Gender disparities in survival and life expectancy are indicative of broader health and socio-economic inequalities. This study examines temporal trends and subnational variations in gendered survival outcomes across Thailand's 77 provinces between 2015 and 2023, emphasizing the impacts of the COVID-19 pandemic and environmental factors. For the first time, comprehensive provincial life tables have been produced for all Thai provinces for this period. Using civil registration data, life tables were constructed, from which life expectancy at birth ([Formula: see text]), at age 65 ([Formula: see text]), and survival probabilities between ages 20 and 65 were derived. Generalized additive models (GAM) with Poisson likelihood were employed to estimate mortality rates and analyze temporal trends and regional disparities. All analyses were conducted separately by sex. National-level estimates were computed as population-weighted aggregates of provincial estimates, from which life table indicators were derived consistently. Uncertainty intervals were estimated for key indicators. From 2015 to 2023, the national gender gap in life expectancy at birth widened from 7.0 years to a peak of 7.7 years in 2021, primarily driven by increased male mortality during the COVID-19 pandemic. Provincial variations were substantial, with gender gaps ranging from 5.7 to 9.5 years. PM2.5 exposure measured in 2024 showed a moderate to strong negative correlation with life expectancy, highlighting significant environmental impacts. Persistent and geographically uneven gender disparities in life expectancy underscore the necessity for localized, gender-sensitive interventions targeting male mortality and environmental health risks.

What is this plain language review about?Cortisol is a hormone that supports many essential functions. When cortisol remains too high for too long (a condition called hypercortisolism), it has negative effects throughout the body, including on organs and tissues involved with regulating blood sugar (glucose). CATALYST was a large study in adults with type 2 diabetes who didn&#x2019;t meet blood glucose targets despite taking multiple medications (&#x2018;difficult to control type 2 diabetes&#x2019;).What were the results?The CATALYST study found that about 1 in 4 (24%) of 1,057 adults with difficult-to-control type 2 diabetes had hypercortisolism due to excess cortisol being produced within the body. Additional evaluations confirmed who was eligible to receive treatment in the study. One hundred thirty-six people with difficult-to-control type 2 diabetes and hypercortisolism entered the treatment phase; 91 took mifepristone (Korlym), a medicine that reduces cortisol activity, and 45 took placebo for 24 weeks along with their usual treatments. The main result that the study looked at was HbA1c, a blood test that shows a person&#x2019;s average blood glucose level over the prior few months.Mifepristone led to a larger decrease in HbA1c (&#x2013;1.47%) than placebo (&#x2013;0.15%), and many people taking mifepristone could lower or stop some glucose-lowering medicines like insulin. They also lost about 10 pounds on average and had greater reductions in waist size than people taking placebo. More people stopped mifepristone than placebo (46% vs 18%). The most common reason people stopped mifepristone treatment was because of side effects (26 people who took mifepristone stopped vs 1 person who took placebo). Side effects for people taking mifepristone were typically mild or moderate. Common side effects in people taking mifepristone included low potassium and symptoms of cortisol withdrawal (tiredness, nausea, vomiting, headache, diarrhea, dizziness) that occur when cortisol activity drops quickly after being high for a long time. These side effects were expected with mifepristone, were temporary, were not dangerous, and were manageable with dose changes and education.What do the results mean?The CATALYST results suggest that people with difficult-to-control type 2 diabetes may benefit from being tested for hypercortisolism. For people with hypercortisolism, treatments targeting cortisol overactivity (such as mifepristone) may help them lower their HbA1c and lose weight.[Box: see text][Box: see text]Link to original article here.

Forecasting how human hands move in egocentric views is critical for applications like augmented reality, human-robot policy transfer, and service/assistive technologies. Recently, several hand trajectory prediction (HTP) methods have been developed to generate future possible hand waypoints, which still suffer from insufficient prediction targets, inherent modality gaps, entangled hand-head motion, and limited validation in downstream tasks. To address these limitations, we present Uni-Hand, a universal hand motion forecasting framework considering multi-modal input, multi-dimensional and multi-target prediction patterns, and multi-task affordances for downstream applications. We harmonize multiple modalities by vision-language fusion, global context incorporation, and task-aware text embedding injection, to forecast hand waypoints in both 2D and 3D spaces. A novel dual-branch diffusion is proposed to concurrently predict human head and hand movements, capturing their motion synergy in egocentric vision. By introducing target indicators, the prediction model can forecast the specific joint waypoints of the wrist or the fingers, besides the widely studied hand center points. In addition, we enable Uni-Hand to additionally predict hand-object interaction states (contact/separation) to facilitate downstream tasks better. To incorporate comprehensive downstream task evaluations in the literature, we build novel benchmarks to assess the real-world applicability of hand motion forecasting algorithms. The experimental results on multiple publicly available datasets and our newly proposed benchmarks demonstrate that Uni-Hand achieves the state-of-the-art performance in multi-dimensional and multi-target hand motion forecasting. Extensive validation in multiple downstream tasks also presents its impressive human-robot policy transfer to enable robotic manipulation, and effective feature enhancement for action anticipation/recognition.

To develop and validate the 8-item Student Quality of Life Index (IQoL), a concise, multidimensional instrument for assessing quality of life (QoL) among higher education students in Brazil, encompassing psychological well-being, vitality and perceived functional capacity. Cross-sectional psychometric validation study using a split-sample approach for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), followed by bifactor item response theory (IRT) modelling and measurement invariance testing. A large-scale survey conducted across 32 private higher education institutions in 14 Brazilian states, covering diverse academic fields. To ensure representativeness, the medical student subgroup was calibrated using post-stratification weights to align sex and age distributions with national medical education demographics. A total of 10 844 undergraduate students completed the survey. Psychometric model development used 10 793 respondents with complete data for the candidate item pool included in the EFA/CFA/IRT pipeline. Score distributions and subgroup comparisons used 10 838 respondents with complete information for sex, age group and course (3656 medical; 7182 other). The sample was predominantly female (69.0%) and white (47.3%) or mixed-race (41.2%), with an age range predominantly between 18 and 29 years. For medical-student comparisons, a stratified, calibrated analytic subset was created to match national sex and age margins, which led to a small reduction in the medical subgroup denominator in those analyses. Psychometric properties of the 8-item IQoL scale. Structural validity was assessed via CFA (weighted least squares mean and variance adjusted estimator) and internal consistency via Cronbach's alpha and Omega coefficients. Item-level performance was evaluated using Samejima's Graded Response Model. Scalar measurement invariance was tested across sex, age and course. The parsimonious 8-item, three-factor model demonstrated superior fit indices (comparative fit index=0.996; Tucker-Lewis index=0.993; root mean square error of approximation=0.058 (90% CI 0.052 to 0.064); standardised root mean square residual=0.031). Internal consistency was high (&#x3b1;=0.88; [Formula: see text]). Bifactor IRT analysis supported a dominant general QoL factor, with item discrimination parameters ([Formula: see text]) ranging from 1.37 to 3.48. Scalar measurement invariance was established for sex, age and academic field, supporting valid group comparisons. Medical students reported slightly higher psychological well-being than non-medical peers, though effect sizes were trivial (Hedges' g=0.074). Within the medical subgroup, females scored higher in vitality and psychological well-being (g up to 0.225). Lower income and self-reported depression were significantly associated with lower global IQoL scores. The Student Quality of Life Index (IQoL) is a psychometrically robust, invariant and efficient tool for large-scale monitoring of student well-being. The establishment of scalar invariance ensures that the observed differences across demographic and academic subgroups reflect true differences in the latent construct, reinforcing the instrument's utility for institutional assessment and mental health policy-making in higher education.