Abstract: ATP-binding cassette (ABC) transporters constitute one of the largest membrane pro-tein superfamilies, yet the structural and evolutionary properties of their non-domain re-gions remain poorly characterized. To elucidate the diversity of these non-canonical re-gions across evolutionary lineages, we analyzed intrinsic disorder, site-specific selection, and predicted post-translational modification (PTM) sites across five architectural classes comprising 1,581 prokaryotic and eukaryotic sequences. Linker and flanking regions were consistently more disordered than transmembrane and nucleotide-binding domains across all architectures. Disorder fraction differed significantly among region types after phyloge-netic correction (Pagel's λ ≈ 0.97). Predicted PTM sites are enriched in disordered non-domain segments, with N-linked glycosylation and phosphoserine showing the strongest positive enrichment; 140 sites satisfied a tiered conservation criterion (Mu-siteDeep score ≥ 0.5; cross-species conservancy ≥ 30%), including 40 high-confidence or moderate-confidence sites (conservancy ≥ 50%) as well as novel phosphotyrosine candi-dates in half transporters and NBD-only proteins. Site-specific selection analyses indicated that episodic positive selection was concentrated at inter-domain boundaries, whereas NBD cores were subject to pervasive purifying selection. Together, these findings establish that non-canonical regions of ABC transporters are evolutionarily dynamic and harbor conserved predicted modification sites, supporting their roles as potential regulatory inter-faces rather than passive structural linkers.

Abstract: The role of the gut-brain axis is crucial in maintaining homeostasis and regulating neural, hormonal, and immunological activity. This study aimed to evaluate the effects of prebiotic or synbiotic on serum markers related to emotional disorders in individuals with morbid obesity in a triple-blind randomized trial. The sample consisted of 22 subjects, 16 women and 6 men, with a mean age of 41.8 ± 8.5 years and a mean BMI of 47.7 ± 6.8 kg/m². Serum BDNF concentrations decreased significantly after 30 days of prebiotic supplementation (p=0.017), and when analyzing the difference between the evaluated moments, only this group showed a reduction in this parameter. Serum cortisol concentrations were increased in all groups between the moments evaluated, being statistically significant in the synbiotic supplemented group (p=0.028). Serum TNF-α concentrations increased significantly after 30 days of prebiotic supplementation when compared to the baseline of the group itself (p=0.035): however, this variation did not promote significant difference between the groups evaluated after 30 days of supplementation. The results suggest that low grade chronic inflammatory state may be related to neuroendocrine changes present in emotional disorders, but studies with greater sampling power and correlations with clinical findings are necessary to strengthen this evidence.

Abstract: This article reviews current knowledge in comparative immunology and presents updated hypotheses on the evolution of the immune system in jawed vertebrates. It focuses on the co-option of the RAG transposon in the origin of the V(D)J recombination system, proposed to have occurred in two stages. Initially, the RAG transposon, along with other eukaryote-specific transposon such as HAT, interacted with host genes in early eukaryotes, leading to a new transposition mechanism. Subsequently, RAG and host genes were integrated into the V(D)J recombination system, representing a major evolutionary innovation. The broader implications of this events are also considered. Earlier hypothesis suggest that the establishment of the V(D)J recombination system contributed to MHC polymorphism. Phylogenomic evidence indicates that key immune components, including MHC, T-cell receptors (α,β and γ,δ), and immunoglobulins, existed in ancestors and later expanded through gene duplication, forming multigene families with diverse functions. Their proteins products interact with other immune molecules to regulate immune responses. While some retained original functions, others evolved new roles through neo-functionalization. Overall, the co-option of the RAG transposon played a critical role in shaping the immune system of jawed vertebrates by driving innovation in both adaptive and innate immunity.

Abstract: ASI should be guided by open-world alignment. While human labels, preferences, and benchmarks remain indispensable, they are too narrow to serve as master objectives for frontier systems. The road to ASI is inherently a frontier supervision problem. As humans become structurally weak supervisors, the technical challenge shifts from imitating judgments to building evaluation loops answerable to reality, explicit constraints, and post-deployment evidence. We critique human-signal monism— the assumption that human-facing signals sufficiently proxy overall system quality—and diagnose its failure modes, including evaluator weakness and proxy over-optimization. As a constructive alternative, open-world alignment centers human intent while coupling it to verifier ecologies: layered task verifiers, hard constraints, uncertainty gates, and monitoring. This framework contributes an operational ASI definition, an actionable formalization, a coding agent case study, an analysis of open-world alignment’s own failure modes, and the Open-World Evaluation Card reporting artifact. Ultimately, ASI must be guided by objectives that remain answerable to the world.

Abstract: Objectives: Sarcopenia impairs physical function and increases healthcare burden among older adults with chronic musculoskeletal disorders. This study estimated the prevalence of sarcopenia and explored factors associated with sarcopenia among Vietnamese elderly outpatients. Methods: A hospital-based cross-sectional study was conducted among 88 outpatients aged 60 years or older with knee osteoarthritis and/or chronic spinal pain at a tertiary hospital in Northern Vietnam from May 2024 to October 2025. Sarcopenia and severe sarcopenia were defined according to the Asian Working Group for Sarcopenia 2019 criteria. Muscle mass, muscle strength, and physical performance were assessed using bioelectrical impedance analysis, handgrip dynamometry, and usual gait speed, respectively. Multivariable logistic regression and an exploratory chi-square automatic interaction detection decision tree were applied. Results: The prevalence of sarcopenia was 40.9%, including 23.9% with sarcopenia and 17.0% with severe sarcopenia. Age >70 years (adjusted odds ratio [AOR] 9.00, 95% confidence interval [CI] 2.40-33.60), history of falls (AOR 6.33, 95% CI 2.77-14.45), low educational attainment (AOR 2.86, 95% CI 1.46-5.61), and poor sleep quality (AOR 1.16, 95% CI 1.02-1.32) were independently associated with sarcopenia. Conclusions: Sarcopenia was common in this outpatient population. Routine case-finding may be particularly relevant in older patients with falls, lower educational attainment, and poor sleep quality. The decision-tree findings should be interpreted as exploratory because of the cross-sectional, single-center design and modest sample size.

Abstract: The valorization of agricultural plant waste as a sustainable source of natural fibers has gained increasing attention due to environmental and economic concerns. This study investigates the feasibility of extracting bast fibers from Egyptian Corchorus olitorius L. (Molokhia) plant residues and evaluates the influence of different extraction methods on fiber properties. Fibers were extracted using biological retting, cold al-kaline chemical treatment (4% NaOH), and manual scraping, followed by comprehensive characterization of their morphological, chemical, crystalline, mechanical, thermal, and environmental properties. The results showed that the extraction method significantly affected fiber performance. Chemically extracted fibers exhibited the smallest average diameter (13.76 ± 0.44 μm), the highest cellulose content (72.23%), and the lowest lignin content (3.20%), indicating effective removal of amorphous components. XRD analysis revealed the highest crystallinity index for chemically extracted fibers (70.0%), compared to bi-ological (60.0%) and manual extraction (64.0%). These structural improvements resulted in superior mechanical properties, with tensile strength and Young’s modulus reaching 600.67 ± 11.73 MPa and 38.96 ± 0.64 GPa, respectively, compared to lower values for biologically and manually extracted fibers. Weight loss analysis indicated optimal extraction durations of 21 days for biological retting and 9 days for chemical treatment. ICP-MS analysis confirmed that heavy metal contents were well below Oeko-Tex® Standard 100 limits. Overall, the findings demonstrate that Molokhia plant waste is a promising and environmentally safe source of natural fibers, with cold chemical extraction offering the most effective route for producing high-quality fibers suitable for bio-based composite applications.

Abstract: Peptides are a rapidly expanding drug class with a parallel and largely unregulated gray market that sells preparations directly to consumers for self-administration. The use of gray market peptides has grown substantially, with patients self-administering these compounds for purported benefits including accelerated musculoskeletal injury recovery, muscle hypertrophy, fat loss, and athletic performance enhancement. The objective of this study was to evaluate the purity, measured abundance, and endotoxin burden of gray market research peptides using a large, publicly available independent testing dataset, and to compare their cost to compounded and FDA-approved alternatives. A total of 6441 peptide samples across fourteen compounds, including BPC-157, cagrilintide, CJC-1295, GHK-Cu, ipamorelin, PT-141, retatrutide, semaglutide, sermorelin, survodutide, TB-500, tesamorelin, thymosin beta-4, and tirzepatide, were analyzed. Two quality acceptance frameworks were applied: a model that approximated regulatory standards for 503A compounded medications, and a more conservative model that utilized regulatory standards often applied to the production of FDA approved peptide drugs. Between the two models, 41.6% to 71.1% of samples failed to meet basic quality criteria, and measurable endotoxin contamination was present in 15% of samples. Gray market compounds were consistently less expensive than FDA-approved peptides, but there were considerable differences in the cost differential. Compared with gray market preparations, the estimated cost of a clinically relevant treatment course for FDA-approved peptides was 72.8% higher for tirzepatide, and 3850% higher for PT-141. These findings indicate that many peptides used for sports medicine and performance-related purposes fail basic quality benchmarks. Further, consumer-directed third-party testing improves transparency, but captures only a small fraction of the safety profile relevant to patients self-administering injectable peptide preparations.

Abstract: Introduction: The clinical relationship between sexual violence and paraphilia is complex, with research indicating several risk factors; however, having paraphilia does not automatically mean an individual will commit sexual violence. Children are considered more vulnerable than able-bodied adults. This is why investigating the subjective psychopathological profile of paraphilia is important, both for prevention and punishment. The use of the term "paraphilia" refers to an intense and persistent psychophysical state of sexual interest in activities or objects that by conventional nature do not have that specific connotation or use. In literature, there is a clear difference between paraphilia and paraphilic disorder. The current validation study aims to introduce the “Perrotta Paraphilic Global Spectrum of Gradation”, which is the basis of a questionnaire that considers both intensity and frequency of the symptoms described by the patient and therefore can distinguish between the functional condition and the disorder, distinguishing between paraphilic interest, paraphilia and paraphilic disorder, in particular for the protection of minors and vulnerable minorities. Method: A validation study with 198 participants was set up, with statistical analysis of the data relating to the population sample. Results: In the case of content validity, all items obtained a CVR score greater than the cut-off value, with a minimum score of 0.902. Therefore, all items of the scale were considered essential. Also, regarding the relevance of the items in exploring the constructs investigated, optimal values of I-CVI (1.000) and scale (0.918) were obtained. Cronbach's alpha is 0.901 and McDonald’s Omega is 0.871. Therefore, all items were rated as relevant. Using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) we achieve construct validity and therefore the validity of the 9 clinical elements. Conclusion: The paraphilic manifestation has clinical relevance and the instrumental proposal that distinguishes the high-functioning forms from the low-functioning ones is suggestive for use on a larger scale.

Abstract: Background: Accurate assessment of neonatal sleep is critical for monitoring brain development and identifying potential neurological disorders, yet manual scoring of multi-channel EEG recordings is labor-intensive and prone to variability. Methods: To address this, we propose a lightweight temporal-spatial feature fusion network for automatic neonatal sleep staging. The model employs a dual-branch architecture to separately capture temporal dependencies and spatial correlations in EEG signals, which are then integrated via an adaptive fusion module to obtain comprehensive feature representations while maintaining low computational complexity. Results: The framework was evaluated on a clinical neonatal dataset (CHFD) for tasks including sleep–wake classification, quiet sleep detection, and three-stage sleep staging, achieving superior performance compared with several state-of-the-art methods. Additional experiments on the MASS-S3 adult dataset demonstrate that the model retains competitive accuracy and F1-score, indicating strong generalization across populations. Conclusions: These results suggest that jointly modeling temporal and spatial features enables robust and efficient automatic sleep staging. The proposed approach offers a practical solution for clinical applications and edge deployment, providing reliable, multi-dimensional assessment of neonatal brain activity and laying the groundwork for future studies integrating larger datasets or multimodal physiological signals.

Abstract: Background and Objectives: Catheter-related bladder discomfort (CRBD) commonly arises as a direct consequence of perioperative urinary catheterization. A fixed dose combination of 1000mg acetaminophen and 300mg ibuprofen provides multimodal analgesia. Accordingly, we assessed the impact of this fixed dose combination on mitigating CRBD in patients undergoing urological procedures. Materials and Methods: In this prospective pilot study, 23 patients undergoing urological surgery requiring urinary catheterization were randomized into 2 groups; approximately 20 minutes before the anticipated end of surgery, patients were administered a combination of 1000 mg acetaminophen and 300 mg ibuprofen (maxigesic group, n = 11) or saline (control group, n = 12). The primary endpoint was the incidence of CRBD immediately after the patient arrived at the post-anesthetic care unit (PACU). The incidence of CRBD at 1,2,6 hours postoperatively, the severity of CRBD at each time point were also assessed. Results: The incidence of CRBD immediately after arrival at the PACU was significantly lower in the maxigesic group (54.5% vs. 100%, p = 0.014), whereas no significant differences were observed at later time points. The incidence of moderate PONV was significantly lower in the maxigesic group at 0 hour and 1hour (p = 0.036, 0.037, respectively). Conclusions: This pilot study indicates that intravenous acetaminophen and ibuprofen could be an effective, well-tolerated strategy for mitigating early postoperative CRBD in urological surgery. While these preliminary results are promising, larger randomized trials are warranted to validate the clinical efficacy of this multimodal regimen.

Abstract: Background/Objectives: Advanced technologies in type 1 diabetes mellitus (T1DM) management have reshaped the strategies used to achieve optimal glucose control. Continuous Subcutaneous Insulin Infusion (CSII) and Automated Insulin Delivery (AID) systems are effective alternatives to multiple daily injections (MDI). This study aims to evaluate glycemic regulation in children and adolescents transitioning from MDI to in-sulin pumps and to raise awareness among patients and their families regarding the benefits of these systems. Methods: 50 pediatric patients with T1DM (24 males, 26 fe-males; mean age 10.76 ± 3.2 years) were evaluated. Cycle 1 established MDI metrics 3 months pre-transition. In cycle 2, patients transitioned either to an AID system (Medtronic MiniMed 780G, 78%), or a non-automated system (Omnipod DASH, 22%). Data were assessed at 3- and 6-months post-initiation. Parameters assessed were: Glycosylated hemoglobin (HbA1c), Time In Range (TIR), Time Above Range (TAR), Time Below Range (TBR), Glucose Management Indicator (GMI), Coefficient of Variation (CV). Results: The cohort exhibited a statistically significant increase in TIR (p=0.0038) with mean values of 70.9% at 3 months and 70.8% at 6 months. TAR significantly reduced (p=0.033) to 26.5% and 24.3% at 3 and 6 months, respectively. Sub-analysis in the AID group, revealed a marked increase in TIR (p=0.0001) alongside significant reductions in TAR (P=0.0009) and GMI (p=0.03). Conclusion: Transitioning from MDI to insulin pump therapy, particularly AID systems, is transforming the clinical landscape of T1DM management. The con-sistency of these results across age groups indicates that AID systems can successfully overcome pediatric and adolescent diabetes management challenges.

Abstract: Regular graphs are classical symmetric structures in graph theory, where each vertex has identical degree and the overall topology often exhibits strong automorphism properties. However, practical systems frequently require heterogeneous constraints, which can be modeled by introducing vertex colorings and non-uniform degree requirements, leading to controlled symmetry breaking. In this paper, we investigate two-tone factors in edge-connected regular graphs and claw-free cubic graphs under arbitrary red-blue vertex colorings. Using the framework of parity (g,f)-factors, we establish two main existence results. First, we prove that every λ-edge connected r-regular graph admits a two-tone ({k},{k,k+2})-factor for any coloring, provided that r/λ⩽k⩽r−r/λ, k⩽r−2, and k|G| is even. Second, we show that every 3-edge connected claw-free cubic graph admits a two-tone ({0,1},{2,3})-factor regardless of the coloring configuration. Beyond existence, we provide a constructive algorithm by reducing the parity factor problem to an exact f-factor problem and further to a perfect matching problem via vertex-splitting techniques. We rigorously justify the correctness of this reduction and show that the desired factor can be computed in polynomial time. From a structural perspective, our results reveal that edge-connectivity serves as a stabilizing mechanism that preserves parity feasibility under arbitrary color-induced perturbations, while claw-free constraints enforce local density that prevents parity imbalance. This provides a symmetry-based interpretation of two-tone factors as a balance between global regularity and local asymmetry. These findings contribute to both the theoretical development of factor theory and its algorithmic realization, with potential implications for deterministic network design and resource allocation in structured systems.

Abstract: Galaxy classification is essential for understanding the formation and evolution of cosmic structures. However, faced with the explosive growth of astronomical observation data, traditional single-modality classification methods relying solely on spectroscopy or imaging have struggled to meet high-precision demands due to insufficient feature utilization and limited generalization capability. Therefore, multimodal fusion has emerged as a promising direction by leveraging information complementarity to overcome the limitations of single data sources. Accordingly, this paper proposes a model named Galaxy CosineNet (GCSNet), which integrates imaging, spectroscopic, and tabular data for high-precision galaxy classification. Specifically, the model employs dedicated encoders to process the three modalities separately and utilizes skip connections to preserve raw features. Furthermore, it incorporates a multi-head self-attention mechanism to deeply mine global cross-modal complementary information. Finally, these features are concatenated and fed into a cosine similarity classification head. Experimental results demonstrate that GCSNet achieves 97.15% accuracy in classifying star-forming, composite, active galactic nuclei (AGNs), and normal galaxies. This performance outperforms the best single-modal baseline, GaSNet, by 0.76% and mainstream multi-modal models such as MB-ISTL and the Transformer by over 1.6%. Consequently, the proposed GCSNet offers an effective and novel approach for research on automatic galaxy classification.

Abstract: Understanding the extent to which anthropogenic activity shapes vegetation dynamics is a central challenge in palaeoecology. In the Eastern Mediterranean, pollen-based studies have traditionally identified human impact through qualitative interpretations of anthropogenic indicators, particularly within the framework of the Beyşehir Occupation Phase (BOP) . However, quantitative comparison of anthropogenic signals across multiple sites remains limited. This study compiles pollen datasets from multiple lacustrine records across Anatolia (Türkiye) to construct a regional multi-site dataset and evaluates anthropogenic influence using a quantitative BOP period anthropogenic taxa integrated with Principal Component Analysis (PCA). Anthropogenic impact was quantified using a composite pollen index based on Olea, Juglans, Plantago lanceolata-type, Cerealia and Rumex acetosa-type taxa. The results reveal substantial spatial variability in anthropogenic signals, with combined pollen percentages ranging from less than 1% to 16% among lakes. PCA results show clear inter-site differentiation, with the first two components explaining 42.94% and 21.95% of the total variance, respectively. In particular Olea emerges as the most influential indicator, strongly contributing to the primary ecological gradient. These findings provide a quantitative extension of the traditionally qualitative BOP concept and demonstrate that anthropogenic influence is a fundamental and spatially heterogeneous component of vegetation dynamics across Anatolia. By integrating a composite anthropogenic index with multivariate analysis, this study offers a robust and transferable framework for comparing human–environment interactions across different regions and ecological settings.

Abstract: Accurate detection of special targets in unmanned aerial vehicle (UAV) remote sensing imagery under complex degradation conditions remains a critical challenge for intelligent surveillance systems. Existing detectors exhibit significant performance degradation when confronted with composite degradation factors such as blur, rain, snow, fog, low illumination, strong light, and electromagnetic interference. To address this limitation, we propose RHG-DETR (Riemannian Hyper-Graph Detection Transformer), a novel detection framework for robust special target detection under multi-type degradation in UAV remote sensing imagery. Using RT-DETR as the baseline, three synergistic innovations are introduced at the backbone, neck, and encoder levels. The Dynamic Receptive-field Hyper-graph Attention Network (DRHANet) replaces the conventional ResNet backbone, employing anisotropic dynamic depthwise separable convolution and a Riemannian Hyper-graph Fusion (RHGF) mechanism to model high-order semantic topology dependencies among target components. The Bi-directional Weighted Adaptive Fusion Network (BWAFN) constructs a two-stage bidirectional feature pyramid with learnable scale contribution weights and a lightweight spatial compensation upsampler to maintain cross-scale semantic consistency under atmospheric degradation. The Adaptive Sparse Multi-scale Encoder with Dynamic normalization (ASMED) reconstructs the AIFI encoder module by introducing sparse window self-attention to suppress background interference, a spatial-gated feedforward fusion to preserve geometric topology constraints of target sub-components, and coordinated dynamic normalization modules to stabilize encoding under extreme illumination and electromagnetic interference. On a self-constructed special target dataset comprising tanks, multiple launch rocket systems, and soldiers under seven degradation types, RHG-DETR achieves an mAP50 of 78.5%, surpassing the RT-DETR baseline by 3.7%, while reducing GFLOPs and parameter count by 34.4% and 28.8%, respectively, at an inference speed of 84.2 FPS. Consistent improvements on VisDrone2019 and BDD100K further validate the cross-domain generalization capability of the proposed framework.

Abstract: Real-time quantitative precipitation estimation (QPE) from weather radar is essential for hydrological forecasting, flash flood warning systems, and water resource management. Despite significant advances in radar technology and signal processing, operational QPE systems face persistent challenges including non-meteorological clutter contamination, signal attenuation, vertical profile biases, and systematic errors that require integration with ground-based rain gauge networks. This review synthesizes recent developments in open-source frameworks for radar QPE, spanning the complete processing chain from raw signal correction to operative hydrological validation. We examine state-of-the-art methods for clutter removal (polarimetric fuzzy logic, CLEAN-AP, neural network quality control), C-band attenuation correction (self-consistent and KDP-based approaches), and vertical profile of reflectivity (VPR) correction for warm-rain events. We compare gauge-radar merging techniques including mean field bias adjustment, spatially variable corrections, Kriging with External Drift (KED), and Conditional Merging, with emphasis on real-time applicability and look-back window strategies. The review identifies key open-source Python libraries (wradlib, Py-ART, pySTEPS, radproc, weatherDataHarmonizer) and documents operational latency constraints for flash flood warning systems. A critical research gap is identified: current open-source solutions lack documented workflows for integrating delayed 24-hour manual gauge readings into real-time QPE streams while maintaining low latency. This review provides researchers and practitioners with a comprehensive roadmap for developing robust, open-source, real-time radar QPE systems suitable for operational hydrological applications.

Abstract: This study introduces the Children's Empathy for Older Adults (CEOA) eight-item scale, a novel image-based instrument designed to measure young children's views, empathy, and behavioural intentions toward older adults. CEOA was administered as a pre-test and post-test metric, following storytelling sessions, on 232 children aged 5-6 years in the multi-racial and multi-cultural context, Singapore. Findings revealed that children with regular exposure to grandparents demonstrated clearer, more distinct responses across all three domains, indicating a more developed understanding of older adults’ needs. In contrast, children without such exposure showed less differentiation between cognitive, affective, and behavioral components. These results underscore the importance of intergenerational contact in shaping children’s perceptions and empathy for older adults. The CEOA scale is a valuable tool for future research and interventions aimed at fostering positive intergenerational relationships.

Abstract: This paper examines whether the rise of remote work following the COVID-19 pandemic has generated a structural transformation in urban spatial organization across major metropolitan areas in advanced economies. While much of the existing literature treats COVID-19 as a temporary shock, this study argues that it has induced a persistent reconfiguration of cities toward more polycentric and decentralized spatial structures.Using a multi-source dataset combining Google mobility reports, NASA/VIIRS night-time light satellite data, OECD and national labor force surveys, and urban economic indicators, the study constructs a novel Urban Polycentricity Index (UPI) to measure spatial dispersion of economic activity. The empirical analysis covers New York, London, Paris, Berlin, and Munich over the period 2019–2025.The methodology integrates structural break tests, difference-in-differences estimation, and spatial equilibrium modeling to identify both the timing and magnitude of post-COVID spatial shifts. Results indicate a significant structural break around 2020–2021, followed by a sustained increase in remote work adoption and urban polycentricity. Satellite and mobility data confirm a systematic redistribution of economic activity from central business districts toward suburban and peripheral zones.Findings show that remote work is a statistically significant driver of urban decentralization, associated with flatter density gradients, reduced commuting intensity, and higher polycentricity. Counterfactual simulations further confirm that, without remote work expansion, cities would have remained substantially more monocentric. Overall, the study demonstrates that COVID-19 has permanently altered urban spatial equilibrium, positioning remote work as a key structural force reshaping metropolitan form.

Abstract: Integrated care is increasingly shaped by digital infrastructures, data governance, and AI-enabled analytics, yet the relevant literature remains fragmented across health-services research, digital health, and machine learning. This article presents a conceptual review informed by structured scoping searches across PubMed, Scopus, Semantic Scholar, Crossref, and selected policy sources covering January 2001–March 2026. The search component was used to map the field and identify representative frameworks, implementations, and technical advances rather than to estimate pooled effects. We synthesise the literature across four domains: conceptual foundations of integrated care, AI and multimodal analytics, implementation barriers, and digital-governance requirements. On that basis, we propose a five-level taxonomy ranging from disease-specific programmes to learning integrated care models and argue that most current deployments remain concentrated at digitally integrated but only weakly adaptive Type IV configurations. Across the literature, three recurrent constraints limit progression towards Type V learning systems: temporal blind spots, maintenance debt, and governance misalignment. Overall, the review positions AI-enabled integrated care less as a finished model than as an emerging design space requiring longitudinal data assets, stewarded model lifecycles, and accountable governance to support clinically useful, equitable, and trustworthy learning systems.

Abstract: The global drive towards sustainability and energy conservation has accelerated the development of intelligent buildings utilizing building management system (BMS). Occupants have profound impacts on building environment. Incorporating occupant-related factors into the environmental control process is essential for optimizing the efficiency of BMS, which thus give rise to the concept of occupant-centric control (OCC). Conventional methods rely on simplified models and fixed schedules that fail to satisfy environment control and occupant requirements, while constructing credible models places strict requirements on the dataset. In this paper, we propose a Model-Aware Predictive Control framework named MAPC, which can construct credible models with limited data and provide room-level control strategies allowing for occupant comfort and energy efficiency. Its technological innovations are twofold. On the one hand, we design a model construction and fine-tuning method combining data-driven subspace projection approach with physical priors, which can construct credible thermal dynamic models with limited data. On the other hand, to balance the potential conflicts between enhancing occupant comfort and saving energy, we present a hierarchical decision-making mechanism, which enables room-level global optimal control considering dynamic occupant comfort requirements and energy usage. Experimental results obtained on a typical duplex apartment dataset demonstrate that MAPC is able to provide room-level control strategies based on dynamic occupant requirements and user preferences, achieving improved occupant comfort and energy efficiency. The ablation experiments also demonstrated the superiority of MAPC in constructing reliable models on limited datasets.