Eye-tracking-while-reading corpora are a valuable resource for many different disciplines and use cases. Use cases range from studying the cognitive processes underlying reading to machine-learning-based applications, such as gaze-based assessments of reading comprehension. The past decades have seen an increase in the number and size of eye-tracking-while-reading datasets as well as increasing diversity with regard to the stimulus languages covered, the linguistic background of the participants, or accompanying psychometric or demographic data. The spread of data across different disciplines and the lack of data sharing standards across the communities lead to many existing datasets that cannot be easily reused due to a lack of interoperability. In this work, we aim at creating more transparency and clarity with regards to existing datasets and their features across different disciplines by i) presenting an extensive overview of existing datasets, ii) simplifying the sharing of newly created datasets by publishing a living overview online, https://t.uzh.ch/1Yh , presenting over 55 features for each dataset, and iii) integrating all publicly available datasets into the Python package pymovements which offers an eye-tracking datasets library. By doing so, we aim to strengthen the FAIR principles in eye-tracking-while-reading research and promote good scientific practices, such as reproducing and replicating studies.
Single-particle tracking (SPT) offers excellent spatiotemporal resolution for revealing biomolecular structures and functions, but linking complex, multidimensional trajectories to underlying biophysical mechanisms remains challenging in living cells. We introduce the Adaptive AI-assisted Single-Particle Tracking (AAISPT) framework, an automated solution for rapid, accurate processing of multidimensional SPT data sets. AAISPT integrates an adaptive segmentation network (Adap_Seg) for detecting biophysically meaningful trajectory transitions and a pretrained classification model (Adap_Cls). Adap_Cls maps multidimensional features to motion states using diffusion fingerprints and a Transformer encoder, and it was generalized with minimal fine-tuning. AAISPT was benchmarked on synthetic data sets against representative methods from the first AnDi challenge, demonstrating competitive performance in trajectory segmentation and classification. Cross-platform validation shows that AAISPT can reliably identify diverse motion states and resolve dynamic transitions across single-particle trajectories of varying dimensionality. Finally, AAISPT was utilized to analyze ligand-dependent nanoparticle-membrane interactions, validating its capabilities in elucidating complex dynamic processes in living cells.
Time-resolved X-ray solution scattering (TR-XSS) provides direct access to protein structural dynamics but has largely been restricted from the microsecond range up to approximately 100 milliseconds. As a result, slower enzymatic systems, including many P-type ATPases, remain difficult to probe. Here, we extend the temporal reach of TR-XSS by sequentially positioning radiation damage-free acquisition windows to enable capturing structural evolution across sub-second to second timescales. Implemented at the CoSAXS beamline at MAX IV Laboratory, this strategy enables continuous tracking of slow protein dynamics while preserving structural sensitivity. Using adenylate kinase (AdK) as a benchmark, we observed a single conformational transition accompanied by signal amplitude decay. In contrast, application to the prokaryotic P-type ATPase LMCA1 revealed clear evolution in scattering profiles, consistent with sequential conformational transitions. Kinetic analysis identified two transitions on the 140 ms and 660 ms timescales, which correspond monitoring rise and decay of a rate-limiting step which can symbolize intermediate dynamics in a slow transport cycle. The results demonstrate that extended-time TR-XSS can resolve multi-step reaction pathways in slow membrane proteins. The approach broadens the accessible timescale of TR-XSS and establishes a general framework for studying slow conformational dynamics in P-type ATPases and related systems.
Emotion inference is not purely reactive but is supported by conceptual priors that contribute to active sampling of meaningful cues as social events unfold, yet how children learn to anticipate when and where such cues will emerge remains unclear. This study examined whether conceptual emotion knowledge serves as a prior supporting developmental increase in anticipatory attention, and whether such attention facilitates more adult-like emotion inferences. Children aged 5-11 years (N = 180, Mage = 8.02 years, 84 female; data collected 2024-2025) from China viewed videos while eye movements were simultaneously recorded and continuously rated the target character's emotion on a valence-arousal grid. Anticipatory attention was indexed by the extent to which children's saccades were directed toward regions about to contain meaningful information. Conceptual emotion knowledge was assessed with a composite that combined emotion granularity, diversity, and performance on a standardized emotion understanding test. Multilevel, moderated mediation models revealed that conceptual emotion knowledge mediated age-related increases in anticipatory saccades toward meaningful regions, which in turn predicted more adult-like emotion judgments. Together, these findings suggest that conceptual knowledge acts as a developmental mechanism bridging children's prior experience and growing attentional strategies, enabling more mature emotion understanding in social environments. Successfully navigating social life requires understanding others' emotions as situations unfold, yet how children learn to predict when and where emotionally meaningful information will appear in real-world contexts remains unclear. Using movies combined with eye tracking and mouse tracking, we show that children increasingly learn where and when to look for emotionally meaningful information before it appears, which in turn predicts more adult-like emotion judgments. Critically, this developmental improvement is driven by children's growing emotion knowledge. These findings suggest that emotion understanding develops through an active, predictive process in which conceptual knowledge contributes to attention allocation in real time.
Real-world glucagon-like peptide-1 receptor agonist (GLP-1 RA) therapies face substantial attrition rates in commercial digital weight loss services (DWLSs). Conversational artificial intelligence (AI) has been proposed to enhance patient support at production scale, but robust evidence of its effectiveness in improving medication retention is scarce. To evaluate the effect of integrating an asynchronous AI patient support agent (Junebot) into a commercial DWLS on 6-month medication adherence and to assess the independent relationship of digital engagement on retention. This retrospective analysis evaluated 16 556 adults prescribed semaglutide within an Australian DWLS between May 2024 and October 2025. The primary endpoint was 6-month medication adherence (≥ 6 orders fulfilled within 183 days). Analytical protocols featured a multivariate binary logistic regression on the full intention-to-treat cohort and three distinct 1:1 propensity score matching (PSM) sensitivity analyses to isolate era-based and tier-specific engagement effects. Six-month adherence was higher post-Junebot than in the pre-Junebot control (53.2% vs. 47.3%; p < 0.001). However, the full cohort model (R2 = 0.2236) revealed that the post-Junebot operational era was independently associated with an increase in the odds of attrition (OR: 1.178; 95% CI [1.052-1.318]; p = 0.004), a trend corroborated by the era-matched PSM model (OR of attrition: 1.309; p = 0.038). Non-automation factors dominated retention trajectories: high-intensity tracking during month 1 (> 25 tracks) dramatically predicted attrition (OR: 15.753; p < 0.001), alongside program pauses (OR: 2.508; p < 0.001) and higher program cost (OR: 2.458; p < 0.001). Within-cohort analysis demonstrated a profound, linear curve for active interaction; structured digital engagement spanning 50%-74.99% of weeks significantly optimised adherence odds across both medication-only (OR: 32.016) and medication plus health coaching cohorts (OR: 13.311). Integrating an AI digital assistant did not independently improve medication adherence; programmatic costs, program pauses and initial tracking anxiety were stronger predictors of long-term retention. After matching post-Junebot cohorts, moderate, active digital engagement appeared to correlate with 6-month retention. Digital providers should prioritise proactive behavioural risk screening and financial accessibility over baseline AI integration.
Atopic dermatitis (AD) affects around 20% of children and up to 10% of adults. Its fluctuating course, severe pruritus, and impact on sleep, mental health, and daily functioning highlight the need for innovative approaches to diagnosis and disease management. Digital health technologies have rapidly expanded to fill this gap, yet their clinical readiness remains uncertain. This scoping review aims to map AI-driven digital tools for AD, classify their functionalities, assess methodologies, and identify challenges and opportunities for real-world implementation. A comprehensive search of MEDLINE (Ovid), Embase (Ovid), Web of Science, and Scopus was conducted from database inception to December 2024 using controlled vocabulary and free-text terms related to "atopic dermatitis," "eczema," "artificial intelligence," "digital applications," and "digital tools." Two reviewers independently screened studies and extracted data, with conflicts resolved by a senior reviewer. Eligible studies included primary research on diagnostic, symptom-tracking, predictive, teledermatology, or language-based tools. Methodological quality was assessed using a 20-item standardised framework. Descriptive statistics and summary tables were used to synthesise findings. 52 studies met inclusion criteria with some having multiple applications: diagnostic tools (n=32), symptom-tracking tools (n=8), predictive models (n=16), teledermatology tools (n=3), and Natural Language Processing/Large Language Models (NLP/LLM)-based applications (n=2). While most studies reported methodological transparency (96%) and data partitioning for model development and evaluation (96%); external validation (21%), code availability (21%), skin colour reporting (27%) and multi-expert labelling (38%) were limited. Despite several Convolutional Neural Network (CNN) based diagnostic models achieving >90% accuracy, few tools underwent real-world testing or clinical integration. AI-driven tools for AD show strong promise. However, limited validation, insufficient amounts of variation in skin type in source data, and real-world evaluation restrict clinical translation. Collaborative efforts to strengthen methodologies, improve dataset representativeness, and evaluate tools in clinical settings are essential for effective implementation.
Collective cell migration relies on coordinated cytoskeletal remodeling, yet the impact of live-cell actin filament probes on these dynamics remains poorly characterized. Here, we systematically compared the performance and cellular effects of fluorogenic jasplakinolide-based probes, SiR-actin and SiR-XActin, with the genetically encoded Lifeact reporter in epithelial monolayers. Using an injury-free wound healing assay combined with widefield images, particle image velocimetry, kymographs, and FUCCI-based cell cycle tracking system, we assessed how probe choice, efflux inhibition, and genetic modification influence actin organization, migration, and proliferation. SiR-XActin provided robust labeling of actin filaments with minimal perturbation in migration rate, directionality, or cell cycle progression, enabling stable visualization of cytoskeletal dynamics in monolayers. In contrast, SiR-actin and Lifeact produced either probe- or cell line-specific effects on migration persistence and wound closure, particularly under prolonged imaging. The use of verapamil, a commonly used phenylalkylamine-derived calcium channel blocker, improved probe retention and migratory persistence without altering proliferation. In contrast, FUCCI-expressing monolayers showed impaired motility independent of probe type, potentially reflecting cytoskeletal constraints associated with cell-cycle reporters. Lifeact-expressing cells exhibited an initial increase in migration rate followed by an incomplete wound closure, consistent with mild actin stabilization. Together, these findings identify SiR-XActin as a minimally perturbative, high-fidelity probe for monitoring actin filament dynamics during collective epithelial migration and highlight the importance of evaluating probe-cell compatibility for live cytoskeletal studies.
Athletes living and training in active conflict zones face a combination of distinct biological pressures that disrupt the body's internal 24-hour clock and stress-hormone systems. The constant psychological threat of conflict keeps the body's stress-hormone axis chronically active, raising cortisol levels, heightening alertness, and making it harder to fall and stay asleep. A key problem in the existing research is that nighttime sleep disruption in conflict zones has often been incorrectly described as ordinary screen-related sleep loss. In reality, government-issued missile warning sirens and emergency phone alerts force athletes fully awake, trigger an acute surge of stress hormones, and, over time, condition the nervous system to treat all nighttime sounds as threats. This pathway, which we call conflict-specific acoustic hypervigilance, is biologically distinct from the sleep disruption caused by voluntary smartphone use. For Muslim athletes in these regions, Ramadan fasting adds further strain: eating and drinking only after sunset shifts the body's internal timing, causes progressive daytime dehydration, and may delay the hormonal signals that govern sleep. This review argues that when all three pressures operate at the same time, their combined effect on sleep, hormones, and recovery may exceed what any one pressure would cause alone, a state we call circadian decoherence. Our proposed Tripartite Athletic Stress Framework maps how these three stressors interact and amplify each other. We propose two measurable cortisol markers as practical tools for monitoring biological overload in affected athletes. Practical countermeasures, including noise management, adjusted meal timing, strategic napping, and psychological resilience training, are outlined with direct reference to the framework's intervention targets. Some athletes keep training and competing in places affected by war. They face a mix of pressures that most sports science models were never designed to handle. War brings a steady psychological strain, the weight of living under constant threat. Sleep suffers too, broken at night by warning sirens and emergency alerts on the phone. For those who also fast during Ramadan, the day is reshaped further, since food and drink are limited to the hours after sunset. Each of these problems has been studied on its own. What has not been examined is what happens when all of them affect the same athlete, week after week, during heavy training. This review draws all three together into a single model, the Tripartite Athletic Stress Framework. The main idea is straightforward. The pressures may not simply add up; they may feed one another. They act on the same biological systems, primarily the stress hormone response and the body’s internal 24-hour clock. When one of these systems is disrupted, it tends to drag the others with it. The body can then slip into what we call circadian decoherence, a state in which sleep, hormone release, recovery, and performance all fall out of step. The practical stakes are real. Athletes from countries at war continue to compete, usually without recovery support matched to their situation. This review describes a few simple checks that team doctors and coaches could begin using now, such as tracking a stress hormone in saliva across the day. It also calls for proper studies that follow these athletes over time so that the framework can be tested directly rather than taken on trust.
Manually tracking research trends in extensive conference programs is challenging, so we used a natural language processing approach to automatically extract trending topics from PDF-formatted programs of the Japanese Circulation Society (JCS). Programs from JCS2023 to JCS2026 were analyzed by GiNZA and Latent Dirichlet Allocation. Among the 42,400 extracted text blocks, there were 8 primary research themes, including sustained interests in coronary artery syndrome and heart failure, an increase in interprofessional collaboration and emerging clusters in arrhythmia and valvular intervention. The automated workflow successfully visualized evolving academic trends, providing a robust tool for comprehensive research exploration.
A single acoustic vector sensor (AVS) enables collocated measurements of acoustic pressure and three orthogonal particle velocity components, providing unambiguous azimuth estimation. However, azimuth estimation methods adapted from array signal processing to a single AVS often suffer from limited robustness and angular resolution. Conventional active sound intensity approaches are likewise challenged in multi-target scenarios and require prior knowledge of the signal-to-noise ratio. To address these limitations, this study preserves the frequency-domain processing framework of the complex sound intensity method and introduces a mixture of wrapped Cauchy distribution (MWC) to characterize the statistical distribution of sound intensity-derived direction angles. The expectation-maximization algorithm clusters frequency-dependent azimuth estimates within a specified frequency band, enabling multi-target azimuth estimation without prior SNR knowledge. To assess algorithmic performance, the influence of multi-target signal coherence and energy ratios on azimuth estimation based on the complex sound intensity method is analytically derived and examined. Three simulations are included: MWC goodness-of-fit under different conditions, comparison with existing methods, and assessment of multi-target tracking. Finally, experimental data acquired in the South China Sea are used to validate the proposed method, demonstrating its capability to accurately estimate the azimuths of multiple targets.
Multi-modal sensing platforms have gained significant attention due to enhanced accuracy and self-calibration capabilities, particularly within complex matrices. Targeting the demand for the real-time detection of the food spoilage marker hydrogen sulfide (H2S), we introduce a triple-mode sensing platform engineered via 3,4-dihydroxyhydrocinnamic-acid-directed interfacial assembly of flower-like MOF@UCNPs heterostructures that synergize intense up-conversion luminescence with potent peroxidase-mimetic activity. Sulfide selectively coordinates with the Cu2+ nodes embedded in the MOF, concurrently (i) shutting down nanozyme catalysis, (ii) restoring up-conversion emission, and (iii) attenuating UV-vis absorbance; complementary RGB alterations captured by a smartphone furnish the third read-out. The assay delivers limits of detection of 0.06 μM (fluorescence), 0.22 μM (absorbance) and 0.90 μM (RGB imaging) with excellent linearity, and achieves satisfactory recoveries in complex food matrices. By tracking H2S released from meat and eggs stored at different temperatures, real-time freshness assessment is realized on a smartphone. This work not only provides a robust food freshness monitoring tool but also offers a versatile blueprint for rational design of multifunctional nanomaterials toward point-of-need analyte detection.
This study evaluated whether implementing perpetual inventory automation in decentralized pharmacies at an academic medical center reduces inventory value and optimizes supply chain functions. This quality improvement project was conducted at an 881-bed academic medical center. The financial and operational impact of an automated perpetual inventory management system was assessed in 2 locations: an ambulatory infusion-based cancer center pharmacy and a perioperative satellite pharmacy. The primary outcome was the change in total inventory value before versus after implementation. Secondary outcomes included staff efficiency and nonfinancial medication inventory metrics. Data was collected from June 2024 to January 2025 and analyzed using descriptive statistics. Postimplementation data showed a cumulative reduction in medication inventory value for both pharmacies. The infusion-based pharmacy's total inventory value decreased by $926,291 (22.15%), and the inventory turnover ratio increased from 1.23 to 1.69 (a 37.01% increase). The perioperative satellite pharmacy's total inventory value decreased by $8,271 (12.88%), and the inventory turnover ratio decreased from 1.14 to 0.78 (31.66%) due to a concurrent decrease in medication spend. Overall, the total inventory was reduced from $4,245,693 to $3,311,131 (22.01%). Implementation of an automated perpetual inventory management system resulted in benefits in the reduction of inventory value reduction and increased operational efficiency. Real-time tracking improved inventory oversight, suggesting automated perpetual inventory management can enhance inventory management in academic medical centers from both financial and workflow perspectives.
Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality worldwide, largely due to the supportive role of the tumor microenvironment (TME). Tumor-associated macrophages, particularly the M2 phenotype, are pivotal in promoting NSCLC progression. Exosomes, key mediators of intercellular communication, can transfer functional cargo from M2 macrophages to cancer cells, thereby regulating malignant behaviors. However, the specific mechanisms by which M2 macrophage-derived exosomes modulate NSCLC progression are not fully understood. Bioinformatics analyses were initially performed to identify differentially expressed genes (DEGs) in M2 macrophages and NSCLC tissues using the GSE159112 and GSE268175 datasets. THP-1 cells were induced to differentiate into M2 macrophages, from which exosomes were isolated and characterized via nanoparticle tracking analysis, transmission electron microscopy, and western blotting. NSCLC cell lines (A549 and H23) were co-cultured with these exosomes to assess the transfer of ETS homologous factor (EHF). Functional assays, including 5-Ethynyl-2'-deoxyuridine (EdU), Transwell, flow cytometry, and sphere formation assays, were conducted to evaluate cell proliferation, migration, invasion, apoptosis, and stemness. Glycolytic capacity was determined by measuring glucose uptake, lactate production, and ATP levels. Chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assays were employed to verify the transcriptional regulation of FGFR1 by EHF. Rescue experiments involving FGFR1 overexpression were performed to validate the signaling axis, and a xenograft mouse model was established to confirm the in vivo findings. EHF was identified as a critical upregulated transcription factor in both M2 macrophages and NSCLC tissues. M2 macrophage-derived exosomes were efficiently internalized by NSCLC cells, resulting in subsequent upregulation of its expression in recipient cells. Mechanistically, EHF transcriptionally activated fibroblast growth factor receptor 1 (FGFR1) by binding to its promoter region. Functionally, exosomes derived from EHF-deficient M2 macrophages significantly suppressed NSCLC cell proliferation, migration, invasion, and sphere formation, while promoting apoptosis. Concurrently, the loss of exosomal EHF inhibited glycolysis, evidenced by reduced glucose uptake, lactate production, and ATP levels. Crucially, restoring FGFR1 expression reversed the suppressive effects induced by EHF-deficient exosomes, confirming that the EHF/FGFR1 axis drives these malignant phenotypes. In vivo experiments further demonstrated that exosomes from EHF-silenced M2 macrophages inhibited tumor growth and downregulated proliferation markers. M2 macrophage-derived exosomal EHF promoted NSCLC progression and glycolysis by transcriptionally activating FGFR1. These findings highlight the EHF/FGFR1 axis as a novel molecular link between macrophages and NSCLC cells.
To accurately identify early destabilization signals in emulsions, this study employed multiple light scattering (MLS) technology combined with microscopic observation, particle size analysis and long-term macroscopic stability tests to perform a multi-scale evaluation on a series of emulsions. By comparing the migration of the transmission light inflection point, the average rate of change of backscattered light intensity (BSMRC) and the uniformity index (U) of samples under different temperatures, the intrinsic correlation between MLS signals and emulsion stability was systematically analysed. The results indicated that three typical destabilization phenomena can be predicted based on the characteristics of the inflection point changes. Further dynamic tracking of BSMRC and U revealed that for stable samples, both BSMRC and U remained at low levels. During destabilization at high temperature (45°C), a significant increase in BSMRC served as a precursor indicator of phase separation. During destabilization at low temperature (-16°C), the U value markedly increased while BSMRC changed gradually, reflecting mechanical damage induced by freezing. In conclusion, multiple light scattering technology can capture destabilization signals in emulsions in advance, with sensitivity superior to traditional visual observation, thereby providing an efficient and reliable technical approach for formulation screening and long-term stability prediction of emulsions. Afin d'identifier avec exactitude les signaux avant‐coureurs de déstabilisation dans les émulsions, cette étude a utilisé la technologie de diffusion multiple de la lumière, associée à l'observation microscopique, à l'analyse granulométrique et à des essais de stabilité macroscopique à long terme, pour réaliser une évaluation à plusieurs échelles sur une série d'émulsions. En comparant la migration du point d’inflexion de la lumière transmise, le taux moyen de variation de l'intensité de la lumière rétrodiffusée et l'indice d'uniformité des échantillons à différentes températures, la corrélation intrinsèque entre les signaux de diffusion multiple de la lumière et la stabilité des émulsions a été analysée de manière systématique. Les résultats ont montré que trois phénomènes typiques de déstabilisation peuvent être prédits sur la base des caractéristiques des variations du point d’inflexion. Un suivi dynamique plus approfondi de l'intensité de la lumière rétrodiffusée et de l'indice d'uniformité a révélé que, pour les échantillons stables, l'intensité de la lumière rétrodiffusée et l'indice d'uniformité restaient tous deux à des niveaux faibles. Lors de la déstabilisation à haute température (45 °C), une augmentation significative de l'intensité de la lumière rétrodiffusée a servi d'indicateur précurseur de la séparation des phases. Lors de la déstabilisation à basse température (‐16 °C), la valeur de l'indice d'uniformité a augmenté de manière marquée tandis que l'intensité de la lumière rétrodiffusée évoluait progressivement, reflétant les dommages mécaniques induits par la congélation. En conclusion, la technologie de diffusion multiple de la lumière permet de détecter à l'avance les signaux de déstabilisation dans les émulsions, avec une sensibilité supérieure à celle de l'observation visuelle classique, offrant ainsi une approche technique efficace et fiable pour le criblage des formulations et la prévision de la stabilité à long terme des émulsions.
"Positive" outcomes such as flourishing and personal recovery are important predictors of physical health, mental illness, quality of life, and all-cause mortality. However, behavioral health outcome measures often emphasize symptom reduction, while overlooking "positive" outcomes. The objective of this study is to describe (1) the outcomes most important to individuals with major depressive disorder (MDD) and (2) "positive" outcomes and psychiatric symptoms in a real-world sample of young adults with MDD. Forty-three participants aged 18-35 with a diagnosis of MDD receiving outpatient behavioral health treatment in an urban safety-net psychiatry department completed online surveys identifying factors most important to recovery and standardized measures of flourishing, personal recovery, functioning, and symptoms of depression and anxiety. Most participants (61.9%) had high symptoms of depression (Patient Health Questionnaire-8 (PHQ-8) ≥ 10), 16.7% were flourishing (Flourishing Scale ≥ 48), and 41.9% had high personal recovery (Brief INSPIRE-O ≥ 50). Factors ranked as "most important" for personal recovery included coping well with stressful events (39.5%), functioning well (34.9%), and having hopes and dreams for the future (30.2%). Notably, among individuals with low depressive symptoms, 60.0% reported low flourishing and 31.3% reported low personal recovery. Individuals with MDD value a diverse range of outcomes, and exhibit a large variation in levels of depressive symptoms and characteristics of "positive" mental health. Aligning outcome tracking with patient-defined goals may provide a more comprehensive understanding of overall mental health.
BackgroundEarly and accurate tracking of Alzheimer's disease (AD) progression is critical for timely intervention. However, electrophysiological biomarkers capable of capturing long-term neurodegenerative changes remain largely underexplored.ObjectiveWe investigated whether individual alpha peak frequency (IAPF), an electroencephalography (EEG)-derived measure of dominant neural oscillatory activity, could serve as a longitudinal biomarker of AD progression.MethodsTwenty-seven patients with AD aged 63-91 years underwent annual EEG and cognitive assessments over 2-7 years. IAPF was extracted from eyes-closed resting-state EEG. Longitudinal associations among IAPF, Mini-Mental State Examination (MMSE) scores, age, and follow-up time were evaluated using repeated-measures correlation and linear mixed-effects models. Annual IAPF changes were compared with those of healthy controls (HC) aged 20-70 years, stratified by decade-based age subgroups. Longitudinal changes in relative spectral power were also analyzed.ResultsPatients with AD showed significant longitudinal declines in both IAPF and MMSE scores, with a positive longitudinal association between the two measures. Mixed-effects models indicated that these declines were better explained by follow-up time after accounting for baseline age than by age at assessment alone. Compared with all healthy-control age subgroups, patients with AD exhibited a significantly steeper annual IAPF decline. Relative theta power increased and alpha/beta power decreased over follow-up, consistent with spectral slowing. However, annualized spectral-power changes showed limited disease specificity, with significant AD-HC differences only for delta and alpha power relative to the oldest HC subgroup.ConclusionsThese findings support IAPF as a non-invasive, temporally sensitive, and clinically accessible biomarker for monitoring AD progression.
Scholarship surrounding the Kraepelinian dichotomy of dementia praecox and manic-depressive insanity has overwhelmingly approached Kraepelin's nosology as the product of Kraepelin's clinical method, use of diagnostic cards, and pre-existing disease concepts, rather than his work in experimental psychology. Revisiting the significance of Kraepelin's 1881-1895 concept of intoxication as model psychosis, this article establishes that Kraepelin's earlier psychological experiments with intoxicants were a potential source of latent, synthesizing concepts, informing the organization of clinical data around two discrete forms of psychosis. Tracking Kraepelin's nosological system over the course of the first six editions of his textbook, it examines Kraepelin's classificatory descriptions relative to the content of his experimental studies on the psychometrics of intoxication, in particular the dualities Kraepelin finds in exogenous and endogenous psychoses. More specifically, it addresses the alignment of inhibitory and excitatory effects at the level of sensation and perception found within both Kraepelin's experiments and psychiatric nosology, proposing that manic-depression and dementia praecox conform with excitatory and inhibitory modes of exogenous psychosis.
The proteolytic behavior of chymosins during cheese ripening impacts cheese texture, flavor, and quality. Especially, the specific hydrolysis of αS1-casein to αS1-I-casein is relevant for early cheese texture development, and this activity varies between chymosin variants. Accurate profiling of αS1-casein and αS1-I-casein during cheese ripening is crucial for understanding these differences between chymosins and the impact on cheese quality. Intact-protein liquid chromatography-high-resolution mass spectrometry (LC-HRMS) was employed to monitor the hydrolysis of αS1-casein to αS1-I-casein during mozzarella cheese ripening. Cheese samples, produced with both bovine and camel chymosin variants, were collected at multiple ripening stages. Protein extraction, separation, and quantification were performed using optimized LC-HRMS protocols to assess hydrolysis kinetics and phosphorylation variants. LC-HRMS analysis revealed that the tested bovine chymosins displayed significantly higher rates of αS1-casein hydrolysis to αS1-I-casein compared to camel chymosins throughout mozzarella cheese ripening. Distinct phosphorylation variants were confidently detected and quantified. Kinetic profiles demonstrated consistent proteolytic activity differences between coagulants. LC-HRMS allows tracking of αS1-casein hydrolysis kinetics throughout mozzarella cheese ripening. This approach not only highlights the pronounced and faster proteolytic activity of bovine chymosins compared to camel chymosins, resulting in more rapid αS1-I-casein formation, but also facilitates the monitoring of phosphorylation variants present in the cheese. These findings emphasize the enzyme-specific effects on cheese texture and quality, demonstrating the importance of advanced protein analysis for comprehensively understanding cheese production and maturation processes.
RNA plays a central role in the formation of diverse traits in pigs. Visualizing RNA dynamics at the single-molecule level in living cells is therefore essential for understanding gene-expression regulation in this species. We report an optimized MS2-MCP RNA-labeling system that incorporates a foldon trimerization domain fused to fluorescent protein and MCP, thereby enhancing fluorescence signals through liquid-liquid phase separation.Using this Foldon-MS2/MCP system, we successfully imaged endogenous GFP mRNA foci in both the nucleus and cytoplasm of living pig cells.Compared with the conventional MS2-MCP system, the Foldon-MS2/MCP system showed markedly increased fluorescence intensity and an improved fluorescence signal-to-noise ratio.We further applied this approach to the endogenous porcine SOX2 gene, achieving high-resolution tracking of SOX2 mRNA foci in live pig cells.Moreover, the system enabled real-time visualization of SOX2 mRNA elongation and translation dynamics. The Foldon-MS2/MCP system provides a powerful platform for investigating RNA localization, gene-expression activation, and endogenous mRNA dynamics in living pig cells.
Survival from critical illness is not equivalent to recovery, particularly in low-resource health systems where ICU survivors may return home with weakness, respiratory vulnerability, medication problems, poor wound care, malnutrition, psychological distress, caregiver burden, and limited follow-up. This commentary responds to the post-ICU care gap in Somalia by proposing a Somalia-adapted nurse-led home recovery framework. The framework is not presented as a fully validated intervention, but as a pragmatic implementation model for settings where ICU beds, rehabilitation services, transport, digital access, and specialist follow-up are constrained. It defines nurse-led home care as a tiered pathway coordinated by an ICU discharge nurse, supported by ward nurses, community health workers, physicians, rehabilitation providers, and referral facilities. The model includes risk stratification, discharge preparation, caregiver education, early phone follow-up, selective home visits, multidisciplinary referral, digital communication safeguards, and outcome tracking. Key implementation issues include workforce capacity, financing, supervision, quality indicators, privacy, medico-legal responsibility, transport barriers, gendered caregiver burden, and the digital divide. Strengthening post-ICU home recovery in Somalia requires local empirical evaluation, but immediate low-cost improvements can begin through structured discharge and follow-up systems.