Trajectories Of Change Research Articles

Examining interrelations among trajectories of mindful awareness, acceptance, and values-consistent actions in acceptance-based behavioral therapy for generalized anxiety disorder

ABSTRACT The goal of this study was to understand how mindful awareness, acceptance, and values-consistent action change across acceptance-based behavioral therapy (ABBT) for generalized anxiety disorder (GAD) and determine their effect on symptoms. We examined weekly data from 31 individuals who received ABBT as part of a randomized control trial for individuals with GAD (Hayes-Skelton, Roemer, & Orsillo, 2013). Participants answered questions weekly about three components of ABBT, including the percentages of time they spent over their past week: 1) aware of the present moment, 2) accepting of their internal experiences, and 3) engaging in values. GAD symptoms were examined using two self-report measures (DASS-21 Stress subscale and PSWQ) and two clinician-rated interview measures (CSR and SIGH-A) at pre- and post-treatment. 1 Mindful awareness, acceptance, and values-consistent action increased linearly across ABBT. All three change trajectories were positively correlated (Z’s 2.99 to 8.74, p’s < .001). Mindful awareness, acceptance, and values-consistent action across treatment predicted decreases in GAD symptoms above and beyond baseline for most outcome measures (Z’s −1.95 to −3.03, p’s < .05), with the exception that mindfulness did not predict DASS-stress (Z = −1.39, p = .17). These findings are consistent with the structure and model of ABBT.

Adapting to Individual Differences: An Experimental Study of Language Evolution in Heterogeneous Populations.

Variations in language abilities, use, and production style are ubiquitous within any given population. While research on language evolution has traditionally overlooked the potential importance of such individual differences, these can have an important impact on the trajectory of language evolution and ongoing change. To address this gap, we use a group communication game for studying this mechanism in the lab, in which micro-societies of interacting participants develop and use artificial languages to successfully communicate with each other. Importantly, one participant in the group is assigned a keyboard with a limited inventory of letters (simulating a speech impairment that individuals may encounter in real life), forcing them to communicate differently than the rest. We test how languages evolve in such heterogeneous groups and whether they adapt to accommodate the unique characteristics of individuals with language idiosyncrasies. Our results suggest that language evolves differently in groups where some individuals have distinct language abilities, eliciting more innovative elements at the cost of reduced communicative success and convergence. Furthermore, we observed strong partner-specific accommodation to the minority individual, which carried over to the group level. Importantly, the degree of group-wide adaptation was not uniform and depended on participants' attachment to established language forms. Our findings provide compelling evidence that individual differences can permeate and accumulate within a linguistic community, ultimately driving changes in languages over time. They also underscore the importance of integrating individual differences into future research on language evolution.

Panel-based assessment of ecosystem condition as a platform for adaptive and knowledge driven management.

Ecosystems are subjected to increasing exposure to multiple anthropogenic drivers. This has led to the development of national and international accounting systems describing the condition of ecosystems, often based on few, highly aggregated indicators. Such accounting systems would benefit from a stronger theoretical and empirical underpinning of ecosystem dynamics. Operational tools for ecosystem management require understanding of natural ecosystem dynamics, consideration of uncertainty at all levels, means for quantifying driver-response relationships behind observed and anticipated future trajectories of change, and an efficient and transparent synthesis to inform knowledge-driven decision processes. There is hence a gap between highly aggregated indicator-based accounting tools and the need for explicit understanding and assessment of the links between multiple drivers and ecosystem condition as a foundation for informed and adaptive ecosystem management. We describe here an approach termed PAEC (Panel-based Assessment of Ecosystem Condition) for combining quantitative and qualitative elements of evidence and uncertainties into an integrated assessment of ecosystem condition at spatial scales relevant to management and monitoring. The PAEC protocol is founded on explicit predictions, termed phenomena, of how components of ecosystem structure and functions are changing as a result of acting drivers. The protocol tests these predictions with observations and combines these tests to assess the change in the condition of the ecosystem as a whole. PAEC includes explicit, quantitative or qualitative, assessments of uncertainty at different levels and integrates these in the final assessment. As proofs-of-concept we summarize the application of the PAEC protocol to a marine and a terrestrial ecosystem in Norway.

Human-like Decision Making for autonomous lane change driving: a hybrid inverse reinforcement learning with game-theoretical vehicle interaction model

The development of automated driving vehicles aims to provide safer, comfortable, and more efficient mobility options. However, the decision-making control of autonomous vehicles still embraces limitations on human performance mimicry. These limitations become particularly evident in complex and unfamiliar driving scenarios, where weak decisionmaking abilities and poor adaptation of vehicle behaviour are prominent issues. This paper proposes a game-theoretic decisionmaking algorithm for human-like driving in the vehicle lane change scenario. Firstly, an inverse reinforcement learning (IRL) model is used to quantitatively analyse the lane change trajectories of the natural driving dataset, establishing the human-like human cost function. Subsequently, joint safety, comfort to build the comprehensive decision cost function. Use the combined decision cost function to conduct a non-cooperative game of vehicle lane changing decision to solve the optimal decision of host vehicle lane changing. The host vehicle lane-changing decision problem is formulated as a Stackelberg game optimization problem. To verify the feasibility and effectiveness of the algorithm proposed in this study, a lane change test scenario has been established. Firstly, we analyse the human-like decision-making model derived by the maximum entropy inverse reinforcement learning algorithm to verify the effectiveness and robustness of the IRL algorithm. Secondly, the human-like game decisionmaking algorithm in this paper is validated by conducting an interactive lane-changing experiment with obstacle vehicles of different driving styles. The experimental results prove that the human-like driving decision-making model proposed in this study can make lane-changing behaviours in line with human driving patterns in lane-changing scenarios of expressway.

Application of 3D Motion Graphic Design Technology of Object Simulation Human Body in Sports Teaching

In the current era, the rapid development of computer technology has brought unprecedented opportunities for innovation in physical education teaching. The introduction of computer technology not only improves the efficiency of physical education teaching but also provides more accurate and scientific means for sports training. In response to this trend, this paper proposes an innovative human body model design method and explores in detail the key implementation techniques of computer simulation of 3D motion maps, aiming to further optimize and improve the quality of physical education teaching. The design of human body models plays a crucial role in physical education teaching and training, as it can help students and teachers better understand the mechanisms and principles of human movement. By introducing computer technology, this paper can construct a more precise, dynamic, and realistic human body model that conforms to the actual motion situation. This model can simulate various movement states of the human body, providing students with an intuitive and vivid learning experience. In the classroom, teachers can use projection or virtual reality devices to display 3D motion models, allowing students to intuitively see the details and changes of motion movements, deepening their understanding of the essentials of the movements. Teachers can demonstrate different movements in real time by operating models, and explain the technical points and precautions of each movement. Students can observe the motion trajectory and posture changes of the model from multiple perspectives to improve learning efficiency. The system generates personalized feedback reports based on students’ movement data, pointing out the problems and improvement directions that students have during the exercise process. Teachers can provide more accurate guidance to students based on these feedbacks, helping them correct incorrect movements and improve their skill levels.

Using machine learning methods to identify trajectories of change and predict responders and non-responders to short-term dynamic therapy

Objectives Predicting therapy responders can significantly improve clinical outcomes. This study aims to identify predictors of response to short-term dynamic therapy. Methods Data from 95 patients who underwent 16-session therapy were analyzed using machine learning. Weekly progress was monitored with the Outcome Questionnaire (OQ45) and Target Complaints (TC). A machine learning model identified change trajectories for responders and non-responders, with a random forest algorithm and elastic net modeling predicting trajectory group membership using pre-treatment data. Results A weak positive relationship was found between the trajectories of the two outcome variables. The results of the different analysis methods were compared and discussed. Important predictors of OQ45 trajectories, based on random forest modeling, included initial symptom severity, difficulties in emotion regulation, coldness, avoidant attachment, conscientiousness, interpersonal problems, non-acceptance of negative emotion, neuroticism, emotional clarity, impulsivity, and emotion awareness (72.8% accuracy). Initial problem severity, self-scarifying extraversion, and non-assertiveness were the most dominant predictors for TC trajectories (62.8% accuracy). Conclusions These findings offer data-driven insights for selecting short-term dynamic therapy. Predicting response for the OQ45, a nomothetic measure, does not extend to the TC, an idiographic measure, and vice versa, highlighting the importance of multidimensional outcome evaluations for personalized treatment.

Prognostic staging system correctly identifies high risk groups in cardiac transthyretin amyloidosis treated with tafamidis

Abstract Background The development of different multiparametric staging systems improved the risk assessment of cardiac transthyretin amyloidosis (ATTR-CA) patients substantially. However, to date, all staging systems were validated in treatment-naive ATTR-CA patients. The introduction of tafamidis led to significant changes in the disease trajectory of ATTR-CA, indicating that it may also have an impact on the prognostic accuracy of these staging systems. Purpose Thus, we sought to assess whether the prognostic value of the National Amyloidosis Centre (NAC) staging system, currently considered as the most accurate, is sustained in ATTR-CA patients treated with tafamidis. Methods This retrospective observational study included ATTR-CA patients treated with tafamidis. Patients were continuously followed from treatment initiation to time of death. NT-proBNP and eGFR data collected at baseline were used to stratify patients into low (stage I), intermediate (stage II) and high-risk (stage III) subgroups according to the NAC staging system. Kaplan-Meier analyses were conducted to illustrate overall survival. Differences in overall survival between subgroups were assessed by log rank tests. P-values &amp;lt;0.05 were considered statistically significant. Results A total of 166 ATTR-CA patients (95.2% wild-type) were enrolled. 81 (48.8%), 51 (30.7%) and 34 (20.5%) patients were stratified into stages I, II and III, respectively. Median follow-up was 539 [323-865] days, during which 20 deaths were recorded. Overall survival of the subgroups over time is demonstrated in figure 1. The probability of overall survival was significantly lower for patients in stage III compared to patients in stages I (log rank; p=0.002) and II (log rank; p=0.031). However, no differences in the probability of overall survival were identified between patients in stage I and II (log rank; p=0.679). Conclusions In this cohort of ATTR-CA patients treated with tafamidis, the NAC staging system effectively identified patients at elevated risk of mortality. However, the distinction in survival rates between low- and intermediate-risk groups was less pronounced, suggesting that tafamidis may have a stronger prognostic impact in these groups.Overall Survival across NAC Stages

Risk-Aware Lane Change and Trajectory Planning for Connected Autonomous Vehicles Based on a Potential Field Model

To enhance the safety of lane changes for connected autonomous vehicles in an intelligent transportation environment, this study draws from potential field theory to analyze variations in the risks that vehicles face under different traffic conditions. The safe minimum vehicle distance is dynamically adjusted, and a comprehensive vehicle risk potential field model is developed. This model systematically quantifies the risks encountered by connected autonomous vehicles during the driving process, providing a more accurate assessment of safety conditions. Subsequently, vehicle motion is decoupled into lateral and longitudinal components within the Frenet coordinate system, with quintic polynomials employed to generate clusters of potential trajectories. To improve computational efficiency, trajectory evaluation metrics are developed based on vehicle dynamics, incorporating factors such as acceleration, jerk, and curvature. An initial filtering process is applied to these trajectories, yielding a refined set of candidates. These candidate trajectories are further assessed using a minimum safety distance model derived from potential field theory, with optimization focusing on safety, comfort, and efficiency. The algorithm is tested in a three-lane curved simulation environment that includes both constant-speed and variable-speed lane change scenarios. Results show that the collision risk between the target vehicle and surrounding vehicles remains below the minimum safety distance threshold throughout the lane change process, ensuring a high level of safety. Furthermore, across various driving conditions, the target vehicle’s acceleration, jerk, and trajectory curvature remained well within acceptable limits, demonstrating that the proposed lane change trajectory planning algorithm successfully balances safety, comfort, and smoothness, even in complex traffic environments.

Trajectory of mean platelet volume changes after aneurysmal subarachnoid hemorrhage in patients with or without delayed cerebral ischemia

The morbidity of aneurysmal subarachnoid hemorrhage (aSAH) remains high, particularly because of secondary cerebral lesions that significantly aggravate the primary lesions. The main type of secondary lesions is delayed cerebral ischemia (DCI), in which platelets (PLT) appear to play a key role. Mean platelet volume (MPV) is an indirect marker of platelet activation. We aimed to determine the individual trajectories of MPV over time in patients with and without DCI during the course of aSAH. This is a single-center, retrospective, longitudinal analysis of individual trajectories of MPV over time, in a cohort of aSAH patients included in the Prospective, Observational Registry of Patient with Subarachnoid Hemorrhage in Neurocritical Care Unit (ProReSHA). A mixed-effects linear regression model was used to compare the trajectories of MPV and MPV/PLT ratio between patients who developed a DCI and those who did not. A total of 3634 MPV values were collected in 587 patients. The analysis of MPV as a function of DCI occurrence showed a significant difference in the trajectory over time between patients with DCI and those without, with an estimate of 0.02 (95%CI 0.01, 0.04, p = 0.009). The analysis of the MPV/PLT ratio as a function of DCI occurrence and other covariates showed a significant difference in the trajectory over time only for patients with a modified Fisher score less than 3, with an estimate of -0.59 (95%CI: -0.94, -0.23, p = 0.001). The individual trajectories of MPV over time differ between patients with DCI and those without. However, MPV values vary greatly over time and between patients. Thus it does not appear as a reliable biomarker for stratifying patients based on their specific risk of developing DCI. ClinicalTrials.gov identifier: (NCT02890004), registered in August 2016.

Association between healthy lifestyle on life course and multimorbidity in adults: results from two national prospective cohort studies

ObjectivesTo examine the correlation between healthy lifestyle patterns, their change trajectories, and the risk of multimorbidity in adults.MethodsBased on two representative national cohorts, the English Longitudinal Study of Aging (ELSA) and the Health and Retirement Study (HRS) including adults aged 50 years and over. We employed Cox regression, lifestyle change trajectories, and restricted mean survival times to explore the relationship between lifestyle (assessed by SCORE2, LE’8, and HLS scores) and multimorbidity. We also conducted mediation analysis to investigate the underlying mechanisms.ResultsA healthy lifestyle (higher LE’8, higher HLS, or lower SCORE2) can reduce the risk of multimorbidity. 2-10% lower multimorbidity risk per one-point increase in LE’8 and HLS. The hazard ratio of multimorbidity for improvements in unhealthy lifestyles or deterioration in healthy lifestyles compared to always healthy lifestyles ranged from 1.598 to 5.602. Besides, for LE’8 and HLS, participants with higher scores had a slower decrease in survival probability in ELSA. Triglyceride, C-reaction protein, fibrinogen, and cystatin C partly mediate the association between lifestyle and multimorbidity.ConclusionsKeeping a healthy lifestyle over time can help reduce the risk of multimorbidity.

An Improved RRT Path-Planning Algorithm Based on Vehicle Lane-Change Trajectory Data

The Rapidly-exploring Random Tree (RRT) algorithm faces issues in path planning, including low search efficiency, high randomness, and suboptimal path quality. To overcome these issues, this paper proposes an improved RRT planning algorithm based on vehicle lane change trajectory data. This algorithm dynamically adjusts the sampling area based on road environment and trajectory change laws, allowing the random tree to sample within an effective area, thereby improving the algorithm’s sampling efficiency. After determining the sampling area, a sampling point optimization strategy is used to enhance sampling quality, resulting in a smoother and more executable path. Finally, the vehicle is processed in a standardized manner to further improve path safety. Simulation results indicate that, compared to the original RRT algorithm, the improved version reduces nodes, planning time, and path length by 12.77%, 64.79%, and 12.87%, respectively. It also improves path smoothness and more closely aligns with actual lane-change trajectories, demonstrating the effectiveness and executability of the improved algorithm.

Handover of intensive care patients

An effective patient handover is a core element of high-quality patient care. Communication during patient handover in the intensive care unit is particularly challenging due to the clinical complexity and rapid changes in patient trajectories, complex interdisciplinary and interprofessional interfaces, linguistic barriers, situational and structural disruptive factors, personnel stress factors as well as the communication and error culture of the teams. In addition to avoiding disruptive factors and creating optimal communication conditions and human resources, the use of standardized and structured handovers with the help of protocols and checklists, as recommended in the literature, plays a decisive role as part of a bundle of measures for effective and safe patient care.

Timing of Alzheimer's disease biomarker progressions: A two-decade observational study from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Alzheimer's Disease Neuroimaging Initiative (ADNI) has been pivotal in identifying and refining Alzheimer's disease (AD) biomarkers for clinical trials. This study leverages longitudinal data from participants who have progressed to amyloid-positivity during their study participation to track evolution of biomarkers and cognitive function. We modeled AD biomarker (positron emission tomography [PET], structural, cerebrospinal fluid [CSF], cognition) trajectories before and after observed amyloid-positivity onset time to detect time at which each biomarker had detectable trajectory changes. Analysis of a sub-cohort of the 20-year ADNI study (N=90) recapitulated Alzheimer's progression beginning with amyloid alterations -4.8 to -5.3 years relative to amyloid-positivity, succeeded by neurodegeneration (t=-4.0 to -4.1 years), and CSF tau (t=-0.4 to -0.5 years). Cognitive decline was observed to significantly correspond with emergence of amyloid-positivity (t=0.2 to 2.4 years). Our results corroborate temporal progression curves of AD biomarkers, providing insights on earliest detectable changes in objective and subjective cognitive function assessments.

Trajectories and drivers signalling the end of agricultural abandonment in Trás-os-Montes, Portugal

Agricultural abandonment has given rise to novel landscape dynamics worldwide. This paper investigates abandonment and post-abandonment dynamics in continental Portugal as a hotspot of landscape change. We mapped the spatial patterns and drivers of recent (1995–2018) land use changes in a remote mountainous region as post-abandonment trajectories, based on detailed land use/land cover data made available by the Portuguese government. We showed that ‘Revegetation’ trajectories, indicative of agricultural abandonment, were still widespread between 1995 and 2007. However, between 2007 and 2018, the landscape was much more stable with ‘Return to agriculture’ as the dominant change trajectory. To understand what drives landscape changes after abandonment, we explored the influence of a wide range of potential biogeophysical and socio-economic drivers on the observed trajectories. We contrasted different landscape outcomes in binary logistic regression models with the potential underlying drivers as independent variables. The regressions revealed that the most significant determinants of these alternating dynamics are existing land use, climate, slope, protection regime and accessibility. The results of the regressions are at times counterintuitive and give important indications of the changing spatio-temporal scales at which these variables exert influence on the landscape outcomes. However, the regression models’ limited accuracies highlight the need for deeper investigation of the socio-economic and historic context of the observed changes. Improved understanding of the (drivers of) alternative dynamics following agricultural abandonment can help inform policy decisions regarding agriculture and cultural landscape preservation.

BEFORE THE ORIGIN OF CLINICAL «MASKS» OF THORACIC AND PELVIC TRAUMA OF SHOOTING ORIGIN

Abstract. The imperialist war waged by rusism in Ukraine is aimed at the destruction of the state and its people. Consequently, it (rusism) does not hesitate to act on the front lines and towards the civilian population in the rear. To achieve this goal, invaders employ not only powerful weaponry but also those prohibited by the international community (bullets with displaced center of gravity, etc.). Relevance of the problem. Due to this, predicting the course of injuries in such conditions is impossible, and each observation is of paramount importance, defining the relevance of the issue. Objective. To share the features of diagnosing certain gunshot injuries and the origin of the brachial plexus syndrome. Materials and Methods. Two civilian trauma cases with particular and fortunate outcomes were under our observation. In the diagnostic process, besides standard methods, all radiological ones were applied, aiding in understanding the peculiarities of the injuries. Results. In the thoracic patient, due to the change in bullet trajectory, it passed subcutaneously from the right lumbar region into the left pleural cavity, damaging the diaphragm and lung. In the «urological» patient, a bullet from the gluteal region lodged subcutaneously beneath the pubic bone. Conclusions. Damage to the diaphragmatic nerve in various areas can lead to manifestations of traumatic brachial plexus syndrome in conditions of warfare.

Association between blood glucose level trajectories and 30-day mortality risk in patients with acute ischemic stroke: analysis of the MIMIC database 2001–2019

BackgroundHyperglycemia is one of the most common comorbidities in patients with acute ischemic stroke (AIS). This study aimed to assess the impact of short-term longitudinal blood glucose level change trajectories on the 30-day mortality risk in patients with AIS.MethodsData for AIS patients were obtained from the 2001–2019 Medical Information Mart for Intensive Care (MIMIC) database. The latent growth mixture modeling (LGMM) was utilized to classify a patient’s blood glucose level trajectory within 24 h of admission. Cox regression analyses were applied to examine the relationship between blood glucose levels at admission and blood glucose level trajectories and the risk of 30-day mortality in patients with AIS.ResultsA total of 2,432 patients with AIS were included in this retrospective cohort study, with 30-day mortality occurring in 574 (23.60%) patients. The median glucose levels of all patients were 136.00 (110.00, 178.00) mg/dL. Four blood glucose level trajectories were identified: low level-stable trend (type 1), moderate level-stable trend (type 2), high level-decreasing-increasing trend (type 3), and moderate level-increasing-decreasing trend (type 4). Type 2 blood glucose level trajectory was associated with an increased risk of 30-day mortality compared with type 1 blood glucose level trajectory [hazard ratio (HR) = 1.28, 95% confidence interval (CI): 1.03–1.59), but there were no significant associations between type 3 (HR = 1.16, 95%CI: 0.77–1.74) and type 4 (HR = 1.44, 95%CI: 0.84–2.45) trajectories and 30-day mortality risk. Subgroup analysis demonstrated that the association between type 2 trajectory and 30-day mortality risk was observed in patients aged ≥ 65 years (HR = 1.37, 95%CI: 1.05–1.79), female (HR = 1.42, 95%CI: 1.05–1.94), with (HR = 1.44, 95%CI: 1.02–2.02) or without (HR = 1.42, 95%CI: 1.01–1.99) diabetes, and not using insulin (HR = 2.80, 95%CI: 1.43–5.49).ConclusionAIS patients with consistently high blood glucose levels within 24 h of admission increased the risk of 30-day mortality.

Quantifying the degree of scientific innovation breakthrough: Considering knowledge trajectory change and impact

Scientific breakthroughs have the potential to reshape the trajectory of knowledge flow and significantly impact later research. The aim of this study is to introduce the Degree of Innovation Breakthrough (DIB) metric to more accurately quantify the extent of scientific breakthroughs. The DIB metric takes into account changes in the trajectory of knowledge flow, as well as the deep and width of impact, and it modifies the traditional assumption of equal citation contributions by assigning weighted citation counts. The effectiveness of the DIB metric is assessed using ROC curves and AUC metrics, demonstrating its ability to differentiate between high and low scientific breakthroughs with high sensitivity and minimal false positives. Based on ROC curves, this study proposes a method to calculate the threshold for high scientific breakthrough, reducing subjectivity. The effectiveness of the proposed method is demonstrated through a dataset consisting of 1108 award-winning computer science papers and 9832 matched control papers, showing that the DIB metric surpasses single-dimensional metrics. The study also performs a granular analysis of the innovation breakthrough degree of non-award-winning papers, categorizing them into four types based on originality and impact through 2D histogram visualization, and suggests tailored management strategies. Through the adoption of this refined classification strategy, the management of innovation practices can be optimized, ultimately fostering the enhancement of innovative research outcomes. The quantitative tools introduced in this paper offer guidance for researchers in the fields of science intelligence mining and science trend prediction.

Land struggles, livelihoods, and trajectories of agrarian change in the Polochic valley, Guatemala

ABSTRACT This article assesses the impact of political contestation on trajectories of agrarian change in the Polochic valley, Guatemala. Livelihood surveys were carried out in four Indigenous Maya-Q’eqchi’ agrarian communities, two of which obtained access to land as a direct outcome of grassroots resistance to land grabbing. By situating livelihood strategies within the broader political economy, I argue that political contestation has inadvertently reproduced a functionally dualist agrarian structure that sustains the agroexport plantation economy while leaving little potential for expanded reproduction among campesino households. Agrarian social movements must look beyond land access alone to achieve the transformative change they seek.

Developmental trajectories and heterogeneity of social engagement among Chinese older adults: a growth mixture model

BackgroundSocial engagement is closely related to well-being among older adults. However, studies on the changing trajectory and influencing factors (especially time-varying factors) of social engagement are limited. This study aimed to examine the social engagement trajectory of older Chinese adults and explore its time-fixed and time-varying factors, thus providing evidence for the development of strategies to promote a rational implementation for healthy aging.MethodsThis study included 2,195 participants from a subset of four surveys from the Chinese Longitudinal Healthy Longevity Survey conducted from 2008 to 2018 (with the latest survey completed in 2018), with follow-ups conducted approximately every three years. Growth mixture modeling was used to explore the social engagement trajectory of older adults and the effects of time-varying variables. In addition, multinomial logistic regression was employed to analyze the association between time-fixed variables and latent classes.ResultsThree distinct trajectories of social engagement among older adults in China were identified: slow declining (n = 204; 9.3%), which meant social engagement score decreased continuously, but social engagement level improved; slow rising (n = 1,039; 47.3%), marked by an increased score of social engagement, but with an depressed engagement level; and middle stabilizing (n = 952; 43.4%), which meant social engagement score and engagement level remained quite stable. A time-fixed analysis indicated that age, marital status, educational level, and annual family income had a significant impact on social engagement (P < 0.05). In contrast, the time-varying analysis showed that a decline in functional ability, insufficient exercise (means no exercise at present), deteriorating self-reported health and quality of life, negative mood, monotonous diet, and reduced community services were closely related to the reduction in social engagement levels (P < 0.05).ConclusionThree trends were observed at the social engagement level. Older adults with initially high levels of social engagement exhibited a continuous upward trend, whereas those with initially low levels experienced a decline in their social engagement, and those with initially intermediate levels remained quite stable. Considering the primary heterogeneous factors, it is imperative for governments to enhance basic services and prioritize the well-being of older adults. Additionally, families should diligently monitor the emotional well-being of older adults and make appropriate arrangements for meals.

Aging-dependent loss of functional connectivity in a mouse model of Alzheimer's disease and reversal by mGluR5 modulator.

Amyloid accumulation in Alzheimer's disease (AD) is associated with synaptic damage and altered connectivity in brain networks. While measures of amyloid accumulation and biochemical changes in mouse models have utility for translational studies of certain therapeutics, preclinical analysis of altered brain connectivity using clinically relevant fMRI measures has not been well developed for agents intended to improve neural networks. Here, we conduct a longitudinal study in a double knock-in mouse model for AD (AppNL-G-F/hMapt), monitoring brain connectivity by means of resting-state fMRI. While the 4-month-old AD mice are indistinguishable from wild-type controls (WT), decreased connectivity in the default-mode network is significant for the AD mice relative to WT mice by 6 months of age and is pronounced by 9 months of age. In a second cohort of 20-month-old mice with persistent functional connectivity deficits for AD relative to WT, we assess the impact of two-months of oral treatment with a silent allosteric modulator of mGluR5 (BMS-984923/ALX001) known to rescue synaptic density. Functional connectivity deficits in the aged AD mice are reversed by the mGluR5-directed treatment. The longitudinal application of fMRI has enabled us to define the preclinical time trajectory of AD-related changes in functional connectivity, and to demonstrate a translatable metric for monitoring disease emergence, progression, and response to synapse-rescuing treatment.