Cross-sectional Dataset Research Articles

Differential cross–section measurements for the proton-induced alpha and deuteron reactions and elastic scattering on 9Be, suitable for Ion beam analysis applications

In the current work the differential cross sections for the proton-induced 9Be(p,d0)8Be and 9Be(p,α0)6Li nuclear reactions (Q-values = 559 and 2125 keV respectively), as well as, the 9Be(p,p0)9Be elastic scattering were measured for 6 backscattering detection angles (θ = 120°, 130°, 140°, 150°, 160° and 170°) covering the Ep,lab = 300–3000 keV energy range with an energy step between 10 and 50 keV. The total estimated uncertainties of the determined cross-section values did not exceed 6.4 % for the elastic scattering, 7.0 % for the 9Be(p,α0)6Li and 9.9 % for the 9Be(p,d0)8Be reaction. Benchmarking measurements were also performed with a suitable thick target in order to validate the obtained cross-section values. The present experimental results constitute three coherent differential cross-section datasets suitable for IBA applications. The measurements were performed at the 4 MV Tandem Dynamitron Laboratory of the Ruhr University Bochum in Germany. The experimental, data analysis and benchmarking procedures are presented in detail.

Shrinking Desert Channel Response to Increasing Human Interferences and Changing Natural Factors in the Upper Yellow River

Many rivers are tightly coupled and intersected with aeolian sand dunes, whose geomorphological evolution involves not only fluvial processes but also aeolian processes that pose a new challenge to fluvial geomorphological studies. However, due to few field studies, our overall understanding of the desert channel geomorphic process is limited. In this paper, we present an outstanding example of desert river channel evolution regulated by aeolian–fluvial interactions in the Ulan Buh Desert of the Yellow River, based on a long time series data set (1966–2019) of channel cross-sections. The results indicate that the lateral addition of aeolian sand, the water–sediment relationship and human interference have a significant role at different periods of channel evolution. Before 1986, higher discharge, lower sediment content and greater intensity of aeolian activity caused aeolian–fluvial interactions and a relative scouring and silting balance in the channel, with little human activity. From 1986 to 2000, an increase in large reservoir operation, vegetation coverage and floodplain farming, coupled with water–sediment relationship variation, caused rapid deposition and shrinkage of the river channel. From 2000 to 2014, the channel kept a slight scouring state. With Haibowan reservoir operation beginning in 2014, the talweg experienced rapid scouring and undercut rebound. However, an expanding and stable floodplain accelerated sedimentation on the floodplain and weakened river lateral erosion, indicating that the channel has shown a shrinkage trend. Meanwhile, wavelet analysis results indicate that human interferences and aeolian activities have no significant role in the periodical characteristics of the channel’s longitudinal erosion and deposition. Therefore, on the whole, increasing human interferences and decreasing wind dynamics have driven this desert wandering channel to be stable, and to gradually form a new balance between erosion and sedimentation.

Prevalence and determinants of adequate postnatal care in Ethiopia: evidence from 2019 Ethiopia mini demographic and health survey

BackgroundThe postpartum period is critical for both the mother's and newborn child's health and survival. Rising morbidity and mortality are usually the consequence of absence of adequate, suitable, or timely care during that time period. There is lack of information on the adequacy of postnatal care in Ethiopia and this study was aimed to investigate adequacy of postnatal care and its determinants in the study area.MethodsIn this study, we used a cross-sectional dataset from the 2019 Ethiopia Mini Demographic and Health Survey. A multistage stratified clustered design applied and survey weights were used to take into account the complicated sample design. A multilevel mixed effects logistic regression was fitted on 3772 women who were nested within 305 clusters. The fixed effect models were fitted and expressed as adjusted odds ratios with 95% confidence intervals, while intra-class correlation coefficients, median odds ratio, and proportional change in variance explained measures of variation. As model fitness criteria, the deviance information criterion and the Akaike information criterion were used.ResultsThis study found that only 563(16.14%, 95% CI: 16.05–16.24) women had adequate post natal care. Age of between 25–35 years old (AOR = 1.55, 95%CI = 1.04–2.31), secondary level of education (AOR = 2.23, 95%CI = 1.43–3.45), Having parity of between two and four had (AOR = 0.62, 95%CI = 0.42 0.93), having ANC follow up four and above (AOR = 1.74, 95%CI = 1.31–2.33), being residents of Oromia region (AOR = 0.10, 95CI = 0.02- 0.43) were strong predictors of adequate postnatal care.ConclusionThe study found that prevalence of adequate PNC in Ethiopia was significantly low. To increase postnatal care adequacy, it was recommended to reinforce existing policies and strategies such as increasing number of antenatal care follow up, and scheduling mothers based on the national postnatal care follow-up protocol.

Comparing network structures on three aspects: A permutation test.

Network approaches to psychometric constructs, in which constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure that pertains to a construct, to a more comparative stance, in which the goal is to compare network structures across populations. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT), which uses resampling-based permutation testing to compare network structures from two independent, cross-sectional data sets on invariance of (a) network structure, (b) edge (connection) strength, and (c) global strength. Performance of NCT is evaluated in simulations that show NCT to perform well in various circumstances for all three tests: The Type I error rate is close to the nominal significance level, and power proves sufficiently high if sample size and difference between networks are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Informing Patient-Provider Engagement for Shared Decision Making Through Mobile Health Applications.

The study utilized a cross-sectional dataset to identify demographic and health factors associated with patient utilization of mHealth applications for engaging with healthcare providers. The focus was on adults with chronic health conditions as the primary app user group. The goal was to reveal specific barriers and facilitators to app adoption among smartphone users, with the aim of highlighting opportunities for upgrades that promote patient empowerment as a prerequisite for shared decision-making (SDM). Data from the Health Information National Trends Survey (HINTS 5, Cycle 4, 2020) with 3865 respondents (≥18 years old) stratified analyses and weighted logistic regression were used. The study found that individuals having a wellness app on a smartphone increased the likelihood (OR 2.68, CI: 2.02-3.56, p-value < 0.0001) of discussing health conditions with providers. Furthermore, individuals with multiple chronic health conditions were more likely (OR 1.93, CI 1.26-2.95, p-value < 0.01) to use apps to use mobile health applications to engage with healthcare providers. Other significant variables affecting app usage such as race, marital status, and educational level. Due to difficulties obtaining in-person healthcare, the COVID-19 epidemic forced a swift deployment of mHealth technologies. Even in the absence of a crisis, mobile health applications continue to be crucial for improving patient-provider engagement and developing novel approaches to healthcare delivery. During the pandemic, people with numerous chronic diseases used apps to stay in touch with doctors and maintain their reliance on these platforms. Nonetheless, different smartphone users continue to use mHealth application in different ways. The findings revealing barriers in mHealth app adoption among certain patient subgroups suggest opportunities for developers, in collaboration with users and providers, to enhance inclusion and acceptability when upgrading mHealth application platforms.

Incidence of Human and Free-Ranging Wild Rodent Infections with Leishmania (Viannia) braziliensis, Aetiological Agent of Cutaneous Leishmaniasis.

Human and wild rodent infection rates with Leishmania (Viannia) braziliensis are needed to differentiate transmission pathways in anthropogenically altered habitats. Human participants in northeast Brazil were tested by the leishmanin skin test (LST) and inspected for lesions/scars characteristic of American clinical leishmaniasis (ACL). Molecular (PCR/qPCR) test records of free-ranging rodents were available from a concurrent capture-mark-recapture study. Force of Infection (λ) and recovery (ρ) rates were estimated from cross-sectional and longitudinal datasets. Cumulative prevalences of human LST+ves and ACL scar+ves were 0.343-0.563 (n = 503 participants) and 0.122-0.475 (n = 503), respectively. Active ACL lesions were not detected. Annual rates of LST conversions were λ = 0.03-0.15 and ρ = 0.02-0.07. The probability of infection was independent of sex and associated with increasing age in addition to the period of exposure. Rodents (n = 596 individuals of 6 species) showed high rates of exclusively asymptomatic infection (λ = 0.222/month) and potential infectiousness to the sand fly vector. Spatially concurrent rodent and household human infection prevalences were correlated. Human exposure to L. (V.) braziliensis continues to be high despite the substantial drop in reported ACL cases in recent years. Spill-over transmission risk to humans from rodents in peridomestic habitats is likely supported by a rodent infection/transmission corridor linking houses, plantations, and the Atlantic Forest.

Cortical gray to white matter signal intensity ratio as a sign of neurodegeneration and cognition independent of β-amyloid in dementia.

Cortical gray to white matter signal intensity ratio (GWR) measured from T1-weighted magnetic resonance (MR) images was associated with neurodegeneration and dementia. We characterized topological patterns of GWR during AD pathogenesis and investigated its association with cognitive decline. The study included a cross-sectional dataset and a longitudinal dataset. The cross-sectional dataset included 60 cognitively healthy controls, 61 mild cognitive impairment (MCI), and 63 patients with dementia. The longitudinal dataset included 26 participants who progressed from cognitively normal to dementia and 26 controls that remained cognitively normal. GWR was compared across the cross-sectional groups, adjusted for amyloid PET. The correlation between GWR and cognition performance was also evaluated. The longitudinal dataset was used to investigate GWR alteration during the AD pathogenesis. Dementia with β-amyloid deposition group exhibited the largest area of increased GWR, followed by MCI with β-amyloid deposition, MCI without β-amyloid deposition, and controls. The spatial pattern of GWR-increased regions was not influenced by β-amyloid deposits. Correlation between regional GWR alteration and cognitive decline was only detected among individuals with β-amyloid deposition. GWR showed positive correlation with tau PET in the left supramarginal, lateral occipital gyrus, and right middle frontal cortex. The longitudinal study showed that GWR increased around the fusiform, inferior/superior temporal lobe, and entorhinal cortex in MCI and progressed to larger cortical regions after progression to AD. The spatial pattern of GWR-increased regions was independent of β-amyloid deposits but overlapped with tauopathy. The GWR can serve as a promising biomarker of neurodegeneration in AD.

Data-driven cranial suture growth model enables predicting phenotypes of craniosynostosis

We present the first data-driven pediatric model that explains cranial sutural growth in the pediatric population. We segmented the cranial bones in the neurocranium from the cross-sectional CT images of 2068 normative subjects (age 0–10 years), and we used a 2D manifold-based cranial representation to establish local anatomical correspondences between subjects guided by the location of the cranial sutures. We designed a diffeomorphic spatiotemporal model of cranial bone development as a function of local sutural growth rates, and we inferred its parameters statistically from our cross-sectional dataset. We used the constructed model to predict growth for 51 independent normative patients who had longitudinal images. Moreover, we used our model to simulate the phenotypes of single suture craniosynostosis, which we compared to the observations from 212 patients. We also evaluated the accuracy predicting personalized cranial growth for 10 patients with craniosynostosis who had pre-surgical longitudinal images. Unlike existing statistical and simulation methods, our model was inferred from real image observations, explains cranial bone expansion and displacement as a consequence of sutural growth and it can simulate craniosynostosis. This pediatric cranial suture growth model constitutes a necessary tool to study abnormal development in the presence of cranial suture pathology.

Credit Access and the Firm–Government Connection: Is There Any Link?

Access to credit for businesses is an unresolved issue, especially in developing countries and transition economies. There has been a lot of research exploring factors affecting firms’ credit accessibility. Particularly, factors related to borrowers and lenders are always placed under consideration. However, besides those factors, institutional elements could also play an important role in guiding companies’ operations. In countries where the economy lacks transparency and low-level development is limited, informal institutional factors can have potential impacts. In this paper, we focus on exploring the relationship between firm–government links and credit access, thereby offering managerial implications through utilizing cross-sectional data sets at the firm level, with an initial sample of 26,849 observations from 38 countries at different levels of development around the world. The results show a positive correlation of firm–government connection with credit access. Moreover, this relationship may vary depending on the market in which the business primarily operates. Specifically, firms working internationally are less influenced by links with governments and tend to rely more on their own characteristics and conditions.

Know, comply, and live: the implications of protocols for COVID-19 management in sub-Saharan Africa

The aim of this study is to examine the factors influencing compliance with COVID-19 preventive management strategies in sub-Saharan Africa (SSA). To address the lack of national data associated with this study, a comprehensive cross-sectional panel dataset by GeoPoll, encompassing 12 SSA countries, is used. The study employs a novel index and a cross-sectional pooled logit econometric regression with predicted margins to identify the determinants of behavioural responses to COVID-19 management strategies. The findings reveal that demographic factors, institutional measures, and country-specific characteristics significantly impact compliance with COVID-19 management strategies in SSA. Additionally, a gender analysis of disaggregated COVID-19 pandemic management protocols revealed that females exhibit different compliance behaviours compared to males, specifically in relation to public transport, hygiene practices, handwashing, and social distancing. However, no significant gender differences are observed in behaviours such as avoiding public places and working from home. Surprisingly, the composite index, which measures overall compliance with all protocols, does not demonstrate any gender differences. Based on the findings, policies aimed at enhancing compliance should consider demographic, institutional, and country-specific factors while also accounting for gender differences in specific compliance behaviours.

Theoretical cross sections for electron collisions relevant for ammonia discharges part 1: NH3, NH2, and NH

Besides being the worlds’ most important fertilizer precursor, ammonia could play an important role as hydrogen carrier in a decarbonized future. The efficient production and decomposition (or cracking) of ammonia are essential to this end. An electricity-driven technology of interest for both these processes are non-thermal plasmas. Plasma processes have the advantage of activating—even inert—molecules and initiating chemical reactions through electron collisions, rather than through conventional heating. However, a complete set of low-energy cross section data is not available for the electron collisions with ammonia (NH3) and its radicals, amidogen (NH2) and imidogen (NH). Here, we used the ab initio R-matrix method to determine theoretical cross sections for the low-energy electron collision processes with NH3, NH2, and NH. Additionally, we explored the contribution of the different processes towards dissociation (especially from electronic excited states). Where possible, we compared our theoretical cross section data with experimental data and/or previous recommendations. Lastly, our own recommended cross section data for the electron collisions are presented. Use of this complete set of electron collision data should contribute to a more accurate description of and better insights into the plasma-chemical kinetics behind plasma-assisted ammonia production and decomposition processes.

Factors Affecting the Length of Stay in the Intensive Care Unit among Adults in Saudi Arabia: A Cross-Sectional Study.

This study aimed to assess patient-related factors associated with the LOS among adults admitted to the ICU in Saudi Arabia. The Ministry of Health provided a cross-sectional dataset for 2021, which served as the data source for this study. The data included data on adults admitted to different ICUs at various hospitals. The number of days spent in the ICU was the outcome variable of interest. The potential predictors were age, sex, and nationality, as well as clinical data from the time of admission. Descriptive statistics and bivariate analysis were used to analyse the association between the predictors and the ICU LOS and characterize how they were distributed. We used negative binomial regression to examine the relationship between the study predictors and the ICU LOS. A total of 42,884 individuals were included in this study, of whom 25,520 were men and 17,362 were women. The overall median ICU LOS was three days. This study showed that the ICU LOS was highly influenced by the patient's age, sex, nationality, source of admission, and clinical history. Several predictors that affect how long adults stay in the ICU in Saudi Arabian hospitals were identified in this study. These factors can be attributed to variances in health care delivery systems, patient demographics, and cultural considerations. To allocate resources efficiently, enhance patient outcomes, and create focused treatments to reduce ICU LOS, it is essential to comprehend these elements.

Impact of Uncertainty Reduction on Lead-Bismuth Coolant in Accelerator-Driven System Using Sample Reactivity Experiments

In this study, we have demonstrated that data assimilation (DA) using lead and bismuth sample reactivities measured in the Kyoto University Critical Assembly A-core can successfully reduce the uncertainty of the coolant void reactivity in accelerator-driven systems (ADSs) derived from inelastic scattering cross sections of lead and bismuth. We reevaluated and highlighted the experimental uncertainties and correlations of the sample reactivities for the DA formula. We used the MCNP6.2 code to evaluate the sample reactivities and their uncertainties and performed DA using the reactor analysis code system MARBLE. The high-sensitivity coefficients of the sample reactivities to lead and bismuth allowed us to reduce the cross-section–induced uncertainty of the void reactivity of the ADS from 6.3% to 4.8%, achieving a provisional target accuracy of 5% in this study. Furthermore, we demonstrated that the uncertainties arising from other dominant factors, such as minor actinides and steel, can be effectively reduced by using integral experimental data sets for the unified cross-section dataset ADJ2017.

Change over time in migrantś self-assessed unmet health care need in Finland

Abstract Background Previous studies have shown that people of foreign background (PFB) often experience barriers in accessing and utilizing medical care services. This study examines the potential change in unmet health care need among PFB living in Finland in two time points: 2018 and 2022. Furthermore, we study the potential factors (age, sex, education, treason for migration and country of origin) associated with the unmet health care need. Methods We used two nationally representative cross-sectional data sets: the Survey on well-being among foreign-born population (FinMonik, n = 6836; response rate 53.1%) in 2018-2019 and the National survey on health, well-being and service use among foreign-born population (MoniSuomi, n = 7838; response rate 44.1%) in 2022-2023. The analyses were limited to those aged 20-64-years. Our indicator for unmet health care need was based on the question: “Do you feel that you have adequately received primary care services during the past 12 months?'' Results Unmet health care need among PFB has increased significantly between the years 2018 and 2022 (p = 0.005). The prevalence of unmet health care need among PFB was 24.9% in 2018 and 33.7% in 2022. Preliminary results show that younger age, those who moved to Finland from Middle East and Africa, refugees and Ingrains Finns reported more frequently unmet health care need. Conclusions The increase of unmet health care need among PFB is alarming. More studies are needed to investigate the reasons behind the high rates of unmet health care need. Key messages • Unmet health care need is an indicator of problems in access to care. • Actions are needed to improve access to care among the migrant population in Finland.

The role of rational decisions in technical inefficiency analysis of Spanish pig farms: The influence of water use management

There has been limited research on the degree to which rational inefficiency arises when environmental factors are considered. Thus, our goal is to contribute to the literature by applying the concept of rational inefficiency to pig farming and investigating the relationship between water use and farm technical efficiency levels. This research aims to provide a better understanding of the complex interactions between pig farming production, the natural environment, and economic performance. The stochastic frontier analysis is applied to a cross-section dataset of 523 Catalan farms specialized in pig production in 2018. The main findings of this study are as follows: First, Catalan pig farming exhibits high levels of technical efficiency with an average score of 0.94, which is consistent with the high intensity and concentration of Spanish pork production. Second, there is no compelling evidence to support the rational inefficiency hypothesis, particularly when comparing the rational efficient and multi-efficient groups.

Baseline treatments and metabolic control of 288,913 type 2 diabetes patients in a 10-year retrospective cohort in Malaysia

Diabetes is one of the quickest-growing global health emergencies of the twenty-first century, and data-driven care can improve the quality of diabetes management. We aimed to describe the formation of a 10-year retrospective open cohort of type 2 diabetes patients in Malaysia. We also described the baseline treatment profiles and HbA1c, blood pressure, and lipid control to assess the quality of diabetes care. We used 10 years of cross-sectional audit datasets from the National Diabetes Registry and merged 288,913 patients with the same identifying information into a 10-year open cohort dataset. Treatment targets for HbA1c, blood pressure, LDL-cholesterol, HDL-cholesterol, and triglycerides were based on Malaysian clinical practice guidelines. IBM SPSS Statistics version 23.0 was used, and frequencies and percentages with 95% confidence intervals were reported. In total, 288,913 patients were included, with 62.3% women and 54.1% younger adults. The commonest diabetes treatment modality was oral hypoglycaemic agents (75.9%). Meanwhile, 19.3% of patients had ≥ 3 antihypertensive agents, and 71.2% were on lipid-lowering drugs. Metformin (86.1%), angiotensin-converting enzyme inhibitors (49.6%), and statins (69.2%) were the most prescribed antidiabetic, antihypertensive, and lipid-lowering medications, respectively. The mean HbA1c was 7.96 ± 2.11, and 31.2% had HbA1c > 8.5%. Only 35.8% and 35.2% attained blood pressure < 140/80 mmHg and LDL-cholesterol < 2.6 mmol/L, respectively. About 57.5% and 52.9% achieved their respective triglyceride and HDL-cholesterol goals. In conclusion, data integration is a feasible method in this diabetes registry. HbA1c, blood pressure, and lipids are not optimally controlled, and these findings can be capitalized as a guideline by clinicians, programme managers, and health policymakers to improve the quality of diabetes care and prevent long-term complications in Malaysia.

Chronic pain, cannabis legalisation, and cannabis use disorder among patients in the US Veterans Health Administration system, 2005 to 2019: a repeated, cross-sectional study

Cannabis use disorder is associated with considerable comorbidity and impairment in functioning, and prevalence is increasing among adults with chronic pain. We aimed to assess the effect of introduction of medical cannabis laws (MCL) and recreational cannabis laws (RCL) on the increase in cannabis use disorder among patients in the US Veterans Health Administration (VHA). Data from patients with one or more primary care, emergency, or mental health visit to the VHA in 2005-19 were analysed using 15 repeated cross-sectional VHA electronic health record datasets (ie, one dataset per year). Patients in hospice or palliative care were excluded. Patients were stratified as having chronic pain or not using an American Pain Society taxonomy of painful medical conditions. We used staggered-adoption difference-in-difference analyses to estimate the role of MCL and RCL enactment in the increases in prevalence of diagnosed cannabis use disorder and associations with presence of chronic pain, accounting for the year that state laws were enacted. We did this by fitting a linear binomial regression model stratified by pain, with time-varying cannabis law status, fixed effects for state, categorical year, time-varying state-level sociodemographic covariates, and patient covariates (age group [18-34 years, 35-64 years, and 65-75 years], sex, and race and ethnicity). Between 2005 and 2019, 3 234 382-4 579 994 patients were included per year. Among patients without pain in 2005, 5·1% were female, mean age was 58·3 (SD 12·6) years, and 75·7%, 15·6%, and 3·6% were White, Black, and Hispanic or Latino, respectively. In 2019, 9·3% were female, mean age was 56·7 (SD 15·2) years, and 68·1%, 18·2%, and 6·5% were White, Black, and Hispanic or Latino, respectively. Among patients with pain in 2005, 7·1% were female, mean age was 57·2 (SD 11·4) years, and 74·0%, 17·8%, and 3·9% were White, Black, and Hispanic or Latino, respectively. In 2019, 12·4% were female, mean age was 57·2 (SD 13·8) years, and 65·3%, 21·9%, and 7·0% were White, Black, and Hispanic or Latino, respectively. Among patients with chronic pain, enacting MCL led to a 0·135% (95% CI 0·118-0·153) absolute increase in cannabis use disorder prevalence, with 8·4% of the total increase in MCL-enacting states attributable to MCL. Enacting RCL led to a 0·188% (0·160-0·217) absolute increase in cannabis use disorder prevalence, with 11·5% of the total increase in RCL-enacting states attributable to RCL. In patients without chronic pain, enacting MCL and RCL led to smaller absolute increases in cannabis use disorder prevalence (MCL: 0·037% [0·027-0·048], 5·7% attributable to MCL; RCL: 0·042% [0·023-0·060], 6·0% attributable to RCL). Overall, associations of MCL and RCL with cannabis use disorder were greater in patients with chronic pain than in patients without chronic pain. Increasing cannabis use disorder prevalence among patients with chronic pain following state legalisation is a public health concern, especially among older age groups. Given cannabis commercialisation and widespread public beliefs about its efficacy, clinical monitoring of cannabis use and discussion of the risk of cannabis use disorder among patients with chronic pain is warranted. NIDA grant R01DA048860, New York State Psychiatric Institute, and the VA Centers of Excellence in Substance Addiction Treatment and Education.

Could an optimally fitted categorization of difference between multi-disease score and multi-symptom score be a practical indicator aiding in improving the cost-effectiveness of healthcare delivery for older adults in developing countries?

BackgroundPhysio-psycho-socioeconomical health comprehensively declines during aging, the complexity of which is challenging to measure. Among the complexity, multiple chronic disorders continuously cumulated during aging, further aggravating the challenge.MethodsA population-based survey on Comprehensive Ageing Health Assessment was conducted in older adults (age > = 60) enrolled from hospital settings and community settings in 13 working centers in six subnational regions in China. Cross-sectional datasets of 8,093 older participants with approximately complete assessment results were collected for the present analysis. Individual’s multi-disease or multi-symptom was respectively scored by summing coexistent multiple diseases or multiple symptoms by respective weighting efficient for Self-Rated Health (SRH). Individual’s age-dependent health decline was further summed of four SRH-weighted scores for daily function (activity of daily life, ADL), physical mobility (an average of three metrics), cognitive function (mini mental state examination, MMSE) and mental being (geriatric depression scale, GDS) plus multi-disease score (MDS) and multi-symptom score (MSS).Multi-disease patten among 18 diseases or multi-symptom pattern among 15 symptoms was latent-clustered in the older adults, the optimal outcome of which was categorized into high, moderate or low aging-associated clusters, respectively. Percentage distribution was compared between overall health decline score and multi-disease pattern cluster or multi-symptom patten cluster. A new variable of difference between MDS and MSS (hereinafter terming DMM) that displayed linear variation with socioeconomic factors was further fitted using multilevel regression analyses by substantial adjustments on individual confounders (level-1) and subnational region variation (level-2).ResultsConsistent gradient distribution was shown between health decline and multimorbidity pattern cluster in the older adults. DMM was found linearly varied with personal education attainment and regional socioeconomic status. Using optimally fitted stratification of DMM (DMM interval = 0.02), an independent U-shaped interrelated tendency was shown between health decline, multi-disease and multi-symptom, which could be well explained by regional disparities in socioeconomic status.ConclusionNewly developed metrics for age-dependent health decline and aging-associated multimorbidity patten were preliminarily validated from within. The new variable of optimally fitted categorization of DMM might function as a practical indicator aiding in improving the cost-effectiveness and reduce inequity of healthcare delivery for older adults in developing countries.

Association of Mindfulness with Perfectionism, Exercise Self-Efficacy, and Competitive State Anxiety in Injured Athletes Returning to Sports.

Injured athletes often face performance challenges upon returning to the field, influenced by external factors and negative emotions. This study investigates how mindfulness is associated with perfectionism, exercise self-efficacy, and competitive state anxiety in athletes recovering from injuries. Using snowball sampling and convenience sampling methods with a cross-sectional dataset of 359 participants from southern China (collected between October and November 2022), we employed structural equation modelling to analyse the relationship between mindfulness and competitive state anxiety in returning athletes. The results reveal that mindfulness interventions enhance exercise self-efficacy, boost task-related confidence, reshape perfectionism towards a positive outlook, and decrease competitive state anxiety. This study establishes positive correlations between perfectionism and competitive state anxiety, and a negative correlation between exercise self-efficacy and competitive state anxiety. Moreover, exercise self-efficacy and perfectionism partially mediate mindfulness's positive impact on competitive state anxiety. In conclusion, this research highlights mindfulness's potential to alleviate perfectionism and competitive state anxiety while enhancing exercise self-efficacy among athletes on the road to recovery.

Identification of different MRI atrophy progression trajectories in epilepsy by subtype and stage inference.

Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics.