Association between low cognitive performance and diabetes-related health indicators across racial and ethnic groups in adults with diabetes.

AIM: To examine the associations between low cognitive performance (LCP) and diabetes-related health indicators (including body mass index [BMI], HbA1c, systolic blood pressure [SBP], low-density lipoprotein [LDL] and self-reported poor physical health) and whether these associations vary across racial/ethnic subgroups. METHODS: We identified adults aged 60 years or older with self-reported diabetes from the 2011-2014 National Health and Nutrition Examination Survey. Individuals with cognitive test scores in the lowest quartile were defined as having LCP. We used regression models to measure the associations of LCP with diabetes-related biometrics (BMI, HbA1c, SBP and LDL); and self-reported poor physical health. Moreover, we explored potential variations in these associations across racial/ethnic subgroups. RESULTS: Of 873 (261 with LCP) adults with diabetes, LCP was associated with higher HbA1c, SBP and LDL (adjusted difference: 0.41%, 5.01 mmHg and 5.08 mg/dL, respectively; P < .05), and greater odds of reporting poor physical health (adjusted odds ratio: 1.59, P < .05). The association between LCP and HbA1c was consistent across racial/ethnic groups, and notably pronounced in Hispanic and Other. BMI worsened with LCP, except for non-Hispanic Black. Excluding the Other group, elevated SBP was observed in people with LCP, with Hispanic showing the most significant association. LDL levels were elevated with LCP for Hispanic and Other. Physical health worsened with LCP for both non-Hispanic Black and Hispanic. CONCLUSIONS: We quantified the association between LCP and diabetes-related health indicators. These associations were more pronounced in Hispanic and Other racial/ethnic groups.

Trends in caesarean section rates in Europe from 2015 to 2019 using Robson's Ten Group Classification System: A Euro-Peristat study.

OBJECTIVE: To assess changes in caesarean section (CS) rates in Europe from 2015 to 2019 and utilise the Robson Ten Group Classification System (TGCS) to evaluate the contribution of different obstetric populations to overall CS rates and trends. DESIGN: Observational study utilising routine birth registry data. SETTING: A total of 28 European countries. POPULATION: Births at ≥22 weeks of gestation in 2015 and 2019. METHODS: Using a federated model, individual-level data from routine sources in each country were formatted to a common data model and transformed into anonymised, aggregated data. MAIN OUTCOME MEASURES: By country: overall CS rate. For TGCS groups (by country): CS rate, relative size, relative and absolute contribution to overall CS rate. RESULTS: Among the 28 European countries, both the CS rates (2015, 16.0%-55.9%; 2019, 16.0%-52.2%) and the trends varied (from -3.7% to +4.7%, with decreased rates in nine countries, maintained rates in seven countries (≤ ± 0.2) and with increasing rates in 12 countries). Using the TGCS (for 17 countries), in most countries labour induction increased (groups 2a and 4a), whereas multiple pregnancies (group 8) decreased. In countries with decreasing overall CS rates, CS tended to decrease across all TGCS groups, whereas in countries with increasing rates, CS tended to increase in most groups. In countries with the greatest increase in CS rates (>1%), the absolute contributions of groups 1 (nulliparous term cephalic singletons, spontaneous labour), 2a and 4a (induction of labour), 2b and 4b (prelabour CS) and 10 (preterm cephalic singletons) to the overall CS rate tended to increase. CONCLUSIONS: The TGCS shows varying CS trends and rates among countries of Europe. Comparisons between European countries, particularly those with differing trends, could provide insight into strategies to reduce CS without clinical indication.

Dietary microalgal-fabricated selenium nanoparticles improve Nile tilapia biochemical indices, immune-related gene expression, and intestinal immunity.

BACKGROUND: Feed supplements, including essential trace elements are believed to play an important role in augmenting fish immune response. In this context, selenium nanoparticles (SeNPs) in fish diets via a green biosynthesis strategy have attracted considerable interest. In this investigation, selenium nanoparticles (SeNPs, 79.26 nm) synthesized from the green microalga Pediastrum boryanum were incorporated into Nile tilapia diets to explore its beneficial effects on the immune defense and intestinal integrity, in comparison with control basal diets containing inorganic Se source. Nile tilapia (No. 180, 54-57 g) were fed on three formulated diets at concentrations of 0, 0.75, and 1.5 mg/kg of SeNPs for 8 weeks. After the trial completion, tissue bioaccumulation, biochemical indices, antioxidant and pro-inflammatory cytokine-related genes, and intestinal histological examination were analyzed. RESULTS: Our finding revealed that dietary SeNPs significantly decreased (P < 0.05) serum alkaline phosphatase (ALP), lactate dehydrogenase (LDH), and cholesterol, while increasing (P < 0.05) high-density lipoproteins (HDL). The Se concentration in the muscle tissues showed a dose-dependent increase. SeNPs at a dose of 1.5 mg/kg significantly upregulated intestinal interleukin 8 (IL-8) and interleukin 1 beta (IL-1ß) gene transcription compared with the control diet. Glutathione reductase (GSR) and glutathione synthetase (GSS) genes were significantly upregulated in both SeNPs-supplemented groups compared with the control. No apoptotic changes or cell damages were observed as indicated by proliferating cell nuclear antigen (PCNA) and caspase-3 gene expression and evidenced histopathologically. SeNPs supplementation positively affects mucin-producing goblet cells (GCs), particularly at dose of 1.5 mg/kg. CONCLUSION: Therefore, these results suggest that Green synthesized SeNPs supplementation has promising effects on enhancing Nile tilapia immunity and maintaining their intestinal health.

Primary care performance measurement in Brazil (Previne Brasil Program), 2022-2023.

BACKGROUND: Previne Brasil is a new way of financing Primary Health Care (PHC) in Brazil that places users of public health services at the center of the system's analysis and attention through the use of seven performance indicators. This study aims to analyze the performance of Primary Health Care in Brazil under the new financing model (Previne Brasil) in the period from the first four months of 2022 to the first four months of 2023. METHODS: This is a territorial ecological study using secondary data from the Primary Care Health Information System (SISAB). Seven performance indicators were included and analyzed according to Brazil, its regions, states and capitals. A comparative descriptive analysis was carried out between the spatial units. RESULTS: Of the seven indicators analyzed, only one (Proportion of pregnant women tested for syphilis and HIV) reached the proposed target considering the average of the four-month periods evaluated (target: 60%; Observed: 62.5%). The Northeast was the region with the best performance, surpassing the target in three of the seven indicators (prenatal consultations, tests for syphilis and HIV in pregnant women and dental care for pregnant women). The states and capitals showed progressive improvement in the performance indicators over the four quarters analyzed, although unevenly between the indicators. The indicators related to hypertension and diabetes were the worst. The Final Synthetic Indicator (FSI) showed an increase in the median over the four quarters (4.78; 5.65; 6.02 6.29), which represents an increase of 1.51 in the indicator between the first four months of 2022 and the first four months of 2023. CONCLUSION: Although there have been improvements in the performance of Primary Health Care in Brazil, the achievement of the agreed targets for the indicators seems a long way off. The socio-spatial inequalities in the indicators reflect the country's health complexity and reinforce the need for policies that take into account the local-regional context.

Evaluating the Soil Quality Index Using Three Methods to Assess Soil Fertility.

Soil health plays a crucial role in crop production, both in terms of quality and quantity, highlighting the importance of effective methods for preserving soil quality to ensure global food security. Soil quality indices (SQIs) have been widely utilized as comprehensive measures of soil function by integrating multiple physical, chemical, and biological soil properties. Traditional SQI analysis involves laborious and costly laboratory analyses, which limits its practicality. To overcome this limitation, our study explores the use of visible near-infrared (vis-NIR) spectroscopy as a rapid and non-destructive alternative for predicting soil properties and SQIs. This study specifically focused on seven soil indicators that contribute to soil fertility, including pH, organic matter (OM), potassium (K), calcium (Ca), magnesium (Mg), available phosphorous (P), and total nitrogen (TN). These properties play key roles in nutrient availability, pH regulation, and soil structure, influencing soil fertility and overall soil health. By utilizing vis-NIR spectroscopy, we were able to accurately predict the soil indicators with good accuracy using the Cubist model (R2 = 0.35-0.93), offering a cost-effective and environmentally friendly alternative to traditional laboratory analyses. Using the seven soil indicators, we looked at three different approaches for calculating and predicting the SQI, including: (1) measured SQI (SQI_m), which is derived from laboratory-measured soil properties; (2) predicted SQI (SQI_p), which is calculated using predicted soil properties from spectral data; and (3) direct prediction of SQI (SQI_dp), The findings demonstrated that SQI_dp exhibited a higher accuracy (R2 = 0.90) in predicting soil quality compared to SQI_p (R2 = 0.23).

A systematic benchmarking framework for future assessments of soil health: An example from Denmark.

Based on current evidence and established critical thresholds for soil degradation indicators, it is concerning that over 60-70% of European soils are unhealthy due to unsustainable management and the impact of climate change. Despite European and national efforts to improve soil health, significant gaps remain. The proposal for a Soil Monitoring and Resilience Law, to be implemented by the European Union, seeks to establish a framework for soil monitoring and promote sustainable management practices to achieve healthy soils by 2050. This requires extensive data collection and soil monitoring systems to accurately estimate soil health across Europe, considering the diversity of soil types, climates, and land uses. To establish a framework for soil monitoring, we must understand the site-specific status of soil and the ranges of soil health indicators across specific pedoclimatic regions. In our study, we evaluated the soil status in agricultural areas in Denmark using soil health indicators and a site-specific benchmarking approach. We compiled nationally representative datasets, combining point and model-informed data of soil parameters such as organic carbon content, bulk density, pH, electrical conductivity, clay-to-soil organiccarbon ratio, water erosion, and nitrogen leaching. By categorizing Danish agricultural soils into monitoring units based on textural classes, landscape elements, and wetland types, we calculated benchmarks for these indicators, considering different cropping systems. Our approach provided detailed point-based results and a spatially explicit overview of the status of soil health indicators in Denmark. We identified areas where soil deviates from the benchmarks of different indicators. Such deviations might indicate soil functions operating outside the normal range, posing potential threats to soil health. This proposed framework could support the establishment of a baseline for assessing the directionality of future changes in soil health. Moreover, it is adaptable for implementation by other countries to support assessments of soil health.

Psychometrics of psychosocial behavior items under age 6 years: Evidence from Nebraska, USA.

Because healthy psychosocial development in the first years of life is critical to lifelong well-being, governmental, and nongovernmental organizations are increasingly interested in monitoring psychosocial behaviors among populations of children. In response, the World Health Organization is developing the Global Scales of Early Development Psychosocial Form (GSED PF) to facilitate population-level psychosocial monitoring. Once validated, the GSED PF will be an open-access, caregiver-reported measure of children's psychosocial behaviors that is appropriate for infants and young children. This study examines the psychometric validity evidence from 45 items under consideration for inclusion in the GSED PF. Using data from N = 836 Nebraskan (USA) children aged 180 days to 71 months, results indicate that scores from 44 of the 45 (98%) items exhibit positive evidence of validity and reliability. A bifactor model with one general factor and five specific factors best fit the data, exhibited strong reliability, and acceptable model fit. Criterion associations with known predictors of children's psychosocial behaviors were in the expected direction. These findings suggest that measurement of children's psychosocial behaviors may be feasible, at least in the United States. Data from more culturally and linguistically diverse settings is needed to assess these items for global monitoring.

Debido a que el desarrollo sicosocial en los primeros años de vida es crítico para el bienestar de toda la vida, las organizaciones gubernamentales y no gubernamentales están más y más interesadas en observar vigilantemente las conductas sicosociales en la población infantil. Como respuesta, la Organización Mundial de la Salud está desarrollando el Formulario Sicosocial de las Escalas Globales del Temprano Desarrollo (GSED PF) para facilitar la observación sicosocial alerta al nivel del grupo de población. Una vez que se haya convalidado, el GSED PF será una medida de acceso abierto, que reportará el cuidador, sobre las conductas sicosociales de los niños que son apropiadas para infantes y niños pequeñitos. Este estudio examina la evidencia de la validez sicométrica de 45 puntos bajo consideración para ser incluidos en el GSED PF. Usando datos de N = 836 niños de Nebraska (Estados Unidos), de edad entre 180 días y 71 meses, los resultados indican que los puntajes de 44 de los 45 (98%) puntos muestran evidencia positiva de validez y confiabilidad. Un modelo bifactorial con un factor general y cinco factores específicos, que mejor encaja con los datos, mostró una fuerte confiabilidad y un modelo aceptable que encaja. Las asociaciones de criterio con factores de predicción conocidos acerca de las conductas sicosociales de los niños se encontraban en la dirección esperada. Estos resultados sugieren que la medida de las conductas sicosociales de los niños pudiera ser posible, por lo menos en los Estados Unidos. Se necesitan datos de escenarios más diversos cultural y lingüísticamente para evaluar estos puntos para la estar alerta en la observación global.

Public health indicators for cerebral palsy: A European collaborative study of the Surveillance of Cerebral Palsy in Europe network.

BACKGROUND: Public health indicators (PHIs) play an increasingly important role in health policy decision-making. Although cerebral palsy (CP) is the commonest physical disability in children, its impact at population level has not been systematically measured so far. OBJECTIVES: We aimed to propose six PHIs for CP designed to annually document the extent of CP and effectiveness of perinatal organisation, the burden of this condition, access to health services and preventive health strategies in the post-neonatal period and to report on the latest updated estimations using population-based data routinely collected by European CP registries. METHODS: The study included children with CP born between 2002 and 2011. Harmonised data (number of cases, functional profile, imaging) were extracted from the Surveillance of Cerebral Palsy in Europe (SCPE) database. Eligibility criteria for analyses were applied separately for each indicator by selecting registries, birth years and CP cases. Current estimates were based on the last 3 birth years, while trends were reported over a 10-year period. All analyses were descriptive. Sensitivity analyses were carried out to examine the stability of the results using various thresholds of percentages of missing values. RESULTS: Analyses were performed on a total of 8621 children with CP from 12 to 17 SCPE registries. A decreasing prevalence of pre/perinatal CP overall, as well as in preterm and full-term-born children, was observed. The burden of the condition was strongly dependent on CP subtype and the presence of associated impairments. Access to brain imaging ranged from 80% to 100% depending on registries. The overall prevalence of post-neonatally acquired CP was approximately 0.8 per 10,000 live births over the study period. CONCLUSIONS: Population-based CP registries can provide data that are relevant for generating key outcomes of interest at the population level, thus potentially contributing to improving public health policies for children with disabilities.

Design of effective outpatient sentinel surveillance for COVID-19 decision-making: a modeling study.

BACKGROUND: Decision-makers impose COVID-19 mitigations based on public health indicators such as reported cases, which are sensitive to fluctuations in supply and demand for diagnostic testing, and hospital admissions, which lag infections by up to two weeks. Imposing mitigations too early has unnecessary economic costs while imposing too late leads to uncontrolled epidemics with unnecessary cases and deaths. Sentinel surveillance of recently-symptomatic individuals in outpatient testing sites may overcome biases and lags in conventional indicators, but the minimal outpatient sentinel surveillance system needed for reliable trend estimation remains unknown. METHODS: We used a stochastic, compartmental transmission model to evaluate the performance of various surveillance indicators at reliably triggering an alarm in response to, but not before, a step increase in transmission of SARS-CoV-2. The surveillance indicators included hospital admissions, hospital occupancy, and sentinel cases with varying levels of sampling effort capturing 5, 10, 20, 50, or 100% of incident mild cases. We tested 3 levels of transmission increase, 3 population sizes, and conditions of either simultaneous transmission increase or lagged increase in the older population. We compared the indicators' performance at triggering alarm soon after, but not prior, to the transmission increase. RESULTS: Compared to surveillance based on hospital admissions, outpatient sentinel surveillance that captured at least 20% of incident mild cases could trigger an alarm 2 to 5 days earlier for a mild increase in transmission and 6 days earlier for a moderate or strong increase. Sentinel surveillance triggered fewer false alarms and averted more deaths per day spent in mitigation. When transmission increase in older populations lagged the increase in younger populations by 14 days, sentinel surveillance extended its lead time over hospital admissions by an additional 2 days. CONCLUSIONS: Sentinel surveillance of mild symptomatic cases can provide more timely and reliable information on changes in transmission to inform decision-makers in an epidemic like COVID-19.

Healthy lifespan inequality: morbidity compression from a global perspective.

Current measures of population health lack indicators capturing the variability in age-at-morbidity onset, an important marker to assess the timing patterns of individuals' health deterioration and evaluate the compression of morbidity. We provide global, regional, and national estimates of the variability in morbidity onset from 1990 to 2019 using indicators of healthy lifespan inequality (HLI). Using data from the Global Burden of Disease Study 2019, we reconstruct age-at-death distributions to calculate lifespan inequality (LI), and age-at-morbidity onset distributions to calculate HLI. We measure LI and HLI with the standard deviation. Between 1990 and 2019, global HLI decreased from 24.74 years to 21.92, and has been decreasing in all regions except in high-income countries, where it has remained stable. Countries with high HLI are more present in sub-Saharan Africa and south Asia, whereas low HLI values are predominant in high-income countries and central and eastern Europe. HLI tends to be higher for females than for males, and HLI tends to be higher than LI. Globally, between 1990 and 2019 HLI at age 65 increased from 6.83 years to 7.44 for females, and from 6.23 to 6.96 for males. Improvements in longevity are not necessarily accompanied by further reductions in HLI among longevity vanguard countries. Morbidity is compressing, except in high-income countries, where it stagnates. The variability in the ages at morbidity onset tends to be larger than the variability in lifespans, and such divergence broadens over time. As longevity increases worldwide, the locus of health inequality is moving from death-related inequalities to disease- and disability-centered ones.

Effects of short-term exposure to gaseous pollutants on metabolic health indicators of patients with metabolic syndrome in Northwest China.

Currently few studies have explored the relationship between exposure to gaseous pollutants and metabolic health indicators in patients, especially in patients with metabolic syndrome (Mets). This study collected 15,520 patients with Mets in a prospective cohort of nearly 50,000 people with 7 years of follow-up from 2011 to 2017, and matched air pollutants and meteorological data during the same period. The mixed effects model was used to analyze the relationship between different short exposure windows (1-week, 1-month, 2-month, and 3-month) of gaseous pollutants (SO2, NO2, and O3) and the metabolic health indicators of patients after controlled the confounding factors. Stratified analysis was performed by demographic characteristics and behavioral factors. The effects of gaseous pollutants on patients with different Met components were also analyzed. The results showed that the short-term exposure to SO2, NO2, and O3 had a certain effect on the metabolic health indicators of patients with Mets in different exposure windows, and with the extension of the exposure window period, the effects increased. The stratified analysis showed that gender, age, and life behaviors might modify these detrimental effects. In addition, the effects of gaseous pollutants on metabolic health indicators in G4 and G7 were more obvious than other Met components, and the effects of gaseous pollutants on the level of LDL-C were found to be statistically significant in most components. Therefore, patients with Mets should pay more attention to the influence of gaseous pollutants to take appropriate protection to reduce potential health risk.

Enhanced passive surveillance for early detection of African and classical swine fevers.

African swine fever (ASF) and classical swine fever (CSF) are transboundary animal diseases (TADs) of pigs. Much effort and resources are regularly put into preventing these diseases' introduction in free areas. Passive surveillance activities bring the highest chances for the early detection of TAD incursions because they are routinely and widely conducted at farms, and because these activities focus on the time between introduction and when the first sample is sent for diagnostic testing. The authors proposed the implementation of an enhanced passive surveillance (EPS) protocol based on collecting data through participatory surveillance actions using an objective and adaptable scoring system to aid the early detection of ASF or CSF at the farm level. The protocol was applied in two commercial pig farms for ten weeks in the Dominican Republic, which is a CSF- and ASF-infected country. This study was a proof of concept, based on the EPS protocol to aid detection of substantial variations in the risk score triggering testing. One of the followed farms had score variation, which triggered testing of the animals, although the test results were negative. The study enables assessment of some of the weaknesses associated with passive surveillance and provides lessons applicable to the problem. Results demonstrate the potential for overcoming some issues preventing the broad application of EPS protocols and suggest that standardised approaches may contribute to the early detection of CSF and ASF introductions.

La peste porcine africaine (PPA) et la peste porcine classique (PPC) sont des maladies animales transfrontalières touchant les porcs. De nombreux efforts et ressources sont régulièrement alloués pour prévenir l'introduction de ces maladies dans des zones indemnes. Les activités de surveillance passive offrent les meilleures perspectives de détection précoce des incursions de maladies animales transfrontalières parce qu'elles sont menées de manière systématique et exhaustive dans les élevages, et parce qu'elles se concentrent sur la période entre l'introduction de la maladie et le moment où le premier échantillon est envoyé au laboratoire pour analyse. Les auteurs proposent la mise en oeuvre d'un protocole de surveillance passive renforcée fondé sur la collecte de données via des actions de surveillance participative utilisant un système de notation objectif et adaptable, en vue d'une détection précoce de la PPA et de la PPC dans les élevages. Ce protocole a été appliqué en République dominicaine, pays infecté par la PPA et la PPC, dans deux élevages porcins commerciaux pendant dix semaines. Cette étude était destinée à valider le principe de la méthode et se fondait sur le protocole de surveillance passive renforcée pour mieux détecter les variations substantielles de la note de risque qui conduisent à tester les animaux. L'un des élevages suivis a présenté une variation de cette note, ce qui a conduit à tester les animaux mais les tests se sont révélés négatifs. L'étude permet d'évaluer certaines des faiblesses associées à la surveillance passive et apporte des enseignements applicables à ce problème. Les résultats illustrent le potentiel de l'approche à surmonter certaines des problématiques empêchant l'application extensive des protocoles de surveillance passive renforcée. Ils suggèrent également que des approches normalisées pourraient contribuer à la détection précoce des cas d'introduction de la PPC et de la PPA.

La peste porcina africana (PPA) y la peste porcina clásica (PPC) son enfermedades animales transfronterizas que afectan al cerdo. Periódicamente se dedican grandes esfuerzos y cuantiosos recursos a evitar que estas enfermedades penetren en zonas que están exentas de ellas. Las actividades de vigilancia pasiva son las más eficaces para detectar con prontitud toda incursión de enfermedades animales transfronterizas, no solo por la regularidad y amplitud con que se llevan a cabo en las explotaciones, sino también porque inciden específicamente en el intervalo entre la penetración de una enfermedad y el momento en que se envía la primera muestra para que sea sometida a pruebas de diagnóstico. Los autores propusieron que se aplicara un protocolo de vigilancia pasiva reforzada que reposaba en la obtención de datos mediante actividades de vigilancia participativa, empleando para ello un sistema objetivo y adaptable de puntuación que ayudaba a detectar con prontitud la presencia en las explotaciones de PPA o PPC. Dicho protocolo fue aplicado a lo largo de diez semanas en dos explotaciones porcinas industriales de la República Dominicana, país en el que ambas infecciones están presentes. El estudio, que sirvió para poner a prueba la idea, pasaba por la aplicación del protocolo de vigilancia pasiva reforzada para ayudar a detectar variaciones sustanciales de la puntuación del nivel de riesgo que activa la realización de pruebas. En una de las explotaciones estudiadas se produjo una variación de la puntuación, cosa que activó la realización de pruebas en los animales, aunque estas arrojaron resultado negativo. El estudio aquí descrito permite evaluar algunos de los puntos débiles de la vigilancia pasiva y extraer enseñanzas aplicables al problema. Los resultados demuestran que es posible salvar algunas de las dificultades que impiden la aplicación generalizada de protocolos de vigilancia pasiva reforzada y dejan pensar que quizá el uso de planteamientos normalizados pueda ayudar a detectar con prontitud los casos de penetración de PPC o PPA.

A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model.

Bearing is the critical basic component of rotating machinery and its remaining life prediction is very important for mechanical equipment's smooth and healthy operation. However, fast and accurate bearing life prediction has always been a difficult point in industry and academia. This paper proposes a new strategy for bearing health assessment based on a model-driven dynamic interval prediction model. Firstly, the mapping proportion algorithm is used to determine whether the measured data are in the degradation stage. After finding the starting point of prediction, the improved annealing algorithm is used to determine the shortest data interval that can be used for accurate prediction. Then, based on the bearing degradation curve and the information fusion inverse health index, the health index is obtained from 36 general indexes in the time domain and frequency domain through screening, fusion, and inversion. Finally, the state space equation is constructed based on the Paris-DSSM formula and the particle filter is used to iterate the state space equation parameters with the minimum interval data to construct the life prediction model. The proposed method is verified by XJTU-SY rolling bearing life data. The results show that the prediction accuracy of the proposed strategy for the remaining life of the bearing can reach more than 90%. It is verified that the improved simulated annealing algorithm selects limited interval data, reconstructs health indicators based on bearing degradation curve and information fusion, and updates the Paris-DSSM state space equation through the particle filter algorithm. The bearing life prediction model constructed on this basis is accurate and effective.

A Smart System for an Assessment of the Remaining Useful Life of Ball Bearings by Applying Chaos-Based Health Indicators and a Self-Selective Regression Model.

Bearings are the most commonly used components in rotating machines and the ability to diagnose their faults and predict their remaining useful life (RUL) is critical for system maintenance. This paper proposes a smart system combined with a regression model to predict the RUL of bearings. The method converts the azimuth signal through low-pass filtering (LPF) and a chaotic mapping system, and uses Euclidean feature values (EFVs) to extract features in order to construct useful health indicators (HIs). In fault detection, the iterative cumulative moving average (ICMA) is used to smooth the HIs, and the Euclidean norm is used to find the time-to-start prediction (TSP). In terms of prediction, this paper uses a self-selective regression model to select the most suitable regression model to predict the RUL of the bearing. The dataset provided by the Center for Intelligent Maintenance Systems (IMS) is applied for performance evaluation; in comparison with previous research, better prediction results can be achieved by applying the proposed smart assessment system. The proposed system is also applied to the PRONOSTIA (also called FEMTO-ST) bearing dataset in this paper, demonstrating that acceptable prediction performance can be obtained.

School life in COVID-19 post-lockdown: differential effects of socio-demographics and adjustments on mental health indicators among university students.

We examined the differential effects of some socio-demographic; and the separate and interactive effects of types of adjustments on university students' mental health indicators (somatic symptoms, anxiety/insomnia, social dysfunction and depression). In a cross-sectional approach, we selected 603 undergraduates (Mage = 21.88, SD = 2.25) from a Nigerian university who answered a structured questionnaire that included measures of studied variables from June to August 2021. No gender difference was observed in students' mental health indicators. Social dysfunction was most reported by 3rd year students; while those from separated homes reported both anxiety and depression most. Somatic symptoms, social dysfunction, anxiety, and depression were higher among students with poor academic, social, emotional, or attachment adjustments. Students who struggled academically and socially reported more social symptoms; whereas those who struggled academically and emotionally were more depressed. Additionally, students with low social and attachment adjustments reported higher social dysfunction and depression; while those with low academic, emotional, and attachment adjustments reported higher anxiety and depression. Adjustment mechanisms are essential for students to enjoy good mental health during COVID-19 post-lockdown irrespective of their gender; especially those from broken homes. Psychological supports are regularly required to improve students' mental health during COVID-19 post-lockdown.

The H9c2(2-1) cell-based sulforhodamine B assay is a non-animal alternative to evaluate municipal wastewater quality over time.

The present study validates the potential of the in vitro H9c2(2-1) cell-based sulforhodamine B (SRB) assay to evaluate the temporal variability of wastewater quality. The impact of effluent disposal on water quality and the efficiency of the wastewater treatment process were also assessed. To correlate standard analytical method results with in vitro results, a total of 16 physicochemical parameters, such as nutrients, pH, chemical oxygen demand, total suspended solids and metals, were determined in both raw and treated wastewater samples. Results revealed that the H9c2(2-1) cell-based SRB assay has an enormous potential to evaluate municipal wastewater quality over time and to discriminate influent and effluent toxic characteristics, as well as for water quality monitoring and surveillance of the efficacy of treatment processes. Finally, the gathered results alerted to the impact of phosphates in a biological system, leading us to recommend the selection of this parameter as a potential environmental health indicator.

Development and validation of a health and nutrition module for the project-level Women's Empowerment in Agriculture Index (pro-WEAI+HN).

Agricultural development projects increasingly aim to improve health and nutrition outcomes, often by engaging women. Although evidence shows such projects can improve women's and children's health and nutrition and empower women, little is known about their impacts on women's health- and nutrition-related agency and the extent to which impacts emerge through women's empowerment, largely due to a lack of instruments that measure the dimensions of women's agency that are directly relevant to health and nutrition outcomes. We developed an optional, complementary module for the project-level women's empowerment in agriculture index (pro-WEAI) to measure health- and nutrition-related agency (pro-WEAI + HN). Our method for developing related indicators used data collected from six agricultural development programmes implemented across Bangladesh, Burkina Faso and Mali (pooled sample = 12,114) and applied psychometric analysis (exploratory and confirmatory factor analysis) and the Alkire-Foster methodology. Results revealed seven indicators covering women's agency in the areas of her own health and diet; her health and diet during pregnancy; her child's diet; breastfeeding and weaning; purchasing food and health products; and acquiring food and health products. Multigroup confirmatory factor analysis revealed measurement invariance across contexts and samples. Tests of association (Cramer's V) and redundancy suggest that the pro-WEAI + HN indicators measured aspects of agency that are distinct from the core pro-WEAI. The uptake of these indicators in studies of nutrition-sensitive agricultural development projects may strengthen the evidence on how such programming can enhance women's empowerment to improve health and nutrition outcomes for themselves and their children.

[WORLD EXPERIENCE: HOW THE DEVELOPMENT OF THE PRINCIPLES OF ACADEMICIAN OF THE ACADEMY OF MEDICAL SCIENCES OF THE USSR N. A. SEMASHKO HELPED ICELAND TO CREATE ONE OF THE MOST EFFECTIVE HEALTHCARE SYSTEMS IN THE WORLD. PART 1. HISTORICAL OVERVIEW].

Annotation. The review article is devoted to the healthcare system of Iceland, which demonstrates some of the best indicators of public health in the world at relatively low costs for a developed European country. The successful experience of Icelandic healthcare is important for Russia, on the one hand, due to the proximity of this country to many Russian regions in terms of climatic and geographical location, demography and territorial development. On the other hand, the success of Iceland's healthcare is closely linked to the development of a model, in many ways similar to the Soviet model of Nikolai Semashko, which Russia has abandoned. The authors believe that Iceland's experience can be used in the modernization of regional healthcare systems in Eastern Siberia, the Far East and the Arctic, as well as Russian healthcare in general.

[WORLD EXPERIENCE: HOW THE DEVELOPMENT OF THE PRINCIPLES OF ACADEMICIAN OF THE ACADEMY OF MEDICAL SCIENCES OF THE USSR N. A. SEMASHKO HELPED ICELAND TO CREATE ONE OF THE MOST EFFECTIVE HEALTHCARE SYSTEMS IN THE WORLD. PART 2. PRINCIPLES FUNCTIONING OF THE ICELANDIC HEALTH SYSTEM].

Annotation. The review article is devoted to the healthcare system of Iceland, which demonstrates some of the best indicators of public health in the world at relatively low costs for a developed European country. The successful experience of Icelandic healthcare is important for Russia, on the one hand, due to the proximity of this country to many Russian regions in terms of climatic and geographical location, demography and territorial development. On the other hand, the success of Iceland's healthcare is closely linked to the development of a model, in many ways similar to the Soviet model of Nikolai Semashko, which Russia has abandoned. The authors believe that Iceland's experience can be used in the modernization of regional healthcare systems in Eastern Siberia, the Far East and the Arctic, as well as Russian healthcare in general.

Do changes in frailty, physical functioning, and cognitive functioning predict mortality in old age? Results from the Longitudinal Aging Study Amsterdam.

BACKGROUND: The ability to accurately predict survival in older adults is crucial as it guides clinical decision making. The added value of using various health indicators as well as changes in these indicators for predicting mortality remains unclear. The aim of this study was to investigate whether changes in health indicators such as frailty and physical performance improve mortality predictions in old age. METHODS: This is a population based prospective cohort study on 995 community-dwelling people aged 68-92 years from the Longitudinal Aging Study Amsterdam. Two measurements at a three-year interval (1995/1996 and 1998/1999) were available for the frailty index, frailty phenotype, grip strength, walking speed, and Mini-Mental State Examination (MMSE). Cox regression was used to analyze mortality risks associated with the current health status and changes in health, with mortality data up to 2017. The extent to which these health indicators improved mortality predictions compared to models with age and sex only was assessed by the area under the receiver operating characteristic curve (AUC). RESULTS: The AUC of age and sex for five-year mortality was 72.8% (95% CI 69.0 - 76.5) and was the lowest in the oldest old (age > 80.5 years). The added AUC of the current status of health indicators ranged from 0.7 to 3.3%. The added AUC of the three-year change was lower, ranging from -0.0 to 1.1%, whereas the added AUC of three-year change and current status combined was similar to current status alone, ranging from 0.6 to 3.2%. Across age, the added AUC of current status was highest in the oldest old, however there was no such pattern using three-year change. Overall, the frailty index appeared to improve mortality predictions the most, followed by the frailty phenotype, MMSE, grip strength, and walking speed. CONCLUSIONS: Current health status improved mortality predictions better than changes in health. Its contribution was highest in the oldest old, but the added value to models with age and sex only was limited.