Browse Articles

Compassion for others and well-being: a meta-analysis

Abstract Compassion has been shown to improve well-being across multiple settings. While the link between self-compassion and well-being is well-established, findings on compassion for others have been more mixed. Using a multilevel approach, this meta-analysis of 54 effect sizes examined the relationship between compassion for others and well-being. The results revealed a moderate, statistically significant positive association (r = .26), suggesting that compassion for others contributes meaningfully to overall well-being. This relationship was consistent for psychological well-being, cognitive well-being, social well-being, and positive affect, whereas the results were weaker for negative affect. Furthermore, results showed that this relationship is not moderated by age, gender, or region. In addition, we examined if there is a causal relationship between compassion for others and well-being examining six effect sizes for state and trait compassion interventions. The results showed moderate improvements in well-being following compassion-based training, indicating promising but preliminary evidence for a causal effect. However, the absence of consistent control group comparisons limits firm conclusions about causality. This meta-analysis presents the first rigorous synthesis of evidence linking compassion for others to well-being, highlighting compassion for others as a distinct and meaningful contributor.

Determinants of empiric combination antibiotic therapy for hospital associated bloodstream infections in the intensive care unit

Abstract Empiric combination antibiotic therapy (ECAT) is commonly used to treat healthcare-associated bloodstream infections (HA-BSIs) and sepsis. However, the level of supporting evidence is low and clinical practice varies significantly. We conducted a post hoc analysis using the EUROBACT-2 international cohort study database, which contained data on 2406 adult patients from 328 intensive care units (ICUs) across 52 countries, collected between June 2019 and January 2021. The main outcome was the proportion of patients receiving ECAT for HA-BSIs. Patient and institutional factors influencing the use of ECAT were examined using Markov-Chain Monte Carlo estimation. Three quarters of patients (75.2%; n = 1810) received empiric antibiotic therapy, with ECAT used in approximately half of cases (52.5%; n = 950). Most patients receiving ECAT (70.4%; n = 669) were treated with two antibiotics, beta-lactams plus glycopeptides being the most common combination (40.2%; n = 382). The odds of ECAT were increased by immune deficiency (OR 1.35 [95% CrI 1.03–1.75]), SOFA scores > 11 (OR 1.77 [95% CrI 1.28–2.46]), uncommon sources of infection (OR 1.63 [95% CrI 1.02–2.59]), and admission to ICUs where > 25% of Enterobacteriaceae isolates produce carbapenemases (OR 2.46 [95% CrI 1.37–4.41). The intra-class correlation coefficients at the ICU and country levels were 23.2% and 4.4%, respectively. In conclusion, factors at the individual, institutional, and national levels may affect the use of ECAT to treat HA-BSIs. Given the impact of institutional variables on the use of ECAT and the inconclusive evidence regarding its potential risks, it is of great importance that treatment is tailored based on local antibiotic stewardship programs and the needs of the individual patient.

Ex vivo evaluation of corneal filler injection for enhancement after small incision lenticule extraction

Abstract To investigate the feasibility of transparent corneal filler injection for enhancement after myopic Small Incision Lenticule Extraction (SMILE) overcorrection in an ex vivo eye model. Myopic SMILE procedure with an anticipated correction of -5.6 dpt was performed in 46 whole porcine eyes ex vivo. A hyaluronic acid filler was injected into the SMILE interface through the incision, which was subsequently sealed with fibrin glue. Three-dimensional optical coherence tomography (OCT) was acquired pre- and postoperatively, assessing the central filler thickness and refractive power changes. Based on the central pocket thickness and radius, the refractive power change after filler injection was calculated using the well-known Munnerlyn formula. The filler volume (0.4 to 1.7 µl) correlated linearly with the central pocket thickness (Pearson’s r = 0.90, p < 0.0001; R² = 0.96). Based on the filler volume and central pocket thickness, a filler radius was calculated as 2.77 mm. According to the Munnerlyn formula, a central pocket thickness change from 30 μm to 148 μm corresponded to a calculated increase in refractive power from 2.9 to 14.5 dpt. The injection of a corneal filler via the primary SMILE-incision into the interface following myopic SMILE is a feasible method to partially reverse the refractive change ex vivo. Further development for clinical use is warranted.

CRISPR/Cas9 generated DSB clusters mimic complex lesions induced by high-LET radiation and shift repair from c-NHEJ to mutagenic repair pathways

Abstract DNA double-strand break (DSB) clusters are a hallmark of high-linear energy transfer (high-LET) radiation and are associated with pronounced biological effects, including reduced cell survival and elevated genomic instability. Our previous work in Chinese hamster cells, engineered with variably designed clusters of I-SceI recognition sites, integrated at multiple genomic locations, revealed that DSB clusters suppress classical non-homologous end-joining (c-NHEJ) and induce chromosomal translocations that ultimately increase cell lethality. Here, we extend this line of investigation to human cell lines and generate DSB clusters using alternative approaches that do not require prior genetic manipulation of the test cell lines. We employ CRISPR/Cas9-technology to generate DSB clusters of specific design at a selected genomic locus and examine their consequences on locus integrity. We target Exon 3 of the human HPRT (hHPRT) gene and introduce single DSBs or DSB clusters of varying numbers and inter-DSB distances. Alterations at the locus reflecting hHPRT gene inactivation, are quantified as mutations causing resistance to 6-thioguanine (6TG). Our results show that DSB clusters are markedly more potent inducers of mutations than single DSBs and that DSBs spaced within ~ 600 base pairs synergize in mutation induction. Mechanistic analyses using small-molecule inhibitors and engineered gene knockout cell lines reveal that the increased mutagenicity of clustered DSBs is primarily mediated by DNA end resection and PARP1-dependent alternative end-joining (alt-EJ) pathways. These findings reinforce the biological relevance of DSB clusters as a severe form of complex DNA damage and provide mechanistic insights into high-LET radiation-induced increased cell killing and genomic instability.

f(Q) gravity as a possible resolution of the H0 and S8 tensions with DESI DR2

Optimizing nutrient stoichiometry for enhanced carbon sequestration in agricultural soils

The impact of the quality and inclusion of tourism services on the lives of people with disabilities in Saudi Arabia

Resting-state functional connectivity correlates of gait and turning performance in multiple sclerosis: a multivariate pattern analysis

Abstract Multiple sclerosis (MS) often leads to mobility impairments, yet the neural mechanisms underlying these deficits remain poorly understood. This study examined whether resting-state functional connectivity (rs-FC) differs between people with MS (PwMS) and healthy controls in relation to spatiotemporal mobility performance. We hypothesized that group differences within the default mode (DMN), frontoparietal (FPN), somatomotor (SN), and visual (VIS) networks would be associated with gait and turning metrics. Twenty-nine PwMS and 28 matched controls completed a two-minute walk test, 180° walking turns, and 360° in-place turns at natural and fast speeds. fMRI data were analyzed using multivariate pattern analysis (MVPA) and post-hoc seed-to-voxel analyses for gait speed, cadence, double support time, stride length, turn duration, peak velocity, and turn angle. PwMS exhibited slower gait speed, shorter stride length, and impaired 360° turning, but no group differences in cadence, double support, or 180° turn metrics. MVPA revealed rs-FC differences across DMN, FPN, SN, and VIS networks. While rs-FC differences were evident for walking metrics, within-group associations were not significant. In contrast, 360° turn angle showed distinct within-group rs-FC associations, particularly involving VAN and DAN networks. These findings highlight turning as a sensitive task for capturing functional neural differences in MS.

A hybrid machine learning-enhanced MCDM model for transport safety engineering

Abstract Delivering reliable decision recommendations and policy inferences is essential for multi-criteria decision-making (MCDM) processes, particularly for transport safety engineering. This study proposes a hybrid machine learning-enhanced MCDM model that integrates distance correlation-based criteria importance through intercriteria correlation (DCRITIC), weighted aggregated sum product assessment (WASPAS), and K-means clustering, referred to as the DCRITIC–WASPAS–K-means model. In particular, we incorporated a machine learning tool (i.e., a graph-based technique) into the model to effectively and robustly select initial centroids. This integration addresses the uncertainty in traditional k-means clustering, which arises from varying initial centroids and its sensitivity to outliers, especially in datasets with noisy or skewed data points, and, more importantly, reduces the number of iterations and runtime cost. This approach improves the robustness and reliability of decision outcomes, thereby supporting more credible and actionable policy interventions. A case study involving transport safety engineering in the Organization of American States (OAS) region validates the model’s practical utility. Comparative analyses demonstrate its superior performance in ensuring consistent decision outputs and communicating policy implications effectively. The proposed framework provides public administrators, policymakers, and government agencies with a reliable, scalable, and data-driven tool for strategic planning and resource allocation in uncertain environments.

Neurophysiological correlates to the human brain complexity through q-statistical analysis of electroencephalogram

Intersubjectivity and value reproducibility of outcomes of quantum measurements

Abstract Every measurement determines a single value as its outcome, and yet quantum mechanics predicts it only probabilistically. The Kochen–Specker theorem and Bell’s inequality are often considered to reject a realist view but favor a skeptical view that measuring an observable does not mean ascertaining the value that it has, but producing the outcome, having only a personal meaning. However, precise analysis supporting this view is unknown. Here, we show that a quantum mechanical analysis turns down this view. Supposing that two observers simultaneously measure the same observable, we can well pose the question as to whether they always obtain the same outcome, or whether the probability distributions are the same, but the outcomes are uncorrelated. Contrary to the widespread view in favor of the second, we shall show that quantum mechanics predicts that only the first case occurs. We further show that any measurement establishes a time-like entanglement between the observable to be measured and the meter after the measurement, which causes the space-like entanglement between the meters of different observers. We also show that our conclusion cannot be extended to measurements of so-called ‘generalized’ or ‘unsharp’ observables, suggesting a demand for reconsidering the notion of observables in foundations of quantum mechanics.

Probing the mechanisms of flavonoids in corn silk for treating type 2 diabetes by in Silico and in vitro experiments

A machine learning tool for predicting newly diagnosed osteoporosis in primary healthcare in the Stockholm Region

Abstract Improving accuracy and timeliness for osteoporosis diagnosis could help prevent fragility fractures, morbidity, and mortality for older individuals. Osteoporosis is an often silent health condition, especially as regards vertebral fractures, and WHO issued a call to action for primary care to lead efforts in screening, assessing, and managing diseases such as osteoporosis. We used a machine learning method, Stochastic Gradient Boosting (SGB), to identify what diagnoses in a primary care setting predict a new osteoporosis diagnosis, using a sex- and age-matched case–control design. Cases of new osteoporosis (ICD-10 code: M80, M81, M82) were identified across all outpatient care settings during 2012–2019. We included individuals aged ≥ 40 years old, stratified by sex and age-groups 40–65 years and > 65 years old. Controls were sampled from outpatients that did not have osteoporosis at any time during 2010–2019. Using the SGB model, we ranked the most important diagnoses related to newly diagnosed osteoporosis, presented as the normalized relative influence (NRI) score with a corresponding odds ratio of marginal effects (OR ME ) of being newly diagnosed with osteoporosis. A train-test approach was used to develop the model, with the performance evaluated using area under the curve (AUC). In total , we included 30,741 patients with osteoporosis aged ≥ 40 years. AUC was high, > 0.899 for all age and sex stratas. The number of visits to primary care in the year prior to the osteoporosis diagnosis contributed with the most predictive information for all age and sex stratas. For all age groups several other factors also showed high NRI and OR ME and among them many unspecific diagnoses such as Dorsalgia showed high NRI, (2.6–9.0%) and other painful musculoskeletal disorders. However, our study also showed that the diagnosis of Hypertension had a very high NRI for patients aged > 65 years but not in patients 40–65 years of age. In this AI study, including only diagnoses from patients seen in primary health care centres, we found that the number of consultations in primary care had high predictive information as well unspecific diagnoses including muscle and skeletal pain predicted high risk for osteoporosis in all age groups.

Multiscale structural and crystallographic characterisation of pigeon eggshells and membranes as an evolutionary model for biomimetic applications

Adaptive resource aware and privacy preserving federated edge learning framework for real time internet of medical things applications

Compact dual-band antenna array for environmental monitoring systems

Abstract A portable, high-resolution, ground-based synthetic aperture radar is essential for effective environmental monitoring. This paper presents a compact microstrip antenna array with dual bands and dual polarizations. This antenna will be integrated into a radar system to aid disaster mitigation efforts. The proposed antenna array consists of 1 × 4 driven elements for each of the three input/output ports; each driven element is coupled to four parasitic patches for bandwidth enhancement, with two shared parasitic patches between each pair of adjacent driven patches. Two of these ports support vertical polarization and operate in the X-band, achieving a bandwidth of 0.52 GHz. In contrast, the third port supports horizontal polarization and operates in the Ku-band, achieving a bandwidth of 0.67 GHz. The proposed design comprises four stacked layers: the lowest layer is dedicated to the feeding network, followed by a ground layer with rectangular apertures for excitation. On top of the ground layer, a third layer contains the driven patches, and the fourth layer holds the parasitic elements. Using two different substrates, the prospective antenna has a 67.2 mm × 71.1 mm size. The dielectric constant for ground plane layer is 3.48 with height of 0.762 mm while the driven and parasitic patches are on another substrate with dielectric constant of 2.2 for better radiation and height of 1.57 mm. In the X-band, the half-power beamwidth (HPBW) is 80° in the E-plane and 28.5° in the H-plane. In the Ku-band, the HPBW is 35.2° in the elevation plane and 15.6° in the horizontal plane. The antenna acquires gain of 11.17 dBi in the X-band and 14.22 dBi in the Ku-band which is suitable for environmental monitoring system applications.

More sedentary behavior and lower physical activity levels in pregnant women during the COVID-19 pandemic

Abstract This Swedish cohort study performed 2016‒2022 aimed to evaluate activity patterns before and during the COVID-19 pandemic among 1405 pregnant women. Sedentary behavior and physical activity levels were objectively measured during seven consecutive days by an accelerometer in early to mid-pregnancy. Linear regression models adjusted for age, parity, BMI, smoking, country of birth, and timing of measurements were used. A subgroup analysis was performed to evaluate whether activity patterns returned to pre-pandemic levels during year 2022. Compared with before COVID-19, daily sedentary behavior increased by 6.6% points (β 6.6, CI 5.4, 7.8), and daily physical activity levels decreased by 6.7% points (β − 6.7, CI − 7.8, − 5.7) during the pandemic. In 2022, daily sedentary behavior was still increased by 7.2% points (β 7.2, CI 5.3, 9.2), and daily physical activity levels decreased by 7.3% points (β − 7.3, CI − 9.1, − 5.5), compared with before COVID-19. Hence, there was a modest increase in sedentary behavior and a small decrease in physical activity after the pandemic outbreak, and these changes were still present during 2022. It is important that health providers are aware of the potential negative changes in activity patterns among pregnant women following a pandemic or a similar situation.

Modular esterification of unstrained carbonyls through palladium-catalyzed alkyne bridging C-C bond activation

Global sampling decline erodes science potential of natural history collections

Abstract The world’s natural history collections hold over two billion specimens, representing a unique spatial and taxonomic record of biodiversity on Earth over time. In recent decades, the accessibility and value of collections data have grown through specimen digitisation, enhanced connectivity, and enriched information from new genotyping and digital trait extraction. These advances are expanding the relevance of collections beyond taxonomy and evolutionary biology to fields like environmental monitoring, agriculture, biosecurity, and public health. However, their utility for addressing major global challenges relies on mobilising legacy data and continuing specimen collection and digitisation. Here we show substantial declines in the rates of collection of specimen data over recent decades, from analysis of over 150 million records from the Global Biodiversity Information Facility (GBIF) spanning more than two centuries. The degree and timing of decline varies across taxonomic groups and geographical regions. Overall, these findings suggest that the value of natural history collections as global research infrastructure is eroding due to decreased collecting of specimen data across species, locations, and time. This is occurring precisely when applications for these data have never been more important, and advances in data analytics, AI and genomics promise to unlock deeper insights from natural history collections.

11-bit two-dimensional floating-gate memories