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Correction: Association between socioeconomic background and cancer: An ecological study using cancer registry and various community socioeconomic status indicators in Kanagawa, Japan
pH-triggered liposomal strategy for cisplatin in lung cancer therapy
Sampled-data velocity-free consensus of Multiple Euler-Lagrange systems under irregular communication delays
This paper addresses the challenging problem of achieving sampled-data, velocity-free consensus for multiple Euler-Lagrange systems under irregular communication delays. While passivity-based control (PBC) is a powerful framework for such systems, existing works fundamentally require continuous feedback from neighbors, as their stability proofs cannot handle the discontinuous right-hand-side dynamics generated by sampled-data and abrupt delays. This limitation renders conventional PBC methods inapplicable in many realistic networked scenarios. This work bridges that theoretical gap by introducing a novel control and analysis method. Our strategy treats the system dynamics over continuous intervals separately from the discrete instants of discontinuity, allowing us to rigorously prove consensus. The control strategy incorporates a virtual system framework to operate without velocity measurements and successfully relaxes the impractical requirement that delays must have finite derivatives. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed consensus algorithm. Index terms: Euler-Lagrange system, Multi-agent system, Sampled-data control.
Exploring behavioral determinants of antimicrobial dispensing in drug retail outlets of Addis Ababa, Ethiopia: a mixed methods study
Abstract Antimicrobial resistance (AMR) is a critical global health threat, exacerbated by inappropriate dispensing practices. Despite efforts to improve antimicrobial stewardship, behavioral determinants influencing antimicrobial dispensing remain understudied, particularly in low-resource settings like Ethiopia. This study was aimed to explore the behavioral drivers of antimicrobial dispensing among pharmacy professionals working at community drug retail outlets of Addis Ababa. This convergent, parallel mixed-methods study was conducted in three randomly selected sub-cities of Addis Ababa from March to May 2025. The quantitative component addressed the prevalence and predictors of dispensing behavior, surveying 240 pharmacy professionals selected via multi-stage sampling from 120 drug retail outlets. Data were collected using digital (Google Forms) and paper-based questionnaires, measuring TDF-based behavioral determinants and dispensing behavior. Participants were pharmacists and druggists with at least three months of dispensing experience, selected from private pharmacies and drug stores. Hierarchical multiple regression and confirmatory factor analysis were performed using SPSS and AMOS. The qualitative component explored contextual nuances through in-depth interviews with 16 professionals until thematic saturation, analyzed using Dedoose software with a thematic approach, without a predefined framework to allow free expression of feelings. Data quality was ensured through pre-testing, digital validation, and transcription cross-checking. Quantitative findings revealed inappropriate dispensing in 53.75% of cases (129/240). One-sample t-tests showed significant positive deviations for most TDF domains (p < .001), except environmental context (p = .642). Hierarchical regression indicated demographic and behavioral factors explained 62% of dispensing behavior variance, with age (β = 0.630, p < .001), female gender (β = 0.343, p < .001), and behavioral factors; (Professional Role and Self-Regulation: β = 0.456, p < .001; Emotional and Social Dynamics: (β = 0.359, p < .001)) as positive predictors, while Skills and Belief Systems (β=-0.431, p < .001) and experience (≥ 5 years) negatively predicted appropriate dispensing (β=-0.304, p < .001). Qualitative insights highlighted systemic barriers, including patient pressure, profit motives, and weak regulatory enforcement, which often overrode professional knowledge. Triangulation revealed that while pharmacists understood guidelines, workplace pressures and financial incentives drove non-compliance. Inappropriate antimicrobial dispensing was prevalent and significantly influenced by key determinants: longer experience negatively predicted appropriate dispensing, while female gender positively predicted it. Behavioral factors, particularly social and commercial pressures like patient demand and competition, were stronger drivers than knowledge alone.
Development & application of a wearable non-differential calorimeter for skin heat transfer analysis
The thermal properties of human skin are of great interest for understanding local and global body heat loss, various physiological responses or even skin injuries. This study presents a wearable, non-invasive skin calorimeter designed for in vivo measurement of skin heat flux, heat capacity, and thermal resistance. The device, based on the principle of non-differential heat conduction calorimetry, consists of a programmable thermostat, a heat flux sensor and a Peltier cooling system. To operate the device, we propose and calibrate a calorimetric thermal model that includes the skin. This new model approach allows to estimate the core temperature of the tissue where the measurement is performed. Experimental validation of the device was carried out on localized skin areas, both at rest and during moderate physical activity. This skin calorimeter allows determination of thermal properties in different skin regions, with an accuracy of ± 2 mW for the heat flux, ± 1 K/W for the thermal resistance, and ± 0.05 J/K for the heat capacity, for a 2 × 2 cm² skin region. The results confirm the applicability of these devices in sports medicine, thermoregulation studies, and medical diagnostics. This work also includes simulations of the calorimeter’s operation, which help to define its operating range and to study the interaction between the device and the human skin.
miR-378, miR-20a, and miR-520a-3p can be used in a novel serum prognostic panel for cervical cancer
The role of implementation climate in shaping early essential newborn care practice: Insights from a multi-center cross-sectional study in China
Background The World Health Organization (WHO) recommends Early Essential Newborn Care (EENC) to improve newborn outcomes. However, uptake remains suboptimal in many low-resource settings. Organisational factors, such as implementation climate, are crucial but understudied in relation to EENC implementation. Objective To explore how implementation climate mediates the relationship between knowledge, attitudes, and EENC practices. Design Multi-site, cross-sectional study. Setting Twelve tertiary maternity hospitals in China (December 2022–April 2023). Participants 433 nurse-midwives. Methods Validated questionnaires were use to assess knowledge, attitudes, practices, and perceived implementation climate related to EENC. Path analysis and logistic regression were employed to explore direct and indirect relationships. Results A total of 69.3% participants reported good EENC practice. Significant predictors included good knowledge (adjusted odds ratio [AOR] = 2.75; 95% confidence interval [CI]: 1.76–4.31), positive attitudes (AOR = 2.00; 95% CI: 1.17–3.41), in-service training (AOR = 1.88; 95% CI: 1.17–3.02), holding a middle leadership role (AOR = 2.24; 95% CI: 1.20–4.17), and perceived workload. Nurse-midwives who reported heavier workloads were 48% less likely to hold positive attitudes towards EENC (AOR = 0.52; 95% CI: 0.28–0.94), which subsequently affected their EENC practice. The mean score of implementation climate was moderately favorable (3.30 ± 0.77), with the lowest in the rewards domain (3.02 ± 1.11). A one-point increase in climate score was associated with significantly higher odds of a positive attitude (AOR = 4.56; 95% CI: 2.98–6.99). Implementation climate influenced EENC practice indirectly through attitudes (RMSEA = 0.039). Conclusions This study highlights the importance of both individual factors and organizational climate in shaping EENC practices. To improve EENC implementation, healthcare systems should prioritize enhancing the implementation climate through leadership support, establishing appropriate reward systems, and addressing workload challenges. Additionally, integrating EENC training into continuous professional development programs and strengthening support for mid-level leadership are key strategies.
Correction: Magnetic susceptibility components reveal different aspects of neurodegeneration in alpha-synucleinopathies
Analysis of heavy metal contamination in topsoils across land use types within the Manghe River watershed in South Taihang and its source attribution
To investigate the characteristics of soil heavy metal pollution in the Manghe River watershed, a typical industrial and mining complex area in the Yellow River Basin, concentrations of Hg, Cr, Cu, Ni, Pb, Zn, Cd, and pH were measured in 121 topsoil samples (0–20 cm) collected from the study area. Geostatistical methods were employed to analyze the spatial distribution patterns of heavy metals. The pollution status was assessed using the pollution load index (PLI), while correlation analysis, principal component analysis (PCA), and a positive matrix factorization (PMF) model were applied to identify the sources of heavy metals. The results indicated that: (1) The concentrations of Hg, As, Ni, Cu, Pb, Zn, and Cd exceeded their respective background values, with Hg, Pb and Cd reaching 3.52, 4.85, and 46.4 times of the background levels, respectively.(2) Different elements exhibited distinct spatial distribution and diffusion patterns, revealing their respective sources and influencing factors. (3) The overall PLI was 0.785, reflecting a mild pollution level across the region, while industrial and mining lands exhibited severe pollution (PLI = 4.3). The relative contribution of each heavy metal to the pollution load was ranked as follows: Cd (30.35)> Pb (4.76)> Hg (3.62)> Zn (2.18)> As (1.77)> Cu (1.53). (4) Principal component analysis categorized the sources of heavy metals into anthropogenic activities and natural origins. Further analysis using the PMF model delineated four specific sources: coal combustion (10.87%), natural and agricultural contributions (27.37%), transportation and agricultural actives (26.81%), and industrial emissions (34.95%). Finally, the study identified the following feasible strategies for controlling heavy metal pollution: blocking and remediating industrial pollution sources; treating agricultural non-point source pollution through biological methods; and substituting traditional transportation sources with new energy alternatives. This research could support decision-making processes related to the prevention and control of heavy metal pollution in the study area, as well as regional sustainable development.
Generating detectors from anomaly samples via negative selection for network intrusion detection
Development of Ensemble Steric and Electrostatic Chirality (ESEC) descriptors for modelling chromatographic enantioseparations
In this work, chiral molecular descriptors were defined using 2 distinct approaches: (1) scalar triple products of vectorial molecular properties, and (2) descriptors that attempt to quantify the amount of twist in the overall molecular shape. Because both approaches give rise to conformation dependence, descriptor values were averaged over a conformational ensemble obtained by Molecular Dynamics. In addition, a method is introduced that attempts to quantify the asymmetry of the distribution of the descriptor values over the conformational ensemble. The totality of the resulting descriptors were named “Ensemble Steric and Electrostatic Chirality (ESEC) descriptors”. A pilot validation study was performed by building Quantitative Structure-Enantioselectivity Relationships (QSER), i.e. mathematical models to predict the chromatographic separation of enantiomers, using a test set of 43 structurally diverse pharmaceuticals analyzed on a polysaccharide-based chiral stationary phase. The best linear regression model (7 descriptors) for the chiral separation (expressed as selectivity factor) featured a low leave-one-out cross validation error (0.0814), a well-predicted elution sequence of the separated enantiomers (21 out of 23 molecules) and a well-predicted α RS for 27 out of 42 molecules. To the best of our knowledge, this is the first time that acceptable linear QSER models were obtained for chiral chromatographic separations of such a chemically diverse set of pharmaceuticals.
Mapping the distribution of phlebotomine sand fly species with emphasis on Leishmania vectors in Nepal and exploring the potential of DNA barcoding for their identification
Are remittances a buffer against food insecurity? Lessons from a national survey in Bangladesh
Food insecurity continues to be a major global challenge, affecting many people worldwide. Bangladesh is particularly vulnerable due to its susceptibility to frequent climate shocks and socioeconomic challenges. This study investigates the causal relationship between remittance receipt and food security through a comprehensive analysis. Using data from the Household Income and Expenditure Survey (HIES) 2022, we developed a food security index incorporating calorie intake, dietary diversity, food expenditure, and the Food Insecurity Experience Scale (FIES) score. Advanced statistical methods, including Seemingly Unrelated Regression (SUR), Zero-Inflated Negative Binomial, linear regression model, inverse-probability-weighting (IPW), and doubly robust method were employed to identify the factors associated with food security and assess the causal effect of remittance earning. Our findings reveal a strong and positive causal effect of remittance receipt on food security. The observed causal effect remained robust against model misspecification and unmeasured confounders, as confirmed through sensitivity analysis. Key factors such as wealth index, residence type, regional differences, household head’s education, and number of earners also influenced food security outcomes. However, the significance of variables like land ownership, household head’s age, and sex varied across measures. This study highlights the transformative role of remittances in reducing food insecurity. Policies that support remittance flows, improve rural infrastructure, and promote skill development and financial literacy can further strengthen their impact.
Comparative evaluation of the cytotoxic effect of two different gutta percha solvents
Abstract Gutta percha solvents play an important role in removing filling materials from dentinal tubules, allowing irrigation solution to penetrate the tubules. The solvents should be biocompatible and have minimal effects on the viability of periapical tissues. Otherwise, they will result in intense inflammatory reactions and interfere with periapical healing. Aim This in vitro study was to evaluate and compare the cytotoxicity of two gutta percha solvents; grapefruit oil and orange oil. Biological testing was carried out on human fibroblasts that were retrieved from the cell bank and then cultured. Two gutta percha solvents (grapefruit oil and orange oil) were added to the cultured cells. Cell viability was evaluated using a WST-1 assay. The effect was evaluated after exposure to various concentrations at 24 h and 72 h. Statistical analysis was performed using two-way analysis of variance (ANOVA). Grapefruit oil had the most significant cytotoxicity followed by orange oil which had the least significant cytotoxicity. The cytotoxicity of the two solvents used was directly proportional to their concentration. Orange oil could be recommended as a solvent for gutta percha because of its low toxicity.
Characteristics and genesis of permian chert in the middle and upper Yangtze Region, China
Cherts, as chemical sedimentary formations, serve as repositories of historical evolutionary data encompassing paleotectonics, paleogeography, and paleoclimate. Furthermore, they play a crucial role as geological foundations for oil and gas exploration. In the Upper Yangtze region, the origin and underlying genesis mechanisms of unstratified cherts from the Permian period have been subject to ongoing debate. This study employs lithological analyses including outcrop profiles and thin-section observations alongside geochemical analyses of macronutrients, trace elements, and rare earth elements to investigate the depositional environment of laminated cherts from the Permian era. Additionally explored are the siliciclastic origins of non-laminated cherts and the diagenetic mechanisms at play in this area. The findings indicate that stratified chert in the Middle and Upper Yangtze regions originate from basin sedimentation below the carbonate compensation depth interface while unstratified chert primarily form through dissolution of carbonates attributable to both hydrothermal activity and seawater processes. This comprehensive investigation provides a robust geological foundation for oil and gas exploration within this study area while also serving as a valuable reference for future research on studies related to chert.
Hepatitis B virus infection and associated risk factors among mothers attending public health facilities in Bahir Dar, Northwest Ethiopia
Correction: Reasons for stopping Pressurized IntraPeritoneal Aerosol Chemotherapy (PIPAC): A retrospective study to improve future patient selection
Small defect detection in printed circuit boards based on the multiscale edge strengthening and an improved YOLOv10
High throughput machine learning pipeline to characterize larval zebrafish motor behavior
Using machine learning, we developed models that rigorously detect and classify larval zebrafish spontaneous and stimulus-evoked behaviors in various well plate formats. Zebrafish are an ideal model system for investigating the neural substrates underlying behavior due to their simple nervous system and well-documented responses to environmental stimuli. To track movement, we utilized an 8 key point pose estimation model, allowing precise capture of zebrafish kinematics. Using this kinematic data, we trained two random forest classifiers in a semi-supervised learning framework to classify various discreet behavioral outputs including stationary, scoot, turn, acoustic-startle like behavior, and visual-startle like behavior. The classifiers were trained on a manually labeled dataset, and their accuracy was validated showing high precision. To validate our machine learning models, we analyzed behavioral outputs during various stimulus evoked responses and during spontaneous behavior. For additional validation, and to show the utility of our recording and analysis pipeline, we investigated the locomotor effects of several established drugs with well-defined impacts on neurophysiology. Here we show that machine learning model development, enabled by semi-supervised learning developed classification models, provide detailed insights into the behavioral phenotypes of zebrafish, offering a powerful, high throughput method for studying neural control of behavior.