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Single-cell RNA transcriptome analysis reveals regulatory programs of RNA binding proteins in diabetic foot ulcers
Quantum secured blockchain framework for enhancing post quantum data security
Characterizing plasma lipid species in metabolic dysfunction associated steatotic liver disease in persons with type 1 diabetes
Manipulating dietary protein and amino acids in the common marmoset Callithrix jacchus impacts circulating metabolites and FGF21 levels
Multi-objective particle swarm algorithm based on angular segmentation archive and dynamic update tactics
Friction and wear properties of Cu-CuG nanocomposite fabricated by accumulative roll bonding
Immunoglobulin G dynamics and outcomes of hypogammaglobulinemia in liver transplant recipients
Steering strategies for wasp inspired self propelled needles
Abstract Positioning a thin needle into a solid substrate near a target region is difficult because the needle can easily bend and buckle. Nevertheless, in nature, female parasitic wasps can do this by using buckling prevention and steering mechanisms. This study presents a self-propelled needle that incorporates wasp-inspired steering mechanisms, specifically, the use of pretension and asymmetry within the needle segments. The needle with an outer diameter of 0.89 millimeters comprises seven parallel needle segments, with the central needle segment being either straight for a forward trajectory or prebent for steering purposes. By retracting and rotating the prebent central needle segment, the needle is capable of omnidirectional steering. The performance of the needle in tissue-mimicking phantoms was evaluated in terms of its propulsion efficiency and steering performance. The propulsion efficiency, affected by slippage of the needle segments with respect to the tissue-mimicking phantoms, was, on average, 63% ± 4% for forward motion and 55% ± 7% for steering motion. Moreover, the needle successfully steered with a mean deflection-to-insertion ratio of 0.41 ± 0.11 (i.e., radius-of-curvature of 44 mm). The proposed bioinspired needle design is a relevant step toward developing steerable needles for percutaneous interventions.
Human activity-aware coverage path planning for robot-based mosquito control
Tonsillectomy and/or adenoidectomy improves macular microcirculation in children with obstructive sleep apnea
Knowledge, attitudes, and practices regarding home-based cardiac rehabilitation in patients with chronic heart failure
Hybrid deep learning-enabled framework for enhancing security, data integrity, and operational performance in Healthcare Internet of Things (H-IoT) environments
Abstract The increasing reliance on Human-centric Internet of Things (H-IoT) systems in healthcare and smart environments has raised critical concerns regarding data integrity, real-time anomaly detection, and adaptive access control. Traditional security mechanisms lack dynamic adaptability to streaming multimodal physiological data, making them ineffective in safeguarding H-IoT devices against evolving threats and tampering. This paper proposes a novel trust-aware hybrid framework integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models, and Variational Autoencoders (VAE) to analyze spatial, temporal, and latent characteristics of physiological signals. A dynamic Trust-Aware Controller (TAC) is introduced to compute real-time trust scores using anomaly likelihood, context entropy, and historical behavior. Access decisions are enforced via threshold-based logic with a quarantine mechanism. The system is evaluated on benchmark datasets and proprietary H-IoT signals under diverse attack and noise scenarios. Experiments are conducted on edge devices including Raspberry Pi and Jetson Nano to assess scalability. The proposed framework achieved an average F1-score of 94.3% for anomaly detection and a 96.1% accuracy in access decision classification. Comparative results against rule-based and statistical baselines showed a 12–18% improvement in detection sensitivity. Real-time inference latency was maintained under 160 ms on edge hardware, validating feasibility for critical H-IoT deployments. Trust scores exhibited high stability under adversarial data fluctuations. This research delivers a scientifically grounded, practically scalable solution for adaptive security in H-IoT networks. Its novel fusion of deep learning and trust modeling enhances both responsiveness and resilience, paving the way for next-generation secure health and wearable ecosystems.
Moderating effect of self compassion on compassion fatigue and satisfaction among counselors
Spatial and multilevel analysis of determinant factors for safely managed sanitation services in Ethiopia
Adaptation and validation of the Indonesian version of the early feeding skill assessment tool for low birth weight infants
Abstract Initiating oral feeding at inappropriate times can cause stress and complications for Low Birth Weight (LBW) infants. It is essential to assess the infant’s readiness for oral feeding to minimize stress and support the transition to full oral feeding. An objective assessment tool is necessary to improve the accuracy of determining the right time to start oral feedings. This study aimed to translate, adapt, and psychometrically validate the original English version of the Early Feeding Skill Assessment Tool (EFS) into the Indonesian language. The EFS questionnaire, an observation instrument with 19 items, uses a 3-option scoring structure: not yet evident, emerging, or consistently observed. The following EFS adaptation phases: (1) forward translation; (2) synthesis of forward translation; (3) back translation; (4) harmonization of the original and back translation; (4) pre-testing with the expert committee for content and relevance; (5) pre-testing with target user population for clarity; and (6) field testing. The translation was carried out by a certified translator in conjunction with a bilingual health professional. Seven neonatal experts participated in the pre-testing phase, while six nurses engaged with the target population for further evaluation. A total of 128 observations were conducted on infants exhibiting stable hemodynamics during the field testing phase. The two forward translations differed in 12 items, leading to expert discussions and consensus on the translation. The pre-testing among seven expert for content validity showed The EFS I-CVI and S-CVI/ave were 1 for all questions, indicating full expert agreement. The pre-testing among six nurses for face validity showed clear instructions (93%), with question agreement ranging from 60 to 100%. Six items had < 80% agreement and were rewritten as suggest from nurses. In field testing from 128 observations among 52 LBW infants revealed 19 items were valid and their reliability was 0.918. The 19-item EFS Indonesian version is valid and reliable for LBW infants. It can be used to implement as an assessment for a common language for infant feeding skills among interprofessional teams, contributing to infant feeding success.
Novel insights into electrical double layers in carbonate reservoirs under low-salinity water injection using molecular dynamics simulation
Abstract Understanding how mineral heterogeneity and brine salinity influence the electrical double layer (EDL) structure is crucial for optimizing low-salinity water injection strategies in carbonate reservoirs. In this study, molecular dynamics (MD) simulations were conducted to elucidate the mechanisms governing EDL behavior at the interface of a novel composite carbonate surface (87% calcite, 13% quartz) in contact with diluted water (DW), seawater (SW), and high-salinity formation water (FW) in presence of complex model oil including wide variety of nonpolar oil hydrocarbon. Results indicated that increasing brine salinity compressed the Stern layer significantly, strengthening electrostatic interactions at the mineral surface and promoting oil adhesion due to enhanced ion bridging by divalent cations (Ca²⁺, Mg²⁺). This process resulted in a pronounced shift toward hydrophobic (oil-wet) surface characteristics under FW conditions, compared to SW and DW. Additionally, the observed differences in water–surface interactions between calcite and quartz were explained mechanistically: the highly charged calcite surface enabled strong inner-sphere coordination of water molecules at 2.37 Å, whereas the neutral quartz surface supported only weaker outer-sphere hydration at 3.1 Å. Ion mobility analysis further revealed enhanced dynamics of Na⁺ ions in SW system, facilitating disruption of oil–surface ionic bridges, while FW promoted ion clustering and reduced ionic mobility, stabilizing oil attachment. These insights emphasize the critical role of mineral surface chemistry and salinity-dependent electrostatic interactions in governing wettability and oil recovery efficiency during low-salinity waterflooding.
Knowledge on HIV postexposure prophylaxis and associated factors among healthcare workers in Kigoma region, Tanzania
AI-enhanced rapid diagnostic testing platform for mass opisthorchiasis screening
Abstract Cholangiocarcinoma (CCA) is a prevalent malignancy in countries along Mekong basin, closely linked to chronic infections caused by Opisthorchis viverrini (OV). Early detection of OV-infected individuals holds significant promise for screening at-risk populations in endemic regions. Recent advancements in immunochromatographic methods have led to the development of a rapid diagnostic test (RDT) based on urinary antigens. However, the current interpretation relies on visual assessment of T-band color intensity, which can be subjective and prone to variability. Furthermore, aggregating data at the regional/country level demands data digitization, a time and resource intense task that introduces further errors. To address this limitation, we introduce the OV-RDT platform, a cloud-based system incorporating artificial intelligence (AI) designed to standardize the reading and interpretation of OV-RDT results while facilitating mass screening campaigns for opisthorchiasis. This cross-platform solution, available on Android and iOS devices, consists of three key components: a mobile application, an intelligent dashboard, and a cloud server cluster. The server cluster has two main components the data processing server and the AI server. The AI server operates two AI-based models systematically developed and validated for image quality assessment and T-band grading of OV-RDT test kit images. The data processing server periodically retrieves and processes data from the cloud database, enabling comprehensive daily visualization through the intelligent dashboard. Validation through extensive field testing was conducted specifically in the northeastern region of Thailand, where opisthorchiasis prevalence is among the highest globally, demonstrating remarkable effectiveness by processing over 100,000 samples. While our platform shows excellent performance in this endemic region, external validation in other geographical areas would be necessary to establish broader generalizability. The EfficientNet-B5-based deep learning model used in the platform exhibited impressive performance in both image quality assessment (98% accuracy) and infection grading classification (95% accuracy in detecting OV infection status). The platform’s user-friendly interface has achieved high satisfaction rates (4.41/5.00) among healthcare workers, while its intelligent dashboard offers real-time analytics and geospatial visualization capabilities. This integrated approach marks a significant advancement in mass screening for opisthorchiasis, potentially enhancing early detection rates and supporting more effective public health interventions in Southeast Asia and the Mekong Basin countries. This study addresses the critical need for mass screening in northeastern Thailand, where liver fluke infection rates are particularly severe; however, the platform’s performance in other regions requires future validation studies.