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Enhancing diesel engine efficiency with waste cooking oil biodiesel and nano additives for sustainable fuel applications
A baby benefits from personalized gene editing in the clinic
Geospatial analysis of vegetation and land surface temperature for urban heat island mitigation in Hawassa City, Ethiopia
Multi-biological activity evaluation of Sn(П), Zn(П) and Fe(П) complexes based on thiocarbohydrazide schiff bases: synthesis, spectroscopic investigations and fluorescence studies
Abstract Two new Schiff base ligands (L1 and L2) were synthesized by condensing thiocarbohydrazide (TCH) with o-anisaldehyde or p-anisaldehyde in ethanol. Their mono- and bi-nuclear complexes with Sn(II), Zn(II), and Fe(II) were prepared for potential fluorescence and biological applications. Characterization was performed using FT-IR, NMR, UV-Vis spectroscopy, mass spectrometry, molar conductance, TGA, X-ray diffraction and SEM. XRD results indicated good crystallinity with crystallite sizes of 20–50 nm. Fluorescent intensity of free TCH ligands increased upon complexation with Sn, Zn, and Fe, suggesting their potential as fluorescence chemosensors. The compounds exhibited variable antimicrobial activities against Staphylococcus aureus, Escherichia coli, and Candida albicans, but lower than commercial drugs. L1Fe and L1Zn enhanced L1’s cytotoxicity in four colorectal malignancy cells and L1Zn in skin cancer cells (A375), lung cancer cells (A549), uterine cervix cells (HeLa), and glioblastoma cells (U87). L1Fe showed enhanced activity in mammary adenocarcinoma cells (T47D) and triple-negative breast cancer cells (MDA-MB-231). L2Sn exhibited 70fold increase in L2’s DPPH radical scavenging compared to the antioxidant ascorbic acid. L1Zn and L2Zn complexes outperformed indomethacin in reducing inflammation in RAW macrophages, enhancing the nanomolar efficacy of L1 and L2. These complexes have promising utility in cancer diagnosis, monitoring and highly selective duality of anti-inflammatory/cytotoxicity treatments.
Collaborative filtering models an experimental and detailed comparative study
Assessing the feasibility of HPV screening for cervical cancer in pregnant women in Ethiopia
Abstract Pregnant women have historically and are currently being excluded from cervical cancer screening in most low and middle-income countries (LMICs). The aim of this study was to assess the feasibility and outcomes of including pregnant women in a HPV self-sampling-based screening program in Ethiopia. Pregnant women, recruited from a previously established cohort, were included. They answered a questionnaire and provided HPV self-samples. If the woman was HR-HPV positive, she underwent triage with VIA with or without Iodine. If positive in triage, the woman was re-scheduled after delivery for a new exam. Primary outcome was screening participation. The participation rate of pregnant women was 92.1% (117/127) (95% CI 86.0–96.1%). They had the same knowledge about cervical cancer and acceptance rate to the study as their non-pregnant peers. Pregnant women had less history of previous screening (p = 0.08). The HPV prevalence was 25.4% (29/114) in self-samples. 93.1% (27/29) attended follow-up, where only 11 had not delivered, and 54.6% (6/11) had detectable HPV infection in their cervical samples. Including pregnant women in HPV self-sampling-based screening is feasible and highly accepted. The findings support integrating pregnant women into cervical cancer screening programs in to enhance prevention and early detection efforts. Clinical trials ID: NCT05125380.
Perception of health professionals towards electronic prescription in a teaching hospital
The antitumor effect of tlr4 inhibition in head and neck cancer cell lines
Morphodynamics of human early brain organoid development
Abstract Brain organoids enable the mechanistic study of human brain development and provide opportunities to explore self-organization in unconstrained developmental systems 1–3 . Here we establish long-term, live light-sheet microscopy on unguided brain organoids generated from fluorescently labelled human induced pluripotent stem cells, which enables tracking of tissue morphology, cell behaviours and subcellular features over weeks of organoid development 4 . We provide a novel dual-channel, multi-mosaic and multi-protein labelling strategy combined with a computational demultiplexing approach to enable simultaneous quantification of distinct subcellular features during organoid development. We track actin, tubulin, plasma membrane, nucleus and nuclear envelope dynamics, and quantify cell morphometric and alignment changes during tissue-state transitions including neuroepithelial induction, maturation, lumenization and brain regionalization. On the basis of imaging and single-cell transcriptome modalities, we find that lumenal expansion and cell morphotype composition within the developing neuroepithelium are associated with modulation of gene expression programs involving extracellular matrix pathway regulators and mechanosensing. We show that an extrinsically provided matrix enhances lumen expansion as well as telencephalon formation, and unguided organoids grown in the absence of an extrinsic matrix have altered morphologies with increased neural crest and caudalized tissue identity. Matrix-induced regional guidance and lumen morphogenesis are linked to the WNT and Hippo (YAP1) signalling pathways, including spatially restricted induction of the WNT ligand secretion mediator (WLS) that marks the earliest emergence of non-telencephalic brain regions. Together, our work provides an inroad into studying human brain morphodynamics and supports a view that matrix-linked mechanosensing dynamics have a central role during brain regionalization.
Synergistic insecticidal effects of zinc-loaded zeolite nanoparticles combined with essential oils against Callosobruchus maculatus
Abstract Callosobruchus maculatus (F.) is a serious pest that causes post-harvest losses, which is a threat to global food security, therefore there is need to develop sustainable pest management strategies. This study investigates the synergistic insecticidal effects of zinc-loaded zeolite nanoparticles in combination with essential oils from Rosmarinus officinalis (L.) and Pimpinella anisum (L.) against C. maculatus adults and their progeny. Zeolite-A and zeolite-X were synthesized and characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS), and found to be highly crystalline and successfully zinc functionalized. The chemical profiles of the essential oils were elucidated by gas chromatography-mass spectrometry (GC-MS). The results showed that zeolites alone had moderate insecticidal activity against the tested insect. Zeolites loaded with zinc enhanced insecticidal activity on C. maculatus. Combining zeolites with essential oils further increases insecticidal activity, with LC50 values ranging from 161 to 306 mg/kg. Zeolite nanoparticles and P. anisum essential oil formulation was the most effective in killing C. maculatus adults and progeny. Co-toxicity factor analysis indicated that there were synergistic effects between the essential oils and zeolites, especially between P. anisum and Zn-zeolite-A. Morphological examination of treated C. maculatus adults via SEM revealed cuticle abrasions, desiccation areas, and damage to sensilla, indicating a physical mode of action for the zeolites. This study suggests that zeolite nanoparticles and essential oil combinations can be used as eco-friendly insecticides for the management of C. maculatus in stored cowpea seeds.
Design of a new method for occupancy monitoring in smart home care with autonomous mobile robot within Internet of Things
Impacts of immune checkpoint inhibitors use on the HIV reservoir are linked to provirus sequences but not integration sites
Leveraging blockchain for cybersecurity detection using hybridization of prairie dog optimization with differential evolution on internet of things environment
Tiny Australian predator defies drought to recover from near-extinction
Scattering and dynamic stress concentration analysis of elastic waves around arbitrarily shaped holes in piezoelectric smart building materials
Hybrid quantum-classical-quantum convolutional neural networks
Correction: Online optimization of continuous casting cutting
Dual-model approach for accurate chest disease detection using GViT and swin transformer V2
Structure analysis in an octocopter using piezoelectric sensors and machine learning
Abstract This study presents a novel diagnostic methodology for assessing drive system damage and its propagation in an unmanned aerial vehicle (UAV) using piezoelectric sensors mounted on each arm of the drone. In contrast to existing studies that focus solely on fault localization, this work investigates the spatial propagation of structural responses to localized motor faults under varying operating conditions. By varying the PWM control signal duty cycle on one motor, different degrees of damage (from 20% to 80%) were simulated. Voltage signals were recorded on each arm of the drone to identify damage and to optimize the number and placement of the sensors. Statistical features extracted in both the time and frequency domains were calculated within sliding time windows. These features (e.g., mean, variance, spectral skewness, spectral kurtosis) from voltage time-series were used as input data for machine learning models (e.g., Random Forest and K-Nearest Neighbors), which are widely applied in the diagnostics of rotary systems for binary classification problems (distinguishing between intact and damaged states of varying damage level). The highest classification accuracy was achieved for the arm where the electric motor failure was induced (from 93% to 94% depending on the degree of damage), while the lowest accuracy was obtained for the opposite arm (from 50% to 57% depending on the degree of damage). It was found that diagnostic accuracy increases when frequency-domain features of the signals are used, particularly for the opposite arms. The proposed methodology provides valuable insights into the structural behavior of the drone in both ground and flight conditions, illustrating the propagation of local damage to other components. The results contribute to the development of robust diagnostic techniques for health monitoring and structural reliability assessment of unmanned aerial vehicles (UAVs).