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Magnetic nanoparticles: Emerging technologies for biomedical applications
No full textNanotechnology has transformed biomedical research, making it possible to design innovative materials with precise properties for healing, treatment, and diagnosis. Among its breakthroughs, magnetic nanoparticles (MNPs) stand out for their unique magnetic, chemical, and physical traits. These tiny particles hold promise for delivering drugs directly to targeted areas, generating heat to destroy tumors (magnetic hyperthermia), and supporting bone tissue repair. However, questions remain about how safe they are for the body, their potential toxicity, and their effects over time. This article reviews recent progress in creating and improving MNPs, focusing on how size, shape, and magnetic performance are factors that directly influence how they behave in the body and how these factors are affected by hydrothermal, sol-gel, and environmentally friendly synthesis techniques. Studies assessing their effectiveness, safety, and impact on living cells in both laboratories and animal models are explored. The article also investigates how 3D and 4D printing can embed MNPs into “smart” biomaterials for better tissue regeneration. When combined with triggers such as magnetic fields or heat, functionalized MNPs have shown improved targeted treatments, controlled hyperthermia, and faster bone healing. Despite their promise, regulatory issues, safety concerns, and large-scale production challenges limit the clinical use of MNPs, making multidisciplinary efforts essential to ensure this technology becomes safe, practical, and widely available
Disease burden attributable to intimate partner violence against females and sexual violence against children in 204 countries and territories, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023
No full textBackgroundViolence against women and against children are human rights violations with lasting harms to survivors and societies at large. Intimate partner violence (IPV) and sexual violence against children (SVAC) are two major forms of such abuse. Despite their wide-reaching effects on individual and community health, these risk factors have not been adequately prioritised as key drivers of global health burden. Comprehensive x§and reliable estimates of the comparative health burden of IPV and SVAC are urgently needed to inform investments in prevention and support for survivors at both national and global levels. MethodsWe estimated the prevalence and attributable burden of IPV among females and SVAC among males and females for 204 countries and territories, by age and sex, from 1990 to 2023, as part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2023. We searched several global databases for data on self-reported exposure to IPV and SVAC and undertook a systematic review to identify the health outcomes associated with each of these risk factors. We modelled IPV and SVAC prevalence using spatiotemporal Gaussian process regression, applying data adjustments to account for measurement heterogeneity. We employed burden-of-proof methodology to estimate relative risks for outcomes associated with IPV and SVAC. These estimates informed the calculation of population attributable fractions, which were then used to quantify disability-adjusted life-years (DALYs) attributable to each risk factor. FindingsGlobally, in 2023, we estimated that 608 million (95% uncertainty interval 518–724) females aged 15 years and older had ever been exposed to IPV, and 1·01 billion (0·764–1·48) individuals aged 15 years and older had experienced sexual violence during childhood. 18·5 million (8·74–30·0) DALYs were attributed to IPV among females and 32·2 million (16·4–52·5) DALYs were attributed to SVAC among males and females in 2023. IPV and SVAC were among the top contributors to the global disease burden in 2023, particularly among females aged 15–49 years, ranking as the fourth and fifth leading risk factors, respectively, for DALYs in this group. Among the eight health outcomes found to be associated with IPV, anxiety disorders and major depressive disorder were the leading causes of IPV-attributed DALYs, accounting for 5·43 million (–1·25 to 14·6) and 3·96 million (1·71 to 6·92) DALYs in 2023, respectively. SVAC was associated with 14 health outcomes, including mental health disorder, substance use disorder, and chronic and infectious disease outcomes. Self-harm and schizophrenia were the leading causes of SVAC-attributed burden, with SVAC accounting for 6·71 million (2·00 to 12·7) DALYs due to self-harm and 4·15 million (–1·92 to 13·1) DALYs due to schizophrenia in 2023. InterpretationIPV and SVAC are substantial contributors to global health burden, and their health consequences span a variety of individual health outcomes. Importantly, mental health disorders account for the greatest share of disease burden among survivors. Investing in prevention of these avoidable risk factors has the potential to avert millions of DALYs and considerable premature mortality each year. Our findings represent strong evidence for global and national leaders to elevate IPV and SVAC among public health priorities. Sustained investments are needed to prevent IPV and SVAC and to implement interventions focused on supporting the complex social and health needs of survivors. FundingGates Foundation.This paper was developed as part of the GBD Collaborator Network and GBD Protocol with support from the GBD Secretariat, IHME, and the GBD Collaborator Network under the IHME ID 6064. This study was funded by the Gates Foundation
Mapping the Processes of Pharmacist Therapeutic Reasoning: A Scoping Review and Development of the Pharmacist Therapeutic Reasoning Model
No full textBackgroundPharmacists make complex therapeutic decisions. Yet the reasoning processes that result in these choices, therapeutic reasoning (TR), are poorly defined. Existing models of clinical reasoning often overlook how pharmacists weigh risks and benefits of treatment options. AimTo develop a conceptual model that characterizes the processes, subprocesses, and cognitive strategies used during pharmacist TR based on current literature. MethodsA scoping review was conducted in February 2024 to identify studies describing pharmacist or pharmacy student reasoning during therapeutic decision-making. Data were extracted by two researchers using a standardized form and inductively analyzed. Codes were thematically organized based on shared properties: discrete knowledge, reasoning connections, or modifying influences. Theory use was assessed using the Continuum of Theory Talk framework. ResultsTen studies met inclusion criteria representing diverse contexts, scope, and reasoning stimuli. A total of 109 unique codes were identified and synthesized into a conceptual pharmacist therapeutic reasoning model (Pharm-TRv1). It consists of three knowledge domains (drug, disease, and patient information), three core reasoning processes connecting these domains (drug–patient, drug–disease, patient–disease), and three to four related subprocesses. The model includes five influencing factors: two external (decision context and entry and exit from reasoning) and three internal cognitive modifiers (metacognition, closing a knowledge gap, and reflection). ConclusionPharm-TRv1 provides a foundational model of pharmacist therapeutic reasoning grounded in current literature. It offers a structured way to describe, teach, and study how pharmacists evaluate treatment options. Future research should further explore specific processes and subprocesses, validate the model, and explore broader theoretical perspectives
UNDERSTANDING THE ROLE OF SESTRIN2 IN ANGIOGENESIS AND RISK OF ISCHEMIC DISEASES ASSOCIATED WITH DIABETES: AN IN-VITRO STUDY AND HUMAN INVESTIGATION IN PARTICIPANTS OF QATAR BIOBANK
No full textDiabetes mellitus and cardiovascular disease represent converging global epidemics, primarily driven by diabetes-induced endothelial dysfunction and impaired reparative angiogenesis. The stress-inducible protein Sestrin2 (SESN2) has been identified as a potential guardian of vascular health, but its precise role and clinical significance in the diabetic milieu remain poorly defined. This dissertation tested the central hypothesis that SESN2 is a critical regulator of endothelial angiogenesis and that its dysregulation is a key contributor to diabetic vascular complications, employing a multi-scale approach from molecular dynamics to population health. In-vitro, using human endothelial cells under glycative stress, this work established that SESN2 is indispensable for angiogenic potential. SESN2 overexpression preserved cell proliferation, invasion, and tube formation by activating the nuclear factor erythroid 2-related factor 2 (NRF2)/heme oxygenase-1 (HO-1) antioxidant axis, upregulating vascular endothelial growth factor (VEGF), and maintaining pro-survival AKT signaling while preventing mechanistic target of rapamycin (mTOR) and mitogen-activated protein kinase (MAPK) hyperactivation. Conversely, silencing SESN2 exacerbated stress-induced apoptosis, an outcome supported by transcriptomic analyses that showed SESN2 loss suppresses angiogenic gene networks while promoting apoptotic pathways. A study of the Qatar Biobank cohort revealed a critical clinical paradox. Higher circulating SESN2 was protective in healthy individuals but was associated with a greater cardiometabolic burden in patients with type 2 diabetes. This suggests SESN2's role shifts from a marker of resilience to a distress signal of overwhelming cellular stress. This finding was corroborated by deep learning-based retinal analysis, where high SESN2 expression correlated with increased vascular density and systemic VEGF levels. At the atomic level, computational modeling elucidated the mechanism of SESN2 as a specific leucine sensor, uncovering a novel allosteric compaction mechanism essential for its function. This structural insight enabled the successful virtual screening and identification of novel, potent small-molecule binders. In conclusion, this dissertation establishes SESN2 as a critical, context-dependent regulator of vascular homeostasis. Its role transitions from a protective guardian in health to a biomarker of severe metabolic distress in chronic diabetes. These findings resolve a key clinical paradox and position SESN2 as a promising target for both diagnostic and therapeutic interventions in diabetic vascular complications
ENHANCING UAV SECURITY: A HYBRID MACHINE LEARNING APPROACH TO INTRUSION DETECTION IN UAV NETWORKS
No full textThe widespread integration of Unmanned Aerial Vehicle (UAV) in intelligent transportation, military reconnaissance, and critical infrastructure monitoring has introduced heightened vulnerabilities to sophisticated cyber-physical attacks. Conventional Intrusion Detection System (IDS) typically isolate cyber and physical layers, failing to capture correlations between telemetry anomalies and network threats, and thus struggle to detect multi-vector attacks spanning both domains. This study proposes a hybrid deep learning-based IDS that fuses synchronized telemetry and MAVLink traffic to model spatiotemporal intrusion signatures with high fidelity. The architecture integrates one-dimensional Convolutional Neural Network (1D-CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Temporal Convolutional Network (TCN), jointly capturing local anomalies, sequential dependencies, and long-range deviations, with the final intrusion decision obtained via probability-level ensemble averaging across the three branches. A cyber-physical dataset was developed using ArduPilot Softwarein- the-Loop (SITL) across one-, two-, and four-UAV swarm deployments under three topologies: drone-to-base station (D2BS), leader-follower, and drone-to-drone (D2D) relay. Four representative attack types were included-Denial of Service (DoS), Replay, False Data Injection (FDI), and Evil Twin. The dataset will be made publicly available upon publication to support reproducibility and further research. Beyond cyber-physical fusion, this work addresses key swarm-specific challenges absent in prior studies: syniii chronizing asynchronous data streams and accounting for response-time feasibility in multi-hop UAV communications. Experimental results show over 95% classification accuracy with reduced false positives, outperforming state-of-the-art unimodal baselines. These contributions highlight not only the role of cyber-physical integration but also the importance of swarm-scale evaluation and open datasets in advancing resilient, real-time UAV security systems
Examining the Effects of Wavelength and Light Intensity on Astaxanthin Production in Haematococcus lacustris
No full textThis thesis studies the effects of light quality and light wavelength on growth performance, nutrient uptake, and pigment accumulation of the freshwater microalga Haematococcus lacustris under mixed nutrient and salinity stresses. The research highlights the potential of microalgae as renewable and sustainable biological resources against the background of global concerns associated with the rapid pace of industrialization, population growth, and urbanization that collectively increase stress on natural ecosystems. Due to their high photosynthetic efficiency, fast growth rates, and ability to acclimate to varying conditions, microalgae are a potentially suitable source of sustainable biomass and value-added metabolite generation. The process optimization of culture conditions for biomass and astaxanthin production under operational and environmental constraints at large-scale cultivation sites in arid circumstances like Qatar is the main emphasis of this work.
The experiments were conducted under controlled light conditions (Red, Blue, Red–Blue, and shifting the light from Blue→Red) with known nitrogen and phosphorus levels and increasing salinities. Growth was estimated using an optical density (OD₇₅₀), specific growth rate (μ), and total pigment analysis, while nutrient assimilation, in particular total nitrogen and total phosphorus, was measured to determine shifts in redox metabolism under different light quality spectra and stress conditions.
The findings indicated that the light spectrum and order had noticeably affected the growth kinetics as well as the nutrient removal rate. Highest biomass production and P-use efficiency were supported by blue and red light alone or their combination (Red–Blue), while sequential Blue→Red treatment led to low nutrient assimilation and metabolic shift toward increased carotenoid (astaxanthin) accumulation. Nitrogen and phosphorus uptake significantly decreased under salinity stress, indicating a physiological tradeoff between stress tolerance and nutrient assimilation capacity.
When combined, the findings show that stress and light quality can work together to promote photosynthetic competence and balance the generation of secondary metabolites with vegetative growth. When compared to culture in autotrophic or red+warm light, cultivation of H. lacustris with green+cool/warm spectrum combined with moderate nutrient supply results in higher biomass production and nutrient uptake, whilst sequential or stress-inducing irradiation conditions lead to astaxanthin accumulation at the expense of growth. Therefore, this study provides scientific justification for the development of ideal two-stage cultivation regimes tailored to Qatar's climate, which may align with more general national strategic goals on bioeconomic and environmental sustainability, with an emphasis on value-added products from microalgae
Intranasal CD40-targeted recombinant MERS-CoV S1 protein is superior to intramuscular immunization in eliciting systemic and mucosal immune responses in mice
No full textThe Middle East respiratory syndrome coronavirus (MERS-CoV) is a global One Health challenge with a potential pandemic threat, with no approved vaccines or antiviral drugs available to date. Thus, there is an urgent need for a safe and effective vaccine. Herein, we developed a recombinant subunit self-adjuvanted fusion protein vaccine targeting the MERS-CoV S1 subunit to CD40-expessing APCs (S1-F/CD40L). We found that intramuscular injection of S1-F/CD40L in conjunction with Alum and CpG was superior to Alum or CpG alone in terms of systemic Ag-specific humoral and cellular responses and Th1-dominant phenotype. Furthermore, the immunogenicity of co-adjuvanted S1-F/CD40L was compared to that of S1 alone via both intramuscular and intranasal immunization. Two intramuscular and intranasal doses of S1-F/CD40L and S1 were immunogenic in eliciting systemic and mucosal humoral and cellular immunity, including IgG, IgA, neutralizing antibodies (nAbs), and T cell responses in mice, with a greater response in the CD40-targeted S1 group. Intranasal vaccination with S1-F/CD40L induced systemic humoral and cellular immune responses comparable to those induced by intramuscular vaccination, including binding and nAbs and T cell responses. Importantly, intranasal vaccine was able to elicit significantly higher local mucosal humoral and cellular immune responses in mouse lungs and markedly elevated circulating IgA levels compared to intramuscular vaccination. Collectively, our results suggest that the S1-F/CD40L vaccine co-adjuvanted with Alum and CpG can be used as an effective and safe mucosal candidate vaccine against MERS-CoV. Furthermore, these data demonstrate that the incorporation of CD40L as APCs targeting ligand and molecular adjuvant enhances immunogenicity, thus offering a promising platform that could be explored further to respond to future emerging pathogens and possible outbreaks.This work was funded by Community Jameel - Saudi Arabia under Jameel Fund for Infectious Disease Research and Innovation under grant no. JML: 004-141-2024. The authors would also like to thank the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia for their support
Deep learning-based cross-device standardization of surface-enhanced Raman spectroscopy for enhanced bacterial recognition
No full textSurface-enhanced Raman spectroscopy (SERS) is a powerful, label-free technique for pathogen detection; however, its broader adoption in clinical diagnostics is hindered by inconsistent spectral quality across portable and laboratory-grade instruments, limited cross-device reproducibility, and the poor generalizability of existing machine learning approaches. These limitations restrict reliable and rapid pathogen identification at the point of care. To address this gap, we collected SERS spectra from analytes spread on silver nanorod (AgNR) substrates using four portable Raman systems (Enwave, Tec5, First Defender, and Rapid ID) and one laboratory-grade reference device (Renishaw). The dataset included 20 analyte classes representing clinically relevant bacterial signatures and reference compounds. We propose a deep learning framework comprising: (1) SERS-D2DNet, a one-dimensional sequence-to-sequence neural network that transforms spectra from portable devices into high-fidelity laboratory-grade equivalents, and (2) SuperRaman, a lightweight super-operational neural network (Super-ONN) for efficient multiclass bacterial classification. Primary and ablation studies confirm the complementary role of domain transformation and classification, demonstrating improved feature separability and reduced misclassification rates. Quantitative results show that SERS-D2DNet reduced mean absolute error to 0.01 and increased R2 to over 98 % across devices, while SuperRaman achieved up to 100 % classification accuracy post-transformation. Compared to existing approaches, SERS-D2DNet delivered the lowest MAE (0.024 to 0.034), while SuperRaman surpassed state-of-the-art classifiers. The combined framework requires only 6.6 million parameters, a compact 9 MB footprint, and a 3.27 ms inference time, making it well-suited for portable deployment. This study establishes a scalable, real-time solution for rapid sepsis detection and pathogen identification, bridging the performance gap between portable and laboratory-grade SERS systems.This work was supported by Qatar Research and Development Institute (QRDI) under grant number NPRP12S-0224-190144 (awarded to Susu M Zughaier)
EFFECT OF NANOENCAPSULATED THYMOL, CARVACROL, AND TRANS-CINNAMALDEHYDE IN HUMAN CELLS AND ZEBRAFISH EMBRYOS
No full textThymol (THY; a monoterpenoid), carvacrol (CARV; a monoterpenoid), and trans-cinnamaldehyde (TC; a phenylpropanoid) are key bioactive constituents of essential oils with diverse pharmacological activities, including anticancer, antimicrobial, preservative, and insecticidal properties. However, their practical applications are limited by poor solubility, high volatility, rapid degradation, low bioavailability, and potential toxicity. Nanoencapsulation offers a promising strategy to overcome these limitations by improving stability and enabling controlled release. This study evaluated the toxicity of free (FT, FC, FTC) and nanoencapsulated [monolayer (ML) and layer-by-layer (LBL)] forms of THY, CARV, and TC in vitro using human cell lines (A549, HT-29, HCT-116) and zebrafish embryos (ZFEs) in vivo. In vitro assays demonstrated that ML and LBL nanoencapsulation significantly reduced the concentration-dependent cytotoxicity of the free compounds in human cells. In vivo, nanoencapsulation enhanced survival and hatching rates of ZFEs, and mitigated the teratogenic and neurotoxic effects observed with the free compounds. The LC₅₀ values of the free compounds were 46.9 μg/mL, 6.78 μg/mL, and 0.91 μg/mL for FT, FC, and FTC, respectively, whereas nanoencapsulation increased LC₅₀ by approximately 10–20-fold, with LBL outperforming ML. Based on U.S. Fish and Wildlife Service (USFWS) guidelines, toxicity classification shifted from moderately–highly toxic for the free forms to slightly–practically non-toxic for the nanocapsules, indicating reduced ecotoxicity. Except for FT and ML-FT, which induced cardiotoxicity at 25 and 900 μg/mL, respectively, none of the tested forms caused cardiotoxicity. Neurotoxicity was induced by the free compounds at concentrations starting at 25 μg/mL for FT, 1.56 μg/mL for FC and 0.195 μg/mL for FTC, yet around 4- to17-fold higher concentrations of the nanoencapsulated forms were required to induce similar effects. Overall, nanoencapsulation markedly reduced the cytotoxicity and developmental toxicity of THY, CARV, and TC, enhancing their safety and environmental compatibility for pharmaceutical, food, and biotechnological applications
Exploring Arid Soils as a Source of Bacillus thuringiensis Biocontrol Agents Active Against Dipteran and Lepidopteran Larvae
No full textMicrobial communities found in arid environments often exhibit unique genetic and metabolic adaptations that enable them to synthesize potent bioactive compounds. Bacillus thuringiensis (Bt) is widely recognized for its biocontrol potential against various insects. This study aims to investigate the insecticidal potential of Bt strains isolated from Qatar’s soil against dipteran and lepidopteran larvae. The microscopic analysis identified distinct crystal types, including bipyramidal, cuboidal, spherical smooth, and spherical rough forms, with distinct cry, cyt, and vip genes. Strains producing bipyramidal crystals carry cry1A, cry2A, and vip3A genes, while only two strains producing spherical crystals carry cry4B and cyt1A genes. Bipyramidal crystal-producing strains (QBT552 and QBT877) showed potent insecticidal activity, achieving 100% mortality against Corcyra cephalonica larvae, with LC50 values of 25 µg/g. Spherical smooth crystal-producing strain (QBT862) exhibited high toxicity against Culex pipiens insect larvae (LC50 = 2 µg/L). The quantification of bipyramidal crystal protein production of strains QBT877 and QBT552 exhibited the highest δ-endotoxin yield (1334.4 ± 6.7 and 1188.7 ± 5.0 µg/mL, respectively), while smooth spherical crystal strains QBT758 and QBT862 were 577.5 ± 8.4 and 567.6 ± 8.4 µg/mL, respectively. These findings highlighted the potential of Bt QBT strains for biocontrol applications, with strains showing promise for producing effective δ-endotoxins