SAID SHEHATA

INTRODUCTION: Vulvovaginal candidiasis (VVC) is one of the most common fungal infections requiring more effective and patient-friendly therapies. This study introduces repurposed Ketoprofen (KPN) Quatsomes (QS) as a novel nano-platform for localized antifungal treatment.

METHODS: KPN-QS were prepared using quaternary ammonium surfactants and cholesterol via probe sonication and optimized through a 3 × 2 mixed factorial design using Design-Expert software. The effects of quaternary ammonium surfactant type (factor A), amount of vesicle-forming materials (factor B), and cholesterol-to-surfactant ratio (factor C) were evaluated to maximize entrapment efficiency and zeta potential, while minimizing particle size.

RESULTS: The optimized QS exhibited spherical nano-sized vesicles (113.7 nm) with high entrapment efficiency (96.8%) and strong positive zeta potential (72.5 mV), ensuring stability and enhanced mucosal adhesion. TEM confirmed the spherical morphology, and release showed biphasic behaviour with 86.5% release after 8 h, alongside excellent storage stability. Repurposing KPN as an antifungal agent significantly enhanced both and microbiological efficacy. The formulation displayed promising MIC values against and markedly improved antifungal performance VVC model. The KPN-QS group exhibited 4.807 and 2.941 log reductions in fungal count compared to the negative control and KPN suspension, respectively, with complete eradication in three rats after 72 h. Histopathological analysis confirmed the safety of QS on vaginal mucosa.

CONCLUSION: Collectively, repurposed KPN-QS constitute a stable, biocompatible nanocarrier for targeted vaginal delivery, demonstrating superior antifungal activity and therapeutic potential in VVC.

The West Africa region faces a growing threat from land degradation and climate change, both of which are deeply interconnected. The region is highly vulnerable due to its dependence on rain-fed agriculture, rapid population growth, and fragile ecosystems such as the Sahel. This paper assesses land degradation and deforestation in West African lands, emphasizing the urgent need for sustainable agricultural development. The paper also discusses the role of human activities, such as deforestation, in exacerbating these issues. The study utilizes moderate resolution images spectrophotometer (MODIS) imagery processing techniques and spatial distribution in remote sensing (RS) and geographic information systems (GIS) environment based on Google Earth Engine (GEE) as a computing cloud to evaluate land degradation levels, incorporating various environmental variables such as precipitation, elevation, and soil properties Landcover changes of the investigated area, West Africa, were extracted using MODIS data over four time series (2005, 2010, 2015, and 2020). Results showed that the many regions of West Africa are seeing significant changes in land-cover at spatial and temporal scales as a result of human activity and climate variability, and the Normalized Difference Vegetation Index (NDVI) for studied area over four times (2005, 2010, 2015, and 2020) which created using MODIS data, is classified into none, low, and moderate vegetation cover in addition to the findings reveal that land degradation processes can be categorized into physical, chemical, and biological degradation, each contributing to the decline in soil health and agricultural output. The analysis indicates that understanding land cover dynamics over time is essential for identifying rapidly changing regions, which could be targeted for future conservation efforts. The study underscores that a thorough understanding of land degradation requires investigating the mechanisms behind vegetation changes and the interactions within human-environmental systems. Furthermore, it underscores the importance of understanding coupled-environmental systems to effectively address land degradation and enhance ecosystem services. The study concludes with recommendations for integrated land management strategies that promote restoration and sustainability in West Africa’s agricultural landscapes.

Multisystem inflammatory syndrome in children (MIS-C) is a post-infectious hyperinflammatory condition that develops after SARS-CoV-2 infection and may involve multiple organ systems, including the cardiovascular, hematologic, neurologic, and gastrointestinal systems. Because MIS-C is characterized by immune dysregulation, genetic factors affecting inflammasome pathways may contribute to disease susceptibility or phenotypic variation.

IntroductionPlant viruses inflict annual economic losses exceeding $30 billion and pose a significant threat to global food security. Discovering reliable antiviral targets is therefore essential to developing effective agents that can substantially reduce these agricultural losses. Despite ongoing efforts, the rapid discovery of such antiviral targets remains a significant challenge.
Objectives
This study employs an accelerated framework that integrates a machine learning-driven (MLD) approach with bioinformatics and in silico chemical screening to rapidly predict essential plant viral genes and validate novel antiviral targets.
Methods
A MLD web-based prediction tool, Vgep, was developed to predict the viral essential gene. Subsequent phylogenetic, structural, and target-likeness analyses assessed the targetability of the essential protein it encodes. Finally, in silico virtual screening, biological activity evaluation, virus morphology observation, gene expression analysis, and molecular simulations were employed to identify a chemical probe for evaluating the druggability of the essential protein.
Results
Using the created Vgep tool, we predicted viral essential genes and identified the viral helicase as a key target in tobamoviruses. Viral helicase reveals high conservation and similarity to benchmark antiviral targets. The chemical probe Amidoca, identified through virtual screening exhibits strong binding affinity (Kd = 4.59 µM) to tobacco mosaic virus helicase. In antiviral bioassays, Amidoca outperformed Ribavirin, exhibiting EC50 values of 155.85 mg/L (inactive), 244.32 mg/L (curative), and 344.59 mg/L (protective), thereby confirming the druggability of the tobamovirus helicase. Mechanistic studies revealed that Amidoca may competitively bind at the helicase’s NTP-binding site. This interaction may interfere with the energy release required for unwinding viral dsRNA and lead to a dramatic reduction in helicase accumulation by nearly 95%.
Conclusion
This work establishes a systematic framework for the rapid discovery and validation of next-generation targets in plant virus therapies, highlighting RNA helicase as a promising antiviral candidate and advancing antiviral agent development efforts.

IntroductionPlant viruses inflict annual economic losses exceeding $30 billion and pose a significant threat to global food security. Discovering reliable antiviral targets is therefore essential to developing effective agents that can substantially reduce these agricultural losses. Despite ongoing efforts, the rapid discovery of such antiviral targets remains a significant challenge.
Objectives
This study employs an accelerated framework that integrates a machine learning-driven (MLD) approach with bioinformatics and in silico chemical screening to rapidly predict essential plant viral genes and validate novel antiviral targets.
Methods
A MLD web-based prediction tool, Vgep, was developed to predict the viral essential gene. Subsequent phylogenetic, structural, and target-likeness analyses assessed the targetability of the essential protein it encodes. Finally, in silico virtual screening, biological activity evaluation, virus morphology observation, gene expression analysis, and molecular simulations were employed to identify a chemical probe for evaluating the druggability of the essential protein.
Results
Using the created Vgep tool, we predicted viral essential genes and identified the viral helicase as a key target in tobamoviruses. Viral helicase reveals high conservation and similarity to benchmark antiviral targets. The chemical probe Amidoca, identified through virtual screening exhibits strong binding affinity (Kd = 4.59 µM) to tobacco mosaic virus helicase. In antiviral bioassays, Amidoca outperformed Ribavirin, exhibiting EC50 values of 155.85 mg/L (inactive), 244.32 mg/L (curative), and 344.59 mg/L (protective), thereby confirming the druggability of the tobamovirus helicase. Mechanistic studies revealed that Amidoca may competitively bind at the helicase’s NTP-binding site. This interaction may interfere with the energy release required for unwinding viral dsRNA and lead to a dramatic reduction in helicase accumulation by nearly 95%.
Conclusion
This work establishes a systematic framework for the rapid discovery and validation of next-generation targets in plant virus therapies, highlighting RNA helicase as a promising antiviral candidate and advancing antiviral agent development efforts.

Marine algae polysaccharides are gaining attention in anticancer drug delivery because they are biocompatible, demonstrate unique biological activities, and are multifunctional. These are naturally derived polysaccharides that come from various sea algae species and include alginates, fucoidans, carrageenans, ulvans, and agars. The polysaccharides have their own inherent anticancer, anti-inflammatory, and immunomodulatory properties. Given that they can form hydrogels, nanoparticles, and capsules, marine algae polysaccharides can lead to controlled and sustained drug release, which is advantageous for treatment efficacy while reducing potential toxicity. Marine algae also respond dynamically to environmental stimuli (pH and enzymes), which enhances targeted delivery to the tumor site while avoiding off-target effects. Although there are potential challenges related to scalability and structural complexity, improvements and advancements in extraction and biotechnological development continue to diminish these challenges. Overall, marine algae polysaccharides represent a novel and more organic strategy to enhance the safety and efficacy of anticancer drug therapy, with development avenues toward further integration into personalized medicine and the creation of combination therapies.

Breast cancer remains a leading cause of cancer-related mortality worldwide, with obesity markedly increasing the risk in affected individuals. Liquid biopsy-based extracellular vesicles (EVs) offer a minimally invasive platform for molecular profiling of tumor-derived markers. Plasma small-EVs from obese, chemotherapy-naïve breast cancer patients (n = 76; stages I-III) and age-matched obese controls (n = 36) were enriched and characterized by high-resolution transmission electron microscopy, dynamic light scattering (DLS) and specific EV markers. Proteomic profile of the enriched small-EVs using nanoLC-MS/MS identified Fibronectin 1 (FN1) and von Willebrand Factor (VWF) as candidate markers. Bioinformatics and STRING networks revealed interactions with Syndecan-2 (SDC2) and Galectin-3 (Gal-3). As an independent validation, western blot confirmed that FN1, VWF, and SDC2 were higher enriched in the small-EVs of breast cancer with different stages than in those of normal and that high content of small-EVs FN1 and SDC2 was primarily associated with the aggressive triple-negative breast cancer (TNBC) subtype. Interestingly, Gal-3 was reduced in small-EVs but elevated in breast carcinoma tissues and microvesicle (MV)-enriched EVs. Functionally, treatment with TNBC-derived plasma small-EVs not only downregulated expression of epithelial marker CDH1, and upregulated expression of the mesenchymal markers ZEB2 and FN1 in low-invasive MCF-7 breast cancer cells, but also elevated expression of inflammatory and matrix-remodeling mediators (Il-6, Tnf-α, and Mmp-9) in BNL CL.2 normal liver cells. ROC-Plotter and drug-gene interaction analyses indicated associations with therapy response, with approved compounds targeting FN1 and VWF. Overall, these findings reveal proteomic signatures of minimally invasive plasma small-EVs as promising markers associated with diagnosis, molecular subtyping, disease progression, and guiding therapeutic strategies in obese breast cancer patients.

Fusarium oxysporum is one of the most harmful soilborne phytopathogens due to wide-host range leading to sustainable global agricultural losses and food insecurity risk. Marine actinomycetes offer a promising sustainable resource of novel antifungal metabolites. This study aimed to isolate a marine Streptomyces with strong inhibitory effect against F. oxysporum f. sp. Lycopersici, identify its active metabolite, optimize fermentation conditions, and in silico validate the molecular mechanism via docking. Marine actinomycete isolated from Mediterranean Sea sediments was identified by morphological, and physio-biochemical characterization. Response surface methodology (central composite design, 23 runs) was used to optimize fermentation parameters, temperature (25‒30°C), seawater concentration (25‒75%) and agitation speed (110‒150 rpm), to enhance antifungal metabolite production by Streptomyces sp. MWM14 for maximal inhibition of F. oxysporum f. sp. Lycopersici in well diffusion method. The bioactive metabolite was extracted and profiled by LC MS/MS. In silico-docking was validated to F. oxysporum f. sp. Lycopersici Avr1 (SIX4) effector protein (PDB 7T6A) via AutoDock Vina. The isolate was tentatively assigned to Streptomyces sp. based on its Gram positive, filamentous morphology, powdery colonies, enzymatic profile (amylase, protease, catalase), and halotolerance (up to 3% NaCl). Under optimized conditions (27.8°C, 84.6% seawater, 143.5 rpm), the inhibition zone reached 2.43 cm (desirability= 0.87). LC MS/MS putatively identified alizarin (1,2 dihydroxyanthraquinone) as a major metabolite, with [M‒H]⁻ at m/z 239.1276 and characteristic fragments at m/z 221.1187, 211.1383, 193.1249, and 137.0958. In silico‒docking predicted possible binding interactions; these results provide supportive in silico evidence rather than definitive experimental validation of alizarin inhibition to the binding pocket Avr1 (SIX4) effector protein from F. oxysporum f. sp. Lycopersici (∆G = –6.9 kcal/mol), with hydrogen bonds to VAL145, THR151 and VAL158, and hydrophobic interactions with LYS87 and ARG160. These findings demonstrate that alizarin from marine Streptomyces sp. is a promising biofungicidal agent against Fusarium activity by targeting fungal Avr1 (SIX4) effector protein signalling. The potential approach of bioprocess optimization and validation establish a cornerstone framework for eco-friendly antifungal agents to combat Fusarium wilt disease.

Fusarium oxysporum is one of the most harmful soilborne phytopathogens due to wide-host range leading to sustainable global agricultural losses and food insecurity risk. Marine actinomycetes offer a promising sustainable resource of novel antifungal metabolites. This study aimed to isolate a marine Streptomyces with strong inhibitory effect against F. oxysporum f. sp. Lycopersici, identify its active metabolite, optimize fermentation conditions, and in silico validate the molecular mechanism via docking. Marine actinomycete isolated from Mediterranean Sea sediments was identified by morphological, and physio-biochemical characterization. Response surface methodology (central composite design, 23 runs) was used to optimize fermentation parameters, temperature (25‒30°C), seawater concentration (25‒75%) and agitation speed (110‒150 rpm), to enhance antifungal metabolite production by Streptomyces sp. MWM14 for maximal inhibition of F. oxysporum f. sp. Lycopersici in well diffusion method. The bioactive metabolite was extracted and profiled by LC MS/MS. In silico-docking was validated to F. oxysporum f. sp. Lycopersici Avr1 (SIX4) effector protein (PDB 7T6A) via AutoDock Vina. The isolate was tentatively assigned to Streptomyces sp. based on its Gram positive, filamentous morphology, powdery colonies, enzymatic profile (amylase, protease, catalase), and halotolerance (up to 3% NaCl). Under optimized conditions (27.8°C, 84.6% seawater, 143.5 rpm), the inhibition zone reached 2.43 cm (desirability= 0.87). LC MS/MS putatively identified alizarin (1,2 dihydroxyanthraquinone) as a major metabolite, with [M‒H]⁻ at m/z 239.1276 and characteristic fragments at m/z 221.1187, 211.1383, 193.1249, and 137.0958. In silico‒docking predicted possible binding interactions; these results provide supportive in silico evidence rather than definitive experimental validation of alizarin inhibition to the binding pocket Avr1 (SIX4) effector protein from F. oxysporum f. sp. Lycopersici (∆G = –6.9 kcal/mol), with hydrogen bonds to VAL145, THR151 and VAL158, and hydrophobic interactions with LYS87 and ARG160. These findings demonstrate that alizarin from marine Streptomyces sp. is a promising biofungicidal agent against Fusarium activity by targeting fungal Avr1 (SIX4) effector protein signalling. The potential approach of bioprocess optimization and validation establish a cornerstone framework for eco-friendly antifungal agents to combat Fusarium wilt disease.

BackgroundResearchers discovered that microRNA 223-3p (miR-223-3p) was elevated in sepsis due to its dysregulation in infection, so the aim of our study was to identify the role of miR-223-3p expression as a potential biomarker and predictor of outcome in sepsis.

Patients and methods

Our study was a prospective observational cohort model consisting of 46 patients diagnosed as either sepsis or septic shock according to sepsis score 3, who were admitted to ICU. Four healthy individuals participated as a reference group for the expression of miR-223-3p. Serum samples were obtained for miR-223-3p gene expression by real-time PCR technology.

Results

The current study demonstrated that there was a higher mean value of miR-223-3p expression in sepsis cases (1.10±1.35) than in healthy persons (0.85±0.41). Pneumonia was the most common diagnosis in 24 (52.2%) cases. Klebsiella was the most predominant bacteria detected in this study 21 (45.7%) patients followed by 10 (21.7%) patients with Acinetobacter. There was a statistical significance difference between males and females, being higher in males (P0.046), also between smokers and nonsmokers, being higher in smokers than nonsmoker (P0.017). It was noticed that high expression of miRNA expression in Klebsiella and E coli infections. Correlation coefficient of miR-223-3p expression in relation to clinical and laboratory data showed the following: in spite of the expression being negatively correlated to body temperature, mean arterial blood pressure, white blood cells, platelet count, and liver functions but there was no statistically significant P value. Despite higher miRNA expression 223-3p in sepsis patients with disturbed consciousness, septic shock, nonsurvivors, and patients with positive fibrin degradation products, there was no statistically significant difference.

Conclusions

MiR-223-3p could be considered a promising biomarker for both marker of severity and predictor of outcomes in sepsis, providing perspectives to the exact molecular mechanisms and potential therapeutic targets. MiR-223-3pp showed higher expression with bacterial infection especially E-coli and klebsiella. These findings pay attention to the potentiality of miR-223-3p as both a diagnostic modality and tool of differentiation between sepsis etiologies and leading precision management.

Dopamine (DA) is a key neurotransmitter involved in numerous mental and motor functions, with abnormal dopamine levels linked to disorders such as parkinsonism, schizophrenia, and attention-deficit hyperactivity disorder (ADHD). This study details the development of a straightforward, precise smartphone-based colorimetric method to quantify dopamine using plasmonic gold nanoparticles (AuNPs) functionalized with N-acetyl-L-cysteine (N-AC). This method utilizes dopamine-induced aggregation of modified AuNPs, causing a color transition from pink to blue detectable by the naked eye. Image acquisition is performed using a smartphone, followed by data preprocessing that incorporates the U2-Net deep learning model along with principal component analysis (PCA). To estimate dopamine concentrations, the random forest regression machine learning algorithm is employed. Various conditions, including buffer type, strength, pH, and reaction time, were optimized for effective results. The proposed method demonstrated linearity for dopamine concentrations ranging from 75 to 400 μM, showing high selectivity against interfering substances like homovanillic acid, epinephrine, and ascorbic acid. Validation of the method adhered to International Council on Harmonisation (ICH) guidelines, successfully determining dopamine levels in both pure and spiked plasma samples, achieving a limit of detection of 17.2 μM. This study marks the first implementation of U2-Net for smartphone-derived data preprocessing and highlights the innovative combination of smartphone technology with machine learning through a user-friendly Streamlit web application for estimating dopamine concentrations.