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Abstract The formation of biofilm by foodborne pathogens increases the risk of foodborne diseases, resulting in major health risks. Research on strategies for eliminating biofilm formation by foodborne pathogens is urgently needed. Therefore, the objective of this study was to construct a new technique for controlling foodborne bacteria and inhibiting the biosynthesis of biofilm via using natural products. The essential orange oil (EOO) and cell-free filtrate of Lactobacillus pentosus RS2 were used as antibacterial and antibiofilm agents against B. cereus RS1, the strongest biofilm-forming strain. The mixture of cell-free filtrate (CFF) and EOO (CFF/EOO) was the best antibiofilm agent under all tested conditions. The minimal inhibitory concentration (MIC) test revealed that 400 μl ml−1 CFF and 16 μl ml−1 EOO completely inhibited the growth of B. cereus. The treatment of three commercial surfaces with CFF/EOO resulted in a high reduction in biofilm synthesis, with adhesion percentages of 33.3, 36.3, and 40.8% on stainless steel, aluminum foil, and aluminum, respectively. The aluminum surface had the greatest adhesion with B. cereus RS1 among the three tested surfaces. These results were confirmed by expression analysis of three essential coding genes, sinR, calY, and spo0A, participating in biofilm formation in B. cereus. The biofilm-negative regulator gene sinR was overexpressed, whereas the biofilm-positive regulator genes calY and spo0A were down-expressed in B. cereus RS1 after treatment with antibiofilm agents, compared with those in the untreated sample. This study revealed that CFF/EOO was more effective at activating sinR (2.099 ± 0.167-fold increase) and suppressing calY and spo0A (0.314 ± 0.058 and0.238 ± 0.04-fold decrease, respectively) compared to control. This result confirmed the biochemical estimation of biofilm formation in B. cereus after treatment with all the experimental agents. The EOO and CFF of L. pentosus RS2 can be used as strong antibacterial and antibiofilm agents against foodborne bacteria. These products reduced the biofilm formation on trade surfaces affecting the expression of three essential biofilm regulatory genes. This study considered novel research concerning the potential antibiofilm activity of EOO combined with CFF of L. pentosus and the molecular analysis of genes regulating biofilm production under stress of CFF/EOO.

Abstract This present study deals with the cross-linking of fungal β-glucosidase on Al2O3 nanocrystals (NCs) synthesized in C. cajan for in-suit genistein production. The Cajanus cajan leaves were dried and used to prepare their extract at 65 °C by agitation for 30 min. For enzyme production under submerged culture, 50 mL of medium at pH 8.6 with an inoculum volume of 2 mL; was incubated for 72 h with optimized parameters at 30 °C. The Al2O3 NCs were synthesized by adding 30 mM Al2NO3 to 25 mL of leaf extract with NaOH at 65 °C for 50 min which enhanced the β-glucosidase specific activity when immobilized. Genistein by biotransformation was obtained using both free (0.67 ± 0.42 mg/mL) and Al2O3 immobilized β-glucosidase (1.3 ± 0.66 mg/mL) for 48 h. The substrate level and enzyme concentration were 2.5 and 1 mL respectively. The UV visible spectra for leaf extract; free and cross-linked β-glucosidase and Al2O3 NCs were at 225, 235, 300, and 210 nm. The bands for Al2O3 NCs were achieved at 500–750 cm− 1 which showed the FTIR analysis to check the change in functional groups of free and Al2O3 cross-linked β-glucosidase. In XRD analysis, peaks depicted the crystalline structure of Al2O3 NCs ranging from 10–50°. The size of NCs was confirmed by using different magnifications (1.01, 2.00, 3.00, 5.00, 7.02, and 10 K X) of SEM images obtained. For zeta potential measurements, the peak was obtained at ˗21.0 mV.

Abstract Overuse of synthetic pesticides causes problems for humans and the environment or leads to insect resistance to insecticides, so plant extracts and essential oils have gained popularity as an environmentally acceptable alternative to chemical pesticides. This study investigated the toxicity of Delonix regia leaf and seed extracts, protein patterns, and genetic distance analysis against the 5th instar larvae of cotton leafworm, Spodoptera littoralis Boisaduval. Methanol, petroleum ether, and acetone extracts of leaves and seeds of D. regia were used to coat castor leaves for ingestion by the 5th instar of S. littoralis larvae. The seeds’ methanol and petroleum ether extracts were the most effective (100 and 98 Mortality%), with LC50 values of 0.887 and 1.795 g/L, respectively, 24 h post-treatments. Data showed that D. regia extracts affected consequences of protein changes compared to untreated S. littoralis larvae resulted in genetic changes, as well as inhibition of the insect’s important α-amylases, forming protein complexes, and influencing normal growth and development. GC-MS analysis of the chemical composition of the seed extract revealed 18 compounds with high levels of stigmasterol, 2-methyl-4-vinylphenol, benzoic acid, 3-hydroxy, and squalene with percentages of 43.07%, 21.33%, 14.51%, and 14.12%, respectively. As a result, we concluded that D. regia seed extracts had the potential to control Spodoptera littoralis larvae.

Abstract As a new investigation for renewable energy generation, the recent three-layer solar structure is composited with three effective material usages of Al (resonator), InSb (substrate), and Ag (based). The overall wavelength of overall 2800 nm with the bandwidth order from 0.2 to 3.0 μm is 91.68%, the extracted percentage is 97.07% at 800 nm (1.41–2.21) µm, and the output is 95.02% with the 1420 nm wavelength by the bandwidth separation of 1.09 and 2.51 μm respectively. With the graphene accumulation in this absorber, the radiated output can be determined in the first visible area, the second ultraviolet region, and the near-infrared spectra respectively. The multi-step exploration of the absorbed rate in design performance, the selected materials and parameter assignments, and the analyzed parametric numbers for each layer will be included in the proposed presentation. The demonstrated solar structure can be performed at the low-temperate rate of less than 100 °C for heating processes of water or space, between 100 and 250 °C for industrial processes, and above 250 °C to release the steam for running turbines in the electricity generation.

Abstract A nanocomposite composed of rGO and γ-Fe2O3 was prepared using ultrasonication for the adsorption of malachite green (MG) dye. The preferential plane diffractions at 2θ values of 35.54° and 26.45° about γ-Fe2O3 (311) and rGO (002) with 19.85 and 20.92 nm crystallite sizes, respectively confirmed the successful formation of nanocomposite nature of the adsorbent. Moreover, XPS and FTIR results also confirmed the composite formation due to the existence of peaks relevant to the composite’s components. The adsorbent’s surface charge (pHPZC = ~ 7.1) was also estimated using the salt addition technique. To minimize experiments and optimize adsorption parameters for the removal of MG by the nanocomposite at 25 °C, central composite design (CCD) using response surface methodology (RSM) was used. The optimal adsorption parameters obtained from the within-range numerical optimization based on 0.923 Derringer’s desirability function were 200 mg/100 mL dose, 80 ppm dye solution, 7.99 pH and 112.68 min. contact time for ~ 90% MG dye removal and 40.64 mg/g adsorption capacity (qm) by rGO/γ-Fe2O3. However, ~ 98% MG dye removal with 64.26 mg/g (qm) was achieved upon extended-range estimation of adsorption parameters. The adsorption data exhibited the best co-relationship with Freundlich isotherm and pseudo-2nd order kinetic model. According to a thermodynamic analysis, the MG adsorption process on the rGO/γ-Fe2O3 surface is exothermic, spontaneous, and less random. Moreover, the thermal stability, desorption, regeneration and reusability of rGO/γ-Fe2O3 nanocomposite were also explored. Finally, this study shows that RSM can be an excellent technique to optimize the dye adsorption process at industrial scale.

Abstract Loss and absence of melanocytes due to a number of factors is responsible for vitiligo; known to be the commonest disorder of pigmentation. The aim of the study was to assess clinically and dermoscopically the efficacy of topical trichloroacetic acid 70% versus methoxsalen 0.2% paint in stable acral vitiligo. The patients were randomly divided into 2 groups. Group a (n = 35) received topical 0.2% methoxsalen every other day for 4 months duration with dermoscopic follow up while group b (n = 35) received received topical TCA 70% application at the clinic every two weeks for 4 months with dermoscopic follow up. The majority of subjects in both groups experienced either no or mild improvement. In TCA group, mean improvement was 4.0 ± 11.6% with range of 0–60% while in the methoxsalen group, mean improvement was 0.57 ± 3.3% with range of 0–20% (p = 0.051). The majority of patients reported poor satisfaction. Both modalities did not demonstrate a significant clinical nor dermoscopic response. TCA 70% had a lower effective rate than other studies probably due to resistance of acral vitiliginous lesions to treatment in comparison to other sites of the body. Further larger multi centre studies with different concentration and combination modalities are required to detect promising treatments for vitligo.

Abstract Because energy interest demands clean and sustainability in the last ten years. Solar thermal energy conversion, where sunlight can be absorbed to convert it into heat can stand as an alternative for this purpose. Graphene dispersed with different substrates enables us to get torsion control over light absorption and heat transport. This work discusses the optothermal properties of graphene-based coatings on different substrates such as CuO, MAPBI3, Fe, etc. The optothermal properties of such CuO-graphene, MAPBI3-graphene, and Fe-graphene combinations display the highest average absorptance of 96.8% across the solar spectrum between 0.2 and 2.5 μm followed by 86.7% by MAPBI3-graphene. However, Fe-graphene depicts a significantly lower value of 24.3%. A critical inspection of these optothermal properties would enrich one with critical knowledge of design optimisation in graphene-coated solar absorbers. Thus, the data collection time is greatly reduced using ML compared to running simulations which have a step size of about 8 h per change. Where the machine learning efficacy is 98% for the thickness optimization of Fe, CuO, and MAPBI3 with 25% test data. Of much potential interest are the solar absorbers developed using these materials in fields such as solar thermal energy harvesting, air/water heaters, and industrial heating systems.

Abstract Although detailed analytical models for droop-controlled microgrids are available, they are computationally complex and do not consider real-time variations in microgrid parameters and operating conditions. This paper proposes Kurtosis-Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) to identify the dominant modes in droop-controlled inverter-based microgrids (IBMGs) using local real-time measurements. In the proposed approach, a short-duration small disturbance is applied to the selected DG’s active power droop gain, and then, the system’s dominant modes are estimated from its local measurements. Additionally, a kurtosis measure is proposed as a quick measure to assess the estimation signal’s characteristics and evaluate the presence and prominence of significant modes within the signal. The effectiveness of the developed approach is validated via MATLAB/SIMULINK simulations. Four case studies were conducted to verify the robustness of the proposed algorithm as follows: under different values of active power droop gains, several variations of lines’ X/R ratios, various levels of noise, and under large load changes and topological disturbances. Besides, a controller-in-the-loop (CIL) experiment was conducted using OPAL-RT to provide a real-time validation of the results. The modes obtained from the proposed algorithm are validated against the analytically derived modes and the estimation accuracy is compared to the recent methods: Prony, Matrix Pencil, and Subspace Identification techniques. Results show higher estimation accuracy for the proposed approach with a robust performance in noisy environments, across varying load conditions, and under different network configurations.

Abstract To illustrate the anti-diabetic properties of Berberis orthobotrys seeds was the aim of the current study. After a series of experiments, two doses of aqueous methanolic extract of the seeds were selected i.e., 151 mg/kg and 301 mg/kg to assess the anti-diabetic potential in normoglycemic, alloxan-induced and glucose-loaded diabetic rats. The effect of the seeds extract on total cholesterol and triglycerides was also assessed. Histological examination of the pancreas and the GC-MS analysis of blood and urine samples of rats were also carried out. At 151 mg/kg and 301 mg/kg doses, the aqueous methanolic extracts of Berberis orthobotrys seeds showed promising results for their anti-diabetic potential comparable to Glibenclamide. Studies on the phytochemistry of AMEBO also exposed the existence of alkaloids, steroids, glycosides, flavonoids and resins. Serum lipid measurements showed a substantial reduction in triglycerides and total cholesterol on days 7 and 15. The pancreas’s islets were protected by the extract, as shown by a histological analysis. The primary metabolite Berberine, which was qualitatively detected by GC-MS in blood and urine samples of rats, may be the alkaloid in command of insulin release or recovery of beta cells after alloxan destruction. More study is necessary to comprehend the underlying signaling process.

Abstract In this study, we report the synthesis of iron oxide nanoparticles (FeONPs) using micro-emulsion-hydrothermal method. By adjusting the synthesis temperature, we successfully produced FeO nanorods and nanospheres. In addition, the 2-octanol, and the surfactant cetyltrimethylammonium bromide served as a solvent in the synthesis process. Using iron nitrate hexahydrate as the salt precursor allowed for the formation of FeONPs with varied sizes, shapes and phases. The synthesized materials were extensively characterized using XRD, SEM, TEM, EDS, FTIR, and XPS techniques. However, the structural analysis revealed rhombohedral (hematite), and (magnetite) crystal structures in the materials synthesized at different temperature and durations, with particles ranged in size from 12 to 97 nm. More importantly, the magnetic characterization, performed with a vibrating sample magnetometer and SQUID magnetometer, indicated that the NPs showed not clear superparamagnetic behavior. In conclusion, this work demonstrates the synthesis of FeONPs with controlled size, shape and phase using microemulsion hydrothermal technique, with detailed characterization offering valuable insights into their magnetic and structural properties.

Abstract Visual attribution in medical imaging seeks to make evident the diagnostically-relevant components of a medical image, in contrast to the more common detection of diseased tissue deployed in standard machine vision pipelines (which are less straightforwardly interpretable/explainable to clinicians). We here present a novel generative visual attribution technique, one that leverages latent diffusion models in combination with domain-specific large language models, in order to generate normal counterparts of abnormal images. The discrepancy between the two hence gives rise to a mapping indicating the diagnostically-relevant image components. To achieve this, we deploy image priors in conjunction with appropriate conditioning mechanisms in order to control the image generative process, including natural language text prompts acquired from medical science and applied radiology. We perform experiments and quantitatively evaluate our results on the COVID-19 Radiography Database containing labelled chest X-rays with differing pathologies via the Frechet Inception Distance (FID), Structural Similarity (SSIM) and Multi Scale Structural Similarity Metric (MS-SSIM) metrics obtained between real and generated images. The resulting system also exhibits a range of latent capabilities including zero-shot localized disease induction, which are evaluated with real examples from the cheXpert dataset.

Abstract This in vitro study aims to evaluate various surface treatments on the shear bond strength and failure mode of CAD/CAM PMMA teeth to the heat-polymerized acrylic denture base. The study sample consisted of 100 teeth that were divided equally into five groups: Group 1: denture artificial teeth (control), Group 2: PMMA teeth without surface treatment, Group 3: PMMA teeth with MMA etching, Group 4: PMMA teeth with sandblasting (aluminum oxide particles), and Group 5: PMMA teeth with perpendicular grooves. The shear bond strength test was performed using a universal testing machine and the failure mode was recorded. Data were analyzed using ANOVA followed by Tukey’s post-hoc tests (α = 0.05). MMA increased the shear bond strength compared with other surface treatments (sandblasting and perpendicular grooves; P

Abstract The cotton leafworm, Spodoptra littoralis, causes great damage to cotton crops. A new, safer method than insecticide is necessary for its control. Selenium nanoparticles (SeNPs) are metalloid nanomaterial, with extensive biological activities. They have low toxicity and can be used safely in plant disease management. In this study, we successfully bio-fabricated selenium nanoparticles and chitosan-selenium nanocomposite (Ch-SeNPs) using a fungal cell-free filtrate of Penicillium griseofulvum. The biosynthesized nanomaterials were initially detected optically by the formation of a red color in the solution mixture and the appearance of a strong plasmon resonance peak at 240–300 nm. The biosynthesized nanomaterials were fully characterized by UV–visible spectroscopy, transmission electron microscopy, dynamic light scattering, energy dispersive X-ray, inductively coupled plasma spectroscopy, and Fourier transform infrared. We tested the anti-insect activities of SeNPs, and Ch-SeNPs against larvae of S. littoralis compared to spore suspensions of P. griseofulvum. The results indicated that Ch-SeNPs followed by SeNPs gave a significantly higher mortality percentage than the spore suspension of the tested fungus. The highest production of all biosynthesized nanomaterials was detected after 7 days at 40 °C under alkaline conditions (pH 9). The average size diameter of SeNPs and Ch-SeNPs were 91.25 and 67.41 nm with zeta potential − 8.05 and + 41 mV, respectively. Both Ch-SeNPs and SeNPs gave high mortality rates and low values of LC50 and LC90 for both larvae and pupae. Ch-SeNPs showed stronger activity against S. littoralis than SeNPs and spore suspension at all experimental conditions. Cytotoxicity experiments indicated their safety against honeybee populations. The current study reveals the significant ultrastructure impact of SeNPs on larvae. These findings suggest that selenium nanoparticles and nanocomposite can be fabricated with a costless easy route using fungal filtrate, and they can be used safely in pest control systems that are safe for honeybee populations. It is the first report about the application of Ch-SeNPs as an anti-insect agent.

Abstract The emergence of 6G cognitive radio IoT networks introduces both opportunities and complexities in spectrum access and security. Blockchain technology has emerged as a viable solution to address these challenges, offering enhanced security, transparency, and efficiency in spectrum management. This survey paper offers a thorough analysis of recent advancements in blockchain-enabled security mechanisms specifically for spectrum access within 6G cognitive radio IoT networks. Covering literature from 2019 to the present, the paper highlights significant contributions and developments in integrating blockchain technology with cognitive radio and IoT systems. It reviews spectrum access security and shows how blockchain’s decentralized approach can solve related issues. Key areas of focus include secure authentication systems, tamper-resistant spectrum sensing, decentralized databases, and smart contracts for spectrum management. The paper also addresses ongoing challenges like interoperability, scalability, and the need for comprehensive security frameworks. Future research directions are proposed, emphasizing the development of advanced blockchain protocols, integration with machine learning, and addressing regulatory and standardization concerns. This paper provides valuable insights for researchers and practitioners aiming to leverage blockchain technology, alongside ML/AI, to enhance security and efficiency in next-generation cognitive radio IoT networks.

Abstract No previous study has examined the association between coffee and caffeine intake and odds of functional dyspepsia (FD). The aim of this study was to investigate the association between coffee and caffeine intake and odds of FD and its components in a large sample of Iranian adults. In this cross-sectional study on 3362 adults aged 18–55 years, a validated food frequency questionnaire (DS-FFQ) was used to assess dietary intakes. A validated modified Persian version of the Rome III questionnaire was used for assessment of FD. Logistic regression was applied to compute odds ratios for FD and its components considering a wide range of covariates. Mean age of study population was 36.2 ± 7.8 years, of them 58.3% were females. The prevalence of FD among study participants was 14.5%. The prevalence of post-prandial fullness, early satiation, and epigastric pain was 7.6, 5.8, and 7.6%, respectively. After controlling for potential confounders, no significant association was observed between coffee (OR: 1.27; 95% CI: 0.86–1.87), and caffeine (OR: 1.00; 95% CI: 0.99–1.02) consumption and odds of FD. In addition, coffee and caffeine consumption was not significantly associated with odds of FD symptoms such as early satiation, post-prandial fullness, and epigastric pain. This was also the case when the analysis was done stratified by gender and BMI status. In conclusion, we found no significant association between coffee and caffeine consumption with odds of FD and its symptoms. Further research in other populations with high coffee consumption is needed.

Abstract The Crimean Congo virus has been reported to be a part of the spherical RNA-enveloped viruses from the Bunyaviridae family. Crimean Congo fever (CCHF) is a fatal disease with having fatality rate of up to 40%. It is declared endemic by the World Health Organization. Many outbreaks of CCHF have been reported over the years. Former studies on CCHF have reported that the nucleoprotein of CCHF, being a pivotal protein in the replication process of the virus, is a potential target for antiviral drugs. However, there is no specific drug that can be used to treat this fatal disease and laboratory testing is prohibited due to its pathogen level 4. This study aims to find a possible potential inhibitor of the nucleoprotein of CCHFV using modern techniques leading ultimately to the development of effective and natural drugs. In this study, a virtual screening procedure involving a docking process followed by the Molecular Dynamics method is used to find out the potential inhibitors of the nucleoprotein of CCHFV. Phytochemicals having pharmacological properties and approved by the Food and Drug Administration are docked over the nucleoprotein of CCHFV. The study signifies the use of Withanolide E as a drug for the treatment of CCHFV as the study depicts the potential of Withanolide E to inhibit the nucleoprotein of CCHFV using reliable and modern techniques.

Abstract A medication error is an inadvertent failure in the drug therapy process that can cause serious harm to patients by increasing morbidity and mortality and are associated with significant economic costs to the healthcare system. Medication reconciliation is the most cost-effective intervention and can result in a 66% reduction in medication errors. To improve patient safety, we developed a machine learning-based tool that prioritizes patients at risk of medication errors upon admission to the hospital to ensure that they undergo medication reconciliation by clinical pharmacists. The data were collected from the electronic health records of patients admitted to Reims University Hospital who underwent medication reconciliation between 2017 and 2023. The data from 7200 patients were used to train four machine learning-based models based on 52 variables in the development dataset. These models were used to prioritize admitted patients according to their likelihood of being exposed to a medication error. Our models, particularly the voting classifier model, demonstrated good performance (a recall of 0.75, precision of 0.65, F1 score of 0.70, AUROC of 0.74 and AUCPR of 0.75). In a retrospective evaluation simulating real-life use, the voting classifier model successfully identified 45% of the total patients selected who were found to have at least one unintended discrepancy compared to 21% when using the existing tool. The positive experimental results of this tool showed a superior improvement of 113% over the existing tool by targeting patients at risk of medication errors upon admission to Reims University Hospital.

Abstract The contributed absorber design in graphene addition with the displacement of three materials for resonator design in Aluminum (Al), the middle substrate position with Titanium nitride (TiN), and the ground layer deposition by Iron (Fe) respectively. For the absorption validation highlight, the best four absorption wavelengths (µm) of 0.29, 0.58, 1, and 2 are also selected to indicate the changes in radiation outputs for every observation. With the displacement of wavelength and bandwidth configuration, the current absorber is observed at 97.32% (more than 97%) for 1.5–2.5 µm wavelength range (1000 nm bandwidth) and above 95% rate (95.38%) is displayed by the 2000 nm bandwidth due to 0.5 and 2.5 µm wavelength separation. The 2800 nm band rate demonstration by 0.2–3 µm wavelength separation verifies 92.42%. For every analysis in this work, the output radiation is shown in ultraviolet region (UV), visible spectrum (Vis), and near-infrared (NIR) area respectively. In the following distribution of the current absorber, the design development in lithography and step-by-step, parametric assignment and AM configuration, radiation analysis for each parameter changes in graph presentation and conclusion. Additionally, the ML approach is applied to reduce the time required in the study. The current solar absorber in the new design can be generated for the multi-solar purposes of water heating, lighting, ventilation, charging for electronic devices, and electric vehicle transportation.

Abstract We designed an ultra-broadband graphene absorber structure with the applied resonator design based on the Al-AlSb-Cr structure, and a thin effective layer of graphene is inserted. To develop the role of the graphene in solar absorbers, the current structure investigates above 98% for 1500 nm bandwidth and 2800 nm (overall bandwidth) for 93.68%. In this study, the procedure of the investigated design in flow chat configuration, the multi-step presentation of the developed layers, and the analysis of the used parameters will be involved. The design is optimized using machine learning algorithm. The optimized design shows good performance compared to the other system. The newly investigated graphene design can be absorbed not only in visible places but also in near-infrared energy and ultraviolet zones. The other applications of the light trapping process, photovoltaic devices, and energy harvesting can also be used.

Abstract Hepatocellular carcinoma (HCC) represents a significant health burden in Egypt, largely attributable to the endemic prevalence of hepatitis B and C viruses. Early identification of HCC remains a challenge due to the lack of widespread screening among at-risk populations. The objective of this study was to assess the utility of machine learning in predicting HCC by analyzing the combined expression of lncRNAs and conventional laboratory biomarkers. Plasma levels of four lncRNAs (LINC00152, LINC00853, UCA1, and GAS5) were quantified in a cohort of 52 HCC patients and 30 age-matched controls. The individual diagnostic performance of each lncRNA was assessed using ROC curve analysis. Subsequently, a machine learning model was constructed using Python’s Scikit-learn platform to integrate these lncRNAs with additional clinical laboratory parameters for HCC diagnosis. Individual lncRNAs exhibited moderate diagnostic accuracy, with sensitivity and specificity ranging from 60 to 83% and 53–67%, respectively. In contrast, the machine learning model demonstrated superior performance, achieving 100% sensitivity and 97% specificity. Notably, a higher LINC00152 to GAS5 expression ratio significantly correlated with increased mortality risk. The integration of lncRNA biomarkers with conventional laboratory data within a machine learning framework demonstrates significant potential for developing a precise and cost-effective diagnostic tool for HCC. To enhance the model’s robustness and prognostic capabilities, future studies should incorporate larger cohorts and explore a wider array of lncRNAs.

Abstract Studies have used extensive clinical information to predict time-to-heart failure (HF) in patients with and without diabetes mellitus (DM). We aimed to determine a screening method using only computed tomography calcium scoring (CTCS) to assess HF risk. We analyzed CTCS scans from 1,998 patients (336 with type 2 diabetes) from a no-charge coronary artery calcium score registry (CLARIFY Study, Clinicaltrials.gov NCT04075162). We used deep learning to segment epicardial adipose tissue (EAT) and engineered radiomic features of calcifications (“calcium-omics”) and EAT (“fat-omics”). We developed models incorporating radiomics to predict risk of incident HF in patients with and without type 2 diabetes. At a median follow-up of 1.7 years, 5% had incident HF. In the overall cohort, fat-omics (C-index: 77.3) outperformed models using clinical factors, EAT volume, Agatston score, calcium-omics, and calcium-and-fat-omics to predict HF. For DM patients, the calcium-omics model (C-index: 81.8) outperformed other models. In conclusion, CTCS-based models combining calcium and fat-omics can predict incident HF, outperforming prediction scores based on clinical factors.Please check article title if captured correctly.YesPlease check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.Yes

Abstract The demand for eco-friendly materials in automotive components has spurred research into natural fibers as sustainable alternatives for brake pads. This study examines the potential of date palm fibers, particularly the palm frond midrib (PFM), in brake pad composites. The effects of epoxy, PFM, and calcium carbonate on the composites’ mechanical and tribological properties were analyzed. The optimal formulation (25% epoxy, 30% PFM, 35% calcium carbonate) exhibited superior properties, including a hardness of 87 HRB, wear rate of 1.5E-03 mg/mm, and COF of 0.73, surpassing commercial pads. Additionally, an inverse relationship between PFM/calcium carbonate content and compressibility was observed, with increased calcium carbonate enhancing wear resistance. This research underscores the potential of utilizing date palm resources in eco-friendly brake manufacturing, reducing the environmental and health impacts of traditional materials.

Abstract Detergents and disinfectants for dairy industry must meet a variety of characteristics, including low toxicity, high antibacterial activity, and excellent rinsing of pollutants from working surfaces. This work presents an innovative detergent-disinfectant agent based on whey mineralizate and silver nanoparticles (Ag NPs), which allows reducing production costs and ensuring high cleanliness of treated surfaces compared to analogues. For this purpose, a method for obtaining sols of Ag NPs stabilized with didecyldimethylammonium bromide (Ag NPs-DDAB) was developed and optimized using neural network algorithms. Characterization of Ag NPs-DDAB showed particles with a radius of 4.5 nm and 20 nm, stable in the pH range from 2 to 11. An acute toxicity study of Ag NPs in mice showed LD50 = 4230 μg/kg. Based on the degree of accumulation and inhalation toxicity, Ag NPs-DDAB are classified as low-hazard chemicals. The developed detergent-disinfectant had a washability of about 90%, high antimicrobial activity (0.005 mg/mL) against Penicillium roqueforti and a sanitary and hygienic effect on coliforms, general contamination and pathogenic microorganisms, a low-corrosive effect and low toxicity (315 mg/mL) to Danio rerio. It was concluded that the use of detergent-disinfectant agent will completely eliminate the consumption of water for the equipment cleaning process and can be used to clean an electrodialysis unit’s circuits, enabling the utilization of secondary waste from membrane milk processing and promoting resource efficiency and cleaner production in the dairy industry.

Abstract This study aimed to evaluate the effectiveness of different pain mitigation methods during orthodontic debonding and to evaluate pain sensitivity across various regions of the dentition. A total of 144 participants (50 males and 94 females) with metal brackets were randomly assigned to one of four groups: High-Frequency Vibration (V), Cotton Roll (CR), Elastomeric Wafer (EW), and Open Mouth group (OM). Pain levels were measured using the Visual Analog Scale (VAS) across different sextants of the dentition. The Kruskal-Wallis test and post hoc analyses were conducted to compare VAS scores between groups. The Mann-Whitney test was used to analyse sex-based differences. The V group, utilizing high-frequency vibration, had the lowest total VAS score, indicating superior pain relief compared to CR, EW, and OM groups. No significant difference was observed between the CR and EW groups. Median VAS scores were highest in the lower front sextant, followed by the upper front sextants, and lowest in the posterior regions, indicating greater pain sensitivity in the anterior regions during debonding. High-frequency vibration was the most effective method for reducing pain during orthodontic debonding, particularly in the anterior dental regions. Both CR and EW methods were also effective but to a lesser extent. These findings suggest that high-frequency vibration could significantly improve patient comfort during orthodontic procedures. Utilizing high-frequency vibration for orthodontic debonding can enhance patient comfort, especially in the more sensitive anterior dental regions, thereby potentially improving treatment compliance and experience. Trial registration: NCT05904587.

Abstract Purple blotch disease is a major fungal disease of Allium cepa L. plants which is caused by the fungus Alternaria porri. The best conditions for the growth of Alternaria porri are temperatures between 22 °C and 25 °C and relatively high humidity. The Hydrotime, Thermal Time, and Hydrothermal Time models were used to measure different parameters of seed germination; therefore, we used them to measure the interactive effects of temperature and water potential on the germination conidia of Alternaria porri. The laboratory experiments were carried out at five constant temperatures, between 5 and 30 °C, and five different water potentials between 0 MPa and − 6 MPa. The germination of Alternaria porri conidia was highest at 25 °C and 0 MPa and lowest at 5 °C and − 6 MPa. The percentage of conidia germination decreased rapidly after 25 oC. Conidia germination was also affected by different water potentials, decreasing at lower water potential. Models based on HTT showed a reasonable fit to the germination and growth rate datasets. The best fitting model for conidia germination (R2 = 0.98491) was based on variable base and maximum temperature as a function of water potential. Based on the TT, HT, and models, the highest and lowest values for θT1 were observed at -6.0 MPa at 30 °C, and 0 MPa at 5 °C and the highest and lowest θT2 values were recorded at -6.0 MPa at 5 °C and 0 MPa at 30 °C while the lowest and highest θH values were recorded at -6.0 MPa at 5 °C and 0 MPa at 25 °C, respectively, for the HTT model, the predicted θHTT average value is 16.32 (MPa°Ch-1). Based on the statistical analysis, the cardinal hydrothermal time constant (θHTT) accurately explains the interactive effect of T and Ψ on the germination of Alternaria porri conidia under different environmental conditions.

Abstract The organic enrichment effects on the meiofauna and nematofauna were assessed for field sediment and other experimental ones enriched with organic matters conducted in the laboratory for 4 weeks. Also, dissolved oxygen (DO) and pH were monitored for each one. The abundance and diversity of meiofaunal groups and nematofauna varied. Strong significant correlations were found between DO and the studied items. Nematoda was the most abundant group in the field sediment and other experimental ones; their counts increased with the increase in organic enrichments and were dominated by deposit feeders. Amphipoda, Ostracoda and predator/omnivore nematodes disappeared in highly organic-enriched sediments. Changes in DO and organic enrichments might be the more attributable reasons for the alteration of the meiobenthic assemblages. The generic compositions of Nematoda provide a good indicator for environmental alterations.

Abstract Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,θ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.

Abstract Medical diagnosis through prediction and analysis is par excellence in integrating modern technologies such as the Internet of Things (IoT). With the aid of such technologies, clinical assessments are eased with protracted computing. Specifically, cancer research through structure prediction and analysis is improved through human and machine interventions sustaining precision improvements. This article, therefore, introduces a Protein Structure Prediction Technique based on Three-Dimensional Sequence. This sequence is modeled using amino acids and their folds observed during the pre-initial cancer stages. The observed sequences and the inflammatory response score of the structure are used to predict the impact of cancer. In this process, ensemble learning is used to identify sequence and folding responses to improve inflammations. This score is correlated with the clinical data for structures and their folds independently for determining the structure changes. Such changes through different sequences are handled using repeated ensemble learning for matching and unmatching response scores. The introduced idea integrated with deep ensemble learning and IoT combination, notably employing stacking method for enhanced cancer prediction precision and interdisciplinary collaboration. The proposed technique improves prediction precision, data correlation, and change detection by 11.83%, 8.48%, and 13.23%, respectively. This technique reduces correlation time and complexity by 10.43% and 12.33%, respectively.

Abstract Tension-type headache (TTH) is the most common type of headache worldwide. It is defined and classified according to the International Classification of Headache Disorders. TTH is treated with over-the-counter medications, mostly paracetamol or ibuprofen. The purpose was to assess the effectiveness of paracetamol versus ibuprofen in treating episodic tension-type headache (ETTH) through direct and indirect comparisons of randomized controlled trials (RCTs). We included RCTs comparing paracetamol with a placebo, ibuprofen with a placebo, or paracetamol with ibuprofen for acute ETTH treatment that were published between 1988 and 2022. We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, and the Web of Science. The Cochrane Collaboration risk of bias tool was used to assess the risk of bias. We identified 14 studies including 6521 people with ETTH. None of the studies had a low risk of bias for all domains; this was most likely due to inadequate reporting and a small sample size. Ibuprofen (odds ratio (OR): 1.73, 95% confidence interval (CI): 1.17–2.56) showed better efficacy than paracetamol (OR: 1.62, 95% CI 1.24–2.13) for pain-free status at 2 h, while paracetamol (OR: 1.42, 95% CI 0.87–2.30) showed better efficacy than ibuprofen (OR: 1.20, 95% CI 0.58–2.48) for pain-free status at 1 h. Paracetamol was associated with the lowest likelihood of rescue medication use (OR: 0.49, 95% CI 0.37–0.65). Ibuprofen was associated with a lower likelihood of the occurrence of any events and gastrointestinal adverse events compared with placebo and paracetamol (OR: 0.95, 95% CI 0.64–1.41 and OR: 0.81, 95% CI 0.44–1.50, respectively). Paracetamol and ibuprofen showed better efficacy than placebo in treating ETTH; there was no statistically significant difference in efficacy between the two drugs. For individuals at a higher risk (like renal insufficiency or risk of GI bleeding), paracetamol may be considered as a preferred option instead of Ibuprofen. Further meta-analyses of head-to-head trials are needed for direct comparisons in the future. PROSPERO registration number: CRD42022340936.

Abstract A new chitosan Schiff base was developed via the reaction of chitosan (CH) with 2-chloro-3-formyl-7-ethoxy quinoline (Q) derivative. The alteration in the chemical structure and morphology of CHQ derivative was confirmed by 1H NMR, FT-IR spectroscopy and SEM analysis. The antibacterial activity was considerably promoted with increasing quinoline concentration up to 1 M with maximal inhibition reached 96 and 77% against Staphylococcus haemolyticus and Escherichia coli, respectively. Additionally, CHQ derivative afforded higher ABTS·+ radical scavenging activity reached 59% compared to 13% for native chitosan, approving its acceptable antioxidant activity. Moreover, the developed CHQ derivative can stimulate the glucose uptake in HepG-2 and yeast cells, while better inhibition of α-amylase and α-glucosidase was accomplished with maximum values of 99.78 and 92.10%, respectively. Furthermore, the molecular docking simulation clarified the binding mode of CHQ derivative inside the active site of α-amylase and α-glucosidase, suggesting its potential use as diabetes mellitus drug. The DFT calculations indicated an improvement in the electronic properties of CHQ with a lower energy band gap reached 4.05eV compared to 5.94eV for CH. The cytotoxicity assay revealed the safety of CHQ towards normal HSF cells, hypothesizing its possible application as non-toxic antibacterial, antioxidant, and antidiabetic agent for biomedical applications.