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Abstract Background Congenital toxoplasmosis occurs when a pregnant woman becomes infected with Toxoplasma gondii (T. gondii) for the first time. Treatment typically involves antimicrobial medications, with spiramycin commonly used to prevent transmission. However, spiramycin's effectiveness is limited due to poor placental penetration. Clindamycin, another antibiotic, can cross the placenta but reaches the fetus at only half the maternal concentration. Encapsulating the drug in chitosan-coated niosomes (Cs-Nio) could enhance its effectiveness by targeting specific organs and ensuring sustained release. To address the challenges of using clindamycin, a niosome-coated chitosan system was investigated for treating congenital toxoplasmosis caused by the VEG strain of T. gondii in an animal model. Methods Pregnant mice were infected with VEG strain of T. gondii on the 12th day of pregnancy, followed by treatment with various drugs across six groups. The treatments included chitosan-coated niosomes loaded clindamycin (Cs-Nio-Cli) and other controls. Parasitological evaluations (microscopic examination and real-time PCR), along with histopathological and immunological assessments were conducted to assess treatment efficacy. Finally, statistical analysis was conducted using GraphPad Prism 8.0 and SPSS 26, comparing test and control groups with T test and Mann–Whitney test. A p ≤ 0.05 was considered statistically significant. Results The study found that treatment with Cs-Nio-Cli significantly reduced the number of T. gondii cysts in the brain and eyes (97.59% and 92.68%, respectively) compared to the negative control group. It also mitigated inflammatory changes, prevented cell death, and reduced vascular cuffs in the brain. In addition, Cs-Nio-Cli treatment decreased bleeding, placental thrombosis, and inflammatory cell infiltration in the placenta while improving eye tissue health by reducing retinal folds and bleeds. Immunologically, nanoclindamycin treatment resulted in lower TNF-α cytokine levels and higher IL-10 levels, indicating an enhanced anti-inflammatory response. Conclusions Although Cs-Nio-Cli demonstrates promise in reducing the transmission of congenital toxoplasmosis and mitigating the effects of congenital toxoplasmosis, additional research is necessary to determine the optimal treatment regimens for the complete eradication of the parasite in the fetus.

Abstract One of the important problems in the environment is heavy metal pollution, and fluorescence is one of the best methods for their detection due to its sensitivity, selectivity, and relatively rapid and easy operation. In this study, 1,8-diaminonaphthalene functionalized super-stable mesoporous silica (DAN-LUS-1) was synthesized and used as a fluorescence probe to identify Hg2+ and Fe3+ in food samples. The TGA and FT-IR spectra illustrated that 1,8-diaminonaphthalene was grafted into LUS-1. XRD patterns verified that the LUS-1 and functionalized mesoporous silica have a hexagonal symmetrical array of nano-channels. SEM images showed that the rod-like morphology of LUS-1 was preserved in DAN-LUS-1. Also, surface area and pore diameter decreased from 824 m2 g⁻1 and 3.61 nm for the pure LUS-1 to 748 m2 g⁻1 and 3.43 nm for the DAN-LUS-1, as determined by N₂ adsorption–desorption isotherms. This reduction demonstrated that 1,8-diaminonaphthalene immobilized into the pore of LUS-1. The DAN-LUS-1 fluorescence properties as a chemical sensor were studied with a 340/407 nm excitation/emission wavelength that was quenched by Hg2+ and Fe3+ ions. Hg2+ and Fe3+ were quantified using the fluorescence response in the working range 8.25–13.79 × 10–6 and 3.84–10.71 × 10–6 mol/L, with detection limits of 8.5 × 10–8 M and 1.3 × 10–7 M, respectively. Hg2+ and Fe3+ were measured in vetiver grass and spinach. Since the Fe3+ quenching can move in the opposite direction with sodium hexametaphosphate (SHMP) as a hiding compound for Fe3+, consequently, the circuit logic system was established with Fe3+, Hg2+, and SHMP as inputs and the fluorescent quench as the output.

In the present study, the integration of electrochemically mediated amine regeneration (EMAR) with amine thermal swing process is investigated as a novel method for CO2 capture, utilizing experimental practical data. The aim is to increase the absorption-desorption temperature difference in order to improve the energy efficiency of the capture process and hamper the amine degradation issue at high desorption temperatures. A comprehensive experimental procedure is presented, and an experimental process setup is designed and constructed. As the main novelty of the study a special heating-cooling subsystem is incorporated with the base EMAR process in order to devise a combined electrochemical-thermal system. The performance of the system is evaluated at sequential incremental desorption temperatures while the absorption temperature is kept constant. Relevant data, including the desorbed CO2 flow, absorbed CO2 flow, stream points temperature, and cell voltage are collected. Based on the data collected, two performance parameters are calculated, including normalized carbon separation work, and CO2 desorption density. Based on these performance parameters the system's capability is assessed, and the optimal desorption temperature is ultimately selected. The presented electrochemical-thermal CO2 separation system, operating with a chloride salt system, demonstrates its best energetics performance at a desorption temperature of 44 °C, resulting in a normalized capture work of 95.2 kJ/molCO2. Under these optimal conditions, the cell's average voltage is measured to be 0.37 V, and the CO2 desorption density is determined to be 0.71 l.min−1.m−2.

Abstract In this study, the deep learning algorithm of Convolutional Neural Network long short-term memory (CNN–LSTM) is used to classify various jewelry rocks such as agate, turquoise, calcites, and azure from various historical periods and styles related to Shahr-e Sokhteh. Here, the CNN–LSTM architecture includes utilizing CNN layers for the extraction of features from input data mixed with LSTMs for supporting sequence forecasting. It should be mentioned that interpretable deep learning-assisted laser induced breakdown spectroscopy helped achieve excellent performance. For the first time, this paper interprets the Convolutional LSTM effectiveness layer by layer in self-adaptively obtaining LIBS features and the quantitative data of major chemical elements in jewelry rocks. Moreover, Lasso method is applied on data as a factor for investigation of interoperability. The results demonstrated that LIBS can be essentially combined with a deep learning algorithm for the classification of different jewelry songs. The proposed methodology yielded high accuracy, confirming the effectiveness and suitability of the approach in the discrimination process.

The real and symbolic tale of the encounter between Prophet Moses and Prophet Khidr in verses 60 to 82 of Surah Al-Kahf presents an inner, spiritual, and ascendant journey towards perfection. Beyond its artistic aspects, the narrative has been interpreted from an Anfusi perspective, exploring various dimensions. Artistic interpreters have highlighted some of these enigmatic signs in Quranic visual art, while introspective interpreters, aligning these concepts with the inner world of humans, have unveiled its secrets beautifully. This descriptive-analytical-comparative study, employing artistic elements such as principles of imagery and artistic devices like dialogue and character portrayal, along with the utilization of Anfusi interpretation involving mystical insights from within the Quran and the interpretation of verses, aims to reveal the truths behind the meanings of this mysterious story. The findings suggest that the meanings of words in this narrative, such as Moses, Fata, the junction of the two seas, servant, ship, boy, and wall, are not merely confined to their apparent meanings. Instead, infused with vitality, movement, and color, these words resonate within hearts and minds. However, artistic interpretation elucidates reality, not imagination, as falsehood does not exist in the Quran. Anfusi interpreters consider this story as a cryptic narrative, interpreting its verses to unveil the states of the human soul and aligning them with the stages of spiritual journeying. Thus, the relationship between the outward and inward aspects of the Quran is not contradictory; rather, a strong conceptual connection exists.

The exploitation of nanofluids is the most noteworthy way to make better the rate of heat transfer in solar collectors. Moreover, recently utilizing thermoelectric generators are widely studied to direct the conversion of heat into electricity. The objective of the present study is to deal with a novel multigeneration system that includes a nanofluid-based parabolic trough collector integrated with a quadruple effect absorption refrigeration cycle (cooling), a thermoelectric generator (power), a PEM electrolyzer (hydrogen), vapor generator and domestic water heater. A parametric study is accomplished to consider the effect of significant parameters such as the volume concentration of nanoparticles, solar radiation, absorption system’s generator load, strong solution concentration, and TEG’s figure of merit on the overall system performance, hydrogen production rate, cooling load, COP and useful energy obtained by the collector. It is observed that the power generated by the system is 18.78 kW and the collector energy and exergy efficiency are 82.21% and 80.48%, respectively. Furthermore, the results showed that the highest exergy destruction rate occurs in the solar system at the rate of 4461 kW. The energy and exergy COPs of the absorption chiller are discovered to be 1.527 and 0.936, respectively. By increasing the concentration of nanoparticles and the amount of solar radiation, the amount of collector useful energy increases while the hydrogen production rate and the generated power in the TEG decreased. The cooling capacity and COPs of the absorption system increased with an increase in VHTG load and decreased with an increase in concentration of the strong solution.

The present study simulates Invelox in a three-dimensional and stable way. The flow regime is turbulent flow and an unorganized grid with 350000 cells was utilized. This work has studied the modeling of invelox with conventional dimensions and four different sizes in the form of four modes for use in a residential building. The numerical data with an error of less than 6% are in good agreement with the available experimental and analytical data. The results show that considering the average velocity of mode 2 with a velocity of 6.54 m/s and a 5% difference from the other two modes, it can be operated in a residential building. It is worth noting that in this investigation, in addition, the effect of dust on the turbine performance was evaluated. The results represent that the oscillation frequency of the blades increases with the increase of the rotational speed. In the case of not considering dust particles on blades, this amount increases by 25%, while considering dust particles with an amount of 0.1%, it increases up to 300%, and this can cause irreparable damage to the turbine as well as the power generation system.

Failure Mode and Effects Analysis (FMEA) is utilized for risk appraisal in various domains. In the FMEA methodology, each failure mode is evaluated by considering three risk factors: severity (S), occurrence (O), and detection (D). Subsequently, the Risk Priority Number (RPN) is obtained by multiplying these listed factors. This study introduces the Deviation Value Step-Wise Method (DVSM) as a new mathematical model for determining the scores of the SOD factors. This methodology consists of three main steps. Firstly, the FMEA technique is used to identify failure modes. Then, the DVSM is employed to assign weights to the SOD components. In this step, relative importance is determined based on linguistic variables. The third step involves ranking failure modes using the weighted RPN. Two general examples and a case study of two-pipe heat exchanger failure modes are considered to validate the proposed model and test the obtained results. The results demonstrate that the suggested approach has enhanced the overall prioritization of failure modes. This enables the Decision-Maker (DM) to identify primary failure modes and formulate corrective/preventive actions. Finally, both sensitivity analysis and energy efficiency investigation have been performed.

Briquettes as a product of biomass compaction is considered a renewable product for replacing fossil fuels. Briquettes can be produced from several sources using the development of technology. In this study, the effect of walnut shells as an organic binder on the properties of biofuel briquettes made of industrial and garden wastes was investigated. Cylindrical briquettes from walnut shell flour with weight ratios of 5%, 10%, 15%, 20%, and 25% were prepared from industrial sawdust and ground garden residue with a weight ratio of 50/50. Briquettes were compacted under a temperature of 170 °C and pressure of 150 kg/cm2 for 30 s. Results indicated that the chemical analyses of compounds, compaction ratio, and density of the briquettes containing different proportions of walnut shells were not significantly different. The lowest fixed carbon was measured for briquettes containing 5% of walnut shell, and increasing the ratio of walnut shell significantly increased the compressive strength of the resulting briquette.

The research examines how silica aerogel and paraffin behaved as phase-change materials in thermal energy storage systems using CuO nanoparticles. The significance is in meeting the need for effective energy storage techniques in response to increasing air pollution levels and rising conventional fuel expenses. The study used molecular dynamics simulations to examine the atomic and thermal properties of a silica aerogel/paraffin/CuO nanostructure within a cylindrical duct. This modeling setup allows for a comprehensive exploration of the interactions among different materials within the thermal energy storage system, offering insights into their combined effects on thermal performance and efficiency. Key results include a nanostructure reaching a maximum (Max) velocity (Velo), temperature (Temp), and density of 0.0096 Å/fs, 626 K, and 0.1365 atom/Å3. The thermal performance represented a sample exhibiting a thermal conductivity and heat flux of 1.74 W/m.K and 66.43 W/m2. The significance of this study utilizing the proposed model lied in its potential to advance our understanding of thermal energy storage systems and their applications. Through an examination of the atomic and thermal behavior of silica aerogel/PCM when CuO nanoparticles were present in a cylinder duct, this study made a valuable contribution to the advancement of energy storage solutions that were both more efficient and effective. The findings of this study may serve as a potential for innovative developments in thermal energy storage and aid in the creation of more environmentally friendly and functional energy solutions.

Abstract Neuromyelitis optica spectrum disorder (NMOSD) is an immune-related demyelinating defect. Long non-coding RNAs (lncRNAs) might influence the pathobiology and progression of NMOSD. The current study assessed expression level of NEAT1, PANDAR, MEG3 and TUG1 lncRNAs in the peripheral blood of NMOSD patients compared with healthy individuals. All mentioned lncRNAs were shown to be over-expressed in total NMOSD cases, male NMOSD cases and female NMOSD cases compared with the matching control subgroups. MEG3 had the most robust over-expression in patients subgroups compared with normal subjects. There was no noteworthy difference in the expression of any of lncRNAs between female and male patients. MEG3 had an ideal performance in the differentiation of NMOSD cases from healthy persons (Sensitivity and specificity values = 100%). Other lncRNAs could also efficiently separate NMOSD cases from control subjects (AUC values = 0.97, 0.89 and 0.88 for PANDAR, NEAT1 and TUG1, respectively). Cumulatively, NEAT1, PANDAR, MEG3 and TUG1 lncRNAs can be considered as appropriate disease markers for NMOSD.

In the near future, hydrogen is expected to become a significant fuel that will largely contribute to the quality of atmospheric air. Hydrogen global production has so far been dominated by fossil fuels. Pure hydrogen is also produced by electrolysis of water, an energy demanding process. In this study a novel multigeneration system is introduced using nanofluid in the solar system. The proposed system includes a quadruple effect absorption refrigeration cycle, a thermoelectric generator, a PEM electrolyzer, vapor generator and domestic water heater. A parametric study is accomplished to consider the effect of significant parameters on the efficiency of the system. It is observed that the power generated by the system is 18.78 kW and the collector energy and exergy efficiency are 82.21% and 80.48%, respectively. Furthermore, the results showed that the highest exergy destruction rate occurs in the solar system at the rate of 4461 kW. The energy and exergy COPs of the absorption chiller are discovered to be 1.527 and 0.936, respectively. The amount of hydrogen production rate decreases by increasing the volume concentration of the nanoparticles, the solar radiation and the figure of merit index.

This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complicated by overlapping symptoms. To overcome this, a multimodal machine learning approach was employed, incorporating both electroencephalography (EEG) patterns and cognitive abnormalities for enhanced classification. Data were collected from 45 participants, including 20 with BD II and 25 with BPD. Analysis involved utilizing EEG signals and cognitive tests, specifically the Wisconsin Card Sorting Test and Integrated Cognitive Assessment. The k-nearest neighbors (KNN) algorithm achieved a balanced accuracy of 93%, with EEG features proving to be crucial, while cognitive features had a lesser impact. Despite the strengths, such as diverse model usage, it's important to note limitations, including a small sample size and reliance on DSM diagnoses. The study suggests that future research should explore multimodal data integration and employ advanced techniques to improve classification accuracy and gain a better understanding of the neurobiological distinctions between BD II and BPD.

We investigated age-related trends in the topology and hierarchical organization of brain structural and functional networks using diffusion-weighted imaging and resting-state fMRI data from a large cohort of healthy aging adults. At the cross-modal level, we explored age-related patterns in the RC involvement of different functional subsystems using a high-resolution functional parcellation. We further assessed age-related differences in the structure–function coupling as well as the network vulnerability to damage to rich club connectivity.Regardless of age, the structural and functional brain networks exhibited a rich club organization and small-world topology. In older individuals, we observed reduced integration and segregation within the frontal-occipital regions and the cerebellum along the brain's medial axis. Additionally, functional brain networks displayed decreased integration and increased segregation in the prefrontal, centrotemporal, and occipital regions, and the cerebellum. In older subjects, structural networks also exhibited decreased within-network and increased between-network RC connectivity. Furthermore, both within-network and between-network RC connectivity decreased in functional networks with age. An age-related decline in structure–function coupling was observed within sensory-motor, cognitive, and subcortical networks. The structural network exhibited greater vulnerability to damage to RC connectivity within the language-auditory, visual, and subcortical networks. Similarly, for functional networks, increased vulnerability was observed with damage to RC connectivity in the cerebellum, language-auditory, and sensory-motor networks. Overall, the network vulnerability decreased significantly in subjects older than 70 in both networks. Our findings underscore significant age-related differences in both brain functional and structural RC connectivity, with distinct patterns observed across the adult lifespan.