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Fasciolosis, a neglected tropical disease caused by Fasciola spp., is a global parasitic infection whose prevalence is on the rise, particularly in warmer regions with favorable temperature and humidity conditions. This study examines the temporal presence of Fasciola spp. in various climatic zones across Iran, focusing on Gilan province as an endemic area. We calculated the Malone index for 5-year intervals spanning significant epidemics in 1988 and 1999 in Gilan, as well as 2005, 2010, 2015, and 2020. The results revealed a significant increase in Malone indices during August, September, and October in 1988, indicating a higher risk for Fasciola presence than in other months. These months were categorized as moderate risk. In 1999, September exhibited moderate risk, while October was classified as low risk. The risk levels remained relatively low in 2005, with the highest value in September and October. Similarly, October and September showed the highest risks in 2010, while October was identified as highly risky in 2015. In 2020, August displayed a higher risk, while September, October, and November indicated low risk. The analysis highlights the variability of risk levels across different years, with September and October consistently appearing as months with a higher probability of Fasciola presence. Conversely, the risk diminishes from January to July, which is considered a low-risk period in the region. Interestingly, a comparison with three different geographical regions in central, southern, and western Iran revealed that the Malone index was zero in all months of the year, except for two cases, suggesting unfavorable conditions for Fasciola presence in these areas. This research sheds light on the complex interplay between climatic variables and the presence of Fasciola spp., with the potential to inform the development of targeted strategies for preventing and controlling this parasite in Gilan province, a known hotspot for fasciolosis, as well as in other regions with similar climatic conditions., Competing Interests: Declarations. Ethical approval: Not applicable. Consent to participate: Not applicable. Consent for publication: All authors gave explicit consent to submit the work. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

The fabrication of scaffolds for bone tissue engineering (BTE) applications often involves the utilization of two distinct categories of biomaterials, namely calcium phosphates and calcium silicates. The selection of these materials is based on their biocompatibility, bioactivity, and mechanical characteristics that closely resemble those of natural bone. The present research examined the utilization of hydroxyapatite (HAP) and tri-calcium silicate (TCS), which are among the most commonly utilized materials in calcium phosphates and calcium silicates, in the context of bone scaffolding applications. A molecular dynamics simulation was conducted to investigate the impact of different concentrations of ceramic nanoparticles, when combined with sodium alginate (SA) hydrogel, on the fabrication of bone scaffolds.The stability and self-assembly were assessed through several parameters, such as the solvent-accessible surface area (SASA), radius of gyration (Rg), radial distribution function (g(r)), root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), hydrogen bonding, van der Waals, electrostatic, and total energies. The findings indicate that the addition of 10 wt% HAP and TCS to the SA hydrogel matrix results in a more compact, stable, and potentially less hydrated structure. Accordingly, the experimental validation of these simulation approved our in silico findings. Experimental rheology and mechanical properties evaluation validate our simulation results, indicating a superior characteristic of TCS10 and HAP10 inks and 3D-printed scaffolds among other composition ratios. This could potentially benefit the in vitro and in vivo performance of the scaffold and its interaction with cells. The aforementioned traits are considered fundamental for the successful execution of the scaffold in the field of BTE. The findings indicate that TCS samples exhibit superior properties when compared to HAP samples, specifically in terms of composition with SA hydrogel., Competing Interests: Declarations. Ethics approval and consent to participate: This article does not contain any studies with human participants or animals performed by any of the authors. Research was approved by ethical committee of Tabriz University of Medical Sciences [ethics code: IR.TBZMED.VCR.REC.1403.220]. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Traumatic brain injury (TBI) is a leading cause of mortality and morbidity worldwide, presenting a significant challenge due to the lack of effective therapies. Neural stem cells (NSCs) have shown promising potential in preclinical studies as a therapy for TBI. However, their application is limited by challenges related to poor survival and integration within the injured brain. This study investigated the effect of a novel nano-scaffold containing stromal cell-derived factor 1 (SDF-1) on NSC behavior and synaptogenesis after TBI. Using an innovative design, we successfully fabricated a nano-scaffold with Young's modulus of approximately 3.21 kPa, which aligns closely with the mechanical properties exhibited by neural tissue. This achievement marks the first time such a scaffold has been created and has promising implications for its potential use in neural tissue engineering applications. Our findings demonstrate that the nano-scaffold enhances NSC proliferation, migration, and differentiation capacity in vitro. Moreover, when transplanted into the injured brain, the nano-scaffold promotes the survival and integration of NSCs, leading to increased synaptogenesis and functional recovery. These findings suggest that using the novel nano-scaffold containing SDF-1 could provide a promising approach to treating TBI by improving NSC behavior and promoting synaptogenesis., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethics approval and consent to participate: All animal experiments were approved by the Animal Ethics Committee of Mashhad Medical University, Iran, and were performed under the supervision of the National Institutes of Health Guide for the Care and Use of Laboratory Animals. The authors complied with the ARRIVE guidelines. All methods were performed in accordance with the relevant guidelines and regulations.  Consent for publication: Not applicable., (© 2025. The Author(s).)

Gradient tissues are anisotropic structure with gradual transition in structural and biological properties. The gradient in structural, mechanical and biochemical properties of osteochondral and meniscal tissues play a major role in defining tissue functions. Designing tissue substitutes that replicate these gradient properties is crucial to facilitate regeneration of tissue functions following injuries. Advanced manufacturing technologies such as 3D bioprinting hold great potentials for recreating gradient nature of tissues through using zone-specific bioinks and layer-by-layer deposition of spatially defined biomaterials, cell types and bioactive cues. This review highlighted the gradients in osteochondral and meniscal tissues in detail, elaborated on individual components of the bioink, and reviewed recent advancements in 3D gradient-based osteochondral and meniscal tissue substitutes. Finally, key challenges of the field and future perspectives for developing gradient-based tissue substitutes were discussed. The insights from these advances can broaden the possibilities for engineering gradient tissues., (© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Auxetic textile sensors represent a new generation of wearable sensors, offering advantages such as high sensitivity, enhanced mechanical properties, and greater comfort due to their suitable physical features. Research in this field remains limited and is still in its nascent stages. In this work, a piezoresistive sensor with a negative Poisson's ratio was developed using the design concept of semi-auxetic yarn, where a stretchable band replaced the core to serve as a substrate for the piezoresistive sensors. A semi-auxetic piezoresistive textronic structure was created by stitching the sensors onto the substrate in a zig-zag pattern. Electromechanical analysis highlighted a trade-off between auxetic behaviour and sensitivity, with the highest sensitivity (-4.8) observed in a structure with a stitching length of 2 cm, and the highest auxeticity (-14.1) found in a structure with a stitching length of 3 cm. This type of piezoresistive textronic is suitable for applications such as yoga straps, driving safety belts, compression bandages, and breathable belly belts for pregnant women., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

A mere glance at the foundation of the sericulture industry to produce silk and the consequent establishment of the Silk Road to transport it; elucidates the significant role that this material has played in human history. Owing to its exceptional robustness, silk was introduced into medicine as a surgical suture approximately two millennia ago. During the last decades, silk has garnered attention as a possible source of biological-based materials that can be effectively used in regenerative medicine. Silk's unique characteristics, like its low immunogenicity, suitable adhesive properties, exceptional tensile strength, perfect hemostatic properties, adequate permeability to oxygen and water, resistance to microbial colonization, and most importantly, excellent biodegradability; make it an outstanding choice for biomedical applications. Although there are many different types of silk in nature, Bombyx mori (B. mori) silk accounts for about 90% of global production and is the most thoroughly investigated and the most commonly used. Silk fibroin (SF) and silk sericin (SS) are the two main protein constituents of silk. SF has been manufactured in various morphologic forms (e.g., hydrogels, sponges, films, etc.) and has been widely used in the biomedical field, especially as a scaffold in tissue engineering. Similarly, SS has demonstrated a vast potential as a suitable biomaterial in tissue engineering and regenerative medicine. Initial studies on SF and SS as wound dressings have shown encouraging results. This review aims to comprehensively discuss the potential role of silk proteins in refining wound healing and skin regeneration.&#xD., (© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Significant progress has been made in the field of image reconstruction using functional magnetic resonance imaging (fMRI). Certain investigations reconstructed images with visual information decoded from brain signals, yielding insufficient accuracy and quality. The combination of semantic information in the reconstruction was recommended to improve performance. However, this issue continues to come across numerous difficulties. To address such problems, we proposed an approach that combines semantically complex details with visual details for reconstruction. Our proposed method consists of two main modules: visual reconstruction and semantic reconstruction. In the visual reconstruction module, visual information is decoded from brain data using a decoder. This module employs a deep generator network (DGN) to produce images and utilizes a VGG19 network to extract visual features from the generated images. Image optimization is performed iteratively to minimize the error between features decoded from brain data and features extracted from the generated image. In the semantic reconstruction module, two models BLIP and LDM are employed. Using the BLIP model, we generate 10 captions for each training image. The semantic features extracted from the image captions, along with brain data obtained from training sessions, are used to train a decoder. The trained decoder is then utilized to decode semantic features from human brain activity. Finally, the reconstructed image from the visual reconstruction module is used as input to the LDM model, while the semantic features decoded from brain activity are provided as conditional input for semantic reconstruction. Including decoded semantic features improves reconstruction quality, as confirmed by our ablation study. Our strategy is superior both qualitatively and quantitatively to Shen et al.'s method, which utilizes a similar dataset. Our methodology achieved an accuracy of 0.812 and 0.815 for the inception and contrastive language-image pre-training (CLIP) metrics, respectively, which are excellent for the quantitative evaluation of semantic content. We achieved an accuracy of 0.328 in the structural similarity index measure (SSIM), indicating superior performance as a low-level metric. Moreover, our proposed approach for semantic reconstruction of artificial shapes and imagined images achieved acceptable success, attaining accuracies of 0.566 and 0.627 based on the CLIP metric, and 0.671 and 0.565 based on the SSIM metric, respectively., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors., (© 2025. The Author(s).)

Hydrogen is recognized as a clean energy replacement for non-renewable fossil fuels, and the utilization of metal-organic frameworks (MOFs) for hydrogen storage has gained considerable interest in recent years. In this study, hydrogen storage in MOFs was estimated using white-box methods, namely group method of data handling (GMDH), genetic programming (GP), and gene expression programming (GEP), which are robust soft-computing methods known for generating innovative correlations. To this end, temperature, pressure, pore volume, and surface area were implemented as input parameters for constructing these robust correlations. After that, the superiority of the established correlations was demonstrated through multiple statistical and graphical error assessment. The results indicated, the GMDH model demonstrates the highest accuracy with root mean square error (RMSE), and mean absolute error (MAE) values of 0.410 and 0.307, respectively. However, the GEP model's accuracy was comparable to that of the GMDH model. In addition, sensitivity assessment showed that the pore volume and the pressure exhibit the strongest linear and non-linear relationships, respectively, with the H 2 storage in MOFs. This was demonstrated by a Pearson correlation coefficient of 0.5 and a Spearman correlation coefficient of 0.56, respectively. Furthermore, temperature had a minimal negative impact on the H 2 storage in MOFs according to Pearson, Spearman, and Kendall coefficients. Finally, to confirm the findings of the GMDH model, the leverage approach was applied, demonstrating that 96% of the data falls within the acceptable region, confirming the statistical reliability of the developed models., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Transdermal drug delivery presents a promising noninvasive approach, bypassing first-pass metabolism and gastrointestinal degradation. However, the stratum corneum (SC) barrier limits drug absorption, necessitating the development of effective delivery systems. Microneedles, particularly polymer-based ones, offer a solution by penetrating the SC while avoiding critical nerves and capillaries. These microneedles are biodegradable, nontoxic, and easily manufacturable, making them a highly attractive platform for transdermal drug delivery. However, their clinical application remains limited due to suboptimal therapeutic efficacy and slow drug release rates. Recent advancements have introduced the incorporation of nanodrugs, such as nanoparticles and encapsulated drugs, into microneedles to enhance drug delivery efficiency. Among the materials explored, graphene and its derivatives, including graphene oxide (GO) and reduced graphene oxide (rGO), have garnered significant attention. Their exceptional mechanical strength, electrical conductivity, and antibacterial properties not only improve the mechanical performance of microneedles but also enhance drug release rates and biocompatibility. This review synthesizes the current state of microneedle technologies, focusing on the materials, fabrication techniques, and performance challenges. It particularly examines the potential of graphene-based microneedles, comparing them to traditional polymer-based microneedles in terms of drug release efficiency and stability. The review highlights key challenges, such as scalability, biocompatibility, and fabrication complexity, and suggests future research directions to address these issues. The incorporation of graphene quantum dots (GQDs) is identified as a promising avenue for improving drug release profiles, stability, and real-time tracking of drug diffusion. Finally, the review outlines emerging applications, including smart drug delivery systems, biosensing, and real-time monitoring, urging further exploration to unlock the full potential of graphene-enhanced microneedles in clinical settings.

There is a growing need for research on Parkinson's disease (PD), a neurological condition that often affects the elderly. By examining brain network connectivity, researchers are able to discover how different brain regions interact during various cognitive and behavioral tasks. They can also understand how changes in nonlinear connections may be linked to neurological and mental illnesses. In this paper, the synchrony levels of 59 EEG channels from 26 Parkinson's patients and 13 healthy subjects is examined by utilizing Phase-Lag Index (PLI) during a motor task and resting-state conditions. Examining different EEG frequency bands shows that whole-brain synchronization in the delta band is significantly lower in PD patients compared to healthy subjects during the task. PD patients also exhibit a lower clustering coefficient and a higher shortest path length in this band during the task, which shows the higher small-worldness in Parkinson's patients compared to healthy individuals. Moreover, the global efficiency in the delta band is significantly reduced in PD patients during the task. An analysis of local efficiency shows that PD and control groups differ in 57 channels. These results reveal that Parkinson's patients appear to have considerable pathological abnormalities in their delta band connectivity and its characteristic features., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Aniline, a hazardous aromatic compound, is a notable contaminant in various industrial wastewater. As a simple and convenient process, adsorption could facilitate aniline adsorption from sewage. Magnetic activated carbon adsorbent (MAC) was produced using the co-precipitation method and applied to the adsorption of aniline from water. The characterization of this adsorbent was surveyed using Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), Brunauer-Emmett-Teller (BET), and Vibrating-Sample Magnetometer (VSM). Key operational parameters, including adsorbent dosage, initial aniline concentration, and initial pH, have been systematically investigated using the Central Composite Design (CCD) approach in the Response Surface Method (RSM). According to the results, the optimal adsorption percentage was achieved with a 2.9 g/L dosage, pH 7.4, 52.7 mg/L initial concentration, and 300 min of contact time. A study on Langmuir, Freundlich, and Sips isotherms was conducted, alongside evaluating pseudo-first-order and pseudo-second-order kinetics. The Sips isotherm and pseudo-second-order models showed the best fit, with R 2 values > 0.98 and 0.99, respectively. The thermodynamic study indicated a negative ΔG, confirming that aniline adsorption is spontaneous on both commercial and magnetic activated carbon., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

The widespread utilization of fossil fuels cause to significantly elevate greenhouse gas emissions. Consequently, the developments of the innovative methods are essential to convert methane into the green energy. Recently, significant effort is made to enhance the performance of the plasma-based conversion technologies. Here, the dissociation rate of methane into heavier hydrocarbon compounds are carefully determined by making use of a hybrid laser-induced plasma (LIP) and spark discharge (SD). Fourier-transform infrared (FTIR) spectroscopy reveals a couple of characteristic peaks after laser triggering, whose intensities notably increase at higher applied voltages. The corresponding peaks indicate the formation of heavier compounds including sp and sp 2 C-H stretching bonds. The findings elucidate that the methane decomposition rate notably elevates in favor of hybrid SD-LIP against that of traditional LIP. It is worth noting that the simultaneous ablative effect of the catalyst surface to remove the carbon soot by the successive laser shots could prevent the catalytic deactivation leading to the sustained performance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Fabricating scaffolds using three-dimensional (3D) printing is an emerging approach in tissue engineering (TE), where filaments with a controlled arrangement are printed. Using fused deposition modeling in bone replacement enables the simulation of bone structure. However, the microenvironment created by the scaffold must meet specific requirements. These requirements aim to create an environment that promotes adhesion, proliferation, differentiation, and cell migration. One of the challenges in creating polylactic acid scaffolds is controlling the degradation rate to match the target tissue. This study investigates the degradation of scaffolds with different geometries and the relationship between scaffolds' geometry and degradation rate. These scaffolds are made of polylactic acid and prepared using 3D printing. The lattice geometry was exposed to acidic media with varying pH levels for 1 month, and pH2 was selected for all geometries for further investigation. The five selected geometries were then immersed in the desired acid for 2 months, and measurements were taken for wet weight, dry weight, morphology, molecular weight, and crystallinity during degradation. The results showed that the hexagonal sample had a 1.5% increase in wet weight, and the gyroid sample had a 1.2% increase, indicating that the wavy shapes had a higher fluid-holding capacity. The degradation analysis indicated that the hexagonal geometry had accelerated degradation compared to the other geometries. Based on these findings, it can be concluded that filament separation not only results in rapid cooling and prevents the recovery of the crystalline arrangement but also increases the surface area to volume ratio, allowing for more acid penetration and faster degradation. Finally, mechanical properties and in vitro evaluation were assessed for three selected geometries. On the 60th day, the hexagonal scaffold had the highest elastic modulus value of 105 ± 0.45 MPa, while the gyroid scaffold had the lowest value of 58.8 ± 0.40 MPa. The lattice scaffold had the highest amount of cell attachment, with 210.88 ± 0.35 cells surviving after 24 hours and 94.01 ± 0.18 cells surviving after 72 hours. These high viability rates indicate that the three scaffolds with the selected geometries are suitable for promoting cell growth., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The interaction of lipopolysaccharide with a recombinant protein is a serious bottleneck, particularly in the purification step of bioprocessing. Recombinant hepatitis B surface antigen (rHBsAg), the active ingredient of the hepatitis B vaccine, is probably contaminated by extrinsic LPS like other biopharmaceuticals. This research intends to eliminate LPS from its mixture with rHBsAg efficiently. Immobilized polymyxin B on magnetic nanoparticles (PMB-MNPs) was synthesized and implemented as an enhanced LPS affinity adsorbent (LAA). The 20-80 EU/dose binary samples with and without surfactant were applied to PMB-MNPs. Formerly, dynamic light scattering (DLS) and transmission electron microscopy (TEM) were examined on the samples to qualitatively show the dissociation effect of the surfactant. Considering the high potential interaction of LPS with HBsAg, the dissociation effects of 0.5 and 1.5% Tween 20 on the binary samples were assessed using immunoaffinity chromatography (IAC) as a quantification tool. The dissociation effect of Tween 20 substantially diminished the interaction, leading to a proportional increase of free LPS up to 66%. The synergetic effect of Tween 20 and privileged LAA was highly effective in eliminating more than 80% of LPS with a remarkable LPS clearance factor of 5.8 and a substantial protein recovery rate of 97%.

The present study investigated effects of coupling electrocoagulation (EC) process with an anaerobic digestion bioreactor, namely up-flow anaerobic sludge blanket (UASB), for the synthetic wastewater treatment. The EC-UASB mode of operation consisted of one anode and two cathodes subjected to an intermittent electrical current (10 min ON/30 min OFF) with current density of 1.5 mA/cm 2 . In light of this integration, the concentration of mixed liquor suspended solids and mixed liquor volatile suspended solids within anaerobic granular sludge (AGS) increased by 20.0 ± 1.4% and 12.8 ± 0.8%, respectively. The results of sludge volume index, loosely and tightly bound extracellular polymeric substances and their constituents (protein and carbohydrate) revealed that through this integration the quality of AGS has been improved. Furthermore, results of scanning electron microscopy and Fourier-transform infrared spectroscopy showed alteration in the morphology and functional groups of AGS, respectively. Additionally, this combination has demonstrated promising results in terms of performance improvement by increasing the removal efficiency of total dissolved solids by 12.1 ± 0.3% and reducing the ionic pollution in treated wastewater. However, the integration of the EC system within the UASB resulted in energy consumption and operating cost of 1.33 kWh/m 3 and 0.099 USD/m 3 , respectively., Competing Interests: Declarations. Conflict of interest: The authors declare that they have no conflict of interest., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The initial interaction between COVID-19 and the human body involves the receptor-binding domain (RBD) of the viral spike protein with the angiotensin-converting enzyme 2 (ACE2) receptor. Likewise, the spike protein can engage with immune-related proteins, such as toll-like receptors (TLRs) and pulmonary surfactant proteins A (SP-A) and D (SP-D), thereby triggering immune responses. In this study, we utilize computational methods to investigate the interactions between the spike protein and TLRs (specifically TLR2 and TLR4), as well as (SP-A) and (SP-D). The study is conducted on four variants of concern (VOC) to differentiate and identify common virus behaviours. An assessment of the structural stability of various variants indicates slight changes attributed to mutations, yet overall structural integrity remains preserved. Our findings reveal the spike protein's ability to bind with TLR4 and TLR2, prompting immune activation. In addition, our in-silico results reveal almost similar docking scores and therefore affinity for both ACE2-spike and TLR4-spike complexes. We demonstrate that even minor changes due to mutations in all variants, surfactant A and D proteins can function as inhibitors against the spike in all variants, hindering the ACE2-RBD interaction.Communicated by Ramaswamy H. Sarma.

Lung cancer is a leading cause of cancer death worldwide. The survival rate is generally higher when this disease is detected in its early stages. Advances in artificial intelligence (AI) have enabled the development of decision support systems that help physicians diagnose diseases. However, these systems often provide final predictions without clarifying how those decisions are reached, raising concerns about trust and adaptation in life-threatening diseases. To address these issues, this study proposes an explainable case-based reasoning (XCBR) approach that considers both physicians' tendency to base their decisions on past cases and the case complexity in its predictions and explanations. The proposed XCBR is enhanced with naïve Bayes (NB) and multilayer perceptron (MLP) classifiers which are processed hierarchically: when the NB deems its predictions to be unlikely, the MLP classifier is employed to verify or update the predictions. This approach incorporates Shapley additive explanations values to elucidate the solutions offered by the MLP. Furthermore, it utilizes the Harris hawks optimization algorithm for feature selection and feature weighting. The proposed XCBR achieved high accuracies of 94.47 % and 100 % on two different datasets, demonstrating its generalization capability. Based on Wilcoxon signed-rank test, its classification accuracy is comparable to that of other state-of-the-art approaches and commonly used classifiers. Moreover, since this approach prioritizes case complexity in its predictions and explanations, it offers better explainability and is particularly suited for serious diseases., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Strabismus, or eye misalignment, is a common condition affecting individuals of all ages. Early detection and accurate classification are essential for proper treatment and avoiding long-term complications. This research presents a new deep-learning-based approach for automatically identifying and classifying strabismus from facial images. The proposed methodology leverages Convolutional Neural Networks (CNNs) to achieve high accuracy in both binary (strabismus vs. normal) and multi-class (eight-class deviation angle for esotropia and exotropia) classification tasks. The dataset for binary classification consisted of 4,257 facial images, including 1,599 normal cases and 2,658 strabismus cases, while the multi-class classification involved 480 strabismic and 142 non-strabismic images. These images were labeled based on ophthalmologist measurements using the Alternate Prism Cover Test (APCT) or the Modified Krimsky Test (MK). Five-fold cross-validation was employed, and performance was evaluated using sensitivity, accuracy, F1-score, and recall metrics. The proposed deep learning model achieved an accuracy of 86.38% for binary classification and 92.7% for multi-class classification. These results demonstrate the potential of our approach to assist healthcare professionals in early strabismus detection and treatment planning, ultimately improving patient outcomes., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethical approval and consent to participate: The local ethics committee of Tehran University of Medical Sciences approved the study in accordance with Helsinki’s ethical principles (IR.TUMS.RARABIH.REC.1401.032)., (© 2025. The Author(s).)

The heavy metals in the effluents of industries threatens the human health. In this research, aqueous two-phase systems based on two deep eutectic solvents, choline chloride/urea and betaine/urea, along with dipotassium hydrogen phosphate, were used as environmentally friendly systems to extract hexavalent chromium. Binodal curves and tie lines were obtained for two systems. The effect of temperature, pH, metal concentration and salt composition was investigated on the extraction efficiency of chromium. Increasing the temperature, the concentration of metal and the amount of salt increased the extraction efficiency. The system consisting of choline chloride/urea had better extraction than other and showed extraction efficiency of 86.82 ± 1.99 at pH = 10 and temperature of 25 ºC and metal concentration of 80 mg/l. the system based on betaine/urea showed extraction efficiency of 89.51 ± 1.69 at pH = 8.9 and temperature of 25 °C and metal concentration of 80 mg/l. Considering the same cost of two systems, the system including betaine/urea was evaluated as a better system for the separation of chromium. These systems were also used to separate lead, cobalt, nickel and cadmium that showed their high tendency to the low phase., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Purpose/aim: The developing brain undergoes a remarkable process of synaptic changes., Material and Methods: To investigate the developmental changes in glutamatergic synaptic connections using the whole-cell patch clamp method, evoked excitatory postsynaptic currents (eEPSCs) were recorded from locus coeruleus (LC) neurons, a brain region crucial for cognitive functions, in rats at ages 7, 14, and 21 days. We employed fractal analysis to compute fractal dimensions of AMPA and NMDA receptors, serving as markers for synaptic maturation., Results: Our findings revealed a significant increase in fractal dimensions during the third postnatal week and hence a developmental chenge of synaptic connections. A strong positive correlation between amplitude and fractal dimensions, in Pearson correlation analysis suggested that the synaptic currents' amplitude is closely related to the fractal properties of the receptors. A linear relationship between fractal dimensions and age indicated that fractal analysis can be a robust tool for predicting developmental changes. Additionally, we employed machine learning techniques to predict developmental changes based on AMPA and NMDA receptors. Support Vector Machine (SVM) models outperformed random forest models in accurately predicting age-dependent developmental changes, as indicated by the area under the curve (AUC) values. SVM achieved an AUC of 0.89 for AMPA receptors and 0.86 for NMDA receptors, demonstrating the effectiveness of fractal-based features in characterizing synaptic maturation., Conclusion: This study offers valuable insights into synaptic development in the LC nucleus and demonstrates the potential of fractal analysis as a tool to understand brain plasticity and early development. Fractal dimensions play a crucial role in characterizing the maturation of glutamatergic synapses and neural circuitry development.

Hydrogels have emerged as a promising material for the removal of heavy metal ions from contaminated water owing to their high water absorption capacity and biocompatibility. Despite notable advancements in improving the adsorptive capacity of hydrogels, the demand for a more efficient structure persists. Here, we explore the ion adsorption performance of crosslinked hydrogels based on chitosan and sodium alginate with various ratios of carbon nanotubes (CNT) and graphene platelets (GNP). This study highlights the adsorption of chromium ions and the thermal stability of hydrogels for pure, single-particle, and hybrid nanocomposites. The results depict a uniform microstructure attained when CNT, GNP, or both are implemented into the hydrogel due to the strong interaction of functional moieties. The incorporation of CNT and GNP manipulates the crystalline structure of the hydrogels, lowering their crystallinity by around 28% and 13%, respectively. The synergistic effect of CNT and GNP in hybrid hydrogels raises the decomposition temperature by 16%, indicating a favorable interplay interaction between nanoparticles and polymers. Calculations of the adsorption capacity accentuate such a mutual effect between CNT and GNP in various loads of ion capture from aqueous solutions. Kinetic models fitted to the hydrogel nanocomposites reveal that the pseudo-second-order model aligns better with the experimental data in comparison to the pseudo-first-order and intraparticle diffusion models, addressing the adsorption mechanisms while capturing chromium ions.

This study investigates the mechanical and microstructural properties of loose sandy soil stabilized with alkali-activated Ground Granulated Blast Furnace Slag (GGBFS). To examine the effects of varying GGBFS contents, curing times, and confining pressures on mechanical behavior, undrained triaxial and unconfined compressive strength (UCS) tests were conducted. Microstructural analyses using FE-SEM, EDX, and FTIR were performed to elucidate the nature and development of cementation. The results of mechanical behavior demonstrate that even with limited GGBFS content (1-6%), the treated samples exhibited significant improvements in strength, stiffness, and energy absorption, underscoring the efficiency of alkali-activated GGBFS as a soil stabilizer. Moreover, mechanical parameters from triaxial tests revealed a nearly constant internal friction angle with increasing GGBFS content and curing duration, while cohesion showed remarkable enhancement. A strong linear correlation between UCS and cohesion was also identified, enabling cost-effective estimation of shear strength parameters. These findings highlight the potential of alkali-activated GGBFS for improving granular soils, offering practical implications for sustainable geotechnical applications, particularly in road construction., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Several mechanisms were postulated to reduce drilling problems, improve hole cleaning characteristics, and keep the bit in good condition for the second usage. This study was conducted on Majnoon Field in southeastern Iraq to optimize the bit and drilling parameters. The results indicated that the 16" SFD75D bit proved the preferred bit for both vertical and deviated wells due to its directional capabilities. MMD and MME were the best bit types for 12.25" and 8.5" sections and proved more successful than the FX and EQ types. The maximum weight on bit (WOB), Round per Minute (RPM), mud weight, and flow rate must be 25.45-73 klbs, 50-179.22, 10.5-10.8 ppg, and 662-940 gpm respectively, according to the drilling section. The wells with the J-shape showed a better penetration rate (increased 50-100%) and steadier torque than those with the S-shape for all drilling sections, with some deviations in the Shiranish, Sadi, and Tanuma Formations. The wells in the south have a sliding percentage of drilling hours more than in the north of the Field, and the inclination of the wells in the south was generally more stable and had lower hydraulics requirements than in the north of the Field., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Braided composites are gaining attention in the most industrial applications. To design rods with optimal tensile properties against combined loads, experimental studies were conducted to investigate the effect of using axial yarn and core in different categories on the tensile properties of braided reinforced composite rods. In this study, six types of braided composite rods with different arrangements of braid components (axial yarn or core type) were produced using glass and polyester fibers with epoxy resin as the matrix. The elastic modulus, ultimate tensile strength, and work of fracture of these rods were tested, and the experimental elastic modulus of samples were compared with previously developed models. Moreover, comparing the stress-strain results of the samples revealed that the produced hybrid samples demonstrate pseudo-ductile tensile behavior, showing varying trends as the type of reinforcement is altered. The results indicate that using a double-layer triaxial braid as reinforcement for the composite can increase the elastic modulus up to 13% compared to the similar single-layer sample. Additionally, employing a double-layer triaxial braid as reinforcement for the composite leads to a greater work of fracture. However, in terms of ultimate tensile strength, biaxial braid reinforcement demonstrates better performance in reinforcing the composite rods., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

This paper presents a thorough literature review on devulcanization methods applied to waste tire rubber: "microwave devulcanization" and "biological desulfurization". To do so, 80 papers published from the year 1990 to 2024 in journals with subscription and open access status across 12 databases were reviewed. This paper compares the efficacy and reviews the basic concepts, advantages, processes, and variable parameters of these two methods. In microwave devulcanization, microwave energy breaks the sulfur crosslinks between polymer chains. The latter breakage is mainly enabled by the presence of carbon black in the tire, which is an excellent microwave absorbent. In biological desulfurization, bacteria or fungi convert the crosslinks to elemental sulfur substances or sulfate. In general, microwave devulcanization of rubber leads to a lower crosslink density and thus a higher degree of devulcanization. On the one hand, breaking the crosslinks requires a significantly shorter time than biological desulfurization. Crosslink scission occurs throughout the sample in microwave devulcanization but only on the sample surface in biological desulfurization. Microwave devulcanization is not sensitive to rubber additives and does not require detoxification before devulcanization. On the other hand, biological desulfurization requires detoxification before devulcanization since it involves living organisms that may not tolerate certain rubber additives.

Heroin as a derivative of morphine contains the alkaloids and flavonoids with plenty of three to five aromatic rings. The latter is known as the main source of fluorescence emission after laser excitation. Here, laser induced fluorescence (LIF) spectroscopy at excitation line of 405 nm with the solvent densitometry method is introduced based on modified Beer-Lambert (MBL), for the rapid and reliable identification of street heroin samples. Not only this hybrid method is cost effective, portable and easy to operate, but also it demonstrates an accurate technique based on the four unique parameters of the concentration at peak (C p ), self-quenching (K), and extinction coefficients (α) as well as the Stocks shift (S S )., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier B.V. All rights reserved.)

In recent years, attempts were made to develop biomaterials using synthetic and natural polymers to induce osteogenesis of human mesenchymal stem cells (hMSCs). Poly(ε-caprolactone) (PCL) is one of the few synthetic polymers with the potential to differentiate hMSCs to bone. However, its potential is limited, attributed to its low strength; its fast crystallization rate also compromises its dimensional stability. Polylactic acid (PLA) is another synthetic biocompatible polymer with low crystallinity and high rigidity widely used in biomedical engineering. It's commonly added to PCL to limit its shrinkage. However, they are incompatible. Here, the PCL: PLA (70: 30) blend was prepared and three PCL-PEG x -PCL block copolymers (BCPs) and cellulose nanocrystals (CNCs) were employed to improve their compatibility. Bone is a natural porous structure and here, by limiting shrinkage of PCL, potential of the blends to yield stable three- dimensional (3D) scaffolds with interconnected pores was studied. Salt leaching and thermally- induced phase separation was used to produce 3D scaffolds. CNCs were efficient in tuning pore morphology through cell- wall stabilization once the optimal BCP improved the interfacial interaction and reduced the viscosity. The results showed the blends possessed osteoinductive potential. However, the CNC nanocomposites were quite successful at intensifying biomineralization process., Competing Interests: Declaration of competing interest The authors declare they have no conflict of interest., (Copyright © 2025 Elsevier B.V. All rights reserved.)

Water quality management is a critical aspect of environmental sustainability, particularly in arid and semi-arid regions such as Iran where water scarcity is compounded by quality degradation. This study delves into the causal relationships influencing water quality, focusing on Total Dissolved Solids (TDS) as a primary indicator in the Karkheh River, southwest Iran. Utilizing a comprehensive dataset spanning 50 years (1968-2018), this research integrates Machine Learning (ML) techniques to examine correlations and infer causality among multiple parameters, including flow rate (Q), Sodium (Na + ), Magnesium (Mg 2+ ), Calcium (Ca 2+ ), Chloride (Cl - ), Sulfate (SO 4 2- ), Bicarbonates (HCO 3 - and pH exerting negative (inverse) effects. Unlike correlations, causal relationships demonstrate directional and often unequal influences, highlighting Mg as a critical driver of TDS levels. This novel application of ML-based causal inference in water quality research provides a cost-effective and time-efficient alternative to traditional experimental methods. The results underscore the potential of ML-driven causal analysis to guide water resource management and policy-making. By identifying the key drivers of TDS, this study proposes targeted interventions to mitigate water quality deterioration. Moreover, the insights gained lay the foundation for developing early warning systems, ensuring proactive and sustainable water quality management in similar hydrological contexts.4 , with HCO 3 and pH exerting negative (inverse) effects. Unlike correlations, causal relationships demonstrate directional and often unequal influences, highlighting Mg as a critical driver of TDS levels. This novel application of ML-based causal inference in water quality research provides a cost-effective and time-efficient alternative to traditional experimental methods. The results underscore the potential of ML-driven causal analysis to guide water resource management and policy-making. By identifying the key drivers of TDS, this study proposes targeted interventions to mitigate water quality deterioration. Moreover, the insights gained lay the foundation for developing early warning systems, ensuring proactive and sustainable water quality management in similar hydrological contexts., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Introduction: Human amniotic membrane (hAM) has a highly biocompatible natural scaffold that is abundant in several extracellular matrix (ECM) components, including but not limited to platelet-derived growth factor (PDGF), transforming growth factor (TGF), and fibroblast growth factor (FGF). In our study, we have focused on a mixture of hAM and demineralized bone matrix (DBM) as an allo-hybrid graft to deliver it into the site of bone defect to decrease bone remodeling time., Methods: Allo-hybrid grafts were prepared by coating the jelly made of decellularized and lyophilized hAM (AMJ) on the surface of DBM and subsequently underwent in vitro studies, such as alkaline phosphatase activity, MTT assay, and SEM analysis. Twenty-four male rats were included in the study, and after creating calvarial defects, rats were divided into four groups: DBM implanted, allo-hybrid implanted, AMJ injection, and a negative control (NC). Bone regeneration was assessed using computed tomography (CT scan) and histological analysis at 1, 2, and 3 months after surgery., Results: CT scan analysis clearly showed improved new bone growth in the allo-hybrid group compared to the NC group. Also, the Hounsfield unit of the allo-hybrid group (774.91 ± 47.8) after 90 days confirms CT scans. Histological staining revealed immature bone in allo-hybrid and DBM groups, along with the creation of a medullary cavity and bone marrow two months after surgery. Three months after surgery, the allo-hybrid group showed signs of new, mature bone, while no sign of healing could be seen in the NC group at any time points. Over a 90-day period, the allo-hybrid group recovered the bone defect area near 90 %. It is relatively twice as much as AMJ group., Conclusion: Histological properties of bone defects and bone regeneration can both be improved by allo-hybrid grafts coated with AMJ., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Ltd. All rights reserved.)

After the emergence of turbo-machines in the industrial field, researchers have always been seeking to increase their efficiency. Centrifugal compressors are widely used in industries, hence understanding their flow and attempting to increase their efficiency has always been a focus. In recent years, using two splitter blades alongside the main blades has been one of the novel methods used to improve the performance of centrifugal compressors. In this study, after validating the NASA-CC3 centrifugal compressor, a centrifugal compressor design with two splitter blades was carried out. Then, by defining eight variables, an attempt was made to optimize the design. The genetic algorithm was used as the optimization algorithm in this process. Since the optimization process only using genetic algorithms is very time-consuming and has high computational costs, artificial neural networks were used to reduce costs. The objective function in this optimization process was to increase efficiency while maintaining the flow rate and pressure ratio at the design point. After the optimization process was completed, the performance curve of the optimized compressor was compared with the NASA-CC3 compressor at and outside the design point, and its advantages and disadvantages were stated. At the end of the above optimization process, the efficiency of the centrifugal compressor increased by 1.06% at the design point. To investigate the effect of the number of computational networks on the independence of results, further analysis is required., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2025 The Authors.)

Oxidant (OX), the sum of ozone (O 3 ) and nitrogen dioxide (NO 2 ), is used to determine nitrogen oxides (NO x )-independent regional contribution and NO x -dependent local contribution. This study investigates OX trends and its local and regional levels in Tehran, Iran using the data from 21 monitoring stations from 2012 to 2022 and satellite remote sensing data (TROPOMI) for 2022. The spatiotemporal trends of O 3 , NO 2 , and OX are first examined using ground-based and remote sensing data, and then the polar plots are employed to identify the dominant directions of OX transport and its sources. Subsequently, the local and regional contributions of OX were determined using OX-NO x relationship. The findings indicated that the OX trend is primarily affected by O 3 fluctuations during the warmer months, whereas NO 2 variations have a stronger correlation with OX during the remaining months. The emissions from the central zone of the city were mostly caused by the transportation fleet. The emissions from industries in the south also have a significant effect on the city's OX concentration. Moreover, the local contribution of OX, reaching a maximum value of 0.15 ppb/ppb NO x (for both monthly and yearly analysis), contributes to the elevation of OX levels during nighttime. In contrast, the OX local contribution at daylight hours is mostly negative, with minimum values of - 0.15 ppb/ppb NOx for July and - 0.07 ppb/ppb NO x for 2012, indicating a decrease in the OX concentration with an increase in the local NO x emission. Furthermore, the OX levels, especially for daylight hours, is mostly related to its regional contribution with maximum of 100 ppb in July and 95 ppb in 2017, suggesting the necessity of considering regional emissions in developing mitigation scenarios. Lastly, our results indicate that the transportation fleet plays an undeniable role in contributing to the local emissions in Tehran., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Motion disorders affect a significant portion of the global population. While some symptoms can be managed with medications, these treatments often impact all muscles uniformly, not just the affected ones, leading to potential side effects including involuntary movements, confusion, and decreased short-term memory. Currently, there is no dedicated application for differentiating healthy muscles from abnormal ones. Existing analysis applications, designed for other purposes, often lack essential software engineering features such as a user-friendly interface, infrastructure independence, usability and learning ability, cloud computing capabilities, and AI-based assistance. This research proposes a computer-based methodology to analyze human motion and differentiate between healthy and unhealthy muscles. First, an IoT-based approach is proposed to digitize human motion using smartphones instead of hardly accessible wearable sensors and markers. The motion data is then simulated to analyze the neuromusculoskeletal system. An agent-driven modeling method ensures the naturalness, accuracy, and interpretability of the simulation, incorporating neuromuscular details such as Henneman's size principle, action potentials, motor units, and biomechanical principles. The results are then provided to medical and clinical experts to aid in differentiating between healthy and unhealthy muscles and for further investigation. Additionally, a deep learning-based ensemble framework is proposed to assist in the analysis of the simulation results, offering both accuracy and interpretability. A user-friendly graphical interface enhances the application's usability. Being fully cloud-based, the application is infrastructure-independent and can be accessed on smartphones, PCs, and other devices without installation. This strategy not only addresses the current challenges in treating motion disorders but also paves the way for other clinical simulations by considering both scientific and computational requirements., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper. No financial support was received for the conduct of this study or preparation of this manuscript. The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed., (© 2025 The Authors.)

The depletion of fossil fuel reserves, increasing environmental concerns, and energy demands of remote communities have increased the acceptance of using hybrid renewable energy systems (HRES). However, choosing an optimal HRES from economic, environmental, reliability, and sustainability aspects is still challenging. To solve this challenge, this study introduces a novel multi-objective optimization approach using the Gravitational Search Algorithm (GSA) and non-dominated sorting techniques. The proposed framework addresses four objectives: minimizing the loss of power supply probability, reducing total costs, increasing renewable energy fraction, and lowering CO 2 emissions. A carbon tax sensitivity analysis evaluates the system's economic performance under varying scenarios. Also, the amount of damage caused by the release of carbon dioxide on human health and the ecosystem is examined. In this way, an optimal configuration consisting of wind turbines, photovoltaic panels, and diesel generators is introduced to satisfy the above objectives. Results demonstrate that the GSA outperforms established methods, such as multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II in Pareto front diversity and convergence. In this work, the optimal system achieves an 18.4% increase in renewable energy share, reducing ecosystem and human health damage by 14.2%. Notably, with a 20% increase in the carbon tax, system costs increased by 3%. These findings underscore the potential of multi-objective optimization combined with carbon tax policies to enhance energy system sustainability and affordability., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

This paper investigates the state estimation problem for an islanded DC microgrid. The microgrid consists of energy storage units, as well as wind and solar generation units, all designed to supply power to a variable DC load, specifically an electric vehicle charging station. To ensure accurate state estimation, a third-order model is utilized for the battery storage system. A DC/DC boost converter is implemented for the photovoltaic system, resulting in a second-order state-space model. The wind generation unit, which incorporates a three-phase synchronous generator, is represented by a fourth-order nonlinear state-space model. Consequently, the entire microgrid is described by a ninth-order nonlinear state-space model, which is simplified by excluding two states from the battery model. This study also includes a comprehensive analysis of the load's dynamic modeling and behavior. A second-order state-space model is derived for the electric vehicle charging station as the load. A variant of the Kalman filter is employed as the state estimation algorithm, which is model-based. The algorithm is implemented to estimate the unknown or unmeasurable states of the microgrid, incorporating known inputs including load, solar irradiation, wind speed, and photovoltaic panel temperature. The algorithm employs an unscented transform to eliminate the need for system linearization, with the Kalman filter playing a pivotal role in the estimation process. The robustness of this convergence is evaluated under three scenarios: first, when encountering sudden load variations; second, when improperly initializing the estimation process; and third, during abrupt changes and noisy measurements in solar irradiation, one of the system inputs. Simulations conducted in MATLAB/SIMULINK demonstrate that the algorithm's estimations converge to actual values with a satisfactory level of accuracy and precision within a desirable time interval., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2025 The Authors.)

This study investigated the performance of various spectrophotometric methods in predicting visually perceived translucency and evaluated the efficiency of imaging techniques in quantifying translucency. We conducted the visual assessment in two stages using the pair comparison method. In the first stage, pairs of samples with identical colors but differing levels of translucency were compared. In the second stage, we compared pairs of samples with different colors but almost identical translucency. In the first stage, spectrophotometric methods exhibited high Pearson correlation coefficients, ranging from 0.96 to 0.99, with visual perceptions, demonstrating their accuracy in estimating translucency. Examination samples of different colors also revealed that among spectrophotometric methods, L*, absorption, and contrast ratio, with Pearson correlation coefficients of approximately 0.96, 0.96, and 0.98, respectively, had the strongest correlations with perceptual data. Using imaging techniques, the best result was obtained by comparing the lightness of the sample image on a black background to the same value on a white background, yielding a Pearson correlation coefficient of approximately 0.87. Additionally, we employed imaging-based methods for predicting translucency in real 3-D objects with simple shapes and limited colors. Despite the limitations, these methods produced promising results., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The application of Activated Carbon Fabrics for adsorption of gas contaminants, including Benzene and Toluene, was performed in a fixed bed column at room temperature. The effect of process parameters such as initial inlet concentration, flow rate, and adsorbent mass on the efficiency of the column was evaluated. Results indicated that increasing adsorbent mass and decreasing initial toluene concentration increase adsorption capacity and breakthrough time. Doubling the adsorbent mass led to an increase in breakthrough time from 48 min to 132 min and from 147 min to 340 min. Reducing the flow rate causes the adsorption capacity and the breakthrough time to increase. The adsorption capacity of toluene is always higher than that of benzene, which can be explained by the higher relative pressure of toluene compared to benzene, which has increased the adsorption capacity. Experimental data were analyzed using the Adams-Bohart, Thomas, and Yoon-Nelson models. The Thomas and Yoon-Nelson models, with correlation coefficients above 0.9, effectively predicted gas adsorption performance in a fixed bed column. However, the Yoon-Nelson model cannot correctly predict the data's behavior in the early moments. It can be seen, in general, that the Thomas model is well able to describe the behavior of the data., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2025 The Authors.)

Although tissue engineering science has made great progress, wound healing has remained a significant clinical challenge, especially in cases of severe injuries requiring advanced treatment strategies. This study aimed to develop patient-friendly in situ gelling nanofibers composed of oxidized carboxymethyl cellulose (OCMC) and gelatin for wound healing applications. A two-axial electrospinning technique was employed to fabricate OCMC/PVA-Gelatin hybrid nanofibers. Because of the nanofibers' high surface area, the resulting scaffold can quickly absorb wound exudates, resulting in a cross-linked hydrogel via the Schiff-base reaction between OCMC and gelatin. The nanofibers were thoroughly characterized using SEM, FTIR, XRD, and TGA, confirming their successful fabrication and structural integrity. Notably, the nanofibers demonstrated full degradation within 21 days, aligning with the requirements for skin tissue regeneration. Antibacterial assays revealed 99.9 % efficacy against Pseudomonas aeruginosa and Methicillin-Resistant Staphylococcus aureus (MRSA). The electrospun OCMC/PVA and OCMC/PVA-Gel samples exhibited 91 ± 10 % and 92 ± 7 % cell viability after 7 days of L929 fibroblast cell culture, confirming their excellent biocompatibility. These findings highlight the potential of the developed nanofibers as antimicrobial and biocompatible wound dressings with an appropriate degradation profile, presenting a practical and effective alternative to conventional hydrogel systems for advanced wound healing applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025. Published by Elsevier B.V.)

Silver nanotriangles (AgNTs) were successfully synthesized as a colorimetric probe for selective and sensitive histidine detection in aqueous media within a 15-100 µM range and a detection limit of 330 nM using UV-Vis spectroscopy. The interaction of HgCl 2 with AgNTs would lead to the formation of disk-shaped Ag/Hg amalgam as observed from the transmission electron images and X-ray diffraction patterns. Histidine prevents these structural and morphological changes and accordingly, the detection approach was developed based on the correlation between the histidine concentration and the in-plane dipole plasmon resonance (DPR) intensity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

To enhance therapeutic strategies for cardiovascular diseases, the development of more reliable in vitro preclinical systems is imperative. These models, crucial for disease modeling and drug testing, must accurately replicate the 3D architecture of native heart tissue. In this study, we engineered a scaffold with aligned poly(lactic- co -glycolic acid) (PLGA) microfilaments to induce cellular alignment in the engineered cardiac microtissue (ECMT). Consequently, the coculture of three cell types, including cardiac progenitor cells (CPC), human umbilical cord endothelial cells (HUVEC), and human foreskin fibroblasts (HFF), within this 3D scaffold significantly improved cellular alignment compared to the control. Additionally, cells in the ECMT exhibited a more uniaxial anisotropic and oriented cytoskeleton, characterized by immunostaining of F-actin. This approach not only enhanced cell structure and microtissue architecture but also improved functionality, evident in synchronized electrophysiological signals. Therefore, our engineered cardiac microtissue using this aligned microfilament scaffold (AMFS) holds great potential for pharmaceutical research and other biomedical applications.

In this research, tartaric acid was used to enhance the hydroxyapatite coating on AZ31 Mg alloy substrate through post-treatment and direct addition methods, and the corrosion resistance and biological activity of the samples were investigated. The parameters of concentration, immersion time, and pH of the coating solution were optimized by Electrochemical Impedance Spectroscopy (EIS) and Direct Current (DC) Polarization techniques. According to EIS results in the post-treatment method, tartaric acid with a concentration of 1 g/L, pH = 9 and immersion time of 2 min, increased the corrosion resistance of hydroxyapatite coating from 3630 to about 18,763 Ω.cm 2 . In the direct addition method, this value increased to 26,577 Ω.cm 2 . The optimal sample obtained from this method has a concentration of 1 g/L of tartaric acid and an immersion time of 2 h. Further, the morphology, crystal structure, and chemical bonds on the surface were investigated via FE-SEM, XRD, and ATR-FTIR analysis, respectively. Moreover, samples were immersed in SBF solution and after 3 weeks of immersion, the post-treatment sample exhibited the highest bio-corrosion resistance and the least morphological changes compared to other samples. Also, using antibacterial tests, it was concluded that the post-treatment method provides a coating with better antibacterial properties than other coatings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier B.V. All rights reserved.)

Electrospun nanofibers with a high surface area are attractive materials for biomedical applications. They have potential use in scaffolds, drug delivery, bio face masks, wound dressings, and biosensors. Various surface modifications have been developed to improve their chemical and physical properties. These modifications include physical, chemical, biological, and nano-treatment approaches. The physical modification includes annealing, stretching, and plasma treatment. Chemical modification includes functionalization with chemical groups, while biological modification involves coating with proteins, enzymes, or antibodies. Nanotreatment approaches use nanomaterials to modify the surface of nanofibers. These modifications enhance the characteristics of the biomedical nanofibers, making them more effective and efficient for their intended applications. The review summarizes the latest research on electrospun nanofiber modification procedures, strategies, and utilities for various biomedical applications. It provides insights into the conditions and requirements of each modification approach and their effects on the properties of the nanofibers. Moreover, it emphasizes the importance of functionalizing nanofibers to meet the most important specific requirements and the potential of electrospun nanofibers in various biomedical applications., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2025 The Authors. Published by Elsevier Ltd.)

Tiny machine learning (TinyML) and edge intelligence have emerged as pivotal paradigms for enabling machine learning on resource-constrained devices situated at the extreme edge of networks. In this paper, we explore the transformative potential of TinyML in facilitating pervasive, low-power cardiovascular monitoring and real-time analytics for patients with cardiac anomalies, leveraging wearable devices as the primary interface. To begin with, we provide an overview of TinyML software and hardware enablers, accompanied by an examination of networking solutions such as Low-power Wide area network (LPWAN) that facilitate the seamless deployment of TinyML frameworks. Following this, we delve into the methodologies of knowledge distillation, quantization, and pruning, which represent the cornerstone strategies for optimizing machine learning models to operate efficiently within resource-constrained environments. Furthermore, our discussion extends to the role of efficient deep neural networks tailored specifically for cardiovascular monitoring on wearable devices with limited computational resources. Through a comprehensive review, we analyze the applications of prominent artificial neural network architectures including Convolutional Neural Networks (CNNs), Autoencoders, Deep Belief Networks (DBNs), and Transformers in the domain of Electrocardiogram (ECG) analytics, shedding light on their efficacy and potential in advancing healthcare technology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Ltd. All rights reserved.)

Metallic implants have been considered as promising alternatives to traditional implants due to their biocompatibility and favorable biodegradability properties. However, one of the major challenges in using these implants is the relatively fast degradation rate of metal alloys in the body's electrolyte environment, which can lead to early loss of performance and the release of undesirable degradation products. Applying appropriate coatings with suitable performance on the surface of metal implants can be an effective solution to control the rate of deterioration and increase their stability in the body environment. In this comprehensive study, various methods of coating metal implants with calcium phosphate or hydroxyapatite structures, including sol-gel, chemical deposition (such as hydrothermal deposition), and thermal spraying (such as plasma spray) methods have been fully investigated. The benefits and drawbacks of each of these techniques in relation to the properties of the resulting coating such as surface morphology, chemical composition, adhesion to the substrate, porosity and crystal structure, anti-corrosion performance, their impact on the biological performance of the implant in terms of biocompatibility, degradation rate control, and mechanical properties, as well as limitations related to the coating process are described. The results of this comprehensive study provide valuable and key guidance for choosing the most suitable coating and coating method according to the type of medical application considered for metal implants., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2025 Published by Elsevier Ltd.)

By gaining insights into how brain activity is encoded and decoded, we enhance our understanding of brain function. This study introduces a method for classifying EEG signals related to visual objects, employing a combination of an LSTM network and nonlinear interval type-2 fuzzy regression (NIT2FR). Here, ResNet is utilized for feature extraction from images, the LSTM network for feature extraction from EEG signals, and NIT2FR for mapping image features to EEG signal features. The application of type-2 fuzzy logic addresses uncertainties arising from EEG signal nonlinearity, noise, limited data sample size, and diverse mental states among participants. The Stanford database was used for implementation, evaluating effectiveness through metrics like classification accuracy, precision, recall, and F1 score. According to the findings, the LSTM network achieved an accuracy of 55.83% in categorizing images using raw EEG data. When compared to other methods like linear type-2, linear/nonlinear type-1 fuzzy, neural network, and polynomial regression, NIT2FR coupled with an SVM classifier outperformed with a 68.05% accuracy. Thus, NIT2FR demonstrates superiority in handling high uncertainty environments. Moreover, the 6.03% improvement in accuracy over the best previous study using the same dataset underscores its effectiveness. Precision, recall, and F1 score results for NIT2FR were 68.93%, 68.08%, and 68.49% respectively, surpassing outcomes from linear type-2, linear/nonlinear type-1 fuzzy regression methods., Competing Interests: Declarations. Competing Interests: The authors declare no competing interests., (© 2025. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

This investigation addresses the reinforcement of rammed earth (RE) structures by integrating carpet polyacrylic yarn waste (CPYW) generated from the carpet production process and employing Ground Granulated Blast-Furnace Slag (GGBS) as a stabilizer, in conjunction with alkali activators potassium hydroxide (KOH), to enhance their mechanical properties. The study included conducting Unconfined Compressive Strength (UCS) tests and Brazilian Tensile Strength (BTS) tests on plain samples, GGBS-stabilized (SS) samples, CPYW-reinforced (CFS) samples, and samples reinforced with a combination of GGBS and CPYW (SCFS). The results showed that the mechanical and resistance properties of the CFS and SCFS samples were improved; these findings were confirmed by the presence of more cohesive GGBS gel and fibers as seen in FE-SEM and microscopic images. Therefore, the use of GGBS and CPYW, both separately and in combination, is suggested as a viable approach to enhance mechanical performance and reduce the brittle failure propensity of RE structures. This study achieved significant improvements in the mechanical behavior of RE structures by integrating CPYW and alkali-activated GGBS. Results showed a 370% improvement in UCS and a 638% increase in BTS than the plain sample. These enhancements demonstrate the potential for using industrial waste in eco-friendly, high-performance construction materials., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)