Limited Size Research Articles

Inborn errors of metabolism in neonates and pediatrics on varying dialysis modalities: a systematic review and meta-analysis

Abstract Background Some inborn errors of metabolism (IEMs) resulting in aberrations to blood leucine and ammonia levels are commonly treated with kidney replacement therapy (KRT). Children with IEMs require prompt treatment, as delayed treatment results in increased neurological and developmental morbidity. Objectives Our systematic review in neonates and pediatrics evaluates survival rates and reductions in ammonia and leucine levels across different KRT modalities (continuous KRT (CKRT), hemodialysis (HD), peritoneal dialysis (PD)). Data sources A literature search was conducted through PubMed, Web of Science, and Embase databases for articles including survival rate and toxic metabolite clearance data in pediatric patients with IEM undergoing KRT. Study eligibility criteria Cross-sectional, prospective, and retrospective studies with survival rates reported in patients with IEM with an intervention of CKRT, PD, or HD were included. Studies with patients receiving unclear or multiple KRT modalities were excluded. Study appraisal and synthesis methods Analysis variables included efficacy outcomes [% reduction in ammonia (RIA) from pre- to post-dialysis and time to 50% RIA] and mortality. The Newcastle Ottawa Risk of Bias quality assessment was used to assess bias. All statistical analyses were performed with MedCalc Statistical Software version 19.2.6. Results A total of 37 studies (n = 642) were included. The pooled proportion (95% CI) of mortality on CKRT was 24.84% (20.93–29.08), PD was 34.42% (26.24–43.33), and HD 34.14% (24.19–45.23). A lower trend of pooled (95% CI) time to 50% RIA was observed with CKRT [6.5 (5.1–7.8)] vs. PD [14.4 (13.3–15.5)]. A higher mortality was observed with greater plasma ammonia level before CKRT (31.94% for ≥ 1000 µmol/L vs. 15.04% for < 1000 µmol/L). Conclusions and implications of key findings Despite the limitations in sample size, trends emerged suggesting that CKRT may be associated with lower mortality rates compared to HD or PD, with potential benefits including prevention of rebound hyperammonemia and improved hemodynamic control. While HD showed a trend towards faster achievement of 50% RIA, all modalities demonstrated comparable efficacy in reducing ammonia and leucine levels. Prospero registration CRD42023418842. Graphical abstract

Size and Surface Effects in the Ultrafast Dynamics of Strongly Cooperative Spin‐Crossover Nanoparticles

AbstractCooperative photoinduced switching of molecular materials at the nanoscale is still in its infancy. Strongly cooperative spin‐crossover nanomaterials are arguably the best prototypes of photomagnetic and volume‐changing materials that can be manipulated by short pulses of light. Open questions remain regarding their non‐equilibrium dynamics upon light excitation and the role of cooperative elastic interactions in nanoscale systems that are characterized by large surface/volume ratios. Femtosecond‐resolved broadband spectroscopy is performed on nanorods of the strongly cooperative Fe‐triazole, which undergoes a reversible low‐spin to high‐spin (HS) phase transition ≈360 K. Supported by density functional theory and mechano‐elastic Monte Carlo simulations, a marked difference is observed in the photoswitching dynamics at the surface of the nanoparticles compared with the core. Surprisingly, under low excitation (<2%) conditions, there occurs a transient increase in the HS population at the surface on the picosecond time scale, while the HS population in the core decays concomitantly. These results shed light onto the importance of surface properties and dynamical size limits of nanoscale photoresponsive nanomaterials that can be used in a broad range of applications.

The role of automated controls and streamlined compliance in managing risks in digital finance

This study investigates the role of automated controls and streamlined compliance in managing risk in digital finance institutions. Through a mixed-methods approach, combining quantitative analysis and qualitative insights from leading Indian digital finance companies (CRED, ZEST MONEY and PHONE PAY), the research examines the effectiveness of advanced technologies in enhancing risk management practices. The study found strong positive correlations between the adoption of automated controls and key performance metrics, including the fraud detection rate (0.68), regulatory reporting accuracy (0.72), and overall risk management effectiveness (0.75). Regression analysis revealed that automated control implementation was the strongest predictor of risk management effectiveness. A comparative analysis of different digital finance models highlights the varying challenges and benefits of implementing these technologies. This study proposes a holistic, future-proof framework that integrates AI, machine learning and blockchain for real-time risk monitoring, automated regulatory reporting and dynamic compliance management. This framework addresses the limitations of the current fragmented approaches and offers a scalable solution that is adaptable to evolving regulatory landscapes. The findings of this study have significant implications for practitioners and policymakers, emphasizing the need for investment in strong automated systems and adaptive regulatory frameworks. While acknowledging the limitations in sample size and longitudinal scope, this research provides a foundation for future studies and offers practical insights for enhancing risk management in the rapidly evolving digital finance sector. The study concludes that effective integration of automated controls and streamlined compliance is crucial for ensuring the resilience, integrity and sustainable growth of the digital finance ecosystem.

Plasma self-driven current in tokamaks with magnetic islands

Abstract Magnetic island perturbations may cause a reduction in plasma self-driven current that is needed for tokamak operation.
 A novel effect on tokamak self-driven current revealed by global gyrokinetic simulations is due to magnetic-island-induced
3D electric potential structures, which have the same dominant mode numbers as that of the magnetic island,
whereas centered at both the inner and outer edge of the island. The non-resonant potential islands
are shown to drive a current through an efficient nonlinear parallel acceleration of electrons.
In large aspect ratio (large-A) tokamak devices, this new effect can result in a significant
global reduction of the electron bootstrap current when the island size is sufficiently large,
in addition to the local current loss across the island region due to the pressure profile flattening.
It is shown that there exists a critical magnetic island width for large-A tokamaks beyond which
the electron bootstrap current loss is global and increases rapidly with the island size.
As such, this process may introduce a size limit for tolerable magnetic islands in large-A tokamak
devices in the context of steady state operation. On the other hand, the current loss caused by magnetic islands in
low-A tokamaks such as spherical tokamak (ST) NSTX/U is minor.
The reduction of the axisymmetric current by magnetic islands scales with the square of island width.
However, the loss of the current is mainly local to the island region, and the pace of current
loss as the island size increases is substantially slower compared to large-A tokamaks.
In particular, the bootstrap current reduction in STs is even smaller in the reactor-relevant
high-$\beta_p$ regime where neoclassical tearing modes are more likely to develop.

Corporate Social Responsibility and Financial Performance of Automotive Companies: A Basis for Corporate Social Responsibility Framework

This study determined the relationship between corporate social responsibility (CSR) activities and the financial performance of automotive companies in Tuguegarao City, Cagayan, using data from 2020-2022. Descriptive statistics, financial metrics analysis, ANOVA, and Chi-Square Pearson tests were employed. Results showed that while environmental and ethical CSR initiatives were emphasized, economic community development was less prioritized. No significant differences were found in CSR activities based on firm profiles, and there was no significant link between CSR activities and financial performance. The study recommends a more balanced CSR approach, with greater emphasis on local economic development and partnerships with community and educational institutions. Due to limitations in sample size and data variety, future research should involve a broader participant base.

Feasibility of endoscopic submucosal dissection including papilla (with video).

Endoscopic papillectomy (EP) is a low-invasive treatment for duodenal tumors including papilla. The limit of lesion size and local recurrence have been issues in EP. We developed endoscopic submucosal dissection (ESD) for the duodenal tumors including papilla (ESDIP: ESD including papilla) to overcome the problems. The aim of this study was to evaluate the feasibility of ESDIP. We included the patients who underwent ESDIP from August 2010 to January 2024 in this study. We evaluated the retrospective characteristics of patients and lesions, clinical results of ESDIP and of endoscopic retrograde cholangiopancreatography (ERCP) as prevention for delayed adverse events, and pathological findings. We also calculated the cumulative recurrence rate and overall survival rate at 12 months after ESDIP. Fifty-four patients were included in this study. The mean lesion size was 39 mm. The third-quarter cases revealed a less than half-circumferential lesion, and the one case with a full-circumferential lesion. Resection was accomplished in 96% (n = 52), and also the en-bloc resection rate was 96%. Of the cases in accomplished resection, 98% of patients were intubated with a pancreaticobiliary drainage tube by ERCP. Intraprocedural perforation occurred in eight cases. Delayed bleeding occurred in 10 cases. Delayed perforation was seen only in one case. The incidence of post-ERCP pancreatitis was 25%. Cumulative local recurrence rate and the overall survival rate were 15% and 96%, respectively. ESDIP may be feasible for duodenal tumors including papilla, and is a potential alternative option to avoid pancreaticoduodenectomy.

Meta-analysis of nifedipine and enalapril combination therapy for hypertensive patients with coronary heart disease

Background: Numerous clinical studies have demonstrated that the therapeutic efficacy of combining nifedipine and enalapril in treating hypertension with coronary heart disease surpasses that of nifedipine as a stand-alone treatment. However, the current evidence is not yet sufficient due to limitations in the sample size, and further validation is needed. To analyze and assess the clinical impact of nifedipine combined with enalapril on hypertension complicated with coronary heart disease, and provide evidence for rational drug use in clinic. Methods: We employed a revised version of the Jadad scale to assess the quality of the research literature following a rigorous screening process. The statistical analysis was performed utilizing the software RevMan 5.4.1 for data analysis. Heterogeneity in the studies was evaluated based on the results of the Q test (P value), and the OR value of the combined effect was calculated using either the model with fixed effects or the one with random effects, with the results presented in a forest plot. Furthermore, a sensitivity analysis was conducted by excluding articles with the highest impact, and potential bias in publication was assessed through the utilization of a funnel plot. Results: A total of 183 articles were initially identified, and after a comprehensive review, 14 clinical randomized controlled trials were chosen for analysis. The meta-analysis findings revealed that the trial group displayed a significantly higher overall effectiveness rate compared to the control group (OR=3.47, 95%CI 2.40-5.03). Additionally, the trial group exhibited a more pronounced reduction in blood pressure and greater enhancement in cardiac function (OR=5.55, 95%CI). Conversely, the control group had a lower occurrence of ischemic events compared to the trial group (OR=0.35, 95%CI 0.24-0.50). Sensitivity analysis confirmed the stability and reliability of the combined effect size results (OR=3.91, 95%CI 2.51-6.09, P<0.00001). However, based on an assessment using funnel plot results suggested potential bias in publication. Conclusion: The combined administration of nifedipine and enalapril exhibits enhanced effectiveness compared to the sole use of nifedipine in individuals diagnosed with hypertension and coronary heart disease, rendering it a valuable alternative for clinical application.

Exact results on the dynamics of the stochastic Floquet-East model

Abstract We introduce a stochastic generalisation of the classical deterministic Floquet-East model, a discrete circuit with the same kinetic constraint as the East model of glasses. We prove exactly that, in the limit of long time and large size, this model has a large deviation phase transition between active and inactive dynamical phases. We also compute the finite time and size scaling of general space-time fluctuations, which for the case of inactive regions gives rise to dynamical hydrophobicity. We also discuss how, through the Trotter limit, these exact results also hold for the continuous-time East model, thus proving long-standing observations in kinetically constrained models. Our results here illustrate the applicability of exact tensor network methods for solving problems in many-body stochastic systems.

Ultraflat Cu(111) foils by surface acoustic wave-assisted annealing

Ultraflat metal foils are essential for semiconductor nanoelectronics applications and nanomaterial epitaxial growth. Numerous efforts have been devoted to metal surface engineering studies in the past decades. However, various challenges persist, including size limitations, polishing non-uniformities, and undesired contaminants. Thus, further exploration of advanced metal surface treatment techniques is essential. Here, we report a physical strategy that utilizes surface acoustic wave assisted annealing to flatten metal foils by eliminating the surface steps, eventually transforming commercial rough metal foils into ultraflat substrates. Large-area, high-quality, smooth 2D materials, including graphene and hexagonal boron nitride (hBN), were successfully grown on the resulting flat metal substrates. Further investigation into the oxidation of 2D-material-coated metal foils, both rough and flat, revealed that the hBN-coated flat metal foil exhibits enhanced anti-corrosion properties. Molecular dynamics simulations and density functional theory validate our experimental observations.

The multi-objective economic statistical design of the p-chart: NSGA II approach

A control chart is a crucially important tool in statistical process control that is essentially used to differentiate between assignable and chance causes of variation. With the advent of Six Sigma in the parlance of quality management, the control chart assumes more significance to hold the gain in the control phase after attaining process improvement in the improve phase. The sample size, sampling interval, and control limits’ multiplier are three important parameters required to design an effective as well as efficient control chart. Many approaches like economic design, statistical design, and economic statistical design of control charts have been studied by several researchers. All these approaches consider a single objective function. In this paper, we have proposed a multi-objective design of the p-chart, where we are minimizing both out-of-control Average Run Length ( ARL δ ) and the expected cost per cycle ( C E ). Non-dominated Sorting Genetic Algorithm II (NSGA II) is used to solve the proposed multi-objective model. The proposed approach is demonstrated with the help of two numerical examples and the corresponding results are found to be quite encouraging for minimizing both objectives in comparison to another pertinent model given in the literature. Sensitivity analysis has been carried out to give credence to the worth of the proposed approach.

Advancements in Data Augmentation and Transfer Learning: A Comprehensive Survey to Address Data Scarcity Challenges

Abstract: Deep Learning (DL) models have demonstrated remarkable proficiency in image classification and recognition tasks, surpassing human capabilities. The observed enhancement in performance can be attributed to the utilization of extensive datasets. Nevertheless, DL models have huge data requirements. Widening the learning capability of such models from limited samples even today remains a challenge, given the intrinsic constraints of small datasets. The trifecta of challenges, encompassing limited labeled datasets, privacy, poor generalization performance, and the costliness of annotations, further compounds the difficulty in achieving robust model performance. Overcoming the challenge of expanding the learning capabilities of Deep Learning models with limited sample sizes remains a pressing concern even today. To address this critical issue, our study conducts a meticulous examination of established methodologies, such as Data Augmentation and Transfer Learning, which offer promising solutions to data scarcity dilemmas. Data Augmentation, a powerful technique, amplifies the size of small datasets through a diverse array of strategies. These encompass geometric transformations, kernel filter manipulations, neural style transfer amalgamation, random erasing, Generative Adversarial Networks, augmentations in feature space, and adversarial and meta- learning training paradigms. : Furthermore, Transfer Learning emerges as a crucial tool, leveraging pre-trained models to facilitate knowledge transfer between models or enabling the retraining of models on analogous datasets. Through our comprehensive investigation, we provide profound insights into how the synergistic application of these two techniques can significantly enhance the performance of classification tasks, effectively magnifying scarce datasets. This augmentation in data availability not only addresses the immediate challenges posed by limited datasets but also unlocks the full potential of working with Big Data in a new era of possibilities in DL applications.

A Study on the Relationship between Field Cognitive Style and English Reading Strategy Choice

In recent years, China has accelerated its internationalization process and made more and more achievements in cross-border exchanges and cooperation. Learning English is essential for the current generation of college students. English reading is an important means to acquire English language knowledge, understand external information, and improve English language practice skills. Since the 70s of the 20th centuries, many scholars at home and abroad have been conducting a lot of research on the correlation between reading strategies and cognitive styles, but most of them are descriptive analysis, and there are few empirical studies. In this study, 324 first-year students majoring in science and engineering of Beijing Institute of Petrochemical Technology were selected by random sampling method, and the questionnaires of mosaic pattern test (Chinese version) and reading strategies were conducted, and data processing was carried out by SPSS 26.0, and descriptive statistics, independent samples t-test and Pearson correlation analysis were performed. The results show that there are significant differences in the use of reading strategies among field-independent and field-dependent science and engineering freshmen. Among them, field-independent individuals are more inclined to choose metacognitive strategies, and field-dependent individuals are more inclined to choose social/emotional strategies. Teachers should give full consideration to the characteristics of students' cognitive styles in English teaching, scientifically adjust the design of activities, the selection of teaching materials and teaching methods, and provide diversified guidance to help students correctly understand themselves and improve their learning efficiency. Due to the limitation of time and sample size, there are still many shortcomings in this study, but to a certain extent, it reflects the current situation of science and engineering English learners in China, and provides empirical data support for teachers' "teaching" and students' "learning".

A method for constructing demographic profiles of Capra aegagrus/C. hircus using logarithm size index scaling

Sex-specific demographic profiles of archaeological animal bone assemblages are key to being able to reconstruct the varied strategies used by ancient hunters and herders to harvest animals from both wild and managed herds. Nearly 25 years ago Zeder developed a method for constructing these profiles based on the study of a large collection of modern wild and domestic caprines that combined metric data and a refined system for determining age of death of archaeological caprine bones (Zeder, 2001, 2006a, 2008; Zeder and Hesse, 2000). The utility of this method was limited, however, by its reliance on individual dimensions from single elements, subjecting the method to sample size limitations. This paper presents a new method for constructing demographic profiles for assemblages of goat remains from archaeological sites drawing on a method developed by Zeder and Lemoine for Sus scrofa (Zeder and Lemoine, 2020a). The method uses normalized metric data following the Logarithm Size Index (LSI) scaling technique designed to address sample size issues. LSI values of post-crania metric data are shown to reflect the strong sexual dimorphism in body size in modern wild goats which is evident in the post-crania of animals over one year of age. Once adjusted for regional body size differences, as well as differences in the scaling of different post-cranial elements, the mid-point of the range of LSI values is shown to be a reliable and highly accurate way of distinguishing between the elements of females and males and for constructing sex-specific age profiles based on these identifications. This method is applied to metric data of goat assemblages from four archaeological sites in the Zagros — two representing hunted animals (Yafteh Cave and Asiab) and two representing early managed herds (Ganj Dareh and Ali Kosh). The paper concludes by offering recommendations, and caveats, for the use of this method in building sex-specific demographic profiles of ancient goat assemblages.

Per Segment Plane Sweep Line Segment Intersection on the GPU

Polygon overlay operations are used for various purposes such as GIS searches and queries, VLSI and basic geometric operations of intersection, union and difference. There have been recent research articles presenting algorithms using the GPU to perform line segment intersection for geometric operations. We present two parallel algorithms implemented on the GPU that focus on the active list portion of the traditional serial plane sweep algorithm. The first algorithm uses a single block of threads to simulate the active list data structure in hardware; this algorithm is slow due to GPU thread block size limitations and synchronization points, but demonstrates favorable time complexity. The second algorithm uses dynamic parallelism to remove synchronization and scales to utilize available GPU hardware (single GPU). We perform experiments on both synthetic and real world data sets. The presented results show improvement in execution time with respect to recent algorithms, and low memory usage compared to recent algorithms. We achieve speedups of up to 38.8 over the serial sweep line algorithm on real world data.

Elevated risk for stroke in genetically predisposed bicuspid aortic valve disease: evidences from a mendelian randomization study

Abstract Background Patients with bicuspid aortic valve (BAV) have been recognized to have an elevated risk for stroke. However, the exact causal association between BAV and stroke remains elusive due to stroke-related comorbidities, residual confounding bias and sample size limitations of observational studies. Purpose To identify the causal relationship between genetically predisposed BAV and 15 cardiovascular diseases (CVDs), and whether calcific aortic valve stenosis (CAVS) or atrial fibrillation (AF) could serve as the mediator on the pathway from BAV to stroke by mendelian randomization (MR) approach. Methods Uncorrelated (r² < 0.001) and genome-wide significant (P < 5×10^-8) single nucleotide polymorphisms were extracted from the largest genome-wide association study statistics (2,236 patients and 11,604 controls) of BAV by Gehlen et al. Summary-level statistics for 15 CVDs were obtained from the UK Biobank study, FinnGen study, HERMES consortium, CARDIoGRAMplusC4D consortium and GIGASTROKE consortium. We used 2-sample MR to estimate the causal association between BAV and 15 CVDs, evaluated 12 potential mediators and mediated proportions within 2-step MR framework. Inverse variance weighted was utilized as primary MR analyses method. Results for each outcome from different sources were pooled using the random effect meta-analysis. False discovery rate (FDR) was calculated by Benjamini-Hochberg method to account for multiple comparisons. Results Genetically predicted per log-odds liability increase of BAV was significantly associated with aortic aneurysm (OR, 1.26; 95% CI, 1.13-1.40; FDR-adjusted P = 3.54 × 10^-4), any stroke (OR, 1.06; 95% CI, 1.02-1.09; FDR-adjusted P = 0.004), ischemic stroke (IS) (OR, 1.17; 95% CI, 1.03-1.11; FDR-adjusted P = 0.001) and cardioembolic stroke (CES) (OR, 1.15; 95% CI, 1.06-1.25; FDR-adjusted P = 0.004). No significant association were observed between BAV and another 11 CVD outcomes. Instead of CAVS, genetic liability to AF mediated the total effect of BAV on any stroke (proportion mediated = 13.2% [95% CI, 3.1% - 23.2%]), IS (proportion mediated = 10.8% [95% CI, 2.5% - 19.1%]) and CES (proportion mediated = 21.5% [95% CI, 5.3% - 37.7%]). Conclusions Genetically predisposed BAV is associated with a higher risk of any stroke, IS and CES, which could be mediated by AF. This study implies the pathophysiological processes from BAV towards stroke and highlights the clinical significance of AF intervention in reducing the incidence of stroke for BAV patients.Causal association between BAV and CVDs

Elucidation of endogenous and exogenous chemicals in maternal serum using high-resolution mass spectrometry

The increasing exposure to environmental chemicals calls for comprehensive non-targeted analysis to detect unrecognized substances in human samples. We examined human serum samples to classify compounds as endogenous or exogenous using public databases and to explore the relationships between exposure markers and metabolic patterns. Serum samples from 84 pregnant women at 32 weeks gestation were analyzed using LC-QToFMS. Using the PubChemLite for Exposomics database, we annotated and classified 106 compounds (51 endogenous, 55 exogenous). The compound patterns were analyzed using three dimensional reduction methods: Principal Component Analysis (PCA), regularized Generalized Canonical Correlation Analysis (rGCCA), and Uniform Manifold Approximation and Projection (UMAP). OPTICS clustering applied to these methods revealed two distinct clusters, with 89 % of significant compounds overlapping between clusters. The detected exogenous compounds included dietary substances, phthalates, nitrogenous compounds, and parabens. Pathway enrichment analysis showed that chemical exposure was linked to changes in amino acid metabolism, protein and mineral transport, and energy metabolism. While we found associations between exposure and metabolite changes, we could not establish causality. Our approach of analyzing both exogenous and endogenous chemicals from the same dataset using PubChemLite database presents a new method for exposome research, despite limitations in sample size and peak annotation accuracy. These findings contribute to understanding multiple chemical exposures and their metabolic effects in human biomonitoring.

Transferable Percolated Particle Monolayers Derived from Marangoni Flows for Thermally Conductive Dielectric Coatings.

Thermal management is becoming one of the most significant design and size limitations for high power density electronics, including motherboards, power converters, and phased array antennas for 5G communications. There are few options for conducting heat away with dielectric materials that avoid shortening or distorting the performance of these electronics. Certain highly thermally conductive 2D and 3D materials, including hexagonal boron nitride and diamond, offer ideal material properties to address these issues but are extremely challenging to process. This work studies highly oriented single-particle thick films of hexagonal boron nitride, manufactured through a modified Langmuir-Blodgett process and densified further using the Marangoni effect to attain remarkable thermal conductivity enhancement with minimal coating thickness. High loadings of hexagonal boron nitride (∼60 vol %) in dense, castable films are also produced to compare thermal spreading ability in a comparatively simpler but less-coordinated percolated system to the highly percolated particle monolayers. These procedures were applied to glass fiber reinforced polymers used in aerospace and radome applications as well as to a single-board computer to demonstrate enhanced thermal management.

An optimization DEM modelling method for soil-fibre undrained cyclic loading tests model based on the influence range of fibre

The computational cost of discrete element modelling is high owing to the limitations of particle size and contact in fibre modelling. This paper proposes an optimised discrete element method (DEM) for a hybrid model of soil and fibres based on the fibre influence range. First, a relative velocity state function is established based on the relative motion state between the fibres and soil particles under undrained cyclic loading. Subsequently, the influence range of the fibres is determined using the relative velocity function based on the first few cycles of the undrained cyclic loading numerical tests. Cluster and clump models of the fibre are then generated based on the influence range of the fibre. Finally, a symmetrical shape of the optimised model is developed by extracting the distribution length of the edge curve of the influence range along the vertical direction of the axis. In this study, the proposed optimised DEM was validated through a series of undrained cyclic loading numerical tests on fibre-reinforced soil. The results of the optimised model were highly consistent with those of the traditional model, and the computational time was significantly reduced. The cyclic loading timing for determining the range of influence of the fibre was analysed. The optimised model based on the influence range of the 15th cycles not only restored almost the same results but also saved the calculation cost by nearly eight times. The optimised model established based on the influence range after the 15th cycles had a slight influence on the results. In addition, the applicability of the optimised model is discussed. This paper provides new insights into the establishment of a hybrid model of soil and fibres.

Recent Innovations and Future Perspectives in Transferosomes for Transdermal Drug Delivery in Therapeutic and Pharmacological Applications.

There have been several non-invasive administrations that have emerged recently to replace conventional needle injections. With its minimal rejection rate, remarkable ease of administration, and remarkable patient comfort and perseverance, the transdermal drug delivery system (TDDS) is the most attractive of them all. The skincare industry, which includes cosmetics, may also find use for TDDS in addition to the pharmaceutical industry. As this strategy mainly entails local drug administration, it can prevent untargeted drug delivery to tissues not intended for the treatment and buildup of localized drug concentrations. Transdermal delivery is hampered by a number of physicochemical characteristics of the skin, which have led to a great deal of research into ways to get over these barriers. The majority of transdermal medicines that have proved effective do so by using smaller lipophilic compounds, which have a molecular weight of a few 100 Daltons. Transferosomes have proven to be an effective method for transdermal distribution of a range of therapies, including hydrophilic actives, bigger molecules, peptides, proteins, and nucleic acids, in order to get around the medications' size and lipophilicity limits. Because of their flexible form and increased surface hydrophilicity, transferosomes are essential for the delivery of medicines and other solutes through and into the skin by exploiting hydration gradients a source of energy. As a result, the medication is released into the skin layers under regulated conditions and has improved overall penetration. In this section we outline the development of transferosomes from liposomes and solid lipid nanoparticles, as well as their subsequent advancements as commercially available dosage forms, physical-chemical characteristics, and cutaneous kinetics.

From microplastics to pixels: testing the robustness of two machine learning approaches for automated, Nile red-based marine microplastic identification.

Despite the urgent need for accurate and robust observations of microplastics in the marine environment to assess current and future environmental risks, existing procedures remain labour-intensive, especially for smaller-sized microplastics. In addition to this, microplastic analysis faces challenges due to environmental weathering, impacting the reliability of research relying on pristine plastics. This study addresses these knowledge gaps by testing the robustness of two automated analysis techniques which combine machine learning algorithms with fluorescent colouration of Nile red (NR)-stained particles. Heterogeneously shaped uncoloured microplastics of various polymers-polyethylene (PE), polyethylene terephthalate (PET), polypropylene (PP), polystyrene (PS), and polyvinyl chloride (PVC)-ranging from 100 to 1000µm in size and weathered under semi-controlled surface and deep-sea conditions, were stained with NR and imaged using fluorescence stereomicroscopy. This study assessed and compared the accuracy of decision tree (DT) and random forest (RF) models in detecting and identifying these weathered plastics. Additionally, their analysis time and model complexity were evaluated, as well as the lower size limit (2-4µm) and the interoperability of the approach. Decision tree and RF models were comparably accurate in detecting and identifying pristine plastic polymers (both > 90%). For the detection of weathered microplastics, both yielded sufficiently high accuracies (> 77%), although only RF models were reliable for polymer identification (> 70%), except for PET particles. The RF models showed an accuracy > 90% for particle predictions based on 12-30 pixels, which translated to microplastics sized < 10µm. Although the RF classifier did not produce consistent results across different labs, the inherent flexibility of the method allows for its swift adaptation and optimisation, ensuring the possibility to fine-tune the method to specific research goals through customised datasets, thereby strengthening its robustness. The developed method is particularly relevant due to its ability to accurately analyse microplasticsweathered under various marine conditions, as well as ecotoxicologically relevant microplastic sizes, making it highly applicable to real-world environmental samples.