Comparing continuum and direct fiber models of soft tissues. An ocular biomechanics example reveals that continuum models may artificially disrupt the strains at both the tissue and fiber levels.

Collagen fibers are the main load-bearing component of soft tissues but difficult to incorporate into models. Whilst simplified homogenization models suffice for some applications, a thorough mechanistic understanding requires accurate prediction of fiber behavior, including both detailed fiber-level strains and long-distance transmission. Our goal was to compare the performance of a continuum model of the optic nerve head (ONH) built using conventional techniques with a fiber model we recently introduced which explicitly incorporates the complex 3D organization and interaction of collagen fiber bundles [1]. To ensure a fair comparison, we constructed the continuum model with identical geometrical, structural, and boundary specifications as for the fiber model. We found that: 1) although both models accurately matched the intraocular pressure (IOP)-induced globally averaged displacement responses observed in experiments, they diverged significantly in their ability to replicate specific 3D tissue-level strain patterns. Notably, the fiber model faithfully replicated the experimentally observed depth-dependent variability of radial strain, the ring-like pattern of meridional strain, and the radial pattern of circumferential strain, whereas the continuum model failed to do so; 2) the continuum model disrupted the strain transmission along each fiber, a feature captured well by the fiber model. These results demonstrate limitations of the conventional continuum models that rely on homogenization and affine deformation assumptions, which render them incapable of capturing some complex tissue-level and fiber-level deformations. Our results show that the strengths of explicit fiber modeling help capture intricate ONH biomechanics. They potentially also help modeling other fibrous tissues.

Synthesis of Non-canonical Tryptophan Variants via Rh-catalyzed C-H Functionalization of Anilines.

Tryptophan and its non-canonical variants play critical roles in pharmaceutical molecules and enzymes. Facile access to this privileged class of amino acids from readily available building blocks remains a long-standing challenge. Here, we report a regioselective synthesis of non-canonical tryptophans bearing C4-C7 substituents via Rh-catalyzed annulation between structurally diverse tert-butyloxycarbonyl (Boc)-protected anilines and alkynyl chlorides readily prepared from amino acid building blocks. This transformation harnesses Boc-directed C-H metalation and demetalation to afford a wide range of C2-unsubstituted indole products in a redox-neutral fashion. This umpolung approach compared to the classic Larock indole synthesis offers a novel mechanism for heteroarene annulation and will be useful for the synthesis of natural products and drug molecules containing non-canonical tryptophan residues in a highly regioselective manner.

Fuzzy Synchronization Likelihood Graph in Deep Neural Networks for Human Motion Time Series Analysis.

Variable interactivity is crucial in biological multivariate time series analysis. This research suggests using graph structures to represent such interactions for more explainable decision-making processes. However, measuring the variable interaction in a graph is an open problem with no unique solution. Existing graph construction methods are either computationally costly, require extensive training, or disregard the inherent data nonlinearities and nonstationarity. We propose using the Fuzzy Synchronization Likelihood (FSL) criterion to address these challenges in constructing a graph and examining the qualitative similarity and dependency among variables. We propose applying this strategy to automated rehabilitation exercise evaluations based on human joint motion data. This multivariate time series application benefits from FSL-constructed graphs by offering further insight into the kinematics of joint interactions. Finally, we extend the convolutional layer in the Deep Mixture Density Neural Network (DMDN) to process the FSL-constructed graph, extracting practical information regarding task-based variable dependencies. An ablation study shows that the proposed FSL Graph-based Deep Neural Network (FSLGDN) outperforms its competitive approaches that use linear correlations and human anatomy for graph construction. Results also indicate that task-based consideration of joint motion data interactions is more beneficial than anatomy. Furthermore, the inherent nonstationarity of motion data leads to the extraction of more information than its linear correlation counterpart. Finally, while the proposed approach ranks competitively with a DMDN, the proposed approach's graph construction and representation of feature dependencies are more intuitive, leading to more explainable decision-making processes.

An Evidence-Based Measure to Assess Self-Efficacy Among Adolescents With Type 1 Diabetes Mellitus in Jordan.

PURPOSE: The purpose of the study was to assess self-efficacy among adolescents with type 1 diabetes mellitus and to identify its contributing factors using a new measure based on the International Society for Pediatric and Adolescent Diabetes Guidelines: the Type 1 Diabetes Mellitus Self-Efficacy Scale (T1DM-SES). METHOD: A descriptive, cross-sectional design was used to collect data from 161 adolescents ages between 12 and 18 via an online questionnaire survey, including demographic and management-related variables and the 21 items of T1DM-SES. RESULTS: Results demonstrated that adolescents had high self-efficacy levels regarding the basic needs to manage their diabetes and relatively moderate and low levels regarding more sophisticated needs, such as adjusting insulin dose to correct fluctuated glucose levels, covering carbohydrates, and managing ketoacidosis at home. Adolescent females and adolescents who have working mothers or caregivers demonstrated higher levels of self-efficacy, whereas adolescents who have another family member with T1DM reported lower levels. CONCLUSION: Assessing adolescents' self-efficacy using evidence-based measures is crucial for informing health education plans. There should be a greater focus on acquiring the advanced knowledge and skills necessary for adolescents to manage the constantly evolving challenges of diabetes management. Access to health care and sufficient health insurance coverage that encompasses modern technology are fundamental for the effective management of T1DM.

Deciphering the intrinsically disordered characteristics of the FG-Nups through the lens of polymer physics.

The nuclear pore complex (NPC) is a critical gateway regulating molecular transport between the nucleus and cytoplasm. It allows small molecules to pass freely, while larger molecules require nuclear transport receptors to traverse the barrier. This selective permeability is maintained by phenylalanine-glycine-rich nucleoporins (FG-Nups), intrinsically disordered proteins that fill the NPC's central channel. The disordered and flexible nature of FG-Nups complicates their spatial characterization with conventional structural biology techniques. To address this challenge, polymer physics offers a valuable framework for describing FG-Nup behavior, reducing their complex structures to a few key parameters. In this review, we explore how polymer physics models FG-Nups using these parameters and discuss experimental efforts to quantify them in various contexts, providing insights into the conformational properties of FG-Nups.

Utility of dynamic contrast enhancement for clinically significant prostate cancer detection.

Objective: This study aimed to evaluate the association of dynamic contrast enhancement (DCE) with clinically significant prostate cancer (csPCa, Gleason Grade Group ≥2) and compare biparametric magnetic resonance imaging (bpMRI) and multiparametric MRI (mpMRI) nomograms. Subjects/patients and methods: We identified a retrospective cohort of biopsy naïve patients who underwent pre-biopsy MRI separated by individual MRI series from 2018 to 2022. csPCa detection rates were calculated for patients with peripheral zone (PZ) lesions scored 3-5 on diffusion weighted imaging (DWI) with available DCE (annotated as - or +). bpMRI Prostate Imaging Reporting and Data System (PIRADS) (3 = 3-, 3+; 4 = 4-, 4+; 5 = 5-, 5+) and mpMRI PIRADS (3 = 3-; 4 = 3+, 4-, 4+; 5 = 5-, 5+) approaches were compared in multivariable logistic regression models. Nomograms for detection of csPCa and ≥GG3 PCa incorporating all biopsy naïve patients who underwent prostate MRI were generated based on available serum biomarkers [PHI, % free prostate-specific antigen (PSA), or total PSA] and validated with an independent cohort. Results: Patients (n = 1010) with highest PIRADS lesion in PZ were included in initial analysis with 127 (12.6%) classified as PIRADS 3+ (PIRADS 3 on bpMRI but PIRADS 4 on mpMRI). On multivariable analysis, PIRADS 3+ lesions were associated with higher csPCa rates compared to PIRADS 3- (3+ vs. 3-: OR 1.86, p = 0.024), but lower csPCa rates compared to PIRADS DWI 4 lesions (4 vs. 3+: OR 2.39, p < 0.001). csPCa rates were 19% (3-), 31% (3+), 41.5% (4-), 65.9% (4+), 62.5% (5-), and 92.3% (5+). bpMRI nomograms were non-inferior to mpMRI nomograms in the development (n = 1410) and independent validation (n = 353) cohorts. Risk calculators available at: https://rossnm1.shinyapps.io/MynMRIskCalculator/. Conclusion: While DCE positivity by itself was associated with csPCa among patients with highest PIRADS lesions in the PZ, nomogram comparisons suggest that there is no significant difference in performance of bpMRI and mpMRI. bpMRI may be considered as an alternative to mpMRI for prostate cancer evaluation in many situations.

Spatial correlation assessment of multiple earthquake intensity measures using physics-based simulated ground motions.

Predictive models for spatial correlation play an effective role in the assessment of seismic risk associated with distributed infrastructure and building portfolios. However, existing models often rely on simplified approaches, assuming isotropy and stationarity. This paper verifies these assumptions by presenting a comprehensive study using a database of 3D physics-based simulated broadband ground motions for Istanbul, generated by the SPEED software. The results reveal significant event-to-event variability and nonstationary and anisotropic characteristics of spatial correlation influenced by source, path, and site effects. The development of nonstationary correlation models requires exploring influential metrics beyond spatial proximity and gaining a deep understanding of their impact, which is the focus of this study. Analysis of the spatial correlations of peak ground displacement, peak ground velocity, peak ground acceleration, and response spectral accelerations at different periods, employing both stationary and nonstationary correlation modelling methods and considering the finite fault model, indicates that the slip distribution pattern, direction and distance of station pairs relative to earthquake rupture, soil softness, and homogeneity of soil properties significantly influence the spatial correlations of near-field earthquake ground motions. Implementation of the introduced parameters in predictive spatial correlation models enhances the precision of regional seismic hazard assessments.

Rapid and non-invasive diagnosis of hyperkalemia in patients with systolic myocardial failure using a model based on machine learning algorithms.

Background: Hyperkalemia is a potentially life-threatening electrolyte disturbance that if not diagnosed on time may lead to devastating conditions and sudden cardiac death. Blood sampling for potassium level checks is time-consuming and can delay the treatment of severe hyperkalemia on time. So, we propose a non-invasive method for correct and rapid hyperkalemia detection. Methods: The cardiac signal of patients referred to the Pediatrics Emergency room of Shahid Rejaee Hospital was measured by a 12-lead Philips electrocardiogram (ECG) device. Immediately, the blood samples of the patients were sent to the laboratory for potassium serum level determination. We defined 16 features for each cardiac signal at lead 2 and extracted them automatically using the algorithm developed. With the help of the principal component analysis (PCA) algorithm, the dimension reduction operation was performed. The algorithms of decision tree (DT), random forest (RF), logistic regression, and support vector machine (SVM) were used to classify serum potassium levels. Finally, we used the receiver operation characteristic (ROC) curve to display the results. Results: In the period of 5 months, 126 patients with a serum level above 4.5 (hyperkalemia) and 152 patients with a serum potassium level below 4.5 (normal potassium) were included in the study. Classification with the help of a RF algorithm has the best result. Accuracy, Precision, Recall, F1, and area under the curve (AUC) of this algorithm are 0.71, 0.87, 0.53, 0.66, and 0.69, respectively. Conclusions: A lead2-based RF classification model may help clinicians to rapidly detect severe dyskalemias as a non-invasive method and prevent life-threatening cardiac conditions due to hyperkalemia.

Family strategies for managing childhood cancer: Using traditional and complementary medicine in Southern Egypt.

BACKGROUND AND PURPOSE: The use of traditional and complementary medicine (T&CM) is common in children with cancer globally. We aimed to assess the prevalence, types, reasons, perceived effectiveness, and disclosure rate of T&CM use among children with cancer in Southern Egypt. We also investigated whether T&CM use contributed to delays in initial presentation and treatment. MATERIALS AND METHODS: A cross-sectional design was utilized. Data were collected via an interviewer-administered questionnaire. Eligible children and their caregivers at the South Egypt Cancer Institute were invited to participate. RESULTS: Eighty-six children completed the study (response rate = 86%). T&CM use was reported by 52 (60.5%) patients, with six (11.5%) experienced delayed presentation. The reasons for T&CM use were complementary for 37 (71%) and alternative for 15 (29%) of the participants. The types of T&CM used included herbal (63%), nutritional (33%), witchcraft (29%), and religious (19%) therapies. Approximately 48% of users employed multiple T&CM types. Family members recommended T&CM for 60% of the users. Most patients (65%) perceived T&CM as effective, with 71% initiating its use during the early phases of treatment. Almost all participants (98%) reported that healthcare providers did not inquire about T&CM use. T&CM usage was more prevalent among wealthier families (p = .023). There was no significant relationship between T&CM use and patient gender, diagnosis, residence, or paternal educational level. CONCLUSIONS: The significant utilization of T&CM among children with cancer highlights the need for healthcare providers to engage in open and early discussions with families regarding T&CM use.

The efficacy of interventions to prevent type 2 diabetes among women with recent gestational diabetes mellitus-A living systematic review and meta-analysis.

BACKGROUND: While previously considered a transient condition, with no lasting adverse impact, gestational diabetes mellitus (GDM) is now a well-established risk factor for developing type 2 diabetes mellitus (T2DM). The risk of developing T2DM appears to be particularly high in the first few years after childbirth, providing a compelling case for early intervention. This review provides an up-to-date systematic review and meta-analysis to assess the effectiveness of interventions to reduce incidence of T2DM in women with a recent history of GDM. METHODS: The search was conducted on October 20, 2023 with an annual surveillance planned for the next 5 years to maintain a living systematic review. The inclusion criteria were randomized controlled trials of any type in women within 5 years of GDM-complicated pregnancy that reported outcomes of T2DM diagnosis or measures of dysglycemia with a follow-up of at least 12 months. RESULTS: Seventeen studies met our inclusion criteria and have been included in this review. There were 3 pharmacological and 14 lifestyle interventions. Intervention was not associated with significant reduction in the primary outcome of T2DM (risk ratio, 0.78; 95% confidence interval [CI]: 0.43-1.41; p = 0.41; I2 = 79%) compared with the control group (placebo or usual care). However, meta-analysis of the four studies reporting hazard ratios suggested a reduction in diabetes incidence (hazard ratio, 0.68; 95% CI: 0.48-0.97; p = 0.03; I2 = 31%). CONCLUSION: This review provides equivocal evidence about the efficacy of interventions to reduce the risk of T2DM in women within 5 years of GDM-complicated pregnancy and highlights the need for further studies, including pharmacotherapy.

A study on value addition of cow dung-based anaerobic sludge for biomethane and bio-oil production via co-liquefaction with rice straw and clam shells as a catalyst.

The waste management sector is moving towards sustainable approaches for facilitating resource-recovery possibilities. Agriculture residue (rice straw), cow dung (cattle waste), and clam shells from the ocean are the primary waste materials possessing a huge value addition opportunity. In this study, the effective usage of rice straw and anaerobic sludge from cow dung for bio-energy production was studied. Cow dung was initially anaerobically processed for the generation of biomethane and sludge in a digester for a retention time of 40 days. The anaerobic sludge with rice straw was hydrothermally processed in varying proportions of 1 : 0, 0 : 1, 1 : 1,1 : 2, 2 : 1, 3 : 1, 1 : 3 and temperatures of 240-360 °C for 1 hour with varying biomass loads of 50, 75, 100, 125, and 150 g. Additionally, clam shells, one of the best bioresources, were used as a catalyst in the hydrothermal process at concentrations of 0.2-1 wt%. The maximum bio-oil produced was 36.23 wt% at a temperature of 320 °C, with a biomass load of 100 g, mixed proportion of 2 : 1 and catalyst loading of 0.6 wt%. The produced bio-oil comprised hydrocarbons, aldehydes, and carboxylic acids, as confirmed through GC-MS. In the anaerobic study, ≈0.018 m3 cumulative gas was produced at a retention time of 40 days. The biochar had a higher carbon content and its feasibility for further usage shows promise towards sustainability.

Anterior odontoid screw fixation of a type III odontoid fracture aided by transoral digital manipulation: illustrative case.

BACKGROUND: Odontoid process fractures make up 10%-20% of all cervical spine fractures, with type III fractures having a considerable amount of heterogeneity. Most simple type III fractures can be managed nonoperatively. However, 21% of complex type III fractures with significant displacement and angulation are inadequately treated with external immobilization and require surgery. Achieving a reduction via traction can pose a great challenge during intraoperative manipulation, especially when presentation is delayed. OBSERVATIONS: A 36-year-old male patient, who presented 2 weeks after a motor vehicle crash, had a Glasgow Coma Scale score of 14 and intact motor and sensory function. A craniocervical computed tomograpy scan revealed a normal brain with a type III odontoid fracture. The patient underwent anterior odontoid screw fixation (AOSF) aided by a transoral digital manipulation to achieve a reduction of the irreducible proximal fracture segment at 8 weeks postinjury. The treatment resulted in preserved neurological function and a satisfactory odontoid fracture reduction. LESSONS: Delayed presentation of a complex type III odontoid fracture can be challenging to treat; however, AOSF with the aid of transoral digital manipulation of the irreducible proximal segment can help to achieve good reduction and fusion with the preservation of neurological function in a young patient. https://thejns.org/doi/10.3171/CASE24294.

Discovery of the selenium-containing antioxidant ovoselenol derived from convergent evolution.

Selenium is an essential micronutrient, but its presence in biology has been limited to protein and nucleic acid biopolymers. The recent identification of a biosynthetic pathway for selenium-containing small molecules suggests that there is a larger family of selenometabolites that remains to be discovered. Here we identify a recently evolved branch of abundant and uncharacterized metalloenzymes that we predict are involved in selenometabolite biosynthesis using a bioinformatic search strategy that relies on the mapping of composite active site motifs. Biochemical studies confirm this prediction and show that these enzymes form an unusual C-Se bond onto histidine, thus giving rise to a distinct selenometabolite and potent antioxidant that we have termed ovoselenol. Aside from providing insights into the evolution of this enzyme class and the structural basis of C-Se bond formation, our work offers a blueprint for charting the microbial selenometabolome in the future.

Intra-canopy LED lighting outperformed top LED lighting in improving tomato yield and expression of the genes responsible for lycopene, phytoene and vitamin C synthesis.

Greenhouses located at high latitudes and in cloudy areas often experience a low quality and quantity of light, especially during autumn and winter. This low daily light integral (DLI) reduces production rate, quality, and nutritional value of many crops. This study was conducted on Sakhiya RZ F1 tomato plants to evaluate the impact of LED lights on the growth and nutritional value of tomatoes in a greenhouse with low daily light due to cloudy weather. The treatments included LED growth lights in three modes: top lighting, intra-canopy lighting, and combined top and intra-canopy lighting. The results showed that although the combined top and intra-canopy lighting reached the maximum increase in tomato yield, exposure to intra-canopy LED lighting alone outperformed in tomato fruit yield increase (28.46%) than exposure to top LED lighting alone (12.12%) when compared to no supplemental lighting during the entire production year. Intra-canopy exposure demonstrated the highest increase in tomato lycopene (31.3%), while top and intra-canopy lighting exhibited the highest increase in vitamin C content (123.4%) compared to the control. The LED light treatment also had a very positive effect on the expression of genes responsible for metabolic cycles, including Psy1, LCY-ß, and VTC2 genes, which had collinearity with the increase in tomato fruit production.

Structural insights into the convergent evolution of sulfoxide synthase EgtB-IV, an ergothioneine-biosynthetic homolog of ovothiol synthase OvoA.

Non-heme iron-dependent sulfoxide/selenoxide synthases (NHISS) constitute a unique metalloenzyme class capable of installing a C-S/Se bond onto histidine to generate thio/selenoimidazole antioxidants, such as ergothioneine and ovothiol. These natural products are increasingly recognized for their health benefits. Among associated ergothioneine-biosynthetic enzymes, type IV EgtBs stand out, as they exhibit low sequence similarity with other EgtB subfamilies due to their recent divergence from the ovothiol-biosynthetic enzyme OvoA. Herein, we present crystal structures of two representative EgtB-IV enzymes, offering insights into the basis for this evolutionary convergence and enhancing our understanding of NHISS active site organization more broadly. The ability to interpret how key residues modulate substrate specificity and regioselectivity has implications for downstream identification of divergent reactivity within the NHISS family. To this end, we identify a previously unclassified clade of OvoA-like enzymes with a seemingly hybrid set of characteristics, suggesting they may represent an evolutionary intermediate between OvoA and EgtB-IV.

Gene therapy clinical trials worldwide to 2023-an update.

To date, 3,900 gene therapy clinical trials have been completed, are ongoing or have been approved worldwide. Our database brings together global information on gene therapy clinical activity from trial databases, official agency sources, published literature, conference presentations and posters kindly provided to us by individual investigators or trial sponsors. This review presents our analysis of clinical trials that, to the best of our knowledge, have been or are being performed worldwide. As of our March 2023 update, we have entries on 3,900 trials undertaken in 46 countries. We have analyzed the geographical distribution of trials, the disease indications (or other reasons) for trials, the proportions to which different vector types are used, and which genes have been transferred. Details of the analyses presented, and our searchable database are on The Journal of Gene Medicine Gene Therapy Clinical Trials Worldwide website at https://a873679.fmphost.com/fmi/webd/GTCT. We also provide an overview of the progress being made around the world, and discuss key trends since the previous review, namely the unprecedented increase in gene therapy clinical trial activity, including the implementation of genome editing technology with the potential to transform the field moving forward.

Somatic RIT1 delins in arteriovenous malformations hyperactivate RAS-MAPK signaling amenable to MEK inhibition.

Arteriovenous malformations (AVM) are benign vascular anomalies prone to pain, bleeding, and progressive growth. AVM are mainly caused by mosaic pathogenic variants of the RAS-MAPK pathway. However, a causative variant is not identified in all patients. Using ultra-deep sequencing, we identified novel somatic RIT1 delins variants in lesional tissue of three AVM patients. RIT1 encodes a RAS-like protein that can modulate RAS-MAPK signaling. We expressed RIT1 variants in HEK293T cells, which led to a strong increase in ERK1/2 phosphorylation. Endothelial-specific mosaic overexpression of RIT1 delins in zebrafish embryos induced AVM formation, highlighting their functional importance in vascular development. Both ERK1/2 hyperactivation in vitro and AVM formation in vivo could be suppressed by pharmacological MEK inhibition. Treatment with the MEK inhibitor trametinib led to a significant decrease in bleeding episodes and AVM size in one patient. Our findings implicate RIT1 in AVM formation and provide a rationale for clinical trials with targeted treatments.

A configuration for cooling assisted organic solvent coated thin film microextraction after dispersive liquid-liquid microextraction method: A microextraction method for ultra-trace analyzing of volatile sample.

A combination of the dispersive liquid-liquid microextraction (DLLME) method based on the total vaporization procedure and cooling-assisted organic solvent-coated thin film microextraction (TFME) was applied for extracting chlorpyrifos (as the model compound). Based on the high thermal conductivity, a nickel foam thin film with the dimensions of 5.0 mm × 5.0 mm was used as a substrate for holding the organic solvent. Supporting thin film by organic solvent increases the thickness and contact area of the film relative to TFME or single drop microextraction (SDME) alone, resulting in a dramatic increase in the extraction efficiency. To protect the organic solvent and enhance the analyte distribution coefficient between the film and the vapor phase, a cooling system was applied. The proposed design was effective due to condensing the target analyte only on the uniform cooled thin film and not on the other regions in the extraction chamber. A corona discharge ionization source-ion mobility spectrometer was employed to identify the analyte. After optimizing the effective parameters, the limits of quantification (S/N = 10) and detection (S/N = 3) were calculated 0.1 and 0.03 µg L-1, respectively, and the dynamic range was measured between 0.1 and 7.0 µg L-1, with a determination coefficient of 0.9997. For three concentration levels of 0.1, 3.0, and 7.0 µg L-1, the relative standard deviations (n = 3) as the repeatability index were to be 6 %, 5 %, and 4 % for intra-day and 9 %, 6 %, and 5 % for inter-day, respectively. The enrichment factor was also calculated to be 3630 for the analyte concentration of 1.0 µg L-1. Well water, potato, and agricultural wastewater were analyzed as the real samples and the relative recovery values were measured between 92 % and 99 %. The accuracy of the proposed technique was validated by the European Standards EN 12393 method. In this approach, two steps of analyte extraction (DLLME and TFME) were used consecutively, resulting in better preconcentration and reduced matrix interference during cleaning-up.

Increased Permeability of the Blood-Brain Barrier in a Diabetic Mouse Model (Leprdb/db Mice).

Type 2 Diabetes Mellitus (T2DM) is linked to multiple complications, including cognitive impairment, and the prevalence of memory-related neurodegenerative diseases is higher in T2DM patients. One possible theory is the alteration of the microvascular and macrovascular environment of the blood-brain barrier (BBB). In this study, we employed different approaches, including RT-PCR, functional pharmacokinetic studies using sodium fluorescein (NaFL), and confocal microscopy, to characterize the functional and molecular integrity of the BBB in a T2DM animal model, leptin receptor-deficient mutant mice (Leprdb/db mice). As a result, VCAM-1, ICAM-1, MMP-9, and S100b (BBB-related markers) dysregulation was observed in the Leprdb/db animal model compared to littermate wild-type mice. The brain concentration of sodium fluorescein (NaFL) increased significantly in Leprdb/db untreated mice compared to insulin-treated mice. Therefore, the permeability of NaFL was higher in Leprdb/db control mice than in all remaining groups. Identifying the factors that increase the BBB in Leprdb/db mice will provide a better understanding of the BBB microvasculature and present previously undescribed findings of T2DM-related brain illnesses, filling knowledge gaps in this emerging field of research.

Optimizing solar power efficiency in smart grids using hybrid machine learning models for accurate energy generation prediction.

The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. Hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas. However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR). The HCLN model demonstrates superior accuracy with the RMSE values of 0.012027 for MWh, 0.013734 for POA and 0.003055 for PR, along with the lowest MAE values of 0.069523 for MWh, 0.082813 for POA, and 0.042815 for PR. The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements.