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Abstract Hepatic steatosis is strongly associated with chronic liver disease and systemic metabolic disorder. Adipose lipolysis is a recognized principal source of intrahepatic fat in various metabolic disorders, including non-alcoholic fatty liver disease. We hypothesized that, in the premorbid state, hepatic de novo lipogenesis (DNL) driven by excess carbohydrates abundance might play a more significant role. We employed a novel nutritional model in sheep of two distinct carbohydrates abundances. During 4 months of the dietary treatment, lambs were monitored for metabolic and terminal liver parameters. Lambs grown on the high-calorie (HC) diet were consistently more hyperglycemic and hyperinsulinemic than lambs grown on the lower-calorie (LC) diet (P

We investigate implicit vocabulary learning by adults who are exposed to a language in their ambient environment. Most New Zealanders do not speak Māori, yet are exposed to it throughout their lifetime. We show that this exposure leads to a large proto-lexicon - implicit knowledge of the existence of words and sub-word units without any associated meaning. Despite not explicitly knowing many Māori words, non-Māori-speaking New Zealanders are able to access this proto-lexicon to distinguish Māori words from Māori-like nonwords. What's more, they are able to generalize over the proto-lexicon to generate sophisticated phonotactic knowledge, which lets them evaluate the well-formedness of Māori-like nonwords just as well as fluent Māori speakers.

In Arabidopsis, a large subset of heat responsive genes exhibits diurnal or circadian oscillations. However, to what extent the dimension of time and/or the circadian clock contribute to heat stress responses remains largely unknown. To determine the direct contribution of time of day and/or the clock to differential heat stress responses, we probed wild-type and mutants of the circadian clock genes CCA1, LHY, PRR7, and PRR9 following exposure to heat (37 °C) and moderate cold (10 °C) in the early morning (ZT1) and afternoon (ZT6). Thousands of genes were differentially expressed in response to temperature, time of day, and/or the clock mutation. Approximately 30% more genes were differentially expressed in the afternoon compared to the morning, and heat stress significantly perturbed the transcriptome. Of the DEGs (~3000) specifically responsive to heat stress, ~70% showed time of day (ZT1 or ZT6) occurrence of the transcriptional response. For the DEGs (~1400) that are shared between ZT1 and ZT6, we observed changes to the magnitude of the transcriptional response. In addition, ~2% of all DEGs showed differential responses to temperature stress in the clock mutants. The findings in this study highlight a significant role for time of day in the heat stress responsive transcriptome, and the clock through CCA1 and LHY, appears to have a more profound role than PRR7 and PRR9 in modulating heat stress responses during the day. Our results emphasize the importance of considering the dimension of time in studies on abiotic stress responses in Arabidopsis.

Perioperative bleeding is a potentially devastating complication in neurosurgical patients, and plasma fibrinogen concentration has been identified as a potential modifiable risk factor for perioperative bleeding. The aim of this study was to evaluate preconditioning with Crotalus atrox venom (Cv-PC) as potential preventive therapy for reducing perioperative hemorrhage in the rodent model of surgical brain injury (SBI). C. atrox venom contains snake venom metalloproteinases that cleave fibrinogen into fibrin split products without inducing clotting. Separately, fibrinogen split products induce fibrinogen production, thereby elevating plasma fibrinogen levels. Thus, the hypothesis was that preconditioning with C. atrox venom will produce fibrinogen spilt products, thereby upregulating fibrinogen levels, ultimately improving perioperative hemostasis during SBI. We observed that Cv-PC SBI animals had significantly reduced intraoperative hemorrhage and postoperative hematoma volumes compared to those of vehicle preconditioned SBI animals. Cv-PC animals were also found to have higher levels of plasma fibrinogen at the time of surgery, with unchanged prothrombin time. Cv-PC studies with fractions of C. atrox venom suggest that snake venom metalloproteinases are largely responsible for the improved hemostasis by Cv-PC. Our findings indicate that Cv-PC increases plasma fibrinogen levels and may provide a promising therapy for reducing perioperative hemorrhage in elective surgeries.

The mosquito Aedes aegypti is the principal vector for the yellow fever and dengue viruses, and is also responsible for recent outbreaks of the alphavirus chikungunya. Vector control strategies utilizing engineered gene drive systems are being developed as a means of replacing wild, pathogen transmitting mosquitoes with individuals refractory to disease transmission, or bringing about population suppression. Several of these systems, including Medea, UD(MEL), and site-specific nucleases, which can be used to drive genes into populations or bring about population suppression, utilize transcriptional regulatory elements that drive germline-specific expression. Here we report the identification of multiple regulatory elements able to drive gene expression specifically in the female germline, or in the male and female germline, in the mosquito Aedes aegypti. These elements can also be used as tools with which to probe the roles of specific genes in germline function and in the early embryo, through overexpression or RNA interference.

Abstract Heavy metal contamination in wastewater poses severe environmental challenges, highlighting the urgent need for efficient and cost-effective solutions. While bentonite incorporation in concrete mixtures has shown promise in adsorbing heavy metals, its experimental validation—through Bentonite Plastic Concrete (BPC)—is hindered by high costs, labor-intensive procedures, and the need for specialized equipment. This study overcomes these barriers by introducing hybrid ensemble learning models, optimized with Forensic-Based Investigation Optimization (FBIO), to predict BPC’s workability and mechanical properties, including slump (S), tensile strength (TS), and elastic modulus (E). Using input parameters such as gravel, bentonite, silty clay, curing time, sand, cement, and water, models including Random Forest (RF), Adaptive Boosting (ADB), Extreme Gradient Boosting (XGB), and Gradient Boosting Regression Tree (GBRT) were developed. Notably, GBRT-FBIO achieved the highest accuracy for E predictions, while XGB-FBIO excelled for TS and S. Shapley Additive Explanation (SHAP) analysis identified water as the most critical factor influencing slump (+ 0.11) predictions while curing time emerged as the key determinant for TS (+ 0.18) and E (+ 0.12) predictions. Additionally, a user-friendly online tool was developed to enable the real-time application of these models, reducing reliance on costly experimental methods. This work addresses key challenges in experimental BPC testing, offering a transformative computational approach for advancing civil engineering materials research.

Abstract Being one of the most important tools in industrial and medical fields, the red-NIR laser has caught a lot of attention in research studies to improve its emission efficiency and the stability of its lasing materials, both thermally and mechanically. In this regard, the emission intensity of the red-NIR laser generated from Er3+ ions doped glass network was enhanced by adding Yb3+, Nd3+, and Ce3+ ions. A host glass network of 44P2O5–15ZnO–10Pb3O4–15NaF–15MgF2–1Er2O3 was proposed as a red-NIR lasing material and was reinforced by 0.5 and 1 mol% of Yb3+, Nd3+, or Ce3+ ions (labeled as PZPbNMEr3+:RE3+). The structural variations resulting from compositional changes, which were examined by XRD, density, FTIR, and Raman measurements, showed an increase in the glass network tightness, leading to better thermal stability and elasticity of the considered PZPbNMEr3+:RE3+ glasses, indicating their suitability as lasing materials. Optically, all the distinctive optical absorption bands of the Er3+, Yb3+, Nd3+, and Ce3+ ions appeared in the spectral region of 200–2500 nm. Additionally, a significant impact of the induced strain or crystal field of the added RE3+ ions on the optical properties was observed. A successful excitation was conducted by the green wavelength of 525 nm to the host glass PZPbNMEr3+, generating five wavelengths, one in the red region at 631 nm and four in the NIR at 748, 801, 1034, and 1527 nm. A significant enhancement in the intensity and redshift of the formed bands were observed with the inclusion of Yb3+, Nd3+, and Ce3+ ions, and new NIR bands were generated. Additionally, an energy transfer between Er3+/Yb3+ and Er3+/Nd3+ was observed. Accordingly, the considered PZPbNMEr3+:RE3+ glasses had highly efficient photoluminescence in the red-NIR region alongside their high thermal stability and good elasticity, making them a suitable choice for laser media.

Abstract Neonicotinoids are one of the most widely used classes of insecticides in agriculture. They are systemic insecticides mainly used as seed treatments to manage sucking insect pests in a wide range of field crops. Environment and growing conditions can impact the efficacy of neonicotinoid seed treatments. To evaluate this, an experiment was conducted to study the interactive effect of soil, water levels on the neonicotinoid concentration and plant growth in cotton. The experiment was laid out in a split-plot design, with three water levels, 30%, 60%, and 100% (control) of recommended irrigation and four seed treatments, including clothianidin, thiamethoxam, imidacloprid and an untreated control. Cotton seedlings were collected and analyzed for physiological changes and neonicotinoid concentrations at 15, 30 and 45 days after germination (DAG). Data were collected on plant height, fresh biomass and leaf area. The neonicotinoid concentrations were analyzed in leaf tissues using LC-MS/MS. The 30% and 60% of recommended irrigation treatments resulted in significantly taller seedlings with greater biomass and leaf area compared to the 100% water saturation (control) treatment. Thiamethoxam-treated seedlings had greater plant height and shoot fresh mass compared to clothianidin and the untreated control, whereas imidacloprid-treated seedlings had greater leaf area than thiamethoxam, clothianidin, and control at all water levels. 30% recommended irrigation increased neonicotinoid concentrations in leaf tissues, with clothianidin showing higher levels compared to other neonicotinoids at 45 DAG. Neonicotinoid seed treatment efficacy may vary with environmental factors, impacting sustainable pest control.

Abstract The rapid deployment of COVID-19 vaccines has necessitated the ongoing surveillance of adverse events, with abnormal uterine bleeding (AUB) emerging as a reported concern in vaccinated females. We aimed to develop a machine learning (ML) model to predict post-vaccination AUB in women aged less than 50 years. A large-scale national cohort, the Korean Nationwide Cohort (K-COV-N cohort), was utilized, comprising over 7 million participants. The study employed advanced ML techniques, including ensemble models combining gradient boosting machine and logistic regression, and conducted feature importance analysis. The dataset was meticulously curated, focusing on relevant demographics and variables, and balanced using Synthetic Minority Over-sampling Technique. Using a national cohort of over 2 million COVID-19 vaccinated cases in South Korea, we developed a ML model for AUB prediction. Our study is the first to develop a predictive model for post-vaccination AUB, employing feature importance analysis to identify the key contributing factors. The analysis revealed three primary predictive features: COVID-19 vaccination frequency, NVX-CoV2373 (Novavax) COVID-19 vaccination count, and hemoglobin levels. These findings provide valuable insights into predicting the risk AUB following COVID-19 vaccination, potentially enhancing post-vaccination monitoring strategies.

Abstract This research presents the best power management of flexible-renewable integrated energy systems (FRIESs) with smart distribution networks (SDNs) by taking nonlinear load harmonic compensation into account. A deterministic model that optimizes for three distinct goals serves as the foundation for the proposed system. The goal is to minimize the combined impact of the network’s operational costs, energy losses, and voltage harmonic distortion, taking into account their respective weights. In the FRIES framework, the goal function serves as a constraint on the operation of flexible and renewable sources, as well as the AC optimum harmonic power flow model. The suggested design is first formulated using nonlinear programming, and it is then approximated to a linear model in order to quickly arrive at the one and only optimum solution to the issue by different solvers. Furthermore, there is inherent uncertainty in the design of this work about the output power of renewable sources, load demand, energy consumption of mobile storage devices, and energy costs. Adaptive robust optimization has been applied to develop solutions that effectively address these uncertainties. Ultimately, the results show that, even with the aforementioned uncertainties, the SDN operation is resilient up to a maximum prediction error of 45%. Furthermore, if the distribution substation power factor is maintained at or above 0.9, the worst-case implemented design may enhance the network’s energy losses, voltage profile, and harmonic status by 8.2%, 43.5%, and 51.2%, respectively, as compared to load flow studies.

Abstract Confined columns, such as round-ended concrete-filled steel tubular (CFST) columns, are integral to modern infrastructure due to their high load-bearing capacity and structural efficiency. The primary objective of this study is to develop accurate, data-driven approaches for predicting the axial load-carrying capacity (P cc​) of these columns and to benchmark their performance against existing analytical solutions. Using an extensive dataset of 200 CFST stub column tests, this research evaluates three machine learning (ML) models – LightGBM, XGBoost, and CatBoost – and three deep learning (DL) models – Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). Key input features include concrete strength, column length, cross-sectional dimensions, steel tube thickness, and yield strength, which were analysed to uncover underlying relationships. The results indicate that CatBoost delivers the highest predictive accuracy, achieving an RMSE of 396.50 kN and an R 2 of 0.932, surpassing XGBoost (RMSE: 449.57 kN, R 2: 0.906) and LightGBM (RMSE: 449.57 kN, R2: 0.916). Deep learning models were less effective, with the DNN attaining an RMSE of 496.19 kN and R 2 of 0.958, while the LSTM underperformed substantially (RMSE: 2010.46 kN, R 2: 0.891). SHapley Additive exPlanations (SHAP) identified cross-sectional width as the most critical feature, contributing positively to capacity, and column length as a significant negative influencer. A user-friendly, Python-based interface was also developed, enabling real-time predictions for practical engineering applications. Comparison with 10 analytical models demonstrates that these traditional methods, though deterministic, struggle to capture the nonlinear interactions inherent in CFST columns, thus yielding lower accuracy and higher variability. In contrast, the data-driven models presented here offer robust, adaptable, and interpretable solutions, underscoring their potential to transform design and analysis practices for CFST columns, ultimately fostering safer and more efficient structural systems.

Abstract Globally, one in every four individuals faces a deficiency in essential micronutrients. Harvested early from various vegetables, grains, and herbs, microgreens have rich nutritional profiles that can mitigate nutrient deficiencies. Here, we analyzed six microgreens’ nutritional profiles for broccoli, black radish, red beet, pea, sunflower, and bean. Ascorbic acid content varied widely, from 32.72 mg/100 g fresh weight (FW) in red beet to 80.45 mg/100 g FW in beans. All microgreens exhibited high macro elements (mg/100 g FW), with potassium ranging from 187.07 to 416.05, magnesium from 45.96 to 86.83, calcium from 67.18 to 148.63, and phosphorus from 2.57 to 4.88. They also contained significant microelements (µg/100 g FW), including iron from 524 to 2610, manganese from 176.32 to 350.56, zinc from 31.92 to 129.78, and copper from 458.84 to 956.34. Glucose content surpassed sucrose and fructose, ranging from 0.114 to 0.580 mg/100 g FW. Among organic acids, citric acid was highest in red beet, succinic acid in beans, and fumaric acid in sunflower. Broccoli microgreens had the highest total phenolic content (825.53 mg GA/100 g FW), while beans had the highest total flavonoid content (758.0 mg RU/100 g FW). Black radish microgreens demonstrated the highest antioxidant capacity. Additionally, volatile aromatic compounds were analyzed across the six microgreen species. These findings highlight the nutritional potential of microgreens, advocating for their inclusion in diets to enhance human health. Red beet microgreens were the richest in organic acids, particularly citric acid, and flavonoids, supporting antioxidant activity, while black radish microgreens exhibited the highest DPPH antioxidant capacity and phenolic content. Bean microgreens stood out for their high ascorbic acid content. Sunflower microgreens had the highest levels of calcium and fumaric acid. Broccoli microgreens were abundant in phenolic compounds and contained high concentrations of iron and manganese. Finally, pea microgreens excelled in phosphorus and copper content.

Abstract Interstitial lung disease (ILD) leads to worse outcomes in subjects with rheumatoid arthritis (RA). Few large-scale longitudinal studies have provided comprehensive data on the incidence and risk of ILD in RA compared with non-RA populations. We compare the risk of incident ILD in subjects with RA and matched controls, while also evaluating the impact of RA serologic status. Using the Korean National Health Insurance Service Data between 2010 and 2017, we identified 52,325 individuals with RA and 261,625 without RA. During a median follow-up period of 4.4 years, 3.7% of the RA cohort and 0.5% of the matched cohort developed ILD, with incidence rates of 8.30 and 1.01 per 1,000 person-years. Compared to controls, the risk of incident ILD was significantly higher in the RA cohort (adjusted hazard ratio [aHR] 7.84 [7.29–8.44]), and seropositive and seronegative RA exhibited aHRs of 9.00 (8.34–9.72) and 4.81 (4.25–5.44). Although the risk of ILD was higher in seropositive RA than seronegative RA, the risk of developing ILD was also higher in subjects with seronegative RA than in matched controls, suggesting that close monitoring for ILD should be performed in this population.

Abstract Global evidence on the association between vaccines and renal adverse events (AEs) is inconclusive. This pharmacovigilance study analyzed a total of 120,715,116 reports from VigiBase collected between 1967 and 2022. We evaluated the global reporting of acute kidney injury (AKI), glomerulonephritis (GN), and tubulointerstitial nephritis (TIN) and assessed disproportionate signals between vaccines and renal AEs using reporting odds ratios (ROR) and the lower limit of the 95% confidence interval of the information component (IC025) in comparison with the entire database. The number and proportion of reports on AKI, GN, and TIN gradually increased, with a substantial increase after 2020. Disproportionate reporting of AKI was significant for COVID-19 mRNA vaccines (ROR, 2.38; IC025, 1.09). Fourteen vaccines were significantly disproportionate for higher GN reporting, and the highest disproportionality for GN reporting was observed for COVID-19 mRNA (ROR, 13.41; IC025, 2.90) and hepatitis B vaccines (ROR, 11.35; IC025, 3.18). Disproportionate TIN reporting was significant for COVID-19 mRNA (ROR, 2.43; IC025, 0.99) and human papillomavirus (ROR, 1.75; IC025, 0.19) vaccines. Significant disproportionality in the reporting of AKI, GN, and TIN was observed in patients exposed to multiple vaccines, including COVID-19 mRNA vaccines, alongside increasing global reports of vaccine-associated renal AEs.

Abstract Numerous efforts have been directed toward promoting nutrition in Arab countries. Implementing national nutrition plans and strategies is essential for addressing a country’s nutritional situation synergistically and cost-effectively. Many Arabic-speaking countries have developed national nutrition plans and/or strategies. Thus, an Arabic national nutrition plans assessment tool is necessary to revise current plans and improve the quality of future plans in the region. This study aimed to translate and evaluate the validity and reliability of the Arabic-translated National Nutrition Plans checklist. After translating the checklist into Arabic, the Delphi technique was used to evaluate face validity using the Face Validity Index (FVI). Test-retest reliability was evaluated by calculating the intraclass correlation coefficient and inter-rater reliability using Fleiss’ Kappa. This resulted in acceptable face validity (FVI values > 0.83). The test-retest reliability was excellent (ICC = 0.98, SEM = 1.96, MDC = 5.43), and the inter-rater reliability achieved substantial agreement between the raters (Fleiss’ kappa [95% CI] = 0.61 [0.59–0.63]). This demonstrates that the Arabic-translated National Nutrition Plans checklist is valid and reliable. The Arabic-translated National Nutrition Plans checklist lays the foundation for the systematic assessment and development of national nutrition plans and strategies in Arab countries.

Abstract Acute appendicitis is the most common cause of surgical acute abdomen, and its classification into uncomplicated, phlegmonous, and complicated forms is crucial for guiding treatment strategies. This study aims to compare the preoperative laboratory findings and postoperative outcomes of phlegmonous appendicitis with uncomplicated and complicated appendicitis. This retrospective cohort study included 1,441 patients who underwent appendectomy between January 2019 and March 2021 at Ankara Bilkent City Hospital. Patients were classified based on histopathological findings into four groups: normal appendix, uncomplicated appendicitis, phlegmonous appendicitis, and complicated appendicitis. Preoperative laboratory values, postoperative complications, and length of hospital stay were analyzed. The distribution of patients was as follows: Group A (normal appendix, 7.6%), Group B (uncomplicated appendicitis, 59.8%), Group C (phlegmonous appendicitis, 17.6%), and Group D (complicated appendicitis, 15%). Phlegmonous appendicitis exhibited significantly higher leukocyte counts and appendix diameters compared to uncomplicated appendicitis but had lower CRP values than complicated appendicitis. The median hospital stay was similar for Groups A, B, and C (1 day) but significantly longer for Group D (2.1 days, p < 0,001). Complication rates were highest in Group D (24.1%) compared to the other groups. While phlegmonous appendicitis shares some clinical and laboratory characteristics with complicated appendicitis, its postoperative outcomes align more closely with those of uncomplicated appendicitis. This suggests that phlegmonous appendicitis may be more accurately classified as a variant of uncomplicated appendicitis, emphasizing the need for consistent classification to improve treatment strategies and patient outcomes.

Abstract Retinopathy of prematurity (ROP) is a major cause of preventable blindness in preterm infants. The association between red blood cell (RBC) parameters and the development of ROP remains unclear. The objectives of the present study were to evaluate the association between RBC parameters and ROP treatment. This single-center, retrospective cohort study included preterm infants born at 117.3 fL at birth (adjusted odds ratio [aOR]:2.3; 95% confidence intervals CI 1.0–5.3). Additionally, on DOL 28, hemoglobin (Hb) values 18.5% (aOR:2.6; 95% CI 1.1–6.2) were associated with ROP treatment. In conclusion, our study indicated that infants born at 117.3 fL at birth, along with Hb 18.5% on DOL 28, had an increased risk of requiring ROP, warranting treatment.

Abstract Artificial intelligence (AI) is being increasingly applied in healthcare to improve patient care and clinical outcomes. We previously developed an AI model using ICD-10 (International Classification of Diseases, Tenth Revision) codes with other clinical variables to predict in-hospital mortality among trauma patients from a nationwide database. This study aimed to externally validate the performance of the AI model. Validation was conducted using a multicenter retrospective cohort study design, analyzing patient data from January 2020 to December 2021. The study included trauma patients based on specific ICD-10 codes, with other clinical variables. The performance of the AI model was evaluated against conventional metrics, including the ISS, and the ICISS (ICD-based ISS), using sensitivity, specificity, accuracy, balanced accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUROC) analyses. Data from 4,439 patients were analyzed. The AI model demonstrated high overall performance, achieving an AUROC of 0.9448 and a balanced accuracy of 85.08%, thereby outperforming traditional scoring systems such as ISS, or ICISS. Furthermore, the model accurately predicted mortality across datasets from each hospital (AUROCs of 0.9234 and 0.9653, respectively) despite significant differences in hospital characteristics. In the subset of patients with ISS

Abstract This research presents a novel framework for distinguishing between actual and non-suicidal ideation in social media interactions using an ensemble technique. The prompt identification of sentiments on social networking platforms is crucial for timely intervention serving as a key tactic in suicide prevention efforts. However, conventional AI models often mask their decision-making processes primarily designed for classification purposes. Our methodology, along with an updated ensemble method, bridges the gap between Explainable AI and leverages a variety of machine learning algorithms to improve predictive accuracy. By leveraging Explainable AI’s interpretability to analyze the features, the model elucidates the reasoning behind its classifications leading to a comprehension of hidden patterns associated with suicidal ideations. Our system is compared to cutting-edge methods on several social media datasets using experimental evaluations, demonstrating that it is superior, since it detects suicidal content more accurately than others. Consequently, this study presents a more reliable and interpretable strategy (F1-score for suicidal = 95.5% and Non-Suicidal = 99%), for monitoring and intervening in suicide-related online discussions.

Abstract Insulin receptor substrate (IRS)-1 and IRS-2 are major molecules that transduce signals from insulin and insulin-like growth factor-I receptors. The physiological functions of these proteins have been intensively investigated in mice, while little is known in other animals. Our previous study showed that the disruption of IRS-2 impairs body growth but not glucose tolerance or insulin sensitivity in rats, which led us to hypothesize that IRS-1 plays more pivotal roles in insulin functions than IRS-2. Here, we created IRS-1 knockout (KO) rats to elucidate the physiological roles of IRS-1 in rats. The body weight of IRS-1 KO rats at birth was lower than that of wild-type (WT) littermates, and postnatal growth of IRS-1 KO rats was severely impaired. Compared with WT rats, IRS-1 KO rats displayed insulin resistance but maintained euglycemia because of compensatory hyperinsulinemia. In addition, despite the increased activity of insulin-stimulated IRS-2-associated phosphatidylinositol-3 kinase (PI3K), insulin-induced phosphorylation of the kinases downstream of PI3K was suppressed in the liver and skeletal muscle of IRS-1 KO rats. Taken together, these results indicate that in rats, IRS-1 is essential for normal growth and the glucose-lowering effects of insulin. IRS-1 appears to be more important than IRS-2 for insulin functions in rats.

Abstract With the advancement of this digital era and the emergence of DApps and Blockchain, secure, robust and transparent network transaction has become invaluable today. These traditional methods of securing the transactions and maintaining transparency have encountered many challenges. It includes some such issues as follows: data privacy, centralized vulnerability, inefficiency in fraud detection and much more. To that effect, and to address such limitations, this paper provides a blockchain technology framework that is driven by advanced machine learning techniques, which will enhance security and transparency throughout the network of transactions. We begin with a design framework based on Federated Learning for Blockchain Integration where distributed datasets across blockchain nodes contribute to a global machine learning model but do not share raw data samples. Different nodes learn their own models. After that, these local models are aggregated towards a common, global model using secure aggregation methods, which makes sure that there is nozza of data privacy and hence, in the process making sure that more accurate models can be obtained due to diversified data sets. With LSTMs Autoencoders, more excellent security protocols are created for anomaly detection and fraud. So, by training the autoencoder on normal transaction data, the system can alert transactions with high reconstruction errors, meaning real-time anomalies. This proactive detection of anomalies reduces fraudulent activities significantly as most of the threats are recognized early. To this end, this paper proposes Smart Contract-based Model Management for machine learning models in a decentralized environment. Smart contracts are responsible for the submission, validation, and execution of the locally updated models in a decentralized fashion such that the management process is transparent and tamper resistant. Integrity and authenticity requirements are fulfilled by enforcing consensus mechanisms. Privacy in Machine Learning is guaranteed through Differential Privacy and Homomorphic Encryption. Differential privacy techniques, so as to ensure individual transaction data privacy in the updates of the local model before aggregation. In homomorphic encryption, computations are made in the encrypted form so when forming privacy preserving global model, privacy is preserved. The Real-time analysis of the transactions can be done with CNNs to detect fraud. Streaming transaction data is analyzed by CNNs leveraging the privacy-preserving global model and producing immediate alerts and actions for detected fraud. This real timing makes the network even more reliable and trustworthy. Our proposed framework is effective according to the interim outcomes where the aggregation of local models occurred without data leakage, detected anomalies very efficiently, managed models very transparently, with privacy of data at a very high level, and easily detected fraudulent transactions. The work presented here provides a great boost to send secure and very easily transparent transactions across the network, and thus resulted in enhanced network trust and decentralization.