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The dismal success rate of clinical trials for Alzheimer's disease (AD) motivates us to develop model systems of AD pathology that have higher predictive validity. The advent of induced pluripotent stem cells (iPSCs) allows us to model pathology and study disease mechanisms directly in human neural cells from healthy individual as well as AD patients. However, two-dimensional culture systems do not recapitulate the complexity of neural tissue, and phenotypes such as extracellular protein aggregation are difficult to observe. We report brain organoids that use pluripotent stem cells derived from AD patients and recapitulate AD-like pathologies such as amyloid aggregation, hyperphosphorylated tau protein, and endosome abnormalities. These pathologies are observed in an age-dependent manner in organoids derived from multiple familial AD (fAD) patients harboring amyloid precursor protein (APP) duplication or presenilin1 (PSEN1) mutation, compared to controls. The incidence of AD pathology was consistent amongst several fAD lines, which carried different mutations. Although these are complex assemblies of neural tissue, they are also highly amenable to experimental manipulation. We find that treatment of patient-derived organoids with β- and γ-secretase inhibitors significantly reduces amyloid and tau pathology. Moreover, these results show the potential of this model system to greatly increase the translatability of pre-clinical drug discovery in AD.

There are currently no preventive or disease-modifying therapies for Parkinson’s Disease (PD). Failures in clinical trials necessitate a re-evaluation of existing pre-clinical models in order to adopt systems that better recapitulate underlying disease mechanisms and better predict clinical outcomes. In recent years, models utilizing patient-derived induced pluripotent stem cells (iPSC) have emerged as attractive models to recapitulate disease-relevant neuropathology in vitro without exogenous overexpression of disease-related pathologic proteins. Here, we utilized iPSC derived from patients with early-onset PD and dementia phenotypes that harbored either a point mutation (A53T) or multiplication at the α-synuclein/ SNCA gene locus. We generated a three-dimensional (3D) cortical neurosphere culture model to better mimic the tissue microenvironment of the brain. We extensively characterized the differentiation process using quantitative PCR, Western immunoblotting and immunofluorescence staining. Differentiated and aged neurospheres revealed alterations in fatty acid profiles and elevated total and pathogenic phospho-α-synuclein levels in both A53T and the triplication lines compared to their isogenic control lines. Furthermore, treatment of the neurospheres with a small molecule inhibitor of stearoyl CoA desaturase (SCD) attenuated the protein accumulation and aberrant fatty acid profile phenotypes. Our findings suggest that the 3D cortical neurosphere model is a useful tool to interrogate targets for PD and amenable to test small molecule therapeutics.

Smart healthcare environments are growing at a rapid pace due to the services and benefits offered to healthcare practitioners and to patients. At the same time, smart healthcare environments are becoming increasingly complex environments where a plethora of devices are linked with each other, to deliver services to patients, and they require special security measures to protect the privacy and integrity of user data. Moreover, these environments are exposed to various kinds of security risks, threats, and attacks. Firewalls are considered as the first line of defense for securing smart healthcare networks and addressing the challenges mentioned above. Firewalls are applied at different levels in networks, and range from conventional server-based to cloud-based firewalls. However, the selection and implementation of a proper firewall to get the maximum benefit is a challenging task. Therefore, understanding firewall types, the services offered, and analyzing underlying vulnerabilities are important design considerations that need addressing before implementing a firewall in a smart healthcare environment. The paper provides a comprehensive review and best practices of firewall types, with offered benefits and drawbacks, which may help to define a comprehensive set of policies for smart healthcare devices and environments.

Intrusion detection in Internet of Things (IoT)-based wireless sensor networks (WSNs) is essential due to their widespread use and inherent vulnerability to security breaches. Traditional centralized intrusion detection systems (IDS) face significant challenges in data privacy, computational efficiency, and scalability, particularly in resource-constrained IoT environments. This study aims to create and assess a federated learning (FL) framework that integrates with long short-term memory (LSTM) networks for efficient intrusion detection in IoT-based WSNs. We design the framework to enhance detection accuracy, minimize false positive rates (FPR), and ensure data privacy, while maintaining system scalability. Using an FL approach, multiple IoT nodes collaboratively train a global LSTM model without exchanging raw data, thereby addressing privacy concerns and improving detection capabilities. The proposed model was tested on three widely used datasets: WSN-DS, CIC-IDS-2017, and UNSW-NB15. The evaluation metrics for its performance included accuracy, F1 score, FPR, and root mean square error (RMSE). We evaluated the performance of the FL-based LSTM model against traditional centralized models, finding significant improvements in intrusion detection. The FL-based LSTM model achieved higher accuracy and a lower FPR across all datasets than centralized models. It effectively managed sequential data in WSNs, ensuring data privacy while maintaining competitive performance, particularly in complex attack scenarios. FL and LSTM networks work well together to make a strong way to find intrusions in IoT-based WSNs, which improves both privacy and detection. This study underscores the potential of FL-based systems to address key challenges in IoT security, including data privacy, scalability, and performance, making the proposed framework suitable for real-world IoT applications.

Internet availability and its integration with smart technologies have favored everyday objects and things and offered new areas, such as the Internet of Things (IoT). IoT refers to a concept where smart devices or things are connected and create a network. This new area has suffered from big data handling and security issues. There is a need to design a data analytics model by using new 5G technologies, architecture, and a security model. Reliable data communication in the presence of legitimate nodes is always one of the challenges in these networks. Malicious nodes are generating inaccurate information and breach the user’s security. In this paper, a data analytics model and self-organizing architecture for IoT networks are proposed to understand the different layers of technologies and processes. The proposed model is designed for smart environmental monitoring systems. This paper also proposes a security model based on an authentication, detection, and prediction mechanism for IoT networks. The proposed model enhances security and protects the network from DoS and DDoS attacks. The proposed model evaluates in terms of accuracy, sensitivity, and specificity by using machine learning algorithms.

Background/Objectives: Stroke is considered the second-leading cause of mortality and a primary contributor to adult disability among both men and women. The primary aim of this research is to conduct a comprehensive investigation into gender disparities and stroke subtypes concerning symptoms, risk factors, and clinical and laboratory aspects of stroke, with a specific focus on young stroke patients. Methods: In this retrospective comparative study, a total of 185 stroke patients were selected through random sampling from the neurology department of a local hospital in Pakistan between August 2022 and March 2024. Data collection was carried out using a standardized questionnaire, and the collected data were cleaned, processed, input, and analyzed using SPSS software version 24.0. Statistical analysis was performed using Pearson’s chi-square test for categorical variables, and descriptive statistics were utilized to present the frequency, percentages, means, and standard deviations of the variables. Statistical significance was set at a p-value of <0.05. Results: Out of the 185 participants in this study, 122 (65.9%) were male and 63 (34.1%) were female. The comparison of laboratory, clinical, and risk factors between males and females revealed a higher prevalence of smoking in males compared to females (p = 0.014). Additionally, higher levels of LDL and triglycerides were noted in males, while females showed a greater prevalence of vertigo (p = 0.002). No statistically significant differences were found in the comparison of laboratory and clinical characteristics among stroke types. In ischemic stroke patients, significant associations were found with symptoms such as loss of strength or weakness (p = 0.002), headache (p = 0.00001), and fever (p < 0.00001), although these associations did not differ by gender. Conclusions: The outcomes of this study underscore the disparities in stroke types and risk factors between genders, providing valuable insights for the development of gender-specific approaches for stroke assessment and prevention among young individuals in Pakistan.

The global scarcity of irrigation water poses a significant challenge to the sustainable production of rice and its availability worldwide. With a growing population driving increased demand for rice, it is crucial to enhance rice production while minimizing water usage. Achieving this requires a comprehensive understanding of the complex interactions between water and nitrogen dynamics and the formulation of strategies to optimize the application of irrigation water and nitrogen fertilizers. This study aims to investigate the impact of varying irrigation regimes and nitrogen application rates on rice growth attributes, yield performance, overall crop productivity, and economic returns. In the 2021 and 2022 rice growing season, two field experiments were carried out in split plot design with four nitrogen levels in sub plots [N0: Control, N1: 75% RDN (Recommended dose of nitrogen; @ 120 kg N ha −1 ), N2: 100% RDN, and N3: 125% RDN] and four irrigation treatments in main plots [I1: recommended irrigation scheduling, I2: at field capacity (20 L m −2 ), I3: 10% depletion from field capacity (20 L m −2 ), and I4: 20% depletion from field capacity (20 L m −2 ). The experiments were replicated three times. The suggested irrigation scheduling treatment (flooded) showed improved growth characteristics, such as plant height, dry matter accumulation, leaf area index, tiller count, SPAD (Soil Plant Analysis Development) value, NDVI (Normalized Difference Vegetation Index) value, leaf relative water content, and yield attributes; however, these were comparable to the application of irrigation water at field capacity. Due to improved plant growth and yield-attributing characteristics, the I1 treatment recorded the highest grain yield of 8.58 t ha −1 and 8.4 t ha −1 , although it was comparable to the I2 treatment, which had grain yields of 8.27 t ha −1 and 8.15 t ha −1 in 2021 and 2022. The grain yield reported by the N3 treatment were significantly greater than those of the N2 treatment, IN 2021 and 2022 respectively. Applying nitrogen at 125% RDN (Recommended dose of nitrogen) and irrigation water at field capacity produced the highest benefit–cost ratio (1.64), which was closely followed by the same irrigation regime and 100% RDN application (1.60 BC ratio). Comparable to irrigation at field capacity, the suggested irrigation schedule demonstrated enhanced growth features, yield attributes, productivity, and profitability. The best way to achieve the optimum growth, productivity, and profitability in transplanted rice was to provide irrigation water at field capacity and nitrogen @ 100% RDN.

Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations. Due to the increase in precision image-based diagnostic tools, driven by advancements in artificial intelligence (AI) and deep learning, there has been potential to improve diagnostic accuracy, especially with Magnetic Resonance Imaging (MRI). However, traditional state-of-the-art models lack the sensitivity essential for reliable tumor identification and segmentation. Thus, our research aims to enhance brain tumor diagnosis in MRI by proposing an advanced model. The proposed model incorporates dilated convolutions to optimize the brain tumor segmentation and classification. The proposed model is first trained and later evaluated using the BraTS 2020 dataset. In our proposed model preprocessing consists of normalization, noise reduction, and data augmentation to improve model robustness. The attention mechanism and dilated convolutions were introduced to increase the model’s focus on critical regions and capture finer spatial details without compromising image resolution. We have performed experimentation to measure efficiency. For this, we have used various metrics including accuracy, sensitivity, and curve (AUC-ROC). The proposed model achieved a high accuracy of 94%, a sensitivity of 93%, a specificity of 92%, and an AUC-ROC of 0.98, outperforming traditional diagnostic models in brain tumor detection. The proposed model accurately identifies tumor regions, while dilated convolutions enhanced the segmentation accuracy, especially for complex tumor structures. The proposed model demonstrates significant potential for clinical application, providing reliable and precise brain tumor detection in MRI.

The present study aimed to evaluate nitrogen dynamics and use efficiency of transplanted rice under variable irrigation regimes and nitrogen levels. Two field experiments were conducted during the 2021 and 2022 rice-growing seasons using a split-plot design with four irrigation treatments in the main plots and four nitrogen levels in the sub-plots, each replicated thrice. Results indicated that nitrogen concentration and uptake by grain and straw were significantly influenced by both irrigation scheduling and nitrogen application. Among irrigation treatments, recommended scheduling and irrigation at field capacity produced the highest nitrogen concentration and uptake, whereas 10 and 20% depletion from field capacity resulted in lower values. For nitrogen levels, 125% of the recommended dose (RDN) recorded the highest grain nitrogen content and uptake, but values were statistically similar to 100% RDN. Flooded rice cultivation led to the greatest nitrogen removal from soil, followed by field capacity and deficit irrigation treatments. The highest nitrogen use efficiency was observed under deficit irrigation, followed by field capacity, while flooded irrigation was the least efficient. Adequate irrigation (I 1 /I 2 ) resulted in the highest nitrogen uptake and grain yield, while deficit irrigation (I 4 ) saved water but led to lower yield and nitrogen use efficiency, with greater amounts of residual nitrogen remaining in the soil. Overall, applying irrigation at field capacity combined with 100% RDN was found optimal for maximizing nutrient uptake and nitrogen use efficiency in transplanted rice, suggesting a sustainable approach to improve resource use without over-application of water or fertilizer.

Additive manufacturing (AM) has greatly revolutionized manufacturing due to its ability to manufacture complex shapes without the need for additional tooling. Most AM applications are based on geometries comprising curved shapes subjected to impact loads. The main focus of this study was on investigating the influence of infill density and the radius of curvature on the impact strength of parts manufactured via an FDM process. Standard geometrical specimens with varying part infill densities and radii of curvature were produced and subjected to Charpy impact tests to evaluate their impact strength. The results suggest that the impact strength increases with the increased density caused by higher amounts of material as well as by the changing cross-sectional areas of the beads. Also, the radius of curvature of the parts shows a clear inverse relationship with the impact energy absorbed by the specimens (i.e., increasing the radius decreased the impact energy of the parts) produced via an FDM process, which can be explained using the beam theory of structural mechanics. The maximum value of impact strength obtained was 287 KJ/m2, and this was achieved at the highest infill density (i.e., solid) and for the smallest radius of curvature.