Search Results (529)

Sustainable property development in developing economies requires a careful balance between attracting foreign capital and maintaining housing affordability for local residents. While foreign direct investment (FDI) serves as a crucial engine for economic growth by enhancing productive capacity and international competitiveness, its effects on local housing markets remain inadequately understood in policy frameworks. This study examines how economic development strategies can be designed to harness FDI benefits while preventing residential market distortions in rapidly industrializing regions. Using Malaysia’s Kulim Hi-Tech Park and Batu Kawan Industrial Park as empirical cases, we analyze the relationship between foreign capital inflows and residential property prices from 2000 to 2022 through time-series regression analysis supplemented by stakeholder consultations. Our findings reveal that FDI significantly influences housing price dynamics in industrial zones, with both positive economic spillovers and challenges for housing affordability. The results demonstrate that targeted policy interventions—including affordable housing mandates, developer incentives, and strategic land use planning—can effectively moderate price appreciation while maintaining investment attractiveness. This research contributes to evidence-based policymaking by identifying integrated mechanisms that promote sustainable and inclusive growth in emerging economies seeking to balance industrial advancement with equitable housing access. The Malaysian experience offers valuable practical insights for policymakers in developing nations navigating the complex relationship between international investment, housing markets, and social welfare. Full article

The integration of artificial intelligence in interventional radiology is an emerging field with transformative potential, aiming to make a great contribution to the health domain. This overview of reviews seeks to identify prevailing themes, opportunities, challenges, and recommendations related to the process of integration. Utilizing a standardized checklist and quality control procedures, this review examines recent advancements in, and future implications of, this domain. In total, 27 review studies were selected through the systematic process. Based on the overview, the integration of artificial intelligence (AI) in interventional radiology (IR) presents significant opportunities to enhance precision, efficiency, and personalization of procedures. AI automates tasks like catheter manipulation and needle placement, improving accuracy and reducing variability. It also integrates multiple imaging modalities, optimizing treatment planning and outcomes. AI aids intra-procedural guidance with advanced needle tracking and real-time image fusion. Robotics and automation in IR are advancing, though full autonomy in AI-guided systems has not been achieved. Despite these advancements, the integration of AI in IR is complex, involving imaging systems, robotics, and other technologies. This complexity requires a comprehensive certification and integration process. The role of regulatory bodies, scientific societies, and clinicians is essential to address these challenges. Standardized guidelines, clinician education, and careful AI assessment are necessary for safe integration. The future of AI in IR depends on developing standardized guidelines for medical devices and AI applications. Collaboration between certifying bodies, scientific societies, and legislative entities, as seen in the EU AI Act, will be crucial to tackling AI-specific challenges. Focusing on transparency, data governance, human oversight, and post-market monitoring will ensure AI integration in IR proceeds with safeguards, benefiting patient outcomes and advancing the field. Full article

Forecast risk management is central to the financial management process. This study aims to apply Monte Carlo simulation to solve three classic probabilistic paradoxes and discuss their implementation in corporate financial management. The article presents Monte Carlo simulation as an advanced tool for risk management in financial management processes. This method allows for a comprehensive risk analysis of financial forecasts, making it possible to assess potential errors in cash flow forecasts and predict the value of corporate treasury growth under various future scenarios. In the investment decision-making process, Monte Carlo simulation supports the evaluation of the effectiveness of financial projects by calculating the expected net value and identifying the risks associated with investments, allowing more informed decisions to be made in project implementation. The method is used in reducing cash flow volatility, which contributes to lowering the cost of capital and increasing the value of a company. Simulation also enables more accurate liquidity planning, including forecasting cash availability and determining appropriate financial reserves based on probability distributions. Monte Carlo also supports the management of credit and interest rate risk, enabling the simulation of the impact of various economic scenarios on a company’s financial obligations. In the context of strategic planning, the method is an extension of decision tree analysis, where subsequent decisions are made based on the results of earlier ones. Creating probabilistic models based on Monte Carlo simulations makes it possible to take into account random variables and their impact on key financial management indicators, such as free cash flow (FCF). Compared to traditional methods, Monte Carlo simulation offers a more detailed and precise approach to risk analysis and decision-making, providing companies with vital information for financial management under uncertainty. This article emphasizes that the use of Monte Carlo simulation in financial management not only enhances the effectiveness of risk management, but also supports the long-term growth of corporate value. The entire process of financial management is able to move into the future based on predicting future free cash flows discounted at the cost of capital. We used both numerical and analytical methods to solve veridical paradoxes. Veridical paradoxes are a type of paradox in which the result of the analysis is counterintuitive, but turns out to be true after careful examination. This means that although the initial reasoning may lead to a wrong conclusion, a correct mathematical or logical analysis confirms the correctness of the results. An example is Monty Hall’s problem, where the intuitive answer suggests an equal probability of success, while probabilistic analysis shows that changing the decision increases the chances of winning. We used Monte Carlo simulation as the numerical method. The following analytical methods were used: conditional probability, Bayes’ rule and Bayes’ rule with multiple conditions. We solved truth-type paradoxes and discovered why the Monty Hall problem was so widely discussed in the 1990s. We differentiated Monty Hall problems using different numbers of doors and prizes. Full article

Background: Advancements in surgical care have made it possible to offer cardiac surgery to an older and frailer patient cohort. Frailty has been recognised as a prognostic indicator that impacts post-operative recovery and patient outcomes. The aim of this study is to identify frailty assessment tools, evaluate the impact of frailty on post-operative outcomes, and explore strategies to optimise care for frail patients undergoing cardiac surgery. Methods: A comprehensive literature search was performed across PubMed, MEDLINE, and SCOPUS to identify articles reporting post-operative outcomes related to frail patients undergoing cardiac surgery. Results: Measurement tools such as gait speed, the Clinical Frailty Scale, Fried frailty phenotype, deficit accumulation frailty index and the Short Physical Performance Battery can be used to assess frailty. Frailty has been reported to increase the risk of post-operative morbidity and mortality. Multiple studies have also reported the association between frailty and an increased length of intensive care unit and hospital stays, as well as an increased risk of post-operative delirium. It is important to perform a comprehensive frailty assessment and implement perioperative optimisation strategies to improve outcomes in this patient population. Pre-operative strategies that can be considered include adequate nutritional support, cardiac prehabilitation, and assessing patients using a multidisciplinary team approach with geriatric involvement. Post-operatively, interventions such as early recognition and treatment of post-operative delirium, nutrition optimisation, early planning for cardiac rehabilitation, and occupational therapy can support patients’ recovery and reintegration into daily activities. Conclusions: The early identification of frail patients during the perioperative period is essential for risk stratification and tailored management strategies to minimise the impact of frailty on outcomes following cardiac surgery. Full article

Background: Chronic obstructive pulmonary disease (COPD) is a significant burden in Saudi Arabia. Improving the attitudes, awareness, and knowledge of healthcare providers toward advance directives and/or advanced care planning (ACP) can increase the use of advance directives. This study aims to investigate healthcare providers’ knowledge, attitudes, and practices concerning advance directives for COPD patients in Riyadh, Saudi Arabia. Methods: This cross-sectional study was employed to assess the knowledge, attitudes, and practices of healthcare providers towards ACP for COPD patients in Riyadh between June and December 2024. The questionnaire was adapted from previous research. Multiple logistic regression was performed to assess the factors associated with good knowledge and positive attitude. Results: A total of 268 participants were included in the analysis. The total mean of knowledge score was 6.96 ± 2.22 out of 12. A total of 161 participants (60.1%) had a poor knowledge score, and 107 participants (39.9%) had a good knowledge score. The total mean of attitude score was 16.23 ± 23.21 out of 26. A total of 148 participants (55.2%) had a poor attitude score and 120 participants (44.8%) had positive attitude. Participants with over 15 years of experience exhibited significantly higher odds of good knowledge (OR = 6.76, 95% CI = 1.03−44.21, p = 0.04). Participants who lived in the Western region had significantly lower odds of good knowledge (OR = 0.32, 95% CI = 0.14–0.71, p = 0.005). Nurses and respiratory therapists had significantly lower odds of having positive attitude (OR = 0.19, 95% CI = 0.09−0.42, p = 0.0001 and OR = 0.34, 95% CI = 0.16–0.75, p = 0.007, respectively). Participants who lived in the Western region had significantly lower odds of having positive attitude (OR = 0.42, 95% CI = 0.19–0.95, p = 0.005). Conclusions: Healthcare providers in Saudi Arabia demonstrated a moderate level of knowledge of ACP for COPD patients. This was accompanied by a moderately positive attitude towards this practice. Future studies should examine effective educational and professional interventions to enhance ACP practices. Full article

Cystic fibrosis (CF), a genetic disorder characterized by mutations in the CFTR gene, has seen significant advances in treatment through cutting-edge approaches such as gene therapy and personalized medicine. This review examines the current and emerging strategies shaping CF care, focusing on novel therapies that target the root cause of CF and optimize patient outcomes. CFTR modulators have transformed cystic fibrosis management by enhancing protein function for specific mutations, leading to improved lung function and quality of life. Concurrently, gene therapy offers transformative potential by aiming to correct CFTR mutations using tools like CRISPR/Cas9 or prime editing, though challenges remain in delivery and long-term efficacy. The integration of precision medicine, facilitated by genomic and computational technologies, allows for personalized treatment plans that account for genetic variability and disease severity. Complementing these approaches, holistic management emphasizes the importance of psychological support and nutritional optimization, acknowledging CF’s multi-system impact. Future directions include exploring anti-inflammatory agents and microbiome modulation to further mitigate disease morbidity. However, global disparities in treatment access continue to challenge equitable healthcare delivery, underscoring the need for policy reform and international cooperation. By synthesizing these developments, this review highlights the transformative potential of modern CF treatments, advocating for continued innovation and global healthcare equity, with the ultimate goal of dramatically improving life expectancy and quality of life for individuals with CF. Full article

Background/Objectives: Pontine cavernomas are rare and challenging vascular malformations, representing a critical subset of brainstem lesions due to their deep location and proximity to essential neural structures. When hemorrhagic, these lesions can cause rapid neurological deterioration, posing life-threatening risks. Management requires a delicate balance between aggressive intervention and preserving vital functions. This case report presents the successful surgical treatment of a giant hemorrhagic pontine cavernoma, highlighting the integration of advanced imaging, precision surgical techniques, and multidisciplinary care to achieve an exceptional patient outcome. Methods: A 47-year-old female presented with acute neurological deterioration, including severe right-sided hemiparesis, dysphagia, and obnubilation. High-resolution MRI, including susceptibility-weighted imaging, confirmed a giant hemorrhagic pontine cavernoma causing brainstem compression. An urgent left-sided pterional craniotomy with a transsylvian approach was performed to access the lesion. Subtotal resection and hematoma evacuation were carried out to relieve brainstem compression while preserving critical structures. Postoperative recovery and lesion stability were evaluated through clinical assessments and imaging after three months. Results: Postoperatively, the patient exhibited marked neurological recovery, with near-complete resolution of hemiparesis, restored swallowing function, and significant functional improvement. Follow-up imaging confirmed a stable residual lesion, no recurrence of hemorrhage, and a well-preserved ventricular system. The combination of early intervention and tailored surgical strategies resulted in a highly favorable outcome. Conclusions: This case underscores the complexity of managing giant hemorrhagic pontine cavernomas and demonstrates that carefully planned surgical intervention, combined with advanced imaging and patient-focused care, can yield remarkable outcomes. It highlights the critical importance of early diagnosis, meticulous surgical planning, and future innovations in neurovascular surgery to improve outcomes in these rare but high-stakes cases. Full article

Background: Advancements in artificial intelligence (AI) diagnostics for renal cell carcinoma (RCC) provide valuable information for classification and subtyping, which improve treatment options and patient care. RCC diagnoses are most commonly incidental due to a lack of specific characterizations of subtypes, often leading to overtreatment. Accurate diagnosis also allows for personalized patient management. Different diagnostic methods, such as histopathology, multi-omics, imaging, and perioperative diagnostics, show a lot of promise for AI. Objective: This literature review focuses on developments in RCC diagnostics and their outcomes, efficacy, and accuracy in classification. Method: We conducted a non-systematic review of the published literature to explore advancements in the diagnostics of RCC. The PubMed and Google Scholar databases were reviewed to extract relevant information. The literature shows that AI can help distinguish RCC from other kidney lesions and track tumor growth. The integration of radiomic features with clinical metadata further enhances the results. This enables clinicians to implement personalized treatment plans. The application of artificial intelligence in perioperative diagnostics enhances decision-making, improves patient safety, mitigates intraoperative complications, and accelerates recovery. Alongside the advancements in AI-assisted diagnostics, there are problems that need to be addressed, including selection bias, demand for larger and diverse datasets, and reliable validation. Conclusions: Despite the challenges, using AI to help with RCC diagnosis could lead to better patient outcomes, a new standard of care for RCC patients, and more personalized cancer management for each patient. Full article

Retinal thickness analysis using optical coherence tomography (OCT) has become an indispensable tool in retinal disease management, providing high-resolution quantitative data for diagnosis, monitoring, and treatment planning. This analysis has been found to be particularly useful for both diagnostic and monitoring purposes across a wide range of retinal diseases, enabling precise disease characterization and treatment evaluation. This paper explores its applications across major retinal conditions, including age-related macular degeneration, diabetic retinopathy, retinal vein occlusion, and inherited retinal diseases. Emerging roles in other diseases such as neurodegenerative diseases and retinal drug toxicity are also highlighted. Despite challenges such as variability in measurements, segmentation errors, and interpretation difficulties, advancements in artificial intelligence and machine learning have significantly improved accuracy and efficiency. The integration of retinal thickness analysis with telemedicine platforms and standardized protocols further underscores its potential in delivering personalized care and enabling the early detection of ocular and systemic diseases. Retinal thickness analysis continues to play a pivotal and growing role in both clinical practice and research, bridging the gap between ophthalmology and broader medical fields. Full article

Artificial intelligence is rapidly transforming pediatric craniofacial surgery by enhancing diagnostic accuracy, improving surgical precision, and optimizing postoperative care. Machine learning and deep learning models are increasingly used to analyze complex craniofacial imaging, enabling early detection of congenital anomalies such as craniosynostosis, and cleft lip and palate. AI-driven algorithms assist in preoperative planning by identifying anatomical abnormalities, predicting surgical outcomes, and guiding personalized treatment strategies. In cleft lip and palate care, AI enhances prenatal detection, severity classification, and the design of custom therapeutic devices, while also refining speech evaluation. For craniosynostosis, AI supports automated morphology classification, severity scoring, and the assessment of surgical indications, thereby promoting diagnostic consistency and predictive outcome modeling. In orthognathic surgery, AI-driven analyses, including skeletal maturity evaluation and cephalometric assessment, inform optimal timing and diagnosis. Furthermore, in cases of craniofacial microsomia and microtia, AI improves phenotypic classification and surgical planning through precise intraoperative navigation. These advancements underscore AI’s transformative role in diagnostic accuracy, and clinical decision-making, highlighting its potential to significantly enhance evidence-based pediatric craniofacial care. Full article

Green carbon dots (GCDs) have emerged as a revolutionary tool in precision medicine, offering transformative capabilities for personalized diagnostics and therapeutic strategies. Their unique optical and biocompatible properties make them ideal for non-invasive imaging, real-time monitoring, and integration with genomics, proteomics, and bioinformatics, enabling accurate diagnosis and tailored treatments based on patients’ genetic and molecular profiles. This study explores the potential of GCDs in advancing individualized patient care by examining their applications in precision medicine. It evaluates their utility in non-invasive diagnostic imaging, targeted therapy delivery, and the formulation of personalized treatment plans, emphasizing their interaction with advanced genomic, proteomic, and bioinformatics platforms. GCDs demonstrated exceptional versatility in enabling precise diagnostics and delivering targeted therapies. Their integration with cutting-edge technologies showed significant promise in crafting personalized treatment strategies, enhancing their functionality and effectiveness in real-time monitoring and patient-specific applications. The findings underscore the pivotal role of GCDs in reshaping healthcare by advancing precision medicine and improving patient outcomes. The ongoing development and integration of GCDs with emerging technologies promise to further enhance their capabilities, paving the way for more effective, individualized medical care. Full article

Background: The bonding protocol for lithium disilicate veneers in the esthetic zone plays a crucial role in modern dental restoration techniques, focusing on the replication of natural tooth properties and esthetics. This process involves several meticulous steps on both ceramic and tooth surfaces to optimize material performance and bond strength. Methods: The objective of this article is to provide an updated review of the literature on the clinical steps for bonding lithium disilicate veneers in the anterior dentition and to document a clinical case where these advanced restorative techniques were applied to treat a female patient seeking to improve her smile. A preliminary review was conducted on the existing literature regarding the clinical protocols for bonding lithium disilicate veneers in the esthetic zone. The main advantage of careful bonding procedures is that they maximize the full potential of the materials’ properties. Results: A review of the literature reveals some minor differences in cleaning the veneers prior to cementation and in the number of steps involved when combining certain materials in a single application process. However, well-executed bonding procedures, following the manufacturer’s recommendations, can maximize the adhesion between the ceramic and the tooth, allowing the restorations to meet the patient’s esthetic demands. Conclusions: Effective bonding of lithium disilicate veneers in the esthetic zone requires multiple treatments on both the ceramic and tooth surfaces. When procedures are followed carefully, long-term esthetic and functional outcomes can be achieved. It is essential that clinicians are familiar with these steps. Proper patient selection, thoughtful treatment planning, and methodical execution of the case can lead to highly esthetic results that satisfy the patient’s demands and ensure long-term success. Full article

This review evaluates the application of artificial intelligence (AI), particularly neural networks, in diagnosing and staging periodontal diseases through radiographic analysis. Using a systematic review of 22 studies published between 2017 and 2024, it examines various AI models, including convolutional neural networks (CNNs), hybrid networks, generative adversarial networks (GANs), and transformer networks. The studies analyzed diverse datasets from panoramic, periapical, and hybrid imaging techniques, assessing diagnostic accuracy, sensitivity, specificity, and interpretability. CNN models like Deetal-Perio and YOLOv5 achieved high accuracy in detecting alveolar bone loss (ABL), with F1 scores up to 0.894. Hybrid networks demonstrate strength in handling complex cases, such as molars and vertical bone loss. Despite these advancements, challenges persist, including reduced performance in severe cases, limited datasets for vertical bone loss, and the need for 3D imaging integration. AI-driven tools offer transformative potential in periodontology by rivaling clinician performance, improving diagnostic consistency, and streamlining workflows. Addressing current limitations with large, diverse datasets and advanced imaging techniques will further optimize their clinical utility. AI stands poised to revolutionize periodontal care, enabling early diagnosis, personalized treatment planning, and better patient outcomes. Full article

As advancements in digital technologies reshape the modern healthcare, integrating digital impression techniques, such as intraoral 3D scanning and 3D analysis and treatment planning, is becoming increasingly essential in the standard care of infants with cleft lip and palate. This study introduces a novel digital measurement method designed specifically for analyzing digital models of infants with unilateral cleft lip and palate, utilizing OnyxCeph³™ software within the interdisciplinary framework of the Frankfurt approach. To support its integration into routine clinical practice, we outlined the complete digital workflow—from data acquisition through intraoral 3D scanning to model preparation and processing. Our method involves precise anatomical and constructed measurement points and diverse distances and angles to evaluate sagittal, transverse, and vertical parameters comprehensively. By leveraging OnyxCeph³™ software for computer-aided measurements of digital 3D models, this approach aims to enhance the accuracy and efficiency of cleft treatment planning, facilitating more informed, personalized care decisions and ultimately improving patient outcomes. Full article

Image segmentation is one of the key factors in diagnosing glioma patients with brain tumors. It helps doctors identify the types of tumor that a patient is carrying and will lead to a prognosis that will help save the lives of patients. The analysis of medical images is a specialized domain in computer vision and image processing. This process extracts meaningful information from medical images that helps in treatment planning and monitoring the condition of patients. Deep learning models like CNN have shown promising results in image segmentation by identifying complex patterns in the image data. These methods have also shown great results in tumor segmentation and the identification of anomalies, which assist health care professionals in treatment planning. Despite advancements made in the domain of deep learning for medical image segmentation, the precise segmentation of tumors remains challenging because of the complex structures of tumors across patients. Existing models, such as traditional U-Net- and SAM-based architectures, either lack efficiency in handling class-specific segmentation or require extensive computational resources. This study aims to bridge this gap by proposing Segment Anything Model 2-UNetwork, a hybrid model that leverages the strengths of both architectures to improve segmentation accuracy and consumes less computational resources by maintaining efficiency. The proposed model possesses the ability to perform explicitly well on scarce data, and we trained this model on the Brain Tumor Segmentation Challenge 2020 (BraTS) dataset. This architecture is inspired by U-Networks that are based on the encoder and decoder architecture. The Hiera pre-trained model is set as a backbone to this architecture to capture multi-scale features. Adapters are embedded into the encoder to achieve parameter-efficient fine-tuning. The dataset contains four channels of MRI scans of 369 glioma patients as T1, T1ce, T2, and T2-flair and a segmentation mask for each patient consisting of non-tumor (NT), necrotic and non-enhancing tumor (NCR/NET), and peritumoral edema or GD-enhancing tumor (ET) as the ground-truth value. These experiments yielded good segmentation performance and achieved balanced performance based on the metrics discussed next in this paragraph for each tumor region. Our experiments yielded the following results with minimal hardware resources, i.e., 16 GB RAM with 30 epochs: a mean Dice score (mDice) of 0.771, a mean Intersection over Union (mIoU) of 0.569, an $S_{\alpha }$ score of 0.692, a weighted F-beta score ($F_{\beta }^w$) of 0.267, a F-beta score ($F_{\beta }$) of 0.261, an $E_{\varphi }$ score of 0.857, and a Mean Absolute Error (MAE) of 0.04 on the BraTS 2020 dataset. Full article