Search Results (9)

Male-factor infertility accounts for approxiamately half of all infertility cases globally, yet therapeutic options remain limited for individuals with no retrievable spermatozoa, such as those with non-obstructive azoospermia (NOA). In recent years, artificial gametogenesis has emerged as a promising avenue for fertility restoration, driven by advances in two complementary strategies: organotypic in vitro spermatogenesis (IVS), which aims to complete spermatogenesis ex vivo using native testicular tissue, and in vitro gametogenesis (IVG), which seeks to generate male gametes de novo from pluripotent or reprogrammed somatic stem cells. To evaluate the current landscape and future potential of these approaches, a narrative, semi-systematic literature search was conducted in PubMed and Scopus for the period January 2010 to February 2025. Additionally, landmark studies published prior to 2010 that contributed foundational knowledge in spermatogenesis and testicular tissue modeling were reviewed to provide historical context. This narrative review synthesizes multidisciplinary evidence from cell biology, tissue engineering, and translational medicine to benchmark IVS and IVG technologies against species-specific developmental milestones, ranging from rodent models to non-human primates and emerging human systems. Key challenges—such as the reconstitution of the blood–testis barrier, stage-specific endocrine signaling, and epigenetic reprogramming—are discussed alongside critical performance metrics of various platforms, including air–liquid interface slice cultures, three-dimensional organoids, microfluidic “testis-on-chip” devices, and stem cell-derived gametogenic protocols. Particular attention is given to clinical applicability in contexts such as NOA, oncofertility preservation in prepubertal patients, genetic syndromes, and reprocutive scenarios involving same-sex or unpartnered individuals. Safety, regulatory, and ethical considerations are critically appraised, and a translational framework is outlined that emphasizes biomimetic scaffold design, multi-omics-guided media optimization, and rigorous genomic and epigenomic quality control. While the generation of functionally mature sperm in vitro remains unachieved, converging progress in animal models and early human systems suggests that clinically revelant IVS and IVG applications are approaching feasibility, offering a paradigm shift in reproductive medicine. Full article

Structural cracks significantly threaten the safety and longevity of historical buildings, which are essential parts of cultural heritage. Conventional inspection techniques, which depend heavily on manual visual evaluations, tend to be inefficient and subjective. This research introduces an automated framework for crack and damage detection using advanced YOLO (You Only Look Once) models, aiming to improve both the accuracy and efficiency of monitoring heritage structures. A dataset comprising 2500 high-resolution images was gathered from historical buildings and categorized into four levels of damage: no damage, minor, moderate, and severe. Following preprocessing and data augmentation, a total of 5000 labeled images were utilized to train and evaluate four YOLO variants: YOLOv5, YOLOv8, YOLOv10, and YOLOv11. The models’ performances were measured using metrics such as precision, recall, mAP@50, mAP@50–95, as well as losses related to bounding box regression, classification, and distribution. Experimental findings reveal that YOLOv10 surpasses other models in multi-target detection and identifying minor damage, achieving higher localization accuracy and faster inference speeds. YOLOv8 and YOLOv11 demonstrate consistent performance and strong adaptability, whereas YOLOv5 converges rapidly but shows weaker validation results. Further testing confirms YOLOv10’s effectiveness across different structural components, including walls, beams, and ceilings. This study highlights the practicality of deep learning-based crack detection methods for preserving building heritage. Future advancements could include combining semantic segmentation networks (e.g., U-Net) with attention mechanisms to further refine detection accuracy in complex scenarios. Full article

Urban sustainability is closely linked to walkability, as it impacts social interaction, public health, and economic development. In megacities like Riyadh, where automobiles dominate mobility, the development of pedestrian infrastructure remains inadequate. An online survey was conducted through public sampling to evaluate walking conditions in central Riyadh’s commercial districts. The 302 participants evaluated 49 critical walkability indicators to determine their significance and satisfaction with the current conditions. Gap analysis and a displeasure measurement framework identified pedestrian challenges. Participants acknowledged the importance of walkability aspects but expressed strong dissatisfaction with existing conditions. Key barriers to pedestrian comfort included inadequate facilities, environmental discomfort, weak safety measures, and cultural driving preferences. The study highlighted crucial walkability issues requiring improvement, such as public toilets, shaded pathways, air quality, and pedestrian-friendly infrastructure. The findings emphasize the need for targeted policy interventions in Riyadh’s commercial districts to enhance pedestrian accessibility and comfort, to promote urban sustainability through improved walkability. This study offers a methodological advancement by combining Relative Importance Index, gap analysis, and a novel disgruntlement measurement framework to assess walkability. The use of 49 Delphi-derived indicators contextualized within a GCC megacity adds a unique perspective to urban sustainability research. The findings inform both local policy and global urban studies by demonstrating how culturally and climatically adaptive walkability metrics can guide equitable, data-driven interventions. Full article

Background/Objectives: The kidneys are essential for eliminating drugs and chemicals from the human body and renal epithelial cells are particularly vulnerable to damage caused by xenobiotics and their metabolites. Drug-induced kidney toxicity is a major cause of drug attrition during preclinical and clinical development and the ability to predict renal toxicity remains a pressing challenge, necessitating more predictive in vitro models. However, the abundance of commercially available renal proximal tubule epithelial cell (RPTEC) sources complicates the selection of the most predictive cell types. Methods: This study compared a wide range of RPTEC sources, including primary cells (Lonza) and various RPTEC lines from different vendors, such as ciPTECs (Cell4Pharma), TERT1/RPTECs (ATCC), and HEK293 (GenoMembrane), including OAT1-overexpressing variants. HepG2 cells were included for a comparison of organ specificity. The different cells were cultured in 96- or 384-well plates and exposed to 12 drugs for 72 h at a concentration yielding a response (0.3–300 µM) to evaluate their ability to predict clinical outcomes. The CellTiterGlo^® assay was used to measure cell viability, and transcriptome data from unexposed cells was analyzed using the TempO-seq^® S1500+ platform. Results: Gene expression data showed that the primary kidney cells most closely matched the transcriptome of the human kidney medulla, followed by the TERT1 and ciPTEC lines, with the HEK lines showing the lowest similarity. The RPTEC sources showed clustering by cell type, with OAT1 overexpression driving changes in metabolic, detoxification, and immune pathways, especially in TERT1 cells. Cell viability data were used to determine points of departure (PODs) which were compared to human serum Cmax values to assess safety margins. The TERT1 and ciPTEC RPTEC lines demonstrated the highest predictive performance for nephrotoxicity, with OAT1 overexpression significantly enhancing sensitivity, accuracy, and overall predictive power (MCC scores: 0.764 and 0.667, respectively). In contrast, HepG2 cells showed the lowest performance across all metrics, highlighting the critical role of cell type and transporter expression in nephrotoxicity prediction. Conclusions: This study highlights important differences among RPTEC sources and their utility in drug safety studies of the renal proximal tubule. We show that while improved cell options for renal proximal tubule are needed, OAT1-overexpressing RPTECs are a superior model to the background cell type. Full article

There is a pressing need for healthcare conversational agents with domain-specific expertise to ensure the provision of accurate and reliable information tailored to specific medical contexts. Moreover, there is a notable gap in research ensuring the credibility and trustworthiness of the information provided by these healthcare agents, particularly in critical scenarios such as medical emergencies. Pilgrims come from diverse cultural and linguistic backgrounds, often facing difficulties in accessing medical advice and information. Establishing an AI-powered multilingual chatbot can bridge this gap by providing readily available medical guidance and support, contributing to the well-being and safety of pilgrims. In this paper, we present a comprehensive methodology aimed at enhancing the reliability and efficacy of healthcare conversational agents, with a specific focus on addressing the needs of Hajj pilgrims. Our approach leverages domain-specific fine-tuning techniques on a large language model, alongside synthetic data augmentation strategies, to optimize performance in delivering contextually relevant healthcare information by introducing the HajjHealthQA dataset. Additionally, we employ a retrieval-augmented generation (RAG) module as a crucial component to validate uncertain generated responses, which improves model performance by 5%. Moreover, we train a secondary AI agent on a well-known health fact-checking dataset and use it to validate medical information in the generated responses. Our approach significantly elevates the chatbot’s accuracy, demonstrating its adaptability to a wide range of pilgrim queries. We evaluate the chatbot’s performance using quantitative and qualitative metrics, highlighting its proficiency in generating accurate responses and achieve competitive results compared to state-of-the-art models, in addition to mitigating the risk of misinformation and providing users with trustworthy health information. Full article

Culture shapes how general principles, rules and knowledge concretise in a specific context. Therefore, broadening the approach to disaster risk management by incorporating cultural variability is a key factor in strengthening resilience in our societies. This article offers a theoretical framework to contextualise the concept of “Safety Culture” in the field of public safety and disaster risk reduction (DRR), with the aim of supporting its understanding and measurement in our communities and finally attaining more disaster-resilient societies. The work discusses the role of cultural dimensions in risk management and DRR. It highlights the relevance of building knowledge and practices based on the consideration of culture and cultural variability in all phases of the risk-management process. After an overview of the safety culture concept, including its origins, developments and applications, the text explores the transferability of this concept to the context of public safety. Then, based on the existing safety culture models, metrics and indicators from other sectors, the paper proposes an original definition of safety culture for the DRR context, shaped by a series of elements and dimensions specifically outlined for this context. Finally, the expected benefits of applying safety culture concepts and techniques for further advances in DRR are discussed. Full article

A lack of data collection on conscious mobility behaviors has been identified in current sustainable and smart mobility planning, development and implementation strategies. This leads to technocentric solutions that do not place people and their behavior at the center of new mobility solutions in urban centers around the globe. This paper introduces the concept of conscious mobility to link techno-economic analyses with user awareness on the impact of their travel decisions on other people, local urban infrastructure and the environment through systematic big data collection. A preliminary conscious mobility indicator framework is presented to leverage behavioral considerations to enhance urban-community mobility systems. Key factors for conscious mobility analysis have been derived from five case studies. The sample offers regional diversity (i.e., local, regional and the global urban contexts), as well as different goals in the transformation of conventional urban transport systems, from improving public transport efficiency and equipment electrification to mitigate pollution and climate risks, to focusing on equity, access and people safety. The case studies selected provide useful metrics on the adoption of cleaner, smarter, safer and more autonomous mobility technologies, along with novel people-centric program designs to build an initial set of conscious mobility indicators frameworks. The parameters were applied to the city of Monterrey, Nuevo Leon in Mexico focusing on the needs of the communities that work, study and live around the local urban campus of the Tecnologico de Monterrey’s Distrito Tec. This case study, served as an example of how conscious mobility indicators could be applied and customized to a community and region of interest. This paper introduces the first application of the conscious mobility framework for urban communities’ mobility system analysis. This more holistic assessment approach includes dimensions such as society and culture, infrastructure and urban spaces, technology, government, normativity, economy and politics, and the environment. The expectation is that the conscious mobility framework of analysis will become a useful tool for smarter and sustainable urban and mobility problem solving and decision making to enhance the quality of life all living in urban communities. Full article

The term “smart lab” refers to a system that provides a novel and flexible approach to automating and connecting current laboratory processes. In education, laboratory safety is an essential component of undergraduate laboratory classes. The institution provides formal training for the students working in the labs that involve potential exposure to a wide range of hazards, including chemical, biological, and physical agents. During the laboratory safety lessons, the instructor explains the lab safety protocols and the use of personal protective equipment (PPE) to prevent unwanted accidents. However, it is not always guaranteed that students follow safety procedures throughout all lab sessions. Currently, the lab supervisors monitor the use of PPE, which is time consuming, laborious, and impossible to see each student. Consequently, students may unintentionally commit unrecognizable unsafe acts, which can lead to unwanted situations. Therefore, the aim of the research article was to propose a real-time smart vision-based lab-safety monitoring system to verify the PPE compliance of students, i.e., whether the student is wearing a mask, gloves, lab coat, and goggles, from image/video in real time. The YOLOv5 (YOLOv5l, YOLOv5m, YOLOv5n, YOLOv5s, and YOLOv5x) and YOLOv7 models were trained using a self-created novel dataset named SLS (Students Lab Safety). The dataset comprises four classes, namely, gloves, helmets, masks, and goggles, and 481 images, having a resolution of 835 × 1000, acquired from various research laboratories of the United Arab Emirates University. The performance of the different YOLOv5 and YOLOv7 versions is compared based on instances’ size using evaluation metrics such as precision, F1 score, recall, and mAP (mean average precision). The experimental results demonstrated that all the models showed promising performance in detecting PPE in educational labs. The YOLOv5n approach achieved the highest mAP of 77.40% for small and large instances, followed by the YOLOv5m model having a mAP of 75.30%. A report detailing each student’s PPE compliance in the lab can be prepared based on data collected in real time and stored in the proposed system. Overall, the proposed approach can be utilized to make laboratories smarter by enhancing the efficacy of safety in research settings; this, in turn, will aid the students in establishing a health and safety culture among students. Full article

Road accidents are increasing every year in Malaysia, and it is always challenging to collect reliable pre-crash data in the transportation community. Existing studies relied on simulators, police crash reports, questionnaires, and surveys to study Malaysia’s drivers’ behavior. Researchers previously criticized such methods for being biased and unreliable. To fill in the literature gap, this study presents the first naturalistic driving study in Malaysia. Thirty drivers were recruited to drive an instrumented vehicle for 750 km while collecting continuous driving data. The data acquisition system consists of various sensors such as OBDII, lidar, ultrasonic sensors, IMU, and GPS. Irrelevant data were filtered, and experts helped identify safety criteria regarding multiple driving metrics such as maximum acceptable speed limits, safe accelerations, safe decelerations, acceptable distances to vehicles ahead, and safe steering behavior. These thresholds were used to investigate the influence of social and cultural factors on driving in Malaysia. The findings show statistically significant differences between drivers based on gender, age, and cultural background. There are also significant differences in the results for those who drove on weekends rather than weekdays. The study presents several recommendations to various public and governmental sectors to help prevent future accidents and improve traffic safety. Full article