Search Results (1,471)

An accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safety and reliability of energy storage systems and electric vehicles. However, existing methods face challenges: physics-based models are computationally complex, traditional data-driven methods rely heavily on manual feature engineering, and single models lack the ability to capture both local and global degradation patterns. To address these issues, this paper proposes a novel hybrid LSTM–Transformer model for LIB SOH estimation using actual measurement data. The model integrates Long Short-Term Memory (LSTM) networks to capture local temporal dependencies with the Trans-former architecture to model global degradation trends through self-attention mechanisms. Experimental validation was conducted using eight 18650 Nickel Cobalt Manganese (NCM) LIBs subjected to 750 charge–discharge cycles under room temperature conditions. Sixteen statistical features were extracted from voltage and current data during constant current–constant voltage (CC-CV) phases, with feature selection based on the Pearson correlation coefficient and maximum information coefficient analysis. The proposed LSTM–Transformer model demonstrated superior performance compared to the standalone LSTM and Transformer models, achieving a mean absolute error (MAE) as low as 0.001775, root mean square error (RMSE) of 0.002147, and mean absolute percentage error (MAPE) of 0.196% for individual batteries. Core features including cumulative charge (CC Q), charging time, and voltage slope during the constant current phase showed a strong correlation with the SOH (absolute PCC > 0.8). The hybrid model exhibited excellent generalization across different battery cells with consistent error distributions and nearly overlapping prediction curves with actual SOH trajectories. The symmetrical LSTM–Transformer hybrid architecture provides an accurate, robust, and generalizable solution for LIB SOH assessment, effectively overcoming the limitations of traditional methods while offering potential for real-time battery management system applications. This approach enables health feature learning without manual feature engineering, representing an advancement in data-driven battery health monitoring. Full article

Background/Objectives: Parkinson’s disease is the second most common neurodegenerative disorder, affecting patients’ daily lives in multiple domains, including functional status, health-related quality of life, and psychological well-being. This study examined the relationship between self-reported global activity level, functional status, Health Related QoL (HRQoL), and psychological state among patients with Parkinson’s disease in Greece. Methods: Thirty volunteers (mean age = 69.07, SD = 11.24), members of the Greek Parkinson’s Patients and Caregivers Association, completed (a) the Parkinson’s Disease Questionnaire to evaluate HRQoL and (b) the Hospital Anxiety and Depression Scale (HADS) to assess psychological state. Participants then performed (a) the Five Times Sit to Stand Test (FTSST) and (b) the Berg Balance Scale (BBS) to evaluate functional status. All questionnaires and the test used in the present study have been validated in Greek. Correlation analysis with Spearman r tests with Bonferroni correction was performed between the above variables. Subsequent linear regression models were used to identify independent predictors of HRQoL and balance using SPSS 29.0.2.0. Results: Participants reported elevated anxiety (M = 9.67, SD = 4.44) and depressive symptoms (M = 8.97, SD = 4.08), alongside relatively high HRQoL scores (M = 40.09, SD = 18.40). Impaired functional performance was observed, with 22 participants failing to complete the FTSST within 16 s and 16 scoring below 40 on the BBS. Functional status was strongly correlated with HRQoL (r = −0.696, p < 0.001) and activity level (r = −0.521, p < 0.008). Depression was also significantly associated with poorer HRQoL (r = 0.618, p < 0.008) and lower activity levels (r = −0.545, p < 0.008). Regression analyses revealed that balance (β = −0.526), disease duration (β = 0.437), anxiety (β = 0.411), and lower limb function (β = −0.351) were significant independent predictors of HRQoL (R² = 0.785; F(9, 20) = 12.69; p < 0.001), while HRQoL (β = −0.738) and lower limb function (β = −0.391) independently predicted balance (R² = 0.699; F(9, 20) = 4.72; p = 0.002), suggesting a bidirectional relationship between physical function and subjective well-being. Conclusions: Activity level, HRQoL, functional status, and psychological state in patients with Parkinson’s disease are interrelated factors. Increased levels of anxiety and depression, as well as reduced HRQoL, were observed. The findings point to a potentially reinforcing cycle between poor balance and diminished quality of life, with anxiety and age playing key roles. Overall, the results illustrate that functional, psychological, and HRQoL measures interact in complex ways, emphasizing the multidimensional profile of patients with Parkinson’s disease. Further studies with larger samples are required to confirm these findings. Full article

Under the combined influence of global climate change and intensified human activities, quantifying ecological compensation (EC) amounts between regions and formulating scientifically sound and rational policies have become critical strategies for addressing the imbalance between economic development and ecological conservation. This study focuses on the Yangtze River Economic Belt (YREB) as the research subject, assesses ecosystem service supply and demand (ESSD) in the years 2000, 2010, and 2020 from the perspective of the water-energy-food nexus (WEF-Nexus), identifies ecosystem service flows (ESF) between supply and demand areas, develops an integrated EC model incorporating ecological, economic, and social dimensions to estimate EC amounts, and ultimately employs the XGBoost-SHAP model to analyze the underlying driving mechanisms. The results indicate the following: (1) From 2000 to 2020, the spatio-temporal variations in the three ESSDs in the YREB were substantial. Additionally, imbalances in ESSDs were observed, predominantly in economically advanced regions. (2) A total of 183 ESFs were identified among cities within the YREB, reflecting relatively active exchanges of ecosystem services (ESs). (3) Over the past two decades, the average annual total EC of the YREB amounted to 46,866.35 million yuan, with EC capital flows occurring in 117 cities. The proportion of water area in each city constitutes the primary driver of the EC amount. The EC model based on the “water-energy-food” ecosystem service flow (WEF-ESF) proposed in this study provides a valuable reference and scientific basis for formulating EC policies among YREB cities. Full article

Obesity is among the top contributing factors for non-communicable chronic disease development and has attained menacing global proportions, affecting approximately one of eight adults. Phytochemicals that support energy metabolism and prevent obesity development have been the subject of intense research endeavors over the past several decades. Cycloastragenol is a natural triterpenoid compound and aglycon of astragaloside IV, known for activating telomerase and mitigating cellular aging. Here, we aim to characterize the effect of cycloastragenol on lipid metabolism in a glucose-induced obesity model in Caenorhabditis elegans. We assessed the changes in the body length, width, and area in C. elegans maintained under elevated glucose through automated WormLab system. Lipid accumulation in the presence of either cycloastragenol (100 μM) or orlistat (12 μM), used as a positive anti-obesity control drug, was quantified through Nile Red fluorescent staining. Furthermore, we evaluated the changes in key energy metabolism molecular players in GFP-reporter transgenic strains. Our results revealed that cycloastragenol treatment decreased mean body area and reduced lipid accumulation in the C. elegans glucose-induced model. The mechanistic data indicated that cycloastragenol suppresses the nuclear hormone receptor family member NHR-49 and the delta(9)-fatty-acid desaturase 7 (FAT-7) enzyme, and activates the 5′-AMP-activated protein kinase catalytic subunit alpha-2 (AAK-2) and the protein skinhead 1 (SKN-1) signaling. Collectively, our findings highlight that cycloastragenol reprograms lipid metabolism by down-regulating the insulin-like receptor (daf-2)/phosphatidylinositol 3-kinase (age-1)/NHR-49 signaling while simultaneously enhancing the activity of the AAK-2/NAD-dependent protein deacetylase (SIR-2.1) pathway. The anti-obesogenic potential of cycloastragenol rationalizes further validation in the context of metabolic diseases and obesity management. Full article

The paper introduces ‘transformative’ curriculum initiatives such as education for sustainable development (ESD) and global citizenship education (GCED), which address ‘macro’ challenges such as climate change, together with ‘holistic’ approaches to student learning such as ‘social and emotional learning’ (SEL) and education for ‘life skills’, ‘21st century skills’, ‘transversal competencies’, AI-related ethics, and ‘health and well-being.’ These are reflected in Section 6 of the 2023 UNESCO Recommendation on Education for Peace, Human Rights and Sustainable Development. It is suggested that such broad goals put forward at global policy level may serve as inspiration for national context-specific programming, while also needing better integration of national insights and cultural differences into global discourse. The paper aims to provide insights to education policy-makers responsible for national curriculum, textbooks and other learning resources, teacher training and examination processes, helping them to promote the human values, integrity and sense of agency needed by students in a time of multiple global and personal challenges. This requires an innovative approach to curricula for established school subjects and can be included in curricula being developed for AI literacy and related ethics. Research into the integration of transformative and holistic dimensions into curricula, materials, teacher preparation, and assessment is needed, as well as ongoing monitoring and feedback. AI-supported networking and resource sharing at local, national and international level can support implementation of transformative and holistic learning, to maintain and strengthen the human dimensions of learning. Full article

The prevailing challenges of fading characteristics and identity crises in historical and cultural districts of small and medium-sized cities have been identified. Traditional analytical methods have been found to be deficient in systematically capturing the unique forms and urban memory of these districts. The present study thus adopts the Shangcheng Historical and Cultural District of Huangling County as a case study, proposing a comprehensive analytical framework that integrates urban memory and multi-dimensional methods such as space syntax, grounded-theory-inspired coding, and urban image analysis. The district is subject to a systematic assessment of its spatial form, structural design, and the mechanisms by which urban memory is conveyed. The proposal sets out targeted renewal strategies for four aspects: paths, edges, nodes and landmarks, and districts. The research findings are as follows: (1) Paths with high integration and connection degrees simultaneously serve as both sacrificial axes and carriers of folk narratives. (2) Edges are composed of the city wall ruins, Loess Plateau landform, and street spaces. The fishbone-like street structure leads to significant differences in the connection degrees of main and secondary roads. (3) Nodes such as Guanyv Temple-Confucian Temple, the South Gate, and the North City Wall Ruins Square have high visual control, while the visual integration and visual control of the Qiaoshan Middle School and Gongsun Road historical nodes are relatively low, and their spatial accessibility is insufficient. (4) Based on the “memory–space” coupling relationship, the district is divided into the Academy Life Area, the Historical and Cultural Core Experience Area, and the Comprehensive Service Area, providing an effective path to alleviate the problem of functional homogenization. The present study proffers a novel perspective on the revitalization mechanisms of historical districts in small and medium-sized cities, encompassing both theoretical integration and practical strategy levels. It further contributes methodological inspirations and localized planning experiences for addressing the cultural disconnection and spatial inactivity problems of historical urban areas on a global scale. Full article

Groundwater vulnerability assessments serve as essential tools for sustainable groundwater management, particularly in regions with intensive anthropogenic activities. However, improving the objectivity and predictive reliability of vulnerability assessment frameworks remains a critical scientific challenge in groundwater science, especially for coastal aquifer systems characterized by strong heterogeneity and complex hydrogeological processes. The traditional DRASTIC model is a widely recognized method but suffers from subjectivity in assigning parameter ratings and weights, often leading to arbitrary and potentially inaccurate vulnerability maps. This limitation also restricts its applicability in areas with complex hydrogeological conditions. To enhance the accuracy and adaptability of the traditional DRASTIC model, a hybrid PSO-BP-DRASTIC framework was developed and applied it to a coastal city in China. Specifically, the model employs a backpropagation neural network (BP-NN) to optimize indicator weights and integrates the particle swarm optimization (PSO) algorithm to refine the initial weights and thresholds of the BP-NN. By introducing a data-driven and globally optimized weighting mechanism, the proposed framework effectively overcomes the inherent subjectivity of conventional empirical weighting schemes. Using ten-fold cross-validation and observed nitrate concentration data, the traditional DRASTIC, BP-DRASTIC, and PSO-BP-DRASTIC models were systematically validated and compared. The results demonstrate that (1) the PSO-BP-DRASTIC model achieved the highest classification accuracy on the test set, the highest stability across ten-fold cross-validation, and the strongest correlation with the nitrate concentrations; (2) the importance analysis identified the aquifer thickness and depth to the groundwater table as the most influential factors affecting groundwater vulnerability in Yantai; and (3) the spatial assessments revealed that high-vulnerability zones (7.85% of the total area) are primarily located in regions with intensive agricultural activities and high aquifer permeability. The hybrid PSO-BP-DRASTIC model effectively mitigates the subjectivity of the traditional DRASTIC method and the local optimum issues inherent in BP-NNs, significantly improving the assessment accuracy, stability, and objectivity. From a scientific perspective, this study demonstrates the feasibility of integrating swarm intelligence and neural learning into groundwater vulnerability assessment, providing a transferable and high-precision methodological paradigm for data-driven hydrogeological risk evaluation. This novel hybrid model provides a reliable scientific basis for the reasonable assessment of groundwater vulnerability. Moreover, these findings highlight the importance of integrating a hybrid optimization strategy into the traditional DRASTIC model to enhance its feasibility in coastal cities and other regions with complex hydrogeological conditions. Full article

Aquatic environments have been a critical source of nutrition for millennia, with wild fisheries supplying protein and nutrients to populations worldwide. A notable shift has occurred in recent decades with the expansion of aquaculture, now representing a fast-growing sector in food production. Aquaculture plays a key role in mitigating the depletion of wild fish stocks and addressing issues related to overfishing. Despite its potential benefits, the sustainability of both wild and farmed aquatic food systems is challenged by anthropogenic pollution. Contaminants from agricultural runoff, industrial discharges, and domestic effluents enter freshwater systems and eventually reach marine environments, where they may be transported globally through ocean currents. Maintaining water quality is paramount to food safety, environmental integrity, and long-term food security. In addition to conventional seafood products such as fish and shellfish, foods such as those derived from microalgae are gaining attention in Western markets for their high nutritional value and potential functional properties. These organisms have been consumed in Asia for generations and are now being explored as sustainable foods and ingredients as an alternative source of protein. Contaminants in aquatic food products include residues of agrochemicals, persistent organic pollutants (POPs) such as dioxins, polychlorinated biphenyls (PCBs), and per- and polyfluoroalkyl substances (PFASs), as well as brominated flame retardants and heavy metals. Public and scientific attention has intensified around plastic pollution, particularly microplastics and nanoplastics, which are increasingly detected in aquatic organisms and are the subject of ongoing toxicological and ecological risk assessments. While the presence of these hazards necessitates robust risk assessment and regulatory oversight, it is important to balance these concerns against the health benefits of aquatic foods, which are rich in omega-3 fatty acids, high-quality proteins, vitamins, and trace elements. Furthermore, beyond direct human health implications, the environmental impact of pollutant sources must be addressed through integrated management approaches to ensure the long-term sustainability of aquatic ecosystems and the food systems they support. This review covers regulatory frameworks, risk assessments, and management issues relating to aquatic environments, including the impact of climate change. It aims to serve as a comprehensive resource for researchers, policymakers, food businesses who harvest food from aquatic systems and other stakeholders. Full article

Background/Objectives: Colorectal cancer (CRC) is associated with anorexia–cachexia syndrome, which negatively affects health-related quality of life (HRQoL). This study aimed to evaluate HRQoL and functional status in CRC patients undergoing chemotherapy who were eligible for oral nutritional supplementation (ONS). Methods: In this prospective, randomized study, 72 patients with stage II–IV CRC were enrolled (40 intervention group [IG], 32 control group [CG]). IG received ONS (2 × 125 mL/day, 600 kcal, 36 g protein) for 12 weeks, while CG received dietary counseling only. HRQoL was assessed every 4 weeks with the Functional Assessment of Anorexia/Cachexia Therapy (FAACT, version 4.0). Functional status was evaluated with the Karnofsky scale. Nutritional status was assessed using the Subjective Global Assessment (SGA), Nutritional Risk Screening (NRS-2002), and body mass index (BMI), and appetite was assessed on a visual analogue scale (VAS). Clinical Trial Registration: ClinicalTrials.gov, NCT02848807. Results: Mean FAACT score did not differ significantly between groups over 12 weeks (101.0 ± 22.8, 95% CI: 94.6–107.4 vs. 105.1 ± 21.4, 95% CI: 99.1–111.1; p = 0.06). However, the observed difference corresponded to an effect size at the lower bound of the moderate range. However, minimally important difference (MID) analysis demonstrated that clinically meaningful improvement was significantly more frequent in IG than in CG for global FAACT (32% vs. 8%; p = 0.03, OR = 5.50, 95% CI: 1.10–27.62, φ = 0.29), physical well-being (32% vs. 8%; p = 0.03, OR = 5.50, 95% CI: 1.10–27.62, φ = 0.29), and emotional well-being (38% vs. 4%; p = 0.002, OR = 14.86, 95% CI: 1.79–123.36, φ = 0.40). Functional well-being and anorexia/cachexia concerns showed favorable, but nonsignificant, trends (FWB improvement: 29% vs. 8%, p = 0.05, OR = 4.79, 95% CI: 0.95–24.27, φ = 0.26; ACS deterioration: 3% vs. 20%, p = 0.07, OR = 0.12, 95% CI: 0.01–1.11, φ = 0.28). HRQoL correlated positively with nutritional status, appetite, and functional performance, while Karnofsky scores remained stable in both groups. Conclusions: ONS did not significantly change the mean QoL scores at the group level but increased the proportion of patients achieving clinically meaningful improvement, particularly in the physical and emotional domains. These findings suggest that ONS may benefit selected patients who respond to nutritional interventions, underscoring the clinical relevance of individualized nutrition strategies in oncology. Full article

The sustained growth of electricity demand and the global transition toward low-carbon energy systems have intensified the need for efficient, flexible, and reliable operation of electrical distribution networks. In this context, the coordinated integration of distributed renewable energy resources and demand-side flexibility has emerged as a key strategy to improve technical performance and economic efficiency. This work proposes an integrated optimization framework for active power supply in a radial, distribution-like network through the optimal siting and sizing of photovoltaic (PV) units and wind turbines (WTs), combined with a real-time pricing (RTP)-based demand-side response (DSR) program. The problem is formulated using the branch-flow (DistFlow) model, which explicitly represents voltage drops, branch power flows, and thermal limits in radial feeders. A multiobjective function is defined to jointly minimize annual operating costs, active power losses, and voltage deviations, subject to network operating constraints and inverter capability limits. Uncertainty associated with solar irradiance, wind speed, ambient temperature, load demand, and electricity prices is captured through probabilistic modeling and scenario-based analysis. To solve the resulting nonlinear and constrained optimization problem, an Improved Whale Optimization Algorithm (I-WaOA) is employed. The proposed algorithm enhances the classical Whale Optimization Algorithm by incorporating diversification and feasibility-oriented mechanisms, including Cauchy mutation, Fitness–Distance Balance (FDB), quasi-oppositional-based learning (QOBL), and quadratic penalty functions for constraint handling. These features promote robust convergence toward admissible solutions under stochastic operating conditions. The methodology is validated on a large-scale radialized network derived from the IEEE 118-bus benchmark, enabling a DistFlow-consistent assessment of technical and economic performance under realistic operating scenarios. The results demonstrate that the coordinated integration of PV, WT, and RTP-driven demand response leads to a reduction in feeder losses, an improvement in voltage profiles, and an enhanced voltage stability margin, as quantified through standard voltage deviation and fast voltage stability indices. Overall, the proposed framework provides a practical and scalable tool for supporting planning and operational decisions in modern power distribution networks with high renewable penetration and demand flexibility. Full article

Experiential learning involves gaining knowledge and understanding from real-life experiences, which helps develop new theories through fresh insights. Kolb described learning as the process of creating knowledge through transforming experience. Its main idea is that challenges and experiences, followed by reflection, lead to learning and growth. An experiential learning module (ELM) is a type of simulation that replicates a real-world situation, simplified to help participants understand complex problems from their perspective. It is based specifically on Kolb’s experiential learning cycle. ELMs use pictures, videos, and voice-over presentations to create a rich, contextually relevant, vicarious learning experience for classroom learners. In this study, the main ELM developed in Haiti was based on Kolb’s learning cycle. The primary goal of the ELM was to help global agriculturalists tackle complex issues related to food insecurity in developing countries. The purpose of this paper is to explain what experiential learning modules are and how to implement them in a study abroad program. An ELM on plantain production in Haiti was used as a case example. Students completed pre- and post-reflection surveys to evaluate their initial assumptions, expectations, and knowledge about the subject, as well as what they learned. A learning assessment measured their understanding of the ELM content. By analyzing the participants’ comments, the instructional approach proved effective in providing a vicarious experience within the classroom. The results from the initial classroom use of the banana and plantain learning module, along with student reactions, offered valuable feedback that led to proposed revisions and improvements to the tool. Full article

Background/Objectives: Diabetes mellitus is a serious global disease characterized by chronic hyperglycemia, resulting from defects in insulin secretion, insulin action, or both. It represents a major health concern affecting millions of people worldwide. This condition can lead to severe complications significantly affecting patients’ quality of life. Due to the limitations and side effects of current therapies, the search for safer and more effective antidiabetic agents, particularly from natural sources, has gained considerable attention. This study investigates the antidiabetic potential of seaweed-derived compounds through structure-based virtual screening targeting α-glucosidase. Methods: A library of compounds derived from the Seaweed Metabolite Database was subjected to a hierarchical molecular docking protocol against α-glucosidase. Extra Precision (XP) docking was employed to identify the top-ranked ligands based on their binding affinities. Drug-likeness was assessed according to Lipinski’s Rule of Five, followed by pharmacokinetic and toxicity predictions to evaluate ADMET properties. Density Functional Theory (DFT) calculations were performed to analyze the electronic properties and chemical reactivity of the selected compounds. Furthermore, molecular dynamics simulations were carried out to examine the stability and dynamic behavior of the ligand–enzyme complexes. Results: Following XP docking and ADMET prediction, four promising compounds were selected: Colensolide A, Rhodomelol, Callophycin A, and 7-(2,3-dibromo-4,5-dihydroxybenzyl)-3,7-dihydro-1H-purine-2,6-dione. Molecular dynamics simulations further confirmed the structural stability and strong binding interactions of these compounds within the α-glucosidase active site. Conclusions: This investigation demonstrated the important role of seaweed-derived compounds in inhibiting α-glucosidase activity. Further experimental validation is warranted to confirm their biological activity and therapeutic potential. Full article

AI enhances aquatic resource management by automating species detection, optimizing feed, forecasting water quality, protecting species interactions, and strengthening the detection of illegal, unreported, and unregulated fishing activities. However, these advancements are inconsistently employed, subject to domain shifts, limited by the availability of labeled data, and poorly benchmarked across operational contexts. Recent developments in technology and applications in fisheries genetics and monitoring, precision aquaculture, management, and sensing infrastructure are summarized in this paper. We studied automated species recognition, genomic trait inference, environmental DNA metabarcoding, acoustic analysis, and trait-based population modeling in fisheries genetics and monitoring. We used digital-twin frameworks for supervised learning in feed optimization, reinforcement learning for water quality control, vision-based welfare monitoring, and harvest forecasting in aquaculture. We explored automatic identification system trajectory analysis for illicit fishing detection, global effort mapping, electronic bycatch monitoring, protected species tracking, and multi-sensor vessel surveillance in fisheries management. Acoustic echogram automation, convolutional neural network-based fish detection, edge-computing architectures, and marine-domain foundation models are foundational developments in sensing infrastructure. Implementation challenges include performance degradation across habitat and seasonal transitions, insufficient standardized multi-region datasets for rare and protected taxa, inadequate incorporation of model uncertainty into management decisions, and structural inequalities in data access and technology adoption among smallholder producers. Standardized multi-region benchmarks with rare-taxa coverage, calibrated uncertainty quantification in assessment and control systems, domain-robust energy-efficient algorithms, and privacy-preserving data partnerships are our priorities. These integrated priorities enable transition from experimental prototypes to a reliable, collaborative infrastructure for sustainable wild capture and farmed aquatic systems. Full article

Background: Iron-deficient anemia (IDA) in pregnant women is a significant health issue globally. Oral iron supplementation is the primary treatment for IDA during pregnancy. For women who do not respond to or cannot tolerate oral iron treatment, intravenous (IV) iron preparations may offer a viable therapeutic option in the third trimester of pregnancy. Ferric carboxymaltose (FCM; Ferinject^®) is an IV iron preparation that allows rapid administration of high single doses of iron with a favorable safety profile. This study evaluated the potential impact of FCM therapy on fetal well-being by recording cardiotocography (CTG) before, during, and after iron infusions. Materials and Methods: We examined 105 women with IDA in the third trimester of pregnancy. During the initial evaluation, each patient was assessed for complete blood count, iron metabolism, B12, folates, hemoglobinopathies, CRP, kidney and liver function, and glucose levels. Each subject received intravenous ferric carboxymaltose (FCM), 500 mg. The study focused on the maternal and fetal safety of FCM infusion. The primary endpoint for maternal safety was the observation of adverse effects of iron infusion. For fetal safety, the primary endpoint was the assessment of CTG. Results: We considered 105 women, comprising 101 singleton and 4 twin pregnancies. The median hemoglobin (Hb) at initial observation was 95 g/L and 117 g/L post-therapy. Regarding maternal safety, side effects were observed during or after FCM infusion in four subjects; three cases involved local symptoms, while one case included nausea and skin rash. Concerning fetal safety, 100% of the cardiotocography records were deemed “normal” using the Dawes–Redman criteria. Conclusions: In conclusion, FCM proved effective in treating anemia in this clinically complex population of pregnant women in the third trimester and appeared safe in this cohort, though larger prospective studies are warranted. Full article

As demand for biomarker-based companion diagnostics (CDx) tests in clinical oncology of precision medicine increases, a clear understanding of the regulatory framework (especially analytical and clinical performance) is imperative to ensure the safety and efficacy of CDx in enhancing patient quality of life and aiding in treatment decisions. This study analyzes the regulatory policies and approval reports in major countries and identifies regulatory checklists for the pre- and post-marketing analytical and clinical performance to ensure safety and efficacy of CDx. It categorizes the pre-marketing analysis into four commonly used techniques, IHC, FISH, PCR, and NGS, reflecting the diversity of CDx types. All analyses are grounded in the latest regulations and guidelines. The developed checklists were subjected to feasibility assessment by industry experts. Our analysis revealed that there are differences in the pre- and post-marketing regulatory frameworks for CDx, reflecting unique characteristics of each country. In particular, differences were observed in the safety and efficacy assessment methods applied to the platform based on technological principle. Evidence-based checklists are established, which support manufacturers in implementing efficient practices and creating systematic regulatory strategies. Furthermore, these checklists facilitate global market access, activate R&D, enhance clinical implementation, and improve licensing practices. Full article