Comprehensive Evaluation Strategy Research Articles

Performance of co-composting Pholiota nameko spent mushroom substrate and pig manure at different proportions: Chemical properties and humification process.

Co-composting is the controlled aerobic degradation of organics, using more than one feedstock. By combining the spent mushroom substrate of Pholiota nameko (SMS) and pig manure (PM), the benefits of each could be used to optimize the composting process and the final product. This study introduced a comprehensive evaluation strategy aimed at identifying the optimal co-composting ratio for these two substrates. A 120-day composting trial was conducted, blending SMS and PM in various ratios to evaluate the benefits of co-composting SMS-PM. The results indicated that dissolved organic matter (DOM) in SMS-derived compost primarily originated from plants, whereas PM-derived compost predominantly consisted of microbial metabolic products, and co-composting combined both sources. An increase in aromaticity and humification degree of DOM occurred during the composting process itself rather than being derived from autochthonous origin. Carbohydrates like phenols and alcohols broke down during composting, and microbes utilized polysaccharides as an energy source for humus formation. As co-composting progressed, the treatments with varying mass ratios of SMS to PM, including 8:2, 7:3, 6:4, 5:5, 4:6, and 3:7 were observed to result in a decline in aliphatic hydroxylated chains alongside an enhancement in aromaticity within the compost. Additionally, there was a conversion from organic carbon (C) to carboxyl C within humic acid (HA) due to oxidation and dehydrogenation processes that facilitated the formation of stable nitrogen-containing compounds characterized by condensed aromatic structures. Following thorough evaluation, it was determined that optimal composting efficacy occurred at a mass ratio of SMS to PM equal to 6:4. Post-compost analysis revealed increases in nutrient content; specifically, germination index (GI) value reached 132.7%, while organic matter content attained 45.3%. Conversely, electrical conductivity (EC), C contents of water-soluble substances and humin (Cwss and CHu) decreased by approximately 11.8%, 73.4%, and 29.8% respectively; meanwhile, C contents of humic-extracted acid and HA (CHE and CHA), along with degree of polymerization (DP), increased by 17.3%, 20.3% and 9.9% respectively. The proposed co-compost formula not only facilitated simultaneous recycling of both SMS and PM waste but also transformed them into high-quality organic fertilizers suitable for soil enrichment-effectively addressing challenges faced by both edible fungi cultivation and livestock industries while augmenting organic fertilizer sources for Black land protection.

Exploring heart failure prevalence and overlooked dimensions: a comprehensive NT Pro-BNP study in high-risk primary care patients

Abstract Background Heart failure (HF) is a prevalent syndrome linked to significant morbidity and mortality, especially in those with comorbid conditions like diabetes, hypertension, and atherosclerotic cardiovascular disease. Early detection and intervention are crucial. Natriuretic peptides, particularly NT-proBNP, are promising biomarkers for identifying at-risk individuals, but their use in primary care settings is not fully understood. Methods This prospective cohort study enrolled 874 participants aged 40 and above with documented risk factors for HF, such as diabetes mellitus, hypertension, metabolic syndrome, obesity, atherosclerotic cardiovascular disease, genetic variants for cardiomyopathy, or exposure to cardiotoxic agents. NT-proBNP levels were measured in blood samples collected at primary care clinics, and participants underwent comprehensive cardiac evaluations at a referral hospital. Clinical characteristics, laboratory findings, electrocardiography, and echocardiography results were recorded. Results The study included 874 patients with at least one risk factor for HF, with a mean age of 62.5 ± 9.1 years and 51.9% female. According to the 2022 AHA/ACC/HFSA HF Guideline, 69.1% were Stage A HF, 21.9% Stage B HF, and 9% Stage C HF. Clinical characteristics, laboratory and echocardiographic findings are shown in Picture 1. The majority had hypertension (71.1%), while other prevalent comorbidities included diabetes mellitus (43.4%), dyslipidemia (35.2%), obesity (27.1%), and atherosclerotic cardiovascular disease (26.2%). Laboratory findings showed a significant decrease in HbA1C, eGFR, LDL, triglycerides, and hemoglobin from Stage A to C HF. Median NT-proBNP levels rose from Stage A to C HF (46.4 pg/mL in Stage A, 219 pg/mL in Stage B, and 268.5 pg/mL in stage C), with 84.8% of Stage B HF patients exhibiting elevated levels. NT-proBNP levels were correlated positively with left atrial volume index, left ventricular mass index, age, E/e’, and estimated systolic pulmonary artery pressure levels. Conversely, NT-proBNP levels showed a negative correlation with estimated glomerular filtration rate and left ventricular ejection fraction. Among Stage C patients, 92.4% had HFpEF. The Picture 2 depicted the study's design and outcomes, providing a comprehensive visual representation. Conclusion NT-proBNP screening in primary care settings identified a substantial proportion of individuals with undiagnosed HF, particularly those in Stage B and Stage C. The high prevalence of HFpEF underscores the need for comprehensive evaluation and management strategies targeting this population. Early detection facilitated by NT-proBNP screening may lead to timely interventions and improved outcomes in individuals at risk for HF.

Exploring Adiposity and Chronic Kidney Disease: Clinical Implications, Management Strategies, Prognostic Considerations.

Obesity poses a significant and growing risk factor for chronic kidney disease (CKD), requiring comprehensive evaluation and management strategies. This review explores the intricate relationship between obesity and CKD, emphasizing the diverse phenotypes of obesity, including sarcopenic obesity and metabolically healthy versus unhealthy obesity, and their differential impact on kidney function. We discuss the epidemiological evidence linking elevated body mass index (BMI) with CKD risk while also addressing the paradoxical survival benefits observed in obese CKD patients. Various measures of obesity, such as BMI, waist circumference, and visceral fat assessment, are evaluated in the context of CKD progression and outcomes. Mechanistic insights into how obesity promotes renal dysfunction through lipid metabolism, inflammation, and altered renal hemodynamics are elucidated, underscoring the role of adipokines and the renin-angiotensin-aldosterone system. Furthermore, the review examines current strategies for assessing kidney function in obese individuals, including the strengths and limitations of filtration markers and predictive equations. The management of obesity and associated comorbidities like arterial hypertension, type 2 diabetes mellitus, and non-alcoholic fatty liver disease in CKD patients is discussed. Finally, gaps in the current literature and future research directions aimed at optimizing the management of obesity-related CKD are highlighted, emphasizing the need for personalized therapeutic approaches to mitigate the growing burden of this intertwined epidemic.

Interpretable machine learning for allergic rhinitis prediction among preschool children in Urumqi, China.

This study aimed to investigate the advantages and applications of machine learning models in predicting the risk of allergic rhinitis (AR) in children aged 2-8, compared to traditional logistic regression. The study analyzed questionnaire data from 7131 children aged 2-8, which was randomly divided into training, validation, and testing sets in a ratio of 55:15:30, repeated 100 times. Predictor variables included parental allergy, medical history during the child's first year (cfy), and early life environmental factors. The time of first onset of AR was restricted to after the age of 1 year to establish a clear temporal relationship between the predictor variables and the outcome. Feature engineering utilized the chi-square test and the Boruta algorithm, refining the dataset for analysis. The construction utilized Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Tree (XGBoost) as the models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), and the optimal decision threshold was determined by weighing multiple metrics on the validation sets and reporting results on the testing set. Additionally, the strengths and limitations of the different models were comprehensively analyzed by stratifying gender, mode of birth, and age subgroups, as well as by varying the number of predictor variables. Furthermore, methods such as Shapley additive explanations (SHAP) and purity of node partition in Random Forest were employed to assess feature importance, along with exploring model stability through alterations in the number of features. In this study, 7131 children aged 2-8 were analyzed, with 524 (7.35%) diagnosed with AR, with an onset age ranging from 2 to 8 years. Optimal parameters were refined using the validation set, and a rigorous process of 100 random divisions and repeated training ensured robust evaluation of the models on the testing set. The model construction involved incorporating fourteen variables, including the history of allergy-related diseases during the child's first year, familial genetic factors, and early-life indoor environmental factors. The performance of LR, SVM, RF, and XGBoost on the unstratified data test set was 0.715 (standard deviation = 0.023), 0.723 (0.022), 0.747 (0.015), and 0.733 (0.019), respectively; the performance of each model was stable on the stratified data, and the RF performance was significantly better than that of LR (paired samples t-test: p < 0.001). Different techniques for evaluating the importance of features showed that the top5 variables were father or mother with AR, having older siblings, history of food allergy and father's educational level. Utilizing strategies like stratification and adjusting the number of features, this study constructed a random forest model that outperforms traditional logistic regression. Specifically designed to detect the occurrence of allergic rhinitis (AR) in children aged 2-8, the model incorporates parental allergic history and early life environmental factors. The selection of the optimal cut-off value was determined through a comprehensive evaluation strategy. Additionally, we identified the top 5 crucial features that greatly influence the model's performance. This study serves as a valuable reference for implementing machine learning-based AR prediction in pediatric populations.

Advancing Sustainable Cyber-Physical System Development with a Digital Twins and Language Engineering Approach: Smart Greenhouse Applications

In recent years, the integration of Internet of Things technologies in smart agriculture has become critical for sustainability and efficiency, to the extent that recent improvements have transformed greenhouse farming. This study investigated the complexity of IoT architecture in smart greenhouses by introducing a greenhouse language family (GreenH) that comprises three domain-specific languages designed to address various tasks in this domain. The purpose of this research was to streamline the creation, simulation, and monitoring of digital twins, an essential tool for optimizing greenhouse operations. A three-stage methodology was employed to develop the GreenH DSLs, a detailed metamodel for enhanced smart monitoring systems. Our approach used high-level metamodels and extended Backus–Naur form notation to define the DSL syntax and semantics. Through a comprehensive evaluation strategy and a selected language usability metrics, the expressiveness, consistency, readability, correctness, and scalability of the DSL were affirmed, and areas for usability improvement were highlighted. The findings suggest that GreenH languages hold significant potential for advancing digital twin modeling in smart agriculture. Future work should be aimed at refining usability and extending its application range. The anticipated integration with additional model-drive engineering and code generation tools will improve interoperability and contribute to digital transformation in the smart greenhouse domain and promote more sustainable food production systems.

Advancements and challenges of industrial‐level acidic CO2 electrolysis

AbstractThe use of green renewable energy to convert carbon dioxide (CO2) into valuable chemicals and fuels through CO2 electrolysis technology (also known as electrochemical CO2 reduction reaction, eCO2RR) is an advantageous technology, which could greatly aid the global carbon‐neutral goal. Although progress has been made in alkaline/neutral media, low carbon conversion efficiency to target products, carbonate/bicarbonate salt precipitation, and blockage of electrode holes caused by CO2 are not conducive to industrial applications. Acidic media could address these issues; however, in these conditions, there are other challenges that need to be addressed, such as hydrogen evolution, poor tolerance of electrocatalysts, and electrolysers. This review discusses recent advances in industrial‐level acidic CO2 electrolysis, including reaction mechanisms, electrocatalysts, and device design, aiming to promote its commercialization. In addition, a comprehensive evaluation strategy of an acidic eCO2RR system is proposed, and perspectives are provided based on related discussion.

Comprehensive evaluation and optimization strategy of solar-driven methanol steam reforming for hydrogen production

In the current era of green energy transformation and hydrogen utilization, there is a pervasive interest in thermochemical hydrogen production using solar energy. This study proposes an innovative strategy for the comprehensive evaluation and optimization of a novel solar-driven methanol steam reforming hydrogen production system. The solar-to-chemical conversion and utilization process were simulated via a comprehensive model, incorporating a kinetic model to calculate the reaction process within the photothermal reactor. Furthermore, an integrated evaluation system based on Taguchi's design, ANOVA, and gray relational analysis was constructed to optimize the objectives of methanol conversion, hydrogen yield, and CO selectivity. The results revealed that the catalyst weight to inlet methanol molar rate operating parameter significantly influenced system performance compared to the inlet temperature and steam-methanol ratio. The multi-objective optimized operating factors resulted in the highest methanol conversion (99.99 %), the highest hydrogen yield (3.196), and the lowest CO selectivity (1.71E-3). Additionally, a interaction factor analysis model is introduced to evaluate the interaction effects between each factor. The results indicate consistent optimal values and corresponding operating conditions across these methods for the system proposed in this study. This study offers valuable insights and guidance for the practical operation of solar-driven thermochemical hydrogen production reactions.

Evaluating emotional and subjective responses in synthetic art-related dialogues: A multi-stage framework with large language models

The appearance of Large Language Models (LLM) has implied a qualitative step forward in the performance of conversational agents, and even in the generation of creative texts. However, previous applications of these models in generating dialogues neglected the impact of ‘hallucinations’ in the context of generating synthetic dialogues, thus omitting this central aspect in their evaluations. For this reason, we propose an open-source and flexible framework called GenEvalGPT framework: a comprehensive multi-stage evaluation strategy utilizing diverse metrics. The objective is two-fold: first, the goal is to assess the extent to which synthetic dialogues between a chatbot and a human align with the specified commands, determining the successful creation of these dialogues based on the provided specifications; and second, to evaluate various aspects of emotional and subjective responses. Assuming that dialogues to be evaluated were synthetically produced from specific profiles, the first evaluation stage utilizes LLMs to reconstruct the original templates employed in dialogue creation. The success of this reconstruction is then assessed in a second stage using lexical and semantic objective metrics. On the other hand, crafting a chatbot’s behaviors demands careful consideration to encompass a diverse range of interactions it is meant to engage in. Synthetic dialogues play a pivotal role in this context, as they can be deliberately synthesized to emulate various behaviors. This is precisely the objective of the third stage: evaluating whether the generated dialogues adhere to the required aspects concerning emotional and subjective responses. To validate the capabilities of the proposed framework, we applied it to recognize whether the chatbot exhibited one of two distinct behaviors in the synthetically generated dialogues: being emotional and providing subjective responses, or remaining neutral. This evaluation will encompass traditional metrics and automatic metrics generated by the LLM. In our use case of art-related dialogues, our findings reveal that the capacity to recover templates or profiles is more effective for information or profile items that are objective and factual, in contrast to those related to mental states or subjective facts. For the emotional and subjective behavior assessment, rule-based metrics achieved a 79% of accuracy in detecting emotions or subjectivity (anthropic), and an 82% on the LLM automatic metrics. The combination of these metrics and stages could help to decide which of the generated dialogues should be maintained depending on the applied policy, which could vary from preserving between 57% to 93% of the initial dialogues.

Effectiveness of Sports-Based Intervention Programs for At-Risk Youth

Purpose: The general purpose of this study was to assess the effectiveness of sports- based intervention programs for at-risk youth. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the effectiveness of sports- based intervention programs for at-risk youth. Preliminary empirical review revealed that improvements in physical health, psychosocial well-being, academic achievement, and prosocial behavior among participants. Key conclusions underscored the importance of thoughtful program design, culturally tailored approaches, and comprehensive evaluation strategies to maximize effectiveness. Moreover, targeted interventions addressing diverse needs and experiences were found essential for inclusivity and equity within programs. While promising, further research is needed to explore long-term outcomes and contextual influences. Overall, these findings emphasize the significant impact of sports-based interventions in supporting the well-being and success of at-risk youth. Unique Contribution to Theory, Practice and Policy: The Social Learning Theory, Positive Youth Development and Ecological Systems Theory may be used to anchor future studies on the effectiveness of sports- based intervention programs for at-risk youth. The study yielded recommendations that contributed significantly to theory, practice, and policy. It advised researchers to delve deeper into the mechanisms through which sports participation influences youth well-being. Practitioners were urged to design participant-centered programs tailored to the diverse needs of at-risk youth, emphasizing safe and inclusive environments and mentorship. Policy implications highlighted the need for increased funding and integration of sports-based interventions into existing youth development initiatives. Collaboration among stakeholders was recommended to enhance program reach and effectiveness, while ongoing monitoring and evaluation were deemed essential for assessing program impacts. Advocacy efforts were also emphasized to raise awareness and mobilize support for youth development initiatives.

BILATERAL PULMONARY EMBOLISM MIMICKING ACUTE CHEST SYNDROME IN A PATIENT WITH SICKLE CELL DISEASE: ACASE REPORT AND REVIEW OF THE LITERATURE

Abstract This case report discusses a 24-year-old female with sickle cell disease (SCD) who developed bilateral pulmonary embolism (PE), mimicking acute chest syndrome (ACS) following a cholecystectomy. Despite initial treatment for ACS, the patient\\\'s condition deteriorated, leading to a diagnosis of massive bilateral PE via chest CT angiography. The report underscores the importance of considering PE in SCD patients with respiratory symptoms unresponsive to standard ACS treatment. It highlights the hypercoagulable state in SCD, which increases the risk of venous thromboembolism, including PE. The case emphasizes the necessity for heightened clinical suspicion and appropriate diagnostic measures to improve patient outcomes. Unfortunately, the patient succumbed to septic shock despite aggressive management. This report advocates for comprehensive evaluation strategies in similar clinical scenarios to prevent misdiagnosis and improve survival rates.

The safety and environmental impacts of battery storage systems in renewable energy

The integration of battery storage systems in renewable energy infrastructure has garnered significant attention due to its potential to enhance energy reliability, efficiency, and sustainability. However, alongside these benefits, concerns persist regarding the safety and environmental impacts associated with the deployment and operation of such systems. This review explores the multifaceted aspects of safety and environmental considerations in battery storage systems within the context of renewable energy. Firstly, safety concerns encompass a range of factors, including thermal runaway, fire hazards, and chemical leakage, which pose risks to both human life and property. Mitigation strategies such as advanced battery management systems and fire suppression technologies are critical for addressing these risks effectively. Secondly, environmental impacts arise throughout the lifecycle of battery storage systems, from raw material extraction to end-of-life disposal. Key issues include resource depletion, greenhouse gas emissions, and pollution from mining activities. Sustainable practices such as responsible sourcing of materials, recycling initiatives, and the development of second-life applications are essential for minimizing environmental footprints. Furthermore, the interaction between battery storage systems and renewable energy sources introduces complexities in assessing environmental impacts. While battery storage facilitates the integration of intermittent renewables like solar and wind by providing grid stabilization and energy storage capabilities, its environmental benefits may be compromised by factors such as energy-intensive manufacturing processes and reliance on non-renewable resources. The safety and environmental impacts of battery storage systems in renewable energy demand comprehensive evaluation and management strategies to maximize benefits while minimizing risks. Collaboration among stakeholders, technological innovation, and regulatory frameworks are crucial for promoting the sustainable deployment and operation of these systems in the transition towards a cleaner and more resilient energy future.

Exploring the impact of recent COVID-19 infection on perfusion and functional parameters derived from gated myocardial perfusion imaging in patients undergoing evaluation for coronary artery disease.

This study seeks to evaluate how recent COVID-19 infection affects myocardial perfusion and functional parameters derived from gated myocardial perfusion imaging in patients undergoing evaluation for coronary artery disease. The goal is to enhance our understanding of COVID-19's influence on the cardiovascular system. Conducted at Farshchian Heart Hospital from 2022 to 2023, this case-control study enrolled patients suspected of coronary artery disease, stratified into two groups: those with confirmed COVID-19 infection within the past 6months (study group) and those without prior COVID-19 infection (control group). Employing a 2-day protocol, stress testing and gated SPECT MPI were performed. Statistical analysis included descriptive statistics, Chi-square test, Student's t test, and Mann-Whitney U test. Among the 86 patients included, 43 were in each group. Significantly higher summed stress core and summed difference score values were observed in the study group compared to the control group (p < 0.05). In addition, the study group exhibited significantly altered global left ventricular ejection fraction, end-diastolic volume, and end-systolic volume (p < 0.05). Non-perfusion findings, including transient ischemic dilation and transient right ventricular visualization, were more prevalent in the study group. Recent COVID-19 infection is associated with impaired myocardial perfusion and altered functional parameters as detected by MPI. These findings underscore the intricate interplay between COVID-19 and cardiovascular health, emphasizing the importance of comprehensive evaluation and management strategies to address cardiac complications in affected individuals. Further research is warranted to elucidate the underlying mechanisms and optimize patient care in the context of COVID-19-associated cardiovascular manifestations.

Exploring the Multifaceted Landscape of Pediatric Obstructive Sleep Apnea: Insights into Prevalence, Severity, and Coexisting Conditions.

Pediatric obstructive sleep apnea (OSA) is a multifaceted disorder marked by recurrent upper airway obstruction during sleep, often coexisting with various medical conditions. This study, aimed to comprehensively analyze the Multifaceted Landscape of Pediatric Insights into Prevalence, Severity, and Coexisting Conditions. With a sample of 1928 participants, our study sought to determine the prevalence, severity, and associations between OSA and diverse conditions. Conducted retrospectively from February 2019 to April 2023, the study included pediatric patients. Data were collected through electronic health records, involving clinical assessments, medical histories, and diagnostic tests to establish OSA and coexisting condition diagnoses. Relationships between sleep parameters, apnea types, and severity indices were evaluated. High OSA prevalence was evident across age groups, with severity peaking between 3 to 12 years. Among the participants, coexisting conditions included allergic rhinitis (59.6%), tonsillar hypertrophy (49.7%), adenoid hypertrophy (28.4%), and obesity (15.3%). Analysis revealed intriguing relationships between different sleep parameters and apnea types. Notable associations were observed between Obstructive Apnea (OA) and Central Apnea (CA), and Mixed Apnea (MA) displayed associations with both OA and CA. Hypopnea correlated directly with the Apnea-Hypopnea Index (AHI), reflecting its role in OSA severity. This study provides a comprehensive understanding of the intricate dynamics between pediatric OSA and coexisting conditions. The prevalence of OSA and its coexistence with various conditions underscore the need for comprehensive evaluation and management strategies. By revealing associations between different sleep parameters and apnea types, the study emphasizes the complexity of OSA diagnosis and management. These findings hold the potential to enhance clinical approaches, ultimately leading to improved care and outcomes for affected children.

Managing a difficult airway due to supraglottic masses: successful videolaryngoscopic intubation after induction of general anesthesia.

While awake, flexible bronchoscopic intubation has long been considered the gold standard for managing anticipated difficult airways, the videolaryngoscope has emerged as a viable alternative. In addition, the decision to perform awake intubation or to proceed with airway management after induction of general anesthesia should be grounded in a comprehensive assessment of risks and benefits. A 41-year old female patient was scheduled for excision of bilateral, mobile, and pedunculated masses on both aryepiglottic folds, which covered almost the entire upper part of the glottis. We conducted a comprehensive evaluation of the patient's signs and symptoms, which included neither stridor nor dyspnea in any position, along with the otolaryngologist's opinion and the findings from the laryngeal fiberscopic examination. Given the potential challenges and risks associated with awake flexible bronchoscopic intubation for this patient, we decided to proceed with gentle tracheal intubation using a videolaryngoscope under general anesthesia. In case of failed mask ventilation and tracheal intubation, we had preplanned strategies, including awakening the patient or performing an emergent tracheostomy, along with preparations to support these strategies. Ensuring that mask ventilation was maintained with ease, the patient was sequentially administered intravenous propofol, remifentanil, and rocuronium. Under sufficient depth of anesthesia, intubation using a videolaryngoscope was successfully performed without any complications. Videolaryngoscopic intubation after induction of general anesthesia can be a feasible alternative for managing difficult airways in patients with supraglottic masses. This approachshould be based on a comprehensive preoperative evaluation, adequate preparation, and preplanned strategies to address potential challenges, such as inadequate oxygenation and unsuccessful tracheal intubation.

Research on Inconsistency Evaluation of Retired Battery Systems in Real-World Vehicles

Inconsistency is a key factor triggering safety problems in battery packs. The inconsistency evaluation of retired batteries is of great significance to ensure the safe and stable operation of batteries during subsequent gradual use. This paper summaries the commonly used diagnostic methods for battery inconsistency assessment. The local outlier factor (LOF) algorithm and the improved Shannon entropy (ImEn) algorithm are selected for validation based on the individual voltage data from real-world vehicles. Then, a comprehensive inconsistency evaluation strategy for retired batteries with many levels and indicators is established based on the three parameters of LOF, ImEn, and cell voltage range. Finally, the evaluation strategy is validated using two real-world vehicle samples of retired batteries. The results show that the proposed method can achieve the inconsistency evaluation of retired batteries quickly and effectively.

Advancing botanical safety: A strategy for selecting, sourcing, and characterizing botanicals for developing toxicological tools

Increases in botanical use, encompassing herbal medicines and dietary supplements, have underlined a critical need for an advancement in safety assessment methodologies. However, botanicals present unique challenges for safety assessment due to their complex and variable composition arising from diverse growing conditions, processing methods, and plant varieties. Historically, botanicals have been largely evaluated based on their history of use information, based primarily on traditional use or dietary history. However, this presumption lacks comprehensive toxicological evaluation, demanding innovative and consistent assessment strategies. To address these challenges, the Botanical Safety Consortium (BSC) was formed as an international, cross-sector forum of experts to identify fit-for purpose assays that can be used to evaluate botanical safety. This global effort aims to assess botanical safety assessment methodologies, merging traditional knowledge with modern in vitro and in silico assays. The ultimate goal is to champion the development of toxicity tools for botanicals. This manuscript highlights: 1) BSC's strategy for botanical selection, sourcing, and preparation of extracts to be used in in vitro assays, and 2) the approach utilized to characterize botanical extracts, using green tea and Asian ginseng as examples, to build confidence for use in biological assays.

A comprehensive strategy for quality evaluation of Changan powder by fingerprinting combined with rapid qualitative and quantitative multi-ingredients profiling.

Changan powder (CAP) is mainly used to treat various intestinal diseases. Few studies on CAP have been reported; therefore, it is necessary to clarify the material basis of CAP to lay the foundation for further elucidating its functional mechanism and support the rational use of drugs. In the present study, we aimed to propose a methodology for the quality control of CAP based on qualitative and quantitative analysis of its components. An ultra-performance liquid chromatography coupled with Fourier transform ion cyclotron resonance mass spectrometry (UPLC-FT-ICR-MS) method was developed to identify chemical components in CAP. In addition, fingerprints of 10 different batches of CAP were established, and quantitative analysis based on UPLC was performed to analyze the quality of CAP. A total of 58 compounds were preliminarily characterized. The similarity of 10 batches of CAP was greater than 0.995, and 23 common peaks were calibrated. Investigation of the quantitative analytical methodology showed that the four components had good linear relationships within their respective concentration ranges (r2 ≥ 0.9992), and the relative standard deviation (RSD) of precision and stability was less than 2%. The RSD of sample recovery ranged from 0.78% to 1.52%. The established method can quickly and effectively identify the chemical components of CAP and accurately quantify the known components in CAP. The established fingerprinting and content determination method is stable, reliable, and easy to operate and can be applied in quality control and in vivo research on CAP.

A comprehensive quality evaluation strategy of Shensong Yangxin capsules based on qualitative, fingerprint and quantitative analyses.

Shensong Yangxin capsule (SSYXC), an effective Chinese patent medicine, has been recorded in the Chinese Pharmacopeia, mainly for the treatment of coronary heart disease and ventricular premature beat. To further complete the quality evaluation of SSYXC, a comprehensive analysis strategy was established. Firstly, the components of SSYXC were qualitatively analysed using ultra-high- performance liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry. A total of 134 compounds were identified or tentatively characterized. Additionally, the fingerprint of SSYXC was established by HPLC, and the similarity of 10 batches of SSYXC was elucidated by similarity analysis. The result indicated that the consistency of chemical composition is good. Finally, to enhance the quality control of SSYXC, according to the results of the fingerprint analysis, the contents of the seven active components was determined, comprising morroniside, loganin, paeoniflorin, salvianolic acid B, palmatine hydrochloride, berberine hydrochloride and tanshinone IIA. In conclusion, the established method, comprising identification of components, fingerprint analysis and quantification of multicomponents, can be sensitively and comprehensively applied to the quality evaluation of SSYXC, which can provide chemical ingredients bases for quality control and the pharmacodynamic mechanism of SSYXC, which could serve as a benchmark for controlling the quality of other Chinese patent medicines.

Strategies for the quality control of Chrysanthemi Flos: Rapid quantification and end-to-end fingerprint conversion based on FT-NIR spectroscopy.

Chrysanthemi Flos (CF) is widely used as a natural medicine or tea. Due to its diverse cultivation regions, CF exhibits varying quality. Therefore, the quality and swiftness in evaluation holds paramount significance for CF. The aim of the study was to construct a comprehensive evaluation strategy for assessing CF quality using HPLC, near-infrared (NIR) spectroscopy, and chemometrics, which included the rapid quantification analyses of chemical components and the Fourier transform (FT)-NIR to HPLC conversion of fingerprints. A total of 145 CF samples were utilised for data collection via NIR spectroscopy and HPLC. The partial least squares regression (PLSR) models were optimised using various spectral preprocessing and variable selection methods to predict the chemical composition content in CF. Both direct standardisation (DS) and PLSR algorithms were employed to establish the fingerprint conversion model from the FT-NIR spectrum to HPLC, and the model's performance was assessed through similarity and cluster analysis. The optimised PLSR quantitative models can effectively predict the content of eight chemical components in CF. Both DS and PLSR algorithms achieve the calibration conversion of CF fingerprints from FT-NIR to HPLC, and the predicted and measured HPLC fingerprints are highly similar. Notably, the best model relies on CF powder FT-NIR spectra and DS algorithm [root mean square error of prediction (RMSEP) = 2.7590, R2 = 0.8558]. A high average similarity (0.9184) prevails between predicted and measured fingerprints of test set samples, and the results of the clustering analysis exhibit a high level of consistency. This comprehensive strategy provides a novel and dependable approach for the rapid quality evaluation of CF.

Identification of the Bioactive Compounds of Raw and Honey-Processed Farfarae Flos by High-Performance Liquid Chromatography-Mass Spectrometry (HPLC-MS) with the Spectrum-Effect Relationship and Chemometrics

Farfarae Flos (FF), also known as “Kuandonghua” in Chinese, is a commonly used traditional Chinese medicine (TCM) with various therapeutic properties, including antitussive, expectorant, antioxidant, and anti-inflammatory effects. Despite the widespread consumption of processed FF, there has been limited progress in improving quality control measures for both raw FF and honey-processed Farfarae Flos (PFF). This study aimed to establish a comprehensive strategy for quality evaluation and distinguish between FF and PFF by integrating high-performance liquid chromatography-mass spectrometry (HPLC-MS) analysis with chemometric techniques. Additionally, the antioxidant activities of FF and PFF were assessed using radical-scavenging experiments. The spectrum-effect relationship is the association of chemical composition information of the fingerprint with antioxidant activity, which was used to predict the primary active components that influence the antioxidant activity of FF. The results indicated that FF and PFF samples showed a clear distinction, and the most potent pharmacologically active ingredients were hyperoside, quercetin, caffeic acid, isoquercitrin, neochlorogenic acid, rutin, cryptochlorogenic acid, and isochlorogenic acid C. Moreover, the comprehensive efficacy of the honey-processed FF was better than for raw FF. In conclusion, the findings of this study provide a scientific foundation for future evaluation of the quality, further research, and practical use of FF herbs.