Quality Of System Research Articles

Association of seminal plasma zinc levels with human semen quality and its toxic effects on sperm motility

Infertility has become one of the most common chronic diseases among reproductive- aged individuals, and male factors account for about 50 %. Zinc is a trace element that is essential for sperm quality and male reproductive system. Although several current studies with small sample sizes have investigated this relationship, the conclusions have been still controversial. Here, using a large population sample involved 25,915 participants from Changzhou Maternity and Child Health Care Hospital, we revealed an inverted “U”-shaped trend between seminal plasma zinc concentrations and sperm motility PR/PR + NP (PR, progressive motility; NP, non-progressive motility). The results showed that the highest values of sperm PR/PR + NP observed in the group with intermediate concentrations of zinc (Group 2, 0.25–2.11 mmol/L). The mean values were 43.17 ± 19.03 % and 56.64 ± 20.28 %, respectively. And the lowest values came out in the highest zinc levels group (Group 4, > 3.04 mmol/L). In vitro cell experiments also showed that zinc caused dose-dependent cytotoxicity for GC-2 cells at a threshold value. Furthermore, RNA-seq analysis demonstrated that high concentrations of zinc exerted toxic effects on GC-2 cells through immune injury. Taken together, our findings suggested that moderate amounts of zinc are crucial for human reproduction and excessive concentrations may have adverse effects on male fertility.

Exploring the Impact of Mobile App Quality on consumers' Online Intention to Transact

ABSTRACTThis study constructed a research framework for exploring the relationship between the three dimensions of app quality (system quality, information quality, and service quality), flow experience, brand trust, and intention to transact based on the business‐to‐consumer e‐commerce platform. The proposed model was tested by analyzing questionnaire survey data from a sample of 503 consumers with experience of mobile online shopping using the variance‐based structural equation modeling technique. The results showed that all three dimensions of app quality were positively associated with flow experience and brand trust. Flow experience was positively associated with intention to transact. The effect of brand trust on consumers' intention to transact was greater than that of flow experience, suggesting that when shopping on a mobile app platform, consumers may be more concerned about trusting the brand than about pleasure in making a purchase decision. The results enrich business‐to‐consumer e‐commerce theory and provide meaningful managerial implications.

Flow cytometry assay modifications: Recommendations for method validation based on CLSI H62 guidelines.

The Clinical and Laboratory Standards Institute (CLSI) H62-Validation of Assays Performed by Flow Cytometry guideline, released in 2021, provides recommendations for platform workflow and quality system essentials, instrument setup and standardization, assay development and optimization and fit-for-purpose analytical method validation. In addition, CLSI H62 includes some recommendations for the validation strategies after a validated flow cytometric method has been modified. This manuscript builds on those recommendations and discusses the impact of different types of assay modifications on assay performance. Recommendations regarding which validation parameters to evaluate depending on the type of modification are provided. The impact of assay modification on the assay's intended use is discussed. When recommending minor deviations from the CLSI H62 process for a laboratory-initiated assay revision (e.g., specimen numbers for sensitivity, specificity, or precision studies), a rationale based on expert opinion is provided with the understanding that not every laboratory, assay type, and circumstance can be comprehensively addressed in this paper. These recommendations are meant as a practical recommendation and are not intended to be restrictive, prescriptive, or understood as necessarily sufficient to meet every specific requirement from regulatory bodies (e.g., FDA or New York State Department of Health).

Pharmaceutical Product Development: Quality by Design (QbD)

A popular modern strategy in pharmaceutical development is called "quality by design," or QBD, which started with established goals including the product's safety and efficacy. Safety and product efficacy are the primary goals of QBD in pharmaceutical development. With QBD, the design itself is emphasized for excellence. QBD was introduced by the Food and Drug Administration in order to comprehend the production process. An overview of the pharmaceutical quality system, quality risk management, and international council for harmonization (ICH) rules for product development is provided in this article in order to preserve product quality. The scientific foundation of QBD, regulatory industry perspectives, and QBD technologies were briefly discussed. There was a focus on excipients, manufacturing process development, container closure systems, and pharmacological substances. The Q9 guidelines for quality risk management, Q10 guidelines for pharmaceutical quality systems, and Q8 guidelines for pharmaceutical development form its basis. In the research and production of pharmaceuticals, it also provides an application of Quality by Design.

Evaluation of a data-driven youngstock rearing quality system in Dutch dairy herds (2019–2022)

In recent years, the Netherlands exhibited an increased focus on improving youngstock rearing. Several data-driven tools were developed to assess and benchmark calf rearing practices based on key indicators, stimulate farmers to improve their youngstock rearing and reduce calf mortality. KalfOK is such a data-driven tool in which a farm-level overview with 15 indicators of youngstock rearing quality is communicated to participating farmers. In this study we assessed which farm characteristics are associated with A) a continuously high or low KalfOK score and B) frequent usage of KalfOK. Data from KalfOK and the identification and registration system were available for all participating herds over a period between 2019 and 2022 (Dataset A). Additionally, a sample of 324 dairy farmers, randomly selected from the entire Dutch dairy population (Dataset B), participated in a voluntary survey. Multivariable (logistic) regression models were used to A) identify differences in characteristics between farms with a continuously high- and farms with a continuously low KalfOK score and B) assess which farm characteristics were associated with frequent use of the KalfOK report. The results of analysis A showed multiple factors associated with having a low or high KalfOK score, including replacement rate, closed farming system, limited growth in herd size and the region where a farm was located. Sociological factors might also partly explain the difference between high and low scoring farms. Analysis B showed that approximately half of the surveyed farmers indicated that they do not frequently use their KalfOK overview in their calf rearing management. The KalfOK score of farmers who regularly use their KalfOK report was higher – and consequently better - compared to the KalfOK score of farmers who sometimes or never use KalfOK. Additionally, farmers using a milk robot and those discussing KalfOK regularly with their veterinarian, used KalfOK more often. Thirty four percent of the surveyed farmers made adjustments to their youngstock rearing management since the start of participation. The study’s insights into patterns in KalfOK utilization and associations with farm characteristics provide valuable information for ongoing efforts to enhance calf rearing practices via the use of KalfOK. It is important to consider what is needed to convince farmers that do not use the KalfOK tool to frequently start using their KalfOK report. Future research incorporating sociological aspects can provide a more comprehensive understanding of other dynamics influencing calf rearing and contribute valuable insights for enhancing the effectiveness of programs like KalfOK in promoting calf health and welfare.

Achieving Excellence in Cloud-Based E-Learning Systems: Exploring Quality Factors and Benefits

Purpose: This study examines the effectiveness and advantages of cloud-based e-learning platforms in Kuwait, integrating concepts from Information System Success Model (ISSM) and Technology Acceptance Model (TAM). It investigates the impact of system quality (SQ), service quality (SRQ), and information quality (IQ) in cloud computing (CC) on user satisfaction (SAT), perceived ease of use (PEOU), and perceived usefulness (PU). Furthermore, it examines how PEOU and usefulness affect SAT and the role of SAT in mediating benefits. Study design/methodology/approach: Survey data were used to analyze relationships in 15 hypotheses using SmartPLS structural equation modelling. Sample and data: The empirical data were gathered from a sample of 221 college students. Results: The analysis revealed that several hypotheses were rejected, including SRQ on SAT, PU, and PEOU, which were found to be insignificant. Similarly, IQ did not exhibit a significant effect on SAT and PU, yet it did have an impact on PEOU. Consequently, it was found that the SAT only mediates the relationship between SQ and benefits. The study further explores potential explanations for these insignificant findings. Originality/value: Despite limited research on cloud computing learning platforms, this study sheds light on the intricate dynamics among these variables and the overall benefits of such platforms in Kuwait.Research limitations/implications: The unexpected insignificant relationships, such as SRQ, suggest practical implications, including the need to re-evaluate the providers’ approaches to customer service. Using services that are more tailored to specific contexts may be beneficial. The timing and context of the study could limit its generalizability.

REAL TIME MONITORING OF AIR POLLUTION USING ARTIFICIAL INTELLIGENCE BASED IOT AND NOVEL SARIMA TECHNIQUE IN SIVAKASI - INDIA

<p style="text-align:justify; margin-bottom:13px"><span style="font-size:11pt"><span style="line-height:200%"><span style="font-family:Calibri,sans-serif"><span style="font-size:12.0pt"><span style="line-height:200%"><span style="font-family:"Times New Roman",serif">Air pollution, a harmful or excessive quantity of pollutants from natural sources and human activities, poses risks to human health, the environment, and ecosystems. AI breakthroughs have allowed for the incorporation of technologies into performance indices, resulting in the development of an AI-based air quality system that evaluates water quality in real time using WHO-defined parameters. This article describes the implementation and planning of AI-based IoT for air pollution tracking and forecasting utilizing AI methodologies, as well as a dashboard on the internet for real-time tracking of air pollutants via Google Cloud servers. Air pollutants such as NO<sub>2</sub>, NO<sub>x</sub>, NH<sub>3</sub>, CO, SO<sub>2</sub>, and O<sub>3</sub> are gathered from IoT sensor nodes in Sivakasi, Tamil Nadu, India, utilizing artificial intelligence algorithms. Individual pollutants are forecasted using time series modeling approaches such as Artificial Neural Network (ANN), Naive Bayes Model, k-nearest neighbour (k-NN), Support Vector Machine (SVM), and Seasonal Autoregressive Interated Moving Average (SARIMA). The data from the IoT sensor node is utilized to train the model, resulting in optimal parameters. The derived model parameters are validated using new, previously unknown data for time. The performances of several Time Series models are examined using performance metrics such as Mean Absolute Error (MAE), coefficient of determination (R<sup>2</sup>), and Root Mean Square Error (RMSE). An AI-based algorithm has been flashed in the Raspberry Pi 3. The present air pollution data and anticipated data are monitored throughout a 7days from 10 p.m. to 4 a.m. using a digital dashboard built in an open-source using Google cloud services. Finally comparing to all above AI based algorithms, SARIMA performed well and h+ad a 95% accuracy level. </span></span></span></span></span></span></p>

AI support for colonoscopy quality control using CNN and transformer architectures

BackgroundConstruct deep learning models for colonoscopy quality control using different architectures and explore their decision-making mechanisms.MethodsA total of 4,189 colonoscopy images were collected from two medical centers, covering different levels of bowel cleanliness, the presence of polyps, and the cecum. Using these data, eight pre-trained models based on CNN and Transformer architectures underwent transfer learning and fine-tuning. The models’ performance was evaluated using metrics such as AUC, Precision, and F1 score. Perceptual hash functions were employed to detect image changes, enabling real-time monitoring of colonoscopy withdrawal speed. Model interpretability was analyzed using techniques such as Grad-CAM and SHAP. Finally, the best-performing model was converted to ONNX format and deployed on device terminals.ResultsThe EfficientNetB2 model outperformed other architectures on the validation set, achieving an accuracy of 0.992. It surpassed models based on other CNN and Transformer architectures. The model’s precision, recall, and F1 score were 0.991, 0.989, and 0.990, respectively. On the test set, the EfficientNetB2 model achieved an average AUC of 0.996, with a precision of 0.948 and a recall of 0.952. Interpretability analysis showed the specific image regions the model used for decision-making. The model was converted to ONNX format and deployed on device terminals, achieving an average inference speed of over 60 frames per second.ConclusionsThe AI-assisted quality system, based on the EfficientNetB2 model, integrates four key quality control indicators for colonoscopy. This integration enables medical institutions to comprehensively manage and enhance these indicators using a single model, showcasing promising potential for clinical applications.

Interpretable prediction, classification and regulation of water quality: A case study of Poyang Lake, China

Effective identification and regulation of water quality impact factors is essential for water resource management and environmental protection. However, the complex coupling of water quality systems poses a significant challenge to this task. This study proposes coherent model for water quality prediction, classification and regulation based on interpretable machine learning. The decomposition-reconstruction module is used to transform non-stationary water quality series into stationary series while effectively reducing the feature dimensions. Spatiotemporal multi-source data is introduced by using the Maximum Information Coefficient (MIC) for feature selection. The Temporal Convolutional Network (TCN) is used to extract the temporal features of different variables, followed by the introduction of External Attention mechanism (EA) to construct the relationship between these features. Finally, the target water quality sequence is simulated using Gated Recurrent Unit (GRU). The proposed model was applied to Poyang Lake in China to predict six water quality indicators: ammonia nitrogen (NH3-N), dissolved oxygen (DO), pH, total nitrogen (TN), total phosphorus (TP), water temperature (WT). The water quality was then classified based on the prediction results using the XGBoost algorithm. The findings indicate that the proposed model's Nash-Sutcliff Efficiency (NSE) value ranges from 0.88 to 0.99, surpassing that of the benchmark model, and demonstrates strong interval prediction performance. The results highlight the superior performance of the XGBoost algorithm (with an accuracy of 0.89) in addressing water quality classification issues, particularly in cases of category imbalance. Subsequently, interpretability analysis using the SHapley Additive exPlanation (SHAP) method revealed that the model is capable of learning relationships between different variables and there exists a possibility of learning the physical laws. Ultimately, this study proposes a water quality regulation mechanism that improves TN and DO levels by stepwise changing the magnitude of water temperature, which significantly improves in the case of data limitations. In conclusion, this study presents an overall framework for integrating water quality prediction, classification and improvement for the first time, forming a complete set of water quality early warning and improvement management strategies. This framework provides new ideas and ways for lake water quality management.

Effects of Virtual Reality (VR)-Based Meditation on Sleep Quality, Stress, and Autonomic Nervous System Balance in Nursing Students.

This study aimed to determine the effects of virtual reality (VR)-based meditation on the sleep quality, stress, and autonomic nervous system balance of nursing students. Nursing students were categorized into experimental groups I (VR-based meditation) and II (concentration meditation) and a control group. Before the study's initiation, we measured the participants' general characteristics, and a sleep measurement device was used to measure sleep quality. Stress levels and heart rate variability were measured before and after study completion. On the first day, all three groups slept without any intervention. On days 2-6, only experimental groups I and II implemented the intervention before sleeping. We found that the subjective sleep quality, wakefulness after sleep onset, sleep efficiency, deep sleep quality, subjective stress, objective stress, and autonomic nervous system balance of the VR meditation group were significantly better than those of the other groups. Our results reveal that the participants who underwent VR-based meditation experienced better sleep quality, lower stress levels, and improved autonomic nervous system balance compared with those in the concentration meditation and control groups. Thus, VR-based meditation effectively enhances sleep quality, lowers stress levels, and improves autonomic nervous system balance in nursing students.

“Moving beyond silos”: focus groups to understand the impact of an adapted project ECHO model for a multidisciplinary statewide forum of substance use disorder care leaders

BackgroundAlthough clinical substance use disorder (SUD) care is multidisciplinary there are few opportunities to collaborate for quality improvement or systems change. In Oregon, the Project ECHO (Extension for Community Healthcare Outcomes) model was adapted to create a novel multidisciplinary SUD Leadership ECHO. The objective of this study was to understand the unique effects of the adapted ECHO model, determine if the SUD Leadership ECHO could promote systems change, and identify elements that enabled participant-leaders to make changes.MethodsFour focus groups were conducted between August and September of 2022 with a purposive sample of participants from the second cohort of the Oregon ECHO Network’s SUD Leadership ECHO that ran January to June 2022. Focus group domains addressed the benefits of the adapted ECHO model, whether and why participants were able to make systems change following participation in the ECHO, and recommendations for improvement. Thematic analysis developed emergent themes.Results16 of the 53 ECHO participants participated in the focus groups. We found that the SUD Leadership ECHO built a multi-disciplinary community of practice among leaders and reduced isolation and burnout. Three participants reported making organizational changes following participation in the ECHO. Those who successfully made changes heard best practices and how other organizations approached problems. Barriers to initiating practice and policy changes included lack of formal leadership authority, time constraints, and higher-level systemic issues. Participants desired for future iterations of the ECHO more focused presentations on a singular topic, and asked for a greater focus on solutions, advocacy, and next steps.ConclusionsThe adapted ECHO model was well received by focus group participants, with mixed reports on whether participation equipped them to initiate organizational or policy changes. Our findings suggest that the SUD Leadership ECHO model, with fine-tuning, is a promising avenue to support SUD leaders in promoting systems change and reducing isolation among SUD leaders.

The Camp fire and perinatal health: An example of the generalized synthetic control method to identify susceptible windows of exposure.

The November 2018 Camp fire was the most destructive wildfire in California history, but its effects on reproductive health are not known. We linked California birth records from 2017-2019 to daily smoke levels using U.S. EPA Air Quality System (AQS) PM2.5 data and NOAA Hazard Mapping System smoke plume polygons during the Camp fire. In the main analysis, pregnancies were considered exposed if they had median AQS PM2.5 levels above 50 μg/m3 for at least 7 days during November 8-22, 2018. We calculated rates of preterm birth and the infant sex ratio based on week of conception and used the generalized synthetic control method to estimate the average treatment effect on the treated and to propose a novel approach to identify potential critical weeks of exposure during pregnancy. We found associations between Camp fire-related smoke exposure and rates of preterm birth, with a risk difference (RD) = 0.005, 95% CI 0.001, 0.010. Exposure during week 10 of pregnancy was consistently associated with increased preterm birth (RD = 0.030, 95% CI 0.004, 0.056). We did not observe differences in the infant sex ratio. Camp fire smoke exposure was associated with increased rates of preterm birth, with sensitive windows in the first trimester.

The Influence of Car Rental Product Quality, Prices, and Complaint Handling System on Decisions to Use Car Rental Services at Botana Transport Rent Car & Tour Travel

The aim of this research is to determine the influence of product service quality, price, rental car complaint handling system on the decision to use car rental services at Botana Transport Rent Cars & Tours travel. This research was conducted at Botana Transport Rent Cars & Tours travel located on Jl. Raya Kemang, Cilegon City. The sample in this study consisted of 100 respondents using Accidental Sampling. The data analysis used is multiple linear regression. The results of the research are that product quality and the complaint handling system have a positive influence, while price has a negative influence on the decision to use a rental car at Botana Transport Rent. Cars & Tours travel. Simultaneously or at the same time, product quality, price and car rental handling system at Botana Transport Rent Cars & Tours travel. Meanwhile, the R Square value explains the decision to use car rental services at Botana Transport Rent Cars & Tours Travel is influenced by product quality, price and complaint handling system by 44.2% while the remaining 55.8% is influenced by other variables not used in this research.

THE ANALYSIS OF STRATEGIC BUSINESS ABOUT RANGERS FRIED CHICKEN IN TAIPEI, TAIWAN

This study uses SWOT analysis and Porter's five forces model to analyze the strategic environment of 德州美墨炸雞 Rangers Fried Chicken (RFC) in Taipei, Taiwan. The study aims to evaluate the internal and external factors of RFC and provide insights into RFC's competitiveness in the Taiwanese fast food market. The results not only highlight RFC's strengths such as strong brand, product quality and service system, but also significant challenges such as intense competition and changing consumer preferences. The study provides strategic recommendations to enhance RFC's competitive advantage and ensure long-term sustainability.

External quality assurance experience with Royal College of Pathologists of Australasia Program at an academic hospital in South Africa.

Laboratories use their performance in external quality assurance (EQA) to establish quality planning strategies and to assess whether testing processes require improvement. The EQA performance of the hematology and coagulation test parameters on the Royal College of Pathologists of Australasia EQA program was evaluated over a 4-year cycle at an academic hospital in Johannesburg, South Africa. The test performance was determined from analytical quality specification (APS) and/or z-scores. Bias and imprecision were used to calculate sigma (σ) metric scores. Specifications from European Federation of Laboratory Medicine and/or biological variation were applied. The laboratory achieved a mean testing score of 98.7±4.0%. There were 103 (10.7%) unacceptable results. On investigation, root causes included: presurvey issues (83%), transcription errors (9%), random errors (6%), and test performance errors (3%). All test parameters evaluated achieved an acceptable median APS during the study period. The mean z-scores, however, were >2 and unacceptable for mean cell hemoglobin concentration and hematocrit. On investigation, this was attributed to significant delay in transport and storage of full blood count samples. White cell count and d-dimer achieved a σ ≥ 6. EQA participation assisted the laboratory in maintaining a quality system. Close monitoring is necessary for international laboratories to avoid sample delays that can affect result quality.

Vendors And Multiple Virtual Stores On Cooperative ECommerce

The Vendors and Multiple Virtual Stores on Cooperative E-Commerce is integrated with the Cooperative Loan Servicing System, Stakeholders Management System, and Events Venue and Hostel Management System. The lack of provisions to offer other services on the side of the customers or buyers can result in less income, resulting in poor scalability of business growth for the grocery store. The researcher gathered data through a series of interviews and by providing questionnaires to the employees and members. Results showed a positive response from employees and members, with strongly Agreed-upon results regarding the system's System Usefulness, Information Quality, and Interface Quality. Using the Vendors and Multiple Virtual Stores on Cooperative ECommerce brought extra income to the Cooperative, which resulted in higher profit. At the same time, it provides convenience to customers by offering other services besides the existing old service, which can result in more customers and lead to higher profits and incomes. Background: A cooperative is an organization run by its members who share the benefits and profits, with productivity enhanced by integrating systems like E-commerce Multi-Store, Hostel Management, Stakeholders Management, and Loan Servicing, along with a Forecasting Model for sales trends. The Internet's evolution has transformed shopping styles, making online shopping increasingly popular and allowing consumers to shop without visiting physical stores. The Vendors and Multiple Virtual Stores on Cooperative E-Commerce aims to address operational issues, expand service reach, and generate additional income for the cooperative and its members by integrating the system into operations. Materials and Methods: The study presents an operational framework encompassing input, process, output, and outcome and emphasizes the use of the Software Development Life Cycle (SDLC) with a focus on prototyping for user feedback and refinement. Data from cooperative members and employees were gathered through interviews and questionnaires, leading to developing, testing, and validating modules for a Cooperative E-Commerce system. Results: The study involved 50 respondents, including 20 cooperative employees and 30 members. The evaluation of system usability through the Post-Study System Usability Questionnaire (PSSUQ) revealed that respondents strongly agreed on the system's usefulness (mean score: 1.29), information quality (mean score: 1.34), and interface quality (mean score: 1.28). High scores indicate that the system is easy to use, provides clear and helpful information, and has a user-friendly interface, contributing to efficient decision-making and competitive advantage for the organization. Conclusion: The integration of Vendors and Multiple Virtual Stores on the Cooperative E-Commerce platform significantly enhances cooperatives' operational efficiency, market reach, and overall business strategy, with respondents rating the system highly for usefulness, information quality, and interface quality

Determination of the Reliability of the Automated System for Organoleptic Water Quality

Determination of the Reliability of the Automated System for Organoleptic Water Quality

The Role of Quality Assurance in Clinical Trials: Safeguarding Data Integrity and Compliance.

Clinical trials, which investigate the effects of drugs in humans, aim to determine safety and efficacy while identifying adverse reactions. Data consistency and subject safety are crucial factors that determine the quality of clinical trials, necessitating overall quality management. There is a growing emphasis on implementing quality systems during the planning stages of clinical trials. Regulatory frameworks have evolved to ensure patient protection and data reliability, underscoring the need for systematic quality management in health research. A clinical trial quality management plan (CTQMP) is essential to describe the tools and methods used to ensure study quality. Globalization has led to an increase in conducting clinical trials in developing nations, presenting challenges due to procedural and ethical disparities. To manage these complexities, outsourcing trial management has become common. Adherence to good clinical practice (GCP) principles, as defined by the International Conference on Harmonization (ICH), is critical for safeguarding participant rights and ensuring credible data. Quality by design (QbD) and quality risk management are now central to clinical trial management, as advocated by the FDA. Technological advancements and robust protocols further enhance trial processes. Effective QA activities, including monitoring and data management, are vital for maintaining compliance, participant safety, and data integrity, highlighting the indispensable role of QA in clinical trial success.

The relationship between psychiatric inpatient length of stay and suicide prevention

Suicide prevention remains a critical concern within mental health care, particularly for individuals with psychiatric disorders. This review examines the relationship between the length of inpatient psychiatric admission and its effectiveness in preventing suicide. The study analyzes existing literature on admission duration, in-hospital and post-discharge suicide rates, and factors influencing these outcomes such as treatment quality, patient characteristics, and post-discharge support systems. While some evidence suggests longer stays may be beneficial for certain diagnostic groups, the quality and intensity of care during admission and robust post-discharge support appear equally crucial. The review highlights the importance of individualized care plans, comprehensive suicide prevention programs, and strong transitional support. It also discusses international perspectives, ethical considerations, and special populations. The paper concludes that an effective approach to suicide prevention should focus on tailored admission lengths, high-quality care, and seamless transitions to community support, rather than solely on extending lengths of stay.

Thoughts on Evaluation of Culture Medium for Human Assisted Reproduction Technology

As the direct microenvironment of assisted reproductive technology, the evaluation of culture medium for human assisted reproduction technology is particularly important. This paper discussed the classification management, technical review points, clinical evaluation and review thinking, focusing on the basic requirements, raw material control, biological evaluation, sterilization process of culture medium for human assisted reproduction technology, combined with some material limit, new added material and quality system control to thoroughly assess management risk of the whole life cycle of culture medium for human assisted reproduction technology.