In January 2024, the International Council for Standardization in Hematology (ICSH) released the "ICSH guidance for internal quality control policy for hematology analyzers". Based on current practices and requirements for internal quality control in different countries, as well as the diversity of laboratory practice and recommendations from commercial internal quality control providers, the guidance provides recommendations on quality control methods, sources and concentration levels of control materials, quality control frequency, and statistical decision rules. This article interprets the guidance and in line with the requirements of China's industry standard WS/T 641-2018, focuses on the selection of control materials and concentration levels, quality control frequency, and statistical interpretation rules for hematology analyzers, aiming to help medical laboratories and manufacturers better understand and apply the guidance.

To conduct a bibliometric visualization analysis of research on liquid chromatography-tandem mass spectrometry (LC-MS/MS) in blood concentration detection using Chinese databases, aiming to reveal its research status, evolution trajectory and frontier hotspots.

Relevant literature was systematically retrieved from the CNKI, the VIP Chinese Journal Service Platform, and Wan fang Database. Visualization analysis was performed using CiteSpace 6.3.R1 software.

A total of 2743 articles were included, with annual publication volume showing a phased characteristic of slow growth, rapid increase and subsequent stabilization. The top three institutions by publication volume were China Pharmaceutical University, the Second Xiangya Hospital of Central South University, and Central South University. The main contributing authors were research teams led by Wen Yuguan and Wen Aidong as core members. Keywords with high frequency included "pharmacokinetics", "LC-MS/MS" and "blood drug concentration". Keyword evolution analysis indicated that the research has progressed through three stages: Methodology establishment, application expansion and clinical precision. Current hotspots focus on therapeutic drug monitoring, ultra-performance liquid chromatography-tandem mass spectrometry and personalized medication research targeting specific drugs and populations.

LC-MS/MS technology has become an indispensable core analytical platform for blood drug concentration detection. The research system is mature and continuously deepening its integration into clinical practice. Future efforts should strengthen collaboration among institutions and authors, continuously improve techniques, refine methodological standardization, and expand application scenarios to further promote the development of individualized drug therapy.

To explore the current landscape of artificial intelligence (AI) models built on clinical laboratory data for predicting chronic kidney disease (CKD)-related outcomes.

Following the PRISMA-ScR guidelines, a systematic search was conducted across PubMed, Web of Science, Scopus, China National Knowledge Infrastructure(CNKI), and Wanfang Databases to identify studies employing AI algorithms with clinical laboratory data to predict CKD outcomes.

16 studies were ultimately included, spanning data from 6 countries, with sample sizes ranging from 308 to 4.8 million participants. Random forest and gradient boosting algorithms are the most widely adopted modeling algorithms. Beyond estimated glomerular filtration rate and serum creatinine, several auxiliary biomarkers demonstrated independent predictive value including urine albumin-to-creatinine ratio, serum albumin, hemoglobin, cystatin C, and glycosylated hemoglobin spanning the domains of renal function, metabolism, nutrition, and hematology. The best-performing models across included studies achieved area under curve values ranging from 0.689 to 0.993.

AI predictive models built on clinical laboratory data demonstrate promising performance across multiple CKD outcome categories, with the integration of multidimensional laboratory biomarkers offering a clear advantage over conventional single-marker assessment frameworks.

It is essential to establish an end-to-end, precision-oriented risk management system, so as to ensure the legal compliance, biospecimen quality and safety, data and information security, as well as facility, equipment and biosafety of the biobank in a general hospital.

Guided by GB/T 37864-2019/ISO 20387:2018 "Biotechnology-Biobanking-General requirements for biobanking" and based on an analysis of practical work cases, this paper summarizes the strategies, processes and key points for the construction of the risk management system.

The construction strategies and a four-step risk control process (risk identification, risk assessment, risk control, and effectiveness analysis) are clarified. Taking the construction practice of the risk management system in the Clinical Biobank Centre of Zhujiang Hospital, Southern Medical University as a case study, the construction path and key points are further elaborated, including a four-level document system, a four-level organizational structure, a quantitative risk assessment matrix, the integration of daily risk identification and control with annual systematic review, and the strengthening of training for proactive risk management. Adhering to the principles of standard guidance, practical verification and systematic integration, a total of 107 biobank risk factors covering 13 modules were established. This paper also provides an empirical illustration of risk management processes and effectiveness analysis methods by assessing the top 20 high-risk factors in the center, formulating and implementing corresponding control measures, and comparing the incidence rates of risk events before and after implementation.

The methodology for establishing a risk management system elaborated in this paper integrates theory with practice, featuring strong operability, and is applicable for reference and continuous improvement in the construction of biobank risk management systems.

Hematology analyzers serve as the most fundamental and widely used disease screening tool in clinical practice, offering a multidimensional detection system that provides numerical results such as complete blood count, white blood cell differential count and cell population data (CPD), alongside graphical data including scattergrams and histograms. However, universal review criteria based on cell counts and instrument flags fail to adapt to the specimen characteristics of different medical institutions, thus resulting in high false-negative risks and a heavy review burden. In recent years, artificial intelligence (AI) has achieved breakthrough advances in medical image recognition, providing a novel approach for the intelligent analysis of hematological graphical data. This paper systematically examines the feasibility of integrating multidimensional signal features from hematology analyzers with traditional review criteria, and divides the development of AI-assisted re-examination workflow into 4 stages: Traditional rule implementation, feature-parameter modeling, intelligent graphical data analysis, and multimodal data fusion. It elaborates on CPD-based machine learning screening methods, the strengths of deep learning in graphical analysis, and the clinical efficacy of multimodal fusion, while analyzing core challenges including the scarcity of high-quality datasets, the algorithmic "black box" issue, and the lack of standardized evaluation frameworks. Looking ahead, the construction of an integrated intelligent review system incorporating numerical parameters, graphical features and clinical information will enhance the detection capacity for abnormal specimens via human-machine collaboration, thereby reducing unnecessary manual reviews.

The development of smart clinical laboratories has gradually evolved from isolated functional applications toward integrated, end-to-end process collaboration. However, three core challenges remain: weak data governance infrastructure, inadequate organizational coordination, and limited data sharing and reuse. These factors collectively constrain the substantive transition from "informatization" to "intelligentization". This article, grounded in the ISO 15189 quality management framework, conducts a systematic analysis across 4 dimensions: Connotative characteristics, current development status, challenges faced, and implementation strategies. Accordingly, it proposes a phased construction pathway based on a "unified data foundation plus scenario-based applications" an organizational collaboration mechanism characterized by "laboratory-led, IT-coordinated, and management-involved" governance, and a data governance framework that ensures security, compliance, and trusted sharing. The value of this article lies in advancing the emerging concept of smart clinical laboratories from vague understanding to actionable strategic design, offering practical references for medical institutions pursuing intelligent transformation.

In recent years, the autoverification system has emerged as an essential part of the construction of intelligent medical laboratories, serving as a core technology for efficient review of test results and standardized quality of laboratory reports. With the continuous development of laboratory automation and information technology, autoverification is gradually shifting from the traditional fixed rule-based model toward multi-dimensional, standardized and intelligent development. This paper reviews relevant industrial guidelines and research at home and abroad, and elaborates on the construction of autoverification systems, application progress in various professional fields and its integration with information technologies. It also discusses the current challenges and future development trends of autoverification, aiming to provide references for the construction and optimization of autoverification systems in medical laboratories.

Against the backdrop of rapid development of informatization and artificial intelligence, the traditional quality assurance model for medical laboratories is increasingly unable to meet the demands of regional medical collaboration. Building a quality management system for regional networked medical laboratories characterized by standardization, intelligence and continuous improvement has become a vital direction for facilitating mutual recognition of laboratory test results, safeguarding public medical safety, and conserving social medical resources. This paper reviews the construction models, key technologies, system establishment and existing challenges of regional networked medical laboratories, so as to provide references for the high quality development of laboratory medicine.

As a strategically vital resource of significant national importance, the protection and utilization of biological resources are intricately linked not only to national biological security but also to the expansion of the biological industry. Experimental cell resources, as a pivotal constituent of biological resources, play an indispensable role in life science research and the development of biomedicine. The establishment of a sharing platform for experimental cell resources in China continues to confront numerous challenges, including insufficient resource integration, an imperfect quality control system, limited technical service capabilities, and an inadequate data management framework. Consequently, this paper, adopting a national strategic perspective, examines the necessity and feasibility of constructing a multimodal governance system for biological resource repositories, using the national model and the sharing platform of distinctive experimental cell resource repositories as illustrative cases. Simultaneously, it offers pertinent recommendations aimed at providing theoretical underpinnings and practical guidance for the development and governance of biological resource repositories in China.

To integrate the five core requirements of the ISO 15189:2022 quality system throughout the entire teaching process, and adopt multimodal teaching methods, aiming to enhance the teaching quality of the Clinical Laboratory Management course and students' job competency.

Centered on the philosophy of "student-centered, competency-oriented, and standards-led", the reform restructured the course content system according to the ISO 15189:2022 quality framework. It innovatively adopted multimodal teaching methods including case-based learning, problem-based learning, flipped classroom, and role-playing. An integrated assessment system combining formative and summative evaluation was established.

Teaching practice demonstrated that the reform effectively enhanced students' depth of theoretical understanding of quality management, practical application skills, and awareness of quality. The average score of the final exam was 82.1±9.4, representing an increase of approximately 6.9% compared to the previous year, with a statistically significant difference（P<0.05）. Questionnaire surveys indicated that over 98% of students found the course content and design, teaching methods and techniques, competency improvement, and learning gains to be "somewhat helpful" or "very helpful". Student satisfaction with teaching reached 98.4% at the end of the semester.

The multimodal teaching reform in the Clinical Laboratory Management course, based on the ISO 15189:2022 quality system, was recognized by both students and teachers. It holds significant practical importance for cultivating high-quality medical laboratory talents who meet international industry standards and for promoting the close integration of teaching and clinical practice.

To adapt to the development of molecular diagnostics, this study explores a theory-practice integrated three-dimensional teaching innovation model guided by textbook construction, aiming to cultivate interdisciplinary and competent medical laboratory talents who are proficient in molecular diagnostics and clinical laboratory practice, capable of biomedical research, and equipped with lifelong learning capacity.

In response to the relatively static knowledge systems, unidimensional organizational frameworks, insufficient clinical application orientation, and lack of digital resources observed in existing molecular diagnostics textbooks, a series of new textbooks were developed with updated content that prioritizes the integration of recent advances and authentic clinical cases, thus establishing a robust foundation for theoretical learning in molecular diagnostics. Meanwhile, by fully utilizing multimedia and online teaching tools, a variety of novel teaching methods such as case-based learning (CBL), problem-based learning (PBL), team-based learning (TBL), micro-lessons and flipped classrooms were introduced. Basic learning was closely integrated with clinical application and students were encouraged to be exposed to clinical practice as early as possible. Students were provided with various summer internships, including placements in hospital clinical laboratories, in vitro diagnostic technology companies, and independent laboratories, as well as multiple opportunities for learning and practice before graduation. The number of comprehensive and design-oriented experimental projects was increased, and students were encouraged to participate in scientific research activities early to cultivate scientific and innovative thinking. A complete teaching management system was established for the entire process. Teaching quality was monitored from multiple perspectives via process monitoring, summative outcome evaluation, and classroom observation systems.

Since the implementation of the innovative model, the outcome has been remarkable. 24 textbooks were compiled, including eight directly related to molecular diagnostics. Over 40 SCI-indexed papers on molecular diagnostics have been published, and 12 awards for outstanding undergraduate graduation theses have been received. Additionally, 34 undergraduate research projects were funded at the institutional level, and 14 papers on teaching reform have been published. Satisfaction surveys revealed that the three-dimensional teaching model enhanced students' learning satisfaction and motivation, strengthened theory-practice integration, and was regarded as beneficial for future practice. The average final exam scores also showed significant improvement after the reform.

This new model represents an effective approach that promotes theoretical advancement and pedagogical innovation in molecular diagnostics education within medical laboratory science, offering an effective direction for educational reform in this field.