ObjectiveTo establish a model that can quickly identify the aroma components in different parts of Nardostachys jatamansi， so as to provide a quality control basis for the market circulation and clinical use of N. jatamansi. MethodsHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） combined with Smart aroma database and National Institute of Standards and Technology（NIST） database were used to characterize the aroma components in different parts of N. jatamansi， and the aroma components were quantified according to relative response factor（RRF） and three internal standards， and the markers of aroma differences in different parts of N. jatamansi were identified by orthogonal partial least squares-discriminant analysis（OPLS-DA） and cluster thermal analysis based on variable importance in the projection（VIP） value >1 and P<0.01. The odor data of different parts of N. jatamansi were collected by Heracles Ⅱ Neo ultra-fast gas phase electronic nose， and the correlation between compound types of aroma components collected by the ultra-fast gas phase electronic nose and the detection results of HS-SPME-GC-MS was investigated by drawing odor fingerprints and odor response radargrams. Chromatographic peak information with distinguishing ability≥0.700 and peak area≥200 was selected as sensor data， and the rapid identification model of different parts of N. jatamansi was established by principal component analysis（PCA）， discriminant factor alysis（DFA）， soft independent modeling of class analogies（SIMCA） and statistical quality control analysis（SQCA）. ResultsThe HS-SPME-GC-MS results showed that there were 28 common components in the underground and aboveground parts of N. jatamansi， of which 22 could be quantified and 12 significantly different components were screened out. Among these 12 components， the contents of five components（ethyl isovalerate， 2-pentylfuran， benzyl alcohol， nonanal and glacial acetic acid，） in the aboveground part of N. jatamansi were significantly higher than those in the underground part（P<0.01）， the contents of β-ionone， patchouli alcohol， α-caryophyllene， linalyl butyrate， valencene， 1，8-cineole and p-cymene in the underground part of N. jatamansi were significantly higher than those in the aboveground part（P<0.01）. Heracles Ⅱ Neo electronic nose results showed that the PCA discrimination index of the underground and aboveground parts of N. jatamansi was 82， and the contribution rates of the principal component factors were 99.94% and 99.89% when 2 and 3 principal components were extracted， respectively. The contribution rate of the discriminant factor 1 of the DFA model constructed on the basis of PCA was 100%， the validation score of the SIMCA model for discrimination of the two parts was 99， and SQCA could clearly distinguish different parts of N. jatamansi. ConclusionHS-SPME-GC-MS can clarify the differential markers of underground and aboveground parts of N. jatamansi. The four analytical models provided by Heracles Ⅱ Neo electronic nose（PCA， DFA， SIMCA and SQCA） can realize the rapid identification of different parts of N. jatamansi. Combining the two results， it is speculated that terpenes and carboxylic acids may be the main factors contributing to the difference in aroma between the underground and aboveground parts of N. jatamansi.

This comprehensive review systematically explores the multifaceted applications, inherent challenges, and promising future directions of artificial intelligence (AI) within the medical domain. It meticulously examines AI's specific contributions to basic medical research, disease prevention, intelligent diagnosis, treatment, rehabilitation, nursing, and health management. Furthermore, the review delves into AI's innovative practices and pivotal roles in clinical trials, hospital administration, medical education, as well as the realms of medical ethics and policy formulation. Notably, the review identifies several key challenges confronting AI in healthcare, encompassing issues such as inadequate algorithm transparency, data privacy concerns, absent regulatory standards, and incomplete risk assessment frameworks. Looking ahead, the future trajectory of AI in healthcare encompasses enhancing algorithm interpretability, propelling generative AI applications, establishing robust data-sharing mechanisms, refining regulatory policies and standards, nurturing interdisciplinary talent, fostering collaboration among industry, academia, and medical institutions, and advancing inclusive, personalized precision medicine. Emphasizing the synergy between AI and emerging technologies like 5G, big data, and cloud computing, this review anticipates a new era of intelligent collaboration and inclusive sharing in healthcare. Through a multidimensional analysis, it presents a holistic overview of AI's medical applications and development prospects, catering to researchers, practitioners, and policymakers in the healthcare sector. Ultimately, this review aims to catalyze the deep integration and innovative deployment of AI technology in healthcare, thereby driving the sustainable advancement of smart healthcare.

Children， as a special group， frequently experience of off-label drug use worldwide. Common reasons for off-label drug use in children include the lack of data on pediatric patients during the clinical trial stage of drug development， delayed updates to drug instructions， and the non-standard professional behavior of some doctors. Off-label drug use in children is a double-edged sword. It could save lives and provide a way to explore additional functions of drugs， while it may also lead to the phenomenon of hyper-indication abuse， increasing the risk of adverse drug events. Regulating off-label drug use in children can safeguard the best treatment rights and interests of children. It is recommended to encourage pharmaceutical enterprises to conduct research and development of pediatric new drugs， simplify the approval process for drug instructions amendments， accumulate evidence-based medical evidence for off-label drug use in children， standardize the process of off-label drug use in children in medical institutions， continuously improve the standardized diagnosis and treatment capabilities of pediatricians， and actively cooperate with the families of pediatric patients in diagnosis and treatment， so as to comprehensively safeguard the rights and interests of both doctors and patients.

Objective To analyze the distribution characteristics and trends of occupational exposure types of medical radiation workers in Gansu Province, China, and to provide a basis for administrative departments to formulate and adjust radiation protection policies. Methods According to the radiation health information platform, the data of occupational exposure types of radiation workers in Gansu Province from 2014 to 2023 were obtained. The proportions of occupational exposure types in each physical examination year was statistically analyzed. Results From 2014 to 2023, the number of medical radiation workers accounted for more than 70% of the total number of radiation workers. The proportion of X-ray imaging diagnostic radiation workers in the total number of medical radiation workers gradually decreased but remained the highest, exceeding 68% annually. The proportion of interventional radiology workers in the total number of medical radiation workers increased from 13.8% to 25.5%. The proportions of radiation therapy and nuclear medicine workers in the total number of medical radiation workers increased slowly. The numbers of interventional radiology and radiotherapy workers in tertiary hospitals both accounted for more than 70% of the total number of such workers in the province. The proportion of interventional radiology workers increased and then decreased. The proportion of radiotherapy workers increased significantly from 70.5% to 93.0%. The number of nuclear medicine workers in tertiary hospitals accounted for more than 80% of such workers in the province. Conclusion Radiation protection policies and measures should be adjusted according to the changes in the types of occupational exposure. The focus of these policies and measures should differ depending on the level of healthcare institutions, the type of radiological diagnostic and therapeutic services, and the characteristics of various occupational exposure types .

This article systematically analyzes the historical evolution of the name， origin， quality evaluation， harvesting， processing and other aspects of Picrorhizae Rhizoma by referring to the medical books， prescription books， and other documents of the past dynasties， combined with relevant modern research materials， in order to provide a basis for the development and utilization of famous classical formulas containing this medicinal herb. The research results indicate that Picrorhizae Rhizoma was first recorded in New Revised Materia Medica from the Tang dynasty. Throughout history， Huhuanglian has been used as its official name， and there are also aliases such as Gehu Luze， Jiahuanglian and Hulian. The main source of past dynasties is the the rhizomes of Picrorhiza kurrooa and P. scrophulariiflora. In ancient times， Picrorhizae Rhizoma was mainly imported by foreign traders via Guangzhou and other regions， and also produced in China， mainly in Xizang. In ancient times， it was harvested and dried in early August of the lunar calendar， while in modern times， it is mostly harvested from July to September， with the best quality being those with thick and crispy rhizomes without impurities， and bitter taste. Throughout history， Picrorhizae Rhizoma was collected， washed， sliced， and dried before being used as a raw material for medicine， it has a bitter and cold taste， mainly used to treat bone steaming， hot flashes， infantile chancre fever， and dysentery. There is no significant difference in taste and efficacy between ancient and modern times. Based on the research results， it is recommended that the rhizomes of P. scrophulariiflora in the 2020 edition of Chinese Pharmacopoeia， or the rhizomes of P. kurrooa， can be used in famous classical formulas containing this medicinal herb， which can be processed according to the processing requirements marked by the original formula. For those without clear processing requirements， the dried raw products are used as medicine.

Objective: To investigate the genetic basis of RhD-negative phenotype in the blood donor population of Nantong City. Methods: RHD genotyping was performed on 386 randomly selected RhD-negative donor samples (from a total of 676 RhD-negative donors identified between January 20, 2023, and June 28, 2024) using polymerase chain reaction (PCR), and the inconclusive results were confirmed by nucleotide sequencing. Results: Ten RHD allele types were identified: The complete deletion variant RHD 01N.01 was predominant (64.25%, 248/386); followed by RHD 01EL.01 (19.69%, 76/386). RHD 01N.03, RHD 01N.04, RHD 01N.16 and RHD 01EL.32 were frequently observed., RHD 01EL.02, RHD 01EL.08, RHD 01EL.37 and RHD 01N.25 were rare, and two exon deletion variants remained uncharacterized. The phenotypic distribution of RhD-negative blood donors was ccee (55.44%)>Ccee(31.09%)>ccEe(5.96%)>CCee(5.44%)>CcEe(1.81%)>CcEE(0.26%), and the antigen distribution trend was e(99.74%)>c(94.56%)>C(38.60%)>E(8.03%). A correlation was observed between RHD genotypes and RhCE phenotypes. Conclusion: The Nantong blood donor population exhibits unique RHD gene polymorphisms. Integrating RhCE serological phenotyping with RHD genotyping is essential for ensuring transfusion safety.

Objective To explore the mechanism of Medicoscab tincture in the treatment of second-degree burns based on network pharmacology and molecular docking technology. Methods The effective components of the tincture were screened by the TCMSP; the effective components of the tincture and burn related targets were screened by GeneCards and OMIM database; Cytoscape 3.7.2 software was used to draw “Chinese medicine-disease-effective components-targets” network diagram; the related gene ontology （GO） functions and pathways of the tincture were obtained through GO enrichment analysis and Kyoto encyclo-pedia of genes and geomes （KEGG） pathway analysis on the targets through Metascape platform; the pathway bubble diagram was constructed by the pathways enriched in the KEGG database, and finally verified by AutoDockTools for molecular alignment. Results 19 effective components and 179 target intersections of Medicoscab tincture were selected. GO analysis showed the intervention burns process mainly involved the reaction of inorganic substances, the reaction of cells to nitrogen compounds, and the response to xenobiotic stimuli, as well as biological processes such as membrane rafts, vesicular cavities, transcriptional regulatory factor complexes, receptor complexes, and endoplasmic reticulum cavities. KEGG analysis showed the function mainly includes AGE-RAGE signal pathway, PI3K-Akt signal pathway, IL-17 signal pathway, TNF signal pathway, etc. Analysis of cytoscape software showed the core targets were AKT1, TNF, IL-6, GAPDH, TP53, etc. Molecular docking showed that the active components of Medicoscab tincture were docking with multiple targets, among which β- sitosterol had strong binding activity with AKT1, GAPDH and TP53. Conclusion Quercetin, kaempferol, baicalein, β-sitosterol and other core active ingredients in the tincture of making scabs, which could assist in the relief of burns, regulate the signalling pathways such as AGE-RAGE, PI3K-Akt, IL-17, TNF and so on by acting on the targets of AKT1, GAPDH, TP53, IL-6 and so on. This study laid a theoretical foundation for clarifying the mechanism of action of tincture of scab making for the treatment of burn-like diseases.

Objective: To investigate the genetic basis of RhD-negative phenotype in the blood donor population of Nantong City. Methods: RHD genotyping was performed on 386 randomly selected RhD-negative donor samples (from a total of 676 RhD-negative donors identified between January 20, 2023, and June 28, 2024) using polymerase chain reaction (PCR), and the inconclusive results were confirmed by nucleotide sequencing. Results: Ten RHD allele types were identified: The complete deletion variant RHD 01N.01 was predominant (64.25%, 248/386); followed by RHD 01EL.01 (19.69%, 76/386). RHD 01N.03, RHD 01N.04, RHD 01N.16 and RHD 01EL.32 were frequently observed., RHD 01EL.02, RHD 01EL.08, RHD 01EL.37 and RHD 01N.25 were rare, and two exon deletion variants remained uncharacterized. The phenotypic distribution of RhD-negative blood donors was ccee (55.44%)>Ccee(31.09%)>ccEe(5.96%)>CCee(5.44%)>CcEe(1.81%)>CcEE(0.26%), and the antigen distribution trend was e(99.74%)>c(94.56%)>C(38.60%)>E(8.03%). A correlation was observed between RHD genotypes and RhCE phenotypes. Conclusion: The Nantong blood donor population exhibits unique RHD gene polymorphisms. Integrating RhCE serological phenotyping with RHD genotyping is essential for ensuring transfusion safety.

The Consolidated Standards of Reporting Trials (CONSORT) statement aims to enhance the quality of reporting for randomized controlled trial (RCT) by providing a minimum item checklist. It was first published in 1996, and updated in 2001 and 2010, respectively. The latest version was released in April 2025, continuously reflecting new evidence, methodological advancements, and user feedback. CONSORT 2025 includes 30 essential checklist items and a template for a participant flow diagram. The main changes to the checklist include the addition of 7 items, revision of 3 items, and deletion of 1 item, as well as the integration of multiple key extensions. This article provides a comprehensive interpretation of the statement, aiming to help clinical trial staff, journal editors, and reviewers fully understand the essence of CONSORT 2025, correctly apply it in writing RCT reports and evaluating RCT quality, and provide guidance for conducting high-level RCT research in China.

As the volume of medical research using large language models (LLM) surges, the need for standardized and transparent reporting standards becomes increasingly critical. In January 2025, Nature Medicine published statement titled by TRIPOD-LLM reporting guideline for studies using large language models. This represents the first comprehensive reporting framework specifically tailored for studies that develop prediction models based on LLM. It comprises a checklist with 19 main items (encompassing 50 sub-items), a flowchart, and an abstract checklist (containing 12 items). This article provides an interpretation of TRIPOD-LLM’s development methods, primary content, scope, and the specific details of its items. The goal is to help researchers, clinicians, editors, and healthcare decision-makers to deeply understand and correctly apply TRIPOD-LLM, thereby improving the quality and transparency of LLM medical research reporting and promoting the standardized and ethical integration of LLM into healthcare.