Phase Ⅰ clinical trials play a crucial role in the research and development of new drugs, serving as the initial studies to assess their safety, tolerability, effectiveness, and pharmacokinetic properties in humans. These trials involve uncertainties regarding safety and efficacy. Comprehensive management of all aspects of phase Ⅰ clinical trials for anti-tumor drugs is crucial to protect the rights and safety of participants. This article provides an in-depth analysis of the key points and precautions necessary for effective quality control throughout the process. The analysis is informed by guidelines such as the “Good Clinical Practice for Drugs” “Key Points and Judgment Principles for Drug Registration Verification” “Key Points and Judgment Principles for Supervision and Inspection of Drug Clinical Trial Institutions” and the standard operating procedures for quality control of the center. Topics discussed include informed consent, inclusion criteria, experimental drugs, biological samples, adverse events, and serious adverse events. The goal is to standardize quality control in phase Ⅰ clinical trials of anti-tumor drugs, ensure the authenticity and reliability of clinical trial data, and protect the rights and safety of participants.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

ObjectiveTo explore quality difference factors of Perillae Caulis based on the contents of multiple chemical components and comprehensively evaluate the quality. MethodsA total of 32 batches of Perillae Caulis samples were collected from 12 producing areas such as Hebei， Anhui and Guangdong， and their diameter range， epidermis color and producing areas were recorded. Total flavonoids， total phenols， volatile oils， 5 active components and 84 volatile components in 32 batches of samples were quantitatively or semi-quantitatively determined by colorimetry， ultra performance liquid chromatography-photodiode array detector（UPLC-PDA） and gas chromatography-mass spectrometry（GC-MS）. Then the differences between the contents of these components were analyzed by principal component analysis（PCA） and non-parametric test. According to the weights of the index components determined by PCA model， entropy weight-technique for order preference by similarity to ideal solution（TOPSIS） model was constructed to evaluate the quality of Perillae Caulis with different characters and origins. ResultsThere were significant differences in the composition of Perillae Caulis with different diameters， epidermis colors and producing areas， and 9 differential components were screened out， including 6 index constituents（total flavonoids， total phenols， caffeic acid， scutellarin， rosmarinic acid and luteolin） and 3 volatile components（caryophyllene oxide， （-）-humulene epoxide Ⅱ， 14-hydroxycaryophyllene）， of which 6 index constituents were higher in samples with small diameter， purple-brown epidermis and southern origin， while the contents of 3 volatile components were higher in samples with large diameter， dark-brown epidermis and northern origin. A significant difference was shown in the model scores of different diameters， epidermis colors and origins（P<0.05）， and the scores of Perillae Caulis with small diameter and purple-brown epidermis from southern area， especially Guangdong， had a high score. ConclusionThere are significant differences in the composition and content of chemical constituents between different diameters， epidermal colors and production areas of Perillae Caulis， samples showing small diameter， owing purple-brown epidermis， and originating from Guangdong were of higher-quality due to their higher content of 8 key indices.

ObjectiveTo explore quality difference factors of Perillae Caulis based on the contents of multiple chemical components and comprehensively evaluate the quality. MethodsA total of 32 batches of Perillae Caulis samples were collected from 12 producing areas such as Hebei， Anhui and Guangdong， and their diameter range， epidermis color and producing areas were recorded. Total flavonoids， total phenols， volatile oils， 5 active components and 84 volatile components in 32 batches of samples were quantitatively or semi-quantitatively determined by colorimetry， ultra performance liquid chromatography-photodiode array detector（UPLC-PDA） and gas chromatography-mass spectrometry（GC-MS）. Then the differences between the contents of these components were analyzed by principal component analysis（PCA） and non-parametric test. According to the weights of the index components determined by PCA model， entropy weight-technique for order preference by similarity to ideal solution（TOPSIS） model was constructed to evaluate the quality of Perillae Caulis with different characters and origins. ResultsThere were significant differences in the composition of Perillae Caulis with different diameters， epidermis colors and producing areas， and 9 differential components were screened out， including 6 index constituents（total flavonoids， total phenols， caffeic acid， scutellarin， rosmarinic acid and luteolin） and 3 volatile components（caryophyllene oxide， （-）-humulene epoxide Ⅱ， 14-hydroxycaryophyllene）， of which 6 index constituents were higher in samples with small diameter， purple-brown epidermis and southern origin， while the contents of 3 volatile components were higher in samples with large diameter， dark-brown epidermis and northern origin. A significant difference was shown in the model scores of different diameters， epidermis colors and origins（P<0.05）， and the scores of Perillae Caulis with small diameter and purple-brown epidermis from southern area， especially Guangdong， had a high score. ConclusionThere are significant differences in the composition and content of chemical constituents between different diameters， epidermal colors and production areas of Perillae Caulis， samples showing small diameter， owing purple-brown epidermis， and originating from Guangdong were of higher-quality due to their higher content of 8 key indices.

Objective: To explore the laboratory testing methods and clinical management strategies for immune hemolytic anemia induced by Ceftizoxime sodium drug-dependent antibodies. Methods: Patient blood samples were subjected to blood typing, direct antiglobulin test, and unexpected antibody identification. Ceftizoxime sodium drug-dependent antibodies were detected using the immune complex method and drug-sensitized red cell method. The properties and titers of the drug antibodies were further assessed. Flow cytometry was used to assess the complement activation capacity of the drug antibodies in vitro. Results: Direct antiglobulin tests (IgG and C3d) were positive. Ceftizoxime sodium drug-dependent antibodies were identified using both the immune complex method and the sensitized red cell method, their titers significantly increased following the addition of the drug. Flow cytometry confirmed the complement activation capability of these antibodies and identified 30 minutes as the optimal time for activation in vitro. The patient's condition improved rapidly after drug withdrawal and supportive transfusion, resulting in a favorable outcome. Conclusion: Ceftizoxime sodium can cause drug-induced immune hemolytic anemia via complement activation mediated by drug-dependent antibodies. Serological testing is essential for diagnosing drug-induced hemolytic anemia. Clinicians should be vigilant for this adverse reaction. The offending drug must be promptly discontinued, and supportive care should be initiated upon the onset of symptoms.

OBJECTIVE: To explore serological detection and blood transfusion strategies of mimicking antibodies, so as to provide appropriate transfusion strategies. METHODS: Detailed serological tests, including ABO blood group, Rh typing, antibody specificity, etc,were performed on two patients with autoimmune hemolytic anemia(AIHA). Meanwhile, the references about blood transfusion from mimicking antibody patients published from 1977 to 2024 in China and abroad were retrospectively summarized and analyzed. RESULTS: The patient 1 blood type was AB,CCDee and the antibody is mimicking anti-e, transfusion the e-negative red blood cells (RBCs) was effective. After two transfusions of e-RBCs, hemoglobin levels significantly increased from 48 g/L to 91 g/L, with complete resolution of hemolytic symptoms. The patient 2 blood type was O,CcDee, and the antibody was mimicking anti-c, the patient was diagnosed with AIHA and treated with hormone. No blood products were transfused during hospitalization, and his hemolysis was relieved. CONCLUSION Strictly grasping the indication of blood transfusion, blood transfusion should not be performed in the unnecessary conditions, and the corresponding antigen-negative RBC should be screened for transfusion in the necessay conditions.
Humans ; Blood Transfusion ; Anemia, Hemolytic, Autoimmune/therapy* ; ABO Blood-Group System ; Retrospective Studies ; Antibodies ; Male ; Blood Grouping and Crossmatching

OBJECTIVE: To investigate the expression of RNA binding protein ELAVL1 in prostate cancer (PCa), especially hormone-sensitive prostate cancer (HSPC), and its relationship with tumor proliferation. This study further aims to reveal the molecular mechanism by which ELAVL1 promotes HSPC proliferation by stabilizing SOX4 mRNA in an m6A-dependent manner. METHODS: The expression of ELAVL1 in PCa tissues and its relationship with prognosis were analyzed in the Cancer Genome Atlas (TCGA) database, and the differences in HSPC and hormone-resistant prostate cancer (HRPC) were compared. And its relationship with prognosis were analyzed in the Cancer Genome Atlas (TCGA) database, and the differences in HSPC and hormone-resistant prostate cancer (HRPC) were compared. Western blot was used to detect ELAVL1 protein expression in PCa cell lines. After ELAVL1 knockdown by siRNA, cell proliferation was evaluated using CCK-8 assays, and changes in downstream target genes were detected by RT-qPCR. Tumor xenograft experiments in nude mice were performed to further assess the impact of ELAVL1 on tumor growth. The interaction between ELAVL1 and SOX4 mRNA was verified by RIP-seq. And the mRNA and protein levels of SOX4 after knockdown of ELAVL1 were detected by RT-qPCR and Western blot, respectively. CCK-8 was used to evaluate the effect of SOX4 knockdown on cell proliferation. MeRIP-qPCR was used to detect the m6A modification level of SOX4 and the effect of knocking down METTL3. RNA pull-down experiments verified the interaction between SOX4 RNA fragments and ELAVL1 protein. RNA stability experiments evaluated the effect of ELAVL1 knockdown on SOX4 mRNA stability. RESULTS: The expression of ELAVL1 in PCa cells was higher than that in normal prostate epithelial cells. The prognosis of patients with high expression of ELAVL1 was significantly worse than that of patients with low expression. In the GSE32269 dataset, the expression level of ELAVL1 in HSPC was significantly higher than that in HRPC. After knocking down of ELAVL1 in LNCaP and VCaP cells, CCK-8 experiments showed that the cell proliferation ability was significantly affected after knocking down ELAVL1, and overexpressed ELAVL1 promoted the proliferation of HSPC cells. The results of in vivo studies showed that knockdown of ELAVL1 significantly inhibited the tumorigenic capacity of LNCaP cells and resulted in a marked reduction in xenograft tumor mass. The levels of SOX4 mRNA and protein in LNCaP and VCaP cells were significantly higher than those in normal prostate epithelial cells RWPE-1. RIP-qPCR confirmed the interaction between ELAVL1 protein and SOX4 mRNA. After knocking down of ELAVL1, the expression levels of SOX4 mRNA and protein were significantly decreased. After knocking down of SOX4, the proliferation ability of LNCaP and VCaP cells was significantly inhibited. CONCLUSION ELAVL1 is highly expressed in HSPC. High expression of ELAVL1 is associated with the proliferation of HSPC. SOX4 is a downstream molecule of ELAVL1 which promotes the proliferation of HSPC. ELAVL1 enhances the stability of SOX4 mRNA through an m6A-dependent mechanism.
Male ; Humans ; SOXC Transcription Factors/genetics* ; ELAV-Like Protein 1/metabolism* ; Cell Proliferation ; Prostatic Neoplasms/genetics* ; Animals ; Mice, Nude ; Cell Line, Tumor ; Mice ; Gene Expression Regulation, Neoplastic ; RNA, Messenger/metabolism* ; Prognosis

OBJECTIVES: Osteoarthritis (OA) and sarcopenia are significant health concerns in the elderly, substantially impacting their daily activities and quality of life. However, the relationship between them remains poorly understood. This study aims to uncover common biomarkers and pathways associated with both OA and sarcopenia. METHODS: Gene expression profiles related to OA and sarcopenia were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control groups were identified using R software. Common DEGs were extracted via Venn diagram analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify biological processes and pathways associated with shared DEGs. Protein-protein interaction (PPI) networks were constructed, and candidate hub genes were ranked using the maximal clique centrality (MCC) algorithm. Further validation of hub gene expression was performed using 2 independent datasets. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of key genes for OA and sarcopenia. Mouse models of OA and sarcopenia were established. Hematoxylin-eosin and Safranin O/Fast Green staining were used to validate the OA model. The sarcopenia model was validated via rotarod testing and quadriceps muscle mass measurement. Real-time reverse transcription PCR (real-time RT-PCR) was employed to assess the mRNA expression levels of candidate key genes in both models. Gene set enrichment analysis (GSEA) was conducted to identify pathways associated with the selected shared key genes in both diseases. RESULTS: A total of 89 common DEGs were identified in the gene expression profiles of OA and sarcopenia, including 76 upregulated and 13 downregulated genes. These 89 DEGs were significantly enriched in protein digestion and absorption, the PI3K-Akt signaling pathway, and extracellular matrix-receptor interaction. PPI network analysis and MCC algorithm analysis of the 89 common DEGs identified the top 17 candidate hub genes. Based on the differential expression analysis of these 17 candidate hub genes in the validation datasets, AEBP1 and COL8A2 were ultimately selected as the common key genes for both diseases, both of which showed a significant upregulation trend in the disease groups (all P<0.05). The value of area under the curve (AUC) for AEBP1 and COL8A2 in the OA and sarcopenia datasets were all greater than 0.7, indicating that both genes have potential value in predicting OA and sarcopenia. Real-time RT-PCR results showed that the mRNA expression levels of AEBP1 and COL8A2 were significantly upregulated in the disease groups (all P<0.05), consistent with the results observed in the bioinformatics analysis. GSEA revealed that AEBP1 and COL8A2 were closely related to extracellular matrix-receptor interaction, ribosome, and oxidative phosphorylation in OA and sarcopenia. CONCLUSIONS AEBP1 and COL8A2 have the potential to serve as common biomarkers for OA and sarcopenia. The extracellular matrix-receptor interaction pathway may represent a potential target for the prevention and treatment of both OA and sarcopenia.
Sarcopenia/genetics* ; Osteoarthritis/genetics* ; Computational Biology/methods* ; Humans ; Protein Interaction Maps/genetics* ; Animals ; Mice ; Gene Expression Profiling ; Gene Ontology ; Transcriptome ; Male ; Signal Transduction/genetics* ; Gene Regulatory Networks