The Pharmacovigilance Guidelines for Clinical Application of Oral Chinese Patent Medicines （hereinafter referred to as the Guidelines） is first specialized in the field of drug safety for oral Chinese patent medicines （OCPMs） in China. Rooted in China's healthcare context， the Guidelines address the unique usage patterns and risk characteristics of OCPMs， filling a regulatory gap in the pharmacovigilance framework specific to this category. To facilitate accurate understanding and effective implementation of the Guidelines， and to promote the standardized development of pharmacovigilance practices for OCPMs， this study offered a systematic interpretation based on its three core components. In the domain of risk monitoring and reporting， the paper analyzed the rationale for multi-source information integration and clarified the criteria for identifying key products and target populations for intensive monitoring. Regarding risk assessment， the Guidelines were examined from three dimensions of formulation components， medication behaviors， and population to address complex safety issues arising from medicinal constituents， irrational use， and individual susceptibility. In the area of risk control， the analysis focused on context-based interventions and dynamic closed-loop management strategies， exploring practical pathways to shift from passive response to proactive risk mitigation. Furthermore， this paper evaluated the applied value of the Guidelines and identified implementation challenges， such as insufficient capacity at the primary-care level and limited digital infrastructure. In response， the study proposed optimization strategies including establishing a dynamic updating mechanism， strengthening training at the grassroots level， and incorporating artificial intelligence to enhance pharmacovigilance capacity. This interpretation aims to provide actionable insights for marketing authorization holders （including manufacturers）， pharmaceutical distributors， healthcare institutions， and research organizations， ultimately supporting the establishment and refinement of a full lifecycle pharmacovigilance system for OCPMs.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

OBJECTIVE To construct three-class （insufficient， normal， excessive） and two-class （insufficient， normal） models for predicting plasma concentration of valproic acid （VPA）， and compare the performance of these two models， with the aim of providing a reference for formulating clinical medication strategies. METHODS The clinical data of 480 patients who received VPA treatment and underwent blood concentration test at the Xi’an International Medical Center Hospital were collected from November 2022 to September 2024 （a total of 695 sets of data）. In this study， predictive models were constructed for target variables of three-class and two-class models. Feature ranking and selection were carried out using XGBoost scores. Twelve different machine learning algorithms were used for training and validation， and the performance of the models was evaluated using three indexes: accuracy， F1 score， and the area under the working characteristic curve of the subject （AUC）. RESULTS XGBoost feature importance scores revealed that in the three-class model， the importance ranking of kidney disease and electrolyte disorders was higher. However， in the two-class model， the importance ranking of these features significantly decreased， suggesting a close association with the excessive blood concentration of VPA. In the three-class model， Random Forest method performed best， with F1 score of 0.704 0 and AUC of 0.519 3 on the test set； while in the two-class model， CatBoost method performed optimally， with F1 score of 0.785 7 and AUC of 0.819 5 on the test set. CONCLUSIONS The constructed three-class model has the ability to predict excessive VPA blood concentration， but its prediction and model generalization abilities are poor； the constructed two-class model can only perform classification prediction for insufficient and normal blood concentration cases， but its model performance is stronger.

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract, which increases the incidence of colorectal cancer (CRC). In the pathophysiology of IBD, ubiquitination/deubiquitination plays a critical regulatory function. Josephin domain containing 2 (JOSD2), a deubiquitinating enzyme, controls cell proliferation and carcinogenesis. However, its role in IBD remains unknown. Colitis mice model developed by dextran sodium sulfate (DSS) or colon tissues from individuals with ulcerative colitis and Crohn's disease showed a significant upregulation of JOSD2 expression in the macrophages. JOSD2 deficiency exacerbated the phenotypes of DSS-induced colitis by enhancing colon inflammation. DSS-challenged mice with myeloid-specific JOSD2 deletion developed severe colitis after bone marrow transplantation. Mechanistically, JOSD2 binds to the C-terminal of inosine-5'-monophosphate dehydrogenase 2 (IMPDH2) and preferentially cleaves K63-linked polyubiquitin chains at the K134 site, suppressing IMPDH2 activity and preventing activation of nuclear factor kappa B (NF-κB) and inflammation in macrophages. It was also shown that JOSD2 knockout significantly exacerbated increased azoxymethane (AOM)/DSS-induced CRC, and AAV6-mediated JOSD2 overexpression in macrophages prevented the development of colitis in mice. These outcomes reveal a novel role for JOSD2 in colitis through deubiquitinating IMPDH2, suggesting that targeting JOSD2 is a potential strategy for treating IBD.

The interaction between m⁶A-methylated RNA and chromatin modification remains largely unknown. We found that targeted inhibition of bromodomain-containing protein 4 (BRD4) by siRNA or its inhibitor (JQ1) significantly decreases mRNA m⁶A levels and suppresses the malignancy of breast cancer (BC) cells via increased expression of demethylase AlkB homolog 5 (ALKBH5). Mechanistically, inhibition of BRD4 increases the mRNA stability of ALKBH5 via enhanced binding between its 3' untranslated regions (3'UTRs) with RNA-binding protein RALY. Further, BRD4 serves as a scaffold for ubiquitin enzymes tripartite motif containing-21 (TRIM21) and ALKBH5, resulting in the ubiquitination and degradation of ALKBH5 protein. JQ1-increased ALKBH5 then demethylates mRNA of extra spindle pole bodies like 1 (ESPL1) and reduces binding between ESPL1 mRNA and m⁶A reader insulin like growth factor 2 mRNA binding protein 3 (IGF2BP3), leading to decay of ESPL1 mRNA. Animal and clinical studies confirm a critical role of BRD4/ALKBH5/ESPL1 pathway in BC progression. Further, our study sheds light on the crosstalks between histone modification and RNA methylation.

OBJECTIVES: To compare the efficacy of toric implantable collamer lens (Toric-ICL) and femtosecond laser-assisted in situ keratomileusis (FS-LASIK) for myopia correction in patients with moderate to high myopia complicated with astigmatism. METHODS: We retrospectively collected data from 64 patients (aged 18-42 years) with moderate to high myopia complicated with astigmatism (128 eyes) undergoing either Toric-ICL (28 patients/56 eyes) or FS-LASIK (36 patients/72 eyes) at our department between January, 2019 and December, 2020. The changes of uncorrected distance visual acuity (UCVA), spherical equivalent (SE), mean astigmatism correction index (CI), corneal endothelial cell density (ECD) and intraocular pressure (IOP) following the procedures were compared between the two groups. RESULTS: In FS-LASIK group, all the eyes (72/72) achieved an UCVA≥1.0, similar to the rate in Toric-ICL group (55/56 eyes; P=0.2374). The postoperative SE was also comparable between FS-LASIK and Toric-ICL groups [0.43±0.06 D (range: -1.0 to 1.50 D) vs 0.38±0.05 D (range: -0.75 to 1.00 D); P=0.56]. The mean astigmatism CI was significantly higher in FS-LASIK group than in Toric-ICL group (0.8561 vs 0.7176; P<0.0001), and 88.89% of the eyes in FS-LASIK group and 69.64% in Toric-ICL group had postoperative astigmatism ≤0.50 D. No significant changes were observed in postoperative corneal ECD in FS-LASIK group, whereas ECD decreased significantly after the procedure in Toric-ICL group (P=0.0057). The patients undergoing Toric-ICL exhibited no significant changes of postoperative IOP, but the patients receiving FS-LASIK had significantly reduced IOP after the procedure (P<0.001). CONCLUSIONS Although the patients included in Toric-ICL group had higher myopia and astigmatism, Toric-ICL still showed better predictability and efficacy for astigmatic correction in Toric-ICL group. Toric-ICL is an effective and safe equivalent of FS-LASIK for correcting moderate myopia but can be more advantageous for correcting high myopia with astigmatism.
Humans ; Astigmatism/complications* ; Myopia/complications* ; Keratomileusis, Laser In Situ/methods* ; Retrospective Studies ; Adult ; Visual Acuity ; Adolescent ; Young Adult ; Treatment Outcome ; Male ; Lens Implantation, Intraocular/methods* ; Female ; Phakic Intraocular Lenses ; Intraocular Pressure

Glutamine provides carbon and nitrogen to support the proliferation of cancer cells. However, the precise reason why cancer cells are particularly dependent on glutamine remains unclear. In this study, we report that glutamine modulates the tumor suppressor F-box and WD repeat domain-containing 7 (FBW7) to promote cancer cell proliferation and survival. Specifically, lysine 604 (K604) in the sixth of the 7 substrate-recruiting WD repeats of FBW7 undergoes glutaminylation (Gln-K604) by glutaminyl tRNA synthetase. Gln-K604 inhibits SCFFBW7-mediated degradation of c-Myc and Mcl-1, enhances glutamine utilization, and stimulates nucleotide and DNA biosynthesis through the activation of c-Myc. Additionally, Gln-K604 promotes resistance to apoptosis by activating Mcl-1. In contrast, SIRT1 deglutaminylates Gln-K604, thereby reversing its effects. Cancer cells lacking Gln-K604 exhibit overexpression of c-Myc and Mcl-1 and display resistance to chemotherapy-induced apoptosis. Silencing both c-MYC and MCL-1 in these cells sensitizes them to chemotherapy. These findings indicate that the glutamine-mediated signal via Gln-K604 is a key driver of cancer progression and suggest potential strategies for targeted cancer therapies based on varying Gln-K604 status.
Glutamine/metabolism* ; Myeloid Cell Leukemia Sequence 1 Protein/genetics* ; Humans ; Proto-Oncogene Proteins c-myc/genetics* ; Cell Proliferation ; Signal Transduction ; Neoplasms/pathology* ; F-Box-WD Repeat-Containing Protein 7/genetics* ; Cell Survival ; Cell Line, Tumor ; Apoptosis