With the rapid development of artificial intelligence (AI) technologies, and the large amount of pharmacovigilance-related data stored in an electronic manner, data-driven automatic methods need to be urgently applied to all aspects of pharmacovigilance to assist healthcare professionals. However, the quantity and quality of data directly affect the performance of AI, and there are particular challenges to implementing AI in limited-resource settings. Analyzing challenges and solutions for AI-based pharmacovigilance in resource-limited settings can improve pharmacovigilance frameworks and capabilities in these settings. In this review, we summarize the challenges into four categories: establishing a database for an AI-based pharmacovigilance system, lack of human resources, weak AI technology and insufficient government support. This study also discusses possible solutions and future perspectives on AI-based pharmacovigilance in resource-limited settings.
LINC00461 represents a new long noncoding RNA. However, it is unclear whether LINC00461 is associated with glucose metabolism and proliferation in triple-negative breast cancer. Here, we show that LINC00461 overexpression induces glucose metabolism and proliferation in TNBC, whereas its downregulation markedly reduces glucose metabolism and proliferation. Mechanistically, LINC00461 might function in TNBC by binding with HSP90. Then, it enhances the interaction between HSP90 and c-Myc and inhibits ubiquitination and degradation of c-Myc to regulate c-Myc target genes-LDHA. Clinically, LINC00461 has tight associations with tumor grade and TNM in cancer patients. There is compelling evidence LINC00461 may be exploited as a possible novel molecular marker and therapeutic target in TNBC.
Objective. To evaluate the diagnostic value of the nanometer carbon suspension tracer staining technique in sentinel lymph node biopsy of breast cancer is the objective of this study. Methods. The PubMed, Embase, Cochrane Library (Central), and Web of Science (SCI Expanded), and Chinese databases (CNKI, VIP, Wan Fang, and CBM) were systematically searched for studies on the diagnostic value of nanocarbon suspension in sentinel lymph node biopsy of breast cancer. Two reviewers independently assessed the methodological quality of each study using the QUADAS-2 tool. The extracted valid data were calculated using Meta-Disc1.4 software and tested for heterogeneity. STATA14.0 software was selected for sensitivity analysis of the included studies, and publication bias was assessed using Deeks’ forest plot asymmetry test. Results. A total of 10 studies were obtained. The pooled data were as follows: sensitivity, 0.92 (0.88~0.95); specificity, 0.99 (0.98~1.00); positive likelihood ratio, 69.24 (30.34~158.02); negative likelihood ratio, 0.09 (0.06~0.13); and the combined diagnostic odds ratio, 747.40 (285.77~1954.76), AUC = 0.9881 . Nanocarbon suspension tracers have an accuracy rate of 98.81% in the diagnosis of sentinel lymph nodes in breast cancer. Conclusion. Tracer staining technology based on nanocarbon suspension can accurately assess the status of lymph nodes in sentinel lymph node biopsy of breast cancer and has good stability and operability, which is worthy of clinical promotion.
2614 Background: PAK (P21 activated kinase) is a family of serine threonine kinases, that consist of 6 members, PAKs 1-6, which are positioned at an intersection of multiple signaling pathways implicated in oncogenesis. Recent studies have found relationship between mutations of some PAK family genes and antitumor immunity. Here, we explored the associations of PAK family gene mutations with ICI response based on multidimensional data from multiple solid tumors. Methods: An immunotherapy cohort (Broad/Dana-Farber, Nat Genet 2018, N = 249) within 7 types of tumors as discovery stage, was used to explore the association of PAK family genes mutations with tumor mutation burden (TMB) and efficacy of immunotherapy. To further validate the predictive value of PAK family genes, we employed another ICI-treated cohort, MSKCC cohort (Samstein, Nat Genet 2019, n = 1661), as a validation cohort. TMB was calculated as the total count of nonsynonymous mutations in coding sequence. Results: In discovery cohort, compared to that in the PAK-Wt group, longer OS was observed in the PAK-Mut group (mOS: 31.3 vs 15.4 months, HR = 0.59, 95% CI: 0.36 to 0.96, P= 0.03). In addition, PAK-Mut group had higher TMB ( P< 0.001). Median TMB in PAK-Mut group and PAK-Wt group is 17.79 (IQR, 7.45-36.55) Mut/Mb and 5.47 (IQR, 2.46-9.09) Mut/Mb, separately. In validation cohort, PAK-Mut patients also achieved significant improved OS compared with PAK-Wt patients (mOS: 44.0 vs 17.0 months, HR = 0.51, 95% CI: 0.37 to 0.69, P< 0.001). Moreover, multivariable analysis of validation cohort demonstrated that PAK-Mut was associated with better OS (HR = 0.71; 95%CI, 0.52-0.98; P = 0.036), after adjusting for Age, Gender, Metastasis, Treatment, TMB, Cancer type. Compared with PAK-Wt group (median [IQR]: 5.90 [2.95-10.04]), PAK-Mut group (median [IQR]: 21.19 [11.81-43.29]) also had higher TMB ( P< 0.001). Conclusions: The results indicated that PAK family genes mutation was associated with a higher TMB both discovery and validation cohort. Analysis of discover and validation immunotherapy cohort data showed PAK family was associated with better OS. These findings indicate that these genes mutation may serve as a potential predictive biomarker for ICI in solid tumor patients.
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