Biomedical Journal最新文献

Background: Electrocardiogram (ECG) abnormalities have demonstrated potential as prognostic indicators of patient survival. However, the traditional statistical approach is constrained by structured data input, limiting its ability to fully leverage the predictive value of ECG data in prognostic modeling.

Methods: This study aims to introduce and evaluate a deep-learning model to simultaneously handle censored data and unstructured ECG data for survival analysis. We herein introduce a novel deep neural network called ECG-surv, which includes a feature extraction neural network and a time-to-event analysis neural network. The proposed model is specifically designed to predict the time to 1-year mortality by extracting and analyzing unique features from 12-lead ECG data. ECG-surv was evaluated using both an independent test set and an external set, which were collected using different ECG devices.

Results: The performance of ECG-surv surpassed that of the Cox proportional model, which included demographics and ECG waveform parameters, in predicting 1-year all-cause mortality, with a significantly higher concordance index (C-index) in ECG-surv than in the Cox model using both the independent test set (0.860 [95% CI: 0.859- 0.861] vs. 0.796 [95% CI: 0.791- 0.800]) and the external test set (0.813 [95% CI: 0.807- 0.814] vs. 0.764 [95% CI: 0.755- 0.770]). ECG-surv also demonstrated exceptional predictive ability for cardiovascular death (C-index of 0.891 [95% CI: 0.890- 0.893]), outperforming the Framingham risk Cox model (C-index of 0.734 [95% CI: 0.715-0.752]).

Conclusion: ECG-surv effectively utilized unstructured ECG data in a survival analysis. It outperformed traditional statistical approaches in predicting 1-year all-cause mortality and cardiovascular death, which makes it a valuable tool for predicting patient survival.

Objective: The aim of this study was twofold: to assess the annual pharmaceutical savings associated with the treatment of cancer patients at Marqués de Valdecilla University Hospital and to estimate the cost of innovative antineoplastic therapies that patients receive as experimental treatment, both during clinical trials throughout 2020.

Material and methods: An observational and financial analysis of the drug cost related to clinical trials was applied. Direct cost savings to the Regional Health System of Cantabria and the cost of innovative therapies used as an experimental treatment in clinical trials were quantified.

Results: This study includes 38 clinical trials with a sample of 101 patients. The clinical trials analyzed provide a total cost savings of €603,350.21 and an average cost saving of €6,630.22 per patient. Furthermore, the total investment amounts to €789,892.67, with an average investment of €15,488.09 per patient.

Conclusions: Clinical trials are essential for the advancement of science. Furthermore, clinical trials can be a significant source of income for both hospitals and Regional Health Systems, contributing to their financial sustainability.

Diagnostic imaging is essential in modern trauma care for initial evaluation and identifying injuries requiring intervention. Deep learning (DL) has become mainstream in medical image analysis and has shown promising efficacy for classification, segmentation, and lesion detection. This narrative review provides the fundamental concepts for developing DL algorithms in trauma imaging and presents an overview of current progress in each modality. DL has been applied to detect free fluid on Focused Assessment with Sonography for Trauma (FAST), traumatic findings on chest and pelvic X-rays, and computed tomography (CT) scans, identify intracranial hemorrhage on head CT, detect vertebral fractures, and identify injuries to organs like the spleen, liver, and lungs on abdominal and chest CT. Future directions involve expanding dataset size and diversity through federated learning, enhancing model explainability and transparency to build clinician trust, and integrating multimodal data to provide more meaningful insights into traumatic injuries. Though some commercial artificial intelligence products are Food and Drug Administration-approved for clinical use in the trauma field, adoption remains limited, highlighting the need for multi-disciplinary teams to engineer practical, real-world solutions. Overall, DL shows immense potential to improve the efficiency and accuracy of trauma imaging, but thoughtful development and validation are critical to ensure these technologies positively impact patient care.

Background: The impact and underlying mechanisms of astragalus polysaccharide (APS) on prostate cancer, particularly its role in immunomodulation, remain inadequately elucidated.

Methods: This study employed the XTT assay for assessing proliferation in prostate cancer cells and macrophages. T cell proliferation was determined using the Carboxyfluorescein diacetate succinimidyl ester labeling assay. APS's effect on T cells and macrophages was scrutinized via flow cytometry, Western blot analysis, ELISA, quantitative PCR and cytokine membrane arrays. The effect of APS on interaction between PD-L1 and PD-1 was investigated by the PD-L1/PD-1 homogeneous assay. Additionally, the impact of conditioned medium from T cells and macrophages on PC-3 cell migration was explored through migration assays.

Results: It was observed that APS at concentrations of 1 and 5 mg/mL enhanced the proliferation of CD8+ T cells. At a concentration of 5 mg/mL, APS activated both CD4+ and CD8+ T cells, attenuated PD-L1 expression in prostate cancer cells stimulated with interferon gamma (IFN-γ) or oxaliplatin, and moderately decreased the population of PD-1+ CD4+ and PD-1+ CD8+ T cells. Furthermore, APS at this concentration impeded the interaction between PD-L1 and PD-1, inhibited the promotion of prostate cancer migration mediated by RAW 264.7 cells, THP-1 cells, CD4+ T cells, and CD8+ T cells, and initiated apoptosis in prostate cancer cells treated with conditioned medium from APS (5 mg/mL)-treated CD8+ T cells, RAW 264.7 cells, or THP-1 cells.

Conclusion: The findings indicate a potential role of 5 mg/mL APS in modulating the PD-1/PD-L1 pathway and influencing the immune response, encompassing T cells and macrophages. Consequently, further in vivo research is recommended to assess the efficacy of APS.

Background: Autoimmune hepatitis (AIH) is an immune-mediated hepatic disease associated with intense complications. AIH is more common in females and needs effective drugs to treat. Guizhi Fuling Wan (GZFLW) is a traditional Chinese herbal formula used to treat various gynecologic diseases. In this study, we aim to extend the new use of GZFLW for AIH.

Methods: The tandem MS-based analysis was used to identify secondary metabolites in GZFLW. Therapeutic effects of GZFLW were tested in a concanavalin A (Con A)-induced AIH model in mice. Ethnopharmacological mechanisms underlying the antiapoptotic, antioxidant, and immunomodulatory protective effects were determined.

Results: Oral administration of GZFLW attenuates AIH in a Con A-induced hepatotoxic model in vivo. The tandem MS-based analysis identified 15 secondary metabolites in GZFLW. The Con A-induced AIH syndromes, including hepatic apoptosis, inflammation, reactive oxygen species accumulation, function failure, and mortality, were significantly alleviated by GZFLW in mice. Mechanistically, GZFLW restrained the caspase-dependent apoptosis, restored the antioxidant system, and decreased pro-inflammatory cytokine production in the livers of Con A-treated mice. Besides, GZFLW repressed the Con A-induced hepatic infiltration of inflammatory cells, splenic T cell activation, and splenomegaly in mice.

Conclusions: Our findings demonstrate the applicable potential of GZFLW in treating AIH. It prompts further investigation of GZFLW as a treatment option for AIH and possibly other hepatic diseases.

N6-methyladenosine (m⁶A) is a mRNA modification with important roles in gene expression. In African trypanosomes, this post-transcriptional modification is detected in hundreds of transcripts and it affects the stability of the variant surface glycoprotein (VSG) transcript in the proliferating blood stream form. However, how the m⁶A landscape varies across the life cycle remains poorly defined. Using full-length, non-fragmented RNA, we immunoprecipitated and sequenced m⁶A-modified transcripts across three life cycle stages of Trypanosoma brucei - slender (proliferative), stumpy (quiescent), and procyclic forms (proliferative). We found that 1037 transcripts are methylated in at least one of these three life cycle stages. While 21% of methylated transcripts are common in the three stages of the life cycle, globally each stage has a distinct methylome. Interestingly, 47% of methylated transcripts are detected in the quiescent stumpy form only, suggesting a critical role for m⁶A when parasites exit the cell cycle and prepare for transmission by the Tsetse fly. In this stage, we found that a significant proportion of methylated transcripts encodes for proteins involved in RNA metabolism, which is consistent with their reduced transcription and translation. Moreover, we found that not all major surface proteins are regulated by m⁶A, as procyclins are not methylated, and that, within the VSG repertoire, not all VSG transcripts are demethylated upon parasite differentiation to procyclic form. This study reveals that the m⁶A regulatory landscape is specific to each life cycle stage, becoming more pervasive as T. brucei exits the cell cycle.