RESUMEN
Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA-SVM, SVM, UMAP-SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.
Asunto(s)
Espectrometría Raman , Espectrometría Raman/métodos , Humanos , Femenino , Neoplasias Endometriales/patología , Neoplasias Endometriales/diagnóstico , Neoplasias Endometriales/química , Aprendizaje Automático , Melanoma/patología , Melanoma/diagnóstico , Melanoma/clasificación , Vesículas Extracelulares/química , Máquina de Vectores de Soporte , Bacterias/clasificación , Bacterias/aislamiento & purificación , Inteligencia ArtificialRESUMEN
OBJECTIVE: To investigate the safety and efficacy of mitoxantrone liposome in the treatment of children with high-risk acute myeloid leukemia (AML). METHODS: The children with high-risk AML who received the mitoxantrone liposome regimen at Wuhan Children's Hospital from January 2022 to February 2023 were collected as the observation group, and the children with high-risk AML who received idarubicin regimen were enrolled as controls, and their clinical data were analyzed. Time to bone marrow recovery, the complete remission rate of bone marrow cytology, the clearance rate of minimal residual disease, and treatment-related adverse reactions were compared between the two groups. RESULTS: The patients treated with mitoxantrone liposome showed shorter time to recovery of leukocytes(17 vs 21 day), granulocytes(18 vs 24 day), platelets(17 vs 24 day), and hemoglobin(20 vs 26 day) compared with those treated with idarubicin, there were statistical differences (P <0.05). The effective rate and MRD turning negative rate in the observation group were 90.9% and 72.7%, respectively, while those in the control group were 94.1% and 76.4%, with no statistical difference (P >0.05). The overall response rate of the two groups of patients was similar. CONCLUSION: The efficacy of mitoxantrone liposome is not inferior to that of idarubicin in children with high-risk AML, but mitoxantrone liposome allows a significantly shorter duration of bone marrow suppression and the safety is better.
Asunto(s)
Leucemia Mieloide Aguda , Liposomas , Mitoxantrona , Humanos , Mitoxantrona/administración & dosificación , Leucemia Mieloide Aguda/tratamiento farmacológico , Niño , Idarrubicina/administración & dosificación , Masculino , Femenino , AdolescenteRESUMEN
The aim of this study was to investigate the effect of hesperidin on the liver and kidney dysfunctions induced by nickel. The mice were divided into six groups: nickel treatment with 80 mg/kg, 160 mg/kg, 320 mg/kg hesperidin groups, 0.5% CMC-Na group, nickel group, and blank control group. Histopathological techniques, biochemistry, immunohistochemistry, and the TUNEL method were used to study the changes in structure, functions, oxidative injuries, and apoptosis of the liver and kidney. The results showed that hesperidin could alleviate the weight loss and histological injuries of the liver and kidney induced by nickel, and increase the levels of lactate dehydrogenase (LDH), alanine aminotransferase (GPT), glutamic oxaloacetic transaminase (GOT) in liver and blood urea nitrogen (BUN), creatinine (Cr) and N-acetylglucosidase (NAG) in kidney. In addition, hesperidin could increase the activities of superoxide dismutase (SOD), catalase (CAT), glutathione (GSH), and glutathione peroxidase (GSH-Px) in the liver and kidney, decrease the content of malondialdehyde (MDA) and inhibit cell apoptosis. It is suggested that hesperidin could help inhibit the toxic effect of nickel on the liver and kidney.
RESUMEN
Recent studies suggest that PARP inhibitors and POLQ inhibitors confer synthetic lethality in BRCA1-deficient tumors by accumulation of single-stranded DNA (ssDNA) gaps at replication forks. Loss of USP1, a deubiquitinating enzyme, is also synthetic lethal with BRCA1 deficiency, and USP1 inhibitors are now undergoing clinical development for these cancers. Here, we show that USP1 inhibitors also promote the accumulation of ssDNA gaps during replication in BRCA1-deficient cells, and this phenotype correlates with the drug sensitivity. USP1 inhibition increased monoubiquitinated PCNA at replication forks, mediated by the ubiquitin ligase RAD18, and knockdown of RAD18 caused USP1 inhibitor resistance and suppression of ssDNA gaps. USP1 inhibition overcame PARP inhibitor resistance in a BRCA1-mutated xenograft model and induced ssDNA gaps. Furthermore, USP1 inhibition was synergistic with PARP and POLQ inhibition in BRCA1-mutant cells, with enhanced ssDNA gap accumulation. Finally, in patient-derived ovarian tumor organoids, sensitivity to USP1 inhibition alone or in combination correlated with the accumulation of ssDNA gaps. Assessment of ssDNA gaps in ovarian tumor organoids therefore represents a rapid approach for predicting response to USP1 inhibition in ongoing clinical trials.