Goffmadden1384
Selection of high yielding and stable maize hybrid requires effective method of evaluation. Multienvironment evaluation is a critical step in plant breeding programs that is aimed at selecting the ideal genotype in a wide range of environments. A method of evaluation that combines a variety parameter of stability could provide more accurate information to select the ideal genotype. The aims of the study were (i) to identify the effect of genotype, environment, and genotype × environment interactions (GEIs) on maize hybrid yields and (ii) to select and to compare maize hybrids that have high and stable yields in diverse environments in Sumatra Island based on combined analysis, selection index, and GGE biplot. The study was conducted in five different environments in Sumatra Island, Indonesia, using a randomized complete block design repeated three times. Data were estimated using combined variance analysis, parametric and nonparametric stability, sustainability index, and GGE biplot. The results showed that the genotype had a significant effect on maize hybrid yields with a contribution of 41.797%. The environment contributed to 24.314%, and GEIs contributed 33.889% of the total variation. E1 (Karo, South Sumatra; dry season) and E3 (Tanjung Bintang, Lampung; dry season) were identified as the most ideal environments (representative) for testing the hybrids for wider adaptability. The maize hybrid with high and stable yields can be selected based on combined stability analysis and sustainability index as well as GGE biplot. These three methods are effectively selected high yielding and stable genotypes when they are used together. The three maize hybrids, namely, MH2, MH8, and MH9, are recommended as high yielding and stable genotype candidates.The diagnosis and treatment of patients in the healthcare industry are greatly aided by data analytics. Massive amounts of data should be handled using machine learning approaches to provide tools for prediction and categorization to support practitioner decision-making. Based on the kind of tumor, disorders like breast cancer can be categorized. The difficulties associated with evaluating vast amounts of data should be overcome by discovering an efficient method for categorization. Based on the Bayesian method, we analyzed the influence of clinic pathological indicators on the prognosis and survival rate of breast cancer patients and compared the local resection value directly using the lymph node ratio (LNR) and the overall value using the LNR differences in effect between estimates. Logistic regression was used to estimate the overall LNR of patients. After that, a probabilistic Bayesian classifier-based dynamic regression model for prognosis analysis is built to capture the dynamic effect of multiple clinic pathological markers on patient prognosis. The dynamic regression model employing the total estimated value of LNR had the best fitting impact on the data, according to the simulation findings. In comparison to other models, this model has the greatest overall survival forecast accuracy. These prognostic techniques shed light on the nodal survival and status particular to the patient. Additionally, the framework is flexible and may be used with various cancer types and datasets.Uremia is a manifestation of end-stage renal failure and is a clinical syndrome shared by various advanced renal diseases. In recent years, the prevalence of uremia has been increasing. Maintenance hemodialysis therapy is used as the treatment of choice for uremia and is widely used. For uremia, the day-to-day treatment has a serious impact on the patient's symptoms, causing a lot of unnecessary burdens to life. While imposing a severe symptom burden on patients, excessive sleep deprivation repair time can cause various risk injuries and further increase the probability of patient mortality. The resulting sleep deprivation can lead to the functional damage of multiple organs and systems such as the cardiovascular system and the immune system. Therefore, improving sleep quality can increase the survival rate of patients to a certain extent. There is less domestic attention to its related research, and there are not many discussions on the pathogenesis. Based on this, this paper proposes to explore the effect of hemodialysis on the sleep quality of patients with uremia under the support of intelligent data. In this study, it was found that MHD patients had various sleep quality problems, and the incidence of sleep disturbance was 61.78%. The experimental results in this paper show that the incidence of sleep disturbance in diabetic nephropathy is 89.7%. This may be combined with a series of complications in patients with diabetes, which in turn affects the quality of sleep in patients. It may increase patient mortality and affect patient quality of life and survival. Therefore, we should pay close attention to the prevention of various complications in diabetic dialysis patients, which will help to improve sleep quality.MRG-binding protein (MRGBP) is a transcription factor widely involved in physiological and pathological processes. Many studies have discussed the relationship between the expression level of MRGBP and the prognosis of various malignant tumours. However, the role and clinicopathological significance of MRGBP in head and neck squamous cell carcinoma (HNSC) are unclear. In this study, the Wilcoxon signed-rank test and logistic regression were used to analyze the relationship between clinical characteristics and MRGBP expression in HNSC. The Kaplan-Meier plotter analysis and Cox regression analysis were established to evaluate the effect of MRGBP on prognosis, and the receiver operating characteristic (ROC) curve and nomogram was constructed. Gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) were used to analyze the correlation between MRGBP and immune infiltration. The results showed that the expression of MRGBP in HNSC tissues was significantly higher than that in normal tissues. The KM plotter analysis showed that the OS of HNSC patients was shorter. The multivariate Cox analysis further confirmed that increased expression of MRGBP was an independent risk factor for OS in HNSC patients. In addition, ROC analysis confirmed its diagnostic value and constructed prognostic nomograms, including age, T, M, N classification, pathological stage, and MRGBP. GSEA showed that MRGBP was associated with high expression of GPCR ligand binding, interleukin receptor binding, and neutrophil degranulation, and ssGSEA showed that MRGBP was associated with T cells and mast cells. In conclusion, MRGBP can serve as an independent prognostic biomarker related to immune invasion of head and neck squamous cell carcinoma.
The present study aimed to develop gefitinib-loaded solid lipid nanoparticles (GEF-SLN), and GEF-loaded PEGylated SLN (GEF-P-SLN) for targeting metastatic lung cancer through the lymphatic system.
The prepared SLNs were characterized in terms of physicochemical properties, entrapment efficiency, and in-vitro release. Furthermore, ex-vivo permeability was investigated using the rabbit intestine. Cytotoxicity and apoptotic effects were studied against A549 cell lines as a model for lung cancer.
The present results revealed that the particle size and polydispersity index of the prepared formulations range from 114 to 310 nm and 0.066 to 0.350, respectively, with negative zeta-potential (-14 to -27.6). Additionally, SLN and P-SLN showed remarkable entrapment efficiency above 89% and exhibited sustained-release profiles. The permeability study showed that GEF-SLN and GEF-P-SLN enhanced the permeability of GEF by 1.71 and 2.64-fold, respectively, compared with GEF suspension. Cytotoxicity showed that IC
of pure GEF was 3.5 μg/mL, which decreased to 1.95 and 1.8 μg/mL for GEF-SLN and GEF-P-SLN, respectively. Finally, the apoptotic study revealed that GEF-P-SLN decreased the number of living cells from 49.47 to 3.43 when compared with pure GEF.
These results concluded that GEF-P-SLN is a promising approach to improving the therapeutic outcomes of GEF in the treatment of metastatic lung cancer.
These results concluded that GEF-P-SLN is a promising approach to improving the therapeutic outcomes of GEF in the treatment of metastatic lung cancer.
Naringin is a naturally occurring flavanone that promotes osteogenesis. Owing to the high lipophilicity, poor in vivo bioavailability, and extensive metabolic alteration upon administration, the clinical efficacy of naringin is understudied. Additionally, information on the molecular mechanism by which it promotes osteogenesis is limited.
In this study, we prepared TAT & RGD peptide-modified naringin-loaded nanoparticles (TAT-RGD-NAR-NPs), evaluated their potency on the osteogenic differentiation of human dental pulp stem cells (hDPSCs), and studied its mechanism of action through metabolomic analysis.
The particle size and zeta potential of TAT-RGD-NAR-NPs were 160.70±2.05 mm and -20.77±0.47mV, respectively. The result of cell uptake assay showed that TAT-RGD-NAR-NPs could effectively enter hDPSCs. TAT-RGD-NAR-NPs had a more significant effect on cell proliferation and osteogenic differentiation promotion. Furthermore, in metabolomic analysis, naringin particles showed a strong influence on the glycerophospholipid metabolism pathway of hDPSCs. Specifically, it upregulated the expression of
and
(two isozymes of phospholipase A2, PLA2), increased the biosynthesis of lysophosphatidic acid (LPA).
These results suggested that TAT-RGD-NPs might be used for transporting naringin to hDPSCs for modulating stem cell osteogenic differentiation. The metabolomic analysis was used for the first time to elucidate the mechanism by which naringin promotes hDPSCs osteogenesis by upregulating PLA2G3 and PLA2G1B.
These results suggested that TAT-RGD-NPs might be used for transporting naringin to hDPSCs for modulating stem cell osteogenic differentiation. The metabolomic analysis was used for the first time to elucidate the mechanism by which naringin promotes hDPSCs osteogenesis by upregulating PLA2G3 and PLA2G1B.
With high malignancy, retinoblastoma (RB) commonly occurs in infants and has incredible difficulty with the early diagnosis. E7766 in vitro In recent years, the integrated theranostics of multimodal imaging-guided therapy has shown promising potential for oncotherapy.
To prepare folate/magnetic dual-target theranostic nanoparticles integrating with US/PA/MR imaging and the synergistic photothermal treatment (PTT)/photodynamic treatment (PDT) for the early diagnosis and timely intervention of RB cancer.
Folate/magnetic dual-target cationic nanoliposomes (CN) encapsulating indocyanine green (ICG) and perfluorohexane(PFH)(FA-CN-PFH-ICG-Fe
O
, FCNPIFE) were synthesized and characterized. Then we evaluated their targeting ability, US/PA/MR imaging effects, and the efficacy of synergistic PTT/PDT in vitro and in vivo. Finally, we explored the mechanism of synergistic PTT/PDT in Y79 tumor-bearing mice.
FCNPIFEs were stable and uniform in 7 days. They showed excellent in vitro targeting ability with a 95.29% cell uptake rate. The in vitro US/PA/MRI imaging results of FCNPIFEs showed a concentration-dependent manner, and in vitro therapy FCNPIFEs exhibited an enhanced anticancer efficacy against Y79 cells. In vivo analysis confirmed that FCNPIFEs enabled a targeted synergistic PTT/PDT under US/PA/MR imaging guidance in Y79 tumor-bearing mice, achieving almost complete tumor regression. Immunofluorescence results displayed weaker fluorescence intensity compared with other single treatment groups, confirming that PTT/PDT synergistic therapy effect was achieved by down-regulating the expression of HIF-1α and HSP70.
FCNPIFEs were verified as promising theranostic nanoliposomes for RB oncotherapy and showed great potential in clinical application.
FCNPIFEs were verified as promising theranostic nanoliposomes for RB oncotherapy and showed great potential in clinical application.
