Background: Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive disease with a poor prognosis for advanced tumors. Anoikis play a key role in cancer metastasis, facilitating the detachment and survival of cancer cells from the primary tumor site. However, few studies have focused on the role of anoikis in HNSC, especially on the prognosis.Methods: Anoikis-related genes (ANRGs) integrated from Genecards and Harmonizome portals were used to identify HNSCC subtypes and to construct a prognostic model for HNSCC patients. Also, we explored the immune microenvironment and enrichment pathways between different subtypes. Finally, we provide clinical experts with a novel nomogram based on ANRGs, with DCA curves indicating the potential clinical benefit of the model for clinical strategies.Results: We identified 69 survival-related HNSCC anoikis-related DEGs, from which 7 genes were selected to construct prognostic models. The prognostic risk score was identified as an independent prognostic factor. Functional analysis showed that these high and low risk groups had different immune status and drug sensitivity. Next risk scores were combined with HNSCC clinicopathological features together to construct a nomogram, and DCA analysis showed that the model could benefit patients from clinical treatment strategies.Conclusion: The predictive seven-gene signature and nomogram established in this study can assist clinicians in selecting personalized treatment for patients with HNSCC.
BackgroundEmergence of blaKPC and blaNDM co-producing Klebsiella pneumoniae strains have led to the limited therapeutic options for clinical treatment. Understanding the diversity and frequency of resistance and virulence genes of these isolates is of great significance.PurposeThe aim of this study is to research the diversity and frequency of resistance and virulence genes in the blaKPC and blaNDM co-producing Klebsiella pneumoniae strains.Methods and ResultsIn this study, 117 K. pneumonia strains were isolated from China, and among of which, 24 were found to be blaKPC and blaNDM co-producing with significant resistance against almost all the commonly used antibiotics. Additionally, 4 strains were hypermucoviscous and 8 showed high serum resistance. Overall, blaSHV, blaCTX-M, tetA and sul1 resistance genes found in 100% of the isolates, followed by blaTEM (95.8%), oqxA/B (91.7%), qnrB (87.5%), aac(6’)Ib-cr (83.3%), blaDHA (79.2%), rmtB (66.7%), qnrS (54.2%), cat(54.2%), floR (50.0%), sul2 (45.8%) cmlA (20.8%)andblaCMY (8.33%), respectively. What’ more, seven blaCTX-M subtypes [blaCTX-M-14 (n=18), blaCTX-M-3(n=11), blaCTX-M-65 (n=4), blaCTX-M-15 (n=3), blaCTX-M-28 (n=2), blaCTX-M-55 (n=2), blaCTX-M-22 (n=1)] and six blaSHV subtypes [blaSHV-12(n=16), blaSHV-11 (n=4), blaSHV-2a(n=1), blaSHV-1(n=1), blaSHV-38(n=1) and blaSHV-28(n=1)] were detected. The frequency of virulence genes was as follows: 100% for entB, ybtS and irp, 95.8% for mrkD, 91.66% for fimH, 79.2% for iutA, 62.5% for iroBCDE, aerobactin and kfu, 66.7% for allS, 45.8% for wcaG, 37.5% for rmpA, 20.8% for pagO and 16.7% for magA.ConclusionFrom this study, we concluded that the blaKPC and blaNDM co-producing Klebsiella pneumoniae strains have a high diversity and frequency of resistance and virulence genes. This study may offer hospitals important information about the control of infections caused by blaKPC and blaNDM co-producing Klebsiella pneumoniae.
Context Zhibai Dihuang pill (ZD), a traditional Chinese medicine nourishes Yin and reduces internal heat, is believed to have therapeutic effects on urinary tract infections (UTIs). Objective To explore the effects and mechanism of modified ZD (MZD) on UTI induced by extended-spectrum β-lactamase (ESBLs) Escherichia coli . Materials and methods Thirty Sprague-Dawley rats were randomly divided into control, model (0.5 mL 1.5 × 10 8 CFU/mL ESBLs E. coli ), MZD (20 g/kg MZD), LVFX (0.025 g/kg LVFX), and MZD + LVFX groups (20 g/kg MZD + 0.025 g/kg LVFX), n = 6. After 14 days of treatment, serum biochemical indicators, renal function indicators, bladder and renal histopathology, and urine bacterial counts in rats were determined. Additionally, the effects of MZD on ESBLs E. coli biofilm formation and related gene expression were analyzed. Results MZD significantly decreased the count of white blood cells (from 13.12 to 9.13), the proportion of neutrophils (from 43.53 to 23.18), C-reactive protein (from 13.21 to 9.71), serum creatinine (from 35.78 to 30.15), and urea nitrogen (from 12.56 to 10.15), relieved the inflammation and fibrosis of bladder and kidney tissues, and reduced the number of bacteria in urine (from 2174 to 559). In addition, MZD inhibited the formation of ESBLs E. coli biofilms (2.04-fold) and decreased the gene expressions of luxS , pfS and ompA (1.41–1.62-fold). Discussion and conclusion MZD treated ESBLs E. coli- induced UTI inhibited biofilm formation, providing a theoretical basis for the clinical application of MZD. Further study on the clinical effect of MZD may provide a novel therapy option for UTI.
Aiming at the problem of logistic division based on genetic algorithm, it is planned to study the improvement of logistic distribution methods. We first meet the requirements of the genetic algorithm of logistic development, use the division method to divide the delivery area of the gene, and formulate a functional delivery plan, which generally includes weight measurement, measurement time, customer value measurement, instrument measurement time, and the whole process index. We set weight goals and find the best way to improve genetic algorithm delivery. The experimental comparison results show that the optimal method takes less than 2 minutes to find the optimal method, while the normal process takes 4 minutes to find the optimal method, and the longest can reach 5 minutes. The comparison shows that the traditional algorithm takes longer to find the correct way than the algorithm developed this time. Finally, the simple logistic distribution optimization method model and the soft time-limited logistic distribution processing optimization model are calculated and simulated by the genetic testing algorithm and genetic algorithm development. The effectiveness of the improved genetic algorithm in local research and the effectiveness of the logistic transportation allocation solution are determined.
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