Abstract Background Insulin resistance (IR) induces hyperinsulinemia, which activates downstream signaling pathways such as the phosphatidylinositol-3-kinase/protein kinase B (PI3K/AKT) pathway, ultimately leading to abnormal proliferation and apoptosis of endometrial cells. This is thought to be a key pathogenic mechanism underlying the development of endometrial polyps (EP). This study aims to investigate the relationship between IR and the development of EP, the expression levels of downstream signaling molecules, including PI3K and AKT, and related laboratory parameters were examined. Methods A total of 100 patients who visited the gynecology outpatient clinic of Zhongda Hospital affiliated with Southeast University from May 2021 to March 2023 and were diagnosed with abnormal endometrial echoes by vaginal ultrasound and underwent hysteroscopic diagnostic curettage were enrolled in this study. General data and relevant hematological indicators were compared, and intraoperative specimens were obtained for pathological examination. Possible factors influencing the development of endometrial polyps were analyzed using Pearson correlation analysis and logistic regression analysis. Results In terms of body mass index, waist circumference, fasting insulin, insulin resistance index, serum total testosterone, and free testosterone index, women of childbearing age in the endometrial polyp group had higher values than those in the non-polyp group, while sex hormone-binding globulin in the endometrial polyp group was lower than that in the non-polyp group, and the differences were statistically significant ( P < 0.05). The expression scores and mRNA expression levels of PI3K and AKT proteins were higher in the EP group than in the non-EP group ( p < 0.05). Pearson correlation analysis showed a positive correlation between HOMA-IR and the expression scores of PI3K and AKT proteins ( p < 0.01). Conclusions Insulin resistance and abnormal activation of the phosphatidylinositol 3-kinase/protein kinase B signaling pathway may be potential pathogenic mechanisms for the development of endometrial polyps.
Accurately identifying multidrug-resistant (MDR) bacteria from clinical samples has long been a challenge. Herein, we report a simple and programmable dual-mode aptasensor called DAPT to reliably detect MDR bacteria. The DAPT method comprises two elements, namely the mode of dynamic light scattering (Mode-DLS) for ultrasensitive detection and the mode of fluorescence (Mode-Flu) for reliable quantification as a potent complement. Benefiting from the states of aptamer-modified gold nanoparticles (AptGNPs) sensitively changing from dispersion to aggregation, the proposed Mode-DLS achieved the rapid, specific, and ultrasensitive detection of methicillin-resistant Staphylococcus aureus (MRSA) at the limit of detection (LOD) of 4.63 CFU mL-1 in a proof-of-concept experiment. Simultaneously, the Mode-Flu ensured the accuracy of the detection, especially at a high concentration of bacteria. Moreover, the feasibility and universality of the DAPT platform was validated with four other superbugs by simply reprogramming the corresponding sequence. Overall, the proposed DAPT method based on a dual-mode aptasensor can provide a universal platform for the rapid and ultrasensitive detection of pathogenic bacteria due to its superior programmability.
A novel multifunctional nano-drug delivery system based on reversal of peptide charge was successfully developed for anticancer drug delivery and imaging. Mesoporous silica nano-particles (MSN) ~50 nm in diameter were chosen as the drug reservoirs, and their surfaces were modified with HIV-1 transactivator peptide-fluorescein isothiocyanate (TAT-FITC) and YSA-BHQ1. The short TAT peptide labeled with FITC was used to facilitate intranuclear delivery, while the YSA peptide tagged with the BHQ1 quencher group was used to specifically bind to the tumor EphA2 membrane receptor. Citraconic anhydride (Cit) was used to invert the charge of the TAT peptide in neutral or weak alkaline conditions so that the positively charged YSA peptide could combine with the TAT peptide through electrostatic attraction. The FITC fluorescence was quenched by the spatial approach of BHQ1 after the two peptides bound to each other. However, the Cit-amino bond was unstable in the acidic atmosphere, so the positive charge of the TAT peptide was restored and the positively charged YSA moiety was repelled. The FITC fluorescence was recovered after the YSA-BHQ1 moiety was removed, and the TAT peptide led the nano-particles into the nucleolus. This nano-drug delivery system was stable at physiological pH, rapidly released the drug in acidic buffer, and was easily taken up by MCF-7 cells. Compared with free doxorubicin hydrochloride at an equal concentration, this modified MSN loaded with doxorubicin molecules had an equivalent inhibitory effect on MCF-7 cells. This nano-drug delivery system is thus a promising method for simultaneous cancer diagnosis and therapy.
In this work, we propose a data-driven reduced-order model (ROM) for high dimensional flow fields by combining flow modal decomposition and multiple regression. SVD-based proper orthogonal decomposition (POD) is employed to extract principal spatial modes representing energy and dynamics level of flow field. The temporal coefficient regression for flow modal series is realized through intelligent algorithms: light gradient boosting machine (LGBM), long short-term memory (LSTM), and temporal convolutional neural network (TCN). The performance of the ROMs are assessed by predicting and analyzing low Reynolds number flow around a circular cylinder and transonic flow around a airfoil. The experiments show that vortex flow and shock flow are both well predicted with the POD-LGBM, POD-LSTM and POD-TCN, whereas the prediction result of POD-TCN is the closest to the numerical solution, with the minimum root mean squared error. Also, it should be noted that the prediction accuracy depends on the reduced-order results of flow field.
Unacceptable amounts of computation can be generated in massive multiple-input multiple-output (MIMO) systems with minimum mean squared error (MMSE) detection at the received side. A weighted Gauss-Seidel iterative algorithm with fast convergence is launched. The proposed algorithm uses a mixture of Conjugate Gradient and Jacobi iterations to select the optimal search direction. Then weighting factor is used to accelerate the traditional Gauss-Seidel iterative algorithm. The results show that the detection capability of the scheme has been improved. The theoretical analysis verifies that the proposed algorithm has a lower computational complexity compared to the MMSE algorithm. After simulation analysis, the recommended algorithm can gain better convergence speed and BER function with fewer iterations. If user antennas setting values is similar to base station antennas, the proposed algorithm is significantly better.
Ureaplasma urealyticum is a common genital mycoplasma in men and women, which can cause reproductive tract infection and infertility, and is also related to adverse pregnancy outcomes and neonatal diseases. Pathogen culture and polymerase chain reaction (PCR) are the main methods for the diagnosis of U. urealyticum. However, pathogen culture takes too long, and PCR requires professional personnel and sophisticated instruments. Here, we report a simple, convenient, sensitive, and specific detection method, which combines catalytic hairpin assembly with a lateral flow immunoassay strip. Only a water bath and a fluorescence reader are needed to detect the results in 30 min. We can realize the point-of-care testing of U. urealyticum by this method. To verify this method, we selected 10 clinical samples for testing, and the test results were exactly the same as the clinical report.
To explore the regularity of abnormal telomeric associations in cellular chromosome of human lung cancer.The rate of telomeric associations was detected in human lung adenocarcinoma cell line A549 , peripheral blood lymphocytes in 15 patients with lung cancer , and in patients with non-cancerous diseases and normal adults as control by chromosome preparation assay.A highly significant difference in the rate of telomeric associations was found between lung cancer group and control group ( P < 0. 005) . Human lung adenocarcinoma cell line A549 had the highest rate of telomeric associations in all groups ( P < 0. 005) . Moreover , there was significant difference in the rate of telomeric associations in idiogramB , idiogram C and idiogram D between lung cancer group and control group (idiogram B , P < 0. 05 ; idiogram C , idiogram D , P < 0. 005) .Compared with control group , human lung adenocarcinoma cell line A549 and lung cancer chromosome has higher telomeric associations and most of themoccured in idiogramB , idiogram C and idiogram D. It will provide worthwhile data of cell genetics for further research work on early diagnosis and prognosis in human lung cancer.
We report a universal dynamic light scattering (DLS) immunosensor by using aptamers-modified gold nanoparticles (GNPs) as a probe for ultrasensitive detection of pathogenic bacteria in complex samples. This DLS immunoassay comprises two elements, namely aptamers-modified GNPs (GNPs@aptamers) for targeted capture and DLS signal transduction for monitoring the average hydrodynamic diameter change. Target pathogenic bacteria are first selectively captured by GNPs@aptamers to form “GNP-coated bacteria” complexes, then subjected to DLS-based measurement after simple wash. Benefiting from the states of GNPs@aptamers sensitively change from dispersion to aggregation, the proposed immunosensor guarantees rapid, specific and ultrasensitive detection of bacteria at the limit of detection (LOD) of 3.9 CFU/mL (Staphylococcus aureus detected as an example). In addition, we employ point-of-care-detection (POCT) of our DLS immunosensor to detect bacteria in clinical samples by multiple aptamers modified GNPs. Overall, these findings indicate that the proposed GNPs@aptamers-enhanced DLS immunosensor is a promising and feasible platform for rapid and ultrasensitive detection of pathogenic bacteria both in the field and at POCT.
Ever since the catalytic hairpin assembly (CHA) circuit has been highlighted as a powerful nucleic acid detection tool, nucleic acid detection methods based on CHA have been widely studied. However, the detection sensitivity of CHA-based methods is insufficient. The relatively high background signals resulting from the spontaneous reaction between the two hairpin probes is one of the major reasons for limiting the sensitivity of CHA. In this study, we established that the addition of deoxynucleotide triphosphates (dNTPs) to the reaction system can significantly reduce the background leakage of CHA. The dNTPs-CHA, coupled with a fluorescence lateral flow assay strip, is used for the rapid and highly sensitive detection of miRNA. It is capable of reliably detecting miRNA in serum samples down to a limit of 100 aM, which is an improvement in the lower detection limit by nearly five orders of magnitude compared to that of the pure CHA.
BackgroundGestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. MethodsHigh-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. ResultsSignificant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. ConclusionHsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.
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