Positive Number Research Articles

Multi-class vulnerability prediction using value flow and graph neural networks

AbstractIn recent years, machine learning models have been increasingly used to detect security vulnerabilities in software, due to their ability to achieve high performance and lower false positive rates compared to traditional program analysis tools. However, these models often lack the capability to provide a clear explanation for why a program has been flagged as vulnerable, leaving developers with little reasoning to work with. We present a new method which not only identifies the presence of vulnerabilities in a program, but also the specific type of error, considering the whole program rather than just individual functions. Our approach utilizes graph neural networks that employ inter-procedural value flow graphs, and instruction embedding from the LLVM Intermediate Representation, to predict a class. By mapping these classes to the Common Weakness Enumeration list, we provide a clear indication of the security issue found, saving developers valuable time which would otherwise be spent analyzing a binary vulnerable/non-vulnerable label. To evaluate our method’s effectiveness, we used two datasets: one containing memory-related errors (out of bound array accesses), and the other a range of vulnerabilities from the Juliet Test Suite, including buffer and integer overflows, format strings, and invalid frees. Our model, implemented using PyTorch and the Gated Graph Sequence Neural Network from Torch-Geometric, achieved a precision of 96.35 and 91.59% on the two datasets, respectively. Compared to common static analysis tools, our method produced roughly half the number of false positives, while identifying approximately three times the number of vulnerable samples. Compared to recent machine learning systems, we achieve similar performance while offering the added benefit of differentiating between classes. Overall, our approach represents a meaningful improvement in software vulnerability detection, providing developers with valuable insights to better secure their code.

Prognostic outcomes in Japanese patients with metastatic castration-sensitive prostate cancer: Comparative assessments between conventional androgen deprivation therapy (ADT) and ADT with novel androgen receptor signal inhibitor.

The objective of this study was to compare the prognostic outcomes between metastatic castration-sensitive prostate cancer (mCSPC) patients receiving conventional androgen deprivation therapy (ADT) and those receiving ADT plus a novel androgen-receptor signaling inhibitor (ARSI) in routine clinical practice in Japan. This was conducted as a retrospective multicenter study including 581 mCSPC patients, consisting of 305 receiving ADT alone or in combination with bicalutamide (group 1) and 276 receiving ADT plus one of the following ARSIs: abiraterone acetate, apalutamide, or enzalutamide (group 2). Prognostic outcomes between these 2 groups were comprehensively compared. In the entire cohort, prostate-specific antigen-progression-free survival (PSA-PFS) in group 2 was significantly longer than that in group 1, while no significant difference was noted in overall survival (OS) between the two groups. In patients corresponding to the LATITUDE high-risk group, however, both PSA-PFS and OS in group 2 were significantly longer than those in group 1. Of several factors examined, the following were identified as independent predictors of poor PSA-PFS in the entire cohort as well as the LATITUDE high-risk group: high C-reactive protein, high lactate dehydrogenase, high alkaline phosphatase, high Gleason score, and group 1. Furthermore, it was possible to precisely classify both the entire cohort and LATITUDE high-risk group into 3 risk groups regarding PSA-PFS according to the positive numbers of independent factors: positive for ≤1 factor, favorable; 2 factors, intermediate; and ≥3 factors, poor. Combined use of ARSIs with ADT could improve the prognostic outcomes of mCSPC patients, particularly those in the LATITUDE high-risk group, in real-world clinical practice in Japan.

Hierarchical multinomial processing tree models for meta-analysis of diagnostic accuracy studies

Abstract Meta-analysis represents a widely accepted approach for evaluating the accuracy of diagnostic tools in clinical and psychological investigations. This article investigates the applicability of multinomial tree models recently suggested in the literature under a fixed-effects formulation for assessing the accuracy of binary classification tools, where the study specific disease prevalences are taken into account. The model proposed in this article extends previous results to a hierarchical structure accounting for the variability between the studies included in the meta-analysis. Interestingly, by exploiting the parameter separability of the complete likelihood function, the resulting hierarchical multinomial tree model is shown to coincide, in its interest parameter component, with the well-known bivariate random-effects model under an exact within-study distribution for the number of true positives and true negatives subjects. The proposal is in line with a latent-trait approach, where inference follows a frequentist point of view. The applicability of the proposed model and its performance with respect to its fixed-effects counterpart and to the approximate bivariate random-effects model based on normality assumptions commonly used in the literature is evaluated in a series of simulation studies. Methods are applied to a real meta-analysis about the accuracy of the confusion assessment method as delirium screening tool.

WHEN NO PRICE IS RIGHT

WHEN NO PRICE IS RIGHT

On a lower bound of Hausdorff dimension of weighted singular vectors

AbstractLet be a ‐tuple of positive real numbers such that and . A ‐dimensional vector is said to be ‐singular if for every , there exists such that for all , the system of inequalities has an integer solution . We prove that the Hausdorff dimension of the set of ‐singular vectors in is bounded below by . Our result partially extends the previous result of Liao et al. [Hausdorff dimension of weighted singular vectors in , J. Eur. Math. Soc. 22 (2020), 833–875].

Preoperative CT-based radiomic prognostic index to predict the benefit of postoperative radiotherapy in patients with non-small cell lung cancer: a multicenter study

BackgroundThe value of postoperative radiotherapy (PORT) for patients with non-small cell lung cancer (NSCLC) remains controversial. A subset of patients may benefit from PORT. We aimed to identify patients with NSCLC who could benefit from PORT.MethodsPatients from cohorts 1 and 2 with pathological Tany N2 M0 NSCLC were included, as well as patients with non-metastatic NSCLC from cohorts 3 to 6. The radiomic prognostic index (RPI) was developed using radiomic texture features extracted from the primary lung nodule in preoperative chest CT scans in cohort 1 and validated in other cohorts. We employed a least absolute shrinkage and selection operator-Cox regularisation model for data dimension reduction, feature selection, and the construction of the RPI. We created a lymph-radiomic prognostic index (LRPI) by combining RPI and positive lymph node number (PLN). We compared the outcomes of patients who received PORT against those who did not in the subgroups determined by the LRPI.ResultsIn total, 228, 1003, 144, 422, 19, and 21 patients were eligible in cohorts 1–6. RPI predicted overall survival (OS) in all six cohorts: cohort 1 (HR = 2.31, 95% CI: 1.18–4.52), cohort 2 (HR = 1.64, 95% CI: 1.26–2.14), cohort 3 (HR = 2.53, 95% CI: 1.45–4.3), cohort 4 (HR = 1.24, 95% CI: 1.01–1.52), cohort 5 (HR = 2.56, 95% CI: 0.73–9.02), cohort 6 (HR = 2.30, 95% CI: 0.53–10.03). LRPI predicted OS (C-index: 0.68, 95% CI: 0.60–0.75) better than the pT stage (C-index: 0.57, 95% CI: 0.50–0.63), pT + PLN (C-index: 0.58, 95% CI: 0.46–0.70), and RPI (C-index: 0.65, 95% CI: 0.54–0.75). The LRPI was used to categorize individuals into three risk groups; patients in the moderate-risk group benefited from PORT (HR = 0.60, 95% CI: 0.40–0.91; p = 0.02), while patients in the low-risk and high-risk groups did not.ConclusionsWe developed preoperative CT-based radiomic and lymph-radiomic prognostic indexes capable of predicting OS and the benefits of PORT for patients with NSCLC.

Application Study on the Reinforcement Learning Strategies in the Network Awareness Risk Perception and Prevention

The intricacy of wireless network ecosystems and Internet of Things (IoT) connected devices have increased rapidly as technology advances and cyber threats increase. The existing methods cannot make sequential decisions in complex network environments, particularly in scenarios with partial observability and non-stationarity. Network awareness monitors and comprehends the network's assets, vulnerabilities, and ongoing activities in real-time. Advanced analytics, machine learning algorithms, and artificial intelligence are used to improve risk perception by analyzing massive amounts of information, identifying trends, and anticipating future security breaches. Hence, this study suggests the Deep Reinforcement Learning-assisted Network Awareness Risk Perception and Prevention Model (DRL-NARPP) for detecting malicious activity in cybersecurity. The proposed system begins with the concept of network awareness, which uses DRL algorithms to constantly monitor and evaluate the condition of the network in terms of factors like asset configurations, traffic patterns, and vulnerabilities. DRL provides autonomous learning and adaptation to changing network settings, revealing the ever-changing nature of network awareness risks in real time. Incorporating DRL into risk perception increases the system's capacity to recognize advanced attack methods while simultaneously decreasing the number of false positives and enhancing the reliability of risk assessments. DRL algorithms drive dynamic and context-aware response mechanisms, making up the adaptive network prevention component of the development. Predicting new threats and proactively deploying preventive measures, such as changing firewall rules, isolating compromised devices, or dynamically reallocating resources to reduce developing risks, is made possible by the system's ability to learn from historical data and prevailing network activity. The suggested DRL-NARPP model increases the anomaly detection rate by 98.3%, the attack prediction accuracy rate by 97.4%, and the network risk assessment ratio by 96.4%, reducing the false positive ratio by 11.2% compared to other popular methodologies.

Ephemeris Matching Reveals False Positive Validated and Candidate Planets from the K2 Mission

Data from the Kepler space telescope have led to the discovery of thousands of planet candidates. Most of these candidates are likely to be real exoplanets, but a significant number of false positives still contaminate the sample, especially in candidate lists from the K2 mission. Identifying and rejecting the false positives lurking in the planet candidates sample is important for prioritizing follow-up resources and measuring the most accurate population statistics. Here, we identify false positives in the K2 planet candidate sample using a technique called “ephemeris matching,” in which we compare the period and transit time of different signals. When signals from different stars show the same period and time of transit, we can conclude that at least one of the two signals is contamination. We identify 43 false positives among published K2 planet candidates (nearly 2% of the complete list), one of which (K2-256 b) was previously validated as genuine exoplanet. This work increases the reliability of the K2 planet sample and helps boost confidence in the surviving planet candidates.

Recalcitrant dermatophyte infections: identification and risk factors.

Recalcitrant dermatophytosis is an emerging phenomenon that occurs worldwide, and Trichophyton indotineae is currently the prominent cause. Skin specimens from patients with tinea infection were obtained by scrubbing and then sectioned into three fragments. Two fragments were subjected to direct microscopic examination and culture, while the third portion was utilized in the PCR method. Isolates were morphologically identified as Trichophyton mentagrophytes/interdigitale complex (n = 60 [83.33%]), Microsporum canis (n = 8 [11.11%]), Trichophyton rubrum (n = 3 [4.16%]), and Epidermophyton floccosum (n = 1 [1.38%]). Among 60 T. mentagrophytes complex isolates, 53 (88.33%) were classified as T. indotineae and seven as T. interdigitale genotype II. The disease duration was longer in the T. indotineae group (P = 0.035). Both Gradient PCR and skin-sampling methods yield similar results in terms of positive and negative cases (P = 1.0000). The time patients stopped their medication did not impact the positive case numbers (P = 0.803). Gender had no effects on the frequency (P = 0.699). Familial contamination, dermatologic disorder, and other underlying conditions did not differ in the two group infections (P > 0.05). Steroid usage is strongly associated with the emergence of tinea infection (P < 0.04). The duration of antifungal administration had a substantial effect on the emergence of resistant organisms (P = 0.05). Steroid usage, T. indotineae involvement, and prolonged exposure to antifungals were the solid and influential factors in recalcitrant involvement. Regarding quick and suitable diagnosis and treatment, which is essential in preventing recalcitrant cases, we suggest that direct skin sample PCR can meet the demands.

Dominant eigenvalue and universal winners of digraphs

Let A be a nonnegative irreducible matrix of order n and Ai(t) be the matrix obtained by increasing its ith diagonal entry by a positive number t. An index i∈[n]={1,…,n} is called a universal winner for A, if, letting ρ(⋅) denote the spectral radius, ρ(Ai(t))≥ρ(Aj(t)) for j∈[n] and for t∈(0,∞).Let G be a strongly connected digraph with vertex set [n]. Let WG be the set of all nonnegative matrices whose underlying graph structure is G. We say a vertex u structurally dominates another vertex v in G, if ρ(Au(t))≥ρ(Av(t)) for all t∈(0,∞) and for all A∈WG. We characterize the class of digraphs G that do not have a vertex that structurally dominates all other vertices. We say two vertices u and v structurally tie if they structurally dominate each other in G. We supply an equivalent graph theoretic condition for the structural tying of two vertices in G. Let S⊆[n] be nonempty. We characterize the class of strongly connected digraphs G with vertex set [n] such that S is the set of universal winners for each A∈WG. We also characterize the strongly connected digraphs whose vertex set can be partitioned into subsets P1,P2,…,Pk such that vertices inside a part Pi structurally tie with each other and vertices of Pi structurally dominate vertices of Pj strictly (without tying) for i<j.

Central amygdala is related to the reduction of aggressive behavior by monosodium glutamate ingestion during the period of development in an ADHD model rat.

Monosodium glutamate (MSG), an umami substance, stimulates the gut-brain axis communication via gut umami receptors and the subsequent vagus nerves. However, the brain mechanism underlying the effect of MSG ingestion during the developmental period on aggression has not yet been clarified. We first tried to establish new experimental conditions to be more appropriate for detailed analysis of the brain, and then investigated the effects of MSG ingestion on aggressive behavior during the developmental stage of an ADHD rat model. Long-Evans, WKY/Izm, SHR/Izm, and SHR-SP/Ezo were individually housed from postnatal day 25 for 5 weeks. Post-weaning social isolation (PWSI) was given to escalate aggressive behavior. The resident-intruder test, that is conducted during the subjective night, was used for a detailed analysis of aggression, including the frequency, duration, and latency of anogenital sniffing, aggressive grooming, and attack behavior. Immunohistochemistry of c-Fos expression was conducted in all strains to predict potential aggression-related brain areas. Finally, the most aggressive strain, SHR/Izm, a known model of attention-deficit hyperactivity disorder (ADHD), was used to investigate the effect of MSG ingestion (60 mM solution) on aggression, followed by c-Fos immunostaining in aggression-related areas. Bilateral subdiaphragmatic vagotomy was performed to verify the importance of gut-brain interactions in the effect of MSG. The resident intruder test revealed that SHR/Izm rats were the most aggressive among the four strains for all aggression parameters tested. SHR/Izm rats also showed the highest number of c-Fos + cells in aggression-related brain areas, including the central amygdala (CeA). MSG ingestion significantly decreased the frequency and duration of aggressive grooming and attack behavior and increased the latency of attack behavior. Furthermore, MSG administration successfully increased c-Fos positive cell number in the intermediate nucleus of the solitary tract (iNTS), a terminal of the gastrointestinal sensory afferent fiber of the vagus nerve, and modulated c-Fos positive cells in the CeA. Interestingly, vagotomy diminished the MSG effects on aggression and c-Fos expression in the iNTS and CeA. MSG ingestion decreased PWSI-induced aggression in SHR/Izm, which was mediated by the vagus nerve related to the stimulation of iNTS and modulation of CeA activity.

Abstract PS01-01: Feasibility and oncological safety of targeted axillary dissection or sentinel lymph node biopsy in patients with clinically node-positive disease after neoadjuvant chemotherapy in the prospective MF-1803 NEOSENTITURK-study

Abstract PS01-01: Feasibility and oncological safety of targeted axillary dissection or sentinel lymph node biopsy in patients with clinically node-positive disease after neoadjuvant chemotherapy in the prospective MF-1803 NEOSENTITURK-study

Abstract PO3-02-08: Risk factors and a new predictive nomogram for predicting the Non-sentinel Lymph Node Metastasis in 708 early-stage Breast Cancer Patients with Positive Sentinel Nodes in China

Abstract Objective: The management of positive sentinel lymph node biopsies (SLNB) in patients with early-stage breast cancer remains a matter of controversy. Some studies have shown that not all breast cancer patients with positive sentinel lymph nodes (SLNs) benefit from further axillary lymph node dissection (ALND). Our study aims to analyse the risk factors and construct a new nomogram to predict non-sentinel lymph node (NSLN) metastasis in Chinese early-stage breast cancer patients with positive sentinel lymph nodes. Methods: The clinical data of 708 patients with early-stage breast cancer who attended The Fourth Hospital of Hebei Medical University for surgical treatment from January 2016 to December 2021 was retrospectively analyzed. All of them were female breast cancer patients with positive sentinel lymph nodes and underwent ALND. The patients were divided into modeling group and validation group according to the time of treatment. The patients in the modeling group were divided into a positive NSLN group and a negative NSLN group. The characteristics: age, maximum tumor diameter, the number of positive SLNs, proportion of positive SLNs, vessel carcinoma embolus, nerve invasion, status of ER, PR, HER-2 and Ki-67 were included in the analysis. And the independent risk factors of NSLN metastasis were screened by univariate (chi-square test) and multivariate (logistic regression) analysis. A nomogram model was established according to the screened independent risk factors, and the model was externally validated by the validation group. The ROC curve was plotted to calculate the area under the curve (AUC) and evaluate the predictive power of the nomogram model. A calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the model. Results: Among the 708 patients, (modeling group n=410, validation group n=298), the average age, average number of positive SLNs, and NSLN positive rate were 50.9±11.1, 1.55±0.87 and 25.71%. The univariate analysis revealed statistically significant differences in the number of positive SLNs (P&amp;lt; 0.001), the proportion of positive SLNs (P &amp;lt; 0.001), vessel carcinoma embolus (P=0.001), nerve invasion (P=0.012), and ER status (P=0.029) in the NSLN-positive group compared with the negative group. The multifactorial logistic regression analysis revealed that the number of positive SLN (P=0.002; OR: 1.700; 95% CI: 1.219-2.370), the proportion of positive SLN (P=0.048; OR: 3.214; 95% CI: 1.001-10.490), vessel carcinoma embolus (P=0.010; OR: 2.076; 95% CI: 1.194-3.612), nerve invasion (P=0.024; OR: 2.119; 95% CI: 1.221-3.678), and ER status (P=0.045; OR: 2.978; 95% CI: 1.022-8.675) were independent risk factors for NSLN positivity. Furthermore, the nomogram was built using these 5 factors and validated on 298 patients in the validation group. The AUC was 0.728 (P&amp;lt; 0.001; 95%CI: 0.673-0.784) in the modeling group and 0.810 (P&amp;lt;0.001; 95%CI: 0.735-0.886) in the validation group. The calibration curves of the modeling and validation groups were close to the ideal curve, and DCA also showed that the model could be applied in clinical practice. Conclusion: For early-stage breast cancer with positive SLNs, these factors as the positive number of SLNs &amp;gt; 2, the ratio of positive SLNs &amp;gt; 50%, vessel carcinoma embolus, nerve invasion and ER-positive could predict NSLN metastasis well. The proposed nomogram appears to accurately estimate the likelihood of positive NSLNs and may greatly help the surgeon to decide intraoperatively whether to perform further ALND and avoid non-essential ALND as well as postoperative complications. Citation Format: Zhenchuan Song, Xueyi Zhao, Liu Yang. Risk factors and a new predictive nomogram for predicting the Non-sentinel Lymph Node Metastasis in 708 early-stage Breast Cancer Patients with Positive Sentinel Nodes in China [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-02-08.

Convolutional neural networks and Internet of Things for fault detection by aerial monitoring of photovoltaic solar plants

Aerial thermographic inspection is performed with thermal cameras embedded in unmanned aerial vehicles, being one of the most relevant monitoring techniques for photovoltaic panels. This technique allows the detection of thermal patterns associated with faults in the photovoltaic panels, although image analysis and fault detection require novel processing methodologies. The volume and variety of aerial thermal data together with the need for faster and more efficient analysis for early maintenance operations require efficient Internet of Things architectures to provide fast and effective diagnostics with easy access.This paper presents a new approach based on image analysis with two consecutive convolutional neural networks to detect hot spots in an Internet of Things platform and reduce the number of false positives. The architecture of the platform is designed to automatically process the received data through the implementation of different Convolutional Neural Networks. A real case study is proposed with thermograms from three photovoltaic solar plants with different sizes, shapes of panels and a wide temperature range. The results show an accuracy of 99 % for panel detection and 96 % for hot spot detection with a reduction of false positives compared to other studies, such as Support Vector Machine or different Artificial Neural Networks, demonstrating the robustness of the method.

Strategic Retesting to Reduce False Positive SARS-CoV-2 PCR Test Results.

Nucleic acid amplification testing is the gold standard for SARS-CoV-2 diagnostics, although it may produce a certain number of false positive results. There has not been much published about the characteristics of false positive results. In this study, based on retesting, specimens that initially tested positive for SARS-CoV-2 were classified as true or false positive groups to characterize the distribution of cycle threshold (CT) values for N1 and N2 targets and number of targets detected for each group. Specimens that were positive for N-gene on retesting and accompanied with S-gene were identified as true positives (true positive based on retesting, rTP), while specimens that retested negative were classified as false positives (false positive based on retesting, rFP). Of the specimens retested, 85/127 (66.9%) were rFP, 16/47 (34.0%) specimens with both N1 and N2 targets initially detected were rFP, and the CT values for each target was higher in rFP than in rTP. ROC curve analysis showed that optimal cutoff values of CT to differentiate between rTP and rFP were 34.8 for N1 and 33.0 for N2. With the optimal cutoff values of CT for each target, out of the 24 specimens that were positive for both N1 and N2 targets and classified as rTP, 23 (95.8%) were correctly identified as true positives. rFP specimens had a single N1 target in 52/61 (85.2%) and a single N2 target in 17/19 (89.5%). Notably, no true positive results were obtained from any specimens with only N2 target detected. These results suggest that retesting should be performed for positive results with a CT value greater than optimal cutoff value for each target or with a single N1 target amplified, considering the possibility of a false positive. This may provide guidance on indications to perform retesting to minimize the number of false positives.

Application and subgroup analysis of competing risks model based on different lymph node staging systems in differentiated thyroid cancer.

Differentiated thyroid cancer (DTC) is the most common endocrine malignancy, with a rising incidence worldwide. Accurate prognostic models are essential for effective patient management. This study evaluates the prognostic value of various lymph node staging systems in DTC using a competing risks model. We used SEER database records (1998-2016) of 16,527 DTC patients, analyzing N stage, positive lymph node numbers (PLNNs), metastatic lymph node ratio (MLNR), log odds of positive lymph nodes (LODDS), and log odds of the negative lymph node (NLN)/T stage ratio (LONT). Univariate and multivariate analyses in a competing risks model were performed, along with subgroup analyses based on demographic and clinical characteristics. In this study of 16,527 patients with DTC, different lymph node staging systems showed different prognostic correlations in univariate and multivariate analyses. In particular, PLNNs showed significant prognostic correlations in several subgroups. Additionally, PLNNs were more suitable as a lymph node staging system for DTC than LODDS and MLNR in N1 stage subgroups, with an optimal cut-off of 13. Receiver operating characteristic curves, calibration curves and nomograms improved the clinical utility of the prognostic model based on PLNNs. Using competing risks model and subgroup analyses, we found that PLNNs had the best prognostic discriminatory efficacy for patients with DTC, especially those with N1 stage disease, and had an optimal cut-off value of 13.

Patient tolerance and complication rates of transperineal prostate biopsy with coaxial technique under local anesthesia: initial experience of a single institution.

Due to infectious complications of transrectal prostate biopsy (TRBx), the transperineal prostate biopsy (TPBx) technique is gaining popularity and is the first-line method in many institutions. We share our experience of the first 100 patients with TPBx, performed using the coaxial needle technique under local anesthesia. We retrospectively reviewed the records of the first 100 patients who had undergone TPBx between December 2022 and September 2023. Complication rates, cancer detection rates, patient tolerance, and pain response to the TPBx under local anesthesia at different steps of the procedure were collected. The mean age, total prostate-specific antigen (PSA), prostate volume, and PSA density were 64.5±7.5 years, 8.82±12 ng/mL, 58.4±26.4 mL, and 0.17±0.18 ng/mL2. Prostate cancer (PCa) was detected at histopathological evaluation in 51 patients. The mean positive core number and percentage of cancer involvement per core in patients who have PCa were 5.4±3.2 and 68.5±29.1, respectively. The mean pain score during the entire procedure was 2.85±1.48. When the steps are evaluated separately, the mean pain score during the probe placement step, local anesthetic, and sampling steps were 3.35±1.65, 2.54±1.45, and 0.9±0.82, respectively. Transperineal prostate biopsy with coaxial needle technique under local anesthesia is a well-tolerated procedure with feasible complication rates and patient discomfort.

Downregulation of lncRNA MIR17HG reduced tumorigenicity and Treg-mediated immune escape of non-small-cell lung cancer cells through targeting the miR-17-5p/RUNX3 axis.

Long noncoding RNA MIR17HG was involved with the progression of non-small-cell lung cancer (NSCLC), but specific mechanisms of MIR17HG-mediated immune escape of NSCLC cells were still unknown. The present study investigated the function of MIR17HG on regulatory T cell (Treg)-mediated immune escape and the underlying mechanisms in NSCLC. Expression of MIR17HG and miR-17-5p in NSCLC tissue samples were detected using quantitative real-time PCR (qRT-PCR). A549 and H1299 cells were transfected with sh-MIR17HG, miR-17-5p inhibitor, or sh-MIR17HG + miR-17-5p inhibitor, followed by cocultured with Tregs. Cell proliferation was measured using 5-ethynyl-20-deoxyuridine (Edu) staining assay and cell counting kit-8 (CCK-8) assay. Flow cytometry was used for determining positive numbers of FOXP3+CD4+/CD25+/CD8+ Tregs. Through subcutaneous injection with transfected A549 cells, a xenograft nude mouse model was established. Weights and volumes of xenograft tumors were evaluated. Additionally, the expressions of immune-related factors includingtransforming growth factor beta (TGF-β), vascular endothelial growth factor A (VEGF-A), interleukin-10 (IL-10), IL-4, and interferon-gamma (IFN-γ) in cultured cells, were evaluated by enzyme-linked immunosorbent assay and western blot analysis. Then, miR-17-5p was decreased and MIR17HG was enhanced in both NSCLC tissues and cell lines. MIR17HG knockdown significantly suppressed cell proliferation, tumorigenicity, and immune capacity of Tregs in A549 and H1299 cells, whereas sh-MIR17HG significantly reduced expression levels of VEGF-A, TGF-β, IL-4, and IL-10 but promoted the IFN-γ level in vitro and in vivo. Moreover, downregulation of miR-17-5p significantly reversed the effects of sh-MIR17HG. Additionally, we identified that runt- related transcription factor 3 (RUNX3) was a target of miR-17-5p, and sh-MIR17HG and miR-17-5p mimics downregulated RUNX3 expression. In conclusion, downregulation of MIR17HG suppresses tumorigenicity and Treg-mediated immune escape in NSCLC through downregulating the miR-17-5p/RUNX3 axis, indicating that this axis contains potential biomarkers for NSCLC.

Optimization of Dendritic Polypeptide Delivery System for Antisense Antibacterial Agents Targeting ftsZ.

There is an urgent requirement for a novel treatment strategy for drug-resistant Staphylococcus aureus (S. aureus) infection. Antisense antimicrobials are promising antimicrobials, and efficient drug delivery systems are necessary for the further development of antisense antimicrobials. To develop new antisense drugs and further improve delivery efficiency and safety, we designed and screened new antisense sequences and optimized dendritic polypeptide nanoparticles (DP-AD) discovered in previous studies. The N/P ratio is optimized from 8:1 to 6:1, and the positive charge number of the optimized DP-AD is studied comprehensively. The results show that the N/P ratio and positive charge number have no significant effect on the particle size distribution and transport efficiency of DP-AD. Reducing the N/P ratio can significantly reduce the cytotoxicity of DP-AD, but it does not affect its delivery efficiency and antibacterial activity. However, in drug-resistant strains, the antibacterial activity of DP-AD76:1 with 10 positive charges is higher than that of DP-AD86:1 with 8 positive charges. Our research discovered a novel ASOs targeting ftsZ and concluded that DP-AD76:1 with 10 positive charges was the optimal choice at the current stage, which provided a promising strategy for the treatment of drug-resistant S. aureus.

On eigenvalues of certain special matrices

Let g : R → [ 0 , ∞ ) be a conditionally negative definite function and f : [ 0 , ∞ ) → [ 0 , ∞ ) be a Bernstein function. We prove that the function h = f ∘ g is conditionally negative definite and that for distinct real numbers p 1 , p 2 , … , p n the inertia of the matrix [ h ( p i − p j ) ] is ( 1 , 0 , n − 1 ) if f is non-linear and g ( x ) = 0 only for x = 0. We also present a new and simple proof to show that the matrix [ log ⁡ ( 1 − p i p j ) ] is negative definite for n distinct positive real numbers p i < 1 , ∀ i . These results supplement and unify earlier results proved by a number of authors including Dyn, Goodman and Michelli, Bhatia and Jain, Garg and Aujla, Garg and Agarwal for operator monotone functions.