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Background The features and survival of stage IV breast cancer patients with different metastatic sites are poorly understood. This study aims to examine the clinicopathological features and survival of stage IV breast cancer patients according to different metastatic sites. Methods Using the Surveillance, Epidemiology, and End Results database, we restricted our study population to stage IV breast cancer patients diagnosed between 2010 to 2015. The clinicopathological features were examined by chi-square tests. Breast cancer-specific survival (BCSS) and overall survival (OS) were compared among patients with different metastatic sites by the Kaplan-Meier method with log-rank test. Univariable and multivariable analyses were also performed using the Cox proportional hazard model to identify statistically significant prognostic factors. Results A total of 18,322 patients were identified for survival analysis. Bone-only metastasis accounted for 39.80% of patients, followed by multiple metastasis (33.07%), lung metastasis (10.94%), liver metastasis (7.34%), other metastasis (7.34%), and brain metastasis (1.51%). The Kaplan-Meier plots showed that patients with bone metastasis had the best survival, while patients with brain metastasis had the worst survival in both BCSS and OS ( p  < 0.001, for both). Multivariable analyses showed that age, race, marital status, grade, tumor subtype, tumor size, surgery of primary cancer, and a history of radiotherapy or chemotherapy were independent prognostic factors. Conclusion Stage IV breast cancer patients have different clinicopathological characteristics and survival outcomes according to different metastatic sites. Patients with bone metastasis have the best prognosis, and brain metastasis is the most aggressive subgroup.

Background Glioblastoma (GBM) is a highly malignant brain tumor with poor survival and prognosis. Randomized trials have demonstrated that chemotherapy improves survival in patients with GBM. This study aims to examine the clinical characteristics that are potentially associated with the efficacy of chemotherapy and the risk factors of GBM. Methods A total of 25,698 patients diagnosed with GBM were identified between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER). The clinical and demographic variables between groups were examined by Student’s t-test and Pearson’s chi-square test. GBM-specific survival (GBMSS) and overall survival (OS) were evaluated using the Kaplan-Meier method with the log-rank test. Univariable and multivariable analyses were also performed using the Cox proportional hazards model to identify statistically significant prognostic factors. Results Patients who received chemotherapy had better overall survival (median OS 13 vs. Three months, HR = 1.9224, 95%CI 1.8571–1.9900, p  < 0.0001) and better GBMSS (median GBMSS of 12 vs. Three months, HR = 1.9379, 95%CI 1.8632–2.0156, p  < 0.0001), compared to patients who did not. Further subgroup analysis revealed that among patients who underwent chemotherapy, those who were younger, with a supratentorial tumor, received surgery, or radiotherapy had both improved OS and GBMSS. Age, race, tumor location, tumor size, and treatments were identified as independent prognostic factors by multivariable analyses for patients with glioblastoma. Conclusion Patients with GBM who were younger (< 65 years), underwent surgery, or radiotherapy can benefit more from chemotherapeutic regimens. Age, race, tumor size, tumor location, surgery, radiotherapy, and chemotherapy were factors associated with the prognosis of patients with GBM.

The intrusion detection method of power industrial control systems is a crucial aspect of assuring power security. However, traditional intrusion detection methods have two drawbacks: first, they are mainly used for defending information systems and lack the ability to detect attacks against power industrial control systems; and second, although machine learning-based intrusion detection methods perform well with the default hyperparameters, optimizing the hyperparameters can significantly improve its performance. In response to these limitations, a random forest (RF)-based intrusion detection model for power industrial control systems is proposed. Simultaneously, this paper proposes an improved grid search algorithm (IGSA) for optimizing the hyperparameters of the RF intrusion detection model to improve its efficiency and effectiveness. The proposed IGSA boosts the speed of calculation from O(nm) to O(n × m). The suggested model is evaluated based on the public power industrial control system dataset after hyperparameter optimization. The experiment results show that our method achieves a superior detection performance with the accuracy of 98% and has more outstanding performance than the same type of work.

Randomness is an important research topic in the field of information security, especially in cryptography. Randomness test techniques are used to examine the quality of random numbers so that they meet the requirements of the application. The randomness of cryptographic algorithms is one of the key concerns in the algorithm design. People have put forward different standards and test requirements for the randomness of cryptographic algorithms, as well as developed corresponding randomness test kits. This paper analyzes the randomness test technologies of cryptographic algorithms and the general randomness test methods, and compares them on this basis. At the end, the actual application scenarios to apply these randomness test methods are discussed.

Background Circulating tumor cells (CTCs) hold great potential in both clinical application and basic research for the managements of cancer. However, it remains to be an enormous challenge to obtain efficient detection of pancreatic CTCs. New detection platforms for the detection of pancreatic CTCs are urgently required. Methods In the present study, we applied a newly-developed platform integrated subtraction enrichment and immunostaining-fluorescence in situ hybridization (SE-iFISH) to analyze clinical significance of pancreatic CTCs. Immunostaining of CK, CD45, DAPI and FISH with the centromere of chromosome 8 (CEP8) were utilized to identify CTCs. Cells with features of CK+/CD45-/DAPI+/CEP8 = 2, CK+/CD45-/DAPI+/CEP8 > 2, CK-/CD45-/DAPI+/CEP8 > 2 were defined as pancreatic CTCs. The Kaplan-Meier method and Cox proportional hazards model were used to analyze the relationship of CTC level and other clinicopathological factors with pancreatic cancer clinical outcomes. Results CTC count in pancreatic cancer was higher than healthy individuals (median, 3 vs 0 per 7.5 ml; P  < 0.001). SE-iFISH platform yielded a sensitivity of 88 % and specificity of 90 % in pancreatic cancer at the cutoff value of 2 cells/7.5 ml. Pancreatic cancer patients with lower CTC count (<3/7.5 ml) had substantially better overall survival (OS) compared with these with higher CTC count (≥3/7.5 ml) (15.2 vs 10.2 months, P  = 0.023). Multivariate analysis indicated that higher CTC count was a strong indicator for worse OS (HR = 4.547, P  = 0.016). Conclusion Our current data showed that CTCs could be detected in pancreatic cancer patients in various stages, whether localized, locally advanced and metastatic. Besides, CTCs have shown the potential implication in predicting prognosis of pancreatic cancer.

Background or Purpose Pancreatic ductal adenocarcinoma (PDAC) is commonly diagnosed by endoscopic ultrasound-guided fine needle aspiration (EUS-FNA). However, the diagnostic adequacy of EUS-FNA is often limited by low cellularity leading to inconclusive results. We aimed to investigate the feasibility and added utility of targeted next-generation sequencing (NGS) on PDAC EUS-FNAs. Methods EUS-FNAs were prospectively performed on 59 patients with suspected PDAC (2014-2017) at a high-volume center. FNAs were analyzed for the presence of somatic mutations using NGS to supplement cytopathologic evaluations and were compared to surgical specimens and circulating tumor DNA (ctDNA). Results Fifty-nine patients with suspected PDAC were evaluated, and 52 were diagnosed with PDAC on EUS-FNA. Four of the remaining seven patients had inconclusive EUS-FNAs and were ultimately diagnosed with PDAC after surgical resection. Of these 56 cases of PDAC, 48 (85.7%) and 18 (32.1%) harbored a KRAS and/or TP53 mutation on FNA NGS, respectively. Particularly, in the four inconclusive FNA PDAC diagnoses (false negatives), half harbored KRAS mutations on FNA. No KRAS/TP53 mutation was found in remaining three non-PDAC cases. All EUS-FNA detected KRAS mutations were detected in 16 patients that underwent primary tumor NGS (100% concordance), while 75% KRAS concordance was found between FNA and ctDNA NGS. Conclusion Targeted NGS can reliably detect KRAS mutations from EUS-FNA samples and exhibits high KRAS mutational concordance with primary tumor and ctDNA. This suggests targeted NGS of EUS-FNA samples may enable preoperative ctDNA prognostication using digital droplet PCR and supplement diagnoses in patients with inconclusive EUS-FNA.

Background Solid pseudopapillary tumors (SPTs) are rare, benign tumors of the pancreas that present as heterogeneous masses. We sought to evaluate the short- and long-term outcomes of surgical resected SPTs. Patients managed via initial surveillance were compared to those who underwent upfront resection. Methods A prospectively maintained institutional database was used to identify patients who underwent surgical resection for a SPT between 1988 and 2018. Data on clinicopathological features and outcomes were collected and analyzed. Results Seventy-eight patients underwent surgical resection for SPT during the study period. The mean age was 34.0 ± 14.6 years and a majority were female ( N  = 67, 85.9%) and white ( N  = 46, 58.9%). Thirty patients (37.9%) were diagnosed incidentally. Imaging-based presumed diagnosis was SPT in 49 patients (62.8%). A majority were located in the body or tail of the pancreas ( N  = 47, 60.3%), and 48 patients (61.5%) underwent a distal pancreatectomy. The median tumor size was 4.0 cm (IQR, 3.0–6.0), nodal disease was present in three patients (3.9%), and R0 resection was performed in all patients. No difference was observed in clinicopathological features and outcomes between patients who were initially managed via surveillance and those who underwent upfront resection. None of the patients under surveillance had nodal disease or metastasis at the time of resection; however, one of them developed recurrence of disease 95.1 months after resection. At a median follow-up of 36.1 months (IQR, 8.1–62.1), 77 (%) patients were alive and one patient (1.3%) had a recurrence of disease at 95.1 months after resection and subsequently died due to disease. Conclusions SPTs are rare pancreatic tumors that are diagnosed most frequently in young females. While a majority are benign and have an indolent course, malignant behavior has been observed. Surgical resection can result in exceptional outcomes.

In the planar perovskite solar cells (PSCs), the electron transport layer (ETL) plays a critical role in electron extraction and transport. Widely utilized TiO2 ETLs suffer from the low conductivity and high surface defect density. To address these problems, for the first time, two types of ETLs based on TiO2 phase junction are designed and fabricated distributed in the opposite space, namely anatase/rutile and rutile/anatase. The champion efficiency of PSCs based on phase junction ETL is over 15%, which is much higher than that of cells with single anatase (9.8%) and rutile (11.8%) TiO2 as ETL. The phase junction based PSCs also demonstrated obviously reduced hysteresis. The enhanced performance is discussed and mainly ascribed to the excellent capability of carrier extraction, defect passivation, and reduced recombination at the ETL/perovskite interface. This work opens a new phase junction ETL strategy toward interfacial energy band manipulation for improved PSC performance. Perovskite solar cells using TiO2 anatase and rutile phase junctions as the electron transport layers demonstrate improved efficiency and reduced hysteresis compared with the single phase counterparts, resulting from the excellent capability of carrier extraction, defect passivation, and reduced recombination at the phase junction/perovskite interface.

End-stage liver diseases, such as cirrhosis and liver cancer caused by hepatitis B, are often combined with hepatic encephalopathy (HE); ammonia poisoning is posited as one of its main pathogenesis mechanisms. Ammonia is closely related to autophagy, but the molecular mechanism of ammonia’s regulatory effect on autophagy in HE remains unclear. Sialylation is an essential form of glycosylation. In the nervous system, abnormal sialylation affects various physiological processes, such as neural development and synapse formation. ST3 β-galactoside α2,3-sialyltransferase 6 (ST3GAL6) is one of the significant glycosyltransferases responsible for adding α2,3-linked sialic acid to substrates and generating glycan structures. We found that the expression of ST3GAL6 was upregulated in the brains of mice with HE and in astrocytes after ammonia induction, and the expression levels of α2,3-sialylated glycans and autophagy-related proteins microtubule-associated protein light chain 3 (LC3) and Beclin-1 were upregulated in ammonia-induced astrocytes. These findings suggest that ST3GAL6 is related to autophagy in HE. Therefore, we aimed to determine the regulatory relationship between ST3GAL6 and autophagy. We found that silencing ST3GAL6 and blocking or degrading α2,3-sialylated glycans by way of Maackia amurensis lectin-II (MAL-II) and neuraminidase can inhibit autophagy. In addition, silencing the expression of ST3GAL6 can downregulate the expression of heat shock protein β8 (HSPB8) and Bcl2-associated athanogene 3 (BAG3). Notably, the overexpression of HSPB8 partially restored the reduced autophagy levels caused by silencing ST3GAL6 expression. Our results indicate that ST3GAL6 regulates autophagy through the HSPB8-BAG3 complex.

The open and distributed connection of the power system makes it vulnerable to various potential cyber-attacks, which may lead to power outages and even casualties. Therefore, the construction of attack and defense drill (ADD) platforms for attack mechanism investigation and protection strategy evaluation has become a research hotspot. However, for the massive and heterogeneous security analysis data generated during the drill, it is rare to have a comprehensive and intuitive method to visually and efficiently display the perspective of the attacker and defender. In order to solve this problem, this paper proposes a visual analysis scheme of an ADD framework for a grid cyber-physical system (GCPS) based on the interactive visual analysis method. Specifically, it realizes system weakness discovery based on knowledge visualization, optimization of the detection model and visualization interaction. Finally, the case study on the simulation platform of ADD proves the effectiveness of the proposed method.