Search

Swarm intelligence optimization methods have steadily gained popularity as a solution to multi-objective optimization issues in recent years. Their study has garnered a lot of attention since multi-objective optimization problems have a hard high-dimensional goal space. The black-winged kite optimization algorithm still suffers from the imbalance between global search and local development capabilities, and it is prone to local optimization even though it combines Cauchy mutation to enhance the algorithm’s optimization ability. The heuristic optimization algorithm of the black-winged kite fused with osprey (OCBKA), which initializes the population by logistic chaotic mapping and fuses the osprey optimization algorithm to improve the search performance of the algorithm, is proposed as a means of enhancing the search ability of the black-winged kite algorithm (BKA). By using numerical comparisons between the CEC2005 and CEC2021 benchmark functions, along with other swarm intelligence optimization methods and the solutions to three engineering optimization problems, the upgraded strategy’s efficacy is confirmed. Based on numerical experiment findings, the revised OCBKA is very competitive because it can handle complicated engineering optimization problems with a high convergence accuracy and quick convergence time when compared to other comparable algorithms.

Adaptive spiral flight and multi-strategy fusion are the foundations of a new FOX optimization algorithm that aims to address the drawbacks of the original method, including weak starting individual ergodicity, low diversity, and an easy way to slip into local optimum. In order to enhance the population, inertial weight is added along with Levy flight and variable spiral strategy once the population is initialized using a tent chaotic map. To begin the process of implementing the method, the fox population position is initialized using the created Tent chaotic map in order to provide more ergodic and varied individual beginning locations. To improve the quality of the solution, the inertial weight is added in the second place. The fox random walk mode is then updated using a variable spiral position updating approach. Subsequently, the algorithm’s global and local searches are balanced, and the Levy flying method and greedy approach are incorporated to update the fox location. The enhanced FOX optimization technique is then thoroughly contrasted with various swarm intelligence algorithms using engineering application optimization issues and the CEC2017 benchmark test functions. According to the simulation findings, there have been notable advancements in the convergence speed, accuracy, and stability, as well as the jumping out of the local optimum, of the upgraded FOX optimization algorithm.

Abstract Background Cardiac remodeling and dysfunction can be caused by atrial fibrillation (AF). The aim of this research is to investigate the relationship between the systemic inflammatory response index (SIRI) and left ventricular (LV) remodeling and systolic function in individuals with AF. Methods 416 patients with AF who were admitted to the Second Department of Cardiology in the East Ward of the Qingdao Municipal Hospital between January 2020 and May 2022 were included in the present retrospective research. The relationship between SIRI and various cardiac parameters was analyzed. The patients’ left atrial (LA) enlargement and left ventricular (LV) hypertrophy and systolic dysfunction were evaluated. SIRI was calculated by the formula: neutrophil × monocyte/lymphocyte. Results SIRI significantly correlated with LV end-diastolic diameter (LVDd), LV posterior wall thickness at end-diastole (LVPWTd), interventricular septal thickness at end-diastole (IVSTd), LV mass index (LVMI), LV ejection fraction (LVEF), LA diameter (LAD), C-reactive protein (CRP), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) in patients with AF. In multivariate linear regression analyses, SIRI was discovered to be significantly related to LVMI (ln-transformed) (p = 0.025), LVEF (ln-transformed) (p = 0.005), and LAD (ln-transformed) (p = 0.007). In multivariate logistic regression, the highest quartile of SIRI (SIRI > 1.62) was significantly associated with LV hypertrophy (p = 0.026), impaired LV systolic function (p = 0.002), and LA enlargement (p = 0.025). Conclusions SIRI was significantly associated with LV remodeling and systolic function impairment in patients with AF. SIRI may serve as a reliable and convenient inflammatory biomarker for detecting impaired cardiac structure and systolic function in patients with AF.

Total polysaccharides of Pinus massoniana Lamb. pollen (PPPSt) is a natural carbohydrate with known anti-inflammatory effects. Inflammation was the main cause of death due to myocardial damage caused by sepsis. The aim of this study was to investigate the potential protective effect of PPPSt on the septic myocardial injury. Lipopolysaccharide (LPS) was injected intraperitoneally to establish the myocardial injury model and PPPS was used for intervention, then the changes in myocardial enzymes, myocardial tissue morphology, oxidative stress, and inflammation levels were evaluated experimentally. The results showed that PPPSt could alleviate the LPS-induced increase of aspartate transaminase (AST), alanine transaminase (ALT), and creatine kinase (CK) levels, and also ameliorate the histopathological damage of myocardium, but PPPSt had no protective effect on oxidative stress injury. In terms of inflammation, PPPSt treatment can significantly reduce the levels of IL-1β and NO in the serum. In addition, PPPSt can also down-regulate the phosphorylation of Nuclear factor kappa beta (NFκB) and NFκB inhibitor alpha (IKBα) in the septic myocardial tissue, and inhibit the activation of NFκB signaling pathway. In conclusion, PPPSt can protect against LPS-induced septic myocardial injury in chicks, which provides a theoretical basis for the potential treatment of septic myocardial injury.

Abstract Leucocyte immunoglobulin‐like receptors (LILRs) are closely related to tumourigenesis, but their clinical value in early‐stage pancreatic ductal adenocarcinoma (PDAC) after pancreaticoduodenectomy remains unknown. Kaplan–Meier and Cox proportional hazards regression models is used to investigate the association between LILR expression and prognosis in tumour biopsies and peripheral blood mononuclear cells. Risk score was calculated for each patient based on the prognostic model. DAVID, STRING, GeneMANIA, and GSEA were used to conduct pathway and functional analyses. The CIBERSORT algorithm is used to analyse tumour‐infiltrating immune cells. Survival analysis showed that high levels of LILRA4 (p = 0.006) and LILRB4 (p = 0.04) were significantly associated with better overall survival. High levels of LILRA2 (p = 0.008) and LILRB4 (p = 0.038) were significantly associated with better relapse‐free survival. JAK‐STAT signalling pathway, regulation of T cell activation, regulation of the immune effector process, and tumour necrosis factor superfamily cytokine production were involved in molecular mechanisms that affected poor prognoses in the high‐risk group in GSEA. CIBERSORT demonstrated that the high‐risk group had significantly higher infiltrating fraction of memory‐activated CD4 T cells and activated NK cells and lower fraction of resting dendritic cells and neutrophils. LILRB4 plays crucial roles in affecting the clinical outcomes of early‐stage PDAC.

The vibration signals from rotating machinery are constantly mixed with other noises during the acquisition process, which has a negative impact on the accuracy of signal feature extraction. For vibration signals from rotating machinery, the conventional linear filtering-based denoising method is ineffective. To address this issue, this paper suggests an enhanced signal denoising method based on maximum overlap discrete wavelet packet transform (MODWPT) and variational mode decomposition (VMD). VMD decomposes the vibration signal of rotating machinery to produce a set of intrinsic mode functions (IMFs). By computing the composite weighted entropy (CWE), the phantom IMF component is then removed. In the end, the sensitive component is obtained by computing the value of the degree of difference (DID) after the high-frequency noise component has been decomposed through MODWPT. The denoised signal reconstructs the signal’s intrinsic characteristics as well as the denoised high-frequency IMF component. This technique was used to analyze the simulated and real-world signals of gear faults and it was compared to wavelet threshold denoising (WTD), empirical mode decomposition reconstruction denoising (EMD-RD), and ensemble empirical mode decomposition wavelet threshold denoising (EEMD-WTD). The outcomes demonstrate that this method can accurately extract the signal feature information while filtering out the noise components in the signal.

The Internet of Things technology provides convenience for data acquisition in environmental monitoring and environmental protection and can also avoid invasive damage caused by traditional data acquisition methods. An adaptive cooperative optimization seagull algorithm for optimal coverage of heterogeneous sensor networks is proposed in order to address the issue of coverage blind zone and coverage redundancy in the initial random deployment of heterogeneous sensor network nodes in the sensing layer of the Internet of Things. Calculate the individual fitness value according to the total number of nodes, coverage radius, and area edge length, select the initial population, and aim at the maximum coverage rate to determine the position of the current optimal solution. After continuous updating, when the number of iterations is maximum, the global output is output. The optimal solution is the node’s mobile position. A scaling factor is introduced to dynamically adjust the relative displacement between the current seagull individual and the optimal individual, which improves the exploration and development ability of the algorithm. Finally, the optimal seagull individual position is fine-tuned by random opposite learning, leading the whole seagull to move to the correct position in the given search space, improving the ability to jump out of the local optimum, and further increasing the optimization accuracy. The experimental simulation results demonstrate that, compared with the coverage and network energy consumption of the PSO algorithm, the GWO algorithm, and the basic SOA algorithm, the coverage of the PSO-SOA algorithm proposed in this paper is 6.1%, 4.8%, and 1.2% higher than them, respectively, and the energy consumption of the network is reduced by 86.8%, 68.4%, and 52.6%, respectively. The optimal deployment method based on the adaptive cooperative optimization seagull algorithm can improve the network coverage and reduce the network cost, and effectively avoid the coverage blind zone and coverage redundancy in the network.

In the past few decades, tumor diagnosis and treatment theory have developed in a variety of directions. The number of people dying from pancreatic cancer increases while the mortality rate of other common tumors decreases. Traditional imaging methods show the boundaries of pancreatic tumor, but they are not sufficient to judge early micrometastasis. Although carcinoembryonic antigen (CEA) and carbohydrate antigen19-9 (CA19-9) have the obvious advantages of simplicity and minimal invasiveness, these biomarkers obviously lack sensitivity and specificity. Circulating tumor cells (CTCs) have attracted attention as a non-invasive, dynamic, and real-time liquid biopsy technique for analyzing tumor characteristics. With the continuous development of new CTCs enrichment technologies, substantial progress has been made in the basic research of CTCs clinical application prospects. In many metastatic cancers, CTCs have been studied as an independent prognostic factor. This article reviews the research progress of CTCs in the treatment and prognosis of pancreatic cancer.

Abstract Background The effect of time delay from diagnosis to surgery on the prognosis of elderly patients with liver cancer is not well known. We investigated the effect of surgical timing on the prognosis of elderly hepatocellular carcinoma patients undergoing surgical resection and constructed a Nomogram model to predict the overall survival of patients. Methods A retrospective analysis was performed on elderly patients with primary liver cancer after hepatectomy from 2012 to 2018. The effect of surgical timing on the prognosis of elderly patients with liver cancer was analyzed using the cut-off times of 18 days, 30 days, and 60 days. Cox was used to analyze the independent influencing factors of overall survival in patients, and a prognostic model was constructed. Results A total of 232 elderly hepatocellular carcinoma patients who underwent hepatectomy were enrolled in this study. The cut-off times of 18, 30, and 60 days were used. The duration of surgery had no significant effect on overall survival. Body Mass Index, Child-Pugh classification, Tumor size Max, and Length of stay were independent influencing factors for overall survival in the elderly Liver cancer patients after surgery. These factors combined with Liver cirrhosis and Venous tumor emboli were incorporated into a Nomogram. The nomogram was validated using the clinical data of the study patients, and exhibited better prediction for 1-year, 3-year, and 5-year overall survival. Conclusions We demonstrated that the operative time has no significant effect on delayed operation in the elderly patients with hepatocellular carcinoma, and a moderate delay may benefit some patients. The constructed Nomogram model is a good predictor of overall survival in elderly patients with hepatectomy.

ABSTRACT: Duck circovirus (DuCV) infection occurs frequently in ducks in China and is generally believed to lead to immunosuppression and secondary infection, though there has been a lack of detailed research and direct evidence. In this study, one-day-old Cherry Valley ducklings were artificially infected with DuCV alone and co-infected with DuCV and Avian Pathogenic Escherichia coli (APEC). The immune indexes at 32 d old were systematically monitored, including immune organ weight, lymphocyte transformation rate, IL-10, IL-12, soluble CD4 (sCD4), soluble CD8 (sCD8), IFN-γ, viral loads in each organ, APEC colonization, and so on. The results showed the development of immune organs in ducklings was affected, resulting in a decrease in the lymphocyte transformation rate (LTR), IL-12, sCD4, sCD8, IFN-γ and an increase in IL-10 content at 8 to 32 d postinfection (dpi). In the detection of virus loads in some organs, it was found that 8 dpi, DuCV existed stably in various organs, suggesting the importance of preventing and controlling the virus in the early stage of culture. The results of exploring the DuCV infection that shows some influence on secondary infection by APEC. The results showed that DuCV infection could significantly enhance the pathogenicity of APEC and the colonization ability of APEC in vivo. DuCV can induce more serious APEC infection in 24 dpi than in 14 dpi. Based on the above results, it can be concluded that DuCV infection will affect the immune system, cause immunosuppression, and lead to more serious secondary infection.

Graft versus host disease after solid organ transplantation is very rare. This article reports a case of graft versus host disease after liver transplantation following targeted therapy and radiotherapy for the treatment of hepatocellular carcinoma. The patient developed a symptomatic skin rash and pancytopenia 13 days after surgery, which was confirmed as graft versus host disease after liver transplantation by histopathology and fluorescence in situ hybridization. Early diagnosis of graft versus host disease after solid organ transplantation is difficult and often delayed due to nonspecific manifestations that overlap with other diseases. Currently, the treatment of graft versus host disease after liver transplantation occurs by either strengthening the immune suppression or weakening the immune suppression; however, there is no unified standard treatment strategy. We found that in addition to age, gender, and human leukocyte antigen type, preoperative radiotherapy is a likely risk factor for graft versus host disease after liver transplantation.

Abstract The purpose of this investigation was to explore the prognostic value of phospholipase C delta (PLCD) genes in early stage pancreatic ductal adenocarcinoma (PDAC) and its potential molecular mechanisms. The prognostic value of PLCD genes in early stage PDAC was assessed using the Kaplan‐Meier method and multivariate Cox proportional hazards regression model. Genome‐wide correlation analysis was performed on PLCD3 to identify the highly correlated genes in the transcriptome. Then, PLCD3 and these correlated genes together underwent a bioinformatics analysis to elucidate the potential molecular biological functions of PLCD3 in PDAC. PLCD1 and PLCD3 are significantly overexpressed in PDAC. In PDAC patients, PLCD3 is overexpressed in certain groups of people with a history of alcoholism (P = .032). High expression of PLCD3 was found to be associated with lower overall survival (OS) of patients with early stage PDAC (P = .020; adjusted P = .016). A combination of PLCD3 and clinical variables was able to better predict the outcome of patients with early stage PDAC. These clinical variables are histological grade (P = .001; adjusted P = .001), targeted molecular therapy (P

Rotor fault diagnosis has attracted much attention due to its difficulties such as non-stationarity of fault signals, difficulty in fault feature extraction and low diagnostic accuracy of small samples. In order to extract fault feature information of rotors more effectively and to improve fault diagnosis precision, this paper proposed a fault diagnosis method based on variational mode decomposition (VMD) symmetrical polar image and fuzzy neural network. Firstly, the original rotor vibration signal is decomposed by using the VMD method and the relevant parameter selection algorithm of the VMD method is also proposed. Secondly, the intrinsic mode functions (IMF), which are sensitive to the signal characteristics, are selected for signal reconstruction based on a comprehensive evaluation factor method. As well, the reconstructed signal is transformed into a two-dimensional snowflake image through using the symmetrical polar coordinate method. Finally, the image features are extracted by the gray level co-occurrence matrix to form the state feature vector, which is input into the fuzzy neural network to realize the rotor fault diagnosis. Through the analysis of measured signals, the experimental results show that the proposed method can reach a higher recognition rate of 98% and the k-cross-validation experiment is used to demonstrate the robustness of the fuzzy neural network, and the average recognition accuracy of this experiment is 99.2%. Compared with some similar methods, the proposed method still has the highest fault recognition precision 98.4%, and the smallest standard deviation 0.5477.

Introduction: The aim of this study was to select qualified patients with hepatocellular carcinoma (HCC) who underwent right hepatectomy (RH) via intraoperative indocyanine green retention test at 15 min (ICG-R15) of the left hemiliver, which prevents severe posthepatectomy liver failure (PHLF).Methods: Twenty HCC patients who were preoperatively planned to undergo RH were enrolled. Intraoperative ICG-R15 of left hemiliver was measured after the right Glissonean pedicle was completely blocked. Patients then underwent RH if intraoperative ICG-R15 was ≤ 10%. Otherwise, patients underwent staged RH (SRH), either associating liver partitioning and portal vein ligation for staged hepatectomy (ALPPS) or portal vein ligation (PVL), followed by stage-2 RH. The comparison group consisted of patients with a ratio of standard left liver volume (SLLV) of > 40% and preoperative ICG-R15 ≤ 10% who underwent RH. The clinical outcomes of these two groups were compared.Results: Of the 20 patients, six underwent stage-1 RH, six underwent ALPPS, five underwent PVL followed by stage-2 RH, and three failed to proceed to stage-2 RH after PVL. No significant differences were found among the 17 patients who underwent stage-1 or stage-2 RH in the study group, the 19 patients in the comparison group, the 11 patients in the stage-2 RH group, and the six patients in the stage-1 RH group in incidences of PHLF, postoperative complications, hospital stay, and HCC recurrence within 1 year after RH. Compared with the stage-1 ALPPS group, the mean operative time and blood loss of the stage-1 PVL group were significantly less (p

ObjectiveThe present study used the RNA sequencing (RNA-seq) dataset to identify prognostic snoRNAs and construct a prognostic signature of The Cancer Genome Atla (TCGA) lower grade glioma (LGG) cohort, and comprehensive analysis of this signature.MethodsRNA-seq dataset of 488 patients from TCGA LGG cohort were included in this study. Comprehensive analysis including function enrichment, gene set enrichment analysis (GSEA), immune infiltration, cancer immune microenvironment, and connectivity map (CMap) were used to evaluate the snoRNAs prognostic signature.ResultsWe identified 21 LGG prognostic snoRNAs and constructed a novel eleven-snoRNA prognostic signature for LGG patients. Survival analysis suggests that this signature is an independent prognostic risk factor for LGG, and the prognosis of LGG patients with a high-risk phenotype is poor (adjusted P = 0.003, adjusted hazard ratio = 2.076, 95% confidence interval = 1.290–3.340). GSEA and functional enrichment analysis suggest that this signature may be involved in the following biological processes and signaling pathways: such as cell cycle, Wnt, mitogen-activated protein kinase, janus kinase/signal transducer and activator of tran-ions, T cell receptor, nuclear factor-kappa B signaling pathway. CMap analysis screened out ten targeted therapy drugs for this signature: 15-delta prostaglandin J2, MG-262, vorinostat, 5155877, puromycin, anisomycin, withaferin A, ciclopirox, chloropyrazine and megestrol. We also found that high- and low-risk score phenotypes of LGG patients have significant differences in immune infiltration and cancer immune microenvironment.ConclusionsThe present study identified a novel eleven-snoRNA prognostic signature of LGG and performed a integrative analysis of its molecular mechanisms and relationship with tumor immunity.

Abstract Background The aim of this study was to investigate the potential prognostic value of Kinesin‐4 family genes mRNA expression in early‐stage pancreatic ductal adenocarcinoma (PDAC) patients after pancreaticoduodenectomy. Methods Kaplan‐Meier survival analysis method with log‐rank test and Cox proportional hazards regression analysis were performed to figure out the association between Kinesin‐4 family genes expression and PDAC patients overall survival time. Joint‐effect survival analysis and stratified survival analysis were carried out to assess the prognosis prediction value of prognosis‐related gene. Nomogram was constructed for the individualized prognosis prediction. In addition, we had used the gene set enrichment analysis and genome‐wide co‐expression analysis to further explore the potential mechanism. Results KIF21A expression level was significantly associated with PDAC patient clinical prognosis outcome and patient with a high expression of KIF21A would have a shorter overall survival time. The prognosis prediction significance of KIF21A was well validated by the joint‐effect survival analysis, stratified survival analysis, and nomogram. Meanwhile, the gene set enrichment analysis and genome‐wide co‐expression analysis revealed that KIF21A might involve in DNA damage and repair, transcription and translation process, post‐translation protein modification, cell cycle, carcinogensis genes and pathways. Conclusions Our current research demonstrated that KIF21A could serve as a potential prognostic biomarker for patient with early‐stage PDAC after pancreaticoduodenectomy.

Low-power wide-area (LPWA) communication has gained increasing attention in recent years with the rapid growth of fifth generation evolution (5G), the Internet of Things (IoT), and mobile computing. Narrowband Internet of Things (NB-IoT) is one kind of LPWA technology based on cellular IoT, which supports massive connections, wide area coverage, ultra-low power consumption, and ultra-low cost. Research on NB-IoT communication is increasingly attractive. Network calculus theory facilitates the performance analysis and network optimization of the NB-IoT system. We aim to analyze and optimize the NB-IoT networks. In this paper, we construct a random access traffic model including the NB-IoT user equipment (UE) arrival process and eNB service process. Then, we utilize the stochastic network calculus (SNC) to analyze the network delay in NB-IoT traffic model. Random latency bounds in different arrival processes are derived. Simulations show that SNC can evaluate the system delay under different distributions effectively. For the condition that numerous UEs access simultaneously following the Beta distribution, we first propose an improved K-means algorithm to cluster the NB-IoT terminals. Then, we raise the scheduling strategy on the basis of priority. It consists of the priority generation algorithm IPGNTQ and the NB-IoT task scheduling algorithm SANTQ. The extensive experiment results verify that our proposed optimized strategy can alleviate the network congestion effectually. Moreover, we compare our proposed optimized scheme with four existing uplink traffic scheduling schemes, showing that ours outperforms all of them.

Background/Aims: The aim of the current study was to identify potential prognostic long non-coding RNA (lncRNA) biomarkers for predicting survival in patients with hepatocellular carcinoma (HCC) using The Cancer Genome Atlas (TCGA) dataset and bioinformatics analysis. Methods: RNA sequencing and clinical data of HCC patients from TCGA were used for prognostic association assessment by univariate Cox analysis. A prognostic signature was built using stepwise multivariable Cox analysis, and a comprehensive analysis was performed to evaluate its prognostic value. The prognostic signature was further evaluated by functional assessment and bioinformatics analysis. Results: Thirteen differentially expressed lncRNAs (DELs) were identified and used to construct a single prognostic signature. Patients with high risk scores showed a significantly increased risk of death (adjusted P < 0.0001, adjusted hazard ratio = 3.522, 95% confidence interval = 2.307–5.376). In the time-dependent receiver operating characteristic analysis, the prognostic signature performed well for HCC survival prediction with an area under curve of 0.809, 0.782 and 0.79 for 1-, 3- and 5-year survival, respectively. Comprehensive survival analysis of the 13-DEL prognostic signature suggested that it serves as an independent factor in HCC, showing a better performance for prognosis prediction than traditional clinical indicators. Functional assessment and bioinformatics analysis suggested that the prognostic signature was associated with the cell cycle and peroxisome proliferator-activated receptor signaling pathway. Conclusions: The novel lncRNA expression signature identified in the present study may be a potential biomarker for predicting the prognosis of HCC patients.

Abstract Cytochrome P2C (CYP2C) subfamily members (CYP2C8, CYP2C9, CYP2C18, and CYP2C19) are known to participate in clinical drug metabolism. However, the association between CYP2C subfamily members and hepatocellular carcinoma (HCC) remains unclear. This study investigated the prognostic value of CYP2C subfamily gene expression levels with HCC prognosis. Data of 360 HCC patients in The Cancer Genome Atlas database and 231 in the Gene Expression Omnibus database were analyzed. Kaplan–Meier analysis and a Cox regression model were used to ascertain overall survival and recurrence‐free survival, and to calculate median survival time using hazard ratios (HR) and 95% confidence intervals (CI). In TCGA database, low expression of CYP2C8, CYP2C9, and CYP2C19 in tumor tissue was associated with a short median survival time (all crude P = 0.001, adjusted P = 0.004, P = 0.047, and P = 0.020, respectively). In TCGA database, joint effects analysis of the combinations of CYP2C8 and CYP2C9, CYP2C8 and CYP2C19, and CYP2C9 and CYP2C19 revealed that high expression of two genes (group 4; group IV, group d) was associated with a reduced risk of death as compared to low expression (group 1, group I, and group a) (adjusted P = 0.005, P = 0.013, and P = 0.016, respectively). In TCGA database, joint effects analysis of CYP2C8, CYP2C9, and CYP2C19 showed that the risk of death from HCC was lower for groups C and D than for group A (adjusted P = 0.012 and P = 0.008, respectively). CYP2C8, CYP2C9, and CYP2C19 gene expression levels are potential prognostic markers of HCC following hepatectomy.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive lethal malignancy. Identification of potential alternative splicing (AS) prognostic indicators and related splicing pathways for the prediction of PDAC outcomes is lacking but urgently needed. A combined strategy of prognostic assessment and computational biology was performed to investigate survival-related AS signatures and their correlation with splicing factors. The prognostic signatures of each type were conducted according to the top 10 prognosis-related AS events, which were filtered through univariate Cox regression analysis. A time-dependent receiver operating characteristic curve was constructed to access the predictive accuracy of prognostic signatures. The independent predictors were identified using multivariate Cox regression analysis. Potential regulation mechanisms between splicing factors and splicing events were investigated through regulatory networks and correlation analyses. A total of 915 overall survival (OS) and 480 recurrence-free survival (RFS)-related AS events were identified in 120 patients with PDAC. The independent prognostic signatures for each type displayed favorable accuracy for the prediction of OS and short-term RFS [area under the curves were >0.6] except for the Exclusive Exons type. The splicing regulatory networks showed potential interactions between splicing factors and AS parent genes. Moreover, a positive relationship was detected among each splicing factor and Percent Spliced In values of prognostic signatures. Our results provide a view of the landscape of prognosis-related AS events and reveal the potential correlation between splicing factors and prognostic signatures, which may represent novel outcome-predictor markers and opportunities for targeted therapy for PDAC.