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Emerging contaminants (ECs) are a diverse group of unregulated pollutants increasingly present in the environment. These contaminants, including pharmaceuticals, personal care products, endocrine disruptors, and industrial chemicals, can enter the environment through various pathways and persist, accumulating in the food chain and posing risks to ecosystems and human health. This comprehensive review examines the chemical characteristics, sources, and varieties of ECs. It critically evaluates the current understanding of their environmental and health impacts, highlighting recent advancements and challenges in detection and analysis. The review also assesses existing regulations and policies, identifying shortcomings and proposing potential enhancements. ECs pose significant risks to wildlife and ecosystems by disrupting animal hormones, causing genetic alterations that diminish diversity and resilience, and altering soil nutrient dynamics and the physical environment. Furthermore, ECs present increasing risks to human health, including hormonal disruptions, antibiotic resistance, endocrine disruption, neurological effects, carcinogenic effects, and other long-term impacts. To address these critical issues, the review offers recommendations for future research, emphasizing areas requiring further investigation to comprehend the full implications of these contaminants. It also suggests increased funding and support for research, development of advanced detection technologies, establishment of standardized methods, adoption of precautionary regulations, enhanced public awareness and education, cross-sectoral collaboration, and integration of scientific research into policy-making. By implementing these solutions, we can improve our ability to detect, monitor, and manage ECs, reducing environmental and public health risks.

Polygonatum kingianum Coll. et (Hemsl) is a famous Chinese traditional food and medicine analogous plant. The rhizome of P. kingianum showed a decrease in levels of alkaloids, amino acids and derivatives, terpenoids, and an increase in organic acid and saccharides when it was processed by the traditional method of “Nine Cycles of Steaming and Sun-Drying”. The relative content of 341 metabolites were increased (fold change, FC > 2; variable importance in projection, VIP > 1 and P-value, P 1, and P

Abstract Enhancers and the enhancer RNAs (eRNAs) have been strongly implicated in regulations of transcriptions. Based the multi-omics data (ATAC-seq, ChIP-seq and RNA-seq) from public databases, Pig-eRNAdb is a dataset that comprehensively integrates enhancers and eRNAs for pigs using the machine learning strategy, which incorporates 82,399 enhancers and 37,803 eRNAs from 607 samples across 15 tissues of pigs. This user-friendly dataset covers a comprehensive depth of enhancers and eRNAs annotation for pigs. The coordinates of enhancers and the expression patterns of eRNAs are downloadable. Besides, thousands of regulators on eRNAs, the target genes of eRNAs, the tissue-specific eRNAs, and the housekeeping eRNAs are also accessible as well as the sequence similarity of eRNAs with humans. Moreover, the tissue-specific eRNA-trait associations encompass 652 traits are also provided. It will crucially facilitate investigations on enhancers and eRNAs with Pig-eRNAdb as a reference dataset in pigs.

Abstract This study aimed to investigate the predictive factors of therapeutic efficacy for chronic subdural hematoma (CSDH) patients receiving atorvastatin combined with dexamethasone therapy by using clinical imaging characteristics in conjunction with computed tomography (CT) texture analysis (CTTA). Clinical imaging characteristics and CT texture parameters at admission were retrospectively investigated in 141 CSDH patients who received atorvastatin combined with dexamethasone therapy from June 2019 to December 2022. The patients were divided into a training set (n = 81) and a validation set (n = 60). Patients in the training data were divided into two groups based on the effectiveness of the treatment. Univariate and multivariate analyses were performed to assess the potential factors that could indicate the prognosis of CSDH patients in the training set. The receiver operating characteristic (ROC) curve was used to analyze the predictive efficacy of the significant factors in predicting the prognosis of CSDH patients and was validated using a validation set. The multivariate analysis showed that the hematoma density to brain parenchyma density ratio, singal min (minimum) and singal standard deviation of the pixel distribution histogram, and inhomogeneity were independent predictors for the prognosis of CSDH patients based on atorvastatin and dexamethasone therapy. The area under the ROC curve between the two groups was between 0.716 and 0.806. As determined by significant factors, the validation's accuracy range was 0.816 to 0.952. Clinical imaging characteristics in conjunction with CTTA could aid in distinguishing patients with CSDH who responded well to atorvastatin combined with dexamethasone.

With the application of intelligent technology in the medical field, patient rehabilitation training is gradually developing towards intelligence. Aiming at the lower limb rehabilitation robots, a lower limb gait curve prediction model based on nonlinear auto-regressive neural network and a compensation control model for rehabilitation robots based on radial basis function neural network are constructed. Firstly, inertial sensors are used to collect upper limb motion data. A nonlinear auto-regressive neural network is used to build a lower limb gait curve prediction model. Then, to design trajectory tracking control for the rehabilitation robot, a radial basis function neural network is applied to build a compensation control model for the rehabilitation robot. The results indicated that the prediction results of the lower limb gait curve prediction model were relatively close to the actual situation. The error between the predicted curve of the hip joint and the actual curve was within 7°. The prediction curve error of the knee joint was within 10°. After adding radial basis function neural network compensation, the motion trajectory and velocity tracking error of the hip joint were significantly reduced. The tracking error remained stable at around 0.007rad and 0.003rad/s, respectively. The motion trajectory and velocity tracking error of the knee joint were stable at around 0.011rad and 0.008rad/s, respectively. According to the research results, the constructed model has good application effects, which is beneficial for improving the rehabilitation effect of lower limb rehabilitation robots on patients.

In recent years, collaborative classification of multimodal data, e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR), has been widely used to improve remote sensing image classification accuracy. However, existing fusion approaches for HSI and LiDAR suffer from limitations. Fusing the heterogeneous features of HSI and LiDAR proved to be challenging, leading to incomplete utilization of information for category representation. In addition, during the extraction of spatial features from HSI, the spectral and spatial information are often disjointed. It leads to the difficulty of fully exploiting the rich spectral information in hyperspectral data. To address these issues, we proposed a multimodal data fusion framework specifically designed for HSI and LiDAR fusion classification, called modality fusion vision transformer. We have designed a stackable modality fusion block as the core of our model. Specifically, these blocks mainly consist of multimodal cross-attention modules and spectral self-attention modules. The proposed novel multimodal cross-attention module for feature fusion addresses the issue of insufficient fusion of heterogeneous features from HSI and LiDAR for category representation. Compared to other cross-attention methods, it reduces the alignment requirements between modal feature spaces during cross-modal fusion. The spectral self-attention module can preserve spatial features while exploiting the rich spectral information and participating in the process of extracting spatial features from HSI. Ultimately, we achieve overall classification accuracies of 99.91%, 99.59%, and 96.98% on three benchmark datasets respectively, surpassing all state-of-the-art methods, demonstrating the stability and effectiveness of our model.

With the evolution of the sixth generation (6G) mobile communication technology, the terahertz (THz) spectrum has attracted much attention in wireless communication applications due to its high bandwidth and low signal transmission delay. However, the introduction of the THz spectrum leads to higher path transmission losses and complex signal attenuation. This makes user sensing and localization in THz massive multiple-input multiple-output (MIMO) systems more challenging. In this paper, we investigate the user sensing and localization problem in THz massive MIMO systems with the assistance of reconfigurable intelligent surfaces (RIS). Firstly, the received signal is modeled as a tensor, and a parallel factor (PARAFAC) method is proposed. The minimum description length (MDL) is then utilized to detect the number of scattering paths in the channel. On this basis, the alternating least squares (ALS) algorithm is employed to estimate the factor matrices, followed by the utilization of a straightforward correlation-based approach to obtain channel parameter information. Finally, the positions of users and scattering points are estimated based on the geometric relationship between channel parameters and location coordinates. The simulation results have verified the effectiveness of the proposed scheme compared to the existing competitive algorithms, and indicate that the proposed scheme exhibits superior parameter estimation performance and can achieve localization accuracy at the decimeter level.

Utilizing unmanned aerial vehicle (UAV) technology in communication holds promise for meeting the increasing data rate demands in future wireless systems due to its flexibility. Meanwhile, reconfigurable intelligent surface (RIS) has garnered increased attention for their potential to enhance wireless communication performance through intelligent control of the transmission environment. In this paper, we first combine the UAV and the RISs to construct an Internet of Things (IoT) uplink transmission system, where the UAV serves as an aerial relay to collect data from IoT terminal (IT) and forward it to base stations (BS), while RISs assist communication to reduce congestion. Then, a parallel factor (PARAFAC) tensor model is formulated at the BS. At last, the iterative alternating least squares (ALS) algorithm and the closed-form singular value decomposition (SVD) algorithm are derived to fit the constructed tensor model for joint channel estimation and symbol detection. Compared with the competitive algorithms, the two proposed algorithms offer lower computational complexity and superior channel estimation performance. Furthermore, the proposed algorithms exhibit good symbol detection capabilities even at higher transmission rates. The numerical results demonstrate the effectiveness of the proposed algorithms.

In the pursuit of enhancing harmonic detection precision within microgrids, this paper introduces a pioneering algorithm, VMD-DCHHO-HD, which amalgamates Variational Mode Decomposition (VMD) with an advanced Harris Hawk Optimization algorithm characterized by dynamic opposition-based learning and Cauchy mutation (DCHHO). This study establishes a fitness function based on Shannon entropy, thereby minimizing the Local Minimum Entropy (LME) as the optimization objective for DCHHO. Building upon this, the VMD crucial parameters are efficiently identified using the enhanced HHO algorithm (DCHHO), enabling precise decomposition of complex voltage signals. The proposed method effectively addresses issues commonly encountered in traditional Empirical Mode Decomposition (EMD) during harmonic analysis, such as mode mixing, endpoint effects, and significant errors. Notably, it adeptly captures harmonic components spanning diverse frequencies, offering a nuanced solution to common pitfalls in traditional methodologies. In simulation experiments, VMD-DCHHO-HD showcases remarkable proficiency in extracting microgrid voltage signals, excelling at discerning high-order, low-amplitude harmonic components amid noise. The algorithm’s superior precision and heightened reliability, as affirmed by comparative analyses against existing methods, position it as an advanced tool for precise and robust harmonic analysis in microgrid systems.

Abstract Atherosclerosis is a chronic, progressive, inflammatory disease that occurs in the arterial wall. Despite recent advancements in treatment aimed at improving efficacy and prolonging survival, atherosclerosis remains largely incurable. In this review, we discuss emerging single-cell sequencing techniques and their novel insights into atherosclerosis. We provide examples of single-cell profiling studies that reveal phenotypic characteristics of atherosclerosis plaques, blood, liver, and the intestinal tract. Additionally, we highlight the potential clinical applications of single-cell analysis and propose that combining this approach with other techniques can facilitate early diagnosis and treatment, leading to more accurate medical interventions.

Although rare, trans-kingdom infection features an interesting infection biology concept, in which highly versatile pathogenic attributes allow successful infections in evolutionarily highly divergent species. Corynespora cassiicola is a phytopathogenic fungus and occasionally causes human infections. Herein, we report a phaeohyphomycosis case caused by C. cassiicola. Given that sporadic reports may contribute to a lack of awareness of the transmission route, clinical manifestations, and diagnostic and clinical management, we systematically reviewed the cases reported thus far. Nine patients were identified and included in the pooled analysis, 88.9% (8/9) of whom were reported after 2010. All patients were from Asian, African, and Latin American countries, among whom 77.8% (7/9) were farmers or lived in areas with active agriculture. Exposed body parts were the major affected infection area, and clinical manifestations were mainly non-specific inflammatory reactions. Although biochemical and morphological examinations confirmed the presence of fungal infection, molecular analysis was used for the final diagnosis, with 77.8% (7/9) being identified by internal transcribed spacer sequencing. Whereas voriconazole, terbinafine, and AmB, either alone or in combination, resulted in successful infection resolution in most cases (5/9; 55.5%), those suffering from invasive facial infections and CARD9 deficiency showed poor outcomes. Our patient is the third case of invasive facial infection caused by C. cassiicola and was successfully treated with intravenous LAmB followed by oral voriconazole combined with topical antifungal irrigation. Molecular identification of fungus and prompt antifungal treatment is pivotal in the clinical success of patients suspected to have phaeohyphomycosis. Moreover, as evidenced by our data, itraconazole treatment is not recommended.

Abstract Background We aim to compare the differences in growth characteristics between part-solid and solid lung adenocarcinoma, and to investigate the value of volume doubling time (VDT) or mass doubling time (MDT) in predicting lymph node (LN) metastasis and preoperative evaluation in patients of early-stage (IA) non-small cell lung cancer (NSCLC). Method We reviewed 8,653 cases of surgically resected stage IA lung adenocarcinoma between 2018 and 2022, with two follow-up visits at least 3 months apart, comparing diameter, volume, and mass growth of pSN and SN. VDT and MDT calculations for nodules with a volume change of at least 25%. Univariable or multivariable analysis was used to identify the risk factors. The area under the curve (AUC) for the receiver operating characteristic (ROC) curves was used to evaluate the diagnostic value. Results A total of 144 patients were included 114 with solid nodules (SN) and 25 with part-solid nodules (pSN). During the follow-up period, the mean VDTt and MDTt of SN were shorter than those of pSN, 337 vs. 541 days (p = 0.005), 298 vs. 458 days (p = 0.018), respectively. Without considering the ground-glass component, the mean VDTc and MDTc of SN were shorter than the solid component of pSN, 337 vs. 498 days (p = 0.004) and 298 vs. 453 days (p = 0.003), respectively. 27 nodules were clinically and pathologically diagnosed as N1/N2. Logistic regression identified initial diameter (p

In recent years, satellite networks have been proposed as an essential part of next-generation mobile communication systems. Software defined networking techniques are introduced in satellite networks to handle the growing challenges induced by time-varying topology, intermittent inter-satellite link and dramatically increased satellite constellation size. This survey covers the latest progress of software defined satellite networks, including key techniques, existing solutions, challenges, opportunities, and simulation tools. To the best of our knowledge, this paper is the most comprehensive survey that covers the latest progress of software defined satellite networks. An open GitHub repository is further created where the latest papers on this topic will be tracked and updated periodically. Compared with these existing surveys, this survey contributes from three aspects: (1) an up-to-date SDN-oriented review for the latest progress of key techniques and solutions in software defined satellite networks; (2) an inspiring summary of existing challenges, new research opportunities and publicly available simulation tools for follow-up studies; (3) an effort of building a public repository to track new results.

Abstract Background Despite the critical progress of non-small cell lung cancer (NSCLC) therapeutic approaches, the clinical outcomes remain considerably poor. The requirement of developing novel therapeutic interventions is still urgent. In this study, we showed for the first time that diosbulbin C, a natural diterpene lactone component extracted from traditional Chinese medicine Dioscorea bulbifera L., possesses high anticancer activity in NSCLC. Methods A549 and NCI-H1299 cells were used. The inhibitory effects of the diosbulbin C on NSCLC cell proliferation were evaluated using cytotoxicity, clone formation, EdU assay, and flow cytometry. Network pharmacology methods were used to explore the targets through which the diosbulbin C inhibited NSCLC cell proliferation. Molecular docking, qRT-PCR, and western blotting were used to validate the molecular targets and regulated molecules of diosbulbin C in NSCLC. Results Diosbulbin C treatment in NSCLC cells results in a remarkable reduction in cell proliferation and induces significant G0/G1 phase cell cycle arrest. AKT1, DHFR, and TYMS were identified as the potential targets of diosbulbin C. Diosbulbin C may inhibit NSCLC cell proliferation by downregulating the expression/activation of AKT, DHFR, and TYMS. In addition, diosbulbin C was predicted to exhibit high drug-likeness properties with good water solubility and intestinal absorption, highlighting its potential value in the discovery and development of anti-lung cancer drugs. Conclusions Diosbulbin C induces cell cycle arrest and inhibits the proliferation of NSCLC cells, possibly by downregulating the expression/activation of AKT, DHFR, and TYMS.

Abstract Background Gut microbiota (GM) comprises a vast and diverse community of microorganisms, and recent studies have highlighted the crucial regulatory roles of various GM and their secreted metabolites in pancreatic cancer (PC). However, the causal relationship between GM and PC has yet to be confirmed. Methods In the present study, we used two-sample Mendelian randomization (MR) analysis to investigate the causal effect between GM and PC, with genome-wide association study (GWAS) from MiBioGen consortium as an exposure factor and PC GWAS data from FinnGen as an outcome factor. Inverse variance weighted (IVW) was used as the primary method for this study. Results At the genus level, we observed that Senegalimassilia (OR: 0.635, 95% CI: 0.403–0.998, P = 0.049) exhibited a protective effect against PC, while Odoribacter (OR:1.899, 95%CI:1.157–3.116, P = 0.011), Ruminiclostridium 9(OR:1.976,95%CI:1.128–3.461, P = 0.017), Ruminococcaceae (UCG011)(OR:1.433, 95%CI:1.072–1.916, P = 0.015), and Streptococcus(OR:1.712, 95%CI:1.071–1.736, P = 0.025) were identified as causative factors for PC. Additionally, sensitivity analysis, Cochran’s Q test, the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO), and MR-Egger regression indicated no heterogeneity, horizontal pleiotropy, or reverse causality between GM and PC. Conclusions Our analysis establishes a causal effect between specific GM and PC, which may provide new insights into the potential pathogenic mechanisms of GM in PC and the assignment of effective therapeutic strategies.

Abstract Backscatter communication relies on passive reflections of the existing radio frequency (RF) signals, making it suitable for low-power and low-complexity communication in IoT applications. However, the performance of existing systems severely degrades in real-life environments, due to irregular “on” and “off” states of ambient signals like WiFi, which are not controllable. In this paper, we propose a joint coding and framing scheme for the backscattering physical layer to fight against the off-state in the excitation signal. We first design transmission schemes including both the Reed-Solomon (RS) codes and the frame structure, to correct the burst error caused by the off states. In order to implement the codes at the resource-constrained tag, we design a look-up table for the encoding process. We prototype our system NuWa that could efficiently backscatter with uncontrolled traffics generated randomly. We demonstrate that NuWa could achieve a 1 Mbps transmission throughput when the tag is over 1 m away from the receiver in high traffic load and 150 kbps in low traffic load. Finally, we evaluate the throughput with respect to the distance change between the tag and the receiver, and 950 kbps is achieved at a distance of $$6\,m$$ 6 m .

Objective: To screen and optimize the method for preparing three-dimensional structural samples of restriction endonuclease Bsa I. Methods: In this study, the Escherichia coli expression system was employed to express the Bsa I protein and its selenomethionine derivative. Firstly, a recombinant expression vector pBAD-Bsa I was constructed and transformed into Escherichia coli (E.coli) ER2566 for expression. Purification was carried out using affinity chromatography and anion exchange chromatography. Subsequently, selenomethionine derivation situation was assessed using mass spectrometry and circular dichroism spectroscopy, followed by enzyme activity determination. Lastly, preliminary crystal growth studies were conducted using the sitting-drop method. Results: Through a two-step purification approach, recombinant Bsa I and Se-Bsa I selenomethionine derivative with a purity exceeding 90% were obtained. Mass spectrometry analysis revealed that all 11 methionine residues in the recombinant Se-Bsa I protein were selenomethionine-incorporated. Circular dichroism spectroscopy and enzyme activity testing confirmed that the selenomethionine incorporation had no significant impact on the structure and activity of Bsa I protein. Crystallization experiments demonstrated that the recombinant Bsa I protein could not only form granular crystals under conditions of 0.2 mol/L magnesium acetate tetrahydrate at pH6.5, 0.1 mol/L sodium cacodylate trihydrate with 20% polyethylene glycol 8000, but also form spherical structures under conditions of 0.1 mol/L sodium acetate trihydrate at pH4.6 and 2 mol/L ammonium sulfate. Conclusion: This study successfully achieved the recombinant expression of Bsa I and Se-Bsa I selenomethionine derivative, conducted preliminary screening of protein crystal conditions, aiming to provide valuable insights for deciphering the three-dimensional structure of Bsa I protein.

The semi-dry fermentation processing (SFP) of Coffea arabica is a traditional primary processing method in the coffee industry, which crucially impacts the coffee’s flavor. To further obtain useful information on microbial communities and chemical compounds during the SFP of C. arabica from Yunnan, China, the microbial community structures and the differentially changed non-volatile compounds (DCnVCs) were comprehensively analyzed. The results showed that Tatumella, Staphylococcus, Klebsiella, Brevundimonas, and Gluconobacter were the most prevalent bacteria genera, and Candida, Hannaella, Hanseniaspora, Pichia, and Lachancea were the most abundant fungal genera. Furthermore, 117 DCnVCs were found in the fermentation-finished samples compared to the raw materials. Therefore, this study can provide useful information for understanding the SFP of coffee beans, and its impact on coffee’s quality parameters.

Elastic polymer-based conductive composites (EPCCs) are of great potential in the field of flexible sensors due to the advantages of designable functionality and thermal and chemical stability. As one of the popular choices for sensor electrodes and sensitive materials, considerable progress in EPCCs used in sensors has been made in recent years. In this review, we introduce the types and the conductive mechanisms of EPCCs. Furthermore, the recent advances in the application of EPCCs to sensors are also summarized. This review will provide guidance for the design and optimization of EPCCs and offer more possibilities for the development and application of flexible sensors.

ABSTRACTForest Stock Volume (FSV) is one of the key indicators in forestry resource investigation and management on local, regional, and national scales. Limited by the saturation problems of optical satellite remote-sensing imagery in the retrieving of stock volume, and the high cost of Light Detection And Ranging (LiDAR) data, it is still challenging to estimate FSV in a large area using single-sensor remote-sensing data. In this paper, a method integrated multispectral satellite imagery and LiDAR data was developed to map stock volume in a large area. A random forest model was adopted to estimate the stock volume of larch forest in China based on the training samples from the Airborne Laser Scanning (ALS)-derived stock volume and corresponding Sentinel-2 imagery. Validation using National Forest Inventory (NFI) data, ALS-derived stock volume and ground investigation data demonstrated that the estimated stock volume had a high accuracy (R2 = 0.59, RMSE = 59.69 m3/ha, MD = 39.96 m3/ha when validated with NFI data; R2 ranged from 0.77 to 0.85, RMSE ranged from 38.68 m3/ha to 67.38 m3/ha, MD ranged from 24.90 m3/ha to 37.27 m3/ha when validated with ALS stock volume; R2 = 0.42, RMSE = 79.10 m3/ha, MD = 62.06 m3/ha when validated with field investigation data). Results of this paper indicated the applicability of estimating stock volume of larch forest in a large area by combining Sentinel-2 data and airborne LiDAR data.

Abstract Background . The Mycobacterium tuberculosis Beijing genotype is globally spread lineage with important medical properties that however vary among its subtypes. M. tuberculosis Beijing 14717-15-cluster was recently discovered as both multidrug-resistant, hypervirulent, and highly-lethal strain circulating in the Far Eastern region of Russia. Here, we aimed to analyze its pathogenomic features and phylogeographic pattern. Results . The study collection included M. tuberculosis DNA collected between 1996 and 2020 in different world regions. The bacterial DNA was subjected to genotyping and whole genome sequencing followed by bioinformatics and phylogenetic analysis. The PCR-based assay to detect specific SNPs of the Beijing 14717-15-cluster was developed and used for its screening in the global collections. Phylogenomic and phylogeographic analysis confirmed endemic prevalence of the Beijing 14717-15-cluster in the Asian part of Russia, and distant common ancestor with isolates from Korea (> 115 SNPs). The Beijing 14717-15-cluster isolates had two common resistance mutations RpsL Lys88Arg and KatG Ser315Thr and belonged to spoligotype SIT269. The Russian isolates of this cluster were from the Asian Russia while 4 isolates were from the Netherlands and Spain. The cluster-specific SNPs that significantly affect the protein function were identified in silico in genes within different categories (lipid metabolism, regulatory proteins, intermediary metabolism and respiration, PE/PPE, cell wall and cell processes). Conclusions . We developed a simple method based on real-time PCR to detect clinically significant MDR and hypervirulent Beijing 14717-15-cluster. Most of the identified cluster-specific mutations were previously unreported and could potentially be associated with increased pathogenic properties of this hypervirulent M. tuberculosis strain. Further experimental study to assess the pathobiological role of these mutations is warranted.

Abstract Background Recurrence after resection is the main factor for poor survival. The relationship between clinicopathological factors and recurrence after curative distal pancreatectomy for PDAC has rarely been reported separately. Methods Patients with PDAC after left‑sided pancreatectomy between May 2015 and August 2021 were retrospectively identified. Results One hundred forty-one patients were included. Recurrence was observed in 97 patients (68.8%), while 44 (31.2%) patients had no recurrence. The median RFS was 8.8 months. The median OS was 24.9 months. Local recurrence was the predominant first detected recurrence site (n = 36, 37.1%), closely followed by liver recurrence (n = 35, 36.1%). Multiple recurrences occurred in 16 (16.5%) patients, peritoneal recurrence in 6 (6.2%) patients, and lung recurrence in 4 (4.1%) patients. High CA19-9 value after surgery, poor differentiation grade, and positive lymph nodes were found to be independently associated with recurrence. The patients receiving adjuvant chemotherapy had a decreased likelihood of recurrence. In the high CA19-9 value cohort, the median PFS and OS of the patients with or without chemotherapy were 8.0 VS. 5.7 months and 15.6 VS. 13.8 months, respectively. In the normal CA19-9 value cohort, there was no significant difference in PFS with or without chemotherapy (11.7 VS. 10.0 months, P = 0.147). However, OS was significantly longer in the patients with chemotherapy (26.4 VS. 13.8 months, P = 0.019). Conclusions Tumor biologic characteristics, such as T stage, tumor differentiation and positive lymph nodes, affecting CA19-9 value after surgery are associated with patterns and timing of recurrence. Adjuvant chemotherapy significantly reduced recurrence and improved survival. Chemotherapy is strongly recommended in patients with high CA199 after surgery.