EGFR is a potential therapeutic target for highly glycosylated and aggressive pancreatic neuroendocrine neoplasms.

pNENs are relative indolent tumors with heterogeneous clinical presentation at diagnosis. It is important to establish aggressive subgroups of pNENs and identify potential therapeutic targets. Patients with pNEN (322 cases) were included to examine the association between glycosylation biomarkers and clinical/pathological traits. The molecular and metabolic features stratified by glycosylation status were assessed by RNA-seq/whole exome sequencing and immunohistochemistry. A considerable proportion of patients had elevated glycosylation biomarkers (carbohydrate antigen [CA] 19-9, 11.9%; CA125, 7.5%; carcinoembryonic antigen [CEA], 12.8%). CA19-9 (hazard ratio [HR] = 2.26, P = .019), CA125 (HR = 3.79, P = .004) and CEA (HR = 3.16, P = .002) were each independent prognostic variables for overall survival. High glycosylation group, defined as pNENs with elevated level of circulating CA19-9, CA125 or CEA, accounted for 23.4% of all pNENs. High glycosylation (HR = 3.14, P = .001) was an independent prognostic variable for overall survival and correlated with G3 grade (P < .001), poor differentiation (P = .001), perineural invasion (P = .004) and distant metastasis (P < .001). Epidermal growth factor receptor (EGFR) was enriched in high glycosylation pNENs using RNA-seq. EGFR was expressed in 21.2% of pNENs using immunohistochemistry and associated with poor overall survival (P = .020). A clinical trial focusing on EGFR expressed pNENs was initiated (NCT05316480). Thus, pNEN with aberrant glycosylation correlates with a dismal outcome and suggests potential therapeutic target of EGFR.

Pancreatic Cancer: An Exocrine Tumor with Endocrine Characteristics.

OBJECTIVE: To examine the characteristics of pancreatic cancer patients with long-term survival. BACKGROUND: Although pancreatic cancer is a highly lethal malignancy, a minority of patients experience long-term survival. The characteristics of these patients remain largely unidentified. METHODS: An indolent subgroup was established using carbohydrate antigen 19-9 (CA19-9), which is the best-validated biomarker for pancreatic cancer. Of 1558 patients, 13.9% were included in the CA19-9-normal (≤ 37 U/mL) subgroup. RESULTS: A normal A19-9 level was an independent variable for overall survival (median survival, 18.1 vs. 9.7 months, hazard ratio = 0.53, P < 0.001). The 5-year survival of patients with stage IV CA19-9-normal cancer was higher than that of patients with stage I-IV CA19-9-high cancer (22.4% vs. 6.8%, P = 0.034). The CA19-9-normal subgroup exhibited reduced levels of circulating glucose (P < 0.001) and increased expression of insulin (P < 0.001) compared with the CA19-9-high subgroup. Glucose was a substrate for CA19-9 biosynthesis through the hexosamine biosynthesis pathway. In addition, in pancreatic cancer animal models of diabetes, glucose control decreased CA19-9 levels and improved overall survival. In a clinical trial (NCT05306028) of patients before undergoing major anticancer treatments, glucose control decreased CA19-9 levels in 90.9% of the patients. CONCLUSIONS: CA19-9-normal pancreatic cancer is a strikingly indolent subgroup with low glucose and high insulin. Glucose control is a promising therapeutic strategy for pancreatic cancer.

Experimental Demonstration of Laser Guiding and Wakefield Acceleration in a Curved Plasma Channel.

Curved plasma channels have been proposed to guide intense lasers for various applications, such as x-ray laser emission, compact synchrotron radiation, and multistage laser wakefield acceleration [e.g. J. Luo et al., Phys. Rev. Lett. 120, 154801 (2018)PRLTAO0031-900710.1103/PhysRevLett.120.154801]. Here, a carefully designed experiment shows evidences of intense laser guidance and wakefield acceleration in a centimeter-scale curved plasma channel. Both experiments and simulations indicate that when the channel curvature radius is gradually increased and the laser incidence offset is optimized, the transverse oscillation of the laser beam can be mitigated, and the stably guided laser pulse excites wakefields and accelerates electrons along the curved plasma channel to a maximum energy of 0.7 GeV. Our results also show that such a channel exhibits good potential for seamless multistage laser wakefield acceleration.

Efficacy of vitamin C on chemotherapy-related anemia in pancreatic cancer: study protocol for a randomized controlled trial.

BACKGROUND: In the treatment of advanced pancreatic cancer, chemotherapy plays a pivotal role. Despite its effectiveness, this regimen is often marred by side effects such as anemia, neuropathy, fatigue, nausea, and malnutrition, which significantly affect patients' tolerance to the treatment. Some studies have shown that vitamin C could potentially augment chemotherapy's tolerability, notably by boosting iron absorption, ameliorating anemia, and relieving pain and numbness in hands and feet. Nevertheless, the integration of vitamin C with chemotherapy to mitigate toxic side effects and enhance the quality of life for advanced pancreatic cancer patients has not been examined in any randomized controlled trials to date. METHODS: A prospective, single-center, open-label, randomized controlled trial will be conducted at Fudan University Shanghai Cancer Center from September 2023 to September 2026. A total of at least 100 patients with advanced pancreatic adenocarcinoma exhibiting distant metastases will be recruited and randomly assigned to the chemotherapy group or the chemotherapy plus vitamin C group. The primary endpoint is the rate of anemia. Secondary endpoints include the rate of grade 3 neuropathy, change of numeric rating scale, quality of life, and overall survival. DISCUSSION: This study aims to assess the impact of low-dose vitamin C on enhancing the quality of life for patients with metastatic pancreatic cancer undergoing gemcitabine and nab-paclitaxel chemotherapy. TRIAL REGISTRATION: The trial was registered with the ClinicalTrials.gov (NCT06018883) on August 31, 2023.

Coupling machine learning and theoretical models to compare key properties of biochar in adsorption kinetics rate and maximum adsorption capacity for emerging contaminants.

Insights into key properties of biochar with a fast adsorption rate and high adsorption capacity are urgent to design biochar as an adsorbent in pollution emergency treatment. Machine learning (ML) incorporating classical theoretical adsorption models was applied to build prediction models for adsorption kinetics rate (i.e., K) and maximum adsorption capacity (i.e., Qm) of emerging contaminants (ECs) on biochar. Results demonstrated that the prediction performance of adaptive boosting algorithm significantly improved after data preprocessing (i.e., log-transformation) in the small unbalanced datasets with R2 of 0.865 and 0.874 for K and Qm, respectively. The surface chemistry, primarily led by ash content of biochar significantly influenced the K, while surface porous structure of biochar showed a dominant role in predicting Qm. An interactive platform was deployed for relevant scientists to predict K and Qm of new biochar for ECs. The research provided practical references for future engineered biochar design for ECs removal.

Iron driven organic carbon capture, pretreatment, recovery and upgrade in wastewater: Process technologies, mechanisms, and implications.

Wastewater treatment plants face significant challenges in transitioning from energy-intensive systems to carbon-neutral, energy-saving systems, and a large amount of chemical energy in wastewater remains untapped. Iron is widely used in modern wastewater treatment. Research shows that leveraging the coupled redox relationship of iron and carbon can redirect this energy (in the form of carbon) towards resource utilization. Therefore, re-examining the application of iron in existing wastewater carbon processes is particularly important. In this review, we investigate the latest research progress on iron for wastewater carbon flow restructuring. During the iron-based chemically enhanced primary treatment (CEPT) process, organic carbon is captured into sludge and its bioavailability is enhanced through iron-based advanced oxidation processes (AOP) pretreatment, further being recovered or upgraded to value-added products in anaerobic biological processes. We discuss the roles and mechanisms of iron in CEPT, AOP, anaerobic biological processes, and biorefining in driving organic carbon conversion. The dosage of iron, as a critical parameter, significantly affects the recovery and utilization of sludge carbon resources, particularly by promoting effective electron transfer. We propose a pathway for beneficial conversion of wastewater organic carbon driven by iron and analyze the benefits of the main products in detail. Through this review, we hope to provide new insights into the application of iron chemicals and current wastewater treatment models.

Machine learning for municipal sludge recycling by thermochemical conversion towards sustainability.

The sustainable disposal of high-moisture municipal sludge (MS) has received increasing attention. Thermochemical conversion technologies can be used to recycle MS into liquid/gas bio-fuel and value-added solid products. In this review, we compared energy recovery potential of common thermochemical technologies (i.e., incineration, pyrolysis, hydrothermal conversion) for MS disposal via statistical methods, which indicated that hydrothermal conversion had a great potential in achieving energy recovery from MS. The application of machine learning (ML) in MS recycling was discussed to decipher complex relationships among MS components, process parameters and physicochemical reactions. Comprehensive ML models should be developed considering successive reaction processes of thermochemical conversion in future studies. Furthermore, challenges and prospects were proposed to improve effectiveness of ML for energizing thermochemical conversion of MS regarding data collection and preprocessing, model optimization and interpretability. This review sheds light on mechanism exploration of MS thermochemical recycling by ML, and provide practical guidance for MS recycling.

Hyperglycemia predicts adverse prognosis in advanced pancreatic cancer patients.

BACKGROUND: Diabetes mellitus is a prevalent comorbidity in pancreatic cancer. Previous studies have mainly concentrated on the association between diabetes and pancreatic cancer outcomes. However, research on the impact of hyperglycemia on the prognosis of patients with advanced pancreatic cancer is limited. METHODS: Information on patients with advanced pancreatic cancer was collected from a prospectively maintained database, and the patients were divided into the hyperglycemia group (fasting blood glucose ≥7.0 mmol/L) and the normoglycemia group (fasting blood glucose < 7.0 mmol/L). Patients with preexisting diabetes were not included in these groups. The associations between hyperglycemia and clinical variables or prognosis were analyzed. RESULTS: Among 697 patients with advanced pancreatic cancer and no prior history of diabetes, 25.3% were diagnosed with hyperglycemia. Patients older than 65 years had a higher risk of developing hyperglycemia (P = 0.044). Patients with hyperglycemia had a worse prognosis than those with normoglycemia (median survival, 7.5 vs. 8.8 months, P < 0.001). Hyperglycemia was associated with increased mortality (hazard ratio = 1.38; P = 0.003). CONCLUSIONS: Hyperglycemia predicts worse overall survival in patients with advanced pancreatic cancer.

Machine learning-assisted exploration for carbon neutrality potential of municipal sludge recycling via hydrothermal carbonization.

In the context of advocating carbon neutrality, there are new requirements for sustainable management of municipal sludge (MS). Hydrothermal carbonization (HTC) is a promising technology to deal with high-moisture MS considering its low energy consumption (without drying pretreatment) and value-added products (i.e., hydrochar). This study applied machine learning (ML) methods to conduct a holistic assessment with higher heating value (HHV) of hydrochar, carbon recovery (CR), and energy recovery (ER) as model targets, yielding accurate prediction models with R2 of 0.983, 0.844 and 0.858, respectively. Furthermore, MS properties showed positive (e.g., carbon content, HHV) and negative (e.g., ash content, O/C, and N/C) influences on the hydrochar HHV. By comparison, HTC parameters play a critical role for CR (51.7%) and ER (52.5%) prediction. The primary sludge was an optimal HTC feedstock while anaerobic digestion sludge had the lowest potential. This study provided a comprehensive reference for sustainable MS treatment and industrial application.

Deep learning-assisted automated sewage pipe defect detection for urban water environment management.

A healthy sewage pipe system plays a significant role in urban water management by collecting and transporting wastewater and stormwater, which can be assessed by hydraulic model. However, sewage pipe defects have been observed frequently in recent years during regular pipe maintenance according to the captured interior videos of underground pipes by closed-circuit television (CCTV) robots. In this case, hydraulic model constructed based on a healthy pipe would produce large deviations with that in real hydraulic performance and even be out of work, which can result in unanticipated damages such as blockage collapse or stormwater overflows. Quick defect evaluation and defect quantification are the precondition to achieve risk assessment and model calibration of urban water management, but currently pipe defects assessment still largely relies on technicians to check the CCTV videos/images. An automated sewage pipe defect detection system is necessary to timely determine pipe issues and then rehabilitate or renew sewage pipes, while the rapid development of deep learning especially in recent five years provides a fantastic opportunity to construct automated pipe defect detection system by image recognition. Given the initial success of deep learning application in CCTV interpretation, the review (i) integrated the methodological framework of automated sewage pipe defect detection, including data acquisition, image pre-processing, feature extraction, model construction and evaluation metrics, (ii) discussed the state-of-the-art performance of deep learning in pipe defects classification, location, and severity rating evaluation (e.g., up to ~96 % of accuracy and 140 FPS of processing speed), and (iii) proposed risk assessment and model calibration in urban water management by considering pipe defects. This review introduces a novel practical application-oriented methodology including defect data acquisition by CCTV, model construction by deep learning, and model application, provides references for further improving accuracy and generalization ability of urban water management models in practical application.

Hyperglycemia is associated with adverse prognosis in patients with pancreatic neuroendocrine neoplasms.

BACKGROUND: Although glucose has a well-recognized protumoral role and the pancreas is a critical organ in adjusting glucose metabolism, the clinical value of hyperglycemia in pancreatic neuroendocrine neoplasms (pNENs) remains largely unidentified. METHODS: A retrospective study including 335 patients with pathologically confirmed pNENs was conducted. A baseline fasting blood glucose concentration ≥5.6 mmol/L was defined as hyperglycemia (otherwise, normal). Survival and regression analyses were performed. RESULTS: Compared with patients with normal glucose, patients with hyperglycemia (47.8%) had a higher proportion of preexisting diabetes mellitus (DM) (36.9% vs. 4.6%, p < 0.001), lymph node involvement (31.0% vs. 14.6%, p = 0.002), distant metastasis (34.4% vs. 22.9%, p = 0.019), and carbohydrate antigen 19-9 (CA19-9) ≥ 37 U/mL (16.6% vs. 7.2%, p = 0.009). Hyperglycemia was associated with CA19-9 ≥ 37 U/mL (Odds Ratio (OR) = 3.19, 95% CI: 1.11-9.17, p = 0.031), lymph node involvement (OR = 2.32, 95% CI: 1.02-5.28, p = 0.045), nonfunctional tumors (OR = 9.90, 95% CI: 2.11-46.34, p = 0.004), and preexisting diabetes (OR = 18.24, 95% CI: 4.06-81.95, p < 0.001). Hyperglycemia was an independent determinant for overall survival in the multivariate analysis (hazard ratio (HR) = 2.65, 95% CI: 1.31-5.34, p = 0.006). CONCLUSION: Hyperglycemia is an independent predictor of overall survival and is associated with preexisting DM or lymphatic metastasis in patients with pNENs. Patients with hyperglycemia and resectable pNENs may benefit from radical resection with dissection of regional lymph nodes.

Insights into the adsorption of pharmaceuticals and personal care products (PPCPs) on biochar and activated carbon with the aid of machine learning.

The science-informed design of 'green' carbonaceous materials (e.g., biochar and activated carbon) with high removal capacity of recalcitrant organic contaminants (e.g., pharmaceuticals and personal care products (PPCPs)) is indispensable for promoting sustainable wastewater treatment. In this study, machine learning (ML) incorporating PPCPs and biochar properties as well as adsorption conditions were applied to build adsorption prediction models and explore the contributions of various biochar's inherent properties to their PPCPs adsorption capacity. The results demonstrated that the models developed by detailed biochar properties (e.g., surface functionality and hierarchical porous structure) from advanced microscopic and spectroscopic techniques were more accurate (i.e., the root-mean-square error decreased by 18-24%) than those by general information such as bulk elemental composition and total pore volume. The relative importance of surface carbon functionalities ranked in the order of C-O bond > CO bond > non-polar carbon for predicting the adsorption capacity. According to the partial dependence analysis, the average pore diameters of adsorbents that were larger than the maximum diameter of PPCPs molecules by 1.5-fold to 2.5-fold favored the PPCPs adsorption. This study reveals new insights into the adsorption of PPCPs and provides a comprehensive reference for the sustainable engineering of biochar adsorbents for PPCPs wastewater treatment.

The application of machine learning methods for prediction of metal sorption onto biochars.

The adsorption of six heavy metals (lead, cadmium, nickel, arsenic, copper, and zinc) on 44 biochars were modeled using artificial neural network (ANN) and random forest (RF) based on 353 dataset of adsorption experiments from literatures. The regression models were trained and optimized to predict the adsorption capacity according to biochar characteristics, metal sources, environmental conditions (e.g. temperature and pH), and the initial concentration ratio of metals to biochars. The RF model showed better accuracy and predictive performance for adsorption efficiency (R2 = 0.973) than ANN model (R2 = 0.948). The biochar characteristics were most significant for adsorption efficiency, in which the contribution of cation exchange capacity (CEC) and pHH2O of biochars accounted for 66% in the biochar characteristics. However, surface area of the biochars provided only 2% of adsorption efficiency. Meanwhile, the models developed by RF had better generalization ability than ANN model. The accurate predicted ability of developed models could significantly reduce experiment workload such as predicting the removal efficiency of biochars for target metal according to biochar characteristics, so as to select more efficient biochar without increasing experimental times. The relative importance of variables could provide a right direction for better treatments of heavy metals in the real water and wastewater.

Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions.

In the study, machine learning was used to develop prediction models for yield and carbon contents of biochar (C-char) based on the pyrolysis data of lignocellulosic biomass, and explore inside information underlying the models. The results suggested that random forest could accurately predict biochar yield and C-char according to biomass characteristics and pyrolysis conditions. Furthermore, the relative contribution of pyrolysis conditions was higher than that of biomass characteristics for both yield (65%) and C-char (53%). For biomass characteristics, structural information was more important than elements compositions for accurately predicting biochar yield and it was inverse for C-char. The partial dependence plot analysis showed the impact way of each influential factor on the target variable and the interactions among these factors in the pyrolysis process. The present work provided new insights for understanding pyrolysis process of biomass and improving biochar yield and C-char.

Insights into asphaltene aggregation in the Na-montmorillonite interlayer.

This study aimed to provide insights into the diffusion and aggregation of asphaltenes in the Na-montmorillonite (MMT) interlayer with different water saturation, salinity, interlayer space and humic substances. The molecular configuration, density profile, diffusion coefficient and aggregation intensity were determined by molecular dynamic simulation, while the 3D topography and particle size of the aggregates were characterized by atomic force microscopy. Results indicated that the diffusivity of asphaltenes was up to 5-fold higher in the MMT interlayer filled with fresh water than with saline water (salinity: 35‰). However, salinity had little impact on the asphaltene aggregation. This study also showed a marked decrease in the mobility of asphaltenes with decrease in the pore water content and the interlayer space of MMT. This was more pronounced in the organo-MMT where the humic substances were present. The co-aggregation process resulted in the sequestration of asphaltenes in the hollow cone-shaped cavity of humic substances in the MMT interlayer, which decreased the asphaltene diffusion by up to one-order of magnitude and increased the asphaltene aggregation by about 33%. These findings have important ramifications for evaluating the fate and transport of heavy fractions of the residual oil in the contaminated soils.

Molecular dynamics simulation of cyclodextrin aggregation and extraction of Anthracene from non-aqueous liquid phase.

Cyclodextrin (CD) extraction is widely used for the remediation of polycyclic aromatic hydrocarbons (PAH) pollution, but it remains unclear about the influence of CD aggregation on the PAH transport from non-aqueous liquid phase to water. The atomistic adsorption and complexation of PAHs (32 anthracenes) by CD aggregates (48 ß-cyclodextrins) were studied by molecular dynamics simulations at hundreds of nanoseconds time scale. Results indicated that high temperature promoted the ßCD aggregation in bulk oil, which was not found in bulk water. Nevertheless, the fractions of anthracenes entrapped inside the ßCDs cavity in both scenarios were significantly increased when temperature increased from 298 to 328K. Free energy calculation for the sub-steps of CD extraction demonstrated that the anthracenes could be extracted when the ßCDs arrived at the water-oil interface or after the ßCDs entered the bulk oil. The former was kinetic-controlled while the latter was thermodynamic-limited process. Results also highlighted the formation of porous structures by CD aggregates in water, which was able to sequestrate PAH clusters with the size obviously larger than the cavity diameter of individual CD. This provided an opportunity for the extraction of recalcitrant PAHs with molecular size larger than anthracenes by cyclodextrins.

Modeling the adsorption of PAH mixture in silica nanopores by molecular dynamic simulation combined with machine learning.

The persistence of polycyclic aromatic hydrocarbons (PAHs) in contaminated soils is largely controlled by their molecular fate in soil pores. The adsorption and diffusion of 16 PAHs mixture in silica nanopore with diameter of 2.0, 2.5, 3.0 and 3.5 nm, respectively, were characterized by adsorption energy, mean square displacement, free surface area and free volume fraction using molecular dynamic (MD) simulation. Results suggested that PAHs adsorption in silica nanopores was associated with diffusion process while competitive sorption was not the dominant mechanism in context of this study. The partial least squares (PLS) regression and machine learning (ML) methods (i.e. support vector regression, M5 decision tree and multilayer perceptrons) were used to correlate the adsorption energy with the pore diameter and PAH properties (number of carbon atoms, aromatic ring number, boiling point, molecular weight, octanol-water partition coefficient, octanol-organic carbon partition coefficient, solvent accessible area, solvent accessible volume and polarization). Results indicated that the PAH adsorption could not be predicted by linear regression as the R(2)Y and Q(2)Y coefficients of PLS analysis was 0.375 and 0.199, respectively. The nonlinearity was well recognized by ML with correlation coefficient up to 0.9. Overall, the combination of MD simulation and ML approaches can assist in interpreting the sequestration of organic contaminants in the soil nanopores.

Molecular dynamic simulation of asphaltene co-aggregation with humic acid during oil spill.

Humic acid in water and sediment plays a key role in the fate and transport of the spilled oil, but little is known about its influence on the aggregation of heavy oil asphaltenes which is adverse for remediation. Molecular dynamic simulation was performed to characterize the co-aggregation of asphaltenes (continental model and Violanthrone-79 model) with Leonardite humic acid (LHA) at the toluene-water interface and in bulk water, respectively, to simulate the transport of asphaltenes from oil to water. At the toluene-water interface, a LHA layer tended to form and bind to the water by hydrogen bonding which provided a surface for the accumulation of asphaltenes by parallel or T-shape stacking. After entering the bulk water, asphaltene aggregates stacked in parallel were tightly sequestrated inside the inner cavity of LHA aggregates following surface adsorption and structure deformation. Asphaltene aggregation in water was 2-fold higher than at the toluene-water interface. The presence of LHA increased the intensity of asphaltene aggregation by up to 83% in bulk water while relatively less influence was observed at the toluene-water interface. Overall results suggested that the co-aggregation of asphaltene with humic acid should be incorporated to the current oil spill models for better interpreting the overall environmental risks of oil spill.

Molecular modeling of interactions between heavy crude oil and the soil organic matter coated quartz surface.

Molecular dynamic (MD) simulation was applied to evaluate the mobility, diffusivity and partitioning of SARA (saturates, aromatics, resins, asphaltenes) fractions of heavy crude oil on soil organic matter (SOM) coated quartz surface. Four types of SOM were investigated including Leonardite humic acid, Temple-Northeastern-Birmingham humic acid, Chelsea soil humic acid and Suwannee river fulvic acid. The SOM aggregation at oil-quartz interface decreased the adsorption of SARA on the quartz surface by 13-83%. Although the SOM tended to promote asphaltenes aggregation, the overall mobility of SARA was significantly greater on SOM-quartz complex than on pure quartz. Particularly, the diffusion coefficient of asphaltenes and resins increased by up to one-order of magnitude after SOM addition. The SOM increased the overall oil adsorption capacity but also mobilized SARA by driving them from the viscous oil phase and rigid quartz to the elastic SOM. This highlighted the potential of SOM addition for increasing the bioavailability of heavy crude oil without necessarily increasing the environmental risks. The MD simulation was demonstrated to be helpful for interpreting the role of SOM and the host oil phase for the adsorption and partitioning of SARA molecules, which is the key for developing more realistic remediation appraisal for heavy crude oil in soils.