Novel artemisinin derivative P31 inhibits VEGF-induced corneal neovascularization through AKT and ERK1/2 pathways.

Corneal neovascularization (CoNV)is a major cause of blindness in many ocular diseases. Substantial evidence indicates that vascular endothelial growth factor (VEGF) plays an important role in the pathogenesis of corneal neovascularization. Previous evidence showed that artemisinin may inhibit angiogenesis through down regulation of the VEGF receptors. We designed and synthesized artemisinin derivatives, and validated their inhibitory effect on neovascularization in cell and animal models, and explored the mechanisms by which they exert an inhibitory effect on CoNV. Among these derivatives, P31 demonstrated significant anti-angiogenic effects in vivo and in vitro. Besides, P31 inhibited VEGF-induced HUVECs angiogenesis and neovascularization in rabbit model via AKT and ERK pathways. Moreover, P31 alleviated angiogenic and inflammatory responses in suture rabbit cornea. In conclusion, as a novel artemisinin derivative, P31 attenuates corneal neovascularization and has a promising application in ocular diseases.

A ResNet mini architecture for brain age prediction.

The brain presents age-related structural and functional changes in the human life, with different extends between subjects and groups. Brain age prediction can be used to evaluate the development and aging of human brain, as well as providing valuable information for neurodevelopment and disease diagnosis. Many contributions have been made for this purpose, resorting to different machine learning methods. To solve this task and reduce memory resource consumption, we develop a mini architecture of only 10 layers by modifying the deep residual neural network (ResNet), named ResNet mini architecture. To support the ResNet mini architecture in brain age prediction, the brain age dataset (OpenNeuro #ds000228) that consists of 155 study participants (three classes) and the Alzheimer MRI preprocessed dataset that consists of 6400 images (four classes) are employed. We compared the performance of the ResNet mini architecture with other popular networks using the two considered datasets. Experimental results show that the proposed architecture exhibits generality and robustness with high accuracy and less parameter number.

A multi-view fusion lightweight network for CRSwNPs prediction on CT images.

Accurate preoperative differentiation of the chronic rhinosinusitis (CRS) endotype between eosinophilic CRS (eCRS) and non-eosinophilic CRS (non-eCRS) is an important topic in predicting postoperative outcomes and administering personalized treatment. To this end, we have constructed a sinus CT dataset, which comprises CT scan data and pathological biopsy results from 192 patients of chronic rhinosinusitis with nasal polyps (CRSwNP), treated at the Second Affiliated Hospital of Shantou University Medical College between 2020 and 2022. To differentiate CRSwNP endotype on preoperative CT and improve efficiency at the same time, we developed a multi-view fusion model that contains a mini-architecture with each network of 10 layers by modifying the deep residual neural network. The proposed model is trained on a training set and evaluated on a test set. The multi-view deep learning fusion model achieved the area under the receiver-operating characteristics curve (AUC) of 0.991, accuracy of 0.965 and F1-Score of 0.970 in test set. We compared the performance of the mini-architecture with other lightweight networks on the same Sinus CT dataset. The experimental results demonstrate that the developed ResMini architecture contribute to competitive CRSwNP endotype identification modeling in terms of accuracy and parameter number.

Carbothermal synthesis of sulfurized nano zero-valent iron from sulfate-reducing bacteria biomass for mercury removal: The first application of biomass sulfur source.

The development of low-cost, highly efficient adsorbent materials is of significant importance for environmental remediation. In this study, a novel material, sulfurized nano zero-valent iron loaded biomass carbon (S-nZVI/BC), was successfully synthesized by a simple manufacturing process. The preparation of S-nZVI/BC does not require the use of expensive and hazardous chemicals. Instead, residual sludge, a solid waste product, is used as feedstock. The sludge is rich in Sulfate-Reducing Bacteria (SRB), which can provide carbon and sulfur sources for the synthesis of S-nZVI/BC. It was observed that S-nZVI particles formed in situ were dispersed within BC and covered by it. Additionally, S-nZVI/BC inherited the large specific surface area and porosity of BC. The adsorption capacity of S-nZVI/BC can reach 857.55 mg g-1 Hg (II) during the remediation of mercury-polluted water. This research offers new perspectives for developing composites in terms of the low cost and harmlessness of raw materials.

High-Performance Anode Material Based on Zinc Naphthalocyanine/Graphene Composite.

Transition metal oxides are a potential anode material owing to their high theoretical capacity. Nonetheless, their large volume changes and low electrical conductivities lead to poor cycling performance and rate capabilities. In this article, an effective strategy is proposed and developed for preparing a ZnO/N-doped graphene composite (ZnNc/GO-5). The key point of this strategy is to use zinc tetra tert-butyl-naphthalocyanine (ZnNc) as a codoped source of N atoms and zinc ions, and graphene oxide (GO) which is combined with ZnNc by π-π deposition as a carbon matrix. After calcination, ZnO microcrystals coated with N-doped graphene are obtained. The unique features of the composite and synergistic effect between N-doped reduced graphene oxide and ZnO microcrystals enable good electrochemical performance by the composites when used in lithium-ion batteries. As an anode material, the as-synthesized ZnNc/GO-5 composite delivers a high first capacity of 1942.9 mAh g-1 and excellent cyclic stability of 861.4 mAh g-1 after 150 cycles at 100 mA g-1. This strategy may offer a new method of designing the anode materials of lithium-ion batteries and promote the practical use of organic molecules in next-generation lithium-ion batteries.

Impact of frailty on short-term postoperative outcomes in patients undergoing colorectal cancer surgery: A systematic review and meta-analysis.

BACKGROUND: Colorectal cancer is a major global health challenge that predominantly affects older people. Surgical management, despite advancements, requires careful consideration of preoperative patient status for optimal outcomes. AIM: To summarize existing evidence on the association of frailty with short-term postoperative outcomes in patients undergoing colorectal cancer surgery. METHODS: A literature search was conducted using PubMed, EMBASE and Scopus databases for observational studies in adult patients aged ≥ 18 years undergoing planned or elective colorectal surgery for primary carcinoma and/or secondary metastasis. Only studies that conducted frailty assessment using recognized frailty assessment tools and had a comparator group, comprising nonfrail patients, were included. Pooled effect sizes were reported as weighted mean difference or relative risk (RR) with 95% confidence intervals (CIs). RESULTS: A total of 24 studies were included. Compared with nonfrail patients, frailty was associated with an increased risk of mortality at 30 d (RR: 1.99, 95%CI: 1.47-2.69), at 90 d (RR: 4.76, 95%CI: 1.56-14.6) and at 1 year (RR: 5.73, 95%CI: 2.74-12.0) of follow up. Frail patients had an increased risk of any complications (RR: 1.81, 95%CI: 1.57-2.10) as well as major complications (Clavien-Dindo classification grade ≥ III) (RR: 2.87, 95%CI: 1.65-4.99) compared with the control group. The risk of reoperation (RR: 1.18, 95%CI: 1.07-1.31), readmission (RR: 1.70, 95%CI: 1.36-2.12), need for blood transfusion (RR: 1.67, 95%CI: 1.52-1.85), wound complications (RR: 1.49, 95%CI: 1.11-1.99), delirium (RR: 4.60, 95%CI: 2.31-9.16), risk of prolonged hospitalization (RR: 2.09, 95%CI: 1.22-3.60) and discharge to a skilled nursing facility or rehabilitation center (RR: 3.19, 95%CI: 2.0-5.08) was all higher in frail patients. CONCLUSION: Frailty in colorectal cancer surgery patients was associated with more complications, longer hospital stays, higher reoperation risk, and increased mortality. Integrating frailty assessment appears crucial for tailored surgical management.

Advancing Drug-Target Interaction prediction with BERT and subsequence embedding.

Exploring the relationship between proteins and drugs plays a significant role in discovering new synthetic drugs. The Drug-Target Interaction (DTI) prediction is a fundamental task in the relationship between proteins and drugs. Unlike encoding proteins by amino acids, we use amino acid subsequence to encode proteins, which simulates the biological process of DTI better. For this research purpose, we proposed a novel deep learning framework based on Bidirectional Encoder Representation from Transformers (BERT), which integrates high-frequency subsequence embedding and transfer learning methods to complete the DTI prediction task. As the first key module, subsequence embedding allows to explore the functional interaction units from drug and protein sequences and then contribute to finding DTI modules. As the second key module, transfer learning promotes the model learn the common DTI features from protein and drug sequences in a large dataset. Overall, the BERT-based model can learn two kinds features through the multi-head self-attention mechanism: internal features of sequence and interaction features of both proteins and drugs, respectively. Compared with other methods, BERT-based methods enable more DTI-related features to be discovered by means of attention scores which associated with tokenized protein/drug subsequences. We conducted extensive experiments for the DTI prediction task on three different benchmark datasets. The experimental results show that the model achieves an average prediction metrics higher than most baseline methods. In order to verify the importance of transfer learning, we conducted an ablation study on datasets, and the results show the superiority of transfer learning. In addition, we test the scalability of the model on the dataset in unseen drugs and proteins, and the results of the experiments show that it is acceptable in scalability.

Nondestructive detection of SSC in multiple pear (Pyrus pyrifolia Nakai) cultivars using Vis-NIR spectroscopy coupled with the Grad-CAM method.

Vis-NIR spectroscopy coupled with chemometric models is frequently used for pear soluble solid content (SSC) prediction. However, the model robustness is challenged by the variations in pear cultivars. This study explored the feasibility of developing universal models for predicting SSC of multiple pear varieties to improve the model's generalizability. The mature fruits of 6 pear cultivars with green skin (Pyrus pyrifolia Nakai cv. 'Cuiyu', 'Sucui No.1' and 'Cuiguan') and brown skin (Pyrus pyrifolia Nakai cv. 'Hosui','Syusui' and 'Wakahikari') were used to establish single-cultivar models and multi-cultivar universal models using convolutional neural network (CNN), partial least square (PLS), and support vector regression (SVR) approaches. Multi-cultivar universal models were built using full spectra and important variables extracted by gradient-weighted class activation mapping (Grad-CAM), respectively. The universal models based on important variables obtained satisfactory performances with RMSEPs of 0.76, 0.59, 0.80, 1.64, 0.98, and 1.03°Brix on 6 cultivars, respectively.

Recovery of biochar particles laden with lead in saturated porous media by DC electric field.

The co-transport behavior of environmental pollutants with biochar particles has aroused great interests from researchers due to the concerns about pollutant diffusion and environmental exposure after biochar is applied to soil. In this work, the recovery and co-transport behavior of biochar micron-/nano-particles (BCMP and BCNP) and lead (Pb2+) in saturated porous media were investigated under different ionic strength conditions (IS = 1, 5 and 10 mM) under a direct current electric field. The results showed that the electric field could significantly enhance the mobility of Pb adsorbed biochar particles, particularly BCNP. The recovery of Pb laden biochar particles was improved by 1.8 folds, reaching 78.8% at maximum under favorable condition at +0.5 V cm-1. According to the CDE (Convection-Dispersion-Equation) model and DLVO (Derjaguin-Landau-Verwey-Overbeek) theory analysis, the electric field facilitated the transport of Pb carried biochar mainly by increasing the negative charges on biochar surface and improving the repulsive force between biochar and porous media. High IS was favorable for biochar transport under the electric field, but inhibited desorbing Pb2+ from biochar (18% by maximum at IS = 10 mM). By switching the electric field power, a two-stage strategy was established to maximize the recovery of both biochar particles and Pb, where BCNP and Pb recovery were higher than electric field free case by 90% and 35%, respectively. The findings of this study can help build a biochar recovery approach to prevent potential risks from biochar application in heavy metal contaminated soil remediation.

The manufacturing process provides green teas with differentiated nonvolatile profiles and influences the deterioration of flavor during storage at room temperature.

Hundreds of green tea products are available on the tea market and exhibit different characteristics. In the present study, seven types of green tea were processed, and their nonvolatile profiles were analyzed by liquid chromatography-mass spectrometry. Non-spreading green tea contained higher concentrations of catechins and flavonoid glycosides, but lower concentrations of amino acids, caffeine, and theaflavins. Non-rolling green teas with a straight appearance contained higher concentrations of flavonoid glycosides and theaflavins. In contrast, leaf-rolling green teas contained much lower concentrations of flavonoid glycosides and catechins. These seven green tea qualities all decreased following prolonged storage, concurrent with increasing concentrations of proanthocyanidins, catechins dimers, theaflavins, and organic acids. The leaf-rolling green teas exhibited reduced levels of deterioration during storage in terms of their nonvolatile profile and sensory quality. Findings show that moderate destruction on tea leaves during green tea processing is beneficial to both tea flavor and quality maintenance during storage.

Deciphering the role of non-coding RNAs involved in sorafenib resistance.

Sorafenib is an important treatment strategy for advanced hepatocellular carcinoma (HCC). Unfortunately, drug resistance has become a major obstacle in sorafenib application. In this study, whole transcriptome sequencing (WTS) was conducted to compare the paired differences between non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), microRNAs (miRNAs), and mRNAs, in sorafenib-resistant and parental cells. The overlap of differentially expressed ncRNAs (DENs) between the SMMC7721/S and Huh7/S cells and their parental cells was determined. 2 upregulated and 3 downregulated lncRNAs, 2 upregulated and 1 downregulated circRNAs, as well as 10 upregulated and 2 downregulated miRNAs, in both SMMC7721/S and Huh7/S cells, attracted more attention. The target genes of these DENs were then identified as the overlaps between the differentially expressed mRNAs achieved using the WTS analysis and the predicted genes of DENs obtained using the "co-localization" or "co-expression," miRanda, and RNAhybrid analysis. Consequently, the potential regulatory network between overlapping DENs and their target genes in both SMMC7721/S and Huh7/S cells was explored. The "lncRNA-miRNA-mRNA" and "circRNA-miRNA-mRNA" networks were constructed based on the competitive endogenous RNA (ceRNA) theory using the Cytoscape software. In particular, lncRNA MED17-203-miRNA (miR-193a-5p, miR-197-3p, miR-27a-5p, miR-320b, miR-767-3p, miR-767-5p, miR-92a-3p, let-7c-5p)-mRNA," "circ_0002874-miR-27a-5p-mRNA" and "circ_0078607-miR-320b-mRNA" networks were first introduced in sorafenib-resistant HCC. Furthermore, these networks were most probably connected to the process of metabolic reprogramming, where the activation of the PPAR, HIF-1, Hippo, and TGF-ß signaling pathways is governed. Alternatively, the network "circ_0002874-miR-27a-5p-mRNA" was also involved in the regulation of the activation of TGF-ß signaling pathways, thus advancing Epithelial-mesenchymal transition (EMT). These findings provide a theoretical basis for exploring the mechanisms underlying sorafenib resistance mediated by metabolic reprogramming and EMT in HCC.

The CXCR4 might be a potential biomarker for esophageal squamous cell carcinoma: A meta-analysis.

OBJECTIVE: To evaluate the relationship between CXCL12/CXCR4 and the progress, prognosis of esophageal squamous cell carcinoma (ESCC), providing evidence for potential early diagnosis, clinical treatment, prognosis evaluation, and therapeutic target of ESCC. METHODS: Databases of PubMed, the Cochrane Library, Embase, and Web of Science were searched for the relationship between CXCL12/CXCR4 and clinicopathological characteristics and survival time of ESCC. Stata16.0 software was used to conduct meta-analysis. RESULTS: A total of 10 studies involving 1216 cases of patients with ESCC were included in our study. The results indicated that high-level expression of CXCR4 was significantly correlated with tumor differentiation [OR = 0.69, 95% confidence interval (CI): (0.50, 0.97)], tumor infiltration [OR = 0.39, 95% CI: (0.25, 0.61)], lymph node metastasis [OR = 0.36, 95% CI: (0.21, 0.61)], clinical stage [OR = 0.33, 95% CI: (0.24, 0.45)] of ESCC. The expression of CXCR4 was also significantly correlated with OS [HR = 2.00, 95% CI: (1.63, 2.45)] and disease-free survival [HR = 1.76, 95% CI: (1.44, 2.15)] in patients of ESCC after surgical resection. No significant relationship was observed between the expression of CXCL12 and the clinicopathological characteristics of ESCC. CONCLUSION: CXCR4 might be a potential biomarker for the progress and prognosis evaluation, and therapeutic target for ESCC.

Novel STAT3 oligonucleotide compounds suppress tumor growth and overcome the acquired resistance to sorafenib in hepatocellular carcinoma.

Signal transducer and activator of transcription 3 (STAT3) plays an important role in the occurrence and progression of tumors, leading to resistance and poor prognosis. Activation of STAT3 signaling is frequently detected in hepatocellular carcinoma (HCC), but potent and less toxic STAT3 inhibitors have not been discovered. Here, based on antisense technology, we designed a series of stabilized modified antisense oligonucleotides targeting STAT3 mRNA (STAT3 ASOs). Treatment with STAT3 ASOs decreased the STAT3 mRNA and protein levels in HCC cells. STAT3 ASOs significantly inhibited the proliferation, survival, migration, and invasion of cancer cells by specifically perturbing STAT3 signaling. Treatment with STAT3 ASOs decreased the tumor burden in an HCC xenograft model. Moreover, aberrant STAT3 signaling activation is one of multiple signaling pathways involved in sorafenib resistance in HCC. STAT3 ASOs effectively sensitized resistant HCC cell lines to sorafenib in vitro and improved the inhibitory potency of sorafenib in a resistant HCC xenograft model. The developed STAT3 ASOs enrich the tools capable of targeting STAT3 and modulating STAT3 activity, serve as a promising strategy for treating HCC and other STAT3-addicted tumors, and alleviate the acquired resistance to sorafenib in HCC patients. A series of novel STAT3 antisense oligonucleotide were designed and showed potent anti-cancer efficacy in hepatocellular carcinoma in vitro and in vivo by targeting STAT3 signaling. Moreover, the selected STAT3 ASOs enhance sorafenib sensitivity in resistant cell model and xenograft model.

Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD.

Currently, clinically available coronary CT angiography (CCTA) derived fractional flow reserve (CT-FFR) is time-consuming and complex. We propose a novel artificial intelligence-based fully-automated, on-site CT-FFR technology, which combines the automated coronary plaque segmentation and luminal extraction model with reduced order 3 dimentional (3D) computational fluid dynamics. A total of 463 consecutive patients with 600 vessels from the updated China CT-FFR study in Cohort 1 undergoing both CCTA and invasive fractional flow reserve (FFR) within 90 d were collected for diagnostic performance evaluation. For Cohort 2, a total of 901 chronic coronary syndromes patients with index CT-FFR and clinical outcomes at 3-year follow-up were retrospectively analyzed. In Cohort 3, the association between index CT-FFR from triple-rule-out CTA and major adverse cardiac events in patients with acute chest pain from the emergency department was further evaluated. The diagnostic accuracy of this CT-FFR in Cohort 1 was 0.82 with an area under the curve of 0.82 on a per-patient level. Compared with the manually dependent CT-FFR techniques, the operation time of this technique was substantially shortened by 3 times and the number of clicks from about 60 to 1. This CT-FFR technique has a highly successful (> 99%) calculation rate and also provides superior prediction value for major adverse cardiac events than CCTA alone both in patients with chronic coronary syndromes and acute chest pain. Thus, the novel artificial intelligence-based fully automated, on-site CT-FFR technique can function as an objective and convenient tool for coronary stenosis functional evaluation in the real-world clinical setting.

Passivation of 2D Cs2PbI2Cl2 Nanosheets for Efficient and Stable CsPbI3 Quantum Dot Solar Cells.

Inorganic CsPbI3 perovskite quantum dots (PQDs) possess remarkable optical properties, making them highly promising for photovoltaic applications. However, the inadequate stability resulting from internal structural instability and the complex external surface chemical environment of CsPbI3 PQDs has hindered the development of CsPbI3 PQD solar cells (PQDSCs). In this work, the capping layer composed of inorganic two-dimensional (2D) Ruddlesden-Popper (RP) phase Cs2PbI2Cl2 nanosheets (NSs) is introduced, which may be effectively treated to improve the surface properties of the CsPbI3 PQD film. This modification serves to passivate defects by filling cesium and iodine vacancies while optimizing the energy band arrangement and preventing humidity intrusion, leading to the meliorative stability and photovoltaic performance. The optimized CsPbI3 PQDSCs achieve an enhanced power conversion efficiency (PCE) of 14.73%, with the superb stability of only a 16% efficiency loss after being exposed to ambient conditions (30 ± 5% RH) for 432 h.

Quantitative differentiation of toxicity contributions and predicted global risk of fipronil and its transformation products to aquatic invertebrates.

Biotransformation often alters chemical toxicity, yet its impacts on risk assessment are hardly quantified due to the challenges in acquiring internal exposure-based thresholds for chemicals that are readily metabolizable. Here, we integrated toxic unit and toxicokinetics to quantitatively assess toxicity contributions and potential risk of both parent compound and transformation products (TPs) to aquatic organisms, using fipronil (FIP) as a representative toxicant. In aquatic invertebrates Chironomus dilutus and Hyalella azteca, approximately 90 % of FIP was transformed to fipronil sulfone (SUL). FIP and SUL exhibited similar intrinsic toxicity to these organisms, which was contrary to conventional perception that SUL was more toxic than FIP. However, biotransformation was still important in risk assessment because the TP had 10-fold slower depuration rate than FIP. The amphipod H. azteca was found to be as sensitive to FIPs as the insect C. dilutus, which was previously considered ten times more sensitive based on external thresholds. This discrepancy has led to overlooking the toxicity of FIP to H. azteca in regional risk assessments. Lastly, we predicted the lethal risk of FIPs in global surface water. When using external thresholds for prediction, FIPs in 3.4 % of the water samples were lethally toxic to H. azteca, and the percentage of water samples at risk increased to 14 % when internal thresholds were used and SUF dominated the risk. This study presents an improved method for quantifying aquatic risk of readily metabolized toxicants. Our findings underscore the urgency of considering TPs in water quality assessments, especially for sensitive species that are at risk in the environment.

Prognostic Value of a Combined Nomogram Model Integrating 3-Dimensional Deep Learning and Radiomics for Head and Neck Cancer.

OBJECTIVE: The preoperative prediction of the overall survival (OS) status of patients with head and neck cancer (HNC) is significant value for their individualized treatment and prognosis. This study aims to evaluate the impact of adding 3D deep learning features to radiomics models for predicting 5-year OS status. METHODS: Two hundred twenty cases from The Cancer Imaging Archive public dataset were included in this study; 2212 radiomics features and 304 deep features were extracted from each case. The features were selected by univariate analysis and the least absolute shrinkage and selection operator, and then grouped into a radiomics model containing Positron Emission Tomography /Computed Tomography (PET/CT) radiomics features score, a deep model containing deep features score, and a combined model containing PET/CT radiomics features score +3D deep features score. TumorStage model was also constructed using initial patient tumor node metastasis stage to compare the performance of the combined model. A nomogram was constructed to analyze the influence of deep features on the performance of the model. The 10-fold cross-validation of the average area under the receiver operating characteristic curve and calibration curve were used to evaluate performance, and Shapley Additive exPlanations (SHAP) was developed for interpretation. RESULTS: The TumorStage model, radiomics model, deep model, and the combined model achieved areas under the receiver operating characteristic curve of 0.604, 0.851, 0.840, and 0.895 on the train set and 0.571, 0.849, 0.832, and 0.900 on the test set. The combined model showed better performance of predicting the 5-year OS status of HNC patients than the radiomics model and deep model. The combined model was shown to provide a favorable fit in calibration curves and be clinically useful in decision curve analysis. SHAP summary plot and SHAP The SHAP summary plot and SHAP force plot visually interpreted the influence of deep features and radiomics features on the model results. CONCLUSIONS: In predicting 5-year OS status in patients with HNC, 3D deep features could provide richer features for combined model, which showed outperformance compared with the radiomics model and deep model.

Assessing the wastewater reclaim system consisted of wastewater plant - hybrid constructed wetland - ultra filtration and reverse osmosis in a chemical industrial park, a multi-criteria decision-making analysis.

Wastewater reclaim in industrial parks can effectively reduce the dependence on external water resources, few literatures evaluated the reclaim system from perspectives of economy, technology, and environmental impact. It is very popular across China that a constructed wetland is linked with a wastewater plant and then discharged the tailwater into surface waters, based on current situation, pilot experiment, and other available techniques, six scenarios for wastewater reclaim system were designed for Shanghai Chemical Industrial Park. Using life cycle assessment, it was found that in scenario of pilot experiment, most environmental impact was derived from wastewater plant and ultra filtration - reverse osmosis, in which ultra filtration - reverse osmosis accounted >20 % for POCP, AP, and EP, Wastewater plant accounted >86 % for ADP, ODP. It was showed that electricity and sludge were most important parameters affecting LCA, when electricity consumption was reduced by 20 %, total standardized environmental impact would be changed in the range of 1.40 %-1.65 %, the most significant change was HTP (6.12 %-6.32 %) when 20 % up and downward change in sludge amount, followed by MAETP (5.27 %-5.36 %). A multi-criteria decision-making analysis was conducted on all the scenarios based on environmental impact, life cycle cost, technical efficiency, it was showed that the scenario designed for pilot experiment was the best available technique, which was consisted of wastewater plant, hybrid constructed wetland, ultra-filtration and reverse osmosis, and reused as desalted water. A wastewater reclaim plant is suggested from the result of this paper. It was believed that this study could provide references for construction of wastewater reclaim system in industrial parks of the world.

Metabolism pathway-based subtyping in endometrial cancer: An integrated study by multi-omics analysis and machine learning algorithms.

Endometrial cancer (EC), the second most common malignancy in the female reproductive system, has garnered increasing attention for its genomic heterogeneity, but understanding of its metabolic characteristics is still poor. We explored metabolic dysfunctions in EC through a comprehensive multi-omics analysis (RNA-seq datasets from The Cancer Genome Atlas [TCGA], Cancer Cell Line Encyclopedia [CCLE], and GEO datasets; the Clinical Proteomic Tumor Analysis Consortium [CPTAC] proteomics; CCLE metabolomics) to develop useful molecular targets for precision therapy. Unsupervised consensus clustering was performed to categorize EC patients into three metabolism-pathway-based subgroups (MPSs). These MPS subgroups had distinct clinical prognoses, transcriptomic and genomic alterations, immune microenvironment landscape, and unique patterns of chemotherapy sensitivity. Moreover, the MPS2 subgroup had a better response to immunotherapy. Finally, three machine learning algorithms (LASSO, random forest, and stepwise multivariate Cox regression) were used for developing a prognostic metagene signature based on metabolic molecules. Thus, a 13-hub gene-based classifier was constructed to predict patients' MPS subtypes, offering a more accessible and practical approach. This metabolism-based classification system can enhance prognostic predictions and guide clinical strategies for immunotherapy and metabolism-targeted therapy in EC.

Aging and mitigation of microplastics during sewage sludge treatments: An overview.

Wastewater treatment plants (WWTPs) receive large quantities of microplastics (MPs) from raw wastewater, but many MPs are trapped in the sludge. Land application of sludge is a significant source of MP pollution. Existing reviews have summarized the analysis methods of MPs in sludge and the effect of MPs on sludge treatments. However, MP aging and mitigation during sludge treatment processes are not fully reviewed. Treatment processes used to remove water, pathogenic microorganisms, and other pollutants in sewage sludge also cause surface changes and degradation in the sludge MPs, affecting the potential risk of MPs. This study integrates MP abundance and distribution in sludge and their aging and mitigation characteristics during sludge treatment processes. The abundance, composition, and distribution of sludge MPs vary significantly with WWTPs. Furthermore, MPs exhibit variable degrees of aging, including rough surfaces, enhanced adsorption potentials for pollutants, and increased leaching behavior. Various sludge treatment processes further intensify these aging characteristics. Some sludge treatments, such as hydrothermal treatment, have efficiently removed MPs from sewage sludge. It is crucial to understand the potential risk of MP aging in sludge and the degradation properties of the MP-derived products from MP degradation in-depth and develop novel MP mitigation strategies in sludge, such as combining hydrothermal treatment and biological processes.