Microbial life-history strategies mediate microbial carbon pump efficacy in response to N management depending on stoichiometry of microbial demand.

The soil microbial carbon pump (MCP) is increasingly acknowledged as being directly linked to soil organic carbon (SOC) accumulation and stability. Given the close coupling of carbon (C) and nitrogen (N) cycles and the constraints imposed by their stoichiometry on microbial growth, N addition might affect microbial growth strategies with potential consequences for necromass formation and carbon stability. However, this topic remains largely unexplored. Based on two multi-level N fertilizer experiments over 10 years in two soils with contrasting soil fertility located in the North (Cambisol, carbon-poor) and Southwest (Luvisol, carbon-rich), we hypothesized that different resource demands of microorganism elicit a trade-off in microbial growth potential (Y-strategy) and resource-acquisition (A-strategy) in response to N addition, and consequently on necromass formation and soil carbon stability. We combined measurements of necromass metrics (MCP efficacy) and soil carbon stability (chemical composition and mineral associated organic carbon) with potential changes in microbial life history strategies (assessed via soil metagenomes and enzymatic activity analyses). The contribution of microbial necromass to SOC decreased with N addition in the Cambisol, but increased in the Luvisol. Soil microbial life strategies displayed two distinct responses in two soils after N amendment: shift toward A-strategy (Cambisol) or Y-strategy (Luvisol). These divergent responses are owing to the stoichiometric imbalance between microbial demands and resource availability for C and N, which presented very distinct patterns in the two soils. The partial correlation analysis further confirmed that high N addition aggravated stoichiometric carbon demand, shifting the microbial community strategy toward resource-acquisition which reduced carbon stability in Cambisol. In contrast, the microbial Y-strategy had the positive direct effect on MCP efficacy in Luvisol, which greatly enhanced carbon stability. Such findings provide mechanistic insights into the stoichiometric regulation of MCP efficacy, and how this is mediated by site-specific trade-offs in microbial life strategies, which contribute to improving our comprehension of soil microbial C sequestration and potential optimization of agricultural N management.

Tracking the changes of dissolved organic matter throughout the city water supply system with optical indices.

Dissolved organic matter (DOM) is important in determining the drinking water treatment and the supplied water quality. However, a comprehensive DOM study for the whole water supply system is lacking and the potential effects of secondary water supply are largely unknown. This was studied using dissolved organic carbon (DOC), absorption spectroscopy, and fluorescence excitation-emission matrices-parallel factor analysis (EEM-PARAFAC). Four fluorescent components were identified, including humic-like C1-C2, tryptophan-like C3, and tyrosine-like C4. In the drinking water treatment plants, the advanced treatment using ozone and biological activated carbon (O3-BAC) was more effective in removing DOC than the conventional process, with the removals of C1 and C3 improved by 17.7%-25.1% and 19.2%-27.0%. The absorption coefficient and C1-C4 correlated significantly with DOC in water treatments, suggesting that absorption and fluorescence could effectively track the changes in bulk DOM. DOM generally remained stable in each drinking water distribution system, suggesting the importance of the treated water quality in determining that of the corresponding network. The optical indices changed notably between distribution networks of different treatment plants, which enabled the identification of changing water sources. A comparison of DOM in the direct and secondary water supplies suggested limited impacts of secondary water supply, although the changes in organic carbon and absorption indices were detected in some locations. These results have implications for better understanding the changes of DOM in the whole water supply system to help ensure the supplied water quality.

CT-based radiomics model using stability selection for predicting the World Health Organization/International Society of Urological Pathology grade of clear cell renal cell carcinoma.

OBJECTIVES: This study aimed to develop a model to predict World Health Organization/International Society of Urological Pathology (WHO/ISUP) low-grade or high-grade clear cell renal cell carcinoma (ccRCC) using 3D multiphase enhanced CT radiomics features (RFs). METHODS: CT data of 138 low-grade and 60 high-grade ccRCC cases were included. RFs were extracted from four CT phases: non-contrast phase (NCP), corticomedullary phase, nephrographic phase, and excretory phase (EP). Models were developed using various combinations of RFs and subjected to cross-validation. RESULTS: There were 107 RFs extracted from each phase of the CT images. The NCP-EP model had the best overall predictive value (AUC = 0.78), but did not significantly differ from that of the NCP model (AUC = 0.76). By considering the predictive ability of the model, the level of radiation exposure, and model simplicity, the overall best model was the Conventional image and clinical features (CICFs)-NCP model (AUC = 0.77; sensitivity 0.75, specificity 0.69, positive predictive value 0.85, negative predictive value 0.54, accuracy 0.73). The second-best model was the NCP model (AUC = 0.76). CONCLUSIONS: Combining clinical features with unenhanced CT images of the kidneys seems to be optimal for prediction of WHO/ISUP grade of ccRCC. This noninvasive method may assist in guiding more accurate treatment decisions for ccRCC. ADVANCES IN KNOWLEDGE: This study innovatively employed stability selection for RFs, enhancing model reliability. The CICFs-NCP model's simplicity and efficacy mark a significant advancement, offering a practical tool for clinical decision-making in ccRCC management.

Unveiling changes in the complexation of dissolved organic matter with Pb(II) by photochemical and microbial degradation using fluorescence EEMs-PARAFAC.

Dissolved organic matter (DOM) is very important in determining the speciation, behaviors, and risk of metal pollutants in aquatic ecosystems. Photochemical and microbial degradation are key processes in the cycling of DOM, yet their effects on the DOM-Pb(II) interaction remain largely unknown. This was studied by examining the complexation of river DOM with Pb(II) after degradation, using fluorescence quenching titration and excitation-emission matrices-parallel factor analysis (EEMs-PARAFAC). Three humic-like and two protein-like components were identified, with strong removals of humic-like components and decreasing average molecular weight and humification degree of DOM by photo- and photo-microbial degradation. The changes in humic-like abundance and structure resulted in notable weakening of their interaction with Pb(II). The tryptophan-like C2 was also mainly removed by photo-degradation, while the tyrosine-like C3 could be either removed or accumulated. The Pb(II)-binding of protein-like components was generally weaker but was enhanced in some degradation groups, which might be related to the lowering competition from humic-like components. The binding parameters correlated significantly with the DOM indices, which were dominated by photo-degradation for humic-like components but by seasonal variations for the tyrosine-like component. These results have implications for understanding the key mechanisms underlying the variability of the DOM-metal interaction in aquatic environments.

Engineered probiotics introduced to improve intestinal microecology for the treatment of chronic diseases: present state and perspectives.

Purpose: Correcting intestinal microecological imbalance has become one of the core strategies to treat chronic diseases. Some traditional microecology-based therapies targeting intestine, such as prebiotic therapy, probiotic therapy and fecal microbiota transplantation therapy, have been used in the prevention and treatment of clinical chronic diseases, which still facing low safety and poor controllability problems. The development of synthetic biology technology has promoted the development of intestinal microecology-based therapeutics for chronic diseases, which exhibiting higher robustness and controllability, and become an important part of the next generation of microecological therapy. The purpose of this review is to summarize the application of synthetic biology in intestinal microecology-based therapeutics for chronic diseases. Methods: The available literatures were searched to find out experimental studies and relevant review articles on the application of synthetic biology in intestinal microecology-based therapeutics for chronic diseases from year 1990 to 2023. Results: Evidence proposed that synthetic biology has been applied in the intestinal microecology-based therapeutics for chronic diseases, covering metabolic diseases (e.g. diabetes, obesity, nonalcoholic fatty liver disease and phenylketonuria), digestive diseases (e.g. inflammatory bowel disease and colorectal cancer), and neurodegenerative diseases (e.g. Alzheimer's disease and Parkinson's disease). Conclusion: This review summarizes the application of synthetic biology in intestinal microecology-based therapeutics for major chronic diseases and discusses the opportunities and challenges in the above process, providing clinical possibilities of synthetic biology technology applied in microecological therapies.

Wuzi Yanzong Pill Plays A Neuroprotective Role in Parkinson's Disease Mice via Regulating Unfolded Protein Response Mediated by Endoplasmic Reticulum Stress.

OBJECTIVE: To investigate the protective effects and its possible mechanism of Wuzi Yanzong Pill (WYP) on Parkinson's disease (PD) model mice. METHODS: Thirty-six C57BL/6 male mice were randomly assigned to 3 groups including normal, PD, and PD+WYP groups, 12 mice in each group. One week of intraperitoneal injection of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) was used to establish the classical PD model in mice. Meanwhile, mice in the PD+WYP group were administrated with 16 g/kg WYP, twice daily by gavage. After 14 days of administration, gait test, open field test and pole test were measured to evaluate the movement function. Tyrosine hydroxylase (TH) neurons in substantia nigra of midbrain and binding immunoglobulin heavy chain protein (GRP78) in striatum and cortex were observed by immunohistochemistry. The levels of TH, GRP78, p-PERK, p-eIF2α, ATF4, p-IRE1α, XBP1, ATF6, CHOP, ASK1, p-JNK, Caspase-12, -9 and -3 in brain were detected by Western blot. RESULTS: Compared with the PD group, WYP treatment ameliorated gait balance ability in PD mice (P<0.05). Similarly, WYP increased the total distance and average speed (P<0.05 or P<0.01), reduced rest time and pole time (P<0.05). Moreover, WYP significantly increased TH positive cells (P<0.01). Immunofluorescence showed WYP attenuated the levels of GRP78 in striatum and cortex. Meanwhile, WYP treatment significantly decreased the protein expressions of GRP78, p-PERK, p-eIF2α, ATF4, p-IRE1 α, XBP1, CHOP, Caspase-12 and Caspase-9 (P<0.05 or P<0.01). CONCLUSIONS: WYP ameliorated motor symptoms and pathological lesion of PD mice, which may be related to the regulation of unfolded protein response-mediated signaling pathway and inhibiting the endoplasmic reticulum stress-mediated neuronal apoptosis pathway.

Optical and molecular indices of dissolved organic matter for estimating biodegradability and resulting carbon dioxide production in inland waters: A review.

Biodegradable dissolved organic carbon (BDOC) constitutes the most labile fraction of dissolved organic matter (DOM), which also functions as a source of CO2 emissions from inland waters. However, no systematic review is available on DOM indicators of BDOC and CO2 production potential. Optical and molecular indices can be used to track small changes in DOM composition during biodegradation. In this review, we identified four different methods for measuring BDOC together with their strengths and limitations. In addition, we discuss the potential of using documented optical indices based on absorption and fluorescence spectroscopy and molecular indices based on Fourier transform ion cyclotron mass spectrometry as proxies for estimating BDOC and biodegradation-induced CO2 production based on previously reported relationships in the literature. Many absorbance- and fluorescence-based indices showed inconsistent relationships with BDOC depending on watershed characteristics, hydrology, and anthropogenic impacts. Nevertheless, several indices, including specific UV absorbance at 254 nm (SUVA254), humification index (HIX), and terrestrial humic-like fluorescent DOM (FDOM) components, tended to have negative relationships with BDOC in tropical and temperate watersheds under baseflow or drought periods. Protein-like FDOM exhibited the strongest correlation with BDOC in different systems, except during storms and flood events. Despite the limited number of studies, DOM molecular indices exhibited consistent relationships with BDOC, suggesting that the relative abundance of aliphatic formulas and the molecular lability index could act as reliable proxies. The DOM optical indices explain up to 96% and 78% variability in BDOC and CO2, respectively; nonetheless, there were limited studies on molecular indices, which explain up to 74% variability in BDOC. Based on literature survey, we recommend several sensitive indices such as SUVA254, HIX, and terrestrial humic- and protein-like FDOM, which could be useful indicators of BDOC and dissolved CO2 in inland water. Future research should incorporate a wider range of geographic regions with various land use, hydrology, and anthropogenic disturbances to develop system- or condition-specific DOM optical or molecular proxies for better prediction of BDOC and CO2 emissions.

Effects of Photodegradation on the Optical Indices of Chromophoric Dissolved Organic Matter from Typical Sources.

Chromophoric dissolved organic matter (CDOM) plays important roles in aquatic environments, and its optical properties provide a series of indices for evaluating the source and composition of dissolved organic matter (DOM). However, little is known about the varying photodegradation of CDOM from different sources and the effects on the optical indices of DOM composition. This was studied for typical natural and anthropogenic sources (plant and leaf litter leachates, the influent and effluent of a wastewater treatment plant, and a river). The CDOM absorption (a280) showed a lower degradability for the plant leachate than other sources, mainly due to its low molecular weight and aromaticity. Four fluorescent components were identified with excitation-emission matrices-parallel factor analysis (EEMs-PARAFAC), namely benzoic acid/monolignol-like C1, humic-like C2 and C3, and tryptophan-like C4. The plant leachate contained mainly C1, which was photodegraded moderately, while other sources had more C2 and C3 with higher photodegradability. C4 was photodegraded in most sources but was photoproduced in the leaf litter leachate. The absorption slope (S275-295) and slope ratio (SR) increased while the humification index (HIX) decreased, suggesting a decreasing molecular weight and humic content by photodegradation. This was consistent with the decreasing %C2 and %C3 but increasing %C4, which indicated preferential removal of humic-like components. The %C1, %C2, biological index (BIX), and fluorescence index (FI) were less affected by photodegradation than other indices for most sources. These results have implications for a better understanding of the photochemistry of CDOM and the applications of optical indices.

An exploratory study of CT radiomics using differential network feature selection for WHO/ISUP grading and progression-free survival prediction of clear cell renal cell carcinoma.

Objectives: To explore the feasibility of predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade and progression-free survival (PFS) of clear cell renal cell cancer (ccRCC) using the radiomics features (RFs) based on the differential network feature selection (FS) method using the maximum-entropy probability model (MEPM). Methods: 175 ccRCC patients were divided into a training set (125) and a test set (50). The non-contrast phase (NCP), cortico-medullary phase, nephrographic phase, excretory phase phases, and all-phase WHO/ISUP grade prediction models were constructed based on a new differential network FS method using the MEPM. The diagnostic performance of the best phase model was compared with the other state-of-the-art machine learning models and the clinical models. The RFs of the best phase model were used for survival analysis and visualized using risk scores and nomograms. The performance of the above models was tested in both cross-validated and independent validation and checked by the Hosmer-Lemeshow test. Results: The NCP RFs model was the best phase model, with an AUC of 0.89 in the test set, and performed superior to other machine learning models and the clinical models (all p <0.05). Kaplan-Meier survival analysis, univariate and multivariate cox regression results, and risk score analyses showed the NCP RFs could predict PFS well (almost all p < 0.05). The nomogram model incorporated the best two RFs and showed good discrimination, a C-index of 0.71 and 0.69 in the training and test set, and good calibration. Conclusion: The NCP CT-based RFs selected by differential network FS could predict the WHO/ISUP grade and PFS of RCC.

Critical evaluation of the interaction between fluorescent dissolved organic matter and Pb(II) under variable environmental conditions.

Dissolved organic matter (DOM) can strongly influence the behavior and risk of metal pollutants in aquatic ecosystems. However, a comprehensive study on the effects of DOM level and environmental factors on the binding of DOM with Pb(II) is lacking. This study examined the DOM-Pb(II) interaction in the river water under variable DOM level, pH, and major ions, using fluorescence excitation-emission matrices-parallel factor analysis (EEMs-PARAFAC). Four humic-like and one protein-like component were identified, and the abundant humic-like components showed higher Pb(II)-binding fractions (f) than the protein-like component. The f of PARAFAC components decreased while the conditional stability constants (logKM) increased for the diluted DOM, indicating the influence of DOM level on its metal binding. The DOM-Pb(II) interaction was sensitive to changes in pH, with generally higher f and lower logKM at the alkaline condition due to changes in the DOM conformation. The addition of major ions significantly decreased the fluorescence quenching by Pb(II), due to competitive effects and potential DOM conformation changes at elevated ions. Overall, our results show that the DOM-Pb(II) complexation is highly dependent on both the DOM properties and environmental factors, which have implications for optimizing the experimental conditions and for comparing the results in different environments.

Effects of coastal aquaculture on sediment organic matter: Assessed with multiple spectral and isotopic indices.

Sediment organic matter (SOM) is important in the biogeochemical cycling of carbon, nutrients, and pollutants in the coastal environment, which is increasingly disturbed by aquaculture that is particularly intense in China. However, the identification of aquaculture signals in SOM is rather challenging in the complex coastal environment that receives materials from a variety of sources. This was studied in a typical culture area of shellfish and algae in SE China from July 2019 to October 2020, using a combination of elemental (OC, TN, N/C), isotopic (δ13C and δ15N), spectral (absorption spectroscopy and fluorescence EEMs-PARAFAC), and statistical analysis (principal component analysis, PCA). All indices of SOM quantity and several spectral indices for the SOM composition correlated significantly with grain size, with lower SOM quantity and higher autochthonous contribution in coarse sediments. The strong correlations between elemental and spectral indices suggested that optical analysis could provide valuable indices for assessing the quantity of bulk organic matter. The comparison of SOM indices between different zones and between different months showed an overall limited influence of shellfish and laver culture. This indicated the sustainability of these types of aquaculture that require no manual addition of feeds and thus are generally clean. The further applications of end-member mixing analysis using the IsoSource program and PCA were more sensitive, which identified the removal of SOM by shellfish in the growing season and the contribution from shellfish residuals after the harvest and the cultured laver at some locations. Overall, our results have implications for a better understanding of the biogeochemical processes and ecosystem sustainability in the coastal environment under intense aquaculture activities.

Real-time automatic prediction of treatment response to transcatheter arterial chemoembolization in patients with hepatocellular carcinoma using deep learning based on digital subtraction angiography videos.

BACKGROUND: Transcatheter arterial chemoembolization (TACE) is the mainstay of therapy for intermediate-stage hepatocellular carcinoma (HCC); yet its efficacy varies between patients with the same tumor stage. Accurate prediction of TACE response remains a major concern to avoid overtreatment. Thus, we aimed to develop and validate an artificial intelligence system for real-time automatic prediction of TACE response in HCC patients based on digital subtraction angiography (DSA) videos via a deep learning approach. METHODS: This retrospective cohort study included a total of 605 patients with intermediate-stage HCC who received TACE as their initial therapy. A fully automated framework (i.e., DSA-Net) contained a U-net model for automatic tumor segmentation (Model 1) and a ResNet model for the prediction of treatment response to the first TACE (Model 2). The two models were trained in 360 patients, internally validated in 124 patients, and externally validated in 121 patients. Dice coefficient and receiver operating characteristic curves were used to evaluate the performance of Models 1 and 2, respectively. RESULTS: Model 1 yielded a Dice coefficient of 0.75 (95% confidence interval [CI]: 0.73-0.78) and 0.73 (95% CI: 0.71-0.75) for the internal validation and external validation cohorts, respectively. Integrating the DSA videos, segmentation results, and clinical variables (mainly demographics and liver function parameters), Model 2 predicted treatment response to first TACE with an accuracy of 78.2% (95%CI: 74.2-82.3), sensitivity of 77.6% (95%CI: 70.7-84.0), and specificity of 78.7% (95%CI: 72.9-84.1) for the internal validation cohort, and accuracy of 75.1% (95% CI: 73.1-81.7), sensitivity of 50.5% (95%CI: 40.0-61.5), and specificity of 83.5% (95%CI: 79.2-87.7) for the external validation cohort. Kaplan-Meier curves showed a significant difference in progression-free survival between the responders and non-responders divided by Model 2 (p = 0.002). CONCLUSIONS: Our multi-task deep learning framework provided a real-time effective approach for decoding DSA videos and can offer clinical-decision support for TACE treatment in intermediate-stage HCC patients in real-world settings.

Comparing the Pb(II) binding with different fluorescent components of dissolved organic matter from typical sources.

Dissolved organic matter (DOM) is important for determining the speciation, environmental behavior, and effects of metal pollutants in aquatic environments. However, little is known about the difference between DOM from natural and anthropogenic sources for binding Pb(II). This study examined the Pb(II) binding with DOM from four typical sources including river, leaf litter leachate, and the influent and effluent of a wastewater treatment plant, using fluorescence quenching titration and excitation-emission matrices-parallel factor analysis (EEMs-PARAFAC). Four humic-like and one protein-like fluorescent components were identified, with much higher protein-like fraction and lower humification degree for the influent than for other sources. In the river water and leaf litter leachate, the abundant humic-like components were quenched by 6-17% while the protein-like component kept stable (2-4%) by the addition of Pb(II). In contrast, the influent DOM showed stronger fluorescence quenching of the protein-like component (46%) with higher conditional stability constant and binding fraction of fluorophore than the humic-like components (15-21%). The effluent DOM displayed weak quenching for all fluorescent components (4-6%) and thus weak complexation with Pb(II), indicating notable changes in the chemical composition and metal-binding affinity of DOM by wastewater treatments. These results demonstrated significant impacts of DOM source and chemical composition on its Pb(II) complexation properties, which have implications for understanding the interactions between DOM and heavy metals.

Feasible synthesis of a novel and low-cost seawater-modified biochar and its potential application in phosphate removal/recovery from wastewater.

In this study, a novel and low-cost seawater-modified biochar (SBC) was fabricated via the pyrolysis of fir wood waste followed by co-precipitation modification using seawater as the Ca/Mg source. The co-precipitation pH was a vital factor during modification, and the optimal pH was 10.50 according to calculations using PHREEQC 2.5 and experiments. The characterizations indicated that Ca and Mg were loaded on the SBC as irregular CaCO3 and nanoflake-like Mg(OH)2, respectively, with the latter dominating. The SBC exhibited a high maximum adsorption capacity of 181.07 mg/g for phosphate, calculated using the Langmuir model, excellent adsorption performance under acidic and neutral conditions (pH = 3.00-7.00), and remarkable selectivity against Cl-, NO3-, and SO42-. The presence of HCO3- promoted adsorption. The mechanisms behind phosphate adsorption involved electrostatic attraction, ligand exchange, precipitation, and inner-sphere complexation. Mg, rather than Ca, was served as the main adsorptive sites for phosphate. Additionally, the feasibility of treating real-world wastewater was tested in batch (using SBC powders) and fixed-bed column (using SBC granules) experiments. The results indicate that the SBC powders could reduce the phosphate concentration from 1.26 mg P/L to below 0.5 mg P/L at a low dose of 0.50 g/L, and the SBC granules exhibited a high removal efficiency with excellent recyclability; the capacity still remained at 78.92% of the initial capacity after five adsorption-desorption runs. Furthermore, the modification process almost did not increase the production cost of the SBC, which was estimated to be 0.41 $/kg. Our results demonstrate that seawater is a low-cost and efficient modifier for biochar modification, and the resultant SBC demonstrates great potential for treating actual phosphate-containing wastewater.

Computed Tomography Image under Three-Dimensional Reconstruction Algorithm Based in Diagnosis of Renal Tumors and Retroperitoneal Laparoscopic Partial Nephrectomy.

This study was to explore the clinical application value of computed tomography (CT) images based on a three-dimensional (3D) reconstruction algorithm for laparoscopic partial nephrectomy (LPN) in patients with renal tumors. 30 cases of renal cell carcinoma (RCC) patients admitted to the hospital were selected as the research objects and were rolled into two groups using a random table method. The patients who received PLN under the three-dimensional reconstruction and laparoscopic technique were included in the experimental group (group A), and the patients who received LPN using CT images only were included in the control group (group B). In addition, the treatment results of the two groups of patients were compared and analyzed. Results. The effective rate of the established model was 93.3%; the total renal arteriovenous variability of group A (13.3%) was higher than that of group B (6.7%), and the operation time (131.5 ± 32.1 minutes) was much lower than that of group B (158.7 ± 36.2 minutes), showing statistical significance (P < 0.05). Conclusion. CT images based on 3D reconstruction algorithms had high clinical application value for LPN in patients with renal tumors, which could improve the efficiency and safety of LPN.

A Reliable Prediction Model for Renal Cell Carcinoma Subtype Based on Radiomic Features from 3D Multiphase Enhanced CT Images.

BACKGROUND: This study aimed to develop a prediction model to distinguish renal cell carcinoma (RCC) subtypes. METHODS: The radiomic features (RFs) from 5 different computed tomography (CT) phases were used in the prediction models: noncontrast phase (NCP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP), and all-phase (ALL-P). RESULTS: For the ALL-P model, all of the RFs obtained from the 4 single-phase images were combined to 420 RFs. The ALL-P model performed the best of all models, with an accuracy of 0.80; the sensitivity and specificity for clear cell RCC (ccRCC) were 0.85 and 0.83; those for papillary RCC (pRCC) were 0.60 and 0.91; those for chromophobe RCC (cRCC) were 0.66 and 0.91, respectively. Binary classification experiments showed for distinguishing ccRCC vs. not-ccRCC that the area under the receiver operating characteristic curve (AUC) of the ALL-P and CMP models was 0.89, but the overall sensitivity/specificity/accuracy of the ALL-P model was better. For cRCC vs. non-cRCC, the ALL-P model had the best performance. CONCLUSIONS: A reliable prediction model for RCC subtypes was constructed. The performance of the ALL-P prediction model was the best as compared to individual single-phase models and the traditional prediction model.

Microbes as a production host to produce natural activecompounds from mushrooms: biosynthetic pathway elucidationand metabolic engineering.

Mushrooms are abundant in bioactive natural compounds. Due to strict growth conditions and long fermentation-time, microbe as a production host is an alternative and sustainable approach for the production of natural compounds. This review focuses on the biosynthetic pathways of mushroom originated natural compounds and microbes as the production host for the production of the above natural compounds.

Biological roles and potential clinical values of circular RNAs in gastrointestinal malignancies.

Circular RNAs (circRNAs), a class of endogenous RNA molecules, are produced by alternative splicing of precursor RNA and are covalently linked at the 5' and 3' ends. Recent studies have revealed that dysregulated circRNAs are closely related to the occurrence and progression of gastrointestinal malignancies. Accumulating evidence indicates that circRNAs, including circPVT1, circLARP4, circ-SFMBT2, cir-ITCH, circRNA_100782, circ_100395, circ-DONSON, hsa_circ_0001368, circNRIP1, circFAT1(e2), circCCDC66, circSMARCA5, circ-ZNF652, and circ_0030235 play important roles in the proliferation, differentiation, invasion, and metastasis of cancer cells through a variety of mechanisms, such as acting as microRNA sponges, interacting with RNA-binding proteins, regulating gene transcription and alternative splicing, and being translated into proteins. With the characteristics of high abundance, high stability, extensive functions, and certain tissue-, time- and disease-specific expressions, circRNAs are expected to provide novel perspectives for the diagnoses and treatments of gastrointestinal malignancies.

Monitoring dissolved organic matter in wastewater and drinking water treatments using spectroscopic analysis and ultra-high resolution mass spectrometry.

Dissolved organic matter (DOM) plays a critical role in determining the quality of wastewater and the safety of drinking water. This is the first review to compare two types of popular DOM monitoring techniques, including absorption spectroscopy and fluorescence excitation-emission matrices (EEMs) coupled with parallel factor analysis (PARAFAC) vs. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), for the applications in wastewater and drinking water treatments. The optical techniques provide a series of indices for tracking the quantity and quality of chromophoric and fluorescent DOM, while FT-ICR-MS is capable of identifying thousands of DOM compounds in wastewater and drinking water at the molecule level. Both types of monitoring techniques are increasingly used in studying DOM in wastewater and drinking water treatments. They provide valuable insights into the variability of DOM composition in wastewater and drinking water. The complexity and diversity of DOM highlight the challenges for effective water treatments. Different effects of various treatment processes on DOM are also assessed, which indicates that the information on DOM composition and its removal is key to optimize the treatment processes. Considering notable progress in advanced treatment processes and novel materials for removing DOM, it is important to continuously utilize these powerful monitoring tools for assessing the responses of different DOM constituents to a series of treatment processes, which can achieve an effective removal of DOM and the quality of treated water.

Development and Validation of a CT-Based Radiomics Nomogram for Predicting Postoperative Progression-Free Survival in Stage I-III Renal Cell Carcinoma.

BACKGROUND: Many patients experience recurrence of renal cell carcinoma (RCC) after radical and partial nephrectomy. Radiomics nomogram is a newly used noninvasive tool that could predict tumor phenotypes. OBJECTIVE: To investigate Radiomics Features (RFs) associated with progression-free survival (PFS) of RCC, assessing its incremental value over clinical factors, and to develop a visual nomogram in order to provide reference for individualized treatment. METHODS: The RFs and clinicopathological data of 175 patients (125 in the training set and 50 in the validation set) with clear cell RCC (ccRCC) were retrospectively analyzed. In the training set, RFs were extracted from multiphase enhanced CT tumor volume and selected using the stability LASSO feature selection algorithm. A radiomics nomogram final model was developed that incorporated the RFs weighted sum and selected clinical predictors based on the multivariate Cox proportional hazard regression. The performances of a clinical variables-only model, RFs-only model, and the final model were compared by receiver operator characteristic (ROC) analysis and DeLong test. Nomogram performance was determined and validated with respect to its discrimination, calibration, reclassification, and clinical usefulness. RESULTS: The radiomics nomogram included age, clinical stage, KPS score, and RFs weighted sum, which consisted of 6 selected RFs. The final model showed good discrimination, with a C-index of 0.836 and 0.706 in training and validation, and good calibration. In the training set, the C-index of the final model was significantly larger than the clinical-only model (DeLong test, p = 0.008). From the clinical variables-only model to the final model, the reclassification of net reclassification improvement was 18.03%, and the integrated discrimination improvement was 19.08%. Decision curve analysis demonstrated the clinical usefulness of the radiomics nomogram. CONCLUSION: The CT-based RF is an improvement factor for clinical variables-only model. The radiomics nomogram provides individualized risk assessment of postoperative PFS for patients with RCC.