Machine learning-based decision support model for selecting intra-arterial therapies for unresectable hepatocellular carcinoma: A national real-world evidence-based study.

IMPORTANCE: Intra-arterial therapies(IATs) are promising options for unresectable hepatocellular carcinoma(HCC). Stratifying the prognostic risk before administering IAT is important for clinical decision-making and for designing future clinical trials. OBJECTIVE: To develop and validate a machine learning(ML)-based decision support model(MLDSM) for recommending IAT modalities for unresectable HCC. DESIGN, SETTING, AND PARTICIPANTS: Between October 2014 and October 2022, a total of 2,959 patients with HCC who underwent initial IATs were enroled retrospectively from 13 tertiary hospitals. These patients were divided into the training cohort (n = 1700), validation cohort (n = 428), and test cohort (n = 200). MAIN OUTCOMES AND MEASURES: Thirty-two clinical variables were input, and five supervised ML algorithms, including eXtreme Gradient Boosting (XGBoost), Categorical Gradient Boosting (CatBoost), Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LGBM) and Random Forest (RF), were compared using the areas under the receiver operating characteristic curve (AUC) with the DeLong test. RESULTS: A total of 1856 patients were assigned to the IAT alone Group(I-A), and 1103 patients were assigned to the IAT combination Group(I-C). The 12-month death rates were 31.9% (352/1103) in the I-A group and 50.4% (936/1856) in the I-C group. For the test cohort, in the I-C group, the CatBoost model achieved the best discrimination when 30 variables were input, with an AUC of 0.776 (95% confidence intervals [CI], 0.833-0.868). In the I-A group, the LGBM model achieved the best discrimination when 24 variables were input, with an AUC of 0.776 (95% CI, 0.833-0.868). According to the decision trees, BCLC grade, local therapy, and diameter as top three variables were used to guide clinical decisions between IAT modalities. CONCLUSIONS AND RELEVANCE: The MLDSM can accurately stratify prognostic risk for HCC patients who received IATs, thus helping physicians to make decisions about IAT and providing guidance for surveillance strategies in clinical practice.

Thermoelectric Performance Enhancement in SnS Polycrystals Owing to Hole Doping Combined with Textured Microstructures.

Recently, the earth-abundant tin sulfide (SnS) has emerged as a promising thermoelectric material due to its phonon and electron structure similar to that of tin selenide (SnSe). However, compared with SnSe, limited progress has been achieved in the thermoelectric property enhancement of SnS. Textured SnS polycrystals with an enhanced thermoelectric performance have been developed in this work. The high carrier mobility benefited from the enhanced texture through the repressing strategy of spark plasma sintering, improving the electrical conductivity. In addition, Sn atom deficiencies in the texture sample led to an increased hole concentration, further boosting the electrical conductivity and power factor. The power factor exceeded 4.10 µW/cm·K2 at 423 K and 5.50 µW/cm·K2 at 850 K. The phonon scattering was strengthened by adjusting the multiscale microstructures including dislocations, defect clusters, etc., leading to an ultralow lattice thermal conductivity of 0.23 W/m·K at 850 K. A figure of merit zT > 1.3 at 850 K and an average zTave of 0.58 in the temperature range 373-850 K were achieved in the SnS polycrystal.

A Novel Machine Learning Model for Predicting Stroke-Associated Pneumonia After Spontaneous Intracerebral Hemorrhage.

BACKGROUND: Pneumonia is one of the most common complications after spontaneous intracerebral hemorrhage (sICH), i.e., stroke-associated pneumonia (SAP). Timely identification of targeted patients is beneficial to reduce poor prognosis. So far, there is no consensus on SAP prediction, and application of existing predictors is limited. The aim of this study was to develop a machine learning model to predict SAP after sICH. METHODS: We retrospectively reviewed 748 patients diagnosed with sICH and collected data from 4 dimensions-demographic features, clinical features, medical history, and laboratory tests. Five machine learning algorithms-logistic regression, gradient boosting decision tree, random forest, extreme gradient boosting, and category boosting-were used to build and validate the predictive model. We also applied recursive feature elimination with cross-validation to obtain the best feature combination for each model. Predictive performance was evaluated by area under the receiver operating characteristic curve. RESULTS: SAP was diagnosed in 237 patients. The model developed by category boosting yielded the most satisfactory outcomes overall with area under the receiver operating characteristic curves in the training set and test set of 0.8307 and 0.8178, respectively. CONCLUSIONS: The incidence of SAP after sICH in our center was 31.68%. Machine learning could potentially provide assistance in the prediction of SAP after sICH.

Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy.

INTRODUCTION: The correlation between radiation exposure before pregnancy and abnormal birth weight has been previously proven. However, for large-for-gestational-age (LGA) babies in women exposed to radiation before becoming pregnant, there is no prediction model yet. MATERIAL AND METHODS: The data were collected from the National Free Preconception Health Examination Project in China. A sum of 455 neonates (42 SGA births and 423 non-LGA births) were included. A training set (n = 319) and a test set (n = 136) were created from the dataset at random. To develop prediction models for LGA neonates, conventional logistic regression (LR) method and six machine learning methods were used in this study. Recursive feature elimination approach was performed by choosing 10 features which made a big contribution to the prediction models. And the Shapley Additive Explanation model was applied to interpret the most important characteristics that affected forecast outputs. RESULTS: The random forest (RF) model had the highest average area under the receiver-operating-characteristic curve (AUC) for predicting LGA in the test set (0.843, 95% confidence interval [CI]: 0.714-0.974). Except for the logistic regression model (AUC: 0.603, 95%CI: 0.440-0.767), other models' AUCs displayed well. Thereinto, the RF algorithm's final prediction model using 10 characteristics achieved an average AUC of 0.821 (95% CI: 0.693-0.949). CONCLUSION: The prediction model based on machine learning might be a promising tool for the prenatal prediction of LGA births in women with radiation exposure before pregnancy.

Swimming ability of Schizothoracinae fishes in Yarlung Zangbo River of China.

The Yarlung Zangbo River is a river with abundant hydropower resources but fragile biodiversity in China. As an important benchmark for both research and ecological management, there is still a lack of knowledge about the swimming ability of fishes in the Yarlung Zangbo River. The induced flow velocity (Uind), critical swimming speed (Ucrit), and burst swimming speed (Uburst) of five Schizothoracinae species were tested in this study. Relative swimming ability related to body length and body shape was calculated. The results indicated that the average absolute swimming speeds (Uind-a, Ucrit-a, and Uburst-a) of all the experimental fish were 10.20 ± 0.01, 57.58 ± 3.28, and 69.54 ± 2.94 cm/s, respectively, and the corresponding relative Uind, Ucrit, and Uburst related to body length (Uind-l, Ucrit-l, Uburst-l) were 1.15 ± 0.07, 5.04 ± 0.26, and 7.23 ± 0.28 BL/s, respectively. Moreover, relative Uind, Ucrit, and Uburst related to body shape (Uind-s, Ucrit-s, and Uburst-s) were 0.80 ± 0.13, 2.49 ± 0.51, and 4.32 ± 0.57 cm-2/s, respectively. No significantly differences in relative swimming speeds existed among five species. Only Oxygymnocypris stewartii was significantly weaker in Uburst-s than Schizothorax o'connori. The body shape showed a stronger relationship with swimming speed than the body length did. Schizothoracinae fish in the Yarlung Zangbo River basin are less sensitive to the water flow and performed weaker Ucrit and Uburst compared to those in the Yangtze River basin, indicating that Schizothoracinae fish in the Yarlung Zangbo River may be more susceptible to threats from environmental changes. The paper enriched the research on the swimming ability of Schizothoracinae fishes and provided efficient data for the fish conservation in the Yarlung Zangbo River.

Improvement of the Thermoelectric Properties of p-Type Mg3Sb2 by Mg-Site Double Substitution.

A P-type Mg3Sb2-based Zintl phase compound has been considered a promising candidate for thermoelectric applications. Alloying, which introduces a high concentration of point defects, is particularly effective in scattering phonons and reducing lattice thermal conductivity. Herein, alloying in p-type Mg2.995Na0.005Sb2 via the introduction of elements like Yb, Eu, Ca, and Ba was realized, and the room-temperature lattice thermal conductivity has been effectively reduced to ∼1.1 W m-1 K-1. To further intensify the phonon scattering, two groups of elements (Eu and Cd, and Yb and Cd) were chosen for heavy alloying at the Mg site, and the lattice thermal conductivity of Mg1.49Eu0.5Cd1Na0.01Sb2 was further reduced to ∼0.45 W m-1 K-1. Eventually, a peak zT as high as ∼1.0 was achieved at 773 K, and the compound outperforms the previously reported p-type Mg3Sb2 compounds.

Terminally differentiated cytotoxic CD4+ T cells were clonally expanded in the brain lesion of radiation-induced brain injury.

BACKGROUND: Accumulating evidence supports the involvement of adaptive immunity in the development of radiation-induced brain injury (RIBI). Our previous work has emphasized the cytotoxic function of CD8+ T cells in RIBI. In this study, we aimed to investigate the presence and potential roles of cytotoxic CD4+ T cells (CD4+ CTLs) in RIBI to gain a more comprehensive understanding of adaptive immunity in this context. MAIN TEXT: Utilizing single-cell RNA sequencing (scRNA-seq), we analyzed 3934 CD4+ T cells from the brain lesions of four RIBI patients and identified six subclusters within this population. A notable subset, the cytotoxic CD4+ T cells (CD4+ CTLs), was marked with high expression of cytotoxicity-related genes (NKG7, GZMH, GNLY, FGFBP2, and GZMB) and several chemokine and chemokine receptors (CCL5, CX3CR1, and CCL4L2). Through in-depth pseudotime analysis, which simulates the development of CD4+ T cells, we observed that the CD4+ CTLs exhibited signatures of terminal differentiation. Their functions were enriched in protein serine/threonine kinase activity, GTPase regulator activity, phosphoprotein phosphatase activity, and cysteine-type endopeptidase activity involved in the apoptotic signaling pathway. Correspondingly, mice subjected to gamma knife irradiation on the brain showed a time-dependent infiltration of CD4+ T cells, an increase of MHCII+ cells, and the existence of CD4+ CTLs in lesions, along with an elevation of apoptotic-related proteins. Finally, and most crucially, single-cell T-cell receptor sequencing (scTCR-seq) analysis at the patient level determined a large clonal expansion of CD4+ CTLs in lesion tissues of RIBI. Transcriptional factor-encoding genes TBX21, RORB, and EOMES showed positive correlations with the cytotoxic functions of CD4+ T cells, suggesting their potential to distinguish RIBI-related CD4+ CTLs from other subsets. CONCLUSION: The present study enriches the understanding of the transcriptional landscape of adaptive immune cells in RIBI patients. It provides the first description of a clonally expanded CD4+ CTL subset in RIBI lesions, which may illuminate new mechanisms in the development of RIBI and offer potential biomarkers or therapeutic targets for the disease.

APOE ε4 allele status modulates the spatial patterns of progressive atrophy in the temporal lobes after mild traumatic brain injury.

INTRODUCTION: We evaluated how the apolipoprotein E (APOE) ε4 allele modulated the spatial patterns of longitudinal atrophy in the Alzheimer's disease-vulnerable brain areas of patients with mild traumatic brain injury (mTBI) from the acute to chronic phase post injury. METHODS: Fifty-nine adult patients with acute mTBI and 48 healthy controls with APOE ε4 allele testing underwent T1-weighted magnetic resonance imaging and neuropsychological assessments with 6 to 12 months of follow-up. Progressive brain volume loss was compared voxel-wise in the temporal lobes. RESULTS: Patients with the APOE ε4 allele presented significant longitudinal atrophy in the left superior and middle temporal gyri, where the progressive gray matter volume loss predicted longitudinal impairment in language fluency, whereas mTBI APOE ε4 allele noncarriers showed mainly significant longitudinal atrophy in the medial temporal lobes, without significant neuropsychological relevance. DISCUSSION: The atrophy progression observed in mTBI patients with the APOE ε4 allele may increase the possibility of developing a specific phenotype of Alzheimer's disease with language dysfunction. Highlights: The apolipoprotein E (APOE) ε4 allele and mild traumatic brain injury (mTBI) are risk factors for Alzheimer's disease (AD) progression.It is unclear how the interaction of mTBI with the APOE ε4 allele impacts the progressive atrophy topography in AD-vulnerable brain regions.In this study, patients with the APOE ε4 allele showed progressive atrophy patterns similar to the early stage of logopenic variant of primary progressive aphasia (lvPPA) phenotype of AD. APOE ε4 allele carriers with mTBI history may be at the risk of developing a given AD phenotype with language dysfunction.

Intensified Phonon Scattering in ZrCoBi Half-Heusler by Noble Metals Doping.

ZrCoBi-based half-Heuslers have great potential in power generation applications because of their high thermoelectric performance in both p- and n-type constituents. In this work, n-type ZrCoBi with improved thermoelectric performance has been realized by intensifying the phonon scattering via noble metal doping, e.g., Pd and Pt doping. The carrier concentration was effectively tuned to the optimal range, and the lattice thermal conductivity was greatly suppressed via the strong strain field and mass fluctuation scattering brought about by the large difference in atomic size and mass between Pd or Pt and Co. Consequently, the state-of-art figure of merit zT ∼1 was achieved in Pd- or Pt-doped ZrCoBi. In addition, the average zTavg values for ZrCo0.95Pd0.05Bi and ZrCo0.925Pt0.075Bi have reached 0.58 and 0.51, respectively, which are higher than those of most of the reported n-type ZrCoBi-based and ZrCoSb-based half-Heusler alloys.

Profiling of RNA editing events in plant organellar transcriptomes with high-throughput sequencing.

RNA editing is a crucial post-transcriptional modification process in plant organellar RNA metabolism. rRNA removal-based total RNA-seq is one of the most common methods to study this event. However, the lack of commercial kits to remove rRNAs limits the usage of this method, especially for non-model plant species. DSN-seq is a transcriptome sequencing method utilizing duplex-specific nuclease (DSN) to degrade highly abundant cDNA species especially those from rRNAs while keeping the robustness of transcript levels of the majority of other mRNAs, and has not been applied to study RNA editing in plants before. In this study, we evaluated the capability of DSN-seq to reduce rRNA content and profile organellar RNA editing events in plants, as well we used commercial Ribo-off-seq and standard mRNA-seq as comparisons. Our results demonstrated that DSN-seq efficiently reduced rRNA content and enriched organellar transcriptomes in rice. With high sensitivity to RNA editing events, DSN-seq and Ribo-off-seq provided a more complete and accurate RNA editing profile of rice, which was further validated by Sanger sequencing. Furthermore, DSN-seq also demonstrated efficient organellar transcriptome enrichment and high sensitivity for profiling RNA editing events in Arabidopsis thaliana. Our study highlights the capability of rRNA removal-based total RNA-seq for profiling RNA editing events in plant organellar transcriptomes and also suggests DSN-seq as a widely accessible RNA editing profiling method for various plant species.

A Synthetic Steroid 5α-Androst-3ß, 5, 6ß-triol Alleviates Radiation-Induced Brain Injury in Mice via Inhibiting GBP5/NF-κB/NLRP3 Signal Axis.

Radiotherapy for head and neck tumors can lead to a severe complication known as radiation-induced brain injury (RIBI). However, the underlying mechanism of RIBI development remains unclear, and limited prevention and treatment options are available. Neuroactive steroids have shown potential in treating neurological disorders. 5α-Androst-3ß, 5, 6ß-triol (TRIOL), a synthetic neuroprotective steroid, holds promise as a treatment candidate for RIBI patients. However, the neuroprotective effects and underlying mechanism of TRIOL on RIBI treatment are yet to be elucidated. In the present study, our findings demonstrate TRIOL's potential as a neuroprotective agent against RIBI. In gamma knife irradiation mouse model, TRIOL treatment significantly reduced brain necrosis volume, microglial activation, and neuronal loss. RNA-sequencing, immunofluorescence, real-time quantitative polymerase chain reaction, siRNA transfection, and western blotting techniques revealed that TRIOL effectively decreased microglial activation, proinflammatory cytokine release, neuron loss, and guanylate-binding protein 5 (GBP5) expression, along with its downstream signaling pathways NF-κB and NLRP3 activation in vitro. In summary, TRIOL effectively alleviate RIBI by inhibiting the GBP5/NF-κB/NLRP3 signal axis, reducing microglia activation and pro-inflammation cytokines release, rescuing neuron loss. This study highlights the potential of TRIOL as a novel and promising therapy drug for RIBI treatment.

Donafenib and GSK-J4 Synergistically Induce Ferroptosis in Liver Cancer by Upregulating HMOX1 Expression.

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide. Donafenib is a multi-receptor tyrosine kinase inhibitor approved for the treatment of patients with advanced HCC, but its clinical effect is very limited. Here, through integrated screening of a small-molecule inhibitor library and a druggable CRISPR library, that GSK-J4 is synthetically lethal with donafenib in liver cancer is shown. This synergistic lethality is validated in multiple HCC models, including xenograft, orthotopically induced HCC, patient-derived xenograft, and organoid models. Furthermore, co-treatment with donafenib and GSK-J4 resulted in cell death mainly via ferroptosis. Mechanistically, through integrated RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin with high throughput sequencing (ATAC-seq) analyses, that donafenib and GSK-J4 synergistically promoted the expression of HMOX1 and increased the intracellular Fe2+ level is found, eventually leading to ferroptosis. Additionally, through cleavage under targets & tagmentation followed by sequencing (CUT&Tag-seq), it is found that the enhancer regions upstream of HMOX1 promoter significantly increased under donafenib and GSK-J4 co-treatment. A chromosome conformation capture assay confirmed that the increased expression of HMOX1 is caused by the significantly enhanced interaction between the promoter and upstream enhancer under dual-drug combination. Taken together, this study elucidates a new synergistic lethal interaction in liver cancer.

Exploring the Association between Low-dose Aspirin Intake and Hyperuricemia in Individuals over 40: A Cross-Sectional Study using NHANES Data (2011-2018).

BACKGROUND Long-term aspirin treatment was recommended for secondary prevention of cardiovascular and cerebrovascular disease. However, some studies reveal low-dose aspirin (LDA) can raise serum uric acid (SUA) levels. Thus, the purpose of this study was to analyze whether LDA intake is associated with hyperuricemia. MATERIAL AND METHODS Data was collected from the National Health and Nutrition Examination Survey (NHANES) between 2011 and 2018. All participants over 40 years old and who selected "preventive aspirin use" were included in the study. Logistic regression analyses were used to evaluate the relationship between LDA intake and hyperuricemia. The stratified analysis was based on race and estimated glomerular filtration rate (eGFR). RESULTS A total of 3540 participants were included in the study. Of them, 805 (22.7%) took LDA, and 190 (31.6%) had hyperuricemia. There was no significant association between hyperuricemia and LDA intake (OR=1.22, 95% CI: 0.97-1.54) after adjusting for confounding factors. However, further subgroup analysis by age showed a significant association between LDA intake and hyperuricemia (OR=3.44, 95% CI: 1.88-6.27) among those 40 to 50 years of age. After adjusting for confounding factors, the relationship was still significant (OR=2.28, 95% CI: 1.10-4.73); we also found that race (Hispanic American, OR=1.84, 95% CI: 1.11-3.06) and eGFR under 60 mL/min/1.73 m² (OR=1.94, 95% CI: 1.04-3.62) may play important roles in the development of hyperuricemia. CONCLUSIONS LDA does not increase the hyperuricemia risk in people over 40 years. However, those aged between 40 and 50 years, Hispanic American, and with impaired renal function should have careful evaluation during LDA treatment.

Therapeutic effect and mechanism prediction of Fuzi-Gancao Herb couple on non-alcoholic fatty liver disease (NAFLD) based on network pharmacology and molecular docking.

BACKGROUND: Fuzi-Gancao herb couple is one of the most common herb couples involved in the TCM formula, which was used for the treatment of chronic diseases. The herb couple has a hepatoprotective effect. However, its main components and therapeutic mechanism are not yet clear. This study aims to elucidate the therapeutic effect and mechanism of the Fuzi-Gancao herb couple on NAFLD from animal experiments, network pharmacology, and molecular docking. METHODS: 60 Male C57BL/6 mice (20 g ± 2 g) were randomly divided into six groups including the blank group (n=10) and NALFD group (n=50). The mice of the NALFD group were fed with a high-fat diet for 20 weeks to establish the NAFLD model and the NALFD mice were randomly divided into five groups including positive group (berberine), model group and F-G groups with three dosages (0.257, 0.514, 0.771 g/kg) (n=10). After 10 weeks of administration, the serum was collected for the analysis of ALT, AST, LDL-c, HDL-c, and TC, and liver tissues were collected for pathological analysis. The TCMAS database was used to collect the main components and targets of the Fuzi-Gancao herb couple. The GeneCards database was used to collect NAFLD-related targets, and the key targets were obtained by intersecting with herbal targets. The disease-component-target relationship diagram was constructed by Cytoscape 3.9.1. The obtained key targets were imported into the String database to obtain the PPI network, and imported into the DAVID database for KEGG pathway analysis and GO analysis. Finally, the key targets and key gene proteins were imported into Discovery Studio 2019 for molecular docking verification. RESULTS: In this study, H-E staining indicated the pathological changes of liver tissue in Fuzi-Gancao groups were significantly improved, and the levels of AST, ALT, TC, HDL-c, and LDL-c in serum of Fuzi-Gancao groups decreased in a dose-dependent manner, compared with the model group. 103 active components and 299 targets in the Fuzi-Gancao herb couple were confirmed in the TCMSP database and 2062 disease targets in NAFLD were obtained. 142 key targets and 167 signal pathways were screened, such as the AGE-RAGE signaling pathway in diabetic complications, HIF-1 signaling pathway, IL-17 signaling pathway, TNF signaling pathway, and so on. The main bioactive ingredients of Fuzi-Gancao herb couple in the treatment of NAFLD are quercetin, kaempferol, naringenin, inermine, (R)-norcoclaurine, isorhamnetin, ignavine, 2,7-Dideacetyl-2,7-dibenzoyl-taxayunnanine F, glycyrol mainly involving IL6, AKT1, TNF, TP53, IL1B, VEGFA and other core targets. Molecular docking analysis indicated that there is a good affinity between the key components and the key targets. CONCLUSION: This study preliminarily explained the main components and mechanism of the Fuzi-Gancao herb couple in the treatment of NAFLD and provided an idea for subsequent research.

Amifostine-Loaded Nanocarrier Traverses the Blood-Brain Barrier and Prevents Radiation-Induced Brain Injury.

Radiation-induced brain injury (RIBI) is a severe, irreversible, or even life-threatening cerebral complication of radiotherapy in patients with head and neck tumors, and there is no satisfying prevention and effective treatment available for these patients. Amifostine (AMF) is a well-known free radical scavenger with demonstrated effectiveness in preventing radiation-induced toxicity. However, the limited permeability of AMF across the blood-brain barrier (BBB) when administered intravenously reduces the effectiveness of AMF in preventing RIBI. Herein, we construct a nanoparticle (NP) platform for BBB delivery of AMF. AMF is conjugated with 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine-n-[poly(ethylene glycol)]-hydroxy succinamide [DSPE-PEG-NHS, PEG M 2000], and the product is DSPE-PEG-AMF. Then, the nanoparticles (DAPP NPs) were formed by self-assembly of poly(lactic-co-glycolic acid) (PLGA), DSPE-PEG-AMF, and polysorbate 80 (PS 80). PEG shields the nanoparticles from blood clearance by the reticuloendothelial system and lengthens the drug circulation time. PS 80 is used to encapsulate nanoparticles for medication delivery to the brain. The results of our study showed that DAPP NPs were able to effectively penetrate the blood-brain barrier (BBB) in healthy C57BL/6 mice. Furthermore, in a well-established mouse model of X-knife-induced brain injury, treatment with DAPP NPs (corresponding to 250 mg/kg AMF) was found to significantly reduce the volume of brain necrosis compared to mice treated with AMF (250 mg/kg). Importantly, the use of DAPP NPs was also shown to significantly mitigate the effects of radiation-induced neuronal damage and glial activation. This work presents a convenient brain-targeted AMF delivery system to achieve effective radioprotection for the brain, providing a promising strategy with tremendous clinical translation potential.

Mitochondrial SENP2 regulates the assembly of SDH complex under metabolic stress.

Succinate dehydrogenase (SDH) is a heterotetrameric enzyme complex belonging to the mitochondrial respiratory chain and uniquely links the tricarboxylic acid (TCA) cycle with oxidative phosphorylation. Cancer-related SDH mutations promote succinate accumulation, which is regarded as an oncometabolite. Post-translational modifications of SDH complex components are known to regulate SDH activity, although the contribution of SUMOylation remains unclear. Here, we show that SDHA is SUMOylated by PIAS3 and deSUMOylated by SENP2, events dictating the assembly and activity of the SDH complex. Moreover, CBP acetylation of SENP2 negatively regulates its deSUMOylation activity. Under glutamine deprivation, CBP levels decrease, and the ensuing SENP2 activation and SDHA deSUMOylation serve to concurrently dampen the TCA cycle and electron transport chain (ETC) activity. Along with succinate accumulation, this mechanism avoids excessive reactive oxygen species (ROS) production to promote cancer cell survival. This study elucidates a major function of mitochondrial-localized SENP2 and expands our understanding of the role of SUMOylation in resolving metabolic stress.

The Impact of Preservation Techniques on Methane-Arrested Anaerobic Digestion of Nutrient-Rich Feedstocks.

The carboxylate platform is a promising biomass-to-energy pathway that uses methane-arrested anaerobic digestion (MAAD) to convert biomass to carboxylic acids, which can be chemically converted to industrial chemicals and liquid fuels. Lignocellulose is an energy-rich carbon source, but lacks nutrients necessary for microbial growth. Chicken manure (rural waste) and sewage sludge (urban waste) are rich in nitrogen and useful macronutrients; therefore, co-digesting these wastes with lignocellulose improves MAAD performance. However, waste nutrients must be digested immediately, or preserved. This study investigated the effects of various preservation techniques - frozen (fresh), air-dried, and baked - on chicken manure and sewage sludge. Batch experiments were performed with office paper (carbon source) and chicken manure or sewage sludge (nutrient source) with different methods of preservation. Fresh substrates produced higher acid yields and biomass conversion (the amount of biomass consumed during digestion) than dried substrates. Baked chicken manure showed reduced conversion and total acid production, which suggests that oven-drying reduces digestibility. From the batch data, the Continuum Particle Distribution Model (CPDM) predicted results of a four-stage countercurrent digestion. The data are displayed on maps showing the impact of liquid residence time (LRT) and volatile solids loading rate (VSLR) on conversion and product concentration. Co-digesting office paper and wet chicken manure at a non-acid volatile solid (NAVS) concentration of 300 g/Lliq, the model predicted a high total acid concentration of 52.8 g/L and conversion of 0.89 g NAVSdigested/NAVSfed at a volatile solid loading rate of 4 g/(Lliq·day) and liquid retention time of 35 days.

Serum metabolite signatures in normal individuals and patients with colorectal adenoma or colorectal cancer using UPLC-MS/MS method.

Colorectal cancer (CRC) is one of the main causes of cancer-related deaths worldwide. Sporadic CRC develops from normal mucosa via adenoma to adenocarcinoma, which provides a long screening window for clinical detection. However, early diagnosis of sporadic colorectal adenoma (CRA) and CRC using serum metabolic screening remains unclear. The purpose of this study was to identify some promising signatures for distinguishing the different pathological metabolites of colorectal mucosal malignant transformation. A total of 238 endogenous metabolites were elected. We found that CRA and CRC patients had 72 and 73 different metabolites compared with healthy controls, respectively. There were 20 different metabolites between CRA and CRC patients. The potential metabolites of tumor growth (including patients with CRA and CRC) were found, such as A-d-glucose, D-mannose, N-acetyl-D-glucosamine, L-cystine, Sarcosine, TXB 2, 12-Hete, and chenodeoxycholic acid. Compared with CRA, 3,4,5-trimethoxybenzoic acid was significantly higher in CRC patients. There results prompt us to use the potential serum signatures to screen CRC as the novel strategy. Serum metabolite screening is useful for early detection of mucosal intestinal malignancy. We will further investigate the roles of these promising biomarkers during intestinal tumorigenesis in future. SIGNIFICANCE: CRC is one of the main causes of cancer-related deaths worldwide. Sporadic CRC develops from normal mucosa via adenomas to adenocarcinoma, which provides a long screening window for about 5-10 years. We adopt the metabolic analysis of extensive targeted metabolic technology. The main purpose of the metabolic group analysis is to detect and screen the different metabolites, thereby performing related functional prediction and analysis of the differential metabolites. In our study, 30 samples are selected, divided into 3 groups for metabolic analysis, and 238 metabolites are elected. In 238 metabolites, we find that CRA patients have 72 different metabolites compared with health control. Compared with health control, CRC have 73 different metabolites. Compared with CRA and CRC patients, there are 20 different metabolites. The annotation results of the significantly different metabolites are classified according to the KEGG pathway type. The potential metabolites of tumor growth stage (including patients with CRA and CRC) are found, such as A-d-glucose, D-mannose, N-acetyl-D-glucosamine, L-cystine, sarcosine, TXB 2, 12-Hete and chenodeoxycholic acid. Compared with CRA patients, CRC patients had significantly higher 3,4,5-trimethoxybenzoic acid level. It is prompted to use serum different metabolites to screen CRC to provide new possibilities.

Prediction of hyperuricemia in people taking low-dose aspirin using a machine learning algorithm: a cross-sectional study of the National Health and Nutrition Examination Survey.

Background: Hyperuricemia is a serious health problem related to not only gout but also cardiovascular diseases (CVDs). Low-dose aspirin was reported to inhibit uric acid excretion, which leads to hyperuricemia. To decrease hyperuricemia-related CVD, this study aimed to identify the risk of hyperuricemia in people taking aspirin. Method: The original data of this cross-sectional study were obtained from the National Health and Nutrition Examination Survey between 2011 and 2018. Participants who filled in the "Preventive Aspirin Use" questionnaire with a positive answer were included in the analysis. Six machine learning algorithms were screened, and eXtreme Gradient Boosting (XGBoost) was employed to establish a model to predict the risk of hyperuricemia. Results: A total of 805 participants were enrolled in the final analysis, of which 190 participants had hyperuricemia. The participants were divided into a training set and testing set at a ratio of 8:2. The area under the curve for the training set was 0.864 and for the testing set was 0.811. The SHapley Additive exPlanations (SHAP) method was used to evaluate the performances of the modeling. Based on the SHAP results, the feature ranking interpretation showed that the estimated glomerular filtration rate, body mass index, and waist circumference were the three most important features for hyperuricemia in individuals taking aspirin. In addition, triglyceride, hypertension, total cholesterol, high-density lipoprotein, low-density lipoprotein, age, race, and smoking were also correlated with the development of hyperuricemia. Conclusion: A predictive model established by XGBoost algorithms can potentially help clinicians make an early detection of hyperuricemia risk in people taking low-dose aspirin.

Optimizing bowel preparation for colonoscopy: A cross-sectional study of the Chinese population.

Background: The quality of bowel preparation is an important factor in the success of colonoscopy. However, the quality of bowel preparation is often affected by multiple factors. The main objective of this study was to explore the specific factors that affect the quality of bowel preparation. Methods: Patients were consecutively recruited from the gastroenterology department in Union Hospital, Tongji Medical College, Huazhong University of Science and Technology in Wuhan from May 2018 to December 2018. All patients were undergoing colonoscopy. Bowel preparation was evaluated by the Ottawa Bowel preparation Scale (OBPS) and all patients were categorized into 2 groups according to the OBPS. Multivariate analysis was conducted to identify the factors associated with bowel preparation quality. Results: A total of 910 patients were included in the analysis with an average age of 48.62 ± 13.57 years. Patient source (P < 0.001) and the preparation method (P = 0.029) were correlated with OBPS adequacy. In addition, after stratified by age, preparation method (P = 0.022) was a significant factor among patients under 50 years old; whereas waiting time (P = 0.005) was a significant factor among patients over 50 years old. Conclusion: Bowel preparation should be tailored based on the age of the patients to determine the most appropriate plan, including the most appropriate waiting time and the most appropriate purgative combination. Doctors should also focus more on the quality of bowel preparation in inpatients, who are more likely than outpatients to have an inadequate bowel preparation.