Magnetic-assisted alignment of nanofibers in a polymer nanocomposite for high-temperature capacitive energy storage applications.

Flexible polymer-based dielectrics with high energy storage characteristics over a wide temperature range are crucial for advanced electrical and electronic systems. However, the intrinsic low dielectric constant and drastically degraded breakdown strength hinder the development of polymer-based dielectrics at elevated temperatures. Here, we propose a magnetic-assisted approach for fabricating a polyethyleneimine (PEI)-based nanocomposite with precisely aligned nanofibers within the polymer matrix, and with Al2O3 deposition layers applied on the surface. The resulting polymer nanocomposite exhibits simultaneously increased dielectric constant and enhanced breakdown strength at high temperatures compared to pristine PEI. The enhanced dielectric constant is contributed by the depolarization effect of out-of-plane orientated nanofibers in composite films, while the surficial Al2O3 layers, with a wide bandgap, effectively prevent charge injection and transport at the electrode/dielectric interface. The optimally aligned composite films exhibit a significantly enhanced discharged energy density of 6.5 J cm-3 at 150 °C, with approximately 41% and 132% enhancement compared to random films and pristine PEI films, respectively. Additionally, a metalized multilayer polymer-based capacitor utilizing this composite film produces a high capacitance of 88 nF, while demonstrating excellent cyclability and flexibility at 150 °C. This work offers an effective strategy for developing polymer-based composite dielectrics with superior capacitive performance at elevated temperatures and demonstrates the potential of fabricating high-quality flexible capacitors.

Genome-wide characterization and metabolite profiling of Cyathus olla: insights into the biosynthesis of medicinal compounds.

Cyathus olla, belonging to the genus Cyathus within the order Agaricales, is renowned for its bird's nest-like fruiting bodies and has been utilized in folk medicine. However, its genome remains poorly understood. To investigate genomic diversity within the genus Cyathus and elucidate biosynthetic pathways for medicinal compounds, we generated a high-quality genome assembly of C. olla with fourteen chromosomes. The comparative genome analysis revealed variations in both genomes and specific functional genes within the genus Cyathus. Phylogenomic and gene family variation analyses provided insights into evolutionary divergence, as well as genome expansion and contraction in individual Cyathus species and 36 typical Basidiomycota. Furthermore, analysis of LTR-RT and Ka/Ks revealed apparent whole-genome duplication (WGD) events its genome. Through genome mining and metabolite profiling, we identified the biosynthetic gene cluster (BGC) for cyathane diterpenes from C. olla. Furthermore, we predicted 32 BGCs, containing 41 core genes, involved in other bioactive metabolites. These findings represent a valuable genomic resource that will enhance our understanding of Cyathus species genetic diversity. The genome analysis of C. olla provides insights into the biosynthesis of medicinal compounds and establishes a fundamental basis for future investigations into the genetic basis of chemodiversity in this significant medicinal fungus.

A Cysteinyl-tRNA Synthetase Mutation Causes Novel Autosomal-Dominant Inheritance of a Parkinsonism/Spinocerebellar-Ataxia Complex.

This study aimed to identify possible pathogenic genes in a 90-member family with a rare combination of multiple neurodegenerative disease phenotypes, which has not been depicted by the known neurodegenerative disease. We performed physical and neurological examinations with International Rating Scales to assess signs of ataxia, Parkinsonism, and cognitive function, as well as brain magnetic resonance imaging scans with seven sequences. We searched for co-segregations of abnormal repeat-expansion loci, pathogenic variants in known spinocerebellar ataxia-related genes, and novel rare mutations via whole-genome sequencing and linkage analysis. A rare co-segregating missense mutation in the CARS gene was validated by Sanger sequencing and the aminoacylation activity of mutant CARS was measured by spectrophotometric assay. This pedigree presented novel late-onset core characteristics including cerebellar ataxia, Parkinsonism, and pyramidal signs in all nine affected members. Brain magnetic resonance imaging showed cerebellar/pons atrophy, pontine-midline linear hyperintensity, decreased rCBF in the bilateral basal ganglia and cerebellar dentate nucleus, and hypo-intensities of the cerebellar dentate nuclei, basal ganglia, mesencephalic red nuclei, and substantia nigra, all of which suggested neurodegeneration. Whole-genome sequencing identified a novel pathogenic heterozygous mutation (E795V) in the CARS gene, meanwhile, exhibited none of the known repeat-expansions or point mutations in pathogenic genes. Remarkably, this CARS mutation causes a 20% decrease in aminoacylation activity to charge tRNACys with L-cysteine in protein synthesis compared with that of the wild type. All family members carrying a heterozygous mutation CARS (E795V) had the same clinical manifestations and neuropathological changes of Parkinsonism and spinocerebellar-ataxia. These findings identify novel pathogenesis of Parkinsonism-spinocerebellar ataxia and provide insights into its genetic architecture.

Glucose-6-phosphate dehydrogenase and transketolase: Key factors in breast cancer progression and therapy.

Breast cancer is one of the most common malignant tumors in women and is a serious threat to women's health. The pentose phosphate pathway (PPP) is a mode of oxidative breakdown of glucose that can be divided into oxidative (oxPPP) and non-oxidative (non-oxPPP) stages and is necessary for cell and body survival. However, abnormal activation of PPP often leads to proliferation, migration, invasion, and chemotherapy resistance in breast cancer. Glucose-6-phosphate dehydrogenase (G6PD) is the rate-limiting enzyme in PPP oxidation. Nicotinamide adenine dinucleotide phosphate hydrogen (NADPH) produced by G6PD is the raw material for cholesterol and lipid synthesis and can resist the production of oxygen species (ROS) and reduce oxidative stress damage to tumor cells. Transketolase (TKT) is a key enzyme in non-oxPPP. Ribose 5-phosphate (R5P), produced by TKT, is a raw material for DNA and RNA synthesis, and is essential for tumor cell proliferation and DNA damage repair. In this review, we describe the role and specific mechanism of the PPP and the two most important enzymes of the PPP, G6PD and TKT, in the malignant progression of breast cancer, providing strategies for future clinical treatment of breast cancer and a theoretical basis for breast cancer research.

Molecular and clinical characterization of a founder mutation causing G6PC3 deficiency.

Background: G6PC3 deficiency is a rare genetic disorder that causes syndromic congenital neutropenia. It is driven by the intracellular accumulation of a metabolite named 1,5-anhydroglucitol-6-phosphate (1,5-AG6P) that inhibits glycolysis. Patients display heterogeneous extra-hematological manifestations, contributing to delayed diagnosis. Objective: The G6PC3 c.210delC variant has been identified in patients of Mexican origin. We set out to study the origin and functional consequence of this mutation. Furthermore, we sought to characterize the clinical phenotypes caused by it. Methods: Using whole-genome sequencing data, we conducted haplotype analysis to estimate the age of this allele and traced its ancestral origin. We examined how this mutation affected G6PC3 protein expression and performed extracellular flux assays on patient-derived cells to characterize how this mutation impacts glycolysis. Finally, we compared the clinical presentations of patients with the c.210delC mutation relative to other G6PC3 deficient patients published to date. Results: Based on the length of haplotypes shared amongst ten carriers of the G6PC3 c.210delC mutation, we estimated that this variant originated in a common ancestor of indigenous American origin. The mutation causes a frameshift that introduces a premature stop codon, leading to a complete loss of G6PC3 protein expression. When treated with 1,5-anhydroglucitol (1,5-AG), the precursor to 1,5-AG6P, patient-derived cells exhibited markedly reduced engagement of glycolysis. Clinically, c.210delC carriers display all the clinical features of syndromic severe congenital neutropenia type 4 observed in prior reports of G6PC3 deficiency. Conclusion: The G6PC3 c.210delC is a loss-of-function mutation that arose from a founder effect in the indigenous Mexican population. These findings may facilitate the diagnosis of additional patients in this geographical area. Moreover, the in vitro 1,5-AG-dependent functional assay used in our study could be employed to assess the pathogenicity of additional G6PC3 variants.

Long-term surgical outcomes of bile duct tumor thrombus versus portal vein tumor thrombus for hepatocellular carcinoma: a propensity score matching analysis.

Background: Portal vein tumor thrombus (PVTT) seriously affects the prognosis of hepatocellular carcinoma (HCC). However, whether bile duct tumor thrombus (BDTT) significantly affects the prognosis of HCC as much as PVTT remains unclear. We aimed to compare the long-term surgical outcomes of HCC with macroscopic PVTT (macro-PVTT) and macroscopic BDTT (macro-BDTT). Methods: The data of HCC patients with macro-BDTT or macro-PVTT who underwent hemihepatectomy were retrospectively reviewed. A propensity score matching (PSM) analysis was performed to reduce the baseline imbalance. The recurrence-free survival (RFS) and overall survival (OS) rates were compared between the cohorts. Results: Before PSM, the PVTT group had worse RFS and OS rates than the BDTT group (P = 0.043 and P = 0.008, respectively). Multivariate analyses identified PVTT (hazard ratio [HR] = 1.835, P = 0.016) and large HCC (HR = 1.553, P = 0.039) as independent risk factors for poor OS and RFS, respectively. After PSM, the PVTT group had worse RFS and OS rates than the BDTT group (P = 0.037 and P = 0.004, respectively). The 3- and 5-year OS rates were significantly higher in the BDTT group (59.5% and 52.1%, respectively) than in the PVTT group (33.3% and 20.2%, respectively). Conclusion: Aggressive hemihepatectomy provides an acceptable prognosis for HCC patients with macro-BDTT. Furthermore, the long-term surgical outcomes of HCC patients with macro-BDTT were significantly better than those of HCC patients with macro-PVTT.

Blood-based CNS regionally and neuronally enriched extracellular vesicles carrying pTau217 for Alzheimer's disease diagnosis and differential diagnosis.

Accurate differential diagnosis among various dementias is crucial for effective treatment of Alzheimer's disease (AD). The study began with searching for novel blood-based neuronal extracellular vesicles (EVs) that are more enriched in the brain regions vulnerable to AD development and progression. With extensive proteomic profiling, GABRD and GPR162 were identified as novel brain regionally enriched plasma EVs markers. The performance of GABRD and GPR162, along with the AD molecule pTau217, was tested using the self-developed and optimized nanoflow cytometry-based technology, which not only detected the positive ratio of EVs but also concurrently presented the corresponding particle size of the EVs, in discovery (n = 310) and validation (n = 213) cohorts. Plasma GABRD+- or GPR162+-carrying pTau217-EVs were significantly reduced in AD compared with healthy control (HC). Additionally, the size distribution of GABRD+- and GPR162+-carrying pTau217-EVs were significantly different between AD and non-AD dementia (NAD). An integrative model, combining age, the number and corresponding size of the distribution of GABRD+- or GPR162+-carrying pTau217-EVs, accurately and sensitively discriminated AD from HC [discovery cohort, area under the curve (AUC) = 0.96; validation cohort, AUC = 0.93] and effectively differentiated AD from NAD (discovery cohort, AUC = 0.91; validation cohort, AUC = 0.90). This study showed that brain regionally enriched neuronal EVs carrying pTau217 in plasma may serve as a robust diagnostic and differential diagnostic tool in both clinical practice and trials for AD.

A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI.

PURPOSE: To explore a subregion-based RadioFusionOmics (RFO) model for discrimination between adult-type grade 4 astrocytoma and glioblastoma according to the 2021 WHO CNS5 classification. METHODS: 329 patients (40 grade 4 astrocytomas and 289 glioblastomas) with histologic diagnosis was retrospectively collected from our local institution and The Cancer Imaging Archive (TCIA). The volumes of interests (VOIs) were obtained from four multiparametric MRI sequences (T1WI, T1WI + C, T2WI, T2-FLAIR) using (1) manual segmentation of the non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE), and (2) K-means clustering of four habitats (H1: high T1WI + C, high T2-FLAIR; (2) H2: high T1WI + C, low T2-FLAIR; (3) H3: low T1WI + C, high T2-FLAIR; and (4) H4: low T1WI + C, low T2-FLAIR). The optimal VOI and best MRI sequence combination were determined. The performance of the RFO model was evaluated using the area under the precision-recall curve (AUPRC) and the best signatures were identified. RESULTS: The two best VOIs were manual VOI3 (putative peritumoral edema) and clustering H34 (low T1WI + C, high T2-FLAIR (H3) combined with low T1WI + C and low T2-FLAIR (H4)). Features fused from four MRI sequences ([Formula: see text]) outperformed those from either a single sequence or other sequence combinations. The RFO model that was trained using fused features [Formula: see text] achieved the AUPRC of 0.972 (VOI3) and 0.976 (H34) in the primary cohort (p = 0.905), and 0.971 (VOI3) and 0.974 (H34) in the testing cohort (p = 0.402). CONCLUSION: The performance of subregions defined by clustering was comparable to that of subregions that were manually defined. Fusion of features from the edematous subregions of multiple MRI sequences by the RFO model resulted in differentiation between grade 4 astrocytoma and glioblastoma.

Multiparametric MR-based feature fusion radiomics combined with ADC maps-based tumor proliferative burden in distinguishing TNBC versus non-TNBC.

Objective.To investigate the incremental value of quantitative stratified apparent diffusion coefficient (ADC) defined tumor habitats for differentiating triple negative breast cancer (TNBC) from non-TNBC on multiparametric MRI (mpMRI) based feature-fusion radiomics (RFF) model.Approach.466 breast cancer patients (54 TNBC, 412 non-TNBC) who underwent routine breast MRIs in our hospital were retrospectively analyzed. Radiomics features were extracted from whole tumor on T2WI, diffusion-weighted imaging, ADC maps and the 2nd phase of dynamic contrast-enhanced MRI. Four models including the RFFmodel (fused features from all MRI sequences), RADCmodel (ADC radiomics feature), StratifiedADCmodel (tumor habitas defined on stratified ADC parameters) and combinational RFF-StratifiedADCmodel were constructed to distinguish TNBC versus non-TNBC. All cases were randomly divided into a training (n= 337) and test set (n= 129). The four competing models were validated using the area under the curve (AUC), sensitivity, specificity and accuracy.Main results.Both the RFFand StratifiedADCmodels demonstrated good performance in distinguishing TNBC from non-TNBC, with best AUCs of 0.818 and 0.773 in the training and test sets. StratifiedADCmodel revealed significant different tumor habitats (necrosis/cysts habitat, chaotic habitat or proliferative tumor core) between TNBC and non-TNBC with its top three discriminative parameters (p <0.05). The integrated RFF-StratifiedADCmodel demonstrated superior accuracy over the other three models, with higher AUCs of 0.832 and 0.784 in the training and test set, respectively (p <0.05).Significance.The RFF-StratifiedADCmodel through integrating various tumor habitats' information from whole-tumor ADC maps-based StratifiedADCmodel and radiomics information from mpMRI-based RFFmodel, exhibits tremendous promise for identifying TNBC.

Increased cysteinyl-tRNA synthetase drives neuroinflammation in Alzheimer's disease.

BACKGROUND: Microglia-mediated neuroinflammation in Alzheimer's disease (AD) is not only a response to pathophysiological events, but also plays a causative role in neurodegeneration. Cytoplasmic cysteinyl-tRNA synthetase (CARS) is considered to be a stimulant for immune responses to diseases; however, it remains unknown whether CARS is involved in the pathogenesis of AD. METHODS: Postmortem human temporal cortical tissues at different Braak stages and AD patient-derived serum samples were used to investigate the changes of CARS levels in AD by immunocytochemical staining, real-time PCR, western blotting and ELISA. After that, C57BL/6J and APP/PS1 transgenic mice and BV-2 cell line were used to explore the role of CARS protein in memory and neuroinflammation, as well as the underlying mechanisms. Finally, the associations of morphological features among CARS protein, microglia and dense-core plaques were examined by immunocytochemical staining. RESULTS: A positive correlation was found between aging and the intensity of CARS immunoreactivity in the temporal cortex. Both protein and mRNA levels of CARS were increased in the temporal cortex of AD patients. Immunocytochemical staining revealed increased CARS immunoreactivity in neurons of the temporal cortex in AD patients. Moreover, overexpression of CARS in hippocampal neurons induced and aggravated cognitive dysfunction in C57BL/6J and APP/PS1 mice, respectively, accompanied by activation of microglia and the TLR2/MyD88 signaling pathway as well as upregulation of proinflammatory cytokines. In vitro experiments showed that CARS treatment facilitated the production of proinflammatory cytokines and the activation of the TLR2/MyD88 signaling pathway of BV-2 cells. The accumulation of CARS protein occurred within dense-core Aß plaques accompanied by recruitment of ameboid microglia. Significant upregulation of TLR2/MyD88 proteins was also observed in the temporal cortex of AD. CONCLUSIONS: The findings suggest that the neuronal CARS drives neuroinflammation and induces memory deficits, which might be involved in the pathogenesis of AD.

Investigation of causal relationships between cortical structure and osteoporosis using two-sample Mendelian randomization.

The mutual interaction between bone characteristics and brain had been reported previously, yet whether the cortical structure has any relevance to osteoporosis is questionable. Therefore, we applied a two-sample bidirectional Mendelian randomization analysis to investigate this relationship. We utilized the bone mineral density measurements of femoral neck (n = 32,735) and lumbar spine (n = 28,498) and data on osteoporosis (7300 cases and 358,014 controls). The global surficial area and thickness and 34 specific functional regions of 51,665 patients were screened by magnetic resonance imaging. For the primary estimate, we utilized the inverse-variance weighted method. The Mendelian randomization-Egger intercept test, MR-PRESSO, Cochran's Q test, and "leave-one-out" sensitivity analysis were conducted to assess heterogeneity and pleiotropy. We observed suggestive associations between decreased thickness in the precentral region (OR = 0.034, P = 0.003) and increased chance of having osteoporosis. The results also revealed suggestive causality of decreased bone mineral density in femoral neck to declined total cortical surface area (ß = 1400.230 mm2, P = 0.003), as well as the vulnerability to osteoporosis and reduced thickness in the Parstriangularis region (ß = -0.006 mm, P = 0.002). Our study supports that the brain and skeleton exhibit bidirectional crosstalk, indicating the presence of a mutual brain-bone interaction.

Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction.

Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future. Machine learning models are applied for MSW-related trend prediction to provide insights on future waste generation or carbon emissions trends and assist the formulation of effective low-carbon policies. Yet, the existing machine learning models are diverse and scattered. This inconsistency poses challenges for researchers in the MSW domain who seek to identify and optimize the machine learning techniques and configurations for their applications. This systematic review focuses on MSW-related trend prediction using the most frequently applied machine learning model, artificial neural network (ANN), while addressing potential methodological improvements for reducing prediction uncertainty. Thirty-two papers published from 2013 to 2023 are included in this review, all applying ANN for MSW-related trend prediction. Observing a decrease in the size of data samples used in studies from daily to annual timescales, the summarized statistics suggest that well-performing ANN models can still be developed with approximately 33 annual data samples. This indicates promising opportunities for modeling macroscale greenhouse gas emissions in future works. Existing literature commonly used the grid search (manual) technique for hyperparameter (e.g., learning rate, number of neurons) optimization and should explore more time-efficient automated optimization techniques. Since there are no one-size-fits-all performance indicators, it is crucial to report the model's predictive performance based on more than one performance indicator and examine its uncertainty. The predictive performance of newly-developed integrated models should also be benchmarked to show performance improvement clearly and promote similar applications in future works. The review analyzed the shortcomings, best practices, and prospects of ANNs for MSW-related trend predictions, supporting the realization of practical applications of ANNs to enhance waste management practices and reduce carbon emissions.

Artificial intelligence-based models enabling accurate diagnosis of ovarian cancer using laboratory tests in China: a multicentre, retrospective cohort study.

BACKGROUND: Ovarian cancer is the most lethal gynecological malignancy. Timely diagnosis of ovarian cancer is difficult due to the lack of effective biomarkers. Laboratory tests are widely applied in clinical practice, and some have shown diagnostic and prognostic relevance to ovarian cancer. We aimed to systematically evaluate the value of routine laboratory tests on the prediction of ovarian cancer, and develop a robust and generalisable ensemble artificial intelligence (AI) model to assist in identifying patients with ovarian cancer. METHODS: In this multicentre, retrospective cohort study, we collected 98 laboratory tests and clinical features of women with or without ovarian cancer admitted to three hospitals in China during Jan 1, 2012 and April 4, 2021. A multi-criteria decision making-based classification fusion (MCF) risk prediction framework was used to make a model that combined estimations from 20 AI classification models to reach an integrated prediction tool developed for ovarian cancer diagnosis. It was evaluated on an internal validation set (3007 individuals) and two external validation sets (5641 and 2344 individuals). The primary outcome was the prediction accuracy of the model in identifying ovarian cancer. FINDINGS: Based on 52 features (51 laboratory tests and age), the MCF achieved an area under the receiver-operating characteristic curve (AUC) of 0·949 (95% CI 0·948-0·950) in the internal validation set, and AUCs of 0·882 (0·880-0·885) and 0·884 (0·882-0·887) in the two external validation sets. The model showed higher AUC and sensitivity compared with CA125 and HE4 in identifying ovarian cancer, especially in patients with early-stage ovarian cancer. The MCF also yielded acceptable prediction accuracy with the exclusion of highly ranked laboratory tests that indicate ovarian cancer, such as CA125 and other tumour markers, and outperformed state-of-the-art models in ovarian cancer prediction. The MCF was wrapped as an ovarian cancer prediction tool, and made publicly available to provide estimated probability of ovarian cancer with input laboratory test values. INTERPRETATION: The MCF model consistently achieved satisfactory performance in ovarian cancer prediction when using laboratory tests from the three validation sets. This model offers a low-cost, easily accessible, and accurate diagnostic tool for ovarian cancer. The included laboratory tests, not only CA125 which was the highest ranked laboratory test in importance of diagnostic assistance, contributed to the characterisation of patients with ovarian cancer. FUNDING: Ministry of Science and Technology of China; National Natural Science Foundation of China; Natural Science Foundation of Guangdong Province, China; and Science and Technology Project of Guangzhou, China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

The Complete Genomic Sequence of Microbial Transglutaminase Producer, Streptomyces mobaraensis DSM40587.

Actinomycetes are remarkable natural sources of active natural molecules and enzymes of considerable industrial value. Streptomyces mobaraensis is the first microorganism found to produce transglutaminase with broad industrial applications. Although transglutaminase in S. mobaraensis has been well studied over the past three decades, the genome of S. mobaraensis and its secondary metabolic potential were poorly reported. Here, we presented the complete genome of S. mobaraensis DSM40587 obtained from the German Collection of Microorganisms and Cell Cultures GmbH. It contains a linear chromosome of 7,633,041 bp and a circular plasmid of 23,857 bp. The chromosome with an average GC content of 73.49% was predicted to harbour 6683 protein-coding genes, seven rRNA and 69 tRNA genes. Comparative genomic analysis reveals its meaningful genomic characterisation. A comprehensive bioinformatics investigation identifies 35 putative BGCs (biosynthesis gene clusters) involved in synthesising various secondary metabolites. Of these, 13 clusters showed high similarity (> 55%) to known BGCs coding for polyketides, nonribosomal peptides, hopene, RiPP (Ribosomally synthesized and post-translationally modified peptides), and others. Furthermore, these BGCs with over 65% similarity to the known BGCs were analysed in detail. The complete genome of S. mobaraensis DSM40587 reveals its capacity to yield diverse bioactive natural products and provides additional insights into discovering novel secondary metabolites.

Visible-Light-Driven NO Release from Postmodified MOFs via Photoinduced Electron Transfer for Antibacterial Application.

Photoresponsive nitric oxide (NO)-releasing materials (NORMs) enable the spatiotemporal delivery of NO to facilitate their potential applications in physiological conditions. Here two novel metal-organic frameworks (MOFs)-based photoactive NORMs achieved by the incorporation of prefunctionalized NO donors into the photosensitive Fe-MOFs via a postmodification strategy is reported. The modified Fe-MOFs display superior photoactivity of NO release when exposed to visible light (up to 720 nm). Significantly, the visible-light-driven NO release properties are further corroborated by their efficient antibacterial performance.

Von Willebrand factor promotes radiation-induced intestinal injury (RIII) development and its cleavage enzyme rhADAMTS13 protects against RIII by reducing inflammation and oxidative stress.

Patients with abdominopelvic cancer undergoing radiotherapy commonly develop radiation-induced intestinal injury (RIII); however, its underlying pathogenesis remains elusive. The von Willebrand factor (vWF)/a disintegrin and metalloproteinase with a thrombospondin type 1 motif, member 13 (ADAMTS13) axis has been implicated in thrombosis, inflammation, and oxidative stress. However, its role in RIII remains unclear. In this study, the effect of radiation on vWF and ADAMTS13 expression was firstly evaluated in patients with cervical cancer undergoing radiotherapy and C57BL/6J mice exposed to different doses of total abdominal irradiation. Then, mice with the specific deletion of vWF in the platelets and endothelium were established to demonstrate the contribution of vWF to RIII. Additionally, the radioprotective effect of recombinant human (rh) ADAMTS13 against RIII was assessed. Results showed that both the patients with cervical cancer undergoing radiotherapy and RIII mouse model exhibited increased vWF levels and decreased ADAMTS13 levels. The knockout of platelet- and endothelium-derived vWF rectified the vWF/ADAMTS13 axis imbalance; improved intestinal structural damage; increased crypt epithelial cell proliferation; and reduced radiation-induced apoptosis, inflammation, and oxidative stress, thereby alleviating RIII. Administration of rhADAMTS13 could equally alleviate RIII. Our results demonstrated that abdominal irradiation affected the balance of the vWF/ADAMTS13 axis. vWF exerted a deleterious role and ADAMTS13 exhibited a protective role in RIII progression. rhADAMTS13 has the potential to be developed into a radioprotective agent.

EHD1 impaired decidualization of endometrial stromal cells in recurrent implantation failure: role of SENP1 in modulating progesterone receptor signalling†.

Recurrent implantation failure (RIF) patients exhibit poor endometrial receptivity and abnormal decidualization with reduced effectiveness and exposure to progesterone, which is an intractable clinical problem. However, the associated molecular mechanisms remain elusive. We found that EH domain containing 1 (EHD1) expression was abnormally elevated in RIF and linked to aberrant endometrial decidualization. Here we show that EHD1 overexpressed in human endometrial stromal cells significantly inhibited progesterone receptor (PGR) transcriptional activity and the responsiveness to progesterone. No significant changes were observed in PGR mRNA levels, while a significant decrease in progesterone receptor B (PRB) protein level. Indeed, EHD1 binds to the PRB protein, with the K388 site crucial for this interaction. Overexpression of EHD1 promotes the SUMOylation and ubiquitination of PRB, leading to the degradation of the PRB protein. Supplementation with the de-SUMOylated protease SENP1 ameliorated EHD1-repressed PRB transcriptional activity. To establish a functional link between EHD1 and the PGR signalling pathway, sg-EHD1 were utilized to suppress EHD1 expression in HESCs from RIF patients. A significant increase in the expression of prolactin and insulin-like growth factor-binding protein 1 was detected by interfering with the EHD1. In conclusion, we demonstrated that abnormally high expression of EHD1 in endometrial stromal cells attenuated the activity of PRB associated with progesterone resistance in a subset of women with RIF.

The TAE score predicts prognosis of unresectable HCC patients treated with TACE plus lenvatinib with PD-1 inhibitors.

BACKGROUND AND AIMS: Transcatheter arterial chemoembolization combined with lenvatinib and PD-1 inhibitors (triple therapy) exhibits promising efficacy for unresectable hepatocellular carcinoma (uHCC). We aimed to evaluate the prognosis of patients with uHCC who received triple therapy and develop a prognostic scoring model to identify patients who benefit the most from triple therapy. METHODS: A total of 246 patients with uHCC who received triple therapy at eight centers were included and assigned to the training and validation cohorts. Prognosis was evaluated by the Kaplan-Meier curves. The prognostic model was developed by utilizing predictors of overall survival (OS), which were identified through the Cox proportional hazards model. RESULTS: In the training cohort, the 3-year OS was 52.0%, with a corresponding progression-free survival (PFS) of 30.6%. The median PFS was 13.2 months [95% confidence interval, 9.7-16.7]. Three variables (total bilirubin ≥ 17 µmol/L, alpha-fetoprotein ≥ 400 ng/mL, and extrahepatic metastasis) were predictors of poor survival and were used for developing a prognostic model (TAE score). The 2-year OS rates in the favorable (0 points), intermediate (1 point), and dismal groups (2-3 points) were 96.9%, 61.4%, and 11.4%, respectively (p < 0.001). The PFS was also stratified according to the TAE score. These findings were confirmed in an external validation cohort. CONCLUSIONS: Triple therapy showed encouraging clinical outcomes, and the TAE score aids in identifying patients who would benefit the most from triple therapy.

Forecasting meteorological impacts on the environmental sustainability of a large-scale solar plant via artificial intelligence-based life cycle assessment.

Although large-scale solar (LSS) is a promising renewable energy technology, it causes adverse impacts on the ecosystem, human health, and resource depletion throughout its upstream (i.e., raw material extraction to solar panel production) and downstream (i.e., plant demolition and waste management) processes. The LSS operational performance also fluctuates due to meteorological conditions, leading to uncertainty in electricity generation and raising concerns about its overall environmental performance. Hitherto, there has been no evidence-backed study that evaluates the ecological sustainability of LSS with the consideration of meteorological uncertainties. In this study, a novel integrated Life Cycle Assessment (LCA) and Artificial Neural Network (ANN) framework is developed to forecast the meteorological impacts on LSS's electricity generation and its life cycle environmental sustainability. For LCA, 18 impact categories and three damage categories are characterised and assessed by ReCiPe 2016 via SimaPro v. 9.1. For ANN, a feedforward neural network is applied via Neural Designer 5.9.3. Taking an LSS plant in Malaysia as a case study, the photovoltaic panel production stage contributes the highest environmental impact in LSS (30 % of human health, 30 % of ecosystem quality, and 34 % of resource scarcity). Aluminium recycling reduces by 10 % for human health, 10 % for ecosystem quality, and 9 % for resource scarcity. The emissions avoided by the forecasted LSS-generated electricity offset the environmental burden for human health, ecosystem quality, and resource scarcity 12-68 times, 13-73 times, and 18-98 times, respectively. The developed ANN-LCA framework can provide LSS stakeholders with data-backed insights to effectively design an environmentally conscious LSS facility, considering meteorological influences.