Prediction of cognitive decline in Parkinson's disease based on MRI radiomics and clinical features: A multicenter study.

OBJECTIVE: To develop and validate a multimodal combinatorial model based on whole-brain magnetic resonance imaging (MRI) radiomic features for predicting cognitive decline in patients with Parkinson's disease (PD). METHODS: This study included a total of 222 PD patients with normal baseline cognition, of whom 68 had cognitive impairment during a 4-year follow-up period. All patients underwent MRI scans, and radiomic features were extracted from the whole-brain MRI images of the training set, and dimensionality reduction was performed to construct a radiomics model. Subsequently, Screening predictive factors for cognitive decline from clinical features and then combining those with a radiomics model to construct a multimodal combinatorial model for predicting cognitive decline in PD patients. Evaluate the performance of the comprehensive model using the receiver-operating characteristic curve, confusion matrix, F1 score, and survival curve. In addition, the quantitative characteristics of diffusion tensor imaging (DTI) from corpus callosum were selected from 52 PD patients to further validate the clinical efficacy of the model. RESULTS: The multimodal combinatorial model has good classification performance, with areas under the curve of 0.842, 0.829, and 0.860 in the training, test, and validation sets, respectively. Significant differences were observed in the number of cognitive decline PD patients and corpus callosum-related DTI parameters between the low-risk and high-risk groups distinguished by the model (p < 0.05). The survival curve analysis showed a statistically significant difference in the progression time of mild cognitive impairment between the low-risk and the high-risk groups. CONCLUSIONS: The building of a multimodal combinatorial model based on radiomic features from MRI can predict cognitive decline in PD patients, thus providing adaptive strategies for clinical practice.

DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery.

Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on modern drug discovery, but there is still an urgent need for user-friendly interfaces that bridge the gap between these sophisticated tools and scientists, particularly those who are less computer savvy. Herein, we present DrugFlow, an AI-driven one-stop platform that offers a clean, convenient, and cloud-based interface to streamline early drug discovery workflows. By seamlessly integrating a range of innovative AI algorithms, covering molecular docking, quantitative structure-activity relationship modeling, molecular generation, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction, and virtual screening, DrugFlow can offer effective AI solutions for almost all crucial stages in early drug discovery, including hit identification and hit/lead optimization. We hope that the platform can provide sufficiently valuable guidance to aid real-word drug design and discovery. The platform is available at https://drugflow.com.

Genome mining of sulfonated lanthipeptides reveals unique cyclic peptide sulfotransferases.

Although sulfonation plays crucial roles in various biological processes and is frequently utilized in medicinal chemistry to improve water solubility and chemical diversity of drug leads, it is rare and underexplored in ribosomally synthesized and post-translationally modified peptides (RiPPs). Biosynthesis of RiPPs typically entails modification of hydrophilic residues, which substantially increases their chemical stability and bioactivity, albeit at the expense of reducing water solubility. To explore sulfonated RiPPs that may have improved solubility, we conducted co-occurrence analysis of RiPP class-defining enzymes and sulfotransferase (ST), and discovered two distinctive biosynthetic gene clusters (BGCs) encoding both lanthipeptide synthetase (LanM) and ST. Upon expressing these BGCs, we characterized the structures of novel sulfonated lanthipeptides and determined the catalytic details of LanM and ST. We demonstrate that SslST-catalyzed sulfonation is leader-independent but relies on the presence of A ring formed by LanM. Both LanM and ST are promiscuous towards residues in the A ring, but ST displays strict regioselectivity toward Tyr5. The recognition of cyclic peptide by ST was further discussed. Bioactivity evaluation underscores the significance of the ST-catalyzed sulfonation. This study sets up the starting point to engineering the novel lanthipeptide STs as biocatalysts for hydrophobic lanthipeptides improvement.

Associations of Long-term Sleep Duration Trajectories with Overall and Cause-Specific Mortality Among Middle-to-older Aged Black and White Adults.

Background: Both short and long sleep durations are adversely associated with numerous chronic conditions, including cardiovascular disease (CVD), diabetes, hypertension, and mortality. The American Academy of Sleep Medicine recommends adults in the United States sleep at least 7 hours and less than 9 hours per night to maintain optimal health. It remains unclear how sleep duration trajectories over time are associated with mortality. Methods: This observational cohort study includes 46,928 Black and White adults (mean age: 53 ± 9 years) who enrolled in the Southern Community Cohort Study between 2002-2009 and completed a follow-up survey in 2008-2013. Participants were categorized into nine sleep duration trajectory categories based on the reported average sleep duration between study enrollment and at follow-up. Participant vital status and date and cause of death were ascertained via linkage to the National Death Index through 2022. Cox regression analysis was performed to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the association between sleep duration trajectory and all-cause and cause-specific mortality (CVD, cancer, and neurodegenerative disease) after adjustment for sociodemographic characteristics, health behaviors, and clinical factors. Results: During a median 12.6 years of follow-up, we documented 13,579 deaths, including 4,135 from CVD, 3,067 from cancer, and 544 from neurodegenerative diseases. Compared to the optimal sleep duration trajectory (maintaining 7-9 hours), all sub-optimal trajectories were associated with significant 6 to 33% greater risk of all-cause mortality in fully adjusted models. Compared to the optimal sleep trajectory, three of the sub-optimal trajectories were associated with increased CVD mortality, with HRs ranging from 1.20 to 1.34. The short-long trajectory was associated with the greatest risk of all-cause mortality (HR:1.33; 95%CI: 1.21, 1.46) and the long-short trajectory was associated with the greatest CVD mortality risk (HR:1.34; 95%CI: 1.10, 1.65). The healthy-long trajectory was associated with the greatest risk of cancer mortality (HR: 1.19; 95%CI:1.00, 1.41). None of the sub-optimal trajectories was associated with neurodegenerative disease mortality. Conclusions: Suboptimal sleep duration trajectories were associated with increased risk of all-cause mortality as well as CVD mortality. Findings highlight the importance of maintaining healthy sleep duration throughout midlife to reduce mortality risk.

Hepatocyte-specific Selenoi deficiency predisposes mice to hepatic steatosis and obesity.

Selenoprotein I (Selenoi) is highly expressed in liver and plays a key role in lipid metabolism as a phosphatidylethanolamine (PE) synthase. However, the precise function of Selenoi in the liver remains elusive. In the study, we generated hepatocyte-specific Selenoi conditional knockout (cKO) mice on a high-fat diet to identify the physiological function of Selenoi. The cKO group exhibited a significant increase in body weight, with a 15.6% and 13.7% increase in fat accumulation in white adipose tissue (WAT) and the liver, respectively. Downregulation of the lipolysis-related protein (p-Hsl) and upregulation of the adipogenesis-related protein (Fasn) were observed in the liver of cKO mice. The cKO group also showed decreased oxygen consumption (VO2), carbon dioxide production (VCO2), and energy expenditure (p < .05). Moreover, various metabolites of the steroid hormone synthesis pathway were affected in the liver of cKO mice. A potential cascade of Selenoi-phosphatidylethanolamine-steroid hormone synthesis might serve as a core mechanism that links hepatocyte-specific Selenoi cKO to biochemical and molecular reactions. In conclusion, we revealed that Selenoi inhibits body fat accumulation and hepatic steatosis and elevates energy consumption; this protein could also be considered a therapeutic target for such related diseases.

Single-cell RNA transcriptomic reveal the mechanism of MSC derived small extracellular vesicles against DKD fibrosis.

Diabetic kidney disease (DKD), a chronic kidney disease, is characterized by progressive fibrosis caused due to persistent hyperglycemia. The development of fibrosis in DKD determines the patient prognosis, but no particularly effective treatment. Here, small extracellular vesicles derived from mesenchymal stem cells (MSC-sEV) have been used to treat DKD fibrosis. Single-cell RNA sequencing was used to analyze 27,424 cells of the kidney, we have found that a novel fibrosis-associated TGF-ß1+Arg1+ macrophage subpopulation, which expanded and polarized in DKD and was noted to be profibrogenic. Additionally, Actin+Col4a5+ mesangial cells in DKD differentiated into myofibroblasts. Multilineage ligand-receptor and cell-communication analysis showed that fibrosis-associated macrophages activated the TGF-ß1/Smad2/3/YAP signal axis, which promotes mesangial fibrosis-like change and accelerates renal fibrosis niche. Subsequently, the transcriptome sequencing and LC-MS/MS analysis indicated that MSC-sEV intervention could restore the levels of the kinase ubiquitin system in DKD and attenuate renal interstitial fibrosis via delivering CK1δ/ß-TRCP to mediate YAP ubiquitination degradation in mesangial cells. Our findings demonstrate the unique cellular and molecular mechanisms of MSC-sEV in treating the DKD fibrosis niche at a single-cell level and provide a novel therapeutic strategy for renal fibrosis.

Distinct Perception Mechanisms of BACH1 Quaternary Structure Degrons by Two F-box Proteins under Oxidative Stress.

The transcription factor BACH1 regulates heme homeostasis and oxidative stress responses and promotes cancer metastasis upon aberrant accumulation. Its stability is controlled by two F-box protein ubiquitin ligases, FBXO22 and FBXL17. Here we show that the homodimeric BTB domain of BACH1 functions as a previously undescribed quaternary structure degron, which is deciphered by the two F-box proteins via distinct mechanisms. After BACH1 is released from chromatin by heme, FBXO22 asymmetrically recognizes a cross-protomer interface of the intact BACH1 BTB dimer, which is otherwise masked by the co-repressor NCOR1. If the BACH1 BTB dimer escapes the surveillance by FBXO22 due to oxidative modifications, its quaternary structure integrity is probed by a pair of FBXL17, which simultaneously engage and remodel the two BTB protomers into E3-bound monomers for ubiquitination. By unveiling the multifaceted regulatory mechanisms of BACH1 stability, our studies highlight the abilities of ubiquitin ligases to decode high-order protein assemblies and reveal therapeutic opportunities to block cancer invasion via compound-induced BACH1 destabilization.

A telomere-related gene risk model for predicting prognosis and treatment response in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a prevalent hematological malignancy among adults. Recent studies suggest that the length of telomeres could significantly affect both the risk of developing AML and the overall survival (OS). Despite the limited focus on the prognostic value of telomere-related genes (TRGs) in AML, our study aims at addressing this gap by compiling a list of TRGs from TelNet, as well as collecting clinical information and TRGs expression data through the Gene Expression Omnibus (GEO) database. The GSE37642 dataset, sourced from GEO and based on the GPL96 platform, was divided into training and validation sets at a 6:4 ratio. Additionally, the GSE71014 dataset (based on the GPL10558 platform), GSE12417 dataset (based on the GPL96 and GPL570 platforms), and another portion of the GSE37642 dataset (based on the GPL570 platform) were designated as external testing sets. Univariate Cox regression analysis identified 96 TRGs significantly associated with OS. Subsequent Lasso-Cox stepwise regression analysis pinpointed eight TRGs (MCPH1, SLC25A6, STK19, PSAT1, KCTD15, DNMT3B, PSMD5, and TAF2) exhibiting robust predictive potential for patient survival. Both univariate and multivariate survival analyses unveiled TRG risk scores and age as independent prognostic variables. To refine the accuracy of survival prognosis, we developed both a nomogram integrating clinical parameters and a predictive risk score model based on TRGs. In subsequent investigations, associations were emphasized not solely regarding the TRG risk score and immune infiltration patterns but also concerning the response to immune-checkpoint inhibitor (ICI) therapy. In summary, the establishment of a telomere-associated genetic risk model offers a valuable tool for prognosticating AML outcomes, thereby facilitating informed treatment decisions.

Adolescents and Children Age Estimation Using Machine Learning Based on Pulp and Tooth Volumes on CBCT Images.

OBJECTIVES: To estimate adolescents and children age using stepwise regression and machine learning methods based on the pulp and tooth volumes of the left maxillary central incisor and cuspid on cone beam computed tomography (CBCT) images, and to compare and analyze the estimation results. METHODS: A total of 498 Shanghai Han adolescents and children CBCT images of the oral and maxillofacial regions were collected. The pulp and tooth volumes of the left maxillary central incisor and cuspid were measured and calculated. Three machine learning algorithms (K-nearest neighbor, ridge regression, and decision tree) and stepwise regression were used to establish four age estimation models. The coefficient of determination, mean error, root mean square error, mean square error and mean absolute error were computed and compared. A correlation heatmap was drawn to visualize and the monotonic relationship between parameters was visually analyzed. RESULTS: The K-nearest neighbor model (R2=0.779) and the ridge regression model (R2=0.729) outperformed stepwise regression (R2=0.617), while the decision tree model (R2=0.494) showed poor fitting. The correlation heatmap demonstrated a monotonically negative correlation between age and the parameters including pulp volume, the ratio of pulp volume to hard tissue volume, and the ratio of pulp volume to tooth volume. CONCLUSIONS: Pulp volume and pulp volume proportion are closely related to age. The application of CBCT-based machine learning methods can provide more accurate age estimation results, which lays a foundation for further CBCT-based deep learning dental age estimation research.

Perceived chronic social adversity and cyberbullying perpetration among adolescents: the mediating role of rumination and moderating role of mindfulness.

Backgrounds: The prevalence of cyberbullying has brought about many adverse effects on adolescents' mental health. Although current studies have shown that perceived chronic social adversity (PCSA) is closely related to cyberbullying perpetration among adolescents, the underlying mechanism of the relationship between the two remains relatively unclear. This study investigated the association of PCSA, rumination, mindfulness, and cyberbullying perpetration among adolescents, building upon the general strain theory, the general aggressive model, and the limited resource of self-control theory. Methods: A sample of 477 Chinese high school students (M age = 15.84 years, SD age = 0.67, 49.69% female) completed the Perceived Chronic Social Adversity Questionnaire, the Ruminative Responses Scale, the Child and Adolescent Mindfulness Measure, and the cyberbullying subscale of the Revised Cyber Bullying Inventory. The current study constructed a moderated mediation model to examine the relationship between PCSA and cyberbullying perpetration among adolescents and assessed the mediating role of rumination and the moderating role of mindfulness. Results: The results revealed a significant positive correlation between PCSA and cyberbullying perpetration. Rumination mediated the relationship between PCSA and cyberbullying perpetration, whereas mindfulness moderated the latter half of the mediation pathway. Specifically, compared to adolescents with higher mindfulness, the association between rumination and cyberbullying perpetration is greater for adolescents with lower mindfulness. Conclusion: The results further deepen our understanding of the mechanisms linking subjective perception of negative life events and cyberbullying perpetration among adolescents from the interaction of multiple factors, thus providing a basis for future interventions to encourage adolescents to properly cope with social adversity and promote positive mental health to reduce the risk of cyberbullying.

Comparative safety assessment of nasogastric versus nasojejunal feeding initiated within 48 hours post-admission versus unrestricted timing in moderate or severe acute pancreatitis: a systematic review and meta-analysis.

BACKGROUND: The primary objective of this study is to comparatively assess the safety of nasogastric (NG) feeding versus nasojejunal (NJ) feeding in patients with acute pancreatitis (AP), with a special focus on the initiation of these feeding methods within the first 48 h of hospital admission. METHODS: Studies were identified through a systematic search in PubMed, EMbase, Cochrane Central Register of Controlled Trials, and Web of Science. Four studies involving 217 patients were included. This systematic review assesses the safety and efficacy of nasogastric versus nasojejunal feeding initiated within 48 h post-admission in moderate/severe acute pancreatitis, with a specific focus on the timing of initiation and patient age as influential factors. RESULTS: The results showed that the mortality rates were similar between NG and NJ feeding groups (RR 0.86, 95% CI 0.42 to 1.77, P = 0.68). Significant differences were observed in the incidence of diarrhea (RR 2.75, 95% CI 1.21 to 6.25, P = 0.02) and pain (RR 2.91, 95% CI 1.50 to 5.64, P = 0.002) in the NG group. The NG group also showed a higher probability of infection (6.67% vs. 3.33%, P = 0.027) and a higher frequency of multiple organ failures. Subgroup analysis for early intervention (within 48 h) showed a higher risk of diarrhea in the NG group (RR 2.80, P = 0.02). No significant differences were found in the need for surgical intervention, parenteral nutrition, or success rates of feeding procedures. CONCLUSION: This meta-analysis highlights the importance of considering the method and timing of nutritional support in acute pancreatitis. While NG feeding within 48 h of admission increases the risk of certain complications such as diarrhea and infection, it does not significantly impact mortality or the need for surgical intervention.

Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention.

BACKGROUND: Accurately identifying the risk level of drug combinations is of great significance in investigating the mechanisms of combination medication and adverse reactions. Most existing methods can only predict whether there is an interaction between two drugs, but cannot directly determine their accurate risk level. METHODS: In this study, we propose a multi-class drug combination risk prediction model named AERGCN-DDI, utilizing a relational graph convolutional network with a multi-head attention mechanism. Drug-drug interaction events with varying risk levels are modeled as a heterogeneous information graph. Attribute features of drug nodes and links are learned based on compound chemical structure information. Finally, the AERGCN-DDI model is proposed to predict drug combination risk level based on heterogenous graph neural network and multi-head attention modules. RESULTS: To evaluate the effectiveness of the proposed method, five-fold cross-validation and ablation study were conducted. Furthermore, we compared its predictive performance with baseline models and other state-of-the-art methods on two benchmark datasets. Empirical studies demonstrated the superior performances of AERGCN-DDI. CONCLUSIONS: AERGCN-DDI emerges as a valuable tool for predicting the risk levels of drug combinations, thereby aiding in clinical medication decision-making, mitigating severe drug side effects, and enhancing patient clinical prognosis.

A hydrogel-functionalized silver nanocluster for bacterial-infected wound healing.

The ever-growing challenges of traditional antibiotic therapy and chronic wound healing have created a hot topic for the development and application of new antimicrobial agents. Silver nanoclusters (Ag NCs) with ultrasmall sizes (<2 nm) and antibacterial effects are promising candidates for next-generation antibiotics, particularly against multi-drug resistant strains. However, the biosafety in the clinical application of Ag NCs remains suboptimal despite some existing studies of Ag NCs for biomedical applications. Considering this, an ultrasmall Ag NC with excellent water solubility was synthesized by a two-phase ligand-exchange method, which exhibits broad-spectrum antibacterial performance. The minimum inhibitory concentrations of Ag NCs against MRSA, S. aureus, P. aeruginosa and E. coli were evaluated as 50, 80, 5 and 5 µg mL-1, respectively. Furthermore, a carbomer hydrogel was prepared to be incorporated into the Ag NCs for achieving excellent biocompatibility and biosafety. In vitro experiments demonstrate that the Ag NC-gel exhibits good antibacterial properties with lower cytotoxicity. Finally, in vivo experiments suggest that this ultrasmall Ag NC functionalized with the hydrogel can serve as an effective and safe antimicrobial agent to aid in wound healing.

Assessment of the 2023 ACR/EULAR antiphospholipid syndrome classification criteria in a Chinese cohort: Impact on clinical practice.

OBJECTIVES: To evaluate the effectiveness of the 2023 ACR/EULAR criteria for antiphospholipid syndrome (APS) in a Chinese cohort, and compare them with the Sapporo and revised Sapporo criteria. METHODS: A cohort comprising 436 patients diagnosed with APS and 514 control subjects was enrolled, including 83 with seronegative APS and 86 classified as antiphospholipid antibody (aPL) carriers. We assessed IgG and IgM anticardiolipin antibodies (aCL) and anti-ß2-glycoprotein I (aß2GPI) antibodies using ELISA, along with a systematic collection of lupus anticoagulant data. Subsequently, we compared the sensitivity and specificity across the three classification criteria. RESULTS: The 2023 ACR/EULAR criteria exhibited improved specificity at 98 %, surpassing the revised Sapporo (90 %) and original Sapporo (91 %) criteria. However, this came with decreased sensitivity at 82 %, in contrast to higher sensitivities in the revised Sapporo (98 %) and Sapporo (91 %) criteria. Examining individual components sheds light on the scoring system's rationale within the new criteria. The inclusion of microvascular thrombosis, cardiac valve disease, and thrombocytopenia improved the identification of nine patients previously classified as "probable APS". Insufficient scoring in 78 previously diagnosed APS individuals was linked to traditional risk factor evaluations for thrombotic events, the emphasis on determining whether obstetric events are linked to severe preeclampsia (PEC) or placental insufficiency (PI), and the lower scores assigned to IgM aCL and/or aß2GPI antibody. Seronegative APS remained a challenge, as non-criteria aPL and other methods were not included. CONCLUSIONS: The new criteria presented notable advancements in specificity. This study provides detailed insights into the strengths and possible challenges of the 2023 ACR/EULAR criteria, enhancing our understanding of their impact on clinical practice.

[A multicenter study of neonatal stroke in Shenzhen, China]. / 深圳市新生儿脑卒中多中心现况调查.

OBJECTIVES: To investigate the incidence rate, clinical characteristics, and prognosis of neonatal stroke in Shenzhen, China. METHODS: Led by Shenzhen Children's Hospital, the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022. The incidence, clinical characteristics, treatment, and prognosis of neonatal stroke in Shenzhen were analyzed. RESULTS: The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137, 1/6 060, and 1/7 704, respectively. Ischemic stroke accounted for 75% (27/36); boys accounted for 64% (23/36). Among the 36 neonates, 31 (86%) had disease onset within 3 days after birth, and 19 (53%) had convulsion as the initial presentation. Cerebral MRI showed that 22 neonates (61%) had left cerebral infarction and 13 (36%) had basal ganglia infarction. Magnetic resonance angiography was performed for 12 neonates, among whom 9 (75%) had involvement of the middle cerebral artery. Electroencephalography was performed for 29 neonates, with sharp waves in 21 neonates (72%) and seizures in 10 neonates (34%). Symptomatic/supportive treatment varied across different hospitals. Neonatal Behavioral Neurological Assessment was performed for 12 neonates (33%, 12/36), with a mean score of (32±4) points. The prognosis of 27 neonates was followed up to around 12 months of age, with 44% (12/27) of the neonates having a good prognosis. CONCLUSIONS: Ischemic stroke is the main type of neonatal stroke, often with convulsions as the initial presentation, involvement of the middle cerebral artery, sharp waves on electroencephalography, and a relatively low neurodevelopment score. Symptomatic/supportive treatment is the main treatment method, and some neonates tend to have a poor prognosis.

Evaluating a maladaptive personality-informed model of social support and post-traumatic stress disorder.

BACKGROUND: Social support is a robust predictor of post-traumatic stress disorder (PTSD). Although the inverse relationship between perceived social support and PTSD (social causation model) is supported, less is understood about the antecedents of the social causation model. Further, there is limited research in non-Western psychiatric populations that experience elevated rates of trauma and PTSD (e.g., mood disorders). The present study evaluated whether cumulative traumatic life events influenced current PTSD symptoms through maladaptive personality traits and perceptions of social support among Asian patients with mood disorders. METHODS: A total of 200 Asian patients (77.5 % Chinese) with mood disorders were assessed for maladaptive personality traits, perceptions of social support, cumulative traumatic life events, PTSD, and depressive symptoms. Structural equation modelling was conducted to evaluate the extended social causation model. RESULTS: The extended social causation model demonstrated acceptable fit to the data (Comparative Fit Index [CFI] = 0.90; absolute Root Mean Square Error of Approximation [RMSEA] = 0.08). There were significant indirect effects of cumulative traumatic life events on current PTSD symptoms (ß = 0.29, p < .001; 85 % variance explained) and depressive symptoms (ß = 0.28, p < .001; 69 % variance explained). LIMITATIONS: Results may not be generalizable beyond the Singapore population due to the socio-cultural and environmental context. CONCLUSIONS: The present findings provide conceptual support for a maladaptive personality-informed model of social support and PTSD, which could better inform trauma-focused interventions in preventing and treating the debilitating effects of PTSD in psychiatric populations.

Heteropolyacid promoted lignin-MOF derived spherical catalyst for catalytic hydrogen transfer of 5-hydroxymethylfurfural.

Catalytic conversion of biomass-derived value-added chemicals was of great significance for the utilization of renewable biomass resources to instead of fossil chemicals. Biomass-derived lignin was regarded as an important support and 5-hydroxymethylfurfural (HMF) was a vital platform chemical derived from cellulose. Herein, a series of lignin-MOF hybrid catalysts were prepared and modified with different heteropolyacids (HPAs), which were then successfully introduced into the selective conversion of HMF to 5-hydroxymethylfurfuryl alcohol (MFA). The effect of different HPA, calcination temperature, etc. were all studied, and all catalysts were well characterized. It was confirmed that silicotungstic acid modified catalyst (Ni3Co-MOF-LS@HSiW) exhibited the best catalytic performance, while the highest conversion of HMF was up to 100%, with the best MFA yield of 86.5%. The finding in this study could provide novel insights for the utilization of lignin and preparation of value-added biomass-derived chemicals.

EGCG-vanadium nanomedicine with neutral pH Fenton reaction activity inhibits heat shock proteins for enhanced photothermal/chemodynamic therapy.

A burgeoning interest has recently focused on the development of nanomedicine to integrate noninvasive photothermal therapy (PTT) and chemodynamic therapy (CDT) for synergistic tumor treatments, owing to PTT's amplification effect on CDT. However, challenges emerge as hyperthermia often induces an unwarranted overexpression of cytoprotective heat shock proteins (HSPs), thereby curtailing PTT efficacy. Additionally, the nearly neutral tumor intracellular pH (pHi ≈ 7.2) that handicaps the Fenton reaction poses a leading limitation to CDT. Addressing these hurdles, we introduce EVP, a nanomedicine developed through the straightforward assembly of epigallocatechin gallate (EGCG), vanadium sulfate (VOSO4), and Pluronic F-127 (PF127). EVP comprehensively downregulates overexpressed HSPs (HSP 60, 70, 90) through the collaborative action of EGCG and vanadyl (VO2+). Moreover, the tumor intracellular pH-processed Fenton-like reaction by VO2+ ensures highly efficient hydroxyl radicals (OH) production in cytosols, overcoming the stringent acidity requirement for CDT. Additionally, the hyperthermia induced by PTT augments OH production, further enhancing CDT efficacy. In vitro and in vivo experiments validate EVP's excellent biocompatibility and potent tumor inhibition, highlighting its substantial potential in tumor therapy.

Revisiting the pathophysiology of intracranial hemorrhage in fetuses with Chiari II malformation - novel imaging biomarkers of disease severity?

BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) has emerged as a notable concern in Chiari II malformation (CM II), yet its origins and clinical implications remain elusive. This study aims to validate the in-utero prevalence of ICH in CM II and investigate contributing factors, and visualize the findings in a network format. MATERIALS AND METHODS: A single-center retrospective review of fetal MRI scans obtained in fetuses with CM II presenting (January 2007 to December 2022) was performed for ICH utilizing EPI-T2* blood-sensitive sequence. Fetuses with aqueduct stenosis (AS) were included as a control group. The incidence of ICH and corresponding gestational ages were compared between CM II and AS cases, and morphometric measurements (inner/outer CSF spaces, posterior fossa, venous structure) were compared among the four 1:1 age-matched groups: CM II+ICH, CM II-ICH, AS+ICH, and AS-ICH. Additionally, a co-occurrence network was constructed to visualize associations between phenotypic features in ICH cases. RESULTS: A total of 101 fetuses with CM II and 90 controls with AS at a median gestational age of 24.4 weeks and 22.8 weeks (P=.138) were included. Prevalence of ICH in fetuses with CM II was higher compared to the AS cases (28.7% vs 18.9%, P=.023), accompanied by congested veins (deep vein congestion mainly in young fetuses, and cortical veins may also be affected in older fetuses). ICH was notably correlated with specific anatomical features, essentially characterized by reduced outer cerebrospinal fluid spaces and clivus-supraocciput angle. The co-occurrence network analysis reveals complex connections including bony defects, small posterior fossa dimensions, vermis ectopia, reduced CSF spaces as well as venous congestion and venous sinus stenosis as pivotal components within the network. CONCLUSIONS: The high prevalence of ICH - detected by fetal MRI -among fetuses with CM emphasizes the pathophysiological importance of venous congestion, ICH, and vasogenic edema. As indicators of disease severity, these features may serve as helpful additional imaging biomarkers for the identification of potential candidates for fetal surgery.ABBREVIATIONS: CM II＝Chiari type II malformation; AS＝aqueduct stenosis; ICH ＝Intracranial hemorrhage.