Chitosan oligosaccharides alleviate macrophage pyroptosis and protect sepsis mice via activating the Nrf2/GPX4 pathway.

In the process of sepsis, excessive occurrence of pyroptosis, a form of programmed cell death acting as a defense mechanism against pathogens, can disrupt immune responses, thus leading to tissue damage and organ dysfunction. Chitosan oligosaccharide (COS), derived from chitosan degradation, has demonstrated diverse beneficial effects. However, its impact on sepsis-induced pyroptosis remains unexplored. In the present study, ATP/LPS was utilized to induce canonical-pyroptosis in THP-1 cells, while bacterial outer membrane vesicles (OMV) were employed to trigger non-canonical pyroptosis in RAW264.7 cells. Our results revealed a dose-dependent effect of COS on both types of pyroptosis. This was evidenced by a reduction in the expression of pro-inflammatory cytokines, as well as crucial regulatory proteins involved in pyroptosis. In addition, COS inhibited the cleavage of caspase-1 and GSDMD, and reduced ASC oligomerization. The underlying mechanism revealed that COS acts an antioxidant, reducing the release of pyroptosis-induced ROS and malondialdehyde (MDA) by upregulation the expression and promoting the nuclear translocation of nuclear factor erythroid-2-related factor 2 (Nrf2), which led to an elevation of glutathione peroxidase 4 (GPX4) and superoxide dismutase (SOD). Notably, the actions of COS were completely reversed by the Nrf2 inhibitor. Consequently, COS intervention increased the survival rate of sepsis.

Particle Size-Tunable Polydopamine Nanoparticles for Optical and Electrochemical Imaging of Latent Fingerprints on Various Surfaces.

Powder dusting method is the most widely used approach due to its low cost, simplicity, minimal instrument dependence, and extensive applicability for developing latent fingerprints (LFPs). Herein, a novel optical and electrochemical dual-mode method for high-resolution LFP enhancement has been explored based on size-tunable polydopamine (PDA) nanoparticles (NPs) and scanning electrochemical microscopy (SECM). Dark PDAs rich in functional groups and negative charges can combine with the residues of LFPs on various surfaces with high sensitivity and selectivity to realize high-resolution visual fingerprint physical patterns on various porous and nonporous substrates with light color. However, optical visualization is not feasible for LFPs on dark or multicolored surfaces. Fortunately, based on the differences in electrochemical reactivity between ridges and furrows caused by the conductivity and reducibility of PDA powders, SECM can serve as a powerful supplement to optical methods to effectively overcome background color interference and distinctly display fingerprint patterns. Intriguingly, it is noteworthy that the binding amount and particle size of PDA powder significantly affected the optical and electrochemical visualization of LFPs: more powder binding amounts provided darker ridges in optical, and more surface reaction sites (larger powder binding mass at the same particle size or smaller particle size at the same mass) provided higher currents of ridges in electrochemical imaging. It demonstrates that the PDA powder as a dual-mode developer for LFPs offers a promising method for individual identification in forensics.

Modular Synthesis of Tetrasubstituted Vinyl Sulfides via One-Pot Sequential Carbene Transfer Reaction from Thiols with α-Diazo Carbonyl Compounds.

We present a one-pot reaction that offers an efficient approach to synthesizing tetrasubstituted vinyl sulfides with high stereoselectivity. This method involves the sequential Wolff rearrangement, ylide formation, and [1,4]-aryl transfer by utilizing aryl and alkyl thiols and α-diazo carbonyl compounds as substrates. Notably, this reaction features commercially available materials, straightforward operation, atom economy, and broad substrate scope. Moreover, the primary photophysical properties (aggregation-induced emission effect) of the products were also investigated, which might be useful in functional materials via structural modification.

Rheumatoid meningitis in the absence of rheumatoid arthritis: 2 cases.

Rheumatoid meningitis (RM) is a rare extra-articular manifestation of rheumatoid arthritis (RA) that has been increasingly recognized by neurologists. However, the diversity of its clinical manifestations makes its diagnosis difficult. RM does not have a unified diagnostic standard, and its link with RA needs to be studied further. Here we report two cases of RM without a history of RA. The first patient, an 80-year-old woman, presented with sudden unilateral limb weakness, with brain MR showing abnormal signals in the leptomeningeal of the right frontal parietal. Subarachnoid hemorrhage was excluded after imaging examination, and infectious meningitis was ruled out after cerebrospinal fluid (CSF) examination. The patient was diagnosed as having RM, she had increased levels of CCP and AKA, the markers of RA, but no history of the disease or other clinical manifestations of it. Another case, a 65-year-old man, was hospitalized with Bell's palsy. We found that he had intracranial imaging changes highly consistent with those characteristic of RM during his routine examination. Except for the left peripheral facial palsy, the patient had no other neurological signs or symptoms and no RA history. After a careful physical examination, we found no joint or other manifestations or serological abnormalities consistent with RA (RF, CCP, AKA, etc.). However, after excluding infection meningitis and considering the patient's unique imaging results, we diagnosed him as having RM. We report these two cases as references for clinical diagnosis and treatment of RM, providing a discussion of our rationale.

Bottle Nanomotors Amplify Tumor Oxidative Stress for Enhanced Calcium Overload/Chemodynamic Therapy.

Developing multifunctional, stimuli-responsive nanomedicine is intriguing because it has the potential to effectively treat cancer. Yet, poor tumor penetration of nanodrugs results in limited antitumor efficacy. Herein, an oxygen-driven silicon-based nanomotor (Si-motor) loaded with MnO and CaO2 nanoparticles is developed, which can move in tumor microenvironment (TME) by the cascade reaction of CaO2 and MnO. Under acidic TME, CaO2 reacts with acid to release Ca2+ to induce mitochondrial damage and simultaneously produces O2 and H2O2, when the loaded MnO exerts Fenton-like activity to produce ·OH and O2 based on the produced H2O2. The generated O2 drives Si-motor forward, thus endowing active delivery capability of the formed motors in TME. Meanwhile, MnO with glutathione (GSH) depletion ability further prevents reactive oxygen species (ROS) from being destroyed. Such TME actuated Si-motor with enhanced cellular uptake and deep penetration provides amplification of synergistic oxidative stresscaused by intracellular Ca2 + overloading, GSH depletion induced by Mn2+, and Mn2+ mediated chemodynamic treatment (CDT), leading to excellent tumor cell death. The created nanomotor may offer an effective platform for active synergistic cancer treatment.

In situ forest with lycopsid trees bearing lobed rhizomorphs from the Upper Devonian of Lincheng, China.

The earliest forests in the Devonian were reported from only four localities worldwide. The tree lycopsids, sometimes as the primary elements of Devonian forests, had evolved several types of rooting systems. In recent years, we found and excavated a Late Devonian (Famennian, 374-359 Ma) lycopsid forest from Zhejiang Province, China. The fossil forest occurs at seven locations of Lincheng Town of Changxing County and mainly consists of in situ small tree lycopsid (Heliodendron longshanense gen. et sp. nov.) stems usually connected to lobed cormose rhizomorphs. The four short lobes of each rhizomorph often branch once and bear roots arranged radially. Allometry is observed between the trunk diameter of Heliodendron and the length of its rhizomorphic lobes, indicating that the trunk develops later than the rhizomorph in tree lycopsid plants. The Devonian witnessed the transformation from clastic nonlycopsid dominated forests to Carboniferous swampy forests dominated by giant lycopsid trees. These trees form a multigenerational community, as shown by the in situ preserved stems at various levels within the same area due to frequent sedimentation events.

De Novo Synthesis of α-Ketoamides via Pd/TBD Synergistic Catalysis.

Precisely controlling the product selectivity of a reaction is an important objective in organic synthesis. α-Ketoamides are vital intermediates in chemical transformations and privileged motifs in numerous drugs, natural products, and biologically active molecules. The selective synthesis of α-ketoamides from feedstock chemicals in a safe and operationally simple manner under mild conditions is a long-standing catalysis challenge. Herein, an unprecedented TBD-switched Pd-catalyzed double isocyanide insertion reaction for assembling ketoamides in aqueous DMSO from (hetero)aryl halides and pseudohalides under mild conditions is reported. The effectiveness and utility of this protocol are demonstrated by its diverse substrate scope (93 examples), the ability to late-stage modify pharmaceuticals, scalability to large-scale synthesis, and the synthesis of pharmaceutically active molecules. Mechanistic studies indicate that TBD is a key ligand that modulates the Pd-catalyzed double isocyanide insertion process, thereby selectively providing the desired α-ketoamides in a unique manner. In addition, the imidoylpalladium(II) complex and α-ketoimine amide are successfully isolated and determined by X-ray analysis, confirming that they are probable intermediates in the catalytic pathway.

Study on the Prediction of Liver Injury in Acute Pancreatitis Patients by Radiomic Model Based on Contrast-Enhanced Computed Tomography.

OBJECTIVES: to predict liver injury in acute pancreatitis (AP) patients by establishing a radiomics model based on contrast-enhanced computed tomography (CECT). METHODS: a total of 1223 radiomic features were extracted from late arterial-phase pancreatic CECT images of 209 AP patients (146 in the training cohort and 63 in the test cohort), and the optimal radiomic features retained after dimensionality reduction by least absolute shrinkage and selection operator (LASSO) were used to construct a radiomic model through logistic regression analysis. In addition, clinical features were collected to develop a clinical model, and a joint model was established by combining the best radiomic features and clinical features to evaluate the practicality and application value of the radiomic models, clinical model and combined model. RESULTS: four potential features were selected from the pancreatic parenchyma to construct the radiomic model, and the area under the receiver operating characteristic curve (AUC) of the radiomic model was significantly greater than that of the clinical model for both the training cohort (0.993 vs. 0.653, p = 0.000) and test cohort (0.910 vs. 0.574, p = 0.000). The joint model had a greater AUC than the radiomics model for both the training cohort (0.997 vs. 0.993, p = 0.357) and test cohort (0.925 vs. 0.910, p = 0.302). CONCLUSIONS: the radiomic model based on CECT has good performance in predicting liver injury in AP patients and can guide clinical decision-making and improve the prognosis of patients with AP.

Uprighting and protraction of two unilateral mandibular molars using a cantilever arm through a sophisticated biomechanical system with clear aligner: A case report.

Although clear aligner can be efficient and predictable in some tooth movements, mandibular molar protraction is the least predictable owing to biological and biomechanical limitations. This case report presents a 20-year-old female with poor prognosed left mandibular first molar (#36), deviated dental midline and mild crowding. After extraction of #36, clear aligners and an Albert cantilever were used for treatment. Through carefully designed dual mechanical system, we achieved uprighting and protraction of #37 within 27 months, with crown and root movements of 9.9mm and 12.1mm, respectively. The predictability of the crown and root movement was 107.6% and 84.6%. Coincident dental and facial midline, class I molar and canine relationship and good root parallelism were also achieved. Large-distance mandibular molar protraction can be achieved effectively with a combination of Albert cantilever arm and clear aligner.

Predicting bowel function after diverting stoma closure in patients with rectal cancer.

Background: Patients with rectal cancer undergoing laparoscopic anterior resection and diverting stomas often suffer from bowel dysfunction after stoma closure, impairing their quality of life. This study aims to develop a machine learning tool to predict bowel function after diverting stoma closure. Methods: Clinicopathological data and post-operative follow-up information from patients with mid-low rectal cancer after diverting stoma closure were collected and analyzed. Patients were randomly divided into training and test sets in a 7:3 ratio. A machine learning model was developed in the training set to predict major low anterior resection syndrome (LARS) and evaluated in the test set. Decision curve analysis (DCA) was used to assess clinical utility. Results: The study included 396 eligible patients who underwent laparoscopic anterior resection and diverting stoma in Tongji Hospital affiliated with Huazhong University of Science and Technology from 1 January 2012 to 31 December 2020. The interval between stoma creation and closure, neoadjuvant therapy, and body mass index were identified as the three most crucial characteristics associated with patients experiencing major LARS in our cohort. The machine learning model achieved an area under the receiver operating characteristic curve (AUC) of 0.78 [95% confidence interval (CI): 0.74-0.83] in the training set (n=277) and 0.74 (95% CI: 0.70-0.79) in the test set (n=119), and area under the precision-recall curve (AUPRC) of 0.73 and 0.69, respectively, with sensitivity of 0.67 and specificity of 0.66 for the test set. DCA confirmed clinical applicability. Conclusions: This study developed a machine learning model to predict major LARS in rectal cancer patients after diverting stoma closure, aiding their decision-making and counseling.

Preparation of environmentally responsive PDA&DOX@LAC live drug carrier for synergistic tumor therapy.

The development of intelligent, environmentally responsive and biocompatible photothermal system holds significant importance for the photothermal combined therapy of tumors. In this study, inspired by Lactobacillus (LAC), we prepared a biomimetic nanoplatform PDA&DOX@LAC for tumor photothermal-chemotherapy by integrating the chemotherapeutic drug doxorubicin (DOX) with dopamine through oxidative polymerization to form polydopamine (PDA) on the surface of LAC. The PDA&DOX@LAC nanoplatform not only achieves precise and controlled release of DOX based on the slightly acidic microenvironment of tumor tissues, but also exhibits enzyme-like properties to alleviate tumor hypoxia. Under near-infrared light irradiation, it effectively induces photothermal ablation of tumor cells, enhances cellular uptake of DOX with increasing temperature, and thus efficiently inhibits tumor cell growth. Moreover, it is further confirmed in vivo experiments that photothermal therapy combined with PDA&DOX@LAC induces tumor cells apoptosis, releases tumor-associated antigens, which is engulfed by dendritic cells to activate cytotoxic T lymphocytes, thereby effectively suppressing tumor growth and prolonging the survival period of 4T1 tumor-bearing mice. Therefore, the PDA&DOX@LAC nanoplatform holds immense potential in precise tumor targeting as well as photothermal combined therapy and provides valuable insights and theoretical foundations for the development of novel tumor treatment strategies based on endogenous substances within the body.

Highly Efficient Phosphazene-Derivative-Based Flame Retardant with Comprehensive and Enhanced Fire Safety and Mechanical Performance for Polycarbonate.

Polycarbonate (PC) as a widely used engineering plastic that shows disadvantages of flammability and large smoke production during combustion. Although many flame-retardant PCs have been developed, most of them show enhanced flame retardancy but poor smoke suppression or worsened mechanical performance. In this work, a novel nitrogen-phosphorus-sulfur synergistic flame retardant (Pc-FR) was synthesized and incorporated into PC with polytetrafluoroethylene (PTFE). The extremely low content of PC-FR (0.1-0.5 wt%) contributes significantly to the flame retardancy, smoke suppression and mechanical performance of PC. PC/0.3 wt% Pc-FR/0.3 wt% PTFE (PC-P0.3) shows the UL-94 V-0 and LOI of 33.5%. The PHRR, THR, PSPR, PCO and TCO of PC-P0.3 decreased by 39.44%, 14.38%, 17.45%, 54.75% and 30.61%, respectively. The impact strength and storage modulus of PC-P0.1 increased by 7.7 kJ/m2 and 26 MPa, respectively. The pyrolysis mechanism of PC-P0.3 is also revealed. The pyrolysis mechanism of PC-P0.3 is stochastic nucleation and subsequent growth and satisfies the Aevrami-Erofeev equation. The reaction order of PC-P0.3 is 1/2. The activation energy of PC-P0.3 is larger than PC-0, which proves that the Pc-FR can suppress the pyrolysis of the PC. This work offers a direction on how to design high-performance PC.

Deep learning for intracranial aneurysm segmentation using CT angiography.

OBJECTIVE: This study aimed to employ a two-stage deep learning method to accurately detect small aneurysms (4-10 mm in size) in computed tomography angiography (CTA) images. Methods: This study included 956 patients from 6 hospitals and a public dataset obtained with 6 CT scanners from different manufacturers. The proposed method consists of two components: a lightweight and fast head region selection (HRS) algorithm and an adaptive 3D nnU-Net network, which is used as the main architecture for segmenting aneurysms. Segments generated by the deep neural network were compared with expert-generated manual segmentation results and assessed using Dice scores. Results: The area under the curve (AUC) exceeded 79% across all datasets. In particular, the precision and AUC reached 85.2% and 87.6%, respectively, on certain datasets. The experimental results demonstrated the promising performance of this approach, which reduced the inference time by more than 50% compared to direct inference without HRS. Conclusion: Compared with a model without HRS, the deep learning approach we developed can accurately segment aneurysms by automatically localizing brain regions and can accelerate aneurysm inference by more than 50%. Keywords: Deep Learning, CT Angiography, Aneurisms.

Consumption of dietary turmeric promotes fat browning and thermogenesis in association with gut microbiota regulation in high-fat diet-fed mice.

This study was designed to verify the anti-obesity effect of dietary turmeric powder (TP) as a traditional cooking spice and its underlying mechanism. The HFD-fed C57BL/6J mice were supplemented with or without TP (8%) for 12 weeks. The results indicated that the glucolipid metabolism disorder of high-fat diet (HFD)-fed mice was significantly ameliorated through the supplementation of TP. The consumption of TP also induced beige-fat development and brown adipose tissue (BAT)-derived nonshivering thermogenesis in HFD-fed obese mice. 16S rDNA-based microbiota or targeted metabolomics analysis indicated that TP ameliorated the intestinal microbiota dysbiosis and microbial metabolism abnormality caused by HFD, reflected by dramatically increasing the relative abundance of Muribaculaceae, Candidatus_Saccharimonas, and Bifidobacterium and production of short-chain fatty acids (SCFAs) and succinate. Interestingly, TP-induced BAT thermogenesis and iWAT browning were highly correlated with the reconstruction of the gut microbiome and formation of SCFAs and succinate. Collectively, these findings manifest beneficial actions of TP on the promotion of adipose browning and thermogenesis in association with gut microbiota reconstruction, and our findings may provide a promising way for preventing obesity.

Diagnostic accuracy of deep learning using speech samples in depression: a systematic review and meta-analysis.

OBJECTIVE: This study aims to conduct a systematic review and meta-analysis of the diagnostic accuracy of deep learning (DL) using speech samples in depression. MATERIALS AND METHODS: This review included studies reporting diagnostic results of DL algorithms in depression using speech data, published from inception to January 31, 2024, on PubMed, Medline, Embase, PsycINFO, Scopus, IEEE, and Web of Science databases. Pooled accuracy, sensitivity, and specificity were obtained by random-effect models. The diagnostic Precision Study Quality Assessment Tool (QUADAS-2) was used to assess the risk of bias. RESULTS: A total of 25 studies met the inclusion criteria and 8 of them were used in the meta-analysis. The pooled estimates of accuracy, specificity, and sensitivity for depression detection models were 0.87 (95% CI, 0.81-0.93), 0.85 (95% CI, 0.78-0.91), and 0.82 (95% CI, 0.71-0.94), respectively. When stratified by model structure, the highest pooled diagnostic accuracy was 0.89 (95% CI, 0.81-0.97) in the handcrafted group. DISCUSSION: To our knowledge, our study is the first meta-analysis on the diagnostic performance of DL for depression detection from speech samples. All studies included in the meta-analysis used convolutional neural network (CNN) models, posing problems in deciphering the performance of other DL algorithms. The handcrafted model performed better than the end-to-end model in speech depression detection. CONCLUSIONS: The application of DL in speech provided a useful tool for depression detection. CNN models with handcrafted acoustic features could help to improve the diagnostic performance. PROTOCOL REGISTRATION: The study protocol was registered on PROSPERO (CRD42023423603).

Comparison of vonoprazan bismuth-containing triple therapy with quadruple therapy in Helicobacter pylori-infected treatment-naive patients: a prospective multicenter randomized controlled trial.

BACKGROUND AND AIM: Helicobacter pylori infection is linked to various gastrointestinal conditions, such as chronic active gastritis, peptic ulcers, and gastric cancer. Traditional treatment options encounter difficulties due to antibiotic resistance and adverse effects. Therefore, the aim of this study was to explore the effectiveness of a new treatment plan that combines vonoprazan (VPZ), amoxicillin, and bismuth for the eradication of H. pylori. METHODS: A total of 600 patients infected with H. pylori were recruited for this multicenter randomized controlled trial. Patients treated for H. pylori elimination were randomly assigned at a 1:1 ratio to receive 14 days of vonoprazan-based triple therapy (vonoprazan + amoxicillin + bismuth, group A) or standard quadruple therapy (esomeprazole + clarithromycin + amoxicillin + bismuth, group B). Compliance and adverse effects were tracked through daily medication and side effect records. All patients underwent a 13C/14C-urea breath test 4 weeks after treatment completion. RESULTS: Intention-to-treat (ITT) and per-protocol (PP) analyses revealed no substantial differences in H. pylori eradication rates between groups A and B (ITT: 83.7% vs 83.2%; PP: 90.9% vs 89.7%). However, significant differences were observed in the assessment of side effects (13.7% vs 28.6%, P < 0.001). Specifically, group A had significantly fewer "bitter mouths" than group B did (3.7% vs 16.2%, P < 0.001). CONCLUSION: Triple therapy comprising vonoprazan (20 mg), amoxicillin (750 mg), and bismuth potassium citrate (220 mg) achieved a PP eradication rate ≥90%, paralleling standard quadruple therapy, and had fewer adverse events and lower costs (¥306.8 vs ¥645.8) for treatment-naive patients.

Genetic evidence for causal association between migraine and dementia: a mendelian randomization study.

BACKGROUND: There is an association between migraine and dementia, however, their causal relationship remains unclear. This study employed bidirectional two-sample Mendelian randomization (MR) to investigate the potential causal relationship between migraine and dementia and its subtypes: Alzheimer's disease (AD), vascular dementia (VaD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB). METHODS: Summary-level statistics data were obtained from publicly available genome-wide association studies (GWAS) for both migraine and five types of dementia. Single nucleotide polymorphisms (SNPs) associated with migraine and each dementia subtype were selected. MR analysis was conducted using inverse variance weighting (IVW) and weighted median (WM) methods. Sensitivity analyses included Cochran's Q test, MR pleiotropy residual sum and outlier (MR-PRESSO) analysis, the intercept of MR-Egger, and leave-one-out analysis. RESULTS: Migraine showed a significant causal relationship with AD and VaD, whereas no causal relationship was observed with all-cause dementia, FTD, or DLB. Migraine may be a potential risk factor for AD (odds ratio [OR]: 1.09; 95% confidence interval [CI]: 0.02-0.14; P = 0.007), while VaD may be a potential risk factor for migraine (OR: 1.04; 95% CI: 0.02-0.06; P = 7.760E-5). Sensitivity analyses demonstrated the robustness of our findings. CONCLUSION: Our study suggest that migraine may have potential causal relationships with AD and VaD. Migraine may be a risk factor for AD, and VaD may be a risk factor for migraine. Our study contributes to unraveling the comprehensive genetic associations between migraine and various types of dementia, and our findings will enhance the academic understanding of the comorbidity between migraine and dementia.