Ophthalmic Artery Morphology and Hemodynamics Associated with White Matter Hyperintensity.

Purpose: To investigate morphological and hemodynamic characteristics of the ophthalmic artery (OA) in patients with white matter hyperintensity (WMH), and the association of the presence and severity of WMH with OA characteristics. Methods: This cross-sectional study included 44 eyes of 25 patients with WMH and 38 eyes of 19 controls. The Fazekas scale was adopted as criteria for evaluating the severity of white matter hyperintensities. The morphological characteristics of the OA were measured on the basis of three-dimensional reconstruction. The hemodynamic parameters of the OA were calculated using computational fluid dynamics simulations. Results: Compared with the control group, the diameter (16.0±0.27 mm vs. 1.71±0.18 mm, P=0.029), median blood flow velocity (0.12 m/s vs. 0.22 m/s, P<0.001), mass flow ratio (2.16% vs. 3.94%, P=0.012) and wall shear stress (2.65 Pa vs. 9.31 Pa, P<0.001) of the OA in patients with WMH were significantly decreased. After adjusting for confounding factors, the diameter, blood flow velocity, wall shear stress, and mass flow ratio of the OA were significantly associated with the presence of WMH. Male sex and high low-density protein level were associated with moderate-to-severe total WMH, and smoking was associated with the moderate-to-severe periventricular WMH. Conclusions: The diameter, blood flow velocity, mass flow ratio, and wall shear stress of the OA were independently associated with the presence of WMH. Atherosclerosis might be involved in the common mechanism of the occurrence of WMH and the OA changes.

Genetic constraint at single amino acid resolution in protein domains improves missense variant prioritisation and gene discovery.

BACKGROUND: One of the major hurdles in clinical genetics is interpreting the clinical consequences associated with germline missense variants in humans. Recent significant advances have leveraged natural variation observed in large-scale human populations to uncover genes or genomic regions that show a depletion of natural variation, indicative of selection pressure. We refer to this as "genetic constraint". Although existing genetic constraint metrics have been demonstrated to be successful in prioritising genes or genomic regions associated with diseases, their spatial resolution is limited in distinguishing pathogenic variants from benign variants within genes. METHODS: We aim to identify missense variants that are significantly depleted in the general human population. Given the size of currently available human populations with exome or genome sequencing data, it is not possible to directly detect depletion of individual missense variants, since the average expected number of observations of a variant at most positions is less than one. We instead focus on protein domains, grouping homologous variants with similar functional impacts to examine the depletion of natural variations within these comparable sets. To accomplish this, we develop the Homologous Missense Constraint (HMC) score. We utilise the Genome Aggregation Database (gnomAD) 125 K exome sequencing data and evaluate genetic constraint at quasi amino-acid resolution by combining signals across protein homologues. RESULTS: We identify one million possible missense variants under strong negative selection within protein domains. Though our approach annotates only protein domains, it nonetheless allows us to assess 22% of the exome confidently. It precisely distinguishes pathogenic variants from benign variants for both early-onset and adult-onset disorders. It outperforms existing constraint metrics and pathogenicity meta-predictors in prioritising de novo mutations from probands with developmental disorders (DD). It is also methodologically independent of these, adding power to predict variant pathogenicity when used in combination. We demonstrate utility for gene discovery by identifying seven genes newly significantly associated with DD that could act through an altered-function mechanism. CONCLUSIONS: Grouping variants of comparable functional impacts is effective in evaluating their genetic constraint. HMC is a novel and accurate predictor of missense consequence for improved variant interpretation.

Visualization of Blood-Brain Barrier Disruption in Septic Mice with the New Method Based on in Vivo Imaging Technology.

BACKGROUND: Dynamic monitoring of the blood-brain barrier (BBB) functional status in septic mice can help to explore the pathological mechanisms. Therefore, we proposed a new method for monitoring BBB permeability and applied it to the detection of sepsis models. METHODS: The new method involves the construction of an optical cranial window and in vivo imaging. We performed dynamic monitoring of BBB permeability and cerebral blood flow (CBF) in cecal ligation puncture (CLP) and endotoxemia (lipopolysaccharide [LPS]) mice. RESULTS: The sensitivity and accuracy of this method were higher than those of Evans blue evaluation. The increase of BBB permeability in the group of CLP mice was relatively mild and correlated with overall survival, and the damage was irreversible. Contrarily, BBB damage in the LPS group was more acute and severe, unrelated to overall survival, but recoverable. The CBF decreased significantly in both model mouse groups 24 h after modeling, but only the CBF proportion decrease in the LPS group was significantly correlated with an increase in BBB permeability. Within 24 h after both models were established, the decrease in blood flow in the digestive organs occurred earlier than in the brain and kidneys, and the decrease in small intestine blood flow in the LPS group progressed faster. CONCLUSIONS: We have successfully demonstrated the feasibility of our novel method to detect BBB permeability in mice. Our results revealed a significant difference in the BBB permeability change trend between the CLP and LPS model mice when survival curves were consistent. Notably, the CLP-model mice demonstrated a closer resemblance to clinical patients. Our findings suggest that early-stage brain tissue hypoperfusion has a greater impact on BBB function damage in endotoxemia mice, which is related to the faster progression of blood flow redistribution.

[Identification of the binding proteins of organic acid metabolites by matrix thermal shift assay].

Organic acid metabolites exhibit acidic properties. These metabolites serve as intermediates in major carbon metabolic pathways and are involved in several biochemical pathways, including the tricarboxylic acid (TCA) cycle and glycolysis. They also regulate cellular activity and play crucial roles in epigenetics, tumorigenesis, and cellular signal transduction. Knowledge of the binding proteins of organic acid metabolites is crucial for understanding their biological functions. However, identifying the binding proteins of these metabolites has long been a challenging task owing to the transient and weak nature of their interactions. Moreover, traditional methods are unsuitable for the structural modification of the ligands of organic acid metabolites because these metabolites have simple and similar structures. Even minor structural modifications can significantly affect protein interactions. Thermal proteome profiling (TPP) provides a promising avenue for identifying binding proteins without the need for structural modifications. This approach has been successfully applied to the identification of the binding proteins of several metabolites. In this study, we investigated the binding proteins of two TCA cycle intermediates, i.e., succinate and fumarate, and lactate, an end-product of glycolysis, using the matrix thermal shift assay (mTSA) technique. This technique involves combining single-temperature (52 ℃) TPP and dose-response curve analysis to identify ligand-binding proteins with high levels of confidence and determine the binding affinity between ligands and proteins. To this end, HeLa cells were lysed, followed by protein desalting to remove endogenous metabolites from the cell lysates. The desalted cell lysates were treated with fumarate or succinate at final concentrations of 0.004, 0.04, 0.4, and 2 mmol/L in the experimental groups or 2 mmol/L sodium chloride in the control group. Considering that the cellular concentration of lactate can be as high as 2-30 mmol/L, we then applied lactate at final concentrations of 0.2, 1, 5, 10, and 25 mmol/L in the experimental groups or 25 mmol/L sodium chloride in the control group. Using high-sensitivity mass spectrometry coupled with data-independent acquisition (DIA) quantification, we quantified 5870, 5744, and 5816 proteins in succinate, fumarate, and lactate mTSA experiments, respectively. By setting stringent cut-off values (i.e., significance of changes in protein thermal stability (p-value)<0.001 and quality of the dose-response curve fitting (square of Pearson's correlation coefficient, R2)>0.95), multiple binding proteins for these organic acid metabolites from background proteins were confidently determined. Several known binding proteins were identified, notably fumarate hydratase (FH) as a binding protein for fumarate, and α-ketoglutarate-dependent dioxygenase (FTO) as a binding protein for both fumarate and succinate. Additionally, the affinity data for the interactions between these metabolites and their binding proteins were obtained, which closely matched those reported in the literature. Interestingly, ornithine aminotransferase (OAT), which is involved in amino acid biosynthesis, and 3-mercaptopyruvate sulfurtransferase (MPST), which acts as an antioxidant in cells, were identified as lactate-binding proteins. Subsequently, an orthogonal assay technique developed in our laboratory, the solvent-induced precipitation (SIP) technique, was used to validate the mTSA results. SIP identified OAT as the top target candidate, validating the mTSA-based finding that OAT is a novel lactate-binding protein. Although MPST was not identified as a lactate-binding protein by SIP, statistical analysis of MPST in the mTSA experiments with 10 or 25 mmol/L lactate revealed that MPST is a lactate-binding protein with a high level of confidence. Peptide-level empirical Bayes t-tests combined with Fisher's exact test also supported the conclusion that MPST is a lactate-binding protein. Lactate is structurally similar to pyruvate, the known binding protein of MPST. Therefore, assuming that lactate could potentially occupy the binding site of pyruvate on MPST. Overall, the novel binding proteins identified for lactate suggest their potential involvement in amino acid synthesis and redox balance regulation.

An End-to-End CRSwNP Prediction with Multichannel ResNet on Computed Tomography.

Chronic rhinosinusitis (CRS) is a global disease characterized by poor treatment outcomes and high recurrence rates, significantly affecting patients' quality of life. Due to its complex pathophysiology and diverse clinical presentations, CRS is categorized into various subtypes to facilitate more precise diagnosis, treatment, and prognosis prediction. Among these, CRS with nasal polyps (CRSwNP) is further divided into eosinophilic CRSwNP (eCRSwNP) and noneosinophilic CRSwNP (non-eCRSwNP). However, there is a lack of precise predictive diagnostic and treatment methods, making research into accurate diagnostic techniques for CRSwNP endotypes crucial for achieving precision medicine in CRSwNP. This paper proposes a method using multiangle sinus computed tomography (CT) images combined with artificial intelligence (AI) to predict CRSwNP endotypes, distinguishing between patients with eCRSwNP and non-eCRSwNP. The considered dataset comprises 22,265 CT images from 192 CRSwNP patients, including 13,203 images from non-eCRSwNP patients and 9,062 images from eCRSwNP patients. Test results from the network model demonstrate that multiangle images provide more useful information for the network, achieving an accuracy of 98.43%, precision of 98.1%, recall of 98.1%, specificity of 98.7%, and an AUC value of 0.984. Compared to the limited learning capacity of single-channel neural networks, our proposed multichannel feature adaptive fusion model captures multiscale spatial features, enhancing the model's focus on crucial sinus information within the CT images to maximize detection accuracy. This deep learning-based diagnostic model for CRSwNP endotypes offers excellent classification performance, providing a noninvasive method for accurately predicting CRSwNP endotypes before treatment and paving the way for precision medicine in the new era of CRSwNP.

Planning biosynthetic pathways of target molecules based on metabolic reaction prediction and AND-OR tree search.

Bioretrosynthesis problem is to predict synthetic routes using substrates for given natural products (NPs). However, the huge number of metabolic reactions leads to a combinatorial explosion of searching space, which is high time-consuming and costly. Here, we propose a framework called BioRetro to predict bioretrosynthesis pathways using a one-step bioretrosynthesis network, termed HybridMLP combined with AND-OR tree heuristic search. The HybridMLP predicts precursors that will produce the target NPs, while the AND-OR tree generates the iterative multi-step biosynthetic pathways. The one-step bioretrosynthesis prediction experiments are conducted on MetaNetX dataset by using HybridMLP, which achieves 46.5%, 74.6%, 81.6% in terms of the top-1, top-5, top-10 accuracies. The great performance demonstrates the effectiveness of HybridMLP in one-step bioretrosynthesis. Besides, the evaluation of two benchmark datasets reveals that BioRetro can significantly improve the speed and success rate in predicting biosynthesis pathways. In addition, the BioRetro is further shown to find the synthetic pathway of compounds, such as ginsenoside F1 with the same substrates as reported but different enzymes, which may be the novel potential enzyme to have better catalytic performance.

Erastin promotes random-pattern skin flaps survival by inducing mTORC1-TFEB mediated autophagy.

Random-pattern skin flaps are important method for skin reconstruction after defect; however, the distal end of flaps is not easily viable due to inadequate nutrient supply. Erastin is a well-established ferroptosis inducer, but our study found that low-dose of erastin (2 µM) may reduce nutrient deficiency induced cell death in human umbilical vein endothelial cells (HUVECs). RNA-seq analysis suggested that its role was related to autophagy regulation. Follow-up studies have shown that the use of autophagy inhibitors or the knockdown of TFEB in HUVECs can both reduce the anti-apoptotic effect of erastin in HUVECs. Mechanism study demonstrated that erastin can suppress mTORC1 and promote TFEB activity in HUVECs, suggesting that the effect of erastin on the survival of HUVECs under nutrient deprivation conditions is regulated by mTORC1/TFEB. Subsequently, we evaluated the effect of erastin on the survival of random-pattern skin flaps in mice in vivo. On the postoperative day 7, we observed a significant increase in flap survival area, blood perfusion, and microvascular density after erastin treatment; also, erastin treatment showed enhanced autophagy within the ischemic region. In summary, our study demonstrates that low-dose of erastin may suppress cell death in endothelial cells under nutrient deficiency condition, and its effects may relate to the mTORC1-TFEB medicated autophagy regulation, erastin treatment may be a potential therapy for random-pattern skin flaps.

Differences in Kondo Splitting of Surface Quantum Systems Induced by Two Distinct Magnetic Tips: A Joint Method of DFT and HEOM.

The interactions between a magnetic tip and local spin impurities initiate unconventional Kondo phenomena, such as asymmetric suppression or even splitting of the Kondo peak. However, a lack of realistic theoretical models and comprehensive explanations for this phenomenon persists due to the complexity of the interactions. This research employs a joint method of density functional theory (DFT) and hierarchical equation of motion (HEOM) to simulate and contrast the modulation of the spin state and Kondo behavior in the Fe/Cu(100) system with two distinct magnetic tips. A cobalt tip, possessing a larger magnetic moment, incites greater atomic displacement of the iron atom, more notable alterations in electronic structure, and enhanced charge transfer with the environment compared with the control process utilizing a nickel tip. Furthermore, the Kondo resonance undergoes asymmetric splitting as a result of the ferromagnetic correlation between the iron atom and the magnetic tip. The Co tip's higher spin polarization results in a wider spacing between the splitting peaks. This investigation underscores the precision of the DFT + HEOM approach in predicting complex quantum phenomena and explaining the underlying physical principles. This provides valuable theoretical support for developing more sophisticated quantum regulation experiments.

The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers.

BACKGROUND: This study investigated whether the Combat compensation method can remove the variability of radiomic features extracted from different scanners, while also examining its impact on the subsequent predictive performance of machine learning models. MATERIALS AND METHODS: 135 CT images of Credence Cartridge Radiomic phantoms were collected and screened from three scanners manufactured by Siemens, Philips, and GE. 100 radiomic features were extracted and 20 radiomic features were screened according to the Lasso regression method. The radiomic features extracted from the rubber and resin-filled regions in the cartridges were labeled into different categories for evaluating the performance of the machine learning model. Radiomics features were divided into three groups based on the different scanner manufacturers. The radiomic features were randomly divided into training and test sets with a ratio of 8:2. Five machine learning models (lasso, logistic regression, random forest, support vector machine, neural network) were employed to evaluate the impact of Combat on radiomic features. The variability among radiomic features were assessed using analysis of variance (ANOVA) and principal component analysis (PCA). Accuracy, precision, recall, and area under the receiver curve (AUC) were used as evaluation metrics for model classification. RESULTS: The principal component and ANOVA analysis results show that the variability of different scanner manufacturers in radiomic features was removed (P˃0.05). After harmonization with the Combat algorithm, the distributions of radiomic features were aligned in terms of location and scale. The performance of machine learning models for classification improved, with the Random Forest model showing the most significant enhancement. The AUC value increased from 0.88 to 0.92. CONCLUSIONS: The Combat algorithm has reduced variability in radiomic features from different scanners. In the phantom CT dataset, it appears that the machine learning model's classification performance may have improved after Combat harmonization. However, further investigation and validation are required to fully comprehend Combat's impact on radiomic features in medical imaging.

The Mutual Regulatory Role of Ferroptosis and Immunotherapy in Anti-tumor Therapy.

Ferroptosis is a form of cell death that is triggered by the presence of ferrous ions and is characterized by lipid peroxidation induced by these ions. The mechanism exhibits distinct morphological characteristics compared to apoptosis, autophagy, and necrosis. A notable aspect of ferroptosis is its ability to inhibit uncontrolled tumor replication and immortalization, especially in malignant, drug-resistant, and metastatic tumors. Additionally, immunotherapy, a novel therapeutic approach for tumors, has been found to have a reciprocal regulatory relationship with ferroptosis in the context of anti-tumor therapy. A comprehensive analysis of ferroptosis and immunotherapy in tumor therapy is presented in this paper, highlighting the potential for mutual adjuvant effects. Specifically, we discuss the mechanisms underlying ferroptosis and immunotherapy, emphasizing their ability to improve the tumor immune microenvironment and enhance immunotherapeutic effects. Furthermore, we investigate how immunotherapeutic factors may increase the sensitivity of tumor cells to ferroptosis. We aim to provide a prospective view of the promising value of combined ferroptosis and immunotherapy in anticancer therapy by elucidating the mutual regulatory network between each.

20-Deoxyingenol Activates Mitophagy Through TFEB and Promotes Functional Recovery After Spinal Cord Injury.

Spinal cord injury (SCI) can lead to permanent paralysis and various motor, sensory and autonomic nervous system dysfunction. The complex pathophysiological processes limit the effectiveness of many clinical treatments. Mitochondria has been reported to play a key role in the pathogenesis of SCI; while mitophagy is a protective mechanism against mitochondrial dysfunction. However, there is recently little drugs that may targeted activate mitophagy to treat SCI. In this study, we evaluated the role of 20-Deoxyingenol (20-DOI) in SCI and explored its potential mechanisms. We used a SCI rat model and evaluated the functional outcomes after the injury. Western blotting and immunofluorescence techniques were used to analyze the levels of mitophagy, apoptosis, and TFEB-related signaling pathways. Our research results show that 20-DOI significantly improves the apoptosis of neural cells after TBHP stimulation and functional recovery after spinal cord injury. In addition, mitophagy, TFEB levels, and apoptosis are related to the mechanism of 20-DOI treatment for spinal cord injury. Specifically, our research results indicate that 20-DOI restored the autophagic flux after injury, thereby inducing mitophagy, eliminating the accumulation of Cyto C, and inhibiting apoptosis. Further mechanism research suggests that 20-DOI may regulate mitophagy by promoting TFEB nuclear translocation. These results indicate that 20-DOI can significantly promote recovery after spinal cord injury, which may be a promising treatment method for spinal cord injury.

SENP1 exacerbates acute myeloid leukemia by enhancing BECN1-dependent autophagy through PTBP1 deSUMOylation.

Genetic association between SUMO-specific protease 1 (SENP1) and acute myeloid leukemia (AML) has been validated. However, the mechanism by which SENP1 affects AML proliferation, apoptosis, and autophagy remains unknown. The levels of SENP1 and polypyrimidine tract-binding protein 1 (PTBP1) were measured in AML patients, AML cell lines, and xenograft tissues. The effects of SENP1 on AML proliferation, apoptosis, and BECN1-dependent autophagy were assessed through in vitro and in vivo loss- or gain-of-function experiments. SUMOylation analysis using immunoprecipitation (IP), RNA pull-down, RIP, and RNA stability assays were used to explore the molecular mechanism of SENP1 in AML development. The SENP1 level was elevated in AML samples. Silencing SENP1 impeded the development of AML, as evidenced by the inhibition of proliferation and promotion of G1 phase arrest and apoptosis resulting from SENP1 depletion in AML cells. Moreover, silencing of SENP1 restrained BECN1-depentent autophagy in AML cells. In addition, the overexpression of BECN1 or PTBP1 partially neutralized the effect of SENP1 knockdown on AML cell behavior. Mechanistically, SENP1 mediated PTBP1 deSUMOylation, which then directly interacted with BECN1 mRNA and enhanced its stability. In vivo experiments further confirmed the repressive effects of SENP1 suppression on AML development. Collectively, the SENP1/PTBP1/BECN1 signaling axis has been identified as a significant therapeutic target for enhancing AML treatment.

Novel artemisinin derivative P31 inhibits VEGF-induced corneal neovascularization through AKT and ERK1/2 pathways.

Corneal neovascularization (CoNV)is a major cause of blindness in many ocular diseases. Substantial evidence indicates that vascular endothelial growth factor (VEGF) plays an important role in the pathogenesis of corneal neovascularization. Previous evidence showed that artemisinin may inhibit angiogenesis through down regulation of the VEGF receptors. We designed and synthesized artemisinin derivatives, and validated their inhibitory effect on neovascularization in cell and animal models, and explored the mechanisms by which they exert an inhibitory effect on CoNV. Among these derivatives, P31 demonstrated significant anti-angiogenic effects in vivo and in vitro. Besides, P31 inhibited VEGF-induced HUVECs angiogenesis and neovascularization in rabbit model via AKT and ERK pathways. Moreover, P31 alleviated angiogenic and inflammatory responses in suture rabbit cornea. In conclusion, as a novel artemisinin derivative, P31 attenuates corneal neovascularization and has a promising application in ocular diseases.

Screening of NSAIDs library identifies Tinoridine as a novel ferroptosis inhibitor for potential intervertebral disc degeneration therapy.

Low back pain (LBP) may profoundly impact the quality of life across the globe, and intervertebral disc degeneration (IVDD) is the major cause of LBP; however, targeted pharmaceutical interventions for IVDD are still lacking. Ferroptosis is a novel form of iron-dependent programmed cell death. Studies have showed that ferroptosis may closely associate with IVDD; thus, targeting ferroptosis may have great potential for IVDD therapy. Non-steroidal anti-inflammatory drugs (NSAIDs) are the first-line medications for LBP, while nuclear factor-erythroid 2-related factor-2 (Nrf2) is a key inhibitory protein for ferroptosis. In the current study, we conducted a molecular docking screening between NSAIDs library and Nrf2 protein. Tinoridine was shown to have a high binding affinity to Nrf2. The in vitro study in nucleus pulposus (NP) cells showed that Tinoridine may promote the expression and activity of Nrf2, it may also rescue RSL3-induced ferroptosis in NP cells. Knockdown of Nrf2 reverses the protective effect of Tinoridine on RSL3-induced ferroptosis in NP cells, suggesting that the inhibitory effect of Tinoridine on ferroptosis is through Nrf2. In vivo study demonstrated that Tinoridine may attenuate the progression of IVDD in rats. As NSAIDs are already clinically used for LBP therapy, the current study supports Tinoridine's application from the view of ferroptosis inhibition.

Prognostic impact and immunotherapeutic implications of NETosis-related prognostic model in clear cell renal cell carcinoma.

BACKGROUND: The ramifications of necroptosis on the prognostication of clear cell renal cell carcinoma (ccRCC) remain inadequately expounded. METHODS: A prognostic model delineating the facets of necroptosis in ccRCC was constructed, employing a compendium of algorithms. External validation was effectuated using the E-MTAB-1980 dataset. The exploration of immune infiltration scores was undertaken through the exploitation of multiple algorithms. Single-cell RNA sequencing data were procured from the GSE171306 dataset. Real-time quantitative PCR (RT-qPCR) was engaged to scrutinize the differential expression of SLC25A37 across cancer and paracancer tissues, as well as diverse cell lines. Assessments of proliferative and metastatic alterations in 769-P and 786-O cells were accomplished through Cell Counting Kit-8 (CCK8) and wound healing assays. RESULTS: The necroptosis-related signature (NRS) emerges as a discerning metric, delineating patients' immune attributes, tumor mutation burden, immunotherapy response, and drug susceptibility. Single-cell RNA sequencing analysis unveils the marked enrichment of SLC25A37 in tumor cells. Concurrently, RT-qPCR discloses the overexpression of SLC25A37 in both ccRCC tissues and cell lines. SLC25A37 knockdown mitigates the proliferative and metastatic propensities of 769-P and 786-O cells, as evidenced by CCK8 and wound healing assays. CONCLUSION: The NRS assumes a pivotal role in ascertaining the prognosis, tumor mutation burden, immunotherapy response, drug susceptibility, and immune cell infiltration features of ccRCC patients. SLC25A37 emerges as a putative player in immunosuppressive microenvironments, thereby providing a prospective avenue for the design of innovative immunotherapeutic targets for ccRCC.

A ResNet mini architecture for brain age prediction.

The brain presents age-related structural and functional changes in the human life, with different extends between subjects and groups. Brain age prediction can be used to evaluate the development and aging of human brain, as well as providing valuable information for neurodevelopment and disease diagnosis. Many contributions have been made for this purpose, resorting to different machine learning methods. To solve this task and reduce memory resource consumption, we develop a mini architecture of only 10 layers by modifying the deep residual neural network (ResNet), named ResNet mini architecture. To support the ResNet mini architecture in brain age prediction, the brain age dataset (OpenNeuro #ds000228) that consists of 155 study participants (three classes) and the Alzheimer MRI preprocessed dataset that consists of 6400 images (four classes) are employed. We compared the performance of the ResNet mini architecture with other popular networks using the two considered datasets. Experimental results show that the proposed architecture exhibits generality and robustness with high accuracy and less parameter number.

A multi-view fusion lightweight network for CRSwNPs prediction on CT images.

Accurate preoperative differentiation of the chronic rhinosinusitis (CRS) endotype between eosinophilic CRS (eCRS) and non-eosinophilic CRS (non-eCRS) is an important topic in predicting postoperative outcomes and administering personalized treatment. To this end, we have constructed a sinus CT dataset, which comprises CT scan data and pathological biopsy results from 192 patients of chronic rhinosinusitis with nasal polyps (CRSwNP), treated at the Second Affiliated Hospital of Shantou University Medical College between 2020 and 2022. To differentiate CRSwNP endotype on preoperative CT and improve efficiency at the same time, we developed a multi-view fusion model that contains a mini-architecture with each network of 10 layers by modifying the deep residual neural network. The proposed model is trained on a training set and evaluated on a test set. The multi-view deep learning fusion model achieved the area under the receiver-operating characteristics curve (AUC) of 0.991, accuracy of 0.965 and F1-Score of 0.970 in test set. We compared the performance of the mini-architecture with other lightweight networks on the same Sinus CT dataset. The experimental results demonstrate that the developed ResMini architecture contribute to competitive CRSwNP endotype identification modeling in terms of accuracy and parameter number.

Carbothermal synthesis of sulfurized nano zero-valent iron from sulfate-reducing bacteria biomass for mercury removal: The first application of biomass sulfur source.

The development of low-cost, highly efficient adsorbent materials is of significant importance for environmental remediation. In this study, a novel material, sulfurized nano zero-valent iron loaded biomass carbon (S-nZVI/BC), was successfully synthesized by a simple manufacturing process. The preparation of S-nZVI/BC does not require the use of expensive and hazardous chemicals. Instead, residual sludge, a solid waste product, is used as feedstock. The sludge is rich in Sulfate-Reducing Bacteria (SRB), which can provide carbon and sulfur sources for the synthesis of S-nZVI/BC. It was observed that S-nZVI particles formed in situ were dispersed within BC and covered by it. Additionally, S-nZVI/BC inherited the large specific surface area and porosity of BC. The adsorption capacity of S-nZVI/BC can reach 857.55 mg g-1 Hg (II) during the remediation of mercury-polluted water. This research offers new perspectives for developing composites in terms of the low cost and harmlessness of raw materials.

High-Performance Anode Material Based on Zinc Naphthalocyanine/Graphene Composite.

Transition metal oxides are a potential anode material owing to their high theoretical capacity. Nonetheless, their large volume changes and low electrical conductivities lead to poor cycling performance and rate capabilities. In this article, an effective strategy is proposed and developed for preparing a ZnO/N-doped graphene composite (ZnNc/GO-5). The key point of this strategy is to use zinc tetra tert-butyl-naphthalocyanine (ZnNc) as a codoped source of N atoms and zinc ions, and graphene oxide (GO) which is combined with ZnNc by π-π deposition as a carbon matrix. After calcination, ZnO microcrystals coated with N-doped graphene are obtained. The unique features of the composite and synergistic effect between N-doped reduced graphene oxide and ZnO microcrystals enable good electrochemical performance by the composites when used in lithium-ion batteries. As an anode material, the as-synthesized ZnNc/GO-5 composite delivers a high first capacity of 1942.9 mAh g-1 and excellent cyclic stability of 861.4 mAh g-1 after 150 cycles at 100 mA g-1. This strategy may offer a new method of designing the anode materials of lithium-ion batteries and promote the practical use of organic molecules in next-generation lithium-ion batteries.

All-in-one bio-derived poly(L-lactic acid)-based composite with fire-resistance and smoke-suppression performance.

The flammability of bio-derived poly(L-lactic acid) (PLA) greatly limits its application and eco-friendly multifunctional fire-fighting PLA-based composites are highly desired. In this work, a fully bio-based modified CS (C-CS) and commercially available eco-friendly ammonium polyphosphate (APP) were used as a synergistic flame retardant agent (C-CS/APP) to investigate its effects on fire-proofing performance and diverse properties of the PLA. The PLA/5%C-CS/5%APP composite exhibited excellent fire-resistant performance with anti-droplet, smoke-suppression and self-extinguishing property, and its limited oxygen index enhanced by 37 % (compared with neat PLA). This composite reached the highest V-0 fire safety rating, and its peak of heat release rate and total smoke production reduced by 26.5 % and 68.3 %, respectively. In addition, the char residue yield after the cone calorimeter test increased by 46 times in the composite, indicating an outstanding char-forming capacity. The condensed phase flame retardancy played a crucial role on the fire-fighting of this composite, that is, significantly enhanced char residue (as a physical barrier) blocked the heat exchange and O2 entry, and further suppressed the combustion reaction. Additionally, the PLA-based composite showed outstanding UV-absorption property, good anti-bacterial effect, and increased hydrophilicity and crystallizability.