An innovative short process for extracting Li and other metals from a lepidolite ore is demonstrated, realizing near-zero residue recovery. This process, which solves two key limitations of conventional processes (high acid utilization and Li/Al separation complexity), is based on sulfuric acid curing and efficiently extracts valuable elements (e.g., Li, K, Al, Rb, Cs, Si, Be, and Mn) from the ore. The optimal parameters, which yielded high leaching efficiencies for the target alkali metals were determined. To enhance selectivity, the feasibility of separating alkali metals from Al and Fe through intermediate product roasting was confirmed via thermodynamic calculations, and the optimal conditions were experimentally validated. Roasting selectively realized high leaching rates for the target products while considerably limiting those of Al and Fe, demonstrating exceptional separation efficiency. Integrated analysis offered mechanistic insights into the thermal-decomposition behaviors of intermediate products, revealing the decomposition of potassium aluminum sulfate (KAl(SO4)(2)) into potassium sulfate (K2SO4) and alumina (Al2O3) at 700 degrees C-800 degrees C to form a eutectic phase of potassium lithium sulfate (KLiSO4) with lithium sulfate(Li2SO4). Additionally, above 800 degrees C, the further decomposition of KAl(SO4)(2) produced additional Al2O3, enabling selective water leaching to concurrently separate the alkali metals from Al/Fe and concentrate the alkali-metal sulfates. A subsequent pH-gradient adjustment enriched the neutralization residues with Be and Mn, streamlining their recovery. The process also yielded high-value byproducts, such as construction-grade silica and smelting-grade alumina residues. Overall, compared with conventional acidification methods, the proposed process reduced acid/alkali consumption while maximizing resource recovery.

Al2(SO4)3 solution is a potential new generation of ionic rare earth (RE) mineral ammonia-free leaching agent. It has the advantages of increasing the mineral Zeta potential, reducing soil and water pollution, weakly acidic, easily desorbed, Al3+ can be recycled, and environmentally friendly. The efficient separation of Al from ion-adsorption type rare earth ore is a key industrial challenge. This study systematically investigated the occurrence states, selective extraction, and resource utilization of rare earth elements (REEs) and Al in fluoritekaolinite associated ion-adsorption type ore. The results indicate that REEs occur dominantly as exchangeable ions adsorbed on minerals. The RE desorption yield reached 95.4% under optimized conditions (0.03 mol/L Al2(SO4)3, liquid-to-solid ratio of 10:1, pH 3.2, 30 min, and 25 degrees C). Co-precipitation using NaHCO3 achieved precipitation yields of 99.7 and 99.2% of RE and Al, respectively. The dissolved Al is subsequently subjected to an integrated atmospheric alkali leaching-acid hydrolysis sequence, which achieved a 99.3% dissolution efficiency and yielded pseudo-boehmite that meets commercial specifications. The developed process establishes an efficient technological pathway for the synergistic recovery of both REEs and Al from complex associated ores.

Combined hepatocellular-cholangiocarcinoma (CHC) is a rare primary liver cancer with low incidence and poor prognosis. CHC can develop distant metastasis (DM) at an early stage, which severely shortens the survival time of patients. We aimed to use machine learning (ML) to construct a decision-making system to assess risk factors for the development of DM in CHC patients. 1180 CHC patients from the SEER database between 2000 and 2020 were collected and randomized into the train set and internal test group in a 7:3 ratio. Patients diagnosed with CHC at our hospital between 2011 and 2018 were also collected as the external validation set (N = 125). Patients with CHC were divided into a metastasis group and a non-metastasis group according to the occurrence of DM. Univariate and multivariate logistic regression analyses were conducted to evaluate the risk factors influencing DM of CHC patients in the training set. We screened the feature variables based on random forest (RF) and forward feature importance sequences. Then we incorporated them into six ML algorithms for constructing machine learning models and used 10-fold cross-validation for internal validation. Receiver operating characteristic (ROC) curves, precision-recall curve (PRC), calibration curve (CC), and confusion Matrix (CM) were used to evaluate the predictive ability of the models. We determined the importance ranking of risk factors for DM in CHC patients using Shapley additive explanations (SHAP) and further used SHAP to analyze the interpretability of the black-box model. Finally, we built a web risk calculator based on the optimal performance model to facilitate its clinical application. A total of six variables were selected for constructing the decision model. Extreme gradient boosting (XGB) obtained the most significant recognition ability, with ROCAUC = 0.863, accuracy of 0.802, sensitivity of 0.875, and PRAUC of 0.642 in the internal test set. 10-fold cross-validation results showed that the ROCAUC of XGB was 0.989, with a standard error of 0.019. The SHAP method revealed that node, surgery, age, grade, primary site, and race are the top 6 key variables that contribute to the occurrence of DM in CHC. In addition, the analysis of two typical cases proved the reliability of the model. The XGB model outperforms other machine learning methods in recognizing the occurrence of distant metastases in CHC patients, and has a high degree of utility and reliability to inform clinical treatment decisions for patients.

In industrial production, approximately half of the cesium present in the lepidolite concentrate remains unextracted and remains in the lepidolite lithium-extraction tailings, resulting in resource loss. Chlorination roasting was employed to effectively extract cesium from lithium-extraction tailings, followed by a successful separation of cesium using t-BAMBP extraction to improve the utilization efficiency of lepidolite mineral resources. The effects of auxiliary materials dosage, roasting temperature, and leaching liquid-to-solid ratio on the migration and transformation behavior of cesium during the roasting-leaching process are investigated, and the optimal process conditions are obtained, achieving a cesium extraction efficiency of 93.3% from the tailings. A systematic study is conducted on the parameters that influence the extraction and separation behavior of cesium, such as the extractant solvent system and oscillation time. The extraction scheme is simplified, and the washing strategy is optimized to some extent. Following two-stage countercurrent extraction, a cesium extraction efficiency of 98.3% is attained. Following eleven-stage cross-flow scrubbing, Rb reached a scrubbing efficiency of 99.3%, with only a 9.73% loss of Cs. Finally, calculations show that the purity of cesium in the stripping solution can reach 99% and the comprehensive recovery efficiency of cesium from the lithium-extraction tailings can reach 82.7%, effectively achieving the extraction and separation of cesium from tailings. The experimental results provide theoretical and experimental references for the effective extraction and successful separation of cesium from lepidolite minerals.

Background Intrahepatic cholangiocarcinoma (ICC) is a highly aggressive liver cancer with a poor prognosis and rapid metastatic potential. Although circular RNAs (circRNAs) have emerged as important regulators in cancer biology, their translational potential and mechanistic contributions to ICC metastasis remain largely unexplored. Methods CircRNA-seq was performed on paired primary and recurrent ICC tissues to identify the differentially expressed circRNAs. Mass spectrometry and functional assays were used to characterize the novel protein encoded by circPICALM.The molecular mechanisms and biological functions of circPICALM and its encoded proteins were evaluated using in vitro and in vivo models, respectively. Results CircPICALM is significantly upregulated in recurrent ICC tumors and is associated with poor patient prognosis. Its biogenesis and expression are regulated by N6-methyladenosine (m6A) modifications within the introns flanking the circulating exons, facilitated by the m6A reader protein YTHDC1. Additionally, the RNA-binding protein, DEAD-box helicase 3 (DDX3), promotes circPICALM accumulation. Importantly, circPICALM encodes a novel protein, circPICALM-219aa, that drives ICC metastasis. Mechanistically, circPICALM-219aa disrupted the inhibitory interaction between SOCS3 and STAT3 by directly binding to both proteins. This interference alleviates SOCS3-mediated suppression of JAK activity and enhances IL-6/JAK/STAT3 signaling. Silencing circPICALM-219aa expression significantly suppressed the activation of this signaling pathway and metastatic potential. Conclusions This study identified circPICALM-219aa as a novel oncoprotein translated from an m6A-modified circRNA, and a key driver of ICC metastasis. Our findings uncover a previously unrecognized mechanism of m6A-mediated circRNA translation in ICC and highlight circPICALM-219aa as a promising therapeutic target for improving patient outcomes.

Depression after a stroke is the most frequent and burdensome neuropsychiatric post-stroke complication. This study aimed to determine the relationship between post-stroke depression (PSD) and the levels of homocysteine (HCY) in patients with spontaneous intracerebral hemorrhage (SICH). We collected data from patients with hemorrhagic stroke (HS) admitted to the hospital and recorded their demographic and clinical characteristics. We also searched for information regarding HAM-D17 (Hamilton Depression) scores and HCY levels at 3 m, the use of antidepressant medications and folic acid during the follow-up period in the group of patients diagnosed with PSD and hyperhomocysteinemia (HHcy) in the acute phase. A total of 1,852 patients were included. 642 (34.7

Background and aimsMalnutrition is a well-recognized predictor of poor prognosis in malignancies. Recent studies suggest that the geriatric nutritional risk index (GNRI) is a more accurate determinant of prognosis in elderly patients than conventional body mass index (BMI). This study aimed to evaluate the GNRI and body composition parameters in elderly patients with intrahepatic cholangiocarcinoma (ICC) and assess their prognostic impact on long-term outcomes.MethodsA total of 157 elderly ICC patients (aged ≥65 years) who underwent radical resection between 2009 and 2018 were retrospectively analyzed. Skeletal muscle index (SMI), muscle attenuation (MA), visceral adipose tissue index (VATI), subcutaneous adipose tissue index (SATI), and visceral-to-subcutaneous fat ratio (VSR) were quantified using computed tomography. Prognostic analyses were conducted using the Kaplan–Meier method, with adjustments using inverse probability weighting. A nomogram based on multivariate Cox regression was constructed and internally validated, comparing its prognostic accuracy with the TNM staging system.ResultsAmong the body composition parameters, low SMI (sarcopenia, 56.1%), high VSR (visceral adiposity, 54.8%), and low MA (intramuscular fat deposition, 50.3%) were significantly associated with overall survival (OS) and recurrence-free survival (RFS) (all p < 0.05). Low GNRI was also a strong predictor of poor prognosis (p < 0.001). Multivariate analysis identified low GNRI (p = 0.009), sarcopenia (p = 0.020), visceral adiposity (p = 0.033), and intramuscular fat deposition (p = 0.036) as independent prognostic factors for OS and RFS. The nomogram, incorporating GNRI, SMI, VSR, MA, microvascular invasion (MVI), CA19-9 levels, and lymph node invasion, demonstrated superior prognostic performance compared to the TNM stage, with a C-index of 0.734 (OS) and 0.704 (RFS) and an AUC of 0.809 (OS) and 0.815 (RFS).ConclusionGNRI, sarcopenia, IMF deposition, and visceral adiposity independently predict mortality and tumor recurrence in elderly ICC patients. Body composition is a major determinant of prognosis in patients with ICC. Our nomogram based on body composition reveals superior prognostic efficacy over TNM stages.

To address the critical challenges in pyrometallurgical recycling processes—such as poor feedstock adaptability, high energy consumption during roasting conversion, and the low added value of rare earth products—this study systematically investigated the mechanism and process optimization of ammonium bifluoride (NH4HF2) roasting for the recovery of neodymium–iron–boron (NdFeB) waste. Thermodynamic analysis confirmed the feasibility of the conversion reaction between NH4HF2 and the rare earth components in NdFeB waste. Single-factor experiments were conducted to examine the effects of roasting temperature, reaction time, and NH4HF2 dosage on rare earth recovery. The optimal conditions were a roasting temperature of 600 °C, a reaction time of 120 min, and a NH4HF2 dosage of 75 wt%, achieving a rare earth recovery rate of 98.81%. Furthermore, the response surface methodology (RSM) was employed to establish a quantitative model correlating process parameters with recovery efficiency. Variance analysis demonstrated that the model was highly significant (F = 136.94, p < 0.0001), with excellent agreement between actual and predicted values (R2 = 0.9944). Factor contribution analysis revealed that NH4HF2 dosage had the most pronounced impact on rare earth fluorination, followed by roasting temperature and reaction time. Under optimized conditions, the purified rare earth fluoride obtained after acid leaching reached a purity of 99.43%, providing high-quality raw material for producing high-value-added rare earth products.

Pyrometallurgical recovery of rare-earth elements from NdFeB wastes is affected by the quality of the raw materials, and mixed rare-earth products add little value. Therefore, this study investigates NaBF4 fluoride roasting for the recovery of rare-earth elements and its underlying mechanisms. Reasonable roasting temperatures were determined based on thermodynamic calculations, and single-factor experiments were conducted. When roasted at 600 degrees C for 30 min with 65 % NaBF4, the fluorination rate of rare-earth elements reached 95.83 %. The composition of the clinker mesophase after roasting under different conditions was analyzed. At high temperatures, oxygen heteroatoms entered the crystal lattice of the rare-earth fluorides, which were subsequently removed during acid leaching, thereby reducing the recovery rate. A three-factor, three-level Box-Behnken test was conducted using the response surface methodology to analyze the effects of various factors and their interactions on the fluorination rate. Thus, an experimental basis for the recovery of rare-earth elements from NdFeB by fluoridation roasting was established. The roasting temperature had the greatest effect on the fluorination rate, followed by the roasting time and amount of NaBF4. The model predicted an optimal fluorination rate of 98.63 % when the material was roasted at 573 degrees C for 25 min with 59 % NaBF4. The clinker was acid-leached under optimal conditions (2.5 h at 80 degrees C with 9 M HCl and a liquid-solid ratio of 4 mL/g), and the Purity of fluorinated rare earths reached 99.39%. These results provide theoretical and experimental support for the application of pyrometallurgy in the recovery of rare-earth elements from NdFeB wastes.

Background: Combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer characterized by a low incidence but a poor prognosis. The purpose of the study was to develop a clinical prediction model utilizing non-invasive blood markers to effectively evaluate the prognosis of cHCC-CCA patients following hepatic resection. Methods: The retrospective analysis was conducted on 125 patients with cHCC-CCA who underwent hepatic resection between April 2013 and October 2022. All cHCC-CCA patients were randomly assigned to the training group (n = 63) and the validation group (n =62). A nomogram based on patient clinical factors was established using cox regression analysis. Receiver operating characteristic curves (ROCs) were used to assess the predictive performance of the model. Calibration and decision curves were employed to evaluate the model's prediction accuracy and goodness of fit. Results: Multivariate analysis revealed significant associations between lymphatic metastasis, microvascular invasion (MVI), gamma-glutamyl transpeptidase to albumin ratio (GAR), carcinoembryonic antigen (CEA), prothrombin time (PT), alpha-fetoprotein (AFP), hepatitis B virus (HBV), and overall survival. Based on these prognostic factors, a nomogram model was established and validated using the validation set. Calibration curves demonstrated good consistency in the 1-year, 3-year, and 5-year survival rates of patients. Additionally, the ROC analysis indicated the model's strong predictive ability, and the decision curves confirmed its clinical applicability. Conclusion: This study successfully developed a nomogram model for predicting survival outcomes in patients with cHCC-CCA following hepatectomy.

Due to the lack of effective screening systems in the rare earth waste recycling industry, the composition of rare earth elements in rare earth waste is complex and difficult to separate. In response to such problems, by studying the reaction behavior between various elements in rare earth waste and cobalt chloride, we propose a process path for the separation and recovery of iron, cerium and other rare earth elements using cobalt chloride roasting. The experiments on simulated wastes show that the leaching rates of the Nd, Sm, Gd, Pr can reach 98.31%, 94.5%, 93.87% and 72.05% under the optimal process conditions, respectively. Ce and iron remain in the leaching residue in the form of CeO2 and CoFe2O4, respectively. And through a simple magnetic separation process, CeO2 and CoFe2O4 can be enriched in non-magnetic leaching residue and magnetic leaching residue, respectively. The cerium content in the leaching residue composed of cobalt ferrite is only 1.95%. Therefore, this method is beneficial to the separation and high-value utilization of iron, cerium, and other rare earth elements in the waste system. The research results can provide theoretical reference for the low-cost and high-value recovery of rare earth secondary resources.

Background Medical image segmentation is a critical task for the early detection and diagnosis of various conditions, such as skin cancer, polyps, thyroid nodules, and pancreatic tumors. Recently, deep learning architectures have achieved significant success in this field. However, they face a critical trade-off between local feature extraction and global context modeling. Method To address this limitation, we present DCM-Net, a dual-encoder architecture that integrates pretrained CNN layers with Visual State Space (VSS) blocks through a Cross-Branch Feature Fusion Module (CBFFM). A Decoder Feature Enhancement Module (DFEM) combines depth-wise separable convolutions with MLP-based semantic rectification to extract enhanced decoded features and improve the segmentation performance. Additionally, we present a new 2D pancreas and pancreatic tumor dataset (CCH-PCT-CT) collected from Chongqing University Cancer Hospital, comprising 3,547 annotated CT slices, which is used to validate the proposed model. Results The proposed DCM-Net architecture achieves competitive performance across all datasets investigated in this study. Conclusions We develop a novel DCM-Net architecture that generates robust features for tumor and organ segmentation in medical images. DCM-Net significantly outperforms all baseline models in segmentation tasks, with higher Dice Similarity Coefficient (DSC) and mean Intersection over Union (mIoU) scores. Its robustness confirms strong potential for clinical use.

Background: Acute ischemic stroke, especially hemorrhage cerebral infarction (HCI), resulted in the leading causes of mortality and long-term disability across populations. However, fewer researches have focused on the risk factors of first admission and recurrence of HCI. Methods: The study included 1857 patients who underwent cerebral infarction with or without hemorrhagic transformation. Clinical characteristics were collected, and univariate and multivariate analysis were performed to explore the risk factors. The subgroup analysis of cerebral infarction recurrence was performed. ROC analysis was utilized, and AUCs were showed the diagnostic values of the risk factors. Results: Compared to the patients with non-hemorrhage cerebral infarction, the patients with hemorrhage cerebral infarction were older and had higher Neutrophil infiltration, AST expression, globulin and BUN, while had lower ALT expression, triglyceride, PT, APTT, homocysteine, d-dimer, CRP and glycosylated hemoglobin. Utilizing univariate and multivariate analysis, age, thrombolytic, Hb, AST and glycosylated hemoglobin were the risk factors between the patients with hemorrhagic cerebral infarction and non-hemorrhagic cerebral infarction. ROC analysis was performed to demonstrate that glycosylated hemoglobin was a diagnostic biomarker for the patients with hemorrhagic cerebral infarction and non-hemorrhagic cerebral infarction (AUC = 0.808). Utilizing univariate and multivariate analysis, age, hypertension history, LDL and MRS Score on admission were the risk factors between non-hemorrhagic cerebral infarction patients with first admission or the cerebral infarction recurrence. ROC analysis was performed to demonstrate MRS Score on admission was a diagnostic biomarker for recurrence of cerebral infarction in patients with non-hemorrhagic cerebral infarction (AUC = 0.708). Utilizing univariate and multivariate analysis, only hypertension history was the risk factors between hemorrhagic cerebral infarction patients with first admission or the cerebral infarction recurrence. Conclusion: In conclusion, age, hypertension history, LDL and MRS Score on admission were the risk factors between cerebral infarction patients with first admission or the cerebral infarction recurrence.

BackgroundInfant, junior, and adult patients with neuronal intranuclear inclusion disease (NIID) present with various types of seizures. We aimed to conduct a systematic literature review on the clinical characteristics of NIID with seizures to provide novel insight for early diagnosis and treatment and to improve prognosis of these patients.MethodsWe used keywords to screen articles related to NIID and seizures, and data concerning the clinical characteristics of patients, including demographic features, disease characteristics of the seizures, treatment responses, imaging examinations, and other auxiliary examination results were extracted.ResultsThe included studies comprised 21 patients with NIID with seizures. The most common clinical phenotypes were cognitive impairment (76.20%) and impaired consciousness (57.14%), and generalized onset motor seizures (46.15%) represented the most common type. Compared with infantile and juvenile cases, the use of antiepileptic drugs in adults led to significant seizure control and symptom improvement, in addition to providing a better prognosis. The number of GGC sequence repeats in the NOTCH2NLC gene in six NIID patients with seizures who underwent genetic testing ranged 72–134.ConclusionThe most common clinical phenotypes in patients with NIID with seizures were cognitive impairment and consciousness disorders. Patients with NIID presented with various types of seizures, with the most common being generalized onset motor seizures. Adult patients had a better prognosis and were relatively stable. The early diagnosis of NIID with seizures is of great significance for treatment and to improve prognosis.

Tumour morphology (tumour burden score (TBS)) and liver function (albumin-to-alkaline phosphatase ratio (AAPR)) have been shown to correlate with outcomes in intrahepatic cholangiocarcinoma (ICC). This study aimed to evaluate the combined predictive effect of TBS and AAPR on survival outcomes in ICC patients. We conducted a retrospective analysis using a multicentre database of ICC patients who underwent curative surgery from 2011 to 2018. The Kaplan-Meier method was employed to examine the relationship between a new index (combining TBS and AAPR) and long-term outcomes. The predictive efficacy of this index was compared to other conventional indicators. A total of 560 patients were included in the study. Based on TBS and AAPR stratification, patients were classified into three groups. Kaplan-Meier curves demonstrated that 124 patients with low TBS and high AAPR had the best overall survival (OS) and recurrence-free survival (RFS), while 170 patients with high TBS and low AAPR had the worst outcomes (log-rank p < 0.001). Multivariate analyses identified the combined index as an independent predictor of OS and RFS. Furthermore, the index showed superior accuracy in predicting OS and RFS compared to other conventional indicators. Collectively, this study demonstrated that the combination of liver function and tumour morphology provides a synergistic effect in evaluating the prognosis of ICC patients. The novel index combining TBS and AAPR effectively stratified postoperative survival outcomes in ICC patients undergoing curative resection.

Abstract Bionic lubricant materials are a class of materials inspired by natural organisms and offer excellent lubrication properties and biocompatibility. In the field of sports medicine, their application opens up new possibilities for the prevention and treatment of sports‐related diseases. The authors will introduce the existing theoretical models of friction in the locomotor system, the characteristics and advantages of biomimetic lubrication materials and discuss in depth their applications in the field of sports medicine. The development of bionic lubrication materials opens up unprecedented opportunities for sports medicine to provide more effective and long‐lasting treatment options for patients.

Ischemic stroke (IS) is a serious central nervous system disease. Post-IS complications, such as post-stroke cognitive impairment (PSCI), post-stroke depression (PSD), hemorrhagic transformation (HT), gastrointestinal dysfunction, cardiovascular events, and post-stroke infection (PSI), result in neurological deficits. The microbiota-gut-brain axis (MGBA) facilitates bidirectional signal transduction and communication between the intestines and the brain. Recent studies have reported alterations in gut microbiota diversity post-IS, suggesting the involvement of gut microbiota in post-IS complications through various mechanisms such as bacterial translocation, immune regulation, and production of gut bacterial metabolites, thereby affecting disease prognosis. In this review, to provide insights into the prevention and treatment of post-IS complications and improvement of the long-term prognosis of IS, we summarize the interaction between the gut microbiota and IS, along with the effects of the gut microbiota on post-IS complications.

We conducted a mechanistic and experimental study on zinc fluoride roasting for the recovery of NdFeB waste to address the difficulties faced during this pyrometallurgical recovery process, such as the high dependence on the quality of raw materials, the high energy consumption involved in roasting transformations, and the low added value of mixed rare earth products. Thermodynamic calculations showed the feasibility of fluorinating rare earths in NdFeB waste, and one-factor experiments were performed. The results showed that at a roasting temperature of 850 °C, a reaction time of 90 min, and 100% ZnF2 addition, the fluorination rate of rare earths could reach 95.69%. In addition, after analyzing the mesophase composition of a clinker under different roasting temperature conditions, it was found that, when the roasting temperature exceeded 850 °C, the fluorination rate of rare earths was reduced, which was consistent with the thermodynamic results. On this basis, response surface methodology (RSM) was used to carry out experiments to investigate in depth the effects of various factors and their interactions on the fluorination rate of rare earths, which provides a sufficient experimental basis for the recovery of NdFeB waste via fluorination roasting. The results of this study show that ZnF2 addition had the greatest influence on the rare earth fluorination reaction, followed by roasting temperature and roasting time. According to the optimization results of the model, the optimal roasting conditions were determined as follows: 119% ZnF2 addition at 828 °C, a roasting time of 91 min, and a rare earth element fluorination rate of 97.29%. The purity of the mixed fluorinated rare earths was as high as 98.92% after leaching the roasted clinker with 9 M hydrochloric acid at a leaching temperature of 80 °C, a liquid–solid ratio of 4 mL/g, and a leaching time of 2.5 h. This study will lay the foundation for promoting the application of pyrometallurgical technology in the recycling of NdFeB waste.

BackgroundLower-grade glioma (LGG) is one of the most common malignant tumors in the central nervous system (CNS). Accumulating evidence have demonstrated that tryptophan metabolism is significant in tumor. Therefore, this study aims to comprehensively clarify the relationship between tryptophan metabolism-related genes (TRGs) and LGGs.MethodsThe expression level of TRGs in LGG and normal tissues was first analyzed. Next, the key TRGs with prognostic value and differential expression in LGGs were identified using the least absolute shrinkage and selection operator (LASSO) regression analysis. Subsequently, a risk model was constructed and Consensus clustering analysis was conducted based on the expression level of key TRGs. Then, the prognostic value, clinicopathological factors, and tumor immune microenvironment (TIME) characteristics between different risk groups and molecular subtypes were analyzed. Finally, the expression, prognosis, and TIME of each key TRGs were analyzed separately in LGG patients.ResultsA total of 510 patients with LGG from The Cancer Genome Atlas (TCGA) dataset and 1,152 normal tissues from the Genotype-Tissue Expression (GTEx) dataset were included to evaluate the expression level of TRGs. After LASSO regression analysis, we identified six key TRGs and constructed a TRGs risk model. The survival analysis revealed that the risk model was the independent predictor in LGG patients. And the nomogram containing risk scores and independent clinicopathological factors could accurately predict the prognosis of LGG patients. In addition, the results of the Consensus cluster analysis based on the expression of the six TRGs showed that it could classify the LGG patients into two distinct clusters, with significant differences in prognosis, clinicopathological factors and TIME between these two clusters. Finally, we validated the expression, prognosis and immune infiltration of six key TRGs in patients with LGG.ConclusionThis study demonstrated that tryptophan metabolism plays an important role in the progression of LGG. In addition, the risk model and the molecular subtypes we constructed not only could be used as an indicator to predict the prognosis of LGG patients but also were closely related to the clinicopathological factors and TIME of LGG patients. Overall, our study provides theoretical support for the ultimate realization of precision treatment for patients with LGG.

NdFeB magnet scraps contain large amounts of iron, which poses challenges in recycling and greatly hinders the recovery of rare earths through direct hydrometallurgical treatment. To address this issue, we conducted tests using a flash furnace to explore the low-temperature reduction behavior of NdFeB magnet scraps under an H2 atmosphere based on thermodynamic calculations comparing the reduction properties of rare earth oxides (REOs) and iron oxide (FeOx). The results demonstrated that the reduction rate of FeOx surpassed 95% under optimal conditions including a reduction temperature of 723 K, a particle size (D90) of 0.45 μm, and an H2 flow rate of 2 L/min. X-ray diffraction and electron probe microanalysis of the reduction product revealed that the flash reduction at 723 K facilitated the selective reduction of FeOx, owing to efficient mass and heat transfer. Consequently, a two-step magnetic separation process was employed to separate metallic Fe and REOs from the reduction product. Fe-rich phase, obtained with a remarkable Fe distribution ratio of 90.2%, can serve as an economical raw material for weathering steel. Additionally, the REOs are enriched in REO-rich phase, achieving a distribution ratio of 93.9% and significantly boosting the REO concentration from 30.2 to 82.8 wt%.