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Black tea is the second most common type of tea in China. Fermentation is one of the most critical processes in its production, and it affects the quality of the finished product, whether it is insufficient or excessive. At present, the determination of black tea fermentation degree completely relies on artificial experience. It leads to inconsistent quality of black tea. To solve this problem, we use machine vision technology to distinguish the degree of fermentation of black tea based on images, this paper proposes a lightweight convolutional neural network (CNN) combined with knowledge distillation to discriminate the degree of fermentation of black tea. After comparing 12 kinds of CNN models, taking into account the size of the model and the performance of discrimination, as well as the selection principle of teacher models, Shufflenet_v2_x1.0 is selected as the student model, and Efficientnet_v2 is selected as the teacher model. Then, CrossEntropy Loss is replaced by Focal Loss. Finally, for Distillation Loss ratios of 0.6, 0.7, 0.8, 0.9, Soft Target Knowledge Distillation (ST), Masked Generative Distillation (MGD), Similarity-Preserving Knowledge Distillation (SPKD), and Attention Transfer (AT) four knowledge distillation methods are tested for their performance in distilling knowledge from the Shufflenet_v2_x1.0 model. The results show that the model discrimination performance after distillation is the best when the Distillation Loss ratio is 0.8 and the MGD method is used. This setup effectively improves the discrimination performance without increasing the number of parameters and computation volume. The model's P, R and F1 values reach 0.9208, 0.9190 and 0.9192, respectively. It achieves precise discrimination of the fermentation degree of black tea. This meets the requirements of objective black tea fermentation judgment and provides technical support for the intelligent processing of black tea.

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Soil respiration (Rs) is projected to be substantially affected by climate change, impacting the storage, equilibrium, and movement of terrestrial carbon (C). However, uncertainties surrounding the responses of Rs to climate change and soil nitrogen (N) enrichment are linked to mechanisms specific to diverse climate zones. A comprehensive meta-analysis was conducted to address this, evaluating the global effects of warming, increased precipitation, and N enrichment on Rs across various climate zones and ecosystems. Data from 123 studies, encompassing a total of 10,377 worldwide observations, were synthesized for this purpose. Annual Rs were modeled and their uncertainties were associated with a 1-km2 resolution global Rs database spanning from 1961 to 2022. Calibrating Rs using ensemble machine learning (EML) and employing 10-fold cross-validation, 13 environmental covariates were utilized. The meta-analysis findings revealed an upsurge in Rs rates in response to warming, with tropical, arid, and temperate climate zones exhibiting increases of 12 %, 13 %, and 16 %, respectively. Furthermore, increased precipitation led to stimulated Rs rates of 11 % and 9 % in tropical and temperate zones, respectively, while N deposition affected Rs in cold (+6 %) and tropical (+5 %) climate zones. The machine learning technique estimated the global soil respiration to range from 91 to 171 Pg C yr-1, with an average Rs of 700 ± 300 g C m-2 yr-1. The values ranged between 314 and 2500 g C m-2 yr-1, with the lowest and highest values observed in cold and tropical zones, respectively. Spatial variation in Rs was most pronounced in low-latitude areas, particularly in tropical rainforests and monsoon zones. Temperature, precipitation, and N deposition were identified as crucial environmental factors exerting significant influences on Rs rates worldwide. These factors underscore the interconnectedness between climate and ecosystem processes, therefore requiring explicit considerations of different climate zones when assessing responses of Rs to global change.

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The integration of visual simulation and biorehabilitation devices promises great applications for sustainable electronics, on-demand integration and neuroscience. However, achieving a multifunctional synergistic biomimetic system with tunable optoelectronic properties at the individual device level remains a challenge. Here, an electro-optically configurable transistor employing conjugated-polymer as semiconductor layer and an insulating polymer (poly(1,8-octanediol-co-citrate) (POC)) with clusterization-triggered photoactive properties as dielectric layer is shown. These devices realize adeptly transition from electrical to optical synapses, featuring multiwavelength and multilevel optical synaptic memory properties exceeding 3 bits. Utilizing enhanced optical memory, the images learning and memory function for visual simulation are achieved. Benefiting from rapid electrical response akin to biological muscle activation, increased actuation occurs under increased stimulus frequency of gate voltage. Additionally, the transistor on POC substrate can be effectively degraded in NaOH solution due to degradation of POC. Pioneeringly, the electro-optically configurability stems from light absorption and photoluminescence of the aggregation cluster in POC layer after 200 °C annealing. The enhancement of optical synaptic plasticity and integration of motion-activation functions within a single device opens new avenues at the intersection of optoelectronics, synaptic computing, and bioengineering.

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Although research on nitrosyl (NO) heme complexes and their one-electron reduced form, nitroxyl (or nitroxyl anion, NO-) derivatives, has been going on for decades, there are still disagreements about the electrical configuration of nitroxyl complexes, and the majority of the work on this topic is based on theoretical calculations. Following the initial nitroxyl iron porphyrin crystal structure, we present two further polymorphic forms of [CoCp2][Fe(TFPPBr8)(NO)]. Using the same completely halogenated porphyrin ligand, we also present two polymorphic forms of nitrosyl cobalt(II) complexes, which are another sort of {MNO}8 structure. In addition to the EXANES and EPR studies of these {FeNO}7 and {CoNO}8 complexes, the {FeNO}8 [CoCp2][Fe(TFPPBr8)(NO)] complex is also investigated by temperature-dependent Mössbauer experiments for the first time with the {FeNO}7 precursor as a control sample. The analysis of the Mössbauer and crystal structural parameters between these two types of {MNO}8 (M = Fe or Co) species and previously reported analogous ones allow us to conclude that the electronic configuration of [Fe(TFPPBr8)(NO)]- is best described as an intermediate between low-spin Fe(II)-NO- and Fe(I)-NO•.

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PURPOSE: The aim of this study is to describe the maximum exposure of the infraorbital region via the orbital floor using the transnasal prelacrimal recess approach (PLRA), and to provide an anatomical basis for treating lesions in the infraorbital region. METHODS: Ten freshly injected frozen heads were dissected using the PLRA. The orbital floor was removed along the border of the medial infraorbital quadrangle, and the periorbita was opened to expose the infraorbital region. The areas of the medial infraorbital quadrangles were measured and analyzed. The PLRA was applied separately on the left and right sides of each cadaver head, resulting in a total of 20 prelacrimal recess approaches. RESULTS: The PLRA enabled visualization of the optic nerve and the central retinal artery through the orbital floor. By integrating both medial and lateral approaches in relation to the inferior rectus muscle, all crucial anatomical structures within the infraorbital region could be clearly identified. The area of the medial infraorbital quadrangle was 420.65 ± 24.03 mm2. CONCLUSION: The PLRA provides access through the orbital floor to the maximum boundary of the infraorbital region, including the lateral orbital wall at the outermost level, the superior rectus muscle at the topmost level, and the medial orbital wall at the innermost level.

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Cancer immunotherapy, as an emerging cancer treatment approach, harnesses the patient's own immune system to effectively prevent tumor recurrence or metastasis. However, its clinical application has been significantly hindered by relatively low immune response rates. In recent years, metal-based biomaterials have been extensively studied as effective immunomodulators and potential tools for enhancing anti-tumor immune responses, enabling the reversal of immune suppression without inducing toxic side effects. This review introduces the classification of bioactive metal elements and summarizes their immune regulatory mechanisms. In addition, we discuss the immunomodulatory roles of biomaterials constructed from various metals, including aluminum, manganese, gold, calcium, zinc, iron, magnesium, and copper. More importantly, a systematic overview of their applications in enhancing immunotherapy is provided. Finally, the prospects and challenges of metal-based biomaterials with immunomodulatory functions in cancer immunotherapy are outlined.

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Drug-induced gene expression profiles can identify potential mechanisms of toxicity. We focus on obtaining signatures for cardiotoxicity of FDA-approved tyrosine kinase inhibitors (TKIs) in human induced-pluripotent-stem-cell-derived cardiomyocytes, using bulk transcriptomic profiles. We use singular value decomposition to identify drug-selective patterns across cell lines obtained from multiple healthy human subjects. Cellular pathways affected by cardiotoxic TKIs include energy metabolism, contractile, and extracellular matrix dynamics. Projecting these pathways to published single cell expression profiles indicates that TKI responses can be evoked in both cardiomyocytes and fibroblasts. Integration of transcriptomic outlier analysis with whole genomic sequencing of our six cell lines enables us to correctly reidentify a genomic variant causally linked to anthracycline-induced cardiotoxicity and predict genomic variants potentially associated with TKI-induced cardiotoxicity. We conclude that mRNA expression profiles when integrated with publicly available genomic, pathway, and single cell transcriptomic datasets, provide multiscale signatures for cardiotoxicity that could be used for drug development and patient stratification.

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Background: Microwave ablation (MWA) is an important method for the treatment of lung cancer, but there is still a lack of standard guidelines for the selection of power. This study aimed to explore the effectiveness and safety of MWA at different power levels. Methods: The study gathered individuals underwent MWA for lung cancer between January 2012 and December 2020. All patients were divided into low power group and high power group based on the power of MWA. By intergroup comparisons, we clarified the differences between the two groups. Results: In this study, 265 participants were involved, with 192 in the low power group and 73 in the high power group. Compared to the low power group, the high power group had a significantly higher incidence of postoperative complications (63.0% vs. 24.0%). In the Kaplan-Meier analysis, overall survival (OS) and disease-free survival (DFS) of the high power group were both better than the low power group. We found through Cox regression analysis that smoking, tumor volume, tumor differentiation, gene mutation, neutrophil count, and lymphocyte count were independent factors affecting the OS of patients. Based on the above factors, we constructed a nomogram, with areas under the curve (AUCs) of 0.941, 0.903, and 0.905 for predicting 1-, 2-, and 3-year OS after MWA, respectively. Conclusions: While high-power MWA brings better long-term prognosis to patients, it also leads to an increase in postoperative complications. The application of a nomogram for stratifying the prognosis of patients may be a more feasible approach to further develop individualized treatment plans.

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Coulombic efficiency and cycle life require further improvement in an ever-growing practical demand for lithium-ion batteries (LIBs), which are one of the most prevalent electrochemical energy storage systems. In this work, a more stable and highly lithium-ion (Li-ion) conductive artificial solid electrolyte interface (A-SEI) is constructed by coating polythiophene (PTh) on the surface of a graphite anode based on molecular dynamic simulations. Our findings reveal that PTh chains effectively prevent direct contact between the electrolyte and the negative electrode while providing a rapid transport channel for lithium ions (Li-ions), resulting in significantly shorter trapping times for Li-ions-at least two orders of magnitude shorter than those in the predominant component of traditional SEI layers.

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Controlling the position, size, and shape of pores is a limitation of traditional monolithic preparation methods. The application of 3D printing technology offers high customizability, allowing the precise printing of pore positions, sizes, and shapes according to the designer's 3D model. Herein, by using Projection Microstereolithography (PµSL), we prepared a 3D-printed monolithic array with post-modification of thiol-functionalized metal-organic framework (MOF), and combined it with inductively coupled plasma mass spectrometry (ICP-MS) for the online analysis of trace Cd and Pb in human urine. To achieve array monolithic microextraction, six 3D-printed monolithic columns were modified with thiol-functionalized MOF-808 (MOF-808-SH), and were then assembled in the 3D printed extraction device incorporating gas valve and scaffold. The MOF-808-SH modified 3D-printed monolithic column exhibits excellent extraction performance to Cd2+ and Pb2+ due to rich active adsorption sites and hierarchical porous structure, and has long life span (>100 reused times). Under the optimized conditions, the limits of detection (LODs) are 3.5 and 17.6 ng L-1 for Cd2+ and Pb2+, respectively, with the relative standard deviations of 4.9 % and 8.2 % (0.1 µg L-1, n = 7), and the sample throughput is 11 h-1. To validate the accuracy of the method, the method was used to determine Cd and Pb in Certified Reference Materials of freeze-dried human urine, the determined results agree well with the certified values. This method was also successfully applied to the determination of trace Cd and Pb in real human urine samples. The developed method offers low LODs, robust anti-interference capability, high sample throughput, long reuse cycles, and automation analysis, showing great potential for the analysis of trace heavy metals in biological samples.

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Background: Depression represents a pressing global public health concern, impacting the physical and mental well-being of hundreds of millions worldwide. Notwithstanding advances in clinical practice, an alarming number of individuals at risk for depression continue to face significant barriers to timely diagnosis and effective treatment, thereby exacerbating a burgeoning social health crisis. Objective: This study seeks to develop a novel online depression risk detection method using natural language processing technology to identify individuals at risk of depression on the Chinese social media platform Sina Weibo. Methods: First, we collected approximately 527,333 posts publicly shared over 1 year from 1600 individuals with depression and 1600 individuals without depression on the Sina Weibo platform. We then developed a hierarchical transformer network for learning user-level semantic representations, which consists of 3 primary components: a word-level encoder, a post-level encoder, and a semantic aggregation encoder. The word-level encoder learns semantic embeddings from individual posts, while the post-level encoder explores features in user post sequences. The semantic aggregation encoder aggregates post sequence semantics to generate a user-level semantic representation that can be classified as depressed or nondepressed. Next, a classifier is employed to predict the risk of depression. Finally, we conducted statistical and linguistic analyses of the post content from individuals with and without depression using the Chinese Linguistic Inquiry and Word Count. Results: We divided the original data set into training, validation, and test sets. The training set consisted of 1000 individuals with depression and 1000 individuals without depression. Similarly, each validation and test set comprised 600 users, with 300 individuals from both cohorts (depression and nondepression). Our method achieved an accuracy of 84.62%, precision of 84.43%, recall of 84.50%, and F1-score of 84.32% on the test set without employing sampling techniques. However, by applying our proposed retrieval-based sampling strategy, we observed significant improvements in performance: an accuracy of 95.46%, precision of 95.30%, recall of 95.70%, and F1-score of 95.43%. These outstanding results clearly demonstrate the effectiveness and superiority of our proposed depression risk detection model and retrieval-based sampling technique. This breakthrough provides new insights for large-scale depression detection through social media. Through language behavior analysis, we discovered that individuals with depression are more likely to use negation words (the value of "swear" is 0.001253). This may indicate the presence of negative emotions, rejection, doubt, disagreement, or aversion in individuals with depression. Additionally, our analysis revealed that individuals with depression tend to use negative emotional vocabulary in their expressions ("NegEmo": 0.022306; "Anx": 0.003829; "Anger": 0.004327; "Sad": 0.005740), which may reflect their internal negative emotions and psychological state. This frequent use of negative vocabulary could be a way for individuals with depression to express negative feelings toward life, themselves, or their surrounding environment. Conclusions: The research results indicate the feasibility and effectiveness of using deep learning methods to detect the risk of depression. These findings provide insights into the potential for large-scale, automated, and noninvasive prediction of depression among online social media users.

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Cardiovascular diseases are a prominent cause of mortality, emphasizing the need for early prevention and diagnosis. Utilizing artificial intelligence (AI) models, heart sound analysis emerges as a noninvasive and universally applicable approach for assessing cardiovascular health conditions. However, real-world medical data are dispersed across medical institutions, forming "data islands" due to data sharing limitations for security reasons. To this end, federated learning (FL) has been extensively employed in the medical field, which can effectively model across multiple institutions. Additionally, conventional supervised classification methods require fully labeled data classes, e.g., binary classification requires labeling of positive and negative samples. Nevertheless, the process of labeling healthcare data is time-consuming and labor-intensive, leading to the possibility of mislabeling negative samples. In this study, we validate an FL framework with a naive positive-unlabeled (PU) learning strategy. Semisupervised FL model can directly learn from a limited set of positive samples and an extensive pool of unlabeled samples. Our emphasis is on vertical-FL to enhance collaboration across institutions with different medical record feature spaces. Additionally, our contribution extends to feature importance analysis, where we explore 6 methods and provide practical recommendations for detecting abnormal heart sounds. The study demonstrated an impressive accuracy of 84%, comparable to outcomes in supervised learning, thereby advancing the application of FL in abnormal heart sound detection.

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Background: Sphaerirostris picae is a parasitic species known for its ability to infect and transmit between hosts in the gastrointestinal tracts of wild avian species. However, there is limited information on its presence and impact on urban avian populations, particularly in China. Materials and Methods: In this study, morphological observations were conducted to detect the presence of Sphaerirostris sp. within the intestinal tract of the Oriental Magpie (Pica serica) collected in Beijing, China. Further confirmation of the parasite's identity was achieved through phylogenetic analysis using COX1 gene sequencing to compare with previously documented Sphaerirostris picae isolates. Results: The morphological and molecular analyses confirmed the presence of Sphaerirostris picae in the Oriental Magpie. Phylogenetic analysis indicated a close relationship with known Sphaerirostris picae isolates. This represents the first reported case of Sphaerirostris picae infection in magpies from Beijing, China. Conclusion: The findings highlight the potential health hazards posed by Sphaerirostris picae to urban avian populations and public health. The study suggests that additional research and surveillance efforts are necessary to better understand the risks associated with this parasite and to develop effective mitigation strategies.

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RATIONALE AND OBJECTIVES: Current radiomics research primarily focuses on intratumoral regions and fixed peritumoral areas, lacking optimization for accurate Ki-67 prediction. This study aimed to develop machine learning (ML) models to analyze radiomic features from Automated Breast Volume Scanning (ABVS) images of different peritumoral region sizes to identify the optimal size for accurate preoperative Ki-67 prediction. MATERIALS AND METHODS: A total of 668 breast cancer patients were enrolled and divided into training (486) and testing (182) cohorts. In the training cohort, ML models were developed for intratumoral and peritumoral regions (2, 4, 6, 8, and 10 mm). Relevant Ki-67 features for each ROI were identified, and different models were compared to determine the optimal one. These models were validated using a testing cohort to find the most accurate peritumoral region for Ki-67 prediction. SHAP (Shapley Additive Explanations) analysis was performed to identify key radiomic features from the optimal model. RESULTS: The Extreme Gradient Boosting (XGBoost) model for the intratumoral region combined with the 6 mm peritumoral region achieved the highest predictive accuracy, with an AUC of 0.957 in the training cohort and 0.920 in the testing cohort. Calibration curves confirmed reliability, and decision curve analysis demonstrated the highest net benefit. SHAP indicated that 6 mm peritumoral radiomic features are more significant than intratumoral features. CONCLUSION: The XGBoost model using ABVS-derived radiomic features from both the intratumoral and 6 mm peritumoral regions provides the most accurate preoperative Ki-67 prediction.

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BACKGROUND: Previous research has highlighted correlations between blood cell counts and chronic liver disease. Nonetheless, the causal relationships remain unknown. AIM: To evaluate the causal effect of blood cell traits on liver enzymes and nonalcoholic fatty liver disease (NAFLD) risk. METHODS: Independent genetic variants strongly associated with blood cell traits were extracted from a genome-wide association study (GWAS) conducted by the Blood Cell Consortium. Summary-level data for liver enzymes were obtained from the United Kingdom Biobank. NAFLD data were obtained from a GWAS meta-analysis (8434 cases and 770180 controls, discovery dataset) and the Fingen GWAS (2275 cases and 372727 controls, replication dataset). This analysis was conducted using the inverse-variance weighted method, followed by various sensitivity analyses. RESULTS: One SD increase in the genetically predicted haemoglobin concentration (HGB) was associated with a ß of 0.0078 (95%CI: 0.0059-0.0096), 0.0108 (95%CI: 0.0080-0.0136), 0.0361 (95%CI: 0.0156-0.0567), and 0.0083 (95%CI: 00046-0.0121) for alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase, and gamma-glutamyl transferase, respectively. Genetically predicted haematocrit was associated with ALP (ß = 0.0078, 95%CI: 0.0052-0.0104) and ALT (ß = 0.0057, 95%CI: 0.0039-0.0075). Genetically determined HGB and the reticulocyte fraction of red blood cells increased the risk of NAFLD [odds ratio (OR) = 1.199, 95%CI: 1.087-1.322] and (OR = 1.157, 95%CI: 1.071-1.250). The results of the sensitivity analyses remained significant. CONCLUSION: Novel causal blood cell traits related to liver enzymes and NAFLD development were revealed through Mendelian randomization analysis, which may facilitate the diagnosis and prevention of NAFLD.

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BACKGROUND: Some heavy metals could be ingested into human body through breathing besides diet and drinking. Atmospheric particulates and smoke are main sources of this kind for the metals' exposure to human. Compared with environmental water, the methodologies for trace metals in particulates and smoke samples with more complex matrix are much less. Magnetic functional sorbents can be designed to remove complex matrix and enrich target analytes. The combination of magnetic solid phase extraction (MSPE) with highly sensitive inductively coupled plasma mass spectrometry (ICP-MS) detection is a good alternative for the analytical purpose. (92). RESULTS: Magnetic polymers were synthesized through free radical polymerization with Fe3O4 nanoparticles as the core and 2-methyl-2-hydroxyethyl 2-acrylate-2-hydroxyethyl ester phosphate as external modifier. The sorbent showed a high phosphorus content (2.7 wt%) and good selectivity to target REEs, along with good reusability (at least 45 times) and chemical stability. With the consumption of 150 mL aqueous solution, an enrichment factor of 300 was obtained by the proposed method, leading to low detection limits (0.001-0.2 ng L-1) for 15 REEs. The application potential of the method was further evaluated by analyzing local atmospheric particulate and cigar smoke samples. Recovery of 86.3-107 % in digested total suspended particulate (TSP) was obtained for 15 REEs, demonstrating a good anti-interference ability of the method. Target REEs in TSP, PM2.5 and PM10 samples were found to be 0.01-2.81, 0.006-1.09 and 0.009-2.46 ng m-3, respectively, and none of them were detected in the collected cigar smoke. (148) SIGNIFICANCE: The method of MSPE-ICP-MS was demonstrated with good potential for trace analysis in complex sample matrix, probably due to the good selectivity of the functionalized polymers. With the design and fabrication of specific functionalized magnetic sorbents, other heavy metals can be monitored in those samples which would be intake by human breathing. It provided an efficient strategy for the evaluation of metals' health risk in particulates and smoke samples. (69).

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BACKGROUND: Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by dopaminergic neuron degeneration and diverse motor and nonmotor symptoms. Early diagnosis and intervention are crucial but challenging due to reliance on clinical presentation. Recent research suggests potential biomarkers for early detection, including plasma netrin-1 (NTN-1), a protein implicated in neuronal survival. METHODS: This cross-sectional study recruited 105 PD patients and 65 healthy controls, assessing plasma NTN-1 levels and correlating them with clinical characteristics. Statistical analyses explored associations between NTN-1 levels and PD symptoms, considering demographic factors. RESULTS: PD patients exhibited significantly lower plasma NTN-1 levels compared to controls. NTN-1 demonstrated moderate potential as a PD biomarker. Positive correlations were found between NTN-1 levels and motor, depression, and cognitive symptoms. Multiple regression analysis revealed disease duration and NTN-1 levels as key factors influencing symptom severity. Gender also impacted symptom scores. CONCLUSION: Reduced plasma NTN-1 levels correlate with PD severity, suggesting its potential as a biomarker. However, further research is needed to elucidate the roles of NTN-1 in PD pathophysiology and validate its diagnostic and therapeutic implications. Understanding the involvement of NTN-1 may lead to personalized management strategies for PD.

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Circulating tumor cells (CTCs) are recognized as promising targets for liquid biopsy, which play an important role in early diagnosis and efficacy monitoring of cancer. However, due to the extreme scarcity of CTCs and partial size overlap between CTCs and white blood cells (WBCs), the separation and detection of CTCs from blood remain a big challenge. To address this issue, we fabricated a microfluidic chip by integrating a passive contraction-expansion array (CEA) inertial sorting zone and an active magnetophoresis zone with the trapezoidal groove and online coupled it with inductively coupled plasma mass spectrometry (ICP-MS) for rapid separation and precise detection of MCF-7 cells (as a model CTC) in blood samples. In the integrated microfluidic chip, most of the small-sized WBCs can be rapidly removed in the circular CEA inertial sorter, while the rest of the magnetically labeled WBCs can be further captured in the trapezoidal groove under the magnetic field. As a result, the rapid separation of MCF-7 cells from blood samples was achieved with an average recovery of 91.6% at a sample flow rate of 200 µL min-1. The developed online integrated inertial-magnetophoresis microfluidic chip-ICP-MS system has been applied for the detection of CTCs in real clinical blood samples with a fast analysis speed (5 min per 1 mL blood). CTCs were detected in all 24 blood samples from patients with different types of cancer, exhibiting excellent application potential in clinical diagnosis.

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Heterogeneity has been one of the main barriers to understanding and treatment of autism spectrum disorder (ASD). Previous studies have identified several subtypes of ASD through unsupervised clustering analysis. However, most of them primarily depicted the pairwise similarity between individuals through second-order relationships, relying solely on patient data for their calculation. This leads to an underestimation of the complexity inherent in inter-individual relationships and the diagnostic information provided by typical development (TD). To address this, we utilized an elastic net model to construct an individual deviation-based hypergraph (ID-Hypergraph) based on functional MRI data. We then conducted a novel community detection clustering algorithm to the ID-Hypergraph, with the aim of identifying subtypes of ASD. By applying this framework to the Autism Brain Imaging Data Exchange repository data (discovery: 147/125, ASD/TD; replication: 134/132, ASD/TD), we identified four reproducible ASD subtypes with roughly similar patterns of ALFF between the discovery and replication datasets. Moreover, these subtypes significantly varied in communication domains. In addition, we achieved over 80% accuracy for the classification between these subtypes. Taken together, our study demonstrated the effectiveness of identifying subtypes of ASD through the ID-hypergraph, highlighting its potential in elucidating the heterogeneity of ASD and diagnosing ASD subtypes.