Loss of nephric augmenter of liver regeneration facilitates acute kidney injury via ACSL4-mediated ferroptosis.

Ferroptosis, characterized by lipid accumulation in intracellular compartments, is related to acute kidney injury (AKI), but the mechanism remains obscure. In our previous study, the protective effect of augmenter of liver regeneration (ALR) on AKI was not fully clarified. In this study, we established an AKI mouse model by knocking out proximal tubule-specific ALR and an AKI cell model by inducing hypoxia, as well as enrolled AKI patients, to investigate the effects of ALR on ferroptosis and the progression of AKI. We found that ALR knockout aggravated ferroptosis and increased ROS accumulation and mitochondrial damage, whereas ALR overexpression attenuated ferroptosis through clearance of ROS and maintenance of mitochondrial morphology. Mechanistically, we demonstrated that ALR could directly bind to long-chain-fatty-acid-CoA ligase 4 (ACSL4) and further inhibit the expression of ACSL4 by interacting with certain regions. By resolution liquid chromatography coupled with triple quadruple mass spectrometry, we found that ALR could reduce the contents of polyunsaturated fatty acids, especially arachidonic acid. In addition, we showed that ALR binds to ACSL4 and attenuates oxylipin accumulation, exerting a protective effect against ferroptosis in AKI. Therefore, targeting renal ALR can attenuate ferroptosis and can offer a promising strategy for the treatment of AKI.

Ultrasensitive and Highly Selective Detection of Staphylococcus aureus at the Single-Cell Level Using Bacteria-Imprinted Polymer and Vancomycin-Conjugated MnO2 Nanozyme.

Pathogenic bacterial infections, even at extremely low concentrations, pose significant threats to human health. However, the challenge persists in achieving high-sensitivity bacterial detection, particularly in complex samples. Herein, we present a novel sandwich-type electrochemical sensor utilizing bacteria-imprinted polymer (BIP) coupled with vancomycin-conjugated MnO2 nanozyme (Van@BSA-MnO2) for the ultrasensitive detection of pathogenic bacteria, exemplified by Staphylococcus aureus (S. aureus). The BIP, in situ prepared on the electrode surface, acts as a highly specific capture probe by replicating the surface features of S. aureus. Vancomycin (Van), known for its affinity to bacterial cell walls, is conjugated with a Bovine serum albumin (BSA)-templated MnO2 nanozyme through EDC/NHS chemistry. The resulting Van@BSA-MnO2 complex, serving as a detection probe, provides an efficient catalytic platform for signal amplification. Upon binding with the captured S. aureus, the Van@BSA-MnO2 complex catalyzes a substrate reaction, generating a current signal proportional to the target bacterial concentration. The sensor displays remarkable sensitivity, capable of detecting a single bacterial cell in a phosphate buffer solution. Even in complex milk matrices, it maintains outstanding performance, identifying S. aureus at concentrations as low as 10 CFU mL-1 without requiring intricate sample pretreatment. Moreover, the sensor demonstrates excellent selectivity, particularly in distinguishing target S. aureus from interfering bacteria of the same genus at concentrations 100-fold higher. This innovative method, employing entirely synthetic materials, provides a versatile and low-cost detection platform for Gram-positive bacteria. In comparison to existing nanozyme-based bacterial sensors with biological recognition materials, our assay offers distinct advantages, including enhanced sensitivity, ease of preparation, and cost-effectiveness, thereby holding significant promise for applications in food safety and environmental monitoring.

Constraining long-term model predictions for woody growth using tropical tree rings.

The strength and persistence of the tropical carbon sink hinges on the long-term responses of woody growth to climatic variations and increasing CO2 . However, the sensitivity of tropical woody growth to these environmental changes is poorly understood, leading to large uncertainties in growth predictions. Here, we used tree ring records from a Southeast Asian tropical forest to constrain ED2.2-hydro, a terrestrial biosphere model with explicit vegetation demography. Specifically, we assessed individual-level woody growth responses to historical climate variability and increases in atmospheric CO2 (Ca ). When forced with historical Ca , ED2.2-hydro reproduced the magnitude of increases in intercellular CO2 concentration (a major determinant of photosynthesis) estimated from tree ring carbon isotope records. In contrast, simulated growth trends were considerably larger than those obtained from tree rings, suggesting that woody biomass production efficiency (WBPE = woody biomass production:gross primary productivity) was overestimated by the model. The estimated WBPE decline under increasing Ca based on model-data discrepancy was comparable to or stronger than (depending on tree species and size) the observed WBPE changes from a multi-year mature-forest CO2 fertilization experiment. In addition, we found that ED2.2-hydro generally overestimated climatic sensitivity of woody growth, especially for late-successional plant functional types. The model-data discrepancy in growth sensitivity to climate was likely caused by underestimating WBPE in hot and dry years due to commonly used model assumptions on carbon use efficiency and allocation. To our knowledge, this is the first study to constrain model predictions of individual tree-level growth sensitivity to Ca and climate against tropical tree-ring data. Our results suggest that improving model processes related to WBPE is crucial to obtain better predictions of tropical forest responses to droughts and increasing Ca . More accurate parameterization of WBPE will likely reduce the stimulation of woody growth by Ca rise predicted by biosphere models.

Variational Tensor Wave Functions for the Interacting Quantum Spin Hall Phase.

The quantum spin hall (QSH) phase, also known as the 2D topological insulator, is characterized by protected helical edge modes arising from time reversal symmetry. While initially proposed as band insulators, this phase can also manifest in strongly correlated systems where conventional band theory fails. To overcome the challenge of simulating this phase in realistic correlated models, we propose a novel framework utilizing fermionic tensor network states. Our approach involves constructing a tensor representation of the fixed-point wave function based on an exact solvable model, enabling us to derive a set of tensor equations governing the transformation rules of local tensors under symmetry operations. These tensor equations lead to the anomalous edge theory, which provides a comprehensive description of the QSH phase. By solving these tensor equations, we obtain variational ansatz for the QSH phase, which we subsequently verify its topological properties through numerical calculations. This method serves as an initial step toward employing tensor algorithms to simulate the QSH phase in strongly correlated systems, opening new avenues for investigating and understanding topological phenomena in complex materials.

Renal damage and old age: risk factors for thrombosis in patients with ANCA-associated vasculitis.

INTRODUCTION: Thrombosis in ANCA-associated vasculitis (AAV) was prevalent and has been neglected in Chinese patients. This study tried to describe the clinical characteristics, identify the risk factors, and investigate the causal relationship between AAV and venous thromboembolism (VTE) by two-sample Mendelian randomization (MR) analysis. METHODS: In this retrospective, observational study, we included all hospitalized AAV patients from Jan 2013 to Apr 2022 in Peking Union Medical College Hospital. We collected their clinical data for multivariate regression analysis to determine the risk factors for thrombosis. The nomogram was constructed by applying these risk factors to predict thrombosis in AAV patients. As for MR analysis, we selected single nucleotide polymorphisms (SNPs) related to AAV from published genome-wide association studies and extracted the outcome data containing deep vein thrombosis (DVT) and pulmonary embolism (PE) from the UK biobank. RESULTS: 1203 primary AAV patients were enrolled, and thrombosis occurred in 11.3%. Multivariate regression suggested that older than 65 years, EGPA, neurological involvement, lung involvement, significantly elevated serum creatinine (> 500µmol/L), and elevated D-dimer were associated with thrombosis in AAV patients. The model demonstrated satisfied discrimination with an AUC of 0.769 (95% CI, 0.726-0.812). MR analysis showed that EGPA could increase the risk of developing DVT and PE (OR = 1.0038, 95%CI = 1.0035-1.0041, P = 0.009). CONCLUSION: Thrombosis was not rare in Chinese patients with AAV. Renal damage and old age emerged as critical risk factors for thrombosis. EGPA might have a potential causal relationship with DVT and PE.

Development and validation of a prediction model for frailty in breast cancer patients with extended survival.

BACKGROUND: Breast cancer (BC) patients with extended survival show a higher incidence of frailty. This study aimed to develop and validate a novel model combining sociodemographic factors (SF) and disease-related factors (DRF) to identify frailty in BC patients with extended survival. METHODS: This cross-sectional study examined data from 1167 patients admitted to a large urban academic medical centre. Three types of predictive models were constructed in the training set (817 patients): the SF model, the DRF model, and the SF + DRF model (combined model). The model performance and effectiveness were assessed using receiver operating characteristic (ROC) curves, calibration plots and decision curves analysis (DCA). Then the model was subsequently validated on the validation set. RESULTS: The incidence of frailty in BC patients with extended survival was 35.8%. We identified six independent risk factors including age, health status, chemotherapy, endocrine therapy, number of comorbidities and oral medications. Ultimately, we constructed an optimal model (combined model C) for frailty. The predictive model showed significantly high discriminative accuracy in the training set AUC: 0.754, (95% CI, 0.719-0.789; sensitivity: 76.8%, specificity: 62.2%) and validation set AUC: 0.805, (95% CI, 0.76-0.85), sensitivity: 60.8%, specificity: 87.1%) respectively. A prediction nomogram was constructed for the training and validation sets. Calibration and DCA were performed, which indicated that the clinical model presented satisfactory calibration and clinical utility. Ultimately, we implemented the prediction model into a mobile-friendly web application that provides an accurate and individualized prediction for BC. CONCLUSIONS: The present study demonstrated that the prevalence of frailty in BC patients with extended survival was 35.8%. We developed a novel model for screening frailty, which may provide evidence for frailty screening and prevention.

Column-based cortical depth analysis of the diffusion anisotropy and radiality in submillimeter whole-brain diffusion tensor imaging of the human cortical gray matter in vivo.

High-resolution diffusion tensor imaging (DTI) can noninvasively probe the microstructure of cortical gray matter in vivo. In this study, 0.9-mm isotropic whole-brain DTI data were acquired in healthy subjects with an efficient multi-band multi-shot echo-planar imaging sequence. A column-based analysis that samples the fractional anisotropy (FA) and radiality index (RI) along radially oriented cortical columns was then performed to quantitatively analyze the FA and RI dependence on the cortical depth, cortical region, cortical curvature, and cortical thickness across the whole brain, which has not been simultaneously and systematically investigated in previous studies. The results showed characteristic FA and RI vs. cortical depth profiles, with an FA local maximum and minimum (or two inflection points) and a single RI maximum at intermediate cortical depths in most cortical regions, except for the postcentral gyrus where no FA peaks and a lower RI were observed. These results were consistent between repeated scans from the same subjects and across different subjects. They were also dependent on the cortical curvature and cortical thickness in that the characteristic FA and RI peaks were more pronounced i) at the banks than at the crown of gyri or at the fundus of sulci and ii) as the cortical thickness increases. This methodology can help characterize variations in microstructure along the cortical depth and across the whole brain in vivo, potentially providing quantitative biomarkers for neurological disorders.

Leaf angle as a leaf and canopy trait: Rejuvenating its role in ecology with new technology.

Life on Earth depends on the conversion of solar energy to chemical energy by plants through photosynthesis. A fundamental challenge in optimizing photosynthesis is to adjust leaf angles to efficiently use the intercepted sunlight under the constraints of heat stress, water loss and competition. Despite the importance of leaf angle, until recently, we have lacked data and frameworks to describe and predict leaf angle dynamics and their impacts on leaves to the globe. We review the role of leaf angle in studies of ecophysiology, ecosystem ecology and earth system science, and highlight the essential yet understudied role of leaf angle as an ecological strategy to regulate plant carbon-water-energy nexus and to bridge leaf, canopy and earth system processes. Using two models, we show that leaf angle variations have significant impacts on not only canopy-scale photosynthesis, energy balance and water use efficiency but also light competition within the forest canopy. New techniques to measure leaf angles are emerging, opening opportunities to understand the rarely-measured intraspecific, interspecific, seasonal and interannual variations of leaf angles and their implications to plant biology and earth system science. We conclude by proposing three directions for future research.

Histopathological auxiliary system for brain tumour (HAS-Bt) based on weakly supervised learning using a WHO CNS5-style pipeline.

PURPOSE: Classification and grading of central nervous system (CNS) tumours play a critical role in the clinic. When WHO CNS5 simplifies the histopathology diagnosis and places greater emphasis on molecular pathology, artificial intelligence (AI) has been widely used to meet the increased need for an automatic histopathology scheme that could liberate pathologists from laborious work. This study was to explore the diagnosis scope and practicality of AI. METHODS: A one-stop Histopathology Auxiliary System for Brain tumours (HAS-Bt) is introduced based on a pipeline-structured multiple instance learning (pMIL) framework developed with 1,385,163 patches from 1038 hematoxylin and eosin (H&E) slides. The system provides a streamlined service including slide scanning, whole-slide image (WSI) analysis and information management. A logical algorithm is used when molecular profiles are available. RESULTS: The pMIL achieved an accuracy of 0.94 in a 9-type classification task on an independent dataset composed of 268 H&E slides. Three auxiliary functions are developed and a built-in decision tree with multiple molecular markers is used to automatically formed integrated diagnosis. The processing efficiency was 443.0 s per slide. CONCLUSION: HAS-Bt shows outstanding performance and provides a novel aid for the integrated neuropathological diagnostic workflow of brain tumours using CNS 5 pipeline.

Synthesis of a water-soluble naphthalene-based macrocycle and its host-guest properties.

Reported here is the synthesis of a naphthalene-based macrocycle bearing anionic carboxylato groups on the rims along with its complexation with cationic guests in aqueous media. The macrocycle could strongly bind guests in a molecular clip model with association constants of 106-107 M-1.

[Discovery of biomarkers related to abnormal lipid metabolism in liver and serum and intervention mechanism of ginsenoside Rb_1 in hyperlipidemia rats based on non-targeted metabolomics].

Through the non-targeted metabolomics study of endogenous substances in the liver and serum of hyperlipidemia rats, the biomarkers related to abnormal lipid metabolism in hyperlipidemia rats were found, and the target of ginsenoside Rb_1 in improving hyperlipidemia was explored and its mechanism was elucidated. The content of serum biochemical indexes of rats in each group was detected by the automatic biochemical analyzer. The metabolite profiles of liver tissues and serum of rats were analyzed by HPLC-MS. Principal component analysis(PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) were used to compare and analyze the metabolic data in the normal group, the hyperlipidemia group, and the ginsenoside Rb_1 group, and screen potential biomar-kers. The related metabolic pathways were further constructed by KEGG database analysis. The results showed that hyperlipemia induced dyslipidemia in rats, which was alleviated by ginsenoside Rb_1. The non-targeted metabolomics results showed that there were 297 differential metabolites in the liver tissues of hyperlipidemia rats, 294 differential metabolites in the serum samples, and 560 diffe-rential metabolites in the hyperlipidemia rats treated by ginsenoside Rb_1. Perillic acid and N-ornithyl-L-taurine were common metabolites in the liver and serum samples, which could be used as potential biomarkers for ginsenoside Rb_1 in the improvement of hyperlipidemia. As revealed by pathway enrichment in the liver and serum, ginsenoside Rb_1 could participate in the metabolic pathway of choline in both the liver and serum. In addition, ginsenoside Rb_1 also participated in the ABC transporter, alanine, aspartic acid, and glutamate metabolism, protein digestion and absorption, ß-alanine metabolism, taurine and hypotaurine metabolism, caffeine metabolism, valine, leucine, and isoleucine biosynthesis, arachidonic acid metabolism, and methionine and cysteine metabolism to improve dyslipidemia in rats.

[Ethics considerations on brain-computer interface technology].

The development and potential application of brain-computer interface (BCI) technology is closely related to the human brain, so that the ethical regulation of BCI has become an important issue attracting the consideration of society. Existing literatures have discussed the ethical norms of BCI technology from the perspectives of non-BCI developers and scientific ethics, while few discussions have been launched from the perspective of BCI developers. Therefore, there is a great need to study and discuss the ethical norms of BCI technology from the perspective of BCI developers. In this paper, we present the user-centered and non-harmful BCI technology ethics, and then discuss and look forward on them. This paper argues that human beings can cope with the ethical issues arising from BCI technology, and as BCI technology develops, its ethical norms will be improved continuously. It is expected that this paper can provide thoughts and references for the formulation of ethical norms related to BCI technology.

Removing nutrients from wastewater by constructed wetlands under perfluoroalkyl acids stress.

Constructed wetlands (CWs) are often used to treat wastewater discharged from wastewater treatment plants (WWTPs), while emerging contaminants (such as perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS)) have been commonly discovered in WWTPs. However, no research has examined whether PFOA/OS (i.e. PFOA and PFOS) affects the performance of CW. Therefore, this study compared the nutrient removal efficiencies of four CWs with varied configurations under PFOA/OS and no PFOA/OS stress conditions. We found that CW containing plants or/and iron-carbon had higher removal efficiency for nutrients (except NH4+-N) than conventional CW in stable operation under wastewater without PFOA/OS. Plants or/and iron increased the nutrient removal efficiency by plant uptake, chemical reaction, and co-precipitation of iron hydroxides. In contrast, the iron-carbon inhibited the nitrification of nitrifying bacteria by consuming dissolved oxygen, converting NO3--N to NH4+-N. Although the removal efficiencies of nutrients by CWs differed after introducing PFOA/OS, the removal order was consistent with those before adding PFOA/OS. Plants or/and iron-carbon effectively increased CWs' resistance to PFOA/OS loading and toxicity, and the function of iron-carbon was superior to the plants. In addition, PFOA/OS reduced the abundances of microbes Hydrogenophaga, Pseudomonas, Sphingomonas, Nitrospira, and Candidatus_Accumulibacter that contributed to nutrient removal.

Identification of biomarkers, immune infiltration landscape, and treatment targets of ischemia-reperfusion acute kidney injury at an early stage by bioinformatics methods.

BACKGROUND: Mechanisms underlying ischemia/reperfusion injury-acute kidney injury (IRI-AKI) are not fully elucidated. We conducted an integrative analysis of IRI-AKI by bioinformatics methods. METHODS: We screened gene expression profiles of the IRI-AKI at early phase from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and enrichment pathways were conducted based on gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and Gene set enrichment analysis (GSEA). Immune cell infiltration analysis was performed to reveal the change of the microenvironment cell types. We constructed protein-protein interaction (PPI), and Cytoscape with plug-ins to find hub genes and modules. We performed robust rank aggregation (RRA) to combine DEGs and analyzed the target genes for miRNA/transcription factor (TF) and drug-gene interaction networks. RESULTS: A total of 239 and 384 DEGs were identified in GSE87024 and GSE34351 separately, with the 73 common DEGs. Enrichment analysis revealed that the significant pathways involve mitogen-activated protein kinase (MAPK) signaling, interleukin-17, and tumor necrosis factor (TNF) signaling pathway, etc. RRA analysis detected a total of 27 common DEGs. Immune cell infiltration analysis showed the plasma cells reduced and T cells increased in IRI-AKI. We identified JUN, ATF3, FOS, EGR1, HMOX1, DDIT3, JUNB, NFKBIZ, PPP1R15A, CXCL1, ATF4, and HSPA1B as hub genes. The target genes interacted with 23 miRNAs and 116 drugs or molecular compounds such as curcumin, staurosporine, and deferoxamine. CONCLUSION: Our study first focused on the early IRI-AKI adopting RRA analysis to combine DEGs in different datasets. We identified significant biomarkers and crucial pathways involved in IRI-AKI and first construct the immune landscape and detected the potential therapeutic targets of the IRI-AKI by drug-gene network.

Shift in the distribution and fate of perfluoroalkyl acids by sluice gates in the multi-environment media of rivers.

The impact of sluice operations on the distribution and fate of perfluoroalkyl acids (PFAAs) remains poorly understood. In this study, the distribution of PFAAs was investigated in water, suspended particles, sediment, and pore water from the upstream and downstream sections of six sluice gates along the Wangyu River, China. The target PFAAs were widely distributed in the dissolved phase (∑PFAAs: 447.61 ± 180.26 ng/L), particle phase (∑PFAAs: 2040.95 ± 1870.88 ng/g dw), sedimentary phase (∑PFAAs: 39.42 ± 35.38 ng/g dw), and pore water phase (∑PFAAs: 8172.54 ± 4278.60 ng/L). Our data suggest predominant detections of short-chain PFAAs such as perfluorobutanoic acid (PFBA) and perfluorohexanoic acid (PFHxA) in the four environmental media. Sediment pore water appeared as an essential repository and potential source for PFAA re-release to the river environment. The levels of PFAAs in the dissolved and suspended particle phase upstream of the sluices were significantly lower than those downstream, while the situation in the sediment and pore water phase was the opposite. Sluice operation caused PFAA redistribution among the multi-environment media but did not change the PFAA composition, which had the significant effect on the partition behavior of perfluoroalkyl carboxylic acids (PFCAs) between particles and water, as well as changed the migration pattern of PFOA, PFNA and PFOS from equilibrium to the migration state. Quantitative prediction models were developed for simulating fate of PFAAs in gate-controlled river, and the major factors affecting the distribution and fate of PFAAs were identified. Our findings provide insights into the redistribution mechanisms of PFAAs and an understanding of their environmental fate.

Brain-derived neurotrophic factor Val66Met polymorphism is associated with mild cognitive impairment in elderly patients with type 2 diabetes: a case-controlled study.

BACKGROUND: Brain-derived neurotrophic factor (BDNF) Val66Met polymorphism is reported to be associated with cognitive dysfunction, an important comorbidity factor in patients with type 2 diabetes mellitus (T2DM), especially in elderly populations, however, the underlying pathophysiological mechanisms are unclear. AIM: This study was performed to investigate the association between BDNF Val66Met polymorphism and mild cognitive impairment (MCI) in elderly patients with T2DM. METHODS: In total, 105 MCI and 105 normal cognition controls of T2DM patients were enrolled; all of the patients underwent neuropsychological assessments. BDNF Val66Met polymorphism was genotyped via TaqMan SNP genotyping assay. Data from clinical and laboratory-based examinations were collected. RESULTS: The frequency of the BDNF Met allele was significantly higher in the MCI group than in the controls. Multiple regression analysis indicated an association of the Met allele with MCI in patients with T2DM (OR = 2.54; 95% CI 1.33-4.84; p = 0.005). Stratified by educational level, the BDNF Met allele was significantly associated with MCI in elderly T2DM patients (OR = 3.29; 95% CI 1.26-8.57; p = 0.015) among the group of low educational levels (< 12 years); however, the association was insignificant among those with higher educational levels. DISCUSSION: BDNF Met allele carriers showed a higher frequency of MCI than Val/Val homozygotes in elderly T2DM patients. However, this association was only significant in patients with low education levels. CONCLUSION: BDNF Val66Met polymorphism may have a potential role in MCI in elderly T2DM patients, especially those with low educational levels.

Impact of a pandemic on surgical neuro-oncology-maintaining functionality in the early phase of crisis.

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has extraordinarily impacted global healthcare. Neuro-oncological surgery units have peculiar features that make them highly relevant in the strategic reaction to the pandemic. In this Chinese Society of Neuro-Oncology (CSNO) initiated survey, we appraise the changes implemented in neuro-oncological surgery hospitals across different Asian countries and provide expert recommendations for responses at different stages of the pandemic. METHODS: We performed a 42-question survey of the early experience of neuro-oncological surgery practice in hospitals across different Asian countries on April 1, 2020, with responses closed on April 18, 2020. RESULTS: 144 hospitals completed the questionnaire. Most were in WHO post-peak phase of the pandemic and reported a median reduction in neuro-oncological surgery volume of 25-50%. Most (67.4%) resumed elective surgery in only COVID-19 negative patients;11.1% performed only emergency cases irrespective of COVID-19 status;2.1% suspended all surgical activity. Ninety-one (63.2%) relocated personnel from neurosurgery to other departments. Fifty-two (36.1%) hospitals suspended post-operative adjuvant therapy and 94 (65.2%) instituted different measures to administer post-operative adjuvant therapy. Majority (59.0%) of the hospitals suspended research activity. Most (70%) respondents anticipate that current neurosurgery restrictions will continue to remain for > 1 month. CONCLUSIONS: Majority of the respondents to our survey reported reduced neuro-oncological surgery activity, policy modification, personnel reallocation, and curtailment of educational/research activities in response to the COVID-19 pandemic. The persistent widespread interruption of surgical neuro-oncology in even post-peak phases of the pandemic raises serious concerns about the long-term impact of the pandemic on neuro-oncological patients and highlights the essence of timely measures for pandemic preparedness, patient triage, and workforce protection.

A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder. Mild cognitive impairment (MCI) is a clinical precursor of AD. Although some treatments can delay its progression, no effective cures are available for AD. Accurate early-stage diagnosis of AD is vital for the prevention and intervention of the disease progression. Hippocampus is one of the first affected brain regions in AD. To help AD diagnosis, the shape and volume of the hippocampus are often measured using structural magnetic resonance imaging (MRI). However, these features encode limited information and may suffer from segmentation errors. Additionally, the extraction of these features is independent of the classification model, which could result in sub-optimal performance. In this study, we propose a multi-model deep learning framework based on convolutional neural network (CNN) for joint automatic hippocampal segmentation and AD classification using structural MRI data. Firstly, a multi-task deep CNN model is constructed for jointly learning hippocampal segmentation and disease classification. Then, we construct a 3D Densely Connected Convolutional Networks (3D DenseNet) to learn features of the 3D patches extracted based on the hippocampal segmentation results for the classification task. Finally, the learned features from the multi-task CNN and DenseNet models are combined to classify disease status. Our method is evaluated on the baseline T1-weighted structural MRI data collected from 97 AD, 233 MCI, 119 Normal Control (NC) subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The proposed method achieves a dice similarity coefficient of 87.0% for hippocampal segmentation. In addition, the proposed method achieves an accuracy of 88.9% and an AUC (area under the ROC curve) of 92.5% for classifying AD vs. NC subjects, and an accuracy of 76.2% and an AUC of 77.5% for classifying MCI vs. NC subjects. Our empirical study also demonstrates that the proposed multi-model method outperforms the single-model methods and several other competing methods.

[Optimal template selecting combined with non-liner template matching for Doppler fetal heart rate extraction].

The ultrasound Doppler fetal heart rate measurement is the gold standard of fetal heart rate counting. However, the existing fetal heart rate extraction algorithms are not designed specifically to suppress the high maternal interference during the second stage of labor, and false detection occurrences are common during labor. With this background, a method combining time-frequency frame template library optimal selecting and non-linear template matching is proposed. The method contributes a template library, and the optimal template can be selected to match the signal frame. After the short-time Fourier transform of the signal, the difference between the signal and the template is optimized by leaky rectified linear unit (LReLU) function frame by frame. The heart rate was calculated from the peak of the matching curve and the heart rate was calculated. By comparing the proposed method with the autocorrelation method, the results show that the detection accuracy of the proposed method is improved by 20% on average, and the non-linear template matching of 23% samples is at least 50% higher than the autocorrelation method. This paper designs the algorithm by analyzing the characteristics of the interference and signal mixing. We hope that this paper will provide a new idea for fetal heart rate extraction which not only focuses on the original signal.

[Anti-disturbance Fetal Heart Rate System Based on Combined Maternal-fetal Monitoring].

The existing fetal monitor is prone to false detection in the case of high maternal interference in the second stage of labor. With this background, the article designs and implements a combined maternal-fetal fetal heart monitoring system. The system obtains the Doppler signal of the abdominal fetal heart and the blood oxygen signal of the mother's finger, and estimates the maternal interference degree in the fetal heart rate Doppler signal according to the maximum correlation value between the maternal finger blood oxygen signal and the abdominal fetal heart Doppler signal, and switches the fetal heart rate extraction algorithm between the autocorrelation method suitable for lower interference and improved template method suitable for higher interference according to the maternal interference degree. The accuracy of our method is 9.2% which is higher than that of the improved template matching method and 6.1% higher than that of the autocorrelation method.