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OBJECTIVES: To investigate the risk factors for bronchopulmonary dysplasia (BPD) in twin preterm infants with a gestational age of <34 weeks, and to provide a basis for early identification of BPD in twin preterm infants in clinical practice. METHODS: A retrospective analysis was performed for the twin preterm infants with a gestational age of <34 weeks who were admitted to 22 hospitals nationwide from January 2018 to December 2020. According to their conditions, they were divided into group A (both twins had BPD), group B (only one twin had BPD), and group C (neither twin had BPD). The risk factors for BPD in twin preterm infants were analyzed. Further analysis was conducted on group B to investigate the postnatal risk factors for BPD within twins. RESULTS: A total of 904 pairs of twins with a gestational age of <34 weeks were included in this study. The multivariate logistic regression analysis showed that compared with group C, birth weight discordance of >25% between the twins was an independent risk factor for BPD in one of the twins (OR=3.370, 95%CI: 1.500-7.568, P<0.05), and high gestational age at birth was a protective factor against BPD (P<0.05). The conditional logistic regression analysis of group B showed that small-for-gestational-age (SGA) birth was an independent risk factor for BPD in individual twins (OR=5.017, 95%CI: 1.040-24.190, P<0.05). CONCLUSIONS: The development of BPD in twin preterm infants is associated with gestational age, birth weight discordance between the twins, and SGA birth.

A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning.

Objective: The purpose of this study is to explore whether machine learning can be used to establish an effective model for the diagnosis of Parkinson's disease (PD) by using texture features extracted from cerebellar gray matter and white matter, so as to identify subtle changes that cannot be observed by the naked eye. Method: This study involved a data collection period from June 2010 to March 2023, including 374 subjects from two cohorts. The Parkinson's Progression Markers Initiative (PPMI) served as the training set, with control group and PD patients (HC: 102 and PD: 102) from 24 global sites. Our institution's data was utilized as the test set (HC: 91 and PD: 79). Machine learning was employed to establish multiple models for PD diagnosis based on texture features of the cerebellum's gray and white matter. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP). Results: The AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Kruskal-Wallis (KW) and logistic regression via Lasso (LRLasso), the AUC was highest using the "one-standard error" rule. 'WM_original_glrlm_GrayLevelNonUniformity' was considered the most stable and predictive feature. Conclusion: The texture features of cerebellar gray matter and white matter combined with machine learning may have potential value in the diagnosis of Parkinson's disease, in which the heterogeneity of white matter may be a more valuable imaging marker.

Fast Pure Shift NMR Spectroscopy Using Attention-Assisted Deep Neural Network.

Pure shift NMR spectroscopy enables the robust probing on molecular structure and dynamics, benefiting from great resolution enhancements. Despite extensive application landscapes in various branches of chemistry, the long experimental times induced by the additional time dimension generally hinder its further developments and practical deployments, especially for multi-dimensional pure shift NMR. Herein, this study proposes and implements the fast, reliable, and robust reconstruction for accelerated pure shift NMR spectroscopy with lightweight attention-assisted deep neural network. This deep learning protocol allows one to regain high-resolution signals and suppress undersampling artifacts, as well as furnish high-fidelity signal intensities along with the accelerated pure shift acquisition, benefitting from the introduction of the attention mechanism to highlight the spectral feature and information of interest. Extensive results of simulated and experimental NMR data demonstrate that this attention-assisted deep learning protocol enables the effective recovery of weak signals that are almost drown in the serious undersampling artifacts, and the distinction and recognition of close chemical shifts even though using merely 5.4% data, highlighting its huge potentials on fast pure shift NMR spectroscopy. As a result, this study affords a promising paradigm for the AI-assisted NMR protocols toward broader applications in chemistry, biology, materials, and life sciences, and among others.

Design, synthesis and biological evaluation of new RNF126-based p300/CBP degraders.

Histone acetyltransferase CREB-binding protein (CBP) and its homologous protein p300 are key transcriptional activators that can activate oncogene transcription, which present promising targets for cancer therapy. Here, we designed and synthesized a series of p300/CBP targeted low molecular weight PROTACs by assembling the covalent ligand of RNF126 E3 ubiquitin ligase and the bromodomain ligand of the p300/CBP. The optimal molecule A8 could effectively degrade p300 and CBP through the ubiquitin-proteasome system in time- and concentration-dependent manners, with half-maximal degradation (DC50) concentrations of 208.35/454.35 nM and 82.24/79.45 nM for p300/CBP in MV4-11 and Molm13 cell lines after 72 h of treatment. And the degradation of p300/CBP by A8 is dependent on the ubiquitin-proteasome pathway and its simultaneous interactions with the target proteins and RNF126. A8 exhibits good antiproliferative activity in a series of p300/CBP-dependent cancer cells. It could transcriptionally inhibit the expression of c-Myc, induce cell cycle arrest in the G0/G1 phase and apoptosis in MV4-11 cells. This study thus provided us a new chemotype for the development of drug-like PROTACs targeting p300/CBP, which is expected to be applied in cancer therapy.

Comprehensive analysis of the mitochondrial genome of Rehmannia glutinosa: insights into repeat-mediated recombinations and RNA editing-induced stop codon acquisition.

Rehmannia glutinosa is an economically significant medicinal plant. Yet, the structure and sequence of its mitochondrial genome has not been published, which plays a crucial role in evolutionary analysis and regulating respiratory-related macromolecule synthesis. In this study, the R. glutinosa mitogenome was sequenced employing a combination of Illumina short reads and Nanopore long reads, with subsequent assembly using a hybrid strategy. We found that the predominant configuration of the R. glutinosa mitogenome comprises two circular chromosomes. The primary structure of the mitogenome encompasses two mitochondrial chromosomes corresponding to the two major configurations, Mac1-1 and Mac1-2. The R. glutinosa mitogenome encoded an angiosperm-typical set of 24 core genes, nine variable genes, three rRNA genes, and 15 tRNA genes. A phylogenetic analysis using the 16 shared protein-coding genes (PCG) yielded a tree consistent with the phylogeny of Lamiales species and two outgroup taxa. Mapping RNA-seq data to the coding sequences (CDS) of the PCGs revealed 507 C-to-U RNA editing sites across 31 PCGs of the R. glutinosa mitogenome. Furthermore, one start codon (nad4L) and two stop codons (rpl10 and atp6) were identified as products of RNA editing events in the R. glutinosa mitogenome.

Development of interpenetrating network hydrogels: Enhancing the release and bioaccessibility of green tea polyphenols.

Green Tea polyphenols (GTP) are important bioactive compounds with excellent physiological regulation functions. However, they are easily destroyed by the gastric environment during digestion. In this work, a sodium alginate (SA)-gellan gum (GG) interpenetrating network (IPN) hydrogel was synthesized to protect and delivery GTP. The ratio of SA/GG significantly affects the network structure of IPN hydrogels and the performance of delivering GTP. The hydrogel formed by interpenetrating 20 % GG with 80 % SA as the main network had the highest water uptake (55 g/g), holding capacity (950 mg/g), and freeze-thaw stability, with springiness reaching 0.933 and hardness reaching 1300 g, which due to the filling effect and non-covalent interaction. Rheological tests showed that the crosslink density of IPN hydrogel in SA-dominated network was improved by the addition of GG to make it better bound to GTP, and the higher water uptake meant that the system could absorb more GTP-containing solution. This IPN hydrogel maintained 917.3 mg/g encapsulation efficiency at the highest loading capacity (1080 mg/g) in tests as delivery system. In in vitro digestion simulations, owing to the pH responsiveness, the IPN hydrogel reduced the loss of GTP in gastric fluid, achieving a bioaccessibility of 71.6 % in the intestinal tract.

Hyperdimensional computing with holographic and adaptive encoder.

Introduction: Brain-inspired computing has become an emerging field, where a growing number of works focus on developing algorithms that bring machine learning closer to human brains at the functional level. As one of the promising directions, Hyperdimensional Computing (HDC) is centered around the idea of having holographic and high-dimensional representation as the neural activities in our brains. Such representation is the fundamental enabler for the efficiency and robustness of HDC. However, existing HDC-based algorithms suffer from limitations within the encoder. To some extent, they all rely on manually selected encoders, meaning that the resulting representation is never adapted to the tasks at hand. Methods: In this paper, we propose FLASH, a novel hyperdimensional learning method that incorporates an adaptive and learnable encoder design, aiming at better overall learning performance while maintaining good properties of HDC representation. Current HDC encoders leverage Random Fourier Features (RFF) for kernel correspondence and enable locality-preserving encoding. We propose to learn the encoder matrix distribution via gradient descent and effectively adapt the kernel for a more suitable HDC encoding. Results: Our experiments on various regression datasets show that tuning the HDC encoder can significantly boost the accuracy, surpassing the current HDC-based algorithm and providing faster inference than other baselines, including RFF-based kernel ridge regression. Discussion: The results indicate the importance of an adaptive encoder and customized high-dimensional representation in HDC.

Joint Bayesian estimation of cell dependence and gene associations in spatially resolved transcriptomic data.

Recent technologies such as spatial transcriptomics, enable the measurement of gene expressions at the single-cell level along with the spatial locations of these cells in the tissue. Spatial clustering of the cells provides valuable insights into the understanding of the functional organization of the tissue. However, most such clustering methods involve some dimension reduction that leads to a loss of the inherent dependency structure among genes at any spatial location in the tissue. This destroys valuable insights of gene co-expression patterns apart from possibly impacting spatial clustering performance. In spatial transcriptomics, the matrix-variate gene expression data, along with spatial coordinates of the single cells, provides information on both gene expression dependencies and cell spatial dependencies through its row and column covariances. In this work, we propose a joint Bayesian approach to simultaneously estimate these gene and spatial cell correlations. These estimates provide data summaries for downstream analyses. We illustrate our method with simulations and analysis of several real spatial transcriptomic datasets. Our work elucidates gene co-expression networks as well as clear spatial clustering patterns of the cells. Furthermore, our analysis reveals that downstream spatial-differential analysis may aid in the discovery of unknown cell types from known marker genes.

The effects of adding various starches on the structures of restructured potato-based dough and the oil uptake of potato chips.

BACKGROUND: The material composition significantly influences the oil absorption and quality characteristics of fried food products. The oil absorption of restructured potato chips is highly dependent on the structural properties of the restructured potato-based dough produced prior to frying. In this study, three types of starch were added to modify the structure of restructured potato-based dough, allowing the production of potato chips with less oil absorption. RESULTS: Distinct differences were observed among the three types of starch in terms of amylose content, chain length distribution, swelling power, solubility, crystalline structure and pasting properties. The addition of wheat starch, corn starch and tapioca starch changed the rheological properties, water distribution and strength of the restructured dough. Importantly, adding wheat starch and corn starch significantly lowered the oil content of potato chips by 7.94% and 13.06%, respectively. The reduction in oil absorption by potato chips was attributed to the increased strength of the starchy gel network of the dough, a slower rate of water evaporation and a limitation of dough expansion during frying. CONCLUSION: Adding wheat starch or corn starch to restructured potato-based dough resulted in a decrease in the oil absorption of potato chips by creating a stronger starchy gel network in the dough. This study could guide the development of suitable material compositions, which are important for producing fried food products with lower oil content. © 2024 Society of Chemical Industry.

Modulating charge oriented accumulation via interfacial chemical-bond on In2O3/Bi2MoO6 heterostructures for photocatalytic nitrogen fixation.

Photocatalytic nitrogen fixation presents an eco-friendly approach to converting atmospheric nitrogen into ammonia (NH3), but the process faces challenges due to rapid interface charge recombination. Here, we report an innovative charge transfer and oriented accumulation strategy using an In-O-Mo bond-modulated S-scheme heterostructure composed of In2O3/Bi2MoO6 (In/BMO) synthesized using a simple electrostatic assembly. The unique interfacial arrangement with optimal photocatalyst configuration (3 % In/BMO) enabled enhanced photogenerated electron separation and transfer, leading to a remarkable nitrogen fixation rate of approximately 150.9 µmol·gcat-1·h-1 under visible light irradiation. The performance of the photocatalyst was 9-fold and 27-fold higher than that of its pristine components, Bi2MoO6 and In2O3, respectively. The experimental and theoretical evaluation deemed interfacial In-O-Mo bonds crucial for rapid transfer and charge-oriented accumulation. Whereas the generated internal electric field drove the spatial separation and transfer of photo-generated electrons and holes, significantly enhancing the photocatalytic N2-to-NH3 conversion efficiency. The proposed work lays the foundation for designing S-scheme heterostructures with highly efficient interfacial bonds, offering a promising avenue for substantial improvements in photocatalytic nitrogen fixation.

Comparative chloroplast genomes study of five officinal Ardisia Species: Unraveling interspecific diversity and evolutionary insights in Ardisia.

Ardisia S.W. (Primulaceae), naturally distributed in tropical and subtropical regions, has edible and medicinal values and is prevalent in clinical and daily use in China. More genetic information for distinct species delineation is needed to support the development and utilization of the genus Ardisia. We sequenced, annotated, and compared the chloroplast genomes of five Ardisia species: A. brunnescens, A. pusilla, A. squamulosa, A. crenata, and A. brevicaulis in this study. We found a typical quadripartite structure in all five chloroplast genomes, with lengths ranging from 155,045 to 156,943 bp. Except for A. pusilla, which lacked the ycf15 gene, the other four Ardisia species contained 114 unique genes, including 79 protein-coding genes, 30 tRNAs, and four rRNAs. In addition, the rps19 pseudogene gene was present only in A. brunnescens. Five highly variable DNA barcodes were identified for five Ardisia species, including trnT-GGU-psbD, trnT-UGU-trnL-UAA, rps4-trnT-UGU, rpl32-trnL-UAG, and rpoB-trnC-GAA. The RNA editiing sites of protein-coding genes in the five Ardisia plastome were characterized and compared, and 274 (A. crenata)-288 (A. brevicaulis) were found. The results of the phylogenetic analysis were consistent with the morphological classification. Sequence alignment and phylogenetic analysis showed that ycf15 genes were highly divergent in Primulaceae. Reconstructions of ancestral character states indicated that leaf margin morphology is critical for classifying the genus Ardisia, with a rodent-like character being the most primitive. These results provide valuable information on the taxonomy and evolution of Ardisia plants.

Plant mitochondrial genome map (PMGmap): A software tool for the comprehensive visualization of coding, noncoding and genome features of plant mitochondrial genomes.

Tools for visualizing genomes are essential for investigating genomic features and their interactions. Currently, tools designed originally for animal mitogenomes and plant plastomes are used to visualize the mitogens of plants but cannot accurately display features specific to plant mitogenomes, such as nonlinear exon arrangement for genes, the prevalence of functional noncoding features and complex chromosomal architecture. To address these problems, a software package, plant mitochondrial genome map (PMGmap), was developed using the Python programming language. PMGmap can draw genes at exon levels; draw cis- and trans-splicing gene maps, noncoding features and repetitive sequences; and scale genic regions by using the scaling of the genic regions on the mitogenome (SAGM) algorithm. It can also draw multiple chromosomes simultaneously. Compared with other state-of-the-art tools, PMGmap showed better performance in visualizing 405 plant mitogenomes, showing potential as an invaluable tool for plant mitogenome research. The web and container versions and the source code of PMGmap can be accessed through the following link: http://www.1kmpg.cn/pmgmap.

The Complete Mitochondrial Genome of Paeonia lactiflora Pall. (Saxifragales: Paeoniaceae): Evidence of Gene Transfer from Chloroplast to Mitochondrial Genome.

Paeonia lactiflora (P. lactiflora), a perennial plant renowned for its medicinal roots, provides a unique case for studying the phylogenetic relationships of species based on organelle genomes, as well as the transference of DNA across organelle genomes. In order to investigate this matter, we sequenced and characterized the mitochondrial genome (mitogenome) of P. lactiflora. Similar to the chloroplast genome (cpgenome), the mitogenome of P. lactiflora extends across 181,688 base pairs (bp). Its unique quadripartite structure results from a pair of extensive inverted repeats, each measuring 25,680 bp in length. The annotated mitogenome includes 27 protein-coding genes, 37 tRNAs, 8 rRNAs, and two pseudogenes (rpl5, rpl16). Phylogenetic analysis was performed to identify phylogenetic trees consistent with Paeonia species phylogeny in the APG Ⅳ system. Moreover, a total of 12 MTPT events were identified and 32 RNA editing sites were detected during mitogenome analysis of P. lactiflora. Our research successfully compiled and annotated the mitogenome of P. lactiflora. The study provides valuable insights regarding the taxonomic classification and molecular evolution within the Paeoniaceae family.

A Bayesian approach for investigating the pharmacogenetics of combination antiretroviral therapy in people with HIV.

Combination antiretroviral therapy (ART) with at least three different drugs has become the standard of care for people with HIV (PWH) due to its exceptional effectiveness in viral suppression. However, many ART drugs have been reported to associate with neuropsychiatric adverse effects including depression, especially when certain genetic polymorphisms exist. Pharmacogenetics is an important consideration for administering combination ART as it may influence drug efficacy and increase risk for neuropsychiatric conditions. Large-scale longitudinal HIV databases provide researchers opportunities to investigate the pharmacogenetics of combination ART in a data-driven manner. However, with more than 30 FDA-approved ART drugs, the interplay between the large number of possible ART drug combinations and genetic polymorphisms imposes statistical modeling challenges. We develop a Bayesian approach to examine the longitudinal effects of combination ART and their interactions with genetic polymorphisms on depressive symptoms in PWH. The proposed method utilizes a Gaussian process with a composite kernel function to capture the longitudinal combination ART effects by directly incorporating individuals' treatment histories, and a Bayesian classification and regression tree to account for individual heterogeneity. Through both simulation studies and an application to a dataset from the Women's Interagency HIV Study, we demonstrate the clinical utility of the proposed approach in investigating the pharmacogenetics of combination ART and assisting physicians to make effective individualized treatment decisions that can improve health outcomes for PWH.

The body mass index and the risk of ectopic pregnancy: a 5-year retrospective case-control study.

PURPOSE: Acknowledging the associated risk factors may have a positive impact on reducing the incidence of ectopic pregnancy (EP). In recent years, body mass index (BMI) has been mentioned in research. However, few studies are available and controversial on the relationship between EP and BMI. METHODS: We retrospectively studied the EP women as a case group and the deliveries as a control group in the central hospital of Wuhan during 2017 ~ 2021. χ2 test of variables associated with ectopic pregnancy was performed to find differences. Univariate and multivariate binary logistic regression analysis was conducted to analyze the association of the variables of age, parity, history of induced abortion, history of ectopic pregnancy, history of spontaneous abortion, history of appendectomy surgery and BMI (< 18.5 kg/m2, 18.5 ~ 24.9 kg/m2, 25 kg/m2 ~ 29.9 kg/m2, ≥ 30 kg /m2) with EP. RESULTS: They were 659 EP and 1460 deliveries. The variables of age, parity, history of induced abortion, history of ectopic pregnancy and BMI were different significantly(P < 0.05). Multivariate analysis showed that the variables of age > 35 years old [(OR (Odds Ratio), 5.415; 95%CI (Confidence Interval), 4.006 ~ 7.320, P < 0.001], history of ectopic pregnancy (OR, 3.944; 95%CI, 2.405 ~ 6.467; P < 0.001), history of induced abortion(OR, 3.365; 95%CI, 2.724 ~ 4.158, P < 0.001) and low BMI (< 18.5 kg/m2) (OR, 1.929; 95%CI, 1.416 ~ 2.628, P < 0.001])increased the risk of EP. CONCLUSION: The history of ectopic pregnancy, history of induced abortion and age > 35 years old were the risk factors with EP. In addition to these traditional factors, we found low BMI (< 18.5 kg/m2) with women may increase the risk to EP.

The association between passive smoking and sleep quality in a Chinese hypertensive population: A cross-sectional study.

INTRODUCTION: This study evaluates the association between passive smoking, specifically secondhand smoke (SHS) and thirdhand smoke (THS) exposure, and sleep quality in a hypertensive population. METHODS: We enrolled 1427 eligible hypertensive patients from a 2022 national cross-sectional survey in China. Data on tobacco smoke exposure and sleep were collected via questionnaires. Multiple logistic regression and linear regression were employed to assess the relationship between passive smoking and sleep quality characteristics, as well as the correlation between passive smoking exposure characteristics and sleep quality. RESULTS: Among 589 hypertensive patients with no tobacco smoke exposure, 679 exposed to SHS, and 159 exposed to THS, SHS exposure was associated with a higher risk of poor sleep quality, even after adjusting for potential confounding factors (ß=0.10; 95% CI: 0.32-0.95). No significant relationship was observed between THS exposure and sleep quality. SHS exposure was associated with various sleep quality characteristics, including shorter sleep duration (AOR=1.71; 95% CI: 1.06-2.76) and increased frequency of 1-2 sleep disturbances per week (AOR=1.68; 95% CI: 1.25-2.26). Individuals exposed to SHS were more likely to experience poorer subjective sleep quality (AOR=1.53; 95% CI: 1.07-2.21) and have sleep efficiency <65% (AOR=1.82; 95% CI: 1.22-2.71). Exposure to passive smoking at home, in the community, in public places, exposure to passive smoking with family and friends, and increased frequency of exposure, were all associated with a higher risk of poor sleep quality. CONCLUSIONS: Our study suggests that SHS exposure in hypertensive populations is associated with poor sleep quality and various characteristics of sleep quality. No significant association was found between THS exposure and sleep quality. These findings underscore the need to enhance tobacco control efforts in China, particularly for individuals with chronic diseases, to safeguard public health.

Independent association of the Meckel's cave with trigeminal neuralgia and development of a screening tool.

PURPOSE: To 1) investigate the association of the properties of the Meckel's cave (MC) with TN occurrence (i.e., affected vs. unaffected nerves) and whether such association was independent of neurovascular contact (NVC); and 2) develop an objective screening tool for TN. MATERIALS AND METHODS: Two hundred and nineteen trigeminal nerves were included. (The severity of) NVC was identified for individual nerve, and a set of 107 radiomic features were extracted to characterize various properties of each MC. Both procedures were primarily based on magnetic resonance imaging sequences. A radiomic score (Rad-score) was constructed for each MC to integrate the features associated with TN occurrence. Independent t-test and logistic regression were conducted to assess the association and develop the screening tool mentioned above. RESULTS: Twelve features were selected to build the Rad-score, with the Inverse Difference Moment Normalized (IDMN) having the greatest weight. The Rad-score was significantly (p ≤ 0.05) higher in the affected compared to the unaffected nerves, irrespective of NVC. The Rad-score and NVC were incorporated in the regression model/screening tool, which demonstrated an acceptable discriminating ability (C-statistic = 0.84). CONCLUSION: This study has identified a potential association of the properties/features of the MC with TN occurrence, probably involving the demyelination and axonal injury of the trigeminal ganglion within the MC as suggested by the IDMN. Such association may be independent of NVC. This finding may provide new insight into the etiology and/or pathophysiology of TN. The screening tool, which demonstrated an acceptable discriminating ability, may contribute to an improvement in its diagnosis.

Tannic acid-enriched nanocellulose hydrogels improve physical and oxidative stability of high-internal-phase Pickering emulsions.

A cellulose suspension and tannic acid (TA) were co-sonicated to prepare TA-incorporated nanocellulose hydrogels with the aim of improving the physical and oxidative stability of high-internal-phase emulsions (HIPEs). Cellulose nanocrystal (CNC) hydrogels were used to stabilize HIPEs, relying on the interfacial adsorption behavior of CNCs and the reversible gelation properties of hydrogels. TA was incorporated due to its ability to improve emulsification performance and antioxidant properties. Introducing TA enhanced the gel strength of hydrogels by decreasing the interfibrillar distance. The utilization of CNC-TA hydrogels effectively improved physical properties of HIPEs. This improvement included a reduction in droplet size from the initial 103.41 µm to 39.66 µm, an enhancement of the gel structure, and an improvement in storage stability. A denser and orderly interfacial structure was formed in CNCs-TA hydrogel stabilized HIPEs due to anchoring TA at the interface driven by the hydrogen-bonding interaction between CNCs and TA. This densely interfacial layer with good antioxidant activity markedly enhanced the oxidative stability of emulsions, as evidenced by the low level of oxidation products in HIPEs. This study has the potential to extend the utilization of CNC-stabilized emulsions to new applications in the food, cosmetic, and pharmaceutical industries.

Scalar-Function Causal Discovery for Generating Causal Hypotheses with Observational Wearable Device Data.

Digital health technologies such as wearable devices have transformed health data analytics, providing continuous, high-resolution functional data on various health metrics, thereby opening new avenues for innovative research. In this work, we introduce a new approach for generating causal hypotheses for a pair of a continuous functional variable (e.g., physical activities recorded over time) and a binary scalar variable (e.g., mobility condition indicator). Our method goes beyond traditional association-focused approaches and has the potential to reveal the underlying causal mechanism. We theoretically show that the proposed scalar-function causal model is identifiable with observational data alone. Our identifiability theory justifies the use of a simple yet principled algorithm to discern the causal relationship by comparing the likelihood functions of competing causal hypotheses. The robustness and applicability of our method are demonstrated through simulation studies and a real-world application using wearable device data from the National Health and Nutrition Examination Survey.

The role of Nurr1-miR-30e-5p-NLRP3 axis in inflammation-mediated neurodegeneration: insights from mouse models and patients' studies in Parkinson's disease.

Nuclear receptor related-1 (Nurr1), a ligand-activated transcription factor, is considered a potential susceptibility gene for Parkinson's disease (PD), and has been demonstrated to possess protective effects against inflammation-induced neuronal damage. Despite the evidence showing decreased NURR1 level and increased pro-inflammatory cytokines in cell and animal models as well as in PD patients' peripheral blood mononuclear cells (PBMCs), the underlying mechanism remains elusive. In this study, we investigated the molecular mechanism of Nurr1 in PD-related inflammation. Through the miRNA-sequencing and verification in PBMCs from a cohort of 450 individuals, we identified a significant change of a Nurr1-dependent miRNA miR-30e-5p in PD patients compared to healthy controls (HC). Additionally, PD patients exhibited an elevated plasma interleukin-1ß (IL-1ß) level and increased nucleotide-binding domain-like receptor protein 3 (NLRP3) expression in PBMCs compared to HC. Statistical analyses revealed significant correlations among NURR1, miR-30e-5p, and NLRP3 levels in the PBMCs of PD patients. To further explore the involvement of Nurr1-miR-30e-5p-NLRP3 axis in the inflammation-mediated PD pathology, we developed a mouse model (Nurr1flox+/Cd11b-cre+, Nurr1cKO) conditionally knocking out Nurr1 in Cd11b-expressing cells. Our investigations in Nurr1cKO mice unveiled significant dopaminergic neurodegeneration following lipopolysaccharide-induced inflammation. Remarkably, Nurr1 deficiency triggered microglial activation and activated NLRP3 inflammasome, resulting in increased IL-1ß secretion. Coincidently, we found that miR-30e-5p level was significantly decreased in the PBMCs and primary microglia of Nurr1cKO mice compared to the controls. Furthermore, our in vitro experiments demonstrated that miR-30e-5p specifically targeted NLRP3. In Nurr1-knockdown microglia, NLRP3 expression was upregulated via miR-30e-5p. In summary, our findings highlight the involvement of Nurr1-miR-30e-5p-NLRP3 axis in the inflammation-mediated neurodegeneration in PD, the results of which may offer promising prospects for developing PD biomarkers and targeted therapeutic interventions.