[Cystic fibrosis primarily presenting with pseudo-Bartter syndrome: a report of three cases and literature review]. / 以假性Bartter综合征为主要表现的囊性纤维化患儿3ä¾‹å¹¶æ–‡çŒ®å¤ä¹ .

OBJECTIVES: To summarize the clinical characteristics and genetic variations in children with cystic fibrosis (CF) primarily presenting with pseudo-Bartter syndrome (CF-PBS), with the aim to enhance understanding of this disorder. METHODS: A retrospective analysis was performed on the clinical data of three children who were diagnosed with CF-PBS in Hunan Children's Hospital from January 2018 to August 2023, and a literature review was performed. RESULTS: All three children had the onset of the disease in infancy. Tests after admission showed hyponatremia, hypokalemia, hypochloremia, and metabolic alkalosis, and genetic testing showed the presence of compound heterozygous mutation in the CFTR gene. All three children were diagnosed with CF. Literature review obtained 33 Chinese children with CF-PBS, with an age of onset of 1-36 months and an age of diagnosis of 3-144 months. Among these children, there were 29 children with recurrent respiratory infection or persistent pneumonia (88%), 26 with malnutrition (79%), 23 with developmental retardation (70%), and 18 with pancreatitis or extrapancreatic insufficiency (55%). Genetic testing showed that c.2909G>A was the most common mutation site of the CFTR gene, with a frequency of allelic variation of 23% (15/66). CONCLUSIONS: CF may have no typical respiratory symptoms in the early stage. The possibility of CF-PBS should be considered for infants with recurrent hyponatremia, hypokalemia, hypochloremia, and metabolic alkalosis, especially those with malnutrition and developmental retardation. CFTR genetic testing should be performed as soon as possible to help with the diagnosis of CF.

Quantifying the potential of cascade outbreaks via early infected nodes using network percolation.

In many fields, accurate prediction of cascade outbreaks during their early stages of propagation is of paramount importance. Based on percolation theory, we propose a global propagation probability algorithm that effectively estimates the probability of information spreading from source nodes to the giant component. Building on this, we further introduce an early prediction method for cascade outbreaks, which provides quantitative predictions of both the probability and scope of cascade outbreaks by fully considering the network structure data and propagation dynamics. Through our research, we observe that cascade outbreaks resemble a phase transition. When approaching the critical point of an outbreak, a few specific activating nodes typically facilitate the transmission of information throughout the entire network, thus enabling early inference of a cascading outbreak. To validate our findings, we conducted experiments on diverse network structures using a classical propagation model and applied our proposed method to analyze a real microblog cascade dataset. The experimental results robustly demonstrate the superiority of our approach over baseline methods in terms of effectively predicting cascade outbreaks with high precision and early detection capability.

Higher-order Granger reservoir computing: simultaneously achieving scalable complex structures inference and accurate dynamics prediction.

Recently, machine learning methods, including reservoir computing (RC), have been tremendously successful in predicting complex dynamics in many fields. However, a present challenge lies in pushing for the limit of prediction accuracy while maintaining the low complexity of the model. Here, we design a data-driven, model-free framework named higher-order Granger reservoir computing (HoGRC), which owns two major missions: The first is to infer the higher-order structures incorporating the idea of Granger causality with the RC, and, simultaneously, the second is to realize multi-step prediction by feeding the time series and the inferred higher-order information into HoGRC. We demonstrate the efficacy and robustness of the HoGRC using several representative systems, including the classical chaotic systems, the network dynamical systems, and the UK power grid system. In the era of machine learning and complex systems, we anticipate a broad application of the HoGRC framework in structure inference and dynamics prediction.

Skin-Interfaced Bifluidic Paper-Based Device for Quantitative Sweat Analysis.

The erratic, intermittent, and unpredictable nature of sweat production, resulting from physiological or psychological fluctuations, poses intricacies to consistently and accurately sample and evaluate sweat biomarkers. Skin-interfaced microfluidic devices that rely on colorimetric mechanisms for semi-quantitative detection are particularly susceptible to these inaccuracies due to variations in sweat secretion rate or instantaneous volume. This work introduces a skin-interfaced colorimetric bifluidic sweat device with two synchronous channels to quantify sweat rate and biomarkers in real-time, even during uncertain sweat activities. In the proposed bifluidic-distance metric approach, with one channel to measure sweat rate and quantify collected sweat volume, the other channel can provide an accurate analysis of the biomarkers based on the collected sweat volume. The closed channel design also reduces evaporation and resists contamination from the external environment. The feasibility of the device is highlighted in a proof-of-the-concept demonstration to analyze sweat chloride for evaluating hydration status and sweat glucose for assessing glucose levels. The low-cost yet highly accurate device provides opportunities for clinical sweat analysis and disease screening in remote and low-resource settings. The developed device platform can be facilely adapted for the other biomarkers when corresponding colorimetric reagents are exploited.

Skin-interfaced microfluidic sweat collection devices for personalized hydration management through thermal feedback.

Non-electronic wearables that utilize skin-interfaced microfluidic technology have revolutionized the collection and analysis of human sweat, providing valuable biochemical information and indicating body hydration status. However, existing microfluidic devices often require constant monitoring of data during sweat assessment, thereby impeding the user experience and potentially missing anomalous physiological events, such as excessive sweating. Moreover, the complex manufacturing process hampers the scalability and large-scale production of such devices. Herein, we present a self-feedback microfluidic device with a unique dehydration reminder through a cost-effective "CAD-to-3D device" approach. It incorporates two independent systems for sweat collection and thermal feedback, including serpentine microchannels, reservoirs, petal-like bursting valves and heating chambers. The device operates by sequentially collecting sweat in the channels and reservoirs, and then activating thermal stimulators in the heating chambers through breaking the valves, initiating a chemical exothermic reaction. Human trials validate that the devices effectively alert users to potential dehydration by inducing skin thermal sensations triggered by sweat sampling. The proposed device offers facile scalability and customizable fabrication, and holds promise for managing hydration strategies in real-world scenarios, benefiting individuals engaged in sporting activities or exposed to high-temperature settings.

Impact of Allee and fear effects in a fractional order prey-predator system with group defense and prey refuge.

This paper presents a novel fractional-order model of a prey-predator system that incorporates group defense and prey refuge mechanisms, along with Allee and fear effects. First, we examine the existence, uniqueness, non-negativity, and boundedness of the solution of the system. Second, a comprehensive analysis is conducted on the existence, stability, and coexistence of equilibrium states in the system, which are crucial for comprehending prey-predator system behavior. Our investigation reveals that the coexistence equilibrium undergoes a Hopf bifurcation under five key parameters. Specifically, an increased threshold for the transition between group and individual behavior, influenced by different strengths of the Allee effect, enhances the stability of both populations. This discovery sheds light on the role of group effects in shaping prey-predator interactions and ecosystem stability. Third, system discretization is employed to explore the impact of step size on stimulating stability and to investigate the Neimark-Sacker bifurcation, providing a more comprehensive understanding of system behavior. The role of step size as a constraint on stability is examined, revealing the system's progression from stability to chaos. Consequently, our results offer a more flexible mechanism for adjusting the stability and dynamics of the two species. Finally, numerical simulations are utilized to validate the reasonableness of the research findings.

Arthroscopic treatment of ankle impingement syndrome.

Arthroscopic treatment of ankle impingement syndrome (AIS) is a minimally invasive surgical procedure used to address symptoms caused by impingement in the ankle joint. This syndrome occurs when there is abnormal contact between certain bones or soft tissues in the ankle, leading to pain, swelling, or limited range of motion. Traditionally, open surgery was the standard approach for treating AIS. However, with advancements in technology and surgical techniques, arthroscopic treatment has become a preferred method for many patients and surgeons. With improved visualization and precise treatment of the arthroscopy, patients can experience reduced pain and improved functionality, allowing them to return to their daily activities sooner. In this paper, we reviewed the application and clinical efficacy the of arthroscopic approach for treating AIS, hoping to provide a reference for its future promotion.

Identification of specific prognostic markers for lung squamous cell carcinoma based on tumor progression, immune infiltration, and stem index.

Introduction: Lung squamous cell carcinoma (LUSC) is a unique subform of nonsmall cell lung cancer (NSCLC). The lack of specific driver genes as therapeutic targets leads to worse prognoses in patients with LUSC, even with chemotherapy, radiotherapy, or immune checkpoint inhibitors. Furthermore, research on the LUSC-specific prognosis genes is lacking. This study aimed to develop a comprehensive LUSC-specific differentially expressed genes (DEGs) signature for prognosis correlated with tumor progression, immune infiltration,and stem index. Methods: RNA sequencing data for LUSC and lung adenocarcinoma (LUAD) were extracted from The Cancer Genome Atlas (TCGA) data portal, and DEGs analyses were conducted in TCGA-LUSC and TCGA-LUAD cohorts to identify specific DEGs associated with LUSC. Functional analysis and protein-protein interaction network were performed to annotate the roles of LUSC-specific DEGs and select the top 100 LUSC-specific DEGs. Univariate Cox regression and least absolute shrinkage and selection operator regression analyses were performed to select prognosis-related DEGs. Results: Overall, 1,604 LUSC-specific DEGs were obtained, and a validated seven-gene signature was constructed comprising FGG, C3, FGA, JUN, CST3, CPSF4, and HIST1H2BH. FGG, C3, FGA, JUN, and CST3 were correlated with poor LUSC prognosis, whereas CPSF4 and HIST1H2BH were potential positive prognosis markers in patients with LUSC. Receiver operating characteristic analysis further confirmed that the genetic profile could accurately estimate the overall survival of LUSC patients. Analysis of immune infiltration demonstrated that the high risk (HR) LUSC patients exhibited accelerated tumor infiltration, relative to low risk (LR) LUSC patients. Molecular expressions of immune checkpoint genes differed significantly between the HR and LR cohorts. A ceRNA network containing 19 lncRNAs, 50 miRNAs, and 7 prognostic DEGs was constructed to demonstrate the prognostic value of novel biomarkers of LUSC-specific DEGs based on tumor progression, stemindex, and immune infiltration. In vitro experimental models confirmed that LUSC-specific DEG FGG expression was significantly higher in tumor cells and correlated with immune tumor progression, immune infiltration, and stem index. In vitro experimental models confirmed that LUSC-specific DEG FGG expression was significantly higher in tumor cells and correlated with immune tumor progression, immune infiltration, and stem index. Conclusion: Our study demonstrated the potential clinical implication of the 7- DEGs signature for prognosis prediction of LUSC patients based on tumor progression, immune infiltration, and stem index. And the FGG could be an independent prognostic biomarker of LUSC promoting cell proliferation, migration, invasion, THP-1 cell infiltration, and stem cell maintenance.

Effectiveness and safety of arthroscopy combined with radial extracorporeal shockwave therapy for osteochondritis of the talus: a prospective, single-centre, randomized, double-blind study.

Aims: Arthroscopic microfracture is a conventional form of treatment for patients with osteochondritis of the talus, involving an area of < 1.5 cm2. However, some patients have persistent pain and limitation of movement in the early postoperative period. No studies have investigated the combined treatment of microfracture and shortwave treatment in these patients. The aim of this prospective single-centre, randomized, double-blind, placebo-controlled trial was to compare the outcome in patients treated with arthroscopic microfracture combined with radial extracorporeal shockwave therapy (rESWT) and arthroscopic microfracture alone, in patients with ostechondritis of the talus. Methods: Patients were randomly enrolled into two groups. At three weeks postoperatively, the rESWT group was given shockwave treatment, once every other day, for five treatments. In the control group the head of the device which delivered the treatment had no energy output. The two groups were evaluated before surgery and at six weeks and three, six and 12 months postoperatively. The primary outcome measure was the American Orthopaedic Foot and Ankle Society (AOFAS) Ankle-Hindfoot Scale. Secondary outcome measures included a visual analogue scale (VAS) score for pain and the area of bone marrow oedema of the talus as identified on sagittal fat suppression sequence MRI scans. Results: A total of 40 patients were enrolled and randomly divided into the two groups, with 20 in each. There was no statistically significant difference in the baseline characteristics of the groups. No complications, such as wound infection or neurovascular injury, were found during follow-up of 12 months. The mean AOFAS scores in the rESWT group were significantly higher than those in the control group at three, six, and 12 months postoperatively (p < 0.05). The mean VAS pain scores in the rESWT group were also significantly lower than those in the control group at these times (p < 0.05). The mean area of bone marrow oedema in the rESWT group was significantly smaller at six and 12 months than in the control group at these times (p < 0.05). Conclusion: Local shockwave therapy was safe and effective in patients with osteochondiritis of the talus who were treated with a combination of arthroscopic surgery and rESWT. Preliminary results showed that, compared with arthroscopic microfracture alone, those treated with arthroscopic microfracture combined with rESWT had better relief of pain at three months postoperatively and improved weightbearing and motor function of the ankle.

Tipping Point Detection Using Reservoir Computing.

Detection in high fidelity of tipping points, the emergence of which is often induced by invisible changes in internal structures or/and external interferences, is paramountly beneficial to understanding and predicting complex dynamical systems (CDSs). Detection approaches, which have been fruitfully developed from several perspectives (e.g., statistics, dynamics, and machine learning), have their own advantages but still encounter difficulties in the face of high-dimensional, fluctuating datasets. Here, using the reservoir computing (RC), a recently notable, resource-conserving machine learning method for reconstructing and predicting CDSs, we articulate a model-free framework to accomplish the detection only using the time series observationally recorded from the underlying unknown CDSs. Specifically, we encode the information of the CDS in consecutive time durations of finite length into the weights of the readout layer in an RC, and then we use the learned weights as the dynamical features and establish a mapping from these features to the system's changes. Our designed framework can not only efficiently detect the changing positions of the system but also accurately predict the intensity change as the intensity information is available in the training data. We demonstrate the efficacy of our supervised framework using the dataset produced by representative physical, biological, and real-world systems, showing that our framework outperforms those traditional methods on the short-term data produced by the time-varying or/and noise-perturbed systems. We believe that our framework, on one hand, complements the major functions of the notable RC intelligent machine and, on the other hand, becomes one of the indispensable methods for deciphering complex systems.

Robustness measurement of multiplex networks based on graph spectrum.

Networks can provide effective representations of the relationships between elements in complex systems through nodes and links. On this basis, relationships between multiple systems are often characterized as multilayer networks (or networks of networks). As a typical representative, a multiplex network is often used to describe a system in which there are many replaceable or dependent relationships among elements in different layers. This paper studies robustness measures for different types of multiplex networks by generalizing the natural connectivity calculated from the graph spectrum. Experiments on model and real multiplex networks show a close correlation between the robustness of multiplex networks consisting of connective or dependent layers and the natural connectivity of aggregated networks or intersections between layers. These indicators can effectively measure or estimate the robustness of multiplex networks according to the topology of each layer. Our findings shed new light on the design and protection of coupled complex systems.

Novel Loss of Function (G15D) Mutation on RAC2 in a Family with Combined Immunodeficiency and Increased Levels of Immunoglobulin G, A, and E.

Ras-related C3 botulinum toxin substrate 2 (RAC2) is a small guanine nucleotide binding molecule that is exclusively expressed in hematopoietic cell lineages as a switcher. Based on in vivo and/or in vitro model experiments, RAC2 plays important roles in different cells through proliferation, secretion, and phagocytosis. It also performs a suppressing function in immunoglobulin (Ig) switching in Rac2-/- animals or cells. Several RAC2 natural mutations have been described in patients with primary immunodeficiency. RAC2 mutations can be classified into loss-of-function inactivating (LoF-I) and gain-of-function activating mutations according to their functional effects. Only two LoF-I mutations on RAC2 have been reported, including a dominant D57N mutation in several cases that exhibit granulocyte function defects and a recessive D56X mutation in cases with common variable immunodeficiency. Regardless of the type of mutation, most of the reported RAC2 mutant cases have shown reduced IgG, IgA, and IgM levels. Herein, we report on a family with three members that suffer from persistent HPV infection, recurrent respiratory infections, bronchiectasis, and autoimmune disease. The immunologic profile suggests that the family was affected by combined immunodeficiency (CID) with increased serum levels of IgG, IgA, and IgE. Exome sequencing identified a de novo RAC2 mutation (c.44G > A/p.G15D) that was co-segregated with the disease in the family. Gene functional experiments identified that such mutation results in reduced guanosine triphosphate binding activity and RAC2 protein expression. In patients' lymphocytes, impaired aggregation and proliferation effects, decreased mitochondrial membrane potential, and increased levels of cell apoptosis were observed, although no functional abnormalities were detected in neutrophils. To our knowledge, this study was the first to identify a LoF-I mutation of RAC2 affecting lymphocyte function that consequently led to CID and increased levels of serum IgG, IgE, and IgA. This study presents a novel subtype of RAC2-related immune disorder.

Change point detection in multi-agent systems based on higher-order features.

Change point detection (CPD) for multi-agent systems helps one to evaluate the state and better control the system. Multivariate CPD methods solve the d × T time series well; however, the multi-agent systems often produce the N × d × T dimensional data, where d is the dimension of multivariate observations, T is the total observation time, and N is the number of agents. In this paper, we propose two valid approaches based on higher-order features, namely, the Betti number feature extraction and the Persistence feature extraction, to compress the d-dimensional features into one dimension so that general CPD methods can be applied to higher-dimensional data. First, a topological structure based on the Vietoris-Rips complex is constructed on each time-slice snapshot. Then, the Betti number and persistence of the topological structures are obtained to separately constitute two feature matrices for change point estimates. Higher-order features primarily describe the data distribution on each snapshot and are, therefore, independent of the node correspondence cross snapshots, which gives our methods unique advantages in processing missing data. Experiments in multi-agent systems demonstrate the significant performance of our methods. We believe that our methods not only provide a new tool for dimensionality reduction and missing data in multi-agent systems but also have the potential to be applied to a wider range of fields, such as complex networks.

Comprehensive Analysis of FASN in Tumor Immune Infiltration and Prognostic Value for Immunotherapy and Promoter DNA Methylation.

Fatty acid synthase (FASN) promotes tumor progression in multiple cancers. In this study, we comprehensively examined the expression, prognostic significance, and promoter methylation of FASN, and its correlation with immune cell infiltration in pan-cancer. Our results demonstrated that elevated FASN expression was significantly associated with an unfavorable prognosis in many cancer types. Furthermore, FASN promoter DNA methylation can be used as a tumor prognosis marker. Importantly, high levels of FASN were significantly negatively correlated with tumor immune infiltration in 35 different cancers. Additionally, FASN was significantly associated with tumor mutational burden (TMB) and microsatellite instability (MSI) in multiple malignancies, suggesting that it may be essential for tumor immunity. We also investigated the effects of FASN expression on immunotherapy efficacy and prognosis. In up to 15 tumors, it was significantly negatively correlated with immunotherapy-related genes, such as PD-1, PD-L1, and CTLA-4. Moreover, we found that tumors with high FASN expression may be more sensitive to immunotherapy and have a good prognosis with PD-L1 treatment. Finally, we confirmed the tumor-suppressive effect of mir-195-5p through FASN. Altogether, our results suggested that FASN may serve as a novel prognostic indicator and immunotherapeutic target in various malignancies.

The structure inference of flocking systems based on the trajectories.

The interaction between the swarm individuals affects the dynamic behavior of the swarm, but it is difficult to obtain directly from outside observation. Therefore, the problem we focus on is inferring the structure of the interactions in the swarm from the individual behavior trajectories. Similar inference problems that existed in network science are named network reconstruction or network inference. It is a fundamental problem pervading research on complex systems. In this paper, a new method, called Motion Trajectory Similarity, is developed for inferring direct interactions from the motion state of individuals in the swarm. It constructs correlations by combining the similarity of the motion trajectories of each cross section of the time series, in which individuals with highly similar motion states are more likely to interact with each other. Experiments on the flocking systems demonstrate that our method can produce a reliable interaction inference and outperform traditional network inference methods. It can withstand a high level of noise and time delay introduced into flocking models, as well as parameter variation in the flocking system, to achieve robust reconstruction. The proposed method provides a new perspective for inferring the interaction structure of a swarm, which helps us to explore the mechanisms of collective movement in swarms and paves the way for developing the flocking models that can be quantified and predicted.

3D Printing-Assisted Supramalleolar Osteotomy for Ankle Osteoarthritis.

Ankle osteoarthritis (OA) is an important factor that causes pain and dysfunction after ankle joint movement. In early and mid-term ankle OA, supramalleolar osteotomy can delay the progression of disease and maximize the preservation of ankle joint function. Three-dimensional printing (3DP) technology has brought us new hope, which can improve the accuracy of osteotomy, reduce the number of fluoroscopy, reduce the amount of blood loss, and achieve personalized and accurate treatment. The data of 16 patients with ankle OA in our center from January 2003 to July 2020 were retrospectively analyzed and divided into the 3DP group and the traditional group according to different treatment methods. Seven patients in the 3DP group used the 3DP personalized osteotomy guide; nine patients were treated by traditional osteotomy. All patients were followed up for 13.9 ± 3.1 months after the operation. The operation time in the 3DP group was 126.4 ± 11.1 min, its intraoperative blood loss was 85.7 ± 24.1 mL, and its intraoperative fluoroscopy time was 2.4 ± 0.2, which were all significantly less than 167.3 ± 12.2 min, 158.3 ± 22.8 mL, and 5.8 ± 0.2 times in the traditional group (P < 0.05), respectively. In the 3DP group, its postoperative tibial anterior surface (TAS) angle was 90.6 ± 0.3° and the talar tilt (TT) angle was 2.2 ± 0.6°, which were all significantly different compared with its preoperative data of 83.4 ± 1.7 and 8.0 ± 1.5°, respectively (P < 0.05). Compared with traditional osteotomy, 3DP-assisted supramalleolar osteotomy for varus and valgus ankle OA can significantly shorten the operation time and reduce intraoperative bleeding and the frequency of intraoperative fluoroscopy; personalized 3DP osteotomy guides and models can assist in the accurate correction of varus deformity during operation, restore the lower limb alignment, and improve the biomechanical status of the lower limbs. In addition, the 3DP of porous tantalum has good histocompatibility, and its interface structure and porosity are more conducive to bone ingrowth. For complex bone defects and revision prostheses, matching implants can be printed individually, which could realize the personalized precise treatment.

Twist2 Reduced NLRP3-Induced Inflammation of Infantile Pneumonia via Regulation of Mitochondrial Permeability Transition by FOXO1.

BACKGROUND: Infantile pneumonia is an acute inflammatory lesion of the lung caused by mycoplasma pneumonia. Indeed, Twist2 signaling pathway controls inflammatory reaction, oxidative stress, and other biological reaction. However, the regulation of Twist2 on the inflammation in infantile pneumonia remains unclear. This study explained that the function and mechanism of Twist2 in infantile pneumonia. METHODS: The subjects included the serum samples of 12 patients with infantile pneumonia and normal healthy volunteers from Hunan Children's Hospital. Besides, mice were given with lipopolysaccharide (LPS) into the lung. Moreover, RAW264.7 macrophages were stimulated with LPS for 4 h and added to the culture medium. RESULTS: In present study, in serum of patients with infantile pneumonia or lung tissue of mice model with infantile pneumonia, TWIST2 expression was lessened. Apart from that, TWIST2 protein could reduce the inflammatory reaction in mice model with infantile pneumonia, resulting in an inhibition in lung injury. Conversely, over-expression of TWIST2 also decreased inflammatory reaction in macrophages model via the regulation of FOXO1/NLRP3 pathway. Downregulation of TWIST2 promoted the inflammation in macrophages model by the regulation of FOXO1/NLRP3 pathway. CONCLUSION: According to the findings, present study have identified that the TWIST2 could reduce the inflammation of infantile pneumonia by NLRP3 inflammasome through the regulation of mitochondrial permeability transition and the induction of FOXO1 expression.

lncRNA NEAT1 promotes hypoxia-induced inflammation and fibrosis of alveolar epithelial cells via targeting miR-29a/NFATc3 axis.

The objective of the present study was to explore the function and mechanism of long noncoding RNA (lncRNA) nuclear paraspeckle assembly transcript 1 (NEAT1) in pulmonary fibrosis (PF) progression. HPAEpic cells and A549 cells were exposed to hypoxic conditions to establish an in vitro model. Cell apoptosis was detected by TUNEL assay, and inflammatory cytokine levels were detected by ELISA. Gene and protein expression levels were identified by qRT-PCR and Western blot assays, respectively. The interaction among NEAT1, miR-29a, and NFATc3 was identified by dual-luciferase reporter and RNA pull-down assays. In hypoxia-treated cells, hypoxia markers (HIF-1α and HIF-2α), cytokines (TNF-α, IL-1ß, and IL-6) and fibrotic markers (α-SMA, collagen I and collagen III) were significantly enhanced. Consistently, the expression levels of NEAT1 and NFATc3 were increased, but miR-29a was decreased in hypoxia-stimulated cells. Knockdown of NEAT1 significantly decreased cell apoptosis and the releases of TNF-α, IL-1ß, and IL-6 as well as reduced the levels of α-SMA, collagen I, and collagen III. Moreover, NEAT1 positively regulated NFATc3 expression by directly targeting miR-29a. Functional experiments showed that the anti-apoptotic, anti-inflammatory, and anti-fibrotic effects mediated by NETA1 silencing were impeded by miR-29a inhibition or NFATc3 overexpression in hypoxia-stimulated HPAEpic and A549 cells. Collectively, these data demonstrated that NEAT1 knockdown inhibited hypoxia-induced cell apoptosis, inflammation, and fibrosis by targeting the miR-29a/NFATc3 axis in PF, suggesting that NEAT1 might be a potential therapeutic target for relieving PF progression.

Particulate matter exposure at urban traffic intersection during haze episodes: A case study in Changsha.

Urban intersection has been identified as a major contributor to the total personal exposure and short-term high exposure of particulate matter (PM) in modern cities. The main aim of this study was to get a better understanding of the determinants of traffic-related PM temporal variations and personal exposure to PMs at a viaduct-covered intersection controlled by traffic signals during the winter haze episodes. A two-day field sampling campaign was conducted with a portable device during evening rush hour and measured the PMs in the 0.3-10 µm size range both on the surface crosswalk and underground passage. PM variations and related cumulative respiratory deposition dose (RDD) along two routes with six road crossing scenarios were estimated on a severe pollution day and a typical day for both adults and children, respectively. The PM concentration on the severe pollution day ranged 59.2-67.9 µg/m3 for PM1, 163.8-257.0 µg/m3 for PM2, and 258.2-469.1 µg/m3 for PM10, respectively, as compared to 47.9-57.9 µg/m3for PM1, 112.7-199.8 µg/m3 for PM2, and 151.0-301.0 µg/m3 for PM10 on the typical day, respectively. The variability could be explained largely by the built-up environment, traffic component, signal setting, and ventilation condition. Our data suggest that an appropriate setting of the traffic signal would help reduce the personal exposure dose on the surface crosswalk at urban intersections and the ventilation condition had a significant influence on local PM distributions inside the underground passage. Results here provide possible suggestions for the future design of a walkable city.

Event detection in temporal social networks using a higher-order network model.

Event detection is one of the most important areas of complex network research. It aims to identify abnormal points in time corresponding to social events. Traditional methods of event detection, based on first-order network models, are poor at describing the multivariate sequential interactions of components in complex systems and at accurately identifying anomalies in temporal social networks. In this article, we propose two valid approaches, based on a higher-order network model, namely, the recovery higher-order network algorithm and the innovation higher-order network algorithm, to help with event detection in temporal social networks. Given binary sequential data, we take advantage of chronological order to recover the multivariate sequential data first. Meanwhile, we develop new multivariate sequential data using logical sequence. Through the efficient modeling of multivariate sequential data using a higher-order network model, some common multivariate interaction patterns are obtained, which are used to determine the anomaly degree of a social event. Experiments in temporal social networks demonstrate the significant performance of our methods finally. We believe that our methods could provide a new perspective on the interplay between event detection and the application of higher-order network models to temporal networks.