Multi-omics analysis of the prognostic and biological role of cuproptosis-related gene in gastric cancer.

Background: A considerable number of gastric cancer (GC) patients cannot receive benefits from current treatments. We aimed to identify possible biomarkers of cuproptosis-related genes (CRGs) in GC patients, which may help guide precision medicine-based decision-making. Methods: RNA sequencing data, copy number variations (CNVs) data, and single nucleotide variant (SNV) data were obtained from The Cancer Genome Atlas (TCGA) database and Gene Set Cancer Analysis (GSCA) database. Chi-squared test was adopted to screen differentially expressed CRGs (DE-CRGs) between samples from 14 kinds of carcinoma and adjacent tissue samples. Then, GC samples were divided into high- and low-expressed groups based on DE-CRGs for further survival analyses and the selection of biomarkers. Methylation sites related with biomarkers were acquired. The correlation between immune cells and biomarkers was verified. Finally, miRNA-mRNA, TFs-mRNA, and co-expression networks were established to detect factors with regulating effects on biomarkers. Results: Three CRGs including LIAS, GLS, and CDKN2A were identified as biomarkers in GC patients. Three methylation sites with a significant survival effect including cg13601799, 07562918, and 07253264 were acquired. Then, we found that B cells native was significantly correlated with CDKN2A, four immune cells such as T cells regulatory are significantly correlated with GLS, and two immune cells such as T cells CD4 memory activated were significantly correlated with LIAS. Moreover, 10 miRNAs in the miRNA-mRNA network and three transcription factors (TFs) in the TFs-mRNA network had a significant correlation with overall survival (OS). Finally, 20 enrichment functions were obtained on the basis of the co-expression network. Conclusions: Three biomarkers with a prognosis prediction value of GC were found, and multi-factor regulatory networks were constructed to screen out 13 factors with regulating influences of biomarkers.

Machine learning vs. regression models to predict the risk of Legionella contamination in a hospital water network.

Introduction: The periodic monitoring of Legionella in hospital water networks allows preventive measures to be taken to avoid the risk of legionellosis to patients and healthcare workers. Study design: The aim of the study is to standardize a method for predicting the risk of Legionella contamination in the water supply of a hospital facility, by comparing Machine Learning, conventional and combined models. Methods: During the period July 2021- October 2022, water sampling for Legionella detection was performed in the rooms of an Italian hospital pavilion (89.9% of the total number of rooms). Fifty-eight parameters regarding the structural and environmental characteristics of the water network were collected. Models were built on 70% of the dataset and tested on the remaining 30% to evaluate accuracy, sensitivity, and specificity. Results: A total of 1,053 water samples were analyzed and 57 (5.4%) were positive for Legionella. Of the Machine Learning models tested, the most efficient had an input layer (56 neurons), hidden layer (30 neurons), and output layer (two neurons). Accuracy was 93.4%, sensitivity was 43.8%, and specificity was 96%. The regression model had an accuracy of 82.9%, sensitivity of 20.3%, and specificity of 97.3%. The combination of the models achieved an accuracy of 82.3%, sensitivity of 22.4%, and specificity of 98.4%. The most important parameters that influenced the model results were the type of water network (hot/cold), the replacement of filter valves, and atmospheric temperature. Among the models tested, Machine Learning obtained the best results in terms of accuracy and sensitivity. Conclusions: Future studies are required to improve these predictive models by expanding the dataset using other parameters and other pavilions of the same hospital.

Physiological network approach to prognosis in cirrhosis: A shifting paradigm.

Decompensated liver disease is complicated by multi-organ failure and poor prognosis. The prognosis of patients with liver failure often dictates clinical management. Current prognostic models have focused on biomarkers considered as individual isolated units. Network physiology assesses the interactions among multiple physiological systems in health and disease irrespective of anatomical connectivity and defines the influence or dependence of one organ system on another. Indeed, recent applications of network mapping methods to patient data have shown improved prediction of response to therapy or prognosis in cirrhosis. Initially, different physical markers have been used to assess physiological coupling in cirrhosis including heart rate variability, heart rate turbulence, and skin temperature variability measures. Further, the parenclitic network analysis was recently applied showing that organ systems connectivity is impaired in patients with decompensated cirrhosis and can predict mortality in cirrhosis independent of current prognostic models while also providing valuable insights into the associated pathological pathways. Moreover, network mapping also predicts response to intravenous albumin in patients hospitalized with decompensated cirrhosis. Thus, this review highlights the importance of evaluating decompensated cirrhosis through the network physiologic prism. It emphasizes the limitations of current prognostic models and the values of network physiologic techniques in cirrhosis.

siRNA Delivery from Cationic Nanocarriers Prepared by Diffusion-assisted Loading in the Presence and Absence of Electrostatic Interactions.

In this study, we use modified cationic nanocarriers as vehicles for the intracellular delivery of therapeutic siRNA. After developing nanocarrier formulations with appropriate pKa, size, swellability, and cytocompatibility, we investigated the importance of siRNA loading methods by studying the impact of the pH and time over which siRNA is loaded into the nanocarriers. We concentrate on diffusion-based loading in the presence and absence of electrostatic interactions. siRNA release kinetics were studied using samples prepared from nanocarriers loaded by both mechanisms. In addition, siRNA delivery was evaluated for two formulations. While previous studies were conducted with samples prepared by siRNA loading at low pH values, this research provides evidence that loading conditions of siRNA affect the release behavior. This study concludes that this concept could prove advantageous for eliciting prolonged intracellular release of nucleic acids and negatively charged molecules, effectively decreasing dose frequency and contributing to more effective therapies and improved patient outcomes. In addition, our findings could be leveraged for enhanced control over siRNA release kinetics, providing novel methods for the continued optimization of cationic nanoparticles in a wide array of RNA interference-based applications.

Design patterns for the construction of computational biological models.

Computational biological models have proven to be an invaluable tool for understanding and predicting the behaviour of many biological systems. While it may not be too challenging for experienced researchers to construct such models from scratch, it is not a straightforward task for early stage researchers. Design patterns are well-known techniques widely applied in software engineering as they provide a set of typical solutions to common problems in software design. In this paper, we collect and discuss common patterns that are usually used during the construction and execution of computational biological models. We adopt Petri nets as a modelling language to provide a visual illustration of each pattern; however, the ideas presented in this paper can also be implemented using other modelling formalisms. We provide two case studies for illustration purposes and show how these models can be built up from the presented smaller modules. We hope that the ideas discussed in this paper will help many researchers in building their own future models.

Analysis of Microbiome Data.

The microbiome represents a hidden world of tiny organisms populating not only our surroundings but also our own bodies. By enabling comprehensive profiling of these invisible creatures, modern genomic sequencing tools have given us an unprecedented ability to characterize these populations and uncover their outsize impact on our environment and health. Statistical analysis of microbiome data is critical to infer patterns from the observed abundances. The application and development of analytical methods in this area require careful consideration of the unique aspects of microbiome profiles. We begin this review with a brief overview of microbiome data collection and processing and describe the resulting data structure. We then provide an overview of statistical methods for key tasks in microbiome data analysis, including data visualization, comparison of microbial abundance across groups, regression modeling, and network inference. We conclude with a discussion and highlight interesting future directions.

Apigenin inhibits proliferation and differentiation of cardiac fibroblasts through AKT/GSK3ß signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Salvia miltiorrhiza Bunge (S. miltiorrhiza) is an important Traditional Chinese herbal Medicine (TCM) used to treat cardio-cerebrovascular diseases. Based on the pharmacodynamic substance of S. miltiorrhiza, the aim of present study was to investigate the underlying mechanism of S. miltiorrhiza against cardiac fibrosis (CF) through a systematic network pharmacology approach, molecular docking and dynamics simulation as well as experimental investigation in vitro. MATERIALS AND METHODS: A systematic pharmacological analysis was conducted using the Traditional Chinese Medicine Pharmacology (TCMSP) database to screen the effective chemical components of S. miltiorrhiza, then the corresponding potential target genes of the compounds were obtained by the Swiss Target Prediction and TCMSP databases. Meanwhile, GeneCards, DisGeNET, OMIM, and TTD disease databases were used to screen CF targets, and a protein-protein interaction (PPI) network of drug-disease targets was constructed on S. miltiorrhiza/CF targets by Search Tool for the Retrieval of Interacting Genes/Proteins (STING) database. After that, the component-disease-target network was constructed by software Cytoscape 3.7. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed for the intersection targets between drug and disease. The relationship between active ingredient of S. miltiorrhiza and disease targets of CF was assessed via molecular docking and molecular dynamics simulation. Subsequently, the underlying mechanism of the hub compound on CF was experimentally investigated in vitro. RESULTS: 206 corresponding targets to effective chemical components from S. miltiorrhiza were determined, and among them, there were 82 targets that overlapped with targets of CF. Further, through PPI analysis, AKT1 and GSK3ß were the hub targets, and which were both enriched in the PI3K/AKT signaling pathway, it was the sub-pathways of the lipid and atherosclerosis pathway. Subsequently, compound-disease-genes-pathways diagram is constructed, apigenin (APi) was a top ingredients and AKT1 (51) and GSK3ß (22) were the hub genes according to the degree value. The results of molecular docking and dynamics simulation showed that APi has strong affinities with AKT and GSK3ß. The results of cell experiments showed that APi inhibited cells viability, proliferation, proteins expression of α-SMA and collagen I/III, phosphorylation of AKT1 and GSK3ß in MCFs induced by TGFß1. CONCLUSION: Through a systematic network pharmacology approach, molecular docking and dynamics simulation, and confirmed by in vitro cell experiments, these results indicated that APi interacts with AKT and GSK3ß to disrupt the phosphorylation of AKT and GSK3ß, thereby inhibiting the proliferation and differentiation of MCFs induced by TGFß1, which providing new insights into the pharmacological mechanism of S. miltiorrhiza in the treatment of CF.

Which intervention is optimal to control blood glucose and improve physical performance in the elderly living with type 2 diabetes mellitus? A network meta-analysis.

BACKGROUND AND OBJECTIVES: This study aimed to find the optimal intervention available to both control blood glucose and improve physical function in the geriatric population with T2DM. METHODS AND STUDY DESIGN: A systemic review and network meta-analysis (NMA) was conducted to assess and rank the comparative efficacy of different interventions on glycosylated hemoglobin A1c (HbAc1), fasting blood glucose (FBG), muscle mass, grip strength, gait speed, lower body muscle strength, and dynamic balance. A total of eight databases were searched for eligible randomized controlled trials (RCTs) that the elderly aged more than 60 years or with mean age ≥ 55 years, the minimal duration of the RCT intervention was 6 weeks, and those lacking data about glycemic level and at least one indicator of physical performance were excluded. The Cochrane risk of bias tool was used to assess the bias of each study included. Bayesian NMA was performed as the main results, the Bayesian meta regression and the frequentist NMA as sensitivity analysis. RESULTS: Of the 2266 literature retrieved, 27 RCTs with a total of 2289 older adults were included. Health management provided by health workers exerts beneficial effects that is superior to other interventions at achieving glycemic control, but less marked improvement in physical performance. Exercise combined with cognitive training showed more pronounced improvement in muscle strength, gait speed, and dynamic balance, but ranked behind in decreasing the HbAc1 and FBG. CONCLUSIONS: Personalized health management combined with physical and cognitive training might be the optimal intervention to both accomplish glycemic control and improvement of physical performance. Further RCTs are needed to validate and assess the confidence of our results from this NMA.

Emergent scale-free networks.

Many complex systems-from the Internet to social, biological, and communication networks-are thought to exhibit scale-free structure. However, prevailing explanations require that networks grow over time, an assumption that fails in some real-world settings. Here, we explain how scale-free structure can emerge without growth through network self-organization. Beginning with an arbitrary network, we allow connections to detach from random nodes and then reconnect under a mixture of preferential and random attachment. While the numbers of nodes and edges remain fixed, the degree distribution evolves toward a power-law with an exponent γ = 1 + 1 p that depends only on the proportion p of preferential (rather than random) attachment. Applying our model to several real networks, we infer p directly from data and predict the relationship between network size and degree heterogeneity. Together, these results establish how scale-free structure can arise in networks of constant size and density, with broad implications for the structure and function of complex systems.

Anti-PD-1/L1 antibody plus anti-VEGF antibody vs. plus VEGFR-targeted TKI as first-line therapy for unresectable hepatocellular carcinoma: a network meta-analysis.

Background: This article is based on our previous research, which was presented at the 2023 ASCO Annual Meeting I and published in Journal of Clinical Oncology as Conference Abstract (JCO. 2023;41:e16148. doi: 10.1200/JCO.2023.41.16_suppl.e16148). Both anti-programmed death 1/ligand-1 (PD-1/L1) antibody + anti-vascular endothelial growth factor (VEGF) antibody (A + A) and anti-PD-1/L1 antibody + VEGF receptor (VEGFR)-targeted tyrosine kinase inhibitor (A + T) are effective first-line therapies for unresectable hepatocellular carcinoma. However, there lacks evidence from head-to-head comparisons between these two treatments. We conducted a network meta-analysis on the efficacy and safety of them. Methods: After a rigorous literature research, 6 phase III trials were identified for the final analysis, including IMbrave150, ORIENT-32, COSMIC-312, CARES-310, LEAP-002, and REFLECT. The experiments were classified into three groups: A + A, A + T, and intermediate reference group. The primary endpoint was overall survival (OS), and secondary endpoints included progression-free survival (PFS), objective response rate (ORR), and incidence of treatment-related adverse events (TRAEs). Hazard ratio (HR) with 95% confidence intervals (CI) for OS and PFS, odds ratio (OR) for ORR, and relative risk (RR) for all grade and grade ≥3 TRAEs were calculated. Under Bayesian framework, the meta-analysis was conducted using sorafenib as intermediate reference. Results: With the rank probability of 96%, A + A showed the greatest reduction in the risk of death, without significant difference from A + T (HR: 0.82, 95% CI: 0.65-1.04). A + T showed the greatest effect in prolonging PFS and improving ORR with the rank probability of 77%, but there were no statistical differences with A + A. A + A was safer than A + T in terms of all grade of TRAEs (RR: 0.91, 95% CI: 0.82-1.00) and particularly in those grade ≥3 (RR: 0.65, 95% CI: 0.54-0.77). Conclusions: A + A had the greatest probability of delivering the longest OS, while A + T was correlated with larger PFS benefits at the cost of a lower safety rate.

Renshen Yangrong decoction for secondary malaise and fatigue: network pharmacology and Mendelian randomization study.

Background: Renshen Yangrong decoction (RSYRD) has been shown therapeutic effects on secondary malaise and fatigue (SMF). However, to date, its bioactive ingredients and potential targets remain unclear. Purpose: The purpose of this study is to assess the potential ingredients and targets of RSYRD on SMF through a comprehensive strategy integrating network pharmacology, Mendelian randomization as well as molecular docking verification. Methods: Search for potential active ingredients and corresponding protein targets of RSYRD on TCMSP and BATMAN-TCM for network pharmacology analysis. Mendelian randomization (MR) was performed to find therapeutic targets for SMF. The eQTLGen Consortium (sample sizes: 31,684) provided data on cis-expression quantitative trait loci (cis-eQTL, exposure). The summary data on SMF (outcome) from genome-wide association studies (GWAS) were gathered from the MRC-IEU Consortium (sample sizes: 463,010). We built a target interaction network between the probable active ingredient targets of RSYRD and the therapeutic targets of SMF. We next used drug prediction and molecular docking to confirm the therapeutic value of the therapeutic targets. Results: In RSYRD, network pharmacology investigations revealed 193 possible active compounds and 234 associated protein targets. The genetically predicted amounts of 176 proteins were related to SMF risk in the MR analysis. Thirty-seven overlapping targets for RSYRD in treating SMF, among which six (NOS3, GAA, IMPA1, P4HTM, RB1, and SLC16A1) were prioritized with the most convincing evidence. Finally, the 14 active ingredients of RSYRD were identified as potential drug molecules. The strong affinity between active components and putative protein targets was established by molecular docking. Conclusion: This study revealed several active components and possible RSYRD protein targets for the therapy of SMF and provided novel insights into the feasibility of using Mendelian randomization for causal inference between Chinese medical formula and disease.

Enhanced cardiovascular disease prediction through self-improved Aquila optimized feature selection in quantum neural network & LSTM model.

Introduction: Cardiovascular disease (CVD) stands as a pervasive catalyst for illness and mortality on a global scale, underscoring the imperative for sophisticated prediction methodologies within the ambit of healthcare data analysis. The vast volume of medical data available necessitates effective data mining techniques to extract valuable insights for decision-making and prediction. While machine learning algorithms are commonly employed for CVD diagnosis and prediction, the high dimensionality of datasets poses a performance challenge. Methods: This research paper presents a novel hybrid model for predicting CVD, focusing on an optimal feature set. The proposed model encompasses four main stages namely: preprocessing, feature extraction, feature selection (FS), and classification. Initially, data preprocessing eliminates missing and duplicate values. Subsequently, feature extraction is performed to address dimensionality issues, utilizing measures such as central tendency, qualitative variation, degree of dispersion, and symmetrical uncertainty. FS is optimized using the self-improved Aquila optimization approach. Finally, a hybridized model combining long short-term memory and a quantum neural network is trained using the selected features. An algorithm is devised to optimize the LSTM model's weights. Performance evaluation of the proposed approach is conducted against existing models using specific performance measures. Results: Far dataset-1, accuracy-96.69%, sensitivity-96.62%, specifity-96.77%, precision-96.03%, recall-97.86%, F1-score-96.84%, MCC-96.37%, NPV-96.25%, FPR-3.2%, FNR-3.37% and for dataset-2, accuracy-95.54%, sensitivity-95.86%, specifity-94.51%, precision-96.03%, F1-score-96.94%, MCC-93.03%, NPV-94.66%, FPR-5.4%, FNR-4.1%. The findings of this study contribute to improved CVD prediction by utilizing an efficient hybrid model with an optimized feature set. Discussion: We have proven that our method accurately predicts cardiovascular disease (CVD) with unmatched precision by conducting extensive experiments and validating our methodology on a large dataset of patient demographics and clinical factors. QNN and LSTM frameworks with Aquila feature tuning increase forecast accuracy and reveal cardiovascular risk-related physiological pathways. Our research shows how advanced computational tools may alter sickness prediction and management, contributing to the emerging field of machine learning in healthcare. Our research used a revolutionary methodology and produced significant advances in cardiovascular disease prediction.

NoC simulation steered by NEST: McAERsim and a Noxim patch.

Introduction: Great knowledge was gained about the computational substrate of the brain, but the way in which components and entities interact to perform information processing still remains a secret. Complex and large-scale network models have been developed to unveil processes at the ensemble level taking place over a large range of timescales. They challenge any kind of simulation platform, so that efficient implementations need to be developed that gain from focusing on a set of relevant models. With increasing network sizes imposed by these models, low latency inter-node communication becomes a critical aspect. This situation is even accentuated, if slow processes like learning should be covered, that require faster than real-time simulation. Methods: Therefore, this article presents two simulation frameworks, in which network-on-chip simulators are interfaced with the neuroscientific development environment NEST. This combination yields network traffic that is directly defined by the relevant neural network models and used to steer the network-on-chip simulations. As one of the outcomes, instructive statistics on network latencies are obtained. Since time stamps of different granularity are used by the simulators, a conversion is required that can be exploited to emulate an intended acceleration factor. Results: By application of the frameworks to scaled versions of the cortical microcircuit model-selected because of its unique properties as well as challenging demands-performance curves, latency, and traffic distributions could be determined. Discussion: The distinct characteristic of the second framework is its tree-based source-address driven multicast support, which, in connection with the torus topology, always led to the best results. Although currently biased by some inherent assumptions of the network-on-chip simulators, the results suit well to those of previous work dealing with node internals and suggesting accelerated simulations to be in reach.

Deep-KEDI: Deep learning-based zigzag generative adversarial network for encryption and decryption of medical images.

BACKGROUND: Medical imaging techniques have improved to the point where security has become a basic requirement for all applications to ensure data security and data transmission over the internet. However, clinical images hold personal and sensitive data related to the patients and their disclosure has a negative impact on their right to privacy as well as legal ramifications for hospitals. OBJECTIVE: In this research, a novel deep learning-based key generation network (Deep-KEDI) is designed to produce the secure key used for decrypting and encrypting medical images. METHODS: Initially, medical images are pre-processed by adding the speckle noise using discrete ripplet transform before encryption and are removed after decryption for more security. In the Deep-KEDI model, the zigzag generative adversarial network (ZZ-GAN) is used as the learning network to generate the secret key. RESULTS: The proposed ZZ-GAN is used for secure encryption by generating three different zigzag patterns (vertical, horizontal, diagonal) of encrypted images with its key. The zigzag cipher uses an XOR operation in both encryption and decryption using the proposed ZZ-GAN. Encrypting the original image requires a secret key generated during encryption. After identification, the encrypted image is decrypted using the generated key to reverse the encryption process. Finally, speckle noise is removed from the encrypted image in order to reconstruct the original image. CONCLUSION: According to the experiments, the Deep-KEDI model generates secret keys with an information entropy of 7.45 that is particularly suitable for securing medical images.

Unraveling the mesoscale organization induced by network-driven processes.

Complex systems are characterized by emergent patterns created by the nontrivial interplay between dynamical processes and the networks of interactions on which these processes unfold. Topological or dynamical descriptors alone are not enough to fully embrace this interplay in all its complexity, and many times one has to resort to dynamics-specific approaches that limit a comprehension of general principles. To address this challenge, we employ a metric-that we name Jacobian distance-which captures the spatiotemporal spreading of perturbations, enabling us to uncover the latent geometry inherent in network-driven processes. We compute the Jacobian distance for a broad set of nonlinear dynamical models on synthetic and real-world networks of high interest for applications from biological to ecological and social contexts. We show, analytically and computationally, that the process-driven latent geometry of a complex network is sensitive to both the specific features of the dynamics and the topological properties of the network. This translates into potential mismatches between the functional and the topological mesoscale organization, which we explain by means of the spectrum of the Jacobian matrix. Finally, we demonstrate that the Jacobian distance offers a clear advantage with respect to traditional methods when studying human brain networks. In particular, we show that it outperforms classical network communication models in explaining functional communities from structural data, therefore highlighting its potential in linking structure and function in the brain.

GCPDFFNet: Small Object Detection for Rice Blast Recognition.

Early detection of rice blast disease is pivotal to ensure rice yield. We collected in situ images of rice blast and constructed a rice blast dataset based on variations in lesion shape, size, and color. Given that rice blast lesions are small and typically exhibit round, oval, and fusiform shapes, we proposed a small object detection model named GCPDFFNet (global context-based parallel differentiation feature fusion network) for rice blast recognition. The GCPDFFNet model has three global context feature extraction modules and two parallel differentiation feature fusion modules. The global context modules are employed to focus on the lesion areas; the parallel differentiation feature fusion modules are used to enhance the recognition effect of small-sized lesions. In addition, we proposed the SCYLLA normalized Wasserstein distance loss function, specifically designed to accelerate model convergence and improve the detection accuracy of rice blast disease. Comparative experiments were conducted on the rice blast dataset to evaluate the performance of the model. The proposed GCPDFFNet model outperformed the baseline network CenterNet, with a significant increase in mean average precision from 83.6 to 95.4% on the rice blast test set while maintaining a satisfactory frames per second drop from 147.9 to 122.1. Our results suggest that the GCPDFFNet model can accurately detect in situ rice blast disease while ensuring the inference speed meets the real-time requirements.

Design and Synthesis of a Robust and Multifunctional Superhydrophobic Coating with a Three-Dimensional Network Structure on a Paper-Based Material.

A fundamental challenge in artificial superhydrophobic papers is their poor resistance to mechanical abrasion, which limits their practical application in different fields. Herein, a robust and multifunctional superhydrophobic paper is successfully fabricated via a facile spraying method by combining silver nanowires and fluorinated titania nanoparticles through a common paper sizing agent (alkyl ketene dimer) onto paper. It is shown that the surface of the paper-based material presents a three-dimensional network structure due to the cross-linking of silver nanowires with a high aspect ratio. Further hydrophilic and hydrophobic performance test results show that it exhibits exceptional water repellency, with a desirable static contact angle of 165° and roll-off angle of 6.2°. The superhydrophobic paper showcases excellent mechanical durability and maintains its superhydrophobicity even after enduring 130 linear sandpaper abrasion cycles or high-velocity water jetting impact benefited from interfacial van der Waals and hydrogen bonding. Simultaneously, the robust superhydrophobic surface can effectively prevent the penetration of acid or alkali solutions, as well as UV light, resulting in excellent chemical stability. Additionally, the superhydrophobic paper offers supplementary features such as self-cleaning, electrical conductivity, and antibacterial capability. Further development of this strategy paves a way toward next-generation superhydrophobic paper composed of nanostructures and characterized by multiple (or additional) functionalities.

Comparing ventriculoatrial and ventriculopleural shunts in pediatric hydrocephalus: a Hydrocephalus Clinical Research Network study.

OBJECTIVE: When the peritoneal cavity cannot serve as the distal shunt terminus, nonperitoneal shunts, typically terminating in the atrium or pleural space, are used. The comparative effectiveness of these two terminus options has not been evaluated. The authors directly compared shunt survival and complication rates for ventriculoatrial (VA) and ventriculopleural (VPl) shunts in a pediatric cohort. METHODS: The Hydrocephalus Clinical Research Network Core Data Project was used to identify children ≤ 18 years of age who underwent either VA or VPl shunt insertion. The primary outcome was time to shunt failure. Secondary outcomes included distal site complications and frequency of shunt failure at 6, 12, and 24 months. RESULTS: The search criteria yielded 416 children from 14 centers with either a VA (n = 318) or VPl (n = 98) shunt, including those converted from ventriculoperitoneal shunts. Children with VA shunts had a lower median age at insertion (6.1 years vs 12.4 years, p < 0.001). Among those children with VA shunts, a hydrocephalus etiology of intraventricular hemorrhage (IVH) secondary to prematurity comprised a higher proportion (47.0% vs 31.2%) and myelomeningocele comprised a lower proportion (17.8% vs 27.3%) (p = 0.024) compared with those with VPl shunts. At 24 months, there was a higher cumulative number of revisions for VA shunts (48.6% vs 38.9%, p = 0.038). When stratified by patient age at shunt insertion, VA shunts in children < 6 years had the lowest shunt survival rate (p < 0.001, log-rank test). After controlling for age and etiology, multivariable analysis did not find that shunt type (VA vs VPl) was predictive of time to shunt failure. No differences were found in the cumulative frequency of complications (VA 6.0% vs VPl 9.2%, p = 0.257), but there was a higher rate of pneumothorax in the VPl cohort (3.1% vs 0%, p = 0.013). CONCLUSIONS: Shunt survival was similar between VA and VPl shunts, although VA shunts are used more often, particularly in younger patients. Children < 6 years with VA shunts appeared to have the shortest shunt survival, which may be a result of the VA group having more cases of IVH secondary to prematurity; however, when age and etiology were included in a multivariable model, shunt location (atrium vs pleural space) was not associated with time to failure. The baseline differences between children treated with a VA versus a VPl shunt likely explain current practice patterns.

"The influence of auditory stimulation on whole body variability in healthy older adults during gait".

Acoustic stimulation appears to be a promising strategy in reducing the risk of falling in older adults, demonstrating effectiveness in improving stability. However, its impact on movement variability, another crucial indicator of fall risk, seems to be limited. This study aims to assess movement variability during walking in a cohort of healthy older adults exposed to three different frequencies of acoustic stimulation (90%, 100% and 110% of each subject's average cadence). Using a systemic approach based on network theory, which considers the intricate relationships between all body segments, we constructed connectivity matrices composed of nodes, represented by bony landmarks, and edges, consisting of the standardised covariance of accelerations between each pair of nodes. By introducing a new metric called Similarity Score (S-score), we quantified the ability of each individual to repeat the same motor pattern at each gait cycle under different experimental conditions. The study revealed that rhythmic auditory stimulation (RAS) at 100% and 90% of the mean cadence significantly increased the S-scores compared to the baseline. These results highlight the effects of RAS in increasing gait repeatability in healthy older adults, with a focus on global kinematics.

Network analysis of the urban-rural differences in depressive symptoms among older adults with multiple chronic conditions: Evidence from a national survey.

BACKGROUND: Evidence on the differences in depressive symptoms among older adults with multiple chronic conditions (MCCs) in urban and rural areas is limited. METHODS: Measures of depressive symptoms (Center for Epidemiologic Studies Depression Scale-10) and demographic factors (age, gender, and urban-rural distribution) were used. RESULTS: A total of 4021 older adults with MCCs were included in this study. Significant differences were observed in both network global strength (Urban: 3.989 vs. Rural: 3.703, S = 0.286, p = 0.003) and network structure (M = 0.139, p = 0.002) between urban and rural residents. CONCLUSIONS: The study highlights the need for region-specific approaches to understanding and addressing depression and holds the potential to enhance understanding of the psychological health status of older adults with MCCs in urban and rural settings.