Speaking the Same Language - The Development of a Glossary of Terms for Social Prescribing in Wales.

Introduction: Social prescribing can facilitate the integration of health, social care and community support but has a diverse and confusing terminology that impairs cross-sectoral communication and creates barriers to engagement. Methods: To address this issue a mixed-methods approach that incorporated a scoping review, a group concept mapping study and consultation was employed to identify and classify the terminology associated with social prescribing. The findings were then used to inform the development of a glossary of terms for social prescribing. Results: Many terms are used interchangeably to describe the same specific aspects of social prescribing. Much of the terminology originates from the health and social care literature of England. Discussion: The terminology used in the academic literature may not accurately reflect the terminology used by the social prescribing workforce. The innovative and interactive glossary of terms identifies the terminology associated with social prescribing and provides additional contextual information. The process of developing the dual language glossary presented several considerations and challenges. Conclusion: The glossary of terms will facilitate cross-sector communication and reduce barriers to engagement with social prescribing. It takes an important first step to help clarify and standardise the language associated with social prescribing, for professionals and members of the public alike.

Hybrid controller with neural network PID/FOPID operations for two-link rigid robot manipulator based on the zebra optimization algorithm.

The performance of the robotic manipulator is negatively impacted by outside disturbances and uncertain parameters. The system's variables are also highly coupled, complex, and nonlinear, indicating that it is a multi-input, multi-output system. Therefore, it is necessary to develop a controller that can control the variables in the system in order to handle these complications. This work proposes six control structures based on neural networks (NNs) with proportional integral derivative (PID) and fractional-order PID (FOPID) controllers to operate a 2-link rigid robot manipulator (2-LRRM) for trajectory tracking. These are named as set-point-weighted PID (W-PID), set-point weighted FOPID (W-FOPID), recurrent neural network (RNN)-like PID (RNNPID), RNN-like FOPID (RNN-FOPID), NN+PID, and NN+FOPID controllers. The zebra optimization algorithm (ZOA) was used to adjust the parameters of the proposed controllers while reducing the integral-time-square error (ITSE). A new objective function was proposed for tuning to generate controllers with minimal chattering in the control signal. After implementing the proposed controller designs, a comparative robustness study was conducted among these controllers by altering the initial conditions, disturbances, and model uncertainties. The simulation results demonstrate that the NN+FOPID controller has the best trajectory tracking performance with the minimum ITSE and best robustness against changes in the initial states, external disturbances, and parameter uncertainties compared to the other controllers.

Leveraging a self-cleaving peptide for tailored control in proximity labeling proteomics.

Protein-protein interactions play an important biological role in every aspect of cellular homeostasis and functioning. Proximity labeling mass spectrometry-based proteomics overcomes challenges typically associated with other methods and has quickly become the current state of the art in the field. Nevertheless, tight control of proximity-labeling enzymatic activity and expression levels is crucial to accurately identify protein interactors. Here, we leverage a T2A self-cleaving peptide and a non-cleaving mutant to accommodate the protein of interest in the experimental and control TurboID setup. To allow easy and streamlined plasmid assembly, we built a Golden Gate modular cloning system to generate plasmids for transient expression and stable integration. To highlight our T2A Split/link design, we applied it to identify protein interactions of the glucocorticoid receptor and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid and non-structural protein 7 (NSP7) proteins by TurboID proximity labeling. Our results demonstrate that our T2A split/link provides an opportune control that builds upon previously established control requirements in the field.

Virtual bracket removal: a comparative assessment of different software packages.

BACKGROUND: High precision intra-oral scans, coupled with advanced software, enable virtual bracket removal (VBR) from digital models. VBR allows the delivery of retainers and clear aligners promptly following debonding, thus reducing the patients' appointments and minimizing the likelihood of tooth movement. The objective of this study was to compare the enamel surface before bonding and after VBR using three different Computer-aided design (CAD) software and to compare their accuracy. METHODS: Maxillary scans of 20 participants starting orthodontic treatment were selected for inclusion in the study, who exhibited mild to moderate crowding and required bonding of brackets on the labial surface of permanent maxillary teeth (from the maxillary left first molar to the maxillary right first molar). Two intra-oral scans were conducted on the same day, before bonding and immediately after bonding using CEREC Omnicam (Sirona Dental Systems, Bensheim, Germany). The virtual removal of the brackets from the post-bonding models was performed using OrthoAnalyzer (3Shape, Copenhagen, Denmark), Meshmixer (Autodesk, San Rafael, Calif, USA), and EasyRx (LLC, Atlanta, GA, USA) software. The models that underwent VBR were superimposed on the pre-bonding models by Medit Link App (Medit, Seoul, South Korea) using surface-based registration. The changes in the enamel surface following VBR using the three software packages were quantified using the Medit Link App. RESULTS: There was a significant difference among the 3Shape, Meshmixer, and EasyRx software in tooth surface change following VBR. Specifically, EasyRx exhibited lower levels of accuracy compared to the other two VBR software programs (p<.001, p<.001). A significant difference in enamel surface change was observed between tooth segments across all software groups, in both incisors and molars, with VBR of the molars exhibiting the lowest level of accuracy (3Shape p=.002, Meshmixer p<.001, EasyRx p<.001). Regarding the direction of tooth surface changes following VBR, it was observed that all three groups exhibited a significant increase in the percentage of inadequate bracket removal across all teeth segments. CONCLUSIONS: 3Shape and Meshmixer manual VBR software were found to be more accurate than EasyRx automated software, however, the differences were minimal and clinically insignificant.

A meta-ethnography of the factors that shape link workers' experiences of social prescribing.

BACKGROUND: Social prescribing is gaining traction internationally. It is an approach which seeks to address non-medical and health-related social needs through taking a holistic person-centred and community-based approach. This involves connecting people with and supporting them to access groups and organisations within their local communities. It is hoped that social prescribing might improve health inequities and reduce reliance on healthcare services. In the UK, social prescribing link workers have become core parts of primary care teams. Despite growing literature on the implementation of social prescribing, to date there has been no synthesis that develops a theoretical understanding of the factors that shape link workers' experiences of their role. METHODS: We undertook a meta-ethnographic evidence synthesis of qualitative literature to develop a novel conceptual framework that explains how link workers experience their roles. We identified studies using a systematic search of key databases, Google alerts, and through scanning reference lists of included studies. We followed the eMERGe guidance when conducting and reporting this meta-ethnography. RESULTS: Our synthesis included 21 studies and developed a "line of argument" or overarching conceptual framework which highlighted inherent and interacting tensions present at each of the levels that social prescribing operates. These tensions may arise from a mismatch between the policy logic of social prescribing and the material and structural reality, shaped by social, political, and economic forces, into which it is being implemented. CONCLUSIONS: The tensions highlighted in our review shape link workers' experiences of their role. They may call into question the sustainability of social prescribing and the link worker role as currently implemented, as well as their ability to deliver desired outcomes such as reducing health inequities or healthcare service utilisation. Greater consideration should be given to how the link worker role is defined, deployed, and trained. Furthermore, thought should be given to ensuring that the infrastructure into which social prescribing is being implemented is sufficient to meet needs. Should social prescribing seek to improve outcomes for those experiencing social and economic disadvantage, it may be necessary for social prescribing models to allow for more intensive and longer-term modes of support.

A dynamically cross-linked catechol-grafted chitosan/gelatin hydrogel dressing synergised with photothermal therapy and baicalin reduces wound infection and accelerates wound healing.

Superior multifunctional hydrogel dressings are of considerable interest in wound healing. In clinical practice, it is useful to investigate hydrogel dressings that offer multifunctional benefits to expedite the process of wound healing. In this study, Catechol-grafted Chitosan, Gelatin, and Fe3+ as substrates to construct a hydrogel network. The network was dynamically cross-linked to form Ccg@Fe hydrogel substrate. Fe3O4 nanoparticles and baicalin, which possess antimicrobial and anti-inflammatory properties, were loaded onto the substrate to form a photothermal antibacterial composite hydrogel dressing (Ccg@Fe/Bai@Fe3O4 NPs). The Ccg@Fe hydrogel was characterised using Fourier transform infrared spectroscopy (FTIR) and Ultraviolet-visible spectrophotometry (UV-Vis). The morphological, mechanical, and adhesion properties of the hydrogel were determined using scanning electron microscopy (SEM) and a universal testing machine. The hydrogel's swelling, hemostasis, and self-healing properties were also evaluated. Additionally, the study determined the release rate of hydrogel-loaded antimicrobial and anti-inflammatory Baicalin (Ccg@Fe/Bai) and evaluated the photothermal antimicrobial properties of hydrogel-loaded Fe3O4 nanoparticles (Ccg@Fe/Bai@Fe3O4 NPs) through synergistic photothermal therapy (PTT). Histological staining of mice skin wound tissues using Masson and H&E revealed that the Ccg@Fe/Bai@Fe3O4 NPs hydrogel dressing demonstrated potential healing ability with the aid of PTT. The study suggests that this multifunctional hydrogel dressing has great potential for wound healing.

Link Prediction in Complex Networks Using Average Centrality-Based Similarity Score.

Link prediction plays a crucial role in identifying future connections within complex networks, facilitating the analysis of network evolution across various domains such as biological networks, social networks, recommender systems, and more. Researchers have proposed various centrality measures, such as degree, clustering coefficient, betweenness, and closeness centralities, to compute similarity scores for predicting links in these networks. These centrality measures leverage both the local and global information of nodes within the network. In this study, we present a novel approach to link prediction using similarity score by utilizing average centrality measures based on local and global centralities, namely Similarity based on Average Degree (SACD), Similarity based on Average Betweenness (SACB), Similarity based on Average Closeness (SACC), and Similarity based on Average Clustering Coefficient (SACCC). Our approach involved determining centrality scores for each node, calculating the average centrality for the entire graph, and deriving similarity scores through common neighbors. We then applied centrality scores to these common neighbors and identified nodes with above average centrality. To evaluate our approach, we compared proposed measures with existing local similarity-based link prediction measures, including common neighbors, the Jaccard coefficient, Adamic-Adar, resource allocation, preferential attachment, as well as recent measures like common neighbor and the Centrality-based Parameterized Algorithm (CCPA), and keyword network link prediction (KNLP). We conducted experiments on four real-world datasets. The proposed similarity scores based on average centralities demonstrate significant improvements. We observed an average enhancement of 24% in terms of Area Under the Receiver Operating Characteristic (AUROC) compared to existing local similarity measures, and a 31% improvement over recent measures. Furthermore, we witnessed an average improvement of 49% and 51% in the Area Under Precision-Recall (AUPR) compared to existing and recent measures. Our comprehensive experiments highlight the superior performance of the proposed method.

Link Prediction in Dynamic Social Networks Combining Entropy, Causality, and a Graph Convolutional Network Model.

Link prediction is recognized as a crucial means to analyze dynamic social networks, revealing the principles of social relationship evolution. However, the complex topology and temporal evolution characteristics of dynamic social networks pose significant research challenges. This study introduces an innovative fusion framework that incorporates entropy, causality, and a GCN model, focusing specifically on link prediction in dynamic social networks. Firstly, the framework preprocesses the raw data, extracting and recording timestamp information between interactions. It then introduces the concept of "Temporal Information Entropy (TIE)", integrating it into the Node2Vec algorithm's random walk to generate initial feature vectors for nodes in the graph. A causality analysis model is subsequently applied for secondary processing of the generated feature vectors. Following this, an equal dataset is constructed by adjusting the ratio of positive and negative samples. Lastly, a dedicated GCN model is used for model training. Through extensive experimentation in multiple real social networks, the framework proposed in this study demonstrated a better performance than other methods in key evaluation indicators such as precision, recall, F1 score, and accuracy. This study provides a fresh perspective for understanding and predicting link dynamics in social networks and has significant practical value.

Wall-climbing performance of gecko-inspired robot with soft feet and digits enhanced by gravity compensation.

Gravitational forces can induce deviations in body posture from desired configurations in multi-legged arboreal robot locomotion with low leg stiffness, affecting the contact angle between the swing leg's end-effector and the climbing surface during the gait cycle. The relationship between desired and actual foot positions is investigated here in a leg-stiffness-enhanced model under external forces, focusing on the challenge of unreliable end-effector attachment on climbing surfaces in such robots. Inspired by the difference in ceiling attachment postures of dead and living geckos, feedforward compensation of the stance phase legs is the key to solving this problem. A feedforward gravity compensation (FGC) strategy, complemented by leg coordination, is proposed to correct gravity-influenced body posture and improve adhesion stability by reducing body inclination. The efficacy of this strategy is validated using a quadrupedal climbing robot, EF-I, as the experimental platform. Experimental validation on an inverted surface (ceiling walking) highlights the benefits of the FGC strategy, demonstrating its role in enhancing stability and ensuring reliable end-effector attachment without external assistance. In the experiment, robots without FGC only completed 3 out of 10 trials, while robots with FGC achieved a 100% success rate in the same trials. The speed was substantially greater with FGC, achieving 9.2 mm s-1in the trot gait. This underscores the proposed potential of the FGC strategy in overcoming the challenges associated with inconsistent end-effector attachment in robots with low leg stiffness, thereby facilitating stable locomotion even at an inverted body attitude.

Streamlined Full-Length Total RNA Sequencing of Paraformaldehyde-Fixed Brain Tissues.

Paraformaldehyde (PFA) fixation is the preferred method for preserving tissue architecture for anatomical and pathological observations. Meanwhile, PFA reacts with the amine groups of biomolecules to form chemical cross-linking, which preserves RNA within the tissue. This has great prospects for RNA sequencing to characterize the molecular underpinnings after anatomical and pathological observations. However, RNA is inaccessible due to cross-linked adducts forming between RNA and other biomolecules in prolonged PFA-fixed tissue. It is also difficult to perform reverse transcription and PCR, resulting in low sequencing sensitivity and reduced reproducibility. Here, we developed a method to perform RNA sequencing in PFA-fixed tissue, which is easy to use, cost-effective, and allows efficient sample multiplexing. We employ cross-link reversal to recover RNA and library construction using random primers without artificial fragmentation. The yield and quality of recovered RNA significantly increased through our method, and sequencing quality metrics and detected genes did not show any major differences compared with matched fresh samples. Moreover, we applied our method for gene expression analysis in different regions of the mouse brain and identified unique gene expression profiles with varied functional implications. We also find significant dysregulation of genes involved in Alzheimer's disease (AD) pathogenesis within the medial septum (MS)/vertical diagonal band of Broca (VDB) of the 5×FAD mouse brain. Our method can thus increase the performance of high-throughput RNA sequencing with PFA-fixed samples and allows longitudinal studies of small tissue regions isolated by their in situ context.

Energy-Efficient and Highly Reliable Geographic Routing Based on Link Detection and Node Collaborative Scheduling in WSN.

Energy efficiency and data reliability are important indicators to measure network performance in wireless sensor networks. In existing research schemes of routing protocols, the impact of node coverage on the network is often ignored, and the possibility that multiple sensor nodes may sense the same spatial point is not taken into account, which results in a waste of network resources, especially in large-scale networks. Apart from that, the blindness of geographic routing in data transmission has been troubling researchers, which means that the nodes are unable to determine the validity of data transmission. In order to solve the above problems, this paper innovatively combines the routing protocol with the coverage control technique and proposes the node collaborative scheduling algorithm, which fully considers the correlation characteristics between sensor nodes to reduce the number of active working nodes and the number of packets generated, to further reduce energy consumption and network delay and improve packet delivery rate. In order to solve the problem of unreliability of geographic routing, a highly reliable link detection and repair scheme is proposed to check the communication link status and repair the invalid link, which can greatly improve the packet delivery rate and throughput of the network, and has good robustness. A large number of experiments demonstrate the effectiveness and superiority of our proposed scheme and algorithm.

High-Precision Fiber Noise Detection and Comparison over a 260 km Field Fiber Link.

In this paper, we present a high-precision optical frequency noise detection and comparison technique using a two-way transfer method over a 260 km field fiber link. This method allows for the comparison of optical frequencies between remote optical references without the need for data transfer through communication. We extend a previously established two-way comparison technique to obtain all data at the local site. Two optical carrier signals are injected into the bidirectional fiber from both ends, and one carrier is reflected back from the remote end. This enables the phase comparison of the two carrier signals at a single site without the need to transmit experimental data. The common-mode frequency noise induced by the bidirectional fiber link is detected and effectively suppressed without the need for sophisticated active fiber noise control. Our demonstration system, which uses a 260 km field fiber link and a common laser source, achieves a fractional instability of 2.5×10-17 at 1 s averaging time and scales down to 3.5×10-21 at 8000 s. This scheme offers the distinct advantage of completing the comparison at a single site, eliminating the need for remote data transfer via communication. This method is expected to enhance reliability for high-precision frequency comparisons between remote optical clocks and advanced atomic clocks.

Contention-Less Multi-Link Synchronous Transmission for Throughput Enhancement and Heterogeneous Fairness in Wi-Fi 7.

Multi-link operation (MLO) is a new and essential mechanism of IEEE 802.11be Extremely High Throughput (Wi-Fi 7) that can increase throughput and decrease latency in Wireless Local Area Networks (WLANs). The MLO enables a Multi-Link Device (MLD) to perform Simultaneous Transmission and Reception (STR) in different frequency bands. However, not all MLDs can support STR due to cross-link or in-device coexistence interference, while an STR-unable MLD (NSTR-MLD) can transmit multiple frames simultaneously in more than two links. This study focuses on the problems when NSTR-MLDs share a link with Single-Link Devices (SLDs). We propose a Contention-Less Synchronous Transmission (CLST) mechanism to improve fairness between NSTR-MLDs and SLDs while increasing the total network throughput. The proposed mechanism classifies links as MLD Dominant Links (MDLs) and Heterogeneous Coexistence Links (HCLs). In the proposed mechanism, an NSTR-MLD obtains a Synchronous Transmission Token (STT) through a virtual channel contention in the HCL but does not actually transmit a frame in the HCL, which is compensated for by a synchronous transmission triggered in the MDL. Moreover, the CLST mechanism allows additional subsequent transmissions up to the accumulated STT without further contention. Extensive simulation results confirm the outstanding performance of the CLST mechanism in terms of total throughput and fairness compared to existing synchronous transmission mechanisms.

Recent advancements in implantable neural links based on organic synaptic transistors.

The progress of brain synaptic devices has witnessed an era of rapid and explosive growth. Because of their integrated storage, excellent plasticity and parallel computing, and system information processing abilities, various field effect transistors have been used to replicate the synapses of a human brain. Organic semiconductors are characterized by simplicity of processing, mechanical flexibility, low cost, biocompatibility, and flexibility, making them the most promising materials for implanted brain synaptic bioelectronics. Despite being used in numerous intelligent integrated circuits and implantable neural linkages with multiple terminals, organic synaptic transistors still face many obstacles that must be overcome to advance their development. A comprehensive review would be an excellent tool in this respect. Therefore, the latest advancements in implantable neural links based on organic synaptic transistors are outlined. First, the distinction between conventional and synaptic transistors are highlighted. Next, the existing implanted organic synaptic transistors and their applicability to the brain as a neural link are summarized. Finally, the potential research directions are discussed.

Understanding the Meat-Masculinity Link: Traditional and Non-Traditional Masculine Norms Predicting Men's Meat Consumption.

Conformity to masculinity ideology predicts men's meat consumption and willingness to reduce their meat intake, but it is unknown which specific masculine norms account for these relationships. This study investigated which traditional and non-traditional masculine norms predict meat consumption, red and processed meat consumption, and willingness to reduce meat consumption in 557 Australian and English males. Men who support the use of physical violence and place high importance on sex ate more meat. Willingness to reduce was highest among men with gender egalitarian views. Targeting these specific masculine norms may be important for mitigating men's overconsumption of meat.

Tailoring Mechanical Properties of a Ceramic Nanowire Aerogel with Pyrolytic Carbon for In Situ Resilience at 1400 °C.

Resilient ceramic aerogels with a unique combination of lightweight, good high-temperature stability, high specific area, and thermal insulation properties are known for their promising applications in various fields. However, the mechanical properties of traditional ceramic aerogels are often constrained by insufficient interlocking of the building blocks. Here, we report a strategy to largely increase the interlocking degree of the building blocks by depositing a pyrolytic carbon (PyC) coating onto Si3N4 nanowires. The results show that the mechanical performances of the Si3N4 nanowire aerogels are intricately linked to the microstructure of the PyC nodes. The compression resilience of the Si3N4@PyC nanowire aerogels increases with an increase of the interlayer cross-linking in PyC. Additionally, benefiting from the excellent high-temperature stability of PyC, the Si3N4@PyC nanowire aerogels demonstrate significantly superior in situ resilience up to 1400 °C. The integrated mechanical and high-temperature properties of the Si3N4@PyC nanowire aerogels make them highly appealing for applications in harsh conditions. The facile method of manipulating the microstructure of the nodes may offer a perspective for tailoring the mechanical properties of ceramic aerogels.

The effectiveness of intervention measures on MERS-CoV transmission by using the contact networks reconstructed from link prediction data.

Introduction: Mitigating the spread of infectious diseases is of paramount concern for societal safety, necessitating the development of effective intervention measures. Epidemic simulation is widely used to evaluate the efficacy of such measures, but realistic simulation environments are crucial for meaningful insights. Despite the common use of contact-tracing data to construct realistic networks, they have inherent limitations. This study explores reconstructing simulation networks using link prediction methods as an alternative approach. Methods: The primary objective of this study is to assess the effectiveness of intervention measures on the reconstructed network, focusing on the 2015 MERS-CoV outbreak in South Korea. Contact-tracing data were acquired, and simulation networks were reconstructed using the graph autoencoder (GAE)-based link prediction method. A scale-free (SF) network was employed for comparison purposes. Epidemic simulations were conducted to evaluate three intervention strategies: Mass Quarantine (MQ), Isolation, and Isolation combined with Acquaintance Quarantine (AQ + Isolation). Results: Simulation results showed that AQ + Isolation was the most effective intervention on the GAE network, resulting in consistent epidemic curves due to high clustering coefficients. Conversely, MQ and AQ + Isolation were highly effective on the SF network, attributed to its low clustering coefficient and intervention sensitivity. Isolation alone exhibited reduced effectiveness. These findings emphasize the significant impact of network structure on intervention outcomes and suggest a potential overestimation of effectiveness in SF networks. Additionally, they highlight the complementary use of link prediction methods. Discussion: This innovative methodology provides inspiration for enhancing simulation environments in future endeavors. It also offers valuable insights for informing public health decision-making processes, emphasizing the importance of realistic simulation environments and the potential of link prediction methods.

Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique.

BACKGROUND: Automated hypothesis generation (HG) focuses on uncovering hidden connections within the extensive information that is publicly available. This domain has become increasingly popular, thanks to modern machine learning algorithms. However, the automated evaluation of HG systems is still an open problem, especially on a larger scale. RESULTS: This paper presents a novel benchmarking framework Dyport for evaluating biomedical hypothesis generation systems. Utilizing curated datasets, our approach tests these systems under realistic conditions, enhancing the relevance of our evaluations. We integrate knowledge from the curated databases into a dynamic graph, accompanied by a method to quantify discovery importance. This not only assesses hypotheses accuracy but also their potential impact in biomedical research which significantly extends traditional link prediction benchmarks. Applicability of our benchmarking process is demonstrated on several link prediction systems applied on biomedical semantic knowledge graphs. Being flexible, our benchmarking system is designed for broad application in hypothesis generation quality verification, aiming to expand the scope of scientific discovery within the biomedical research community. CONCLUSIONS: Dyport is an open-source benchmarking framework designed for biomedical hypothesis generation systems evaluation, which takes into account knowledge dynamics, semantics and impact. All code and datasets are available at: https://github.com/IlyaTyagin/Dyport .

Identification and validation of biomarkers in membranous nephropathy and pan-cancer analysis.

Background: Membranous nephropathy (MN) is an autoimmune disease and represents the most prevalent type of renal pathology in adult patients afflicted with nephrotic syndrome. Despite substantial evidence suggesting a possible link between MN and cancer, the precise underlying mechanisms remain elusive. Methods: In this study, we acquired and integrated two MN datasets (comprising a single-cell dataset and a bulk RNA-seq dataset) from the Gene Expression Omnibus database for differential expression gene (DEG) analysis, hub genes were obtained by LASSO and random forest algorithms, the diagnostic ability of hub genes was assessed using ROC curves, and the degree of immune cell infiltration was evaluated using the ssGSEA function. Concurrently, we gathered pan-cancer-related genes from the TCGA and GTEx databases, to analyze the expression, mutation status, drug sensitivity and prognosis of hub genes in pan-cancer. Results: We conducted intersections between the set of 318 senescence-related genes and the 366 DEGs, resulting in the identification of 13 senescence-related DEGs. Afterwards, we meticulously analyzed these genes using the LASSO and random forest algorithms, which ultimately led to the discovery of six hub genes through intersection (PIK3R1, CCND1, TERF2IP, SLC25A4, CAPN2, and TXN). ROC curves suggest that these hub genes have good recognition of MN. After performing correlation analysis, examining immune infiltration, and conducting a comprehensive pan-cancer investigation, we validated these six hub genes through immunohistochemical analysis using human renal biopsy tissues. The pan-cancer analysis notably accentuates the robust association between these hub genes and the prognoses of individuals afflicted by diverse cancer types, further underscoring the importance of mutations within these hub genes across various cancers. Conclusion: This evidence indicates that these genes could potentially play a pivotal role as a critical link connecting MN and cancer. As a result, they may hold promise as valuable targets for intervention in cases of both MN and cancer.

The effectiveness of social prescribing in the management of long-term conditions in community-based adults: A systematic review and meta-analysis.

OBJECTIVE: The objective of this systematic review and meta-analysis was to evaluate the effectiveness of social prescribing interventions in the management of long-term conditions in adults. DATA SOURCES: Eleven electronic databases were searched for randomised and quasi-randomised controlled trials. REVIEW METHODS: Outcomes of interest were quality of life, physical activity, psychological well-being and disease-specific measures. Bias was assessed with the Cochrane Risk of Bias 2 tool. A narrative synthesis and meta-analysis were performed. RESULTS: Twelve studies (n = 3566) were included in this review. Social prescribing interventions were heterogeneous and the most common risks of bias were poor blinding and high attrition. Social prescribing interventions designed to target specific long-term conditions i.e., cancer and diabetes demonstrated significant improvements in quality of life (n = 2 studies) and disease-specific psychological outcomes respectively (n = 3 studies). There was some evidence for improvement in physical activity (n = 2 studies) but most changes were within group only (n = 4 studies). Social prescribing interventions did not demonstrate any significant changes in general psychological well-being. CONCLUSION: Social prescribing interventions demonstrated some improvements across a range of outcomes although the quality of evidence remains poor.