True Causality Research Articles

Two fractional order cumulative residual time series measures based on Rényi entropy

In the study of time series analysis, Kullback-Leibler divergence can measure the difference between the distribution diverges of time series, and transfer entropy is usually used to quantify the causal influence among time series. In order to analyze the relationship between time series more comprehensively, two novel fractional order times series measures, i.e., Rényi cumulative residual Kullback-Leibler (RCRKL) and Rényi cumulative residual transfer entropy (RCRTE), are proposed in this paper. Some mathematical properties of RCRKL are proved as a distance metric, and the zeros and bounds of RCRTE are studied. Then, some examples on synthetic data are provided to study the effect of parameter changes on metric values. Finally, the proposed two fractional-order time series measures are applied to pavement rutting time series, for which a RCRKL-based clustering is designed and the causal relationships between influence factors and rutting are detected by RCETE. The results demonstrate that the RCRKL-based clustering algorithm performs better than those based on the baseline distance measures. Meanwhile, RCRTE can also detect the true causality between rutting and its influence factors and reject the false causality. In summary, the proposed RCRKL and RCRTE take the advantages of both cumulative residual entropy and Rényi entropy, and provide a more comprehensive insight for time series analysis.

Tracing the evolving X-ray reverberation lags within an individual AGN light curve

Abstract We present the Granger causality (GC) test for the X-ray reverberation analysis of Active Galactic Nuclei (AGN). If the light curves in the continuum-dominated band help predict (Granger cause) those dominated by reflection, the Granger lags that associate to the intrinsic reverberation lags can be inferred. We focus on six AGN observed by XMM-Newton, including the sources well-known to exhibit clear X-ray reverberation lags (IRAS 13224–3809 and 1H 0707–495) and those in which reverberation signatures are not well confirmed (MCG–6-30-15, IZW1, Mrk 704 and Mrk 1040). We employ the sliding-window algorithm and estimate the Granger (intrinsic) Fe-L lags along the light curve as the window moves through. This reveals the evolving lags towards the end of some individual observations, suggesting that the corona varies progressively. Occasionally, we observe two clearly separate lags that suggest an extended corona consisting of two zones while producing competing reverberation of two lags. While the GC test is purely hypothetical and might not explain true causality, our conclusion is that the lags are present and could be understood as reverberation lags. Assuming the lags changing solely with the corona, we find that the IRAS 13224–3809 corona varies between ∼10–25 rg and sometimes move to ≳ 50 rg. The corona of 1H 0707–495 and MCG–6-30-15 may be analogous to that of IRAS 13224–3809, while in IZw1, Mrk 704 and Mrk 1040 a more compact corona is expected.

Bayesian-Based Causal Structure Inference With a Domain Knowledge Prior for Stable and Interpretable Soft Sensing.

Due to the high-stakes nature of industrial processes, there is an immediate and pressing need on soft sensors for stability and interpretability. In this regard, causality-inspired modeling aims to learn causal features corresponding to the direct causes of quality variables, exhibiting great potential in terms of both stability and interpretability. However, most existing causality-inspired methods overlook temporal modeling and domain knowledge integration, which hinders their real-world application in industrial soft sensing. To this end, this article proposes a novel causality-inspired stable long short-term memory (Stable-LSTM), which leverages Bayesian-based causal structure inference and incorporates domain knowledge as a prior to enhance the performance stability and physical interpretability of soft sensors. After extracting temporal features via long short-term memory (LSTM), a Bayesian-based causal structure inference approach is developed by leveraging variational inference to learn the underlying hidden causal structure within the industrial processes. Through a hidden explanation of domain knowledge, a prior distribution is placed on the hidden causal structure, which will greatly enhance the physical interpretability and facilitate the exploration for true causality. Moreover, we also introduce a global sample reweighting strategy to remove spurious correlations and reveal causal effects between time series hidden features and quality variables. Finally, the performance stability and physical interpretability of the proposed Stable-LSTM are verified using a three-phase flow facility and a m-phenylenediamine distillation process. The results show that the Stable-LSTM achieves the highest soft sensing accuracy under distribution shift, and the inferred causal structure exhibits the greatest consistency with the domain knowledge, when compared with the seven existing methods.

Diabetes and bacterial co-infection are two independent risk factors for respiratory syncytial virus disease severity.

Diabetes mellitus (DM) is common among older adults hospitalized with lower respiratory tract infection, yet information on the impact of DM on disease severity is limited. This study retrospectively analyzed 46 Turkish patients infected with respiratory syncytial virus (RSV), with information on their comorbidities, co-infection status, and symptoms. Patients are grouped into four severity levels from mild to severe, according to lung parenchymal infiltration status and oxygen level. Similar to previously published studies, we found that comorbidities of diabetes, heart failure, hypertension, co-infection of any type, bacterial co-infection, and age are associated with the disease severity. Cough is the most common symptom (89%) followed by fever (26%) and myalgia, dyspnea, and weakness (around 20%). Using a second-order analysis (two-variable regression), we identified two independent risks for disease severity, the first is represented by diabetes, and the second is represented by bacterial co-infection. We observed two patients whose more severe symptoms were not associated with an older age, but associated with a combination of diabetes and bacterial co-infection. To confirm the true causality from the statistical correlation, further studies are needed.

The causal link between cardiometabolic risk factors and gray matter atrophy: An exploratory study

Although gray matter atrophy is commonly observed with aging, it is highly variable, even among healthy people of the same age. This raises the question of what other factors may contribute to gray matter atrophy. Previous studies have reported that risk factors for cardiometabolic diseases are associated with accelerated brain aging. However, these studies were primarily based on standard correlation analyses, which do not unveil a causal relationship. While randomized controlled trials are typically required to investigate true causality, in this work, we investigated an alternative method by exploring data-driven causal discovery and inference techniques on observational data. Accordingly, this feasibility study used clinical and quantified gray matter volume data from 22,793 subjects from the UK biobank cohort without any known neurological disease. Our method identified that age, sex, body mass index (BMI), body fat percentage (BFP), and smoking exhibit a causal relationship with gray matter volume. Interventions on the causal network revealed that higher BMI and BFP values significantly increased the chance of gray matter atrophy in males, whereas this was not the case in females.

Enhanced brain efficiency network by integrating the new causality with fMRI and its application for Alzheimer’s Disease study

The analysis of influence flow is crucial for the topological characterization of functional brain efficiency networks. Directional interaction among areal BOLD signals can be modeled by the causality method. Despite being widely used for studying Alzheimer's Disease in the brain, the Granger Causality (GC) has been shown to be unable to reveal true causality relationships through mathematical proof and simulation in EEG experiments. To evaluate its effectiveness in fMRI studies, first, we integrated a novel causality method called the New Causality (NC) with fMRI data. Both strong and weak causal impacts between stochastic processes were simulated and tested by GC and NC methods. Additionally, 1,893 patients in different stages of progression toward Alzheimer s disease were acquired and analyzed through the causality-based connectivity study. Finally, machine learning was employed to explore the performance in classification under these two methods. Simulation results show that compared to the GC, the NC method is more sensitive and reasonable to address causality relationships, especially for those weak causal impacts. Both the brain efficiency network and the classification performance can be enhanced through the NC introduction. Furthermore, it provides additional evidence supporting the critical involvement of the middle insular cortex, along with the temporal, parietal, and frontal lobes, in consciousness and functional diversion, with the help of NC integration.

PS01 Prevalence of baseline anxiety and depression in patients attending a tertiary virtual acne service

Abstract Evidence suggests that the prevalence of depression and suicide in those with acne is higher than in the general population. There have also been case reports of suicide in patients taking isotretinoin for acne, although there is a lack of evidence to suggest a true causality. We know that many people with acne and acne scars struggle with impacts on emotions, social/daily activities, study, work and relationships. A prospective cohort study was undertaken, including 150 consecutive new patients in our acne clinics from 7 December 2021 to 5 July 2022 as part of a service evaluation. This project was registered with our local research and innovation team. We captured a snapshot of the demographics of our patients, including baseline Generalised Anxiety Disorder Assessments (GAD-7), Patient Health Questionnaires (PHQ-9) and pre-existing mental health problems. Mean age was 26 years (range 17–46). Sixty-four per cent of patients were women and 36% were men. Thirty per cent of patients reported a previous/current mental health problem, the most common being anxiety (19.3%) and depression (18.0%). The most common medications taken previously or currently were sertraline (4.0%) and propranolol (4.0%). Initial PHQ-9 scores ranged from 0 to 29 points: 49.1% had a score of ≥ 5; 29.1% a score of ≥ 10; and 18.2% a score of ≥ 15. As a comparison, in July–August 2021, the prevalence of depression (classified as a PHQ-8 of ≥ 10) was 32% in women and 20% in men aged 16–29 years in the UK. Rates of depression in our patient cohort were therefore similar to those in the general population in this age group. Interestingly, in the general population, 17% of those aged ≥ 16 years experienced depression during this time. This is significantly higher than the prepandemic prevalence rate of 10%. Initial GAD-7 scores in our cohort ranged from 0 to 21 points: 58.7% had a score of ≥ 5; 30.3% a score of ≥ 10; and 11.0% a score of ≥ 15. As a comparison, in September–October 2021, an estimated 28% of those 16–29 years old experienced moderate–severe anxiety in the UK (GAD-7 equivalent to ≥ 10), and the prevalence was higher in women. Anxiety levels were therefore similar in our cohort compared with the general population. The prevalence of anxiety and depression is high in young adults, which has increased with the COVID-19 pandemic, and the prevalence in our acne cohort is similar to that of the general population of a similar age. It remains that those with moderate–severe acne, regardless of what treatment is received, should be screened and monitored for anxiety and depression.

Hepatitis B Virus Reactivation After COVID-19 Vaccination

Abstract Reactivation of different viruses after COVID-19 vaccination has been identified, such as hepatitis C virus, herpes viruses, and Epstein Barr virus. In this case, a middle-aged female patient treated for hepatitis B virus (HBV) by tenofovir and developed persistent hepatitis B envelope antigen seroconversion with lack of viremia. She received the first dose of an inactivated COVID-19 vaccine. Three days later, manifestations of acute hepatitis occurred. The liver enzymes were elevated; alanine aminotransferase was 286 U/L, aspartate aminotransferase 160 U/L, HBV DNA level 208,000 IU/L and HBs Ag level 2944 IU/L. Retreatment by tenofovir was initiated and symptoms improved with reduction of liver enzymes. Three months later, liver enzymes became at normal level. This is considered the first report of HBV reactivation after COVID-19 vaccine. Patients with HBV infection who stopped HBV treatment after hepatitis B envelope antigen seroconversion should be closely monitored after COVID-19 vaccination for the possibility of HBV reactivation to determine if it is a true causality or just an association.

Appraising the Causal Association between Systemic Iron Status and Heart Failure Risk: A Mendelian Randomisation Study.

Although observational studies have shown that abnormal systemic iron status is associated with an increased risk of heart failure (HF), it remains unclear whether this relationship represents true causality. We aimed to explore the causal relationship between iron status and HF risk. Two-sample Mendelian randomisation (MR) was applied to obtain a causal estimate. Genetic summary statistical data for the associations (p < 5 × 10−8) between single nucleotide polymorphisms (SNPs) and four iron status parameters were obtained from the Genetics of Iron Status Consortium in genome-wide association studies involving 48,972 subjects. Statistical data on the association of SNPs with HF were extracted from the UK biobank consortium (including 1088 HF cases and 360,106 controls). The results were further tested using MR based on the Bayesian model averaging (MR-BMA) and multivariate MR (MVMR). Of the twelve SNPs considered to be valid instrumental variables, three SNPs (rs1800562, rs855791, and rs1799945) were associated with all four iron biomarkers. Genetically predicted iron status biomarkers were not causally associated with HF risk (all p > 0.05). Sensitivity analysis did not show evidence of potential heterogeneity and horizontal pleiotropy. Convincing evidence to support a causal relationship between iron status and HF risk was not found. The strong relationship between abnormal iron status and HF risk may be explained by an indirect mechanism.

Causality Analysis: The study of Size and Power based on riz-PC Algorithm of Graph Theoretic Approach

This paper proposes modified Peter and Clark (PC) algorithm of graph-theoretic approach to study causality correlated data. The proposed algorithm is derived to determine the directions of the casual correlated complex variables. The PC algorithm treats VAR residuals as original variables while the proposed algorithm riz-PC uses modified R recursive residuals to find the correct causal direction among policy variables. This study evaluates the performance of these causal search algorithms in term of size and power properties. Our findings suggest that the newly proposed modified riz-PC algorithm can test causality better, as it successfully depicted the correct causal direction and was best at differentiating between true and spurious causality in routine Monte Carlo experiments.

Modeling multiscale causal interactions between spiking and field potential signals during behavior

Objective. Brain recordings exhibit dynamics at multiple spatiotemporal scales, which are measured with spike trains and larger-scale field potential signals. To study neural processes, it is important to identify and model causal interactions not only at a single scale of activity, but also across multiple scales, i.e. between spike trains and field potential signals. Standard causality measures are not directly applicable here because spike trains are binary-valued but field potentials are continuous-valued. It is thus important to develop computational tools to recover multiscale neural causality during behavior, assess their performance on neural datasets, and study whether modeling multiscale causalities can improve the prediction of neural signals beyond what is possible with single-scale causality. Approach. We design a multiscale model-based Granger-like causality method based on directed information and evaluate its success both in realistic biophysical spike-field simulations and in motor cortical datasets from two non-human primates (NHP) performing a motor behavior. To compute multiscale causality, we learn point-process generalized linear models that predict the spike events at a given time based on the history of both spike trains and field potential signals. We also learn linear Gaussian models that predict the field potential signals at a given time based on their own history as well as either the history of binary spike events or that of latent firing rates. Main results. We find that our method reveals the true multiscale causality network structure in biophysical simulations despite the presence of model mismatch. Further, models with the identified multiscale causalities in the NHP neural datasets lead to better prediction of both spike trains and field potential signals compared to just modeling single-scale causalities. Finally, we find that latent firing rates are better predictors of field potential signals compared with the binary spike events in the NHP datasets. Significance. This multiscale causality method can reveal the directed functional interactions across spatiotemporal scales of brain activity to inform basic science investigations and neurotechnologies.

Sustainable continuous improvement programs

Continuous improvement programs in their various forms have proven their capabilities in creating value for a company, but often fail to have a lasting impact. The reason is its management and engineers gradually lose incentive and fall back into longstanding ways because they fail to consistently meet their objectives, solving ill-posed inverse problems. The unsustainable executions and results are costly and take more unnecessary time for the company to attain high return on investment. This article examines three key tenets of the continuous improvement programs, i.e., deployment, methodology, and common tool. Behavioural economics approach, scientific principles, and causal inferences based on Bayesian statistics are applied to guide us in evaluating the key tenets. The guidance is used to extract the prerequisites of the mindset development of understanding and improving the mechanism of the true causality and reasoning process to uphold continuous improvement intended goals, i.e., perpetually better quality, and capability improvement.

Weakly-Supervised Video Object Grounding via Causal Intervention.

We target at the task of weakly-supervised video object grounding (WSVOG), where only video-sentence annotations are available during model learning. It aims to localize objects described in the sentence to visual regions in the video, which is a fundamental capability needed in pattern analysis and machine learning. Despite the recent progress, existing methods all suffer from the severe problem of spurious association, which will harm the grounding performance. In this paper, we start from the definition of WSVOG and pinpoint the spurious association from two aspects: (1) the association itself is not object-relevant but extremely ambiguous due to weak supervision; and (2) the association is unavoidably confounded by the observational bias when taking the statistics-based matching strategy in existing methods. With this in mind, we design a unified causal framework to learn the deconfounded object-relevant association for more accurate and robust video object grounding. Specifically, we learn the object-relevant association by causal intervention from the perspective of video data generation process. To overcome the problems of lacking fine-grained supervision in terms of intervention, we propose a novel spatial-temporal adversarial contrastive learning paradigm. To further remove the accompanying confounding effect within the object-relevant association, we pursue the true causality by conducting causal intervention via backdoor adjustment. Finally, the deconfounded object-relevant association is learned and optimized under a unified causal framework in an end-to-end manner. Extensive experiments on both IID and OOD testing sets of three benchmarks demonstrate its accurate and robust grounding performance against state-of-the-arts.

More exposure opportunities for promoting freshwater conservation

Abstract Freshwater ecosystems have a higher percentage of threatened and extinct species than terrestrial or marine realms, but remain under‐represented in conservation research and actions arguably as a consequence of less popularity and promotion. Cover images of conservation journals were used as a proxy of exposure and potential promotion opportunities provided for different ecosystems and species. To examine whether articles related to cover images received more attention, citations and Altmetric scores of cover‐featured articles were compared with non‐featured ones within the same host journal issue. Freshwater ecosystems (10.4%) were featured less often than marine (15.2%) or terrestrial (74.4%) ecosystems on covers of 18 conservation journals from 1997 to 2016. All 15 most featured species are from terrestrial or marine ecosystems. In addition, cover‐featured studies showed higher citations and Altmetric scores than non‐featured ones within the same host journal issue, indicating that cover‐featured articles received more attention. Further investigations are needed to examine the relationship (i.e. whether there is a true causality) between being featured on the cover, and citations and Altmetric scores received by articles, as well as potentially resulting in greater conservation efforts. Nevertheless, we believe that providing exposure opportunities is likely to better inform the public about the continuing degradation of freshwater ecosystems and its impacts on human well‐being, including economic loss and danger to public health. Journal editors can contribute by balancing their selection of featured ecosystems and species when opportunities arise. Increasing exposure opportunities for freshwater ecosystems through various channels seems a promising approach to raise public awareness and appreciation of freshwater biodiversity. Scientists can play an active role and form an alliance with journal editors, conservation organizations, and media, to increase momentum in society for fresh waters to be experienced as essential ecosystems and prevent further degradation of freshwater habitats and biodiversity loss.

Political Engagement and Crime Victimization: A Causal Analysis

In recent years, research has demonstrated that crime victimization serves as a catalyst to political engagement. However, much of this work has not addressed two key issues: 1) the true causality of this relationship, and 2) how victims’ identities and personal experiences might influence this relationship. This paper tackles these issues by testing the effect of victimization on non-electoral engagement using the Two-City, Six-Wave panel survey administered in Brazil between 2002 and 2006. It finds that the causal relationship between victimization and engagement only exists for participation in political party meetings. Furthermore, when exploring the role of individual identity and community context, only men, those who live in safe neighborhoods, and White Brazilians experience an increase in their engagement. Meanwhile, women, those in unsafe neighborhoods, and Afro-Brazilians do not experience such an increase. Those who have not experienced discrimination also increase their participation, while those who have experienced discrimination do not.

Cerebral Palsy and Criteria Implicating Intrapartum Hypoxia in Neonatal Encephalopathy - An Obstetric Perspective for the South African Setting.

The science surrounding cerebral palsy indicates that it is a complex medical condition with multiple contributing variables and factors, and causal pathways are often extremely difficult to delineate. The pathophysiological processes are often juxtaposed on antenatal factors, genetics, toxins, fetal priming, failure of neuroscientific autoregulatory mechanisms, abnormal biochemistry and abnormal metabolic pathways. Placing this primed compromised compensated brain through the stresses of an intrapartum process could be the final straw in the pathway to brain injury and later CP. It is thus simplistic to base causation of cerebral palsy on only an intrapartum perspective with radiological 'confirmation', as is often the practice in medicolegal cases in South African courts. The present modalities (MRI and CTG when available) that retrospectively attempt to determine causation in courts are inadequate when used in isolation. Unless a holistic scientific review of the case including all contributing clinical factors (antepartum, intrapartum and neonatal), fetal heart rate monitoring, neonatal MRI if possible (and preferred) or late MRI, and histology (placental histology if performed) are taken into account, success for plaintiff or defendant currently in a court of law will depend on eloquent legal argument rather than true scientific causality. The 10 criteria set out in this document to implicate acute intrapartum hypoxia in hypoxic ischaemic encephalopathy/neonatal encephalopathy serve as a guideline in the medicolegal setting.

Cerebral Palsy and Criteria Implicating Acute Intrapartum Hypoxia in Neonatal Encephalopathy - An Obstetric Perspective for the South African Setting.

The science surrounding cerebral palsy indicates that it is a complex medical condition with multiple contributing variables and factors, and causal pathways are often extremely difficult to delineate. The pathophysiological processes are often juxtaposed on antenatal factors, genetics, toxins, fetal priming, failure of neuroscientific autoregulatory mechanisms, abnormal biochemistry and abnormal metabolic pathways. Placing this primed compromised compensated brain through the stresses of an intrapartum process could be the final straw in the pathway to brain injury and later CP. It is thus simplistic to base causation of cerebral palsy on only an intrapartum perspective with radiological 'confirmation', as is often the practice in medicolegal cases in South African courts. The present modalities (MRI and CTG when available) that retrospectively attempt to determine causation in courts are inadequate when used in isolation. Unless a holistic scientific review of the case including all contributing clinical factors (antepartum, intrapartum and neonatal), fetal heart rate monitoring, neonatal MRI if possible (and preferred) or late MRI, and histology (placental histology if performed) are taken into account, success for plaintiff or defendant currently in a court of law will depend on eloquent legal argument rather than true scientific causality. The 10 criteria set out in this document to implicate acute intrapartum hypoxia in hypoxic ischaemic encephalopathy/neonatal encephalopathy serve as a guideline in the medicolegal setting.

Rethinking Climate, Climate Change, and Their Relationship with Water

We revisit the notion of climate, along with its historical evolution, tracing the origin of the modern concerns about climate. The notion (and the scientific term) of climate was established during the Greek antiquity in a geographical context and it acquired its statistical content (average weather) in modern times after meteorological measurements had become common. Yet the modern definitions of climate are seriously affected by the wrong perception of the previous two centuries that climate should regularly be constant, unless an external agent acts upon it. Therefore, we attempt to give a more rigorous definition of climate, consistent with the modern body of stochastics. We illustrate the definition by real-world data, which also exemplify the large climatic variability. Given this variability, the term “climate change” turns out to be scientifically unjustified. Specifically, it is a pleonasm as climate, like weather, has been ever-changing. Indeed, a historical investigation reveals that the aim in using that term is not scientific but political. Within the political aims, water issues have been greatly promoted by projecting future catastrophes while reversing true roles and causality directions. For this reason, we provide arguments that water is the main element that drives climate, and not the opposite.

Prevalence and risk indicators of peri‐implantitis: an university‐representative cross‐sectional study

Background: Despite some epidemiological studies are available on the prevalence and risk indicators of peri-implant diseases, there is still a need of studies in different settings since the role of some risk factors is still putative. To minimize selection bias, representative samples should be used. However, only two epidemiological studies have analysed the epidemiology of peri-implantitis using representative samples so far, both referring to private settings and not to university clinics. Aim/Hypothesis: The aim of this cross-sectional study was to analyse the prevalence of peri-implantitis and its risk indicators in a university-based representative sample. Materials and Methods: 240 patients who received at least one dental implant in an EFP Program in Periodontology between the years 2000 and 2017 and in function by at least 1 year were randomly selected. Those who accepted to participate received a detailed medical and dental history assessment, a clinical examination and a standardized peri-apical radiograph. Peri-implantitis was both defined according to the 2018 World Workshop (bone levels≧3 mm & BoP/SoP) and according to the case definitions of Sanz & Chapple 2012 (bone levels≧2 mm & BoP/SoP). The prevalence of peri-implantitis was then calculated, as well as a multilevel multivariate logistic regression analysis was carried out to identify the risk indicators associated with peri-implantitis. Results: 109 patients with a total of 508 implants accepted to participate. The prevalence of peri-implantitis according to the definition of the 2018 World Workshop was 6.52% at implant-level and 16.19% at patient-level. The prevalence of peri-implantitis according to the Sanz & Chapple 2012 case definition was 33.86% at implant-level and 60.75% at patient level. The following factors were identified as risk indicators for peri-implantitis (Sanz & Chapple 2012 case definition): - smoking (OR = 3.3; 95% CI: 1.2–8.9); - diabetes (OR = 12.66; 95% CI: 1.78–90.16); - moderate/severe periodontitis (AAP) (OR = 3.7; 95% CI: 1–40–9.74); - use of interproximal brushes (OR = 0.18; 95% CI: 0.06–0.62); - implant brand (Nb vs. S: OR = 5.12; 95% CI: 1.90 – 13.90); - type of super-structure (multi-unit restorations vs. single crown: OR = 2.37; 95% CI: 1.22–4.60); - plaque (six sites vs. 0 sites: OR = 3.4; 95% CI: 1.02–11.32). Conclusions and Clinical Implications: According to the present study, peri-implantitis is a highly prevalent disease at patient-level. Diabetes, smoking, moderate/severe periodontitis, implant brand, multi-unit restorations and plaque were identified as risk indicators for peri-implantitis, while the use of interproximal brushes was inversely associated. Randomized clinical studies or, when not possible for ethical reasons prospective cohort studies, are needed to demonstrate true causality of the identified risk indicators. Keywords: peri-implantitis, cross-sectional, risk indicators, risk factor, prevalence.

Recurrent glomerulonephritis after renal transplantation.

The current understanding of the incidence, predisposing factors, pathophysiology and effective treatment of recurrent glomerulonephritis (RGN) in renal transplants remains at best patchy and at worst, completely lacking. Current reports have been limited by inconsistencies in study design, sample populations and lengths of follow-up. Making sense of the available evidence will provide the tools to support transplant nephrologists in their management of allograft donors and recipients. With better survival of renal allografts, RGN has become a dominant factor influencing allograft survival. Evidently, the risk of recurrence is proportional to the incremental time posttransplantation. The proposed risk factors for RGN include but are not limited to the severity of primary glomerulonephritis (PGN), younger recipient age, live-related donor allograft, minimal HLA mismatch, steroid avoidance and nonuse of induction therapy. Unfortunately, these findings are derived from retrospective cohort and registry studies; hence, true causality for RGN is hard to prove. The management of RGN is improving, as we gain greater understanding of its pathophysiology, including the genetic, alloimmune and autoimmune contributions to recurrence. With better pretransplant risk stratification, posttransplant surveillance, novel biomarkers and new treatment strategies, we hope the transplant community will eventually have the tools to predict risk, prevent recurrence and personalise treatment of RGN.