Causal Connection Research Articles

Modeling Dynamic Processes in the Black Sea Pelagic Habitat—Causal Connections between Abiotic and Biotic Factors in Two Climate Change Scenarios

The paper contributes to the Sustainable Development Goals (SDGs) targeting Life Below Water by introducing user-friendly modeling approaches. It delves into the impact of abiotic factors on the first two trophic levels within the marine ecosystem, both naturally and due to human influence. Specifically, the study examines the connections between environmental parameters (e.g., temperature, salinity, nutrients) and plankton along the Romanian Black Sea coast during the warm season over a decade. The research develops models to forecast zooplankton proliferation using machine learning (ML) algorithms and gathered data. Water temperature significantly affects copepods and “other groups” of zooplankton densities during the warm season. Conversely, no discernible impact is observed on dinoflagellate Noctiluca scintillans blooms. Salinity fluctuations notably influence typical phytoplankton proliferation, with phosphate concentrations primarily driving widespread blooms. The study explores two scenarios for forecasting zooplankton growth: Business as Usual, predicting modest increases in temperature, salinity, and constant nutrient levels, and the Mild scenario, projecting substantial temperature and salinity increases alongside significant nutrient decrease by 2042. The findings underscore high densities of Noctiluca scintillans under both scenarios, particularly pronounced in the second scenario, surpassing the first by around 70%. These findings, indicative of a eutrophic ecosystem, underscore the potential implications of altered abiotic factors on ecosystem health, aligning with SDGs focused on Life Below Water.

Association of sleep traits with male fertility: a two-sample Mendelian randomization study.

Background: Previous observational studies have investigated the association between sleep-related traits and male fertility; however, conclusive evidence of a causal connection is lacking. This study aimed to explore the causal relationship between sleep and male fertility using Mendelian randomisation. Methods: Eight sleep-related traits (chronotype, sleep duration, insomnia, snoring, dozing, daytime nap, oversleeping, and undersleeping) and three descriptors representing male fertility (male infertility, abnormal sperm, and bioavailable testosterone levels) were selected from published Genome-Wide Association Studies. The causal relationship between sleep-related traits and male fertility was evaluated using multiple methods, including inverse variance weighting (IVW), weighted median, Mendelian randomisation-Egger, weighted model, and simple model through two-sample Mendelian randomisation analysis. Mendelian randomisation-Egger regression was used to assess pleiotropy, Cochrane's Q test was employed to detect heterogeneity, and a leave-one-out sensitivity analysis was conducted. Results: Genetically-predicted chronotype (IVW,OR = 1.07; 95%CL = 1.04-1.12; p = 0.0002) was suggestively associated with bioavailable testosterone levels. However, using the IVW method, we found no evidence of a causal association between other sleep traits and male fertility. Conclusion: This study found that chronotype affects testosterone secretion levels. However, further studies are needed to explain this mechanism.

Crosstalk between Oxidative Stress and Inflammation Caused by Noise and Air Pollution-Implications for Neurodegenerative Diseases.

Neurodegenerative diseases are often referred to as diseases of old age, and with the aging population, they are gaining scientific and medical interest. Environmental stressors, most notably traffic noise and air pollution, have recently come to the forefront, and have emerged as disease risk factors. The evidence for a connection between environmental risk factors and neurodegenerative disease is growing. In this review, the most common neurodegenerative diseases and their epidemiological association with traffic noise and air pollution are presented. Also, the most important mechanisms involved in neurodegenerative disease development, oxidative stress, and neuroinflammation are highlighted. An overview of the in vivo findings will provide a mechanistic link between noise, air pollution, and neurodegenerative pathology. Finally, the importance of the direct and indirect pathways, by which noise and air pollution cause cerebral damage, is discussed. More high-quality data are still needed from both epidemiological and basic science studies in order to better understand the causal connection between neurodegenerative diseases and environmental risk factors.

Association of immune cell traits with Parkinson's disease: a Mendelian randomization study.

Immunity and neuroinflammation play crucial roles in the pathogenesis of Parkinson's disease (PD). Nonetheless, prior investigations into the correlation between immune inflammation and PD have produced varying results. Identifying specific immune cell phenotypes that are truly associated with PD is challenging, and the causal relationship between immune cells and PD remains elusive. This study conducted a comprehensive two-sample Mendelian randomization (MR) analysis, employing five distinct analytical approaches, to clarify the causal connection between immune cell characteristics and the risk of PD. Utilizing GWAS data, we investigated the causal relationship between 731 immune cell traits and PD. These immune cell phenotypes encompass absolute cell (AC) counts, median fluorescence intensity (MFI), and relative cell (RC) counts for B cells, cDCs, mature stage T cells, monocytes, myeloid cells, TBNK (T cells, B cells, and natural killer cells), and Tregs, as well as the logistic parameter (MP) for cDCs and TBNK. The inverse variance weighted (IVW) analysis indicated that Myeloid DCs (p = 0.004), HVEM expression on CD45RA- CD4+ T cells (p = 0.007), CD62L- CD86+ Myeloid DCs (p = 0.015), and HLA DR expression on monocytes (p = 0.019) were associated with a reduced risk of PD. CD14+ CD16+ monocytes (p = 0.005), HLA DR+ NK cells within CD3- lymphocytes (p = 0.023), and CD28 expression on activated & secreting Tregs (p = 0.032) were associated with an increased risk of PD. This study establishes a causal link between immune cell phenotype and the pathogenesis of PD, identifying several specific immune cell characteristics associated with PD. This could inspire researchers to delve into the pathogenesis of PD at the cellular subtype level, and aid in the identification of potential pharmacological protein targets for PD.

Neoatherosclerosis And Paleoatherosclerosis: Complications After Stenting

Neoatherosclerosis is a process of new in-stent atherosclerosis progression, it is now considered a common complication of percutaneous coronary intervention with drug-eluting stent. The development of neoatherosclerosis is a serious matter and can lead to the occurrence of late and very late stent thrombosis. Neoatherosclerosis poses a significant threat and increases the likelihood of experiencing acute coronary syndrome and angina. Paleoatherosclerosis is an advancement of pre-existing atherosclerosis, and it is also able to cause complications after stent implantation. In this review we concentrate on the atherosclerosis impact on stent restenosis and stent thrombosis, focusing on the differences between neoatherosclerosis and paleoatherosclerosis. They can lead to various complications including stent failure, although there is still much uncertainty surrounding these conditions. Numerous risk factors have been recognized that raise the likelihood of neoatherosclerosis progression, including the type of stent, kidney dysfunction, tobacco use, and low-density-lipoprotein (LDL) concentrations. These and other risk factors for neoatherosclerosis and paleoatherosclerosis development ought to be investigated in future studies in order to find causal connections and possible ways of alleviating these conditions.

Elucidating the causal landscape: Mendelian randomization analysis of lifestyle and physiological factors in stress urinary incontinence.

To explore the potential causal links between obesity, type 2 diabetes (T2D), and lifestyle choices (such as smoking, alcohol and coffee consumption, and vigorous physical activity) on stress urinary incontinence (SUI), this study employs a Mendelian Randomization approach. This research aims to clarify these associations, which have been suggested but not conclusively established in prior observational studies. Genetic instruments associated with the exposures at the genome-wide significance (p < 5 × 10-8) were selected from corresponding genome-wide association studies. Summary-level data for SUI, was obtained from the UK Biobank. A two-sample MR analysis was employed to estimate causal effects, utilizing the inverse-variance weighted (IVW) method as the primary analytical approach. Complementary sensitivity analyses including MR-PRESSO, MR-Egger, and weighted median methods were performed. The horizontal pleiotropy was detected by using MR-Egger intercept and MR-PRESSO methods, and the heterogeneity was assessed using Cochran's Q statistics. Our findings demonstrate a significant causal relationship between higher body mass index (BMI) and the risk of SUI, with increased abdominal adiposity (WHRadjBMI) similarly linked to SUI. Smoking initiation is also causally associated with an elevated risk. However, our analysis did not find definitive causal connections for other factors, including T2D, alcohol consumption, coffee intake, and vigorous physical activity. These findings provide valuable insights for clinical strategies targeting SUI, suggesting a need for heightened awareness and potential intervention in individuals with higher BMI, WHR, and smoking habits. Further research is warranted to explore the complex interplay between genetic predisposition and lifestyle choices in the pathogenesis of SUI.

Kausalsätze verstehen: Schülervorstellungen zu kausalen Zusammenhängen und kausalen Konnektoren

Research on connectors is an indispensable part of German didactics, since they contribute decisively to the creation of coherence and facilitate the reception of texts. What concepts students have about (causal) connectors is rarely addressed in didactic research. Yet, the elicitation of students' concepts is a central starting point to make new knowledge connectable and to enable knowledge construction (cf. Gropengießer &amp; Marohn, 2018). In this paper, we will discuss the results of an exploratory qualitative study in which language-related student concepts of causality and causal connectors were collected, which is an important starting point of the conceptual change approach (cf. Möller, 2007; Benesch &amp; Winkler, 2016). Via cooperative thinking aloud (cf. Schuttkowski, Rothstein, Schmitz &amp; Gräsel, 2015), the concepts of five student tandems (n = 8 students) on causal connections and causal connectors were videographically elicited and analyzed using content structuring qualitative content analysis (cf. Kuckartz, 2018). The meta-linguistic utterances show that sixth grade students have a broad range of different concepts about causal connections and the function of causal connectors.

Regulation mechanisms of disulfidptosis-related genes in ankylosing spondylitis and inflammatory bowel disease.

Disulfidptosis is a recently identified form of cell death that contributes to maintaining the internal environment balance of an organism. However, the molecular basis of disulfidptosis in ulcerative colitis (UC), ankylosing spondylitis (AS), and Crohn's disease (CD) has not been thoroughly explored. Firstly, the differentially expressed genes (DEGs) and disulfidptosis-associated genes (DAGs) were obtained through differential analysis between diseases (AS, CD, and UC) and control groups. After the disulfidptosis score was acquired using the single-sample gene set enrichment analysis (ssGSEA) algorithm, the DE-DAGs were screened by overlapping DAGs and DEGs of the three diseases. Next, the feature genes were selected through a combination of machine learning algorithms, receiver operating characteristic (ROC) curves, and expression analysis. Based on these feature genes, nomograms were created for AS, CD and UC. The co-feature genes were then identified by taking the intersections of the genes featured in all three diseases. Meanwhile, single-gene set enrichment analysis (GSEA) and the TF-mRNA-miRNA network were utilized to investigate the molecular mechanisms of the co-feature genes. To validate the expression differences of the co-feature genes between healthy controls and patients (AS and IBD), RT-PCR was performed. Lastly, mendelian randomization (MR) analysis was utilized to explore the causality between genetic variants of S100A12 with AS, UC and CD. In this study, 11 DE-DAGs were obtained. Functional enrichment analysis revealed their involvement in cytokine production and fatty acid biosynthesis. Latterly, AS/CD/UC -feature genes were derived, and they all had decent diagnostic performance. Through evaluation, the performance of the nomogram was decent for three diseases. Then, 2 co-feature genes (S100A12 and LILRA5) were obtained. The GSEA enrichment results indicated that the co-feature genes were mainly enriched in the cytokine-cytokine receptor interaction and drug metabolism cytochrome P450. As shown by functional experiments, there was a correlation between the mRNA expression of S100A12 with AS, UC and CD. Additionally, a causal connection between S100A12 and IBD was detected through MR analysis. In this study, 2 co-feature genes (S100A12 and LILRA5) were screened, and their functions were investigated in AS, CD and UC, providing a basis for further research into diagnosis and treatment.

Investigating the shared genetic architecture between frailty and insomnia.

The epidemiological association between frailty and insomnia is well established, yet the presence of a common genetic etiology is still uncertain. Further exploration is needed to ascertain the causal relationship between frailty and insomnia. Utilizing data obtained from genome-wide association studies (GWAS) summaries, we utilized the linkage disequilibrium score regression (LDSC) to determine the genetic correlation existing between frailty and insomnia. The determination of causality was achieved through the application of two-sample Mendelian randomization. We investigated the enrichment of single nucleotide polymorphism (SNP) at various tissue types utilizing stratified LD score regression (S-LDSC) and multimarker analysis of genome annotation (MAGMA). Common risk SNPs were identified using Multi-Trait Analysis of GWAS (MTAG) and Cross-Phenotype Association (CPASSOC). We further investigated the expression profiles of risk genes in tissues using Summary-data-based Mendelian randomization(SMR) based on pooled data, to explore potential functional genes. Our findings indicated a significant genetic correlation between frailty and insomnia, highlighting SNPs sharing risk (rs34290943, rs10865954), with a pronounced correlation in the localized genomic region 3p21.31. Partitioned genetic analysis revealed 24 functional elements significantly associated with both frailty and insomnia. Furthermore, mendelian randomization revealed a causal connection between frailty and insomnia. The genetic correlation between frailty and insomnia showed enrichment in 11 brain regions (S-LDSC) and 9 brain regions (MAGMA), where four functional genes (RMB6, MST1R, RF123, and FAM212A) were identified. This study suggests the existence of a genetic correlation and common risk genes between frailty and insomnia, contributing to a deeper comprehension of their pathogenesis and assists in identifying potential therapeutic targets.

Elucidating shared biomarkers in gastroesophageal reflux disease and idiopathic pulmonary fibrosis: insights into novel therapeutic targets and the role of angelicae sinensis radix.

Background: The etiological underpinnings of gastroesophageal reflux disease (GERD) and idiopathic pulmonary fibrosis (IPF) remain elusive, coupled with a scarcity of effective therapeutic interventions for IPF. Angelicae sinensis radix (ASR, also named Danggui) is a Chinese herb with potential anti-fibrotic properties, that holds promise as a therapeutic agent for IPF. Objective: This study seeks to elucidate the causal interplay and potential mechanisms underlying the coexistence of GERD and IPF. Furthermore, it aims to investigate the regulatory effect of ASR on this complex relationship. Methods: A two-sample Mendelian randomization (TSMR) approach was employed to delineate the causal connection between gastroesophageal reflux disease and IPF, with Phennoscanner V2 employed to mitigate confounding factors. Utilizing single nucleotide polymorphism (SNPs) and publicly available microarray data, we analyzed potential targets and mechanisms related to IPF in GERD. Network pharmacology and molecular docking were employed to explore the targets and efficacy of ASR in treating GERD-related IPF. External datasets were subsequently utilized to identify potential diagnostic biomarkers for GERD-related IPF. Results: The IVW analysis demonstrated a positive causal relationship between GERD and IPF (IVW: OR = 1.002, 95%CI: 1.001, 1.003; p < 0.001). Twenty-five shared differentially expressed genes (DEGs) were identified. GO functional analysis revealed enrichment in neural, cellular, and brain development processes, concentrated in chromosomes and plasma membranes, with protein binding and activation involvement. KEGG analysis unveiled enrichment in proteoglycan, ERBB, and neuroactive ligand-receptor interaction pathways in cancer. Protein-protein interaction (PPI) analysis identified seven hub genes. Network pharmacology analysis demonstrated that 104 components of ASR targeted five hub genes (PDE4B, DRD2, ERBB4, ESR1, GRM8), with molecular docking confirming their excellent binding efficiency. GRM8 and ESR1 emerged as potential diagnostic biomarkers for GERD-related IPF (ESR1: AUCGERD = 0.762, AUCIPF = 0.725; GRM8: AUCGERD = 0.717, AUCIPF = 0.908). GRM8 and ESR1 emerged as potential diagnostic biomarkers for GERD-related IPF, validated in external datasets. Conclusion: This study establishes a causal link between GERD and IPF, identifying five key targets and two potential diagnostic biomarkers for GERD-related IPF. ASR exhibits intervention efficacy and favorable binding characteristics, positioning it as a promising candidate for treating GERD-related IPF. The potential regulatory mechanisms may involve cell responses to fibroblast growth factor stimulation and steroidal hormone-mediated signaling pathways.

Money Supply, Inflation and Budget Deficit in Russia Compared to the United States

The author examines the causes and sources of the depreciation of money in Russia compared with that in the United States. The subject is causal connections between the budget deficit, money supply, and the depreciation of money. The relevance of the research for Russia is determined by concerns about macroeconomic stability and high inflation. In the case of the United States, an increase in the money supply and an inflation spike occurred because of the debt financing of the federal budget deficit. The scientific novelty of the paper lies in considering the two main options for monetary policy to support the liquidity of public debt: hard and soft, and the analytical methods and results of the research. One of the important scientific results is that the burden of public debt should be measured not as the ratio of public debt to gross domestic product (GDP) but as the share of public debt in a bond market. The second scientific result is very important for the practice: during 2011–2022, in the eight biennial periods, the GDP deflator was approximately equal to the growth of the money supply M2 minus GDP growth. Thus, the depreciation of money was directly caused by monetary policy. In the other three biennial periods, a substantial difference was observed, probably because of external shocks. As the method of the study, the author estimated the effect of interest rates caused by crowding out corporate debt by public debt. It was substantiated that to obtain the effects of soft monetary policy and thus the increase of M2 to GDP deflator, it is essential to use biennial periods. Based on the results of the analysis, it was revealed that, particularly in 2021–2022, the growth of the GDP deflator amounted to 139.8% and was due to the growth of the money supply M2 by 140.5%. At the same time, the effect on GDP growth was insignificant, at 3.4%. The key conclusion is that for the implementation of macroeconomic stability policies, it is necessary to manage the expansion of the M2 money supply, the exchange rate, and to use the GDP deflator as an important indicator in addition to the inflation index — consumer price index. A good way to achieve this is to adopt a special law for controlling inflation, similar to the USA Inflation Reduction Act.

COPD and T2DM: a Mendelian randomization study.

Type 2 diabetes (T2DM) stands as a global chronic illness, exerting a profound impact on health due to its complications and generating a significant economic burden. Recently, observational studies have pointed toward a potential link between Chronic Obstructive Pulmonary Disease (COPD) and T2DM. To elucidate this causal connection, we employed the Mendelian randomization analysis. Our study involved a two-sample Mendelian randomization (MR) analysis on COPD and T2DM. Additionally, tests for heterogeneity and horizontal pleiotropy were performed. For the MR analysis, 26 independent single nucleotides polymorphisms (SNPs) with strong associations to COPD were chosen as instrumental variables. Our findings suggest a pronounced causal relationship between COPD and T2DM. Specifically, COPD emerges as a risk factor for T2DM, with an odds ratio (OR) of 1.06 and a 95% confidence interval ranging from 1.01 to 1.11 (P = 0.006). Notably, all results were devoid of any heterogeneity or pleiotropy. The MR analysis underscores a significant causal relationship between COPD and T2DM, highlighting COPD as a prominent risk factor for T2DM.

Separating the effects of life course adiposity on diabetic nephropathy: a comprehensive multivariable Mendelian randomization study.

Previous Mendelian randomization (MR) of obesity and diabetic nephropathy (DN) risk used small sample sizes or focused on a single adiposity metric. We explored the independent causal connection between obesity-related factors and DN risk using the most extensive GWAS summary data available, considering the distribution of adiposity across childhood and adulthood. To evaluate the overall effect of each obesity-related exposure on DN (Ncase = 3,676, Ncontrol = 283,456), a two-sample univariate MR (UVMR) analysis was performed. The independent causal influence of each obesity-related feature on DN was estimated using multivariable MR (MVMR) when accounting for confounding variables. It was also used to examine the independent effects of adult and pediatric obesity, adjusting for their interrelationships. We used data from genome-wide association studies, including overall general (body mass index, BMI) and abdominal obesity (waist-to-hip ratio with and without adjustment for BMI, i.e., WHR and WHRadjBMI), along with childhood obesity (childhood BMI). UVMR revealed a significant association between adult BMI (OR=1.24, 95%CI=1.03-1.49, P=2.06×10-2) and pediatric BMI (OR=1.97, 95%CI=1.59-2.45, P=8.55×10-10) with DN risk. At the same time, adult WHR showed a marginally significant increase in DN (OR =1.27, 95%CI = 1.01-1.60, P=3.80×10-2). However, the outcomes were adverse when the influence of BMI was taken out of the WHR (WHRadjBMI). After adjusting for childhood BMI, the causal effects of adult BMI and adult abdominal obesity (WHR) on DN were significantly attenuated and became nonsignificant in MVMR models. In contrast, childhood BMI had a constant and robust independent effect on DN risk(adjusted for adult BMI: IVW, OR=1.90, 95% CI=1.60-2.25, P=2.03×10-13; LASSO, OR=1.91, 95% CI=1.65-2.21, P=3.80×10-18; adjusted for adult WHR: IVW, OR=1.80, 95% CI=1.40-2.31, P=4.20×10-6; LASSO, OR=1.90, 95% CI=1.56-2.32, P=2.76×10-10). Our comprehensive analysis illustrated the hazard effect of obesity-related exposures for DN. In addition, we showed that childhood obesity plays a separate function in influencing the risk of DN and that the adverse effects of adult obesity (adult BMI and adult WHR) can be substantially attributed to it. Thus, several obesity-related traits deserve more attention and may become a new target for the prevention and treatment of DN and warrant further clinical investigation, especially in childhood obesity.

Causal association of gut microbiota on spondyloarthritis and its subtypes: a Mendelian randomization analysis.

Despite establishing an association between gut microbiota and spondyloarthritis (SpA) subtypes, the causal relationship between them remains unclear. Gut microbiota data were obtained from the MiBioGen collaboration, and SpA genome-wide association study (GWAS) summary data were obtained from the FinnGen collaboration. We conducted a two-sample Mendelian randomization (MR) analysis using the inverse-variance-weighted method supplemented with four additional MR methods (MR-Egger, weighted median, simple mode, and weighted mode). Pleiotropy and heterogeneity were also assessed. Reverse MR analysis was used to detect reverse causal relationships. We identified 23 causal links between specific gut microbiota taxa and SpA levels. Of these, 22 displayed nominal causal associations, and only one demonstrated a robust causal connection. Actinobacteria id.419 increased the risk of ankylosing spondylitis (AS) (odds ratio (OR) = 1.86 (95% confidence interval (CI): 1.29-2.69); p = 8.63E-04). The family Rikenellaceae id.967 was associated with a reduced risk of both AS (OR = 0.66 (95% CI: 0.47-0.93); p = 1.81E-02) and psoriatic arthritis (OR = 0.70 (95% CI: 0.50-0.97); p = 3.00E-02). Bacillales id.1674 increased the risk of AS (OR = 1.23 (95% CI: 1.00-1.51); p = 4.94E-02) and decreased the risk of enteropathic arthritis (OR = 0.56 (95% CI: 0.35-0.88); p = 1.14E-02). Directional pleiotropy, or heterogeneity, was not observed. No reverse causal associations were observed between the diseases and the gut microbiota. Our MR analysis suggested a genetic-level causal relationship between specific gut microbiota and SpA, providing insights into the underlying mechanisms behind SpA development mediated by gut microbiota.

Repetitive magnetic stimuli over the motor cortex impair long-term consolidation of a balance task by suppressing up-regulation of intracortical inhibition

Introduction&#x0D; Recent findings have demonstrated that low-frequency repetitive magnetic stimulations (rTMS) over the primary motor cortex (M1) impaired short-term consolidation of a balance task, underscoring the causal connection between M1 and the consolidation of balancing skills (Egger et al., 2023). However, the underlying neural mechanisms induced by rTMS and whether these adaptations endure over an extended period, encompassing multiple acquisition sessions, remain insufficiently elucidated (Censor &amp; Cohen, 2011). So far, its is widely acknowledged that GABAergic processes play an important role for consolidation (Sanes &amp; Donoghue, 2000), at the same time, are affected by learning balance skills (Mouthon &amp; Taube, 2019; Taube et al., 2020). Therefore, the present study aimed to investigate the impact of rTMS on GABA-mediated short-interval intracortical inhibition (SICI) and to explore the role of M1 in the long-term consolidation of a balance task (i.e., across multiple acquisition sessions).&#x0D; Methods&#x0D; Thirty-one volunteers underwent six balance acquisition sessions on a rocker-board, each followed by either rTMS or sham rTMS based on group affiliation. During the first and last training session, SICI was measured twice; before the balance acquisition and after the application of rTMS or sham-rTMS to investigate potential short- and long-term adaptations in intracortical inhibition. Adaptations were assessed during the execution of the learned balance task and in a non-learning postural control task (i.e., stable upright stance).&#x0D; Results&#x0D; Regardless of group affiliation, all participants achieved comparable improvements within the balance acquisition sessions. However, consolidation varied between groups. In particular, between the third and the fourth acquisition session, as Tukey corrected post-hoc tests showed a significant decline in performance for the rTMS group (p = 0.006). Both short- (p = 0.014) and long-term (p = 0.038) adaptations in SICI were affected by rTMS: while the sham rTMS group upregulated SICI, rTMS led to reduced levels of inhibition. No neurophysiological effects were observed in the non-learning control task (upright stance).&#x0D; Discussion/Conclusion&#x0D; The interfering effect of rTMS on balance consolidation and on upregulation of SICI indicates that increased intracortical inhibition is an important mechanism to protect and engrave newly acquired motor memory. Importantly, adaptations in SICI were only apparent during the execution of the learned task.&#x0D; References&#x0D; Censor, N., &amp; Cohen, L. G. (2011). Using repetitive transcranial magnetic stimulation to study the underlying neural mechanisms of human motor learning and memory. The Journal of Physiology, 589(1), 21-28. https://doi.org/10.1113/jphysiol.2010.198077&#x0D; Egger, S., Wälchli, M., Rüeger, E., &amp; Taube, W. (2023). Short-term balance consolidation relies on the primary motor cortex: A rTMS study. Scientific Reports, 13, Article 5169. https://doi.org/10.1038/s41598-023-32065-x&#x0D; Mouthon, A., &amp; Taube, W. (2019). Intracortical inhibition increases during postural task execution in response to balance training. Neuroscience, 401, 35-42. https://doi.org/10.1016/j.neuroscience.2019.01.007&#x0D; Sanes, J. N., &amp; Donoghue, J. P. (2000). Plasticity and primary motor cortex. Annual Review of Neuroscience, 23, 393-415. https://doi.org/10.1146/annurev.neuro.23.1.393&#x0D; Taube, W., Gollhofer, A., &amp; Lauber, B. (2020). Training-, muscle- and task-specific up- and downregulation of cortical inhibitory processes. The European Journal of Neuroscience, 51(6), 1428-1440. https://doi.org/10.1111/ejn.14538

The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery

BackgroundRetinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP.MethodsBy mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa.ResultsA mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa.ConclusionsThe possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.

SLE serum induces altered goblet cell differentiation and leakiness in human intestinal organoids

Human intestinal epithelial cells are the interface between luminal content and basally residing immune cells. They form a tight monolayer that constantly secretes mucus creating a multilayered protective barrier. Alterations in this barrier can lead to increased permeability which is common in systemic lupus erythematosus (SLE) patients. However, it remains unexplored how the barrier is affected. Here, we present an in vitro model specifically designed to examine the effects of SLE on epithelial cells. We utilize human colon organoids that are stimulated with serum from SLE patients. Combining transcriptomic with functional analyses revealed that SLE serum induced an expression profile marked by a reduction of goblet cell markers and changed mucus composition. In addition, organoids exhibited imbalanced cellular composition along with enhanced permeability, altered mitochondrial function, and an interferon gene signature. Similarly, transcriptomic analysis of SLE colon biopsies revealed a downregulation of secretory markers. Our work uncovers a crucial connection between SLE and intestinal homeostasis that might be promoted in vivo through the blood, offering insights into the causal connection of barrier dysfunction and autoimmune diseases.

Fuzzy‐set qualitative comparative analysis (fsQCA) for validating causal relationships in system dynamics models

AbstractModelers often create diverse system dynamics models for the same issue, depending on their viewpoints, which can decrease stakeholder assurance. Validating system dynamics may enhance stakeholder confidence. This study suggests using fuzzy‐set qualitative comparative analysis (fsQCA) as a technique based on a set theory approach to validate the causal connections between entities in causal loop diagram (CLD) models. This case study analyzed the issue of Indonesian mobile network operators with limited sample data, utilizing the fsQCA method to test causal connections between entities in the CLD model that require validation. Following the creation of the CLD model through the system dynamics methodology, fsQCA was employed to enhance the previously formed model. The fsQCA method fuses qualitative comparative analysis (QCA) with fuzzy set theory, permitting partial membership, and can identify causal links among entities in the CLD model. It assists in testing causal relationships using limited sample data and boosts stakeholder confidence in the CLD model.

Symptom patterns in the co-occurrence of depressive and generalized anxiety symptoms: A network analysis of a Canadian nationally representative sample

BackgroundThis study aimed to investigate the symptom patterns of major depressive disorder (MDD) and generalized anxiety disorder (GAD) in a matched nationally representative sample of the Canadian population. We also tested whether childhood maltreatment (CM) exposures and sex would be linked with different symptom patterns. MethodsA total of 3296 participants from the Canadian Community Health Survey-Mental Health with complete information on MDD and GAD symptoms and being matched on the studied sociodemographic characteristics were included in the current study. Network analysis was performed to examine the MDD-GAD symptom network, network stability and centrality indices were also estimated. Finally, network comparison in connectivity patterns was conducted to explore the impact of maltreatment experience and sex differences in the MDD-GAD symptom networks. ResultsThe CM group had stronger network connections and showed differences in the network structures from the non-CM group. In the CM group, depressed mood and diminished interest were central symptoms and strongly connected with other symptoms. Additionally, females had stronger connections in the MDD-GAD symptom network than males, and sleep disturbance was a central symptom for females, alongside depressed mood and diminished interest. LimitationsThe cross-sectional design restricts our capacity to establish longitudinal or causal connections between symptoms. ConclusionsDepressed mood was the most central node that was strongly connected with other symptoms in the network. Distinct MDD-GAD symptom networks were discovered in the CM and the female group when compared to their counterparts. Noteworthy, individuals with CM had a stronger correlation between worry and suicidal ideation. Clinical management and intervention efforts should pay close attention to these core symptoms to yield optimal treatment effects, particularly for females and individuals with CM.

Causal Connectivity Network Analysis of Ictal Electrocorticogram With Temporal Lobe Epilepsy Based on Dynamic Phase Transfer Entropy.

Temporallobe epilepsy (TLE) has been conceptualized as a brain network disease, which generates brain connectivity dynamics within and beyond the temporal lobe structures in seizures. The hippocampus is a representative epileptogenic focus in TLE. Understanding the causal connectivity in terms of brain network during seizures is crucial in revealing the triggering mechanism of epileptic seizures originating from the hippocampus (HPC) spread to the lateral temporal cortex (LTC) by ictal electrocorticogram (ECoG), particularly in high-frequency oscillations (HFOs) bands. In this study, we proposed the unified-epoch dynamic causality analysis method to investigate the causal influence dynamics between two brain regions (HPC and LTC) at interictal and ictal phases in the frequency range of 1-500 Hz by introducing the phase transfer entropy (PTE) out/in-ratio and sliding window. We also proposed PTE-based machine learning algorithms to identify epileptogenic zone (EZ). Nine patients with a total of 26 seizures were included in this study. We hypothesized that: 1) HPC is the focus with the stronger causal connectivity than that in LTC in the ictal state at gamma and HFOs bands. 2) Causal connectivity in the ictal phase shows significant changes compared to that in the interictal phase. 3) The PTE out/in-ratio in the HFOs band can identify the EZ with the best prediction performance.