Early seizures and risk of epilepsy and death after intracerebral haemorrhage: The MUCH Italy.

INTRODUCTION: It is unclear which patients with non-traumatic (spontaneous) intracerebral haemorrhage (ICH) are at risk of developing acute symptomatic seizures (provoked seizures occurring within the first week after stroke onset; early seizures, ES) and whether ES predispose to the occurrence of remote symptomatic seizures (unprovoked seizures occurring more than 1 week after stroke; post-stroke epilepsy, PSE) and long-term mortality. PATIENTS AND METHODS: In the setting of the Multicenter Study on Cerebral Haemorrhage in Italy (MUCH-Italy) we examined the risk of ES and whether they predict the occurrence of PSE and all-cause mortality in a cohort of patients with first-ever spontaneous ICH and no previous history of epilepsy, consecutively hospitalized in 12 Italian neurological centers from 2002 to 2014. RESULTS: Among 2570 patients (mean age, 73.4 ± 12.5 years; males, 55.4%) 228 (8.9%) had acute ES (183 (7.1%) short seizures and 45 (1.8%) status epilepticus (SE)). Lobar location of the hematoma (OR, 1.49; 95% CI, 1.06-2.08) was independently associated with the occurrence of ES. Of the 2,037 patients who were followed-up (median follow-up time, 68.0 months (25th-75th percentile, 77.0)), 155 (7.6%) developed PSE. ES (aHR, 2.34; 95% CI, 1.42-3.85), especially when presenting as short seizures (aHR, 2.35; 95% CI, 1.38-4.00) were associated to PSE occurrence. Unlike short seizures, SE was an independent predictor of all-cause mortality (aHR, 1.50; 95% CI, 1.005-2.26). DISCUSSION AND CONCLUSION: The long-term risk of PSE and death after an ICH vary according to ES subtype. This might have implications for the design of future clinical trials targeting post-ICH epileptic seizures.

Mucus Structure, Viscoelastic Properties, and Composition in Chronic Respiratory Diseases.

The respiratory mucus, a viscoelastic gel, effectuates a primary line of the airway defense when operated by the mucociliary clearance. In chronic respiratory diseases (CRDs), such as asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis (CF), the mucus is overproduced and its solid content augments, changing its structure and viscoelastic properties and determining a derangement of essential defense mechanisms against opportunistic microbial (virus and bacteria) pathogens. This ensues in damaging of the airways, leading to a vicious cycle of obstruction and infection responsible for the harsh clinical evolution of these CRDs. Here, we review the essential features of normal and pathological mucus (i.e., sputum in CF, COPD, and asthma), i.e., mucin content, structure (mesh size), micro/macro-rheology, pH, and osmotic pressure, ending with the awareness that sputum biomarkers (mucins, inflammatory proteins and peptides, and metabolites) might serve to indicate acute exacerbation and response to therapies. There are some indications that old and novel treatments may change the structure, viscoelastic properties, and biomarker content of sputum; however, a wealth of work is still needed to embrace these measures as correlates of disease severity in association with (or even as substitutes of) pulmonary functional tests.

Ionic Strength Impacts the Physical Properties of Agarose Hydrogels.

Agarose is a natural polysaccharide known for its ability to form thermoreversible hydrogels. While the effects of curing temperature and polysaccharide concentration on mechanical properties have been discussed in the literature, the role of ionic strength has been less studied. In the present manuscript, we investigate the effects of supporting salt concentration and the role of cation (i.e. Na+ or Li+, neighbors in the Hofmeister series), on the setting and performance of agarose hydrogels. Compressive and rheological measurements show that the supporting salts reduce the immediate elastic response of agarose hydrogels, with Li+ showing a stronger effect than Na+ at high ionic strength, while they significantly increase the extent of linear stress-strain response (i.e., linear elasticity). The presence of increasing amounts of added supporting salt also leads to a reduction in hysteresis during mechanical deformation due to loading and unloading cycles, which is more pronounced with Li+ than with Na+. The combination of rheological measurements and NMR relaxometry shows a mesh size in agarose hydrogels in the order of 6-17 nm, with a thickness of the water layer bound to the biopolymer of about 3 nm. Of note, the different structuring of the water within the hydrogel network due to the different alkali seems to play a role for the final performance of the hydrogels.

Long-Term Risk of Arterial Thrombosis After Intracerebral Hemorrhage: MUCH-Italy.

BACKGROUND: The identification of patients surviving an acute intracerebral hemorrhage who are at a long-term risk of arterial thrombosis is a poorly defined, crucial issue for clinicians. METHODS: In the setting of the MUCH-Italy (Multicenter Study on Cerebral Haemorrhage in Italy) prospective observational cohort, we enrolled and followed up consecutive 30-day intracerebral hemorrhage survivors to assess the long-term incidence of arterial thrombotic events, to assess the impact of clinical and radiological variables on the risk of these events, and to develop a tool for estimating such a risk at the individual level. Primary end point was a composite of ischemic stroke, myocardial infarction, or other arterial thrombotic events. A point-scoring system was generated by the ß-coefficients of the variables independently associated with the long-term risk of arterial thrombosis, and the predictive MUCH score was calculated as the sum of the weighted scores. RESULTS: Overall, 1729 patients (median follow-up time, 43 months [25th to 75th percentile, 69.0]) qualified for inclusion. Arterial thrombotic events occurred in 169 (9.7%) patients. Male sex, diabetes, hypercholesterolemia, atrial fibrillation, and personal history of coronary artery disease were associated with increased long-term risk of arterial thrombosis, whereas the use of statins and antithrombotic medications after the acute intracerebral hemorrhage was associated with a reduced risk. The area under the receiver operating characteristic curve of the MUCH score predictive validity was 0.716 (95% CI, 0.56-0.81) for the 0- to 1-year score, 0.672 (95% CI, 0.58-0.73) for the 0- to 5-year score, and 0.744 (95% CI, 0.65-0.81) for the 0- to 10-year score. C statistic for the prediction of events that occur from 0 to 10 years was 0.69 (95% CI, 0.64-0.74). CONCLUSIONS: Intracerebral hemorrhage survivors are at high long-term risk of arterial thrombosis. The MUCH score may serve as a simple tool for risk estimation.

Diagnostic accuracy of research criteria for prodromal frontotemporal dementia.

BACKGROUND: The Genetic Frontotemporal Initiative Staging Group has proposed clinical criteria for the diagnosis of prodromal frontotemporal dementia (FTD), termed mild cognitive and/or behavioral and/or motor impairment (MCBMI). The objective of the study was to validate the proposed research criteria for MCBMI-FTD in a cohort of genetically confirmed FTD cases against healthy controls. METHODS: A total of 398 participants were enrolled, 117 of whom were carriers of an FTD pathogenic variant with mild clinical symptoms, while 281 were non-carrier family members (healthy controls (HC)). A subgroup of patients underwent blood neurofilament light (NfL) levels and anterior cingulate atrophy assessment. RESULTS: The core clinical criteria correctly classified MCBMI vs HC with an AUC of 0.79 (p < 0.001), while the addition of either blood NfL or anterior cingulate atrophy significantly increased the AUC to 0.84 and 0.82, respectively (p < 0.001). The addition of both markers further increased the AUC to 0.90 (p < 0.001). CONCLUSIONS: The proposed MCBMI criteria showed very good classification accuracy for identifying the prodromal stage of FTD.

Cortico-spinal tDCS in amyotrophic lateral sclerosis: A randomized, double-blind, sham-controlled trial followed by an open-label phase.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive disease for which no curative treatment is currently available. OBJECTIVE: This study aimed to investigate whether cortico-spinal transcranial direct current stimulation (tDCS) could mitigate symptoms in ALS patients via a randomized, double-blind, sham-controlled trial, followed by an open-label phase. METHODS: Thirty-one participants were randomized into two groups for the initial controlled phase. At baseline (T0), Group 1 received placebo stimulation (sham tDCS), while Group 2 received cortico-spinal stimulation (real tDCS) for five days/week for two weeks (T1), with an 8-week (T2) follow-up (randomized, double-blind, sham-controlled phase). At the 24-week follow-up (T3), all participants (Groups 1 and 2) received a second treatment of anodal bilateral motor cortex and cathodal spinal stimulation (real tDCS) for five days/week for two weeks (T4). Follow-up evaluations were performed at 32-weeks (T5) and 48-weeks (T6) (open-label phase). At each time point, clinical assessment, blood sampling, and intracortical connectivity measures using transcranial magnetic stimulation (TMS) were evaluated. Additionally, we evaluated survival rates. RESULTS: Compared to sham stimulation, cortico-spinal tDCS significantly improved global strength, caregiver burden, and quality of life scores, which correlated with the restoration of intracortical connectivity measures. Serum neurofilament light levels decreased among patients who underwent real tDCS but not in those receiving sham tDCS. The number of completed 2-week tDCS treatments significantly influenced patient survival. CONCLUSIONS: Cortico-spinal tDCS may represent a promising therapeutic and rehabilitative approach for patients with ALS. Further larger-scale studies are necessary to evaluate whether tDCS could potentially impact patient survival. CLINICAL TRIAL REGISTRATION: NCT04293484.

Agarose Cryogels: Production Process Modeling and Structural Characterization.

A cryogel is a cross-linked polymer network with different properties that are determined by its manufacturing technique. The formation of a cryogel occurs at low temperatures and results in a porous structure whose pore size is affected by thermal conditions. The adjustable pore sizes of cryogels make them attractive for diverse applications. In this study, the influence of the external operational temperature, which affects the cooling and freezing rates, on the production of cryogels with 2% w/w agarose is investigated. Moreover, a mathematical model is developed to simulate the cryogel production process and provide an initial estimate of the pore size within the structure. The predictions of the model, supported by qualitative light microscopy images, demonstrate that cryogels produced at higher process temperatures exhibit larger pore sizes. Moreover, the existence of pore size distribution within the gel structure is confirmed. Finally, stress relaxation tests, coupled with an image analysis, validates that cryogels produced at lower temperatures possess a higher stiffness and slower water release rates.

Nanocomposite Hydrogels with Self-Assembling Peptide-Functionalized Carbon Nanostructures.

Carbon nanostructures (CNSs) are attractive components to attain nanocomposites, yet their hydrophobic nature and strong tendency to aggregate often limit their use in aqueous conditions and negatively impact their properties. In this work, carbon nanohorns (CNHs), multi-walled carbon nanotubes (CNTs), and graphene (G) are first oxidized, and then reacted to covalently anchor the self-assembling tripeptide L-Leu-D-Phe-D-Phe to improve their dispersibility in phosphate buffer, and favor the formation of hydrogels formed by the self-organizing L-Leu-D-Phe-D-Phe present in solution. The obtained nanocomposites are then characterized by transmission electron microscopy (TEM), oscillatory rheology, and conductivity measurements to gain useful insights as to the key factors that determine self-healing ability for the future design of this type of nanocomposites.

Interpreting permeability as a function of free drug fraction: The case studies of cyclodextrins and liposomes.

In order to solubilize poorly soluble active pharmaceutical ingredients, various strategies have been implemented over the years, including the use of nanocarriers, such as cyclodextrins and liposomes. However, improving a drug's apparent solubility does not always translate to enhanced bioavailability. This work aimed to investigate to which extent complexation with cyclodextrins and incorporation into liposomes influence drug in vitro permeability and to find a mechanistic description of the permeation process. For this purpose, we investigated hydroxypropyl-ß-cyclodextrin (HP-ß-CD) and phosphatidylcholine liposomes formulations of three chemically diverse compounds (atenolol, ketoprofen and hydrocortisone). We studied drug diffusion of the formulations by UV-localized spectroscopy and advanced data fitting to extract parameters such as diffusivity and bound-/free drug fractions. We then correlated this information with in vitro drug permeability obtained with the novel PermeaPadⓇ barrier. The results showed that increased concentration of HP-ß-CD leads to increased solubilization of the poorly soluble unionized ketoprofen, as well as hydrocortisone. However, this net increment of apparent solubility was not proportional to the increased flux measured. On the other hand, normalising the flux over the empirical free drug concentration, i.e., the free fraction, gave a meaningful absolute permeability coefficient. The results achieved for the liposomal formulation were consistent with the finding on cyclodextrins. In conclusion, we proved the adequacy and usefulness of our method for calculating free drug fractions in the examined enabling formulations, supporting the validity of the established drug diffusion/permeation theory that the unbounded drug fraction is the main driver for drug permeation across a membrane.

Risk Profile of Patients with Spontaneous Cervical Artery Dissection.

OBJECTIVE: Epidemiological data to characterize the individual risk profile of patients with spontaneous cervical artery dissection (sCeAD) are rather inconsistent. METHODS AND RESULTS: In the setting of the Italian Project on Stroke in Young Adults Cervical Artery Dissection (IPSYS CeAD), we compared the characteristics of 1,468 patients with sCeAD (mean age = 47.3 ± 11.3 years, men = 56.7%) prospectively recruited at 39 Italian centers with those of 2 control groups, composed of (1) patients whose ischemic stroke was caused by mechanisms other than dissection (non-CeAD IS) selected from the prospective IPSYS registry and Brescia Stroke Registry and (2) stroke-free individuals selected from the staff members of participating hospitals, matched 1:1:1 by sex, age, and race. Compared to stroke-free subjects, patients with sCeAD were more likely to be hypertensive (odds ratio [OR] = 1.65, 95% confidence interval [CI] = 1.37-1.98), to have personal history of migraine with aura (OR = 2.45, 95% CI = 1.74-3.34), without aura (OR = 2.67, 95% CI = 2.15-3.32), and family history of vascular disease in first-degree relatives (OR = 1.69, 95% CI = 1.39-2.05), and less likely to be diabetic (OR = 0.65, 95% CI = 0.47-0.91), hypercholesterolemic (OR = 0.75, 95% CI = 0.62-0.91), and obese (OR = 0.41, 95% CI = 0.31-0.54). Migraine without aura was also associated with sCeAD (OR = 1.81, 95% CI = 1.47-2.22) in comparison with patients with non-CeAD IS. In the subgroup of patients with migraine, patients with sCeAD had higher frequency of migraine attacks and were less likely to take anti-migraine preventive medications, especially beta-blockers, compared with the other groups. INTERPRETATION: The risk of sCeAD is influenced by migraine, especially migraine without aura, more than by other factors, increases with increasing frequency of attacks, and seems to be reduced by migraine preventive medications, namely beta-blockers. ANN NEUROL 2023;94:585-595.

SEMtree: tree-based structure learning methods with structural equation models.

MOTIVATION: With the exponential growth of expression and protein-protein interaction (PPI) data, the identification of functional modules in PPI networks that show striking changes in molecular activity or phenotypic signatures becomes of particular interest to reveal process-specific information that is correlated with cellular or disease states. This requires both the identification of network nodes with reliability scores and the availability of an efficient technique to locate the network regions with the highest scores. In the literature, a number of heuristic methods have been suggested. We propose SEMtree(), a set of tree-based structure discovery algorithms, combining graph and statistically interpretable parameters together with a user-friendly R package based on structural equation models framework. RESULTS: Condition-specific changes from differential expression and gene-gene co-expression are recovered with statistical testing of node, directed edge, and directed path difference between groups. In the end, from a list of seed (i.e. disease) genes or gene P-values, the perturbed modules with undirected edges are generated with five state-of-the-art active subnetwork detection methods. The latter are supplied to causal additive trees based on Chu-Liu-Edmonds' algorithm (Chow and Liu, Approximating discrete probability distributions with dependence trees. IEEE Trans Inform Theory 1968;14:462-7) in SEMtree() to be converted in directed trees. This conversion allows to compare the methods in terms of directed active subnetworks. We applied SEMtree() to both Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) and simulated datasets with various differential expression patterns. Compared to existing methods, SEMtree() is able to capture biologically relevant subnetworks with simple visualization of directed paths, good perturbation extraction, and classifier performance. AVAILABILITY AND IMPLEMENTATION: SEMtree() function is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph.

Targeting Non-Coding RNAs for the Development of Novel Hepatocellular Carcinoma Therapeutic Approaches.

Hepatocellular carcinoma (HCC) remains a global health challenge, representing the third leading cause of cancer deaths worldwide. Although therapeutic advances have been made in the few last years, the prognosis remains poor. Thus, there is a dire need to develop novel therapeutic strategies. In this regard, two approaches can be considered: (1) the identification of tumor-targeted delivery systems and (2) the targeting of molecule(s) whose aberrant expression is confined to tumor cells. In this work, we focused on the second approach. Among the different kinds of possible target molecules, we discuss the potential therapeutic value of targeting non-coding RNAs (ncRNAs), which include micro interfering RNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). These molecules represent the most significant RNA transcripts in cells and can regulate many HCC features, including proliferation, apoptosis, invasion and metastasis. In the first part of the review, the main characteristics of HCC and ncRNAs are described. The involvement of ncRNAs in HCC is then presented over five sections: (a) miRNAs, (b) lncRNAs, (c) circRNAs, (d) ncRNAs and drug resistance and (e) ncRNAs and liver fibrosis. Overall, this work provides the reader with the most recent state-of-the-art approaches in this field, highlighting key trends and opportunities for more advanced and efficacious HCC treatments.

Rheological and low field NMR characterization of hydrophobically-modified PEG hydrogels for drug delivery.

The focus of this work is on the characterization of hydrophobically-modified polyethylene glycol hydrogels, to be used as drug delivery systems, by means of the combined used of rheology and low field Nuclear Magnetic Resonance. Indeed, these two techniques allowed understanding how the transient physical bonds deriving from hydrophobic association superimpose to the pre-existing covalent bonds. We found that the improvement of physical bonds can be achieved not only by increasing the content of hydrophobic segments but also by using thermal treatments after hydrogel preparation. Moreover, we proved the reliability of an overall interpretative model linking the dependence of the shear modulus and the average magnetic relaxation time. Finally, we proposed a new mathematical approach for the determination of the magnetic relaxation spectrum. This approach reduced the computational heaviness of the procedure and allowed to easily discern the different contributes nested in the overall magnetic relaxation spectrum, an aspect that the traditional approach cannot provide directly.

Use of low-field NMR and rheology to evaluate the microstructure and stability of a poly(d,l-lactide-co-glycolide)-based W/O emulsion to be processed by spray drying.

Drug-loaded emulsions for spray drying should be optimised for their rheological behaviour and stability under operating conditions, as this is essential for achieving the desired physicochemical properties of the final dry product. Our aim was therefore to investigate the structure and stability of a water-in-oil (W/O) emulsion containing vancomycin hydrochloride as the active ingredient in the aqueous phase, poly(d,l-lactide-co-glycolide) as the structural polymer in the dichloromethane-based organic phase, and various stabilisers using low-field nuclear magnetic resonance (LF NMR) and rheological characterisation. Four emulsions were tested, namely-one without stabiliser, one with Poloxamer® 407, one with chitosan and Span™ 80 and one with chitosan only. The theoretical interpretation of the rheological data allowed the determination of the velocity and the shear rate/stress profiles inside the feed path of the W/O emulsion, aspects that are critical for the industrial scale-up of the emulsion drying process. In addition, LF NMR demonstrated that shaking was sufficient to restore the original emulsion structure and that the droplet size of all emulsions was in the range of 1-10 µm, although the emulsion with chitosan had the narrowest droplet size distribution and the higher zero shear viscosity, which accounts for the increased long-term stability due to impeded droplets movement.

Boron Nitride-Doped Polyphenylenic Organogels.

Herein, we describe the synthesis of the first boron nitride-doped polyphenylenic material obtained through a [4 + 2] cycloaddition reaction between a triethynyl borazine unit and a biscyclopentadienone derivative, which undergoes organogel formation in chlorinated solvents (the critical jellification concentration is 4% w/w in CHCl3). The polymer has been characterized extensively by Fourier-transform infrared spectroscopy, solid-state 13C NMR, solid-state 11B NMR, and by comparison with the isolated monomeric unit. Furthermore, the polymer gels formed in chlorinated solvents have been thoroughly characterized and studied, showing rheological properties comparable to those of polyacrylamide gels with a low crosslinker percentage. Given the thermal and chemical stability, the material was studied as a potential support for solid-state electrolytes. showing properties comparable to those of polyethylene glycol-based electrolytes, thus presenting great potential for the application of this new class of material in lithium-ion batteries.

Potential Application of Small Interfering RNA in Gastro-Intestinal Tumors.

Despite the progress made in the diagnoses and therapy of gastrointestinal cancers, these diseases are still plagued by a high mortality. Thus, novel therapeutic approaches are urgently required. In this regard, small interfering RNA (siRNA), double-stranded RNA molecules able to specifically target the mRNA of pathological genes, have the potential to be of therapeutic value. To be effective in the human body, siRNAs need to be protected against degradation. Additionally, they need to target the tumor, leaving the normal tissue untouched in an effort to preserve organ function. To accomplish these tasks, siRNAs have been formulated with smart delivery systems such has polymers and lipids. While siRNA protection is not particularly difficult to achieve, their targeting of tumor cells remains problematic. Here, after introducing the general features of gastrointestinal cancers, we describe siRNA characteristics together with representative delivery systems developed for gastrointestinal cancers. Afterward, we present a selection of research papers employing siRNAs against upper- and lower- gastrointestinal cancers. For the liver, we also consider papers using siRNAs to combat liver cirrhosis, a relevant risk factor for liver cancer development. Finally, we present a brief description of clinical trials employing siRNAs for gastrointestinal cancers.

SEMgraph: an R package for causal network inference of high-throughput data with structural equation models.

MOTIVATION: With the advent of high-throughput sequencing in molecular biology and medicine, the need for scalable statistical solutions for modeling complex biological systems has become of critical importance. The increasing number of platforms and possible experimental scenarios raised the problem of integrating large amounts of new heterogeneous data and current knowledge, to test novel hypotheses and improve our comprehension of physiological processes and diseases. RESULTS: Combining network analysis and causal inference within the framework of structural equation modeling (SEM), we developed the R package SEMgraph. It provides a fully automated toolkit, managing complex biological systems as multivariate networks, ensuring robustness and reproducibility through data-driven evaluation of model architecture and perturbation, which is readily interpretable in terms of causal effects among system components. AVAILABILITY AND IMPLEMENTATION: SEMgraph package is available at https://cran.r-project.org/web/packages/SEMgraph. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modelling drug diffusion through unstirred water layers allows real-time quantification of free/loaded drug fractions and release kinetics from colloidal-based formulations.

The correlation between in vivo and in vitro data is yet not sufficiently optimized to allow a significant reduction and replacement of animal testing in pharmaceutical development. One of the main reasons for this lies in the poor mechanistic understanding and interpretation of the physical mechanisms enabling formulation rely on for deploying the drug. One mechanism that still lacks a proper interpretation is the kinetics of drug release from nanocarriers. In this work, we investigate two different types of classical enabling formulations - i) cyclodextrin solutions and ii) liposomal dispersions - by a combination of an experimental method (i.e. UV-Vis localized spectroscopy) and mathematical modelling/numerical data fitting. With this approach, we are able to discriminate precisely between the amount of drug bound to nanocarriers or freely dissolved at any time point; in addition, we can precisely estimate the binding and diffusivity constants of all chemical species (free drug/bound drug). The results obtained should serve as the first milestone for the further development of reliable in vitro/in silico models for the prediction of in vivo drug bioavailability when enabling formulations are used.

SEMgsa: topology-based pathway enrichment analysis with structural equation models.

BACKGROUND: Pathway enrichment analysis is extensively used in high-throughput experimental studies to gain insight into the functional roles of pre-defined subsets of genes, proteins and metabolites. Methods that leverages information on the topology of the underlying pathways outperform simpler methods that only consider pathway membership, leading to improved performance. Among all the proposed software tools, there's the need to combine high statistical power together with a user-friendly framework, making it difficult to choose the best method for a particular experimental environment. RESULTS: We propose SEMgsa, a topology-based algorithm developed into the framework of structural equation models. SEMgsa combine the SEM p values regarding node-specific group effect estimates in terms of activation or inhibition, after statistically controlling biological relations among genes within pathways. We used SEMgsa to identify biologically relevant results in a Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) together with a frontotemporal dementia (FTD) DNA methylation dataset (GEO accession: GSE53740) and compared its performance with some existing methods. SEMgsa is highly sensitive to the pathways designed for the specific disease, showing low p values ([Formula: see text]) and ranking in high positions, outperforming existing software tools. Three pathway dysregulation mechanisms were used to generate simulated expression data and evaluate the performance of methods in terms of type I error followed by their statistical power. Simulation results confirm best overall performance of SEMgsa. CONCLUSIONS: SEMgsa is a novel yet powerful method for identifying enrichment with regard to gene expression data. It takes into account topological information and exploits pathway perturbation statistics to reveal biological information. SEMgsa is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph .