Schizophrenia is associated with cognitive deficits and higher stress sensitivity, potentially related to gut-brain axis disturbances, partially due to dysbiosis. To ascertain this phenomenon, we aimed to evaluate these behavioral phenotypes in association with microbiota profile in the triple-hit Wisket rat model of schizophrenia. Furthermore, as a bidirectional approach, the effects of clozapine (CLO) and/or prebiotic (galactooligosaccharide, B-GOS) treatment were also investigated. Male Wistar (control) and Wisket rats were treated for 3 weeks: CLO or its vehicle was administered intraperitoneally, while B-GOS or water was provided ad libitum in drinking bottle. The food-rewarded Ambitus test was used to assess cognition-related behaviors before and during the third week of the treatment. Afterwards, fecal samples were collected to analyse microbiota composition, and smooth muscle electromyography was performed to assess immobilization-induced stress response. B-GOS monotreatment resulted in the highest improvement in cognition-related parameters in Wisket model rats; however, it never reached the performance of control animals. Wisket animals showed higher vulnerability to immobilization-induced stress condition. This group-difference disappeared by each pharmacological treatment, with the most prominent effect of the CLO + B-GOS combination treatment. β-diversity analysis revealed an overall compositional difference of fecal microbiota between treatment groups. Several taxa associated with schizophrenia-model or treatment were significantly correlated with behavioral parameters. Consistent with clinical findings, cognitive impairment with increased stress sensitivity were highlighted in Wisket model rats. To our knowledge, this is the first study on B-GOS prebiotic in a triple-hit schizophrenia model, suggesting microbiome-targeted therapy may aid some schizophrenia-related symptoms.

In addition to aortic manifestations, Marfan syndrome can affect retinal vessels. Our aim was to evaluate retinal circulation, and its correlation with genotype and cardiovascular manifestation identifying predictors of aortic involvement (dilation and/or dissection). In the retrospective, cross-sectional study, 39 Marfan syndrome patients with optical coherence tomography angiography records were included. Retinal thickness, superficial and deep vessel density in total retina, fovea, parafovea, perifovea, area and perimeter of foveal avascular zone, fractal dimension were measured. Two groups were created by mutation type: haploinsufficient, dominant negative. Latter were divided into two subgroups according to whether mutation resulted in cysteine elimination. Subjects were assigned into cardiovascular risk based on previous aortic surgery. Retina of haploinsufficient patients was thinner in total, foveal, parafoveal, perifoveal areas compared to dominant negative subjects (p ≤ 0.047). Retinal thickness of haploinsufficient individuals was thinner in total, parafoveal areas compared to dominant negative without (LSD p = 0.038, Bonferroni p = 0.027, respectively) and with cysteine elimination variants (LSD p = 0.032, Bonferroni p = 0.002, respectively). In fovea and perifovea, retinal thickness was decreased in haploinsufficient patients in comparison to dominant negative with cysteine elimination group (Bonferroni p ≤ 0.029). Total, parafoveal, perifoveal superficial and total, parafoveal deep vessel density of subjects who underwent aortic surgery were lower compared to non-operated patients (p ≤ 0.043). To the best of our knowledge, our study is the first to describe a relationship between genotype and optical coherence tomography angiography parameters in Marfan syndrome. These findings along with correlations between genetics and cardiovascular manifestations reported previously, suggest that these parameters may be indirect predictors of increased cardiovascular risk. Here we demonstrated associations between these parameters and aortic involvement.

Marfan syndrome (MFS) is an autosomal dominant connective tissue disorder caused by mutations in the gene encoding fibrillin-1 (FBN1), the main component of extracellular microfibrils. In the aortic wall, these microfibrils maintain structural integrity and sustain hemodynamic load. Pathogenic FBN1 variants are thought to structurally and functionally impair fibrillin-1 microfibrils, leading to progressive aortic aneurysm and dissection, the major causes of morbidity and mortality in MFS. However, the molecular mechanisms whereby these genetic variants translate into structural and mechanical defects are far from being understood.Here we explored the morphology and the nanomechanical characteristics of individual aortic fibrillin-1 microfibrils from MFS patients and non-MFS controls by atomic force microscopy. The topographical assessment revealed a preserved overall pattern and periodicity of the microfibrils, but with morphological irregularities in MFS microfibril beads and interbead segments, consistent with presumed structural fragility. Force spectroscopy revealed a reduction of transverse elastic modulus in patients harboring haploinsufficient FBN1 variants. Nanoindentation analysis was indicative of localized deformation, occurring at markedly lower forces in MFS microfibril beads, suggesting diminished load-bearing capacity.These data provide direct nanoscale evidence of structural and mechanical consequences of FBN1 mutations on human aortic tissue. Altered fibrillin-1 microfibril morphology and reduced stiffness in MFS support a pathogenetic mechanism in which compromised microfibrillar integrity weakens the aortic wall, predisposing it to progressive dilation.

Post-marketing surveillance is crucial for drug safety, yet the tools of pharmacovigilance rely solely on text-based data that may limit contemporary machine learning methodologies in the support of decision-making. With the recent surge of employing large language models (LLMs) for text-based tasks, there also arises an unmet need for a different approach which is not grounded in the linguistic patterns of unfiltered natural text, like LLMs, but rather based on real-world drug safety data. Here, we adapt contrastive learning algorithms to generate adverse event vector representations from spontaneous adverse event reports to serve as machine-readable (i.e. numerical) resources for downstream pharmacovigilance applications, such as drug-event association prediction for signal detection or causality assessment. We present comprehensive interpretability analyses of the resulting representations through density-based clustering, semantic evaluation, and comparison of multivariate dispersions, revealing patterns that reflect both functional and causal relations of the adverse events while also capturing drug-safety-related information better than existing medical terminologies and encoder-only LLMs. Furthermore, we demonstrate the applicability of our representations as input features in our downstream classifier model, outperforming the reporting odds ratio method, commonly used by regulatory agencies, and also LLM-generated representations (area under the receiver operating characteristic curve: 0.88 versus 0.76-0.83) on drug-event association prediction benchmarks. Therefore, we propose an interpretable adverse event vector representation, serving as a general resource that could enable the development of a wide array of machine learning applications to support decision-making in pharmacovigilance and facilitate patient safety.

Background Myocardial infarct size (IS) is the gold standard end-point in shorth-term studies on cardioprotection. However, IS quantification in rodent models with standard Evans Blue and 2,3,5-triphenyltetrazolium chloride (TTC) staining is time-consuming and prone to inter-observer variance. Therefore, we aimed to develop an artificial intelligence (AI)-based application to reduce time and inter-observer variability of IS analysis in rodent acute myocardial infarction (MI) models. Methods We used TTC/Evans blue-stained heart slice images of independent laboratories from previously published projects. Rat (n = 325 and 248 slices) and mouse (n = 77 slices) datasets were used to train deep learning segmentation models with three different neural network architectures, which were combined into a single AI analysis. AI analysis was compared with manual analysis on rat data from a training laboratory (internal data, n = 496 slices, n = 41 whole-hearts) and data from independent laboratories (external data, n = 60 and 62 slices). Additionally, two independent evaluators performed manual and AI-assisted analysis, consisting of AI-analysis and its manual correction, on internal (n = 36 slices) and external data (n = 37 slices). Results Lin’s concordance correlation coefficient (CCC) between IS/AAR values from manual and AI analysis was 0.844 with 95% CI of [0.814; 0.869] for images of internal data heart slices. On external data heart slices, AI accurately annotated slice area and AAR but failed to annotate infarcted area. On internal whole-heart data, CCC between AI and AI-assisted IS/AAR was 0.894 with 95% CI of [0.812; 0.942]. AI-assisted analysis reduced evaluation time on both internal and external datasets and increased region overlap for AAR between the two independent evaluators on dependent data. Conclusions AI-assisted analysis significantly reduced analysis time and inter-observer variability. For optimal performance, lab-specific AI training is recommended. Infarctsize-AI™ is available at . Translational perspective Myocardial infarct size (IS) is the gold-standard end-point in shorth-term studies to assess potential cardioprotective therapies against acute myocardial infarction (AMI). However, IS quantification in rodent AMI models is time-consuming and prone to inter-observer variance. Therefore, we developed an AI-based software that can reduce analysis time and inter-observer variability and facilitate documentation, which facilitates the clinical translation of potential cardioprotective therapies. ![Figure][1] ### Competing Interest Statement TK is founder and director of Camoxis Ltd. PF is the founder and CEO, and ZG is the Translational Program Director of Pharmahungary Group, a group of R&D companies. All other authors declare no competing interests. European Cooperation in Science and Technology, CA22169 Ministry for Innovation and Technology in Hungary, 2020-4.1.1.-TKP2020 National Research, Development and Innovation Office (NKFIH) of Hungary, K139105, FK138223, EFOP-3.6.3-VEKOP-16-2017-00009 European Union, RRF-2.3.1-21-2022-00003 the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund, 2024-2.1.1-EKÖP-2024-00004 Ministry of Health, Singapore Ministry of Health’s National Medical Research Council, MOH-STaR21jun-0003, NMRC CG21APR1006 CArdiovascular DiseasE National Collaborative Enterprise, MOH-001277-01 [1]: pending:yes

BACKGROUND AND PURPOSE:Hidden cardiotoxicity is defined as drug-induced cardiotoxicity that becomes obvious only in the presence of comorbidities. However, the molecular mechanisms of hidden cardiotoxicity are not always known. Therefore, unbiased multi-omics approaches could assist in revealing regulatory pathways. The most notable representative of hidden cardiotoxic drugs is the cyclooxygenase-2-inhibitor, rofecoxib. We previously reported increased mortality in rats because of proarrhythmic effects of rofecoxib in ischaemic hearts. Here, we aimed to identify molecular mechanisms of hidden cardiotoxicity exemplified by rofecoxib that present prior to comorbidities. EXPERIMENTAL APPROACH:Rats were treated with rofecoxib or its vehicle for 4 weeks. RNA sequencing and proteomic datasets of heart samples were used for differential expression and pathway reconstruction analyses. KEY RESULTS:In this model, mechanisms of hidden cardiotoxicity could not be revealed by transcriptomic analyses. However, mass-spectrometry-based proteomics showed conspicuous changes, revealing 132 proteins that were dysregulated in expression or on phosphorylation sites. Importantly, the phospho-proteomics allowed us to identify two kinases that may mediate cardiotoxicity. Finally, pathway reconstruction maps a complex molecular machinery whose clustered proteins regulate processes involving cytoskeleton binding, mRNA processing, proteolysis, translation, citrate acid cycle and calcium ion signalling. CONCLUSION AND IMPLICATIONS:This is the first demonstration that multi-omics characterisation can reveal underlying regulatory pathways of hidden cardiotoxicity. Importantly, our study shows that transcriptomics gives limited information on the hidden cardiotoxic effects of rofecoxib, which are mainly mediated by changes in posttranslational modifications and protein expression. These changes, among other mechanisms, may disturb the cardiac calcium handling, which could explain the fatal arrhythmias following ischaemia/reperfusion observed with rofecoxib.

Background Atrial fibrillation (AF) is common in patients with chronic heart failure (HF). Nevertheless, some patients with HF remain in sinus rhythm (SR) even with marked left atrial (LA) dilatation and fibrosis. The underlying mechanisms for the differences in atrial arrhythmogenicity are poorly uncovered. Recent findings indicate that distinct microRNAs (miRNA) might induce left atrial (LA) structural and molecular alterations. However, the impact of miRNA dysregulation on AF development in the context of HF has not been studied independently of LA remodeling. Objective This study aimed to evaluate the differences in LA miRNA expressions in patients with HF with AF or SR. Methods LA myocardial samples were obtained from patients with advanced HF with AF (n = 12; paroxysmal n = 4, chronic as persistent/permanent n = 8) or SR (n = 12) undergoing heart transplantation. The extent of LA interstitial fibrosis was evaluated using picrosirius red staining. The LA load was estimated by measuring LA mRNA expression of the NPPA gene encoding atrial natriuretic peptide with quantitative real-time polymerase chain reaction and circulating N-terminal pro-atrial natriuretic peptide (NT-proANP) by enzyme-linked immunosorbent assay. The LA miRNA screening was performed using the NanoString technology. Results LA dilatation, fibrosis, NPPA gene expression, as well as circulating NT-proANP levels were similar between the AF and SR groups, suggesting a comparable extent of atrial remodeling and load among the study groups. The miRNA analysis revealed no differences in atrial miRNA expression between the groups, even after AF subgroup analysis. Conclusions The LA miRNA expression profile shows no distinction between AF and SR in patients with advanced HF with similar levels of pathological atrial remodeling.

Marfan syndrome (MFS) is an autosomal dominant disease caused by mutations in the gene (FBN1) of fibrillin-1, a major determinant of the extracellular matrix (ECM). Functional impairment in the cardiac left ventricle (LV) of these patients is usually a consequence of aortic valve disease. However, LV passive stiffness may also be affected by chronic changes in mechanical load and ECM dysfunction. Passive stiffness is determined by the giant sarcomeric protein titin that has two main cardiac splice isoforms: the shorter and stiffer N2B and the longer and more compliant N2BA. Their ratio is thought to reflect myocardial response to pathologies. Whether this ratio and titin's sarcomeric layout is altered in MFS is currently unknown. Here, we studied LV samples from MFS patients carrying FBN1 mutation, collected during aortic root replacement surgery. We found that the N2BA:N2B titin ratio was elevated, indicating a shift toward the more compliant isoform. However, there were no alterations in the total titin content compared with healthy humans based on literature data. Additionally, while the gross sarcomeric structure was unaltered, the M-band was more extended in the MFS sarcomere. We propose that the elevated N2BA:N2B titin ratio reflects a general adaptation mechanism to the increased volume overload resulting from the valvular disease and the direct ECM disturbances so as to reduce myocardial passive stiffness and maintain diastolic function in MFS.

Comorbidities between gastrointestinal diseases and psychiatric disorders have been widely reported, with the gut-brain axis implicated as a potential biological basis. Thus, dysbiosis may play an important role in the etiology of schizophrenia, which is barely detected. Triple-hit Wisket model rats exhibit various schizophrenialike behavioral phenotypes. The present study aimed to compare the diversity and abundance of gut microbiota in Wisket model and control rats; furthermore, to correlate the microbial taxonomic profiles to indices of behavioral change. Tail-flick and Ambitus tests were used to assess acute heat pain sensitivity, and record exploration and locomotor activity along with motivation in young adult, control and Wisket model rats. Fecal microbiota composition was profiled by deep sequencing of bacterial 16S rRNA, and it was correlated to behavioral phenotype. Wisket rats exhibited significantly decreased pain sensitivity, lower locomotor activity and exploration, and impaired motivation compared with controls. No significant differences were observed in bacterial alpha diversity between the groups; however, clear differences in community structure were observed. Wisket rats showed decreases in several genera of Firmicutes and Saccharimonas, and increases in Bacteriodetes and Helicobacter phyla compared with controls. Correlation analysis revealed significant associations between the microbiota profile and the behavioral phenotype. This is the first demonstration that fecal microbiota composition is markedly altered in a triple-hit schizophrenia rat model, suggesting the contribution of the microbiota-gut-brain axis in the development of the schizophrenia-like behavioral phenotype. Thus targeting the gut microbiota may be a novel approach to treat such impairments.

Diabetic cardiomyopathy (DCM) is a complex condition linked to diabetes, characterized by cardiac and vascular dysfunction, frequently concomitant with heart failure with preserved ejection fraction. The extracellular matrix glycoprotein Tenascin-C (TNC) has been found to be upregulated under diabetic conditions. However, the potential contributory role of TNC in the progression of DCM remains largely unclear. This study was designed to elucidate the role of TNC in the pathogenesis of DCM. Diabetes was induced in adult male wild-type (WT) and TNC knockout (TNC-KO) mice, through the administration of streptozotocin (50 mg/kg) for five consecutive days. At 18 weeks cardiac and aortic vascular function was evaluated using echocardiography and wire myography. Myocardium and plasma samples were collected for biochemical, histological, and molecular analyses. Cardiomyocytes and cardiac fibroblasts were used to investigate the impact of diabetes on TNC expression, inflammation, myocardial stiffness and function. Additionally, transcriptomic analysis of cardiac tissue by RNA-sequencing was conducted. Plasma TNC levels were assessed by enzyme-linked immunosorbent assay in cohorts of heart failure patients and type 2 diabetes mellitus. TNC-KO diabetic mice showed preserved left ventricular systolic and diastolic function, significantly reduced cardiac fibrosis and mitigated endothelial dysfunction compared to WT diabetic animals. Compared with cardiomyocytes of diabetic WT animals, cardiomyocytes of TNC-KO mice developed less stiffness (Fpassive). Additionally, exposing mouse cardiomyocytes and human cardiac fibroblasts to high glucose stress (30 mM) led to a significant increase in TNC expression. Conversely, recombinant human TNC promoted pro-inflammatory and oxidative stress markers in cardiomyocytes. The role of TNC in fibrosis and DCM was found to involve pathways related to p53 signaling and Serpin1k, Ccn1, Cpt1a, and Slc27a1, as identified by RNA sequencing analysis. Additionally, plasma TNC levels were significantly elevated in patients with heart failure, irrespective of diabetes status, compared to healthy individuals. Our findings indicate that in diabetes, TNC contributes to cardiac contractile dysfunction, myocardial fibrosis, oxidative stress, inflammation, and metabolic disturbances in diabetic mouse heart. These results implicate the potential of TNC inhibition as a novel therapeutic approach for treating DCM.

Hidden cardiotoxicity is defined as cardiotoxicity of a drug that manifests only in the diseased heart. We have previously shown that the proarrhythmic hidden cardiotoxic properties of a model drug, the selective cyclooxygenase-2 inhibitor rofecoxib, can be revealed in preclinical models of ischemia/reperfusion injury. As metabolic comorbidities, such as hypercholesterolemia (HC), may exacerbate hidden cardiotoxicity, we aimed to investigate the hidden cardiotoxic effects of the model drug, rofecoxib, in the presence of hypercholesterolemia. Rats were fed a high-cholesterol diet for 12 weeks and treated with 5.12 mg/kg rofecoxib. Four weeks of rofecoxib treatment surprisingly improved HC-induced mild cardiac dysfunction by restoring end-diastolic pressure, stroke work, and mechanical efficiency. Then, RNA sequencing revealed that the expression of 28 miRNAs and 300 genes was significantly altered in the HC-fed group. The HC-induced expression changes of miR-27a-5p and miR-30d-5p were reversed by rofecoxib treatment. Cdc42ep4, Cox5, and Cxcl9 genes were also counter-regulated following rofecoxib treatment compared to HC-induced changes. This is the first demonstration that rofecoxib improves HC-induced cardiac dysfunction, with the mechanism involving changes in the gene expression profile, including some key regulators of rofecoxib action.

Neuropathic pain (NP), a disease affecting the somatosensory nervous system, causes a high socioeconomic burden on healthcare systems and society; yet, its management has not been fully solved. Post COVID-19, with sequelae of the somatosensory system, further increases the number of individuals with chronic pain, thus further exacerbating the social burden related to NP. The pro-inflammatory mediators, such as TNF-α, IL-1β, and IL-6, are massively released in patients with COVID-19 and are known to be involved in the pathomechanisms of NP, particularly in relation to neuronal sensitization. Specifically, the release of large amounts of these pro-inflammatory cytokines from immune and non-immune effector cells during the acute and post COVID-19 phases prone to have substantial involvement in the development of post COVID-19 NP. Several preclinical and clinical studies have shown that vitamins' supplementation, namely vitamins A, B1, B2, B3, B5, B6, B7, B9, B12, C, D, E, and K can attenuate the enhanced release of TNF-α, IL-1β, and IL-6. The early supplementation of these vitamins may mitigate the neuronal sensitization evoked by cytokine storm due to COVID-19 or other viruses. This strategy may provide a novel approach to prevent the development of post COVID-19 NP. This review highlights the mechanisms by which COVID-19 can induce NP and how vitamins may help to alleviate these post COVID-19 sequelae.

Background Obesity is a major risk factor for the development of cardiovascular disease. However, recent research shows that moderate obesity reduces the risk of developing cardiovascular disease. We evidenced before that MAO-B inhibitor selegiline reduced visceral adiposity. Aim Therefore, our aim was to investigate cardiac effects of selegiline in moderate obesity in rats treated with a high-fat diet (HFD). Key Findings We demonstrated that HFD improved cardiac contractility parameters, which were reversed by selegiline. Enhanced contractility might be attributed to an increased sarcoplasmic/endoplasmic reticulum Ca2+-ATPase (SERCA2a) expression and phospholamban pentamerization. Selegiline reduced SERCA2a expression in HFD. HFD increased Tumor necrosis factor and Nuclear factor-kappa B expression which were not affected by selegiline. HFD induced proapoptotic processes, which were restored by selegiline. Conclusion In conclusion, moderate obesity improves cardiac function through Ca2+ homeostasis and inflammatory processes and MAO-B inhibition reverses these effects.

Background and aims Nonsteroidal anti-inflammatory drugs (NSAIDs) can cause small intestinal injury and dysbiosis. Although NSAID-induced dysbiosis is well-characterized and contributes to enteropathy, the changes in host-bacterial interactions during enteropathy remain largely unexplored. Here we assessed the expression pattern of six toll-like receptors (TLRs) and three antimicrobial peptides (AMPs) over the course of indomethacin (IND)-induced enteropathy in rats, and evaluated their correlations with inflammation and dysbiosis. In addition, we assessed for the first time the effect of IND on small intestinal mucosal biofilm structure. Materials and methods Mucosal injury, inflammation and expression of TLR and AMP genes were evaluated at five time points following IND administration. Gut microbiota composition was determined by 16S rRNA gene sequencing. Small intestinal mucosal biofilms were visualized using fluorescent in situ hybridisation. Key findings We found that TLR1, TLR2 and cathelicidin were upregulated, TLR5 was downregulated, whereas TLR6 and TLR9 were not altered in enteropathy. TLR4 expression showed only subtle differences, but correlated with α-defensin 5 and β-defensin 2 levels. We found several correlations between TLRs, AMPs, inflammation and gut bacteria in severe enteropathy, but in early disease stage TLR1, TLR2, TLR5 and cathelicidin expression were more strongly associated with inflammation, whereas TLR4 and defensins were more dependent on gut dysbiosis. IND treatment also caused mild damage to the mucosal microbiota biofilm. Significance This is the first comprehensive characterization of the time-dependent changes in TLRs, AMPs and mucosal biofilm in NSAID-treated rats, which may help to identify new strategies for the treatment of enteropathy.

Aortic dissection occurs rarely during pregnancy but carries a significantly high vital risk for both the mother and the fetus. Early diagnosis and treatment are critical for a successful outcome. A 32-year-old pregnant woman at 31 weeks of gestation began experiencing shortness of breath, chest pain, and palpitations, which were attributed to an anxiety disorder she had been previously diagnosed with. The symptoms continued to worsen following the delivery when a chest computed tomography investigation revealed signs of chronic type A aortic dissection and an 80 mm post-dissection aneurysm of the ascending aorta. Aortic repair via composite aortic root replacement surgery (Bentall procedure) and partial aortic arch replacement was performed. The patient’s postoperative evolution was notable for a series of complications. Our report highlights the importance of thorough maternal examination during pregnancy. The high mortality rates associated with aortic dissection occurring in pregnant women and the possibility of missed intervention due to atypical clinical presentation warrant the need for standardized international protocols aimed at the prevention and timely diagnosis of prenatal aortic disease.

Safety signals for potential drug-induced adverse events (AEs) typically emerge from multiple data sources, primarily spontaneous reporting systems, despite known limitations. Increasingly, real-world data from sources such as electronic health records (EHRs) and administrative databases are leveraged for signal detection. Although network analysis has shown promise in mapping relationships between clinical attributes for signal detection in spontaneous reporting system databases, its application in real-world data from EHRs and administrative databases remains limited. This study aimed to evaluate the performance of network analysis in detecting safety signals within Italian administrative databases, using drug-induced acute myocardial infarction (AMI) as a proof of concept. We employed a case–crossover design to explore the association between drug exposure and AMI using the Healthcare Administrative Database of Mantova, Italy, from 2014 to 2018. Patients with their first AMI hospitalization were identified after a 365-day washout period to exclude prior hospitalizations. We constructed a network to analyse the relationships between prescribed drugs and diagnoses, represented as nodes, with undirected edges illustrating their interactions. For each patient with AMI, we identified all diagnoses and drugs recorded or redeemed within 365 days of the first AMI episode and generated various drug–diagnosis, drug–drug, and diagnosis–diagnosis pairs. We calculated the frequency of these pairs, and three types of edge weights quantified the strength of connections. We identified outlier drug–AMI pairs using a predictive score (F) based on frequency (C) and full edge weights (WF), with validation for known AMI associations. We prioritized signals using the F score, C of AMI, and WF, analysed through k-means clustering to identify patterns in the data. From 2014 to 2018, a total of 3918 patients had an AMI, with 4686 AMI diagnoses. Of those, 2866 had prescriptions in the previous year, totalling 498,591 prescriptions. A network analysis identified 2968 unique nodes, revealing 529,935 diagnosis–diagnosis connections, 235,380 drug–diagnosis connections, and 102,831 drug–drug connections. The median number of connections (C) was 404 (Q1–Q3: 194–671) for drug nodes and 380 (Q1–Q3: 216–664) for diagnosis nodes. The median WF was 11.8 (Q1–Q3: 9–14), and the median F score across pairs was 0.1 (Q1–Q3: 0.1–0.3). A total of 249 potential safety signals were detected, with 63.4