Integration of mass cytometry and mass spectrometry imaging for spatially resolved single-cell metabolic profiling.

The integration of spatial omics technologies can provide important insights into the biology of tissues. Here we combined mass spectrometry imaging-based metabolomics and imaging mass cytometry-based immunophenotyping on a single tissue section to reveal metabolic heterogeneity at single-cell resolution within tissues and its association with specific cell populations such as cancer cells or immune cells. This approach has the potential to greatly increase our understanding of tissue-level interplay between metabolic processes and their cellular components.

snRNA-seq analysis in multinucleated myogenic FSHD cells identifies heterogeneous FSHD transcriptome signatures associated with embryonic-like program activation and oxidative stress-induced apoptosis.

The sporadic nature of DUX4 expression in FSHD muscle challenges comparative transcriptome analyses between FSHD and control samples. A variety of DUX4 and FSHD-associated transcriptional changes have been identified, but bulk RNA-seq strategies prohibit comprehensive analysis of their spatiotemporal relation, interdependence and role in the disease process. In this study, we used single-nucleus RNA-sequencing of nuclei isolated from patient- and control-derived multinucleated primary myotubes to investigate the cellular heterogeneity in FSHD. Taking advantage of the increased resolution in snRNA-sequencing of fully differentiated myotubes, two distinct populations of DUX4-affected nuclei could be defined by their transcriptional profiles. Our data provides insights into the differences between these two populations and suggests heterogeneity in two well-known FSHD-associated transcriptional aberrations: increased oxidative stress and inhibition of myogenic differentiation. Additionally, we provide evidence that DUX4-affected nuclei share transcriptome features with early embryonic cells beyond the well-described cleavage stage, progressing into the 8-cell and blastocyst stages. Altogether, our data suggests that the FSHD transcriptional profile is defined by a mixture of individual and sometimes mutually exclusive DUX4-induced responses and cellular state-dependent downstream effects.

Conserved cell types with divergent features in human versus mouse cortex.

Elucidating the cellular architecture of the human cerebral cortex is central to understanding our cognitive abilities and susceptibility to disease. Here we used single-nucleus RNA-sequencing analysis to perform a comprehensive study of cell types in the middle temporal gyrus of human cortex. We identified a highly diverse set of excitatory and inhibitory neuron types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to similar mouse cortex single-cell RNA-sequencing datasets revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of properties of human cell types. Despite this general conservation, we also found extensive differences between homologous human and mouse cell types, including marked alterations in proportions, laminar distributions, gene expression and morphology. These species-specific features emphasize the importance of directly studying human brain.

Cell type matching across species using protein embeddings and transfer learning.

MOTIVATION: Knowing the relation between cell types is crucial for translating experimental results from mice to humans. Establishing cell type matches, however, is hindered by the biological differences between the species. A substantial amount of evolutionary information between genes that could be used to align the species is discarded by most of the current methods since they only use one-to-one orthologous genes. Some methods try to retain the information by explicitly including the relation between genes, however, not without caveats. RESULTS: In this work, we present a model to transfer and align cell types in cross-species analysis (TACTiCS). First, TACTiCS uses a natural language processing model to match genes using their protein sequences. Next, TACTiCS employs a neural network to classify cell types within a species. Afterward, TACTiCS uses transfer learning to propagate cell type labels between species. We applied TACTiCS on scRNA-seq data of the primary motor cortex of human, mouse, and marmoset. Our model can accurately match and align cell types on these datasets. Moreover, our model outperforms Seurat and the state-of-the-art method SAMap. Finally, we show that our gene matching method results in better cell type matches than BLAST in our model. AVAILABILITY AND IMPLEMENTATION: The implementation is available on GitHub (https://github.com/kbiharie/TACTiCS). The preprocessed datasets and trained models can be downloaded from Zenodo (https://doi.org/10.5281/zenodo.7582460).

Single-Cell Transcriptomics Reveals Discrete Steps in Regulatory T Cell Development in the Human Thymus.

CD4+CD25+FOXP3+ regulatory T (Treg) cells control immunological tolerance. Treg cells are generated in the thymus (tTreg) or in the periphery. Their superior lineage fidelity makes tTregs the preferred cell type for adoptive cell therapy (ACT). How human tTreg cells develop is incompletely understood. By combining single-cell transcriptomics and flow cytometry, we in this study delineated three major Treg developmental stages in the human thymus. At the first stage, which we propose to name pre-Treg I, cells still express lineage-inappropriate genes and exhibit signs of TCR signaling, presumably reflecting recognition of self-antigen. The subsequent pre-Treg II stage is marked by the sharp appearance of transcription factor FOXO1 and features induction of KLF2 and CCR7, in apparent preparation for thymic exit. The pre-Treg II stage can further be refined based on the sequential acquisition of surface markers CD31 and GPA33. The expression of CD45RA, finally, completes the phenotype also found on mature recent thymic emigrant Treg cells. Remarkably, the thymus contains a substantial fraction of recirculating mature effector Treg cells, distinguishable by expression of inflammatory chemokine receptors and absence of CCR7. The developmental origin of these cells is unclear and warrants caution when using thymic tissue as a source of stable cells for ACT. We show that cells in the major developmental stages can be distinguished using the surface markers CD1a, CD27, CCR7, and CD39, allowing for their viable isolation. These insights help identify fully mature tTreg cells for ACT and can serve as a basis for further mechanistic studies into tTreg development.

In-silico and in-vitro studies revealed alpha-amyrin as a potent pnhibitor of TLR2 for the therapeutics of bacterial infection and sepsis.

This study employed a multifaceted approach to investigate the inhibitory potential of alpha-amyrin against TLR2, a key player in bacterial infection and sepsis. A high-resolution TLR2 model was constructed using Swiss-MODEL, exhibiting excellent quality with 100% sequence identity and coverage. Cavity detection revealed five significant cavities on TLR2. Molecular docking identifies alpha-amyrin as a potent inhibitor, displaying a strong binding affinity of -8.6 kcal/mol. Comprehensive analyses, including ADMET predictions, PASS analysis, and SwissTargetPrediction, affirm alpha-amyrin's drug-like properties and diverse biological activities. Cytotoxicity assays on HEK-293 cells confirm its safety, and fluorescence-based inhibition assays provide empirical evidence of its inhibitory potency on TLR2 enzymatic activity. Further validations in HUVECs show a significant decrease in TLR2 mRNA expression (p<0.01) and activity (p<0.05) upon alpha-amyrin treatment. In conclusion, this integrative study positions alpha-amyrin as a promising therapeutic candidate for TLR2 inhibition, emphasizing its potential in combating bacterial infections with safety and efficacy.

Prevalence of Obesity and Associated Dietary Habits among Medical Students at King Khalid University, Southwestern Saudi Arabia.

Background and Objectives: Healthy eating is a crucial approach to improving overall health, encompassing a well-balanced diet of natural and fresh foods, plenty of fruits and vegetables, and foods rich in minerals and vitamins. This study aimed to assess the prevalence of obesity and associated dietary habits among medical students at King Khalid University, Aseer, Saudi Arabia. Materials and Methods: This observational cross-sectional study was conducted through face-to-face interviews. A structured predesigned questionnaire was used to collect data. Results: A total of 540 medical students were included; 43.3% of participants were aged 20-22 years, 24.8% were in the 3rd year, and 82.0% had an average income level. Of them, 21.9% were overweight and 14.6% were obese. There was a significant association between obesity and grade (p = 0.004). Significant differences were observed between males and females in adding sugar to beverages, the frequency of eating out, cooking meat, and drinking water (p < 0.05). The predictors of obesity were being male (OR = 3.5, 95% CI [1.6-7.8], p = 0.002), age (OR = 1.8, 95% CI [1.1-3.0, p = 0.019], being at grade 2 (OR = 38.8, 95% CI [4.0-375.8], p = 0.002), having grilled meat (OR = 0.42, 95% CI, [0.20-0.99], p = 0.048), using artificial sweeteners [OR = 0.24, 95% CI [0.08-0.73], p = 0.012], and drinking sparkling bottled water (OR 8.6, 95% CI [1.2 333-63.8], p = 0.034). Conclusions: The study revealed a high prevalence of obesity and overweight among medical students of both sexes. It recommends education on healthy eating habits, balanced nutrition, and regular physical activity, as well as gender-specific health initiatives, nutritional counseling, and the inclusion of physical activity.

Trends in prescribing and outcomes in obese versus non-obese patients receiving rivaroxaban therapy: an observational study using real-world data.

PURPOSE: To investigate real-world prescribing trends and clinical outcomes based on body mass index (BMI) categorization in patients who received rivaroxaban therapy. METHODS: This was a retrospective cohort study involving all patients who received rivaroxaban therapy across all Hamad Medical Corporation (HMC) hospitals from 2015 to 2020. RESULTS: The number of patients initiated on rivaroxaban therapy significantly increased from 152 (3.3%) in 2015 to 1342 (28.9%) in 2020 (p <0.001). Within BMI categories, a similar increasing trend was observed in underweight, normal, and overweight patients, while from 2018 to 2020, there was a decreasing trend in rivaroxaban prescribing in all obese classes. The prevalence rate of all-cause mortality differed significantly between the BMI groups, with the highest mortality being among morbidly obese patients (BMI ≥ 40 kg/m2) (p< 0.001). On the other hand, no significant differences were found between the BMI groups in terms of bleeding, pulmonary embolism, deep vein thrombosis and stroke incidences. Multivariate logistic regression analyses showed that the likelihood of all-cause mortality was significantly higher in overweight and all categories of obese patients compared to underweight patients: overweight (OR: 5.3, 95% CI: 2.3-11.9, p< 0.001); obese class 1 (OR: 5.4, 95% CI: 2.3 - 12.2, p< 0.001); obese class 2 (OR: 6.5, 95% CI: 2.7 - 15.6, p< 0.001); and obese class 3 (OR: 3.7, 95% CI: 1.6 - 8.7, p = 0.003). CONCLUSIONS: Rivaroxaban prescribing has significantly increased over the years across general population, with a noticeable decline in obese population during the last few years (from 2018 onwards). Furthermore, an appreciable association was evident between all-cause mortality and BMI of these patients.

SpaGE: Spatial Gene Enhancement using scRNA-seq.

Single-cell technologies are emerging fast due to their ability to unravel the heterogeneity of biological systems. While scRNA-seq is a powerful tool that measures whole-transcriptome expression of single cells, it lacks their spatial localization. Novel spatial transcriptomics methods do retain cells spatial information but some methods can only measure tens to hundreds of transcripts. To resolve this discrepancy, we developed SpaGE, a method that integrates spatial and scRNA-seq datasets to predict whole-transcriptome expressions in their spatial configuration. Using five dataset-pairs, SpaGE outperformed previously published methods and showed scalability to large datasets. Moreover, SpaGE predicted new spatial gene patterns that are confirmed independently using in situ hybridization data from the Allen Mouse Brain Atlas.

Immunomodulatory Efficacy-Mediated Anti-HCV and Anti-HBV Potential of Kefir Grains; Unveiling the In Vitro Antibacterial, Antifungal, and Wound Healing Activities.

The utilization of fermented foods with health-promoting properties is becoming more popular around the world. Consequently, kefir, a fermented milk beverage made from kefir grains, was shown in numerous studies to be a probiotic product providing significant health benefits. Herein, we assessed the antibacterial and antifungal potential of kefir against a variety of pathogenic bacteria and fungi. This study also showed the effectiveness of kefir in healing wounds in human gastric epithelial cells (GES-1) by (80.78%) compared with control (55.75%) within 48 h. The quantitative polymerase chain reaction (qPCR) results of kefir-treated HCV- or HBV- infected cells found that 200 µg/mL of kefir can eliminate 92.36% of HCV and 75.71% of HBV relative to the untreated infected cells, whereas 800 µg/mL (the highest concentration) completely eradicated HCV and HBV. Moreover, the estimated IC50 values of kefir, at which HCV and HBV were eradicated by 50%, were 63.84 ± 5.81 µg/mL and 224.02 ± 14.36 µg/mL, correspondingly. Kefir can significantly suppress the elevation of TNF-α and upregulate IL-10 and INF-γ in both treated HCV- and HBV-infected cells. High-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS) analysis of kefir revealed the presence of numerous active metabolites which mainly contribute to the antimicrobial, antiviral, and immunomodulatory activities. This study demonstrated, for the first time, the anti-HBV efficacy of kefir while also illustrating the immunomodulatory impact in the treated HBV-infected cells. Accordingly, kefir represents a potent antiviral agent against both viral hepatitis C and B, as well as having antimicrobial and wound healing potential.

Single-cell RNA sequencing in facioscapulohumeral muscular dystrophy disease etiology and development.

Facioscapulohumeral muscular dystrophy (FSHD) is characterized by sporadic de-repression of the transcription factor DUX4 in skeletal muscle. DUX4 activates a cascade of muscle disrupting events, eventually leading to muscle atrophy and apoptosis. Yet, how sporadic DUX4 expression leads to the generalized muscle wasting remains unclear. Transcriptome analyses have systematically been challenged by the majority of nuclei being DUX4neg, weakening the DUX4 transcriptome signature. Moreover, DUX4 has been shown to be expressed in a highly dynamic burst-like manner, likely resulting in the detection of the downstream cascade of events long after DUX4 expression itself has faded. Identifying the FSHD transcriptome in individual cells and unraveling the cascade of events leading to FSHD development may therefore provide important insights in the disease process. We employed single-cell RNA sequencing, combined with pseudotime trajectory modeling, to study FSHD disease etiology and cellular progression in human primary myocytes. We identified a small FSHD-specific cell population in all tested patient-derived cultures and detected new genes associated with DUX4 de-repression. We furthermore generated an FSHD cellular progression model, reflecting both the early burst-like DUX4 expression as well as the downstream activation of various FSHD-associated pathways, which allowed us to correlate DUX4 expression signature dynamics with that of regulatory complexes, thereby facilitating the prioritization of epigenetic targets for DUX4 silencing. Single-cell transcriptomics combined with pseudotime modeling thus holds valuable information on FSHD disease etiology and progression that can potentially guide biomarker and target selection for therapy.

Timing and localization of myasthenia gravis-related gene expression.

Myasthenia gravis (MG) is an acquired autoimmune disorder caused by autoantibodies binding acetylcholine receptors (AChR), muscle-specific kinase (MuSK), agrin or low-density lipoprotein receptor-related protein 4 (Lrp4). These autoantibodies inhibit neuromuscular transmission by blocking the function of these proteins and thereby cause fluctuating skeletal muscle weakness. Several reports suggest that these autoantibodies might also affect the central nervous system (CNS) in MG patients. A comprehensive overview of the timing and localization of the expression of MG-related antigens in other organs is currently lacking. To investigate the spatio-temporal expression of MG-related genes outside skeletal muscle, we used in silico tools to assess public expression databases. Acetylcholine esterase, nicotinic AChR α1 subunit, agrin, collagen Q, downstream of kinase-7, Lrp4, MuSK and rapsyn were included as MG-related genes because of their well-known involvement in either congenital or autoimmune MG. We investigated expression of MG-related genes in (1) all human tissues using GTEx data, (2) specific brain regions, (3) neurodevelopmental stages, and (4) cell types using datasets from the Allen Institute for Brain Sciences. MG-related genes show heterogenous spatio-temporal expression patterns in the human body as well as in the CNS. For each of these genes, several (new) tissues, brain areas and cortical cell types with (relatively) high expression were identified suggesting a potential role for these genes outside skeletal muscle. The possible presence of MG-related antigens outside skeletal muscle suggests that autoimmune MG, congenital MG or treatments targeting the same proteins may affect MG-related protein function in other organs.

Cingulate networks associated with gray matter loss in Parkinson's disease show high expression of cholinergic genes in the healthy brain.

Structural covariance networks are able to identify functionally organized brain regions by gray matter volume covariance across a population. We examined the transcriptomic signature of such anatomical networks in the healthy brain using postmortem microarray data from the Allen Human Brain Atlas. A previous study revealed that a posterior cingulate network and anterior cingulate network showed decreased gray matter in brains of Parkinson's disease patients. Therefore, we examined these two anatomical networks to understand the underlying molecular processes that may be involved in Parkinson's disease. Whole brain transcriptomics from the healthy brain revealed upregulation of genes associated with serotonin, GPCR, GABA, glutamate, and RAS-signaling pathways. Our results also suggest involvement of the cholinergic circuit, in which genes NPPA, SOSTDC1, and TYRP1 may play a functional role. Finally, both networks were enriched for genes associated with neuropsychiatric disorders that overlap with Parkinson's disease symptoms. The identified genes and pathways contribute to healthy functions of the posterior and anterior cingulate networks and disruptions to these functions may in turn contribute to the pathological and clinical events observed in Parkinson's disease.

SCHNEL: scalable clustering of high dimensional single-cell data.

MOTIVATION: Single cell data measures multiple cellular markers at the single-cell level for thousands to millions of cells. Identification of distinct cell populations is a key step for further biological understanding, usually performed by clustering this data. Dimensionality reduction based clustering tools are either not scalable to large datasets containing millions of cells, or not fully automated requiring an initial manual estimation of the number of clusters. Graph clustering tools provide automated and reliable clustering for single cell data, but suffer heavily from scalability to large datasets. RESULTS: We developed SCHNEL, a scalable, reliable and automated clustering tool for high-dimensional single-cell data. SCHNEL transforms large high-dimensional data to a hierarchy of datasets containing subsets of data points following the original data manifold. The novel approach of SCHNEL combines this hierarchical representation of the data with graph clustering, making graph clustering scalable to millions of cells. Using seven different cytometry datasets, SCHNEL outperformed three popular clustering tools for cytometry data, and was able to produce meaningful clustering results for datasets of 3.5 and 17.2 million cells within workable time frames. In addition, we show that SCHNEL is a general clustering tool by applying it to single-cell RNA sequencing data, as well as a popular machine learning benchmark dataset MNIST. AVAILABILITY AND IMPLEMENTATION: Implementation is available on GitHub (https://github.com/biovault/SCHNELpy). All datasets used in this study are publicly available. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification and characterization of two consistent osteoarthritis subtypes by transcriptome and clinical data integration.

OBJECTIVE: To identify OA subtypes based on cartilage transcriptomic data in cartilage tissue and characterize their underlying pathophysiological processes and/or clinically relevant characteristics. METHODS: This study includes n = 66 primary OA patients (41 knees and 25 hips), who underwent a joint replacement surgery, from which macroscopically unaffected (preserved, n = 56) and lesioned (n = 45) OA articular cartilage were collected [Research Arthritis and Articular Cartilage (RAAK) study]. Unsupervised hierarchical clustering analysis on preserved cartilage transcriptome followed by clinical data integration was performed. Protein-protein interaction (PPI) followed by pathway enrichment analysis were done for genes significant differentially expressed between subgroups with interactions in the PPI network. RESULTS: Analysis of preserved samples (n = 56) resulted in two OA subtypes with n = 41 (cluster A) and n = 15 (cluster B) patients. The transcriptomic profile of cluster B cartilage, relative to cluster A (DE-AB genes) showed among others a pronounced upregulation of multiple genes involved in chemokine pathways. Nevertheless, upon investigating the OA pathophysiology in cluster B patients as reflected by differentially expressed genes between preserved and lesioned OA cartilage (DE-OA-B genes), the chemokine genes were significantly downregulated with OA pathophysiology. Upon integrating radiographic OA data, we showed that the OA phenotype among cluster B patients, relative to cluster A, may be characterized by higher joint space narrowing (JSN) scores and low osteophyte (OP) scores. CONCLUSION: Based on whole-transcriptome profiling, we identified two robust OA subtypes characterized by unique OA, pathophysiological processes in cartilage as well as a clinical phenotype. We advocate that further characterization, confirmation and clinical data integration is a prerequisite to allow for development of treatments towards personalized care with concurrently more effective treatment response.

An educational guide for nanopore sequencing in the classroom.

The last decade has witnessed a remarkable increase in our ability to measure genetic information. Advancements of sequencing technologies are challenging the existing methods of data storage and analysis. While methods to cope with the data deluge are progressing, many biologists have lagged behind due to the fast pace of computational advancements and tools available to address their scientific questions. Future generations of biologists must be more computationally aware and capable. This means they should be trained to give them the computational skills to keep pace with technological developments. Here, we propose a model that bridges experimental and bioinformatics concepts using the Oxford Nanopore Technologies (ONT) sequencing platform. We provide both a guide to begin to empower the new generation of educators, scientists, and students in performing long-read assembly of bacterial and bacteriophage genomes and a standalone virtual machine containing all the required software and learning materials for the course.

Clinical outcomes of high-intensity doses of atorvastatin in patients with acute coronary syndrome: A retrospective cohort study using real-world data.

AIMS: To compare the effectiveness and safety of 2 high-intensity atorvastatin doses (40 mg vs 80 mg) among acute coronary syndrome (ACS) patients. METHODS: This retrospective observational cohort study using real-world data included patients admitted with ACS to the Heart Hospital in Qatar between 1 January 2017 and 31 December 2018. The primary endpoint was a composite of cardiovascular disease-associated death, nonfatal ACS and nonfatal stroke. Cox proportional hazard regression analysis was used to determine the association between the 2 high-intensity atorvastatin dosing regimens and the primary outcome at 1 month and 12 months postdischarge. RESULTS: Of the 626 patients included in the analyses, 475 (75.9%) received atorvastatin 40 mg, while 151 (24.1%) received atorvastatin 80 mg following ACS. Most of the patients were Asian (73%), male (97%) with a mean age of 50 years and presented with ST-elevation myocardial infarction (60%). The incidence of the primary effectiveness outcome did not differ between the atorvastatin 40-and 80-mg groups at 1 month (0.8 vs 1.3%; adjusted hazard ratio = 0.59, 95% confidence interval 0.04-8.13, P = .690) and at 12 months (3.2 vs 4%; adjusted hazard ratio = 0.57, 95% confidence interval 0.18-1.80, P = .340). Similarly, the use of the 2 doses of atorvastatin resulted in comparable safety outcomes, including liver toxicity, myopathy and rhabdomyolysis with an event rate of <1% in both groups. CONCLUSION: The use of atorvastatin 40 mg in comparison to atorvastatin 80 mg in patients with ACS resulted in similar cardiovascular effectiveness and safety outcomes.

Prevalence and clinical significance of antiphospholipid antibodies in patients with coronavirus disease 2019 admitted to intensive care units: a prospective observational study.

Coronavirus disease 2019 (COVID-19) increases the risk of coagulopathy. Although the presence of antiphospholipid antibodies (aPLs) has been proposed as a possible mechanism of COVID-19-induced coagulopathy, its clinical significance remains uncertain. Therefore, this study aimed to evaluate the prevalence and clinical significance of aPLs among critically ill patients with COVID-19. This prospective observational study included 60 patients with COVID-19 admitted to intensive care units (ICU). The study outcomes included prevalence of aPLs, and a primary composite outcome of all-cause mortality and arterial or venous thrombosis between antiphospholipid-positive and antiphospholipid-negative patients during their ICU stay. Multiple logistic regression was used to assess the influence of aPLs on the primary composite outcome of mortality and thrombosis. A total of 60 critically ill patients were enrolled. Among them, 57 (95%) were men, with a mean age of 52.8 ± 12.2 years, and the majority were from Asia (68%). Twenty-two patients (37%) were found be antiphospholipid-positive; 21 of them were positive for lupus anticoagulant, whereas one patient was positive for anti-ß2-glycoprotein IgG/IgM. The composite outcome of mortality and thrombosis during their ICU stay did not differ between antiphospholipid-positive and antiphospholipid-negative patients (4 [18%] vs. 6 [16%], adjusted odds ratio 0.98, 95% confidence interval 0.1-6.7; p value = 0.986). The presence of aPLs does not seem to affect the outcomes of critically ill patients with COVID-19 in terms of all-cause mortality and thrombosis. Therefore, clinicians may not screen critically ill patients with COVID-19 for aPLs unless deemed clinically appropriate.

Medications adherence post-primary percutaneous coronary intervention in acute myocardial infarction: A population-based cohort study.

WHAT IS KNOWN AND OBJECTIVE: The use of medications for secondary prevention is the cornerstone in the treatment of coronary artery disease (CAD). However, adherence to these medications is still suboptimal worldwide. This retrospective observational study aimed to assess the adherence to post-percutaneous coronary intervention (PCI) medications, along with predictors of non-adherence. METHODS: We conducted a retrospective observational cohort study to assess the adherence to post-PCI medications by determining the rate of prescription refills for 12 months after discharge among STEMI patients, as well as predictors of non-adherence. Adherence was assessed by medication availability 80% of the time monitored by the prescription refills rate for 1 year post-discharge. RESULTS AND DISCUSSION: A total of 1334 patients who presented with STEMI and underwent primary PCI were included in our retrospective analysis. The majority of patients included were male (96%) with a mean age of 51 ± 10.2 years. The overall adherence rate for all medications was only 28.4%, with an individual adherence rate of 50.5% for aspirin, 49.9% for P2 Y12 inhibitors, 48.1% for statins, 39.6% for beta-blockers and 42.9% for angiotensin-converting enzyme (ACE) inhibitors/angiotensin II receptor blockers (ARB). Factors that increased the likelihood of non-adherence were prolonged hospital length of stay and getting the medications with charge (aOR = 1.94, 95% CI 1.1-3.3; p-value = 0.017, aOR = 1.87, 95% CI 1.1-3.3; p-value = 0.029, respectively), while having a regular follow-up after discharge and attending the first clinic appointment were significantly associated with decreased likelihood of non-adherence (aOR = 0.01, 95% CI 0.004-0.04; p-value < 0.001, aOR = 0.06, 95% CI 0.03-0.1; p-value < 0.001, respectively). WHAT IS NEW AND CONCLUSION: The adherence rate to post-PCI medications among patients with STEMI was relatively low; however, attending the first outpatient clinic appointment and having a regular follow-up reduced the likelihood of non-adherence.

Prevalence and factors associated with gastroesophageal reflux disease among primary health care attendants at Abha city, southwestern Saudi Arabia.

BACKGROUND: Gastroesophageal reflux disease (GERD) is an abnormal reflux of the gastric content into the esophagus. In Saudi Arabia the GERD prevalence is not recently well studied. OBJECTIVE: To investigate the prevalence of gastroesophageal reflux disease and associated factors among attendants of primary health care centers (PHCCs) at Abha city, Saudi Arabia. METHOD: A descriptive cross- sectional study was performed using GerdQ as diagnostic tool for the GERD. The GerdQ consisted of six questions. Four questions were about the positive GERD predictors. The other two questions were about the negative GERD predictors. The scoring of GerdQ relies on the frequency of GERD symptoms during the last seven days. Using stratified random sample technique a representative sample was slected from the study PHCCs taking into conmsideration the relative catchment population in each center among adult males and females attending the selected PHCCs for any reason. RESULTS: The present study included 320 persons. The study showed a prevalence of GERD of 67.8%. The prevalence of GERD with high impact on daily life (HIDL) was found to be 50%. By multiple logistic regression (enter method) only four significant independent factors associated with GERD were identified; being unmarried (aOR = 1.85, 95% CI:1.02-3.23); smoking (aOR = 2.11, 95% CI: 1.41-5.98), fast food intake (OR = 1.28, 95% CI:1.01-1.71), and subjective perception of stress (OR = 3.0, 95% CI:1.68-5.26). CONCLUSIONS: GERD is a public health problem among adults in the region. Community level awareness programs are recommended. Healthcare providers must be aware of community perceptions and practices.