Identifying Similar Allergens and Potentially Cross-Reacting Areas Using Structural Database of Allergenic Proteins (SDAP) Tools and D-Graph.

The Structural Database of Allergenic Proteins (SDAP) provides rapid search tools to identify similarities among allergens, their IgE epitopes, and to determine the potential allergenicity of any novel protein. Many labs have identified IgE-binding proteins and their antibody binding or T cell epitopes using dotspots or microarrays. This chapter describes how to determine the relationship of these proteins and peptides to known allergens using the tools implemented in SDAP. One can also search with these smaller peptide similarity search tool implemented in SDAP to find similar sequences with low property distance (PD) values in the over 1500 sequences of allergens. The sequences can be compared by mapping on the surface of the protein structures provided for nearly all the allergens in SDAP. Once the user has a unique list of similar sequences, they can be graphed in 2D according to interpeptide PD values calculated automatically by the D-graph program. This chapter provides a step-by-step description of how to do this, starting from a protein similar to the Ole e 1 (olive pollen) allergen family.

Expanding the Landscape of Amyloid Sequences with CARs-DB: A Database of Polar Amyloidogenic Peptides from Disordered Proteins.

Several databases collecting amyloidogenic regions have been released to provide information on protein sequences able to form amyloid fibrils. However, most of these resources are built with data from experiments that detect highly hydrophobic stretches located within transiently exposed protein segments. We recently demonstrated that cryptic amyloidogenic regions (CARs) of polar nature have the potential to form amyloid fibrils in vitro. Given the underrepresentation of these types of sequences in current amyloid databases, we developed CARs-DB, the first repository that collects thousands of predicted CARs from intrinsically disordered regions. This protocol chapter describes how to use CARs-DB to search for sequences of interest that might be connected to disease or functional protein-protein interactions. In addition, we provide study cases to illustrate the database's features to users. The CARs-DB is readily accessible at http://carsdb.ppmclab.com/ .

Automated Virtual Screening.

Computational methods in modern drug discovery have become ubiquitous, with methods that cover most of the discovery stages: from hit finding and lead identification to lead optimization. The overall aim of these computational methods is to obtain a more efficient discovery process, by reducing the number of "wet" experiments required to produce therapeutics that have higher probability of succeeding in clinical development and subsequently benefitting end patients by developing highly effective therapeutics having minimal side effects. Virtual Screening is usually applied at the early stage of drug discovery, looking to find chemical matter having desired properties, such as molecular shape, electrostatics, and pharmacophores at desired three-dimensional positions. The aim of this stage is to search in a wide chemical space, including chemistry available from commercial suppliers and virtual databases of predicted reaction products, to identify molecules that would exert a particular biochemical response. This initial stage of the discovery process is very important since the subsequent stages will use the initial chemical motifs that have been found at the hit finding stage, and therefore the most suitable the compound is found, the more likely it is that subsequent stages will be successful and less time and resource consuming. This chapter provides a summary of various Virtual Screening methods, including shape match and molecular docking, and these methods are used in a Virtual Screening workflow that is provided as an example which is described to be run automatically in cloud resources. This automatic in-depth exploration of the chemical space using validated Virtual Screening methods can lead to a more streamlined and efficient discovery process, aiming to deliver chemical matter of high quality and maximizing the required biological effects while minimizing adverse effects. Surely, Virtual Screening pipelines of this nature will continue to play a central role in producing much needed therapeutics for the health challenges of the future.

High-Throughput Structure-Based Drug Design (HT-SBDD) Using Drug Docking, Fragment Molecular Orbital Calculations, and Molecular Dynamic Techniques.

Structure-based drug design (SBDD) is rapidly evolving to be a fundamental tool for faster and more cost-effective methods of lead drug discovery. SBDD aims to offer a computational replacement to traditional high-throughput screening (HTS) methods of drug discovery. This "virtual screening" technique utilizes the structural data of a target protein in conjunction with large databases of potential drug candidates and then applies a range of different computational techniques to determine which potential candidates are likely to bind with high affinity and efficacy. It is proposed that high-throughput SBDD (HT-SBDD) will significantly enrich the success rate of HTS methods, which currently fluctuates around ~1%. In this chapter, we focus on the theory and utility of high-throughput drug docking, fragment molecular orbital calculations, and molecular dynamics techniques. We also offer a comparative review of the benefits and limitations of traditional methods against more recent SBDD advances. As HT-SBDD is computationally intensive, we will also cover the important role high-performance computing (HPC) clusters play in the future of computational drug discovery.

Telemedicine in Primary Practice in the Age of the COVID-19 Pandemic-Review.

Background and Objectives: In the era of the COVID-19 pandemic, telemedicine, so far underestimated, has gained in value. Currently, telemedicine is not only a telephone or chat consultation, but also the possibility of the remote recording of signals (such as ECG, saturation, and heart rate) or even remote auscultation of the lungs. The objective of this review article is to present a potential role for, and disseminate knowledge of, telemedicine during the COVID-19 pandemic. Material and Methods: In order to analyze the research material in accordance with PRISMA guidelines, a systematic search of the ScienceDirect, Web of Science, and PubMed databases was conducted. Out of the total number of 363 papers identified, 22 original articles were subjected to analysis. Results: This article presents the possibilities of remote patient registration, which contributes to an improvement in remote diagnostics and diagnoses. Conclusions: Telemedicine is, although not always and not by everyone, an accepted form of providing medical services. It cannot replace direct patient-doctor contact, but it can undoubtedly contribute to accelerating diagnoses and improving their quality at a distance.

Prognostic Tools for Older Women with Breast Cancer: A Systematic Review.

Background: Breast cancer is the most common cancer in women, and older patients comprise an increasing proportion of patients with this disease. The older breast cancer population is heterogenous with unique factors affecting clinical decision making. While many models have been developed and tested for breast cancer patients of all ages, tools specifically developed for older patients with breast cancer have not been recently reviewed. We systematically reviewed prognostic models developed and/or validated for older patients with breast cancer. Methods: We conducted a systematic search in 3 electronic databases. We identified original studies that were published prior to 8 November 2022 and presented the development and/or validation of models based mainly on clinico-pathological factors to predict response to treatment, recurrence, and/or mortality in older patients with breast cancer. The PROBAST was used to assess the ROB and applicability of each included tool. Results: We screened titles and abstracts of 7316 records. This generated 126 studies for a full text review. We identified 17 eligible articles, all of which presented tool development. The models were developed between 1996 and 2022, mostly using national registry data. The prognostic models were mainly developed in the United States (n = 7; 41%). For the derivation cohorts, the median sample size was 213 (interquartile range, 81-845). For the 17 included modes, the median number of predictive factors was 7 (4.5-10). Conclusions: There have been several studies focused on developing prognostic tools specifically for older patients with breast cancer, and the predictions made by these tools vary widely to include response to treatment, recurrence, and mortality. While external validation was rare, we found that it was typically concordant with interval validation results. Studies that were not validated or only internally validated still require external validation. However, most of the models presented in this review represent promising tools for clinical application in the care of older patients with breast cancer.

Indirect and Direct 65+ Patient Reporting of Non-Steroidal Anti-Inflammatory Drug-Induced Adverse Drug Reactions as a Source of Information on Polypharmacy and Polypharmacy-Related Risk.

Background and Objectives: Non-steroidal anti-inflammatory drugs (NSAIDs), which have anti-inflammatory and analgesic properties, are commonly used in the treatment of various, particularly frequent, as well as chronic, conditions in older patients. Due to common polypragmasia in these patients and a high risk of adverse drug reactions (ADRs) and drug interactions, pain management poses a therapeutic challenge. This study describes the importance of ADR reports in the identification of polypharmacy and the ensuing interactions. Materials and Methods: Both healthcare professionals (HPs) and non-healthcare professionals (non-HPs) reports collected in the EudraVigilance database of NSAIDs, including most commonly co-reported medications and reported reactions, were analysed and differences between HPs and non-HPs reports were identified. Results: In the analysed period and group, non-HPs reported more reactions but indicated fewer drugs as suspect or concomitant. The outcomes of our analysis indicate more HP engagement and more detailed reports of serious ADRs when compared to non-serious individual case safety reports (ICSRs) by non-HPs, which appeared more detailed. Such reactions as kidney failure and increased risk of bleeding are known adverse reactions to NSAIDs and common symptoms of their interactions, which were described in the available literature. They were much more frequently reported by HPs than by non-HPs. Non-HPs more frequently reported reactions that may have been considered less significant by HPs. Conclusions: The differences between healthcare professionals' (HPs) and non-healthcare professionals' (non-HPs) reports may result from the fact that the reports from patients and their caregivers require a professional medical diagnosis based on symptoms described by the patient or additional diagnostic tests. This means that when appropriately classified, medically verified, and statistically analysed, the data may provide new evidence for the risks of medication use or drug interactions.

Traditional, Complementary and Alternative Medicines in the Treatment of Ejaculatory Disorders: A Systematic Review.

Background and Objectives: Ejaculatory dysfunction (EjD) is a common male sexual disorder that includes premature ejaculation, delayed ejaculation, retrograde ejaculation, and anejaculation. Although psychological and pharmacological treatments are available, traditional, complementary, and alternative medicine (TCAM) is reportedly used. However, the clinical evidence for TCAM in EjD remains unclear. Therefore, this study aims to systematically review human clinical trials investigating the use of TCAM to treat EjD. Materials and Methods: A systematic review of the literature following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted by searching Scopus and PubMed databases. Controlled clinical trials investigating a cohort of male patients diagnosed primarily with EjD and undergoing any TCAM intervention compared to any comparison group were included. Quality of the studies was assessed using the Cochrane Risk of Bias tool for randomized controlled trials. Results: Following article screening, 22 articles were included. Of these, 21 investigated TCAM in premature ejaculation, and only 1 investigated TCAM in retrograde ejaculation. Different TCAM categories included studies that investigated lifestyle, exercise and/or physical activities (n = 7); herbal medicine supplements (n = 5); topical herbal applications (n = 4); acupuncture or electroacupuncture (n = 3); vitamin, mineral and/or nutraceutical supplements (n = 1); hyaluronic acid penile injection (n = 1); and music therapy (n = 1). Only 31.8% (n = 7) of the included studies were found to have a low risk of bias. The available studies were widely heterogenous in the TCAM intervention investigated and comparison groups used. However, the included studies generally showed improved outcomes intra-group and when compared to placebo. Conclusions: Different TCAM interventions may have an important role particularly in the management of PE. However, more studies using standardized interventions are needed.

Insights into Microbiota in Sjögren's Syndrome.

Primary Sjögren's syndrome (pSS) is a heterogeneous chronic autoimmune disorder with multiple clinical manifestations that can develop into non-Hodgkin's lymphoma in mucosa-associated lymphoid tissue. The pathogenesis of Sjögren's syndrome (SS) is not completely understood, but it is assumed that pathogenesis of SS is multifactorial. The microbiota plays a notable role in the development of autoimmune disorders, including Sjögren's syndrome. Molecular mimicry, metabolite changes and epithelial tolerance breakdown are pathways that might help to clarify the potential contribution of the microbiota to SS pathogenesis. This review aims to provide an overview of recent studies describing microbiota changes and microbiota mechanisms associated with Sjögren's syndrome. Data on the microbiota in SS from PubMed, Web of Science, Scopus and the Cochrane Library databases are summarized. Overall, the microbiota makes a major contribution to the development of Sjögren's syndrome and progression. Future microbiota studies should improve the management of this heterogeneous autoimmune disease.

Research Trends of Patient-Reported Outcome Measures in Orthopedic Medical Practices: A Bibliometric and Visualized Study.

Background and Objectives: Patient-reported outcome measures (PROMs), also known as self-report measures, are critical tools for evaluating health outcomes by gathering information directly from patients without external interpretation. There has been a growing trend in the number of publications focusing on PROMs in orthopedic-related research. This study aims to identify the most valuable publications, influential journals, leading researchers, and core countries in this field using bibliometric analysis, providing researchers with an understanding of the current state and future trends of PROMs in orthopedic research. Materials and Methods: All PROMs in orthopedic-related publications from 1991 to 2022 were obtained from the WoSCC database. R software (version 4.2.2), VOSviewer (version 1.6.17), and Microsoft Excel (version 2303) were used for the bibliometric and visual analysis. Results: A total of 2273 publication records were found from 1991 to 2022. The results indicated that the United States (US) has made significant contributions to orthopedic-related PROMs. The majority of active research institutions are located in the US. J ORTHOP RES has published the most articles. J BONE JOINT SURG AM has the highest total citations. Conclusions: Our study provides a valuable reference for further exploration of the application of PROMs in orthopedics. PROMs have emerged as an increasingly popular area of research within the field of orthopedics, both in clinical practice and academic research. We conducted a bibliometric analysis in terms of journals, authors, countries, and institutions in this field. Additionally, we analyzed the potentialities and advantages of using PROMs in orthopedic research. There is an increasing trend towards using network-based or short message service (SMS)-based electronic patient-reported outcome measures (ePROMs) in orthopedic medical practices. It is anticipated that the role of PROMs in psychological and mental health research and telemedicine will continue to grow in importance.

Oral Nutritional Supplements Reduce Body Weight Loss after Gastrectomy in Patients with Gastric Cancer: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

This systematic review and meta-analysis aimed to summarize the effects of oral nutritional supplements (ONSs) on body weight loss (BWL) after gastrectomy. A systematic search was conducted across the PubMed, Cochrane, and Embase databases through May 2023. The study inclusion criteria were as follows: (1) studies on interventions including ONSs after gastrectomy in patients with gastric cancer; (2) studies in which comparisons were specified according to standard, regular, or usual postoperative diets; and (3) randomized controlled trial studies including outcomes measured as mean differences in BWL. The data were pooled using the random-effects model and expressed as mean differences with 95% confidence intervals (CI). Based on data from seven studies including 1743 patients (891 for ONSs and 852 for the control), the overall pooled mean difference was 0.848 (95% CI: 0.466 to 1.230) and the Higgins I2 value was 86.0%. This systematic review and meta-analysis is the first study to show that ONSs are significantly associated with reducing BWL, compared with standard diets, after gastrectomy in patients with gastric cancer. Furthermore, we found that ONSs were more effective in patients with lower nutritional kilocalorie intake after gastrectomy.

The First 1000 Days of Life: How Changes in the Microbiota Can Influence Food Allergy Onset in Children.

BACKGROUND: Allergic disease, including food allergies (FA)s, has been identified as a major global disease. The first 1000 days of life can be a "window of opportunity" or a "window of susceptibility", during which several factors can predispose children to FA development. Changes in the composition of the gut microbiota from pregnancy to infancy may play a pivotal role in this regard: some bacterial genera, such as Lactobacillus and Bifidobacterium, seem to be protective against FA development. On the contrary, Clostridium and Staphylococcus appear to be unprotective. METHODS: We conducted research on the most recent literature (2013-2023) using the PubMed and Scopus databases. We included original papers, clinical trials, meta-analyses, and reviews in English. Case reports, series, and letters were excluded. RESULTS: During pregnancy, the maternal diet can play a fundamental role in influencing the gut microbiota composition of newborns. After birth, human milk can promote the development of protective microbial species via human milk oligosaccharides (HMOs), which play a prebiotic role. Moreover, complementary feeding can modify the gut microbiota's composition. CONCLUSIONS: The first two years of life are a critical period, during which several factors can increase the risk of FA development in genetically predisposed children.

The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review.

The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering medical professionals to evaluate patient recovery and predict disease development more efficiently. This systematic review aims to study the application of wearable sensor technology and machine learning algorithms in different disease rehabilitation training programs, obtain the best sensors and algorithms that meet different disease rehabilitation conditions, and provide ideas for future research and development. A total of 1490 studies were retrieved from two databases, the Web of Science and IEEE Xplore, and finally 32 articles were selected. In this review, the selected papers employ different wearable sensors and machine learning algorithms to address different disease rehabilitation problems. Our analysis focuses on the types of wearable sensors employed, the application of machine learning algorithms, and the approach to rehabilitation training for different medical conditions. It summarizes the usage of different sensors and compares different machine learning algorithms. It can be observed that the combination of these two technologies can optimize the disease rehabilitation process and provide more possibilities for future home rehabilitation scenarios. Finally, the present limitations and suggestions for future developments are presented in the study.

Neuromorphic Sentiment Analysis Using Spiking Neural Networks.

Over the past decade, the artificial neural networks domain has seen a considerable embracement of deep neural networks among many applications. However, deep neural networks are typically computationally complex and consume high power, hindering their applicability for resource-constrained applications, such as self-driving vehicles, drones, and robotics. Spiking neural networks, often employed to bridge the gap between machine learning and neuroscience fields, are considered a promising solution for resource-constrained applications. Since deploying spiking neural networks on traditional von-Newman architectures requires significant processing time and high power, typically, neuromorphic hardware is created to execute spiking neural networks. The objective of neuromorphic devices is to mimic the distinctive functionalities of the human brain in terms of energy efficiency, computational power, and robust learning. Furthermore, natural language processing, a machine learning technique, has been widely utilized to aid machines in comprehending human language. However, natural language processing techniques cannot also be deployed efficiently on traditional computing platforms. In this research work, we strive to enhance the natural language processing traits/abilities by harnessing and integrating the SNNs traits, as well as deploying the integrated solution on neuromorphic hardware, efficiently and effectively. To facilitate this endeavor, we propose a novel, unique, and efficient sentiment analysis model created using a large-scale SNN model on SpiNNaker neuromorphic hardware that responds to user inputs. SpiNNaker neuromorphic hardware typically can simulate large spiking neural networks in real time and consumes low power. We initially create an artificial neural networks model, and then train the model using an Internet Movie Database (IMDB) dataset. Next, the pre-trained artificial neural networks model is converted into our proposed spiking neural networks model, called a spiking sentiment analysis (SSA) model. Our SSA model using SpiNNaker, called SSA-SpiNNaker, is created in such a way to respond to user inputs with a positive or negative response. Our proposed SSA-SpiNNaker model achieves 100% accuracy and only consumes 3970 Joules of energy, while processing around 10,000 words and predicting a positive/negative review. Our experimental results and analysis demonstrate that by leveraging the parallel and distributed capabilities of SpiNNaker, our proposed SSA-SpiNNaker model achieves better performance compared to artificial neural networks models. Our investigation into existing works revealed that no similar models exist in the published literature, demonstrating the uniqueness of our proposed model. Our proposed work would offer a synergy between SNNs and NLP within the neuromorphic computing domain, in order to address many challenges in this domain, including computational complexity and power consumption. Our proposed model would not only enhance the capabilities of sentiment analysis but also contribute to the advancement of brain-inspired computing. Our proposed model could be utilized in other resource-constrained and low-power applications, such as robotics, autonomous, and smart systems.

Impact of single versus multiple spliceosome mutations on myelodysplastic syndrome.

Myelodysplastic syndromes (MDS) are myeloid neoplasms that are driven by genetic mutations. Generally, it is thought that a higher number of mutations is associated with worse prognosis. However, the impact of genetic mutations when they occur in the same functional class has not been well studied. Here we investigated the impact of multiple spliceosome mutations on prognosis in MDS patients, hypothesizing that multiple mutations in the same class are biologically redundant and would not affect prognosis. Departmental Next Generation Sequencing (NGS) database (>6000 cases) was queried and the data was analyzed to identify cases with spliceosome mutations (SF3B1, SRSF2, U2AF1, ZRSR2, U2AF1). Overall, 71 patients met criteria for the study. Cases with single spliceosome mutations (i.e., no other co-mutations whatsoever) were as follows: SF3B1 (38), SRSF2 (5), U2AF2 (11), and ZRSR2 (1). Cases with concurrent spliceosome mutations were as follows: SF3B1 + SRSF2 (5), SF3B1 + U2AF1 (1), SF3B1 + ZRSR2 (3), SRSF2 + U2AF1 (2), SRSF2 + ZRSR2 (1), U2AF1 + ZRSR2 (4). Four of 55 (7.3%) of patients in the single mutation group vs. 4 of 16 (25%) of patients in the concurrent mutation group progressed to acute myeloid leukemia (AML). Mean OS in the single mutation group was 103.5 months vs. 71.6 months in the multiple concurrent mutation group (χ2= 2.404; p= 0.12). Our results challenge the current dogma that increased mutation in MDS portend worse survival. We demonstrate that multiple mutations bear no impact on clinical prognosis when the additional mutations occur in same spliceosome class.

Building a large gene expression-cancer knowledge base with limited human annotations.

Cancer prevention is one of the most pressing challenges that public health needs to face. In this regard, data-driven research is central to assist medical solutions targeting cancer. To fully harness the power of data-driven research, it is imperative to have well-organized machine-readable facts into a knowledge base (KB). Motivated by this urgent need, we introduce the Collaborative Oriented Relation Extraction (CORE) system for building KBs with limited manual annotations. CORE is based on the combination of distant supervision and active learning paradigms and offers a seamless, transparent, modular architecture equipped for large-scale processing. We focus on precision medicine and build the largest KB on 'fine-grained' gene expression-cancer associations-a key to complement and validate experimental data for cancer research. We show the robustness of CORE and discuss the usefulness of the provided KB. Database URL https://zenodo.org/record/7577127.

Dynamic associations of relational conflicts at work and consequent negative emotion dynamics with diurnal cortisol variations.

This study examines the predictive value of conflict and conflict-related variations in negative emotion dynamics, with respect to three cortisol indicators (cortisol awakening responses; overall cortisol output; diurnal cortisol slopes). A total of 166 workers provided momentary reports on conflict(s) with colleagues and negative emotions 10 times a day for 2 workdays and salivary cortisol samples 5 times a day. The results of latent growth curve piecewise multilevel models revealed that the occurrence of a conflict and the number of conflicts introduced significant variations in specific cortisol parameters indicating greater cortisol levels throughout the day. Moreover, the conflict-elicited negative emotion boost predicted a lower reduction of cortisol levels from morning to evening. Last, the postconflict decline in negative emotions was negatively associated with overall cortisol production. This study contributes to establishing a potential association between naturally occurring episodic conflicts at work and daily cortisol patterns, identifying within-person fluctuations in negative emotions as psychological mechanisms through which this occurs. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The effects of leadership levels and gender on leader well-being.

Previous research examining differences in levels of well-being between leaders and nonleaders has yielded mixed results. To explain the inconsistencies, we compare levels of well-being among nonleaders, mid-level leaders, and high-level leaders. Drawing from the job demands-resources model (Bakker & Demerouti, 2007, 2017) and the expanded version proposed by Crawford et al. (2010), we anticipate mid-level leaders will have lower levels of well-being compared to senior leaders and nonleaders, and females will be more vulnerable than males in mid-level leadership. In Study 1, we use multilevel models and propensity score matching (N = 24,067) and find mid-level leaders have worse general health conditions compared to nonleaders and high-level leaders, and that this effect is more pronounced among females. In Study 2, we collect experience sampling data from workers (N = 86; 1,634 observations) who completed a short survey four times daily for five consecutive working days. Mid-level leaders report more end-of-day negative emotions than high-level leaders and nonleaders, mediated by higher job demands and lower levels of job control to combat the negative effects of job demands; an effect that is particularly pronounced among female respondents. In Study 3, we use a two-wave time-lagged survey study (N = 330) and find middle managers have more challenge and hindrance job demands than nonleaders, and insufficient job control to offset the negative effects of job demands. Female middle managers report the most physical health symptoms and enjoy less eudaemonic well-being than their male counterparts. We conclude that leadership levels and gender have important and overlooked impacts on well-being. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Serum Ferritin and Non-alcoholic Fatty Liver Disease: A Meta-analysis and Systematic Review.

BACKGROUND/AIMS: Previous studies have shown that hyperferritinemia is a common phenomenon in non-alcoholic fatty liver dis- ease patients. We aim to further explore the relationship between serum ferritin levels and non-alcoholic fatty liver disease using a meta-analysis. MATERIALS AND METHODS: Four Library databases were electronically searched from inception until December 2021 to find prospective cohort or case-control studies examining the relationship between serum ferritin levels and non-alcoholic fatty liver disease, and all kinds of literature were screened according to the inclusion and exclusion criteria. The odds ratio and other related data were extracted, and a meta-analysis was performed. RESULTS: Eleven studies examining the relationship between serum ferritin levels and non-alcoholic fatty liver disease were included. The serum ferritin levels in the non-alcoholic fatty liver disease group were significantly higher than those without non-alcoholic fatty liver disease group (1.54 ng/mL, 95% CI: 0.85-2.23, P < .001). Serum ferritin levels were significantly associated with the risk of non-alcoholic fatty liver disease in both men and women (odds ratio = 2.36, 95% CI: 1.41-3.93, P = .001 and odds ratio = 2.93, 95% CI: 1.83-4.69, P < .001, respectively), and after adjusting for the parameters, the relationships were still shown to be significant in men and women (odds ratio = 2.24, 95% CI: 1.64-3.05, P < .001 and odds ratio = 3.30, 95% CI: 2.13-5.11, P < .001, respectively). CONCLUSION: Serum ferritin levels were higher in patients with non-alcoholic fatty liver disease than in those without non-alcoholic fatty liver disease and were associated with the risk of non-alcoholic fatty liver disease in both men and women.