Engineering innovations in medicine and biology: Revolutionizing patient care through mechanical solutions.

The overlap between mechanical engineering and medicine is expanding more and more over the years. Engineers are now using their expertise to design and create functional biomaterials and are continually collaborating with physicians to improve patient health. In this review, we explore the state of scientific knowledge in the areas of biomaterials, biomechanics, nanomechanics, and computational fluid dynamics (CFD) in relation to the pharmaceutical and medical industry. Focusing on current research and breakthroughs, we provide an overview of how these fields are being used to create new technologies for medical treatments of human patients. Barriers and constraints in these fields, as well as ways to overcome them, are also described in this review. Finally, the potential for future advances in biomaterials to fundamentally change the current approach to medicine and biology is also discussed.

Uncovering Key Factors in Graphene Aerogel-Based Electrocatalysts for Sustainable Hydrogen Production: An Unsupervised Machine Learning Approach.

The application of principal component analysis (PCA) as an unsupervised learning method has been used in uncovering correlations among diverse features of aerogel-based electrocatalysts. This analytical approach facilitates a comprehensive exploration of catalytic activity, revealing intricate relationships with various physical and electrochemical properties. The first two principal components (PCs), collectively capturing nearly 70% of the total variance, attested the reliability and efficacy of PCA in unveiling meaningful patterns. This study challenges the conventional understanding that a material's reactivity is solely dictated by the quantity of catalyst loaded. Instead, it unveils a complex perspective, highlighting that reactivity is intricately influenced by the material's overall design and structure. The PCA bi-plot uncovers correlations between pH and Tafel slope, suggesting an interdependence between these variables and providing valuable insights into the complex interactions among physical and electrochemical properties. Tafel slope stands to be positively correlated with PC1 and PC2, showing an evident positive correlation with the pH. These findings showed that the pH can have a positive correlation with the Tafel slope, however, it does not necessarily reflect a direct positive correlation with the overpotential. The impact of pH on current density (j)and Tafel slope underscores the importance of adjusting pH to lower overpotential effectively, enhancing catalytic activity. Surface area (from 30 to 533 m2 g-1) emerges as a key physical property, inclusively inverse correlation with overpotential, indicating its direct role in lowering overpotential and increasing catalytic activity. The introduction of PC3, in conjunction with PC1, enriches the analysis by revealing consistent trends despite a slightly lower variance (60%). This reinforces the robustness of PCA in delineating distinct characteristics of graphene aerogels, affirming their potential implications in diverse electrocatalytic applications. In summary, PCA proves to be a valuable tool for unraveling complex relationships within aerogel-based electrocatalysts, extending insights beyond catalytic sites to emphasize the broader spectrum of material properties. This approach enhances comprehension of dataset intricacies and holds promise for guiding the development of more effective and versatile electrocatalytic materials.

A versatile pressure-cell design for studying ultrafast molecular-dynamics in supercritical fluids using coherent multi-pulse x-ray scattering.

Supercritical fluids (SCFs) can be found in a variety of environmental and industrial processes. They exhibit an anomalous thermodynamic behavior, which originates from their fluctuating heterogeneous micro-structure. Characterizing the dynamics of these fluids at high temperature and high pressure with nanometer spatial and picosecond temporal resolution has been very challenging. The advent of hard x-ray free electron lasers has enabled the development of novel multi-pulse ultrafast x-ray scattering techniques, such as x-ray photon correlation spectroscopy (XPCS) and x-ray pump x-ray probe (XPXP). These techniques offer new opportunities for resolving the ultrafast microscopic behavior in SCFs at unprecedented spatiotemporal resolution, unraveling the dynamics of their micro-structure. However, harnessing these capabilities requires a bespoke high-pressure and high-temperature sample system that is optimized to maximize signal intensity and address instrument-specific challenges, such as drift in beamline components, x-ray scattering background, and multi-x-ray-beam overlap. We present a pressure cell compatible with a wide range of SCFs with built-in optical access for XPCS and XPXP and discuss critical aspects of the pressure cell design, with a particular focus on the design optimization for XPCS.

Towards Understanding Aerogels' Effect on Construction Materials: A Principal Component Analysis Approach.

This study investigates the applicability of Principal Component Analysis (PCA) for distinguishing construction materials. The approach enhances data presentation, revealing distinct clusters and variable impacts on materials. This perspective provides valuable insights into concrete materials, guiding materials science and engineering practices. Our findings show the capacity of PCA to show a clear distinction between concrete and non-concrete composites. Compressive strength significantly affects certain composites, being influenced by aerogel loading. The peculiar role of aerogel density among the other factors is attributed to their possession of the smallest thermal conductivity. To address moderate total variance of PCA, segregation into concrete (C) and non-concrete (NC) categories is explored, offering a more robust distinction and higher clustering. Concrete materials show higher variance, emphasizing the effectiveness of the segregation approach. PCA highlights aerogel density's influence on thermal conductivity on concrete materials. For non-concrete materials, a moderately higher variance is noted, emphasizing the critical role of aerogel-related properties (size and density). These findings underscore the importance of aerogel characteristics in shaping material behaviour.

Exploring Gel-Point Identification in Epoxy Resin Using Rheology and Unsupervised Learning.

Any thermoset resin's processing properties and end-use performance are heavily influenced by the gel time. The complicated viscosity of resin as a function of temperature is investigated in this work, with a particular emphasis on identifying the gel point and comprehending polymerization. Rheology studies carried out using a plate-plate controlled stress rheometer under isothermal conditions were used to compare three experimental techniques for figuring out an epoxy resin's gel point. We also look at the basic modifications that take place during polymerization. We verify the reliability of the three strategies by including Principal Component Analysis (PCA), an unsupervised machine learning methodology. PCA assists in uncovering hidden connections between these methods and various affecting factors. PCA serves a dual role in our study, confirming method validity and identifying patterns. It sheds light on the intricate relationships between experimental techniques and material properties. This concise study expands our understanding of resin behavior and provides insights that are essential for optimizing resin-based processes in a variety of industrial applications.

Investigating Machine Learning and Control Theory Approaches for Process Fault Detection: A Comparative Study of KPCA and the Observer-Based Method.

The paper focuses on the importance of prompt and efficient process fault detection in contemporary manufacturing industries, where product quality and safety protocols are critical. The study compares the efficiencies of two techniques for process fault detection: Kernel Principal Component Analysis (KPCA) and the observer method. Both techniques are applied to observe water volume variation within a hydraulic system comprising three tanks. PCA is an unsupervised learning technique used for dimensionality reduction and pattern recognition. It is an extension of Principal Component Analysis (PCA) that utilizes kernel functions to transform data into higher-dimensional spaces, where it becomes easier to separate classes or identify patterns. In this paper, KPCA is applied to detect faults in the hydraulic system by analyzing the variation in water volume. The observer method originates from control theory and is utilized to estimate the internal states of a system based on its output measurements. It is commonly used in control systems to estimate the unmeasurable or hidden states of a system, which is crucial for ensuring proper control and fault detection. In this study, the observer method is applied to the hydraulic system to estimate the water volume variations within the three tanks. The paper presents a comparative study of these two techniques applied to the hydraulic system. The results show that both KPCA and the observer method perform similarly in detecting faults within the system. This similarity in performance highlights the efficacy of these techniques and their potential adaptability in various fault diagnosis scenarios within modern manufacturing processes.

Towards Understanding Aerogels' Efficiency for Oil Removal-A Principal Component Analysis Approach.

In this study, our aim was to estimate the adsorption potential of three families of aerogels: nanocellulose (NC), chitosan (CS), and graphene (G) oxide-based aerogels. The emphasized efficiency to seek here concerns oil and organic contaminant removal. In order to achieve this goal, principal component analysis (PCA) was used as a data mining tool. PCA showed hidden patterns that were not possible to seek by the bi-dimensional conventional perspective. In fact, higher total variance was scored in this study compared with previous findings (an increase of nearly 15%). Different approaches and data pre-treatments have provided different findings for PCA. When the whole dataset was taken into consideration, PCA was able to reveal the discrepancy between nanocellulose-based aerogel from one part and chitosan-based and graphene-based aerogels from another part. In order to overcome the bias yielded by the outliers and to probably increase the degree of representativeness, a separation of individuals was adopted. This approach allowed an increase in the total variance of the PCA approach from 64.02% (for the whole dataset) to 69.42% (outliers excluded dataset) and 79.82% (outliers only dataset). This reveals the effectiveness of the followed approach and the high bias yielded from the outliers.

Application of Unsupervised Machine Learning for the Evaluation of Aerogels' Efficiency towards Ion Removal-A Principal Component Analysis (PCA) Approach.

Water scarcity is a global problem affecting millions of people. It can lead to severe economic, social, and environmental consequences. It can also have several impacts on agriculture, industry, and households, leading to a decrease in human quality of life. To address water scarcity, governments, communities, and individuals must work in synergy for the sake of water resources conservation and the implementation of sustainable water management practices. Following this urge, the enhancement of water treatment processes and the development of novel ones is a must. Here, we have investigated the potential of the applicability of "Green Aerogels" in water treatment's ion removal section. Three families of aerogels originating from nanocellulose (NC), chitosan (CS), and graphene (G) are investigated. In order to reveal the difference between aerogel samples in-hand, a "Principal Component Analysis" (PCA) has been performed on the physical/chemical properties of aerogels, from one side, and the adsorption features, from another side. Several approaches and data pre-treatments have been considered to overcome any bias of the statistical method. Following the different followed approaches, the aerogel samples were located in the center of the biplot and were surrounded by different physical/chemical and adsorption properties. This would probably indicate a similar efficiency in the ion removal of the aerogels in-hand, whether they were nanocellulose-based, chitosan-based, or even graphene-based. In brief, PCA has shown a similar efficiency of all the investigated aerogels towards ion removal. The advantage of this method is its capacity to engage and seek similarities/dissimilarities between multiple factors, with the elimination of the shortcomings for the tedious and time-consuming bidimensional data visualization.

Application of Unsupervised Learning for the Evaluation of Aerogels' Efficiency towards Dye Removal-A Principal Component Analysis (PCA) Approach.

Water scarcity is a growing global issue, particularly in areas with limited freshwater sources, urging for sustainable water management practices to insure equitable access for all people. One way to address this problem is to implement advanced methods for treating existing contaminated water to offer more clean water. Adsorption through membranes technology is an important water treatment technique, and nanocellulose (NC)-, chitosan (CS)-, and graphene (G)- based aerogels are considered good adsorbents. To estimate the efficiency of dye removal for the mentioned aerogels, we intend to use an unsupervised machine learning approach known as "Principal Component Analysis". PCA showed that the chitosan-based ones have the lowest regeneration efficiencies, along with a moderate number of regenerations. NC2, NC9, and G5 are preferred where there is high adsorption energy to the membrane, and high porosities could be tolerated, but this allows lower removal efficiencies of dye contaminants. NC3, NC5, NC6, and NC11 have high removal efficiencies even with low porosities and surface area. In brief, PCA presents a powerful tool to unravel the efficiency of aerogels towards dye removal. Hence, several conditions need to be considered when employing or even manufacturing the investigated aerogels.

Application of Unsupervised Learning for the Evaluation of Burial Behavior of Geomaterials in Peatlands: Case of Lignin Moieties Yielded by Alkaline Oxidative Cleavage.

Tropical Peatlands accumulate organic matter (OM) and a significant source of carbon dioxide (CO2) and methane (CH4) under anoxic conditions. However, it is still ambiguous where in the peat profile these OM and gases are produced. The composition of organic macromolecules that are present in peatland ecosystems are mainly lignin and polysaccharides. As greater concentrations of lignin are found to be strongly related to the high CO2 and CH4 concentrations under anoxic conditions in the surface peat, the need to study the degradation of lignin under anoxic and oxic conditions has emerged. In this study, we found that the "Wet Chemical Degradation" approach is the most preferable and qualified to evaluate the lignin degradation in soils accurately. Then, we applied PCA for the molecular fingerprint consisting of 11 major phenolic sub-units produced by alkaline oxidation using cupric oxide (II) along with alkaline hydrolysis of the lignin sample presented in the investigated peat column called "Sagnes". The development of various characteristic indicators for lignin degradation state on the basis of the relative distribution of lignin phenols was measured by chromatography after CuO-NaOH oxidation. In order to achieve this aim, the so-called Principal Component Analysis (PCA) has been applied for the molecular fingerprint composed of the phenolic sub-units, yielded by CuO-NaOH oxidation. This approach aims to seek the efficiency of the already available proxies and potentially create new ones for the investigation of lignin burial along a peatland. Lignin phenol vegetation index (LPVI) is used for comparison. LPVI showed a higher correlation with PC1 rather than PC2. This confirms the potential of the application of LPVI to decipher vegetation change, even in a dynamic system as the peatland. The population is composed of the depth peat samples, and the variables are the proxies and relative contributions of the 11 yielded phenolic sub-units.

Metal Oxide Hydrogel Composites for Remediation of Dye-Contaminated Wastewater: Principal Component Analysis.

Water pollution is caused by multiple factors, such as industrial dye wastewater. Dye-contaminated water can be treated using hydrogels as adsorbent materials. Recently, composite hydrogels containing metal oxide nanoparticles (MONPs) have been used extensively in wastewater remediation. In this study, we use a statistical and artificial intelligence method, based on principal component analysis (PCA) with different applied parameters, to evaluate the adsorption efficiency of 27 different MONP composite hydrogels for wastewater dye treatment. PCA showed that the hydrogel composites CTS@Fe3O4, PAAm/TiO2, and PEGDMA-rGO/Fe3O4@cellulose should be used in situations involving high pH, time to reach equilibrium, and adsorption capacity. However, as the composites PAAm-co-AAc/TiO2, PVPA/Fe3O4@SiO2, PMOA/ATP/Fe3O4, and PVPA/Fe3O4@SiO2, are preferred when all physical and chemical properties investigated have low magnitudes. To conclude, PCA is a strong method for highlighting the essential factors affecting hydrogel composite selection for dye-contaminated water treatment.

Evaluation of the Adsorption Efficiency of Graphene Oxide Hydrogels in Wastewater Dye Removal: Application of Principal Component Analysis.

Industrial dye wastewater is one of the major water pollution problems. Adsorbent materials are promising strategies for the removal of water dye contaminants. Herein, we provide a statistical and artificial intelligence study to evaluate the adsorption efficiency of graphene oxide-based hydrogels in wastewater dye removal by applying Principal Component Analysis (PCA). This study aims to assess the adsorption quality of 35 different hydrogels. We adopted different approaches and showed the pros and cons of each one of them. PCA showed that alginate graphene oxide-based hydrogel (without polyvinyl alcohol) had better tolerance in a basic medium and provided higher adsorption capacity. Polyvinyl alcohol sulfonated graphene oxide-based hydrogels are suitable when higher adsorbent doses are required. In conclusion, PCA represents a robust way to delineate factors affecting hydrogel selection for pollutant removal from aqueous solutions.

BH3-Only Proteins Noxa and Puma Are Key Regulators of Induced Apoptosis.

Apoptosis is an evolutionarily conserved and tightly regulated cell death pathway. Physiological cell death is important for maintaining homeostasis and optimal biological conditions by continuous elimination of undesired or superfluous cells. The BH3-only pro-apoptotic members are strong inducers of apoptosis. The pro-apoptotic BH3-only protein Noxa activates multiple death pathways by inhibiting the anti-apoptotic Bcl-2 family protein, Mcl-1, and other protein members leading to Bax and Bak activation and MOMP. On the other hand, Puma is induced by p53-dependent and p53-independent apoptotic stimuli in several cancer cell lines. Moreover, this protein is involved in several physiological and pathological processes, such as immunity, cancer, and neurodegenerative diseases. Future heat shock research could disclose the effect of hyperthermia on both Noxa and BH3-only proteins. This suggests post-transcriptional mechanisms controlling the translation of both Puma and Noxa mRNA in heat-shocked cells. This study was also the chance to recapitulate the different reactional mechanisms investigated for caspases.

Assessment of the Efficiency of Chemical and Thermochemical Depolymerization Methods for Lignin Valorization: Principal Component Analysis (PCA) Approach.

Energy demand and the use of commodity consumer products, such as chemicals, plastics, and transportation fuels, are growing nowadays. These products, which are mainly derived from fossil resources and contribute to environmental pollution and CO2 emissions, will be used up eventually. Therefore, a renewable inexhaustible energy source is required. Plant biomass resources can be used as a suitable alternative source due to their green, clean attributes and low carbon emissions. Lignin is a class of complex aromatic polymers. It is highly abundant and a major constituent in the structural cell walls of all higher vascular land plants. Lignin can be used as an alternative source for fine chemicals and raw material for biofuel production. There are many chemical processes that can be potentially utilized to increase the degradation rate of lignin into biofuels or value-added chemicals. In this study, two lignin degradation methods, CuO-NaOH oxidation and tetramethyl ammonium hydroxide (TMAH) thermochemolysis, will be addressed. Both methods showed a high capacity to produce a large molecular dataset, resulting in tedious and time-consuming data analysis. To overcome this issue, an unsupervised machine learning technique called principal component analysis (PCA) is implemented.

The Application of Principal Component Analysis (PCA) for the Optimization of the Conditions of Fabrication of Electrospun Nanofibrous Membrane for Desalination and Ion Removal.

Nowadays, acquiring a water supply for urban and industrial uses is one of the greatest challenges facing humanity for ensuring sustainability. Membrane technology has been considered cost-effective, encompasses lower energy requirements, and at the same time, offers acceptable performance. Electrospun nanofibrous membranes (ENMs) are considered a novel and promising strategy for the production of membranes that could be applied in several treatment processes, especially desalination and ion removal. In this study, we apply an unsupervised machine-learning strategy, the so-called principal component analysis (PCA), for the purpose of seeking discrepancies and similarities between different ENMs. The main purpose was to investigate the influence of membrane fabrication conditions, characteristics, and process conditions in order to seek the relevance of the application of different electrospun nanofibrous membranes (ENMs). Membranes were majorly classified into single polymers/layers, from one side, and dual multiple layer ENMs, from another side. For both classes, variables related to membrane fabrication conditions were not separated from membrane characterization variables. This reveals that membranes' characteristics not only depend on the chemical composition, but also on the fabrication conditions. On the other hand, the process conditions of ENM fabrication showed an extensive effect on membranes' performance.

Assessment of actinobacteria use in the elimination of multidrug-resistant bacteria of Ibn Tofail hospital wastewater (Marrakesh, Morocco): a chemometric data analysis approach.

The efficiency of the treatment of hospital wastewater by actinobacteria was investigated using two chemometric data analysis methods. Six strains of multi-resistant bacteria isolated from Marrakesh hospital wastewater and four strains of antagonistic actinobacteria isolated from Moroccan marine environment were characterized by fatty acids released as methyl esters by thermochemolysis-GC/MS. The hierarchical cluster analysis (HCA) and the principal component analysis (PCA) were used to correlate fatty acids (FA) distributions within strains. HCA allowed to discriminate between bacteria and actinobacteria. A lower Euclidean distance is noted for bacteria. With PCA, linear and branched-chained FAs correlated with bacteria whereas mono unsaturated FAs correlated more specifically with Gram (-) bacteria. Terminally branched-chained FAs correlated most likely with actinobacteria. A co-culture of actinobacteria and bacteria monitored during 15 days demonstrated the efficiency of the biological treatment for 2 of the 4 studied actinobacteria. The effect is more important on Gram-negative bacteria. Antagonistic actinobacteria seem to be poorly efficient against Gram-positive bacteria.

Antihypertensive efficacy and safety of a standardized herbal medicinal product of Hibiscus sabdariffa and Olea europaea extracts (NW Roselle): A phase-II, randomized, double-blind, captopril-controlled clinical trial.

Hypertension is a public health concern that needs immediate attention upon diagnosis. The demand for natural alternatives is on the rise; Hibiscus sabdariffa and Olea europaea are traditionally used for hypertension management in Egypt. In this study, we aimed to investigate the antihypertensive efficacy and safety of two doses of an herbal product of Hibiscus sabdariffa calyxes and Olea europaea leaves (NW Roselle) in Egyptian patients with grade 1 essential hypertension. We equally randomized 134 patients to receive captopril 25 mg, low-dose NW Roselle, or high-dose NW Roselle BID for 8 weeks. No significant decrease was found in systolic blood pressure or diastolic blood pressure when we compared low-dose NW Roselle and high-dose NW Roselle to captopril (p > .05). In all groups, mean reduction in BP at 8 weeks was significant; 16.4/9.9 mmHg (p < .0001), 15.4/9.6 mmHg (p < .0001), and 14.9/9.4 mmHg (p < .0001) with captopril, low-dose NW Roselle, and high-dose NW Roselle respectively. In addition, low-dose NW Roselle induced a significant reduction in the mean level of triglycerides (17.56 mg/dL; p = .038). In conclusion, NW Roselle had comparable antihypertensive efficacy and safety to captopril in Egyptian patients with grade 1 essential hypertension.

Establishing an ECMO program in a developing country: challenges and lessons learned.

AIM: The ECMO (extracorporeal membrane oxygenation) Program at the American University of Beirut Medical Center was established in November 2015 as the first program serving adult and pediatric population in a low-resource setting. The aim of the study is to describe the challenges faced during the establishment of the program and factors leading to its success. METHODS: The program establishment is described. The preparation phase, included the strategic, financial, and clinical planning by administration, nursing, and a multidisciplinary team of physicians. The training and education phase included all the involved nurses, perfusionists, and physicians. Concerns were heard from various stakeholders, and the challenges were analyzed and discussed. RESULTS: The preparation committee chose the adequate equipment, responded to the concerns, defined roles and responsibilities through credentialing and privileging, wrote policies and protocols, and established a strategy to decide for the ECMO indication. Selected team of nurses, physicians, and perfusionists are identified and trained locally, and abroad. A full-time ECMO physician was recruited to launch the program. Twelve patients (6 adults, 3 children, and 3 neonates) were supported by ECMO, for cardiac and respiratory indications. Eleven patients were supported by veno-arterial ECMO, and 1 patient (a neonate) with veno-venous ECMO. Overall, 75% survived to decannulation and 41% survived to discharge. CONCLUSION: With limited human and financial resources, new ECMO centers need to carefully establish selection criteria that may differ from those used in developed countries. Indications should be discussed on a case by case basis, taking into account clinical, social, and financial issues. This experience might help other institutions in developing countries to build their own program despite financial and human limitations.

The role of bone morphogenetic proteins 2 and 4 in mouse dentinogenesis.

OBJECTIVE: The bone morphogenetic proteins (BMPs) play crucial roles in tooth development. However, several BMPs retain expression in the dentin of the fully patterned and differentiated tooth. We hypothesized that BMP signaling therefore plays a role in the function of the differentiated odontoblast, the job of which is to lay down and mineralize the dentin matrix. DESIGN: We generated mice deficient in Bmp2 and 4 using a dentin matrix protein 1 (Dmp1) promoter-driven cre recombinase that was expressed in differentiated odontoblasts. RESULTS: The first and second molars of these Bmp2 and Bmp4 double conditional knockout (DcKO) mice displayed reduced dentin and enlarged pulp chambers compared to cre-negative littermate controls. DcKO mouse dentin in first molars was characterized by small, disorganized dentinal fibers, a wider predentin layer, and reduced expression of dentin sialophosphoprotein (DSPP), dentin matrix protein 1 (DMP1), and bone sialoprotein (BSP). DcKO mouse odontoblasts demonstrated increased type I collagen mRNA production, indicating that the loss of BMP signaling altered the rate of collagen gene expression in these cells. Bmp2 and Bmp4 single Dmp1-cre knockout mice displayed no discernable dentin phenotype. CONCLUSIONS: These data demonstrate that BMP signaling in differentiated odontoblasts is necessary for proper dentin production in mature teeth.

Anti-apolipoprotein A-I antibodies and paraoxonase 1 activity in Systemic Lupus Erythematosus.

Systemic lupus erythematosus (SLE) patients have an increased risk of atherosclerosis. Identification of at-risk patients and the pathogenesis of atherosclerosis in SLE remain elusive. Paraoxonase 1 (PON1) and anti-apolipoprotein A-I antibody (anti-Apo A-I) appear to have a potential role in premature atherosclerosis in SLE. The aim of this work was to study PON1 activity and anti-Apo A-I antibody in SLE female patients and to demonstrate their relations to disease activity as well as disease related damage. Forty SLE female patients and 40 apparently healthy volunteers were included. Anti-Apo A-I antibodies levels and PON1 activity levels were assessed. Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) and systemic Lupus International Collaboration Clinics (SLICC)/American College of Rheumatology (ACR) damage index were preformed in all patients. Compared with controls, SLE patients showed significantly lower PON1 activity and significantly higher titers of anti-Apo A-I. Anti-Apo A-I antibody titers correlated inversely with PON1 activity. Elevated titers of anti-Apo A-I antibody and reduced PON activity were related to increased SLEDAI and (SLICC/ACR) damage index scores. We concluded that there is decreased PON1 activity and formation of anti-Apo A-I antibodies in female patients with SLE. SLE-disease activity assessed by SLEDAI and SLE disease related organ damage assessed by SLICC/ACR damage index are negatively correlated with PON1 activity and positively correlated with anti-Apo A-I antibodies. PON1 activity and anti-Apo A-I antibodies might be involved in the pathogenesis of atherosclerosis in SLE patients.