Expanding the antiprotozoal activity and the mechanism of action of n-butyl and iso-butyl ester of quinoxaline-1,4-di-N-oxide derivatives against Giardia lamblia, Trichomonas vaginalis, and Entamoeba histolytica. An in vitro and in silico approach.

In this study, n-butyl and iso-butyl quinoxaline-7-carboxylate-1,4-di-N-oxide derivatives were evaluated in vitro against Giardia lamblia (G. lamblia), Trichomonas vaginalis (T. vaginalis), and Entamoeba histolytica (E. histolytica). The potential mechanism of action determination was approached by in silico analysis on G. lamblia and T. vaginalis triosephosphate isomerase (GlTIM and TvTIM, respectively), and on E. histolytica thioredoxin reductase (EhTrxR). Enzyme inactivation assays were performed on recombinant GlTIM and EhTrxR. Compound T-167 showed the best giardicidal activity (IC50 = 25.53 nM) and the highest inactivation efficiency against GlTIM without significantly perturbing its human homolog. Compounds T-142 and T-143 showed the best amoebicidal (IC50 = 9.20 nM) and trichomonacidal (IC50 = 45.20 nM) activity, respectively. Additionally, T-143 had a high activity as giardicial (IC50 = 29.13 nM) and amoebicidal (IC50 = 15.14 nM), proposing it as a broad-spectrum antiparasitic agent. Compounds T-145, and T-161 were the best EhTrxR inhibitors with IC50 of 16 µM, and 18 µM, respectively.

Systemic Inflammation but not Oxidative Stress Is Associated with Physical Performance in Moderate Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) patients manifest muscle dysfunction and impaired muscle oxidative capacity, which result in reduced exercise capacity and poor health status. The aim of this study was to compare the physical performance, systemic inflammation, and oxidative stress of patients with moderate COPD, and to associate physical performance with inflammatory and oxidative stress plasma markers. Twenty CONTROL (n = 10) and moderate COPD (n = 10) patients participated in this study. Systematic inflammation and oxidative stress plasma markers, maximal aerobic capacity (VO2peak), and maximal isometric strength (MVIC) of the knee extensor (KE) muscles were measured. VO2peak was 31.3% greater in CONTROL compared to COPD (P = 0.006). The MVIC strength of the KE was 43.9% greater in CONTROL compared to COPD (P = 0.002). Tumor necrosis factor-alpha (TNF-α) was 79.6% greater in COPD compared to CONTROL (P < 0.001). Glutathione peroxidase activity (GPx) activity was 27.5% lesser in COPD compared to CONTROL (P = 0.05). TNF-α concentration was correlated with KE MVC strength (R = -0.48; P = 0.045) and VO2peak (R = -0.58; P = 0.01). Meanwhile, malondialdehyde (MDA) and GPx activity were not associated with KE strength or VO2peak (P = 0.74 and P = 0.14, respectively). COPD patients showed lesser muscle strength and aerobic capacity than healthy control individuals. Furthermore, patients with COPD showed greater systemic inflammation and lesser antioxidant capacity than healthy counterparts. A moderate association was evident between levels of systemic inflammation and physical performance variables.

A systematic benchmarking framework for future assessments of soil health: An example from Denmark.

Based on current evidence and established critical thresholds for soil degradation indicators, it is concerning that over 60-70% of European soils are unhealthy due to unsustainable management and the impact of climate change. Despite European and national efforts to improve soil health, significant gaps remain. The proposal for a Soil Monitoring and Resilience Law, to be implemented by the European Union, seeks to establish a framework for soil monitoring and promote sustainable management practices to achieve healthy soils by 2050. This requires extensive data collection and soil monitoring systems to accurately estimate soil health across Europe, considering the diversity of soil types, climates, and land uses. To establish a framework for soil monitoring, we must understand the site-specific status of soil and the ranges of soil health indicators across specific pedoclimatic regions. In our study, we evaluated the soil status in agricultural areas in Denmark using soil health indicators and a site-specific benchmarking approach. We compiled nationally representative datasets, combining point and model-informed data of soil parameters such as organic carbon content, bulk density, pH, electrical conductivity, clay-to-soil organiccarbon ratio, water erosion, and nitrogen leaching. By categorizing Danish agricultural soils into monitoring units based on textural classes, landscape elements, and wetland types, we calculated benchmarks for these indicators, considering different cropping systems. Our approach provided detailed point-based results and a spatially explicit overview of the status of soil health indicators in Denmark. We identified areas where soil deviates from the benchmarks of different indicators. Such deviations might indicate soil functions operating outside the normal range, posing potential threats to soil health. This proposed framework could support the establishment of a baseline for assessing the directionality of future changes in soil health. Moreover, it is adaptable for implementation by other countries to support assessments of soil health.

A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks.

This paper presents a systematic approach for solving complex prediction problems with a focus on energy efficiency. The approach involves using neural networks, specifically recurrent and sequential networks, as the main tool for prediction. In order to test the methodology, a case study was conducted in the telecommunications industry to address the problem of energy efficiency in data centers. The case study involved comparing four recurrent and sequential neural networks, including recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), and online sequential extreme learning machine (OS-ELM), to determine the best network in terms of prediction accuracy and computational time. The results show that OS-ELM outperformed the other networks in both accuracy and computational efficiency. The simulation was applied to real traffic data and showed potential energy savings of up to 12.2% in a single day. This highlights the importance of energy efficiency and the potential for the methodology to be applied to other industries. The methodology can be further developed as technology and data continue to advance, making it a promising solution for a wide range of prediction problems.

An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions.

Wireless sensor networks (WSN) involve large number of sensor nodes distributed at diverse locations. The collected data are prone to be inaccurate and faulty due to internal or external influences, such as, environmental interference or sensor aging. Intelligent failure detection is necessary for the effective functioning of the sensor network. In this paper, we propose a supervised learning method that is named artificial hydrocarbon networks (AHN), to predict temperature in a remote location and detect failures in sensors. It allows predicting the temperature and detecting failure in sensor node of remote locations using information from a web service comparing it with field temperature sensors. For experimentation, we implemented a small WSN to test our sensor in order to measure failure detection, identification and accommodation proposal. In our experiments, 94.18% of the testing data were recovered and accommodated allowing of validation our proposed approach that is based on AHN, which detects, identify and accommodate sensor failures accurately.

An intelligent climate monitoring system for hygrothermal virtual measurement in closed buildings using Internet-of-things and artificial hydrocarbon networks.

Studies analyzing indoor thermal environments comprising temperature and humidity may be insufficient when obtaining data from sensors, which may be susceptible to inaccurate or failed information from internal and external factors. Therefore, this study proposes an intelligent climate monitoring using a supervised learning method for virtual hygrothermal measurement in enclosed buildings used to predict temperature and relative humidity when a sensor failure is detected. The methodology comprises the data collection from a wireless sensor network, the building of the learning model for predicting the dynamics of environmental variables, and the implementation of a sensor failure detection model. We use an artificial hydrocarbon network as the learning model for their simplicity and effectiveness under uncertain and noisy data. The experiments use data acquired in two settings: (1) a laboratory office and (2) a museum storage room. The first scenario has multiple workstations, and the staff turns on or off the air conditioning depending on the feeling of comfort, generating an uncontrolled environment for the variables of interest. The second scenario has controlled temperature and humidity to ensure the conservation conditions of the museum pieces. Both scenarios used 12 sensors that acquired data for one month, providing an average of 58,300 values for each variable. Results of the proposed methodology provide 95% of accuracy in terms of sensor failure detection and identification, and less than 0.22% of tolerance variability in temperature and humidity after sensor accommodation in both scenarios.

Integrative genome-centric metagenomics for surface water surveillance: Elucidating microbiomes, antimicrobial resistance, and their associations.

Surface water ecosystems are intimately intertwined with anthropogenic activities and have significant public health implications as primary sources of irrigation water in agricultural production. Our extensive metagenomic analysis examined 404 surface water samples from four different geological regions in Chile and Brazil, spanning irrigation canals (n = 135), rivers (n = 121), creeks (n = 74), reservoirs (n = 66), and ponds (n = 8). Overall, 50.25 % of the surface water samples contained at least one of the pathogenic or contaminant bacterial genera (Salmonella: 29.21 %; Listeria: 6.19 %; Escherichia: 35.64 %). Furthermore, a total of 1,582 antimicrobial resistance (AMR) gene clusters encoding resistance to 25 antimicrobial classes were identified, with samples from Brazil exhibiting an elevated AMR burden. Samples from stagnant water sources were characterized by dominant Cyanobacteriota populations, resulting in significantly reduced biodiversity and more uniform community compositions. A significant association between taxonomic composition and the resistome was supported by a Procrustes analysis (p < 0.001). Notably, regional signatures were observed regarding the taxonomic and resistome profiles, as samples from the same region clustered together on both ordinates. Additionally, network analysis illuminated the intricate links between taxonomy and AMR at the contig level. Our deep sequencing efforts not only mapped the microbial landscape but also expanded the genomic catalog with newly characterized metagenome-assembled genomes (MAGs), boosting the classification of reads by 12.85 %. In conclusion, this study underscores the value of metagenomic approaches in surveillance of surface waters, enhancing our understanding of microbial and AMR dynamics with far-reaching public health and ecological ramifications.

Unleashing the sequestration potential of soil organic carbon under climate and land use change scenarios in Danish agroecosystems.

Future global climate changes are expected to increase soil organic carbon (SOC) decomposition. However, the combined effect of C inputs, land use changes, and climate on SOC turnover is still unclear. Exploring this SOC-climate-land use interaction allows us to understand the SOC stabilization mechanisms and examine whether the soil can act as a source or a sink for CO2. The current study estimates the SOC sequestration potential in the topsoil layer of Danish agricultural lands by 2038, considering the effect of land use change and future climate scenarios using the Rothamsted Carbon (RothC) model. Additionally, we quantified the loss vulnerability of existing and projected SOC based on the soil capacity to stabilize OC. We used the quantile random forest model to estimate the initial SOC stock by 2018, and we simulated the SOC sequestration potential with RothC for a business-as-usual (BAU) scenario and a crop rotation change (LUC) scenario under climate change conditions by 2038. We compared the projected SOC stocks with the carbon saturation deficit. The initial SOC stock ranged from 10 to 181 Mg C ha-1 in different parts of the country. The projections showed a SOC loss of 8.1 Mg C ha-1 for the BAU scenario and 6 Mg C ha-1 after the LUC adoption. This SOC loss was strongly influenced by warmer temperatures and clay content. The proposed crop rotation became a mitigation measure against the negative effect of climate change on SOC accumulation, especially in sandy soils with a high livestock density. A high C accumulation in C-saturated soils suggests an increase in non-complexed SOC, which is vulnerable to being lost into the atmosphere as CO2. With these results, we provide information to prioritize areas where different soil management practices can be adopted to enhance SOC sequestration in stable forms and preserve the labile-existing SOC stocks.

Influence of Bacillus safensis and Bacillus pumilus on the electrochemical behavior of 2024-T3 aluminum alloy.

In this work, electrochemical techniques were employed to evaluate the contribution to the corrosion and corrosion inhibition of 2024-T3 aluminum alloy by two Gram-positive bacteria. In addition, polarized impedance was used to determine the microbial effect on the cathodic and anodic reactions. These microorganisms were collected from a tropical environment due to the favorable bacterial growth of this kind of climate. The alloy was exposed to the sterile medium and inoculated for up to 12 days evaluating the microbiological and electrochemical behavior. The results by linear scanning voltammetry showed that the B. safensis and B. pumilus caused a dual effect of increase and decrease currents, and through electrochemical impedance spectroscopy, showed in some cases, inductive loop, which could be associated with local corrosion and another case, an increasing impedance could be related to protection. In addition, a morphological characterization was performed by scanning electron microscopy before and after exposure, showing an increase in copper precipitation in the vicinity of the intermetallic phases by bacteria, attributed to local corrosion, but, in general, a significant effect of damages was not observed.

Longitudinal Study after Sputnik V Vaccination Shows Durable SARS-CoV-2 Neutralizing Antibodies and Reduced Viral Variant Escape to Neutralization over Time.

Recent studies have shown a temporal increase in the neutralizing antibody potency and breadth to SARS-CoV-2 variants in coronavirus disease 2019 (COVID-19) convalescent individuals. Here, we examined longitudinal antibody responses and viral neutralizing capacity to the B.1 lineage virus (Wuhan related), to variants of concern (VOC; Alpha, Beta, Gamma, and Delta), and to a local variant of interest (VOI; Lambda) in volunteers receiving the Sputnik V vaccine in Argentina. Longitudinal serum samples (N = 536) collected from 118 volunteers obtained between January and October 2021 were used. The analysis indicates that while anti-spike IgG levels significantly wane over time, the neutralizing capacity for the Wuhan-related lineages of SARS-CoV-2 and VOC is maintained within 6 months of vaccination. In addition, an improved antibody cross-neutralizing ability for circulating variants of concern (Beta and Gamma) was observed over time postvaccination. The viral variants that displayed higher escape to neutralizing antibodies with respect to the original virus (Beta and Gamma variants) were the ones showing the largest increase in susceptibility to neutralization over time after vaccination. Our observations indicate that serum neutralizing antibodies are maintained for at least 6 months and show a reduction of VOC escape to neutralizing antibodies over time after vaccination. IMPORTANCE Vaccines have been produced in record time for SARS-CoV-2, offering the possibility of halting the global pandemic. However, inequalities in vaccine accessibility in different regions of the world create a need to increase international cooperation. Sputnik V is a recombinant adenovirus-based vaccine that has been widely used in Argentina and other developing countries, but limited information is available about its elicited immune responses. Here, we examined longitudinal antibody levels and viral neutralizing capacity elicited by Sputnik V vaccination. Using a cohort of 118 volunteers, we found that while anti-spike antibodies wane over time, the neutralizing capacity to viral variants of concern and local variants of interest is maintained within 4 months of vaccination. In addition, we observed an increased cross-neutralization activity over time for the Beta and Gamma variants. This study provides valuable information about the immune response generated by a vaccine platform used in many parts of the world.

Immunogenicity and reactogenicity of heterologous immunization against SARS CoV-2 using Sputnik V, ChAdOx1-S, BBIBP-CorV, Ad5-nCoV, and mRNA-1273.

Heterologous vaccination against coronavirus disease 2019 (COVID-19) provides a rational strategy to rapidly increase vaccination coverage in many regions of the world. Although data regarding messenger RNA (mRNA) and ChAdOx1 vaccine combinations are available, there is limited information about the combination of these platforms with other vaccines widely used in developing countries, such as BBIBP-CorV and Sputnik V. Here, we assess the immunogenicity and reactogenicity of 15 vaccine combinations in 1,314 participants. We evaluate immunoglobulin G (IgG) anti-spike response and virus neutralizing titers and observe that a number of heterologous vaccine combinations are equivalent or superior to homologous schemes. For all cohorts in this study, the highest antibody response is induced by mRNA-1273 as the second dose. No serious adverse events are detected in any of the schedules analyzed. Our observations provide rational support for the use of different vaccine combinations to achieve wide vaccine coverage in the shortest possible time.

Reliability and repeatability of a smartphone-based 6-min walk test as a patient-centred outcome measure.

Aims: The 6-min-walk test (6MWT) is a validated proxy for frailty and a predictor of clinical outcomes, yet is not widely used due to implementation challenges. This comparative effectiveness study assesses the reliability and repeatability of a home-based 6MWT compared to in-clinic 6MWTs in patients with cardiovascular disease. Methods and results: One hundred and ten (110) patients scheduled for cardiac or vascular surgery were enrolled during a study period from June 2018 to December 2019 at the Palo Alto VA Hospital. Subjects were provided with an Apple iPhone 7 and Apple Watch Series 3 loaded with the VascTrac research study application and performed a supervised in-clinic 6MWT during enrolment, at 2 weeks, 1, 3, and 6 months post-operatively. Subjects also received notifications to perform at-home smartphone-based 6MWTs once a week for a duration of 6 months. Test-retest reliability of in-clinic measurements and at-home measurements was assessed with an industry standard Cronbach's alpha reliability test. Test-retest reliability for in-clinic ground truth 6MWT steps vs. in-clinic iPhone 6MWT steps was 0·99, showing high reliability between the two tested measurements. When comparing for in-clinic ground truth 6MWT steps vs. neighbouring at-home iPhone 6MWT steps, reliability was 0·74. Conclusion: Running the test-reliability test on both measurements shows that an iPhone 6MWT test is reliable compared to an in-clinic ground truth measurement in patients with cardiovascular disease.

National Health Systems and COVID-19 Death Toll Doubling Time.

Coronavirus disease 2019 (COVID-19) has placed stress on all National Health Systems (NHSs) worldwide. Recent studies on the disease have evaluated different variables, namely, quarantine models, mitigation efforts, damage to mental health, mortality of the population with chronic diseases, diagnosis, use of masks and social distancing, and mortality based on age. This study focused on the four NHSs recognized by the WHO. These systems are as follows: (1) The Beveridge model, (2) the Bismarck model, (3) the National Health Insurance (NHI) model, and (4) the "Out-of-Pocket" model. The study analyzes the response of the health systems to the pandemic by comparing the time in days required to double the number of disease-related deaths. The statistical analysis was limited to 56 countries representing 70% of the global population. Each country was grouped into the health system defined by the WHO. The study compared the median death toll DT, between health systems using Mood's median test method. The results show high variability of the temporal trends in each group; none of the health systems for the three analyzed periods maintain stable interquartile ranges (IQRs). Nevertheless, the results obtained show similar medians between the study groups. The COVID-19 pandemic saturates health systems regardless of their management structures, and the result measured with the time for doubling death rate variable is similar among the four NHSs.

Diversity of Non-O157 Shiga Toxin-Producing Escherichia coli Isolated from Cattle from Central and Southern Chile.

Cattle are the main reservoir of Shiga toxin-producing Escherichia coli (STEC), one of the world's most important foodborne pathogens. The pathogen causes severe human diseases and outbreaks. This study aimed to identify and characterize non-O157 STEC isolated from cattle feces from central and southern Chile. We analyzed 446 cattle fecal samples and isolated non-O157 STEC from 12.6% (56/446); a total of 93 different isolates were recovered. Most isolates displayed ß-glucuronidase activity (96.8%; 90/93) and fermented sorbitol (86.0%; 80/93), whereas only 39.8% (37/93) were resistant to tellurite. A subgroup of 30 representative non-O157 STEC isolates was selected for whole-genome sequencing and bioinformatics analysis. In silico analysis showed that they grouped into 16 different sequence types and 17 serotypes; the serotypes most frequently identified were O116:H21 and O168:H8 (13% each). A single isolate of serotype O26:H11 was recovered. One isolate was resistant to tetracycline and carried resistance genes tet(A) and tet(R); no other isolate displayed antimicrobial resistance or carried antimicrobial resistance genes. The intimin gene (eae) was identified in 13.3% (4/30) of the genomes and 90% (27/30) carried the stx2 gene. A phylogenetic reconstruction demonstrated that the isolates clustered based on serotypes, independent of geographical origin. These results indicate that cattle in Chile carry a wide diversity of STEC potentially pathogenic for humans based on the presence of critical virulence genes.

Toward Tailor-Made Biocide Materials Based on Poly(propylene)/Copper Nanoparticles.

A set of poly(propylene) composites containing different amounts of copper nanoparticles (CNP) were prepared by the melt mixed method and their antimicrobial behavior was quantitatively studied. The time needed to reduce the bacteria to 50% dropped to half with only 1 v/v % of CNP, compared to the polymer without CNP. After 4 h, this composite killed more than 99.9% of the bacteria. The biocide kinetics can be controlled by the nanofiller content; composites with CNP concentrations higher than 10 v/v % eliminated 99% of the bacteria in less than 2 h. X-ray photoelectron spectroscopy did not detect CNP at the surface, therefore the biocide behavior was attributed to copper in the bulk of the composite.

Whole-Genome Phylogenetic Analysis Reveals a Wide Diversity of Non-O157 STEC Isolated From Ground Beef and Cattle Feces.

Shiga toxin-producing Escherichia coli (STEC) causes foodborne outbreaks that can lead to complications such as hemolytic uremic syndrome. Their main reservoir is cattle, and ground beef has been frequently associated with disease and outbreaks. In this study, we attempted to understand the genetic relationship among STEC isolated in Chile from different sources, their relationship to STEC from the rest of the world, and to identify molecular markers of Chilean STEC. We sequenced 62 STEC isolated in Chile using MiSeq Illumina. In silico typing was determined using tools of the Center Genomic Epidemiology, Denmark University (CGE/DTU). Genomes of our local STEC collection were compared with 113 STEC isolated worldwide through a core genome MLST (cgMLST) approach, and we also searched for distinct genes to be used as molecular markers of Chilean isolates. Genomes in our local collection were grouped based on serogroup and sequence type, and clusters were formed within local STEC. In the worldwide STEC analysis, Chilean STEC did not cluster with genomes of the rest of the world suggesting that they are not phylogenetically related to previously described STEC. The pangenome of our STEC collection was 11,650 genes, but we did not identify distinct molecular markers of local STEC. Our results showed that there may be local emerging STEC with unique features, nevertheless, no molecular markers were detected. Therefore, there might be elements such as a syntenic organization that might explain differential clustering detected between local and worldwide STEC.

[Celiac crisis: presentation as bleeding diathesis]. / Crisis celíaca: presentación con diátesis hemorrágica.

Celiac crisis is a severe and potentially fatal complication of celiac disease. Unusual at present, it has been described mainly in children younger than 2-years-old, but reports in adults do exist. We report a 26-years-old lady with tetany and bleeding diathesis at presentation. In spite of it rareness, it is important to consider celiac crisis among the multiple manifestations of celiac disease.

A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset.

The automatic recognition of human falls is currently an important topic of research for the computer vision and artificial intelligence communities. In image analysis, it is common to use a vision-based approach for fall detection and classification systems due to the recent exponential increase in the use of cameras. Moreover, deep learning techniques have revolutionized vision-based approaches. These techniques are considered robust and reliable solutions for detection and classification problems, mostly using convolutional neural networks (CNNs). Recently, our research group released a public multimodal dataset for fall detection called the UP-Fall Detection dataset, and studies on modality approaches for fall detection and classification are required. Focusing only on a vision-based approach, in this paper, we present a fall detection system based on a 2D CNN inference method and multiple cameras. This approach analyzes images in fixed time windows and extracts features using an optical flow method that obtains information on the relative motion between two consecutive images. We tested this approach on our public dataset, and the results showed that our proposed multi-vision-based approach detects human falls and achieves an accuracy of 95.64% compared to state-of-the-art methods with a simple CNN network architecture.

Liking Product Landscape: Going Deeper into Understanding Consumers' Hedonic Evaluations.

The use of graphical mapping for understanding the comparison of products based on consumers' perceptions is beneficial and easy to interpret. Internal preference mapping (IPM) and landscape segmentation analysis (LSA) have successfully been used for this propose. However, including all the consumers' evaluations in one map, with products' overall liking and attributes' perceptions, is complicated; because data is in a high dimensional space some information can be lost. To provide as much information as possible, we propose the liking product landscape (LPL) methodology where several maps are used for representing the consumers' distribution and evaluations. LPL shows the consumers' distribution, like LSA, and also it superimposes the consumers' evaluations. However, instead of superimposing the average overall liking in one map, this methodology uses different maps for each consumer's evaluation. Two experiments were performed where LPL was used for understanding the consumers' perceptions and compared with classic methodologies, IPM and cluster analysis, in order to validate the results. LPL can be successfully used for identifying consumers' segments, consumers' preferences, recognizing perception of product attributes by consumers' segments and identifying the attributes that need to be optimized.

Hypercholesterolemia Due to Lipoprotein X: Case Report and Thematic Review.

The liver is a key organ in lipid and lipoprotein metabolism, hence hepatic diseases often manifest as lipid disturbances. Cholestatic liver diseases are frequently associated with an important increase in total cholesterol at the expense of lipoprotein X (LpX), an abnormal lipoprotein isolated and characterized in the 1960s to 1970s in patients with obstructive jaundice. Lipoprotein X is rich in phospholipids, albumin, and free cholesterol, has a density similar to low-density lipoprotein (LDL), and a size similar to very low-density lipoprotein (VLDL), which has hampered its detection through routine laboratory tests. Unlike LDL, LpX has no apoB-100, so it is not removed from circulation via the LDL receptor, and it is not clear whether or not it can be atherogenic. Although LpX was initially described in patients with cholestasis, it has also been found in patients with genetic deficiency of lecithin-cholesterol acyltransferase (LCAT), in patients who receive lipid-rich parenteral nutrition and most recently in patients with graft versus host disease of the liver. In the presence of LpX, plasma total cholesterol can rise up to 1000 mg/dL, which may lead to the development of skin xanthomas and hyperviscosity syndrome. Treatment of LpX-dependent hypercholesterolemia with conventional hypolipidemic drugs is frequently ineffective, and definitive treatment relies on correction of the underlying cause of cholestasis. Here, we present the case of a patient with LpX-dependent hypercholesterolemia in the context of primary biliary cholangitis.