The lipidome has a broad range of biological and signaling functions, including serving as a structural scaffold for membranes and initiating and resolving inflammation. To investigate the biological activity of phospholipids and their bioactive metabolites, precise analytical techniques are necessary to identify specific lipids and quantify their levels. Simultaneous quantification of a set of lipids can be achieved using high sensitivity mass spectrometry (MS) techniques, whose technological advancements have significantly improved over the last decade. This has unlocked the power of metabolomics/lipidomics allowing the dynamic characterization of metabolic systems. Lipidomics is a subset of metabolomics for multianalyte identification and quantification of endogenous lipids and their metabolites. Lipidomics-based technology has the potential to drive novel biomarker discovery and therapeutic development programs; however, appropriate standards have not been established for the field. Standardization would improve lipidomic analyses and accelerate the development of innovative therapies. This review aims to summarize considerations for lipidomic study designs including instrumentation, sample stabilization, data validation, and data analysis. In addition, this review highlights how lipidomics can be applied to biomarker discovery and drug mechanism dissection in various inflammatory diseases including cardiovascular disease, neurodegeneration, lung disease, and autoimmune disease. Phenotypic lipid profiling techniques have dramatically improved in the last decade. This technological advancement has ushered in a movement towards multianalyte identification and quantification of endogenous lipids and their metabolites which are potent regulators of inflammation and pro-resolving processes. The ability to comprehensively understand the metabolism of these important structural components and cellular signaling molecules has deepened our insights into disease pathophysiology and helped identify drug targets for potential therapeutic development. Additionally, lipidomic profiling has identified imbalances in lipid homeostasis in several diseases of global health concern including metabolic syndrome, cardiovascular disease (CVD), neurodegenerative disease, respiratory disease, and autoimmune disease ,2. ImporMetabolomics is an analytical approach that utilizes mass detection systems to provide a cross-sectional snapshot of the metabolites involved in a particular biological system in order to dissect metabolic pathways as well as quantify analytes of interest. Lipidomics represents a category of metabolomics centered on the metabolomic evaluation of lipids and their metabolites. While lipids are known to be potent mediators of inflammation and other cell signaling cascades, lipidomics continues to be underutilized in guiding our understanding of disease pathophysiology and the impact of therapeutic interventions.As clinical trials have become more reliant on biomarkers as disease modifying endpoints, lipidomic monitoring may be highly applicable in drug development. Currently, there is a lack of guidance as to how lipidomics can be deployed to understand pharmacological interventions, and how it can be used to facilitate safety and efficacy assessments in clinical trials. To address this lack of standardized workflows for lipidomics, the National Institute of Standards and Technology (NIST) conducted surveys and studies of laboratories in pursuit of creating harmonization and improved standardization of lipidomic methodologies and findings ,6. This The ability to simultaneously detect multiple analytes has been denoted as “Omics” approaches. This powerful technique combines layered information derived from genetics, transcriptomics, and proteomics, where metabolic data can improve our understanding of the molecular mechanisms driving disease progression. This approach can be globally assessed to identify changes in biomatrices as well as tissues, where correlations can be made between analyte concentrations and other measures of disease severity. Accurate correlations with disease severity highlight the power of this approach that can be transformed into diagnostic biomarkers .Although proteins and nucleic acids have been thoroughly evaluated using proteomics and genomics, respectively, broad spectrum determination of genes and proteins may be limited due to a lack of contextualization. Metabolomics/lipidomics has the advantage of relating phenotype to alterations in an entire metabolic pathway, such as increased or decreased enzymatic activities. Metabolomic/lipidomic approaches, which can determine either the relative levels or precise concentrations of targeted analytes within a specific biomatrix, are most important where gene expression cannot determine the levels of active metabolites. Examples of this would include structural lipids  found in the cellular membranes which can be metabolized to form bioactive lipids capable of regulating or promoting the resolution of inflammation. The ability to quantify levels of precursor molecules and their metabolites provides a more complete picture of the relationships between molecular changes and phenotypical outcomes.Inflammatory responses are mediated by various chemical entities including histamine, eicosanoid lipids, and cytokines ,9. MetabAt the center of these metabolomics/lipidomics analyses is the use of mass spectrometry (MS) technology . This poCurrently there is a lack of uniformity in the types of MS technologies employed. The diversity of MS methods used includes liquid chromatography (LC)-triple quadrupole MS, LC-quadrupole time-of-flight MS (LC-QTOF/MS), LC-Orbitrap, nano-electrospray ionization MS (nano-ESI–MS), gas chromatography MS (GC–MS), acoustic ejection MS, and matrix-assisted laser desorption ionization-time of flight (MALDI-TOF). Each of these techniques has their own specific advantages and limitations as to the types of data produced . A critiOne limitation of “omic” approaches is analyte stabilization. Prior to analyzing any lipidomics sample, proper sample processing is imperative. There are several factors that affect the sample during lipidomic studies, especially the preprocessing, storage, selection conditions, and matrix specific sample processing . CurrentFor effective controls, it is important to know the age and condition of biological samples, as well as the extraction and preservation processes used. Since lipids are highly sensitive to oxidation, which can be introduced during sample preparation, the stabilization of susceptible analytes is of critical importance. Drying processed samples under nitrogen gas, as opposed to other techniques, is one approach to minimize the extent of lipid oxidation. Another important consideration is conducting stability studies which assess rates of deterioration. This is rarely evaluated, where known analytes are stored over time and the time-dependent degradation is evaluated after stabilization . Using AData validation protocols should follow guidelines recognized by the FDA to expedite the drug development process. The Clinical and Laboratory Standards Institute (CLSI), a not-for-profit organization which develops global standards, has produced an FDA recognized guidance on evaluation of precise quantitative methods (EP05A3) . To moniThe fundamental uniqueness of each biological system makes the analysis of metabolomic data more complicated despite shared biochemical reactions and metabolites . LipidomLipidomic data dimension reduction may employ unsupervised methods, such as PCA, or supervised ones, like partial least squares regression (PLSR), PLS discriminant analysis (PLS-DA), and orthogonal projections to latent structures discriminant analysis (OPLS-DA). The primary goal of PCA and PLS is to identify group differences from a multivariate dataset. A class can refer to any biologically relevant classification, such as specific diet or drug interventions. PCA is the most widely used multivariate analysis method for feature-dependent sample classification. However, it is important for variables to be tested for the assumptions of predictive regression modeling before any meaningful conclusion can be extrapolated. These approaches are highly applicable to personalized medicine and therapeutic development.More recently, machine learning algorithms have been employed to analyze data generated by newer bioanalytical techniques. Since metabolites can interact in a non-linear manner , data structures from lipidomics data can become complex, making machine learning algorithms a practical approach to dissect data in a time-efficient manner . These aFollowing the development of predictive tools for neurological imaging, machine learning using Python has become an integral means for data analysis. Advantages of the Python language for lipidomics-based machine learning models include the wide variety of libraries available for data processing. NumPy and Pandas are data manipulation libraries built for mathematical computations, data cleansing, and data merging. The latter library can be further used to visualize and plot data along with Matplotlib and Seaborn libraries. Once data processing is complete, machine learning models can be built using Python libraries based on model and algorithm type . Libraries such as sci-kitlearn (built upon SciPy) are open-source tools for predictive analyses and cover algorithms involved in both classification and regression such as support vector machine (SVM), k-nearest neighbors (KNN), and random forest. Furthermore, k-means, spectral clustering, feature selection, and cross validation algorithms are available for model selection and the optimization of features and parameters. These tools can be utilized to look at a fixed outcome of interest. Alternatively, deep learning using neural networks is a common example of unsupervised machine learning in which the algorithm is not provided with pre-defined labels or scores. Python libraries such as Keras (built upon Tensorflow) offer a neural network application programming interface (API) for which users can build layers and objectives into. These data analysis tools are useful in interpreting lipidomics data and presenting them in a form that can be used to extrapolate disease pathophysiology or for pharmacologic dissection.Lipidomic analyses can be aimed at certain subgroups of lipid species such as steroids, triglycerides, phospholipids, polyunsaturated fatty acids (PUFA) and their bioactive metabolites . Phospholipids are amphiphilic lipids characterized by a polar phosphate head group attached to two hydrophobic tails. These membrane-bound lipids are important structural components that support cellular membrane integrity and fluidity, but also serve as precursor molecules for various lipid subtypes by storing them in their esterified forms. Mammalian cells can contain thousands of distinct phospholipid species; however, the most abundant and widely characterized subgroups include the phosphatidylcholines (PC), phosphatidylethanolamines (PE), phosphatidylserines (PS), and sphingomyelins (SM) .The hydrolysis of phospholipids and subsequent release of free PUFA into the cell is mediated by phospholipase enzymes, of which there are two major types. Calcium-dependent cytosolic phospholipase A2 (cPLA2) preferentially cleaves ω-6 arachidonic acid (AA) from phospholipids, whereas calcium-independent phospholipase A2 (iPLA2) more efficiently releases eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA) and docosapentaenoic acid (DPA) ω-3 PUFAs. AA, EPA and their metabolites are termed eicosanoids, whereas DHA, DPA and their metabolites are termed docosanoids. Collectively, eicosanoids and docosanoids serve as potent regulators of inflammation both in systemic circulation and local microenvironments. These lipid metabolites exert their biological effects through binding to G-protein-coupled receptors (GPCR) found on the surface of various immune and glial cell types. Binding initiates signaling cascades to elicit cell-specific responses. Based on the type of immunologic response elicited, eicosanoids and docosanoids are classified as either pro-inflammatory or pro-resolving. While pro-inflammatory lipid mediators promote leukocyte recruitment, reactive oxygen species (ROS) generation and inflammatory cytokine release, pro-resolving lipids attenuate the inflammatory response by inducing leukocyte apoptosis and promoting the clearance of cellular debris by monocyte-mediated efferocytosis .Most pro-inflammatory eicosanoids are products of AA metabolism . These iUnlike eicosanoids, the docosanoids are predominantly pro-resolving mediators . The 15-Although hundreds of structurally diverse lipid mediators have been identified, their distinct physiological roles have only recently begun to be understood as technological advancements have enabled more comprehensive lipid profiling ,47. SincCVD is the leading cause of death regardless of gender or ethnic background . It accoAberrant levels of lipids and lipoproteins play an important role in the pathogenesis of CVD. Their association with atherosclerosis is well studied; however, lipids and lipoproteins also have central functions in cell signaling and the regulation of pro-inflammatory pathways . In partCholesterols are both synthesized endogenously as well as gained from diet. Steroids like cholesterol are cellular membrane components that maintain the integrity and fluidity of cell membranes. Additionally, cholesterol is also a key precursor that forms bile acids and steroidal hormones. Both classes of steroidal structures are activators of nuclear factors  and thus regulate metabolic enzymes such as the CYP system. In the blood, cholesterol, fatty acids (FAs), and triglycerides (TGs) are transported in hydrophilic lipoprotein complexes.The contributions of cholesterols and lipoproteins to the development of atherosclerosis are well established. Although the development of atherosclerotic plaques is complex, dysregulation of lipid pathways plays a central role. Given the strong associations between atherosclerotic burden, ischemic cardiac disease, and stroke outcomes ,53,54,55Plasma lipids are measured in several ways; however, LC-MS remains a popular research methodology because of its accuracy and high sensitivity . While LFuture lipidomic applications will more accurately identify the cholesterol and lipoprotein metabolites that mediate ASCVD. If plasma LC-MS profiling can correlate unstable plaque lipid composition, this would establish a precise “liquid biopsy” of disease. A greater understanding of the biologic interactions between cholesterols and lipoproteins, and this how this transition leads to atherogenesis would provide insights as how to develop novel therapeutic interventions. The combination of LC-MS lipidomics in parallel with other approaches would allow a comprehensive “multi-omic” approach that potentially will yield significant scientific and clinical findings in CVD.de novo biosynthesis. The complexity and variety of sphingolipids is representative of its diverse roles in the body. Like other lipid types, sphingolipids are structural lipids important in maintaining membrane integrity and function. In circulation, they help preserve lipoprotein structure and function. As signaling molecules, sphingolipids have been implicated in various essential physiological functions, such as growth inhibition, apoptosis, proliferation, differentiation, and inflammation [Sphingolipids are a class of lipids that contain an aliphatic amino alcohol on the sphingoid backbone. Sphingolipids are either diet derived or produced through ammation .The precise mechanistic role of sphingolipids in CVDs has not been fully elucidated, there is a growing body of evidence that supports the prognostic value of sphingolipids as predictive biomarkers for CVD outcomes including cardiovascular mortality, risk of heart failure, and recovery from MI ,66,67. CAside from risk factor detection, circulatory sphingolipid profiles can change dynamically and are indicative of immunological response post-MI. The increases of different ceramide species have corresponded to upregulation of acute phase proteins and pro-inflammatory activation. Alterations in sphingolipid metabolism occurred during the reparative phase, 7 days after induced MI. In particular, ceramide-1-phosphate (Cer1p) to ceramide ratio in myocardial tissue corresponded with increased ceramide kinase (Cerk) expression . As lipiThe important role ceramides play in CVD progression also makes it an attractive target for treatment. Circulatory sphingolipid profiles and sphingolipid concentrations in plasma and in extracellular vesicles (EV) are known to fluctuate post-MI. Through the administration of the neutral sphingomyelinase inhibitor, GW4869, hydrolysis of sphingomyelins into ceramides is inhibited, which in turn impairs EV biogenesis. As a result, the suppression of circulating inflammatory EV post-MI in rats was able to preserve left ventricular ejection fraction . This fiWhile there is an abundance of existing lipidomic studies that employ a comparative and systemic approach to elucidating the disease state of CVD at the serum and tissue level, there is limited research with the analytical resolution capable of quantitating CVD metabolites in single cells . AtherosIschemic stroke is the most common form of stroke and a leading cause of death and disability worldwide . DespiteLipidomic analysis of unstable carotid atheromatous plaques might provide a lipid signature to identify high-risk patients. Several lipid families associated with cell-signaling and inflammation have been studied. CEs are differentially associated with unstable plaques, with large polyunsaturated CEs showing the greatest vessel wall accumulation ,63. ProfPost-stroke lipidomics produces a unique metabolomic profile that might also differentiate stroke etiologies. LC-MS of serum samples after stroke demonstrated significant differences in FA metabolism of oleic acid, linoleic acid, and AA compared to healthy controls, while changes in phosphoglyceride metabolism was shown to differentiate small artery versus large artery occlusion . Rodent Lipidomic studies in both human AIS and animal models of disease have identified potential therapeutic targets for stroke prevention and treatment. Growing interest has been in the administration of PUFAs and their anti-inflammatory metabolites. In a mouse model of ASCVD, oral supplementation of RvD1 restored RvD1: LTB4 ratios and reduced markers of oxidative stress and necrosis . In a SpAntithrombotic drugs have been the main pharmacotherapy for CVD prevention. Among drugs that inhibit platelet activity, aspirin is the most well studied and most widely used therapeutic. In CVD, aspirins primary mechanism of action is the irreversible inhibition of platelet COX-1, which has been attributed to reducing TXA2 formation. Under normal conditions, TXA2 activates platelets and causes vasoconstriction through calcium-dependent pathways and promoting clot formation. Lipidomics has further elucidated the anti-inflammatory pharmacodynamics of aspirin. In addition to blocking endothelial cell PG synthesis by COX-1 and COX-2 inhibition, aspirin reduces pro-inflammatory oxylipins generated from PUFA oxygenation. In a study of healthy volunteers, low-dose aspirin was associated with a broad decrease in serum FA levels and reductions in linoleic acid and AA pro-inflammatory metabolites .Aspirin can also promote resolution of inflammation by triggering the biosynthesis of SPMs by acetylation of COX-2. In AA metabolism, aspirin-treated COX-2 results in a 15-lipoxygenase (15-LOX)-like reaction that promotes the synthesis of aspirin-triggered lipoxins (ATLs) . In expeIn CVD populations at the highest risk of cardioembolic disease, anticoagulant therapy is associated with reduced stroke risk. Among novel oral anticoagulants (NOACs), the direct thrombin inhibitor, dabigatran, has gained special interest for potential anti-inflammatory properties resulting from reduced thrombin activation of the PAR-1 receptor. Protease-activated receptors (PARs) are increasingly recognized for their role in mediating both acute and chronic inflammation. G protein-coupled signaling induced by PAR-1 activation promotes the expression of vascular cell adhesion molecule-1 (VCAM-1), intercellular adhesion molecule-1 (ICAM-1), and E-selectin, mediating vascular permeability to immune cells ,100. PARStatins, or HMGCR inhibitors, are indicated to treat hypercholesterolemia and improve lipoprotein profiles in patients with CVD. Their anti-inflammatory and antioxidant effects have been well studied, including reduced levels of C-reactive protein, atherogenic LDL, TNF-α, interferon gamma (IFNγ), inhibition of T-helper cell inflammatory signaling, and by regulation of leukocyte-endothelial cell interactions ,106,107.Current research has additionally used a lipidomic approach to describe the differential effects of various HMGCR inhibitors and variabilities in patient response. In LC-MS plasma profiling of patients receiving statin therapy, rosuvastatin was associated with increased PC levels and lower sphingomyelin:(sphingomyelin + PCs) ratios when compared to atorvastatin , which mNeuroinflammation is a common feature among many neurodegenerative diseases, such as AD, Parkinson’s disease (PD), and multiple sclerosis (MS). Considering that the brain is a lipid-rich environment where lipid uptake and metabolism is tightly regulated, lipidomics-based approaches are becoming more widely recognized as effective strategies to investigate neurodegenerative disease pathophysiology and identify clinical biomarkers. This section describes the interplay between lipid mediators of inflammation and hallmarks of neurodegenerative disease pathologies, how these interactions may influence disease progression, and how lipidomic profiling has been utilized to evaluate the efficacy of pharmacological interventions against neuroinflammation and neurodegeneration.AD progression has been closely linked to the accumulation of beta amyloid (Aβ) peptides and neurofibrillary tangles for several decades, yet therapeutics developed to target these aggregates have not corresponded to effective therapeutics. More recently, chronic neuroinflammation has become recognized as a central hallmark of AD pathology, where glial cell activation  results in the release of pro-inflammatory cytokines and eicosanoids to promote neurodegeneration .Of the eicosanoids generated from glial cell activation, LTs and PGs have been the most widely studied in AD pathogenesis. They have been detected at higher levels in the AD brain and implicated as mediators of AD-related neuroinflammation . LTB4 haNeurotoxic aldehydes generated from PUFA peroxidation such as 4-hydroxy-2-nonenal (4-HNE) and malondialdehyde (MDA) have been linked to AD pathogenesis . These rThe ω-3 PUFAs  and their metabolites are recognized as protective in the context of AD. These metabolites include the Rvs, MaRs, and NPDs, the majority of which are derived from DHA, which constitutes up to 60% of all esterified fatty acids in neuronal plasma membranes . In clinIn clinical AD studies, the ratio of ω-3:ω-6 PUFA is often measured to assess the degree of inflammation, where a lower ratio is indicative of a more proinflammatory state ,129. ThiSimilar to AD, PD is a neurodegenerative disorder characterized by neuroinflammation and oxidative stress in the brain, which is driven by chronic glial cell activation . This isFor example, increased levels of PGD2 and PDE2 in brains with PD have been detected, which has coincided with increased COX-2, prostaglandin E synthase and prostaglandin D synthase activities ,138,139.Lewy bodies, which are protein aggregates comprised of α-synuclein, represent an important neuropathological hallmark of PD. Structurally, α-synuclein is very similar to A2 lipoproteins such as cPLA2, and it has been shown to be involved in phospholipid metabolism ,145. BotLipidomic profiling can support the development of therapeutics for neurodegenerative diseases by identifying enzymatic drug targets responsible for glial cell class switching toward pathological phenotypes. Additionally, precise and accurate lipidomics can be used to monitor efficacy of potential drug candidates. As our understanding of lipid mediators and their involvement in neurodegenerative diseases continues to unfold, many have investigated different approaches to regulate neuroinflammation by PUFA supplementation or targeting proteins within the PUFA metabolism pathways such as PLAs, COXs, and LOXs.Due to clinical evidence showing lower levels of DHA and its metabolites in AD patient brains compared to age-matched controls, supplementation with DHA and other ⍵-3 PUFAs has been widely explored as a possible treatment or preventative action against AD-associated cognitive decline. Although dietary DHA supplementation in transgenic mouse models has been shown to mitigate Aꞵ pathology and restore cerebral blood volume, DHA supplementation in humans has yielded inconclusive results. Meta-analyses of several observational studies and clinical trials did not find conclusive evidence of improved cognitive function or protection against AD pathologies after short, medium, or long term (>6 months) daily DHA supplementation ,147,148.The overactivation of cPLA2 in brains of patients with AD and PD, and its selective induction of the pro-inflammatory AA pathway, has led many to hypothesize that cPLA2 inhibition may ameliorate neurodegenerative disease outcomes by shifting lipid metabolism toward the production of pro-resolving mediators derived from ω-3 PUFAs. Inhibition of cPLA2 by Annexin A1 has been shown to elicit neuroprotective effects by mitigating neuroinflammation and neuronal damage in the CNS following spinal cord injury to rats, and cPLA2 knockout in AD mouse models was shown to ameliorate cognitive deficits despite Aꞵ accumulation ,151. In Aspirin, a COX inhibitor, has been widely explored as a potential treatment for protection against neurodegenerative diseases such as AD because of its ability to block PG biosynthesis and promote the formation of ATLAs. A meta-analysis of 12 cohort studies and three clinical trials reported that although low doses of aspirin coincided with reduced incidence of dementia in cohort studies, these findings were not confirmed in clinical trials . SimilarIn addition to COX enzymes, the inhibition of LOXs have been proposed for the treatment or prevention of neurodegenerative diseases such as AD. The inhibition of 5-LOX prevents LT formation from AA, and in an AD mouse model, 5-LOX inhibition has been shown to reduce cognitive impairment, Aꞵ deposition, and neuronal loss . FurtherIn the quest to identify anti-inflammatory drugs for AD and PD, minocycline has been tested in clinical trials because of its ability to suppress oxygen radical formation and microglial activation . The druInflammation is a common pathological feature for several lung diseases. There is growing evidence supporting the role of lipids in mediating inflammation of bacterial and viral infections, chronic obstructive pulmonary disease (COPD), asthma, cystic fibrosis, and cancer ,174,175.4, and PGE2 were significantly increased in the sputum of COPD patients [COPD is one of the leading causes of mortality in the US and worldwide and lacks effective treatments . Additiopatients . Lipidompatients . HoweverLipidomic studies have identified critical changes of the lipidome in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in relation to disease severity . GloballIn particular, dysregulation of eicosanoids and docosanoids in the serum and lungs from SARS-CoV2 patients showed changes in sphingomyelins, phospholipids, glycerolipids, and CEs which have been associated with other lung injuries and cardiovascular diseases as described previously . Of spec2 and PGE2) were increased while DHA metabolites increased on day 7 during the inflammatory-to-fibrosis phase. Interestingly, no plasma lipidome changes were observed with bleomycin treatment, indicating the need for appropriate sample collection to properly study drug-induced lipidome alterations. LOX inhibition using nordihydroguaiaretic acid has been shown to attenuate bleomycin induced lung fibrosis, which suggests that bleomycin may exert its pulmonary toxic activity through dysregulating lipid metabolism [Lipidomics can also be used to monitor toxicological responses of therapeutic interventions. For example, it has been used to evaluate bleomycin, an anticancer agent, induced lung toxicity in a mouse model . In the tabolism . UtiliziLung injury is also a common adverse effect of radiation therapy where no approved treatment or medical countermeasures for radiation-induced lung injury (RILI) exist. Decreased pulmonary surfactant lipids  and heme b were evident following RILI in rhesus macaques regardless of pathological presentation . AdditioIt is well established that lipid profiles are altered in respiratory infections due to an increase in COX-2 expression. COX inhibitors such as non-steroidal anti-inflammatory drugs (NSAIDs) are routinely used to treat inflammation and pain associated with a myriad of conditions . Several®, Bayer, Tokyo, Japan), a dual receptor antagonist of D-prostanoid receptor 2 (DPr2) and TX receptors (TDRs) has been used in Japan to boost interferon lambda (IFN-λ), thereby suppressing SARS-CoV-2 replication. In a small cohort of patients, 75 mg twice daily of Ramatroban administration was found to rapidly improve both respiratory distress and hypoxemia. Reduction in disease severity was able to prevent hospitalization and promote recovery from acute disease [Pharmacologic intervention such as Ramatroban  and irritable bowel disease (IBD) are some of the most persistent autoimmune diseases that have no highly specific diagnostic tests available, impairing treatment and recovery times. To overcome this, lipidomics was identified as a novel approach to provide more insights into autoimmune disorders and to identify potential lipid biomarkers which would further help characterize and understand the underlying disease pathophysiology.SLE is a chronic autoimmune disorder found predominantly in women which can affect several organs in the body including skin, kidney, liver, and brain. The underlying molecular mechanisms of this disease are largely unknown; however, SLE pathogenesis is attributed to oxidative stress and dysfunction of the immune system. ROS contributes to dyslipidemia and dyslipoproteinemia which are characterized by high levels of VLDL and TG but lower levels of HDL. Dyslipidemia occurs because of declining renal function, which is one of the most common comorbidities for SLE. Therefore, SLE patients also have a higher susceptibility towards developing CVD. HDL promotes oxidation of LDL and is a cell efflux promoter, which can promote NETosis and compromise lipid metabolism when dysregulated . SLE pat2 resulted in reduction of PGE2 and LTB4. However, chronic use of corticosteroids  could potentially lead to a flare-up and failure to treat the inflammatory disease state [Oleic acid, EPA, and AA have also been measured at lower levels in SLE patients, where reductions of these lipids were associated with higher disease severity. Early stages of autoimmune diseases showed PGs and LTs induce influx of neutrophils and increase biosynthesis of PGE2 and LTB4 . Selectise state . This efse state . The praCrohn’s disease (CD) and ulcerative colitis (UC) are IBD subtypes . While bSPMs effects on intestinal inflammation include reduction of NF-ĸB activation, decreased neutrophil infiltration, and the phenotypic switch of macrophages from pro-inflammatory to pro-resolving ,211,212.Abnormal FA metabolism has been shown in IBD patients with both active and quiescent states. In particular, PUFA dysregulation is found in the bowel inflammation process through eicosanoids derived from AA corresponding to increased colonic inflammatory cytokines and increased serum FA ,221,222.This review is meant to give readers an understanding of lipidomic methodologies and considerations with examples of how lipidomics can drive pathophysiology and pharmacologic dissections. However, due to the vast amount of literature, the review aims to be concise to capture the multitude of factors involved in lipidomic studies but is not exhaustive. Additionally, this review is limited to focus on selected inflammatory diseases, while there are several other inflammatory diseases showing dysregulation of the eicosanoid pathways that were not addressed. Another limitation concerns methods both for data production and treatment. Sample preparation, experimental conditions, and analytical methods were highly variable between studies, which made it difficult to draw comparisons between them. For example, different lipid stabilization methods, storage times, and extraction techniques were utilized. Additionally, this review does not address isobaric or isomeric lipids which may coelute together. The studies reviewed were conducted with different MS technologies and across different biomatrices, which made it difficult to form uniform assessments. None of the studies evaluated address the issues of comprehensive medication history for study participants, and thus drug utilization impact on lipid concentrations cannot be delineated. Lastly, the review does not address other metabolomic or proteomic mediators that facilitate the lipidomic signaling process. Metabolites involved in the generation of ROS work closely with the inflammatory lipids but have been excluded from the scope of this review. A summary of the lipidomic studies discussed in this review can be found in Lipidomic profiling has advanced our understanding of several diseases. Although specific analytes and their changes in concentration have been important to identify potential dysregulation, the ratio of the precursors in relation to bioactive metabolites may be more informative. This highlights the delicate balance between inflammatory and pro-resolving mediators, but also the importance of enzymes involved in lipid metabolism pathways. In the lipidomic profiling of the various diseases discussed in this review, active inflammation is associated with lower EPA:AA and DPA:AA ratios in the early onset of acute inflammation, where a counterbalance with pro-resolving lipid mediators can promote disease resolution. In chronic inflammation, however, persistent lowering of EPA:AA and DPA:AA ratios and the inability to realign this metabolic imbalance is apparent. There are efforts utilizing this data to develop therapeutic strategies; where dysregulation in the lipidome may also present an opportunity to develop precise biomarkers for disease monitoring. These opportunities can only be realized when the affected tissues have lipidome changes that can be detected in the circulation or affected tissues can be readily sampled as in BAL lung samples. In order to do these types of correlations, machine learning and deeper learning tools are required to improve their predictive values. It is key that advancement of analytical techniques is integrated with machine learning analysis correlating clinical outcomes with these biochemical markers. Standardization of lipidomic analyses will be critical in the drug development process to ensure accurate and reproducible findings from various academic and industry laboratories. Researchers interested in utilizing lipidomics should refer to agency recognized guidelines on assay development.This review highlighted several considerations for lipidomic analyses including advantages of various MS instruments, sample stabilization, and data analysis and validation. Furthermore, this article reviewed how lipidomics has revealed novel biomarkers of inflammatory diseases and driven drug development by elucidating molecular mechanisms of various pharmaceutical agents. Lipidomic findings have shown strong overlap among these inflammatory diseases and allow researchers to utilize analyses in other systems to drive their own research and development. More comprehensive studies in the future can result in larger data-banking and, with the use of advanced computing, accelerate development of novel therapeutic interventions in inflammatory diseases on a global scale.


Parkinson’s disease (PD) is a multisystem alpha-synucleinopathic neurodegenerative disease and the most prevalent neurodegenerative disorder after Alzheimer’s disease with a high incidence rate in the elderly population. PD is highly multifactorial in etiology and has complex and wide-ranging pathogenic mechanisms. Environmental exposures and genetic predisposition are prominent risk factors. However, current evidence suggests that an intimate link may exist between the risk factor of sleep disturbance and PD pathogenesis. PD is characterized by the pathological hallmarks of alpha-synuclein aggregations and dopaminergic neuron degeneration in the substantia nigra. The loss of dopamine-producing neurons results in both motor and non-motor symptoms, most commonly, bradykinesia, tremor, rigidity, psychiatric disorders, sleep disorders and gastrointestinal problems. Factors that may exacerbate alpha-synuclein accumulation and dopamine neuron loss include neuroinflammation and glymphatic system impairment. Extracellular alpha-synuclein can induce an inflammatory response which can lead to neural cell death and inhibition of neurogenesis. The glymphatic system functions most optimally to remove extracellular brain solutes during sleep and therefore sleep disruption may be a crucial progression factor as well as a risk factor. This literature review interprets and analyses data from experimental and epidemiological studies to determine the recent advances in establishing a relationship between glymphatic system dysfunction, sleep disturbance, and PD pathogenesis and progression. This review addresses current limitations surrounding the ability to affirm a causal link between improved glymphatic clearance by increased sleep quality in PD prevention and management. Furthermore, this review proposes potential therapeutic approaches that could utilize the protective mechanism of sleep, to promote glymphatic clearance that therefore may reduce disease progression as well as symptom severity in PD patients. Parkinson’s disease (PD) is one of the world’s most rapidly developing neurodegenerative diseases, with prevalence projected to double to twelve million by 2040, after an increase to six million between 1990 and 2015 .The results of the meta-analysis by Noyce and colleages found that the following risk factors have a positive association with PD pathogenesis: family history, constipation, mood disorders, pesticides, head injury, rural living, beta-blockers, farming or agriculture, well water and depression . FurtherPD targets the extrapyramidal system in the brain but can progress to having wide-ranging effects on both the central and peripheral nervous systems. PD pathophysiology is predominantly characterised by the progressive dopaminergic neurodegeneration in the substantia nigra (SN) in the midbrain which is responsible for motor control, cognitive functions, and limbic activity ,8. TherePD is referred to as a synucleinopathy, which is a term that encompasses neurodegenerative disorders that have pathogenic lesions comprised of insoluble aggregations of the presynaptic protein, alpha-synuclein . The patIdiopathic PD is the most prevalent form of Parkinsonism. However, there are other forms of parkinsonian disorders that include atypical parkinsonian disorders, which is a term used to distinguish neurodegenerative disorders which have similar signs and symptoms as PD but differ slightly in pathophysiology. The atypical parkinsonism syndromes include synucleinopathies; Multiple System Atrophy and Dementia with Lewy bodies, and tauopathies; Progressive Supranuclear Palsy and Corticobasal Degeneration . Levin aThus, this review hypothesises that glymphatic dysfunction and sleep disturbance are intimately linked to inducing PD pathology. This review aims to provide insight into the research question; to what extent does glymphatic system dysfunction and sleep disturbance play a role in PD pathogenesis and progression?PD is characterised by pathogenic forms of the intraneural protein called alpha-synuclein. When alpha-synuclein accumulates, this stimulates an aggressive inflammatory response from glial cells which are primed to defend the brain from pathogenic threats. If neuroinflammation becomes uncontrolled, this can lead to dopaminergic neurodegeneration and therefore cause PD .Under normal physiology, alpha-synuclein is an abundant protein in the brain found in neuronal presynaptic terminals and plays a role in regulating presynaptic signalling, the stability of the neural cell membrane and membrane trafficking operation. Alpha-synuclein becomes pathogenic when it misfolds, is overexpressed due to genetic mutations or when it adopts oligomeric conformations or produces amyloid fibrils . A studySeveral studies implicate neuroinflammation as playing a pivotal role in both PD onset and progression ,24,25,26Microglia are phagocytic cells that form the innate immune system defence in the central nervous system (CNS) and have both neuroprotective and neurotoxic functions . These cAlpha-synuclein aggregation is a powerful inflammatory stimulator of microglia, which are primed to phagocytose or degrade the abnormal protein build-up . When chThe most abundant glial cell in the CNS, astroglia are located in synaptic regions of neurons and have cytoplasmic projections with end-feet that cover approximately 97% of all cerebral vasculature. Astroglia have a neuroprotective role in maintaining neuronal metabolite homeostasis and regulating the permeability between the cerebral vasculature, interstitial fluid (ISF) and cerebrospinal fluid (CSF). Astroglia facilitate permeability through membrane water channels called aquaporin-4 (AQP4) which are highly localised in astrocytic end feet .The study by Trudler and colleagues, showed that alpha-synuclein oligomers cause astrocytes to release glutamate which induces an inflammatory cascade that leads to synaptic damage and neuronal loss . SimilarAquaporin-4 (AQP4) channels facilitate the CSF-ISF exchange of fluid and extracellular solutes between the perivascular space of brain vasculature and the interstitium . Zhang aNamed because of its high dependence on glial cells and its pseudo-lymphatic characteristic of clearing interstitial waste, the glymphatic system was first proposed by the study from Iliff and colleagues, who discovered a brain-wide fluid transport pathway in mice . Before Lining the perivascular spaces are astrocytic end feet that are highly polarized with AQP4 water channels. AQP4 channels facilitate the movement of the CSF-ISF interchange whereby, CSF flows into the interstitium from the periarterial space and ISF, carrying all interstitial solutes, flows out of the brain parenchyma and into perivenous spaces to be cleared from the brain . Other sIliff and colleagues found that periarterial influx did not significantly reduce in KO mice due to the periarterial end-feet having significantly less AQP4 polarization compared to pericapillary and perivenous end-feet, leading to their conclusion that bulk flow is present in the periarterial influx pathway . The stuThe fluid dynamics responsible for glymphatic flow are still heavily debated within the literature. Current theories of fluid movement mechanisms include diffusion, bulk flow, dispersion and arterial pulsatility. Some studies using tracer experiments in rodents supported that bulk flow  is a prominent mechanism of CSF and ISF movement, due to the rapid clearance of tracers along the perivasculature, irrespective of molecular size or concentration gradients ,36,37.Conversely, Faghih and Sharp found bulk flow to be implausible as it exceeded maximum perivascular resistance and pressure values that were determined using vascular tree models . Jin andAsgari and colleagues supported that pulsation driven bulk flow would be reliant on a valve mechanism which has not been found to date . This stIn light of the above literature, multiple mechanisms are likely at play in driving glymphatic fluid and transporting solutes in various regions in the brain. However, establishing these mechanisms holds particular relevance in neurodegenerative diseases due to their association with toxic solute clearance. Discrepancies in this area likely arise due to the difficulty in studying the glymphatic system and therefore a wide variety of methodical approaches have been used as human studies are not currently possible.Due to the complex physiology and crucial role the glymphatic system plays in brain health, emerging evidence has implicated glymphatic system dysfunction in neurological diseases, especially proteinopathies . Brain hLi and colleagues established that dilated or enlarged perivascular spaces (PVS) have been associated with over-abundance of extracellular proteins such as alpha-synuclein and tau as well as dopaminergic neurodegeneration in the SN in PD . Park anIn conflict with the aforementioned studies, Donahue and colleagues investigated both global and regional changes in PVS and found that there was no significant difference between these regions between idiopathic PD and healthy controls . HoweverThe ALPS index is another indicator of a compromised glymphatic system and specifically, a low ALPS index indicates reduced diffusivity along the PVS. PD patients, especially those in the later PD stages, have a significantly lower APLS score than early stage PD, essential tremor patients and healthy patients ,55. ThesAQP4 deficiency or altered polarity can induce neuroinflammation and also hinder glymphatic exchange in the parenchyma and these factors play a key role in PD pathology. Although alpha-synuclein is a dominant pathological hallmark, the relationship between AQP4 channels and alpha-synuclein has not been thoroughly examined in the literature. According to current experimental evidence there may be a bidirectional relationship between impaired glymphatic flow and AQP4 depolarisation, whereby reduced glymphatic flow through the brain can directly exacerbate alpha-synuclein pathology and also compromise AQP4 polarity . However+/−) and control mice that were subjected to alpha-synuclein preformed fibril (PFF) injections and the effect on AQP4 expression and subsequently alpha-synuclein deposition in three brain regions were measured over six months [The experimental study, by Zou and colleages, blocked meningeal lymphatic drainage at the cervical lymph nodes in 24-week-old A53T and control mice models to investigate the correlation between glymphatic system dysfunction, AQP4 polarization and alpha-synuclein accumulation in PD pathology . A more x months .+/−-PFFs mice models showed the highest AQP4 depolarisation, alpha-synuclein aggregation, dopaminergic neurodegeneration and inflammatory activity compared to the other mice models, indicating that impaired glymphatic flow is a powerful progression factor in a developing synucleinopathy [+/−) and alpha-synuclein accumulation are working independently in the mice models, they still have pathogenic potential but precipitate effects of smaller magnitude. Furthermore, both studies tested the motor capability of the different mice models and found that motor coordination and balance was most significantly diminished in mice with a predisposition to excess alpha-synuclein (A53T or PFF) and with compromised glymphatic flow (either by LDclns or AQP4 deficiency), compared to the other mice models. Evidently, impaired glymphatic exchange by altered AQP4 polarity or direct blockage of fluid movement are prominent pathogenic and progression factors in PD pathology.The A53T-LDclns and AQP4inopathy ,33. HoweThe discovery of the glymphatic system and its role in clearing metabolic waste from the parenchyma has provided new insight into the neuroprotective role of sleep in facilitating glymphatic activity . During Additionally, anaesthesia increased glymphatic influx and efflux, indicating that the sleep-wake state may be more responsible for determining the interstitial volume and glymphatic solute clearance efficacy compared to the circadian rhythm . Convers. Shokri-Kojori and colleagues found that even after one night of sleep deprivation there were substantial increases in amyloid-beta accumulation in the hippocampus and thalamus in human subjects [The pathological link between sleep disruption and neurodegenerative diseases was first investigated in Alzheimer’s Disease (AD) . AD typisubjects . Due to Bmal1, whereas the expression of Bmal1 remained predominantly constant in PD patients. There was no observable difference between cases and controls in the other two studied clock genes, Per2 and RevErba, however, in comparison to Bmal1, these clock genes do not play a significant role in circadian rhythm regulation [Bmal1 expression, hence the dopamine depletion in PD may have an indirect effect on circadian rhythm disruption [Breen and colleagues investigated hormone levels and clock gene expression to investigate altered circadian rhythm in PD patients using polysomnography and serum analysis . Controlgulation . PD patisruption . In addisruption ,70. Ono and colleagues found using in vitro studies that melatonin was able to inhibit alpha-synuclein assembly and destabilise fibrils and therefore prevent alpha-synuclein cytotoxicity and thus neurodegeneration . Hence, Epidemiological studies have found sleep disturbance to be a strong risk factor in PD ,73,74. THsiao and colleagues examined the relationship between non-apnoea sleep disorders (NSD) and PD development by conducting a cohort study that recruited 91,273 adult patients with non-apnoea sleep disorders and no pre-existing PD from the Taiwan National Health Insurance Research Database between 2000 and 2003 . A matchAnother prominent sleep disorder that is currently receiving significant attention in the literature as a potential prodromal biomarker to PD is REM behaviour disorder (RBD). This is because RBD disrupts the REM sleep stage and can lead to a vicious cycle of neuroinflammation, alpha-synuclein deposition and EPVS burden . In a MRDepression is another potential prodromal factor of PD that also has strong associations with sleep disturbance. According to polysomnography psychiatric research by Riemann and colleagues, patients with depression exhibited a decline in delta power, disinhibition of REM sleep, decrease in REM latency and an increase in REM density and total REM sleep time . As prevThere are limited primary source data on human neuropathology due to ethical constraints. Consequently, the majority of experimental studies used in this review are animal studies. Human and intervention studies, which would provide the highest quality of evidence, are lacking. Epidemiological studies overall were also lacking, esspecially intervention studies which are crucial in research being the only epidemiological study design that can establish causality. Of the epidemiological studies used, many were cohort or case–control studies, which although they have acceptable scientific rigor, are only able to establish an association.Of the human studies, many utilised MRI imaging or ex vivo brain tissue due to the difficulty and unethical nature of conducting invasive experiments on the live human brain or using complex human brain surrogates . Ex vivoAmong the animal studies, many involved genetically engineered animals where certain genes have been knocked out  or overexpressed  and other animals studies involved CSF tracers. Not all aspects of human neurological disease can be recapitulated in animal models due to the fundamental differences in neurodevelopment, genetics, pathology, and disease mechanisms . For insThe lack of human and intervention studies which would provide primary and high-quality data on relationships between sleep, glymphatic impairment and PD, have led to the difficulties in establishing a strong causal link in this review. Because the glymphatic system is still a recently described phenomenon and its relationship with PD and sleep is still actively being studied, many studies cited within this review did not provide definitive conclusions, rather the majority of studies suggested further research is needed.Bmal1 expression. As previously mentioned, melatonin may be neuroprotective in alpha-synuclein pathology. However, recent studies found that melatonin was ineffective in eliminating insomnia and excessive daytime sleepiness [If increased glymphatic flow and higher quality sleep are neuroprotective factors of PD, then this may give insight into future therapeutic approaches that could be implemented to reduce the risk and progression of PD. Firstly, deep brain stimulation has been found to significantly increase sleep quality and motor function in PD patients by increasing deep sleep and slow-wave activity. However, it is uncertain whether this technique normalises the sleep cycle as opposed to alleviating sleep disruption ,95,96. Aeepiness ,99. Videeepiness . There iAnother avenue for a therapeutic approach is treating prodromal disorders and comorbidities of PD. For instance, bright light therapy has been used in RCT studies and has been found to significantly improve insomnia, sleep quality, iRBD and depression in PD patients ,101,102.There is complex aetiology surrounding sleep and its disease-modifying role in PD pathogenesis and progression; causal links are yet to be established by human studies. Thus, AQP4 knockout in experimental PD resulted in the failure to increase TGF-β1 production which may exacerbate neuroinflammation . Whereas


Dental implants made of titanium (Ti) material is recognized as the leadingtreatment option for edentulous patients’ rehabilitation, showing a high successrate and clinical longevity. However, dental implant surface acts as a platformfor microbial adhesion and accumulation once exposed to the oral cavity. Biofilmformation on implant surfaces has been considered the main etiologic factor toinduce inflammatory diseases, known as peri-implant mucositis andperi-implantitis; the latter being recognized as the key reason for late dentalimplant failure. Different factors, such as biofilm matrix production, source ofcarbohydrate exposure, and cross-kingdom interactions, have encouraged increasedmicrobial accumulation on dental implants, leading to a microbiologicalcommunity shift from a healthy to a pathogenic state, increasing inflammationand favoring tissue damage. These factors combined with the spatial organizationof biofilms, reduced antimicrobial susceptibility, complex microbiologicalcomposition, and the irregular topography of implants hamper biofilm control andmicrobial killing. In spite of the well-known etiology, there is still noconsensus regarding the best clinical protocol to control microbial accumulationon dental implant surfaces and treat peri-implant disease. In this sense,different coatings and Ti surface treatments have been proposed in order toreduce microbial loads and control polymicrobial infections on implantabledevices. Therefore, this critical review aims to discuss the current evidence onbiofilm accumulation on dental implants and central factors related to thepathogenesis process of implant-related infections. Moreover, the potentialsurface modifications with anti-biofilm properties for dental implant devices isdiscussed to shed light on further promising strategies to controlperi-implantitis. In addition, weprospect this review of available scientific evidence will stimulate researchers torationally engineer safer implant surfaces, focusing on translation to real-worldapplications.,,Biofilms are well-organized microbial communities enmeshed in a three-dimensionalextracellular matrix ,Protein adsorption on biomaterials has been pointed as the initial biologicalresponse in the human body, and this process is responsible for intercedingsuccessive cellular events, such as microbial and host cell adhesion Streptococcus, Fusobacterium, andCapnocytophaga species have been described as initialcolonizers immediately after implant placement Neisseria, Rothia, andGemella. ,,Knowing that the oral cavity hosts nearly a thousand bacterial species detectableby high throughput 16S rRNA gene sequencing ,According to the last classification of dental implant diseases published in2018, the current evidence shows that polymicrobial biofilms are responsible forinducing inflammatory disease processes on peri-implant sites named asperi-implant mucositis and peri-implantitis Candidaalbicans, all that leading to subsequent environmental changes anda higher proinflammatory response ,,,Importantly, a recent adaptation from the “ecological plaque hypothesis” on toothsurfaces was used to depict a switch from a healthy to a disease state in theperi-implant mucosa and bone tissues ,,19). These implant surface modifications can be performed bynumerous techniques, which may be mechanical, physical, chemical, or acombination of all i) microbial biofilm modulation(bioactive surfaces); ii) microbial repelling/non-adheringproperties (antifouling surfaces), iii) contact-killing orrelease-killing compounds , andiv) ‘on-demand’ antimicrobial agent delivery (smartsurfaces) ,Ti surface modifications have been widely proposed to improve the quality of theosseointegration process and/or exert an antimicrobial property to avoid implantinfections in vitro apatite growth ,,The initial research focus on implant surfaces was the so-called ‘bioactivesurfaces’ Regarding the antimicrobial attributes of bioactive surfaces, researchers are notonly thinking about developing surfaces that could kill bacteria, but alsocreating a positive effect on the composition of the attached biofilm byselecting health associated bacterial species ,et al.) (in vitro and in situ (~ 8-fold decreasevs. non-treated surface)  , reducin,Streptococcus aureus and Escherichia coli)and represents a valuable strategy to be tested against oral polymicrobialbiofilms related to dental implant surfaces using in vivomechanistic studies.In addition to superhydrophobic surfaces, nature-inspired biomimetic surfacesalso have strong antifouling activities to prevent microbial accumulation ,,,Antimicrobial-loaded surfaces have been a major focus of implantology research inthe last 20 years 2) Considering that implant-related infections are chronic conditions associatedwith continuous pathogenic and multi-resistant biofilm accumulation 2 level, andpH) stimulus have been tested in biomedical applications to activate thisdeveloped smart surfaces, showing to be a reliable drug delivery strategy withreusability property to guarantee the activation of the surface several times,as needed. Furthermore, to enhance the optimization of these surfaces,dual-stimulus approaches have been recently proposed to faster both preventionand clinical treatment stages of dental Based on the current limitations related to the short-term effect ofantimicrobial-loaded surfaces, smart surfaces engineering is growing as a novelgeneration of antimicrobial approaches for implant applications A significant concern emerging from current Implant Dentistry is the increasedprevalence of peri-implant infections and the lack of consensus on the mosteffective therapeutic technique to treat such diseases. Often, diseased implantsneed to be removed, damaging bone and supporting tissues and creating a biggerproblem to be solved prior the possibility of new implant placement ,in vitro and in vivo modelsthat fail to mimic the oral environment, the human microbiota, and the complexnature of the disease. No wonder these surfaces have not been extensively tested inrandomized controlled clinical trials nor gained the implant market, even though itmight be a turning point to fight such disease.There is no doubt that the first developed antimicrobial surfaces associated with theincorporation of bioactive elements or antimicrobial drugs have frustrated us tosome extent. Such surfaces work in the principle of contact-killing or drug release,,,Currently, in an interdisciplinary and multifaceted approach, researchers havededicated their efforts to developing the so-called smart/intelligent surfaces.Those surfaces respond to certain stimuli to release the antimicrobial agent whenprecisely needed, in a site-specific manner. However, considering that theperi-implant disease has an immune-inflammatory nature, just controlling the biofilmis not enough and there is a need to incorporate immunomodulatory agents withanti-inflammatory properties to control the host response as well Recent evidence is accumulating and suggesting that the presence of Ti productsaround dental implants may contribute to microbial dysbiosis and peri-implantitisin vivo compatibility. In thisregard, machine-learning algorithms to engineer reliable surfaces are in-handstrategies to better explore novel surfaces architecture Another field of investigation with promising outcomes is the nature-inspiredsurfaces to provide antifouling activity under safe conditions Finally, to smartly win the never-ending war against polymicrobial infections ondental implants and gain potential breakthroughs in the surface-engineering implantfield is important to deeply recognize the strategies of our enemy, understand theprecise mechanism of etiopathogeneses, and the risk factors of the peri-implantdisease. Will we win the war? We do not know precisely, but significant knowledge isstill to be explored by well-designed studies.


Drug-induced liver injury (DILI) is a major cause of the withdrawal of pre-marketed drugs, typically attributed to oxidative stress, mitochondrial damage, disrupted bile acid homeostasis, and innate immune-related inflammation. DILI can be divided into intrinsic and idiosyncratic DILI with cholestatic liver injury as an important manifestation. The diagnosis of DILI remains a challenge today and relies on clinical judgment and knowledge of the insulting agent. Early prediction of hepatotoxicity is an important but still unfulfilled component of drug development. In response, in silico modeling has shown good potential to fill the missing puzzle. Computer algorithms, with machine learning and artificial intelligence as a representative, can be established to initiate a reaction on the given condition to predict DILI. DILIsym is a mechanistic approach that integrates physiologically based pharmacokinetic modeling with the mechanisms of hepatoxicity and has gained increasing popularity for DILI prediction. This article reviews existing in silico approaches utilized to predict DILI risks in clinical medication and provides an overview of the underlying principles and related practical applications. More than 30,000 drugs have been developed for diverse diseases, of which 1100 drugs could potentially cause liver injury. In the United Kingdom, the incidence of drug-induced liver injury (DILI) was reported as 13.9 per 100,000 inhabitants . As one Currently, there are emerging preclinical human-relevant in vitro models used to evaluate the toxic injury of drug candidates to the liver. In these models, either single-cell type or multi-cell type assays can be performed . The maiAnimal models also play an important role in the pharmacokinetics and toxicity researches of drug metabolism in vivo relevant to DILI. Several models of chimeric mice with humanized hepatocytes have been developed over the years, most of which require the damage of endogenous mouse hepatocytes followed by a transplant of human liver cells. Highly immunodeficient NOG mice (TK-NOG) are a humanized liver model expressing a herpes simplex virus type 1 thymidine kinase (HSVtk) transgene and mouse liver cells that were ablated after exposure to ganciclovir . HumanizCommonly used tests to assess DILI, including in vitro experiments or in vivo animal models mentioned above, are sometimes inaccurate when the data were extrapolated to humans. In addition, it is difficult to detect DILI during prospective clinical trials as the relatively low DILI occurrence rate compared to other serious adverse events (SAEs) requires a disproportionately large sample size for detection. Ethical considerations also forbid re-challenge tests for DILI ,20. AlthDILI is defined as a series of reactions triggered by exposure to any artificial or natural compound that leads to acute or chronic liver injury according to the course of the disease. It can also be divided into intrinsic DILI (InDILI) and idiosyncratic DILI (IDILI) based on the pathophysiological mechanism. Intrinsic liver injury is usually dose-dependent and can be predicted directly from human or animal models . InDILI Oxidative stress results from the excessive generation of reactive oxygen species (ROS), which are harmful to cells. For example, hydroxyl radicals can react directly with DNA components, phospholipids, and protein side chains to damage macromolecular structures and cause cell necrosis and apoptosis . In addiAcetaminophen (APAP), extensively used as an antipyretic, is a dose-dependent hepatotoxic drug. The main metabolic pathway of APAP at the therapeutic dose is phase II conjugation, where APAP is converted to nontoxic compounds including glucuronide derivatives  and sulfate derivatives . These polar metabolites are then excreted via urine. Only approximately 5% of APAP is oxidized by CYP450 enzymes in phaseImetabolism to form N-acetyl-P-benzoquinone imine (NAPQI), a strong oxidant, and subsequently conjugates with glutathione (GSH) . HoweverMitochondria are essential organelles in eukaryotes. They are responsible for not only the production of energy—in the form of adenosine triphosphate (ATP)—but also the regulation of cellular biological activities including biosynthetic processes, calcium homeostasis, stress responses, and cell death . DysfuncThe generation and secretion of hepatic bile depend on several transporters for bile acid transport and transmembrane water flow, such as sodium ion-dependent cholic acid transporters, bile salt removal pumps, and a number of water channels . CholestDysfunctional water channel proteins can also contribute to bile flow impairment. Aquaporin-8 (AQP8), a member of the aquaporin family of membrane channel proteins that facilitate osmotic pressure-driven water transport, is highly expressed on the canalicular membrane of hepatocytes . Many stHepatic inflammation is the common manifestation of many liver diseases, including drug-induced hepatotoxicity . The livCytokines are important to regulate innate and specific immunity and also function as signals for potential danger. These messenger proteins bind to specific target cell receptors and can stimulate or inhibit cellular involvement in immune responses . Some stComputer algorithms can be established to predict DILI based on a series of training data, which can be applied clinically or during drug development. To facilitate analysis, most DILI events can be divided into the input cause and output result. Input causes include the properties of drugs, such as chemical structures, gene expression profiles, and cell and tissue images. These properties are used to determine the probability of DILI occurrence. Output results could include in vitro and in vivo hepatotoxicity, changes in biomarkers, or clinical adverse events related to DILI . By summChemical structures are commonly associated with the bioactivity of drugs and closely relate to the occurrence and severity of DILI. As a result, the development of Quantitative Structure-Activity Relationship (QSAR) via in silico models plays an important role in the prediction and assessment of DILI. In QSAR, the structure of each chemical compound will be treated as a vector known as a molecular descriptor. Then the functional relationship between molecular descriptors and a DILI-related biological activity of molecules  will be constructed . DiffereThere are different computational programs to process the input information, which can be divided into explicitly coded decision rules and implicitly defined rules. Explicitly coded decision rules are commonly used when expert toxicology or hepatotoxicity knowledge is available, in which the assessment of hepatotoxicity is determined by a fixed classification algorithm based on chemical structures . HoweverWith the expert knowledge approaches (explicitly coded decision rules), known drug information is used to identify specific fragments of molecules that are associated with DILI. These are generally called structural alerts . Egan etBesides structural alerts, some classification algorithms are developed to utilize existing drug knowledge for the DILI judgment. For example, Zhu and Kruhlak proposed a scoring rule for post-marketing DILI data of 2029 drugs with 13,555 drug-adverse event pairs and classified them as DILI-positive or -negative according to their respective scores .As mentioned above, the decision-making algorithms of machine learning (ML) are implicitly defined and typically obtained by algorithm optimization. Machine learning can be further classified as shallow ML methods  and deep learning methods based on the so-called deep artificial neural networks with at least two hidden layers .Due to their good accuracy, there are increasing numbers of predictive statistical models using machine learning methods. Chen et al. established a QSAR model using the Decision Forest algorithm, to calculate molecular descriptors from 2D chemical structures by Mold2. The model was trained by 197 drugs and assessed by 3 validation data sets with an overall estimated accuracy of 73.6% in high-confidence therapeutic subgroups . Zhang eDeep learning algorithms are increasingly popular in the prediction of DILI. Xu et al. constructed the DILI prediction model using the undirected graph recursive neural networks (UGRNN) method, relying only on a few suitable molecular descriptors with suitable representations that are learned automatically from the data. The DL-Combined model, which performed better than previous DILI prediction models, was trained on 475 drugs and predicted 198 drugs with 86.9% accuracy, 82.5% sensitivity, and 92.9% specificity . Nguyen-To complement and improve chemical structure-based models, additional information in the form of biological drug properties such as gene expression data  and cellular indicators are necessary. Most gene expression data are accessible in some large-scale biomedical datasets such as the Connectivity Map (CMap) project, which is a collection of transcriptional expression data derived from cultured human cells treated with various compounds  and GenoIn addition, in vitro indicators determined by imaging assays can be a feature of chemical compounds, such as mitochondrial damage, oxidative stress, and intracellular glutathione. For example, Zhu et al. used human hepatocyte imaging assay technology (HIAT) descriptors that included several biochemical indicators  of 156 DILI-positive and 136 DILI-negative compounds to build a DILI predictive model. Compared to the chemical structure-based model alone, the hybrid models combined with chemical structures and in vitro biological data could enhance the prediction accuracy of human hepatotoxicity . Puri etMechanisms of drug action can also be considered during the modeling process. Wu et al. incorporated the mode of action of 333 drugs into the QSAR model, which was divided into active and inactive and yielded a predictive accuracy of 0.711 .A mechanistic approach that is currently being developed—known as DILIsym—could provide a bottom-up prediction of liver safety liabilities in new drug candidates. It integrates pharmacokinetics exposure, mechanisms of hepatotoxicity, and interpatient variability into the modeling process to demonstrate the frequency and the extent of a new DILI in an average patient or a specific population  Figure .DILIsym can simulate the occurrent process of hepatotoxicity, incorporating submodels for the production of reactive metabolites, and generation of ROS (oxidative stress). Mitochondrial dysfunction can be further investigated in MITO-sym , accumulDuring the modeling process of DILIsym, a physiologically based pharmacokinetic (PBPK) model is created using available parameters related to the drug properties and physiological structures to estimate the time-dependent exposure of the drug in the region of interest. With the development of modeling theory, technology in the engineering field, and the popularization of computer technology and computing software, PBPK modeling techniques have matured considerably since their inception in the early 1930s . The PBPThe first drug modeled by DILIsym was APAP where the model generated oxidative stress accounting for APAP overdosed hepatotoxicity. The modeling was used to propose the optimal treatment protocol with N-acetyl cysteine . Subsequmax of tolvaptan had a minor impact on tolvaptan pharmacokinetics and hepatotoxicity, but that of DM-4103 resulted in marked hepatic accumulation of DM-4103 and bile acids, reductions in hepatic ETC activity and ATP concentrations and increase in hepatotoxicity plasma biomarkers. This DILIsym model supported the hypothesis that impaired biliary efflux increased susceptibility to tolvaptan-associated hepatotoxicity observed in patients with ADPKD, and MRP2 played a more prominent role in tolvaptan-associated liver injury owing to the inhibition of DM-4103 on MRP2 [Several fundamental DILI mechanisms are integrated into DILIsym, including mitochondrial toxicity, bile acid-mediated toxicity, and oxidative stress. By sequentially turning off each of these hepatotoxicity mechanisms and observing the DILI outcome change degree of each alteration, the predominant mechanism of hepatotoxicity can be found . By adju on MRP2 .According to the General Practice Research Database (GPRD), the crude incidence of drug hepatotoxicity in the UK is 2.4 cases per 100,000 people per year [Knowledge-based predictive models use the computational algorithm and specific descriptors to develop the relationship between the drug properties and DILI outcome and further predict the DILI risk. The most challenging problem of knowledge-based predictive models is the lack of DILI annotation datasets, which can be defined as a comprehensive collation of drug information, including the hepatotoxicity descriptions from available data, dosing regimen, basic properties, related study results, and the risk classification schemes of DILI into a single database. There are various public DILI annotation datasets with different drugs and content, for example, the LiverTox Dataset , the LTKIn addition, DILI is often simplified to a qualitative classification problem in the case of computational predictions. This approach, however, does not provide sufficient information on essential factors such as dose dependency or affected patient population during model development. Consequently, the practical applicability of such models is limited. Mechanism-based prediction algorithms such as DILIsym can combine cell assays, PBPK modeling, and interindividual variation into a single model to predict the time course of drug hepatotoxicity. The ability to simulate different dosing regimens and to predict the corresponding DILI risk of each regimen can help to clarify some fundamental clinical questions, including the dose-dependency of a particular drug and determination of safety margin can be undertaken through dose escalations in the virtual subjects within DILIsym . AdditioDILI in China accounts for about 20% of the hospitalization rate of acute liver injury , which cTheoretically, all subjects who received a drug share the same risks of experiencing hepatotoxic drugs. However, certain populations will be more vulnerable to hepatotoxic drugs. Applying in silico models mentioned above in the analyses of DILI risk factors among vulnerable patient groups is of great significance for DILI research. Related genes have always been studied to understand the pathogenesis of specific DILIs. Moore et al. used machine learning approaches, including multivariate adaptive regression splines (MARS), multifactor dimensionality reduction (MDR), and logistic regression to investigate single-nucleotide polymorphism (SNP)–SNP interaction as a potential DILI risk factor . MoreoveIn conclusion, we have introduced two main types of in silico models for DILI prediction: knowledge-based models using the computational algorithm and mechanism-based models using DILIsym, which could be used to evaluate the risk of drug-associated liver adverse events in both clinical settings and drug development processes.


The recent methodological advances in multi-omics approaches, including genomic, transcriptomic, metabolomic, lipidomic, and proteomic, have revolutionized the research field by generating “big data” which greatly enhanced our understanding of the molecular complexity of the brain and disease states. Network approaches have been routinely applied to single-omics data to provide critical insight into disease biology. Furthermore, multi-omics integration has emerged as both a vital need and a new direction to connect the different layers of information underlying disease mechanisms. In this review article, we summarize popular network analytic approaches for single-omics data and multi-omics integration and discuss how these approaches have been utilized in studying neurodegenerative diseases. Human diseases have enormous levels of complexity. The vast majority of conditions, such as Alzheimer’s disease (AD), are caused by a combination of genetic, environmental, and lifestyle factors, most of which have not yet been identified . To undeNetwork-based approaches to studying human disease have had promising applications. Networks constitute the foundation of biological systems. A network can involve different levels of biological entities that connect to one another through direct or indirect interactions. The central idea of applying network analysis is to reduce the dimensionality of data from thousands of altered genes, proteins, metabolites, lipids, or other biological entities to a smaller and more interpretable set of altered processes .The fast development of omics technology combined with a decrease in cost has made multi-omics readily available to researchers. These different omics data types are unique but complementary with each set containing information that is not represented in others. Taken together as a collective group, these various omics representations constitute the biological networks that drive disease mechanisms. Multi-omics integration using a network approach helps to bridge the gap between genotype and disease, which remains an essential task in the current era of big data analytics. In the following sections, we summarize popular network analytic approaches for omics studies of neurodegenerative diseases from the standpoints of both single-omics and multi-omics integration.BioRender.com).As most neurodegenerative disease studies focused on the analysis of single-omics for many years, successful network approaches have been developed to fit the data type. In fact, network frameworks can often be adapted to fit multiple data types. Here, we summarize several common network approaches for single-omics analysis   methodology was developed for quantifying the putative genetic interactions in cancer. This approach has been demonstrated to uncover differential genetic interaction burdens between mutated or known cancer genes compared to other essential genes. The network-predicted putative genetic interactions were also correlated with patient survival . While tTranscriptomics has evolved from the traditional bulk RNA sequencing to single cell/single nuclei RNA sequencing (sc/snRNA-seq) allowing the molecular mechanism of the disease to be directly measured and explored at the cellular level. More recent spatial transcriptomics technologies measure cell-level expression activity in their morphological context. In the context of AD, it has been demonstrated that the strongest disease-associated transcriptomic changes appeared early in pathological progression and were highly cell-type specific, whereas genes upregulated at late stages were common across cell types and primarily involved in the global stress response . RegardlTarget-list-based approaches rely on a list of specific gene targets, often selected from differentially expressed genes between conditions, to identify enriched gene ontologies, canonical pathways, and regulatory networks. This approach is intuitive, and the gene targets identified can often be validated in wet labs . HoweverTrem2 and Tyrobp, and an apoE-related module with multiple Serpina3 genes , relies on calculating correlation coefficients between gene pairs, grouping highly correlated genes into modules , and then correlating the expression pattern of the module with traits of interest. The module hub genes, those with the highest connectivity to all genes within the module, can be nominated as biomarkers or therapeutic targets. For example, a3 genes . Notablya3 genes   are often regarded as hub genes because they bind to specific DNA sequences, form interactions with many genes, and conduct most of the regulation activities in the human genome, Additionally, cancer genes such as oncogenes and tumor suppressors are often hub genes in the tumor genetics network . While hProteomics analyses can utilize many of the same network approaches as transcriptomics due to the easy mapping of genes to proteins . Of noteMany major studies have relied on protein networks to investigate disease biology. In one multicenter consortium study, the investigator analyzed more than 2,000 human brain tissues via quantitative mass spectrometry (MS)-based proteomics and found a protein network module linked to sugar metabolism emerged as one of the protein modules most significantly associated with AD pathology and cognitive impairment. This module was enriched in AD genetic risk factors as well as microglia and astrocyte protein markers associated with an anti-inflammatory state, suggesting that the biological functions it represents serve a protective role in AD . FurtherABCA7 haplodeficiency affected glycerophospholipid metabolism, linoleic acid metabolism, and α-linolenic acid metabolism. Our group found that aging regulated mouse serum metabolomics and affected networks involving long chain fatty acids, amino acids, and biogenic amines , and many tools using this concept have been reviewed . NetworkThe Similarity Network Fusion (SNF) method is one of the few computational network approaches for multi-omics integration. It constructs a sample-similarity network for each data type and integrates these networks into a single comprehensive similarity network via a nonlinear combination method. During this process, weak similarities are removed while strong similarities are added, making the combined network more coherent and robust. SNF has been shown to work well to integrate mRNA, miRNA, and DNA methylation data for five cancer datasets . Lemon-TWhile the true computational network approaches for multi-omics integration are under-developed, many studies that successfully drew valuable conclusions from multi-omics data used network analysis differently. For example, HDAC1, BIN1, PICALM, and APOE. Interestingly, the involvement of HDAC1, which contributes to epigenetic silencing of active chromatin, was found to be the hub gene in transcriptomics data of all four diseases, suggesting a crucial role of epigenomics in unveiling the mechanisms of neurodegenerative diseases (Neurodegenerative diseases share many common features such as the accumulation of misfolded proteins and the progressive loss of neurons. These events are usually regulated on multiple omics’ levels . To compdiseases .In this review, we summarized several common network analysis approaches used in the studies of neurodegenerative diseases. The network approaches for different omics are mainly built on the assumption that the biological entities that function in the same network or pathway correlate. As a result of this assumption, network methods initially designed for a particular type of omics can be adapted to other omics modalities, often with minimal difficulty. At the same time, multi-omics integration approaches have been utilized in cancer studies for over a decade, thanks to the large magnitude of data types available in cancer databases . Multi-o


Paget-Schroetter syndrome (PSS) is a rare form of spontaneous upper extremity deep vein thrombosis associated with vigorous activity of the upper extremities. We present a rare case of a young swimmer who presented with a painful right upper extremity swelling, with compression ultrasound (CUS) revealing extensive venous clots in the basilic, axillary, and subclavian veins. Venous duplex revealed extrinsic compression of the subclavian vein, and catheter-based contrast venography confirmed our diagnosis of PSS. The patient was started on a therapeutic dose of subcutaneous enoxaparin and referred to a higher center for further intervention. Paget-Schroetter syndrome (PSS) is a rare cause of spontaneous unilateral upper extremity deep venous thrombosis (UEDVT) ,2, occurA 32-year-old nonsmoker, fairly active lady, and an avid swimmer, without comorbidities, was initially seen in the emergency department (ED) one week ago and diagnosed with right UEDVT (axillary thrombus) and right lower lobe segmental pulmonary embolism (PE) after presenting with a day of painful swelling of the right upper extremity. It was thought to be provoked by oral contraceptives (OCP) with estrogen, and she was subsequently discharged on apixaban from the ED. Despite stopping the OCP and being religiously compliant with apixaban, she reported persistent symptoms for one week, thus prompting her second visit to the hospital. She denied significant trauma, immobilization, or major surgery to her affected arm. She usually goes swimming during her leisure time and recalled going for a swim on the day before the onset of symptoms. She denied a history of venous access to the right upper extremity, personal history of DVT, abortion, or miscarriages. She also denied a history of fever, insect bite, or rash. She was fully vaccinated for coronavirus disease 2019 (COVID-19), the last dose received three months back. She had no family history of bleeding disorders or clotting disorders, and she denied intravenous recreational drugs use.On presentation, she had a temperature of 36.8°C, heart rate of 66 beats per minute, blood pressure of 125/95 mmHg, respiratory rate of 19 breaths per minute, and saturation of 96% on room air. On physical examination, she had tender right upper arm swelling with overlying erythema and a cord-like superficial vein extending from the lower cervical region to the elbow. She had an unremarkable neurological examination in the affected extremity, with intact distal pulses. The rest of the physical examination was normal.She underwent an extensive thrombophilia workup Table . Flow cyVenous duplex showed extrinsic compression of the subclavian vein as it passed under the first rib, confirmed by catheter-based contrast venography Figures , 5. She Paget-Schroetter syndrome (PSS) was introduced in 1949 AD by Hughes after a compilation of numerous case reports of upper extremity deep vein thrombosis (UEDVT), which was assembled in recognition of the work by Paget in 1875 and Schroetter in 1884 where both described an association of acute venous stasis in a healthy person caused by repeated movements of shoulder girdle damaging the axillary vein, thus responsible for the formation of thrombus also known as traumatic thrombosis of the axillary vein . The narEven though the role of inherited and acquired causes of thrombophilic events is unclear, it is not unreasonable to test the patients for these abnormalities as they might help predict postoperative anticoagulation . The totPSS is a classic example of a clinical condition needing a high index of suspicion and timely diagnosis. Prompt treatment has a great outcome with minimal long-term sequelae but, if missed, is complicated by significant long-term morbidity. Therefore, physicians should be aware of this rare entity for early recognition and timely referral to vascular surgery. Further studies are required to compare the efficacy of thrombolysis with surgical options.


Disease staging systems allow for the measurement of disease severity based on the identification of important milestones in the natural history of a disease. Staging systems can be used to help guide clinical care, serve as inclusion criteria for therapeutic trials, and furthermore provide a consistent framework for interpreting findings from different studies in different populations. Disease staging systems can also be used to model the progression of clinical and biomarker changes in relation to important disease landmarks. In Alzheimer’s disease (AD), which is defined by the presence of amyloid-β plaques and tau neurofibrillary tangles ,2, stagiin vivo research has focused on dichotomous classification of amyloid-PET and tau-PET imaging into +/- groups, the spatial resolution of PET provides an opportunity for staging based on the anatomical distribution of pathology, similar to post-mortem staging systems [18F]MK6240 tau-PET data [in vivo disease staging, as opposed to neuropathology, is the ability to track the evolution of disease in the same individual over time. Using longitudinal imaging, we observed that amyloid-PET abnormality at baseline was associated with different rates of Braak stage progression. Progression from early Braak stages (0-II) took place in the presence or absence of abnormal amyloid. However, progression from Braak stage III and above during two-year follow-up occurred exclusively in the presence of abnormal amyloid-β.Although most  systems . Due to  systems  providesPET data . After ain vivo disease staging is the ability to monitor the severity of AD specifically. This is in contrast to clinical dysfunction, which is often the result of multiple neuropathological processes. Furthermore, cognitive reserve, which can influence the extent to which individuals can carry brain pathology without symptoms, also complicates clinical staging. PET-based Braak staging can also identify preclinical disease . Therefore, in vivo staging of Alzheimer’s disease has greater sensitivity and specificity over clinical assessment of symptoms [Taken together, these results showcase how applying disease staging systems to living humans can provide new insights into disease progression . Anothersymptoms .PET-based Braak staging may also prove useful for clinical trial enrolment. Identifying individuals at a specific stage of tau abnormality will allow for a more homogeneous study population, potentially reducing the variance in longitudinal cognitive decline. Furthermore, studies aimed at halting the progression of tau pathology may seek to recruit patients at a specific early stage. Finally, tau-PET can be used to monitor disease progression in clinical trials, with lack of tau progression as a possible signal of effectiveness. This is similar to the recent phase II donanemab trials which recruited amyloid-positive individuals with intermediate levels of tau-PET uptake, which met primary endpoints in phase II .The recent approval of aducanumab (Aduhelm) highlights the need for biomarkers to assess the severity of AD . If evidin vivo PET staging and ex vivo neuropathological staging are very different: in vivo PET-based Braak staging relies on surpassing predefined thresholds of abnormality with specific brain ROIs representing specific Braak stages. In contrast, histopathological Braak staging relies on the detection of neurofibrillary tangles using staining techniques. Therefore, identification of even small numbers of neurofibrillary tangles using histopathological techniques can place an individual at a more advanced Braak stage, as a higher concentration of neurofibrillary tangles would be needed for the same individual to exceed a predefined threshold for abnormality. Finally, it is important to emphasize that staging systems involve dimensionality reduction. Despite the fact that atypical clinical variants of AD largely conform to the Braak system, some information about the spatial distribution of tau in atypical AD [Several unanswered questions remain. The first is the degree of correspondence between PET-based Braak stage and Braak stage at autopsy. Because of limitations of PET’s sensitivity, some discordance between antemortem and postmortem Braak stage is expected. Furthermore, it must be highlighted that the methodology of pical AD  is lost