Complex Disease States Research Articles

Insights and Opportunities from Multimarker Evaluation of Heart Failure: Lessons from BIOSTAT-HF.

Heart failure is a complex and heterogenous disease state that affects millions worldwide. Over recent decades, advancements in medical therapy and device implementation have significantly transformed the landscape of heart failure outcomes, while improvements in imaging modalities and greater accessibility to genome sequencing have led to increasing recognition of distinct heart failure endotypes. There is rising evidence to suggest all patients do not benefit equally from intensification of guideline directed medical therapy (GDMT). Efforts to personalize medical therapy to maximize benefits while minimizing side effects remains an ongoing challenge. We review key manuscripts from The BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF), a multicenter observational study conducted across Europe, which prospectively enrolled patients with acute or worsening heart failure. BIOSTAT-CHF was designed to characterize biological pathways associated with varied patient responses to GDMT for heart failure. Utilizing a diverse cohort of European patients, the authors were able to develop risk models to predict mortality and heart failure hospitalization from clinically available data plus standard and novel biomarkers. They also utilized modeling to refine characterization of heart failure subtypes and personalization of GDMT titration. In this review we highlight key insights from the BIOSTAT-CHF cohort and how they relate to: (1) prognosis and monitoring treatment response in heart failure, (2) personalization of heart failure treatment, and (3) elucidation of biological pathways and future directions for research. These insights summarize how BIOSTAT-CHF has contributed to a deeper understanding of heart failure, focusing on using biomarkers personalizing treatment approaches, with a goal of ultimately improving patient outcomes.

Real world application of the 2023 ACR/EULAR antiphospholipid antibody syndrome classification criteria in a pharmacist directed anticoagulation clinic.

To determine the number of patients that met classification for antiphospholipid antibody syndrome (APS) after applying the 2023 American College of Rheumatology and the European Alliance for Associations of Rheumatology (ACR/EULAR) classification criteria, to identify reasons patients did not meet the new criteria, and determine the number of patients who were single, double, or triple positive based on laboratory criteria. A single center, retrospective chart review of patients with APS on anticoagulation managed by ambulatory care clinical pharmacists. Data collected included patient demographics, type of anticoagulation, and clinical and laboratory criteria for APS as defined by the 2023 ACR/EULAR criteria. Data is presented using descriptive statistics. A total of 51 patients previously diagnosed with APS were included. There were 42 patients on warfarin (82.3%), 4 patients on direct oral anticoagulants (19%), 3 patients on low molecular weight heparin (5.8%), and 2 patients on fondaparinux (3.9%). Of the 51 patients, 12 (23.5%) met classification criteria, 33 (64.7%) did not meet classification criteria and 6 (11.7%) had insufficient data. Of the 27 patients that did not meet criteria, 13 patients did not meet the laboratory criteria (39.4%), 6 patients did not meet the clinical criteria (18.2%) and 14 patients did not meet both laboratory and clinical criteria (42.4%). Of the 12 patients that met classification criteria, 2 patients were triple positive (16.7%), 3 were double positive (25%), and 7 were single positive (58.3%). Results from this study indicate that APS continues to be a complex disease state with challenges in diagnosis and classification. Since only a small number of patients in our clinic continued to meet the classification criteria, opportunities for patient re-evaluation of management strategies at our institution could be considered.

Treatment of Long COVID With Enoxaparin

Background and Purpose: Long COVID is a complex multisystemic disease state, which represents a huge economic and health burden worldwide. Hypercoagulation and the formation of fibrinaloid microclots have been proposed as an underlying pathology, which may underpin a wide variety of presenting symptoms, via reduced blood flow and cellular energy production. Postexertional malaise (PEM) is common and exercise therapy should not be recommended without addressing the underlying pathology. Case Presentation: Patient was female, 43, with Long COVID for 21 months characterized primarily by extreme fatigue, exercise intolerance, PEM, cognitive dysfunction, and postural orthostatic tachycardia syndrome. She was unable to work or leave the house unless for essential medical appointments. Before her initial severe acute respiratory syndrome coronavirus 2 infection, she had no prior health conditions or comorbidities, a body mass index of 20.8, and engaged in regular vigorous exercise. Intervention: This case report describes a patient treated with enoxaparin, a low molecular weight heparin, at 20 mg per day subcutaneously, with assessment at 8 weeks. Outcomes: The patient's function and quality of life improved markedly, as reported subjectively, and with objective measurements at 8 weeks. Levels of activity improved markedly with no adverse events. The severity of PEM decreased dramatically, and postural orthostatic tachycardia syndrome was better controlled. The FUNCAP score, used to measure functional capacity, increased from 3.3 (moderately affected) to 5.07 (mildly affected) after treatment. Discussion: It is recommended that this treatment should be further investigated to elucidate the role of enoxaparin in addressing underlying pathology in Long COVID, including hypercoagulation and microclots.

The Latest Trends in the Management of Type 1 and Type 2 Diabetes Mellitus

Over the past few years, we have witnessed many advances in the understanding of diabetes and its management. Greater insight into pathogenesis has led to the approval of the first immunopreventative therapy for T1DM. We are using non-insulin agents more for nephro- and cardioprotection than glucose-lowering effects while leaning on advancing technology to use insulin more safely. We now recognize that over half of T1DM is diagnosed in adulthood, the prevalence of obesity in patients with T1DM matches that of the general population, and rates of pediatric T2DM have dramatically risen amongst marginalized youths in recent years. Diabetes is now considered more of a heterogenous disease state than ever before, and practitioners will need to be familiar with these endotypes as personalized medicine replaces standardized treatment approaches. To this end, this article aims to summarize recent findings in an easily digestible manner so that providers may be more familiar with this ever-growing complex disease state.

Novel Machine Learning of DNA Methylation Patterns to Diagnose Complex Disease: Identification of Cerebral Palsy with Concurrent Epilepsy.

Spastic cerebral palsy (CP) is a common pediatric-onset disability with an estimated prevalence of 0.2%. It is a complex condition characterized by muscle stiffness, contractures, and abnormal movement. Spastic CP is difficult to diagnose. Although nearly all affected children are born with it or acquire it immediately after birth, many are not identified until after 19 months of age with the diagnosis often not confirmed until 5 years of age. In addition, CP frequently co-occurs with other complex conditions that can complicate diagnosis and treatment. For example, an estimated 42% of spastic CP cases have co-occurring epilepsy. Recent studies indicate that altered DNA methylation patterns in peripheral blood cells are associated with CP and may have diagnostic value.Accordingly, the purpose of this study is to assess the diagnostic value of methylation in CP with more complex disease states. We evaluated machine learning classification for detecting CP based on DNA methylation pattern analysis in the context of co-occurrent epilepsy. Blood samples from 30 study participants diagnosed with epilepsy (n=4), spastic CP (n=10), both (n=8), or neither (n=8) were analyzed by Illumina MethylationEpic arrays. A novel machine learning algorithm using a Support Vector Machine (SVM) or Linear Discriminant Analysis (LDA) was developed to identify methylation loci that classified CP from controls and to measure the classification ability of identified methylation loci. The isolation of informative methylation loci was performed in a binary comparison between CP and controls, as well as in a 4-way comparison that included epilepsy. Median F1 scores for SVM-based analysis were 0.67 in 4-class comparison, and 1.0 in the binary classification. SVM outperformed LDA (median F1 0.57 and 0.86, respectively). Overall, the novel machine learning based algorithm was able to classify study participants with spastic CP and/or epilepsy from controls with significant performance.

Romosozumab rescues impaired bone mass and strength in a murine model of diabetic kidney disease

As international incidence of diabetes and diabetes-driven comorbidities such as chronic kidney disease (CKD) continue to climb, interventions are needed that address the high-risk skeletal fragility of what is a complex disease state. Romosozumab (Romo) is an FDA-approved sclerostin inhibitor that has been shown to increase bone mineral density and decrease fracture rates in osteoporotic patients with mild to severe CKD, but its effect on diabetes-weakened bone is unknown. We aimed to test Romo's performance in a model of combined diabetes and CKD. 6-week old male C57BL/6 mice were randomly divided into control (CON) and disease model (STZ-Ad) groups, using a previously established streptozotocin- and adenine-diet-induced model. After 16 weeks of disease induction, both CON and STZ-Ad groups were subdivided into two treatment groups and given weekly subcutaneous injections of 100 μL vehicle (phosphorus buffered saline, PBS) or 10 mg/kg Romo. Mice were euthanized after 4 weeks of treatment via cardiac exsanguination and cervical dislocation. Hindlimb bones and L4 vertebrae were cleaned of soft tissue, wrapped in PBS-soaked gauze and stored at 20C. Right tibiae, femora, and L4s were scanned via microcomputed tomography; tibiae were then tested to failure in 4-pt bending while L4s were compression tested. Romo treatment significantly increased cortical and trabecular bone mass in both STZ-Ad and CON animals. These morphological improvements created corresponding increases in cortical bending strength and trabecular compression strength, with STZ-Ad treated mice surpassing vehicle CON mice in all trabecular mechanics measures. These results suggest that Romo retains its efficacy at increasing bone mass and strength in diabetic kidney disease.

Pharmacotherapeutic Considerations in the Treatment of Nontuberculous Mycobacterial Infections: A Primer for Clinicians.

Nontuberculous mycobacteria (NTM) can cause a variety of infections, including serious pulmonary disease. Treatment encompasses polypharmacy, with a targeted regimen of 2-5 active medications, depending on site of infection, species, and clinical characteristics. Medications may include oral, intravenous, and inhalational routes. Medication acquisition can be challenging for numerous reasons, including investigational status, limited distribution models, and insurance prior authorization. Additionally, monitoring and managing adverse reactions and drug interactions is a unique skill set. While NTM is primarily medically managed, clinicians may not be familiar with the intricacies of medication selection, procurement, and monitoring. This review offers insights into the pharmacotherapeutic considerations of this highly complex disease state, including regimen design, medication acquisition, safety monitoring, relevant drug-drug interactions, and adverse drug reactions.

Peptide-Drug Conjugates: An Emerging Direction for the Next Generation of Peptide Therapeutics.

Building on recent advances in peptide science, medicinal chemists have developed a hybrid class of bioconjugates, called peptide-drug conjugates, that demonstrate improved efficacy compared to peptides and small molecules independently. In this Perspective, we discuss how the conjugation of synergistic peptides and small molecules can be used to overcome complex disease states and resistance mechanisms that have eluded contemporary therapies because of their multi-component activity. We highlight how peptide-drug conjugates display a multi-factor therapeutic mechanism similar to that of antibody-drug conjugates but also demonstrate improved therapeutic properties such as less-severe off-target effects and conjugation strategies with greater site-specificity. The many considerations that go into peptide-drug conjugate design and optimization, such as peptide/small-molecule pairing and chemo-selective chemistries, are discussed. We also examine several peptide-drug conjugate series that demonstrate notable activity toward complex disease states such as neurodegenerative disorders and inflammation, as well as viral and bacterial targets with established resistance mechanisms.

Single-cell transcriptomics in thyroid eye disease.

Thyroid eye disease (TED) is a poorly understood autoimmune condition affecting the retroorbital tissue. Tissue inflammation, expansion, and fibrosis can potentially lead to debilitating sequelae such as vision loss, painful eye movement, proptosis, and eyelid retraction. Current treatment modalities for TED include systemic glucocorticoids, thioamides, methimazole, teprotumumab, beta-blockers, and radioactive iodine; however, it has been reported that up to 10%-20% of TED patients relapse after treatment withdrawal and 20%-30% are unresponsive to mainstay therapy for reasons that have yet to be more clearly elucidated. In the past 4 years, vision researchers have harnessed high-throughput single-cell RNA sequencing to elucidate the diversity of cell types and molecular mechanisms driving the pathogenesis of TED at single-cell resolution. Such studies have provided unprecedented insight regarding novel biomarkers and therapeutic targets in TED. This timely review summarizes recent breakthroughs and emerging opportunities for using single-cell and single-nuclei transcriptomic data to characterize this highly complex disease state. We also provide an overview of current challenges and future applications of this technology to potentially improve patient quality of life and facilitate reversal of disease endpoints.

Specialty pharmacy interventions benefit patients receiving HIV postexposure prophylaxis

BackgroundSpecialty pharmacies service many different complex disease states that require high-cost medication, including the treatment of patients prescribed human immunodeficiency virus postexposure prophylaxis (PEP). PEP requires time-sensitive initiation and patient counseling for therapeutic efficacy. ObjectiveThis study aimed to examine all PEP referrals received at a specialty pharmacy and demonstrate how they aided in interventions including assisting in obtaining financial assistance, making clinical interventions, and offering counseling to patients. MethodsThis is an observational retrospective chart review of patients who received PEP from one specialty pharmacy. All patients who filled PEP at the pharmacy between January 1, 2017, and July 1, 2022, were included. Information was collected from documentation provided in the electronic medication record used by the pharmacy. The PEP regimen prescribed were raltegravir (RAL) + emtricitabine/tenofovir disoproxil fumarate (FTC/TDF) and dolutegravir (DTG) + FTC/TDF. ResultsA total of 52 patients were treated with PEP during the measurement period. Patients who received a PEP regimen of RAL + FTC/TDF experienced a total cost savings of $1692.60 and $218.40 for those who were fully insured and uninsured, respectively. Patients who received a PEP regimen of DTG + FTC/TDF experienced a total cost savings of $676.20 and $2725.50 for those who were fully insured and uninsured, respectively. Counseling by a pharmacist was offered to all patients and 74.5% of patients accepted. Pharmacists made clinical interventions on 29.4% of PEP referrals. ConclusionPEP medications are expensive and time sensitive and can require clinical interventions and specific patient counseling. This study indicates that specialty pharmacies can provide and ensure access to care in the areas of financial assistance, patient counseling, and clinical interventions.

The Role of ADAMTS Proteoglycanases in Thoracic Aortic Disease.

Thoracic aortic aneurysm and dissection (TAAD) are complex disease states with high morbidity and mortality that pose significant challenges to early diagnosis. Patients with an aneurysm are asymptomatic and typically present to the emergency department only after the development of a dissection. The extracellular matrix (ECM) plays a crucial role in regulating the aortic structure and function. The histopathologic hallmark termed medial degeneration is characterised by smooth muscle cell (SMC) loss, the degradation of elastic and collagen fibres and proteoglycan (PG) accumulation. Covalently attached to the protein core of PGs are a number of glycosaminoglycan chains, negatively charged molecules that provide flexibility, compressibility, and viscoelasticity to the aorta. PG pooling in the media can produce discontinuities in the aortic wall leading to increased local stress. The accumulation of PGs is likely due to an imbalance between their synthesis by SMCs and decreased proteolysis by A Disintegrin-like and Metalloproteinase with Thrombospondin motifs (ADAMTS) proteoglycanases in the ECM. Mouse models of TAAD indicated that these proteases exert a crucial, albeit complex and not fully elucidated, role in this disease. This has led to a mounting interest in utilising ADAMTS proteoglycanases as biomarkers of TAAD. In this review, we discuss the role of ADAMTSs in thoracic aortic disease and their potential use in facilitating the clinical diagnosis of TAAD and disease progression.

Toward the analysis of functional proteoforms using mass spectrometry-based stability proteomics.

Functional proteomics aims to elucidate biological functions, mechanisms, and pathways of proteins and proteoforms at the molecular level to examine complex cellular systems and disease states. A series of stability proteomics methods have been developed to examine protein functionality by measuring the resistance of a protein to chemical or thermal denaturation or proteolysis. These methods can be applied to measure the thermal stability of thousands of proteins in complex biological samples such as cell lysate, intact cells, tissues, and other biological fluids to measure proteome stability. Stability proteomics methods have been popularly applied to observe stability shifts upon ligand binding for drug target identification. More recently, these methods have been applied to characterize the effect of structural changes in proteins such as those caused by post-translational modifications (PTMs) and mutations, which can affect protein structures or interactions and diversify protein functions. Here, we discussed the current application of a suite of stability proteomics methods, including thermal proteome profiling (TPP), stability of proteomics from rates of oxidation (SPROX), and limited proteolysis (LiP) methods, to observe PTM-induced structural changes on protein stability. We also discuss future perspectives highlighting the integration of top-down mass spectrometry and stability proteomics methods to characterize intact proteoform stability and understand the function of variable protein modifications.

Unsupervised neural network for single cell Multi-omics INTegration (UMINT): an application to health and disease.

Multi-omics studies have enabled us to understand the mechanistic drivers behind complex disease states and progressions, thereby providing novel and actionable biological insights into health status. However, integrating data from multiple modalities is challenging due to high dimensionality and diverse nature of data, and noise associated with each platform. Sparsity in data, non-overlapping features and technical batch effects make the task of learning more complicated. Conventional machine learning (ML) tools are not quite effective against such data integration hazards due to their simplistic nature with less capacity. In addition, existing methods for single cell multi-omics integration are computationally expensive. Therefore, in this work, we have introduced a novel Unsupervised neural network for single cell Multi-omics INTegration (UMINT). UMINT serves as a promising model for integrating variable number of single cell omics layers with high dimensions. It has a light-weight architecture with substantially reduced number of parameters. The proposed model is capable of learning a latent low-dimensional embedding that can extract useful features from the data facilitating further downstream analyses. UMINT has been applied to integrate healthy and disease CITE-seq (paired RNA and surface proteins) datasets including a rare disease Mucosa-Associated Lymphoid Tissue (MALT) tumor. It has been benchmarked against existing state-of-the-art methods for single cell multi-omics integration. Furthermore, UMINT is capable of integrating paired single cell gene expression and ATAC-seq (Transposase-Accessible Chromatin) assays as well.

Implementation of an integrated ambulatory care pharmacist collaborative care workflow model in specialty clinics in a large academic health system.

Ambulatory care and specialty pharmacy practices are rapidly growing in tandem with the accelerated development of advanced therapies for complex disease states. A coordinated and standardized interprofessional team-based approach is critical to providing high-quality care to specialty patients on complex, expensive, and high-risk therapies. Yale New Haven Health System dedicated resources to the creation of a medication management clinic under a unique care model that integrates ambulatory care pharmacists within specialty clinics who coordinate with centralized specialty pharmacists. The new care model workflow encompasses ambulatory care pharmacists, specialty pharmacists, ambulatory care pharmacy technicians, specialty pharmacy liaisons, clinicians, and clinic support staff. The strategies employed to design, implement, and optimize this workflow to meet the increasing demand for pharmacy support in specialty care is discussed. The workflow incorporated key activities from highly diverse existing specialty pharmacy, ambulatory care pharmacy, and specialty clinic practices. Standard processes were developed for patient identification, referral placement, visit scheduling, encounter documentation, medication fulfillment, and clinical follow-up. Resources were created or optimized to support successful implementation, including an electronic pharmacy referral, specialty collaborative practice agreements to facilitate pharmacist-led comprehensive medication management, and a standardized note template. Communication strategies were developed to facilitate feedback and process updates. Enhancements focused on eliminating documentation redundancies and delegating nonclinical tasks to a dedicated ambulatory care pharmacy technician. The workflow was implemented in 5 ambulatory rheumatology, digestive health, and infectious diseases clinics. Pharmacists utilized this workflow to complete 1,237 patient visits, serving 550 individual patients over 11 months. This initiative created a standard workflow to support an interdisciplinary standard of specialty patient care that is robust to accommodate planned expansion. This workflow implementation approach can serve as a road map for other healthcare systems with integrated specialty and ambulatory pharmacy departments undertaking similar models for specialty patient management.

Energy Dynamics in Chronic Heart Failure, Chronic Kidney Disease & the Cardiorenal Syndrome: A New Causal Paradigm

In this paper we examine HF and CKD through the lens of a new causal paradigm: as a conjoined energy deficiency syndrome whose progression is governed by impaired energy generation by the cardiovascular system leading to subsequent deterioration of blood energy dynamics. Due to the immense complexity of this topic our intention is not to go into unnecessary and laborious detail on specific cellular and molecular aspects of the syndromes but, instead, to use illustrative examples to establish overarching principles and to highlight the connection between the deteriorating energy field and all resultant aberrations. Indeed, once the energy dynamics are recognized cellular and molecular descriptions become superfluous. Our use of the term 'causal' is meant to imply that future breakthroughs in the treatment (and potential reversal) of these complex disease states necessarily entails recognition and correction of underlying energy deficits.

Multiomics Analysis Reveals Novel Genetic Determinants for Lens Differentiation, Structure, and Transparency.

Recent advances in next-generation sequencing and data analysis have provided new gateways for identification of novel genome-wide genetic determinants governing tissue development and disease. These advances have revolutionized our understanding of cellular differentiation, homeostasis, and specialized function in multiple tissues. Bioinformatic and functional analysis of these genetic determinants and the pathways they regulate have provided a novel basis for the design of functional experiments to answer a wide range of long-sought biological questions. A well-characterized model for the application of these emerging technologies is the development and differentiation of the ocular lens and how individual pathways regulate lens morphogenesis, gene expression, transparency, and refraction. Recent applications of next-generation sequencing analysis on well-characterized chicken and mouse lens differentiation models using a variety of omics techniques including RNA-seq, ATAC-seq, whole-genome bisulfite sequencing (WGBS), chip-seq, and CUT&RUN have revealed a wide range of essential biological pathways and chromatin features governing lens structure and function. Multiomics integration of these data has established new gene functions and cellular processes essential for lens formation, homeostasis, and transparency including the identification of novel transcription control pathways, autophagy remodeling pathways, and signal transduction pathways, among others. This review summarizes recent omics technologies applied to the lens, methods for integrating multiomics data, and how these recent technologies have advanced our understanding ocular biology and function. The approach and analysis are relevant to identifying the features and functional requirements of more complex tissues and disease states.

Abstract 2392: Multi-dimensional morphology analysis enables identification and label-free enrichment of heterogeneous tumor cell populations

Abstract Studying tumor heterogeneity at single cell resolution is crucial for elucidating tumor progression and treatment response. Classically, tumor cells are characterized with a limited set of biomarkers that cannot cover the full extent of the tumor cell heterogeneity. The advent of single cell genomics has enabled scientists to study heterogeneity at high resolution, but gene expression signatures may not translate to readily available biomarkers for functional studies. Multi-dimensional morphology analysis has the potential for higher resolution than cell surface biomarkers while also reducing cell manipulations. The Deepcell platform enables multi-dimensional morphology analysis of unlabeled single cells using artificial intelligence (AI), imaging, and microfluidics, allowing for higher resolution of population heterogeneity beyond protein expression markers. Using the Deepcell platform, we trained an AI model to identify and sort for malignant cells from non-small cell lung cancer cells (NSCLC) tumor biopsies based on morphology. scRNA-seq, CNV, and targeted mutation analysis verified enrichment of carcinoma cells. Sorted cells had gene expression signatures indicative of tumor cells (e.g. EpCAM expression), increased genomic alterations by CNV analysis, and increased allele frequency of P53 and KRAS mutations relative to pre-sorted samples. Overall, the data indicate brightfield images of cells can be used to detect macro-level changes in cell morphology resulting from the molecular events involved in tumorigenesis. In addition, we induced chemotherapeutic drug resistance of lung cancer cell lines in vitro, imaged the resulting cultures on the Deepcell platform, and used the bright-field cell images to generate multi-dimensional morphological embeddings that can be visualized by UMAP. The resulting UMAPs showed distinct clusters of cells for both the parent and resistant cell lines, indicating that there are morphological differences associated with drug resistance. We developed a random forest classifier to identify the top differential morphological features between parental and resistant cell lines. These top features can distinguish between the populations with high accuracy (87%). Finally, we performed multi-dimensional morphology analysis to compare lung adenocarcinoma (LA) and squamous cell carcinoma (SCC) cell lines. The classifier predicted the correct cell type with 75% accuracy, suggesting that differences between these two carcinomas are reflected in their morphology. Together, our data suggest that AI-detected morphological differences between cell populations may show a biologically significant link between morphology and phenotype. As such, multi-dimensional morphology analysis will bolster the understanding of complex disease states and tumor microenvironment, particularly in patient derived biopsies. Citation Format: Andreja Jovic, Kiran Saini, Ryan Carelli, Tiffine Pham, Christian Corona, Jeanette Mei, Michael Phelan, Stephane C. Boutet, Kevn Jacobs, Julie Kim, Manisha Ray, Chassidy Johnson, Nianzhen Li, Mahyar Salek, Maddison Masaeli, Matt Barnes, Cyril Ramathal. Multi-dimensional morphology analysis enables identification and label-free enrichment of heterogeneous tumor cell populations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2392.

Role of the Endocannabinoid System in Metabolic Control Processes and in the Pathogenesis of Metabolic Syndrome: An Update

Metabolic syndrome is a complex disease state, which appears mostly as a consequence of an unhealthy, sedentary lifestyle. Metabolic complications include insulin resistance (IR), diabetes, dyslipidemia, hypertension, and atherosclerosis, impairing life standards and reducing life expectancy. The endocannabinoid system (ECS) has an important role in signalization processes, not only in the central nervous system, but also in the peripheral tissues. Several physiological functions are affected, and overexpression or downregulation contributes to several diseases. A better understanding of the functions of cannabinoid (CB) receptors may propose potential therapeutic effects by influencing receptor signaling and enzymes involved in downstream pathways. In this review, we summarize recent information regarding the roles of the ECS and the CB1 receptor signaling in the physiology and pathophysiology of energy and metabolic homeostasis, in the development of obesity by enhancing food intake, upregulating energy balance and fat accumulation, increasing lipogenesis and glucose production, and impairing insulin sensitivity and secretion. By analyzing the roles of the ECS in physiological and pathophysiological mechanisms, we introduce some recently identified signaling pathways in the mechanism of the pathogenesis of metabolic syndrome. Our review emphasizes that the presence of such recently identified ECS signaling steps raises new therapeutic potential in the treatment of complex metabolic diseases such as diabetes, insulin resistance, obesity, and hypertension.

Autoimmune encephalitis-Challenges and management in intensive medical care

Despite relevant improvements in the diagnostics and treatment of autoimmune encephalitis (AE), severely affected patients still need treatment on the intensive care unit (ICU). Such complex disease states are sometimes difficult to bring under control and ICU complications have a negative influence on the outcome of treatment. A rapid diagnosis and timely initiation of immunotherapy are crucial to minimize ICU treatment and to avoid potentially severe complications. This article outlines the ICU treatment of autoimmune encephalitis and describes the most common challenges and complications of (neuro)intensive medical care and their management.

A Discussion of the Contemporary Prediction Models for Atrial Fibrillation.

Atrial Fibrillation is a complex disease state with many environmental and genetic risk factors. While there are environmental factors that have been shown to increase an individual's risk of atrial fibrillation, it has become clear that atrial fibrillation has a genetic component that influences why some patients are at a higher risk of developing atrial fibrillation compared to others. This review will first discuss the clinical diagnosis of atrial fibrillation and the corresponding rhythm atrial flutter. We will then discuss how a patients' risk of stroke can be assessed by using other clinical co-morbidities. We will then review the clinical risk factors that can be used to help predict an individual patient's risk of atrial fibrillation. Many of the clinical risk factors have been used to create several different risk scoring methods that will be reviewed. We will then discuss how genetics can be used to identify individuals who are at higher risk for developing atrial fibrillation. We will discuss genome-wide association studies and other sequencing high-throughput sequencing studies. Finally, we will touch on how genetic variants derived from a genome-wide association studies can be used to calculate an individual's polygenic risk score for atrial fibrillation. An atrial fibrillation polygenic risk score can be used to identify patients at higher risk of developing atrial fibrillation and may allow for a reduction in some of the complications associated with atrial fibrillation such as cerebrovascular accidents and the development of heart failure. Finally, there is a brief discussion of how artificial intelligence models can be used to predict which patients will develop atrial fibrillation.