Personalized Disease Management Research Articles

Pharmacotherapy from Pre-COVID to Post-COVID: Longitudinal Trends and Predictive Indicators for Long COVID Symptoms

Background/objectives: A significant number of COVID-19 cases experience persistent symptoms after the acute infection phase, a condition known as long COVID or post-acute sequelae of COVID-19. Approved prevention and treatment options for long COVID are currently lacking. Given the heterogeneous nature of long COVID, a personalized medicine approach is essential for effective disease management. This study aimed to describe trends in pharmacotherapy from pre-COVID to post-COVID phases to gain insights into COVID-19 treatment strategies and assess whether pre-COVID pharmacotherapy can predict long COVID symptoms as a health status indicator. Methods: In the Precision Medicine for more Oxygen (P4O2) COVID-19 study, 95 long COVID patients were comprehensively evaluated through post-COVID outpatient clinics and study visits. This study focused on descriptive analysis of the pharmacotherapy patterns across different phases: pre-COVID-19, acute COVID, and post-COVID. Furthermore, associations between pre-COVID medication and long COVID outcomes were analyzed with regression analyses. Results: We observed peaks in the use of certain medications during the acute infection phase, including corticosteroids and antithrombotic agents, with a decrease in the use of renin–angiotensin system inhibitors. Consistently high use of alimentary tract medications was found across all phases. Pre-COVID respiratory medications were associated with fatigue symptoms, while antiinfectives and cardiovascular drugs were linked to fewer persisting long COVID symptom categories. Conclusion: Our findings provide longitudinal, descriptive pharmacotherapy insights and suggest that medication history can be a valuable health status indicator in characterizing patients for personalized disease management strategies, considering the heterogeneous nature of long COVID.

AI-driven ethical issues in the development of personalised therapy for pediatric SIRS patients

Abstract Background Systemic Inflammatory Response Syndrome (SIRS) is a rare disease often presenting emergency. While the aetiology varies, current diagnostic criteria do not allow differentiation. Hopes are placed on OMICs research and development of Artificial intelligence (AI) algorithms for personalised disease management. Our report aims at identification and analysis of AI-driven ethical issues within the bigger ERA PerMed funded project of development of tailored immunotherapy for paediatric SIRS patients (TIPS) coordinated by Prof. Catharina Schütz from Technic University Dresden. Methods Systematic literature review followed by qualitative research methodology. Ten experts with different backgrounds were interviewed. Subsequent thematic analysis was performed by two independent researchers. Results Experts were on the opinion that AI can lead to a depart from individual approach in the medicine, as the physician may be induced to only act based on algorithms that are more or less representative (N = 2). Interviewees were aware that AI can lead to changes in the assessment of reality, as it is dependent on data quality (N = 4). AI-aided decision-making might be useful for special therapeutic situations, although it may not always capture the subtleties of individual situations (N = 2). Positive implications of AI were significantly more emphasised, though current frameworks were perceived as more limiting than enabling. Conclusions Personalised therapy is making its first steps while the ethical apprehensions has already been there with a strong emphasis on precautionary principle and resulting limitations on the application of AI-algorithms. The multifaceted nature of the AI-driven ethical issues in personalised medicine research requires an interdisciplinary discourse, strengthened ethics training of all professionals involved in the technology development and more active involvement of physicians in the shaping of AI. Key messages • AI algorithms promise therapeutic benefits, not achievable by other means, for specific categories of vulnerable pediatrics patients. • The multifaceted nature of the AI-driven ethical issues in personalised medicine research requires an interdisciplinary discourse and further ethics training of all stakeholders.

Precision health: Determining the capacity of practicing nurses across the United States.

Precision Health (PH) holds the promise of revolutionizing healthcare by enabling personalized disease prevention and management through the integration of genomic data, lifestyle factors, environmental influences, and other social determinants of health (SDoH). However, the absence of a baseline assessment of knowledge, skills, and attitudes (KSAs) of practicing nurses' capacity for PH hinders its integration. The purpose of this study is to determine the capacity of practicing Registered Nurses (RNs) for PH across the United States and to assess the validity and reliability of a tool designed for this use-the Precision Health Nurse Capacity Scale (PHNCS). A descriptive exploratory study was conducted to evaluate the capacity of practicing RNs for this evolving phenomenon, PH, using a convenience sample. The survey was sent via email and made available to all members of the American Nurses Association (ANA) who work in a variety of practice environments. The ANA represents the over 4 million nurses practicing in the United States. The majority of nurse respondents felt it is important for nurses to become more educated about all aspects of PH including SDoH but they lack confidence in the integration of PH. The PHNCS was found to be a valid and reliable tool in measuring the capacity of nurses to practice PH. The incorporation of PH into nursing practice suffers an immediate impediment: the lack of know-how of the US nursing workforce. This inaugural data on KSAs for PH establishes a logical baseline from which the requisite education and training should commence. Precision Health is an emerging healthcare approach in the United States and globally. Enabling it will require a nursing workforce prepared with the requisite KSAs. Determining the capacity of the nursing workforce is a foundational step to begin this process.

Experiences and perceptions of patients with ankylosing spondylitis: A systematic review and meta-synthesis of qualitative studies.

The systematic evaluation of relevant qualitative studies on the experiences of patients with ankylosing spondylitis provides a foundation for the clinical development of personalized disease management programs for this patient category. Multiple databases, including PubMed, Web of Science, Embase, Cochrane Library, CINAHL, Scopus, CNKI, Wanfang, CBM, and VIP, were searched for qualitative research literature on the experiences of patients with ankylosing spondylitis from the inception of databases to March 2024. Eligible studies were included, and quality was assessed using the quality evaluation standard of qualitative research at the Joanna Briggs Institute (JBI), Australia (2016). The results were integrated using the meta-aggregation approach. A total of 11 papers were included in the review. Four themes and 10 subthemes were synthesized: (1) difficulties in diagnosis and treatment; (2) effects of disease symptoms; (3) maladjustment of social roles; and (4) lack of support for disease response. Medical staff should leverage the advantages of the Internet to enhance knowledge and education on ankylosing spondylitis. They should focus on patients' mental health, assist in active self-management, provide personalized interventions, promote recovery, and improve the quality of life for patients. Additionally, society should offer a diverse range of support.

Advancements in electrochemical immunosensors towards point-of-care detection of cardiac biomarkers.

Cardiovascular disease remains the leading cause of death worldwide, with mortality rates increasing annually. This underscores the urgent need for accurate diagnostic and monitoring tools. Electrochemical detection has emerged as a promising method for swiftly and precisely measuring specific biomarkers in bodily fluids. This approach is not only cost-effective and efficient compared to traditional clinical methods, but it can also be tailored to detect individual biomarkers, which makes it particularly well-suited for point-of-care (POC) applications. The ability to conduct testing at the point of care is crucial for timely interventions and personalized disease management, empowering healthcare providers to tailor treatment plans based on real-time biomarker data. Thanks to recent advancements in nanomaterials, we've seen significant progress in electrochemical detection, leading to the development of specialized rapid immunoassay systems. These systems utilize specific antibodies to target molecules, expanding the range of detectable biomarkers. This innovation has the potential to revolutionize the diagnosis and treatment of cardiovascular diseases by enhancing detection sensitivity and specificity. Ultimately, these advancements aim to improve patient outcomes by enabling earlier diagnosis, more precise monitoring, and personalized therapeutic interventions, which will contribute to more effective management of cardiovascular health globally.

A population-scale analysis of 36 gut microbiome studies reveals universal species signatures for common diseases

The gut microbiome has been implicated in various human diseases, though findings across studies have shown considerable variability. In this study, we reanalyzed 6314 publicly available fecal metagenomes from 36 case-control studies on different diseases to investigate microbial diversity and disease-shared signatures. Using a unified analysis pipeline, we observed reduced microbial diversity in many diseases, while some exhibited increased diversity. Significant alterations in microbial communities were detected across most diseases. A meta-analysis identified 277 disease-associated gut species, including numerous opportunistic pathogens enriched in patients and a depletion of beneficial microbes. A random forest classifier based on these signatures achieved high accuracy in distinguishing diseased individuals from controls (AUC = 0.776) and high-risk patients from controls (AUC = 0.825), and it also performed well in external cohorts. These results offer insights into the gut microbiome’s role in common diseases in the Chinese population and will guide personalized disease management strategies.

Development of an immunoprecipitation assay for detecting anti‐3‐hydroxy‐3‐methylglutaryl‐coenzyme A reductase autoantibodies using a non‐radioactive biotinylated recombinant protein

AbstractObjectivesA variety of myositis‐specific autoantibodies have been identified in sera from patients with idiopathic inflammatory myopathies, and they play a crucial role in tailoring personalized disease management. In particular, autoantibodies against 3‐hydroxy‐3‐methylglutaryl‐coenzyme A reductase (HMGCR) are now recognized as a key tool for diagnosing immune‐mediated necrotizing myopathy. The current gold standard for detecting anti‐HMGCR autoantibodies involves immunoprecipitation (IP) using radiolabeled proteins from cell extracts or purified proteins produced by in vitro transcription/translation (IVTT). Unfortunately, this radioisotope labeling is technically intricate and not suitable for routine laboratory use. To address this, we developed a novel assay called “Bio‐IVTT‐IP” for detecting anti‐HMGCR autoantibodies, which uses a nonradioactive biotinylated recombinant HMGCR protein produced by IVTT.MethodsWe collected 14 clinical specimens containing anti‐HMGCR autoantibodies from patients with immune‐mediated necrotizing myopathy, which were validated using the gold standard IP assay, and a set of 35 control samples from patients with other autoimmune diseases, such as systemic lupus erythematosus.ResultsThe Bio‐IVTT‐IP assay successfully identified 14 clinical samples positive for anti‐HMGCR. We confirmed that the performance of the Bio‐IVTT‐IP assay was completely consistent with that of the gold standard IP assay using radiolabeled proteins. Notably, no immunoprecipitates were found in control samples from patients with other autoimmune diseases.ConclusionsThese findings show that the Bio‐IVTT‐IP assay can serve as a potential alternative to the gold standard IP assay for detecting anti‐HMGCR autoantibodies. It can be considered a practical immunodiagnostic tool for diagnosing immune‐mediated necrotizing myopathy, particularly owing to its accessibility in routine laboratory settings.

AI Approach for Enhanced Thalassemia Diagnosis Using Blood Smear Images.

This paper aims to propose an approach leveraging Artificial Intelligence (AI) to diagnose thalassemia through medical imaging. The idea is to employ a U-net neural network architecture for precise erythrocyte morphology detection and classification in thalassemia diagnosis. This accomplishment was realized by developing and assessing a supervised semantic segmentation model of blood smear images, coupled with the deployment of various data engineering techniques. This methodology enables new applications in tailored medical interventions and contributes to the evolution of AI within precision healthcare, establishing new benchmarks in personalized treatment planning and disease management.

Translational Utility of Organoid Models for Biomedical Research on Gastrointestinal Diseases.

Organoid models have recently been utilized to study 3D human-derived tissue systems to uncover tissuearchitecture and adultstem cellbiology. Patient-derived organoids unambiguously provide the most suitable in vitro system to study disease biology with the actual genetic background. With the advent of much improved and innovative approaches, patient-derived organoids can potentially be used in regenerative medicine. Various human tissues were explored to develop organoids due to their multifold advantage over the conventional in vitro cell line culture approach and in vivo models. Gastrointestinal (GI) tissues have been widely studied to establish organoids and organ-on-chip for screening drugs, nutraceuticals, and other small molecules having therapeutic potential. The function of channel proteins, transporters, and transmembrane proteins was also explained. The successful application of genome editing in organoids using the CRISPR-Cas approach has been reported recently. GI diseases such as Celiac disease (CeD), Inflammatory bowel disease (IBD), and common GI cancers have been investigated using several patient-derived organoid models. Recent advancements on organoid bio-banking and 3D bio-printing contributed significantly in personalized disease management and therapeutics. This article reviews the available literature on investigations and translational applications of patient-derived GI organoid models, notably on elucidating gut-microbial interaction and epigenetic modifications.

404-error "Disease not found": Unleashing the translational potential of -omics approaches beyond traditional disease classification in heart failure research.

The emergence of personalized medicine, facilitated by the progress in -omics technologies, has initiated a new era in medical diagnostics and treatment. This review examines the potential of -omics approaches in heart failure, a condition that has not yet fully capitalized on personalized strategies compared to other medical fields like cancer therapy. Here, we argue that integrating multi-omics technology with systems medicine approaches could fundamentally transform heart failure management, moving away from the traditional paradigm of 'one size fits all'. Our review examines how omics can enhance understanding of heart failure's molecular foundations and contribute to a more comprehensive disease classification. We draw attention to the current state of medical practice that only relies on clinical evidence and a number of standard laboratory tests. At the same time, we propose a shift towards a universal approach that uses quantitative data from multi-omics to unravel complex molecular interactions. The discussion centres around the potential of the transition as a means to enhance individual risk assessment and emphasizes management within clinical settings. While the use of omics in cardiovascular research is not recent, many past studies have focused only on a single omics approach. In order to achieve a better understanding of disease mechanisms, we explore more holistic approaches using genomics, transcriptomics, epigenomics, and proteomics. This review concludes with a call to action to adopt multi-omics in clinical trials and practice to pave the way for more personalized disease management and more effective heart failure interventions.

Direct Laser Processing and Functionalizing PI/PDMS Composites for an On-Demand, Programmable, Recyclable Device Platform.

Skin-interfaced high-sensitive biosensing systems to detect electrophysiological and biochemical signals have shown great potential in personal health monitoring and disease management. However, the integration of 3D porous nanostructures for improved sensitivity and various functional composites for signal transduction/processing/transmission often relies on different materials and complex fabrication processes, leading to weak interfaces prone to failure upon fatigue or mechanical deformations. The integrated system also needs additional adhesive to strongly conform to the human skin, which can also cause irritation, alignment issues, and motion artifacts. This work introduces a skin-attachable, reprogrammable, multifunctional, adhesive device patch fabricated by simple and low-cost laser scribing of an adhesive composite with polyimide powders and amine-based ethoxylated polyethylenimine dispersed in the silicone elastomer. The obtained laser-induced graphene in the adhesive composite can be further selectively functionalized with conductive nanomaterials or enzymes for enhanced electrical conductivity or selective sensing of various sweat biomarkers. The possible combination of the sensors for real-time biofluid analysis and electrophysiological signal monitoring with RF energy harvesting and communication promises a standalone stretchable adhesive device platform based on the same material system and fabrication process.

Personalized decision support system for tailoring IgA nephropathy treatment strategies

BackgroundThe ongoing debate surrounding the use of immunosuppressive treatments for IgA nephropathy (IgAN) underscores the demand for personalized and effective strategies. MethodsAnalyzed data from 807 IgAN patients over 5+ years using three methods: Random Forest with molecular biomarkers, network biomarkers with graph engineering, and an auto-encoder model. All models were trained using identical demographic, clinical, and pathological data, employing an 80–20 split for training and testing purposes. ResultsIn the comprehensive assessment of IgAN prognosis, the Random Forest model, employing molecular biomarkers, demonstrated strong performance metrics (AUC = 0.83, sensitivity = 0.51, specificity = 0.96). However, traditional graph feature engineering on patient-specific networks outperformed these results with an AUC of 0.90, sensitivity of 0.64, and specificity of 0.94. The Auto-encoder model showed the best accuracy (AUC = 0.91, sensitivity = 0.46, specificity = 0.96). The findings highlighted the superior predictive capabilities of network biomarkers over molecular biomarkers for adverse renal outcome prediction in IgAN. Consequently, we integrated Auto-encoder-derived Network Biomarkers with Random Forest Models to enhance prognostic precision in diverse IgAN treatment scenarios. The prediction for the prognosis of patients receiving supportive care, glucocorticoid therapy, and immunosuppressant treatment yielded AUC values of 0.95, 0.96, and 1, respectively, indicating high specificity. Drawing from these insights, we pioneered the development of an innovative decision support model for IgAN treatment. This model demonstrated the ability to make medical decisions comparable to those by experienced nephrologists, enabling the customization of personalized disease management strategies. ConclusionOur system accurately predicted IgAN prognosis and evaluated various treatment efficacies, aiding physicians in devising optimal therapeutic strategies for patients.

Stratification of Kidney Transplant Recipients Into Five Subgroups Based on Temporal Disease Trajectories.

Kidney transplantation is the treatment of choice for patients with end-stage renal disease. Considerable clinical research has focused on improving graft survival and an increasing number of kidney recipients die with a functioning graft. There is a need to improve patient survival and to better understand the individualized risk of comorbidities and complications. Here, we developed a method to stratify recipients into similar subgroups based on previous comorbidities and subsequently identify complications and for a subpopulation, laboratory test values associated with survival. First, we identified significant disease patterns based on all hospital diagnoses from the Danish National Patient Registry for 5752 kidney transplant recipients from 1977 to 2018. Using hierarchical clustering, these longitudinal patterns of diseases segregate into 3 main clusters of glomerulonephritis, hypertension, and diabetes. As some recipients are diagnosed with diseases from >1 cluster, recipients are further stratified into 5 more fine-grained trajectory subgroups for which survival, stratified complication patterns as well as laboratory test values are analyzed. The study replicated known associations indicating that diabetes and low levels of albumin are associated with worse survival when investigating all recipients. However, stratification of recipients by trajectory subgroup showed additional associations. For recipients with glomerulonephritis, higher levels of basophils are significantly associated with poor survival, and these patients are more often diagnosed with bacterial infections. Additional associations were also found. This study demonstrates that disease trajectories can confirm known comorbidities and furthermore stratify kidney transplant recipients into clinical subgroups in which we can characterize stratified risk factors. We hope to motivate future studies to stratify recipients into more fine-grained, homogenous subgroups to better discover associations relevant for the individual patient and thereby enable more personalized disease-management and improve long-term outcomes and survival.

Quantification of smooth muscle in human airways by polarization-sensitive optical coherence tomography requires correction for perichondrium.

Quantifying airway smooth muscle (ASM) in patients with asthma raises the possibility of improved and personalized disease management. Endobronchial polarization-sensitive optical coherence tomography (PS-OCT) is a promising quantitative imaging approach that is in the early stages of clinical translation. To date, only animal tissues have been used to assess the accuracy of PS-OCT to quantify absolute (rather than relative) ASM in cross sections with directly matched histological cross sections as validation. We report the use of whole fresh human and pig airways to perform a detailed side-by-side qualitative and quantitative validation of PS-OCT against gold-standard histology. We matched and quantified 120 sections from five human and seven pig (small and large) airways and linked PS-OCT signatures of ASM to the tissue structural appearance in histology. Notably, we found that human cartilage perichondrium can share with ASM the properties of birefringence and circumferential alignment of fibers, making it a significant confounder for ASM detection. Measurements not corrected for perichondrium overestimated ASM content several-fold (P < 0.001, paired t test). After careful exclusion of perichondrium, we found a strong positive correlation (r = 0.96, P < 0.00001) of ASM area measured by PS-OCT and histology, supporting the method's application in human subjects. Matching human histology further indicated that PS-OCT allows conclusions on the intralayer composition and in turn potential contractile capacity of ASM bands. Together these results form a reliable basis for future clinical studies.NEW & NOTEWORTHY Polarization-sensitive optical coherence tomography (PS-OCT) may facilitate in vivo measurement of airway smooth muscle (ASM). We present a quantitative validation correlating absolute ASM area from PS-OCT to directly matched histological cross sections using human tissue. A major confounder for ASM quantification was observed and resolved: fibrous perichondrium surrounding hyaline cartilage in human airways presents a PS-OCT signature similar to ASM for birefringence and optic axis orientation. Findings impact the development of automated methods for ASM segmentation.

High-Grade Infection after Branched Endovascular Aortic Repair in Patient with Recent COVID-19 Hospitalization.

In the context of the COVID-19 pandemic, the global healthcare landscape has undergone significant transformations, particularly impacting the management of complex medical conditions such as aortic aneurysms. This study focuses on a 76-year-old female patient with a history of extensive cardiovascular surgeries, including aortic valve replacement, Bentall operation, and Frozen Elephant Trunk procedure, who presented with a type II thoracoabdominal aortic aneurysm post-COVID-19 recovery. A comprehensive frailty assessment using the Modified Frailty Index and a two-phase endovascular approach for aneurysm treatment, considering the patient's frailty and complex medical history was performed. Upon successful aneurysm management, the patient's postoperative course was complicated by COVID-19 reinfection and Enterococcus faecalis superinfection, highlighting the increased risk of bacterial superinfections and the challenges posed by antimicrobial resistance in COVID-19 patients. The study underscores the necessity of vigilant postoperative surveillance and a multidisciplinary approach in managing such complex cases, highlighting the importance of personalized care strategies, integrating cardiovascular and infectious disease management, and adapting healthcare practices to the unique challenges of the pandemic. This case contributes to the evolution of knowledge on managing aortic aneurysms in the COVID-19 era, advocating for patient-centric treatment approaches and continuous research into long-term patient outcomes.

Effects of cumulative cognitive function within 1-year of discharge on subsequent mortality among patients hospitalized for acute heart failure: A nationwide prospective cohort study.

To examine the association between cumulative cognitive function and subsequent mortality among patients hospitalized for acute heart failure (AHF). Based on a prospective cohort of patients hospitalized for AHF, cognitive function was measured by using Mini-Cog test at admission, 1- and 12-month following discharge. Cumulative cognitive function was interpreted by cumulative Mini-Cog score and cumulative times of cognitive impairment. Outcomes included subsequent all-cause and cardiovascular mortality. 1454 patients hospitalized for AHF with median follow-up of 4.76 (interquartile range [IQR]: 4.18-5.07) years were included. Tertile 1 of cumulative Mini-Cog score had the highest risk of all-cause (hazard ratio [HR]: 1.52, 95% confidence interval [CI]: 1.14-2.03) and cardiovascular mortality (HR: 1.40, 95% CI: 1.02-1.93) compared with Tertile 3; patients with ≥ 2 times of cognitive impairment had the highest risk of all-cause (HR: 1.34, 95% CI: 1.03-1.73) and cardiovascular mortality (HR: 1.25, 95% CI: 0.93-1.67) compared with patients without any cognitive impairment. Cumulative Mini-Cog score provided the highest incremental prognostic ability in predicting all-cause (C-statistics: 0.64, 95% CI: 0.61-0.66) and cardiovascular mortality (C-statistics: 0.63, 95% CI: 0.60-0.67) risk on the basis of Get With The Guidelines-Heart Failure score. Poor cumulative cognitive function was associated with increased risk of subsequent mortality and provided incremental prognostic ability for the outcomes among patients with AHF. Longitudinal assessment and monitoring of cognitive function among patients with AHF would be of great importance in identifying patients at greater risk of self-care absence for optimizing personal disease management in clinical practice.

Exploring Patterns of COPD Exacerbations and Comorbid Flare-Ups.

Comorbidities are known to complicate disease management in patients with Chronic Obstructive Pulmonary Disease (COPD). This is partly due to lack of insight into the interplay of acute exacerbations of COPD (AECOPD) and comorbid flare-ups. This study aimed to explore patterns of AECOPDs and comorbid flare-ups. Data of increased symptoms were extracted from a 12-month daily symptom follow-up database including patients with COPD and comorbidities (chronic heart failure (CHF), anxiety, depression) and transformed to visualizations of AECOPDs and comorbid flare-up patterns over time. Patterns were subsequently categorized using an inductive approach, based on both predominance (ie, which occurs most often) of AECOPDs or comorbid flare-ups, and their simultaneous (ie, simultaneous start in ≥ 50%) occurrence. We included 48 COPD patients (68 ± 9 years; comorbid CHF: 52%, anxiety: 40%, depression: 38%). In 25 patients with AECOPDs and CHF flare-ups, the following patterns were identified: AECOPDs predominant (n = 14), CHF flare-ups predominant (n = 5), AECOPDs nor CHF flare-ups predominant (n = 6). Of the 24 patients with AECOPDs and anxiety and/or depression flare-ups, anxiety and depression flare-ups occurred simultaneously in 15 patients. In 9 of these 24 patients, anxiety or depression flare-ups were observed independently from each other. In 31 of the included 48 patients, AECOPDs and comorbid flare-ups occurred mostly simultaneously. Patients with COPD and common comorbidities show a variety of patterns of AECOPDs and comorbid flare-ups. Some patients, however, show repetitive patterns that could potentially be used to improve personalized disease management, if recognized.

The mycosis fungoides cutaneous microenvironment shapes dysfunctional cell trafficking, antitumor immunity, matrix interactions, and angiogenesis.

Malignant T lymphocyte proliferation in mycosis fungoides (MF) is largely restricted to the skin, implying that malignant cells are dependent on their specific cutaneous tumor microenvironment (TME), including interactions with non-malignant immune and stromal cells, cytokines, and other immunomodulatory factors. To explore these interactions, we performed a comprehensive transcriptome analysis of the TME in advanced-stage MF skin tumors by single-cell RNA sequencing. Our analysis identified cell-type compositions, cellular functions, and cell-to-cell interactions in the MF TME that were distinct from those from healthy skin and benign dermatoses. While patterns of gene expression were common among patient samples, high transcriptional diversity was also observed in immune and stromal cells, with dynamic interactions and crosstalk between these cells and malignant T lymphocytes. This heterogeneity mapped to processes such as cell trafficking, matrix interactions, angiogenesis, immune functions, and metabolism that affect cancer cell growth, migration, and invasion, as well as antitumor immunity. By comprehensively characterizing the transcriptomes of immune and stromal cells within the cutaneous microenvironment of individual MF tumors, we have identified patterns of dysfunction common to all tumors that represent a resource for identifying candidates with therapeutic potential as well as patient-specific heterogeneity that has important implications for personalized disease management.

Treat-to-target strategies for the management of familial Mediterranean Fever in children

BackgroundThe objective of this initiative was to develop a treat-to-target (T2T) approach for the management of patients with Familial Mediterranean Fever (FMF), including the definition of a complex treatment target, and establish strategies that improve patient care and long-term outcome.MethodsAn initial set of statements as well as a flow chart visualising the proposed concept was developed. To adapt the preliminary statements to the current state of knowledge, a systematic literature search was performed and the modified statements were subject to a Delphi approach. To ensure the applicability of the statements in daily practice, an online survey was conducted among paediatric rheumatologists in Germany. In addition, data from the national AID-NET registry were analysed with respect to therapeutic response.ResultsThis T2T initiative yielded a total of 26 statements guiding FMF management with respect to diagnosis, treatment targets, treatment strategies and monitoring. The online survey identified cut-off values for inflammatory markers indicating treatment intensification and appropriate measures in case of colchicine intolerance or non-adherence. The analysis of data derived from the national AID-NET showed that colchicine therapy was successfully terminated in 61% of patients (27 out of 44) with heterozygous MEFV mutations. Multidimensional treatment targets incorporating objective and subjective reported outcome measures were developed. These provide the basis for stratifying patients into the following treatment paths: continue colchicine, persisting attacks / inflammation, colchicine intolerance, persisting arthritis, colchicine reduction and adjustment/reduction of biologics.ConclusionsThe proposed consensus treatment plan for the management of FMF incorporates multidimensional targets allowing transparent treatment decisions, which will promote personalised disease management and increase adherence to therapy.

Determination of prognostic predictors in patients with solitary hepatocellular carcinoma: histogram analysis of multiparametric MRI.

To evaluate the histogram parameters of preoperative multiparametric magnetic resonance imaging (MRI) and clinical-radiological (CR) characteristics as prognostic predictors in patients with solitary hepatocellular carcinoma ≤ 5 cm and to determine the optimal time window for histogram analysis. We retrospectively included 151 patients who underwent preoperative MRI between January 2012 and December 2017. All patients were randomly separated into training and validation cohorts (n = 105 and 46). Eight whole-lesion histogram parameters were extracted from T2-weighted images, apparent diffusion coefficient maps, and dynamic contrast-enhanced images. Univariate and multivariate logistic regression analyses were performed to evaluate these histogram parameters and CR variables related to early recurrence (ER) and recurrence-free survival. A nomogram was derived from the clinical-radiological-histogram (CRH) model that incorporated these risk factors. Kaplan-Meier survival analysis was performed to evaluate the prognostic performance of the CRH model. In total, 151 patients (male: female, 130: 21; median age, 54.46 ± 9.09 years) were evaluated. Multivariate logistic regression analysis revealed that the significant risk factors of ER were Mean Absolute Deviation and Minimum in the histogram analysis of the delayed phase images, as well as three important CR variables: albumin-bilirubin grade, microvascular invasion, and tumor size. The nomogram built by incorporating these risk factors showed satisfactory predictive ability in the training and validation cohorts with AUC values of 0.747 and 0.765, respectively. Furthermore, the prognostic nomogram can effectively classify patients into high- and low-risk groups (p < 0.05). Multiparametric MRI-derived histogram parameters provide additional value in predicting patient prognosis. The CRH model may be a useful and noninvasive method for achieving prognostic stratification and personalized disease management.