Complex Disease Research Articles

Network medicine informed multiomics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. We developed a network medicine methodology via integrating human brain multi-omics data to prioritize drug targets and repurposable treatments for ALS. We leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on human brain expression quantitative trait loci (QTL) (eQTL), protein QTL (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Using a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in known ALS pathobiological pathways. Applying network proximity analysis of predicted ALS-associated genes and drug-target networks under the human protein-protein interactome (PPI) model, we identified potential repurposable drugs (i.e., Diazoxide and Gefitinib) for ALS. Subsequent validation established preclinical evidence for top-prioritized drugs. In summary, we presented a network-based multi-omics framework to identify drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

A Narrative Review of Congenital Syphilis in the United States: Innovative Perspectives on a Complex Public Health and Medical Disease.

Over the past two decades, congenital syphilis cases have risen 11-fold in the United States. While disparities across geography, race, and ethnicity exist, lack of timely screening or treatment is identified in 88% of cases nationally. Congenital syphilis is a public health and medical problem rooted in systematic and societal structural determinants of health and healthcare limitations. Early syphilis in pregnancy leads to congenital syphilis if untreated in 50 - 70% of cases, with risk for fetal demise, and among survivors, congenital anomalies, organ damage, and central nervous system disease. Prevention of congenital syphilis lies in early detection and treatment in pregnant persons. In this narrative review, we describe the evolving epidemiology of syphilis and congenital syphilis, highlighting unique aspects among women. We explore the role of novel screening and treatment strategies, public health policy, and medical considerations in terms of congenital syphilis prevention. Readers of this review will understand CS as a complex public health and medical disease that can be prevented through innovative and coordinated strategies in public health policy, expanded screening and research opportunities.

Evaluation of the anticancer properties of the phytochemicals present in <em>Annona muricata</em>

Cancer is a complex disease characterized by the unrestrained proliferation and dissemination of abnormal cells. The issue continues to be a notable public health concern on a global scale, with millions of new cases and fatalities being reported annually. Despite significant advances in cancer treatment, it remains a major challenge due to the high cost and toxicity of current therapies. Phytochemicals naturally found in plants are crucial resources for developing novel drugs and potential cancer therapies. The clinical efficacy of these anti-cancer agents has been demonstrated against diverse neoplastic cell lines. Additional investigation in this field can result in improved cancer therapies. Among many other plants effective against cancer, this literature review explores the anti-cancer properties of Annona muricata, a plant commonly known as soursop, which is a tropical plant species native to Central and South America, the Caribbean, and West Africa known for its edible fruit which has some medicinal merits, but also some toxicological effects. Annona muricata has been traditionally used for medicinal purposes, including cancer treatment. Acetogenins, alkaloids, phenolic compounds, and other active compounds are only a few of the phytochemicals that this review cites as being present in A. muricata. The review also looks into A. muricata’s biological abilities to fight lung, liver, prostate, colon, pancreas, and breast cancers. The toxicity of A. muricata and possible adverse effects are also covered in the review. The review also looks at the usage of nanoparticles with A. muricata as a cancer treatment. Overall, this literature review provides valuable insights into the potential anti-cancer properties of A. muricata and its potential as a therapeutic agent in cancer treatment.

Evaluating Advanced Machine Learning Models for Histopathological Diagnosis of Hansen Disease.

Leprosy is a neglected infectious disease caused by Mycobacterium leprae and Mycobacterium lepromatosis and remains a public health challenge in tropical regions. Therefore, the development of technological tools such as machine learning (ML) offers an opportunity to innovate strategies for improving the diagnosis of this complex disease. To validate the utility of different ML models for the histopathological diagnosis of Hansen disease. An observational study was conducted where 55 H&E-stained tissue slides from leprosy patients and 51 healthy skin controls were analyzed using microphotographs captured at various magnifications. These images were categorized based on histopathological findings and processed using the Cross-Industry Standard Process for Data Mining methodology for designing and training ML models. Five types of ML models were evaluated using standard metrics such as accuracy, sensitivity, and specificity, emphasizing data normalization as a fundamental step in optimizing model performance. The artificial neural network (ANN) model demonstrated an accuracy of 70%, sensitivity of 74%, and specificity of 65%, demonstrating its ability to identify leprosy cases with moderate precision. The receiver operating characteristic curve of the ANN model showed an area under the curve of 0.71. Conversely, models such as decision trees, logistic regression, and random forests showed similar accuracy results but with slightly lower sensitivity, potentially indicating a higher risk of false negatives in detecting leprosy-positive cases. The ANN model emerges as a promising alternative for leprosy detection. However, further refinement of these models is necessary to enhance their adaptability across different clinical settings and participation in patient care.

Triangulation for causal loop diagrams: constructing biopsychosocial models using group model building, literature review, and causal discovery

AbstractThe complex nature of many health problems necessitates the use of systems thinking tools like causal loop diagrams (CLDs) to visualize the underlying causal network and facilitate computational simulations of potential interventions. However, the construction of CLDs is limited by the constraints and biases of specific sources of evidence. To address this, we propose a triangulation approach that integrates expert and theory-driven group model building, literature review, and data-driven causal discovery. We demonstrate the utility of this triangulation approach using a case example focused on the trajectory of depressive symptoms in response to a stressor in healthy adults. After triangulation with causal discovery, the CLD exhibited (1) greater comprehensiveness, encompassing multiple research fields; (2) a modified feedback structure; and (3) increased transparency regarding the uncertainty of evidence in the model structure. These findings suggest that triangulation can produce higher-quality CLDs, potentially advancing our understanding of complex diseases.

A rare case of splenic marginal zone lymphoma with MYD88 mutation transformed into diffuse large B-cell lymphoma: case report and literature review.

In indolent lymphomas, histological transformation (HT) often results in a poor prognosis and presents a significant challenge in the management of these lymphomas. Previous studies have indicated that MYD88 mutations are associated with transformation in certain haematologic malignancies. We report a rare case of splenic marginal zone lymphoma (SMZL) harbouring an MYD88 mutation, which was transformed into diffuse large B-cell lymphoma (DLBCL) and accompanied by newly emerging genetic abnormalities. The role of the MYD88 gene in SMZL is currently unclear. Through this case, we reviewed relevant studies, which indicated that MYD88 mutations, along with other genetic anomalies, may play a significant role in this process. In the future, it is essential to collect more of these rare cases for further research.

Evaluation of Bayesian Linear Regression models for gene set prioritization in complex diseases.

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the model's performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Comparative analyses with MAGMA (Multi-marker Analysis of GenoMic Annotation) demonstrate BLR's superior performance, especially in highly overlapped gene sets. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR model's ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits.

GRIN2A and Schizophrenia: Scientific Evidence and Biological Mechanisms.

Schizophrenia is a severe psychiatric disorder and a complex polygenic inherited disease that affects nearly 1% of the global population. Although considerable progress has been made over the past 10 years in the treatment of schizophrenia, antipsychotics are not universally effective and may have serious side effects. The hypofunction of glutamate NMDA receptors (NMDARs) in GABAergic interneurons has long been postulated to be the principal pathophysiology of schizophrenia. A recent study has shown that GRIN2A pathogenic variants are closely related to the aetiology of the disorder. GRIN2A encodes the GluN2A protein, which is a subunit of NMDAR. Most GRIN2A variants have been predicted to cause protein truncation, which results in reduced gene expression. Preclinical studies have indicated that GRIN2A mutations lead to NMDAR loss of function and substantially increase the risk of schizophrenia; however, their role in schizophrenia is not well understood. We hypothesise that the heterozygous loss of GRIN2A induces NMDAR hypofunction sufficient to confer a substantial risk of schizophrenia. Therefore, this review focuses on GRIN2A as a target for novel antipsychotics and discusses the mechanisms by which GRIN2A modulates antischizophrenic activities. Moreover, our review contributes to the understanding of the pathophysiology of schizophrenia to facilitate finding treatments for the cognitive and negative symptoms of schizophrenia.

Development of Novel 3D Spheroids for Discrete Subaortic Stenosis.

In this study, we propose a new method for bioprinting 3D Spheroids to study complex congenital heart disease known as discrete subaortic stenosis (DSS). The bioprinter allows us to manipulate the extrusion pressure to change the size of the spheroids, and the alginate porosity increases in size over time. The spheroids are composed of human umbilical vein endothelial cells (HUVECs), and we demonstrated that pressure and time during the bioprinting process can modulate the diameter of the spheroids. In addition, we used Pluronic acid to maintain the shape and position of the spheroids. Characterization of HUVECs in the spheroids confirmed their uniform distribution and we demonstrated cell viability as a function of time. Compared to traditional 2D cell cultures, the 3D spheroids model provides more relevant physiological environments, making it valuable for drug testing and therapeutic applications.

Renewed Insight into Cancer Mechanism and Therapy: Advances, Challenges, and Future Directions

Cancer remains a complex and multifactorial disease characterized by genetic mutations, epigenetic changes, and alterations in cellular signaling pathways. While traditional treatments such as surgery, chemotherapy, and radiation have been the backbone of cancer therapy, their limitations, including drug resistance, recurrence, and significant side effects, have driven the search for more targeted and personalized treatment approaches. This review provides an in-depth exploration of recent advances in cancer research and therapy, focusing on molecular mechanisms, genetic alterations, and the tumor microenvironment, all of which play pivotal roles in tumor initiation and progression. Key discoveries in oncogenes, tumor suppressor genes, and immune evasion strategies have led to the development of targeted therapies and immunotherapies. These advancements have revolutionized the treatment landscape, with therapies like immune checkpoint inhibitors, CAR T-cell therapy, and personalized medicine offering improved patient outcomes. However, challenges such as tumor heterogeneity, acquired drug resistance, therapy-related toxicities, and the high costs of these novel treatments continue to limit their effectiveness and accessibility. The review also delves into emerging therapeutic strategies, including next-generation immunotherapies, precision oncology enhanced by artificial intelligence, tumor vaccines, microbiome-based therapies, and the application of nanotechnology for drug delivery. As cancer therapies evolve, addressing these challenges is crucial for future breakthroughs. The review emphasizes the need for continued research into overcoming resistance mechanisms, reducing treatment-related side effects, and enhancing global access to cutting-edge treatments. Looking ahead, the integration of new technologies and the development of more equitable healthcare strategies will be essential to ensuring that advances in cancer therapy translate into improved survival and quality of life for patients worldwide. In conclusion, while remarkable progress has been made in cancer treatment, a multifaceted approach that blends scientific innovation, precision medicine, and healthcare accessibility will be key to overcoming the ongoing challenges in the fight against cancer. Keywords: Cancer Mechanism, Therapy, Advances, Challenges, Future Directions

Leveraging Organ-on-Chip Models to Investigate Host-Microbiota Dynamics and Targeted Therapies for Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD) is an idiopathic gastrointestinal disease with drastically increasing incidence rates. Due to its multifactorial etiology, a precise investigation of the pathogenesis is extremely difficult. Although reductionist cell culture models and more complex disease models in animals have clarified the understanding of individual disease mechanisms and contributing factors of IBD in the past, it remains challenging to bridge research and clinical practice. Conventional 2D cell culture models cannot replicate complex host-microbiota interactions and stable long-term microbial culture. Further, extrapolating data from animal models to patients remains challenging due to genetic and environmental diversity leading to differences in immune responses. Human intestine organ-on-chip (OoC) models have emerged as an alternative in vitro model approach to investigate IBD. OoC models not only recapitulate the human intestinal microenvironment more accurately than 2D cultures yet may also be advantageous for the identification of important disease-driving factors and pharmacological interventions targets due to the possibility of emulating different complexities. The predispositions and biological hallmarks of IBD focusing on host-microbiota interactions at the intestinal mucosal barrier are elucidated here. Additionally, the potential of OoCs to explore microbiota-related therapies and personalized medicine for IBD treatment is discussed.

Neuroinflammatory Proteins in Huntington's Disease: Insights into Mechanisms, Diagnosis, and Therapeutic Implications.

Huntington's disease (HD) is a hereditary neurodegenerative disorder caused by a CAG tract expansion in the huntingtin gene (HTT). HD is characterized by involuntary movements, cognitive decline, and behavioral changes. Pathologically, patients with HD show selective striatal neuronal vulnerability at the early disease stage, although the mutant protein is ubiquitously expressed. Activation of the immune system and glial cell-mediated neuroinflammatory responses are early pathological features and have been found in all neurodegenerative diseases (NDDs), including HD. However, the role of inflammation in HD, as well as its therapeutic significance, has been less extensively studied compared to other NDDs. This review highlights the significantly elevated levels of inflammatory proteins and cellular markers observed in various HD animal models and HD patient tissues, emphasizing the critical roles of microglia, astrocytes, and oligodendrocytes in mediating neuroinflammation in HD. Moreover, it expands on recent discoveries related to the peripheral immune system's involvement in HD. Although current immunomodulatory treatments and inflammatory biomarkers for adjunctive diagnosis in HD are limited, targeting inflammation in combination with other therapies, along with comprehensive personalized treatment approaches, shows promising therapeutic potential.

Epigenetic signatures of asthma: a comprehensive study of DNA methylation and clinical markers

BackgroundAsthma, a complex respiratory disease, presents with inflammatory symptoms in the lungs, blood, and other tissues. We investigated the relationship between DNA methylation and 35 clinical markers of asthma.MethodsThe Illumina Infinium EPIC v1 methylation array was used to evaluate 742,442 CpGs in whole blood from 319 participants from 94 families. They were part of the Netherlands Twin Register from families with at least one member suffering from severe asthma. Repeat blood samples were taken after 10 years from 182 individuals. Principal component analysis on the clinical asthma markers yielded ten principal components (PCs) that explained 92.8% of the total variance. We performed epigenome-wide association studies (EWAS) for each of the ten PCs correcting for familial structure and other covariates.Results221 unique CpGs reached genome-wide significance at timepoint 1 after Bonferroni correction. PC7, which correlated with loadings of eosinophil counts and immunoglobulin levels, accounted for the majority of associations (204). Enrichment analysis via the EWAS Atlas identified 190 of these CpGs to be previously identified in EWASs of asthma and asthma-related traits. Proximity assessment to previously identified SNPs associated with asthma identified 17 unique SNPs within 1 MB of two of the 221 CpGs. EWAS in 182 individuals with epigenetic data at a second timepoint identified 49 significant CpGs. EWAS Atlas enrichment analysis indicated that 4 of the 49 were previously associated with asthma or asthma-related traits. Comparing the estimates of all the significant associations identified across the two time points yielded a correlation of 0.81.ConclusionWe identified 270 unique CpGs that were associated with PC scores generated from 35 clinical markers of asthma, either cross-sectionally or 10 years later. A strong correlation was present between effect sizes at the 2 timepoints. Most associations were identified for PC7, which captured blood eosinophil counts and immunoglobulin levels and many of these CpGs have previous associations in earlier studies of asthma and asthma-related traits. The results point to a robust DNA methylation profile as a new, stable biomarker for asthma.

Dosing Patterns of Dulaglutide and Semaglutide in Patients with Type2 Diabetes Mellitus in France and Italy: A Retrospective Cohort Study.

Type2 diabetes mellitus (T2DM) is a complex metabolic disease with several treatment options. Some glucagon-like peptide1 receptor agonists (GLP-1 RAs) approved by the European Medicines Agency include dulaglutide, subcutaneous (s.c.) semaglutide, and oral semaglutide. This study examines dulaglutide and semaglutide dosing patterns for T2DM in France and Italy. IQVIA Longitudinal Prescription Data identified adults with T2DM prescribed dulaglutide or semaglutide between August 1, 2020 and December 31, 2022. Cohort1 (incident) and cohort2 (prevalent) users were followed for 12months. In France and Italy, 255,571 and 350,853 patients, respectively, received at least one study GLP-1 RA. Most dulaglutide-naïve patients in France (62%) and approximately half in Italy (49%) started on 1.5mg and remained on this dose for up to 12months (France: 66% cohort1, 88% cohort2; Italy: 73% cohort1, 87% cohort2). In cohort1, s.c. semaglutide users mostly started on 0.25mg (France, 78%; Italy, 61%). At 12months, s.c. semaglutide 1.0mg was most prescribed (France: 58% cohort1, 75% cohort2; Italy: 59% cohort2), with cohort1 in Italy mostly receiving 0.5mg (57%). Most oral semaglutide users in Italy started on 3.0mg (78% cohort1; 68% cohort2), which was increased to 7.0mg (62% cohort1) and 14.0mg (48% cohort2) at 12months. GLP-1 RA dosing patterns, although similar between France and Italy, were heterogeneous over time. As oral semaglutide and higher dulaglutide doses are recent to the market, additional real-world evidence is required to evaluate utilization patterns. Graphical abstract available for this article.

Unexpected normal ROTEM results in protein C deficiency with IVC thrombosis

Protein C deficiency is a hereditary disorder that increases the risk of thrombotic events but has unclear effects on rotational thromboelastometry (ROTEM) analysis. A man in his 60s with a history of protein C deficiency and multiple thrombotic events underwent inferior vena cava (IVC) filter removal, iliocaval thrombectomy and infrarenal IVC placement for IVC-related complete iliocaval and common femoral vein thromboses. A ROTEM analysis showed normal coagulation in NATEM and EXTEM and only a slight shortening in A10 and A20 in INTEM, which was unexpected given his diagnosis of protein C deficiency. Normal results indicate that there is complexity and variability of coagulation to maintain a balanced state even in individuals with underlying coagulation disorders.

Sequence of events leading to medical abortion for fetal indications after 34 weeks' gestation: 23 years of experience in a single medical center.

Technical advances are rapidly improving the ability to detect anatomical malformations and genetic abnormalities during pregnancy. We aimed to identify the sequence of events leading to medical abortion ≥ 34 weeks' gestation, to determine whether the procedure could have been carried out earlier. Retrospective study of medical abortions in singleton pregnancies carried out in our department over a 23-year period from1998 to 2021. 36/4055 (0.88%) abortions were carried out ≥ 34 weeks' gestation. The indications were anatomical in 20 (55%), chromosomal or genetic in 14 (39%) and CMV infection In two. Evaluation of the sequence of events suggests that an earlier diagnosis would have been unfeasible before the third trimester in 18 cases (50%) because the disorder was developmental and ultrasound findings would not have presented earlier. By contrast, certain other cases might have been diagnosed earlier if the patient had not delayed routine screening or if screening had been administered on schedule. In this series, full adherence to local screening tests and protocols, and timely decision-making could have substantially reduced the late abortion rate by as much as half.

Unveiling Macrosomia Risks of Non-Diabetic Women: Insights from Second Trimester Maternal Lipid Profiles.

Macrosomia, characterized by excessive fetal growth, is common in infants born to women with pre-gestational diabetes and gestational diabetes mellitus (GDM). However, macrosomia, which leads to birth-related maternal and fetal complications and metabolic complications in the adolescence of the affected fetuses, also occurs in the pregnancies of non-diabetic women. This study aims to identify the association between second-trimester lipid profiles and macrosomia in non-diabetic pregnant women to aid in early diagnosis. This retrospective cohort study included 8,956 patients who delivered at a tertiary care center between 2017 and 2019. Exclusion criteria encompassed pre-existing diabetes, GDM, preeclampsia (PE), intrahepatic cholestasis of pregnancy, obesity, fetal chromosomal or genetic abnormalities, tobacco, alcohol, or drug use affecting lipid metabolism. Participants were divided into two groups: 621 with macrosomia and 873 controls. Second trimester maternal lipid profiles and demographic variables such as age, pregnancy week, and gender were assessed. In the study cohort, maternal age (P=0.002), gestational week (P=0.003), and cesarean section rate (P<0.001) were higher in the macrosomic group. High-density lipoprotein-cholesterol (HDL-C) was significantly lower, while total cholesterol (TC), triglycerides (TG), and low-density lipoprotein-cholesterol (LDL-C) were significantly higher in the macrosomic group (P<0.001). Univariate analysis revealed positive associations between second-trimester TG (OR 1.023, 95% CI: 1.020‒1.033, P<0.001), TC (OR 1.023, 95% CI: 1.016‒1.030, P<0.001) and LDL-C (OR 1.036, 95% CI: 1.018-1.054, P<0.001) with macrosomia and a negative association with HDL-C (OR 0.954, 95% CI: 0.923‒0.976, P<0.001). However, after adjusted multivariable logistic analysis, only TG remained statistically significantly associated with macrosomia (OR 1.054, 95% CI: 1.033‒1.076, P<0.001). Our study emphasizes the importance of early recognition and prevention of macrosomia. Structured prospective studies are needed to enhance macrosomia prediction and implement preventive measures, such as dietary modifications. These strategies will be crucial in preventing birth-related complications and long-term health risks, including diabetes, obesity, and cardiovascular diseases, associated with macrosomia.

GPS-Net: Discovering prognostic pathway modules based on network regularized kernel learning.

The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely focused on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions. By innovatively incorporating advanced multiple kernel learning techniques and network-based regularization, the proposed method not only enhances the accuracy of biomarker and pathway identification but also significantly reduces computational complexity, as demonstrated by extensive simulation studies. Applying GPS-Net, we identified key pathways that are predictive of patient outcomes in a cancer immunotherapy study. Overall, our approach provides a novel framework that renders genome-wide pathway-level prognostic analysis both feasible and scalable, synergizing both mechanism-driven and data-driven methodologies for precision genomics.

Neutrophil-to-Lymphocyte Ratio as a Biomarker for Clinical Response After Autologous Hematopoietic Stem Cell Transplantation in Systemic Sclerosis.

Systemic sclerosis is a complex disease that affects various target organs, making it difficult to assess response and determine remission or relapse. A baseline Neutrophil-to-Lymphocyte Ratio (NLR) >2.95 is associated with severe progressive skin and lung disease and decreased 5-year survival in systemic sclerosis (SSc). However, it is unknown whether NLR changes in response to treatment. To retrospectively evaluate NLR changes as a biomarker for treatment response in SSc. Progressive diffuse SSc patients who were treated with autologous hematopoietic stem cell transplantation (AHSCT group), with combination therapy of rituximab and mycophenolate mofetil (combination group) or CAR-T cell therapy (CAR-T group) were recruited, along with healthy controls (HC group). NLR, modified Rodnan Skin Score (mRSS) and forced vital capacity (FVC)% predicted were repeatedly assessed over 2 years. Fifteen patients were recruited in the AHSCT group, 15 in the combination group, and six patients in the CAR-T group. Baseline mean NLR was high (>2.95) in AHSCT, combination groups, and CAR-T compared with HC. All treatment arms showed a statistically significant decrease in mRSS values and an increase in FVC% at each time point up to 12 months. In a linear mixed model, NLR significantly decreased up to 24 months only in the AHSCT group. NLR correlated with mRSS and FVC exclusively in the AHSCT group. NLR increased above 3 in two patients who experienced a relapse after AHSCT. NLR is a simple biomarker that correlated with outcome measures in SSc following AHSCT, but not with conventional therapy or CAR-T therapy. It is suggested that a decrease in NLR following AHSCT may indicate remission, whereas an increase may be associated with exacerbation. Further research is needed to validate these novel findings.

Recent updates on systemic treatment of atopic dermatitis.

Atopic dermatitis (AD) is a complex disease with multifactorial pathogenesis and variable clinical presentation. Up to one-fifth of patients with AD develop moderate to severe disease that is often refractory to classical therapies and can compromise quality of life. This review summarizes recent clinical evidence on biological agents and small-molecule immunotherapies for the treatment of AD.