Thyroid Eye Disease and Glaucoma: A Cross-Sectional Study Comparing Clinical Characteristics and Disease Severity

Retinal and Choroidal Alterations in Thyroid-Associated Ophthalmopathy: A Systematic Review

Thyroid-associated ophthalmopathy (TAO), or Graves' orbitopathy (GO), is a complex autoimmune disorder affecting orbital tissues, often leading to vision-threatening complications such as dysthyroid optic neuropathy (DON). In this systematic review, conducted following PRISMA guidelines, 22 studies were evaluated to investigate the role of optical coherence tomography (OCT) in assessing retinal and choroidal changes in TAO. Parameters such as the retinal nerve fiber layer (RNFL), ganglion cell complex (GCC), ganglion cell layer (GCL), and choroidal thickness were analyzed. RNFL changes varied by disease severity, with significant thinning in DON due to nerve fiber loss and thickening in early DON due to optic disk edema. Subfoveal choroidal thickness (SFCT) was consistently higher in active TAO, correlating positively with the clinical activity score (CAS) and proptosis, suggesting its role as a marker of disease activity. Subgroup analysis revealed that spectral-domain OCT (SD-OCT) was the most sensitive for detecting retinal changes. The findings highlight the effectiveness of OCT in detecting minor retinal and choroidal alterations in TAO. However, the variability of study designs, as well as the lack of longitudinal data, limits the ability to draw broad conclusions. Further standardized, long-term investigations are required to properly understand OCT's diagnostic and prognostic value in TAO.

Unveiling the Dynamics of Antimicrobial Resistance: A Year-Long Surveillance (2023) at the Largest Infectious Disease Profile Hospital in Western Romania

Background/Objectives: Antimicrobial resistance (AMR) is a critical global health threat, leading to increased morbidity, mortality, and healthcare costs. This study aimed to identify the most common bacterial pathogens and their resistance profiles from 2179 positive clinical cultures from inpatients at "Victor Babes" Hospital of Infectious Disease and Pneumoftiziology Timisoara in 2023. Methods: Samples were collected from sputum, bronchial aspiration, hemoculture, urine, wound secretions, catheter samples, and other clinical specimens. Results: Key pathogens identified included Klebsiella pneumoniae, Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Acinetobacter baumannii, with notable resistance patterns, observed K. pneumoniae exhibited high resistance rates, notably 41.41% in Quarter 1, while E. coli showed 35.93% resistance in the same period. S. aureus, particularly MRSA, remained a persistent challenge, with 169 cases recorded over the year. A. baumannii and P. aeruginosa displayed alarming levels of multi-drug resistance, especially in Quarter 3 (88.24% and 22.02%, respectively). Although there was a general decline in resistance rates by Quarter 4, critical pathogens such as S. aureus and K. pneumoniae continued to exhibit significant resistance (81.25% and 21.74%, respectively). Conclusions: The study's findings align with the broader antimicrobial resistance trends observed in Romania, where high resistance rates in K. pneumoniae, E. coli, S. aureus (MRSA), Acinetobacter, and Pseudomonas species have been widely reported, reflecting the country's ongoing struggle with multi-drug-resistant infections. Despite some reductions in resistance rates across quarters, the persistent presence of these resistant strains underscores the critical need for strengthened antimicrobial stewardship, infection control measures, and continuous surveillance to combat the growing threat of AMR in Romania and similar healthcare settings.

Impact of Gut Microbiome Interventions on Glucose and Lipid Metabolism in Metabolic Diseases: A Systematic Review and Meta-Analysis

BACKGROUND

The gut microbiome is increasingly recognized as a key player in metabolic health, influencing glucose and lipid metabolism through various mechanisms. However, the efficacy of gut microbiota-targeted interventions, such as probiotics, prebiotics, fecal microbiota transplantation (FMT), and diet-based treatments, remains unclear for specific metabolic outcomes. In this study, the aim was to evaluate the impact of these interventions on the glucose and lipid parameters in individuals with metabolic diseases such as diabetes mellitus (DM), obesity, and metabolic syndrome.

METHODS

This systematic review and meta-analysis included 41 randomized controlled trials that investigated the effects of gut microbiota-targeted treatments on metabolic parameters such as fasting glucose, glycated hemoglobin (HbA1c), homeostatic model assessment for insulin resistance (HOMA-IR), total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides. A comprehensive search was conducted using databases like PubMed, Google Scholar, and Scopus, focusing on interventions targeting the gut microbiota. A meta-analysis was performed using random-effects models, with effect sizes calculated for each outcome. Risk of bias was assessed using the Cochrane Risk of Bias tool.

RESULTS

Gut microbiota-targeted interventions significantly reduced fasting glucose, HbA1c, HOMA-IR, total cholesterol, LDL-C, and triglycerides, with moderate heterogeneity observed across studies. The interventions also led to modest increases in HDL-C levels. Probiotic and synbiotic interventions showed the most consistent benefits in improving both glucose and lipid profiles, while FMT yielded mixed results. Short-term interventions showed rapid microbial shifts but less pronounced metabolic improvements, whereas longer-term interventions had more substantial metabolic benefits.

CONCLUSIONS

In this study, it is demonstrated that gut microbiota-targeted interventions can improve key metabolic outcomes, offering a potential therapeutic strategy for managing metabolic diseases. However, the effectiveness of these interventions varies depending on the type, duration, and population characteristics, highlighting the need for further long-term studies to assess the sustained effects of microbiota modulation on metabolic health.

COVID-19 and Its Potential Impact on Children Born to Mothers Infected During Pregnancy: A Comprehensive Review

Pregnancy is a vulnerable period of time during which pregnant people are prone to infections like COVID-19, which can increase risks for both the mother and fetus. These infections may lead to complications such as preterm birth, developmental delays, and congenital abnormalities. While COVID-19 poses additional risks like placental dysfunction and neonatal infections, studies on long-term effects remain limited. Ongoing research and monitoring are essential to understand and mitigate potential cognitive and developmental challenges in children born to mothers infected with COVID-19. This review aims to guide clinicians in managing these risks throughout childhood. Maternal COVID-19 infection during pregnancy can have significant implications for fetal development, even if the newborn is not infected at birth. The release of inflammatory cytokines may cross the placental barrier, potentially disrupting fetal brain development and increasing the risk of long-term cognitive and behavioral issues, such as ADHD or autism. Placental dysfunction, caused by inflammation or thrombosis, can lead to intrauterine growth restriction (IUGR), preterm birth, or hypoxia, affecting both neurological and respiratory health in newborns. Furthermore, a compromised fetal immune system can increase susceptibility to autoimmune conditions and infections. The early diagnosis and management of infections during pregnancy are crucial in mitigating risks to both the mother and fetus. Swift intervention can prevent complications like preterm birth and long-term developmental challenges, ensuring better health outcomes for both the mother and child. Long-term monitoring of children born to mothers infected with COVID-19 is necessary to understand the full extent of the virus's impact. This review evaluates the long-term systemic effects of maternal COVID-19 infection during pregnancy on fetuses, newborns, and children, focusing beyond vertical transmission. It highlights the broader impacts on fetal development, offering insights to help clinicians manage potential issues that may arise later in life.

CMV Retinitis in the Context of SARS-CoV-2 Infection: A Case Study and Comprehensive Review of Viral Interactions

PURPOSE

Cytomegalovirus (CMV) retinitis is a sight-threatening condition predominantly affecting immunocompromised individuals, such as those with Human Immunodeficiency Virus (HIV)/Acquired Immunodeficiency Syndrome (AIDS). We aimed to present an observational case report on CMV retinitis following Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and to review the literature on the molecular and cellular changes in CMV and SARS-CoV-2 infections and how they may influence each other. Case Description: A 32-year-old man with a history of AIDS presented with decreased vision and ocular pain exacerbated by movement, beginning a day prior. Ocular examination revealed anterior uveitis, corneal endothelial edema, and retinal necrosis in the left eye. CMV retinitis was diagnosed based on positive serologic testing and a low cluster of differentiation 4 (CD4) count, with concurrent SARS-CoV-2 infection detected. Treatment included valganciclovir and topical agents, with a focus on managing CMV complications. This case highlights the potential role of SARS-CoV-2 in reactivating dormant CMV in severely immunocompromised individuals. We also discuss the implications of this interaction for immunocompromised patients, emphasizing the need for vigilant monitoring and personalized treatment strategies. Conclusions: Our case suggests that SARS-CoV-2 may trigger reactivation of CMV infection, leading to bilateral involvement in patients with low CD4 lymphocyte counts, which can result in severe visual impairment. The review discusses the molecular and cellular interactions between CMV and SARS-CoV-2, as well as risk factors, pathophysiology, and diagnostic methods for CMV retinitis, providing recommendations based on the literature findings.

Cardiovascular Risk Factors as Independent Predictors of Diabetic Retinopathy in Type II Diabetes Mellitus: The Development of a Predictive Model

Background: Diabetic retinopathy (DR) is a leading cause of blindness in patients with type 2 diabetes mellitus (T2DM). Cardiovascular risk factors, such as hypertension, obesity, and dyslipidemia, may play a crucial role in the development and progression of DR, though the evidence remains mixed. This study aimed to assess cardiovascular risk factors as independent predictors of DR and to develop a predictive model for DR progression in T2DM patients. Methods: A retrospective cross-sectional study was conducted on 377 patients with T2DM who underwent a comprehensive eye exam. Clinical data, including blood pressure, lipid profile, BMI, and smoking status, were collected. DR staging was determined through fundus photography and classified as No DR, Non-Proliferative DR (NPDR), and Mild, Moderate, Severe, or Proliferative DR (PDR). A Multivariate Logistic Regression was used to evaluate the association between cardiovascular risk factors and DR presence. Several machine learning models, including Random Forest, XGBoost, and Support Vector Machines, were applied to assess the predictive value of cardiovascular risk factors and identify key predictors. Model performance was evaluated using accuracy, precision, recall, and ROC-AUC. Results: The prevalence of DR in the cohort was 41.6%, with 34.5% having NPDR and 7.1% having PDR. A multivariate analysis identified systolic blood pressure (SBP), LDL cholesterol, and body mass index (BMI) as independent predictors of DR progression (p < 0.05). The Random Forest model showed a moderate predictive ability, with an AUC of 0.62 for distinguishing between the presence and absence of DR XGBoost showing a better performance, featuring a ROC-AUC of 0.68, while SBP, HDL cholesterol, and BMI were consistently identified as the most important predictors across models. After tuning, the XGBoost model showed a notable improvement, with an ROC-AUC of 0.72. Conclusions: Cardiovascular risk factors, particularly BP and BMI, play a significant role in the progression of DR in patients with T2DM. The predictive models, especially XGBoost, showed moderate accuracy in identifying DR stages, suggesting that integrating these risk factors into clinical practice may improve early detection and intervention strategies for DR.

Retinal Imaging-Based Oculomics: Artificial Intelligence as a Tool in the Diagnosis of Cardiovascular and Metabolic Diseases

Cardiovascular diseases (CVDs) are a major cause of mortality globally, emphasizing the need for early detection and effective risk assessment to improve patient outcomes. Advances in oculomics, which utilize the relationship between retinal microvascular changes and systemic vascular health, offer a promising non-invasive approach to assessing CVD risk. Retinal fundus imaging and optical coherence tomography/angiography (OCT/OCTA) provides critical information for early diagnosis, with retinal vascular parameters such as vessel caliber, tortuosity, and branching patterns identified as key biomarkers. Given the large volume of data generated during routine eye exams, there is a growing need for automated tools to aid in diagnosis and risk prediction. The study demonstrates that AI-driven analysis of retinal images can accurately predict cardiovascular risk factors, cardiovascular events, and metabolic diseases, surpassing traditional diagnostic methods in some cases. These models achieved area under the curve (AUC) values ranging from 0.71 to 0.87, sensitivity between 71% and 89%, and specificity between 40% and 70%, surpassing traditional diagnostic methods in some cases. This approach highlights the potential of retinal imaging as a key component in personalized medicine, enabling more precise risk assessment and earlier intervention. It not only aids in detecting vascular abnormalities that may precede cardiovascular events but also offers a scalable, non-invasive, and cost-effective solution for widespread screening. However, the article also emphasizes the need for further research to standardize imaging protocols and validate the clinical utility of these biomarkers across different populations. By integrating oculomics into routine clinical practice, healthcare providers could significantly enhance early detection and management of systemic diseases, ultimately improving patient outcomes. Fundus image analysis thus represents a valuable tool in the future of precision medicine and cardiovascular health management.