Mitra Abbasifard

Infertility is a major public health concern with substantial biological, psychological, and social consequences for women. Accumulating evidence suggests that chronic low-grade inflammation may contribute to female reproductive dysfunction. Diet is an important modifiable determinant of systemic inflammation, and the Dietary Inflammatory Index (DII) has been developed to quantify the inflammatory potential of habitual dietary intake. However, evidence regarding the association between DII and female infertility remains limited, particularly in Middle Eastern populations. This study aimed to examine the association between DII and self-reported female infertility among women participating in the Fasa Adults Cohort Study, Iran. This cross-sectional analysis included 2,036 women aged 35-45 years. Dietary intake was assessed using a validated 118-item food frequency questionnaire, and DII scores were calculated from 31 available dietary components. Infertility was defined as self-reported inability to conceive after at least 12 months of regular unprotected sexual intercourse. Logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for infertility according to DII, after adjustment for demographic, anthropometric, and lifestyle covariates. Among the study participants, 595 women (29.2%) reported a history of infertility. Mean DII score was significantly higher among women with infertility than among fertile women. In the crude model, DII was not significantly associated with infertility. However, after multivariable adjustment, each one-unit increase in DII was associated with higher odds of infertility (adjusted OR = 1.29; 95% CI: 1.13-1.63). This association remained after adjustment for BMI, smoking status, age, educational level, waist circumference, and total energy intake. Higher DII scores were independently associated with greater odds of self-reported infertility in this Iranian cohort. These findings suggest that the inflammatory potential of diet may be related to female reproductive health. However, because of the cross-sectional design, causality and temporality cannot be inferred, and further prospective and mechanistic studies are warranted.

Pericardial effusion (PE) is a frequent yet underdiagnosed complication of rheumatoid arthritis (RA), with substantial mortality risk. Nevertheless, early detection remains challenging due to nonspecific presentations and the limited feasibility of routine echocardiography. We aimed to identify simple clinical and laboratory features associated with the presence of PE using advanced machine learning (ML) approaches. In this cross-sectional study, 958 RA patients were evaluated, all of whom underwent transthoracic echocardiography for PE detection. A comprehensive set of demographic, clinical, and laboratory variables was analyzed. Eight ML algorithms were developed and evaluated using 45 repeated train-test splits to enhance robustness. Model performance was assessed using area under the precision-recall curve (AUC-PR) and receiver operating characteristic curve (AUC-ROC). Feature importance was determined using SHAP analysis. PE was present in 126 patients (13.2%). The random forest (RF) model achieved the best performance (mean AUC-PR, 0.674 [95% CI 0.504-0.824]; mean AUC-ROC, 0.943 [95% CI 0.903-0.968]). The most influential features were palpitation, chest pain, RA disease duration, and age. Patients with PE were older (62.8 ± 12.15 vs. 57.7 ± 12.65 years, p ≤ 0.001), and several clinical features differed significantly between groups (p < 0.05). SHAP analysis demonstrated a consistent positive association of palpitation, chest pain, longer disease duration, and older age with PE detection. These findings support the use of readily available clinical features by ML to increase diagnostic suspicion for PE in patients with RA. RA patients presenting with palpitations or chest pain, particularly those of older age or with long-standing disease, may warrant closer consideration for echocardiographic evaluation. Key Points • Pericardial effusion is a common yet often underrecognized and potentially life-threatening complication in patients with rheumatoid arthritis. • This study is the first to apply machine learning techniques to identify simple clinical and laboratory indicators associated with pericardial effusion in rheumatoid arthritis. • Palpitation, chest pain, older age, and longer disease duration were identified as the clinical features most strongly associated with the presence of pericardial effusion. • These findings may help clinicians identify patients in whom suspicion for pericardial effusion should be heightened and echocardiographic evaluation more strongly considered.

Background Rheumatoid arthritis (RA) is a chronic inflammatory disease marked by painful, progressive joint damage that leads to disability and a significant socioeconomic burden. Given the growing interest in data-driven healthcare, this study applied machine learning (ML) techniques to identify patterns of joint involvement associated with greater disease activity and functional impairment in patients with RA. Method In this cross-sectional study, 936 individuals with RA were assessed using demographic and clinical data, along with detailed joint examinations, including tenderness, swelling, and limitation of motion. Multiple ML models, including random forest, extreme gradient boosting (XGB), k-nearest neighbors, CatBoost (CAT), and LightGBM, were evaluated to predict disease severity and disability. Disease activity and disability were measured using Disease Activity Score based on 28 joints (DAS28) and Health Assessment Questionnaire Disability Index (HAQ-DI) scores, respectively. Results For disability prediction, the XGB model performed best (F1 score, 0.60; AUC, 0.85), while the CAT model showed the highest performance for disease activity classification (F1 score, 0.63; AUC, 0.81). Key joint features associated with higher disability included the knee, wrist, acromioclavicular joint, and several hand joints. In contrast, high disease activity was primarily linked to tenderness and swelling in the knee and small hand joints. Conclusion Overall, involvement of the knee, wrist, shoulder, and small hand joints was associated with functional disability, whereas disease activity was mainly reflected in the knee and hand joint involvement. These findings emphasize the importance of recognizing specific joint involvement patterns to improve clinical assessment and guide treatment strategies in RA. Key Points • This study is the first to apply machine learning to comprehensively evaluate joint-specific patterns of involvement and their association with both functional disability and disease activity in rheumatoid arthritis. • Large joints, particularly the knee, wrist, and shoulder, were identified as the strongest determinants of significant functional disability, alongside key small hand joints involved in grip and fine motor function. • High disease activity was primarily driven by small hand joints (especially MCP and PIP joints), with additional contributions from selected large joints such as the knee, wrist, and acromioclavicular joint. • These findings highlight the clinical value of detailed joint-level assessment beyond traditional composite scores, supporting more targeted and personalized management strategies in rheumatoid arthritis.

Background: Assessing bone mineral density (BMD) at a minimum of two skeletal sites is crucial for diagnosing osteoporosis. This research aimed to examine the discordance in BMD measurements between the lumbar spine and proximal femur using dual-energy X-ray absorptiometry (DXA), along with several influencing factors. Methods: This cross-sectional analysis included 1270 patients from the Rafsanjan BMD center in 2021. Eligible participants were at least 20 years old and possessed one or more risk factors for osteoporosis. Bone mineral density was assessed in the femoral region and the L1-L4 lumbar vertebrae using the DXA technique. Based on the T-score, values below -2.5 were classified as osteoporosis, while scores between -1 and -2.5 indicated osteopenia. The data were analyzed using SPSS 22 software, with a significance threshold of P<0.05 for statistical analyses. Results: Females constituted 83.5% of the study population, with 46.9% of all participants aged between 50 and 65 years. T-score analysis revealed 48.6% (617 cases) concordance, 44.5% (565 cases) minor discordance, and 6.9% (88 cases) major discordance. According to the Z-score, the results showed 60% (762 cases) concordance, 37.5% (476 cases) minor discordance, and 2.5% (32 cases) major discordance. Conclusion: The proportional odds model indicated that gender, age, tea consumption, residence location, and physical activity level were significant predictors affecting the likelihood of BMD discordance. Consequently, clinicians should anticipate discrepant T-score and Z-score results between the spine and hip in approximately 50% of patients.

Osteoporosis (OsP) is a bone-associated pathologic state defined by low bone mass and reduced bone mineral density (BMD) with susceptibility to fracture. However, conventional therapies have mainly relied on hormonal therapy and resorptive or anabolic drugs. As a novel scientific discipline, osteoimmunology has shown the immune system's role in bone tissue turnover. Osteoclastogenesis can be influenced by pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α), interleukin-1beta ( IL-1β), and IL-6. Meanwhile, OsP has been considered a protective factor comprising anti-inflammatory cytokines such as IL-10 and transforming growth factor beta (TGF-beta) and regulatory T cells (Tregs). Therefore, novel immunotherapeutic approaches that are presently available include the use of monoclonal antibodies (mAbs) against the receptor activator of nuclear factor-kappa B (RANK)/RANKL/osteoprotegerin (OPG) system, cytokine manipulation, and Treg expansion. Possible avenues for future research are the ability to manipulate the gut–immune axis using probiotics, the potential cross-talk between bisphosphonates and the immune system, as well as targeting Tregs, macrophages, and mesenchymal stem cells (MSCs). However, there are still concerns about these treatments' effectiveness and side effects. Nevertheless, the patient's immunological status is not constant, and the relationship between aging and immune dysfunction is not fully understood. This review article offers an update on the current understanding of immunotherapeutic approaches in managing OsP and next-generation strategies that aim to develop tailored therapies for bone disorders.

Background and Objectives: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects various organs of the body, including the kidneys. This study aimed to determine the prevalence of renal manifestations and associated factors in patients with SLE. Materials and Methods: In this descriptive cross-sectional study carried out in 2022, ...