Background: Varicocoele is a correctable cause of male infertility. Although physical examination is still being used in diagnosis and grading, it gives conflicting results when compared to ultrasonography‐based varicocoele grading.Objectives: We aimed to develop a multi‐class machine learning model for the grading of varicocoeles based on ultrasonographic measurements.Method: Between January and May 2024, we enrolled unilateral varicocoele patients at an infertility clinic, assessing their varicocoele stages using the Dubin and Amelar system. We measured vascular diameter and reflux time at the testicular apex and the subinguinal region ultrasonography in both the supine and standing positions. Using these measurements, we developed four multi‐class machine learning models, evaluating their performance metrics and determining which patient position and projection were most influential in varicocoele grading.Results: We included 248 patients with unilateral varicocoele in the study, their average age was 26.61 ± 4.95 years old. Of these, 212 had left‐sided and 36 had right‐sided varicocoeles. According to the Dubin and Amelar system, there were 66 grade I, 96 grade II, and 86 grade III varicocoeles. Among the models we created, the random forest (RF) model performed best, with an overall accuracy of 0.81 ± 0.06, an F1 score of 0.79 ± 0.02, a sensitivity of 0.69 ± 0.02, and a specificity of 0.8 ± 0.03.Vascular diameter measurement at the testicular apex in the supine position had the most impact on grading across all models. In support vector machine and multi‐layer perceptron models, reflux time measurements from the subinguinal projection in the standing position contributed the most, while in RF and k‐nearest neighbors models, measurements from the subinguinal projection in the supine position were the most influential.Conclusions: Machine learning methods have demonstrated superior accuracy in predicting disease compared to traditional statistical regressions and nomograms. These advancements hold promise for clinically automated prediction of varicocoele grades in patients. Tailored varicocoele grading for individuals has the potential to enhance treatment effectiveness and overall quality of life.

Background: In this study, we aimed to report long‐term follow‐up of our pediatric and adult patients with DCLRE1C (DNA cross‐link repair 1C) hypomorphic mutation who were diagnosed leaky severe combined immunodeficiency (SCID).Methods: Eighteen patients (13 children and five adults), aged between 6 and 29 years were included. Clinical and immunological features, including immunoglobulin levels, T and B cells, natural killer cell subsets, regulator T (Treg) cell ratios/markers, and cytokines, were assessed before and after hematopoietic stem cell transplantation (HSCT) and compared with healthy controls.Results: Recurrent infections (78%) and skin manifestations (61%) such as granulomatous skin lesions, warts, and vitiligo were the most common clinical findings. Autoimmune diseases were observed in 33% and malignancy in 17%. Most patients had low serum IgA and B‐ and T‐cell lymphopenia at the first admission. Recent thymic emigrants (RTE), T_naive, B_naive, CD56^dimCD16⁺ cell ratios were significantly lower in the patients than in control; however, follicular helper T T_FH and Th1 [interferon gamma (IFN‐γ)] cell ratios were significantly higher than the control. Although, Treg ratio and its functional receptors tend to be high but not significant. Eleven patients (61.1%) were treated with HSCT. Median follow‐up times of transplant patients was 56 (9–67) months.Conclusion: Patients with hypomorphic DCLRE1C mutations may present with variable clinical and laboratory findings at different ages. Our study showed a helper T (Th)1‐dominant immune response before and after HSCT. Increased IFN‐γ and T_FH cells ratio could be a reason of chronic inflammation and autoimmunity developing before and after HSCT. Long‐term follow‐up of these patients after HSCT will help to better understand the disease and its pathophysiology.

Background: Inborn errors of immunity (IEIs) are a heterogeneous group of rare disorders caused by genetic defects in one or more components of the immune system. The Jeffrey Modell Foundation’s (JMF) Ten Warning Signs are widely used for early detection; however, their diagnostic sensitivity is limited. Machine learning (ML) approaches may improve prediction accuracy by integrating additional clinical variables into decision-making frameworks. Methods: This retrospective study included 298 participants (98 IEI, 200 non-IEI) evaluated at a university-affiliated clinical immunology clinic between January and December 2020. IEI diagnoses were confirmed using European Society for Immunodeficiencies (ESID) criteria. Two datasets were constructed: one containing only JMF criteria and another combining JMF criteria with additional clinical variables. Four ML algorithms—random forest (RF), k-nearest neighbors (k-NN), support vector machine (SVM), and naive Bayes (NB)—were trained and optimized using nested 5-fold stratified cross-validation repeated three times. Performance metrics included accuracy, sensitivity, specificity, F1 score, Youden Index, and the area under the receiver operating characteristic curve (AUROC). SHapley Additive exPlanations (SHAP) were applied to evaluate feature importance. Results: Using only JMF criteria, the best-performing model was SVM (accuracy: 0.90 ± 0.04, sensitivity: 0.93 ± 0.05, AUROC: 0.91 ± 0.02). With the addition of clinical variables, the SVM achieved superior performance (accuracy: 0.94 ± 0.03, sensitivity: 0.97 ± 0.03, AUROC: 0.99 ± 0.00), outperforming both the classical JMF criteria (accuracy: 0.91, sensitivity: 0.87, AUROC: 0.90) and the JMF-only SVM model. SHAP analysis identified family history of early death, pneumonia history, and ICU admission as the most influential predictors. Conclusions: ML models, particularly SVM integrating JMF criteria with additional clinical variables, substantially improve IEI prediction compared with classical JMF criteria. Implementation of such models in clinical settings may facilitate earlier diagnosis and timely intervention, potentially reducing morbidity and healthcare burden in IEI patients.