Background: Lung cancer is a leading cause of cancer-related deaths, with Non-Small Cell Lung Cancer (NSCLC) comprising 85–90% of cases, most commonly adenocarcinoma. Key mutations include EGFR Exon 19 deletions and Exon 21 L858R. While third-generation TKIs like osimertinib, lazertinib, and aumolertinib are effective, resistance often arises. Amivantamab, a bispecific monoclonal antibody targeting EGFR and c-MET, shows promise, particularly against EGFR Exon 20 insertions. Objective: To provide a comprehensive evaluation of the adverse effects associated with amivantamab in EGFR-mutant NSCLC, including their underlying pathophysiological mechanisms and evidence-based management strategies. In addition, this review aims to contextualize the clinical relevance of amivantamab by briefly outlining the therapeutic evolution of EGFR-targeted treatments, highlighting the rationale for its development, and current positioning in treatment paradigms. Methods: A comprehensive review of clinical trials, including CHRYSALIS, PAPILLON, MARIPOSA, and PALOMA-III, was conducted to assess the safety and efficacy of amivantamab. Practical and early insights into managing adverse effects of amivantamab are critical to better adherence and quality of life. Results: Adverse effects were observed in most of the patients treated with amivantamab. Common side effects included cutaneous toxicities, diarrhea, infusion-related reactions (IRRs), vascular thrombosis and pneumonitis. The most frequent cutaneous side effects were rash, paronychia, pruritis, and stomatitis. Diarrhea occurred in patients primarily due to EGFR inhibition. IRRs were predominantly mild to moderate, occurring mainly during the first cycle. Thrombosis was a notable adverse effect observed, even in patients receiving anticoagulant prophylaxis. Pneumonitis was less common but severe. Management: Adverse effects of amivantamab are managed based on severity. Cutaneous toxicities are treated with antibiotics, topical steroids, and dose adjustments. Diarrhea is managed with hydration, loperamide, and dose interruption. Infusion-related reactions (IRRs) are treated symptomatically, with epinephrine in severe cases. Anticoagulants are used for deep vein thrombosis or thromboembolism, and fibrinolytics or thrombectomy may be considered. Pneumonitis is managed by discontinuing amivantamab and using glucocorticoids. Conclusions: Amivantamab is effective for EGFR mutant NSCLC but can cause adverse effects. Understanding these effects and implementing management strategies can optimize outcomes, maintaining treatment efficacy.

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.

Background/aim: The aim of our study was to investigate the effectiveness of lumbar stabilization exercises on serum biomarker levels and clinical outcomes in lumbar disc herniation (LDH) patients with and without neurological deficit. Materials and methods: 40 patients with neurological deficits (WND) and 34 patients without neurological deficits (WOND) diagnosed with LDH were included in this study. The patients WND and WOND were randomized into stabilization exercises (SE) and general exercises (GE) groups. Stabilization exercises or general exercises were applied for six weeks. Serum levels of tumor necrosis factor alpha (TNF-α), interleukin (IL)-1β, IL-6, IL-4, beta-endorphin (BE), anandamide (AEA), and 2-arachidonoylglycerol (2-AG) were measured using the enzyme-linked immunosorbent assay. All patients filled out the Visual Analog Scale, Oswestry Disability Index, Beck Depression Index and Beck Anxiety Index. Results: In patients WND and WOND, SE did not show any statistically significant difference in relation to TNF-α, IL-1β, IL-6, IL-4, BE, AEA, and 2-AG (p>0.05). There were no statistically significant differences between the SE and GE groups in pain intensity and disability outcomes in patients WND (p>0.05). SE group showed greater reductions in depression and anxiety scores compared to the GE group (p0.05), whereas the decrease in anxiety score was greater in the SE group (p<0.05). Conclusion: Our results demonstrate that no effect of lumbar stabilization exercise is seen on circulating levels of TNF-α, IL-1β, IL-6, IL-4, BE, AEA, and 2-AG. Further exercise trials are needed to investigate what type, duration, and intensity of exercise is relevant for biomarkers that may play a role in the immune process.