Newborns born to diabetic mothers had increased oxidative stress and DNA damage compared to those born to mothers with euglycemia.

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Background: Synovial sarcoma is a translocation-associated mesenchymal neoplasm that represents around 10% of all soft tissue sarcomas. Diagnosing biphasic synovial sarcoma is generally straight forward, owing to distinctive histologic features. Transducer-like enhancer of split 1 (TLE1) is overexpressed in synovial sarcomas. Study aimed to evaluate sensitivity and specificity of TLE1 immunohistochemical expression in synovial sarcoma and its histological mimics. Methods: Conventional sections from 30 cases of synovial sarcoma, 24 cases of monophasic synovial sarcoma mimics and 6 cases of poorly differentiated sarcoma mimics were subjected to TLE1 IHC staining. TLE1 immunostaining was graded from 0, 1+, 2+, 3+, with 2+ or 3+ grades interpreted as positive staining. Results: Of the 60 tumours, majority are monophasic spindle cell type (56.6%), followed by biphasic (16.6%), monophasic epithelial (6.6%), poorly differentiated (13.3%) and calcifying type (6.6%). Upon expression of TLE1 in tumors, 20 cases showed Grade 3, 8 cases shown Grade 2, 2 cases shown Grade 1 TLE1 Expression in Synovial sarcoma. 2 cases shown Grade 3 and 2 cases shown Grade 2 TLE1 expression in Schwannoma. Whereas 1case shown grade 2 in Rhabdomyosarcoma. 1case shown grade 2 in Hemangiopericytoma. TLE1 sensitivity for diagnosis of synovial sarcomas was 93.3%, and specificity of 73.3% with positive predictive value of 77.77% and negative predictive value of 91.6%. Conclusion: Specificity can be increased with optimal IHC panel which includes BCL2, Pan Cytokeratin, EMA, CD99 and CD34. Molecular confirmation is the diagnostic gold standard for synovial sarcoma, TLE1, in view of its high sensitivity may be a useful marker within the optimal IHC panel for substantiating a diagnosis of synovial sarcoma. Awareness of TLE1 expression in other tumours and its correct interpretation are necessary.

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The clinical significance of the tumor‐immune interaction in breast cancer is now established, and tumor‐infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple‐negative (estrogen receptor, progesterone receptor, and HER2‐negative) breast cancer and HER2‐positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state‐of‐the‐art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false‐positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in‐depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple‐negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.