Gamze Sonmez

Background/Objectives: Vitamin B12 (cobalamin) plays an essential role in DNA synthesis, erythropoiesis, and cellular metabolism. Its deficiency is common in the general population and may influence various biochemical markers. Tumor markers such as CA 125, CA 15-3, CA 19-9, CEA, and AFP are widely used in oncology, yet their levels can also be affected by non-malignant conditions. This study aimed to investigate the relationship between serum vitamin B12 levels and tumor marker concentrations in non-cancerous patients. Methods: This retrospective study included 250 adult participants (141 with low serum vitamin B12 < 200 ng/L and 109 with normal B12 ≥ 200 ng/L) who presented to Etlik City Hospital between June 2022 and June 2025. All participants were non-smokers and had no history of chronic kidney disease or malignancy. Hematologic, biochemical, thyroid, and iron parameters were analyzed alongside tumor markers (CA 15-3, CA 125, CA 19-9, CEA, and AFP) using standard immunoassay techniques. Statistical analyses included group comparisons, correlation testing, and logistic regression to identify factors independently associated with low vitamin B12 levels. Results: Patients with low vitamin B12 levels exhibited significantly lower hemoglobin, hematocrit, white blood cell, and neutrophil counts (p < 0.01 for all). Free T4 and serum iron were independently associated with low B12 levels in multivariable analysis (p < 0.001). Among tumor markers, CA 125 and CEA were significantly reduced in patients with vitamin B12 deficiency (p < 0.0001), while CA 15-3 showed a moderate inverse correlation with B12 concentration (r = –0.36, p < 0.0001). Conclusions: Vitamin B12 deficiency is associated with alterations in hematologic, thyroid, and iron parameters and may be associated with serum tumor marker levels even in non-cancerous individuals. Recognizing the potential association between B12 status and tumor marker variability may contribute to more cautious interpretation of tumor marker results in clinical practice.

X-linked agammaglobulinemia (XLA) is a rare immunodeficiency caused by a mutation in Bruton’s tyrosine kinase, resulting in profoundly low immunoglobulin levels and susceptibility to recurrent infections. We report a 14-year-old patient with XLA who presented with persistent fever, bloody diarrhea, and headache suggestive of pachymeningitis. The patient had chronic giardiasis. His clinical condition deteriorated with worsening thrombocytopenia, hepatitis, and minimal diffuse dural thickening on brain magnetic resonance imaging, interpreted as pachymeningitis. Empirical immunosuppressive therapy was initiated, but the patient ultimately succumbed to illness. Although Giardia has been linked to various extra-intestinal manifestations, its association with meningitis or pachymeningitis is not reported. This case highlights the complexity of managing immunocompromised patients with unexplained fever and neurological symptoms, emphasizing the need for further research into the potential neurological effects of Giardia infection.

Central nervous system (CNS) tuberculosis (TB) carries high mortality and neurologic sequelae, especially when unrecognized inborn errors of immunity are present. We report a young woman with refractory TB meningitis, multiple intracranial tuberculomas, hydrocephalus, and intracranial hypertension despite 10 months of standard anti-TB therapy and corticosteroids. Severe vomiting led to poor adherence and severe weight loss. As immunologic evaluation suggested atypical combined immunodeficiency (CID); monthly intravenous immunoglobulin (IVIG) was started. Neurosurgery did not give indication for shunt surgery. Given the severity and presumed impaired antimycobacterial immunity, adjuvant interferon-γ (IFN-γ; 50 µg/m² 3 times weekly) was added to therapy with steroids. Vomiting resolved, weight improved, and treatment was tolerated. Serial magnetic resonance imaging showed reduction in leptomeningeal enhancement and tuberculoma burden without surgery; no neurologic sequelae developed. Next-generation sequencing identified a homozygous NHEJ1 variant, not confirmed by Sanger, but findings supported CID. In refractory CNS-TB with suspected immunodeficiency, IFN-γ, steroids, IVIG in addition to anti-TB therapy may improve outcomes and avoid neurosurgical intervention. Early immunological evaluation and host-directed therapy should be considered.

Objective: Alzheimer’s disease (AD) is characterised by the gradual decline of cognitive function and impaired cholinergic neurotransmission due to reduced brain acetylcholine levels. AChE and BChE regulate acetylcholine degradation and are important targets in AD. This research investigated the inhibitory effects of indole-3-propionic acid (IPA) and indole-3-butyric acid (IBA) on human cholinesterases, using enzyme kinetics and molecular docking. Methods: We assessed the inhibitory effects of purified human BChE and commercially sourced erythrocyte-derived AChE using a modified Ellman spectrophotometric assay in a 96-well microplate. Kinetic parameters (Km, Vmax, and Ki) were determined by nonlinear regression and the Michaelis–Menten model, with Lineweaver–Burk plots used to analyse inhibition patterns. IC50 values were calculated by nonlinear regression analysis. Molecular docking simulations were performed to predict the binding positions of the indole derivatives and the reference Alzheimer’s drugs donepezil and tacrine analogue in the active sites of AChE and BChE. Results: According to docking simulations, both indole derivatives bind to the catalytic gorge of cholinesterases, but with lower predicted affinity than reference inhibitors. Enzyme inhibition assays showed that IBA exhibited weak inhibitory activity against both enzymes, with IC50 values of 15.61 mM for BChE and 54.28 mM for AChE. IPA exhibited stronger inhibition, particularly against BChE, with an IC50 of 2056 µM. Kinetic analysis indicated that IPA displays mixed-type inhibition of BChE (Km = 170.1 µM, Ki = 1774 µM), while its inhibition of AChE is competitive (IC50 = 3773 µM, Km = 268.5 µM, Ki = 6727 µM). Conclusion: These findings suggest that short-chain indole metabolites can interact with cholinesterases, but are not potent inhibitors. Compared to IBA, IPA showed higher activity and distinct inhibition profiles for BChE and AChE. The development of new indole-based cholinesterase inhibitors may prove to be potent modulators of cholinergic enzymes.

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is now the leading cause of chronic liver disease globally, mirroring the increasing prevalence of obesity, insulin resistance, and type 2 diabetes. Early detection of hepatic steatosis is vital for cardiometabolic risk assessment; however, conventional imaging is costly and impractical for population screening. This study aimed to develop interpretable machine-learning models to predict ultrasound-detected hepatic steatosis within the MASLD spectrum using routinely available clinical and biochemical data. Methods: We analyzed data from 644 adults, 50% of whom had ultrasound-detected hepatic steatosis. Preprocessing, imputation, and feature selection were implemented within a single scikit-learn pipeline to avoid information leakage. An Elastic Net-regularized logistic regression identified the top 20 predictors, which were subsequently used across nine supervised machine learning (ML) classifiers. Model performance was evaluated via repeated stratified 5-fold cross-validation (25 resamples) using accuracy, F1 score, sensitivity, specificity, Youden’s J, balanced accuracy, and Area Under the Receiver Operating Characteristic Curve (AUROC). Interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: Participants with ultrasound-detected hepatic steatosis exhibited greater adiposity, insulin resistance, and dyslipidemia compared with controls [p < 0.05 for body mass index (BMI), waist circumference, glucose, glycated hemoglobin (HbA1c), triglycerides]. Elastic Net selection highlighted Weight, Ponderal Index, Fibrosis-4 Index (FIB-4), blood urea nitrogen (BUN)/Creatinine ratio, Aspartate Aminotransferase to Platelet Ratio Index (APRI), and Visceral Adiposity Index as the strongest predictors. Logistic Regression and Gradient Boosting achieved the best performance (accuracy = 0.65 ± 0.03; AUROC = 0.71 ± 0.04; balanced accuracy = 0.66 ± 0.06), outperforming rule-based indices such as Fatty Liver Index (FLI) and Hepatic Steatosis Index (HSI) reported in the literature. SHAP analysis confirmed clinically coherent feature effects, with higher anthropometric and hepatic injury indices increasing the predicted probability of ultrasound-detected hepatic steatosis. Conclusions: Routinely available clinical and biochemical parameters can predict hepatic steatosis with moderate accuracy using transparent, interpretable ML models. Logistic Regression and Gradient Boosting provided best discrimination and robust internal performance, offering a pragmatic, low-cost approach for early identification of ultrasound-detected hepatic steatosis within the MASLD spectrum in primary and metabolic care settings.

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is now the leading cause of chronic liver disease globally, mirroring the in-creasing prevalence of obesity, insulin resistance, and type 2 diabetes. Early detection of hepatic steatosis is vital for cardiometabolic risk assessment; however, conventional imaging is costly and impractical for population screening. This study aimed to devel-op interpretable machine-learning models to predict ultrasound-detected hepatic ste-atosis within the MASLD spectrum using routinely available clinical and biochemical data. Methods: We analyzed data from 644 adults, 50% of whom had ultra-sound-detected hepatic steatosis. Preprocessing, imputation, and feature selection were implemented within a single scikit-learn pipeline to avoid information leakage. An Elastic Net–regularized logistic regression identified the top 20 predictors, which were subsequently used across nine supervised machine learning (ML) classifiers. Model performance was evaluated via repeated stratified 5-fold cross-validation (25 resamples) using accuracy, F1 score, sensitivity, specificity, Youden’s J, balanced accu-racy, and Area Under the Receiver Operating Characteristic Curve (AUROC). Inter-pretability was assessed using SHapley Additive exPlanations (SHAP). Results: Par-ticipants with ultrasound-detected hepatic steatosis exhibited greater adiposity, insu-lin resistance, and dyslipidemia compared with controls [p < 0.05 for body mass index (BMI), waist circumference, glucose, glycated hemoglobin (HbA1c), triglycerides]. Elastic Net selection highlighted Weight, Ponderal Index, Fibrosis-4 Index (FIB-4), blood urea nitrogen (BUN)/Creatinine ratio, Aspartate Aminotransferase to Platelet Ratio Index (APRI), and Visceral Adiposity Index as the strongest predictors. Logistic Regression and Gradient Boosting achieved the best performance (accuracy = 0.65 ± 0.03; AUROC = 0.71 ± 0.04; balanced accuracy = 0.66 ± 0.06), outperforming rule-based indices such as Fatty Liver Index (FLI) and Hepatic Steatosis Index (HSI) reported in the literature. SHAP analysis confirmed clinically coherent feature effects, with higher anthropometric and hepatic injury indices increasing the predicted probability of ul-trasound-detected hepatic steatosis. Conclusions: Routinely available clinical and bi-ochemical parameters can predict hepatic steatosis with moderate accuracy using transparent, interpretable ML models. Logistic Regression and Gradient Boosting pro-vided best discrimination and robust internal performance, offering a pragmatic, low-cost approach for early identification of ultrasound-detected hepatic steatosis within the MASLD spectrum in primary and metabolic care settings.

Response to the Letter to the Editor

Aims: Diabetes mellitus is a well-recognized risk factor for rotator cuff pathology, yet the specific clinical and metabolic determinants of tendinopathy in this population remain poorly defined. This study aimed to identify the ultrasonographic, biochemical, and clinical factors associated with the development of supraspinatus tendinopathy among patients with diabetes. Methods: We performed a single-center, retrospective cross-sectional study of 63 patients with diabetes mellitus. All participants underwent shoulder ultrasonography to measure supraspinatus tendon thickness and assess tendinitis. Patients were grouped according to ultrasonographic evidence of tendinitis. Demographic data, glycemic parameters [hemoglobin A1c (HbA1c), fasting glucose], renal function indices [estimated glomerular filtration rate (eGFR), creatinine], lipid profiles, and diabetic complications (neuropathy, retinopathy, nephropathy) were compared between groups. Logistic regression and ROC curve analyses were used to identify and evaluate diagnostic predictors.Results: Supraspinatus tendinopathy was present in 29 patients (46%). Compared with patients without tendinitis, affected individuals had significantly greater supraspinatus tendon thickness (7.66±1.29 vs. 6.95±1.22 mm; p=0.03), lower eGFR (p=0.005), higher serum creatinine (p=0.03), and a higher prevalence of diabetic neuropathy (65% vs. 35%; p=0.02). Although tendon thickness correlated positively with body mass index, HbA1c, and C-reactive protein, these metabolic and inflammatory markers did not differ significantly between groups. ROC analysis showed supraspinatus tendon thickness had the highest diagnostic accuracy (AUC=0.70).Conclusion: In our study, supraspinatus tendinopathy in diabetes mellitus was associated with tendon thickening, renal dysfunction, and neuropathy rather than glycemic or lipid measures, highlighting microvascular complications as key drivers of diabetic rotator cuff tendinopathy.