Turkish Society of Hematology

Lung cancer is the most prevalent malignancy worldwide and the leading cause of cancer-related deaths. The adrenal gland is a frequent site of metastasis in non-small cell lung cancer (NSCLC). The differentiation of adrenal metastases from benign lesions is imperative for accurate staging and treatment planning. Imaging methods such as FDG-PET/CT, CT, and MRI provide important diagnostic information; however, false negative and false positive results may occur with these methods. The employment of clinical, biochemical, and radiological predictors has the potential to enhance diagnostic precision. A retrospective cross-sectional study was performed on NSCLC patients diagnosed between 2015 and 2025. From 2,194 lung cancer cases, 121 patients who exhibited adrenal lesions on initial PET/CT and had no extrathoracic metastases were included. Clinical data (age, sex, T-N stage), biochemical markers (CEA, CA125) and radiological parameters (SUVmax, lesion size, laterality) were obtained. The relationship of these parameters with the presence of adrenal metastasis was systematically evaluated in order to identify potential predictors. Of 121 patients, 86 had adrenal metastases and 35 had benign adrenal lesions. Malignant lesions demonstrated significantly higher SUVmax, larger size, and elevated CEA and CA125 levels. ROC analysis revealed optimal cut-off values: SUVmax > 3.05 (AUC = 0.86, sensitivity 89%, specificity 69%), CEA > 4.45 ng/mL, CA125 > 24.5 U/mL, and lesion size > 14.5 mm. Multivariate regression identified N3 stage (OR = 12.75, p = 0.043), elevated CEA (OR = 1.02, p = 0.038), and lesion size (OR = 1.09, p = 0.037) as independent predictors of metastasis. Most patients with metastasis were diagnosed at advanced stages, with 91.9% being T3–4 or N2–3. The present study demonstrates that SUVmax, lesion size, CEA, T stage, and N stage are significantly associated with adrenal metastasis in NSCLC. Integrated assessment of these clinical, biochemical, and radiological parameters can improve the differentiation of adrenal metastases from benign lesions, facilitating more accurate staging and treatment planning.

This study aimed to comparatively reveal the effects of laughter therapy versus walking on prenatal attachment and mental well-being of pregnant women. The sample was identified through snowball sampling among eligible pregnant women and subsequently randomized into the laughter therapy and walking groups using random.org. This randomized controlled experimental study was conducted with 78 pregnant women (40 received laughter therapy, 38 walked regularly) in their 20-30th weeks of pregnancy. The study was conducted between April 25th, 2024, and June 25th, 2024. The pregnant women in the laughter therapy group received 20-30-minute laughter therapy sessions once a week for 8 weeks. The pregnant women in the walking group were given a walking program to follow for 8 weeks. In the laughter therapy group, the post-test Prenatal Attachment scores and the mean Mental Well-Being scores were found to be statistically significantly higher than in the walking group (Cohen d=0.59 ≈ 0.50; Cohen d=0.94 ≈ 1.0, respectively). We concluded that pregnant women who received laughter therapy had higher levels of prenatal attachment and mental well-being than those who walked.

Background/Objectives: Attention-Deficit/Hyperactivity Disorder (ADHD) shows substantial cognitive heterogeneity, complicating individualized clinical formulation. This study examined whether Cognitive Disengagement Syndrome (CDS), anxiety, and ADHD symptom severity are associated with memory functions and visuospatial skills in children with ADHD. Methods: The sample included 120 children aged 6-12 years with ADHD (ADHD + CDS: n = 40; ADHD-only: n = 80). Memory was assessed with the Oktem Verbal Memory Processes Test (OVMPT) and Wechsler Memory Scale-Visual Reproduction (WMS-VR), and visuospatial skills with WISC-IV Block Design and Judgment of Line Orientation (JLO). ADHD symptoms were rated using combined parent-teacher Turgay-Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition-Based Disruptive Behavior Disorders Scale (T-DSM-IV-S) scores; CDS symptoms with the Barkley Child Attention Scale; and anxiety with the SCARED-Child Form. Group comparisons, correlation analyses, and multivariable linear regression models were conducted. Results: The ADHD + CDS group performed worse on WISC-IV Block Design than the ADHD-only group (p = 0.005). In the ADHD + CDS group, inattention severity showed a strong negative association with WMS-VR short-term memory (r = -0.560, p < 0.001). In the ADHD-only group, inattention severity was negatively associated with OVMPT Spontaneous Recall (ρ = -0.319, p = 0.004) and JLO total score (ρ = -0.348, p = 0.002). Anxiety severity in the ADHD-only group was positively associated with OVMPT Total Learning (ρ = 0.350, p = 0.001), Highest Learning (ρ = 0.370, p = 0.001), and WMS-VR short-term memory (ρ = 0.304, p = 0.006). In regression analyses, the presence of CDS independently and negatively predicted WMS-VR short-term memory (β = -0.187, p = 0.018) and Block Design performances (β = -0.226, p = 0.016). Inattention symptom severity was also independently and negatively associated with Block Design performance (β = -0.243, p = 0.013). Conclusions: CDS status and symptom dimensions contribute to cognitive variability in pediatric ADHD, with CDS showing independent associations with timed visuospatial construction and short-term visual memory. Inattention severity emerged as a robust dimensional predictor of cognitive inefficiency across domains, supporting the clinical utility of symptom-based cognitive profiling in ADHD diagnostic evaluations. In addition, mild anxiety symptoms demonstrated meaningful associations with some learning and memory performances within the ADHD-only group, indicating that affective factors may modulate cognitive outcomes in ADHD. Taken together, these findings support considering CDS status and symptom dimensions jointly when characterizing cognitive variability in ADHD.

The share of data center electricity consumption in national energy demand, along with the associated carbon emissions, has been increasing significantly in several leading countries. Beyond monthly or annual emission calculations using yearly average emission rates at the data center facility scale, and even beyond rack-level calculations, fine-grained power distribution unit per-outlet level assessment with hourly dynamic grid mix can enable user-, server-, application- or virtual machine (VM)-specific detailed tracking of carbon emissions, supporting effective emission management. This study investigates a year-long PDU per-outlet level hourly emission assessment using real data center and grid mix data, employing unsupervised data-driven clustering. The results of the case study provide valuable insights into the enhanced visibility and evaluation capabilities enabled by the proposed methodology.

Objectives: This study presents the development of a bilingual, expert-evaluated question-answer (Q&A) dataset, named PMR-Q&A, designed for training large language models (LLMs) in the field of Physical Medicine and Rehabilitation (PMR). Methods: The dataset was created through a systematic and semi-automated framework that converts unstructured scientific texts into structured Q&A pairs. Source materials included eight core reference books, 2310 academic publications, and 323 theses covering 15 disease categories commonly encountered in PMR clinical practice. Texts were digitized using layout-aware optical character recognition (OCR), semantically segmented, and distilled through a two-pass LLM strategy employing GPT-4.1 and GPT-4.1-mini models. Results: The resulting dataset consists of 143,712 bilingual Q&A pairs, each annotated with metadata including disease category, reference source, and keywords. A representative subset of 3000 Q&A pairs was extracted for expert validation to evaluate the dataset's reliability and representativeness. Statistical analyses showed that the validation sample accurately reflected the thematic and linguistic structure of the full dataset, with an average score of 1.90. Conclusions: The PMR-Q&A dataset is a structured and expert-evaluated resource for developing and fine-tuning domain-specific large language models, supporting research and educational efforts in the field of physical medicine and rehabilitation.