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Real-world effectiveness and safety of sacituzumab govitecan in patients with metastatic or unresectable locally advanced triple-negative breast cancer: A multicenter study by the Turkish Oncology Group.
No full textSacituzumab govitecan (SG) is an antibody–drug conjugate approved for metastatic or unresectable locally advanced triple-negative breast cancer (mTNBC) after at least two prior systemic therapies, yet real-world evidence remains limited. We conducted a retrospective, multicenter study including 285 patients with unresectable locally advanced or metastatic TNBC treated with SG across 52 oncology centers in Turkey. Median progression-free and overall survival were 5.4 months (95% confidence interval [CI], 4.6–6.2) and 12.2 months (95% CI, 10.5–13.9), respectively, with a 12-month overall survival rate of 49.2%. The objective response rate and disease control rate in evaluable patients were 36.8% and 63.9%. Grades 3–4 adverse events, mainly neutropenia, occurred in 44.2% of patients. Dose reductions were needed in 20% of cases; no treatment-related deaths were reported. Our large real-world cohort reinforces the effectiveness and manageable safety profile of SG, mirroring pivotal trials, and highlights its value as a therapeutic option in diverse and heavily pretreated mTNBC populations
GÖĞÜS AĞRISI ŞİKAYETİ İLE ÇOCUK KARDİYOLOJİ POLİKLİNİĞİNE BAŞVURAN HASTALARDA PSİKİYATRİK TANI, PSİKOLOJİK SAĞLAMLIK DÜZEYİ VE EBEVEYN KABUL REDDİNİN İNCELENMESİ
No full textSustainable electrospun MXene-biopolymer composite membranes for efficient bioelectricity generation in microbial fuel cells
No full textIn this study, electrospun poly(3-hydroxybutyrate-co-4-hydroxybutyrate) (P34HB) composite membranes containing MXene (Ti3C2Tx) at different concentrations (0.5-5 wt%) are produced, and their performance is evaluated as proton exchange membranes in microbial fuel cells (MFCs). The addition of MXene significantly improves the physicochemical and electrochemical performance of the membranes due to its high electrical conductivity, surface hydrophilicity, and layered structure. The composite membrane containing 3 wt% MXene demonstrates superior proton conductivity (19.5 mS/cm at 80 degrees C), ion exchange capacity (0.87 meq/g), and water retention capacity (101.9 %), exceeding the performance of the pure P34HB membrane by more than three times. The MFC tests reveal an exceptional open-circuit voltage of 795 mV, a maximum power density of 301 mW/m2, a Coulombic efficiency of 18.7 %, and a chemical oxygen demand (COD) removal efficiency of 85 %. The results confirm that the incorporation of MXene effectively enhances proton conduction and electron recovery by reducing internal resistance. To the authors' knowledge, this study is the first to report electrospun MXene-P34HB nanofiber membranes used in MFCs and presents a sustainable and biodegradable alternative for next-generation bioelectrochemical systems
Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics
No full textThe integration of artificial intelligence, protein engineering, and sustainable nanomedicine is driving a paradigm shift in theranostics by enabling highly precise disease diagnosis and targeted therapy. AI-driven methodologies, including machine learning and deep learning, facilitate the rapid analysis of complex biological and chemical datasets, accelerating protein structure prediction, molecular docking, and structure-activity relationship modeling. These capabilities support the rational design of proteins and peptides with enhanced specificity, therapeutic efficacy, and safety, while enabling personalized treatment strategies tailored to individual molecular profiles. In parallel, sustainable nanomedicine focuses on the development of biodegradable, biocompatible, and environmentally benign nanomaterials to improve drug bioavailability, stability, and controlled release. AI-assisted optimization further refines nanocarrier design by balancing therapeutic performance with safety and environmental impact. Advanced intelligent nanocarriers capable of real-time monitoring, adaptive drug release, and degradation into non-toxic by-products represent a significant advancement over conventional static systems. The theranostic paradigm has become central to precision medicine, particularly in oncology, especially where AI-designed nanoplatforms enable targeted delivery of imaging agents and therapeutics to tumors, while allowing continuous treatment monitoring and minimizing off-target effects. Emerging applications in neurological, infectious, and cardiovascular diseases further highlight the broad clinical potential of this approach. Accordingly, this review summarizes AI-driven protein design strategies, sustainable nanocarrier engineering, and their convergence in next-generation theranostic systems, critically discussing mechanistic insights, translational challenges, and design principles required for developing safe, scalable, and clinically adaptable intelligent nanomedicines
A Multimodular AI Algorithm for Automated Assessment of Left Ventricular Function in Ischemic Heart Disease: Ejection Fraction, Wall Motion, and Regional Myocardial Segmentation
No full textBackground: Ischemic heart damage reduces the pumping efficiency of the heart by affecting the left ventricular ejection fraction (LVEF) and causing wall motion abnormality (WMA). In daily clinical practice, these parameters are interpreted by physicians using two dimensional transthoracic echocardiography (2D-TTE). Because 2D-TTE reports rely on visual evaluations, they are subject to experience-based limitations and exhibit low reproducibility. Aims: To develop an artificial intelligence algorithm composed of two modules that enable automatic LVEF calculation and WMA detection for analyzing 2D-TTE images. Study Design: Diagnostic accuracy study. Methods: A total of 600 adult patients were retrospectively included. The model combined static frame segmentation with dynamic tracking using a hybrid Simpson’s method applied to apical 2- and 4-chamber views. Model performance was assessed against cardiologist measurements using Bland-Altman analysis. The YOLOv8 and ResNet50 models were employed for the wall motion module. Performance metrics, including accuracy, precision, F1 score, and area under the curve, were evaluated. Results: In the Bland-Altman analysis, the mean bias between the LVEF module and cardiologist measurements was -4, with limits of agreement ranging from -15 to -3. Regression analysis demonstrated a strong correlation between the LVEF module and cardiologist measurements (r = 0.71, p < 0.001). In the wall motion module, the YOLOv8 segmentation model exhibited high accuracy, while ResNet50 achieved superior performance with an accuracy of 95%. The algorithm’s color coding contributed to standardized interpretation among operators, enhancing consistency. Conclusion: This is the first study to integrate automated EF calculation and WMA detection within a single workflow. SafeHeart offers accurate, reproducible, and rapid analysis, with the potential to support routine echocardiography practice. Color-coded region segmentation can facilitate more standardized and reliable results
Optimization of Punch Shaft Design for Reduced Punching Force and Enhanced Tool Life in S500MC Steel Sheet Forming
No full textSiyasal Özneleşmenin Sınırları: Hatice Sabiha Görkey ve Erken Cumhuriyet Dönemi Kadın Temsiliyeti
No full textFrom Thesis to Publication: A Five-Year Cross-Disciplinary Analysis in Pathology, Urology, and Endocrinology (2018-2022) From Thesis to Publication: A Five-Year Cross-Disciplinary Analysis in Pathology, Urology, and Endocrinology (2018-2022)
No full textOBJECTIVE: Despite the legal requirement to complete a thesis during residency training in Türkiye, the extent to which these theses are translated into high-quality scientific publications remains unclear. Disciplinary differences in research culture, resource availability, and clinical workload may influence these outcomes. MATERIAL AND METHODS: This cross-sectional study analyzed 1245 open access residency theses completed between 2018 and 2022 in the fields of pathology (n=344), endocrinology (n=525), and urology (n=376). Theses were retrieved from the National Thesis Center of the Council of Higher Education. Their publication status was identified via searches in PubMed and Google Scholar. Data collected included journal index status (SCI-E, ESCI, ULAKBIM), Journal Impact Factor™ (JIF), citation count, and time to publication. Statistical comparisons were made using chi-squared and Kruskal-Wallis tests with p < 0.05 considered significant. RESULTS: Among the 1245 residency theses analyzed, 344 (27.6%) were in pathology, 525 (42.2%) in endocrinology and metabolic diseases, and 376 (30.2%) in urology. The conversion rate to publication significantly differed across specialties (p = 0.0002): 86 of 344 pathology theses (25.0%), 115 of 525 endocrinology theses (21.9%), and 139 of 376 urology theses (37.0%) were published. Urology theses had the highest representation in SCI-E indexed journals (72.7%), while endocrinology demonstrated the highest mean Journal Impact Factor (2.3; p < 0.0001). The average number of citations per publication was also highest in urology (4.5), although this difference was not statistically significant (p = 0.0673). Median time to publication ranged from 2.3 to 2.7 years, with no significant difference between specialties (p = 0.1287). Differences in the distribution of Q2, Q3, and Q4 journal publications were statistically significant between specialties. CONCLUSION: Endocrinology had the highest number of theses, whereas urology had the highest publication rate and number of citations per publication