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Causes of diagnostic and treatment delays in locally advanced breast cancer: a nationwide multicenter survey and electronic health records analysis in Turkiye
Full text linkDelays in breast cancer (BC) diagnosis and treatment negatively impact survival outcomes. Understanding patient- and provider-related factors behind these delays is crucial. This study aimed to identify nationwide reasons for delayed diagnosis and treatment of locally advanced BC in Turkiye. A prospective, multicenter hospital-based survey was conducted across 35 institutions between 2023 and 2024. Patient- and provider-related delays were assessed via a structured 61-item face-to-face survey, supplemented by clinical data from electronic health records. Delays exceeding 3 months were clinically categorized as significant. A total of 1322 women participated from seven regions across Turkiye. Factors contributing to diagnostic delays on a national level included economic reasons (5.5%), lack of family support (3.3%), lack of knowledge (12.4%), lack of time due to household work (3.8%), difficulty in finding an appointment (6.7%), pregnancy-related reasons (1.1%), fear of losing the breast (8.9%), fear of death (9.8%), and transportation difficulties (5.1%). Provider-related delays were infrequent. About 89.3% of the patients had the initial doctor appointment and 89.6% had the first specialist consultation within one month. Treatment planning was predominantly based on a multidisciplinary team decision in 88.3% of patients. Regarding treatment initiation, 93.2% started required treatment within 1 month of decision. Patient-related factors are the major causes of diagnostic delay in Turkiye. On the other hand, from the provider's perspective, the presence of multidisciplinary teams, including dedicated breast surgeons, represents a key factor in ensuring the timely implementation of diagnostic procedures and treatment strategies
IOT and AI Integration for Smart Prosthetic Limb Systems
No full textThis project studies the combination of the Internet of Things (IoT) with artificial intelligence (AI) to increase the functionality and flexibility of smart prosthetic limbs. The work takes a mixed-method approach, combining simulation-based modeling and real-world experimental testing, to design a prosthetic system that leverages advanced AI algorithms and IoT technology. Key techniques incorporate the use of machine learning for adaptive control and real-time data processing using IoT sensors. The results reveal considerable improvements in prosthetic limb performance, including increased responsiveness, user experience, and system dependability. Findings reveal that the combination of IoT and AI not only enhances prosthesis control but also provides a more customized and intuitive user interface. The study concluded that this new technique has the potential to transform prosthetic technology, presenting a roadmap for future research into further refinement and broader implementation of smart prostheses. Future work will focus on improving the AI algorithms, increasing real-world testing, and finding potential applications in customized healthcare
A Review on Underwater Optical Wireless Communication (UOWC) Technology: Current Trends and Future Prospects
No full textConference name : 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, ICHORA 2025
Conference city : Ankara
Conference date : 23 May 2025 - 24 May 2025
Conference code : 209351There has been a lot of attention to UOWC's ability to offer an alternative to expensive broadband submarine communications due to its capacity to operate underwater. UOWCs are comparable to FSO communications and laser satellite links, among others, since they all rely on optical wavelengths to send encrypted data across secure, one-to-one connections. Nevertheless, the intricacy of the undersea medium causes many hurdles to deploying UOWC systems. Light absorption, scattering, and turbulence-induced fading are all examples of such phenomena. This paper thoroughly assesses important channel parameters, including water type, attenuation coefficients, scattering quality, and turbulence effects. We thoroughly analyzed and reviewed all the new models and concepts for underwater optical transmission. ML and DL have also been evaluated, and distinct benefits have been demonstrated for UOWC. In order to equalize signals in various undersea channels, noise levels, and system geometric configurations, they can acquire from the received signal. More specifically, ML can better dampen nonlinear possessions in UOWC setups. ML may also manage numerous impairments concurrently by addressing the interplay between several impairments
Treatment of pulmonary hydatid cysts: a single-centre analysis of 872 cases
No full textArticle number : ezaf114OBJECTIVES: The objective of this study was to investigate the importance of pulmonary parenchyma preservation, the results of cystotomy and the capitonnage technique and the efficacy of postoperative albendazole treatment. METHODS: A retrospective study was conducted at a single centre between 2000 and 2024, encompassing 872 consecutive patients with pulmonary hydatid cysts. RESULTS: Of the cases studied, 394 (45.2%) were female and 478 (54.8%) were male, with a mean age of 26.8 ± 19.7 years (range: 2–86 years). Isolated lung involvement was observed in 553 (63.4%) cases. In general, a single hydatid cyst was detected in 665 (76.3%) patients, and 466 (53.4%) of these had isolated lung involvement. In 452 cases (51.8%), only the right lung was affected, whereas 294 (33.7%) had left lung involvement, and 126 (14.5%) had bilateral lung involvement. An operation was performed in 807 (92.5%) cases. Cystotomy and capitonnage were performed in 782 (89.7%) patients. Thoracoscopic wedge resection was performed in 13 cases (1.5%), cystotomy alone in 5 cases (0.6%), lobectomy in 5 cases (0.6%) and enucleation with capitonnage in 2 cases (0.2%). Postoperative complications included atelectasis in 45 cases (5.6%), prolonged air leak in 8 cases (1.0%), empyema in 6 cases (0.7%), wound infection in 3 cases (0.4%) and bleeding in 2 cases (0.2%). Recurrence was observed in 4 (0.5%) surgically treated cases, and 1 case (0.1%) resulted in death in the postoperative period. CONCLUSIONS: The management of pulmonary hydatid cysts with cystotomy and capitonnage is feasible in the majority of patients and results in acceptable success and complication rates. Administration of albendazole postoperatively has been shown to be an effective method of preventing recurrence
A Review of Solar Panel Cooling Methods and Efficiencies
No full textPhotovoltaics is one of the most widely employed clean energy sources on earth. However, when the temperature of the PV cell rises, its electrical power decreases, which makes it essential to find ways to develop the module's efficiency in high-temperature situations. One of the techniques used to raise efficiency and performance is cooling. Researchers have used a variety of ways to cool solar PV panels, including active and passive methods. Researchers used a forced air stream, PCM, a heat exchanger, water, and many other methods to make a solar PV thermal system work better. The principal purpose of this chapter is to look at the significant information the researchers found in their research about how to improve the efficiency and performance of PV cells, how to cool them, and other reasons that affect the output of solar cells
An analysis of the AKP’s bureaucratic tutelage discourse in Turkey
No full textThe Justice and Development Party (AKP) governments have been using a bureaucratic tutelage discourse. The literature on populism acknowledges the instrumental use of anti-bureaucratic discourse to mobilize masses. However, there is no systematic study on the ideological functions of the populist discourse against bureaucracy to sustain/transform the existing structures and power relations. The analysis of the bureaucratic tutelage discourse of AKP governments between 2002 and 2023 reveals that the bureaucratic tutelage discourse reproduces an antagonistic relation between politics and bureaucracy, and builds a common sense of bureaucracy that should be fully obedient to politics
A fast and efficient machine learning assisted prediction of urea and its derivatives to screen crystal propensity with experimental validation
No full textPredicting crystal propensity is crucial yet challenging in various industries where it significantly influences product stability, performance, and efficacy. Predicting a crystal propensity identifies their optimal chemical structures for desired properties including solubility, bioavailability, shelf-life stability etc. Herein, A machine learning (ML) assisted analysis is performed to predict their crystal propensity by collecting a dataset of 6000 non-crystalline and over 200 crystalline urea and its derivatives. The data is trained by employing a Support Vector Machine (SVM) with its Radial Basis Function (RBF) and linear kernels along with Random Forest regression analysis. The trained data is compared with four other ML models, including Linear Regression, Gradient Boosting, Random Forest and Decision Tree Regressions to predict their crystal propensity. It yields an accuracy of 79 % for identifying their non-crystalline compounds and 59 % in predicting crystallization failure. Their dimensionality reduction via t-SNE reveals their distinct clustering patterns to underscore their complex interplay between molecular structure and crystal propensity. Their experimental validation also corroborates the current findings to demonstrate their efficacy to streamline their crystal engineering for pharmaceutical formulation-based workflows. Notably, the number of rotatable bonds and molecular connectivity index (χov) emerges as pivotal descriptors for enabling their accurate classification with minimal input features. This study elucidates its quantitative structure-crystallinity relationship to provide a valuable tool for crystal design and optimization
Early detection of ventricular dysfunction in LADA using novel tissue Doppler parameters: A case-control study
No full textIntroduction & ObjectiveLatent autoimmune diabetes mellitus (LADA), a heterogeneous disease, is much more common in society than thought. Although it has been claimed that LADA is similar to type 2 diabetes mellitus (T2DM) from a cardiovascular perspective, there is no clear consensus. In this context, the objective of this study is to assess subclinical dysfunction in the left and right ventricles in patients with LADA using novel tissue Doppler imaging (TDI) parameters.Materials &MethodsThe sample of this observational case-control study consisted of 57 consecutive patients aged between 30 and 70 years who applied to the endocrinology and metabolism outpatient clinics, were diagnosed with LADA, and were referred to the cardiology clinic for echocardiographic (ECHO) evaluation. The control group consisted of 60 healthy volunteers. Participants' demographic and clinical characteristics and laboratory findings were recorded. All participants underwent ECHO using conventional ECHO and TDI. Subclinical left ventricular dysfunction was assessed using the myocardial performance index (MPI) and isovolumic myocardial acceleration (IVA).ResultsThere were no significant differences between the patient and control groups in terms of conventional ECHO parameters. The left ventricular (LV) MPI was significantly higher in the patient group than in the control group (0.54 ± 0.11 vs 0.47 ± 0.07, p = 0.008). There was no significant difference between the groups in right ventricular (RV) MPI (0.49 ± 0.10 vs 0.46 ± 0.70, p = 0.217). IVA was decreased in both ventricles (IVA mitral: 3.03 ± 1.44 vs 3.78 ± 1.66, p = 0.008 and IVA tricuspid: 2.67 ± 0.88 vs 3.42 ± 0.97, p = 0.034). Both LV IVA and RV IVA were found to be significantly correlated with glutamic acid decarboxylase antibodies' (GADA) levels in the negative direction (R = -0.290, p = 0.005 and R = -0.340, p = 0.001).ConclusionsIt was observed that LADA negatively affected the systolic and diastolic functions of both ventricles, with its effect being more pronounced in the left ventricle. Glycemic control and autoantibody titers were found to be correlated with TDI parameters, emphasizing their relevance in assessing cardiac dysfunction
An intelligent atrous convolution-based cascaded deep learning framework for enhanced privacy preservation performance in edge computing
No full textA system without any communication delays, called edge computing, has been introduced for nearer and faster services. The major concern in the edge computing scenario is its privacy risks. A user, as well as a cloud data preservation scheme, is the main aim of this paperwork. Test data is given by the user to access the cloud-based data processing framework. The training of the suitable model is carried out by utilizing the data stored in the cloud. The suggested model divides the entire model into two sections, namely, the untrusted cloud and the trusted edge. On the trusted edge side the data is directly provided to the developed advanced deep learning model called the Atrous Convolution based Cascaded Deep Temporal Convolution Network (ACC-DTCN) for the data analysis process. However, instead of giving the whole data directly to the untrusted cloud side, the test data is protected on the cloud side by utilizing a hybrid encryption technique called the Optimal Hybrid Encryption Model (OHEM). Both Attribute-Based Encryption (ABE) and Homomorphic Encryption (HE) are utilized in the recommended OHEM scheme. The OHEM variables are tuned with the help of an advanced algorithm called the Enhanced Ladybug Beetle Optimization algorithm (ELBOA). The confidence score vector among the testing and training data is predicted by the implemented ACC-DTCN model by utilizing the encrypted data on the cloud side. The suggested privacy preservation scheme provides higher prediction accuracy and prevents interference attacks while contrasting it against conventional methods during extensive experimentations
Automatic Identification of Dental Implant Brands with Deep Learning Algorithms
No full textObjectives: To reduce the problems arising from the inability to identify dental implant brands, this study aims to classify various dental implant brands using deep learning algorithms on panoramic radiographs.
Methods: Images of four different dental implant systems (NucleOSS, Medentika, Nobel, and Implance) were used from a total of 5,375 cropped panoramic radiographs. To enhance image clarity and reduce blurriness, the Contrast Limited Adaptive Histogram Equalization (CLAHE) filter was applied. GoogleNet, ResNet-18, VGG16, and ShuffleNet deep learning algorithms were utilized to classify the four different dental implant systems. To evaluate the classification performance of the algorithms, ROC curves and confusion matrices were generated. Based on these confusion matrices, accuracy, precision, sensitivity, and F1 score were calculated. The Z-test was used to compare the performance metrics across different algorithms.
Results: The accuracy rates of the deep learning algorithms were obtained as 96.00% for GoogleNet, 84.40% for ResNet-18, 98.90% for VGG16, and 84.80% for ShuffleNet. A statistically significant difference was found between the accuracy rate of the VGG16 algorithm and those of GoogleNet, ShuffleNet, and ResNet-18 (p < 0.001, p < 0.001, and p < 0.001, respectively).
Conclusions: With the achievement of high accuracy rates, deep learning algorithms are considered a valuable and powerful method for identifying dental implant brands