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Nasal polyps are edematous polypoid masses covered by smooth, gray, shiny, soft and gelatinous mucosa. They often pose a threat for the patients to result in allergic rhinitis, sinus infections and asthma. The aim of this paper is to design a reliable rhinology assistance system for recognizing the nasal polyps in endoscopic videos. We introduce NP-80, a novel dataset that contains high-quality endoscopy video-frames of 80 participants with and without nasal polyps (NP). We benchmark vanilla machine learning and deep learning-based classifiers on the proposed dataset with respect to robustness and accuracy. We conduct a series of classification experiments and an exhaustive empirical comparison on handcrafted features (texture features -Local Binary Patterns (LBP) and shape features- Histogram of Oriented Gradients (HOG) and Convolutional Neural Network (CNN) features for recognizing nasal polyps automatically. The classification experiments are carried out by K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT) and CNN classifiers. The best obtained precision, recall, and accuracy rates are 99%, 98%, and 98.3%, respectively. The classifier methods built with handcrafted features have shown poor recognition performance (best accuracy of %96.3) from the proposed CNN classifier (best accuracy of %98.3). The empirical results of the proposed learning techniques on NP-80 dataset are promising to support clinical decision systems. We make our dataset publicly available to encourage further research on rhinology experiments. The major research objective accomplished in this study is the creation of a high-accuracy deep learning based nasal polyps classification model using easily obtainable portable rhino fiberoscope images to be integrated into an otolaryngologist decision support system. We conclude from the research that using appropriate image processing techniques along with suitable deep learning networks allow researchers to obtain high accuracy recommendations in identifying nasal polyps. Furthermore, the results from the study encourages us to develop deep learning models for various other medical conditions.

The enzyme-linked immunosorbent assay (ELISA) detects antigen-antibody interactions by using enzyme-labelled conjugates and enzyme substrates that generate colour changes. This review aims to provide an overview of ELISA, its various types, and its applications in detecting metabolites in biological fluids. The article discusses the history of the assay, its underlying principles and procedures, common ELISA protocols, and the most accurate and reliable techniques for measuring peptide molecules in biological fluids. Additionally, we emphasize best laboratory practices to achieve consistent, high-quality results and outline the essential materials for setting up an ELISA laboratory, drawing from our over 30 years of experience in the field.

Background/Objectives: Video laryngoscopy is one of the primary methods used by otolaryngologists for detecting and classifying laryngeal lesions. However, the diagnostic process of these images largely relies on clinicians’ visual inspection, which can lead to overlooked small structural changes, delayed diagnosis, and interpretation errors. Methods: AI-based approaches are becoming increasingly critical for accelerating early-stage diagnosis and improving reliability. This study proposes a hybrid Convolutional Neural Network (CNN) architecture that eliminates repetitive and clinically insignificant frames from videos, utilizing only meaningful key frames. Video data from healthy individuals, patients with vocal fold nodules, and those with vocal fold polyps were summarized using three different threshold values with the Structural Similarity Index Measure (SSIM). Results: The resulting key frames were then classified using a hybrid CNN. Experimental findings demonstrate that selecting an appropriate threshold can significantly reduce the model’s memory usage and processing load while maintaining accuracy. In particular, a threshold value of 0.90 provided richer information content thanks to the selection of a wider variety of frames, resulting in the highest success rate. Fine-tuning the last 20 layers of the MobileNetV2 and Xception backbones, combined with the fusion of extracted features, yielded an overall classification accuracy of 98%. Conclusions: The proposed approach provides a mechanism that eliminates unnecessary data and prioritizes only critical information in video-based diagnostic processes, thus helping physicians accelerate diagnostic decisions and reduce memory requirements.

Objective The neurological causes of suicide remain poorly understood. This study sought to ascertain whether there is a correlation between amygdala volume and suicidal behavior. Methods This case–control retrospective study included 193 participants—108 healthy controls and 85 individuals with a history of suicide attempts. Sagittal magnetic resonance imaging was conducted for each participant, and cross-sectional areas of the left and right amygdala were determined independently using freehand tracing. The total volume of the amygdala was determined by multiplying the thickness of the slices by the sum of the regions. Results Patients with a history of suicide attempts had significantly decreased amygdala volumes on both the right (p = 0.018) and left (p = 0.036) sides compared with controls. Receiver operating characteristic analysis determined a cutoff value of 1825 mm³ for both hemispheres. The cutoff sensitivity and specificity for the right amygdala were 30.6% and 88%, whereas the values for the left amygdala were 31.8% and 84.3%, respectively. Conclusions This is the first study to show that individuals who had attempted suicide had considerably decreased left and right amygdala volumes, with receiver operating characteristic analysis indicating good specificity (84.3%–88%). According to these findings, a reduced amygdala volume might be a useful biomarker for identifying those who are at risk of attempting suicide.

Background and Objectives: This study aimed to examine the relationship between serum calprotectin levels and facial paralysis in patients with Bell’s palsy and to determine its prognostic significance. Materials and Methods: This study included 40 patients diagnosed with Bell’s palsy and 20 healthy individuals as controls. The patients were categorized into three groups based on their response to treatment: complete response, partial response, and no response. Blood samples were taken before treatment and in the third month after treatment to measure C-reactive protein, white blood cell count, lymphocyte count, neutrophil count, neutrophil-to-lymphocyte ratio, and calprotectin levels. Results: Serum calprotectin levels were found to be elevated in patients with BP compared to the healthy controls; however, no significant correlation was observed between calprotectin levels and disease prognosis. Conclusions: The findings suggest that Bell’s palsy patients have elevated serum calprotectin levels compared to healthy individuals, indicating the potential use of calprotectin as a biomarker in Bell’s palsy. However, no significant difference in calprotectin levels was observed between patients with varying degrees of treatment response, suggesting that calprotectin may be limited in predicting disease prognosis.

The study determined the damage caused by formaldehyde (FA) exposure in blood and liver samples using biochemical markers. Histopathological analysis was performed using the terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) method and measurement of CD68 cell density. To what extent the antioxidant molecules thymoquinone (TQ) and ozone (O ) reversed the damage caused by FA exposure was investigated, both when used alone and combined. Fifty-six Sprague-Dawley male rats of eight to ten weeks of age were used in the experiment. The rats were divided into eight groups, with seven rats in each group: the untreated control group, the group treated with TQ (10 mg/kg/day), the group treated with O (150 μg/kg/day), the group treated with TQ+O , the group exposed to FA (10 ppm 8 h/day), the group receiving FA+TQ, the group receiving FA+O , and the group receiving FA+TQ+O . Serum aspartate transaminase (AST), alanine transaminase (ALT), total antioxidant (TAS, U/mL), and total oxidant (TOS, nmol/mL) levels were analyzed. TAS and TOS levels, CD68 cell density, and apoptotic cells were determined in liver tissues. FA exposure caused an increase in serum AST and ALT levels of (p<0.05) experimental animals, a decrease in TAS levels in serum (p=0.03) and liver (p>0.05) and an increase in TOS levels (p>0.05), TUNEL positivity (p<0.001), and CD68 cell density (p=0.004). Administration of TQ and O  as antioxidants significantly reversed biochemical and histopathological alterations in the serum and liver. TQ and ozone therapy suppressed oxidative stress caused by FA exposure and reversed the emerging histopathological deteriorations. Ozone therapy did not suppress the effects of TQ. Therefore, ozone therapy can be given as a supportive therapy along with the main therapeutic agents. We think TQ and ozone therapy may be useful to protect individuals exposed to FA.

Background/Objectives: Artificial intelligence (AI) has attracted great interest due to its applicability in many fields. The adoption of visual illustration techniques produced by AI in the field of graphic design has further expanded the field of use of this technology. This study focuses on foot anatomy illustrations generated by Adobe Firefly and Microsoft Designer Image Creator applications, evaluating them based on detail, clarity, anatomical realism, accuracy, and aesthetic appeal. Methods: The illustrations were created using text-based scripts, and five anatomists compared them to traditional illustrations from the Sobotta Atlas of Human Anatomy. Results: Fleiss’ Kappa statistic was used to analyze consistency among expert evaluations. For the four figures generated by both AI applications, Fleiss’ Kappa agreement was high. Adobe Firefly performed slightly better in illustrating phalanx and ankle bones, but its anatomical accuracy was lower for tarsal and metatarsal bones. Microsoft Designer Image Creator excelled in illustrating metatarsal bones, while its tarsal and phalanx illustrations were less anatomically accurate than Adobe Firefly and the atlas drawings. Both programs showed average realism in ankle structures, while the tarsal bones had low realism. Conclusions: Artificial intelligence applications within the scope of the study showed fast performance. Aesthetic appeal is dominant at first glance in the resulting drawings. In general, both applications have struggled to reflect anatomical reality.

A persistent mental illness, schizophrenia has a complicated etiopathogenesis that includes both environmental and genetic elements. This study examined the possibility of diagnosing schizophrenia by utilizing computed tomography (CT) images of the orbit and its structures, which were then examined by artificial neural networks (ANNs) and machine learning (ML) algorithms. A retrospective analysis of the CT scans of 90 healthy people and 90 people with schizophrenia was conducted. Prior to measurement, all CT images underwent preprocessing steps to ensure align-ment and standardization. Height, width, depth, wall length, aperture area, interorbital width, biorbital width, bimalar width, skull transverse diameter, and optic nerve sheath width were among the orbital parameters that were measured. Statistical analysis revealed significant differences between the groups in left orbital width, left orbital aperture area, right optic nerve sheath width, transverse skull diameter, bimalar width, biorbital width, and left medial wall length. ML algorithms and ANNs were applied to the data, with the Extra Tree Classifier (ETC) algorithm achieving the highest accuracy of 0.78 and the Multilayer Perceptron Classifier (MLCP) model of ANN achieving an accuracy of 0.75 after 1000 training iterations. The Random Forest algorithm's SHAP analyzer determined that the left orbital width had the biggest impact on the final outcome. These results add to the expanding field of machine learning applications in psychiatry by indicating that AI-based models that analyze orbital morphometry may be useful instruments for detecting schizophrenia.

Cancer is a devastating disease that has significant psychological and biological impacts. Generally, lung cancer primarily affects men while breast cancer primarily affects women. Thus, this study aimed to investigate the levels of anxiety and depression in patients with these prevalent cancer types, as well as their perceptions of the illness and any potential connections between them. The study included a total of 252 participants, consisting of 110 breast cancer patients, 112 lung cancer patients, and 30 healthy individuals as controls. The Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI) were administered to assess mood, while the Illness Perception Questionnaire (IPQ) was used to evaluate cancer perceptions. Results revealed that both breast cancer and lung cancer patients had significantly higher BDI and BAI scores compared to the control group. Furthermore, the BDI and BAI scores were lower in breast cancer patients compared to lung cancer patients. The IPQ causal representation–immunity score was significantly higher in lung cancer patients than in breast cancer patients (p = 0.01). Positive correlations were found between BDI scores and BAI scores, as well as between BDI scores and certain subscale scores of the IPQ related to illness representation and causal representation. Additionally, a positive correlation was observed between BAI scores and the IPQ illness representation–timeline acute/chronic subscale, while a negative correlation was found between BAI scores and the IPQ causal representation–accident or chance scores. Overall, the study findings demonstrated that breast and lung cancer patients possess negative perceptions of their disease and experience high levels of anxiety and depression. To enhance the quality of life and promote resilience in these patients, it is recommended to incorporate psychological interventions that consider anxiety, depression, and disease perception.

Cumhuriyetin ilan edilmesinin ardından, Batılı bir yönetim anlayışı benimsenmiş ve toplumun bu yönde dönüştürülmesine girişilmiştir. Bu noktada temel alınan bazı ilkeler belirlenmiş, bunlar bugünkü bilinen haliyle altı temel başlıkta toplanmıştır. Bunların içerinde ön plana alınan, devletin ve toplumun yapılanmasında esas alınan halkçılık prensibi olmuştur. Eşitsizliklerin kaldırılmasına, ayrılıkların önlenmesine ve halkın kendisinin hâkim konuma getirilmesine dayanan bu görüş çerçevesinde, mevcut yapılanmadaki var olan aksaklıkların bertarafı yoluna gidilmiştir. Bu anlamda girişilen adımlardan biri de, soyadı kullanımının mecburi hale getirilmesi, lakap ve unvanların kaldırılmasıdır. Soyadı kullanımının öteden beri var olan bu toplulukta, yasal bir zorunluluğa dönüştürülme girişiminde başarılı olunabilmesi için yönetimin siyasal bir ikna süreci içine girdiği ve bu yönde dönemin kanaat önderleri olarak kabul edebileceğimiz öğretmenleri, din adamlarını, memurları ve basını kullandığı görülmüştür. Çalışma, soyadı kanununun hazırlanma ve çıkış süreci ile soyadlarının kayıt altına alınması için verilen yasal sürenin bitimine kadar geçen süreyi kapsamaktadır. Çalışmada, konuya ilişkin mevcut literatür incelenmiş ve bunun yanı sıra, TBMM Zabıt Cerideleri ve dönemin basını üzerinde taramalar yapılmıştır. Çalışmada, kanunun çıkışından, kanunda öngörülen sürenin dolmasına kadar geçen sürede yürütülen siyasal iletişim ortaya konmaya çalışılmış, böylelikle hem konunun siyasi amaçları, hem de bu amaca giderken kullanılan araçlar incelenmiştir. Bu yönüyle çalışma, siyasi ve basın tarihine bir katkıyı hedeflemiştir.