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In digital pathology, accurate mitosis detection in histopathological images is critical for cancer diagnosis and prognosis. However, this remains challenging due to the inherent variability in cell morphology and the domain shift problem. This study introduces ConvNext Mitosis Identification-You Only Look Once (CNMI-YOLO), a new 2-stage deep learning method that uses the YOLOv7 architecture for cell detection and the ConvNeXt architecture for cell classification. The goal is to improve the identification of mitosis in different types of cancers. We utilized the Mitosis Domain Generalization Challenge 2022 data set in the experiments to ensure the model's robustness and success across various scanners, species, and cancer types. The CNMI-YOLO model demonstrates superior performance in accurately detecting mitotic cells, significantly outperforming existing models in terms of precision, recall, and F1 score. The CNMI-YOLO model achieved an F1 score of 0.795 on the Mitosis Domain Generalization Challenge 2022 and demonstrated robust generalization with F1 scores of 0.783 and 0.759 on the external melanoma and sarcoma test sets, respectively. Additionally, the study included ablation studies to evaluate various object detection and classification models, such as Faster-RCNN and Swin Transformer. Furthermore, we assessed the model's robustness performance on unseen data, confirming its ability to generalize and its potential for real-world use in digital pathology, using soft tissue sarcoma and melanoma samples not included in the training data set., (Copyright © 2024 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights reserved.)

This study aimed to investigate the change in diet quality in addition to dietary adherence to the planetary health diet during night shifts in healthcare workers. This observational follow-up study involved 450 healthcare workers working night shifts (327 females, 123 males). A survey form requesting sociodemographic information (gender, age, marital status), job title, sleeping duration during the night shift, 24 h dietary records for pre-night-shift, during night shift, and post-night-shift, and anthropometric measurements (body weight and height) was applied. The scores of the Planetary Health Diet Index (PHDI) and the Healthy Eating Index 2020 (HEI-2020) were calculated according to the dietary records. The total HEI-2020 and PHDI scores decreased significantly ( p < 0.05) during the night shift (44.0 ± 8.8 and 48.3 ± 13.2, respectively) compared to pre-night-shift (46.1 ± 9.2 and 51.9 ± 13.4, respectively) and increased post-night-shift (44.7 ± 9.9 and 50.6 ± 14.9, respectively), with no statistically significant difference between pre- and post-night-shift. There was a significant main effect of night shift working on total PHDI (F(896, 2) = 8.208, p < 0.001, η p 2 = 0.018) and HEI-2020 scores (F(894, 2) = 6.277, p = 0.002, η p 2 = 0.014). Despite healthcare workers' knowledge of health factors, night shifts lead to poor dietary choices. To improve diet quality and sustainability, it is crucial to enhance access to healthy food options in their work environment.

This study aimed to determine changes in energy and nutrient intakes over three consecutive days, including the day of the shift, and the days before and after the shift, in healthcare workers working in a 24 h shift system. This study is an observational follow-up study conducted with a total of 500 volunteer healthcare professionals. Food consumption records were taken over 3 consecutive days: pre-shift (off day), shift day (24 h shift), and post-shift (off day). Mean daily intakes of energy, carbohydrate, protein, fat, saturated fat, caffeine, vitamins B 1 , B 2 , niacin, B 6 , folate, and B 12 , potassium, magnesium, phosphorus, iron, and zinc are listed from highest to lowest as shift day > pre-shift > post-shift ( p < 0.05 for all pairwise comparisons). While fiber, vitamin C, and calcium intakes were similar on the shift day and pre-shift day, they were significantly lower on the post-shift day ( p < 0.05). The lowest dietary reference intake percentages on the post-shift day were calcium, fiber, and folate, respectively. In the present study, significant differences were detected in the energy, micronutrient-intake, and macronutrient-intake levels between the pre-shift day, shift day, and post-shift day of healthcare workers. Awareness should be increased regarding the decreased nutrient intake seen especially on the first day after a 24 h shift, and appropriate precautions should be taken to increase calcium, fiber, and folate intake levels.

Allium species are among the most widely cultivated vegetables for centuries for their positive effects on human health and their variety of uses in food preparation and cooking. Preparation and cooking processes create chemical changes that can affect the concentration and bioavailability of bioactive molecules. Understanding the changes in bioactive compounds and bioactive activities in Allium vegetables resulting from preparation and cooking processes is essential for better retention of these compounds and better utilization of their health benefits. This study aimed to investigate the effects of different preparation and cooking processes on the bioactive molecules of Allium vegetables. This review concludes that bioactive compounds in Allium vegetables are affected by each preparation and cooking process depending on variables including method, time, temperature. Owing to differences in the matrix and structure of the plant, preparation and cooking processes show different results on bioactive compounds and bioactive activities for different vegetables. Continued research is needed to help fill gaps in current knowledge, such as the optimal preparation and cooking processes for each Allium vegetable., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Katırcıoglu and Navruz-Varlı.)

Objectives: The artificial intelligence competition in healthcare at TEKNOFEST-2022 provided a platform to address the complex multi-class classification challenge of abdominal emergencies using computer vision techniques. This manuscript aimed to comprehensively present the methodologies for data preparation, annotation procedures, and rigorous evaluation metrics. Moreover, it was conducted to introduce a meticulously curated abdominal emergencies data set to the researchers., Methods: The data set underwent a comprehensive central screening procedure employing diverse algorithms extracted from the e-Nabız (Pulse) and National Teleradiology System of the Republic of Türkiye, Ministry of Health. Full anonymization of the data set was conducted. Subsequently, the data set was annotated by a group of ten experienced radiologists. The evaluation process was executed by calculating F 1 scores, which were derived from the intersection over union values between the predicted bounding boxes and the corresponding ground truth (GT) bounding boxes. The establishment of baseline performance metrics involved computing the average of the highest five F 1 scores., Results: Observations indicated a progressive decline in F 1 scores as the threshold value increased. Furthermore, it could be deduced that class 6 (abdominal aortic aneurysm/dissection) was relatively straightforward to detect compared to other classes, with class 5 (acute diverticulitis) presenting the most formidable challenge. It is noteworthy, however, that if all achieved outcomes for all classes were considered with a threshold of 0.5, the data set's complexity and associated challenges became pronounced., Conclusion: This data set's significance lies in its pioneering provision of labels and GT-boxes for six classes, fostering opportunities for researchers., Clinical Relevance Statement: The prompt identification and timely intervention in cases of emergent medical conditions hold paramount significance. The handling of patients' care can be augmented, while the potential for errors is minimized, particularly amidst high caseload scenarios, through the application of AI., Key Points: • The data set used in artificial intelligence competition in healthcare (TEKNOFEST-2022) provides a 6-class data set of abdominal CT images consisting of a great variety of abdominal emergencies. • This data set is compiled from the National Teleradiology System data repository of emergency radiology departments of 459 hospitals. • Radiological data on abdominal emergencies is scarce in literature and this annotated competition data set can be a valuable resource for further studies and new AI models., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

This study aimed to investigate the effects of sociodemographic parameters on healthy and sustainable nutrition in nutrition students. This cross-sectional study was conducted with 601 students. Researchers administered questionnaire forms to gather sociodemographic information such as age, gender, geographical region, residence area, accommodation, BMI, and income level. Participants' 24 h dietary records were used to evaluate Healthy Eating Index-2020 (HEI-2020) and Planetary Health Diet Index (PHDI). The mean PHDI scores of the Marmara (53.4 ± 14.9), Aegean (58.2 ± 18.3), Mediterranean (55.3 ± 15.5), and Black Sea (55.5 ± 15.7) regions, which are the coastal regions of Türkiye, were significantly higher than for the Central Anatolia region (46.7 ± 15.1). The PHDI and HEI-2020 score means of students living in metropolitan cities and rural areas were significantly higher than those living in urban areas ( p < 0.05). Being in the 20-25 years age group increased the probability of being in a lower PHDI group (AOR 1.82; 95% CI 1.07:3.12; p = 0.028). While a similar result was found in the 20-25 years age group for HEI-2020, income level and gender did not have a statistically significant effect on these scores. Since students' ages, geographical regions, and residence areas affect PHDI and HEI-2020, it is considered important to take these sociodemographic variables into consideration in guidelines and studies.

To advance both human health and environmental sustainability, it's crucial to assess the adaptation to new dietary trends emerging in this field. This study aimed to explore the relationship between diet quality and the principles of planetary health diet in young adults studying at university. This cross-sectional study consisted of 945 young adults with a mean age of 20.1 ± 1.34 y (582 females, 363 males). A questionnaire form containing socio-demographic information (age, gender, education level), anthropometric measurements (body weight and height), and a 24 h dietary record form for three consecutive days was applied. The scores of the Planetary Health Diet Index (PHDI) and the Healthy Eating Index-2020 (HEI-2020) were calculated according to the dietary records. The mean total scores of the PHDI and HEI-2020 were 59.9 ± 14.16 and 54.2 ± 10.87, respectively. The association between the HEI-2020 score and the PHDI score was significant ( p = 0.003). A one-unit increase in the unadjusted HEI-2020 score caused a 0.429 unit decrease in the PHDI score (95% CI: -0.709; -0.149). The findings underscore the imperative for targeted interventions and educational programs to enhance the PHDI and HEI-2020 scores, promoting individual well-being and environmental sustainability in the university.

The re-identification (ReID) of objects in images is a widely studied topic in computer vision, with significant relevance to various applications. The ReID of players in broadcast videos of team sports is the focus of this study. We specifically focus on identifying the same player in images taken at any given moment during a game from various camera angles. This work varies from other person ReID apps since the same team wears very similar clothes, there are few samples for each identification, and image resolutions are low. One of the hardest parts of object ReID is robust feature representation extraction. Despite the great success of current convolutional neural network-based (CNN) methods, most studies only consider learning representations from images, neglecting long-range dependency. Transformer-based model studies are increasing and yielding encouraging results. Transformers still have trouble extracting features from small objects and visual cues. To address these issues, we enhanced the Swin Transformer with the levering of CNNs. We created a regional feature extraction Swin Transformer (RFES) backbone to increase local feature extraction and small-scale object feature extraction. We also use three loss functions to handle imbalanced data and highlight challenging situations. Re-ranking with k-reciprocal encoding was used in this study's retrieval phase, and its assessment findings were provided. Finally, we conducted experiments on the Market-1501 and SoccerNet-v3 ReID datasets. Experimental results show that the proposed re-ID method reaches rank-1 accuracy of 96.2% with mAP: 89.1 and rank-1 accuracy of 84.1% with mAP: 86.7 on the Market-1501 and SoccerNet-v3 datasets, respectively, outperforming the state-of-the-art approaches., (© 2024. The Author(s).)

Introduction: Present study investigates the effects of different home pre-treatment processes and cooking techniques on the acrylamide content of fried potatoes., Methods: Potato sticks were prepared in two different pre-treatment ways (washing and soaking) and cooked with three other techniques (air frying, deep frying, and oven frying). Acrylamide analyses were performed on cooked potatoes using an LC-MS/MS method., Results: The highest acrylamide content was found in potatoes cooked using the air fryer (12.19 ± 7.03 μg/kg). This was followed by deep frying (8.94 ± 9.21 μg/kg) and oven frying (7.43 ± 3.75 μg/kg). However, the difference between the acrylamide contents of the potatoes according to the cooking methods was not statistically significant. The acrylamide content of the potatoes that were subjected to soaking in all three ways was lower than the potatoes that were not soaked and only washed. In the deep-frying method, it was found statistically significant that the soaked potatoes contained less acrylamide ( p = 0.029)., Discussion: It is important to highlight the relatively low acrylamide levels found in oven-frying, lower than air frying in both washing and soaking groups in the present study. Although air fryers, which have become widely used as an alternative to deep frying in recent years, provide French fries with less oil, their role in the formation of acrylamide should be further investigated., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Navruz-Varlı and Mortaş.)

Aim: Many studies have examined the relationship between chronotype and general health. Because healthy eating is the basis of health aim of this study is to evaluate the relationship between healthy nutrition attitudes and chronotype in young adults. Methods: This cross-sectional study was conducted with 1,400 young adults (936 females and 464 males). Chronotype was assessed by the Turkish version of the Morningness-Eveningness Questionnaire (MEQ) and the Attitude Scale for Healthy Nutrition (ASHN) was applied by face-to-face interview. Furthermore, 24-h dietary recall was collected. The scores of nutrient adequacy ratio (NAR) and mean adequacy ratio (MAR) were calculated. The anthropometric measurements including body weight and height were taken., Results: The chronotype distribution of participants according to the MEQ score and MAR score were not significantly different between females and males ( p  > 0.05). The percentage of participants with high healthy eating attitude was lowest in the evening chronotype and highest in the morning chronotype (49.7, 37.7 and 25.4% in morning-, intermediate-, evening-types, respectively; p  = 0.000 for all groups according to the reciprocal comparisons). There was a positive correlation of the MEQ scores with the scores of ASHN ( r  = 0.282, p  < 0.01). ASHN score was shown to be a predictor for MEQ score ( β  = 0.280, p  = 0.000). Moreover, the score of "poor eating habits" was shown to be highest in the evening chronotype and lowest in the morning chronotype (14.9 ± 3.0 and 13.1 ± 3.0, respectively; p  < 0.01)., Conclusion: The results of this study indicate that individuals with the evening chronotype are more deficient in terms of healthy nutrition attitudes. Further studies with larger populations will potentially guide the development of health promotion strategies to prevent and treat chronic diseases based on an individual's chronotype., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Mortaş, Ayhan, Navruz Varlı, Köse, Ağagündüz and Bilici.)

Objective: The artificial intelligence competition in healthcare was organized for the first time at the annual aviation, space, and technology festival (TEKNOFEST), Istanbul/Türkiye, in September 2021. In this article, the data set preparation and competition processes were explained in detail; the anonymized and annotated data set is also provided via official website for further research., Materials and Methods: Data set recorded over the period covering 2019 and 2020 were centrally screened from the e-Pulse and Teleradiology System of the Republic of Türkiye, Ministry of Health using various codes and filtering criteria. The data set was anonymized. The data set was prepared, pooled, curated, and annotated by 7 radiologists. The training data set was shared with the teams via a dedicated file transfer protocol server, which could be accessed using private usernames and passwords given to the teams under a nondisclosure agreement signed by the representative of each team., Results: The competition consisted of 2 stages. In the first stage, teams were given 192 digital imaging and communications in medicine images that belong to 1 of 3 possible categories namely, hemorrhage, ischemic, or non-stroke. Teams were asked to classify each image as either stroke present or absent. In the second stage of the competition, qualifying 36 teams were given 97 digital imaging and communications in medicine images that contained hemorrhage, ischemia, or both lesions. Among the employed methods, Unet and DeepLabv3 were the most frequently observed ones., Conclusion: Artificial intelligence competitions in healthcare offer good opportunities to collect data reflecting various cases and problems. Especially, annotated data set by domain experts is more valuable.

Purpose: The aim was to determine sleep quality and related factors including restless leg syndrome in the 6th year medical students and medical residents in Pamukkale University, Faculty of Medicine, Denizli, Turkey., Methods: The data collection phase of this cross-sectional study was completed between November 1st and December 31st, 2019, with a questionnaire including 59 items. Three hundred forty-one medical students and residents participated in the study. The Pittsburgh Sleep Quality Index was used to determine the participants' sleep quality. The International Restless Legs Syndrome Study Group Diagnostic Criteria and the Restless Leg Syndrome Rating Scale were used for the diagnosis and for determining the seriousness of restless leg syndrome, respectively., Results: The mean age (SD ±) was 26.6 ± 3.2. Men (50%) and medical residents (59%) constituted the majority. Most of the participants (70%) had poor sleep quality, and 9% had restless leg syndrome. Despite losing their significance in multivariable analysis, a relationship existed between restless leg syndrome and sleep quality in bivariate comparisons (p = 0.04). According to the multiple variable analysis, age (p = 0.008), female gender (p = 0.04), insufficient income (p = 0.009), being on-medication (p = 0.007), being a current smoker (p = 0.01), not sleeping at the usual time (p = 0.04), and listening to music before sleeping (p = 0.03) were identified as independent risk factors for poor sleep quality., Conclusions: The prevalence of poor sleep quality and restless leg syndrome are high in the 6th year medical students and residents. Necessary measures should be taken to reduce the effect of negative conditions on these vulnerable groups., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

As a result of most of the bone disorders seen in hip joints, shape deformities occur in the structural form of the hip joint components. Image-based quantitative analysis and assessment of these deformities in bone shapes are very important for the evaluation, treatment, and prognosis of the various hip joint bone disorders. In this article, a novel approach for the image-based computerized quantitative analysis of proximal femur shape deformities is presented. In the proposed approach, shape deformities of the pathological proximal femurs were quantified over the contralateral healthy proximal femur shape structure of the same patient in 2D by taking the hip joint symmetry property of human anatomy into consideration. It is based on the idea that if the right and left proximal femurs in bilateral hip joints are highly symmetrical and also if one of the proximal femurs is healthy and the contralateral one is pathological, the non-overlapping bone shape regions can represent the deformities in pathological proximal femurs when both proximal femurs are registered to overlap each other. In the methodological process of the proposed study, a set of image preprocessing operations was primarily performed on the raw magnetic resonance imaging (MRI) data. Then, the segmented proximal femurs in bilateral hip joint images were automatically aligned with the Iterative Closest Point (ICP) rigid registration method. Following the registration, a set of image postprocessing operations was performed on the images of proximal femurs aligned. In the quantification phase, the bone shape deformities in pathological proximal femurs were quantified simply in terms of the mismatching area in 2D by measuring a shape variation index representing the total bone shape deformity ratio. To evaluate the proposed quantitative shape analysis approach, bilateral hip joints in a total of 13 coronal MRI sections of 13 patients with Legg-Calve-Perthes disease (LCPD) were used. Experimental studies have shown that the proposed approach has quite promising results in the quantitative representation of the pathological proximal femur shape deformities. Furthermore, consistent results have been observed for the Waldenström classification stages of the disease. The shape deformity ratios in pathological proximal femurs were quantified as 9.44% (±1.40), 18.38% (±6.30), 24.73% (±12.42), and 27.66% (±10.41), respectively for the Initial, Fragmentation, Reossification, and Remodelling stages of LCPD with the quantification error rates of 0.29% (±0.16), 0.58% (±0.71), 1.12% (±0.82), and 0.80% (±0.98). Additionally, a mean error rate of 0.65% (±0.68) was observed for the quantified shape deformity ratios of all samples., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Medical image segmentation is one of the most crucial issues in medical image processing and analysis. In general, segmentation of the various structures in medical images is performed for the further image analyzes such as quantification, assessment, diagnosis, prognosis and classification. In this paper, a research study for the 2D semantic segmentation of the multiform, both spheric and aspheric, femoral head and proximal femur bones in magnetic resonance imaging (MRI) sections of the patients with Legg-Calve-Perthes disease (LCPD) with the deep convolutional neural networks (CNNs) is presented. In the scope of the proposed study, bilateral hip MRI sections acquired in coronal plane were used. The main characteristic of the MRI sections that were used is to be low quality images which were obtained in different MRI protocols by using 3 different MRI scanners with 1.5 T imaging capability. In performance evaluations, promising segmentation results were achieved with deep CNNs in low quality MRI sections acquired in different MRI protocols. A success rate about 90% was observed in semantic segmentation of the multiform femoral head and proximal femur bones in a total of 194 MRI sections obtained from 33 MRI sequences of 13 patients with deep CNNs., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The aim of this study was to adapt the rapid assessment method (RAM) and International Osteoporosis Foundation Food Frequency Questionnaire (IOF FFQ) tools, used for the assessment of daily calcium intake in university students, and to compare the data obtained using 24-h recall (24-HR) data. There was a moderate positive correlation between the RAM and IOF., Purpose/introduction: Calcium is an essential mineral that plays vital roles in metabolism and it is very important to accurately assess the amount of calcium intake on the diet. It was aimed to assess the daily calcium intake of university students by two different food frequency questionnaires (FFQs) developed specifically for evaluating calcium intake, and 24-h recall method., Method: The study was conducted with a total of 183 female university students aged 18-31. In the study, a questionnaire including sociodemographic information, a calcium calculator, IOF FFQ, prepared in seven different languages, and RAM was applied. In addition, 1-day food consumption records were obtained using 24-HR method., Results: The daily average calcium intakes of individuals according to two different food frequency questionnaires (RAM FFQ and IOF FFQ) and 24-h food consumption records are respectively 1594.2 ± 810.9, 897.9 ± 368.4, and 605.6 ± 278.3 mg. When the daily average calcium intake was compared with the other two methods, RAM was found to be statistically higher (p < 0.001). There was a moderate positive correlation between the RAM and IOF FFQ methods (Pearson r = 0.528, p < 0.01) and the intra-class correlation coefficient between these two methods was found to be significant and moderate (ICC r = 0.452, p < 0.01)., Conclusion: In terms of protecting and improving health, it is important to make suggestions using fast and short tools to ensure adequate calcium intake from young age. It is thought that FFQs are the most appropriate methods in assessing daily calcium intake for this study group because it is observed that the list of foods and the amount of portions in FFQs reduce the problem of remembering at 24-h method to a great extent.