The aim of this systematic review was to evaluate the state of the art of radiomics in testicular imaging by assessing the quality of radiomic workflow using the Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). A systematic literature search was performed to find potentially relevant articles on the applications of radiomics in testicular imaging, and 6 final articles were extracted. The mean RQS was 11,33 ± 3,88 resulting in a percentage of 31,48% ± 10,78%. Regarding QUADAS-2 criteria, no relevant biases were found in the included papers in the patient selection, index test, reference standard criteria and flow-and-timing domain. In conclusion, despite the publication of promising studies, radiomic research on testicular imaging is in its very beginning and still hindered by methodological limitations, and the potential applications of radiomics for this field are still largely unexplored. In receTesticular cancer is a relative rare disease  and it is predominant in young/middle-aged males , 15. Man2.2.1.To identify records of interest, two reviewers (S.C.F. and M.F.) independently performed a systematic literature search for potentially relevant articles about Radiomics applications in testicular imaging.The examined medical literature archives were PubMed, Scopus, and Web of Science, using the following search terms: testicular AND radiomics. Filters were applied to include only original research published in English. No restrictions in country of publication, study design, and outcomes were applied. The last search was run in September 2022. Duplicates have been removed and all the selected articles were initially screened reviewing the titles and abstracts. After the screening, the authors read the full text of the studies and any disagreement was overcome by discussion to reach a mutual agreement. From each study, the following data were extracted: publication year, number of patients, study design, study aim, journal topic, and professional role of the first author.2.2.The methodological quality of the included studies was carried out by two readers (M.F. and L.C.) using the Radiomics Quality Score (RQS), as proposed by Lambin et al., and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), as proposed by Whiting et al. , 22. ConRadiomics Quality Score (RQS) is a tool made up of 16 items, categorized by Park et al. into 6 domains, with different possible scores in relation to its importance . Accordi3.After the exclusion of duplicates (19) and unrelated papers (2), 6 articles were finally included in the review. The study selection flow-chart is resumed in Out of 6 articles, none were published before 2018, four  were published in clinical journals and only two  in radiological journals. The mean patient number was 70 ± 23,86 (range 39–97). Many of the studies had a retrospective study design , while only one was a prospective observational study . In half of the papers included in the review, the first author was a radiologist. Most of the articles addressed the oncologic topic , focusing on differential diagnosis between benign and malignant testicular masses or prediction of lymph nodes histopathology after chemotherapy. Only one article investigated potential role of radiomics as gonadal function biomarker. Characteristics of included articles are resumed in Overall, the included articles achieved a mean RQS total of 11,33 ± 3,88 (range 6–18) and a percentage of 31,48% ± 10,78% . Imaging protocols, features reduction, discrimination statistics and comparison to gold standard are well-documented in all the included articles. Validation without retraining was performed in all the studies, even though only on dataset from the same institute. Half of the studies reported multiple segmentation, to analyze feature robustness to segmentation variabilities, while only two provided more holistic models combining radiomics with clinical variables. None of the studies performed phantom studies or imaging at multiple time-points. Similarly, no study reported cut-off analyses, calibration statistics, decision curve or cost-effectiveness analysis. Finally, none of the included articles made code and data publicly available to facilitate reproducibility of the study. The detailed RQS score for all included articles for each RQS item is shown in The risk of bias and applicability concerns according to the QUADAS-2 are summarized in 4.Radiomics is a new engineering approach based on automated high-throughput extraction of quantitative features from medical images . RadiomiDespite promising studies, the potential applications of radiomics for testicular imaging are still largely unexplored. Indeed, testicular tumors are relatively rare, as they represent only the 1% of neoplasm and the 5% of urological tumors in males , 15. CohThe most common testicular cancers are testicular germ tumors (TGCTs), accounting for approximately the 90%–95%, and are split into two categories: seminomas (SGTs) and non-seminomas (NSGTs) . The maiAnother relevant difference between SGCTs and NGCTs is that nearly half of NSGCTs already show metastases at the time of first diagnosis . CurrentBaessler et al. trained a machine learning classifier to differentiate “benign” (fibrotic/necrotic) from “malignant” (viable cancer/teratoma) lymph nodes on contrast-enhanced CT in patients with NSGCT post-chemotherapy. The classifier achieved an accuracy of 0.81 and outperformed the commonly used “size” criterion (0.68) . The modin vivo imaging measurement of testicular function. The unavailability of standard methods and reference ranges for US testicular volume measurement prompts to look for other parameters, such as testicular echostructure  resulting in a percentage of 31,48% ± 10,78% . This percentage is slightly higher compared to the average result (18.87%) reported by Spadarella et al. in a systematic review of RQS applications. However, it would be premature to draw firm conclusions from this difference, as it could be largely explained by the limitations of the RQS tool itself and particularly by its lack of reproducibility –48. RegaRegarding QUADAS-2 criteria, no relevant biases were found in the included papers in the patient selection, reference standard criteria and flow-and-timing domains.In conclusion, radiomics application in testicular imaging is a promising area of research, despite being in its early stages, and should be pursued to improve the accuracy of testicular tumor diagnosis, staging, and treatment planning. Compared to other oncologic diseases, invasive procedures such as testicular biopsy are not recommended, and currently represent a limitation in the pre-operative characterization of testicular cancer and therapy planning. Radiomics has already been shown able to fill this gap when applied to cross-sectional imaging, but it could be even more useful if employed in ultrasound, which represents a first-line diagnostic imaging technique. However, clinical implementation is hindered by methodological limitations, including the lack of external validation and the prevalence of retrospective studies. Large-scale multicenter studies and prospective designs are needed to overcome these limitations and push this field forward.


The pathological mechanism for a disorder of consciousness (DoC) is still not fully understood. Based on traditional behavioral scales, there is a high rate of misdiagnosis for subtypes of DoC. We aimed to explore whether topological characterization may explain the pathological mechanisms of DoC and be effective in diagnosing the subtypes of DoC.Using resting-state functional magnetic resonance imaging data, the weighted brain functional networks for normal control subjects and patients with vegetative state (VS) and minimally conscious state (MCS) were constructed. Global and local network characteristics of each group were analyzed. A support vector machine was employed to identify MCS and VS patients.p < 0.05). For patients of both groups, global network measures were not significantly different (p > 0.05). Nodal efficiency, nodal local efficiency, and nodal clustering coefficient were reduced in frontoparietal brain areas, limbic structures, and occipital and temporal brain areas (p < 0.05). The comparison of nodal centrality suggested that DoC causes reorganization of the network structure on a large scale, especially the thalamus. Lobal network measures emphasized that the differences between the two groups of patients mainly involved frontoparietal brain areas. The accuracy, sensitivity, and specificity of the classifier for identifying MCS and VS patients were 89.83, 78.95, and 95%, respectively.The average connection strength was reduced in DoC patients and roughly equivalent in MCS and VS groups. Global efficiency, local efficiency, and clustering coefficients were reduced, and characteristic path length was increased in DoC patients (There is an association between altered network structures and clinical symptoms of DoC. With the help of network metrics, it is feasible to differentiate MCS and VS patients. Every year, more than 50 million people in the world suffer a variety of brain injuries  or unresponsive wakefulness syndrome, minimally conscious state (MCS), etc. VS patients resume the sleep–wake cycle and open their eyes spontaneously or under stimulation, but they have no obvious signs of consciousness. MCS patients show a weak and unstable state of consciousness.Consciousness cannot be measured directly. At present, traditional behavioral scales , we constructed the weighted brain functional networks for the normal control (NC) group, VS group, and MCS group and analyzed the corresponding structural characteristics of the brain network. Then, the network characteristics of MCS and VS patients were fed into a support vector machine to verify whether the network representations could provide useful information to effectively differentiate the two types of patients.One hundred and four patients with DoC were recruited from Beijing Tiantan Hospital, Capital Medical University, China. Each patient suffered severe brain injury for more than 1 month. Based on the currently most recognized Coma Recovery Scale—Revised (CRS-R) assessment, these patients were divided into a group of 80 VS patients and a group of 24 MCS. patients by performing multiple evaluations within 2 weeks. None of the patients had any contraindications to MRI scans, and no sedation or anesthesia was performed before MRI acquisition. Due to the excessive head motion (translation > 3 mm or rotation > 3°) and terrible image quality, the data of 40 VS and 5 MCS patients were discarded in the follow-up analysis. The specific demographic and clinical information for the remaining 40 VS  and 19 MCS patients  is listed in 3, and flip angle = 90°. The scan time lasted 7 min, generating a total of 210 volumes. T1-weighted high-resolution structural images were acquired for registration of functional images using a sagittal brain volume imaging sequence with the following parameters, TR/TE = 8.16/3.18 ms, thickness/gap = 1.0/0 mm, matrix size = 256 × 256 × 188, voxel size = 1 × 1 × 1 mm3, and flip angle = 7°. During data acquisition, each participant was instructed to keep their eyes closed, lie motionless, and not think about anything. Head movements were minimized using wedge-shaped foam padding.All images were collected using a Discovery MR750 3.0-T scanner  equipped with an eight-channel head coil. fMRI images were obtained axially using a T2-weighted echo-planar imaging sequence sensitive to the blood oxygenation level-dependent contrast. The detailed acquisition parameters were as follows: repetition time (TR)/echo time (TE) = 2000/30 ms, thickness/gap = 4.0/0.6 mm, matrix size = 64 × 64 × 39, voxel size = 3.75 × 3.75 × 4 mm3. A 4-mm full-width at half maximum Gaussian kernel was applied to smooth the spatially normalized image. The time series of all voxels were bandpass filtered between 0.01 to 0.1 Hz.Data preprocessing is an essential step to reduce the noises of physiological and instrument sources and increase the signal-to-noise ratio. In the present study, data were screened and preprocessed by running Data Processing and Analysis for Brain Imaging (DPABI) Version 6.0 210501 software package  atlas was employed, which parcellates the brain excluding the cerebellum into 90 separated brain functional areas (45 in each brain hemisphere). The 90 areas are abstracted into network nodes. Then, the average time series of all voxels in each brain area were extracted. The relationship between the network nodes for each subject was revealed by a 90 × 90 matrix, which was obtained from calculating the Pearson correlation coefficient Ten metrics were adopted to characterize the topological organization of the brain function network for healthy subjects, VS patients, and MCS patients, including global efficiency, local efficiency, clustering coefficient, characteristic path length, nodal efficiency, nodal local efficiency, nodal clustering coefficient, degree centrality, betweenness centrality, and eigenvector centrality. The first four are called global network metrics. The last six are referred to as local network metrics. The subsequent discussion was carried out in the weighted form of all network measures. Nodal efficiency reflects the difficulty for a node to transmit information to its neighbor. Nodal local efficiency describes the ability of information exchange between the first-hop neighbors of a node. Nodal clustering coefficient of a node quantifies the clustering degree for its first-hop neighbors. Nodal centrality reveals the importance of nodes in the whole network. Degree centrality evaluates node importance based on edge weight. Betweenness centrality assesses the status of nodes according to their contribution to information flow in the network. The eigenvector centrality of a node emphasizes the quantity and quality of its neighbors. In other words, if a node has a large number of important neighbors, the node is also very important. The characteristic path length is the average shortest path length of all node pairs. Global efficiency is defined as the average nodal efficiency of all nodes. These two indicators represent the capability of information transmission at the network level, depicting the functional integration from the side. Similarly, local efficiency and clustering coefficient are the average representations for nodal local efficiency and nodal clustering coefficient of all nodes, respectively. In addition to information interaction, they measure functional separation to some extent.Combining the selected complex network measures, the length of the final feature vector for each subject was 544. Feature selection is a very important process in the classification task, which optimizes the original feature set by eliminating redundant features. Feature selection helps to improve the classification performance and, at the same time, reduces the training time and storage requirements. Common feature selection can be roughly divided into filtered type, wrapped type, and embedded type. The filtered type relies on the importance score of the feature to filter the features that meet the conditions, independent of the training process of the classifier. The wrapped type is closely linked to the training process. This method treats classification performance as an evaluation criterion and searches for the optimal subset of features by multiple iterations. Unlike the previous two approaches, the embedded type integrates the feature selection within the training process, enabling automatic and simultaneous feature selection during the training process. Therefore, the features were selected in this study using an embedded method , , ⋯ , }, where x ∈ Rd, y ∈ R, the loss function of the logistic regression isGiven the data set D = { The distance between each sample and the other samples was calculated to obtain the N is set according to the imbalance ratio of the sample,(b) The sampling rate k nearest neighbors of each sample for linear interpolation, and the new sample is synthesized as(c) One sample is randomly selected from the xnew is the new sample synthesized, xi is a sample of the minority class, k nearest neighbors, δ is a random factor in the range .where N times.(d) The process is repeated After feature selection and oversampling, a support vector machine was employed to identify MCS and VS patients. Usually, a small number of samples for training and testing may result in low robustness and generalization of the model. To obtain a stable and reliable performance evaluation, cross-validation is an essential step. Here, leave-one-out cross-validation was used. In each iteration, the method selects one sample as the test data, while the rest are classified as training data. Until each sample has acted as test data once, the cross-validation is over. Owing to the class imbalance, it is not reasonable to adopt accuracy alone to describe model performance. Therefore, sensitivity and specificity, two common metrics, were also complemented to evaluate the classification performance. The formula for each indicator is listed below,TP denotes the number of MCS patients correctly predicted, TN represents the number of VS patients correctly predicted, FP is the number of VS patients incorrectly identified as MCS patients, and FN symbolizes the number of MCS patients incorrectly identified as VS patients. The main implementation pipeline of this study is illustrated in where r-to-z transformation. The group-averaged corrected correlation coefficient matrix is shown in z-value distribution was estimated with the help of histograms to confirm this hypothesis, as shown in z-values of the NC group, MCS group, and VS group roughly followed a normal distribution with means μ of 0.31, 0.22, and 0.21 and standard deviation σ of 0.18, 0.14, and 0.14, respectively. The group-averaged z-values of the MCS group and VS group were mainly concentrated in the interval . The number of z-values >0.3 was reduced in the MCS group and VS group compared with the NC group. Therefore, when we constructed the brain functional network using the first 5% to 40% of z-values with an interval of 1%, the average connection strength of the NC group was necessarily higher than that of the remaining two groups. In addition, To improve the normality of the Pearson correlation coefficient, a non-linear correction to the correlation coefficient matrix was performed using Fisher's p < 0.05). The characteristic path length of the MCS and VS groups was significantly higher than that of the NC group (p < 0.05). However, there was no difference in these measures between the MCS and VS groups (p > 0.05).Subsequently, the area under the curve (AUC) for each network measure was calculated. The AUC of the network measure is a summary scalar reflecting the structure of the brain network. With this approach, the network parameters of various thresholds can be integrated. A non-parametric permutation test was applied to assess the between-group difference in the AUC of each network measure. The number of permutations was 10,000. The significance level α was 0.05. The between-group comparison of global network measures for the NC group, MCS group, and VS group is given in Equally, between-group differences in each local network measure were examined using non-parametric permutation tests, which were mapped to the brain surface using BrainNet Viewer software  in multiple frontoparietal brain areas of MCS and VS patients. These areas mainly include the dorsolateral and medial prefrontal cortex, orbitofrontal cortex, precentral gyrus, postcentral gyrus, paracentral lobule, supplementary motor area, precuneus, inferior parietal lobule, Rolandic operculum, supramarginal gyrus, and angular gyrus. Analogous changes in nodal measures were observed in the insula, amygdala, parahippocampal gyrus, and cingulate gyrus (p < 0.05). Furthermore, nodal efficiency, nodal local efficiency, and nodal clustering coefficient were significantly reduced in some occipital and temporal regions (p < 0.05), such as the superior occipital gyrus, middle occipital gyrus, inferior occipital gyrus, cuneus, lingual gyrus, calcarine fissure and surrounding cortex, fusiform gyrus, superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, and Heschl's gyrus. Compared with the NC group, the nodal efficiency, nodal local efficiency, and nodal clustering coefficient of the bilateral caudate nucleus were significantly increased in the VS group (p < 0.05). Differently, there were no such changes in the MCS group yet (p > 0.05).Nodal efficiency, nodal local efficiency, and nodal clustering coefficient quantify the ability of neural information transmission in the node neighborhood. Compared with the NC group, nodal efficiency, nodal local efficiency, and nodal clustering coefficient were significantly reduced (p < 0.05). The nodal local efficiency and nodal clustering coefficient of the left caudate nucleus were smaller in the MCS group than those of the VS group (p < 0.05). The nodal local efficiency of the left Rolandic operculum and the left supramarginal gyrus was larger in the MCS group than that of the VS group (p < 0.05). The nodal clustering coefficient of the left Rolandic operculum was larger in the MCS group than that of the VS group (p < 0.05).Compared with the VS group, the nodal efficiency of the right middle frontal gyrus and the right caudate nucleus decreased, and the nodal efficiency of the left precentral gyrus, left postcentral gyrus, left inferior parietal lobule, right paracentral lobule, and left Heschl's gyrus increased in the MCS group (p < 0.05) in MCS and VS patients, indicating an elevated status of the thalamus in the network.Nodal centrality is a key indicator to measure the influence of a node in the network. The NC group, MCS group, and VS group were compared using three common centrality measures, namely degree centrality, betweenness centrality, and eigenvector centrality. Despite the perspectives of these centrality metrics to quantify the importance of nodes being different, comparative analyses based on nodal centrality consistently showed a general readjustment of brain functional network structure in MCS and VS patients compared with the NC group. In particular, the degree centrality, betweenness centrality, and eigenvector centrality of the thalamus were significantly increased (p < 0.05). The betweenness centrality of the right dorsolateral superior frontal gyrus, right middle frontal gyrus, left orbital inferior frontal gyrus, left cuneus, bilateral angular gyrus, and right inferior temporal gyrus was lower in the MCS group than that in the VS group, and the betweenness centrality of the right Rolandic operculum, bilateral olfactory cortex, right posterior cingulate gyrus, bilateral hippocampus, right parahippocampal gyrus, right putamen, and right middle temporal gyrus was higher than that in the VS group (p < 0.05). The eigenvector centrality of the right dorsolateral superior frontal gyrus, right middle frontal gyrus, and right caudate nucleus was lower in the MCS group than that in the VS group, and the eigenvector centrality of the left precentral gyrus, left Rolandic operculum, right superior occipital gyrus, left postcentral gyrus, left supramarginal gyrus, right paracentral lobule, left Heschl's gyrus, and left superior temporal gyrus were higher than that in the VS group (p < 0.05).As for the MCS group and VS group, the degree centrality of the right dorsolateral superior frontal gyrus, right middle frontal gyrus, and right caudate nucleus in the MCS group was lower than that in the VS group, and the degree centrality of the left precentral gyrus, left postcentral gyrus, left inferior parietal lobule, right paracentral lobule, and left Heschl's gyrus were higher than that in the VS group  for the publication of any potentially identifiable images or data included in this article.YD proposed conceptualization, methodology, completed a formal analysis, and wrote the original draft of the manuscript. YY, XC, XG, and JH designed the experiment and collected data. YY, YD, QH, SW, and FD reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.


Cities play an important role in achieving sustainable development goals (SDGs) to promote economic growth and meet social needs. Especially satellite imagery is a potential data source for studying sustainable urban development. However, a comprehensive dataset in the United States (U.S.) covering multiple cities, multiple years, multiple scales, and multiple indicators for SDG monitoring is lacking. To support the research on SDGs in U.S. cities, we develop a satellite imagery dataset using deep learning models for five SDGs containing 25 sustainable development indicators. The proposed dataset covers the 100 most populated U.S. cities and corresponding Census Block Groups from 2014 to 2023. Specifically, we collect satellite imagery and identify objects with state-of-the-art object detection and semantic segmentation models to observe cities’ bird’s-eye view. We further gather population, nighttime light, survey, and built environment data to depict SDGs regarding poverty, health, education, inequality, and living environment. We anticipate the dataset to help urban policymakers and researchers to advance SDGs-related studies, especially applying satellite imagery to monitor long-term and multi-scale SDGs in cities. Therefore, cities play an increasingly important role in achieving United Nations Sustainable Development Goals3 (SDGs), which aim to prosper economic growth and meet social needs. According to the report “The United States Sustainable Development Report4”, cities in the United States (U.S.) perform poorly on a series of SDGs . Currently, monitoring sustainable development in U.S. cities heavily relies on door-to-door surveys such as the American Community Survey7 (ACS) data. First, ACS data for constructing the SDG index in U.S. cities is economically costly as the annual budget can reach millions of dollars8. Second, the current SDG index dataset for U.S. cities is meant for a single year and only focuses on the city level, which hinders monitoring of multi-scale and multi-year SDG progress9. Alternatively, built upon the rapid development of remote sensing and deep learning techniques, satellite imagery showing nearly real-time and bird’s-eye view information in cities has been broadly investigated as a data source for SDG monitoring17. Therefore, monitoring SDGs in cities with satellite imagery is of great significance in promoting sustainable urban development.Nowadays, more than 50% of the population lives in cities, producing 80% of the GDP worldwide18, which barely match the requirements of long-term and multi-scale SDG monitoring in cities. Other open-source satellite imagery datasets, such as SpaceNet19 or ForestNet20, merely contain the dataset for one single SDG. Besides, other survey data for SDG, such as UNESCO Survey on Public Access to Information and Survey Module21 on SDG Indicator 16.b.1 & 10.3.1, are based on questionnaires. At last, although plenty of survey data in the U.S. will aid in SDG monitoring, it would be difficult for urban policymakers and researchers to extract the critical information easily. Motivated by the SustainBench22 in low-income countries, and to fulfill the data requirements for SDG monitoring in U.S. cities, we propose a comprehensive long-term and multi-scale satellite imagery dataset with 25 SDG indicators for five SDGs . Moreover, the dataset covers about 45,000 Census Block Groups (CBGs) in the 100 most populated U.S. cities from 2014 to 2023. Using satellite images and SDG data in the U.S., urban policymakers and researchers can develop various models or assumptions regarding SDG monitoring remotely. And further, the dataset from the U.S. can aid urban policymakers and researchers in inferring SDG progress in low-income countries, which mostly lack SDG-related survey data.However, a long-term and multi-scale satellite imagery dataset, which reveals the yearly change in SDGs in multiple years and in different spatial (administrative) scales for city SDG monitoring, is still lacking. For instance, some satellite imagery datasets about SDGs focus on either country level or cluster level (25–30 households) spatially and only contain data of a single year26) detected as well as several land cover semantics (forest and road27) inferred with the models transferred from the computer vision community. The detected objects refer to the countable artificial objects and venues visible in satellite images in cities, while the land cover is mostly the uncountable environmental information. For SDGs, we collect the indicators that can be inferred from satellite images in urban scenarios. Specifically, indicators for SDG 1 “No poverty”, SDG 3 “Good health and well-being”, SDG 4 “Quality education”, SDG 10 “Reduced inequalities”, and SDG 11 “Sustainable cities and communities”28 are included in our dataset. The indicators are generated from multi-source data, including nighttime light (NTL) data from Earth Observation Group (EOG)29, WorldPop population data30, ASC data7, and OpenStreetMap (OSM) built infrastructure32.Figure Researchers can design deep learning models to predict various long-term SDGs  in cities from historical satellite images.Researchers can also estimate various SDG progresses by utilizing multi-scale satellite imagery visual attributes at the CBG and city levels and reveal the linkage between the multi-scale satellite imagery and SDGs.Researchers can propose a spatiotemporal framework that simultaneously utilizes long-term and multi-scale satellite imagery for SDG monitoring, which sheds light on satellite imagery fusion of temporal and spatial dimensions.At last, this paper advances the SDG-related community by generating a long-term and multi-scale satellite imagery dataset in urban scenarios by collecting and processing satellite images and SDG indicators from multiple sources, which is a time-consuming and laborious work, and the alignment of satellite image visual attributes and SDG data. The dataset aims to help urban policymakers and researchers, who might not have the platform to collect and process the large volume of data, to conduct numerous SDG studies spanning poverty, health, education, inequality, and built environment. More importantly, as the first urban satellite imagery dataset for multiple SDGs monitoring with interpretable visual attributes , it can aid in further achieving sustainable cities with high interpretability and advancing urban sustainability progress. The satellite imagery and the visual attributes extracted by the computer vision models in the dataset can serve as the input for various kinds of research regarding SDGs, and the SDG indicators act as output. Specifically, we recommend the following potential applications:We aim to provide a comprehensive and representative dataset that includes satellite imagery and corresponding SDG indicators covering long terms and multiple scales. To ensure that the indicators can thoroughly depict sustainable urban development, we select five SDGs altogether: SDG 1 No poverty (five indicators), SDG 3 Good health and well-being (five indicators), SDG 4 Quality education (five indicators), SDG 10 Reduced inequalities (two indicators), and SDG 11 Sustainable cities and communities (eight indicators). Overall, the target dataset generation process includes collecting, processing, and aligning multi-source data, and the overall workflow is presented in Fig. 33. The population in the 100 cities varies from 222,194 to 8,467,513, with a mean population of 642,002. The city-of-interests and population in descending order are shown in Table We select the 100 cities with the most population in the contiguous United States, which is explored on the ACS 2021 population data34. The shapefiles are divided by states, and each shapefile contains the city name , state name, Federal Information Processing Standard state code, and geographical boundary coordinates. We use the python packet shapely to access the shapefiles and extract the boundary coordinates using the city and state names. The geographic coordinate system is WGS84.Then we collect city geographic boundary files from the U.S. Census Bureau TIGER/Line shapefiles7. The CBG boundaries for the years 2014~2019 are the same, and the U.S. government adjusts the CBG boundary for the year 2020.Next, we determine the corresponding CBGs within the cities. The boundaries of all CBGs in the U.S. are gathered from SafeGraph Open Census Datashapely and geopandas. The geographic lookup table between cities and corresponding CBGs is shown in Table For each city, we overlap the CBG boundaries on the city boundary, and every CBG whose area intersection with city boundary takes up more than 10% of the corresponding CBG area is considered contained in the city. This process uses Python packets 17, urban pattern mining15, commercial activity prediction35, and population prediction36. Inspired by the interpretable feature generation from satellite imagery14, we provide satellite imagery visual attributes in our dataset to promote the research of SDG monitoring. The processing of satellite imagery consists of three parts: imagery collection, object detection, and semantic segmentation.Satellite imagery provides a near real-time bird’s-eye view of the earth’s surface. Combined with machine learning techniques, satellite imagery has been widely used in predicting socioeconomic status, especially in urban research, which includes poverty/asset prediction37. It provides users access to the World Imagery of different versions created over time. The imagery is in RGB format collected from different satellites and of different spatial resolutions marked by different zoom levels, which split the entire world into different numbers of tiles. Overall, the imagery collection process includes generating image tile numbers according to the boundary of each city as well as the desired zoom level  and downloading images with the tile numbers from the satellite imagery archive. In our target dataset, we set the zoom level to 19, which is about 0.3 m/pixel. We also select the Esri World Imagery archive of June from 2014 to 2023 to collect the satellite images of the 100 most populated cities, which generates altogether 12,269,976 images each year.First, we collect the satellite imagery in our dataset from Esri World Imagery38, and further, transportation and mobility were recognized as central to sustainable development at the 2012 United Nations Conference on Sustainable Development39. Sports & leisure are highly correlated to citizens’ life quality41. Children and young people benefit largely from sports, which are inseparable from a quality school education, promoting SDG 3 and SDG 442. The building characteristics  can reveal the population and income status in urban areas44, and the impact of buildings on human well-being can not be neglected. Therefore, the buildings, cars, and other objects in satellite imagery contain certain correlations with SDG indicators. In our dataset, we consider 17 objects from the abovementioned aspects: transportation, sports & leisure, and building.Second, many aspects of cities are related to people’s lives and can reveal SDG progress. Transportation in the city is integral to urban development46 pre-trained on the MS COCO dataset47 and finetune it on xView dataset23 and DOTA v2 dataset25 to detect objects in the collected satellite imagery. The default parameters48 are used for finetuning the object detection models. We aggregate the number of objects detected from satellite images at the CBG and city levels to show visual object attributes at multiple scales.The urban object categories are presented in Table 51 pre-trained on the ADE20K dataset52 and finetune it on LoveDA dataset27 to generate semantic information from the collected satellite imagery, which includes background, building, road, water, barren, forest, and agriculture. Moreover, we compute the pixel-level percentage of each semantic information presented in Table Third, land cover information such as forests or water can also depict the urban environment and is not included in the detected objects. Therefore, we add the land cover semantic information inferred from satellite imagery in our generated dataset. We use the Vision Transformer (ViT)-Adapter-based semantic segmentation model53. The population data is downloaded at a resolution of 3 arc (approximately 100 m at the Equator). We use Python packets shapely and gdal to crop the population data with the CBG/city geographic boundary and sum up the cropped pixel values as the total population. The area (km2) is calculated from the CBG/city boundary data with Python packet geopandas. The geographic centroid can also be computed with Python packet geopandas.For each CBG/city, we present the population, area, centroid coordinates, and geographic boundary, which describe the essential information for the selected area-of-interests. Specifically, we collect the population data from 2014 to 2020 from the WorldPop project54. Finally, SDG 11 “Sustainable cities and communities” reflects the living conditions in CBG/city, and the related indicators are calculated from OSM historical data and ACS data. Altogether, we collect 25 indicators across five SDGs. The indicators and relevant SDG targets are described in Table Target 1.2: By 2030, reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definitions.Target 1.4: By 2030, ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of property, inheritance, natural resources, appropriate new technology and financial services, including microfinance.Target 3.8: Achieve universal health coverage, including financial risk protection, access to quality essential healthcare services, and access to safe, effective, quality, and affordable essential medicines and vaccines for all.Target 4.1: By 2030, ensure that all girls and boys complete free, equitable, and quality primary and secondary education leading to relevant and effective learning outcomes.Target 4.3: By 2030, ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including university.Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status.Target 11.2: By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all, improving road safety, notably by expanding public transport, with special attention to the needs of those in vulnerable situations, women, children, persons with disabilities and older persons.Target 11.3: By 2030, enhance inclusive and sustainable urbanization and capacity for participatory, integrated and sustainable human settlement planning and management in all countries.There are five SDGs  concerning poverty, health, education, inequality, and built environment collected in our produced dataset at the CBG/city level. SDG 1 “No poverty” focuses on income and population in poverty status. The indicators for “No poverty” are collected from ACS data. SDG 3 “Good health and well-being” and SDG 4 “Quality education” highlight people’s health insurance status and population with different academic degrees, and corresponding indicators are extracted from ACS data. SDG 10 “Reduced inequalities” intends to reduce inequality, and the indicators are from ACS data and from NTL combined with population data with a recent algorithm for monitoring regional inequality through NTL3. Our target dataset incorporates income and poverty status data to represent the SDG 1 indicators in cities. Specifically, median household income, population above poverty (number of population whose income in the past 12 months is at or above poverty level), population below poverty (number of population whose income in the past 12 months is below poverty level), and population with a ratio of income to poverty level  under 0.5 and between 0.5 to 0.99 are collected to describe the income & poverty in CBG/city. The poverty threshold is computed by the Census Bureau according to the family size and ages of family members every year with variations to Consumer Price Index. The threshold is a country-specific value and does not change geographically55. Moreover, population above/below poverty and population with different ratios of income to poverty level are measurements of poverty status.SDG 1 aims to end poverty in all its forms everywhere56. Then, we generate the city-level indicators: population above/below poverty and population with a ratio of income to poverty level under 0.5 and between 0.5 to 0.99 by aggregating all the CBG data within the city. Median household income at the city level is related to the income distribution of the population in cities and is gathered directly from ACS data57. The boundary files and ACS data are both collected from the U.S. Census Bureau. And ACS data denotes the city as “place” as in the boundary files, and the ACS definition of a city boundary is the same as the U.S. Census Bureau TIGER/Line shapefiles.We collect the median household income, population above/below poverty, and population with a ratio of income to poverty level under 0.5 and between 0.5 to 0.99 at the CBG level from the ACS data3. In our target dataset, we use the population data with no health insurance covering all ages to represent SDG 3 indicators because health insurance is correlated to the health status of the population in urban regions59. Specifically, civilian noninstitutionalized population, population with no health insurance under 18, between 18 to 34, between 35 to 64, and over 65 years old are collected from ACS data7 to describe the health insurance at the CBG and city levels.SDG 3 aims to ensure healthy lives and promote well-being for all populations at all ages3. Therefore, indicators directly depicting city education status can be selected here. In dataset generation, we collect from ACS data7 population enrolled in college, population that graduated from high school, population with a bachelor’s degree, a master’s degree, and a doctorate for indicators of school enrollment & education attainment to monitor SDG 4.SDG 4 aims to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all3. We use income Gini60 and light Gini54 to monitor the process of SDG 10. The income Gini reveals the inequality status of income and is collected from ACS data. Light Gini can present the distribution of NTL per person and thus indirectly reveal regional development inequality. Similar to the income Gini, the lower the light Gini is, the more equally the region develops, which means the region moves towards eliminating inequality in SDG 10. The results in the original paper54 report the light Gini at a 1-degree grid cell, which can not be directly used in urban scenarios. Therefore, we calculate the light Gini following the method54. Specifically, the NTL per person is calculated by dividing the NTL value by the population number in all grids in each CBG/city. Then, the Gini index60 of NTL per person in the CBG/city boundary is computed as the light Gini. The NTL is the Visible Infrared Imaging Radiometer Suite (VIIRS) data54 with a spatial resolution of 15 arc seconds (500 m at the Equator). We download the VIIRS Nighttime Lights version 2 Median monthly radiance (the unit for light intensity is nW /cm2/sr) with background masked from EOG63. Compared with income Gini from traditional income survey data, light Gini measures the NTL inequality in urban regions by considering NTL as an indicator for economic development, which is a different measurement of inequality54.SDG 10 aims to reduce inequality within and among countries3. We incorporate indicators related to the built environment and land use in the target dataset. Specifically, we generate building density, driving/cycling/walking road density, POI density, land use information, and residential segregation (index of dissimilarity and entropy index) as indicators to monitor SDG 11.SDG 11 aims to make cities and human settlements inclusive, safe, resilient, and sustainable65. We collect the U.S. state-level historical Protocolbuffer Binary Format files from Geofabrik32 from 2014 to 2023. Then we apply Python packet pyrosm to extract the building, driving road, cycling road, walking road, POI, and land use information in cities and CBGs by corresponding boundary polygons. For calculating building density, we divide the number of buildings by the area of CBG/city. For each of the three kinds of road density, we divide the total length of each kind of road by the corresponding area of CBG/city. The POI density, which is defined as the ratio of the number of all POIs and the area of CBG/city, can show urban venues with human information. The OSM POIs include all OSM elements with tags “amenity”, “shop” or “tourism”. The amenity tag is useful and important facilities for the urban population, which include Sustenance, Education, Transportation, Financial, Healthcare, Entertainment, Arts & Culture, Public Services, Waste Management, and Others. The shop tag includes locations of all kinds of shops and the sold products, such as Food & Beverages, General Store, Mall, Clothing, Shoes, Accessories, Furniture, etc. The tourism tag is the places for tourists, such as Museum, Gallery, Theme Park, Zoo, etc. Moreover, we generate the land use indicators  by calculating the area percentage of each kind of land use in the area of CBG/city.The source data of urban built environment and land use is collected from OSMThe indicators for the built environment quantitatively measure the density of buildings and roads. It should be noted that the indicators for SDG 11 are imperfect since the actual quality of buildings and roads is not provided in the dataset. Users can use the building/road/POI indicators as side information for depicting urban development.66. We calculate the index of dissimilarity67n is the number of CBGs in a city, wi is the number of race “w”  in CBG i, wT is the total number of race “w” in the city, bi is the number of race “b”  in CBG i, and BT is the total number of race “b” in the city. We calculate the index of dissimilarity for four racial or ethnic groups: Non-Hispanic White (White), Non-Hispanic Black or African American (Black), Non-Hispanic Asian (Asian), and Hispanic66. There are altogether six categories of indices of dissimilarity: White-Black, White-Asian, White-Hispanic, Black-Asian, Black-Hispanic, and Asian-Hispanic.Residential segregation is related to inclusivity in U.S. cities68k is the number of racial/ethnic groups, pij is the proportion of jth race/ethnicity in CBG/city i. We include groups of the White, Black, Asian, and Hispanic population at the CBG or city level.Next, we calculate the entropy indexThe limitations of our dataset include errors from multiple data sources, partial coverage of SDG progress, and the shortcomings of selected indicators.69 for ACS data, literature73 for OSM, literature74 for WorldPop, and literature62 for NTL data. ACS data uses sampling error to measure the difference between the true values for the entire population and the estimate based on the sample population. And the magnitude of sampling error is measured by the margin of error69. ACS provides a margin of error for all ACS estimate data which we collect as SDG indicators in our dataset. The dataset users can freely access the margin of error values of the ACS-oriented indicators in our dataset from the ACS official website. OSM data is a Volunteered Geographic Information (VGI) and is frequently updated by volunteers. In terms of the road network, OSM is about 83% complete globally70. The building completeness for OSM in San Jose city in the U.S. is about 72% and confirms the validity of OSM building density in our dataset71. Some cities show a large jump in building number in a consecutive year due to lagging annotations. The POIs in OSM are compared with the Foursquare POIs and 60% of the POIs can be matched with high accuracy72. At last, the accuracy of the OSM land use dataset73 for the U.S. is above 60%. The population data from WorldPop has a coefficients of determination75R2 greater than 0.95 when evaluated on the population data in China74. The nighttime light intensity also shows a high consistency (R2 greater than 0.97) compared with different nighttime light datasets62.The errors from data sources include measurement errors in satellite imagery, ACS data collection, OSM, WorldPop population, and NTL data. The measurement errors in satellite imagery processing are mainly from the object detection and semantic segmentation tasks, and the accuracy metrics are shown in Table And the provided dataset does not cover the whole SDG aspects, and thus cannot be used as the sole measurement for SDG monitoring. However, the dataset still has great reference value and aids decision-making for urban researchers and policymakers.At last, some indicators cannot always be the best indicators for corresponding SDGs. For example, the indicator health insurance for SDG 3  may not be the best measurement of health status because health insurance usage is affected by the income or wealth of the insurance owners.76 and is stored in tabular format. The live version with potential updates is available in the GitHub repository (https://github.com/axin1301/Satellite-imagery-dataset). We split the output dataset into seven categories, as shown in Fig. The produced dataset can be accessed through the Figshare repositoryTables We have object numbers and land cover semantic attributes processed from satellite imagery of the years 2014 to 2023. The object categories include planes, airports, passenger vehicles, trucks, railway vehicles, ships, engineering vehicles, bridges, roundabouts, vehicle lots, swimming pools, soccer fields, basketball courts, ground track fields, baseball diamonds, tennis courts, and buildings (number of buildings). The land cover semantic attributes contain background, building (pixel percentage), road, water, barren, forest, and agriculture. There are altogether 24 visual attributes obtained from satellite imagery, which are shown in Table We provide median household income, population above/below poverty, population with a ratio of income to poverty level under 0.5, and population with a ratio of income to poverty level between 0.5 to 0.99 for “No poverty” indicators in Table We offer civilian noninstitutionalized population, population with no health insurance under 18, population with no health insurance between 18 to 34, population with no health insurance between 35 to 64, and population with no health insurance over 65 years old for “Good health and well-being” indicators in Table We provide population enrolled in college, population that graduated from high school, population with a bachelor’s degree, a master’s degree, and a doctorate for “Quality education” indicators in Table We provide income Gini and light Gini for “Reduced inequalities” indicators in Table We provide building density, driving/cycling/walking road density, POI density, land use information , and residential segregation (index of dissimilarity and entropy index) for “Sustainable cities and communities” indicators in Table 33 in Fig. Our dataset selects the 100 most populated cities in the contiguous United States. We demonstrate the comparison of overall population in our city-of-interests and in all U.S. cities78, we also present the evaluation metrics for the object detection and semantic segmentation models on the evaluation datasets in Table 79 reaches a mAP@0.5 of 27.2%, which is lower than the 37.1% of our object detection model on xView dataset. For the DOTA v2.0 dataset, the best mAP is obtained by DCFL80 reaching 57.66%. However, since the image split and experiment settings in the object detection model training processes might be different, the direct comparison of performance metrics of our models and the state-of-the-art literature on xView and DOTA v2.0 datasets is for reference only. For the semantic segmentation task, the mIoU of our model 52.8% is close to the state-of-the-art model UperNet81 with an mIoU of 52.44%. Such results guarantee the usefulness and credibility of our produced data. In addition, to test the robustness of our trained models qualitatively, we randomly select satellite images with their corresponding object detection and semantic segmentation results, which are shown in Fig. We use state-of-the-art object detection and semantic segmentation models in the computer vision community to extract the visual attributes from satellite imagery. The training datasets, i.e., xView, DOTA v2, and LoveDA datasets, for the deep learning models are frequently used in satellite imagery interpretation tasks. We prepare the training datasets according to the models’ requirements and transfer the trained models to the satellite images we collect. Following the evaluation methods in the computer vision community82, commercial activity86, education level12, and health outcome87. Therefore, we validate the possibility of inferring SDG indicators from corresponding satellite imagery. Specifically, for each CBG, the visual attributes  and commercial activity (POI density in SDG 11). The studies for inferring regional health or education status are very few, and the performance of relevant prediction models is much lower than the performance of income prediction (see Fig. Visual information in satellite imagery correlate with income/daily consumptionThis study aims to provide a long-term and multi-scale dataset in cities covering the satellite imagery attributes and SDG indicators for urban policymakers and researchers to advance SDG monitoring. Specifically, the satellite imagery attributes in our dataset can be used as input for proposing machine learning models to predict the SDG indicators. Moreover, the SDG data in our dataset can also provide insights into how SDGs evolve in time or scale. Since our dataset contains various aspects of cities, we recommend the following potential research applications: introducing new methods for predicting poverty/income, health, education, inequality, and living environment status of people in cities from long-term or multi-scale satellite images. Researchers are also encouraged to discover the underlying relationship between various SDG progresses and satellite images in cities.The dataset files at the CBG level has about 400,000 lines of data, which might take a long time to load in Excel. Thus, we recommend loading the data with a Python script that can handle large datasets. For the object numbers inferred from the satellite imagery, the confidence level above 0.2 is counted.


During the frozen-thawed embryo transfer (FET) method, controlled ovarian hyperstimulation is used. At the same time, progesterone support is given for luteal phase support. In this study, we investigated the effects of various luteal phase support agents administered orally, intramuscularly (IM), and vaginally during FET on pregnancy rates.  The files of 166 patients between the ages of 21 and 44 in the Assisted Reproductive Techniques Center of Acıbadem Mehmet Ali Aydınlar University Atakent Hospital were analyzed retrospectively between 2016 and 2022. The patients' FSH, LH, E2, P4, AMH, and TSH levels were measured. The GnRH antagonist protocol was initiated on the 2nd or 3rd day of menstruation. Three types of progesterone agents were used in females with PCOS. Three different methods were applied: 50 mg/ml of IM progesterone daily, 90 mg of progesterone gel 2∗1 vaginally, and dydrogesterone acetate tb. orally 3∗1. FET was performed on women who received 21 days of treatment by thawing 5th-day embryos. B-hCG was performed on the 12th day after the transfer, and evaluations were made. The study results were evaluated as follows: for the whole study group, for those <30 years of age, for those 30-35 years of age, and for those >35 years of age.  A total of 164 patients, 57 females using vaginal progesterone gel, 30 females using oral progesterone tablet, and 77 females using IM progesterone, who met the inclusion criteria, were included in the study. The pregnancy outcomes of IM progesterone application were statistically significantly higher in the entire study group and the >35 age group when compared to the vaginal progesterone gel application. It was found that the pregnancy outcomes of IM progesterone application increased statistically significantly in the <30 age group when compared to outcomes in the other groups, using vaginal progesterone gel and oral progesterone tb.  We found that IM progesterone application was more effective than vaginal progesterone gel application for luteal phase support. Many randomized controlled, especially live birth rate studies, are required before results can more closely approximate those for the general population. Frozen-thawed embryo transfer (FET) is preferred over fresh IVF due to the lower risk of ovarian hyperstimulation syndrome . The majAlthough progesterone taken orally has a positive effect, it is believed that a high dose is required to achieve effective blood concentrations due to the first-pass effect through the liver. There are also systemic side effects. It is possible to obtain effective blood concentrations easily in IM applications. However, injection necessitates medical personnel, and injections carry risks. Vaginal progesterone, on the other hand, acts directly on the uterus and prepares the endometrium. The main point of concern in relation to the use of vaginal progesterone is whether it is sufficiently absorbed from the vaginal tissue .In this study, we investigated the effects of various luteal phase support agents administered orally, IM, and vaginally during FET on pregnancy rates. Our limitation in this study is that only clinical pregnancy rates were determinatem, and live birth rates were not given. In addition, the other limitation is that subcutaneous progesterone agents have not been compared. We also evaluated endometrial thickness, BMI, and serum estradiol (E2) and progesterone (P4) levels on transfer day.The study was accomplished following ethical principles according to the Declaration of Helsinki 1964. This retrospective study was approved by the Ethics Committee of the Acıbadem Mehmet Ali Aydınlar University, School of Medicine.∗1. Finally, dydrogesterone acetate  was applied orally as 3∗1. FET was performed on women, who received 21 days of treatment by thawing 5th-day embryos. The B-hCG was performed on the 12th day after the transfer, and evaluations were made. Inclusion criteria were being primarily infertile, being between the ages of 21 and 44, having undergone the FET protocol, and having a BMI between 18 and 40 kg/m2. The exclusion criteria were as follows: a live birth history (secondary infertile couple), a history of chemoradiotherapy, the presence of a chronic illness, substance abuse, and severe ovarian failure.The study was conducted by retrospectively analyzing the files of 166 patients between the ages of 21 and 44 at the Assisted Reproductive Techniques Center of the Acıbadem Mehmet Ali Aydınlar University Atakent Hospital between 2016 and 2022. The patients' FSH, LH, E2, P4, AMH, and TSH levels were measured. The GnRH antagonist protocol was initiated on either the 2nd or 3rd day of menstruation. The patients were called to the outpatient clinic at an interval of 3-4 days. The patients' blood E2 levels were monitored. After the follicles, which reached 17 mm in size, were cracked with the ovulation induction agent Ovitrelle 250 micrograms/0.5 ml . Oocyte collection was performed under general anesthesia. All embryos developed in vitro for 5 days were frozen. Then, estrogen, which was initiated on the 2nd day of the menstrual period, was given to all patients as 2 mg 3∗1 . Apart from the basal E2 hormone, the patients' weekly E2 values were also measured. Transvaginal ultrasonography was also used to measure endometrial thickness. After 15 days of estrofem treatment, 3 types of progesterone agents were administered to the patients. Progesterone , one of which is natural micronized progesterone, was administered daily IM. The other method was vaginal; 90 mg of progesterone gel  was applied vaginally at a dose of 2p value of less than 0.05 was considered significant. Based on the statistical comparison results made according to reference publications, the number of samples was calculated as follows: n = 68 . Type 1 error was determined as 5%, and the power of the study was 80%.In the analysis of the variables, SPSS 25.0 , PAST 3 , and Medcalc 14  programs were used. The conformity of univariate data to normal distribution was evaluated with the Shapiro-Wilk-Francia test, while the homogeneity of variance was evaluated with the Levene test. While the Mardia (Dornik and Hansen Omnibus) test was used for the conformity of multivariate data to the normal distribution, the Box-M test was used for variance homogeneity. In order to compare more than two independent groups with each other based on quantitative variables, the one-way ANOVA test was used with the Bootstrap test, and the Kruskal-Wallis H test was used in conjunction with the Monte Carlo simulation; Dunn's test was used in post hoc analyses. The Wilcoxon test (Monte Carlo) was used to compare dependent quantitative variables with two repeated measurements. In the comparison of categorical variables with each other, the Fisher-Freeman-Halton and Pearson chi-square tests were tested using the Monte Carlo simulation technique. The Backward method was used in conjunction with the logistic regression test to determine the cause-effect relationship of the groups with the explanatory variables. Quantitative variables were expressed as mean (standard deviation) and median (minimum/maximum) in the tables, while categorical variables were shown as n (%). The variables were analyzed at a 95% confidence level, and a Pr (Y = 1|X = 1) H0 = 0.33, our smallest odd ratio was calculated as 1.74, and the power at the 0.05 significance level was 94%.Power analysis G power version 3.1 of our study was used for all included patients. For the logistic regression with a sample size of 164 observations, A total of 164 patients, 57 females using vaginal progesterone gel, 30 females using oral progesterone tablet, and 77 females using IM progesterone, who met the inclusion criteria, were included in the study. The pregnancy rate and serum LH levels differed statistically significantly between groups based on the factors assessed. Pregnancy was observed at a rate of 66.2% in the P4 IM group, 56.7% in the oral progesterone group, and 42.1% in the vaginal progesterone gel group. When compared to the vaginal progesterone group, the progesterone IM group had a statistically significant higher rate. In the evaluation of serum LH levels, the median values were found to be 5.21 IU/l in the IM progesterone group, 6.72 IU/l in the oral progesterone group, and 5 IU/l in the vaginal progesterone group .Age, BMI, infertility causes, pregnancy rates, endometrial echogenicity thicknesses, IVF trials, serum FSH, LH, AMH, TSH, basal and transfer days E2, and P4 levels for different luteal phase support agents for the group <30 years of age are specified in Age, BMI, infertility causes, pregnancy rates, endometrial echogenicity thicknesses, IVF trials, serum FSH, LH, AMH, TSH, basal and transfer days E2, and P4 levels for different luteal phase support agents for the group 30-35 years of age are specified in Age, BMI, infertility causes, pregnancy rates, endometrial echogenicity thicknesses, IVF trials, serum FSH, LH, AMH, TSH, basal and transfer days E2, and P4 levels for different luteal phase support agents for the group <35 years of age are specified in In order to better understand the pregnancy outcomes, the relationship between age, treatment agents used, FSH, transfer day progesterone values, and pregnancy outcomes was evaluated . A negatIn multiple logistic regression analysis, a difference in the pregnancy success rate was found between oral progesterone tb and vaginal progesterone gel applications in females aged >35 years (OR 7.29 (1.58-33.58)). A difference was found between the serum progesterone level on the transfer day and the basal serum progesterone level in females 30-35 years of age (OR 1.06 (1.01-1.11)) .Estrogen applications started in the early follicular phase suppress follicle selection and the LH peak, preventing ovulation. However, the serum LH level is crucial for endometrial receptivity . In theiSerum AMH, antral follicle count, and serum FSH measurement between the 2nd and 5th days of menstruation are crucial for ovarian reserve evaluation . TherefoThe luteal phase of menstruation is the phase that occurs after the ovulation phase and is responsible for the formation of the corpus luteum through progesterone. It is significant for implantation. For this reason, the serum P4 level and progesterone support, if necessary, are important in patients, who will undergo FET . VolovskWang et al. applied vaginal progesterone gel (90 mg) or IM progesterone (40 mg/day) for luteal phase support to females, who had received oral dydrogesterone (20 mg/day) and estradiol valerate (4–8 mg/day) . In termSC administration is a relatively new progesterone administration route that we did not apply in our study but is one which has been compared to other methods in the literature. Türkgeldi et al. compared the pregnancy outcomes of females, who had received 50 mg of SC progesterone and 180 mg/day of vaginal progesterone in females who had undergone FET . No statWe found that IM progesterone application was more effective than vaginal progesterone gel application for luteal phase support. Many randomized controlled, especially live birth rate studies, are required for the results to more closely approximate those for the general population. We plan to conduct randomized controlled trials encompassing the use of SC progesterone agents in the following stages.


Hyalinising clear cell carcinoma (HCCC) of the lung is a rare tumor, with only 12 reported cases. To improve the differential diagnosis, the aim of this study was to clarify the clinicopathological characteristics, immunophenotype, and molecular characteristics of HCCC of the lung and relate these to prognosis.Sections of HCCC of the lung were collected from a patient for pathological observation, immunohistochemistry, histochemistry, and fluorescence in situ hybridization; the clinical, pathological, and molecular characteristics were compared with those reported in the literature.The tumor had a well-demarcated border nodule with a maximal diameter of 2.5 cm. Microscopic findings showed either clear or eosinophilic cytoplasm in the tumor cells. Growth was predominantly in the sheets, nests, and trabeculae in a background of hyalinised, fibrotic stroma, and mucus degeneration. Immunohistochemistry showed that the tumor cells expressed cytokeratin 7, P63, P40, CK5/6, Pan Cytokeratin (PCK), and epithelial membrane antigen, whereas they were negative for thyroid transcription factor-1, napsin A, CD10, vimentin, and smooth muscle actin. The Ki67 proliferation index was 5%. The tumor was positive for both period acid–Schiff (PAS) and Alcian blue–PAS, with a small amount of mucus staining positive for PAS–diastase. Fluorescence in situ hybridization revealed Ewing sarcoma breakpoint region 1 rearrangement and Ewing sarcoma breakpoint region 1-activating transcription factor 1 fusion.HCCC is a low-grade carcinoma with excellent prognosis. Tumour necrosis may be a potential risk factor for recurrence and metastasis. Our review of reported cases suggests that regional lymph node dissection combined with lobectomy is a safer treatment than only lobectomy for HCCC of the lung. The upper jaw is the most frequently affected site, followed by the buccal mucosa, base of the tongue, floor of the mouth, lips, posterior molars, and tonsils. This tumor typically presents as a painless mass under the mucosa, with ulcers on the surface mucosa. The clinical symptoms vary depending on the location of the primary tumor.Hyalinising clear cell carcinoma (HCCC) is a rare malignant tumor of salivary gland origin, often occurring in the minor salivary glands. This tumor was first described and named by Milchgrub et al In the 2021 version of the World Health Organisation (WHO) classification of chest tumors, salivary gland lung epithelial tumors included pleomorphic adenoma, mucoepidermoid carcinoma, adenoid cystic carcinoma, epithelial-myoepithelial carcinoma, myoepithelioma, myoepithelial carcinoma, and HCCC. HCCC of the lung is the rarest salivary gland tumor, and it is mainly located in the central airway bronchus. It was first reported in 2015 by García et al and only 12 cases have been reported to date in global English-language literature.–12Primary lung salivary gland cancer is especially rare, accounting for less than 1% of all lung tumors. It originates from the small salivary glands under the bronchial mucosa.A nonsmoker, 54-year-old female patient was admitted to our hospital. A nodule in the anterior segment of the right upper lobe was incidentally identified via chest computed tomography (CT). The patient occasionally suffered from chest tightness but had no cough, sputum, hemoptysis, shortness of breath, or other discomforts. A chest CT scan revealed a single solid nodule of 24 × 15 mm located in the anterior segment of the right upper lobe; it presented lobulation signs, uneven enhancement, and reduced central density of the venous-phase lesion. Right upper lobectomy and regional lymph node dissection were performed, and no evidence of lymph node metastasis was found. The surgical margin was negative, and no further treatment was given.The surgical specimen was fixed in 10% neutral buffered formalin, embedded in paraffin, and cut into 2.5 to 4 μm-thick sections. The sections were processed for hematoxylin and eosin staining, immunohistochemistry, and histochemistry. The DAKO automatic immunohistochemistry instrument  was used. The antibodies and kits used were purchased from DAKO (Agilent) and Beijing Zhongshan Jinqiao Biotechnology Co., Ltd.  and used according to the manufacturers’ instructions. The kits for histochemistry of periodic acid–Schiff (PAS), Alcian blue/PAS (AB–PAS), and PAS-diastase (D–PAS) were purchased from Guangzhou Anbiping Pharmaceutical Technology Co., Ltd.  and were operated according to the manufacturer’s instructions.EWSR1) gene indicates that the gene is broken. When >15% of the tumor cells showed this signal, it was judged as a positive gene rearrangement. Double staining of the EWSR1 gene (green) and activating transcription factor 1 (ATF1) gene (red) was interpreted as gene fusion when >5% of tumor cells showed a positive signal; if the ratio of double fusion was below this threshold, single fusion in >30% of tumor cells was also interpreted as a positive signal for gene fusion. The probes and various reagents used in fluorescence in situ hybridization detection were purchased from Guangzhou Anbiping Pharmaceutical Technology Co., Ltd. , and the specific operation steps were performed in strict accordance with the manufacturer’s instructions.Sections were first stained with hematoxylin and eosin to locate the tumor area. Then, to enable the observation of 100 tumor cells, the areas with clearly visible cell nuclei were selected under a fluorescent microscope. A red-green separation signal in the Ewing sarcoma breakpoint region 1  was harvested from the patient with a volume of 10.5 × 10 × 2.5 cmImmunohistochemistry showed that the tumor cells were positive for CK7, P63 Fig. , P40, CKEWSR1, the proportion of positive cells was 65%. This is above the 15% threshold; thus, the tissue demonstrated EWSR1 gene rearrangement. The ratio of positivity for the double-color fusion probes for EWSR1 and ATF1 was 7%, which was greater than the 5% threshold; the ratio of single fusion-positive cells was 71%, which was greater than 30%. These results, thus, confirmed the presence of EWSR1–ATF1 gene fusion  of the reported cases were female patients. A slight majority  of the patients had no history of smoking, and none of the patients had a history of salivary gland tumors. Clinically, some patients had varying degrees of cough and shortness of breath, and a few patients had other symptoms such as intermittent chest pain, persistent back pain, hemoptysis, and dyspnea. CT or X-ray showed solid nodules in the lungs with clear nodules, some of which protruded into the bronchus, lung, or around the hilar. In 8 cases, the tumors were in the right lung; in 2 cases, they were in the left lung; and in the other 3 cases, they were in the trachea. The maximum tumor diameter ranged from 0.9 to 3.5 cm. The average tumor size was 2.4 cm, and the median maximum diameter was 2.5 cm. The tumor typically has a clear border, and the cut surface is white or off-white.The histological morphology of HCCC of the lung appears to be common among the reported cases and the present case. The tumor cells are distributed in irregular nests, cords, and trabeculae, with round or oval nuclei. Most of the nucleoli are not obvious, the chromatin is fine, the nuclear atypia is small, and mitotic phases are rare. Pseudo-inclusion bodies in the nucleus are occasionally seen (2 of the 13 cases). The cytoplasm is transparent or eosinophilic, distributed in the hyaline or fibrotic stroma, and lymphocyte infiltration can be seen around and within the tumor. Occasionally, glands are formed, and mucin is secreted in the cavity. There is no formation of keratinized beads detected in any case. Regarding the immunophenotype, most cases of HCCC of the lung express epithelial markers such as PCK, CK7, CK5/6, P63, and P40, but are typically negative for TTF-1, napsin A, CgA, Syn, and S-100. The Ki-67 proliferation index is usually low at around 5%, as in the present case.EWSR1 gene rearrangement.–15EWSR1–ATF1 fusion is the most common fusion form, which is also a characteristic of HCCC of the lung. EWSR1 gene rearrangement was detected in all 13 reported cases (100%), and EWSR1–ATF1 fusion was detected in 6 patients (46%), including the present case. The EWSR1–CREM fusion was also detected in 1 patient. However, detection of EWSR1 and a related partner was not performed for six of the reported cases; MAML2 genetic testing was performed for three of these cases, and no rearrangement was found.Recent studies have confirmed that 87% to 91% of HCCC cases have  indicating that the overall prognosis is relatively good. When considering the prognostic factors of the disease, we found that in the relapsed case, the patient had been previously diagnosed with lung cancer (16 years prior to the HCCC of the lung diagnosis) and had a right upper lobectomy at that time. The chest CT scan performed 16 years later showed that the paratracheal lymph nodes were enlarged, with a diameter of 3.4 cm. Both lungs showed multiple nodules with a maximum diameter of 1.5 cm. Wedge resection and paratracheal lymph node dissection were performed using thoracoscopy. Microscopic review of the primary lung tumor slices excised 16 years previously showed that the tumor cells had similar histological characteristics, and both EWSR1 rearrangement and EWSR1–ATF1 gene fusion were detected using fluorescence in situ hybridization. This confirmed that the case was a primary HCCC of the lung with delayed lung metastasis after 16 years. More importantly, necrosis of the tumor cells was observed in the lung metastases and paratracheal lymph nodes. According to the reports of HCCC occurring in the head and neck,,17 tumor necrosis is a potential risk factor for recurrence and metastasis. Therefore, we speculate that the potential risk factors for HCCC of the lung recurrence and metastasis may also be related to tumor necrosis.HCCC is generally considered to be a low-grade tumor, which is indolent in most case reports. According to the reported literature, recurrence and lymph node metastasis after a follow-up of 8 to 181 months was found in only 1 case (8%),Regarding the treatment of HCCC of the lung, six of the reported cases were mainly treated by local surgical resection, 5 cases received local surgical resection and regional lymph node dissection, 1 case received adjuvant radiotherapy at the same time as resection, and 1 case received laser therapy and tumor debulking with cryotherapy to the tumor base. In the case of recurrence, only local surgical resection had been performed 16 years prior. Therefore, we speculate that, for this type of tumor, lobectomy combined with selected regional lymph node dissection may be a safer treatment, especially if there is necrosis in the tumor with an increased histological grade. In the present case, both right upper lobectomy and regional lymph node dissection were performed, with no evidence of lymph node metastasis. As the surgical margin was negative, no further treatment was given. The current follow-up is 8 months, and there has been no recurrence or metastasis. However, long-term follow-up is needed to make any inferences regarding the effectiveness of our treatment strategy in this case. demonstrated that MEC, as the most common salivary gland tumor in the lung system, is similar to HCCC tumor cells in terms of immunohistochemistry, with both tumors showing positive staining for CK5/6, CK7, P63, and P40. Histologically, MEC tumor cells are usually mainly composed of mucous cells, epidermal cells (squamous cells), and intermediate cells; clear cells and eosinophils may also be present. Incontrast, HCCC components are simpler, mainly comprising clear cells in the hyaline stroma. In terms of molecular genetics, MEC is mainly associated with MECT1–MAML2 fusion, whereas HCCC is mainly associated with a rearrangement of the EWSR1 gene. The differential diagnosis with PEComa lies in the fact that tumor cells of PEComa are rich in glycogen, although its cytoplasmic transparency is similar to that of HCCC. However, PEComa has characteristic expression of the melanin markers HMB45 and/or melan A but does not express markers characteristic for HCCC such as CK and squamous epithelium-related markers ; HCCC generally does not express HMB45, which can help to distinguish it from PEComa.The histological characteristics of HCCC of the lung may be difficult to distinguish from those of other tumors with clear cell characteristics, including mucoepidermoid carcinoma (MEC), squamous cell carcinoma, adenocarcinoma with clear cell characteristics, metastatic clear cell renal cell carcinoma, and perivascular epithelioidcell tumors (PEComa). Falk et alBecause HCCC usually expresses P63, P40, and CK5/6, another important differential diagnosis is squamous cell carcinoma. However, compared with typical squamous cell carcinoma, HCCC shows low-grade cytological characteristics, the cell atypia is not obvious, and mitoses are easily identified. HCCC also has a lower cell proliferation index and non-angularity than squamous cell carcinoma.EWSR1 rearrangement or EWSR1–ATF1 fusion. In addition, compared with most lung adenocarcinomas, immunohistochemical features are the main distinguishing point for HCCC, which is negative for TTF-1 and napsin A, in contrast to lung adenocarcinomas.Other differential diagnoses include adenocarcinoma with clear cell characteristics and metastatic clear-cell renal cell carcinoma. For this distinction, it is important to consider the main clinical history and molecular features such as the presence of EWSR1gene rearrangement is also a strong indicator for auxiliary diagnosis. HCCC of the lung is an indolent salivary gland cancer with a good prognosis; however, the factors affecting the prognosis are uncertain. Tumour necrosis may be a potential risk factor for recurrence and metastasis. Owing to the limited information, more cases are needed to identify common and distinguishing features, which can help to improve differential diagnosis. Our review and case report suggest that regional lymph node dissection combined with lobectomy may be a safer treatment than only lobectomy for HCCC of the lung; however, it remains to be verified whether this treatment influences prognosis.In summary, we have reported another case of a very rare HCCC of the lung and summarized the cases reported to date. To our knowledge, this is the most complete literature review on HCCC of the lung. With a total of 13 cases now reported, our case helps to provide new context for this rare disease. HCCC of the lung has similar histological, immunophenotypic, and molecular characteristics to those of HCCC of the head and neck. Therefore, understanding key histomorphological features and the location of tumors is essential for the diagnosis of the disease, and the characteristic www.editage.cn) for English language editing.We would like to thank Editage (Conceptualization: Liqiao Chen, Ning Zhou.Data curation: Liqiao Chen, Fanrong Wang, Tingting Xu, Shuya Hu, Ying Chen.Methodology: Tao Li, Yangyan Fu, Yiman Luo.Validation: Liqiao Chen, Ning Zhou, Shuya Hu.Writing – original draft: Liqiao Chen.Writing – review & editing: Ning Zhou.


The paper presents the analysis of two compaction methods for obtaining W/Cu Functional Graded Materials (FGMs) consisting of three layers with the following compositions (% weight): first layer 80 W/20 Cu, second layer 75 W/25 Cu, and third layer 65 W/35 Cu. Each layer composition was obtained using powders obtained through mechanical milling. The two compaction methods were Spark Plasma Sintering (SPS) and Conventional Sintering (CS). The samples obtained after the SPS and CS were investigated from morphological (scanning electron microscopy-SEM) and compositional (energy dispersive X-ray spectroscopy-EDX) points of views. Additionally, the porosities and the densities of each layer in both cases were studied. It was found that the densities of the sample’s layers obtained through SPS are superior to those obtained through CS. The research emphasizes that, from a morphological point of view, the SPS process is recommended for W/Cu-FGMs, having raw materials as fine-graded powders against the CS process. Materials, energy, and modern sciences are the three great pillars of modern technology. In recent years, materials science has developed rapidly due to interdisciplinarity and due to new theories and technologies. The development of materials that respond to practical applications is of great interest, especially if their manufacturing technologies ensure low energy consumption and environmental protection .Materials based on W/Cu are widely used for electrical contacts, heat sink materials for high-power microelectronic devices, welding electrodes, and thermal management devices ,3. Due tThere are several methods to produce W/Cu materials: by infiltrating the liquid copper into open pores of W skeleton and by liquid or solid phase sintering. The infiltration method requires higher consumption of energy, especially for complex shape parts, so it is very expensive ,4. An alAdditionally, in ref. , researcBesides a simple W/Cu mix, an improvement of this material class is obtaining functional graded materials. FGMs represent a class of materials composed of one or more different materials whose properties and structure vary throughout their volume. The component materials can vary chemically from metallic, ceramic, and polymeric materials to inorganic/organic materials, such as cells and tissues. A concrete example of biological materials with a structural and functional gradient is bones, teeth, and bamboo wood .From a geometric point of view, the materials that make up an FGM can be in the form of lamella, granules, fibers, etc. .Due to their high performance and many of their functionalities, FGMs have been successfully used in the construction of new generations of spacecraft. The most familiar FGMs are those composed of refractory ceramic materials and metals because they combine properties very well, such as resistance to high temperatures and wear and oxidation offered by ceramic materials with properties such as mechanical strength, machining, and toughness offered by metallic materials . Due to W/Cu FGMs represent a class of materials which combine the properties of W such as high melting point, low coefficient of thermal expansion, and high mechanical resistance with those of copper, namely high electrical and thermal conductivity. Due to these properties, W/Cu FGMs are suitable for plasma-facing materials (PFM) for fusion reactors ,16,17.In the literature, there are some processes presented that are used for fabricating W/Cu FGMs, as follows: one-step resistance sintering method , laser sWithin the methods used by the PM technique, plasma sintering (SPS) is one of the modern processes, which allows for the compaction and sintering of powders at the same time. It is a modern process with great potential ,24, yet In ref. , experimIn ref. , elaboraThis study is focused on the W/Cu FGMs synthetized by SPS and CS, which were used as starting materials and mechanically milled powders, respectively.In order to prepare W/Cu FGM, tungsten nanopowders (44 nm) and copper micron powders (type SE Pometon) were used. The particle size distribution of W nanopowders was determined by Dynamic Laser scattering (DLS), using a 90 Plus particle size analyzer, Brookhaven Instruments Corporation, Holtsville, NY, USA, equipped with 35 mW solid state laser, having 660 nm wavelength. The powder was in suspension with distilled water and the suspension was subjected to an ultrasound process for 5 min. Compared to SEM analysis, DLS measures the hydrodynamic diameter, which can be different because it depends on several factors such as the concentration of the dispersion medium, the type of ions in the dispersion medium, the size of the particles core, and the surface structure of the particles ,28.In As it can be seen in In Copper powders, as seen in Three types of mixtures with the following compositions—(% weight) 80 W/20 Cu, 75 W/25 Cu, and 65 W/35 Cu—were subjected to the mechanical milling (MM) process for 20 h in order to obtain composite powder used for preparing FGMs. For the MM process, a vario-planetary ball mill Pulverisette 4 made by Fritsch was used. The MM parameters were the following: the material of vials was stainless steel, vial volume was 250 mL, the material of the balls was stainless steels, the ball diameter was Φ = 10 mm, the balls to powder ratio was 2/1, the rotational speed of the main disk was 400 rpm, the rotational speed of the vials was 800 rpm, the milling atmosphere was argon, and the milling type was dry.The mixtures obtained after MM process were used to fabricate the FGM materials by SPS and CS, according to the flow chart presented in For the SPS process the mixtures were stacked layer by layer into a graphite die (Φ = 20 mm) that was cylindrically shaped, according to In order to obtain W/Cu FGMs by CS process, the powders were stacked layer by layer into a metallic die (Φ = 12 mm) and were pressed at 600 MPa using LBG testing equipment, which was connected to the computer for registering the results by TCSoft2004Plus software. The force that was applied was equal to 68,293 Kn, the pressing speed was 50 mm/minute, and there was no dwell time at the higher force. In the first experiment, the powders were pressed without any lubricant addition and, because the particles were smaller (less than 500 nm), the green part had no mechanical strength a. FollowThe CS process was carried out according to the diagram given in The samples were axially cut using a Metkon Metacut-M 250 machine and were heat-mounted in transparent resin (30 mm diameter) using a Metkon Metapress-A machine. In order to study the microstructural aspects, the mounted samples were ground with silicon carbide papers with the following grits: 80, 120, 240, 400, 800, 1200, 2000, and 2500. They were polished with a GALAXY Polishing cloth PHI and Dia-Duo 2 suspension using Metkon Forcipol 2V machine. SEM characterization was performed using a JEOL JSM5600LV microscope equipped with an EDX spectrometer .To determine the densities, the porosities of each layer were measured by image analysis using ImageJ software. In For each layer, the composition and theoretical density were determined by applying the following Equation:Dc—density of composite material; Dr—density of reinforcement material; Vr—volume fraction of reinforcement material; Dm—density of matrix material; Vm—volume fraction of matrix material.In order to convert weight percentage to volume percentage, we applied the following equations:AG—% weight of tungsten; BG—% weight of copper; AV—% volume of tungsten; BV—% volume of copper.3, the theoretical density —14.96 [g/cm3], for layer 3 (65 W/35 Cu)—13.73 [g/cm3].Applying the Equations (1)–(3) and considering the density of W and Cu as 19.25 and 8.96 g/cmThe densities were calculated according to the following Equation:In As can be seen from After milling together for 20 h, the morphologies of the three W/Cu compositions are shown in As can be seen from The distribution maps of W/Cu FGM obtained by CS, In the case of SPS sample, the EDX line distribution, as can be seen from According to the maps, it was observed that the W and Cu phase are distributed relatively uniformly in each layer. Analyzing Analyzing the distribution of elements and the images of the interfaces, it can be concluded that, using the SPS method, compact W/Cu FGM materials are obtained.Functional graded materials based on W/Cu were successfully fabricated by the SPS process. To obtain green W/Cu FGMs by the CS method it is necessary to add binder, otherwise the green part of the sample has no mechanical strength. The elemental distribution maps of the samples obtained by SPS are more homogenous compared with the samples obtained by the CS process. In the cross-section of the CS samples, the presence of some dimples and cracks was observed, which are due to the pressing process. The cracks are presented in each layer, and in the interfaces. The CS process is time-consuming (~125 min until sintering temperature) compared to SPS sintering (~3 min). In contrast, through SPS a uniform and crack-free sample was obtained. It had lower porosity and high density. According to the morphological analysis, the SPS process is more suitable to obtain W/Cu FGMs compared with the CS process in the case of powders with fine granulation that are used as raw materials.