Diabetic retinopathy (DR) is one of the serious complications of diabetes mellitus. Without further treatment, it can evolve into the stage of proliferation, which will lead to the formation of new blood vessels, vitreous hemorrhage, or anterior retinal hemorrhage, which will lead to severe vision loss and increase the risk of blindness.The research literature on the risk factors of diabetic retinopathy published as of July 1, 2020 was searched through MEDLINE, Embase, ovid, Web of Science, Wanfang, CNKI, and other databases, The search strategy has been first developed in MEDLINE using MeSH subject headings combined with free-text terms and Stata12.0 software was used for meta-analysis.This study is ongoing and the results will be submitted to a peer-reviewed journal for publication.Ethical approval is not applicable, since this is an overview based on published articles.The registration number is INPLASY202070107, the DOI number is 10.37766/inplasy2020.7.0107. According to statistics, there were 425 million people with diabetes in the world aged 20 to 79 years in 2017, and it is expected to increase to 629 million by 2045. Nearly 80% of patients live in low- and middle-income countries  According to the WHO study, in 2011, the number of diabetic patients worldwide has reached 366 million, and by 2025, there will be >500 million diabetic patients in the world, and about one-third of them will develop diabetic retinopathy (DR).,4Diabetes mellitus (DM) is a group of chronic metabolic diseases caused by genetic, environmental, and autoimmune diseases. Long-term metabolic disorders can lead to microvascular and macrovascular diseases, neurological complications, among others. Its main pathological changes are the proliferation of capillary endothelial cells, thickening of basement membrane, and selective loss of pericytes, which ultimately leads to the formation of micro angioma, the increase of microvascular permeability, and the blood-retinal barrier destruction, atresia or blockage of capillaries, and formation of new blood vessels.,7 At present, the global prevalence rate of DR is 34.6%, and the prevalence rate of DR in developed countries is close to 40.3%; 3.6% of patients with type 1 DM and 1.6% of patients with type 2 DM will be blind. DR seriously threatens the quality of life of diabetic patients, and at the same time brings a serious economic burden to society.,11DR is one of the most common and serious microvascular complications in diabetes. Therefore, further research on the risk factors of DR and effective preventive measures is necessary. Provide corresponding interventions for high-risk factors to prevent the occurrence and development of DR. The occurrence and development of DR are affected by many factors. Most studies have shown that DR is related to blood sugar level and disease course, but the results of research on the correlation between blood pressure, blood lipids, UAE and body mass index (BMI) and other factors and DR are inconsistent.–15The risk factors of DR are many and complex. It can make the patient progress from asymptomatic step by step and eventually lead to irreversible blindness. However, it is still difficult to effectively prevent the visual impairment caused by it.–18 Adopt the method of meta-analysis of published on angiotensin gene polymorphism, diabetes duration, glycosylated hemoglobin, fasting plasma glucose, postprandial 2 hours blood sugar, triglyceride, cholesterol, uric acid, the relationship between the BMI and DR make a comprehensive analysis of literature, to determine whether the risk factors associated with diabetic retinopathy.Systematic reviews and meta-analysis can provide scientific evidence for health decisions and can also form higher-level recommendations in the guidelines.22.1This systematic review and meta-analysis has been registered on the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY). The registration number is INPLASY202070107, the DOI number is 10.37766/inplasy2020.7.0107.2.22.2.1Inclusion: Randomized controlled trial (RCT); cohort studies; case–control studies.Exclusion: non-Chinese and English literature; incomplete or missing research data; unable to obtain original documents; repeated publication of literature; editorials; commentaries.2.2.2DR.2.2.3Advanced age, male sex, DM duration, insulin treatment, fasting blood glucose, 2-hour postprandial blood glucose, glycated haemoglobin A1c, total cholesterol, triglyceride, BMI, systolic blood pressure.2.2.4Incidence of DR.2.32.3.1“Diabetic retinopathy” was used as the English search term, database retrieval was carried out on MEDLINE, Embase, ovid, Web of Science, Wanfang, CNKI database, and literatures on diabetic retinopathy published from the establishment of the database to July 2019 were collected systematically.2.3.2Other resources are: manual and other search: search relevant literature by Baidu, Google, Yahoo, and other search engines; document tracing method as an auxiliary retrieval.2.4All search results are imported into EndNote X9 literature management software, 2 reviewers (YYH and ZMJ) will screen the titles and abstracts of literature independently, then read the full text to assess literature according to the inclusion and exclusion criteria, any disagreements will be resolved by a third reviewer (SLS). Study selection will be summarized in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram.2.5Two researchers (YYH and ZMJ) independently screened the literature in strict accordance with the inclusion and exclusion criteria. During the screening, they first read the title, eliminated the obviously irrelevant literature, and then further read the abstract of the literature and the full text to determine whether to include it or not. If necessary, contact the original study author via email or other means for information. If there is any difference in the content of data extraction, the third party (SLS) shall be consulted.2.6,20 This is a specific method for assessing the quality of cohort and case–control study. There are 8 entries in 3 modules, among which 4 points are selected for study population, 2 points for comparability between groups, and 3 points for measurement of results. The total score ≥6 points is considered as high-quality research literature.Two reviewers will independently assess the quality of included studies by using the Newcastle-Ottawa Scale (NOS) for nonrandomized studies. For each of the above items, make a judgment of “low risk of bias,” “high risk of bias,” and “uncertain risk of bias.” Disagreement will be solved by discussion or by consulting the third person (SLS).The Cochrane bias risk assessment tool was used to evaluate the final included RCTs: random allocation method; allocation plan concealment; blinding of research subjects and experimenters; blinding of outcome evaluators; completeness of result data; selective reporting of studies Results; other sources of bias, including potential bias related to the specific research design of the study.2.7I2 and P values to test the heterogeneity of the combined literature. If P ≥ .1, I2 < 50%, it indicates that there is homogeneity among the studies or the heterogeneity is within the acceptable range, and the fixed-effects model is used to merge the calculation of the effect size; on the contrary, it is considered that there is heterogeneity between the studies. Egger method and Begg method were used to assess publication bias.Statistical analysis was performed on the extracted data using Stata 12.0 software. For measurement data, the weighted mean difference is used as the combined effect size; for binary variable data, the odds ratio (OR) is used as the combined effect size. Use the statistics 2.8If the evidence is sufficient, we will conduct a subgroup analysis to determine the difference between different sex, age  among others.2.9Two reviewers (YYH and ZMJ) will use the GRADE  method to assess the quality of evidence of included studies. The evidence levels are classified into 4 levels: high, moderate, low, or very low.3To investigate the risk factors for any DR in people with DM, a random-effects meta-analysis was employed a priori because of anticipated variation in study populations, geography, and study design. As a rule, we only included risk factors that were investigated in at least 3 studies using multivariate design, and the definitions of the same risk factor should be similar across all included studies. Finally, 11 factors met our criteria and were included in meta-analysis.Conceptualization: Yuying Hou.Methodology: Yitong Cai.Software: Yuying Hou, Zhumin Jia, Suling Shi.Writing – original draft: Yuying Hou, Suling Shi, Zhumin Jia.Writing – review & editing: Yuying Hou, Suling Shi, Zhumin Jia.


Asynchronous movement of the carotid atheromatous plaque from B-mode ultrasound has been previously reported, and associated with higher risk of stroke, but not quantitatively estimated. Based on the hypothesis that asynchronous plaque motion is associated with vulnerable plaque, in this study, synchronisation patterns of different tissue areas were estimated using cross-correlations of displacement waveforms. In 135 plaques (77 subjects), plaque radial deformation was synchronised by approximately 50% with the arterial diameter, and the mean phase shift was 0.4 s. Within the plaque, the mean phase shifts between the displacements of the top and bottom surfaces were 0.2 s and 0.3 s, in the radial and longitudinal directions, respectively, and the synchronisation about 80% in both directions. Classification of phase-shift-based features using Random Forests yielded Area-Under-the-Curve scores of 0.81, 0.79, 0.89 and 0.90 for echogenicity, symptomaticity, stenosis degree and plaque risk, respectively. Statistical analysis showed that echolucent, high-stenosis and high-risk plaques exhibited higher phase shifts between the radial displacements of their top and bottom surfaces. These findings are useful in the study of plaque kinematics. More severe damage may be caused by vulnerable plaques, i.e. plaques prone to rupture. These are strongly associated with the formation of blood clots and the release of plaque fragments into the systemic circulation, which may cause a cerebrovascular event, such as stroke or transient ischemic attack (TIA)2. Given the substantial burden of stroke , investigating the behaviour of carotid plaque towards improving stroke prevention is of utmost importance.The carotid atheromatous plaque is a lesion of the carotid artery wall and typically consists of a fibrous cap  of varying thickness and a lipid core (mostly cholesterol and cellular debris). In cases of advanced degeneration, plaque lesions present a more complicated structure, including calcification, intraplaque hemorrhage and ulceration4. Currently, clinical management of carotid plaque is based on the degree of stenosis, i.e., the percentage of lumen area occupied by atheromatous material, and the prior occurrence of symptoms5. Although the degree of stenosis is a validated marker for management of carotid plaques, some studies have indicated that a high degree of stenosis is not necessarily related to a high risk of a cerebrovascular event7. These facts indicate that there is room for improving the current clinical scheme for assessing plaque vulnerability, possibly through the identification of noninvasive, low-cost and reliable imaging markers for predicting strokes8.Ultrasound imaging is the preferred imaging modality for the diagnosis of carotid atheromatous plaque, owing to a number of advantages, including noninvasiveness, bedside availability, short examination times, lack of radiation exposure, and low cost12. Motion analysis can be defined as the estimation of arterial tissue displacement during one or more cardiac cycles. It has been shown that carotid atheromatous plaque performs a complex, multidirectional, often periodic, motion during the cardiac cycle13. Despite the technical challenges, such as the low image resolution in ultrasound imaging and the complexity of the local tissue geometry and mechanics, several studies have suggested a number of kinematic and strain indices associated with plaque rupture risk14.For instance, carotid motion analysis estimated with ultrasound image sequences has gained increasing attention as a potential index of plaque vulnerability16 and pathological conditions, such as hypertension, diabetes and coronary artery disease18, as well as the motion of the wall adjacent to carotid plaque20. These studies have studied the expected cyclical motion in the radial direction and have also identified a longitudinal component of wall motion. It has also been observed that decreased longitudinal movement of the common carotid artery is associated with higher plaque burden20. Significantly lower amplitudes of both radial and longitudinal displacements have been found in older diabetic subjects, compared to healthy young adults18. Recently, the feasibility of assessing tissue motion inhomogeneities was demonstrated along with their association with the presence of coronary artery disease21. Blood pressure has been positively correlated with common carotid artery displacement17. Other studies have suggested that the severity of carotid stenosis is associated to axial wall stresses and accelerations19, as well as to the presence of an anterograde component in the longitudinal direction of wall motion12.A number of efforts have focused on motion of non-atheromatous segments of the arterial wall in normal13, maximal (discrepant) surface velocities22 and displacement vector maps23. A group of studies have also qualitatively described the so called “jellyfish sign” phenomenon, according to which the carotid plaque surface rises and falls in a manner inconsistent with arterial pulsatile wall motion26. Other similar phenomena include motion of intraplaque contents27, mobility at the edge of the plaque, mobility in all parts of the plaque and mobility at the bottom of an ulcer on the plaque26. Studies have also investigated tissue strain, i.e. the change of displacement with respect to some initial reference status32. These studies have converged to the general conclusion that softer, echolucent plaques undergoing higher strains tend to be more prone to rupture and they are associated with poorer patient cognition. The concept of concordant and discordant motion was recently introduced to describe the spread of motion of different plaque areas33.Related studies have proposed various metrics to quantify plaque motion patterns, including statistical measures of velocities, motion amplitudes and diastole-to-systole displacements of the entire plaque area during the cardiac cycle26, as well as within the plaque itself27 has been reported in some studies. The patterns of synchronisation of such relative movements have only been estimated qualitatively in a few studies26 and have shown that asynchronous motion of the plaque relative to the adjacent wall is associated with plaque instability and stroke recurrence.Among the investigated phenomena, relative motion between the plaque and the adjacent wallTo the best of our knowledge, there is no study focused on investigating synchronisation patterns of carotid plaque motion in an automated and quantitative way. Therefore, the purpose of this study was to quantify synchronisation patterns of the carotid plaque, in relation to its adjacent wall and within itself, and investigate potential associations of these synchronisation patterns with different plaque phenotypes, including echogenicity, stenosis degree, patient symptoms and plaque risk. The major contributions of this work are to (a) suggest a systematic approach for assessing such patterns, (b) provide specific numerical indices (measured in seconds) for the related phenomena, i.e. the phase shifts between plaque and wall, and within plaque in radial and longitudinal directions, and (c) evaluate the derived indices in different plaque phenotypes, based on the hypothesis that asynchronous plaque motion is associated with phenotypes characterising vulnerable plaque, namely echolucency, symptomaticity, high stenosis degree and high risk. These contributions will provide new knowledge about plaque biomechanics, which is important and necessary for future studies, including prognostic follow-up assessments.Seventy seven consecutive patients  with carotid atherosclerosis were included in the study, free from comorbidities, including heart failure, liver dysfunction, cancer, chronic diseases etc. Subjects were on statin-based, anti-platelet and lipid-lowering medication. The dataset included 18 symptomatic patients , 57 asymptomatic patients  and 2 patients (6 plaques) whose symptomaticity or stenosis degree was unknown; the latter were only included in the association-with-echogenicity study. The symptomatic subjects, for whom only the ipsilateral artery was studied, had experienced a stroke or a TIA, within 6 months prior to the examination. A number of asymptomatic subjects had plaque in both the right and left carotids and in both types of subjects more than one plaque may be present in an artery (tandem lesions); tandem lesions were treated as separate plaques. The patients’ ages were 70 ± 9 years (range 43–85 years), and their stenosis degrees 75% ± 17% (range 20–99%), based on Doppler ultrasound measurements.B- mode ultrasound images were acquired in longitudinal section using a LOGIQ Book  scanner and a linear array 4–10 MHz transducer. Subjects were examined in a supine position, with a slight backward inclination of the head, towards the opposite side of the carotid under examination. Patients rested for at least 5 minutes before the examination, to stabilise their heart rate and blood pressure. To minimise movements due to factors other than haemodynamic forces, the operator held the transducer as stable as possible, exerting minimal pressure, and the patients were asked to breath-hold during recordings. Scanner and transducer settings included a high dynamic range (60 or 75 dB) and zero persistence, and 10 MHz centre frequency. At least three cardiac cycles were recorded at a rate of 25 frames/s. Image resolution was 12 pixels/mm in the radial and longitudinal directions. The room temperature was kept constant at 26 °C.All ultrasound examinations were performed by 4 experienced physicians in the Vascular Surgery Department of the University Hospital “ATTIKON”, Athens, Greece. Data collection was approved by the ATTIKON hospital institutional review board and all subjects included in the study gave their informed consent to the scientific use of the data. The methods were carried out in accordance with the relevant guidelines and regulations.Plaque motion synchronisation patterns relative to the adjacent normal wall as well as within the plaque were estimated through cross-correlations of pairs of waveforms representing displacements of plaque and wall tissue.34. If cross-correlation is calculated for all lags Cross-correlation If the peaks (or the troughs) of two time-varying signals coincide in time, their cross-correlation has a high positive value. These signals are considered synchronous, or in-phase, or with a 0° phase shift. If the peaks of one signal coincide in time with the troughs of the other signal, their cross-correlation has a high negative value. These signals are considered asynchronous, or out-of-phase, or with a 180° phase shift. A cross-correlation value equal to 0 indicates uncorrelated signals.The main steps of the methodology are described below and illustrated in Fig. For each plaque image sequence (video), an experienced physician marked manually in the first frame the following four ROIs: the posterior and anterior wall-lumen interfaces , and the plaque top and bottom surfaces  Fig. . PWL and35. This algorithm was evaluated in an in silico framework consisting of 13 simulated sequences, and has been shown to be accurate and robust in motion tracking of the arterial wall from B-mode ultrasound images13. For each ROI, 1.6 × 1 mm2 reference blocks were selected in the first frame, centred at ROI pixels. Figure The radial and longitudinal positions of all pixels included in the selected ROIs were estimated across all frames with an adaptive block-matching algorithm, which incorporates Kalman filteringROI positions were used to estimate six sets of waveforms for each plaque:  (i)wall diameter, which was selected as the most representative waveform, i.e. the one in which the most clear cyclic motion was observed, among the distances of vertical pairs of AWL and PWL pixels,(ii)radial displacements of all PTS pixels, namely their radial positions along consecutive frames,(iii)longitudinal displacements of all PTS pixels, namely their longitudinal positions along consecutive frames,(iv)radial displacements of all PBS pixels,(v)longitudinal displacements of all PBS pixels, and(vi)radial distances of PTS and PBS pixel pairs, defined as the absolute differences of waveforms (ii), (iv) across vertical pixel pairs.Twenty five pixels from the right and 25 from the left edge of the plaque PTS and PBS were removed to ensure that only plaque pixels, and no normal (non-plaque) wall area, were included in the analysis. The number of removed pixels (25) was heuristically determined, following visual inspection and testing. Figure th order Butterworth filter with a cutoff frequency of 0.6 Hz was applied to the displacement waveforms36, so as to remove unwanted offsets or abrupt fluctuations present in the low-frequency band. The cutoff value was selected to ensure that heart rates above approximately 40 beats per minute remain unaffected after filtering. Independent component analysis (ICA) demonstrated that the suggested methodology is robust against external motion (Supplementary methods).A high-pass 4Cross-correlation 1 (CC1): Radial deformation of the plaque with wall diameter, i.e. waveforms (i) and (vi),Cross-correlation 2 (CC2): Radial displacements of plaque top and bottom surfaces, i.e. waveforms (ii) and (iv), andCross-correlation 3 (CC3): Longitudinal displacements of plaque top and bottom surfaces, i.e. waveforms (iii) and (v).Three types of cross-correlations were calculated using the previously described waveforms:CC2 and CC3 describe intra-plaque kinematics, whereas CC1 was considered, so as to provide a measure with respect to a well-known arterial parameter.Figure Signals to be correlated were confined within an average cycle window, estimated from the dominant frequency of the wall diameter waveform.dmax, in seconds  measures  of the lags From each cross-correlation waveform, two types of measurements were obtained: (a) the sign corresponding to the maximum absolute cross-correlation, and (b) the corresponding lag nds Fig. . For eacTherefore a total of 24 features were extracted for each plaque, namely 8 features  for each of the 3 cross-correlation types.37, the physician selected a region in the blood and one in the adventitia, and the median pixel values of these regions  were set as the lowest (black) and the highest (white) values in the image, respectively. Then, the image grayscale intensities were linearly adjusted so that GSMblood was 0, and GSMadv was 19037.To normalise ultrasound images according to widely accepted procedures38. Plaque echogenicity was estimated as follows: the plaque was located automatically in each frame of the sequence after the first frame, using motion analysis of PBS and PTS areas, and the corresponding grayscale median (GSM) values were calculated. Plaque GSM was defined as the mean value of the GSMs of all frames. Echolucent plaques were considered those with a GSM < 2539 and echogenic those with GSM ≥ 25.An echolucent plaque is a dark appearing plaque in the ultrasound recording, while an echogenic plaque is a bright appearing oneIntra and inter-observer variability were assessed by means of phase shift measurements performed for plaque boundaries displaced by 0–2 pixels with respect to the original (expert-annotated) ones. This experiment was designed based on the assumption that different observers, or the same observer at different times, produce different tissue outlines, which are displaced versions of a given contour. The range of the displacements  was selected heuristically, based on observations that tissue outlines derived by different experts were not more than 2 pixels apart. Differences between original and displaced versions in all cases were assessed statistically.The four associations investigated were validated through classification schemes using supervised machine learning. The purpose of classification was to evaluate the overall potential of the extracted features, which, can alternatively be considered as a “motion synchronisation signature”, through their association with the four clinical phenotypes. Subsequently, statistical analysis was performed, to identify the features with the highest discriminatory ability.40. For this study, as many principal components as necessary were retained to cover 95% of the initial set’s variance.Feature selection was applied using Principal Component Analysis (PCA), whereby the initial feature set is converted into a reduced set of linearly uncorrelated features, orthogonal to each other , which retains most of the initial set’s variance, namely, its information content41. Among the algorithms available in Weka, the Random Forest (RF) algorithm was used, due to its superior performance and its robustness to overfitting42. The RF algorithm uses a number of parameters that need to be tuned properly, before training, to avoid overfitting or underfitting. The two parameters that were tuned included the number of features to be used in random selection , and the number of trees to be generated . For parameter tuning, 10-fold cross-validation was used. The parameters that were tuned included the number of data points, the number of features of each tree of the forest, and the number of the trees that we build for the forest.Classification models for each association were implemented using the Weka workbench version 3.6 43 was applied to create synthetic samples for the minority class, i.e. the class with the lowest number of cases. Of note, these synthetic samples were used only for training the model, not for testing.To address the problem of class imbalance that is present in our data, the ADASYN algorithm44.For the evaluation of each model, leave-one-out cross-validation (LOOCV) was chosen, because the medium size of our dataset indicated it as the optimal choice in terms of computational cost, as well as bias-variance trade-off45.To evaluate the performance of the classification models, a set of metrics was calculated, including accuracy (ACC), sensitivity (SENS), specificity (SPEC), precision (PREC), negative predictive value (NPV), F1 score (F1SC) and the area under the Receiver Operating Characteristics (ROC) curve (AUC)Statistical analysis was performed using the non-parametric Wilcoxon rank sum test and statistical significance was considered for a p-value equal to or lower than 0.05.All analyses were performed using Matlab R2016a  and a computer with an Intel Core i5 220 GHz CPU.Table Regarding the variability study, all indices were similar between the original and the displaced versions. As an example, the p-values for the mean phase shifts were 0.46 for CC1 and CC2 and 0.39 for CC3.In the following subsections detailed results are presented for the statistical analysis of the entire dataset, for each of the investigated scenarios. Tables showing statistical analysis results present values for synchronisation percentages and mean phase shifts, even if they were not found statistically different, so as to provide a feel for these measures, given they are reported for the first time.Of the 135 plaques of the dataset, 37 were echolucent (GSM < 25) and 98 were echogenic (GSM ≥ 25). The stenosis degrees and ages were not statistically different in the two groups .The application of PCA identified 13 features as the principal components satisfying the 95% variance coverage criterion for this association.CC2 and medianCC2). Also, the mean phase shifts between top and bottom surfaces of both echogenic and echolucent plaques were significantly higher in the longitudinal direction, compared to the radial direction.Table Of the 124 plaques used in this substudy, 93 caused a degree of stenosis higher than or equal to 70%. Of these 93 high-stenosis plaques, 71 were asymptomatic and 22 were symptomatic. The stenosis degrees and ages were not statistically different in the two groups .The application of PCA identified 11 features as the principal components satisfying the 95% variance coverage criterion for this association.Table Of the 124 plaques used in this substudy, 97 were asymptomatic. Of these 97 asymptomatic plaques, 26 caused a low degree of stenosis (<70%) and 71 caused a high degree of stenosis (≥70%). The ages of the patients were not statistically different in the two groups . By definition, the high-stenosis group in this study is the same as the asymptomatic group in the previous study.The application of PCA identified 13 features as the principal components satisfying the 95% variance coverage criterion, for this association.CC2, higher meanCC2) and less uniformly (higher stdevCC2) relative to the bottom surface, than in low-stenosis plaques, in the radial direction. Also, the mean phase shifts between top and bottom surfaces of both low- and high-stenosis plaques were significantly higher in the longitudinal direction, compared to the radial direction.Table 46 and asymptomatic subjects with stenosis degrees ≥70%47; otherwise subjects are considered low-risk5.Of the 124 plaques used in this substudy, 26 were low-risk and 98 were high-risk. The ages of the patients were not statistically different in the two groups . According to the current clinical decision-making scheme, high-risk subjects are symptomatic ones with stenosis degrees ≥50%The application of PCA identified 12 features as the principal components satisfying the 95% variance coverage criterion for this association.CC2) and less uniformly (higher stdevCC2) relative to the bottom surface, than in low-risk plaques, in the radial direction. In addition to this, most of the significantly different features (3 out of 4) were derived from cross-correlation type 2, namely between radial motion of top and bottom plaque surfaces. Also, the mean phase shifts between top and bottom surfaces of both low-risk and high-risk plaques were significantly higher in the longitudinal direction, compared to the radial direction.Table CC2 in Table CC1 in Tables As it can be observed, a few of the features in the previous Tables Figures This study showed that the synchronisation percentages in our dataset were approximately 50%, 80% and 80%, for CC1, CC2 and CC3, respectively, and the mean phase shifts were 0.4 s, 0.2 s and 0.3 s, respectively. To the best of our knowledge, such features characterising phase shifts and synchronisation percentages of the motion of carotid atheromatous plaque from B-mode ultrasound have not been previously quantified. The RF algorithm yielded AUC scores of 0.81, 0.79, 0.89 and 0.90, for the association with echogenicity, symptomaticity, stenosis degree and plaque risk, respectively. It was also observed that echolucent, high-stenosis and high-risk plaques had significantly higher phase shifts between the radial displacements of their top and bottom surfaces (0.23–0.26 s on average), compared to echogenic, low-stenosis and low-risk plaques (0.16–0.20 s on average).5. Asymptomatic subjects with low- and high-stenoses are offered different treatments; conservative treatment with medication for the former, while carotid revascularisation for the latter5.The interrogated phenotypes were selected on the grounds of their associations with plaque vulnerability and selection of treatment. Specifically, echogenicity has been associated with increased vulnerability. Symptomatic and asymptomatic plaques with stenosis degrees higher than 70% are currently offered carotid revascularisation48 for more information. The performances of these classifiers were inferior compared to the RF algorithm.Feature selection identified the same set of features for most association scenarios . Also, in all association studies, NPV had the lowest value among all evaluation metrics. This is expected, because the “negative” class was the minority class, namely it was outnumbered by the “positive” class, therefore, this metric reflects the inferiority of the “negative” class in terms of sample size. It is pointed out that 3 additional classifiers, besides RF, were benchmarked on the same dataset, namely Multilayer Perceptron, Nearest Neighbours and Support Vector Machines (SVMs). These algorithms perform supervised machine learning, i.e. their inputs and outputs are known; seeAlthough feature selection identified 12–13 features for each association scenario, statistical analysis yielded fewer features, namely 2–4 depending on the scenario. This indicates that despite the relatively low number of statistically significant features in a specific association, there is additional, potentially discriminatory, information which is uniformly distributed among the entire set of the 24 features, and is revealed with classification. The good performance of the classifiers, ranging from 79% to 90%, indicates that there is sufficient information present in the datasets of all association scenarios.27, while machine learning methodologies have been introduced in fewer cases24. Specifically, Gastounioti et al.11 compared multiple classifiers and feature selection methods, as well as combinations of them, and concluded that the SVM classifier combined with the Fisher Discriminant Ratio for feature selection were optimal in discriminating symptomatic and asymptomatic patients. Meshram et al.30 and Wang et al.32 implemented a logistic regression classifier and ROC analysis, towards correlation of plaque strain indices with patient cognitive function. Finally, Ichinose et al.24 implemented a multiple linear regression analysis , followed by a machine learning analysis using an Artificial Neural Network based on the Log-Linearised Gaussian Mixture Network, to correlate the “jellyfish sign” of motion with the presence of new lesions, detected by diffusion-weighted imaging. The generation of these lesions is the most common complication caused by carotid artery stenting. Machine learning is appropriate for the study of complex relations, whereas statistical tests are limited to simpler cases. The combination of both machine learning and statistical analysis methodologies, which is implemented in the current study, allows the design of a robust, multi-level validation scheme and, thus, the extraction of reliable results about the complex phenomenon of plaque motion synchronisation.Most of the previous studies have used statistical tests to validate their resultsEcholucent, high-stenosis and high-risk plaques presented significantly higher phase shifts between the radial displacements of their top and bottom surfaces, compared to echogenic, low-stenosis and low-risk plaques. A potential implication of these findings is that asynchronous motion patterns are associated with higher plaque vulnerability, given their association with its determinants, including echolucency, high-stenosis and presumed high risk. These results and related implications should be confirmed in follow-up studies. In contrast, statistical analysis between symptomatic and asymptomatic plaques did not reveal any differences. This finding may imply that echogenicity and stenosis degree hold more information and, thus, are more crucial clinical parameters, than symptomaticity, as far as plaque kinematics are concerned. Moreover, the significantly higher phase shifts in the longitudinal direction, in the majority of interrogated groups (7 out of 8), indicate more asynchronous intra-plaque motion in the longitudinal direction, than in the radial direction.et al.13 reported that symptomatic plaques presented 37% higher radial motion range of PTS and 50% higher relative movement between PTS and PBS. Moreover, Kume et al.25, Ogata et al.26 and Ichinose et al.24 showed that the jellyfish sign, a pattern that characterises the asynchronous motion of the plaque relative to the adjacent wall, is associated with plaque vulnerability and stroke recurrence. Gastounioti et al.49 found that echolucent plaque segments moved more intensely in the radial direction, compared to echogenic plaque segments. Finally, Tat et al.12 reported that patients with severe plaque stenosis presented greater longitudinal anterograde wall motion than those with moderate stenosis. In combination with our finding that high-stenosis plaques had significantly higher and more dispersed phase shifts between the radial displacement of their top and bottom surfaces, this suggests that irregular wall dynamics characterising high-stenosis cases may be reflected not only within plaque but also in relative movement with the adjacent wall.The main findings of this research, namely that echolucent, high-stenosis and high-risk plaques are characterised by higher phase shifts and, thus, less synchronous motion patterns between the radial motion of their top and bottom surfaces than echogenic, low-stenosis and low-risk plaques, qualitatively agree with other studies on plaque kinematics. Gastounioti 32, B-mode has also been used for motion measurements29. In this work, only B-mode data were available in the commercial scanning device that was used. It has been shown that radiofrequency ultrasound outperforms B-mode, due to its reduced variability in cardiac strain estimation50. A more recent study however showed that local arterial characteristics can be assessed equally reliably and accurately with B-mode technology51. Advantages of B-mode include relatively low-cost and widespread use in clinical practice, while radiofrequency devices are higher-cost and mostly used for research purposes. It is therefore important to be able to extract as much information as possible from the widely available B-mode devices allowing to address a wider range of clinical applications. B-mode-ultrasound-based tissue kinematics could be further combined with other plaque properties, such as neovascularisation and elasticity, assessed using contrast-enhanced ultrasound and elastography, respectively, towards providing an overall valid plaque characterisation52.This work is one of the studies demonstrating the ability to extract features characterising tissue kinematics from B-mode ultrasound images. Although radiofrequency ultrasound is being widely used for tissue motion and strain estimationMotion of the arterial wall and plaque during the cardiac cycle is a particularly complex phenomenon, resulting from the combined effect of a number of different forces/stresses, including translation, rotation, shear, tethering, etc. Taking into account the complexity of this phenomenon, in this study we selected to address representative plaque motion patterns, namely in relation to adjacent wall as well as in the radial and longitudinal directions within itself.The limitations of this study include the medium size and the heterogeneity of the dataset. Compared to previous studies on ultrasound-based carotid plaque kinematics, in which dataset sizes ranged from 11 to 165 patients, our 77-patient (135-plaque) dataset was considered adequate for benchmarking our methodology. Dataset heterogeneity consists in including subjects of both gender and with lesions located in both the left and right carotids. Although larger and more homogeneous datasets are always desirable to reach safer conclusions, we believe that the medium size of our dataset and the grouping into smaller, somewhat more homogeneous, datasets has allowed us to make some reliable and interesting observations.The findings presented in this study are promising for further in-depth study of carotid plaque kinematics from B-mode ultrasound. Future work in this area might focus on the combination of phase-shift features with other ultrasound-based kinematic features towards extracting valuable information about plaque mechanics. The application of the proposed classification model to substantially larger datasets, including follow-up patient data, will allow the identification of potential novel markers for improved risk stratification.In conclusion, this study quantified synchronisation patterns of the carotid atheromatous plaque from B-mode ultrasound, and associated them with echogenicity, symptomaticity, stenosis degree and plaque risk. Synchronisation percentages in our dataset were approximately 50%, 80% and 80% and the mean phase shifts 0.4 s, 0.2 s and 0.3 s, for cross-correlation types 1, 2 and 3, respectively. The RF algorithm, combined with PCA, achieved very good performance in the benchmarking procedures, yielding AUC scores of 0.81, 0.79, 0.89 and 0.90, for the association with echogenicity, symptomaticity, stenosis degree and plaque risk, respectively. Statistical analysis showed that echolucent, high-stenosis and high-risk plaques exhibited higher phase shifts between the radial displacements of their top and bottom surfaces. These findings are promising for further in-depth study of ultrasound-based carotid plaque kinematics, towards improving risk stratification.Video 1.Video 2.Independent Component Analysis.


Introduction: Uterus transplantation (UTx) is a rapidly evolving treatment of uterine-factor infertility. We report the results of the first 10 UTx procedures performed at our institution. Methods: The program started in April 2016 as a two-arm study comparing the efficacy of UTx from live donors (LD) and deceased donors (DD). Results: Between April 2016 and April 2018, we performed five DD UTx and five LD UTx. Two grafts had to be removed early due to thrombosis. One graft was removed due to chronic rejection and previous herpes simplex infection at month 7. Graft survival is 70% at one year. Recipient survival is 100% at two years. Live donor survival is 100% at three years. Three live-births have been achieved, two from a LD and one from a graft from a nulliparous DD. Vaginal anastomotic stenosis occurred in 63% (5/8) of grafts. Self-expanding stents have shown preliminary suitability for the treatment of vaginal stenosis. Three recipients developed severe acute rejection. Conclusion: The interim results of our study demonstrate mid-term viability in 70% of grafts. The LD UTx produced two live births and the DD UTx produced one live birth. Nulliparous donors should be considered for donation. Uterus transplantation (UTx) has evolved from a purely experimental to a single method of treatment for women affected with absolute uterine-factor infertility (AUFI) ,2,3. AlbOur team obtained approval with the trial from the following institutions: 1) the Ministry of Health, Czech Republic , and 2) the Joint Ethics Committee of the Institute for Clinical and Experimental Medicine (IKEM) and Thomayer Hospital (number 2044/15 (NM-15-01)). We registered the trial at Clinical Trials Database (identifier NCT03277430). The study approval was for 20 UTx, 10 from LDs, and 10 from DDs.The trial was performed at the IKEM hospital, Prague, in collaboration with Prof. Michael Olausson, M.D., Ph.D., from the Department of Obstetrics and Gynecology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. Additionally, a team of obstetricians and gynecologists from University Hospital Motol, Prague, collaborated in the first half of the trial. All retrieval and transplant procedures were carried out by a senior transplant surgeon and principal investigator of the study (J.F.).Requests for participation were received by phone or email. After initial phone screening, candidates were invited for a thorough evaluation. The final decision was made by a multi-disciplinary team. Candidate recipients who had no suitable LD were listed for a DD UTx. The nature of the study, including potential risks, was thoroughly explained to all study participants . Written informed consent with the trial and with the publication of the results of the trial was obtained from all graft recipients and LDs. In the Czech Republic, the principle of presumed consent with regards to multi-organ recovery from DDs applies. Additionally, as of December 2020, there is no special legislation regarding recovery of vascular composite allografts (VCA). However, approval of uterus procurement was still obtained from the DD’s next-of-kin in all cases.Inclusion criteria for a LD comprised the following: female 18–60 years of age, ≤4 childbirths, ≤1 caesarian section, and good general health assessed by the study team. The advantages of a LD are a planned procedure, detailed donor evaluation and selection, and shorter warm and cold ischemia time. On the other hand, it does burden the donor with the surgery, which may also result in complications.A DD provides a graft with longer vascular pedicles and is free of potential complications that could otherwise arise with a LD. After brain death (DBD), all DDs were evaluated according to the protocol of multi-organ donors. No donors are accepted after circulatory death. Eligibility criteria for a DBD comprised the following: general suitability for multi-organ donation, permission for uterus recovery, institutional review board approval, clinical trial registration, AB0 compatibility, negative cross-match, female 18–60 years of age, no previous hysterectomy, and no previous uterus malignancy. The full criteria of uterine DD are published elsewhere . A full Before a uterine donor candidate can be accepted, she requires a thorough assessement, which includes determining the quality of her uterine vessels. At our institution, computed tomography angiography (CTA) is used for evaluation of arteries, i.e., their course, diameter, degree of atherosclerosis, variant anatomy, etc. According to our preliminary results, CTA was insufficient in the evaluation of uterine venous outflow (uterine and utero-ovarian veins) in three of six uterine LD candidates. Whereas all of the LDs were assessed using CTA, only three of five DDs were evaluated in this manner. Magnetic resonance angiography (MRA) is reported to achieve better results in the assessment of uterine veins ,20. HoweInclusion criteria for recipients comprised the following: female 18–40 years of age, absolute uterine factor infertility based on congenital or acquired uterus absence, desire for a child, having a male partner and good general health.The uterus was transplanted in an orthotopic position in the lesser pelvis with arterial and venous anastomoses to the iliac vessels and vaginal anastomosis to the vault of vagina . VasculaTo prevent graft thrombosis, all recipients were treated with low-molecular-weight heparin (LMWH) in a prophylactic dose in the first postoperative month. The recipients received 100 mg of acetylsalicylic acid (ASA) daily during the entire post-transplantation period. The antibiotic/antimycotic/chemotherapeutic prophylaxis comprised ampicillin/sulbactam for 24 h, fluconazole for five days, and trimethoprim/sulfamethoxazole for four months (three times a week). Cytomegalovirus (CMV) prophylaxis follows the recommendation for “surveillance after prophylaxis” , i.e., 9The follow-up comprises four indispensable components: (1) clinical examination; (2) laboratory examination; (3) assessment of perfusion; and (4) cervical biopsies. The frequency of the visits is also defined per-protocol. The recipients are checked twice a week postoperatively in the first month, once every two weeks postoperatively in months 2–6, and once every three weeks in the further course. The visit involves a laboratory examination, a Doppler transvaginal or transabdominal ultrasound (US), and a clinical examination. The clinical examination consists of an abdominal examination and an inspection of the vagina and cervix to look for discoloration or pathological vaginal discharge. Doppler US is a crucial means of follow-up of the perfusion of the graft. Biopsies are planned for postoperative days 7 and 14, and later for months 1, 2, 3, 4, 6, 7, 8, 10, and 12 post-transplantation. During pregnancy, a per-protocol biopsy is performed at the gestational age of 20 weeks. In case of rejection, therapy is determined based on the severity of rejection . While bIn the course of two years, i.e., between April 2016 and April 2018, 10 recipients underwent UTx, namely five DD UTx and five LD UTx. Nine recipients suffered from Mayer-Rokitansky-Küster-Hauser syndrome (MRKHS) type I, and one recipient (Case 7) suffered from MRKHS type II (including agenesis of the right kidney). The mean age of the recipients was 28 ± 3 years . All transplantations were blood type compatible. The characteristics of the recipients and corresponding donors is provided in In all but one case, the arterial and venous anastomoses were sutured on the recipient´s external iliac vessels. In Case 1, the internal iliac artery and vein were used for right-sided anastomoses. In all but one case, the arterial anastomosis of the graft´s uterine artery was performed using the donor internal iliac artery patch or segment. In Case 1, the anastomosis was carried out directly with the ends of the uterine arteries. Venous outflow was established with two anastomoses in five cases: either two uterine veins or two ovarian/utero-ovarian veins depending on vein size, length and quality. All 4 venous outflow anastomoses were able to be carried out in the remaining five cases. In Case 8, an adjunct third venous outflow anastomosis was added to the two previous ones during venous thrombectomy on day 5. Detailed characteristics of the vascular anastomoses are shown in Operative details and post-operative complications including open or vaginal revision surgeries are listed in ©, Dr Franz Kohler Chemie, GmBH, Germany), and excision of all other organs are complete, the uterine graft is finally procured. In this way, there is no prolongation of the cold ischemia time (CIT) of life-saving organs. At the back-table, the graft is inspected for perfusion and vessel quality. A hysteroscopy ex vivo is performed on the bench, and the graft, including the vascular pedicles, is prepared for the transplantation. The mean age of DDs was 40 ± 18 years (range 19–57). The average duration of multi-organ procurement, including uterus, from a DBD was 6 h 46 min ± 65 min .Firstly, a transvaginal ultrasound is performed to rule out any pathologies of the uterus, such as tumors, scars, intraluminal adhesions, etc. . A mediaThe mean age of all donors was 46 ± 14 years (range 19–59) . The meaIn two cases (LD3 and LD5), adjunct oophorectomy needed to be performed in premenopausal women, resulting in preterm menopause, which was treated pharmacologically. Intra-operative iatrogenic ureteric laceration occurred in LD5. It was treated with a suture and placement of a temporary ureteric JJ stent. The LD4 suffered from urinary bladder hypotonia, which resulted in urinary retention. It was treated with a suprapubic catheter, which was inserted under local anesthesia. The urinary retention issue resolved three months after donation. The altruistic LD6 suffered from hypesthesia of the dorsolateral aspect of her calf in the early and late postoperative course. This was attributed to the defective application of compression stockings. The neural defect resolved slowly through therapy with B-complex vitamins. The three-year survival rate of our live donors was 100%.© assay) against human leukocyte antigens (HLA) class I ).Early graft loss occurred twice and mid-term graft loss occurred once. In Case 2, a hematoma was removed and the right uterine artery was re-anastomosed on day 1. Hysterectomy of the graft was carried out on day 7 for an arterial thrombosis that possibly occurred, according to explant histology, in part due to atherosclerosis. In Case 8, removal of a hematoma and thrombectomy of the left uterine vein were performed on day 5. Additional anastomosis of the right ovarian vein was also carried out. However, the graft had to be removed on day 15 due to complete thrombosis. Histology indicated hemorrhagic graft necrosis. Moreover, immunological examination on day 18 revealed positive T-FACS cross-match and the presence of de novo donor-specific antibodies (DSA)  infection of the graft was revealed in a protocol cervix biopsy on day 40. This infection was treated with i.v. and p.o. antiviral drugs. No growth of endometrium was noted on US and menstrual bleeding never started. A graft hysterectomy was performed on day 213 post-transplantation. Histology revealed complete fibrous obliteration of the cervical canal and the uterine cavity due to a possible combination of protracted rejection changes , vascular thrombosis, graft atherosclerosis and HSV infection. The 30-day graft survival rate or technical success rate was 80%, while the one-year graft survival rate was 70%. In Case 5, completion hysterectomy was performed after successful delivery on day 1109 post-transplant. Six of the remaining grafts are currently viable.All recipients are alive to this day. The two-year survival rate for recipients is 100%. To date, recipient Cases 1–4 have survived 4 years, Cases 5–9 have survived 3 years and Case 10 has survived 2.7 years. Details on graft and recipient survival are given in Seven out of 10 of our recipients were born with vaginal agenesis as part of the MRKHS. Six of them underwent a previous laparoscopic Vecchietti procedure in order to create a neo-vagina, and one (Case 5) underwent a dilation procedure. Three recipients were born with a vagina . One specific complication of UTx surgery is a stenosis of the vaginal anastomosis. Details on vagina type and the occurrence of post-transplant anastomotic stenosis are listed in In Case 1, the stenosis was first dilated by a gynecologist on day 267 and hemostasis was carried out for bleeding caused by a cervix biopsy. Additionally, the gynecologist twice attempted to cut and resect the vaginal stenosis using a diathermy knife. After the last attempt, the patient developed vesico-vaginal fistula; the gynecologist recommended a graft hysterectomy. The transplant team, however, disagreed with the plan, and on day 272, vesico-vaginal fistula closure was attempted by applying a suture trans-vaginally. Unfortunately, the fistula did not heal on a permanent urinary catheter. Therefore, open repair of the fistula, stenosis excision and a vaginal re-anastomosis were carried out on day 341. This procedure was performed by a transplant surgeon and an urologist. The patient healed without complications, yet the stenosis recurred and a stent was inserted on day 383. A total of two more stent insertions were required after the second surgery and vaginal re-anastomosis, after which there was no longer a need for a stent. In Case 5, repeated dilation and stenting was necessary only for the first 10 months post-transplantation. A scheduled transplant hysterectomy was performed five months after successful delivery. In Case 6, repeated dilation and stenting is still necessary to this day. In Case 7, a stent was also inserted; however, this graft was removed due to chronic rejection and HSV infection. The stenosis healed two months prior to the hysterectomy. In Case 10, repeated stenting contiues to this day. This recipient became pregnant after her fourth attempt and lost the child at week 20. After this miscarriage, the stent was inserted only once. After couple of weeks there is no need for further stenting, the vaginal anastomosis stays wide open.Uncomplicated urinary tract infection (UTI) was treated with antibiotics in 4 recipients , and repeatedly in Cases 1 and 3. In Case 1, UTI was associated with vesico-vaginal fistula. Description and time occurrence of infections, leukopenia, rejection episodes and renal impairments are listed in Leukopenia episodes were treated by adjusting existing, and potentially myelo-toxic, therapy as well as by administering filgrastim. In Cases 3 and 5, examination of a bone marrow aspirate was performed, revealing post medication suppression.The human leukocyte antigen (HLA) mismatch between the donors and the recipients, percentage of panel-reactive antibodies, acute rejection episodes and their treatments are presented in Chronic kidney disease (CKD) emerged in recipient Case 1 as early as day 27  0.83 mL/s). The patient gradually developed CKD G3A1 caused by nefrotoxic medication including calcineurin. Her renal function is permanently impaired and oscillating. Her maximum serum creatinine was 154 μmol/L and her minimum eGFR was 0.63 mL/s on day 1101. Her pregnancy passed without a significant worsening of CKD.Recipient Case 4 also developed CKD on day 104, and she has sustained a mild reduction in renal function . Recipient Cases 5 and 6 suffered from a decline in renal function on days 502 and 68, respectively, necessitating hospitalization and i.v. fluids. This occurred in both cases, however, during an episode of inter-current gastroenteritis and exhibited a prerenal etiology and temporary nature.©, Chiesi) and TAC twice daily was used in the remaining cases . Gynecologic data on transplanted uterine grafts, embryo transfers, pregnancies and livebirths are presented in All seven recipients with viable grafts underwent embryo transfers (ET). To date, five have become pregnant, of which two have suffered miscarriages (Cases 3 and 10) and three have given birth . There were no congenital malformations nor neonatal complications. None of the recipients are currently pregnant. The medication of the recipients before commencing ET and/or pregnancy was as follows: Case 1: TAC, Pr, AZA, ASA, Case 3: TAC, Pr, AZA, ASA, Case 4: TAC, ASA, Case 5: TAC, ASA, Case 6: TAC, Pr, ASA, Case 9: TAC, Pr, AZA, ASA, Case 10: TAC, AZA, ASA. A prolonged-release TAC once daily was used in Case 1 (EnvarsusIn Case 1 (LD UTx), the ETs were slightly delayed due to a vesico-vaginal fistula which required repair. She underwent seven unsuccessful ETs. Owing to numerous unsuccessful ETs, as well as gaps between the transfers, resulting in prolonged exposure to immune suppression, the recipient developed renal impairment. The eighth ET was successful and she gave birth to a healthy baby girl with a birthweight of 2115 g (10–50 percentile) and an Apgar score of 9-10-10. Apart from gestational diabetes treated with diet and placenta praevia marginalis the pregnancy was uneventful. In Case 3 (UTx from a nulliparous DD), the recipient underwent 11 ETs, three of which resulted in clinical pregnancy; however, all three of these ended in miscarriage for unknown reasons. She also suffered from three episodes of AR, the last of which was severe. In Case 4 (LD UTx), the recipient suffered from no complications at all (except for mild CKD). She underwent 11 ETs but failed to achieve pregnancy. A new ovum pick up will be necessary.Clostridium difficile colitis with perforated appendicitis [In Case 5 (UTx from a nulliparous DD), the recipient suffered from numerous late complications: vaginal stenosis, three episodes of mild AR, four episodes of leukopenia and ndicitis . She gotndicitis . After tIn Case 6 (LD UTx), the patient underwent five ETs. Pregnancy has yet to be achieved. She suffered from both moderate and severe episodes of AR. Surgery to remove intrauterine adhesions is planned.In Case 9 (LD UTx), the patient suffered from one moderate and one severe episode of AR. A third ET led to pregnancy. Pregnancy-associated hypertension was treated pharmacologically. The patient gave birth to a healthy baby girl at a gestational age of 36 + 2, with a birth weight of 2300 g (10–50 percentile) and an Apgar score of 10-10-10. The patient has already undergone 2 ETs with the intention of having a second child.In Case 10 (DD UTx), the recipient had one episode of moderate AR. She went through six ETs. The second ET included two embryos. The fifth ET resulted in pregnancy, but it was discontinued through septic abortion for unknown reason at week 22.In this clinical trial, a total of 20 UTx procedures were intended. Ten of the uterine grafts were meant to be retrieved from live donors and another 10 grafts from deceased donors. The first 10 transplants of this study  were performed in women with MRKH syndrome and comprised five UTx from LDs and five UTx from DDs. Three recipients experienced graft loss. Three live births of healthy children were achieved, comprising two from LD UTx and one from nulliparous DD UTx.n = 10). However, we feel it is relevant to publish interim results since UTx is still a novel and rapidly evolving field. Moreover, the number of child births in our study (n = 3) is still relatively low. One patient had her graft removed after a successful delivery, and another desires a second child and is currently undergoing embryo implants. The remaining four recipients continue receiving embryo transfers. Regrettably, two of them have experienced miscarriages.The present study is limited by the relatively low number of UTx procedures  and Case 8 (LD UTx), graft loss was attributable to early vascular thrombosis occurring on day 7 and on day 15, respectively. In Case 8, additional immunological examination revealed a newly positive T-FACS cross-match and the presence of de novo DSA against HLA class I. This may suggest immune rejection involvement in the development of the thrombosis. The mid-term loss of the third graft on day 213 was related to an HSV infection. Our technical success rate of 80% is consistent with that of other studies ,15,17. TStricture of vaginal anastomosis was a relatively common and protracted complication. It occurred in five of eight (63%) viable grafts. None of the pre-transplant vagina type in MRKHS, i.e., own vagina, dilation or the L-Vecchietti procedure, seemed to affect the incidence of post-transplant stenosis. In fact, stenosis occurred in all three of these scenarios. However, stenosis occurred in all there recipients with a vagina , in only one recipient after dilation (Case 5), and in only one out of four recipients who received the L-Vecchiettti procedure (Case 6). The other there recipients who received an L-Vecchiettti neovagina were stenosis-free. The presence of stenosis did not appear to significantly affect conception or pregnancy outcomes. Indeed, all recipients who gave birth had stenosis . Nevertheless, the number of procedures is too low to be able to draw a definitive conclusion.In the largest series worldwide the rates of mid-term graft viability, vaginal strictures, graft rejections, miscarriages and livebirths were as follows. In the pioneering Gothenburg (Sweden) cohort by Brannstrom, Olausson et al., nine LD UTx have been accomplished and two graft losses have been experienced, one due to thrombosis on day 3 and another one due to infection on day 105 . During In a subsequent Gothenburg study, 8 robot-assisted uterus retrievals from LDs andopen UTx have been carried out, with mid-term viability in 6/8 cases . The intIn the study by Brucker et al. , four LD UTx have been done with full mid-term graft viability in all of the four recipients. Stenosis of the vaginal anastomosis was not reported and mild graft rejection happened in 2/4 recipients. One early miscarriage at eight weeks and four days of pregnancy was experienced. Two livebirths have been achieved in the first two UTx women, including the one with previous pregnancy loss .In the DUETS  by Testa, Johannesson et al., 20 UTx have been performed, 18 from a LD and two from a DD. Six graft failures occurred. In phase 1, 50% of recipients (5/10) had a technically successful uterus transplant, compared to 90% (9/10) in phase 2 . In the One challenging aspect of UTx is utilizing nulliparous grafts for transplantation. The main issue is that these grafts have yet to prove their ability to promote childbirth. The first UTx from a DD ever had used a nulliparous DD and this was also the second UTx case worldwide, carried out in Turkey in 2011. This case produced clinical pregnancy but ended in a miscarriage ,32. In oRecently, more than 70 UTx procedures and 23 livebirths from 21 recipients have been reported worldwide . UterineTen uterus transplants, comprising five from live donors and five from deceased donors, have been performed at our institution. The interim results of our study demonstrate mid-term viability for 70% of uterine grafts. Stenosis of vaginal anastomosis was encountered in five of eight grafts (63%) and necessitated repeated stent placement. Immunosuppression side effects, infectious complications and recurrent rejection may have led to significant delays in embryo transfers.Three live births have been achieved, two from a LD and one from a nulliparous DD. Therefore, nulliparous donors can be considered for donation; however, their potential needs further clarification. We also observed four miscarriages in two recipients. This preliminary report gives further evidence of the feasibility of uterine transplantation from both living and deceased donors.


Hyperspectral cameras onboard unmanned aerial vehicles (UAVs) have recently emerged for monitoring crop traits at the sub-field scale. Different physical, statistical, and hybrid methods for crop trait retrieval have been developed. However, spectra collected from UAVs can be confounded by various issues, including illumination variation throughout the crop growing season, the effect of which on the retrieval performance is not well understood at present. In this study, four retrieval methods are compared, in terms of retrieving the leaf area index (LAI), fractional vegetation cover (fCover), and canopy chlorophyll content (CCC) of potato plants over an agricultural field for six dates during the growing season. We analyzed: (1) The standard look-up table method (LUTstd), (2) an improved (regularized) LUT method that involves variable correlation (LUTreg), (3) hybrid methods, and (4) random forest regression without (RF) and with (RFexp) the exposure time as an additional explanatory variable. The Soil–Leaf–Canopy (SLC) model was used in association with the LUT-based inversion and hybrid methods, while the statistical modelling methods (RF and RFexp) relied entirely on in situ data. The results revealed that RFexp was the best-performing method, yielding the highest accuracies, in terms of the normalized root mean square error (NRMSE), for LAI (5.36%), fCover (5.87%), and CCC (15.01%). RFexp was able to reduce the effects of illumination variability and cloud shadows. LUTreg outperformed the other two retrieval methods (hybrid methods and LUTstd), with an NRMSE of 9.18% for LAI, 10.46% for fCover, and 12.16% for CCC. Conversely, LUTreg led to lower accuracies than those derived from RF for LAI (5.51%) and for fCover (6.23%), but not for CCC (16.21%). Therefore, the machine learning approaches—in particular, RF—appear to be the most promising retrieval methods for application to UAV-based hyperspectral data. By provRemote sensing observations are valuable data sources for mapping the spatial variation of crop traits in precision agriculture applications . In partprior information on the cross-correlations between variables , which can be collected from field measurements, may reduce the probability of unrealistic parameter combinations and simulated spectra [Over the last few decades, a diversity of retrieval methods has been developed, enabling the conversion of reflectance data into certain variables of interest. These models can be broadly classified into three general categories: Physically based, statistical, and hybrid methods. The physically based methods , are considered generic, transferable, and independent of in situ measurements –26. Howe spectra .Progressing along this line, a comparison of the LUTreg method with other retrieval methods is needed. Statistical methods involve parametric and non-parametric regression methods ,33. MachThe last category of retrieval methods is hybrid methods, which use ML algorithms for training spectra simulated by an RTM. Hybrid methods appear promising, as they combine the universality and robustness of physical models with the advantages of non-parametric methods  –42. To tA specific challenge arises when UAV-based hyperspectral data are acquired under sub-optimal illumination conditions, a condition that is quite abundant in, for instance, Central and Northern Europe, featuring a mixture of full or partial cloud cover and clear sky during a growing season. Indeed, processing such images taken under variable illumination throughout the flight mission and the growing season of the crop is not straightforward. In the studies of ,57, addiOverall, this study set out to investigate the performances of statistical, physical, and hybrid methods under variable illumination for estimating LAI, fCover, and CCC from UAV-based hyperspectral data throughout the growth cycle of a potato crop. The following specific objectives are addressed: (1) To test whether the regularized LUT (LUTreg) developed in the study of Abdelbaki et al. , which w22.1Solanum tuberosum L. cv. Victoria) was cultivated in the spring/summer season of 2016. The predominant soil type was sandy loams. The annual mean temperature and annual total precipitation of the study area were 9.8 °C and 865 mm, respectively. Six field sampling dates for the potato crop were conducted, from 8 July to 10 August, resulting in 156 sampling plots . The Formula (1) developed by [−2) for each plot was then determined, using multiplication between LAI and LCC values at leaf level. Non-destructive measurements were taken in the center area of each plot, along with the positions , in ordeloped by  was appl2.22 on the ground was observed. For each plot, the eight measurements at distributed positions  at six critical growth stages with a spectral range from 300–2500 nm. The spectral resolution of 2151 spectral bands was 3 nm between 350–1000 nm and 10 nm between 1000–2500 nm. Before carrying out the UAV flight missions, ASD canopy measurements were taken at nadir position under direct solar illumination between 10:30 a.m. and 2:00 p.m. (one hour before and after on the same day of the flight mission), from a distance of about 80 cm above the canopy. With a field of view of 25°, a footprint diameter of 0.88 cm2.3Prior to the hyperspectral flight missions, a fixed-wing aircraft UAV (Mavinci Sirius) equipped with a Real-Time Kinematic Global Positioning System (RTK-GPS) was flown over the larger study site, in order to obtain a reference orthomosaic of the area, on 22 June 2016. The UAV contained an RGB-camera. The captured images were processed to an orthomosaic, using the Agisoft Photoscan Processional software , to a final ground resolution of about 1.7 cm and registered to the local Universal Transverse Mercator Zone 31 North projection based on the World Geodetic System (UTM 31N WGS 1984).xy of 0.78 to 0.14 pixels.Aerial images for the experiment were acquired during six flight missions in 2016  using an2 and the residuals (RMSE) (2 = 0.99 (RMSE = 0.01) for the reference date (19 July) to R2 = 0.94 (RMSE = 0.04) for the following flight date (27 July), where the lightning conditions varied the most, indicating mean reflectance errors below 2% vs. 5% for the worst case.In our campaigns, UAV data acquisition was carried out bi-weekly and deliberately under less favorable illumination conditions . For rads (RMSE) . All hypWhen comparing the canopy reflectances collected in situ to the spectra obtained by the OXI VNIR-40 sensor, spectral shifts between distinct spectral features were detected due to a broad bandwidth of the spectral bands of Gamaya-camera. The resulting overlap of individual spectral bands results in spectral smoothing and a related shift in spectral band positions. This is comparable to a low-pass filtering effect, as shown in 33.1The Soil–Leaf–Canopy (SLC) model , a combiCv is the vertical crown cover, –k*LAIe is the gap fraction following the Lambert–Beer law, and k is the extinction coefficient in the vertical direction, which depends on the leaf inclination distribution function (LIDF) and the viewing angle (Θ).SLC simulates canopy reflectance over the spectral range between 400 and 2500 nm with a spectral resolution of 1 nm. The PROSPECT-4 model simulates directional-hemispherical reflectance and transmittance for a single leaf. The input variables of the model are the leaf structure parameter (N) and leaf biochemical constituents, including leaf chlorophyll content (LCC), leaf dry matter content (Cm), leaf water content (Cw), and leaf senescent matter content (Cs). The 4SAIL2 model, which is an amended version of the turbid medium SAIL model, simulates the top of the canopy reflectance. This model is a function of a series of variables: The fraction of brown canopy area (fB), the dissociation factor (D), hotspot (hot), tree shape factor (Zeta), crown cover (Cv), leaf area index (LAI), and leaf inclination distribution function (LIDF a and b). The latter three input variables were used to retrieve the effective fraction of vegetation cover (fCover) , as follprior knowledge from ground measurements, while the remaining parameters were fixed based on the literature, as shown in Moreover, in the Hapke model, the soil moisture content (SM) and Hapke parameters are employed for simulating soil spectra . To observe the geometry of the UAV image capture, the observed zenith angle (tto), relative azimuth angles (psi), and the solar zenith (tts)  parameters were fixed at nadir-viewing position under different field conditions. To constrain the inversion result, the ranges and distributions of free variables  were set, based on 3.2Extending a previous study , we gene(1)Initialize the number of canopy simulations  and the number of correlated variables with their normal distributions.(2)Generate the Latin Hypercube Samples (Z) with the size of n × 3, considering the number of canopy simulations (n) and correlated variables (3) that divide into samples (N), with the same probability of 1/N, and selecting one sampling value of these samples in each partition randomly .(3)m), following the size of the correlated variables.Define the correlation matrices between three measured variables (M) and between the generated values of LHS ((4)L) of the measured variables by using the Cholesky decomposition method (LU) for the correlation or covariance matrix (M), which satisfies: (3 × 3)M is a Hermitian positive-definite matrix, which is decomposed into lower triangular (L) and upper triangular (TL) matrices; R is the correlation coefficient used in the correlation matrix M; iσ is the standard deviation of the variable ix; ρi,j is the covariance between xi and j; a, c, and d could be positive, negative or zero values of off-diagonal values; and b and e cannot be equal to zero.Calculate the non-singular lower triangular matrix ((5)Q) from the correlation matrix of the LHS realizations ((3 × 3)m): Calculate the non-singular lower triangular matrix ((6)Z) to a new matrix, denoted Z1, with size n × 3. Simulate the correlated random variate, which is based on transforming the realization matrix of LHS ((7)Z1 to the normal distribution function, as defined before for three variables (Step 1). Then, each product of Z1 represents (Z1i) for LAI, (Z1j) for Cv, and (Z1k) for LCC.Convert the uniform correlated variables of The detailed calculations of the proposed algorithm used in LUTreg are described as follows, broken down into steps and implemented using Matlab 2019: More details about the implementation of the Cholesky method (LU) and LHS to correlate the model inputs using the ground measurements can be found in ,76. The As the fully green leaves of the potato crop were observed during the growing season, the value of the fraction of the brown canopy area (fB) was set to zero . Further3.33.3.1The retrievals from LUTstd- and LUTreg-based inversion were applied to 156 of the measured spectra recorded during six observation dates and at three levels of nitrogen fertilizers . The RMSE was used as a cost function, in order to find the closest match between measured and simulated spectra, sorted in ascending order . Consequently, the best single solution for a possible variable combination could be defined. However, this solution may not be the unique or optimal result, due to measurement errors and model inadequacies . In an a3.3.2From the available ML algorithms in the MLRA toolbox of ARTMO, three approaches were selected for this study . They we3.3.3Among the ML methods presented in With the aim of reducing the effect of spatial autocorrelation in the 156 experimental samples, leaving-one-sampling-date-out (n = 27) as unseen data in the model was performed. This procedure was repeated for each sampling date, except for the first date, in which the model was trained with 135 samples and the remaining samples (n = 21) were used for validation. As image acquisition in our experiment was carried out under varying illumination conditions, the exposure time was added as independent variable besides the measured spectra to estimate LAI, fCover and CCC variables during the training process (hereafter denoted as RFexp).3.42), normalized root mean square error (NRMSE (%), and root mean square error (RMSE) , the paired t-test was applied to evaluate the significance between different levels of nitrogen. The LAI, fCover, and CCC estimates obtained from the retrieval methods were assessed using common statistical indicators, such as the coefficient of determination (Rr (RMSE) . In hybrAfter evaluating the accuracy assessment between retrieval strategies, the best method was used to map the canopy traits using UAV images taken under sunny conditions (19 July). The non-potato crop area (soil and weeds) was manually masked out from the UAV image using the ENVI software . Then, the best retrieval method was applied to the potato crop-masked images.44.14.22 and NRMSE (%), improved in the estimated LAI and CCC; however, when increasing the level of nitrogen (N280), their accuracies started to decrease. For fCover, the observation was the same as LAI and CCC, where the accuracy improved slightly in LUTreg with the standard level of nitrogen (N180).The performance of LUTreg was evaluated against LUTstd, in terms of LAI, fCover, and CCC predictions, using the whole data of the six observation dates. At different levels of nitrogen fertilization, there was a clear enhancement in LAI and CCC estimates, but not for the fCover estimate  and 4. Wp < 0.05. Significant differences between the three levels of nitrogen in the estimations were also found between LUTreg and LUTstd. Using the paired t-test, significant differences between the two types of LUTs (LUTstd and LUTrteg) were observed for LAI, fCover, and CCC, at 4.32 = 0.70, NRMSE% = 9.80). RF and CCF were the best methods for fCover and CCC when using sample sizes of 500 and 1000, respectively .The validation accuracies of the predicted LAI, fCover, and CCC by the three selected ML methods are presented to identify the best method  and bestThere were significant differences between the three hybrid methods (p < 0.05) for LAI (0.0045), fCover (0.00063), and CCC (0.000018). When assessing the significant difference between LUTreg and the best method of ML, LAI did not show any difference (0.082), as compared to fCover (0.00013) and CCC (0.0000029). Scatter plots  show tha4.4RFexp > RF > LUTreg > hybrid; while the performance of the methods for CCC were ordered as LUTreg > hybrid > RFexp > RF.The results presented in Variation in illumination obviously impacted LAI retrieval, where sunny days were always the best for statistical methods, as compared to other dates. However, when comparing two methods (LUTreg and hybrid methods) under cloudy conditions and at the maturity stage (5 August), the best accuracy of LAI was obtained from LUTreg. For CCC, the result was inconsistent with LAI using statistical methods (RF and RFexp), where the CCC estimates were not optimal under sunny conditions. Nevertheless, when using hybrid and LUTreg methods, the results of CCC obtained on the date of 5 August were of the same order of magnitude as those obtained for LAI. In the last case for fCover, it was shown that on 27 July (under cloudy conditions), four methods  yielded the best accuracy, as compared to other observation dates. A final observation was that on 10 August , the accuracy of all estimates  started to degrade, which was remarkably consistent with the measured data. 55.1In this study, the regularization scheme based on the introduction of variable correlation obtained from the in situ data into LUTreg successfully improved the estimated variables of LAI and CCC at different levels of nitrogen, as compared to fCover . The res2 = 0.83 and NRMSE =10.46%) was slightly increased in LUTreg, as compared to LUTstd (R2 = 0.80 and NRMSE = 13.13%). However, for LAI and CCC obtained from LUTreg, the accuracies were considerably improved  rather than LUTstd . LUTreg underestimated the high values of LAI (above 4) due to saturation; this result is in line with previous studies [When combining the whole data from the six observation dates , the imp studies ,89,90. T studies ,72.2 = 0.73 and NRMSE = 13.87% for LAI, R2 = 0.74 and NRMSE = 14.99% for fCover, and R2 = 0.64 and NRMSE = 16.11% for CCC. The accuracy of our results was higher than that in previous studies [2 = 0.70 and NRMSE = 17.85%) did not show any improvement, as compared to the estimated LAI and CCC (R2 = 0.71 and NRMSE = 25.57% for LAI and R2 = 0.70 and NRMSE = 14.01% for CCC). The reason for having a low improvement for estimated fCover in the present study might be that the value of the LIDF parameter was fixed in LUTreg, in order to simplify the model parameterization and avoid confusion with other free variables. The LAI and Cv variables used for quantifying fCover have a great influence on the NIR spectrum and, thus, a problem of the linear spectral mixture of soil and canopy might be introduced in the model simulation for certain plots [On the date of 19 July, under sunny conditions, using UAV-based hyperspectral data , the acc studies . The latin plots . Moreovein plots  on 19 Ju5.2For the crop growing season, over the six observation dates, retrieval methods  were used to predict crop traits. There are three aspects that cannot be differentiated to study their effects on the estimates: Illumination variation, growth cycle, and the variation in structural crop traits .2/m2) with a previous study [2/m2).For LAI, statistical methods (RF and RFexp) performed optimally under sunny conditions, as compared to LUTreg inversion and hybrid methods. However, under cloudy conditions and the late growing season dates (5 and 10 August), LUTreg inversion turned out to be the best. This indicates that statistical methods (RF and RFexp) were apparently more affected by illumination conditions than LUTreg and hybrid methods. The results obtained from statistical method (RFexp) under sunny conditions was consistent (NRMSE = 9.33% and RMSE = 0.32 mus study  that use2 = 0.46), as compared to other studied plant types [The best prediction for fCover was observed for the RFexp method under partial cloud conditions (27 July) at the maturity stage. Furthermore, the other methods  also yielded the best predictions on that date (27 July). This observation was contradictory with our expectation, as the prediction of fCover under sunny conditions was not the best. This might partly be attributed to the impact of phenological growth stages on crop status, rather than the cloud shadow effect. In fact, using any retrieval method can yield an accurate result when the crop is near closure and covering the soil background. Our results (RMSE below 10%) are supported by a similar retrieval study , where tnt types .prior knowledge from previous studies to fix some parameters (as mentioned previously) was possibly anticipated to lead to some error in the simulated canopy spectra. Thus, tuning the model input parameters  are of tremendous importance in improving canopy simulations during crop development over time, as UAV-hyperspectral data has a very high spatial resolution (1.7 cm). At this resolution, the shadowing effect, cast by plants, and the bidirectional effects might have an impact. (2) ML optimization. Each algorithm has its own set of parameters; thus, they need to be tuned properly to having optimal values. Finally, (3) the proper choice of RTM type. The SLC radiative transfer model treats the canopy as a horizontally uniform layer (1D), which does not accommodate for row crops and, as such, does not fully represent the actual canopy architecture of the row-structure of the potato crop. For instance, at the early stage, the soil background is exposed within a plot, representing the vegetation in both the row and the furrow [2 and NRMSE (%), as opposed to CCF, when used for the CCC estimate. The latter might be affected due to the limitation of ML methods in predicting vegetation variables from remote sensing data [We noticed that across the observation dates, LUTreg inversion could deliver generally superior results, compared to the hybrid method. These findings were expected, as a hybrid method inevitably is influenced by confounding factors, including: (1) LUT parameterization. Using e furrow ,93. Howeing data ,94 or thThe results revealed that the four retrieval methods—hybrid, LUTreg, RF, and RFexp—were affected by two main issues. First, the measured spectra still had some uncertainties. These may have originated from: (1) The exposure time, which was not adjusted carefully during the cloudy dates ; (2) instrumental issues, such as the spectral correction method was carried out for Gamaya to reduce the mismatch between ASD FieldSpec3 and UAV spectral band positions. This correction was not an accurate solution to solve the overlapping bandwidths of the spectral bands of Gamaya sensor. Second, uncertainties could have been induced by the in situ measurements. Using the LAI-2000 instrument often leads to LAI underestimation in the case of potato crops, as this instrument relies on several assumptions  which we6The purpose of this study was to evaluate the LUTreg-based inversion method integrated with the Cholesky method, in terms of providing improved LAI, fCover, and CCC estimates for a potato crop using UAV-hyperspectral data through the crop’s development over time. This study built upon an earlier study , where tOur major finding was that the LUTreg method was able to improve the accuracy of LAI and CCC, either using the whole data of the six observation dates or under different levels of nitrogen. However, the estimated fCover from LUTreg was improved slightly, as compared to LUTstd. Moreover, at each growth stage, LUTreg delivered superior accuracies, compared to the hybrid method. However, when comparing LUTreg with the statistical method (RF), the accuracy of LAI and fCover estimates decreased, while that for the CCC estimate did not. Finally, the use of exposure time as an explanatory variable in the RF method (RFexp) was successfully able to alleviate the influences of illumination variability during image acquisition and decreasing the errors in all predictions .These findings open an avenue for further studies. The use of the Cholesky method in LUTreg using PROSAIL , INFORM Appendix