Finally, we show its efficacy to discriminate between the two groups and demonstrate competitive performance in comparison to existing deterministic and probabilistic biomechanical indices.We present a data-informed, highly personalized, probabilistic approach for the quantification of abdominal aortic aneurysm (AAA) rupture risk. Our novel framework builds upon a comprehensive database of tensile test results that were carried out on 305 AAA tissue samples from 139 patients, as well as corresponding non-invasively and clinically accessible patient-specific data. Based on this, a multivariate regression model is created to obtain a probabilistic description of personalized vessel wall properties associated with a prospective AAA patient. We formulate a probabilistic rupture risk index that consistently incorporates the available statistical information and generalizes existing approaches. For the efficient evaluation of this index, a flexible Kriging-based surrogate model with an active training process is proposed. In a case-control study, the methodology is applied on a total of 36 retrospective, diameter matched asymptomatic (group 1,  An abdominal aortic aneurysm (AAA) is a slowly progressing vascular disease, causing an enlargement of the infrarenal aorta and is considered pathological if the aortic diameter exceeds 30 mm . AAA preTo prevent such a disastrous scenario, the clinical guidelines from the US-based Society for Vascular Surgery recommend elective repair for AAA patients with an aortic diameter greater or equal to 55 mm, regular screening intervals for patients with smaller-sized AAAs and one-time screenings for AAAs in men and women above a certain age and based on established risk factors . This maNonetheless, a biomechanical rupture risk assessment can provide an additional important piece of information. It enables the possibility to provide patient-specific screening guidelines, avoid unnecessary interventions  and suppIn this paper, we present a highly personalized, probabilistic framework for the biomechanical quantification of AAA rupture risk. The framework builds upon a comprehensive database, consisting of tensile experiments that were carried out on 305 AAA tissue samples from 139 patients and corresponding non-invasively and clinically accessible patient data. The approach consistently incorporates the available statistical information in terms of probability distributions in order to account for patient-specific uncertainties about relevant vessel wall properties. We emphasize the importance of accounting for these uncertainties and demonstrate that this leads to a more accurate individualized rupture risk assessment as compared to deterministic approaches.Our work builds upon previous efforts by our group and collaborators regarding the biomechanical modeling and characterization of AAA in-vivo behavior –16, as wn = 18) and known symptomatic/ruptured  cohort of AAA patients.The paper is organized as follows. Motivated by a failure-based criterion, our rupture risk index is formulated in Section 2.1 incorporating patient-specific statistical information. Section 2.2 defines the biomechanical AAA model and specifies the probabilistic regression model to obtain personalized vessel wall properties. In Section 2.3, a method for the efficient evaluation of the rupture risk index is proposed and in Section 3, the framework is applied on a total of 36 retrospective, diameter matched asymptomatic  and the failure criterionFrom a mechanical point of view, rupture represents an event of local material failure at a point σ(x) exceeds the local wall strength σγ(x). This results in the failure function φ(x) = σ(x) − σγ(x), or the criterionWe limit ourselves to stress-based failure and define rupture as an event where the local wall stress measure vm(x) as the local stress measure σ(x) and an assumed spatially constant wall strength σγ, this criterion can be evaluated asUsing the equivalent von Mises stress σIt is important to note that the above definition does not incorporate any aspect about failure over time. In order to be able to include time in the analysis, i.e. to make a statement about the risk of rupture in the next year, one would require knowledge about the future progression of the AAA for this patient, such as a model for the aneurysm growth and change in vessel wall properties. Since there is hardly any knowledge about these aspects, we limit the further discussion to a rupture risk assessment at the point of time of the acquired data. While there are sudden events like calcification-induced formation of saccular aneurysms, we assume that in most cases an AAA is a slowly progressing disease and thus our approach has, at least for the near future, sufficient predictive capability.d to a critical maximum diameter dmax. While established in clinical practice and easy to apply using CT or ultrasound imaging, this criterion has often been criticized T, i.e.Θi ∼ p(log Θ), i = 1…neval.Since the calculation of the probability of rupture wall cf.  and alloThe Kriging surrogate training process is carried out under the following two demands:As few as possible high-fidelity model evaluations.Ensure that the Kriging model is accurate where necessary.p(log Θ) is the patient-specific probability distribution for the invasive model parameters Θ = T from the regression model in Section 2.2.4. The reasoning behind this choice follows from the ALM approach, where only the predictive standard deviations p(log Θ), so we can attribute a higher weight to the more probable regions in Θ. Additionally, we pay special attention to points in the input space, where the predicted maximum von Mises stresses θnext for evaluation results in the optimization problemTo that end, we adopt and extend the Active Learning-MacKay (ALM) strategy from  and chooθnext = T can then simply be extracted from Θnext. During the active learning, we monitor the averageThe next evaluation point Based on our retrospective AAA database of non-invasive and invasive data pairs and a multi-output Gaussian process model fitted to this dataset (cf. Section 2.2.4), the necessary steps to estimate the probability of rupture for a prospective patient are:Step 1: Data generation in the clinic: CT imaging, determination of the non-invasive parameters ξ from Table 2Step 2: Geometry creation: segmentation and meshing of the AAA geometryStep 3: Model specification: modeling of the invasive properties Θ(ξ) using the multi-output Gaussian process model from Sections 2.2.4 and A.1.Step 4: Surrogate training: fitting of the Kriging model using active learningStep 5: Post-processing: estimating the probability of ruptureξ are available for a prospective patient by using cohort statistics  without personalization. The computational procedure is summarized in Algorithm 1. In practice, it has proven feasible to choose ninit = 8 , ngrid = neval = 10, 000 and tol = 1.0 × 10−4.While CT imaging is essential for geometry creation, the rupture risk analysis can also be carried out if no non-invasive properties Algorithm 1 Calculating the probability of rupture Input: Input uncertainties p(logΘ(ξ)), simulation model ninit, ngrid, neval1: 2: Set ninit samples 3: Generate 4: Train a Kriging surrogate using the training data 5: Create a grid  ψ^1 cf. whiledo6: θnext using 7:  Determine 8:   Update the Kriging model with the new data point 9:  Set iter = iter + 1end while10: neval samples 11: Generate Output:12: p(logΘ):Before demonstrating the framework in full detail, a brief comparison between the multi-output Gaussian process regression model (cf. Section 2.2.4) with existing probabilistic modeling approaches used in the context of AAA rupture risk is provided. To that end, we employ leave-one-out-cross-validation (LOOCV) on our dataset κ ∈ {t, α, β, σγ}. This corresponds to the approach chosen in . Co. Co45]. tion cf. .dmax, is one important non-invasive parameter in our framework (cf. Section 2.2.3), we emphasize that its influence has been rendered ineffective through the study design. A similar trend as in The obtained values for the relative mean and median differences in The maximum diameter criterion, by design, clearly fails to separate the two groups in all our comparisons.The proposed index The results indicate that the more statistical information taken into account, the better the capability to distinguish between group 1 and group 2.n = 18, group 2: n = 18) and the database described in Section 2.2.3. Second, there was no matching based on other risk factors such as sex, age or family history, which could be a confounder. Third, since we only have access to electively repaired or symptomatic/ruptured AAAs for mechanical testing, the mean diameters of the two groups  are larger than the Society for Vascular Surgery’s decision criterion for elective repair (55 mm) T, with Kriging can be regarded as a special case of a Gaussian process, where data points are assumed noise-free to interpolate the high-fidelity model at the provided high-fidelity evaluations. To find an adequate function for this purpose, we use a Kriging interpolation model that incorporates explicit basis functions as described in . Using sb and B can be resolved, if a vague prior for η is chosen, i.e. if the limiting case is considered, where B−1 approaches the zero matrix 0. In that case, the predicted mean for a new point θ⋆ becomesr = h⋆ − HK−1k⋆ and neval model evaluations. K is the data covariance matrix between all The dependence on the prior parameters k⋆ is a vector with the covariances between training and test points, i.e.H a matrix containing vectors h(θ) at all training data points and h⋆ = h(θ⋆). It is interesting to note, how the terms in Eqs k⋆ is a vodel cf.  and for S1 TableComputed correlations between all non-invasive and invasive AAA vessel wall properties using Spearman’s rank correlation coefficient.(CSV)Click here for additional data file.S2 TableDataset containing the eight non-invasive AAA vessel wall properties: maximum AAA diameter, maximum thrombus thickness, AAA length, subrenal diameter, thrombocytes, hemoglobin, mean corpuscular hemoglobin (MCH), mean corpuscular volume (MCV). All values are provided in their respective unit as specified in (CSV)Click here for additional data file.S3 TableDataset containing the four invasive AAA vessel wall properties: wall thickness, alpha stiffness, beta stiffness, wall strength. All values are provided in their respective unit as specified in (CSV)Click here for additional data file.S4 TableComputed true positive rates (TPR), false positive rates (FPR) and corresponding threshold values for (CSV)Click here for additional data file.


Principal component analysis showed possible clustering according to polyphenol amounts. According to the observed diversity of quality traits and bioactive polyphenol contents, the traditional varieties have potential for consumer acceptance and increased cultivation.Earlier studies suggested that traditional apple varieties have quality traits well accepted by consumers and beneficial effects on human health. The aim was to collect 25 traditional apple varieties grown in Croatia and to determine, for the first time in so many details, their external , internal quality traits , and seed characteristics. In addition, individual polyphenols were determined in the flesh and peel, by using RP-HPLC. All was compared to the commercial variety ‘Idared’. Quality parameters of these varieties were similar to those of the commercial variety. The flesh and peel contained flavan-3-ols, dihydrochalcones, phenolic acids, and flavonols, while anthocyanins were additionally found in the peel. Total polyphenols in the peel (536–3801 mg kg Apples are among the most consumed fruits in the world. After China, the European Union is one of the biggest producers of apples. Inside the EU, the biggest producers are Poland, Italy, France, and Germany . The totOver the centuries, numerous different varieties of apples with different quality traits have been developed . QualityThe quality traits of commercial apples grown in large quantities such as ‘Fuji’, ‘Golden Delicious’, ‘Gala’, and ‘Idared’ are well known ,18,19 anThere is much that remains unknown about these old, traditional, indigenous varieties. Quality indicators are not as well known for these varieties as they are for the commercial varieties ,18,19. RThe importance of old varieties of apples can be seen from studies that highlight their beneficial effects when compared to those of commercial ones. Namely, old apple varieties have been mentioned as varieties better tolerated than new varieties by individuals who have developed intolerance to apples . FurtherThe aim of this study was to collect traditional, indigenous apple varieties grown in orchards in Croatia and determine their internal and external quality parameters . To the best of our knowledge, the quality traits of these apples were not previously studied. The other aim was to determine the amounts of the various polyphenolic compounds found in the peel and flesh of the apples. For some of the varieties studied here, polyphenols have never been studied before. All of the quality parameters and polyphenols were compared with the commercial cultivar ‘Idared’.p-coumaric acid (C9008), and chlorogenic acid (C3878), and from Extrasynthese  procyanidin B1 (0983), procyanidin B2 (0984), quercetin-3-O-galactoside (1027 S), quercetin-3-O-rhamnoside (1236 S), phloretin-2’-O-glucoside (1361 S), phloretin (1360 S), cyanidin-3-galactoside chloride (O923 S). Orto-phosphoric acid (85% HPLC-grade) was from Fluka . Methanol (HPLC grade) was from J.T. Baker .Polyphenol standards were purchased from several firms. From Sigma-Aldrich , the standards obtained were (+)-catechin hydrate (C1251), (-)-epicatechin (E1753), quercetin dihydrate (Q0125), quercetin-3-rutinoside hydrate (R5143), quercetin-3-glucoside (17793), Around two kg of each variety of apple were harvested from orchards in Croatia  owned by Mirko Veić, and orchards in the town Gornji Tkalec (45°58’24.0”N 16°27’12.0”E) and the village Rude (45°46’35.6”N 15°40’35.8”E)) in the following varieties: ‘Crvenka’, ‘Crveni Boskop’, ‘Pisanika’, ‘Lještarka’, ‘Božićnica’, ‘Božićnica 2’, ‘Cox’s Orange Pippin’, ‘Ivanlija’, ‘Boskop’, ‘Bobovac’, ‘Slavonska srčika’, ‘Kolerova srčika’, ‘Batulenka’, ‘Gravenstein’, ‘Kandil Sinap’, ‘Citronka’, ‘Zimnjara’, ‘Zlatica’, ‘Mašanka’, ‘Kanada’, ‘Gloria Mundi’, ‘Zelenika’, ‘Krastavka’, ‘Adamova zvijezda’, and ‘Wild apple’. The variety Božićnica was harvested from two orchards , so the names used here are ‘Božićnica’ and ‘Božićnica 2’, respectively. The commercial variety ‘Idared’ was harvested in the orchard in the village of Rude (Samobor). This variety was chosen because it is one of the most often grown and popular commercial varieties in Croatia and in this study it served as a control. Apples of each variety were divided into two groups. The group of apples for quality determination consisted of approximately one kg of each variety. It was transferred to the laboratory of the Faculty of Agriculture, University of Zagreb, and kept in a refrigerator before internal and external quality traits were determined. The other group of approximately one kg of each variety was transferred to the laboratories of the Faculty of Food Technology, University of Osijek, where they were kept in a refrigerator for no more than 5 days before extracts for the determination of polyphenolic compounds were made. Polyphenol contents of the varieties ‘Bobovac’, ‘Batulenka’, ‘Mašanka’, and ‘Krastavka’ were never studied before to the best of our knowledge. The quality traits and polyphenolic compounds of all varieties were never studied before in so much detail.All apples (5 to 10 apples) in the quality determination group were measured for weight, height, and width. The weight of the fruit was determined by a digital two-decimal laboratory scale  and expressed in grams (g). The height and the width of the fruit were measured by the digital caliper , and the values were expressed in millimeters (mm). From the height and width measurements, the fruit shape index was represented by the ratio of height/width. Means were calculated for each variety. Color was measured according to CIE Lab system on a colorimeter . Colorimeter calibration was done with black and white plates supplied with the instrument.−2. These apples were then cut in half and the starch index was determined by using a starch-iodine test. Apples were then peeled, cut, and cored, and the seeds were removed from cores. The seeds were used for seed characteristic determination and the remaining apple flesh was mixed and squeezed to get juice. The juice was used for internal quality determination. A refractometer  was used to measure the soluble solid concentration in apple juice and expressed as °Brix. Furthermore, total acids were measured with titration by neutralization with 0.1% NaOH. Data were expressed as % of malic acid. Additionally, the pH was measured with pH meter  in the apple juice. All results were expressed as means ± standard deviations.All apples (5 to 10 apples) in the quality determination group were examined to determine their firmness by using a penetrometer  with 11 mm probe as the average value from four measurements made at opposite fruit sides at the equatorial fruit zone. The values were expressed in kg∙cmAll seeds were inspected to determine which were healthy and which were bad seed, and the number of each were counted. The resulting total seed number per apple, total healthy seed number per apple, and bad seed number per apple were expressed as mean ± standard deviation. For each apple, each seed as well as all seeds were weighed by the four-decimal analytical scales . The total seed weight and one seed weight were expressed as means ± SD.For the apples in the polyphenol determination group (about one kg of each variety), the procedure was as follows. The apples were peeled with a commercial peeler. For each variety, the peel was pooled together and homogenized with the help of a coffee grinder. The remaining apple flesh was then cut into four equal parts, the seeds were removed and the flesh pieces were pooled together. The flesh was homogenized with a stick blender. These samples were frozen, stored at −18 ° C and extracts were made the second day.Briefly, for each extract, 0.2 g of peel or flesh was weighed and added to a plastic cuvette. Then, 1.5 mL of solvent consisting of 80% methanol and 20% water was added into each cuvette. The extraction was performed on an ultrasonic bath  for 15 min. The solution was centrifuged  for 10 min at 10,000 rpm. Extracts were separated from the residue. Two parallel samples were prepared for each variety. Extracts were filtered through a 0.45 μm PTFE filters before injection into the HPLC system.3PO4 (mobile phase A) and 100% methanol (mobile phase B). The flow was adjusted to 0.8 ml min−1. The gradient was used as follows: 0 mL 5% B, 5 min 25% B, 14 min 34% B, 25 min 37% B, 30 min 40% B, 34 min 49% B, 35 min 50% B, 58 min 51% B, 60 min 55% B, 62 min 80% B, 65 min 80% B, 67 min 5% B, 72 min 5% B. A volume of 10 μL of standards and samples was injected into the system. Calibration curves were constructed by analyzing authentic polyphenol standards in different concentration ranges. All standards showed linear calibration curves with r2 from 0.9927 to 1.0000. Limits of detection (mg L−1) and quantification (mg L−1) were as follows: , procyanidin B2 , (+)-catechin , (−)-epicatechin , phloretin-2-glucoside , phloretin , chlorogenic acid , p-coumaric acid , quercetin-3-glucoside , quercetin-3-galactoside , quercetin-3-rhamnoside , quercetin , cyanidin-3-galactoside . Polyphenols were tentatively identified by comparing the retention times and spectrum of peaks (200 and 600 nm) in extracts with those of authentic standards. Additional confirmation of the identifications was made by spiking polyphenol extracts with authentic standards. Some polyphenols were tentatively identified as phloretin-2-xylosylglucoside, phloretin derivative, chlorogenic acid isomer, p-coumaric acid derivative, and quercetin derivative 1 and 2. Those compounds were quantified using calibrations curves of phloretin, chlorogenic acid, p-coumaric acid, and quercetin, respectively.For the characterization of individual polyphenols, the HPLC system 1260 Infinity II  with a quaternary pump, a PDA detector, and a vialsampler was used. Polyphenols were separated using a Poroshell 120 EC C-18 column, 4.6 × 100 mm, 2.7 μm and a Poroshell 120 EC-C18 4.6 mm guard-column. The mobile phases were 0.1% Hn = 2). All results were expressed as mean ± standard deviation. For the statistical analysis of the results, the Minitab statistical program  and MS Excel  were used. Results of internal and external quality traits were shown as histograms to visualize grouping of the data. Correlation was checked by Pearson correlation coefficients. Results of polyphenol amounts were analyzed by using post-hoc Tukey tests to see possible differences within varieties. In addition, principal component analysis (PCA) and dendograms were used to visualize clustering of varieties according to all internal and external quality parameters and polyphenol amounts.The number of samples for internal and external parameter determination depended on the number of apples of each variety in the quality determination group. For polyphenol determination, the number of samples for each variety was 2 and each was injected into the HPLC system once  1 to 5, soluble solid concentration (SSC) 12–17 °Brix, maturity index (MI) 0.1 to 0.4, total acids 0.3%–1.1%, SSC/TA 13 to 66, pH 2.4–3.8) and in the commercial variety . Quality parameters reported in the literature [−2 (22 varieties), starch decomposition index 2.3–5 (24 varieties), maturity index 0.04–0.24 (24 varieties), SSC 13–17.4 °Brix (21 varieties), TA 0.27%–0.68 % (20 varieties), and SSC/TA ratio 13–39 (23 varieties). Only some varieties were outside of those values. In addition, two varieties can be mentioned because they showed lower starch decomposition index and higher maturity index (‘Božićnica 2’ and ‘Zelenika’).Internal quality parameters are shown in terature ,23,38 coInternal parameters are important for fruit maturity and when deciding about harvest time, and they are also important for consumer acceptance of apples. Fruit firmness is a textural property which decreases with maturity as apples become softer . Apples r2 = 0.77), weight and width (r2 = 0.94), and height and width (r2 = 0.72). Those are logical correlations which show that as fruit width and height are increasing, the weight is increasing too. Fruit width is increasing with the increase of fruit height. Fruit height correlates with the shape (r2 = 0.63), which is also a logical correlations showing the connection of fruit dimensions and fruit shape. Some correlations could also be seen between internal quality parameters. There is a negative correlation between starch decomposition index and maturity index (r2 = −0.82). As a fruit becomes mature, the starch is decreasing. And there is a negative correlation between TA and SSC/TA (r2 = −0.77). This is also a logical correlation showing the increase of the SSC/TA ratio as TA is decreasing. Looking at the seeds of apple varieties, some correlations are showing connections between total seeds and the numbers of healthy and bad seeds. Namely, as apples have higher total number of seeds, the number of healthy seeds is increasing too (r2 = 0.66). As the total healthy seed number is higher, the bad seed number is decreasing  and the total seed weight is higher (r2 = 0.78). As the number of bad seeds is decreasing, the total weight of seeds increases (r2 = −0.65).All external and internal quality parameters and seed characteristics were correlated to see if there is some connection amongst them . A positIn general, the majority of old varieties in this study are of the size that consumers accept. They represent a diversity of shape or taste which can also be positive for consumer acceptance. According to external and internal quality parameters, it can be suggested that old varieties have the quality necessary for acceptance in the market. The majority of old varieties were similar in internal and external quality parameters to the commercial variety ‘Idared’. The color of apples is also very diverse, which also contributes to the acceptance in the market.−1 FW), dihydrochalcones (19–287 mg kg−1 FW), phenolic acids (14–547 mg kg−1 FW), flavonols (119–2513 mg kg−1 FW), and anthocyanins (0–200 mg kg−1 FW) are in agreement with amounts found in the literature [terature  and withterature ,25. Applterature ,41. Almop-coumaroylquinic acid, and p-coumaric acid derivative), and flavonols . These identifications agree with the literature [−1 FW), dihydrochalcones (2–98 mg kg−1 FW), phenolic acids (55–1058 mg kg−1 FW), flavonols (8–27 mg kg−1 FW) are in agreement with literature data [terature ,23,32. Ture data  and withure data ,25. ApplFurthermore, As stated, the majority of old, traditional varieties contain higher polyphenol amounts in the flesh and in the peel than the commercial variety. This is a quality trait that should be highlighted for those old varieties. Almost all old, traditional varieties contain higher dihydrochalcone content.Although the amounts in the peel of old, traditional varieties do not differ statistically, some varieties can be highlighted according to higher polyphenol content in the peel, namely, ‘Pisanika’, ‘Adamova zvijezda’, ‘Zelenika’, ‘Wild apple’, and ‘Kanada’. Moreover, the flesh of the varieties ‘Božićnica 2’, ‘Boskop’, ‘Zimnjara’, and ‘Crveni boskop’ contained high content of polyphenols.To visualize some differences between old varieties according to their internal and external quality characteristics and polyphenol content, principal component analysis (PCA) was used to analyze the data .−1 FW of total polyphenols in the peel are grouped in one, and all other in a second cluster. Green apples can be grouped into one cluster that overlaps with two other clusters  and the other with varieties with a lower total polyphenol content. These results were consistent with principal component analysis and also with dendograms. But the final analysis with principal component analysis, which took into account all internal and external quality parameters and polyphenol content, showed a difference of the group of varieties ‘Božićnica 2’, ‘Boskop’, ‘Wild apple’, ‘Adamova zvijezda’, and ‘Crvenka’ when compared to other varieties. The other varieties clustered into a second group.In general, it can be suggested that varieties cluster into two main clusters according to total polyphenol content, one is a cluster of varieties with higher total polyphenol content  . LanzersStudies are beginning to show the beneficial effects of old varieties and their polyphenols. Serra et al.  demonstrDihydrochalcones are a specific group of polyphenolic compounds from apples with potential to reduce blood glucose levels ,46 whichEarlier studies have shown that there are differences in old apple varieties . But stuDue to all of that, old, traditional varieties of apples are very valuable fruits. Apples studied here for the first time, showed some differences according to quality characteristics and polyphenols in the flesh and peel. Those differences should be further studied with genetic tests of the varieties to separate those unique and especially valuable for preservation.In twenty-five traditional, indigenous apple varieties, sixteen different quality traits and color were determined for the first time and compared to a commercial variety ‘Idared’, the commercial variety most grown in Croatia . This is


This cross-sectional study examines public beliefs about the coronavirus disease 2019 (COVID-19) pandemic in response to President Trump’s social media posts during and after his infection with the virus. Surveys and interviews, typically used to gauge public perspectives, have collection lag times.5 Accordingly, we sought to use near real-time social media data to capture the public’s changing COVID-19–related attitudes during President Trump’s recent infection.Misinformation has hampered health officials’ ability to control the coronavirus disease 2019 (COVID-19) pandemic.COVID-19, coronavirus) within 15 characters of a supporting  or contradicting  adjective or hashtag. Tweets were collected from “swing states”  (STROBE) reporting guideline.For this cross-sectional study, from September 23 to October 8, 2020, we collected 22 800 tweets regarding public views on COVID-19 using Twitter’s free application programming interface. Search criteria included COVID-19–related keywords  , were reBox. “Covid is fake”“Pandemic = myth. #CoronaHoax”“It's time to get real about Covid. Getting it is far from a death sentence. In fact the survival rate is greater than 98%. We should be vigilant but if we get it we get it. We don't like the flu but we manage. Time to go back to life as we knew it before this hype”“cdc has said most of the mask people wear are not even effective there has been no social distancing during riots no one seems to care. Many of those 210k people didn't even really die from the actual Chinese virus just go back to getting false info from fake news”“Stay home...even if you don’t care or believe this entire Pandemic is a hoax! It’s time to CHANGE your behavior #Covid19isarealthing”“Wow, a little covid and Twitter goes silent.....come on man, it's all a hoax right? #GotWhatYouDeserved”“Like i said to my mom last night. how come real tax payers are paying thousands in medical bills for covid. why isn’t trump paying for his medical bills?”“Not as fake as Trump claiming to have Covid #TrumpCovidHoax #TrumpNeverHadCovid19”Abbreviation: COVID-19, coronavirus disease 2019.aTaken from 5945 tweets identified from September 23, 2020, to October 8, 2020. Tweets were restricted to “swing states” , individual accounts, and non-retweets.2 test was used to assess differences in tweet proportions for each category across periods. Significance was set at a 2-sided P = .05. Analyses were performed using Excel, version 1808 (Microsoft Corp).We randomly selected and manually labeled 3000 tweets from each period into 1 of 4 categories: (1) COVID-19 is not real, (2) COVID-19 is real but not serious, (3) COVID-19 is real and serious, and (4) unrelated or no stance . Interra2 = 569.9; P < .001), 2 and 3 , and overall  (with Bonferroni correction)  . Most tw3 Second, social media can be used to assess the public’s changing responses to public health–related communications. Third, social media analysis can and should be explored to identify sources of and ways to combat misinformation.These results suggest that some members of the public adapted beliefs in response to President Trump’s tweets and that social media may be used as a near real-time data source to capture these changing perspectives, including COVID-19–related misinformation. This analysis has several immediate public health implications. First, health departments, political leaders, and influencers should leverage social media to support science on health issues, especially during pandemics.6 Although we manually reviewed tweets, restricted data to individual accounts, excluded retweets, and limited tweets per Twitter username, online manipulation  remains a potential issue. In addition, we did not have demographic data, such as political affiliation, limiting more detailed analyses.6 Overall, the results suggest that social media can be used as a tool to capture public perspectives, identify misinformation, and aid public health efforts.Limitations of this study include Twitter not being representative of the general population.


C. reticulata “Chachi” except for C. reticulata “Tangerina” and C. reticulata “Dahongpao” recorded in Chinese Pharmacopoeia. Most importantly, the peels of C. reticulata “Shiyueju,” C. reticulata “Ponkan,” C. reticulata “Tribute,” and C. reticulata “Bayueju,” traditionally rarely used for medicinal food, were highly similar to that of C. reticulata “Chachi” and rich in bioactive flavonoids, which can be considered the effective medicinal resources of CRP.Citri Reticulatae Pericarpium (CRP), known as Chenpi (CP) in Chinese, is a medicinal food for health and fitness. In order to find out the characteristic activity chemicals distinguishing various cultivars of CRP and provide a reference for effective development of citrus resources, an “activity fingerprint” of CRP from 21 different cultivars was established based on the evaluation of antitussive and expectorant activities. There were 18 common peaks in the HPLC fingerprint, of which 3 flavonoid glycosides and 14 polymethoxyflavonoids (PMFs) were identified by LC/MS-IT-TOF. Furthermore, five characteristic chemicals were determined and similarity calculation with principal component analysis (PCA) was combined together to compare the similarity and difference among various cultivars. The results showed that some other cultivars were also similar to  Citrus reticulata Blanco or its cultivated varieties  2013-0002). Animals were fasted for 12 hours before the first and the final administration but given water ad libitum. All the experimental procedures were performed in accordance with the Regulation on the Administration of Laboratory Animals issued by the State Council of China. All animals were killed by cervical dislocation, and all efforts were made to minimize suffering.The positive control drug, dextromethorphan hydrobromide (DH) tablets, for the antitussive activity assay was purchased from Baiyunshan Guanghua (China). The ambroxol hydrochloride (AH) tablets, used for the expectorant activity assay, were obtained from Boehringer Ingelheim . The solvents for HPLC and LC/MS-IT-TOF analysis, HPLC-grade acetonitrile and purified water, used after filtration through membranes, were procured from Honeywell (America) and Yibao (China), respectively. Hesperidin , nobiletin , 3,5,6,7,8,3′,4′-heptamethoxyflavone , tangeretin , and 5-hydroxy-6,7,8,3′,4′-pentamethoxyflavone  were all of reference standard grade, with the high purity above 98%. All other reagents were of analytical grade and pure.μm membrane for HPLC-UV and LC/MS-IT-TOF analysis.Each reference of the five flavonoids was dissolved together in methanol at an appropriate concentration to obtain the mixed standard solution. Each sample powder was extracted ultrasonically with 100 times of methanol (w/v) for 30 minutes at 320 W . The solFifty Kunming mice were randomly allocated to five groups of half male and half female each: control, MECRP-20, MECRP-40, MECRP-80, and DH-60. The control group and standard group received normal saline (0.9%) and DH (60 mg/kg) daily for 7 continuous days, respectively, while the test groups were administered orally with the MECRP . After final administration for an hour, each animal was put in a sealed glass container and exposed to 25% ammonia water aerosol for 20 s by an ultrasonic nebulizer . The micn = 10/group): control, MECRP-20, MECRP-40, MECRP-80, and AH-40, and each of the groups was medicated orally continuously for 7 days (1 time/day). The control group and treated groups were administrated based on the method of the antitussive activity assay, whereas the standard group received AH 40 mg/kg orally. Thirty minutes after the final administration, each mouse was injected intraperitoneally with 0.5 mL phenol red solution in saline and killed by cervical dislocation 30 minutes later. The tracheas of the same length were taken from the thyroid cartilage to the trachea branch of the mice and washed ultrasonically with 3 mL NaOH solution (0.03 mol/L) in saline for 10 minutes rum data .PMFs are significant flavonoids particularly presented in citrus species, containing multiple methoxyl groups, low polarity, and planar structure . With a + ion at m/z 403 and yielded fragment ions at m/z 388 ([M + H − CH3]+), 373 ([M + H − 2CH3]+), and 355 ([M + H − 2CH3 − H2O]+). Similarly, compound 11 was deduced as 3,5,6,7,8,3′,4′-heptamethoxyflavone by comparing to the reference standard with the [M + H]+ ion at m/z 433.Compound 9 was confirmed as nobiletin by comparison to the reference standard with the same fragmentation information. Nobiletin gave an [M + H]+ at m/z 389 and the product fragment ion at m/z 374 ([M + H − CH3]+), 359 ([M + H − 2CH3]+), and 341 ([M + H − 2CH3 − H2O]+). Compounds 6 and 8 were known as isosinensetin and sinensetin combining with the mass data of the previous research [20H20O8 and similar fragment of compound 14. Similarly, compound 15 was inferred to be dihydroxy-pentamethoxyflavone differed by the molecular weight of 16 of compound 7.Compounds 6, 7, 8, 12, 14, and 15 were considered pentamethoxyflavones based on their molecular formula and fragment ions . Compariresearch . CompounCompound 12 was identified as tangeretin by comparing the retention time and fragmentation information of the reference standard. Moreover, the fragmentation information of compound 17 was similar to that of compound 12 and differed by the molecular weight of 14. Combining with the fragment information previously reported , compounR2 > 0.9990), and the RSDs of accuracy, repeatability, stability, and recovery were all below 3%, indicating that the assay method was feasible.Hesperidin and other four main PMFs selected from 17 characteristic compounds profiled above were determined, and the results are given in PCA is a well-known analysis method with the purpose of dimensionality reduction to transform multiple indicators into a few comprehensive indicators , 33. DefC. reticulata “Unshiu,” C. reticulata “Erythrosa,” C. reticulata “Yichangju,” C. reticulata “Qingjiangju,” C. reticulata “Linhaiju,” C. reticulata “Xunwuju,” and C. reticulata “Biangan,” with the high content of hesperidin and low PMFs. C. reticulata “Tankan” was in group 2 due to its lower content of flavonoids, while C. reticulata “Suavissima” with a higher content of flavonoids was in group 3. Other eleven cultivars were clustered in group 4 with C. reticulata “Chachi,” showing parts of similarities and relationships with the traditional genuine herb. The result of PCA was approximately consistent with that of similarity analysis.As the result of the PCA plot shows , 88 sampSince an LC/MS-IT-TOF was used, 17 characteristic compounds were finally profiled on the 18 common peaks at the “activity fingerprint,” and all of them were identified belonging to flavonoids, suggesting that there may be a potential relationship between the differences among varieties and flavonoid compositions. Then, hesperidin and four main PMFs were selected for quantitative determination and further analysis. And the PCA results confirmed that the differences among varieties were significantly correlated with flavonoids, not only including hesperidin, the single indicator for CRP quality control in Chinese Pharmacopoeia (version 2015) [C. reticulata “Chachi,” C. reticulata “Tangerina,” and C. reticulata “Dahongpao” were quite similar to each other except for C. reticulata “Unshiu,” which indicated that the selection of the traditional medicinal herb of CRP was rational related to the high content of PMFs. In addition, there are some other cultivars significantly similar to C. reticulata “Chachi,” such as C. reticulata “Kinokuni,” C. reticulata “Yangshanju,” C. reticulata “Wuyueju,” and C. reticulata “Nianju,” and so on. Among them, it was worth noting that the cultivars of C. reticulata “Shiyueju,” C. reticulata “Ponkan,” C. reticulata “Tribute,” and C. reticulata “Bayueju” were rarely used for traditional medicine but exhibited a relatively high content of hesperidin and PMFs, even exceeding that of C. reticulata “Chachi.” Therefore, these four cultivars can be considered effective resources for the application of CRP, which provide a reference for citrus resource development.In the present study, an effective “activity fingerprint” on CRP from different cultivars involving antitussive and expectorant activity assays, similarity analysis, LC/MS-IT-TOF analysis, quantitative analysis, and PCA was perfectly established, which was proven to relate to pharmacological activity and conducted to find the characteristic chemicals distinguishing various cultivars of CRP for quality control. From the results above, the recorded cultivars of


The activity concentration used for therapy in mice (170 kBq/mL) was chosen to be in agreement with an activity concentration that is anticipated to be clinically relevant in patients (200 MBq/L).Antibodies labeled with alpha-emitter astatine-211 have previously shown effective in intraperitoneal (i.p.) treatments of ovarian cancer. In the present work we explore the use of investigational farletuzumab, aimed at the folate receptor alpha. The aim was to evaluate the biodistribution and therapeutic effect of 211At-farletuzumab (n = 16); or with a combination of 125I-farletuzumab and 211At-MX35 (n = 12). At 1, 3, 10 and 22 h, mice were euthanized and s.c.-tumors and organs weighted and measured for radioactivity. To evaluate therapeutic efficacy, mice were inoculated i.p. with 2 × 106 NIH:OVCAR-3 cells. Twelve days later, the treatments were initiated by i.p.-administration. Specific treatment was given by 211At-labeled farletuzumab  which is specific for OVCAR-3 cells. Control treatments were given by either 211At-labeled rituximab which is unspecific for OVCAR-3 , non-radiolabeled farletuzumab  or PBS only .For biodistribution, using intravenous injections and mice carrying subcutaneous (s.c.) tumors, the animals were administered either 211At-farletuzumab was similar to that with 125I as radiolabel, and also to that of 211At-labeled MX35 antibody. The tumor-free fraction (TFF) of the three control groups were all low . TFF following treatment with 211At-farletuzumab was 91%.The biodistribution of 211At-farletuzumab, delivered at clinically relevant 211At-mAb radioactivity concentrations and specific activities, showed a 6 to 10-fold increase (treated versus controls) in antitumor efficacy. This observation warrants further clinical testing.The current investigation of intraperitoneal therapy with  To explore this, we have focused on evaluating and optimizing adjuvant TAT of ovarian cancer, where any remaining microscopic tumors are typically located within the peritoneal cavity. We have previously translated promising in-vitro and pre-clinical evaluation of intraperitoneally administered 211At-MX35 [In the quest for new and better treatments of disseminated cancers, targeted alpha-particle therapy (TAT) hold promise . Use of 1At-MX35 .211At, is unlikely to penetrate substantially into microtumors, due to the so-called binding barrier. The result will be that the core of microtumors with diameters larger than ~200 μm will escape irradiation.The generated knowledge has allowed us to construct predictive models ,8 that cTo partly overcome this shortcoming, we have herein evaluated farletuzumab  as a candidate vector for intraperitoneal TAT. Farletuzumab is an optimized anti-folate receptor alpha (FRA) antibody and is the humanized version of the murine LK26 . It targFarletuzumab has relatively low binding affinity, 2 nM , which c211At-farletuzumab in in-vitro and in-vivo experiments and, using models for radiation dosimetry, to translate the findings to expected results in the clinical setting. The experiments were therefore designed to mimic the clinical situation regarding mAb concentration and radiation doses.The aim of the current study was to evaluate the biodistribution and therapeutic efficacy of The investigational antibody farletuzumab  is directed against human folate receptor alpha (FRA) which has elevated expression in approximately 90% of ovarian cancers . Farletul-glutamine and 1% penicillin and streptomycin. Cultures were adherent, fed twice a week, and split once a week. In all subsequent experiments, where relevant, RPMI 1640 cell media without folic acid was used.NIH:OVCAR-3 cells were obtained from ATTC . The cells were cultured in RPMI 1640 cell media without folic acid (Biowest) and supplemented with 10% fetal bovine serum, 1% 209Bi211At reaction in a Scanditronix MC32 cyclotron. After courier transport to Gothenburg (Sweden), 211At was isolated using a dry distillation procedure and recovered in chloroform, as previously described [Astatine-211 was produced at Copenhagen University Hospital, Denmark, via the escribed ,18. Iodi211At chloroform solution was evaporated to form a dry residue. For labeling the 211At, dry residue was activated with N-iodosuccinimide in methanol/1% acetic acid. The m-MeATE-immunoconjugate was acidified to a pH of ~5.5 using citric acid and then added to the activated 211At. An astatine activity of 50–200 MBq was used depending on the desired specific activity. The activated astatine was contacted with the immunoconjugate for 1 min on a vortex shaker. The reaction was quenched with sodium ascorbate and the astatine-labeled immunoconjugate was then purified into PBS by size exclusion chromatography . Radiolabeling with 125I was done as previously described [The antibodies were conjugated with N-succinimidyl 3-(trimethylstannyl)benzoate (m-MeATE) and radiolabeled as previously described . BrieflySix-weeks-old female BALB/c nude mice were purchased from Janvier Labs (France) and housed at the local animal research facility and kept according to the Animal Welfare Act (2018:1192) under the Swedish Board of Agriculture. Mice were allowed one week of acclimatization before tumor-cell inoculation. Water and food was provided ad lib. This study was approved by the local animal experiments committee .kon, koff, and the number of antigens available per cell needed to be established. Several cell experiments (outlined below) were designed for this purpose.A model for antibody binding to cells as well as penetration in microtumors has previously been presented for antibody concentrations used in human intraperitoneal TAT . In orde211At-farletuzumab therapy, i.e. and antibody concentration of 0.4 μg/mL. OVCAR-3 cells, 2 × 106 cells in 2 mL, were incubated at 37 °C with 125I-farletuzumab for 1, 2, 3, 4, 5, or 24 h. The cell suspension was then transferred to a new tube and centrifuged 4 min. Cells were washed twice in medium and the cell pellet and the final supernatant were separately measured for radioactivity.The cell-binding kinetics was determined using the same antibody concentration as that which is proposed  for clinkoff. In these, 2.6 × 107 OVCAR-3 cells in 13 mL supplemented RPMI-1640 medium was added to a 50-mL tube and incubated with 125I-farletuzumab (0.4 μg/mL) for 1–5 h. Cells were then washed by first adding 35 mL medium and then centrifuged for 4 min followed by removal of the supernatant. Another 13 mL medium was added to the cell pellet to form a cell suspension that was transferred to six tubes  for koff determination at six time-points, 1–24 h, when 2 or 40 mL were added to the respective tube. Cells were washed and the cell pellet and the supernatant were each measured for radioactivity.Separate experiments (n = 15) were designed to establish the release component One set of experiments (n = 5) were designed to evaluate the number of relevant antigens available per cell. Antibody concentrations ranged from what is anticipated for clinical used, i.e.0.4 μg/mL up to 40 μg/mL, i.e. 100 fold higher. Cell binding were first evaluated at 1, 2, 3, 4, 5, and 24 h. Since saturation was reached within 1 h, repeat experiments were evaluated only at 2 h.Surface Plasmon Resonance (SPR) investigations of both unconjugated and m-MeATE-conjugated farletuzumab were performed using a Biacore 2000 system. The antibody/antibody conjugate was immobilized in two channels on a Protein A chip  and the Folate alpha antigen was dissolved in the running buffer ). Between each folate alpha concentration  the chip was completely regenerated  and the protein immobilized again. This was repeated with low variability of the response for the same concentration. Binding kinetics data were calculated using a 1:1 Langmuir isotherm binding model.211At-labeled farletuzumab was determined according to Lindmo et al. [6 cells/mL) were serially diluted  using PBS/BSA (0.1% BSA) in duplicates for each experiment. A constant amount (0.5–5 ng) of radiolabeled immunoconjugate was added to every sample. The association was allowed to proceed for 180 min at room temperature under gentle agitation. The cells were then centrifuged  to form a pellet and the supernatant removed. PBS was then added whereby the cells were re-suspended and subsequently centrifuged again before removal of the supernatant and measuring for radioactivity. The total radioactivity added to each sample (determined through reference samples) was compared to the activity of the cell samples to determine the bound immunoconjugate fraction.The immunoreactive fraction of 7 NIH:OVCAR-3 cells in 0.1 mL. Four weeks thereafter, the longest axes of the tumors were 5–7 mm, and mice were i.v. injected (0.1 mL in the tail vein) with radiolabeled antibodies.Mice (n = 28) were subcutaneously inoculated at one or two sites, each with 1 × 10211At-farletuzumab (n = 16) and another group with a combination of 150 kBq 125I-farletuzumab and 450 kBq 211At-MX35 (n = 12). The MX35 was included as positive control of specific tumor uptake known from previous studies. At 1, 3, 10 and 22 h, mice were euthanized and tumors and organs weighed and measured for radioactivity in a gamma-well counter . The measurements were corrected for physical decay and dead-time errors. Activity of 211At was determined by measuring the 77–93 keV characteristic X-rays and correcting for their abundance (43 photons per 100 211At disintegrations). Since the samples contained both 125I (t½ = 59.4 days) and 211At (t½ = 7.21 h) they were all measured at two times in two separate energy windows. The first measurement was made directly after dissection of the organs and the second after three days when all 211At had decayed. This way the 125I activity could be measured without spill-over errors from 211At to the 125I-window, while for 211At the correct activity could be derived by subtracting the spill-over counts from 125I to the 211At-window.One group was administered with 450 kBq 6 NIH:OVCAR-3 cells in a single-cell suspension (0.2 mL PBS). Active or control (sham) treatments, all in 0.7 mL, were delivered i.p. twelve days after cell inoculation. The experiment was designed so that by the chosen injected activity, the microtumors in mice would receive roughly the same absorbed dose as microtumors in patients . The rate of antibody escape from the abdominal cavity in mice [Therapeutic efficacy was investigated on sixty-three mice sorted into four groups. All mice were inoculated i.p. with 2 × 10 in mice  is highe211At-farletuzumab together with trace amounts (10 kBq) of 125I-rituximab. One control group  received 0.7 MBq unspecific 211At-Rituximab. Another control group  received unlabeled farletuzumab; and one group  received PBS only.The experimental group  received 700 kBq The mice were inspected for well-being every second day and weighed every 7 to 10 days. At signs of deteriorating health, animals were euthanized and the abdominal cavity opened to investigate any presence of ascites and/or macroscopic tumors. Remaining mice were euthanized and evaluated 5 months (154 days) after therapy. To investigate occurrence of microscopic tumors in mice without established ascites or macroscopic tumor, tissues from the abdominal wall, mesentery, and spleen were taken for paraffin sectioning and hematoxylin and eosin (H&E) staining. Additional samples were taken from suspected lesions at other locations. Between 10 and 30 sections at 100 μm distance were processed for microscopy. Both the animal dissection and the histological analysis were blinded, i.e., performed without any knowledge of the treatment received.211At-farletuzumab and each of the three control groups.Fisher Exact Test (Graphpad PRISM) was used for determining the significance of difference in TFF between mice treated with 211At-labeled antibodies' uptake in intraperitoneal microtumors was conducted using the Alpha Camera system [211At-farletuzumab or 211At-MX35. Within each group, half the mice were administered an activity concentration of 170 kBq/mL and the other half 5.7 MBq/mL. Alpha imaging was conducted at 4 h and 9 h after injection, as previously described [In a separate experiment, imaging and quantification of a system . Two weeescribed  Since prescribed , the splAn in-house-developed model of the biodistribution of i.p. infused antibodies in humans  was modiMean absorbed dose to organs and macrotumors was calculated using the biodistribution data, using methods described previously .Astatine-211 labeling of m-MeATE-conjugates resulted in 65 (±7) % labeling yield and 96 (±3) % purity, both for farletuzumab and MX35. The overall radiochemical yield was 63 (±7) %. Specific activities up to 2.3 GBq/mg, corresponding to 1 in 45 antibodies being radiolabeled, were achieved. The iodine-125 labeling of the MeATE reagent and subsequent conjugation to farletuzumab resulted in an overall radiochemical yield of 38% (±5) and the radiochemical purity was always >95%. The specific activity of the iodinated antibodies was adjusted to match the specific activity of the astatinated farletuzumab.125I-farletuzumab on OVCAR-3 cells reached equilibrium within 1 h (211At- and 125I-labeled farletuzumab yielded similar results. Remaining uptake at various times after the incubation is shown in −1 s−1 and koff = 5.61 × 10–3 s−1 and the other set using kon = 2.25 × 105 M−1 s−1 and koff = 5.02 × 10–4 s−1) for the mAb binding kinetics to 600,000 antigens per cell, and ignoring possible internalization.For antibody concentrations anticipated for clinical intraperitoneal TAT, i.e. 0.4 μg/mL, the uptake of thin 1 h A. The mekon = 5.06 × 105 M−1 s−1 and koff = 5.61 × 10−3 s−1. Results for unconjugated farletuzumab resulted in similar values, i.e. 4.66 × 105 M−1 s−1 and 5.81 × 10−3 s−1, respectively . Ebel et al. have previously presented kinetics data from SPR measurements using another method, resulting in kon = 2.25 × 105 M−1 s−1 and koff = 5.02 × 10−4 s−1 [SPR measurements for m-MeATE-conjugated farletuzumab resulted in 10−4 s−1 .Results from the cell experiments were analyzed and compared with both the parameter values derived from the herein presented SPR studies and with those of Ebel et al. The results indicated that the mAb binding and release kinetics were best described by a combination of the two sets of binding parameters. A good fit to all experimental results could be observed when the binding kinetics data was set with an assumption that half the available antigens bind mAb according to the kinetics described by our SPR measurements, and half bind mAb according to kinetics derived from Ebel et al . With thkon = 5.06 × 105 M−1 s−1 and koff = 5.61 × 10−3 s−1 and the other set using Ebel et al.'s data, i.e. kon = 2.25 × 105 M−1 s−1 and koff = 5.02 × 10−4 s−1 [211At-farletuzumab on OVCAR-3. This value was lower than observed in later studies, most likely due to the probable loss of some component of bound mAbs in the washing steps in the IRF-assay. The unspecific At-rituximab was also tested on OVCAR-3 and had a IRF lower than 0.01.To estimate binding and penetration into microtumors, our previously presented model  were use10−4 s−1 . Model r211At-farletuzumab was similar to that of 125I-farletuzumab, and also to that of 211At-labeled MX35 antibody  and stomach at later time-points indicate accumulation of unlabeled, free, 211At. A comparison of the biodistribution of the different groups in terms of residence time is given in Suppl. Table 2.The biodistribution of antibody . For the211At-farletuzumab and 211At-MX35 in microtumors, with levels up to a factor of 500 higher than for the mean for the spleen itself. 211At-farletuzumab at 4 h after i.p. injection. Using H&E-slides from the alpha imaging cohort of mice, the estimated microtumor size  at the day of therapy in the efficacy study was 44 (±19) μm with a range from 19 to 82 μm. Alpha imaging verified a high uptake of both 211At-farletuzumab at 4 h following i.p. injection, the activity uptake per cell was estimated for several (n = 23) microtumors. A linear relation with uptake was found for a wide range of microtumor sizes, correlating to 5–400 cells , three mice at d120, d134, and d134. In the control group D (PBS only), two mice died atd99 and d134. All these animals presented with tumors upon dissection. Since the large majority of the mice survived the full study period any estimation of median survival was not allowed. However, when the apparent survival curves  were compared a significant difference (Log-rank Mantel-Cox test) was found between 211At-farletuzumab and unlabeled farletuzumab  as well as between 211At-farletuzumab and PBS . The difference between 211At-farletuzumab and 211At-rituximab was not significant.A total of seven mice were euthanized before the end of the experiment, due to deteriorating health. This was evident by swollen abdomen, indicating presence of ascites. In control group C (unlabeled farletuzumab), two mice were both euthanized 99 days after therapy (d99). In control group B (unspecific 211At-rituximab) was 12%, 9% and 14%, respectively. TFF after treatment with 211At-farletuzumab was 91% ; unlabeled farletuzumab (P < 0.0001****); and PBS (P = 0.0001***).At the end of experiment, the tumor-free fraction (TFF) of the three control groups of mice , to the nucleus of a single cell within the mouse peritoneum, was 7.6 Gy. Using the binding kinetics derived within this current work, an additional 9.6 Gy would result from 211At-farletuzumab bound to the cell surface. The modeled dose profiles to spherical microscopic tumors of various sizes are shown in As estimated by dosimetry modeling, the radiation dose contribution from the surrounding liquid containing 211At-farletuzumab. Mean absorbed doses to organs and macrotumors in the biodistribution are presented in Suppl. Table 3.With an assumed eradicative dose at 10 Gy from alpha-particle irradiation (26), single cells and microtumors with diameters up to at least 300 μm are expected to be sterilized following treatment with 211At-farletuzumab. This concentration has been established as safe with a likely therapeutic effect [This study was designed to mimic the irradiation within the peritoneal cavity in patients treated with 200 kBq/mL intraperitoneally injected c effect ,5. Sincec effect ,22 show The main purpose of this study was to conduct experiments and modeling that were aimed at predicting efficacy in patients. Initially, our attempts to determine the number of binding sites per OVCAR-3 cell resulted in atypical saturation curves. This made it difficult to estimate absorbed dose to microtumors in the mice, and therefore also to the patient situation. To generate results that allow an estimate of the dose to microtumors in a patient, the cell experiments were made at antibody concentrations used for clinical intraperitoneal TAT . The derA previous study of the binding properties of farletuzumab to IGROV1 and SW620 cells concluded that it is directed against a single-class antigen . Our cur125I and 211At as radiolabel. The organ and tumor uptakes were also similar to that of 211At-MX35. Another study have shown that expression of FRA on normal tissue is generally very low and mainly confined to retina, placenta, choroid plexus, and the luminal surface of kidneys and lungs [211At-farletuzumab to them will be delayed and any radiation damage therefore relatively low. We observed an elevated uptake in thyroid(throat) both for 125I and 211At. This is a common finding. While pre-treatment with substances such as sodium perchlorate has been investigated to mitigate this, we did not use any blocking agent in our study. We have preliminary data (unpublished) suggesting that blocking agents could infer alterations in the biodistribution causing increased toxicity in mice.The biodistribution study of farletuzumab showed similar behavior for both nd lungs . These aThe tumor model for the therapy study was developed to simulate the clinical situation where adjuvant intraperitoneal TAT is relevant, i.e. minimal amounts of disseminated ovarian cancer within the peritoneal cavity. We have previously used this model, where a single-cell suspension is inoculated intraperitoneally, to evaluate the efficacy of other targeted alpha therapies on disseminated ovarian cancer ,3. Resul211At-bound unspecific antibody. Since roughly 10 Gy is required for microtumor eradication [211At decay in the peritoneal fluid.By allowing two weeks from inoculation of the single-cell suspension to therapy microtumors are formed. An indirect measure of the sizes of these microtumors comes from the therapeutic efficacy of the dication ,26, our We have previously used two months as follow-up time for the mice. In the current work we followed the mice for five months in order to facilitate a more accurate detection of remaining tumor, and thereby provide a more reliable estimate of the tumor-free fraction. Signs of toxicity were monitored at least weekly by visually inspecting and weighting the animals. In other animal models, loss of weight can indicate deteriorating health. However, in our model, such loss often occurs simultaneously with appearance of peritoneal ascites (that adds to weight) resulting in a near-zero net effect on weight. The seven mice that were euthanized before the end of experiment had all swollen abdomen, indicating presence of ascites, and presented with tumor upon dissection.211At-farletuzumab in various sizes microtumors. These estimates were then compared with actual distribution in tumor tissue using our alpha camera [By using our previously presented modeling for the biodistribution of intraperitoneally infused antibodies in patients , as wella camera . Togethea camera , we coul211At-MX35 in a mouse the blood concentration reaches 25%IA/g already at 3 h after administration, while for a patient, using data from the previous phase I study [4 lower. Correspondingly, the absorbed dose to the bone marrow following an i.p.-infusion is markedly lower in a patient than in a mouse. Accounting for their respective anticipated therapeutic administered activity , the dose rates to the bone marrow after infusion of 211At-MX35, mouse versus patient, is presented in Suppl Fig. 3B. At these activities, the dose to the bone marrow for mice can be >2000 mGy, while for patients it will be approximately 15 mGy [In translation from mice to the patient situation, the experimental observations we have made using the intraperitoneal mouse model of ovarian cancer cohere with modeling and dosimetry calculations. This suggests that data derived in mice could be translated to what is expected in patient, when it comes to therapeutic effect on intraperitoneal microtumors. For toxicity, however, inherent differences in the pharmacological kinetics mouse versus patient make adequate translations more difficult. The main reason for this is the strong difference in passage time of a radiolabeled mAb from the intraperitoneal cavity to its systemic distribution, as illustrated in Suppl Fig. 3A. Following an i.p.-infusion of  I study , the con211At-MX35 F(ab′)2 fragments, no radiation-related toxicity was seen [211At-farletuzumab on patients will be less than 1 mg in total. Toxicity of 211At-farletuzumab must, however, be properly studied in a clinical phase I trial. In the clinical study using the 211At-MX35 F(ab′)2 compound, we did not reach the maximal tolerated dose (MTD) [In a clinical study on i.p. delivery of was seen . Similarwas seen , and these (MTD) ,5. We dise (MTD) . These a211At-farletuzumab for adjuvant intraperitoneal therapy in as clinically relevant settings as possible, some unavoidable shortcomings remains. The number of relevant antigens might vary considerable on patients' tumor cells; there might be uptake in healthy organs that does not present in mice; the stability of the compound might be compromised; and the presented models might be too simple to predict real distribution in patient. In summary, the current investigation of intraperitoneal therapy with 211At-farletuzumab, delivered at clinically relevant 211At-mAb radioactivity concentrations and specific activities, shows much promise and warrants further clinical testing.While we have attempted to evaluate the efficacy of 10.13039/501100004359Swedish Research Council (grant number 02275-2017), the 10.13039/501100002794Swedish Cancer Society , the 10.13039/501100010223King Gustav V Jubilee Clinic Research Foundation  and grants from the Swedish state under the agreement between the Swedish government and the county councils, the ALF-agreement ALFGBG-435001. The investigational antibody farletuzumab was provided free of charge from Morphotek Inc., Exton, PA, USA.This study was funded by the All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. This article does not contain any studies with human participants performed by any of the authors.Stig Palm: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration, Funding acquisition.Tom Bäck: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing - review & editing, Visualization.Emma Aneheim: Methodology, Formal analysis, Investigation, Writing - review & editing, Visualization.Andreas Hallqvist: Writing - review & editing.Ragnar Hultborn: Conceptualization, Writing - review & editing, Supervision.Lars Jacobsson: Conceptualization, Methodology, Software, Validation, Formal analysis, Writing - review & editing, Visualization, Supervision.Holger Jensen: Resources, Writing - review & editing.Sture Lindegren: Methodology, Investigation, Writing - review & editing, Funding acquisition.Per Albertsson: Conceptualization, Methodology, Writing - review & editing, Visualization, Supervision, Project administration, Funding acquisition.The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Stig Palm, Tom Bäck, Lars Jacobsson, Sture Lindegren and Per Albertsson declare ownership in the Swedish commercial company Aprit Biotech AB. The data in this manuscript was derived prior to the declared ownership.


The clinical success demonstrates the enormous potential of immunotherapy in cancer treatment.This article presented research linking gastric cancer to immune cells, based on RNA-seq data of Stomach adenocarcinoma (STAD) and gene expression profile of GSE84437, 24 kinds of tumor-infiltrating immune cells were quantified by single-sample gene set enrichment analysis.Th2 cells, T helper cells, and Mast cells were identified as prognostic immune cells in both TCGA and GEO groups. Then SUPV3L1 and SLC22A17 were identified as hub genes which may affect immune cell infiltration by correlation analysis. Survival analysis further proved that hub genes and prognostic immune cells are associated with the prognosis of gastric cancer. In gastrointestinal tumors, hub genes and prognostic immune cells also found differences in non-tumor and tumor tissues.We found that three immune cells infiltration are associated with the prognosis of gastric cancer and further identify two hub genes. These two key genes may affect immune cell infiltration, result in the different prognosis of patients. Many experimental and theoretical studies indicate that most solid tumors are associated with immune infiltrate, as early as 15 years ago, immune response within colorectal cancers are associated with early metastatic invasion and survival were introduced by Franck Pagès et al. . With thTumors are composed of many types of cells, the main part of which is a large number of malignant cells. Tumor-infiltrating immune cells are also one of the types that play an important role , 6, for We download gene expression data, somatic mutation data and clinical data of stomach adenocarcinoma (STAD) from the cancer genome atlas (TCGA) database by TCGAbiolinks and maftools packages in R 3.5.1, , 12). In. In12]).Gastric cancer patients died of non-malignant disease and other malignancies were excluded, and samples with complete survival data were included. According to Gabriela Bindea et al., we obtained the marker genes of 24 immune cells, including aDC, B cells, CD8 T cells, Cytotoxic cells, DC, Eosinophils, iDC, Macrophages, Mast cells, Neutrophils, NK CD56 bright cells, NK CD56 dim cells, NK cells, pDC, T cells, T helper cells, Tcm, Tem, TFH, Tgd, Th1 cells, Th17 cells, Th2 cells and TReg  . Then, P < 0.05) in both groups be considered as effects of prognosis. For further evaluate the impact of the immune cells with statistically significant, patients were divided into 2 groups according to the method of best separation in “survminer” R package, then, overall survival were analyzed by “survival” R package. Kaplan Meier-plotter  could assess the effect of hub genes on survival [P value were calculated and displayed on the plot.The association between immune cells and overall survival was carried out using univariate Cox regression, immune cells with statistically significant were selected as hub genes. The method of survival analysis of hub genes is the same as the previous step.Hub genes were several genes that are related to immune cells. The methods of Pearson correlation coefficient and Spearman’s rank correlation coefficient were used for calculation of the correlation between gene expression and immune cells, genes with p < 0.05).Data of tumor mutational burden were downloaded by “TCGAbiolinks” R package, “Maftools” R package was used to read the maf files and count the number of variants in each sample. We tried to analyze whether there are differences in tumor mutational burden (TMB) between the high and low expression of hub genes and prognostic immune cells. 322 samples with complete survival information, gene expression data and TMB were included. According to the method of best separation in “survminer” R package, patients were divided into groups of high and low, and the Wilcoxon test was used to identify differences of tumor mutational burden(https://xenabrowser.net/datapages/), Normal tissue data is from Genotype tissue expression (GTEx) database, tumor tissue data is from TCGA database, and infiltrating immune cells were quantified by ssGSEA.Gastric cancer is one of digestive system tumor, we further compare the differences of immune cells and hub gene expression between digestive tumors and normal tissue. Gene transcripts per million (TPM) of digestive system normal and tumors tissues were downloaded from UCSC Xena  screened between the high and low group, gene set enrichment analysis (GSEA)  and enriP < 0.05) related to the survival of patients with gastric cancer in two datasets, and the three immune cells showed the same effect. Th2 cells and T helper cells were protective factors, and Mast cells were risk factors. The survival plot based on the best separation of high and low infiltration of each immune cell in TCGA and GEO datasets. As shown in Fig. After quantification of infiltrating immune cells, univariate Cox regression was used to screen immune cells that affect prognosis. Results of TCGA and GEO datasets were shown in Fig. P < 0.01 and correlation> 0.3 were selected. In TCGA group, 4844 genes which associated with Mast cells, 2160 genes which associated with T helper cells and 2984 genes which associated with Th2 cells were screened out, in GEO group, 2128 genes which associated with Mast cells, 372 genes which associated with T helper cells and 1590 genes which associated with Th2 cells were screened out. SUPV3L1 and SLC22A17  and SUPV3L1low(188 samples) groups and SUPV3L1 expression was used to divide patients into SLC22A17high (243 samples) and SLC22A17low(117 samples) groups. As shown in Fig. http://kmplot.com/analysis/). It was found that expression of SLC22A17 (HR = 1.58 (1.18–2.12), P = 0.0022) was associated with worse overall survival (OS) for gastric cancer patients,expression of SUPV3L1 (HR = 0.6 (0.45–0.8) P = 0.00034) was associated with good overall survival (OS) for gastric cancer patients  in tumor mutational burden between patients in the high and low group, high expression of SUPV3L1 and infiltration of Th2 cells and T helper cells and low expression of SLC22A17 and infiltration of Mast cells coupled with a high mutational burden  in the expression of SUPV3L1 and SLC22A17  were identified significantly associated with SLC22A17 expression  were identified significantly associated with SUPV3L1 expression ”, “ECM-receptor interaction”, “Glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate”, “Malaria”, “Protein digestion and absorption” and “Regulation of lipolysis in adipocytes”, Six representative pathways about SLC22A17 were “Apelin signaling pathway”, “Cushing syndrome”, “MAPK signaling pathway”, “Oxytocin signaling pathway”, “PI3K-Akt signaling pathway” and “Platelet activation” Fig. . 3 up-reion Fig. , 125 up-ion Fig. a, KEGG aion Fig. b.Fig. 4The clinical success of immune checkpoint therapy recently, demonstrate the enormous potential of immunotherapy in cancer treatment. Currently, the main treatment method of patients with cancer is blocking CTLA-4 and PD-1 pathways and CAR T cell therapy. These methods were dependent on a sequence of basic science discoveries , Dong H Single-sample gene set enrichment analysis (ssGSEA) can be used to quantify immune infiltrating cell types based on the marker genes of immune cells , 10, basThrough the analysis, Th2 cells and T helper cells were identified as protective factors, and Mast cells as a risk factor, but immune cells may play a dual role in cancers, even one kind of immune cells has a dual role. It has reported that Th2 cells can be used to eradicate cancer , and Th2However, the concrete mechanism is still unknown, further analysis was performed and two related hub genes (SUPV3L1 and SLC22A17) in three immune cells types of TCGA and GEO groups were regarded as hub genes for further validation, indicating that the two hub genes had a high connection with infiltration as well as prognosis. It has been reported that overexpression of SLC22A17 associated with poor prognosis of cancer, such as endometrial carcinoma, gliomas and hepatocellular –30, and Below, we illustrated the differences between hub genes and prognostic immune cells in non-tumor tissues and tumor tissues within the context of specific gastrointestinal tumors. We can see the different infiltration of 3 kinds immune cells in normal and tumor tissues, Mast cells is less in tumor tissue, and Th2 cells is more in tumor tissue, it further suggested that immune cell infiltration may related to gastrointestinal tumors.In this paper, we found that three immune cells infiltration are associated with the prognosis of gastric cancer and further identify two hub genes. These two key genes may affect immune cell infiltration, result in the different prognosis of patients.