Efficacy of treosulfan, used in the treatment of marrow disorders, depends on the activity of its monoepoxy—(EBDM) and diepoxy compounds. The study aimed to describe the pharmacokinetics of treosulfan and EBDM in the rat plasma and brain by means of mixed-effects modelling.The study had a one-animal-per-sample design and included ninty-six 10-week-old Wistar rats of both sexes. Treosulfan and EBDM concentrations in the brain and plasma were measured by an HPLC–MS/MS method. The population pharmacokinetic model was established in NONMEM software with a first-order estimation method with interaction.One-compartment pharmacokinetic model best described changes in the concentrations of treosulfan in plasma, and EBDM concentrations in plasma and in the brain. Treosulfan concentrations in the brain followed a two-compartment model. Both treosulfan and EBDM poorly penetrated the blood–brain barrier . Treosulfan plasma clearance was significantly lower in male rats than in females (0.273 L/h/kg vs 0.419 L/h/kg).The developed population pharmacokinetic model is the first that allows the prediction of treosulfan and EBDM concentrations in rat plasma and brain. These results provide directions for future studies on treosulfan regarding the contribution of transport proteins or the development of a physiological-based model.The online version of this article (10.1007/s43440-020-00115-0) contains supplementary material, which is available to authorized users. Treosulfan is a small, polar molecule (molecular weight = 279.29 g/mol) that gains importance in the treatment of marrow disorders. Treosulfan itself does not exert a pharmacodynamic effect, and its cytotoxic properties result from the activity of its transformers. These molecules with one -1,2-epoxybutane-3,4-diol-4-methanesulfonate, EBDM) or two epoxide rings -1,2:3,4-diepoxybutane, DEB) are formed in a pH and temperature-dependent reaction . The results of clinical trials indicate that treosulfan is effective and well-tolerated in patients with acute myeloid leukemia [p = 0.0000164) within 24 months post-transplantation [p = 0.0082) and transplantation-related mortality  were also better in treosulfan-treated patients. In June 2019 treosulfan was authorized for marketing by the European Commission as a part of conditioning treatment prior to allogeneic hematopoietic progenitor cell transplantation under the name TRECONDI [At first, treosulfan was registered for the treatment of ovarian cancer, under the name Ovastatleukemia . Also, iTRECONDI .The mechanism of transport of treosulfan through the blood–brain barrier is not well known. Several issues hint that there is a need for a further, more thorough investigation of this matter. First, an advantage of treosulfan over busulfan is its low neurotoxicity . RecentlThe low penetration of treosulfan into the central nervous system is advantageous in terms of limited neurotoxicity, but may also pose some challenges. The central nervous system is often seen as a “sanctuary site” because drugs do not usually penetrate into it . The cauThese results indicate that pharmacokinetics of treosulfan need further investigation, especially in the aspect of penetration into the central nervous system. Therefore the present study aimed to construct a joint population pharmacokinetic model that would describe the pharmacokinetics of both treosulfan and EBDM in plasma and the brain, on the basis of the data gathered from the aforementioned animal-model study.As mentioned before, the present study presents a population pharmacokinetic approach and an in-depth analysis of data gathered in our previously published study, with a focus on plasma and brain concentrations of treosulfan and EBDM . The genThe study included a total of 96 Wistar rats: 48 male and 48 female. Local Ethics Committee for Experimental on Animals approved the design of the study. All of the procedures were conducted in full compliance with the European Community guidelines, as well as national, ethical, and regulatory principles, per the spirit of Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC). Every possible effort was made to minimize animal suffering.The animals adapted to the conditions for 2 weeks in standard cages under controlled temperature (22 ± 1 °C), humidity (55 ± 15%), and lightning (12 h light/dark cycle). Rats had ad libitum access to water and a standard commercial feed. On the day of the study, the animals were 10-week old, with a mean bodyweight of 306 ± 25 g and 188 ± 15 g for males and females, respectively.Certified reference material of treosulfan was kindly supplied by medac GmbH . The solution for injection was prepared by dissolving 1000 mg of the drug powder in 20 mL sterile water. A 500 mg/kg b.w. the dose of freshly prepared treosulfan solution was injected intraperitoneally into the lower left quadrant of the abdomen. Before blood and tissue collection, the animals underwent general anesthesia with an intraperitoneal bolus of 50 mg/kg b.w. ketamine  and 10 mg/kg b.w. xylazine .via heart puncture. The blood was immediately acidified by the addition of 50 µL of 1 M citric acid per 1 mL of blood  to halt ex vivo conversion of treosulfan to EBDM and further transformation of EBDM to DEB. After blood collection, the animals were sacrificed for the collection of organs. Brains were rinsed with 0.9% NaCl, weighed, and mechanically homogenized with 0.05 M citric acid (5 mL/g of the tissue). Full blood and tissue homogenates were centrifuged at 4000 × g for 10 min. Resulting plasma and particle-free supernatants were stored at − 80 °C until further analysis.The study followed a one-point per animal design. The samples were collected predose and 0.25, 0.5, 1, 2, 4, 6, and 24 h after the treosulfan injection. From each animal 5 mL of blood was withdrawn v/v). The analytes were detected in a multiple reaction monitoring modes, and the electrospray ionization interface operated in a positive mode. Drying gas (nitrogen) flew at a rate of 10 L/min at 300 °C. The mass transitions used for monitoring treosulfan were 296.0 → 183.1 (for quantification), 296.0 → 279.1, and 296.0 → 87.1 (for identification); the mass transition used for monitoring and quantitation of EBDM was 296.0 → 87.1. The corresponding collision energies were 5 eV, 5 eV, 9 eV, and 5 eV, respectively.Treosulfan and monoepoxide were determined with a validated LC–MS/MS method . The samQuality control was assured by the use of calibration standards, prepared by spiking a drug-free matrix with known concentrations of the analytical standards. The lower limits of quantitation for treosulfan in plasma and brain homogenate supernatant were 0.22 µM and 0.11 µM, respectively; limits for monoepoxide were 0.93 µM and 0.19 µM, respectively. Concentrations of treosulfan and EBDM in the rat brain were calculated knowing that 1 µM in the brain homogenate supernatant was equivalent to 6 µmol/kg in the brain tissue . Then, cNONMEM software package  was used for pharmacokinetic modeling. Diagnostic plots were generated with the R program  and Xpose . Perl Speaks NONMEM scripts  , 18 werep < 0.05) in the objective function value (OFV) between two nested models. Model fit was also assessed by visual examination of diagnostic plots. For each model following plots were evaluated: individual (IPRED) and population- predicted (PRED) concentrations versus observed concentrations, IPRED and PRED concentrations versus time, weighted (WRES) and conditional-weighted residuals (CWRES) versus predicted concentrations, WRES and CWRES versus time and the distribution of CWRES.Pharmacokinetic parameters in linear models were estimated with a first-order estimation method with interaction (FOCE-I) through an ADVAN5 subroutine. A significant improvement in the model fit was marked by a 3.84 decrease (V). Unless a defined kinetic constant value was used, the transfer between adjacent compartment was parametrized in terms of intercompartmental clearance (Q). The dose of treosulfan was specified in the model as 1797 µmol/kg for each animal. Therefore, all of the distribution volume and clearance parameters are also expressed per kg body weight. The pharmacokinetic parameters were assumed to be log-normally distributed [θij is a value of the j-th pharmacokinetic parameter for an i-th individual, θj is the population parameter estimate, and ηij is a random variable characterizing IIV, which is normally distributed with mean zero and variance of ω2.The model was parametrized in terms of clearance (Cl) and distribution volumes  in plasma up to the total dose of 47 g/m2 (1161 mg/kg b.w.) [Pharmacokinetics of treosulfan and EBDM is linear because previously published research showed linear changes of the area under the time-concentration curve (AUC) and maximum concentration (kg b.w.) ;Unbound fraction of treosulfan and EBDM in the plasma and brain homogenate is above 0.94 . TherefoTransformation of treosulfan to EBDM is irreversible ;Transformation rate constant of treosulfan to EBDM in plasma (pH = 7.40) equals to 0.451 1/h ;kf is the transformation rate constant. It was not possible to measure the exact pH of each brain sample. Therefore it was assumed that it has a pH similar to the intracellular pH in the brain, which is equal to 7.2 [The transformation rate constant of treosulfan to EBDM in brain homogenate was calculated according to the following equation :\documenl to 7.2 ;Treosulfan can be eliminated either in the unchanged form via the urinary pathway or transformed into EBDM; other pathways of elimination are negligible –28;in) and the extent of the transport (BBB), according to the following equations:in and CLout are influx and efflux clearances, respectively, and kin and kout represent the transport rate constants between the central plasma and the central brain compartments with the respective volumes of Vcpl,, and Vb,c.Blood–brain barrier transport was estimated similarly as presented by Borström et al. . It was Following assumptions were made before model development:The model was developed sequentially. No preliminary data on the population pharmacokinetic parameters of treosulfan in rats were available. Therefore it was not possible to input previously reported values as fixed. First, only the data on treosulfan plasma concentrations were included, then EBDM plasma concentrations were added, followed by treosulfan brain concentration and finally EBDM brain concentrations. At each step, the models were examined as described in the previous section. The volume parameters of the model that best fitted the observed data were fixed to the estimated values and included in the next step of the analysis. With this approach, along with fixed transformation rate constants, it was possible to avoid problems with the identifiability of clearance parameters.p < 0.05). In the second step, the covariates were sequentially removed. A relationship was kept in the model if the OFV increased by more than 6.67 (p < 0.01). Only the most significant parameter-covariate relationships were included in the final pharmacokinetic model.The influence of sex on pharmacokinetic parameters was investigated. A PsN script scm (stepwise covariate model building from NONMEM model) was utilized. The significance of covariates was evaluated in a “forward-inclusion backward elimination” process. First, the sex was added as a covariate for all of the parameters, and the OFV of the nested models was calculated. The most significant parameter-covariate relationship that was associated with the largest decrease in OFV was retained. The process was repeated until no further relationships were significant  was performed for 1000 simulated observations. 95% prediction intervals (PI) were constructed upon the simulated time-concentration data and compared with the observed data. Second, the bootstrap analysis was performed. 1000 bootstrapped datasets were used for the determination of a median value and 5th–95th confidence intervals (CI) for each parameter included in the model. To better resemble the structure of the original dataset, the resampling procedure was stratified on time points and animal sex. Therefore, each of the bootstrapped sets included at each time point the same number of animals of the same sex, as in the original dataset. Bootstrapped values of pharmacokinetic parameters were compared with the final model estimations.Inspection of the measured concentrations revealed that one of the female rats had exceptionally high concentrations of treosulfan in plasma 0.25 h after administration (3059.7 µM). This concentration was almost threefold higher than the average concentration in the rest of the samples (1147.0 µM), and the associated absolute value of CWRES exceeded 5. Therefore we decided to remove the data from this individual from the analysis. Also, samples collected 24 h after the administration had concentrations of the analytes below their respective quantitation limits, and this time point was excluded from the further analysis.The final structural model is presented in Fig. k12 = 5.12 1/h), but the IIV of this parameter is large and exceeds 50% (Table F).Peritoneal administration of treosulfan allowed rapid absorption of the drug .V6/F) had to be included to describe the pharmacokinetics of treosulfan in brain adequately. It significantly improved the fit of the model and lowered the OFV (ΔOFV = − 126.04 between the two discussed models). The estimated value of V4/F, which is associated with the concentrations of treosulfan in the brain homogenate, was small compared with the other compartments and equalled approximately 3 mL/kg. This value was similar to the average volume (6.56 mL/kg) of extravascular space in rat brain [As presented in the VPC graphs Fig. , changesat brain . TherefoV5/F) was also set to the physiological value of 6.56 mL/kg because in the first estimation V5/F was very similar (5.6 mL/kg). We tested models that assumed exclusive formation of EBDM from treosulfan in the brain tissue, but the models that well fitted the data included both the transport of EBDM across the blood–brain barrier and its formation from treosulfan. VPC graphs  of the final model was 44.5, and it indicates that the model is not over-parametrized or ill-conditioned.During the model refinement step we inspected the IIV values, their standard errors, and shrinkage. We excluded from the model the IIV parameters that could not be adequately and precisely estimated from the model (e.g. RSE > 100%) or had a high shrinkage. After removing such a parameter, we inspected the potential changes in the OFV, the diagnostic plots, and if the values for other estimated parameters were reasonable. The final model includes IIV estimates for only trations . However1) and sex remained significant after the backward-elimination process. The model that included the slower elimination of treosulfan from plasma in males had the OFV lower by 9.50 , and the interindividual variability of CL1 decreased by 7.9%.Sex was the only covariate for which we could evaluate an influence on treosulfan and EBDM pharmacokinetics. In the forward-inclusion step of the stepwise covariate modeling method, the inclusion of sex significantly lowered the values IIV of plasma clearances of treosulfan and EBDM Fig. . However1 and BBB2) were below 1  estimated with the final model was similar to the values reported by Romański et al.  [The distribution volume of treosulfan (le rats) . The autle rats) –35.Gender differences in the expression of organic anion transporters (OATP) might explain the differences observed in our study. Expression of the gene encoding OATP1 transporter, localized at the apical plasma membrane of the kidney, is much higher in female rats; as a result, systemic clearance of OATP1 substrates, such as zenarestat, is elevated . AnotherPo/w) is less than 5 [P-glycoprotein, multidrug resistance-associated protein or even OATP, might effectively remove treosulfan from the brain. However, no study so far evaluated the involvement of these transporters in treosulfan pharmacokinetics. Also, the saturability of the process was not assessed. Another finding from the study is a two-compartment disposition of treosulfan in the brain. The distribution volume of the peripheral brain compartment was over 50-fold higher than the volume of the central one. Results from a previous study indicate that for small compounds two compartments may exist in the brain—one that equilibrates rapidly, and the second one that equilibrates for up to 30 min [Several studies provide data on the blood–brain transport of treosulfan and EBDM. In an in vitro study, Linz et al. found that transport of treosulfan in the apical-to-basolateral direction (influx) is lower than the basolateral-to-apical (efflux), with permeability values of 1.6% and 3.0%, respectively . The auts than 5 . Also, to 30 min . The autPo/w = − 1.18 for EBDM vs. log Po/w = − 1.58 for treosulfan, respectively) [kt = 37 °CEBDM → DEB, pH = 7.4,  = 0.206 1/h) and undergoes hydrolysis  [S-transferase and epoxide hydrolase—are involved in the elimination of epoxides. One may hypothesize that the activity of these enzymes, particularly in the liver, contributes to a high systemic clearance of EBDM. Glutathione S-transferase also is present in the brain and may play a role in the observed low exposure of EBDM in this organ. Higher expression of this enzyme in endothelial cells and astrocytes may contribute to the resistance to some antiepileptic drugs [The permeability of EBDM through the blood–brain barrier was almost threefold larger than treosulfan (0.317 vs 0.120). It could be explained by lower hydrophilicity of EBDM (log ctively) . Also, a269 1/h) . It mighic drugs . Solubleic drugs , 44. HowThe limitations of this study also have to be noted. The sampling protocol was destructive, and each data point represents a single individual. Therefore random effects had to be fixed to the error associated with the analytical method. Estimation of random effects could be possible if treosulfan and EBDM concentrations were measured via a microdialysis method and a full pharmacokinetic profile would be provided for each animal. Another advantage of the microdialysis approach would be to apply a prolonged and denser sampling protocol without sacrificing an animal for each data point. Also, the exact pH of brain homogenates was unknown, and the value of transformation constant had to be approximated. Another aspect is the age uniformity. As earlier described, the penetration of treosulfan and EBDM is higher in younger animals. Hence the age might be a significant covariate of BBB parameters.SLCO1B1 gene (rs4149056) corresponds with decreased clearance of methotrexate and occurrence of statin-induced myopathy [The results of the present study provide several implications and directions for future studies on treosulfan. If OATPs participate in treosulfan transport via cell membranes, genetic polymorphisms of these proteins might significantly influence the pharmacokinetics of treosulfan or partially explain its variability. Some known polymorphisms in genes encoding OATPs have an established, clinically relevant impact on the safety of drugs. For example, the presence of 512C>T variant in myopathy , 46. Alsmyopathy . FinallyThe developed population pharmacokinetic model allows accurate prediction of treosulfan and EBDM concentrations in rat plasma and brain homogenates. The systemic clearance of treosulfan is significantly lower in male rats. The efflux of treosulfan and EBDM through the blood–brain barrier markedly exceeds their influx into the brain.Supplementary file1 (DOCX 312 kb)Below is the link to the electronic supplementary material.


Globally, water scarcity has become a common challenge across many regions. Digital surveillance holds promise for monitoring environmental threats to population health due to severe drought. The 2019 Chennai water crisis in India resulted in severe disruptions to social order and daily life, with local residents suffering due to water shortages. This case study explored public opinion captured through the Twitter social media platform, and whether this information could help local governments with emergency response. Sentiment analysis and topic modeling were used to explore public opinion through Twitter during the 2019 Chennai water crisis. The latent Dirichlet allocation (LDA) method identified topics that were most frequently discussed. A naïve Tweet classification method was built, and Twitter posts  were allocated to identified topics. Topics were ranked, and corresponding emotions were calculated. A cross-correlation was performed to examine the relationship between online posts about the water crisis and actual rainfall, determined by precipitation levels. During the Chennai water crisis, Twitter users posted content that appeared to show anxiety about the impact of the drought, and also expressed concerns about the government response. Twitter users also mentioned causes for the drought and potential sustainable solutions, which appeared to be mainly positive in tone. Discussion on Twitter can reflect popular public opinion related to emerging environmental health threats. Twitter posts appear viable for informing crisis management as real-time data can be collected and analyzed. Governments and public health officials should adjust their policies and public communication by leveraging online data sources, which could inform disaster prevention measures. Water is essential for human life and a key part of any society. The United Nations describes water as “serving a crucial link between society and the environment” . Fresh wCrisis brought by water shortage is not only detrimental to everyday activities, as mentioned above, but also negatively impacts economic development. For example, during a water crisis in Brazil, the shortage hindered hospital and business operations, as well as school activities . As a reWater scarcity is a growing problem that disproportionately impacts lower-income countries , yet govGovernments are in need of effective approaches for monitoring water supply as well as the ability to obtain real-time information to inform early response to mitigate potential water crises. The recent emergence of digital surveillance techniques may offer a novel approach that is ideally suited for addressing this need. Digital surveillance is performed through collecting vast streams of data from the digital trace, which refers to the data that are generated from everyday use of digital devices, that people leave on a continuous basis following their interaction with modern day technologies, and then extracting information from this data . Social There have been cases where digital surveillance has been used in disaster detection and water management. These approaches build on an extensive body of prior computational studies, where sentiment analysis involves extracting text data from social media platforms in order to capture user opinions and attitudes about a wide variety of topics . For insTo illustrate the potential use of digital surveillance for monitoring water shortages and for managing water crises, the purpose of this paper was to present a case study exploring the use of digital surveillance techniques for monitoring the 2019 water crisis in Chennai, India. This water shortage crisis lasted for several weeks and strongly impacted Chennai, the capital city of the state of Tamil Nadu, India. The water reservoir levels were shrinking steadily over the past year before the crisis, and the trigger of the crisis was the delay of the monsoon season . In thisThe Chennai water crisis started on 19 June 2019  due to tFor this initial exploratory study, we used data collected from Twitter. Twitter is a popular social network platform where people can interact and discuss trending topics with posts containing up to 280 character limits, and the messages posted on the Twitter platform are called tweets ,37. By TThe hashtag symbol (#) is used in front of the keywords in a tweet and can be used to identify the topic of a tweet . To collThe Google translation API was used to translate non-English tweet texts . The GooTo further check the relationship between the tweets and the drought, precipitation was used as a comparison . PrecipiIn order to identify what people were talking about on Twitter regarding the Chennai water crisis, and how they were feeling towards these issues, we used sentiment analysis and topic modeling. Sentiment analysis can calculate how positive or negative each Tweet is, whereas topic modeling can identify different major topics among all the Tweets and categorize them under these topics. We also used topic heat, which makes it possible to categorize each tweet to an identified topic so as to further look into each topic.We examined the number of tweets per day as well as average tweet sentiment to see how people were feeling overall towards the Chennai water crisis. Sentiment analysis is a computational method that can identify to what extent a piece of text is positive or negative, and in this study, it was carried out using VADER . VADER iTopic modeling belongs to natural language processing, and it identifies patterns in a text . We emplGensim returns topics in the format of clusters “weight*word” connected by “+” signs (see example below). The number of topics returned is not a pre-fixed parameter, and in this study was selected manually based on interpretability and performance. If the number of topics was too small, the model with the higher number of topics was going to return more interpretable topics; if the number of topics was too big, some of the topics were going to be too similar to be identified. In this study, the number of topics was set to 8. Many times, the words in a topic appear confusing. To aid the interpretation of the topic, words returned by the LDA model were manually selected to represent that topic, and the following rules were applied in this study: (1) terms that were common in more than two different topics were dismissed as redundant; (2) verbs that did not specify a topic were also excluded; (3) if there were synonyms mentioned in one topic, only the one with the highest weight (the number before each word) was kept; (4) words that could not be found in VADER word collections were excluded; and (5) the top three weighted words were selected, after applying all the previous four rules. The lowest number of words that a topic has left after applying these first four rules is three; therefore, to keep all topics consistent, the number of words that represents the topic was capped at three. For instance, below we illustrate one of the topics returned by the LDA model:Words: 0.036*“conserv” + 0.031*“crisi” + 0.028*“need” + 0.023*“home” + 0.019*“govern” + 0.018*“citi” + 0.018*“save” + 0.017*“go” + 0.016*“savewat” + 0.016*“wast”Note that only the root of each word is kept within the topics returned by the LDA model, so the complete words from the original text are: conserve, crisis, need, home, government, city, save, go, savewater (as a Twitter hashtag), and waste. According to rules 1, 2, and 4: (1) the words “crisis”, “home”, “city”, and “save” are seen in several other topics; (2) the words “need” and “go” are used as verbs, and do not contain much information; and (4) the word “savewater” is not found in VADER. Therefore, the words representing this topic would be: “conserve”, “government”, and “waste”. It can be understood that this topic would be about conservation, government, and waste. To examine this interpretation, tweets under the topic can be sampled. The next section addresses how individual tweets were categorized into the topics identified.One single tweet can discuss one single topic or several topics at once. To determine which of the topics the tweet is most relevant to, we used a method called topic heat. Topic heat is a quantitative measure that describes how much a topic is being discussed. Topic heat is calculated using a similarity score generated by WordNet ,51. WordTopics are word stems, which are not always included in the database of WordNet. Therefore, topic words generated by LDA are manually transferred into nouns. Tweets were tokenized, and each word was processed similarly, but using WordNet. WordNet can find a set of synonyms of the specific word (including the word itself), ranking in similarity with the word given, and the first noun will be selected. After this, all words that are accounted for in the similarity calculation will be nouns.Topic heat is calculated in the form of total similarity of the words related to the topic. Consider a table, where column heads are the words in the topic and the row head are words from the targeted word corpus, as illustrated in To see which topic an individual tweet is mainly talking about, topic heat calculated in the last step is used. For every single tweet, its topic heat regarding all topics is available. The topic corresponding with the highest heat will be selected as the main topic of a given tweet. In other words, the tweet is labeled with that topic.p = 0.040), where the correlation is higher than the significance threshold labeled by the blue line, is at zero lag. In other words, the fraction of negative sentiments appeared to increase as the number of discussions on Twitter increased simultaneously.To explore the relationship between daily Twitter frequency and sentiment further, the daily negative sentiment fraction and number of tweets are lined up and cross-correlation was performed . Cross-cIn p = 0.119), it is observed that topic 1 (“Lake save future”), 2 (“Conserve government waste”), 6 (“Pond rainwater nature”), and 7 (“Tap world important”), which are with high numbers of tweets, appear to be negatively correlated with precipitation (p = 0.043) of −0.446 with precipitation simultaneously. In other words, tweets mentioning topics 1, 2, 6, or 7 decreased in number when precipitation levels increased during the Chennai water crisis.Although the correlation between the total number of tweets per day and precipitation is not significant (pitation . Throughpitation , it is cDigital surveillance has been previously used for monitoring water resources and natural disasters across diverse settings ,20,21,22Generally, the tweets collected in this study were less negative when referring to water bodies or advocating for support and protection and water conservation. However, the tweets appeared to be more negative when talking about human behavior and the resulting impacts of the water crisis on daily life. From the sentiment analysis for different topics and samples from different tweets, people appeared to positively seek solutions to the water shortage problems, while generally not showing satisfaction with government action but feeling for the suffering of people during the crisis. These findings suggest that Twitter can be a potentially useful resource for local governments, as they could use the platform to collect authentic views from the public and draw from these insights to strengthen their communication campaigns, disaster response, and outreach efforts. This is consistent with prior studies showing that local governments can use social media content to inform decision-making. For example, tweets have been used after mass disasters for enabling quick damage assessment, and delivering messages through social media platforms and text messages, as was demonstrated in the 2010 Haiti earthquake for crisis mapping ,56,57.Exploring the relationship between the sentiment of the tweets collected in this study and actual precipitation levels is an important methodological strength, as it offers insights about how the number of discussions on Twitter fluctuates in response to changing rainfall levels, which is directly relevant to the source of the crisis. This corresponds to prior research showing that abnormal weather events can lead to increases in climate change-related conversations on Twitter . The negThere have been studies using digital surveillance for emergency management when facing disasters, including from Australia, China, and the United States ,21,22. HAnother study using Twitter examined the 2015 Chennai flooding , which hThe methods employed in this study can be executed swiftly, while the massive real-time information sharing on Twitter and accessibility to its data make timely reporting potentially more efficient and able to capture public perceptions . This isThere are also limitations in this study that warrant consideration. For example, in terms of data, analysis results from tweets can only represent Twitter users, which limits the generalizability of these findings. This indicates that our results would exclude the segment of the population who does not have access to the internet and who do not use social media platforms like Twitter. This concern is particularly relevant in lower-resource settings, such as in India, where many individuals, especially those living in impoverished settings, do not have access to digital technologies or use social media. In terms of methodology, the key words needed for the tweet collection, number of topics returned by LDA, as well as topic key words were obtained through manual browsing and selection, which might not be applicable to all cases. Tweets are in different languages and possible inaccuracies in Google Translate may affect the outcome. Further, as we are expecting public opinion from local people who are impacted by the water crisis, there is concern that the locations of most tweets are not possible to be identified, as many users do not make their location information publicly available. This is an inherent limitation with data captured from Twitter. The topic model proposed is still naïve and issues need to be studied further, such as an alternative method that better quantifies word connections between verbs and nouns. This highlights an important area to advance natural language processing research, by ensuring better accuracy through detecting differences and similarities between nouns and verbs used in online posts. It is noteworthy that while there are limitations with using Twitter for digital surveillance, our findings add to an increasing number of studies showing that social media can effectively capture public opinions about natural disasters in lower-resource settings such as India. Social media platforms like Twitter can offer valuable insights that would be difficult to collect using traditional surveys or interview methods, and could be used to supplement existing public health and government response efforts.This study sheds light on the topics that people are most concerned about and their emotions regarding these specific topics throughout a natural disaster in a low-resource setting. The method employed in this paper is flexible and can potentially be applied in different disaster settings for emergency response purposes, as well as emergency situations that extend beyond natural disasters to other public health threats. Future research can optimize the topic model proposed in this study, and try to incorporate tailored poststratification methods to alleviate the problem of underrepresentation. This is likely a particularly significant concern in the context of India, where many individuals may not have access to social media, and who are therefore not represented in the data captured from these popular social media platforms as part of digital surveillance efforts. Furthermore, our study highlights the need for research to expand on this approach and generate more in-depth and granular characterization of the impacts of a changing environment and crises on population health outcomes. When the accuracy of the method is improved, it can be adapted for a broader range of contexts and settings to enable prompt evaluation of public opinion and to inform disaster response. In the future, governments and local authorities could draw from the methods described in this case study to better understand public opinion and regions of greatest need as a reference to policy-making and communication with their people, especially in emergency situations where immediate information is desired.


Electrospinning is one of the most successful and efficient techniques for the fabrication of one-dimensional nanofibrous materials as they have widely been utilized in multiple application fields due to their intrinsic properties like high porosity, large surface area, good connectivity, wettability, and ease of fabrication from various materials. Together with current trends on energy conservation and environment remediation, a number of researchers have focused on the applications of nanofibers and their composites in this field as they have achieved some key results along the way with multiple materials and designs. In this review, recent advances on the application of nanofibers in the areas—including energy conversion, energy storage, and environmental aspects—are summarized with an outlook on their materials and structural designs. Also, this will provide a detailed overview on the future directions of demanding energy and environment fields. The world’s population is growing annually, raising concerns for energy consumption and its impact on the environment. According to recent publications ,2,3, 80%In current years, nanomaterials are quite known in being potential candidates for energy materials and for removing the pollutants due to their intrinsic properties like high porosity, large surface area, and reduced dimensions . Herein,Until now, one-dimensional (1D) nanomaterials—such as nanofibers (NFs), nanowires (NWs), nanotubes (NTs), nanoneedles (NNs), and nanorods (NRs)—have been synthesized and the potential of their applications in diverse fields associated with energy and environmental crisis including ion infused batteries, biosensors, high performance filtration, heavy metal adsorption have been reported. Many synthetic approaches have been employed to produce nanomaterials, where electrospinning technique has outshined as a promising route. Electrospun nanomaterials with unique properties—including high surface-to-volume ratio, low weight, high porosity, tunable morphologies, and strength—can be synthesized through a simple and cost-effective process. They can be finely tuned depending on the solution and electrospinning process parameters . A wide Electrospinning offers opportunities for designing and manufacturing new materials for improving the energy generation, conversion, and storage devices. Application of electrospun nanofibrous materials for energy and environmental issues have been studied in several reviews ,18,19,20The world is facing the exponentially increasing energy demands. Apart from the unprecedented decline in global energy demand (3.8%) in 2020 owing to the impacts of the Covid-19 crises and lockdowns, world total energy demands are forecasted to increase by 1.3% each year according to the international energy agency’s annual projections (the current policies scenario) . TherefoElectrical energy conversion from renewable sources—such as wind, sun, and tide—is one of the considerable concerns in the 21st century world. Converting sunlight directly into electrical energy using photovoltaic technology have been considered as one of the low-cost and high efficiency methods. For the first time, B. O’Regan and M. Grätzel reported dye-sensitized solar cells (DSSCs) in 1991 for obtaining electricity . During In dye-sensitized solar cells, the photoanode has a vital role in the performance of photovoltaic cells as it carries dye and transports the photo-generated electrons of excited dye molecules to the collection electrode. The photoanode consists of a transparent conducting oxide covered by a layer of semiconductor, hence, the structure and morphology of the semiconductor are very important in cell fabrication ,27,28,292 ..−1 in diZ. Jiang et al. prepared magnetic composite nanofibrous mats using oleophilic and hydrophobic polymers such as polystyrene/polyvinylidene fluoride for oil-in-water separation (oil sorption capacity of 35–46 g/g) .3O4 nanoparticles on or in the nanofibers enables magnetic recovery of the mats using an external magnet and increases the mechanical strength of the composite nanofibrous mats compared to that of neat polystyrene mat.Incorporation of magnetic Fe−1 for silicone oil) . P. P181]. −4 L/m−2/h−1 and an equilibrium particle suspension flux of 0.545 × 10−4 L/m−2/h−1 under extremely low pressure (0.025 MPa).Studies showed that antifouling property in hydrophobic polymer membranes could be improved by blending with hydrophilic polymers. In one study, intrinsic fouling features of hydrophobic polyvinylidene fluoride (PVDF) was solved by hydrophilic modification of PVDF membrane . Li et aAs the permeability decreases from microfiltration to ultrafiltration membranes, the operating pressure increases to guarantee a sufficient flow of liquid stream (the flux). Ultrafiltration membranes (pore size around 0.1 micron to 0.01 micron) requiring an applying pressure of 1–10 bar ,179 are Schiffman et al. displayed the enhanced performance of ultrafiltration membranes by electrospun nanofibers. Composite ultrafiltration membranes with highly porous nanofiber layers of hydrophilic cellulose (WCA 0°) or robust hydrophobic polysulfone (WCA 110 ± 5°)  were evaIn another article, Wang et al. proposed the improved potential of membranes made of thermoplastic nanofibers and skeleton nanofibers in ultrafiltration upon hot-pressing . Hot-prePressure-driven membranes with a mean pore size around 0.001 micron and applied pressure of 10–15 bar ,179 reta4 solution and 76% against NaCl solution) were attributed to formation of an interface between the PVDF and PA matrix due to small amounts of tree-like branched structures that inserted into the polyamide layer and acting as directed water channels. In another study, composite electrospun polyvinylidene fluoride (PVDF)/poly(vinylpyrrolidone) (PVP) nanofiltration membranes containing chitosan/activated carbon/silver nanoparticles were synthesized by Nakhowong’s group. They showed that incorporation of Ag nanoparticles with intrinsic antibacterial characteristics into composite membranes improve the antibacterial activity of membranes against E. coli and S. aureus . I. I215]. Very good performance in an AGMD, reported to be achieved by modifying the hydrophobicity and the surface structure of the membranes. Attia et al. synthesized composite nanofibers by embedding alumina nanoparticles in nanofibrous structures . The hie−2/h−1 for 7 days DCMD duration).Potential applicability of electrospun nanofibers as supporting material in dual layer membrane distillation was demonstrated by Deka et al. . They shBiological membrane bioreactors are alternative to challenging conventional activated sludge processes. Integration of membrane-based technologies in conventional anaerobic biological treatments has been caused replacing large clarifiers (settling basins) and therefore reducing the cost. Moreover, high quality effluents and lower quantity of sludge are superior features of this method. Membrane bioreactor (MBR) technology in water reclamation and recycling is promising for saving large quantity of water from industrial and municipal wastewater that can be used for gardening, car washing, and cooling processes. Potential applications of this technology have also been evaluated for removing compounds of emerging concerns ,223 and Membrane bioreactor technology relies on biodegradation of a mixed liquid (a mixture of raw or settled wastewater and activated sludge ) employiElectrospun membranes draw growing interest in water treatment bioreactors as well. Functionalized nanofibrous membrane with special wettability for irreversible fouling control, chemical, and physical strength of membrane have been used in bioreactors . L.F. Re2/h) was related to the surface hydrophilicity due to the extra hydroxyl and carboxylate groups on the surface of Fum-A. Hydrophilic membranes form a water layer on their surface that acts as a barrier for foulants. Modification of tubular electrospun polyacrylonitrile nanofibers was carried out by heat treatment followed by coating a polyamide layer on electrospun backing layer [2/h) and salt rejecting performance compared to conventional osmotic membranes at external and submerged bioreactors under pressure. The low salt flux value of 0.5 g/m2/h may be a beneficial feature for suppressing salt accumulation in bioreactors. Furthermore, unusual results were obtained compared to osmotic membranes. Increasing the applied pressure on the feed side accompanied by improving the water flux with no salt accumulation in the feed side. The salt content of feed side is kept constant compared to osmotic membranes because of the increased flow of salt to the concentrate side with no reverse salt stream.Moradi et al.  reportedng layer . The modNanofiltration and reverse osmosis are specific procedures for eliminating microorganisms from water . Neverth2 bacteriophage removal. In another article, a three-layered multifunctional portable device was suggested for water purification by Taheran et al. [Escherichia coli and Staphylococcus aureus.Biocidal agents including metal or metal oxide nanoparticles , synthetic or bio-based disinfectants like N-halamines, quaternary ammonium compounds, and chitosan have been studied widely ,242,243.n et al. . The firn et al. . Nanofib2, ZnO, and TiO2 and organic-based chemiresistive sensors are two main groups of this category. Mechanisms of sensing and resistive responses depends on the oxidizing or reducing nature of the target gas and charge carriers on the surface of semiconductors (electrons or holes) [Gaseous pollutants and volatile organic compounds (VOCs) are two major contributors to air pollution. Gas sensors or gas detectors have been designed for sensing odorless gases or trace concentration of vapors which cannot be detected by human nose . The mosr holes) .Besides, other types of sensors have been developed. Optical gas sensors measure the changes of light property like wavelength in the sensing material. For example, IR point sensors measures the absorption and reflection of IR waves through the interaction of analyte gas. In calorimetric sensors heat is generated through the interaction of target gas with the sensing materials. Quartz crystal microbalances (QCM) as a major type of acoustic wave sensors has been widely applied for chemical sensing. Quartz crystal microbalance technology is principally based on the changes in resonance frequency of piezoelectric quartz crystals with the mass uptake on the sensing layer. A schematic view of these sensors is depicted in Various kinds of materials including conductive polymers, metal-oxide semiconductors and composite materials have been applied as sensing materials. Gas sensors based on electrospun membranes have proven higher sensitivity and reduced the energy requirements of metal-oxide based sensors, thanks to their larger specific surface area, highly porous structures, and special electrochemical characteristics ,250. QCM2/PVP nanofibers (NFs) (mean diameter of 41–281 nm) [2 gas with very low power consumption (60 mW) was attributed to highly porous structure and large surface to volume ratio of one-dimensional nanofibers of TiO2/PVP.Al-Hazeem et al., fabricated an n-type semiconductor hydrogen detector based on electrospun TiO–281 nm) . Sensing2O3 nanowires with Pt core was synthesized by co-electrospinning method and coated with a silica mesoporous SBA15 (santa barbara amorphous-15) molecular sieve layer as the moisture barrier [Doping electrospun nanofibrous membrane with pH sensitive dyes have been used for fabrication of colorimetric sensors  as well. barrier . The sen2 nanoplates via electrospinning combined with post heat treatments of polyvinyl pyrrolidone incorporated metal salts 3·6H2O). PtNi alloy/CeO2 plates/N-doped carbon nanofibers showed stable structure and superior sensing functions (high sensitivity of 345.0 µA/mM−1/cm−2) for hydrogen peroxide detection in cosmetic products . Electrocatalytic activity of the fibers was attributed to highly porous structure, high surface area of the fibers and the small size and uniform distribution of PtNi alloy nanoparticles and CeO2 plates because of the facilitated electron transfer.Electrospun nanofibrous-based sensors have also been entered in the field of detection of liquid analytes due to their excellent properties. Developing selective and sensitive sensors and biosensors with low detection limits and selective detection of a desired molecule among interfering molecules have great importance for disease detection , immune-sensing (detection of serum or other body fluids), hydrogen peroxide, and toxin detection in food and cosmetic industry and for environmental monitoring applications . It is nBoth short-term and long-term exposures to airborne particulate matter (PM) pollution contributes to respiratory and cardiovascular disease and cancers . PM10  and PM2.5  are two major components of particulate matter that pose health risks. Electrospun nanofibrous filters with nanosized fiber diameters, highly porous structures, large pore size, tortuous channels, along with their light weight are good candidates for PM filtration compared to conventional micro-sized fiber-based filters. However, their low mechanical strengths limit their application independently and therefore, they supported on nonwoven substrates or used as composite materials. Pure polymeric filters and composite polymer-based filters have been studied. Composite polymer-based filters promote the mechanical and tensile strength of the membrane . ElectroAs an example, superlight weight air filters using superhydrophobic nanofiber net structures with sieving and surface adhesion property was reported by Liu et al., to filter particles under 0.3 microns (PM0.3) . PolyvinAnother study conducted by the same authors reported the fabrication of surface-modified nanofiber net filters inspired by the nature . Wet-adh3O4, PU-CNT-NiFe2O4, PU-CNT-Ni0.5, Zn0.5Fe2O4) [2S filtration. The superior performance of PU-CNT-Fe3O4 nanofibers in H2S removal (498 mgH2S/g) compared to pure PU nanofiber and other PU nanofibers containing CNT-ferrite composites was ascribed to sulfurization (sulfide formation from the reaction of iron ions with hydrogen sulfide).Organic/inorganic nanoparticle incorporated polymer-based filters improves their adsorptive capacities as well. Maddah et al. fabricated a series of electrospun polyurethane nanofibers based on composite carbon nanotubes incorporated Ni-Zn ferrite particles  . The capE. coli bacteria. Highly porous nanofibers with 1% and 10% AgNPs exhibited excellent filtration efficiency (≈100%) and good quality factor (0.04 Pa−1) for removing sodium chloride (NaCl) aerosol particles (9–300 nm diameters). Filters with 50% AgNPs showed the lowest pressure drop and high filtration efficiency (>98.6%) in elimination of nanoparticles (9–300 nm) from the air (E11 according to standards of EN 1822). The lowest pressure drops, high permeability and larger void space in nanofibers with 50% AgNPs content were related to the higher concentration of silver ions in the electrospinning solution that increases the viscosity of the solution, generating a lower rate of nanofiber deposition on the collector.Biocidal activity was induced into polyacrylonitrile nanofibers by incorporating silver nanoparticles . NanofibSupported catalysts have demonstrated interesting benefits including facile recovery and reusability of the heterogenized catalyst either magnetically or gravitationally and preventing leakage of the catalyst especially in the case of precious metal catalysts. Application of electrospun nanofibers directly as catalyst or catalyst supporting materials are one of the most studied research areas . Encapsu3/g and a high thermal conductivity (4.17 Wm−1/K−1 at room temperature). Multiple inter-layer reflections in multi-layer structure of the hot-pressed electrospun mats caused absorption of EMI wave to be the major energy loss mechanism, compared to bulk material. The authors attributed higher thermal conductivity of the hot-pressed electrospun mats to the more aligned arrangement of nylon 66 chains after electrospinning and increased crystallinity of the polymer. Huang et al. prepared a flexible composite film comprising electrospun TiO2/SiO2 (TS) nanofibers as the substrate [2/SiO2 nanofiber and polypyrrole shell. Thin reduced graphene oxide sheets were further attached on the surface of the nanofibers. The fabricated films exhibited EMI shielding effectiveness of ~30 dB in X band region (8–12 GHz) and a higher specific EMI shielding effectiveness (SE) of ~13829 dB·cm2/g−1 compared to most of metallic EMI materials.Electromagnetic radiation or interference (EMI) emitted from surrounding electronics is another concerning pollution along with water and air pollution in recent years. The great importance of protecting sensitive electronic equipment against electromagnetic interference in practical applications including portable devices, critical defense systems, and aerospace has provoked attempts for developing flexible high-performance EMI shielding materials with light weight and wide absorption bandwidth while maintaining mechanical strength. EMI shielding materials capable of absorption or reflecting electromagnetic interference playing a shielding role against electromagnetic waves. Conductive materials such as metals, conductive polymers, graphene , and comubstrate . Polypyr2 emissions to the atmosphere, carbon capture and storage (CCS) technologies have been developed and referred to capture of emitted carbon dioxide from industrial processes and then releasing the captured CO2 and transportation to a secure storage. A vast number of chemical and physical based systems [2 liquefaction [Carbon dioxide with the ability to trap heat is the leading contributor to global warming and ocean acidification and mainly entered to the atmosphere through fossil fuel combustion according to IEA  . Regardi systems  have beeefaction ; high prefaction .2 capture technologies and industrially practical CO2 separation materials for highly selective capture of CO2 from a mixed gas stream having good CO2-capturing capacity and stable adsorption/desorption performance has spawned significant effort around the world. Electrospun nanofibrous materials with their unique structures—including high surface area to volume, ease of functionalization, high flexibility, tailorable pore size, and surface chemistry—have motivated researchers to explore and assess the potential application and performance of such materials and their modified forms as CO2 capturing agents. Surface functionalization of the adsorbents endows active functional groups with intrinsic affinity to CO2 and provides more active sites for selective CO2 adsorption as well. Amine-bearing nanofibers have displayed high CO2-capturing capacity due to the high affinity of NH2 group toward CO2 molecules that provides further CO2 adsorbing sites and enhances selectivity of the material. For instance, Abbasi et al. reported grafting poly glycidyl methacrylate onto electrospun syndiotactic polypropylene nanofibers via radiation-induced grafting [2 adsorption capacities of 2.87, 2.06, and 0.94 mmol/g respectively.Designing and developing COgrafting . Nanofib2/g and 0.404 cm3/g). Compared with non-activated composite, CO2 capture capacity was significantly improved . In a similar study, activated carbon nanofibers by carbonization and activation of reduced graphene oxide incorporated-PAN nanofibers were synthesized by Othman et al. [2 capture capacity of 58 mmol/g at 15 bar and 25 °C. Electrospun materials containing MOF nanoparticles have developed as well and demonstrated mechanical properties along with improved CO2 adsorption capacities. Organized and open porous structure of MOFs coupled with high surface area of electrospun nanomaterials supply more active sites enables them to improve capturing performance. Choi et al. reported fabrication of polyacrylonitrile nanofiber immobilized with different loading of ZIF-8 MOF and HKUST-1 MOF particles through electrospinning [2 adsorption capacities for MOF-loaded nanofibers assigned to increased pore volume due to the additional porous structure of MOFs. Further functionalization of MOF-loaded nanofibers by impregnation of polyethyleneimine remarkably improved CO2 capture capacity and CO2/N2 selectivity of PAN ZIF-8 mats. Besides, CO2 adsorption capacities were decreased by increasing the polyethyleneimine content because of the decreased pore volume due to the pore blocking by polyethyleneimine molecules.Various methods have been used for increasing specific surface area and porosity of the fibers. As an example, Heo et al. fabricated a porous carbonaceous material by carbonization of PAN/PVDF hybrid composites . Carbonin et al. . Meso- aspinning . ImproveIon exchange is a simple strategy for selective removal of cationic or anionic species depending on the charge of ionic active sites. Ion exchange nanofibrous membranes have revealed excellent features to be good candidates for removal of dye and dissolved poisonous heavy metal ions compared to precipitation, chelation, and adsorption-based technologies. For instance, zeolite  nanopart−4 S/cm) depending on the sulfonation time [Nanofiber-based ion exchange membranes have been studied in delivery systems, water treatments, medical applications, and catalysis. These materials have also been used in energy-related areas. Electrospinning of materials with intrinsic ion exchange ability or functionalized by grafting or encapsulation of ion exchange agents into polymeric matrix have indicated great potential of application in batteries, fuel cells, electrodialysis and reverse electrodialysis . Low torion time .2+ ions were proposed with the adsorption of Cd2+ ions by replacing Al3+ and Mg2+ ions in layered double hydroxide component of the composite membrane. In another study, Johns et al. prepared functionalized polyacrylonitrile nanofibers deposited on a layer of PVDF nanofibers [® 336 on nanofibers appeared to have good U uptake at nearly neutral pH. PAN nanofibers bearing amidoxime group (–C(NH2) = N–OH) exhibited the highest uptake (at pH 6.8).Electrospun micro- and nanofibers of polystyrene loaded with metal-layered double hydroxide particles showed higher stability, provided by thermal stability of the metal-layered double hydroxide, coupled with chemical stability from embedding layered double hydroxides into PS polymer matrix . Removalnofibers . SurfaceElectrospinning is the most successful techniques to fabricate nanofibrous materials with a great control of final outcome. Together with their intrinsic properties like high surface-to-volume ratio, micro/nano-sized fibers, process simplicity and process scalability, electrospinning can provide competitive advantages. As presented in this review, electrospinning has provided significant results in both DSSCs and perovskite solar cell applications, while it has shown its potential in fuel cell fabrication with electrospun nanofiber-based electrode and electrolyte membranes. In addition, larger surface area of nanofiber materials has become crucial in energy storage facilities, since rechargeable batteries and supercapacitors have utilized nanofiber membranes constantly. In this review, a number of electrospinning applications have been discussed with details on their materials and specially the design. On the other hand, controllable physical and chemical properties of nanofiber membranes have attracted environmental remediation field as well. Water treatment, filtration, contaminant removal, and related treatment approaches were reviewed thoroughly while focusing on the applications of nanofibers and various related designs. Also, applications of air filtration and environmental monitoring were considered and summarized.There is no doubt that electrospinning technique has contributed the advancement of energy conversion, energy storage, and environmental applications throughout past decades. Its capability of fabricating nanofiber in composite with multiple materials has become one of key properties in recent years, it can fuel future directions as well. With the introduction of novel and innovative materials in the energy field, electrospinning provides unique material properties with cost-effective and scalable fabrication. With growing concerns about the environment, water treatment, and water and air filtration will become a key requirement in the future. Significantly, the recent COVID-19 outbreak has proved the necessity of having competitive advantage over dangerous pathogens, in all possible ways.In the future, it will be crucial to keep working on the safety and well-being of the humans and other living species. Recyclability of nanofiber membranes should become a key concern in all application fields, together with the performance enhancement it can provide. Use of recycled materials will also be an interesting topic of discussion in the future, since it can be highly beneficial.


Staphylococcus aureus, 456 Pseudomonas aeruginosa, and 1588 Escherichia coli genomes. We find that feature selection by RSE detects known AMR associations more reliably than common statistical tests and previous ensemble approaches, identifying a total of 45 known AMR-conferring genes and alleles across the three organisms, as well as 25 candidate associations backed by domain-level annotations. Furthermore, we find that results from the RSE approach are consistent with existing understanding of fluoroquinolone (FQ) resistance due to mutations in the main drug targets, gyrA and parC, in all three organisms, and suggest the mutational landscape of those genes with respect to FQ resistance is simple. As larger datasets become available, we expect this approach to more reliably predict AMR determinants for a wider range of microbial pathogens.The evolution of antimicrobial resistance (AMR) poses a persistent threat to global public health. Sequencing efforts have already yielded genome sequences for thousands of resistant microbial isolates and require robust computational tools to systematically elucidate the genetic basis for AMR. Here, we present a generalizable machine learning workflow for identifying genetic features driving AMR based on constructing reference strain-agnostic pan-genomes and training random subspace ensembles (RSEs). This workflow was applied to the resistance profiles of 14 antimicrobials across three urgent threat pathogens encompassing 288  Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli), our approach outperformed common statistical methods for detecting gene-level associations, identifying a total of 45 known resistance-conferring genes, as well as 25 candidate genes potentially involved in new mechanisms of resistance. These results show that this method can generalize to other drugs and pathogens to predict and explain resistance profiles at the gene level.Antimicrobial resistance remains a persistent threat to global public health, with 700,000 deaths each year attributable to resistant bacterial infections. The falling cost of genome sequencing offers an avenue for rapidly predicting and elucidating the resistance profiles of infectious isolates, which is necessary for the design of more effective antimicrobial therapies from existing drugs. As such, clinical surveillance programs have already yielded sequences for thousands of distinct, resistant strains of most major pathogens. Here, we have developed a workflow for training machine learning models capable of not just predicting resistance profiles from genome sequences, but also identifying the responsible genes. When applied to 14 drugs and three urgent threat pathogens ( The emergence of antimicrobial resistance (AMR) remains a persistent problem in the treatment of bacterial infections. Since the discovery of penicillin in 1928, pathogens have developed resistance to almost all major antibiotics, often within a few years of their introduction , and anaStaphylococcus aureus Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: In this submitted manuscript the authors  propose a method that predicts antimicrobial resistant (AMR) genes using SVM ensembles trained using random subspace ensemble (RSE). The SVM-RSE predictors were built for 3 of the main multi-drug resistant Bacterial species and 10 antibiotic classes extracting features from the 3 pan-genomes.The authors show that the SVM-RSEs are more accurate in predicting AMR genes than well-established association tests. Furthermore the technique is reference-agnostic and has the potential to be improved further.The manuscript is generally nice to read and has ample supporting material.Following is a list of minor issues that I encountered reading the manuscript:I) In lines 229-231 the authors state that oversampling or undersampling did not affect the performance. this does not seem so clear from Fig3b, were ranks for SVM-RSE-O and SVM-RSE-U are sensibly higher than those given from SVM-RSE. Please rephrase accordingly or explain in more details.II) In figures (main and supplementary) change "# of X" with "Number of X"III) In Fig. 1 it would be nice to have a more descriptive legend (e.g. near-core: missing from <= 10 strains)IV) In Fig2a, specify the distance used in the y-axisV) In Fig2b and c, add a legend for the resistance patternsVI) Fig. S2, and S3: add "Functional category" above the legendsVII) Lines 173-175: there is probably a missing/wrong word in the sentence  Fig 3b: in the x-axis change "ImsR" with "ImrS"Reviewer #2: In this work, Hyun et al. have developed a Support Vector Machine (SVM)-based method to identify and predict antimicrobial resistance determinants from (pan)genomic data.First, they build pangenomes for three species of interest  and then they use such gene sets to train and apply models for AMR determinants search.They succeed in the identification of known determinants and their method seems to outperform common association tests and to achieve prediction accuracies competitive withprevious machine learning approaches . The paper is well written and the amount of data analyses is notable.Overall I have a few concerns regarding the structure of the paper and the lack of experimental validation on some of the predictions.- The first part of the results is devoted to “standard” pangenomic analyses. It is no news that core and accessory gene sets do not show interesting and significantly enriched COG categories. Those categories are to broad, especially for the purpose of the current analysis. I would suggest to move the first paragraph  to SM and start the Results section from the current “Support vector machine ensembles identify known AMR genes more…”.This would allow going straight to the point.- It is interesting that the SVM-RSE method identifies novel putative AMR determinants. This, however, calls for an experimental validation of the capability of a few of such genes  to confer resistance to the organism harboring them. This would render the manuscript and the implemented approach much more reliable. Do those substitutions confer resistance to otherwise susceptible strains? Additionally, hflX or oppD are ideal targets to validate the predictions made by the authors.- I have a (minor) concern about the criterion used to classify a gene into core, near-core, accessory, unique or near-unique. The authors have shown a threshold based on a fixed number of strains . However, the genome datasets are highly variable among the selected species  so I think It would be worth replacing such a fixed threshold with a relative amount (10%?). I would be curious to see whether the numbers and the results would change applying such a relative criterion.Reviewer #3: Hyun and colleagues provide a novel machine learning-based approach for finding enriched sets of alleles involved in AMR. These are compared to known AMR gene alleles, and some discussion is provided for the genes that are implicated by the models that were previously unrecognized in these species. The paper is well written and I enjoyed reading it. However, there were several places in the manuscript where I became very confused. In my opinion text requires a major revision that centers on clarifying certain points, and directly focusing on the message of the paper. That being said, I think that it is scientifically sound and that this paper will be of importance to readers.Here are my comments:In the intro, has this RSE approach been used with success elsewhere? Was the choice inspired in some way or invented by the authors?The last paragraph of the intro, line 88, says that RSEs aggregate classifiers trained on random subsets of both the sample set and the feature set. This requires a sentence explaining what the sample set and feature set are in this context. At first I thought samples were genomes, but since this is a study of pan genomes that was not clear (at this point in the paper).Figure 1a says strains, but I think the authors mean genomes since two genomes of the same strain could exist in the training set.Did the authors do anything to account for the phylogenetic distribution of the strains of each species?Step 4 of figure 3a says “Train SVMs on AMR phenotypes, average weights”. I can’t find exactly how the authors processed the AMR phenotypes. In the case of MICs, are they predicting MICs? Was everything converted to susceptible vs. resistant, S vs. I vs. R, susceptible vs. non-susceptible etc. If so, did they use CLSI or EUCAST?Similarly, this looks like they built an ensemble of SVMs for predicting the key features for one antibiotic at a time, analyzing one species at a time, but that also is not clear until you get very deep into the paper and it is not made clear in the methods.For any core gene, each mutational variant observed would create another dimension in the vector. Some important core genes would be expected to have very few variants and hence dimensions, and some might have many. Do the authors have any insights on how this might affect accuracy or the ultimate selection of AMR-related features in either a regular SVM or a RSE? Are the important genes, simply those that have few variants?Coloring and/or scale should be defined in Figure 1c.The authors use the term allele, throughout, which I believe means alternative forms of the same gene. However, on line 166 they jump to protein space by defining alleles based on blastp. Is the feature vector based on proteins or genes? I think it is proteins. They should clarify, and I recommend careful use of terms here. This also makes Table 3 a bit confusing because I think the table is describing nucleotide SNPs in column 2 (ie. gene space), but I don’t understand how the model is identifying a nucleotide here if it is based on proteins.Along these lines, the results section starting on line 181 sets up a comparison of statistical tests vs. the SVM method. Some lead in is required here. I think that the authors are performing statistics over the same matrix that they are performing the svm analysis but that isn’t clear in the text.Based on my recollection, I think that set of antimicrobials and their associated alleles for the 3 species tends to skew toward chromosomally encoded AMR genes (Lines 162-165). Is that the case? I don’t think that action is required in terms of the experimental approach but that should be mentioned, with some discussion of implications, if it is indeed true.In figure 2A and B I don’t understand how the data can cluster if there is no information on susceptible vs. resistant phenotypes (2B) for many of the strains in the purple clade on the right-hand side of the plot. Were the purple strains used in the model?The rationale for the computation of pan-genome openness (line 120) and its depiction in Figure 1 and the supplemental figure are not entire clear to me. If it is required, please clarify. If not, it might help the paper if this went in the supplement. This is also true of the extensive examination of overlap in function between core and accessory genes. This is important but not of primary importance, unless I have missed something.The authors previously wrote a really nice paper on M. tuberculosis (ref 6). Why was this organism excluded from the analysis? Is there something different about this organism that makes it less amenable to the method?I understand that shifting from an optimization for model accuracy to a focus on gene identification is important, but the accuracy of the models should still be described in greater detail. It only appears on line 265. I think that the precision vs. recall data are still important to this study because they will provide information that the models are correctly balanced and capable of predicting both classes accurately. This in turn lends support to the idea that we should believe the feature list that has been implicated in AMR. To this end, I think that Figure S4 (counts of SR genomes) and S7 (Accuracy vs MCC) should be brought back into the main text, and that either precision and recall, or F1 should be showed as well.It seems like this method casts a wide net. That is, the top features are enriched by known AMR variants, but that there is still a lot of genes that are being pulled in and that some analysis is required to determine their biological relevance. It would be nice to get these lists. It would bring interest to the bench scientists.Small things:Line 154 define DHFRLine 249, I think the authors mean trimethoprim/sulfamethoxazole, but I’m not 100% sure.Line 265 “an accuracies”In Dataset S1, the underscores in the PATRIC ids should be converted to periods, or a table legend is required. Might be good to convert cells to text format first.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: No: I couldn't find spreadsheets for most of the figures in this submission Reviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article .One of the goals of PLOS is to make science accessible to educators and the public. PLOS staff issue occasional press releases and make early versions of PLOS Computational Biology articles available to science writers and journalists. 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Due to the anisotropy of the shape and the interaction between the particles, the Hc could not be enhanced. Therefore, the Ms and Hc of the nanoparticles were 80.23 Am2/kg and 979.3 Oe, respectively. The hysteresis loop and the change of magnetic moment during the demagnetization of the CoFe2O4 nanofiber array were simulated via micromagnetic software. The simulated Hc was 1480 Oe, which was similar to the experimental value.The morphology of magnetic materials has a great influence on the properties, which is attributed to the magnetic anisotropy of the materials. Therefore, it is worth studying the fabrication of the aligned fiber and the change of its domain distribution. Nanoparticles and nanofibers were prepared by the hydrothermal and electrospinning methods, respectively. At the same time, the arranged nanofibers were collected by the drum collecting device. After the same annealing at 700 °C, it was found that the diameter of fibers collected by different collecting drums is similar. By studying the hysteresis loops of nanoarrays, it was found that they had strong anisotropy. The easy axis was parallel to the long axis, the Hc and Mr of the easy axis and the hard axis were 1330.5 Oe, 32.39 Am With the development of science and technology, ferrite materials are known as promising materials. Due of their good magnetism, different applications in electrical and optoelectronic devices, significant resistance, and low eddy current loss. Cobalt ferrites are known as attractive magnetic materials in other oxides due to their special properties and low-cost production. Cobalt ferrite has good mechanical hardness, high coercive force, wear anisotropy, high mechanical strength, medium saturation magnetization, high chemical stability, and high-temperature magnetic order . Its appIn recent years, the applications of magnetic nanomaterials in ultra-high-density magnetic recording media ,3, bioseHowever, these methods require complex equipment and expensive instruments for precise preparation, and the length of samples prepared is limited. Electrospinning technology ,19 has tDue to the uncontrollable spray of the fibers, it is still challenging to prepare nanofiber arrays. In the electrospinning process, the filamentation process of polymer nanomaterials includes three stages: (1) the beginning and extension of the charged jet along a straight line; (2) the increase of electric bending instability and the further extension of jet; and (3) the jet solidifies into one-dimensional polymer nanomaterials on the collecting drum .By adjusting the collection device of the electrospinning method, directional fibers can be obtained. The parallel electrode collecting device is a simple method to prepare oriented fibers. Park et al.  placed tThrough the above analysis, in order to make the device simple and the collection effect better, we will use the drum collecting device. We believe that the Taylor cone angle can be reduced by reducing the collection width of the collection bucket to make the spray nanofibers more concentrated, or by changing the linear speed of the drum, so the rotation speed and the filament winding speed are close to each other. The Taylor taper refers to the fact that the solution is subjected to surface tension and the electric field force at the nozzle of the needle tube. When the voltage reaches the critical value, the solution at the needle nozzle will become conical .2O4 nanofibers with a high orientation will be prepared by controlling the diameter and width of the drum collecting device. The influence of the shape parameters of the collecting drum on the fiber shape orientation was further discussed and verified. The particles’ diameters, which were similar to the fibers’ diameters, were prepared by the hydrothermal method. The magnetic properties of nanofibers and nanoparticles were compared. So that the pollution and costs are reduced, our solvent only uses water and alcohol to achieve environmental friendliness. Finally, the domain changes of the CoFe2O4 nanofiber array during demagnetization were simulated by the object oriented micro magnetic framework (OOMMF).In this paper, CoFe3)3·6H2O  and Fe(NO3)3·9H2O  with a Co/Fe molar ratio of 1:2 were dissolved in 5 mL deionized water and ethanol . Then, 0.5 g polyvinyl pyrrolidone  was added to the precursor solution and stirred for 6 h. A syringe pump was used to deliver the precursor solution to a stainless steel needle with a constant flow rate of 0.3 mL/h. The needle was connected to a high-voltage power supply. In our experiment, the voltage and the distance between the syringe needle and the grounding collector were 17 kV and 15 cm, respectively. The speed of the collecting drum was 1500 r/min. Finally, the precursor nanofibers were cut into small pieces and placed in a tubular furnace at 700 °C for 4 h heat treatment. The treatment was carried out in an air atmosphere. The experimental principle of electrospinning is shown in All other chemicals used in this work were of analytical grade. The Co(NO3)3·6H2O  and Fe(NO3)3·9H2O  with a Co/Fe molar ratio of 1:2 were added to 40 mL deionized water and dissolved completely, then the solution was poured into the reactor. The reactor was held at 200 °C for 4 h and then sintered at 700 °C for 4 h. The treatment was carried out in an air atmosphere. A sample of nanoparticles was obtained and were named CFO-P.Hydrothermal synthesis of nanoparticles is a process in which Co. The morphology and energy dispersive spectrometer (EDS) pictures of the samples were observed by a JEM-6701F scanning electron microscopy . The magnetic properties were measured by a MicroSense EV-9 vibrating sample magnetometer .2O4 nanofibers and nanoparticle samples calcinated at 700 °C were analyzed by XRD. The XRD spectra of these four samples are given as shown in The structural characteristics of three kinds of CoFeFrom For nanofibers, alignment fibers and disordered fibers have great changes in magnetic properties due to the influence of shape anisotropy. 2/kg and 25.12 Am2/kg. For aligned nanofibers, the Hc of the parallel field configuration and perpendicular field configuration was 1330.5 Oe and 857.2 Oe, respectively, while the Mr was 32.39 Am2/kg and 24.80 Am2/kg, respectively. In addition, the Mr/Ms of the parallel arrangement was higher than that of the perpendicular arrangement. This shows that the aligned nanofibers had obvious magnetic anisotropy.r changes of the two samples at 0–180° (5° apart from each point) were measured. The trend of Mr in the 180° field was obtained by fitting the points. r of the non-oriented nanofibers, it was found that the curve fitted by each point is straight, indicating that non-oriented nanofibers have isotropy. r curve of aligned fibers was a parabola, indicating that the aligned nanofibers have anisotropy. This means that aligning of the hard and easy-to-magnetize axes of nanofibers were perpendicular and parallel to the long axes, respectively. Although the anisotropy of nano-magnetic materials is the result of the interaction between shape anisotropy and magnetocrystalline anisotropy, shape anisotropy has a greater influence on magnetic anisotropy for aligned nanofibers.In order to observe the difference between anisotropy and isotropy more clearly and intuitively, the M2O4 nanoparticles and nanofibers prepared by the hydrothermal method and electrospinning method are shown in s data, when magnetic nanoparticles are magnetized, there are no other interaction constraints, making it easy to be magnetized by the external magnetic field, so it has a high saturation magnetization. However, for nanofibers, due to the different magnetic domain arrangements from the nanofibers, in the magnetization process, nanofibers interact with adjacent nanofibers to minimize energy, in addition to the long-axis particles. As a result, the Ms of the nanofibers was smaller than that of the nanoparticles. It is for these reasons that nanofibers have a higher Mr/Ms ratio.For magnetic nanoparticles and nanofibers, their magnetic properties are different due to the interaction of shape anisotropy. The hysteresis loops and magnetic properties of the CoFec than the nanoparticles. This is due to the fact that under the influence of shape anisotropy, the domains of nanofibers are connected in series along the long axis. When the material is in the demagnetization state, on account of the arrangement and distribution of magnetic domains in the nanofibers, the magnetic induction strength of nanofibers is affected by the morphology of nanofibers, so it is difficult to reduce the magnetic induction strength of nanofibers to zero. Therefore, the nanoparticles are easier to demagnetize and have lower Hc.We found that the nanofibers had higher H2O4 with different morphologies. It was found that the fiber had high Hc. This was due to the longest axial ratio of one-dimensional nanomaterials, so the shape anisotropy of fibers must be considered. It was found that the system with a magnetic dipole in the linear chain will enhance the coercivity. This dipole–dipole interaction between grains in one-dimensional fibers plays an important role in the magnetization process [2O4 nanofibers in new fields such as biomagnetism, magnetic recording materials, and microwave absorbers, and is of great significance [Similar findings were found in the studies by Li  and Mord process . Therefoificance ,36,37,382O4 nanofiber array were studied using OOMMF micromagnetic simulation technology. CoFe2O4 exhibits hard magnetic behavior with positive and a much larger magnetocrystalline anisotropy constant, which leads to <100> as the easy-axis and <111> as the hard-axis [s is the saturation magnetization and K is the effective magnetic anisotropy. The first numerical coefficient value of 0.07619 was used due to the cubic anisotropy of CoFe2O4. Using the experimental data of aligned nanofibers with the field parallel to the fiber direction, Ms = 2.65 × 105 A/m , Hc = 0.13 mT, the effective magnetic anisotropy value K of CoFe2O4 nanofibers was about 0.836 × 105 J/m3. In order to verify the experimental process, the simulated external magnetic field took 0.1 kOe as the step and scanned the magnetic field from +20 kOe to −20 kOe. When the magnetization was rapidly relaxed to equilibrium, the damping constant α was set to 0.5. On the basis of the finite difference method, the size of the fiber cell should be lower than the exchange length M(H)=ion time . Figure 2O4 nanofiber array was studied. To further understand the magnetization reversal mechanism of the nanofibers, the demagnetization process of the CoFec was obtained. In the simulation process, it was found that the magnetic moment of some nanofibers in the array was reversed in an instant, while the other nanofibers needed a higher external magnetic field, and the demagnetization process was finally completed.By comparing the magnetic properties of non-oriented nanofibers with oriented nanofibers, it was found that aligned nanofibers are anisotropic. Due to shape anisotropy, the hard axis and easy axis of the magnetization axis were perpendicular and parallel to the long axis. By comparing the magnetic properties of nanofibers with nanoparticles, it was found that the difference between them was mainly due to the different morphology. The hysteresis loop and domain motion in the demagnetization process of nanofibers were simulated by the micro magnetic software, and a similar H


The rs311103C disrupts the GATA-binding site, resulting in decreased CD99 expression. We aimed to investigate the association between PE and the allele frequency of rs311103 in pregnancies in a fetal sex-dependent fashion. In a case-controlled study, we included 241 pregnant women, i.e., 105 PE cases and 136 normotensive controls. A SNP allelic discrimination analysis was performed on DNA from maternal venous blood and fetal cord blood by qPCR. A statistically significant association was observed between rs311103 allele frequency and PE in mothers carrying male fetuses. Therefore, the rs311103 genotype may play a role in the pathogenesis of PE in a fetal sex-specific manner.Preeclampsia (PE) is a human specific syndrome with unknown etiology causing maternal and fetal morbidities and mortalities. In PE, maternal inflammatory responses are more exaggerated if the fetus is male than female. Other pregnancy complications such as spontaneous abortions are also more common if the fetus is male. Recent transcriptome findings showed an increased expression of CD99 in erythroid cells from male cord blood in PE. The single nucleotide polymorphism (SNP) rs311103, located in a GATA-binding site in a regulatory region on the X/Y chromosomes, governs a coordinated expression of the Xg blood group members CD99 and Xg The maternal mortality rates in sub-Saharan African countries including Ethiopia  remain the highest in the world . The synSeveral studies have shown an association between fetal sex and PE. The rate of death of male fetuses in early gestation is higher than that of female fetuses . The obsThe proteins CD99 and Xg belong to the Xg blood group system. The Xg glycoprotein shows ~48% sequence homology with CD99 , howeverThe Paired Ig-like type 2 Receptors (PILRs) of CD99 are expressed on immune cells as transmembrane proteins. PILRs are one of the paired receptor families, which are composed of an activating β- and an inhibitory α-isoform. The β isoform is expressed at higher levels on Natural Killer (NK) cells, B cells, T cells, dendritic cells, granulocytes and macrophages, while the α-isoform is expressed at very low levels on NK cells . Takheawa blood group antigen [a and CD99 proteins [CD99 expression is not completely controlled by this transcription factor binding site, and CD99 is strongly expressed in individuals of the GG and GC genotypes, while it is low in the CC genotype [The CD99 gene is located within the pseudo-autosomal regions of the X and Y chromosomes  and is s antigen . The sinproteins . The minproteins  to have genotype . While Ggenotype . GATA3 igenotype ,36.Pregnancies with male fetuses have been associated with increased risk for PE, and are believed to be confronted by stronger intrauterine selection forces compared to pregnancies with female fetuses ,38. Howen = 105, controls, n = 136)  . In the We tabulated stepwise frequency distributions of the SNP rs311103 genotypes among mothers and fetuses, and Chi-square was used to test the relationship between genotypes and PE. First, frequency tabulation was done between different maternal groups  and amonp-value < 0.05 at X2 = 3.94, percentage point 3.84) (High CD99 expressing genotypes (GG and GC), designated as G+, and low CD99 expresser genotype (CC) were tabulated against subgroups of mothers. The relationships were tested using the Chi-square test. The frequency distribution of genotypes was compared between PE and control mothers of male fetuses; there was a statistically significant association in the genotype frequencies (nt 3.84) . Howevernt 3.84) .The frequency distribution of genotypes between male or female fetuses of PE and control mothers was also compared, but no statistically significant associations was found .In addition, the different groups of fetal SNP rs311103 genotypes were tabulated against the mothers . The disOur study showed a statistically significant association between the maternal SNP rs311103 genotype frequency and PE pregnancies carrying male fetuses. The maternal SNP rs311103 genotype CC that results in low CD99 expression was underrepresented in PE cases with male fetuses, while there was no difference in frequencies in female pregnancies. There is a lack of evidence regarding a molecular basis for the association between fetal sex and PE. Our previous study showed that CD99 mRNA was upregulated in umbilical cord blood erythroid cells from male fetuses in PE pregnancies , and we Fetal sex is a risk factor for adverse pregnancy outcomes ; pregnanThe SNP rs311103 is situated in a GATA-binding site located in the pseudo-autosomal regions of the sex chromosomes, and has been shown to have high affinity for GATA1, GATA2 and GATA3 transcription factors . The binThe GATA3 transcription factor binding to the GATA-binding site would regulate the CD99 expression in both maternal T and NK cells present in the uterine decidua, and the maternal CC genotype would then result in low levels of CD99 expression and protein on these cells. Since both T and NK cells are important for a normal placentation, a low CD99 expression on these cells might have negative effects on the process, and may contribute to a failed implantation. Maternal low CD99 levels in immune cells such as Th cells might disturb the balance between Th1 and Th2 responses, and thereby, further increase the maternal inflammatory response seen during PE pregnancies. Low levels of CD99 on endothelial cells in maternal blood vessels would have a negative effect on the recruitment of leukocytes to the uterus, and further disturb the balance between the important immune cells needed during placentation. Furthermore, it is possible that low levels of CD99 on the endothelial cells lining the spiral arteries might affect the remodeling process and contribute to poor placentation. However, our results suggest that low levels of maternal CD99 are only important when the embryo is male. Our recent study showed an association between PE and a maternal killer cell immunoglobulin-like receptor (KIR) inhibitory genotype expressed in the uNK cells, demonstrating the important role played by uNK cells in pregnancy outcome . HoweverA weakness of our study is the incomplete collection of data during recruitment, resulting in a reduction of the number of samples included in the final analyses.In conclusion, our study suggests that mothers with the CC genotype developing PE have an increased rate of implantation failure or failed pregnancy if the embryo is male in a fetal sex-dependent fashion. The molecular mechanisms that link CD99 expression in PE pregnancies with male fetuses is currently lacking, but our data may be helpful in directing future studies towards investigations of exaggerated sex-dependent maternal immune responses towards male fetuses.A case-control study design was employed. Study participants were recruited at Adama Regional Referral Hospital, Ethiopia. Data collection was performed by midwife nurses and laboratory staff who received training about data collection. The principal investigator and head nurse supervised the data collection process. A standard operating procedure was prepared and followed for data collection. A national protocol was used by the Obstetrics and Gynecology specialist physicians and medical residents for the identification of PE cases during their routine hospital practice . Participant recruitment, the recording of clinical and demographic data and blood sample collections took place from December 2016 to August 2017. A total of 241 pregnant women, of which 105 were PE cases and 136 normotensive controls, were included. The participants were recruited based on the order of their visit to the Obstetrics and Gynecology delivery ward. The cohort has been used in a previous study .Ethical clearance was secured from Department of Biochemistry, School of Medicine, College of Health Sciences, Addis Ababa University, Ethiopia ; AHRI-ALERT Ethical Review Committee ; NRERC, the Federal Democratic Republic of Ethiopia Ministry of Science and Technology  at or after 20 weeks of gestation with proteinuria (spot urine protein ≥ 2+ on dipstick testing or ≥30 mg/mg protein/creatinine) or findings of maternal organ dysfunction or utero-placental dysfunction  . In the TM Genotyping master mix . Briefly, the maternal and fetal genomic DNA was amplified in a 10-µL, real time quantitative polymerase chain reaction containing 1 µL of 10 ng genomic DNA, 5 µL of 2× TaqMan Genotyping master mix, 0.25 µL of 40× primer assay and 3.75 µL of distilled water. Cycling was carried out in a QuantStudio 3 thermocycler (Applied Biosystems by Thermo Fisher Scientific). Positive controls for the three genotypes, i.e., GG, GC and CC, and a negative control (DNase-free water) were included in each assay, together with test samples. All samples were run in duplicate.The maternal venous blood and cord blood samples were collected in EDTA test tubes. The samples were stored at –20 °C until extractions of genomic DNA were performed using QIAGEN DNA Mini Blood Kit  from 200 μL of maternal blood or 200 μL of cord blood. The DNA was used for SNP rs311103 genotyping based on the primers and methods previously described [https://pregnancycolab.tghn.org/collect/). The statistical analysis was done using IBM SPSS version 26.0. The data was analyzed using the Chi-square test. p-value < 0.05, at 95% confidence interval, was considered statistically significant.Demographic and clinical data were collected using the COLLECT database developed by the Global Pregnancy Collaboration (CoLab;