Next-generation sequencing technology has revolutionised pathogen surveillance over the last two decades. However, the benefits are not equitably distributed, with developing countries lagging far behind in acquiring the required technology and analytical capacity. Recent declines in the cost associated with sequencing—equipment and running consumables have created an opportunity for broader adoption. During the COVID-19 pandemic, rapid diagnostics development and DNA sequencing revolutionised the ability to diagnose and sequence SARS-CoV-2 rapidly. Socioeconomic inequalities substantially impact the ability to sequence SARS-CoV-2 strains and undermine a developing country’s pandemic preparedness. Low- and middle-income countries face additional challenges in establishing, maintaining and expanding genomic surveillance. We present our experience of establishing a genomic surveillance system at the Aga Khan University, Karachi, Pakistan. Despite being at a leading health sciences research institute in the country, we encountered significant challenges. These were related to collecting standardised contextual data for SARS-CoV-2 samples, procuring sequencing reagents and consumables, and challenges with library preparation, sequencing and submission of high-quality SARS-CoV-2 genomes. Several technical roadblocks ensued during the implementation of the genomic surveillance framework, which were resolved in collaboration with our partners. High-quality genome sequences were then deposited on open-access platforms per the best practices. Subsequently, these efforts culminated in deploying Pakistan’s first SARS-CoV-2 phyllo surveillance map as a Nextstrain build. Our experience offers lessons for the successful development of Genomic Surveillance Infrastructure in resource-limited settings struck by a pandemic. The use of Next-Generation Sequencing is essential for surveillance of the emergence and spread of SARS-CoV-2 variants.For informed national responses now and in the future, expanding sequencing capacity in resource-limited settings is required.Here we present our experiences, challenges and remedial strategies in building genomic surveillance, sequencing capacity and bioinformatics analysis in an academic setting.Positive COVID-19 samples were collected along with the metadata and sequenced, and bioinformatics analysis was performed afterward.High-quality genome and related metadata were submitted to Global Initiative on Sharing All Influenza Data, National Center for Biotechnology Information (NCBI) GenBank and the (European Nucleotide Archive at the European Bioinformatics Institute (ENA-EBI) COVID-19 portal.Challenges regarding procurement, deployment of the analysis pipeline and submission were encountered and resolved.The importance of genomics in controlling a disease outbreak has been learnt from the ongoing global pandemic.Our experience can be referenced in other low- and middle-income countries and for future pandemic preparedness.Next-Generation Sequencing (NGS) is a powerful technique that can enable the rapid high-throughput sequencing of the SARS-CoV-2 RNA genome. Expanding SARS-CoV-2 genomic surveillance data along with clinically relevant contextual data will yield insights on the transmission patterns and pathophysiology of COVID-19.Genomic surveillance of the evolving SARS-CoV-2 strains is vital for helping control the pandemic.7 8Sequencing rapidly is not sufficient, especially in a pandemic setting. Modern public health systems are needed, equipped with robust surveillance capabilities to cater to the complexity of metadata collection during lockdowns, etc, as well as readiness to respond to pandemics, much like in the UK and Denmark. Countries may lack public health laboratories capable of handling samples of high infectious potential, or be overly centralised system and restricted to handle such samples (availability of biosafety facilities), which would cause sequencing and submission to be delayed.With over 220 million inhabitants, 65.6% aged over 15 years,13Traditional North–South collaborations have involved HICs (the North) providing laboratory reagents to LMICs (the South), or LMICs sending biological samples to HICs for analysis. This one-sided exchange misses the essence of genuine, long-term partnership. Ideally, North-South collaborations should include knowledge and skill transfer, infrastructure development and empowerment of local institutions in LMICs. This way, these countries can gradually build their own capacity for research, rather than relying on resources and expertise from HICs.We share our experience of an ideal collaboration, for instance with Chan Zuckerberg Biohub (CZB) and the Chan Zuckerberg Initiative (CZI), aimed at advancing in-house testing and analysis of sequencing data. In this context, we detail our journey of establishing genomic surveillance, sequencing infrastructure and the public dissemination of SARS-CoV-2 data under resource-restricted conditions. We also discussed the challenges we faced and how we resolved them. Our experiences and insights could serve as a blueprint for other LMICs exploring similar paths in the future.The Aga Khan University (AKU), a key private-sector educational institution in Pakistan, has significantly contributed to the local pandemic response through its clinical services, public outreach and extensive research activities. Prepandemic, the Department of Pediatrics and Child Health had ordered an Illumina iSeq100 for research, which arrived in March 2020. We received assistance from CZB/CZI in establishing our genomic infrastructure and training through webinars and Slack channel, reflecting true North-to-South collaboration. In late 2020, AKU chose to prioritise SARS-CoV-2 sequencing for the nation’s benefit.dx.doi.org/10.17504/protocols.io.br77m9rn). Briefly, the Reverse Transcription Polymerase Chain Reaction (RT-PCR) based ARTIC protocol was used to obtain the amplicon from the extracted RNA, followed by the NEB FS DNA library preparation protocol to make the amplicon library. We successfully sequenced 705 SARS-CoV-2 on several batches using the Illumina iSeq100 and MiSeq platforms and collected the minimum required metadata  were collected from the AKU Hospital (AKUH) clinical laboratories and sequenced at the Infectious Diseases Research Lab, Department of Pediatrics and Child Health, Medical College, AKU, Karachi, Pakistan, after informed consent. SARS-CoV-2 sequencing was performed using ARTIC-NEB (New England BioLabs) V.3 and V.4 protocols , delivery timelines (5–6 months in our case for getting a sequencer), lack of on-site application and engineering support. We worked with our collaborators at CZB/CZI and the principal vendor (based in the USA) to overcome after-sales support and instrument troubleshooting issues. Since many distributors simply act as the primary source of technical support, it is essential to have complex technical support that is tailored to the requirements of the laboratory (availability of instrument spares and facility of troubleshooting within the country). There are, however, sporadic gaps in this support. The AKU’s engineering department and local vendors recently worked alongside the team from Illumina, UK, to remotely instal  NextSeq2000 in our laboratory to overcome this gap. Forming partnerships or consortiums, pooling resources and expertise to overcome the technical and logistical hurdles associated with such complex technology can help address these challenges in LMICs.Obtaining sequencing reagents and consumables posed significant challenges. Distributors lacked local inventory, necessitating overseas imports, which were hampered by pandemic-related shortages and the recent Russia–Ukraine conflict. We are now collaborating with Illumina and their local distributor to secure a steady supply of sequencing reagents by building a local stockpile. This established relationship will facilitate other regional groups access to sequencing resources, thereby enhancing the region’s overall capacity.Even before the pandemic, our laboratory faced issues with post-purchase support and services to install, maintain or calibrate systems to high standards. Our iSeq100 sequencer came with a 1-year warranty, but post-warranty, we could only replace the equipment under a special service contract. We did not experience any significant issues during the warranty, but it alerted us to the need for backup plans and adequate funds for equipment maintenance. We also secured a service contract with Illumina via our local distributor, stipulating that if the device fails, the days will not count towards the service contract and that the vendor would face a penalty if they were unable to address the malfunction. This arrangement meant that if our equipment failed for 3 months, our warranty was extended by an additional 4.5 months past its original termination date.The key lesson from this experience is the critical importance of proactive planning and contractual protection for equipment maintenance and support in the context of genomic laboratories, particularly in regions with limited resources or infrastructure. Despite having a warranty, the foresight to secure a comprehensive service contract mitigated potential disruptions due to equipment failure and incentivised prompt vendor response. This highlights the necessity of strategic planning, adequate funding for equipment upkeep and strong contractual agreements to ensure continuous operation and service delivery in genomic research and public health scenarios.As recipients of the Gates Grand Challenges round 22 grant in May 2019, we had intended to visit CZB/CZI for training in sequencing and assay development for our metagenomics project, given our lack of previous in-house sequencing experience. However, the pandemic disrupted travel plans and we transitioned to online training, heavily relying on asynchronous communication through Slack due to time zone differences with the CZB/CZI team. Once travel restrictions eased, we were able to visit CZB/CZI in September 2022 for a 2-week intensive training on metagenomics, encompassing crucial aspects of laboratory work, analysis and epidemiology.Remote learning and digital collaboration tools can be effectively leveraged when physical presence is not possible. However, our teams subsequent in-person visit for intensive training underscores the continued value of hands-on learning and direct interaction in building skills and knowledge in a complex field like metagenomics.While preparing the library, we encountered problems related to adapter dimerisation, excessive DNA fragmentation and library quality control mandating adjustments to the sequencing protocol. These changes included increasing the number of magnetic bead cleanups to eliminate adapter dimers, reducing fragmentation time to prevent over-fragmentation of DNA and implementing a TapeStation system (a type of capillary electrophoresis system) to enhance library quality control .One must be prepared to conduct multiple experiments and adjust protocols as needed to ensure the quality of the results. In this case, interventions such as increasing the number of magnetic bead cleanups, adjusting the fragmentation time and integrating a TapeStation system for quality control were all crucial adjustments. This highlights the need for continuous learning, experimentation and adaptability in genomic research.The AKU core laboratory is a hive of activity, with devoted researchers, PhD students and PostDocs working in bacteriology, virology, parasitology and tissue banking sectors in the BSL-2 (Biosafety Level-2) and BSL-3 facilities. Despite these resources, operating in a core, multipurpose research laboratory designed to accommodate a wide array of life sciences research presented a significant challenge, particularly due to the lack of dedicated areas for nucleic acid extraction, PCR clean space, post-PCR space, sequencing space and so on. Initially, our collaborators recommended a BSL-3 space, prompting us to create segregated areas within the BSL-3 facility for RNA extraction, minimising contamination risks between samples and library preparations.Specific processes like nucleic acid extraction, PCR and sequencing require isolated spaces to prevent cross-contamination and ensure data integrity. Adaptive solutions, like creating separated areas within existing facilities when setting up new, dedicated spaces is not feasible.With the work we have done so far, we have been funded to establish a state-of-the-art molecular and sequencing facility within the new Women and Child Health Building, AKU Karachi.https://waqasnayab.gitbook.io/pipeline/master/implementation-of-cz-id-mini-wdl-based-sars-cov-2-consensus-genome-workflow-pipeline-at-aku.On 16 April 2021, we received a grant from PHA4GE to implement standardised bioinformatics practices, pipelines and data structures in SARS-CoV-2 sequencing laboratories in Pakistan. Sequences were analysed to obtain SARS-CoV-2 consensus genome assemblies using the in-house deployment of Chan Zuckerberg ID (CZ ID) consensus genome mini-Workflow Descriptive Language (WDL)-based pipeline on the AKU server (~8–9 mins on each sample). We uploaded our experiences of installation of bioinformatics protocols at https://pha4ge-subgrant.slack.com/archives/C029C32JGKX) with PHA4GE Bioinformatics Pipelines and Vizualisation working group. The shortage of bioinformatics expertise in Pakistan is another obstacle. As part of another Bill & Melinda Gates Foundatiuon (BMGF)-funded project, we had been building the bioinformatics capacity for the last couple of years preceding the pandemic. This came to good use during the pandemic. During the implementation of the CZ ID mini-WDL-based command line interface (CLI) pipeline, we faced several roadblocks that were discussed and resolved at PHA4GE #pakistan-aga-khan slack channel .Consensus genomes are widely deposited in open-access databases to use for public surveillance purposes. On the other hand, raw data is critical for comparing methods and assessing reproducibility, as well as identifying minor variants. Most SARS-CoV-2 sequences (consensus genomes) have only been deposited in GISAID, with a proportion of submitters also depositing matching raw read data in the International Nucleotide Sequence Database Collaboration that includes (1) National Center for Biotechnology Information (NCBI), (2) European Molecular Biology Laboratory–European Bioinformatics Institute and (3) DNA Data Bank of Japan. Linkage of contextual data to consensus sequences as well as raw data in public repositories is vital. At present, we successfully uploaded high-quality SARS-CoV-2 viral sequences and related metadata to open-source sequencing databases, that is, GISAID, GenBank and European Bioinformatics Institute’s COVID-19 portal. We are the first laboratory from Pakistan that submitted sequencing data in the form of raw paired-end fastq files for researchers’ community benchmarking of evolving bioinformatics tools and algorithms (BioProject accession ID: https://clades.nextstrain.org/), sequences with Ns more than 3000 were discarded altogether, however, sequences having mixed sites and frameshift issues were corrected and proceeded for submission. To improve the genome quality, the following in-house methods were used:A major challenge that was encountered with the submission of SARS-CoV-2 consensus genome sequences on GISAID was the presence of frameshift mutations and mixed sites observed in some samples. The quality of viral FASTA sequences was evaluated using Nextclade  in October 2021 . Both builds are updated after every 2–3 weeks .SARS-CoV-2 has swept globally, and Pakistan is no exception. Where the virus was introduced, and how it was transmitted in the early stage of the epidemic in Pakistan is largely unknown. Genomic epidemiology is a powerful tool to answer these questions. However, owing to the scarcity of testing and lack of sequencing of samples in an LMIC setting, Pakistan was poorly represented on the Nextstrain global build. Our research group under the umbrella of CITRIC Center for Bioinformatics and Computational Biology, Karachi campus, decided to construct a Pakistan-specific build to get a complete map of SARS-CoV-2 genomic epidemiology in Pakistan. For this, a snakemake-based Nextstrain’s CLI pipeline was installed on AKU’s server. We successfully deployed a phylosurveillance map of Pakistan  as a Nextstrain build  with a reliable internet connection (weekly update to latest versions of pipelines). These infrastructure elements are crucial for handling and processing Nextstrain analysis. Overcoming these challenges necessitated seeking external support and exploring avenues for training programmes to enhance local bioinformatics capabilities. In our case, a meeting was arranged with Nextstrain’s developer, who provided valuable guidance in resolving the encountered issues:GISAID has a few sequences submitted from Pakistan which created hurdles in building the tree using the default main script (builds.yml). The yml script was edited and customised to make it executable on Pakistani sequences data.https://github.com/nextstrain/nextstrain.org/pull/416).Deployment of Pakistan build on the Nextstrain webpage was also a challenge. A pull request was made on Nextstrain’s GitHub page and was fetched by a member of the developer team to merge and deploy the tree on Nextstrain (Implementing a phylosurveillance map in Pakistan was an important local contribution to the field of genomic epidemiology, demonstrating that even in resource-limited settings, significant contributions to global understanding can be made. Using open-source tools like Nextstrain’s CLI pipeline can help resource-limited settings to implement genomic surveillance and contribute to global understanding of the virus. Empowerment through self-initiative can be an enabler.Pneumococcal, Klebsiella and other enteric pathogens.Since the initiation of this work, the team has started SARS-CoV-2 sequencing for wastewater-based epidemiology, another area of a public health laboratory research and implementation, that needs immediate attention. We have also embarked on other microbial sequencing including microbiome, nasopharyngeal swabs for In establishing these processes, sustainability has been a major challenge with funding being the biggest concern. Advocacy is a pivotal component; promoting understanding and importance of genomics in policy discussions can garner more sustained funding and support. Diversification is also key where the reliance must go beyond external grants. Funding from public–private partnerships, local government bodies or collaborations with international universities or research institutions can play a huge role. Concurrently, we need to ensure our research is translated into tangible applications in various sectors, such as public health, agriculture and environmental science, to attract significant funding. Transforming the sequencing centre into a service provider could generate income that supports the continuation of our operations.It is crucial to invest in the training and development of local scientists and technicians to cultivate domestic expertise and reduce reliance on external resources. A proactive approach is required to plan for maintenance and updates of sequencing technology to prevent expensive and disruptive system failures or replacements active engagement with the global genomics community is essential to do this as it offers avenues for resource exchange, learning opportunities and potential partnerships.17The government must employ critical steps to establish a centralised procurement system for wet-lab reagents and NGS platform components, such as those from Illumina. Collaborations with initiative teams, like CZB/CZI, can add significant value.Going forward, we aim to apply the lessons from the COVID-19 pandemic to future strategies against emerging and evolving pathogens. The value of genomics in controlling disease outbreaks has been highlighted, and our goal is to establish robust genomic response programmes for future pandemics and disease outbreaks. By leveraging our updated equipment and high-performance computing infrastructure, we aim to develop tools and pipelines for diverse pathogen genomic analyses, enabling us to face future challenges more effectively.


Catheter ablation (CA) has become the first-line treatment strategy for atrial fibrillation (AF) but remains with a substantial recurrence rate. The aim of this meta-analysis was to determine the association between baseline natriuretic peptide levels and AF recurrence after CA.We systematically searched PubMed, EMBASE, Web of Science, and Wiley-Cochrane Library for relevant studies published up until May 2022. Overall effect analysis and subgroup analysis were performed with Review Manager software.Finally, 61 studies that met the inclusion criteria were included in our meta-analysis. Compared with the nonrecurrence group, the recurrence group had increased baseline level of atrial natriuretic peptide (ANP) , brain natriuretic peptide (BNP) , N-terminal pro-BNP , and midregional N-terminal pro-ANP .Increased baseline natriuretic peptide levels, including ANP, BNP, N-terminal pro-BNP, and midregional N-terminal pro-ANP, are associated with a higher risk of AF recurrence after CA. Nonetheless, further studies are needed to elucidate the predictive value of baseline natriuretic peptides in AF patients undergoing CA. The disease remains an increasing public health concern throughout the world. Approximately 3.9 million individuals aged over 60 years suffer from AF in China. As a progressive disease, AF is associated with a higher risk of all-cause and cardiovascular death. Nowadays, catheter ablation (CA) is recommended as the first-line treatment strategy for rhythm control due to its effectiveness in sustaining sinus rhythm.,5 However, it still has a substantial recurrence rate, and the success rate ranges from 30 to 85%. Therefore, how to judge the prognosis of CA is particularly important.Atrial fibrillation (AF) is a common cardiac rhythm disturbance associated with serious complications including strokes, thromboembolism, and cognitive impairments that can influence patients’ quality of life and impose higher medical care costs. Meanwhile, studies have shown that AF patients have higher plasma natriuretic peptide concentrations than that in the control group, and the elevated natriuretic peptide levels decrease after CA restores sinus rhythm.,9 However, whether increased natriuretic peptide is a true predictor of AF recurrence still remains controversial. Therefore, we conducted this meta-analysis to include more studies, discover different kinds of natriuretic peptides, and identify the association between baseline natriuretic peptide levels and AF recurrence after CA.Natriuretic peptides are primarily secreted by myocytes under various stimuli and have been widely used as important diagnostic and monitoring tools for cardiovascular diseases.In order to aggregate all of the relevant published studies, Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used for all peer-reviewed studies. We searched PubMed, EMBASE, Web of Science, and Wiley-Cochrane Library for relevant studies published up until May 2022. The following search items were used: natriuretic peptide, AF, CA, and recurrence. The “related article” function was also used during the search; the references for retrieved articles were manually searched to avoid initial misses.The included studies had to fulfill the following inclusion criteria: studies were designed as randomized controlled trials, prospective observational studies, or retrospective observational studies; the study enrolled patients who underwent their first CA; post-ablation AF recurrence was assessed as an outcome; atrial arrhythmia including AF, atrial flutter, or atrial tachycardia confirmed by 12-lead electrocardiography or Holter electrocardiography is used in the assessment of AF recurrence; and the number of patients, mean (standard deviation [SD]) or median (range or interquartile range) level of natriuretic peptides within recurrence and nonrecurrence groups were provided.The exclusion criteria were: non-human study; abstracts, reviews, unpublished reports, overlapped publications, non-English language articles; and AF patients underwent rate control strategies or surgical ablation.Two reviewers independently extracted data, including publication information , study design, characteristics of participants , follow-up duration, recurrence rates, and pre-ablation natriuretic peptide level of recurrence and nonrecurrence groups. Disagreement data were resolved by consensus or adjudication by a third author. was used to evaluate the quality of each study. It allocates 0 or 1 point to each numbered item in each category with the exception of comparability where up to 2 points can be assigned. The assessments were done independently by 2 authors. Any disagreements encountered were resolved by discussion. When no consensus could be achieved, a third reviewer was consulted for reconciliation.The Newcastle–Ottawa Scale and McGrath et al. Heterogeneity was assessed using the Cochrane Q test and I2 index. When the heterogeneity test was P ≥ .05, or I2 < 50% indicating low statistical heterogeneity, a fixed effect model was used; otherwise, a random effect model was chosen. The source of heterogeneity was analyzed using subgroup analysis. Sensitivity analysis was evaluated by determining whether the remaining results would be markedly affected after removing the relatively low-quality study. Overall effect analysis and subgroup analysis were performed with Review Manager software . Publication bias was appraised by visual inspection of funnel plots and Egger test conducted by Stata 15 . A trim-and-fill analysis was applied to investigate the potential influence of publication bias. P < .05 was considered statistically significant.Standardized mean difference (SMD) was used to present the pooled effect with a 95% confidence interval (CI). The SMD is an estimate of the effect size and can be interpreted as the difference between means of groups divided by the pooled SD. For baseline characteristics represented by median and range/IQR, mean and SD values were estimated using the method described by Luo et alAn ethics statement is not applicable because this study is based exclusively on published literature. This study protocol conforms to the provisions of the Helsinki Declaration as revised in 2013.http://links.lww.com/MD/I921. For quality assessment, all included studies were of relatively high quality with the Newcastle–Ottawa Scale scores ranging from 6 to 8 points.One thousand three hundred eighty-five studies were acquired after a primary search, and then 851 duplicates were excluded. Three hundred twenty-seven studies underwent the first sift-prescreening by scanning the title and abstracts, among which 221 were excluded. The remaining 106 studies were retrieved for the second selection, and the full texts were read and evaluated carefully. The flow diagram of studies screening was listed in Figure P < .0001; Fig. I2 = 45% (P = .08). The heterogeneity was still present after subgroup analysis by follow-up duration, AF type, or sample size . Since these underlying confounding factors could not explain the heterogeneity, sensitivity analysis was performed by removing 2 relatively low-quality studies to evaluate the stability and reliability of our results. We found that the heterogeneity decreased significantly by excluding 2 relatively low-quality studies (I2 = 0%), but the overall pooled effects did not change statistically , suggesting the result reliable.After meta-analysis of 8 related studies, the pooled results indicated that there was a statistically significant association between baseline ANP level and post-ablation AF recurrence . Moderate or high heterogeneity still existed after subgroup analysis by follow-up duration, AF type, sample size, or geographic region . In sensitivity analysis, the result was not significantly altered after excluding 7 relatively low-quality studies , demonstrating the results were stable.Based on the 37 related studies, our meta-analysis showed that the AF recurrence group had a significantly greater baseline BNP level than the nonrecurrence group, and the pooled SMD was 0.51 . After subgroup analysis by follow-up duration, AF type, sample size, or geographic region, moderate or high heterogeneity still existed . To evaluate the stability and reliability of our results, sensitivity analysis was performed by removing 4 relatively low-quality studies, and our result was not altered .Significant association between pre-ablation baseline NT-proBNP level and post-ablation AF recurrence was found in our meta-analysis of 25 related studies. The pooled SMD was 0.71 . In sensitivity analysis, the result was not significantly altered after excluding the relatively low-quality study , demonstrating the results were stable.There were 4 studies that provided data about baseline MR-proANP levels. A statistically significant association was found between baseline MR-proANP level and post-ablation AF recurrence. The pooled SMD was 0.91 . Nevertheless, the asymmetrical funnel plots and the Egger test for NT-proBNP suggested the presence of publication bias (P = .004) . However, based on the trim-and-fill analysis, no trimming was performed and data was unchanged . Combined with the results of the sensitivity analysis, in which the result was not altered, we considered the final results were reliable.Publication bias analysis was performed when 10 or more studies were retrieved (BNP and NT-proBNP). The funnel plots of BNP were symmetrical with Egger test  but the prognosis after therapy is complex, this includes recurrence or recurrence-free, short-term or long-term recurrence. Predictors for the prognosis are also miscellaneous.,14,15 Therefore, identifying and predicting high-risk patients with AF recurrence can help doctors to make an optimized patient selection, inform patients of the risk-benefit ratio, guide surgeons to select the best ablation strategy, and implement individualized treatment strategies. Natriuretic peptides refer to a class of cardiac neurohormones secreted from myocardium cells mostly in response to increased wall tension due to pressure or volume overload. The value of natriuretic peptides as biomarkers useful for cardiovascular diseases was first described in patients with heart failure and continued in those with acute coronary syndrome presentations. Over the past decade, there has also been growing evidence of the use of natriuretic peptides in AF.,18 Although the utilization of NPs in heart failure management has been well established, the importance of these biomarkers in relation to AF has not been fully clarified. Consequently, comprehensively analyzing the role of different kinds of natriuretic peptides in AF management may thus facilitate the integration of these widely available biomarkers in clinical applications.CA, mainly through PVI, is an effective treatment to restore sinus rhythm for patients with AF,It is worth mentioning that this is one of the few articles to investigate the effect of different natriuretic peptides on the recurrence of AF after ablation with the widest variety of natriuretic peptides. This meta-analysis identified 61 observational studies that investigated the potential association between baseline natriuretic peptides and AF recurrence after CA. The present results suggested that patients with AF recurrence have greater baseline ANP, BNP, NT-proBNP, and MR-proANP levels, indicating a predictive role for natriuretic peptides in the recurrence of AF after CA. The plasma level of ANP in healthy individuals is approximately 20 pg/mL and is evaluated to be 10- to 100-fold higher in patients with heart failure. Meanwhile, it is also well known that ANP is increased in the setting of atrial tachyarrhythmias, including AF, independent of the left atrium diameter. The elevated concentration of ANP in the peripheral plasma obtained during persistent AF is considered to be caused by the loss of atrial contraction and the rapid ventricular rate, which leads to an increased central volume loading and atrial stretch. In this meta-analysis, 8 studies were pooled and a significant association between baseline ANP level and post-ablation AF recurrence was found. Previous meta-analysis had also revealed the same result.ANP is primarily expressed and stored in the atrium and synthesized as pre-prohormones. The primary stimulus for ANP release is atrial wall stretch resulting from increased intravascular volume. The plasma level of BNP in healthy individuals is approximately 3.5 pg/mL and is evaluated to be 100-fold higher in patients with heart failure. Atrial dysrhythmia would also increase BNP secretion. Asynchronous contraction of the atrial myocardium could produce a tethering effect of atrial myocardial fibers that may stimulate the secretion of BNP. Our findings highlight a strong association between elevated BNP and AF recurrence following CA. Another important finding is that BNP levels were affected by follow-up time and region in the subgroup analysis. Patients with short follow-up time (≤3 months) within the cohorts reduced the strength of the association of BNP levels with ablation outcomes. Furthermore, the pooled results for the American region still show no significant association between them , even though Shaikh et al showed that circulating BNP levels were independently associated with late AF recurrence after pulmonary vein isolation.BNP is minimally stored in granules in the ventricles and secreted directly in large bursts following stimulation. Thus, plasma NT-proBNP level as a cardiac biomarker may be an interesting alternative to BNP. Based on 25 related studies, our meta-analysis shows that the AF recurrence group had a significantly higher pre-ablation level of NT-proBNP. In 2009, Hwang et al demonstrated that NT-proBNP levels were an independent predictor of AF recurrence in a group of patients with paroxysmal AF and persistent AF after multivariate analysis. Besides, NT-proBNP may be a possible biomarker of the hormonal status subsequently reflecting the hormonal remodeling of the atria in patients with AF. Meanwhile, some studies have failed to find a significant association between NT-proBNP and AF recurrence. Giannopolous et al performed a post hoc analysis of a prospective study of hypertensive patients with paroxysmal AF. Baseline NT-proBNP levels were higher in patients with recurrence than in those who remained arrhythmia-free, but the association was rendered non-significant when adjusted for variables.NT-proBNP, coexisting in circulation with BNP in 1 to 1, is easy to be determined because of its longer half-life (3 to 4 times longer than BNP), higher quantity (16 to 20-fold higher than BNP), and more stable concentration in the blood, which makes NT-proBNP concentrations relatively more stable than BNP over brief time periods. and proANP is stored and released by atrial cardiomyocytes and is cleaved into mature ANP and its N-terminal fragment, NT-proANP. Within this fragment is the midregional section, MR-proANP, which has a longer half-life than mature ANP, making its assessment more reliable. It was previously shown that MR-proANP is elevated in numerous pathologies specifically affecting the left atrium, such as mitral stenosis.,33 Meanwhile, MR-proANP is increased in patients with AF, independently of hemodynamic conditions, and also that there is a link between AF burden and MR-proANP levels. Other studies have also reported that CA for AF significantly reduced plasma concentrations of ANP and MR-proANP in the long term. In our meta-analysis, 4 studies were pooled and showed a statistically significant association between baseline MR-proANP level and post-ablation AF recurrence.MR-proANP is a stable peptide that results from the cleavage of proANP (pro-ANP) Secondly, natriuretic peptide is recognized as a diagnostic marker of heart failure. The increase of natriuretic peptide levels before ablation may reflect cardiac dysfunction, which may cause AF through intracellular calcium overload, worsening myocardial fibrosis, decreased conduction velocity, and increased dispersity of the refractory period.The explanation for the predictive role of natriuretic peptides in the recurrence of AF may be as follows: Firstly, it has been reported that elevated levels of natriuretic peptides are associated with an increase in left atrial size, which may increase the risk of recurrence of AF due to atrial fibrosis and remodeling after ablation.Undoubtedly, there were several potential limitations in this meta-analysis. First, the retrieved studies in our meta-analysis were observational studies rather than randomized control trials, in which comparability between groups was not easy to be controlled. Some of them had a relatively small sample size. Second, Moderate or high heterogeneity existed among studies that were utilized for assessments. The contributing factors to heterogeneity include variation in follow-up time (from 3 to 63 months), sample size (from 26 to 1410), and AF type. Third, our inclusion criteria focused on studies in English, which led to selection or allocation biases, and affected the results of our meta-analysis. Fourth, we did not assess the prognostic value of baseline natriuretic peptides level by continuous variable due to insufficient such data. Finally, publication bias was found in the studies pooling NT-proBNP outcomes. However, the “trim-and-fill” analysis indicated that the predictive value of NT-proBNP level had not changed obviously after adjustment of publication bias.In conclusion, our meta-analysis indicated that increased baseline natriuretic peptide levels, including ANP, BNP, NT-proBNP, and MR-proANP, are associated with a higher risk of AF recurrence after CA. Measurement of these baseline levels should be recommended among AF patients before CA. Considering the limitations described above, further well-designed, larger, and long-term studies are needed to elucidate the predictive value of baseline natriuretic peptides in AF patients undergoing CA.Conceptualization: Yujing Yuan, Boyuan Nie.Data curation: Boyuan Nie, Li Li.Formal analysis: Yujing Yuan, Boyuan Nie, Li Li.Investigation: Caixia Guo.Methodology: Yujing Yuan, Boyuan Nie, Binbin Gao.Resources: Caixia Guo.Software: Binbin Gao.Supervision: Boyuan Nie, Binbin Gao, Caixia Guo.Writing – original draft: Yujing Yuan.Writing – review & editing: Yujing Yuan, Binbin Gao.


Atrial fibrillation (AF) costs are expected to be substantial, but cost comparisons with the general population are scarce. Using data from the prospective Swiss-AF cohort study and population-based controls, we estimated the impact of AF on direct healthcare costs from the Swiss statutory health insurance perspective.Swiss-AF patients, enrolled from 2014 to 2017, had documented, prevalent AF. We analysed 5 years of follow-up, where clinical data, and health insurance claims in 42% of the patients were collected on a yearly basis. Controls from a health insurance claims database were matched for demographics and region. The cost impact of AF was estimated using five different methods: (1) ordinary least square regression (OLS), (2) OLS-based two-part modelling, (3) generalised linear model-based two-part modelling, (4) 1:1 nearest neighbour propensity score matching and (5) a cost adjudication algorithm using Swiss-AF data non-comparatively and considering clinical data. Cost of illness at the Swiss national level was modelled using obtained cost estimates, prevalence from the Global Burden of Disease Project, and Swiss population data.The 1024 Swiss-AF patients with available claims data were compared with 16 556 controls without known AF. AF patients accrued CHF5600 (EUR5091) of AF-related direct healthcare costs per year, in addition to non-AF-related healthcare costs of CHF11100 (EUR10 091) per year accrued by AF patients and controls. All five methods yielded comparable results. AF-related costs at the national level were estimated to amount to 1% of Swiss healthcare expenditure.We robustly found direct medical costs of AF patients were 50% higher than those of population-based controls. Such information on the incremental cost burden of AF may support healthcare capacity planning. This study used 5 years of follow-up data from a large prospective cohort of prevalent atrial fibrillation (AF) patients.The direct medical cost impact of AF was assessed by comparison with population-based controls drawn from a large health insurance database.Several regression-based and propensity score-based methods were used to judge robustness and AF costs were also assessed using a non-comparative approach.The cohort of AF patients may not be fully representative of all AF patients.A limited degree of residual presence of AF in the control population cannot be ruled out.Atrial fibrillation (AF) is the most common form of serious arrhythmia worldwide, and a major cause of stroke and heart failure. More than 11 million people live with AF in Europe.1Several studies on cost of illness of AF have estimated costs at the patient or nationwide levels. Direct healthcare costs per patient were estimated to range from EUR2315 to EUR3307 annually in Europe,12 14–16So far, most attempts assessing the cost impact of AF remained descriptive. To our knowledge, only two studiesWe used a recent real-world dataset from a large prospective cohort study of AF patients to assess the yearly cost impact of AF. Comparing with a population-based control sample, direct healthcare costs of AF were estimated at the patient level and transferred to the national level. Results were compared with estimates resulting from an adjudication algorithm only using the cohort data in a non-comparative approach.Swiss-AF is a large, ongoing prospective observational cohort study across 14 clinical centres in Switzerland, investigating AF-related cognition, complications and economic aspects. Patients were enrolled between 2014 and 2017 if they had a history of documented AF and were older than 65 years; 228 patients were enrolled aged 45–64 to enhance the study of socioeconomic aspects. A data cut of 2014–2020 was used in this analysis. The detailed study setup has been published earlier.17Alongside clinical data, health economic data were collected. These included medical resource use at the study centres, and health insurance claims from four cooperating health insurers covering 42% of the study sample. In Switzerland, health insurance is compulsory and offered to anyone, covering inpatient and outpatient services. The benefit package is uniform across the country and defined by law.To assess the cost impact of AF, a population-based reference sample was provided by Helsana, an insurer covering about 15% of the Swiss population. Helsana enrolees were eligible for the reference sample if they were not Swiss-AF patients, were in the same age range as the Swiss-AF population, and had statutory health insurance claims data available for a period equivalent to the one available for Swiss-AF patients. For the reference sample a subset of 19 002 patients was randomly selected, frequency matched to the Swiss-AF patients by age, gender and geographical region . To ensu10.1136/bmjopen-2023-072080.supp1Supplementary dataEquivalent claims data were available for the Swiss-AF and control patients, reflecting all claims for reimbursement by the Swiss statutory health insurance. The claims data included detailed information on outpatient services and drugs, and less detailed information on inpatient services based on SwissDRG.23Our main outcome of interest was the AF-induced part of direct medical healthcare costs from the perspective of the Swiss statutory health insurance. To assess the cost impact of AF, the Swiss-AF patients were compared with the population-based controls, using different multivariable regression methods: (1) ordinary least square regression (OLS), (2) OLS-based two-part modelling, (3) generalised linear model (GLM)-based two-part modelling and (4) 1:1 nearest neighbour propensity score matching. Furthermore, (5) estimates were compared with AF costs estimated using a previously developed adjudication algorithm.Covariates available for both the Swiss-AF and control population included the following types: first, patient characteristics: age, sex and area of residence (greater regions of Switzerland); second, PCGs as proxies for comorbidities: acid-related disorders , bone diseases, cancer, dementia, epilepsy, respiratory illness, rheumatic conditions, glaucoma, gout, iron deficiency, chronic pain, psychiatric diseases, use of antipsychotic drugs, thyroid disease and other rare diseases and third, a year of follow-up. Insurance characteristics were obtained from three of four insurers and considered in a sensitivity analysis.First, the characteristics of the included Swiss-AF and control patients were described with standard methods. Healthcare costs per patient and cost trajectories over time were descriptively analysed for both populations, distinguishing between total, outpatient and inpatient costs. Cost trajectories over time were depicted as line plots not considering missing data points.Second, the mentioned multivariable regression approaches were pursued to assess the cost impact of AF, using the above-listed covariates as independent variables. All approaches included a time fixed effect for month of observation.The two-part alternatives to OLS were pursued because healthcare costs are characterised by a significant proportion of zero values and right-skewed distributions of non-zero costs.Third, the different regression-based estimates of AF costs were compared with the estimates of AF costs resulting from applying the AF adjudication algorithm to the Swiss-AF patients’ claims data.24Fourth, cost of illness of AF for Switzerland was roughly approximated as total costs per year, and costs per inhabitant and year, for the time period 2000–2019. Mean annual AF-related costs were taken from the GLM-based two-part model and assumed to follow the trend of healthcare expenditures in Switzerland for the period (index 2019=100%). AF prevalence was taken from the database of the Global Burden of Disease Project for the Swiss population older than 30.29All analyses were conducted by using R V.3.6.3.Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.The evolution of mean annual costs by cohort and cost component is depicted in This study presents up-to-date evidence of real-world AF-related healthcare costs. To the best of our knowledge, it is the first study comparing AF-related cost estimates using population-based controls with a data-derived bottom-up approach to adjudication of AF costs. We obtained similar results for all estimation methods used: mean annual AF-related costs amounted to CHF5600 (EUR5091); indicating roughly 50% higher direct medical costs of Swiss AF patients compared with the population-based controls. At the national level, AF-related costs amounted to CHF700 million (EUR636 million) in 2019, equivalent to about 1% of Swiss healthcare expenditure.et al’s studyOur estimates of AF-related direct medical costs of CHF5600 annually are consistent with previously published estimates, despite notable differences in study designs and data collection approaches. In Europe, annual direct medical cost estimates at the patient level ranged from EUR2315 to EUR3785 (Spain EUR2315 (2006),Transferred to the Swiss national level, direct medical AF costs amounted to CHF700 million in 2019. AF-related cost estimates for European countries ranged from EUR660 to EUR2548 million (Germany EUR660 million (2004),16Our estimates of AF-related costs in the large, prospective Swiss-AF cohort were highly consistent and robust. In particular, the regression-based estimates of AF costs using a matched control population were remarkably similar to the cost estimates based on direct adjudication to AF. The adjudication algorithm was derived using clinical and claims data for the Swiss AF sample only, without comparison to the population-based controls. So far, most literature has focused on estimating costs from clinical or claims dataThere are still several limitations of our work requiring discussion. Most importantly, the Swiss-AF study population is not truly representative of all AF patients in Switzerland, given enrolment in inpatient and outpatient clinical centres and an expected under-representation of patients younger than 65 years driven by eligibility criteria. It would in fact be extremely difficult, if not impossible, to recruit a truly representative sample of AF patients into any study. We still expect our cost estimates to provide a reasonable approximation of the typical AF-related costs of Swiss patients with clinically diagnosed AF. The decision to enrol patients independently of time since diagnosis supports this notion, all the more given the observed high degree of stability of our results over time. However, we cannot exclude that enrolment of the Swiss-AF patients in clinical centres may have led to a certain overestimation of inpatient cost in the first year of observation. Second, the selection algorithm used to define the control population is likely to have missed some patients with AF. However, the lack of exclusion of these patients should not have biased the results strongly, as they did not display indicators of AF-related hospitalisation or major procedures. If anything, a moderate underestimation of AF costs may have occurred. Third, cost calculations were based on claims data, and not all claims may have been handed in for reimbursement. However, in patients with a chronic disease and substantial healthcare costs, this is rather not expected. We could not acquire insurance characteristics from one insurer and have considered these in a sensitivity analysis without distortion of our results. Fourth, the controls were provided by one health insurance only. Major differences between insurers are not expected in the Swiss statutory health insurance, as the primary benefit package is uniform across the country and defined by law. A further limitation affects the estimation of the cost of illness at the national level. There were several assumptions made: (A) AF-related cost estimates were based on the results of the GLM-based two-part model, (B) the development of costs per patient over time was assumed to follow the development of healthcare expenditures in Switzerland and (C) AF patients under the age of 30 were not considered in the prevalence estimates. As a last limitation, this analysis focused on direct medical costs from the perspective of the Swiss statutory health insurance. Costs of lost productivity were not considered and the total impact of AF on the economy was thus not captured. Separate work will address the topic of impact of AF on productivity in younger Swiss-AF patients.In conclusion, the results of this study indicate that AF patients incur 50% higher costs than comparable population-based controls. Costs were at a comparable level as reported by other cost-of-illness studies for AF. Different regression-based approaches to estimating AF-related costs led to similar results, confirming the robustness of our findings. A well-defined bottom-up approach using clinical and claims data but no control population also yielded similar results. This finding is valuable for the interpretation of the existing cost-of-illness literature and may inform decisions on investments in healthcare policies. To control the high costs of AF, future steps may include conducting real-world analyses to understand contributing factors and services, assessing the cost-effectiveness of AF-related treatments to guide resource allocation, and studying risk factors to develop targeted interventions aimed at reducing AF incidence and improving healthcare efficiency.


Sequencing technologies have rapidly evolved over the past two decades, and new technologies are being continually developed and commercialized. The emerging sequencing technologies target generating more data with fewer inputs and at lower costs. This has also translated to an increase in the number and type of corresponding applications in genomics besides enhanced computational capacities (both hardware and software). Alongside the evolving DNA sequencing landscape, bioinformatics research teams have also evolved to accommodate the increasingly demanding techniques used to combine and interpret data, leading to many researchers moving from the lab to the computer. The rich history of DNA sequencing has paved the way for new insights and the development of new analysis methods. Understanding and learning from past technologies can help with the progress of future applications. This review focuses on the evolution of sequencing technologies, their significant enabling role in generating plant genome assemblies and downstream applications, and the parallel development of bioinformatics tools and skills, filling the gap in data analysis techniques. With more than 40 years of remarkable DNA sequencing improvements, today, the development of cost-reducing and higher throughput sequencing technologies, along with relevant bioinformatics tools, have made it possible to produce high-quality genome assemblies in a much-reduced timeline, which has subsequently led to the mapping of the genetic variations in thousands of individuals, providing genetic insights into population histories and domestication events. The multinational and multi-institutional consortium the Earth BioGenome Project (EBP) aims to unify the phylogenetic networks across all eukaryotic life derived from their complete de novo genomes ,2. This Complete reference genome assemblies of the entire plant kingdom will open new scientific views on the evolution and speciation events on earth and genetic control of plant traits, both at intra- and inter-species levels. They will also enhance the understanding of how plants function in ecosystems, lead to the discovery of natural botanical compounds for human medicine, and will aid an increase in food production to curb global hunger while respecting planetary boundaries and adapting to climate change.Here, we provide an overview of the improvements in sequencing technologies, the development of the associated bioinformatics tools, and advancements in plant genomics. We also outline the progress achieved in assembling plant genomes, sequence technologies, and assemblers used to contribute towards crop improvement.Over 40 years of consistent development of reliable sequencing technology emerging to make considerable progress in accuracy, cost, and reduced sequencing time has been improved.. From first-generation to third-generation sequencing, the combined technologies developed have significantly increased the read length, improved quality, and provided massive increases in throughput with significant cost reductions. However, currently, second-generation  sequencing technology dominates. It is during this phase that the sequencing process advanced as a technology to help understand the genetic basis behind the phenotype. This first-generation sequencing technology is based on the dideoxynucleotide chain termination method developed by Sanger and Coulson in 1975, commonly known as the Sanger method  and nuclWhile the Sanger method was continuously popular, particularly for accurate sequencing of specific sequences, such as genes, many other sequencing technologies emerged at around the same time, such as (i) the pyrophosphate sequencing used by Roche for the 454 sequencing platform , (ii) the ligation enzyme method used for the SOLID technique by ABI sequencing company, (iii) single-molecule sequencing with HeliScope from Helicos Biosciences, and (iv) DNA colony sequencing technology from Illumina.https://doi.org/10.48550/arXiv.1711.11004), HiSeq [SGS sequencing was conducted in either a stepwise iterative process or in a real-time manner, producing a combination of qualitative and quantitative sequence information, which was not possible with FGS data. The second generation of sequencing technology was symbolized by several approaches, all fundamentally based on parallel data production with individual sequences identified by position on a flow cell or microarray. Roche’s 454, Illumina’s Solexa, Hiseq technology, and ABI’s Solid technologies not only reduced sequencing costs but also increased the speed of sequencing . It is p), HiSeq , and Novde novo genome assemblies, especially where genomes are large or contain repetitive DNA, as is the case with many plants [Third-generation sequencing (TGS) technologies have focused on increasing the read length while maintaining the sequencing throughput. The single-molecule real-time sequencing-by-synthesis (SMRT) technology from Pacific Biosciences introduced read lengths of single DNA fragments exceeding 10 Kb, and long sequences are particularly useful for y plants , and Revwww.10xgenomics.com) integrated with GemCode technology supplied with the Supernova2 genome assembler. This technology was specifically designed for diploid and low-complex genomes, such as Corylus avellana, where its implementation produced a chromosome-level genome assembly [With continual upgrades in sequencing chemistry and technology, it is possible to generate longer reads of over 100 Kb in length. Nanopore is a technology that takes a different approach to sequencing via synthesis adopted by PacBio. This technology  decodes the DNA molecule by detecting electrical fluctuations as a nucleic acid molecule passes through a small diameter biological “pore”. Continuous sequences from single molecules up to 500 Kb have been reported, although generally, a single molecule sequence is likely to average around 20 Kb . By haviassembly .https://bionanogenomics.com/products/) and OpGen (http://www.opgen.com/about-us/opgen-overview/).Similarly, optical mapping and Dovetail Hi-C technologies are useful to complete the ordering of various DNA contigs in a genome by creating a visual physical map along large DNA molecules, which assist in correlating a DNA sequence with a physical location ,16. ThisSequencing technologies, mainly using high-throughput NGS sequencers, generate significant amounts of data. For example, the recent sequencer from Illumina (NovaSeq 6000) has a higher output than the earlier generation of sequencing machines producing between 1300–20,000 million reads (65 Gb to 3 Tb). The long reads from PacBio reach up to a maximum of 300 Kb, and the data generated with Sequel I, II (CLR), II (HiFi) range from 0.5 million to 400 million reads (15 Gb to 100 Gb), with the nanopore sequencing technology (Minion and Promethion) sequencing ranging from 2.5–12 million reads (40 Gb to 180 Gb). Genlisea margaretae with a 1C value of 0.07 pg (65 Mb) and the canopy plant Paris japonica with a 1C value of 152.2 pg (148.9 Gb) are equally accessible in terms of raw sequence generation and coverage [https://cvalues.science.kew.org/). Generating several-fold coverage of genomic data produces potentially massive datasets, ranging from Gb to Tb of sequence information. Depending on the scope of the project, handling such large datasets is a major concern for small (or even big) research labs. Decades ago, geneticists were mostly involved in lab work; now, the most limiting factor is the analysis of the data to derive meaning or interpretation out of it using computational tools. Understanding the algorithms and processing the data are a crucial part of genetics and genomics data analysis when searching for biological meaning. With this capacity, sequencing land plants having a wide range of genome size DNA content can, in theory, possibly generate good coverage of the entire genome sequence data. For example, the corkscrew plant coverage  , and processing the data for large population-wide studies, such as finding deeper scientific insights, marker–trait association, analyzing diversity, domestication, and assessing data from gene-editing technologies, requires robust storage and computing capacities. To maintain the uniformity of the data in the global databases, the members’ databases  of the International Nucleotide Sequence Database Collaboration (INSDC)  share anncbi.nlm.nih.gov)) (The recent stats release of GenBank reports having 16.7 trillion nucleotide bases for 1.7 million whole genome sequences (as of June 2022) (GenBank and WGS Statistics (ih.gov)) . Of whicftp.ncbi.nlm.nih.gov) or downloading data programmatically with the provided public API to the Entrez system (https://eutils.ncbi.nlm.nih.gov). With the increasing complexity of genomic data themselves, the major databases also integrate other genomic features and provide tools to search and retrieve these datasets. The Entrez system of NCBI is one such tool allowing users to search, view, and download the sequences from GenBank. Other modes of data accessibility allow for downloading from the FTP site (https://www.legumeinfo.org), to smaller databases containing crop/plant-specific information, such as for the chickpea SSR database (https://cegresources.icrisat.org/CicArMiSatDB/index.html) [https://cegresources.icrisat.org/cicarvardb/) [Numerous databases have been developed for genomic data to suit a variety of different purposes . Based oex.html)  and chicrvardb/) . Howeverrvardb/) , and mayrvardb/) .Genome assembly refers to aligning the small fragments of a DNA sequence to reconstruct the genome sequence in the original order and orientation. High-throughput sequencing through first- and second-generation sequences has enabled the assembly of many plant genomes. The highly fragmented genome assemblies generated with short reads have been improved with long read sequence assemblies, simplifying and improving the ability to generate chromosome-level assemblies with reduced reliance on dedicated research experts.Thanks to the NGS technology and increased computational power, the standard of the genome assemblies available has improved significantly. Genomics has accelerated its growth in the past decade from draft-level genome assemblies to reference-level genome assemblies ,79,80. www.plabipd.de)  . Based o, potato , or the , potato ,85 and s, potato . The devIn recent years, gold-standard and platinum-standard chromosome-level genome assemblies are being achieved in prominent model crop plants ,90,91,92www.plabipd.de) of the total assembly number of 1143 flowering plants, and 125 are non-flowering plants (Chromosome-level genome assemblies were initiated with Arabidopsis in 2000  and lateg plants . Most ofwww.bionanogenomics.com) and chromosome conformation capture sequencing (Hi-C) [A. alpina [Several novel technologies have emerged , the Irg (Hi-C) ) to imprg (Hi-C)  and lateg (Hi-C) ,109, ricg (Hi-C) , and bang (Hi-C)   to handle such challenges. Additionally, handling the terabytes of sequence data and storage and managing the computing clusters and complexity of the algorithms also need to be addressed.In addition to improving the quality of reference genomes to platinum-standard, present-day technologies paved the way for the transformational shift from the representative single genotype’s genome sequence to the pan-genome sequence as a reference for a better understanding of the variability present within a species . The advDrosophila melanogaster) and human genome in 2000–2003; later, AMOS was launched under an open-source framework. These assemblers were developed based on overlap–layout–consensus on an overlap graph [de Bruijn graph-based assembly was developed [de Bruijn graph-based assemblers have difficulties handling sequencing errors and need high computational power (100+ Gb of memory). The challenge with uneven genome coverage and reads too short to span repeated regions can be addressed by a combination of many short reads and fewer longer reads or mate–pair reads . Multiplex de Bruijn graphs automate the assemblies of long HiFi reads [de novo assembly. After the release of the SHARCGS assembler for Solexa reads, other assemblers were released and became the most popular assembly tools  and assembly approaches have removed the limitation of genome sequencing for not only the crops with small genome sizes but also enabled sequencing and assembly of large genome crops, like wheat (~17 Gbp) ,140,141,The genome assembly quality has improved as the sequencing technologies and assembling tools improved , especiaThe initial assembly version of the sorghum genome assembly released in 2009  with shode novo genome assembly produced 1.29 million scaffolds, capturing 2.8 Gbp of sequence [For a large genome (~8 Gb) rye , initially, a virtual linear gene order model  was established with high-throughput transcript mapping and chromosome survey sequencing ; followisequence  and latesequence .In addition to the chromosome-scale assemblies, TGS has enabled the assembly of polyploid genomes, such as bread wheat , potato With the emerging sequence technology and bioinformatics tools, it is possible to assemble a nearly complete genome sequence. With cytogenetic advances to measure the genome size (such as flow cytometry), a genome size estimation is a useful first step in a complete genome sequencing project. The amount of sequencing data required to produce a given level of coverage depends on the 1C amount of DNA per cell (including ploidy level), and for most species, this can be found in the Kew Plant Genome Database. Most plant genome assemblies are smaller than the cytogenetic genome estimation size; this may be because of assembly errors or difficult-to-approach genomic regions, like centromeric and repetitive regions in the plant genome, where assemblers struggle . Some of the assembled plant genome sizes are quite close to the cytogenetic estimated size, indicating the assembler has captured the majority of the genome content. Assemblies above the estimated size, however, may need refinement to reduce contaminants or alter the assembly parameters. The genome assembly provides the coordinate system for the gene models and other genomic features, like SNPs, Indels, SSRs, etc. Predicting the gene models with ab initio gene findings and supporting evidence in the form of RNA data increases the accuracy. However, this may not list out the complete complement of genes of the species for which resequencing a wide range of diverse accessions will reveal more genes that are genotype-specific. For example, the resequencing of >1000 wild and cultivated rice accessions has predicted the presence of thousands of genes with lower sequence diversity in cultivated rice, indicating a rice domestication genetic bottleneck ,152. MorThe most commonly used downstream analysis with pan-genome assemblies is to identify the genetic variation of any DNA segment in a genome or a gene (including gene fragments) that can be used as a marker for genotyping. Bioinformatics resources enhancing crop genomics for downstream analysis include copy number variations (CNV), identification of variations based on the length , a set of SNPs used as a unit in the form of a haplotype to increase the resolution of GWAS, k-mer analysis, linkage disequilibrium (LD), presence–absence variations, pan-genome-wide association studies (PWAS), genotyping-by-sequencing, reduced representation sequencing, domestication, and diversity analysis . With thhttps://investors.fluidigm.com/node/13686/pdf), IntelliQube (https://www.myebpl.com/intelliqube.html), MassARRAY [https://www.illumina.com/science/technology/microarray.html), TaqMan [https://www.diversityarrays.com/). Genotyping by sequencing (GBS) is a highly multiplexed system for constructing reduced representation libraries from the sequencing platform with low-cost, reduced sample handling with no need for a reference genome. GBS (including the single digest RAD and double digest RAD and skim-sequencing) are tools for genomics-assisted breeding in a range of plant species through the applications of SNPs identification, gene/QTL mapping, molecular diversity, GWAS, construction of high-density genome maps, haplotype maps, phylogenetics, identification of candidate genes, genetic linkage analysis, molecular marker discovery, and genome sequencing and selection. Such genetic resources assist in predicting the genetic value of selected candidates based on the genomic estimated breeding values (GEBV) from high-density and quality markers. Genomic selection (GS) is an approach to exploit genetic markers to develop new markers-based models to increase the genetic gain of complex traits for breeding programs. High-throughput marker technologies have changed the entire scenario of marker applications and enabled the use of GS routine work for crop improvement.High-throughput genotyping enables the genotyping of thousands of targeted loci (genetic markers) on thousands of samples. Depending on the number of markers and the sample size, different genotyping techniques can call genotypes in different ranges. Some of the technologies include Illumina golden gate, Affymetrix SNP, reduced-representation genome sequencing, exome-seq, Fluidigm  (“phenomics”) has unique advantages in facilitating accurate, automated, high-quality data collection techniques, including visible light imaging, X-ray computed tomography, visible and near-infrared spectroscopy, multispectral imaging, chlorophyll fluorescence, fluorescence imaging, and nuclear magnetic resonance (NMR)  Xiao et. Plant pGenomics data science is a field that needs powerful computational and statistical methods to decode the information in plant genomic DNA. Having a better understanding of genomics with these data science tools helps researchers to uncover the differences between the varieties at a DNA level and enhance crop improvement. Bioinformatics has emerged to bring in vivo experimentation and in vitro data analysis with statistical and computational tools to process the data by developing and implementing the algorithms as software tools to make predictions based on the experimental data.Researchers are now generating more genomic data to understand genome functions and mine genetic information to explore novel insights from the vast amounts of generated genomic data. Sequencing huge numbers of individuals of a species generates terabytes of data, and processing such large amounts of data needs additional terabytes to petabytes of storage and working computational infrastructure. Researchers need special computational and software tools to mine and interpret hidden biological information through assembling the sequence data, aligning the sequence reads, and mining the variation, association studies and other genomic insights .Artificial intelligence (AI) tools help researchers process vast quantities of genomic sequence data to find patterns in a genome . AI typiMachine learning (ML) is a subset of artificial intelligence (AI) involving the development of algorithms that learn to perform a specific task based on given inputs. ML is implemented in either supervised learning (predicting output based on the given input features describing the object) or unsupervised learning (seeking patterns comparison and grouping the data) . Supervihttps://www.cs.waikato.ac.nz/ml/weka/) and Orange (https://orange.biolab.si/) as user-friendly graphical interfaces, scikit learn (https://scikit-learn.org/) [https://www.tensorflow.org/overview/) [https://cran.r-project.org/web/packages/caret/vignettes/caret.html) [ML method implementations are available in the form of a Weka (rn.org/) , Keras [rn.org/) , and PyTrn.org/) . In Advaerview/)  in Pythoet.html) .ML is widely used for crop improvement; some of the case studies include plant–pathogen interactions , traits,The applications of ML have been widely used in phenotyping through high-throughput, image-based plant phenotyping which uses a convolutional neural network (CNN)  and deepML can improve plant breeding ,233, withttps://www.agspeak.in/), AutoML (https://www.automl.org/), aWhere (https://www.climateshot.earth/awhere), Farm at Hand (https://www.farmathand.com/), Plantix (https://plantix.net/en/), Tumaini (https://ciat.cgiar.org/phenomics-platform/tumaini/), and Xarvio .Mobile apps have been designed to collect data, record details, predict plant disease, predict weather changes, and other miscellaneous applications. The apps and the underlying algorithms interpret images captured through the devices, thus reporting the health condition of plants, soil color and other phenotypes. The availability of these apps for farmers assists in detecting disorders and suggests suitable measures to protect the crop. Some of the apps are AgSpeak  require less power, reagents, maintenance, and storage and have an easy processing format. It is also equally important to have advanced and compatible bioinformatics tools to analyze the big data generated from the agriculture sector.First- and second-generation sequencing technologies generated short-sequence reads resulting in highly fragmented reference genome assemblies  but were used to generate the first reference genomes for plants. Such low-quality assemblies (compared to third-generation assemblies) have many gaps and do not represent the actual genome structure. On the other hand, combined second- and third-generation sequence data have contributed to generating full chromosome-level (CL) to T2T-level reference sequences. Only in a few plant genomes have high-quality, gapless chromosome levels to T2T quality assemblies been generated; therefore, further improvements are necessary to generate high-quality standards.T2T-level genome assemblies will provide insights into the genetic diversity, identification of domestication events, and the investigation of the evolutionary history of plant species.The sequencing of multiple accessions of a plant species is expected to allow the assembly of a pan-genome which represents the collection of core and dispensable genes present in a species . In addiThe recent advances and developments in bioinformatics applications for plant genomes provide huge potential for plant genome research. As sequencing technology has become much more affordable and portable to handle, the importance of bioinformatics tools increases to analyze and manage the data. More plant species genome databases are being established with a variety of analysis methods. Phylogenomics and GWAS now generate more accurate results with the tools developed with newer algorithms. Moreover, high-throughput phenotyping needs to provide results with a high resolution to meet the density of genotype information.de novo assembly, scaffolding, annotation, and downstream analysis. This is key for gold- or platinum-standard genome assemblies.The genetic information in the form of sequence data or optical maps needs to be as error-free as possible, selecting the appropriate informatics tools for With the rate of the growing world population, there is a constant increase in demand for food, and AI will play a vital role in meeting these demands, coupled with computational power through robotics, smartphone apps, and image processing algorithms. AI provides automation in agriculture. Technology is being developed in agriculture for automated methods, crop improvement, and crop protection. With computational advances, including AI, ML, and DL, the future GAB, including marker-assisted selection (MAS), MABC, marker-assisted recurrent selection (MARS) , haplotyIn the past two decades, the parallel advances in sequencing technology and bioinformatics tools have enabled plant researchers to generate genomics resources for economically important plants, which is critical for crop improvement and to develop a greater scientific understanding of the gene underlying critical traits for future agriculture.


K. pneumoniae-KPC infections. Methods: We conducted an analysis based on the real-life data of our ten years retrospective cohort to assess the cost-effectiveness and cost-utility of a short course of CAZ/AVI plus source control compared to a long course plus source control. A Markov model was structured. Patient transition between health states was modeled, each transition has a probability, and each state has a cost and a utility. Incremental cost-effectiveness ratios (ICERs) were obtained by dividing the difference in costs by the difference in utilities between the two courses. Input parameter uncertainty was investigated through sensitivity analysis. We launched 1000 Monte Carlo simulations by iteratively perturbing variables within estimated variation ranges, obtaining an ICER result for each simulation. Results: In the first model (old appropriate treatment), a short course of treatment was associated with reduced costs per patient per year of €4818.60 and reduced effects (0.10 QALYs), compared to a long course. In the CAZ/AVI model, the short course was associated with increased costs of €1297.9 and with increased effects (0.04 QALYs), resulting in an ICER of €32,317.82 per QALY gained, below the WTP threshold of €40,000. Conclusions: Our findings highlight additional evidence regarding the cost-effectiveness of CAZ/AVI for policy-makers. We outline that CAZ/AVI could be cost-effective compared to old appropriate antibiotic therapies for KPC-Kp BSI.Background: Evidence has shown that short courses of antibiotic therapy are at least as effective as long courses with better clinical outcomes. CAZ/AVI has demonstrated its clinical efficacy in treating  Several studies have compared the impact of a short versus long course of antimicrobial therapy aiming to limit adverse effects, promote early treatment discontinuation and discharge, and avert the ecological pressure leading to the emergence of antimicrobial resistance ,5,6,7,8.Klebsiella pneumoniae carbapenemase (KPC), class C and some OXA β-lactamases, but it has no activity against metallo-β-lactamases (MBLs). It was approved by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) for treating complicated urinary tract infections (cUTIs), complicated intraabdominal infections (cIAIs), hospital-acquired pneumonia (HAP), ventilator-acquired pneumonia (VAP), and other infections due to Gram-negative bacteria with limited treatment options [Enterobacterales infections, several observational studies after its commercialization have shown an association with CAZ/AVI therapy and lower mortality rates compared to previously used drug regimens including meropenem, colistin, tigecycline, or their combinations [Ceftazidime/avibactam (CAZ/AVI) is a relatively new third-generation cephalosporin combined with a non-β-lactam-β-lactamase inhibitor which inhibits class A enzymes, including extended-spectrum beta-lactamase (ESBLs),  options ,10. In a options . Despiteinations ,15,16,17K. pneumoniae-KPC bloodstream infections. Since CAZ/AVI has demonstrated its clinical efficacy and its use is now consolidated for the treatment of K. pneumoniae-KPC infections, the economic benefits of this drug have been assessed through cost-effectiveness and cost-utility analyses comparing CAZ/AVI to the previously available treatments. Tichy et al. [In previous work by our group , source y et al.  have demy et al.  that CAZy et al.  have demy et al.  trial daK. pneumoniae-KPC.We conducted an analysis based on the real-life data of our ten years retrospective cohort to assess the cost-effectiveness and cost-utility of a short course (<7 days) of CAZ/AVI plus source control compared to a long course (≥7 days) of CAZ/AVI plus source control. Then we further compared the CAZ/AVI therapy versus a short (<7 days) or long course (≥7 days) of old appropriate treatment in bloodstream infections sustained by K. pneumoniae-KPC such as Italy. We built a model incorporating data from the literature and retrospectively collected data from our tertiary care center, to determine the difference in costs of short (<7 days) vs. long-course (≥7 days) (standard of care) of targeted antibiotic therapy and the difference in effects in terms of quality-adjusted life years (QALYs). Two models were built, comparing (1) short vs. long course of old appropriate treatment and (2) short vs. long course of CAZ/AVI either with source control for the treatment of bloodstream infections sustained by K. pneumoniae-KPC (KPC-Kp BSI).The aim of our analysis was to investigate whether the results of the recent meta-analysis by Turjeman et al. about the duration of antibiotic treatment for Gram-negative bacteremia  would trA Markov model structure was chosen as it allows us to consider recurrent events. Patients begin each cycle in state A: KPC-Kp BSI. Patients then transition to four other states: state B , state E , state C , and state D (death). Patients in state C can remain in state C, or transition towards states D, B, or E. Patients in state B can remain in state B, return to state A (relapse), or transition to states D and E. States D and E are absorbing states as mortality and recurrent infections after discharge were not incorporated in the model.Probabilities. Point estimates were obtained from the studies included in the meta-analysis by Turjeman et al. [n et al.  and fromCosts. Costs were evaluated from the hospital perspective and were obtained from our setting. We did not apply discounting as models were run for 12 months. We evaluated the following costs: costs associated with a hospital stay, distinguishing admission to intensive care units (ICUs) and regular wards, and drug costs. The latter were considered only in the CAZ/AVI model, as drug costs for older antibiotics are negligible compared to costs associated with a hospital stay. Costs were assigned to states A, B, and C, whereas we did not consider any costs for states D and E .To evaluate costs associated with a hospital stay, we evaluated the proportion of patients in our cohort in states A, B, and C who were first diagnosed in ICUs vs. regular wards . The cosThe cost of a daily dose of ceftazidime-avibactam was obtained from our hospital pharmacy and resulted in €173.16. We assumed a short course of treatment to be 7 days, whereas we considered the actual length of treatment of patients in our cohort receiving CAZ/AVI for a long course: median of 11 days . Utilities. Utilities were obtained from the study by Koukoubani et al. [i et al. , which eBase-case. Base-case results were obtained by inputting point estimates for each parameter . Incremental cost-effectiveness ratios (ICERs) were obtained by dividing the difference in costs by the difference in utilities between short vs. long course treatment obtained in both models. We set willingness to pay (WTP) thresholds at €30,000 and €40,000 per QALY [per QALY .Deterministic one-way sensitivity analysis. Input parameter uncertainty was investigated through sensitivity analysis. We varied the following transition probabilities according to ranges reported in Probabilistic sensitivity analysis. We launched 1000 Monte Carlo simulations by iteratively perturbing variables within estimated variation ranges, obtaining an ICER result for each simulation. Each ICER was calculated by randomly selecting values within variable ranges simultaneously. We assumed a beta distribution for transition probabilities and QALYs and a gamma distribution for costs. We then calculated the proportion of ICERs under both WTP thresholds and calculated 95% confidence intervals (CIs) for both proportions through bootstrap resampling (1000 iterations). We used Microsoft Excel 2016 to build models and performed statistical analyses using IBM SPSS version 28.0.Base-case. Deterministic sensitivity analysis. Probabilistic sensitivity analysis. ICER results for 1000 Monte Carlo simulations are shown in K. pneumoniae-KPC such as Italy. Moreover, potential obstacles in the use of newer antibiotics such as CAZ/AVI might be represented by costs despite the demonstrated efficacy in RCTs and retrospective studies compared to old appropriate treatments with an approximate value of $7500–$15,000 for a 7–14-day therapy [Several studies supported the use of short antibiotic therapy in different clinical conditions, showing no differences in mortality and improving adverse effects, and promoting early discharge. Our analysis aimed to investigate whether these results emerging from growing literature would translate into benefits from a cost-utility perspective in a highly endemic setting for  therapy ,16,23.For this reason, we conducted a cost-utility analysis comparing short vs. long courses of CAZ/AVI or old appropriate antibiotic regimens in our real-life cohort of patients with KPC-Kp BSI in a tertiary hospital in Northern Italy. The results of our analysis show that a short course of old antibiotic treatment is not cost-effective compared to long course treatment, but CAZ/AVI short course treatment could be cost-effective compared to a long course treatment. This finding might support the consideration of a short course of CAZ/AVI as a first-line option in KPC-Kp bloodstream infections, discouraging the use of combinations of old drugs both for the well-known clinical inferiority, for carbapenem-sparing and for cost-saving in the setting of stewardship interventions and optimization of resources ,16,17,18In a similar base case analysis published by Simon et al. , CAZ/AVIOur analysis has several important limitations that should be considered when interpreting results. Due to the retrospective nature of the analyzed cohort, potential biases could be possible such as recall bias and missing data, the lack of a severity score such as SOFA, and the possible change of treatments and approaches across 10 years of observation. As with any model, our analysis was limited by the validity of our assumptions and the accuracy of our estimates. Regarding assumptions underlying our model, as stated in the Methods section two tunnel states were added to consider the duration of infection, as we assumed this would affect the length of hospital stay and the probability of dying. We incorporated in our model what we considered were the most relevant elements of clinical pathways and aimed to avoid unnecessary complexity, and therefore we did not incorporate the full spectrum of possible health states and outcomes for patients with KPC-Kp BSI, in particular, post-discharge events were not considered. Concerning input parameters, an important limitation of this study is the limited availability of transition probability estimates, in particular for mortality (as stated in the methods section). However, we extracted data from studies included in a recent meta-analysis and performed sensitivity analyses over broad ranges. Further, we applied mortality estimates from what we expected would be the worst-case scenario in terms of patient case-mix , therefore we can suppose results for short course treatment are conservative estimates, in particular for the CAZ/AVI model. Costs estimates are limited by having been obtained by a single center, and could have limited representativeness in countries without single-payer health systems. QALY estimates were obtained from a single Greek study , howeverTo conclude, our aim was to provide a preliminary assessment of short course treatment for patients with KPC-Kp BSI, which are difficult to include in randomized clinical trials, in order to help guide future clinical investigations. Our findings highlight additional evidence regarding the cost-effectiveness of CAZ/AVI for policy-makers. We outline that CAZ/AVI could be cost-effective compared to old appropriate antibiotic therapies for KPC-Kp BSI. Our model with input parameters from a real-life cohort of KPC-Kp BSI patients and published data could be transferred to other similar settings with different willingness-to-pay thresholds to assess the cost-effectiveness of using up-to-date antibiotic regimens, such as CAZ/AVI. Future research on CAZ/AVI use in practice for CRE infections would aid in refining conclusions about the economic impact of CAZ/AVI.