Polyketide retrobiosynthesis,where the biosynthetic pathway ofa given polyketide can be reversibly engineered due to the colinearityof the polyketide synthase (PKS) structure and function, has the potentialto produce millions of organic molecules. Mixing and matching modulesfrom natural PKSs is one of the routes to produce many of these molecules.Evolutionary analysis of PKSs suggests that traditionally used moduleboundaries may not lead to the most productive hybrid PKSs and thatnew boundaries around and within the ketosynthase domain may be moreactive when constructing hybrid PKSs. As this is still a nascent areaof research, the generality of these design principles based on existingengineering efforts remains inconclusive. Recent advances in structuralmodeling and synthetic biology present an opportunity to acceleratePKS engineering by re-evaluating insights gained from previous engineeringefforts with cutting edge tools. Thus, the interest and potential in engineering PKSs isbased on the valuable activities of these complex compounds, and theircolinear biosynthetic logic, meaning that the chemical structure ofthe compounds produced can be directly inferred by the order and typeof the enzymes in the pathway.Retrosynthesis is the concept of designingsynthetic organic chemistryroutes by working backward from the final product to define a seriesof achievable reactions from simpler building blocks.6 The mechanismof polyketide elongation has long been compared to fatty acid synthesis8 in that an acyl-CoA extender unit is first primed by an acyl transferase(AT) onto its cognate acyl carrier protein (ACP) before undergoinga decarboxylative Claisen condensation reaction with the polyketidechain tethered to a β-keto synthase (KS). The polyketide β-carbonis then optionally reduced by a NADPH-dependent ketoreductase (KR)to a hydroxyl group, then dehydrated by a dehydratase (DH) to an enoylgroup, and finally reduced to a fully saturated β-carbon byan enoyl reductase (ER). After undergoing elongation and reduction,the polyketide chain is shuffled by the ACP to the KS domain of thedownstream module where the assembly line continues with the condensationof a new extender unit. At the end of the assembly line, the mostcommon termination domain is a thioesterase (TE), which either hydrolyzesthe linear product off the PKS or catalyzes a cyclization reaction.11 The regio- and stereochemistry of each ketide building block isdetermined by the combination of conserved catalytic motifs in theKS, KR, DH, and ER.12 The structure ofthe final ketide product can be inferred from the order in which thesemodules interact, which is directed by both protein–proteininteractions and substrate specificity of each domain. Therefore,there is a direct relationship between the DNA sequence of the biosyntheticcluster and the structure of the molecule produced.PKSs are composed of modulesthat act as part of an assembly linecarrying out stepwise decarboxylative Claisen condensation reactionsof acyl-CoA building blocks.13 polymer precursors,14 specialty chemicals,15 and biofuels.16 Nevertheless, many engineered PKSs suffer fromreduced catalytic activities.9 Two primaryoverarching issues have prevented PKSs from reaching their full potential:neither the protein–protein interactions nor the substrate–proteininteractions within PKSs are well characterized.19 However, recent structural,21 evolutionary,23 biochemical,24 and metabolic engineering16 studiessuggest that the traditional boundaries used for module–moduleconnections may not provide optimal interactions between modules,and that there are alternative boundaries that may boost success rates.25 Furthermore, recent progress in the field of machine learning hasenabled impressive models in a diverse range of fields including proteinstructure prediction. The release of Alphafold226 and RoseTTAFold27 for proteinstructure prediction has prompted a swath of studies investigatingcomplex macromolecular phenomena including protein complex analyses.28 In this perspective, we discuss how previousmodule-based PKS engineering efforts can be re-evaluated in the contextof modern structural modeling and how the insights gained may initiatea new generation of PKS design principles.PKSs representan attractive avenue to the access of truly complexcarboxylic molecules, and their unique organization has prompted numerousengineering efforts by mutating or shuffling catalytic parts for thebiosynthesis of novel products. They have been successfully engineeredto produce numerous interesting bioproducts including potential therapeutics,31 Demonstrating the inherent promiscuity with PKS parts, Menzellaet al. successfully biosynthesized over a dozen novel triketide lactonesby recombining dozens of PKS modules.31 However, the dramatically reduced product titers from most of theirengineered constructs showed that there are unknown fundamental principlesthat govern the interactions between PKS modules.The natural homology existing betweenPKS modules from differentbiosynthetic gene clusters (BGCs) within diverse organisms sparkedone of the most ambitious goals of PKS engineering, the lego-izationof PKSs. The lego-ization of PKSs involves the combinatorial expressionof PKS parts for the biosynthesis of novel bioproducts 1A.30,31 32 Thoughthe failures in KS-catalyzed chain extension are presumably multifacetedand still have not been logically resolved, the gatekeeping behaviorof KSs has been supported by in-depth biochemical33 and evolutionary analyses.23 The evolutionaryanalyses provide evidence that KS domains more strongly coevolve withupstream domains, suggesting that an alternative definition of a PKSmodule would have the KS domain as the last domain of a PKS moduleinstead of the first. This alternative definition of a PKS module(starting at AT and ending after KS) is known as the PKS exchangeunit (XU),34 analogous to the definitionused in non-ribosomal peptide synthetases (NRPSs).36Subsequent studies that investigated the KS domainfrom downstreammodules revealed that the KS domain plays a critical role in the interactionsbetween chimeric modules, and that swapping the KS can, in some cases,resuscitate inactive interactions.37 Notably, thisKS gatekeeping activity is much more prevalent in trans-AT PKS assemblylines than in cis-AT PKS systems and has been used to identify generalizabledesign principles for such systems.40 Trans-AT PKSs are differentiatedfrom cis-AT PKSs in that the AT domain, which is responsible for loadingan extender unit onto the ACP, is not located within the same modulethat performs chain elongation. Instead, the AT domain is either foundas a separate, standalone enzyme or occasionally within a differentmodule. Though closely related to cis-AT PKSs, the unique architecturalnuances of trans-AT PKSs mean that some but not all engineering strategiescan be directly generalized between systems.This XU model contrasts with the genetic organization of thedomainswithin open reading frames but is logical given that the structuralcharacteristics of a substrate entering a KS active site are determinedby the AT and reducing domains. The biochemical analyses provide evidencethat the condensation reaction catalyzed by the KS domain is indeedthe rate-limiting step.44 Furthermore, this model implies that many evolutionary means existto alleviate reduced activity at module boundaries via convergentevolution. It should continue to be a key area of focus to determinewhat biochemical aspects of PKS evolution can be generalized.Insights aboutthe mechanism of PKS diversity have emerged fromthe observation that the phylogeny of KS domains aligns closely withthe host organism phylogeny and that KS domains are most closely relatedto other KS domains within the same PKS, and more broadly to otherKS domains within the same host organism. This suggests a model thatPKSs evolved primarily through horizontal gene/BGC transfer, recombination,gene conversion, and genetic drift, rather than through gene duplication.13 Notably, the mutant assembly lines were not generated through targeted in vitro cloning, but rather through spontaneous in vivo homologous recombination. The authors reported thatwhen modules were deleted from the native PKS, the place where recombinationgenerally occurred was not particularly on either end of the KS butrather throughout the KS domain, AT domain, and the interdomain linkerregion upstream of the ACP, with a notable hot spot for recombinationwithin the middle of the KS that takes advantage of the high sequencehomology between KS domains. The Pikromycin PKS is another significantcase study system providing evidence that utilizing XU module boundariescan improve activity of chimeric PKSs.45 Moreover, in vivo homologous recombination engineering strategieshave also been applied to the Pikromycin PKS and similarly demonstratesuccessful recombination throughout the KS and AT domains.46 The Aureothin and Neoaureothin PKSs are twohomologous BGCs that were successfully engineered to produce non-nativeproducts by utilizing XU module boundaries.47The consideration of these factors has led to some recent evolutionary-inspiredPKS engineering strategies 1B,C. For34 The goal of Su et al. was to engineer a successfulinteraction between module 13 and module 21 of the Stambomycin PKS.34 They compared six strategies for engineeringthis interaction including the following: swapping the C-terminaldocking domain of module 13 for the C-terminal docking domain of module20 [nonfunctional]; swapping the C-terminal docking domain and performinga mutation in the KS-ACP interface region of the ACP in module 13[functional]; swapping the KS domain of module 21 for the KS domainof module 14 utilizing XU boundaries [functional]; swapping the KSdomain of module 21 for the KS domain of module 14 utilizing the recombinationhot spot boundaries [functional]; swapping the KS domain of module21 for the KS domain of module 14 utilizing XU boundaries and swappingthe KS-ACP interface region of the ACP in module 21 for the KS-ACPinterface region of the ACP in module 14 [nonfunctional]; and lastswapping the KS domain of module 21 for the KS domain of module 14utilizing XU boundaries and performing a G to D mutation in the ACPof module 21 [functional].34 The functionalvariants were successfully able to produce measurable quantities oftruncated final product; notably, the variant that swapped the downstreamKS domain using the recombination hot spot junction in the middleof the KS domain achieved the best kinetics.34The Stambomycin PKS has been subject to one of the most systematicstudies of module boundaries to date, where six different approachesto generating a truncated version of the assembly line were applied.Figure S1) have exhibited some success in engineered systems; however,without a systematic study examining module boundaries in a varietyof systems, it is difficult to identify generalizable design principles.One promising approach that has recently been applied for the optimizationof AT-swap junctions that could potentially be used for more systematicallyanalyzing module–module interactions involves the use of oligonucleotidepools for generating libraries of junctions, a solubility biosensorfor performing a preliminary high throughput screen of high expressingvariants, and a kinetic analysis of a diverse subset of the library.48 This approach is amenable to high throughputexperimentation and could enable more generalizable junctions by systematicallycharacterizing the space of possible junctions. For now, recent evidence the ability of an ACP domain to translocate a polyketide chainto a downstream KS domain, (ii) the ability of a KS domain to catalyzethe condensation of a polyketide chain with an extender unit, (iii)and the ability of an ACP domain to translocate a ketide extenderunit with an upstream KS domain.PKSs are very large andhighly dynamic homodimeric proteins, whichmeans that structural characterization of PKS assembly lines was untilrecently limited to individual domains or didomains. For example,docking domains,62 This revealed conformations that active PKSmodules undergo; however, many key details about intermodule communicationcould not be elucidated due to the resolution. Cryogenic electronmicroscopy (cryo-EM) has additionally been used to capture conformationaldata about a full-length PKS module in the presence of an upstreamACP, resulting in the hypothesis that there are two distinct activesites within the KS domain for intramodule and intermodule ACP interactions.64 However, the arch-shaped structure observed in this system is differentfrom previous extended-shaped models and is being further rebuttedby recent structural data.65 Nonetheless,cryo-EM has been used in tandem with the first high-resolution X-raycrystallographic structure of a full-length PKS module to illuminatemany of the dynamic events that must happen for intramodule transacylationand chain extension to occur.20 Moleculardynamics simulations have been employed to visualize the small-subunitstructural changes that occur as an AT domain accepts an extendersubstrate.66 High-resolution cryo-EM structuresof an Erythromycin PKS module further reveal dynamics of how an ATdomains’ position relative to its cognate KS domain enabletransacylation.21 In type II fatty acidsynthases, several key residues have been identified at the interactioninterface between ACP and KS domains that alter the behavior of substratesin the KS active site,68 and detailed mechanisms of KScatalytic activity have been elucidated.70 Though the KS-ACP interface is not identical between type II fattyacid synthases and type I PKSs,71 the keyinterplay observed between the ACP domain and its corresponding ATand KS domains sustains the idea that domain–domain interactionsincorporating the ACP are crucial to PKS activity. The essentialityof compatible ACP interactions, in particular with the KS domain ofa downstream module, has been corroborated in several reports.33 The recent improvement in the resolution of cryo-EM has enabledcritical perspectives to the structural dynamics of PKS enzymes andshould continue to be employed to further elucidate trajectories ofcatalytic activity, as has been done in the NRPS field.72There are several recent reportsof larger multidomain structuralcharacterization efforts that reveal insights into how to potentiallyimprove module-based engineering. The first direct description ofmodule–module interactions utilized the ability of small-angleX-ray scattering (SAXS) to capture low-resolution structures of proteinsin the Erythromycin PKS without the need for crystallization.74 and RoseTTAFold27 as highly accurateprotein structure prediction algorithms based on deep-learning modelshas enabled numerous impressive protein engineering feats including de novo protein design75 andproteome-scale protein interaction screening.28 Similar deep-learning models have been used to develop MutCompute,76 a generalizable protein design algorithm thathas successfully been applied to optimize the thermostability, pHtolerance, and kinetics of a PET hydrolase.77 Furthermore, the advancement of large language models has led tothe development of programs including ESMFold,78 a rapid protein structure prediction algorithm, and ProGen,79 a generative protein sequence algorithm capableof hallucinating proteins with custom properties. Additional toolshave enabled more accessible use to AlphaFold2 including ColabFold74 and Foldy.80 Thoughthese modern protein modeling algorithms have enabled incredible achievements,challenges remain in the modeling of large proteins (>3000 aminoacids)and in high accuracy protein complex modeling. Nonetheless, thesemodern protein modeling algorithms may have the potential to augmentour understanding and ability to engineer PKSs.The release of AlphaFold2Figure S2) and in some cases demonstrates asymmetric reaction chambers. Each PKS model generated byAlphaFold2 has an extended-shape conformation, and we have not observedany models that have an arch-shaped conformation. This is likely dueto the majority of experimentally derived structures of KS-AT domains,which form part of the data set on which AlphaFold2 is trained, beingin an extended-shape conformation. An additional attractive featurewould be the ability to model multiple modules at the same time. Unfortunately,we have been unable to obtain an AlphaFold2 model of two full-lengthmodules at the same time due to two main reasons: (i) the GPU memoryrequired scales quadratically with the length of the protein leadingto out-of-memory errors for protein complexes that are too large and(ii) reducing the memory usage by reducing the size of the multiplesequence alignments input into AlphaFold2 leads to artifacts in theresultant structure such as overlapping chains . Alternatively, we have been able to demonstratethat it is possible to truncate the upstream module to facilitatemodeling with a downstream module, thus making it possible to visualizean ACP interacting with both a downstream KS-AT  and an upstream KS-AT .In a useful demonstration of how KS-ACP modeling can inform and boostengineering success, Buyachuihan et al. utilized AlphaFold2 modelsof the Venemycin synthase to improve product yields by optimizingdocking domains that take into account the flexibility required atthe KS-ACP interface.81Here we demonstratethe potential applicability of AlphaFold2 tothe modeling of PKSs. We have shown that AlphaFold2 is capable ofreasonably modeling an entire PKS module S2 and in31 and applied the state-of-the-artprotein complex prediction algorithm described by Humphreys et al.28 to calculate the probability that a given upstreamACP would successfully interact with a given downstream KS, the hypothesisbeing that the predicted interaction probability between nativelyinteracting modules would be higher than non-natively interactingmodules and that it would correlate with overall production titers.Overall, we observed limited correlation between the predicted interactionprobability and the observed production data from Menzella et al.31 . Thisresult is not surprising as it is clear that the product titer dependson many factors including non-native substrate tolerance and chimericprotein stability. Also, despite our initial hypothesis that the predictedinteraction probability between natively interacting domains wouldbe higher than that between non-natively interacting domains, we seea limited correlation between the predicted interaction probabilityand whether the interaction between an ACP and KS is natively occurring(82 annotations to updatemultiple sequence alignments. Additionally, the advancement and incorporationof deep-learning tools capable of handling substrate–proteininteractions, such as DiffDock,83 shouldenable more comprehensive modeling of PKS behavior. Lastly, the abilityof AlphaFold2 to generate unique stable conformations of PKSs shouldenable detailed visualizations of PKS dynamics. Ultimately, thesetools are at the stage where unique and novel insights can alreadybe obtained, and further improvements will only accelerate what ispossible in PKS engineering.These capabilities beg the question of whetherstructural modelingcould be used to predict the likelihood that a given ACP could successfullyinteract with a given KS domain. We used Alphafold to model the 144intersubunit ACP and KS-AT interactions experimentally characterizedby Menzella et al.et al.31 S6. ThisPKSs have shown promiseas a retrobiosynthesis platform for productionof a wide variety of small molecules. PKS intermodule communicationhas naturally been a topic of significant interest as it is frequentlythe bottleneck in engineered systems. PKS collinearity presents aninteresting opportunity for machine-learning scientists to test modernprotein design strategies on a system with a reduced design space.Modern protein design programs present a complementary opportunityfor PKS engineers to obtain optimized PKS designs that take into accountevolutionary signals, structural elements, and empirical evidence.The collaboration between these groups should prove beneficial.


TERT, and WRAP53 expression in a total of 54 pre-diabetic subjects from the EPIRDEM study. We regressed TL, TERT, and WRAP53 on 12 lipoprotein subclasses, employing a Gaussian linear regression method with Lasso penalty to determine a lipoprotein profile associated with telomere-related parameters. The covariates included age, sex, body mass index (BMI), dyslipidemia, statin consumption, and physical activity leisure time. We identified a lipoprotein profile composed of four lipoprotein subfractions associated with TL , two lipoprotein subfractions associated with TERT expression , and five lipoprotein subfractions associated with WRAP53 expression . After adjusting for known confounding factors, most lipoprotein profiles maintained the association with TL, TERT, and WRAP53. Overall, medium and small-sized HDL particles were associated with shorter telomeres and lower expression of TERT and WRAP53. Large HDL particles were associated with longer telomere and lower expression of WRAP53, but not with TERT. Our results suggest that the lipoprotein profiles are associated with telomere length, TERT, and WRAP53 expression and should be considered when assessing the risk of chronic diseases.Telomere length (TL) is a well-known marker of age-related diseases. Oxidative stress and inflammation increase the rate of telomere shortening, triggering cellular senescence. Although lipoproteins could have anti-inflammatory and proinflammatory functional properties, the relationship between lipoprotein particles with TL and telomerase activity-related genes has not been investigated much. In this study, we assessed the associations of lipoprotein subfractions with telomere length,  Aging is associated with a progressive decline in several physiological processes, leading to an increased risk for several non-communicable diseases including cardiometabolic diseases and cancer. Telomere length is nowadays considered one of the best biomarkers of aging, and its shortening is a well-known marker in age-related diseases . Telomer-/- mice found fewer atherosclerotic lesions development compared with their normal counterparts, suggesting that the absence of TA is protective for atherosclerotic disease . Categorical variables are presented as frequency and percentage (%). Initially, a thorough examination of the lipoproteins was conducted to ensure that an excessive proportion of missing values (>20%) was not present . All lipTERT, and WRAP53—the 99th percentile of each outlier statistic was used as the cut-off point to identify significant outliers, resulting in the exclusion of 1 observation for each variable. A logarithmic base 10 transformation was applied to TERT and WRAP53 to improve their distribution. Using a multiple imputation with chained equations by the R “MICE” package [For the three dependent variables—TL,  package , we impuTERT, and WRAP53 on the 12 lipoproteins and lipoprotein subclasses. Given the dimensionality and the collinear nature of the data  were retained. Based on the available data, we calculated the 95% confidence interval. Finally, for each chosen lipoprotein particle, we determined the lipoprotein score (lipoprotein profile) as the weighted sum of the averaged coefficients from the 54 iterations. The effectiveness of the lipoprotein profile in determining its relation to telomere-related parameters was assessed using Pearson correlation coefficients between the reported and predicted. TERT, and WRAP53, we conducted a sensitivity analysis by adding them as covariates. All analyses were performed using R version 4.2.1 (2022-06-23) . To address potential confounding effects of age (continuous), sex, BMI (continuous), dyslipidemia , statins , and physical activity leisure time  on the association between lipoproteins and TL, 2. Furthermore, half of the participants had dyslipidemia. From a total of 54 participants allocated to the EPIRDEM study, nucleic acid samples were not available in 5 of them. Missing data were imputed using the methodological approach detailed in the Materials and Methods section. Participants’ characteristics are summarized in Lipoprotein particles descriptive analysis revealed an imbalance in the concentrations of different HDL particle sizes, with the concentration of S-HDL-P being up to 28 times higher than that of L-HDL-P. Furthermore, concentrations of the different LDL and VLDL particle sizes increased as the size decreased . TERT, and WRAP53 and selected 48 times in the LOOCV of the Lasso regressions. Large LDL and HDL particles were positively associated with TL, whereas total and medium HDL-P displayed inverse associations. Large HDL particles were positively associated with TERT expression, while medium HDL-P were negatively associated with TERT expression. For WRAP53 expression, only small LDL particles were positively associated, whereas small and large HDL-P, S-VLDL-P, and L-LDL-P were inversely associated.TERT, WRAP53, and their corresponding lipoprotein profiles were 0.350 , 0.316 , and 0.375 , respectively  may contribute to shortening telomeres [Telomere length shortens in each cell division and is regularly repaired by telomerase which consists of two major components, TERT and TERC, and other proteins, such as WRAP53. Therefore, a reduction in elomeres . Althougelomeres . Previouelomeres . Furtherelomeres ,33. A moelomeres . No assoelomeres .TERT, but we found an inverse association between small VLDL particles and WRAP53 expression, one of the proteins involved in the telomerase trafficking and assembly, that would apparently reduce the telomerase activity [WRAP53 via atherogenic effects is unknown, and further research is needed to elucidate this issue [Low HDLc and high concentrations of LDLc have been strongly associated with a higher risk of CVD . Howeveractivity . Whetheris issue . TERT expression. On the other hand, medium and total HDL particles were inversely associated with TL, whereas small-sized HDL particles were inversely associated with TERT. Small HDL particles have been positively associated with incident hypertension in a prospective cohort study of 17,527 initially healthy women followed for 8 years [We found large HDL particles positively associated with telomere length and  8 years . Further 8 years , and thi 8 years . These rDespite the considerable attention given to the role of HDL in cardiovascular risk assessment, the relationship between these two factors remains controversial . SmallerWRAP53 expression. Mutations in WRAP53 gene results in shortening telomeres [WRAP53 expression substantially reduces the activity of the telomerase complex leading to shorter telomeres [Moreover, small-sized and large HDL particles were inversely associated with elomeres  and reduelomeres . Howeverelomeres . WRAP53. A recent longitudinal study of metabolomic data from 1162 participants within the Hortega Study [WRAP53 gene expression. Large-sized LDL particles were positively associated with TL and inversely associated with ga Study  identifiTERT and WRAP53 with the size of lipoprotein particles assessed via a robust NMR-based metabolic platform. The study has some limitations, including the relatively small sample size that could limit the statistical power for additional findings. Another limitation is that participants included in this analysis were pre-diabetic, which makes it difficult to generalize our findings. Replication of the finding in an independent study with a large sample size, and wider analyses of telomerase complex will strengthen the study conclusions. Longitudinal data and a more detailed approach for covering telomerase complex and activity might provide additional and valuable information into telomere length and lipid profile interaction, and a possible reverse causation. To the best of our knowledge, this is the first study evaluating, in the same study participants, the associations between leukocyte telomere length and the expression of TERT, and WRAP53. Overall, total and medium HDL particles were associated with shorter telomeres and lower expression of TERT and WRAP53. Large HDL particles were associated with longer telomeres, and higher TERT expression but lower expression of WRAP53. Despite these findings, the underlying mechanisms are not fully understood, and further research is needed to elucidate the relationship between lipid profile and telomeres, as well as its potential clinical implications. In summary, we found several lipoprotein particles associated with TL,


The field of molecular diagnostics is experiencing a remarkable revolution, propelling us into an era of unparalleled advancements that are reshaping our understanding of diseases and revolutionizing healthcare. With every passing day, breakthroughs in molecular diagnostics are pushing the boundaries of our ability to detect, diagnose, and treat a vast array of medical conditions. In this captivating collection, we present a series of articles that serve as a testament to the astonishing progress made in this field across various disciplines, encompassing infectious diseases, cancer, and genetic disorders. These articles provide a window into the latest techniques, methodologies, and discoveries that are transforming the landscape of molecular diagnostics and opening new horizons for improved patient care and outcomes.Sorokina et al.) delves into the importance of detecting and quantifying mutant alleles in heterogeneous cell mixtures, particularly in the context of HIV research. The authors presented a new methodology that utilizes multiplex droplet digital polymerase chain reaction (ddPCR) to accurately quantify the presence of cells carrying the CCR5Δ32 mutation. This approach enables swift and precise measurement of mutant CCR5Δ32 alleles in cell mixtures, even at low levels as low as 0.8%. The sensitivity and potential of droplet digital PCR demonstrated in this study hold promise for application in other disease settings.The journey of molecular diagnostics can be traced back to the ground-breaking discovery of the DNA double helix structure by Watson and Crick in the 1950s . This piYin et al.) focuses on hepatocellular carcinoma (HCC), which is one of the most common types of malignancies worldwide. The authors performed a comprehensive analysis of 186 m6a-related lncRNAs to identify clusters associated with overall prognosis in HCC. They developed prognostic signatures based on four m6A-related lncRNAs that could predict the overall survival of HCC patients. This study highlights the power of exploring lncRNAs in cancer classification and prediction. Additionally, the study “Development of next-generation sequencing (NGS) has provided useful genetic information to redefine diagnostic, prognostic, and therapeutic strategies for the management of acute leukemia (AL)”  demonstrates the utility of targeted NGS panels in pediatric acute leukemia. The authors validated the AmpliSeq™ for Illumina® Childhood Cancer Panel, which includes common genes associated with childhood cancer, and assessed its feasibility in routine diagnostics. The panel showed high sensitivity and specificity for detecting genetic variants and fusion genes, and its clinical utility was demonstrated in refining diagnosis and identifying targetable mutations. This study highlights the application of NGS in improving diagnostic accuracy and guiding therapeutic decisions in pediatric cancer.Building upon the foundation laid by PCR, DNA sequencing techniques pioneered by Sanger introduced a new paradigm of precision by facilitating the determination of precise DNA sequences. This breakthrough has propelled the field of molecular diagnostics forward, enabling comprehensive analyses of inherited disorders, identification of disease-associated genetic variations, and the emergence of personalized medicine based on an individual’s unique genetic profile. By enabling the determination of precise DNA sequences, these breakthroughs have propelled the field of molecular diagnostics forward, revolutionizing cancer research and patient care. Fuelled by the power of Next-Generation Sequencing (NGS) technology, DNA sequencing has become a cornerstone of molecular diagnostics in cancer research. This had enabled the rapid and cost-effective sequencing of millions of DNA fragments simultaneously, providing comprehensive genomic information with unprecedented depth and accuracy. This technological breakthrough has significantly accelerated our understanding of the genetic landscape of cancer, shedding light on the complex genomic architecture of tumours. The small RNA molecules and non-coding transcripts have been found to play crucial roles in regulating gene expression and influencing various cellular processes, including tumorigenesis. By studying the expression patterns and functional roles of miRNAs and lncRNAs, researchers have identified specific signatures and dysregulated molecules that contribute to tumour development and progression. For instance, the study “Classification of Hepatocellular Carcinoma Based on N6-Methylandenosine–Related lncRNAs Profiling”  like PCR, have emerged as powerful tools capable of rapid and accurate pathogen identification. This capability facilitates early diagnosis, leading to enhanced patient care and effective epidemiological surveillance for prompt outbreak investigations and monitoring of antimicrobial resistance, thus safeguarding public health on a global scale. Molecular diagnostics has witnessed the emergence of a remarkable innovation known as point-of-care testing (POCT), revolutionizing the field by enabling rapid and on-site diagnostic tests without the need for specialized laboratory equipment. Molecular POCT devices possess compact form factors, user-friendly interfaces, and leverage techniques like isothermal amplification and microfluidics, empowering immediate diagnosis and treatment decisions at the point of care. The transformative potential of POCT is particularly evident in resource-limited settings, remote locations, and emergency situations, greatly enhancing healthcare accessibility and improving patient outcomes. A noteworthy example of the application of molecular diagnostics in point-of-care testing is demonstrated in the research focusing on the development of diagnostic methods for African swine fever virus (ASFV) and classical swine fever virus (CSFV), which pose significant threats to the pig industry. The researchers introduced Recombinase-aided amplification (RAA) technology combined with a nucleic acid test strip (RAA-strip) to specifically detect ASFV/CSFV. This method exhibited high sensitivity and specificity, capable of detecting low levels of viral DNA/cDNA.In the pursuit of medical precision, biomarkers have emerged as indispensable allies, providing measurable indicators of biological processes and conditions. Real-time PCR and microarray analysis have played pivotal roles in identifying and quantifying specific biomarkers. These advancements have propelled the development of companion diagnostic tests, revolutionizing disease diagnosis, prognosis, and treatment monitoring. By unravelling the intricate tapestry of biomarkers, molecular diagnostics empowers clinicians to identify patients who are most likely to benefit from targeted therapies, ultimately optimizing treatment outcomes.In conclusion, the field of molecular diagnostics has undergone a breath-taking evolution, propelled by pioneering techniques, transformative breakthroughs, and cutting-edge applications. These advancements have reshaped healthcare, empowering clinicians with precise tools for disease detection, genetic analysis, and personalized treatment. As we embrace the ongoing advancements in this field, the future holds limitless possibilities for molecular diagnostics, promising further revolutions in healthcare and improved patient outcomes.


Topic models are a useful and popular method to find latent topics of documents. However, the short and sparse texts in social media micro-blogs such as Twitter are challenging for the most commonly used Latent Dirichlet Allocation (LDA) topic model. We compare the performance of the standard LDA topic model with the Gibbs Sampler Dirichlet Multinomial Model (GSDMM) and the Gamma Poisson Mixture Model (GPM), which are specifically designed for sparse data. To compare the performance of the three models, we propose the simulation of pseudo-documents as a novel evaluation method. In a case study with short and sparse text, the models are evaluated on tweets filtered by keywords relating to the Covid-19 pandemic. We find that standard coherence scores that are often used for the evaluation of topic models perform poorly as an evaluation metric. The results of our simulation-based approach suggest that the GSDMM and GPM topic models may generate better topics than the standard LDA model. Topic models are widely used to extract latent topics in texts, but the most regularly applied models are not well tuned for sparse documents. However, with the rising importance of social media platforms such as Twitter, extracting latent topics from short and sparse texts has become increasingly relevant. Tweets are relatively short, which creates challenges when using standard topic models relying on the inherent assumption that texts are composed as mixtures of latent topics  topic model with the Gibbs Sampler Dirichlet Multinomial Model (GSDMM) and the Gamma Poisson Mixture Model (GPM), which are specifically designed for sparse data and hence presumably more suitable for Twitter data than the LDA with the Pseudo-Document Simulation method.For evaluating and comparing topic models, standard approaches such as the likelihood-based perplexity metric, coherence scores and top words are insufficient. Chang et al.  shows thWe propose the simulation of pseudo-documents as a new evaluation method to compare LDA, GSDMM and GPM and contrast the results of our method with standard evaluation approaches.The LDA is a generative process, which assumes that each document in a corpus is generated by a mixture of topics  given topic z.determine the corresponding words To generate the The hyper-parameters of the LDA are the Dirichlet parameters d was generated by a topic k. Marginalizing over the latent topics, the generating process for the words of a document d can be written asLatent Dirichlet Allocation (LDA) is the standard model for detecting latent topics in documents, implemented, for example, in the Python-package gensim (Řehůřek and Sojka d can be written as product of word probabilities The generating process for a document d in a corpus consisting of K topic distributions as k.Determine Determine the distribution over topics for the whole corpus as d in the corpus with choose a topic p(w|z) given topic z.determine the corresponding words For document The hyper-parameters of the GSDMM are The collapsed Gibbs sampler algorithm for the Dirichlet Multinomial Model (GSDMM) is described by Yin and Wang  as a modd by first selecting a mixture component with regards to the mixture weights d. The likelihood of document d with the sum of the total probability over all mixture components can be characterized byK is the number of mixture components or topics. The probabilities of the words are independent of their position within a document. Through this, the probability of document d generated by topic k is given by:The DMM generates a document and Wang  proposesn in document d, as a multinomial distribution over the vocabulary, the absolute frequency v in document d is modelled with a Poisson distribution. Most topic models in the literature using the Poisson distribution assume that the documents are generated from a mixture of topics as in the LDA. As the GSDMM, the GPM model is a mixture model, assuming that each document is only generated from a single topic instead of a mixture of topics and is hence especially constructed to deal with short text corpora  model is a topic model proposed by Mazarura et al. , in whicThe main difference between the GSDMM and the GPM is that the word frequencies mentclass2pt{minimIn general, Variational Bayesian methods are based on the idea of approximating posterior densities by optimization over a previously posited family of distributions. The online Variational Bayes algorithm for LDA was proposed by Hoffman et al.  and is bThe collapsed Gibbs sampler, introduced by Liu  for Bayek, and k. Additionally, w in topic k and w appears in d. The additional index d is neglected for the corresponding count. Based on this result, the conditional distribution of the components of The goal of inference is to determine the posterior distribution and Wang  for detamentclass2pt{minimBecause a Dirichlet distribution is used as prior for the multinomial distribution in GSDMM, the posteriors for Additionally, experimental results Yin and Wang  show thak, one obtains the following result:N is a length fixed uniformly for all documents and As the structure of the GPM is very similar to GSDMM, and because this model also relies on conjugate priors for the hyper-parameters, the conditional distributions for the collapsed Gibbs sampling algorithm in this case can be derived similarly to the previous examples via integrating out the a et al. , is idenmentclass2pt{minima et al.  since PoMoreover, experimental results indicate that the collapsed Gibbs sampling procedure for GPM is even better suited for identification of the true number of topics than GSDMM when provided with an appropriately large number of initial topics  included in the tweets.For our source documents, we stream tweets from the social media platform Twitter. The Tweepy API for Python used in this work collects about 1% of all tweets drawn in real time , divided by commas. Fourth, all parts in the data which do not contain useful information, so-called stopwords, are removed, using the library of the We propose a novel method for topic model evaluation by simulating labelled pseudo-documents. For every model, a unique pseudo-document corpus is generated. Initially, the LDA, GSDMM and GPM topic model are fitted on the source data, such that a word-probability matrix The scraped tweets are used to generate a representative distribution for the number of words in our sample of tweets. Based on the distribution in Fig. k from k. Thus, we create labelled documents and simulate a fixed number of 5000 documents per topic. In detail, for one topic model the Pseudo-Document Simulation proceeds as follows: Determine k in topics s in pseudo-documents (i) Determine the number of words k from (ii) Determine For pseudo-document For topic The rows of the theoretical matrix The topic models are then applied again on the respective simulated pseudo-corpora. A well performing topic model, that is applied on the simulated data, should generate topics each containing about the same number of documents, because for each topic the same number of documents are created. To evaluate how the topic models perform on the simulated data, it is hence firstly evaluated, if the number of documents are equally distributed over topics. While the resulting topics are hopefully closely related to the topics from which the simulated documents are created, direct topic classification of the documents is not possible, as the resulting topics are not necessarily identical to the initial topics from the document simulation. Hence, measures like accuracy or precision cannot be calculated as all three topic modelling approaches do not yield a direct classification. As a remedy, we calculate the column correlations of the mentclass2pt{minimOur proposed method allows us to evaluate and compare the overall performance of the different topic models objectively on concrete data. Therefore, we provide an alternative to the conventional evaluation methods that have various shortcomings and will be discussed in the next section. To compare the models, we estimate the word-probability matrix This section discusses conventional evaluation methods. First, the UMass-coherence score, which is a widely used metric for topic model evaluation, despite of its various shortcomings, is briefly described. Second, the labelling of the top words of the topics as a subjective but also common method is discussed.bad topic. Rosner et al. (z, since the coherence metric is calculated for each topic. M denotes the number of words with the highest probabilities in topic z that are evaluated. These words are obtained by ordering the probabilities in M, otherwise the results may be biased. Choosing a very small value of M of e.g. 2 would thus only take the two most probable words into account and could lead to a topic consisting of two very coherent words but otherwise incoherent words. In line with Rosner et al. (M to 20 such that topic z’s 20 most probable words are taken into account as Röder et al. (M is small. Coherence scores are often used as an automatic scoring metric for evaluating a topic models performance and hence are frequently used for hyper-parameter optimization. Coherence, a systematic or logical consistency, is defined in terms of topics as the co-occurrence of words with similar semantic meaning within the same document. The coherence score hence measures to which extend a topic consists of words with high probabilities in entclass1pt{minimar et al.  proposesr et al. , we set r et al.  found ths et al.  found thr et al.  providesA subjective evaluation method of topic models is the interpretation of top words, i.e. words with the highest probability in a topic and labelling of topics by humans. Chang et al.  tests thK are compared between the models. Lastly, we apply the Pseudo-Document Simulation method and analyze the model performance based on the pseudo-documents.In the following, the empirical results of the LDA, GSDMM, and GPM models are discussed. The top words for 20 topics are compared and the clusters are manually labeled in order to compare the interpretability of the topics for each model. The coherence scores for different numbers of topics For the LDA model fixed symmetric priors are used for the hyper-parameters The 10 top words for each of the three models over 20 topics are visualized in Tables Figure To avoid the problems associated with the use of coherence scores and the issues of manually evaluating the models results, we suggest a method that compares the model performance based on pseudo-documents. The documents for the pseudo-corpora are simulated as described in Sect. The deviation from the 5000 documents that a perfect model should find is represented in Fig. In addition, we compute the column correlations of the We simulate pseudo-documents and use standard topic model evaluation methods to compare the performance of the LDA topic model with the GSDMM and GPM models for sparse and short text. In a case study, we use tweets filtered by keywords relating to the Covid-19 pandemic. The coherence scores suggest that the LDA clearly outperforms both the GPM and the GSDMM model, although GPM and GSDMM are expected to perform better, since there are specifically designed for sparse and small text data. In contrast, the model comparison with the Pseudo-Document Simulation method shows that the LDA model is outperformed by the GSDMM and the GPM models. The Pseudo-Document Simulation enables researchers to compare topic models, but our proposed solution still requires the separate hyperparameter optimization for the individual models with coherence scores or the use of priors that are recommended in the literature for the specific models. In further research, the Pseudo-document simulation approach could be also used to optimize hyperparameters of individual models with respect to the average column correlations of the


Similarly to the previous Special Issue entitled “Molecular Mechanisms of Allergy and Asthma” , this SpConsidering their pivotal role in allergies, T cell-related molecular and cellular mechanisms were the focus of four independent investigational works. Perveen et al.  showed tSeveral other papers focused on the molecular mechanisms related to mast cells (MCs), the pivotal effector cells behind allergic responses. Ashikari and coworkers  identifiFurthermore, while Nguyen and colleagues  focused Finally, Zhernov et al. exhaustively described the molecular mechanisms behind eosinophilic esophagitis  and scomIt is with great pleasure that I am presenting the articles included in this Special Issue to the asthma and allergy research community.


The objective of this research was to develop an environment-friendly and scalable method for the production of self-sanitizing electrospun nanofibers. This was achieved by immobilizing silver nanoparticles (Ag NPs) onto plasma-treated surfaces of biodegradable polycaprolactone (PCL) nanofibers. The plasma deposited polymer layer containing carboxyl groups played a critical role in providing a uniform distribution of Ag NPs on the nanofiber surface. Ag ions were absorbed by electrostatic interaction and then reduced under the action of UV-light. The concentration and release of Ag ions were analyzed using the EDXS/XPS and ICP AES methods, respectively. Although high levels of Ag ions were detected after 3 h of immersion in water, the material retained a sufficient amount of silver nanoparticles on the surface (~2.3 vs. 3.5 at.% as determined by XPS), and the release rate subsequently decreased over the next 69 h. The antipathogenic properties of PCL-Ag were tested against gram-negative and gram-positive bacteria, fungi, and biofilm formation. The results showed that the PCL-Ag nanofibers exhibit significant antimicrobial activity against a wide range of microorganisms, including those that cause human infections. The incorporation of Ag NPs into PCL nanofibers resulted in a self-sanitizing material that can be used in variety of applications, including wound dressings, water treatment, and air filtration. The development of a simple, scalable, and environmentally friendly method for the fabrication of these nanofibers is essential to ensure their widespread use in various industries. The ability to control the concentration and release rate of Ag ions in the PCL nanofibers will be critical to optimize their efficacy while minimizing their potential toxicity to human cells and the environment. The COVID-19 pandemic, caused by the rapid spread of the severe acute respiratory syndrome coronavirus SARS-CoV-2), has led to the widespread use of facemasks as a preventive measure. According to reports from the World Health Organization (WHO), facemasks act as a barrier to prevent the transmission of infectious droplets and aerosols between individuals. However, recent studies have shown that widely used masks, including cotton masks, surgical masks, and N95 masks, do not provide 100% protective efficacy, but rather reduce the risk of contamination within a range of 20-90% when compared to not wearing a mask . All the composite samples (PCL:PEO/Ag NPs) in that study demonstrated high levels of antimicrobial activity, suppressing the growth of antibiotic-resistant GFP E. coli bacteria. The authors detected the rapid release Ag ions in the first 20 h (40% of loading), followed by a slow increase in the concentration of Ag in the next 76 h.Dubey et al.  describe3 . A plasma treatment was carried out using a RF power of 200 W, a chamber pressure of 200 mTorr, and an argon flow of 32 cm3/min. The optimum time for the plasma treatment was determined to be 1.5 min. To increase the stability of the samples, after the plasma treatment, they were immersed in an aqueous TPP solution. The average size of the Ag NPs was determined to be 1.5 nm. The addition of Ag increased the antimicrobial properties of prepared materials against E. coli bacteria by seven times.Annur et al.  used Ar E. coli and S. aureus bacteria were represented by the activities of Ag NPs, curcumin, and chitosan.Foroushani et al.  synthesiThe incorporation of silver in the form of nanoparticles (AgNPs) into PCL nanofibers enhances their antibacterial properties. One of the key advantages of using PCL nanofibers with AgNPs is their ability to release silver ions very rapidly ,39,40. TSecondly, the small size of AgNPs in the range of 5–50 nm facilitates their diffusion through the PCL-nanofiber matrix, allowing the more rapid release of silver ions ,42. The Finally, the release of silver ions from PCL nanofibers with AgNPs can be affected by the concentration of silver and the morphology of the nanofibers. Higher concentrations of silver and nanofibers with smaller diameters can lead to the more rapid release of silver ions.The swift release of silver ions from PCL nanofibers with AgNPs is desirable for wound-dressing applications as it can provide a rapid antibacterial effect, preventing the growth and spread of bacteria at the wound site. It is worth noting that the rapid release of silver ions may also increase the risk of toxicity to human cells and, therefore, it is important to find an optimal silver-loading concentration and release rate that provides effective antibacterial properties without harming human cells .2/C2H4 gas mixture contained carboxyl groups that played a crucial role in ensuring the even distribution of the Ag NPs on the nanofiber surface. Electrostatic interactions with negatively changed COOH groups allowed the Ag ions to be absorbed and reduced under UV light.The aim of this study was to create an eco-friendly and scalable method to produce self-sanitizing electrospun nanofibers. The Ag NPs were immobilized on the surfaces of plasma-modified biodegradable PCL nanofibers. The plasma-polymerized layer deposited from the Ar/COE. coli U20, Candida auris CBS10913, Candida parapsilosis ATCC90018, Candida albicans ATCC90028, and S. aureus MW2. We found that the PCL-Ag samples demonstrated potent antibacterial activity against all the tested strains.Here, we investigated the effectiveness of PCL-Ag electrospun nanofibers against different pathogenic strains, including Candida auris CBS10913. This means that no colonies of these pathogens were found, indicating the complete inhibition of their growth. The other strains, including Candida parapsilosis ATCC90018, Candida albicans ATCC90028, and S. aureus MW2, also showed a significant reduction in the number of colonies formed, ranging from 2 to 4 log scales.After six hours of incubation, 100% antipathogen activity was observed for the E.coli U20 and In contrast, the unmodified tissue samples showed biofilm formation with varying densities. Biofilms are common defense mechanisms used by pathogens to protect themselves from the host’s immune system and antimicrobial agents. The biofilms formed on the unmodified tissue samples may have made them more resistant to antibiotics and antimicrobial agents, making infections more difficult to treat.It is worth noting that none of the Ag-modified tissue samples showed biofilm formation. The incorporation of silver nanoparticles into the PCL-Ag electrospun nanofibers may have contributed to the prevention of biofilm formation. Silver nanoparticles have been shown to have antimicrobial properties and can inhibit biofilm formation by disrupting bacterial and fungal cell membranes. Therefore, PCL-Ag electrospun nanofibers can effectively inhibit the growth of various pathogenic strains, including those that are resistant to antibiotics. The incorporation of silver nanoparticles into nanofibers may provide additional antimicrobial properties, preventing biofilm formation and potentially reducing the risk of infections.The high levels of antibacterial and antifungal activity of PCL-Ag membranes can be attributed to several factors: the presence of Ag nanoparticles on the surfaces of the nanofibers, which act as potent antipathogenic agents; some silver nitrates, as detected by XPS; and 0.6 at.% of nitrogen from the nitrate environment present on the surface.During washing or immersion in water, the unreacted silver nitrate could be released, resulting in the decay of the silver concentration. This means that the silver ions that were initially present on the surfaces of the nanofibers may have been released into the water. The release of silver ions can have both positive and negative effects. On one hand, it can lead to burst-silver-ion release in the first hour, which can increase the antibacterial and antifungal efficacy of PCL-Ag electrospun nanofibers. This means that a high concentration of silver ions is released initially, which can provide an immediate antimicrobial effect. However, it should be remembered that on the other hand, the release of silver ions can also have negative effects. Silver ions are toxic to both bacteria and human cells, and their release can cause damage to healthy cells. Therefore, the amount and rate of silver ions released from PCL-Ag electrospun nanofibers need to be carefully controlled to balance the desired antimicrobial effect with the potential negative effects.3- in the PCL-Ag electrospun nanofibers indicates the presence of unreacted silver nitrate on the surface, which can be released during washing or immersion in water. This release of silver ions can have both positive and negative effects, and careful control of the release rate is necessary to maximize the antibacterial and antifungal efficacy of nanofibers while minimizing potential negative effects.Overall, the presence of nitrate NOFinally, the results of the study confirm that PCL-Ag nanofibers have the potential to be utilized as effective antipathogenic materials in the development of face masks and other medical devices, as well as in wound dressings and other biomedical applications. The lack of biofilm formation in Ag-modified tissue samples is particularly relevant to hospital environments, where multidrug-resistant bacteria are significant sources of health problems. The use of PCL-Ag nanofibers could provide a solution to this problem by inhibiting the formation of biofilms and providing sterile environments for patients. Further studies can be conducted to optimize the Ag loading and the release rate of PCL-Ag nanofibers to improve their overall antibacterial efficacy and to explore their potential for clinical applications.In the present study we successfully developed a scalable and environmentally friendly method to fabricate self-sanitizing electrospun nanofibers. The immobilization of silver nanoparticles (Ag NPs) onto the surfaces of plasma-treated biodegradable polycaprolactone (PCL) nanofibers, through electrostatic interaction and UV-light reduction, resulted in a material with significant antimicrobial activity against a wide range of microorganisms, including those that cause infections in humans. The plasma-deposited polymer layer containing carboxyl groups played a critical role in ensuring the even distribution of Ag NPs on the nanofiber surface. A high concentration of Ag+ ions was released within the first three hours of the immersion in water, but this ion release subsequently decreased and stabilized. The incorporation of the Ag NPs into the PCL nanofibers resulted in a self-sanitizing material that can be used in various applications, including wound dressings, water treatment, and air filtration. The ability to control the concentration and release rate of Ag NPs in the PCL nanofibers will be critical to optimize their efficacy while minimizing their potential toxicity to human cells and the environment. Overall, the results suggest that PCL-Ag nanofibers have significant potential for use in various biomedical and environmental applications.


During the continuous charge and discharge process in lithium-sulfur batteries, one of the next-generation batteries, polysulfides are generated in the battery’s electrolyte, and impact its performance in terms of power and capacity by involving the process. The amount of polysulfides in the electrolyte could be estimated by the change of the Gibbs free energy of the electrolyte,  During the charge/discharge process of the battery, lithium polysulfides (LiPS), Lithium sulfur (LiS) batteries have been rapidly receiving attention as the next-generation secondary battery that can surpass lithium-ion batteries in perspective of cell capacity, lightness, and cost6, physical or chemical properties of organic molecules or inorganic materials8. In real-world applications like pharmaceuticals and battery electrolytes, mixtures are more prevalent than single substances. However, most existing machine learning research has focused on single substances. Recently, Wang et al.9 made a progress in this area by introducing an attention-based model specifically designed to predict the properties of inorganic mixtures. The attention mechanism has been utilized not only in language and vision tasks but also in predicting chemical properties. Attention mechanisms offer both precise predictions and the ability to provide explanations for the results. For example, Attentive FP10, which utilized graph attention mechanisms, achieved state-of-the-art performance in predicting chemical properties and remains one of the best models in the field.Machine learning has been extensively adopted for various applications, ranging from predicting material properties to designing new materials. Common examples include predicting properties of small molecules such as estimating the binding affinity of a molecule to proteinsThe highly accurate By analyzing the interactions between electrolyte chemicals at the molecular level, we explored what physical properties are involved in the interactions.Here, we developed a model using molecular representations and the attention mechanism to predict The overall outline of this study consisting of three parts is shown in Fig. 12, MolCLR(MCR)13, Attentive FP(AF)10) with two regressors(simple Multi Layer Perceptrons(MLP), Transformer Encoder(TE)14). More details of data, representations and regressors are written in the method section. In Table We compared frameworks to find the best way to predict mentclass2pt{minimHowever, when conducting electrolyte researches, researchers test new chemicals in addition to the conventional chemicals in our dataset, so the model can be practically used when it can predict the properties of electrolytes containing new chemicals well. To evaluate the model in this respect, we divided the 69 solvents into seven groups, each of which has 10, 10, 10, 10 ,10, 10, and 9 solvents. The list of 69 solvents can be found in the supplementary information. For the seven cases where formulations containing solvents from each group were designated as the test set, we trained the models by taking each of the remaining six groups as the validation set and the rest as the training set. Thus, we checked the performance of each model on a total of 42 cases, and the average L1 losses and R2 scores are recorded in Table The transformer encoder not only showed good performance in predicting For example, attention scores were examined on common electrolytes in lithium-sulfur batteries such as 1,3-dioxolane (DOL), 1,2-dimethoxyethane (DME), lithium;bis(trifluoromethylsulfonyl)azanide (LiTFSI) and lithium;nitrate (LiNO3). The average of attention scores from 1881 formulations generated by grid search were used. In the Fig. 16. While our analysis did not consider the ionic conductivity of the electrolyte, it explained the effects of co-solvents on polysulfide solubility through attention values. To investigate the effects of co-solvents, the degree to which DME refers to other chemicals can be obtained from the self-attention map, which can be understood as how much other chemicals affect DME. The five highest and five lowest contributors to DME were compared to find commonalities, and contributions to DME of each chemical were correlated with the properties of the chemicals to determine which properties were associated with these. Since the average attention scores vary depending on the number of chemicals in the electrolyte when extracting attention scores, we used only electrolytes composed of 6 chemicals (48k out of 100k data). In the supplementary information, chemicals are listed in decreasing order of average attention values from all heads contributing to the DME.In order to obtain practical perspective, further attention analysis was conducted by comparing attention values of chemicals dedicating to conventional solvent 1,2-dimethoxyethane (DME). DME is a primary component of commonly used electrolytes due to its high performance in stabilizing a lithium metal anode during charge/discharge cycles. However, its high polysulfide solubility leads to the shuttling effect, prompting researches to explore new co-solvent candidates which can reduce the solubility of LiPS without decreasing electrolyte’s ionic conductivity17. And it has been reported that 1-methylimidazole can dissolve 18. On the other hand, five chemicals with the lowest contribution have fluorine groups. Having a fluorine group results in very low reactivity due to the stability of the C-F bond. A previous study19 suggested that fluorinated ethers have lower solubility than general ethers because the fluorine has more electronegativity and stronger steric hindrance than the hydrogen. Based on the five highest and lowest contributing chemicals, it can be inferred that contributions are related to the solubility of polysulfides. We compared data distributions of The top five chemicals with the highest contributions and the five with the lowest can be seen in the Fig. 20 that developed novel methods for predicting logP value using diverse molecular structures, it was found that logP highly correlates with molecular polarizability and partial atomic charges on nitrogen and oxygen, with a correlation coefficient of 0.89. And molecular weight affecting the size of the molecular size, is also related to the molecular polarizability. From these, we consider that the self-attention heads can capture the chemical property(polarizability) related to Additionally, we correlated how the contribution to DME, obtained from attention scores, relates with physical properties. These properties include logP, molecular weight, atomic polarizability, bond polarizability, and the smr (Wildman–Crippen MR descriptor) which are highly related to the solubility. Among several properties, logP and molecular weight have pearson correlation coefficients of We developed a framework to predict 22. The chemical potential, The electrolyte formulation 10 is a graph neural network architecture for molecular representations that accurately extracts information from chemical structures and offers interpretability. It employs two steps of attention layers for atom and molecule embedding and has demonstrated high predictive performances on diverse datasets. The network learns non-local intramolecular interactions strongly related to specific properties, as supported by feature visualization. Therefore, we pretrained Attractive FP using the values of apol, bpol, slogp, smr related to 23.Attentive FP13 is a framework that applies self-supervised graph neural network-based contrastive learning to molecules. First, it converts molecular smiles into a molecular graph. Atom masking, bond deletion, and subgraph removal are applied to these molecule graphs to create augmented graphs. In these graphs, the features that have undergone the convolutions and readout processes are trained to exhibit high similarity for features from the same molecule and low similarity for features from different molecules through contrastive loss, as employed in the previous study24. The biggest advantage of this framework is that this model can be pre-trained using a lot of data without additional labeling.MolCLR12 is the one of the most popular molecular fingerprint. It encodes structural information of a molecule as a vector, bit strings and we used RDKit25 to convert a molecule from SMILES to the vector. Therefore, it is widely used in the field of new drug development, especially when finding similarities between molecules.Morgan fingerprint26 is a type of artificial neural network composed of multiple layers and nodes, capable of learning complex non-linear patterns. It consists of an input layer, one or more hidden layers, and an output layer. Nodes in each layer are connected to the previous layer through weights and activation functions. In this study, molecular embeddings were multiplied by fractions of each component and used as input.The multi layer perceptron14, first introduced in the natural language processing field, has demonstrated high performance across various domains. Transformer encoder can flexibly encode words by considering the degree of influence between them depending on the context. To encode chemicals in each formulation, we used molcular embededding to represent each molecule and fractional encoding instead of positional encoding to represnet each fraction, as done in a previous study27. The rest of the model remains identical to the original one. Additionally, by analyzing multi-head attention scores, we were able to visualize interactions between components in each formulation.Transformer28 to pretrain Attentive FP and MolCLR to get the embeddings of chemicals, which contains 480 million molecules. The formulation dataset was split with the ratio of train: valid: test at 8:1:1 Fig. For a fair evaluation of the six architectures compared in the result part, training was conducted by setting the hyperparameters to be the same as possible. The dimension of embeddings used for training was set to 256. We used molport databaseSupplementary Information.


Detailed information about the impact of bacterial antimicrobial resistance (AMR) on Lower Respiratory Tract and other thorax Infections (LRTIs) is currently unavailable. It is important to have precise data on bacterial AMR to develop effective programs and policies for controlling AMR and to use antibiotics prudently for optimal treatment of LRTI patients. The primary objective of this research is to present extensive estimates of the mortality due to "bacterial pathogens" and its "AMR" of LRTIs in the United States in 2019.Mortality data associated with bacterial antimicrobial resistance and lower respiratory tract infection (LRTI) pathogens were acquired from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) in 2019.In United States, there were 88089 deaths  due to bacterial pathogens in LRIT in 2019. Highest number of deaths observed due to Staphylococcus aureus (34.04%), followed by Streptococcus pneumoniae (18.30%), Pseudomonas aeruginosa (12.48%) in LRTI in 2019. There were 10984 deaths attributed to and 46482 deaths associated with bacterial AMR in LRTI in 2019.Bacterial pathogens causing LRTIs are a significant cause of mortality in the United States. Moreover, antimicrobial resistance (AMR) among these pathogens has become a growing concern, exacerbating the burden of LRTI-related deaths. The implementation of such measures, along with the promotion of antimicrobial stewardship, can help to mitigate the impact of LRTI-associated deaths and preserve the effectiveness of available antimicrobial agents for the future.All Authors: No reported disclosures