Cancer genome sequencing enables accurate classification of tumours and tumour subtypes. However, prediction performance is still limited using exome-only sequencing and for tumour types with low somatic mutation burden such as many paediatric tumours. Moreover, the ability to leverage deep representation learning in discovery of tumour entities remains unknown.We introduce here Mutation-Attention (MuAt), a deep neural network to learn representations of simple and complex somatic alterations for prediction of tumour types and subtypes. In contrast to many previous methods, MuAt utilizes the attention mechanism on individual mutations instead of aggregated mutation counts.SPOP-associated prostate cancer, microsatellite instability, POLE proofreading deficiency, and MUTYH-associated pancreatic endocrine tumours without these tumour subtypes and subgroups being provided as training labels. Finally, scrunity of MuAt attention matrices revealed both ubiquitous and tumour-type specific patterns of simple and complex somatic mutations.We trained MuAt models on 2587 whole cancer genomes (24 tumour types) from the Pan-Cancer Analysis of Whole Genomes (PCAWG) and 7352 cancer exomes (20 types) from the Cancer Genome Atlas (TCGA). MuAt achieved prediction accuracy of 89% for whole genomes and 64% for whole exomes, and a top-5 accuracy of 97% and 90%, respectively. MuAt models were found to be well-calibrated and perform well in three independent whole cancer genome cohorts with 10,361 tumours in total. We show MuAt to be able to learn clinically and biologically relevant tumour entities including acral melanoma, SHH-activated medulloblastoma, Integrated representations of somatic alterations learnt by MuAt were able to accurately identify histological tumour types and identify tumour entities, with potential to impact precision cancer medicine.The online version contains supplementary material available at 10.1186/s13073-023-01204-4. Accurate identification of tumour histological type and molecular subtype is crucial to determining cancer diagnosis, prognosis and treatment choice , 2. In pSomatic mutations in a cancer cell are the consequence of the mutational processes which acted on its ancestors in the somatic cell tree . Many suRecently, several computational methods have been developed to predict tumour types by analysing somatic driver and passenger mutation patterns in next-generation sequencing data , 17. TumWhile supervised approaches have been developed to predict tumour subtypes –25, unsuHere, we developed a novel deep neural network (DNN) model, termed Mutation-Attention (MuAt), which allows us to predict tumour types from cancer whole-genome and whole-exome sequencing data. It leverages the ability of DNNs to learn in a supervised setting representations that can be used to explore and explain the structure of input data beyond class labels. MuAt utilizes the attention mechanism , 33, whiIn this work, we demonstrate the utility of a supervised approach to compute tumour-level representations from mutation-level representations with the attention mechanism for precision cancer medicine tasks. These tasks include tumour type identification and distinguishing diverse set of tumour characteristics such as molecular subtypes and DNA repair deficiencies without specifically training for each individual characteristic.SPOP mutations and pancreatic endocrine tumours with germline MUTYH mutations, hypermutable subtypes such as microsatellite-unstable cancers and polymerase CCND1-amplified acral melanomas, and Sonic Hedgehog (SHH)-activated medulloblastomas.Our models achieve high accuracy in predicting tumour types, with top-1 and top-3 accuracies of 88.8% and 96.1% in the 24 tumour types that were studied within the PCAWG consortium. MuAt further outperforms the previous state-of-the-art approaches for cancer types that have been challenging to predict such as tumours with MSI. We investigate the utility of our models in tumour exome sequencing data from the TCGA consortium, achieving 64.1% accuracy across 20 tumour types. Exploring the representations learnt by MuAt, we show that the model learns to differentiate tumour subtypes which were not given as input information. These subtypes include tumours driven by somatic and germline mutations such prostate cancers with somatic https://github.com/primasanjaya/mutation-attention).The use of attention mechanism together with the ability to learn representations for different data modalities such as mutation types and positions allows MuAt to represent each mutation as a combination of these modalities. To gain insight into model results, we show that the trained model learns to focus its attention to mutations that are characteristic for each tumour type. MuAt models trained with cancer genomes from PCAWG and TCGA consortiums, an interactive browser, and the source code are available under a permissible licence at GitHub  project and WES data from the Pan-Cancer Atlas project of TCGA. PCAWG analysed whole genomes of 2658 human tumours and matched normal samples across 38 tumour types obtained from International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) donors . We usedThe Pan-Cancer Atlas project of TCGA  releasedTo validate our results with data which was not used in training, we used the whole genomes available in ICGC that were not included in the PCAWG dataset above. Out of 16 tumour types, 5 tumour types were matched to PCAWG tumour types. There were 309 tumours, with a total of 2,806,722 mutations . SVs were not available in this dataset.Furthermore, we evaluated our models on cancer whole genome sequences available through Genomics England (GEL)  . This daFinally, we carried out validation in a cohort of colorectal cancer (CRC) whole genomes, which not used to train the models. This cohort consisted of 256 cancers with somatic variants called with MuTect v1.1.4 (GRCh37) . SomaticMuAt is a DNN model, which predicts tumour types based on a catalogue of somatic alterations that are observed in a single cancer genome Fig. . We descQ, K, V are called the query, key and value matrices, respectively, and Q, K and V are all l is the number of mutations and d is the feature dimension; these matrices are obtained as linear transformations of the input. Product d-dimensional feature space defined in terms of similarity to other mutations in the same tumour. Unlike in many natural language processing applications which employ attention [In the second module, an attention mechanism is used to assign more weight (“soft-select”) to pairs of mutations which are informative in predicting the tumour type, and compute input features for the third module. Attention is defined as softmax , 33. Q, The third module combines the mutation features with fully connected layers yielding tumour-level features. These features are used to compute the final output of the model, which are probabilities over the tumour type labels. The three modules constitute a single model, where all parameters are trained end-to-end with backpropagation and stochastic gradient descent. The trained MuAt model can be interrogated by extracting mutation-level features from the attention module and tumour-level features from the last module. We can project the latter onto a two-dimensional space with UMAP  for discWe were interested in (1) evaluating the contribution of different mutation types in prediction performance, (2) finding hyperparameters which result in best prediction performance, (3) comparing MuAt with existing models, (4) how to best interpret the trained MuAt models, and whether the features learnt by MuAt are compatible with previous findings. This section provides details how the experiments to answer these questions were prepared.For each somatic variant call in the datasets, mutational sequences In experiments, we also considered non-mutation events as negative examples. These are constructed by randomly selecting positions where there is a mutation in one tumour , encoding this position without mutational symbols  and placing it into another tumour’s mutational catalogue. When injecting negative examples into a tumour, we add the median number of variants per type in the dataset as negatives. Specifically, if the dataset has a median of 1000 SNVs and 100 indels, then each tumour will receive a total of 1100 negative examples, where 1000 were picked from random SNVs in other tumours, and the remainder from random indels. Genomic positions are represented in 1-Mbp bins. For instance, the position  would be encoded as the token “chr1_11”. This token is used to encode all mutations occurring in chr:11,000,000–11,999,999. Since all models were trained on somatic variants with genomic positions in GRCh37 coordinates, we converted positions to GRCh37 coordinates with Python package “liftover” for any data in GRCh38.We experimented with mutational annotations consisting of indicators whether the mutation occurs in a gene (“genic”) or in an exon (“exonic”). In addition, we categorize each mutation into one of four mutually exclusive classes (“strand”): mutation’s pyrimidine reference base is on the (1) same or (2) opposite strand as a gene, or (3) mutation overlaps two genes on opposite strands, or (4) mutation is intergenic. This annotation attempted to capture transcriptional strand biases associated with some mutational mechanisms .Each of the three input modalities  was one-hot encoded separately using token dictionaries. The dictionary of positions consisted 2915 tokens for all 1-Mb genomic bins. Mutational motif dictionary consisted of 3692 tokens including 96 SNVs, 2170 MNVs, 1160 indels, 233 SVs and 33 MEIs. Finally, the annotation dictionary contained 2i, and i is the correct tumour type.We performed search for MuAt hyperparameters over embedding dimensions {128, 256, 512}, number of encoder layers {1, 2, 4}, number of attention heads {1, 2}, number of fully connected layers {1, 2} and mutation types to be included in the input. Additional file i’th folds from each cancer type. The exact same splits were used to train all models . In each combined fold, we trained a model for each combination of the hyperparameter choices listed above, and the model achieving the best accuracy in the validation set was selected from each fold. The hyperparameters and prediction performance for each fold are given in Additional file To perform cross-validation, we split the data equally into ten folds in each cancer type and combined the To create the MuAt and DNN models used to evaluate prediction performance, and to investigate the tumour-level features learnt by MuAt, we created ensemble models from models obtained in cross-validation. We first chose the best models from each fold as the components of the ensemble. Then, ensemble prediction score was computed by summing the class prediction scores (logits) of these component models, with ensemble prediction corresponding to the tumour type receiving the highest ensemble prediction score.The model selected for analysis of tumour typing performance in sparse data was trained on SNVs, MNVs, indels and SVs/MEIs, genomic positions and mutation annotations using the PCAWG dataset. This model contained one attention head and two encoder layers, embedding dimension 512, resulting in 28,458,520 trainable parameters.We compared MuAt to deep neural network and random forest (RF) models proposed by Jiao et al. . We furtTo investigate whether mutational signatures could be used to predict tumour types in PCAWG, we created a random forest model on single-base substitution (SBS), doublet-base substitution (DBS) and small insertion-and-deletion signatures (ID) extracted in PCAWG tumours . We usedTP, TN, FP, FN are the number of true positives and negatives, and false positives and negatives, respectively. Top-k accuracies were calculated such that the prediction was deemed correct when the correct class is among the k highest scoring predictions.We report the performance in terms of accuracy To test the performance of MuAt in sparse data, and further to test transfer learning from WGS to WES data, we selected 13 common tumour types existing in both PCAWG and TCGA dataset  before the final prediction from all ten ensemble component models . NMF analysis was carried out with Python sklearn library, minimizing the Frobenius norm. A hyperparameter search was carried out over the number of components to extract in PCAWG data, revealing a knee point at approximately 14 components, with 50 components showing reconstruction error convergence. These 50 components, called MuAt factors M1,s as Association of MuAt factors with mutational signatures was examined by fitting a least-squares linear model for each PCAWG single-base signature exposure A correspond to mutations of a tumour. We can thus visualize the matrices with respect to different mutational data modalities; in our experiments, we visualized mutational motifs and genomic positions  Fig. a, althouWe then benchmarked our method against a recent deep neural network model . Here, We compared the deep neural network-based methods to random forest (RF) models trained on somatic mutations and mutation signatures. The accuracies of RF models on somatic mutations were substantially worse than either MuAt or DNN . RF models trained on PCAWG mutational signatures yielded better performance than on mutation counts, but failed to reach accuracies of either MuAt or DNN. RF model trained on combined SBS, DBS and ID signature features achieved accuracy of 81.4%. Individual signature type models had substantially worse accuracies . Full results are available in Additional file To gauge the abilities of MuAt models trained with PCAWG data to generalize to unseen data, we evaluated MuAt with whole-genome data not used in training. Here, we evaluated both the performance of models obtained from a cross-validation procedure (“fold models”) and models formed by combining the fold models  showed the poorest prediction accuracy with 81% of tumours correctly predicted  were more difficult to predict (accuracy 85.5%) than the tumours with intermediate burdens . This group included many colorectal, stomach and uterine cancers with DNA repair defects leading to hypermutability. Since MuAt capacity is being limited to 5000 mutations that are randomly sampled from the mutation catalogue of the tumour, and this might explain the difficulty in predicting tumours with high mutational burden, we tested the consistency of MuAt predictions by predicting each PCAWG tumour 100 times. Figure Similarly, the tumours with the highest burden  learnt in PCAWG data by projecting the features to a two-dimensional space with UMAP [SPOP  and 1.4x  more SVs as well as excess of 10–100 bp deletions , SPOP tumours did not display excess of other somatic mutation types. Instead, SPOP tumours constituted a molecular subgroup of prostate cancer characterized by increased somatic structural alteration burden, compatible with previous reports [ERG driver mutations (n=85), mutually exclusive to SPOP , were ePTCH1, DDX3X and SMO driver mutations characteristic to SHH medulloblastomas, and PRDM6 enhancer hijacking driver events found in Group 4-medulloblastomas associated with MuAt features  were confined to one of MuAt feature clusters  resulting in microsatellite instability as well as tumours exhibiting very high burden of mutations, especially TpCpT>TpApT substitutions characteristic of polymerase urs Fig. c. MuAt fmentclass2pt{minimmentclass2pt{minimmentclass2pt{minimCCND1, a common alteration in acral melanomas associating with ulceration and localized metastasis [CCND1 amplification and clustered with the acral melanomas. The remaining melanomas, mostly of the cutaneous subtype, had a high number of CpC>TpT dinucleotide substitutions compatible with signature DBS1 due to UV light exposure. Finally, in chronic lymphocytic leukemias, MuAt differentiated tumours with patterns of somatic mutations that had occurred in B cells during IGH gene rearrangements  , we discovered four patients with tumours clustering in MuAt feature space to harbour germline mutations in  cancers , 53. ConLastly, we projected both PCAWG and GEL tumour-level features onto the same UMAP to examine how well the two cohorts aligned in the MuAt feature space. Most GEL tumours were found to cluster together with PCAWG tumours in tumour type specific clusters, with a small number of tumours with low prediction confidence placed in-between. GEL squamous cell lung cancers (SCC) clustered together with PCAWG SCCs , a deep neural network designed to predict tumour types from somatic mutation catalogues while learning informative representations of tumour subtypes. Unlike other recent models, MuAt integrates a variety of heterogeneous information such as mutation type, genomic position and individual mutation annotations rather than representing mutations as aggregated counts. Trained with cancer genomes from the PCAWG consortium, MuAt achieved 89% accuracy in predicting the tumour type in a held-out set of PCAWG tumours (24 types). To evaluate MuAt’s performance with exome sequencing, which is often used in clinical settings , 55, we We benchmarked MuAt against other machine learning (ML) methods utilizing somatic mutations and mutation signatures to predict tumour types. Here, MuAt improved over the previous state-of-the-art in PCAWG, GEL and ICGC tumour genomes, and TCGA tumour exomes. We also observed relatively good performance with downsampled WGS data, which suggests MuAt may be useful for low-coverage WGS data, paediatric tumours, and in cell-free DNA applications where only a fraction of the somatic mutation catalogue of a tumour is captured .Deep learning models have been reported to be fragile, or sensitive to small changes in input data, leading to incorrect or misleading conclusions drawn from model outputs , 57. As SPOP mutations [BRCA1 and BRCA2 mutations. SPOP has a role in DNA damage response [SPOP mutated prostate cancers have elevated levels of genomic instability [ SPOP mutations have been associated with better response to therapies [We showed MuAt tumour-level features to distinguish between tumour subtypes, even if these labels were not available during training. By associating MuAt features with driver events identified in PCAWG, MuAt highlighted prostate cancers driven by utations , charactresponse , and SPOtability , 59.  SPherapies , 60, potPRDM6 with a 3.7-fold increase in SV burden. PRDM6 is activated by enhancer hijacking events in Group 4 medulloblastomas [PRDM6 activation, or vice versa, remains to be explored in detail. MuAt also clustered together a group of four pancreatic neuroendocrine tumours harbouring germline mutations of MUTYH with loss-of-heterozygosity in the tumours, demonstrating the ability of MuAt to identify tumours with similar germline DNA repair defects [MuAt stratified medulloblastomas into four clusters, one of which contained seven tumours driven by enhancer hijacking events involving lastomas . Whether defects .Since MuAt was able to accurately perform tumour typing with somatic mutation catalogues, we hypothesized that the learnt tumour-level features would share a degree of similarity with mutational signatures , 15. We MuAt’s attention mechanism allowed us to discover aspects of mutation data such as the type and position—or combinations of these—which were informative in predicting each tumour type. We found various types of mutations occurring in the IGH locus to be driving predictions of B cell malignancies. Here, MuAt was able to capture the interaction between specific mutation types and the genomic region characteristic to somatic hypermutation in B cells. MuAt was also able to leverage the rarer mutation types such as L1 retrotranspositions to help identify cancers such as esophageal and other epithelial cancers where these events are relatively common –67. WhilReformer  or LinfoIn conclusion, we have demonstrated how deep representation learning can in large cancer datasets yield features which are useful beyond labelled data, for instance in tumour subtyping. Our method, MuAt, is already able to contribute to multiomics data integration to drive biological discovery and clinical applications by providing informative representations of somatic mutation catalogues of tumours. This can be extended to incorporate additional data on somatic mutations, such as epigenetics, potentially enabling scrutiny of the role of epigenetic interactions in somatic mutagenesis , 70–72. Additional file 1:Table S1. Dataset Information for PCAWG, TCGA, ICGC, and common tumour subtypes in PCAWG and TCGA datasets. Table S2. Precision, recall, and f1 scores in the PCAWG and TCGA datasets. Table S3. Classification accuracy of the different models benchmarked. Table S4. Coefficients with 95% confidence intervals, p-values and FDR-adjusted q-values of a PCA model associating driver mutation statuses with MuAt features in the PCAWG dataset tumours. Table S5. Coefficients with 95% confidence intervals, p-values and FDR-adjusted q-values of a negative binomial model associating driver mutation statuses with mutation counts in the PCAWG dataset tumours. Table S6. Coefficients with 95% confidence intervals, p-values and FDR-adjusted q-values of a least-squares linear regression model associating MuAt tumour-level features with the fraction of mutated microsatellitesin the PCAWG dataset tumours. Table S7. Coefficients with 95% confidence intervals, p-values and FDR-adjusted q-values of a negative binomial model associating MuAt tumour-level features with mutation counts in the PCAWG dataset tumours. Table S8. Pancreatic neuroendocrine tumours in the PCAWG dataset with germline MUTYH driver mutations. Table S9. Linear association of MuAt factors with mutation counts. Table S10. Linear association of MuAt factors with mutational signatures. Table S11. Attention values for mutation type pairs, averaged over tumour types.Additional file 2:Figure S1. Architecture of MuAt. Figure S2. Top-1, top-3 and top-5 accuracy, precision, recall and F1 scores of MuAt models trained with different mutation types in PCAWG and TCGA data. Figure S3. Transfer learning performance. Figure S4. MuAt model performance with and without the attention module in PCAWG data with different combinations of mutation types. Figure S5. Confusion matrix of MuAt in TCGA data. Figure S6. Calibration curve of MuAt trained with PCAWG. Figure S7. Confusion matrix for MuAt and DNN in the independent ICGC dataset. Figure S8. Confusion matrix for MuAt and DNN trained on PCAWG in GEL. Figure S9. Confusion matrix for MuAt trained on PCAWG in GEL. Figure S10. Confusion matrix for MuAt and DNN in the Katainen et al dataset. Figure S11. Linear association of PCAWG driver events with MuAt tumour-level feature principal components. Figure S12. Prostate cancers with SPOP driver events. Figure S13. MuAt tumour-level feature UMAP showing medulloblastomas in the PCAWG dataset. Figure S14. Pancreatic endocrine tumours of four patients clustering with kidney cancers in MuAt tumour-level feature UMAP. Figure S15. MuAt SNV+pos tumour level features on PCAWG and GEL. Figure S16. A clustered heatmap of MuAt tumour-level feature in each PCAWG tumours. Figure S17. Association of MuAt tumour-level features with mutation counts by type. Figure S18. Association of MuAt PCAWG model tumour-level features with COSMIC mutational signatures. Figure S19. Variance in the number of mutations attributed to signaturesexplained by MuAt tumour-level features. Figure S20. Mean attention values of genomic positions per tumour type in the MuAt PCAWG model.Additional file 3. The Genomics England Research Consortium.


The extracellular microenvironment modulates glioma behaviour. It remains unknown if blood-brain barrier disruption merely reflects or functionally supports glioma aggressiveness. We utilised intra-operative microdialysis to sample the extracellular metabolome of radiographically diverse regions of gliomas and evaluated the global extracellular metabolome via ultra-performance liquid chromatography tandem mass spectrometry. Among 162 named metabolites, guanidinoacetate (GAA) was 126.32x higher in enhancing tumour than in adjacent brain. 48 additional metabolites were 2.05–10.18x more abundant in enhancing tumour than brain. With exception of GAA, and 2-hydroxyglutarate in IDH-mutant gliomas, differences between non-enhancing tumour and brain microdialysate were modest and less consistent. The enhancing, but not the non-enhancing glioma metabolome, was significantly enriched for plasma-associated metabolites largely comprising amino acids and carnitines. Our findings suggest that metabolite diffusion through a disrupted blood-brain barrier may largely define the enhancing extracellular glioma metabolome. Future studies will determine how the altered extracellular metabolome impacts glioma behaviour. Metabolite diffusion through a disrupted blood-brain barrier defines the enhancing extracellular glioma metabolome, providing insights into how cancer behaviour may modulate the extracellular microenvironment of high-grade gliomas. Isocitrate dehydrogenase (IDH) mutations occur in all oligodendrogliomas and a subset of astrocytomas, affording improved prognosis due to typically slower growth and more favorable response to standard-of-care radiation and alkylating chemotherapy2. Glioma heterogeneity hampers therapeutic generalisations across diverse patient cohorts. Moreover, diverse genetic phenotypes accumulate within individual patients’ tumour ecosystems, hampering therapeutic efforts to target glioma-associated mutations4. Conversely, molecularly diverse gliomas may resort to similar metabolic pathways in the setting of nutrient deprivation, hypoxia, and genotoxic stress6. Although glioma metabolism is increasingly scrutinised for therapeutic targets, few strategies currently exist to interrogate the metabolic microenvironment of human gliomas in situ, and there is a relative paucity of global metabolomic data from live human gliomas.Astrocytomas and oligodendrogliomas comprise most adult primary malignant brain tumours and remain incurable, regardless of the best available therapies9 and has been used longitudinally to study metabolites present in human gliomas when compared to adjacent brain11. While a limited subset of patients may be willing to undergo post-operative microdialysis, it is usually hoped that there will be minimal tumour left to sample following surgery, often preventing sampling from multiple regions of the glioma. As such, intra-operative microdialysis may be of interest for evaluating diverse regions of gliomas. However, while some studies have utilised intraoperative low molecular weight microdialysis (8–20 kDA) to quantify select extracellular metabolites14, no study thus far has deployed intra-operative microdialysis to characterise the global extracellular glioma metabolome, nor to compare the metabolome of contrast-enhancing versus non-enhancing tumour regions. We used variable flow-rate pumps set to 2 μL/min and high molecular weight catheters (100 kDa) to maximise the volume of microdialysate recovered during 15 surgeries in 14 patients. We report that contrast-enhancing  disrupted) regions of high-grade gliomas (HGGs) exhibited a conserved extracellular metabolome across both IDH-mutant and IDH-WT astrocytomas, similar to findings from other groups utilising intra-or-post-operative microdialysis16. However, comparison to the radiographically BBB intact (non-enhancing) portions of these tumours revealed that this enhancing glioma signature was significantly enriched for plasma-associated metabolites, suggesting that BBB disruption may contribute to the glioma extracellular metabolome.Microdialysis has been used to quantify human extracellular biomarkers of traumatic and hypoxic brain injury in neurocritical care units. This is most commonly performed in the post-operative setting with low-molecular weight catheters, including an FDA-approved system consisting of 20 kDA catheters perfused at 0.3 μL/min using the M-dialysis 106 pump. Microdialysis is also a well-established method to quantify central nervous system (CNS) drug delivery in early phase clinical trials14 to recover a usable volume of microdialysate containing diverse analytes, including metabolites, during a standard-of-care surgery. To that end, we initiated our intra-operative microdialysis trial in an initial cohort of five patients. To evaluate the feasibility of the intra-operative microdialysis approach, we aimed to measure microdialysate levels of the well-characterised and dialysable oncometabolite, D-2-hydroxyglutarate (D-2-HG), known to be produced by IDH-mutant gliomas17. As such, patients were enrolled whose tumours could plausibly be IDH-mutant. Subsequent pathology revealed that three of these initial five patients indeed had IDH-mutant gliomas ; two patients were found to have IDH-wild type (WT) glioblastomas   tumours (median: 27.29 versus 0.63 μM)  and brain adjacent to tumour (831.80 versus 0.68 μM). These data demonstrated the feasibility of intraoperative HMW microdialysis to sample a dialysable oncometabolite during standard-of-care surgery for tumour resection.Intra-operative microdialysis can enable sampling of diverse regions within the live extracellular glioma microenvironment during standard-of-care glioma resections. However, low molecular weight catheters preclude collection of most extracellular analytes; slow flowrates combined with short intra-operative sampling times prevent acquisition of enough microdialysate volumes to enable multi-omic analyses. We hypothesised that we could safely and feasibly deploy intra-operative high molecular weight HMW, 100 kDA) microdialysis using an elevated perfusion rate of 2 μL/min00 kDA miIDH status has been reported to impact the metabolism of IDH-mutant gliomas, we further analysed these samples via untargeted metabolomic analyses--an increasingly leveraged strategy to gain more comprehensive insights regarding the global metabolome. We utilised 20 μL of microdialysate from each catheter for untargeted metabolomic analysis via the Metabolon platform18. Among hundreds of other metabolites, untargeted analysis reported total 2-HG. When plotted against the combined stereoisomer (D-2-HG + L-2-HG) levels from targeted analyses, a robust correlation (R2 = 0.9989) between the two platforms was observed across a 2500-fold concentration range   Fig. . HoweverIDH-mutant astrocytomas, the extracellular abundance of 2-HG was several-fold less in enhancing than non-enhancing tumour . This observation raised the possibility that 2-HG could be lost down its concentration gradient into systemic circulation. Indeed, elevated 2-HG was previously reported in venous outflow of IDH-mutant high-grade gliomas20. An alternate explanation could be that enhancing tumour regions from IDH-mutant gliomas generate less 2-HG due to changes in the IDH metabolism of the tumour core. Arguing against this alternate explanation, a third patient with an enhancing grade 4 IDH-mutant astrocytoma showed enhancement throughout the lesion, precluding placement of a catheter in non-enhancing tumour. Although both tumour catheters were placed within the margins of contrast-enhancing tumour, the second tumour catheter targeted a region of less intense enhancement, suggestive of poorer central perfusion or relative necrosis . We first asked how patient’s catheters correlated to each other to identify possible patterns in the global metabolome based on patient identity, radiographic location, IDH mutation, and primary vs. recurrent status. Spearman correlation of all 44 catheters revealed that all samples from each patient with an oligodendroglioma  clustered based on patient identity. Conversely, 9/10 catheters from enhancing regions of WHO grade 4 astrocytomas clustered together , while others clustered with brain catheters . Although Patient GemAstro3-mut’s tumour did have enhancing components, these areas were extremely fibrous, hampering catheter placement. As such, both catheters were placed into non-enhancing regions. Interestingly, however, this patient’s catheters clustered with the enhancing tumour catheters, potentially suggesting flow of metabolites from nearby enhancing tumour regions  was most strongly correlated with the enhancing catheter from their repeat resection (GBMWT2B: Catheter X). In sum, results of correlation analyses suggest that the extracellular metabolome of molecularly diverse contrast-enhancing astrocytomas clustered by catheter location, whereas that of oligodendrogliomas clustered by patient. Perhaps unsurprisingly, the extracellular metabolome of non-enhancing tumour regions of WHO grade 4 astrocytomas appeared to exist on a spectrum, appearing in some cases more similar to the enhancing tumour and in other patients, closer to that of the “brain” catheter. However, visual inspection of relative metabolite abundance as a function of catheter demonstrated substantial heterogeneity across patients within each region . We used the average ranks from these 3 patients to then evaluate the reproducibility of findings in the second cohort of 5 new patients and one patient’s recurrent (GBMWT2B) disease 10 months later. The ranked order of metabolites in the enhancing versus brain comparison appeared visually similar for all patients in both cohorts when ranked based on the average of the 9 cases  to statistically identify similarities between and across patients’ catheters. Although more commonly applied to gene-based analyses, we repurposed this for metabolomic analysis. Using EA, we selected the top and bottom 35 metabolites based on fold change from each patient’s tumour vs. brain or enhancing vs. non-enhancing tumour ranked metabolite list (“metabolite sets”) . Metabolites defining the brain metabolome included arabitol (FC = 0.04) and arabonate (FC = 0.08), in addition to canonical brain-associated metabolites, such as N-acetylaspartate (NAA)22 .After demonstrating that enhancing astrocytomas exhibit a conserved extracellular metabolome via rank-based and formal enrichment analyses Fig. , we next≥ 2 Fig. ; Table 2. The pooled bloody versus clean CSF ranked list was positively enriched for bloody metabolites in the paired CSF sample and negatively enriched for clean metabolites (FDR < 1 × 10−5), suggesting robust metabolic similarities between both analysis methods , and initially found no significantly differentially abundant metabolites. By removing the FC criterion, 4 significant metabolites were identified. Taurine was more abundant in venous than arterial glioma blood, suggesting production by the tumour. In agreement with this result, we found taurine to be significantly more abundant in both enhancing and non-enhancing tumour as compared to brain . Three metabolites were more abundant in glioma arterial than venous blood, suggesting utilisation by the tumour  GAA is a previously unreported extracellular glioma metabolite that highly differentially abundant in enhancing (126.32x) and non-enhancing (13.63x) glioma as compared to brain; 3) the extracellular metabolome of enhancing glioma is conserved across patients, comprises largely amino acids and carnitines, and is enriched for plasma-derived metabolites.We utilised an intraoperative window-of opportunity to perform microdialysis, sampling the extracellular metabolome of radiographically distinct regions of human gliomas in situ. Findings revealed that 1) 2-HG is robustly elevated in IDH-mutant gliomas epitomise the oncogenic relevance of altered metabolism26. We observed elevated 2-HG in the microdialysate of IDH-mutant tumours via both targeted (D vs L-2-HG) and untargeted  analysis   Fig. , suggestIDH-mutations are thought to drive early tumourigenic metabolic and epigenetic changes32, the global extracellular metabolomes of enhancing IDH-mutant versus IDH-WT grade 4 astrocytomas were otherwise indistinguishable , but not enhancing tumour (Table p = 0.004)  remains in clinical trials for glioma (NCT02796261); though upregulation of polyamine transporters may mediate metabolic compensation leading to improved survival with dual blockade of ODC and polyamine transporters (via AMXT-1501), in preclinical models of diffuse midline gliomas and other cancers61. To determine if GAA can be used as a dynamic biomarker of ODC production in situ, we are opening a post-operative microdialysis study under an IND, monitoring microdialysate levels of GAA in unresectable regions of residual tumour with or without DFMO +/− AMXT150162. Further dynamic insights may be achievable with isotope tracing and are under development.Although we used UPLC-MS/MS to maximise our yield of potentially novel glioma metabolites, we had not expected to find a new extracellular glioma metabolite that could outperform all other enhancing glioma metabolites by over 10-fold. Although GAA was present at a low abundance in bloody CSF and not detected in clean CSF, its disproportionately high fold change in both enhancing and non-enhancing tumour versus brain  hope that the data and insights shared can help inspire patient-centric translational paradigms. By leveraging neurosurgical access to live human disease, we aim to accelerate tangible progress within the lifetimes of individual patients.n = 14; 15 surgeries; Table 14, with collection vials exchanged every 20 min. See Supplementary methods for procedural details, pathology, and molecular tumour analyses. Each patient is referred to via a unique de-identified label reflecting their histology, IDH-status, and WHO grade according to 2021 WHO classification63  and the sample size then expanded once the safety and feasibility of the study had been demonstrated.All study procedures were approved by the Mayo Clinic Institutional Review Board (IRB) and performed according to relevant ethical regulations. Patients provided written informed consent to participate in NCT04047264--an ongoing study utilising intraoperative high molecular weight 100 kDa) microdialysis under an investigational device exemption. Study eligibility included adults ( > 18yo) undergoing a clinically indicated surgery for known or suspected glioma. Each patient  using slight modifications to previously described methodsUntargeted metabolomic analysis was performed by Metabolon, Inc. Ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS), which combines physical separation of liquid chromatography with the mass analysis capabilities of mass spectrometry. See supplementary methods for details.2) given all catheters were located within tumour. In patients with both enhancing and non-enhancing tumour catheters (n = 7 patients), a ranked metabolite lists of enhancing vs. non-enhancing tumour was generated (Catheter X/Y). To understand the potential impact of extracellular blood-derived metabolites on microdialysate metabolic signatures, we additionally generated a ranked list of bloodiness-associated metabolites using a pair of cranial CSF samples obtained from a single patient prior to resection of a 4th ventricular ependymoma. Both samples were obtained within approximately 1 min of each other—one with and one without blood contamination from the surgical wound. Twenty-six other intracranial CSF samples were collected with variable blood-contamination. Samples were centrifuged at 400 Gy for 10 min to remove red blood cells and other debris and aliquoted. For the paired bloody and clean CSF sample, the ranked metabolite list was based on metabolite fold-change (bloody/non-bloody) between samples, after filtering for metabolites present in both CSF and > 90% of microdialysate samples. For the pooled bloody and clean CSF samples, CSF samples were ranked from greatest to least blood contamination based on heme content. Excluding the ependymoma sample, CSF from the top (bloody) and bottom (clean) quartiles were utilized (n = 7 each), filtering for metabolites present > 90% of microdialysate samples and > 85% of CSF samples. Ranked metabolite lists were then generated based on the metabolite fold-change for the average of the bloody CSF samples versus the average of the clean CSF samples. See supplementary methods for details on enrichment ranked lists analyses.Ranked metabolite lists of enhancing or non-enhancing tumour (Catheter X or Y) versus brain (Catheter Z) were generated from fifteen cases based on calculated fold changes of the peak area  for each metabolite. A tumour versus brain ranked metabolite list could not be generated for one patient . See supplementary methods for enrichment analysis details.Gene Set Enrichment Analysis (GSEA) is the most widely utilised analytical method for rank-based analysis of data sets in biomedicine. GSEA is frequently performed using curated gene sets from the molecular signatures database (MSigDB), though custom analyte sets can be used, enabling enrichment analysis of metabolites rather than genes. In GSEA, custom analyte sets are queried against a ranked list of analytes to determine where those analytes fall on the ranked list  and samples.Metabolomic analyses were performed using MetaboAnalyst (5.0), a publicly available web-based tool to analyse and visualise metabolomic datap ≤ 0.05. Graphs were generated using GraphPad PRISM 9.1. FDR ≤ 0.05 was considered statistically significant for enrichment analysis. FDR values given as 0 by GSEA were reported as <1 × 10−5.Data are presented as normalised peak areas , repurposed for metabolite set analysis using custom metabolite sets. The normal distribution of metabolites in Fig. Further information on research design is available in the Peer Review FileSupplementary MaterialsDescription of Additional Supplementary FilesSupplementary DataReporting Summary


Due to its high viscosity, SGL has the potential as a gel-forming and release-rate-controlled material for application in swellable and floating gastroretentive drug delivery systems (sfGRDDS). The aim of this study was to develop ciprofloxacin (CIP)-loaded sfGRDDS tablets comprised of SGL and HPMC in order to extend CIP exposure in the body and achieve optimal antibiotic treatment regimes. Results illustrated that SGL-HPMC-based sfGRDDS could swell to a diameter above 11 mm and showed a short floating lag time (<4 s) and long total floating time (>24 h) to prevent gastric emptying. In dissolution studies, CIP-loaded SGL-HPMC sfGRDDS demonstrated a specific biphasic release effect. Among the formulations, the SGL/type-K HPMC 15,000 cps (HPMC 15K) (50:50) group exhibited typical biphasic release profiles, with F4-CIP and F10-CIP individually releasing 72.36% and 64.14% CIP within 2 h dissolution, and sustaining release to 12 h. In pharmacokinetic studies, the SGL-HPMC-based sfGRDDS demonstrated higher maxC (1.56–1.73 fold) and shorter maxT (0.67 fold) than HPMC-based sfGRDDS. Furthermore, SGL 90L in GRDDS indicated an excellent biphasic release effect and a maximum elevation of relative bioavailability (3.87 fold). This study successfully combined SGL and HPMC to manufacture sfGRDDS that retain CIP in the stomach for an optimal duration while improving its pharmacokinetic characteristics. It was concluded that the SGL-HPMC-based sfGRDDS is a promising biphasic antibiotic delivery system that can both rapidly achieve the therapeutic antibiotic concentration and maintain the plasma antibiotic concentration for an extended period to maximize antibiotic exposure in the body.Sangelose The oral drug delivery system is recognized as the most convenient and costly expedient for daily usage . HoweversfGRDDS) to prolong the dissolution and enhance the oral bioavailability of nilotinib [GRDDS with perpetuated gastric residence time is of particular interest for drugs that are restrictedly active in the stomach  ,6. GRDDSilotinib . Hydroxyilotinib ,11. Unfoilotinib ,12. Accoilotinib . Hence, ® (SGL) is a commercial HPMC derivative that has been hydrophobically modified by the introduction of a stearyl group. It is widely used in cosmetics as a thickening agent [sfGRDDS.Sangeloseng agent ,15. In rng agent ,17. It wng agent . The strng agent . Hence, Bacillus anthracis-induced anthrax or treat the lower respiratory tract, otitis, sinusitis and cystitis [Bacillus anthracis-induced anthrax in the vulnerable domain [Bacillus anthracis-induced anthrax is a potentially fatal infectious disease in humans and livestock, prevention and treatment are critical issues to curtail the mortality. From the perspective of infection prevention, sufficient exposure to prophylactic antibiotics is an important prerequisite for bacteria elimination [Ciprofloxacin hydrochloride (CIP), 1-cyclopropyl-6-fluoro-4-oxo-7-piperazin-1-ylquinoline-3-carboxylic acid hydrochloride, is the second generation of broad-spectrum fluoroquinolone antibiotics . The checystitis ,21. Accoe domain . Since Bmination . Unfortumination . Furthermination . CIP is mination . Hence, sfGRDDS tablets comprised of SGL and HPMC. The first part of the study was to confirm the gastroretentive capability of SGL-HPMC sfGRDDS tablets through swelling and floating capacities. The second part aimed to evaluate the suitability of CIP loaded into SGL-HPMC sfGRDDS tablets. The physical characterizations of tablets, including weight variation, thickness and tensile strength, were well evaluated. Following in vitro dissolution, studies investigated the release characterizations, and further release kinetic models were assessed to comprehend the release mechanism of tablets. The in vivo pharmacokinetic studies of optimized formulations were then evaluated through orally administrated CIP-loaded GRDDS tablets in New Zealand rabbits to confirm the improvement of in vivo characterizations.The aim of the present study is to develop CIP-loaded ®, were purchased from Bayer Pharma AG . Hydroxypropyl methylcellulose stearoxy ether  were supplied from DAIDO Chemical Corporation . K-type hydroxypropyl methylcellulose 4000 and 15,000 cps (HPMC 4K and 15K) and crospovidone were purchased from Shin-Etsu Chemical Co., Ltd. . Sodium bicarbonate was obtained from Natural Soda, LLC . Microcrystalline cellulose (MCC 102) was purchased from Wei Ming Pharmaceutical Mfg. Co., Ltd. . All the reagents used in experiments were either pharmaceutical or chromatographic grade.Ciprofloxacin hydrochloride (CIP) was obtained from Shangyu Jingxin Pharmaceutical Co., Ltd. . Commercial CIP tablets, CiproxinsfGRDDS tablets were prepared via the direct compression method, and the tablets were used to verify the gastroretentive effect of SGL [sfGRDDS using colloidal formers at three different ratios  and varying molecular weights of SGL and HPMC. The composition of formulations is shown in The formulations of SGL-HPMC t of SGL . In the st) was measured, and the weight-gain swelling ratio (rW) was calculated through the following equations:iW and tW demonstrate the initial weight of dry tablets and that of swollen tablets at time t , respectively.Swelling studies were conducted via the USP dissolution apparatus II method . The paddle speed was maintained at 50 rpm, and the dissolution medium used was 900 mL of simulated gastric fluid consisting of 0.1 N HCl at pH 1.2, which was kept at 37.0 ± 0.5 °C. The swelling was represented as the water-absorbing capacity and could be determined by either the diameter increment or weight gain of swollen tablets. The tablets were withdrawn at predetermined intervals  and surface droplets were immediately removed by wiping with a paper towel. The diameter of swelling tablets . The paddle speed was maintained at 50 rpm, and the dissolution medium used was 900 mL of simulated gastric fluid consisting of 0.1 N HCl at pH 1.2, which was kept at 37.0 ± 0.5 °C. Floating capacities were divided into two indices of floating lag time (FLT) and total floating time (TFT). Individually, floating lag time is defined as the duration that immersed tablets require to rise to the water surface. The total floating time is the period of tablets continuously floating on the water’s surface.sfGRDDS were tableted by the direct compression method [sfGRDDS formulations, 250 mg ciprofloxacin was added to the optimized formulations to conduct further evaluations. The composition of formulations is shown in The formulations of CIP-loaded SGL-HPMC n method . After tWeight variation was measured by weighing 3 CIP-loaded tablets of each formulation via an electronic balance  and calculating the average weight to compare with the theoretical weight.Thickness was determined by a digimatic micrometer  for 3 CIP-loaded tablets in each formulation. The thickness enabled us to compare the variation of the tableting compress process.The tensile strength of CIP-loaded tablets was estimated via a tensile strength testing machine . The results were triplicated in each formulation. The tensile strength indicated the extent to which the tablet can cope with compression force.R2 value of 0.9999, and the concentration range was 0.15–15 μg/mL.In vitro dissolution studies were performed using the USP dissolution apparatus II method . The paddle speed was maintained at 50 rpm, and the dissolution medium used was 900 mL of simulated gastric fluid consisting of 0.1 N HCl at pH 1.2, which was kept at 37.0 ± 0.5 °C. Five-milliliter aliquots were collected at predetermined intervals , and the same volume of fresh dissolution medium was replaced simultaneously. The drug concentrations of withdrawn samples were analyzed via a UV/VIS spectrophotometer  at a wavelength of 277 nm. The calibration curve of y = 0.1241x + 0.0015 was validated with an Release kinetic models were analyzed by DDSolver add-in software (an add-in program for Microsoft Excel) to evaluate the release mechanism ,28,29. TZero-order modelFirst-order modelHiguchi modelKorsmeyer–Peppas modelQ indicates the fraction of drug released at time t and the Q0 demonstrates the initial value of Q. The k0, k1, Hk, kpK and HCk represent the release rate constant of the various kinetic models, respectively. The n value of the Korsmeyer–Peppas model is the diffusion exponent and could indicate the releasing mechanism of formulations.Hixson–Crowell modelIn vivo pharmacokinetic studies were performed on male New Zealand rabbits (body weight 3–4 kg) due to the pH value of the gastrointestinal environment being similar to humans . All pha®); (b) F4-CIP; (c) F10-CIP; (d) F16-CIP. Each group contained 3 rabbits who were orally administrated the formulations containing 291 mg CIP . Blood samples were withdrawn at 0, 0.5, 1, 1.5, 2, 4, 6, 8 and 12 h through the marginal ear vein of rabbits after oral tablet administration. Blood samples were collected into heparin-coated tubes and centrifuged at 4000 rpm for 10 min. After centrifuging, the supernatants of each sample were collected separately, and further plasma extraction processes were conducted.The rabbits were divided into 4 groups: (a) commercial tablet (CiproxinThe blood samples adopted the protein precipitation method to extract the CIP from plasma. The protein precipitation was conducted by acetonitrile (ACN) . In brie® 100 RP-18 column  to separate samples. The mobile phase consisted of 0.02 M disodium hydrogen phosphate buffer (adjusted to pH 2.7 with phosphoric acid) and acetonitrile (ACN) at the ratio of 80:20 (v/v) through isocratic elution, and the flow rate was controlled at 1.5 mL per min. The injection volume of samples was set at 40 µL, and the detection wavelength was situated at 277 nm. The calibration curve of y = 117633x − 9894.8 was validated with an R2 value of 0.9998, and the concentration range was 0.05–5 μg/mL.The plasma samples were analyzed and quantified via HPLC. The JASCO HPLC system  was utilized; it comprised a PU-4180 pump, AS-4150 autosampler, UV-4075 UV/Vis detector and LC-NET II/ADC interface box. The stationary phase employed a LiChrospherp < 0.05 demonstrated statistical significance.All experimental data are presented as the mean ± standard deviation (SD). The determination of statistical significance between means was performed by one-way analysis of variance (ANOVA). The value of sfGRDDS tablets comprised of SGL and HPMC to elongate the period of gastric retention by curtailing the floating lag time, prolonging the floating duration and further sustaining the dissolution release pattern, proving the in vivo characterizations through a rabbit pharmacokinetic model. To attain the goal of elongating the period of gastric retention, one method is floating on the gastric fluid in the stomach immediately and for a substantial period, since it could prevent the risk of gastric emptying; the method other is swelling of the tablets to over about 11 mm diameter to avoid gastric emptying through the pyloric sphincter [The aim of the present study is to develop CIP-loaded phincter .sfGRDDS tablets experienced more than a 3-fold increase in weight-gain swelling ratio (rW) and about 1.3-fold increase of diameter after 8 h of sinking in the dissolution medium. The capacity of water absorption in GRDDS is an imperative mechanism that could produce swelling to avoid gastric emptying through the pylorus [rW measurements are shown in rW ranged from 314.5 to 421.1%. This demonstrated whether SGL 60L or 90L combined with HPMC had the capacity of water absorption. Similar results were related to the diameter of swelling tablets (Dst). Plots of Dst measurements are presented in st to un-swelling tablets ranged from 137.6 to 162.6%. This also indicated the improvement of the water absorption capacity of SGL-HPMC sfGRDDS tablets. The swelling mechanism of SGL-HPMC tablets is dominated by the interaction of intra- and intermolecular stearyl groups and hydroxy groups on SGL and HPMC [All the SGL-HPMC  pylorus ,34. Plotand HPMC . Furtherand HPMC . The potGao et al. investigated the swelling characterizations of HPMC-based tablets with various proportions and different types of HPMC . In thissfGRDDS tablets were less than 4 s and steadily more than 24 h, respectively. The lower FLT and longer TFT could lower the risk of gastric emptying after swallowing the tablets into the stomach. In terms of the results, the sodium bicarbonate would generate carbon dioxide bubbles when encountered in the hydrochloric acid solution. Further, the bubbles were immediately entrapped in hydrated SGL and HPMC, resulting in a short period of FLT and a long duration of TFT. In summary, SGL combined with HPMC in GRDDS exhibited stable performance in swelling and floating capacities. The formulations were loaded with CIP to evaluate subsequent dissolution properties.The FLT indicated the ability to refloat tablets, while the longer TFT represented the sustainability of the floating capacity of tablets. The data of FLT and TFT are presented in sfGRDDS, CIP was loaded into SGL-HPMC sfGRDDS tablets to evaluate the in vitro dissolution and in vivo pharmacokinetic properties. Through dissolution and pharmacokinetic studies, the further release efficacy could be established and the role of SGL in sfGRDDS could be clarified.After verifying the gastroretentive effect of SGL combined with HPMC in sfGRDDS, facilitating water uptake into tablets to accelerate gel formation [The physical characterization and floating capacities are illustrated in ormation ,41. Moreormation . In floasfGRDDS tablets are shown in sfGRDDS. In a recent study, SGL-based gel was reported to have lower viscosity than HPMC-based gel at low polymer concentration; conversely, SGL-based gel had higher viscosity versus HPMC-based gel at high polymer concentration [sfGRDDS has a biphasic release effect. Furthermore, the biphasic release effect is decreased with the increment of the HPMC amount. This study demonstrated that HPMC forms a high-viscosity gel layer when it initially encounters water, resulting in a decrease in the initial release characteristics.The dissolution profiles of the CIP-loaded ntration . The gelntration ,44. ThesThe dissolution results of the formulations composed of SGL/HPMC 4K (50:50) are shown in sfGRDDS tablets are shown in 2h, Q12h and Q24h in each group, the biphasic release effect was also observed on SGL-HPMC sfGRDDS formulations. MDT values were increased with the accruement of the HPMC amount. In contrast, the DE value was decreased with the increment of the HPMC amount due to a lower ratio of hydrophilic polymers in the tablet matrix accelerating the dissolution rate. To sum up, the results of MDT and DE supported that the molecular weight of HPMC-related polymers influenced the dissolution, with the higher molecular weight showing a decreased dissolution rate [The dissolution parameters of the CIP-loaded ion rate ,47.sfGRDDS, and the formulations were mainly composed of different molecular weight HPMC and disintegrants [sfGRDDS demonstrated that had a higher FLT and lower TFT led to dissolution of the CIP at 12 h. In our presented study, the addition of a high viscosity polymer (SGL 60L or 90L) to sfGRDDS could facilitate rapid release at the initial dissolution phase and also improve the sustained release effect of dissolution up to 24 h.Gaikwad, V. D. et al. developed CIP-loaded tegrants . FurtherIn summary, SGL demonstrated the ability of biphasic release of CIP in HPMC-based GRDDS. In addition to the high viscosity of SGL materials providing high gel strength in the GRDDS structure, the particular biphasic releasing effect was noticed; the immediate release supplied quick drug concentration to deliver therapeutic effects within a rapid period, and further extended release contributed to maintaining drug concentration with slight fluctuation and prolonged the therapeutic effects. The SGL-HPMC 15K (50:50) group exhibited the typical biphasic release character; pharmacokinetic studies were evaluated to clarify the relationship of pharmacokinetic parameters between different hydrophilic polymer (SGL and HPMC) compositions.R2), where the value of R2 close to 1 represented good fit, while an R2 value below 0 demonstrated poor fit [R2 formula, a negative value indicated the that regression model performed inadequately and was an inappropriate fit [The release kinetics of dissolution were evaluated by fitting five mathematical models, including zero-order, first-order, Higuchi, Korsmeyer–Peppas and Hixson-Crowell models. The best-fitting release model was chosen using the coefficient of determination  of the first-order model. This indicated the release rate of F1-CIP, F4-CIP and F7-CIP was dependent on the concentration of CIP. F2-CIP, F3-CIP, F5-CIP, F6-CIP, F8-CIP, F9-CIP, F10-CIP, F11-CIP and F12-CIP were fitted to the Korsmeyer–Peppas model . In the Korsmeyer–Peppas model, the value of n represented the mechanism of release. An n value below 0.45 indicated the diffusion-type drug release mechanism, and an n value above 0.89 indicated the swelling-type drug release mechanism. However, an n value between 0.45 to 0.89 would be considered as the dual release mechanism , containing diffusion and swelling [n value of F2-CIP, F5-CIP and F10-CIP, respectively, was 0.418, 0.443 and 0.197, demonstrating that diffusion was the main release mechanism. In F2-CIP and F5-CIP, the lower molecular weight of SGL formed a loose gel layer that would not entrap the CIP, effectively resulting in drug release controlled by a diffusion mechanism. F10-CIP facilitated the diffusion-releasing mechanism due to the lower content of gelling polymers, which could not construct a sufficient gelling layer to snare the CIP. Furthermore, the n value of F3-CIP, F6-CIP, F8-CIP, F9-CIP, F11-CIP and F12-CIP  was between 0.45 and 0.89, which indicated that both diffusion and swelling were the mechanisms controlling drug release. Compared with F2-CIP and F5-CIP, F8-CIP and F11-CIP had the same amount of gelling polymers, but the higher molecular weight of SGL predominated the swelling-releasing mechanism via generating the robust gelling layer to entrap the CIP release. Meanwhile, F3-CIP, F6-CIP, F9-CIP and F12-CIP comprised a high content of hydrophilic polymers, which originated from the thick hydrate gel layers. It could ensnare the CIP release from the polymeric gel layer and further contribute to the release mechanisms of diffusion and swelling. In addition, the n value in each SGL-HPMC formulation group exhibited a correlation with the concentration of HPMC. When the concentration of HPMC increased in each formulation group, the n value was also increased simultaneously. This indicated that a higher hydrophilic polymer content in sfGRDDS systems could form stauncher gelling layers, influencing the release kinetics through the swelling mechanism.From the results, F1-CIP, F4-CIP and F7-CIP, consisting of lower molecular weight polymers (SGL 60L or HPMC 4K), were demonstrated as the best fit  were significantly higher than those of the commercial tablets and F19-CIP (0.86 ± 0.24 and 1.60 ± 0.36 μg/mL). The maxT of commercial tablets F4-CIP and F10-CIP (1.33 ± 0.29 h) were shorter than F16-CIP (2.0 ± 0.0 h). There was no statistically significant difference in the MRT values among the groups, but F4-CIP exhibited the shortest MRT due to its weak gel strength, which resulted in ineffective entrapment of CIP, causing high initial burst release and an inability to sustain the release of CIP. The AUC0-t of F10-CIP (12.78 ± 3.25 μg/mL/h) was significantly higher than that of the commercial tablets (3.30 ± 0.63 μg/mL/h). Furthermore, the AUC0-∞ of F10-CIP and F16-CIP  was significantly higher than that of the commercial tablets (4.72 ± 0.85 μg/mL/h).Pharmacokinetic studies were conducted to comprehend the in vivo characteristics of SGL in GRDDS. The plasma concentration–time curves after oral administration of 250 mg ciprofloxacin commercial tablets and sfGRDDS formulations raised AUC values and improved relative bioavailability compared to non-gastroretentive commercial tablets, indicating that sfGRDDS is a promising strategy to ameliorate pharmacokinetic characteristics. Furthermore, from the pharmacokinetic parameters, the SGL-HPMC-based sfGRDDS demonstrated significantly higher maxC (1.56–1.73 fold) and shorter maxT (0.67 fold) than HPMC-based sfGRDDS. It contributed to the particular biphasic release effect of SGL. Although SGL had the capability of rapid release in the initial dissolution phase, SGL 90L exhibited an excellent sustained release effect compared to SGL 60L, suggesting that high molecular weight SGL could form the higher viscosity gel layer to entrap the CIP release from the tablets.Compared to commercial tablets, F4-CIP, F10-CIP and F16-CIP demonstrated elevated relative bioavailability by about 2.83–4.79 fold due to rapid gastric emptying of commercial tablets, resulting in poor drug absorption in the lower part of the GI tract. Among F4-CIP, F10-CIP and F16-CIP, F10-CIP exhibited the highest increase of relative bioavailability (387.33 ± 98.43%), which was attributed to the crucial biphasic drug release characteristics of SGL. Although F4-CIP was also composed of SGL, F4-CIP was limited to an increase in relative bioavailability of 238.35 ± 45.94%, since SGL 60L had a relatively lower molecular weight than SGL 90L, leading to rapid release rather than effectually entrapping the drug release due to the gel layer. A similar value of relative bioavailability was shown for the F16-CIP group, which demonstrated 247.34 ± 45.46% relative bioavailability compared to the commercial tablets. From the perspective of pharmacokinetic curves, F4-CIP manifested rapid absorption and elimination; nevertheless, F16-CIP exhibited a lag in absorption at the initial phase and maintained the plasma concentration for 12 h. Though F4-CIP and F16-CIP demonstrated comparable relative bioavailability, they possessed different pharmacokinetic characteristics. In our pharmacokinetic studies, sfGRDDS, in order to retain CIP in the stomach for an optimal duration while improving its pharmacokinetic characteristics. The SGL-HPMC-loaded sfGRDDS exhibited excellent swelling and floating capacities while simultaneously possessing the unique biphasic drug release potential. In terms of pharmacokinetics, we verified that the SGL-HPMC sfGRDDS could shorten the initial lag onset of drug concentration and improve the relative bioavailability. Due to the specific biphasic drug release feature of SGL, the formulations effectively prolonged the exposure of CIP in vivo. Overall, the SGL-HPMC-based sfGRDDS represents a promising biphasic-released antibiotic delivery system that could rapidly achieve therapeutic antibiotic concentrations while maintaining plasma antibiotic concentration for an extended period, thereby maximizing exposure in the body and optimizing therapeutic efficacy.This study successfully combined hydrophobically modified HPMC (SGL) and HPMC, loaded to


In shallow subduction zones, fluid behavior impacts various geodynamic processes capable of regulating slip behaviors and forming mud volcanoes. However, evidence of structures that control the fluid transfer within an overriding plate is limited and the physical properties at the source faults of slow earthquakes are poorly understood. Here we present high-resolution seismic velocity models and reflection images of the Hyuga-nada area, Japan, where the Kyushu-Palau ridge subducts. We image distinct kilometer-wide columns in the upper plate with reduced velocities that extend vertically from the seafloor down to 10–13 km depth. We interpret the low-velocity columns as damaged zones caused by seamount subduction and suggest that they serve as conduits, facilitating vertical fluid migration from the plate boundary. The lateral variation in upper-plate velocity and seismic reflectivity along the plate boundary correlates with the distribution of slow earthquakes, indicating that the upper-plate drainage system controls the complex pattern of seismic slip at subduction faults. Vertical fluid pathways in the upper plate of the Hyuga-nada subduction zone, Japan, facilitate upward fluid migration from the plate boundary that host slow earthquakes and produce seafloor mud volcanoes. High fluid pressure is a prerequisite condition for slow earthquakes to nucleate at subduction faults4. At shallow parts of subduction zones (depth < 15 km), the pore fluid pressure is primarily controlled by the compaction/dehydration reactions of sediments entering the subduction zone3. Once released from the source, the fluid migrates upward via permeable zones into the forearc wedge owing to its buoyancy6, forming mud volcanoes and cold seeps on the forearc basins and trench slopes7. Subducting seamounts (topographic highs at the plate boundary) impact the hydrological system in subduction zones by causing a heterogeneous distribution of permeability in the upper plate and forming a subduction channel filled with fluid-rich sediments10. However, the evidence on fluid distribution directly linked to subducting seamounts is limited. In particular, the along-strike extent of the impact of the subducting seamount is poorly constrained. In addition, our understanding of how the complex structure formed by subducting seamounts modulates the slip pattern at subduction faults remains unclear11.Plate boundaries in subduction zones portray a wide spectrum of slip behavior, from day-to-year-long slow slip events to instantaneous faulting, governed by diverse physical factors. Pore fluid is one of the most influential parameters of seismic slips, and pore fluid behavior has been intensively studied in recent years13, the source areas of which are delineated by long-term slow slip events on the downdip side14 16 implies that these events occur at the plate interface, driven by the spatiotemporal variation in the fluid pressure therein19. Another fluid-related phenomenon in the Hyuga-nada area is abundant mud volcanoes on the seafloor22. Although geochemical analyses of core samples suggested that these mud volcanoes are fed by the dehydration of clay minerals at shallow crustal depths21, the origin of the fluid is poorly understood. Moreover, the hydrological system responsible for the transfer of deep fluids is unknown.The Hyuga-nada subduction zone in southwest Japan is an excellent area to study the relationship between subduction earthquakes and forearc hydrology. Adjacent to the Nankai Trough, where megathrust earthquakes of magnitudes greater than 8 occur frequently, the Hyuga-nada area historically hosted several interplate earthquakes of magnitudes ~7e14 Fig. . On its 25. We compare the Vp model with the reflection image on the coincident line for structural interpretations. Based on the seismic results, we suggest the origin of deep fluids and their pathways up to the seafloor mud volcanoes. We also compare them with the spatial distribution of slow and regular earthquakes to discuss the structural controls on their occurrence. The structural characterization on the subduction megathrust faults using our study method can provide important information to evaluate the spatial extent of future megathrust earthquakes in the region.To better understand the geodynamic system in the Hyuga-nada subduction zone, we performed a dense seismic refraction experiment using ocean bottom seismographs (OBSs) and a multichannel seismic (MCS) reflection survey in the summer of 2020. For the refraction study, we laid out 50 OBSs at 2-km intervals on the seismic line HYU01) Fig. . The sei Fig. . T26, suggesting that the subduction of the Kyushu-Palau Ridge increases the porosity of the forearc wedge over a broad region (about four times broader than the ridge itself). Interestingly, some of the low-velocity columns dip slightly toward the summit of the ridge located in the middle of the study area. Based on their fan-shaped geometry radiating from the seamount, we interpret that the low-velocity features represent an intensive fracture network; previous studies utilizing sandbox experiments explain that such networks consisting of subvertical faults can develop above subducting seamounts27. The dense distribution of fracture zones in the vicinity of the Kyushu-Palau Ridge may reflect the stress concentration in the region caused by seamount subduction, and their lower seismic velocities in the western part  close to the seismic profile, indicating another typical feature of mud volcanoes  in the region. For example, while tremors and VLFEs actively occur around the subducting Kyushu-Palau Ridge, where the plate boundary is reflective and exhibits low seismic velocities (Vp of 3.5–4.5 km/s), their activities disappear in the western part of the profile , it may also be true for shallow subduction zones .Several episodes of tremors and VLFEs are observed within a localized area of an ~30-km rangeThe seismic data were acquired through two research cruises conducted in August 2020. During the KM20-05 cruise by the R/V Kaimei of Japan Agency for Marine-Earth Science and Technology (JAMSTEC), we deployed 50 OBSs along the HYU01 profile at 2-km intervals. Each OBS contained three‐component geophone and hydrophone sensors . The OBSs recorded the acoustic signals from 596 airgun shots triggered every 200 m along the 120-km-long seismic line. All the OBSs were recovered by the R/V Kairei during the KR20-10 cruise, providing the seismic records of the airgun signals. We also collected the MCS reflection data on the same line as the OBS refraction survey, using a 5.6-km-long 444-channel streamer cable onboard the Kaimei; the shooting interval for the reflection survey was 50 m. For both the surveys, the Kaimei’s tuned airgun arrays  were used as the source.41. Then, we determined the first breaks in the refraction waves manually, using the vertical component of the geophone data. The picking error ranging from 30 ms to 60 ms were assigned to each pick, depending on the offset and the noise level of the data. A total of 27,902 first arrivals were used for a traveltime tomography analysis42 to derive a preferable Vp model. The initial Vp model for the first-arrival traveltime tomography (FAT) was created by referring to the seismic refraction studies close to the survey line considered in this study25 . The data were processed following a standard prestack flow that included trace editing, tidal static correction, and low-cut filtering (of 1–3 Hz). The processing also included swell-noise attenuation, deghosting, source signature deconvolution, 2D surface-related multiple elimination, least-squares adaptive subtraction, and high-resolution radon demultiple for noise reduction. Velocity analyses and prestack Kirchhoff depth migration were performed to produce the final seismic reflection image Fig. .https://www.ngdc.noaa.gov/mgg/gdas/). The data were gridded at 1arc-min (bin size), and then were transformed to the vertical component of the magnetic field produced by the same source magnetized in the vertical direction [reduced-to-pole (RTP) anomaly]. The paleo latitude of the study area was assumed to be 26° N, while considering the northward convergence direction of the Philippine Sea Plate.The magnetic anomaly data were downloaded from the Marine Trackline Geophysical Database of the National Centers for Environmental Information (Supplementary Information FilePeer Review File


Diligent fluid management is an instrumental part of Enhanced Recovery After Surgery. However, the effect of a ward regimen to limit intravenous fluid administration on outcome remains unclear. We performed a meta-analysis investigating the effect of a restrictive versus a conventional fluid regimen on complications in patients after non-cardiac surgery in the postoperative period on the clinical ward.We performed a systematic search in MEDLINE, Embase, Cochrane Library, and CINAHL databases, from the start of indexing until June 2022, with constraints for English language and adult human study participants. Data were combined using classic methods of meta-analyses and were expressed as weighted pooled risk ratio (RR) or odds ratio (OR) with 95% confidence interval (CI). Quality assessment and risk of bias analyses was performed according to PRISMA guidelines.P < .03; I2 = 35%). This reduction in overall complication rate was not consistent in the non-randomized studies . No significant association was found for mortality using a restrictive fluid regimen . A restrictive fluid regimen is significantly associated with a reduction in postoperative length of stay in the non-randomized studies  but not in the RCTs . Risk of bias was moderate to high. Methodological quality was very low to moderate.Seven records, three randomized controlled trials, and four non-randomized studies were included with a total of 883 patients. A restrictive fluid regimen was associated with a reduction in overall complication rate in the RCTs (RR 0.46, 95% CI 0.23 to 0.95; This meta-analysis suggests restrictive fluid therapy on the ward may be associated with an effect on postoperative complication rate. However, the quality of evidence was moderate to low, the sample size was small, and the data came from both RCTs and non-randomized studies.The online version contains supplementary material available at 10.1186/s13741-023-00337-9. Currently, one in every four patients undergoing surgery suffer from one or more complications , and intensive care units (ICU). Therefore, a reduction of the amount of fluid given to patients on the ward might contribute substantially to outcome. This systematic review aims to meta-analyze the available evidence for a restrictive fluid regimen on the ward.http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42017075304.We performed a systematic review and meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines  randomized controlled trials (RCTs) and non-randomized studies, (2) the studied population should include adult patients undergoing elective non-cardiac surgery in the postoperative period on the ward (≥ 2 h after surgery and ≤ 30 days) without further restrictions for type of anesthesia, (3) the intervention should include a restrictive fluid regimen (solely or as part of an ERAS protocol) compared to a conventional fluid regimen, and (4) the studies should report on the incidence of complications. Studies investigating fluid regimens at high care units  were excluded.Title and abstract of the records were screened for relevance with the use of a systematic review system , Doha, Qatar) , mortality , and severe complications  using a standardized form. This form was an adapted version of the Data Extraction and Assessment Template from the Cochrane Public Health Group. The variables extracted included the year of publication, type of study, country, number of patients, type of patients, type of surgery, type of hospital, the age of patients, gender, ASA score, the aim of the study, the definition of complications used, the complications looked at in the study, the definition of restricted and conventional fluid regimen, and outcomes reported as mentioned above.RCTs were assessed for the risk of bias according to a ten-point checklist (Cochrane). Risk of bias due to missing results in the synthesis was assessed according the Cochrane Handbook Sect. 13.3.3 Higgins . For nonFor each study, the risk ratio (RR) for common events  or Peto odds ratio (OR) method for rare events, and the 95% confidence intervals for complications and mortality comparing the restrictive and the standard groups were calculated    or in the non-randomized studies   but not in the RCTs   Fig. a. This r5%) Fig. b. Mortal5%) Fig. , 90-day 5%) Fig. , in-hosp5%) Fig. , or over5%) Fig. . A restrIn this meta-analysis, we reviewed and compared fluid management strategies in the ward. The main finding is that a restrictive fluid regimen is significantly associated with a reduction in complications in RCTs and a reduction in PLOS in non-randomized studies. However, a restrictive fluid regimen is not significantly associated with a reduction in complications in non-randomized studies or a decrease in mortality in the under 1000 patients studied.Restricted or patient-tailored fluid therapy is part of ERAS (Gustafsson et al. We performed a TSA to determine if the results of this met-analysis are mathematically supported (Wetterslev et al. Despite several studies and the “The British Consensus Guidelines concerning postoperative fluid therapy,” there is no widely accepted appropriate fluid therapy on the ward (Powell-Tuck Overall, it seems that a restrictive fluid regimen may help in improving post-surgical patient outcome. Still, some challenges for prescription of fluids apply. A fluid balance is difficult to obtain (Walsh et al. Our meta-analysis suggests that reduction of complications with more diligent fluid therapy on the ward may be feasible. Individualized fluid prescription in a more structured approach may be beneficial, based on protocols addressing both the volume status and the clinical response of the patient (Myles et al. Our study suggests a restrictive approach towards intravenous fluid use on the ward following major gastrointestinal surgery may be associated with a statistically significant effect on complication rate in RCTs and postoperative length of stay in non-randomized studies. However, the quality of evidence was moderate to low, the sample size was small, and the data came from both RCTs and non-randomized studies. The association did not translate into reduced mortality.Additional file 1. Search documents.Additional file 2. PRISMA flow diagram.Additional file 3. Full text screening—exclusion criteria.Additional file 4. Intraoperative fluid regimens.Additional file 5. Risk of bias summary.Additional file 6. Mortality.Additional file 7. PLOS.


But details of how swarming individuals interact with the fluid environment are often under-examined. How do fluid forces shape aquatic swarms? How do fish use their flow-sensing capabilities to coordinate with their schooling mates? We propose viewing underwater collective behaviour from the framework of fluid stigmergy, which considers both physical interactions and information transfer in fluid environments. Understanding the role of hydrodynamics in aquatic collectives requires multi-disciplinary efforts across fluid mechanics, biology and biomimetic robotics. To facilitate future collaborations, we synthesize key studies in these fields.Collective behaviour defines the lives of many animal species on the Earth. Underwater swarms span several orders of magnitude in size, from coral larvae and krill to tunas and dolphins. Agent-based algorithms have modelled collective movements of animal groups by use of  UnderwaThis question has been investigated by the use of agent-based models, which simulate emergent group behaviour based on assumed rules for individual agents. Such models often impose a social force that either attracts or repels an individual to its neighbour based on their distance as if they were connected via a virtual spring . Social Going beyond such simplifications, the sensory systems of swarming animals must be considered. Aquatic organisms perceive the world through synthesizing a wide range of sensory information, including visual, hydrodynamic, proprioceptive, electric and magnetic cues. Classical models such as ,6 were aStigmergy was initially coined to describe the collective construction of termite mounds . I. Ia) [37a) ,127–130. channel  , making them effective flow sensing probes for measurements in the field [Sensing robots without fish-like beating tails benefited from a higher signal-to-noise ratio and enabled theoretical analyses. Researchers have increased the number of pressure sensors from 9 ,131, 11 figure 5he field ,133. Thehe field –137 alsohe field ,139 and he field ,141. An he field .d) [e) [Flow sensing has also been used in robots to gather information about their neighbours. For a pair of robots towed in a flume, the follower robot can use the sensed hydrodynamic pressure variations to estimate the states of the leader robot, including its relative location, orientation, oscillating frequency, amplitude, etc. d [125,14d) . A free-d) [e) . The estd) [e) . These sd) [e)  remains . 5Understanding underwater collectives requires knowledge from various disciplines. Research using live animals and robots presents two distinct philosophies for tackling complexity. In animal research, it is often difficult to control and isolate variables of interest, leading to an often messy but realistic picture. On the other hand, a biomimetic approach builds the complex system from the ground up and is excellent for studying each contributing factor in isolation. However, constructing these underwater systems is challenging and the fidelity of bio-mimicry is always compromised to some extent. Future studies that join different approaches would reveal novel insights into underwater collectives .Figure . 5.1a). Such a coupled model will capture the realistic interaction between agents and their fluid environments: agents’ movements disturb the fluid field, and the fluid in turn propels the agents. Incorporating fluid field simulations also creates the opportunity to model agents’ behavioural rules based on their fluid surroundings. Such sensory-based multi-agent models will enable studies of how agents react to hydrodynamic cues, and how variations of this feedback mechanism affect the cohesiveness of the school (figure 6b).Agent-based models are critical for exploring and understanding collective phenomena, but the dynamics of the fluid environment has often been ignored. While this may be valid for vision-dominated animals such as starlings –147, undfigure 6The most glaring challenge with coupling collective behaviour to a flow solver is the computational cost. Current advances in computational fluid dynamics allow resolutions of fine details in flow structures. However, high-fidelity algorithms require a considerable amount of computational resources and time. One direct numerical simulation of a fish school swimming for a few seconds can take weeks to complete on a computer cluster. To reduce the computational cost, most existing fluid simulations of fish schools prescribe the fish’s movements and ignore the force balance around each individual and Newton’s second law. If the prescribed conditions such as the formation, tail-beat amplitude and frequency are inherently unstable, the formation cannot be sustained and any solutions reached would not be representative of biological collectives.To ensure a force balance around individual agents while keeping computational cost low, the fidelity of the simulations must be compromised. This can be done by using potential flow theories ,148. How. 5.2c). Most existing experiments used small freshwater fish (a few centimetres in length) in shallow aquaria, forcing formations to be planer which made tracking manageable [Menidia beryllina yields at best 70% mean average precision (mAP) [One fundamental challenge of studying fish schools is the difficulty of conducting animal experiments. Keeping, experimenting with, and tracking fish schools in three dimensions even in a laboratory environment requires expertise in both biology and computer vision c. Most enageable ,150. Whinageable –153 and nageable –156, theon (mAP) . For schon (mAP) ,115, or on (mAP) . The recon (mAP)  also proDoryteuthis opalescens) and Pacific sardine (Sardinops sagax), which have distinct propulsive mechanisms [To gain a deeper understanding of underwater collective behaviour, studies of a wider range of species are also critical . Our knochanisms . Using rchanisms . Adaptivchanisms ,163 and chanisms –167 haved)? How stable are these formations? It will be interesting to see if other patterns of coherent movement will emerge from systematic studies like these.Studies of underwater swarms can also benefit from rigorous characterizations of their spatial–temporal features. Many influential studies that shaped our understanding of schooling formations use experiments that are based on trials under a minute long ,59. At tfigure 6Tracking fish schools provides a crucial step toward understanding their collective behaviour. Since the flow field depends only on the movement of the school, it can be reconstructed using fluid simulations or robotic experiments once the tracks are obtained. Through such hybrid approaches, one can obtain detailed flow information such as the fluid stress and pressure field next to each individual’s lateral line systems without directly experimenting with the animals. In addition, other sensory information such as vision and proprioception may also be inferred. Without further measurements, it will be tangible to derive a mapping from what a fish may sense to its behaviour. Note that both the input and output spaces of this mapping are large and the relationship may be nonlinear. Searching for low-dimensional structures from high-dimensional data is a recurring theme in biophysics . Dimensi. 5.3deforming their body. In the absence of external control, free-swimming robots have to generate thrust through deformation and thus move more like real fish [Bioinspired robots enable rigorous studies of hydrodynamic effects on underwater swimmers, isolating factors related to animal behaviour. While tethered robots and hydrofoils provide better control for experimental work, they prevent natural movements that arise from hydrodynamic interactions. Air bearing systems allow a tethered object to move freely in two dimensions, but movements in the other four degrees of freedom are constrained. Critically, the pitch angles of hydrofoils are often controlled. In fact, rigid hydrofoils can only generate thrust by pitching, which is driven by the motor it is attached to. This is distinct from fish, who generate thrust by contracting their muscles and eal fish ,176–178.eal fish ,179–183.e,f). With the added flow-sensing capabilities and knowledge of fish’s hydrodynamic feedback algorithm, future robots may be able to perform vortex matching and increase energy efficiency. Development of miniature artificial lateral line sensors that are scalable and easily deployable will also significantly advance our understanding of the hydrodynamic information embedded in the environment.Bioinspired robots also provide unique opportunities to peek into the sensory world of biological organisms. If the morphology and kinematics of fish are mimicked, a fish robot in a swarm will sense the same fluid pressure and shear that a fish in such a school must sense. While electronic sensors may differ from neuromasts in their sensitivity and range, the hydrodynamic information accessible to them is identical. Adding arrays of flow sensors to systems like BlueSwarm  would refigure 6. 6Fish schools are mesmerizing. They have inspired people from all walks of life and are an excellent example of collective behaviour. They are also easy to observe; most pet stores sell dozens of tropical species that readily school in aquaria. Despite the ubiquity, it remains mysterious how they coordinate with each other so well and how fluid environment dictates their collective behaviour.To some, the mystery may seem to have been solved when the Vicsek and Boids models showed that by keeping a certain distance from their neighbour, a flock of agents could avoid obstacles and move cohesively. Nonetheless, these models overlooked limitations of sensing in biological organisms. The range, resolution and speed of information acquisition are all limited; an animal cannot perfectly estimate its neighbours’ locations and their distances from it. This gap in our understanding of swarms becomes glaring as roboticists struggle to translate the social force models to robots—robots also have limited sensory capabilities.In bridging the gap, interdisciplinary collaboration is essential. Fluid mechanics researchers have long pondered the fluid–structure interactions of hydrofoils; biologists have meticulously described the morphology and sensing mechanism of lateral line systems; roboticists can now assemble fleets of underwater vehicles. To gain deeper insights into underwater coordination, we must gather a diverse team of experts from different backgrounds.fluid stigmergy in other collective systems where signals are transported by dynamic fluid fields. In fact, many organisms from microscopic to macroscopic coordinate with each other using chemical signals that are suspended in fluids like water or air. The role of fluid flow in these collectives remains to be investigated.Finally, we look forward to applications of . 6.1https://github.com/dalejn/cleanBib. Our references contain 8.2% woman (first author)/woman (last author), 12.0% man/woman, 18.6% woman/man and 61.2% man/man. By race, our references contain 14.8% author of colour (first)/author of colour (last), 6.0% white author/author of colour, 18.0% author of colour/white author, and 61.2% white author/white author. Following the original method, a colour author can be Asian, Hispanic, or Black. Of all 366 first/last authors (counting duplicates), 73.2% are white, 22.4% are Asian and only 4.4% are Hispanic and Black combined. This method is limited in that the predictions made by gender-api.com are not always correct. We have found that for a few authors of Asian origins, gender-api.com predicted the incorrect gender while claiming to have a near-perfect confidence score. We manually corrected a few predictions upon looking up researchers’ profiles, but the results cannot be assumed to be completely accurate. The statistics we report fail to include intersex, non-binary, transgender people, indigenous and mixed-race authors, or those who may face differential biases due to the ambiguous racialization or ethnicization of their names. Under-representations of other dimensions such as sexual orientation, (dis)ability, class and their intersections are difficult to reveal. We look forward to future work that could help us better understand how to support equitable practices in science.This statement is inspired by a recent initiative . The acc


Prevention of severe respiratory syncytial virus (RSV) disease in people at high risk for severe RSV infection  is a significant unmet medical need. Data are sparse on the natural history  of the infection in patients initially managed in the outpatient setting. To address this gap, the natural history of RSV infection in outpatient adults was examined in a real-world electronic health record (EHR) data source with daily data updates, enabling analysis of the current season.In US-based Truveta EHR database, adults with a positive RSV laboratory test result  in the outpatient setting (emergency department (ED), office/clinic, or urgent care) were identified and excluded if immediately hospitalized (within 24 hours of index). To accurately capture preexisting comorbidities, patients were required to have at least one healthcare encounter in the 6 months prior to index. Analyses were stratified by time: pre-COVID-19 (2017-2019), COVID-19 (2020-2021), and current season (2022-January 2023) and restricted to health systems participating for the entire study period. Occurrences of all-cause hospitalization, ED visits, and pneumonia were assessed during 28 days of follow-up (index not included), stratified by risk factors .Among the 6 health systems, 5,807 adults were identified from 2017-2023. Mean ages (standard deviation) were as follows: 52 (20) years (2017-2019); 52 (20) years (2020-2021); 62 (18) years (2022-January 2023). Hospitalization proportions ranged from 7.2-13.2% for all older adults (≥65 years) and were approximately 50% lower when restricting to older adults without other risk factors (Table 1). Expanding to adults 18+ years with risk factors brought hospitalization proportions to 5.7-10.4% (Table 1).The proportion of high risk adults hospitalized ranged from 3.3-13.2%, with substantial variation by period. Future analyses will include expansion of the study period to include the entire 2022-2023 season and validation in other EHRs in the US.Diana Garofalo, PhD MPH, Pfizer: Stocks/Bonds Joshua T. Swan, PharmD, MPH, BCPS, FCCM, CareDx: Grant/Research Support|Genentech: Grant/Research Support|Grifols Share Services North America: Grant/Research Support|Heron Therapeutics: Grant/Research Support|Kedrion Biopharma: Advisor/Consultant|Kedrion Biopharma: Grant/Research Support|Pacira Pharmaceuticals: Grant/Research Support|Pfizer: Grant/Research Support|Pfizer: Employee|Pfizer: Stocks/Bonds|VigiLanz Corporation: Grant/Research Support Margaret Tawadrous, MD, MS, Pfizer: Full time employee|Pfizer: Full-time employee|Pfizer: Full-time employee|Pfizer: Full -time employee|Pfizer: Ownership Interest|Pfizer: Ownership Interest|Pfizer: Ownership Interest|Pfizer: Ownership Interest|Pfizer: Stocks/Bonds|Pfizer: Stocks/Bonds|Pfizer: Stocks/Bonds|Pfizer: Stocks/Bonds Glenn Pixton, MS, Abbott: Stocks/Bonds|Abbvie: Stocks/Bonds|Pfizer: employee|Pfizer: Stocks/Bonds|Viatris: Stocks/Bonds Stephen E. Schachterle, Pfizer Inc., Pfizer Inc.: Full time employee|Pfizer Inc.: Ownership Interest|Pfizer Inc.: Stocks/Bonds Suzanne Landi, PhD, Pfizer, Inc: Employment|Pfizer, Inc: Stocks/Bonds Sudeepta Aggarwal, Ph.D, Pfizer: Employee of and may hold stock/stock options in Anindita Banerjee, Phd, Pfizer: Employee and stockholder Niki Alami, MD, Pfizer: Employee|Pfizer: Stocks/Bonds Scott P. Kelly, PhD, Pfizer: Employee|Pfizer: Stocks/Bonds