The development of in vitro/in vivo translational methods for synergistically acting drug combinations is needed to identify the most effective therapeutic strategies. We performed PBPK/PD modelling for siremadlin, trametinib, and their combination at various dose levels and dosing schedules in an A375 xenografted mouse model (melanoma cells). In this study, we built models based on in vitro ADME and in vivo PK/PD data determined from the literature or estimated by the Simcyp Animal simulator (V21). The developed PBPK/PD models allowed us to account for the interactions between siremadlin and trametinib at PK and PD levels. The interaction at the PK level was described by an interplay between absorption and tumour disposition levels, whereas the PD interaction was based on the in vitro results. This approach allowed us to reasonably estimate the most synergistic and efficacious dosing schedules and dose levels for combinations of siremadlin and trametinib in mice. PBPK/PD modelling is a powerful tool that allows researchers to properly estimate the in vivo efficacy of the anticancer drug combination based on the results of in vitro studies. Such an approach based on in vitro and in vivo extrapolation may help researchers determine the most efficacious dosing strategies and will allow for the extrapolation of animal PBPK/PD models into clinical settings. Metastatic melanoma is a cancer condition that is dangerous and difficult to treat due to its ability to spread early and aggressively. Before the development of new therapeutic strategies, the median survival of patients with metastatic melanoma was only 6–9 months  and the Analysis of the interactions between two or more drugs at the PD level is difficult because of a lack of consensus on which theoretical model should be used to describe the drug interaction type. As previously discussed, this issue is usually related to a dilemma when a drug combination is classified as synergistic according to one model but antagonistic in the other ,10,11,12Our main goal in this study was to develop and optimise a PBPK/PD model which could allow the translation of the in vitro drug combination results to an in vivo situation. An approach that involves using the results from in vitro studies coupled with PBPK/PD modelling may accurately describe the observed tumour growth inhibition (TGI) data and may suggest the most synergistic and efficacious schedules and dose levels for siremadlin and trametinib in mice in vivo. This modus operandi may lead to more accurate estimations of drug combination efficacy in virtual clinical trials (VCTs), which might be performed on a virtual representation of cancer patients and ultimately provide the rationale for using this drug combination in clinical trials on real patients with melanoma cancer.AUC0–24h, Cmax, and Tmax, which, in most cases, were within the 2-fold error range 0.5–2.0 , as shown in  0.5–2.0 .AUC0–24h, the PBPK simulation for siremadlin indicated that this parameter was accurately predicted for all tissues within a fold error of 0.93, except for the A375 tumour tissue, which was slightly overestimated (1.22), as shown in AUC0–24h. The estimated fold errors were also close to unity (0.93) in all tissues except for the A375 tumour, for which we noted a 0.96-fold error , A375 tumour (1.00), brain (1.31), skin (1.57), and gut (1.15); please refer to Cmax was somewhat underpredicted in the muscles (0.89), spleen (0.95), heart (0.89), kidney (0.97), lungs (0.99), and liver (0.94), as shown in Concerning the predicted Tmax parameter for siremadlin, the estimations for this parameter were generally overpredicted for all the tissues (1 < fold error < 2). The exceptions were the gut and A375 tumour tissue, for which this parameter was slightly underpredicted (0.74) and remarkably overpredicted (4.26), respectively. This may have been caused by unoptimised tumour blood perfusion in the mouse model; alternatively, the observed Tmax values might not have been not properly determined due to sparse sampling, as depicted in Tmax for trametinib, the calculated fold errors for most of the tissues were within a 2-fold error range. The only exception was for the brain tissue (fold error 0.11), which may have been caused by suboptimal blood perfusion for this tissue in the constructed PBPK model . In most cases, those parameters were within the 2-fold error range (0.5–2.0). Regarding the estimated AUC0–24h for the siremadlin model assuming PK interaction, this parameter was slightly underestimated for all the tissues (within a fold error range of 0.76–0.86), except for the A375 tumour tissue, for which this parameter was slightly overestimated (1.54), as shown in AUC0–24h were also slightly underestimated (fold error range: 0.76–0.88) in all the tissues except for the A375 tumour and plasma, for which we noted 1.22- and 1.02-fold errors, respectively , A375 tumour (1.00), brain (1.02), skin (1.31), and gut (1.00); please refer to Cmax was somewhat underpredicted in the studied tissues, including the muscle (0.89), spleen (0.90), heart (0.94), kidney (0.84), lungs (0.86), and liver (0.86), as shown in Cmax was slightly underestimated in the muscle (0.88), heart (0.70), kidney (0.96), lungs (0.61), gut (0.77), and liver (0.85). However, in this model, we also noted that the Cmax value of the skin was underestimated , except for the liver and A375 tumour tissues, for which this parameter was slightly underpredicted (0.81) and considerably overpredicted (3.38), respectively. This may have been caused by unoptimised tumour blood perfusion in the mouse model, or the observed Tmax values might not have been not properly determined due to sparse sampling, as depicted in Tmax for the trametinib model accounting for PK interaction, the calculated fold errors in most of the tissues were within a 2-fold error range. The only exceptions were for the brain tissue (fold error 0.17), which may have been caused by suboptimal blood perfusion for this tissue, and the gut (fold error 2.79), which may have been due to one or more of the following reasons: the combined effect of the high modification of the ka parameter, sparse sampling of the gut tissue homogenate, or homogenisation of the initial fragment of the intestine where the trametinib concentrations more quickly appeared  tumour-cell-killing hypothesis, drug effects described by the exponential drug-killing model, acquired resistance to the therapy, and treatment effect delay described by the signal distribution model with four transit compartments. For drugs administered in monotherapy, those models were able to accurately capture the changes in tumour volume in time, with a mean relative error (RE) < 20%, as shown in To properly fit the tumour volume data, TGI modelling for drug combinations required additional parameters for the drug interactions at the PK and PD levels. We tested two different parameters that were determined to be drug interaction parameters at the PD level during the in vitro data analysis . We seleWe successfully verified the TGI models with external efficacy data, as described in The key input parameters for the final TGI models for the single drug and drug combination are shown in 3). Additionally, the results from simulations for two therapy cycles suggested that the synergistic efficacy of the siremadlin and trametinib drug combination reduces the number of trametinib doses needed to achieve tumour stasis (tumour volume ≤ 170 mm3) to only 21 doses in a treatment cycle. In general simulations of combinations accounting for continuous siremadlin dosing, the results indicated that qdx7 or qdx14 had a higher efficacy than intermittent dosing schedules qwx2 or qdx1, as shown in We created a universal model based on a visual inspection of the data from the current study, validation studies, and score analysis. Determining the relationships between the TGI model parameters allowed us to carefully extrapolate the values of the model parameters for different doses and dosing regimens . As showAUC0–24h, Cmax, and Tmax, which were mainly within the 2-fold error range for all tissues except the A375 tumour and brain tissues. This may have been caused by suboptimal blood perfusion in those tissues. In the case of the A375 tumour, a slower distribution and, consequently, a higher predicted Tmax than that observed was probably related to insufficient blood perfusion. Applying higher blood perfusion would be possible, but it might be out of the range of measured perfusion in human melanoma xenografts [AUC0–24h, Cmax, and Tmax mainly within the 2-fold range for all the tissues except for the A375 tumour, brain, and gut. Regarding the A375 tumour and brain tissues, the high Tmax fold error might be related to unoptimised blood perfusion in this model. The higher calculated trametinib Tmax for the gut may have been related to the combined effect of the considerable modification of the ka parameter with the sparse sampling of the gut tissue homogenate, or to the homogenisation of the initial fragment of the intestine (where the concentrations more quickly appeared), which could have impacted the predicted Tmax for this tissue. The Tmax was the PK parameter most often mispredicted. Such a discrepancy between the predicted and observed values for this parameter might have been related to the fact that we used the observed Tmax values on the highest observed Cmax, which might not have been accurately determined because of the sparse sampling of the observed data in this study. We successfully verified the obtained PBPK models with external PD data extracted from the literature [The developed mouse PBPK/PD models for the MDM2 inhibitor siremadlin, the MEK inhibitor trametinib, and their therapeutic combination were able to describe both the pharmacokinetic and pharmacodynamic profiles of those drugs. The models consider the oral (P.O.) administration of siremadlin and trametinib, full-body distribution model, hepatic metabolism for siremadlin, and intravenous clearance for trametinib. Additionally, the models implement a permeability-limited tumour distribution model and drug interactions at absorption and tumour distribution levels, which allowed us to capture changes in siremadlin and trametinib concentrations in plasma and other tissues  when we separately or simultaneously administered both compounds. The interactions at the absorption level include changes in the absorption rate constant (ka) for siremadlin and alterations in the absorption rate constant (ka), fraction absorbed (fa), and lag time (tlag) in the case of trametinib. We hypothesised that those two drugs are competing for intestinal transporters related to absorption. Whereas siremadlin might be preferentially transported, a saturation of absorption and transport mechanisms may decrease trametinib absorption. In turn, in tumour interaction depending on the shift in passive permeability and efflux P-gp transporter clearances, an increased disposition of both compounds may occur, which may additionally abolish tumour resistance, which is often related to increased efflux transporter abundance and activity ,19,20,21nografts ,23,24. Iterature ,14.The developed TGI models describe the time course of siremadlin and trametinib efficacy when administered separately and together to mice xenografted with A375 melanoma cells. The logistic growth model best described the unperturbed tumour growth using data from the current study and validation sets.Based on previously performed in vitro studies, it is known that compounds’ killing effect is concentration- and time-dependent with an initial delay in the response, and resistance that might arise. These initial assumptions led us to develop final perturbed TGI models for siremadlin and trametinib. The models assume logistic tumour growth, the Skipper–Schabel–Wilcox (log-kill) tumour-cell-killing hypothesis, drug effect described by the exponential drug killing model, acquired resistance to the therapy, and treatment effect delay described by the signal distribution model with four transit compartments. The delay in the effect of these drugs is most likely related to the duration of the signal transduction associated with the activation of the p53–MDM2 and MAPK pathways, resulting in cell death. Resistance is an inherent part of anticancer treatment; therefore, a population of resistant cells was assessed for both drugs separately and when combined in the performed study, and its description may play a critical role in predicting and optimising the treatment response and may improve therapy scheduling ,26. Moden = 6); additionally, no data from other mouse melanoma xenografts were available. Furthermore, differences in the exposure ratios (AUC ratio) in the models using external PD data may have been caused by the different formulations used in those studies, but they could also be explained by the variability in the resistance parameter lambda. However, because the used cell lines were authenticated, the possibility that the genomic drift impacted the arising resistance is unlikely. The selected TGI model for the MDM2 inhibitor is similar to already reported models for RG7388 (idasanutlin) [Among the most relevant limitations of the present work is that the current models are restricted only to the experimental data available from a single mouse study with a limited number of animals (anutlin)  and HDM2anutlin)  molecule3). Moreover, these simulations suggested that the synergistic efficacy of the siremadlin and trametinib drug combination reduces the number of successive trametinib doses needed to achieve tumour stasis (tumour volume ≤ 170 mm3) to only 21 doses in the treatment cycle. This might be especially useful for patients who may develop hypersensitivity, a serious skin rash, or other adverse events grade 2 or higher after using trametinib [The results of an in-depth analysis of the TGI models’ parameter dependencies allowed us to extrapolate PD model predictions for different doses and dosing frequencies of the studied drugs after one or two cycles of the therapy, which allowed us to select the most optimal therapeutic strategies that ensure a high efficacy. The modelling and simulation approach suggested several effective dosing schedules; however, the most important are those assuming tumour stasis and complete tumour regression. The simulations of the tumour volume after two cycles of the combined therapy at HEDs revealed that at least 40 days of continuous dosing of trametinib combined with siremadlin (regardless of the siremadlin dosing regimen) is needed for the tumour to completely regress . Additionally, by comparing the results from the efficacy simulations after 40 doses of trametinib  with datFurther analyses are encouraged to externally validate the developed PBPK/PD models for siremadlin, trametinib, and their combination toward predicting tumour volume after human equivalent dose administrations.The in vitro/in vivo translational approach presented in this study facilitated the determination of the most synergistic and efficacious schedules and dose levels for the siremadlin and trametinib combination in mice and may provide a rationale for planned translational modelling between mice and melanoma-bearing patients.Recently published clinical data on siremadlin ,33 and tPBPK/PD modelling is a powerful tool that allows researchers to properly estimate the in vivo efficacy of anticancer drug combinations based on the results of in vitro studies. Such an approach may indicate the most efficacious dosing strategies. This method allows for better planning of the clinical trials and estimation of drug combination efficacy in such trials on the virtual representation of cancer patients.Siremadlin  and trametinib  used in this study were obtained from MedChemExpress. PEG 400  and Cremophor RH40  were provided by Merck (formerly Sigma-Aldrich), EtOH  was provided by POCH, and Labrafil M1944CS  was provided by Gattefosse. The A375 cell line used in the single drug administration in vivo studies was obtained from American Type Culture Collection (CRL-1619). For drug combination in vivo studies, the A375 cell line was provided by European Collection of Authenticated Cell Cultures (88113005).https://www.office.com). Digitalisation of the literature-derived data was performed with the use of WebPlotDigitizer software . PD modelling was performed with Monolix software . Monolix custom PD model in Mlxtran can be found in Code S1. PBPK/PD modelling was performed in Simcyp simulator software . Custom PK interaction and PD models in Lua can be found in Codes S2 and S3. The relationship between PD parameters in the universal PD model was determined by 2D curve fitting in Microsoft Excel and 3D curve fitting in Python ZunZun3 tool .PK parameters were estimated with Microsoft Excel . Pharmacokinetic parameters  and tissue:plasma partition coefficients (Kp) were calculated using MS Excel 2016. Area under the concentration versus time curve was calculated using the linear trapezoidal rule. Tissue:plasma partition coefficients (Kp) were calculated as proposed by Rodgers and Rowland [Crl:CD-1- Rowland  with an  Rowland  (Equatiov/v), 10% Cremophor RH40 (v/v), 10% EtOH (v/v), and 20% Labrafil M1944CS (v/v)), siremadlin, trametinib, or their combination in vehicle. The volume of the administration (10 mL/kg) of the compounds was always adjusted to the mouse body weight. Initial tumour volumes, doses, dose schedules, and numbers of animals in particular in vivo studies are summarised in Determination of tumour growth was performed after oral gavage of vehicle (60% PEG 400 (3 = 1 mL.Tumour volume (V) was recorded with an electronic calliper twice or thrice a week and was calculated based on its length and width using the prolate ellipsoid equation (Equation (2)).max) and area under the concentration vs. time curve (AUC) and Tmax [The modelling strategy was based on “middle-out” approach combining advantages of “bottom-up” and “top-down” approaches, whereby some parameters were fixed  for siremadlin and fa and CLiv for trametinib in the ranges presented in 0–24h, Cmax, or Tmax to the observed AUC0–24h, Cmax, or Tmax respectively (Equation (3)). AUC0–24h was calculated by the linear trapezoidal rule. Both visual checks and numerical analyses were performed in Microsoft Excel 2016.The final siremadlin and trametinib PBPK models were compared with external PK data ,14 throuOptimal PD models used further in PBPK/PD modelling were established in Monolix software. Due to the comprehensive library of Monolix PD (TGI) models , the fir−2LL and BICc were estimated by linearisation method to accelerate calculations. For TGI models further developed in Simcyp Animal, the goodness of TGI model fit was evaluated based on the mean relative error (RE) value (Equation (5)) being < 20% as proposed in :RE (%) =The first stage of TGI model development was focused on the selection of a mathematical model describing properly unperturbed tumour growth of A375 xenografts. Different tumour growth models assuming growth saturation such as logistic, generalised logistic, hybrid Simeoni–logistic, Gompertz, exponential Gompertz, and von Bertalanffy were evaluated and fitted to the data from the vehicle group in the Monolix software. The selected unperturbed logistic growth model assumes an exponential growth rate (kge) which decelerates linearly with respect to the initial tumour size (TS0) and is described by the following Equation (6):After the characterisation of tumour growth, the tumour growth inhibition models (perturbed models) were developed for siremadlin, trametinib, and their combination separately based on observed tumour volume data. Several structural TGIs were compared in terms of model score in order to select the best fit to the averaged tumour volume data.A screen of multiple models incorporating introduction of the type of killing hypothesis (Norton–Simon or Skipper–Schabel–Wilcox log-kill), dynamics of treatment effect , type of treatment effect delay ,where reposed in . The durbda*TSr),where reposed in  (Equatio0–24h) in external data, the value of the killing-effect-related parameter (kkill) was adjusted with the AUC ratio parameter which was calculated for siremadlin based on previously performed PK studies  and trametinib efficacy data digitised from the literature ,55 stud. Due to In the last step, unperturbed and perturbed tumour growth inhibition models for siremadlin, trametinib, and their combination previously developed in Monolix were translated into Lua programming language and applied within the Simcyp Animal V21 for further development to achieve a mean relative error (RE) value of < 20% (Equation (5)). A graphical summary of TGI model development and verification is presented in After the construction of the final PD models, relationships between input parameters were established in order to construct universal TGI models which allowed the proper prediction of tumour volume even after using different doses and dosing frequencies of the studied drugs.Relationships between input parameters were developed for siremadlin, trametinib, and combination TGI models separately and only for selected input parameters , while the other ones determined experimentally or estimated through TGI model development  were fixed at specific values .Development of the dependency of the parameters was based on the initial assumption that values of input parameters scaled with the dose and were summed up in the combination TGI model. Various mathematical equations were screened in the ZunZun3 standard 3D equations library  in order to find the best fit describing dependencies between input parameter data. Then, the best models (in terms of the lowest sum of squared absolute error) were verified on the siremadlin and trametinib verification datasets.3 (0.17 mL); therefore, the cut-off for tumour stasis was set to be ≤ 170 mm3 at the end of the simulation (960 h). Following complete eradication of the tumour, scarring often occurs at the site of tumour implantation, leaving behind connective tissue that may be mistaken for a small tumour; thus, tumour width or length below the limit of detection (4 mm) resulting in tumour volume of 32 mm3 was selected as the cut-off for complete tumour regression at the end of the simulation (960 h). The chosen cut-off for complete response (tumour volume ≤ 32 mm3) is in line with reported values [Tumour volume for studied drugs and their combination was estimated using universal PBPK/PD model within a 0–960 h simulation timeframe at the human equivalent doses and clinically examined dosing regimens for each drug . For simd values ,57,58. TAnimal doses equivalent to human doses were calculated according to Equation (29) .Animal


Captopril treatment reduced populations of myeloid-derived suppressor cells (MDSCs)  and increased PD-1 expression on infiltrating hepatic tissue-resident memory (TRM)-like CD8+ (p < 0.001) and double-negative  T cells; (4) RASi reduced CRLM growth in the regenerating liver and altered immune cell composition by reducing populations of immunosuppressive MDSCs and boosting populations of PD-1+ hepatic TRMs. Thus, RASi should be explored as an adjunct therapy for patients undergoing partial hepatectomy for CRLM.(1) Liver regeneration following partial hepatectomy for colorectal liver metastasis (CRLM) has been linked to tumour recurrence. Inhibition of the renin–angiotensin system (RASi) attenuates CRLM growth in the non-regenerating liver. This study investigates whether RASi exerts an antitumour effect within the regenerating liver following partial hepatectomy for CRLM and examines RASi-induced changes in the tumour immune microenvironment; (2) CRLM in mice was induced via intrasplenic injection of mouse colorectal tumour cells, followed by splenectomy on Day 0. Mice were treated with RASi captopril (250 mg/kg/day), or saline (control) from Day 4 to Day 16 (endpoint) and underwent 70% partial hepatectomy on Day 7. Liver and tumour samples were characterised by flow cytometry and immunofluorescence; (3) captopril treatment reduced tumour burden in mice following partial hepatectomy ( Liver resection offers the best chance of cure for patients with colorectal liver metastasis (CRLM); however, tumour recurrence in the future liver remnant (FLR) is not infrequent and affects 40% of patients ,2. The bc-myc and cyclin D1 downregulation [+ cytotoxic and CD4-CD8- (double-negative (DN)) T cells [Renin–angiotensin system inhibitors (RASis), commonly used to treat cardiac failure and hypertension, have been shown to prolong survival, improve response to neoadjuvant therapy, and reduce recurrence in a range of solid cancers ,8,9. Stugulation . In the  T cells ; howeverCaptopril treatment significantly reduced CRLM tumour burden in the FLR following partial hepatectomy, compared with the control group at Day 16 a,b. ThisWe recently reported that the RASi captopril induces changes in T-lymphocyte subpopulations and their activation status in a non-regenerating model of CRLM . The pre+:CD8+   cells in liver (Changes observed in the regenerating liver were complimentary to those previously observed in the non-regenerating liver . Captopr = 0.01)  and CD4+in liver .+ and double-negative  T-cell populations expressing PD-1, and the intensity of PD-1 expression, in both regenerating liver and tumour specimens  [RM, defined by the expression of murine hepatic TRM markers, CD44 and CD69 (termed “TRM-like cells”), and assessed the effect of captopril on these populations and their PD-1 expression. We found that, although the proportion of TRM-like CD8+ T cells did not increase with captopril treatment in the liver  , are incRM)  and breaRM) . We invehe liver a, the rancreased d–f. Convreatment a, while reatment c,e,f.RM-like”) in captopril-treated regenerating liver tissue significantly increased, and this corresponded with an increase in PD-1 expression on DN TRM-like cells in captopril-treated regenerating liver and tumour , compared with that observed in the CLRM model (p = 0.2) and this was also accompanied by a large reduction in the CD4+ TRM-like:CD8+ TRM-like ratio , compared with the result seen in the presence of a tumour in which case this population was significantly increased (p < 0.05). Like the CRLM model, we observed that captopril treatment increased PD-1 expression on both CD8+ and DN T TRM-like cells (RM-like CD8+ T cells (but not DN T cells) and increases the expression of PD-1 in CD8+ and DN TRM-like populations in physiological liver regeneration. This suggests that the expansion of DN and PD-1+ DN TRM-like populations is a tumour-specific response.As tumour induces many changes in immune cell infiltrates and responses, we sought to extract the effect of captopril on tumour-related changes by comparing them with those changes that occurred in response to liver regeneration alone. In the absence of a tumour, captopril significantly increased the proportion of CD8phocytes a. Additiphocytes b. Furtheke cells c and PD1ke cells d in regeke cells b. These Myeloid-derived suppressor cells (MDSCs) have been recognised to have immunosuppressive effects and are associated with cancer progression and poorer prognosis .+ leukocytes in the liver (Hi (monocytic MDSCs), Ly6CInt (intermediate), and Ly6CLo (granulocytic MDSCs) [Hi) and granulocytic (Ly6CLo) MDSC populations  ,19. Captulations a.+ MDSCs or the MDSC subtypes   a,b. Inte+ = 34%, a, back d+ = 34%, b. Taken + hepatic-TRM-like T lymphocytes. Further research is required to determine whether the changes in the immune microenvironment induced by captopril therapy directly reduce tumour burden in the regenerating liver and how these immune changes relate to other known antitumour effects of captopril [Tumour recurrence stimulated by the process of liver regeneration, following liver resection, is a major barrier to the long-term survival of patients with primary or secondary liver tumours. Presently, 40% of patients undergoing liver resection will experience intrahepatic recurrence. In this study, using a homogenous tumour mouse model that closely mimics the clinical scenario, we showed that RASi therapy with captopril inhibits the growth of CRLM in the regenerating FLR and is associated with phenotypic changes in immune cells in the tumour microenvironment. These changes included a reduction in populations of MDSCs and enhanced populations of PD-1aptopril ,10,11. NRMs are involved in maintaining effective antitumour immune responses and correlate positively with survival for a range of solid cancers [RM expressing PD-1 have been shown to maintain cytotoxicity in the tumour microenvironment (TME) [RM for antitumour activity, or there is an abundance of MDSCs. These findings correlate with those revealed in our study, in which a significant reduction in tumour burden was observed alongside the expansion of PD-1+ TRM populations. Our findings support a role for PD1+ CD8+ TRM-like lymphocytes in maintaining cytotoxicity against CRLM. This may explain why some cancers, including mismatch repair proficient colorectal cancers, have exhibited poor responses to checkpoint inhibitor therapy and suggests that RASi could play a role in the treatment of CRC by modulating the immune infiltrate.Studies have demonstrated that T cancers ,16. In c cancers ,22, TRM nt (TME) ,16,23,24Lo) and monocytic (Ly6CHi) MDSCs.MDSCs are crucial components of TME that facilitate cancer growth by secreting immunosuppressive cytokines such as TNF and TGFβ, inadvertently allowing cancer cells to evade the patient’s immune system ,26. ClinRM-cell populations are enhanced, while MDSC populations are reduced—is not likely to be coincidental. Studies in mice have demonstrated that both granulocytic and monocytic MDSCs suppress cytotoxic T cells [RM populations. To date, immunotherapy has largely focused on improving the cytotoxic function of effector T cells, rather than reducing populations of MDSCs, but the latter is also emerging as a viable approach to improve clinical cancer outcomes and trials examining this are underway [In acute inflammation, the adaptive and innate arms of the immune system work in concert with one another to restore homeostasis. In contrast, during chronic inflammation, such as within the TME, the immune response is dysregulated and characterised by the accumulation of immunoinhibitory cells, including MDSCs. Therefore, the finding we report here—namely, that certain T T cells . Howeverunderway . Clinicaunderway . However+ T cells, including TRM-like CD8+ T cells, in the regenerating liver in the absence of a tumour. This is in line with other studies that have also demonstrated that CD8+ T cells are key for hepatocyte proliferation and successful liver regeneration [To our knowledge, this is the first study to demonstrate that the RASi captopril modulates the immune responses in the regenerating liver, in the absence of a tumour. We found that captopril significantly increased populations of CD8neration . Convers+ TRM-like DN T cells was unique to the CRLM model, suggestive of tumour specificity. This is in agreement with other studies that have demonstrated DN T cells possess important antitumour functions [Our study also demonstrated a crucial role for DN T cells in attaining effective antitumour immunity. The expansion of PD-1unctions , and adounctions .RM-like lymphocytes. These results contribute to a growing body of literature suggesting that perioperative RASis may improve treatment outcomes for patients undergoing CRLM resection and should be explored further.In conclusion, this study showed that RASis reduced CRLM growth in the regenerating liver and altered immune cell composition by reducing populations of immunosuppressive MDSCs and enhancing populations of PD-1+ hepatic TAnimal experiments were approved by Austin Health Animal Ethics Committee (AEC-05435). Male CBA mice aged 10 weeks old and weighing 20–25 g were obtained from the Animal Resources Centre, Western Australia.A mouse colorectal cancer cell line (MoCR), developed via dimethyl-hydrazine induction of colon carcinoma in CBA mice , was useThe groups of mice not undergoing tumour induction followed the same treatment and hepatectomy timeline as outlined above but did not undergo tumour inoculation or splenectomy.Livers were fixed in 10% formalin for 24 h and stereometric tumour burden analysis was calculated, as previously described . Briefly7 cells/mL in cold FACS wash buffer . Briefly, 1 × 106 cell aliquots were stained with either Cocktail 1: CD45-BV510, CD4-FITC, CD3-PE, CD44-PerCP, CD8-PECy7, PD-1-APC, CD69-APC-Cy7  and viability dye DAPI, Cocktail 2: CD45-BV510, CD103-FITC, CD3-PE, CD8-PECy7, PD-1-APC, CD4-APC-Cy7  and DAPI, or Cocktail 3: CD45-BV510, F4/80-FITC, Ly6C-PE, PD-L1-PECy7, CD11b-APC-Cy7 , and DAPI. Samples were run on the Canto II (BD Biosciences) and analysed using FlowJo™ software.Tumour and liver samples were enzymatically digested (0.25 mg/mL liberase (Sigma) and 1 mg/mL DNAse (Sigma) in DMEM) at 37 °C for 40 min. Cell suspensions were filtered , treated with red blood cell lysis buffer for 2 min at 37 °C, washed, and resuspended at 1 × 10Following formalin fixation, livers were processed into paraffin blocks using standard techniques  and 4 μm sections were slide-mounted. Slides were dewaxed, and antigen retrieval  at 99 °C for 30 min was performed. Goat serum (10%) was used for blocking. Primary antibodies to PD-1  and CD8  were incubated overnight at 4 °C. Corresponding secondary antibodies were applied at 1:250 . DAPI 1:5000 was used for nuclear staining. Images were processed using confocal microscopy .t-test. Graphical data are expressed as the mean +/− standard error of the mean.GraphPad Prism was used for statistical analysis. Data were analysed using a two-tailed Student’s


This study aimed to determine the influence of fluid overload on bronchopulmonary dysplasia (BPD) in very low-birth-weight infants (VLBWI) within 1 week after birth.n = 60) and a non-BPD group (n = 97) according to whether BPD was present. The general condition, fluid intake, and fluid overload of the two groups of neonates within 1 week after birth were compared. The logistic regression was used to assess the association between infant characteristics and BPD. The ROC curve was used to assess how well the 7 day cumulative fluid overload predicted BPD, and to identify an optimal cut off for prediction.This was a retrospective case control study conducted in the Jingzhou Central Hospital. The clinical data of VLBWI  who were admitted to the neonatal intensive care unit of this hospital from January 2016 to December 2021 were analyzed retrospectively. A total of 157 cases were enrolled and divided into a BPD group (P < 0.05). The differences in the other indicators were not statistically significant between the two groups. The logistic regression analysis revealed that a younger GA, the presence of intrauterine infection, and a 7-day cumulative fluid overload were the risk factors for the development of BPD. A ROC curve was plotted with the 7-day cumulative fluid overload as the test variable and BPD as the status variable. The area under the curve was 0.75 , with a sensitivity of 76.7% and a specificity of 70.1%, corresponding to a 7-day cumulative fluid overload of 36.2%.The comparison of the patients' general condition revealed that the neonates in the BPD group had a younger GA, lower BW, lower 5-min Apgar score, longer duration of invasive mechanical ventilation, and higher incidence of intrauterine infections and administration of surfactants (A younger GA, the presence of intrauterine infection, and a 7-day cumulative fluid overload were risk factors for the development of BPD. A 7 day cumulative fluid overload threshold of 36.2% best predicted the development of BPD. With the advances in neonatal intensive care unit (NICU) care and perinatal technology, the birth and survival rates of very low-birth-weight infants (VLBWI) and extremely low-birth-weight infants (ELBWI) have increased considerably, and the incidence of bronchopulmonary dysplasia (BPD) also increases along with it . CurrentAbnormal postnatal fluid status may promote the development of BPD. The 1st week after birth is a critical period for intrauterine to extrauterine transition, and although the relevant guidelines provide recommendations for fluid management, infants are prone to developing occult fluid overload (FO) due to differences in healthcare centers and units, individual patients, and diseases. Fluid overload may lead to reduced pulmonary compliance and an increased need for oxygen and mechanical ventilation (MV) , while MPrevious studies , 5 foundThis was a retrospective case control study, following the STROBE guidelines . The stuThe inclusion criteria were the following concomitant conditions: (1) neonates who were born in our obstetrics department, (2) newborns admitted to our NICU within 24 h after birth, (3) neonates with a birth weight (BW) < 1,500 g, (4) 26 weeks ≤ GA < 32 weeks, and (5) newborns with a hospital stay of ≥14 days.Those who met at least one of the following exclusion criteria were excluded from the study: (1) newborns with severe asphyxia, (2) newborns with inherited metabolic diseases or severe congenital malformations, (3) newborns with severe congenital cardiovascular diseases, (4) newborns with gastrointestinal perforation that was not caused by neonatal necrotizing enterocolitis (NEC) and required surgery, and (5) newborns with incomplete data. The present study was approved by the ethics committee of the hospital.The enrolled newborns were divided into two groups based on the presence of BPD. After admission, all the newborns were treated with respiratory support, enteral and parenteral nutrition, maintenance of vital signs, stabilization of the internal environment, and symptomatic support. Neonates with hemodynamic abnormalities caused by patent ductus arteriosus (hs PDA) were treated with medications, and no special treatments were administered.For this study, the diagnosis of BPD was based on the diagnostic criteria of NICHD in 2001. BPD was defined as death due to pulmonary parenchymal disease and respiratory failure with the exclusion of NEC, severe intraventricular hemorrhage (IVH), sepsis between 14 days of postnatal age and 36 weeks of adjusted GA, and those in whom oxygen inhalation was still required 28 days after birth , 10. In With the use of the hospital information system database in our hospital, the patients' general information , daily fluid intake  and output , daily weight, and adverse outcomes  were collected.Generally, the daily fluid overload is calculated using the following method :However, considering BW differences, the formula used in the present study was as follows:The daily fluid overload, 3-day cumulative fluid overload, and 7-day cumulative fluid overload were calculated separately to reflect the early fluid overload.During the study period, the nursing standard included an initial total liquid intake of 80–100 mL/kg/D, followed by an increase of 20 mL/kg/D per day. No sodium was added to the parenteral nutrition supplemented in the initial 72 h. The sodium intake is usually supplemented with the physiological requirement of 3 mmol/kg/day from the 4th day and adjusted according to the blood sodium concentration. The infants were placed in an incubator, and the relative humidity was generally 70% in the first 7 days and 50% later. The nurse weighed the baby with the same scale at 8:00 every morning, and counted the liquid intake and output of the previous day.PDA was diagnosed by echocardiography; NEC was diagnosed according to modified Bell stage by combining clinical manifestation and imaging examination ; Grade IDiagnostic criteria of LOS: The disease usually starts after 72 h of birth, including clinical diagnosis and a definite diagnosis. The former is one of the following conditions under the premise of clinical abnormal manifestations: blood non-specific examination ≥ 2 positive; abnormal cerebrospinal fluid examination; DNA or antigen of special bacteria were detected in blood. The latter is positive in blood culture or cerebrospinal fluid (or other sterile cavity fluid) culture under the premise of clinical abnormal manifestations.The diagnostic criteria of PH: sudden dyspnea and/or increased thin moist rale in the lungs; bleeding red liquid at the mouth and nose and/or bloody liquid in the endotracheal tube.x ± s) or median (M) (min–max). Countable data were expressed as the number of cases (n) (%), and the Chi-squared test or Fisher's exact probability method was used for intergroup comparisons. A value of P < 0.05 was considered statistically significant. Binary logistic regression was used to explore the relationship between the early fluid overload and BPD, and the receiver operator characteristic (ROC) curve was used to determine the optimal threshold.The measurement data were expressed as mean ± standard deviation  . Besides, the differences were not statistically significant (.05) see .P < 0.05). The daily fluid intake from days 6 to 7 after birth was higher in the BPD group than in the non-BPD group, and the differences were not statistically significant (P > 0.05) (see The daily fluid intake in the BPD group was higher than in the non-BPD group from days 1 to 5 after birth (.05) see . From thP < 0.05). The daily fluid overload was higher in the BPD group than in the non-BPD group on day 1 after birth, but the difference was not statistically significant (P > 0.05). The 3 and 7-day cumulative fluid overloads (41.5 ± 9.6) were significantly higher in the BPD group than in the non-BPD group (33.4 ± 9.0) (P < 0.01) (see The daily fluid overloads were higher in the BPD group than in the non-BPD group from days 2 to 7 after birth (.01) see .A univariate analysis was used to screen the factors correlated with BPD, including GA, BW, 5-min Apgar score, presence of intrauterine infection, use of surfactant, and duration of invasive MV as well as the 3 and 7-day cumulative fluid overloads. A multivariate logistic regression analysis model was constructed. The details was showed in P = 0.042), with a sensitivity of 76.7%, a specificity of 70.1%, and a Youden index of 0.468; these results corresponded to a 7-day cumulative fluid overload of 36.2%.An ROC curve was plotted using the 7-day cumulative fluid overload as the test variable and BPD as the status variable. The area under the curve (AUC) was 0.75 , as shown in via cytokine-mediated inflammatory response , and the daily fluid intake of the BPD group was higher than that of the non-BPD group on the 6th to 7th day after birth, but the difference was not statistically significant (P > 0.05). This may be due to the fact that most clinicians may be aware of the problem of fluid overload and make appropriate adjustments on days 6-7, especially when no significant physical weight loss is found. This finding is similar to the results of Oh et al., . The daily fluid overload was higher in the BPD group than in the non-BPD group on day 1 after birth, but the difference was not statistically significant (P > 0.05). This might have been due to the inconsistent admission times of the patients on the first day. The 3 and 7-day cumulative fluid overloads were significantly higher in the BPD group than in the non-BPD group (P < 0.01), and the increased 7-day cumulative fluid overload was an independent risk factor for the development of BPD . In a previous study concerning the fluid overload in neonates, Matsushita et al.  It was a retrospective clinical study spanning 5 years, with some missing data. (2) The small sample size of the VLBWI might have limited the emergence of meaningful results due to the limitations of the inclusion criteria. (3) The relationship between fluid overload and caloric intake was not addressed in the present study.In this study, we determined the influence of fluid overload on bronchopulmonary dysplasia (BPD) in very low-birth-weight infants (VLBWI) within 1 week after birth. It was found that a young GA, the presence of intrauterine infection, and the 7-day cumulative fluid overload were the independent risk factors for the development of BPD in ELBWI. A 7-day cumulative fluid overload threshold of 36.2% best predicted the development of BPD among VLBWIs.The original contributions presented in the study are included in the article/The study was conducted in accordance with the Declaration of Helsinki (as was revised in 2013). The study was approved by Ethics Committee of the Jingzhou Hospital Affiliated to Yangtze University. The parents of all newborns provided informed consent for their infants' medical records to be used.Y-JL and X-FZ: Conception and design of the research. X-QY and HH: Acquisition of data. Y-JL: Analysis and interpretation of the data. Y-JL and J-HL: Statistical analysis. Y-JL: Writing of the manuscript. X-FZ: Critical revision of the manuscript for intellectual content. All authors read and approved the final draft. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Bacillus subtilis is a model bacterial system used to uncover the regulatory pathways and key building blocks required for biofilm growth and development. In this work, we report on the emergence of a highly active population of bacteria during the early stages of biofilm formation, facilitated by the extraction of fluid from the underlying agar substrate. We trace the origin of this fluid extraction to the production of poly-γ-glutamic acid (PGA). The flagella-dependent activity develops behind a moving front of fluid that propagates from the boundary of the biofilm towards the interior. The extent of fluid proliferation is controlled by the presence of extracellular polysaccharides (EPS). We also find that PGA production is positively correlated with higher temperatures, resulting in high-temperature mature biofilm morphologies that are distinct from the rugose colony biofilm architecture typically associated with B. subtilis. Although previous reports have suggested that PGA production does not play a major role in biofilm morphology in the undomesticated isolate NCIB 3610, our results suggest that this strain produces distinct biofilm matrices in response to environmental conditions.A hallmark of microbial biofilms is the self-production of an extracellular molecular matrix that encases the resident cells. The matrix provides protection from the environment, while spatial heterogeneity of gene expression influences the structural morphology and colony spreading dynamics.  This behaviour is ubiquitous across the microbial world and a clear understanding of biofilm genesis, development, and maturation is important both from a fundamental microbiological perspective, but also due to their impact on many industrial, clinical, and biotechnological sectors. For example, biofilms act as sources for many chronic infections and their physical characteristics make them difficult to eradicate7. This intransigence can also impact industrial processes, where biofilms may result in pipe blockages, induce corrosion, or contaminate products10. However, while there are many negative consequences of biofilm formation, microbial biofilms play vital roles in waste water treatment and other bioremediation processes14 and understanding their formation as well as their disruption is of fundamental interest.A common strategy employed by bacteria to mitigate stresses imposed by their environment is to co-exist in sessile communities known as biofilms. The transition from unicellular to multi-cellular life allows the residents to coordinate responses to stimuli, share metabolic burdensBacillus subtilis is a Gram-positive bacterium that has been extensively used as a model organism to investigate the genetic regulation and molecular mechanisms of biofilm formation16. The major components of the matrix produced by B. subtilis are the fibrillar protein TasA17, the hydrophobin-like protein surfactant BslA20, and the polysaccharide synthesized by products of the epsA-O operon21. One of the principle regulatory pathways that controls the expression of these components is modulated by the transcription factor Spo0A, with moderate levels of phosphorylated Spo0A activating transcription of the sinI-sinR operon23. SinR is a DNA-binding transcription factor that controls matrix production by interacting with the epsA-O and the tapA-sipW-tasA promoters24. When SinR binds to its antagonist proteins (SinI and SlrR), repression is alleviated from these operons and biofilm formation can proceed26.B. subtilis colony biofilm formation. Following initial deposition of founding cells, a highly motile population of bacteria emerges that swims in fluid extracted from the underlying agar substrate. We genetically link this moving front of fluid to the production of the polymer poly-γ-glutamic acid (PGA). We find the influx of fluid is dependent on both bacterial density and environmental temperature, and that the extent of fluid infiltration is in turn modulated by the production of extracellular polysaccharide (EPS). The production of PGA at higher temperatures, and the concomitant extraction of fluid, significantly impacts the mature biofilm morphology, which diverges from the typical structure associated with B. subtilis NCIB 3610. Our results suggest that B. subtilis has the ability to produce an alternative extracellular matrix in response to the environmental conditions.In this work we report on the observation of multiple travelling fluid fronts that develop during the early stage of B. subtilis cells (OD600 = 1) was deposited onto MSgg agar, a biofilm-promoting minimal media27. After inoculation the droplet evaporates, which results in a ‘coffee ring’ deposition pattern: a high-density ring of bacterial cells forms at the edge of the initial droplet while the interior is more sparsely populated  to further assess this motion. We found localized regions of high vorticity, and velocities ranging from 1 to 10 μm/s with a mean of 2.1 μm/s , however it travels much faster than the initial propagation of fluid, and predominantly moves from the interior ring of the annulus back towards the outer initial coffee ring −1 , is deleted. Without sinR, the bacteria over-produce the extracellular matrix and highly wrinkled colony biofilms occupy a smaller footprint 24. As predicted, the maximum distance that the fluid encroached into the interior of the biofilm relative to the outer edge of the colony , TasA (tasA), and EPS (epsA-O), again measuring the distance of fluid travel. We found that the fluid traveled further in the bslA strain compared to NCIB 3610, while the fluid flow in the tasA strain traveled less  of each feature, and the relative distances were normalized by their maximum value to allow direct comparison of their time-dependent evolution. Firstly, after a lag time, the outer edge of the colony begins to expand at a constant rate, as shown in Fig. Imaging the fluid flow in colony biofilms in this manner allowed us to fully track three key features of the dynamics: (1) the distance travelled by the expanding outer edge of the growing colony; (2) the distance travelled by the fluid flux, as above; and 3) the motility arrest front. All measurements were taken relative to the initial position  always extract fluid with a directionality: fluid always propagates from high density to low density regions . At 30 ∘C we did not observe any fluid flow into the colony biofilm and no fluidisation plateau in the edge expansion for any of the strains, even the wild-type and epsA-O strains  spreading may facilitate escape and the search for more suitable environs to colonise.From our experiments it is clear that high temperatures induce the colony to withdraw a considerable volume of fluid from the agar substrate. Previous work has shown that PGA can confer protection to bacteria and increase survival under many different environmental stressesB. subtilis and V. cholerae has been modelled as a viscous hydrogel network that facilitates biofilm expansion via osmotic fluid influx61. The localization of EPS production at the propagating boundary of a growing colony is thought to drive the outward expansion of the biofilm. Concomitant production of osmolytes stimulates fluid extraction that swells the matrix at the growing boundary, driving motion forward. In our experiments, PGA seems to have the opposite effect: colony expansion stalls due to the colony entering a ‘liquid-like’ state when fluid is extracted from the substrate. Previous work has shown that colony biofilm expansion is strongly governed by mechanical contact forces between neighbouring cells and friction with the underlying substrate64. In our experiments, expansion only recommences when the fluid environment dissipates, physical contacts are restored, and non-PGA matrix production begins. This raises a further question: how does colony expansion occur when PGA is the primary matrix component? Our experiments at high temperatures show that the wild-type 3610 strain does expand significantly beyond the initial deposition footprint. Strains that do not produce EPS or surfactin do not expand as much as the wild-type hinting that these components have a role to play in facilitating colony expansion  biofilms. We speculate that in our experimental system, the B. subtilis cells accumulate at the front of the incoming fluid wave, modifying the interfacial energetics and destabilising the interface between the fluid and the air, and giving rise to the observed fingering instability. Localisation of cells to these interfaces may also give rise to cell density gradients, which in turn may evolve as patterns in the mature biofilm. More work will need to be done to uncover the biological and physical mechanisms that cause this unusual phenomena and any possible benefit or function in ecological settings.The travelling waves of fluid in the ∘C) are suggestive of a spatial separation between PGA-producing cells at the edge of the colony biofilm and matrix-producing cells in the middle (as has previously been reported67), resulting in the annular confinement of fluid. Such spatial and temporal heterogeneity is a common feature in biofilms where the local microenvironment can strongly influence the phenotypic state of the cells. Phenotypic heterogeneity within a biofilm can be generated from variations in the chemical or physical environment69, genotypic variations, and stochastic gene expression68.Our experiments at intermediate temperatures  govern this switch-like phenomenon.The spatial heterogeneity is transient at 38 ilm Fig. b. The moB. subtilis biofilm  and that this was due to PGA production71. Therefore, a spo0A mutant broadly mimics the wild-type biofilm phenotype that we observe at high temperatures. This implies that Spo0A may be the regulatory component that controls temperature-dependent production of a PGA-rich or EPS/TasA-rich biofilm matrix.Previous work has shown that strains possessing a deletion of B. subtilis has the ability to produce different biofilm matrices with distinct properties to adapt to disparate environmental conditions. Such a strategy may be employed in natural environments given B. subtilis can grow at a wide range of temperatures: it is found in desert soils and within composts which can easily reach temperatures of 50 ∘C. Until now, PGA was not thought to be an important factor as a matrix component in B. subtilis NCIB 3610 biofilms37. However, it appears, under the right conditions, PGA can become an alternative matrix component with distinct structural and physical characteristics that may aid the biofilm to survive in high temperature conditions.Taken together, these results imply that B. subtilis strains were initially grown in LB medium . Biofilms were grown on MSgg agar . When appropriate, antibiotics were used as required at the following concentrations: chloramphenicol at 5 μg ml−1, kanamycin at 25 μg ml−1, spectinomycin at 100 μg ml−1, and tetracycline at 10 μg ml−1.B. subtilis strains used in this work are derived from the wild-type laboratory isolate NCIB 3610 and constructed using standard protocols. SPP1 phage transduction was used for transfer of genomic DNA from the donor strain into the recipient NCIB 3610.All strains used in this study are provided in Supplementary Table Ⓡ Mastermix (Merck). The material was disrupted via repeated passage through a 23-gauge needle and then sonication . The samples were incubated at 21 ∘C (with agitation) for 20 min and centrifuged at 4 ∘C  for 10 min. The supernatant was retained and contained PGA and extracted proteins. The samples were quantified using a Pierce BCA protein assay kit (Thermo Scientific) and stored at −20 ∘C prior to further analysis.Colony biofilms were grown for 24 h, at which point the biomass was harvested from the agar surface and placed into a 500 μl aliquot of BugBusterSamples containing 10 μg of total protein  were analysed by SDS-PAGE (12% w/v resolving gel and 7% w/v stacking gel) alongside a molecular weight ladder ). All gels were processed in parallel and were run at 180 V until the blue dye front in the sample loading dye reached the bottom of the gel. The acrylamide gels were thoroughly rinsed in water and soaked in Instant Blue Coomassie protein stain (Abcam) for 1 h with gentle rocking. After incubation in the stain, the gels were rinsed with deionized water and imaged using a Molecular Imager Gel Doc XR system (Biorad). The gels were further soaked in 0.5% (w/v) methylene blue in 3% (v/v) acetic acid for 10 min then repeatedly washed in deionized water until background non-bound dye was removed.B. subtilis strains were inoculated into 3 mL LB from a single colony grown on 1.5% w/v LB agar. The bacteria were allowed to grow at 30 ∘C with 200 rpm orbital shaking until reaching an OD between 1.5 and 2. The cell culture was diluted to OD600 1.0 in phosphate-buffered saline. A 3 μL droplet of bacteria was deposited onto a 35 mm petri dish (Corning) containing MSgg agar. The droplets of bacteria were allowed to dry for 10 min. For samples prepared via spin-coating, a petri dish containing MSgg agar was placed onto a vacuum spin-coater (Cammax Precima) rotating at 2000 rpm. A 10 μL droplet of bacteria was placed on the rotating agar. For all experiments, the petri dish was placed on a Nikon Ti inverted microscope that is temperature controlled. All microscopy images and movies were captured using a CoolSNAP HQ2 CCD camera controlled by μManager software. Brightfield movies and images of the biofilms were captured from the underside of the petri dish and are imaged through the agar (thickness ~ 4 mm). Nikon Plan 2x UW and 10X Plan Fluor objectives were used in image acquisition. Additional biofilm imaging was performed using a Leica MZ16 stereoscope. For the bead tracking experiments, 1 μm diameter latex carboxylate-modified polystyrene yellow fluorescent beads (Sigma-Aldrich) were diluted into PBS from the stock solution in a 1:1000 ratio. 1 μL of the working solution was added to the 1 mL of the diluted cell culture just prior to deposition. Movies were acquired in bright and epifluorescent channels (Nikon GFP fluorescent filter cube) and the beads were tracked using the ImageJ plugin TrackMate (v3.8.0)72. Image analysis was performed using the Fiji distribution of ImageJ. Whole biofilm microscopy images were captured as above but in multiple tiles that were stitched together using the ‘Pairwise Stitching’ plug-in. The images of the fingering instabilities were generated by first using the ‘straighten’ tool to transform a circular to a linear region. The default ImageJ threshold method was applied to binarize the images. In cases where the intensity varied across the image, the image was partitioned into regions of similar intensity and then thresholding was performed. The ’edge finding’ tool was used to locate the edges of the thresholded images. After edges were identified the interior was filled to form a representation of the fingers. If the image was partitioned, the image was stitched together using the stitching tool in ImageJ. Measurement of fluid travel distance was manually tracked in ImageJ and the mean displacement was averaged over 10 separate measurements for each experiment. For each strain studied, three separate experiments were performed. Edge and front displacement measurements were similarly performed in ImageJ by manually tracking the movement of the front over successive frames. Displacements were always measured relative to the initial position of each feature. PIV analysis was performed using PIVlab (v2.39)74. Images were first processed using a CLAHE filter with a window size of 20 pixels. Three interrogation passes were used with window sizes of 64/32 pixels, 32/16 pixels, and 16/8 pixels. The subpixel estimator used was the Gauss 2x3-point. Image masks were applied to limit spurious vector estimation and vectors were rejected if they exceeded 5 standard deviations from the mean.Further information on research design is available in the Supplementary InformationReporting SummarySupplementary Movie 1Supplementary Movie 2Supplementary Movie 3Supplementary Movie 4Supplementary Movie 5Supplementary Movie 6Supplementary Movie 7Supplementary Movie 8Supplementary Movie 9Supplementary Movie 10Supplementary Movie 11Supplementary Movie 12Supplementary Movie 13Supplementary Movie 14Supplementary Movie 15Supplementary Movie 16Supplementary Movie 17Supplementary Movie 18Supplementary Movie 19Supplementary Movie 20Supplementary Movie 21Supplementary Movie 22Supplementary Movie 23


The aim of the study was to develop an algorithm for the selection of discriminating probes to identify a wide range of causative agents of human infectious diseases.Chlamydophila (Chlamydia) pneumoniae — an atypical bacterial pathogen causing community-acquired pneumonia (CAP). Nucleotide sequences for analysis were downloaded from the NCBI databank.The algorithm for selecting the probes was implemented in the form of the disprose (DIScrimination PRObe SElection) computer program written in the R language. Additionally, third-party software was used: the BLAST+ and ViennaRNA Package programs. The developed algorithm was tested by selecting specific probes for detecting Chlamydophila pneumoniae. The specificity of the selected probes calculated in silico indicated a low risk of their nonspecific binding and a high potential of using them as molecular genetic diagnostic tools .An algorithm for the selection of specific probes capable of detecting human infectious pathogens has been developed. The algorithm is implemented in the form of the disprose modular program, which allows for performing all stages of the probe selection process: loading the nucleotide sequences and their metadata from available databanks, creating local databases, forming a pool of probes, calculating their physicochemical parameters, aligning the probes and sequences contained in local databases, processing and evaluating the alignment results. The algorithm was successfully tested and its performance was confirmed by selecting a set of probes for the specific detection of An algorithm for the selection of specific probes detecting a wide range of human pathogens in clinical biomaterial has been developed and implemented in the form of the disprose modular program. The probes selected using this program can serve as the functional basis of DNA-oriented microarrays able to identify causative agents of polyetiological diseases, such as CAP. Due to the flexibility and openness of the program, the scope of its application can be expanded. In view of the current epidemic situation, the need for diagnostic DNA microarrays capable of detecting bacterial and viral pathogens has become urgent.The diagnostic value of a DNA microarray depends on the selection of specific oligonucleotide probes. There are difficulties with this selection, which are due to the chemical complexity and species specificity of biological samples; these factors contribute to the high risk of probe cross-hybridization with a non-target DNA, and the appearance of false positive results .The existing algorithms for the probe selection are based on assessing the conformity of candidate sequences to the criteria of specificity and homogeneity –5. As a The aim of the study was to develop an algorithm for the selection of discriminating probes to identify a wide range of causative agents of human infectious diseases.Chlamydophila (Chlamydia) pneumoniae.In this study, we pursued the following specific goals: to develop an algorithm for the selection of specific probes, to implement it in the form of a computer program, to optimize the performance of the algorithm, and to test it by selecting probes for the detection of https://CRAN.R-project.org/package=disprose).The algorithm for the selection of probes intended for the mass differential detection of bacterial and viral pathogens is implemented in the form of the disprose computer program written in the R programming language and designed as a package of functions. The package is distributed under the GNU GPL-3 license (2007) and is available for downloading from The Comprehensive R Archive Network official international repository (C. pneumoniae. The genetic sequences of C. pneumoniae were obtained from the NCBI Nucleotide database  OR Chlamydophila pneumoniae * [organism]” were downloaded from the NCBI Nucleotide collection database. The resulting metadata set contained a total of 9062 records. According to further analysis, 17 whole genome sequences and 165 sequences of the WGS project were selected as targets. The target base had a size of 0.02 Gb. The list of target sequences is presented in Appendix 3. The nonspecific sequence base was made up of previously selected sequences associated with human genetic material, representatives of its normal flora and microbiota.We used the proposed algorithm and the disprose program to search for specific probes that allow the detection of The sequence “Chlamydia pneumoniae TW-183, complete sequence” (ID number NC_005043 NCBI RefSeq) was chosen as the “parent” sequence to create the candidate probe pool. The parent sequence was sliced into segments of all possible lengths from 24 to 32 nucleotide bases (nb), which constituted the pool of candidate probes .m value of the probes was calculated. Most probes had Tm values close to 57°C. Due to that, we were able to accelerate the selection process by reducing the number of probes while keeping only those with Tm within 56.97–57.03°C. Thus, the total number of candidate probes selected in such way for the next stage was 6571.When testing the physicochemical properties, we used the following selection criteria: the content of G and C in the range of 40–60%, the absence of homo-repeats of 5 nb and longer, and the MEF — not less than 0 kcal/mol. In the end, the TTo test the ability of candidate probes to hybridize specifically with target sequences, the probes were aligned with the either target or nonspecific sequences using the BLAST algorithm. To obtain the most specific probes, we established the following conditions for efficient hybridization: for hybridization with the target sequences, the identity should be 100% in the absence of point mismatches and nucleotide gaps; for hybridization with nonspecific sequences, the identity is required to be 50% or higher.in silico. To shorten the time required for detecting possible nonspecific hybridizations, the number of candidate probes was reduced to 1050 by narrowing the range of acceptable Tm values to 56.994–57.006°C. Probes that efficiently hybridized with at least one nonspecific sequence were excluded from the final pool.Of the candidate pool, 6380 probes effectively interacted with all target sequences C. pneumoniae among other pathogens. Due to their high specificity, the selected probes can be used as a functional basis for DNA microarrays designed to identify actual causative agents of CAP. The total time for selecting the probes using the disprose program was 130 min. A list of selected probes and their characteristics is presented in Appendix 4.The above operations of the algorithm resulted in 100 specific discriminating probes, which provided the differential detection of The proposed algorithm for selecting probes for a DNA microarray involves the identification of target sequences. The specificity and discriminatory potential of the probes (i.e. the ability of a DNA microarray to detect a specific pathogen) directly depend on the conservatism of the target sequence.16S and 23S rRNA genes, although less specific, allow one to distinguish between bacteria belonging to different species. Probes for less conservative sequences, for example, bacterial genes recA, gyrB, rpoB, are able to distinguish between strains of bacteria within the same species [Probes for highly conserved regions of the genome, such as the  species .If there is no information on the degree of conservatism in the given genome, it is possible to search for these data using a set of target sequences and the method of their multiple alignment. However, multiple alignment of a large number of long sequences  requires significant computing power and takes a long time .In the disprose program, a different approach to searching for target regions is implemented; that is to generate a large number of short probes and align them into a set of sequences using the BLAST algorithm. This process is less demanding in terms of equipment, it is well implemented using a parallel mode, and it is ten times less time-consuming. Probes aligned to the full target sequence list with 100% coverage are considered to be specific to conserved regions of the sequence pool. Thus, varying the list of target sequences using the disprose program, makes it possible to select probes for designing DNA microarrays with different discriminatory potential.C. pneumoniae in clinical samples. C. pneumoniae is one of the many microorganisms that cause CAP. The cumulative share of this and other “atypical” causative agents of CAP vary from 8 to 30% of cases [C. pneumoniae — demonstrated the potential of the developed algorithm.We tested the developed algorithm by searching for specific probes that would allow for differential detection of of cases , 22, andof cases . This apC. pneumoniae and may be of interest not only for the diagnostic purpose but also for phylogenetic studies.As a result of the algorithm operation, one hundred probes with high specificity for the target pathogen were selected from the large-size pool of candidate probes. After comparing between the sites of origin of the probes and fragments of the annotated reference genome, we found that most of the probes originated from the genes that encoded for enzymes, chaperone proteins, and also regulators of the cell cycle (see Appendix 4). These genes contain regions highly conserved for C. pneumoniae. The algorithm and program for the selection of probes have a number of advantages:An algorithm has been developed to search for specific probes able to identify human pathogens of bacterial and viral origin. The algorithm is implemented as the disprose computer program written in the R language; its performance has been demonstrated by identifying the probes for detecting universality — the algorithm is aimed at finding specific areas in a set of sequences of any size and can be easily adapted to solve a wide range of tasks;modularity — the execution of the algorithm occurs in several stages, their order is determined by the user, while any stage can be skipped or performed using a third-party software product;openness — the initial code of the disprose package is publicly available and can be modified in accordance with the task;usability — the algorithm can work with popular databases of genetic information  and local databases.The flexibility and openness of the program provide for expansion of the scope of its application.


Tenosynovial giant cell tumour (TGCT) is a rare soft-tissue tumour originating from synovial lining of joints, bursae and tendon sheaths. The tumour comprises two subtypes: the localised-type (L-TGCT) is characterised by a single, well-defined lesion, whereas the diffuse-type (D-TGCT) consists of multiple lesions without clear margins. D-TGCT was previously known as pigmented villonodular synovitis. Although benign, TGCT can behave locally aggressive, especially the diffuse-type. Magnetic resonance imaging (MRI) is the modality of choice to diagnose TGCT and discriminate between subtypes. MRI can also provide a preoperative map before synovectomy, the mainstay of treatment. Finally, since the arrival of colony-stimulating factor 1-receptor inhibitors, a novel systemic therapy for D-TGCT patients with relapsed or inoperable disease, MRI is key in assessing treatment response. As recurrence after treatment of D-TGCT occurs more often than in L-TGCT, follow-up imaging plays an important role in D-TGCT. Reading follow-up MRIs of these diffuse synovial tumours may be a daunting task. Therefore, this educational review focuses on MRI findings in D-TGCT of the knee, which represents the most involved joint site (approximately 70% of patients). We aim to provide a systematic approach to assess the knee synovial recesses, highlight D-TGCT imaging findings, and combine these into a structured report. In addition, differential diagnoses mimicking D-TGCT, potential pitfalls and evaluation of tumour response following systemic therapies are discussed. Finally, we propose automated volumetric quantification of D-TGCT as the next step in quantitative treatment response assessment as an alternative to current radiological assessment criteria.The online version contains supplementary material available at 10.1186/s13244-023-01367-z. TGCT is categorised according to its site of involvement and growth pattern.D-TGCT is characterised by irregular synovial proliferation infiltrating multiple synovial knee recesses.Systematic MRI approach is provided for preoperative and systemic treatment response assessment.An overview of synovial knee recesses guides the radiologist in assessing blind spots.Machine learning-based tumour segmentation may be applied to assess D-TGCT tumour volume in the near future, so that treatment response assessment can be quantified and improved by artificial intelligence.The online version contains supplementary material available at 10.1186/s13244-023-01367-z. Tenosynovial giant cell tumour (TGCT) is a fibro-histiocytic soft-tissue tumour that involves anatomical structures covered by a synovial membrane . It can also affect extra-synovial locations, such as subcutaneous and intramuscular lesions . Common TGCT can be classified according to its site (intra- and extra-articular) and growth pattern . In the L-TGCT occurs in an extra-articular location in 90% of cases, involving tendon sheaths of the volar aspect of fingers 85%), followed by foot and knee locations (15%) . These t%, followCSF1) translocation resulting in CSF1 overexpression. CSF1 overexpression causes an increase in neoplastic cells by binding to CSF1-receptors (CSF1R) and accumulating CSF1R presenting cells [TGCT subtypes share a common underlying pathogenesis, mainly related to a Colony-Stimulating Factor 1 articular soft-tissue calcifications pleads against the diagnosis of D-TGCT and differential diagnoses such as gout or synovial chondromatosis should be considered .Ultrasound is not part of the standard diagnostic workup of D-TGCT; however, it can be helpful in performing image-guided biopsies . AppearaMRI is the modality of choice to diagnose D-TGCT. The scanning protocol applied at our tertiary referral centre for bone and soft tissue tumours  is shown in Additional file D-TGCT findings include irregular synovial thickening (> 5 mm), typically described as “frond-like” with villous or nodular morphology . This syD-TGCT intra-articular forms are likely to spread diffusely, developing a multicompartmental growth pattern involving at least two contiguous intra-articular synovial recesses. In the knee, several recesses may be involved, as illustrated in the detailed description of Fig. D-TGCT’s extra-articular growth pattern mainly occurs secondary to intra-articular extension through transcapsular fenestrations . MastbooD-TGCT signal intensity is heterogeneous. T1-weighted imaging (T1-WI) shows a hypointense to iso-intense signal, whereas fluid-sensitive sequences show a hyperintense signal with foci of low signal intensity corresponding to hemosiderin in the tumour Fig. . D-TGCT Areas of intralesional fat with high signal intensity on T1-WI related to deposition of lipid-laden macrophages (xanthoma cells) are a classic feature of D-TGCT, however, this finding is uncommon , 23. ThiGout tophi can present as both intra- and extra-articular or peri-articular nodules. Typical locations such as the subcutaneous fat or within the distal quadriceps or proximal patellar tendon can help distinguish gout from D-TGCT . Gout anSynovial chondromatosis can present either as multiple round bodies similar in size and shape with a “snowstorm” or “cobblestone” pattern and a variable degree of calcification  or coalesce into multiple intra-articular synovial masses  gadolinium (Gd) side-to-side for anatomy and enhancement of lesions). Secondly, side-to-side comparison of axial fluid sensitive sequences (we use T2 DIXON) and T1-WI FS Gd images helps distinguish tumour from (rim-)enhancing synovial fluid or cyst-like components. Coronal images add value for assessment of erosions and femorotibial chondropathy.The same MRI protocol should be used for pre-and post-treatment assessment  and pes anserinus are not connected to the knee joint and are atypical locations for D-TGCT.The middle compartment is a tight area where distension is limited.The central synovial recess is situated in front of the ACL.The intercruciate space is challenging to assess on MRI, as D-TGCT is generally underestimated in this area due to the similar low signal intensity compared to the cruciate ligaments. Intra-articular pericruciate ganglion cysts should be differentiated from tumour. Bone erosions caused by the tumour in the intercondylar notch can be undercalled as cystic change at the insertion of the cruciate ligaments, which is a common finding on normal knee MRIs . Bone erThe femorotibial synovial recesses, both medial and lateral, are occupied in severe D-TGCT cases when tumour volume generally exceeds 80–100 cc. These areas are prone to develop cortical erosions as they are virtually not distensible. The MRI is the modality of choice to assess residual D-TGCT after synovectomy and for postoperative follow-up. Common postoperative changes include skin thickening, fat stranding or inflammation in Hoffa , subcutaneous and intramuscular oedema with susceptibility artefacts secondary to surgical clips Fig. B. Joint With the arrival of systemic therapies targeting CSF1/CSF1R in D-TGCT patients not regarded amenable to surgery, MRI is required to objectively assess treatment response , 17, 41.Peterfy et al. developed a semiquantitative, joint-specific, visual tumour volume score (TVS) for D-TGCT. This score was developed analogous to and based on arthritis visual scores used in clinical trials. TVS expresses tumour volume as a percentage of the estimated volume of the maximally distended normal synovial cavity of the involved joint. TVS can incorporate all tumour regions and defines tumour size relative to the joint size . HoweverAside from changes in tumour size, other specific MRI findings following CSF1R inhibitors have been described in pilot studies. These findings include a decrease in signal intensity on fluid-sensitive sequences with a reduction of capsular distension and joint effusion and an increase in hemosiderin deposition . DecreasOf note, patients may experience complete symptomatic relief even in the setting of what appears to be an incomplete response based on imaging . So-callFollowing the limitations of the current response criteria, volumetric quantification techniques integrated with Artificial Intelligence  methods may help measure tumour change. However, developing such an automated quantification method is challenging due to irregular growth and heterogeneous signal intensity of TGCT .We performed a first step towards objective volumetric quantification of D-TGCT by segmenting 3D tumour volume in 40 treatment-naïve patients with D-TGCT. All imaging data were anonymised, and informed consent was waived by the institutional review board (G19.127). Measuring tumour volume was done in Brainlab (Elements), an application that is commonly used for preoperative planning of bone and soft-tissue tumours.3 (range 3.2–208.8 cm3). The mean volume located in the anterior compartment was 21 cm3 (range 0.0–205.8 cm3) and in the posterior compartment 23 cm3 (range 0.0–172.9 cm3). Examples of volumetric segmentation in three different patients are shown in Fig. For 3D segmentation, a sagittal T1 spectral inversion recovery (SPIR) Gd and an axial T2 DIXON (water only) scan were utilised. In addition to the total volume, the volumes in the anterior and posterior compartments of the knee were calculated separately. The mean total volume was 44 cmWe showed that 3D MR segmentation allows to objectively quantify the tumour burden of TGCT and provides a quick visual assessment of TGCT lesion distribution throughout the knee. However, since 3D segmentation of TGCT is time-consuming and operator-dependent, future steps will focus on automating this process.MRI is the modality of choice in diagnosing D-TGCT, providing preoperative mapping and assessment of response to systemic therapies. However, due to its irregular shape, extensive growth and low signal intensity, D-TGCT disease extent can be challenging for the radiologist. We highlighted imaging characteristics of D-TGCT affecting the knee and provided a structured report template Table . In addiAdditional file 1: Table S1. MRI protocol for D-TGCT of the knee.


Hypoxia, as a typical hallmark of the tumour microenvironment (TME), has been verified to exist in most malignancies and greatly hinders the outcome of tumour treatments, including chemotherapy, photodynamic therapy, radiotherapy, and immunotherapy. Various approaches to alleviate tumour hypoxia have been reported. Among them, biomimetic nanomaterial-facilitated tumour oxygenation strategies, based on the engagement of human endogenous proteins, red blood cells, the cell membrane, and catalase, are the most impressive due to their excellent tumour active-targeting ability and superior tumour-selective capability, which, however, have not yet been systematically reviewed. Herein, we are ready to describe the current progress in biomimetic nanomaterial-facilitated tumour oxygenation strategies and corresponding improvements in tumour treatment outputs. In this review, the underlying mechanism behind the superior effect of these biomimetic nanomaterials, compared with other materials, on alleviating the hypoxic TME is highlighted. Additionally, the ongoing problems and potential solutions are also discussed. Tumour hypoxia, which is usually characterized mainly by insufficient oxygen supply and caused by the rapid proliferation of tumour cells and incomplete development of the vascular system, has been shown to exist in most solid tumors . Moreove2) delivery, have been extensively reported. While these strategies indeed have the ability to alleviate tumour hypoxia to some extent, they also lead to inescapable side effects on normal tissues due to their nontargeting and nonselectivity toward tumour cells  are encased by human endogenous proteins, erythrocytes, red blood membranes, platelet membranes, cancer cell membranes, or exosomes, their tumour accessibility and blood half-life can be greatly improved  and the in situ generation of O2 , albumin (HSA), glutathione S-transferase (GST), and bovine serum albumin (BSA), have long been employed to fabricate biomimetic nanomaterials through an eco-friendly biomimetic synthesis technology due to their wonderful biocompatibility, good flexibility, and green synthesis features . Recentl2+ (ferrous) to Fe3+ (ferric) under high O2 pressure (oxidative environment), allowing for efficient O2 binding and transport (2 pressure (reductive environment), the iron state returns to Fe2+, which is responsible for the rapid O2-unleashing capacity. By virtue of their superior O2-carrying/release capability, Hb-based biomimetic nanomaterials have been applied to tumour oxygenation to boost antitumor therapy. Recently, Zhang’s group designed Hb-mediated biomimetic nanomaterials (Gd@HbCe6-PEG NPs) to overcome the hypoxic TME-limited efficacy of PDT  through PA imaging. HbO2, as a positive indicator of tumour oxygenation, displayed an increased signal intensity over time after the i.v. injection of oxy-Gd@HbCe6-PEG NPs (6-PEG is an applicable O2 nanocarrier. Diffusion-weighted (DW) imaging in vivo is a powerful technology for measuring the direction and extent of the localized diffusion of water molecules. As a functional magnetic resonance (MR) imaging modality, DW imaging can reflect visible changes in tumour size and morphology. According to 6-PEG NP-rendered PDT group, indicating a noticeable treatment outcome.Hb is a special protein that has an iron-containing haem group . The iroransport . Howevery of PDT   . The conThe principal function of red blood cells (RBCs) is delivering oxygen to all tissues in the body, including tumors . Inspire4:Yb,Er@NaGdF4 nanocrystals) were synthesized for translating 980 nm laser irradiation into 550 nm luminescence and demonstrated a core-shell nanostructure  and surrounded by the phospholipid bilayer of RBCs, bursting the RBS membranes can release oxygen to overcome hypoxia in the tumour. Due to its high spatial and temporal precision, laser light is a widely studied remote trigger in the drug release process. To achieve better controlled release outcomes, the Food and Drug Administration (FDA)-approved agent indocyanine green (ICG) was used. Based on this, an RBC-based biomimetic nanomaterial (UCNPs@RB@RGD@avidin crosslinked with RBC@ICG@biotin) was fabricated for near-infrared (NIR) II fluorescence bioimaging-guided tumour surgery and tumour oxygenation-boosted PDT  Wang et. In thistructure . Additior 10 min . Moreover 10 min . The resin vivo  would promote pore formation on RBCms. In normoglycaemia, the Endo release would be suppressed as the pore closed, thus contributing to a pulsatile release manner and sustained high plasma levels (Antiangiogenic drugs (such as Endo) have been demonstrated to be able to alter the oxygenation status of a tumour, but most of them fail to acquire satisfactory clinical therapeutic effects, mainly due to their unstoppable degradation and unstable features in vivo . To solvin vivo  . GDYO@i-RBM showed prolonged blood circulation and enlarged extravascular and hypoxic region penetration because of the functional iRGD peptide. When exposed to irradiation with a 660 nm laser, GDYO nanosheets can evolve sufficient O2 to relieve perfusion-limited hypoxia; at the same time, the hyperthermia effect of GDYO overcomes perfusion-limited hypoxia because of the dilation of vessels and blood perfusion  and further enhanced PDT, resulting from the changes in tumour volume and weight and pathological changes . This work sheds new light on RBCm-based biomimetic nanomaterials for overcoming hypoxia.As we know RBCs coursing through veins primarily transport oxygen throughout the body, including the tumour. This has inspired the utilization of RBCms as oxygen carriers to overcome both diffusion-limited and perfusion-limited hypoxia  Jiang e. In detaerfusion . After ierfusion  and furchanges . This wIn addition to RBC membranes, platelet membranes have also been utilized to design biomimetic nanomaterials . These b6@ZIF-8 by the cancer cell membrane. The in vivo results revealed that GCZ@M could accumulate in tumours with the help of the homologous tumour cell membrane. The encapsulated drug that accumulates in tumours could be sustainably released, triggered by pH and ultrasound. Then, the therapeutic effect of sonodynamic therapy could be improved by the effect of overcoming hypoxia in tumours through the use of ultrasound and the therapeutic properties of GCZ  is overexpressed in the TME  or CAT-like nanozymes  and the decreased populations of Treg cells (a kind of immunosuppressive cell). TNF-α and IFN-γ, two major indicators related to antitumor immune responses, were also detected in sera after different treatments. The TNF-α and IFN-γ concentrations were both significantly increased in the RPNPs/Ce6-CAT/PEGDA group, which is useful for effectively preventing tumour recurrence -based biomimetic nanomaterial (Ru@CeO2-RBT/Res-DPEG) to alleviate tumour hypoxia and inhibit tumour metastasis and recurrence  and O (yellow) were the shell components of Ru@CeO2  In human endogenous protein-facilitated tumour oxygenation strategies, the protein structure may be altered due to covalent or noncovalent modifications and the application of organic solvents. Additionally, the release of the delivery systems from the human endogenous protein is also a challenge .(2) In red blood cell-facilitated tumour oxygenation strategies, the limited proliferative ability of red blood cells extremely confines the mass production of red blood cell-based biomimetic nanomaterials and thus cannot meet clinical needs .(3) In cell membrane-facilitated tumour oxygenation strategies, the cell membrane has difficulty completely covering the surface of nanomaterials and is easily detached from the surface of nanomaterials due to the lack of covalent bond constraints, thus limiting clinical applications .2O2 amount is limited, which greatly restricts the ability of biomimetic nanomaterials to generate sufficient in situ O2 ((4) In catalase-facilitated tumour oxygenation strategies, the intratumourally endogenous H situ O2 .Although the current biomimetic nanomaterial-driven tumour oxygenation strategies have remarkably surmounted hypoxia-associated resistance in tumour treatment, there are still some ongoing challenges that should be solved before these strategies can be applied to clinical cancer treatment.We envision that the ongoing challenges will be solved in the future and that biomimetic nanomaterial-empowered tumour oxygenation strategies will promote the development of cancer treatment in the clinic.