Each of these subsets is distinguished from each other by the expression of distinct surface markers and by their functions in homeostasis and disease. In this review, we discuss the most up-to-date phenotypic classification of human monocytes that has been greatly aided by the application of novel single-cell transcriptomic and mass cytometry technologies. Furthermore, we shed light on the role of these plastic immune cells in already recognized and emerging human chronic diseases, such as obesity, atherosclerosis, chronic obstructive pulmonary disease, lung fibrosis, lung cancer, and Alzheimer's disease. Our aim is to provide an insight into the contribution of human monocytes to the progression of these diseases and highlight their candidacy as potential therapeutic cell targets.Human monocytes are divided in three major populations; classical (CD14 Human monocytes were originally defined by their distinctive morphology at the beginning of the previous century by Paul Ehrlich and Ilya Metchnikoff [reviewed in ]. The in++CD16+) and the non-classical (CD14dimCD16+) (+CD16+HLA-DR+CD86+CD11c+) monocyte population with a distinct transcriptomic profile  was discovered  . Subsequscovered . At this+ . Subseeostasis .3CL1  in comparison to classical monocytes which utilize carbohydrate metabolism as their energy source . Similarrocesses . Furtherrocesses  and do nrocesses . Finally disease . While SIn recent years the concept of trained immunity has been introduced . MonocytTaken together, human monocyte subsets display remarkable heterogeneity in their surface marker expression and function; classical monocytes exhibit a more pro-inflammatory phenotype via their ability to secrete soluble mediators and to differentiate into monocyte-derived DCs to bridge innate and adaptive immune responses, intermediate monocytes are specialized in antigen presentation and play an important role in HIV infections, while non-classical monocytes are responsible for the anti-viral responses of this lineage . In thisNon-communicable diseases (NCDs) are among the main causes of death in western countries. The close correlation between dietary habits and morbidity and mortality of chronic NCDs has been already extensively characterized , 47. In TLR4 and TLR8 and secretion of pro-inflammatory cytokines, such as IL-1β and TNF in response to LPS or ssRNA stimulation  will contribute to understanding primary and secondary effects of diet on the monocyte compartment, possibly dissecting the influence of single macronutrients.in vitro activation of human monocytes with 6-formylin-dolocarbazole (FICZ), an endogenous ligand for the environmental sensor aryl hydrocarbon receptor  reprograms the cells to enhance the expression of pro-inflammatory cytokines and chemokines  displayed a long-lasting pro-inflammatory phenotype , 82, 83. regions . Similar regions .+CXCR6+ non-classical monocytes are more frequent in patients with atherosclerosis. This subset presents with a higher capacity to migrate toward the chemokine CXCL16 secreted by macrophages in plaques and is probably involved in the clearance of apoptotic cells from the necrotic core  monocyte-derived cells , non-classical , and neutrophil-like . Nevertheless, although these monocyte populations matched to the three major peripheral blood monocyte populations from the same study, on average, considerable transcriptional differences were seen, such as those related to expressed chemokines and chemokine receptors , cytokine receptors (CD115) and scavenger receptors . Further phenotyping revealed that monocyte/TAM localization is driven by microenvironmental cues and thus HLA-DRhi TAMs are found at the tumor lesion, whereas HLA-DRlo/− monocytes reside at distant sites . They may affect the structure or the survival of the neurons, which are unable to regenerate after the damage, thus leading to cognitive or motor dysfunction. The immune system was only recently found to play an important role in neuronal injury that occurs in an inflammatory milieu through a complex interplay between resident (microglia) and infiltrating myeloid cells (monocytes) , 133. ThAD is characterized by the accumulation of insoluble amyloid beta (Aβ) in the extracellular matrix which forms plaques and of hyperphosphorylated tau protein in the cytoplasm which forms neurofibrillary tangles . StudiesmidCD11b+) and moMϕs as (CD45hiCD11b+), a common strategy also used in other studies  and cell surface receptors   . More reresponse .In none of these single-cell sequencing studies on AD presented above have researchers been able to identify bone marrow-derived monocytes. The reason for this could be monocyte exclusion in sorting panels, such as in Galatro et al.  or the uHuman monocytes are still widely studied in context of peripheral blood and the advent of novel single-cell technologies, including sequencing-based methods have fueled new interest in these cells. While higher heterogeneity has been suggested, we still propose classical, intermediate, and non-classical monocytes as the three major subsets within the monocyte cell space. We would suggest further heterogeneity being explained by functional states of these important immune cells. However, this requires a community effort with guidelines on how to define such newly defined cell states in the monocyte compartment. This will also be important in view of the increasing interest in tissue-associated monocytes and their ability to differentiate into moMϕs or moDCs. Of particular interest are current and future studies on spatio-temporal behaviors of monocyte-derived cells within diseased tissues and organs. We are convinced that the new single-cell technologies can help to decipher the role of these important cells during the major chronic, but also acute inflammatory diseases.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.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.


The first-line therapy for rheumatoid arthritis (RA) is weekly oral methotrexate (MTX) at low dosages (7.5–25 mg/week). However, ~40% of patients are non-adherent which may explain why some do not respond and need to start more expensive biological therapies. To monitor adherence more accurately and develop strategies to improve it, a validated objective MTX adherence test is required.To develop and validate the diagnostic accuracy of a novel MTX adherence assay using high-performance liquid chromatography–selected reaction monitoring– mass spectrometry (HPLC-SRM-MS) based biochemical analysis of drug levels.20 patients with RA underwent MTX pharmacokinetic assessment using HPLC-SRM-MS MTX plasma concentration analysis over a 6-day period. Directly observed therapy was the reference standard. Pharmacokinetic model validation was performed using independent plasma samples from real-world patients (n=50) with self-reported times of drug administration. Following assay optimisation, the sensitivity of the assay to detect adherence was established using samples from an observational cohort study (n=138).A two-compartment pharmacokinetic model was developed and validated. Simulations described the sensitivity required for MTX detection over 7 days; subsequent assay optimisation and retesting of samples confirmed that all patients were correctly identified as MTX adherers. Using real-world samples, the assays sensitivity was 95%.Non-adherence to MTX is common and can have a significant effect on disease activity. HPLC-SRM-MS plasma analysis accurately detects MTX adherence. The validated objective test could easily be used in clinic to identify patients requiring adherence support. About 40% patients are non-adherent to methotrexate, up until now there has been no objective direct method to measure methotrexate non-adherence.This study demonstrated that methotrexate adherence can be measured using a novel high-performance liquid chromatography–selected reaction monitoring–mass spectrometry assay which could easily be used in the clinic.In the future, the developed test could be used as part of a biofeedback tool to improve non-adherence healthy behaviour in patients.The antifolate drug methotrexate (MTX) is the first-line therapy for rheumatoid arthritis (RA) in European and American guidelines.72) where it is routine practice to measure MTX levels.In oncology, MTX is used at high doses in leukaemia eg, 1000 mg/m00 mg/m2 The aim of this study was to develop and investigate the diagnostic accuracy of an HPLC-SRM-MS assay for the detection of MTX drug levels as a direct measurement of adherence. This required (1) the development of an MTX adherence assay, (2) a pharmacokinetic (PK) study of MTX to evaluate the ability of the assay to measure adherence, (3) validation of the PK model in an observational cohort, (4) further optimisation of the assay to the required performance criteria and finally (5) investigation of the ability of the assay to detect adherence in samples from real-world patients.An overview of the study procedures is shown in 10.1136/annrheumdis-2019-215446.supp1Supplementary dataThe assay had an initial lower limit of quantification (LLOQ) of 0.5 nM and 0.75 nM for MTX and 7-OH-MTX, respectively. Samples were tested in triplicate and the measurement accepted if coefficient of variation (CV) ≤15% and the result was within the calibration curve. Samples not passing quality control were excluded from the analysis. Mean measurements were multiplied by their dilution factor to obtain the final concentration. The assay was subsequently validated for measurement in serum samples; drug-free serum was prepared as per the plasma preparation protocol. The linearity, LLOQ, carryover and precision of the assay were determined in the plasma samples.The study design was developed in collaboration with a Research User Group (RUG) of patients and carers living with musculoskeletal conditions, including RA. The RUG assessed the patient information resources that were appropriately worded and assessed the burden of the study to the participants.Measurement of MTX and 7-OH-MTX metabolites in urine of patients with rheumatoid arthritis; the MEMO study) included patients who were at least 18 years of age, had a physician diagnosis of RA and were commencing MTX as part of their usual care. Patients were identified from the Rheumatoid Arthritis Medication Study (RAMS) study, a 1-year prospective multicentre observational study in the UK designed to identify predictors of response to MTX in patients with RA.13 14The single-centre diagnostic accuracy study  in cryovials (Greiner) and frozen by placing in a −80°C freezer. Samples were labelled with the patient ID, date and time of collection. Clinical information and reference standard results were available to the performers/readers of the index test.Participants were screened and attended three visits at the CRF, Manchester, UK. Plasma samples were collected for the measurement of MTX concentrations from all patients prior to and following directly observed therapy of MTX at baseline (the reference standard). Samples were collected in KPK model validation was undertaken, to replicate the model, using independent samples from the RAMS study. Briefly, a specially designed diary was used to collect adherence data. Each week for 26 weeks, patients recorded their MTX use including day and time of ingestion and/or usual time of MTX ingestion in the case report form. Three-month and 6-month blood samples were collected, the date/time of sampling recorded and the samples were posted to the coordinating centre in Manchester. Samples were processed as described previously and stored at −80°C until measurement. Three-month and 6-month samples were used to validate the PK model and ability of the assay to measure adherence, respectively.A population PK model was developed using mixed-effect modelling software NONMEM version 7.3.0 .Following PK model validation, simulations were used to determine adherence cut-offs required for the correct detection of adherence according to dose of MTX ingested with a proportion of samples predicted to be true positives ≥80%. Assay optimisation was undertaken to improve the LLOQ online .3 was not detected in two or more samples or the measurement was outside the calibration range when taking into account the expected minimum concentration according to the PK model and the patients’ MTX dose. Triplicates where one sample failed were included if the sample failed due to non-detection of the internal standard MTX-d3. Mean measured concentration was used to detect adherence.Six-month RAMS blood samples were used to assess the sensitivity of the assay to detect adherence. Samples were measured in triplicate and rejected if CV ≥25%; MTX-dTwenty RA patients were recruited  were collected from the MEMO study and measured in triplicate from 20 patients with blank plasma samples in each assay run. The median time from MTX ingestion to last plasma sample was 101 hours (IQR: 94–142 hours). No MTX-free plasma samples falsely detected MTX. Rejection of samples due to high CV or measurement less than LLOQ was 1.1 and 9.8 for MTX and 2.3% and 12.6% for 7-OH-MTX, respectively. MTX absorption was rapid, with plasma concentrations peaking at around 2 hours after oral administration. A two- compartment model for MTX and one-compartment model for 7-OH-MTX was fitted to the data online . The effSimulation data of 1000 hypothetical individuals after ingesting 5, 10, 15 and 20 mg MTX to predict the proportion of subjects with measured MTX and 7-OH-MTX levels below the LLOQ is shown in In total, 51 plasma samples were collected where time of MTX ingestion was diarised and date/time of venepuncture was recorded from the RAMS cohort . Baseline clinical and demographic characteristics are shown in Simulations were performed and confirmed that an LLOQ of 0.1 nM was sufficient to detect adherence at 7 days for each dose of MTX ≥5 mg with a predicted proportion above the LLOQ of ≥80% online . Based oThe assay was subsequently reoptimised for sensitivity to generate a new LLOQ of 0.1 nM by further optimising the mass spectrometer parameters online . SeventeFor further validation from an independent cohort, 159 6-month RAMS samples were available. Following quality control, 138 samples remained. Out of 138 samples, only 7 were below the adherence limit  resultinEvidence consistently suggests that RA medication adherence is low in adults.19We present an HPLC-SRM-MS method developed and validated for the detection of MTX adherence. There are a number of strengths of the current assay: first, there is limited sample preparation required and that required is simple and straightforward compared with other assays; second, the assay has been shown to be sensitive for MTX detection and uses a technology that can be implemented in healthcare settings; third, the assay may be used in other disease in which low-dose MTX is prescribed such as psoriasis.et al, no correlation of measurement of MTXPG was found with a Medication Event Monitoring System that registers openings of the medication package; the findings did not, therefore, support the measurement of MTXPG as a biomarker of adherence.1/2 of MTXPG; the time when MTXPG levels become undetectable, can range from 2 to 32 weeks.22There are a number of different methods which have been developed to measure MTX and its metabolites including assays that can detect MTX polyglutamate (MTXPG). However, in a recent study by Pasma et al.While the sample size of the initial MEMO study was modest (n=20), the resultant PK profile of a two-compartment model has been suggested previously in several studies.et al measured antihypertensive drug levels in the urine and/or serum of hypertensive patients with feedback to patients of their results.Limitations of the current study include the fact that the assay can only detect whether the drug was taken and the correct dose within the preceding 6 days and does not reflect long-term adherence behaviours, and the assay would be unable to detect for example, patients who were regularly non-adherent but adherent in the few days preceding their appointments .Further work to test the assay in a clinical environment is required to assess whether identification of MTX non-adherent patients will improve adherence and whether the intervention would be cost-effective. Low-dose MTX is the first-line drug for the treatment of RA and is used in other diseases such as psoriasis and psoriatic arthritis. Non-adherence to treatment may be a significant barrier to achieving full treatment response. If non-adherence is identified, support programmes could be considered as the use of a patient support programme to improve adherence to adalimumab has previously demonstrated greater adherence, improved persistence and reduced total healthcare costs.30In conclusion, we have developed and validated an HPLC-SRM-MS assay to monitor MTX adherence. The assay has demonstrated a high sensitivity required for adherence detection to low weekly doses of MTX used in several chronic inflammatory conditions and the assay has been validated in independent real-world samples. The next vital work to implementation is a clinical trial to investigate whether measurement of MTX adherence using the assay can improve MTX adherence.


The Endosomal Sorting Complex Required for Transport (ESCRT) proteins form an evolutionarily conserved membrane remodelling machinery. Identified originally for their role in cargo sorting and remodelling of endosomal membranes during yeast vacuolar sorting, an extensive body of work now implicates a sub-complex of this machinery (ESCRT-III), as a transplantable membrane fission machinery that is dispatched to various cellular locations to achieve a topologically unique membrane separation. Surprisingly, several ESCRT-III-regulated processes occur during cell division, when cells undergo a dramatic and co-ordinated remodelling of their membranes to allow the physical processes of division to occur. The ESCRT machinery functions in regeneration of the nuclear envelope during open mitosis and in the abscission phase of cytokinesis, where daughter cells are separated from each other in the last act of division. Roles for the ESCRT machinery in cell division are conserved as far back as Archaea, suggesting that the ancestral role of these proteins was as a membrane remodelling machinery that facilitated division and that was co-opted throughout evolution to perform a variety of other cell biological functions. Here, we will explore the function and regulation of the ESCRT machinery in cell division. The exS. cerevisiae or S. pombe undergo a closed mitosis, assembling an intra-nuclear spindle without breaking open their nuclear envelopes S. japonicus, a fission yeast strain related to S. pombe undergoes a semi-open mitosis and provides an important comparative evolutionary model for analysis of mitotic programmes ArchaeaWhilst the above description describes the situation in metazoans, related remodelling events occur in all organisms. However, key players and necessary physical processes can differ markedly. For example, yeast such as 3Archaea division was described in strains lacking FtsZ or Mre1, the prototypical actin and tubulin paralogues, suggesting that ESCRT-III may represent an ancestral filament forming machinery that may function in place of these cytoskeletal members Whilst not the goal of this review, it is perhaps helpful to introduce the individual complexes and subdivision of activities within the ESCRT machinery. In the context of endosomal sorting, and learning from examples described in yeast, the ESCRT machinery performs both cargo sorting and membrane remodelling functions. There exist 4 ESCRT complexes . These proteins bind specifically to the Shrub/CHMP4 N-terminus proteins through their third DM14 domain D. melanogaster CHMP4 orthologue (Shrub) showed that these proteins polymerise in a head-to-head orientation, with salt bridges and complementary inter-subunit electrostatic interactions driving filament assembly As well as core ESCRT-III subunits, evidence is emerging that polymerisation of ESCRT-III may be controlled through the engagement of regulatory Coiled-Coil and C2-domain containing 1A/1B proteins  in 4The exact mechanism by which membranous stalks are severed and membranes separated awaits experimental validation, however, it is clear that ESCRT-III is the main driver of this process. In a cell biological context, this machinery is recruited to distinct sites of action through interaction with a variety of adaptor proteins 5In the final stages of cytokinesis, the midbody (a membrane and microtubule-rich intercellular bridge connecting daughter cells) is severed through the process of abscission. This process bears a topological equivalence to the membrane separation events performed by ESCRT-III Figs.  and 2B. d-like fold and the ability of MITD1 to bind membranes through this domain was essential for abscission Whilst ALIX seems the major adaptor for cytokinesis in HeLa cells, the existence of ESCRT-II dependent cytokinesis routes that are exposed in the absence of an ALIX:ESCRT-III interaction highlight the robustness of this axis for the completion of cell division 6in-toto – a situation that has not yet been achievable in-vitro. Indeed, co-polymers of CHMP1B and the N-terminal domain of IST1 have recently been described, that result in 24 nm diameter filaments Excitingly, using state of the art electron tomography, spiral filaments have been visualised at the abscission site that may correspond to filaments of ESCRT-III Filament remodelling and subunit exchange could also be observed in-vitro, using purified yeast proteins, and here, the authors observed that Vps24 and Vps2 could suppress Snf7 polymerisation, suggesting that these subunits act to regulate assembly of the membrane remodelling filament. However, rather than acting as a ‘cap’ to the growing Snf7 spiral, as has been previously proposed 7During cytokinesis, progression from midbody formation to abscission takes upwards of an hour (in cultured HeLa cells), rendering abscission a G1 event Progressive removal of tubulin filaments by microtubule-severing ATPases such as Spastin As well as reorganisation of the microtubule cytoskeleton, the actin cytoskeleton must also be remodelled. F-actin plays a critical role in cleavage furrow ingression and has been observed to persist in the furrow until the time of abscission 2 plays an important role in cytokinesis 22-dephosphorylation elevates the relative cytokinetic pool of this lipid Alongside changes to the actin and microtubule cytoskeletons, the plasma membrane also gets remodelled during cytokinesis. The plasma membrane lipid PtdInsPAs such, an interplay between actin and microtubule cytoskeletons, and plasma membrane lipids, underlies the process of midbody maturation . We beli8Mitosis follows a temporally coordinated unidirectional programme, with progression between sequential phases being rapid but with a series of control points inserted to ensure progression occurs only when subsequent stages have been performed satisfactorily. The major control point in mitosis, the SAC, delays anaphase onset until bi-orientation of sister chromatids is achieved. However, in recent years, an additional control point has been identified during the final stages of cytokinesis – the abscission checkpoint. This checkpoint is thought to allow the cell to resolve any post-SAC defects accompanying chromosome segregation, and appears to exploit the natural control mechanisms governing abscission timing and midbody maturation.D. melanogaster GSCs, ALIX and ESCRT-III are necessary for abscission The existence of such a checkpoint was described first in yeast that exhibited an abscission delay in response to anaphase defects. This delay, deemed a control mechanism called NoCut, was shown to depend upon Aurora/Ipl1 in the spindle midzone and the presence of chromatin in the cleavage plane In addition to Aurora B- and ESCRT-III-dependent control of abscission timing, it also appears that regulation of actin remodelling can contribute to this process. As described above, actin must be cleared from the midbody to allow cytokinesis to proceed 9Whilst Aurora B may regulate abscission timing, the mechanistic basis for this is only recently coming to light. One Aurora B substrate has been revealed to be the ESCRT-III subunit, CHMP4C The mechanism by which CHMP4C delays abscission is a matter of ongoing investigation; CHMP4C localises early in cytokinesis and occupies the Flemming body However, there are alternate explanations for the mechanistic basis of CHMP4C-dependent abscission control; the Chromosomal Passenger Complex component Borealin binds directly to CHMP4C’s N-terminus The ESCRT machinery has evolved to include additional members that help regulate this checkpoint; the Unc51-like kinase3 (ULK3) can bind MIM-containing ESCRT-III components via tandem MIT-domains and phosphorylates these proteins 10Thus the abscission checkpoint can be effected through regulation ESCRT-III − how then, is it engaged? Lagging chromosomes have been discussed as a mechanism for engaging an abscission delay, but there are a number of other events that are integrated into this checkpoint. Decatenation errors stemming from improper DNA-replication in S-phase can lead to ultrafine bridges (UFBs) of single-stranded DNA during anaphase A variety of stresses independent of DNA-replication or chromosome segregation can also engage the abscission checkpoint. Tension in the intracellular bridge delays abscission and tension release triggers ESCRT-III assembly, suggesting a biophysical mechanism exists to retard or accelerate abscission, depending on the physical environment sensed by the cells de-novo insertion during open mitosis has forced the adoption of this surveillance strategy in these organisms. Again, the exact mechanism of surveillance remains to be established, with suggestions that ESCRT-III functions either to extract, or cause budding of these structures into the intermembrane space In yeast, a related situation exists whereby malformed NPCs accumulate in the absence of ESCRT-III activity It is important to note that there is unexplained variability in the outcome of disrupting the abscission checkpoint, even within similar experimental systems. Sustained Aurora B activity was originally observed to stabilise intercellular canals and prevent tetraploidisation induced by cleavage furrow regression 11Archaea, they appear dispensable for cytokinesis in a number of organisms such as yeasts and C. elegans. In worms, high resolution tomographic approaches investigating the 1st mitotic division in the C. elegans embryo have demonstrated that membrane sealing occurs around the midbody after furrow ingression, leading to cytoplasmic separation D. melanogaster, ALIX and ESCRT-III are necessary for cytokinetic abscission of germline stem cell bridges in-vivoTEX14 is found in a locus that contributes to the susceptibility for Testicular Germ Cell Tumours Whilst ESCRTs have a clear role in cytokinesis in mammalian cells and flies and are necessary for division in 12Accepted roles for ESCRT-III as the cytokinetic abscission machinery have, in recent years, become established. However, early reports indicating the presence of CHMPs on mitotic chromatin de-novo from vesicles produced during nuclear envelope fragmentation at prophase. However, this theory was developed in X. laevis cell-free extracts, within which, unlike in intact cells, membranes begin in a fragmented state. It is now more widely accepted that the ER is the source of nuclear membranes, with contact sites between ER tubules and chromatin observed shortly after anaphase, followed by the coating of chromatin with ER membranes It was initially suggested that daughter nuclear membranes were formed In recent years, a startling relocalisation of ESCRT-III to the reforming nuclear envelope has been observed for a brief period during mitotic exit Nuclear envelope reformation around newly segregated chromatin occurs in parallel with the disassembly of spindle microtubules which traverse the nuclear envelope. Mechanisms to remove these microtubules remained unclear until recently, but the role of ESCRT-III in sealing nuclear membranes provides a route through which this could be accomplished. As in cytokinesis, ESCRT-III subunits can recruit the microtubule severing AAA-ATPase, Spastin to disassemble these fibres. In the context of nuclear envelope reformation, Spastin recruitment is dependent upon its MIT-domain and on the presence of IST1 Due to the numerous membrane remodelling roles of ESCRT-III in the cell, the machinery must be recruited to its site of action by location-specific adaptor proteins. As described, in the case of cytokinesis, ESCRT-III is recruited by the CEP55 midbody protein and during viral budding and multivesicular body biogenesis it is recruited by Gag and ESCRT-0 respectively. In the context of nuclear envelope reformation, the Stenmark laboratory identified that the poorly studied ESCRT-III protein, CHMP7, was necessary for ESCRT-III assembly at the nuclear envelope, pointing to a specific role for this protein in this specialised process CHMP7 is somewhat of a hybrid protein, with a C-terminal CHMP-like domain that resembles other ESCRT-III subunits and an N-terminal tandem winged-helix (WH) domain, which resembles well the tandem-WH domain containing, ESCRT-II subunits In mammalian cells, endogenous CHMP7 localises to the ER S. cerevisiae and Cmp7 in S. pombe, with data suggesting an ER function for this protein Whilst yeast typically undergo a closed mitosis, they too express a CHMP7 homologue: Chm7 in 13S. cerevisiae, albeit during interphase. Here, they act to survey for mal-assembled, or poorly functioning NPCs, whose existence would lead to a breakdown in nucleocytoplasmic compartmentalisation. These NPCs are cleared in a manner requiring ubiquitin and the proteasome, and in strains lacking ESCRT-III components, these defective NPCs aggregate into a single focus, deemed the Storage of Improperly Assembled NPCs (SINC) compartment. Once more, the LEM-domain-containing INM proteins are implicated in this control; Heh2 can bind to early NPC assembly products, and acts to recruit Snf7/CHMP4 and Vps4, indicating that ESCRT-III function acts somehow to remove these defective NPCs As described earlier, ESCRT-III proteins also act at the nuclear envelope of It is unclear whether a similar surveillance role exists in mammalian cells, given that they have ample opportunity to remodel their NPCs during each mitosis. However, as described above, telling data indicating that NPC mis-assembly can signal through Aurora B to the ESCRT-III mediated abscission checkpoint, to impose a temporal delay upon cytokinesis, suggests that such a quality control mechanism may exist A number of important questions regarding ESCRT function on the nuclear envelope remain. We have little understanding of the lipid environment necessary for CHMP7 to assemble ESCRT-III and, whilst interaction with LEM-domain containing proteins is important, we don’t fully understand how ESCRT-III assembly is restricted to sites of annular fusion in the nuclear envelope subdomain. Interaction with spindle microtubules is likely important, but the finding that ESCRT-III operates to repair interphase nuclear envelope ruptures, which likely occur in the absence of traversing microtubules, suggests that additional factors may be required. Our understanding of how membrane remodelling is coordinated with insertion of NPCs 14S. pombe lacking Vps4, karyokinesis defects were observed and the duplicated nuclear envelope-embedded spindle pole bodies failed to separate Mitotic roles for ESCRTs have largely stemmed from their ability to remodel membranes, however, intriguing findings suggest that more expansive mitotic roles may be played by these proteins. Prior to its relocalisation to the midbody, CEP55, the key recruiter of ESCRT-III, localises to centrosomes 15Archaea suggests that mitotic functions were the ancestral role of this machinery that has been co-opted throughout evolution to effect completion of a variety of cell biological processes. These processes all require the resolution of a cytoplasm filled stalk connecting two membranes, with ESCRT-III acting to sever of this stalk. Telling cartoons hypothesise the narrowing of an ESCRT-III spiral filament bringing the walls of these stalks closer together until wall-fusion becomes inevitable, but it would be prudent to remember that we have very little idea of how the membrane fission reaction actually occurs. We do know that ESCRT-III acts to separate membranes previously tethered by stalks . It is hoped that biophysical lines of enquiry will soon shed light on the mechanics by which these membrane fission reactions are performed.The prevalence of ESCRT functions in recent years has taken many by surprise; it truly feels as though ‘ESCRTs are everywhere’ C. elegans leads us to question how these species complete cytokinesis. These are exciting times for ESCRT-biology and it is hoped that our deeper understanding of roles for this machinery in cell division will illuminate the workings of fundamental cellular processes that are essential for life, growth and development.Mitotic biology relies upon proper completion of precisely temporally coordinated events, and the surprising involvement of ESCRTs in a wealth of mitotic processes will hopefully provide routes by which regulation and activation of ESCRT activity can be explored. Documented roles for ESCRTs in nuclear envelope biology and cytokinesis have been established, although whether these processes are linked by co-regulatory mechanisms remain unclear. For example, it will be important to discover if deregulated ESCRT biology at the nuclear envelope engages the abscission checkpoint, or, for example, whether there is coordination between nuclear envelope sealing on lagging chromosomes and engagement of this checkpoint. For nuclear envelope-relevant ESCRT-function, it will also be important to examine how membrane sealing is coordinated with functionalisation of this organelle through re-establishment of NPC function and regeneration of INM and ONM identity, and to understand further the evolutionary conservation of this function. Regards cytokinesis, a scaling problem between the staging of ESCRT-III at the relatively large midbody ring and its eventual transition to the abscission site needs to be understood, as does the relatively unexplored process of midbody maturation. Moreover, the apparent absence of involvement of ESCRTs in abscission in yeast or


Multiparametric magnetic resonance imaging (mpMRI) has become increasingly important for the clinical assessment of prostate cancer (PCa), but its interpretation is generally variable due to its relatively subjective nature. Radiomics and classification methods have shown potential for improving the accuracy and objectivity of mpMRI-based PCa assessment. However, these studies are limited to a small number of classification methods, evaluation using the AUC score only, and a non-rigorous assessment of all possible combinations of radiomics and classification methods. This paper presents a systematic and rigorous framework comprised of classification, cross-validation and statistical analyses that was developed to identify the best performing classifier for PCa risk stratification based on mpMRI-derived radiomic features derived from a sizeable cohort. This classifier performed well in an independent validation set, including performing better than PI-RADS v2 in some aspects, indicating the value of objectively interpreting mpMRI images using radiomics and classification methods for PCa risk assessment. In 2018, the American Cancer Society (ACS) estimates 164,690 new PCa cases  and 29,430 PCa-related deaths in the United States, imposing a substantial socioeconomic burden. The ability to accurately assess the aggressiveness risk of a diagnosed PCa could improve the selection of appropriate treatment for these patients, leading to improved outcomes, including PCa-specific mortality2.Prostate cancer (PCa) is the third most common cause of death and the most prevalent male malignancy worldwide4. In combination with the Prostate Imaging Reporting and Data System Version 2 (PI-RADS v2), encouraging results have been reported for the prediction of the likelihood of intermediate- and high-grade cancers6. However, despite the high sensitivity of mpMRI, the assessment of PCa is based on visual qualification and is therefore subjective6. The reported inter-observer agreement has only been moderate to good6, with several multi-reader studies finding an overall inter-reader agreement ranging from poor (0.5) to reasonable (0.71), depending on the study and reader experience8.Over the past decade, multi-parametric magnetic resonance imaging (mpMRI) has become increasingly important for the evaluation, localization, and staging of PCa9, as well as PCa aggressiveness11. T2W and DW/ADC signal intensities have also been found to correlate with Gleason scores, a histologic scale of PCa dysplasia 13; however, the ranges of ADC values for different Gleason scores overlapped considerably, limiting their utility in differentiating between these scores, and clinical decision-making12.To make mpMRI imaging more objective and reliable, researchers have attempted to identify quantitative imaging parameters extracted from T2-weighted (T2W) and diffusion-weighted (DW) images. T2W and DW (including apparent diffusion coefficient [ADC] map) signal intensities have been shown to correlate with histopathology-based nuclear cell-density16. The underlying assumption is that images collected during routine clinical care contain latent information regarding tumor behavior that can be extracted using a variety of quantitative image characterization algorithms14. The extraction of these radiomic features enables the conversion of collections of digital clinical images into structured quantitative data that can help model tumor behavior. For example, in radiomic studies conducted using mpMRI, a variety of tumor phenotypes relating to texture aided in the diagnosis of PCa19. Using five different Haralick texture features20  extracted from T2W and DW images, Wibmer et al. showed significant differences in all the features between cancerous and non-cancerous tissue in the peripheral zone19. Lv et al. used fractal-based features to distinguish prostate tumor tissue from normal peripheral zone tissue18.The field of radiomics deals with the extraction of quantifiable features such as texture, size and shape from clinical images22. Due to these abilities, ML methods, especially those for classification22, are increasingly being incorporated into radiomic studies to improve PCa24 assessment and make it less subjective. A probabilistic Support Vector Machine (SVM)25 applied to a combination of Haralick features20 was found to perform well in discriminating benign lesions from malignant PCa26. For the same problem, Liu et al. reported an AUC27 of 0.73 based on mpMRI-based radiomic features and SVM, which increased to 0.82 when combined with targeted prostate biopsy results, although the results were based on a small 18 patient validation set28. Tiwari et al. used a similar approach for distinguishing benign lesions from PCa regions, as well as high- from low-grade PCa29.Machine Learning (ML) methods are designed to sift through large amounts of high-dimensional data, without any particular guiding  hypothesis, to directly discover potentially actionable knowledge24 have demonstrated the utility of combining radiomics and ML for PCa assessment, they have only explored this combination in a limited manner. These limitations include the utilization of a small number of classification methods, often of the same type (e.g. SVM), evaluation using the AUC score only, and a non-rigorous assessment of all possible combinations of radiomics and classification methods to identify the best possible classifier. This study presents a systematic and rigorous ML-based framework comprised of classification30, cross-validation31 and statistical analyses32 designed to identify the best performing classifier for PCa risk stratification based on mpMRI-derived radiomic features derived from a sizeable cohort. Note that risk stratification of PCa using NCCN guidelines33 is a more challenging task compared to other PCa outcomes predicted in the studies discussed above, such as pathologic grade, as it includes an interplay of multiple factors. To conduct a comprehensive assessment of the candidate classifiers tested in this framework, the Precision-Recall-F-measure family of evaluation measures was employed in addition to the AUC score27. This family is more informative about classifier performance in scenarios of unbalanced class distributions35, typical in biomedical studies like PCa risk stratification, as is also the case in this and other studies’ cohorts37. The performance of the final classifier developed by the framework for assessing risk was evaluated in an independent cohort of PCa patients, and compared to the PI-RADS v2 system5 to assess the relative utility of a well-developed combination of radiomics and ML for objective and accurate PCa risk stratification.Although these and other studiesIn this section, we present the results of the application of our ML-based framework and its components on radiomics features derived from our various study cohorts.To address the substantial class imbalance in the development set, i.e., a much higher number of lower-risk patients (54) than high-risk ones 14), the performance of the classification algorithms constituting the framework was first evaluated with and without random over-sampling. For all the algorithms, random over-sampling led to significantly improved performance across all the evaluation measures as compared to no oversampling  across the ten cross-validation runs. In terms class-specific measures also, QSVM attained the highest average values of Fmax (0.89), Pmax (0.82) and Rmax (0.98) for the high-risk class , Pmax (0.79) and Rmax (0.97) for the lower-risk class  plots32  in terms of all the evaluation measures for both the classes. Thus, QSVM was chosen as the algorithm to learn the final radiomics-based classifier over the whole development set. The associated threshold for binarizing the probabilistic predictions generated by the classifier into discrete high/lower-risk labels was determined to be 0.17253, the average of the thresholds found to maximize Fmax for QSVM in the ten cross-validation runs.Since absolute performance measures can sometimes be misleading, a comprehensive statistical comparison of the performance of the classification algorithms tested in the framework was also conducted using Friedman-Nemenyi testss32 Fig.  show tha39. The radiomics classifier also performed much better on the real validation set than its randomized versions  to reasonable (0.71), depending on the study and reader experience8. This variation has been attributed to experience of the imaging and pathology readers, variable interpretation of PI-RADS v2 guidelines and diversity of standards used for histopathological staging41.Identifying patients with clinically significant PCa remains a challenging problem42. Furthermore, radiomics, in combination with machine learning (ML), can help achieve objective classification of clinical images that can be a valuable tool to aid clinicians in identifying appropriate treatment options for patients without subjecting them to unnecessary interventions4. In contrast to prior studies following the radiomics followed by ML methods approach29, which generally utilized only a single or small number of ML methods, this study developed a systematic and rigorous ML-based framework for deriving a reliable and objective risk classifier by finding the optimal classification method(s) for radiomic features derived a given set of imaging data. This framework was used to develop a radiomics-based classifier operating on mpMRI images of PCa tumors that accurately distinguishes between subjects with high-risk PCa and those with lower-risk PCa. Our classifier, built on top of 110 radiomic features interpreted via a Quadratic kernel-based Support Vector Machine (QSVM)25, performed with reasonably high precision or predictive value (PPV = 0.57 and NPV = 0.94) and high recall or sensitivity (0.86 and 0.72 for the high and lower-risk classes respectively) for classifying PCa patients in an independent validation set 27, indicating the value of objective assessment of PCa risk as compared to a more subjective one.Radiomics can analyze a large number of features of images that are difficult to study solely by visual assessment27, including in radiomics studies47. However, AUC weighs classification errors in the two classes being evaluated equally in a cumulative manner, which can lead to misleading results in situations where the study data may have substantially imbalanced numbers of samples in the two classes35. Since this study’s cohorts were similarly imbalanced, i.e. many more lower-risk patients as compared high-risk ones, the performance of all the classifiers tested in the framework, both candidate and final, was also assessed in terms of class-specific measures, namely recall (sensitivity), precision , and F-measure  is routinely used to assess the performance of classification models53, and the work is relatively limited for MRI-based radiomic features. Mayerhoefer et al. performed basic investigations using simple polystyrene/agar gel-based phantoms to evaluate the effect of MRI acquisition parameters such as repetition time, echo time, number of acquisitions and sampling bandwidth on image texture, an important radiomic feature54. The study summarized significant changes in this feature with changes in the tested MRI acquisition parameters. Collewet et al. drew similar conclusions during their investigation of the reliability of texture-based classification of a small number of old and new cheese samples55. In summary owing to the increased number of imaging variables within MRI compared to CT, as well as the lack of reliable phantoms (imaging standards) and standardized reliability tests, it was beyond the scope of our study to assess the reliability of the radiomics features used. Further studies are warranted on this topic.The design of our single-center, retrospective study has some limitations. First, the study only used data from a single type of scanner from a single MRI vendor, obtained using the same imaging protocol. This was done to keep the radiomics features as comparable as possible across patients. It is well-recognized that radiomic features need to be reliable, i.e., reproducible and repeatable across different imaging and image-processing protocols, as well as scanners. However, currently, a lot of the work assessing reliability has only been reported on CT-based radiomic features56. Furthermore, the number of samples in the development set in our study (n = 68) was comparable to (≈) the number of features (p = 110), similar to exploratory studies56 using MRI data. Although, some radiomics studies use feature selection techniques to reduce the number of features57, the n ≈ p nature of our study doesn’t necessitate the use of these techniques58. Indeed, with larger values of n and p, this step may become much more important and useful, as has been demonstrated in other studies60.Also, due to our stringent approach to acquire controlled data, our sample size, while quite moderate, was comparable to similar exploratory studies36. We addressed the imbalance problem by using the commonly used method of randomly oversampling the minority class48 (high-risk PCa) during classifier training, which yielded significant performance improvements  patient stratification that can eventually translate to more effective and personalized patient treatments, wherein decisions regarding active surveillance or intensified therapy can be made more objectively and reliably.While PI-RADS is more easy-to-do and practical than running machine learning from a scratch, the technique is based on qualitative evaluation and therefore subjective in nature33. These categories were subsequently combined into 2 classes - “high risk” (high risk and above as per the above guidelines) and “lower risk” (intermediate and low risk as per the above guidelines) - to make the data amenable for traditional classification algorithms.Seventy three prostate cancer (PCa) patients presenting between March 2013 and May 2016 were included in this single institution, retrospective study. Inclusion criteria were: (1) histopathologic diagnosis of PCa, (2) mpMRI of the prostate, and (3) transrectal ultrasound-magnetic resonance (TRUS-MR) imaging fusion guided biopsy of the prostate within 2 months of mpMRI. Five patients were excluded from the original set due to marked imaging artifacts from hip prostheses, resulting in the final development set consisting of 68 patients. For patients with multiple lesions identified on mpMRI, the dominant lesion was chosen. The patients were divided into high, intermediate and low categories per National Comprehensive Cancer Network guidelinesThe development set was used to train and evaluate all the candidate classifier for differentiating between the above classes, as well as for training the final classifier. This classifier was applied to an independent validation set of 53 PCa patients who presented at the USC Keck Medical Center between June 2016 and July 2017 after applying the same inclusion and exclusion criteria used for constructing the development set. Baseline characteristics of the development and validation sets are provided in Table 5. Images containing the largest lesion diameter in transaxial, coronal and sagittal planes were saved for radiomics analysis.MRI examinations were performed on a 3-Tesla MRI system  using a standard protocol compliant with PI-RADS v26 who was blinded to all clinic-pathological information about the patients to avoid bias.The acquired images were transferred to a dedicated Synapse 3D workstation . A radiologist experienced in prostate imaging identified regions of interest (ROI), and an ROI was manually placed within the margins of the tumor in a two-dimensional (2D) plane. The mpMRI images were also qualitatively evaluated by the same radiologist using PI-RADS v2), and Fast Fourier transform (FFT)-based frequency features from, totaling 110 radiomic features . MATLAB®  was used to process the voxel-wise data for deriving the features.2D radiomic analysis was conducted on the orientation that provided the largest tumor diameter in each phase in the axial, coronal or sagittal dimension of both the T2W and ADC images. Fifty-five different texture features each were derived from both T2W and ADC images using four different methods, namely histogram analysis, Gray-Level Co-occurrence and Difference Matrices 38, Linear (L), Quadratic (Q), Cubic (C) and Gaussian (G) kernel-based Support Vector Machine (SVM)25, Linear Discriminant Analysis (LDA)64 and Random Forest (RF)65, were examined for learning candidate classification models from the development set. These algorithms were chosen given their widespread use in classification studies and easy-to-use implementations in the MATLAB® classification-learner package.Random oversampling to address class imbalance: Many clinical outcomes exhibit an unbalanced natural distribution of patients between different classes, e.g. cancer-afflicted and cancer-free, in a general sampling of the population66. This was the case in the development set, where only 14 samples belonged to the high-risk class, as compared to 54 in the lower-risk one. This may create a challenge for traditional classification algorithms, as they are generally designed for  balanced classes48. Thus, to address this problem, the random oversampling method48, where multiple copies of the minority class samples  in the development set were created to equal the number of the majority class samples , was used. This resampled development set was then used for training the candidate classifier(s).Cross-validation: A 5-fold cross-validation (CV) procedure31 was applied to the development set to train and evaluate candidate classifiers using the algorithms listed above, supplanted with random oversampling during training. In each CV round, candidate classifiers were trained using 80% of the development set (4 folds), and evaluated on the remaining 20% (1 fold). Repeating the process over all the folds and collecting the resulting predictions generated a vector of the same length as the size of the development set. This vector was then compared with true labels of the patients, using the evaluation measures discussed below, to assess the classification performance of the algorithm being considered. This CV process was repeated ten times to reduce the unlikely chance of getting over-optimistic results with just one run.Statistical analysis of classifier performance: To determine the best performing classification algorithm among those tested in the 10 CV rounds, a statistical analysis of the performance of all the tested classification models was conducted using the Friedman-Nemenyi tests32. These tests, which also account for multiple hypothesis testing, are used to assess the statistical significance of the relative difference of performance of the algorithms and their resultant models in terms of their relative ranks across the 10 CV runs. Critical Difference (CD) plots32, implemented using open-source Matlab code67, were used to visualize the results of these tests and enabled us to identify the best performing classification algorithm.Final classifier development: The final classifier was trained by applying the above best performing algorithm to the whole development set.Based on our previous work on developing classifiers from high-dimensional data27. Although the Area under the ROC Curve (AUC) has been the most commonly used performance measures in radiomics studies69, it is not reliable in cases of substantially unbalanced classes35, which is the case in this study. Thus, in addition to AUC, class-specific Precision, Recall and F-measure evaluation measures, which are more suited for unbalanced class situations35 (Supplementary Fig. max72, as well as the associated values of Precision and Recall, termed Pmax and Rmax respectively, were used to evaluate the candidate classifiers tested in the framework. The corresponding classification score threshold that yielded this value of Fmax was also recorded for each of the classifiers. The threshold for the final classifier obtained by averaging the threshold that yielded the highest Fmax value for the corresponding classification algorithm in each of the ten cross-validation rounds. The final classifier was applied in combination with this threshold to the independent validation set to obtain binary predicted labels for the constituent patients, which were then evaluated in terms of AUC, F-measure, Precision and Recall.Classifier performance can be evaluated in terms of a variety of measuresPI-RADS v2: The images, including T2W and ADC of all ROIs, were scored using PI-RADS v2 by two radiologists with 17 and 4 years of experience in prostate imaging, respectively5. The readers were blinded to initial mpMRI reports, clinical data, and pathologic outcomes. Each reader reviewed the MR images, identifying all lesions in each patient suspicious for clinically significant cancer and assigning a score for each lesion based on PI-RADS v2 classification. In scenarios where a discrepancy in score was observed, a consensus was obtained based on a discussion by the two readers. For all patients, the PI-RADS v2 scores following consensus was used for validation tests. A threshold of 3 was used for converting these scores to classify the patients as high and lower risk33.Permutation-based randomized validation sets: To determine the extent to which the performance of our final classifier could have been due to random chance, its performance was also evaluated on 100 randomized versions of the validation set. These versions were obtained by randomly permuting the labels of the samples in the validation set, which is expected to break the relationship between the features and true labels. The final classifier is then applied to each of these randomized versions, and the average of the resultant performance of the classifier was used as the representative evaluation measure of this benchmark.The best performing radiomics-based classifier identified by our framework was applied to the independent validation set of 53 PCa patients to assess the classifier’s generalizability to new patient populations. This performance was compared to those of two benchmarks as a part of this assessment:These performance assessments were carried using the classifier evaluation measures discussed above. To assess the statistical significance of the performance differences between the radiomics classifier and PI-RADS, we created 100 bootstrapped versions of the validation set, and measured the performance of the classifier and PI-RADS on these versions in terms of AUC, recall, precision, and F-measure. Then we evaluated the p-values of the performance differences for each of these measures using the Friedman-Nemenyi tests described above.The institutional review board of the University of Southern California approved the study protocols. Written informed consent was obtained from all subjects and all research was performed in accordance with relevant guidelines and regulations.Supplementary Information


Is inpatient or clinic-based rehabilitation associated with superior outcomes after total knee arthroplasty compared with home programs?This systematic review and meta-analysis included 5 unique studies involving 752 unique participants comparing clinic- and home-based rehabilitation and 1 study comparing inpatient rehabilitation with a home-based program. Based on low- to moderate-quality evidence, no associations between settings, no clinically important differences for mobility or patient-reported pain and function at 10 and 52 postoperative weeks, and no significant differences in quality of life or range of motion were found.For adults who underwent total knee arthroplasty, clinic or inpatient vs home-based rehabilitation appeared to offer no clinically important advantages. This systematic review and meta-analysis assesses whether inpatient or clinic-based rehabilitation is associated with superior function and pain outcomes compared with any home-based rehabilitation among adult patients who underwent total knee arthroplasty. Recent publication of the largest trials to date investigating rehabilitation after total knee arthroplasty (TKA) necessitate an updated evidence review.To determine whether inpatient or clinic-based rehabilitation is associated with superior function and pain outcomes after TKA compared with any home-based program.knee arthroplasty, randomized controlled trial, physiotherapy, and rehabilitation.MEDLINE, Embase, CINAHL, and PubMed were searched from inception to November 5, 2018. Search terms included Published randomized clinical trials of adults who underwent primary unilateral TKA and commenced rehabilitation within 6 postoperative weeks in which those receiving postacute inpatient or clinic-based rehabilitation were compared with those receiving a home-based program.Two reviewers extracted data independently and assessed data quality and validity according to the PRISMA guidelines. Data were pooled using a random-effects model. Data were analyzed from June 1, 2015, through June 4, 2018.Primary outcomes were mobility  and patient-reported pain and function (Oxford knee score [OKS] or Western Ontario and McMaster Universities Osteoarthritis Index) reported at 10 to 12 postoperative weeks. The GRADE assessment  was applied to the primary outcomes.Five unique studies involving 752 unique participants  compared clinic- and home-based rehabilitation, and 1 study involving 165 participants  compared inpatient and home-based rehabilitation. Low-quality evidence showed no clinically important difference between clinic- and home-based programs for mobility at 10 weeks  and 52 weeks . Moderate-quality evidence showed no clinically important difference between clinic- and home-based programs for patient-reported pain and function at 10 weeks  and 52 weeks .Based on low- to moderate-quality evidence, no superiority of clinic-based or inpatient programs compared with home-based programs was found in the early subacute period after TKA. This evidence suggests that home-based rehabilitation is an appropriate first line of therapy after uncomplicated TKA for patients with adequate social supports. Concern about the total episode-of-care costs for arthroplasty, including the rehabilitation period, has led to the introduction of bundled payments in the United States, consisting of a single bundled payment to health care organizations for all services related to the TKA to 90 days after surgery.8 This payment approach has subsequently driven health care providers to reconsider the use of the more expensive inpatient rehabilitation pathways.7The increased volume of surgery constitutes a significant burden on the acute health care budget, but because the surgery is typically followed by a protracted rehabilitation period, the latter can add significantly to the cost of care. Several studies9 Available options include one-to-one or group-based interventions (land or water) and various iterations of home-based care, including domiciliary programs (physiotherapy visits in the home), telerehabilitation, or more simple monitored  or unmonitored home programs.13 Previous systematic reviews of randomized clinical trials15 have concluded that no single setting—clinic- or home-based, in water or on land—appears to be associated with better recovery across a range of outcomes. Despite this finding, to date, no evidence-based clinical practice guideline exists to promote the use of home-based programs after uncomplicated TKA. Trials published since the aforementioned reviews,18 however, have included new comparisons  and constitute the largest TKA rehabilitation trials to date. Thus, a more contemporary review is warranted, potentially as a precursor to development of a much-needed clinical practice guideline.Outside inpatient rehabilitation, the setting, cost, and modes of provision vary greatly when rehabilitation is delivered in the community.The aim of this systematic review and meta-analysis was to investigate the importance of the rehabilitation setting on outcomes for adults after elective, primary, unilateral TKA. Specifically, we aimed to determine whether inpatient or clinic-based rehabilitation is associated with superior function and pain outcomes after TKA compared with any home-based physiotherapy program . Superiority was defined as a change considered to be clinically important for each outcome assessed.Cochrane Handbook for Systematic Reviews of Interventions, Version 5.1.019 and is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline.20 The protocol was updated to include the GRADE  component for assessing quality of evidence.21This systematic review of randomized clinical trials follows the methods described in the Published randomized clinical trials were eligible for inclusion. We excluded studies reported only as abstracts if adequate data could not be obtained from the authors, studies in which TKA data could not be separated from other procedures , and studies for which we were unable to obtain potentially relevant data from the authors on request. No language restrictions were applied.We included studies of adults (age ≥18 years) who had undergone a primary unilateral TKA and commenced rehabilitation within 3 months of surgery. We excluded studies of unicompartmental surgery, revision TKA, or TKA secondary to trauma.24 have investigated this option.We included studies investigating rehabilitation after TKA in which patients who had received postacute inpatient or clinic-based rehabilitation were compared with others who had received a monitored or an unmonitored home-based or domiciliary program after discharge from the acute-care facility. We excluded telerehabilitation because other reviews in progress during conduct of our study and subsequently published25 measured as laps walked on a flat surface in 6 minutes; walking speed, stair ascent and descent tests, and chair rise test), patient-reported pain and function , generic health-related quality-of-life measures , and knee range of motion (ROM), expressed as active or passive ROM, extension, and/or flexion.The goal of physiotherapy-based rehabilitation after TKA is to improve physical function, including walking, activities of daily living, and knee mobility. We grouped outcomes into the following categories that broadly reflect these goals: physical performance test results  and patient-reported pain and function (OKS) measured at 10 to 12 postoperative weeks. Secondary outcomes included mobility and patient-reported pain and function, knee ROM, postoperative complications, and health-related quality of life measured at 10, 26, and/or 52 weeks.knee arthroplasty, randomized controlled trial, physiotherapy and related terms, and rehabilitation  independently screened titles and abstracts of the search output to identify studies suitable for further scrutiny. They discussed inconsistencies in the screening process before a decision was made to review the full text. The same reviewers then screened full-text articles to determine inclusion in the review. Discrepancies in the final list were discussed, and a consensus was reached for all articles.30 which includes the following variables: random allocation sequence, allocation concealment, blinding , attrition , and selective outcome reporting. Disagreements in risk of bias were resolved by discussion, or, when necessary, a third person arbitrated. Included studies were also assessed using the Physiotherapy Evidence Database scale,31 used to identify trials that are more likely to be valid and to contain sufficient information to guide clinical practice.Two reviewers (M.A.B. and J.M.N.) independently assessed study quality using the Cochrane Collaboration Risk of Bias tool,32 Disagreements were resolved by consensus between the 2 reviewers. To ensure reproducibility and consistency, the reviewers used a checklist to rate each component of the GRADE assessment.33 We used GRADEpro software to create summary of findings tables.34 Because 4 of the investigators  were involved in randomized clinical trials relevant to this review, the GRADE assessment was undertaken by reviewers not involved in any of the included studies.Two reviewers (S.A. and A.L.) independently used the GRADE component to categorize the quality and strength of the evidence as high, moderate, low, and very low for the 6MWT and patient-reported pain and function at 10 to 12 postoperative weeks (the primary outcomes) and at 52 weeks.19Four reviewers  independently extracted data. We collected data related to participants ; country; study dates; inclusion and exclusion criteria; setting, timing, duration, and intensity of the intervention and comparison (control) conditions; duration of follow-up; losses to follow-up and reasons; and outcomes. Means (SDs) were extracted for outcomes reported as continuous variables. Proportions were extracted for categorical outcomes. Appropriate conversions were applied when outcomes were reported as medians and interquartile ranges or means and 95% CIs.For studies with incomplete data, we attempted to contact the corresponding author. We also asked whether any outcomes not reported in their publications had been collected. When authors of included studies were unable to provide additional data, all available data were included in the review. If data had been provided by authors to other reviewers in published reviews, these were included in the analyses in the case of failure to retrieve data from the primary source and acknowledged appropriately. Authors of included studies were also contacted when there was incomplete reporting of data. Where possible, we used data from intention-to-treat analyses in our calculations to determine between-group differences.19 of including each comparison separately but with the shared intervention group divided evenly among the comparisons. For continuous outcomes, only the total number of participants was divided, with the means (SDs) left unchanged.For studies with 3 randomized arms, we adopted a strategy described by Higgins and GreenData were analyzed from June 1, 2015, through June 4, 2018. We used the mean differences (MDs) and 95% CIs for continuous outcomes with the same units . We presented continuous outcomes with different units as standardized MDs and 95% CIs. Categorical outcomes were expressed as a number with percentage.35 Because the standardized MD can be artifactually affected by correlation between baseline and follow-up measurements when including the SD of change along with the SD of absolute values, we used the baseline SD for change score values when combining change scores and absolute values. For dichotomous outcomes, we used a pooled odds ratio.The 2 main comparisons (clinic- vs home-based and inpatient vs home-based) were considered separately. Where possible, we pooled data using random-effects meta-analysis.I2 statistic to assess statistical heterogeneity among included studies. We planned to explore publication bias using funnel plots if we had a minimum of 10 included studies, but the number found did not reach this. We used RevMan software (version 5.3)36 to compile data and perform statistical analyses.We used the 40 5 of which were included in the meta-analysis  and titles and abstracts were screened, we retrieved 15 studies for evaluation, of which 9 studies were excluded  included in the meta-analysis compared outpatient rehabilitation  with home-based rehabilitation (monitored or unmonitored). The sixth study16 with 165 patients  compared inpatient rehabilitation with home-based rehabilitation monitored by a health care professional. This study was included in a qualitative synthesis only. In all studies, rehabilitation commenced within 3 months of surgery, and participants were followed up for 26 to 52 weeks. Among studies that reported diagnostic data, the most common diagnosis was osteoarthritis. The mean (SD) age of study patients ranged from 66.2 (8.2) to 70.9 (SD not provided) years among studies reporting age. All studies excluded patients with complications in the acute postoperative period. Four studies reported patient adherence to the program   but with the shared intervention group  divided evenly among the comparisons as previously described in the data extraction section.19Five unique studiesd 61%38) . One stu40 used adequate methods for generating the randomization sequence, with the sixth study37 not providing this information. Four studies39 described the use of adequate methods to conceal allocation. Blinding of participants and therapists was not possible in any of the studies owing to the nature of the intervention, but all included studies blinded assessors of objective outcomes to group allocation. Two studies17 were free of selective outcome reporting. Risk of bias was present in 2 studies38 owing to uneven losses to follow up. Physiotherapy Evidence Database scores assessing study quality ranged from 5 to 8 (maximum of 10)  compared with those who received a home-based program (n = 142) at 10 to 12 weeks and an MD in 6MWT of −3.05 m  compared with those receiving a home-based program in both studies with 243 participants at 26 weeks. At 52 weeks, participants who had undergone clinic-based rehabilitation had walked an MD in 6MWT of −25.37 m  compared with participants who had undergone clinic-based rehabilitation. Based on GRADE assessment, low-quality evidence suggests that there may be no clinically important difference between clinic- and home-based programs for mobility at 10 and 52 weeks . Based on the GRADE component, moderate-quality evidence suggests little or no difference between clinic- and home-based programs for patient-reported pain and function in 457 patients at 10 weeks  and in 388 patients at 52 weeks  (Three studiesto 0.34) .38 reported quality-of-life outcomes at 1 or more follow-up points using the 12- or 36-Item Short Form Health Survey. No superiority of outcomes was found for patients receiving clinic-based rehabilitation compared with those who received a monitored or unmonitored home-based program in 314 participants at 10 to 12 weeks, 313 participants at 26 weeks, and 242 participants at 52 weeks . Per protocol analyses yielded the same results across all points. Moderate-quality evidence suggested that inpatient rehabilitation was not associated with superior mobility and patient-reported pain and functional outcomes compared with the outcomes from a monitored home program; however, this result must be interpreted cautiously because it is based on a single study  determined for a population of older adults and those with stroke by Perera et al.43Home-based rehabilitation provided greater mobility (approximately 25 m more in the 6MWT) at 52 weeks compared with a clinic-based program. However, research indicates that this difference is not clinically important. Using a triangulation of methods, including patient-perceived anchor-based thresholds and distribution-based thresholds, Naylor and colleagues16 This study reported that inpatient rehabilitation was associated with higher levels of patient-reported satisfaction. Understanding the reason for this finding would be useful for informing alternative models of rehabilitation provision.A single study provided evidence that inpatient rehabilitation is not associated with better mobility and patient-reported pain and function outcomes compared with a monitored home-based program among adults undergoing uncomplicated TKA.44 combined data from recipients of total hip and knee arthroplasty and concluded that the combined cohort had no difference in pain, functional outcomes, or patient satisfaction between the 2 treatment groups and that inpatient rehabilitation was not cost-effective. We were not able to obtain individual joint data from the authors, so were not able to include these data in our meta-analysis.No studies included in this review considered whether outcomes of post-TKA rehabilitation delivered in the domiciliary setting differed from those in other rehabilitation settings. One study comparing inpatient with domiciliary rehabilitation18 This study compared usual care with a home-based exercise program; however, usual care consisted of any combination of clinic- or inpatient-based programs, and many in the home-based program also accessed clinic-based care. Thus, we were unable to assign their participants to exclusively home-based or facility-based care. Those authors concluded that a home-based exercise program was not inferior to usual care for a range of patient-reported and objectively measured outcomes, including the Western Ontario and McMaster Universities Osteoarthritis Index, walking speed, and knee ROM.We were also unable to include the largest randomized clinical trial conducted to date concerning rehabilitation after TKA (n = 390).14 published in 2015 examined the effectiveness of physiotherapy exercise after TKA and found no differences for outpatient compared with home-based physiotherapy exercise for physical function or pain outcomes. A short-term benefit that favored home-based physiotherapy exercise for ROM flexion was not clinically important. These findings are consistent with those of our review.A systematic review and meta-analysis38 Another potential source of bias, because of the nature of the intervention, was that participants could not be blinded to their treatment.The risk of bias in the 5 studies included in the review was variable. The primary source of potential bias was from uneven losses to follow-up in 2 studies.45 plans to evaluate unsupervised home exercise with and without a web-based recovery platform compared with traditional outpatient physiotherapy after TKA. Another, with a published protocol46 and feasibility study,47 will compare clinic-based group physiotherapy with usual (home-based) care. However, the method proposed suggests possible crossover between settings in the latter arm because some patients were referred to physiotherapy services on an individual basis at the discretion of the hospital physiotherapist, orthopedic team, or general physician, with no indication of how many such referrals were made. We also updated our search to November 2018, with no new eligible trials identified.We identified a number of ongoing randomized trials comparing rehabilitation settings after TKA for future consideration. One, identified via a search in ClinicalTrials.gov,This review has specific strengths. We included only studies in which treatment assignments were randomized, enhancing the strength of the conclusions that could be drawn from the findings. Our review was also comprehensive because we included non–English-language articles in the search strategy, although none were found to be suitable for inclusion.48 and compared clinic-based rehabilitation with an unmonitored home-based program. However, that study had significant risk of bias, including failure to mask outcome assessors and incomplete outcome data, and data obtained from the author were insufficient for inclusion in the analysis. Despite these efforts, failing to identify unpublished studies may have introduced bias.This review also has several limitations. First, the failure to identify all relevant studies is a common source of bias in systematic reviews. We conducted thorough searches of research databases as well as clinical trial registries, including studies in all languages, using reference list searches of included studies and forward citation tracking, and corresponded with authors of included studies. We identified 1 study that was reported only in a conference proceeding17 only partially overcomes unit-of-analysis error because the resulting comparisons remain correlated,19 meaning that the narrowness of the 95% CIs may have been overestimated. Also, we did not consider complications occurring in the subacute care period as part of this review because of uncertainty around the standardization of reporting in this area. However, our conclusions apply to patients who did not experience major complications during the acute-care period that would have prohibited their involvement in the programs prescribed.Second, the number of studies in this area of research is small, limiting the precision of the findings and influencing the ability to assess for publication bias graphically or statistically. Third, the method used to divide data from the 3-arm studySeveral clinical trials have investigated the influence of setting on the effectiveness of rehabilitation delivered in the early subacute phase after TKA. This review found consistent evidence suggesting no clinically important differences between clinic-based or inpatient rehabilitation after TKA compared with a home-based program across a range of outcomes. Care that aligns with this evidence would incorporate home-based rehabilitation as the first line of therapy, reserving the more intensely supervised approaches for the most impaired patients or those without adequate social supports. In our view, the development of an evidence-based clinical practice guideline appears to be the next step in synthesizing this literature and aligning practice with the most up-to-date evidence.


Three recent studies revealed synergy between immune-checkpoint inhibitors and the microbiome as a new approach in the treatment of cancer. Incidentally, there has been significant progress in understanding the role of polyunsaturated fatty acids (PUFAs) in modulating cancer and the immune system, as well as in regulating the microbiome. Inflammation seems to be the common denominator among these seemingly unrelated biological entities—immune system, the microbiome, and long-chain polyunsaturated fatty acids (LC-PUFAs). This commentary presents a hypothesis proposing the existence of an optimal level of LC-PUFAs that nurtures the suitable gut microbiota preventing dysbiosis. This synergy between optimal LC-PUFAs and gut microbiota helps the immune system overcome the immunosuppressive tumour microenvironment including enhancing the efficacy of immune checkpoint inhibitors. A model on how LC-PUFAs (such as omega(n)-3 and n-6 fatty acids) forms a synergistic triad with the immune system and the microbiome in regulating inflammation to maintain homeostasis is presented. The principles underlying the hypothesis provide a basis in managing and even preventing cancer and other chronic diseases associated with inflammation. The past decade has witnessed exciting developments in the medical field with the advancement of immunotherapy in managing cancer and the microbiome as a potential therapeutic modality. One of the challenges in immunotherapy is the cunning ability of cancer cells to evade the patients’ immune system. This has been attributed to the tumour cells’ ability to exploit the inflammatory microenvironment around the tumour, which promotes immunosuppression or the inability of the host’s immune surveillance system to recognise the tumour as foreign. A breakthrough came with the understanding of a tumour’s escape mechanism using the programmed death PD-1) pathway. In this pathway, programmed death ligand 1 (PD-L1), which is often overexpressed on tumour cells, negatively regulates T-cell mediated response upon binding to the immune cells’ PD-1 receptor, allowing tumours to evade the T-cell immune response. This mechanism of evasion has been termed the immune checkpoint. Development of inhibitors of immune checkpoint pathway has led to successful therapeutic antibodies against PD-1 (nivolumumab and pembrolizumab) that has shown a therapeutic benefit against a variety of cancers. However, it was observed that treatment with these new immune checkpoint inhibitors only worked for a subset of the cancer patients [ pathway.At the same time, metagenomics has elucidated the gut’s complex microbial population and provided insight on how inflammation is modulated by the different types of bacteria and its metabolites. With the link between inflammation and immunosuppression, Routy et al. , GopalkrInflammation is currently accepted to be central to most chronic diseases . InflammLong-chain polyunsaturated fatty acids such as omega (n)-3 and n-6 PUFAs are major components of cellular membranes and necessary for normal brain and neurological development. In addition to providing structural integrity to cells as major components of the cell membranes, these molecules play critical roles in cell signalling and modulating inflammation. Arachidonic acid (AA) is a major component of the cell membrane. The n-3 PUFAs, DHA (docosahexaenoic acid) and EPA (eicosapentaenoic acid), are known to be anti-inflammatory since their metabolites (protectins and resolvins) are key in resolving inflammation .EPA and DHA are not efficiently produced by the human body from the conversion of the dietary essential fatty acid, ALA  and are better sourced from a diet rich in EPA and DHA. On the other hand, n-6 PUFAs especially AA is known to be inflammatory and is synthesised efficiently in humans from the dietary intake of the essential fatty acid, LA (linoleic acid) . Inflammation is triggered by the enzymatic release of AA from cellular membranes upon injury leading to a metabolic cascade where pro-inflammatory eicosanoids activate the production of inflammatory cytokines that recruit immune cells to the site of injury or infection. Once immune cells arrive at the site of injury or infection, they release pro-inflammatory signals to amplify their response and complete the task. This inflammatory pathway is counter-balanced by the metabolism of n-3 fatty acids (DHA and EPA) and its metabolites to resolve the inflammatory process once the immune system has overcome the infiltrating agent or damage.Dietary EPA and DHA, once absorbed is readily incorporated into cellular membranes. Membrane-bound EPA and DHA are released by the same phospholipase A2 enzyme that leads to the formation of anti-inflammatory eicosanoids and pro-resolving lipid mediators (resolvins and protectins), which are responsible for resolving inflammation. Furthermore, EPA directly competes with enzymes involved in AA metabolism, providing an additional handle in controlling inflammation ,7. RecenAnti-inflammatory drugs such as aspirin and NSAIDS , inhibit cyclooxygenase enzymes and prevent the synthesis of key mediators of the inflammation cascade: pro-inflammatory eicosanoids and their metabolites. Further upstream in this pathway, steroids block phospholipase activity thereby shutting both pro-inflammatory and intrinsic inflammatory pathways which explains the serious side-effects associated with steroids ,7. Bifidobacteria produce metabolites, especially short chain fatty acids, like butyrate, strengthen the gut barrier and regulate inflammatory response [Historically, microbes have been viewed mainly as infectious agents that cause disease with minimal benefit to the human host. They have been one of the main triggers of inflammation and cause of disease. However, it is now recognised that there are symbiotic microbes (probiotics) that have a positive effect in human health including the modulation of inflammatory response . The dynresponse .The major function of the immune system is to combat infiltrating harmful infectious agents as well as host cells, such as neoplastic cells, that have gone awry. Cytokines such as TNF-alpha, etc., are activated by the initial inflammatory response facilitating the recruitment and entry of immune cells from the lymphatic vasculature. They further react with immune cells to accelerate the inflammatory cascade by releasing other cytokines such as IL-1, IL-6, etc., as well as free radicals to complete the process of eradicating infection or repairing tissues, as well as resolving the inflammatory response. The immune cells range from members of the innate immune system  to members of the cellular (T-cells) and humoral (B-cells) response network as well as immune suppressor cells. However, the failure of the immune system to resolve inflammation can lead to genesis of the tumour microenvironment. An example of this shift from a pro-inflammatory anti-tumour response and shift to an immunosuppressive tumour micro environment can be gleaned from tumour-associate macrophages . When loHelicobacter pylori was determined to be the cause of gastric cancer by Barry Marshall and Robin Warren, and is the cause of other gastric diseases [The recent discovery of the interaction between the gut microbiota and the immune checkpoint pathway ,3,4 demodiseases .These recent studies independently demonstrated how the composition of gut microbes can determine the efficacy of immune checkpoint inhibitors against cancer. This represents a novel proposition for modulating cancer and potentially other chronic diseases. It was hypothesised that dysbiosis creates an inflammatory environment in the gut and spreads systemically leading to immune suppression and that this state can be overcome by shifting the balance towards favourable microbiota thereby re-activating immune surveillance function ,3,4. These studies not only revealed a new avenue for treating cancer but also identified potential biomarkers (from the microbiome) to tailor the use of immune checkpoint inhibitors and possibly improve the efficacy of immunotherapies. Nevertheless, significant challenges remain. Multitudes of gut microbes still require identification and determination of their respective roles. Once identified, it will be necessary to propagate the desired microbial populations with the desired phenotype (s) preserved and to deliver them in a suitable, therapeutic form. Routy et al. , GopalkrClostridium difficile infections which is a single bacterial species [Faecal microbiome transplantation is an approach that preserves the phenotype of the desired microbiome and has shown to work against  species . HoweverIs there a simpler solution to achieve an optimal balance in the gut microbiome in order to increase the efficacy of cancer immunotherapies?Although the interaction between gut microbiota and the immune checkpoint pathway has been discovered ,3,4, theElucidating direct causality for n-3 PUFAs especially DHA and EPA, provides an opportunity to confirm links with the microbiome and immunotherapy. This hypothesis can be tested because it is possible to measure n-3 PUFA levels vis-à-vis clinical endpoints. If validated, LC-PUFAs can be used as a companion diagnostic to determine whether an intervention of choice such as a particular immunotherapy would yield positive results. Hypothetically, LC-PUFA measurements could also lead to the discovery of simple fatty acid diagnostic biomarkers that would correlate with optimal microbial populations in the gut. This would be far simpler than genotyping entire microbial populations from patient stool samples. The genotyping protocol also runs the risk of missing certain microbial populations during the sampling process. Using n-3 and n-6 PUFAs as proxy for the microbiome has the advantage of direct intervention: the microbiome profile can be modulated prior to immunotherapy to achieve optimal efficacy. Direct intervention with n-3 PUFAs is simpler and more cost-effective compared to the use of probiotic cocktails or faecal transplantation to improve therapeutic outcomes. However, this does not preclude the possibility of combining therapeutic strategies involving n-3 PUFAs, probiotic cocktails and/or faecal transplantation. EPA and DHA could modulate cancer by different mechanisms: (i) apoptosis; (ii) angiogenesis and metastasis; (iii) inflammation; (iv) tumour cell proliferation; (v) epigenetic abnormalities; and (vi) gene expression and cell signalling ,19,20. IIt should also be noted that n-3 PUFAs’ with its strong anti-inflammatory properties should have a window of optimal use before it becomes a double-edged sword with potentially serious adverse events. Based on in vitro and animal models as well as limited clinical data, there are concerns that excess n-3 PUFAs may promote cancer. More clinical trials where precise measurements of n-3 PUFA levels can be taken can hopefully define the optimal window and shed light on the mechanisms on how they affect the microbiome . Propionibacterium acnes, Staphylococcus aureus, and Bacillus cereus [H. pylori infection with direct implications to gastric cancer [A direct link has been established between n-3 PUFAs and modulation of the microbiome especially with respect to inflammation ,18,25. Fs cereus . EPA andc cancer .Caenorhabditis elegans that converts n-6 to n-3 PUFA constitutively without the need for dietary n-3 supplementation. FAT-1 mice fed with an n-6 PUFA enriched diet had reduced low grade inflammation and metabolic endotoxemia. It was observed that levels of intestinal alkaline phosphatase (IAP) was enhanced throughout the animals’ tissues [Salmonella infection [There has also been significant progress in understanding mechanism of how LC-PUFAs regulate the gut’s microbial population. When mice were fed pro-inflammatory n-6 PUFA-enriched diets, it induced metabolic endotoxemia, which is a result of gut dysbiois . Studies tissues . Increas tissues . IAP remnfection . Moreovenfection . n-3 PUFTransgenic zebrafish expressing salmon desaturase genes fish had enhanced levels of n-3 PUFAs, resulting to an increase in anti-bacterial and anti-inflammatory activities . MoreoveN-carbamylglutamate, a metabolite known to have beneficial effects to the mammalian gut [These mechanistic studies are supported by recent observational studies in humans. In middle aged and elderly women, n-3 fatty acid levels correlated with gut microbiome diversity, independent of fibre intake and the production of lian gut . Furtherlian gut . There wlian gut . A randolian gut .Omega-3 PUFAs are known to be anti-inflammatory and able to suppress the immune system. Thus, it has been suggested that n-3 PUFAs should be contraindicated in conditions that require an active immune system such as during an infection and even in cancer since further immunosuppression would worsen the prognosis in cancer therapy . HoweverThe ability to measure levels of the LC-PUFAs especially EPA, DHA and AA in the body is key to testing the hypothesis as well as the applying it in clinical settings. Once absorbed from dietary intake, LC-PUFAs are readily incorporated into cellular membranes. LC-PUFAs are present in plasma as free fatty acid and phospholipids only in a matter of minutes to days, which makes sampling difficult. The other extreme is that in adipose tissues the EPA/DHA ratio remains stable for months, even years but sampling can be very challenging. LC-PUFAs however are present in red blood cells (RBCs) at a steady-state from weeks to months and its membrane composition are a good representative of all tissue types in the body . Both thMeasuring fatty acids in red blood cells traditionally involved the isolation of red blood cells from whole blood and the relative amounts of fatty acids determined by gas chromatography. Whole blood had to be stored at −190 °C to prevent EPA and DHA decay. This method is tedious which has limited its application in clinical trials. Currently, dried blood spot cards can be used to omega-3 levels from a few drops of blood; the EPA and DHA are stabilised at ambient temperature for up to 9 weeks with low background levels ,39. The The omega-3 index which corresponds to the percentage of EPA and DHA in RBC membranes was formally developed and used as a biomarker for cardiovascular health ,41. AlthAnother biomarker associated with the relative fatty acid content in blood is the AA/EPA ratio. Since the two PUFAs are key membrane components that regulate inflammatory response, their relative amounts are used as an inflammation marker where some studies have shown strong links in diseases such as cancer . As prevThe debate on whether omega-3 supplements work continue to rage in spite of numerous clinical trials and meta-analyses ,44. MostValidating the hypothesis that optimal n-3 and n-6 PUFAs would improve cancer immunotherapy outcomes would require the determination of baseline omega-3 indices and the AA/EPA ratios of cancer patients as preliminary baseline data. Cohorts of cancer patients within specified omega-3 indices and AA/EPA ratios will be determined, have their microbiomes genotyped and IAP activity measured to establish any correlations. Subsequently, the subjects will be treated with immunotherapy  and their responses determined. It is hypothesised that patients with high omega-3 indices and low AA/EPA ratios will correlate with increased IAP activity, i.e., favourable microbiome profiles, and respond better to immunotherapy. These results would represent an observational study that would support the hypothesis. Nevertheless, these studies should provide the information needed to define the optimal omega-3 index and AA/EPA ratio that results in a favourable microbiome that makes immunotherapy work. To validate the hypothesis that LC-PUFAs are key modulators of the inflammation triad, it is important to demonstrate causality. Randomised double-blind, placebo-controlled clinical trials involving cancer patients with suboptimal omega-3 indices and AA/EPA ratios with corresponding microbiome profiles, need to be conducted. Although n-3 PUFA treatment of healthy individuals did not lead to major shift in microbial diversity , this isResults from these trials may have implications in developing appropriate cancer treatment protocols as well as in making recommendations for the target omega-3 index and AA/EPA ratio for cancer prevention. It would also be of interest to consider healthy subjects who have inherited genetic predispositions to cancer but with no manifestation yet, and determine whether maintaining optimal levels of n-3 and n-6 PUFAs, healthy microbiome and balanced inflammation can prevent them from developing cancer. This represents a novel example of precision medicine and nutrition.This communication puts forward the hypothesis that LC-PUFAs can act as proxies for determining the state of the gut microbiota. In particular, LC-PUFAs can serve as biomarkers for a cancer patient’s gut microbiome which can be modulated accordingly to improve immunotherapy outcomes. Validation of the hypothesis has implications towards disease prevention. Once validated, in order to prevent disease, cancer in this example, an individual would need to maintain the target level of LC-PUFAs in tissues as well as the level of inflammation required to maintain healthy gut microbiota and immune system. The proposed measurement of LC-PUFAs provides a practical tool to monitor and modulate the microbiome towards increased immunotherapy efficacy. LC-PUFAs and inflammation levels via the AA/EPA ratio are quantifiable parameters and should facilitate achieving and maintaining personal target levels through cognitive behavioural or dietary modification, n-3 PUFA supplementation or therapeutic intervention. One caveat is that achieving optimal n-3 and n-6 levels may take time (months) and should be a consideration in future clinical trial designs. Although the hypothesis has focused on an application in cancer, it is conceivable that the same principles would apply to other inflammatory disorders which have shown similar correlation between LC-PUFA levels and the microbiome: Inflammatory bowel disease ,48, post