I point out fundamental mathematical errors in the recent paper published in this journal ‘Quantum correlations are weaved by the spinors of the Euclidean primitives’ by Joy Christian. In each paper, he proposes a local hidden variables model  that according to him explains those correlations and reproduces them. According to the celebrated result known as Bell’s theorem [S3: the surface of the unit ball in S7, thought of as a quaternionic three-sphere rather than a real three-sphere.Christian , publishGeometric Algebra (GA), based on the interplay between Clifford Algebra (CA), an important part of abstract algebra, and Geometry. It has been promoted as a universal language of physics. Important parts of quantum information theory have already been rewritten in the language of Geometric Algebra, though so far this did not have much impact on the field.A constant theme in the author’s papers is to exploit a very well known and very established part of mathematics, Foundations of Physics. The author had not published any of his papers in peer reviewed journals, so many of the early critiques were not even submitted to regular journals, but remained, like his, as preprints on the preprint server arXiv.org. The author did respond vigorously to all critiques in yet more preprints and has always maintained that all criticism levelled at it had been mistaken and has been adequately refuted by himself.In Gill , I publiIn my paper, I argued that the author’s work was built on a combination of ambiguous notation and elementary errors in logic, algebra and calculus. My paper included a section on an earlier version of the paper which is the topic of discussion here.IEEE Access [IEEE Access. I am grateful for the invitation by RSOS to react to [RSOS [Time did not stand still, and after 2018, the author published a pair of companion papers, Christian 2019, 2020), in the journal E Access ,6. At thE Access  on the s, 2020, ito [RSOS , who indIf physicists want to ‘rescue’ the author’s ideas because they see something in the underlying idea, it is up to them to do so. Physicists have again and again enriched mathematics by intuitively and with deep physical insight discovering patterns and abstract structures hitherto not known to mathematicians. This will surely happen again and again in the future. Debate on Bell’s theorem will also continue for many years to come. In appendix A, I will discuss proposals by two referees of this paper to salvage the author’s program. I argue that they cannot succeed. In fact, there are several other controversial and, in my opinion physically more interesting options for those who want to get around Bell’s theorem. There are plenty of fora where these controversial alternative quantum foundations are vigorously discussed, and a huge (peer-reviewed) literature. I am afraid that the author’s work has not added directly to the ongoing debate, though it did stimulate several original contributions which have made Bell’s own case even stronger than it already was, and made the task of opponents thereby harder still.That is not to say that Bell’s theorem does not have a sound mathematical core. When stated as a mathematical theorem in probability theory, there can be no doubt about its validity. My work on the subject does not challenge this mathematical core, if it is viewed as a piece of mathematics.This is however exactly what the author’s works, and in particular his RSOS paper [not do. He does challenge the core mathematics of Bell’s work, and moreover, in [challenge the metaphysical conclusions regarding locality and realism derived from that mathematical core. My work thus draws a sharp distinction between the mathematical core of Bell’s theorem and the metaphysical conclusions derived from it.But drawing that ‘essential distinction’ is exactly what he does not do. He adopts Bell’s own mathematical framing of the concepts of locality and realism and argues that one of the basic mathematical steps in Bell’s proof is wrong. But it is his own argument which is evidently wrong. He does claim that our conception of space needs revision, but in view of its defects, his own work does not provide much support for that claim.In , the autOS paper , do not over, in , he builover, in  to asser. 2mathematical claims in Christian [Hurwitz theorem  stating that Bell’s theorem, Bell [In this section, l discuss two established mathematical results which contradict hristian . These asee Baez ; and (b)em, Bell  on the iI also discuss two no-go theorems saying that certain quantum correlations cannot be simulated on a network of classical computers, where the allowed connections between computers mimic the spatial-temporal relations involved between physical subsystems of a typical Bell experiment. These can be thought of as theorems of computer science, but they are also merely repackagings of the mathematical core of Bell’s theorem. I bring them up because the author illustrates his mathematical claims by Monte Carlo computer simulations, and I use them to provide more evidence for my own claims, that his theory is badly wrong.Gull’s theorem, Gull [enhanced using martingale theory (probability) and randomization (statistics).These ‘theorems from computer science’  are (c) em, Gull , Bell’s em, Gull , which iRegarding Gull’s theorem (c), astrophysicist Stephen Gull is another powerful promotor of geometric algebra. Readers familiar with Fourier series and time-series analysis might find Gull’s proof of Bell’s theorem much easier than Bell’s. The fact that the result can be proven using a myriad different mathematical approaches is further evidence that it stands like a rock.et al. [Regarding my own contribution (d), recent ‘loophole-free experimental violations of Bell inequalities’,  and otheet al. , was usead verecundam; by authority. Perhaps the author’s work is merely blemished by details of notation or terminology. Fortunately for us, the author also presents computer simulations of his model. If that code faithfully represented his intended interpretation of his formulae, then by looking at the code one could deduce what he was trying to express in his published formulae. Moreover, one could check by inspection whether or not his code faithfully respects locality and realism. The simulations do seem to generate the results predicted by quantum mechanics. This means, in view of results (c) and/or (d), that it is not possible that the code satisfies the specifications agreed by Bell and the author.Computer simulations of Bell-type experiments have become very popular, and here too, the same statistical issues arise. They are popular both among opponents and among supporters of Bell. In the context of the author’s work, some readers might imagine that thanks to mathematical subtleties which they cannot hope to appreciate, the author’s work is still largely correct. An appeal to the Hurwitz theorem or to a mathematical version of Bell’s theorem is an argument It is indeed easy to check that the computer code effectively just draws the cosine curve built into the computer algebra package used by the author; it does not respect the constraints of local realism. It therefore weakens, rather than supports, his claims. His claims are unfounded, and the supporting evidence which he offers is actually further evidence that he has not succeeded in converting possible physical insight into hard science.. 2.1ab = ba, associativity is a(bc) = (ab)c). The useful and important thing to know here is that a division algebra has no zero divisors. There do not exist elements A and B, neither equal to zero, such that AB = 0. However, the author’s algebra has an element called the ‘pseudo-scalar’, I will denote it by M, such that M2 = 1. It follows that 0 = M2 − 1 = (M − 1)(M + 1). Taking norms, 0 = ‖M − 1‖.‖M + 1‖. Hence ‖M − 1‖ = 0 or ‖M + 1‖ = 0. Therefore M − 1 = 0 or M + 1 = 0, which implies that M = 1 or M = −1. That is a contradiction. The author’s algebra is associative, the octonions are not. It is known as The best reference for this theorem is Baez’s award-winning paper  on the osame algebra. The linear spans of those two bases are trivially the same. The multiplication operation is the same. The author’s claims regarding abstract algebra have naturally attracted the interest of algebraists. One of the founders of geometric algebra has published a paper, Lasenby [A curious elementary mathematical error is that he defines two algebras, built from two eight-dimensional real vector spaces  Lasenby , detaili. 2.2Xa and Yb are a family of random variables on a single probability space, taking values in the set {− 1, + 1}, and where a and b denote directions in ordinary three-dimensional Euclidean space, represented by unit vectors a, b. Then it is not possible that a and b.Bell , as rapia and two for b, delivering us four combinations of possible values of the pair . Since four binary random variables are supported by a discrete probability space with just 16 elementary outcomes, a proof  can be framed in absolutely elementary terms [Bell’s proof works by focussing on two choices for ry terms , which wBell used his mathematical result to argue that quantum mechanics violated the meta-physical principles of locality and realism. One can escape from this mathematical obstruction only by redefining the concepts of locality and realism. The author, however, does not take that well trodden route. He claims that the purely mathematical result is wrong. His alleged proof thereof is his explicit construction of a counterexample. I have elsewhere shown that his construction depends on simple errors in elementary algebra and calculus.He also argues that Bell’s proof contains a fundamental error in reasoning: the Bell-CHSH inequality involves correlations obtained from different sub-experiments involving measurements of non-commuting observables, and (he says) therefore cannot be combined. However, in quantum mechanics, even if two observables do not commute, a real linear combination of those observables is another observable. By the linearity encapsulated in the basic rules of quantum mechanics, expectation values of linear combinations of non-commuting observables are the same linear combination of the expectation values of each observable separately. If a local hidden variables model reproduces the statistical predictions of quantum mechanics, then it must reproduce this linearity.. 2.3Consider a computer simulation of a Bell-CHSH-type experiment. Initially, one could imagine three computers, a source computer sending information to two measurement locations, where two computers each simulate an apparatus which receives ‘stuff’ from a source, and a ‘setting’ (a measurement direction) supplied by an experimenter. The ‘measurement station computers’ each output an ‘outcome’ ±1. After a large number of trials, one collects the inputs (settings) and outputs (outcomes) together and computes the correlation  between the outcomes, for each possible pair of input settings.completely separated classical computers, which perform the following task.Now, if that could be done, one could also create a copy of the source measurement station, including all the data which is stored on it at the beginning of the computer simulation, and then merge each of the copies of the source with the two measurement stations, giving us together two N rounds, each computer is supplied an input, and each computer supplies an output. After the complete run of N ‘trials’ is completed, one collects all the data together, and correlates the outputs, for each possible pair of inputs. Gull [a · b? He outlines a proof that this is impossible using an argument from Fourier analysis. Unfortunately he never published a formal proof, so we must make do with his overhead transparencies from a conference. Inspection of Gull’s outline proof shows that he is making a particular ‘no use of memory’ assumption. Each of those computer’s outputs, in trial n, depends only on the initial data stored in the computers and their programs, and on the new setting a or b, and on the trial number n, but not on the previous n − 1 inputs. Full mathematical details can be found in Gill & Karakozak [The two computers are both loaded with data and a computer program, and then disconnected. After that, in ts. Gull  used to arakozak .if (lambda==1) q =(NA NB) else q =(NB NA). One may check the algebra embodied in the code: the program simply computes −a · b. It does not calculate the correlation between the actual ±1 valued measurement outcomes! The program does not implement the mathematical model given in the paper.The author’s paper contains computer code: just one program which simulates many times two measurement settings and the value of some hidden variables which one can imagine created by nature in the source and transmitted to two measurement stations. Measurement outcomes are computed at each measurement station from the relevant setting and from the hidden variables. Next the program goes on to compute the correlation between measurement outcomes for any pair of measurement settings. This is where things go wrong: notice the code line . 2.4nth pair of outcomes from the nth pair of settings might make use of information about the past n − 1 settings and outcomes. Using martingale theory I proved a probability bound showing that deviations from the Bell-CHSH inequality of any size had exponentially small probability, provided only that the binary setting choices at each trial were performed, outside of each measurement station, and again and again, by two fair coin tosses. The measurement station computers were even allowed to communicate with one another and with the source between each trial. This result was later refined and improved and used by all the experimenters in the four famous ‘loophole-free’ Bell experiments of 2015, starting with the Delft experiment [My paper, Gill , was my periment  and contperiment  for furt. 3S3, not of The paper Christian  is irrepClick here for additional data file.


As a stable and reliable artificial intelligence algorithm, ANN can provide new ideas and methods for clinical rehabilitation evaluation.In order to explore the application of artificial neural network in rehabilitation evaluation, a kind of ANN stable and reliable artificial intelligence algorithm is proposed. By learning the existing clinical gait data, this method extracted the gait characteristic parameters of patients with different ages, disease types and course of disease, and repeated data iteration and finally simulated the corresponding gait parameters of patients. Experiments showed that the trained ANN had the same score as the human for most of the data . There was a strong correlation between ANN and improved Ashworth scores as assessed by human raters ( Artificial intelligence (AI) has become a neighborhood with many practical applications and active research topics. Artificial neural network (ANN), as an important branch of modern artificial intelligence technology, has been widely and deeply applied to modern medical activities due to its powerful learning ability and stable feature recognition and prediction functions . RehabilPryor et al. proposed a body evaluation model based on the mixed density neural network. The movement sequences of patients were collected by the Microsoft Kinect sensor as the input of the neural network. The neural network consists of two layers: self-coding layer and mixed density layer. The former is used to reduce the dimension of the motion sequence and extract the features, while the latter is used to express the patient's motion sequence as a Gaussian density function and compare it with the preset motion to evaluate the patient's body function. This model can be applied to the rehabilitation evaluation and training guidance of stroke rehabilitation patients in the family environment . WilkinsBuilding a virtual mechanical model or learning and forecasting existing data through neural network algorithm can better reflect the balance ability of the target population. However, the main problem at present is the lack of large sample and multidisease clinical evaluation model and its clinical feasibility needs further study.Artificial intelligence is an important branch of computer science, which includes many application fields such as machine learning, machine vision, and intelligent search. Machine learning (ML) is the fundamental way to make computers intelligent, to put it simply. ML can be understood as enabling the computer to discover and mine deep rules from massive data and then carry out data classification and prediction, which is similar to the functional model in mathematics. The data sample is taken as the input variable, and the output is the expected result of simulation. ML can be further divided into supervised and unsupervised learning modes . UnsuperRehabilitation assessment is a process of collecting patient history and related data; putting forward hypotheses; carrying out inspection and measurement; comparing, synthesizing, analyzing, and interpreting the results; and finally forming a conclusion and diagnosis of obstacles. Rehabilitation assessment includes all functional or ability disabilities requiring rehabilitation treatment. Through rehabilitation assessment, the location, scope, type, nature, characteristics, degree, causes, and prognosis of obstacles can be found and determined, so as to provide the basis for the formulation of clear rehabilitation goals and rehabilitation treatment plans. Correct and accurate evaluation of rehabilitation is the premise and basis for formulating correct principles, plans, and specific implementation programs of rehabilitation treatment. In addition, the effect of patients' rehabilitation treatment also needs to be reflected by the change of rehabilitation evaluation results –10.The whole process of rehabilitation treatment must rely on rehabilitation assessment. Generally speaking, rehabilitation assessment mainly has the following functions: (1) seek and determine the type, degree, and cause of obstacles; (2) guide the formulation of rehabilitation treatment plan; (3) determine rehabilitation treatment programs; (4) judge the rehabilitation effect; (5) prognostic analysis; and (6) prevent the occurrence and development of obstacles. In the process of assessing various functional disorders, the commonly used rehabilitation evaluation methods include observation method, investigation method, scale method, and instrument measurement method; among them, scale method is the most commonly used evaluation method in clinical practice at present, and it is a method to use standardized scale to classify the severity of dysfunction of patients or to summarize the score.Recurring neural network (RNN), also known as automatic association network or feedback network, is one of the most popular deep learning algorithms at present. RNN units are connected to each other in turn to form a directed cycle, and the internal state of the units makes the network show dynamic temporal behavior. They are particularly powerful in tasks where context is crucial for predicting results and are often applied to solve problems in areas such as speech recognition and natural language processing. The RNN can use its internal memory to process any input sequence. In RNN, signals propagate forward and backward by introducing a loop in the network.t, ah′t−1 is 1 moment h′ neurons' hidden state vectors, which are information from memory units at the previous moment, zht is the input information at time t and the memory unit information at the previous time obtained by weighted product and sum, after nonlinear transformation of the activation function fh(·). It becomes the information spit of the memory unit at the current moment. Common activation functions include Softmax, ReLU, and Tanh. yht is the output vector of K neuron in the output layer at time t. wih, wh′h,  and whk are, respectively, the connection weights between the input layer and hidden layer, hidden layer and hidden layer, and hidden layer and output layer.The forward propagation formula of RNN is shown as follows:Accuracy: accurate rate can be used to represent the ability of the classifier to correctly classify samples in the whole sample set, that is, the ratio of all correctly classified samples to the total number of samples, including true positive and true negative samples, and their calculation is shown as follows:Precision: accuracy is the percentage of samples that are predicted to be positive where the true value is also positive. It is the degree to which the predicted sample is close to the true value, and its calculation is shown in the following formula:False positive rate (FPR) represents how many negative samples were wrongly classified as positive, calculated as follows.The mean square error can be understood as the Euclidean distance between the true value of the sample and the predicted value as follows:Since MSE contains square operation, the value of the calculated result is very large, which is not conducive to comparative analysis. Therefore, RMSE is born, that is, the square sign of MSE is taken to make the order of magnitude of the error result consistent with the order of magnitude of the sample data as follows:This data collection experiment was carried out in the Chinese Medicine Knowledge and Data Engineering Experiment of University of Electronic Science and Technology of China. The actions of the experiment were conducted under the guidance of clinical professionals, and all of them came from rehabilitation doctors with rich experience in the rehabilitation research of stroke patients and stroke rehabilitation treatment. According to the results of existing studies, at least 20 subjects (5 in each group) are required to achieve a statistical power of 0.95 in the test to detect differences in limb mobility among patients with different degrees of impairment. Therefore, a total of 36 healthy subjects  were enrolled in this study, and each participant was subjected to a Brunnstrom II–VI simulation of upper limb motor dysfunction in stroke patients.Starting position: the subject is comfortably seated on the chair, with the torso straight, head straight, neck straight, upper limbs hanging naturally on both sides of the body , lower limbs naturally relaxed, and knees bent 90°Lift the hemiplegic arm on the sagittal plane to horizontal height (shoulder flexion 90°)Bend the elbow and touch the opposite shoulder with the palm of your hand (elbow flexion and forearm pronation)The upper limbs naturally return to the starting position ; therefore, the number of data samples in each period is equal to 36; a total of 180 samples were identifiedThe shoulder touch exercise was used as arm activity assessment, and the motor ability of the subjects' upper limbs was observed. Under normal circumstances, a complete movement process includes the following four steps:In the simulation data collection stage, From In order to further prove the performance effect of the proposed method, we analyze the confusion matrix on the test set of all the methods. r = 0.825, p < 0.01). These characteristics of ANN can help it to learn a large number of clinical data and then generate a stable clinical evaluation model, improve the accuracy and efficiency of rehabilitation evaluation, reduce the work intensity of rehabilitation physicians and therapists, and alleviate the current shortage of rehabilitation resources. Using measurement methods, although the degree of interference is low, it is easy to produce measurement error, affect the quality of the data, missing values, and other problems. In terms of data analysis, the data collected by neural network lack missing processing.Evaluation of rehabilitation is an important link in the process of rehabilitation treatment, which is the basis for the establishment of rehabilitation program and the evaluation of rehabilitation effect. In view of the upper limb motor dysfunction that often occurs after stroke, based on the objectivity and performance defects of the existing clinical rehabilitation evaluation methods and automatic evaluation research, deep learning technology was first applied to the automatic evaluation research of stroke rehabilitation. Firstly, the commonly used clinical scales are analyzed, and the motion measurement system is designed according to the evaluation project content and scoring requirements. Then, different depth learning algorithm models are output according to the data characteristics and expected output. Finally, this automatic evaluation research is compared with relevant depth learning algorithms to verify that the proposed method is a better method for realizing the automatic evaluation task of upper limb motor function. Experiments showed that the trained ANN had the same score as human for most of the data . There was a strong correlation between ANN and improved Ashworth scores as assessed by human raters (


Parafontaria laminata were examined, focusing on Pristionchus spp. The nematodes that propagated on dissected millipedes were genotyped using the D2-D3 expansion segments of the 28S ribosomal RNA gene. Four Pristionchus spp. were detected: P. degawai, P. laevicollis, P. fukushimae, and P. entomophagus. Of the four, P. degawai dominated and it was isolated from more than 90% of the millipedes examined. The haplotypes of partial sequences of mitochondrial cytochrome oxidase subunit I examined for Pristionchus spp. and P. degawai showed high haplotype diversity.Phoretic nematodes associated with two mass-occurring populations of the millipede  Pristionchus  is a satellite model system in many different fields of biology  is used to study phenotypic plasticity, kin recognition, and chemical biology  stage, which can be reared on artificial media, suggesting phoretic association, but the worms also propagate on the carcass of their host/carrier insects utilizing the carcass as the substrate of their food bacteria ’, but is now synonymized to the original subspecies  were isolated, and two Pristionchus spp. that were undescribed at the time were described taxonomically as P. degawai  and Nobeyama (Nob), Minamimaki Village, Nagano, Japan in 2016. These sites are relatively cool mountain areas  in central Japan.Collected millipedes were brought to the laboratory, and kept at 10 to 15°C until dissection.φ = 90 mm Petri dish) and kept at ca. 20°C for 1 week. After the propagation of Pristionchus spp. was confirmed, 3 to 5 individual nematodes were hand-picked from each plate, transferred individually to nematode digestion buffer  were individually dissected on water agar  gene was determined for the established cultures. Millipedes (20 per population) were dissected on the agar plate, and kept at ca. 20°C, as described above. The agar plates were examined under a dissecting microscope  daily for 2 weeks.Pristionchus species was recognized, 5 to 10 first-found gravid females were transferred separately into Nematode Growth Medium (φ = 40 mm Petri dish) previously inoculated with the Escherichia coli OP50 strain. The propagated nematodes were subcultured and kept as laboratory strains. Using this procedure, 215 temporal strains were established. An individual nematode was hand-picked from each culture and genotyped based on the D2-D3 LSU sequence (as described above). In addition, all temporal strains and two strains of P. fukushimae established in the first isolation were genotyped based on the mtCOI sequence according to the methods in Kanzaki and Futai (2002), and the sequences were analyzed phylogenetically with the online version of PhyML (http://www.atgc-montpellier.fr/phyml/) in which the analytical parameters were selected automatically , P. fukushimae , P. laevicollis , and P. degawai  (https://www.ncbi.nlm.nih.gov/genbank/?). The newly determined mtCOI sequences were deposited in GenBank with accession numbers LC589007-LC589060  . The D2-LC589060 . In addiPristionchus spp. from millipedes are summarized in Pristionchus entomophagus is widely distributed in Europe   , 2013b an Nagoya  and Kyotlaminata , and domPristionchus spp.  was confirmed. Pristionchus spp., except for fig associates, is not only phoretic, but also necromenic species. Thus, considering their host as a substrate, host specificity is not necessary to become strict. Contrastingly, many species inhabiting newly-dead wood, e.g., Micoletzkya (diplogastrid bacteria feeder) and Bursaphelenchus , have relatively high host/carrier specificity  for Mat and 3.0  for Nob  will give more detailed population genetics information.The primary purpose of this study was to establish many acificus , and no Pristionchus spp. have been isolated from millipedes, including P. laevicollis, P. degawai, P. riukiariae  and Mononchoides sp. from Chamberlinius hualienensis collected at Hachijojima Island, Japan and P. pacificus, P. arcanus, and Oscheius spp. from Riukiaria spp. collected in three localities in Japan . Further analysis of its diversity and ecology, e.g., whether they are competing or segregating, will give new insight to understand the biological interaction among Pristionchus spp. and other millipede associates.Although many thelastomatid and rhigonematid gut parasites have been described from millipedes, phoretic/necromenic species have not been examined systematically, i.e., in addition to above species descriptions, in Japan . In the  species , and preP. laminata has other populations that do not undergo typical mass-occurrence and has several congeners in Japan (e.g., Pristionchus spp. as well as other phoretic/necromenic species.In addition, an e.g., . Further


Y705), and the activated STAT3 subsequently induces expression of cytokines which in turn activate STAT3, forming an autocrine loop. Clinically, upregulation of Lnc-DC is associated with poor prognosis. In particular, analysis of a tamoxifen-treated patient cohort indicates that Lnc-DC expression can predict the response to tamoxifen. Together, this study demonstrates a previously uncharacterized function of Lnc-DC/STAT3/cytokine axis in estrogen independent growth and tamoxifen resistance, and Lnc-DC may serve as a potential predictor for tamoxifen response.Selective estrogen receptor modulators (SERMs) such as tamoxifen have proven to be effective in the treatment of estrogen receptor (ER) positive breast cancer. However, a major obstacle for such endocrine therapy is estrogen independent growth, leading to resistance, and the underlying mechanism is not fully understood. The purpose of this study was to determine whether long non-coding RNAs (lncRNAs) are involved in regulation of estrogen independent growth and tamoxifen resistance in ER positive breast cancer. Using a CRISPR/Cas9-based SAM (synergistic activation mediator) library against a focus group of lncRNAs, we identify Lnc-DC as a candidate lncRNA. Further analysis suggests that Lnc-DC is able to reduce tamoxifen-induced apoptosis by upregulation of anti-apoptotic genes such as Bcl2 and Bcl-xL. Furthermore, Lnc-DC activates STAT3 by phosphorylation (pSTAT3 Estrogen plays a critical role in breast cancer development because estrogen can stimulate cancer cell growth through estrogen receptor (ER). About 80% breast tumors express ER and thus, specific targeting of the estrogen signaling pathway provides an effective way to treat ER positive breast cancer. Several clinical drugs currently available target either estrogen synthesis such as aromatase inhibitors or ER such as selective estrogen receptor modulators (SERMs) . However, over 30% of these ER positive tumors fail to respond to tamoxifen therapy due to intrinsic resistance ; moreovePrevious studies indicate that multiple factors are attributed to tamoxifen resistance, including AKT , mutatioSpecifically, we explored the utility of CRISPR/Cas9 gene editing system  to identY705, STAT3, and SHP-1 were from Cell Signaling ; GAPDH and Tubulin were from Protein Tech . Secondary antibodies conjugated with IRDye 800CW or IRDye 680 were purchased from LI-COR Biosciences . PCR primers were obtained from IDT . STAT3 siRNAs and control siRNA were purchased from Thermo Fisher Scientific . Cytokine Array kit was obtained from R&D system Inc. .Primary antibodies were purchased from commercial sources: pSTAT3L-glutamine, 100 U/ml penicillin and 100 μg/ml streptomycin. 293 T cells were grown in DMEM with 10% FBS supplemented with 2 mM L-glutamine, 100 U/ml penicillin, and 100 μg/ml streptomycin. The cells were authenticated by DDC Medical  using the short tandem repeat profiling method. All cells were incubated at 37 °C with 5% CO2 in the humidified chamber.MCF-7, T47D, and 293 T cells were obtained from ATCC . MCF-7 and T47D cells were grown in RPMI 1640 (Thermo Fisher Scientific) with 10% FBS  and 2 mM glutamine (Thermo Fisher Scientific). For estrogen deprivation experiments, cells were grown and maintained in phenol-red-free RPMI-1640 medium supplemented with 5% charcoal-stripped FBS (Thermo Fisher Scientific), 2 mM To knock out Lnc-DC, we adopted the dual gRNA approach with CRISPR/Cpf1 and Lnc-DC dual gRNAs were cloned using the approach as previously described . To clonTo construct SAM library, we modified lenti sgRNA(MS2)_zeo backbone vector (Addgene Plasmid #61427) by replacing the Nhe I-BamH I fragment with a gBlock (IDT) fragment (Supplementary Table SAM gRNA library was constructed by designing gRNAs against ~1 kb from promoter regions of 241 different lncRNAs. Five gRNAs were designed for each lncRNAs using Chop-Chop program . OligonuMCF-7 cells were first infected with lenti MS2-P65-HSF1_Hygro (Addgene Plasmid #61426) and lenti dCAS-VP64_Blast (Addgene Plasmid #61425) and then selected with hygromycin and blasticidin. Next, SAM library was introduced into these cells by infection, followed by zeocin selection. The cells were cultured in E2 free medium for 28 days during which the medium was replaced with fresh E2 free medium every other 2 days. After that, the cells were harvested for lncRNA profiling.Since number of lncRNAs in this SAM gRNA library was relatively small, we profiled lncRNA expression using PCR arrays consisting of primer sets derived from each of these lncRNAs included in the library. Total RNA from cells was isolated using Direct-zol™ RNA MiniPrep kit . Reverse transcription was carried out by using RevertAid Reverse Transcriptase (Thermo Fisher Scientific) and random primer mix (New England BioLabs). The expression of lncRNAs was detected by quantitative PCR (qPCR) using SYBR Green method. Analysis of qRT-PCR was performed as described previously . For comCells were transfected with plasmid DNA using DNAfectin  or with siRNAs using RNAfectin reagent  following the manufacturer’s protocol.Lentivirus was packaged in 293 T cells using the third generation of packaging system and the virus containing culture medium was collected 48 h after transfection and spun for 10 min at 3,000 r.p.m. Lentivirus infection was carried out six-well plates by mixing 500 μl virus supernatant + 500 μl medium containing 8 μg polybrene.3 cells per well and grown for 3 days before adding MTT.Cell proliferation was analyzed using MTT assays as described previously . Cells wFor the colony formation assay, cells were seeded in six-well plates. After two weeks of culture, the cells were fixed with 4% PFA (Sigma) and then stained with crystal violet (0.5%). The number of colonies were countered for 5 representative fields. The experiments were repeated 3 times. Relative colony formation was calculated as compared to vector controls as 100%.https://benchling.com) and primer sequences were listed in Supplementary Table We knocked out Lnc-DC by CRISPR/Cpf1 system  using thRNA immunoprecipitation was performed using Magna RIP RNA-Binding Protein Immunoprecipitation Kit  using STAT3 antibody according to the manufacturer’s protocol. After the RNA was recovered from protein A + G beads, qRT-PCR was performed to detect Lnc-DC using primers Lnc-DC-RT-5.1 and Lnc-DC-RT-3.1  according to the manufacturer’s protocol. Briefly, cells were first fixed with 1% formaldehyde to generate DNA–protein cross-links. Then, cells were lysed and sonicated to chromatin fragments of ~200–300 bp and cell lysates were immunoprecipitated with STAT3 antibody. After immunoprecipitation, genomic DNA was isolated and bound protein was digested with proteinase K. PCR was performed using primers listed in Supplementary Table Chromatin immunoprecipitation (ChIP) assays were performed using EZ-ChIPTo detect in vivo interaction between Lnc-DC and STAT3, we constructed S1m-tagged Lnc-DC expression vector, and then transfected it into MCF-7 cells. The cells were harvested 48 h after transfection and then lysed in 1 ml lysis buffer containing protein inhibitors and RNase inhibitor. The supernatant was collected after centrifugation for 20 min at 4 °C and then incubated with streptavidin beads (MilliporeSigma) for 30 min at 4 °C to remove the background. The pre-cleaned cell lysate was transferred to a new tube and 1/10 of the lysate was saved as protein input. The remaining lysate was incubated with streptavidin beads for 4 h at 4 °C before washing 5 times with ice-cold PBS. Finally, the pellet was dissolved in 30 µl 2xSDS sample buffer, followed by SDS-PAGE and Western blot with STAT3 antibody.The cells were treated as per requirement, and protein was extracted form cells. Concentration of protein was estimated by Bradford method . The proTo identify the cytokines secreted in conditioned medium from the cells, we used the cytokine array kit carrying antibodies against 102 cytokines from R&D systems (Proteome Profiler™ Human XL Cytokine Array Kit), and experiments were performed according to the manufacturer’s protocol using Odyssey imaging system (Li-Cor).Female nude (nu/nu) mice (4–5 weeks old) were purchased from Charles River . All animal studies were conducted in accordance with the NIH animal use guidelines and a protocol was approved by the UMMC Animal Care Committee. MCF-7 cells carrying SAM library before or after selection at the exponential stage were harvested at exponential stage of growth and mixed with 50% matrigel (BD Biosciences). One million cells/spot were injected to mammary fat pads of female nude mice. Tumor growth was monitored and size of tumor was measured every other day after 7 day of cell injection.t-test or one way ANOVA with the help of Prism software version 6. A value of P < 0.05 was considered statistically significant.Statistical analysis of data was performed using the Student’s www.lncrandb.org and http://www.cuilab.cn/lncrnadisease) and they are curated from the literature.We adopted a CRISPR/Cas9-based synergistic mediation activator (SAM) system using MS2 bacteriophage coat proteins combined with p65 and HSF1 because it has been shown to greatly enhance the transcription activation . Unlike To determine the involvement of lncRNAs in estrogen-independent growth, we performed gain of function screens. Using the selection procedure outlined in Figs. Next, we injected BS cells or AS cells into mammary fat pads of female nude mice to determine whether they can grow into tumor without supplement of estrogen. As expected, the parental MCF-7 supplemented with E2 (estradiol pellet) resulted in 100% tumor take rate Fig. . Tumor tTo identify which SAM gRNAs are enriched by this selection, we adopted a two-step approach. The first step was to perform lncRNA profiling by qRT-PCR arrays against each of lncRNAs in this library. The second step was to detect the relative abundance of SAM gRNAs by qRT-PCR using gRNA specific primer sets, focusing on those enriched by estrogen deprivation. The lncRNA profiling identified that three lncRNAs  were upregulated Fig.  after sehttp://kmplot.com/analysis/index.php?p=service&cancer=breast) breast cancer gene chip database [Based on the profiling results, we asked whether any of these lncRNAs has any clinical relevance. Searching kmplot  was highly enriched after selection Fig. ; other 4Next, we determined the contribution of Lnc-DC to tamoxifen resistance. We treated the Lnc-DC1 cells and vector control cells with tamoxifen. As shown in Fig. To better define the role of Lnc-DC in tamoxifen resistance, we generated Lnc-DC knockout (KO). It is evident that Lnc-DC KO cells were more sensitive to tamoxifen as determined by TUNEL assay Fig. . Then, wY705 in both normal and E2 free medium  Fig. S. We confAs master gene regulators, lncRNAs have been implicated in regulation of diverse cellular function and disease processes. The present study demonstrates Lnc-DC as a potential lncRNA involved in estrogen independent growth and tamoxifen resistance. Further characterization provides supporting evidence that Lnc-DC is a key regulator for STAT3-cytokine feedback loop, leading to tamoxifen resistance. Dysregulation of Lnc-DC may contribute to the clinical endocrine therapy resistance, and thus, Lnc-DC may serve as a biomarker to predict the response to tamoxifen.ER is not only a good diagnostic marker for breast cancer, but also serves a therapeutic target. Tamoxifen is one of the important SERMs for ER positive breast cancers  and it cMechanisms of tamoxifen resistance are complex and may involve multiple factors such as AKT, HER2, and R-Ras –42. In aY705, whereas Lnc-DC KO suppresses pSTAT3Y705 level. We further show that the interaction of Lnc-DC with STAT3 reduces the binding of SHP-1 to STAT3. In addition, Lnc-DC is able to stimulate the secretion of the cytokines such as GDF15 and IGFBP2. Finally, these Lnc-DC 1 expressing cells also reveal a high level of GDF15 and IGFBP2.Lnc-DC was first reported to be required for the differentiation of monocytes into dendritic cells by regulating STAT3 phosphorylation and it appears to be expressed exclusively in dendritic cells . Lnc-DC STAT3 becomes activated through phosphorylation on tyrosine residue as a DNA binding protein in response to epidermal growth factor and interleukin-6 . Thus, SThe gain of function screening can facilitate identification of new gene function important to different human diseases. Our CRISPR/Cas9-based SAM library screen provides such an example for identification of novel function of lncRNAs. Since overwhelming number of lncRNAs are identified to date, it is difficult to include every one of them in a single library. To tackle this challenge, we focus on a group of lncRNAs which are selected based on literature information. These lncRNAs are associated with physiological function or their aberrant expression is associated with certain disease conditions. Although such a library may miss some important hits, an advantage of the small number of lncRNAs in this library would allow us to design multiple gRNAs per lncRNA to enhance the change of gene activation. This design may be critical for functional screen because not every gRNA is equally able to activate the corresponding lncRNA. For instance, we found that only one of five Lnc-DC SAM gRNAs induces a high level of the endogenous Lnc-DC expression.In summary, we demonstrate a critical role for Lnc-DC in regulation of STAT3-cytokine loop, leading to estrogen independency and tamoxifen resistance. Furthermore, clinical data mining suggests that Lnc-DC may serve as a potential marker for prediction of tamoxifen response. In this regulatory system, JAK promotes STAT3 phosphorylation, whereas SHP-1 promotes dephosphorylation. The interaction of Lnc-DC with STAT3 may prevent SHP-1’s action on pSTAT3, keeping a high level of pSTAT3. STAT3 is known to be capable of upregulating anti-apoptotic genes such as Bcl2 and stimulating cytokine production. Since cytokines such as CXCL12, IGFBP2 and GDF15 can be regulated by both ER and STAT3, it is conceivable that these cytokines, in addition to anti-apoptotic genes, can be kept producing in the elevated Lnc-DC background such that these tumor cells can survive and grow even when ER signaling is shut down by clinical intervention such as estrogen deprivation or tamoxifen treatment Fig. . Given tSupplementary figuresSupplementary table 1Supplementary table 2


Neuropilin-1 (NRP1) is a recently identified glycoprotein that is an important host factor for SARS-CoV-2 infection. On the other hand, angiotensin-converting enzyme-2 (ACE2) acts as a receptor for SARS-CoV-2. Additionally, both NRP1 and ACE2 express in the kidney and are associated with various renal diseases, including renal carcinoma. Therefore, the expression profiles of NRP1 and ACE2 in kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) patients from the various cancer databases were investigated along with their impact on patients’ survivability. In addition, coexpression analysis of genes involved in COVID-19, KIRC, and KIRP concerning NRP1 and ACE2 was performed. The results demonstrated that both t NRP1 and ACE2 expressions are upregulated in KIRC and KIRP compared to healthy conditions and are significantly correlated with the survivability rate of KIRC patients. A total of 128 COVID-19-associated genes are coexpressed, which are positively associated with NRP1 and ACE2 both in KIRC and KIRP. Therefore, it might be suggested that, along with the ACE2, high expression of the newly identified host factor NRP1 in renal carcinomas may play a vital role in the increased risk of SARS-CoV-2 infection and survivability of COVID-19 patients suffering from kidney cancers. The findings of this investigation will be helpful for further molecular studies and prevention and/or treatment strategies for COVID-19 patients associated with renal carcinomas. Neuropilin-1 (NRP1) is a multifaceted transmembrane glycoprotein that acts as a receptor for semaphorins SEMA3A) and vascular endothelial growth factor (VEGF) family members . AngiogeA and vasACE2 is reported to be a receptor for SARS-CoV-2 in kidney injury, while, recently, NRP1 has been identified as a potential host factor for SARS-CoV-2, and NRP1 specifically binds with the C-end rule (CendR) motif of the S1 subunit of the spike protein ,7. MoreoOn the other hand, along with the lung, multiple organs such as the liver, kidneys, heart, and brain might be affected by SARS-CoV-2 in COVID-19 patients ,9. SARS-Therefore, in this study, we investigated the expression profile of NRP1 and ACE2 in kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) patients from various cancer databases. In addition, the relationship of NRP1 and ACE2 expression to patients’ survivability rate followed by the coexpression analysis of genes positively regulated with these factors and associated with COVID-19 were studied.http://gepia2.cancer-pku.cn/#index), a publicly accessible online database, was used to analyze the NRP1 expression profile across thirty-three (33) human cancers and paired normal tissues using the TCGA (The Cancer Genome Atlas) datasets [http://gepia2.cancer-pku.cn/#index).Gene Expression Profiling Interactive Analysis 2 (GEPIA2) server  using the TCGA dataset [http://ualcan.path.uab.edu/index.html) and the TCGA dataset [http://ualcan.path.uab.edu/index.html).The mRNA expression profile of NRP1 and ACE2 in KIRC and KIRP patients are analyzed by the UALCAN website ( dataset . There w dataset . UALCAN http://funrich.org/index.html) and InteractiVenn (http://www.interactivenn.net/) [All the genes positively correlated with NRP1 and ACE2 in both KIPC and KIRP were downloaded from the TCGA dataset of the UALCAN and processed and viewed by FunRich software (nn.net/) ,17. The http://ctdbase.org/) [http://www.interactivenn.net/) [A total of 7703 genes that are reported to be associated with COVID-19 were extracted from the Comparative Toxicogenomics Database (CTD) and processed (se.org/) . CTD pronn.net/) ,17. It iKIRC and KIRP are the commonest type of kidney cancers and might cover up to 90% and 10% of the total renal cell carcinoma cases, respectively ,20,21. AThough increased NRP1 expression was observed both in KIRC and KIRP compared to healthy conditions, the expression of NRP1 was found to be significantly upregulated in KIRC compared to healthy humans as measured using the TCGA dataset of UALCAN A,B. In aThe survivability rate of renal cancer patients depends on the treatment and metastatic condition. In addition, renal cell carcinoma may progress in COVID-19 patients, and the mortality rate is significantly higher than in non-cancer patients . AccordiMany host genes are expressed due to the SARS-CoV-2 infection, and expression variations of these genes regulate immune and other cell signaling pathways; therefore, they are involved in COVID-19 pathogenesis, disease progression, and case fatality . A totalSurprisingly, it was revealed that both the VEGF  and HGF, binding proteins of NRP1, are also associated with COVID-19 . ClinicaThe newly identified SARS-CoV-2 host factors, NRP1 and ACE2, are significantly upregulated in renal carcinomas. The expression of these factors is also correlated with the survivability of patients suffering from renal carcinomas. In addition, a large number of COVID-19-associated genes are coexpressed with the genes positively correlated with ACE2 and NRP1 in renal carcinomas. Therefore, along with the ACE2, a high expression of the newly identified host factor NRP1 in renal carcinomas may play a vital role in the increased risk of SARS-CoV-2 infection and high mortality of COVID-19 patients suffering from kidney cancers. The findings of this bioinformatic-based investigation will be helpful for further molecular studies and prevention and/or treatment strategies for COVID-19 patients associated with renal carcinomas.


Mechanistically, upon estrogen stimulation, estrogen receptor-α (ERα) was recruited to the KDM6B promoter, directly enhancing KDM6B expression. Subsequently, KDM6B was recruited to the BMP2 and HOXC6 promoters, resulting in the removal of H3K27me3 marks and activating the transcription of BMP2 and HOXC6, the master genes of osteogenic differentiation. Furthermore, we found that estrogen enhanced DMSC osteogenesis during calvarial bone regeneration and that estrogen’s pro-osteogenic effect was dependent on KDM6B in vivo. Taken together, our results demonstrate the vital role of the ERα/KDM6B regulatory axis in the epigenetic regulation of the estrogen-dependent osteogenic response.Osteoporosis is a highly prevalent public health burden associated with an increased risk of bone fracture, particularly in aging women. Estrogen, an important medicinal component for the preventative and therapeutic treatment of postmenopausal osteoporosis, induces osteogenesis by activating the estrogen receptor signaling pathway and upregulating the expression of osteogenic genes, such as bone morphogenetic proteins (BMPs). The epigenetic regulation of estrogen-mediated osteogenesis, however, is still unclear. In this report, we found that estrogen significantly induced the expression of lysine-specific demethylase 6B (KDM6B) and that KDM6B depletion by  The disease is multifactorial and caused by an imbalance between bone formation and resorption mediated by osteoblasts and osteoclasts, respectively.11 Although MSCs isolated from adult bone marrow (BMSCs) are best known, current studies using MSCs derived from dental tissues in bone tissue engineering have demonstrated promise for multiple clinical applications, as dental mesenchymal stromal cells (DMSCs) display similar characteristics to BMSCs.13 In fact, compared to BMSCs, DMSCs may be more advantageous as DMSCs are more readily available without significant risk to the donor from discarded dental tissues such as third molars or primary teeth. Furthermore, these cells exhibit increased mineralization potential, a faster proliferation rate, and a higher number of stem/progenitor cells than BMSCs.15Human mesenchymal stromal cells (MSCs) are progenitor cells with the following characteristics: (1) the ability to self-renew and differentiate into various cell types, including osteoblasts, chondrocytes, and adipocytes; (2) ease of isolation; and (3) lack of immunogenicity, making them indispensable tools in tissue engineering and regenerative therapy.16 However, as exhibited in the clinical manifestation of postmenopausal osteoporosis, estrogen also plays an important role in bone homeostasis and has been consistently shown to be protective of bone in all sexes.17 While estrogen deficiency rapidly leads to systemic osteoporosis in both long bones and craniofacial bones, the addition of exogenous estrogen can enhance the osteogenic potential of MSCs, leading to restoration of bone loss.19 On a cellular level, estrogen directs MSC differentiation into osteogenic lineages and osteoblast proliferation, leading to an overall increase in bone mineral density.23 Estrogen induces these pro-osteogenic effects by binding to estrogen receptors, estrogen receptor-α (ERα) and estrogen receptor-β (ERβ), which then act as transcription factors to control the expression of genes related to bone formation.24 While estrogen signaling is regulated through the balance between the activities of ERα and ERβ, ERα has been shown to be more important in estrogen-mediated osteogenesis. Zhou et al. reported that estrogen stimulates bone morphogenetic protein-2 (BMP2) gene transcription in mouse MSCs, leading to enhanced bone formation. Mechanistically, estrogen-activated ERα and ERβ both bind to estrogen-responsive element (ERE) binding sites in the BMP2 promoter. However, ERα was substantially more efficacious than ERβ at activating the BMP2 promoter.20 In another study by Chen et al., primary MSCs were isolated from postmenopausal women with osteoporosis. When these cells were treated with estrogen, ERα and alkaline phosphatase (ALP) mRNA levels were upregulated, while ERβ expression remained unchanged, suggesting that estrogen could promote the osteogenic potential of MSCs from osteoporotic patients via ERα activity.21 As such, the regulatory mechanisms of estrogen-mediated bone formation at the transcriptional level are understood. However, the key epigenetic players in estrogen-mediated osteogenesis remain unknown.Estrogen (E2), a naturally occurring steroid, is best known for its function in reproductive organ development.26 Specifically, histone methylation status is managed by two opposing enzymes, histone methyltransferases, and histone demethylases.27 A high density of repressive epigenetic marks, such as H3K27me3 and H3K9me3, on the promoter region inhibits gene expression.28 When these inhibitory epigenetic signatures on lineage-specific genes are erased by histone demethylases, silenced genes become activated to drive stem cell differentiation into specific lineages.29Stem cell fate determination requires a delicate orchestration of genetic and epigenetic programs. Epigenetic modifiers are largely responsible for the elaborate orchestration of gene activation and inhibition at specific time points, directed toward the terminally differentiated phenotype of MSCs. Among various epigenetic regulators, histone methylation has recently emerged as an integral mechanism in MSC lineage commitment.30 A decade ago, KDM6B’s role in cell differentiation was first identified in ectodermal differentiation, including epidermal differentiation and neural stem cell differentiation in human cells.32 Studies have shown that KDM6B, by directly upregulating the expression of neurogenic genes, including Pax6, Nestin, and Sox, is critical for the neural commitment of embryonic stem cell (ESC)-derived neural stem cells.32 During neural differentiation, KDM6B was also found to interact with Smad2/3 and Smad1 to activate the transcription of target neuronal genes.34 Other studies revealed the involvement of KDM6B in the formation of the endoderm by either modulating the WNT signaling pathway or binding to T-box transcription factors to drive ESCs into a definitive endodermal lineage.36 Our previous studies focused on MSC differentiation characterized KDM6B as a major epigenetic regulator in MSC osteogenic and odontogenic differentiation.37 Moreover, we recently showed that alcohol-induced inhibition of KDM6B is responsible for dysregulation of DMSC osteogenic differentiation following exposure to alcohol.38 Less is known, however, about the role of KDM6B in estrogen-dependent MSC osteogenic commitment. Therefore, this study examined the epigenetic program responsible for the estrogen-dependent osteogenic response by investigating KDM6B and elucidating the mechanism by which KDM6B regulates estrogen-mediated stem cell differentiation.KDM6B, a member of the Jumonji-C domain-containing histone demethylase family, reduces H3K27me3 to H3K27me1 at the pericentric heterochromatin. KDM6B’s diverse functions in stem cell differentiation and development are becoming increasingly elucidated. KDM6B selectively modulates H3K27me3 silencing epigenetic signatures in target genes and lineage-specific genes to direct stem cells to a specific lineage fate.41 To determine whether E2 could promote the osteogenic differentiation of DMSCs, we first induced the cells to undergo osteogenic differentiation in the presence of E2. E2-enhanced osteogenesis in DMSCs from all donors, as demonstrated by increases in ALP activity after treatment for 5 days  treatment without osteogenic inducing medium (OIM) for 2 and 4 h. qRT-PCR revealed that KDM6B was most strongly upregulated by E2  without OIM for 2 h. qRT-PCR revealed that KDM6B expression was significantly upregulated in a dose-dependent manner , both of which resulted in depletion of KDM6B in DMSCs, as confirmed by qRT-PCR , E2 treatment resulted in significant induction of KDM6B expression, which was strongly suppressed upon the introduction of KDM6B shRNA. Flag-tagged KDM6B in the DMSC/shKDM6B/Flag-KDM6B cells restored KDM6B expression to a level similar to that in the DMSC/Scrsh/V cells, as confirmed by western blot analysis  were implanted following DMSC seeding. Micro-CT images demonstrated maximum bone formation at 25 μmol·L-1 E2 compared to the scaffold without E2 loading  showed minimal bone healing of just 3.19% BV/TV only after 8 weeks. In contrast, defects treated with estrogen showed significant increases in defect repair with greater estrogen dosage, amounting to 3.12% at 1 μmol·L-1, 5.48% at 5 μmol·L-1, and 10.36% at 25 μmol·L-1.Our group previously reported the pro-osteogenic effects of estrogen delivery in an animal model of palatal fracture.shKDM6B and DMSC/Scrsh were seeded in scaffolds with and without 25 μmol·L-1 E2 and subsequently transplanted into 3 mm calvarial critical-sized defects. After 8 weeks, crania with transplanted scaffolds were harvested and prepared for micro-CT and histological analysis. Visualization and quantitative analysis of micro-CT images revealed that E2 treatment was unable to promote further bone repair in the absence of KDM6B compared to that in the DMSC/Scrsh cells , which is consistent with a previous report that there is no KDM6B binding site on the RUNX2 promoter in human MSCs.25 During estrogen-induced osteogenesis, HOX may have a direct role in osteogenesis in a RUNX2-independent manner, as reported by Hassan et al.49 Another important difference in this study compared to our previous work is KDM6B’s effect on DLX5 gene expression. In previous studies, KDM6B depletion did not alter the expression of the DLX5 gene, a transcription factor that plays critical roles in osteoblast differentiation and craniofacial development.37 Instead, we showed that BMP-induced KDM4B, a histone demethylase for the repressive H3K9me3 signature, epigenetically regulates and activates DLX5 expression, leading to MSC osteogenic commitment.25 In this study, we found that estrogen-induced DLX5 expression is significantly suppressed in KDM6B-depleted cells during estrogen-mediated osteogenesis, thus suggesting that regulatory mechanistic variations exist among different pathways.Our results confirmed the findings from our previous studies using both BMSCs and DMSCs that osteogenic commitment was mediated by KDM6B through 51 However, our study showed that KDM6B demethylase activity is responsible for the activation of the BMP2 and HOXC6 genes, leading to estrogen-induced MSC osteogenic differentiation. As bone homeostasis by estrogen relies on a delicate balance between osteogenesis and adipogenesis, MSC commitment to an osteogenic fate requires the concurrent inhibition of differentiation toward an adipogenic lineage. Our previous study reported that the depletion of KDM6B significantly induced the expression of the master adipogenic transcription factor PPARG, leading to enhanced adipogenic differentiation of human BMSCs.25 Similarly, another study showed that KDM6B depletion promoted the differentiation of mouse MSCs into adipocytes, while overexpression of KDM6B inhibited adipogenesis.52 These results support the conclusion that KDM6B can direct MSCs into an osteogenic fate by both promoting MSC osteogenic differentiation and restricting adipogenic differentiation. KDM6B’s ability to manage bone-fat balance through estrogen-mediated pathways highlights its potential as a therapy for postmenopausal patients. Beyond its antiadipogenic function, KDM6B likely has additional targets that contribute to estrogen-induced bone formation. For instance, Lu et al. reported that KDM6B removed H3K27me3 at insulin-like growth factor binding protein 5 promoters to activate the target gene, resulting in an MSC-mediated anti-inflammatory effects and bone tissue regeneration.53 In addition, KDM6B was found to suppress osteoblast apoptosis by erasing H3K27me3 marks from the antiapoptotic Bcl-2 gene and regulating Bim phosphorylation.54 KDM6B therefore increases osteoblast number and enhances bone formation by both promoting osteoblastic differentiation and stimulating osteoblastic apoptosis. As bone remodeling involves a complex interplay among the immune system, osteoblasts, and osteoclasts, estrogen deficiency has been shown to be linked with increased inflammation, enhanced osteoclast differentiation and decreased osteoblast activity.1 As such, the examination of KDM6B’s interaction with inflammatory processes such as NF-κB signaling and the osteoblast apoptotic pathway may reveal KDM6B’s role as a main epigenetic player in estrogen-mediated bone homeostasis and its potential to guard against postmenopausal osteoporosis.Previous studies reported that KDM6B can regulate target genes in a demethylase-independent manner.55 In the absence of estrogen, specific inhibitory histone methyltransferases place methyl groups on histones to repress constitutive ERα activity.56 Upon estrogen stimulation, the histone demethylase KDM1A was found to dismiss these repressive epigenetic marks to induce ER activation, ensuring a ligand-dependent and regulated transcriptional response.56 Other studies identified the histone demethylases KDM4B and KDM3A as critical regulators of the ER signaling cascade in cancer cells.58 Mechanistically, KDM4B directly interacts with the ER and components of the ER signaling system, erasing the inhibitory epigenetic mark H3K9me3 in ERα target sites for the expression of MYB, MYC, and CCND1.59 In addition, KDM4B’s association with ERα and the transcriptional regulator mixed-lineage leukemia 2 complex was shown to be a critical mechanism for estrogen-induced G1/S transition of the cell cycle in breast cancer.60 In breast cancer cells, Svotelis et al. uncovered the participation of KDM6B in estrogen/ERα signaling. Upon E2 induction, KDM6B and ERα work in concert to demethylate the H3K27me3 mark from the poised enhancer region of the ERα-regulated antiapoptotic gene BCL2, leading to the prevention of breast cancer cell death.61 These findings highlight the dynamic and important role of histone demethylases in the epigenetic control of estrogen and ERα pathways.While very little is known about the epigenetic programming of estrogen/ER signaling in MSC osteogenic differentiation, several studies have revealed an association between histone demethylases and ER signaling in breast cancer cells. ERα is a ligand-dependent transcription factor that, upon initiation by estrogen binding, undergoes translocation to the nucleus to modulate the gene transcription machinery.KDM6B, indicating the direct regulation of KDM6B by ERα at the transcriptional level. This finding underscores the role of ERα signaling in regulating epigenetic modifiers and possibly the overall chromatin landscape, which may constitute a feedforward loop in the cellular function of ERα. Moreover, as shown by KDM4B’s epigenetic modulation of ERα and KDM6B’s physical interaction with ERα in breast cancer cells, there is a possibility that a positive feedback loop exists whereby KDM6B epigenetically promotes ERα transcription.61 Notably, further mechanisms responsible for regulating KDM6B expression during osteogenic differentiation are being elucidated. Emerging evidence has shown that KDM6B expression can be post-transcriptionally regulated by microRNAs. Tang et al. reported that elevated levels of miR-99a in osteoporotic bone marrow suppress osteoblastic commitment and differentiation of MSCs through its inhibitory effects on KDM6B.62 Conversely, the inhibition of miR-99a promoted bone formation in vivo. Another study revealed that miR-146a directly associates with the 3′ UTR of KDM6B and negatively regulates KDM6B function during MSC osteogenic differentiation.63 As with miR-99a, inhibition of miR-146a restored KDM6B expression and significantly increased osteogenic potential. Therefore, the identification of KDM6B regulatory mechanisms, such as miR-99a and miR-146a, during estrogen-induced osteogenesis may lead to targeted therapeutic approaches for postmenopausal osteoporosis.Information on the direct targets of ER signaling during bone formation remains limited. Our study revealed that estrogen induces KDM6B expression through the recruitment of ERα to the promoter region of BMP2 and HOXC6 transcription during estrogen-mediated osteogenesis. These findings provide a vital epigenetic link between estrogen/ER signaling and bone homeostasis, thereby providing a foundation for future studies.Collectively, our present study demonstrates for the first time that when directly induced by ERα upon estrogen stimulation, the histone demethylase KDM6B epigenetically activates -1 penicillin and 100 U·mL-1 streptomycin in a humidified 5% CO2 incubator at 37 °C. The OIM consisted of αMEM supplemented with 5% charcoal-stripped FBS , 100 μmol·L-1 ascorbic acid , 10 nmol·L-1 dexamethasone  and 2 mmol·L-1 β-glycerophosphate  with or without 17 β-estradiol (E2) . The medium was changed every 2 days.The primary DMSCs used in this study were isolated from craniofacial tissues (IRB#13-000241) from four different donors, two males and two females aged 17–24 years old. Cells were grown in phenol red-free alpha-modified Eagle’s medium (αMEM) supplemented with 10% charcoal-stripped fetal bovine serum (FBS) , 100 U·mL64 For viral infection, DMSCs were plated overnight and then infected with retroviruses or lentiviruses together with polybrene  for 24 h. The cells were then infected again for 24 h. Resistant clones were cultured, and the efficiency of knockdown or overexpression was confirmed by quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) or western blots. In rescue experiments, DMSCs with knockdown were transduced with retroviral constructs containing the Flag- gene of interest. The target sequences for shRNA were as follows: sh1KDM6B: 5′- GCAGTCGGAAACCGTTCTT −3′, sh3KDM6B: 5′- GTGGGAACTGAAATGGTAT -3′. Flag-KDM6B full-length cDNA was cloned into a retroviral construct by PCR.Viral packaging was performed as previously described.-1 Tris buffer (pH 9.6) after 5–7 days of induction. ALP activity assays were performed using alkaline buffer solution  with alkaline phosphate yellow liquid substrate  for 30 min at 37 °C, and the absorbance of the reactions was read at 405 nm and then normalized based on protein concentrations.For ALP staining, cells were fixed with 70% ethanol and incubated with a solution of 0.25% naphthol AS-BI phosphate  and 0.75% Fast Blue BB , which was dissolved in 0.1 mol·L-1 sodium phosphate for 30 min at room temperature. The concentration was determined by absorbance measurement at 562 nm on a multiplate reader and calculated by a standard calcium curve in the same solution. The final calcium level was normalized to the total protein concentrations in each group prepared from a duplicate plate.For detection of the mineralization potential, cells were induced for 2–3 weeks, fixed with 70% ethanol in 1 h and stained with 1% Alizarin Red S . For quantification of calcium mineral deposition, Alizarin Red S was destained using 10% cetylpyridinium chloride in 10 mmol·LALP: forward, 5′- GACCTCCTCGGAAGACACTC -3′; reverse, 5′- TGAAGGGCTTCTTGTCTGTG -3′. OSX: forward, 5′- GGAAGAAGCCCATCCACA -3′; reverse, 5′- AAGCCTTGCCATACACCTTG -3′. DLX5: forward, 5′- CTACAACCGCGTCCCAAG -3′; reverse, 5′- GCCATTCACCATTCTCACCT -3′. KDM6B: forward, 5′- CCTCGAAATCCCATCACAGT -3′; reverse, 5′- GTGCCTGTCAGATCCCAGTT -3′. OCN: forward, 5′- TGAGAGCCCTCACACTCCTC -3′; reverse: 5′- TCCTGCTTGGACACAAAGG -3′. HOXC6: forward, 5′- GAATTCCTACTTCACTAACCC-3′; reverse, 5′-TCATAGGCGGTGGAATTGAG-3′. The primers for epigenetic gene profiling are provided in Supplementary Table Total RNA was isolated from DMSCs using TRIzol reagents . RNA was synthesized from cDNA with random hexamers and reverse transcriptase according to the manufacturer’s protocol . qRT-PCRs were performed using the SYBR Green PCR kit  and the CFX qRT-PCR Detection System. Primer pairs for qRT-PCR were as follows: 6 cells were used. Cells were incubated with a dimethyl 3,3′ dithiobispropionimidate-HCl (DTBP)  solution (5 mmol·L-1) for 10 min at room temperature in a dark area and treated for 15 min at 37 °C in 1% formaldehyde. Total cell lysates were sonicated to generate ~200–500 bp DNA fragments. All resulting precipitated samples were quantified using qPCR. Data are expressed as a percentage of input DNA. Antibodies used for ChIP assays were purchased from the following commercial sources: rabbit polyclonal anti-KDM6B , rabbit monoclonal anti-H3K27me3 , and rabbit monoclonal anti-ERα . Primer pairs were HOXC6: forward, 5′- GGTTGTGCCACAGTCTAGAAG -3′; reverse, 5′- TTTCACAATGGGCAGCGGG -3′. BMP2: forward, 5′- TCCTCTGAGCTGCTAATCGC -3′; reverse, 5′- TGTGGCAGACTGAAGGATGG -3′. KDM6B: forward, 5′- CCCAGTAAGGTCAGATGTGGTC -3′; reverse, 5′- TGGGAGAAGGACTAGGGATG -3′.For each ChIP reaction, 2 × 10-1) in OIM. After 24 and 48 h, cells were collected and washed once using PBS. RIPA buffer  was used to lyse cells for 30 min at 4 °C. The supernatant was collected as total protein after centrifugation at 12 000 r·min-1 for 20 min. Equal amounts of protein were separated by SDS-PAGE and detected for target proteins. Antibodies against ERα  and β-actin  were utilized.Cells were treated with E2 (10 nmol·L-1) treatment for 4 h, the cells were fixed in fresh 4% paraformaldehyde (PFA). The coverslips were washed with PBS for 5 min twice. The cells were then blocked in 1% BSA for 1 h at room temperature, followed by incubation with primary antibody overnight in a 4 °C refrigerator . On the following day, coverslips were washed with PBS for 3 min twice. Then, secondary antibodies were applied to the cells for 1 h at room temperature. After PBS washing, the nucleus was stained with DAPI. The cells were visualized under fluorescence or confocal microscopy.Cells were grown on 10 mm glass coverslips. After OIM with or without E2  to achieve a porosity of 92% (v/v), and the slurry was compressed into Teflon molds. After being lyophilized overnight, the scaffolds were immersed in Milli-Q water to leach out sucrose. The scaffolds were then sterilized in 70% EtOH for 30 min and rinsed with sterile water. Last, the scaffold sheets were shaped into round discs (3 mm in diameter and 0.5 mm in height) with a biopsy punch. The PLGA scaffolds were further coated with apatite layers by incubating scaffolds in simulated body fluid (SBF). The scaffold discs were subjected to glow discharge argon plasma etching (Harrick Scientific). The etched scaffolds were incubated in SBF1 composed of CaCl2, MgCl2•6H2O, NaHCO3, K2HPO4•3H2O, Na2SO4, KCl and NaCl for 24 h and then further incubated in SBF2 composed of CaCl2, K2HPO4•3H2O, KCl and NaCl for another 24 h at 37 °C. The Ap-PLGA scaffolds were loaded with phosphate-buffered saline (PBS) or E2  and lyophilized for further usage.Apatite-coated poly(lactic-coglycolic acid) (Ap-PLGA) scaffolds were prepared through solvent casting and particulate leaching processes following an established protocol as previously described.67 After gentle removal of the circular bone plug, DMSCs (1 × 106 cells) seeded with or without the E2 scaffold were placed on the defect. After surgery, all animals were allowed to fully recover. All mice were euthanized 8 weeks after implantation.The animal protocol for this study was approved by the Animal Research Committee at the University of California, Los Angeles. Eight-week-old male SCID mice (Charles River Laboratories) were randomly divided into four groups. In each mouse, a 3-mm-diameter calvarial critical-sized defect was created on the parietal bone with a trephine drill as previously described.The calvarium was dissected and fixed with 10% v/v formalin for 48 h. High-resolution microcomputed tomography  was used to scan samples at a resolution of 10 μm with a 60-kV and 166-μA X-ray source and a 1-mm aluminum filter. For the analysis, NRecon software (Bruker) with image correction steps was used to reconstruct three-dimensional image datasets from 2D X-ray images, DataViewer software (Bruker) was used to orient with images on a 3D plane, and CTAn software (Bruker) was used to conduct three-dimensional volumetric analysis. The region of interest was outlined with a circular shape on consecutive transaxial sections to create a uniform volume of interest. BV/TV values were quantified.-1, pH 7.1) solution for 2 weeks. After decalcification, the parietal bone was removed from the cranium and embedded in paraffin. Tissue samples were sectioned into 5-μm sections and stained with hematoxylin and eosin at the University of California, Los Angeles, Tissue Procurement Core Lab. Slides were submerged in 0.1% trypsin diluted in 1X PBS for 10–20 min at 37 °C for antigen retrieval and then incubated with anti-KDM6B antibody  as a primary antibody. Tissue sections were visualized using 3-amino-9-ethylcarbazole , followed by counterstaining with hematoxylin. Sections were sealed with Faramount aqueous mounting solution (Dako) as previously described.68Samples were fixed in 4% PFA for 24 h and decalcified in 10% ethylenediaminetetraacetic acid (0.1 mol·LAn Olympus IX73 microscope and cellSens software version 1.6 (Olympus Corporation) were used to visualize and quantify the bone volume of calvarial defects in IHC. Three random fields of view per section sample were randomly taken for each specimen, and the average values were used as the data point for each specimen.t-tests. One-way ANOVA with Tukey’s post hoc test was applied when there were more than two groups. Two-way ANOVA with a Bonferroni post hoc test was applied when there were multiple groups across different points. When a normal data distribution could not be assured, the Wilcoxon/Mann–Whitney test was applied. Using GraphPad Prism 5, the results were considered statistically significant at P < 0.05.Data are expressed as the mean ± SEM. Differences in measured variables between two groups were assessed by two-tailed Student’s Supplementary Information


Allergic rhinitis (AR) is a common disorder affecting up to 40% of the population worldwide and it usually persists throughout life. Nasal epithelial barrier constitutes the first line of defense against invasion of harmful pathogens or aeroallergens. Cell junctions comprising of tight junctions (TJs), adherens junctions, desmosomes and hemidesmosomes form the nasal epithelial barrier. Impairment of TJ molecules plays causative roles in the pathogenesis of AR. In this review, we describe and discuss the components of TJs and their disruption leading to development of AR, as well as regulation of TJs expression by epigenetic changes, neuro-immune interaction, epithelial-derived cytokines , T helper 2 (Th2) cytokines  and innate lymphoid cells. These growing evidence support the development of novel therapeutic approaches to restore nasal epithelial TJs expression in AR patients. Allergic rhinitis (AR) is a global health problem affecting 10–40% of the population worldwide and it usually persists throughout life . AR is ae.g. pro-inflammatory cytokines, growth factors and chemokines)  . Epithelmokines) , 13. Mucmokines) .Apical junctional complexes (AJCs) connect epithelial cells to one another and they consist of TJs, adherens junction, desmosomes and hemidesmosomes . TJs areTJs in the epithelial cells consist of three primary constituents of transmembrane proteins namely occludin (OCLN), claudin (CLDN) and junctional adhesion molecules (JAMs) . OCLN ~, 27. OCLThe CLDN family contains more than 25 protein members . CLDNs ai.e. one transmembrane domain and one cytoplasmic tail of variable length containing a PDZ domain that can interact with ZO  and four related proteins . JAMs are type I transmembrane glycoproteins composed of two immunoglobulin-like domains  with ZO . They ha with ZO .OCLN mRNA expression also occurred in HDM-induced AR patients compared to non-allergic controls  patients  albumin levels  of HDM-induced AR patients compared to non-allergic controls  and exhibited disorderedly arrangement of nasal mucosa epithelium  or Akt inhibitor (MK-2206) reversed the effects of UPM. Through Akt inhibition, it decreased UPM-induced ROS formation and p38 and p65 protein phosphorylation, and restored the expression of ZO-1 and E-cadherin . MeanwhiDermatophagoides pteronyssinus (Der p) and Dermatophagoides farinae (Der f)  has been reported to cleave extracellular domain sites of OCLN and in CLDN1 proteins, resulted in amplified epithelial permeability that allowed the passage of Der p 1 through the epithelial barrier  –55. HDM  barrier , 56, 57.proposed . Treatmeeability .via fungi exposure that facilitates the access of pathogens and directly activates immune cells  and in vivo AR model (rats) downregulated the expression of both mRNA and protein levels of OCLN, CLDN1, ZO-1 and JAM-A as well as increased TER and FD4 permeability compared to control group . This results in the activation and polarization of memory Th2 cells, leading to the secretion of Th2 cytokines such as IL-4, IL-5, and IL-13 , and presence of mast cells and histiocytes. ILCs especially ILC2s are enriched in barrier tissues such as the skin, lung, and intestine and where they regulate immune responses surrounding the epithelium and sensitization to allergens  through IL-13 in asthma  by reducing expression of TJs in epithelial cells  monoclonal antibodies to nasal secretion of AR patients successfully restored the Th2-induced epithelial barrier dysfunction . It was pression . IL-4 wapression .IL6 SNP (rs1800795) was linked with an increased risk of AR (Therapeutic effects of the HDACi SoB on mice with AR and Trichostatin A (TSA) on nasal lavage fluid of AR patients resulted in significantly decreased serum levels of IL-4 and IL-10, and IL-4 and IL-5, respectively , 89. Botsk of AR  and posisk of AR . The prosk of AR .IL13 was detected in the epithelial compartment of the nasal mucosa of perennial AR patients but not observed in normal subjects (miR)-143 where overexpression of miR-143 significantly decreased the expression of pro-inflammatory factors (e.g. eotaxin and mucin) responsible for producing nasal symptoms , and they are found in the nose, trachea and proximal airways. Tuft cells have close proximity with nerve fibre and can promote protective respiratory reflexes such as sneezing, release neurotransmitters (such as acetylcholine), eicosanoids and cytokines (e.g. IL-25 and TSLP) (Solitary chemosensory cells (SCCs) are found in healthy sinus cavity and turbinate tissues of the human upper airway where they function as epithelial sentinels by detecting pathogenic metabolites and initiate protective immune defense . Increasnd TSLP) , 109. Tund TSLP) .in vitro  expressed in epithelial cells and endothelial cells are involved in inflammatory process and have been shown to regulate the permeability of cellular barriers in several tissues –113. TRPin vitro . Hence, in vitro . Decreasin vitro .miR-125b, miR-16, miR-299-5p, miR-126, miR-206 and miR-133b  increased miR-125b expression by increasing the expression and activation of CXCR4 which downregulated FoxP3 expression (miR-125b-overexpressed group and vice versa in the miR-125b inhibitor group compared with the control group. The autophagy inhibitor 3-MA restored TJ proteins expression in the miR-125b-overexpressed group. These findings was demonstrated in both in vitro (RPMI 2650) and in vivo (rat AR model)  . This stvia histone posttranslational modifications (PTMs) such as acetylation  and transmit the information to immune cells which then facilitate chromatin remodeling of epithelial cells, resulting in increased chromatin accessibility while embedding a memory of the experience within their chromatin . This noe.g. imiquimod, abrasion wounding or Candida infection) compared to naïve mice, even 180 days later. This was possible because at 30 days after the exposure, most chromatin changes were still maintained in EpSCs and increased chromatin accessibility in genes encoding molecules involved in inflammation, interleukin signaling, oxidative stress response and proliferation  to hasten barrier restoration after subsequent tissue damage . Accelerferation .RM) cells could be maintained in barrier tissues for prolonged periods, suggesting their roles in preserving epithelial barrier  cells via a homeostatic proliferation mechanism  targeting IL-4 and IL-13 receptors in patients with polyp showed preservation of several disease-associated genes including CTNNB1, a key WNT mediator that influences basal cell proliferation and differentiation . This ex barrier . It was depleted . CD8+ TRechanism .versus normal subjects, or in murine AR models.The structure of nasal epithelial barrier, their associations with ILCs and regulation of TJs expression by cytokines, nerves activation and epigenetic changes, are presented in e.g. air pollution, HDMs) and endogenous  triggers trapping the epithelium in a diseased state contributing to AR development, and protection of the nasal epithelial barrier integrity through restoring TJs expression is a promising therapeutic approach for AR patients.Impairment of nasal epithelial barrier through the loss of TJs expression contributes to the development of AR. The pro-inflammatory, epithelial-derived cytokines TSLP, IL-25 and IL-33 play critical roles in AR inflammation. These cytokines activate and expand ILC2s upon encounter with allergens which subsequently induce the release of other Th2 cytokines leading to TJs breakdown in nasal epithelial of AR patients. Thus, targeted therapy against these cytokines is a therapeutic opportunity for the disease. For instance, tezepelumab, a human monoclonal therapeutic antibody that targets TSLP, is currently being evaluated for the treatment of airway allergic diseases including asthma and AD . AlthougSMNH, HTT, NSMA and KKW conceived the manuscript. SMNH and KKW designed the manuscript, wrote and revised the manuscript. SMNH prepared the figure and table. HTT, NSMA and NMS edited and revised the manuscript. All authors have read and approved the final manuscript.This work was supported by Universiti Sains Malaysia (USM) grants comprising of the Research University Grant (1001/PPSP/8012349) awarded to KKW, Research University Grant (1001.PPSP.8012285) awarded to NSMA and USM Fellowship Scheme awarded to SMNH.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.


Pulmonary rehabilitation is a strongly recommended and effective treatment for people with chronic lung disease. However, access to pulmonary rehabilitation is poor. Globally, pulmonary rehabilitation is accessed by less than 3% of people with chronic lung disease. Barriers to referral, uptake and completion of pulmonary rehabilitation are well documented and linked with organizational, practitioner and patient-related factors. Enhancing the knowledge of health care professionals, family carers, and people with chronic lung disease about the program and its benefits produces modest increases in referral and uptake rates, but evidence of the sustainability of such approaches is limited. Additionally, initiatives focusing on addressing organizational barriers to access, such as expanding services and implementing alternative models to the conventional center-based setting, are not yet widely used in clinical practice. The COVID-19 pandemic has highlighted the urgent need for health care systems to deliver pulmonary rehabilitation programs remotely, safely, and efficiently. This paper will discuss the pressing need to address the issue of the low accessibility of pulmonary rehabilitation. It will also highlight the distinctive challenges to pulmonary rehabilitation delivery in rural and remote regions, as well as low-income countries. Pulmonary rehabilitation is an important treatment for people with chronic lung disease . The offIn people with chronic obstructive pulmonary disease (COPD), strong evidence confirms that pulmonary rehabilitation optimizes muscle function, improves exercise capacity, reduces dyspnea and enhances health-related quality of life . FurtherDecades of research has demonstrated consistent barriers to access to pulmonary rehabilitation. The official ATS/ERS statement of 2015 highlighted important barriers to pulmonary rehabilitation including insufficient funding, limited resources and lack of knowledge and skill of healthcare professionals and patients regarding the benefits of pulmonary rehabilitation . In 2017While there has been a modest improvement over the last decade, current rates of referrals to pulmonary rehabilitation in COPD remain suboptimal worldwide. In 2018, a scoping review of 10 developed countries found referral rates of 35% of eligible patients or lower in over 90% of included studies . ReferraJAMA, Rochester et al. highlighted that one of the key barriers to pulmonary rehabilitation referral was the poor recognition of its benefits by health care organizations [In a recent editorial in izations . This isizations . A US stizations . Additioizations . A Canadizations .The two most common barriers to referral are related to the lack of healthcare professionals’ knowledge: knowledge about the construct of pulmonary rehabilitation and knowledge about its benefits . In the Pulmonary rehabilitation uptake, which has been defined as the percentage of referred patients who attend the program for their initial assessment or attend at least one session , is alsoCompletion of pulmonary rehabilitation has been defined as the percentage of attendees who underwent 70% of sessions or those who completed their end-rehabilitation assessment . It is rRehab sounds like…when we used to take people to rehab centers sort of thing, old people, something like that, which would turn me off actually” [a focus for the delivery of quality care through changes in thinking about power relationships and patients ‘rights’” [this may or may not help you!” [Lack of perceived benefits of rehabilitation is a strong barrier to program uptake . One quactually” . Perceptctually” . Culturarights’” . In the rights’” . Anotherlp you!” .I think sometimes the doctors can be very abrupt with you, ... I don’t know whether it’s my imagination … perhaps my guilt thinking oh perhaps they haven’t got much patience with me because it’s self-inflicted, I don’t know.” [In another qualitative study in people who declined pulmonary rehabilitation post-exacerbation of COPD, guilt, fear of others’ judgment and reduced help-seeking appeared to be a strong barrier to uptake, “t know.” . People t know.” . Additiot know.” .I just can’t make it because I have no car and I have to walk all the way down to X Rd; that takes me about half an hour.” (p3) [Other important barriers to uptake of pulmonary rehabilitation occur at the organizational level. Environmental factors such as travel time, use of public transport, and weather conditions are well-known barriers of rehabilitation uptake ,40,47,48While access to rehabilitation services is poor worldwide, living in remote regions or rural areas acts as an added disadvantage for people with chronic lung disease. A US survey indicated that pulmonary rehabilitation centers are more established in metropolitan counties, while 73% of rural counties do not have an outpatient program . In AustA recent systematic review of 112 publications in 78 developing and underdeveloped countries found that pulmonary rehabilitation is established in only 17 of the included countries . A qualiIf we know what happens (in pulmonary rehabilitation) then we can sell it better (practice nurse)” [Enhancing the knowledge of health care professionals on pulmonary rehabilitation has been prompted by the insights of health care professionals themselves, “ nurse)” . A syste nurse)” : a patie nurse)” , general nurse)” ,15 and a nurse)” . Unfortu nurse)” .One retrospective analysis reported that a Canadian hospital that includes a Living Well with COPD (LWW COPD) program had an 85% referral rate to pulmonary rehabilitation compared to hospitals without LWW COPD  . AdditioTwo systematic reviews appraised interventions to optimize uptake of pulmonary rehabilitation ,64. One From the start of the twenty-first century, tele-medicine has been reported to have many benefits, especially in providing care in a non-emergency setting . BenefitThese novel models of rehabilitation are not broadly adopted in usual care worldwide, although the COVID-19 pandemic has accelerated uptake. Small scale implementation studies carry promising results. In Australia, the HomeBase model from the HomeBase trial  was rollhttps://homebaserehab.net/ (accessed on 13 October 2021). Additional remotely delivered pulmonary rehabilitation recourses include the Canadian Thoracic Society pulmonary rehabilitation-endorsed program and guidance on virtual models of pulmonary rehabilitation from the Agency for Clinical Innovation in New South Wales, Australia [People with chronic lung disease are amongst the most vulnerable groups to developing severe complications with COVID-19 . The Worustralia ,83.Access to pulmonary rehabilitation is suboptimal worldwide. Whilst barriers to referral, uptake and completion are well described, there have been few interventions that successfully modify these factors. Improving access to pulmonary rehabilitation through the use of telerehabilitation and home-based models shows promise but requires policy drivers for widespread implementation. The COVID-19 pandemic has highlighted the need to optimize the remote delivery of pulmonary rehabilitation services, with strong quality assurance to ensure optimal patient outcomes.