Over the past century, there has been an evolution of thought about how “disability” is being viewed in cultural, sociopolitical, and legal terms . This haThe shift from an “individual model” to a relational model of disability is also reflected in the WHO's International Classification of Functioning, Disability, and Health , a modelAs an extension of the relational model, disability can be understood as a complex system challenge or “wicked problem” which transcends simplistic notions of individual impairment characteristics that manifest in homogeneous environments . DisabilFurther significant developments have occurred in public health. Traditionally, public health professionals focus on the prevention of “disabilities” resulting from preventable causes of illness and injury . Over the past 20 years, foremostly in the United States, public health professionals have recognized people with disabilities as a frequently disadvantaged population in relation to primary health care, health promotion programe, health risk behavior counseling, and public health campaigns and interventions . There iThe landscape of disability and rehabilitation is not static. New conditions with more or less complex impairments and rehabilitation requirements are continuously emerging. At present, we are only beginning to understand what the legacy of COVID 19 will be  and its The last 30 years have seen unprecedented changes in the development of new technologies, especially in information and communication but also in relation to home, transportation and other aspects of daily living. People with disabilities are key users of technology in relation to mobility and navigation as well as in information-processing and communication. Increasingly, the boundaries between mainstream and assistive technologies are blurring . If thisThe need for close involvement of stakeholders in the design of services, policies, or research is increasingly being recognized around the world . People Rehabilitation is a complex process and involves multiple stakeholders. Patients or consumers who are being referred to rehabilitation require the optimization of their functioning and a meaningful return to their daily lives including work and employment, life activities, school, and community affairs. Co-existing or multiple morbidities can complicate and make negatively impact a person's activity and participation and thereby will challenge the rehabilitation process . IncreasMoreover, rehabilitation and other health care professionals will be increasingly confronted with complex challenges arising from people with multiple health care conditions (multi-morbidity). Rehabilitation would not only have to take into consideration single , but multiple conditions . A patient-centric rehabilitation approach is critical now more than ever given the landscape of changing and emergent needs in rehabilitation research and practice, and the resultant socioeconomic environment that may magnify the deleterious effect on inclusion of people with disability. Continuing to involve the users and the patients in rehabilitation practice and research will only benefit the dialogue between researchers and users, finding ways that are sensible to the end-users and robust from research and outcome measurement perspective. Ultimately, we want to achieve people-centered, not only condition-specific pathways of care.Public and patient involvement is a cornerstone in disability research and practice. In partnership with patients/users, rehabilitation espouses collaboration between disciplines toward the aim of improving patient outcomes. The philosophy of “nothing about us without us” is fundamental. If we were to understand, assess, and help with the rehabilitation of people who may be excluded from the society because of their disability. Recent efforts in the rehabilitation field have focused on engaging patients or consumers in a way that they participate in the design and conduct of the research and acting as consultants , where pConcrete efforts can be made and promoted in this field. For example, patients can participate as members of the research steering committee, or as members of an Advisory Board . PatientDisability can impact different levels of functioning from the person to the family to the extended family to the community and the society. The nature of this impact requires that rehabilitation also address the different levels of functioning accordingly and appropriately. Recent models have come to light such as the Disability Evaluation, Livelihood, and Employment Rehabilitation Model (DELIVER) . The DELFragmentation and specialization in rehabilitation have distanced the disciplines from the diverse and complex lives of people with disabilities. Simple technological “fixes” will not be able to transform rehabilitative care to address emergent and complex rehabilitative care and support needs. A rehabilitation system of the future would have to return to the root of truly capturing the input of people and communities to identify the needs and influence effective practice of care. Patients, families, and communities are key stakeholders in rehabilitation and need to be recognized as co-designers of health research and services if we are to combat disability. If scientifically robust rehabilitation is co-created, implemented and evaluated in collaboration with diverse community stakeholders, we can create a holistic, context-sensitive approach to people's lived experience of their disability.RE and TK equally contributed in the conceptualization, writing of the manuscript, and approved the manuscript prior to submission.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.


Augmented reality (AR) is a field of technology that has evolved drastically during the last decades, due to its vast range of applications in everyday life. The aim of this paper is to provide researchers with an overview of what has been surveyed since 2010 in terms of AR application areas as well as in terms of its technical aspects, and to discuss the extent to which both application areas and technical aspects have been covered, as well as to examine whether one can extract useful evidence of what aspects have not been covered adequately and whether it is possible to define common taxonomy criteria for performing AR reviews in the future. To this end, a search with inclusion and exclusion criteria has been performed in the Scopus database, producing a representative set of 47 reviews, covering the years from 2010 onwards. A proper taxonomy of the results is introduced, and the findings reveal, among others, the lack of AR application reviews covering all suggested criteria. Augmented Reality (AR) is a technological field that has already fueled diverse application areas for a few decades. A typical definition of AR can be found in : “AugmenEver since that publication, there has been a thriving increase of works in several application areas where AR could be utilized. The first known paper that surveys the state-of-the-art in AR applications is  by AzumaIn the latest years, there has been an apparent rise in published material concerning AR as one can see in As the years advanced, there were even more application fields that found support in AR, such as education, tourism, retail, and geoscience. In each of these fields, many new findings were published. Researchers had to find their way into exploring the current state-of-the-art in each different area, depending on the specific AR application they were aiming at. Thus, reviews of specific AR applications areas, as well as reviews of specific technical aspects of AR, started to emerge. In The increased interest both for AR itself, as well as for surveying its application areas and technical aspects, rendered it crucial, in our mind, to perform a review of what areas have attracted more interest by the researchers, measured by the number of reviews in each area and the extent to which the application characteristics have been analyzed. As a result, the areas which deserve to be further considered by researchers for AR application development will be identified, as well as the weaknesses that existing AR systems reveal in practice, in order to suggest future research directions. In order to do so, a sort of “umbrella” review will be performed, in the sense that existing reviews in all AR application areas will be covered. The only similar attempt made before was that by Nesenbergs et al. , but it RQ1: Are all application areas adequately covered by current AR reviews?RQ2: Are all technical aspects of proposed AR systems covered by AR reviews?RQ3: Is it possible to establish common taxonomy criteria for surveying any AR applications area?RQ4: Is it possible to recognize which technical aspects of AR are considered more significant depending on the AR application area?The research questions that are going to be answered based on the results are the following:The layout of the rest of the paper is as follows. In After the original review paper by Azuma , an incrApart from reviews regarding AR as a whole, several papers have reviewed developments of specific technical aspects. As far as input device technology is concerned, some good examples are papers reviewing skin-integrated vibrohaptic devices ,16, wearIn terms of display technology, a significant effort has been made to report technological advances. Yin et al. , Zhan etZhou et al.  are concKurkovsky et al.  focus onLast but not least, a special mention should be made to surveys on collaborative AR ,42,43,44In order to end up with a decent collection of survey papers in the field of AR applications, the Scopus database was chosen for searching. This specific database includes a large number of exclusively peer-reviewed journals  covering all fields of science over a large period of time (1970–). The search was made using the expression:The title has to contain the words: “augmented”, “reality”, and either “review” or “survey”. 467 papers were originally returned as hits.The search is limited to a year range of 2010–2022 so that relatively recent results are also taken into account by the review paper (20 papers removed).Results were limited once again by demanding the document type to be designated as “article” or “review”. Sometimes, the item is characterized as a research article by the publisher, although it clearly contains a review (153 papers removed).The results are, subsequently, limited to source type of “journal”: review papers are traditionally lengthy and do not normally fit to a conference (2 papers removed).This means that:Other commercial databases, such as Web of Science, were not considered due to access limitations through the author’s institution. Free ones such as Google Scholar were also not utilized due to their automatic mechanism of item inclusion . Thus, iQualitative taxonomy per article: The review paper should contain a taxonomy of AR application works in tabular format, based on several criteria. A researcher needs to be able to pinpoint all necessary technical aspects of an existing AR application field in order to be able to easily identify strengths and weaknesses of existing methods and propose ones with real novelty. Based on this, 37 articles were excluded for not presenting such a taxonomy.Application oriented reviews: Only reviews focusing on a specific AR application field are sought, and not ones focusing on specific technical aspects of applications. 5 articles were excluded by this criterion.No framework/protocol: No existing frameworks or protocols for performing reviews are to be assessed. This way, 2 articles were excluded.English language: Only articles written in English were considered, as it is the most common and accessible language to the great majority of researchers. Three (3) articles were excluded because they were written in Spanish.No umbrella reviews: The present work can be considered an “umbrella” review, in the sense that it covers other review works. However, it probably is the first one to conduct such an analysis. In fact, only a single such article was found, and it was only covering a broader application area, not all possible AR application areas, as in the present case.Sufficient number of reviewed AR works: Since, sometimes, reviews cover also other technological areas such as VR together with AR, it is possible that some reviews do not cover a sufficient number of AR application works, and their taxonomy is thus not tailored to AR applications. This way, one review paper was excluded, which only referenced a single AR application.The remaining 292 papers, resulting after the above-referenced exclusion process, were all sought for downloading via the Scopus Document Download Manager, and only 96 of them could eventually be retrieved. The subsequent manual screening process was based on the following criteria:After this careful screening, the remaining 47 articles underwent the subsequent analysis.The 47 papers that were eventually selected for reviewing contained several taxonomy criteria. After a careful study, it was concluded that there are several criteria appearing in different works that could be useful for reference by future research works. It should also be noted that the criteria names vary from one article to another. Another point is that no criteria that were not deemed as useful for drawing conclusions were taken into account, such as publication name, authors’ country of origin, or participants’ ages. In the following, each of the selected criteria is presented and defined, using a standard name each time.Head-worn: The  see-through display is very close to the user’s eyes, since it is attached to their head . Probably the most expensive technology, but the hands are left free for interaction with the surrounding environment.Hand-held: The users hold the see-through display in their hands. This technology acts like a magnifying lens and its cost is definitely lower than that of head-worn displays, but at least one hand is occupied.Spatial: The display is positioned at a specific location and it is either video, optical, or projective. Usually, spatial displays are intended for applications with minimum interaction .The specific criterion basically refers to the display type employed. Most commonly encountered types are :Head-worThis criterion refers to the use case of the application and its description varies, depending on the specific area. For example, in , which iThis refers to the kind of approach followed by authors to conduct their research, such as pilot study, qualitative study, quantitative study, prototype description , a game engine , a 3D graphics API  or, simply, the language in which the application was developed from scratch . The software implements all interaction, visualization, and registration tasks.Magnetic tracking: A device that bases its function in magnetic field properties is used to calculate the position of a receiver with respect to a transmitter.Vision-based tracking: An optical sensor is employed in order to decide on the pose of the viewer. Based on the electromagnetic spectrum range and the object dimensionality utilized, a further categorization can be introduced:-Infrared tracking: Infrared light sources (LEDs) are, usually, attached to the target of interest and a proper camera in the environment receives the emitted light (configuration can be inverted with respect the LED and camera positions).-Visible Light Tracking: The most common optical sensor type, found practically in any consumer electronic device  receives visible light from the environment. Another level of taxonomy can be introduced, based on what kind of feature is the visual trigger:*Fiducial or marker-based tracking: A static planar image (marker) attached to specific targets is required to activate the augmented content. Examples are QR codes, logos, and product packaging. In this case, the virtual content is anchored to the marker (it is displayed in a specific location with regard to the marker).*Natural feature or markerless tracking: Instead of resorting to a marker, these techniques scan the whole environment for naturally existing features that are unique and might trigger the superposition of the virtual items. It is preferable that the natural image contains enough edges and corners to make it easier to be recognized.*Model based tracking: A 3D object model instead of a planar marker is used to trigger the augmentation.-3D structure tracking: This category is based on creating range images, usually by means of a pair of an infrared projector and an infrared sensor . Such a device can perform full 3D reconstruction of a scene.Inertial tracking: Sensors such as gyroscopes and accelerometers are employed in order to measure all three angles of rotation of the object being tracked, as well as its change of position.GPS or Location-based tracking: This trigger type is simply based on current geographic coordinates, rendering such techniques suitable for wide area applications, such as those demanding directional guidance.Hybrid tracking: As one expects, this category fuses input from different kinds of sensors in order to improve tracking performance and overcome problems of specific sensor types.This refers to the way the presentation of the AR content is triggered. A common categorization of tracking techniques is :MagneticThis criterion concerns the weaknesses of the proposed work. For example, in an image-guided therapy system , a drawbInput modalities: Channels originating from humans and destined at computers:-Vision: For example, when the camera tracks an AR marker.-Tactility: Clicking a mouse button is an example.-Audition: e.g., a voice command.-Kinesthetics: For example, sensing the position or movement of hands.Output modalities: Channels originating from computers and destined at humans:-Vision: For example, presentation of 3D graphics on a screen.-Audition: e.g., sound effects accompanying a visual augmented item.Modalities refer to the different input or output sensory channels for human–computer interaction :Input moSince the gathered papers concerned reviews of specific AR application areas, the first thing that comes into question is how these articles are distributed with respect to application field. The pie chart in Starting with healthcare, which regards 41% of total articles, or 19 out of 47 (plus the one mentioned above), it should be first stressed that, under the term “healthcare”, several subcategories are considered, such as surgery, nursing, dentistry, rehabilitation, physical therapy, emergency medicine, and so on. In It is evident from In We can see that researchers in this area tend to pay more attention to the field of interest (targeted application), the method employed to perform the research, and the hardware (mostly display type) in use. On the contrary, software, limitations, tracking, and modalities seem to attract reviewers’ attention less.Second in order of interest to researchers comes the field of education in all its forms, including professional training and learning. Seventeen out of forty-seven surveys, a significant 36%, refer to this application area. In Again, there is no article to score 10 out of 10 in presenting a clear tabular taxonomy based on all selected criteria. The one closest to achieving it is the review paper by Ajit et al. , with a In Two of the three top criteria found in healthcare oriented reviews are also among the first three here: hardware and field of interest. However, in the third position we find the method and the main outcomes, which are descriptive fields capturing a great percent of the essence of any work. Modalities, limitations, software, and tracking are still at the lower end of reviewers’ preferences.Although, in reality, all other application areas could prove equally important for assessing AR usage in them, as it was done with healthcare and education, the findings after the inclusion and exclusion process described above were too few to perform a quantitative analysis per criterion. For this reason, it was decided to consider all of them together in the current subsection. For convenience, a column was added, showing the specific application area to which each paper belongs, as one can see in Although, in this case, there is increased diversity in the nature of applications, since they come from different areas, the ones achieving the highest score in providing the selected taxonomy criteria are the publications by Li et al.  in the fAgain, it is obvious that the field of interest and the hardware are the two of the three top trends in criteria selection by previous authors, together with tracking, which shares third place with method. To no surprise, the tracking method is referenced by reviews in the fields of industry, engineering, and robotics, which have a tendency to provide more technical details. In the last four places, we find the main outcomes, aim, limitations, and modalities.After all the above-described analysis, we are ready to answer the research questions posed in RQ1: Are all application areas adequately covered by current AR reviews?: The answer is obviously negative. Even in the most widely covered application areas, such as healthcare and education, there is no single review paper that covers all taxonomy criteria that were considered to be significant for including. At this point, it should be noted once again that examined review papers were expected to compare existing works with criteria being presented in a tabular format, so that future researchers can follow the state-of-the-art easily. Some of the selected criteria may be descriptive by nature  but an experienced researcher can capture their essence in a few words. Some of the papers not selected after screening may contain useful comparison information in a totally textual format, making it difficult for the reader to come up with a conclusion about the pros and cons of the referenced works. Eventually, there are certain areas apart from all the ones encountered in the selected papers which deserve more attention. In fact, there are works proposing reviews in the field of e-commerce [commerce , architecommerce , construcommerce , or humacommerce  that do RQ2: Are all technical aspects of proposed AR systems covered by AR reviews?: Although the focus of each researcher performing a review may be on different technical details of AR technology, it is apparent, from the present study, that certain aspects are considered less significant by different authors, depending on their scientific background. For example, the study by Liang et al. [g et al.  concernig et al.  present RQ3: Is it possible to establish common taxonomy criteria for surveying any AR applications area?: As one can conclude from the results presented in the previous subsections, it is possible to apply all ten selected criteria to create a taxonomy of applications in any scientific area employing AR technology, since hardware, software, tracking, and modalities are technical details specific to AR, and all the rest  are general criteria that could be practically used in any case study, irrespective of technology . These ten criteria comprise a set that enables reviewers to perform a taxonomy that will assist fellow scientists in proposing novelties in the field of AR.RQ4: Is it possible to recognize which technical aspects of AR are considered more significant depending on the AR application area?: In the present paper, it is claimed that there is a degree of correlation between the popularity of a technical aspect of AR as a criterion in existing AR application area reviews and the extent to which this technical aspect is important to authors for the particular application. Indeed, it is evident from the results presented in the previous subsections that the display component is the most popular as a taxonomy criterion for authors conducting a survey in healthcare and education applications, in which the optical stimulus is the most important for the outcome of the AR use, whether the end-user is a patient, a healthcare specialist, or a student. Other aspects that drew authors’ attention to a lesser extent are software, tracking, and modalities. One reason for this may be that, in certain areas, such as healthcare and education, standard (not custom) software , or a standard tracking method , or standard modalities  is usually employed and, thus, the authors may sometimes consider the specific criteria as redundant for taxonomy. Another reason could be that the reviewers come from a scientific area different than Computer Science and, thus, are not interested in exploring the technical aspects of the method, rather than its social implications.In the present umbrella review, it was first attempted to select a representative set of published AR application reviews, after careful screening based on specific eligibility conditions. Afterwards, a set of ten taxonomy criteria was presented that can be used in future reviews in the area. The findings were subsequently presented with respect to the selected criteria, and a further analysis of them followed in the form of answers to four specific research questions. It was thus deduced that no AR application area is fully covered until today according to the proposed taxonomy, and that the technical aspects of employed AR technology are sometimes neglected by the authors. Additionally, it was proved that the ten taxonomy criteria that were selected can be utilized in any AR application area and provide a complete picture of the state-of-the-art. Lastly, it was found out that the hardware components are deemed a more significant technical aspect by reviewers compared to software, tracking, and modalities.It is the strong belief of the author that the present work will help future review authors in the area to conduct their surveys in a way that will assist researchers to identify the strengths and weaknesses of previous methods and introduce technical novelties.Regarding future work, it would be a good idea to perform umbrella reviews per AR application area in order to identify potential lack of proper technical coverage in area reviews. Another point would be to propose a more fine-grained taxonomy that would reveal which previously employed AR systems perform better in practice, and thus decide which research directions would be more fruitful.


Au‐AR pep‐PROTAC results in suppression of AR levels and induces tumor regression in both enzalutamide sensitive and resistant prostate cancer animal models. Further optimization of the Au‐AR pep‐PROTAC can ultimately lead to a new therapy for AR‐V7‐positive CRPC.Androgen receptor splice variant‐7 (AR‐V7), one of the major driving factors, is the most attractive drug target in castration‐resistant prostate cancer (CRPC). Currently, no available drugs efficiently target AR‐V7 in clinical practice. The DNA binding domain (DBD) is indispensable for the transcriptional activity of AR full length and AR splice variants, including AR‐V7. Based on the homodimerization structure of the AR DBD, a novel peptide‐based proteolysis‐targeting chimera (PROTAC) drug is designed to induce AR and AR‐V7 degradation in a DBD and MDM2‐dependent manner, without showing any activity on other hormone receptors. To overcome the short half‐life and poor cell penetrability of peptide PROTAC drugs, an ultrasmall gold (Au)‐peptide complex platform to deliver the AR DBD PROTAC in vivo is developed. The obtained Au‐AR pep‐PROTAC effectively degrades AR and AR‐V7 in prostate cancer cell lines, particularly in CWR22Rv1 cells with DC A novel peptide PROTAC targeting androgen receptor DNA binding domain via AI‐Rosetta assisted design for castration‐resistant prostate cancer therapy is presented. With the development of second‐generation AR antagonists including abiraterone, enzalutamide, and darolutamide, the treatment and survival of prostate cancer patients have been dramatically improved. Although more than 80% of prostate cancer patients initially respond to androgen deprivation therapy (ADT), they eventually develop castration‐resistant prostate cancer (CRPC) after 24–36 months. Molecular mechanisms of ADT resistance are largely driven by AR aberrations including AR protein overexpression, AR gene amplification, AR gene mutations, and AR splice variants. The expression of AR splice variants, especially AR splice variant‐7 (AR‐V7), is a crucial factor in CRPC progression and drug resistance. All second‐generation AR antagonists including enzalutamide and darolutamide target the ligand‐binding domain (LBD) in AR. Compared with the full‐length AR structure, AR‐V7 lacks the LBD. Functionally, the AR‐V7 could be constitutively active without androgen binding, thereby representing an important means of escape from ADT in prostate cancer. Moreover, enzalutamide has been shown to increase the expression of the AR‐V7 variant but not full‐length AR protein level in prostate cancer patients, making AR‐V7 a rational and promising target in CRPC.Prostate cancer is the most common and second leading lethal cancer among men.1 The  While, only AR‐V7 targeting compound EPI‐506 (NCT02606123) and its derivative EPI‐7386  targeting AR N‐terminal domain (NTD) have entered into clinical phase 1/2 study for CRPC patients. However, no drugs targeting AR‐V7 could ultimately enter into clinical use indicating there is still an urgent need for developing novel AR‐V7 targeting therapies. Structural studies have shown that homodimerization of AR via their DNA binding domain (DBD) is essential for its transcriptional activity and that the DBD is highly conserved across different AR mutants and variants, including AR‐V7 and other AR variants. Also, AR DBD forms a dimerization interface distinct from other hormone receptors making the dimerization interface a unique drug target. The homo‐dimeric interaction interface of the AR DBD is a typical protein‐protein interaction interface that adopts a flat, extended conformation; the binding of small molecules is thus energetically difficult, although the binding of peptide drugs is feasible. Based on the protein structure, via Artificial Intelligence (AI)‐aided peptide drug design with a hotspot‐centric Rosetta computational approach, we designed a novel peptide antagonist with high binding affinity for the AR DBD.Some great efforts have been made by scientists to develop novel inhibitors targeting AR‐V7.17 Whi A PROTAC drug is a heterobifunctional molecule containing one ligand that binds to the target protein of interest and a second ligand for an E3 ligase. As the AR protein plays a key role in CRPC, several small‐molecule PROTAC drugs target the full‐length AR, including ARV‐110, ARCC‐4, and ARD‐69, have achieved great efficacy. However, only one PROTAC drug targeting AR‐V7, MTX–23—has been developed. Nevertheless, compared with full‐length AR PROTAC drugs (DC50 < 1 nM), MTX‐23 (DC50 ≈ 2 µM for AR) is much less effective because of the protein structure of the AR DBD. The published data indicated that there is still an unmet need for an AR‐V7‐targeting PROTAC. Among various inhibitors with therapeutic potential, peptide drugs show higher potency, greater selectivity, and lower toxicity than low molecular weight compounds due to their larger interface with the target protein. Therefore, peptide PROTAC drugs have more advantages against targets lacking definite binding pockets such as AR‐V7. Starting from our designed AR DBD‐targeting peptide and a reported peptide sequence targeting the E3 ligase MDM2, we developed a novel peptide‐based PROTAC drug that induced the degradation of both AR and AR‐V7 in addition to other variants.Proteolysis‐targeting chimera (PROTAC) drug design strategy provides a new opportunity for cancer therapeutic development involving the induction of protein degradation.23 A P EGFR, KRAS, etc., which all showed great therapeutic potential. Oligo‐based PROTAC (O'PROTAC) have been developed to target DNA‐binding proteins and transcription factors. These different PROTAC design has greatly enriched the development of the whole research field. Different from small molecules, peptide PROTAC is regarded as bioPROTAC. The concept of PROTAC was proved by a peptide PROTAC design targeting IkBalpha. Poor membrane penetration and short half‐life limit the direct use of peptide PROTAC, while new technologies have been developed to overcome the disadvantages to promote peptide PROTAC development. Cyclic peptide PROTAC and staple peptide PROTAC designs have been proved feasible for peptide PROTAC design. There is still a lack of rational design strategies for peptide PROTAC drugs, although peptide PROTAC has the advantage of better safety and potency. Protein structure prediction and drug screening by artificial intelligence have attracted a lot of attention and greatly improved the efficiency of novel drug discovery. Here, we introduced the Rosetta system developed by Dr. Baker's group and the protocol developed by Colin A. Smith and Tanja Kortemme into peptide PROTAC drug rational design. Separately, we have designed novel targeted peptide sequences for interest protein‐AR DBD and E3 ligase‐MDM2 with Rosetta assisted. Our designed AR DBD targeting peptide PROTAC drug forcibly linked MDM2 to the AR DBD and induced degradation of both AR‐V7 and AR. To overcome the disadvantage of peptide PROTAC – short half‐life and poor cell penetrability, we developed a universal one‐step ultrasmall gold (Au)‐peptide PROTAC protocol for delivery of the peptide PROTAC drug in potential clinical applications, inspired by published protocols.Although PROTAC drugs have become an important modality for certain targets, there are still protein targets that do not have certain small‐molecule binding sites, which is necessary for PROTAC design. In this case, except from small molecular PROTAC drugs other PROTAC forms are beginning to gain attention. Some conventional PROTAC drugs have been designed to target different targets like Bruton's tyrosine kinase,31 EGFIn summary, we report a peptide PROTAC designed by the AI system Rosetta that targets the AR DBD and is doped with novel ultrasmall Au nanoparticles as a delivery system, and we demonstrate its efficient induction of AR and AR‐V7 degradation both in vitro and in vivo, therefore indicating its potential for CRPC therapy and pharmacological application.22.1Figurereported a 12‐aa peptide targeting MDM2 termed PMI identified by phage display  of AR DBD dimer inhibition by AR DBD PROTACr is 1.8 µM, in presence of 200 nM of AR DBD dimer. The maximum FP was taken to indicate 100% dimer of AR DBD, and the minimum was taken to indicate the monomer AR DBD. As the MDM2 recruiting peptide sequence template was from PMI which is a MDM2/MDMX dual‐target peptide, we also performed an ITC experiment between MDMX and AR pep‐PROTAC. As shown in Figure d between MDMX and AR pep‐PROTAC is 1.43 µM.The designed peptide PROTAC drug was achieved by solid‐phase peptide synthesis, analytical high‐performance liquid chromatography (HPLC), and electrospray ionization mass spectrometry (ESI‐MS) results of the synthesized peptide PROTAC drug are shown in Figure protocol.43 Imp2.2FigureTo synthesize AR pep‐PROTAC drug‐conjugated ultrasmall Au nanoparticles with high peptide drug‐loading efficiency and stability, we developed a facile, customizable, one‐step chemistry. As shown in  Au‐AR pep‐PROTAC facilitate controlled drug release in the cellular environment  and measured time‐dependent changes by reversed‐phase HPLC (RP‐HPLC) Figure . The res44 Au‐t Figure . Additio The ultrasmall Au‐AR DBD PROTAC drug complex allows the potential clinical use of the peptide AR DBD PROTAC drug.By constructing an ultrasmall Au‐AR pep‐PROTAC drug complex, we achieved the goal of highly efficient delivery of the peptide AR DBD PROTAC drug. Compared with larger nanoparticles, ultrasmall nanoparticles have been reported to exhibit more efficient renal clearance in vivo, with almost no accumulation in filter organs and enhanced permeability and retention (EPR) in tumors.45 The2.3Figure50) values of the Au‐AR pep‐PROTAC drug in LNCaP, C4‐2, and CWR22Rv1 cells were 230.8 nM, 248.1 nM, and 126.9 nM, respectively. Under high concentration (over 500 nM), Au‐AR pep PROTAC show totally inhibition of the growth of AR‐positive prostate cancer cells. The total inhibition of AR positive cancer cells caused by Au‐AR pep‐PROTAC is probably because AR is completely degraded at the protein level in a short period of time as some other small molecular AR targeting PROTAC also showed total inhibition of AR positive cancer cells. While these data indicate we need to examine more in‐depth the toxicity of Au‐AR pep‐PROTAC in high concentrations. As the toxicity of Au‐AR pep‐PROTAC is mainly because of Au nanoparticles, we examined cell killing ability of Au nanoparticles alone. As shown in Figure To evaluate the proliferation‐inhibiting activity of the Au‐AR pep‐PROTAC drug in vitro, we treated C4‐2, LNCaP, and CWR22Rv1 cells with the Au‐AR DBD PROTAC drug at concentrations ranging from 10 to 1000 nM. Bare Au nanoparticles and reversal peptide‐loaded Au nanoparticles were used as negative controls. As the reversal peptide shared the same physicochemical property with designed AR pep‐PROTAC with no binding ability with AR or MDM2, reversal peptide‐loaded Au nanoparticles could be used as an appropriate negative control. As shown in 50) values of the Au‐AR pep‐PROTAC drug in LNCaP, C4‐2, and CWR22rv1 cells were 175.2 nM, 193.0 nM, and 48.8 nM (AR)/79.2 nM (AR‐V7), respectively. Immunofluorescence staining of AR in C4‐2 cells after 24 h of Au‐AR pep‐PROTAC drug treatment also confirmed the induction of AR degradation by the Au‐AR pep‐PROTAC drug  analysis of AR was performed. The primary antibody  targeting AR we used is raised against amino acids 299–315 of AR, so we could determine both AR and AR‐V7 at the same time. The Au‐DBD PROTAC drug‐induced the degradation of both full‐length AR and AR‐V7 in both dose‐dependent , the Au‐AR DBD PROTAC drug‐induced full‐length AR and AR‐V7 degradation in time‐ and dose‐dependent manners , progesterone receptor (PR), mineralocorticoid receptor (MR), and glucocorticoid receptor (GR) has been performed. Au‐AR pep‐PROTAC did not show any activity on ER, PR, MR, and GR in MCF‐7 or T47D cells  experiments were performed to detect cellular binding between AR and MDM2. First, 293T cells were transfected with HA‐AR or Flag‐MDM2 plasmids, after 24 h 125 nM Au‐AR DBD PROTAC was added to 293T cells. The cell lysate was collected for IP experiments after 48 h. As shown in To determine the specificity of AR pep‐PROTAC, mutation of amino acids (575‐591) in the AR DBD plasmid was conducted. As shown in Figures Considering all the in vitro results, we confirmed that AR pep‐PROTAC specific targeting amino acids (575‐591) in AR DBD and action by hijacking MDM2 to interact with AR DBD.2.56, AR‐positive) and CWR22rv1 cells . In each of these models, 25 tumor‐bearing mice were randomly divided into 5 groups and treated every three days for three weeks with PBS, bare Au nanoparticles, the Au‐AR pep‐PROTAC drug, Au‐Peptide Control, or enzalutamide at the same dose (2 mg kg−1). Two administration routes were tested in the xenograft models, with subcutaneous injection (SC.) in C4‐2 models and intraperitoneal injection (IP.) in CWR‐22rv1 models. All the drug including Au‐AR pep‐PROTAC drug, Au‐Peptide Control and enzalutamide were dissolved in 0.4 mg ml−1 in PBS (pH 7.4). Each injection volume was 100 µl and PBS was used as control.To evaluate the therapeutic efficacy of the Au‐AR pep‐PROTAC drug in vivo, we established two subcutaneous xenograft mouse models of prostate cancer with C4‐2 cells . While for ARD‐61, a higher dosage was used in mouse models than Au‐AR pep‐PROTAC.Compared with AR targeting PROTAC ARV‐110, the animal dosage was 1 mg kgmg kg−1).48 Whi2.6Figure1/2 of Au‐AR DBD PROTAC is 26.3 h. While as shown in publications, the half‐life of majority free peptides is <5 h.To evaluate the pharmacokinetic (PK) properties of Au‐AR pep‐PROTAC drug, AR pep‐PROTAC peptide was first labeled with rhodamine B. We have conducted in vivo mouse pharmacokinetics assay for the peptide PROTAC drug with/without Au with intraperitoneal injection. As shown in 50 of Au‐AR pep‐PROTAC. As shown in Figure To analyze the toxicity of Au‐AR pep‐PROTAC, hERG tests on cardiac muscle cells up to 30 µM of Au‐AR pep‐PROTAC were performed which is 120 times more than IC50 of Au‐AR pep‐PROTAC on AML 12 cells is 2.41 µM which is almost ten‐fold compared to IC50 in prostate cancer cells. Apoptosis assays also confirmed with the results of cell viability tests , ALP, and AST(Aspartate aminotransferase) in AML 12 cell lysate after 24 h treatment of Au‐AR pep‐PROTAC were also performed , ALP, AST(Aspartate aminotransferase), ALT(Alanine aminotransferase), and TBA in mice serum after 24 h injection of Au‐AR pep‐PROTAC were performed. As shown in Figure −1.To further detection of toxicity about Au‐AR pep‐PROTAC on liver tissue, we tested toxicity of Au‐AR pep‐PROTAC on AML 12 cells which is a normal mice liver cell line. As shown in Figure 3 while most available drugs targeting AR bind to the LBD, as it contains a ligand‐binding pocket formed by 12 folded helices making AR‐V7 escape from drug targeting. However, there is a bottleneck in traditional small molecule drug discovery and development toward AR‐V7. Innovatively, we designed a peptide PROTAC drug targeting the AR DBD via AI‐assisted rational design. Via hydrogen bond and charge interactions, peptide drugs can achieve higher binding affinity and specificity than small molecule drugs for targeting the protein‐protein interaction interface.AR‐V7 is the most attractive target in CRPC,50 whi PROTAC drug design takes advantage of the intrinsic protein degradation mechanism of the ubiquitin‐protease system to eliminate specifically targeted proteins. To design our PROTAC, we selected a homodimeric interface, which is essential for AR activity. In addition, MDM2 was selected as the PROTAC‐targeting E3 ligase since it has been reported to be involved in AR ubiquitination and degradation under physiological conditions, which increases the degradation efficiency of the designed PROTAC molecule. The DC50 value of our designed Au‐AR DBD PROTAC was 125 nM for full‐length AR—16‐fold more effective than MTX‐23—and only 62.5 nM for AR‐V7—5‐fold more effective than MTX‐23. These data indicate that peptide PROTAC drugs are more efficient than small‐molecule PROTAC drugs for undruggable targets such as AR‐V7. Peptide PROTAC as an important branch of the field of PROTAC drugs has attracted more and more attention. We firstly report how to induce AI system Rosetta as a common strategy for peptide PROTAC drug design.In all aspects of drug discovery, the PROTAC drug design strategy is a more promising approach than the traditional antagonist design.54 PRO Due to the complexity of peptide molecule side chains, Au nanoparticle‐loaded peptides are usually greater than 50 nm in diameter. We developed a fast, stable ultrasmall Au‐peptide system that could avoid the side effects of Au nanoparticles as their ultrasmall size effect. Moreover, this ultrasmall Au‐peptide system could also be applied for universal peptide PROTAC drug delivery.Also, we applied nanotechnology for peptide PROTAC delivery. Ultrasmall Au nanoparticles (less than 10 nm in diameter) are promising for biomedical applications because of their relatively low systemic toxicity, rapid kidney clearance, and high tumor accumulation.56 DueIn summary, we designed a novel peptide‐based PROTAC drug directly targeting the DBD of AR and could induce both AR and AR‐V7 degradation with potential clinical use and provided a universal strategy for peptide PROTAC drug design and delivery.4Peptide drug design was used Rosetta and “sequence tolerance” protocol. In brief, 10 interface sites was defined at which mutations to all 20 naturally occurring amino acids (except cysteine) will be allowed in the simulations. In a first step, Rosetta calculates an overall interface binding score and a fold stability score for each protein chain in the given protein‐protein complex. Next, Rosetta models and scores nonnative sequences at the interface sites selected for the design. Nonnative sequences at the protein‐protein interface were selected and propagated by a genetic algorithm; for each sequence, the rotameric conformations of each sequence at the interface are optimized by a Monte Carlo simulated annealing protocol and the resulting structural models were scored using the all‐atom Rosetta scoring function. Throughout the simulation, nonnative sequences with folding and binding scores near or significantly better than the starting sequence were saved and considered to be “tolerated”. At the end of a simulation, the frequency with which each amino acid type appeared in the list of tolerated sequences was calculated and used to generate a position‐specific “tolerance profile” for each interface site.Boc amino acids were obtained from Peptides Institute; p‐methyl‐BHA (MBHA) resin was purchased from Applied Biosystems; Tris‐(2‐carboxyethyl) phosphine (TCEP), glutathione (GSH), dichloromethane (DCM), N, N‐dimethylformamide (DMF), acetonitrile, triisopropylsilane (TIPS), N, N‐diisopropylethylamine (DIEA), p‐cresol and ultrapure guanidine hydrochloride (GuHCl) were obtained from Sigma‐Aldrich; and trifluoroacetic acid (TFA) was purchased from Halocarbon.E.Coli expression and purified by affinity chromatography. All peptides were synthesized by solid‐phase peptide synthesis and purified by preparative high‐performance liquid chromatography. For ITC experiments, clean sample cells and injection syringe were cleaned according to the manufacturer's protocol before loading protein and peptide. Rinse the sample cell several times with 2 ml of buffer. Load the sample cell with 0.2 ml of protein solution , being careful to avoid bubble formation, load the syringe with 0.04 ml peptide solution . Choose the temperature at 25 °C for the ITC experiments. a small volume (0.4 µl) for the first injection was used and keep the subsequent injections at the desired volume (2 µl for 19 injections). Once the experiment had finished, the ITC data was fitted with the analysis software for the instrument.ITC  was an established technique that can determine the binding ability between protein and ligands. The signal measured was the heat released upon the interaction of the two reactants. All proteins – MDM2, AR‐V7, or AR ΔDBD were obtained with To achieve the ultrasmall gold (Au)‐peptide PROTAC complex nanoparticle system, 2 mg peptide was first dissolved in 10 ml water and boiled. After boiling for 3 min, 100 µl 1% HAuCl4 was added to the peptide solution. The molar ratio between Au and AR pep‐PROTAC is 6:1. Then 200 µl 1% branched PEI (25 KD) was added to the solution, and the ultrasmall gold (Au)‐peptide PROTAC complex nanoparticle after boiling for 20 min was achieved. The quantitation of loading peptide was performed by analytic HPLC.293T cells were maintained in Dulbecco's modified Eagle medium containing 10% FBS. LNCap, C4‐2, CWR22Rv1, and DU145 cells were cultured in RPMI 1640 medium containing 10% FBS. VCaP cells were cultured in DMEM medium containing 10% FBS.HA‐AR, HA‐AR‐V7, HA‐AR ΔDBD, Flag‐MDM2, and his‐Ub were purchased from Addgene. The plasmids pET‐28a (MDM2), pET‐28a (AR – V7), and pET‐28a (AR ΔDBD) were purchased from GenScript.The anti‐AR (sc7305) and anti‐GAPDH (sc‐47724) antibodies were purchased from Santa Cruz. The anti‐AR V567es (ab200827) antibody was purchased from abcam. The anti‐vinculin antibody (V‐4505), anti‐Flag agarose beads (A‐2220), anti‐HA agarose beads (A‐2095), secondary anti‐mouse antibody, and secondary anti‐rabbit antibody were purchased from Sigma‐Aldrich.E. coli BL21 (DE3) cells transformed with pET28‐AR‐V7 expression plasmids were grown to an OD600 of 0.6 at 37°C in LB medium supplemented with 100 µg ml−1 Kanamycin. After induction with 1 mM IPTG, cultures were incubated for overnight in 18° before harvesting. The frozen bacteria pellets were resuspended in lysis buffer (50 mM Tris (pH 8.0) and 300 mM NaCl) and lysed by sonication at 4 °C. As AR‐V7 was expressed in the inclusion body, the precipitate was dissolved with 8 M urea and the supernatant was discarded.Filtered redissolved precipitate was loaded onto a Ni‐NTA column  pre‐equilibrated with 8 M urea loading buffer and washed with 5% elution buffer  followed by a 5%–100% gradient elution with an ÄKTA purifier system. The fractions containing the eluted proteins were concentrated and loaded on Superdex75 16/60  size exclusion chromatography column in 25 mM Tris pH 8.0, 8 M urea. The main protein fractions were dialysis and freeze‐drying for further usage.−1, followed by a six‐fold dilution with Tris‐Hcl containing 0.1 mM TCEP, pH 7.4, and an overnight dialysis against the Tris‐Hcl. After dialysis, protein precipitation was removed by centrifugation. The concentration of AR‐V7 protein was quantified by UV 280 absorption.For AR‐V7 refolding, AR‐V7 protein powder was dissolved in 6 M GuHCl at 0.2 mg mL3 cells per well in 96‐well plates, and three independent samples were seeded in duplicate for each concentration. Cell viability tests were performed 24 h after treatment with Au, Au‐peptide control, or Au‐AR DBD PROTAC. After a 2 h incubation with CCK‐8 reagent, absorbance values at 450 nm were determined using a microplate reader. The CCK‐8 kit was purchased from Dojindo Laboratories.Cell viability was evaluated by a cell counting kit‐8 (CCK‐8) assay. Cells were plated at a density of 4 ×10Immunoblotting was performed to determine the AR amount after Au‐AR DBD PROTAC treatment. Cells were lysed in EBC buffer (50 mM Tris (pH 7.5), 120 mM NaCl, and 0.5% NP‐40) supplemented with protease inhibitors. Cell lysates were quantified by a Pierce bicinchoninic acid (BCA) Protein Assay Kit (Thermo Fisher). Equal amounts of total proteins were loaded for western blotting analysis. The proteins were separated by SDS‐PAGE gels. Then proteins were transferred to polyvinylidene fluoride (PVDF) membranes and incubated with specific primary antibodies at 4 °C overnight. After incubating with HRP‐conjugated secondary antibody for 1 h at room temperature, the protein immunoreactive signals were tested.For immunoprecipitation (IP) assays, a total of 3 mg of cell lysate was incubated with anti‐flag, anti‐HA, or Ni‐NTA beads for 4 h at 4°C in the absence or presence of AR DBD PROTAC. Subsequently, cell lysates were washed with EBC buffer and the proteins were extracted from the beads by boiling at 95 °C for 5 min. For immunoprecipitation assays, the proteins were separated by SDS‐PAGE gels. Then they have transferred to polyvinylidene fluoride (PVDF) membranes and incubated with specific primary antibodies at 4 °C overnight. After incubating with HRP‐conjugated secondary antibody for 1 h at room temperature, the protein immunoreactive signals were tested.3). Mice in the five medication treatment groups were subjected to a 3‐week treatment regimen with an injection every other day. The tumor length and width were measured with calipers, and the tumor volume was calculated using the following equation: tumor volume (V) = length × width2/2. Treatment of five groups of mice (five mice per group) was initiated  after the tumor had been established after 3 weeks as a palpable mass (around 50 mm3 in size). All IHC staining images were scored using ImageJ profiler as published protocol.Treatment of five groups of mice (5 mice per group) was initiated  after tumors were established in 2 weeks as palpable masses ).t‐test for two group comparison. P‐values of 0.05 or less were considered statistically significant for single comparisons.All data are expressed as the mean ± SD and are representative of triplicate samples, experimental group data were normalized to vehicle treated. Statistics were generated using GraphPad Prism software. Differences among multiple groups and the effects of treatment for data obtained from in vivo and in vitro studies were analyzed by nonparametric Kruskal‐Wallis test for multiple comparisons and The authors declare no conflict of interest.Supporting InformationClick here for additional data file.


Rehabilitation robotics aims to facilitate the rehabilitation procedure for patients and physical therapists. This field has a relatively long history dating back to the 1990s; however, their implementation and the standardisation of their application in the medical field does not follow the same pace, mainly due to their complexity of reproduction and the need for their approval by the authorities. This paper aims to describe architecture that can be applied to industrial robots and promote their application in healthcare ecosystems. The control of the robotic arm is performed using the software called SmartHealth, offering a 2 Degree of Autonomy (DOA). Data are gathered through electromyography (EMG) and force sensors at a frequency of 45 Hz. It also proves the capabilities of such small robots in performing such medical procedures. Four exercises focused on shoulder rehabilitation ) were carried out and confirmed the viability of the proposed architecture and the potential of small robots  in rehabilitation procedure accomplishment. This robot can perform the majority of the default exercises in addition to ADLs but, nevertheless, their limits were also uncovered, mainly due to their limited Range of Motion (ROM) and cost. Rehabilitation appears to be one of the most appealing and effective sub-fields of healthcare for the robot’s application. A typical rehabilitation session includes four steps: identifying the patient’s condition, defining the rehabilitation goals, planning and implementing the treatments and, lastly, evaluating the outcomes . There aRobots have the ability to transform and improve this sector by lowering the physical therapist’s burden, allowing less supervision by therapists, and offering more precise tracking of patient development throughout the sessions. Application with robotics in the rehabilitation process is a challenging task, as a consequence, the development of robotic technology designed to help patients perform rehabilitation is one of the most lagging sub-fields in medical robotics research and experiences many technical hurdles. The main challenges encountered include: cost, safety, efficiency, and ease of use for both the user and therapist .Limiting viral transmission: Since the SARS-CoV-2 outbreak, the use of robots in hospitals has grown significantly, mostly to limit the spread of viruses [ viruses . These a viruses .Patient monitoring and pressure relief: Using onboard and/or external sensors, highlighting improvement or deterioration in the patient’s health, and can even perform some diagnostics for the most advanced robots. In addition, the robotic alternative can allow clinicians to be freed from laborious and repetitive tasks.Nevertheless, Human–Robot Collaboration (HRC) is seen as the solution in a variety of areas, including the medical field. In these fields, robots are seen as the answer to achieving high efficiency and excellent results in several domains. Robots in healthcare have emerged as a result of the various issues that these robots may overcome, which can be divided into two categories:The rehabilitation robotics era started in the 1990s ,7,8; sinThe completion of rehabilitation sessions directly after an incident are very beneficial and can be paramount to a patient’s chances of recovery—the longer a patient delays in rehabilitation sessions, the more their chances of recovery decrease . UnfortuCobots, such as the UR3 and LBR iiwa from Universal Robots and KUKA, respectively, might represent a solution for a fast implementation of these technologies in the rehabilitation sector. These robots are renowned for being fast, easy to program, flexible, and safe , as wellTherefore, the objective of this work is to promote the application and implementation of industrial robots in functional rehabilitation by employing an association of different wireless sensors connected to a single software that is able to offer, through a graphical interface, the possibility for the physiotherapist to select or easily create a rehabilitation strategy according to the needs of the patient, to have the possibility to see the effects of the rehabilitation in real time through the sensors used, and to follow the patient’s progress throughout the sessions thanks to the data saving capabilities. According to the IEC/TR 60601-4-1, defining the DOA of medical electrical equipment and described in , the proIn addition to this introduction, this paper is organized as follows: In this section, a closer look will be taken at some of the recently published works, starting with . The desFurther,  disseminThe same research team, led by Bouteraa, has presented in  a wrist In , a desigThe work in  further According to an analysis of the previously mentioned articles, the GUI is implemented in all of the proposed architecture except for ,19,35; ta to e refer to the elements applied, while the points numbered from 1 to 9 correspond to the data transmitted. The different sensors are implemented with a “plug and play” strategy.The proposed system is composed of several components that communicate with one another and allows the rehabilitation treatment to be completed. (a) SmartHealth software: The software can be installed on any computer using the windows operating system. Several real-time loops are responsible for data reading, displaying, and taking action inside the software;(b) Router: In the case of wireless communication, a router linked via Ethernet cable to the UR3 robotic arm is necessary. The physiotherapist needs only to connect to the computer with the router network. This router act as a bridge to transmit the information between the robot and the computer. Nevertheless, a direct Ethernet connection can also be used, allowing one to ride off the router;(c) Control box and Teach pendant: The teach pendant is generally applied to communicate easily with the robot using the UR programming interface. Through this architecture, the teach pendant is useless. Nevertheless, the control box is still required;(d) UR3: Universal Robots is the robotic arm responsible for performing upper limb rehabilitation and driving the patient’s hand;(e) Robotiq FT300: It is an external 6 DOF force and torque sensor that allows monitoring of the patient’s force during the rehabilitation sessions;(f) EMG sensor: The EMG sensor allows real-time monitoring and transfer of the patient’s muscular activity via Bluetooth.Regarding the description of the communication data:1): The physiotherapist assigns the rehabilitation method to be performed and introduces the patient’s characteristics and specificities;(2): Patient and exercise monitoring via the graphical software interface;(3): Robot command. The command sent can be a moving command, composed of a specific position/speed in (4): The data received following the requested command in (3). It can be an acknowledgment of the requested command, force, torque, position, or joint angle data;(5): Bi-directional data transmission between the router and robotic arm control box;(6): The continuation of the data sequence transmitted in (3);(7) and (8): The origin of the data transmitted to (4);(9): A bi-directional data transmission, consisting of Bytes sent to/received from the EMG Shimmer sensor. and determine the restrictions to be applied to the robot. Once everything has been defined, all devices remain in standby mode until the instructions to start the exercise are initiated. In contrast to some of the solutions proposed to record a personalized exercise , the physiotherapist can directly move the robotic arm without significant effort. During this movement, the software records the path that the robot passes and ensures a high fidelity of path duplication. Depending on the characterization made earlier, the program will launch one of the modules depicted in the above figure. The physiotherapist can view the patient’s data in real-time, including force, robot position regarding the reference, and muscular activity. Thus, it allows the medical professional to assess the patient’s condition in real-time and reassess a strategy for the rehabilitation sessions. The reader should be aware that, due to the significance of the data reception frequency, the control part, the data reception and saving part, and the data display part are all carried out in parallel. In the current version, the data are gathered with an average frequency of 45 Hz.Safety is the most crucial consideration during rehabilitation sessions; as robots interact so closely with individuals, they might result in harming the users. In this regard, the robot must not exceed the patient’s ROMs to prevent injuries. The ROMs can be pre-programmed in advance by restricting the robotic arm’s movements. Furthermore, as for the UR5, the UR3 is equipped with an internal safety system that terminates all robot movements if the force surpasses 250 N within 500 ms, which is ideal for HRC. The ROBOTIQ FT300 force/torque sensor, described below, can also be employed to measure the force released by the patient in real-time; this value can be configured to 50 N for force and 8 Nm for torque according to . Actuallhttps://github.com/RahimCHELLAL/SmartHealth). The data The robot control varies according to the rehabilitation type selected, with the software allowing the user to choose between passive and active-assisted rehabilitation. The UR3 internal control algorithm is also employed to enable the Tool Center Point (TCP) to reach the exact position requested based on a reference sent and completes the motion control. However, this reference varies based on the type of rehabilitation. The UR3 is equipped with a force–torque sensor, as illustrated in For passive rehabilitation, the software sends the reference position For assisted active rehabilitation, the reference is the robot arm speed. The y and z axis for the 2nd and 3rd exercise described in the Results and Discussion section, the error between the actual position (q) and reference trajectory is also added to y and z axis, used for the 2nd and 3rd exercise.It should be noted that, for the control in EMG sensors can monitor the electrical activity of a muscle for a given moment. This information can be essential during rehabilitation, as it can provide physiotherapists with vital clues and the ability to interpret the response, positive or not, of the muscle to the chosen treatment . SHIMMERLateral Shoulder Rotation exercise in a passive mode, where it is intended for the patient to offer less or no force;The first exercise is a Lateral Shoulder Rotation exercise in a restricted active-assisted mode, where the robot movement is restricted only to a 2D planner environment The second exercise is a Lateral Shoulder Rotation exercise in a free active-assisted mode, where the robot’s movements are totally free in The third exercise is a Custom exercise”. With this functionality, it is possible to manually create any exercise by simply moving the robotic arm; its movements are then recorded and reproduced.The fourth exercise is an ADL; the chosen exercise consists of mimicking the action of taking a cup, drinking from it, and putting it back. This exercise can be performed thanks to one of the functionalities offered by the software which is the “Four exercises focused on shoulder rehabilitation were performed to assess the viability and performance of the proposed architecture and the UR3 application in the field of rehabilitation. The difference between the exercises is highlighted as follows:The Lateral Shoulder Rotation carried out in most test exercises is performed as follows. The patient, while standing, bends the elbow at a right angle and moves the arm outward as far as their ROM allows in a horizontal plane while keeping their elbow fixed to their body . Figure The UR3 robot is applied to perform the rehabilitation, and the robotic arm communication with the software is running at the maximum allowed frequency of 125 Hz. Nevertheless, the sampling frequency is evaluated at 45 Hz. The contact point between the patient hand and the robot is achieved through an arm handle that can be seen in In this exercise, the first test is the passive lateral rotation of the shoulder. As previously mentioned, the person exerts virtually no force to assist the movement, with the cobot following the pre-programmed trajectory. The following y-axis and x-axis in the middle of the exercise. This peak is the patient resistance when the robot reaches the ROM’s limit. It is essential to point out to readers that the force data gathered and shown above is a summation of the robot’s force In In The second exercise results are shown in z-axis remains constant with an inaccuracy of 0.0027 m with the reference trajectory. The vector of the patient’s force that is transmitted and taken into account by the robot can also be seen in the same figure; this vector represents the subtraction between the direction of the patient’s force and the error between the actual position and the reference trajectory; this strategy aims to minimize the error between the actual position and the reference trajectory, while also taking the direction desired by the patient into account. In contrast to the preceding picture, the patient’s forces involved in From The results of the third exercise, where the patient’s hand is able to move freely in z-axis. It can be seen that the robot takes into consideration the patient’s force on the z-axis, which was ignored in the second exercise, and moves accordingly in the third one. This force has reached values up to 20 N. The implemented controller helps the movement to be seamless and closer to the reference trajectory.The observations made for As for In a stroke rehabilitation program, strategies are primarily based on a task-oriented approach and aim to improve the patient’s movement control, range of motion, accuracy, and hand-eye coordination . The tashttps://github.com/RahimCHELLAL/SmartHealth) with a link available in the “Data Availability Statement” section.As shown in Rehabilitation robotics essentially combines continuous interaction with the patient’s limb to accomplish therapy treatments, which is thought to be a suitable supplement to regular rehabilitation sessions. However, it still requires physical therapist supervision. Different designs and models have been reported, but a design consensus has yet to be reached. The designs differ from one robot to the next, depending on the robot constraints (DOF), size (ROM), and body part targeted (upper or lower limb).The shortcomings of some of the existing upper limb rehabilitation robotic systems  are mainly due to the use of unitary data collection systems. This paper proposes the use of commercially available robotic arms with their integration into a simple architecture centralized by SmartHealth software capable of collecting different types of data in parallel and in real time. Additionally, this work offers patient profile management and exercise selection. The different data are monitored and recorded, thus offering a classification of 2 DOF. The software also allows the data to be exported, permitting to offer further processing possibilities by using external software such as MATLAB.Youtube: https://youtu.be/TR-I_WQIs_w. A second video summarizing the project can be accessed through this link https://youtu.be/auFwzHZbiBQ (both links were accessed on 29 October 2022).In this work, after completing the architecture design and carrying out their integration and interconnection, an HRC demonstration and a viability study of the application of the UR3 as a rehabilitation robot applying the same architecture was carried out through a preliminary study. The results of the experimental tests first confirm the viability of this architecture and the applicability of the UR3 in the field of rehabilitation; they have demonstrated all the capabilities of the robot associated with the SmartHealth software to perform upper limb rehabilitation. To demonstrate the results, a first video is published and is accessible at The UR3 is a mighty robotic arm and the most affordable variant of the universal robot. However, the cost of these robots is not negligible and cannot be covered by most medical institutions. In addition, while it is an excellent alternative to the larger model (UR5 and UR10), limitations have been noticed while attempting to execute some particular exercises, such as the shoulder abduction exercise with a straight elbow. This constraint is caused by the robot’s ROM being confined to a 1-m diameter sphere.In terms of future work, it is considered to implement an additional tab or a complementary software that permit the processing of the data that were gathered through an intelligent algorithm to assess the patient’s performance during the sessions. In addition, clinical trials with the UR3 robotic arm will be conducted in a local hospital to compare its capabilities to other robotic rehabilitation projects documented in the literature and the traditional rehabilitation technique. The clinical studies will be carried out employing the same architecture, which has been proven to perform exercises while recording patient data and giving export capabilities with a relatively high frequency.Another shortcoming observed in the rehabilitation robotics sector is the limitation of the developed software to a specific robot, and no real connection exists between each solution. Thus, further development of the SmartHealth software permits the possibility of controlling other types of robotic arms, such as KUKA and enables a multi-control aspect, allowing it to control several devices simultaneously.


Testosterone is the canonical growth factor of prostate cancer but can paradoxically suppress its growth when present at supraphysiological levels. We have previously demonstrated that the cyclical administration of supraphysiological androgen (SPA), termed bipolar androgen therapy (BAT), can result in tumor regression and clinical benefit for patients with castration-resistant prostate cancer. However, predictors and mechanisms of response and resistance have been ill defined. Here, we show that growth inhibition of prostate cancer models by SPA required high androgen receptor (AR) activity and were driven in part by downregulation of MYC. Using matched sequential patient biopsies, we show that high pretreatment AR activity predicted downregulation of MYC, improved clinical response, and prolonged progression-free and overall survival for patients on BAT. BAT induced strong downregulation of AR in all patients, which is shown to be a primary mechanism of acquired resistance to SPA. Acquired resistance was overcome by alternating SPA with the AR inhibitor enzalutamide, which induced adaptive upregulation of AR and resensitized prostate cancer to SPA. This work identifies high AR activity as a predictive biomarker of response to BAT and supports a treatment paradigm for prostate cancer involving alternating between AR inhibition and activation.  Signaling through the androgen receptor (AR) is the primary oncogenic driver of prostate adenocarcinoma. Inhibition of AR signaling by androgen deprivation and AR inhibitors produces significant therapeutic and palliative benefit and constitutes the cornerstone of treatment for advanced disease. Yet this therapeutic strategy is not curative, and patients eventually progress with lethal castration-resistant prostate cancer (CRPC). Studies dating back over 30 years indicate that supraphysiological androgens (SPAs) can paradoxically suppress the growth of some prostate cancer cell lines and xenograft models –3. TheseEarly studies of SPA-mediated growth inhibition of prostate cancer models suggested that this effect occurs predominantly after long-term exposure to low androgen conditions and requires treatment with supraphysiological, rather than physiological, doses of androgens . Thus, iWe and others are working to map the downstream molecular events through which SPA results in growth inhibition of SPA-sensitive prostate cancer models. Multiple mechanisms have been proposed, including downregulation of c-MYC  , 13–15, Here, we utilize both paired sequential biopsies of metastases of patients with CRPC who have been treated with BAT and prostate cancer models to identify predictors and molecular mechanisms of prostate cancer regression by BAT. We report that high AR activity is required for growth inhibition of prostate cancer models by SPA, which occurs, in part, through downregulation of MYC. Similarly, in patients, high pretreatment AR activity predicts the downregulation of MYC, tumor regression, and both progression-free and overall survival on BAT. Notably, we found that acquired resistance to SPA could be driven by the downregulation of AR but that this could be overcome by treatment with an AR inhibitor, which induced adaptive upregulation of AR.https://doi.org/10.1172/JCI162396DS1). Among these cell lines, we observed that pretreatment AR abundance and activity (as assessed by expression of the AR target prostate-specific antigen (PSA)) was higher in SPA-sensitive cell lines than SPA-resistant cell lines  (see MW scores than nonresponders (P = 0.011) (MW score was not driven by expression of 1 dominant gene (MW score of 0.6 (selected due to its ability to stratify patients with distinct outcomes), we observed that patients with high (>0.6) ARAMW scores exhibited greater PSA responses (P = 0.010) (P = 0.005) (P = 0.058) (P = 0.002) (P = 0.064) , we evaluated clinical samples from a prospective clinical trial (NCT03554317) that included on-study sequential paired biopsies of soft tissue metastases before (preBAT) and after 3 cycles of BAT (on-BAT) . Twenty-bundance , A–C. Prbundance . To examore) see . Notably= 0.011) . The ARAant gene ; the incant gene ; and theant gene . Thus, eant gene , 28. Str= 0.010) , greater= 0.005) , a trend= 0.058) , and lon= 0.002)  on BAT. = 0.064) . TogetheMW score are that it does not require a reference expression vector and is independent of variations in sequencing depth and processing. Therefore, we applied ARAMW scoring to an independent cohort of 266 patients with mCRPC who were not exposed to BAT (SU2C/PCF cohort) (MW score >0.6 does not predict favorable outcomes independent of BAT treatment (Some strengths of the ARA cohort) . Among t cohort) . Analysireatment  — and isreatment , B–D.MYC mRNA expression  on 8q24   near  on 8q24 . We notectively) , which sAR, ANKRD30A, and LINC00993, and upregulation of RERGL and PARM1 (2TPM less than 3), so the importance of these differences is uncertain.By principal component analysis of the RNA sequencing data, the differences in gene expression profiles between patients was generally much greater than the differences in gene expression induced by BAT . This innd PARM1 . In contnd PARM1 , howeverPrevious studies have suggested that SPA can induce DNA damage and downregulation of homologous recombination repair (HRR) gene expression in prostate cancer cell lines  and thatKLK3 (encodes PSA) and PSA expression, and loss of suppression of MYC  model derived from a metastasis of a patient with CRPC and adapted to grow in a castrated mouse (SkCaP-1R) . This PD–30 days . Notably–30 days . Subcuta–30 days . These dMW score (defined by our cut-off of less than 0.6) who exhibited significant downregulation of MYC and/or clinical evidence of tumor regression by BAT. Moreover, the prevalence of this biomarker — an ARAMW score greater than 0.6 — among patients with mCRPC in the SU2C/PCF data accurately estimated prior assessments of BAT efficacy in larger clinical trials. If the ARAMW score biomarker is required for a PSA50 response to BAT, and the PSA50 response rate among biomarker-positive patients is 70%, then the predicted PSA response rate to BAT among patients with mCRPC is 25.6% . This is consistent with the measured PSA50 BAT-response rate of 24.3% among 173 patients with mCRPC across 2 clinical trials ” NCT04363164). By shifting the treatment strategy to anticipate resistance, this dynamic protocol is an attempt to use game theory . R1881 was obtained from Sigma-Aldrich, enzalutamide from SelleckChem, and testosterone cypionate from Steraloids.LNCaP and VCaP cell lines were obtained from ATCC. LAPC4 and 22Rv1 cell lines were a gift from J Isaacs . LNCaP, LAPC4, and 22Rv1 were grown in RPMI 1640 (Thermo Fisher Scientific) supplemented with 10% FBS (Corning), sodium lactate 1.6 mM, sodium pyruvate 0.5 mM, L-alanine 0.43 mM, and 1% pen-strep (Thermo Fisher Scientific). VCaP was grown in DMEM (ATCC) supplemented with 10% FBS (Corning), and 1% pen-strep (Thermo Fisher Scientific). Full-length AR cDNA was cloned into the BAMH1/Sal1 site of pLenti-CMV-GFP-Puro , and empty vector control was generated through excision of GFP of this plasmid. These vectors, along with pCDH-puro-cMyc vector , pCDH-EF1-FHC empty vector control , piSMARTvector-PGK-TurboGFP-TRE3G-shAR vectors  were transfected into 293T cells (ATCC) along with pMD2.G (Addgene) and psPAX2 (Addgene) packaging vectors using lipofectamine (Invitrogen) to produce AR-puro, cMyc-puro, control-puro, and Tet-On-shAR-puro lentivirus particles. 2 days after transduction with the indicated virus, vector-expressing cells were selected with puromycin 1 μg/mL for 72 hours. To constitutively express MYC in LAPC4-AR cells, LAPC4-AR cells were transfected with pCDNA3-HA-HA-human MYC vector  using lipofectamine (Invitrogen) then selected for stable expression with G418 (Sigma-Aldrich) for 7 days. Cells were maintained at 37°C in 5% CO6 cells/well) or 12-well (0.05 × 106 cells/well) plates and incubated with R1881, enzalutamide, or vehicle control for the indicated duration. Cells were counted using a hemocytometer with viability assessed by trypan blue exclusion. For clonogenic survival assessment, cells were plated on 6-well plates at low density  in 1 mL fresh media and 250 μL conditioned media obtained from a confluent flask of the parental cell line. Colonies were stained with crystal violet  after 10–20 days. For cell cycle analysis, cell pellets were resuspended in cold 70% ethanol for a minimum of overnight, then subsequently washed with PBS and resuspended in 50 μg/mL propidium iodide (Sigma-Aldrich) and 100 μg/mL RNAse (Sigma-Aldrich) and run on the BD FACSCelesta flow cytometer with analysis using FlowJo software 10.4.2.For proliferation and cell viability assays, cells were plated in triplicate on 6-well  containing protease and phosphatase inhibitors (Roche). Protein concentration was determined using the Pierce BCA Protein Assay kit (Thermo Fisher Scientific), and 5–20 μg of lysate was resolved on a SDS-PAGE gel and transferred to a nitrocellulose membrane. Membranes were blocked with 5% milk for 1 hour, then incubated with primary antibody overnight. Primary antibodies used were anti-cMYC , anti-AR , anti-PSA , anti-vinculin . Anti-rabbit IgG HRP-linked antibody  and anti-mouse IgG HRP-linked antibody  were used as secondary antibodies. Please refer to MYC (Hs00153408_m1), AR (Hs00907244_m1), KLK3 (Hs02576345_m1), and ACTB  on an ABI7500 Real-Time PCR System (Thermo Fisher Scientific). Relative gene expression was determined by ΔΔCT.RNA was extracted using the RNeasy kit (Qiagen) and cDNA generated using the high-capacity cDNA reverse transcription kit (Thermo Fisher Scientific). Real-time PCR was performed in triplicate using 500 μg cDNA, 10 μL TaqMan Gene Expression Master Mix (Thermo Fisher Scientific), and 1 μL 20 × TaqMan Gene Expression Assay probe mix for The COMbination of BAT and Nivolumab  clinical trial was a single-arm, multicenter, open-label phase II study of BAT in combination with the anti-PD1 agent nivolumab for patients with CRPC that had progressed on at least 1 novel AR-target therapy. The inclusion and exclusion criteria and prespecified study end points were previously described . PatientMale NSG mice aged 8–12 weeks were obtained from the Sidney Kimmel Comprehensive Cancer Center Animal Core Facility and surgically castrated. SKCaP PDX tissue was minced, mixed with Matrigel (BD Biosciences) and implanted subcutaneously on the flank. Testosterone was administered by implantation of a slow-release subcutaneous pellet in the opposite flank. Pellets were assembled using 25 mm sections of Silastic Laboratory tubing, filled with 30 mg testosterone cypionate , sealed on both ends with Silastic Medical Adhesive Type A, then sterilized. Enzalutamide (SelleckChem) was administered by oral gavage 10 mg/kg/day in 200 μL 1% carboxymethyl cellulose, 0.1% Tween-80, 5% DMSO. Tumors were measured twice weekly using microcalipers, and tumor volume was calculated using the following formula: 0.5236 × L × W × H. At study completion, the mice were euthanized and the tumors were extracted. Tumors were flash frozen for subsequent lysis for immunoblot analyses and formalin-fixed for subsequent IHC analyses. All mice were housed in the Johns Hopkins animal facility.The sequencing data described have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE213444.t test using GraphPad Prism v8.0 software. Statistical comparisons of proportions were performed using a Chi-squared test. Statistical comparisons of time-to-event data were performed using Kaplan-Meier analysis with log-rank test using GraphPad Prism v8.0. Analysis of correlation was performed using Pearson’s correlation calculation. P values less than 0.05 were considered statistically significant.Results are displayed as mean ± SD, unless otherwise indicated. Statistical comparison between groups was performed using a 2-tailed Student’s The COMbination of BAT and Nivolumab  clinical trial was approved by the IRBs at Johns Hopkins, Dana Farber, and University of San Francisco, and all accrued patients provided written informed consent. For animal studies, we have complied with all relevant ethical regulations in accordance and approved by the Johns Hopkins IACUC.LAS, ESA, AMD, JTI, MCM, and SRD conceptualized the study. LAS, RK, DES, ESA, AMD, JTI, MCM, and SRD developed the methodology. LAS, RK, DES, EAT, DMR, SLD, LA, YY, CGA, JLH, TJ, KAB, JNE, JM, AG, and AS performed the experiments. LAS and DES visualized the study. ESA, SKK, and SRD acquired funding for the project. SY, JL, WNB, SKK, ESA, AMD, JTI, MCM, SRD supervised the study. LAS wrote the original draft of the manuscript. All authors were responsible for reviewing and editing the manuscript. The order of co–first authors was determined based on their contributions to the manuscript.