Multiple studies have demonstrated the importance of androgen receptor (AR) splice variants (SVs) in the progression of prostate cancer to the castration-resistant phenotype and their utility as a diagnostic. However, studies on AR expression in non-prostatic malignancies uncovered that AR-SVs are expressed in glioblastoma, breast, salivary, bladder, kidney, and liver cancers, where they have diverse roles in tumorigenesis. AR-SVs also have roles in non-cancer pathologies. In granulosa cells from women with polycystic ovarian syndrome, unique AR-SVs lead to an increase in androgen production. In patients with nonobstructive azoospermia, testicular Sertoli cells exhibit differential expression of AR-SVs, which is associated with impaired spermatogenesis. Moreover, AR-SVs have been identified in normal cells, including blood mononuclear cells, neuronal lipid rafts, and the placenta. The detection and characterization of AR-SVs in mammalian and non-mammalian species argue that AR-SV expression is evolutionarily conserved and that AR-SV-dependent signaling is a fundamental regulatory feature in multiple cellular contexts. These discoveries argue that alternative splicing of the AR transcript is a commonly used mechanism that leads to an expansion in the repertoire of signaling molecules needed in certain tissues. Various malignancies appropriate this mechanism of alternative AR splicing to acquire a proliferative and survival advantage. The androgen receptor (AR) has a dominant role in mammalian physiology from early development, where it is critical in establishing the male phenotype, to post-natal development and adult maintenance of multiple tissues ,2,3,4. WSince the AR is a member of the 48-member steroid receptor superfamily, it has the same basic structure as other family members, consisting of an N-terminal activation (NTD), DNA binding (DBD), and ligand binding domains (LBD), where a hinge region lies between the DBD and LBD ,4,10. ThWhile the AR has a critical role in male development and physiology, it is a pivotal regulatory molecule in prostate cancer (PCa). Its central function in the initiation and progression of this malignancy was proposed by Huggins and Hodge over 80 years ago . Their vThe emergence of AR splice variants (AR-SVs), which retain the NTD and DBD but no longer express the LBD and are therefore refractory to therapies that target this region, is greatly associated with the acquisition of castration resistance. Some AR-SVs retain the ability to translocate into the nucleus, bind to DNA, and regulate gene transcription constitutively ,29,30,31Below, we summarize our comprehensive review of studies on AR-SV expression and function in multiple non-prostatic malignancies, non-cancer pathologies, and normal tissues in humans and non-human species. This comparative analysis underscores the wide prevalence of AR-SVs in differing cells, where these regulatory molecules make unique contributions to defining cellular physiology in various pathologies and normal processes.Historically, lower molecular weight (LMW) AR variants have been reported in studies on AR in androgen insensitivity syndrome and in malignancies, but their role in PCa was established only about a decade ago ,34,35,36In the context of PCa, full-length-AR (FL-AR) and AR-V7 DNA binding site specificities, either homo or heterodimers, have been explored in several studies, but the results are inconsistent; some find that FL-AR and AR-V7 heterodimer and FL-AR homodimer binding sites exhibit significant overlap, while others report a unique AR-V7 binding profile ,39,40,41In PCa, expression of AR-SVs has two well-described etiologies: genomic rearrangements of the AR locus and aberrant RNA splicing. Genomic rearrangements associated with AR-SVs have been described in PCa in vitro models and in human CRPC lesions ,47,48. TAberrant RNA splicing is sensitive to FL-AR signaling and therefore can be rapidly initiated in the absence of androgens but reversed when androgen dependent signaling is reinstated, arguing that this process is an adaptation rather than a clonal selection of AR-SV-expressing cells and that androgen deprivation drives AR-SV-expression ,51,52. N567es in transgenic mice where prostate epithelial cell-specific AR-SV forced expression was driven by a modified probasin promoter [567es drove early hyperplasia and ultimately the development of invasive adenocarcinoma. Transcriptome studies identified enriched expression of inflammatory-related cytokines, transcription factors, and tumorigenesis-associated transcripts. Together, these studies confirm that AR-SV expression reprograms cellular transcription, elevating the expression of genes that promote tumor initiation and progression . Many mpromoter ,59. AR-Vgression .Strides have been made in the development of agents that target the AR-NTD with the goal of inhibiting NTDs containing AR isoforms. Unlike the LBD, the intrinsically disordered AR-NTD makes structure-based drug design difficult, but inhibitor development is underway. In a recent review, Ji and co-authors surveyed the status of NTD-targeting agent development, which can broadly be assigned to four groups :EPI-001 and the analogues are bisphenol A diglyceride derivatives initially isolated from murine sponge. The original isolated compound was reported to target the AR-NTD ,62. WhilBicalutamide-derived selective AR degraders include several compounds that can directly bind to the AR-NTD of FL-AR and AR-SVs to block interactions with co-activators . The mosAgents with dual targeting of STAT3 and AR inhibitory activity have been identified. These compounds interact with the AR-NTD to inhibit recruitment of the AR co-activator SRC-1, resulting in a loss of activity, blocking nuclear translocation of FL-AR and AR-SVs, and causing AR degradation by way of the ubiquitin-mediated pathway .A biphasic antibody molecule that consists of a single-chain variable antibody fragment that recognizes DNA and a single-chain variable fragment that recognizes an epitope in the AR-NTD can accumulate in the nucleus and block signaling by the FL-AR and AR-SVs. This molecule can inhibit the proliferation of AR-dependent cells in a ligand-independent manner .AR signaling has been suggested as a contributing factor in other cancers ,69. VaryCell culture and animal models reiterate the contributions of androgens and/or AR to the development of cancer. However, clinical studies on the efficacy of AR-targeting therapeutics have been disappointing, showing little measurable benefit. Multiple explanations can reconcile the divergent preclinical and clinical results, including the expression of constitutively active AR-SVs, which, as in CRPC, could render cells refractory to current AR targeting therapeutics.Below, we review reports of AR-SV expression in non-prostatic malignancies. All the malignancies, with the exception of breast cancer (BrCa), exhibit a sex disparity where males are more likely to develop disease than females. However, counterintuitively, AR-SVs were detected in tumors from males and females, and males did not have an increase in incidence or levels of AR-SVs. In BrCa, a cancer that overwhelmingly occurs in females, AR-SVs are detected in most high-grade malignancies. It is notable that in the context of BrCa, the tumors that most frequently express AR-SVs are Triple Negative BrCa (TNBC), particularly tumors with apocrine features. Moreover, salivary duct carcinomas, the only salivary gland tumor that displays a sex disparity, also exhibit apocrine features and express AR-SVs. While the transcriptomes regulated by distinct AR-SVs have not been investigated in all contexts, the prevailing pattern suggests that the AR-SV-regulated transcriptome is different from the FL-AR-driven transcriptional program. Moreover, there is little, if any, similarity between the investigated AR-V7-regulated transcriptions in different cellular contexts. Lastly, another emerging common theme is that while the level of an SV is low in comparison to the amount of total AR, depletion of the constitutively active AR-SV has a disproportionate effect on the cells’ physiological properties, suggesting that its role is independent of other AR-SVs and FL-AR and needed for optimal cell survival and growth (Table 1).Breast cancers (BrCa) are highly reliant on estrogens and progesterone, whose activity is mediated by their respective receptors ,73,74. HAR as a prognostic marker of disease progression is complicated and nuanced and appears to depend on whether tumors express ER and PR. In ERα-positive malignancies, AR may be a tumor suppressor, while in ERα-negative tumors, AR may be a tumor promoter . AR-reguIn contrast, in ERα-negative malignancies, including triple negative  BrCa (TNBC), AR has oncogenic properties. Blocking AR signaling in ERα-negative cell models suppressed cell growth . The comSeveral studies reported the presence of AR-SVs in BrCa ,112,113.Salivary duct carcinoma is a rare cancer of the salivary glands, accounting for 2% of salivary gland tumors. This aggressive malignancy is frequently fatal, and for advanced, unresectable lesions, therapeutic options are limited. Most salivary malignancies do not display a sex disparity, but salivary duct carcinomas are an exception and are more common in males than females ,115. ImmAR-SV expression was assessed in a cohort of male and female patients of varying ages . The expGlioblastoma multiforme (GBM) exhibits a sex disparity where males are 1.59 times more likely than females to develop this malignancy, and females exhibit a survival advantage ,131. FemAR gene amplification was detected in 27% of GBMs from men and 38% of tumors from women, and further analysis shows that AR mRNA is overexpressed in 93% of tumor samples . This deEpidemiological studies found that males have a two-fold higher risk of developing kidney cancer than females, and this disparity is unaltered by age, year surveyed, or geographic location . While mThe two most common RCC subtypes are clear-cell RCC (ccRCC) and papillary RCC (pRCC). ccRCC accounts for 75% of all RCCs, while pRCC accounts for approximately 10%. The tumors differ in vascularization; ccRCCs are highly vascularized, while pRCCs are hypovascularized when compared to the surrounding parenchyma ,151. ccRBladder cancer (BlCa) displays a striking sex disparity where males are 3–4 times more likely to develop the disease than females . NumerouMost bladder cancer cells express AR-SV, and in some contexts, this is the major isoform expressed . HoweverThe AR is implicated in the development and pathogenesis of hepatocellular carcinomas (HCC) ,166,167.One explanation for these disappointing clinical results is that the presence of constitutively active AR-SVs enhances AR signaling and renders the cells refractory to therapeutic interventions. Dauki and collaborators re-examined HCC TCGA patient data and found that 78% of patient tumors expressed AR-SVs, which are detected in male and female patients but are higher in males . This anIn this section, we summarize the studies on AR-SVs in non-cancer conditions as well as in normal tissues (Table 1).Polycystic Ovarian Syndrome (PCOS) is the most frequent endocrine disorder of reproductive-aged women, affecting up to 15% of women worldwide . PCOS caHuman genetic studies implicate AR CAG repeat polymorphisms and AR SNP rs615G/A with PCOS, suggesting that alterations of the AR gene have a role in PCOS pathogenesis ,187. A sThe placenta develops at the maternal-fetal interface to support fetal development for nutrient and gas exchange and endocrine signaling. The placenta secretes androgens throughout pregnancy and expresses the AR nuclear receptor and the non-genomic membrane receptor, suggesting a role for androgen signaling in placental function. Androgens and the FL-AR are expressed in the placenta of humans ,190, rodOn average, male fetal growth leads to increased birthweights and birthweight centiles compared with female fetal growth in normal pregnancies . CirculaAR-SVs have recently been reported in the human placenta which include the FL-AR, AR-V1, AR-V7, and AR-45 along with unknown proteins that are detected by anti-AR antibody including 120, 90, and 55 kDa isoforms . These vThe AR is essential for the development of testicular tissue . Sertoli23stop  and ARΔ4 (in-frame deletion of 120 nucleotides of exon 4 resulting in a protein with an incomplete LBD) were only detected in testis, while ARΔ2stop (premature stop encoding only the NTD) and ARΔ3 (in-frame skipping of exon 3 encoding a protein missing the 2nd Zn finger) were only detected in testis. This AR-SV is the same as the AR-SV described in PCOS, in breast cancer samples, and in patients with AIS [Early studies on AR-SV expression in testis identified AR45, an AR-SV that encodes a truncated NTD but a complete DBD, hinge, and DBD. Functional studies of this variant in non-testicular tissue found that AR45 could activate or repress AR transcriptional activity, depending on co-regulator expression, but studies were not conducted in the context of testicular cells . Subsequwith AIS ,212,213,Studies on impaired spermatogenesis found AR-V7  is differentially expressed in testicular tissue from controls and patients with nonobstructive azoospermia (NOA) . The NOAMembrane AR has not been as well studied as classical cytoplasmic/nuclear AR. The AR is expressed in many areas of the brain, including the substantia niagra, hippocampus, and entorhinal cortex ,216,217.Recently, Garza-Contreras et al.  reportedThe immune responses of males and females differ . FemalesThese studies led to an evaluation of AR expression in distinct populations of peripheral blood mononuclear cells (PBMC), including B and T lymphocytes, monocytes/macrophages, neutrophil lineage cells, and natural killer cells . Most ne+ T lymphocytes (helper cells), CD8+ T lymphocytes (effector cells), and macrophages all express AR, while AR expression in B lymphocytes is much lower [+ lymphocytes with physiologically relevant levels of DHT results in increased expression of IL-10, a cytokine that suppresses the function of NK cells and T cells while enhancing B-cell survival. This suggests that androgens modify lymphocyte cytokine expression, altering the male immune system [Sub-populations enriched for CD4ch lower . In vitre system .PBMC obtained from non-cancer patients express not only FL-AR but also AR-V7 . Of the AR protein structure is evolutionarily conserved from fish to humans . Androge2stop and ARΔ3 SVs are also present in phylogenetically distinct vertebrate species—rat, African clawed frog, and gilthead seabream, a teleost fish [Sanches-Hernandez et al.  report aost fish .Androgen signaling has implications for the response to immunotherapy. In preclinical studies, melanoma growth following anti-PD-L1 treatment was significantly greater in female animals than in male animals. This effect could be in part explained by inhibition of Treg upon PD-L1 blockade and thus an enhanced immune response, suggesting that androgens present in the male may have a negative impact on responses to anti-PD-L1 therapy . A meta-Most of the studies on androgen signaling blockade and treatment efficacy focus on prostate cancer, where androgen deprivation therapy is a standard treatment for advanced malignancies . Most imExpression of AR-SVs in human neutrophils has not been explored, but given their role in fish development and response to bacterial stimuli, such studies would further our understanding of the endocrine-immune axis in humans.This comprehensive review of numerous studies that identified AR-SVs in various tissues reveals that AR-SV expression is pervasive across tumor and non-tumor tissues and in diverse species. When analyzed in total, the studies lead to several conclusions: (1) AR-SV expression is conserved through evolution, as evidenced by its presence in fish, frogs, and mammalian species. The AR-SV is similar across species and tissues, where most retain the NTD while the LBD is removed. The exception to this is AR45, which is missing the NTD but retains the DBD and LBD. In most variants, the DBD remains intact, but in some cases, the second ZN finger (encoded by exon 3) may be missing. (2) AR-SVs drive variant-specific transcriptomes that are distinct from those of the FL-AR, but there may be overlapping gene targets. Likewise, the AR-SV binding motifs differ from the FL-AR AREs. Studies in PCOS demonstrated that two different AR-SVs, generated by the insertion and deletion of sequences, drive distinct transcriptional programs; therefore, AR-SV-driven transcriptional programs are specific to a splice variant. Hence, AR-SVs are not simply substituting for FL-AR signaling but complementing, extending, or, in some cases, replacing FL-AR-specific gene expression. (3) AR-SV-driven transcription does not require FL-AR since some cells express only AR-SVs. Moreover, in many cells, a particular AR-SV may constitute a small percentage of total AR present in cells, yet its depletion exerts profound effects on cell proliferation, apoptosis, invasion, and migration, further arguing that AR-SVs have distinct and vital roles that are independent of FL-AR. Furthermore, AR-SVs and FL-ARs can heterodimerize. Is the transcriptional program driven by heterodimers distinct from either homodimer? (4) More recent studies have identified novel constitutively active AR-SVs, but most of the above studies focused on AR-SVs that were originally identified as constitutively active in PCa. It is possible that novel AR-SVs exist but remain undetected, and if true, the prevalence of AR-SVs may be even greater. Malignancies that are not as prevalent or as well studied as those reviewed above may also express these molecules where they may contribute to disease or therapeutic responses, so the prevalence of AR-SVs may be even greater. This requires further study. Moreover, defining the importance of AR-SVs in malignancies may expose a therapeutic opportunity. AR-SVs were detected in PCa cells, which are heavily reliant on AR activity; hence, significant efforts have focused on identifying agents that would target these isoforms along with the FL-AR. Since AR-SVs have a role in promoting the viability and tumorgenicity of multiple malignancies, such agents, when identified, could prove beneficial for the treatment of various malignancies where FL-AR and AR-SVs have a demonstrated role in pathogenesis.


Exercise rehabilitation is crucial for neurological recovery in hemiplegia-induced upper limb dysfunction. Technology-assisted cortical activation in sensorimotor areas has shown potential for restoring motor function. This study assessed the feasibility of mobile serious games for stroke patients' motor rehabilitation.A dedicated mobile application targeted shoulder, elbow, and wrist training. Twelve stroke survivors attempted a motor task under two conditions: serious mobile game-assisted and conventional rehabilitation. Electroencephalography and electromyography measured the therapy effects.p < 0.05). RMS was notably higher in game-assisted rehabilitation, particularly in shoulder training (p < 0.05).Patients undergoing game-assisted rehabilitation showed stronger event-related desynchronization (ERD) in the contralateral hemisphere's motor perception areas compared to conventional rehabilitation (Serious mobile game rehabilitation activated the motor cortex without directly improving muscle activity. This suggests its potential in neurological recovery for stroke patients. Attempting motor tasks is one of the most commonly used experimental paradigms for EEG . The speRehabilitation interventions for UL aim to improve activities of daily living, functional independence, and quality of life . StudyinFor the rehabilitation of stroke survivors, technology has potential therapeutic benefits and offers interesting and effective adjuncts. In multiple studies, the use of novel rehabilitation interventions such as UL rehabilitation robots , virtualWe aimed to study the effects of serious gaming using smartphones on the motor rehabilitation of patients with stroke. In this experiment, we set up two groups of controlled rehabilitation conditions, namely, game-assisted and conventional rehabilitation. Each patient participated in two experiments, which were conducted on two random days under different rehabilitation conditions to minimize possible training effects. The UL rehabilitation content sets three items, namely, shoulder training (a), elbow training (b), and wrist training (c). Each of this rehabilitation training content is divided into several motor tasks and monitors the brain and muscle activity of the patients in real time during the experiment. The results were obtained by comparing ERD/ERS and other data to analyze the sensorimotor rhythm of the same patient under the rehabilitation conditions of the two groups. The related muscle activity intensity was analyzed by comparing MF/RMS and other data. We hypothesized that the brains and motor muscles of patients would respond better to MA tasks during rehabilitation activities assisted by serious mobile games than without app use.2.2.1.In the experimental setup, we used a smartphone that measures 6.1 inches and weighs 218 g. It is equipped with an internal accelerometer, gyroscope, linear motor, and speaker. The dedicated serious game includes three intervention modules focusing on shoulder, elbow, and wrist rehabilitation. The patients engage in rehabilitation courses while holding the smartphone. The team meticulously designed the app interface, which incorporates the following distinctive features. (1) Vibration feedback: The app utilized vibration feedback to enhance the sensory perception of the limbs of the patients. The intensity of the vibrations guided the patients in performing standardized rehabilitation movements. (2) Voice guidance: The app provided real-time audio instructions and rhythmic cues for each step of the exercises. (3) Motion tracking: The app utilized the accelerometer and gyroscope to monitor and record the trajectory of each movement. After each rehabilitation session, the recorded data were visually presented on the interface to motivate the patients in their rehabilitation efforts.Based on a systematic review and meta-analysis conducted by Veerbeek et al.  in 2014,The GRASP requires participants to engage in 60 min of daily exercise, monitored weekly by therapists. The program comprises three exercise levels , correlating to severe, moderate, and mild impairment levels, respectively, according to the Fugl–Meyer injury scale.To develop a rehabilitation exercise regimen, we combined exercises from Levels 2 and 3 of the GRASP, integrating elements from the rehabilitation exercise program of the local hospital. These exercises encompass movements and coordination exercises for various UL joints, along with activities targeting hand function and daily life tasks. Through these exercises, we aim to promote UL functional recovery, enhance the self-care abilities of patients, and improve their quality of life.(a)Shoulder training: The patient sat, and the training was divided into six sub-movements: (1) shoulder abduction, (2) shoulder adduction, (3) horizontal shoulder abduction, (4) shoulder horizontal adduction, (5) external shoulder, and (6) internal shoulder rotation. Each sub-action lasted for 5 s, followed by 15 s of rest after the action was restored, and the training cycle set was performed 20 times.(b)Elbow training: The patient sat, and the training was divided into four sub-movements: (1) arm bending, (2) arm straightening, (3) arm valgus, and (4) arm varus. Rest was allowed for 15 s after the action was restored, and the action set was repeated 20 times.(c)Wrist training: The patient adopted a sitting position, and the training was divided into four sub-actions: (1) wrist joint bending, (2) wrist joint extension, (3) radial deviation, and (4) ulnar deviation. Each sub-action lasted for 5 s, with an interval of 15 s. This set of movements was repeated five times.The specific rehabilitation training content was as follows:2.2.From October 2022 to April 2023, a total of 12 community patients with stroke  were recruited to participate in rehabilitation training. Since the rehabilitation training content is derived from GRASP Levels 2 and 3, it corresponds to mild impairment according to the Fugl–Meyer injury scale. Therefore, the inclusion criteria for stroke participants in our study are as follows: (1) first unilateral stroke ≥2 weeks after stroke confirmed by computed tomography or magnetic resonance imaging; (2) Fugl–Meyer UL assessment (FMA-UE) >36 points and hand movement score >7; (3) basic mental state examination score ≥25; and (4) able to sit independently on a chair for at least 1 h. The exclusion criteria were as follows: (1) unilateral neglect or vision problems, (2) hypersensitivity to the electrode gel, and (3) receiving noninvasive brain stimulation during the study. All participants completed a cardiopulmonary exercise test (CPET) and received physician clearance before starting the intervention.The 12 participants involved in the study exhibit mild impairment in their ULs, with Brunnstrom scores ranging between Level 4 and Level 6. They do not present any additional disabilities, possess basic activities of daily living, and do not show signs of post-stroke depression. These stroke survivors are currently primarily focused on rehabilitating fine motor skills, enhancing coordination, and improving movement speed and accuracy.This study was approved by the Ethics Committee of First Hospital of Jilin University (2022-514) and conducted in accordance with the Declaration of Helsinki. An experienced therapist who was blinded to the study performed all clinical measurements. The FMA-UE is a classic motor measurement scale designed for UL hemiplegia  that ref2.3.2.3.1.The 32-channel ANT neuro eego™ mylab product was used to collect the EEG signals  of each channel, of which C3 and C4 corresponded to the body motor center in the precentral gyrus of the frontal lobe and CP1 and CP2 corresponded to the body motor center in the postcentral gyrus of the frontal lobe, with a sampling rate of 1,000 Hz. The eight-channel MYON Wireless EMG was used to record the corresponding EMG signals, with a sampling rate of 1,000 Hz.The locations of EMG signal acquisition in the different sub-experiments differed. Shoulder rehabilitation training mainly collected EMG signals of the deltoid (DEL), supraspinatus (SSP), biceps brachii (BB), and triceps brachii (TB); elbow rehabilitation training mainly collected signals from the brachioradialis (BRD), brachialis (BR), BB, and TB; wrist rehabilitation mainly included the flexor carpi ulnaris (FCU), extensor carpi ulnaris (ECU), flexor carpi radialis (FCR), and extensor carpi radialis (ECR).In this experiment, four channels  were turned on, and four pairs of electrodes were attached to the skin surface of the abdomen of the relevant muscles, with the centers separated by 2 cm. The impedance of all EEG and EMG electrodes was kept below 10 kΩ. Synchronized with a sampling frequency of 1,000 Hz, the event time was marked using E-prime 3.0.2.3.2.N (where 7 ≤ N ≤ 14) after the initial session, the second session takes place, and the experimental groups are interchanged (G→R or R→G). This approach ensures that each participant undergoes both forms of rehabilitation exercises, allowing for a more comprehensive evaluation of the efficacy and impact of the two rehabilitation methods. The two groups had the same motor tasks, and the patients in the game-assisted rehabilitation group underwent rehabilitation training under the instructions of the game and somatosensory feedback while holding the mobile phone. The patients in the conventional rehabilitation group followed the rehabilitation training instructions given by the staff.Due to the prolonged duration of the rehabilitation exercises, conducting consecutive sessions may potentially influence the experimental outcomes. Therefore, to prevent any sequence effects, the study is designed in a manner where each participant is randomly assigned to either the game-assisted rehabilitation group (G) or the conventional rehabilitation group (R) in the first session. Following a random number of days All motor tasks were divided into a preparation phase (S1), movement phase (S2), and rest phase (S3) to reduce the mutual influence before and after the task. Taking the instructions of the game/staff as the 0 s of the time scale, the arrangement of each motor task was as follows: −3 to 0 s was the preparation stage (S1), which provided the reference measurement in the idle state before the motor task; 0–5 s was the exercise phase (S2), with each sub-action MA classified as a single trial and repeated 20 times to reduce errors during the test; 5–15 s was the rest phase (S3), which calculated the influence of the current MA; and 15–18 s was the preparation stage (S1) for the next motor task. The baseline was set from −2 s to −1 s to prevent the signal from being polluted by the response to the previous stimulus, as shown in The motor task was specifically designed such that the participant performed 14 sub-actions  in shoulder, elbow, and wrist training. The order of the sub-actions that each patient needed to perform was randomly disrupted, and there was a 10-min rest period after each sub-action was completed. A total of 12 total patients with stroke participated in the two experimental groups. Before the experiment officially started, each subject stood still for 5 min, and the subjects started to attempt the motor tasks after receiving instructions.2.4.2.4.1.The EEG signals recorded in the channel were preprocessed in a series of steps. The data analysis was performed offline in MATLAB (R2019a) using the EEGLAB toolbox. The EEG recordings were re-referenced to the average value of channels TP9 and TP10, and basic finite impulse response filtering was carried out with a 30 Hz low-pass and 0.1 Hz high-pass filter, respectively. Segments with low signal-to-noise ratio were rejected, and independent component analysis was implemented using the “pop_runica” function from EEGLAB . ArtifacThe steps included (1) import data; (2) electrode positioning; (3) remove unnecessary electrodes; (4) re-reference; (5) 0.01–30 Hz band-pass filtering; (6) segmentation and baseline correction; (7) run ICA; (8) remove electrooculopathy and artifacts; and (9) resample to 500 Hz.t, F) from −3,000 to 15,000 ms (in steps of 5 ms) in the time domain, 1–30 Hz (in 1 Hz steps) in the frequency domain, and a power spectrum P, P = | f |. We calculated the power spectrum of all channels at alpha (8–13 Hz) and beta (14–30 Hz) to determine the ERD and ERS of the motor task by the affected UL. The ERD/ERS transition was defined as the percentage decrease/increase in instantaneous power density at the “event” compared to the “pre-event” baseline value  and beta (14–30 Hz) frequency ranges were analyzed because motor preparation and execution generate ERD in the sensorimotor regions at 10 and 20 Hz. The time-frequency distribution of the EEG was estimated using a windowed Fourier transform (WFT)  with a fne value .ERD/ERS=2.4.2.The sEMG signal is a bioelectrical current generated by the contraction of surface muscles. The nervous system controls muscle activity—contraction or relaxation—resulting in diverse signals from different muscle fiber motor units in the skin at any given time. sEMG is a non-stationary microelectrical signal with amplitudes ranging from 0 to 1.5 mV, containing pertinent information between 20 and 200 Hz . During Maximum voluntary contraction percentage (MVC%) is a commonly used index for evaluating EMG signals because individual differences have little influence on EMG signals. However, obtaining MVC% is cumbersome and requires participants to repeat maximal isometric contractions, which is not suitable for special populations, such as patients with hemiplegia. The root mean square (RMS) is a time domain index of EMG signals that reflects the muscle load level in real time and measures the load on the participant during the measurement period. It has the advantages of easy operation and real-time response ; therefoN, the myoelectric signal data after filtering and segmentation of each sample channel is Data, and the RMS is calculated asThe total number of myoelectric data sample points filtered by each motor task is 2.4.3.p-value of paired samples in each group was significant (p < 0.05).Statistical analysis was performed using SPSS (IBM). Due to the small sample size, the data were confirmed to have a normal distribution according to the Shapiro–Wilk normality test. Because each subject participated in the game-assisted rehabilitation experiment and the conventional rehabilitation experiment (C), there was no individual difference between the two experiments for the same subject. Therefore, the Wilcoxon signed-rank test was used for the between-group analysis to determine whether the 3.3.1.3.1.1.M = −54.300, SD = 23.371) was higher than that in the conventional rehabilitation group  and had a larger magnitude . The paired-sample Wilcoxon signed-rank test was performed on the two groups of samples. Based on the variable game-assisted rehabilitation ERD paired with conventional rehabilitation ERD, Cohen's d value of the difference was 0.407 . The p-value was 0.008***, which was significant at the level and rejected the null hypothesis. Therefore, there was a significant difference in the ERD of the contralateral hemisphere between the two groups in the (a) shoulder training experiment.The results showed that during the execution of the motor task, the ERD value of the contralateral hemisphere motion perception area in the game-assisted rehabilitation group  under game-assisted rehabilitation (G) was compared with the ERD value  of a larger magnitude . The paired-sample Wilcoxon signed-rank test was performed on the two groups of samples. Based on the variable game-assisted rehabilitation ERD paired with conventional rehabilitation ERD, Cohen's d value of the difference was 0.681 (medium). The p-value was 0.012**, which was significant at the level and rejected the null hypothesis. Therefore, there was a significant difference in the ERD of the ipsilateral hemisphere between the two groups in the shoulder training experiment.During the execution of the motor task, the ERD value of the motion perception area of the ipsilateral hemisphere , SSP , BB , and TB ] and conventional rehabilitation (C) . The four pairs of muscles had larger RMS values under the game-assisted rehabilitation condition than under the conventional rehabilitation condition. The differences were DEL , SSP , BB , and TB .d values of DEL, SSP, BB, and TB were 0.462 , 0.804 (medium), 0.782 (medium), and 1.001 (very large), and their p-values were 0.041** (significant), 0.041** (significant), 0.012** (significant), and 0.028** (significant), respectively. Therefore, there was a significant difference in the RMS between the two groups of muscles in the shoulder training experiment.Two groups of four pairs of muscle RMS samples were subjected to a paired-sample Wilcoxon signed-rank test based on the variable RMS of game-assisted rehabilitation muscles paired with the RMS of conventional rehabilitation muscles. Cohen's 3.2.3.2.1.M = −47.223, SD = 23.122) was higher than that in the conventional rehabilitation group  and had a larger magnitude . The paired-sample Wilcoxon signed-rank test was performed on the two groups of samples. Based on the variable game-assisted rehabilitation ERD paired with conventional rehabilitation ERD, Cohen's d value of the difference was 0.545 (medium). The p-value was 0.023**, which was significant and rejected the null hypothesis. Therefore, there was a significant difference in the ERD of the contralateral hemisphere between the two groups in (b) elbow training experiment.The results showed that during the execution of the motor task, the ERD value of the contralateral hemisphere motion perception area in the game-assisted rehabilitation group  under the condition of game-assisted rehabilitation (G) was compared with the ERD value  of a larger magnitude . The paired-sample Wilcoxon signed-rank test was performed on the two groups of samples. Based on the variable game-assisted rehabilitation ERD paired with conventional rehabilitation ERD, Cohen's d value of the difference was 0.437 . The p-value was 0.071*, which reflected no statistical significance, and the null hypothesis was not rejected. Therefore, there was no significant difference in the ERD of the ipsilateral hemisphere between the two groups in the elbow training experiment.During the execution of the motor task, the ERD value of the motion perception area of the ipsilateral hemisphere , BR , BB , and TB ] and conventional rehabilitation (C) .M = 0.961, SD = 5.008), BR , BB , and TB .The four pairs of muscles had larger RMS values under the game-assisted rehabilitation condition than under the conventional rehabilitation condition. The differences were BRD , 0.239 , 0.430 , and 0.426 , respectively, and their p-values were 0.53, 0.209, 0.158, and 0.084*, respectively. Thus, there were no statistically significant differences at any level, which reflects a lack of statistically significant differences in RMS between the two groups of muscles in the elbow training experiment.Two groups of four pairs of muscle RMS samples were subjected to a paired-sample Wilcoxon signed-rank test based on the variable RMS of game-assisted rehabilitation muscles, paired with the RMS of conventional rehabilitation muscles. Cohen's 3.3.3.3.1.M = −49.428, SD = 23.235) was higher than that in the conventional rehabilitation group  and had a larger magnitude . The paired-sample Wilcoxon signed-rank test was performed on the two groups of samples. Based on the variable game-assisted rehabilitation ERD paired with conventional rehabilitation ERD, Cohen's d value of the difference was 0.682 (medium). The p-value was 0.008***, which was significant and rejected the null hypothesis. Therefore, there was a significant difference in the ERD of the contralateral hemisphere between the two groups in the wrist training experiment.The results showed that during the execution of the motor task, the ERD value of the contralateral hemisphere motion perception area in the game-assisted rehabilitation group  under the condition of game-assisted rehabilitation was compared with the ERD value  with a larger magnitude . The paired-sample Wilcoxon signed-rank test was performed on the two groups of samples. Based on the variable game-assisted rehabilitation ERD paired with conventional rehabilitation ERD, Cohen's d value of the difference was 0.546 (medium). The p-value was 0.002***, which was significant and rejected the null hypothesis. Therefore, there was a significant difference in the ERD of the ipsilateral hemisphere between the two groups in the wrist training experiment.During the execution of the motor task, the ERD value of the motion perception area of the ipsilateral hemisphere , ECU , FCR , and ECR ] and conventional rehabilitation (C) .M = 2.260, SD = 3.893), ECU , FCR , and ECR .The four pairs of muscles had larger RMS values in the game-assisted rehabilitation condition than in the conventional rehabilitation condition, and the differences were FCU , 0.018 , 0.317 , and 0.170 , respectively, and their p-values were 0.117, 0.875, 0.071*, and 0.388, respectively. There were no statistically significant differences at any levels; therefore, there was no significant difference in RMS between the two groups of muscles in the (c) wrist training experiment.Two groups of four pairs of muscle RMS samples were subjected to a paired-sample Wilcoxon signed-rank test based on the variable RMS of game-assisted rehabilitation muscles paired with the RMS of conventional rehabilitation muscles. Cohen's 4.Using technology and design to assist in the rehabilitation of hemiplegia after stroke is an important issue in the health field. This study used EEG and EMG to explore the possibility of serious games assisting the rehabilitation of UL hemiplegia after stroke. In the experiment, 12 patients with UL hemiplegia attempted motor tasks under conditions of serious game-assisted rehabilitation and conventional rehabilitation. We collected and compared the EEG and EMG data.The research primarily compares the differences between the two experiments. Thus, the EEG section selected the motor perception areas  for primary analysis, while the EMG section chose the main muscles for each training group. As the data reflect only local features and are influenced by complex factors, comparisons between the data of different subjects do not lead to valid conclusions. However, for individual subjects, the variables from the two experiments have been controlled, enabling a comparison of EEG and EMG changes after serious game-based rehabilitation interventions (as shown in 4.1.From the experimental results, in the MA tasks of the three rehabilitation training, the motion perception area of the contralateral hemisphere of patients had a stronger ERD in game-assisted rehabilitation than in conventional rehabilitation, and the results were statistically significant. This finding suggests a better activation of the sensorimotor areas of the brain of the patient during the use of serious gaming applications . TactileSensorimotor areas of the ipsilateral hemisphere also had stronger ERD in game-assisted rehabilitation compared to that in conventional rehabilitation, with significant differences in shoulder training and wrist training but not in elbow training. This result was expected because ERD is generally more severe in the contralateral hemisphere than in the ipsilateral hemisphere during MA tasks .Three horizontal comparisons of shoulder, elbow, and wrist training showed that the ERD intensity difference between shoulder training in game-assisted rehabilitation and conventional rehabilitation was the strongest of the three exercises; that is, the serious game app played the largest role in the activation of the motor cortex in shoulder rehabilitation.During the experiment, we found that the patients with hemiplegia observed their rehabilitation movements very carefully during elbow and wrist training. The two training movements had a small range of motion and were observed within range of sight. In mirror visual feedback, vision is often used as an information supplement for limb perception impairments . TherefoIn addition, the voice broadcast function provides auditory stimulation, and the rhythm and instructions of the movement provide patients with supervision and guidance, which encourages them to have a stronger willingness to perform the actions.4.2.From the experimental results, among the three MA rehabilitation training tasks, the RMS of patients with hemiplegia during game-assisted rehabilitation was greater than that during conventional rehabilitation, except for one MA task. However, after the paired-sample Wilcoxon signed-rank test, the RMS significantly differed only for shoulder training. This finding suggests that game-assisted rehabilitation is more intense than conventional rehabilitation in terms of the actual muscle motor performance, although the difference was not statistically significant. Simultaneously, in elbow and wrist training, the patient observed his or her own movements, and the visual feedback encouraged the patient to complete the MA task more properly; therefore, the difference in RMS between the two rehabilitation trainings was small.4.3.RMS amplitude represents the degree of muscle activity. The difference between the experimental conditions of the G and C groups was more obvious in the motor cortex activity but less in terms of the muscle activity change. The ERD in the bilateral hemispheres, ipsilateral hemispheres, contralateral hemispheres, and C3/C4 channels in patients with hemiplegia did not significantly differ between the MA and MI tasks . In acti4.4.This experiment did not observe the rehabilitation changes of the two groups from the complete rehabilitation cycle but rather conducted a short-term comparison of the activation degrees of the cerebral motor cortex and of the exercise muscles during the rehabilitation process, to predict the feasibility of the serious game rehabilitation app. Under the condition of game-assisted rehabilitation, the stronger cerebral motor cortex signal of patients confirmed the feasibility of the serious game app in rehabilitation assistance, to a certain extent. Because the activity intensity of the muscles depends more on objective physical fitness and the actual degree of rehabilitation, it is difficult to make a huge change in a short period. Therefore, it is foreseeable that serious game rehabilitation apps cannot significantly improve muscle activity.4.5.Considering that each participant underwent the first and second experiments on different days, numerous factors might have influenced the measurements of brain activity. These factors include environmental elements such as variations in temperature and humidity within the 2 days, potentially affecting the brain activity of the participants during the experiments. Moreover, the emotional state of the participants could also be impacted, such as the influence of short-term events, leading to different emotional states during the two experiments, thereby affecting the measurement of brain activity. In addition, the medication and sleep status of the participants on the day of the experiment could also influence the measurement of brain activity.Factors influencing EMG signals are similarly complex. Environmental elements, such as variations in temperature and humidity within the 2 days, might impact the muscle activity and endurance of participants during the experiments. Variances in physiological states, emotional conditions, and pain levels may also affect EMG data. Furthermore, the initial state of the muscles and subtle shifts in electrode placement between the two experiments could impact data accuracy.5.In our study, we only conducted comparative trials of the two rehabilitation methods on the same participants without long-term clinical observation and recording. In addition, the limited number of participants and experimental sessions restricted our ability to comprehensively assess the effectiveness of game-assisted rehabilitation. To gain a better understanding of the potential of game-assisted rehabilitation, we recommend increasing the number of participants and experimental sessions in future research. Long-term clinical observation and recording should be incorporated. Moreover, further improvements in experimental design and methodology are essential to enhance the reliability and accuracy of the experiments.


Previously, we reported that the supine and prone positions cause a significant decrease in nasal patency in subjective and objective assessments of healthy individuals. Therefore, we conducted a study to evaluate the effect of posture on nasal patency in patients with allergic rhinitis (AR); (2) Methods: The present study comprised 30 patients diagnosed with AR and 30 healthy subjects without nasal disease (non-AR). Changes in nasal patency were evaluated in the sitting, supine, and prone positions. We used the visual analog scale to evaluate subjective nasal blockage. Acoustic rhinometry and endoscopy were used to objectively measure changes in nasal patency; (3) Results: In the non-AR group, the prone position had a significant effect on subjective nasal blockage compared with the sitting position, with significant decreases in the minimal cross-sectional area (mCSA) measured by acoustic rhinometry. Furthermore, endoscopy demonstrated a significantly increased inferior turbinate hypertrophy in the non-AR group. In the AR group, there was no statistical difference in subjective nasal blockage symptoms between the different positions. However, in objective examinations (acoustic rhinometry and endoscopy), the prone position showed significantly decreased nasal patency; (4) Conclusions: In patients with AR, subjective nasal blockage did not significantly increase in the supine or prone position. Endoscopy demonstrated increased inferior turbinate hypertrophy in supine and prone positions resulting in a significant reduction in nasal cavity mCSA, indicating an objective reduction in nasal patency. There are few studies on sleep position and nasal patency ,2. Howev®; Phadia AB, Uppsala, Sweden [We screened patients for AR in an outpatient clinic after obtaining informed consent. A total of 30 patients with AR were enrolled in the experimental group of the present study. We used the visual analog scale (VAS) to assess rhinitis-related symptoms including nasal blockage, itchy nose, sneezing, and runny nose, with scores ranging from 0 to 100 ,10. The , Sweden ); and a The control group comprised 30 healthy volunteers without AR. The criteria for inclusion in the control group were VAS scores for the four main symptoms of AR of less than 30 points and a serum IgE level lower than 120 kU/L. The exclusion criteria were the same as those described above for patients with AR.t-tests, the sample size of 52 was based on a power of 80%. Using a two-tailed t-test with p < 0.05, we estimated the sample size as 26 participants per group, with an allowance for a potential dropout rate of 20%.By using unpaired Student The present study was reviewed and approved by the Institutional Review Board and was given the number 201801720B0C501.Three positions were assessed in this study: the sitting, supine, and prone positions  Figure . SubjectThe mCSAs of both nasal cavities were measured by acoustic rhinometry, with the sum used to compare objective nasal patency between different postures. In addition, we performed endoscopic examinations to directly observe changes in the inferior turbinate with different postures. The endoscopic examination method used was as described in our previous study . A rigidt-test, with p-values of less than 0.05 considered statistically significant. VAS scores, mCSAs, and endoscopy results for each of the three positions are presented as bar and dot graphs, with comparisons between groups performed using the paired Student’s t-test. * p < 0.05; ** p < 0.01; *** p < 0.0001.Data on age, serum IgE levels, VAS scores, and mCSA were presented as mean ± standard deviation. Data were compared using the unpaired Student’s p = 0.201) (p < 0.001) and rhinorrhea  in the sitting position. While the VAS scores for sneezing and itchy nose were higher in the AR group, the difference between the AR and non-AR groups did not reach statistical significance. In addition, mCSA measured by acoustic rhinometry did not significantly differ between the two groups in the sitting position .A total of 60 subjects were included in the present study, including 30 patients with AR  and 30 healthy individuals without sinonasal disease . No significant differences in sex were observed between the two groups (= 0.201) . Of the p < 0.001; mean score increased by 2.35 times), while the prone position was associated with a significantly higher degree of nasal blockage . The effect of the prone position on nasal blockage was more significant than that of the supine position   A.p = 0.423; prone vs. sitting, 34.33 ± 26.61 vs. 30.33 ± 26.59, p = 0.452)  B.p = 0.002; prone vs. sitting, 0.87 ± 0.32 vs. 1.00 ± 0.23, p = 0.009; mCSA in the AR group: supine vs. sitting, 1.01 ± 0.39 vs. 1.11 ± 0.29, p = 0.014; prone vs. sitting, 0.95 ± 0.29 vs. 1.11 ± 0.29, p < 0.001; p = 0.079; mCSA in the AR group, prone vs. supine: 0.95 ± 0.29 vs. 1.01 ± 0.39, p = 0.022).Individual mCSAs for both nostrils in non-AR and AR subjects were measured using acoustic rhinometry and then summed for comparison. Results demonstrated that the supine and prone positions significantly reduced the mCSA in both non-AR and AR subjects compared with the sitting position . Other comparisons did not reach statistical significance. Inferior turbinate swelling was significantly increased in the prone position compared with the sitting position in both groups for both nostrils .We used endoscopy to detect changes in the degree of inferior turbinate hypertrophy in various postures A–D. We eWe previously investigated the effects of the supine and prone positions on the degree of nasal blockage in healthy individuals without nasal disease . Both suIn the present study, we explored the effect of reduced nasal patency due to postural changes in patients with AR by comparing them with healthy individuals without nasal disease. We observed no significant difference in subjective nasal blockage between the sitting, supine, and prone positions; however, acoustic rhinometry and endoscopy demonstrated significantly decreased nasal patency in the prone position. We speculate that decreased nasal patency in patients with AR may be due to prolonged and compensated nasal blockage . TherefoAs AR is a chronic disease with a high prevalence worldwide, it is likely to coexist with many respiratory diseases, including obstructive sleeping apnea (OSA) and sudden infant death syndrome (SIDS) . An incrThis study evaluated the effect of the supine and prone positions on nasal cavity patency. However, some people sleep in the lateral recumbent position. Thus, further studies are required to determine the effect of the lateral recumbent position on nasal patency, and increased knowledge of the physiological changes caused by this position may contribute to OSA studies.One issue that warrants further investigation is the potential impact of comorbid conditions on the relationship between postural changes and nasal patency in patients with AR. Comorbidities, such as asthma, chronic sinusitis, or gastroesophageal reflux disease (GERD), can often coexist with AR and may affect nasal patency and congestion. Understanding the interplay between these comorbidities, postural changes, and nasal patency could provide valuable insights into the complexity of managing AR in patients with multiple medical conditions. For example, asthma is a common comorbidity in patients with AR, and bronchial hyperresponsiveness has been reported to be associated with increased nasal congestion. Investigating whether the effect of postural changes on nasal patency differs between AR patients with and without asthma could shed light on the possible interactions between respiratory function, nasal patency, and posture. In addition, GERD, which is known to be associated with the supine position and nighttime symptoms, can exacerbate AR symptoms by causing postnasal drip and nasal inflammation. It might be valuable to explore whether the effect of postural changes on nasal patency is more pronounced in AR patients with GERD, particularly during nighttime when reflux symptoms are more likely to occur. Identifying potential differences in the impact of postural changes on nasal patency among AR patients with various comorbidities could help develop more targeted treatment plans and interventions for these patients.The topic worth studying is the potential role of medication use, including intranasal corticosteroids, antihistamines, and decongestants, in modulating the effect of postural changes on nasal patency in AR patients—pharmacological treatments for AR aim to reduce inflammation and relieve symptoms, including nasal congestion. However, the interaction between medication use, postural changes, and nasal patency has yet to be extensively explored. Understanding this relationship could help optimize medication use and improve symptom control in AR patients. For instance, the timing of medication administration concerning postural changes could potentially influence the effectiveness of treatment in alleviating nasal congestion. It might be worth examining whether administering medications at specific times relative to postural changes, such as before bedtime, could help minimize the impact of supine or prone positions on nasal patency in AR patients. Additionally, comparing the effect of different medications on postural changes and nasal patency might reveal specific drug classes that are more effective in mitigating the impact of posture on nasal congestion. This information could aid in developing tailored treatment regimens that consider the timing of medication administration and the choice of drug classes, in addition to postural changes, to improve symptom control in AR patients.A deeper exploration of the influence of comorbid conditions and medication use on the relationship between postural changes and nasal patency in AR patients could help elucidate potential interactions and complexities in managing AR in patients with multiple medical conditions or under pharmacological treatment. A better understanding of these factors could ultimately contribute to developing more personalized and effective treatment strategies for AR patients, considering the presence of comorbidities and medication use, in addition to postural changes.The influence of demographic factors, such as age and sex, on the effect of different positions on nasal patency in AR patients is worthy of being investigated further. Although our study did not find significant differences in sex between the AR and non-AR groups, further data stratification based on age and sex might reveal specific trends or differences in how different positions affect nasal patency in various subpopulations. A more in-depth analysis of the potential interactions between age, sex, and the effect of postural changes on nasal patency could lead to a better understanding of the pathophysiology of AR and help tailor more personalized treatment strategies for different patient populations. For instance, it is known that hormonal fluctuations during menstruation, pregnancy, or menopause can affect nasal congestion and mucosal edema in women. Therefore, it might be valuable to examine whether the effect of postural changes on nasal patency differs between premenopausal and postmenopausal women or during different stages of the menstrual cycle. Similarly, age-related changes in nasal structures and functions, such as reduced mucociliary clearance and nasal airflow, might influence the effect of different positions on nasal patency in older adults. Identifying potential differences in the impact of postural changes on nasal patency among various demographic subgroups could provide valuable insights for developing age- and sex-specific treatment recommendations and interventions for AR patients.Another area of discussion is the potential role of environmental factors, such as allergen exposure, in modulating the effect of postural changes on nasal patency in AR patients. It is well-established that allergen exposure plays a crucial role in developing and exacerbating AR symptoms. However, the interaction between allergen exposure, postural changes, and nasal patency has yet to be thoroughly investigated. Understanding the relationship between these factors might help develop strategies to reduce the impact of allergen exposure on nasal congestion and improve patients’ quality of life. For example, patients with AR might be more sensitive to allergens during sleep due to prolonged exposure in specific positions, such as the prone or supine positions. It could be valuable to explore whether interventions targeting allergen reduction in the sleeping environment, such as using allergen-proof bedding materials or air purifiers, could help mitigate the effects of postural changes on nasal patency in AR patients. Additionally, investigating the temporal relationship between allergen exposure, postural changes, and nasal congestion could reveal valuable insights into the optimal timing of allergen avoidance measures or pharmacological treatments concerning sleep and postural changes.Our study contributes to the broader understanding of nasal physiology and the development of novel interventions for AR and other respiratory disorders. As observed in our study, the discrepancy between the objective findings and subjective perception of nasal blockage in AR patients may be due to various factors, including neuroplasticity, neural adaptation, and altered sensory perception in chronic nasal congestion. Gaining a better understanding of the underlying mechanisms responsible for these phenomena could pave the way for developing new therapeutic approaches targeting nasal blockage’s sensory and neural aspects, in addition to the traditional treatment options focusing on inflammation and congestion. Furthermore, the potential influence of sleeping positions on nasal patency may lead to the development of new positional therapies or devices that aim to optimize nasal airflow during sleep. For instance, personalized positional pillows or wearable devices could be designed to promote specific sleeping positions or maintain an optimal head position that maximizes nasal patency for patients with AR or other respiratory disorders. These innovative interventions could complement existing treatment strategies, such as pharmacological therapies and behavioral modifications, to provide more comprehensive and effective management of these conditions.Our study had several limitations. First, anatomical variations such as NSD may have influenced the effect of postural change on nasal patency. We excluded patients with significant NSD; however, computed tomography of the sinuses was not performed to correct for the effects of NSD. We are therefore considering the use of computed tomography in subsequent studies. Second, the objective analyses used in this study, including acoustic rhinometry and endoscopy, were performed by the same examiner with attempts made to ensure the instruments were used in the same position between subjects and postures. Acoustic rhinometry is a delicate examination that requires the nozzle to be placed tightly against the nostrils to avoid air leakage; however, excessive pressure could cause nasal valve deformation and affect the testing results. The difficulty of performing the test was even higher when the patients were examined in the prone position, and this would increase the risk of producing bias on examination. Third, in order to standardize the testing process, we fixed the testing sequence from sitting, supine, to prone positions; however this approach might cause bias which could be reduced by randomly positional changes.Subjective and objective assessments in the present study demonstrated that the supine and prone positions cause increased nasal blockage compared with the sitting position in non-AR healthy individuals, and that the prone position has more influence on nasal patency than the prone position. In AR patients, although the objective findings were consistent with those for non-AR subjects, postural change had no significant effect on nasal blockage.


These fingerprints were then used to assess receptor subtype expression across human brain cell systems and compared with Δlog(Emax/EC50) values arising from β-arrestin activation or measurements of cAMP response desensitization to assess the possibility of ligand bias among β-AR agonists. Agonist activity profiles were confirmed to be system-independent and, in particular, revealed β2-AR functional expression across several human brain cell types. Broad β2-AR function observed is consistent with noradrenergic tone arising from the locus coeruleus exerting heterocellular neuroexcitatory and homeostatic influence. Notably, Δlog(Emax/EC50) measurements suggest that tested β-AR agonists do not show ligand bias as it pertains to homologous receptor desensitization in the system examined. Δlog(Emax/EC50) agonist fingerprinting is a powerful means of assessing receptor subtype expression regardless of receptor expression levels or assay readout, and the method may be applicable to future use for novel ligands and tissues expressing any receptor with available reference agonists.Noradrenergic projections from the brainstem locus coeruleus drive arousal, attentiveness, mood, and memory, but specific adrenoceptor (AR) function across the varied brain cell types has not been extensively characterized, especially with agonists. This study reports a pharmacological analysis of brain AR function, offering insights for innovative therapeutic interventions that might serve to compensate for locus coeruleus decline, known to develop in the earliest phases of neurodegenerative diseases. First, β-AR agonist activities were measured in recombinant cell systems and compared with those of isoprenaline to generate Δlog(E The β-adrenergic receptor (β-AR) family comprises three established targets of successful therapeutics for patients with respiratory, cardiovascular, and urological disorders . HistoriDetailed characterization of the functional expression of distinct β-AR populations in glial and non-glial cells is important to predict their suitability as potential therapeutic targets. Changes in receptor expression level or coupling efficiency between systems, and the resulting changes in agonist potency and maximal efficacy values, can confound translatability of agonist impact for drug development programs. Should novel, brain directed, β-AR agonist therapeutics be developable, translational methods will be crucial for comparing agonist profiles, understanding differential receptor expression across different tissues, and modeling the impact on brain function from preclinical data to clinical data.max/EC50) (max/EC50) values are derived, allowing cell- or assay-specific features to be neutralized and permitting agonist activity translation across systems. Using cAMP concentration–response curves, we calculated isoprenaline-referenced Δlog(Emax/EC50) values for established β-AR agonists for each of the three different β-AR subtypes , expressed recombinantly. These data generated an “agonist fingerprint” for each β-AR subtype functionally expressed. We then used this fingerprint to assess functional expression in previously uncharacterized central nervous system (CNS) relevant cell lines, iPSC-derived human brain cell types and primary human glia. We also used Δlog(Emax/EC50) to assess β-arrestin recruitment and desensitization, key components of the cycle of GPCR activation and inactivation  , which hax/EC50) , as wellax/EC50) . In thestivation .max/EC50) analysis as a framework for characterizing GPCR expression and function. We demonstrate that β2-AR functional expression is most widespread among isolated cell types from the human brain, and our data suggest that established β-AR agonists are essentially unbiased in their activation of β2-AR for Gs versus β-arrestin and signal desensitization. This characterization provides an important reference for further in vitro functional studies of adrenoceptor activation in the human brain.Together, this work provides an in-depth characterization of signaling responses to β-AR agonists and further tests the use of Δlog, adrenaline (Sigma E4250), noradrenaline (Matrix Scientific 037592), dobutamine (Tocris 0515), prenalterol (Santa Cruz Bio sc-280023), formoterol (Apex Bio B1359), clenbuterol (Sigma C5423), salbutamol (Sigma S8260), tulobuterol (Alfa Aesar J61448), and mirabegron (Med Chem Express CS-0915). Upon arrival, ∼2 mg of the compound was weighed out and resuspended in DMSO to achieve a final concentration of 10 mM. Compounds were aliquoted and stored at −80°C or −20°C until use. A limit of five freeze/thaw cycles was implemented for each aliquot.2 humidified incubator. Any CHO-K1 cell line recombinantly expressing a receptor transgene was grown in media supplemented with 1 mg/mL G418 (Sigma A1720) to maintain selection for transgene expression.All cell sources, exogenous protein expression information, and growth media conditions are detailed in CHO-K1 cell transgene lines engineered at CuraSen were all prepared from human codon-optimized, gene-synthesized constructs (GenScript) corresponding to sequences with protein accession numbers detailed in Monoclonal cell lines were generated by dilution cloning. In brief, single cells were grown in 96-well plates, allowed to proliferate into stable cultures, and assessed for receptor expression by again assessing the responses to established receptor agonists.1-AR or β2-AR, the β2-AR or β1-AR coding gene  was knocked out using CRISPR/Cas9 (Synthego). THP-1 cells were nucleofected with optimized sgRNA/SpCas9 ribonucleoprotein complexes, with the ADRB1 or ADRB2 gRNA target sequences: GCGGCCCCACACCACGAUGG and CGUCUGCAGACGCUCGAACU, respectively. The indel percentage in the cell population after knockout was 65% and 67%, from which monoclonal cell populations were grown in-house as previously described to identify and isolate knockout clones.To create THP-1 cells expressing only β1-AR, the β2-AR coding gene (Adrb2) was knocked out using CRISPR/Cas9 (Synthego). C6 cells were nucleofected with optimized sgRNA/SpCas9 ribonucleoprotein complexes with the Adrb1 or Adrb2 gRNA target sequence: GCUGCUGCCUCCAGCCAGCG and CCUGGCGCUCGGCUUCCAUU. The indel percentage in the cell population after knockout was 65% and 85%, from which monoclonal cell populations were grown in-house as previously described to identify and isolate knockout clones.To create C6 rat glioma cells expressing only β2-AR were generated by infecting ReNcell VM cells with a lentivirus containing the ADRB2 gene (OriGene RC204499L3V) at an MOI of 12.5, and 48 h post-infection, cells were grown in media containing 0.25 μg/mL puromycin.ReNcell VM cells expressing βTo passage adherent cells, cells were released either using EDTA solution  after washing in 1x PBS (Caisson PBL05) or using Accutase (Mediatech 25-058-CI) after washing in 1x PBS.1-AR and β2-AR and 1 μM for β3-AR), and a full isoprenaline dose–response curve were added as controls on all assay plates. β-AR agonists were serially diluted across the plate by nine 5-fold dilutions to generate a 10-point dose–-response curve.cAMP accumulation was measured using the cAMP Gs Dynamic HTRF kit (Cisbio/Perkin Elmer 62AM4PEC), broadly following the manufacturer’s instructions. Compounds were prepared at a 2x final concentration to accommodate the subsequent addition of cells. To prepare compound dose–response curves, compounds were diluted to the necessary highest concentration  in 1x stimulation buffer containing 500 μM 3-isobutyl-1-methylxanthine (IBMX) and dispensed into a 96-well U-bottom polypropylene assay plate (Corning 3365). Vehicle (DMSO), a maximally active isoprenaline dose (0.1 μM for βFive microliters from 96-well compound source plates were stamped into every well of a 384-well white-bottom plate (Corning 3825) using a VIAFLO 384 equipped with a 0.5–12.5 μL pipetting head (Integra), thus creating four technical replicates for each treatment condition. Plates (384-well) containing compounds were covered and stored at room temperature until addition of resuspended cells.D2-labeled cAMP (acceptor) and europium cryptate-labeled cAMP antibody (donor) (Cisbio/Perkin Elmer 62AM4PEC) were dissolved in water following the manufacturer’s instructions, aliquoted, and stored at −80 °C. For detection of cAMP, on the day of the assay prior to addition of cells to compounds, both reagents were diluted 21-fold in the provided lysis buffer (Cisbio/Perkin Elmer), combined, and stored at room temperature prior to addition to assay plates.2 for 30 min. cAMP accumulation was then stopped by adding 10 μL HTRF detection reagents in lysis buffer using the Multidrop Combi and plates were covered with an aluminum plate seal (Axygen PCR-AS-600) and agitated  for at least 2 h, to allow competition between D2-cAMP and stimulated cAMP for the donor-labeled cAMP antibody to reach equilibrium.Resuspended cells were resuspended in a minimal volume of 1x stimulation buffer containing 500 μM IBMX at room temperature. All cell suspensions were further diluted in 1x stimulation buffer containing 500 μM IBMX to a density corresponding to the lower values of the dynamic range of the cAMP HTRF assay, with each cell system optimized individually. Five microliters of cells of correct density were added to the compound in assay plates using a Multidrop Combi (Thermo Fisher). Cells and compounds were covered with a clear plate seal (Axygen PCR-SP) and incubated at 37°C, 5% COHTRF was detected using a Tecan Spark plate reader, operated using SparkControl™ software, using the TR fluorescence intensity setting. Both the donor and acceptor were excited at 320 nm, detected after 100-μs delay, with 400-μs integration time, and 50 flashes per read. Donor fluorescence was detected at 620 nm, and acceptor fluorescence was detected at 665 nm. To standardize reads across experiments, camera gain was manually set to 120 for donor detection and 140 for acceptor detection.2 humidified incubator.cADDis fluorescence assays were performed broadly as described by the manufacturer (Montana Molecular D0200G). A single cADDis infection reaction comprised 25 μL cADDis mix  + 10% FBS, 1% P/S)), combined with 25 μL cADDis media containing 1321N1 cells at a density of 6 × 10^4 cells/mL. A mixture of cADDis reaction and 1321N1 cells was prepared to provide enough volume to seed the middle 240 wells of a 384-well plate (Corning 3764). Using a VIAFILL (Integra) equipped with a 16-channel head, 50 μL of cADDis: 1321N1 cells was added to each well , and the plate was incubated at room temperature for 15 min before overnight incubation in a 37°C, 5% COAfter 24 h of incubation, cells were imaged using an Incucyte S3 (green and phase channel) to measure cADDis fluorescence intensity at baseline (unstimulated). Compounds were diluted to the 5x final concentration in cADDis media, serially diluted while maintaining vehicle (DMSO) concentration, and 12.5 uL of 5x compound was seeded into the cADDis 1321N1 cell plate using a VIAFLO 384 (Integra). The 1321N1 cells were immediately imaged again on the Incucyte .For each well, the cADDis intensity after compound addition was normalized to the mean of the vehicle control wells, and this intensity was corrected for the cADDis intensity of that well at baseline  as it related to the mean baseline intensity of all wells across the test plate.max/EC50) values were calculated for each concentration–response curve using GraphPad Prism and Microsoft Excel. A reference agonist  dose–response curve was generated in the same plate as each test agonist, on the same experimental day with identical cell and assay reagents. Therefore, β-AR agonist Δlog(Emax/EC50) values, relative to isoprenaline, were also calculated on a plate-matched basis. Average isoprenaline Δlog(Emax/EC50) values for each β-AR agonist were used to generate β-AR fingerprint radar plots of system responses to β-AR agonists, again using Microsoft Excel. Therefore, radar plots reflect Δlog(Emax/EC50) mean values calculated from the entire dataset presented in max/EC50) value is calculated on a plate-matched basis.β-AR agonist log(Emax/EC50) error calculations using tandem versus parallel experimental design is shown in max/EC50) values, and the second method follows that described in A comparison between Δlog(E® cells expressing β2-AR (93–0182C2) with a receptor ProLink™ tag and arrestin enzyme acceptor tag were cultured in T75 flasks at 37°C, 5% CO2 in a growth medium containing DMEM/F12 (Caisson Labs DFL13), 10% FBS (Corning 35-010-CV), 1x penicillin/streptomycin (Caisson Labs PSL01), 1 mg/mL G-418 (Caisson Labs G030), and 300 μg/mL hygromycin (Caisson Labs H010) and were passaged before full confluence. Cells were seeded in 384-well plates (Corning 3570) at a density of 5,000 cells per well in 20 μL growth medium. The next day, solutions of β-AR agonist were prepared as 5-fold serial dilutions in a 96-well plate (Corning 3363) at the desired final concentration of 5x. About 5 μL of the drug solution was transferred from this 96-well source plate to the 384-well cell plate using a VIAFLO 384 equipped with a 0.5–12.5 μL pipetting head (Integra), generating four technical replicates for each drug dose. Plates were sealed with clear plastic (Corning AXYPRSP) and incubated for 90 min in a 37°C, 5% CO2 incubator. Meanwhile, the working detection solution (WDS) was freshly prepared at room temperature from kit reagents (93-0001L) as per the manufacturer’s instructions in a 19:5:1 ratio (buffer: component 1: component 2). After drug incubation was complete, 12.5 μL WDS was added per well using a Multidrop Combi. Plates were covered with an aluminum plate seal (Axygen PCR-AS-600) and agitated  for 90 min. Luminescence was detected (360–700 nm) with a Tecan Spark plate reader, operated using SparkControlTM software, using 500 ms integration time.DiscoverX PathHuntermax/EC50). Cells were incubated with 1x drug solution for 24 h. After 24 h of incubation, cells were washed three times by inverting and gently tapping plates onto paper towels, followed by centrifugation of the inverted plate at 30 x g, before adding 40 μL of wash solution containing DPBS, + 0.1% bovine serum albumin (VWR 97061-416) using a VIAFILL dispenser (Integra). After the third wash solution removal, 10 μL of a solution of HTRF Stimulation Buffer 1 (Cisbio/Perkin Elmer 64SB1FDC) containing 500 μM IBMX (Cayman 13347) and 5 μM tulobuterol was added to each well with a VIAFLO pipette. The cell plate was sealed with clear plastic and incubated at 37°C for 30 min. D2-labeled cAMP (acceptor) and europium cryptate-labeled cAMP antibody (donor) (Cisbio/Perkin Elmer 62AM4PEC) were dissolved in water and added to cell plates as previously described for the cAMP HTRF assay, followed by similar signal detection using a Tecan Spark plate reader.1321N1 cells were seeded in 384-well plates (Corning 3570 or 3764) at a density of 5,000 cells per well in 40 μL growth medium. The next day, solutions of β-AR agonists were prepared as 5-fold serial dilutions in a 96-well plate to the desired final concentration of 5x. About 10 μL of the drug solution was transferred from the 96-well source plate to the 384-well cell plate using a VIAFLO 384 equipped with a 0.5–12.5 μL pipetting head (Integra), generating four technical replicates for each drug dose. A full concentration–response curve of isoprenaline was prepared for each cell plate to allow in-plate determination of Δlog(Emax/EC50) values ± 95% CI across n = 3 independent experiments.Concentration–response curves shown in figures display mean ± SEM across n = 4 technical replicates from a single representative dose–response curve. For dose–response curves generated from cAMP HTRF, raw ratiometric HTRF signals, relative to the maximum effect of isoprenaline, were plotted, unless stated. Each compound was assayed a minimum of three times in independent experiments. The exact experimental replicate number is shown in cAMP HTRF ratios were obtained by applying the formulaFor all dose–response curves generated across all assay readouts (cAMP HTRF and cADDis), the potencies of test and control compounds were determined by non-linear regression using GraphPad Prism. HTRF ratios, from quadruplicate measures at each concentration, were plotted versus the log concentration of β-AR agonist and analyzed using the following 4-parameter logistic equation:50) and maximum effect (Emax) relative to the within-plate isoprenaline dose response were reported.All dose–response curves on a given plate were simultaneously analyzed to define a single, shared baseline value, and the Hill slope was constrained to be >0, to avoid false curve fits from inactive compounds. The average (mean) β-AR agonist potency  was dissolved, and a concentration range was prepared by serial dilution in 1x stimulation buffer. The cAMP standard curve was detected by cAMP HTRF contemporaneously with β-AR agonist-stimulated 1321N1 cells, as described previously. The cAMP standard curve was fitted to a non-linear regression 4-parameter logistic equation as described previously, with a separate baseline value from β-AR agonist dose responses in cells. The standard curve was used to interpolate per-well cAMP quantities from raw ratiometric cAMP HTRF values for each dose of each β-AR agonist. β-AR agonist cAMP-normalized concentration dose–response curves were then analyzed using 4-parameter logistic regression, as described previously, to assess the potency and efficacy of compounds.Representative dose–response curves shown were compiled from raw cAMP HTRF ratiometric values, first by selecting individual agonist dose responses that closely resembled average potency and maximal efficacy values. Those selected datasets were normalized to the plate-matched isoprenaline dose–response curve, first by calculating the individual cAMP HTRF ratio value relative to the average of the isoprenaline baseline  and then by dividing that value by the average maximal  isoprenaline response to make the isoprenaline dose–response curve 100%.1-AR versus β2-AR, average β-AR agonist Δlog(Emax/EC50) values, relative to isoprenaline, for β2-AR (in CHO-K1 cells) were subtracted from the average β-AR agonist Δlog(Emax/EC50) values, relative to isoprenaline, for β1-AR (again in CHO-K1 cells) to generate ΔΔlog(Emax/EC50) values (max/EC50) values were also calculated for cAMP, arrestin recruitment, and functional desensitization assays (To assess β-AR agonist selectivity at β) values . This ann assays . 95% con1-, β2-, or β3-AR human homologs. We characterized the endogenous β-AR agonists adrenaline and noradrenaline, the potent and non-selective β1/β2-AR agonist isoprenaline, β1-AR selective agonists dobutamine and prenalterol, β2-AR selective agonists salbutamol, clenbuterol, tulobuterol, and formoterol, and the β3-AR selective agonist mirabegron. As a negative control, CHO-K1 cells lacking recombinant expression showed no cAMP response to any agonist tested . We then subtracted the same metric of the plate-matched reference agonist, isoprenaline, for which Emax is defined as 1.For each agonist in each replicate experiment, we calculated the logmax/EC50), provides a system-independent metric for agonist activity relative to isoprenaline  is, in theory, independent of tissue, assay, and receptor coupling, as variations in receptor expression, amplification, or other variables are canceled by the comparison to a reference agonist (max/EC50) values across a panel of agonists provide a composite readout of receptor activation by those agonists as a profile. Δlog(Emax/EC50) values were arranged in a radar plot to give each β-AR population a distinct visual agonist fingerprint (max/EC50) is 0. The exact shape of the agonist fingerprint is arbitrary, as it depends on the agonist arrangement on the radar axes. Imposing a fixed orientation of agonists on the plot, the shape of the fingerprint is unique for each receptor subtype. The resulting Δlog(Emax/EC50) β-AR agonist fingerprints allow functional expression of the three β-ARs to be easily distinguished from one another.Δlog(E agonist . Accordimax/EC50) analysis may also be used to analyze selectivity for any of the test agonists for one receptor subtype over another, by comparing Δlog(Emax/EC50) values across two assay systems (ΔΔlog(Emax/EC50)) (max/EC50) is relative to the reference agonist, so it is optimal when that reference agonist is thought to be non-selective between receptors being examined. As isoprenaline is thought to be non-selective at β1-AR vs. β2-AR (1-AR vs. β2-AR selectivity using ΔΔlog(Emax/EC50)  are not further discussed here.Δlog(Ex/EC50)) . Interprs. β2-AR , we examax/EC50)  and foun potency . For thimax/EC50) agonist fingerprinting was assessed in a native expression system, 1321N1 astrocytoma cells: a human cell line reported to express β2-AR  β-AR agonist fingerprint  versus the β1-AR selective antagonist CGP-20712A  as a method of assessing β-AR subtype functional expression independent of receptor expression density or coupling efficiency.We represented β-AR agonist activity in 1321N1 cells as a Δlog(Egerprint . AgonistP-20712A . These dmax/EC50) agonist fingerprinting using alternative cAMP detection assays, we employed cADDis (Montana Molecular), a live-cell fluorescent biosensor that decreases in fluorescence intensity when bound to cAMP (max/EC50) β-AR agonist values from cADDis matched Δlog(Emax/EC50) values derived from cAMP HTRF, and therefore generated an identical β-AR agonist fingerprint to that derived from cAMP HTRF (max/EC50) activity determination is agnostic of the reagent used to measure agonist activation of cyclic AMP production.To test the performance of Δlog(E to cAMP . We meas to cAMP . The cADAMP HTRF . These dmax/EC50), we confirmed that dose–response curves derived from HTRF values pre- and post-normalization by a cAMP standard curve gave identical Δlog(Emax/EC50) values (max/EC50) from ratiometric HTRF data, minimizing data transformation steps.Lastly, to ensure that normalization methods  do not a) values . This fimax/EC50) values are affected when more than one β-AR is present. First, we mixed CHO-K1 cells recombinantly expressing either β1-AR or β2-AR, at defined ratios of seeding density, and assessed the combined cAMP response. Mixed CHO-K1 β-AR populations produced intermediate agonist fingerprints versus pure expression populations (max/EC50) method of analysis can detect mixed-receptor expression among cell populations.To determine whether agonist fingerprinting could be useful in systems with expression of multiple receptors, we examined how Δlog(Eulations . These dmax/EC50) β-AR agonist fingerprint that did not correspond to either exclusively human β1-AR or human β2-AR expression profiles  and reassessed functional β-AR expression. C6 Adrb1 KO cells  metric, as expected for AR homologs of different primary sequences and agonist pharmacology  (max/EC50) β-AR agonist fingerprints confirmed functional expression of the respective receptors in KO lines  . SuperimKO lines . We notees , ADRB1 KO, and ADRB2 KO THP-1 cells revealed THP-1 ADRB1 KO cells retain only 5% ± 2% of the cAMP response observed in native THP-1 cells  values remain consistent in low-expression systems.The close alignment between β-AR agonist fingerprints from THP-1 and ADRB2 KO THP-1 cells suggested that β-1 cells . In cont-1 cells . These d2-AR expression in primary astrocytes isolated from either the cortex or the hippocampus , and lower Emax for the partial agonist tulobuterol (max/EC50) and observed a similar agonist fingerprint with tulobuterol as an outlier (max/EC50) removes system-dependent effects, deviation from the line of identity in a plot of Δlog(Emax/EC50)arrestin vs. Δlog(Emax/EC50)cAMP represents ligand bias (max/EC50)   of 0. Th® cells produced concentration–response curves with Hill slopes greater than 1 (® cell system. We therefore tested the correlation of Δlog(Emax/EC50)arrestin (PathHunter™) with Δlog(Emax/EC50)cAMP values obtained from 1321N1 cells, which naturally express β2-AR  values using test β-AR agonist desensitization curves expressed as a percentage of isoprenaline-induced desensitization (max/EC50) desensitization values were plotted against Δlog(Emax/EC50)cAMP values, all measured in 1321N1 cells. In agreement with β-arrestin recruitment (PathHunter®) results, we observed that most tested β-AR agonists fell on the line of identity, suggesting these β-AR agonists are unbiased  transformation indicates a lack of ligand bias.Taken together, our system-independent analysis demonstrates that partial β-AR agonists for cAMP production also show partial agonism for β-arrestin recruitment. Partial β-AR agonists also cause a partial degree of functional desensitization. The panel of agonists presented here display varying strengths in potency and intrinsic activity in both assays, and the use of Δlog(Emax/EC50) parameter, and we provide real-world examples of the utility of Δlog(Emax/EC50) in assessing new cell systems and measuring potential ligand bias. This work builds on that of others (max/EC50) value for a given test agonist–reference agonist pair is a system-independent measure of agonism, regardless of factors such as receptor expression level or signal transduction efficiency, this method provides a functional readout advantage over profiling receptor expression with selective antagonists.The ability to characterize GPCR expression in systems with native levels of expression, transducer coupling, and effector signaling enables robust lead optimization for GPCR drug discovery programs. Here, we present a radar plot visualization for “agonist fingerprinting” of β-AR receptor expression using the Δlog to be effective in identifying receptor expression, as long as the agonist pair shows a relationship specific to the receptor of interest. For example, the isoprenaline-referenced Δlog(Emax/EC50) value of formoterol is significantly different for β1-AR (−0.87 ± 0.15), β2-AR (1.17 ± 0.15), and β3-AR (−0.02 ± 0.1). To test a novel system, a dose-response of isoprenaline and formoterol and a formoterol Δlog(Emax/EC50) calculation should allow assessment of β-AR subtype expression at a population level, agnostic of the expression level or coupling efficiency of that system. This finding extends the utility of the approach to other receptor types with a limited number of known agonists. Applying agonist fingerprinting to novel systems will be most successful when the assay format allows direct comparison of test and reference agonist  to minimize replicate error in Δlog(Emax/EC50) measurements. To provide an example of measurement error if plate-matching is not possible, we have provided a further analysis of a data subset in The use of multiple agonists for profiling lessens the chance that the expression of a closely related receptor is missed due to the use of non-selective ligands. However, it is worth noting that potentially only one test agonist and a single reference agonist may be required for Δlog(Emax/EC50) agonist fingerprints when CHO-K1 cells with single β-AR expression were mixed in different proportions, and the fingerprint changed in a predictable continuum between the pure receptor populations based on those receptor subtype proportions. In addition, we found that rat primary cortical astrocytes, C6 rat glioma cells, and human THP-1 cells naturally express both β1-AR and β2-AR, examples of the endogenous expression of multiple β-ARs. The Δlog(Emax/EC50) agonist fingerprint can detect these co-expression scenarios and can even provide clues about the relative expression of receptor subtypes, though this can be difficult to detect when one subtype is expressed more than the other (2-AR (relative to β1-AR), which is less obvious in the Δlog(Emax/EC50) β-AR agonist fingerprints. Thus, it is essential to carefully examine the dose–response curves in all situations; a shallow Hill slope from biphasic dose–response curves with selective agonists (such as formoterol for THP-1 cells) was the biggest indicator of potential dual expression.We observed unique Δlog(Ehe other . For examax/EC50) calculations to additional signaling pathways, we show that for β2-AR, the Δlog(Emax/EC50) value for cAMP production tracks closely with the Δlog(Emax/EC50) value obtained from β-arrestin recruitment and functional desensitization assays across multiple agonists, assayed with PathHunter® and 1321N1 cells, respectively. With the exception of formoterol and levosalbutamol compared in cAMP versus functional desensitization assays, ΔΔlog(Emax/EC50) values include the value of 0, indicating a lack of bias , but this finding may not be generalizable to all tissue types expressing β2-AR. A major challenge in translating ligand bias to a therapeutic effect is to understand what magnitude of ligand bias in an in vitro system corresponds to a clinically meaningful pharmacodynamic effect. Without a clearly biased ligand, this cannot be tested effectively. Therapeutically selective activation of pathways may still be attained by different molecular and therapeutic properties not studied here, namely, lipophilicity, occupancy at equi-effective concentration, and dosing schedule (Our results suggest that meaningful ligand bias is not present among our tested βschedule . A key tschedule  is that,2-AR expression in human primary pericytes, endothelial cells, vascular smooth muscle cells, cerebral astrocytes, and hippocampal astrocytes. In precursor cell systems, we show β2-AR expression in iPSC microglia and cortex-derived neural progenitor cells. The observed expression of β2-AR on several human non-neuronal cell types provides support for this receptor and these cell types as being recipients of noradrenaline volume transmission, in line with observations of multi-cellular expression of β-ARs in the human brain (1-AR/β2-AR expression are also consistent with those of prior tissue analyses (In the brain, noradrenaline is released broadly in the cortex and cerebellum in a manner reflecting a ‘volume transmission’ process, from varicosities on neuronal noradrenergic axons with cell bodies originating mainly in the locus coeruleus . Using aan brain . Our obsanalyses . Severalanalyses , setting