Background: In minimally invasive spine surgery (MISS), where the surgeon cannot directly see the patient’s internal anatomical structure, the implementation of augmented reality (AR) technology may solve this problem. Methods: We combined AR, artificial intelligence, and optical tracking to enhance the augmented reality minimally invasive spine surgery (AR-MISS) system. The system has three functions: AR radiograph superimposition, AR real-time puncture needle tracking, and AR intraoperative navigation. The three functions of the system were evaluated through beagle animal experiments. Results: The AR radiographs were successfully superimposed on the real intraoperative videos. The anteroposterior (AP) and lateral errors of superimposed AR radiographs were 0.74 ± 0.21 mm and 1.13 ± 0.40 mm, respectively. The puncture needles could be tracked by the AR-MISS system in real time. The AP and lateral errors of the real-time AR needle tracking were 1.26 ± 0.20 mm and 1.22 ± 0.25 mm, respectively. With the help of AR radiographs and AR puncture needles, the puncture procedure could be guided visually by the system in real-time. The anteroposterior and lateral errors of AR-guided puncture were 2.47 ± 0.86 mm and 2.85 ± 1.17 mm, respectively. Conclusions: The results indicate that the AR-MISS system is accurate and applicable. Minimally invasive surgery is a significant trend in surgical development. How to make minimally invasive surgery more accurate is an urgent problem, especially for minimally invasive spinal surgery (MISS). The puncture positioning procedure is crucial in MISS; at present, it still relies on the guidance of X-ray fluoroscopy and the surgeon’s own experience . In thisAlthough navigation and robot-assisted surgical techniques have been applied in clinical practice ,6, they Augmented reality (AR) is a technology that combines virtual information with the real-world environment. It is widely used in various fields, such as education and entertainment . In the In our study, we combined augmented reality, artificial intelligence, and optical tracking to enhance the augmented reality minimally invasive spine surgery (AR-MISS) system. The AR-MISS system has three functions: AR radiograph superimposition, AR real-time puncture needle tracking, and AR intraoperative navigation. The purpose of this study was to evaluate the feasibility and accuracy of this novel technique by animal experiments.All experiments were performed following the guidelines prescribed by the Institutional Animal Care and Use Committee (IACUC) under authorization No. A2019016. Twenty male adult beagles  were used as the experimental animals due to their suitable size, robust physical health, and clear spinal imaging. These beagles were 3 years of age and had an approximate weight of 10 kg. The animals were housed individually and fed with specific standard laboratory chow ad libitum.The beagles were placed in a prone position. Preoperatively, general anesthesia was administered to the animals. After intravenous anesthesia induction with dexmedetomidine (0.008 mg/kg) and Zoleti (0.8 mg/kg), orotracheal intubation was performed. Anesthesia was maintained by continuous inhalation of 2% isoflurane. The anesthetic reaction was observed; if necessary, additional anesthetic could be added. Under anesthesia, the dorsal hair was shaved and prepared. The surgical site was disinfected thoroughly and draped.The AR-MISS system consists of a hardware system, a location kit, and self-developed software (version V1.0). The hardware system includes an infrared positioning device , two depth cameras , and a workstation with a monitor. The location kit includes custom-made noninvasive fiducial markers and a puncture needle locator. The system was implemented with a C-arm  in the animal-specific operation room with lead protection . The worThe AR-MISS system has an AR radiograph superimposition function. The noninvasive AR fiducial markers were placed on the animal’s back and flank in the horizontal direction and perpendicular direction via the radiolucent bracket A. The siThe fiducial markers could be visualized both in the radiographs and in the videos A,C. We tTo evaluate the precision of the AR radiograph superimposition, 10 needles were fastened on the beagles’ backs and sides A. The poThe AR-MISS system can track the puncture needle in real time. The fiducial markers were first placed in different positions. In each position, the location information was collected by the depth camera and the optical tracker, and then spatial registration was performed to achieve correspondence between the two coordinates in the real intraoperative space and the video space. The locator and the retroreflective markers were affixed to the proximal end of the puncture needle; thus, the optical tracker can track the 6-DOF motion data of the puncture needle in the real intraoperative video. After self-adaptive calibration and coordinate transformation were obtained, the AR virtual puncture needle was finally able to be superimposed in the intraoperative video fused with fluoroscopy images.To evaluate the precision of AR real-time puncture needle tracking, the puncture needle was placed on the body surfaces of the beagles’ backs and flanks. In this situation, the real puncture needle was captured by the camera and shown on the screen, while the AR virtual puncture needle was tracked and shown on the same screen in real time . The disWith the realization of AR radiograph superimposition and AR real-time puncture needle tracking, the AR-MISS system can achieve AR real-time navigation.After general anesthesia, AP and lateral radiographs were taken and superimposed on the beagle’s back and flank in the video. The beagle’s back was sterilized and covered by drapes. The puncture needle was tracked by the AR-MISS system, and the tracking accuracy was first verified on the body surface. Then, the puncture needle was inserted into the beagle’s back. The direction of the needle was adjusted according to the relative position between the AR radiographs and the AR puncture needle in real time. Under the guidance of the AR-MISS system, the puncture needle was carefully advanced until the needle tip touched the vertebra.A screenshot was taken, and the final positions of the AR needle in the AR radiographs were recorded. Then, the real radiographs were taken, and the final positions of the real needle in the real radiographs were recorded . The posAfter the operation, the beagles were housed continuously for one week. The overall situation and the motor function of the beagles were observed. In the first three days after the experiment, the beagles were administered subcutaneous meloxicam 0.1 mg per kg body weight daily for analgesia. After one week of observation, the beagles were anesthetized by isoflurane inhalation . The inhalation was maintained for 5 min to achieve deep anesthesia, and 10 mL of 10% potassium chloride solution was injected intravenously for euthanasia.Experimental data are expressed as the mean ± standard error (SE). All statistical computations were performed using SPSS software .In the experiment, all the AR radiographs were successfully superimposed in the real intraoperative videos. The AR radiograph distance error distribution diagram and angle error distribution diagram are shown in Before puncture, the puncture needles were tracked and shown in the real intraoperative videos successfully. The accuracy of the AR real-time puncture needle was evaluated by the tracking experiment. The tracking distance error distribution diagram and angle error distribution diagram are shown in The puncture process was finally guided by the AR-MISS system. The results showed that the position of the AR puncture needle in the AR radiograph was similar to the position of the real puncture needle in the real radiograph . The AR-Recently, AR has become one of the most promising technologies. Unlike virtual reality technology, AR can be used not only for preoperative planning but also for intraoperative interaction with the surgeon . AR naviAccurate registration of the three-dimensional coordinates of the three spaces is a necessary step of the AR navigation system. The current spinal navigation systems often require the fixation of a fiducial marker on the exposed spinous process . For opeThe registration of the three spaces was verified by the AR radiograph superimposition experiment. The results showed that the positions of the needle tips in the AR radiographs and the real radiographs were similar, confirming the success of the registration. In addition, AR radiograph superimposition is a promising function. Fluoroscopy is widely used in MISS for preoperative and intraoperative localization . With thA percutaneous procedure is necessary for minimally invasive spinal surgery. Surgeons can obtain a full view of the instrument before it is inserted into the body. Once the instrument is inserted, the internal part of the instrument is invisible. Intermittent fluoroscopy is usually needed to locate the instrument. The AR real-time puncture needle tracking function could make the internal part of the instrument visible. The AR-MISS system combined the AR radiograph superimposition function and the AR real-time tracking function, allowing the surgeon to see the real information  and the virtual information . The surgeon could adjust the AR instrument toward the target shown in the AR radiograph in real time and complete the percutaneous puncture procedure under unintermittent guidance of the AR-MISS system. This AR guidance process did not require continuous fluoroscopy; only fluoroscopy after the puncture was needed to check the final position of the instrument.Precision is a key point in the application of the AR-MISS system. The precision requirement of AR medical applications is higher than that of applications of AR in other fields, such as entertainment. In our study, precision of up to 1 mm was confirmed in the AR radiograph superimposition experiment and the AR tracking experiment. From these distribution plots, it is shown that the majority of errors are concentrated within an acceptable range, although some outliners do exist. In the AR navigation guided puncture experiment, there are some outliners 9 or 10 mm away from the target. These outliners are likely attributable to the movement or breathing of the beagle, shifts in the camera or system, etc., leading to significant changes in position. As the beagles were kept in the prone position during the experiment, the body exhibited minimal positional changes in the AP direction during respiration, whereas lateral positioning showed notable fluctuations in the respiratory cycle. Consequently, outliners in the lateral position are more prominent than those in the AP position. However, the precision remained at a good level, which could meet the needs of clinical use. In addition, the tracking procedure could respond quickly to avoid the puncture risk caused by system delay.The advantages of using an AR-MISS system include the following: it can provide the surgeon with a real-time view for guidance without continuous fluoroscopy, making the puncture positioning procedure easier; there is no problem with hand–eye coordination; the cost is low, and no complicated equipment is needed; and it is easy to use and does not significantly change the surgical procedure. The limitations of the AR-MISS system and the study include the following: the system solely performs real-time tracking of the puncture needle, and the object is only localized once during the overlay of augmented reality X-ray images. Subsequently, the fiducial markers device must be removed to avoid obstructing the puncture procedure. Thus, slight movement of the body, such as breathing or slight movement caused by puncture, may affect the accuracy. Functions such as movement tracking and correction need to be developed in the future. The functions of the AR-MISS system were verified in animal experiments, and more clinical trials are needed for further evaluation.In summary, we combined augmented reality technology, artificial intelligence technology, and optical tracking technology to enhance the augmented reality minimally invasive spine surgery (AR-MISS) system. The functions of the system  were verified by animal experiments. The results showed that the AR-MISS system was accurate and applicable. Even though augmented reality is not currently widely used in clinics, with the development of technology, there is no doubt that the future of AR in spine surgery is bright.


AV) and the mean pressure gradient (mPGAV) will be affected by AR, whereas the effective orifice area (EOA) and the ratio between maximum velocity of the left ventricular outflow tract and transvalvular flow velocity (maxVLVOT/maxVAV) will not. Furthermore, we hypothesized that EOA (by continuity equation), and the geometric orifice area (GOA) , will not be affected by AR. Methods and Results: In this retrospective study, 335 patients  with severe AS (defined by EOA < 1.0 cm2) who underwent a transthoracic and transesophageal echocardiography were analyzed. Patients with a reduced left ventricular ejection fraction (LVEF < 53%) were excluded (n = 97). The remaining 238 patients were divided into four subgroups depending on AR severity, and they were assessed using pressure half time (PHT) method: no, trace, mild (PHT 500–750 ms), and moderate AR (PHT 250–500 ms). maxVAV, mPGAV and maxVLVOT/maxVAV were assessed in all subgroups. Among the four subgroups (no (n = 101), trace (n = 49), mild (n = 61) and moderate AR (n = 27)), no differences were obtained for EOA  and GOA . In severe AS with moderate AR, compared with patients without AR, maxVAV (p = 0.005) and mPGAV (p = 0.022) were higher, whereas EOA (p = 0.998) and maxVLVOT/maxVAV (p = 0.243) did not differ. The EOA was smaller than the GOA in AS patients with trace , mild , and moderate AR . In 40 (17%) patients with severe AS, according to an EOA < 1.0 cm2, the GOA was ≥ 1.0 cm2. Conclusion: In severe AS with moderate AR, the maxVAV and mPGAV are significantly affected by AR, whereas the EOA and maxVLVOT/maxVAV are not. These results highlight the potential risk of overestimating AS severity in combined aortic valve disease by only assessing transvalvular flow velocity and the mean pressure gradient. Furthermore, in cases of borderline EOA, of approximately 1.0 cm2, AS severity should be verified by determining the GOA.Background: Diagnosing severe aortic stenosis (AS) depends on flow and pressure conditions. It is suspected that concomitant aortic regurgitation (AR) has an impact on the assessment of AS severity. The aim of this study was to analyze the impact of concomitant AR on Doppler-derived guideline criteria. We hypothesized that both transvalvular flow velocity (maxV AV), mean transvalvular pressure gradient (mPGAV), the ratio between the maximum pre-stenotic velocity in the left ventricular outflow tract (maxVLVOT) and maxVAV (maxVLVOT/maxVAV), and the effective orifice area (EOA) that is calculated using the continuity equation, are recommended parameters for grading AS severity [The prevalence of severe aortic valve stenosis (AS) as the most common type of valvular heart disease increases with age >75 years) to 3–4% . Althoug years toThe EOA describes the functional orifice area, which can be indirectly calculated using the continuity equation. When determining the EOA, some methodological limitations must be considered. For example, in comparison to the GOA, the EOA is suspected to be smaller, which is due to the phenomenon of flow constriction caused by flow turbulence at the inner edges of the actual anatomic orifice area . In contMost studies on clinical outcomes reported on patients with isolated AS ,10. The AV, mPGAV, EOA, maxVLVOT/maxVAV) in patients with severe AS, and to detect potential discrepancies between the EOA (by continuity equation) and GOA (by planimetry). Regarding current recommendations concerning grading AS severity, and the increase of total forward flow due to AR, we hypothesized that concomitant AR has no significant impact on the Doppler-derived guideline criteria of severe AS (EOA and maxVLVOT/maxVAV), but it does have a significant impact on maxVAV and mPGAV.The aim of this study was to analyze the impact of concomitant AR on Doppler-derived parameters (maxV2 and indexed EOA < 0.6 cm2/m2) who underwent TTE and TEE at University Hospital Leipzig  between January 2014 and December 2017. Patients with a reduced left ventricular ejection fraction (LVEF) < 55% (n = 97) were excluded. The remaining 238 patients were divided into four subgroups, depending on AR severity, and this was determined by the pressure half time (PHT) method: no, trace, mild (PHT 500–750 ms), and moderate AR (PHT 250–500 ms) (This retrospective study included 335 patients with severe AS (defined by EOA < 1 cm–500 ms) . Severe TTE was performed using a Vivid e9 or Vivid e95 ultrasound system with a M5-S phased array probe . Data analyses were performed with the EchoPac software .2  or >115 g/m2  [Dop). All measurements were performed in accordance with current recommendations [Left ventricular dimensions and LV mass (LVM) were assessed using M-Mode measurements that were obtained using the Devereux formula. Concentric LV hypertrophy was defined by the relative wall thickness:  >  0.42 and LVM index > 95 g/m2  . The LV ndations ,17. Leftndations ,19. Endondations .AV, mPGAV, and the maxVLVOT/maxVAV ratio. The effective orifice area (EOA) was calculated using the continuity equation [LVOT) was determined using TEE along the mid-esophageal long axis at 5–10 mm from the aortic valve during mid-systole. LVOT blood flow velocities were assessed using pulsed wave (pw)-Doppler along the apical long axis, wherein the sample volume was placed at a position that corresponded with the position of the DLVOT measurements. maxVAV was determined using continuous wave (cw)-Doppler along the apical long axis. mPGAV was calculated using the Bernoulli equation [LVOT/maxVAV ratio was calculated using the maximum flow velocities of the LVOT and the aortic valve.In all patients, the echocardiographic parameters recommended for AS evaluation were determined: maxVequation ,4. The dequation ,4. The mThe pressure half time (PHT) was assessed along the apical long axis in diastole using cw-Doppler after the optimal visualization of the regurgitation jet. Trace AR was defined by the following criteria: (1) a pinhead-sized origin of the regurgitation jet; (2) a PHT > 750 ms, if cw-Doppler documented no intercept angle between the ultrasound beam and the direction of the blood flow of the regurgitant velocities; and/or (3) a non-holodiastolic AR documented by an anatomical color-M-Mode. A PHT of 500–750 ms was defined as mild AR, and a PHT of 250–499 ms was defined as moderate AR.AV < 40 mmHg was defined as a low gradient (LG)-AS, and mPGAV ≥ 40 mmHg as a high gradient (HG)-AS. A left ventricular stroke volume index (LVSViDop) ≤ 35 mL/m2 was defined as a low flow (LF)-AS, and LVSViDop > 35 mL/m2 as a normal flow (NF)-AS. Subsequently, severe AS patients were divided into four subgroups: LFLG (low flow-low gradient)-AS, NFLG -AS, LFHG (low flow-high gradient)-AS, and NFHG -AS.In accordance with the proposal of Lancellotti et al. , all patTEE was performed using a Vivid e9 or Vivid e95 ultrasound system with a 6VT transesophageal array probe . The geometric orifice area was determined using planimetry with 3D echocardiography. During the post-processing analyses, sectional planes were manually aligned in the plane of the aortic valve opening, and they were orthogonally parallel to the course of the ascending aorta (“flexi-slice mode”) in order to best visualize the aortic valve opening.t-test. All categorical variables were expressed as numbers with their percentages (%) and compared using the chi-squared or Fisher exact test, as appropriate. The Kolmogorov–Smirnov test was performed to test the normal distribution of the population. Linear regression and Pearson’s r were applied to evaluate the association between two linear variables. Data comparisons between more than two groups were performed by one-way Analysis of Variance (ANOVA). A p value < 0.05 was considered to indicate statistical significance.Statistical analyses were performed using SPSS Statistics version 24.0 . Continuous variables were expressed as the mean value ± standard deviation (SD), and they were compared between groups using the Student’s Intraobserver variability was assessed by repeating all measurements under the same conditions in 20 patients. Furthermore, interobserver variability was assessed using the measurements of a second investigator who was unaware of the results of the first examination.2 (indexed EOA < 0.6 cm2/m2). In 42% (n = 101) of AS patients, no AR was detected, whereas 21% (n = 49) showed trace, 26% (n = 61) mild, and 11% (n = 27) moderate AR. Baseline characteristics are presented in In this retrospective study, 238 patients  showed an EOA < 1 cmBP), and it was significantly higher in patients with moderate AR , trace (p = 0.015), and mild AR (p = 0.044). Accordingly, mPGAV was significantly higher in patients with moderate AR compared with AS patients with no (p = 0.022) and trace AR (p = 0.028). The maxVLVOT/maxVAV ratio did not differ between AR subgroups (p = 0.243).The lowest maxVerate AR . Peak tr2 ± 0.14 vs. 0.79 cm2 ± 0.15, p = 0.024), mild , and moderate AR . No significant differences between EOA and GOA could be observed in patients without AR .The effective (EOA) and geometric (GOA) orifice area did not differ significantly between AR subgroups . In genep = 0.025), and a higher number of patients with NFHG conditions , was observed compared with AS patients without AR (p > 0.05)  .p > 0.05).Among AS patients with concomitant AR, dyspnea was the most common symptom, followed by chest pain and syncope . The numIntra- and interobserver variabilities of all echocardiographic measurements were in the range of 7.2% to 8.6%.AV and mPGAV, were significantly increased in severe AS and concomitant moderate AR, thus confirming the hypothesis that AR has a significant impact on these echocardiographic parameters.Doppler-derived parameters, maxVLVOT/maxVAV, did not differ significantly between AR subgroups. In addition, the hypothesis concerning the idea that in AS patients with combined AR, EOA is significantly lower than GOA, was confirmed. As suspected, there was no significant difference between either parameter in patients with isolated severe AS.The EOA calculated using the continuity equation and maxVIn patients with moderate AR, the number of patients with LFLG conditions was significantly lower than in patients without AR, whereas the number of patients with NFHG conditions was significantly higher in patients with moderate AR compared with patients without AR.In this retrospective study, patients were comparatively old and had a low body height; this was associated with small LV cavities, and consequently, lower LV stroke volumes. These characteristics may explain that although all patients presented a LVEF > 55%, about one third (29%) presented LFLG conditions and fulfilled the criteria for “paradoxical” LFLG-AS, which was first described in 2007 by Hachicha et al. . Higher AV and mPGAV) in AS patients in a small cohort of 25 patients [AV and mPGAV. Although both the maximum velocities increase over the LV outflow tract and at the level of the aortic valve, the ratio between the two (maxVLVOT/maxVAV ratio) remains the same. In contrast, mPGAV increases due to the proportional regurgitant volume, thus leading to an overestimation of AS severity based on mPGAV. Therefore, maxVAV and mPGAV are less suitable compared with EOA and maxVLVOT/maxVAV, which must be considered when estimating AS severity in patients with concomitant AR.In 1988, Grayburn et al. observed the significant impact of AR on doppler-derived parameters  are performed .AV and mPGAV, but also on the echocardiographic parameters of the proposed classification by Lancellotti et al. [Lancellotti et al. demonstrated differences between clinical outcomes in patients with severe AS depending on the transvalvular pressure gradient and indexed forward stroke volume . In accoi et al. . The mori et al. , whereasThe fact that symptoms did not differ between the four subgroups with severe AS, each with different degrees of AR severity, might lead to the assumption that trace to moderate AR is well compensated in severe AS.AV, mPGAV, EOA, and maxVLVOT/maxVAV) is more error-prone if, for example, maxVAV and mPGAV, are significantly influenced by the contribution of the concomitant AR. Thus, AS severity might be overestimated in patients with only moderate AS if the impact of the proportional regurgitant volume of the AR will not be considered.Based on the results of the present study, the question arises as to whether the classification of AS severity based on Doppler-derived parameters . In addition, all patients underwent TEE to confirm the diagnosis. Low intra- and interobserver variabilities highlight the quality of the acquired and analyzed echocardiographic data sets. In addition to the echocardiographic data, data on the patients’ symptoms were also available.In the present study, a relatively high number of well-characterized patients with severe AS (according to the results of a continuity equation) were included (Although the effective regurgitant orifice area (EROA), determined by the proximal isovelocity surface area (PISA) method, may be considered a more reliable parameter, AR severity was assessed semi-quantitatively using PHT in the present study. It should be noted that in patients with severe AS, and with only trace or mild AR (most patients in the present study), flow convergence zones (PISA zones) are difficult to visualize, and the PISA method is often not applicable. As this is a retrospective study, the flow convergence zones could not be well elaborated in many patients with primarily trace and mild AR. On the other hand, the PHT method is also mentioned in current guidelines, despite its main limitation that the alignment of AR jet formation using cw-Doppler is often difficult.AV and mPGAV, whereas the EOA and the ratio of maxVLVOT/maxVAV remain unchanged. Characterization of AS severity using only maxVAV and mPGAV might overestimate AS severity in patients with concomitant moderate AR. Further prospective studies need to clarify the prognostic impact of concomitant AR on AS severity—especially in borderline EOA—using the continuity equation. In addition, TEE should frequently be implemented in the diagnostic algorithm to confirm the diagnosis of severe AS.In severe AS, concomitant AR has a significant impact on the Doppler-derived guideline criteria, maxV


SBI-46, an antiandrogen-equipped histone deacetylase inhibitor, is the lead compound developed for targeting castration-resistant prostate cancer (CRPC). We determined the anticancer effect of SBI-46 on in vitro and in vivo models of CRPC. In our results, SBI-46 downregulates AR and AR-splice variant (SV) expression and its downstream target genes in CRPC cells. Additionally, SBI-46 inhibits AR expression and nuclear localization in the presence of DHT. We further demonstrated that SBI-46 induces apoptosis by activating the pro-apoptotic genes Bax, cleaved PARP, and cleaved caspase-9, as well as downregulating the expression of the antiapoptotic genes Bcl2 and BCl-xL. Additionally, the oral administration of SBI-46 abrogates the growth of C4-2B and 22Rv1 CRPC xenograft tumors that express AR or both AR and AR-SV. Finally, our results demonstrate that SBI-46 exerts an anticancer effect by inhibiting AR and AR-SV in preclinical models of CRPC.+ CRPC cells but not AR- CRPC and normal immortalized prostate epithelial cells (RWPE1) or normal kidney cells (HEK-293 and VERO). Molecular analysis confirmed that SBI-46 downregulated the expressions of both AR+ and AR-splice variants (AR-SVs) in CRPC cells. Further studies revealed the downregulation of AR downstream (PSA) events in CRPC cells. The oral administration of SBI-46 abrogated the growth of C4-2B and 22Rv1 CRPC xenograft tumors that express AR or both AR and AR-SV in xenotransplanted nude mice models. Further, immunohistochemical analysis confirmed that SBI-46 inhibits AR signaling in xenografted tumor tissues. Conclusion: These results demonstrate that SBI-46 is a potent agent that inhibits preclinical models of CRPC by downregulating the expressions of both AR and AR-SV. Furthermore, these results suggest that SBI-46 may be a potent compound for treating CRPC.Background: Epigenetic modification influences androgen receptor (AR) activation, often resulting in prostate cancer (PCa) development and progression. Silencing histone-modifying enzymes (histone deacetylases-HDACs) either genetically or pharmacologically suppresses PCa proliferation in preclinical models of PCa; however, results from clinical studies were not encouraging. Similarly, PCa patients eventually become resistant to androgen ablation therapy (ADT). Our goal is to develop dual-acting small molecules comprising antiandrogen and HDAC-inhibiting moieties that may overcome the resistance of ADT and effectively suppress the growth of castration-resistant prostate cancer (CRPC). Methods: Several rationally designed antiandrogen-equipped HDAC inhibitors (HDACi) were synthesized, and their efficacy on CRPC growth was examined both in vitro and in vivo. Results: While screening our newly developed small molecules, we observed that SBI-46 significantly inhibited the proliferation of AR Castration-resistant prostate cancer (CRPC) is an advanced type of prostate cancer (PCa) that progresses even with low androgen levels . AndrogeSeveral AR variants have been reported in CRPC, among which, splice variant 7 (AR-V7) has been frequently linked to resistance for ADT . Most spRecent studies have suggested that epigenetic events could regulate AR signaling . More sp+) LNCaP than a hormone-independent (AR−) DU145. In addition, the antiproliferative activities of these dual-acting compounds are significantly enhanced relative to the separate template compounds [We have described first-in-class HDAC-inhibiting compounds targeted to prostate tumors by equipping them with the additional ability to bind the androgen receptor (AR). These compounds elicited the signatures of the inhibition of HDACs and AR signaling, and the cohort was selectively more toxic to hormone-dependent  expression, resulting in the growth inhibition of CRPC cells. More importantly, the oral administration of SBI-46 abrogated the growth of AR or both AR and AR-SV-expressing xenografted tumors with no discernible evidence of toxicity.In the pursuit of an extensive structure–activity relationship (SAR) study, we have rationally designed and synthesized several antiandrogen-equipped HDAC inhibitors (HDACi) (a detailed structure-activity relationship (SAR) study will be published in a separate manuscript). While screening our newly developed small molecules, we discovered a novel compound, SBI-46 A, which In vitro HDAC inhibition was performed by the contract research organization (CRO) BPS Bioscience while an AR-binding assay was performed by Eurofins Panlabs. . These assays were performed through contract agreements with these CROs.Human PCa cells (LNCaP and 22Rv1) and normal prostate epithelial (RWPE-1) and kidney (HEK-293 and VERO) cells were obtained from American Type Cell Culture . C4-2B was obtained from the ViroMed Laboratories . The cells were grown on a specified medium previously described . DihydroThe effect of the treatments on prostate cancer and normal prostate epithelial cells’  viability was evaluated using the Alamar blue assay. Cells were seeded into 96-well plates, and after growing to 60–75% confluency, they were treated with vehicle control  or various concentrations of SBI-46 in respective media for 24 h. At the termination of exposure, cells were incubated with 100 μL of 3% Alamar Blue solution in a complete growth medium at 37 °C for 2 h. Absorbance was measured, and data were analyzed following the manufacturer’s instructions .3 cells) that had been exposed to either the vehicle or SBI-46 were harvested and used for the assay following the manufacturer’s instructions. Colonies were manually counted after being stained with 0.005% crystal violet after ten days [The CytoSelectTM 96-well In Vitro Tumor Sensitivity Assay Kit was used to perform the colony formation assay to detect anchorage-independent growth . LNCaP, C4-2B, and 22Rv1 cells  or in combination for 24 h and were fixed. Then, the cells were incubated with AR and AR-V7 (ab273500:EPR15656-290) antibodies, followed by secondary antibodies to detect the localization and expression. The cells were analyzed using a KEYENCE fluorescence microscope (BZ-X800/BZ-X810).6/well in RPMI for 24–48 h until the confluency reached 80%. Then, 5 µM SAHA, 25 and 50 µM Enzalutamide, 0.5 and 5 µM SBI-46 solutions in DMSO were added to separate cell cultures such that the final DMSO level in each culture was 0.1%. Cells were treated for 6 h and scraped out of the well. The cells were washed with cold PBS three times and lysed with RIPA buffer (110 μL)  containing phosphatase inhibitor  and protease inhibitor . The cell lysates were vortexed for 15 s, followed by sonication for 90 s. The lysates were then centrifuged at 14,000 rpm for 10 min, and the supernatants were collected. The total protein concentration was determined using a BCA protein assay kit . Based on the results from the BCA assay, the lysates were diluted to make an equal protein concentration, and 20 μg of each lysate was loaded to each well of the TGX MIDI 4–20% gel  and ran at 150 V for 70 min. Subsequently, the gel was transferred onto Turbo PDVF membrane , and after blocking with 5% BSA for 1–2 h, the membrane was incubated overnight with Ac-Tubulin , Ac-H4 , GAPDH , AR , AR-V7 (ab198394) and (ab273500:EPR15656-290) PSA (ab53774), and β-actin  antibodies. On the second day, the membrane was washed with TBST for 3 × 5 min. A relevant secondary antibody  was added, and the membrane was incubated with agitation for 1 h. Bands were quantified using the Odyssey CLx and Biorad Image system. The image J software was used for densitometry analysis.To probe H4 and tubulin acetylation status, LNCaP cells were seeded into a 6-well plate at 1 × 106, approximately) were injected into separate flanks of the mice (n = 6) for subcutaneous tumor xenograft studies using a final volume of 100 μL of phosphate-buffered saline and Matrigel (1:1). Once the tumors reached ~50–100 mm3, the mice were randomly divided into two groups; one group received a vehicle and the other groups received SB1-46 (20 mg/kg) orally for 7 days for 4 weeks. Mice were monitored twice weekly, and tumor volumes were measured once a week.All animals were maintained in germ-free environments, and the study was carried out per IACUC guidelines that the University of Louisville authorized. Athymic nude Balb/c mice (nu/nu) were obtained from The Jackson Laboratory at 6 weeks of age. C4-2B and 22Rv1 cells  were subjected to immunohistochemical analysis using antibodies against AR, AR-V7, Ki67 and PSA. After dewaxing, blocking sections were incubated with primary antibody  and subsequently incubated with relevant goat antirabbit IgG . Color detection was achieved with 3,3′-Diaminobenzidine and counterstained with hematoxylin. Images were captured via light microscopy (40×) using an Olympus BX43 microscope  [t-tests and a one-way ANOVA analysis were performed for two-group and multiple-group comparisons, respectively. A p-value < 0.05 was considered statistically significant, and values were presented as mean ± SD.GraphPad Prism 8.0 was used to perform all statistical analyses . Unpaired two-tailed Student’s 50 , and AR+ splice variants (AR-SV) and CRPC cells (C4-2B-IC50: 480 nM and 22Rv1 IC50: 560 nM). On the other hand, SBI-46 was not effective against AR-null PC-3 cells (p = 0.001), 22Rv1 (p = 0.001), and LNCaP (p = 0.001)) reduced the colony-forming ability of CRPC cells  and AR-null CRPC cell lines (PC-3) treated for 24 h. We observed that SBI-46 inhibited nanomolar concentrations, the growth of AR+  and α-tubulin (a cytoplasmic HDAC6 substrate) ,26 in LNNext, we investigated if the inhibition of AR signaling contributes to the antiproliferative effects of SBI-46 against AR+ and AR+ SV CRPC cells. In our Western blot analysis, we observed that the expressions of AR-FL (AR-Full length) and splice variant 7 (AR-V7) were downregulated in SBI-46-treated CRPC cells, with the inhibition of PSA expression downstream of the effector of AR F–H. TogeAR is naturally localized in the cytoplasm. Once it binds to its ligand (such as testosterone), AR translocates into the nucleus and transactivates target genes. However, nuclear-localized AR was seen in CRPC patients. Hence, we examined AR’s cytosolic and nuclear expression in SBI-46-treated 22Rv1 and C4-2B cells. As shown in Dihydrotestosterone binds to the AR and elicits the transcriptional activity of AR-regulated genes and functions by inducing proliferation, survival, and differentiation in CRPC . Hence, p = 0.01)  , suggest6 cells), and when the tumors reached 50–100 mm3, either the vehicle or SBI-46 (20 mg/kg b.w.) were given orally for five days/week for four weeks. The oral administration of SBI-46 to mice caused a significant decrease in the volume  effectively inhibits both epigenetic and AR signaling pathways. This results in the upregulation of acetylated H4 and tubulin in LNCaP cells and the inhibition of both AR full-length and AR-V7 expression in CRPC cells. HDAC6 is a well-known regulator of HSP90, which retains and stabilizes full-length AR in the cytosol . InteresWe also confirmed in all three CRPC cell lines  that the SBI-46-induced inhibition of AR-FL and AR-SV expression occurs in a time-dependent manner in PCa. Additionally, the expression of PSA, a direct target of AR, is almost abrogated, suggesting that SBI-46 effectively targets AR signaling in CRPC cells. Previous studies suggested AR-V7 is predominately localized in both the cytoplasm and nucleus ,44. A prIt has been reported that the inhibition of AR-mediated survival signaling by small molecules results in either cell cycle arrest or the induction of apoptosis in CRPC cells . Our stuWe identified a novel AR inhibitor, SBI-46, which significantly inhibited growth and induced apoptosis in CRPC cells without causing significant toxicity to healthy immortalized cells. Furthermore, our in vivo data suggest that SBI-46 is a promising compound that may overcome full-length and AR-SV-mediated resistance to androgen ablation therapy in CRPC. Further analysis may reveal whether SBI-46, in combination with chemotherapy, may enhance sensitivity in CRPC. Other comprehensive preclinical studies are warranted to evaluate the efficacy and toxicity of SBI-46 as a single agent or in combination therapy for treating CRPC.


Stroke is one of the most prevalent health issues that people face today, causing long-term complications such as paresis, hemiparesis, and aphasia. These conditions significantly impact a patient’s physical abilities and cause financial and social hardships. In order to address these challenges, this paper presents a groundbreaking solution—a wearable rehabilitation glove. This motorized glove is designed to provide comfortable and effective rehabilitation for patients with paresis. Its unique soft materials and compact size make it easy to use in clinical settings and at home. The glove can train each finger individually and all fingers together, using assistive force generated by advanced linear integrated actuators controlled by sEMG signals. The glove is also durable and long-lasting, with 4–5 h of battery life. The wearable motorized glove is worn on the affected hand to provide assistive force during rehabilitation training. The key to this glove’s effectiveness is its ability to perform the classified hand gestures acquired from the non-affected hand by integrating four sEMG sensors and a deep learning algorithm . The InceptionTime algorithm classified ten hand gestures’ sEMG signals with an accuracy of 91.60% and 90.09% in the training and verification sets, respectively. The overall accuracy was 90.89%. It showed potential as a tool for developing effective hand gesture recognition systems. The classified hand gestures can be used as a control command for the motorized wearable glove placed on the affected hand, allowing it to mimic the movements of the non-affected hand. This innovative technology performs rehabilitation exercises based on the theory of mirror therapy and task-oriented therapy. Overall, this wearable rehabilitation glove represents a significant step forward in stroke rehabilitation, offering a practical and effective solution to help patients recover from stroke’s physical, financial, and social impact. Stroke has become a major contributor to human death globally, with over 10 million people suffering from it annually. This debilitating disease affects the lives of patients and their families, significantly reducing the quality of life of all involved ,3. With The traditional approach to rehabilitation, which relies on one-on-one training involving a patient and a therapist, has proven ineffective and cost-prohibitive in today’s world. With the outbreak of the COVID-19 epidemic, face-to-face therapy sessions in hospitals had to be suspended, making the traditional approach to rehabilitation untenable . To addrThe availability of assistive devices is critical for many countries’ economic and social development. It enables people with disabilities to participate in community activities, work, and earn an income, reducing the poverty levels . As a reIn 2018, Randazzo et al. proposed a novel hand exoskeleton design to assist and rehabilitate individuals with impaired movement function and improve their quality of life. The device is wearable, portable, and does not impede hand mobility. It utilizes an EEG brain–machine interface to control hand movements through pattern detection. Finger actuation is achieved using Bowden cables, a linear actuator, with each finger having an active DOF for flexion and extension. The user can passively control abduction and adduction. The device is cost-effective, utilizing off-the-shelf parts as well as 3D printing and laser cutting. The actuator is housed in a closed box mounted on the chest, connected to the fingers via Bowden cables. The device is powered by a lithium–polymer (LiPo) battery, capable of continuous operation for up to 3 h .In 2020, Butzer T. et al. designed the RELab tenoexo, a fully wearable, soft hand exoskeleton for daily activities, It consists of a hand module attached to the hand and a backpack containing electronic components, motors, and batteries. The backpack and hand module are connected by a force transmission system based on Bowden cables and can be attached by a clip mechanism. In the hand module, three DOF achieve dynamic, precision, and lateral grasping: a combination of 2–5-digit actuators for flexion/extension, an individual actuator for the thumb flexion/extension, and a manual lateral and oppositional pad for the thumb. Two DC motors are fed forward and controlled to drive the flexion/extension of the fingers and thumb through gear-rack mechanisms. The Myo armband (a wireless sEMG sensor for monitoring) is used .In 2022, Marek Sierotowicz and colleagues from the Robotics and Mechatronics Department at the German Aerospace Center (DLR) designed an EMG-driven machine learning-controlled soft glove for assisting grasp and rehabilitation. The glove can assist the flexion and extension of the index finger and thumb through a tendon-driven system. The two actions, thumb and finger flexion, are independently assisted by electric motors that pull and release their respective tendon lines. The glove has a control system that recognizes attention through sEMG .Despite these efforts, we think that the shape of the device and the fact that it has to be carried on the chest or a backpack still have some negative implications for the user, especially for stroke patients who usually suffer from a lack of self-confidence within a community, which can affect the progress of the training beside the short time of use of 3 h. In addition, the size and the weight of the device have a negative effect on the comfort of the user.This article presents a hand rehabilitation system with a soft glove that supports mirror, task-oriented therapies, and neural plasticity. The actuated glove, integrated with a linear actuator, provides the driving force for the affected hand during rehabilitation training. The sEMG sensor enables cooperation between both hands and was used to control the actuated glove to let the affected and the non-affected hands move simultaneously. An integrated sensor measures the bending angle and the progress of the rehabilitation programmer. A machine learning algorithm was developed to classify the sEMG gestures as a control command for the actuated glove. This gives the affected hand the suitable driving force to perform the corresponding motions. Our glove is superior to previous devices because of its lightweight, the soft material used for its fabrication, its long-lifetime batteries that can support 4–5 h of use, and its low cost, which is a good advantage for low-income countries, as discussed before. The device is built so that all of its parts are hidden under the shoulder and under the clothes, and the only visible part is the glove placed on the wrist, which positively affects the user’s self-confidence.Moreover, our glove can train each finger alone to perform flexion and extension with the integrated sensor to measure the angle of bending that reflects the progress of the rehabilitation program. Finally, our glove is based on two verified therapy theories, mirror therapy and task-oriented therapy, using sEMG. The telecooperation between the affected and the unaffected hand has also a good effect on the progress of the therapy training.The research and development of hand rehabilitation equipment are still in their infancy. Most exoskeleton-hand robots focus on improving finger joints and increasing muscle strength, ignoring the vital role of active brain participation and the sensory function input into the motor function. Therefore, the effect of fine motor rehabilitation on hand function is not considered. At present, the hand-function rehabilitation robots design adopts an exoskeleton structure, which mainly provides a driving force through motors and pneumatics to complete flexion and extension of the fingers and joints. Four main driving methods have been used for hand-function rehabilitation robots. They mainly include a motor drive, a pneumatic artificial muscle drive, a memory alloy drive, and a lasso drive. Because the lasso drive has good flexibility and linearity, we used a lasso drive to design hand-functional rehabilitation robots.The electrical voltages in sEMG signals range from −5 to +5 (mV) and are influenced by both the movements performed and the muscle extraction and contraction level. The continuous availability and variability of the signal can be measured using a suitable detection element ,19,20,21Using a neural network with a backpropagation or fuzzy recognition system ,28 may nA highly accurate decoding and classification method for sEMG signals was developed in this research, which can be used as mirror therapy to mirror the motion from the non-affected hand in the affected hand. The wearable glove developed in this study can perform 16 desired motions, including 6 task-oriented therapy-based motions, and is cost-effective and comfortable. This glove can be used for home-based rehabilitation and is not limited to clinical environments. The individual finger movement and the cooperation of multiple fingers can be trained using this glove. The system design and fabrication process, as well as the methodology used in the research, are described in this article, which begins with sEMG implementation and experiment details in the mirror therapy scenario and ends with data preprocessing and classification.This study aimed to introduce a wearable rehabilitation glove based on flexible lasso transmission driven by a flexible rope and a linear actuator. The glove was designed to assist patients with hand impairments in the rehabilitation process and enable them to perform routine activities in daily life, which can boost their confidence and promote their independence. As shown in The flexible-rope driving method used in the glove design provides several advantages, including a simple structure, a lightweight design, substantial flexibility, comfortable wearing, and convenient use. The flexible rope acts as a transmission mechanism that can transmit power from the linear actuator to the glove without a significant loss of power. This design enables the glove to provide the necessary driving force to assist the patient’s hand movements during the rehabilitation process, while also ensuring the patient’s comfort and safety.The lightweight design of the glove allows patients to wear it for extended periods without feeling uncomfortable or fatigued, making it suitable for long-term use. Additionally, the flexible lasso transmission provides a substantial range of motion, allowing patients to perform various hand movements without feeling restricted or inhibited. This range of motion is essential in promoting hand function and mobility recovery, which can be challenging for patients with hand injuries or conditions.The wearable glove presented in this study is primarily composed of a main control module, a separation module, a glove connection module, and wearing gloves, as illustrated in The system components are shown in The dimensions and weight of the host part were carefully selected to ensure that the wearable glove is compact and lightweight. These features ensure that patients could use the glove comfortably and without undue strain, allowing for the glove to be used over an extended period.The structure of the wearable glove used for hand rehabilitation is shown in To monitor the wearer’s progress, pressure sensors were placed at the fingertips to detect the pressure exerted by the fingers during exercises. This information can be used to adjust the glove’s level of assistance, ensuring that the rehabilitation program is tailored to the wearer’s needs.The wearable glove utilizes linear actuators as a power source to address the need for a portable and compact wearable glove for home-based rehabilitation and daily life assistance. Given that the flexible glove requires bending and straightening each finger, an electric cylinder is not feasible since it can only provide one function. To address this, a specialized linear motor was designed with meshing gears behind the motor reducer, resulting in two outputs in opposite directions, as illustrated in The control machine and separation structure are connected flexibly, as depicted in Consider the decoupling design between the central control module and the glove module of wearable rehabilitation robots. In this paper, a separator was designed between the central control module and the glove module to decouple the movement. The splitter makes the signal transmission part of the sensor separable, through spring pins and contacts on the circuit board. The structure and hardware composition of the separator are shown in The wearable glove controller utilizes the STM32 microcontroller as its central processing unit, with a 12 V lithium battery as the power source for driving the actuators. A serial touch screen enables communication via a serial interface with the central control hardware. Additionally, switch, mode selection, and intensity adjustment buttons were incorporated for selecting the rehabilitation mode during home-based rehabilitation training. A pressure sensor and a resistive linear displacement sensor were integrated into the design to monitor the fingers’ condition and movements within the rehabilitation device. The device is equipped with a lithium battery with a capacity of 11.84 Wh, a standard voltage of 7.4 V, and a nominal capacity of 3200 mAh. During grip mode testing, the power consumption was measured to be 3.7 W, and the device’s continuous exercise time was calculated to be 3.2 h. However, due to the device’s low power consumption during exercise gaps, actual laboratory test rehabilitation training can run for up to 4 h. The WiFi module of USR-C322 was built into the device to communicate with other control modules through UDP. Network communication ensures that the upper computer can control and display the real-time state of the glove, as shown in Equipment control and equipment status monitoring can be realized through the instructions in the figure above.The equipment described in this paper mainly uses finger motion control instructions, and the equipment developed and used in this paper does not include vibration and electrical stimulation accessories.The equipment sends the return instruction of equipment status (data type 0x01) as follows:It includes the protocol frame header 0x00AA00CC, data type 0x01, equipment number 0x0001, three bytes used by a five-way vibration motor 0x111110, five-way displacement status 0xEEEEE0, whether electrical stimulation 0x01, frame tail 0x0D0A0D0A.When we need to control the equipment, we send a control instruction with data type 0xA2 to the corresponding equipment.At the same time, we use the following instructions as the heartbeat packet mechanism of the device.The upper computer sends heartbeat packets (data type A0) to the device regularly:After receiving the heartbeat packet, the device replies to the data with the device number, usage mode, and power information:By adding a WiFi module in the development and compiling the program to realize a UDP device control protocol, the device can be controlled by other devices.Gesture recognition was designed to simulate and fit the complexity of real hand-finger movement ,35,36,37A total of eight healthy subjects, six males and two females, all right-handed except one male and between the ages of 21 and 30 years, participated in the data acquisition. The data were collected using . Four surface electrodes were placed on the forearm muscles, which are four muscles in total , as shown in sEMG signals were collected at a sampling rate of 1000 Hz, and each gesture was performed 12 times, each time consisting of four runs, each one lasting for 4000 ms; and there was a rest period of 3000 ms to avoid causing muscle fatigue, as shown in This work divided pattern recognition into three main steps: data preprocessing, feature extraction, and pattern recognition. Pattern recognition was divided into two stages, i.e., classifier training and classifier prediction, using new signals from the non-training data.Initially, we selected pertinent data located in the intervals of , which contained EMG signals relevant to the targeted motion. Next, we proceeded with the raw data pre-processing, where the raw signals were trimmed by a butter band-pass filter, ranging from 5 to 480 Hz in areas where an sEMG was present, and further filtered by a notch filter between 48–52 Hz.To obtain a large set of training data that could be used for pattern recognition training, we used the segmentation overlapped method for extracting useful time-domain features such as mean square value (MAV), root-mean-square (RMS), variance absolute value (VAV), integrated electromyography (Iemg), simple square integral (SSI), waveform length (WL). The extracted featured data contained useful information such as signal strength and amplitude. Later, the wrapper backward method was used to obtain new features. The wrapper method was also used as a feature selection method to select the optimal feature subset by determining the importance of each feature subset independently using a real training test to identify the feature that positively affected the recognition system accuracy. Therefore, we could select the optimal feature subset from the original features. In the feature extraction process, the window length was 200 ms, and the overlap of the adjacent window was 50 ms. The data collected from a single muscle were divided into 317 windows. The 200 ms data window set was a good choice containing enough information to define the contained gesture fully. The 50 ms overlap window utilized the data set to produce a refined, dense classification technique to meet the real-time classification needs.This article used the PyQt5 framework to build software for EMG acquisition and raw data storage, as shown in We adopted the 1D-CNN algorithm and the InceptionTime algorithm. The above-collected data were intelligently identified. The obtained iteration accuracy and confusion matrix of ten gestures are shown in The sEMG drive of the hand rehabilitation robot using the framework is shown in Based on the above framework, we developed an online recognition software for grasping and extending a hand using the above algorithm, as shown in The corresponding flowchart is shown in The complete hand rehabilitation robot we constructed as described in this study is shown in The control of the rehabilitation robot was realized by sEMG driving, as shown in A highly accurate decoding and classification method for sEMG signals was developed in this research, resulting in a classification accuracy of 90.89%. The contribution of this work surpasses that of previous hand rehabilitation systems. The wearable glove that was created can accurately perform ten desired motions, including six task-oriented therapy-based motions. Additionally, the glove is inexpensive and exceptionally comfortable. It can be used as a home-based rehabilitation system and is not limited to clinical environments. The glove can train individual finger movements and multiple finger cooperation, with a latency time of less than 300 ms, thus satisfying online training requirements. The controller, glove, and glove structure were designed to align with human–machine integration and ergonomics, as hand-function robots are used for rehabilitation and muscle strength enhancement. The host structure has no edges or sharp corners, with round edges made of metallic silver. The wearable device mainly operates through a touch screen, offering good man–machine interaction characteristics.The core actuator of the robot was designed independently and can output a two-way rope-pulling force. Generally, the rope can only transmit the pulling force, and two actuators are needed to realize a bidirectional drive. In this paper, the actuator has two reverse outputs; so, a single actuator can directly drive a finger forward and backward. The transmission adopts an optimized lasso transmission design, which is closer to the movement characteristics of human muscles. Compared with other flexible drives, the lasso drive has the characteristics of fine-grained operation, reliable transmission, and high safety. In this paper, the lasso drive presents a single-tube bidirectional lasso that can realize a finger’s flexion and extension.The glove part considers the palm movement, a challenge for most wearable hand devices, and the design is lighter and more convenient to wear. A flexible thin-film pressure sensor was placed at the fingertips of the glove to collect pressure intended for muscle strength enhancement. The driving rope was sewn into the glove, obtaining a solution close to the real driving force of the muscle. The whole machine has a WIFI communication interface that grants intelligent control through the protocol.In the offline training, the InceptionTime algorithm had the highest accuracy among all the algorithms. It showed less prediction time and required less training time than the 1D-CNN algorithm, and the average difference in accuracy between them was ±9.2%. We decided to use the InceptionTime algorithm for its low prediction time, which is important for our application. The highest recorded accuracy was 90.89%, which satisfies our wearable glove application.In conclusion, the presented wearable hand rehabilitation system has the potential to significantly impact the motor recovery of paresis and hemiparesis patients. Actuated gloves with sEMG sensing are a flexible, wearable technology that is safe, comfortable, and portable. Additionally, the system can be affordable for low- and middle-income countries, which have limited access to rigid exoskeleton devices due to their high cost. Compared to dedicated-data gloves with targeted sensors, the wearable glove utilizing biomedical signals offers improved signal quality and higher accuracy in detecting the desired motions during rehabilitation training. This connection between the desired motion activated by the brain and sent to the muscle and the actual movement executed by the actuated glove shown on a screen can speed up the rehabilitation process based on the mirror therapy technique. However, it is worth noting that achieving a fine-grained classification of the training gestures still requires the precise placement of the EMG electrode. This study presents a promising wearable rehabilitation system for hand motor recovery that can be used in various settings, including clinical and home-based environments.


In this work, we focus on using reinforcement learning and game theory to solve for the optimal strategies for the dice game Pig, in a novel simultaneous playing setting. First, we derived analytically the optimal strategy for the 2-player simultaneous game using dynamic programming, mixed-strategy Nash equilibrium. At the same time, we proposed a new Stackelberg value iteration framework to approximate the near-optimal pure strategy. Next, we developed the corresponding optimal strategy for the multiplayer independent strategy game numerically. Finally, we presented the Nash equilibrium for simultaneous Pig game with infinite number of players. To help promote the learning of and interest in reinforcement learning, game theory and statistics, we have further implemented a website where users can play both the sequential and simultaneous Pig game against the optimal strategies derived in this work. The most common object of a games is to beat the opponents, whether they are computers or other human players. To do so, a player needs to take a sequence of actions as his/her strategy to increase the winning rate and/or decrease the cost (such as time or the number of steps). For some complicated games like Go, despite the great empirical success of Alpha Go based on deep reinforcement learning7, the optimal strategy remains unknown. In fact, a new program Alpha Go Zero has outperformed the previous version of Alpha Go known as Master after 40 days of self-training by playing millions of games against itself in quick succession and with no input from human players8, indicating the current best policy can still be improved. For many simple games, the optimal strategy can be learned via RL algorithms by playing against itself without direct supervision. The dice game Pig is one of them.Reinforcement learning (RL) serves as an important branch of machine learning. As a powerful approach in decision and control theory, RL has attracted extensive focus, with wide applications in the fields of roboticsScarne on Dice9. This simple one-die game was designed on the sequential basis: Two players take turns to roll a single die until one of them (the winner) reaches a certain goal, which usually is 100 accumulated points. At each turn, a player can keep rolling a die with the scores added to his/her turn total, if no 1 is rolled, or the player wishes to hold so that the turn total will be added to his/her total scores. If a 1 is rolled, however, the player will have all his/her turn total wiped out to zero and it becomes the opponent player’s turn. In summary, during a player’s turn, the player can choose “roll” or “hold”. If the player chooses “roll” and rolls a 1, his/her turn ends automatically with his/her total scores unchanged. If the player chooses “roll” and rolls a number other than 1, namely 2–6, the number will be added to his/her turn total, and the player can choose “roll” or “hold” again. If the player chooses “hold”, the turn total will be added to his/her total scores and his/her turn ends. Pig is known as a “jeopardy dice game”, where previous turn total can be jeopardized by continuing to roll for greater gains (if the next roll is not 1) or ruin (if 1 is rolled next) (Knizia10). The optimal strategy to this sequential one-die game is not as simple as the game itself—it has only been solved in recent years using the Markov decision process and dynamic programming (Neller and Presser12).The original dice game Pig was publicized by American magician John Scarne in 1945 in his popular book 13). Apart from increasing the number of the dice rolled, another possible variation is to increase the number of players. The Pig game is originally designed for two players to compete against each other. The game can be easily extended to multiple players without a significant change of the game mechanism. The 3-player sequential Pig game was investigated with the conclusion that the optimal strategy may not exist if the game total points exceed some threshold . As this simple game can be easily played with even more players, say during a party, we are interested in how a certain player will make decisions under such situation. Before we derive the optimal strategy for multiplayers, we first address a potential problem of the current Pig game framework under the multiplayer setting and provide one possible solution.Originally the game is played by turns, i.e sequentially, and hence we called it the sequential Pig game. The one-die sequential Pig game has since evolved into several variations, with the most popular one being the two-dice Pig game where two dice are rolled instead of one. The optimal strategies for the standard two-dice sequential Pig game as well as its variation “Double Trouble” have been derived in our previous paper (Zhu and Ma11) and the two-dice (Zhu and Ma13) sequential Pig games have indicated that the player who plays first has certain advantage in term of the winning probability. The sequential setting is unfair to those played at the end of the playing queue, especially when the number of players increases. Another issue of the sequential game for multiplayer is that if the number of players n is large, taking turns to play can decrease each player’s participation rate, which is defined his/her number of turns divided by the sum of the turns that everybody has played, roughly speaking, m winners, and 0 for the other Both the one-die  reinforcement learning to derive a near-optimal strategy. Next, we develop the optimal strategy for the 3-player simultaneous game under the independent strategy setting, followed by the extension of the n-player simultaneous game optimal strategy. Subsequently, we discuss the asymptotic behavior of the optimal strategy when 13).In this work, we utilize dynamic programming and value iteration by first formulating the game as a Markov Decision Process, and then providing transition probabilities, to find the optimal strategy for the simultaneous dice game Pig. For the fundamental reinforcement learning framework consisting of two main subsections “i, and the opponent player B’s score j, with the state space denoted by S. Given such state s, player A decides the action a he/she should take for this turn and we define A to be the action space. For any two states a will change the state from s to s to a is defined as m is the number of winners, and The simultaneous Pig game can be solved using reinforcement learning as we are interested in the long-term reward–winning the game. In particular, the game can be viewed as a Markov Decision Process (MDP) as the action is not determined by the past states given the current state. For simplicity, we first consider the 2-player game with player A and his/her opponent player B. The state 15). Recently, many more advanced deep RL methods, such as Double Deep Q-network16, Dueling Deep Q-network17 and Soft Actor-Critic18, have been developed.The dice game Pig can be solved using Dynamic Programming (DP) as the exact transition probability from the current state to the next state is known. Given a perfect model, classical DP algorithms can suffer from their great computational costs when the state space is large, which occurs when the number of players increases. However, DP still plays an important role in the field of reinforcement learning as it can be used to compute the exact  solution in a fully known MDP. Without the complete knowledge of the environment, model-free methods such as Monte Carlo (MC) and Temporal Difference (TD) learning, have less limitations and can often achieve similar effect as DP with less computational expense. These basic model-free methods can be integrated with neural networks to create a new subcategory of the deep reinforcement learning (DRL), with a well-known example being the Deep Q-network  to be the state-value function, namely the winning probability of the state i is player A’s score, and j is the opponent player B’s score. Denote the policy that player A follows by k, then player A keeps rolling. As long as player A’s turn total reaches k , player A immediately holds. The action is defined as the turn total k that player A needs to hold at each given state . Obviously, we have the action space q to be the action-value function for the state  and the action k, where k, the turn total he/she will get is not always k. Instead, the turn total is a random variable k points this turn . Then the above Eq.  is set to be 0 for the terminal states  satisfying Define k, i.e. the exactly values of One fundamental issue is how to determine the probability distribution In general, we can recursively compute mentclass2pt{minimAn example is to see the probability distribution of entclass1pt{minimaWe first derive the optimal strategy for player A given the opponent player B’s strategy is independent of A. By independent, we mean that the opponent player B’s strategy will not change if player A’s strategy changes. The optimal strategy is learned by the agent itself without any direct supervision.n is the best choice? We compare the winning rate of the corresponding optimal strategy found by Algorithm 0.1 against the “hold at n” strategy with different reasonable selections of n, as Fig. n, but the winning rate is still above 50%. One interesting fact is that there are several local minimums, namely With the independence strategy assumption, the corresponding optimal strategy mentclass2pt{minimmentclass2pt{minimk) player A will take versus his/her own score is plotted in Fig. For entclass1pt{minimaGiven the opponent’s score is fixed, the turn total a player would hold at should gradually decrease when his/her own score increases, to avoid the risk of losing all turn total.Given the opponent’s score is fixed , the turn total a player would hold at should increase at some points. As the player’s score pass some threshold, i.e., closer to the goal , (s)he should take the risk of rolling higher turn total to increase the winning probability. Those points are typically around 50 and 75, as they are usually the threshold to reduce the number of turns needed to win the game by 1 (on average a player should hold at around 25).The discontinuities are caused by the following two facts:Therefore, those “jumps” can be viewed as the trade-off between the profit (possibly reducing the number of turns needed) and the risk . Those discontinuities demonstrate the power of reinforcement learning because human strategies such as “hold at 25” or “keep pace” can never detect those change points.j (where j is player B’s entire score at the beginning of the turn) if player A’s score is greater than 60. Moreover, the opponent player B can even utilize randomness so that his/her strategy will become a mixed strategy. The algorithm still works in such case with some slight modifications. Since nowq of the state  and the action k for player A according to the following:One remark is that, with the independence assumption, the opponent player B’s strategy can be a more complex pure strategy, i.e. a spline function of player A’s entire score, instead of a simple “hold at n” strategy. An example is player B will hold at 25 if player A’s score is less than 40, hold at 30 if player A’s score is between 40 and 60, and hold at 100-q, i.e. s. Suppose the value iteration converges, so the new greedy policy s, it follows that:Denote the old policy by i, j) we haveIf both players play rationally, i.e. assuming player A and the opponent player B adopt the same strategy, then for all state (mentclasspt{minimae system –8) is t is ti, jSince Even the game is simultaneous, the policy update does have order. Here we update the policy The pure-strategy equilibrium does not exist in this game, because neither player A nor the opponent player B would deviate from any profile of strategies. For example, 20), we can find the mixed-strategy Nash equilibrium of this game. A mixed-strategy combines each pure strategy stochastically with fixed probability. The equilibrium has the property that the payoffs from selecting the policy b, so player B has a, player A will select policy Due to the fact that every finite game has a Nash equilibrium  developed DOA’s generalization form Policy-Space Response Oracle (PSRO)22. While both our SVIMA and DOA involve the minimax operator, SVIMA can be viewed as a generalization of value iteration for multi-agent learning which alternatives evaluate all possible actions and update the value function. For DOA and PSRO, the policy set is restricted at the beginning and the newly found policy is continuously added to the policy set until convergence. As the nature of dynamic programming, SVIMA relies on the full information on the transitions between each state. Thus, SVIMA does not require simulations, unlike PSRO which usually depends on fictious play where a payoff table (or the winning probabilities) is updated through the simulated observed outcomes, as mentioned in one of its application (Muller23).For two players, the divergence is caused by forcing to set the two players’ policies to be the same while one policy is changed slightly later than the other, like “chasing one’s own tail”. However, this loop can be avoided under the multi-agent reinforcement learning setting, from which we developed a Stackelberg Value Iteration for multi-agent reinforcement learning (SVIMA). For multi-agent reinforcement learning, Mcmahan (2003) proposed the Double Oracle Algorithm (DOA) based on game theoryi has one reward function 24).Under the multi-agent RL setting, in specific, for a Markov game, there is a collection of actions sets 25. Considering the nature of a simultaneous game, instead of the Stackelberg competition model where the leader usually has substantial advantage, the Cournot model is more appropriate. However, the actions for some states are saddle points if applying the Cournot model, as the solution space is a subset of integers. The results are consistent with our fore-mentioned mixed-strategy. Under the simultanoues dice game settings, these two players are exactly identical in terms of information and winning rates. Consequently, applying the Stackelberg model to our problem will produce a very close approximation to the exact solution found by the Cournot model, according to a weak version of first-mover’s equilibrium profit (Osborne26).In our simultaneous dice game for two players, we let player A and the opponent player B be two individual agent and they would compete against each other. We use the Stackelberg model to find the subgame perfect Nash equilibrium (SPNE). The Stackelberg competition model was first developed in economics, with its application in many fields, such as system controli, j) can be found by solving the following system:For two policies One advantage of the simultaneous Pig game is that it can be easily extended to multiple players, even with large number of players, without considering the order of play or decreasing players’ game experience. Another advantage is that the game is fair for every player, unlike in the sequential game where the playing order implies advantages or disadvantages towards winning. In the simultaneous game since every player begins their turns at the same time. There is no need to decide the order of play between the players. In this section, we shall first consider the 3-player simultaneous game and then extend which to infinite number of players.i is player A’s entire score, k that a player will hold at given a state p and the action-value function n ranges from 20 to 30. Not surprisingly, the best combination is that both opponents B1 and B2 adopt the “hold at 25” strategy given the 3-player game is independent, which is consistent to our previous result that “hold at 25” is the best simple strategy for the 2-player simultaneous game. The detailed winning probability of the corresponding optimal strategy against different combinations is shown in Supplementary Table Similar to the 2-player simultaneous game, here we have an additional player, denoted by the opponent player B2, with the first opponent player being B1. The state in the 3-player game becomes a 3-element tuple n is the number of players.For the 4-player simultaneous game, we can introduce another variable n, and even push n to infinity, what is the optimal strategy for each player? The limiting behavior of the optimal policy can be analyzed through symmetry. According to the simultaneous nature of the game, the Nash equilibrium or the optimal strategy is found when every player adopts the same strategy. The problem is then reduced to what is this strategy?When we further increase the number of players n is the number of players. For sufficiently large number of players, especially when If every player uses the same strategy, the winning probability is the same for all players, namely entclass1pt{minimam players selecting the “hold at 100” strategy, their expected individual winning probability is at least n is sufficiently large, thus less than m. Consequently, any player not adopting the “hold at 100” strategy will switch his/her strategy to the “hold at 100” strategy. Therefore, the Nash equilibrium is that every player will select the “hold at 100” strategy when there are infinite number of players.In generally, if there are n is greater than or equal to 99, we have In fact, when 11), but with some constraints for the 3-player game . While in the simultaneous game, value iteration does not converge for pure-strategy equilibrium, indicating only the mixed-strategy equilibrium which can be found by game theory exists. If sub-optimal is allowed, Stackelberg value iteration can be applied to find the pure strategy. However, in the case of independent strategies, value iteration can always find the corresponding optimal strategy for one player given the other players’ strategies, regardless of the number of players. In particular, we demonstrate how to use value iteration to find the corresponding optimal strategy for the 3-player simultaneous game. The result can be easily extended to any finite number of players. As for the limiting behavior in terms of the number of players, the pure-strategy Nash equilibrium is simply the “hold at 100” strategy, not surprisingly. Below we have summarized all the results based on related work in Table The dice game Pig was originally designed for 2-player sequential play. In this work, we first discussed some potential issues of the current sequential Pig game and provided the simultaneous game as a solution. Then we thoroughly analyzed different cases of simultaneous Pig game, from independent strategy to optimal strategy, and from 2-player game to multiplayer game, using Markov decision process (MDP), dynamic programming (DP) and game theory, with the first two being classical components of reinforcement learning (RL). Before the reinforcement learning method is applied in the dice game Pig, the simple “hold at n” strategy is considered as a type of heuristic sub-optimal strategy with best the value n chosen through winning probabilities. This type of strategy is not dependent upon the opponent player’s score either. On the other hand, the optimal strategy developed by value iteration maximizes the winning probability at any state and is therefore a function of the opponent’s score, or even a function of the opponent’s strategy. This work has also demonstrated the power of RL in the field of games. In the sequential game, even the Nash equilibrium can be obtained through value iteration for the 2-player game , finance, science, healthcare and so on, by greatly improving the framework for decision making in these fields. Because the pure-strategy equilibrium does not exist, value iteration encounters divergence, and the mixed-strategy equilibrium is obtained through game theory analysis. How to combine these two methods more tightly, i.e. to develop a RL algorithm that can directly find the mixed-strategy, will be our future focus. Furthermore, our work is based on a fixed player’s view, namely player A, and assumes no cooperation between the players. Using multi-agent reinforcement learning to incorporate the cooperative game is another direction worth future investigation.Supplementary Tables.


Editorial on the Research TopicEditorial: Pursuing quality education in Physical and Rehabilitation Medicine-Cannata et al., Tannor et al.);Launching a call for optimizing education in PRM as a tool for relieving the burden of disability in low-income countries ;Providing examples of the current and near-future approaches to PRM education, ;Highlighting strategies to ensure the highest level of postgraduate training as a mean to support high-quality interventions of PRM physicians in disability and health care ; andDescribing how the competency-based education for PRM trainees has changed during the COVID-19 pandemic .Emphasizing the importance of teaching the basics of scientific research to postgraduate PRM trainees to prepare them to choose evidence-based treatments, promote scientific research in PRM and eventually improve management  and why bothering about it would strengthen rehabilitation in health systems and eventually improve health care? The collection of articles in the Special Issue about Pursuing quality education in Physical and Rehabilitation Medicine addresses these fundamentally important questions through different approaches:PRM is defined as the “medicine of functioning”. This is a key concept to understanding the value of this medical specialty, linking its scope to the intrinsic value of rehabilitation. Rehabilitation is a person-centred process including interventions designed to optimize functioning in individuals with health conditions or impairments in interaction with their environment ; as suchOn May 27th 2023, the 76th World Health Assembly (WHA) adopted a landmark resolution on “Strengthening Rehabilitation in Health Systems”, taking a historic step towards acknowledging that rehabilitation is an essential element of universal health coverage, while admitting that worldwide rehabilitation needs are largely unmet, and more than 50% of people miss the rehabilitation services they require in many low and middle-income countries (LMIC).a)Poor consideration of functioning as the third indicator of health, complementing morbidity and mortality, and, consequently, poor integration of its assessment in the health information system and poor attention to functioning by policymakers, when setting health priorities and allocating resources;b)Limited exposure of medical students to training in the care of people with disabilities; this issue is of particular relevance when we consider the emphasis given by the World Health Organization (WHO) on the need to integrate rehabilitation services within all health system levels, including primary health care c)Lack of awareness among healthcare providers of the relevance of rehabilitation across the life course and for a wide range of health conditions;d)Poor promotion of academic capacity in PRM worldwide;e)Poor awareness that quality education and training of rehabilitation professionals (including PRM physicians) is an investment in the health of populations;f)Insufficient workforce and equipment to respond to the increase in rehabilitation needs created by the progressive population aging combined with the impressive expansion of chronic disorders.This inadequacy is the outcome of several interacting factors:•Creating strong leadership and political support for rehabilitation at different levels,•Developing a strong multidisciplinary rehabilitation workforce,•Promoting rehabilitation concepts across all health workforce education, and•Building research capacity to expand the availability of robust evidence for rehabilitationThe Rehabilitation 2030 initiative introduced a “call for action”, gathering stakeholders towards coordinated global actions to scale up rehabilitation . Among tcalls for emphasising the pivotal role of PRM physicians and harmonising post-graduate education in PRM.To accomplish their role of “medical specialists of functioning”, the PRM physicians are called to plan the rehabilitation process, tailoring it to the individual health needs . With moEducation in PRM is also required at the undergraduate level, in medical schools. Virtually all physicians will encounter people with disabilities in their clinical practice across various pathologic conditions. However, studies demonstrate that people with disabilities are inadequately referred for rehabilitation even in developed countries. A systematic analysis of academic medical institution education offers in the United States reported that the undergraduate medical education system does not adequately train students to provide care for people with disabilities. The most common reason for not delivering a disability awareness curriculum was that no one advocated for its inclusion .Advocacy is the mission of the World Rehabilitation Alliance (WRA), a WHO global network of stakeholders, committed to supporting the implementation of the Rehabilitation 2030 Initiative, by increasing the awareness and demand for rehabilitation, and, especially in LMIC, driving investment in quality rehabilitation education and training, and expanding the integration of the rehabilitation workforce into all levels of care and practice settings .In the European Union, harmonising staff education at the undergraduate and postgraduate levels is a mandatory element for ensuring the highest standard of rehabilitation care across different countries .To support the widespread adoption of standards in PRM education, and overcome the current discrepancies in the duration and contents of PRM training throughout Europe, the European Board for PRM has released the European training requirements (ETR) for PRM education, that detail the theoretical knowledge (learning outcomes) and the core competencies to be achieved at the end of training (training outcomes), in preparation for the independent practice of PRM .At the world level, the Education Committee of the International Society of PRM (ISPRM) released the first version of the recommended Core Curriculum and Competency, in 2019  with theDeveloping competency-based education can represent a powerful mechanism to align education and training with health system priorities. This holds particular value for resource-limited countries, where the knowledge and skills of rehabilitation doctors need to reflect not only the population's health profile but also the strengths and weaknesses  of the health system.Tannor et al. commented the IRF initiative shedding light on the ongoing challenges of inadequate availability of PRM trainers, logistics and services for hands-on experience, and funding. They also reported how it becomes especially difficult to set up PRM training programs in countries where not only PRM trainers but also allied rehabilitation professionals are missing.In the African Region, where there is a substantial lack of PRM physician workforce, as PRM training programs are missing, the International Rehabilitation Forum (IRF) developed and implemented a fellowship program to train physicians in rehabilitation medicine, in Ghana, Ethiopia and Cameroon . High-quality rehabilitation care represents a constitutive element of health systems worldwide. The harmonisation of staff education both at the undergraduate and postgraduate level is a key element for ensuring the highest standard of rehabilitation care. International bodies, like the UEMS Board for Physical and Rehabilitation Medicine (PRM) or the ISPRM, have already delivered regulatory documents setting standards in postgraduate PRM education. Implementing such rules is to be validated worldwide with special attention to low and middle-income countries.


The translation of evidence based practice to clinical care in rehabilitation settings has been limited , 2. It cDespite such challenges, the effective implementation of evidence based cognitive rehabilitation treatments is essential and the process of clinical implementation for cognitive rehabilitation has been specifically investigated –7. In anboth before and during the implementation of the intervention are integral to delivering flexible interventions in practice. Additional modifications that may occur in clinical practice include applying an intervention to a clinical population that was not included in the empirical research or to an individual who may not have met inclusion criteria in the research studies associated with the intervention  approach in inpatient stroke rehabilitation, was conducted at five rehabilitation hospitals in Canada . In thisIt is likely that increased integration of implementation science and knowledge translation paradigms in the area of cognitive rehabilitation will allow for increased communication and collaboration between cognitive rehabilitation researchers and practitioners and will contribute to increased development of flexible evidence-based protocols that can flow from the research lab to the more fluid demands of the treatment environment. Such flexibility will allow for guided modifications to evidence-based cognitive rehabilitation treatment protocols and data driven application of cognitive rehabilitation which aligns with clinical recommendations. Formal tracking and documentation of needed and implemented modifications, as well as improved dialogue between researchers and clinicians are necessities. Such dialogue will encourage a common language through which to communicate about cognitive rehabilitation concepts  and to d