Psychological readiness scores have been developed to optimize the return to play in many sports-related injuries. The purpose of this study was to statistically validate the ankle ligament reconstruction-return to sport injury (ALR-RSI) scale after modified Broström-Gould (MBG) procedure.A similar version of the ACL-RSI scale with 12 items was adapted to quantify the psychological readiness to RTS after MBG and to describe construct validity, discriminant validity, feasibility, reliability and internal consistency of the scale, according to the COSMIN methodology. The term “knee” was replaced by “ankle”. The AOFAS and Karlsson scores were used as references patient-related outcome measurements (PROMs).r > 0.5) correlated to the AOFAS and Karlsson scores, with a Pearson coefficient r = 0.69 [0.54–0.80] and 0.72 [0.53–0.82], respectively. The mean ALR-RSI score was significantly greater in the subgroup of 55 patients who resumed sports activity compared to those that no longer practiced sport: 61.9 (43.8–79.6) vs 43.4 (25.0–55.6), (p = 0.01). The test–retest showed an “excellent” reproducibility with a ρ intraclass correlation coefficient of 0.93 [0.86–0.96]. The Cronbach’s alpha statistic was 0.95, attesting an “excellent” internal consistency between the 12 ALR-RSI items.A total of 71 patients were included. The ALR-RSI score after MBG procedure was highly (The ALR-RSI score is a valid and reproducible tool for the assessment of psychological readiness to RTS after an MBG procedure for the management of CLAI, in a young and active population. The ALR-RSI score may help to identify and counsel athletes on their ability to return to sport.III.The online version contains supplementary material available at 10.1007/s00167-022-06895-7. Inversion ankle trauma is one of the most frequently sports-related injury and usually involves partial or complete rupture of the anterior talofibular ligament (ATFL) , 12, 29.The modified Broström-Gould procedure (MBG) remains the gold standard for the management of CLAI and consists in the association of the ATFL repair (retention and direct suture) and the transfer of the extensor retinaculum , 16, 29.In a young and active population who underwent anatomic repair surgery, one of their expectations is the ability to resume sports activity and patients are usually anxious to know the achievable level of play. Although the MBG is safe and allows most patients to resume preinjury sports activities, it has been reported that around 25% are unable to return to sport (RTS) , 21. SomThe main purpose of this study was to statistically validate the ankle ligament reconstruction-return to sport injury (ALR-RSI) scale using a population of patients who underwent an MBG surgery. A similar version of the ACL-RSI scale with 12 items was adapted to quantify the psychological readiness to RTS after MBG and to describe construct validity, discriminant validity, feasibility, reliability and internal consistency of the scale. Physicians and patients could use this tool to ensure psychological readiness to return to sport after MBG procedure.Institutional review board approval (COS-RGDS-2021-06-003) was granted for the study and all patients provided informed consent to participate.This study identified and enrolled patients who underwent ankle ligament repair for the treatment of CLAI, in 2018 and 2019 via database of three surgical units by searching for relevant diagnostic codes. From this group, inclusion criteria comprised the following: minimum follow-up of 2 years, > 18 years old, sport activity prior to surgery and no associated lesion during the procedure. An all inside endoscopic MBG procedure was performed in all cases .The ALR-RSI was similar of the scale validated for the ankle ligament reconstruction and adapted from the ACL-RSI score . The worThe last version of the ALR-RSI was validated following the international Consensus-based Standards for the selection of health status Measurement Instruments (COSMIN) guidelines . An addiA total of 71 patients responded to a questionnaire including the ALR-RSI scale, the Karlsson score and the AOFAS score. Participants were also asked to give consent and to complete components relating to their return to sport. The ALR-RSI was completed twice at 15-day interval.r > 0.5, the correlation was considered as “strong”, “moderate” if 0.5 < r < 0.3 and “weak” if 0.3 < r < 0.1. A Wilcoxon test was used to compare the “patient” and “control” groups to assess the discriminant validity. We also compared the patients who resumed sport and those who had abandon their sport activity. The Cronbach alpha coefficient was calculated to estimate the internal consistency and was “excellent” if α ≥ 0.90. The ρ intraclass correlation coefficient (ICCC) was used to evaluate the reliability. The reproducibility was “excellent” (ρ > 0.75) or “good” (0.75 < ρ < 0.40). The percentage of missing responses, the ceiling and floor effects allowed to evaluate the feasibility  regarding the Karlsson score and r = 0.72 [0.53–0.82] for the AOFAS. Also, the ALR-RSI scores of RTS subgroup were found discriminant. The mean ALR-RSI score was significantly higher among the 55 patients who resumed sports activity compared to those that no longer practiced sport: 61.9 (43.8–79.6) vs 43.4 (25.0–55.6), (p = 0.01).The ALR-RSI scale was highly . In the current study, the ρ intraclass correlation coefficient (ICCC) was found to be 0.93 [0.86–0.96], reflecting a reproducibility that was considered “excellent”. In addition, the mean ALR-RSI score was 58.3 (41.2–77.5) at the first survey completion and 59.19 (37.5–80.4) the second time.The reliability of the ALR-RSI score was explored from the calculation of the The Cronbach’s alpha statistic for the ALR-RSI after MBG procedure was 0.95, attesting an “excellent” internal consistency between the 12 ALR-RSI items.Fifty-five participants (77.5%) returned to sport after MBG procedure. In this group, patients resumed to the same sport with the preinjury level in 21 cases (38.2%) and at a lower performance level in 19 cases (34.5%). Sports practice was modified in 15 patients (27.3%).The main finding of this study was the ALR-RSI score is a valid and reproducible tool for the assessment of psychological readiness to RTS after an MBG procedure for the management of CLAI, in a young and active population.n = 18).In the current study, 77.5% of patients returned to sport after MBG surgery, with a median follow-up of 2.6 years 2.0; 3.7). Previous literature supports this finding. In particular, a prospective study conducted by the French Arthroscopic Society observed an RTS rate around 90% following arthroscopic repair for CLAI in recreational athletes and 73% in competitive athletes. Regarding the RTS at the preinjury level, Maffulli et al.  presente.7. PreviThe RTP timeline and the ability to resume sport at the preinjury level are primarily of concern for young and sportive population. White et al.  reportedThe choice of a survey with numeric answers allows simplifying the collect of data, compared to open questionnaire. Sports surgeons and physicians can easily refer to this questionnaire to counsel patients on RTS. However, Webster and Feller examined the responsiveness of the ACL-RSI score and found a moderate responsiveness over 6 months, using anchor-based methods. Specifically, the authors showed that the ACL-RSI scale had a sufficient responsiveness to detect clinical relevant changes at a group level and was more limited at an individual level.There are several limitations in the current study. First, a possible selection bias may have been introduced, related to the inherent nature of a retrospective study. Second, the ALR-RSI scale was initially based on a modification of the ACL-RSI score and not developed for CLAI. However, the ALR-RSI has recently been validated for ankle instability after anatomic ligament reconstruction . MoreoveThis study validated the ALR-RSI score as a routine practice tool to assess the psychological readiness to RTS after Broström procedure in patients with CLAI.Through the results of this cohort, the ALR-RSI has to be considered as RTS metric tool to provide a clear message to patients who underwent MBG procedure for CLAI. Orthopaedic surgeons may use these findings to counsel and set expectations with their active patients, using evidence-based medicine on their ability to return to their favorite sport.Supplementary file1 (DOCX 18 kb)Below is the link to the electronic supplementary material.


Glycerol-induced hyperhydration (GIH) could improve 2max in recreationally active persons through an optimal increase in plasma volume. Moreover, ingestion of a large bolus of cold fluid before exercise could decrease thermal stress during exercise, potentially contributing to improved performance. We determined the effect of GIH on 5-km running TT performance in 10 recreationally active individuals . Using a randomized and counterbalanced protocol, participants underwent two, 120-min hydration protocols where they ingested a 1) 30 mL/kg fat-free mass (FFM) of cold water (~4 °C) with an artificial sweetener + 1.4 g glycerol/kg FFM over the first 60 min (GIH) or 2) 7.5 mL/kg FFM of cold water with an artificial sweetener over the first 20 min (EUH). Following GIH and EUH, participants underwent a 5-km running TT at 30 °C and 50% relative humidity. After 120 min, GIH was associated with significantly greater fluid retention (846 ± 415 mL) and plasma volume changes (10.1 ± 8.4%) than EUH, but gastrointestinal (GI) temperature did not differ. During exercise, 5-km running TT performance , as well as heart rate, GI temperature and perceived exertion did not significantly differ between conditions. This study demonstrates that the additional body water and plasma volume gains provided by GIH do not improve 5-km running TT performance in the heat in recreationally active individuals.Maximal oxygen consumption ( Indeed, Ramsbottom et al. [2max. This observation is unsurprising, as an all-out 5-km running TT is completed at approximately 90% of 2max and 98% of maximum heart rate [2max depends on both the cardiac output and the arteriovenous oxygen difference [2max, either by manipulating cardiac output, or arteriovenous oxygen difference, or both, could potentially lead to an improved 5-km running TT performance. It is possible to increase cardiac output by artificially increasing plasma volume [Maximal oxygen consumption  a volume . The plaa volume . Therefor et al.  demonstrIn addition, decreased hyperthermia during exercise may help endurance performance . To thisAll observations provided above lead us to believe that GIH may be advantageous for endurance performance under warm ambient conditions. Therefore, the purpose of this study was to compare the effect of pre-exercise GIH to a state of pre-exercise euhydration (EUH) on fluid balance responses, performance, gastrointestinal (GI) temperature, heart rate and rating of perceived exertion during a 5-km running TT conducted in a warm environment in recreationally active individuals. We hypothesized that (1) GIH would produce a state of hyperhydration of at least 800 mL; (2) the increase in plasma volume at the beginning of exercise would be more important and its decline less important immediately following exercise with GIH than EUH; (3) there would be no significant difference in heart rate and rating of perceived exertion during the 5-km running TT between conditions; (4) the GI temperature would be lower with GIH than EUH throughout the hydration and exercise periods; and (5) the 5-km running TT time would be faster with GIH than EUH.2max) participated in this study. Inclusion criteria were: (1) being healthy and between the age of 18–50 years, (2) training at least 3 h per week, (3) practicing an aerobic sport for at least one year, (4) taking no medication that can affect core body temperature and hydration state and (5) having a body mass index < 30 kg/m2. The protocol was thoroughly explained, and participants gave their written informed consent to participate in this study. The CIUSSS Estrie-CHUS Ethics Committee (2020-3606) approved all experimental procedures.Ten  recreationally active individuals ; 190 ± 9 bpm for maximal heart rate; 48 ± 3 mL/kg/min for The study used a randomized and counterbalanced protocol. Participants first underwent a preliminary visit where baseline measurements were taken. No more than 10 days following the first visit, participants realized a familiarization trial to customize themselves with the 5-km running TT. Participants then underwent two experimental trials (GIH and EUH) separated by 7 days and conducted at the same time of day.2max was determined with a metabolic analyzer  calibrated according to the manufacturer instructions using an incremental running protocol on a motorized treadmill. Participants started walking at 5 km/h with 0% grade for 1 min, with further increments of 1 km/h occurring every 1 min until the participants could not continue. The 2max was confirmed when at least two of those criteria were reached: (1) respiratory exchange ratio ≥ 1.1, (2) theoretical maximal heart rate (220-age), (3) 2 plateau concurrent to an increase in running speed [During the preliminary visit, the height of the participants, wearing only socks, was measured to the nearest 0.5 cm using a wall stadiometer, post-void nude BM with a digital scale , fat mass and FFM with the dual-energy X-ray absorptiometry technology  and blood pressure and resting heart rate using a digital sphygmomanometer  after the participants remained seated for a period of 2 min. Finally, ng speed .For the 24-h period preceding the familiarization trial, participants filled a dietary log (nutrition and hydration), which they replicated for the 24-h period preceding the two experimentations. Participants refrained from consuming diuretics, with the exception of caffeine, during the 24-h period preceding the experimentations as well as from taking any supplements in the 48-h period preceding the experimentations ,6,7. To A familiarization trial was executed to minimize any learning effect . During Each experimental trial was divided into three phases: (1) arrival at the laboratory and baseline data collection, (2) 120-min hydration period and, (3) 5-km running TT. Then, the participants started the 120-min hydration period, which consisted of ingesting either (1) 7.5 mL/kg FFM of cold water (~4 °C) with an artificial sweetener + 1.4 g glycerol/kg FFM at times 0, 20, 40 and 60 (GIH) [Following the hydration period, participants were transferred to the environmental chamber where they mount the motorized treadmill and remained silent for 2 min. Then, the participants ran at a pace of their choice on the treadmill for a 5-min period to warm up before the TT. The participants’ heart rates, GI and skin temperatures and subjective perceptions were measured before and after the warmup and then the 5-km running TT started at 0% grade, after the participants had recovered for 2 min. The participants could adjust their speed throughout the running period and did not have access to any metrics, such as speed and elapsed time, except for the distance completed. Standardized verbal encouragement was provided during the TT. Two propeller-type fans  as well as an axial-type fan  simulated wind in front of participants at a velocity closely approximating running speed. Wind speed was verified using an anemometer . Heart rate, GI, skin temperatures and subjective perceptions were measured at the end of each km. A capillary blood sample was taken immediately following the TT. Fluid was not provided during exercise.Heart rate was measured with a Garmin Premium heart rate monitor . Gastrointestinal temperature was measured using calibrated telemetric pills  ingested 10 h prior to the participants’ arrival at the laboratory . Skin teUrine volume was measured gravimetrically using a digital scale  considering that 1 mL of urine equals 1 g. Urine specific gravity was measured using a refractometer , and urine osmolality was measured using the freezing point depression technique . Accumulated fluid retention observed every 20 min during the hydration period was calculated using the following Equation (2) ,6,7:Accu2 and CO2, as well as respiratory water losses were not considered in the calculation of the sweat loss during the TT and were assumed to be similar among the two experimentations [Accumulated urine volume represented the sum of each volume of urine produced during each timepoint. Sweat loss during the TT was determined by subtracting the post- from the pre-TT BM . Losses ntations . Sweat lThis procedure was executed as reported in Goulet et al. . The finPerceived thirst   and heatAll statistical analyses were performed using the IBM SPSS Statistics software . Shapiro–Wilk tests were used to analyze data normality. Dependent t-tests were used to compare results pertaining to hydration state at arrival at the laboratory  and environmental conditions . When normality was not respected, which was the case for urine specific gravity on arrival at the laboratory, a Wilcoxon test was executed to compare the two conditions. Effect sizes were computed for hemoglobin, plasma volume, heart rate, GI temperature, perceived exertion and the 5-km TT performance with this Equation (3) : repeated-measures ANOVAs were used to determine the time, condition and condition × time effects on variables repeatedly measured during the experiments. When sphericity was violated, Greenhouse–Geisser correction was applied. Multiple pairwise comparisons were executed when significant interaction effects were observed. Post-hoc corrections were performed using the false discovery rate procedure. To examine the impact of baseline hydration status on TT performance, a Pearson product-moment correlation was performed between the changes in USG between conditions at baseline and the changes in the 5-km TT performance. A Pearson product-moment correlation was also performed between nning TT , a signip = 0.089) and relative humidity  between conditions during the hydration periods. Temperature , relative humidity , and simulated wind speed  during the TT inside the environmental chamber did not significantly differ between conditions.p < 0.001) with EUH (p = 0.001) with GIH (69.7 ± 8.4 kg) than with EUH (68.6 ± 8.7 kg) at minute 120. p = 0.004). Hence, plasma volume was 10.1 ± 8.4% higher with GIH than with EUH after the hydration period . This increase in plasma volume resulted in a significant reduction in hemoglobin concentration  and hematocrit  in the GIH compared to the EUH condition. Although it was of a lesser magnitude with GIH, at the end of the TT, no significant difference in the decline of plasma volume between the two conditions was observed, compared with baseline . At this timepoint, hemoglobin concentration was not significantly different between conditions . The mean change in plasma volume from baseline during exercise was −7.9 ± 5.1% with EUH compared to −0.4 ± 10.2% with GIH . On the other hand, mean hemoglobin concentration values during exercise were 15.9 ± 1.0 with EUH compared to 15.2 ± 1.1 g/dL for GIH . Sweat loss  and sweat rate  did not significantly differ between conditions during the TT.At the end of the hydration period, accumulated fluid retention with GIH was 727 ± 408 mL, compared to −119 ± 325 mL (with EUH . Consequp = 0.018) was observed, indicating that, on average, GI temperature was lower with GIH than with EUH. However, post-hoc analyses revealed that only at times 60 (p = 0.014) and 80 min (p = 0.007) of the hydration periods were GI temperatures lower with GIH than EUH. Therefore, there was no significant difference in GI temperature at the end of the hydration period between the two conditions  nor at the end of the TT . There were interaction (p = 0.026) and condition (p = 0.045) but not time effects (p = 0.145) for skin temperature during the TT. However, post-hoc analyses revealed that at no time during the TT were the differences between conditions statistically significant (p > 0.095). Heart rate continuously increased and similarly across time between conditions, with no interaction effect (p = 0.876), as illustrated in p = 0.10, effect size = −0.36), 94 ± 4 and 93 ± 3% of maximal heart rate  and 93 ± 6 and 90 ± 5% of 2max (p = 0.11) with EUH and GIH, respectively.The changes in GI temperature throughout time with GIH and EUH are illustrated in p < 0.03). Furthermore, rating of thermal comfort was significantly lower with GIH, compared to EUH, between the time 60 and 100 min of the hydration period  but was similar at the end of the hydration period (p = 0.14). As illustrated in p < 0.01), but no condition effect was observed . Only the pattern of change in the rating of perceived exertion differed between conditions across time , but post-hoc analyses revealed no significant differences. The mean rating of perceived exertion during the TT was not different between conditions . Abdominal bloating and pain did not differ between conditions at the end of the hydration period and were representative of baseline levels.Rating of thirst was similar for GIH and EUH during the first hour of the hydration period, but became significantly lower with GIH during the second hour . There was no relationship between the changes in baseline hydration status and changes in 5-km TT performance . Moreover, in the current study, we observed no relationship  between the 5-km running TT performance achieved with EUH and 2max. As shown in p = 0.275, Δ of 2.2 ± 5.1%). The associated effect size was small with a value of 0.16. Only four participants improved their 5-km running TT performance with GIH, with a mean decrease in time of 42.6 ± 37.8 s, compared with EUH. On the other hand, the remaining six participants were 72.2 ± 41.1 s slower with GIH compared with EUH.No order effect was detected  Based on our calculations, the increase in oxygen uptake provided by GIH would have been too small to confer a performance advantage over EUH during the TT. (2) The fluid load ingested with GIH increased BM, which may have reduced running economy, thereby canceling out the slightly improved O muscles . (3) The muscles . The fac2max.This study has some limitations worth mentioning. A placebo effect cannot be ruled out. Indeed, personal viewpoints may have led some participants to believe that beginning an exercise while euhydrated may be better than GIH . HoweverIn conclusion, this study demonstrates that despite significantly increasing body water and plasma volume, GIH does not improve 5-km running TT performance in the heat in recreationally active individuals. This study is pertinent. In fact, 1) it is the first to examine the impact of GIH on 5-km TT running performance in comparison to a EUH condition and; 2) very few GIH-related studies have focused on performance in recreationally active individuals. There is a genuine interest in studying this population since the vast majority of participants in running events are training on a recreational basis .


Model 2 multivariate analysis further disclosed that LTL (p = 0.021) and CSF glucose (p < 0.001) had independent diagnostic efficacies in PNBM, with areas under the curve (AUC) of 0.703 and 0.922, respectively. In summary, this study provided a new insight into the subject of CSF glycomics concerning bacterial infection in patients with hemorrhagic stroke.Post-neurosurgical bacterial meningitis (PNBM) is one of the severe complications in patients receiving neurosurgical procedures. Recent studies have found microbe-related glycans play important roles in adhesion, invasion, and toxicity toward innate immunological reactions. In this study, we aimed to investigate the glycomic profile and its potential diagnostic efficacy in post-neurosurgical bacterial meningitis (PNBM) patients with hemorrhagic stroke. A total of 136 cerebrospinal fluid (CSF) samples were recruited and divided into a PNBM group and a non-PNBM group based on the clinical diagnostic criteria. A lectin biochip-based method was established for the detection of glycans in CSF. The clinicopathological data and biochemical parameters in CSF from all patients were analyzed. Two models for multivariate analysis investigating glycan changes in the CSF were conducted, aiming at determining the specific expression and diagnostic efficacy of lectin-probing glycans (LPGs) for PNBM. In univariate analysis, we found that 8 out of 11 LPGs were significantly correlated with PNBM. Model 1 multivariate analysis revealed that PNA ( Post-neurosurgical bacterial meningitis (PNBM) is one of the severe complications among patients receiving neurosurgical treatment for the diseases of the central nervous system (CNS) . RegardlGlycosylation is a common procedure that is post-translational modification (PTM) in nature and is widely observed among mammals and microbes ,9. GlycaNeisseria gonorrhoeae [Neisseria meningitides [Streptococcus [Campylobacter jejuni [Burkholderia cepacia [Lactobacillus plantarum [The bacterial surface is abundantly exposing glycans and glycoconjugates, which provide unique antigens against innate immunological reaction for recognition and clearance . Importaorrhoeae , Neisserngitides , Group Btococcus , Campylor jejuni , and Bur cepacia , as welllantarum . An evalIn this study, we detected different glycan levels in CSF among PNBM and non-PNBM patients receiving neurosurgery owing to hemorrhagic stroke by a well-established lectin biochip. We aimed at discovering potential biomarkers for the diagnostic values in PNBM. The results may also provide a novel concept about the profile of glycan in infectious CSF into development of glycoconjugate vaccines.A total of 136 patients were randomly recruited in this study. All patients were diagnosed with hemorrhagic stroke by two senior doctors and received neurosurgical treatments. CSF samples were collected via lumbar cistern drainage or lumbar puncture with aseptic technique and were stored at −80 °C. The study was conducted according to the Declaration of Helsinki and was approved by the Institutional Ethics Board of the First Affiliated Hospital of Anhui Medical University. Informed consent was obtained from all participants or the appropriate relatives.Isolation of high-abundant proteins in CSF was introduced in our previous study . AccordiThe protocol for chemical modification on biochip was described according to our previous study . LectinsTM 10K-A microscanner, Capitalbio Co., Ltd., Beijing, China). The fluorescence intensities on the biochips were recorded.Each lectin was resolved in 10 mM HEPES buffer (pH 8.5) containing 0.001% BSA to the concentration of 1 mg/mL and then was immobilized on individual spot on the biochip surface, incubating at room temperature (RT) for 2 h to have robust conjugations with the surface. The biochips were then washed with a 0.01 M PBST buffer (pH 7.4) and dried with a nitrogen flow. The Cy3-labelled sera from 53 PNBM patients and 83 non-PNBM controls were individually supplied on the lectin-probed biochips and incubated in a humidity chamber and dark environment at RT for 0.5 h. After rinsed in PBST buffer twice at RT for 3 min and dried with a flow of nitrogen, the biochips were scanned with a microarray scanner  or the median with interquartile range (IQR). Univariate analysis was performed using Student’s t-test or the Mann–Whitney U test. The dependent variables that were statistically significant between PNBM and non-PNBM cohort in the univariate analysis were further analyzed with multivariate analysis. Two models of multivariate analyses were conducted using lineage regression analysis. Model 1 investigated the glycan changes that were differentially expressed in the CSF of PNBM patients. Model 2 analyzed all the lectin-probing glycans (LPGs) and biochemical parameters in the CSF to discover independent risks for PNBM. B value in multivariate analysis represents the partial regression coefficient of arguments in the equation of regression. The negative value of B represents that the argument has a negative effect on the dependent variable. Binary data were analyzed using the chi-squared test. The Stenotrophomonas maltophilia, Moderate thermophiles, and Streptococcus agalactiae, respectively. Two patients were infected with Acinetobacter baumannii, and a patient was jointly infected with the bacteria of Pseudomonas aeruginosa and Aeromonas caviae. The remaining 47 patients were diagnosed with PNBM according to the biochemical characters of CSF.This study recruited 136 patients with hemorrhagic stroke of whom 62 were diagnosed with intracerebral hemorrhage (ICH) and 74 were diagnosed with aneurysmal subarachnoid hemorrhage (aSAH). PNBM was diagnosed in 53 out of the 136 patients based on the diagnostic criteria issued by IDSA’s Clinical Practice Guidelines for Healthcare-Associated Ventriculitis and Meningitis 2017  and a Chp = 0.002), LCA (p = 0.002), PNA (p = 0.002), RCA-I (p = 0.027), Jacalin (p < 0.001), SNA (p = 0.009), MAL-I (p = 0.048) and LTL (p < 0.001). The expressions of the remaining LPGs did not reveal statistical significances between the two groups, but two revealed marginal p values, NPL (p = 0.073) and PVL (p = 0.086) (p = 0.034), Jacalin (p = 0.034) and LTL (p = 0.001) in CSF revealed significantly differences between the two groups. In Model 2, biochemical parameters and LPGs in the CSF that were differentially expressed between PNBM and non-PNBM groups in univariate analysis were further analyzed. The results showed that LTL (p = 0.021) and CSF glucose (p < 0.001) had statistical significances for independently distinguishing PNBM from non-PNBM  . We condnon-PNBM .Based on the data from the Model 2 multivariate analysis, we found LTL and glucose in CSF were the independently hazard risks for PNBM. Therefore, we performed a receiver operator characteristic (ROC) analysis for further evaluating the diagnostic values of LTL and CSF glucose between the PNBM group and non-PNBM group. The area under the curve (AUC) was 0.703 for LTL, with sensitivity of 54.2% and specificity of 84.9% A, and foWe performed correlation analyses between the biochemical parameters of CSF and LPGs in patients with PNBM A and witIn this study, we profiled glycan levels in CSF in patients with hemorrhagic stroke via a well-established lectin biochip. Our results indicated that PNA, Jacalin and LTL-probing glycans were differentially expressed in the CSF of PNBM patients, rather than that of non-PNBM patients. In addition, we found that LTL-probing glycans and CSF glucose might have independently diagnostic efficacies in PNBM. This study also provided a new concept about the glycomic profiles of nosocomial bacterial infection in CSF regardless of bacterial types.Mycobacterium tuberculosis infection and could serve as a potential novel diagnostic and therapeutic biomarker. Moreover, Wang et al. [Burkholderia-associated infections.Glycosylation is an important post-transcriptional modification, functioning in cell–cell adhesion, protein folding and protein trafficking. Glycan covers almost all surfaces of cells and microbes . Recent g et al.  demonstrNeisseria spp. Infection, such as N. meningitis and N. gonorrhoeae, glycans involved in all stages of colonization and progression [Klebsiella serotypes averted the recognition by host lectin via altering sugars, such as mannobiose and rhamnobiose in their capsule, leading to a decreasing response of phagocytes in the respiratory tract [The composition of bacterial cell wall varies between taxonomic groups and species, depending on the cell type and the developmental stage. Bacterial surface is enriched in glycocojugates. Molecular structures of glycan on some bacterial cell walls vary from those of glycan produced by humans . Previougression . On the ry tract .Anaplasma marginale regulated the differential expression of tick α--fucosyltransferases in midgut cells, indicating that the pathogen utilized core α--fucose of N-glycan to infect tick midgut cells. Sun et al. [Salmonella neuraminidase-associated compositional shift of macrophage glycocalyx using nano-LC-chip QTOF mass cytometry technology. The shift led to an increase in fucosylation, and a subsequently impairment for both macrophage phagocytosis and galvanotaxis. However, we did find any direct evidence for the association of α-fucose or fucosylation with bacterial infections in CNS. In addition, a previous report indicated that Galβ3GalNAc, which PNA conjugated to, had an intensive interaction with cell wall-glyceraldehyde-3-phosphate dehydrogenases in Lactobacillus reuteri [In our study, we used two models of multiple regression analysis to investigate the differentially expressed LPGs in CSF. In Model 1, we found 3 LPGs, PNA-probing, Jacalin-probing and LTL-probing glycans, Galβ3GalNAc, Galβ3GalNAc-Ser/Thr and α-Fucose, were differentially expressed in the CSF between PNBM and non-PNBM cohorts. These results reflected the differentially expressed molecules concerning the infection, regardless of diagnostic efficacy. In previous studies, fucosylation of bacterial related proteins or lipids, which LTL was conjugated, has been widely investigated in the bacterial infections. Vimonish et al.  found thn et al.  observed reuteri . AlthougStreptococcus pneumoniae was found to be reactive with anti-pneumococcus sera and then it was developed as a hexavalent CPS vaccine, which received a brief license by US Food and Drug Administration [N. gonorrhoeae targeting poly-N-acetylglucosamine [P. aeruginosa targeting alginate [S. aureus targeting β-WTA of S. aureus [The information about the infection-related glycomics in CSF is vital for development of glycoconjugate vaccines and antibody-based drugs. As early as 1923, capsular polysaccharide (CPS) of stration . So far,stration . Furthercosamine , AR-105/alginate , and DST. aureus . Even thIn Model 2, the biochemical characters of CSF, which were related to the diagnostic criteria of meningitis, were included. The results indicated that LTL-probing glycans and glucose in CSF might be served as independently diagnostic biomarkers for PNBM. Clinical decision-making for PNBM is mainly based on the bacterial culture and biochemical factors in CSF. However, low positive rate in conventional bacterial culture and the complicated CSF environment in neurosurgical patients make the diagnosis of PNBM perplexing. We noticed that polymerase chain reaction (PCR) and next generation sequencing (NGS) can also detect the microbial species with a higher sensitivity. However, we did not apply these techniques for detection of the infectious bacteria in CSF. Because of trace abundance of bacterial load, the host DNA may disturb the PCR amplification of bacterial DNA, thus leading to a complex background. On the other hand, due to the high-sensitivity of NGS, multiple microbes can be detected, including commensal microbes and pathogenic microbes. Moreover, because of the microbial interactions, we could not distinguish which microbe contributes to the infection. CSF glucose, a monosaccharide, is routinely tested as one of the well-established biomarkers estimating CNS infections but is less served as an independent application for the diagnosis of PNBM. In this study, we found that both glucose and LTL-probing glycan in CSF would significantly be the independent biomarkers for PNBM, indicating that the glycometabolism might play a key role in the pathogenesis of PNBM. However, whether a combinational test of LTL-probing glycan and glucose in CSF could be served as a novel criterion for the independent diagnosis of PNBM should be further validated in an augmented population.Some limitations in the study should be carefully considered. First, although 6 out of 53 patients showed positive bacterial culture, it was too few to do statistical analysis for individual microbial stains. Moreover, it was not clear what was the detailed pathogenic microbes in the remaining 47 patients negative with PNBM according to the biochemical characters of CSF, thus it is improper to make the comparison of fluorescent intensity between the culture-positive and culture-negative samples. Therefore, the results of the glycan profile in this study did not reflect relationships to a certain bacterial type but in general were the universal biomarkers for bacterial infection. Bacteria-specific glycan profiling analysis will be expected when enough CSF samples with definite information about bacterial types are recruited, and second, the LPGs in our study referred to the molecules that contained structures of specific glycans. However, we did not know about detailed information about the molecules. As indicated from the result of correlation analysis of the LPGs with biochemical characters of CSF, a combination study of proteomics and glycomics for CSF should be conducted to investigate the functional molecules in the pathogenesis of PNBM.In summary, this study provided new information in CSF glycomics concerning bacterial infection in patients with PNBM, which would be of potential value in development of glyco-conjugated vaccines in prevention of meningitis. Additionally, we observed that the LTL-probing glycan and glucose in CSF were two independent risk factors for clinical diagnosis of PNBM.


Pitch, as a sensation of the sound frequency, is a crucial attribute toward constructing a natural voice for communication. Producing intelligible sounds with normal pitches depend on substantive interdependencies among facial and neck muscles. Clarifying the interrelations between the pitches and the corresponding muscular activities would be helpful for evaluating the pitch-related phonating functions, which would play a significant role both in training pronunciation and in assessing dysphonia. In this study, the speech signals and the high-density surface electromyography (HD sEMG) signals were synchronously acquired when phonating [a:], [i:], and [ә:] vowels with increasing pitches, respectively. The HD sEMG energy maps were constructed based on the root mean square values to visualize spatiotemporal characteristics of facial and neck muscle activities. Normalized median frequency (nMF) and root-mean square (nRMS) were correspondingly extracted from the speech and sEMG recordings to quantitatively investigate the correlations between sound frequencies and myoelectric characteristics. The results showed that the frame-wise energy maps built from sEMG recordings presented that the muscle contraction strength increased monotonously across pitch-rising, with left-right symmetrical distribution for the face/neck. Furthermore, the nRMS increased at a similar rate to the nMF when there were rising pitches, and the two parameters had a significant correlation across different vowel tasks . These findings suggested the possibility of utilizing muscle contraction patterns as a reference for evaluating pitch-related phonation functions. The proposed method could open a new window for developing a clinical approach for assessing the muscular functions of dysphonia. This purpose had been achieved by dynamically visualizing the electrophysiological spatiotemporal properties of the facial/neck muscles and quantitatively analyzing the correlations between sound frequencies and myoelectric characteristics when phonating with increasing pitches across different vowel tasks. The proposed method might open a new window for developing a clinically relevant approach for training the pronunciation and diagnosing the dysphonia.Toward reducing the complexity of the subjects, a total of 14 healthy male volunteers (mean age = 24.7 ± 1.5 years) participated in this study involving a set of systematically designed phonation tasks. Before the experiments, all the subjects were enquired to ensure that they had no history of phonation difficulties, and a pure-tone audiometry test was conducted to ensure that they had no hearing problems. Then, the objective and experimental procedures of the experiments were clearly explained before the data collection session. All the subjects gave written informed consent and provided permission to publish their photographs/data for scientific and educational purposes. The protocol of this study was approved by the Institutional Review Board of Shenzhen Institutes of Advanced Technology (#IRB ID: SIAT-IRB-170815-H0178).via a headset-attached microphone placed above the first row of sEMG electrodes on the right side of the face, as seen in In this study, the speech signals and the HD sEMG recordings were simultaneously acquired from all the recruited subjects when a subject phonated the back vowels [a:], front vowels [i:], and central vowels [ә:] with a continuously increased pitch. The speech signals were recorded at a sampling rate of 44,100 Hz Each surface electrode is ~10 mm in diameter, and an inter-electrode distance of about 15 mm was ensured between two neighboring electrode centers to cover the phonation-related muscles in intensive densities. In this regard, a two-dimension (2D) array made up of 80 electrodes in a 5 × 16 grid was placed on the suprahyoid and infrahyoid muscles located in the front neck regions of each subject, as seen in The phonation experiments were individually carried out on all the recruited subjects in an electromagnetic-shielded and soundproofed room that allowed the collection of high-quality electric and acoustic signals. Three different phonating experiments were adopted in the current study to assess the muscle activation patterns and to explore the interrelations between the speech and HD sEMG signals. First, the subjects were asked to maintain a quiet state without phonating or moving their body parts for around 30 s so that the baseline for the speech and the HD sEMG signals could be determined. Then, the subjects were asked to pronounce [a:], [i:], and [ә:] vowels, respectively, in a sequential order with a monotonously rising pitch (from low to high) for three successive repetitions per trial. In each repetition, the target vowel was phonated for ~4 s and followed by a rest period of 6 s that comprised 3 s before the phonation and 3 s after the phonation to prevent the subjects from experiencing either muscle or mental fatigue that may compromise the fidelity of the signals. During the phonation, the speech signals and the HD sEMG recordings were synchronously acquired from all the subjects.The spectrogram of the speech signals during the phonation task was derived by using the short-time Fourier transform (STFT) method to perform the time-frequency analysis. The duration of the STFT analysis window was 20 ms, and the overlap width between two neighboring windows was 10 ms. The formula of the STFT method implemented in this study could be expressed as follows:where x[n] is the segmental speech signal at time frame n and ω[n-m] is a Hamming window with the same duration as x[n].To further illustrate the frequency domain features, the speech signals obtained during the phonation tasks were segmented into a series of frames with a duration of 250 ms, and the amplitude spectrum of each frame was calculated accordingly. Then, frame-by-frame spectral curves could be obtained to demonstrate the frequency-domain characteristics of the speech signals when the subject was phonating vowels in an increasing pitch. Afterward, the median frequency (MF) of the speech signals from all frames was computed. MF is defined as the frequency at which the spectrum is divided into two regions with equal amplitude; in other words, MF is the frequency at half of the power spectral density of the signals  is the MF value in analysis window i, min(MF) is the minimum MF value, and max(MF) is the maximum MF value during the entire phonation.where nMF(i) is the normalized MF value of speech signals, A set of digital filters were used to enhance the quality of the HD sEMG signals obtained during the different phonation tasks. In this regard, we first used a third order Butterworth band-pass filter from 10 to 500 Hz to reduce baseline fluctuations. Then, a Butterworth band-stop filter was also applied to attenuate the power line interferences at 50 Hz and its harmonics. After the pre-processing, the filtered sEMG signal of each channel was sliced into a series of sequential 250 ms analysis windows, and the root-mean square (RMS) was computed for each analysis window to obtain the average energy distribution of the muscular activities as follows:sEMG[n]} is the RMS value of sEMG signals for each analysis window, sEMG[i] is the ith sample in the analysis window, and n is the total sample number of the analysis window.where RMS{Then, the frame-by-frame RMS values of all the 120 channels were computed to investigate the dynamic activities of the facial and neck muscles during the phonation with continuously increasing pitches. Besides, RMS values of the same time frame from different neighboring channels were joined together to form a 2D array according to the electrode positions so that a 2D energy map could be obtained to present the energy distribution of the corresponding region. In this way, an energy map with a size of 4 × 5 could be obtained for the left and right faces, respectively. A 5 × 16 energy map could also be generated for the neck region. Then, the energy maps of all the time frames were globally normalized for the face and neck region, respectively, so that the sequential energy maps in a frame-wise manner could help to demonstrate the spatial and temporal changes in energy and muscular activities during different phonation stages.i, RMS is the RMS value of channel i in analysis window j, min[RMS(i)] is the minimum RMS value of channel i, and max[RMS(i)] is the maximum RMS value of channel i.where nRMS(i) is the normalized RMS value of sEMG signals in channel The nMF of the speech signals, as well as nRMS values of the HD sEMG signals per analysis window, was obtained and compared to investigate the correlations between the speech signals and muscular activities. The entire phonation process with a duration of 10 s was segmented into a series of analysis windows with a length of 250 ms, resulting in a total of 40 nMF and 40 nRMS values. Then, the correlation coefficients between the two groups of nMF and nRMS were calculated to analyze the interrelations between the speech and sEMG signals.In this study, the time-domain and time-frequency characteristics of the speech signals obtained during the phonation process of [a:] vowel using low to high pitch were analyzed as follows. The waveform characteristic of the speech signals in the time-domain is seen in Meanwhile, the time-frequency characteristics of the speech signals along the time axis are presented in A series of waveforms across the phonation process of [a:] vowel was constructed to characterize the amplitude spectrum of the speech signals. In this regard, the speech signals were segmented into 40 continuous windows of a length of 250 ms, otherwise known as frames from which the amplitude spectrum waveforms were later obtained and presented in a successive manner, as seen in The temporal waveforms of HD sEMG signals are displayed in As seen in In this section, dynamic HD energy maps were constructed to analyze the characteristics of the facial and neck muscles during the entire phonation process. For visualizing the dynamic changes in muscular activities associated with facial and neck muscles during rising pitches, the earlier described RMS feature was computed from a series of analysis windows that resulted in feature matrices utilized for generating the HD sEMG energy maps presented in First, a typical dynamic HD sEMG energy map of the facial and neck muscles from an analysis window during the phonation process is displayed in Subsequently, a further analysis that involved the entire phonation process was done to observe if similar results could be obtained from a single channel described earlier. In this regard, the entire phonation task was segmented into 40 analysis windows of a length of 250 ms each, and a sequence of HD sEMG energy maps was constructed to reflect the dynamism of the myoelectric activities associated with the facial and neck muscles. Hence, a total of 40 frames of HD sEMG energy maps representing myoelectric activities of the facial muscles are shown in At the onset of the phonation task (F1F10), the subjects were asked to assume a quiet state without moving. Hence, no activity could be seen for both the facial  and neckAs seen in Then, it was observed that the muscular activities of the neck muscles  were actTo quantitatively investigate the interrelations between the pitches of the speech signals and the intensity of the myoelectric activities while phonating vowel [a:] on a rising pitch scale, the nMF of speech signals and the nRMS values of sEMG recordings were compared across the entire phonating process. Then, the correlation coefficient between the nMF and nRMS features was computed across three repetitions of the phonation task, and the obtained result for a representative subject is presented in r = 0.95 in test 1, r = 0.92 in test 2, and r = 0.94 in test 3) at a p < 0.05 based on Pearson's correlation coefficient analysis. In other words, this analysis indicated that a strong correlation existed between sound frequencies and muscular activities associated with pitch-related phonation.In To further validate the above claim, the correlation coefficient between the nMF of the speech signals and the nRMS across the 120 channels of sEMG recordings in the three repeated tests was computed, and the obtained result is presented in the scatter plots seen in The effects of the facial and neck muscle activities across different vowels ([i:] and [ә:]) during the normal phonating processes were also investigated using the HD sEMG energy maps from another subject. Each phonating task that contained 40 frames of energy maps was truncated to 24 frames (from frame 9 to 32). Then, two of the adjacent frames from the remaining 24 maps were averaged with non-overlapped epochs to construct 12 consecutive energy maps (F1–F12). It can be seen from The HD sEMG energy maps in In this section, the correlations between the nMF of the speech signals and the nRMS of the sEMG recordings were investigated for the three phonating tasks including the back [(a:)], front [(i:)], and central [(ә:)] vowels across subjects when the pitch increased from low to high, and the obtained results are shown in r = 0.95, p < 0.05), [i:] , and [ә:] .In Moreover, to investigate the interrelations between the nMF of the speech signals and the nRMS from the 120 channels of sEMG recordings across different vowel tasks, the correlation coefficient of the two parameters was calculated and displayed as box plots shown in p < 0.05), was achieved across subjects while a maximum correlations value of 0.95 and was recorded by subject 12, indicating a significant correlation between the nRMS and the nMF when phonating vowel [a:]. A mean correlation coefficient of ~0.89 ± 0.04 was recorded across subjects when the vowel [i:] was phonated, and of almost 0.87 ± 0.05 when phonating vowel [ә:], with 0.96 as the highest value (subject 4) also indicating significant interrelations between the nMF of the speech signals and the nRMS of HD sEMG recordings when phonating [i:] vowel with a continuous increase in pitch. Similarly, when the vowel [ә:] was phonated, an average correlation coefficient of 0.87 ± 0.05 was recorded across all participants with subject 5 attaining the maximum value (0.95) at p < 0.05, which also indicated significant correlations between the sound frequencies and muscular activities when the vowel [ә:] was phonated.To further confirm if the correlations between the speech signals and the sEMG recordings are consistent across all of the individuals, the correlation coefficients between the nMF and nRMS values for all the 14 subjects were obtained when [a:], [i:], and [ә:] vowels were phonated from a low to a high pitch . The resThe principal objective of this study was to investigate the interrelations between the pitches and the corresponding muscular activities when phonating different vowels with increasing pitches, which would be helpful for evaluating the pitch-related phonating functions. This objective has been reached by dynamically visualizing the myoelectrical activities of the facial/neck muscles and quantitatively investigating the correlations between sound frequencies and myoelectric characteristics during different phonating tasks.In this study, the speech and the HD sEMG signals were synchronously acquired from 14 subjects when phonating [a:], [i:], and [ә:] vowels with increasing pitches. The synchronous analysis of the speech and HD sEMG signals would clarify the interrelations between the pitches and the myoelectric activities of facial and neck muscles associated with pitch-related phonation. The sEMG signals display the potential difference between two separate electrodes above the muscles on the skin, which can be used to assess muscular function by recording electric potential activities generated by muscle cells that are from the inherent and extrinsic muscles. Moreover, phonating is a complex process controlled by multiple articulatory muscles, which contain the inherent and extrinsic muscles spanning a relatively large area of the face and neck. There were ~30 facial and neck muscles that are involved in a phonating movement. Therefore, a total of 120 channels of closely spaced electrodes were used to construct the 2D electrode arrays for recording the HD sEMG signals that were processed in the space dimension to assess global facial and neck muscle activities corresponding to pitch-related phonation. The use of the HD sEMG signals, which provided spatiotemporal information on muscular activities, could address the limitations that finite myoelectric information obtained from several muscles, which might not afford enough functional characteristics for dynamic assessment of the articulatory muscles associated with an entire phonation process. As it is known, the nMF of the signals is defined as the frequency value at which the signal power spectrum was divided into two sections of equal energy content, and it can afford an acceptably good representation of the sound frequency shift. Meanwhile, the nRMS values of the sEMG signals represent the average power of the myoelectric activities  was divided into a sequence of duration, and the RMS values of the HD sEMG signals in each duration were calculated and visualized as pixels placed at a certain location of the electrode array on the facial and neck muscles. Thus, a series of 2D temporal sEMG energy maps of the facial and neck muscles were constructed for displaying the dynamic activities of the facial and neck muscles when phonating different vowels . Based on the HD energy maps, the high intensity of the maps on the neck region appeared before that of the facial areas, which indicated that the muscular activities from the neck muscles were activated before the facial muscles at the onset of the phonating [a:] and [ә:] vowels , 8. In cConsequently, the dynamic properties of the HD sEMG energy maps were well corresponding to the physiological and biomechanical principles of phonation, which would make the HD sEMG method a simple and non-invasive tool for accurately visualizing the phonating process and assessing the temporal and spatial properties of facial and neck muscles associated with phonation functions. Moreover, it could also help researchers and laryngologists to better understand the dynamic muscular activities of the facial and neck muscles during phonation. The HD sEMG technique might pave the way for developing a clinically relevant approach to screen, diagnose the phonating disorder caused by muscle problems, and even provide a potential method to locate the abnormal muscles that lead to the phonating disorder. Furthermore, the findings from the proposed method are not only applicable to the assessment of phonating function but may also spur positive advancement in other rehabilitation areas, including swallowing function evaluation . The patients/participants provided their written informed consent to participate in this study.SC and GL: conceptualization. MZ and XW: data curation and writing—original draft. MZ, SC, and GL: funding acquisition. MZ and HD: methodology. SC: supervision. XW, YH, and HZ: validation. MZ and ZL: visualization. MZ and MW: writing—review and editing. All authors contributed to the article and approved the submitted version.This work was supported in part by the National Natural Science Foundation of China , the Shenzhen Governmental Basic Research Grant (#JCYJ20180507182241622), the Science and Technology Planning Project of Shenzhen (#JSGG20210713091808027 and #JSGG20211029095801002), and the SIAT Innovation Program for Excellent Young Researchers (E1G027).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Wearable devices based on surface electromyography (sEMG) to detect muscle activity can be used to assess muscle strength with the development of hand rehabilitation applications. However, conventional acquisition devices are usually complicated to operate and poorly comfortable for more medical and scientific application scenarios. Here, we report a flexible sEMG acquisition system that combines a graphene-based flexible electrode with a signal acquisition flexible printed circuit (FPC) board. Our system utilizes a polydimethylsiloxane (PDMS) substrate combined with graphene transfer technology to develop a flexible sEMG sensor. The single-lead sEMG acquisition system was designed and the FPC board was fabricated considering the requirements of flexible bending and twisting. We demonstrate the above design approach and extend this flexible sEMG acquisition system to applications for assessing muscle strength and hand rehabilitation training using a long- and short-term memory network training model trained to predict muscle strength, with 98.81% accuracy in the test set. The device exhibited good flexion and comfort characteristics. In general, the ability to accurately and imperceptibly monitor surface electromyography (EMG) signals is critical for medical professionals and patients. Although existing conventional surface electromyographic signal (sEMG) monitoring devices are able to meet the needs of medical and scientific research in terms of accuracy, they are large, expensive, not portable to detect, and require professional operation. It is also very difficult to guarantee the stability of their muscle electrical signal data to meet the needs of detection, even under stretchable conditions . FlexiblThe sensing system of sEMG mainly consists of an sEMG sensor and an sEMG acquisition circuit ,10,11,12The sEMG acquisition circuit mainly consists of signal acquisition, filtering, amplification, and transmission. sEMG electrodes acquire weak electrical signals that are filtered and amplified and the resulting sEMG signals are processed and transmitted. Fialkoff et al.  proposedMuscle force is an important physiological parameter of the human body; this has important applications in medical diagnosis, motor evaluation, and human–computer interaction ,25. HoweCurrently, the research on sEMG sensors is mainly focused on motion recognition, including limb muscles and facial muscles. Since different limb movements generate different muscle potentials, the acquisition of limb EMG can be used for movement recognition, while the acquisition of facial muscles is generally used for lipographic interaction ,44,45. LIn general, although there are some studies on the application of sEMG acquisition systems, there are still some shortcomings. Some studies use commercially available sEMG development board modules, which are not well-adapted to the characteristics of the human arm. Most studies use wired connections and more wires are very limiting for application scenarios. Many researchers have paid little attention to the overall flexibility of muscle strength assessment systems, including the sensors for sEMG acquisition and the hardware for wireless sEMG acquisition modules. Refs. ,57,58,59In this section, the overall preparation process and several performance test experiments  of the flexible sEMG acquisition system will be presented. By using flexible electronics, flexible sEMG electrodes based on graphene are designed and combined with a flexible stretchable substrate to make sEMG sensors. Flexible sEMG wireless acquisition hardware is designed to collect sEMG signals under different grip forces or different hand gestures. The sEMG system is applied to grip force estimation and rehabilitation by designing algorithms and control programs.The flexible sEMG sensor consists of a square-shaped electrode with a side length of 12 mm and a serpentine wire design for the wire transmission line, thus realizing the tensile performance of the sensor. The electrode part of the sensor for sEMG acquisition is specially designed and is enlarged in 2 laser patterning of porous graphene on PI was conducted with a VLS2.30 universal laser system platform : 10.6 µm wavelength of laser with a beam size of ≈120 µm, a pulse duration of ≈14 µs, a maximum power of 30 W, and a maximum scanning speed of ≈23 in·s−1. The laser system provides the options of controlling image density  from 1 to 7 and changing the PPI (pulse per inch) from 0 to 1000. In all the experiments, the setting was fixed at 10.5% of the maximum power and 11% of the maximum scanning speed. The image density was set as 6. The PPI was set as 1000. All the experiments were performed under ambient conditions. The PI film is laid flat on wafer A, with the PI layer facing upward and the LIG layer in contact with the PDMS. The excess is peeled off from the PDMS of wafer A at this point to complete the patterning process. A 20:1 liquid PDMS is spin coated onto wafer B containing the transferred pattern at 300 r/min and 800 r/min, with spin coating times of 3 s and 5 s. The final ultra-thin encapsulation layer is formed on top of the electrode layer. The PDMS of wafer B is heated and the encapsulation process is completed in 10 min, and the processed flexible sEMG sensor is uncovered from wafer B.For the preparation process of flexible graphene sEMG sensors, the wafers were cleaned with isopropyl alcohol (IPA) and deionized water (DI water) and the residual isopropyl alcohol was blown off with a nitrogen gun, followed by heating the wafers at 110 °C for 5 min. After completing all patterns, PDMS  base solution and curing agent was used to prepare liquid PDMS. For spin coating 10:1, liquid PDMS was spin coated on wafer A at 300 r/min for 3 s. Subsequently, wafer B was placed on a hot plate at 100 °C for 10 min to accelerate the solidification of PDMS. A 20:1 liquid PDMS was spin coated onto wafer B at 300 r/min and 800 r/min for 3 s and 5 s, respectively, followed by heating wafer B on a 100 °C hot plate for 10 min to accelerate PDMS solidification. The COWe completed the preparation of a flexible sEMG sensor based on graphene electrodes. Our paper is to achieve the prediction of muscle strength and active rehabilitation training control of a hand rehabilitation robot; hence, this requires a surface EMG signal acquisition system with good resistance to motion interference as well as with long-term monitoring stability. Toral et al.  have demThe hardware of the sEMG acquisition system used in this paper uses Bluetooth communication, thus reducing the limitation of connection lines. The composition framework of the system hardware is shown in The signal processing circuit generally consists of a preamplifier circuit, a bandpass filter circuit, and an ADC acquisition circuit. The sEMG signal is weak due to its small amplitude and the increased impedance between the electrodes and the skin due to factors such as hair and skin tissue. Generally, the amplitude of the sEMG signal is 0–5 mV, so it cannot be directly converted to analog-to-digital. By using a preamplifier circuit, the sEMG signal is amplified and some interfering signals are filtered out. The most important filtering process is the working frequency of 50 Hz; removing it can improve the fidelity of the sEMG signal. Furthermore, since the effective sEMG signal is generally concentrated near the frequency of 150 Hz, a band-pass filter of 20–400 Hz can well avoid noise interference at high and low frequencies ,62.The surface EMG signal frequency is 0–500 Hz, since most of our concern and useful EMG signal for muscle strength is within 250 Hz. The chosen low-cost low-power Bluetooth data transmission method is limited to 500 Hz for sampling frequency; therefore, TI’s ADS1292R is selected as the single-channel EMG acquisition amplifier chip, with sampling frequency set to 500 Hz, 24-bit analog-to-digital conversion, reference voltage of 2.42 V, system operating voltage of 3.4 V–4.2 V, and operating current of 45 mA during acquisition communication 3.7 V; its supporting front-end circuit principle is shown in The signal transmission part uses a Bluetooth transmission method. Bluetooth is based on low-cost proximity wireless connection; low-power and micro-power consumption are its characteristics. The Bluetooth module used in this paper is an hc-05 Bluetooth module. The schematic diagram of the Bluetooth system is shown in At the same time, considering the subsequent use requirements, this paper adds a 9-axis acceleration sensor chip  and the use schematic diagram is shown in In order to realize the bendable performance of the hardware system, this paper realizes the circuit board through hardware design and flexible circuit technology. In this paper, sEMG hardware is divided into three areas. Each area is connected through wiring and the area without wiring is hollowed out.The external network circuits for power management and charging lamps are placed in area 1. The main chip, indicators, and button interaction are placed in area 2. The Bluetooth module used in this paper is hc-05, which is independent of area 3 due to its size. The design diagram of the flexible partitioned circuit board is shown in After completing the design of the sEMG acquisition circuit, the package is prepared using a flexible printed circuit board manufacturing process. A flexible printed circuit (FPC) has significant advantages as a special electronic interconnection technology. The main reason for the flexibility of FPC is the PI substrate supporting the copper foil and the copper foil with good bending properties.This paper completes the flexible sEMG acquisition hardware. The flexible lead can be connected with the flexible electromyography electrode designed in this paper. The whole flexible sEMG acquisition circuit is shown in Using the finite element software ABAQUS , we simulated the effects of these types of deformations during the stretching, bending, and twisting of the FPCB of the flexible sEMG hardware. First, we performed the experimental analysis of the flexible sEMG acquisition circuit board in an ABAQUS simulation environment for stretching, twisting, and bending. In the ABAQUS pre-processing, we will build the finite element model of the flexible printed circuit board according to the material properties of the copper layer and the polyimide (PI) layer of the required material for FPC . The simAfter setting up the pre-processing parameters, we then simulated the flexible sEMG acquisition board in an ABAQUS simulation environment for tensile, torsional, and bending experiments. In the ABAQUS post-processing, the results of the pre-processing of the flexible sEMG acquisition board are extracted and the stress value cloud of the FPCB is viewed to analyze the deformation trend and to summarize the distribution pattern, from which it is inferred whether the FPCB is damaged by the three deformation simulations.Most current commercial force or torque sensors are bulky, expensive, and not user-friendly, nor do they make full use of the sensors for quantitative analysis of the relationship between lateral force and torque and electromyographic signals. The flexible sEMG acquisition system designed in this paper has the advantages of accurate data acquisition, high reliability, small size, low power consumption, simple operation, and no violation of wearing. In the control process of the hand function rehabilitation robot, two important features should be included, namely the movement of the motion and the strength magnitude of the motion. The current hand rehabilitation robots mostly control the robot after recognizing the motion pattern, ignoring the magnitude of the force applied to the hand, thus increasing the mental stress of the patient. The purpose of this part is to find a method to predict the hand strength magnitude based on EMG signals to provide a basis for the control of the hand rehabilitation force application magnitude. RMS) of the muscle strength information. Here, we used RMS as a characteristic of the EMG signal.The processing of the sEMG signal is divided into time and frequency domains. Time domain analysis treats the EMG signal as a function of time. Statistical analysis is then performed with time as the independent variable, which can provide indicators to assess the characteristics of the change in the EMG curve in the time dimension. The time domain approach best reflects the root mean square (d(i) denotes the single channel sEMG signal sequence and N denotes the length of the sEMG signal sequence used to calculate the RMS value.In the equation, RMS we employ here. In the later sections, we will describe the model in detail. After the completion of the model for predicting the hand grip strength magnitude from the EMG signal, the obtained muscle strength levels are transmitted  to the main control chip of the hand rehabilitation device. Then, the force level is used as the threshold input to control the hand rehabilitation device to grip and open the hand. The muscle strength level 0 is the open position of the hand rehabilitation device. The muscle strength level 1 to 4 is the fist clenching position of the hand rehabilitation device, while the strength of the fist clenching gradually increases according to the rising level. The prototype hand rehabilitation robot appeared in our previous article and we also applied the designed flexible sEMG acquisition system to the training mode of the device to improve the hand rehabilitation effect [We use a long short-term memory network (LSTM) as a hand grasp strength prediction model. LSTM is a special kind of recurrent neural network (RNN), which is mainly designed to solve the gradient disappearance and gradient explosion problems during the training of long sequences. Simply speaking, it means that LSTM can have better performance in longer sequences compared with normal RNN. It is suitable for processing bioelectrical signals with time-series characteristics ,65. Furtn effect .The purpose of this part is to find a method for predicting hand force magnitude based on EMG signals to provide a basis for the control of force magnitude applied by hand rehabilitation equipment. In order to verify the feasibility of the flexible sEMG acquisition system made in this project for detecting muscle force magnitude, we designed a grip strength testing and prediction experiment. The experiment was conducted in a laboratory setting, as shown in After the experiment was prepared, the subjects grasped the force acquisition blocks according to six different levels of force  and the approximate constant force grip strength lasted for 20 s according to the value on the display of the electronic counting grip, with 30 sets of each applied force size. The typical sEMG waveforms at different grip strengths are shown in Firstly, the collected data are cleaned and normalized in order to input the segmentation function. The purpose of the segmentation function is to divide the processed data and labels into a training set and a test set in a 4:1 ratio, where 80% is the training data set and 20% is the test set. The model learning rate is set to 0.02 and the cross entropy loss function is used. Then, the data are transformed into tensor, and the training set and test set data are encapsulated into train by dataloader_ Loader and test_ Loader to obtain data by batch, 200 pieces per batch. Train the training set data for 60 times, output the training loss value and training accuracy once every 200 batches, and save the weight parameter with the smallest loss value as best_ weights. Pt, the purpose is to save the minimum weight parameters of training in the training set, so as to directly take the trained model to test the model in the later stage. We use the long short-term memory (LSTM) algorithm; the algorithm flow is shown in We classified muscle strength into four strength levels  and predicted which level the measurements were close to. It is also compared with the effectiveness of back propagation network (BP-ANN)—an algorithm widely used to train feed-forward neural networks for supervised learning—effectively calculating the gradient of the loss function with respect to the network weights. This makes it possible to train multilayer networks using a gradient approach to update weights to minimize losses. The gradient of the loss function with respect to each weight is computed by a chain rule that iterates backward one at a time starting from the last layer so that the error function decreases in the direction of the negative gradient, thus allowing the BP neural network prediction output to continuously approximate the desired output .The experiment of the stress value cloud, as shown in In this section, we first show a practical scenario of flexible sEMG sensing attached to a human forearm, where the sensor has good adhesion due to intermolecular forces  , as showWe tested two individuals, and a total of 30,946 offline data were validated. Offline validation of the proposed algorithm using back propagation network (BP-ANN) determinations achieved an accuracy of 82.65%. When we used the long short-term memory (LSTM) algorithm, we obtained an accuracy of 98.81%. We use PyQt5 as the framework of the software interface to build a simple sEMG ac-quisition and display software. In the process, the interface will store and process the original data in real time. The single-lead sEMG acquisition software is shown in For the needs of mirror training in general rehabilitation training, this paper uses a flexible sEMG acquisition system to recognize two simple gestures, including grasping and stretching. The flexible sEMG acquisition system is used to collect the sEMG signal of one hand, to judge whether the hand is grasped or extended, and to control the other hand through the exoskeleton hand rehabilitation robot, as shown in The flexible and stretchable sEMG sensor and sensing system designed in this paper has certain flexibility and the system shows good bending and twisting characteristics. By using the flexible electronic system shown in this paper, the hand muscle strength in rehabilitation training or daily life can be monitored. At the same time, because the sEMG system designed in this paper has good electrical performance, it can better use the flexible sEMG system designed in this paper for rehabilitation training or gesture recognition.However, the system in this paper is still in the principle prototype and there is still a certain distance from a fully mature product. The designed hardware shows good bending and twisting characteristics but lacks extensibility. In the follow-up, we can further develop the flexible circuit board. By optimizing the routing design, the lead wire between the three-lead hardware is designed as a serpentine wire in order to realize the tensile characteristics of the whole system.In the follow-up research, we hope to make the flexible sEMG sensor into an integral column sensor and to expand the hardware from a single-lead sEMG to array a multi-lead sEMG system. Through arraying sEMG perception, more muscle-related parameters can be obtained, which will achieve higher accuracy and more motion prediction.In this paper, we developed a flexible sEMG sensor for muscle strength evaluation and rehabilitation training. At the same time, we developed flexible wireless sEMG acquisition hardware to realize the flexibility of the overall sEMG acquisition system. Through the fully flexible acquisition system, the collected signal has good stability. Through the extraction of sEMG characteristics under different muscle strengths, the project realizes the research of muscle strength feedback through sEMG and realizes the mirror rehabilitation.