TBX21 (T-bet) which drives the Th1 immune response, is linked to the major depressive disorder (MDD) phenotype. This study investigated the association between the TBX21 immune gene and the possibility of late-onset Alzheimer’s disease (LOAD) incidence in 194 LOAD and 200 control subjects using the real-time qPCR and the Tetra-ARMS-PCR methods. We also used an in silico approach to analyze the potential effects imparted by TBX21 rs17244587 and rs41515744 polymorphisms in LOAD pathogenesis.The involvement of the peripheral immune system in the etiology of neurodegenerative diseases has recently been emphasized. Genome-wide association studies (GWAS) have recently identified several candidate immune genes linked to development of both Alzheimer’s disease (AD) and depression. TBX21 “immune gene” had significantly elevated mRNA expression levels in the leukocytes of peripheral blood in patients with LOAD (P < 0.0001). We also found an upward trend in TBX21 expression with increasing age in LOAD patients compared to the control group . We noticed that the TT genotype of rs41515744 plays a protective role in LOAD incidence, as it attenuates the expression of TBX21 in the control group. We observed that the dominant model of rs41515744 represented a substantial association with LOAD (P = 0.019).We found that the TBX21 (T-bet) immune gene in LOAD development and that the elevated TBX21 mRNAs in the WBCs of LOAD patients may represent a new easy diagnostic test for Alzheimer’s disease.Our results show for the first time the likely impact of the  Here, wrs41515744 in healthy control samples showed 177 cases with CC, 18 cases with CT, and 5 cases with TT genotypes. The mined data from LOAD samples implied 155 cases with CC, 33 cases with CT, and 6 cases with TT genotypes. The genotype distribution for the rs17244587 SNP for the healthy control individuals was 178 cases with GG, 12 cases with GA, and 10 cases with AA, while in LOAD patients, genotypes were 178 cases with GG, 6 cases with GA and 10 cases with AA. To study the frequency of various genotypes between the groups, a chi-square test was conducted. The results were as follows: F = 0.01; P = 0.96 for rs41515744 which indicates that the difference is not significant  , but this cannot be a reliable significant association  were analyzed to understand the effectiveness of rs17244587 polymorphism, the results showed no significant association between any models of inheritance and the risk of LOAD (P.0.05) (Table For the 5) Table .Table 3Srs41515744 in the LOAD group were 343 (84.4%) and 45 (11.6%), respectively. These allele frequencies in the control group were 372 (93%) (C) and 28 (7%) (T). The results for rs17244587 indicated that the frequencies of major (G) and minor (A) alleles were 362 (93.3%) and 26 (6.7%) in the LOAD group and 368 (97%) and 32 (3%) in the control group, respectively.Statistical analysis revealed that the frequencies of major (C) and minor (T) alleles of rs41515744 (χ2 = 0.003 and P = 0.59) or rs17244587 (χ2 = 0.005 and P = 0.56) polymorphisms  in TBX21 expression levels in the leukocytes of peripheral blood of LOAD patients  compared to normal healthy individuals   between TBX21 expression in cases vs. controls for both CC, and TT genotypes whereas according to the results of Dunn’s multiple comparisons test, there was no significant relationship between CT (p > 0.05) genotypes in the two compared groups  between TBX21 expression in cases vs. controls for both GG and GA genotypes. Based on the results of Dunn’s multiple comparisons test, there was no significant relationship between the AA (P > 0.05) genotype in the two compared groups . A scatter plot of the fold change expression of TBX21 versus the age of the studied population is shown in Fig. We also analyzed the expression levels of rs17244587 (G) and rs41515744 (C), indicating a high linkage disequilibrium (P < 0.05) , and the rarest haplotypes were GT (0.02%) in both case and control samples Table . Based oTFBIND software program (http://www.tfbind.hgc.jp/) to investigate potential TF binding sites on corresponding DNA motifs. Generally, there are 3 models of binding for TFs in the identified ranges (see Table rs17244587 and C for rs41515744); 2) TFs that only bind to the recessive model (A for rs17244587 and T for rs41515744), and finally, 3) TFs that bind to either dominant or recessive models (G/A for rs17244587 and C/T for rs41515744).We examined 15 bp upstream and 15 bp downstream of both SNPs using the rs41515744 was in the enhancer region and rs17244587 in the enhancer flanking region of the TBX21 gene (found on http://asia.ensembl.org/index.html). TFBIND results revealed that the transcription factors NGFIC, NRF2, and MYOD can bind only in the presence of the rs41515744 major allele (C), while the minor allele T can create a new binding site for p300, CAAT, and HNF3B. Moreover, transcription factors, including IK1 and SP1, can only bind when the rs17244587 major allele (G) is present and on the other hand, PAX2 and GR bind with the minor allele A.The bioinformatics analyses for the studied variants indicated that Late-onset Alzheimer’s disease dementia is one of the most widespread age-related neurodegenerative disorders and is triggered by genetic, epigenetic, and environmental factors, and inflammation .TBX21 immunity gene has an overall > 2-fold elevated mRNA expression level in the leucocytes of peripheral blood in patients with late-onset Alzheimer’s disease (LOAD). TBX21 has been shown to be involved in major depressive disorder (MDD) and the antidepressant response [rs41515744 SNP suggests that TT is a protective factor in healthy individuals as it attenuates TBX21 expression in control groups. We also found an upward trend in TBX21 expression in LOAD patients with increasing age compared to the control group . On the opposite side and, inside the LOAD groups, TBX21 could have fluctuated expression, depending on different genotypes. Our results conclusively indicated that the lower TBX21 expression in LOAD patients is associated with the presence of GG and GA genotypes for rs17244587 SNP and further, the CC genotype for rs41515744 SNP. Our results also indicated that inside the studied population, there were no significant differences in either allelic or genotyping levels of rs41515744 and rs1244587. Based on the OR test results, we found that the dominant model of the rs41515744 polymorphism represents a significant association with LOAD inheritance (0.019), while the codominant model of inheritance for this rs was only at the margin of a significant level (P = 0.044). In contrast, in the rs17244587 SNP, there was no significant association between any model of inheritance and the risk of LOAD.In this study, we show that the TBX21 inflammatory gene in peripheral leukocytes of LOAD patients. This study also investigates the possible difference in single nucleotide polymorphisms of rs41515744 and rs17244587 SNPs of the TBX21 gene in LOAD cases compared to healthy individuals. Various studies have previously demonstrated the relationship between mutations in the TBX21 gene and the development of different “immunity”-associated diseases, such as rheumatoid arthritis, asthma, type 1 diabetes called insulin dependent diabetes mellitus (IDDM), and systemic lupus erythematosus (SLE) [TBX21 and EOMES genes in T- and NK cells in patients with multiple sclerosis (MS). Their finding indicates that the expression level of both genes is decreased in MS patients’ T- and NK cells [TBX21 gene has remarkable polymorphisms based on the SNP database of NCBI (https://www.ncbi.nlm.nih.gov/snp/?term=TBX21). Gourh P et al. assessed independent data for the rs11650354 SNP of the TBX21 gene in 902 systemic sclerosis (SSc) patients compared to 4745 controls. Their results showed that the TT genotype of the TBX21 gene had a recessive pattern for susceptibility to the SSc [Recent findings indicate that a consistent in situ inflammation cascade has an enormous effect on the formation of Aβ plaques and NF tangles in both synaptic and cytoplasmic regions of neurons . The trius (SLE) . In animus (SLE) . Iinflamus (SLE) . ParnellNK cells . Our res the SSc . In addi the SSc .Depressive symptoms are common in Alzheimer’s patients and occur in approximately 20–30% of AD cases . Depressrs17244587 in TBX21 and rs2296840 in PSMB4 genes in the American-Mexican population [rs17244587 or TT in rs2296840 will increase the risk of the development of major depressive disorder (MDD) [TBX21 mRNA expression in leukocytes of LOAD patients than in control individuals. We also observed the higher TBX21 mRNA expression was associated with the AA genotype of rs17244587 in LOAD patients, although this difference was not statistically significant. Due to the differences in TBX21 mRNA expression between patients and controls and to achieve a more precise view of the potential impacts influenced by rs17244587 and rs41515744 polymorphisms in TBX21 expression in AD patients, we performed in silico subanalysis for additional investigation of the aforementioned polymorphisms. As will follow, our analysis indicates that a number of transcription factors (TFs) can bind to the site of rs17244587 and, rs41515744 polymorphisms to activate or inhibit transcription of TBX21 in the presence of G, A, or G/A alleles for rs17244587 and C, T, or C/T alleles for rs41515744. This may indicate that the TBX21 “depressive” gene could play a role in the pathogenicity of AD from a molecular view:The team of Wong ML, in 2008, discovered the correlation between depression and two SNPs of rs41515744 in a regulatory role. This can be interpreted as a dual role of the heterozygote genotype as the protector and inducer of AD. The CC genotype had the lowest rate of expression, indicating the protective role of FRF2.Prior studies have demonstrated that SP1, p300, and NRF2 have an impact on AD –43. CitrTBX21 (T-bet) immune gene in late-onset Alzheimer’s’ disease development and that the elevated TBX21 transcripts in the leucocytes of peripheral blood in LOAD patients may represent a new easy diagnostic test for Alzheimer’s disease. An in silico study revealed that few transcription factors can potentially bind in the presence of either major or minor alleles of both rs41515744 and rs17244587 studied SNPs of the TBX21 gene in LOAD cases. However, we know this conclusion is not a direct finding of this study. This study was powered at 80% with a significance level of 5% confidence and a more extensive population analysis may give a more conclusive result. With the sample size used in this study, our result violates the assumption of Hardy-Weinberg equilibrium for the distribution of genotypes in both case and control groups, which indicates the limitation of this study. This violation may indicate a genotype misclassification of polymorphism results however, this would not affect the innovative finding of this study i.e. the overall overexpression of the TBX21 immune gene in the leucocytes of peripheral blood in LOAD patients compared to healthy individuals.Collectively, our results show for the first time, the likely impact of the 10.1212/wnl.34.7.939). A total of 194 patients with LOAD  and 200 control individuals  were enrolled in this case-control study. The patients’ mean age ± standard deviation (SD) was 77.94 ± 8.642, ranging from 65 to 88 years. Controls with a mean age of 82.73 ± 8.395 years (ranging from 65 to 95 years) were non-AD diagnosed and healthy individuals. Patients and control individuals diagnosed with non-AD cognitive impairment, Parkinson’s disease, cardiovascular diseases, and diabetes were excluded from this study. All participants were of Persian ethnicity from or residing in northern Iran provinces; mostly from Guilan province. The blood samples were taken during 2 years of collection between December 2020 and December 2022 from several Disable and Elderly Hospice centers, mostly located in Rasht, the Guilan province’s capital. This study was approved by the University of Medical Sciences of Guilan’s Ethics Committee with international ID NO: IR.GUMS.REC.1399.427. A 5 ml peripheral blood sample of each individual with LOAD and/or healthy people was collected from several Disable and Elderly Hospice centers or elsewhere in Guilan Province, Rasht, Iran. All participants or their representatives provided informed consent. The blood samples were transferred to anticoagulant-containing EDTA K3 Venoject™ tubes  by venipuncture procedure for further use.Eligibility criteria for the study population were set to the individuals older than 65, which subsequently were divided into two groups of patients with LOAD and healthy individuals matched for age and gender. Both LOAD and healthy individuals were diagnosed and identified by their physicians. A neurologist further double-examined each individual who preliminary was diagnosed with LOAD by a physician. The diagnosis of LOAD was performed based on the National Institute of Neurological Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria  by the salting out standard method. Purified genomic DNA was stored at −20 °C until use. The extracted DNAs were used as templates for the TBX21 genotyping study.Genomic DNA from the whole white blood cells (WBCs) of peripheral blood of individuals was extracted using TritonTRIzol™ Reagent . The concentration and purity of extracted RNAs were evaluated by a NanoDrop device  at A260 and A280 nm wavelength ratios. The quality and integrity of RNAs were tested and approved by running RNA samples on gel electrophoresis. All RNAs were treated with DNase I  to ensure that there was no trace of potential genomic DNA contamination. cDNA synthesis (RT_PCR) was carried out by a cDNA synthesis kit  using 2 µg of total RNA, according to the manufacturer’s protocol. The rest of all the DNAs/synthesized cDNA and RNA were stored at -20°C and -80°C, respectively, for further use.On the same day of sampling, total RNA from peripheral blood leukocytes of individuals was extracted using TBX21 SNP genotyping was performed by multiplex amplification refractory mutation system polymerase chain reaction (Tetra-ARMS PCR). Two sets of inner and outer primers were precisely designed using Oligo7 software (v7. 56) presented in Table Metabion International AG, Germany).rs41515744 were a combination of Fouter + Rinner amplifying the normal allele of C with a product length of 185 bp and Fouter + Rinner amplifying the mutation of the T allele with a final product length of 297 bp. Additionally, the outer forward and reverse primers amplified the full TBX21 selected region with a product length of 424 bp. In addittion, the specific primers of rs17244587 were a combination of ROuter + Finner as well as Fouter + Rinner which amplify the normal G allele of 281 bp and mutant A allele of 384 bp, respectively. The combination of forward and reverse primers copies a 611 bp region of the gene.The specific primers for Ampliqon, Denmark) of 12 µL, 1 µL of each primer, 5 µL of DNA template, and DEPC-treated water (4 µL). Thermal Cycler  program was set to initial denaturation (95°C- 5 min) followed by 40 cycles of a 3-step amplification program which consisted of denaturation (95°C- 45 secs), annealing (59°C for rs17244587 and 65°C for rs41515744 primers- 50 secs) and extension (72°C- 45 s) and settled by a final extension (72°C- 5 min) to avoid the synthesis of moiety nonspecific strands. Subsequently, electrophoresis was carried out via 2% agarose gel containing SafeStainTM and a DNA ladder .All amplification reactions were run with a final volume of 25 µL reaction mixtures composed of a Master mix , 1 µL of each forward and revers primer (with a concentration of 5 pmol/µL each), 1 µL of template cDNA (with an adjusted concentration of samples on 1 µg) and 7 µL of DEPC-treated water. The cycling conditions for the thermocycler  program were set up to one cycle of 96 °C for 10 min followed by 45 cycles of 95 °C for 30 s, and 60 °C for 30 s. All primers were designed again using Oligo7 software (v7. 56) and tested in the NCBI primer BLAST tool. An internal GAPDH control was used to measure the relative expression of the TBX21 gene in each sample . The annealing temperatures for all primers were obtained and optimized via the gradient PCR method in prestudy pilot testing. Eventually, the relative fold change in expression of the target TBX21 and internal control (GAPDH) genes was calculated using the 2−ΔΔCt cycle threshold method.The real-time quantitative PCR (qPCR) method was conducted in triplicate sample repeats in a final volume of 20 µL mixture including 10 µL of SYBR green PCR Master mix , odds ratio (OR), P-value, and 95% Confidence Interval (CI) using WEB-Assotest (available at: http://ekstroem.com:3838/webassotest/). Analyses were also performed for dominant, codominant, recessive, and overdominant genetic models. Differences in allele frequency levels between age and gender were assessed by an independent t test and, a one-way ANOVA test that was used to evaluate the association between gender and age at genotype frequency levels. Significance was adjusted to P-values less than 0.05. Correspondingly, GraphPad/Prism software (v8.0.2) was used to apply student t tests on the qPCR data as well as one-way ANOVA for evaluations of various genotypes in each SNP. Linkage disequilibrium (LD) analysis was performed using Haploview (v4.2). The haplotype block for LD analysis has consisted of rs41515744 and rs17244587.To examine the association between the


In plant and animal breeding, sometimes observations are not independently distributed. There may exist a correlated relationship between the observations. In the presence of highly correlated observations, the classical premise of independence between observations is violated. Plant and animal breeders are particularly interested to study the genetic components for different important traits. In general, for estimating heritability, a random component in the model must adhere to specific assumptions, such as random components, including errors, having a normal distribution, and being identically independently distributed. However, in many real-world situations, all of the assumptions are not fulfilled. In this study, correlated error structures are considered errors that are associated to estimate heritability for the full-sib model. The number of immediately preceding observations in an autoregressive series that are used to predict the value at the current observation is defined as the order of the autoregressive models. First-order and second-order autoregressive models i.e., AR(1) and AR(2) error structures, have been considered. In the case of the full-sib model, theoretical derivation of Expected Mean sum square (EMS) considering AR(1) structure has been obtained. A numerical explanation is provided for the derived EMS considering AR(1) structure. The predicted mean squares error (MSE) is obtained after including the AR(1) error structures in the model, and heritability is estimated using the resulting equations. It is noticed that correlated errors have a major influence on heritability estimation. Different correlation patterns, such as AR(1) and AR(2), can be inferred to change heritability estimates and MSE values. To attain better results, several combinations are offered for various scenarios. Genetic improvement in plants and animals is primarily determined by the degree to which desirable characteristics are inherited and depends on the accuracy of selection and selection intensity, as well as on the amount of genetic variation, etc. . The avaIn statistical modelling aspects, prediction of phenotypic variability is influenced by several factors . In someClarias magur) raised in a two-year class, estimate their heritabilities at stocking and harvest, as well as determine the genetic and phenotypic correlations between them. Using information from incredibly small family samples, Ødegård and Meuwissen [h2) estimations from full-sib analysis exceeding unity was described by Prabhakaran and Sharma [Zea mays L.) were examined by Marker and Joshi [Again, numerous approaches are available in the literature for assessing the correlation between observations. Durbin and Watson  providedeuwissen  exploredeuwissen  exploredeuwissen  exploredeuwissen  proposedd Sharma . Two hunnd Joshi  at two dThis paper is organized as follows. One of the primary needs for studying the statistical characteristics of genetic parameters was to simulate statistical-biological models with known population parameters. In general, different procedures can be used to estimate heritability, among them, those based on offspring regression on parents and on sib correlation or sib analysis have several advantages and disadvantages. The accuracy and bias of the estimators are determined by the relationship between them, used in the study. At present, we are solely interested in using sib analysis to estimate heritability. As stated below, data generation for correlated and uncorrelated instances is performed using a two-way classification model. In this work, Roningen  employedThis is commonly referred to as the full-sib analysis model and may be expressed asith sirejth dam mated to ith sirekth member of the ijth full-sib group.µ is considered as a general mean and The simulation model used for generating full-sib data is given as follows:Let, Falconer ).h^2=2tThe genetic composition of this estimate of heritability isAV, DV, AAV, ADV, and DDV are defined as variance due to additive effect, variance due to dominance effect, variance due to interaction of additive components, variance due to interaction of additive and dominance effect, and variance due to interaction of dominance component, respectively. Thus, this estimate is subject to bias from dominance deviations, as well as from non-allelic interactions. The estimate of heritability is the least reliable of all the estimates of heritability.Here, n offspring per dam iskth progeny of the jth dam mated to the ith sire. si is the sire effect common to all the progeny of the ith sire; ijd is the dam’s effect common to all the progeny of the jth dam mated to the ith sire and ijke is random deviation.The statistical model for estimation of heritability based on the full-sib mating design with μ are random and independent, with expectations of zero and variancesAll effects except The data on progeny are then subjected to hierarchical analysis of variance; between sires, between dams within sires, and within dams and within sires to get the estimates of sire and dam components of variance. The form of analysis along with mean squares and their composition in terms of the observational components of variance are shown in Now, we know thatNoting the genetic composition of the variance components Sire components:Dam component:Sire + Dam component:The estimate of heritability by full-sib correlation was obtained by taking the average of sire and dam components and its sampling variance was determined by the approximate formula of sampling variance of an intra-class correlation coefficient.Suppose that sires are independent but within sire, progenies are correlated. Further, assume that the correlated errors follow AR(1) i.e.,From the above equation, In case of AR(2),In a similar fashion, we have generated the correlated data for different error structures other than AR(1) e.g., AR(2), a function of a distance, etc.The usual full-sib model is as follows:μ = Effect common to all individualsis = ith sire effect ijd = Effect due to jth dam mated to the ith sirei = 1, 2, … S→j = 1, 2, … in→k = 1, 2, … ijn→ Assumptions:From the first principal, we have obtained the following equation for the above model.The data were generated from a population with low and high heritability for various sample sizes and family structures. Data have been generated using different heritability values i.e., high, and low , using a full-sib model and different sample sizes 100 and 500 and different correlations of errors A(1) and AR(2). The data were generated from a population with low and high heritability for various sample sizes and family structures. The heritability estimates, along with MSE (Means Square Error), are obtained and shown in The heritability estimates along with MSE (Means Square Error) are obtained and given in .In the present work, a full-sib model is used to generate data. AR(1) and AR(2) errors are employed in this case. In the case of a full-sib model, equations for E(MSE) and heritability estimation in the presence of AR(1) correlation in the error are derived. Different AR(1) values ranging from −1 to +1 are investigated for different heritability values between 0.1 and 0.5. When the correlation is negative, the predicted mean sum of squares is overestimated, and this overestimation increases as the degree of correlation increases.However, if the errors are positively associated, the expected mean sums of squares are underestimated, and they drop with an increasing degree of correlation and approach 0 as ρ tends to unity. On the other hand, when calculating the mean sums of squares due to sire, the expected mean sums of squares are underestimated when ρ is negative and overstated when the correlation is positive. As ρ tends to unity, the expected mean sum of squares due to sire approaches to its maximum value. If the correlation is positive, the heritability values are overestimated. A similar pattern may be seen at all levels of heritability. As the autoregressive coefficients rise from minus one to almost one, heritability rises from zero to nearly four. In the case of AR(2), if the AR(1) value is fixed while the AR(2) values are changed, the MSE value decreases as the correlation value increases in general. Occasionally, random tendencies are observed. We discovered that specific combinations of AR(1) and AR(2) values , , and  provide superior estimates of heritability. As sample sizes are increased, the MSE values are observed to decrease. Biased estimations are obtained in practically all scenarios. Estimates based only on sire components are superior to estimates based solely on dam components. Biased estimations are obtained in virtually all circumstances. Estimates based only on sire components outperform estimates based solely on sire and dam components. In the case of AR(2), if the AR(1) value is fixed while altering the AR(2) values, the MSE value decreases as the correlation value increases in general. Occasionally, random tendencies are discovered. We discovered that combining AR(1) and AR(2) values  and  yields superior estimates of heritability. As sample sizes are increased, the MSE values are observed to decrease. Biased estimates are obtained in all circumstances. Following the pattern, MSE values decline after fixing (AR(1) values increase AR(2) values up to AR(1) values 0.0). The correlation combination  and  produces better results than any other combination. Estimates based only on sire components outperform estimates based solely on sire and dam components. The correlation combination of  and  produces better results than any other combination. The MSE values drop as the sample size increases. Different correlation patterns, such as AR(1) and AR(2), can be inferred to alter heritability estimates and Means Square Error values. Various combinations are proposed for different circumstances to achieve better results.In many plant and animal breeding experiments, it is seen that the observations are not independently distributed and that some type of correlation exists between the observations. In the presence of correlated error structures, the classical assumption of independence between observations is violated. Generally, plant and animal breeders are particularly interested to study the genetic components and the underlying causes of several traits. Thus, evaluating various genetic variations and inferring their inheritance based on estimations of various genetic characteristics is crucial from the perspective of plant and animal breeding programs. In the case of the full-sib model, theoretical derivation of Expected Mean sum square (EMS), considering AR(1) structure has been obtained. A numerical explanation is provided for the derived EMS considering AR(1) structure. MSE appears to be growing in tandem with the increasing trend in the correlation coefficient. It has been discovered that the MSE value increases in tandem with increasing correlation. For combined AR(1) and AR(2) structure, AR (1) is kept fixed, and AR(2) is updated. The trend in MSE behaves the same. When AR(1) and AR(2) are both adjusted, a decent correlation structure combination is found that reduces MSE. According to a simulation study, estimating heritability using only the sire component produced better results than estimating heritability using both the dam and sire components and using only the dam component.


Several recent studies have indicated that upper extremity injuries are classified as a top common workplace injury. Therefore, upper extremity rehabilitation has become a leading research area in the last few decades. However, this high number of upper extremity injuries is viewed as a challenging problem due to the insufficient number of physiotherapists. With the recent advancements in technology, robots have been widely involved in upper extremity rehabilitation exercises. Although robotic technology and its involvement in the rehabilitation field are rapidly evolving, the literature lacks a recent review that addresses the updates in the robotic upper extremity rehabilitation field. Thus, this paper presents a comprehensive review of state-of-the-art robotic upper extremity rehabilitation solutions, with a detailed classification of various rehabilitative robots. The paper also reports some experimental robotic trials and their outcomes in clinics. A study conducted by the Bureau of Labor Statistics agency has revealed that there were more than 4.4 million upper extremity injuries from 1992 to 2018 in the USA, which makes upper extremity injuries the second most common workplace injury in the USA . A strokThe primary objective of rehabilitation is to help people restore their physical abilities to improve their performance and help patients return to a “normal” state where they can perform Activities of Daily Living (ADL) ,7. ClassHowever, due to the nature of traditional therapy and the ever-increasing shortage of physiotherapists, physical rehabilitation has not been accessible to a large number of people. A recent study conducted by the Chartered Society of Physiotherapy (CSP) reported that the number of newly graduated UK physiotherapists should annually grow by 500 graduates for several years to keep pace with the high demand. Moreover, the UK Charity Muscular Dystrophy has revealed that muscular-impairment patients lack an “on life-improving treatment” experience due to the insufficient number of specialist physiotherapists. Based on the charity’s report, 60% of patients having muscle-wasting problems did not receive appropriate physiotherapy treatment. Furthermore, 40% of UK neuromuscular clinks have declared that they urgently need higher physiotherapists capacity  . Rehabilitation exercises are repetitive and a single session can last for multiple hours. Therefore, researchers have been seriously interested in robotic rehabilitation. Moreover, robots as a versatile tool have been proven to be effective and favored by patients over traditional occupational therapy.This article offers a comprehensive review of robotic systems in the assessment and treatment of upper extremity disorders and impairments. The review also goes deeper than most of the literature by exploring how these robots are created, how popular motor learning principles operate, and how they could be applied, as well as exploring some of the clinical trials and their results. The main contribution of this paper is to conduct a comprehensive review of the advancements in robotic systems to treat upper extremity disorders and impairments. The main theme of the paper is to review what has been achieved so far in this field and discuss the gaps and the challenges that need to be addressed to achieve higher dependence on robotic systems to effectively deal with rehabilitation systemsThe rest of the paper is organized as follows. Rehabilitative robots can be classified in two ways, i.e., according to their structure and according to the type of provided therapy. Rehabilitative robots can be classified in similar ways to any other robots . In thisMIT-MANUS, a system with five degrees of freedom (DOF) that helps in the rehabilitation of three degrees of freedom of wrist motion: extension-flexion, abduction-adduction, and pronation-supination ,12;Bi-Manu-Track a, a singMirror-Image Motion enabler (MIME), a six-DoF robot that is used for the shoulder and elbow ,15.The earliest literature on robotic UE therapy was using EE-based systems. These systems only have one interface, the hand/forearm of the patient. This type of system is simpler and easier to manufacture; moreover, they are more flexibly adjustable to fit different arm lengths, although determining the patient’s posture proves difficult with this type of system. It also disallows generating isolated movements at specific joints, since movement at the EE can cause a combination of movements in the entire limb’s joints  . ExampleMore examples of EE-based systems are included in FLEXO-arm1 b: The FLHarmony: The exoskeleton known as Harmony is equipped with a shoulder mechanism that follows the natural anatomy of the human body, allowing for unrestricted movement of all joints. It is capable of bearing the weight of the upper body and applying assisting force to help patients carry out desired movements .ANYexo: The ANYexo exoskeleton is a flexible and adaptable device with six degrees of freedom, intended for use on the upper limb. It is equipped with a series of elastic actuators that allow for low-impedance torque control. The device is primarily used as an experimental platform to test new hardware concepts and algorithms for autonomous therapy of patients with varying degrees of neural impairment. The aim of the device is to provide greater independence and functionality to individuals with arm impairments .ETS-MARSE a: The EThttps://myomo.com/what-is-a-myopro-orthosis/, accessed on 25 April 2023) MyoPro is a powered upper limb orthosis developed by Myomo Inc. . The device is designed to help individuals with upper limb paralysis due to conditions such as stroke, spinal cord injury, or brachial plexus injury regain movement and function in their affected arm.MyoPro:  Neofect Rapael Smart Glove is a wearable glove that uses sensors and haptic feedback to provide interactive training for individuals with hand weakness due to neurological conditions. The device is designed to help patients regain fine motor control and dexterity in their hands.Neofect Rapael Smart Glove:  sensor implemented in Kiguchi et al. a 76], w, w76], wHandCARE  motors and hydraulic and pneumatic actuators. Some robots, such as the T-WREX  do not hThis section examines the different controller designs which were implemented in an upper-extremity rehabilitative robotic system. Several control paradigms were utilized in rehabilitative robotic systems ranging from basic controllers such as the Proportional-Integral-Derivative controller (PID) to hybrid complex controller designs. The variety of the available controller designs gave the developers the flexibility to choose the appropriate controller based on the advantages and disadvantages of each control class. In the literature, the controllers of an upper-extremity rehabilitation robotic system can be classified into PID controllers, robust controllers, adaptive controllers, and Artificial Intelligence (AI) controllers. One of the most simple and basic controllers is the PID controller. A PID controller continuously applies a command signal to the actuators to minimize the error between the desired values and the sensed actual values based on proportional, integral, and derivative terms. The simple design and philosophy of PID controllers have been utilized successfully in many different robotic applications with an acceptable accuracy degree. In , the FirPID controllers have been widely implemented in commercial-level products. The main advantage of the PID controller is the simplicity of the design. The PID simply consists of a single equation and does not need any knowledge of the plant to be controlled. Acceptable performance can be achieved when operating in normal conditions, which is usually the case with rehabilitation robots. Rehabilitation robots are usually operating in stable indoor environments with minimal external disturbances from the environment. Therefore, as presented earlier, the literature shows a number of successful rehabilitation robotic systems have adopted PID controllers. However, it is clear from the presented works that the PID itself was not enough to produce advanced performance, and thus, an additional optimization step was needed to find the best tuning parameters of the PID controller.Even though the successful integration of PID controllers in rehabilitation robots, the performance of PID controllers cannot be always guaranteed. If the system is exposed to a sudden strong disturbance from the environment, the PID controller might not be able to handle and maintain the stability of the robotic system. Moreover, rehabilitation robotic systems are directly connected to the body of a human. A small wrong movement can cause critical injuries. Therefore, to ensure better performance and to achieve advanced abilities to reject disturbances, other classes of controllers are adopted in rehabilitative robots, such as robust controllers, adaptive controllers, and AI-based controllers.The robust control theory focuses on dealing explicitly with uncertainties and disturbances that are defined within a pre-defined boundary. Robust controllers achieve stability in the presence of bounded modeling errors. A well-known robust control technique is the Sliding Mode Control (SMC). It is known that SMC controllers can achieve more robust results compared to basic PID controllers. Therefore, the literature has shown a number of rehabilitative robotic systems controlled using SMC controllers. In , a fractThe third class of controllers used in rehabilitation robots is the adaptive control method. Thus, several recent rehabilitation robots have been proposed to be controlled by an ADRC controller. In , an ADRCWith the recent advancements in the processing power of modern computers, Artificial Intelligence (AI) concepts have been widely adopted in control theory. A popular AI-based tool is Neural Networks (NN). In , a novelalmost possible between the two perspectives.While the control methods are the engineer’s point of view, rehabilitative robotics are a multidisciplinary study that no one short of a polymath can do on their own, and medical personnel’s input is a must for a successful system. This section aims to provide the strategies targeted at the human brain, rather than the robotic devices, to induce motor learning. They are inevitably parallel, and as such, there are many similarities that a bijection is The study in  delved iGame therapy also takes on a more immersive form by utilizing Virtual Reality (VR) technology. Previously, VR was limited by the technologies of the time, but as development advanced, VR technologies have become more accessible, which quickly revealed their potential in medical use cases . In the Unfortunately, a Cochrane review  showed tThis section aims to showcase a number of randomized trials that were carried out over the years. We will first cover some of the popular and quantifiable motor ability tests in the following section, and then some of the trials in Section 5.2In order to assess the effectiveness of the devices used, the effect sizes must be defined. There exists a number of ways to assess motor recovery that are attested by clinicians, physiotherapists, and researchers. The following are some of the most common ways to assess motor recovery, which can be used to understand the results of RCTs and meta-analyses, as they measure different goals.The Fugl-Meyer  scale isThe action research arm test (ARAT)  is a 19-The Wolf Motor Function Test (WMFT) quantifies UE motor ability through timed and functional tasks . The widAnother scale that is used to assess motor recovery is the stroke impact scale. It is a stroke-specific self-reporting and health measure. It is designed to evaluate multidimensional stroke outcomes. The scale was first released as version 2.0, which includes 64 items in 8 domains /instrumental ADL, mobility, communication, emotion, memory and thinking, and participation) . Based oBarthel’s ADL index  is an orThe ARMin III was tested in a parallel-group randomized trial of 24 sessions . The resA single-blinded randomized trial was carried out with the Bi-Manu-Track, where 44 patients were split into a control group and a robot arm trainer (AT) group, who participated in 30 sessions in either conventional therapy (control group) or robotic therapy using the Bi-Manu-Track (AT group). In the robot AT group, FM score was 15 points higher at study end and 13 points higher at 3-month follow-up than the control ES group .The T-WREX also had a randomized controlled trial that had an intervention carried over 24 Sessions. The robotic treatment group saw an FM improvement of (p=0.06) . The oth (p=0.4) .MIME’s Randomized Controlled Trial was 15 Sessions long. Since the MIME has two modes of operation, they were tested as separate groups in addition to a group that used both modes of operation. The FM improvement in the unilateral therapy group was  2.5±0.6 . With th 2.5±0.6 .enhancing poststroke motor recovery, rather than replacing traditional therapy.As seen in This section provides an attempt to answer the question of this paper: “Will your next therapist be a robot?”. To answer this question, a discussion on the current challenges and gaps in the robotic rehabilitation field is conducted. This section also suggests strategies to meet the requirements needed to achieve effective robotic rehabilitation systems. Based on the comprehensive analysis provided in this paper, it is clear that the current design of rehabilitation robots has focused more on the technical aspects of the robotic systems rather than the rehabilitation effectiveness of the variety of operational or training modes available to patients. For example, by comparing Rehabilitation robotic solutions are recommended to be built by considering sensorimotor methods. These methods can help in reducing faulty motor behavior using proprioceptive and cutaneous stimulations . To the Cost: Many of the advanced robots used in rehabilitation are expensive, which limits their accessibility to patients;User-friendliness: Robots used in rehabilitation need to be easy to operate and require minimal training so that they can be used by patients with varying levels of physical and cognitive abilities;Adaptability: Robots need to be adaptable to various patient needs and abilities, which requires sophisticated algorithms and control systems;Safety: Robots must be safe to use, with built-in safety features to prevent accidents and injuries;Evidence-based: There is a need for more research to determine the effectiveness of robots in rehabilitation, and to identify the specific patient populations and conditions for which they are most useful;Ethical considerations: There are ethical considerations to be addressed, such as how to balance the benefits of using robots with the potential loss of human interaction and empathy.Based on the discussion of this section and this paper, the short answer to the question: “Will your next therapist be a robot?” would be no, at least for the next decade for a fully dependent robotic system. Even though it is shown throughout this paper that robots have been effectively integrated into the rehabilitation field, it is clear that there are still some challenges to achieve an entirely independent robotic rehabilitation system. The following aspects need to be addressed and studied in future works:This paper has presented a comprehensive review on robotic rehabilitation systems that have been recently proposed. Various robotic solutions have been presented in the literature with different configurations and approaches, from devices that simply make the patient repeatedly do an exercise to those that go as far as including enriched training environments to unlock the human brain’s true learning capabilities. Overall, when used in conjunction with traditional therapy, robots offer an enhanced experience for the patients. However, there is yet to exist a robotic system that completely replaces traditional therapy. The review had also discovered the advantages and disadvantages of various techniques used in robotic rehabilitation and pointed to the current gaps in the rehabilitation robotic field.


This article has been corrected at the request of the authors to correct the affiliation for Mohammed Khashab from Orthopaedic Surgery, King Abdulaziz Medical City/Ministry of National Guard - Health Affairs, Jeddah, SAU to:1. Orthopedics, King Abdulaziz Medical City, Jeddah, Saudi Arabia2. Research Office, King Abdullah International Medical Research Center, Jeddah, Saudi Arabia.3. Orthopedics, College of Medicine, King Saud bin Abdulaziz University for Health Sciences, Jeddah, Saudi ArabiaThe authors deeply regret that these errors were not identified and addressed prior to publication.


AR amplification and structural rearrangement. These two classes of AR alterations often co-occur in CRPC tumors, but it is unclear whether this reflects intercellular or intracellular heterogeneity of AR. Resolving this is important for developing new therapies and predictive biomarkers. Here, we analyzed 41 CRPC tumors and 6 patient-derived xenografts (PDXs) using linked-read DNA-sequencing, and identified 7 tumors that developed complex, multiply-rearranged AR gene structures in conjunction with very high AR copy number. Analysis of PDX models by optical genome mapping and fluorescence in situ hybridization showed that AR residing on extrachromosomal DNA (ecDNA) was an underlying mechanism, and was associated with elevated levels and diversity of AR expression. This study identifies co-evolution of AR gene copy number and structural complexity via ecDNA as a mechanism associated with endocrine therapy resistance.Androgen receptor (AR) inhibition is standard of care for advanced prostate cancer (PC). However, efficacy is limited by progression to castration-resistant PC (CRPC), usually due to AR re-activation via mechanisms that include   KLK3 gene, indicates that AR transcriptional activity persists in most CRPC tumors and remains an attractive therapeutic target (The androgen receptor (AR) is a nuclear receptor transcription factor activated by androgenic ligands such as testosterone. In the prostate, active AR regulates the expression of genes vital for epithelial cell homeostasis. This AR dependence remains a hallmark of prostate cancer (PC) cells. Therefore, systemic therapies for advanced PC include endocrine therapies that prevent testicular androgen production, the CYP17A1 inhibitor abiraterone that prevents systemic androgen synthesis, and high-affinity competitive AR antagonists such as enzalutamide, apalutamide, and darolutamide . Despitec target .AR gene alterations in 75–90% of patients, consisting of amplification, single nucleotide variants (mutations) and structural rearrangements   with breakpoints that occur within the AR gene body, resulting in altered AR gene architecture. In 5–10% of CRPC cases, AR gene rearrangements exist as the only AR alteration detectable, and in this context they promote synthesis of tumor-unique AR variants (AR-V) that lack the AR ligand binding domain and possess constitutive activity that supports growth of CRPC cells , it remains unclear whether this reflects intercellular or intracellular heterogeneity of AR gene rearrangements. Resolving this question is important for developing new therapeutic regimens and biomarkers to ensure elimination of all cells in a CRPC tumor.Analysis of CRPC metastases using short-read DNA-sequencing (DNA-seq) has identified ngements . Conversagonists . However (ctDNA) . AR genePC cells ,15. Convin silico reconstruction of original high molecular DNA molecules from which DNA-seq reads were derived , which are large circular DNA molecules in cancer cells known to harbor amplified oncogenes  was obtained from American Type Culture Collection (ATCC)  in 2013 and cultured in RPMI 1640 medium (Invitrogen) supplemented with 10% fetal bovine serum (FBS) and 1× (100 μg/ml) penicillin/streptomycin. The CWR-R1 late subline was generated from the CWR-R1 cell line as described ,20, and Patient-derived xenograft (PDX) models LuCaP 86.2, LuCaP 35, LuCaP 35CR, LuCaP 77, LuCaP 77CR, LuCaP 105 and LuCaP 105CR have been described . LuCaP 8PDXs were resected from mice, cut into pieces with dimensions of 2–4 mm, and dissociated enzymatically using a human tumor dissociation kit (Miltenyi Biotec) and a tissue dissociator (Miltenyi Biotec) per manufacturer's specifications. Red blood cells were lysed with ammonium chloride potassium (ACK) buffer (Lonza). The cells were passed through 100 and 40 μm strainers  sequentially to filter coarse particulates. Cultures were established by plating cells in prostate-specific organoid medium without dihydrotestosterone in 6-well tissue culture dishes  without matrigel for five days.Metastatic CRPC samples labeled 00 through 14 were obtained from patients who died of CRPC from 2000 through 2014 and signed written informed consent for a rapid autopsy under the aegis of the Prostate Cancer Donor Program at the University of Washington (IRB protocol no. 2341) . MetastaAR and other defined genomic regions (Genomic DNA (gDNA) was isolated from cell lines and tissues using a Nucleospin Triprep kit . DNA was submitted to the University of Minnesota Genomics Center for linked-read DNA-seq library preparation and barcoding using the Chromium platform (10× Genomics). In brief, gDNA was diluted to 0.8–1.2 ng/μl, and 10 μl of diluted gDNA was added to a Sample Master Mix using the Chromium Genome Reagent Kits with beads (10× Genomics). The Sample Master Mix was loaded onto a genome chip, along with genome gel beads and partitioning oil and loaded on a Chromium controller. Once complete, the gel-bead in emulsions (GEMs) were transferred to new PCR tubes and incubated. After GEM incubation, the mix was cleaned up using DynaBeads (Thermo Fisher Scientific) and SPRIselect beads (Beckman Coulter). Post-GEM quality control (QC) was performed using an Agilent Bioanalyzer High Sensitivity DNA chip (Agilent) to determine yield and fragment size. DNA-seq libraries were then constructed. Following library construction, Hybridization-based enrichment was performed on DNA-seq libraries using a custom SureSelect bait panel (Aligent) designed to target  regions . Post-caAR copy CNV estimates were made as previously described  and used in the Bionano SP Tissue and Tumor DNA Isolation protocol for 400× Human Genome Sample Analysis. Tumor cells were enzymatically digested and lysed, and ultra-high molecular weight genomic DNA was bound by a paramagnetic disk in isopropanol. DNA was washed, eluted and homogenized in buffer, and quantified. A 750 ng aliquot of ultra-high molecular weight DNA was as input for the Bionano Prep Direct Label and Stain (DLS) procedure, wherein the DLE-1 enzyme conjugated fluorescent labels at the occurrence of a 6 bp motif. Excess labels were removed, and the DNA backbone was counterstained overnight. Labeled DNA was transferred to flowcells of a Saphyr Chip, then linearized and scanned with the Saphyr System. Images were digitized into whole-genome molecule and label information in real time, and collected to a minimum genome-wide target coverage of 400×.LuCaP 105 and 105CR tumors sized approximately 1000 mmThe dataset was assessed for quality control based on molecule size, labeling efficiency, and map rate and coverage of the hg19 reference. Data analysis was performed with the Bionano Solve Rare Variant Analysis (v 3.7), which performs alignment of individual molecules to this reference. Molecule clusters capturing putative structural variants (SVs) were identified, assembled locally into consensus maps, and realigned to the reference to generate refined and annotated SV calls. Using a separate algorithm based on depth of coverage, a whole genome copy number was also calculated. The copy number profile was analyzed for segments representing regions of copy number gain or loss. SV and copy number variant (CNV) calls were then presented and dynamically visualized in Bionano Access Software (v 1.7).VCaP and LuCaP 105CR cells were harvested using 0.05% Trypsin/EDTA following overnight arrest in medium containing 50 ng/ml colcemid. Trypsinized cells were treated with 0.75 M KCl, fixed with 3:1 methanol:acetic acid, and spread onto glass slides. Frozen PDX and CRPC tumor tissue was partially thawed and touched to glass slides. BAC DNA probes encompassing the AR locus (RP11-807F19 and RP11-963N10) were labeled by nick translation reaction using a Nick Translation Kit (Abbott Molecular) and red - 500 dUTP (Enzo Life Science). BAC probes encompassing 16p11.2 (RP11-114A14 and RP11-279M12) were labeled using Green – 500 dUTP (Enzo Life Science). The labeled DNA was precipitated in COT-1 DNA, salmon sperm DNA, sodium acetate and 95% ethanol, dried and resuspended in 50% formamide hybridization buffer, and mixed with commercial probes recognizing the centromeric region of chromosome X (Abbott Molecular). The probe/hybridization buffer mix was denatured, probe was applied to glass slides, and slides were hybridized for 48 h at 37°C in a humidified chamber. After hybridization, the FISH slides were washed in a 2× SSC solution at 72°C for 15–30 s, and counterstained with DAPI stain. Fluorescent signals were visualized on an Olympus BX61 microscope workstation  with DAPI and FITC filter sets. FISH images were captured using an interferometer-based CCD cooled camera (ASI) and FISHView ASI software.LuCaP 105 and LuCaP 105CR tumor bits were pulverized under liquid nitrogen, lysed in 1× Laemmli buffer, sheared through a 16-gauge insulin syringe, and boiled. Aliquots of lysates containing 30 μg protein mass were subjected to western blot as described  using prTotal RNA (1 μg) was used for cDNA synthesis using cDNA qScript SuperMix (Quanta Biosciences). cDNA (1 ul) was used for quantitative PCR detection of splice junctions specific to full-length AR, AR-V7 and AR-V9 as described .Total RNA (50 ng) was used for AR 3’RACE reactions using a 5’/3’RACE kit, second generation (Roche) as described . The finAR gene alterations in CRPC . This workflow provided an average 68–524X coverage depth of all targeted regions in these samples, and a median 15–148 linked reads per high molecular weight DNA molecule, yielding phase blocks with median sizes of 17 245–44 955 kb   and one PDX (LuCaP 86.2) that are known to harbor ngements ,20,35 an4 955 kb . We obse Figures , C. Non- Figures , C. Highe Figure .AR gene rearrangements in mapped DNA-seq data from these samples. Long Ranger identifies distant loci containing DNA-seq reads linked by the same barcode, indicating structural variation. NAIBR identifies DNA structural variants from split molecules, which are structurally-altered segments of DNA built in silico from DNA-seq reads linked by the same barcode. Three NAIBR d-parameter settings were tested, which defines a maximum distance allowed between linked reads. Long Ranger did not detect any known AR gene rearrangements, whereas NAIBR identified split molecules and discordant read pairs that correctly resolved the known AR gene rearrangements when multiple d-parameter settings were applied  for AR and the chromosome X centromere revealed that VCaP cells had 2 copies of chromosome X, one of which contained a single AR puncta and the other containing a homogeneously staining region (HSR) of focal AR amplification  and copy number variants (CNV) correctly identified a known s Figure . FluoresAR-targeted linked-read DNA-seq to analyze a cohort of 23 CRPC metastases from surgery or autopsy of 20 patients that had all received androgen depletion therapy, but had variable treatment exposures to abiraterone and enzalutamide , AR SNVs in 3/23 samples (13%), and AR gene rearrangements in 8/23 samples (35%). As observed in previous studies, AR gene rearrangements mainly occurred in samples that harbored AR amplification  mice to model androgen depletion therapy , AR SNVs (2/18 or 11% of samples) and AR gene rearrangements (5/18 or 27% of samples) were similar to the frequencies observed in the cohort we analyzed by targeted linked-read DNA-seq . Integrative analysis of a separate whole genome DNA-seq dataset (non-linked read) from 101 CRPC metastases , indicating this relationship is not an artifact of linked-read DNA-seq  sequenccoverage . Combinitastases  revealed DNA-seq .AR status during treatment with enzalutamide, evidenced by presence of an AR T878A mutation prior to treatment and emergence of an AR gene rearrangement (tandem duplication) after treatment  was found to harbor multiple  Figures , F. Barcn Figure . This suAR-targeted linked-read DNA-seq data from the cohort of 23 CRPC specimens. In this dataset, 4 CRPC specimens (17%) had split molecules with overlapping barcodes, indicating complex AR gene rearrangements. Noteworthy, these 4 CRPC specimens  ranked among the highest in the number of AR gene copies and burden of AR gene rearrangements , and several duplications (barcodes 9–15 and 21–22). A similar pattern was observed in CRPC specimen 10-013K4 wherein barcodes 1–9 connected a network of phased duplications and inversions at the 3’ end of the AR gene and barcodes 10-19 connected a network of phased inversions, deletions, and duplications in the middle of the AR gene . These results indicate an association between very high AR copy number and complex, multiply-rearranged AR gene structures.Across all 41 CRPC specimens analyzed by targeted or whole genome linked-read DNA-seq, phased AR copy number and complex multiply-rearranged AR gene structures, we focused on LuCaP 35 and LuCaP 105 PDX models, which displayed accumulation of AR gene copies and AR gene rearrangements under castration pressure  in LuCa Figures . This AR Figures . Using as Figure . These os Figure . FISH foted mice . Importa Figures –N.AR ecDNA, we assessed SNVs and CNVs in additional CRPC driver genes covered by the linked-read DNA-seq assay , which included a higher relative ratio of AR-V7 splicing  from CRPC patients often observed punctate AR staining patterns, at frequencies similar to HSR staining patterns  is derived from nuclear chromosomal DNA and has been reported in approximately 50% of human cancers, including the PC cell lines PC-3 and DU145 ,42,43. L gliomas . A recenpatterns ,47. It wAR gene structures in CRPC tumors with AR ecDNA indicates that ecDNA amplification can also be a source of continuous gene structural variation to further increase intratumoral heterogeneity. Previous studies have shown that ecDNA can evolve, but mainly via a mechanism of smaller ecDNAs joining to form larger ecDNAs (AR can enhance structural diversity. A limitation of our study is that the genomic region targeted for linked-read DNA-seq did not cover an enhancer located 650 kb upstream of AR (AR enhancer and AR gene body. In previous studies, AR gene rearrangements have been shown to promote expression of diverse AR-V species that lack the AR ligand binding domain (AR gene copies displayed alterations in mRNA splicing and expression of AR-Vs, including but not limited to AR-V7 and AR-V9. Given that AR-V7 expression in CTCs from CRPC patients portends poor outcomes (AR gene copy number in CRPC tissues (AR amplification and/or structural diversification on ecDNA is a mechanism that explains AR-V7 detection in clinical assays.Mathematical modeling has predicted that ecDNA amplification can increase oncogene copy number and intratumoral heterogeneity more effectively than chromosomal amplification . Our finr ecDNAs ,48–50, wam of AR ,51, whicg domain ,20,35,52outcomes ,54, and  tissues ,55, it wzcad045_Supplemental_FilesClick here for additional data file.


Fast and automated reconstruction of retinal hyperreflective foci (HRF) is of great importance for many eye-related disease understanding. In this paper, we introduced a new automated framework, driven by recent advances in deep learning to automatically extract 12 three-dimensional parameters from the segmented hyperreflective foci in optical coherence tomography (OCT).Unlike traditional convolutional neural networks, which struggle with long-range feature correlations, we introduce a spatial and channel attention module within the bottleneck layer, integrated into the nnU-Net architecture. Spatial Attention Block aggregates features across spatial locations to capture related features, while Channel Attention Block heightens channel feature contrasts. The proposed model was trained and tested on 162 retinal OCT volumes of patients with diabetic macular edema (DME), yielding robust segmentation outcomes. We further investigate HRF’s potential as a biomarker of DME.Results unveil notable discrepancies in the amount and volume of HRF subtypes. In the whole retinal layer (WR), the mean distance from HRF to the retinal pigmented epithelium was significantly reduced after treatment. In WR, the improvement in central macular thickness resulting from intravitreal injection treatment was positively correlated with the mean distance from HRF subtypes to the fovea.Our study demonstrates the applicability of OCT for automated quantification of retinal HRF in DME patients, offering an objective, quantitative approach for clinical and research applications. With abIn recent decades, advances in high-resolution fundus imaging techniques have led to the discovery of specific imaging features of retinal diseases, which may serve as diagnostic, predictive, and prognostic biomarkers for this disease . OpticalHowever, manual annotation of HRF in OCT is time-consuming, and sometimes excessively subjective. With the rapid development of computer science, there is great potential for automatic segmentation and quantification of HRF in OCT images, with benefits for clinical practice. The segmentation algorithms for HRF can be categorized into two primary groups: traditional segmentation algorithms and deep learning-based segmentation methods. Traditional HRF segmentation approaches usually require manual parameter tuning and extensive prior knowledge. Okuwobi et al.  employedIn this study, we presented a deep learning-based framework for the quantitative analysis of HRF in OCT images. Specifically, the main contributions of our article can be summarized as follows:We achieve excellent HRF segmentation performance by combining nnU-Net  adaptabiUsing the proposed method, we extracted 12 parameters to characterize HRF morphology and distribution, showing significant differences in volume and amount among the three HRF sub-types in retinal OCT images.Using the extracted 12 HRF parameters, we evaluated changes in HRF before and after treatment and their correlation with central macular thickness (CMT) improvement.2.This is a retrospective, longitudinal study conducted at the Affiliated Ningbo Eye Hospital of Wenzhou Medical University  from November 2020 to July 2022. This study was approved by the ethics committee of the Affiliated Ningbo Eye Hospital of Wenzhou Medical University (ID: 20210327A), and informed written consent was obtained from each participant involved in our study according to the Declaration of Helsinki.2.1.Type 2 diabetes mellitus (DM) patients were recruited and diagnosed by an endocrine specialist. Demographic and clinical information from all patients such as age, gender, duration of DM, and systolic/diastolic blood pressure were recorded. All patients had an extensive ophthalmic examination, involving slit-lamp biomicroscopy, and assessment of intraocular pressure, axial length, and visual acuity. The inclusion criteria of our patients are as follows: 1. Diagnosed with type 2 DM; 2. Age > 18 years; 3. Macular edema, defined clinically and by a retinal thickness of >250 μm in the central subfield ; 4. Coul2.2.2, with 384 B-scans, and 6 × 6 mm2, with 512 B-scans. OCT images showing retinal abnormalities such as age macular degeneration (AMD), severe cataract, and glaucoma; images with signal quality less than 7; or with OCT artifacts present, were excluded. OCT data displayed in our study followed the OSCAR-IB quality criteria . This imaging equipment has a scanning protocol of 40,000 A-scans/s , with ancriteria  and APOScriteria . Patient2.3.We introduced an automatic tool for HRF analysis in OCT images. A deep learning-based approach was employed for precise segmentation of HRF, boundaries of inner retina (IR) and outer retina (OR) in OCT images. The resulting segmentations are then used to calculate HRF parameters.2.3.1.HRF was defined as discrete and well-defined lesions distributed between the in-ternal limiting membrane (ILM) and retinal pigmented epithelium (RPE), with similar reflectivity to the RPE layer . Conside2.3.2.The distribution of HRF in the IR and OR, and their downward shift, have been previously studied . The IR 2.4.2.4.1.In this research, we modified the nnU-Net, to, respectively, perform two segmentation tasks: hyperreflective foci segmentation and retinal layer segmentation. The framework comprises a basic U-Net architecture library that includes 2D and 3D version.For the retinal layer segmentation task, we modified the 2D version of nnU-Net as the underlying network topology. The network architecture is shown in Convolutional neural networks with U-Net structure have higher inductive bias, but lack the ability to capture long-distance dependent features. Inspired by CS2-Net , we embewhere where For the HRF segmentation task, we have extended the modified nnU-Net from 2D to 3D. To achieve this, we have replaced all the 2D operations in both the encoder and decoder modules with 3D ones. Additionally, we have incorporated the 3D version of the SCA module into the bottleneck layer of the network. The detailed network architecture is illustrated in All convolutions in the encoder and decoder adopt the form of Convolution-InstanceNorm-LeakyReLU, which are different from that in the vanilla architecture. Specifically, LeakyReLU (negative slope = 0.01) is used instead of ReLU, and instance normalization  is used The dice loss formula used here is a variant of that used in Drozdzal et al. , and it I × K denotes the softmax output of the network, v ∈ RI × K denotes the one-hot encoding of the ground truth, I represent the number of pixels in a training batch and K represents the number of categories.where u ∈ R2.5.In this study, we analyzed changes in HRF’s morphology and distribution in OCT images before and after IVI treatment. A previous study limited the maximum diameter range of HRF to 20–50 μm, which excludes the other two signals , refers 2.5.1.Noise Signal Quantity : Number of NS within the analyzed region.- Hyperreflective Dots Quantity : Number of HD within the analyzed region.- Hyperreflective Clumps Quantity : Number of HC within the analyzed region.- Noise Signal Volume : Volume of NS within the analyzed region in μm3.- Hyperreflective Dots Volume : Volume of HD within the analyzed region in μm3.- Hyperreflective Clumps Volume : Volume of HC within the analyzed region in μm3.- 2.5.2.Distance between noise signal and fovea : Distance between NS and fovea, indicating average distance of NS pixels from the foveal center in μm.- Distance between hyperreflective dots and fovea : Distance between HD and fovea, indicating average distance of HD pixels from the foveal center in μm.- Distance between hyperreflective clumps and fovea : Distance between HC and fovea, indicating average distance of HC pixels from the foveal center in μm.- Distance between noise signal and RPE : Distance between NS and RPE, indicating average distance of NS pixels from RPE in μm.- Distance between hyperreflective dots and RPE : Distance between HD and RPE, indicating average distance of HD pixels from RPE in μm.- Distance between hyperreflective clumps and RPE : Distance between HC and RPE, indicating average distance of HC pixels from RPE in μm.- 2.6.p < 0.05 (two-sided test) was adopted to express statistical significance.All statistical analysis was performed using version 18.0 of SPSS software . Continuous variables were expressed as mean ± standard deviation (SD) for normal data; and median and interquartile ranges (IQR) for skewed data. Categorical variables were presented as frequencies. To compare the differences among different subtypes of HRF and the differences in HRF parameters before and after treatment, the Wilcoxon signed-rank test was used, and the results were expressed as the median (IQR). To investigate the correlation between the improvement in CMT and given parameters of HRF, Spearman’s rank correlation coefficients were calculated using a non-parametric test for linear correlation. A significance level of 3.3.1.3.1.1.The proposed model was implemented in PyTorch using an NVIDIA GeForce 3,090 GPU with 24GB memory. The training process involved 500 epochs, and employed the following settings: Adam optimization, with an initial learning rate of 0.01; a batch size of 2 for HRF segmentation; and a batch size of 1 for retinal layer segmentation. To enhance training stability, we adopted a poly learning rate policy, with a momentum of 0.9.3.1.2.To quantitatively assess the proposed network’s segmentation performance, we employ the following metrics. The Dice Similarity Coefficient (DSC) quantifies the agreement between HRF manually annotated by expert ophthalmologists and those automatically segmented by the proposed network, which can be defined as:We also assess our method using Intersection over Union (IOU), precision, recall, and F1-Score, defined as:where TP indicates true positives, FP indicates false positives, TN indicates true negatives, and FN indicates false negatives.3.1.3.In order to evaluate the effectiveness of the proposed network, we selected several state-of-the-art neural networks for comparison, including FCN , U-Net , U-Net++As seen in 3.1.4.To demonstrate the effectiveness of the channel attention and spatial attention modules, we compared our proposed method with the baseline method and two variants.Baseline + SAB: We removed the CAB from this variant to assess its contribution.Baseline + CAB: We removed the SAB from this variant to assess its contribution.Baseline: We removed both SAB and CAB to evaluate their combined contribution.3.2.We enrolled 47 eyes from 26 patients with DME, acquired with OCT , and a total of 11 eyes from 8 patients were included in this study. We excluded 36 eyes from 18 patients from the analysis. One eye of one patient was excluded due to poor OCT image quality (motion artifacts on OCT images): 22 eyes of 11 patients were excluded due to lack of follow-up records; and 13 eyes from 7 patients were excluded due to no improvement in CMT after anti-VEGF or dexamethasone IVI treatment. The characteristics and clinical information of our study participants are displayed in 3.2.1.p = 0.003). No significant differences were found between p = 0.131). Among the distance-related parameters, the results showed no significant differences between the HRF classified according to their diameter size.3.2.2.Due to the retrospective design of the study, OCT examinations were not performed at regular intervals. To avoid bias related to the duration of follow-up, only two consecutive follow-up visits with improvement in CMT were selected for each eye. The longitudinal study included 11 eyes from 8 patients, with a follow-up of 1.9 ± 1.6 months . During study period, all eyes were treated with intravitreal injections: 91% (10/11) of eyes received anti-VEGF injections and 9% (1/11) of eyes received dexamethasone injections. The number of intravitreal injections was 1.4 ± 0.7 .p = 0.003, p = 0.033, p = 0.003, p = 0.026, p = 0.008, p = 0.004, p = 0.003, respectively). There were no significant changes in the other six quantitative parameters between the two phases. In IR, p = 0.016, p = 0.016, respectively). There were no significant changes in the other 10 quantitative parameters between the two phases. In OR, p = 0.006, p = 0.047, p = 0.006, p = 0.004, p = 0.004, respectively). There were no significant changes in the other seven quantitative parameters between the two phases.We assessed whether changes in HRF were significant at two consecutive follow-up visits in the presence of improved CMT. 3.2.3.p = 0.015, p = 0.016, p < 0.001, respectively). There was no significant correlation between the other nine baseline quantitative parameters and p = 0.006, p = 0.019, p < 0.001, respectively). There was no significant correlation between the other nine baseline quantitative parameters and We assessed whether there was a significant correlation between improvement in CMT at two consecutive follow-up visits and baseline HRF. 4.The given study aims to quantify HRF in OCT images as part of a retrospective study on patients with DME at baseline and follow-up. Previous studies relied on manual counting methods to quantify HRF, which is time-consuming and less reliable. In examining HRF as a potential biomarker, the existing body of literature has been inconsistent , 35–42, 4.1.Statistical analysis indicated significant disparities in both the number and volume parameters of baseline HRF subtypes. Our study validated HRF discrimination based on diameter range by analyzing baseline HRF morphological parameters. A previous study used 20 μm and 50 μm diameters to differentiate HRF subtypes , found a4.2.We showed the mean distance from HRF to RPE was significantly reduced after IVI treatment. By studying the distribution parameters of HRF in a longitudinal analysis of two consecutive follow-ups, our study indicated the tendency of HRF to migrate from the inner retina to the outer retina after IVI treatment; similar to our findings, Pemp et al. showed that DME uptake triggered the downward migration of HRF  into the4.3.p < 0.001), which could explain why there was no correlation between the concentration of baseline HRF in the fovea in IR and the reduction in CMT, since the convergence of smaller HRF subtypes to larger HRF subtypes mainly occurs in OR according to the discussion above. We speculate that future studies on the differential distribution of HRF aggregated in the fovea may be able to verify whether it produces some physiological changes that affect the outcome of IVI treatment.We also showed that improvement in CMT resulting from IVI treatment was positively correlated with the mean distance from HRF to the fovea in OR and WR, and not significantly correlated with other parameters. We explored the correlation between the therapeutic effect of IVI on foveal edema and the quantitative parameters of HRF at baseline by examining the quantitative parameters of HRF at baseline and the percentage improvement in CMT at two consecutive follow-up visits. A previous study  performe4.4.Our study has certain limitations. Firstly, all participants were Chinese, and enrolled from a single medical center: larger and more various samples would be an advantage. More multi-center studies should therefore be conducted on larger cohorts to confirm the reproducibility of analysis on these parameters of HRF. Secondly, the enrollment criteria for this study only included cases with a positive response to injection therapy, rather than including refractory cases. It would be more convincing to recruit subjects with definitive treatment and make a long-term follow-up comparison. Thirdly, the study design lacked untreated blank controls to derive reasons for changes in parameters before and after treatment, while the small sample size and high homogeneity made the study findings more indicative of a pilot.5.We introduced a deep learning-based approach to quantify hyperreflective foci in OCT images of DME patients. Our retrospective analysis of 11 eyes using this method showed that it effectively quantified baseline and follow-up changes in hyperreflective foci by extracting relevant geometric parameters. In this study, we were able to validate certain findings reported in prior research and uncover novel insights: for instance, our investigation revealed that the concentration of HRF in the fovea region may influence the efficacy of IVI treatment. We believe that accurate quantification and follow-up of HRF in OCT images at baseline and during treatment may enable clinicians to monitor DME disease progression, assess treatment response and identify patients who may benefit from a personalized approach to treatment.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving humans were approved by the ethics committee of the Affiliated Ningbo Eye Hospital of Wenzhou Medical University (ID: 20210327A). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.XW: Conceptualization, Methodology, Software, Writing – original draft, Writing – review & editing, Formal analysis. YaZ: Conceptualization, Formal analysis, Resources, Writing – review & editing. YM: Writing – review & editing. WK: Conceptualization, Formal analysis, Writing – review & editing. JY: Investigation, Writing – review & editing. JL: Software, Writing – review & editing. SM: Conceptualization, Writing – review & editing. QiY: Conceptualization, Validation, Writing – review & editing. QuY: Conceptualization, Resources, Writing – review & editing. YiZ: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Resources, Software, Supervision, Visualization, Writing – original draft, Writing – review & editing.


Flexible pressure sensors have played an increasingly important role in the Internet of Things and human–machine interaction systems. For a sensor device to be commercially viable, it is essential to fabricate a sensor with higher sensitivity and lower power consumption. Polyvinylidene fluoride (PVDF)-based triboelectric nanogenerators (TENGs) prepared by electrospinning are widely used in self-powered electronics owing to their exceptional voltage generation performance and flexible nature. In the present study, aromatic hyperbranched polyester of the third generation (Ar.HBP-3) was added into PVDF as a filler  to prepare nanofibers by electrospinning. The triboelectric performances (open-circuit voltage and short-circuit current) of PVDF-Ar.HBP-3/polyurethane (PU)-based TENG shows better performance than a PVDF/PU pair. Among the various wt.% of Ar.HBP-3, a 10 wt.% sample shows maximum output performances of 107 V which is almost 10 times that of neat PVDF (12 V); whereas, the current slightly increases from 0.5 μA to 1.3 μA. The self-powered TENG is also effective in measuring human motion. Overall, we have reported a simpler technique for producing high-performance TENG using morphological alteration of PVDF, which has the potential for use as mechanical energy harvesters and as effective power sources for wearable and portable electronic devices. Recently, the need for human interaction applications including synthetic e-skin and health management  robots has increased due to the development of self-powered adaptable wearables ,12,13,14An example of such a technique is the nanogenerator, which transforms mechanical energy into electricity, and is the final outcome of a minor physical change . They haTENGs can operate in a variety of ways, but the majority of them have been observed to operate in contact separation, which occurs when two surfaces with two electron affinities encounter each other ,38. The In TENG, there is always a pair of triboelectric materials with different charge affinities that are employed to generate charges. To generate more charges or obtain a higher output from the TENG, a significant difference in the charge affinities of the two materials is preferred. However, in practice, the two materials with the highest difference in charge affinity are not selected. The reason is that the triboelectrification between two materials is based not only on their chemical compositions but also on other physical features such as their elasticity, friction and topographical structure. The triboelectric effect and level of contact electrification performance are based on materials that come into contact. In this case, contact electrification is caused by the surface transfer of electrons or ions between these two materials. Each material has its own ability to lose or gain electrons during the contact electrification process. This ability can be found in a list of materials (triboelectric series), where the polarity and amount of charge for each material are described as shown in β-phase. It is claimed that, in comparison with its as-cast film, PVDF nanofibers produced by the electrospinning technique exhibit a greater coefficient of piezoelectricity and higher energy conversion efficiency [Additionally, the technique used for fabrication is crucial in developing high-performance nanogenerators. Due to its adaptability in applications including chemical and physical sensors, healthcare diagnostics, sensitive robotics and wearable technology, fiber-based electronic devices are becoming more and more popular these days ,29. In tficiency ,49,50,51ficiency ,56,57,58α, β, γ, δ and ε) [β-crystalline phase has the maximum dielectric and piezoelectric response compared to its other polymorphic phases [β-phase fraction may provide the required electrical efficiency in nanogenerators. As a polymer with multiple functions, PVDF also has excellent ferroelectric, piezoelectric, pyroelectric, and dielectric characteristics [β-phase content in PVDF is required, and this is achieved by mechanical stretching, annealing, electrical polling, and by adding diverse additives such as ZnO, RGO: Fe, ALO/RGO, aromatic hyperbranched polyesters (Ar.HBP), etc. The main criteria are the rearrangements of C–F groups to achieve a favorable enhancement in PVDF’s β-phase, which in turn can boost the device conductivity of nanogenerators [Electrospun PVDF nanofibers offer a lot of prospects for applications such as energy harvesters ,59,60,61δ and ε) ,75,76. Pc phases ,77. As aeristics ,68,71. Tnerators ,94,95,96nerators .VOC) and short circuit current (ISC) parameters. We have demonstrated that the triboelectric property of PVDF can be significantly enhanced through the addition of Ar.HBP of the third generation (Ar.HBP-3) and aided by electrospinning. Hence, a high-performance P-Ar.HBP-3 (10 wt.%) TENG has been successfully developed based on this negative triboelectric material. Detailed investigation revealed that the modulation of PVDF composition using Ar.HBP-3 and nanofiber structure fabricated through electrospinning significantly contributed to the triboelectric performance enhancement of P-Ar.HBP-3 nanofiber. Draft comparison for the above-mentioned voltage obtained by P-Ar.HBP-3 (10 wt.%) in comparison with earlier reported materials is given in In this study, we fabricated a TENG using electrospun nanofiber based on PVDF-Ar.HBP of the third generation (P-Ar.HBP-3) blend mixture and non-woven PU film, and used it for harvesting energy under diverse conditions. Electrospun P-Ar.HBP-3 nanofiber and non-woven PU film operate as the corresponding negative and positive triboelectric layers. FTIR, XRD, FESEM and EDS techniques were used to analyze and correlate the structural and morphological characteristics of the blend with its triboelectric behavior. Triboelectric performance of the TENG device was assessed using open-circuit voltage  and acetone were purchased from Sigma-Aldrich, Seoul, Republic of Korea. Synthesis and characterization of aromatic hyperbranched polyester of third generation (Ar.HBP-3) is reported in our earlier study . All thev/v) solvent mixture (16 wt.%) under steady magnetic stirring at 350 rpm speed for 4 to 5 h at 50 °C, and the resultant homogenous solution is further used for the electrospinning process. Along with that, comparative studies were carried out using a neat PVDF solution. Custom-designed electrospinning equipment was used to conduct the electrospinning process. A 10 mL syringe was adapted for filling the prepared solution and transferred through a needle having a diameter of 23 G with a flow rate of 1.2 mL/h by a pumped syringe. The distance between the electrospinning needle tip and the film collector wrapped on a roller was 15 cm, and the electrospinning applied voltage was set at 20 kV. For about 6 h, collection of all the fibers was performed with 60 rpm roller speed. During the electrospinning process, fast evaporation of the solvents takes place. We also took additional care to remove the traces of residual solvents, if any, in the as-spun fibers by keeping the samples in vacuum oven for 5 h at 50 °C to remove any traces of residual solvent. Further, we preserved the samples in a desiccator under closed vacuum for a week before proceeding to electrical measurements.P-Ar.HBP-3 with varying Ar.HBP-3 content (10 to 40 wt.%) was dispersed in DMF/acetone . Across both sides of the mat, two Ni–Cu conductive electrodes were placed as the top and bottom electrodes. Electrodes were then soldered with Cu and finally, two flexible PET substrates of a larger size compared to the size of the fiber mat were fixed on both sides of the electrodes. To construct a compressed device and resolve the gap among materials, the layered structure was finally subjected to proper pressure. This was to prevent the triboelectric effect and to shield the nanogenerator from environmental damage. In addition, this will not allow direct contact of the sensor with the skin and is safe for the user performing the measurements. For the TENG fabrication, P-Ar.HBP-3 (10 to 40 wt.%) electrospun mat and non-woven polyurethane (PU) film were cut into the specified sizes  with an input impedance of 40 MΩ. Furthermore, an oscilloscope was coupled to a low-noise current preamplifier  for ISC measurement. Sensitivity of the TENG was measured using a BIOPAC System, Inc., Goleta, CA, USA MP150 connected to a measurement expertise Piezo Film Lab Amplifier (conditions: Rin of 100 MΩ and gain of 0 dB). These measurements were carried out for healthcare monitoring applications.Polymer structure was confirmed using Fourier-transform infrared  spectroscopy. Morphology of the fibers was examined using a field emission-scanning electron microscope . The nanofiber’s X-ray diffraction (XRD) patterns were measured using an X-ray diffractometer  with a Cu Kα radiation source. An external pushing force was generated by an electrical pushing machine. v/v). Afterwards, the polymeric solution was electrospun onto the substrate (2) and adhered along with the Ni–Cu electrode and PET substrate, respectively. These devices could be used in a variety of healthcare-monitoring applications.The schematic representation for the production of electrospun nanofibers and TENG for applications in energy harvesting and health monitoring is shown in β-phase by blending with Ar.HBP-3 so as to increase the output voltage of the sensor. Hence, we did not perform any mechanical studies for the sensor.Mechanical damage to the sensor setup is a big challenge, preventing TENGs’ practical applications. TENGs have four working modes, i.e., single-electrode, contact-separation, lateral-sliding. and free-standing triboelectric layer modes. For single-electrode and contact-separation modes, TENGs are subjected to constant external tapping, bending, and stretching, which will cause mechanical damage such as cracks, leading to rupture of the sensor structure. In the case of lateral-sliding and free-standing triboelectric modes, TENGs suffer from significant friction, wear, and adhesion of material during sliding. For example, the triboelectric surface of TENGs is prone to being worn out by frictional forces. The worn particles are transferred to the surface of the opposite material, resulting in output degradation and a loss in sensor longevity. Our main focus in this study is the enhancement of the PVDF As shown in β-phase are more desirable. Therefore, the effect of Ar.HBP-3 upon PVDF crystalline forms was investigated using XRD and FTIR measurements. XRD patterns of electrospun PVDF and P-Ar.HBP-3 (0 to 40 wt.%) fibers are shown in α-phase crystallization peaks at 18.3° and 19.8°, which correspond to (020) and (110) reflections, respectively. This outcome demonstrates that the α-phase predominates in neat PVDF samples. In the case of P-Ar.HBP-3 (10 wt.%) fiber, unique crystallization peaks corresponding to (020) and (110) reflections of β-phase is observed at 20.6°, thereby demonstrating the coexistence of both α- and β-phases in the blend case. Electrospinning’s simultaneous stretching and poling assists in the PVDF’s transformation from the α- to β-phase.Due to their higher triboelectric sensitivity, PVDF fibers with a significant proportion of α-phase is represented by the vibrational bands at 763 and 976 cm−1, whereas the electroactive β-phase is represented by the distinctive peaks at 841 cm−1 and 1276 cm−1. The minimal peak intensity of the γ-phase at 1234 cm−1 indicates the majority presence of β- and α-phases in their composition. The prominent PVDF fiber’s α-phase peaks sharply reduce when compared to the P-Ar.HBP-3 fiber, indicating that the electrospinning is an effective way to generate and/or improve the polar β-phase. This trend is also a confirmation of the effect of Ar.HBP-3 in improving the β-phase content in PVDF. Under the condition that the infrared transmittance corresponds with the Lambert–Beer law, the β-phase’s relative content in electrospun fibers is determined using the equation:−1 and 763 cm−1 absorbance are denoted as βA and αA, respectively. β-phase fractions in electrospun nanofiber have varied. β-phase % rises with an increase in Ar.HBP-3 content, reaching a peak value of 93.8 % for the nanofiber, i.e., electrospun PVDF with 10 wt.% Ar.HBP-3 exhibits higher β-crystalline phase content than neat PVDF fibers as shown in β-phase in PVDF. However, decreasing β-phase content with Ar.HBP-3 content beyond 10 wt.% may be attributed to increasing coagulation as observed during sample preparation.Additional analysis on the crystalline phase changes in P-Ar.HBP-3 (0 to 40 wt.%) was performed using FTIR analyses  as shownβ-phase formation on the P-Ar.HBP-3 surface is shown in β-phase in PVDF. Similarly, the interfacial interactions of Ar.HBP-3 can pull PVDF link chains to form crystals on the surface in an all-trans form, thereby enabling the conversion of localized non-polar α-phase to polar β-phase. Ar.HBP-3 serves as a nucleating agent in this circumstance, offering basic units for the creation of PVDF crystalline polymerization and inciting the creation of PVDF’s β-phase fragment through potent interaction at the interface [2/–CF2 dipoles differ. As shown in 2/–CF2 in PVDF tend to align their F atoms towards the Ar.HBP-3 at the P-Ar.HBP-3 interface [2/–CF2 dipoles are affected by these cross-surface links and the dipolar interaction between PVDF and DMF (polar) solvent, thereby causing the formation of the β-phase [β-phase at the P-Ar.HBP-3 interface. As a result, when compared to pure PVDF nanofibers, the electrospun P-Ar.HBP-3 nanofiber contains more β-phase. Furthermore, it is also observed that when Ar.HBP-3 content is raised further, the β-phase proportion is lowered. Increasing the amount of Ar.HBP-3 over 10 wt.% induces agglomeration as well as inhibiting the mobility of PVDF chain links, which may help explain this phenomenon [The anticipated mechanism for nterface ,56. In cnterface . The mob β-phase ,58. Addienomenon . As a reFiber orientation is one of the important factors when processing electrospun fibers, as they are collected on a rotating collector. The two main fiber orientations obtained during electrospinning are ‘aligned’ and ‘random’. While random fibers are randomly oriented at different angles throughout the collected sample, aligned fibers are typically oriented in the same direction. Flat plate and roll-to-roll collectors are the most commonly used collector tools for electrospun nanofibers. The most efficient, and increasingly common method of collecting aligned fibers is by using a rotating drum collector. The degree of fiber alignment is determined by the linear speed of the rotating collector. As linear speed increases, fibers become more aligned. Linear speed on a rotating collector is calculated by υ = r × ω, and is typically represented in revolutions per minute (rpm), where υ = linear velocity, r = radius of drum collector, and ω = angular velocity. In our study using the drum collector, we did not observe any oriented fibers, and instead saw aligned fibers.SEM images of electrospun PVDF and P-Ar.HBP-3 (10 wt.%) nanofibers are shown in The elemental mapping images of P-Ar.HBP-3 (10 wt.%) film are shown in β-phase and dielectric constant of 10 wt.% P-Ar.HBP-3/PU-based TENG was responsible for the enhanced electrical performance.TENG devices were fabricated using the prepared P-Ar.HBP-3 nanofibers with various Ar.HBP-3 concentrations  along with readily available non-woven PU fabric. By applying a constant frequency and force of 1 Hz & 10 N, respectively, the output electrical efficiency of the fabricated TENGs were evaluated. β-phase and dielectric properties along with a corresponding improvement in the TENG’s electrical output [Defects surrounding Ar.HBP-3-filled PVDF were eliminated as observed from the cross-sectional FE-SEM images. This results in increasing the PVDF’s l output . The eleTo demonstrate the viability of P-Ar.HBP-3/PU-based TENG in real-time applications, the fabricated TENG is able to power 12 LEDs without using a capacitor or rectifier when operating under a load and frequency of 10 N at 1 Hz . The cir2) sensitivity is determined (In the ambient environment, mechanical energy is abundant, and the majority is spent in our daily activities. This example illustrates how to use TENGs to collect human motion data. Initially, the sensor is attached to the piezo-amplifier during the measurement, and its output is attached to the Biopac 150 system. This can wirelessly communicate the data to the computer or mobile phone, which uses acknowledgment software during the measurement. The schematic representation of the general circuit used for human health monitoring measurements with Biopac MP150 is shown in termined a. The seThese results indicate that the TENG responds well enough to twisting compared to other activities as demonstrated in As demonstrated in β-phase of PVDF is effectively enhanced using electrospinning technique, aided by the influence of dipole interaction between the functional groups present in PVDF and Ar.HBP-G3. Triboelectric performance of the TENGs is significantly improved in the P-Ar.HBP-3 case compared to neat PVDF. Under the mechanical strain of 10 N at 1 Hz for TENG with 10 wt.% Ar.HBP-3, we could achieve a maximum VOC of 107 V and ISC of 1.3 μW. Further, to power electronics and charge capacitors, the mechanical energy transformed from triboelectric energy was used. In healthcare monitoring applications, the fabricated TENG exhibited high voltage output stability. In addition, 12 LEDs were directly lit from the maximum VOC of 107 V. Additionally, the specified TENG was used to harvest mechanical energy and power portable electronics. In order to track and monitor the patient’s physical conditions, the TENG was also integrated with a variety of health monitoring systems. Due to their low cost, ease of fabrication, and excellent output, the flexible nanofiber TENGs developed in the present study are very promising for powering portable electronics and for healthcare monitoring applications.In conclusion, by employing the synergistic enhancing impact of Ar.HBP-3 on the triboelectric efficiency of electrospun PVDF nanofibers, we were able to successfully fabricate flexible and high voltage output TENGs. The polar