The aggressiveness of renal cell carcinoma (RCC) associated with Xp11.2 translocation/TFE3 gene fusion (Xp11.2 translocation RCC [Xp11.2 tRCC]) is age‐dependent, which is similar to the overall trend of reproductive endocrine hormones. Therefore, this study focused on the effect and potential mechanism of androgen and androgen receptor (AR) on the progression of Xp11.2 tRCC.The effects of androgen and AR on the proliferation and migration of Xp11.2 tRCC cells were first evaluated utilising Xp11.2 tRCC cell lines and tissues. Because Transcription factor enhancer 3 (TFE3) fusion proteins play a key role in Xp11.2 tRCC, we focused on the regulatory role of AR and TFE3 expression and transcriptional activity.When Xp11.2 tRCC cells were treated with dihydrotestosterone, increased cell proliferation, invasion and migration were observed. Compared with clear cell RCC, the positive rate of AR in Xp11.2 tRCC tissues was higher, and its expression was negatively associated with the progression‐free survival of Xp11.2 tRCC. Further studies revealed that AR could positively regulate the transcriptional activity of TFE3 fusion proteins by small ubiquitin‐related modifier (SUMO)‐specific protease 1, inducing the deSUMOylation of TFE3 fusion. On the other hand, UCHL1 negatively regulated by AR plays a role in the deubiquitination degradation of the PRCC‐TFE3 fusion protein. Therefore, the combination of the AR inhibitor MDV3100 and the UCHL1 inhibitor 6RK73 was effective in delaying the progression of Xp11.2 tRCC, especially PRCC‐TFE3 tRCC.Androgen and AR function as facilitators in Xp11.2 tRCC progression and may be a novel therapeutic target for Xp11.2 tRCC.The combined use of AR antagonist MDV3100 and UCHL1 inhibitor 6RK73 increased both the SUMOylation and ubiquitination of the PRCC‐TFE3 fusion protein Androgen receptor (AR) regulates the SUMO modification of the TFE3 fusion proteins by small ubiquitin‐related modifier‐specific protease 1.AR affects the ubiquitination of the PRCC‐TFE3 fusion protein by regulating UCHL1 negatively.Targeting AR and UCHL1 may serve as a novel target for Xp11.2 translocation renal cell carcinoma therapy. Such investigation is important for clarifying the molecular mechanism for the age dependence of Xp11.2 tRCC progression, thus allowing us to provide a theoretical basis for the effective therapeutic target of this rare tumour.22.1From 2013 to 2020, 46 cases of Xp11.2 tRCC patients with tumour issues were identified by TFE3 fluorescence in situ hybridisation at the Affiliated Drum Tower Hospital of Medical School of Nanjing University, and 13 cases of clear cell RCC (ccRCC) were randomly selected as controls. All patients provided signed informed consent for the use of their tissues for scientific research. The current study was approved by the Institutional Review Board.2.2PRCC‐TFE3 translocation and NONO translocation, respectively, were gifts from the Tumour Cell Line Repository of the National Cancer Institute. All cells were cultured in 90% Dulbecco's Modified Eagle Medium  + 10% fetal bovine serum  + 1% penicillin/streptomycin . In the androgen‐deprivation experiment, 10% fetal bovine serum was replaced with 10% charcoal‐stripped media (CSM). For every experiment, at least three different donors were used. All human cell lines were cultured mycoplasma‐free and authenticated by short tandem repeat profiles (or single nucleotide polymorphism) profiling. For treatment, cells were exposed to dihydrotestosterone (DHT) , MDV3100 , ASC‐J9 , 6RK73 , MG‐132  or combinations for either 24, 48 or 72 h.The cell lines HEK293T, 786–O, HK‐2 and ACHN were purchased from ATCC . UOK120 and UOK109, human carcinoma cell lines characterised by To generate AR‐overexpressing, overexpressing, SUMO‐specific protease 1 (SENP1) AR knocked down, SENP1 knocked down or UCHL1 knocked down stable cell populations, UOK109, UOK120, 786‐O or HEK‐293T cells were infected with pCDH‐AR/pCDH‐Vec, pCDH‐SENP1/pCDH‐Vec, pLV‐shAR‐1/pLV‐scr (CACCAATGTCAACTCCAGGAT), pLV‐shAR‐2/pLV‐scr (CACCAATGTCAACTCCAGGAT), pLV‐shAR‐3/pLV‐scr (GAGCGTGGACTTTCCGGAAAT), pLV‐shSENP1/pLV‐scr (CCGAAAGACCTCAAGTGGATT) or pLV‐shUCHL1/pLV‐scr (CGGGTAGATGACAAGGTGAAT) by using the Lipofectamine 2000 reagent . The psPAX2 packaging plasmid pMD2. G envelope plasmid and the transfer plasmid were transfected into HEK293T cells to produce lentiviruses. Virus supernatants were collected 48 and 72 h after transfection.2.3PRCC, NONO and TFE3 genes were obtained from GenBank. PRCC‐TFE3 is fused by the first exon of PRCC and 4–10 exons of TFE3. NONO‐TFE3 is fused by the first exons of exons 1–7 of NONO and exons 6–10 of TFE3. Then, the fusion genes were cloned into the pcDNA3.1‐3FLAG plasmid vector . Then, the pcDNA3.1(+)‐3Flag‐P2A‐EGFP plasmid  was digested with EcoRI and BspEI, and after fused with the HIS sequence, the PRCC‐TFE3 and NONO‐TFE3 genes were introduced into the expression vector pFLAG‐CMV2 using EcoRI and BspEI sites. Full‐length human SUMO1 was amplified by polymerase chain reaction (PCR) and introduced into the pcDNA3.1(+)‐3Flag‐T2A‐mCherry plasmid . All constructs were confirmed by sequencing. Mutations of PRCC‐TFE3 or NONO‐TFE3 at K330R or K460R were generated by using the Fast‐Mutagenesis Kit V2  following the instructions, and the primers used for K330R and K460R are shown in Supplementary Table The sequences of the 2.4After washing with ice‐cold phosphate buffered saline, two million cells were collected, and the IP assay was performed by using a Pierce Magnetic Co‐IP Kit  according to the manufacturer's protocol. To identify the SUMO modification of target proteins, 20 mM N‐ethylmaleimide  supplemented with 200 μM iodoacetamide (IAM)  was added to the lysis buffer. To identify the ubiquitination of target proteins, the proteasome inhibitor MG132 was added 6 h before cell lysis. The soluble fractions were incubated with 4 μg of antibody and magnetic beads for 2 h at room temperature.2.5Total protein was extracted with pre‐cooled radio‐immunoprecipitation assay (RIPA) lysis buffer , 1% NP) for 30 min. Then, 20 mM NEM and 200 μM IAM were added to the lysis buffer to observe the SUMOylated protein. The cell lysate was centrifuged at 13 400 g for 15 min, and then the supernatant was subjected to (SDS) sample buffer  by boiling for 10 min. Cell lysates were separated using SDS–polyacrylamide gel and then transferred to polyvinylidene fluoride (PVDF) membrane  by standard procedures and then immunoblotted using the indicated antibodies  was used to perform ChIP assay following protocol. The DNA level was quantified by quantitative real‐time PCR (qPCR), and the special primers for ChIP are shown in Supplementary Table 2.7Total RNA was isolated using TRIzol extraction reagent  and was then reverse‐transcribed using a Hiscript II Reverse Transcriptase master mixing kit  according to the manufacturer's instructions. All primers (Supplementary Table 2.8The promoter of target genes was cloned into the PGL3‐Basic vector. HEK293T cells were cultured and then transfected with a target promoter plasmid (containing firefly luciferase), AR, PRCC‐TFE3 or NONO‐TFE3 plasmid using Lipofectamine 2000. The PRL‐TK plasmid  was transfected as an internal control. After transfection for 48 h, the cells were lysed with a Dual‐Luciferase Reporter Assay Kit , and luciferase activity was detected with a GloMaxTM 96 Microplate Luminometer (Promega). The primers used for the luciferase reporter assay are provided in Supplementary Table 2.9Paraffin‐embedded tissue specimens were sectioned into 4‐mm sections. Following routine deparaffinisation, rehydration and blocking, the sections were incubated in primary antibodies against proteins of interest. On the second day, the sections were incubated in horseradish‐conjugated rabbit secondary antibodies, followed by diaminobezidin 3 (DAB) and haematoxylin staining.2.10Cells grown on glass‐bottom culture dishes were fixed, permeabilised and blocked sequentially. Then, the cells were incubated with the indicated primary antibodies overnight and with secondary antibodies for 1 h. Glass bottom was mounted with 4',6‐diamidino‐2‐phenylindole (DAPI) . Fluorescent images were examined and photographed on a confocal microscope .2.11Nuclear and cytoplasmic fractions were extracted with NE‐PER Nuclear and Cytoplasmic Extraction Reagents . In brief, cells were collected using trypsin‐EDTA and then centrifuged at 500 g for 5 min. Pre‐cold cytoplasmic protein extraction reagents I and II were added to the cell pellet, and the tube was centrifuged for 5 min at a speed of 16 000 g to obtain a cytoplasmic extract from the supernatant. The pellet containing nuclei was suspended in nuclear protein extraction reagent and centrifuged at 16 000 g for 10 min.2.12Cells were washed and collected routinely for the follow‐up work. PI/RNase Staining Buffer  and an Annexin V‐FITC/PI staining Kit  were used following the manufacturer's protocols. The samples were detected by FlowJo software (v7.0) on a BD FACSCalibur flow cytometer (BD Biosciences).2.13Cell growth was assessed using EdU staining or cell counting kit 8 (CCK8) assays. EdU staining was performed using a BeyoClick EdU‐594 kit (Beyotime). Before EdU staining, UOK 109 and 786‐O cells were incubated with EdU for 3 h, and UOK120 cells were incubated with EdU for 6 h. The positive rate was calculated as the percentage of EdU‐stained cells among the total cell count in random fields. CCK8 assays were performed at 37°C for 2 h, and the absorbance was measured at 450 nm.2.144 cells were seeded into the upper chambers. After 24 h, 4% paraformaldehyde and 1% crystal violet were used to fix and stain the cells that migrated. Cells were observed and counted with a microscope 50i (Nikon). For the cell invasion experiment, the upper chambers were precoated with Matrigel (Biosciences) and then subjected to the protocol used for the cell migration assay.Cell migration ability was measured with a wound scratch experiment or transwell cell migration assay with an 8‐μm pore size (Corning Life Sciences). For the wound scratch experiment, wound closure was quantitated by ImageJ (NIH), and the wound healing percentage was calculated as the ratio between the wound area and initial scratch area. For the transwell cell migration assay, 5 × 102.15Five hundred cells were plated in 3.5‐cm dishes. After 7 days of culture, Giemsa stain solution  was used to stain the cell colonies, and ImageJ software was used to quantify and count the number of visible colonies.2.16t‐test, chi‐square test, Fisher's exact test or one‐way analysis of variance were used to calculate the statistical significance by SPSS 23.0 (SPSS Inc.). Statistical significance was defined as two‐sided p‐values less than .05. Survival data were obtained from electronic medical records. Progression‐free survival (PFS) was defined as the survival time without relapse or progression since the initiation of surgery. Overall survival (OS) refers to the time interval between surgery and death. Kaplan–Meier analysis was performed by log‐rank test. Differences were considered statistically significant when p < .05.Data in the tables are expressed as the mean ± SEM from at least three independent experiments. Student's 33.1S phase cells decreased, and cell apoptosis increased after cells were cultured with CSM ,To evaluate the predictive value of AR for the prognosis of Xp11.2 tRCC patients, the AR expression of 46 cases of Xp11.2 tRCC and 13 cases of ccRCC was detected by IHC, and the results showed that 26 (56.5%) cases of Xp11.2 tRCC and four (30.8%) cases of ccRCC were positive for AR (3.3p < .05) between AR and the wild‐type TFE3 gene , the target genes of wild‐type TFE3 protein or TFE3 fusion proteins involved in the cell cycle and cell–cell adhesion were evaluated. qPCR results showed that with the overexpression or knockdown of AR expression, a large proportion of the target genes of the TFE3 fusion protein and wild‐type TFE3 correspondingly increased or decreased, respectively . The obtained results showed that only a small number of genes were directly regulated by AR and TFE3 fusion proteins .Then, IHC staining of SENP1 and UCHL1 was performed to evaluate their physiological relevance to AR and tumour progression Figure . Among 43.9To assess the therapeutic effects of AR inhibitors in suppressing the progression of Xp11.2 tRCC, UOK109 and UOK120 cells were treated with MDV3100, a second‐generation antiandrogen. The results showed that MDV3100 could improve the SUMOylation rate of TFE3 fusion proteins by reducing the expression of SENP1 Figure . At the 4The crucial role of AR in the development and progression of prostate cancer has been acknowledged, and the expression of AR in susceptible male individuals, such as RCC and melanoma, has also been reported. However, the role of AR in the process of RCC remains controversial. As early as 2004, a study including 182 cases of RCC found that the positive rate of AR reached 14.8%, which was much higher than that of ERα (1.1%) and PR (1.1%). In addition, AR was regarded as a protective factor in RCC because the expression of AR was associated with lower tumour stage, unclear grade and prognosis. In contrast, a study in 2015 found that the mRNA expression level of AR was significantly higher in patients with pT2 tumours than in those with pT1 tumours. Treatment with DHT resulted in proliferation in the AR‐positive cell line HS891. T and CAKI2, while enzalutamide inhibited proliferation and cell viability in a dose‐dependent manner. A meta‐analysis including 11 retrospective studies with 1839 RCCs found no difference between AR expression and metastatic status, metastatic type (lymph or distant metastasis), susceptibility, pathological type or cancer‐specific survival of RCC. However, positive AR expression was demonstrated to be significantly associated with male patients, lower pathological grade and earlier tumour stage of RCC. In our study, relevant data extracted from GEPIA showed that AR expression was positively associated with PFS in conventional RCC, which indicated its tumour suppressor role in RCC. Our study discovered a relatively higher anti‐AR positivity ratio in Xp11.2 tRCC than in ccRCC (56.5% vs. 30.8%), although the difference was not statistically significant. However, AR expression was negatively associated with PFS in Xp11.2 tRCC patients but not the age of onset, patient sex, tumour size, tumour stage or pathological grade. In contrast, there was no significant difference in AR positivity between men and women, which could explain, at least in part, their semblable prognosis.Recently, an in vitro study showed that AR increases haematogenous metastasis but decreases lymphatic metastasis of RCC by enhancing miR‐185‐5p expression, which promoted HIF2α/VEGF‐A and suppressed VEGF‐C expression.SUMOylation and deSUMOylation are reversible and highly dynamic processes that are influenced by cellular stress.SENP1 is significantly elevated with chronic androgen exposure in the androgen‐sensitive human prostate cancer cell line, indicating the feedback mechanism between SENP1 and AR,SENP1 was reported to play an important role in regulating AR‐dependent transcription and hypoxia signalling by removing SUMO isoforms from AR, and the expression of SENP1 directly correlates with the aggressiveness and recurrence of prostate cancer by regulating two critical bone remodelling proteins, MMP2 and MMP9.To date, only three ubiquitin enzymes have been reported to regulate MiT/TFE degradation.5Overall, this study identified that AR was frequently expressed in Xp11.2 tRCC tissues and that the expression of AR was associated with patient PFS. AR induced transcriptional activity of TFE3 fusion proteins and degradation by regulating their deSUMOylation and ubiquitination, respectively. Furthermore, UCHL1 inhibitor 6RK73‐induced degradation of the PRCC‐TFE3 fusion protein led to enhancement of the potency of the AR antagonist enzalutamide in UOK120 cells. These findings provide a rationale for the clinical testing of combination strategies with AR and UCHL1 inhibitors in Xp11.2 tRCC.The authors declare that they have no competing interests.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.


Acoustic radiation is one of the most important white matter fiber bundles of the human auditory system. However, segmenting the acoustic radiation is challenging due to its small size and proximity to several larger fiber bundles. TractSeg is a method that uses a neural network to segment some of the major fiber bundles in the brain. This study aims to train TractSeg to segment the core of acoustic radiation.We propose a methodology to automatically extract the acoustic radiation from human connectome data, which is both of high quality and high resolution. The segmentation masks generated by TractSeg of nearby fiber bundles are used to steer the generation of valid streamlines through tractography. Only streamlines connecting the Heschl's gyrus and the medial geniculate nucleus were considered. These streamlines are then used to create masks of the core of the acoustic radiation that is used to train the neural network of TractSeg. The trained network is used to automatically segment the acoustic radiation from unseen images.The trained neural network successfully extracted anatomically plausible masks of the core of the acoustic radiation in human connectome data. We also applied the method to a dataset of 17 patients with unilateral congenital ear canal atresia and 17 age- and gender-paired controls acquired in a clinical setting. The method was able to extract 53/68 acoustic radiation in the dataset acquired with clinical settings. In 14/68 cases, the method generated fragments of the acoustic radiation and completely failed in a single case. The performance of the method on patients and controls was similar.In most cases, it is possible to segment the core of the acoustic radiations even in images acquired with clinical settings in a few seconds using a pre-trained neural network. The acoustic radiation (AR) is a white matter fiber bundle that connects the Heschl's gyrus (HG) in the cortex with the medial geniculate nucleus (MGN) in the mid-brain , 2. The Extracting the AR with tractography from diffusion MRI (dMRI) is challenging . First, ex vivo studies on macaque monkeys that the AR might have extra layers of fibers that create a “belt” that can go beyond the HG and reach the superior temporal gyrus (STG)  , 15. Theb-values  and high resolution (1.5 mm isotropic) from the MGH adult diffusion dataset of the human connectome project (HCP)  , 18. Howct (HCP) , 16, 19.Two automatic tools include the segmentation of the AR: XTRACT , 21 and TRACULA  is a tooRegarding the AR, Maffei et al.  used a sb-values, is fast (takes a few seconds), does not require a previous registration of images, and, unlike atlases, the results are subject-specific. Due to the aforementioned difficulties in segmenting the AR, the original method did not include the AR. More recently, Wasserthal et al. , or metallic artifacts were excluded from the cohort. In twelve of the patients, the right ear is affected. Eight of the patients are female and nine are male. The patients were all recruited in the Stockholm region. The ethical permit was granted by the Swedish ethical board (Dnr 2012/1661-31/3). The clinical dataset was pre-processed with the standard pre-processing pipeline of MRtrix3  with anatomically-constrained tractography (ACT)  from MRt MRTrix3  with theAs mentioned, one of the challenges in obtaining the AR is that it is very close to other fiber bundles, as shown in As shown in An additional problem is that the HG and the superior temporal gyrus (STG) are very close to each other, as shown in Notice that the described restrictions for generating streamlines are stringent and make the generation of training data computationally expensive. Actually, around 150,000 generated streamlines were discarded per every single accepted one. Thus, as stopping criteria, we set a maximum of 1,000 accepted streamlines, or 150 million generated streamlines in total per seed mask. The maximum length of each streamline was set to 60 mm. The two sets of streamlines per side were combined into a single tractogram. This procedure resulted in tractograms of at least 1,000 streamlines per side of the brain. Finally, a mask of the AR per side was created with the voxels traversed by at least ten streamlines. This procedure was successful in all HCP subjects.It is important to emphasize that the original article of TractSeg  used whoThis section shows the results of the proposed methodology for segmenting the core of the AR applied to HCP data and the diffusion data acquired in a clinical setting on 17 patients with unilateral congenital ear canal atresia and 17 age- and gender-paired controls.We tested the trained network in 20 additional HCP subjects not used for training. As shown in As shown in Using visual inspection, we found that the proposed methodology was able to extract anatomically plausible AR in all 20 subjects used for independent testing.b = 1, 000s/mm2, 60 directions, spatial resolution = 2.3 mm isotropic). This case is more challenging than the segmentation of the HCP data due to the low spatial and angular resolution and the relatively low b-value used in the acquisition. We applied the trained network on dMRI data of 17 subjects with unilateral ear canal atresia and 17 controls. As mentioned, these images were acquired in a clinical setting  from the HCP young adult dataset by using masks of neighboring fiber bundles is also possible. One issue of our approach is that our strategy is very restrictive and time-consuming.Previous studies have shown that extracting the AR is possible 00 s/mm2 . In thisThus, in order to reduce the computation time, we trained the neural network of TractSeg  with theThe trained neural network of TractSeg was able to segment the core of the AR in HCP data in a few seconds instead of several hours. We used a workstation equipped with an Intel Xeon CPU E5-2630 v3 with 8 cores at 2.40 GHz, and a GPU NVIDIA GeForce GTX 1070. The processing of one HCP subject using the methodology described in Section 2.3 was 8–10 h in this workstation. Computing the peaks of the fODFs took approximately 1 min and applying the trained neural network took around 40 s for both the HCP data and the clinical data. The segmentations generated by the trained neural network were anatomically plausible when applied to an independent set of subjects from HCP. The methodology proposed in Section 2.3 is conservative. Thus, the segmentation masks obtained with the neural network are also conservative compared to the publicly available atlases of the AR. We argue that it is important to have a conservative approach to extracting the core of AR. This way, the downstream conclusions drawn from group analyses of the AR will become more meaningful.The trained neural network had more problems with data acquired in a clinical setting. Still, it was able to completely segment the core of the ARs in 77.9% of the cases, yielded fragmented masks in 20.6% of the cases, and only failed in a single subject. The performance was very similar in patients and controls. The neural network tended to reconstruct the core of the left AR better than the core of the right AR.As shown in some cases, the neural network yields a fragmented segmentation. Such fragments can be used as seeds for tractography, which has the advantage of reducing the high cost of running tractography to extract the core of the AR.We compared the proposed methodology with the segmentation generated by TractSeg  trained Unlike our methodology, XTRACT was able to generate the AR in all cases. Since XTRACT uses less restrictive rules for generating the masks, they cover more voxels, which makes TractSeg increase its robustness at the cost of being less specific. In some cases, the XTRACT masks covered parts of the ventricles and the most posterior parts of the STG, almost reaching the medial temporal gyrus. Thus, we recommend a manual review of these masks before any further analysis.Previously, Bertó et al.  added prWe showcased the use of segmentation masks by performing a bundle analysis on the clinical dataset to assess differences in FA between patients and control in the AR. We did not find any statistically significant difference between the groups. The 95% CI was larger than other bundles . This suggests that the intersubject variability is higher for the AR.The results of this study are encouraging but also show that more research is needed toward a fully automatic segmentation of the AR from images acquired in clinical settings. For example, as mentioned, TractSeg uses three peaks of the fODFs . RecentlThis study has many limitations. One of the main issues is that there is not possible to have a personalized ground truth that can be used to assess the accuracy. This is a general limitation of any method based on tractography. The atlas by Bürgel et al.  was creaIn this study, we proposed a methodology to extract the core of the AR in subjects from the HCP young adult dataset by using masks of neighboring fiber bundles obtained with TractSeg. Since the procedure is expensive, we trained TractSeg to extract the AR automatically. For this, we used the masks of the AR extracted from a set of subjects from the HCP young adult dataset. The trained neural network was applied both to unseen subjects of the HCP young adult dataset and a clinical dataset.The main conclusion of this study is that it is possible to segment the core of the AR in most cases, even in images acquired in clinical settings in a few seconds with the trained network. In case it is not possible to reconstruct the core of the AR, the results can be used as masks for tractography.https://doi.org/10.5281/zenodo.7052849. The data from the Karolinska Institute cannot be shared. Requests to access these datasets should be directed to RM, rodmore@kth.se.The data analyzed in this study is subject to the following licenses/restrictions: We used data from the Human Connectome Project (HCP), WU-Minn Consortium  funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The training data and the trained neural network is available at The Human Connnectome Project (HCP) data is publicly available and all authors have accepted the HCP Open Access Data Use Terms. The acquisition of the dataset from Karolinska Institute was reviewed and approved by the Swedish Ethical Board (Etikprövningsmyndigheten) Dnr 2012/1661-31/3. The patients/participants provided their written informed consent to participate in this study.MS: conceptualization, data curation, investigation, methodology, and writing—review and editing. CE: conceptualization, methodology, resources, writing—review and editing, supervision, and funding acquisition. RM: conceptualization, methodology, visualization, resources, project administration, writing—original draft, review and editing, supervision, and funding acquisition. All authors contributed to the article and approved the submitted version.This study was partially supported by VINNOVA, through AIDA, the Center for Innovative Medicine (CIMED), Region Stockholm, and Digital Futures, Project dBrain.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Allergic rhinitis (AR), by definition, denotes an inflammation of the nasal mucosa, mediated by IgE, accompanied by symptoms such as nasal congestion, local itching, sneezing and rhinorrhea.There have been two large studies assessing the prevalence of AR in Brazil. The first, through questionnaires applied to children and adolescents, revealed that nasal and ocular symptoms during the previous year, but without the presence of an upper respiratory infection were present in approximately 12–15% of subjects. The second, also using questionnaires, but evaluating all age ranges with a diagnosis of AR, found a prevalence of 9%.3Interestingly, these studies infer that the nasal symptoms are allergic in nature, since the presence of the specific IgE has not been proven, a basic assumption to determine an allergic etiology (see definition).In 1859, Charles Harrison Blackley applied pollen to his nasal mucosa and that triggered the symptoms of rhinitis. Thus, the nasal challenge test for the diagnosis of allergic rhinitis appeared.One of the first studies to demonstrate the production of specific IgE in nasal secretion only was published in 1975. Fourteen patients with clinical symptoms suggestive of AR caused by dust mites, although with a negative radioallergosorbent test (RAST) were submitted to nasal challenge testing with this antigen. All patients had nasal symptoms, which were absent in the control group.This fact demonstrates that there are two phenotypes of AR. One represents “classic” patients, who exhibit a characteristic clinical picture, the presence of positive systemic specific IgE in skin and serum tests, and have a personal and family history of atopy. The other phenotype contains individuals who have symptoms only after contact with allergens, but no family history of atopy and positive nasal provocation test only for the antigen. These individuals have what is called Local Allergic Rhinitis (LAR).The prevalence of LAR is not known. Some studies show that over 47% of cases of noninfectious, non-allergic rhinitis may be a result of specific IgE production restricted to the nasal mucosa.There is very little in-depth medical literature on LAR; however, the questions of whether LAR represents a possible early onset of AR has been addressed. Follow-up of adult and adolescent patients with LAR for 5 years did not show any difference in the onset of atopy in (6.25%) when compared to controls (5.2%).Dermatophagoides pteronyssinus) is the main associated agent.Patients with LAR have the same classic symptoms of those with AR, such as sneezing, itching, nasal obstruction and rhinorrhea.In addition to the dust mites, other antigens are associated with LAR, such as pollens and fungi, causing symptoms in both children and adults.11From a pathophysiological point of view, LAR is similar to AR. An inflammatory infiltrate pattern with Th2 , immediate phase characterized by the activation and release of basophil mediators and a late phase with eosinophil attraction and activation can be observed.Regarding therapy, LAR patients respond well to both oral and topical medications. Additionally, they also show improvement with allergen-specific subcutaneous immunotherapy, similarly to patients with AR. A pilot study with 20 patients with LAR sensitive to grasses were submitted to allergen-specific subcutaneous immunotherapy. At the end of the study, the authors observed greater tolerance to the allergen and symptom reduction, as well as decreased use of rescue medication.The diagnosis of LAR is corroborated by demonstrating the presence of the nasal specific IgE through nasal provocation test with allergens or the local synthesis of IgE, without the presence of systemic atopy.The authors declare no conflicts of interest.


Rehabilitative care is often overlooked and underfunded despite being a key component of universal health coverage, and now faces further neglect due to indirect impacts of the coronavirus disease 2019 pandemic. Policy-makers can leverage strategic purchasing approaches to make the most of available funds and maximize health gains. To implement more strategic purchasing of rehabilitation, health planners must: (i) develop and prioritize evidence-based rehabilitation service packages; (ii) use fit-for-purpose contracting and provider payment mechanisms to incentivize quality and efficient service delivery; and (iii) strengthen stewardship. This paper examines these three policy priorities by analysing their associated processes, actors and resources based on country experiences. Policy-makers will likely face several obstacles in operationalizing these policy priorities, including: inadequate accountability and coordination among sectors; limited data and research; undefined and non-standardized rehabilitation services, costs and outcomes; and inadequate availability of rehabilitative care. To overcome challenges and institute optimal strategic purchasing practices for rehabilitation, we recommend that policy-makers strengthen health sector stewardship and establish a framework for multisectoral collaboration, invest in data and research and make use of available experience from high-income settings, while creating a body of evidence from low- and middle-income settings. Purchasing refers to the transfer of funds from third-party payers to health-care providers to pay for services delivered to a covered population.Through these considerations, strategic purchasing focuses resources on outcomes that expand financial protection and rehabilitation service coverage and help countries progress towards universal health coverage goals.,,,,Strategic purchasing is technically intricate, capacity intensive and politically complex to implement.Despite these complexities, rehabilitation purchasers can rely on several policy options and instruments to improve purchasing decisions.The transition from passive purchasing to strategic purchasing represents a change in thinking in how rehabilitation services are prioritized, paid for and delivered. We present three key policy priorities that can promote this shift and enable reforms to improve efficiency and quality of care: (i) developing and prioritizing evidence-based benefits packages for rehabilitation; (ii) using contracting and provider payment mechanisms to pay for quality and efficient rehabilitation services; and (iii) instituting and strengthening stewardship for purchasing rehabilitation services.These policy priorities, their implementation components and country examples are based on consultations with health financing and rehabilitation experts and peer-to-peer exchanges with practitioners from over 40 countries.A national health benefits package refers to the set of services that can be feasibly financed and provided within a country’s context.Rehabilitation service packages should be developed within the existing health service prioritization framework. Essential processes include estimating population need, comparing need to currently available services and potential solutions, estimating related costs, setting tariffs and matching the package with available financial resources. This approach will help avoid vague, non-transparent and underfunded benefits packages that are occasionally observed in publicly funded systems in low- and middle-income countries.Rehabilitation benefits should be selected by multistakeholder committees or working groups, and based on prioritization frameworks. Because of the close linkage of rehabilitation to the rights of people living with disabilities, civil society actors such as organizations of people with disabilities can play a fundamental role in ensuring user responsiveness of benefits and advocating to prioritize rehabilitation in financing schemes. In the Philippines, such organizations helped facilitate the prioritization and the inclusion of select mobility, prosthetic, orthotic and rehabilitation services in the Z Benefits package under the national health insurance scheme,Data and evidence are essential resources for the formulation of benefits packages. Rehabilitation need is frequently informed by disability data, or prevalence of health conditions that can cause permanent or temporary functional difficulties. Looking beyond disability data is important in capturing rehabilitation needs of individuals without a disability status or with cognitive functional challenges that may not be adequately captured by self-reported surveys.Contracts are negotiated agreements between purchasers and providers. Provider payment mechanisms can be part of contracts and define how funds are transferred to providers. These mechanisms are key tools for a strategic purchaser to promote the efficiency and quality of rehabilitation services.When contracting for health services, purchasers should develop legal frameworks for collaboration, identify public and private rehabilitation providers to contract, construct an agreement about what will be purchased, determine how it will be purchased, set minimum required standards for facilities and expected outcomes, and develop systems to continuously monitor provider performance.–,One common observation in low- and middle-income countries is that rehabilitation contracts often lack specifications on the expected outcomes of care.,,Implementing contracts and provider payment mechanisms can be resource intensive as they rely heavily on well-defined outcomes of care, information systems and data, as well as the capacity to report and monitor. Purchasers must find a sustainable balance between overly standardized, inflexible and passive payment practices that do not consider service outcomes – such as the voucher system for Georgia’s State Programme for Social Rehabilitation and ChildcareThe scarce evidence on the effectiveness of different contracting and provider payment mechanisms for rehabilitation is limited to high-income settings and remains unexplored for low- and middle-income settings. Therefore, piloting and testing innovative approaches, conducting implementation research and other adaptive learning methods is needed to better understand the feasibility and impact of different contracting approaches and provider payment mechanisms on rehabilitation outcomes. Countries can learn from their own and peer experiences on different purchasing approaches for rehabilitation and the global health community should actively invest in this exchange.,,Effective stewardship for strategic purchasing requires leadership to develop policies, set clear objectives, align various actors to achieve those objectives, and enforce the regulatory environment for purchasing arrangements.One effective strategy is to identify and empower rehabilitation focal points within health ministries to champion health sector stewardship for rehabilitation, including purchasing policies. Rehabilitation services are purchased from health and non-health sectors. The latter include disability-focused programmes in the social, labour, defence or education sectors, or insurance schemes that manage disability, accident and/or workers’ compensation schemes.For example, the Rehabilitation Services Unit of the Kenya Ministry of Health leads the multistakeholder Rehabilitative and Assistive Technology Interagency Coordinating Committee and oversees the rehabilitation technical working group, which shapes county rehabilitation agendas and advises county directors for health on rehabilitation financing reforms – including for strategic purchasing. These reforms are coordinated at the county level with stakeholders from the health and non-health sectors. Reforms are guided by the National Rehabilitation and Assistive Technology Strategy and other national policies.Numerous legal, operational and technical resources can support stewardship of rehabilitation purchasing. These resources include strategic or operational plans, terms of reference for coordination platforms, and legal or policy documents that clarify the roles of various institutions, empower health sector actors to be effective stewards for rehabilitation and enable purchasers to be strategic in their investments.Global momentum created by the Rehabilitation 2030 agendaStrategic purchasing is a powerful but technically demanding approach to promoting quality and equity of services in resource-constrained settings. We propose following certain first-order priorities for policy-makers and managers committed to improving purchasing decisions for rehabilitation.Purchasers need data to make strategic decisions, so an immediate priority should be identification and investment in data that can inform benefits, contracts, provider payment mechanisms and accountability mechanisms. While countries build capacities and cultures of data production, transparency and exchange among rehabilitation providers and multisectoral purchasers, they can adapt global resources and information to their local and/or national context.Strategic purchasing decisions for rehabilitation should be inclusive and multisectoral. Rehabilitation is purchased and provided by multiple sectors. Without clarity on roles and accountability frameworks, everyone and no one is simultaneously accountable. In line with global momentum to strengthen rehabilitation care through a health system lens,Technical and academic stakeholders within the global health community have a key role to play in facilitating learning about and informing strategic health purchasing policies for rehabilitation. There is still a lot to learn about the feasibility and effectiveness of strategic purchasing instruments such as benefits packages, contracts and provider payment mechanisms for rehabilitation in resource-constrained settings and emerging health systems. The global health community should focus on innovating on, piloting, testing and studying these approaches to fill the knowledge gap and enrich the global evidence base with experiences from low- and middle-income countries. Over time, this experience will inform global normative instruments and resources for countries to use across different regions, resource settings and health systems contexts.The need to reinforce strategic use of funds for rehabilitation is urgent – especially in resource-constrained settings and considering post-COVID-19 realities. We present policy priorities that can help health system leaders make strategic purchasing decisions and target investments in ways that accelerate progress towards universal health coverage. We also encourage the global health community to invest in applied research on purchasing rehabilitation services, so policy-makers can assess innovative approaches and leverage high quality evidence throughout this journey.


Allergic rhinitis (AR) is considered to be 1 of the most difficult diseases to treat globally. It has a serious impact on the quality of life and social economy of patients and has become an important global health problem. Several drugs have been recommended to treat AR, but their effectiveness and mechanism of action in these patients remain unclear. The purpose of this study will be to compare the efficacy and mechanism of action of 2 drugs for the treatment of AR (moderate to severe): a Dermatophagoides Farinae Drops Sublingual Immunotherapy and a Momethasone Furoate nasal spray as an adjunct to the treatment of subjects with AR.A randomized, prospective, double-blind  clinical trial. The participants (n = 60) will be randomly distributed into 2 groups. The experimental group will receive a sublingual Immunotherapy for 3 months. The control group will receive the mometasone furoate nasal spray for 3 months. Before treatment, 1 month and 3 months after treatment, total nasal symptom score scale, Visual analogue Scale and Quality of Life questionnaire of rhinoconjunctivitis will be measured and Changes of the serums of IgE, interferon-γ, IL-4, IL-17, tumor necrosis factor-α, IL-5, IL-9, IL-13, IL-25, IL-33, vascular endothelial growth factor, TSLP and IL-22 in both groups. The measurements will be performed by the same researcher who was unaware of the participants’ subgroup.We believe that the treatment of perennial AR with sublingual Immunotherapy and nasal hormones will be more effective in these patients. Furthermore, the sublingual Immunotherapy mainly acts mostly on the cellular immunity, while nasal hormones mainly act on local inflammatory responses. We expect to clarify which treatments are more effective and how they work in improving perennial AR. Numerous epidemiological studies have been shown that AR affects 10% to 40% of the global population. It has become a major chronic inflammatory respiratory disease and is considered 1 of the most difficult diseases to treat globally.Allergic rhinitis (AR) is a common and frequent ailment of otolaryngology. It is a perennial or seasonal disease with symptoms such as sneezing, runny nose, itching nose, nasal congestion and anosmia.–6 it has a serious impact on the quality of life and socio-economic well-being of patients and has become a major global health problem. It is reported that economic losses in EU countries can be between 30 and 50 billion euros per year. A large sample study in the United States showed that AR reduced worker productivity more than any other disease, including hypertension, diabetes, and heart disease.Although AR is not a serious disease, but due to its recurrent, persistent, and complicating characteristics,,10 all kinds of treatment methods have their advantages. However, there are no good measures to avoid dust mite allergy at present. Therefore, it is becoming more and more important for scholars to change patients’ allergic physique through specific immunotherapies.The treatment of AR mainly includes environmental control, drug therapy, immunotherapy, etc. Regulating the immune balance of Th1/Th2/Th17 cells during the occurrence and development of AR is considered to be an important approach in the current treatment of AR. But there is increasing evidence that innate immune response is also the pathogenesis of AR. Innate lymphoid cells (ILCs) are involved in mucosal immune formation, lymphocyte development, tissue damage repair and epithelial barrier protection, and play an important role in fighting infection, regulating inflammation and maintaining immune homeostasis. Some studies have proposed that the 3 subsets of innate lymphocytes  are functionally approximately corresponding to Th1, Th2, and Th17 of T helper cells. However, current studies have not been conclusive, and the specific mechanism of ILCs in the development of AR has not been fully elucidated.AR is a noninfectious chronic inflammatory disease of the nasal mucosa mainly mediated by immunoglobulin E after exposure to allergens in atopic individuals.Our objective was to compare the efficacy of sublingual immunotherapy and nasal hormone in the treatment of perennial AR and the mechanism of action on ILCs, and to reveal the correlation between ILCs  and Th1/Th2/Th17 cell immunity, so as to provide research basis for clinical study of AR.A randomized, prospective, double-blind  clinical trial. The Researchers should not be blinded due to the use of therapeutic method. Figure ,12,13 during that appointment will be referred to a Outpatient physician of otolaryngology Head and neck Surgery, who will check whether they meet the other inclusion and exclusion criteria before offering them the opportunity to participate in the study.We will recruit and include 60 participants with perennial AR from the outpatient Department of Otorhinolaryngology Head and Neck Surgery Sant by the People’s Hospital of Anshun City of Guizhou Province (China). All participants receiving this service must be seen by a physician . All those diagnosed with perennial AR (of more than 1 years duration) according to the Guidelines for ARThe inclusion criteria will be: We will include participants with perennial AR only ; above 18 years old to below 65 years old; both sexes; The participants has not received specific immunotherapy in the past 1 month or has not used any drugs to treatment the AR in the past 1 week; The participants must be examined with a positive result in the IgE sensitization test to aeroallergens ; they must with normal cognitive function and agree to participate in this study and sign the informed consent form.The exclusion criteria will be: Participants who met any of the following criteria were excluded: The participants with severe nasal septum deviation, chronic rhinosinusitis, bronchial asthma, nasal polyps, upper respiratory tract infection, lung infection and other diseases; with severe dysfunction of heart, liver, kidney or autoimmune diseases; Pregnant or lactating women; Allergic constitution and allergic to the experimental drugs and ingredients; With drug addiction history; With major neuropsychiatric diseases who cannot take medication regularly; participating in other clinical trials.The participants who meet all inclusion criteria and not meet exclusion criteria will have the opportunity to participate in the study and provide all relevant information orally and in writing. They will be told to be randomly assigned to study groups, each of which will be treated with a different drug, all of which are appropriate for their condition, and the goal of the study is to determine which drug produces the best results.To maintain this blindness, the participants will not be told which group they had been assigned to.If they decide to participate, they will be asked to sign an informed consent form, and the recruiting physician will conduct a baseline assessment.We will be collected 60 subjects and randomize them into 2 groups  and use a computerized randomization method using EXCEL software and RAND function to generate random numbers . Sixty randomly selected subjects will have a 1/2 chance of being randomly assigned to either the experimental or control groups. According to the order of presentation, participants will be assigned to the corresponding group for treatment according to the generated random number table.The ENT clinic practitioner performing the intervention will be the only 1 with access to the list generated by the computer program. This physician cannot be blinded by using manual techniques, and he/she will know which group the subject is assigned to by referring to the number assigned by the evaluator.To ensure the concealment and blindness of the task, the subjects will not know which group they have been assigned to.The recruiting physician (PhD in otolaryngology with 8 years of experience in the field) will perform all assessments for the study and remain blinded to the group to which subjects are assigned at all times.Table Table   and Changes in the level of serological indicators (Enzyme-linked immunosorbent assay will be used to detect). The secondary outcome indicators are visual analogue scale and Standardized Rhinoconjunctivitis Quality of Life Questionnaire and mitigate changes in drug use. Data will be recorded by Case report forms (CRFs) before treatment, at 1 month of treatment, and at 3 months of treatment.The primary outcome indicator is the decrease in the total nasal symptom scoreFor safety reasons, all unexpected reactions related to the study treatment will be recorded and described in detail, including time to onset, duration of symptoms, severity of symptoms, management measures, time to resolution of adverse effects, and causal classification. All adverse events, whether or not they are related to the study treatment, will be recorded and managed by the investigator. If more than 25% of the patients discontinued the intervention because of adverse events, the superior physician would decide to discontinue the trial. The CRF will be used for data collection to document demographics, assessment, and reasons for patient withdrawal. At the end of the study, the investigator will submit a CRF form to the data Management Committee.Data for this study will be collected by 2 assistants and entered into pre-designed forms. Information and data related to this study will be collected, shared, and stored in a separate repository to protect confidentiality before, during, and after testing. The information was not available to anyone outside the research team. Participants’ study information may not be released outside the study without their written permission.SPSS  is the earliest statistical analysis software in the world. Norman H. Nie, C. Hadlai (Tex) Hull and Dale H. Bent, three graduate students from Stanford University in the United States, successfully researched and developed the software in 1968. Meanwhile, SPSS company was established. In 1975, SPSS headquarters was established as a corporate organization in Chicago.On July 28, 2009, IBM announced it would acquire SPSS, Inc., a provider of statistical analysis software, for $1.2 billion in cash. The latest version of SPSS is now 25 and renamed IBM SPSS Statistics. So far, SPSS company has more than 40 years of growth history.The hospital’s Clinical Trials Administration will be responsible for overseeing the progress of the study, and this organization will have no involvement in the study at all and will not present a conflict of interest. On-site monitoring meetings will be hold once before and 1 month after the start of the study to monitor the progress of the study and to ensure compliance with established protocols, good clinical practice guidelines and applicable regulatory requirements. Although a separate data monitoring committee will not be established, the hospital clinical trial administration will perform this function by regularly monitoring the clinical trial to ensure scientific validity, scientific integrity, and data accuracy. Current studies have shown that Th1/Th2/Th17 cell immune imbalance is an important mechanism of AR pathogenesis.–18AR is a noninfectious chronic inflammatory disease of nasal mucosa mainly mediated by immunoglobulin E after atopic individuals are exposed to allergens. AR can be classified as seasonal or perennial according to the type of allergen. According to the course of disease can be divided into intermittent and persistent; The impact on quality of life is divided into mild and moderate-severe. Th17 is a new type of T helper lymphocyte, which is a pro-inflammatory cell that can activate the body’s inflammatory response and participate in the regulation of the autoimmune system. It was discovered in 2003 and got its name because it can secrete iconic factors such as IL-17 and IL-23, and plays an important role in the body’s self-immune response.In the development of AR, T cells are the only cells that react directly with antigens. Helper T cells are derived from interleukin-2-producing precursor cells. After initial stimulation, these cells develop into Th0 cells (CD4 + T cells), which produce cytokines including IFN-γ, IL-2, IL-4, and IL-5. Th0 cells differentiate into Th1 cells under the induction of IL-12 and IFN-γ, which secrete IFN-γ, IL-2 and tumor necrosis factor-β and participate in cellular immune response. Under the induction of IL-4, they differentiate into Th2 cells and secrete IL-4, IL-5, IL-13, IL-8 and other cytokines to participate in humoral immune response. It has been reported that IL-4 immune inflammatory factor released by Th2 cells has a regulatory effect on the level of IgE. However, IFN-γ released by Th1 cells has an inhibitory effect on IL-4 secretion by Th2 cells. IL-17 is a cytokine secreted by Th17 cells with strong proinflammatory effect. Serum IL-17 in patients is positively correlated with IgE level, and its increased level can be used as an indicator for the diagnosis of AR. Therefore, regulating the immune balance of Th1/Th2/Th17 cells is an important way to treat AR. However, increasing evidence shows that innate immune response is also the pathogenesis of AR.In the occurrence and development of AR, IFN-γ, IL-4 and IL-17 are the main effectors of Th1, Th2 and Th17 respectively. ILCs, as an important effector cell population of innate immunity, are characterized by 3 major characteristics: they do not undergo receptor gene rearrangement and clonal selection, lack of phenotypic markers of myeloid cells and dendritic cells, and their morphology belongs to the lymphoid lineage. ILCs are mostly tissue-resident lymphocytes, mainly distributed in the tonsil, broncho-lung, intestinal tract, skin and other mucosal barrier sites. Ilcs are involved in mucosal immune formation, lymphocyte development, tissue damage repair and epithelial barrier protection, and play an important role in fighting infection, regulating inflammation and maintaining immune homeostasis.The innate immune system is the first line of defense against invading pathogens or antigens, and its response is rapid and nonspecific. Subsequently, the activated adaptive immune system performs complete elimination of specific antigens. which are functionally approximately corresponding to Th1, Th2 and Th17 of Helper T cells. ILC1s includes natural killer cells (NK) and ILC1 cells, which depend on T-box transcription factor and produce large amounts of IFN-γ and tumor necrosis factor-α. The development of ILC2s depends on the transcription factor GATA3 to produce Th2-type cytokines and other effector molecules, such as IL-4, IL-5, IL-9, IL-13 and Vascular endothelial growth factor, which drive the development of type 2 immune response. Moreover, unlike T cells, which recognize specific antigens, ILC2s respond to nonspecific cytokines, including IL-25, IL-33, and Thymic stromal lymphocytes produce hormone. TSLP can stimulate the activation and proliferation of ILC2s to produce a large amount of IL-5 and IL-13, resulting in airway inflammation and airway hyperresponsiveness. ILC3s depend on the transcription factor RORTt to produce cytokines IL-17 and IL-22 similar to Th17.–29According to the phenotype and cytokines secreted by ILCs, ILCs can be divided into 3 subsets of type 1, 2 and 3 innate lymphocytes ,,31 However, the level of IL-25 released by peripheral blood mononuclear cells will be up-regulated after basophils of birch and pollen allergy patients are stimulated by allergens. Other studies have shown that the number of ILC2 in peripheral blood of patients with house dust mite allergy is increased, and its number change is positively correlated with the severity of symptoms.,34 Studies on AR caused by plant allergens found that during the grass pollen season, the number of ILC2 and ILC3 in peripheral blood of patients with grass pollen allergy increased, while the number of ILC1 did not change significantly. However, Bartemes et alfound that the number of ILC2 in peripheral blood of AR patients did not increase, but that of asthma patients increased. There is no consensus on whether the number of ILC2s in peripheral blood of AR patients is increased.Some studies have found that after the nasal epithelium of AR patients is stimulated by allergens, the pro-inflammatory cytokines in the epithelium increase, and IL-25, IL-33 and TSLP can be detected in the nasal lavage fluid of patients with house dust mite allergy.In conclusion, the mechanism of AR is mainly related to the imbalance of Th1/Th2/Th17 cell immunity, but more and more evidence shows that innate immune response is also the pathogenesis of AR, and the specific mechanism of ILCs in the development of AR has not been fully elucidated.Therefore, this study aims to explore the efficacy of sublingual desensitization in the treatment of perennial AR and its mechanism of action on ILCs, reveal the correlation between ILCs  and Th1/Th2/Th17 cell immunity, and provide research basis for clinical research on AR.Conceptualization: Guangjun Tang, Bo Do.Data curation: Kai Yang, Youxing Lan.Formal analysis: Jiafeng Lu.Funding acquisition: Guangjun Tang.Software: Long Chai.Supervision: Fangming Chen.Writing – original draft: Guangjun Tang, Youxing Lan.Writing – review & editing: Guangjun Tang, Li Tian.


This diagnostic study assesses the accuracy and internal validity of a prediction model developed to estimate the risk of perinatal arterial ischemic stroke in term neonates using clinical pregnancy, delivery, and fetal factors. Can common clinical factors be used to develop and internally validate a risk prediction model for perinatal arterial ischemic stroke (PAIS) in term neonates?In this diagnostic study of 2571 term neonates, a prediction model for risk of PAIS that included 1924 neonates and 9 clinical factors  had good discrimination and model fit between case and control individuals.Prediction models for PAIS may help identify neonates at risk of PAIS who should be screened for early diagnosis and intervention. Perinatal arterial ischemic stroke (PAIS) is a focal brain injury in term neonates that is identified postnatally but is presumed to occur near the time of birth. Many pregnancy, delivery, and fetal factors have been associated with PAIS, but early risk detection is lacking; thus, targeted treatment and prevention efforts are currently limited.To develop and validate a diagnostic risk prediction model that uses common clinical factors to predict the probability of PAIS in a term neonate.In this diagnostic study, a prediction model was developed using multivariable logistic regression with registry-based case data collected between January 2003, and March 2020, from the Alberta Perinatal Stroke Project, Canadian Cerebral Palsy Registry, International Pediatric Stroke Study, and Alberta Pregnancy Outcomes and Nutrition study. Criteria for inclusion were term birth and no underlying medical conditions associated with stroke diagnosis. Records with more than 20% missing data were excluded. Variable selection was based on peer-reviewed literature. Data were analyzed in September 2021.Clinical pregnancy, delivery, and neonatal factors associated with PAIS as common data elements across the 4 registries.The primary outcome was the discriminative accuracy of the model predicting PAIS, measured by the concordance statistic (C statistic).P = .20). Internal validation techniques yielded similar C statistics , as did a sensitivity analysis using cases and controls from Alberta, Canada, only .Of 2571 term neonates in the initial analysis , 1389 (54%) were male, with a greater proportion of males among cases compared with controls (318 [60%] vs 1071 [52%]). The final model was developed using 1924 neonates, including 321 cases (17%) and 1603 controls (83%), and 9 clinical factors associated with risk of PAIS in term neonates: maternal age, tobacco exposure, recreational drug exposure, preeclampsia, chorioamnionitis, intrapartum maternal fever, emergency cesarean delivery, low 5-minute Apgar score, and male sex. The model demonstrated good discrimination between cases and controls  and good model fit (Hosmer-Lemeshow The findings suggest that clinical variables can be used to develop and internally validate a model to predict the risk of PAIS in term neonates, with good predictive performance and strong internal validity. Identifying neonates with a high probability of PAIS who could then be screened for early diagnosis and treatment may be associated with reductions in lifelong morbidity for affected individuals and their families. PAIS is most often diagnosed as neonatal AIS in term neonates, which manifests acutely as seizures or encephalopathy.4 PAIS may also go undetected in the neonatal period and present in late infancy or early childhood, when it is termed arterial presumed perinatal ischemic stroke.5 Both are considered within a spectrum of the same disease.Perinatal stroke is a focal vascular brain injury defined as occurring from the fetal period to 28 days of postnatal life.6 PAIS may be associated with an underlying condition such as complex congenital heart disease7 or bacterial meningitis,8 although additional contributing factors may still be present. Studies of PAIS have suggested associations with maternal and pregnancy factors, such as nulliparity, preeclampsia, and gestational diabetes11; intrapartum factors, such as maternal fever and chorioamnionitis13; and fetal or neonatal factors, such as fetal heart rate abnormalities, intrauterine growth restriction, meconium staining, and male sex.16 These findings, however, have been inconsistent and were likely affected by variable terminologies and modest sample sizes. Without a way to identify neonates at risk of PAIS who appear to be healthy, early diagnosis of PAIS and strategies for prevention are challenging.The timing of perinatal stroke allows for unique pathophysiological considerations, including biological factors between the mother and fetus and peripartum-specific factors, such as the placenta, labor and delivery, fetal transition, and adaptive alterations of the coagulation system in both mother and neonate.17 The placenta provides oxygenation and nutrition to the fetus and is a direct source of thromboembolism to the brain via the fetal circulation, which lacks the thrombus-filtering capacity of the postnatal pulmonary circulation. Although histopathology is challenging to obtain, perinatal stroke has been associated with a variety of placental conditions19 through mechanisms of maternal or fetal vascular malperfusion, thromboinflammatory processes, and infection.22 Clinical factors, such as the frequent bilaterality of lesions in PAIS  and the low recurrence risk (<1%) of PAIS,25 are also consistent with a primary placental mechanism.Placental pathology is suspected to be a common factor associated with PAIS.26 and have shown patient factors, such as history of diabetes, hypertension, and atrial fibrillation, to be candidate predictors.27 To our knowledge, such models have not yet been developed or validated for perinatal stroke because the complex and unmeasurable nature of PAIS pathophysiology makes primary prevention in utero a challenge. Prediction and early diagnosis of PAIS could allow close monitoring in the perinatal period, with possible implications for emerging acute treatments28 and early rehabilitation31 to optimize outcomes.Prediction models have proved to be valuable in the primary prevention of adult strokeUsing a large, well-characterized sample of PAIS cases, we sought to develop and validate a diagnostic risk-prediction model based on common clinical perinatal factors that estimates the probability of PAIS in a term neonate. A secondary objective was to explore whether the factors identified a priori in predicting PAIS could support a placental mechanism.32 The Canadian Cerebral Palsy Registry, established in 2003, is a multiregional prospective Canadian registry of children with cerebral palsy.33 A system for confirming and classifying perinatal stroke  in participants with hemiparetic cerebral palsy was recently validated in this registry.34 The International Pediatric Stroke Study, established in 2006, is a clinical research registry of pediatric stroke that stores medical and imaging data for international collaborative research.23 These data are collected using standardized procedures from International Pediatric Stroke Study investigators in more than 15 countries, with approximately 70% of participants residing in Canada and the US.23 Case data for the current study were collected from January 2003 to March 2020, with data analysis completed in September 2021. Because these 3 registries include overlapping catchment areas, source data were cross-referenced to ensure no participants were included more than once. The University of Calgary and Alberta Health Services research ethics boards approved the study and waived informed consent owing to the retrospective study design and analysis. This study followed the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting guideline.35In this diagnostic study, PAIS cases were collected from 3 sources: the Alberta Perinatal Stroke Project, the Canadian Cerebral Palsy Registry, and the International Pediatric Stroke Study. The Alberta Perinatal Stroke Project, established in 2008, is a research cohort with prospective (2008-2017) and retrospective (1990-2008) enrollment at a single tertiary care pediatric center  in Alberta, Canada.36 established in 2009 as a population-based prospective cohort of pregnant women in Alberta. This longitudinal cohort study collected pregnancy, delivery, and neonatal data and has followed child health outcomes to 12 years of age. The Alberta Pregnancy Outcomes and Nutrition common data elements have previously been used as control variables in studies of factors associated with PAIS.34Data for healthy control individuals were obtained from the Alberta Pregnancy Outcomes and Nutrition study,23 Healthy controls required normal motor development at 3 years of age to rule out delayed presentation of stroke. Participants were excluded if caregiver consent was incomplete or if more than 20% of data fields were missing.Registries were reviewed for eligible case and control individuals. Participants were selected based on the following criteria: (1) birth between the year of registry establishment and March 2020, (2) term birth (≥37 weeks’ gestation), and (3) no medical comorbidities associated with a stroke diagnosis . Stroke case definitions were applied across the 3 case registries  and included a magnetic resonance imaging–confirmed diagnosis of AIS presumed to have occurred in the neonatal period as established by clinical-radiographic diagnostic criteria.37 Variables were only included in this study if they were consistently defined and directly comparable across the source registries according to their data codebooks, with any uncertainty resolved with each study coordinator to ensure accuracy. Data for certain variables were recoded from ordinal or continuous scales into binary measures to ensure that the variable’s presence was captured consistently in the prediction model  rather than prompted by the current data.Descriptive statistics were used to evaluate data for completeness and to identify the prevalence of each study variable among cases and controls. Candidate predictors for the model were identified a priori based on peer-reviewed research literature43 Model fit was assessed using the Hosmer-Lemeshow test. Internal validation was done using bootstrap resampling and 10-fold cross-validation. As a sensitivity analysis, the model was applied to only cases and controls from Alberta, Canada, and a C statistic representing a single, local population was obtained. All available data were used for model development, with resampling methods used for internal validation.44 Analyses were conducted using Stata, version 16 (StataCorp LLC). Two-sided P = .05 was considered significant. The final model was presented as a regression formula using the candidate predictors weighted by their coefficients to estimate a term neonate’s predicted risk of PAIS.Univariable analyses using logistic regression were done to explore individual associations between each selected clinical variable and the outcome of PAIS. A diagnostic prediction model was then developed using multivariable logistic regression of main effects of these candidate variables to predict the probability of PAIS. Complete case analysis was used to address missing data, whereby only participants for whom there were no missing data for the candidate predictors were included in the model. The primary outcome was discriminative accuracy of the model in predicting PAIS, measured by the concordance statistic (C statistic), shown as the area under the receiver operating characteristic curve. The C statistic is the probability that a randomly selected individual who experienced the outcome  would have a higher predicted probability of having the outcome occur than would a randomly selected healthy control. Values greater than 0.7 indicate good model discrimination.A total of 2571 participants were included in the initial analysis; 527 (20%) were case patients with PAIS from the 3 case registries and 2044 (80%) were healthy controls . ClinicaTobacco and recreational drug (substance) use in pregnancy were present in increased proportions in the case cohort, as were certain intrapartum factors, such as chorioamnionitis and maternal fever. Clinical chorioamnionitis was present in 22 PAIS cases (4%) and 8 controls (<1%). With regard to mode of delivery, a higher proportion of controls was delivered vaginally (1527 [75%] vs 286 [54%]), whereas a higher proportion of cases was delivered via emergency caesarian delivery (155 [30%] vs 257 [13%]). The percentage of missing data for each clinical factor, where applicable, is also shown in 46: maternal age, tobacco exposure in pregnancy, recreational drug (substance) exposure in pregnancy, preeclampsia, chorioamnionitis, maternal fever intrapartum, emergency cesarean delivery, low Apgar score (<7) at 5 minutes, and male sex. All 9 variables selected as candidate predictors were significantly associated with the outcome in univariable analyses, and no further variable selection was undertaken; all 9 variables were included in the final model.Preliminary univariable analyses were done to show possible associations between each individual predictor and outcome . Of the P = .20). Sensitivity, or detection rate, was 11% with a false-positive rate of 1%. There was also indication of overestimation  without overfitting . Two methods of internal validation, bootstrap resampling and k-fold cross-validation, were used and yielded C statistics similar to that in the original model. Bootstrapped results showed a C statistic of 0.73 , and the mean 10-fold cross-validated area under the curve was 0.72   and 1603 controls (83%). Model performance measures, presented in 69-0.76) B.P = .86).A sensitivity analysis was done using only Alberta Perinatal Stroke Project case data and a randomly selected subset of Alberta Pregnancy Outcomes and Nutrition controls because participants in both groups were from the same Alberta population and thus were presumed to be the most homogeneous. This model included 479 participants (87 cases [18%] and 392 controls [82%]) and demonstrated a C statistic of 0.71  and good model fit , wherepredicted risk of PAIS in a term neonate =  + (0.21 × tobacco exposure) + (1.73 × substance exposure) + (0.86 × preeclampsia) + (1.29 × chorioamnionitis) +  + (0.50 × emergency cesarean delivery) + (1.69 × low 5-minute Apgar score) + , substituting 1 for present and 0 for absent for each variable .2 Biologically, these data provide indirect support for the placental embolism hypothesis for PAIS pathogenesis.In this diagnostic study, we developed an internally valid, diagnostic clinical prediction model to quantify the risk of PAIS in term neonates. Using data from 4 study cohorts of women and infants and 9 commonly available clinical factors, this model showed good predictive performance  and strong internal validity. Similar findings were obtained using a subset of the Alberta data only . The results suggest that simple clinical prediction models may improve estimations of the risk of PAIS occurrence by 20-fold compared with current birth prevalence rates.21 were used as surrogates to consider the role of placental dysfunction. Chorioamnionitis has the most direct association with placental disease,47 and in this study, when chorioamnionitis was present, the odds of PAIS were increased by a factor of 3.63 . The wide 95% CI, however, suggests a low prevalence of this predictor in our data set; it was present in only 30 neonates (1%) in the study population. Of importance, the proportion of chorioamnionitis in the control group  was similar to the known North American population-based prevalence,47 whereas the proportion in the case group was 4% , supporting the importance of this factor in PAIS. This finding also supports well-established associations between chorioamnionitis and perinatal brain injury in term neonates.20Although data on direct examination of the placenta were not available in this large-scale study, key clinical factors associated with abnormal placental physiology and histology in PAIS38 vasoactive drugs such as cocaine or methamphetamine may be more likely to prompt a thromboembolic process in the placenta,45 resulting in a focal arterial ischemic injury in the fetal or neonatal brain. Given the power of the sample in our study and that rates of both recreational substance and tobacco use among controls approximated published rates,36 our findings suggest that these potentially modifiable factors associated with PAIS require further investigation.Recreational drug (substance) exposure in pregnancy was significantly associated with increased odds of PAIS , whereas tobacco exposure was not . Although the effects of tobacco on the placenta are presumed to occur through chronic reduction of blood flow that creates a pathologically hypoxic environment,48 An effective prediction model could help determine which neonates should receive a screening evaluation, such as a cranial ultrasonography, a noninvasive and inexpensive bedside test, if risk of perinatal stroke was found to be present.49 Acute treatments for PAIS, such as stem cell therapy and erythropoietin, are currently being studied,50 and if PAIS is highly suspected in a neonate, earlier diagnosis leading to treatment may optimize outcomes.51 In addition, this study’s model may be particularly useful to identify seemingly asymptomatic neonates with perinatal stroke , because their window for neuroprotection and early therapy is wider and often missed. Unique inflammatory biomarkers collected from acute blood samples from neonates have also been associated with PAIS.52 Application of our clinical prediction model could be used in combination with such biomarkers within the first 48 hours of life to further enhance early identification.Prediction models provide diagnostic probabilities and potentially impact clinical practice when actions can be taken with regard to the prediction. With the inclusion of neonatal clinical factors, some of which can only be collected immediately after birth, the goal of this study’s prediction model was to identify neonates at risk for PAIS for early diagnosis and treatment to prevent secondary complications. Although neuroimaging such as magnetic resonance imaging is required to confirm a PAIS diagnosis, this technique is not always available or suitable for a neonate who is clinically unstable. Delay of a PAIS diagnosis by days, months, or even years is common in the case of presumed perinatal stroke.The next step for this diagnostic clinical prediction model of PAIS would be to assess its parameters at various predicted thresholds to better establish risk groups for optimal sensitivity. The complexity of these data lends itself to higher-level analyses, and efforts are under way to use machine learning techniques to make more accurate data-driven predictions. This study may expand the way clinicians and researchers think about perinatal stroke and stroke prediction and prevention and will hopefully serve as a foundation on which future research can be based.This study has strengths. The sample size was more than 500 PAIS cases, and thus, to our knowledge, the study included the largest case group among existing case-control studies. The unique study methods identified easily measured and well-defined clinical factors as predictors for PAIS. In addition, our approach of using existing literature to define relevant factors and then validating them collectively using this data set supports the consistency in the literature on key variables associated with PAIS. Perinatal stroke is a rare disease, and randomized clinical trials are not possible because many variables  and the outcome of perinatal stroke itself cannot be randomly assigned or manipulated; therefore, we believe that analyses such as ours may be the best way to develop predictive models for early PAIS detection.53 or other populations worldwide. The sensitivity analysis using only cases and controls from Alberta attempted to mitigate some of this imbalance and revealed similar results.This study also has limitations. There was a lack of external validation, which was not feasible with the available data. Certain factors known to be associated with perinatal stroke, such as nulliparity, meconium, and abnormal fetal heart rate, were not consistently captured across the source registries, and thus, the association of these potential predictors with PAIS could not be included in the model. In addition, although PAIS cases were obtained from local, national, and international registries to increase study power, controls were obtained only from the Alberta population and may not have had the same baseline rates of certain factors  as the US population54 and these intuitions might be supported by the addition of a risk prediction model that provides rationale for a higher or lower clinical index of suspicion. Because the prevalence of acutely symptomatic PAIS is less than 1 in 2500 live term births2 and the clinical recognition rate is also low, this model’s detection rate of 11% may substantially improve the identification of cases. In an era of precision medicine, identifying key factors associated with PAIS may have marked clinical impact in reducing the burden of perinatal stroke on patients and families.This diagnostic study showed that clinical variables may have predictive utility in identification of neonates at risk of PAIS. Clinicians often rely on their judgement and limited experiences in predicting the likelihood of PAIS, which is challenging for nonexperts when assessing a rare disease of poorly understood pathogenesis. To date, validated guidelines to aid in such prediction do not exist; however, the clinical variables included in this study are readily available and intuitively considered when making an informed determination of risk in neonates with neurological concerns. The process of updating a clinician’s prior beliefs about whether an individual has PAIS is inherently bayesian,