Among the urinary tract’s malignancies, bladder cancer is the most frequent one: it is at the tenth position of most common cancers worldwide. Currently, the gold standard therapy consists of radical cystectomy, which results in the need to create a urinary diversion using a bowel segment from the patient. Nevertheless, due to several complications associated with bowel resection and anastomosis, which significantly affect patient quality of life, it is becoming extremely important to find an alternative solution. In our recent work, we proposed the decellularized porcine small intestinal submucosa (SIS) as a candidate material for urinary conduit substitution. In the present study, we create SIS-based hybrid membranes that are obtained by coupling decellularized SIS with two commercially available polycarbonate urethanes (Chronoflex AR and Chronoflex AR-LT) to improve SIS mechanical resistance and impermeability. We evaluated the hybrid membranes by means of immunofluorescence, two-photon microscopy, FTIR analysis, and mechanical and cytocompatibility tests. The realization of hybrid membranes did not deteriorate SIS composition, but the presence of polymers ameliorates the mechanical behavior of the hybrid constructs. Moreover, the cytocompatibility tests demonstrated a significant increase in cell growth compared to decellularized SIS alone. In light of the present results, the hybrid membrane-based urinary conduit can be a suitable candidate to realize a urinary diversion in place of an autologous intestinal segment. Further efforts will be performed in order to create a cylindrical-shaped hybrid membrane and to study its hydraulic behavior. Bladder cancer (BC) is the tenth most common form of cancer worldwide, with an estimation of 549,000 new cases and 200,000 deaths annually: in males, it is the seventh most commonly diagnosed cancer ,2. It isMost BCs (70% of cases) are diagnosed as non-muscle invasive (NMIBCs), while the others are muscle-invasive BCs (MIBCs). The guidelines of the European Association of Urology (EAU) classify NMIBCs into low, intermediate, and high risk depending on the treatment options: in the case of low-risk NMIBCs, only transurethral resection of the bladder (TURB) is performed, whereas in the case of intermediate-risk and high-risk NMIBCs, the standard treatment requires TURB with or without Bacillus Calmette–Guèrin (BCG) immune therapy or chemotherapy. A minority of NMIBCs shows unfavorable prognosis, while high-risk NMIBCs have a high rate of disease recurrence and/or progression to muscle-invasive tumors and BCG treatment failure .In the case of MIBCs and in high-risk, unresponsive NMIBCs, radical cystectomy (RC) is performed, creating a urinary diversion using a bowel segment from the patient. However, this surgical approach results in a high risk of complications. The most common ones are associated with the intestinal segment and include strictures, gastrointestinal (GI) tract bleeding, bowel leakage, fistula formations, and the development of metabolic disorders, depending on the dimension of the bowel segment and the type of diversion ,6,7,8,9.Therefore, the necessity for an alternative urinary conduit is becoming more and more prominent in urology in order to avoid the use of autologous intestine and reduce the burden of complications. A promising approach can be focused on tissue engineering techniques, which allow largely improving the surgery by reducing its duration, intra-operatory risks and patient complications  ,11.Thus far, several types of scaffolds have been investigated, both synthetic and biological, with the aim to identify the best option for urinary conduits. Among synthetic materials, polyglycolic acid (PGA) and poly lactic-co-glycolic acid (PLGA) are in use due to their reliable and reproducible mechanical features, but they often lack sufficient biocompatibility, thus eliciting adverse reactions ,14,15,16Acellular tissue matrices  ,30,31,32In the present study, we describe the development of two hybrid membranes; they are obtained by coupling decellularized small intestinal submucosa (SIS) (previously described by our group in ) with twThe results obtained allow hypothesizing the use of the novel hybrid materials for the production of tubular structures to replace urinary conduits.®, E1014, Sigma-Aldrich, Saint Louis, MO, USA) was used to remove DNA residues.Jejunum was collected from an abattoir and treated within 3 hrs. after animal sacrifice. The abattoir’s protocols followed EC guidelines 1099/2009 concerning animal health and protection at the time of sacrifice, was supervised by the Italian government, and was approved by the associated legal authorities of animal welfare (Food and Consumer Product Safety Authority). SIS isolation was obtained accordingly to an already described procedure ,40,41 by2) with the mucosal side facing down. Therefore, a controlled amount of polycarbonate urethane (PCU) solution  was poured on the external side of the SIS. The hybrid membranes and the polymeric membranes used as control in the cytocomptibility tests were dried at 38 °C for 3 days. After desiccation, hybrid and PCU membranes were separated from the frame.Hybrid membranes were produced by solution casting technique and solvent evaporation, following the procedure described by Todesco et al.  for peri−1 are due to carbonate groups, and the peak at 1598 cm−1 suggests the presence of aromatic molecules. The peak around 1637 cm−1 can be related to the diamine content. The signal intensities differ in the range 1200 to 900 cm−1: this may be due to the presence of silica microparticles in CF AR-LT, which makes it less sticky. Thermo gravimetric analysis (TGA) confirmed the presence of this inorganic component (~9%).Polymeric membranes are named CF AR (Chronoflex AR) and CF AR-LT (Chronoflex AR-LT) and hybrid membranes are named SIS AR and SIS AR-LT, depending on the PCU. Components of the poycarbonate urethanes can be found in literature: polycarbonate polyol, methylene diisocyanate (MDI), ethylene diamine (EDA), and 1,3-diaminocyclohexane . ChronofIn order to analyze ECM composition and structural integrity of the SIS after hybrid membrane realization, immunofluorescence was performed following the same procedure that was decribed in our previous paper . For theFor patch immunofluorescence, samples were incubated with phalloidin—Atto 647N  and DAPI to stain F-actin and nuclei, respectively.Leica AF6000 microscope connected to a Leica DC300 digital camera and equipped with LAS AF Software  was used for image acquisition. Further analysis was performed with ImageJ software.Decellularized SIS and hybrid membranes (SIS AR and SIS AR-LT) were analyzed with two-photon to evaluate the effect of the hybrid membranes realization procedure on collagen arrangement by assessing the second harmonic generation (SHG) signal .A custom-made multimodal microscope was used . SeveralThe RAW images were analyzed with ImageJ software . CollageSince we wanted to evaluate polymer interaction with decellularized tissue, Dunnett’s multiple comparisons test were made between decellularized SIS, used as control, and both hybrid membranes. The comparison between native and decellularized SIS was previously reported in .During two-photon microscopy analysis, each hybrid membrane sample was excited with a laser at wavelength of 800 nm, and the detector acquired the emitted light in two channels: one at a wavelength of 400 nm, to which the generation of the second harmonic corresponds, as described above, and in which only the collagen is observed. For the second channel, at a wavelength between 435 and 500 nm (blue channel), only the polymer was detected. Finally, SHG and blue channel intensities were calculated by dependence on the depth of the hybrid membranes samples.2)  were equilibrated for 3–4 h. in deuterium oxide . Samples were then processed with a Nicolet iS-50 spectrometer  equipped with the Attenuated Total Reflectance (ATR) accessory. Infrared spectra of the samples and background were recorded using 64 scans in the range of 4000–500 cm−1 at room temperature and analyzed with a Matlab® script  [In order to check if the polycarbonate urethanes coupled with SIS can alter the secondary structure of tissue proteins and if the polymer penetrates it, hybrid materials were analyzed using the Fourier transform infrared spectroscopy (FTIR). Decellularized SIS and hybrid membranes on the SIS side, and polycarbonate urethanes (CF AR and CF AR-LT) flaps  .2) [Dog-bone-shaped specimens of decellularized SIS, SIS AR, and SIS AR-LT were cut according to the ASTM D1708-13 standard (gauge size 5 × 2 mm2) . SpecimeUniaxial tensile loading tests were performed by means of a custom-made instrument , which is operated with dedicated software implemented in LabVIEW.Tests were executed at room temperature, and specimens were kept wet with saline solution. Specimens were pre-loaded up to 0.1 N, then elongated (0.2 mm/s) to breakage. Decellularized SIS, SIS AR, and SIS AR-LT samples were tested along the circumferential and longitudinal directions as described in our previous study .® script . Mechanical parameters were quantified from the stress–strain curve plotted for each sample. Ultimate tensile strength (UTS) and failure strain (FS) were calculated as the maximum strength and elongation reached by the specimen, while Young’s modulus (E) was determined as the slope of the stress–strain curves in the linear region.Data were analyzed with an in-house-developed MatlabStatistical tests allow comparing decellularized SIS and hybrid membranes to evaluate the effect of polymers when coupled with the decellularized tissue. The comparison between native and decellularized SIS was previously reported elsewhere .Samples of hybrid membranes (SIS AR and SIS ARLT) were subjected to the sterilization procedure whose efficacy was previously demonstrated . It is bSamples (each in duplicate) of sterilized hybrid membranes (SIS AR and SIS AR-LT) were treated according to the European Pharmacopoeia guidelines . Thiogly2 and cultured for 1, 7, and 14 days using the abovementioned media. Cell growth was evaluated through immunofluorescence, live/dead staining, metabolic proliferation assay, and dsDNA quantification.The cytocompatibility of SIS, hybrid membrane (SIS AR and SIS AR-LT) and polycarbonate urethane (CF AR and CF AR-LT) samples was evaluated by a direct contact assay, following the part 5 of ISO 10993 . SamplesDNA content was quantified from decellularized SIS, hybrid membranes (SIS AR and SIS AR-LT) and polymers (CF AR and CF AR-LT) seeded with MSCs after 1, 7, and 14 days using a DNeasy Blood & Tissue Kit . Concentration was measured at 260 nm with a NanoDrop One Spectrophotometer . Values were normalized for the final volume of the solution to calculate ng of DNA in each sample.Cell viability of seeded samples was evaluated after 1, 7, and 14 days with the Live/Dead viability/cytotoxicity kit  to stain live cells in green and dead cells in red. Additionally, the Hoechst 33258  was used to identify the nuclei. Images were taken with an Olympus IX71 microscope.Cultured cell proliferation was assessed by WST-1 at days 1, 7, and 14. Absorbance was read at 450 nm with a microplate reader . The values of the non-seeded samples (control) were averaged and taken from the value of each seeded sample.With the aim to morphologically characterize SIS AR and SIS AR-LT, phase contrast pictures A,B and aWith the aim to evaluate both polymers’ (CF AR and CF AR-LT) penetration in decellularized SIS, two-photon images of phase contrast and autofluorescence (blue channel) were merged A,B. In F−1 and 1251 cm−1: they are specific of the urethane and carbonate groups, respectively. These peaks are not present in the case of decellularized SIS and hybrid membranes. The peak at 1637 cm−1 can be due to the presence of a diamine, while the peak at 1598 cm−1 is caused by the presence of aromatic molecules in the hard segment of the polymers. Finally, peaks at 1251 cm−1, 956 cm−1, and 791 cm−1 are related to the presence of carbonate groups in the soft segment [−1 and 1558 cm−1 due to amide I and amide II binding, respectively. Peaks for collagen are maintained at 1451 cm−1, 1339 cm−1, 1205 cm−1, 1080 cm−1, and 1035 cm−1, as previously described [Chemical composition was further investigated through FTIR-ATR analysis E,F, wher segment . Moreoveescribed . These rUniaxial tests were performed to compare the mechanical response to load of the decellularized SIS and hybrid membranes (SIS AR and SIS AR-LT): With regard to samples thickness, it significantly increased for the hybrid membranes with respect to the decellularized tissue, with a higher increase in the case of SIS AR-LT C. ComparSimilar trends were observed for UTS F with a As for FS E, there No turbidity was detected in thioglycolate and soybean–casein digest media broth for SIS AR and SIS AR-LT demonstrating their effective capacity to remove bacteria and fungi .Decellularized SIS, SIS AR, and SIS AR-LT from the tissue side and CF AR and CF AR-LT alone were seeded with human bone marrow cells (MSCs) and analyzed after 1, 7, and 14 days from seeding. Nuclei and F-actin were marked with DAPI and phalloidin, respectively A–O. CleaLive/dead staining was performed A–O, confThe metabolic activity was quantified in SIS, SIS AR, SIS AR-LT, CF AR, and CF AR-LT seeded with MSCs using the WST-1 assay at days 1, 7, and 14. As control group, cells seeded on plastic were analyzed exhibiting a significant growth between days 1 and 14. Other significant differences were detected between days 1 and 7 and days 1 and 14 in both hybrid membranes and between days 1 and 14 in CF AR-LT, while no significant difference was found in the case of SIS and CF AR. At the same time, both hybrid membranes showed significantly increased values in comparison with cells seeded on plastic, SIS, and polymers alone, especially at days 7 and 14.The urinary diversion that is usually created after radical cystectomy with a segment of the patient’s bowel causes a number of complications, e.g., strictures, bleeding, leakage, fistula formations, and metabolic disorders ,51. In oTo date, several approaches have been investigated starting from synthetic materials  to biological ones . However, both kinds of materials showed some limitations, which impaired their application in clinical practice. To overcome these limitations and maintain the advantages of both approaches, the attention has been recently focused on hybrid materials, which combine the typical features of both types of materials. For instance, scaffold obtained from bonding collagen matrix to PGA was proposed for engineering hollow organs such as urinary bladder and it was seeded with urothelial cells (UCs) and bladder smooth muscle cells (SMCs) before implantation in mice . It causInterestingly, another group created a biodegradable hybrid scaffold consisting of a synthetic polymer  and collagen, seeded with neonatal (foreskin) fibroblasts (NNFs) inside and on the top of collagen gels . Similarstigated . HoweverMoreover, another group tested silk biomaterials in combination with extracellular matrix coatings, which were tested with SMCs and UCs, but murine embryonic stem cells (ESCs) and induced pluripotent stem cells (IPSs) were also evaluated for bladder tissue engineering . AdditioIn addition to the choice of the proper cell lines, another critical issue is due to the scaffold porosity: it was demonstrated that in a hybrid scaffold made of BAM and electrospun PLGA microfibers, the increase of the porosity was an effective way to enhance cell proliferation and distribution in vitro and tissue ingrowth in vivo . SimilarOther groups coupled porous extracellular matrices such as AM or BAM with other biomaterials in order to create novel hybrid materials. For instance, amniotic membrane was used in combination with PLCL and implanted in rats ,. SimilaFollowing these results, we decided to use decellularized SIS for urinary conduit reconstruction in our previous study : it was In order to overcome the impermeability issue, another group tested a tissue-engineered tubular polypropylene mesh scaffold, which was first pre-implanted before the ureter reconstruction model in pig . Howeverw/v). They are declared biocompatible and hemocompatible and differ for the presence of silica microparticles in CF AR-LT to make the polymer less tacky. Moreover, these polymers were chosen since they are suitable for molding, casting and dip-coating fabrication techniques and can resist stress cracking and exhibit high flexural resistance.In the present study, we decided to keep decellularized SIS in order to exploit its biocompatibility, but improving its mechanical features and impermeability by creating two types of hybrid membranes (SIS AR and SIS AR-LT) coupling SIS (whose role would be to accommodate a large number of cells) with two polycarbonate urethanes (CF AR and CF AR-LT) which serve as a barrier, as already performed with decellularized porcine pericardium . CF AR aWe demonstrated the maintenance of collagen fibers in decellularized SIS after the realization of hybrid membranes through immunofluorescence, and two-photon analysis. In both SIS AR and SIS AR-LT, collagen fibers appeared less crimped and more stretched compared to SIS: this may be due to the realization process of hybrid membranes, which requires SIS dehydration with filter paper to prevent polymer coagulation. Moreover, two-photon and FTIR analyses demonstrated a higher penetration of Chronoflex AR-LT in the SIS in comparison with Chronoflex AR.Biomechanical results demonstrated a significant decrease of hybrid membrane stiffness with a reduction of Young’s modulus (E) and ultimate tensile strength (UTS) in both circumferential and longitudinal directions. Correspondingly, the maximum elongation achieved by the materials (FS) in both directions was not significantly different from that of the decellularized tissue. Generally, the mechanical behavior of the hybrid membranes appeared more isotropic in comparison with decellularized SIS, with less prominent differences between the two directions. This can be a key feature for the ideal urinary conduit, which must resist physiologic hydraulic pressures without permanent deformations.Prior to biocompatibility evaluation, it was crucial to confirm the sterilization effectiveness after the realization of hybrid membranes. For this reason, we applied the same chemical sterilization procedure that was demonstrated to be effective in the case of the decellularized SIS : it was Cytocompatibility was then tested by direct contact assay to study attachment, migration and distribution of the seeded cells within the scaffold. We decided to use human MSCs isolated from bone marrow in order to study an adequate cell type to be applied also in cases of malignancy ,70 and aThe results obtained suggest the adequacy of both hybrid membranes to realize a novel urinary conduit.SIS-based hybrid membranes have shown to be a promising scaffold for tissue engineering applications and for urinary conduit substitution. The realization of hybrid membranes did not interfere with the decellularized SIS collagen content (which is the main tissue component), but polymers changed the mechanical properties of the biological tissue, decreasing its stiffness and leveling the mechanical behavior along the circumferential and longitudinal directions. Moreover, the presence of the polymer did not weaken SIS cytocompatibility, but rather it significantly improved them, allowing greater cell growth and organization over time compared to the decellularized SIS alone.In order to satisfy the intended clinical need, the future developments of this research will be to realize a conduit made of hybrid membrane which is repopulated with specific cell types and to pre-clinically evaluate it through in vivo studies on animal models.


Hyperglycemia is rising globally and its associated complications impose heavy health and economic burden on the countries. Developing effective survey-based screening tools for hyperglycemia using reliable surveillance data, such as the WHO STEPs surveys, would be of great importance in early detection and/or prevention of hyperglycemia, especially in low or middle-income regions.In this study, data from the nationwide 2016 STEPs study in Iran were used to identify socioeconomic, lifestyle, and metabolic factors associated with hyperglycemia. Furthermore, the ability of five commonly used machine learning algorithms  in the prediction of hyperglycemia on STEPs dataset were compared via tenfold cross validation in terms of specificity, sensitivity, and the area under the receiver operating characteristic curve.n = 5245) had (undiagnosed) hyperglycemia. Multivariate logistic regression analysis showed that older age , high BMI status , having hypertension (OR = 1.647), consuming fish more than twice per week (OR = 1.496), and abdominal obesity (OR = 1.464) were the five most important risk factors for hyperglycemia. Furthermore, all the five hyperglycemia prediction models achieved AUC around 0.70, and logistic regression  and random forest  had the optimal performance.A total of 17,705 individuals were included in this study, of those 29.624% (This study shows that it is possible to develop survey-based screening tools for early detection of hyperglycemia using data from nationwide surveys, such as WHO STEPs surveys, and machine learning techniques, such as random forest and logistic regression, without using blood tests. Such screening tools can potentially improve hyperglycemia control, especially in low or middle-income countries. Hyperglycemia is a metabolic disorder characterized by a high level of blood glucose, which in the intermediate stages appears in disguise of prediabetes—i.e. presence of impaired fasting blood glucose (IFG) and/or impaired glucose tolerance (IGT), or high glycated hemoglobin HbA1c)—and could develop to diabetes mellitus (DM) if untreated c—and cou.It is well-known that hyperglycemia is a precursor to a wide spectrum of non-communicable diseases (NCDs) such as DM, (cardio)vascular complications, diabetic retinopathy, and chronic kidney disease , 3. ThesSeveral studies have documented an increasingly high prevalence of hyperglycemia in Iran. A recent study has reported the prevalence of prediabetes and diabetes 25.8% and 16.1% respectively among adults aged 20–69 years living in Yazd Province, Iran . AnotherOver the past few years, applications of machine learning (ML) in the screening and/or diagnosis of glycemic-based disordered have been well investigated. While most of the work in this area has focused on screening and/or diagnosis of diabetes , 10, theIn the present work, we aim to apply the five commonly used ML techniques  to the data from the nationwide 2016 STEPs study in Iran to identify non-invasive factors associated with hyperglycemia and develop predictive ML models using these factors. As a result, we propose ML predictive models for survey-based hyperglycemia screening, which can assist healthcare systems, especially in low or middle-income countries.The structure of the paper is as follows: The mythology of the investigation is discussed in n = 30,541 of Iranian population aged 18 and over, collected via cluster sampling from all provinces of Iran (except Qom province) in 2016; details of the Iran 2016 STEPs survey are discussed in [Data from the latest Surveillance of Risk Factors of Non-communicable Disease (STEPs study) in Iran, conducted in 2016 based on WHO STEPs instrument, were used in this study [ussed in . The finBlood sugar level was determined based on FBS measurements in mg/dl, or HbA1c values. Hyperglycemia (the outcome variable) was defined as FBS ≥ 110 mg/dl according to WHO criterion , or HbA1The 18 non-invasive principal input variables were selected based on the literature or their statistical significance in the univariate analysis. The principal input variables comprise socioeconomic variables, lifestyle variables , and metabolic risk factors (body-mass-index (BMI) status; abdominal obesity (AO); blood pressure (BP) status, hypertension history; having hypercholesterolemia (HC); history of HC).Socioeconomic variables include: Living area ; Gender ; Age group (Teenager (age < 20); Young (age 20–45); Middle-aged (age 45–60); Elderly (age ≥ 60)); Income level ; Education level ; Marital status (Married/Single); and Family type (Armed force households/Others).Dietary pattern was determined using diet health level and fish consumption. Diet health level  was determined based on diet health score (DHS) in the range 0–12, calculated as the sum of the scores in the range 0–2 of six items  according to IraPEN instructions . Poor diSmoking status was defined based on daily cigarette smoking status at three levels: Non-smoker; former smoker; current smoker. Alcohol consumption was categorized as regular drinking (i.e. drinking at least 3 times per month) and non-regular drinking . Physical activity (PA) status was determined at four levels  based on weekly physical activity measured in MET-minutes using the Global Physical Activity Questionnaire . Low/insBMI status was determined based on BMI values—calculated as weight measured in kilograms (kg) divided by the square of height measured in meters (m)—at five levels : UnderweBP status was determined based on systolic blood pressure (SBP) and diastolic blood pressure (DBP), measured in mmHg, in three categories : Normal n) and relative frequency (%), and were analyzed by Pearson's chi-squared test. Numerical variables were presented by mean ± standard deviation (SD) and were analyzed by Welch's ANOVA test. Multivariate logistic regression analysis was used to assess the simultaneous effect of the principal variables on the outcome variable (i.e. blood sugar status). In all the statistical tests a p-value less than 0.05 (p < 0.05) was regarded as statistically significant.Statistical analysis of the data was performed by STATA v16.0 and statsmodels v0.13.1 module in Python. Categorical variables were presented by absolute frequency , Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), and Artificial Neural Network (ANN)—were developed to predict hyperglycemia. To optimize the performance of the models, hyperparameter optimization was done using the Bayesian optimization technique, as implemented in Python library scikit-optimize v0.8.1, with fivefold cross validation.The Python machine learning library scikit-learn v0.24 was used to develop LR, RF, and SVM models. The XGBoost open-source software was employed to develop the XGBoost model. The ANN models were developed via Keras, the Python deep learning library, with 1 hidden layer (ANN1) and 2 hidden layers (ANN2) separately. The number of neurons in each hidden layer was determined via hyperparameter tuning while fitting the model. The ReLU function was used as the activation function for the input and hidden layers, and the sigmoid function was used as the activation function for the output layer. For training the ANN models, the categorical cross-entropy loss function was used, and the weights were optimized using Adam optimizer through 200 epochs.For each of the five ML algorithms, four types  of models were developed: Models of Type A are the baseline models built using the 18 principal input, ordinal encoded, categorical variables. Models of Type B were developed using dummy encoding of the principal input variables. Models of Type C and Type D are similar to models of type A and Type B respectively except that some additional numerical variables ; BMI; WC; WHR; pulse rate; pulse pressure; MAP) were included in the respective models.The performance of the models was estimated via tenfold cross validation, and the mean ± SD value for performance metrics across the folds, as well as 95% confidence intervals (CI), were reported. The metrics used to measure the performance of the models include accuracy (Acc), specificity (SP), sensitivity (SN), and F1-score defined as follows in terms of the true positive (TP), false positive (FP), true negative (TN), and false negative (FN) cases :\documenThe model performances were also compared in terms of the area under the receiver operating characteristic curve (AUC), which summarizes model performances in terms of sensitivity and specificity .A total of 17,705 individuals with mean ± SD age 45.57 ± 15.263 were included in this study. The socioeconomic characteristics of the study participants are presented in Table The lifestyle and metabolic characteristics of the study participants are presented in Tables n = 5245), and the prevalence of hyperglycemia among men (30.11%) was slightly higher than women (29.19%), although the difference was not statistically significant. The mean age of the participants was 45.57 ± 15.263, and the mean age of the hyperglycemic subjects (54.36 ± 14.83) was significantly (p < 0.0001) higher than the mean age of the non-hyperglycemic subjects (41.874 ± 13.865).The mean FBS of the subjects was 92.719 ± 20.804 mg/dl and the mean value for HbA1c was 5.485 ± 0.629. The prevalence of hyperglycemia in the sample was 29.624% , and current smoking (OR = 1.306) were identified as significant risk factors for hyperglycemia, whereas intensive PA was found to be a protective factor against hyperglycemia (OR = 0.038). As for metabolic factors, the results indicate that having abnormal BMI and high BP are significantly associated with increased odds of having hyperglycemia. It was also found that subjects with abdominal obesity (OR = 1.464) and HC (OR = 1.27) are more likely to have hyperglycemia.Five commonly used ML algorithms  were used for the prediction of hyperglycemia, and for each algorithm, four types of models  were developed depending on the method of encoding used for categorical variables and inclusion of the numerical variables in the models. Among the four types of models developed for each ML algorithm, the RF Type D, XGB Type D, SVM Type C, LR Type C, ANN1 Type C, and ANN2 Type D were selected as the optimal models in terms of AUC, SN, and SP. The performance of the optimal models is presented in Table In this study, the data of 17,705 records from the nationwide 2016 STEPs study in Iran were used for identifying socioeconomic, lifestyle, and metabolic factors associated with hyperglycemia. Furthermore, prediction models for the diagnosis of hyperglycemia were developed using five ML algorithms, and their performance was compared using tenfold cross validation in terms of accuracy, specificity, sensitivity, and AUC.Multivariate logistic regression analysis showed that socioeconomic factors , lifestyle factors , and metabolic factors  were significantly associated with hyperglycemia. The five most influential risk factors were older age , high BMI status , having hypertension (OR = 1.647), consuming fish more than twice per week (OR = 1.496), and abdominal obesity (OR = 1.464).The direct association between age and risk of having hyperglycemia, as well as the significant role of metabolic risk factors, such as obesity, (pre)hypertension, and having HC, in having hyperglycemia is quite well-established in the literature , 23, 24.The results of the study showed that men are slightly more susceptible to hyperglycemia than women (OR = 1.101), which is in accordance with several previous studies in Iran , 25, andp = 0.0149) between income level and hyperglycemia was observed in the univariate analysis, the multivariate logistic regression analysis did not show a significant association in presence of other factors. This observation is consistent with [p < 0.0001) among married (30.24%) than single (26.76%) subjects is consistent with some studies in Iran [Univariate and multivariate analysis did not show a significant association between the living area and hyperglycemia. However, some studies in Iran and other countries have confirmed that diabetes is more prevalent in urban areas whereas prediabetes is more prevalent in rural areas . Althougent with  but incoent with . The obsn single .76% subj in Iran .We did not find a significant association between diet health levels, determined using IraPEN guidelines, with hyperglycemia. However, frequent fish consumption (more than twice per week) was found to be a significant risk factor for hyperglycemia (OR = 1.496). Inconsistent with this observation, some studies have not reported a significant association between fish consumption and diabetes , and somMultivariate regression analysis showed that subjects with intensive weekly PA are less likely to have hyperglycemia compared to those with low PA (OR = 0.871). Several studies have also reported the benefits of physical activity in the prevention of hyperglycemia , 30.p = 0.0108) among regular alcohol drinkers, no significant association between alcohol consumption and hyperglycemia was found in multivariate logistic regression analysis in the presence of other factors, which is contrary to the findings of some similar studies [The role of smoking as a significant risk factor for hyperglycemia has been confirmed by many previous studies , 24, and studies , 24, 31.Among the models developed using the five ML algorithms, the LR Type C  and RF Type D  had the optimal balanced performance in terms of AUC, sensitivity, and specificity.Logistic regression has previously been proposed as an optimal model for the prediction of hyperglycemia in several similar studies. In a study based on data from Tehran Lipid and Glucose Study in Iran , the proSeveral similar studies have also proposed the RF algorithm as an optimal algorithm for the prediction of hyperglycemia. In a study based on a nationally representative sample of the US population , a modelMaeta et al.  in a stuModels based on the SVM algorithm were reported to perform better than the LR model in the prediction of prediabetes in some studies. Choi et al.  in a stuAlthough many studies, in accordance with the findings of the present study, have reported a weaker performance of ANN models in the prediction of hyperglycemia , 32, in The present study has two main limitations. Firstly, at the time of preparing this work data from Iran's 2021 STEPs study, as well as STEPs studies conducted before 2016, were not available from National Institute for Health Research, Iran. These additional data could have been used for external validation, as well as developing more robust models. Another limitation is concerning the performance of the prediction models. Although all the proposed models achieved a fair performance (AUC ≥ 0.70), further performance improvement is necessary. Several studies have confirmed that prediction of hyperglycemia is more difficult than diabetes alone , 12, andThis study shows that it is possible to develop survey-based screening tools for early detection of hyperglycemia using data from nationwide surveys, such as WHO STEPs surveys, and machine learning techniques, such as random forest and logistic regression, without using blood tests. Such screening tools can potentially improve hyperglycemia control, especially in low or middle-income countries.


Cooking at home is a critical survival skill. We propose a new cooking assistance system in which a user only needs to wear an all-in-one augmented reality (AR) headset without having to install any external sensors or devices in the kitchen. Utilizing the built-in camera and cutting-edge computer vision (CV) technology, the user can direct the AR headset to recognize available food ingredients by simply looking at them. Based on the types of the recognized food ingredients, suitable recipes are suggested accordingly. A step-by-step video tutorial providing details of the selected recipe is then displayed with the AR glasses. The user can conveniently interact with the proposed system using eight kinds of natural hand gestures without needing to touch any devices throughout the entire cooking process. Compared with the deep learning models ResNet and ResNeXt, experimental results show that the YOLOv5 achieves lower accuracy for ingredient recognition, but it can locate and classify multiple ingredients in one shot and make the scanning process easier for users. Twenty participants test the prototype system and provide feedback via two questionnaires. Based on the analysis results, 19 of the 20 participants would recommend others to use the proposed system, and all participants are overall satisfied with the prototype system. Home cooking can be both a healthy hobby and a sustainable activity. Nevertheless, the traditional cooking experience tends to be tedious and unenjoyable, especially for those who are unskilled in the kitchen. Using recipes has been the traditional way to teach and learn how to cook, but this may lead to several practical issues. The first issue is to find recipes for ingredients you already have in the house. Locating available ingredients and matching them with the right recipes is not straightforward, but it is an eco-friendly practice of reducing food waste. Another issue is to read and follow a recipe in the process of cooking. Switching back and forth between preparing the food and reading the recipe is neither convenient nor safe. Fortunately, augmented reality (AR) technology can superimpose a virtual demo video on an actual kitchen scene so the user can follow a recipe more easily. In addition, computer vision (CV) technology can sense the actual kitchen environment so that food ingredients in a refrigerator or cabinet can be detected and recognized automatically. Best of all, both AR and CV technologies can be integrated in an all-in-one AR headset with a built-in camera to significantly enhance the cooking experience.Numerous apps are available to help with cooking by suggesting recipes or providing detailed instructions using either a tablet or smartphone in kitchen. However, these modern gadgets can neither perceive the actual environment nor follow the user around the kitchen. Alternatively, some novel smart kitchens are designed to save time and energy spent on cooking. Typically, they demand that users install several Internet-connected sensors, cameras, projectors, and displays in the kitchen, which makes their deployment challenging and expensive. To improve the cooking experience, we propose an AR cooking assistance system that simply requires the user to put on an all-in-one AR headset called Magic Leap One . As showUsers do not need to install any external devices in the kitchen. All they need is an all-in-one AR headset that costs about 550 USD.Without holding a smartphone or tablet to aim at a specific food ingredient, the user can direct the built-in camera on the AR headset to detect and recognize multiple food ingredients by simply looking at them.No matter where the user moves in the kitchen, the demonstration video is always in the field of view (FOV) of the user.The demonstration video is superimposed on the real-world scene without blocking the line of sight of the actual cooking.Without holding any remote controller or touching any physical button, the user can control the proposed system through non-touch interaction using natural hand gestures.Cooking at home comes with numerous challenges such as classifying food ingredients, searching for potential recipes, and following the recipes for cooking. The contribution of this paper is finding the solutions for these issues, implementing the algorithms, and integrating them in an all-in-one AR headset. The proposed AR cooking assistance system has the following advantages:The remaining parts of the paper are structured in six sections. 2 [Plenty of research works and projects have been proposed, such as a smart kitchen based on the Internet of Things (IoT) , user ce2  leaks, a2 . NeverthThe goal of a smart kitchen is to take away the stress of cooking . A user-Another research direction for cooking assistance is AR. AREasyCooking  is an apIn addition, Hasada et al.  focus onSmart kitchens and AR cooking are two different ways to make cooking easier and more effective, but these ideas can also be integrated to build a more complete system. CounterIntelligence  is an ARThe Magic Leap One is the target AR headset for the proposed cooking assistance system. A PC with Windows 10 is used as the development platform of the proposed AR cooking application. The software engine used to create the proposed application is the Unity 2020.1.6f1 because of its cross-platform compatibility with the Magic Leap One. The Lumin SDK [As shown in Once sufficient food ingredients are detected and recognized, the user is provided with a list of suggested recipes based on the recognized ingredients. The list consists of all recipes with at least one required main ingredient detected and is sorted according to the proportion that is computed as the number of the available essential ingredients divided by the number of the required ingredients: After selecting a recipe, the user is offered an overall recipe screen with a picture of the finished product and the detailed instructions, as shown in C-Gesture”, “L-Gesture”, “Open Hand-Gesture”, “Finger Up-Gesture”, “Fist-Gesture”, “OK-Gesture”, “Pinch-Gesture”, and “Thumbs Up-Gesture”. In addition, it also includes a state where no hand gesture is recognized. As shown in Open Hand-Gesture” is used to lock the recipe window on any designated corner to prevent it from blocking the view of the real environment. The “OK-Gesture” is used to trigger the scanning of food ingredients. It is also used in case the user wants to move on to the next step of the recipe. In contrast, the “L-Gesture” is used if the user wants to move back to the previous step of the recipe. The “Pinch-Gesture” can be used to click on buttons or to select a recipe from the recipe list. In addition, it can be used to move the step-by-step recipe until it is locked into the right place. The “Fist-Gesture” stops a video from playing, and the “Finger Up-Gesture” plays the corresponding video along with the recipe. The “Thumbs Up-Gesture” can be used to take a picture while in the scanning screen for food ingredient recognition and can be used in the title screen to select the button to open the recipe list. It is also used in the recipe list menu to start a step-by-step recipe. Finally, the “C-Gesture” is reserved to exit the system after the cooking is finished. By using these hand gestures, the proposed AR cooking guide is a fully non-touch interactive system.Hands are usually busy and must remain clean in the process of cooking. Instead of using a touch screen or holding a controller in the hand, bare-hand gestures are recognized to control the cooking tutorial and the video playback in the proposed system. An API provided by the Magic Leap One, called Lumin SDK , is utilWith the advance of CV technology, many deep learning models based on the CNN  can be utilized to recognize food ingredients in an image. Usually, the models assume that the target object is the only subject located at the center of the image. To detect and recognize numerous objects with multiple categories in an image, it is necessary to apply models to the image at multiple locations and scales. A location and scale with a high prediction score are considered a detection. This repetitive process makes them inefficient and inconvenient for food ingredient scanning in our application.On the other hand, the deep learning model called YOLO (You Only Look Once)  is an obThe YOLO models have been incrementally improved over earlier versions; thus, the network architecture of YOLOv5 is highly complicated. As shown in In the training stage, we rely on a food ingredient dataset, called Q-100 , consistOpenCV for Unity [In the recognition stage, the constructed network with pre-trained weights can be used directly for food ingredient detection and recognition. The DNN module in the OpenCV supports YOLOv5. However, Unity only supports scripts written in C# and cannot natively run the OpenCV code in C and C++. A third-party asset, called or Unity , is emplbox_loss (error of location), obj_loss (error of detection), cls_loss (error of classification), precision, recall, and mAP (mean average precision), respectively.The YOLOv5 model is trained on the Q-100 food ingredient dataset for 100 epochs, and it takes 9.5 h to complete. The training time can be shortened significantly if a powerful GPU is used instead of only a CPU. true positive is a correct detection made by the model, a false positive is a detection made by the model that turned out to be incorrect, and a false negative is when something is not detected or is missed. A model is good if it has high precision and high recall. A trade-off between precision and recall is determined heuristically in the proposed application.A precision and recall. To more precisely evaluate accuracy, an F-score is computed as the harmonic mean of precision and recall. Overall, the YOLOv5 achieves an F-score of 0.61. To improve the accuracy of the recognition, we have tried other deep learning models such as ResNet [F-score of 0.78. However, they can only classify one ingredient at a time, and the ingredient is expected to be the only subject in the image. It is troublesome and time-consuming for users to aim at each food ingredient and classify them one after another. On the other hand, YOLOv5 can detect and recognize multiple food ingredients at the same time. To make the food scanning process more user-friendly, our AR cooking system adopts YOLOv5 to locate and classify food ingredients efficiently.s ResNet  and ResNs ResNet . Table 3For simplicity, the prototype AR cooking system currently focuses on vegetarian recipes. Twenty people participated in the testing of the prototype system and gave feedback regarding how easy the system was to use via a usability questionnaire , they were asked to fill out a background questionnaire (BQ as shown in After the results from both background questionnaires (BQ) and usability questionnaires (UQ) were collected, we made statistical charts in order to get a more concrete idea of the participant’s answers. By assigning five rating scores (1~5) to the five response options  in the five-point Likert scale, I prefer eating homemade food over eating takeout”, and BQ6, “I enjoy cooking”. This suggests that when one enjoys cooking more, one prefers to eat more homemade food than take-out food.A correlation analysis was conducted over the questions in the background questionnaire. A correlation coefficient  represents how strongly two variables are related to each other. As a correlation coefficient approaches 1, it indicates that there is a positive correlation. This implies that as one variable increases, so does the other. The opposite holds true as well—as a correlation coefficient approaches −1, it indicates that there is a negative correlation, which implies that as one variable increases, the other decreases. The most significant positively correlations (0.98) were for BQ5, “After assigning five rating scores (1~5) to the five response options  in the five-point Likert scale, I would recommend the system to others”, and UQ12, “I am satisfied with the end product”, had a perfect positive correlation coefficient. This suggests that if a user was satisfied with what they had cooked, they were more willing to recommend the system to others. In addition, UQ12, “I am satisfied with the end product”, and UQ13, “Overall, I am satisfied with the system”, had a perfect positive correlation. This implies that if a user was satisfied with what they had cooked, they were satisfied with the system as well.A correlation analysis was conducted with the questions in the usability questionnaire. This indicated that UQ11, “Finally, we also analyzed the correlation between participants’ cooking background and their experience with using the proposed AR cooking system. The most significant correlation coefficient (0.97) was for BQ4 “I am confident in following a simple recipe” and UQ2 “I would use the system again”. This suggests that the more confident the user was in following a recipe, the higher the chance they would like to use the proposed system again, mainly because the proposed system is a step-by-step recipe guide. However, if they did not want to use the proposed system again, that means they might have developed a negative view of the cooking guide system, and hence their confidence in following a recipe may be reduced. On the other hand, the most significant negative correlation coefficient (-0.94) was for BQ6, “I enjoy cooking”, and UQ5, “I needed prior knowledge in order to use the system”. If no prior knowledge is required to use the system, this means the system is easy to use, and if the system is easy to use, the user will enjoy cooking more. This matches the goal of the proposed system to make people enjoy cooking. The opposite is also true: if one needs prior knowledge in order to use the system, this means the system is hard to use, and thus the user will not enjoy cooking. Instead of dining out or buying ready-to-eat food, cooking your own meal is cheaper and healthier. Home-cooked meals gives you greater control over the ingredients and calories in your meals, thus improving weight management, fulfilling personal needs, and reducing illness risk. According to the feedbacks from the received questionnaires, we confirm that the proposed AR cooking guide system is feasible and practical for cooking assistance. Most participants had no trouble learning and using the proposed system. In total, 19 of the 20 participants would recommend the system to others to use (UQ11). All participants were satisfied with their end products from their baking (UQ12), and all participants were overall satisfied with the system (UQ13) (either strongly agreeing or agreeing). Regarding the optional feedbacks, most participants stated that once they were used to the hand gestures, the system gradually became easier to use as time goes on. In addition, the demonstration video for each cooking step was helpful because worded steps can be a bit vague. Several participants believe that making the whole process non-touch is the best feature because having clean hands is an important part of cooking. A non-touch interactive system assures users that their hands touch only the food ingredients, and they can cook while not having to touch anything else. Some participants express that being able to lock the video window in a designated position is another handy feature. This way, it does not interfere with the field of view of the real scene behind, and the user can look back and forth between the virtual video tutorial and their physical working area in order to cook efficiently. In particular, two participants reported that they prefered to be able to minimize the video window in certain cooking steps. Regarding the optional suggestions for improvement, some participants felt that the hand gesture recognition was too sensitive. Sometimes, the system recognized hand gestures accidentally when the participant was actually doing something else, which resulted in unnecessary hassles. In addition, since some hand gestures look alike, the recognition system occasionally misidentified a hand gesture as something else and executed the wrong function. The gesture recognition needs to be more intuitive and less sensitive. A careful tuning of the thresholds could be helpful to achieve a better trade-off between precision and recall. Moreover, instead of recognizing static hand gestures solely based on an image, recognizing dynamic hand actions based on a short-term video has the potential to reduce confusion and should be more robust and reliable. Furthermore, two participants suggested having the system recognize both hands instead of one hand, which can lead to more combinations of gestures that are essential for cooking action recognition. Besides, a participant also suggested some recipe steps could be improved to sound less vague, especially in terms of measurements. A participant also mentioned that “softer colors” would be a better choice to improve the visualization of the interface. In addition to the 10 min preparation time, participants took about 50 min to follow all the steps, mix the ingredients, and bake the cake in the oven, all assisted by the proposed AR cooking system. It is interesting to note that one hour is normally the time it takes for an experienced baker to bake a cake. Even if less than half of the participants had experience of baking a cake (BQ8), the proposed AR cooking system was useful and effective in helping unskilled people to complete the cooking task within the expected time limit. All participants were successful in the baking of their cakes. No destructive mistakes occurred during our experiments. Even if a few participants needed to restart the demo video in some cooking steps due to misunderstandings of the procedures, all participants were satisfied with the cake they made.One problem encountered in our experiment is that users could not wear prescription glasses with the original Magic Leap One. According to the website of Magic Leap, a prescription insert is available, but it is custom-made for each user and requires additional purchase. Another problem is the overheating of the AR headset with prolonged use, which can be felt by the user wearing the headset and possibly causes dizziness for some people. We propose a new prototype AR system for cooking assistance in which a user only needs a pair of all-in-one AR glasses without having to install any external devices or sensors in the kitchen. We try to overcome some common troubles in cooking, implement the algorithms, and integrate them in an all-in-one AR headset. The user can direct the AR headset’s built-in camera to detect and recognize food ingredients by simply glancing over them in the refrigerator or on the kitchen table. Accordingly, the types of the recognized food ingredients are used to match appropriate recipes. Then, the proposed system provides and displays interactive demo videos on how to carry out each cooking step in the chosen recipe. All processes can be controlled via the user’s natural hand gestures in real-time, without the need to hold a controller in the hand. Compared with the deep learning models ResNet and ResNeXt, YOLOv5 achieves lower accuracy for ingredient recognition, but it can locate and classify multiple ingredients at the same time and thus greatly simplify the scanning process for users. Twenty people participated in the testing of the prototype system, provided feedback via questionnaires, and suggested improvements. All participants were overall satisfied with the prototype system, and 19 of the 20 participants would recommend others to use it; hence, the usability of the proposed AR cooking assistance system is confirmed.The prototype could be extended in the future by including more interactive recipes. The list of suggested recipes could also provide more information such as nutrition facts and calorie counts. In addition, implementing a scalable database to manage the addition of recipes for better tracking and storing should make the system more complete. Moreover, the more accurate recognition of a wider variety of food ingredients is another potential area for future research. Finally, the system could be enhanced by recognizing dynamic hand gestures, monitoring cooking actions, detecting procedural mistakes, and guiding users to prevent or recover from potential failure.


The focus of this survey was to understand the current status of implementation of early rehabilitation for critically ill children in China. We also reviewed the available literature on this topic for further insights to inform its future development.via the social media platform “WeChat Questionnaire Star” within the framework of the Rehabilitation Group of the Pediatrics Branch of the Chinese Medical Association. A narrative literature review on the implementation of the early rehabilitation for critically ill pediatric and/or adult patients was carried out.We used a cross-sectional study design to survey tertiary hospitals nationwide. Questionnaires were distributed n = 105, 52.0%) of respondent hospitals reported that they implement early rehabilitation for critically ill children. Among these 105 hospitals, 28 implemented a continuous chain of early rehabilitation. A total of 24 hospitals had set up permanent specialized centralized early rehabilitation units for critically ill children.A total of 202 valid questionnaires were received. About half (Early rehabilitation for critically ill children is not widely available in China and only a minority of hospitals implement a continuous chain of early rehabilitation. To improve this undesirable situation, we suggest creating a two-level integrated system comprising centralized early rehabilitation units and surrounding early rehabilitation networks within a region. With the development of critical care medicine, an increasing number of children have survived critical illness in the past few decades , 2. As aDespite China's massive and urgent clinical needs, little is written about the scope of early rehabilitation for critically ill children. Some initial practice has taken place in a few hospitals in the last 5 years, but its development is unfocused and uneven with limited theoretical support and consideration. The focus of our survey was to understand the current status of implementation of early rehabilitation for critically ill children in China. We also reviewed the available literature on early rehabilitation for critically ill patients, to further illuminate the topic and inform future development.The survey used a cross-sectional observational study design to survey Tertiary care centers nationwide in China. Questionnaires were distributed to directors of the related departments within the framework of the Rehabilitation Group of the Pediatrics Branch of the Chinese Medical Association. Because no director of the related departments of hospitals in the provinces/cities of Hong Kong, Macau and Taiwan were in the Rehabilitation Group, so no questionnaire was expected from these provinces.Only tertiary care centers were surveyed, considering that the implementation of early rehabilitation for critically ill children is still in the initial stage in China. Tertiary care centers in China are those of the highest level and defined as cross-regional, providing comprehensive and specialized medical care with extensive medical education and research functions .via the social media app “WeChat Questionnaire Star.”A questionnaire was developed based on the authors' direct field experiences. To ensure content validity, the draft questionnaire was completed and evaluated by experts in several hospitals and was revised based on their suggestions. The final questionnaire was sent to directors of the related departments The questionnaire consisted of five sections: respondent, department and hospital information and four key components of early rehabilitation for critically ill children:  the statEach returned questionnaire was carefully reviewed for completeness and consistency. For questionnaires with any incomplete responses or suspected errors, data were confirmed by WeChat contact or telephone call. If no answer could be obtained, that questionnaire was excluded as a “no response.”p <0.05.The data were entered into a database using Microsoft Excel  software and cleaned, checked and analyzed using SPSS statistical software for Windows, version 21.0 . Descriptive statistics were used to present the data. Nominal data are reported as proportions. Comparisons of the proportions were made between different types of hospitals using the chi-square test or Fisher's exact test. The level of statistical significance was established at A narrative literature review was carried out by searching PubMed with the search words “intensive care unit,” “critical illness,” “critically ill,” “children,” “child,” “pediatric,” “early rehabilitation,” “early mobilization,” “rehabilitation,” “continuous chain,” “continuum,” “centralized rehabilitation,” “specialized rehabilitation,” “sub,” “critical,” and “acute.” The data search was further augmented by scanning the references of identified papers. We review and analyzed these studies for further insights to inform the future development of the early rehabilitation for critically ill children in China.Between March 25, 2021, and April 7, 2021, 306 returned questionnaires were received, of which 104 were excluded . The data were judged to be doubtful mainly because of the logical contradiction among the answer to several questions in one questionnaire or the obvious mistakes based on common sense, e.g., the number of beds in the specialized centralized early rehabilitation units was over 100. For the repeated questionnaires from the same department, only the questionnaires from the chief director of a department were kept. If there is no first choice, questionnaires from the director of the specialized centralized early rehabilitation unit within a certain department as the second-best choice . This len = 105; 52.0%) of respondent hospitals reported that they implement early rehabilitation for critically ill children; the percentage for children's hospitals was as high as 88.5%, but for general hospitals and maternal and child health hospitals were 44.8 and 52.4%, respectively. The pairwise comparisons were significant between the children's hospitals and general hospitals  and between children's hospitals and maternal and child health hospitals , but not significant between general hospitals and maternal and child health hospitals . Similarly, a pairwise comparison of the number of hospitals that had not implemented early rehabilitation for critically ill children but planned to implement it in the next 3 years was significant between children's hospitals and general hospitals  and between children's hospitals and maternal and child health hospitals , but not significant between general hospitals and maternal and child health hospitals . Only one (3.8%) children's hospital had no plan to implement rehabilitation of critically ill children in the next 3 years, whereas 18 (13.4%) general hospitals and 5 (11.9%) maternal and child health hospitals had no such plan.The status of implementation and planning of early rehabilitation for critically ill children at the time of the survey is shown in Among the 105 hospitals that implemented early rehabilitation for critically ill children, 28 (26.7%) hospitals implemented a continuous chain of early rehabilitation that commenced in the ICU/PICU and continued in the acute care ward when patients were discharged from the ICU/PICU, 65 (61.9%) hospitals implemented early rehabilitation only in the ICU/PICU, and 12 (11.4%) hospitals implemented early rehabilitation only in the acute care ward. In addition, among the 28 hospitals implementing a continuous chain of early rehabilitation, 23 hospitals had permanent centralized early rehabilitation units specifically designed for critically ill children, and the other 5 hospitals only provided temporary bed units for critically ill patients when needed. Meanwhile, among the 12 hospitals implementing early rehabilitation only in the acute care ward, only 1 hospital had a permanent centralized early rehabilitation unit, and the other 11 hospitals would provide temporary bed units for these children when needed. So, a total of 24 hospitals had built permanent centralized early rehabilitation units for critically ill children.Of the 24 early rehabilitation units, 22 (91.7%) had fewer than 20 beds and 18 (75.0%) had 10 or fewer beds; 22 (91.7%) accepted fewer than 100 patients per year and 17 (70.8%) accepted fewer than 50 patients per year.The treatment of motor dysfunction, disturbance of consciousness and swallowing dysfunction were basic skills mastered by nearly all the 24 units. Respiratory and bladder/urethral dysfunction were managed by the majority.Patients with a nasogastric tube or an indwelling catheter were accepted by nearly all the 24 units; 15 units accepted patients with a tracheal tube and 7 units accepted ventilated patients.In the event of deterioration of condition or the development of indications requiring additional medical care, most of the 24 units indicated they were able to deal with the occasional occurrence of seizure in a patient, but would transfer patients to a suitable ward or unit if they developed status epilepticus; similarly, patients with pneumonia were usually treated within the unit but were transferred to a suitable ward or unit if they developed severe pneumonia. Respiratory failure and cardiac failure were also indications for transferring patients for most of the units.Among the 24 units that responded, 15 had independent workgroups comprising doctors, nurses and therapists, and one unit had an independent group of doctors. All the units indicated that they invited doctors from other departments for consultations, including ICU/PICU, neurology, neurosurgery, respiratory medicine, orthopedics and nutrition, et al.The three most frequently involved doctor specialists were those from pediatrics/pediatric surgery, rehabilitation medicine and neurology/pediatric neurology. The three most frequently involved types of the therapist were from the fields of rehabilitation therapeutics, sports medicine and traditional Chinese medicine.This study provides an initial descriptive analysis of the practice characteristics of early rehabilitation of critically ill children in China. The study has several strengths and to our knowledge, this is the first and largest practice analysis on this topic published to date. Responders spanned almost all provinces and cities in China. As mentioned above in Section Study design and participants, no questionnaire was expected from the provinces/cities of Hong Kong, Macau and Taiwan because of the objective limitation of the questionnaire distribution. Valid questionnaires were obtained from all other provinces/cities in China. The survey responses illuminated some notable facts: early rehabilitation for critically ill children is not widely available in China—only about half of the respondent hospitals reported that they implement it, and only a minority of hospitals implement a continuous chain of early rehabilitation that commences in the ICU/PICU and continues in the acute care ward. Specialized centralized early rehabilitation units for critically ill children were even sparser. The survey was carried out within the framework of the Rehabilitation Group of the Pediatrics Branch of the Chinese Medical Association and only tertiary care centers were surveyed; this gave a concentrated view of the situation, and in reality, far fewer than half of the hospitals are capable of implementing the early rehabilitation of critically ill children and most of the existing early rehabilitation is incomplete. Compared to the rate (77.8%) of the implementation of early mobilization within PICUs in Canada according to a national survey conducted 9 years ago , the gapCurrently, “early rehabilitation in ICU” in the literature and in practice frequently refers to exclusive mobility or exercise programs , despiteNevertheless, our survey results suggested that the implementation of early rehabilitation in ICU/PICU is not ideal. Only about half of respondent hospitals reported that they can implement early rehabilitation for critically ill children, and a few of them could only implement early rehabilitation after patients were discharged from the ICU/PICU rather than while they were admitted there. We believe that the usual working framework and protocols of hospitals in China are the reason for this, as rehabilitation staff outside of the ICU/PICU must be formally consulted by ICU/PICU physicians to work with critically ill patients. However, ICU/PICU physicians are often focused on saving the life of the critical patient and may not be aware of patients' loss of functioning and need for rehabilitation. This problem also exists in PICUs in developed countries. Several studies in Europe and North America have revealed that the major barrier to early mobilization in PICUs is the lack of physician order , 19, 20.Therefore, to promote early mobilization in the ICU/PICU in China, we must promote the concept of early mobilization to ICU/PICU physicians. We suggest that rehabilitation departments and ICU/PICUs foster ongoing interdisciplinary cooperation, including holding multidisciplinary ward rounds and communicating academic knowledge regularly.Several studies have further shown that besides early rehabilitation commenced in the ICU, a continuous chain of early rehabilitation on the acute care ward can also lead to an earlier discharge from the hospital and improved functional recovery and is also cost-effective and safe for adult survivors of critical illness. The results of these studies are summarized in However, in our survey, among the 105 hospitals claiming to be able to implement early rehabilitation for critically ill children, only 40 could immediately implement early rehabilitation in acute care wards after patients' discharge from the ICU/PICU. Other hospitals may be limited by a lack of technology and professionals. There is often a large time gap between acute care and inpatient rehabilitation—the so-called “rehab hole” mentioned by Simmel et al. , 27. TheGiven the above, we suggest creating two-level integrated early rehabilitation systems that include specialized centralized early rehabilitation units and surrounding early rehabilitation networks within a region. Specialized centralized early rehabilitation units with professional multidisciplinary team could be built in the regional medical centers. When critically ill children having achieved medical stability in the ICU/PICU and with serious dysfunctions and comorbidities in need of a continuous chain of early rehabilitation, they can be transferred to the units to get not only the rehabilitation therapy to facilitate further functional recovery, but also the identification and management of the comorbidities to achieve a stable physical state for rehabilitation therapy. The surrounding early rehabilitation networks are ICU/PICUs at all types and levels of hospitals within the radiation range of specialized centralized early rehabilitation unit in the regional medical center. They should implement early mobilization and rehabilitation into everyday practice and transfer patients in need of further comprehensive early rehabilitation to the centralized early rehabilitation units. These units should be standardized and accept referral patients from surrounding networks. They should provide training opportunities to staff within their systems. From our practical experience, we have observed that there are critically ill children whose acute hospital needs have been met but who still have physical and cognitive disabilities. If the continuous chain of early rehabilitation is not available to them, the observation period and waiting time to transfer them out of the PICU is longer, which aggravates the patients' financial burden and delays the reunion of these children with their families. It also wastes resources and strains the PICU capacity for new patients.Our findings should be interpreted within the context of several limitations. Firstly, as mentioned previously in Section Discussion, the survey was carried out within the framework of the Rehabilitation Group of the Pediatrics Branch of the Chinese Medical Association and only tertiary care centers were surveyed. We should not ignore that this has caused a concentration effect and we need to acknowledge that the reality is worse than the survey suggests, reinforcing the importance of developing early rehabilitation for critically ill children in China. Secondly, there may be respondent reporting bias because we adopted a self-report method. However, we tried to minimize this bias by limiting the questionnaire respondents to department directors, who should know their departments best. Also, the questionnaire was carefully designed, evaluated and revised before formal use.In conclusion, this survey revealed two notable facts: (1) early rehabilitation for critically ill children is not widely available in China; (2) only a minority of hospitals implement a continuous chain of early rehabilitation. These may result in many critically ill children not receiving the necessary standard early rehabilitation therapy in time, and the discontinuation of early rehabilitation is often inevitable for most patients during the referral period.Given the urgency in China of developing a continuous chain of early rehabilitation for critically ill children, with limited resources, we suggest that a two-level integrated system is created which includes centralized early rehabilitation units and surrounding early rehabilitation networks within a region. ICU/PICUs at all types and levels of hospitals should be encouraged to implement early mobilization and rehabilitation into everyday practice and transfer patients in need of further comprehensive early rehabilitation to centralized early rehabilitation units.Further research on the safety, efficacy and cost-effectiveness of centralized early rehabilitation units for critically ill children is needed.The original contributions presented in the study are included in the article/TZ designed the study, designed the questionnaire, analyzed the data, reviewed the literature, and wrote the manuscript. XD helped to design the questionnaire and draft the manuscript. YF, WJ, and XH helped to design the questionnaire. YC participated in the design of the study and the questionnaire. NX participated in the design of the study and the questionnaire and coordinate the study. All authors have made substantial contributions to this study and manuscript and participated in the data collection, manuscript revision, and approved the final manuscript.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.


Tacrolimus is a key drug in immunosuppressive therapy following lung transplantation. The blood tacrolimus levels are likely to fluctuate in the early postoperative period, and failure to maintain the tacrolimus trough level in target ranges is a risk factor for rejection. However, there is little information about the relationship between the time in therapeutic range (TTR) of the tacrolimus trough level (tacrolimus TTR) and clinical outcomes. This study aimed to evaluate the association between tacrolimus TTR and acute rejection (AR) within the first three months after lung transplantation.This was a retrospective study of patients who underwent lung transplantation at a single center. The target tacrolimus trough levels were 10–15 ng/mL, and tacrolimus TTR was calculated using the Rosendaal method. The cut-off value of the tacrolimus TTR was estimated by receiver operating characteristic analysis based on AR.n = 22) and ‘‘late-AR’’ was defined as any AR after 1-month post-transplant (n = 4). For early AR, the relationship between tacrolimus TTR and the onset of AR was examined. There were no differences in the tacrolimus TTR between the early-AR group and non-AR group . For late-AR, the relationship with tacrolimus TTR was examined every 10 d. The tacrolimus TTR during postoperative days (POD) 21–30 and POD 31–onset was significantly lower in the late-AR group than the no-AR group . The cutoff value of the tacrolimus TTR during POD 21–30 was estimated as 55.0%.The study included 90 patients. AR was observed in 26 patients. In this study, ‘‘early-AR’’ was defined as any AR within 2 weeks post-transplant (Our findings suggest that a lower tacrolimus TTR is a predictor of late AR. A tacrolimus TTR of 55% or higher is necessary to reduce the risk of AR during this period after lung transplantation. Lung transplantation has been established as a therapeutic option for patients with end-stage lung disease . To prevIn general, for drugs whose pharmacological effects are concentration-dependent, the length of time for which the blood concentrations are in the therapeutic range is related to efficacy. For example, the effect of the anticoagulant, warfarin, is concentration dependent. It is well-known that the duration ratio in the target therapeutic range of prothrombin time (time in therapeutic range (TTR), %), a marker of warfarin blood levels, predicts efficacy and adverse events . TherefoThe incidence of AR after lung transplantation is highest in the first three months and decreases with time . In addiPatients who underwent lung transplantation at Kyoto University Hospital between January 2016 and December 2020 were retrospectively analyzed. Patients who concomitantly used basiliximab or changed tacrolimus to cyclosporine were excluded from this analysis because the target range of tacrolimus trough concentration was different from that of other patients. Electronic medical records were reviewed and pertinent data were retrieved. The collected data included patient demographics, medical history, transplantation details, and medications. This study was approved by the Ethics Committee of Kyoto University Graduate School and Faculty of Medicine (R0545-2). The Ethics Committee waived the need for informed consent since this was an observational study using existing data.A combination of tacrolimus, mycophenolate mofetil, and corticosteroids was used in all patients. Tacrolimus was initiated at the discretion of the transplant team. Mycophenolate mofetil was orally administered at 1500 mg/day, and the dosage was reduced in cases such as intolerable gastrointestinal side effects or leukopenia. Corticosteroid therapy was initiated with intravenous methylprednisolone at 125 mg/day for 3 d after transplantation and reduced to a stable dose of oral prednisolone (0.4 mg/kg) using a weekly weaning regimen.The measurements of blood tacrolimus levels were usually performed daily during intensive care unit stay and then twice or thrice weekly after transfer to the general ward until discharge. Micafungin was initiated in the perioperative period, and itraconazole oral solution was administered when the patients tolerated the oral intake. The concentrations of itraconazole and its major metabolite hydroxyitraconazole were measured in all cases. After the sum of the serum itraconazole and hydroxyitraconazole concentrations reached 750 ng/mL, micafungin administration was stopped. After the initiation of itraconazole, the blood tacrolimus levels were measured at least daily until the tacrolimus trough concentration was stable. Tacrolimus levels in the blood were measured using a chemiluminescent immunoassay kit . Trough levels of tacrolimus were measured the morning prior to drug administration. The tacrolimus dosage was adjusted to trough concentrations of 10–15 ng/mL in the first three months after transplantation.The Rosendaal linear interpolation method was used to calculate tacrolimus TTR . The tacThe primary outcome was the clinical AR within the first three months after lung transplantation. The transplant physicians judged the AR based on radiographic and clinical findings without transbronchial lung biopsy. AR episodes were characterized by dyspnea, low-grade fever, leukocytosis, hypoxemia, and diffuse interstitial infiltrates on chest radiographs. Patients with AR were primarily treated with bolus doses of methylprednisolone and were closely monitored for various clinical signs. Rabbit anti-thymocyte globulin was administered in cases of hemodynamic compromise, persistent AR, or severe rejection. In this study, AR was defined as a suspected rejection that was improved after the treatment with methylprednisolone or rabbit-antithymocyte globulin.t-test. A receiver operating characteristic (ROC) curve was constructed to determine the thresholds for the optimal tacrolimus TTR. All analyses were performed using GraphPad Prism . The alpha level of significance was set at P < 0.05.At our institution, 40% of patients developed AR within 5 years after lung transplantation ; howevern = 22] and 38.0 ± 3.1 d [n = 4], respectively). In this study, ‘‘early-AR’’ was defined as any AR within 2 weeks post-transplantation (n = 22) and ‘‘late-AR’’ was defined as any AR after 4 weeks post-transplantation (n = 4).A flowchart of the patient selection process is shown in Fig. n = 64), early AR group (n = 22), and late AR group , length of hospital stay , and the ratio of infection after lung transplantation (P = 0.014).Patient demographics and clinical characteristics were compared among the following three groups: no AR group (P = 0.416). For late AR, the relationship between AR and tacrolimus TTR every 10 d was investigated to determine which period of TTR was associated with AR . The relationship between tacrolimus trough concentration and early or late AR is shown in Tables We assumed an association between AR and tacrolimus TTR in the period preceding AR. Therefore, the tacrolimus TTR to AR onset in patients with early AR was compared with that on day 8 in patients who did not develop AR Table . The resAR Table . The tacP = 0.0237; Fig. The threshold value of tacrolimus TTR during postoperative days 21–30 was evaluated. The cutoff value of the tacrolimus TTR during postoperative days 21–30 was estimated as 55.0 %  from postoperative day 25, and ganciclovir was administered to treat this infection. In case #4, the antifungal prophylaxis was changed from itraconazole to micafungin due to suspected breakthrough fungal infection on postoperative day 30. Infection was observed in cases #1, #2, and #4. Antimicrobial therapy was administered from the day these infections were observed.Figure AR is a major complication from lung transplantation. In previous studies investigating the impact of tacrolimus TTR in renal transplant patients, the cut-off values in the first year after transplantation were estimated as 60% , 78% 1414, and 4Tacrolimus exerts its effect through tertiary complexation with FK506 binding protein 12 and calcineurin, leading to decreased nuclear production of interleukin-2 and thereby reduced lymphocyte proliferation. This complex inhibits the activity of the enzyme calcineurin and thus, interrupts the calcium-dependent signal-transduction pathway in T cells . Since tIn this study, a decrease in tacrolimus TTR was observed 10 d before the onset of late-AR. This suggests that a decrease in tacrolimus TTR may influence/promote AR in lung transplant patients over a short period of time. Of the four patients with late-AR, three developed infections after transplantation. Since immunosuppressive therapy increases the risk of infection, tacrolimus dose reduction is usually considered when the infection is diagnosed. In these cases, it is possible that the tacrolimus blood levels were adjusted to the lower end of the target range. In addition, the antibiotic and antifungal doses were changed prior to the onset of AR, in these cases. Tacrolimus is metabolized by cytochrome P450 (CYP) 3A in the liver and small intestine . ItraconAR is considerably prevalent in the early post-transplant period. In a previous study, AR occurred mostly within the first postoperative month, observed in 26% of patients (63 of 241) . In thisThis study has several important limitations. First, this study had a limited sample size, which makes it difficult to examine the effects of potential confounders and perform subgroup analysis. Second, because this was a retrospective and non-randomized observational investigation, various unmeasured confounders may have resulted in a bias-derived outcome. Third, there was a variation in the measurement intervals of tacrolimus blood levels. Therefore, the estimated tacrolimus concentration may not have accurately reflected the real exposures. Fourth, AR is determined by clinical symptoms and steroid pulse implementation and not by transbronchial lung biopsy. Therefore, the potential for AR may not have been determined. In addition, it is unclear whether AR is acute cellular rejection or not.This study is the first to assess the effect of tacrolimus TTR in the early postoperative period of lung transplantation. Our findings suggest that a lower tacrolimus TTR is a predictor of AR four weeks after lung transplantation. A tacrolimus TTR of 55% or higher is necessary to reduce the risk of AR during this period.


Androgen receptor variant 7 (AR-V7), an AR isoform with a truncated ligand-binding domain, functions as a transcription factor in an androgen-independent manner. AR-V7 is expressed in a subpopulation of hepatocellular carcinoma (HCC), however, its role(s) in this cancer is undefined. In this study, we investigated the potential roles of AR-V7 in hepatocarcinogenesis in vivo in a c-MYC-driven mouse HCC model generated by the hydrodynamic tail-vein injection system. The impacts of AR-V7 on gene expression in mouse HCC were elucidated by RNA-seq transcriptome and ontology analyses. The results showed that AR-V7 significantly exacerbated the c-MYC-mediated oncogenesis in the livers of both sexes. The transcriptome and bioinformatics analyses revealed that AR-V7 and c-MYC synergistically altered the gene sets involved in various cancer-related biological processes, particularly in lipid and steroid/sterol metabolisms. Importantly, AR-V7 suppressed a tumor suppressor Claudin 7 expression, upregulated by c-MYC overexpression via the p53 signaling pathway. Claudin 7 overexpression significantly suppressed the c-MYC-driven HCC development under p53-deficient conditions. Our results suggest that the AR-V7 exacerbates the c-MYC-driven hepatocarcinogenesis by potentiating the oncogenic roles and minimizing the anti-oncogenic functions of c-MYC. Since AR-V7 is expressed in a subpopulation of HCC cases, it could contribute to the inter- and intra-heterogeneity of HCC. Primary liver cancer is the third leading cause of cancer death worldwide with more than 900,000 new cases and 800,000 deaths every year . PrimaryAndrogen receptor (AR) is the male sex hormone receptor and a transcription factor playing pivotal roles in the development of male reproductive organs and masculinization of various somatic organs, including the central nervous system , 16. TheSince the c-MYC pathway is frequently and aberrantly activated in HCC, we investigated the roles of AR-V7 in hepatocarcinogenesis in vivo by utilizing a c-MYC-driven mouse HCC model. The roles of AR-FL were also investigated simultaneously. The c-MYC-driven HCC model mice that co-expressed AR-V7 or AR-FL were generated using the hydrodynamic tail-vein injection technique and the Sleeping Beauty transposon system , 24. TheFLAG vectors were generated in-house, using pDONR221, pENTR-1A, pT3-EF1α (used as a backbone), and the Gateway cloning system   , the SB146 , the FLAG were diluted with 2 μg pCMV-SB100 in 2 mL saline (0.85% NaCl), sterilized through 0.2-μm filter and injected into the lateral tail-vein of a recipient mouse (20 g body size) in 7 seconds. Total amount of injected DNA was adjusted using pT3-EF1α as a control null vector. Animals were monitored every 2 days for tumor growth, harvested at 20 days post-injection for analyses. The Institutional Animal Care and Use Committee approved all experimental procedures accordingly to the NIH Guide for Care and Use of Laboratory Animals.The 5- to 6-weeks-old FVB/N mice  were divided randomly into groups of 6–7 animals for each sex, and hydrodynamic tail-vein injection was performed as described previously , 25. TheImmunohistochemistry and immunofluorescence were performed as described previously , using ahttps://imagej.nih.gov/ij/) as shown in Supplementary Fig. Surface areas of the liver and liver cancer in photographic images were manually selected and quantified using the ImageJ program (version 1.52a)  in bioinformatic analyses. Gene ontology and pathway analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) bioinformatics resources (version 2022) [At 20 days post hydrodynamic injection, liver tumor foci were dissected and pooled for each mouse. Non-tumor liver tissues were collected from the control group mice. Total RNA was isolated from tumor foci and non-tumor tissues using TRIZOL-Plus RNA isolation kit . One μg total RNA from each biological triplicate or quadruplicate sample was subjected to a total mRNA sequencing (RNA-seq) and subsequent data analyses with the Ensembl GRCm39 (release M27) mouse reference genome, as described previously . Genes won 2022) .Total RNA isolated from mouse tissue samples was treated by RQ1-DNase treatment  to remove contaminant DNA and was reverse-transcribed using the SuperScript IV reverse-transcriptase kit (Thermo Fisher Scientific). The reverse-transcribed products were analyzed by quantitative PCR (RT-qPCR) in biological triplicate or quadruplicate for each sample using TaqMan Fast Advanced Master Mix (Thermo Fisher Scientific) and QuantStudio3 real-time PCR detection system (Thermo Fisher Scientific). The TaqMan gene expression assay IDs are described in Supplementary Table De-identified human frozen HCC and cirrhotic liver samples were obtained from the Cooperative Human Tissue Network (CHTN). Consents from donors were obtained by CHTN at the time of procurement, per the established policy of CHTN. RNA isolation and conventional RT-PCR analysis were performed using the primers listed in Supplementary Table Since the c-MYC pathway has been demonstrated to significantly contribute to HCC oncogenesis, we evaluated the effects of AR-V7 expression on the c-MYC-driven hepatocarcinogenesis in vivo. To generate the mouse model, we stably transfected the c-MYC expression vector pT3-c-MYC with the AR-V7 expression vector pT3-AR-V7 (hereby designated as hep-c-MYC/AR-V7) or without pT3-AR-V7 (designated as hep-c-MYC) into mouse hepatocytes using the hydrodynamic tail-vein injection technique Fig. . We alsoAt 20 dpi, the overall tumor volume in the hep-c-MYC/AR-V7 mice was larger than hep-c-MYC mice regardless of the sexes as indicated by increased liver-to-body weight ratio Fig. . SimilarHistopathologic analyses of hematoxylin-eosin (H&E) stained sections revealed that tumors developed in hep-c-MYC mice were moderately steatohepatitic, contained small droplets of fat in cancer cells Fig. , c-MYC. On the other hand, the relative tumor area in hep-c-MYC/AR-FL male mice were morphologically larger than that in hep-c-MYC male mice Fig. . In addiThe c-MYC plays significant roles in many biological processes, including proliferation, protein and ribosomal biosynthesis, metabolism, immune surveillance, cell differentiation, cell adhesion, and senescence. When it is aberrantly overexpressed/activated, it amplifies the biological processes toward oncogenesis, such as gene instability, accelerated cell proliferation, metabolism, and angiogenesis, resulting in cancer development . To explThe macroscopic analyses showed that AR-FL co-expression moderately exacerbated the c-MYC-driven liver cancer in male mice, but not in female mice Fig. . Hence, To evaluate the effects of AR-V7 on c-MYC-driven hepatic oncogenesis, we examined specific genes, i.e., B4galnt1/G4GALNT1, Ffar4/FFAR4, and Il1rl2/IL1RL2, that was further upregulated by AR-V7 co-expression, as indicated in class-D genes Fig. , red. ThWhile c-MYC plays pivotal roles in cell proliferation and cancer development, it also increases the sensitivity to apoptosis in non- and pre-malignant cells , 38. Furn = 4, male mice) at 11 dpi was significantly lower than that of hep-c-MYC cancer at 20 dpi  was localized in cytoplasm, and nucleoli in the hydrodynamically transfected hepatocytes at 2 dpi of pT3-c-MYC, pT3-Cldn7FLAG and pX330-p53 (hep-c-MYC/p53KO/Cldn7FLAG)  Fig. , confirmdpi Fig. . These rThe high cancer heterogeneity of HCC is a major problem in developing effective therapeutic strategies for this deadly cancer . Hence, The full-length AR harboring the C-terminal ligand-binding domain (LBD) functions as a transcription regulator in a ligand-dependent manner , 43. SinEither AR-V7 expression or AR-FL overexpression alone in hepatocytes did not result in tumorigenesis in the mouse liver  exacerbate the c-MYC-mediated hepatocarcinogenesis by potentiating the oncogenic and minimizing the anti-proliferative functions of c-MYC in the liver Fig. , therebySupplementary FiguresSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4