Background: Real-time continuous glucose monitoring (CGM) devices help detect glycemic excursions associated with exercise, meals, and insulin dosing in patients with type 1 diabetes (T1D). However, the delay between interstitial and blood glucose may result in CGM underestimating the true change in glycemia during activity. The purpose of this study was to examine CGM discrepancies during exercise and the meal postexercise versus self-monitoring of blood glucose (SMBG).Methods: Seventeen adults with T1D using insulin pump therapy and CGM completed 60 min of aerobic exercise on three occasions. A standardized meal was given 30 min postexercise. SMBG was measured during exercise and in recovery using OmniPod® Personal Diabetes Manager  with built-in glucose meter , while CGM was measured with Dexcom G4® with 505 algorithm (n = 4) or G5® (n = 13), which were calibrated with subjects' own PDM.Results: SMBG showed a large drop in glycemia during exercise, while CGM showed a lag of 12 ± 11 (mean ± standard deviation) minutes and bias of −7 ± 19 mg/dL/min during activity. Mean absolute relative difference (MARD) for CGM versus SMBG was 13 (6–22)% [median (interquartile range)] during exercise and 8 (5–14)% during mealtime. Clarke error grids showed CGM values were in zones A and B 94%–99% of the time for SMBG.Conclusion: In summary, the drop in CGM lags behind the drop in blood glucose during prolonged aerobic exercise by 12 ± 11 min, and MARD increases to 13 (6–22)% during exercise as well. Therefore, if hypoglycemia is suspected during exercise, individuals should confirm glucose levels with a capillary glucose measurement. Successive generations of devices have improved sensor accuracy when comparing with laboratory standards , thereby leading to nonadjunctive status, so that insulin dosing and decision-making around hypoglycemia treatment can be made using some of these devices.8 CGM was shown to help guide patients on when to initiate carbohydrate feeding during physical activity in active adolescents and young adults with type 1 diabetes (T1D). Although a threshold of 126 mg/dL was set for carbohydrate feeding, however, that study did not determine if the sensor glucose was an accurate reflection of SMBG during exercise. More recently, CGM devices have been heavily scrutinized in the context of exercise due to the apparent lag between interstitial and blood glucose when the blood glucose concentration is changing rapidly.9–12 This lag is often attributed to the following: (1) physiologic lag affected by blood flow10; (2) sensor reaction time to glucose13; and (3) signal processing.14 There may also be a bias between the CGM and reference measurements due to the baseline signal (at a zero glucose concentration) generated by the CGM sensors. These discrepancies between CGM devices and SMBG may impact insulin-dosing decisions and carbohydrate replacement, particularly around exercise and during meals when glucose levels can change rapidly. In line with this, Biagi et al.15 reported an increase in MARD from 9.5% to 16.5% during aerobic exercise, while Moser et al.16 demonstrated a further increase in MARD (∼20%) when blood glucose levels dropped into the hypoglycemic range with exercise. Interestingly, Adolfsson et al.17 reported that MARD values also tended to increase with higher intensity  compared with lower intensity  activities in adolescents with T1D. However, this has not been consistently observed in studies examining intermittent high-intensity exercise,19 likely because glucose levels tend not to change as dramatically because of a rise in glucose counterregulatory hormones and lactate levels.20In an earlier field study,The primary purpose of this study was to assess the accuracy of newer CGM technology compared with SMBG during prolonged aerobic exercise and the meal postexercise in adults living with T1D. Our hypothesis is that CGM accuracy and MARD values will deteriorate during prolonged, steady-state moderate-intensity exercise and during the meal challenge postexercise.clinicaltrials.gov in 2017 (identifier: NCT03130101). A total of 17 individuals  with T1D were recruited for the study. The inclusion criteria included 17–65 years of age; duration of diabetes >1 year; using insulin pump therapy  for at least 1 month; a total daily insulin dose of at least 0.25 U/kg; and glycated hemoglobin (HbA1c) ≤9.9% (85 mmol/mol). The exclusion criteria included unpredictable hypoglycemia; not able to perform regular physical activity due to an injury; having conditions that may make exercise unsafe ; and/or physician diagnosis of active diabetic retinopathy or neuropathy. Written informed consent was obtained from all participants before study initiation.The present study conformed to the standards set by the Declaration of Helsinki and was approved by the Research Ethics Board at York University. The study was registered at 21 using CGM for a secondary analysis. All exercise testing took place at York University in the Clinical Human Exercise Laboratory. Participants took part in one preliminary visit and three experimental visits, in which 60 min of aerobic exercise was performed with different insulin basal rate settings to help mitigate the risk for hypoglycemia. The preliminary testing consisted of questionnaires, anthropometric measurements, and a test of peak oxygen consumption (VO2peak). The experimental visits included three moderate-intensity aerobic exercise bouts with different adjustments in basal insulin, completed in a randomized counterbalanced manner. Participants were asked to refrain from all forms of vigorous-intensity exercise  for 24 h before each visit and were asked to avoid alcohol and caffeine consumption during the monitoring period.These are ancillary data from a previously published study,1c  were measured. The VO2peak test was an incremental-to-maximum treadmill protocol and was measured using a portable metabolic system  and heart rate monitor . The metabolic unit measures breath-by-breath expired oxygen and carbon dioxide concentrations using oxygen sampling line, turbine flowmeter, harness, face mask, and head strap.During the preliminary testing, height, body mass, blood pressure, body fat percentage, and HbA® with 505 algorithm or G5®, San Diego, CA) at least 24 h before testing and were provided with a receiver or mobile app to track glucose levels throughout the study. A total of 7 participants were trained on the operation of CGM and the remaining 10 participants were asked to continue using their own CGM throughout the study duration. Of the 17 participants, four used CGM with Dexcom G4 Platinum with 505 algorithm (started before the study) and the remaining (n = 13) used CGM with Dexcom G5 throughout the study. Participants wore CGM for 1 week at a time and were instructed to use their Personal Diabetes Manager (PDM) for SMBG using the built-in glucose meter and associated blood glucose test strips . Following an initial warm-up period of 2 h after sensor insertion, participants were advised to calibrate at least once every 12 h using their own PDM glucose meter. If experimental visits were scheduled more than 1 week apart, participants were asked to insert a new sensor at least 24 h before the next visit. All CGM data were uploaded to the “Healthcare Professional” Dexcom Clarity® account and the data were later retrieved for analysis.All participants were instrumented with a CGM sensor and transmitter (Dexcom G42peak) walking/light jogging on a treadmill. The activity was broken down into four 15-min bouts with 5-min rest periods in between.All participants completed three prolonged aerobic exercise visits that were separated by at least 24 h and all sessions were pooled for this analysis. Participants were asked to consume the same lunch of their choice and take their usual mealtime bolus insulin , no later than 11:30AM. The three insulin adjustment strategies included (1) pump suspension (100%) at exercise start, for the duration of the activity; (2) a 50% basal rate reduction (BRR), set 90 min pre-exercise for the duration of the activity; and (3) an 80% BRR, set 90 min pre-exercise for the duration of the activity. At 1:30PM participants were reminded to reduce basal insulin until the end of exercise. Exercise began at 3:00PM and consisted of 60 min of moderate-intensity . Additional dextrose was provided if the initial 16 g did not restore blood glucose concentrations. Exercise was resumed when SMBG values reached >81 mg/dL.Capillary glucose was determined at 30 and 10 min pre-exercise, just before exercise onset, and every 15 min during exercise using the OmniPod PDM (Insulet) with built-in glucose meter . Measurements were completed in duplicate and if the duplicate value differed from the first value by >10 mg/dL, a third SMBG was determined. For analysis purposes, if duplicates were performed, then the average of both SMBG values were used, whereas if triplicates were performed, the average of the closest two values was used. If hypoglycemia (<70 mg/dL) developed during exercise, participants were provided with 16 g of dextrose tablets  rate immediately following exercise end. Following a 30-min rest period post-exercise, all participants consumed a standardized meal containing ∼30–50 g of carbohydrates, ∼10–20 g of protein, and ∼5–15 g of fat , and SMBG levels were monitored for 90 min postmeal.The CGM uses mathematical models to convert raw sensor measurements to blood glucose concentrations. The measurements are based on interstitial glucose levels and the models correct for various error sources between the interstitial and blood glucose concentrations, including a physiological time lag and a bias .A single compartment model is used: and  represent the time lag and bias parameters and are identified through least squares fit. The numerical values of the derivative (or ROC) term  are obtained with the Savitzky–Golay filter.22 The filter order and window size are set to 1 and 7, respectively. Least squares fit is used to find the unknowns  and . Three different sets of the unknowns were identified. The first set assumes that the lag and bias between the CGM and SMBG measurements are global over time. The latter two sets assume that the lag and bias are different during exercise and meal.where The pairing determination is based on the SMBG values that are paired with the closest CGM value within the next 5 min. Overall, there were 10 matched pairs of SMBG and CGM values for each participant across all three conditions. The “rest” time points include all the 10-min pre-exercise, exercise start, and premeal data. The “exercise” time points include 15, 30, 45, and 60 min of exercise. The “meal” time points include 30, 60, and 90 min following meal ingestion.P < 0.05 and the data are represented as mean ± standard error of mean as well as median and interquartile range. Absolute glucose concentrations during exercise and meal recovery were pooled across the three experimental conditions. A two-way repeated-measures analysis of variance was conducted to compare SMBG and CGM values during exercise and in the meal recovery postexercise. During rest, exercise, and recovery, there was no difference in Dexcom G4 and G5 glucose data when analyzed separately (data not shown), and therefore, all CGM data were pooled for all analyses. All of the graphics and statistical analyses were completed using GraphPad Prism Version 7.0 . The MARD values were calculated using the absolute relative difference between the glucose meter value and CGM value divided by the glucose meter value, then multiplied by 100.Statistical significance was set at 1c 6.5% ± 0.5% or 47 ± 5 mmol/mol). The duration of diabetes was 14 ± 10 years and VO2peak was 41.6 ± 5.9 mL/(kg·min), indicative of good cardiometabolic fitness, but not of the elite level.The anthropometric measurements for all 17 participants  are summarized below. Participants recruited for this study were all adults , height 168 ± 10 cm, weight 72 ± 10 kg, on insulin pump therapy, and in good glycemic control  was calculated during rest, exercise, and meal recovery. The CV between the two closest blood glucose readings was 2.2% ± 1.9% during rest, 2.3% ± 2.0% during exercise, and 2.2% ± 1.7% during the meal recovery.P < 0.05). Overall, there were 12 documented episodes of hypoglycemia during the 51 total exercise sessions , as measured by SMBG, with a mean SMBG value of 60 ± 5.4 mg/dL. The corresponding CGM value during these documented hypoglycemic events was higher in all occasions, averaging 81 ± 10.2 mg/dL. In recovery, when blood glucose was rising, the average CGM value was significantly lower than the SMBG value 30 min postmeal (P = 0.001).r-squared values for SMBG versus CGM were similar at r2 = 0.82, r2 = 0.82, and r2 = 0.73 during rest, exercise, and meal challenge, respectively (P > 0.05).23 However, as CGM technology and accuracy improve over time, several devices are being recognized as the standard of care for managing T1D and gaining nonadjunctive status, so that insulin-dosing decisions and hypoglycemia management can be made without confirmatory SMBG.5 Since most studies are carried out under tightly controlled conditions in laboratory settings and few studies have assessed the accuracy of CGM and flash glucose monitor devices in outpatient settings,25 future studies should focus on comparing the newest and most advanced glucose-monitoring technology in outpatient settings with a greater emphasis on exercises of varying intensities and durations.In the past, CGM devices were not considered for nonadjunctive use, primarily due to the concern of inaccuracy that could lead to potentially inappropriate treatment and insulin-dosing decisions.19; however, to our knowledge, this is the first study to measure CGM accuracy during a structured 60-min aerobic exercise bout using primarily newer Dexcom G5 technology (n = 13). The remaining participants (n = 4) wore Dexcom G4 Platinum with updated software and 505 algorithm. In addition, our analysis showed no difference with the G4® data removed; therefore, all CGM data were pooled. In the present study, MARD values were higher during exercise [13 (6–22)%] compared with the meal recovery [8 (5–14)%]. More specifically, when we separated the data by BRR condition , we found a greater discrepancy in CGM values relative to SMBG values. Therefore, to ensure safety, our recommendation for individuals with T1D is to use CGM in addition to a standard blood glucose meter during physical activity.Previous literature has looked at CGM performance during both aerobic and anaerobic exerciseondition , we founThe present study assessed the accuracy of CGM compared with SMBG during exercise and in meal recovery. First, we found that CGM had good congruence with SMBG during rest, likely because the CGM was calibrated to this glucose meter. Second, we found that mean CGM values underestimated the drop in mean glucose during exercise and lagged significantly behind SMBG. It is important to highlight that in those individuals who developed hypoglycemia during exercise, as measured by SMBG, the CGM overestimated glucose by about 20 mg/dL, on average, with very few of the CGM readings in the hypoglycemic range . Therefore, based on our findings, we suggest setting a higher CGM threshold to initiate carbohydrate feeding to treat  or prevent  hypoglycemia, if the CGM displays downward trend arrows.10 These findings may have critical implications for the development of hybrid closed-loop or automated insulin delivery systems for exercise since these devices may inadvertently overdeliver insulin when plasma glucose is dropping, but sensor glucose fails to drop or may even rise.26 In contrast, for the meal response after exercise, the delayed rise in sensor glucose may result in insulin underdelivery compared with plasma glucose and/or SMBG values. To overcome some of these challenges, Turksoy et al.27 determined that incorporating wearable technology with automated insulin delivery can better detect the changes in glucose levels and is showing promise in the prevention of hypoglycemia during exercise.It is worth noting that we found significant variation within the identified lag and bias values during exercise and these variations may be due to intersubject or sensor variability.17 while others compare plasma glucose as the reference, using a glucose analyzer.28 The laboratory standard for CGM accuracy is most commonly a glucose analyzer 30; however, we chose SMBG as the reference in this study. Interestingly, according to a recent study that used capillary blood glucose concentration as a reference for CGM accuracy compared with venous blood found that SMBG as the reference was associated with significantly lower MARD values compared with plasma glucose as measured by a glucose analyzer .31 In a real-world setting, patients will not have glucose analyzers available to use as a reference during exercise, and therefore, SMBG is the most common reference measurement.A number of studies have looked at CGM accuracy during exercise; some using SMBG as a reference,31 In the present study, all CGM calibrations were made using the participant's own PDM glucose meter. As such, SMBG and CGM values were not different during rest (P > 0.05), but CGM lag time was apparent, specifically during exercise. As can be observed in Measuring CGM accuracy may be negatively impacted if the reference method is different from the method that is used to calibrate the CGM device.During exercise, as shown in This study has a number of limitations that should be acknowledged. First, we included only SMBG measurements and were lacking measurements from a glucose analyzer . Second, due to the frequent fingerstick glucose sampling during exercise, SMBG values were determined every 30 min during the meal challenge rather than every 15 min such as during exercise. Third, these ancillary data included only one form of exercise  and future studies would benefit from assessing CGM lag time during various intensities and durations of activity. Finally, our study included the use of both Dexcom G4 with 505 algorithm and G5 technology, and therefore, future studies should conduct similar analyses in a real-world setting and use the latest CGM technology available.1c and quality of life in both children and adults with T1D.32–35 However, we conclude that the accuracy of newer CGM technology remains negatively impacted during prolonged aerobic exercise and patients need to be aware of this potential CGM time delay. More specifically in our study, the CGM  lag time was 12 ± 11 min behind SMBG readings during exercise with a bias of −7 ± 19 mg/dL/min. Due to this clinically important delay in CGM versus SMBG, we suggest patients increase vigilance and perform more frequent fingerstick capillary glucose monitoring around exercise.In summary, CGM technology helps patients closely monitor glucose levels and make appropriate changes to prevent dysglycemia. Numerous studies have reported that regular use of CGM lowers the time spent in hypoglycemia, and improves HbA


Hemostatic materials are of great importance in medicine. However, their successful implementation is still challenging as it depends on two, often counteracting, attributes; achieving blood coagulation rapidly, before significant blood loss, and enabling subsequent facile wound-dressing removal, without clot tears and secondary bleeding. Here we illustrate an approach for achieving hemostasis, rationally targeting both attributes, via a superhydrophobic surface with immobilized carbon nanofibers (CNFs). We find that CNFs promote quick fibrin growth and cause rapid clotting, and due to their superhydrophobic nature they severely limit blood wetting to prevent blood loss and drastically reduce bacteria attachment. Furthermore, minimal contact between the clot and the superhydrophobic CNF surface yields an unforced clot detachment after clot shrinkage. All these important attributes are verified in vitro and in vivo with rat experiments. Our work thereby demonstrates that this strategy for designing hemostatic patch materials has great potential. Nanotechnology can bring significant advancements to hemostatic patches. Here, the authors design a superhydrophobic hemostatic surface with immobilized carbon nanofibers that can stop bleeding instantaneously upon application, seal the wound subsequently by promoting quick fibrin formation, and facilitate unforced and facile patch removal without tearing the wound. Improperly dressed wounds will prolong healing time and impose a high infection risk4, leading to significantly increased mortality and economic burden. To exemplify, $10 billion per year is spent on the treatment of complex wounds in North America5, while the global wound care market is estimated to reach $22 billion by 20206. Despite the progress in developing advanced hemostatic materials over the last few decades9, there are still two major challenges to be addressed: excessive blood loss during the period that the clot is forming and strong clot adhesion on the hemostatic dressing that causes pain, secondary bleeding, and possible infection during the wound-dressing removal.Uncontrolled hemorrhage and wound infection are leading causes of death in the medical field of wound care11, which unavoidably absorbs blood and causes unnecessary blood loss and gauze adhesion onto the wound. Blood absorbed in the gauze forms a solid clot-gauze composite, forced peeling of which often tears the wound and causes secondary bleeding and pain. This makes it difficult to replace the old wound dressing without causing secondary infections or hemorrhage, in procedures ranging from common wounds to surgery, and to the extreme case of hemophilic patients12, where excessive bleeding will occur before coagulation. To deal with these problems, active clotting agents (chitosan3 or kaolin7) have been adopted into hemostatic materials, to reduce bleeding by expediting the coagulation process. However, such agents employ free micro-particles, which poses a safety threat of causing micro-thrombosis if they enter the vascular system14. Recently, researchers proposed using superhydrophobic (SHP) or superhydrophilic materials for hemostatic purposes. A superhydrophilic material (graphene sponge8) is reported to absorb water from the blood quickly, forming a dense layer of blood cells and platelets, thus promoting coagulation. Hydrophilic hemostatic material can also be prepared by spray coating β-chitosan on the porous nanofiber mat15, and the hydrophilic β-chitosan coating can increase blood wettability and thus enhance clotting. Alternatively, a SHP coating can be applied on the back of the normal superhydrophilic gauze as an impervious layer to prevent blood loss through the gauze16. However, the core functionality of these approaches is still either based on a blood-absorbing hemostatic material (superhydrophilic) that does not minimize blood loss and secondary bleeding or a blood-repelling material (superhemophobic) that simply repels blood but does not actively trigger clotting. Therefore, the aforementioned two key challenges on wound management still remain poorly addressed.The conventional method to deal with bleeding is mechanically pressing the wound with a cotton gauzeHere we report a strategy for achieving hemostasis by designing a SHP and blood-repelling surface that simultaneously achieves fast clotting with no blood loss, anti-bacterial property, and clot self-detachment. The non-wetting feature of the SHP hemostatic surface can withstand substantial blood pressure and help reduce blood loss and bacteria attachment. We find that carbon nanofibers (CNFs) immobilized on this surface can promote fast fibrin growth and thus clotting. Due to the presence of micro-air pockets within the blood–substrate contact area, there is minimal contact between the clot and the SHP CNF patch, leading to natural clot detachment after clot maturation and shrinkage, which reduces the peeling tension required to peel off the patch by about 1~2 orders of magnitude compared with a normal hydrophilic gauze or commercial hemostatic products. These features have been verified in vitro and in vivo, demonstrating the effectiveness of this strategy for designing hemostatic patch materials.18 , and polytetrafluoroethylene (PTFE)19 or polydimethylsiloxane (PDMS)21 onto a substrate (Table −1 (72 mN m−1 for water)22 and a complex composition, but our SHP CNF surfaces could still repel it, with blood contact angles (CAs) of 153.6 ± 1.4° for the CNF/PTFE Ti surface and 151.4 ± 1.8° for the CNF/PDMS Ti surface  formed rapidly  with anticoagulant ethylenediaminetetraacetic acid (EDTA)dly Fig. –5. In thdly Fig.  and 3. Adly Fig. , in the dly Fig. , alignindly Fig. . Similardly Fig. , and fibdly Fig. .24), and occurred on both the SHP CNF/PTFE and CNF/PDMS surfaces  tests26. We used a high dose (2 mg ml–1) of a thrombin inhibitor, argatroban27, in EDTA or citrated blood and PPP, and found that both blood and PPP droplets (20 μl) quickly rolled off the SHP CNF surface at a very small angle without generating any fibers . This is not customarily observed on SHP surfaces, which typically have low RAs corresponding to low contact-angle hysteresis30. In a recent report, a similar distorted receding edge for blood droplets sliding down an SHP surface was observed but not explained22.Fibrin fibers generated at the receding side Fig.  greatly  θr Fig. . Consequ26. Fibrin monomers would attach onto CNFs and then self-polymerize into long insoluble fibrin fibers, which became visible upon blood/plasma-substrate separation  with green fluorescence protein (GFP) expression plasmid over a glass slide that was half-coated with CNFs and nearly no bacteria was found on the SHP CNF surface , our material design strategy can achieve fast clotting without blood loss. This performance can be especially beneficial for chronic bleeding disorders45.The non-wetting property of our SHP CNF coating can prevent blood loss at the wound site, by keeping blood within the wound. This feature was demonstrated in vitro, with a silicone tube filled with blood that had a hole opened on its side to mimic a bleeding wound. Cotton gauzes, with and without SHP CNF coating, were used to cover the holes , about 54 times smaller than that for the normal hydrophilic gauze . After clot detachment, CNFs that were initially immobilized on cotton fibers were transferred onto the clot  onto the rat skin (hair shaved) with the clinically approved tape for 12 h  were pre-mixed with PTFE powder 19 or PDMS (Slygard 184)21 and were applied on substrates via spray coating58. Before spray coating on a flat Ti substrate , the Ti mesh  before test to make sure there was no free CNF on our material.CNFs59. As EDTA would prevent blood coagulation by irreversibly chelating calcium ions, EDTA blood was not suitable for coagulation test; for in vitro clotting tests , nominal receding CA θr_nom, nominal CA hysteresis CAHnom (CAHnom = θa − θr_nom), and RA θ were measured using the tilting method60, by placing a 20 μl droplet  on the sample surface and tilting the sample till droplet roll-off. For PPP or blood on the SHP CNF surface, due to the existence of fibrin fibers, RA θ was defined as the tilt angle when fibrin fibers fractured and the droplet rolled off quickly. Static and dynamics CAs were averaged over four repetitions (n = 4). Surface morphology of the prepared samples and the surfaces after blood/plasma tests were characterized by SEM  after gold coating.CAs and RAs were measured using a custom-built device. CA was measured using the sessile method by dispensing 5 μl liquids  on flat substrates (the CNF/PTFE Ti and CNF/PDMS Ti surfaces) and 20 μl liquids on the CNF Ti mesh. Dynamic CAs, including advancing CA θr_nom for blood or PPP droplets (20 μl) on the SHP CNF surface was measured at the moment before catastrophic fibrin fiber fracture and droplet rolling down  was first dissolved in 0.9% NaCl solution and then added into EDTA blood or PPP to achieve a final dose of 2 mg ml−1, which was higher than the value used in animal studies to ensure sufficient thrombin inhibition61.The anti-thrombin, argatroban25. Sliding tests with EDTA PPP droplets were respectively performed on three SHP CNF/PTFE and CNF/PDMS Ti surfaces (15 mm by 7 mm); fibrin fiber generation during the sliding test was confirmed using the setup in Supplementary Fig. 25, to verify the existence of fibrin on the CNF surfaces after PPP sliding tests.The porcine fibrin ELISA kit was ordered from MyBioSource, Inc. 36 the samples were then carefully rinsed by dipping into DI water three times37. Samples were air-dried; fibrin structures grown on the surface were observed under SEM after gold coating.Forty microliters of EDTA PPP was dispensed onto a SHP CNF/PTFE Ti surface placed in a plastic petri dish and cultivated for 4 min at 37 °C; the reaction was terminated by slowly adding sufficient DI water into the petri dish;E. coli MG1655 (K-12) (ATCC®700926™), harboring constitutive GFP expression plasmid stored at −80 °C, was added into 3 ml fresh LB broth, supplemented with Kanamycin (Km) (50 μg ml−1). The cells were incubated in a shaking incubator at 37 °C with a shaking speed of 225 r.p.m. The culture was diluted to OD600 of 0.5 with fresh LB (50 μg ml−1 Km). The CNF/PTFE glass slide sample was first sterilized with ultraviolet, then 40 µl cell culture was flushed over the sample surface across areas with and without CNF coating. The sample was subsequently air-dried in Biosafety Cabinet  for 20 min. Bacteria attached on the CNF surface were observed under the confocal laser scanning microscope , with laser wavelength of 473 nm for exciting the GFP42.For convenient observation of bacteria attached on the surface, a SHP CNF/PTFE coating was half-coated on a glass slide following the same procedure for the CNF/PTFE Ti surface. The SHP CNF/PTFE glass surface and the CNF/PTFE Ti surface were shown to have the same performance through water, blood, and PPP testing. Ten microliters of glycerol stock ) of To compare the clotting performance of our CNF gauze, chitosan gauze was prepared by spray coating the chitosan and PDMS composite dispersion onto the pristine gauze. For fair comparison, weight ratio of chitosan to PDMS was 1:2, the same as CNF to PDMS weight ratio for our CNF gauze. Twenty-five milligrams of chitosan  was first dissolved in 5 ml 0.15 M acetic acid with an ultrasonic probe; 50 mg PDMS (pre-polymer to cross linker weight ratio 9:1) was dispersed in 10 ml acetone by the ultrasonic probe. After mixing the two dispersions under ultrasonication for 5 min, the composite dispersion was spray-coated onto a pristine gauze (size: 10 cm by 10 cm) following the same protocol for preparing the CNF gauze. After coating, the chitosan gauze was baked at 80 °C for 1 h to evaporate the solvent.24, clotting test was performed using the citrated blood. Different types of gauzes  were pre-warmed in 20 ml polystyrene (PS) plastic petri dishes (37 °C water bath). After mixing the citrated blood with 0.2 M CaCl2 at a volume ratio of 10:1 to initiate coagulation24, 20 μl blood was immediately dispensed and sandwiched between two gauzes in the petri dish , measured by the spectrophotometer at 540 nm, would represent the amount of hemoglobin from unclotted red blood cells; HA(0), the absolute hemoglobin absorbance of 20 μl blood in 10 ml DI water at t = 0 min, was used as reference; RHA(t), the relative hemoglobin absorbance at clotting time t, was calculated as HA(t)/HA(0) 8. As the hemoglobin came from unclotted red blood cells, a smaller RHA(t) would mean faster clotting24.With reference to published studiesn = 3); NCA on the normal gauze was considered as the red-color area, which was soaked by blood, whereas NCA on the CNF gauze was considered as the clot-gauze contact area  of that on the uncoated pristine gauze ; the results  during the clotting test, following the same protocol, 20 μl blood was placed between two normal gauzes or two CNF gauzes for coagulation to occur. After 1 h, the two pieces of gauze were separated and a picture taken from the top was processed to calculate the NCA  was quickly filled into the silicone tube and placed in a petri dish. After 10 min at room temperature, the tube was removed from the petri dish and the weight increase in petri dish due to blood leakage was measured as the blood loss. The test was repeated three times (n = 3) to get the average blood loss.Mimicking a wound dressed with a medical gauze, an opening representing the wound (8 mm by 5 mm) was made on a silicone tube (inner diameter: 7.8 mm) and was covered with the SHP CNF gauze by medical tape .To test the maximum pressure that the CNF gauze (without a back-supporting impervious membrane) could withstand before blood leakage, citrated blood was slowly filled into a long tube with a hole sealed by the CNF gauze  by 3 M high-strength adhesive tape; the clot on the CNF gauze was peeled from one side by pulling a thin cotton wire which was glued onto the clot by epoxy  was used to compare the clot-peeling tension for the CNF gauze and the uncoated normal gauze.Citrated blood was mixed with 0.2 M CaCl2 solution at a volume ratio of 10:1 was then dispensed onto the substrate to soak it with blood. Hemostatic samples (30 mm long and 10 mm wide) were immediately placed onto the substrate material, allowing the clot to form between the hemostatic sample and the substrate material. Clotting was first allowed to occur at 37 °C for 1 h; clot solidification was subsequently accelerated by exposing the clotted samples to warm air flow from a hair dryer for 30 min. After that, samples were peeled from one side to measure the peeling force . Besides, WCA was measured with 10 μl DI water (n = 5) to provide information on hydrophobicity for different samples  were prepared to be plaster-like for in vivo experiment  were made on the back of the rat, one on the left and one on the right at the same position; upon incision, the plaster-like gauze was applied onto the wound instantly  were ordered from InVivos Pte Ltd, Singapore, with approval from the National University of Singapore Institutional Animal Care and Use Committee (NUS IACUC protocol number: R18-0961), complying with all relevant ethical regulations for animal testing and research. Rats were anesthetized by isofluorane (4–5% in 100% oxygen for induction and 1–3% for maintenance) with thermal pad to prevent hypothermia. Hair on the back was first shaved with an electrical razor; residual hair was removed with VeetAs for the blood loss, it was characterized by measuring the weight increase in gauze. Weight of the gauze peeled at 3 min minus its initial weight was taken as the blood loss. Gauze weight was measured by a high-precision weighing balance (resolution: 1 mg). The data were averaged over six repetitions.n = 5), allowing the clot to sufficiently mature and solidify. Before gauze peeling, the adhesive film around the gauze was carefully trimmed away (Supplementary Fig. As for the peeling force, it was measured by peeling the gauze along the wound after about 2 h (132 ± 10 min and 133 ± 10 min for the control and the CNF gauze, respectively; mean ± SD, Skin compatibility test was also conducted in vivo by attaching our material onto rat skin for 12 h (Supplementary Fig. Further information on research design is available in the Supplementary InformationSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3Supplementary Movie 4Supplementary Movie 5Supplementary Movie 6Supplementary Movie 7Reporting SummaryDescription of Additional Supplementary FilesPeer Review File


In this mini-review, we will summarize the current understanding of the molecular mechanism of Fe–S biogenesis by SUF. The advances in our understanding of the molecular aspects of SUF originate from biochemical, biophysical and recent structural studies. Combined with recent in vivo experiments, the understanding of the Fe–S biogenesis mechanism considerably moved forward.Iron–sulfur clusters (Fe–S) are amongst the most ancient and versatile inorganic cofactors in nature which are used by proteins for fundamental biological processes. Multiprotein machineries  exist for Fe–S cluster biogenesis which are mainly conserved from bacteria to human. SUF system ( So far, the ATPase activity of SufC was not shown to be important for Fe–S assembly in vitro. However, deletion of sufC or mutation in the ATP binding site abolish in vivo function of the SUF pathway , 80Therm3W) Fig. . The oveIn addition, compared to other ABC ATPase structures, a significant displacement occurs at a linker region between the ABC α/β domain and the α-helical domain. The linker conformation is stabilized by a hydrophobic interaction between conserved residues around the Q loop. Finally, the surface of SufC has a cleft different from those observed in other ABC ATPase structures. These results suggest that SufC interacts with its partners, SufB and SufD, in a manner different from that of ABC transporters.2D complex . It. It2D cofication . Negativ SufC2D2  as folloand SufD , 82; (3)and SufD .2D complex. The structures of SufB and SufD are similar and share a common domain organization: an N-terminal helical domain, a core domain which consists of a right-handed parallel β-helix, and a C-terminal helical domain to which SufC interacts. Important structural change of the SufBC2D complex occur, initiated by SufC dimerization in the presence of Mg2+ and ATP. Thanks to a fluorescent experiment, Cys405 of SufB, a strictly conserved amino acid buried at the heterodimer interface between the SufB and SufD heterodimer, was shown to become exposed during ATP hydrolysis. His360 of SufD, localized close to Cys405 of SufB, likely undergoes similar exposure upon conformation change. Finally, two Hg2+ ions are present in the structure at the interface of the SufB–SufD heterodimer. One bound to Cys405 in SufB, and the other bound to Cys358 in SufD, which is located adjacent to His360 of SufD. These ions can bind the authentic Fe–S binding site, and therefore, these three residues Cys405 in SufB, H360 and Cys358 in SufD were proposed as good candidate for Fe–S cluster ligation . Th. ThE. co3 from β . Later, 3 from β . Very re3 from β . It is psuf operon. They mainly consisted in studying the in vivo impact  after inactivation of a single suf gene and allowed to demonstrate that suf operon is involved under oxidative stress and iron limitation [suf operon on the Fe–S assembly process in E. coli. To detect the effect of mutation in vivo, two strategies were used. One strategy was to perform complementation assays using an E. coli mutant strain that can survive without Fe–S clusters [E. coli strain (UT109) the chromosomal suf and isc operons are deleted (ΔsufABCDSE ΔiscUA-hscBA). Deletion of both operons in E. coli is lethal in general; but, UT109 harbors the plasmid pUMV22 that carries three genes for the mevalonate (MVA) pathway, which allows UT109 to grow with an absolute dependence on MVA supplementation [E. coli. The second strategy consists to assess Fe–S assembly in vivo on SufBC2D using the color of host cells overproducing SufBC2D complex that contain mutation on SufB, SufC or SufD proteins. Cells are blackish-green when active SufBC2D complex is overproduced and white for an inactive complex, unable to form a Fe–S cluster [2D complex. For both strategies, a series of mutations was generated on SufB, SufC and SufD proteins. The in vivo complementation assays reveal critical amino acids on SufC: Lys40, Glu171 and His203 [2D complex [2D  proteins are blackish-green [For SufD, the in vivo complementation assay and cells color reveals that His360 is critical for Fe–S metabolism and Fe–S assembly on SufBC2D , 41 in a2D , 50 .Based on biochemical, biophysical, structural and in vivo experiments the current model for Fe–S assembly on SufBC2D complex. In particular, the invariant residue Cys405 in SufB and likely the His360 in SufD, become exposed to the surface to construct the nascent Fe–S cluster. The building of the Fe–S is possible by arrivals of sulfur and iron ions. SufS catalyzed desulfurization of l-cysteine with the formation of persulfide on its Cys364. Nucleophilic attack of SufE Cys51 thiolate allows transpersulfuration reaction to occur and formation of a persulfide on SufE Cys51. A second transpersulfuration between SufE and SufB generates a persulfide on SufB Cys254 residue that serves as the first sulfur acceptor site on SufB. Then, sulfur migrates from SufB Cys254 to SufB Cys405. SufB Cys405 is > 25 Å away from SufB Cys254. An internal hydrophilic tunnel ranging through the β-helix core domain of SufB just between SufB Cys254 and SufB Cys405 may help during sulfur transfer between these two cysteines. Residues Lys303 and Gln285 might be directly involved . If this putative sulfur tunnel is involved in sulfur transfer from Cys254 to Cys405 (that is an interesting hypothesis) that would be the first time that sulfur transfer reaction occurs without transpersulfuration mechanism, which usually is used for sulfur as a strategy to travel long distances under a non-toxic form. SufB Cys405 is the final sulfur acceptor and a good candidate for one of the Fe–S cluster ligands. SufD His360 is likely another one. SufB Glu434 and SufB His433 or SufB Glu432 may be also involved during Fe–S assembly or as Fe–S ligands  on small chelates (ferric citrate) and proposed that it can be involved during Fe–S assembly in the reduction of ferric iron [0 to S2−) even though this was not demonstrated experimentally [2 serves to reduce iron. Considering that SUF system is involved under oxidative stress and iron limitation another possibility would be that the reduced flavin serves as a sensor of oxidative conditions (hypothesis never considered so far). The binding site of the FADH2 is still unknown despite several experiments using mutants in SufB [2 binding site requires further studies. Another next challenge in the future in the Fe–S assembly field involving SUF system is to get structural information of an integrated system containing SufSE–SufBC2D proteins. The SufSE complex interacts with SufBC2D complex to provide sulfur atoms for Fe–S cluster assembly. Sulfur atoms enter SufBC2D complex via SufB protein. Some ITC experiments demonstrated a flip–flop mechanism of allosteric regulation where binding of one SufE to one active site of SufS dimer diminishes further SufE binding to the second active site [2D complex for sulfur transfer: SufS2E, SufS2E2, SufSE? It is reasonable to hypothesize that a stoichiometric SufSE complex is relevant for interaction with SufBC2D since only one sulfur entry to SufB is require (Fig. 2D complexes? As a consequence, it is urgent to stabilize and get structural information on the huge SufSEBC2D complex. This would allow to trap Fe–S intermediate, and therefore, identify Fe–S coordination sites and fully understand the Fe–S assembly mechanism.There are still some remaining questions. How and when iron is delivered to the Fe–S assembly site? is there a specific iron donor protein for the SUF system? Genetic experiments strongly suggest a link between SufD and iron metabolism , 35, 49. complex . Only thric iron . Recentlmentally . Thus, t in SufB ; and theive site . One canire Fig. . Another


Peroxisome proliferator-activated receptors (PPARs) are a family of nuclear hormone receptors that control the transcription of specific genes by binding to regulatory DNA sequences. Among the three subtypes of PPARs, PPARγ modulates a broad range of physiopathological processes, including lipid metabolism, insulin sensitization, cellular differentiation, and cancer. Although predominantly expressed in adipose tissue, PPARγ expression is also found in different regions of the kidney and, upon activation, can redirect metabolism. Recent studies have highlighted important roles for PPARγ in kidney metabolism, such as lipid and glucose metabolism and renal mineral control. PPARγ is also implicated in the renin-angiotensin-aldosterone system and, consequently, in the control of systemic blood pressure. Accordingly, synthetic agonists of PPARγ have reno-protective effects both in diabetic and nondiabetic patients. This review focuses on the role of PPARγ in renal metabolism as a likely key factor in the maintenance of systemic homeostasis. The peroxisome proliferator-activated receptors (PPARs) are ligand-activated nuclear hormone receptors that participate in the transactivation or transrepression of networks of target genes, resulting in complex biological effects. PPARs are class 2 receptors that, upon ligand binding, heterodimerize with retinoid X receptors (RXRs) and translocate to the nucleus, whereupon the PPAR:RXR heterodimer binds to the PPAR response element (PPRE) generally in the promoter region of target genes, to control their expression ,2. The aThere are three known subtypes of PPARs that have distinct physiological roles in energy metabolism in different tissues: PPARα, PPARδ, and PPARγ. Overall, PPARs function as lipid sensors to govern metabolic homeostasis through binding to dietary metabolites: PPARα regulates catabolism, mainly in the liver and the heart, PPARγ regulates anabolism in adipose tissue, and PPARδ is involved in fatty acid transport and oxidation in skeletal muscle. PPARs not only serve critical roles in the control of lipid metabolism, but they are also implicated in the regulation of vascular diseases, cellular differentiation, insulin sensitization, and cancer . In the PPARγ gene is located on chromosome 3 (3p25.2) and contains nine exons spanning more than 100 kb [PPARγ splice variants, generated by alternative splicing and differential promoter usage, are found in human [PPARγ is also present in other animals  [PPARγ lead to dysfunctional lipid and glucose homeostasis and have been directly related to type 2 diabetes mellitus and obesity [In humans, the n 100 kb . Four PPin human ; howeverbrafish) ,12, alth obesity , familia obesity , and als obesity ,16.PPARγ is expressed predominantly in the adipose tissue where, together with the coexpression of C/EBP alpha and other proteins involved in lipid and glucose metabolism, serves as a key regulator both of adipocyte differentiation and triglyceride energy stores, and has pleiotropic vascular effects that are independent of its glucose blood-lowering effect, protecting against the progression of hypertension and atherosclerosis [PPARγ1 is expressed in many tissues and cell types, including white and brown adipose tissue, skeletal muscle, liver, pancreatic β-cells, macrophages, colon, bone, and placenta. By contrast, PPARγ2 has a more restricted pattern of expression with significant amounts found only in white and brown adipose tissue under physiological conditions, although it is induced in liver and skeletal muscle in response to overnutrition or genetic obesity. PPARγ3 mRNA expression appears to be limited to human white adipocytes, but it is also found in a variety of cell lines with different origins, including liver hepatocellular cells (HepG2), human intestinal cells (Caco-2), and cervical cancer (HeLa) cells. PPARγ4 is expressed in human adipose tissue [clerosis ,18. Spece tissue  and mutae tissue .O-GlcNAcylation at Thr-84 has been found to reduce PPARγ transcriptional activity in adipocytes cultured in vitro. Moreover, PPARγ2 is phosphorylated at Ser-112 by MAPK in response to different mitogenic growth factors that inhibit fat cell differentiation. The cdk5-dependent phosphorylation of PPARγ at Ser-273 occurs in inflamed obese white adipose tissue and decreases PPARγ activity. By contrast, PKA phosphorylation has been reported to positively affect the activity of PPARγ [PPARγ expression can be inhibited by miRNAs in specific settings [At the protein level, PPARγ is subject to several post-translational modifications, including glycosylation and phosphorylation, which function to modify its activity . For exaof PPARγ . Furtherof PPARγ ,24. Oversettings , constitIn kidney, PPARγ is expressed in different regions of the renal collecting system under physiological conditions, including connective renal tubules and collecting ducts  Figure . PPARγ iIn this review, we focus on several key observations that illustrate the central role of PPARγ in renal metabolism and maintenance of systemic homeostasis. We will examine the involvement of PPARγ in renal lipid, glucose, and mineral metabolism, and also blood pressure control. Against this background, we will also address the potential use of PPARγ agonists in the clinical setting as therapeutic agents for renal pathologies.Human kidney contains about 3% fat, although this varies greatly among individuals. Much of this (~50%) is in the form of phospholipids that form cell membranes, 15% is in the form of triglycerides, and around 0.3% is nonesterified (free) fatty acids (FFAs) . Under pPPARγ2 isoform in a leptin-deficient obese (ob/ob) background [ob/ob littermates at an early age, POKO mice present renal hypertrophy and dyslipidemia, and with alterations in some proliferation markers. Moreover, they develop incipient insulin resistance associated with a decrease in the expression of renal adiponectin. POKO mice also exhibit faster progression of kidney disease compared with ob/ob mice, accompanied by an increase in the expression of transforming growth factor beta (TGFβ) and also inflammatory and profibrotic markers in glomeruli, which associates with lipotoxicity and insulin resistance. This model has confirmed the key role of PPARγ2 in regulating renal lipid metabolism and insulin sensitivity, which is closely associated with glomerular filtration rate and albuminuria [PPARγ1 expression that correlates with a proinflammatory and proapoptotic state, and with changes in the gene expression of enzymes involved in fatty acid synthesis including a decrease in acyl-CoA carboxylase and fatty acid synthase [Moorhead et al. in 1982 were the first to establish a link between alterations in lipid metabolism and kidney disease . Mutatiockground ,35. Despuminuria . Furthersynthase .In addition to lipid metabolism, PPARγ is involved in adipokine expression from adipose tissue, including adiponectin , a circuPPARγ cause FPLD3 (OMIM 604367), clinically characterized by loss of subcutaneous limb and gluteal fat with preservation of visceral and subcutaneous abdominal fat, fatty infiltration of the liver, and hyperuricemia [Heterozygous mutations in uricemia . All paturicemia . It is kuricemia .Systemic lupus erythematosus is an autoimmune connective tissue disease marked by immune complex-mediated lesions in small blood vessels of different organs, particularly the kidneys. Mice lacking macrophage-specific expression of PPARγ or RXRα develop glomerulonephritis and autoantibodies to nuclear antigens, resembling the nephritis seen in systemic lupus erythematosus . MoreoveOverall, these findings illustrate the crucial role of PPARγ as a master regulator of systemic lipid metabolism in the kidney.The kidney contributes to glucose homeostasis not only through the processes of utilization , but it is also increasingly recognized as having a significant role in gluconeogenesis , and uniquely contributes to plasma glucose regulation by controlling glucose reabsorption from renal tubules following glomerular filtration . WhereasBlood glucose is freely filtered at the glomerulus and is then reabsorbed predominantly in the proximal tubule. This process is performed by two sodium-dependent glucose cotransporter (SGLT) proteins situated in different segments of epithelial cells of the proximal tubule (SGLT2 in the S1 segment and SGLT1 in the S3 segment). Once glucose has been concentrated in the epithelial cells, it diffuses out to the interstitium via specific facilitative glucose transporters (GLUTs) localized at the basolateral membrane . HyperglA recent review has addressed the association of metabolic traits, specifically glucose metabolism, with PPARγ genetic polymorphisms in humans , the mosPPARγ. Through this analysis, two new heterozygous mutations in PPARγ, Pro467Leu, and Val290Met, were identified in three subjects [The discovery of PPARγ as a target for TZDs prompted the screening of a cohort of subjects with severe insulin resistance for mutations in subjects . In addisubjects . Similarsubjects . Moreovesubjects . In addisubjects .Chronic kidney disease is directly related to the development of abnormalities in bone mineral metabolism. The kidney is known to regulate the levels of vitamin D3 by producing its most active form, 1,25 dihydroxyvitamin D3 , which participates in calcium and phosphate metabolism. The key players in this hormonal bone-parathyroid-kidney axis include fibroblast growth factor 23 (FGF23), Klotho, parathyroid hormone (PTH), and calcitriol, and recent work has also implicated the involvement of PPARγ .PPARγ-deficient embryonic stem cells failed to differentiate into adipocytes, but spontaneously differentiated into osteoblasts. They further showed that adipogenesis was restored by reintroduction of the PPARγ, indicating that PPARγ insufficiency stimulates osteoblastogenesis in vivo. The canonical Wnt/β-catenin-PPARγ system determines the molecular switch between osteoblastogenesis and adipogenesis [PPARγ. The osteogenic pathway is linked to the stimulation of Wnt signaling, leading to the final transcriptional activation of early osteogenic markers such as runt-related transcription factor 2 (RUNX-2) and alkaline phosphatase (ALP), which is mediated by β-catenin. Conversely, the adipogenic pathway involves inhibition of the Wnt pathway leading to ubiquitination/degradation of β-catenin, which results in the transcription of PPARγ, a pivotal initiator of adipogenesis [Osteoblasts and adipocytes share a common multipotent mesenchymal stem cell progenitor. Akune et al.  showed togenesis . Activatogenesis .klotho gene is a target of PPARγ [Klotho was initially discovered as an antiaging gene and encodes a single-pass transmembrane protein that forms a complex with the FGF receptor (FGFR) to create a de novo high-affinity binding site for FGF23. In addition to membrane-anchored Klotho, a secreted form of the protein is directly released into the extracellular compartment and is present in body fluids [Klotho is expressed in multiple tissues but is particularly high in the kidney in distal convoluted tubules, proximal convoluted tubules, and also in inner medullary collecting duct-derived cell lines. Recently, two Klotho-related genes were identified based on sequence similarity, βKlotho and γKlotho. The originally described Klotho is now referred to as αKlotho to distinguish it from the β and γ forms. βKlotho is expressed in various tissues, most notably in the liver and white adipose tissue, whereas γKlotho is expressed in the eye [It has been recently shown that the murine of PPARγ . Klotho y fluids . Klotho  the eye .Klotho is involved in the regulation of calcitriol production and modulates urinary phosphate, calcium and potassium excretion, and is downregulated in conditions related to kidney injury [klotho mRNA expression in Otsuka Long–Evans Tokushima Fatty rats and protected against the endothelial dysfunction induced in this model of atherogenesis [y injury . Consistogenesis . Unfortuogenesis ,64. LikePPARγ induce severe hypertension and type 2 diabetes. Mice with mutations in PPARγ in smooth muscle present vascular dysfunction and severe systolic hypertension [PPARγ (In line with a major role in the regulation of vascular tone and blood pressure, mutations in rtension . Cells on [PPARγ  and prodPPARγ also acts as a negative regulator of angiotensin II receptor 1 transcription, another important component of RAAS. In 2004, the angiotensin II receptor 1 antagonist telmisartan, which is to treat hypertension and diabetes, was identified as a partial agonist of PPARγ ,68. By aIt has been also observed that troglitazone has vasodilating effects on efferent and afferent arterioles from rabbit kidney, consistent with PPARγ expression in intima/media renal vasculature, thereby decreasing glomerular capillary pressure and hence excretion of urinary protein .Scnn1g gene, encoding the gamma subunit of the epithelial Na+ channel, was identified as a critical PPARγ target gene in the control of edema [The beneficial effects of PPARγ agonists in the control of blood pressure underscore the pivotal role of PPARγ at this level. PPARγ also has a critical role in systemic fluid retention through the regulation of renal sodium transport in the collecting duct, as the adverse effect of TZD use in the treatment of diabetes is PPARγ-dependent. Thus, the of edema ,73. The PPARγ in mouse suppresses or diminishes diurnal rhythms [nocturnin, a circadian-regulated gene that encodes a deadenylase thought to be involved in the removal of polyA tail from mRNAs, binds to PPARγ and enhances its transcriptional activity. Enhanced activity of PPARγ by nocturnin may result in increased bone marrow adiposity and bone loss [All PPAR isoforms are known to be rhythmically expressed . Specifi rhythms . In thisone loss .The circadian expression of PPARγ has not yet been established in kidney. An evaluation of this phenomenon could be especially interesting to associate the regulation of renal metabolism with central and peripheral circadian networks.PPARγ together with the adverse effects of PPARγ agonists hinders efforts to develop safe clinical treatments. The new challenges with regard to future applications include a comprehensive analysis of PPARγ cell-specific actions in the kidney and the manipulation of PPARγ expression. Finally, a better understanding of the molecular mechanisms of action of PPARγ in specific pathways together with its systemic implications may allow the development of new agonists and modulators to improve the management of kidney disease and consequently global homeostasis.PPARγ is a nuclear receptor that regulates systemic glucose and lipid homeostasis and participates also in immunity and vascular health. In the kidney, PPARγ is expressed in many different types of cells with diverse metabolic specializations, underscoring important roles for this nuclear receptor in renal lipid metabolism, glucose management, mineral metabolism, and the control of systemic blood pressure. PPARγ activation improves insulin sensitivity and reduces cardiovascular complications and renal injury in clinical practice. Nevertheless, the complex regulation of


Recent technological developments have enabled the measurement of a plethora of biomolecular data from various omics domains, and research is ongoing on statistical methods to leverage these omics data to better model and understand biological pathways and genetic architectures of complex phenotypes. Current reviews report that the simultaneous analysis of multiple (i.e. three or more) high dimensional omics data sources is still challenging and suitable statistical methods are unavailable. Often mentioned challenges are the lack of accounting for the hierarchical structure between omics domains and the difficulty of interpretation of genomewide results. This study is motivated to address these challenges. We propose multiset sparse Partial Least Squares path modeling (msPLS), a generalized penalized form of Partial Least Squares path modeling, for the simultaneous modeling of biological pathways across multiple omics domains. msPLS simultaneously models the effect of multiple molecular markers, from multiple omics domains, on the variation of multiple phenotypic variables, while accounting for the relationships between data sources, and provides sparse results. The sparsity in the model helps to provide interpretable results from analyses of hundreds of thousands of biomolecular variables.With simulation studies, we quantified the ability of msPLS to discover associated variables among high dimensional data sources. Furthermore, we analysed high dimensional omics datasets to explore biological pathways associated with Marfan syndrome and with Chronic Lymphocytic Leukaemia. Additionally, we compared the results of msPLS to the results of Multi-Omics Factor Analysis (MOFA), which is an alternative method to analyse this type of data.msPLS is an multiset multivariate method for the integrative analysis of multiple high dimensional omics data sources. It accounts for the relationship between multiple high dimensional data sources while it provides interpretable results through its sparse solutions. The biomarkers found by msPLS in the omics datasets can be interpreted in terms of biological pathways associated with the pathophysiology of Marfan syndrome and of Chronic Lymphocytic Leukaemia. Additionally, msPLS outperforms MOFA in terms of variation explained in the chronic lymphocytic leukaemia dataset while it identifies the two most important clinical markers for Chronic Lymphocytic Leukaemiahttp://uva.csala.me/mspls.https://github.com/acsala/2018_msPLS Technological developments have enabled the measurement and storage of a plethora of biomolecular data extracted from various omics domains, such as data from the genome, epigenome, proteome or metabolome. It has become common to measure hundreds of thousands of biomolecular variables. To explore biological pathways across multiple omics domains, which might be associated with phenotypic (e.g. disease) outcomes, a natural research direction is to simultaneously analyse these omics domains. Complex diseases, such as obesity, diabetes, and schizophrenia have genetic architectures that involve many biological pathways, since they are a result of interactions between genomic, epigenomic and environmental variables , 2. TherThere is ongoing research for suitable statistical methods that could help leverage the available omics data to better model and understand biological pathways and genetic architectures of complex phenotypes on the biomolecular level .Some of the first statistical methods developed for the integrated (i.e. simultaneous) analysis of multiple high dimensional omics datasets are generalizations of well known multivariate methods; e.g. sparse Canonical Correlation Analysis (CCA) –8, sparsTo address those challenges, we propose a multiset multivariate statistical method, called multiset sparse Partial Least Squares path modeling (msPLS). msPLS is the penalised extension of multi-block Partial Least Squares path modeling (PLS-PM). Given the situation where biomolecular variables from multiple omics domains are measured on the same patients with shared phenotypes of interest, msPLS models biological pathways by identifying biomarkers (i.e. biomolecular variables that are associated with the phenotypes of interest) in each omics domain. The omics domains are assumed to have a hierarchical structure between each other, and their relationship is modelled in terms of dependencies through explanatory and response domain pairs. The explanatory and response omics data source pairs can be determined through the hypothesised information transfer between data sources as follows . In an aTo illustrate such an explanatory and response dependency structure, consider that we have biomolecular variables  measured in patients with Marfan syndrome. The goal of this analysis is to use msPLS to explore biological pathways associated with Marfan syndrome, through the simultaneous analysis of the data sources. For this setting, we assume that the proteomic variables are responses for both the epigenomic and transcriptomic variables. Thus the proteome data source has an asymmetric relationship with both the epigenome and the transcriptome data sources. Additionally, there is a symmetric relationship between the epigenome and the transcriptome data sources, assuming a recursive information transfer between the epigenome and transcriptome. These assumptions are based on the special biological sequential information transfers of the central dogma of molecular biology and its elaborated versions , 26. GivTo provide interpretable results from analyses of hundreds of thousands of MVs is addressed through sparse variable selection. msPLS enforces sparse variable selection through penalization methods, such as through the Least Absolute Shrinkage and Selection Operator (LASSO), Ridge, and Elastic Net (ENet) penalization methods . These pThe rest of the paper is structured as follows. In the next section, the results of the real data analyses are described, where msPLS was applied to geneomewide biomolecular variables measured in Marfan patients in order to explain the variance in the phenotypic proteomic variables with the combination of biomarkers from the epigenome and transcriptome, while accounting for a hypothesised relationship in omics domains. Additionally, msPLS was applied to a second omics dataset containing data from patients with Chronic lymphocytic leukaemia, and its results were compared to the results of MOFA. We discuss these findings in the “We applied msPLS to genomewide epigenomic, transcriptomic and proteomic data sources measured in Marfan patients . In addiThe goal of this analysis was to explore biological pathways associated with Marfan disease based on epigenomic, transcriptomic and proteomic data measured in 37 Marfan patients . The 364p-value <0.01 after permutation  was used to test the association of the resulting gene expression markers . For the sake of interpretability, we assessed the pathways with p-values only lower than 5×10−2. From the 208 pathways, 58 (28%) had a p-value <5×10−2 , the “Crosslinking of collagen fibrils” , the “TGF-beta receptor signaling in EMT ” , and the “Loss of Function of TGFBR2”  pathway. The identified pathways can be further appraised in the context of known interactions of genes and genetic phenotypes. We queried the curated database of Online Mendelian Inheritance in Man . The OMIM query yielded 372 results . The mutation in the FBN1 associated with the classic type of Marfan syndrome. Although MFS2 is phenotypically not separable from classic Marfan syndrome, both disease types include thoracic aortic aneurysm, and more generally aortic risk as the main common feature of the disease ,c2=,c3=; indicating X1 has one explanatory, X2 has one explanatory and X3 has two explanatory data sources(i) Let vector Ifq has any explanatory data sources:Z is the matrix of column bind LVs, i.e. Z=, and q. Then where c1 we calculateFor θ11 and θ31 remain 0.c2 we calculateFor θ22 and θ32 remain 0,c3 we calculateand for θ13 and θ23 are obtained from the multiple regression step ′]−1′ζ3, and  is the matrix obtained by column binding ζ1 and ζ2. The value of θ33 remains 0.where the entries q′-th column of C that indicates the data sources that are response for data source q′, i.e. c1=′,c2=′,c3=′; indicating X1 has two responses, X2 has two responses and X3 has no response data sources(ii) Let vector Ifq′ has any response data sources:c1 we calculatei.e., for c2 we calculateand for Θ are;After Steps (b-i) and (b-ii), the entries of θ21 and θ12 in Step (b-i) are overwritten in Step (b-ii). This is because ζ1 and ζ2 are both responses to each other.Notice that Re-estimate the the latent variablesc. Estimate the new w(1) weightsd. Evaluate the convergence criteria and discard the oldw(0) weightse. While Upon convergence, returnandQ related data sources X1,...,Xq,...,XQ with p1,...,pq,...pQ corresponding MVs, measured on n samples , where the entry q is a response data source for data source q′ and 0 otherwise. The goal of the analysis then is to optimise the following objective function  in respect to data source q′;The general case for msPLS can be described as follows. Given ζq is the LV of q′th column of matrix C (i.e. q′ have at least one response data source), ith column of data source ith MV of q′ have at least one response data source, then the squared correlation between q′ does not have any response data sources, the correlation between the MVs of Xq and ζq and ζq and the response MVs in where is (CCA) , 22, 50.is (RDA) , 22, 51.Q data sources.Next we describe the general algorithm for General msPLS algorithmQ data sources X1,..,Xq,...,XQ,Given Θ=C∈{0,1}Q×Q, where and Preliminary stepsX1,..,Xq,...,XQCenter and scale ′ with length pqSet CRT=1 and a small positive tolerance γ=10−6Define convergence criterion Iterative regression stepsCRT≥γ;Estimate initial LVsa. q is the index from 1 to Q and ∝ indicates that ζq is normalised to unit varianceModel the relationship between data sourcesb. cq be the q-th row of C that indicates the data sources that are exploratory for data source q(i) Let vector Ifq has any explanatory data sources:Z is the matrix of column bind LVs, i.e. Z=, and q.where q′-th column of C that indicates the data sources that are response for data source q′(ii) Let vector Ifq′ has any responses:Re-estimate the LVsc. Estimate the new d. IfXq doesn’t have any response data sources:otherwise:Evaluate the convergence criteria and discard the oldweights and calculate OF from Eq.  in the general msPLS algorithm. The subsequent latent variables can be obtained by applying msPLS to the residual data sources, where the residuals data sources are calculated asIt is possible to extract multiple LVs per data source in a way that they explain a different portion of variance in the MVs. The explained variance is based on the wq weights that optimise OF in Eq. , by setting λ2→∞ in Eq.  was selected through 10-fold cross validation. Additionally, we assessed the statistical significance of the resulting models through permutations and the confidence interval of the optimisation criterion was approximated through bootstrapping.We conducted simulation studies to assess msPLS’s ability to identify associated MVs (i.e. explanatory MVs that are highly correlated with their response MVs and thus have the highest explanatory power for the variance in the response MVs) when those MVs are spread over multiple data sources. We repeated the simulations 1000 times and used UST penalisation for which the optimal penalty parameter  from which kq variables were associated with their LVs and response MVs (i.e. k1 = k2 = k3 = 10), and there were jq number of not associated MVs . The number of samples are denoted by n samples , and in the first three simulation studies n varied from 1, 100, and 250.All X1 and X2 were generated from a multivariate normal distribution with mean 0 and covariance matrix Σ, and their response MVs in X3 was generated from LVs ζ1 and ζ2, as follows;Σ=I2000Replace where X1=Dn,1:10001: and X2=Dn,1001:20001:Σ is a p1 + p2 dimensional identity matrix where elements H, where D was sampled from the multivariate normal distribution with mean 0 and covariance matrix Σ, and D was used to generate X1 and X2. Next, the weight vectors were generated;(5)k MVs had higher regression weights with their LVs (with weights jq MVs (i.e. (6)ζ1=X1w1 and ζ2=X2w2The associated X3 was generated with from ζ1 and ζ2. The k3 associated LVs were sampled from the normal distribution with mean θ1ζ1 + θ2ζ2 (where θq is the regression coefficient from Eq. (θ1 = 0.8 and θ2 = 0.7) and standard deviation j3 not associated variables were sampled from the standard normal distribution;(7)(8)i=1,...,k3:Xi)3( distributed For (9)i=k3+1,...,p3:Xi)3.In addition, we designed a fourth simulation study, where the size of the data resambled the size of the omics data sources, described in “n=50, n=100, n=250. To assess msPLS’s ability to identify the kq associated MVs from explanatory data sources X1 and X2, we used the true-positive rate (TPR) and true-negative rate (TNR) measures over 1000 simulations.We generated data as described in above with three different sample sizes, i.e. w weights) to either the number of associated MVs that were generated, or to the total number of non-zero w weights, whichever is smaller , for n=100 the optimised OF resulted in 117.12 , and for n=250 the optimised OF resulted in 123.61 .We assessed the statistical significance of the resulting models in respect to the optimised OFs through permutation, and the confidence intervals of the optimised OFs were constructed through bootstrapping see the “” sectionProject name: msPLS implementationhttp://uva.csala.me/mspls and https://github.com/acsala/2018_msPLSProject home page: Operating system(s): Platform independentProgramming language: ROther requirements: additional R packages listed in the source code, freely available from the Comprehensive R Archive Network onlineLicense: MITAny restrictions to use by non-academics: MIT license appliesAdditional file 1 Appendix: Supplementary materials for the proposed method and results in pdf file format.Additional file 2 Supplementary materials for the gene set enrichment analysis results of the Marfan data, obtained from Reactome in coma separated file format.Additional file 3 Supplementary OMIM Gene Map Retrieval results of the Marfan data, obtained from OMIM in xls file format.Additional file 4 Supplementary co-expression pattern results of the Marfan data, obtained from Gene Mania in txt file format.Additional file 5 Supplementary gene set enrichment analysis results of the Chronic lymphocytic leukaemia data analyzed by MOFA. Results are obtained by the MOFA R package and exported as txt file format.Additional file 6 Supplementary gene set enrichment analysis results of the Chronic lymphocytic leukaemia data analyzed by msPLS. Results are obtained by the MOFA R package and exported as txt file format.Additional file 7 Supplementary material on the overlapping gene set enrichment analysis results between msPLS and MOFA on the Chronic lymphocytic leukaemia data. Results are obtained by the MOFA R package and exported as txt file format.


Enhanced expression of β-III tubulin, TH, and NeuN and an increase in the average neurite extension length were observed when cells were differentiated within collagen-coated GF in comparison with collagen gels. Furthermore, these graphene-based scaffolds were not cytotoxic as MSC seemed to retain viability and proliferated substantially during in vitro culture. In summary, these results suggest the utility of 3D graphene foams towards the differentiation of DA neurons from MSC, which is an important step for neural tissue engineering applications.The implantation of stem cells in vivo is the ideal approach for the restoration of normal life functions, such as replenishing the decreasing levels of affected dopaminergic (DA) neurons during neurodegenerative disease conditions. However, combining stem cells with biomaterial scaffolds provides a promising strategy for engineering tissues or cellular delivery for directed stem cell differentiation as a means of replacing diseased/damaged tissues. In this study, mouse mesenchymal stem cells (MSCs) were differentiated into DA neurons using sonic hedgehog, fibroblast growth factor, basic fibroblast growth factor, and brain-derived neurotrophic factor, while they were cultured within collagen-coated 3D graphene foams (GF). The differentiation into DA neurons within the collagen-coated GF and controls  was confirmed using  Among all organs in our body, the human brain is one of the largest and most complex, consisting of 100 billion nerves that communicate via trillions of synaptic connections . The valBiomimetic 3D scaffolds are preferred tools for culturing neurons as they provide defined mechanical and physicochemical properties with an interconnected porous structure that can enable a higher or more complex organization than traditional two-dimensional monolayer conditions . ChangesIn this study, our objective was to utilize a commercially available 3D graphene scaffold termed as “graphene foam” (GF) for culturing mouse MSCs and differentiating them into DA neurons. We hypothesized that these MSC-differentiated DA neurons when cultured in a 3D scaffold will more closely exhibit morphologies, functions, and other necessary characteristics of in vivo DA neuronal tissues, compared to 2D culture or monolayer substrates. To culture cells on the hydrophobic graphene substrates , they neThis work will significantly contribute by enabling a platform that will allow us to study interactions between healthy DA neurons and their synaptic communications and identify mechanisms involved in DA neuron apoptosis during injury and disease. As studying DA neuronal cell death in human brains is extremely difficult and invasive, the development of such in vitro 3D models of DA neurons would make it feasible to probe cellular and molecular mechanisms of neurodegenerative disorders and implement novel therapeutic strategies. This study is innovative as the technique might be adaptable for engineering other 3D tissue models from different stem cell types.″ × 2″) were purchased from Graphene Supermarket . For cleaning, these foams were washed with 70% ethanol followed by UV exposure for 30 min in a laminar sterile flow hood. Using a sterile biopsy punch , samples were prepared for further processing and experimentation. These pristine GF discs were collagen coated  to locate β-III tubulin and to detect vimentin; the samples were then incubated with a vimentin mouse monoclonal antibody (24 hr at 4°C) followed by a goat polyclonal secondary antibody to mouse IgG1-heavy chain (FITC)  (2 hr at 25°C). For the detection of TH, the samples were incubated with a purified rabbit monoclonal IgG antibody to TH; for the detection of NeuN, the samples were incubated with a NeuN rabbit polyclonal antibody (24 hr at 4°C) followed by a goat anti-rabbit IgG secondary antibody, Alexa Fluor 488 conjugate (2 hr at 25°C), at a dilution of 1 : 1000 in the dark. The samples were then washed with 1x PBS thrice and mounted using Fluoromount-G with DAPI (Thermo Fisher Scientific) and imaged using confocal fluorescence microscopy .For the differentiation of mouse MSCs into DA neurons atop 3D scaffolds or 2D culture plastic, published protocols were followed . For theAs a control, for the differentiation of mouse MSCs into DA neurons, we chose to induce differentiation in human MSCs alongside (online supplement) the mouse MSCs. This was important to confirm the validity of the differentiation induction protocol in mouse MSCs, compared to the human MSCs, and also contrast the response of both cell types to the same differentiation protocol. Further, both mouse and human MSCs were validated by alkaline phosphatase (ALP) staining prior to their differentiation induction, to confirm their stemness and pluripotency (online supplement) .β-III tubulin or NeuN and analyzed using ImageJ software [Axonal extensions on proximal and distal sides of differentiated neurons in collagen-coated GF and in collagen were measured by calculating the axonal outgrowth length, visualized with software . The fint-test was performed to determine if the averages of any two sample datasets compared were significantly different. p values less than 0.05 were considered significant.All samples were present in triplicate unless otherwise mentioned. Numerical data are expressed as the mean ± standard deviation. Microsoft Excel Student's As shown in −1 [−1, fitting well the range of 1625–1690 cm−1 for the general amide-I band position. This was due to the existence of hydrogen bonds in collagen [The FTIR spectra of the acid-soluble collagen extract are shown in −1 . The amicollagen . The helIt was essential to confirm the cross-linking of the collagen atop the GF by a secondary technique, other than by visual confirmation . TherefoI-V (current versus voltage) curve, it is clear that the GF exhibits a nonlinear behavior with a current level of ~0.10 A at 3 V  indicates that the GF was primarily multilayered graphene [D peak appeared near 1350 cm−1 and some other peaks emerged including a minor peak at ~2450 cm−1 (G′ band). The defect was increased from 0.02 to 0.7 in collagen-coated GF which is estimated from the intensity ratio of the D band and G band (D/G) [−1 which is a characteristic of the collagen (graphene . After cnd (D/G) . The amicollagen , though θ = 26.5° and 2θ = 55° correspond to the (002) and (004) planes of graphene, respectively, where the intensity of the peak at 2θ = 26.5° got reduced for the collagen coating [θ = 32° and 2θ = 45.3° indicating a traditional mineralized collagen [XRD spectra of the collagen-coated GF is shown in  coating , 35. Thecollagen . The X-rcollagen , 35, 36.p = 0.05). These results provide an important basis for the development of the GF as a biocompatible substrate for cell culture, therapy, and tissue engineering.Results from FACS analysis  showed tFor the first time in this study, we adopted and optimized a differentiation protocol for the induction of DA neuronal differentiation of mouse MSCs based on other published protocols using human MSCs   . This reAlthough monolayer graphene substrates have been used for MSC culture , it is tNetworks of neurons (in vivo) develop via an elaborate succession of cellular events that, when disrupted, can lead to neuron dysfunction and degeneration , 49–57. The outcomes from this study are both novel and significant because it will help reveal interactions between healthy DA neurons and their synaptic communications. It can also help predict mechanisms involved in injury- or disease-induced DA neuron apoptosis. Outcomes from this study can be extended to model other networks of neurons, such as cortical neurons, to study normal and abnormal corticogenesis in the CNS , periphe


Crystalline layered carbon nitrides can be inter-converted by simple ion exchange process allowing their properties to be tuned. xHCl material that was previously only available by a high-pressure-high-temperature route (c). Our work also shows (d) that the intercalant-free (IF-) PTI material spontaneously absorbs up to 10 weight% H2O from the ambient atmosphere and that this process is reversible, leading to potential applications for membranes and water capture in dry environments.The development of processes to tune the properties of materials is essential for the progression of next-generation technologies for catalysis, optoelectronics and sustainability including energy harvesting and conversion. Layered carbon nitrides have also been identified as of significant interest within these fields of application. However, most carbon nitride materials studied to date have poor crystallinity and therefore their properties cannot be readily controlled or easily related to their molecular level or nanoscale structures. Here we report a process for forming a range of crystalline layered carbon nitrides with polytriazine imide (PTI) structures that can be interconverted by simple ion exchange processes, permitting the tunability of their optoelectronic and chemical properties. Notable outcomes of our work are (a) the creation of a crystalline, guest-ion-free PTI compound that (b) can be re-intercalated with ions or molecules using “soft chemistry” approaches. This includes the intercalation of HCl, demonstrating a new ambient pressure route to the layered PTI· This property leads to its widespread use as the anode for most lithium ion batteries (LIB).Intercalation compounds are formed when atomic or molecular guest species are inserted between sheets of a layered host lattice.Within layered materials the intralayer bonding is significantly stronger than that between the layers, giving rise to highly anisotropic properties. Such a bonding arrangement facilitates the intercalation process as the interlayer (out-of-plane) spacing expands or contracts readily as a function of the insertion or removal of intercalant species. Intercalation events can therefore be detected by monitoring changes in the unit cell dimensions, particularly the lattice parameter normal to the layers, using X-ray diffraction (XRD).3N3) rings, bridged by sp2 N–H groups. They form layers with C6N9H3 stoichiometry that resemble graphitic sheets, but contain regular structural voids  in a molten eutectic LiCl/KCl or LiBr/KBr salt mixture at 600 °C. Although this original study suggested that the layers contained polyheptazine structural units, later work by Wirnhier et al. and others showed that the carbon nitride sheets were based on the same polytriazine imide motif as found for the PTI·HCl compound.– or Br– ions intercalated between the PTI layers  PTI compound, and then replace them using ‘soft chemistry’ techniques with different ionic species. This points the way to the creation of a new family of crystalline layered PTI materials with tunable optoelectronic and chemical properties. Our IF-PTI phase constitutes the first substantiated example of a crystalline layered PTI-structured ion-free carbon nitride compound, with stoichiometry Cvia the previously published molten salt synthesis approach  and pair distribution function (PDF) analysis of total X-ray scattering data, Mesch et al. indicated a more precise location of Li+ and H positions within the PTI·LiCl structure, proposing that the structure could be better modelled using an orthorhombic unit cell with P212121 symmetry.P63cm to P212121, combined with the larger unit cell size, would result in splitting of the (100), (110) and (210) reflections , which we do not observe experimentally. Likewise, several additional peaks that would be expected for the orthorhombic phase are absent. We did attempt to refine our PTI·LiCl data within the suggested orthorhombic space group and structure, but the refinement consistently returned to the hexagonal solution. We have therefore used the hexagonal unit cell model to describe the PTI structures presented in this report.The starting compounds for our study were crystalline PTI·LiCl and PTI·LiBr materials prepared  see ESI, software see ESI,. A hexagθ or dhkl value of the (002) reflection.θ (3.36 Å) for PTI·LiCl to 25.2° 2θ (3.52 Å) in PTI·LiBr, representing the 0.16 Å change in interlayer spacing expected for intercalation of the larger halide ion .– and Br–) dominate the X-ray scattering intensity over the lighter elements  that determine the layered structures.l” Miller index component compared with hk0 reflections in the PTI·LiCl and PTI·LiBr diffraction patterns. Such differences in peak broadening between in-plane and between-plane reflections is commonly observed among layered materials that can exhibit different degrees of structural order within and between the planes.The gallery height, defined as the distance between two adjacent carbon nitride layers, is readily extracted from the 2tion.θ 3. Å for PT+, Br– and Cl– ions were removed from the PTI·LiCl and PTI·LiBr layered structures using continuous Soxhlet extraction in deionized water, typically for 20 days for complete extraction (see ESI6N9H3 (or C2N3H). Neither material contained any detectable residual halide component. Quantification of the Li content was achieved using quantitative solid state 7Li MAS NMR spectroscopy, using the integrated intensity of the central isotropic peak and spinning sidebands. This showed that only 1 ± 0.1% Li remained in the IF-PTI samples compared with the starting compounds (see ESI6N9H3 (C2N3H). The stoichiometry established from elemental analysis of PTI·LiBr and IF-PTI obtained via two different methods  is provided in + ions in the structure by H+ ions derived from the aqueous extraction medium. This is borne out by pH measurements that showed the water reservoir in the Soxhlet reactor to have changed from a starting value of ∼7 to close to 10. This increase in pH can be attributed to the exchange of Li+ from the PTI·LiCl or PTI·LiBr materials with H+ derived from the water, resulting in an increase in OH– concentration within the aqueous phase.39Lin see ESI and 3, ws see ESI. Comparis see ESI demonstrhkl component shifted to higher 2θ values as a result of the substantially reduced interlayer spacing from 3.52 Å to 3.23 Å (l) component were broadened compared with (hk0) reflections (l) component, along with the change in relative peak intensity, causes some of the characteristic peaks from the starting PTI·LiBr phase (such the (102) and (111) reflections) to be reduced in intensity so that they fall below the detection sensitivity of the instrument. We also observed an additional reflection for IF-PTI at ∼18° 2θ, that can be explained by the appearance of the (101) reflection for the hexagonal unit cell, which is not observed for PTI·LiBr or PTI·LiCl. This peak appears because of the change of symmetry that occurs as the layer stacking pattern changes from AA′ in which the voids ‘line up’ along the c axis, to AB with a triazine ring located directly above and below each void. This change in layer stacking motif is clearly seen for the PTI·HCl material  represent a new intercalant-free (IF-) PTI phase, containing layers of triazine rings bridged by secondary imido (–NH–) groups.The hexagonal crystallites observed in TEM images of IF-PTI are identical to those reported for PTI·LiBr and PTI·LiCl materials prepared by similar synthesis routes .23,26 Ou–1 at the same positions as for the PTI·LiCl or PTI·LiBr starting materials, however the peaks were significantly sharpened following the washing procedure  reflection that shifted to a smaller 2θ value indicating a change in interlayer spacing from 3.23 Å to 3.29 Å. This was accompanied by a change in the relative peak intensities, resulting in the ‘emergence’ of peaks that were previously too weak to be observed above the background. We propose that these changes in the IR spectra and PXRD patterns are consistent with the occurrence of rapid and spontaneous intercalation of H2O molecules, which have been absorbed into the IF-PTI structure from the surrounding ambient atmosphere. Examination of our IF-PTI materials that had been exposed to the atmosphere using thermogravimetric analysis  spectroscopy. The IF-PTI samples showed C–N bonding peaks between 1200–1600 cmrocedure . A numbee to air . While tsis TGA,  showed ad IF-PTI . This pr+ containing salt back into the IF-PTI framework, to re-form the initial PTI·LiBr intercalated compound and thus demonstrate that the de-intercalation/re-intercalation process could occur without damaging the carbon nitride backbone. Similar approaches have been applied to create a wide range of graphite intercalated compounds (GICs).Following the initial deintercalation step, samples of dry IF-PTI were then re-exposed to a molten salt (LiBr : KBr) mix in a sealed ampoule under reduced pressure , (110) and (210) reflections and a slight change in the position of the (002) interlayer reflection from 27.7° 2θ to 27.5°, associated with the lowered Cl site occupancy.θ demonstrates the change in layer stacking from AA′ to AB analogous to that observed for IF-PTI (After refluxing IF-PTI with 5 M HCl for 4 days we observed formation of a PTI·HCl phase. Lin 110) and 0 reflectr IF-PTI .Understanding and controlling variations in the electronic structure of layered carbon nitrides is essential to target optoelectronics applications. We measured UV-visible absorption spectra for the various PTI materials prepared in this study . The spe+ and Cl–, Br– ions were introduced during the re-intercalation process.The features at longer wavelength, beginning near 650 nm and culminating in a broad peak at ∼420 nm, have been attributed to n to π* transitions associated with lone pairs on N atoms in the triazine rings becoming excited into the π* orbitals.et al.+ with Li+ at the imide positions should cause splitting to occur in the N valence band states at highest energy.+ so there is no splitting of the N valence band states and less hybridization with Cl orbitals, hence the sharpness of the observed peaks. If the bandgap is taken as the onset of the first π–π* peak near 360 nm, then there is a slight increase in bandgap energy compared with ∼375–380 nm values observed for PTI·LiCl and PTI·LiBr . This is achieved without loss of layer ordering through extraction of the intercalated ions into aqueous solution using a low temperature process. The resulting IF-PTI material can then act as a reversible host material for a range of intercalates, introduced either from molten salts or from aqueous solutions. We have demonstrated the reversibility of the intercalation process to reproduce the starting PTI phases, as well as new compounds by the re-intercalation of other combinations of ion pairs, shown here for the HThere are no conflicts to declare.Supplementary informationClick here for additional data file.