Artificial insemination (AI) is a valuable tool for ex situ wildlife conservation, allowing the re-infusion and dissemination of genetic material, even after death of the donor. However, the application of AI to species conservation is still limited, due mainly to the poor survival of cryopreserved sperm. Recent work demonstrated that oviductal extracellular vesicles (oEVs) improved cat sperm motility and reduced premature acrosomal exocytosis. Here, we build on these findings by describing the protein content of dog and cat oEVs and investigating whether the incubation of cryopreserved red wolf and cheetah sperm with oEVs during thawing improves sperm function. Both red wolf and cheetah sperm thawed with dog and cat oEVs, respectively, had more intact acrosomes than the non-EV controls. Moreover, red wolf sperm thawed in the presence of dog oEVs better maintained sperm motility over time (>15%) though such an improvement was not observed in cheetah sperm. Our work demonstrates that dog and cat oEVs carry proteins important for sperm function and improve post-thaw motility and/or acrosome integrity of red wolf and cheetah sperm in vitro. The findings show how oEVs can be a valuable tool for improving the success of AI with cryopreserved sperm in threatened species. Canis rufus) is a critically endangered American canid [Acinonyx jubatus) is classified as ‘vulnerable’ by the International Union for Conservation of Nature (IUCN) [The red wolf  , and occe (IUCN) ,4,5. Thee (IUCN) . It is ve (IUCN) ,8. Theree (IUCN) . While ne (IUCN) ,12,13,14e (IUCN) ,13,14. We (IUCN) ,12. The e (IUCN) ,16,17,18Despite the potential utility of ARTs, the practical application of AI to carnivores, including wild canids and felids, is still limited. This is mainly due to poor post-freeze and post-thaw sperm survival. The functionality of a spermatozoon generally depends on its motility , acrosomFelis catus) oEVs are enriched in proteins related to energy metabolism, membrane modification, and reproductive function [Canis familiaris) and cat oEVs recovered at different stages of the reproductive cycle and (2) determine whether incubation of red wolf and cheetah sperm with oEVs obtained from their domestic counterparts during thawing could improve their motility and maintain acrosome integrity in vitro.After mating, sperm travel into the uterus, enter the oviduct, and attach to the epithelium of the isthmus, a process which is necessary for extending sperm viability within the female reproductive tract ,30. It hfunction . We havefunction . Althougfunction ,34, and function , there h10 to 1.8 × 1011 particles·mL−1, with an average of 4.5 ± 4.8 × 1010 particles·mL−1 in the dog, and from 2.0 × 1010 to 4.5 × 1010 particles·mL−1, with an average of 3.1 ± 1.3 × 1010 particles·mL−1 in the cat. There were no differences in oEV concentration (as determined by NTA) among reproductive stages, except for oEVs collected during the peri-ovulatory period (PeriO) having higher concentration of oEVs compared to other groups  to recover dog and cat oEVs. This precipitation method was already shown to be superior to ultracentrifugation protocols in terms of EV isolation yield, without changes in EV quality . Similarr groups b. Ultraperformance liquid chromatography and tandem mass spectrometry (UPLC–MS/MS) were used to identify the protein content of dog and cat oEVs among different reproductive stages . Across all reproductive stages, gene ontology (GO) analysis identified cellular component pathways that are related to EVs, including extracellular vesicle (GO:1903561), extracellular exosome (GO:0070062), and secretory vesicle (GO:0099503) in both dogs and cats . In partWe first separated the 1158 identified proteins into three groups, independent of their reproductive stage: (1) proteins shared by dog and cat oEVs ; (2) proteins identified only in cat oEVs (cat only: 234 proteins); (3) proteins present only in dog oEVs (dog only: 331 proteins), as depicted in p = 0.0782, t-test). Post-thaw total motile red wolf sperm incubated in dog PeriO or PostO oEVs was significantly higher than the control (no oEVs) . The presence of cat oEVs during thawing did not influence the post-thaw motility of cheetah spermatozoa, which decreased to less than 10% after 4 h incubation in all treatment groups , pre-ovulatory (PreO), peri-ovulatory (PeriO) or post-ovulatory (PostO) stages. The overall total motile cryopreserved red wolf sperm decreased from 80 ± 10% (fresh) to 61 ± 18% . The presence of cat oEVs during thawing also better supported the post-thaw acrosome integrity of cheetah spermatozoa compared to the no oEV control .Red wolf sperm thawed in the presence of dog oEVs displayed significantly higher proportions of sperm with intact acrosomes compared to the control (no oEVs) (Extracellular vesicles (EVs) are membrane-encapsulated particles containing regulatory molecules that contribute to cell–cell communication by carrying proteins, peptides, RNA species , lipids, and DNA fragments ,40,41. En = 3 males). Because the species’ low heterozygosity [The most effective way of preserving mammalian sperm long term is through cryopreservation ,55,56,57zygosity ,69, wherzygosity . Previouzygosity . A vital event for fertilization in mammals is the ability of spermatozoa to undergo acrosomal exocytosis (AE) . AE occuInterestingly, only PeriO and PostO dog oEVs supported red wolf sperm motility, while the positive effect on acrosome integrity was seen in all oEV groups. Unlike other domestic mammals, which ovulate metaphase-II stage oocytes, canine ovulation is characterized by the release of an immature oocyte that requires an additional 3–4 days to undergo maturation in the oviduct prior to fertilization . FurtherTsga10 gene expression was shown to improve sperm morphology and motility in mice [+2 ATPase 4 (ATP2B4 or PMCA4), PMCA4, a member of a family of calcium efflux pumps. PMCA4 plays crucial roles in calcium homeostasis in mouse sperm by maintaining low resting cytosolic calcium and a calcium gradient across the sperm plasma membrane [In the present study, we found that 51 proteins were exclusively expressed in PeriO and PostO dog oEVs  have a bilayer membrane rich in cholesterol, sphingomyelin, glycosphingolipids, and phosphatidylserine . To dateWe demonstrated that dog and cat oEVs carry proteins important for sperm function, including proteins that have the potential to restore sperm function after cryopreservation. Thawing red wolf and cheetah sperm in the presence of dog and cat oEVs, respectively, improved post-thaw motility in the red wolf and prevented premature acrosome exocytosis in both red wolf and cheetah sperm. Future studies should investigate whether these positive effects can be maximized by incubating sperm with oEVs during the cryopreservation process, and whether they can improve fertilizing ability of frozen-thawed gametes. Nevertheless, the use of oEVs can be a valuable tool to improve sperm cryosurvival and, consequently, the success of artificial insemination with frozen-thawed samples in these threatened carnivores, representing a step forward for successful assisted breeding of these species. n = 5) were maintained at three institutions in the USA and were sampled during the breeding season (February and March 2018 and 2019). The age of sperm donors ranged from 2 to 9 years old at the time of semen collection. Animals were housed singly, in breeding/companion pairs, with multiple male conspecifics, or in family units (packs). All institutions adhered to the husbandry guidelines put forth by the Red Wolf Species Survival Plan® (SSP), including housing animals in enclosures containing natural substrate, foliage, and sheltered dens with bedding material. Adult male cheetahs (n = 5) were maintained at one institution in the USA and were sampled between 2011 and 2019. The age of sperm donors utilized in this study ranged from 3 to 12 years old at the time of semen collection. Animals were housed singly or a group of brothers . Cheetahs received a commercially produced beef-based diet 5 days per week and supplements of whole rabbits and bones 2–3 days per week. The institution adhered to the husbandry guidelines put forth by the Cheetah Species Survival Plan® (SSP), including housing animals in pens of approx. 18,000 ft2 containing natural substrate, foliage, and sheltered dens with bedding material. All animal procedures were approved by the Institutional Animal Care and Use Committee of the Smithsonian’s National Zoological Park . Adult, male red wolves  with medetomidine (0.04 mg/kg im) or dexmedetomidine (0.02 mg/kg im), sometimes also including midazolam and maintained with isoflurane as needed. Prior to the start of electroejaculation, the rectum was evacuated using a well-lubricated gloved hand and the penis carefully exteriorized and washed thoroughly with sterile saline and wiped dry. To avoid urine contamination in semen samples, the urinary bladder was emptied by inserting a 3.5 fr × 22 in polypropylene urinary catheter into the urethra and urine aspirated and bladder flushed with saline solution. Semen collection was performed using an electroejaculation method as previously described for the red wolf  via inseAdult male cheetah fasted for approximately 24 h before inducing anesthesia by the institution veterinary staff using an intramuscular injection of a combination of the following drugs (as deemed most appropriate by the attending veterinarian): medetomidine hydrochloride (22.0–25.0 μg/kg body weight), midazolam (0.2 mg/kg body weight), and/or ketamine HCl (2.0–3.5 mg/kg). Propofol  was administered, as necessary, to maintain an appropriate plane of anesthesia during electroejaculation. Each ejaculate collection and subsequent evaluation were performed according to previously described, rigorous protocols and metrics . Brieflyg for 5 min and supernatant removed and samples processed for cryopreservation as previously described [6 sperm·mL−1. The samples were chilled at 5 °C for at least 30 min. Then, an equal volume of extender containing 8% glycerol (v/v) was added dropwise over 2–3 min to the chilled samples, for a final cryoprotectant concentration of 4% glycerol, and final sperm concentration of 100 × 106 cell·mL−1. Chilled samples were then frozen using a dry ice pelleting method [After assessing for initial total motility, red wolf sperm concentration was determined using a hemocytometer. Ejaculates were centrifuged at 300× escribed . Followig method . Brieflyg to concentrate the sperm pellet and remove the seminal fluid. After centrifugation, the pellet was resuspended in Test Yolk Buffer  with 0% glycerol in a sterile Eppendorf tube. Final sperm concentration was either 50 or 60 × 106 total motile sperm·mL−1. Samples were placed into a water bath and cooled to 5 °C (approx. 2.5 h) in a walk-in refrigerator. After reaching 5 °C, an equal volume of TYB with 8% glycerol was added to the sample step-wise over a period of 30 min . Samples were loaded into 0.25 cc sterile straws (~120 µL/straw) and cryopreserved over liquid nitrogen vapor. Straws were placed 7.62 cm above the liquid for 1 min, then 2.54 cm above the liquid for 1 min and then plunged into the liquid nitrogen. After assessing for initial motility and forward status, cheetah sperm concentration was determined using a hemocytometer. Samples were centrifuged for 8 min at 100× n = 11 and 9, respectively; 11 months to 4 years old) were opportunistically collected from local veterinary clinics after routine ovariohysterectomy of stray and household cats and dogs and transported at 4 °C to the laboratory within 6 h of excision. For the domestic dogs, all were designated as “in heat” by veterinary staff, based on appearance of the vulva (swollen and/or serosanguinous discharge). As previously described [g (room temperature) for 30 min to remove cells and debris, then the supernatant was mixed with 1000 (dog) and 500 (cat) μL of the Total Exosome Isolation Reagent  and incubated overnight at 4 °C. The samples were centrifuged at 10,000× g for 1 h, and the pellet resuspended in 50 μL of PBS. EVs were then aliquoted and kept at −20 °C until use.Post-pubertal domestic dog and cat oviducts  post-ovulatory (PostO)—determined by the presence of one or more corpora lutea without being possible to identify the ovulatory foci [oEV samples were categorized into different reproductive stages, which were determined based on ovarian morphology: (1) proestrus (ProE)—absence of corpus luteum and presence of follicles smaller than 2 mm of diameter; (2) pre-ovulatory (PreO)—absence of corpus luteum and presence of follicles bigger than 3 mm of diameter; (3) peri-ovulatory (PeriO)—corresponded to the period immediately after ovulation, determined by the presence of ory foci ,101,102.Nanoparticle tracking analysis was performed using the ZetaView S/N 17–332  and data were analyzed using this software  by Alpha Nano Tech  as previously described . Each oE10 particles·mL−1, and frozen at −20 °C. Proteins were extracted and prepared via single-pot, solid phase-enhanced sample-preparation (SP3) technology [https://www.thegpm.org/TANDEM) [Felis catus, taxa 9685) and dog . Label-free quantification (MS1-based) was used, and peptide peak areas were calculated using OpenMS (https://www.openms.de/) [E-value scores of 0.01 or less.Oviductal EVs for each estrous stage  were pooled to a final concentration of 2 × 10chnology , and ana/TANDEM)  using bo/TANDEM) , and by /TANDEM) . RAW datnms.de/) . ProteinCanis familiaris) and the cat (Felis catus) genomes. Because protein expression levels were determined via UPLC–MS/MS, small differences in sample concentrations and/or measurement error can introduce artefactual differences across samples. To control for this effect, we used Probabilistic Quotient Normalization (PQN) [Data Entrez Gene IDs were mapped for all identified proteins using the R package rentrez (ver 1.2.1) . The bacon (PQN) , which hon (PQN) . After Pon (PQN)  to identCanis familiaris and the Felis catus genomes. The evidence was set to “Inferred by Curator (IC)”, and the statistical test was set to a right-sided hypergeometrical test with a κ score of 0.7–0.9 using Bonferroni (step down). The function “GO Term fusion” was selected, the GO term restriction levels were set to 3, and a minimum of 3 genes or 5% genes in each GO term were used.The Cytoscape 3.5.1 plugin ClueGO (v.2.5.6)  was usedTEM preparation and imaging was performed at the Alpha Nano Tech . Briefly, oEVs (10 μL) were dropped onto Formvar-coated carbon-coated TEM grids , allowed to be adsorbed to the grid for 10 min, and washed two times with water (30 s each). A 2% Aqueous Uranyl Acetate solution was applied to the grid for 30 seconds (negative staining of exosomes), then whisked off with filter paper, and grids were air dried before imaging on a Jeol JEM1230 transmission electron microscope .−1 bovine serum albumin, 4.78 mM KCL, 1.71 mM CaCl2, 1 mM MgCl2, 1.19 mM KH2PO4, 0.25 mM Na pyruvate, 21.55 mM Na lactate, 2.78 mM glucose, 0.05 g L−1Streptomycin sulphate and 100,000 units of Penicillin G; modified from [6 particles·mL−1 dog oEVs collected from each of the four reproductive stages—ProE, PreO, PeriO and PostO) or without oEVs for 5 min. Sperm were then centrifuged at 300× g for 5 min. The sperm pellet was resuspended in NC-CCM either without dog oEVs or supplemented with 30 × 106 particles·mL−1 dog oEVs , to achieve a final sperm concentration of 5 × 106 sperm·mL-1 dog sperm. All treatment groups were kept in an incubator at 38.5 °C, 5% CO2 in air, and then analyzed for total sperm motility and acrosome integrity. Cheetah sperm straws were cut in four pieces (while submerged in liquid nitrogen), and each piece was thawed for 10 sec in air and then in 1 mL of NC-CCM at 38.5 °C, with  or without cat oEVs. Sperm were then centrifuged at 100× g for 8 min and the pellet resuspended in NC-CCM at 38.5 °C, with  or without cat oEVs to a final sperm concentration of 5 × 106 sperm·mL−1, and kept in an incubator at 38.5 °C, 5% CO2 in air and analyzed for total sperm motility and acrosome integrity  at 38.5ntegrity . Sperm samples were incubated at 38.5 °C in thawing medium with or without oEVs for up to 24 h and samples were collected at 0, 0.5, 1, 2, 3, 4, 6, 8, 10 and 18 h for motility analysis and at 0, 0.5, 1 and 2 h for acrosome integrity analysis. For motility analysis, 3 μL of sperm samples were dropped onto a glass slide, covered with a coverslip and observed at a magnification of 200× on a positive phase-contrast microscope  with a warmed (38 °C) stage. At least five fields per sample were recorded, and a minimum of 200 spermatozoa were assessed for total motility  by two different researchers, who were unaware of the treatment . −1; Thermo Fisher, Rockford, IL, USA) and the acrosome-specific fluorochrome fluorescein isothiocyanate-labeled peanut (Arachis hypogaea) agglutinin . At least two hundred spermatozoa were counted per animal, treatment, and time point at 1000× magnification .For acrosome integrity assessment, 5 μL of sperm were fixed in 5 μL of 4% paraformaldehyde for 15 min and labeled with HOECHST33342 . This allowed us to use the percentage of sperm remaining motile and the number of newly non-motile sperm at each time point as estimates of in and id, respectively. We then used log-rank test statistics to determine whether there was any significant effect of oEV treatment on sperm motility. Survival functions were fit using the R package ‘survival’ (v 3.1-8) [To assess the effect of oEV treatment on sperm total motility, we fit non-parametric Kaplan–Meier survival functions  to the mv 3.1-8) .https://doi.org/10.6084/m9.figshare.12100755.v1.The datasets generated during the current study are available in the Figshare repository, under DOI:


Conclusions: Compared to healthy and age-matched controls, adult individuals with OSAS had significantly higher serum/plasma TNF-α levels. For children with OSAS, significantly higher levels were observed for TNF-α in serum but not in plasma.Background: Obstructive sleep apnea syndrome (OSAS) is associated with a variety of inflammatory factors. Specifically, proinflammatory cytokines appear to be associated with the pathogenesis of OSAS. Methods: For the present meta-analysis and meta-regression on serum and plasma tumor necrosis factor alpha (TNF-α) levels in individuals with and without OSAS, we performed a systematic search without any restrictions of the PubMed/Medline, Scopus, Cochrane Library, and Web of Science databases to find relevant articles published up to 1 February 2020. Results: Fifty-five  were included and analyzed. Always compared to age-matched healthy controls, the pooled MDs were as follows: adults, serum: 10.22 pg/mL (95% CI = 8.86, 11.58;  Obstructive sleep apnea syndrome (OSAS) is a common sleep-related breathing disorder. It is estimated that 4% of both female and male adults suffer from OSAS . SpecifiPeople with objectively assessed OSAS are at increased risk to suffer from atherogenesis , vasculaAs regards the immune system, the tumor necrosis factor alpha (TNF-α) is a common cytokine secreted by adipocytes and the mononuclear-macrophage system , which rThe meta-analysis was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines .One of the authors (M. Sadeghi) systematically searched four databases, namely PubMed/Medline, Scopus, Cochrane Library, and Web of Science, for articles published in English up to 1 February 2020, with no restrictions, and with the following expressions: “sleep apnea” or “OSA” or “obstructive sleep apnea” or “obstructive sleep apnea syndrome” or “OSAS“, with “TNF-alpha” or “TNF-α” or “TNF” or “tumor necrosis factor alpha”; and with “blood” or “plasma” or “serum”. We also manually inspected the reference lists of the articles  for publications related to our topic.Inclusion criteria were as follows: (1) studies evaluating the association between plasma or serum TNF-α levels and OSAS with case-control design without age (≥18 years as adults and <18 years as children), sex or BMI restrictions; (2) OSAS was defined as AHI > 5 events/h diagnosed by polysomnography; (3) participants with OSAS and controls had no other systematic diseases such as diabetes, asthma, neurological disorders such as multiple sclerosis, neurodegenerative disorders such as Alzheimer’s disease, or oral diseases; (4) studies reporting pretreatment morning serum and/or plasma levels of TNF-α (around 6:00–10:00 a.m.); (5) studies reporting sufficient data to compute the MD and 95% confidence interval (CI) in participants with OSAS and controls; (6) studies with more than 10 cases included as individuals with OSAS and controls.Exclusion criteria were: (1) studies with irrelevant or insufficient data or without clinical data; (2) Letter to the editors, review articles, conference papers, meeting abstracts, and book chapters; (3) studies without a control group; (4) studies reporting controls with AHI > 5 events/h; (5) studies with overlapped data with other studies; (6) studies without reporting cut-off AHI values for OSAS; (7) studies mixing pediatric and adult participants.Two authors (M.M.I. and M.E.) read independently the titles and abstracts of the retrieved studies. They then selected the relevant studies, while another author (M.S.) retrieved the full-texts of the articles and excluded several full-texts based on the exclusion criteria mentioned above. If two studies had overlapped data, we selected the study with the most recent publication year.Two authors (S.B. and M.S.) independently extracted the data from each study included in the meta-analysis. If there was a disagreement between the two authors, the third author (M.M.I.) helped to find a final decision. The data extracted for the meta-analysis included basic information including the first author, publication year, country of study, ethnicity of subjects in each study, age, BMI, and AHI of both groups, and mean and standard deviation (SD) of TNF-α levels in plasma and serum. Since some studies considered patients with mild OSAS (AHI: 1–5 events/h) as a control group condition, and since we had to use this protocol for other studies, reporting the quality of studies as for usual case-control studies did not seem to be reasonable.One author (M.S) evaluated the quality of the studies included in the meta-analysis using the Newcastle-Ottawa Scale (NOS) with a total score of nine for each study .p-value < 0.1 and I2 > 50% showed a statistically significant heterogeneity, the analysis was performed by the random-effect model to evaluate the pooled ORs and CI values. Otherwise, we used the fixed-effect model. The weight (%) represents the effect of an individual study on the pooled results obtained. In general, the larger the sample size and the narrower CI are, the higher is the percentage of weight.The data were analyzed by one author. Review Manager 5.3 software  was used to calculate the crude MD and 95% CI, which evaluated the significance of the pooled MD by Z-test. Heterogeneity across the studies was evaluated using both Cochran Q  and I2 mThe results of the Begg’s and Egger’s tests were analyzed by the Comprehensive Meta-Analysis version 2.0 software (CMA 2.0). The Begg’s funnel plot shows the standard error (SE) of the log (MD), and the precision of each study is plotted against its log (MD) . In addip-value (two-tailed) was less than 0.05, then there was a statistically significant difference. The meta-regression is a quantitative method used in meta-analyses to estimate the impact of moderator variables on study effect size. The trim-and-fill method was used to estimate potentially missing studies due to publication bias in the funnel plot and adjusting the overall effect estimate .Four studies compared serum TNF-α concentrations between children with OSAS and without OSAS  and founp = 0.35; I2 = 61% [Ph = 0.04]).Five studies compared serum TNF-α concentrations between children with OSAS and without OSAS  and founp < 0.00001), in Caucasians , and in mixed ethnicities  were significantly higher, compared to healthy controls. Serum TNF-α concentrations in Asians were 16.9 and 86.3 times higher than serum TNF-α concentrations in Caucasian and mixed ethnicities with OSAS, and always compared to controls.Subgroup analysis of serum TNF-α concentrations in adult samples are reported in p < 0.00001), in Caucasians ), and in mixed ethnicities  were significantly higher, compared to healthy controls. Next, in Asians, plasma TNF-α concentrations were 4 and 12.2 times higher than in Caucasian and mixed ethnicities with OSAS, and always compared to controls.For plasma TNF-α concentrations, for those adults with OSAS, plasma TNF-α concentrations in Asians . For adult participants with OSAS and a BMI ≤ 30 km/m2, the pooled MD was 23.07 pg/mL . Thus, results showed that serum TNF-α concentrations were 18.2 times higher participants with OSAS and a BMI > 30 kg/m2, when compared to those participants with a mean BMI ≤30 kg/m2.For serum TNF-α concentrations, and compared to healthy controls, adult participants with OSAS and a BMI > 30 km/m2 showed a pooled MD of 1.06 pg/mL . For adult participants with OSAS and a BMI ≤ 30 km/m2, the pooled MD was 10.82 pg/mL . Thus, results showed that plasma TNF-α concentrations were 10.2 times higher in adults with OSAS and a BMI > 30 kg/m2 than plasma TNF-α concentrations of adults with OSAS and a BMI ≤ 30 kg/m2 and always compared to healthy controls.For plasma TNF-α concentrations, and compared to healthy controls, adult participants with OSAS and a BMI > 30 km/m2 showed a pooled MD of 2.28 pg/mL ; while compared to controls, individuals with OSAS and with a BMI ≤ 30 kg/m2 showed a pooled MD of 18.64 pg/mL . Thus, serum TNF-α levels were 8.2 times higher in studies with participants with OSAS and a BMI >30 kg/m2 than serum TNF-α levels with participants with OSAS and a BMI ≤ 30 kg/m2.For serum TNF-α levels, compared to controls, individuals with OSAS and with a BMI > 30 kg/mp < 0.00001) for participants with OSAS and a BMI > 30 kg/m2; for participants with OSAS and a BMI ≤ 30 kg/m2, the pooled MD was not statistically significant .For plasma TNF-α levels, compared to controls, the pooled MD was 7.32 pg/mL  for studies with more than 100 cases, and 2.15 pg/mL  for studies with less than 100 cases. Thus, serum TNF-α levels were 25.8 times higher in the studies with more 100 cases, compared to studies with less than 100 cases.For serum TNF-α levels, the pooled MD was 55.47 pg/mL  for studies with more than 100 cases, while the pooled MD was 2.87 pg/mL  for studies with less than 100 cases. Thus, plasma TNF-α levels were 2.4 times higher in the studies with more than 100 cases, compared to studies with less than 100 cases.For plasma TNF-α levels, the pooled MD was 6.91 pg/mL , while the pooled MD was 127.01 pg/mL  in individuals with OSAS and a AHI ≤ 30 events/h. Thus, serum TNF-α levels were 22.2 times higher, when participants with OSAS had AHI ≤ 30 events/h, compared to AHI > 30 events/h.For serum TNF-α levels, in individuals with OSAS and a AHI > 30 events/h, the pooled MD was 5.72 pg/mL , while in individuals with OSAS and AHI ≤ 30 events/h, the pooled MD was 2.97 pg/mL . Thus, serum TNF-α levels were 2.7 times higher in participants with OSAS and AHI > 30 events/h, compared to serum TNF-α levels in participants with OSAS and AHI ≤ 30 events/h.For plasma TNF-α levels, in individuals with OSAS and AHI > 30 events/h, the pooled MD was 8.15 pg/mL , whereas for studies with participants with a BMI ≤ 30 kg/m2 serum TNF-α levels did not statistically differ between participants with or without OSAS , whereas no significant MDs were observed in studies including Caucasian participants with OSAS and a BMI > 30 kg/m2.For plasma TNF-α levels, studies with Caucasian participants with OSAS and a BMI ≤ 30 kg/m2, the pooled MD was 2.58 pg/mL . There was no statistically significant MD for serum TNF-α levels between Caucasians with or without OSAS and a mean ≤ 30 kg/m2.For studies with serum TNF-α levels and Caucasian controls with a mean BMI > 30 kg/m2, the pooled MD was 0.56 pg/mL . Studies with controls with a BMI ≤ 30 kg/m2 showed a pooled MD of 5.64 pg/mL . Thus, plasma TNF-α levels were 10.1 times higher in controls with a BMI ≤ 30 kg/m2, compared to plasma TNF-α levels of controls with a BMI > 30 kg/m2For studies with plasma TNF-α levels and controls with a BMI > 30 kg/mp = 0.0006), whereas there was no significant difference for studies with >100 cases.Serum TNF-α levels had a significant difference in studies with ≤ 100 cases , and plasma TNF-α levels were 7.80 pg/mL .In studies with individuals with OSAS and AHI of > 30 events/h, serum TNF-α levels were 1.40 pg/mL  and plasma  TNF-α levels differed statistically significantly  for studies including OSAS patients with a mean BMI of ≤30 kg/m2, and plasma TNF-α levels were 14.92 pg/mL  for studies including OSAS patients with a mean BMI of ≤30 kg/m2.For serum and plasma TNF-α levels, there were no studies including controls with a BMI > 30 kg/mp < 0.00001) for studies with 100 or more participants, and the pooled MD was 7.80 pg/mL  for studies less than 100 participants. Thus, serum TNF-α levels were 11.5 times higher in studies with more than 100 participants, compared to studies with less than 100 participants.For serum TNF-α levels, the pooled MD was 89.88 pg/mL  for studies with 100 or more participants, and the pooled MD was 21.51 pg/mL  for studies less than 100 participants. Thus, plasma TNF-α levels were 1.7 times higher in studies with less than 100 participants, compared to studies with more than 100 participants.For plasma TNF-α levels, the pooled MD was 12.34 pg/mL ; plasma TNF-α levels were 12.48 pg/mL .For studies including AHI > 30 events/h, mean serum TNF-α levels were 12.94 , compared to studies with controls with BMI of controls ≤ 30 kg/m2 (For studies with serum TNF-α levels and controls with BMI of controls >30 kg/m30 kg/m2 .p < 0.0001) and for plasma TNF-α levels , compared to studies with more than 100 participants.For studies with 100 or less participants, there was a significant difference for serum TNF-α levels ; the pooled MD for studies with participants with OSAS with AHI ≤ 30 events/h was 42.13 pg/mL . Thus, serum TNF-α levels were 42.9 times higher in studies with participants with OSAS and AHI ≤ 30 events/h, compared to the serum TNF-α levels in studies including participants with OSAS and AHI > 30 events/h.For serum TNF-α levels, the pooled MD for studies including participants with OSAS with AHI > 30 events/h was 0.96 pg/mL . When studies included participants with OSAS and an AHI ≤ 30 events/h, MDs between studies with participants with or without OSAS were not significant.For plasma TNF-α levels, the pooled MD for studies with participants with OSAS with AHI > 30 events/h was 1.69 pg/mL  for serum, and MD = 10.82 pg/mL  for plasma (For studies including BMI ≤ 30 kg/mr plasma .p < 0.00001), while in studies with ≤ 100 participants, the pooled MD was 4.42 pg/mL . Thus, serum TNF-α levels were 14.4 times higher in studies with more than 100 participants, compared to serum TNF-α levels in studies with ≤100 participants.For serum TNF-α levels and in studies with more than 100 participants, the pooled MD was 63.51 pg/mL  while in studies with ≤100 participants, the pooled MD 13.79 pg/mL . Thus, plasma TNF-α levels were 1.7 times higher in studies with more than 100 participants, compared to plasma TNF-α levels in studies with ≤100 participants.For plasma TNF-α levels and in studies with more than 100 participants, the pooled MD was 8.12 pg/mL ; the pooled MD for studies including participants with OSAS and an AHI ≤ 30 events/h was 173.00 pg/mL . Thus, serum TNF-α levels were 13.4 times higher in participants with OSAS and AHI ≤ 30 events/h, compared to the serum TNF-α levels in participants with OSAS and AHI > 30 events/h.For serum TNF-α levels, the pooled MD for studies including participants with OSAS and an AHI > 30 events/h was 12.94 pg/mL . When studies included participants with OSAS had an AHI ≤ 30 events/h, MDs between studies including participants with or without OSAS were not significant.For plasma TNF-α levels, the pooled MD for studies including participants with OSAS and an AHI > 30 events/h was 16.13 pg/mL  and plasma  TNF-α levels  revealed a publication bias. For plasma TNF-α levels, Begg’s test (p = 0.03943) revealed a publication bias, but Egger’s test (p = 0.19315) did not show any bias between and across the studies.For serum levels, Egger’s and Begg’s tests ; using the trim-fill method, the imputed point estimate was 0.323 . In addition, under the random-effects model, the point estimate and 95% CI for the combined studies was 8.245 ; using the trim-fill method, the imputed point estimate was 0.523 .For plasma TNF-α levels and 3 imputed studies, under the fixed-effects model, the point estimate and 95% CI for the combined studies was 2.463 , and using trim-fill method, the imputed point estimate was 2.294 . In addition, under the random-effects model, the point estimate and pseudo 95% CI for the combined studies was 5.452 ; using the trim-fill method, the imputed point estimate was 2.668 .The overall effect sizes on serum and plasma TNF-α levels reported in the forest plot appeared valid, with trivial publication bias effect based on fixed-effects model, because the observed estimates were similar to the adjusted estimates. In contrast, the overall effect sizes on serum and plasma TNF-α levels reported in the forest plot appeared invalid, with significant publication bias effect based on random-effects model, because the observed estimates had high difference with the adjusted estimates.The present meta-analysis with 55 studies evaluated the serum TNF-α levels (29 studies of adults and 4 studies of children) and plasma TNF-α levels (17 studies of adults and 5 studies of children) in individuals with OSAS, compared to controls. The results showed that plasma and serum TNF-α levels in adult individuals with OSAS were significantly higher than the corresponding levels of control. For children with OSAS serum TNF-α levels, but not plasma TNF-α levels, were statistically significantly higher than those of controls. The present results have clinical importance because elevated serum and plasma TNF-α levels can be a risk factor for the development of further systemic diseases in adults with OSAS. Further, more generally, inflammation may play a crucial role in the pathogenesis of OSAS, and there is evidence that individuals with OSAS have elevated interleukin TNF-α ,48,62,78Elevated TNF-α levels have been associated with the pathophysiology of reoxygenation injury, myocarditis, cardiac allograft vasculopathy, heart failure progression , arthritThe present pattern of results also sheds some light on further new findings: More specifically, for adults, it turned out that compared to healthy controls, TNF-α levels were higher both in blood serum and blood plasma. It follows that at least for adults with OSAS, assessing either blood plasma or blood serum does not appear to be of clinical importance. In contrast, for pediatric samples and compared to healthy controls, higher TNF-α levels were observed for blood serum but not for blood plasma. The quality of the data at hand does not allow a deeper understanding of the underlying physiological mechanisms. Thus, for want of such data, the following admittedly speculative assumptions are advanced. First, it is conceivable that for pediatric samples, the number of four studies on plasma TNF-α was too small, and therefore, the statistical variance of plasma TNF-α was blurred by sample size inconsistencies. Similarly, second, at least among adults, Hirotsu et al.  showed tThe pattern of results described in the present meta-analysis and systematic review consistently showed that higher TNF-α levels were associated with pathologically higher BMI scores as a proxy of obesity. Given that above all childhood overweight and childhood obesity is increasing worldwide and is a major concern of public health , the assThe results of one study  showed tp: 0.034) between higher BMI scores and higher TNF-α levels [Next, TNF-α appears to mediate both a somnogenic activity and fatigue related to excessive daytime somnolence in obese patients with OSAS ,28,30. Tα levels .As regards AHI and TNF-α levels, Kanbay et al.  and otheNext, Minoguchi et al.  reportedDespite the novelty of the results, the following limitations should be considered: (1) In all studies, results have not been adjusted for possible confounding factors such as obesity, smoking, or alcohol consumption. (2) The results of the funnel plots showed a publication bias across the studies; it follows that a systematic bias in the data presentation cannot be ruled out. (3) Studies with a small sample size (less than100 cases) had an inadequate power to detect possibly meaningful associations. (4) There was a high heterogeneity among studies in some analyses. (5) Studies reported different cut-off AHI values, which made comparisons between the studies difficult. (6) In some studies, TNF-α levels were considered as secondary outcome. (7) In some studies, the existence of mixed ethnicities might have blurred the associations between the ethnicity and TNF-α levels.By contrast, the strengths of the meta-analysis were as follows: (1) There were sufficient studies to allow the subgroup analyses. (2) The sensitivity analysis showed a stability of the results. (3) The studies written in other languages than English were included in the meta-analysis.The result of the meta-analysis and meta-regression confirmed that compared to healthy controls, individuals with OSAS (adults) had significant higher serum/plasma TNF-α levels, whereas for children with OSAS, this pattern of results was observed for serum TNF-α levels, but not for plasma TNF-α levels. Last, the results of the present meta-analysis showed that elevated TNF-α levels in individuals with OSAS appeared to be related to the severity of the disease. Future studies might investigate if and to what extent interventions on OSAS  favorably impact on TNF-α levels and possibly also on weight regulation.


Escherichia coli, in drinking and recreational water quality monitoring. For these applications, a standard sample size is 100 mL, which is quite large for magnetic separation microfluidic analysis platforms that typically function with <20 µL/s throughput. Here, we report the use of 1.5-µm-diameter magnetic microdisc to selectively tag target bacteria, and a high-throughput microfluidic device that can potentially isolate the magnetically tagged bacteria from 100 mL water samples in less than 15 min. Simulations and experiments show ~90% capture efficiencies of magnetic particles at flow rates up to 120 µL/s. Also, the platform enables the magnetic microdiscs/bacteria conjugates to be directly imaged, providing a path for quantitative assay.The long-term aim of this work is to develop a biosensing system that rapidly detects bacterial targets of interest, such as  Escherichia, Enterococcus, and Streptococcus) water , which resulted in 436 cases of illness, 65 hospitalizations, and 1 death in the United States [Enterococci and generic Escherichia coli in fresh and marine waters [E. coli is used as the indicator of fecal contamination, although there are a host of pathogenic microorganisms that are responsible for the aforementioned health problems.Detection of bacteria indicative of fecal contamination is central to water quality monitoring for ensuring safe water for human contact and/or drinking. For instance, as reported in the National Outbreak Reporting System (NORS) from the Center for Disease Control and Prevention (CDC), between 2009 and 2017 there were a total of 33 outbreaks associated with exposure to fecal contaminated  below 35 CFU/100 mL for While there has been much work in bacteria detection for water and food samples ,8, thereMicrofluidic devices possess advantages in terms of their size, low-cost fabrication, and the possibility of parallel device operation . Differe0–1.5 × 109 CFU/100 mL and analysis times ranging 0.35–2.5 h [E. coli at levels as low as 100 CFU/100 mL in less than 45 min. However, the isolation (magnetic trapping) step was performed using a bulky apparatus that required multiple successive passes through the device to achieve high capture efficiencies. Here, we present the use of a microfluidic device for faster sample filtering, convenient sample preparation, and ultimately better performance.Microfluidic magnetic separation technologies have aimed to reduce the total analysis time by avoiding long enrichment steps by isolating/concentrating magnetically-tagged bacteria using various magnetic field apparatuses . Most ma35–2.5 h . Previou35–2.5 h ,25,26,2735–2.5 h , aptamerMain challenge of magnetic separation microfluidic devices is their typically small volume capacity. As summarized in In this work, we demonstrate the use of a much improved microfluidic magnetic separation (µFMS) device that offers flow rates of up to 120 µL/s with capture efficiencies of ~94%. This device is capable of analyzing 100 mL water samples in less than 15 min, a significant advancement towards rapid bacteria detection. Instead of integrating the magnets into the microfluidic platform as in ,28,30, oE. coli or other target particles/cells). After co-incubation of these bio-functionalized microdiscs with a 100 mL water sample containing the target particle or cell, the μFMS device is used to isolate the microdisc/target conjugates in a localized area for imaging. Target cells can optionally be stained/labeled with a variety of fluorescent tags, and analysis is carried out using standard fluorescence microscopy.80Fe20), and then 5 nm gold again, was deposited by magnetron sputtering followed by an ultra-sonicated lift-off process using AZ400K developer  diluted in water (1:4) to form the gold-coated permalloy magnetic microdisc array on the substrate. The total thickness of each magnetic microdisc was 80 nm. Finally, the microdiscs were released by dissolving the tungsten sacrificial layer using 30% hydrogen peroxide and washed/decanted three times using deionized (DI) water and a permanent magnet. Magnetic microdiscs were fabricated using standard microfabrication techniques, as described in . BrieflyE. coli [E. coli, using thiolated DNA aptamers  synthesized by GeneLink  was performed. The gold-coated magnetic microdiscs (~6 million discs) were suspended in 200–300 µL of capture probe  solution overnight at room temperature. The discs were then rinsed three times with deionized water using a permanent magnet to decant supernatant. Once magnetic microdiscs were functionalized, they were introduced into water samples containing E. coli for around 30 min at room temperature with occasional agitation. Then, samples were filtered using a simple magnet or a µFMS device followed by fluorescent staining, using the BacLight LIVE/DEAD kit, where 3 µL of SYTO9 per every 1 mL of sample was introduced. The gold-coated microdiscs were bio-functionalized for selective binding to a bacterial/particle target using thiolated capture probes. In previous works, we targeted general coliforms and E. coli  using leAdditionally, for abiotic experimentation, we also tested and followed the same protocol described above using biotin-functionalized microdiscs to target avidin-coated polystyrene particles. Thiolated biotin  purchased from NanoCS Inc.  was used to target avidin-coated particles. Avidin-coated fluorescent particles  were purchased from Spherotech, Inc . The microfluidic device was fabricated using a standard soft-lithography process . BrieflyOnce the microfluidic (µF) component for the µFMS device was fabricated, the magnetic separation (MS) component was integrated considering the magnetic force acting on the magnetic particles. The magnetic force acting on a particle can be defined as: density . Similartization .For the MS component, the magnetic assembly comprised a 3 × 3 array of neodymium (NdFeB) magnets (each a 6.35 mm × 6.35 mm × 6.35 mm cube) arranged with alternating polarizations in a chess-board pattern. The maximum field gradients (and hence magnetic forces acting on the magnetic discs) occurred at the boundaries between the magnets. The magnet array was attached to the glass slide of the microfluidic device using removable adhesive film. A syringe pump was used to control the flow rate of sample solutions into the microfluidic device during experiments. −19 m3) than typical individual IONs , should experience higher magnetic forces resulting in better capture efficiencies during µFMS. In this work, two main experiments were performed to evaluate capture efficiencies of the µFMS device using both microdiscs and iron-oxide nanoparticles (IONs): 1) separation of magnetic particles from 0.2 mL samples at various flow rates and 2) large-volume separation of magnetic particles from 50–100 mL samples. Considering the relationship between magnetic forces acting on particles, the magnetic moment of particles, and their volumes (Equations (1) and (2)), our microdiscs, larger in size and volume . Concentrations of magnetic microdiscs solutions tested were 6.5 µg/mL and 0.065 µg/mL for 0.2 and 100 mL samples, respectively. The concentration of 100 mL sample with microdiscs represented an immeasurable magnetic volume sample, however, it was limited due to microfabrication costs. Therefore, experiments with IONs were replicated for large-sample volumes with much higher concentrations of magnetic particles (12.5–100 µg/mL) in an effort to obtain a measurable magnetic volume sample for vibrating sample magnetometry (VSM) quantification (as will be described in the next sections), as well as to evaluate possible clogging of microchannels of the µFMS device. 2O3 nanopowder, < 50 nm particle size (BET)) purchased from Sigma-Aldrich . Then, five 0.2 mL samples of the disc solution, with a fixed concentration of ~6 million discs/mL, was filtered through the µFMS device at different flow rates: 5, 15, 30, 60, and 120 µL/s. While two 0.2 mL samples of the IONs solution, with a fixed concentration of ~3000 million particles/mL, were filtered through the µFMS device at two different flow rates: 5 and 120 µL/s.Small-volume sample solutions were prepared with 1) microfabricated microdiscs and 2) IONs (FeCapture of microdiscs from large-volume samples (up to 100 mL) was assessed by filtering the samples with the µFMS device at a concentration of 0.065 µg/mL as a proof-of-concept. Then, capture of IONs was performed in 50 mL and 100 mL samples with concentrations of 100 µg/mL and 12.5 µg/mL, respectively. The importance of this experiment relied on our goal of isolating magnetic microdisc/bacteria conjugates from 100 mL samples in a short time period. Hence, these samples were filtered at a flow rate of 120 µL/s (up to 14 min filtering times) using a syringe pump with a 10 mL syringe . E. coli cells at a concentration of 100 CFU/mL from a 0.5 mL sample using aptamer-functionalized microdiscs at a flow rate of 120 µL/s.Finally, avidin-coated particles targeting and capture was assessed by adding the biotin-functionalized microdiscs to the 100 mL particle solution for 30 min at room temperature (protecting from light), followed by filtering using the µFMS device and a flow rate of 120 µL/s. Two 100 mL samples were prepared: 1) 3000 million particles and 2) 3000 particles. Both samples were exposed to the same amount of magnetic microdiscs (6 million). Similarly, a preliminary experiment was performed to isolate E. coli sample) were quantified in order to estimate capture efficiencies, as will be described in the next section.The concentration of the magnetic particles in the stock (before filtration) and the filtrate (after filtration) of each of these samples , similar to a method described by C. M. Earhart, et al. . The mag2). For example, in the case of the first experiment (0.2 mL samples), 0.1 mL filtrate droplets were dried on the substrate. On the other hand, for the second experiment (50 and 100 mL samples) 50 µL and 300 µL filtrate droplets were used for sample preparation. Sample preparation for the VSM measurements consisted of drying a pipetted droplet of known volume (10 to 500 µL) of the sample containing magnetic particles on a silicon substrate  was performed at different flow rates to match the experiment described before for confirmation. Here, the cross-sectional view presented in hd): 70 nm (lower bound), 618 nm , and 1.5 µm (upper bound). In the case of the IONs, they were simulated with hydrodynamic diameters of 50 nm as defined by Sigma-Aldrich (< 50 nm) and 124 nm considering the hydrodynamic diameter measured in [hdD simulated for IONs are shown with their respective capture efficiencies. For the magnetic field, field gradient, and magnetic force calculations, Maxwell’s equations were solved within the ‘magnetic fields, no currents’ module. While the flow and particle velocity, the Navier-Stokes’ equations were solved within the ‘laminar flow’ and ‘particle tracing for fluid flow’ modules. Finally, the drag force acting on the particles and their trajectories were studied within the ‘particle tracing for fluid flow’ module. Magnetic capture of particles using 2D finite element modeling in COMSOL Multiphysicssured in , as wellQ = 5, 15, 30, 60, and 120 µL/s), to match later experimental results. However, considering that the flow rates set experimentally (using the syringe pump) were defined for a single inlet that then divides into 8 parallel channels, then in the simulations (for a single microfluidic channel) the flow rates were defined as: cQ = Q/8 ≈ 0.63, 1.9, 3.8, 7.5, and 15 µL/s. For simulations, 1000 particles were released in the inlet and simulated for 8 s with time steps of 0.01s. Finally, after each simulation, particles in the outlet were counted using the global evaluation , and capture efficiency was computed as follows:Simulations for each of the particles (IONs and microdiscs) were performed by changing the flow rates  capture efficiencies decreased as the flow rates increased. hdD smaller than 124 nm, which experimentally was not matched. However, in order to match experiments, capture efficiency simulations were close to 100% for hdD of 294 nm or higher, which may serve as an indication of particle aggregation during experiments.hdD > 294 nm. All percentages are represented as estimated mean with 95% confidence interval. Experimental results for microdiscs A, showedThe 100 mL sample containing ~6 million magnetic microdiscs was filtered at a flow rate of 120 µL/s using the µFMS device and imaged using an optical microscope. From the large-volume samples experiments with IONs, the magnetic separation microfluidic device was capable of processing > 50 mL samples . The 50 −7 A·m2 for the 300 µL from the stock sample, and 4.2 × 10−8 A·m2 for the 300 µL from the filtrate sample), which resulted in the 72.2 ± 2.0% capture efficiency.VSM data in These experimental results demonstrated that the µFMS device is capable of filtering > 50 mL samples at flow rates of 120 µL/s. Now, it can be observed that the capture efficiencies measured in these large-volume experiments (~72%) are lower than the ~93% reported for the experiments performed on 0.2 mL. However, it is important to consider that the 0.2 mL sample experiments consisted of a lower concentration (~6.47 µg/mL) of particles (microdiscs). Therefore, it is possible that this decreased capture efficiency was related to clogging of the µFMS device microchannels with such high concentrations of IONs in the 50 and 100 mL samples  C. This d−7 particles/mL and the second sample contained 3000 particles/mL. Results show higher fluorescence in the sample with higher concentration of particles . This material was by no means a control experiment, but provides strong insight into the feasibility of the proposed bacteria detection system.Results on the isolation of particle targets were obtained for avidin-coated particles in 100 mL samples. Here, isolation of particles was successfully observed after filtration using the µFMS device and a flow rate of 120 µL/s. Two 100 mL samples with different particle concentrations were filtered using the µFMS device, one sample contained 3 × 10articles  A,B, whearticles  C,D. AltE. coli cells, from a 0.5 mL sample, using aptamer-functionalized microdiscs were obtained using the µFMS device and a flow rate of 120 µL/s. Confirmation of microdisc/bacteria conjugates was achieved via fluorescent microscopy. Finally, preliminary results on the isolation of In this work, a microfluidic magnetic separation (µFMS) device capable of filtering up to 100 mL samples in less than 15 min was developed. Also, capture efficiency studies in (0.2 mL samples) was > 90% at flow rates of up to 120 µL/s flow rates. Preliminary results have also shown promise towards 100 mL filtering using the same microfluidic device as well as isolating microdisc/bacteria conjugates, which at the same time results promising towards water quality monitoring tests. Future works will focus on the integration of the magnetic separation microfluidic device with a portable microscopy platform along with the image-processing algorithm for near-quantification of bacterial targets and microdiscs in field samples.


We here present NOseq, a method to detect m6A residues in defined amplicons by virtue of their resistance to chemical deamination, effected by nitrous acid. Partial deamination in NOseq affects all exocyclic amino groups present in nucleobases and thus also changes sequence information. The method uses a mapping algorithm specifically adapted to the sequence degeneration caused by deamination events. Thus, m6A sites with partial modification levels of ∼50% were detected in defined amplicons, and this threshold can be lowered to ∼10% by combination with m6A immunoprecipitation. NOseq faithfully detected known m6A sites in human rRNA, and the long non-coding RNA MALAT1, and positively validated several m6A candidate sites, drawn from miCLIP data with an m6A antibody, in the transcriptome of Drosophila melanogaster. Conceptually related to bisulfite sequencing, NOseq presents a novel amplicon-based sequencing approach for the validation of m6A sites in defined sequences.Methods for the detection of m As we now know, m6A deposition on nascent RNA is an early and important event in maturation of mRNA, which is orchestrated by a sophisticated catalytic complex, whose components are subject to continuous updates     , with un (NOm6A) .6A site detection by amplicon sequencing. The workflow combines an optimized deamination protocol, target-specific library preparation and a deamination-related alignment algorithm. The latter is based on the well-known seed-and-extend mapping strategy, first introduced in Blast (k-mers (short sequence of k bases) between reads and references. Basically, an alignment score is attributed according to a substitution matrix, taking mismatches and gaps into account. Here, we implemented an asymmetric substitution matrix considering deamination-induced nucleotide conversion, an alignment strategy known from bisulfite sequencing  and female-lethal-2-d (fl(2)d) from Drosophila melanogaster.Here, we present NOseq, an Illumina sequencing-based analysis pipeline for deaminated RNA sequences and min Blast  and mostin Blast  as SOAP in Blast  and BOWTin Blast . The algg. BSMAP ). We suc2 in T-160 cell culture flasks containing growth medium . Cultures at around 80% confluency were routinely splitted, i.e. every 24–48 h in new growth medium until harvesting of the cells and up to 1 month (10 passages). For total RNA extraction, cells were detached from the flasks for 5 min with 3 ml of Trypsin/EDTA (Sigma-Aldrich) and pelleted by low speed centrifugation in a 50 ml tube. The cells at confluency were evaluated (seeding) at ∼18 million, resuspended and vortexed in 2 ml TriReagent (Sigma Aldrich) before adding 800 μl of chloroform allowing phase separation under centrifugation . The upper aqueous phase containing RNA was precipitated with 250 μl of 100% nuclease-free isopropanol. After vortexing, incubation and centrifugation , the pellet was washed twice with 80% ethanol, centrifuged again and the air-dried pellet was finally dissolved in MilliQ water.HEK293 or HeLa cells were cultured at 37°C with 5% CODrosophila melanogaster Oregon-R wild-type strain and nullIme4 knockout strain  and the purity was assessed according to both absorption ratios 6A monoclonal mouse antibody (purified IgG) (Synaptic Systems) was first incubated overnight at 4°C in 1× PBS with Protein G Sepharose 4 Fast Flow antibody purification resin . Around 8 μg of total RNA, mRNA or synthetic oligonucleotide (Table 6A RNA fragments were then isolated from the antibodies by TriReagent (Sigma Aldrich) and following ethanol precipitation.The anti-mRNA (synthetic/isolated/immunoprecipitated) was deaminated under different conditions. Reactions were performed in a total volume of 100 μl, including RNA, 1 M sodium acetate/acetic acid buffer  and 1 M sodium nitrite solution  at 50, 60 or 70°C for 1, 3, 5, 10, 20 or 30 min. The reaction was stopped by adding 0.5 M ammonium acetate solution  and 1 μl glycogen (Thermo Scientific) to a final volume of 250 μl. By adding 750 μl ethanol (−80°C) the treated RNA was ethanol precipitated for 1 h at –80°C. Samples were centrifuged for 45 min at 5° and 15 000 × g. Pellets were washed with 80% ethanol and centrifuged again. Then, the ethanol precipitation step was repeated. Pellets were resuspended with 10 μl MilliQ water.3): δ = 3.77 , 4.86  ppm. 13C NMR : δ = 67.2, 69.3 ppm.Diethylene glycol dinitrite was synthesized in analogy to a known procedure  as folloSynthetic RNA was deaminated by incubation in an aqueous solution containing 1 M diethylene glycol dinitrite, 2 M pyridine and 1 M potassium thiocyanate  for 2, 5 or 10 h at 37°C. The solution was diluted with buffer  and the RNA was precipitated by addition of three volumes of cold ethanol and incubation in liquid nitrogen for 15 min. After centrifugation and washing with 75% ethanol, the pellet was resuspended in MilliQ water, and the integrity of the RNA was checked by analytical PAGE (stained by SYBR gold).N6-methyl-N6-nitrosoadenosine (NOm6A). Mass spectrometer settings used included ion polarity: positive; ion Source: ESI; dry temp: 350°C; nebulizer: 15 psi; dry gas: 12 l/min; trap drive: 43.0; Octapole RF amplitude: 140.3 Vpp; capillary exit: 96.6 V; skimmer: 40.0 V; Oct 1 DC: 12.0 V; Oct 2 DC: 1.7 V; scan range: 105–600 m/z; averages: two spectra; maximum accumulation time: 200 ms; and ICC target: 30 000. Data was analysed with Bruker LC-MS data reading software  and nucleosides were monitored by multiple reaction monitoring (dynamic MRM mode).LC-MS/MS analysis was used to monitor the deamination levels of treated RNA samples. Prior to LC-MS/MS analysis, RNA samples (were digested into nucleosides according to the following protocol: samples were incubated in a buffer of 25 mM ammonium acetate (pH 7.5), 0.3 U nuclease P1, 0.1 U snake venom phosphodiesterase, 1 U fast alkaline phosphatase and 10 U Benzonase overnight at 37°C. The digested samples were analysed on an Agilent 1100 HPLC series equipped with a diode array detector (DAD) and an Agilent 1100 mass selective detector (LC/MSD-Trap). A YMC Triart C18 column  from YMC Europe (Germany) was used at 30°C column temperature. Mobile phase A (MPA) consisted of 5 mM ammonium acetate buffer adjusted to pH 5.4 using acetic acid and mobile phase B (MPB) of pure acetonitrile. The elution was performed at a flow rate of 0.4 ml/min using the following gradient: 3% MBP from 0 to 3 min, 3–15% MPB from 3 to 30 min, 15–90% MPB from 30 to 31 min, 90% MBP from 31 to 35 min, 90–3% MPB from 35 to 36 min and 3% MPB from 36 to 45 min. The effluent from the column was measured photometrically at 254 nm by the DAD for the detection of the nucleosides. In addition to the UV detection, a mass spectrometer was used for the identification of nucleosides, e.g. Around 500 ng of (treated) synthetic RNA oligonucleotide Table  were usevia 10% denaturing polyacrylamide gel electrophoresis for 40 min at 10 W. The corresponding gel area (amplicon size), determined by GeneRuler Low Range DNA Ladder (Thermo Scientific), was cut out and eluted. The gel elution was performed overnight at 25°C in 0.5 M ammonium acetate solution, followed by Nanosep filtering  and subsequent ethanol precipitation.Around 500 ng of (treated) RNA  were incubated at 75°C for 5 min with 2.5 μM of targeted RT primers (Table DNA libraries were quantified using the Qubit dsDNA HS (High Sensitivity) Assay Kit with the Invitrogen benchtop fluorometer Qubit 2.0. Each library (amplicon and synthetic oligonucleotide) was analysed, and quality evaluation by automated electrophoresis on the Agilent 2100 Bioanalyzer system was performed, following the Bioanalyzer High Sensitivity DNA Kit instructions.Libraries were subjected to high-throughput sequencing using an Illumina MiSeq instrument with single-read (SR50) or paired-end (PE75) mode. Prior to loading on the sequencing chip, the multiplexed libraries were diluted to 6–8 pM final concentration.Drosophila melanogaster S2R+ cells and 5 μg of anti-m6A antibody (Synaptic Systems). Immunoprecipitations were performed in quadruplicates and as a control one immunoprecipitation was performed where UV-crosslinking was omitted. Of note, this sample produced a library of limited complexity, reflecting a low amount of background mRNA binding. Briefly, total RNA was isolated using Trizol reagent (Invitrogen) and DNA was removed with DNase-I treatment (NEB). Polyadenylated RNA was purified by two rounds of binding to Oligo (dT)25 magnetic beads (NEB) and fragmented with RNA fragmentation solution (Ambion) using 1 μl of solution per 2 μg of mRNA and with 7 min incubation at 70°C. Immunoprecipitation was performed at 4°C in 500 μl of binding buffer (BB) . First, isolated mRNA and antibody were incubated for 2 h. Samples were then transferred to individual well of a 12-well cell culture plate and crosslinked on ice (two-times at 150 mJ/cm2). Next, 60 μl of magnetic ProteinG beads (Invitrogen) were resuspended in 500 μl of BB and added to the IP sample. Samples were then incubated for additional 2 h at 4°C, before washing with ice-cold solutions was performed: 1× with BB, 2× with high salt buffer , 1× BB, 2× with PNK buffer . All washes were performed by gentle pipetting and with 1 min incubation on ice. Washes with HSB were additionally rotated for 2 min at 4°C. Finally, beads were resuspended in 900 μl of PNK buffer. 40 μl were used for WB analysis to evaluate immunoprecipitation efficiency. Remaining 860 ml were used for library preparation. All steps of library preparation were performed as previously described in Sutandy et al.  and indexed by starting position. In order to increase the algorithm speed, k-mers were alphabetically ordered. The alignment process is relying on seed-and-extend strategy. Sequenced reads were individually segmented into k-mers of size k, scanned against the reference sequence k-mers and scored according to the asymmetric substitution matrix (NOseq Illumina output demultiplexed FastQ files were unzipped. The nucleotide sequences (FASTA format), read information (identity number) and their corresponding quality scores (encoded in ASCII) were extracted, compiled and analysed using Python. The bioinformatic pipeline is single file dependent and each file is associated to a reference sequence. Prior to the alignment of the reads, the whole reference sequence was divided into smaller overlapping segments with identical size , cytidine (C), guanosine (G) and thymidine (T) was summed for each position and corresponding frequencies were plotted using matplotlib python library. In addition, only A positions in the reference sequence were selected and associated A and/or G rates were plotted. From this analysis, the cumulative density function of the normal distribution was calculated for each A position using the python package scipy.stats.norm and plotted together with the detection threshold at 0.95 (red line) . The UMI sequence (7 nt) of every aligned read was determined and the dataset was checked for duplicates (reads with same UMI sequences).et al.  from m6A  sites  signals. For further analysis we separated the adenosine and guanosine fractions into two individual plots, showing the adenosine and the guanosine rate respectively , revealed that m6A sites can be detected in different sequence contexts with the same deamination treatment and analysis pipeline, without additional optimization steps. A probable explanation is that under the milder conditions used, the xanthosine blockage plays a minor role, since the guanosine conversion is slower in comparison to those of cytidine and adenosine  that was varied from 0 to 100%. Partial deamination parameters , representing a compromise featuring optimized mapping yield versus adenosine deamination, were chosen for this test  under these milder conditions is thought to avoid potential RT-arrest at m6A sites. NOseq data showed a statistically valid m6A signal in samples containing down to ∼50% m6A. This de novo detection limit could be lowered to ∼10% by conducting a methylated RNA immunoprecipitation (MeRIP) enrichment , such a calibration would have to rely on synthetic modified RNA, and require considerable effort in terms of time and resources.To evaluate the sensitivity of our method and address its detection threshold for sub-stoichiometric mrichment  prior torelation , we conc6A sites to demonstrate the applicability of the method. To this end, we developed an amplicon sequencing method for target-specific evaluation of m6A sites  and this strategy was therefore not further pursued. Rather, as a compromise of signal-to-noise versus sequencing output, we turned to conventional primers and used milder deamination conditions  of the protein-coding mRNA Hairless (H) in D. melanogaster, which plays a role in Notch signalling d) in D. melanogaster, an associated component of the methyltransferase complex mediating m6A methylation  can be differentiated.In summary, we developed an mecessary . Our metecessary . In thisecessary . The det6A sites subsequent to their validation by NOseq is made plausible by our proof-of-principle data on synthetic RNA. This would comprise synthetic template mixtures with ascending m6A content in the sequence context in question. The sequence-specific calibration differentiates the procedure from known methods .6A33) – 50°C, pH 4.0, 20 min., Sample2.fastq – 18S rRNA amplicon – 60°C, pH 5.0, 10 min.Testdata: Sample1.fastq – 53mer (mgkaa1173_Supplemental_FilesClick here for additional data file.


Combined androgen blockade using bicalutamide (Bic) is a therapeutic choice for treating prostate cancer (PCa). However, even at regular clinical dosages, Bic frequently shows adverse effects associated with cardiovascular and renal damage. Previously, we found that Bic selectively damaged mesangial cells compared to tubular cells and in an in vivo rat model, we also found renal damage caused by Bic. In the present study, a rat mesangial cell model was used to further the investigation. Results indicated that Bic enhanced lactate dehydrogenase release, reactive oxygen species (ROS) production, lysosome population and kidney injury molecule-1 and decreased N-cadherin. Bic elicited mitochondrial swelling and reduced the mitochondrial potential, resulting in severe suppression of the oxygen consumption rate (OCR), maximum respiration and ATP production. The hypoxia-inducible factor (HIF)-1α transcriptional activity and messenger RNA were significantly upregulated in dose-dependent manners. The HIF-1α protein reached a peak value at 24 h then rapidly decayed. BCL2/adenovirus E1B 19-kDa protein-interacting protein 3 and cleaved caspase-3 were dose-dependently upregulated by Bic (60 μM) and that eventually led to cell apoptosis. It is suggested that Bic induces renal damage via ROS and modulates HIF-1α pathway and clinically, some protective agents like antioxidants are recommended for co-treatment. Bicalutamide (Bic) is a non-steroidal antiandrogen which provides blockade at tumor sites to facilitate treatment of prostate cancer (PCa) . Bic posAlthough Bic is deemed to be “nontoxic” and “harmless” , a diverOverall mechanisms of the adverse effects on organs by Bic are not well established. Some speculations indicated that Bic-induced liver injury might be due to its toxic metabolites . OtherwiStudies of men undergoing long-term (≥12 months) combined androgen blockade (CAB) revealed higher prevalence of diabetes mellitus (DM) and metabolic syndrome compared to controls ,19. AdmiAlternately, Bic may exert apoptotic effects independent of its antiandrogenic ability. Bic decreases prostatic blood flow and induces apoptosis via a hypoxic pathway . As usuaConsidering that mitochondria play a critical role in cellular energy production and this phenomenon has gained much attention for its opposing roles in cell survival and cell death , we propLDH release was induced by Bic at 24 h in a dose-dependent manner a. The elIt is worth mentioning that in addition to the biomarkers of KIM-1 and N-cadherin, neutrophil gelatinase-associated lipocalin (NGAL) is a very useful biomarker widely expressed in a variety of cell types, including neutrophils, mesangial cells and tubular cells ,50. NGAL●O2−) and thus constitutes a major site of cellular ROS production [Of all cellular ROS sources, electron leakage from the mitochondrial electron transfer chain to molecular oxygen generates a steady flux of superoxide anion , JC-1 spontaneously forms J-aggregates with emission of intense red fluorescence (fluorescence emission at ~590 nm). While in apoptotic or unhealthy cells with a low ΔψM, JC-1 shows only green fluorescence (fluorescence emission at ~529 nm) [In healthy cells with a high mitochondrial potential (Δψ~529 nm) . Consequ~529 nm) .M)  a. Bic inM) a. Mitochdiseases . An in vdiseases . Consistdiseases . It is kdiseases . Damageddiseases . In an adiseases . Previoudiseases . It was Normally in most eukaryotic cells, the double-membrane structure of mitochondria constitutes three divided regions and two compartments, designated the outer mitochondrial membrane (OMM), the intermembrane space, the inner mitochondrial membrane (IMM) that forms cristae and the matrix . CristaeControversially, a study by Ball et al (2016) reported that ‘Bicalutamide was not found to be a mitochondrial toxicant” and we suspect that different cell lines have different susceptibilities . Ball etp < 0.05)  b–e. ConsWhen electron transport chain inhibitors are added, residual oxygen consumption (ROX) is due only to non-mitochondrial respiration caused by some oxidases and other cellular enzymes . ROX washif-1α transcriptional activity was found to have increased only at 24 h after treatment with Bic and an obvious difference was also found between treatment with 30 and 60 μM Bic (hif-1α mRNA at 24 h was found to have increased in a dose-dependent manner (hif-1α mRNA seemed to occur simultaneously with the increase in hif-1α transcription activity (p < 0.05)  c.Much of the literature mentions some solid supporting evidence for this finding. In response to acute hypoxia, HIF-1α rapidly accumulates in cells due to inactivation of oxygen-sensitive prolyl hydroxylase . Prolongp < 0.05). In parallel, Bic was found to upregulate cleaved-caspase-3 at 30 and 60 μM , an inhibitor of prolyl hydroxylase, to examine whether this compound could abolish the apoptotic effect of HIF-1α. Results showed that Bic caused 1.59, 2.88 and 2.20-fold increase of early, late and total apoptosis compared to control respectively . The sighif-1α transcription and hif-1α mRNA. A summary of Bic-induced renal mesangial cell damage is shown in In summary, Bic exhibited a certain degree of cytotoxicity even at regular doses prescribed for patients. Bic caused LDH release, upregulation of KIM and downregulation of N-cadherin. Bic elicited excessive production of ROS, membrane potential depolarization, mitochondrial swelling and a decreased mitochondrial population and consequently, the OCR and ATP production were dose-dependently inhibited. In parallel, Bic dose- and time-dependently upregulated Meanwhile, to prevent adverse effects induced by Bic, some nutraceutic compounds are suggested to be applied as adjuvant therapy to ameliorate or prevent the mitochondrial dysfunction, such as coenzyme Q, which is also dependent on vitamins B2, B6, B12 and C, folic acid, pantothenic acid and niacinamide . As is wBicalutamide (Bic), 3--2,5-diphenyltetrazolium bromide (MTT), Dulbecco’s modified Eagle medium (DMEM), tetramethyl ethylene diamine (TEMED) and FG4592 were provided by Sigma-Aldrich . The enhanced chemiluminescence (ECL) system was a product of Merck Millipore . The PRO-PREP Protein Extraction Solution was provided by iNtRON Biotech. . JC-1, dihydroethidium (DHE) and dichlorodihydrofluorescein diacetate (DCFDA) were provided by Millipore Sigma . The 2% charcoal-containing fetal bovine serum (FBS) was provided by GeneTex .2 atmosphere. After cells had completely adhered, the medium was replaced with 2% charcoal FBS DMEM and incubated overnight (for 12–16 h). Then the indicated drugs were applied at 30 and 60 μM Bic that was previously prepared in DMEM with 2% charcoal FBS. The medium was replaced with fresh medium and cells were incubated for different periods as indicated.A rat mesangial cell (RMC) line was provided by the Bioresource Collection and Research Center . RMCs were cultured in modified DMEM containing 25 mM glucose, 15% FBS, 4 mM L-glutamine, 1.5 g/L of sodium bicarbonate, 0.4 mg/mL of G418 and 0.4% phosphate-buffered saline (PBS) and incubated at 37 °C under a 5% COCells were cultured as described above. Drugs were applied and the LDH assay was carried out after 48 h using a Cayman LDH Cytotoxicity Assay Kit (cat. No. K601170). The optical density was measured at 490 nm.™ Oxidative Stress Kit .Following a method described by Owusu-Ansah et al. , a DHE o2 atmosphere for 1 h. After removing the medium, cells were rinsed with PBS several times and cells were observed under a fluorescent microscope .According to Owusu-Ansah et al.  and foll2 atmosphere. The medium was removed and observed with a Leica TCS SP5 Confocal Spectral Microscope Imaging System . JC-1 green-fluorescent monomers that formed at depolarized membrane potentials were detected at Ex/Em = 514/529 nm and orange-fluorescent J-aggregates that formed at hyperpolarized membrane potentials were detected at Ex/Em = 585/590 nm.RMCs were trypsinized and seeded onto 3.5-cm culture dishes. When cells had completely adhered, the medium was replaced with fresh 2% charcoal FBS DMEM and incubated overnight. Bic was added at the indicated concentrations and incubated for 24 h. The medium was then removed and replaced with 2% charcoal FBS DMEM containing 1 μg/mL of JC-1. Cells were incubated at 37 °C for 30 min under a 5% COAlternately, FCCP (10 μM), a protonophore, was added to untreated cell cultures at a concentration of 50 nM to depolarize mitochondrial membranes as the positive control. Approximately 10 min after the addition of the uncoupler, cells were illuminated at Ex/Em (nm) = 514/529 nm for green monomers and Ex/Em (nm) = 585/590 nm for J-aggregates.4 cells/dish overnight. After cells had totally adhered, the medium was replaced with 2% charcoal FBS-DMEM and incubated overnight. Then RMCs were treated with 60 μM Bic for 48 h or left untreated, after which cells were transferred to phenol red-free medium. Dishes were then placed on a Nanolive 3D cell explorer  and images are taken at 600 × magnification.RMCs (400 µl) were seeded into 35-mm tissue culture dishes at a density of 6 × 105) were seeded into poly-L-lysine-coated two-well chamber slides and treated with 30 and 60 μM Bic for 24–48 h. Cells were fixed in 0.1 M cacodylate solution containing 2% paraformaldehyde and 2.5% glutaraldehyde for 1 h. After removing the fixative solution, cells were rinsed thrice with buffer solution containing 0.1 M cacodylate and 7% sucrose, for 15 min each time. Specimens were stained with 1% OsO4 contrast solution (in 0.1 M cacodylate buffer) for 1–2 h and then subjected to a gradient ethanol dehydration method . Dehydrated specimens were treated thrice with propylene oxide (PO), each time for 10 min. The resin was evacuated under a vacuum for 8 h and PO/resin (1:1) was mixed and evacuated under a vacuum overnight. Specimens were embedded and heated to 62 °C in the oven for 48–72 h until completely set, then reshaped, sliced and analyzed by HT7700 TEM  for access to intracellular ultrastructures.RMCs (3 × 104 cells/100 μL of medium/well) and incubated overnight (at 37 °C in 5% CO2). Bic at indicated doses was applied for 24 h. Then, cell culture medium was replaced with 175 μL of unbuffered Seahorse XF Base medium supplemented with 25 mM of glucose, 2 mM of L-glutamine and 1 mM of sodium pyruvate at 1 h before the measurement. Prior to the measurement, the Seahorse XF analyzer gently mixed the assay medium in each well for 10 min to enable the oxygen partial pressure to reach equilibrium. The OCR was then measured three times to establish a baseline rate prior to an acute injection of Bic. The following was the procedure for the Cell Mito Stress Test (Agilent Technology): (1) basal respiration was measured in unbuffered medium; (2) oligomycin (1 μM), an inhibitor of ATP synthesis, was injected to determine respiration linked to ATP production; (3) FCCP (2 μM), a mitochondrial oxidative phosphorylation uncoupler, was added to measure maximal respiration; and (4) antimycin A and rotenone (1 μM each) were applied in combination to block respiration due to the simultaneous inhibition of complexes III and I, respectively [The entire protocol was carried out following instructions of the manufacturer . RMCs were seeded onto a collagen-coated XF 96-well cell culture microplate (2.5 × 10ectively . The OCRectively .6 cells/mL) were seeded and grown in 10-cm culture dishes overnight. Then Bic at 30 or 60 μM was applied for 3, 6 and 24 h. Transcription activity of HIF in the nuclear extract was detected at OD 450 nm.HIF-1α transcriptional activity was analyzed by an HIF-1α transcription factor assay kit . RMCs  at 6 and 24 h. After the amount of RNA was quantified using a Nano-Drop 1000 Spectrophotometer , 1 μg of RNA was subjected to a qPCR to produce complementary (c)DNA . A real-time thermocycler  was used to perform the reactions as follows—1 cycle of pre-denaturation at 95 °C for 3 min and then 35 cycles sequentially processed according to the sequence: denaturation at 95 °C for 15 s; annealing at 59 °C for 30 s; and extension at 72 °C for 30 s, followed by one cycle final extension at 72 °C for 10 min and one cycle cool-down at 4 °C for 10 min. The LabStarTM SYBR QPCR Kit  was adopted for fluorescence detection. The respective primers used were: T method .RMCs were treated with Bic for 24–48 h as indicated. Extraction of cytoplasmic and nuclear proteins was carried out as instructed by the cytoplasmic and nuclear protein extraction kit . The proteins obtained were transferred into new Eppendorf flasks and stored at −80 °C. A previously described standard protocol was used . SpecifiIn order to elucidate the role of HIF-1 in cell apoptosis, FG-4592  was used to interfere with the effect of HIF-1. FG-4592 is a small molecule that acts as a modulator of polyhydroxylase which can degrade HIF .® Annexin V and Dead Cell Kit on a Muse Cell Analyzer  following the protocols of the manufacturer. In brief, RMCs were seeded onto six-well plates at a density of 4 × 105 cells/well in 2% charcoal FBS. The medium was aspirated off, replaced with new medium containing the target medicine Bic  in 2% charcoal FBS and co-incubated with FG-4592 (at 2.5 μM) for 24 h. Cells with medium were harvested, treated with trypsin for 3 min, transferred to a centrifuge tube and centrifuged at 1000× g for 5 min. The supernatant was aspirated off. A PBS wash was added, agitated well and centrifuged for 5 min at 1000× g. The supernatant was aspirated off and new medium containing 2% charcoal FBS (1 mL) was added and mixed well. To an aliquot of 100 μL of the culture, 100 μL of the Annexin V solution (Millipore MCH100105) was added and mixed well. The mixture was left to react for 20 min in the dark avoiding direct sunlight. The solution was subjected to Muse and analyzed with Muse 1.3.1. software .The cell apoptosis analysis was carried out with an MCH100105 Muset-test in SPSS 10.0 software . An analysis of variance (ANOVA) was also adopted with Tukey’s test to analyze variances and significances of difference between paired means. The significance level was judged by the p value. * p < 0.05, ** p < 0.01, *** p < 0.005.All data were analyzed by Student’s Bic has been used as one of the effective antiandrogens. The regular dose is around 50–250 mg/day. However, even at such doses, it has frequently been reported to have adverse effects associated with cardiovascular and renal damage. We showed that Bic selectively damaged RMC kidney cells, the etiology of which was due to the immense production of ROS and the mitochondrial damage that resulted in a decreased OCR. Speculatively, patients afflicted with renal diseases and DM may be more severely injured than those without those complications. Moreover, prolonged Bic treatment may induce upregulation of HIF-1α, KIM-1 and BNIP3, resulting in cell apoptosis in the renal organ. Consequently, when Bic is prescribed for patients, it is warranted to take these adverse effects of Bic into consideration, in particular, for DM patients.


Appropriate motor adaptation relies on cerebellar activity –4, and tHere, we wished to replicate the facilitatory effect of cerebellar atDCS on visuomotor adaptation. To increase the likelihood of observing an effect of cerebellar stimulation, we used a within-subject design and a novel adaptation task paradigm designed to bias learning towards primarily cerebellum-dependent processes ,17. In t2Twenty-seven healthy, right-handed  participParticipants controlled a cursor on a screen using a joystick and made fast, accurate and ballistic movements to ‘shoot’ through a target at one of eight possible locations aligned around the central starting position . The curA DC-Stimulator  delivered a 1.5 mA (0.06 mA/cm2) current via two 5 × 5 cm electrodes  using Ten20® conductive paste. The anode was centered on the right cerebellar cortex, 3 cm right of the inion, in line with the pre-auricular point, and the cathode was centered over the right buccinator muscle [Cursor movements were analyzed on a trial-by-trial basis using in-house software written in Matlab . Joystick position was sampled at 60Hz, filtered with a zero-phase filter (25Hz cut-off), and numerically differentiated to determine velocity. For each trial, the angular error (°) was defined as the angle between a line connecting the starting position with the position of peak velocity of the cursor and the line connecting the starting position with the target. Positive values indicate a clockwise error (‘overshooting’). Trials with premeditated or otherwise poorly-defined movements were excluded from further analysis  excluded per subject).Adaptation error was calculated as the mean error across all trials excepting the first 8 of each rotation block, to capture the rapid error reduction of adaptation performance excluding the initial exposure to the rotation ,19. Adap32(1) = 0.58, p = 0.45), nor between real and sham sessions (χ2(1) = 2.15, p = 0.14). Baseline performance did not differ during real and sham stimulation (χ2(1) = 1.36, p = 0.24).Both participant and experimenter blinding was successful . Baseline performance did not differ between sessions (1 v 2: χ2(1) = 4.54, p = 0.03), though this improvement was smaller in the stimulation period alone (χ2(1) = 2.85, p = 0.09). We therefore included session as a covariate of no interest into our model in all further analyses. Adaptation performance did not differ significantly between real and sham stimulation either during stimulation (χ2(1) = 1.05, p = 0.3) or across the whole task (χ2(1) = 0.15, p = 0.7). Finally, to avoid any session effects, we tested for an effect of stimulation in the first session only. Adaptation performance did not differ significantly between subjects receiving real and sham stimulation in the first session  = 0.23, p = 0.64).As expected, participants learned to compensate for the imposed rotation across the experiment  = 5.5, p < 0.001). However, participants performed better in Session 2 than Session 1 across the task (χ2(1) = 3.22,p = 0.07) but this could be explained by baseline differences (χ2(1) = 12.3, p > 0.1).Here, we wanted to probe implicit mechanisms of adaptation. Awareness of any offset between the joystick and the cursor might increase the use of explicit strategies, potentially engaging different neural substrates . When as2(1) = 1.62, p = 0.2) or during the stimulation phase (χ2(1) = 0.9, p = 0.34).We saw no effect of stimulation on retention, either across the whole experiment (χ4Potential explanations for our failure to replicate a significant behavioural effect of cerebellar tDCS, include significant between- session differences. However, given our sample size, and the null finding when only considering the first session, at the most the behavioural effect of cerebellar tDCS appears much smaller than previously reported .While session order effects pose a difficulty for motor adaptation experiments, within-subject studies are crucial in controlling for the physiological variability in tDCS studies. Our results highlight the critical need to investigate the physiological, brainstate and task-dependent factors influencing tDCS to reconcile conflicting evidence on behavioural effects of cerebellar tDCS. An investigation into the precise physiological changes in the cerebellum in response to tDCS could help navigate the vast parameter space and further the development of cerebellar tDCS for clinical applications.


PRR) has been reported, such as through using the combination risk ratio (CRR). However, the CRR does not consider the overlap between the lower limit of the 95% confidence interval of the PRR of concomitant-use drugs and the upper limit of the 95% confidence interval of the PRR of single drugs. In this study, we proposed the concomitant signal score (CSS), with the improved detection criteria, to overcome the issues associated with the CRR. “Hypothetical” true data were generated through a combination of signals detected using three detection algorithms. The signal detection accuracy of the analytical model under investigation was verified using machine learning indicators. The CSS presented improved signal detection when the number of reports was ≥3, with respect to the following metrics: accuracy (CRR: 0.752 → CSS: 0.817), Youden’s index (CRR: 0.555 → CSS: 0.661), and F-measure (CRR: 0.780 → CSS: 0.820). The proposed model significantly improved the accuracy of signal detection for drug-drug interactions using the PRR.There is a current demand for “safety signal” screening, not only for single drugs but also for drug-drug interactions. The detection of drug-drug interaction signals using the proportional reporting ratio ( Pre-marketing randomized clinical trials typically focus on establishing the safety and efficacy of a single drug rather than investigating drug-drug interactions; therefore, patients who use drugs other than the one under investigation are usually excluded. However, unlike pre-marketing trials, it is common to use multiple drugs for treatment post-marketing. Therefore, attention should be paid not only to the adverse events caused by a given drug, but also to those arising as a result of interactions between two or more drugs. In some reports, the proportion of adverse events caused by drug-drug interactions was estimated to be approximately 30% of the unexpected adverse events . The usePRR) [Several detection algorithms ,4,5,6 haPRR)  is oftenPRR) ,8,9.CRR) as a signal detection algorithm for drug-drug interactions [CRR is calculated by dividing the estimated PRR points of concomitant-use drug D1 and drug D2 (=drug DPRR drug D21 ∩ ) by the estimated PRR points of drug D1 or drug D2 (drug DPRR 1 or drug DPRR 2), as shown in Susuta et al. proposed the combination risk ratio  the drug DPRR 1 and the χ2drug D1, (2) the drug DPRR 2 and the χ2drug D2, replace it as follows:However, to calculate the N11 = n111 + n101, N00 = n000 + n010, N10 = n110 + n100, N01 = n001 + n011, N1+ = n11+ + n10+, N+1 = n++1, N0+ = n00+ + n01+, N+0 = n++0,(1) drug D2: N11 = n111 + n011, N00 = n000 + n100, N10 = n110 + n010, N01 = n001 + n101, N1+ = n11+ + n01+, N+1 = n++1, N0+ = n00+ + n10+, N+0 = n++0.(2) n111 ≥ 3, (2) drug DPRR drug D21 ∩  > 2, (3) χ2drug Ddrug D21 ∩  > 4, (4) CRR > 2, this was signal of drug-drug interaction.When (1) drug DPRR drug D21 ∩ . In other words, as shown in drug DPRR drug D21 ∩  (=PRR025drug Ddrug D21 ∩ ) overlaps with the upper limit of the 95%CI of drug DPRR 1 or drug DPRR 2 (=PRR025drug D1 or PRR025drug D2).However, the number of adverse events reported during concomitant use is generally lower than that of single-drug-induced adverse events, and the 95% confidence interval (95%CI) tends to be wider for INTSS) [CSS)  , we propS) [CSS)  and verita et al .CSS are shown in F-measure: 0.638) and 0.570 for PR : 0.645).The receiver operating characteristic (ROC) curve and precision-recall (PR) curve of the CRR (true positive (TP): 473, false positive (FP): 266, true negative (TN): 2735, and false negative (FN): 450). In contrast, the CSS detected 1862 signal pairs .In all cases, 739 pairs were detected using the TP, FP, TN, and FN signal pairs detected for n111 ≥ 3 are shown in CRR , and 621 pairs were detected using the CSS .The numbers of CSS confirmed an improvement in signal detection with respect to the following metrics: (1) Youden’s index (CRR: 0.424 → CSS: 0.629), (2) F-measure (CRR: 0.569 → CSS: 0.633), (3) Recall (sensitivity) (CRR: 0.512 → CSS: 0.956), and (4) negative predictive value (NPV) (CRR: 0.859 → CSS: 0.874), in comparison with CRR.The n111 ≥ 3, the CSS confirmed an improvement in signal detection with respect to the following metrics: (1) Accuracy (CRR: 0.752 → CSS: 0.817), (2) Youden’s index (CRR: 0.555 → CSS: 0.661), (3) F-measure (CRR: 0.780 → CSS: 0.820) (4) Specificity (CRR: 0.557 → CSS: 0.714), and (5) Precision  (CRR: 0.640 → CSS: 0.723). The metrics of the Ω shrinkage measure were as follows: (1) Accuracy (0.880), (2) Youden’s index (0.775), (3) F-measure (0.875), (4) Specificity (0.824), (5) Recall (sensitivity) (0.951), (6) Precision (PPV) (0.810), and (7) NPV (0.956)  .CSS and the Ω shrinkage measure were κ (95%CI): 0.330 (0.314–0.345), Ppositive: 0.505, and Pnegative: 0.759 in all case. On the other hand, for n111 ≥ 3, those values were κ (95%CI): 0.729 (0.708–0750), Ppositive: 0.880, and Pnegative: 0.849.The similarity metrics between the CSS, a newly proposed analytical model to overcome potential issues with the CRR. In this study, 3924 pairs of drug D1–drug D2—Stevens–Johnson syndrome (SJS) in the Japanese Adverse Drug Event Report (JADER) database were evaluated. Unknown adverse event data do not exist, therefore no “real” true data for adverse events were available. Therefore, to verify the accuracy of CSS, we had to prepare “hypothetical” true data pertaining to adverse events. These “hypothetical” true data were also used to validate the subset analysis for detecting drug-drug interaction signals [We evaluated the setting criteria and accuracy of the  signals .CSS. The highest value for Youden’s index was 0.632, and the cutoff value was 1.041 (F-measure: 0.645). Furthermore, the PR curve must be considered when evaluating imbalanced data such as those used in the present study. The F-measure was the highest for a cutoff value of 1.271 . Considering the detection criteria, including the results presented here, the criterion (CSS > 1) proposed by us would be preferable.The ROC curve was generated to determine the cutoff value of CSS, with 1862 pairs , followed by the CRR, with 739 pairs , in all cases. One reason for this difference in the number of detections is that, unlike the CSS, the CRR cannot detect combinations for n111 < 3. Therefore, we also investigated the difference in the number of detections for n111 ≥ 3.The highest number of signals was detected using the CSS , whereas 739 pairs were detected using the CRR . These results demonstrate the significantly improved signal detection accuracy of the newly proposed CSS in comparison with that of the CRR. However, the CSS exhibited slightly lower accuracy for detecting drug-drug interaction signals in comparison with the Ω shrinkage measure.As shown in CSS might be a more suitable method for detecting drug-drug interaction signals using PRR instead of the CRR.These results suggest that the CSS and the Ω shrinkage measure.Our previous studies  have shoCSS and the Ω shrinkage measure were κ (95%CI): 0.330 (0.314–0.345), Ppositive: 0.505, and Pnegative: 0.759, whereas those between the CRR and Ω shrinkage measure were κ: 0.718 (0.703–0.733), Ppositive: 0.771, and Pnegative: 0.948 [CRR cannot detect combinations for n111 < 3, the CSS can. It is considered that this difference in detection criteria affected the similarity.The similarity metrics between the e: 0.948  in all cCSS is more similar to the Ω shrinkage measure than the CRR in n111 ≥ 3; the Ω shrinkage measure and CSS was κ (95%CI): 0.729 (0.708–0750), Ppositive: 0.880, and Pnegative: 0.849, whereas the CRR and Ω shrinkage measure were κ: 0.621 (0.597–0.646), Ppositive: 0.850 and Pnegative: 0.763 [CRR, the CSS has the advantage of being able to detect combinations for n111 < 3, but if a more conservative signal detection is needed, consider adding “n111 ≥ 3” to the criteria.Indeed, the e: 0.763 . Unlike (1)drug D1–drug D2 adverse event (SJS) combination rather than a pharmacology-based combination. Unfortunately, data on unknown adverse events do not exist; thus, it was necessary to use “hypothetical” true data instead of “real” true data for validation. Therefore, we used a combination of signals detected using the three algorithms as “hypothetical” true data for detecting drug-drug interaction signals.The true data used in this study consist of a statistics-based (2)drug D1–drug D2 adverse events are expected, and a study design including all such combinations is not practical. Therefore, SJS was the target adverse event in this study. Although this adverse event has been used previously in other comparative studies by our group [It is important to compare detection trends using all adverse events recorded in the validation datasets. However, numerous combinations of ur group ,13 and our group ,14, it i(3)Differences in the approach adopted by regulatory authorities may result in differences in the tendency to register adverse events in the spontaneous reporting system. For example, JADER has long not accepted reports from patients, whereas the Food and Drug Administration Adverse Events Reporting System (FAERS) includes reports from non-medical professionals. It is unclear how the differences in registration tendencies would affect the results of this study . HoweverHowever, this study, like our previous study , has thrhttp://www.info.pmda.go.jp/fukusayoudb/CsvDownload.jsp) (in Japanese only).The validation dataset was created from the Japanese Adverse Drug Event Report database (JADER), using data from the first quarter of 2004 to the fourth quarter of 2015. It can be accessed directly here:  using the preferred term (PT) in the Medical Dictionary for Regulatory Activities Japanese version (MedDRA/J).There are no “real” true data for unknown adverse events. It was considered that only known adverse events should not be used as “real” true data for validation, because detection algorithms require the power to detect unknown adverse events. Therefore, “hypothetical” true data used in the previous study  were als“Hypothetical” true data used the combination of signals detected by three algorithms  PRR drug D1 ∩ drug D2 > 2, (3) χ2drug D1 ∩ drug D2 > 4, (4) combination risk ratio (CRR) > 2, this was signal of drug-drug interaction.This model proposed by Susuta  is calcuPRR (PRR025) and the upper 95%CI for the PRR (PRR975) was calculated using Two new models and criteria were considered. The lower 95%CI for the CSS (Equation (5)) as a new model. The newly proposed model is the ratio of PRR025drug Ddrug D21 ∩  and PRR975drug D1 (or PRR975drug D2). This principle is similar to the interaction signal score (INTSS) [We proposed the  (INTSS) .PRR025drug D∩ drug D21  > 1, (2) CSS > 1 as the newly proposed criteria.In this study, we considered (1) In this study, we selected the Ω shrinkage measure  as the mE111 is the expected value of adverse event (AEs) caused by the combination of two drugs. Ω025 > 0 is used as a threshold to screen for signals under the combination of two drugsThe calculation is shown in Equations (6) and (7). However, The ROC curve is normally used to judge model performance. To properly analyze imbalanced data, which include a higher proportion of negative data than positive data, evaluation using the PR curve ,20 in adF-measure (Equation (10)), Specificity, Recall (=Sensitivity), Precision , and negative predictive value (NPV).The evaluation indicators that we have set were Accuracy (Equation (8)), Youden’s index (Equation (9)), κ), proportionate agreement for positive rating (Ppositive), and proportionate agreement for negative rating (Pnegative), as reported in previous studies [CRR/CSS).The commonality of the signals detected by each statistical model was evaluated using Cohen’s kappa coefficient  with PRROC package version 1.3.1.The analysis software in this study used Visual Mining Studio  version 8.4, Microsoft Excel 2019 , and R version 4.0.0  and the upper limit of the 95%CI of drug DPRR 1 or drug DPRR 2 (=PRR025drug D1 or PRR025drug D2).The ntensity . HoweverCSS with an improved detection criteria, with reference to the INTSS, to overcome the issues associated with the CRR. Our proposed model significantly improved the accuracy of signal detection for drug-drug interactions using the PRR.In this study, we proposed a