Msx2, Dlx5, and Bmp2 the most, more noticeably in mABDCs than in mLBDCs during osteogenic differentiation. In in vivo models, the BMP4‐treated mABDC group showed massive bone formation and maturation as opposed to its counterpart. Bone sialoprotein expression was also validated in calcified tissues. Overall, our data suggest that alveolar bone and long bone have different responsiveness to EMI by distinct gene regulation. In particular, BMP4 has critical bone formation effects on alveolar bone, but not on long bone. © 2020 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.Alveolar bone is both morphologically and functionally different from other bones of the axial or peripheral skeleton. Because of its sensitive nature to external stimuli including mechanical stress, bone loss stimuli, and medication‐related osteonecrosis of the jaw, alveolar bone rendering is seen as an important factor in various dental surgical processes. Although multiple studies have validated the response of long bone to various factors, how alveolar bone responds to functional stimuli still needs further clarification. To examine the characteristics of bone in vitro, we isolated cells from alveolar, femur, and tibia bone tissue. Although primary cultured mouse alveolar bone‐derived cells (mABDCs) and mouse long bone‐derived cells (mLBDCs) exhibited similar osteoblastic characteristics, morphology, and proliferation rates, both showed distinct expression of neural crest (NC) and epithelial–mesenchymal interaction (EMI)‐related genes. Furthermore, they showed significantly different mineralization rates. RNA sequencing data demonstrated distinct transcriptome profiles of alveolar bone and long bone. Osteogenic, NC‐, and EMI‐related genes showed distinct expression between mABDCs and mLBDCs. When the gene expression patterns during osteogenic differentiation were analyzed, excluding several osteogenic genes, NC‐ and EMI‐related genes showed different expression patterns. Among EMI‐related proteins, BMP4 elevated the expression levels of osteogenic genes,  Against these stressors, periodontium—tissue covering the tooth root—supports the tooth. Among the tissues that makeup periodontium, alveolar bone has interesting conformation changes sensitive to external forces. At times, such bone sensitivity allows rearrangement of the tooth to its proper site. The condition of alveolar bone is clinically significant in many cases, including periodontitis and dental implant surgery. However, the development and underlying genetic characteristics of alveolar bone still require further investigation.Teeth have many mechanical stressors from a variety of external forces, including mastication, tooth brushing, and injury.1 Agai Although bone does generally respond to environmental stimuli, alveolar bone is especially sensitive to mechanical forces. Such sensitivity has been taken advantage of in orthodontic procedures. Another difference in responsiveness can be seen in osteoporosis treatment. When treated with osteoporosis drugs, long bone has shown positive results. In contrast, alveolar bone has shown detrimental effects from the same drugs. These different phenomena infer that fundamental differences exist between alveolar bone and long bones.To understand the genetic and molecular makeup of alveolar bone, we selected long bone for our comparison because of its pre‐existing extensive research. When compared, alveolar and long bone showed different responsiveness. Generally, bone is a dynamic tissue that responds to environmental stimuli; for example, reduction of mechanical loading on bone results in bone loss.6 Alth Early development of both alveolar bone and long bone is closely related to EMI. For long bone, the formation of a limb bud emerges from the interaction between apical ectodermal ridge, a specialized epithelium, and mesenchyme, which is derived from lateral plate mesoderm. For alveolar bone, the development of periodontium results from the interaction between dental epithelium and ectomesenchyme—a form of mesenchyme that migrates from the neural crest (NC). Moreover, recent studies have shown the linkage between alveolar bone remodeling and epithelial cell rests of Malassez in adult periodontium. Using these differences in EMI interaction, we attempt to understand the uniqueness of alveolar bone by using developmental differences.Previous studies have suggested that the developmental process of alveolar and long bone is different. Epithelial–mesenchymal interaction (EMI), especially, is known to play an essential role in organ development, including bone.9 EarlWe found distinct genetic profiles of EMI‐related genes, NC‐related genes, and osteogenic genes by investing transcriptome data in mouse primary cultured alveolar bone‐derived cells (mABDCs) and long bone‐derived cells (mLBDCs). The EMI‐related factors produced by dental epithelium showed different effects in mABDCs and mLBDCs. Finally, our results provided evidence that BMP4 regulates bone formation differently in alveolar bone and long bone.All animal experiments followed protocols approved by the Institutional Animal Care and Use Committee of Seoul National University (SNU‐160509‐6). The head and long bone dissected from C57BL/6 male mice were fixed in 4% paraformaldehyde  at 4°C overnight and decalcified in a 10% EDTA  for 2 weeks at room temperature. Embedded tissues were sectioned at a thickness of 5 μm. To evaluate histologic findings, sections were stained with H&E . For histomorphometric analysis, an optical microscope  connected to a computer and charge‐coupled device (CCD) camera  and an adaptor  was used to take images of the samples. Image analysis of new bone and marrow formation was performed with analySIS LS Starter .Alveolar and long bones were collected from 7‐day‐old C57BL/6 mice. After euthanasia, the alveolar, tibia, and femur bones were removed and cells were released from the matrix using 1‐mL digestion medium  at 37°C with shaking. After 5‐min digestion, the first fraction was collected and discarded, and four subsequent fractions were collected every 10 min for 40 min and pooled. Cells were plated in α‐MEM containing 10% FBS (GIBCO) and antibiotic–antimycotic reagents (GIBCO) in 10‐cm dishes. Cells were allowed to proliferate until 80% to 90% confluency, seeded into 6‐cm dishes, and cultured until confluency (day 0 time point). Osteogenic differentiation was induced with 5mM sodium beta‐glycerophosphate (Sigma‐Aldrich) and 50 μg/μL ascorbic acid  in proliferation medium. Differentiation medium was changed every 2 to 3 days. Samples were collected at 0, 4, 7, 10, and 14 days by thoroughly rinsing the wells with PBS, and stored in −80°C.Bsp, Osteocalcin (Oc), Alp, Runx2, Osx, Bmp2, Bmp4, Nfic, Cpne7, Msx1, Msx2, Dlx5, and Dmp1 were synthesized as listed in Supplementary Table T) method.Total RNA was extracted from cells with Tri reagent according to the manufacturer's instructions . Total RNA (3 μg) was reverse transcribed using Superscript IV Reverse Transcriptase  and oligo (dT) primers (Invitrogen). One μL of the RT product was PCR‐amplified using the primer pairs. For real‐time PCR, the specific primers for 5 cells per dish. Osteogenic differentiation was induced after each dish reached confluency. At a certain time point, cells were fixed with 4% PFA overnight at 4°C, and stained with 40mM Alizarin Red S (Sigma‐Aldrich), pH 4.2 for 30 min at room temperature. For the quantification of mineralized matrix in culture, Alizarin Red stain was eluted using 0.5 mL of 5% sodium dodecyl sulfate (Amresco) in 0.5 N HCl solution, with shaking for 30 min; the absorbance of the eluted dye was measured at 405 nm.Cells were seeded into 6‐cm culture dishes at a density of 0.8 × 102 incubator at 37°C in triplicates; the samples were processed for MTT  assay at day 0, 1, 2, and 3. Then 150 μL of 5‐g/L MTT solution were added to each well for 2 hours at 37°C. The cells were then lysed in DMSO , and absorbance at 570 nm was determined with a microplate reader.Cells were seeded in a 96‐well plate at 5000 per well in a COTo construct cDNA libraries with the TruSeq RNA library kit, 1 μg of total RNA was used. The protocol consisted of polyA‐selected RNA extraction, RNA fragmentation, random hexamer primed reverse transcription, and 100 nt paired‐end sequencing by Illumina HiSeq2500 . The libraries were quantified using qPCR according to the qPCR Quantification Protocol Guide and qualified using an Agilent Technologies 2100 Bioanalyzer .Mus musculus (mm10) using Tophat v2.0.13. Tophat incorporates the Bowtie v2.2.3 algorithm to perform the alignment and mapping. We used Cufflinks for transcript assembly and abundance estimation. After aligning reads to the genome, Cufflinks v2.2.1 was used to assemble aligned reads into transcripts and to estimate their abundance. The transcript counts in isoform level were calculated, and the relative transcript abundances were measured in FPKM (fragments per kilobase of exon per million fragments mapped) from Cufflinks. Gene‐level expression values were also calculated from the transcript counts. We excluded genes with zeroed FPKM values >1 for total samples. We added 1 with the FPKM value of the filtered gene to facilitate log2 transformation. Filtered data were transformed by logarithm and normalized by the quantile normalization method.We processed reads from the sequencer and aligned them to the  v2.0.13.13 Tophttp://david.abcc.ncifcrf.gov/) to understand biological meanings behind large list of genes.We used a multidimensional scaling method to visualize the similarities among samples. Hierarchical clustering analysis also was performed, using complete linkage and Euclidean distance as a measure of similarity to display the expression patterns of differentially expressed genes (DEG) that are satisfied with |fold change| ≥ 2. Biologically gene functional annotation analysis for the DEG list was performed using the DAVID tool (of genes.16https://www.r-project.org/).All data analyses and visualizations of differentially expressed genes were conducted using R . Proteins (20 μg) were resolved using 10% polyacrylamide gel electrophoresis and transferred to a polyvinylidene fluoride (PVDF) membrane . The PVDF membrane was blocked with Tris‐buffered saline Tween‐20 , and 0.1% Tween‐20; Amresco) buffer containing 5% nonfat dry milk  for 1 hour at room temperature. The blots were then washed and incubated with the indicated antibodies for overnight at 4°C with gentle shaking. Affinity‐purified rabbit polyclonal anti‐CPNE7, anti‐NFIC, and anti‐BSP  antibodies were produced as described previously. The anti‐ALP (sc‐30203), anti‐RUNX2 (sc‐10758), and anti‐GAPDH (sc‐25778) antibodies were purchased from Santa Cruz Biotechnology . The anti‐OC (ab223692), anti‐BMP2 (ab14933), anti‐BMP4 (ab39973), anti‐MSX1 (ab174207), anti‐MSX2 (ab223692), and anti‐DLX5 (ab64827) antibodies were purchased from Abcam . The anti‐OSX (PA5‐40509) was purchased from Invitrogen. Blots were washed three times for 10 min each in TBST, followed by incubation with anti‐rabbit or anti‐mouse immunoglobulin G conjugated to horseradish peroxidase (Santa Cruz Biotechnology) in TBST for 1 hour at room temperature. After washing three times in TBST, the blots were analyzed using an enhanced chemiluminescence reagent  according to the manufacturer's guidelines. Protein loading was assessed by the expression of GAPDH.The whole‐cell lysates of cells were harvested using a lysis buffer consisting of 50mM Tris–HCl, pH 7.4, 150mM NaCl, 1% Nonidet P‐40, 1mM EDTA, and 1mM phenylmethylsulfonylfluoride supplemented with protease inhibitors . Following centrifugation at 13,000eviously.18 The6) were mixed with 100‐mg hydroxyapatite/tricalcium phosphate (HA/TCP) ceramic powder  alone or with BMP4  in a 0.5% fibrin gel, and then transplanted s.c. into immunocompromised mice  for 6 and 12 weeks.The primary cultured mouse bone‐derived cells (1 × 10 at a dilution of 1:100 as the primary antibody and a biotin‐labeled goat anti‐rabbit IgG (Vector Labs) as the secondary antibody. Tartrate‐resistant acid phosphatase staining was performed. The total mineralized area among the regenerated bone‐ and marrow‐like tissue was analyzed using the LS starter program (Olympus Soft Imaging Solutions).For histomorphometric analysis of newly formed mineralized tissue, samples were harvested and fixed in 4% PFA, decalcified in 10% EDTA (pH 7.4), embedded in paraffin, and stained with H&E, Masson's trichrome , or processed for immunohistochemistry. For immunohistochemistry, proteins were detected with anti‐BSP17 at t test was used for comparison between two groups. The two‐way ANOVA was used for comparison of more than three groups. Differences were considered statistically significant at *p < 0.05 and **p < 0.005.All values are expressed as mean ± SD of at least three independent experiments. The Student's A). At the bud‐to‐cap transition, E14, mineralized bone was discovered around the site where basal bone usually forms. When enamel organ begins to form cervical loops, dental follicle near the cervical loop started to form alveolar bone . At the cap‐to‐bell stage, E16 and E19, alveolar bone grew more actively near the developing tooth. Incisors began to be seen beneath the bone that covers the molar root . At the hard tissue formation, postnatal day 7 (PN7), molars and incisors were almost fully covered by alveolar bone. Specifically, when the distance between outer and inner dental epithelium became closer, the molar‐region alveolar bone almost fully covered the crown region . At the root formation stage, PN21, the majority of the bones were well‐matured, while small amounts of dental epithelial‐derived tissues remained in the periodontal ligaments .Prior to comparing long and alveolar bone, alveolar bone development stages were explored throughout the tooth development stages of the molar. At the bud stage, embryonic day 12 (E12), multiple mesenchymal stem cells beneath the tooth bud were condensed to form basal bone . At E16 and E19, diaphysis parts transformed to bone tissue . At PN7, bone and marrow cavity became distinguishable . At PN21, cortical bone and marrow cavity were well‐matured . These data elucidate that alveolar bone development begins at the bud‐to‐cap stage and is most actively formed at the hard tissue formation. Simultaneously, long bone was discovered to actively form at the same stage. During the development, epithelial tissue was located more closely and for a longer duration to alveolar bone than to long bone.Long bone was analyzed with the femur, specifically the diaphysis region. At E12 and E14, most of the hind limbs were formed with cartilage . mABDCs and mLBDCs were explanted from each of the bone pieces; their morphology looked similar . Proliferative activity was similar in both bone‐derived cells . To confirm their osteoblastic characteristic, we compared both bone‐derived cells with nonosteogenic cells, specifically mouse primary dermal fibroblasts (mDFs) from the ventral skin. Bsp was highly expressed in both bone‐derived cells when compared with mDFs. Both bone‐derived cells manifested mineralized nodule formation after osteogenic differentiation, but not in mDFs . These results indicate that mABDCs and mLBDCs have osteoblastic characteristics and similar proliferative activity and morphology.To analyze the characteristics of each bone cell in vitro, we harvested bone cells from each tissue. Alveolar bone‐ and long bone‐derived cells were isolated at the hard tissue formation stage, when both bone formations were most active  under four different conditions: mABDC‐only, mLBDC‐only, mABDC with rBMP4, and mLBDC with rBMP4. Six weeks after transplantation, bone‐like tissues were formed at the periphery of HA/TCP particles only in BMP4‐treated groups. The rBMP4‐treated mABDC group exhibited more bone‐like tissues than the rBMP4‐treated mLBDC group. Contrastingly, marrow‐like tissue including adipose tissue presented an opposite response. This phenomenon was more prominent after 12 weeks Fig. A,B. Pro Previous studies mention the beginning stages of mouse alveolar bone formation. However, the development stages of alveolar bone are not defined in detail. For better analysis, the stages were outlined using the tooth‐development stages as a guide. Along with these stages, to compare long to alveolar bone, this study performed a histological analysis. During this analysis, we observed that epithelial tissue was located more closely and for a longer duration to developing alveolar bone than to long bone. From these observations, we infer that alveolar bone formation could be associated with dental epithelium conformational changes. Overall, alveolar bone development could be more related to epithelial tissue than to long bone development.Alveolar bone is a component of the periodontium tissue, which is formed by the ectomesenchyme‐derived dental follicle cell. Some studies reveal that the interaction of ectomesenchyme and dental epithelium is essential during periodontium development, the same goes for tooth development.2 Prev Most craniofacial bones are formed via intramembranous ossification. Long bone is created by endochondral ossification. However, when perichondrium transforms to periosteum, osteoblasts form new bone beneath the perichondrium with intramembranous ossification at the diaphysis region. By selecting a similarly intramembranous ossified bone, we limited the confounding factors that could arise from comparing differently ossified bone.To exclude as many confounding factors as possible, we harvested mLBDCs from the diaphysis region of the long bone for comparison. There are two kinds of ossification during bone development: intramembranous and endochondral.25 MosMsx family—are abundant in mesenchymal NC cells in pharyngeal arches and craniofacial skeleton. When mutated, mouse exhibited an absence and/or malformation of alveolar bone. Another NC‐related gene, Dlx family genes, also plays a significant role in embryonic development and is seen expressed in cranial NC‐derived craniofacial primordia and limbs. The lack of functional Dlx5 in mice results in dysmorphogenesis in almost all cranial bones, including incisive and molar alveolar bone. However, for limbs, single homozygous mutants of Msx1, Msx2, and Dlx5 do not display gross abnormalities. As hinted, NC‐related are known as regulators of EMI during orofacial and limb developments. NC‐related genes were more highly expressed in alveolar bone tissue and cells than in long bone. Also, their expressions were higher in mABDCs than in mLBDCs during differentiation. These results indicate that an abundant expression of NC‐related genes could be a marker for alveolar bone. We theorized that EMI would affect alveolar bone more than long bone because EMI is strongly regulated from the abundant expression of NC‐related genes.To compare alveolar bone to long bone, NC‐ and EMI‐related genes in primary cultured bones between alveolar bone and long bone were analyzed. Primary cultured bone cells showed different characteristics between alveolar bone and long bone in NC‐ and EMI‐related gene expression. Homeobox genes—skeleton.28 WheBmp2, Bmp4, Nfic, or Cpne7 individually in either alveolar or long bone. In this study, we looked at these individual relationships as a group and attempted to understand how these relationships fit in the bigger picture of how correlated or different alveolar and long bone are. Bmp was selected for our analysis because Bmp signaling is crucial for regulating EMI during organogenesis—organs formed include tooth and limb. Specifically, Bmp2 and Bmp4 are frequently codistributed, and their expression shifts between epithelium and mesenchyme. Along with Bmp, Nfic was also observed because a previous study defined it as a component of EMI in ectomesenchyme.Nfic KO mice showed defective alveolar bone formation and osteoporosis like phenotype in long bone. Finally, Cpne7 was explored between the two because it is secreted from dental epithelium, and its effects on odontogenic induction to dental mesenchyme was confirmed in a previous study. Although previous studies merely confirmed the existence of EMI in alveolar and long bone, this study illustrated distinct EMI‐related gene expression patterns in alveolar bone and long bone. Upon seeing the expression patterns, we inferred that the regulation of EMI in alveolar bone and long bone could be different.Previous studies have explored EMI and and limb.36 Spe This discrepancy is thought to result from the different bone cell harvesting method. The other study used primary bone marrow cells, whereas we used primary cells from the bone surface.Along with the differences seen in NC‐ and EMI‐related genes, a difference in mineralization capacity was seen between long and alveolar bone. Mineralization capacity was more elevated in mLBDCs than in mABDCs. In contrast, their morphology and proliferation rate were not significantly different. These results aligned with osteogenic gene expression patterns in mLBDCs through RNA‐seq. Interestingly, a different study showed a more elevated mineralization capacity in mABDCs than in mLBDCs.42 ThiAs mentioned above, although initially inferred, alveolar and long bone showed distinct genetic profiles. Specifically, osteogenic, EMI‐, and NC‐related genes were different in mABDCs and mLBDCs. To confirm these results, RNA sequencing was used, and the difference between a mABDC's and a mLBDC's EMI‐ and NC‐related gene expression was confirmed. Along with the differences seen during standard conditions, EMI‐ and NC‐related gene expression patterns were also different during osteogenic differentiation. Considering how most NC‐related genes are known as transcription factors that regulate osteogenic gene expression, these results hint that different regulation of EMI‐ and NC‐related genes take part in altering bone characteristics to result either in alveolar or long bone.43 To compare the effects, we analyzed gene expression in BMP4‐ and CPNE7‐treated mABDCs and mLBDCs. To recap, despite their correlation as EMI‐related genes, MSX1, MSX2, DLX5, and NFIC were not treated because they are transcription factors inferred to not be secreted from dental epithelial cells.To further compare alveolar and long bone, their osteogenic, NC‐related, and EMI‐related gene responsiveness to developing dental epithelial secretions were compared. Their responsiveness to developing dental epithelial secretions was selected because developing dental epithelial secretions induce differentiation of mesenchyme cells through gene regulation.44 To OC was not regulated. Relatively to mLBDCs, Bsp, Alp, and Osx were more elevated in mABDCs. During differentiation, most osteogenic genes were downregulated in BMP4‐treated mLBDCs. Bsp increased in both mABDCs and mLBDCs. The increment of increase in mABDCs after the BMP4 treatment was greater than the magnitude of increase in mLBDCs after the treatment. In the in vivo model—contrary to the cell‐only group—the BMP4‐treated mABDC group formed more BSP‐positive bonelike tissue than BMP4‐treated mLBDCs. Another osteogenic gene and osteocyte marker, Dmp1, was upregulated in BMP4‐treated mABDCs at the late stages of differentiation. In mLBDCs, Dmp1 was downregulated in mLBDCs during the entire differentiation stage. In the in vivo study, Masson's trichrome‐stained tissue showed a more mature bonelike matrix in the BMP4‐treated mABDC group than the BMP4‐treated mLBDC group. Overall, our findings indicate that BMP4 could accelerate the maturation of alveolar bone by stimulating Dmp1; this effect was not seen in long bone. Our results show that BMP4 induces more bone formation and maturation in alveolar bone than in long bone by upregulating osteogenic genes.Initially, osteogenic gene responsiveness to developing dental epithelial secretions was compared between alveolar and long bone. Between BMP4 and CPNE7, BMP4‐treated cells illustrated a stronger regulation of osteogenic genes. BMP4 elevated mRNA expression of osteogenic genes in mABDCs. However, for BMP4‐treated mLBDCs, Msx2 and Dlx5, in both cells. Msx2 gene expression, expressed in mandibular alveolar bone during skeletal growth, was elevated in response to BMP4 during osteogenic differentiation. Previous studies show Msx2 inducing Alp activity and inhibiting adipogenesis by diminishing the expression of Pparγ. Again, when treated with BMP4, Dlx5 was upregulated in mABDCs during the early stages of differentiation, whereas for mLBDCs, Dlx5 was downregulated during the entire differentiation stage. There was no change in the other NC‐related gene, Msx1. In the in vivo model, volume of marrow‐like tissue including adipose tissue was smaller in BMP4‐treated mABDCs than in BMP4‐treated mLBDCs. BMP4 regulates alveolar bone formation in Msx1 KO mouse via inducing Dlx5 and Runx2 expression. These results suggest that BMP4 induces alveolar bone‐specific bone formation by regulating Msx2 and Dlx5 expression. Furthermore, although previous studies show that Msx1 has an effect on BMP4 in alveolar bone, our results show that BMP4 does not affect Msx1, inferring a downstream relationship. Finally, our results show that elevated levels of Msx2 could also affect the proportion of marrow tissue in bone.Following osteogenic gene comparison, the NC‐related gene responsiveness to developing dental epithelial secretions was compared. BMP4, along with the osteogenic genes, was seen to upregulate NC‐related genes, ntiation.29 PreCpne7, Bmp2, and Bmp4. For CPNE7, we observed that CPNE7 did not regulate any genes in mABDCs and mLBDCs. Cpne7 was observed to be downregulated by BMP4 in both bone‐derived cells during differentiation. These observations infer that Cpne7 may have a distinct function in odontogenic cells and osteogenic cells. Bmp2 is the most well‐known bone‐inducing factor. Interestingly, BMP4 induced Bmp2 expression in both cells. The rate of increase of Bmp2 induced by BMP4 was higher in mABDCs than in mLBDCs during differentiation. Bmp4 expression was downregulated in both BMP4‐treated cells. Based on these results, we inferred the possibility that BMP4 induced bone formation effects resulting from BMP4 upregulating Bmp2, which then affects bone formation. Furthermore, we speculated that the effects of BMP4 could be controlled by negative feedback.Finally, for the comparison, EMI‐related gene responsiveness to developing dental epithelial secretions was observed. The responsiveness that was immediately noticed was from Bsp was higher in mABDCs than in mLBDCs during differentiation. Regarding the NC‐related gene responsiveness, we saw that BMP4 controlled Msx2 and Dlx5 in mesenchyme cells. Although the origin of BMP4 (epithelium or mesenchyme) is unknown, BMP4 seems to be specifically significant in mABDC osteogenic growth, considering how there was a drastic change in EMI‐related gene responsiveness. Overall, once again, EMI‐related gene responsiveness showed that BMP4 could play a significant role in mABDCs by having a steeper increase of BMP4‐induced Bmp2 in mABDCs than in mLBDCs during differentiation. Our findings line‐up with a previous study that reveals that Bmp4 knockout in epithelial cells had a more severe effect on alveolar bone than the knockout in mesenchyme cells. When the damage was compared between alveolar and long bone, the study revealed that damage in alveolar bone was more severe—at times, long bone showed no defect.While observing the differences between alveolar and long bone, we discovered epithelium and BMP4 potentially affecting alveolar bone. To confirm the previous data regarding NC‐ and EMI‐related gene differences between the two bones, RNA sequencing was performed. When the sequencing was executed, significant differences between the two bones were observed as the following: BMP pathways, BMP responsiveness, odontogenesis, epithelium, and mesenchyme. When the osteogenic gene responsiveness was observed, we saw that BMP4 elevated the mRNA expression of osteogenic genes in mABDCs. Furthermore, when the cell was BMP4 treated, we observed that the rate of increase of me cells.47 WheIn summary, we elucidated the different genetic profiles of alveolar bone and long bone. Additionally, EMI‐related epithelial factor, BMP4, was seen to affect alveolar bone formation more than long bone by regulating the expression of osteogenic, EMI‐, and NC‐related genes. By detailing the differences between alveolar and long bone, the present study emphasizes the need for tissue‐specific bone treatment. This can be seen from the different gene responses for BMP4 in long and alveolar bone. More specifically, based on our results, this study highlights the need of a distinct treatment specifically for alveolar bone, considering how distinctly different the bone is.The authors have no conflicts of interest to declare.https://publons.com/publon/10.1002/jbm4.10382.The peer review history for this article is available at Fig. S1 Alveolar bone and long bone development during mouse tooth development stage.  Embryonic day 12 (bud stage).  Embryonic day 14 (bud‐to‐cap transition).  Embryonic day 16  & 1st molar, early bell stage (D)).  Embryonic day 19 (late bell stage).  Postnatal day 7 (hard tissue formation).  Postnatal 21 day (root formation). (A‐G) The region covered by the red dashed line: alveolar bone. The region covered by the black dashed line: basal bone. (H‐M) The region covered by the red dashed line: bone. The region covered by the black dashed line: cartilage. E, embryonic; PN, postnatal.Click here for additional data file.Fig. S2 Characteristics of mouse alveolar bone and long bone derived cells. (A‐D) Morphology of cells derived from alveolar bone and long bone.  Primary cultured cells isolated from alveolar bone pieces and passage 1 cells.  Primary cultured cells isolated from long bone pieces and passage 1 cells. Cells were observed under an optical microscope (X 100). (E) MTT assay was analyzed in both bone derived cells for 3 days. (F) Expression levels of osteoblast marker gene, Bsp, obtained by real‐time PCR from cDNA of mABDC, mLBDC and mDF. Real‐time PCR values are normalized to the internal housekeeping gene, Gapdh. (G) Alizarin Red S staining (ARS). Cells were cultured in osteogenic induction media for 10 and 20 days.Click here for additional data file.Fig. S3 Functional analysis of RNA sequencing data. (A‐F) Bar plot of gene‐enrichment and functional annotation analysis using gene ontology. (A‐D) Terms of biological process category. (A) Development and morphogenesis‐related terms (B) Gene expression and signaling‐related terms. (C) Responsiveness‐related terms. (D) Ossification, differentiation, remodeling, and mineralization‐related terms. (E) Terms of molecular function category. (F) Terms of cellular component category. (G) Top 20 terms in enrichment test of KEGG pathway analysis.Click here for additional data file.Fig. S4 Relative mRNA expression of CNC‐related genes. Relative expression of genes expressed in craniofacial NC cells, NC‐derived craniofacial/ pharyngeal arch mesenchyme, craniofacial skeleton, limb bud mesenchyme. Those genes are known as markers of neural crest stem cell, NC progenitor cell, and craniofacial neural crest cell. All statistical analysis performed by Student t‐test, n = 3, *p < 0.05, **p < 0.005.Click here for additional data file.Fig. S5 Histological analysis of the osteoclast using mABDC and mLBDC in vivo. Samples were stained with TRAP (Tartrate‐resistant acid phosphatase) (A‐D) mLBDC. (E‐H) mABDC.Click here for additional data file.Table S1 Primers used for real‐time PCR.Click here for additional data file.


However, several medications are being given off‐label to patients based on anecdotal reports and uncontrolled studies. Among these, the combination of Chloroquine (CHQ)/Hydroxychloroquine (H‐CHQ) and Azithromycin has garnered the maximum attention, owing partially to the support from the political leaders around the globe. This attention has led to significantly increased use of CHQ/H‐CHQ both inside and outside the hospital.With a busy clinician in mind, we will review and quantify the risk of QTc prolongation, arrhythmias, and torsades de pointes when using the combination of CHQ or H‐CHQ with Azithromycin. CHQ, H‐CHQ, and Azithromycin are known to produce QTc prolongation, torsade de pointes, and sudden cardiac death.P = .574), type of antimalarial drug (P = .452) and duration of drug use (P = .09).Rey et al in 2003, studied 46 rheumatology patients (42 using CHQ and 4 using H‐CHQ). 17.39% of patients developed a prolonged QTc interval. Such abnormalities were not linked to dose  in the dose range 500 mg (10 ms) to 1500 mg (14 ms).A randomized placebo‐controlled parallel trial conducted in 2013 enrolled 116 healthy controls getting 1000 mg of chloroquine alone or in combination with escalating doses of Azithromycin . There was a definite correlation between azithromycin dose and QTc interval increase  as well as various antiarrhythmic, antidepressant, and antipsychotic drugs.A parallel, randomized, double‐masked, phase IIb clinical trial from Brazil in April 2020 compared patients diagnosed with Covid‐19 being put on high dose CQ  or low‐dosage CQ . 18.9% in the high dose group and 11.1% of the low dose group had prolonged QTc from normal to greater than 500 ms. There were no instances of ventricular tachycardia or torsade de pointes.Actively ask all patients about potential off label use of H‐CHQ and azithromycin or any other Covid‐19 medication that they potentially might have taken. This precaution is especially true for psychiatrists, and psychiatric practitioners as many psychotropic agents can potentially prolong the QTc interval.Get regular EKG’s to monitor QTc's along with regular clinical monitoring and discuss emergency precautions in depth. Refer to a cardiologist as needed. Encourage the use of FDA approved mobile device like KardiaMobile to get the regular EKG’s if seeing the patient regularly is not possible.Avoid other QTc prolonging medications to reduce the cumulative risk.CHQ and H‐CHQ have long half‐lives ranging from 30 to 60 days, and so it can stay in the system for a while even after stopping it. Patients need to be aware of that.Educate all patients on the current data about CHQ/H‐CHQ and Azithromycin so that they are well informed about the risks and benefits associated with it.CHQ and H‐CHQ remain pivotal drugs in the treatment of Malaria and other rheumatological disorders in many places in the world. The off label drug use of this drug can create significant drug shortages for patients who need these drugs for rheumatological reasons.Warn about CHQ/H‐CHQ induced hemolytic anemia in patients with G6PD deficiency, the potential of causing hypoglycemia in diabetic patients, and of the higher risk of QTc prolongation in patients with renal insufficiency or failure.The administration of H‐CHQ in the current setting of the COVID‐19 crisis is experimental. There are small trials that are already underway, but until we get more robust research, the use of H‐CHQ with or without Azithromycin is likely to be widespread. In the current review, we found that the risk of QTc prolongation when using CHQ/H‐CHQ alone or in combination with Azithromycin is between 10 and 40 ms for most cases in 11%–33% of the cases. The use of Azithromycin amplifies the risk of QTc prolongation. The critical increase from a normal QTc level to beyond 500 ms is rare in most studies unless there are additional risk factors. There were only sporadic instances of any arrhythmias or torsade de pointes, but since these patients had many underlying medical issues, a cause‐effect relationship cannot be established, and CHQ/H‐CHQ plus Azithromycin cannot be labeled as the main cause. In an ICU and in‐patient setting, intensive telemetry monitoring will help in possibly preventing torsades de pointes and sudden cardiac death. However, in an outpatient setting, we recommend that all clinicians, specifically psychiatrists and psychiatry practitioners, need to be aware of the risk of QTc prolongation associated with this medication so that they can advice their patients on the use of this medication in a better manner. The FDA has come out with a warning in April 2020, where it cautioned against the use of CHQ or H‐CHQ for Covid‐19 outside of the hospital setting or a clinical trial due to risk of heart rhythm problems.Off‐label use of CHQ, H‐CHQ, and Azithromycin has significantly increased in this pandemic in an attempt to reduce mortality and morbidity. The significant risk of QTc prolongation remains a serious concern. Clinicians need to take extra precautions in outpatient settings where cardiac monitoring might not be readily available.1Center for Drug Evaluation and Research, FDA. FDA cautions use of hydroxychloroquine/chloroquine for COVID‐19. 2020 . Available from https://www.fda.gov/drugs/drug‐safety‐and‐availability/fda‐cautions‐against‐use‐hydroxychloroquine‐or‐chloroquine‐covid‐19‐outside‐hospital‐setting‐or2ReyLD, BerneckA, GonçalvesL, SilvaMB, SkareTL, SilvaJA. Prolongamento do intervalo QT do eletrocardiograma em pacientes reumáticos usando antimaláricos. Rev Bras Reumatol Engl Ed. 2003;43(5):275–8.3Pfizer Labs. Zithromax (azithromycin tablets) and azithromycin for oral suspension. 2013 . Available from http://www.accessdata.fda.gov/drugsatfda_docs/label/2013/050710s039,050711s036,050784s023lbl.pdf4Multicenter Collaboration Group of Department of Science and Technology of Guangdong Province and Health Commission of Guangdong Province for Chloroquine in the Treatment of Novel Coronavirus Pneumonia. Expert consensus on chloroquine phosphate for the treatment of novel coronavirus pneumonia. Zhonghua Jie He He Hu Xi Za Zhi. 2020;43:E019–E29.320753655BorbaMGS, ValFFA, SampaioVS, AlexandreMAA, MeloGC, BritoM, et al. Effect of high vs low doses of chloroquine diphosphate as adjunctive therapy for patients hospitalized with severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection: a randomized clinical trial. JAMA Netw Open. 2020;3(4):e208857.


The main aim of this study was to evaluate if the binge eating disorders (BEDs) related to obesity were associated with the severity of non‐alcoholic fatty liver disease (NAFLD).n = 183), between 2011 and 2015, also responded to a Beck Depression Inventory, Hospital Anxiety and Depression Scale, and a Fatigue Impact Scale autoquestionnaire. A control group of 29 healthy people matched by age and gender was included.Severely obese patients who had been referred for bariatric surgery were included in this study at the Nice University Hospital. All patients underwent a liver biopsy at the time of surgery. Between 2008 and 2015, 388 patients had an assessable Bulimia Test (BULIT) self‐questionnaire at the time of surgery. A subgroup , 14 patients had a “probable diagnosis” of BED, and 47 patients had a “high risk” of developing a BED according to the BULIT. Obese patients had significantly more severe BED, depression, anxiety, and fatigue compared to controls. Steatosis, non‐alcoholic steatohepatitis, or fibrosis was not associated with BED. Similarly, the severity of NAFLD was not associated with depression, anxiety, or fatigue.Among the 388 obese patients , are not associated with the severity of liver pathology in severely obese patients. In 2013, the global prevalence of overweight and obese individuals was 36.9% for men and 38% for women,Severe and morbid obesity are associated with the development of non‐alcoholic fatty liver disease (NAFLD).Between September 2008 and December 2015, 388 severely obese patients referred for bariatric surgery in our hospital completed one self‐report to screen for BED. Patients with overt clinical bulimia nervosa, diagnosed by two specialist psychiatrists , were contraindicated for surgery as recommended by French guidelines and were not included in our cohort.et al.Clinicobiological preoperative assessment was performed 2 or 3 weeks before surgery. A systematic review of all the prescribed treatments in the last 6 months was conducted, particularly for anxiolytics, antidepressant therapy, and sleeping pills. No information about job status and education level was obtained from patients. Metabolic syndrome was defined according to Alberti Severely obese patients completed self‐screening questionnaires for BED, depression, anxiety, and fatigue 2–3 weeks prior to surgery, and an exploratory group completed these same questionnaires again 1 year after surgery. The revised Bulimia Test (BULIT) is a validated self‐report for the screening of BEDs, translated to French.From 1 July 2011, three other self‐reports were added to evaluate depression , anxiety [HAD], and fatigue . The BECK is a 13‐item self‐report used for the measurement of deep depression. A score of 0–4 means no depression, 5–7 “mild depression,” 8–15 “moderate depression,” and >15 “severe depression.”The severity of fatigue was measured using the French version of the FIS.Healthy persons working in the scientific institute of our center, matched for age and gender to the patients, without a background of psychological disorders, and without anti‐depressive or anxiolytic drug use, filled out the four self‐reports in July 2016. Written informed consent was obtained from all subjects.t‐test as required. The Chi‐square test was used to compare qualitative values. All calculations were made using NCSS 2012 .Quantitative variables are presented as the median and 25–75th interquartile range. Quantitative values were compared using the Mann–Whitney test or the Student's 2, female gender 81%) were included in our study. Patients with symptomatic BED were contraindicated by a psychiatrist and represented 4% of all the patients (n = 1735) referred for bariatric surgery at our hospital during the period between September 2008 and December 2015. Among the patients included in our study, 196 patients (51%) had metabolic syndrome, and 64 (16%) had type 2 diabetes mellitus. Among the 388 patients, 338 (87%) patients had NAFLD, and 71 (18%) had NASH. Significant fibrosis was present in 28% of patients , and they had more men (P = 0.02) and less steatosis (P = 0.0003) than the first 205 patients who filled out only the BULIT.Among the 388 severely obese patients, 183 filled out three more self‐reports to evaluate depression (BECK + HAD‐Depression), anxiety (HAD‐anxiety), and fatigue (FIS). They were comparable in terms of BULIT and NASH but were older , filled out the same self‐reports. They had no background of depression or psychotropic intake. Lean subjects were comparable to the 388 severely obese patients with respect to gender and age.The control group, consisting by 29 healthy lean subjects  but were comparable to the whole cohort in terms of gender and BMI at baseline (Table An exploratory follow‐up group of 15 patients was recruited to fill out the same four self‐reports 12 months after bariatric surgery. These patients were slightly older (48 P < 0.00001 vs patients), and median BULIT score for the control group was 47 [43–57.5]  patients had a “probable diagnosis” (BULIT ≥104) of bulimia nervosa, 47 12%) had a “high risk” (88 ≤ BULIT ≤ 103) of eating disorders, 85 (22%) had a “moderate risk” (73 ≤ BULIT <88) of eating disorders, and 242 (62%) had no pathological test  showed the same results as the entire cohort. No difference was found between women and men.P < 0.00001 vs patients). According to the HAD‐depression, two subjects had “definite depression,” 3 had “possible depression,” and 24 had a normal test result (P < 0.00001 vs patients).According to BECK, 14 (8%) patients had “severe depression,” 48 26%) had “moderate depression,” 37 (20%) had “mild depression,” and 84 (46%) had no depression  had a diagnosis of anxiety  had anxiety, 35 (19%) had “possible anxiety,” and 127 (69.5%) had a normal test  for the control group , but there was no influence of an anxiolytic or sedative drug.The median FIS was 31 [14–70] for obese patients and 10 3.5–23.5] , HAD‐depression , HAD‐anxiety , and FIS . Moreover, patients with more severe BED (BULIT ≥ 104) had more depression , more anxiety (P = 0.028), and more fatigue (P = 0.027) than patients with BULIT < 104. Similar results were found with different risks of BED (BULIT ≥ 88 or BULIT ≥ 73)  .One year after bariatric surgery, among the exploratory follow‐up group of 15 patients, only 1 patient presented moderate risk of eating disorders, 1 patient had mild depression according to BECK, 1 patient had definite depression, and 2 had potential depression according to HAD. The patient with mild depression according to BECK was the same patient with definite depression according to the HAD. No patient had a diagnosis of anxiety.Here, we report that the prevalence of BED (BULIT ≥ 73) was 38% in a well‐characterized cohort of 388 severely obese patients who had been referred for bariatric surgery.Our cohort includes a majority of women, which is representative of European cohorts of morbidly obese patients.vs no NAFLD, presence of NASH vs no NASH, fibrosis F2–F4 vs F0–F1, fibrosis F3–F4 vs F0–F2) of patients with BED were not more severe than patients without BED. A subgroup of 183 patients underwent further psychological assessment for depression, anxiety, and fatigue. Among these patients, 25–34% had significant depression, and 28% were anxious. A positive correlation between the severity of BED and the severity of depression, anxiety, and fatigue was found. We did not find any association between depression, anxiety, or fatigue and the liver pathology severity . All results were similar between women and men. Compared to healthy persons, severely obese patients had significantly more BED and were more depressed, more anxious, and more tired.Contrary to our hypothesis, the NAFLD features . The differences between their results and our own could be explained because their cohort differed from ours as they included morbidly obese patients (mean BMI 48.3 ± 7.7 kg/m2) with suspected NAFLD and also obese patients (mean BMI 35.6 ± 7.6 kg/m2) who had a clinical indication for percutaneous liver biopsy as determined by one of the attending hepatologists. No information was given about the number of patients in each group. Possibly due to this, their patients were more severely affected than ours: 14% versus 3.9% of F3‐F4 fibrosis and 36% versus 16% with a type 2 diabetes, respectively. Very interestingly, in the subgroup analysis of morbidly obese patients, no significant association was found with liver damage, which is similar to our study.et al. showed that fatigue, using FIS, was increased in 156 biopsy‐proven NAFLD patients with a mean BMI of 32 ± 6 kg/m2 compared to healthy subjects. Consistent with our study, they showed that FIS was not associated with the severity of histological features .A recent meta‐analysis estimated that the prevalence of depression was 19% among patients undergoing bariatric surgery.Some limitations in our study must be noted. First, patients with clinical bulimia nervosa were contraindicated for bariatric surgery and were not included in our study. A control positive group of severely obese bulimic patients may have been an interesting comparison. Secondly, the BULIT was validated to diagnose eating disorders according to DSM‐III. The evolution of the DSM (now DSM‐V) and the new subgroup of eating disorders had not been evaluated. Thirdly, our study may have inherent sampling and reporting biases in that only those patients who completed self‐reported questionnaire data were included. Fourthly, although no information about education level and job status was obtained from patients, previous studies on severely obese patients showed no impact of these factor on the occurrence of BED.Our study has several strengths; first, it included 388 patients with a systematic liver biopsy, reviewed by a single pathologist, while the other studies did not have biopsy or were only on a small number of patients. Second, we used psychological tests that have been validated in the literature. BULIT, Beck, and HAD have been used previously in obese populations and are validated for use in severely obese populations.In conclusion, contrary to our hypothesis, mild BED appeared to be more closely associated with obesity than with liver pathology in severely obese patients. However, more studies could be interesting on patients with symptomatic BED and with nonsevere obese NAFLD.AppendixS1 Supplementary methods.Click here for additional data file.Table S1 Comparison of an exploratory follow‐up cohort of 15 patients versus the patients at baseline.Table S2 Comparison of BECK, HAD‐Depression, HAD‐Anxiety, and FIS according to the severity of BULIT.Click here for additional data file.


Previous research has suggested that worry is negatively associated with working memory performance. However, it is unclear whether these findings would replicate across different worry levels and in individuals with anxiety and depressive disorders .n = 36; clinical group with another anxiety or mood disorders: n = 33; subclinical group: n = 27; control group: n = 42). Trait worry levels were collected from all of the participants. Working memory performance was measured as accuracy and reaction time.One-hundred-thirty-eight participants performed a two-block working memory task . Based on participants` current clinical status, four groups were considered (generalised anxiety disorder group: During the first block, higher worry scores were significantly associated with longer reaction times. Moreover, the generalised anxiety disorder group, clinical group, and subclinical groups demonstrated significantly longer reaction times compared to the control group in Block 1, when age was controlled for. From Block 1 to Block 2, all of the participants demonstrated a significant decrease in accuracy and reaction time, regardless of worry level or clinical status.The results indicate that higher worry levels negatively impact WM processing efficiency. Moreover, when age was controlled for, we found participants` clinical status to be linked with WM impairments. The results highlight the relevance of investigating the impact of different worry levels on cognitive processes across clinical and non-clinical populations. Worry is commonly experienced by a vast majority of people, including healthy children and adults of all age groups, as well as those suffering from anxiety and mood disorders . In its Moreover, the high comorbidity rates among anxiety and depressive disorders point to shared underlying cognitive, emotional and behavioural processes across specific disorders . In lineA growing amount of studies suggests that worry binds cognitive resources, particularly in working memory . The WMRecent evidence provides support for the basic assumptions of ACT , 14. A nTo date, only few studies investigated WM performance in clinical patients showing elevated worry levels , 20. WheThe presented literature is diverse and studies differ to a great extent in regard to the WM measures, sample population, and WM task duration. For example, although a range of different tasks exist to measure WM capacity (e.g. ), the tablock denotes a complete WM task that is usually undertaken by participants as a single task [The present study investigates how worry and clinical status influence WM performance. Participants performed an extended WM task consisting of two WM blocks (Block 1 and Block 2) lasting in total around 30 min. In this study, gle task . WM perfAccording to ACT , we expeWe expect individuals the GAD, clinical and subclinical groups to show similar accuracy, but longer reaction times in WM Block 1, compared to the control group .In general, based on studies investigating learning effects in WM tasks , we expeV = 0.29 indicated by the Pillais-Bartlett-Trace  for cognitive-behavioural therapy for GAD patients . This stoftware, ).M = 27.7, SD = 7.39). The sample was comprised of RCT participants and a control group composed of students from the University of Zurich. In the RCT sample, the Structured Clinical Interview for DSM-IV , with age ranging from 19 to 61 years  was apn = 36), a clinical group (n = 33) and a subclinical group (n = 27). The fourth group was the control group (n = 42). The four groups did not differ in sex, nationality or socio-economic status  = 3.23, p = .01) as indicated by the Kruskal- Wallis test and the Games-Howell correction. Eta square was calculated as a measure of effect size and indicates a moderate effect η2 = 0.081.Based on the current clinical status, three groups were formed: A GAD group , followed by the clinical group  and the subclinical group . The control group reported the lowest worry scores . The Kruskal-Wallis test and the Games-Howell correction revealed significant group differences in worry scores (H [p > .001) and effect size calculations indicated a large effect measured by eta square (η2 = 0.54). The control group reported significantly less worry than the GAD group , the clinical group  and the subclinical group . Further, the subclinical group reported significantly lower worry scores than the GAD group . These results confirm the grouping of the GAD, clinical, subclinical and control individuals. Furthermore, PSWQ scores were comparable to other studies using clinical groups and control groups [Worry at intake, assessed with the Penn State Worry Questionnaire , was hil groups .Participants performed two WM blocks Block 1 and 2) and rated their current worry level at three time points .The Penn State Worry Questionnaire  is a 16-item self-reported questionnaire assessing pathological worry. Total scores in the present study ranged from 23 to 77, with higher scores representing greater worry. Internal consistency in the present study was excellent . Self-administrated measures for subjective phenomena have been proven useful in many fields .The “Memory Updating task”  was usedThe two blocks were alternate versions of the same updating task. Thus, the general structure of the blocks was identical  but the presented numbers and arithmetic operations were randomized for each block in order to avoid a possible learning effect. The WM task was self-paced and participants needed on average 15 min per WM block.The main goal of the current study was to examine how worry and the clinical status influence WM accuracy and reaction time in Block 1 as well as the change from Block 1 to Block 2 (predictor by Block interaction). A multivariate multilevel modelling approach was used to simultaneously analyse the two outcome variables (accuracy and reaction time) as well as the nested data structure . The equations for the multilevel model as well as detailed information on data preparation can be found in the Additional file SD = 11.4) in Block 1 which decreased to 81.8 (SD = 9.4) in Block 2. In addition, mean reaction time across all participants decreased from 2581 ms (SD = 652 ms) in Block 1 to 2451 ms (SD = 611) in Block 2  = 3.31, p = .002; clinical: t (134) = 6.02, p > .000; GAD: t (134) = 4.67, p > .000). Further, in the control group, worry decreased significantly from T1 to T2 (t (268) = − 2.4, p = .016) and from T1 to T3 (t (268) = − 2.7, p = .006). There was no significant Group by Block interaction.Descriptive statistics on the total sample indicate a mean accuracy of 87.6 (t (407) = − 1.3, p = .19; Model 1a). However, higher worry scores were significantly associated with longer reaction times in Block 1 (t (407) = 2.41, p = .01), Fig. t (396) = − 1.43, p = .15). Again, a significant main effect of worry on reaction time was obtained (t (396) = 2.46, p = .01) in Block 1. There was no significant main effect of age on accuracy (t (396) = − 0.66, p = .50) or reaction time (t (396) = −.018, p = .85).In Block 1, there was no significant main effect of worry on accuracy , followed by the control group , the subclinical group  and the GAD group . For reaction time, in Block 1, the control group was the fastest group , followed by the subclinical , and GAD group , with the clinical group being the slowest group . When performing the multivariate multilevel model (Model 2a), there were no significant differences in accuracy and reaction time between the control group and all of the other groups in Block 1. When age was included (Model 2b), accuracy mean scores did not differ between the control group and all of the other groups in Block 1. However, the GAD, clinical and subclinical group showed significantly longer reaction times in Block 1, compared to the control group (GAD: t (380) = 2.15, p = .03; clinical: t (380) = 2.55, p = .01; subclinical: t (380) = 2.08, p = .03). No main effects of age on accuracy (t (380) = 0.92, p = .35) or reaction time (t (380) = − 1.92, p = .055) were obtained.On a descriptive level, mean accuracy in Block 1 was highest in the clinical group (t (407) = − 8.54, p < .000) as well as in reaction time (t (407) = − 4.50, p < .000), regardless of worry levels. There was no significant Block by worry interaction for accuracy or reaction time. When age was included, results again indicated a significant decrease in accuracy (t (396) = − 8.49, p < .000) as well as in reaction time (t (396) = − 4.40, p < .000) across all participants. A significant worry by age interaction was obtained for accuracy (t (396) = − 2.08, p = 0.03) indicating that with higher age, the more negative the effect of worry on accuracy. No further interaction reached statistical significance.When comparing WM performance between Block 1 and Block 2, the participants decreased significantly in accuracy (t (399) = − 4.99, p < .001) as well as in reaction time (t (399) = − 2.62, p = .009). Next, for each group , the change in accuracy and reaction time from Block 1 to Block 2 was computed and this change was compared to the change of the control group’s change in accuracy and reaction time from Block 1 to Block 2 (“Group by Block interaction”). No significant Group by Block interactions for accuracy or reaction time for the GAD, clinical and subclinical group compared to the control group were obtained, indicating that the rate of change in accuracy and reaction time from Block 1 to Block 2 did not differ between the groups. When age was controlled for, the control group showed a significant decrease in accuracy (t (380) = − 4.09, p = < .001) but not in reaction time (t (399) = − 1.09, p = .27) from Block 1 to Block 2. No further interaction reached statistical significance.Second, potential group differences in the change in accuracy and reaction time from Block 1 to Block 2 were explored (Group by Block interactions). From Block 1 to Block 2, the control group showed a significant decrease in accuracy  performance measured by accuracy and reaction time. Based on the Attentional Control Theory , we expected individuals with high worry  to show similar performance effectiveness (accuracy), but longer reaction times in WM Block 1 (reduced efficiency), compared to those with low worry (such as the control group) (Hypothesis 1 & 2). Indeed, our results supported Hypothesis 1 and partly supported Hypothesis 2. In line with ACT, we found that self-reported worry levels were significantly associated with longer reaction times in WM Block 1 but did not affect accuracy.The obtained results support existing research indicating that high trait worry impairs WM performance , 16. Wher = − 0.20). Therefore, future research is needed to further investigate if particular functions of WM (i.e. updating function) are more severely impaired by worry than others WM functions [This study may provide first insight into to the question if worry and its associated WM deficits are only relevant in the context of a mental disorder  or whether worry generally affects WM processing in vulnerable populations (i.e. high worriers) even when the diagnostic criteria for a mental disorder are not fulfilled. We obtained preliminary evidence that worry is generally affecting WM performance in individuals with high worry levels, and that WM deficits associated with worry are not only relevant in the context of a mental disorder. The results are in line within the current Research Domain Criteria era which proposes that disrupted WM processes are thought to play an important role in the maintenance of pathological worry . More spunctions . To concunctions , 39. MorIn this study we used a two-block WM task consisting of more than 300 trials  and the There are several limitations that must be considered when interpreting the results of the present study. First, the GAD, clinical, and subclinical groups were recruited in the context of the same RCT and therefore a particular population of help-seeking individuals might have been recruited. Therefore, in order to further investigate the dimensional nature of worry, it would be helpful to include more heterogeneous transdiagnostic samples recruited from the general population. Second, this study contrasted different clinical and non-clinical samples recruited from different populations. Although this is a common procedure in such designs , 21, 42,This is one of the first studies to investigate the relationship between different worry levels and WM performance across clinical and non-clinical samples using a two-block WM task. The results are in line with ACT  indicatiAdditional file 1: Participants (demographic characteristics) and detailed description of the working memory task. Table S1. Demographic information on the total sample and each group according to the current clinical status.Additional file 2. Data preparation and statistical models.Additional file 3: Descriptive and inferential statistics (including tables and figures). Table S2. Descriptive statistics for accuracy and reaction time Table S3. Descriptive statistic of the perceived level of worry. Table S4. Results of the Multivariate multilevel models 1a and 2a predicting accuracy and reaction time Table S5. Results of the Multivariate multilevel models 1b and 2b predicting accuracy and reaction time Table S6. Proportional reduction of explained variance represented by Pseudo-R2 for accuracy and reaction time for each model comparison Fig. 3. Mean level of self-reported worry for the GAD, clinical, subclinical and control group over the course of the WM task.


P < .0001). p62 accumulation was highest in the desmin (15.6%) and phospholamban (7.2%) groups. P62 accumulation was homogeneously distributed in the myocardium. Fibrosis was not associated with p62 accumulation in subgroup analysis of phospholamban hearts. In conclusion, accumulation of p62‐positive protein aggregates is homogeneously distributed in the myocardium independently of fibrosis distribution and associated with desmin and phospholamban cardiomyopathy. Proteotoxic protein accumulation is a diffuse process in the myocardium while a more localized second hit, such as local strain during exercise, might determine whether this leads to regional myocyte decay.Genetic cardiomyopathy is caused by mutations in various genes. The accumulation of potentially proteotoxic mutant protein aggregates due to insufficient autophagy is a possible mechanism of disease development. The objective of this study was to investigate the distribution in the myocardium of such aggregates in relation to specific pathogenic genetic mutations in cardiomyopathy hearts. Hearts from 32 genetic cardiomyopathy patients, 4 non‐genetic cardiomyopathy patients and 5 controls were studied. Microscopic slices from an entire midventricular heart slice were stained for p62 . The percentage of cardiomyocytes with p62 accumulation was higher in cardiomyopathy hearts (median 3.3%) than in healthy controls (0.3%;  In the normal situation, protein degradation pathways, like autophagy, prevent this accumulation of misfolded proteins. Recent studies suggest an important role for impaired autophagic processes in myocardial diseases such as myocardial infarction, pressure overload hypertrophy and various forms of cardiomyopathies.Autophagy regulates cellular homoeostasis by transporting intracellular cargo to the lysosomes for bulk degradation and recycling of macromolecules.P62 recognizes ubiquitinated proteins and promotes aggregation and sequestration (synonym sequestosome‐1) marking protein aggregates for degradation by autophagy or the ubiquitin proteasome system.PLN), we have recently demonstrated aggregates that are both p62 and phospholamban positive.DES, CRYAB, PLN, PKP2, DSP, LMNA, MYBPC3, MYH7, TNNI3 and TNNT2). In addition, we studied the distribution of these aggregates throughout the myocardium.In hearts of cardiomyopathy patients with a pathogenic variant in the gene encoding phospholamban . Written informed consent for collection and biobanking of tissue samples was obtained prior to transplantation or, in certain cases, approved by the ethics committee when obtaining informed consent was not possible because of death of the patient.DES) and the desmin‐related αB‐crystallin (CRYAB), phospholamban (PLN), desmosomal proteins (PKP2 and DSP), lamin A/C (LMNA), and sarcomeric proteins . Five control hearts were used (four donor hearts not used for transplantation and one heart acquired at autopsy after a non‐cardiac death). At the time of clinical presentation, the cardiomyopathy subtype of these patients had been classified by their cardiologist as arrhythmogenic [ACM] according to the revised task force criteria,Hearts from 32 patients with a genetic cardiomyopathy and 4 patients without known pathogenic mutations were included in the study that were obtained during heart transplantation or at autopsy. Genetic testing was performed in a clinical setting. Patients were classified in functional protein groups encoded by (likely) pathogenic variants (mutations) in genes encoding desmin  was used to stain for p62 using the Ventana BenchMark Ultraplatform (Roche). For p62 quantification, the heart slice was divided into five regions for the free wall of the left ventricle , one region for the septum and two regions for the right ventricle  as we have described previously Figure .8, 92.1Data are expressed as median [interquartile range]. A Mann‐Whitney test was used to compare percentages between two groups and a Kruskal‐Wallis test to compare multiple groups. Dunn's multiple comparison test was used to correct for multiple comparisons. Linear regression was used to test the association between two continues variables.33.1Patient characteristics are summarized in Table 3.2versus 3.3% [1.4‐6.4] in the cardiomyopathy hearts (P < .0001). In the cardiomyopathy hearts, p62 accumulation was highest in the desmin, phospholamban and desmosomal gene groups  followed by, lamin AC (1.3% [1.2‐2.5]) and sarcomeric gene (1.2% [0.3‐4.9]) groups . In the subgroup analysis of the other mutation groups, no significant difference was observed between RV, septum and LV , septum (3.1% [1.5‐6.8]) and LV (3.6%[1.4‐6.4]). In the desmosomal gene group, the percentage of p62‐positive cells in RV (2.1% [1.6‐3.1]) was significantly lower than the percentages in the septum (4.3% [2.8‐5.3]) and LV , RV (P = .9), LV (P = .9) and the three separate layers of the left ventricular wall .Given the relatively high numbers of phospholamban hearts, we decided to perform a subgroup analysis to study the association between p62 and fibrosis. The percentage of p62‐positive cardiomyocytes was not associated with the percentage fibrosis in the myocardium of the whole heart  gene. This observation is in line with the classical histopathological hallmark of many desmin‐related cardiomyopathies, that is abnormal cytoplasmic desmin aggregation in cardiomyocytes.DES p. Asn342Asp variant (15% p62‐positive cells in our study) induced cytoplasmic aggregates and small desmin fibrils without normal filament assembly. However, in a heterozygous co‐expression experiment, that is more comparable to the human situation, mutant and wild‐type desmin molecules were incorporated into the same filamentous structures without aggregate formation.CRYAB and 15% in DES cardiomyopathy replacing large parts of the sarcomeres, suggesting that these aggregates may play an important role in the impaired myocardial function of these cardiomyopathies. In addition, it has been demonstrated that aberrant protein aggregation in desmin cardiomyopathy leads to mitochondrial dysfunction, abnormal metabolism, and altered cardiomyocyte structure.The highest percentages of cardiomyocytes with aggresomes were observed in desmin cardiomyopathy patients, including one patient with a pathogenic mutation in the desmin‐related αB‐crystallin  mouse model where hearts not only showed focal fat infiltrations in the RV (consistent with arrhythmogenic cardiomyopathy), but also cytoplasmic aggregates consisting of intercalated disc proteins such as desmoplakin, plakoglobin and connexin 43. These aggregates coincided with disruption of the intercalated discs, intermediate filaments and microtubules.Also in desmosmal mutation‐positive arrhythmogenic cardiomyopathies, p62‐positive aggregates were observed. This is in line with observations in a recent desmoplakin (MYH7 and 1 TNNI3); therefore, the cardiomyopathy in these patients might have been affected by other variables that we have currently not yet identified. Also, in lamin A/C hearts we observed relatively low percentages of p62‐positive cardiomyocytes which is in concordance with a mouse model study where the increase of p62‐positive cells was only twofold compared to controls.The hearts of the other patient groups revealed the lowest percentages of p62‐positive cardiomyocytes that were comparable to the p62 levels of patients without known pathogenic mutations. Sarcomeric mutations are usually missense and nonsense variants which cause a dysfunctional protein that can still be integrated in the sarcomere of the cardiomyocyte. This might explain why in patients with sarcomeric mutations accumulation of mutated proteins and p62‐positivity appears to be limited. In the sarcomeric group, 3 patients had a variant of unknown significance  are urgently needed. Furthermore, we used explanted hearts, and therefore, our results only represent end‐stage disease.In conclusion, accumulation of p62‐positive protein aggregates is homogeneously distributed in the myocardium independently of fibrosis distribution and strongly associated with desmin and phospholamban cardiomyopathy. Proteotoxic protein accumulation probably is a diffuse process in the myocardium where a second hit, such as local strain during exercise, might determine whether this leads to regional myocyte decay and fibrosis.The authors confirm that there are no conflicts of interest.Joy van der Klooster: Conceptualization ; Data curation (lead); Formal analysis (lead); Methodology ; Project administration ; Resources ; Software ; Validation ; Visualization ; Writing‐original draft (lead). Shahrzad Sepehrkhouy: Data curation ; Methodology ; Software ; Writing‐review & editing . Dennis Dooijes: Data curation ; Resources ; Writing‐review & editing . Wouter te Rijdt: Data curation ; Methodology ; Resources ; Writing‐review & editing . Frederique Schuiringa: Data curation ; Investigation ; Writing‐review & editing . Jolanthe Lingeman: Data curation ; Investigation ; Writing‐review & editing . Peter van Tintelen: Resources ; Supervision ; Validation ; Writing‐review & editing . Magdalena Harakalova: Funding acquisition ; Investigation ; Software ; Writing‐review & editing . Roel Goldschmeding: Supervision ; Writing‐review & editing . Albert Suurmeijer: Conceptualization ; Methodology ; Supervision ; Writing‐review & editing . Folkert Asselbergs: Funding acquisition (lead); Resources ; Supervision ; Writing‐review & editing . Aryan Vink: Conceptualization (lead); Data curation ; Formal analysis ; Investigation ; Methodology ; Project administration ; Resources ; Software ; Supervision ; Validation ; Visualization ; Writing‐review & editing .Table S1Click here for additional data file.


We report a patient with idiopathic hypereosinophilic syndrome (I‐HES) who achieved remission with benralizumab after relapsing on mepolizumab. An 83‐year‐old man was admitted to Showa General Hospital after presenting with hypoxaemia and multiple erythematous lesions. He showed a marked increase in blood eosinophil count. Skin biopsy revealed an invasion of eosinophils in the dermis. He was diagnosed with I‐HES. He was commenced on prednisolone 40 mg/day with a plan to wean this over time after pulse steroid therapy for three days. Mepolizumab was added when the prednisolone dose was 25 mg/day. Unfortunately, at a prednisolone dose of 5 mg/day, there was evidence of disease progression and the patient was switched to benralizumab. Prednisolone was tapered again and, finally, the patient was in remission. Benralizumab targets interleukin (IL)‐5R and induces antibody‐dependent cell‐mediated cytotoxicity, thereby reducing the eosinophil counts in the tissue. This can be attributed to the therapeutic efficacy against I‐HES. We believe this report may help develop novel therapeutic strategies for I‐HES. We report a patient with idiopathic hypereosinophilic syndrome (I‐HES) who achieved remission with benralizumab after relapsing on mepolizumab. We believe this report may help develop novel therapeutic strategies for I‐HES. Hypereosinophilic syndrome (HES) comprises a group of diseases that are characterized by prolonged peripheral eosinophilia and organ damage in the absence of secondary causes for eosinophilia. Recent reports have shown myeloproliferative, lymphoproliferative, overlap, idiopathic, and associated variants of HES. The recent past has seen the evolution of therapeutic strategies for HES.Idiopathic HES (I‐HES) is primarily treated using corticosteroids. However, corticosteroids are associated with adverse events and symptomatic relapse and need to be discontinued. Prednisolone has been discontinued in 42% of patients due to lack of efficacy, intolerance, or other reasons in a study .Monoclonal antibodies targeting interleukin (IL)‐5 or IL‐5R have corticosteroid‐sparing effect and they are analysed as part of ongoing clinical trials. Antibodies have specific receptor targets. Benralizumab and mepolizumab have separate and complementary mechanisms of action useful for the treatment of I‐HES. There are a few reports of benralizumab being used to treat I‐HES after relapse with mepolizumab. A study has described the use of benralizumab in a phase II trial. Only three HES patients have been treated (with one achieving remission) with benralizumab after mepolizumab in the phase II trial .3/μL. He was admitted to Showa General Hospital after presenting with hypoxaemia and multiple erythematous lesions. Physical examination during his initial visit revealed that he was 165.0 cm and 60.7 kg with a body temperature of 38.6°C and had a blood pressure of 138/84 mmHg, radial pulse rate of 106/min, respiratory rate of 28/min, and peripheral capillary oxygen saturation (SpO2) of 75% (room air)–92% (6 L/min oxygen). There were multiple erythematous lesions throughout his whole body. Fine crackles were heard in both his lungs upon auscultation. Laboratory work‐up revealed leucocytosis with 21.8 × 103/μL, 48.0% eosinophils, and C‐reactive protein levels of 12.88 mg/dL. Chest radiographs showed consolidation mainly in the left lung and right upper lung , and multiple erythematous lesions. Chest radiographs and computed tomography showed ground‐glass opacities and pleural effusion in both lungs (Fig. Figure ngs Fig. . We diagngs Fig. .Mepolizumab is a humanized monoclonal antibody that targets IL‐5 and selectively inhibits eosinophilic inflammation while reducing eosinophil content in the sputum and blood. According to a multicentre placebo‐controlled trial of mepolizumab in patients with PDGFRA‐negative HES, mepolizumab was well tolerated and had a significant glucocorticoid‐sparing effect. However, this holds true only in 50% of the airway tissues and bone marrows and has no significant effect on bronchial mucosal staining of the eosinophil major basic protein .Benralizumab is an anti‐human IL5‐Rα antibody that inhibits ligand‐independent IL‐5 receptor signalling. It produces antibody‐directed cell‐mediated cytotoxicity against eosinophils and basophils and depletes IL‐5Rα‐expressing cells. Blocking IL‐5R signalling independent of a ligand and depleting IL‐5Rα‐expressing cells enhance clinical efficacy. Benralizumab treatment depletes eosinophils from the peripheral blood more than that using mepolizumab. Benralizumab depletes eosinophils in the airway mucosa, sputum, and bone marrow. Benralizumab produces a median decrease in the content of peripheral blood eosinophils from a baseline of 95.8% in the airway mucosa, 89.9% in sputum, and 100% in the bone marrow .The life cycle of eosinophils comprises three phases: bone marrow, blood, and tissue phase. They are primarily tissue‐dwelling cells. Once eosinophils have entered the blood, they have a short half‐life ranging from 8 to 18 h. After circulating in the blood, eosinophils migrate into the tissues, where the life span ranges from two to five days. In humans, the eosinophil tissue:blood ratio is ~100:1.14; thus, eosinophils merely “pass through” circulation en route to the tissues .Taken together, benralizumab, not mepolizumab, was effective in depleting tissues of their eosinophils. Benralizumab was more effective in treating I‐HES by targeting IL‐5R rather than IL‐5. This will help develop effective corticosteroid‐sparing treatment methods based on benralizumab alone or in combination with mepolizumab. Future studies should focus on identifying and devising therapeutic interventions that are more efficacious for I‐HES.Appropriate written informed consent was obtained for publication of this case report and accompanying images.


The Pacific Crest Trail (PCT) is a 4265‐km hiking trail that extends from the US‐Mexican border to the US‐Canadian border through the mountain ranges of western North America. Individuals who hike the entire length of the trail in one season (4–6 months) perform long daily exercise durations while exposed to extreme environmental temperatures, high altitudes, intense solar radiation, and the consumption of calorie‐rich, nutrient‐poor diets. This case study reports changes in arterial function and body composition in a subject before and after a 112‐day long‐distance hike of the PCT. Brachial artery flow‐mediated dilation, a measure of vascular endothelial function, decreased from: 6.97% to 5.00%. Carotid‐femoral pulse wave velocity, a measure of aortic stiffness, increased from 5.39 to 5.76 m/s. Dual‐energy x‐ray absorptiometry scans detected no major changes in total‐body bone mineral density, fat mass, or lean mass, although there were minor, unfavorable changes in some subregions of the body. It is important for individuals completing a long‐distance hike to be aware of the potential deleterious changes associated with large volumes of exercise and consuming a high‐calorie, low‐quality diet. The subject trekked 4,052km over 112 days. Tests were performed before and after the trek. The main mountain ranges of the PCT include the Sierra Nevada of the US state of California and the Cascade Range of the states of California, Oregon, and Washington. The trail ascends and descends a total of about 250,000 m with an impressive range from 43 m at The Bridge of the Gods in Cascade Locks, Oregon, to 4009 m at the summit of Forester Pass in California. Of the millions of people who set foot on the PCT each year, only a few hundred attempt and complete a thru‐hike—a single season, continuous hike of the entire length. Thus, completion of the PCT represents an impressive physical feat. This noteworthy accomplishment requires a thru‐hiker to perform long daily exercise durations in extreme environmental temperatures, high altitudes, and intense solar radiation while simultaneously traversing rugged wilderness and carrying a heavy backpack filled with all essential gear and food.A successful thru‐hike also requires increased caloric intake well above basal metabolic rate. Hikers routinely hitchhike into towns at road crossings every 4–5 days to gather food and supplies, repair gear, and recover. Arriving into town with significant reductions in body weight, food resupply is the main priority during town‐stops with the goal of consuming as many calories as possible. For this subject, in‐town calories largely come from foods such as cheeseburgers, pizza, ice cream, and other fast food items. Quintessential food for this hiker to carry on‐trail are lightweight yet calorically dense, to minimize pack weight, such as candy bars, pre‐packaged pastries, chips, nuts, and jerky. While these foods are high in calories, they contain few essential nutrients.Most hikers travel in a northbound direction from Mexico to Canada and require the majority of the 6‐month snow‐free season between April and September  and diastolic blood pressure (DBP) were measured using an automated device .BA), as described previously  was assessed on a treadmill and performed at an altitude of 1655 m using the Balke protocol , a measure of aortic stiffness, was assessed using applanation tonometry  or DBP  and all blood pressures were within the normotensive range. Resting HR was 60 beats per minute pre‐PCT and 68 beats per minute post‐PCT.Resting diameter of the brachial artery was 4.50 mm pre‐PCT and 4.62 mm post‐PCT. FMD3.2Detailed body composition data are presented in Table S1. The hiker experienced small changes in body weight (−0.99%), BMD (−0.56%), and body fat percentage (+3.45%) pre‐ to post‐PCT. Although there was no significant change in whole‐body BMD, there were two subregions of the body that exhibited changes after the 112 days of hiking. The BMD of the spine decreased by 5.17%, and the BMD of the pelvis decreased by 3.76% pre‐ to post‐PCT. Total‐body BMD T‐Score was 0.5 pre‐PCT and 0.4 post‐PCT, and total‐body BMD Z‐Score was 0.4 pre‐PCT and 0.3 post‐PCT, making these changes not substantial to total‐body BMD. The total fat mass of the hiker increased by 1.79% from baseline to post‐PCT. In the legs and arms, there was a decrease in fat mass by 6.3% and 3.8%. Alternatively, there was a 9.0% increase in the fat mass of the trunk from pre‐ to post‐PCT. The hiker experienced a decrease in total body lean mass by 1.2% after the PCT, coming mostly from a 6.2% decrease in the arms and a 4.5% decrease in the legs.Additionally, the subject performed daily push‐ups to maintain upper body strength, completing 282 push‐ups per hiking day during the first 56 days. On the 57th day, 1000 push‐ups were performed, after which, shoulder pain developed. For the following 13 days, daily push‐ups were reduced to 66 per day. After this, all push‐ups were discontinued in an attempt to preserve all energy.4BA, a marker of endothelial function, decreased. Additionally, there were multiple minor changes in CFPWV, BMD, fat mass, and lean mass. In addition to the extreme exercise load, the subject performed to complete the PCT, the individual consumed a very poor diet for the duration of the hike. The results of this case study suggest that the combination of extreme exercise and poor diet may lead to negative changes in health markers.In this case study, we evaluated the effects of a long‐distance hike on the PCT on arterial function and body composition in a highly‐fit, 25‐year‐old male. Our primary finding was that FMD4.1BA from 6.97∆% to 5.00∆% indicates a likely decrease in endothelial function. As FMDBA is a risk factor for cardiovascular diseases  can induce muscle hypertrophy . Additional testing sessions during and after the hike would allow for investigation of the time course of changes in arterial function and body composition.This study was limited by including only a single subject; however, the speed at which this subject completed the PCT allowed a unique look at the physiological effects of large volumes of exercise and a poor‐quality, high‐calorie diet. We cannot uncouple the contributions of these two significant stimuli, and we do not have a detailed dietary record from the subject. FMD6BA, an increase in CFPWV, and minor unfavorable changes to body composition. Our findings suggest that large volumes of exercise do not compensate for a poor diet and/or may provide diminishing returns and prove detrimental to physiological function. These findings have implications for others attempting long‐distance thru‐hikes or similar tests of endurance. Importantly, individuals with poor arterial function or low bone mineral density should take extra precautions when engaging in long‐distance hiking in order to maintain proper physiological function.During a 112‐day hike along the PCT, extreme daily volumes of physical activity coupled with a poor diet resulted in a reduction in FMDThe authors declare no competing interests.T.C.H. and D.H.C. conceived and designed research; T.C.H. and D.H.C. collected data; T.C.H. and D.H.C. analyzed data; T.C.H. wrote manuscript; T.C.H. and D.H.C. edited and revised manuscript and approved final version of manuscript.