The aim of this study was to investigate the peritumoral pseudocapsule (PC) status and identify the factors influencing PC status in small renal cell carcinoma (RCCs).A total of 147 patients with small RCC (≤4 cm) who had undergone tumor enucleation (TE) were assigned into three groups according to PC status: complete PC, PC absence, and PC invasion. Computed tomography (CT) imaging and clinicopathological features were compared among the three groups. Univariate and multivariate analyses were performed to identify factors associated with incomplete PC.p < 0.001). PC absence was most common in female patients (50%), whereas PC invasion was more common in male patients (85%) (p = 0.017). The tumor diameter in the PC absence group (2.24 ± 0.93 cm) was shorter compared with that of the complete PC group (2.88 ± 0.76 cm) and PC invasion group (3.16 ± 0.64 cm) (p < 0.001). Univariate and multivariate analysis showed that hypoenhancement pattern, unclear boundary, and non‐CCRCC subtype were independent risk factors of incomplete PC.The number of patients with complete PC, PC absence, and PC invasion was 87 (59%), 20 (14%), and 40 (27%), respectively. Compared with the other two groups, tumors with complete PC were most common in clear cell RCC (CCRCC) and showed a hyperenhancement pattern (92%) and clear boundary (63%) on CT scanning images (Hypoenhancement pattern, unclear boundary, and non‐CCRCC subtype were significant predictors of incomplete PC in small RCCs. It remains to be established whether TE is an appropriate procedure for patients with incomplete PC. The aim of this study was to investigate the pseudocapsule status and identify the factors influencing PC status in small renal cell carcinomas. Hypoenhancement pattern, unclear boundary, and non‐CCRCC subtype were independent risk factors of incomplete PC. It is composed of dense fibrous connective tissue that limits tumor cells infiltration into the normal surrounding renal parenchyma.2A total of 214 patients with small RCCs who had undergone TE in Nanjing Drum Tower Hospital were analyzed from January 2020 to January 2022. The inclusion criteria were as follows: (1) Patients with 18 or over 18 years old; (2) Pathologically confirmed RCCs; (3) Underwent preoperative contrast‐enhanced computed tomography (CT) scan; (4) Complete clinicopathological data. Exclusion criteria were as follows: (1) Rare subtype of RCCs; (2) Patients with lymph nodes or distant metastasis; (3) Multiple ipsilateral or bilateral RCCs; (4) Patients who received immune or targeted therapy before surgery. Finally, 147 patients were included in the study.The following data were analyzed during the study: age, gender, body mass index (BMI), side, hypertension, smoking history, clinical symptom, tumor diameter, CT, and pathological findings. CT imaging features included necrosis/cystic components, homogeneity, enhancement pattern, and boundary. Heterogeneous and homogeneous enhancement were defined as the presence or absence of unenhanced areas in the tumor lesion, respectively.Peritumoral PC status were evaluated by two independent observers and reviewed by an experienced uropathologist who was blinded to other clinical characteristics. To ensure accuracy and consistency, the evaluation of PC status was performed on the tumor‐parenchyma interface. Three or more slides with tumors surrounded by PC or renal parenchyma were considered to be a sufficient histologic material. The exception occurred when the size of the tumor was less than 2 cm. We classified PC status as complete PC, PC absence, and PC invasion. We defined complete PC as continuous PC without tumor infiltration in all slides, PC absence as the invisible PC with tumor cells immediately adjacent to the normal renal parenchyma in all slides, and PC invasion as tumor cells infiltrating the PC or surrounding renal parenchyma, regardless of whether the infiltration was partial or total. PC absence and PC invasion were defined as incomplete PC in our study. This retrospective study was approved by the institutional review board of Nanjing Drum Tower Hospital. Due to the retrospective nature of the study, the informed consent was waived.2.1p value less than 0.05 was considered statistically significant.Statistical analyses were performed using SPSS 23.0 software. Normality of the data was tested by Kolmogorov–Smirnov test. The difference among groups were compared using one‐way ANOVAs or chi‐square tests. For univariate and multivariate analysis, the logistic regression model was performed. A 3A total of 147 small RCCs, including 117 clear cell RCCs , 18 papillary RCCs , and 12 chromophobe RCCs  were enrolled in the study. All small RCCs had pathologically confirmed negative surgical margins. Of note, 144 (98%) asymptomatic small RCCs were detected during the physical examination or incidental detection. The diameter of small RCCs ranged from 0.8 to 4 cm, with mean diameter of 2.87 ± 0.8 cm. Necrotic or cystic components, homogeneity, hyperenhancement pattern, and clear boundary on CT scan occurred in 26 (18%), 99 (67%), 119 (81%), and 74 (51%) tumors, respectively. The PC was complete in 87 (59%) small RCCs, while PC absence and PC invasion were found in 20 (14%) and 40 (27%) small RCCs, respectively. Other detailed imaging and clinicopathological features data are summarized in Table p < 0.001). Tumor diameter in the PC absence group (2.24 ± 0.93 cm) was smaller compared with that of the complete PC group (2.88 ± 0.76 cm), whereas tumors in the PC invasion group had the largest diameters (3.16 ± 0.64 cm). Of cases of PC invasion 28% were high nuclear grade. The proportion of high nuclear grade was only 7% in the PC absence group, although the difference was not statistically significant (p = 0.054). Compared with the other groups, the complete group showed a hyperenhancement pattern (92%) and clear boundary (63%) on a CT scan. Patients in the PRCC and ChRCC groups were combined to form the non‐CCRCC group. The difference in PC status between the two groups was statistically significant (p < 0.001), with 68% of patients in the CCRCC group having complete PC compared with 23% in the non‐CCRCC group. Specifically, the number of cases in the PRCC group with PC invasion was 13 (72%). Of the 12 ChRCCs, PC absence was present in 5 (42%) cases. Representative radiographic and pathologic images of PC status were shown in Figures Tumor characteristics were divided based on PC status and are shown in Table p < 0.001), unclear boundary (p < 0.001) and non‐ccRCC (p < 0.001) were related to incomplete PC. We further conducted a multivariable logistic analysis to explore the factors influencing incomplete PC. Hypoenhancement pattern , unclear boundary  and non‐CCRCC subtype  were found to be independent risk factors of incomplete PC.Univariate and multivariate analyses were conducted to investigate factors associated with incomplete PC Table . Univari4This is the first study to investigate the PC status in small RCCs after TE. Complete PC was observed in CCRCC, whereas PC absence and PC invasion were common in ChRCC and PRCC, respectively. Further analysis showed that PC absence was related to small size, female, and low nuclear grade tumors, whereas PC invasion was associated with large size, male, and high nuclear grade. Furthermore, hypoenhancement pattern, unclear boundary, and non‐CCRCC subtype were found to be independent risk factors of incomplete PC.The peritumoral PC in RCC was first reported by CahillThe presence of peritumoral PC is considered to be a hallmark of malignant renal tumors because it is rare in benign tumors.p = 0.054).PC invasion was previously reported in 68% and 76% of cases,PC absence and invasion are important factors influencing the surgical options and prognosis of RCC patients.The relationship between prognosis and incomplete PC has been widely investigated in recent years. A study by Xi et al. involving 1307 patients found that incomplete PC resulted in clinically significant adverse effects on the prognosis of patients.At present, most studies about peritumoral PC focused on postoperative pathological features.There are several limitations in this single‐center retrospective study. First, a small sample size was used which limits the generalizability and accuracy of our conclusions, especially the small numbers of patients with PRCC and ChRCC. Second, PC status has not been clearly defined in previous studies. Thus, we categorized PC status at the tumor‐parenchyma interface into three distinct subtypes: complete PC, PC absence, and PC invasion and evaluated the factors influencing PC status. Third, we did not investigate the association between PC status and prognosis due to the short follow‐up period.5This study found that hypoenhancement pattern, unclear boundary, and non‐clear cell RCC subtype were independent risk factors of incomplete PC in small RCCs. Because incomplete PC increases the risk of positive surgical margin during TE and is associated with poor prognosis, tumors with incomplete PC should be closely monitored during preoperative surgical planning or postoperative follow‐up.Xiang Dong and Sheng Pan contributed equally to this research. Xiang Dong: conception, writing, editing. Sheng Pan: Data collection, assembly of the data. Xiaodie Zhou: Reviewed pathologic specimens. Wenliang Ma: Data analysis, review. Hongqian Guo: Conception, design. Weidong Gan: design, supervision, review. All authors approved the final manuscript.The authors confirm that there are no conflicts of interest.The protocol for this research project has been approved by the institutional review board of Nanjing Drum Tower Hospital and it conforms to the provisions of the Declaration of Helsinki. Due to the retrospective nature of the study, the informed consent was waived.


The mycetoma is a granulomatous chronic disease, subcutaneous disease is the common presentation, very few cases are reported affecting central nervous system, but there are not cases in Renal Transplant (RT). Renal transplant recipients have increased risk of invasive fungal infections. Transplant centers in geographic endemic risk areas should suspect the possibility of mycetoma infection in patients. This clinical entity is not commonly seen in the United States, and hence few cases have been reported. Lesions are often associated with trauma. Around 75% of mycetoma is thought to occur in the lower extremity. The incubation period can be found up to 9 years.2A 25‐year‐old male, construction worker in Zapopan, Jalisco, Mexico, with a chronic kidney disease for 7 years was diagnosed with unknown cause. The patient received a renal graft in January 2017, from a related donor (sister): 35 years old, high risk to cytomegalovirus , intermediate risk to Epstein Barr virus , cross match negative; ABO compatible (A+); and sharing three class I antigens  and four class II antigens . Graft (left) has one artery and one vein, warm ischemia time is 1 min 8 s, cold ischemia time is 30 min; mean arterial pressure at clamping is 100 mmHg and after unclamping 105 mmHg, double J catheter was not required. No incidents were reported during RT. After treatment with basiliximab, the maintenance immunosuppressive therapy was tacrolimus (2 mg twice a day [BID]), mycophenolic acid (1 gram BID), and prednisone (5 mg per day). Prophylaxis to CMV valganciclovir 450 mg BID was given; CMV disease  was developed (8 months after RT), polymerase chain reaction (PCR) 73,400 copies/ml. Valganciclovir was re‐started 900 mg BID until no replication.Actinomadura madurae was identified, and then nucleotide sequencing of 16S rRNA PCR analysis was realized after 3 weeks of growth, at 37°C, it was established a certain diagnosis , no fever or neurological alterations were found; leucocytes were 3010 mm/μl, neutrophils 68% 2050 mm/μl), LDH 729 μ/L, and procalcitonin 0.30 ng/ml; computed tomography (CT) scan showed an abscess in the right parietal–temporal area (Figure 050 mm/μl3Mycetoma is a chronic granulomatous disease usually that affects bone, subcutaneous, and deep tissues. There have been some reports that mention the infection could be disseminated. This pathology has recently been classified into a neglected disease, commonly observed in poor, low‐income countries.Actinomadura madurae.Actinomadura madurae has been associated with females most commonly.The patient is a rural community worker,The patient did not have any clinical evidence for mycetoma infection. After first evaluation, we searched for any skin alteration without success. The mycetoma infection has an initial asymptomatic evolution. The final stage is characterized by pain.A. madurae to TMP/SMX is high, as is the efficacy in clinical practice. However, the treatment of actinomycetoma in most cases is prolonged, with complications, often expensive or with adverse events.In vitro, the susceptibility of madurae is characterized by a continuous secretion,Mycetoma caused by Actinomadura Graft function was stable during the evolution. Tacrolimus levels were low during the evaluation. During infection, it was not necessary to decrease the tacrolimus dose.Actynomadura madurae to be the initial causative factor in immunocompromised patients. Transplant centers should suspect the possibility of mycetoma infection in patients, specially those who lives in geographic endemic risk areas, middle age group and farmers, as well, complete clinical evaluations to find mycetoma‐related skin lesions.This clinical case report is focused on the apparition of mycetoma in the central nervous system. No clinical manifestation of a kidney graft receptor was found during the illness. We found CG was involved in primarily treating clinicians, reviewing the manuscript and final approval. AS and MP were involved in treating clinicians. RC was involved in coordinating case selection, reviewing the draft of the paper, interpreting data and final approval. RF was involved in drafting the manuscript. GG was involved in drafting the paper, creating a data set and descriptions for the original draft, revisions, and final approval.The authors declare no conflicts of interest associated with this manuscript.As a single case report with the patient's signed consent, no other ethical review was required.Written informed consent was obtained from the patient to publish this report in accordance with the journal's patient consent policy.


Atrial fibrillation (AF) is a rapid supraventricular arrhythmia. However, the pathogenesis of atrial fibrillation remains controversial. We obtained transcriptome expression profiles GSE41177, GSE115574 and GSE79768 from GEO database. WGCNA was performed, DEGs were screened, PPI network was constructed using STRING database. CTD database was used to identify the reference score of hub genes associated with cardiovascular diseases. Prediction of miRNAs of hub genes was performed by TargetScan. DIANA‐miRPath v3.0 was applied to make functional annotation of miRNA. The animal model of atrial fibrillation was constructed, RT‐PCR was used to verify the expression of hub genes. Immunofluorescence assay for THBS2 and VCAN was made to identify molecular. Design of BP neural network was made to explore the prediction relationship of CXCR4 and TYROBP on AF. The merged datasets contained 104 up‐regulated and 34 down‐regulated genes. GO and KEGG enrichment analysis results of DEGs showed they were mainly enriched in ‘regulation of release of sequestered calcium ion into cytosol’, ‘actin cytoskeleton organization’ and ‘focal adhesion’. The hub genes were CXCR4, SNAI2, S100A4, IGFBP3, CSNK2A1, CHGB, VCAN, APOE, C1QC and TYROBP, which were up‐regulated expression in the AF compared with control tissues. There was strong correlation among the CXCR4, TYROBP and AF based on the BP neural network. Through training, best training performance is 9.6474e‐05 at epoch 14, and the relativity was 0.99998. CXCR4 and TYROBP might be involved in the development of atrial fibrillation by affecting inflammation‐related signalling pathways and may serve as targets for early diagnosis and preventive treatment. It refers to uncoordinated atrial excitation and results in ineffective atrial contraction. AF is a serious cardiovascular condition that endangers human health in the 21st century, and its incidence increases with age.Bioinformatics technology has been widely used to find genetic changes in the processes of disease occurrence and development and can simultaneously analyse multiple changes in gene expression. It is a reliable means to find molecular diagnostic or therapeutic targets of disease. Wang and Wang used bioanalysis to screen multiple genes and molecules related to the occurrence and development of AF, which provided ideas for the early diagnosis and targeted therapy of AF.In the present study, we used bioinformatics technology to mine gene sequencing data, obtained from a public database, of patients with AF and compare these data with those of healthy individuals. We extracted high‐quality genetic datasets for further analysis. Functional enrichment analysis of the DEGs was performed using DAVID, Metascape and Gene Set Enrichment Analysis (GSEA). Weighted gene co‐expression network analysis (WGCNA) of the genes in the merged datasets was also performed. A summary of hub genes and the miRNAs that regulate these hub genes was obtained. Finally, we made a preliminary analysis of the role these DEGs and hub genes play in AF.22.1We obtained the transcriptome expression profiles GSE115574, GSE41177 and GSE79768 from the Gene Expression Omnibus (GEO) database  is an analysis tool that can be used to describe correlation patterns among genes across microarray samples and find modules of highly correlated genes.2.3p‐value < 0.05.The DEGs were screened using the R package ‘limma’, and the cut‐off criterion was a 2.4http://www.genome.jp) analysis can provide specific pathways and link genomic information with higher‐order functional information. GO (Gene Ontology) analysis can annotate the functions of genes using terms from a dynamic, controlled vocabulary, which contains three aspects of biology: BP , CC (cellular components) and MF (molecular functions). GSEA  is a computational method that can execute GO and KEGG analysis for a given gene list. Metascape and DAVID are online analysis tools that provide a comprehensive gene list annotation and analysis resource. In our study, the GO and KEGG analyses of DEGs were performed using GSEA, Metascape and DAVID (p < 0.05).Kyoto Encyclopedia of Genes and Genomes  is an online database that can predict and provide a protein–protein interaction (PPI) network.STRING (2.6http://ctdbase.org/) is a web‐based database that can identify relationships between genes/proteins and disease. In our study, the relationships between gene products and cardiovascular diseases were analysed using this database.The CTD database  is an analysis tool that can perform predictive analyses and determine possible mechanisms for the co‐regulation of the expression of hundreds of genes expressed in different cell types. In our study, the miRNAs that regulated hub genes were screened for using TargetScan.TargetScan (2.8http://www.microrna.gr/miRPathv3) is an online software suite dedicated to conducting functional and pathway enrichment analyses for miRNAs. In our study, GO and KEGG pathway enrichment analyses were performed using miRPath (p < 0.05).DIANA‐miRPath v3.0 (2.9n = 10) and the atrial fibrillation group . The rats in the AF group were narcotized with 4.5% isoflurane (0.8‐1 L/min). The rats were fixed in an upturned position and intubated in the external jugular vein or femoral vein. After that, electrocardiograph oscilloscope and electrocardiograph machine were connected to observe and record the II lead electrocardiograph, respectively. Arrhythmias may occur within 4–5 minutes after constant intravenous injection of aconitine solution at 1 pg/0.1 ml per minute with a micropump. The Animal Care and Use Committee of the Institute of Laboratory Animal Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS&PUMC) authorized the experimental ethics agreement.Twenty rats with a body weight of about 200 g were obtained from the Huafucang Biotechnology Co., Ltd. , which were randomly divided into two groups, including control group  of each sample were calculated and presented as fold change of gene expression relative to the control group. GAPDH was used as an endogenous control.Total RNA was extracted from the blood samples using the TRIzol®  and reverse transcribed into cDNA with the Servicebio®RT First Strand cDNA Synthesis kit  for 60 min at 42°C. Terminate the reaction by heating at 70°C for 5 min. RT‐qPCR was performed in a Light Cycler® 4800 System (Roche Diagnostics) with a specific set of primers for the amplification of secreted hub genes. Primers used were as follows in the Table 2.11Washing three times with PBS (pH 7.4) (5 min/time), immersed the renal sections in EDTA antigen retrieval buffer (pH 8.0)  to make antigen retrieval. Treat with PBS(PH7.4) , adding 3%BSA to block non‐specific binding for 30 min. Throwing away the blocking solution, sections are incubated by CXCR4 antibody  . Washing the sections again with PBS (PH7.4), fluorescent secondary antibodies (diluteon rate = 1:5000) responding to the primary antibodies were added . Then, incubated with DAPI solution   to counterstaining nucleus.Finally, using spontaneous fluorescence quenching reagent (5 min)  to make spontaneous fluorescence quenching and sealing the sections with anti‐fade mounting medium. The detecting process of TYROBP was the same as above using TYROBP antibody . Fluorescence microscopy (Nikon NIKON ECLIPSE C1) showed that the nuclei were blue (excitation wavelength 330–380 nm and emission 420 nm) and the positive expression was red or green .2.12Before performing neural network modelling, the input and output of neural network need to be determined. In this experiment, due to the limitation of data, part of the input feature vectors in the disease risk determination model were reduced, and finally the two features data of CXCR4 and TYROBP, disease of AF were used as input vectors to conduct limited validation of the AF disease risk determination model, which can already reflect the health status of the body to a large extent due to the relatively large impact of these characteristics.Corresponding to the three characteristics data entered is the individual's diagnostic result, and in the health status item, 1 represents that the individual is AF and 0 represents that the individual is normal. To fit the data and give the risk of AF, the model only needs a corresponding output.2.13Normal myocardial cells and AF myocardial cells were purchased from Zhongke Quality Inspection Co., Ltd. The medium condition was DMEM  + 10%FBS  + 1% double antibody . The CXCR4 siRNA and TYROBP siRNA were compounded by Beijing Qingke biotechnology co., LTD. The expression level of CXCR4, TYROBP, PI3K, AKT and TNF‐alpha were detected after intervention of siRNA by RT‐qPCR.2.14p‐value < 0.05 was considered statistically significant.The statistical analyses in this study were conducted using Perl, R software (version 3.5.3) and SPSS 20.0. Associations between hub genes and the status of samples were tested using Pearson's chi‐square test, while genes and their effects on AF based on univariate logistic proportional regression analysis were analysed. A receiver operating characteristic (ROC) curve was obtained using SPSS 20.0 and MedCalc software. Statistical analyses were conducted using SPSS software, version 24.0 (IBM Corp.), and MATLAB . A 33.1The GSE41177 dataset contained 9843 up‐regulated genes and 10 down‐regulated genes Figure A. The GS3.2In our study, WGCNA analysis was conducted using the R package ‘WGCNA’. A power value of 8 was the lowest power for which scale independence was below 0.8, and this was selected to produce a hierarchical clustering tree of 6301 genes . The clu3.3The enrichment results of the GO and KEGG analyses of DEGs were obtained using DAVID; they were mainly enriched in ‘inflammatory response’, ‘inflammatory response to antigenic stimulus’, ‘actin cytoskeleton organization’, ‘focal adhesion’, ‘apical plasma membrane’, ‘cell surface’ and ‘PI3K‐Akt signaling pathway’ .The enrichment results of the GO and KEGG analyses of DEGs performed by Metascape were mainly enriched in ‘Staphylococcus aureus infection’, ‘inflammatory response’, ‘blood microparticle’, ‘endoplasmic reticulum lumen’ and ‘dimeric IgA immunoglobulin complex’ .The enrichment results of GO and KEGG analyses of DEGs, performed by GSEA, were mainly enriched in ‘cell volume homeostasis’, ‘cornified envelope’, ‘nuclear cyclin dependent protein kinase holoenzyme complex’, ‘copper ion homeostasis’, ‘regulation of heart morphogenesis’ and ‘myofibril assembly’  and 4.In summary, through the above enrichment analysis, the DEGs related to the AF were mainly enriched in the biological processes of inflammation and infection. The results manifested that the AF might be correlated with the process of inflammation.3.4The PPI network of DEGs was constructed using the STRING online database and analysed with Cytoscape software . Four di3.5The CTD database showed that the hub genes targeted cardiovascular diseases, as shown in 3.6The miRNAs that regulated the hub genes were screened for using TargetScan Table . GO and 3.7The ROC curve of hub genes is shown in 3.8p < 0.05, As shown in 3.9p < 0.05).By the immunofluorescence assay, protein expression of CXCR4  and TYRO3.10Through training, best training performance is 9.6474e‐05 at epoch 14 , and the3.11p < 0.05), and the inflammation response was strong. After down‐regulated the CXCR4 in the AF myocardial cell, the expressions of CXCR4/PI3K/AKT/TNF‐alpha were decreased significantly (p < 0.05), and the inflammation response was inhibited. , and the inflammation response was strong. After down‐regulated the TYROBP in the AF myocardial cell, the expressions of CXCR4/PI3K/AKT/TNF‐alpha were decreased significantly (p < 0.05), and the inflammation response was inhibited. , and the inflammation response was strong. After down‐regulated the CXCR4 in the AF myocardial cell, the expressions of CXCR4/ TYROBP /PI3K/AKT/TNF‐alpha were decreased significantly (p < 0.05), and the inflammation response was inhibited. The PI3K family plays an important role in the transduction of intracellular signals and the pathogenesis of inflammation, obesity, and immune diseases.Our result found that the CXCR4 and TYROBP might accelerate the inflammation to cause AF by activating the PI3K/AKT signalling pathway. Compared with the control myocardial cell, the expressions of CXCR4/TYROBP/PI3K/AKT/TNF‐alpha were higher in the AF myocardial cell (CXCR4 (C‐X‐C motif chemokine receptor 4) is widely distributed and can be found in various locations within cells.The transmembrane immune signalling adaptor, TYROBP, is mainly located at the plasma membrane, cell surface and integral components of the membrane.Despite the rigorous bioinformatics analysis conducted in this study, there are still some shortcomings. This study lacks an in‐depth verification of the proposed mechanisms. The reliability of our results could be improved by animal experiments and comprehensive verification using clinical samples.In conclusion, bioanalysis can effectively identify differentially expressed genes between patients with atrial fibrillation and healthy individuals with sinus rhythm. Specifically, CXCR4 and TYROBP mediate the development of AF through inflammation. These differentially expressed genes may be involved in the development of AF by affecting inflammation‐related signalling pathways and may serve as targets for early diagnosis and preventive treatment. Our study provides new evidence and ideas for the further exploration of the pathogenesis and treatment of atrial fibrillation.Yan‐fei Zhang: Conceptualization ; project administration ; writing – review and editing . Ling‐bing Meng: Formal analysis ; methodology ; visualization . Meng‐lei Hao: Methodology ; writing – original draft . Xing‐yu Li: Formal analysis ; methodology ; visualization ; writing – original draft . Tong Zou: Data curation ; methodology ; writing – original draft .The authors declare that they have no competing interests.Appendix S1Click here for additional data file.


Urachal carcinoma (UrC) is a rare, aggressive cancer with a poor prognosis that is frequently diagnosed in advanced stages. Due to its rarity, the current staging systems, namely Sheldon, Mayo, and Ontario were established based on relatively small patient cohorts, necessitating further validation. We used a large patient population from the National Cancer Database to model a novel staging system based on the Tumor (T), Node(N), and Metastasis (M) (TNM) staging system and compared it to established staging systems.We identified patients diagnosed with UrC between the years of 2004–2016. To determine median overall survival (OS), a Kaplan–Meier (KM) curve was generated using the Sheldon, Mayo, Ontario, and TNM staging system. A cox proportional‐hazards regression model was developed to highlight predictors of overall survival.p < 0.0001). As compared to the Sheldon, Mayo, and Ontario staging system, our TNM staging system had a more balanced sample and survival distribution per stage and no overlap among stages on KM survival curves. The Mayo, Ontario, and TNM staging systems were more accurate in terms of stage‐survival correlation than the Sheldon staging system .A total of 626 patients were included in the analysis. The OS for the entire cohort was 58.2 months (50.1–67.8) with survival rates at 12, 24, and 60 months of 83%, 70%, and 49%, respectively (The proposed novel TNM staging system for UrC has a more balanced sample distribution and a more accurate stage‐survival correlation than the traditional Mayo, Sheldon, and Ontario staging systems. It is clinically applicable and enables better risk stratification, prognosis, and therapeutic decision‐making. This study proposed a novel TNM staging system for urachal carcinoma based a large number of patients in NCDB. It provides a more balanced sample distribution and a more accurate stage‐survival correlation than the traditional Sheldon, Mayo, and Ontario staging systems. It is clinically applicable and enables better risk stratification, prognosis, and therapeutic decision‐making. The fibrous remnant persists after birth as the median umbilical ligament. Urachal tissue remnants that remain in the ligament have the potential to become neoplastic at any point along the tract.Given the lack of consensus regarding the ideal staging system for UrC, we queried the National Cancer Database (NCDB) in order to compare established staging systems, namely Sheldon, Mayo, and Ontario against our novel, modified Tumor, Nodes, Metastases (TNM) staging system.22.1We conducted a retrospective hospital‐based review using de‐identified patient data from the NCDB, a large clinical oncology database that captures more than 70% of newly diagnosed malignancies in the United States and which is supported by the American Cancer Society, American College of Surgeons (ACS), and Commission on Cancer (CoC).2.2Several staging systems have been proposed for UrC, including those by Sheldon et al., Ashley et al., and Pinthus et al.in‐situ (Tis), 1; absence of nodal involvement or distant metastasis) is confined to the urachus. Stage 2 disease  involves tumor invasion of the bladder muscularis propria or microscopic invasion of bladder perivesical tissue. Stage 3 disease  involves macroscopic invasion of bladder perivesical tissue or invasion of nearby structures, including the uterus, vagina, or prostate. Stage 4 disease  involves tumor invasion of the pelvic/abdominal wall or any nodal or distant site involvement.Our novel, four‐category TNM staging system is described in Tables AJCC Cancer Staging Manual for the urinary bladder.in‐situ, 1, 2, 2a, and 2b were defined as Mayo stage 1 because the tumor is confined to the urachus and/or bladder muscularis propria. T‐stages 3, 3a, 3b, 4, 4a, 4b correlated with Mayo stage 2 due to tumor extension beyond the urachus/bladder muscular layer and with invasion of bladder perivesical tissue or nearby structures including the pelvic/abdominal wall, uterus, or vagina. Any regional lymph node (N) involvement, N1‐N3, was defined as Mayo stage 3 and any distant metastasis (M) was defined as Mayo stage 4. Sheldon stage 1 corresponded with T‐stage 0, a, and in‐situ given no tumor invasion beyond the urachal mucosa. Sheldon stage 2 equated with T‐stage 1 as tumor invasion is still confined to the urachus. Sheldon stages 3A/B/C/D were grouped and corresponded with T‐stages 2, 2a, 2b, 3, 3a, 3b, 4, 4a, and 4b due to tumor invasion of perivesical tissue or nearby structures such as the bladder, abdominal/pelvic wall, uterus, vagina, or other viscera. Any regional lymph node involvement, N1‐N3, was defined as Sheldon 4A and any distant metastasis, M1, corresponded with Sheldon 4B. For the Ontario system, T1 included T‐stages 0, a, in‐situ, and 1 as the tumor is confined to the urachal submucosa. T2 correlated with T‐stage 2 as the tumor is limited to the muscular wall of the bladder. T3 corresponded to T‐stages 3, 3a, 3b as the tumor invades perivesical tissue and T4 correlated to T‐stage 4a and 4b given invasion of adjacent organs.Although an AJCC TNM staging system for the urinary bladder exists, currently in its 8th edition,2.3Data analysis was performed utilizing MedCalc Version 19.6. Kaplan–Meier (KM) curves were created to determine the cumulative probability of survival.33.1The cohort's baseline characteristics included mostly patients who were white (81%), male (56%), and had a median age of 58 years. The stage distribution of UrC cases according to Sheldon, Mayo, Ontario, and TNM staging criteria were as follows: Sheldon stage I (7%), stage II (9%), stage III (56%), stage IV (28%); Mayo stage I 33%), stage II (38%), stage III (7%), stage IV (21%); Ontario T1 (22%), T2 (24%), T3 (45%), T4 (9%); TNM stage I (18%), stage II (46%), stage III (18%), stage IV (18%)  . According to the Mayo staging system . Per the Ontario staging system . The median OS based on the proposed TNM staging system . Table To determine median OS for UrC, a KM curve was generated using the Sheldon, Mayo, Ontario, and TNM staging systems. The OS for the entire cohort of 626 patients was 58.2 months  with survival rates at 12, 24, and 60 months being 83%, 70%, and 49%, respectively (1) Table , the medm Figure , the medm Figure , the medm Figure  was 132.p < 0.05 for all stages) than the Sheldon staging system.A cox proportional‐hazards regression model was developed to identify predictors of overall survival Table . The May4UrC is a rare malignancy accounting for <1% of all bladder malignancies.Our study represents one of the largest known analysis of UrC patients. We found that our proposed TNM staging system may serve as a novel staging system for UrC and aid in disease prognostication and management; however, our staging system requires further validation and comparison against established staging systems.Multiple staging systems for UrC have been proposed; the Sheldon and Mayo staging systems are the most commonly used.Szarvas et al. conducted a meta‐analysis of 1010 UrC cases using single‐institution and population‐based studies; of those patients, 532 had data available for Sheldon staging and 221 were staged according to the Mayo system. It was found that the majority of cases were staged as Sheldon stage III and IV, 68% and 24%, respectively, and Mayo stage I and II, 33% and 40%, respectively. Sheldon staging tends to classify patients as higher stage disease, with nearly 92% of UrC diagnosed at advanced stages (Sheldon stage III and greater) and at which point there is already tumor invasion of the urinary bladder. Mayo staging classifies patients as lower stage and seems to have a more balanced patient distribution by stage.Although an AJCC TNM staging system exists for the urinary bladder, it cannot be applied directly to UrC.Currently, no standard treatment guidelines exist for UrC but rather are extrapolated from small‐series case reports and single‐institution studies. According to The National Comprehensive Cancer Network (NCCN) guideline recommendations, patients with localized UrC should undergo partial or complete cystectomy with en bloc resection of the urachal ligament, umbilicus, and lymph node dissection.An optimal staging system is necessary to help direct treatment algorithm. For the proposed TNM staging system, patients with stage 1, 2, and 3 disease may likely benefit from upfront surgical umbilectomy and partial or radical cystectomy, while stage 2 and 3, especially the latter may potentially benefit from adjuvant systemic therapy. Stage 4 disease would likely require upfront systemic chemotherapy and potentially salvage/consolidative surgical resection for those who responded to chemotherapy. Ongoing effort is being made to link the treatment modalities and disease stage.Analyses utilizing the NCDB database have several limitations, so our results should be interpreted with caution. For instance, the AJCC TNM staging system for the urinary bladder cannot be directly applied to UrC; however, per the NCDB PUF, UrC cases were coded according to the 6th and 7th editions of the AJCC Cancer Staging Manual for the urinary bladder.5UrC is an extremely rare malignancy with advanced stage on presentation that lacks a validated staging system or definitive treatment guidelines, largely in part due to the lack of large, randomized, prospective studies. We describe here a novel, four‐category TNM staging system constructed from a large population‐based analysis. We hope that our proposed staging system allows for improved risk‐stratification, prognostication, and therapeutic decision‐making. Future studies will aim at performing a retrospective review of overall survival, relative to treatment modalities, in UrC.Vladimir Limonnik: Conceptualization, methodology, formal analysis, writing‐ original draft, writing‐ review and editing. Arash Samiei: Methodology, formal analysis, writing‐ review and editing. Stephen Abel: Methodology, formal analysis, writing‐ review and editing. Rodney E. Wegner: Methodology, formal analysis, writing‐ review and editing. Goutham Vemana: Formal analysis, writing‐ review and editing. Shifeng S. Mao: Conceptualization, methodology, formal analysis, writing‐ review and editing, supervision, project administration.There was no funding for this study.All authors declare there are no conflicts of interest.A retrospective review was performed using de‐identified patient data; given patient de‐identification, the study was exempt from institutional review board oversight.


Obesity and ethnicity play important roles in cardiovascular complications in patients with type 2 diabetes mellitus (T2DM). This study aimed to compare cardiometabolic risk profiles between Chinese and Finnish older adults of central obesity with prediabetes or T2DM.n = 1089), and the Chinese subjects came from the Shanghai High-risk Diabetic Screen (SHiDS) study (n = 818). The KIHD and SHiDS studies used similar questionnaires to determine participants’ baseline characteristics regarding the history of medication use and diseases and lifestyle factors. All study subjects participated in glucose tolerance tests and anthropometry assessments, including waist circumference measurements.Study subjects were 60–74 years old and originated from two population samples. The Finnish subjects came from the Kuopio Ischemic Heart Disease (KIHD) study (n = 298), fasting and 2-h glucose, and fasting insulin and insulin resistance were significantly higher in Chinese than in Finnish (p < 0.0001–0.016). In addition, triglyceride (TG) level was higher and the low-density lipoprotein cholesterol (LDL) and LDL to high-density lipoprotein cholesterol (HDL) ratio were lower in Chinese than in Finnish (p < 0.0001–0.003). Among subjects of central obesity with T2DM (n = 251), Chinese subjects had significantly less proportions of antihypertensive, glycaemic control medication, and statin users as well as lower level of physical activity , while higher blood pressure (p = 0.002 for systolic blood pressure and p < 0.0001 for diastolic blood pressure), TG levels (p < 0.05) and HDL (p = 0.002) than the Finnish counterparts. There were no differences in β-cell function (HOMA-β) between Chinese and Finnish both in prediabetes and T2DM.Among study subjects of central obesity with prediabetes (Our results indicated that Chinese and Finnish older adults of central obesity with prediabetes and T2DM had similar β-cell function. However, Chinese individuals with prediabetes are prone to insulin resistance. Meanwhile, lipid metabolism dysfunction is also different between Chinese and Finnish. Chinese older adults of central obesity with prediabetes showed higher TG, but Finnish showed higher LDL and LDL/HDL. Strategic for T2DM prevention and treatment should be ethnically specific. Type 2 diabetes mellitus (T2DM) is the most common metabolic disease, with an increasing prevalence worldwide , 2. AccoEthnicity is an important factor that needs to be considered for T2DM and diabetes-related complications. For some ethnic groups, there is a greater predisposition for developing T2DM and diabetes-related complications , 7–12. CPrevious studies have shown that cardiovascular biomarkers, low-density lipoprotein (LDL), total cholesterol (TC), cardiac troponin T and C-reactive protein are systematically higher in Caucasian diabetic patients with acute coronary syndrome than in their Chinese counterparts . Ethnic While obesity and ethnicity play important roles in cardiovascular complications in patients with T2DM, there is almost no any study that has evaluated ethnicity-specific cardiovascular risk profiles of adults with both glucose impairment and central obesity at the population level. In the context of increasingly ethnically diverse individuals and ageing in many countries, especially European countries, studies on the relationship between ethnicity, obesity, and cardiovascular complications in elderly individuals with glucose impairment could have an immense impact on public health and the socioeconomic burden of CVDs associated with T2DM –29. ThusThe study subjects were derived from two well-defined population samples. The Finnish subjects came from the Kuopio Ischemic Heart Disease (KIHD) study , and theth Hospital (2018-KY-056 (K)). Written informed consent was obtained from all participants, and all study procedures were conducted according to the Declaration of Helsinki.Similar questionnaires examining history of medication use and diseases; lifestyle, including smoking, drinking and physical activity habits; and education level were used to characterize the background of the participants. The Finnish study was approved by the Ethics Committee of Kuopio University and Kuopio University Hospital, Kuopio, Finland (licence number 143/97), and the Chinese study was approved by the Ethics Committee of Shanghai Jiao Tong University 6Body weight and height were measured with standard methods and used to calculate BMI. Blood pressure was measured with the conventional method ). Waist circumference (WC) and hip circumference were measured by tape . The waiBlood samples were collected in the morning between 7:00 and 9:00 a.m. after overnight fasting at both sites. Serum was extracted by centrifugation and stored immediately at  – 80 °C until analysis. Glucose tolerance tests were performed at both sites after overnight fasting, and samples were taken 30 min and 2 h after the intake of 75 g glucose for the assessment of serum insulin (chemiluminescent immunoassay) and glucose . The homeostasis model assessment of insulin resistance index (HOMA-IR) was calculated as (fasting insulin concentration × fasting glucose concentration)/22.5, and the β-cell function index (HOMA-β) was calculated as (20 × fasting insulin concentration/(fasting glucose concentration  – 3.5). The serThe criterion for prediabetes was defined according to the American Diabetes Association criteria as a fasting glucose level from 5.6 mmol/l to 6.9 mmol/l or an OGTT 2 h glucose level from 7.0 mmol/l to 11.0 mmol/l . The crit-test in univariate analysis or Mann–Whitney U tests. Analysis of covariance (ANCOVA) was used to control for confounding factors such as age, sex, BMI and medicine when comparing the variables between the Chinese and Finnish populations. Pearson correlation analysis was performed to evaluate the correlation between central obesity (BMI and waist circumference) and glycaemic and lipid variables. In addition, generalized linear models (GLMs) were used to control for confounding factors such as age, sex, BMI and medicine when assessing the relationships among BMI, WC and glycaemic and lipid variables.Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 21.0. Descriptive statistics were generated and presented for each ethnic group, including percentages for categorical variables and means and 95% confidence intervals for continuous variables. The Shapiro–Wilk test was used to check all data for normality. If the data were not normally distributed, a log transformation was used to ensure a normal distribution. Categorical variables were compared between Chinese and Finnish individuals using the chi-square test for independence. Continuous variables were compared between Chinese and Finnish individuals by using Student’s n = 199) of the Finnish population had prediabetes, and 7.9% (n = 85) had T2DM, while 30.6% (n = 250) had prediabetes and 31.8% (n = 260) had T2DM in the Chinese population. Since the purpose of this report was to examine the differences in the cardiometabolic risk profiles of Finnish and Chinese older adults with glucose impairment and central obesity, the following results are only related to the comparison of prediabetes and T2DM between Finnish and Chinese individuals with central obesity.Among the targeted population, 18.3%  and heavier  and had a larger BMI , but higher participation in physical activity  than Chinese individuals in both the prediabetes and T2DM groups.The general characteristics of the subjects with prediabetes and T2DM and central obesity were shown in Table p = 0.002), but lower in smoking (p < 0.0001), using antihypertensive medication (p < 0.0001) than their Finnish counterparts (p = 0.033).Among prediabetes patients, the proportion of alcohol drinking was significantly higher in Chinese individuals (p = 0.046), lower using antihypertensive and antidiabetic medication as well as statins, but higher blood pressure (p = 0.04 for SBP and p < 0.0001 for diastolic blood pressure (DBP)) than the Finnish population, and after controlling for age, sex, BMI, and the use of antihypertensive medication, the significance remained the same.Among T2DM patients, the Chinese population had a smaller WHR (p = 0.003), for a shorter duration (p < 0.0001) and at a lower intensity (p < 0.0001) than Finnish individuals. In T2DM patients, Chinese patients had a similar frequency of LTPA to Finnish patients but a shorter duration (p = 0.002) and lower intensity (p < 0.0001).Walking was the most common type of leisure time physical activity (LTPA) for both Finnish and Chinese individuals. In addition to walking, hunting, fishing, and gymnastics were common LTPAs among Finnish individuals, while less than 10% of Chinese engaged in Taiji, dancing, and jogging  and 2 h glucose (p = 0.016) (see Table 2 = 0.082) and HOMA-IR . After additional confounding factors, such as antidiabetic medication, SBP, education, physical activity, smoking and alcohol consumption, were added to the model, the significance remained the same.Among prediabetes patients, the Chinese population had higher fasting glucose (p = 0.043). However, after adding SBP, education, LTPA, smoking and alcohol drinking to the model, this significance disappeared.In T2DM patients, after controlling for age, sex, BMI and use of antidiabetic medication, 2-h glucose was significantly higher in Chinese individuals than in Finnish individuals (p = 0.001) but lower LDL  levels and LDL/HDL ratios . No other differences between the Chinese and Finnish populations were observed.In T2DM patients, only TG levels were higher in Chinese patients . In addition, in Finnish individuals, WC was significantly correlated with fasting glucose, TG, HDL and TG/HDL in prediabetes , while TG and TG/HDL ratio were positively correlated with BMI and WC, and HDL was negatively correlated with WC in T2DM . In Chinese individuals, BMI and WC was positively correlated with HOMA-β (both p = 0.0001) and WC with TG/HDL ratio (p = 0.042) in prediabetes patients and BMI with HOMA-β in T2DM patients (p = 0.012), respectively. Controlling for all the confounding factors , the significant associations between WC and HDL in Finnish T2DM and WC and TG/HDL ratio in Chinese and Finnish pre-diabetes disappeared. Interestingly, the associations between HOMA-β and BMI/WC in Finnish individuals and between HOMA-β and WC in Chinese T2DM appeared to be significant.We assessed the correlations between central obesity and glycaemic and lipid profiles. The results are given in Supplementary Table 3. Significant associations were found between BMI/WC and insulin/HOMA-IR in both the Finnish and Chinese populations . However, HDL was correlated with HOMA-β only in Chinese pre-diabetes but not in Finnish. In T2DM, TG levels were correlated with fasting insulin level and HOMA-IR and TG/HLD ratio with 2 h glucose level, fasting insulin level and HOMA-IR only in Finnish T2DM not in Chinese, while HDL was correlated with fasting glucose and 2 h glucose level in Chinese not in Finnish. Controlling for all the confounding factors , the associations in prediabetes and in Finnish T2DM were still significant, while the significant associations between HDL and fasting glucose and 2 h glucose level in Chinese T2DM disappeared.In addition, we assessed the correlations between glycaemic and lipid profiles. The results are given in Supplementary Table 4. Significant associations were found between TG levels and TG/HDL ratio and insulin/HOMA-IR/HOMA-β in both the Finnish and Chinese prediabetes  than in European T2DM populations .When only subjects with central obesity and prediabetes were included, the differences between Finnish and Chinese subjects were related not only to insulin resistance but also to the levels of glucose, TG and LDL and the LDL/HLD ratio. Central obesity rather than obesity defined by BMI has been associated with various detrimental metabolic consequences, such as dysglycaemia and dyslipidaemia, all of which cause cardiovascular mortality . A conseHowever, there are ethnic differences regarding lipid metabolism between Finnish and Chinese individuals with prediabetes or T2DM. Compared with the Finnish, Chinese older adults of central obesity with glucose impairment had higher TG levels. The risk of unhealthy lipid profiles, such as high TG, may lead to cardiovascular diseases , dementiExisting studies have mainly compared the differences between different ethnic groups within the same country, and fewer comparisons of the differences between ethnic groups on different continents have been reported , 47, 48.In conclusion, by comparing the cardiometabolic profiles of Finnish and Chinese older adults of central obesity with prediabetes and T2DM, we found that both Finnish and Chinese had similar β-cell function. However, Chinese individuals with prediabetes are prone to insulin resistance. Meanwhile, lipid metabolism dysfunction is also different between Chinese and Finnish prediabetes. Chinese individuals with prediabetes showed higher TG, but Finnish showed higher LDL and LDL/HDL. Strategic for T2DM prevention and treatment should be ethnically specific.


The epidemiology, clinical features, and socioeconomic burden associated with detection of rhinoviruses (RV)/enteroviruses (EV) from individuals in the community with acute respiratory infections (ARIs) are not fully understood.To assess the clinical and socioeconomic burden associated with RV/EV, a secondary analysis of data collected during a prospective, community‐based ARI surveillance study was performed. From December 2012 to September 2017, adult and pediatric participants with ARIs had nasopharyngeal specimens obtained and tested by multiplex polymerase chain reaction assay. Characteristics and socioeconomic burden including missed school or work and/or antibiotic use among participants who did and did not seek medical care and among participants with and without co‐detection of another respiratory pathogen with RV/EV were compared.n = 686), those seeking medical care (n = 199) had a longer duration of illness (5 vs. 7 days) and were more likely to miss work/school (16.4% vs. 47.7%) and/or use antibiotics (3.6% vs. 34.2%). Co‐detection occurred in 8% of ARIs of which 81% occurred in children. Co‐detection was not associated with longer illness, more missed work/or school, or antibiotic use.Throughout the study period, RV/EV was detected in 54.7% (885/1617) of ARIs with a respiratory pathogen detected. Most ARI episodes associated with RV/EV occurred in females (59.1%) and children ≤17 years old (64.2%). Those ≤17 years were more likely to seek medical care. Compared to those not seeking medical care (Non‐medically attended and medically attended ARIs associated with RV/EV resulted in clinical and socioeconomic burden, regardless of co‐detection of other respiratory pathogens. Data collected during these calls included: duration of ARI symptoms; days of missed school or work for ill participants or their caregivers; seeking medical care for the ARI in primary care clinics, urgent care, or emergency departments (ED); reported diagnosis such as pharyngitis, otitis, pneumonia, sinusitis; hospitalizations; and use of antibiotics.2.3Bordetella pertussis, Chlamydophila pneumoniae, and Mycoplasma pneumoniae.Nasal swab samples were tested for respiratory pathogens in a research laboratory at CUIMC using multiplex RT‐polymerase chain reaction (PCR) . This assay identified the following respiratory pathogens: adenovirus, human coronaviruses , human metapneumovirus, RV/EV, influenza , parainfluenza , RSV, 2.4p < 0.05) with seeking medical care in the bivariate analysis. Analyses were conducted using STATA version 14 .We assessed detection of RV/EV among participants in three age groups: <5, 5–17, and ≥18 years of age. To describe seasonal trends, epidemiological curves of ARIs with RV/EV detected were created for children ≤17 years versus adults ≥18 years of age. Demographic, clinical (selected comorbidities), and socioeconomic characteristics  of participants with RV/EV who did and did not seek medical care were compared using simple logistic regression. The clinical and socioeconomic burden among those who did and did not seek medical care and among those with RV/EV alone vs. RV/EV co‐detected with another respiratory pathogen were compared by Student t‐test, chi‐square, and Wilcoxon‐rank test, as appropriate. We used multiple logistic regression to investigate characteristics of those who did and did not seek medical care and the burden among those with and without co‐detection while controlling for selected characteristics found to be significantly associated  had a swab obtained. Among the 1617 (58.7%) samples with a respiratory pathogen detected, RV/EV were detected in 885 (54.7%) participants. RV/EV were detected throughout the year with annual peaks in early Spring and early Fall  nor level of education  was associated with seeking medical care. Among children, daycare/school attendance vs. home care was not associated with seeking medical care . In multiple logistic regression, only age ≤17 years remained associated with seeking medical care .Medical care was sought by 22.5% 199/885) of participants, including 33.7% (84/249) of children <5 years old, 25.1% (80/319) of children 5–17 years old, and 11.0% (35/317) of adults. Overall, participants who sought medical care were more likely to be male and ≤17 years of age and less likely to have a chronic non‐respiratory condition (Table 99/885 ofp = 0.09). Participants who sought medical care had significantly more days of illness (7 vs. 5 days) and participants/their caretakers were more likely to miss school and/or work (47.7% vs. 16.4%). Of the 199 participants who sought medical care, 68 (34.2%) were prescribed antibiotics including 36.9%, 33.8%, and 28.6% of those <5, 5–17, and ≥18 years of age who sought care, respectively; participants <5 years of age were not more likely to be prescribed antibiotics than those ≥18 years of age (p = 0.44). Fifty‐five reported receiving antibiotics for the following diagnoses: pharyngitis, otitis media, bronchitis, sinusitis, pneumonia, and/or an asthma exacerbation. Among the 686 participants who did not seek medical care, 25 (3.6%) used antibiotics, most of whom were adults .Overall, 23.5% (208/885) of participants with ARIs associated with RV/EV missed at least 1 day of work or school and 10.5% (93/885) used antibiotics (Table 3.2n = 70) of ARIs with RV/EV detected. The following were co‐detected: coronaviruses (n = 22), parainfluenza (n = 12), human metapneumovirus (n = 9), RSV (n = 9), adenovirus (n = 7), M. pneumoniae (n = 5), influenza (n = 4), and C. pneumoniae (n = 2). Most co‐detections (81.4%) occurred in children. The proportion of participants who reported symptoms of runny nose/congestion, cough, sore throat, and muscle/body aches was similar in those with and without co‐detection (data not shown). However, fever/feverishness was reported more frequently in those with co‐detection than without co‐detection . Those with and without co‐detection had a similar duration of illness and similar rates of missing work/school or antibiotics use (Table p = 0.126).Co‐detection of another respiratory pathogen occurred in 8.0%  participants did not seek medical care. While the clinical and socioeconomic burden of ARIs associated with RV/EV detection was higher among those who sought care, participants who did not seek care had a median of 5 days of illness and 16% missed at least 1 day of work and/or school. While direct healthcare costs associated with RV/EV have not been measured, both direct healthcare and indirect costs associated with non‐influenza viral infections are estimated to be $40 billion per year in the United States; this is greater than the costs associated with chronic diseases including hypertension, asthma, or chronic obstructive pulmonary disease.Seeking medical care was associated with antibiotic use in both adults and children. While some participants received antibiotics from their providers to treat other diagnosis, for example, otitis media and pneumonia, antibiotic use for symptoms consistent with viral illness highlights the need to improve antimicrobial stewardship in ambulatory settings. Globally, 85%–95% of all antibiotics are prescribed for outpatients including those cared for in urgent care facilities, clinics, and EDs.Several of our findings corroborated those described previously. More than half of the ARIs with RV/EV detected occurred in children, consistent with previous observations that RV/EV are the most common causes of ARI in children.Co‐detection of another respiratory pathogen with RV/EV occurred in only 8% of ARIs. While those with co‐detection were more likely to report fever/feeling feverish, the clinical symptoms, days of illness, and socioeconomic burden were comparable in those with RV/EV alone versus RV/EV co‐detected with another pathogen, further confirming the substantial burden and clinical impact of RV/EV. As previously described, we also demonstrated that co‐detection was more common in children.This study has limitations. It may lack generalizability as it was performed in New York City with predominantly Hispanic/Latino participants. The burden of illness was reported by a household reporter; ill participants did not provide additional verification. The proportion of participants who missed work/school and symptom duration may be underestimated due to recall bias. Burden may also be underestimated as some adults did not work outside the home as they were unemployed or worked as homemakers. The use of RT‐PCR may overestimate co‐detection due to prolonged viral shedding and/or detection of non‐viable virus. Finally, the RT‐PCR assay used in this study does not distinguish RV from EV so differences associated with different RV/EV types could not be assessed.5This study provides further evidence of the clinical and socioeconomic burden associated with RV/EV in both adults and children. We noted that most participants with RV/EV did not seek medical care. Most did not miss work or school and thus, likely infected others outside the home. We found inappropriate antibiotic use among participants, which supports strengthening antimicrobial stewardship efforts in ambulatory settings. Further research could help assess the socioeconomic burden of RV/EV in other patient populations and prevention strategies for RV/EV should be developed.Katia Halabi: Data curation; formal analysis; investigation; methodology; project administration. Melissa Stockwell: Conceptualization; formal analysis; funding acquisition; investigation; methodology; validation. Luis Alba: Data curation; formal analysis; methodology. Celibell Vargas: Data curation; formal analysis; investigation; methodology. Carrie Reed: Conceptualization; methodology. Lisa Saiman: Conceptualization; formal analysis; funding acquisition; investigation; methodology; project administration; supervision; validation.https://publons.com/publon/10.1111/irv.12989.The peer review history for this article is available at


Arabidopsis and maize was validated with wet lab experiments, of which five substitution sites were accurately predicted. PPVED could facilitate the identification and characterization of genetic variants that explain observed phenotype variations in plants, contributing to solutions for challenges in functional genomics and systems biology. PPVED can be accessed under a CC‐BY (4.0) license via http://www.ppved.org.cn.Single amino acid substitution (SAAS) produces the most common variant of protein function change under physiological conditions. As the number of SAAS events in plants has increased exponentially, an effective prediction tool is required to help identify and distinguish functional SAASs from the whole genome as either potentially causal traits or as variants. Here, we constructed a plant SAAS database that stores 12 865 SAASs in 6172 proteins and developed a tool called Plant Protein Variation Effect Detector (PPVED) that predicts the effect of SAASs on protein function in plants. PPVED achieved an 87% predictive accuracy when applied to plant SAASs, an accuracy that was much higher than those from six human database software: SIFT, PROVEAN, PANTHER‐PSEP, PhD‐SNP, PolyPhen‐2, and MutPred2. The predictive effect of six SAASs from three proteins in  PPVED iA plant SAAS database with 12 865 SAASs in 6172 proteins was constructed with three different sources: UniProt/Swiss‐Prot, NCBI/PubMed, and simulation calculation based on multiple sequence alignments , physicochemical features (631), database annotated features (31), predicted features (328), and coevolutionary features (220). To reduce the feature dimensions and avoid overfitting, 48 features from 1215 candidates were selected using the three‐step feature selection pipeline proposed in this study . Moreover, we also found that the removal of all predicted features caused the most severe performance loss compared with removing other features categories. , was larger than 0.2 for all features, demonstrating the significance of selected variables Figure . Notably. Figure . OverallWith 48 informative features, we built 15 machine learning models based on 4 types of learning algorithms: RF, XGBoost, SVM, and FFNN, which included 4 individual models and 11 ensemble models. The prediction results of ensemble models were synthesized from multiple individual models using the stacking method. The global performance of predictive models was evaluated based on six indicators: sensitivity (SEN), specificity (SPE), precision (PRE), accuracy (ACC), MCC, and area under the curve (AUC). After applying these models in the benchmark dataset with 10,292 SAASs  that link to the human SAAS dataset. Six indicators of global performance for each software are listed in Table Vitis rotundifolia were predicted as functional with the protein being reported to affect NAD+ cleavage activity  gene encodes a mitochondrial protein and is involved in maintaining the mitochondrial electron transport chain function in Arabidopsis  cluster biosynthesis  were predicted to be functional, and one (ISU1T55 M) was predicted to be neutral  is a candidate gene associated with phosphorus deficiency in a genome‐wide association study (GWAS). The highest associated sites included two single‐nucleotide polymorphisms (SNPs) leading to two nonadjacent SAASs, and one indel near the EAR domain, which have been reported to modulate transcriptional inhibitory activity . We selected all complete proteins classified as ‘Viridiplantae’ with mutagenesis information from the UniProt/Swiss‐Prot database and then manually retrieved the mutagenesis annotations of these proteins. We excluded non‐SAASs, including insertions, deletions, and multi‐amino acid substitutions, and finally obtained 5751 SAASs. Of these SAASs, 4964 were functional and 787 were neutral. The second source of SAASs was NCBI/PubMed (https://pubmed.ncbi.nlm.nih.gov). We retrieved literatures that may be associated with plant SAASs from the NCBI/PubMed database using a set of preset keywords . In summary, we obtained a total of 12 865 plant SAASs, of which 6367 were functional and 6498 were neutral. These SAASs had a roughly balanced ratio of 1:1 and were evenly distributed in 6172 proteins. We randomly selected 5094 functional and 5198 neutral SAASs as the benchmark dataset (80% of the dataset) to build and tune the model. The remaining SAASs, comprising 1273 functional and 1300 neutral SAASs, were used for the independent dataset (20% of the dataset) to validate the generalization ability of the model.http://www.ppved.org.cn. Notably, for each SAAS, we recorded the following detailed information: source, organism, protein accession number, protein sequence source database, protein sequence, wildtype amino acid, mutant amino acid, mutation position, PMID of supporting literature, supporting experimental evidence, and label.To further validate the model, 314 newly plant functional SAASs from the UniProt/Swiss‐Prot (release 2020_05) database were collected according to the above method. All datasets used in this study can be downloaded from We comprehensively collected the features that characterize SAASs, and these features were roughly divided into the following five categorieset al., et al., et al., i,jP is the frequency of amino acid j at position i.It is reported that evolutionarily conserved positions are frequently associated with disease‐related mutations in humans PPP we standardized the benchmark dataset and recorded the parameters of each feature, including the mean and standard deviation (SD); and (ii) we applied the parameters to the independent dataset, thereby standardizing the independent dataset. The calculation formula for the z‐score method was as follows:Feature selection can reduce the dimensions of features and avoid model overfitting. However, it is extremely challenging to select important and informative features from among numerous features. Here, we designed a three‐step feature selection pipeline to select the optimal feature combinations for the prediction of SAASs from the above 1215 features. In this pipeline, we used the RF with default hyperparameters provided by the randomForest v4.6‐14 package to build the models. Notably, all RF models were built by repeating stratified fivefold cross‐validation 10 times. The details of the pipeline are shown in Figure B in Figure We used individual feature to build the RF models, thereby maintaining consistency between the number of models and features. Then, we used the out‐of‐bag (OOB) error of the RF models to exclude meaningless features, and the features .A heuristic‐based genetic algorithm (GA) was used for feature selection. In each iteration, we randomly selected a feature C1, C2, C3, C4, C5) produced by GA, from which an optimal feature combination  was selected.Finally, a backward feature selection algorithm (BFSA) was used for feature selection. Here, we only performed BFSA on the five optimal feature combinations  using the stacking method based on the four algorithms. In summary, we built a total of 15 machine learning models, of which 4 were individual models and 11 (C2 4+C3 4+C4 4) were ensemble models.Additionally, to validate the complementarity of the above four algorithms for the prediction of SAASs and to further improve model performance, we also built 11 ensemble models , and three that were developed based on machine learning  were used. The two models provided by PolyPhen‐2, HumDiv, and HumVar were considered. No software developed based on hybrid methods is applicable to plants.http://www.ppved.org.cn.To ensure that our proposed models can be utilized, a user‐friendly online website was developed. We used Apache as the web server and Perl as the backend language to write the common gateway interface. The website operates on a 64‐bit CentOS Linux server with a basic configuration of eight cores and 32G. The homepage of the website is The user interface is shown in Figure G87D, ISU1T55 M, ISU1G106D, and ISU1A143T were amplified and cloned from corresponding suppressor mutants. All complementation constructs were based on the binary vector pCAMBIA1300. Arabidopsis plants were transformed with the agrobacteria‐mediated floral dipping method , and then disrupted by sonication. The suspensions were centrifuged at 10,000 × g for 15 min at 4°C. The supernatants of the His6‐tagged proteins obtained were incubated with 500 μL Ni Sepharose  and then washed twice with buffer A. The recombinant proteins were eluted with buffer B (500 mm imidazole in buffer A). The eluents were further applied to size exclusion chromatography with the Superdex 75 or Superdex 200 column with ÄKTA Purifier 10 FPLC system .Hism HEPES‐KOH, pH 7.5, 150 mm KCl, and 10 mm MgCl2 before being used for the heat‐induced aggregation assay. CS (500 nm) was prepared in a final volume of 150 mL 20 mm HEPES‐KOH (pH 7.5) and 2.8 mm β‐mercaptoethanol with different amounts of tested proteins. The mixtures were loaded onto a 96‐well microplate and heated at 45°C. Light scattering at 340 nm was monitored at 45°C in a Synergy 4 spectrophotometer (BioTek) for 90 min. Control measurements were performed with commercial bovine serum albumin (BSA).All the tested proteins and citrate synthase (CS)  were dialyzed in 20 mZmERF9 were fused with the Gal4‐AD sequence. Then, the fused fragments were cloned into pGBKT7 and further fused with the Gal4‐BD sequence. Gal4‐AD was cloned into pGBKT7 to be fused with the Gal4‐BD sequence, resulting in the complete Gal4,which was used as a control. The transformation was conducted according to the manual of Yeast Protocols Handbook (Clontech). Primers and constructions are listed in Table Different allelotypes of The authors declare that they have no competing interests.Yanli Lu and Yaxi Liu conceived the study and participated in its design and coordination; Xiangjian Gou, Haoran Shi, and Qi Wang analysed the data and developed the PPVED tool; Xuanjun Feng and Rongqian Xie performed the experiments; Xiangjian Gou, Hongxiang Li, Banglie Yang, and Lixue Chen collected the dataset; Xiangjian Gou drafted the manuscript; Xiangjian Gou, Tingting Guo, Xuanjun Feng, Yaxi Liu, and Yanli Lu revised the manuscript. All the authors read and approved of the final version of the manuscript.Figure S1. Performance comparison between 48 features‐based models (after feature selection) and 1,215 features‐based models (before feature selection).Figure S2. Model performance when removed a class of features and used the remaining features to train the models.Figure S3. The filtering rules of simulation‐based single amino acid substitutions (SAASs) used in this study.Figure S4. Threshold of predicted score for distinguishing functional and neutral single amino acid substitutions.Click here for additional data file.Table S1. The 48 features finally selected.Click here for additional data file.Table S2. Prediction results of 314 newly collected SAASs from the UniProt/Swiss‐Prot database.Click here for additional data file.Table S3. Prediction results of 1,515 neutral SAASs from the published literature.Click here for additional data file.Table S4. Preset keywords for retrieving literatures from the NCBI/PubMed database.Click here for additional data file.Table S5. Out‐of‐bag error of each feature in the first stage of feature selection.Click here for additional data file.Table S6. Plasmid constructs and primers used in this study.Click here for additional data file.


American adults have gained weight during the COVID‐19 pandemic. Little is known about how patients who are medically managed for overweight and obesity, including patients who are prescribed antiobesity pharmacotherapy, have fared.To assess the COVID‐19 pandemic's effect on weight, food choices, and health behaviors in patients receiving medical treatment for overweight or obesity.Adult patients treated at an urban academic weight management center between 1 May 2019 and 1 May 2020 were electronically surveyed between 23 February and 23 March 2021. The survey assessed changes in weight, eating, behaviors, and the use of antiobesity medications (AOMs) following issuance of social distancing/stay‐at‐home policies in March 2020.p < 0.001). More participants achieved ≥5% weight loss if they were taking AOMs compared to those who were not , and weight gain ≥5% was also lower in those taking AOMs . Patients with pre‐pandemic BMI ≥30 kg/m2 taking AOMs experienced the greatest weight reduction, and there was greater weight loss associated with increased physical activity.In 970 respondents, median percent weight change for those taking AOMs was −0.459% [interquartile range −5.46%–(+3.73%)] compared to +2.33% [IQR −1.92%–(+6.52%)] for those not taking AOMs (Medical weight management protected against weight gain during this period of the COVID‐19 pandemic. Increased physical activity, decreased alcohol intake, and use of AOMs were factors that contributed to this protective effect. Forty‐five out of 50 states issued stay‐at‐home orders during this time, which led to widespread business closures and a significant increase in working from home.During the COVID‐19 pandemic, many individuals have experienced significant weight gain.Studies have highlighted a connection between stress and anxiety leading to negative effects on health behaviors and weight management during the COVID‐19 pandemic.While studies have been published assessing the impact of COVID‐19 on mental health, behaviors, and weight among patients with overweight or obesity, little is known about how patients who are medically managed for their weight, which includes use of antiobesity pharmacotherapy, have fared. This study evaluated how the pandemic has affected weight, food choices, and health behaviors in patients receiving medical treatment for overweight or obesity at a large, urban, academic weight management center in the northeastern United States. The study's hypothesis was that the pandemic would have a negative impact on food choices and health behaviors but that medical weight management, including the use of antiobesity medications (AOMs) would protect against weight gain.2Patients 18 and older treated for overweight or obesity between 1 May 2019 and 1 May 2020 at the Comprehensive Weight Control Center at Weill Cornell Medicine were identified via electronic health record and invited to complete an anonymous survey between February 23, and 23 March 2021. The survey assessed self‐reported changes in weight, eating, behaviors , and the use of AOMs following issuance of social distancing/stay‐at‐home policies in March 2020. While all of the patients included in the study were being medically managed for their weight, specific treatment was highly individualized. All patients were counseled on diet and physical activity, but not all were prescribed AOMs.p < 0.05) compared to simpler models with the predictors in the chosen model. Data were analyzed in R  using standard R software functions  invoked by the cufunctions package.Multivariable linear regression models were used to identify predictors of body weight change in this cross‐sectional study. Gender, age, ethnicity/race (white vs. non‐white), AOM use, pre‐pandemic BMI, cohabitation, urban setting, physical activity, and alcohol consumption were examined as predictors. Pairs of variables were evaluated for interaction, and identification of effect modification by AOM use and pre‐pandemic BMI led to stratification of the sample into analytical subgroups. Models were fit to each subgroup with sufficient sample size. Models were ranked by the corrected Akaike information criterion, the top‐ranked model was chosen, and likelihood ratio tests were used to confirm the chosen model improved the fit significantly (32 (interquartile range (IQR) 27.1–35), AOM use was common . More participants achieved ≥5% weight loss if they were taking AOMs compared to those who were not , and weight gain ≥5% was lower in those taking AOMs . Greater percent weight change was predicted by higher pre‐pandemic BMI.Of the 4882 patients contacted for the survey, 970 individuals responded (response rate = 19.9%). Median pre‐pandemic BMI was 30.4 kg/m2 vs. BMI ≥30 kg/m2; Figure 2 taking AOMs, 2. pre‐pandemic BMI <30 kg/m2 taking AOMs, 3. pre‐pandemic BMI ≥30 kg/m2 not taking AOMs, 4. pre‐pandemic BMI <30 kg/m2 not taking AOMs. Linear regression models were fitted to each cohort separately. Group 4 was not modeled due to insufficient sample size. Table The relationship between percent weight change and pre‐pandemic BMI differed by AOM use and pre‐pandemic BMI (BMI <30 kg/m2 taking AOMs experienced the greatest weight reduction. Greater weight loss was associated with increased physical activity, with a more pronounced effect at higher BMIs. For example, somewhat more physical activity was predicted to decrease body weight by 4.0% at BMI of 30 kg/m2 and by 19.4% at BMI of 50 kg/m2 (Table 2 not taking AOMs, weight loss was associated with higher pre‐pandemic BMI and increased physical activity (Supp Table 2 taking AOMs, weight loss was associated with pre‐pandemic BMI and decreased alcohol consumption, whereas age over 65, decreased physical activity, non‐white status, and increased alcohol consumption were significantly associated with weight gain (Supp Table 2 and 6.0% at BMI of 28 kg/m2 (Supp Table Patients with a pre‐pandemic BMI ≥30 kg/mIn the entire sample, 73.1% reported more home‐cooked meals and 60.8% reported less physical activity. There was greater weight loss with self‐reported increased physical activity, with a more pronounced effect at higher BMIs Table . Increas4This study illustrates that medical weight management, particularly use of AOMs and increased physical activity, protects against weight gain and can also lead to weight loss during times of stress and environmental change. Increased physical activity, decreased alcohol intake, and use of AOMs were modifiable factors that contributed to this protective effect. Interestingly, the predicted impact of physical activity on weight loss increased exponentially at higher BMIs in those taking AOMs. Both directions of change in alcohol consumption during the pandemic have been reported in the literature.This is the first study to investigate weight change in patients undergoing medical treatment including pharmacotherapy for overweight and obesity during the COVID‐19 pandemic. These findings offer unique insight into the utility of antiobesity pharmacotherapy and its interactions with health behaviors. While morbidity and mortality associated with the virus has decreased there is still uncertainty, anxiety and stress surrounding the pandemic. There are also semi‐permanent to permanent changes in Americans' daily routines which continue to impact overall health behaviors. Addressing both the mental health and behavioral health components is important to mitigate weight gain and promote overall health.Limitations of this study include single center design with participants at different stages of weight‐loss/maintenance, and lack of a control group. The anthropometry data was also self‐reported and retrospective in nature. Intercurrent illness and unintentional weight loss could not be differentiated. Overall health and socioeconomic status of the participants were not controlled for.Obesity is increasing rapidly in the United States and it has been projected that by 2030 nearly 50% of American adults over the age of 18 will have obesity.Dr. Hootman reports being a consultant for Faeth Therapeutics, Inc.; and an educational program reviewer for PESI, Inc.Dr. Saunders reports ownership/stock/management interest in Intellihealth.Dr. Aronne reports receiving consulting fees from/and serving on advisory boards for Gelesis, Jamieson Wellness, Janssen Pharmaceuticals, Jazz Pharmaceuticals, Novo Nordisk, Pfizer, Real Appeal and Eli Lilly; receiving research funding from Allurion, Astra Zeneca, Gelesis, Janssen Pharmaceuticals, Novo Nordisk and Eli Lilly; having equity interests in Allurion, ERX Pharmaceuticals, Gelesis, Intellihealth, Jamieson Wellness, Myos Corp and Zafgen; and serving on a board of directors for Intellihealth and Jamieson Wellness.The rest of the authors report no disclosures.Concept and design: Barenbaum, Shukla, Saunders, Redmond, Hootman, Aronne; Acquisition, analysis, or interpretation of data: All authors; Drafting of the manuscript: Barenbaum, Shukla, Saunders, Chan; Critical revision of the manuscript for important intellectual content: All authors; Statistical analysis: Chan, Crowley, Ramakrishnan; Administrative, technical, or material support: Casper; Study supervision: Shukla, Saunders.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.


Arctocephalus pusillus pusillus) using carbon and nitrogen isotopic compositions of serially sampled whiskers collected at four breeding sites along the coast of South Africa. Drawing on over 900 isotopic measurements, we assessed geographic variability in isotopic niche width between colonies and the degree of individual specialization. We found slight, but clear geographic differences in isotopic ratios and isotopic niche widths, seemingly related to ecological setting, with niche widths being proportional to the area of available shelf and shelf‐slope habitat surrounding the colony. We further identified periodic oscillations in isotopic ratios, which likely reflect temporal patterns in foraging distribution and prey type, linked to shifts in the availability of prey resources and their interaction with constraints on individual females throughout their breeding cycle. Finally, individual specialization indices revealed that each of the study populations contain specialist individuals that utilize only a small subset of the total population niche width. The degree of individual specialization was, however, not consistent across colonies and may reflect an interactive influence between density‐dependent effects and habitat heterogeneity. Overall, this study provides important information on the trophic ecology of Cape fur seals breeding in South Africa and highlights the need to consider geographic and individual variability when assessing the foraging ecology of marine predators.Information on resource use and trophic dynamics of marine predators is important for understanding their role in ecosystem functioning and predicting population‐level responses to environmental change. Where separate populations experience different local environmental conditions, geographic variability in their foraging ecology is often expected. Within populations, individuals also vary in morphology, physiology, and experience, resulting in specialization in resource use. In this context, isotopic compositions of incrementally grown tissues such as keratinous hairs offer a valuable opportunity to study long‐term variation in resource and habitat use. We investigated the trophic ecology of female Cape fur seals ( Using stable isotope compositions of serially sampled whiskers, this paper provides a detailed assessment of Cape fur seal trophic ecology across four South African breeding colonies. We focus on geographic differences in diet and habitat use, long‐term temporal trends in isotopic signatures, and geographic differences in individual specialization. Our results highlight clear geographical gradients in the isotopic composition of whiskers, which support recent evidence of spatial differences in habitat use and foraging strategies. Assessing long‐term patterns at the individual level is especially challenging because this requires repeated sampling of the same individual across several years. In recent years, stable isotope analysis (SIA) of consumer tissues has increasingly gained popularity as a means to monitor the foraging ecology of upper trophic level species , and the Research Ethics Committee at the Nelson Mandela University, then known as the Nelson Mandela Metropolitan University (reference: A13‐SCI‐ZOO‐008).2.2Sample collection was carried out at four South African Cape fur seal breeding colonies, namely Kleinsee, Vondeling Island, Seal Island , and Black Rocks (Algoa Bay) during the austral winters of 2014 and 2015 Figure . At eachSample processing and stable isotope analysis were conducted at the School of Ocean and Earth Science, University of Southampton, UK. Prior to isotopic analyses, whiskers were hand‐washed with 100% ethanol and cleaned using distilled water. They were then dried, measured, and sectioned into 3‐mm‐long segments from the proximal  to the distal end (tip). Each section was weighed on a microbalance, and then subsampled by cutting longitudinally, to produce samples in a mass range of (0.3–1.99 mg) and packaged into tin containers. Carbon and nitrogen isotope analysis was performed simultaneously via continuous‐flow isotope ratio mass spectrometry using a Vario Isotope select elemental analyzer, coupled to an Isoprime 100 isotope mass spectrometer. Replicates using internal laboratory standards (L‐glutamic acid (C), Glutamic acid (CT standard), acetanilide and protein standard OAS) were used for quality control and calibration. Long‐term analytical precision assessed from internal standards was better than 0.2‰ for both isotopes. The average C:N ratios for individuals ranged from 2.6 to 2.8, within the acceptable theoretical range for pure keratin  of carbon and nitrogen were expressed as delta values:X is 13C or 15N, Rsample and Rstandard represent the isotopic ratios of the samples and standards (Vienna PeeDee Belemnite and atmospheric N2), respectively.Isotopic ratios  were determined and used to compare isotopic niche width and overlap. SEACs was set to contain approximately 40% of the data and are increasingly being used as a measure of the core isotopic niche for marine predators  were calculated based on 10,000 simulated posterior ellipses. Posterior distributions and overlap of the 95% credibility intervals for SEABs were assessed by means of density plots and probability estimates for differences in isotopic niche width between groups were computed. The degree of isotopic niche overlap between each group  was calculated as a proportion of the overlapping area between the two SEAC ellipses.Inter‐ and intracolony differences in isotopic niche area and overlap were assessed using Stable Isotope Bayesian Ellipses fitted through the Arctocephalus australis , and for all segments available. A non‐parametric Monte Carlo bootstrap technique was adopted to test the significance of the WIC/TNW ratio, by which 1000 replicates were generated to test the null hypothesis that all individuals were generalists. Unless stated otherwise, results are presented as mean ± standard error (SE).The degree of individual specialization for each colony was calculated separately for δ313C values ranged from −14.4‰ to −12.3‰ while δ15N values ranged from 14.2‰ to 17.4‰.Whiskers from 25 female Cape fur seals were analyzed Table . Individ3.113C and δ15N between colonies , and higher values than individuals at Kleinsee (0.4 ‰)  and False Bay (0.6‰), differences between Kleinsee and Black Rocks were not significant  revealed slight, but clear differences in both δes Table . For δ13) Figure . By conty Figure . In addint Table . Pseudo‐C = 0.4‰, SEAB = 0.4 (0.1–0.2)). Probabilistic analysis based on Bayesian estimation further revealed that the isotopic niche width for False Bay was similar to the Kleinsee grouping , p = .08), but was substantially larger than the Vondeling Island , p = .01) and Black Rocks groupings , p < .001). Furthermore, the isotopic niche area for Black Rocks was substantially smaller than the Kleinsee grouping (p < .01) but similar to the Vondeling Island group (p = .09). Kleinsee and Vondeling Island, however, occupied a similar sized isotopic niche (p = .13). The largest isotopic niche overlap was between Vondeling Island and False Bay, with the SEAC overlapping by 30%  showed that the False Bay grouping occupied the largest area , for which the time series were then adjusted to that of the reference whisker. The time series of the 12 remaining whiskers, for which there were no significant cross‐correlations, were left unchanged. On average, whiskers represented isotopic information spanning a period of 4.5 ± 0.3 y (range: 2.6–7.5 y).Wavelet analysis identified significant periodicity in the δs Figure . Periodis Figure . In 18 o3.315N values than for the δ13C values. At all colonies, individual specialization indices (WIC/TNW) were slightly higher for both δ13C and δ15N values, when calculated using the full whisker compared with the first 24 fragments , showing a westward increase in both δ13C and δ15N values  show a westward increase in δ15N, but an eastward increase in δ13C values  exhibited higher degrees of isotopic niche overlap compared with seals from more distant colonies . This is to be expected given the general baseline differences in both carbon and nitrogen ratios along the southern African coast  than for spatial (δ13C) dimensions, which suggests that females may specialize on certain prey types across a variety of habitats. This is consistent with Roughgarden's indices of female Antarctic and South American fur seals ; formal analysis (lead); funding acquisition ; investigation (lead); methodology (lead); visualization (lead); writing – original draft (lead); writing – review and editing . Clive N. Trueman: Conceptualization ; funding acquisition ; investigation ; supervision ; writing – review and editing . Stephen P. Kirkman: Investigation ; methodology ; supervision ; writing – review and editing . John P. Y. Arnould: Conceptualization ; funding acquisition ; investigation ; methodology ; writing – review and editing . Amanda T. Lombard: Funding acquisition ; supervision ; writing – review and editing . Maëlle Connan: Methodology ; writing – review and editing . G. J. Greg Hofmeyr: Data curation ; methodology ; writing – review and editing . S. Mduduzi Seakamela: Data curation ; writing – review and editing . Pierre A. Pistorius: Conceptualization ; funding acquisition ; supervision ; writing – review and editing .The authors declare that there are no competing interestsFigure A1Figure A2Click here for additional data file.