Osteosarcoma (OS), as a typical kind of bone tumors, has a high incidence among adolescents. Traditional tumor eradication avenues for OS such as chemotherapy, surgical therapy and radiation therapy usually have their own drawbacks including recurrence and metastasis. In addition, another serious issue in the treatment of OS is bone repair because the bone after tumor invasion usually has difficulty in repairing itself. Hydrogels, as a synthetic or natural platform with a porous three-dimensional structure, can be applied as desirable platforms for OS treatment. They can not only be used as carriers for tumor therapeutic drugs but mimic the extracellular matrix for the growth and differentiation of mesenchymal stem cells (MSCs), thus providing tumor treatment and enhancing bone regeneration at the same time. This review focuses the application of hydrogels in OS suppression and bone regeneration, and give some suggests on future development. Osteosarcoma (OS), as a well-known primary bone tumor, involves the invasion of tumors into bone tissue and often occurs in children and adolescents . ReportsAs discussed above, it’s vital to ensure the complete resection of OS after surgery but remains difficulty. Besides, radical resection is dangerous because of the complex anatomical structure and blood vessels in the bone tissues . Along wHerein, we discussed the recent advances in the use of hydrogels to achieve OS therapy, with emphasis on suppressing the tumors and Promoting bone regeneration . We beliThe conventional therapy strategies for OS comprise the combination of chemotherapy with surgical methods . Chemothin vivo by MG63-bearing mice.Several researches have demonstrated that hydrogels are capable of treating tumors due to their porous structure and versatile biocompatibility. It’s acceptable to administer therapeutic drugs or functional cells into the resected OS area with the help of hydrogels . With thBecause of the complexity and diversity of tumor pathogenesis, the effect of single chemical drug may be compromised. Thus, synergistic chemotherapy is needed to solve the problem. For instance, Combretastatin A-4 (CA4) are able to bind the tubulin of endothelial cells, disturbing the formation of blood vessel and ultimately, eliciting tumor necrosis through inhibiting the supply of oxygen and nutrients . Unfortuco-glycolide)-poly (ethylene glycol)-poly (lactide-co-glycolide) (PLGA-PEG-PLGA) for delivering CDDP, MTX, and DOX /MNPs, for local and on-demand injection for delivering drugs (doxorubicin (DOX)) (co-AM)/MNP nanogels (GelMA/(poly (NIPAM-co-AM)/MNPs)). The magnetic field and temperature-dependent DOX release from (GelMA/(poly (NIPAM-co-AM)/MNPs)) were evaluated. Lastly, the efficacy of this new form of DOX-carrying drug on pre-osteoblast and osteosarcoma cells was investigated in vivo , and cisplatin (CDDP) based. The clinical efficacy of this regimen was proved by the systemic administration of the aforementioned drugs . Ma et a and DOX . In the n (DOX)) . In this in vivo .in vitro experiment were studied. The DOX-packed hydrogel’s cytotoxicity was evaluated in vitro, in K-7 (mouse osteosarcoma cancer) and Saos-2 (human osteosarcoma cancer) cells. Lastly, the DOX-loaded hydrogel’s antitumor efficacy was determined in vivo in the K-7 mice tumor model. DOX-loaded hydrogel’s systemic toxicity and the safety of its local delivery were assessed by mice’s organ pathological analysis and their survival rate. Similarly, Yu et al. suggested a procedure for local Sun and chlorin e6 (Ce6) delivery by zwitterionic redox-responsive hydrogels for preventing the relapse of osteosarcoma  is not only important for determining the formulation’s concentration, but also for predicting its success in the clinical setting . Even thosarcoma  (Yu et aosarcoma . Yu syntosarcoma . This coosarcoma . This shnditions .10(PO4)6(OH)2, nHA) has been proven to provide nutrition for bone defects and also help repair bone   . Similar (ExCeL) . The keyIn comparison with traditional biometal scaffolds , Young’s polyetheretherketone (PEEK) model resembles more human cortical bone, thereby, alleviating osteoporosis and osteonecrosis risk triggered by stress shielding . Based ovia poly (ethylene glycol) diacrylate (PEGDA) and GelMA hydrogels  and osteoblasts (bone-producing cells) ydrogels  (Jiang eydrogels . When osydrogels . But in Hydrogels are enormous water meshes that characteristically resemble extracellular matrix. These are very porous and have excellent biological compatibility and degradability. They are capable of introducing growth factors that can repair bone defects . TherefoHowever, hydrogel’s application in osteosarcoma is limited for the following reasons, first, despite extensive literature research on hydrogels, clinical applications have encountered bottlenecks, and only a few hydrogels have been approved and commercialized . Additio


LncRNA) have been found to play essential roles in the tumor, and their functions in the ferroptosis of tumor cells have been partially discovered. However, there is no summary of ferroptosis-related LncRNA and its functions in melanoma. In the present study, we aim to explore the expression profile of ferroptosis-related LncRNA genes and their value in melanoma prognosis by bioinformatics analysis. The expression of ferroptosis-related gene (FRG) from melanoma clinical data was extracted based on the Cancer Genome Atlas (TCGA) database. By screening the RNA expression data of 472 cases of melanoma and 810 cases of normal skin, eighteen ferroptosis-related differential genes were found to be related to the overall survival rate. Furthermore, 384 ferroptosis-related LncRNAs were discovered through constructing the mRNA-LncRNA co-expression network, and ten of them were found with prognostic significance in melanoma by multivariate Cox analysis. Risk assessment showed that the high expression of LncRNA00520 is associated with poor prognosis, while the increased expression of the other LncRNA is beneficial to the prognosis of patients with melanoma. From univariate and multivariate Cox regression analysis, there were ten ferroptosis-related LncRNA risk models towards to be significant independent prognostic factors for patients with melanoma and valuable predictive factors for overall survival (OS)(P<0.05). The ROC curve further suggested that the risk score has relatively reliable predictive ability (AUC=0.718). The protein level of ferroptosis-related genes was verified by the HPA database and IHC test, leading to the discovery that the expressions of ALOX5, PEBP1, ACSL4, and ZEB1 proteins up-regulated in tumor tissues, and existed differences between tumor tissues and normal tissues. In conclusion, we identified ten ferroptosis-related LncRNA and constructed a prognosis model base.Ferroptosis is an iron-dependent programmed cell death related to the biological process of many kinds of tumors. Long noncoding RNAs ( Although melanoma is not the most common malignant tumor in skin cancer, it is the leading cause of skin cancer-related death , 2, The As well known, cancer cells, including melanoma cells, have an anti-apoptotic property and exhibit malignancy. Ferroptosis is an iron-dependent cell death dependent on the intracellular accumulation of reactive oxygen species (ROS), distinct from apoptosis and autophagy . As a noIncreasing evidence indicated that ferroptosis plays an essential role in cancer . DysreguAlthough the study of ferroptosis is a relatively new field with fast development, it is still a lack of studies on ferroptosis-related LncRNA genes in melanoma. Thus, this study is aimed to explore the expression profile of ferroptosis-related LncRNA and their value in the prognosis of melanoma by bioinformatics analysis. Meanwhile, using the HPA protein database, we probed into the proteins linked to ferroptosis-related LncRNA genes.RNA sequence (tumor) data and corresponding clinical information of 1416 skin melanoma patients was extracted from TCGA database. The gene expression profiles were normalized by “Limma” R software installation package. The gene expression of normal skin tissue was extracted from the GTEX data sets from UCXC database. Then the intersection of the genes of TCGA and GTEX data sets was made and merged. Both TCGA data and UCXC data are public, thus the study exempts the approval of local ethics committees.https://cn.string-db.org/) network interaction and co-expression correlation analysis on these genes were performed.The differentially expressed genes in tumor and non-tumor tissues in the TCGA cohort were screened by “LIMMA” R software package. The overall survival rate (OS) was analyzed by univariate Cox analysis to screen ferroptosis-related genes with prognostic value. Subsequently, heat map visualization, PPI  × expr(lncRNA1) + coef(lncRNA2) × expr(lncRNA2) +… + coef (lncRNAn) × expr(lncRNAn). Coef (LncRNAn) is defined as the lncRNAs coefficient related to survival. ExpR (LncRNAn) is defined as the expression of lncRNAs. According to the median risk score, melanoma patients in TCGA were divided into high-risk group and low-risk group. Kaplan-Meier survival analysis used survival and Servicer R package to estimate the survival difference between the two groups.In order to evaluate the relationship between survival and clinicopathological factors and risk score, univariate and multivariate Cox regression analysis was performed using the SurvivalR software package. The time-dependent receiver operating characteristic (ROC) curve was drawn by the Survival ROC R software package, and the prediction accuracy of different clinicopathological factors and risk scores on survival time was estimated.https://www.gSea-msigdb.Org/gsia/index.jsp) is a powerful analytical method to explain the whole genome expression profile, and detect the relationship between several cancer-related pathways, metabolic pathways, transcriptional procedures and stress responses in biological processes. P < 0.05 was considered to be statistically significant.Gene set enrichment analysis (GSEA) was used for functional annotation. GSEA (https://www.proteinatlas.org/) uses transcriptome and proteomics techniques to study protein expression in different human tissues and organs at RNA and protein levels. Up to now, more than 26000 kinds of antibodies have been collected in HPA. The protein expression information in HPA was from the immunohistochemistry (IHC) staining and confirmed by professionals with high accuracy and credibility. Therefore, we use the HPA database to verify the protein level of ferroptosis-related genes.HPA (human protein atlas) protein database  tissues, and used monoclonal antibodies including anti-ALOX5 , Anti-PEBP1 , Anti-ACSL4  and Anti-ZEB1 , which was operated on BENCHMARK automatic immunohistochemical instrument. Positive and negative controls were carried out according to the manufacturers’ recommendations. Immunoreactivity result was recorded as negative without tumor cell expression, conversely as positive.All statistical analyses were carried out using R software (version 4.0.3). Normally and non-normally distributed variables were analyzed using the unpaired student’s t-test and the Wilcoxon test, respectively. The co-expression networks of ferroptosis-related lncRNAs-mRNAs with prognostic value were established. Based on FDR, Benjamini-Hochberg method was used to identify the differently expressed lncRNAs.We uncovered the differentially expressed ferroptosis-related genes in tumor tissues and paracancerous tissues. Univariate Cox regression analysis identified 18 OS-associated genes  with prognostic value. COX regression analysis showed that 46 ferroptosis-related LncRNAs were significantly associated with TCGA melanoma patients (P <0.01), 37 of which were low risk (risk ratio: HR <1), 9 as high-risk (risk ratio: HR> 1). Multivariate Cox analysis further screened 10 prognostic ferroptosis-related LncRNAs. These 10 LncRNAs form an optimal pre-rear risk model of ferroptosis-related LncRNA. As shown in According to the risk score formula and the calculated median risk score, we divided melanoma patients into high-risk and low-risk groups. In this study, we drew ten lncRNA survival curves with prognostic value. Despite that LncRNA00520 represents the poor prognosis of patients with melanoma, the high expression of the other several LncRNAs was beneficial to the prognosis of patients with melanoma Figure 3Kaplan-Meier survival analysis demonstrated that the overall survival rate (OS) of the high-risk group was worse than that in the low-risk group (p <0.001) . The results revealed that most of the ten ferroptosis-related lincRNAs correlate with the metabolism signaling pathway. In the high expression group, ferroptosis-related lincRNAs regulated the glyoxylate and dicarboxylate metabolism signaling pathway, oxidative phosphorylation signaling pathway, and pentose phosphate pathway. As meanwhile, in the low expression group, they trend to affect the JAK-STAT signaling pathway, nod like receptor signaling_pathway, and toll-like receptor signaling pathway Table 1.http://www.proteinatlas.org) database to verify four ferroptosis-related genes. The results showed that all four ferroptosis-related genes were expressed in varying degrees in melanoma, as shown in We used the human protein map , and four ferroptosis-related proteins were obtained to understand further the protein function ALOX5, PEBP1, ACSL4 and ZEB1. Moreover, IHC verification in subsequent clinical specimens showed that these ferroptosis-related proteins were highly expressed in melanoma compared with normal tissues. ALOX5, belonging to the Human lipoxygenases (LOX) family  family –36. It m) family . AlthougMoreover, phosphatidylethanolamine binding protein 1 (PEBP1) is a conserved protein, isolated initially based on its ability to bind phospholipids . PEBP1 aAcyl-CoA synthetase long-chain family member 4 (ACSL4) is a vital isoenzyme in the metabolism of polyunsaturated fatty acids, which determines the sensitivity of ferroptosis. ACSL4 disorders are associated with a variety of malignant tumors, including gastric , breast Zinc-fingerE-box-bindinghomeobox1 (ZEB1) is a transcriptional regulator of epithelial-mesenchymal transformation of (EMT) , 45. ConIn the present study, we systematically employed bioinformatics to analyze the ferroptosis-related differential genes in melanoma and identified independent prognostic differential genes. Through the association of ferroptosis-related mRNA-LncRNA, an effective prognostic model based on the risk score was successfully constructed. Our results provided a direction for prognosis assessment in melanoma patients, and the four ferroptosis-related LncRNA might be used as new biomarkers for melanoma.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.YR and JZ carried out the studies, participated in collecting data, and drafted the manuscript. HZ and WD performed the statistical analysis and participated in its design. QL provided help and advice on administration. YR, JZ, HZ, and QL wrote the manuscript. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript.This work was supported by the Project of biobank (No.YBKB201911) from Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine and the Cross disciplinary Research Projects of Ninth People’s Hospital, Shanghai JiaoTong university School of Medicine (JYJC202106).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Neural oscillations support fundamental mechanisms of information processing in neural networks  signals in cognitively impaired and normal oldest-old adults (90+ years old) in terms of its relationships to their cognitive status. In the impaired oldest-old participants, increased theta but reduced beta power was found in frontoparietal regions and the default mode network, indicative of cortical slowing. Engagement during demanding cognitive tasks (indicator of cognitive reserve) was associated with stronger connectivity in the alpha and beta bands. Overall, the oscillatory changes in the oldest-old could not be readily distinguished from individuals younger than 85 years.Fröhlich et al. examined differences in spectral power and oscillatory reactivity in 80+-year old adults across different cognitive status . No differences in power during eyes-closed condition were found between healthy individuals and those with cognitive impairments. The authors noted that these findings might be related to anatomical changes associated with advanced aging such as cortical thinning which could lower baseline EEG amplitudes. Although this was not directly addressed in the study, the report highlights the importance of additional anatomical information in this population to reliably interpret scalp-level oscillations.Doan et al. examined the utility of resting-state and sensory and task-related EEG measures to predict dementia severity based on MMSE scores. After adjusting for demographic confounds, prefrontal EEG measures were found to be highly correlated with MMSE. Furthermore, relationships within EEG measures, including peak frequency, median frequency, alpha-to-theta ratio, alpha asymmetry, and theta-band power indicated increased risk of dementia. This preliminary evidence suggests a potential role of rsEEG as a screening tool. But larger-scale studies will need to substantiate these findings with cautious study designs, matching dementia and non-dementia groups on important demographic variables.Smailovic et al. investigated the relative potential of rsEEG power, rsEEG connectivity and novel (neurogranin) and conventional CSF markers (amyloid and tau pathology) to differentiate subtypes of amnestic MCI. The strongest discriminator between single-domain and multi-domain MCI was a connectivity measure—global synchrony—in theta and delta bands. Connectivity in slow frequencies was related to early effects of AD-specific molecular pathology, further promising the utility of rsEEG measures as potential biomarkers of dementia.Lopez et al. modeled connectivity hubs from resting-state alpha measurements in AD. Although both controls and individuals diagnosed with AD had significant parietal “degree” and “connector” hubs derived from alpha rhythms, outward directionality of parietal hubs was lower in AD.Spironelli and Borella examined short- and long-term effects of working memory (WM) training among healthy older adults on behavioral and rsEEG-based oscillatory indices. Specific training, maintenance and transfer effects were reported in the WM treatment group compared to the active control group. For oscillatory responses, the treatment group showed increased oscillatory responses in bilateral anterior sites, which were correlated with better post-training performance.Rodríguez-González et al. implemented a network-based approach that considered complex interactions among neurophysiological, cognitive (MMSE), and behavioral  variables to assess treatment outcomes. Together the results indicate that changes in EEG parameters can serve as indicators of treatment-related changes in cognition and behavior.Complementary to the assessment of resting-state power, Eyjolfsdottir et al. examined the effects of targeted encapsulated cell biodelivery of nerve growth factor (NGF-ECB) in the basal forebrain (a treatment approach for AD) on rsEEG parameters and cognition over a 12-month period. Increased theta power was associated with a decrease in CSF cholinergic marker (ChAT), whereas increased alpha power was related to increased ChAT and stabilization of MMSE scores.Zhang et al. examined rsEEG oscillatory and non-oscillatory changes induced by dopaminergic medication in patients with PD. Beta-band phase synchronization was up-regulated by medication. Medication also increased the spectral slope of broadband non-oscillatory component, suggesting that spectral slope could serve as a marker of global efficiency.In summary, cognitive impairment is found to be associated with increases in slow frequency and reduction in higher frequency cortical rhythms. Oscillatory reactivity is also modulated by behavioral and pharmacological interventions, and can serve as an indicator of treatment success alongside with other biomarkers. All studies here examined rsMEEG measures mainly including spectral power, ratio between bands, shifts in bands, and connectivity using phase- or amplitude-based metrics, suggesting possible mechanistic roles of oscillations.This Research Topic provides new methodologies and results with a potential to advance research and clinical practice for aging populations. Most reported results are preliminary, and signal a need for robust, well-designed, large-scale clinical trials with a focus on spectral oscillatory measures to replicate the said findings. Longitudinal studies are needed to validate reliability in predicting cognitive status. Specific to connectivity, approaches that overcome known challenges regarding volume conduction, linear mixing, and signal-to-noise ratio should be used (Palva and Palva, AK: conceptualization, writing, review, and editing. PS-B and LM: writing, review, and editing. TF: review and editing. All authors contributed to the article and approved the submitted version.


This finding emphasizes the importance of concomitant screening of the anal region in case of cervical HR-HPV.High-risk HPV (HR-HPV) infection is an established risk factor of cervical cancer. Cases of anal cancer are increasingly observed in women. Since anal infection by HR-HPV is also a risk factor for anal cancer, the aim of this study was to investigate the relation between cervical and anal HPV and dysplasia, as their interaction remains unclear. The present study identified anal HR-HPV as an independent risk factor for patients with cervical HR-HPV , had earlier sexual intercourse , had more sexual partners , more dysplastic cervical cytology findings  and higher prevalence of anal HR-HPV . Furthermore, the HR-HPV group reported more anal intercourse . Multivariate analysis retained anal HR-HPV as independent risk factor for cervical HR-HPV . The results of this study emphasize that it is of upmost importance to screen women for anal HR-HPV when diagnosing cervical HR-HPV.Anogenital human papillomaviruses (HPV) are highly prevalent in sexually active populations, with HR-HPV being associated with dysplasia and cancers. The consequences of cervical HPV infection are well-known, whereas those of the anus are less clear. The correlation of cervical and anal HPVs with the increasing number of anal cancers in women has not been studied yet. The objective of our prospective study was to determine whether cervical and anal HPV correlated in a cohort of women recruited in a university hospital in Switzerland. Recruitment was conducted in the gynecology clinic, the colposcopy clinic, and the HIV clinic. Cervical and anal HPV genotyping and cytology were performed. Overall, 275 patients were included , and among them, 102 (37%) had cervical HR-HPV. Patients with cervical HR-HPV compared to patients without cervical HR-HPV were significantly younger (39 vs. 44 yrs,  Human papillomaviruses (HPV) are naked, double-stranded DNA viruses that rely on epithelium differentiation for productive infections. More than 200 different genotypes infect humans, having a tropism for specific epithelia such as skin, oral, and anogenital mucosa. Upon infection of the basal cells, viral DNA replication is tightly controlled by the host cell as well as viral factors. Viral production is intimately linked to terminal differentiation of the infected epithelial cell so that virions are produced only in the upper layers of the infected epithelium. Most infections clear spontaneously under immunological host control. However, long-term persistence over years can be observed. In those cases, cell host alteration and viral oncogene expression can lead to cellular abnormalities, and potentially to cancer .Anogenital HPVs are mainly sexually transmitted. They can infect various epithelia such as the vulva, vagina, cervix, penis, oropharynx, and anus. Specific HPV genotypes considered as high-risk (HR-HPV) are a necessary cause of most of cervical cancers . The samHIV is a major risk factor of recurrent HPV infection. Other risks such as tobacco use, men having sex with men (MSM), early onset of sexual activity, or multiple sexual partners have also been identified . CervicaThe aim of the present study was to determine if patients with cervical HR-HPV infection had concomitant infection of the anus with HR-HPV as well.ANGY for AnusGynecology. This prospective, cross-sectional, single-center study included women recruited at the Lausanne University Hospital (CHUV), a tertiary university hospital in Switzerland. Patients to be classified either in the cervical HR-HPV-positive group or cervical HR-HPV-negative group were recruited in three outpatient clinics: (1) gynecology clinic (patients with regular problems and follow-up); (2) specialized colposcopy clinic; and (3) HIV clinic. Total number of patients to include was 360, 120 from each of the three outpatient clinics. Based on the prevalence of anal dysplasia in each of the three groups , a sample size of 330 patients was calculated. Considering 10% margin error, the final sample size was set at 360 patients.The study was named Cervical and anal screening of HPV genotypes and cytology were performed by board-certified gynecologists and colorectal surgeons. The operation was completed during a single appointment. A cervical cytology smear (for PAP-test and HPV test), with biopsies if needed , was conducted after vaginal examination and colposcopy on a gynecological chair. The proctology team conducted a proctology examination with high-resolution anoscopy (HRA), along with cytological smears of the anal canal . Using a dedicated brush, ectocervical, endocervical, and anal samples were performed. HPV tests were performed with the MagNaPure 96 Total Nucleic Acids kit , with genotyping of 28 HPV genotypes  by the Anyplex™ II HPV28 kit .Furthermore, all patients were screened for HIV. A self-assessed questionnaire exploring sexual habits and HPV immunization status was submitted to patients as well . FollowiThe study was approved by the Institutional Review Board of “Commission Cantonale d’éthique Vaud” . Informed consent was obtained from all subjects involved in the study.Recruitment was conducted between September 2016 and October 2020, with premature study discontinuation of the clinical trial due to difficulties in recruiting patients and related to COVID-19 epidemic-related restrictions. Statistical power for the main outcome, cervical HR-HPV, was not impacted in the present statistics due to its high prevalence.p values lower than 0.05 were considered as significant. Variables yielding p values lower than 0.05 by univariate analysis were entered into a multivariate logistic regression model to determine variables independently associated with the presence of cervical HR-HPV. All statistical analyses were carried out using XLstat Biomed software .Patients were divided into two groups according to their cervical HR-HPV: positive or negative. The chi-squared test was applied to compare qualitative variables that were expressed as n (percent). The ANOVA test was used to compare quantitative variables that were reported as mean ± standard deviation (SD). Overall, 275 participants were enrolled until premature study discontinuation. Patient demographics are presented in Univariate analysis of cytopathologic factors associated with cervical HR-HPV is shown in p-value = 0.02), smoking , cervical LR-HPV , and anal HR-HPV  as factors independently associated with cervical HR-HPV  is debated, as the prevalence of cancer is low . The appYounger age was identified as an independent risk factor for cervical HR-HPV. As we know to date, there is a clearing of HPV in human cells over time, with some genotypes being more carcinogenic. Young patients tend to have a higher viral load but also different concomitant subtypes of genotypes .https://monographs.iarc.who.int/wp-content/uploads/2018/06/mono90.pdf (accessed on 17 October 2022)), stating that HR-HPVs, when they are found, are more often present with LR-HPVs. These findings support the hypothesis of coinfection/cohabitation models [Low-risk HPV was retained as risk factor for cervical HR-HPV. This is in line with the literature, as stated by Cogliano et al., along with the IARC classification of HPV genotypes , we should expect to see lower rates of HPV in women and decreasing cancer cases ,17,18. AIt could be very interesting to follow up with our participants, in order to observe the evolution of HPV prevalence over time, and the eventual development of cancers. On the opposite, one should not forget the natural course of HPV infection, with most of the viral load being cleared from cervical and anal cells as time passes . Several limitations of the present study need to be addressed. There is an inherent weakness due to the cross-sectional design of the study. For instance, the difficulty to reliably assess both HPV and dysplasia impedes the identification of a causal inference. However, as highlighted by Wang et al., the design is useful to establish preliminary evidence before planning future large-scale studies . The ratIn our experience, anal screening during a gynecologic exam is not acceptable for many women. The results of this study may help to increase the acceptance of anal screening.The strength of the present study is the prospective collection of concomitant samples from both the anus and the cervix, not only in patients with known cervical dysplasia, but also in a comparative group without any lesions. This study supports the assumption, as suggested by Acevedo-Fontánez et al., that women with gynecological HPV-related malignancies are more likely to develop secondary anal cancer than women in the general population .In conclusion, genital HPV is highly prevalent in women, and high-risk genotypes should be closely monitored in order to prevent cervical and anal dysplasia as well as cancer development. Most importantly, when cervical HR- HPV is diagnosed, anal screening for concomitant HPV or dysplasia should be proposed, as there can be dramatic consequences for the health of our patients if neglected.


However, they may not be optimal for predicting promoter-centered chromatin interactions.Promoter-centered chromatin interactions, which include promoter–enhancer (PE) and promoter–promoter (PP) interactions, are important to decipher gene regulation and disease mechanisms. The development of next-generation sequencing technologies such as promoter capture Hi-C (pcHi-C) leads to the discovery of promoter-centered chromatin interactions. However, pcHi-C experiments are expensive and thus may be unavailable for tissues/cell types of interest. In addition, these experiments may be underpowered due to insufficient sequencing depth or various artifacts, which results in a limited finding of interactions. Most existing computational methods for predicting chromatin interactions are based on We develop a supervised multi-modal deep learning model, which utilizes a comprehensive set of features such as genomic sequence, epigenetic signal, anchor distance, evolutionary features and DNA structural features to predict tissue/cell type-specific PE and PP interactions. We further extend the deep learning model in a multi-task learning and a transfer learning framework and demonstrate that the proposed approach outperforms state-of-the-art deep learning methods. Moreover, the proposed approach can achieve comparable prediction performance using predefined biologically relevant tissues/cell types compared to using all tissues/cell types in the pretraining especially for predicting PE interactions. The prediction performance can be further improved by using computationally inferred biologically relevant tissues/cell types in the pretraining, which are defined based on the common genes in the proximity of two anchors in the chromatin interactions.https://github.com/lichen-lab/DeepPHiC.Bioinformatics online. However, the distance of the two regions on chromosome can be tens of kilobases (kb) away from each other. Various experimental approaches have been developed for detecting chromatin interactions, which can be captured at the level of a single locus by low-throughput 3C, 4C, a set of loci by medium-throughput 5C, ChIA-PET and genome-wide loci by high-throughput itu Hi-C . HoweverPromoter–enhancer (PE) interactions, where enhancer initializes or activates the level of transcription of the targeted gene, is well studied. Promoter–promoter (PP) interactions, which have been reported in cultured mammalian cells and a few primary tissues, also play an important role in transcriptional coordination in the way that proximal and distal genes are active and transcribed cooperatively through PP interactions , which uses DNA sequence in two anchors to predict PE interactions   . ParticuDespite the success of the previous work, there are still several challenges. First, to our best knowledge, there is no deep learning approach yet to include genomic sequence and epigenetic signal in the anchors as well as anchor distance into an integrative framework. Second, deep learning approaches, which target to predict tissue/cell type-specific PP and PE interactions in a broad context, is underdeveloped. Third, the sample size of labelled chromatin interactions can be moderate, which makes the training set less powerful for achieving a genome-wide prediction. In this work, we develop a multi-modal deep learning framework named DeepPHiC, which is based on the densely connected CNN, to predict both PP and PE interactions. The contribution of our work lies on several aspects: (i) DeepPHiC is trained and evaluated on a comprehensive set of pcHi-C data including 27 tissues/cell types; (ii) DeepPHiC utilizes a comprehensive set of informative features, ranging from genomic sequence, epigenetic signal in the anchors and anchor distance; (iii) an extension of DeepPHiC to a multi-task learning and transfer learning framework further improves the prediction performance; (iv) DeepPHiC is released as an open-source python toolkit, which can benefit the genomic research community. As a result, we demonstrate that inclusion of more informative features leads to improved prediction performance; the multi-task learning and transfer learning framework further improve the prediction performance. Importantly, DeepPHiC outperforms state-of-the-art deep learning approaches for the similar purpose and is robust to the inclusion of anchor distance as a feature. Finally, we find that using predefined biologically relevant tissues/cell types for multi-task learning and transfer learning of DeepPHiC can achieve comparable performance to using all tissues/cell types, especially for predicting PE interactions. The prediction performance can be further improved by using biologically relevant tissues/cell types, which are defined based on gene similarity in the proximity of anchors in the chromatin interactions.We collect and curate a deposition of promoter-centered chromatin interactions in 3D-genome Interaction Viewer and database (3DIV)  as the positive set and only keep tissues/cell types with more than 100 significant interactions. As a result, the number of positive samples ranges from 972 to 56 057 with a median of 5071 among 26 tissues/cell types for PE and from 187 to 10 338 with a median of 917 among 25 tissues/cell types for PP. To create the negative set, we select interactions (FDR >0.5) with matched GC contents as the positive set. Without loss of generality, the number of negative interactions is set as the same as the positive interactions. More details of the distribution of positive interactions in tissues/cell types can be found in To create the labelled training set, we filter chromatin interactions with distance more than 10To create the epigenetic signals in the anchors, we collect the matched tissue/cell type-specific epigenetic datasets from Roadmap Epigenomics Project , multi-task paradigms of DeepPHiC (DeepPHiC-ML) and transfer learning paradigms of DeepPHiC (DeepPHiC-TL). DeepPHiC-Base is trained for each tissue independently with 70% labelled data for training, 15% for validation, 15% for testing, and no data from other tissues will be used. For DeepPHiC-ML, chromatin interactions from other tissues except tissue of interest are used to pretrain the shared feature extractor. Chromatin interactions from tissue of interest are used to train the tissue-specific feature extractor for obtaining the private features, together with the common features from the shared feature extractor, to train the tissue-specific classifier. For DeepPHiC-TL, all tissues except the tissue of interest are used to pretrain the shared feature extractor, and fine-tuning of DeepPHiC is performed for the whole network. For both DeepPHiC-ML and DeepPHiC-TL, 85% labelled data are used for training and 15% for validation in pretraining the shared feature extractor, 70% labelled data are used for training and 15% for validation in fine-tuning the whole network, and 15% for independent testing the whole network.To demonstrate the superior performance of DeepPHiC, we compare DeepPHiC to four state-of-the-art deep learning methods with the similar purpose to predict chromatin interactions. Specifically, SPEID is CNN, which only uses genomic sequence in the anchors as the input features. Similarly, DeepMILO also utilizes genomic sequence in the anchors but extends the model by integrating a deep CNN and a bidirectional LSTM network, which will capture both local and long distance sequence dependency. Particularly, ChINN and DeepTACT are two multi-modal deep learning models. ChINN use both anchor distance and genomic sequence in the anchors as the input features. DeepTACT integrates both genomic sequence in the anchors and chromatin accessibility in the anchors as the input features to predict both promoter-enhancer interactions and PP interactions. We use both area under the curve (AUC) and area under the precision recall curve (AUPRC) to evaluate the prediction performance for all methods. To alleviate the bias of random sampling, for each model, we randomly split the whole labelled data 10 times into training, testing and validation sets and report AUC and AUPRC for 10 experiments.Most existing deep learning approaches with the similar purpose adopt the genomic sequence in the anchors as the model input such as SPEID and DeepMILO. Other approaches expand the feature sets by including anchor distance (e.g. ChINN) and epigenetic signal in the anchors (e.g. DeepTACT). Nevertheless, none of these approaches consider all three types of features simultaneously. Without loss of generality, we use labelled chromatin interactions from both PE and PP in Lymphoblastoid cells (GM) to demonstrate that integrating all three types of features together will achieve the best prediction performance. Since DeepPHiC is a multi-modal model, which takes sequence (S), read (R) and distance (D) as the model input, we adapt DeepPHiC to make it accommodate to arbitrary combinations of feature sets in the feature extractor accordingly .As a result, we find that using all three types of features (S + R + D) achieves the highest AUC and AUPRC in predicting both PE and PP, followed by sequence and distance (S + D), and distance (D) . InteresFor each tissue, we train DeepPHiC-Base, which is trained only using labelled chromatin interactions from this tissue. We also train DeepPHiC-ML and DeepPHiC-TL, both of which use labelled data from both tissue of interest and other tissues. We evaluate all three paradigms of DeepPHiC to predict PE in 26 tissues and PP in 25 tissues, and report AUC and AUPRC in 10 experiments, respectively.We use ‘mAUC’ to represent median of AUCs in 10 experiments. For each tissue, we calculate the rank of mAUC for three paradigms of DeepPHiC. For PE prediction, DeepPHiC-TL has the best performance in 22 out of 26 tissues, followed by DeepPHiC-ML, which achieves the second best performance in 17 out of 26 tissues. However, DeepPHiC-Base has the lowest AUC in 16 out of 26 tissues. Similarly, for PP prediction, DeepPHiC-TL has the highest AUC in 23 out of 25 tissues, followed by DeepPHiC-ML, which obtains the second best performance in 21 out of 25 tissues. In contrast, DeepPHiC-Base has the lowest AUC in 21 out of 25 tissues . The aboSince both DeepPHiC-ML and DeepPHiC-TL improve the learning for common feature space by leveraging labelled chromatin interactions from other tissues, the advantage of DeepPHiC-ML and DeepPHiC-TL to DeepPHiC-Base is more evident for tissue with small amount of labeled data. It is because learning common feature space may be underfitting by small amount of labelled data. For PE prediction, the improvement of AUC is more evident for tissues with a small amount of labelled data such as SB (972), LG (1188), LV (1565) and ME (1045), compared to tissues with a large amount of labelled data such as FT2 (56057), TB (83604) and EG2 (20049), where the three paradigms of DeepPHiC show a comparable performance. Similarly, for PP prediction, among tissues with a small amount of labelled data such as OV2 (187), RV (723), SB (704), TH1 (1066), BL1 (555), NPC (546), LG (661), CM (962), LV (968), LI111 (706), TB (444), GA (900), H1 (917), PA (396), AO (788), the improvement of DeepPHiC-ML and DeepPHiC-TL is more apparent compared to tissues with a large sample size such as GM (4446), SX (5866), X5628FC (6224), MSC (4168), IMR90 (4156), HCmerge (2519), ME (3142), AD2 (5421) and SG1 (10338). Overall, these observations indicate that DeepPHiC-ML and DeepPHiC-TL indeed improve the prediction performance for tissues with a small amount of labelled data. For tissues with a large amount of labelled data, DeepPHiC-ML and DeepPHiC-TL are on par with DeepPHiC-Base. Moreover, since PP are much fewer than PE, it is more favorable to apply DeepPHiC-ML and DeepPHiC-TL for predicting PP.Multiple deep learning models, such as DeepTACT, DeepMILO, SPEID and ChINN, have been developed to predict genome-wide chromatin interactions, which vary in the training set, the types of input features and the network architecture. To make a fair comparison between DeepPHiC and these methods, we use the same training, validation and testing sets for all methods, which are derived from labelled PE in 26 tissues/cell types and labelled PP in 25 tissues/cell types. For competing methods, 70% labelled data is used for training, 15% for validation and 15% for testing for each tissue. For DeepPHiC, we use DeepPHiC-TL as it has been demonstrated to outperform DeepPHiC-Base and DeepPHiC-ML.For each tissue, we use ‘mAUC’ to represent median of AUCs in 10 experiments and calculate the rank of mAUC for each method. For each method, we summarize the ranks across all tissues. Consequently, DeepPHiC achieves the best performance in all 26 tissues for PE prediction, followed by ChINN, which obtains the second best performance in 25 out of 26 tissues. Nevertheless, SPEID, DeepMILO and DeepTACT have less desirable but comparable performance. Among all methods, we further compare the median of mAUCs across all tissues by using two-sided Wilcoxon rank sum test .P-value = 4.115P-value = 2.049P-value = 1.504P-value = 4.033P-value = 2.294P-value = 3.006P-value = 1.108P-value = 1.582As expected, DeepPHiC obtains the highest median of mAUCs for PE prediction, followed by ChINN . The improvement of DeepPHiC over other methods is also significant  tops other methods and ChINN (S + D) has the second best performance. This observation is also consistent with the previous finding that S + R + D achieves the best performance, followed by S + D . MoreoveTo evaluate the robustness of DeepPHiC under different FDR cutoffs, we perform additional experiments by changing FDR cutoffs for defining the positive and negative set . Consequently, DeepPHiC still outperform other approaches by achieving higher AUC and AUPRC in the majority of tissues/cell types across different FDR cutoffs . Since tSo far, the training set is created based on the confidence of chromatin interactions with matched GC content between the positive set (FDR <0.1) and the negative set (FDR <0.5). Another way to create the negative set is to match the anchor distance between chromatin interactions in the positive set and the negative set (e.g. DeepTACT). In this case, distance is no longer used as a feature for model input. To demonstrate DeepPHiC is still robust without including anchor distance as a feature, we remove the anchor distance from both DeepPHiC and ChINN and use same aforementioned experimental design to compare all methods in terms of AUC and AUPRC.P-value <0.05 from a two-sided Wilcoxon rank sum test. However, the performance of ChINN declines more compared to DeepPHiC, and ChINN is on par with other approaches. Similarly, for PP prediction . Hence, we evaluate the prediction performance of DeepPHiC-TL and DeepPHiC-ML by utilizing predefined biologically relevant tissues/cell types. Particularly, we consider three groups of tissues/cell types: (i) cell types relevant to H1 (H1 group), which include H1 embryonic stem cell (H1), H1-derived mesendoderm cell (ME), H1-derived mesenchymal stem cell (MSE) and H1-derived neuronal progenitor cell (NPC); (ii) brain regions (Brain group), which include Hippocampus (HCmerge) and Dorsorateral prefrontal cortex (X5628FC); (iii) cell types relevant to heart (Heart group), which include cardiomyocytes (CM), aorta (AO), left ventricle (LV) and right ventricle (RV).For each group of tissues/cell types, we perform the same aforementioned procedure to train DeepPHiC-TL (group) and DeepPHiC-ML (group) respectively. For DeepPHiC-ML (group), chromatin interactions in the group except tissue/cell type of interest are used to pretrain the shared feature extractor, chromatin interactions from tissue/cell type of interest are used to train the tissue-specific feature extractor. The integrated features from both the common features and private features will connect to the tissue-specific classifier for predicting tissue-specific chromatin interactions. For DeepPHiC-TL (group), chromatin interactions in the group except the tissue/cell type of interest is used to pretrain the common feature extractor, and the chromatin interactions from the tissue/cell type of interest is used to fine-tune the DeepPHiC-TL (group).Consequently, for PE prediction, DeepPHiC-TL (group) outperforms DeepPHiC-TL in most cell types in H1 group  and is comparable to DeepPHiC-TL in Brain group and Heart group except left ventricle (LV) . In contIn addition to using predefined biologically relevant tissues/cell types, we design another approach, which can computationally infer the biological similarity among tissues/cell types based on the common genes in the proximity of two anchors in the chromatin interactions. Specifically, we obtain the nearest genes of both anchors in chromatin interactions from the positive set and define the biological similarity between two tissues/cell types as the proportion of common genes among all genes in both tissues/cell types. Consequently, for each tissue/cell type, top tissues/cell types of highest biological similarity to the tissue/cell type of interest can be selected for pretraining. Here, we choose top five tissues/cell types for pretraining DeepPHiC-TL, which is denoted as DeepPHiC-TL (Top5). Then, we compare DeepPHiC-TL (Top5) to the default setting of DeepPHiC-TL, which utilizes all tissues/cell types except the one of interest in the pretraining. As a result, we find that DeepPHiC-TL (Top5) outperforms DeepPHiC-TL in 20 out of 26 tissues/cell types for PE prediction and in 19 out of 25 tissues/cell types for PP prediction in terms of AUPRC. Both approaches also keep a similar trend in terms of AUC . TherefoWe have evaluated that the integration of sequence feature, read feature and distance feature will improve the prediction performance for DeepPHiC. Here, we further evaluate two additional features, evolutionary feature and DNA structural features, which can be directly inferred from DNA sequence, on the prediction performance of DeepPHiC. To obtain the evolutionary feature, we retrieve phastCons scores, which represent probabilities of negative selection and range between 0 and 1 for each nucleotide on the human genome using R/Bioconductor package ‘phastCons100way.UCSC.hg19’ on the DNA sequence of the anchors in each chromatin interaction. We smooth nucleotide-level evolutionary features in a window size of 1000 with a sliding size 50, yielding 21 evolutionary features for each anchor. Then, we integrate the evolutionary feature with sequence feature, read feature and distance feature into DeepPHiC, which is denoted as DeepPHiC-ev. Afterwards, we compare DeepPHiC-ev and DeepPHiC in the transfer learning paradigm and find that DeepPHiC-ev improves the prediction performance for both PE and PP in terms of AUC and AUPRC . TherefoFor DNA structural features . TherefoIn the previous sections, we’ve demonstrated that DeepPHiC improves the prediction for both PE and PP. Here, we explore the possibility of ‘cross prediction’, where DeepPHiC is trained on PE and used to predict PP (PE-to-PP), and vice versa (PP-to-PE). We include all aforementioned deep learning models in comparison and report the prediction performance in terms of AUC . As a reIn this work, we develop a multi-modal deep learning framework named DeepPHiC, which is based on the densely connected CNN, to predict promoter-centered chromatin interactions such as PE interactions and PP interactions by leveraging a comprehensive sets of pcHi-C datasets across multiple tissues/cell types. Different from existing approaches, DeepPHiC utilizes a comprehensive set of informative features, ranging from genomic sequence and epigenetic signal in anchors as well as anchor distance. We find that anchor distance is an informative feature, which indicates that anchors in promoter-centered chromatin interactions are closer to than random anchors in the genome. Using all three feature types together achieves better prediction performance than using arbitrary combinations of feature sets as model input. In addition, we find other informative features such as evolutionary feature and DNA structural features can further improve the prediction performance of DeepPHiC.We further develop two paradigms of DeepPHiC, which is based on multi-task learning (DeepPHiC-ML) and transfer learning (DeepPHiC-TL) respectively. The key idea of DeepPHiC-ML and DeepPHiC-TL lies on using chromatin interactions from other tissues to boost the learning for common feature space and thus fewer labelled data from targeted tissue is needed to train the tissue-specific prediction model, which in turn will improve the prediction for chromatin interactions in the targeted tissue. To demonstrate the effectiveness of the two paradigms of DeepPHiC, we compare both DeepPHiC-ML and DeepPHiC-TL to the baseline of DeepPHiC (DeepPHiC-Base), which is trained only using chromatin interactions in the targeted tissue, for both PE and PP prediction. Consequently, DeepPHiC-ML and DeepPHiC-TL outperforms DeepPHiC-Base in both PE and PP prediction, and DeepPHiC-TL achieves best prediction performance for both PE and PP prediction. Moreover, the advantage of DeepPHiC-ML and DeepPHiC-TL is more evident for tissues with scarce labelled data. As PP is much fewer than PE, DeepPHiC-ML and DeepPHiC-TL hold a more clear advantage in PP prediction than PE prediction. Due to the advantage of DeepPHiC-TL, we adopt DeepPHiC-TL as the default implementation when using DeepPHiC.Furthermore, we compare DeepPHiC to state-of-the-art deep learning approaches with the similar purpose of predicting 3D chromatin interactions. As a result, DeepPHiC outperforms other competing approaches in predicting both PE and PP, which include SPEID and DeepMILO utilizing only genomic sequence in the anchors, ChINN using both anchor distance and genomic sequence in the anchors, and DeepTACT adopting genomic sequence and chromatin accessibility in the anchors. We find that DeepPHiC has the best performance and holds a clear advantage to other methods. Again, the improvement of DeepPHiC is more evident for tissues with a small amount of labelled data, especially for PP prediction. Moreover, we design a ‘cross prediction’ strategy, where DeepPHiC is trained on PE and then used to predict PP, and vice versa. Still, we find a consistent trend that DeepPHiC and ChINN outperform other competing approaches.Two important factors may affect the performance of DeepPHiC. One is the type of input features and the other is the tissues/cell types used for multi-task/transfer learning. Similar to ChINN, DeepPHiC incorporates the informative feature-‘anchor distance’ to improve the prediction performance. First, we perform an experiment to evaluate the robustness of DeepPHiC by removing anchor distance from the model input. Consequently, DeepPHiC without anchor distance still outperforms competing methods. Second, we evaluate the modified DeepPHiC-TL and DeepPHiC-ML by utilizing both predefined and computationally derived biologically relevant tissues/cell types. As a result, we find that utilizing predefined biologically relevant tissues/cell types will achieve comparable performance to using all relevant tissues/cell types when the number of chromatin interactions in predefined biologically relevant tissues/cell types is reasonably large. However, the prediction performance of DeepPHiC-TL and DeepPHiC-ML declines when the number of chromatin interactions from predefined biologically relevant tissues/cell types is limited. In addition, we computationally infer the biological similarity among tissues/cell types based on the common genes in the proximity of two anchors in chromatin interactions, and select the top similar tissues/cell types in the pretraining. We find that using computational inferred biologically similar tissues/cell types in the pretraining will help improve the prediction performance than using all tissues/cell types in the pretraining in the transfer learning paradigm. In addition, the improvement is more evident than using predefined biologically relevant tissues/cell types.btac801_Supplementary_DataClick here for additional data file.


Arthritis is a highly prevalent and disabling condition among U.S. adults  . Age-adjAmong adults with arthritis who reported having received counseling to be physically active, the median age-standardized prevalence was 69.3% (range = 59.9% [North Dakota] to 75.8% [Alaska]) . The ageThe prevalence of self-management class attendance and receipt of health care provider counseling to be physically active among adults with arthritis varied considerably across states and by participant characteristics, with no clear regional patterns. Among adults with arthritis, self-management class attendance was low among all persons. The specific groups identified with low self-management class attendance and receipt of physical activity counseling were men, persons with a high school education or less, and those residing in small cities or rural areas. Opportunities for increasing health care provider counseling and interventions focused on improving self-management class attendance and physical activity among persons living with arthritis should continue for all, but especially for those groups with lower engagement in these activities.The benefits of self-management courses and counseling to engage in physical activity are well established health goals for the nation, each of which was codified and evaluated in Healthy People 2020. The relevant Healthy People 2020 arthritis objective targetAmong the known benefits of physical activity for adults with arthritis are improved mood, strength, and endurance and reduced arthritis-related joint pain, stiffness, and fatigue (Recommending self-management class attendance while counseling persons with arthritis to engage in physical activity might be the most effective strategy for increasing physical activity. A health care provider’s recommendation to attend a self-management workshop is strongly associated with self-management class attendance (The findings in this report are subject to at least three limitations. First, BRFSS data rely on self-report and might be subject to recall, social desirability, and other biases. Second, low response rates that differ by state might bias study findings; however, the weighting methodology accounts for nonresponse. Finally, the question to ascertain self-management class attendance did not establish whether respondents attended an evidence-based self-management course. A strength of this study is the use of recent data with a large sample size that allowed analyses of detailed characteristics in 49 states, DC, and two U.S. territories. In addition, the prevalence estimates generated are representative at the state level.Self-management class attendance and health care provider counseling for physical activity varied by state and sociodemographic characteristics among adults with arthritis. Public health professionals and medical groups can help improve patient self-management behaviors and outcomes among patients with arthritis by equipping health care providersArthritis is a prevalent chronic condition. Self-management education and health care provider counseling encouraging engagement in physical activity can improve the health of adults with arthritis; however, in 2014, only 11.4% and 61.0% of arthritis patients reported engaging in each, respectively.In 2019, a median of 16.2% adults with arthritis attended a self-management class, and 69.3% received provider counseling for physical activity. Prevalences differed by state and sociodemographic characteristics.Equipping health care providers with the tools to counsel arthritis patients about the benefits of physical activity and self-management education and support referrals to evidence-based programs is needed to improve adoption of these behaviors.


Established associations between hearing loss and cognitive decline were primarily defined by pure-tone audiometry, which reflects peripheral hearing ability. Speech-in-noise performance, which reflects central hearing ability, is more limited in prior literature. We examined the longitudinal associations of audiometric hearing and speech-in-noise performance with cognitive decline.We studied 702 participants aged ≥60 years in the Baltimore Longitudinal Study of Aging 2012–2019. Global and domain-specific  cognitive performance were assessed by the cognitive assessment battery. Hearing thresholds at 0.5, 1, 2, and 4 kilohertz obtained from pure-tone audiometry were averaged to calculate better-ear pure-tone average (PTA) and participants were categorized as having hearing loss (>25 decibels hearing level [dB HL]) or normal hearing (≤25 dB HL). Speech-in-noise performance was assessed by the Quick Speech-in-Noise (QuickSIN) test, and participants were categorized as having below-median (worse) or above-median performance. Linear mixed effects models with random intercepts and slopes were used to assess baseline cognitive performance and cognitive decline by hearing status. Models adjusted for demographic, lifestyle and disease factors.Participants with audiometric hearing loss showed similar baseline cognitive performance but faster decline in global cognitive function, language, executive function, and attention. Participants with below-median QuickSIN score showed worse baseline cognitive performance in all domains and faster decline in global cognitive function, language, memory, executive function and attention.Audiometric hearing might be targeted to delay cognitive decline. Speech-in-noise performance might be a novel marker and might be more sensitive to memory decline. Approximately 6.2 million U.S. older adults currently live with Alzheimer's disease and related dementias and this number is expected to rise with population aging . Living Hearing loss, as defined by pure-tone audiometry, is common among older adults: its prevalence increases from 45% among adults in their 60s to nearly 90% among adults 80 years and older . AudiomeAlthough pure-tone audiometry is the gold standard for hearing evaluation that has long been used for defining hearing loss, pure-tone audiometry is primarily a measure of peripheral auditory function, reflective of the initial encoding of auditory signals. Other hearing evaluations like speech-in-noise tests, which characterize central auditory function , instead rely on higher-level cognitive processing. Difficulties understanding speech in the presence of noise are common among older adults and significantly impact daily living. Cognitive functions like working memory and attention have been related to speech-in-noise performance , suggestTo bridge the gaps in longitudinal evidence that examines and compares the impacts of peripheral and central aspects of hearing on cognition in well-established aging cohorts, this study aims to investigate associations of both audiometric hearing and speech-in-noise performance with global and domain-specific  cognitive decline over follow-up among adults aged 60 years and older using data from the Baltimore Longitudinal Study of Aging (BLSA). We hypothesize that worse audiometric hearing and speech-in-noise performance are both associated with accelerated rates of cognitive decline.The Baltimore Longitudinal Study of Aging (BLSA) is an ongoing cohort study of aging conducted by the National Institute on Aging since 1958. BLSA enrolls community-dwelling healthy U.S. adults aged 20 years and older continuously. Enrolled participants are followed for life. Participants undergo comprehensive health assessments during study visits every 1–4 years depending on age . Details of the BLSA design have been published previously , 15. WriHearing evaluations were performed in the BLSA from 2012 onwards. This study included BLSA participants aged 60 years and older with hearing and cognitive measures between 2012 and 2019. We identified 738 participants aged 60 years or older with complete data on pure-tone audiometry and speech-in-noise performance at ≥1 study visit and defined their first visit as the baseline in this analysis. We further excluded 3 participants missing all cognitive assessments and 33 participants with missing data on covariates, leaving a final analytical sample of 702 participants.Global and domain-specific cognitive performance, as our outcomes of interest, were assessed by a battery of neurocognitive tests at each study visit between 2012 and 2019. Descriptions of the cognitive tests are presented in (1) Language was represented by Letter Fluency Test , Categor(2) Memory was represented by immediate and long-delay free recall from the California Verbal Learning Test ;(3) Attention was represented by Trail Making Test Part A  and Digi(4) Executive function was represented by Trail Making Test Part B , Digit S(5) Visuospatial ability was represented by Card Rotations Test  and BentZ scores using the baseline mean and standard deviation (SD) for comparison across tests. Corresponding standardized test scores in each cognitive domain were then used to derive global and domain-specific cognitive factor scores using structural equation modeling for confirmatory factor analysis, where observed covariation in the manifest variables (cognitive test scores) was explained by the latent variables (cognitive factor scores)  so that higher scores for all the cognitive tests reflect better performance. Individual cognitive tests were standardized by converting to  scores) , 25.Global cognitive factor score served as our primary outcome of interest and five domain-specific cognitive factor scores  served as our secondary outcomes of interest.Pure-tone audiometry, as our measure of peripheral auditory function, was conducted using Interacoustics AD629 audiometer with ER3A insert earphones in a sound-attenuating booth according to best-practice procedures . ParticiThe Quick Speech-in-Noise (QuickSIN) test assesses participants' ability to understand speech in the presence of background noise . Particivia self-report, including age (continuous in years), sex , race  and years of education. Education was categorized as high school or less (≤12 years), any college (12–16 years) and beyond college (>16 years). Self-reported smoking status was collected as current, former and never and was combined as ever/never smoker for analysis due to the small number of participants identified as current smokers. Body mass index (BMI) was calculated from measured height and weight and was analyzed continuously in kg/m2. Hypertensive status was defined based on measured systolic blood pressure (SBP) and diastolic blood pressure (DBP): Hypertension was considered present if SBP was ≥140 mmHg or DBP ≥90 mmHg; Prehypertension was considered present if SBP was between 120 and 139 mmHg or DBP was between 80 and 89 mmHg  using ANOVA for continuous variables and Pearson's chi-squared test for categorical variables.Linear mixed effects models with random intercepts, random slopes, unstructured covariance matrix and robust standard errors were fitted to estimate the longitudinal associations between hearing status at baseline and longitudinal trajectories of cognitive performance. To test whether rates of change in cognitive performance vary by baseline hearing status, models included an interaction term between time since baseline and hearing status. Model assumptions were checked using residual diagnostic plots. For each cognitive factor score , which is the outcome of interest, we fit separate models for each hearing measure  as the main exposure of interest. Non-linear trajectories of cognitive performance were explored by graphical representation and were found for QuickSIN score. We therefore included a liner spline term with a knot at 20 points, which assumes different linear relationships between QuickSIN score and cognitive performance among those with QuickSIN score <20 vs. ≥20 points.To explore interactions of hearing status with demographic characteristics predicting cognitive decline, 3-way interaction terms (time since baseline × hearing status × age/sex/race) were included in the models to test whether the longitudinal associations between hearing and cognitive performance vary by demographic characteristics. For exploration of interactions, age was categorized as >75 vs. ≤75 years, race was categorized as White vs. Black (excludes 46 participants in other categories), and sex was still analyzed as males vs. females.Models with better-ear PTA as the main exposure were adjusted for age, sex, race, education, smoking, body mass index, hypertension, diabetes, elevated cholesterol, and depressive symptoms. Models with QuickSIN score as the main exposure were additionally adjusted for better-ear PTA since speech perception in noise depends on the integrity of the auditory signals from the peripheral auditory system.P-value <0.05 was considered statistically significant.Analyses were conducted using Stata, version 15.1 . A two-sided A total of 702 participants  were included in our study. Participants were followed for a mean of 3.5 years (Range: 0–7 years) over a mean number of 3 visits (Range: 1–7). Among 702 participants, 258 (37%) had normal audiometric hearing (Better-ear PTA ≤25 dB HL) and above-median (>23) QuickSIN score, 292 (42%) had both audiometric hearing loss (Better-ear PTA >25 dB HL) and below-median (≤23) QuickSIN score, 65 (9%) had normal audiometric hearing but below-median QuickSIN score, and 87 (12%) had audiometric hearing loss but above-median QuickSIN score. 697 of 702 participants had data on hearing aid use and 117 of them had any hearing aid use .2). We also compared baseline characteristics of participants with QuickSIN score below median (worse performance) to those with QuickSIN score above median (2) and greater level of depressive symptoms (Mean CES-D: 5 vs. 4).Baseline characteristics of participants by audiometric hearing are presented in e median . ParticiBaseline cognitive performance comparing participants with audiometric hearing loss to participants with normal audiometric hearing did not differ significantly. However, participants with audiometric hearing loss had faster annual rates of decline in global , language , executive function  and attention  cognitive factor scores when compared to participants with normal hearing . NeitherParticipants with QuickSIN scores below the median (worse) showed significantly lower cognitive factor scores at baseline across all domains when compared to participants with QuickSIN scores above the median . ParticiWe did not detect an interaction between hearing status and age (>75 vs. ≤75 years), sex  or race (White vs. Black) –6. Only Among 702 BLSA participants aged 60 years or older with up to 7 years of follow-up between 2012 and 2019, after adjusting for demographic, lifestyle and disease factors, we found longitudinal associations of both audiometric hearing and speech-in-noise performance with global and domain-specific cognitive decline. Participants with worse speech-in-noise performance but not audiometric hearing, already showed worse cognitive performance at baseline. Specifically, audiometric hearing loss was associated with faster rates of decline in global cognitive function as well as declines in language, executive function and attention. Participants with speech-in-noise performance below the median (worse) had faster rates of decline in global cognitive function, language, memory, executive function and attention compared to those with scores above the median. In our exploration of interaction, we found an interaction between speech-in-noise performance and race for memory performance, such that the association of speech-in-noise performance with cognitive decline was driven largely by Black participants but not White participants.Hearing relies on both peripheral and central auditory systems: the peripheral auditory system captures sound signals and converts them into electrical signals in the cochlea; the central auditory system carries these electrical signals to the brainstem and cortex for recognition, integration and understanding of the information. As the gold standard, pure-tone audiometry is widely used in prior literature to define hearing loss and is recognized as a risk factor for cognitive decline and dementia . HoweverThough inconclusive, prior evidence has documented associations of both worse audiometric hearing and speech-in-noise performance with smaller brain volumes and worse white matter integrity cross-sectionally –34 as weP = 0.08). It is possible that we were underpowered to detect a difference in memory decline, given our overall sample is healthy older adults.Our finding that participants with normal hearing and hearing loss, as defined by pure-tone audiometry, had similar cognitive performance at baseline was consistent with the cross-sectional finding of a previous study among 313 participants in BLSA using study years 2012–2015 , but wasOur study found associations between worse speech-in-noise performance and worse cognitive performance at baseline as well as faster decline in cognitive performance over time. These significant associations between speech-in-noise performance and cognition remained robust after adjusting for audiometric hearing levels. The limited prior research investigating associations between speech-in-noise performance and cognitive decline demonstrates inconsistent results. One study conducted among 837 participants (Mean baseline age = 65 years) from the Rotterdam Study found baseline differences in cognitive performance, but no significant differences in rates of change associated with speech-in-noise performance after a mean follow-up of 4.4 years ; anotherWhen comparing the results regarding audiometric hearing and speech-in-noise performance, baseline differences in cognitive performance were only found for speech-in-noise performance. The observed associations between speech-in-noise performance and cognitive decline were driven by those with relatively intact speech perception function at baseline. Differences in the findings might reflect underlying brain pathologies not yet captured by audiometric hearing. Moreover, in terms of cognitive domains being impacted by these two hearing measures, both audiometric hearing and speech-in-noise performance are associated with accelerated decline in language, executive function and attention, but memory domain is significantly impacted by worse speech-in-noise performance instead of audiometric hearing. Though there is no well-supported hypothesis, memory, especially those aspects that required learning new materials, might decline earlier with advancing age . AudiomeOur examination of interaction by age, sex and race showed an interaction between speech-in-noise performance and race in the memory domain, where Black participants showed accelerated decline in memory with worse speech-in-noise performance while White participants showed no difference in rates of decline. The observed differential associations among Black and White participants might be explained by health disparities across the lifespan. For example, White participants might have higher cognitive reserve due to positive psychosocial and lifestyle factors and are thus more resilient to cognitive aging and have compensatory strategies . HoweverOur study has a number of strengths: First, in this well-established cohort of community-dwelling older adults, the comprehensive battery of neurocognitive tests measured over a mean follow-up time of 3.5 years enabled longitudinal investigation of global and domain-specific cognitive performance. Second, in addition to pure-tone audiometry that primarily measures peripheral auditory function, we also included speech-in-noise performance assessed using the QuickSIN test, which is a reliable measure of central auditory function. The QuickSIN test is commonly used in clinical settings and more applicable to clinical practices as it is easy and quick to administer. Additionally, we are able to capture both peripheral and central aspects of hearing and compare our findings, which can provide more insights into the underlying pathways. Last, in exploratory analysis, we investigated interactions by a set of demographic characteristics to investigate potential differences by sub-groups who might experience accelerated cognitive decline. Although our study still has a relatively limited sample size and follow-up period, we have expanded upon previous works in the BLSA cohort and added to the currently limited body of literature by investigating speech-in-noise performance and cognition longitudinally. Also, the BLSA cohort consists of healthy adults and our results might not be generalizable to the general U.S. older adult population. In addition, because magnetic resonance imaging (MRI) measures are associated with both hearing and cognition, future studies with MRI measures might consider the roles of brain structure and function.In conclusion, our study demonstrated longitudinal associations of both audiometric hearing and speech-in-noise performance with accelerated decline in cognition among a sample of community-dwelling older adults. Moreover, participants with worse speech-in-noise performance, but not audiometric hearing loss, had worse baseline cognitive performance. Audiometric hearing, primarily as a measure of peripheral auditory function, might be a risk factor for cognitive decline and might be targeted to delay cognitive decline. Comparatively, speech-in-noise performance, as it involves higher-level cognitive processing, might be a novel risk marker of underlying cognitive aging and may be uniquely sensitive to decline in memory function, contributing to early identification of individuals more vulnerable to cognitive decline and might be an easy-to-use tool applicable to clinical settings. Future longitudinal studies with audiometric hearing and speech-in-noise performance are needed.https://blsa.nia.nih.gov/how-apply.The data analyzed in this study was obtained from the Baltimore Longitudinal Study of Aging (BLSA), the following licenses/restrictions apply: Researchers can only access BLSA data after approval of an Analysis Plan. Requests to access these datasets should be directed to BLSA, The studies involving human participants were reviewed and approved by the Institutional Review Board of the Intramural Research Program of the National Institutes of Health. The patients/participants provided their written informed consent to participate in this study.Study concept or design: NA, YA, AG, JS, FL, LF, SR, JD, and DP. Analysis or interpretation of data: KJ, DP, and JD. Drafting the manuscript: KJ and DP. Critical revision of the manuscript: NA, YA, AG, JS, FL, LF, SR, and JD. All authors contributed to the article and approved the submitted version.


These results would suggest that nutritional status should therefore be supervised by specialists and an increase in good quality muscle could improve prognosis.Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are heterogeneous rare diseases causing malnutrition and cachexia in which the study of body composition may have an impact in prognosis. In this context, we aimed to evaluate muscle and fat tissues by computed tomography (CT) at L3 level at diagnosis and at the end of follow-up in a cohort of 98 GET-NET patients and their relationships with clinical and biochemical variables as predictors of survival. Body composition measures and overall mortality correlated with age, ECOG, metastases, LDH, albumin and urea levels. Although there was no relationship between body composition variables at diagnosis and overall and specific mortality, an increase in low-density muscle and a decrease in normal-density muscle during follow-up were independently correlated to overall (p <0.05) and tumor-cause mortality (p < 0.05). Conclusion: Although body composition measures obtained by CT at diagnosis did not impact survival of GEP-NET patients, a loss of good quality muscle during follow-up was associated with an increased overall and tumor-related mortality. Nutritional status should therefore be supervised by nutrition specialists and an increase in good quality muscle could improve prognosis.Background: Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are heterogeneous rare diseases causing malnutrition and cachexia in which the study of body composition may have an impact in prognosis. Aim: Evaluation of muscle and fat tissues by computed tomography (CT) at the level of the third lumbar (L3 level) at diagnosis and at the end of follow-up in GET-NET patients and their relationships with clinical and biochemical variables as predictors of survival. Methodology: Ninety-eight GEP-NET patients were included. Clinical and biochemical parameters were evaluated. Total body, subcutaneous, visceral and total fat areas and very low-density, low-density, normal density, high-density, very high-density and total muscle areas were obtained from CT images. Results: Body composition measures and overall mortality correlated with age, ECOG (Eastern Cooperative Oncology Group performance status) metastases, lactate dehydrogenase (LDH), albumin and urea levels. Although there was no relationship between body composition variables at diagnosis and overall and specific mortality, an increase in low-density muscle and a decrease in normal-density muscle during follow-up were independently correlated to overall ( Gastroenteropancreatic neuroendocrine tumors (GEP-NET) constitute a heterogeneous group of neoplasms with a low incidence and a variable behavior . ContrarRegarding obesity, current results are conflicting as some patients with obesity are more likely to survive (obesity paradox), hence the BMI (body mass index) is a parameter with limitations in the evaluation of lean tissues . NowadayDifferent techniques are available to measure body composition , ultrasound , bioelecThe prevalence of malnutrition in GEP-NETs patients is not well known to date. However, there are already studies that suggest that patients with GEP-NET have nutritional deficits and that their diet is inadequate . Some stThis is a single-center, retrospective, observational study including 159 consecutive patients with gastrointestinal and pancreatic NETs. All patients were evaluated in Hospital Universitario de La Princesa between 2004 and 2021. A complete record including history, physical examination and hormone levels was performed in all cases and interpreted by expert endocrinologists in a multidisciplinary team composed of oncologists, surgeons, radiologists and nuclear medicine physicians, classifying all patients according to World Health Organization (WHO) criteria . Patients were managed following current recommendations and guidelines ,24. All The Internal Ethical Review Committee of the Hospital de La Princesa approved this project. Written informed consent was obtained from all patients before inclusion in accordance with the Declaration of Helsinki.https://github.com/endonutriHUPR/Body-composition-measurement, accessed on 29 September 2022). Then, area subtraction was executed by R version 4.0.3 [An experienced radiologist selected high-quality CT slides of patients at L3 level. Images at diagnosis were used for the analysis. High contrast, unlined images and visible surgeries were exclusion criteria. Two independent experts were trained to perform the processing of CT images with NIH ImageJ version 2.3.0  whose pron 4.0.3 ,29. The following body composition measures were obtained: total body area, visceral fat tissue , subcutaneous fat area , intermuscular fat area , total fat area , very low-density muscle area , low-density muscle area , normal-density muscle area , high-density muscle area , very high-density muscle area  and total muscle area . After the measurement step, we tested the correlation between both analyses with the Spearman’s rho test. Then, we normalized the data by dividing them by the square of patients’ height in meters and, finally, we obtained the mean of both measures.In addition, we collected the L3 CT image at the end of the follow-up of each patient defined as death or last available following the same aforementioned statements. We only considered those with at least one year of difference from the diagnosis image. Finally, the difference between final and initial diagnosis images regarding body composition measures was calculated for each patient.All the statistical analysis was performed using R version 4.0.3 . Normali2. The median follow-up time for patients was 5 years (p25: 2–p75: 8 years) with 33 patients deceased at the end , weight (p = 0.020), BMI (p = 0.010), tumor grade (p < 0.001), ECOG (Eastern Cooperative Oncology Group performance status) (p < 0.001), tumor size (p = 0.003), presence of metastases and/or residual disease after surgery (p < 0.05), and coexistence of non-oncological respiratory diseases . When biochemical values were evaluated, we found unfavorable outcomes when patients had higher levels of GOT/AST (p = 0.034), LDH (lactate dehydrogenase) (p = 0.022) and urea (p = 0.001). Interestingly, patients with low albumin (p = 0.016) also displayed a worse survival rate. Other biochemical variables did not reach significant results , a decrease in muscle density  and lower levels in subcutaneous fat . In addition, we found an increasing tendency in TFA  in relation to survival . Furthermore, there is an inverse correlation between NDMA and low-density muscle, particularly intense with VLDMA , and fat compartments, especially with IMFA . Regarding TMA, moderate positive correlations with NMDA , LDMA , VAT  and body area  were found.When exploring in depth the relationships among body composition, clinical and biochemical variables and survival, Spearman’s rho analysis A showed p < 0.001), neutrophils percentage  and age . In contrast, NMDA had a weak positive correlation with lymphocytes percentage  (p = 0.004), glucose  and triglycerides .Interestingly, we found negative correlations between NDMA with LDH  A. In addp < 0.05), metastases , nodules , and non-functioning tumors . Interestingly, the use of somatostatin analogues or diabetes diagnosis did not reach significant results in body composition and mortality  .2/m2 to 39.07 cm2/m2) with no differences when compared between survival and mortality (p = 0.334). Interestingly, patients with adverse outcomes gained low-density muscle  and lost normal-density muscle  at the end of the follow-up .Therefore, we evaluated the changes in body composition at the end of the follow-up  and their impact in overall mortality through logistic regression analysis using the confounding factors previously identified. Results are displayed in ollow-up . Despitep < 0.05) and a decrease in normal-density muscle area (p < 0.05) compared to survival. Changes are represented in We then performed the analyses specifically in tumor-cause mortality; their results maintained the tendency that we previously observed in all-cause mortality . There wThese results seemed to advise against the importance of muscle quality in GEP-NET patients regarding its relationship with overall and tumor-cause mortality during the follow-up.Emerging evidence suggests that alterations in body composition can predict the survival of patients with cancer ,18,21,22When evaluating patient outcomes in univariate analysis, we found that the amount of muscle and fat compartments and also its quality and fat distribution at diagnosis were related to patient outcomes. Specifically, we found that less total fat, visceral fat, subcutaneous fat, less total muscle, and decreased muscle quality were associated with increased mortality risk. When relationships were assessed, an increase in muscle area of normal-density was negatively correlated with low-density muscle area and, in consequence, with myosteatosis and fat tissue. However, after correction with confounding factors, we observed that the study of body composition using computed tomography technology at diagnosis had no independent correlations with survival of patients with GEP-NETs. Our results are consistent with previous works with the same disease that studied the relationship between body composition and unfavorable outcomes. Sarcopenia was not associated with long-term prognosis, but was an independent risk factor in a small subgroup of patients with gastric mixed adenoneuroendocrine carcinoma (gMANEC) and an independent risk factor after surgery . In anotHowever, during the follow-up of the body composition in our cohort of patients, the results in survival were very interesting and showed that both the loss of normal-density muscle and the gain of low-density muscle were independently related to all-cause mortality. Additionally, when studying tumor-related mortality, we observed that the relationship with the loss of muscle of normal density and increase of muscle of low density was maintained. These changes were adjusted by age, presence of metastases, ECOG, LDH, albumin, and urea. Studies are currently being carried out to assess muscle maintenance as a variable related to survival. In a study of 18 patients with malignant mesothelioma, muscle loss during follow-up measured by DXA was associated with decreased survival . The muscle density loss in patients with NETs could be due to several factors, including paraneoplastic syndromes, hormonal hypersecretion, including the physical presence of cancer, mechanical symptoms related to the disease or treatment, and malabsorption . All theIn our cohort, a higher volume of normal muscle was an independent factor of survival in the follow-up, and it was positively correlated with albumin and lymphocytes and negatively correlated with age at diagnosis. On the contrary, increase of low-density muscle tissue (myosteatosis) had a relationship with mortality in the follow-up, and were positively related to higher levels of fibrinogen, LDH, glucose, and triglycerides at diagnosis, parameters that were previously correlated with poor prognosis ,41. In aThe low-density muscle gain is due to a loss of normal muscle. Therefore, weight gain does not have an impact on mortality if muscle quality is not improved. Nutritional management during the therapeutic itinerary is vital in patients with GEP-NET. For this reason, hopefully, a modifiable factor such as a change in body composition during follow-up may influence the prognosis of patients. Still, more extensive studies are needed to clarify its real potential in clinical practice and which strategies measures  might enhance the quality of muscle during follow-up. In this regard, the collaboration between multidisciplinary teams that include oncologists, endocrinologists and nutritionists are necessary to identify patients at high risk and to implement physical exercise and specific nutrition prevention programs. In this context, the research team agrees with the recommendations previously published by Kikut et al.  for the Finally, one of the main limitations of this study is the observational methodology of the work. Another aspect to consider is that the cohort is part of a single center, which can bias the reproducibility of the study. On the other hand, the cohort is one of the largest published, and the long follow-up period from 2004 to 2021 offers advantages in providing information on patient’s natural history. Still, the appearance of new treatments and the specific effect of any treatment in body composition have not been discussed in this paper and should be evaluated in future studies. Other aspects to consider in future studies are muscle strength  and physical activity information of patients at diagnosis and during the follow-up.For all these reasons, multicenter clinical trials should be conducted to better understand the influence of body composition in patients with GEP-NET. Early nutritional treatment with particular emphasis on maintaining and increasing muscle mass should be a priority in the follow-up of patients with GEP-NET. Therefore, it seems necessary to implement therapies that allow our patients to maintain or increase muscle quality as a primary objective.Body composition analysis is feasible using CT data acquired in routine clinical practice in patients with NETs. Loss in normal-density muscle and increase in low-density muscle during follow-up are independently associated with an increase in mortality. Nutritional management during the follow-up should be mandatory for patients with GEP-NET.