Evaluating the diagnostic efficiency of deep-learning models to distinguish malignant from benign parotid tumors on plain computed tomography (CT) images.The CT images of 283 patients with parotid tumors were enrolled and analyzed retrospectively. Of them, 150 were benign and 133 were malignant according to pathology results. A total of 917 regions of interest of parotid tumors were cropped . Three deep-learning networks  were used for diagnosis (approximately 3:1 for training and testing). The diagnostic efficiencies  of three networks were calculated and compared based on the 917 images. To simulate the process of human diagnosis, a voting model was developed at the end of the networks and the 283 tumors were classified as benign or malignant. Meanwhile, 917 tumor images were classified by two radiologists (A and B) and original CT images were classified by radiologist B. The diagnostic efficiencies of the three deep-learning network models (after voting) and the two radiologists were calculated.For the 917 CT images, ResNet50 presented high accuracy and sensitivity for diagnosing malignant parotid tumors; the accuracy, sensitivity, specificity, and AUC were 90.8%, 91.3%, 90.4%, and 0.96, respectively. For the 283 tumors, the accuracy, sensitivity, and specificity of ResNet50 (after voting) were 92.3%, 93.5% and 91.2%, respectively.ResNet50 presented high sensitivity in distinguishing malignant from benign parotid tumors on plain CT images; this made it a promising auxiliary diagnostic method to screen malignant parotid tumors. Parotid tumor is the most common type of salivary gland tumor. The acinar, ductal, and myoepithelial cells that comprise parotid tissues can give rise to a variety of benign and malignant neoplasms. Pre-operative recognition of malignancy in parotid tumors is useful in that it may alert the surgeon to more stringent attention to the operative margin and hence better tumor clearance .From the clinical aspect, although there are some clues of malignancy-rapid growth, skin fixation, ulceration, facial nerve palsy, pain, or cervical node metastasis, but only 30% malignant parotid tumors present with these features , 2. FineRecently, deep learning methods, especially convolutional neural network (CNN), have demonstrated effectiveness in image recognition tasks. CNN-based tumor segmentation and classification have been widely used in breast cancer , lung caResidual neural network (ResNet) is a CNN network proposed in 2015. The framework reformulates the layers as learning residual functions with reference to the layer inputs to obtain deeper networks with higher accuracy . The ResAn oral radiologist collected the CT images of patients with parotid tumors in our hospital from 2008 to 2020. The inclusion criteria were as follows: (1) primary parotid tumor; (2) definite pathological diagnosis was available after surgery. (3) The CT images were of good quality, without motion artifacts and foreign body artifacts. The approval from the Ethics Committee of our University was obtained prior to performing this study (NJSH-2022NL-069).The plain CT images of 283 patients  with parotid tumors were included. Of them, 150 were benign  and 133 were malignant . No statistical difference of age and gender was found between benign and malignant tumor group. The pathological classification of the 283 tumors was showed in All patients were performed CT examination before surgery; the parameters of CT were as follows: tube potential: 130 kVp, tube current 56 mA, slice thickness: 3 mm, matrix: 512 × 512, window width: 200 Hounsfield units (Hu), window level: 40 Hu.Two radiologists manually selected the region of interest; axial CT images with lesions were randomly selected and then the regions of interest were obtained by square cropping the CT images . For each parotid lesion (three or five CT images), the most classification was counted as the final result of the parotid lesion. Generated activation maps by class activation map (CAM) on test dataset were applied to evaluate the region of interest for further clinical review.Other two neural networks (VGG16_bn and DenseNet169) were used to classify the benign and malignant parotid tumors; the voting model was also added at the end of these networks and the diagnostic efficiency of them was compared with ResNet50. These two networks were also models pre-trained using the ImageNet database –25.After development of the CNN models were complete, the 283 parotid tumors were classified into benign or malignant ones by two radiologists , using the same CT images. These two observers did not take part in the model training process and were blinded to lesion selection. The observers were also unaware of patient names, laboratory results, other imaging findings, or final diagnosis. After 3 months, observer B re-classified the 283 tumors into malignant or benign; this time, all the original CT images without cropping of the 283 patients were used. The following characteristics were used for classification: tumor location, number of tumors (single or multiple), the size of the tumor (the size based on the selected CT images), tumor shape , tumor density , and tumor margins .The diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the three CNN networks was calculated on the 917 CT images. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) of the three networks were constructed and calculated. The diagnostic accuracy, sensitivity, specificity, PPV, and NPV; the three deep-learning network models (after voting); and the two radiologists were also calculated for the 283 tumors. The diagnostic accuracy, sensitivity, and specificity of onefold were compared, and the statistical significance was calculated between VGG19_BN, DenseNet169, radiologist A, radiologist B, and radiologist B (second time) with ResNet50 (after voting) using McNemar’s test. The statistical analyses were conducted using SPSS 23.0 software .The classification performance of three networks was shown in The attention heatmap was generated by CAM and then the heatmap was super-imposed on the original CT image, so that the location of parotid tumor and the region highlighted by ResNet could be compared. As showed in The accuracy, sensitivity and specificity, PPV, and NPV of the three networks after voting and the two radiologists were shown in The pre-operative diagnosis of benign and malignant tumors of the parotid gland is of great clinical significance and can have an important impact on surgical planning. Because of the important function of facial nerve, to preserve facial nerve function is a general and important principle in parotid tumors treatments. For benign lesions, local excision or partial parotidectomy is sufficient and every attempt to preserve facial nerve function should be made during surgery; however, for malignant tumors, a total parotidectomy with sacrifice of any part of the nerves overtly involved in tumor is desirable . For surFine-needle aspiration cytology is used for the pre-operative diagnosis, and high specificity was showed by Piccioni et al.’s study and Dhanani et al.’s study , 28. HowIn clinic, ultrasound, CT, and MRI are widely used in parotid tumors diagnosis . CT imagP < 0.05). Because radiologist B (second time) had a diagnosis using all the original CT images without cropping, the diagnosis of accuracy and sensitivity increased. Our manual classification results were also similar with previous study  was used, and the diagnostic accuracy, sensitivity, and specificity were 71%, 33%, and 87% for malignant tumors. In Xia et al.’s study, a modified ResNet model was developed based on MRI images of 233 parotid tumors (153 benign tumors and 80 adenocarcinoma), and the accuracy, sensitivity, and specificity were 88.2%, 94.6%, and 81.7% for differentiating benign from malignant parotid lesions. And studies using CNN networks based on portal phase CT images (contrast-enhanced CT) and ultrasound images were also published recently , 33. In In this study, in order to simulate the process of human diagnosis, a voting model was built at the end of the three deep-learning network models, and the accuracy, sensitivity, and specificity of the three CNN models were calculated for the 283 tumors. After voting, the three CNN models all showed higher diagnostic efficiency than the models without voting. The pathology diagnosis was a microscopic diagnosis, so the tumors will be diagnosed as malignant if there are malignant cells. However, imaging reflects the macroscopic morphology, so imaging diagnosis is a probabilistic diagnosis and it needs comprehensive analysis. Zhao et al. recently reported a hybrid algorithm, a Bayesian network branch performing probabilistic causal relationship reasoning and a graph convolutional network branch performing more generic relational modeling and reasoning using a feature representation . And theIn this study, we re-analyzed the mis-diagnosed parotid tumors of CNNs. And we found that the accuracy for lymphoma diagnosis was 73.3% (11/15), for ResNet50 (after voting), this was much lower than the whole database. The lymphomas usually appear as homogeneous and sharply demarcated nodes, just like benign tumors; this maybe the reasons of misdiagnosis. Moreover, most mis-diagnosed malignant parotid tumors were small ones; they were homogeneous and well-defined, and similar with benign tumors. So, lymphoma and small tumors are more likely be mis-diagnosed even for CNNs. Furthermore, using the attention heatmap, we can infer which part of the input image is focused on by the neural network. For some tumors, the highlighted areas were on the margins, and for others, the highlighted areas were intra-tumoral, which means that the neural network focused on the texture of images of these tumors  built in the neural networks. A neural network with auto-segmentation or automated detection need to be explored in further study. And networks using a voxel-based domain and the whole CT images without cropping, combining the radiological with clinical findings are also needed to be explored in the future. Third, this study was based on a single center; an external validation study is needed to validate its diagnostic performance and generalizability. Prospective and multi-institutional datasets are also needed in future studies.ResNet50 presented high sensitivity in distinguishing malignant from benign parotid tumors on plain CT images, and this made it a promising auxiliary diagnostic method to screen malignant parotid tumors.linzitong_710@163.com.The data used to support the findings of this study were supplied by Zitong Lin under license and so cannot be made readily available. Requests for access to these data should be made to The approval from the Ethics Committee of our University was obtained prior to performing this study (NJSH-2022NL-069). The data are anonymous, and the requirement for written informed consent was therefore waived.Z.Y. Hu contributed to writing of this manuscript, neural network modeling, data analysis and visualization. B.X. Wang contributed to neural network modeling and optimization, data analysis and visualization. X. Pan contributed to collection of materials and data curation. D.T. Cao contributed to data analysis. A.T. Gao contributed to collection of materials. X.D. Yang contributed to study design and interpretation of the results. Y. Chen contributed to study design and neural network modeling and optimization. Z.T. Lin contributed to conceptualization, CT image consultation, review and editing of this manuscript, and funding acquisition. All authors gave final approval and agree to be accountable for all aspects of the work.This work was supported by the General project of Jiangsu Commission of Health (M2021077), the Jiangsu Province Medical Association Roentgen Imaging Research and Special Project Funds (SYH-3201150-0007), the Medical Science and Technology Development Foundation (YKK19090), and the Nanjing Clinical Research Center for Oral Diseases (no. 2019060009).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.


Tuina can effectively alleviate ulcerative colitis-related symptoms, but the mechanism of action is unknown. The purpose of this research is to explore potential pathways for the treatment of tuina through gut microbiota and proteomics techniques. Thirty-two male BALB/c mice were divided into four groups, the control, model, mesalazine, and tuina groups. The ulcerative colitis model was established by freely drinking a 3% dextran sulphate sodium solution for 7 days. The mesalazine group and the tuina group, respectively, received 7 days of mesalazine and tuina treatment. Subsequently, their body weights, feces properties, colon length, histomorphological changes, gut microbiota, and colon proteomics were determined. Body weights, disease activity index score, colon histological scores, and microbiota diversity were restored in the tuina group. At the phylum level, Firmicutes was increased and Bacteroidota decreased. At the family level, Lachnospiraceae increased and Prevotellaceae decreased. At the genus level, the Lachnospiraceae_NK4A136_group was increased. Proteomics detected 370 differentially expressed proteins regulated by tuina, enriched to a total of 304 pathways, including biotin metabolism, Notch signaling pathway, linoleic acid metabolism, and autophagy. Tuina can effectively improve the symptoms of weight loss, fecal properties, and colon inflammation in ulcerative colitis mice and restore the gut microbiota diversity, adjusting the relative abundance of microbiota. The therapeutic effects of tuina may be achieved by modulating the signaling pathways of biotin metabolism, Notch signaling pathway, linoleic acid metabolism, and autophagy. Ulcerative colitis (UC) is a chronic inflammatory disease of the colon and small intestine caused by a combination of genetic background and environmental factors . The disComplementary and alternative therapies, such as tuina, acupuncture, and moxibustion, are widely used in the treatment of UC. Tuina is a therapy that treats diseases by applying pressure with direction, depth, and intensity to the skin, muscles, and joints of the body through a variety of manipulations. It has been widely used in the treatment of a variety of diseases, such as the musculoskeletal system, nervous system, etc.. In receHere, to explore the potential mechanisms of action for UC treatment using tuina, we combined the gut microbiota and proteomics to investigate whether tuina can alter the dysbiosis of the gut microbiota and regulate the expression of colon proteins in UC model mice. We reported that mesalazine and tuina treatment restored the lost diversity and composition after modeling, with an increased abundance of beneficial taxa associated with the alleviation of inflammatory symptoms. We identified 247 differentially expressed proteins (DEPs) regulated by mesalazine and 370 DEPs regulated by tuina and then performed bioinformatic analyzes. This study preliminary explored the potential mechanisms of tuina for the treatment of UC and provided a basis for follow-up studies.In this experiment, 32 9-week-old male BALB/c mice weighing 20 ± 1 g each were purchased from SPF (Beijing) biotechnology Co., LTD.  for this experiment, the certification number is SCXK (JING) 2019–0010. The mice received sterile maintenance feed, with free access to drinking water. These animals were housed at 25 ± 0.5°C and relative humidity of 60%–70%, and the padding was changed twice a week. All animal experimental procedures were following the principles of the local animal ethics committee .After 3 days of acclimatization, at time point P1, the 32 mice were randomly assigned to the control group, the model group, the mesalazine group and the tuina group, 8 per group. Mice in the control group received distilled water. And experimental UC models were induced by freely drinking 3% dextran sulphate sodium (DSS) solution  for 7 days in the other three groups .After 7 days of DSS solution induction, at time point P8, all groups received distilled water. The mesalazine group was treated with a once daily mesalazine solution  solution for 7 days. The tuina group received a 10-min tuina treatment per day for 7 days .During acclimatization feeding, the manipulator performed daily handling to reduce animal stress. The tuina protocol consisted of two parts: rubbing the abdomen and pointing. The procedure was performed as follows : The manAt time point P15, all animals were assessed for the disease activity index (DAI) score according to the following criteria . Then, aThe colon tissues were fixed in a 4% paraformaldehyde fixing solution at 4°C for 48 h. The tissues were then embedded in paraffin and sectioned, the thickness was 4 μm. The glass slides were hydrated at 65°C for 1 h and stained with HE solution , then observed and photographed through the microscope . Colon histological scores were graded according to the following standard of tissue injury .Gut microbiota analysis was performed by the 16 S rDNA bioinformatics approach. Briefly, the genomic DNA of the microbial community in fecal samples was extracted by a soil DNA kit . After qualifying for the concentration and purity test, proceed to the next operation. The V3-V4 hypervariable region of bacterial 16S rRNA gene was amplified by forwarding primer 338 and reverse primer 806, and the primer sequences are 5′-ACTCCTACGGGAGGCAGCAG-3′ and 5′-GGACTACHVGGGTWTCTAAT-3′. Then triple PCR 16S rRNA gene amplification was performed. According to the standard protocol, the purified amplicons were pooled and end-sequenced using the MiSeq PE 300  platform. Then, using FastQ to quality filter the raw reads of 16S rRNA gene sequencing, and using Fast Length Adjustment of Short reads to merge . The UPAThe DIA quantitative analysis process includes 5 steps: total protein extraction, protein digestion, high pH RP-UPLC separation, liquid chromatography-mass spectrometry analysis, and protein identification . Brieflyp < 0.05 was considered statistical significance. Differential abundance testing between groups and post hoc tests were performed using the Benjamini-Hochberg method and Tukey’s multiple comparisons. The Chao diversity index was used to measure Alpha diversity. The binary-jaccard distance and principal coordinates analysis (PCoA) was used to analyze Beta diversity. The metabolic functional prediction analysis was performed using the PICRUSt analysis platform. In this study, the identification thresholds for DEPs were selected as fold change >1.2 or < 0.83 and p-value <0.05. Using the GO (http://geneontology.org/) and KEGG pathway (https://www.kegg.jp/kegg/) platforms for DEPs annotation and enrichment bioinformatics analysis . Data were presented by mean ± standard error (SEM). A two-way ANOVA and Tukey’s multiple comparisons were used to test the significance differences of the data and analysis .ap = 0.015). At time points P8 to P15, the model group maintained the trend of weight loss. In contrast, treatments with mesalazine or tuina showed effects . Mice that received tuina showed a slightly lower weight but no statistical difference compared with the mesalazine group (p = 0.208). Along with pathological weight loss, changes in the properties of stool, fecal occult blood, or even bloody stool are also caused by DSS . Although the DAI score in the mesalazine group was better than the tuina group, there was no statistical between the two groups (p = 0.496).The body weights of the mice were measured every 2 days during the experiment . As the d by DSS . The stop = 0.612). However, 2 groups were statistically significant different when compared with the model group . The degree of change in the morphological structure of the colon before and after treatments was assessed using the colon histological score (bp < 0.000), at the same time, reduced colon inflammation, preservation of structural integrity, and promotion of cell recovery can be observed. The results of the mesalazine group were similar to the tuina group (p = 0.101). Altogether, these results indicated that tuina could improve damages, reduce inflammation, and protect the structure of the colon.DSS-induced DAI score changes can also be reflected by changes in colon lengths . The colal score ,E. The mCompared with the control group, there was a significant difference in the gut microbiome of the model group. At the phylum level, the predominant phyla of all groups are composed of Firmicutes and Bacteroidota, and the model mice showed an increased relative abundance of Verrucomicrobiota and a decreased relative abundance of Patescibacteria, Desulfobacterota, Actinobacteriota, and Campilobacterota . At the Compared with the model group, at the phylum level, the relative abundance of Patescibacteria and Desulfobacterota increased and Actinobacteriota and Verrucomicrobiota decreased in the tuina group. In the mesalazine group, Proteobacteria and Cyanobacteria increased, Desulfobacterota, Actinobacteriota, and Verrucomicrobiota decreased . At the The results of beta diversity showed that 4 groups were clustered separately in the PCoA, suggesting that modeling and intervention were the primary factors that affected community differences. There were very small areas of overlap between the model and tuina groups, this suggested that the tuina treatment may not be as effective as the mesalazine treatment, which is consistent with other results from this study . HoweverThe functional abundance of metabolic COG in this community was deduced from the 16S compositional data utilizing the PICRUSt analysis platform. The metabolic COG functions of the colony were mainly focused on the transport and metabolism of carbohydrate, amino acid, and inorganic ion, and energy production and conversion . We suggQuantitative proteomics analysis of DIA and bioinformatics analysis can further elucidate the potential regulatory mechanisms of tuina treatment on UC. The principal component analysis (PCA) showed excellent sample aggregation in each group, and the distance was long between the sample points in each group, indicating that the four groups were not similar to each other, the model was successfully established, and tuina or mesalazine treatment was effective . ProteomTo understand the unique mechanism of tuina, it is necessary to identify the DEPs that are regulated by tuina. There were 437 overlapping DEPs between the control group vs. model group and the model group vs. tuina group, of which 370 DEPs were regulated by tuina. And there were 379 overlapping DEPs between the control group vs. model group and the model group vs. mesalazine group, with 247 DEPs regulated by mesalazine treatment .GO enrichment analysis revealed a total of 265 GO terms based on 370 DEPs regulated by tuina treatment, including glandular epithelial cell differentiation, oxygen carrier activity, regulation of cytokine production involved in immune response, etc. . There wDue to the unclear pathogenesis of UC, modern medicine cannot find effective therapeutic targets and methods. Tuina is gradually applied to the complementary alternative treatment of UC. Combined with the gut microbiota and proteomics, the mechanism of tuina treatment can be elucidated. Here, we histologically assessed the extent of damage to the colon morphological structures of the colon tissue and generally explored the overall changes in body weight, stool property, blood feces, and colon length under tuina treatment, mesalazine treatment, and non-treatment conditions in the UC model. We also analyzed the structural changes and diversity of the gut microbiota by 16 s rRNA high-throughput sequencing. Finally, quantitative DIA proteomics analysis was performed on colon samples to study changes in protein expression between all groups.The DSS-induced UC model chosen for this experiment is an acute modeling method commonly used in animal studies, which is similar to the pathology of human UC. After 7 days of drinking the DSS solution, the animals showed weight loss and gross bloody feces, and the colon tissues also showed varying degrees of ulceration, edema, transmural inflammatory cell infiltration, and epithelial cell damage under microscopic observation, which proved the success of the established model . The gutAt present, the symptoms of UC are mainly controlled by aminosalicylic acid preparations (ASAP) and glucocorticoids. The representative drug for ASAP is mesalazine, which reduces PEG2 synthesis in the mucous membrane of the colon and inhibits neutrophil function . The maiTuina treatment used abdominal rubbing manipulation and pressing manipulation of ST 36 and ST 37. Abdomen rubbing manipulation is the main method, it can reduce the volume of gastric residue, reduce distension, increase bowel movements, and improve gastrointestinal functions without side effects . ST36 anThe results of the gut microbiology examination did not show overlap in the expression of the tuina group and the mesalazine group, and we speculated that the treatment of tuina and mesalazine achieves therapeutic effects through different mechanisms. At the phylum level, we found that tuina increased the relative abundance of Firmicutes and decreased Bacteroidota. Firmicutes account for about 30% of the total number of gut microorganisms, which together with Bacteroidota cover more than 90% of gut microbes. It can regulate energy absorption and metabolic conversion by the body and are a relatively stable species in the gastrointestinal flora . FirmicuTuina can regulate biotin metabolism, Notch signaling pathway, linoleic acid metabolism, autophagy, etc. Biotin, a water-soluble vitamin, is involved in the immune and inflammatory response, cellular stress response . PatientIn conclusion, tuina can effectively improve the DAI score in DSS-induced UC mice, relieve colitis and restore colon morphology, as well as restore gut microbiota diversity and adjust microbiota structure. The therapeutic effects of tuina may be related to modulating biotin metabolism, Notch signaling pathway, linoleic acid metabolism, and autophagy signaling pathways.http://www.ncbi.nlm.nih.gov/bioproject/905890 with accession number: PRJNA905890. Further inquiries can be directed to the corresponding author.The original contributions presented in this study are publicly available. The raw data can be found on NCBI at the following link: The animal study was reviewed and approved by the Animal Ethics Committee of the Beijing University of Chinese Medicine.ML and TY: study conception and design of the work. HW, ZL, XW, HD, RJ, DL, YX, and QG: data acquisition. HW and ZL: analysis and data interpretation, drafting of the manuscript. HW, ZL, YZ, and YJ: approval of the final version of the manuscript. All authors contributed to the article and approved the submitted version.The authors have received funding for research, writing, and publication of this paper from the National Natural Science Foundation of China (No. 81704193).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.


Empty nesters in China have limited mental health literacy (MHL), which may lead to poorer health outcomes. Studies demonstrate that MHL is associated with both resilience and positive coping style. However, the potential mechanism of MHL, resilience and positive coping style remain unclear. Therefore, the study aims to investigate the possible mediating role of resilience in the relationship between MHL and positive coping style.In this cross-sectional study, a total of 363 empty nesters from Huzhou, China were surveyed in 2022. The Chinese version of Mental Health Literacy Scale (C-MHLS), the Chinese version of 10-item Connor-Davidson Resilience Scale (CD-RISC-10) and the Simplified Coping Style Questionnaire (SCSQ-19) were used to assess MHL, resilience, and positive coping style, respectively.p < 0.01). MHL can significantly and positively predict the positive coping style, and resilience played a partial intermediary role between MHL and positive coping style, with the intermediary effect of 77.36%.Positive coping style was significantly correlated with MHL and resilience, and MHL was positively correlated with resilience (This study indicates that MHL not only directly affected positive coping style, but also indirectly influences positive coping style by increasing the resilience of empty nesters. The results provide an empirical evidence for the development of intervention programs to improve positive coping style directly and indirectly. Consequently, community health servicers should take targeted measures which focus on MHL and resilience as breakthrough points to stimulate positive coping style of empty nesters, and ultimately achieve their overall well-being. AccordiEmpty nesters refers to the elderly over 60; have no children or have children, but are not around; live alone or only with their spouses. It can be further divided into absolute empty-nesters (those who do not live with their children in the same city or have no children) and relative empty-nesters  Inclusion criteria: age ≥ 60; elderly who lived alone or only with their spouse for more than 6 months each year, and their children were absent or childless; awareness was clear, and communication with investigators was barrier-free; volunteered and signed informed consent. (2) Exclusion criteria: had serious physical diseases, mental disorders or cognitive disorders; unable to take care of themselves; children living nearby could take care of the elderly at any time.2.2.Before the formal survey, 30 empty nesters were selected by convenience sampling to test the reliability and validity of the instruments, as well as the appropriateness of the survey method and field. During the formal survey, with the assistance of community health servicers, we used a combination of centralized investigation , and individual household surveys to conduct on-site investigations. Each data was obtained through a face-to-face survey of about 30 min. The trained researchers used unified instructions to explain the purpose and significance of the study. Prior to the survey, informed consent was obtained from each participant.2.3.The general information questionnaire was used to assess the demographic information, including gender, age, residence, degree of education, living style, marital status, monthly income and other information.Mental health literacy was measured by the Chinese version of Mental Health Literacy Scale (C-MHLS), which was developed by Resilience was measured by the Chinese version of the 10-item Connor-Davidson Resilience Scale (CD-RISC-10). The CD-RISC-10 was first simplified by Professor Campbell-shills, who extracted 10 items from the 25-item Connor-Davidson Resilience Scale . The SCSQ-19 was compiled by 2.4.All statistical analyses were carried out using SPSS 24.0 and AMOS 26.0. The analysis strategy was divided into three steps. Firstly, descriptive statistics was used to process sociodemographic data, of which observable variables were presented as the means ± standard deviations (M ± SD) for continuous variables and frequencies and percentages for categorical variables. Secondly, correlation analysis and multiple linear regression analysis were used to explore the direct relationship among variables. Thirdly, we used the bootstrap-based structural equation modeling analysis in AMOS 26.0 software to test the mediating role of resilience between MHL and positive coping style. For the mediation analysis, the positive coping style was considered a dependent variable, MHL was considered an independent variable, and the resilience was entered as mediating variable.3.3.1.M = 72.69, SD = 8.56) and more than half of them were female (50.7%), married (79.9%), and lived in city (61.4%). The level of education was predominantly primary school . Monthly income was mostly <5,000 RMB  and resilience . Moreover, there was a significant positive correlation between MHL and resilience .Correlation analysis  showed tp < 0.001, p = 0.038, respectively). “knowledge of self-treatment” and “attitudes that promote recognition or appropriate help seeking behavior” of MHL were significantly associated with positive coping style . However, other dimensions of MHL had no statistical significant effect on positive coping style (p > 0.05).Multiple linear regression models were constructed to expose the effects of resilience and MHL on positive coping style . Two dim3.4.3.4.1.We used Harman’s one-factor method to test the common method deviation. All the items of the scale used to measure MHL, resilience and positive coping style were included in SPSS 24.0 software for exploratory factor analysis. The results showed that 12 factors with characteristic root > 1 were extracted, and the maximum factor variance explanation rate was 15.15% (< 40%), so there was no serious common method deviation in this study .3.4.2.2χ), 2χ/df, root mean square error of approximation (RMSEA), adjusted goodness-of-fit index (AGFI), comparative fitting index (CFI), goodness-of-fit index (GFI), and normed fit index (NFI).In this study, the analysis was performed by a bootstrap-based structural equation model to test the mediation effect of the resilience on the correlation between MHL and positive coping style. Several indexes were calculated to evaluate the model fit to the data: chi-square statistic  needed further verification. Bootstrapping was performed to confirm the mediation effect, taking 95% confidence interval (CI) and sampling number of 5,000. The results were shown in 4.As far as we know, this is the first study to investigate the MHL among empty nesters, and analysis the mediating role of resilience on MHL and the positive coping style using a structural equation model. Findings highlighted that positive coping style is influenced by both MHL and resilience, and the indirect pathway is more influential. The scores of MHL, resilience and positive coping style of empty nesters in this study belong to the lower middle level, which were consistent with previous studies .Empty nesters with enough MHL are more likely to adopt positive coping styles. On the one hand, characters with high MHL possess sufficient mental health knowledge, so that they can identify mental diseases accurately in the early stage . FurthmoIn our study, MHL is proved to influence coping style through resilience, which is consistent with the findings of In China, poor MHL among empty nesters is alarming. Studies substantiates that MHL will influence empty nesters’ ability to comprehensively apply mental health knowledge, skills and attitudes to deal with mental diseases , which i5.Several limitations of this research should be noted. First, the sample population of empty nesters included only a few randomly selected communities in a city, which may introduce bias and limit the universality of the research results. Accordingly, it is indispensable to conduct future research with empty nesters from different regions. Second, it is difficult to draw a conclusion on the causal relationship between the research variables because of the cross-sectional design used in this study. Third, the data were collected through self-report questionnaires, which may be subject to reporting bias.6.This study bears out that resilience is an important mediator in the relationship between MHL and positive coping style of empty nesters. The results provide an empirical evidence for the development of intervention programs to improve positive coping style directly and indirectly. Consequently, community health servicers should take targeted measures which focus on MHL and resilience as breakthrough points to stimulate positive coping style of empty nesters, and ultimately achieve their overall well-being.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by the ethics committee of the School of Nursing, College of Medicine, Huzhou University (2022-03-14). The patients/participants provided their written informed consent to participate in this study.QZ was involved in research design and critical revision for intellectual content of the manuscript. LS completed the first draft of the manuscript, submitted the manuscript for publication. YW, JY, and WG contributed to assist in the development of the study protocol. SS and LQ assisted with participant enrollment and consent, and worked out the data acquisition plan. All authors commented on previous versions of the manuscript, read and approved the final manuscript.This work was supported by the National Social Science Fund of China (22BGL251), China Scholarship Council (202008330254), Scientific Research Fund of Zhejiang Provincial Education Department (Y202250172) and Postgraduate Research and Innovation Project of Huzhou University (2022KYCX68).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.


Regional lymph node metastasis is a contributor for poor prognosis in osteosarcoma. However, studies on risk factors for predicting regional lymph node metastasis in osteosarcoma are scarce. This study aimed to develop and validate a model based on machine learning (ML) algorithms.A total of 1201 patients, with 1094 cases from the surveillance epidemiology and end results (SEER) (the training set) and 107 cases  admitted from four medical centers in China, was included in this study. Independent risk factors for the risk of lymph node metastasis were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), the random forest (RF), the decision tree (DT), and the multilayer perceptron (MLP), were used to evaluate the risk of lymph node metastasis. The prediction model was developed based on the bestpredictive performance of ML algorithm and the performance of the model was evaluatedby the area under curve (AUC), prediction accuracy, sensitivity and specificity. A homemade online calculator was capable of estimating the probability of lymph node metastasis in individuals.Of all included patients, 9.41% (113/1201) patients developed regional lymph node metastasis. ML prediction models were developed based on nine variables: age, tumor (T) stage, metastasis (M) stage, laterality, surgery, radiation, chemotherapy, bone metastases, and lung metastases. In multivariate logistic regression analysis, T and M stage, surgery, and chemotherapy were significantly associated with lymph node metastasis. In the six ML algorithms, XGB had the highest AUC (0.882) and was utilized to develop as prediction model. A homemade online calculator was capable of estimating the probability of CLNM in individuals.T and M stage, surgery and Chemotherapy are independent risk factors for predicting lymph node metastasis among osteosarcoma patients. XGB algorithm has the best predictive performance, and the online risk calculator can help clinicians to identify the risk probability of lymph node metastasis among osteosarcoma patients. Lymph node metastasis is one of the most common metastases and has important prognostic implications for many types of cancers , 2. SystOsteosarcoma, also called osteogenic sarcoma, is a common primary malignant bone tumor and is highly malignant and aggressive . OsteosaMachine learning, a new type of artificial intelligence, is beginning to be widely used in healthcare data analysis and is a powerful tool to improve clinical strategies –14. MachRetrospective analysis of the SEER  database and data of patients admitted to the Second Affiliated Hospital of Jilin University, the Second Affiliated Hospital of Dalian Medical University, the Liuzhou People’s Hospital affiliated to Guangxi Medical University, and the Xianyang Central Hospital. SEER is an authoritative source for cancer statistics in the United States. The Surveillance, and it provides information on cancer statistics in an effort to reduce the cancer burden among the U.S. population. Patients were included if they had (1) pathologically confirmed primary osteosarcoma, (2) no concurrent malignancy, and (3) complete clinical information, including age, gender, race, first site, T and M stage, surgery, and chemotherapy. Patients were excluded if they had (1) no complete clinical information, (2) other primary neoplastic disease, and (3) unknown metastatic status. For multicenter data, the study was approved by the ethics review committee of four medical institutions in China, the Second Affiliated Hospital of Jilin University, the Second Affiliated Hospital of Dalian Medical University, Liuzhou People’s Hospital, and Xianyang Central Hospital (No. 2021-00-22) and was conducted in accordance with the guidelines of the Helsinki Declaration.The cohort from the SEER database was included in the traininggroup and the cohort from the four medical centers was includedin the validation group. We compared the pathologicalcharacteristics of the training and validation group andanalyzed the risk factors for predicting lymph node metastasisby the univariate analysis. Subsequently, the multivariate logisticregression analysis was used to evaluate each variable, andindependent predictors associating with lymph node metastasiswere obtained. The independent predictors were included in sixmachine learning algorithms and the AUC was calculated toidentify the highest performing machine learning model. Meanwhile, A web-based calculator was capable of estimating the probability of lymph node metastasis in individuals.The training group was extracted from the SEER database using SEER statistical software (version 8.3.6). All analyses were performed using R software (version 3.6.0). Continuous variables were represented as the median with interquartile range (IQR), while categorical variables were represented as numbers with proportions. Differences of two groups were compared by Wilcoxon rank-sum test for continuous variables, and categorical variables were evaluated using the Chi-Squared test or Fisher’s Exact test. Logistic regression analysis was used to analyze the relationship between various predictor variables  and an outcome which is binary (dichotomous). Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), the random forest (RF), the decision tree (DT), and the multilayer perceptron (MLP), were used to evaluate the risk of lymph node metastasis. The prediction model was developed based on the best predictive performance of ML algorithm and the performance of the model was evaluated by the area under curve (AUC), prediction accuracy, sensitivity and specificity.Bilateral p-values < 0.05 were considered statistically significant.The 1201 patients were divided into two groups according to the presence of lymph node metastases, and the differences between the two groups (lymph node metastases vs no lymph node metastases) in terms of age (P=0.01), T stage (P<0.001), M stage , surgery (P<0.001), chemotherapy (P=0.005), bone metastases (P<0.001), and survival times (P<0.001) were statistically significant Table 1.In the univariate analysis, age (P<0.05), survival time (P<0.01), laterality (P<0.05), T (P<0.001), M (P<0.001), surgery (P<0.001), chemotherapy (P<0.01), bone metastases (P<0.001), and lung metastases (P<0.001) were significantly associated with the occurrence of lymph node metastasis in osteosarcoma, while there were no significant differences in race, sex, primary site, and radiation between the two groups. The multiple logistic regression analysis including statistically significant differences in univariate factors showed that T , M , surgery , chemotherapy  were independent predictors of lymph node metastasis Table 2.The patients in the training group were trained with 10-fold cross-validation, and the data set was divided into 10 parts, of which 9 parts were used for training and 1 part for testing on a rotating basis, and the final accuracy was averaged 10 times. The results Figure 1The relative importance of variables in each ML algorithm for predicting osteosarcoma lymph node metastasis is shown in https://share.streamlit.io/liuwencai123/os_lnm/main/os_lnm.py.In this study, an online calculator based on the best model  was developed to predict the risk of lymph node metastasis in osteosarcoma patients Figure 4Osteosarcoma, Ewing sarcoma, and chondrosarcoma were the three most common types of malignant bone tumors. Of the three tumors, osteosarcoma and Ewing sarcoma occur more frequently in childhood, whereas chondrosarcoma is more common in the elderly . PatholoThis study developed and validated several models using popular machine learning algorithms to predict the risk of lymph node metastasis among osteosarcoma patients and the logistic regression analysis showed that T stage, M stage, surgery, and chemotherapy were independent risk factors for lymph node metastasis among osteosarcoma patients. After comparing the performance of the six ML algorithms, we found that the XGB algorithm had the best performance (AUC=0.882). To increase the feasibility of the application of this model, an online web calculator was further developed for assessing the individual probability of lymph node metastasis in patients with osteosarcoma.The clinical characteristics of age, T stage, laterality, M stage, and surgery were identified by the ML algorithm as the most important predictors of lymph node metastasis in patients with osteosarcoma. The results of this study showed that older patients with osteosarcoma had a greater risk of lymph node metastasis, which may be related to the bimodal nature of the age of predilection for osteosarcoma . T stageIn a study conducted by Dong et al. , gender,The advantage of the present study is that the ML algorithm was used to develop a prediction model to assess the risk of lymph node metastasis in patients with osteosarcoma using readily available clinical data, and the prediction model developed in this study was validated by the validation group, showing strong predictive power compared with the linear model used in previous studies. The prediction model developed in this study showed strong predictive power and some advantages over the linear models used in previous studies. The inclusion of different ethnic groups in the modeling and validation groups also demonstrated the generalizability of the model. Finally, in order to make the prediction model more convenient for clinical use, an online application based on the model was created, which allowed clinicians to predict the risk of lymph node metastasis in patients with osteosarcoma by using the clinical characteristics of the patients available.There were some limitations of this study. Firstly, this study was a retrospective study and there might be some selection bias. Secondly, the results analyzed in this study only demonstrated the association between risk factors and lymph node metastasis, but could not elucidate whether there was a causal relationship. Because the original data in the SEER database have no chronological sequences, thus the causal relationship between variables could not be obtained after analysis. Therefore, this study need further investigations.This study developed and validated the ML algorithm for individualized prediction of whether a patient with osteosarcoma will develop lymph node metastasis by using readily available clinical features. T stage, M stage, surgery and Chemotherapy are independent risk factors for predicting lymph node metastasis among osteosarcoma patients. Among all the six ML algorithms, the XGB algorithm has the best predictive performance, and the online risk calculator was generated based on this algorithm, which can help clinicians to identify the risk probability of lymph node metastasis among osteosarcoma patients.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.The SEER database is a comprehensive data source developed based on population data and updated annually since its launch in 1973. It is public and identifiably accessible that data analysis is treated as non-human subjects by the Office for Human Research Protections. As such, no institutional review board approval and informed consent were required. For multicenter data, the study was approved by the ethics review committee of four medical institutions in China, the Second Affiliated Hospital of Jilin University, the Second Affiliated Hospital of Dalian Medical University, Liuzhou People’s Hospital, and Xianyang Central Hospital (No. 2021-00-22) and was conducted in accordance with the guidelines of the Helsinki Declaration.CLY, CXG and QL carried out the study design. WCL conducted the research and collected and analyzed the data. WLL and YFL performed the statistical analysis and drafted the manuscript. ZRT and STD provided the expert consultations and suggestions. ALL conceived the study, participated in its design and coordination, and helped shape the language. All authors contributed to the article and approved the submitted version.Author CG was employed by Hengpu Yinuo (Beijing) Technology Co., Ltd, Beijing, China.The remaining 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.


The probiotic LAB viability in FM was affected by the addition of the fruit pulp. Phocaeicola was dominant in the FMPF and FMB samples; Bifidobacterium was related to FM formulations, while Alistipes was associated with FMPF and FMB, and Lactobacillus and Lacticaseibacillus were predominant in FMC. Trabulsiella was the central element in the FMC, while Mediterraneibacter was the central one in the FMPF and FMB networks. The FM formulations increased the acetic acid, and a remarkably high amount of propionic and butyric acids were detected in the FMB treatment. All FM formulations decreased the ammonium ions compared to the control; FMPF samples stood out for having lower amounts of ammonia. The probiotic FM with fruit pulp boosted the beneficial effects on the intestinal microbiota of healthy humans in addition to increasing SCFA in SHIME® and decreasing ammonium ions, which could be related to the presence of bioactive compounds.The effect of putative probiotic fermented milk (FM) with buriti pulp (FMB) or passion fruit pulp (FMPF) or without fruit pulp (FMC) on the microbiota of healthy humans was evaluated. FM formulations were administered into a simulator of the human intestinal microbial ecosystem (SHIME The functional food properties are associated with the metabolic or physiological role that the nutrient or non-nutrient has on the growth, development, maintenance, and other normal functions of the human organism . For theAmong functional dairy products, fermented products are relevant since the use of probiotic bacteria for carrying out fermentation is strategic when meeting the consumers’ demand for healthy yet appealing food products. Moreover, probiotic fermented milk (FM) can also modulate the gut microbiota, increasing the presence of beneficial microorganisms and an arsenal of essential metabolites ,4.Another trend in the development of functional dairy products is to supplement them with fruit pulp, which can improve sensory properties, such as taste, aroma, texture, and color, diversifying the products available on the market ,6. TropiInitially, studies focused on the effect of bio-compounds on pathogenic bacteria present in the microbiota; however, in recent years, research has focused on evaluating the effect of polyphenols (PP)—including fruits rich in bioactive compounds—on the modulation of the intestinal microbiota ,10,11. T®).Therefore, the aim of this study was to evaluate the effect of fermented milk with and without fruit pulp on LAB viability, intestinal microbiota composition , and production of short-chain fatty acids and ammonium ions by means of an in vitro test using the Simulator of Human Intestinal Microbial Ecosystem  as a cryoprotector. For revitalization, the isolated strains were inoculated  in 10 mL MRS broth  at 37 °C for 18 h. Subsequently, the activated cultures were centrifuged  and washed twice with 0.85% saline solution before being inoculated in milk. The freeze-dried Streptococcus thermophilus TA 080  was used as a starter culture directly in the product. These proportions yielded initial counts of approximately log 7 CFU/mL after milk inoculation. strains ,21, werePassiflora edulis f. Flavicarpa Deg.), originating from Bahia state, were purchased in São José do Rio Preto-SP, Brazil. The fruits were washed and sanitized in running water and submerged for 10 min in 10% (w/v) sodium hypochlorite (NaClO) solution . Subsequently, the pulp was manually separated from the seed using a sieve. The pulp was then stored in polyethylene plastic bags in the absence of light and frozen (−80 °C). Buriti fruits (Mauritia flexuosa) were harvested and processed in Teresina-PI, Brazil, the typical region for buriti production. The fruits were washed and sanitized, as previously described, manually pulped using a stainless-steel knife, and stored at 4 °C, as described by Moura Filho [Passion fruits (ra Filho . Subsequw/v) passion fruit pulp (FMPF), and FM with 1% (w/v) buriti pulp (FMB). The reconstituted milk  in sterile water) with or without fruit pulp, according to the experiment, was heated to 90 °C for 10 min using a food processor , cooled to inoculation temperature (37 °C), and distributed into sterilized bottles.To evaluate the effect of the fruit pulp on the gut microbiota, three different formulations were prepared: FM without pulp (control—FMC), FM with 1%  at 42 °C until reaching a pH of 4.6. After the fermentation was completed, the product was cooled and the curd was manually broken in a standardized way for all formulations; finally, it was cooled to 4 °C and then stored, as described by Borgonovi et al. [The activated cultures of −8 were prepared with 0.85% saline solution and plated to appropriate solid growth media on Petri dishes, according to Solieri et al. [Streptococcus spp. were counted using M17 agar (Difco) under aerobic conditions at 50 °C, Lb. casei SJRP38 was counted in MRS agar (Difco) at 45 °C, and Lb. plantarum ST8Sh was counted in MRS agar (Difco) at 15 °C, both under anaerobic conditions, all after incubation for 48 h. The incubation temperature of Lb. casei and Lb. plantarum were defined after preliminary trials in which different incubation temperatures  were assessed aiming to count both evaluated strains separately.For the analysis of LAB viability in FM, a 100 μL aliquot of all FM samples was withdrawn; serial dilutions up to 10i et al.  with mod® model was used to simulate the human digestion process. It is a computer-controlled system in which volumetric capacity, pH, temperature (37 °C), and retention time (24 h) were controlled. A schematic description of the SHIME® system [® model consists of five compartments representing the stomach, small intestine, and ascending colon, anaerobically controlled by the addition of nitrogen and the pH value adjusted by using hydrochloric acid or sodium hydroxide, accordingly, in each reactor.The SHIMEion time  h were c® was adapted for this specific study, in such a way that the transverse and descending colon were replaced by the triplicate of the ascending colon, according to Salgaço et al. [SHIMEo et al. .® was prepared with distilled water, supplemented with 3 g/L starch , 2 g/L pectin , 4 g/L gastric mucin type II swine (Sigma-Aldrich), 1 g/L xylan , 1 g/L peptone , 1 g/L arabinogalactan (Sigma-Aldrich), 0.4 g/L glucose , 3 g/L yeast extract (Kasvi), and 0.5 g/L L-cysteine (Sigma-Aldrich) [The feed medium used in SHIMEAldrich) . Stomach conditions were simulated in reactor 1 (R1) and the pH value was adjusted by adding HCl, along with the carbohydrate-based medium. In order to reach the duodenum conditions, in the second reactor (R2), an artificial pancreatic juice was added, composed of 12.5 g/L sodium bicarbonate , 6 g/L ox-bile (Sigma-Aldrich), and 0.9 g/L pancreatin (Sigma-Aldrich) . This ca® reactors were colonized with a pool of feces donated by three healthy male volunteers, aged 18–22 years old. The following criteria were met: donors had not taken antibiotics within a period of six months prior to the study; had not consumed probiotic products over the past 3 to 6 months; and did not have any food allergies or intolerance to dairy products. The stool inoculum was prepared according to the procedures described by Duque et al. [g for 15 min. The supernatant was collected, and 10 mL was added to the three compartments that simulated the ascending colon  and the pancreatic juice (60 mL) were placed into the three compartments for 14 days before the experiments, according to Possemiers et al. [The SHIMEe et al. . The sams et al.  and Van s et al. .After 14 days of stabilization of the human microbiota, the FM formulations  were administered for 5 days, followed by 5 days washout, as described by Duque et al. . The tre® simulator, the samples were collected and quantified using a specific ion meter  coupled to a selective ammonia ion electrode , according to Bianchi et al. [The short-chain fatty acid (SCFA) levels and ammonium ions were determined from samples collected from reactors representing the ascending colon and frozen at −20 °C. This analysis was performed according to Espírito Santo et al.  and Adori et al. .® was performed using next-generation sequencing by Neoprospecta Microbiome Technologies . The V3–V4 hypervariable region of the 16S rRNA gene was amplified using specific primers. The data were organized by microbiota phyla, families, genera, and species, according to Casarotti et al. [The determination of the composition of the intestinal microbiota collected from the SHIME® Safe . Next, the DNA quantification was conducted  based on the ratio 260/280; the quality of the DNA was verified by using the Nanodrop ND-1000 spectrophotometer . The libraries were sequenced using the equipment MiSeq Sequencing System .Initially, the DNA was extracted from 2 mL of samples collected (as described in item 2.4.3) from the reactors that represent the ascending colon in duplicate , using the QIAamp DNA Stool Mini Kit , according to the manufacturer’s protocol, and visualized by agarose gel electrophoresis 1% stained with SYBRAfter sequencing, the FastQC software was used for the initial assessment of the quality of the sequenced data. Additionally, the libraries were analyzed  to detecSuperior quality reads were submitted to the DADA2 pipeline  using thThe rarefaction curves were obtained to evaluate the effectiveness of the sampling depth . Relativ®, were all carried out by analysis of variance (ANOVA) followed by a Tukey test, which was used to compare the formulations. A significance level of 5% probability and Minitab 16 software were adopted.The bacteria viability analyses in the FM, as well as ammonium, SCFA, and viability of bacteria in the reactors representing the ascending colon in SHIMEp ≤ 0.05) was used to compare differences in richness and α-diversity among FM formulations. In case of significance, the Fisher’s LSD post-hoc test was used for pairwise comparison and grouping of means (p≤ 0.05). Both analyses were conducted using the R software [The Kruskal–Wallis test (software .p ≤ 0.05). In the event of a difference, a post-hoc analysis was performed [The beta diversity analysis was performed by calculating Bray–Curtis dissimilarities between samples taken from the FM formulations using the R software. For each cluster, a PERMANOVA analysis  was perferformed  to assesp≤ 0.05). The analyses were graphically represented with the R software [DESeq2 was used for the evaluation of differentially abundant taxa  by compasoftware . For thip ≤ 0.05) in order to restrict the scope of networks only to strongly positive or negative relationships. The construction of the networks and the topological information were obtained through the R software v.1.3.4 [The structural characteristics of microbial communities from the samples of FM formulations were evaluated based on co-occurrence networks considering the taxonomic level of the genus. Those whose relative abundance was lower than 0.1% were filtered out. The nets were obtained independently for each treatment . After calculating Pearson’s coefficients , a minim v.1.3.4 .p ≤ 0.1), linear regression graphs were obtained.Correlations between the microbiota and the levels of SCFAs and ammonia were obtained considering the taxonomic level of family. Pearson’s coefficients were calculated using the R software , and forLb. casei in the FM with pulp fruits was lower than that in FMC  and açaí pulp. The beneficial effect of fruit pulps on the viability of LAB was ascribed to the presence of bioactive compounds, such as phenolic compounds, carotenoids, and organic acids, in addition to fibers, such as fructooligosaccharides (FOS) found in fruits, some of them described as prebiotics [The fruit pulp did not interfere in LAB viability in FM. The population of t in FMC . A similebiotics .Lb. plantarum ST8Sh in FM was similar between the type of fruit and the difference was small compared to the control  was composed of Firmicutes (average of 55.37%), Proteobacteria (average of 28.06%), and a minimal proportion of Actinobacteria (average of 2.15%), when it is desirable that Firmicutes and Bacteroidetes compose 85–90% of the total microbiota and, less abundantly, Actinobacteria, Proteobacteria, and Verrucomicrobia , differeRegarding the taxonomic profiles, there was a similarity between the samples of FM supplemented with fruit pulp (FMPF and FMB) when compared to samples of FM without any fruit pulp (FMC), both at the phylum A and thePhocaeicola presented the highest general abundance, with averages of 24.49%, 61.91%, and 64.61% in FMC, FMPF, and FMB samples, respectively , although this was relatively heterogeneous among the samples of this treatment  for in vitro simulation models. However, we expected that the alpha diversity indices would increase in FMPF and FMB, since the increases in these indices are related to intestinal health . A previp = 0.0197), as well as between the FMC and FMB (p = 0.0228). Among the formulations with fruit pulp, there are no significant differences (p > 0.05). Furthermore, the reduction in dimensionality obtained by PCoA resulted in an explanation of 76% of the variability found in the samples.The beta-diversity analysis evaluated the presence of differences among the microbiota composition resulting from FM formulations . AccordiSince there were differences in the beta diversity between FM and those with added fruit pulp, we may suggest that the FMPF and FMB were able to modulate the microbiota in vitro. Our research group already showed in vitro microbiota modulation by fermented goat milk with passion fruit .Fruits are a source of carbohydrates, fibers, amino acids, acids, minerals, polyphenols, vitamin C, B-complex vitamins, provitamin A, carotenoids, phytosterols, aromatic compounds, and other bioactive compounds that are beneficial to human health ,75,76. TClostridiaceae (Group 1) and Rikenellaceae families, in addition to the Alistipes genus, which were higher in milk with fruit pulp (FMPF and FMB). On the other hand, the Lactobacillaceae family, as well as its associated genera Lacticaseibacillus and Lactobacillus, were depleted in these same formulations when compared to FMC  comparison between fermented milk formulations  stands out. In addition to being enriched in the three types of FM, this taxon is among the most prevalent genera in these samples  produced from lactose through the transgalactosylation activity of the β-galactosidase enzyme. GOS degradation occurs more effectively if associated with probiotic cultures, forming a synergistic combination with some species of Bifidobacteria and Lactobacillus [In the comparison between each FM and its control , the con samples B, which bacillus .Finally, the structuring of the microbiomes of the different types of fermented milk was evaluated by obtaining co-occurrence networks. It was observed that the type of fermented milk exerted changes in the relationships between the identified bacterial genera ; Table 2Trabulsiella (ID 63) was the central element of the FMC without significant co-occurrences in the FMPF and FMB formulations. The networks of FMPF and FMBP formulations show the same central genus: Mediterraneibacter (ID 40). In addition, the genus Mediterraneibacter has a series of conserved and consistent relationships in both networks arising from FM supplemented with fruit pulps, namely: positive relationships with the Lachnospira (ID 37) and Faecalibacterium (ID 35) genera and negative relationships with the Alistipes (ID 44), Parabacteroides (ID 22), Flintibacter (ID 26), and Enterocloster (ID 23) genera. Another notable feature is the positive association between the Salmonella (ID 64) and Enterobacter (ID 18) genera, which was consistent across the three networks.Furthermore, there is a change in the genera considered central in the networks ; Table 3The knowledge on the metabolic interspecies interactions and their functional roles is crucial for improving our understanding and predicting functioning and stability of the human intestinal microbiome in health and disease. However, we are still just beginning to understand how those bacterial species interact; knowing which potential interventions can help make us healthier remains a challenge. These interactions are highly dynamic and can result in competition for resources and/or cross-feeding for cooperative action ,97.Trabulsiella, which were detected in FMC, are Gram-negative microorganisms from the Enterobacteriaceae family and Proteobacteria phylum [Trabulsiella and other genera, showing their role in maintaining gut health by lowering luminal pH and digestion of complex polysaccharides in chickens [Trabulsiella representatives were detected in skin microbiome structure between healthy individuals from different races and ethnicity, especially on arms and hands [The bacterial species representing the genus a phylum . Similarchickens . In addind hands . It was nd hands .FMPF and FMBP formulations, nevertheless, showed a different microbial interaction. In a previous study, it was shown that buriti and passion fruit pulps are rich in bioactive compounds, such as phenolic compounds and carotenoids, in addition to fibers. The buriti pulp contains higher amounts of flavonoids compared to the passion fruit pulp; it is also rich in quercetin, while the major compounds of the passion fruit pulp are orientin, followed by vitexin. The fruit pulps are rich in carotenoids, and β-carotene is the major carotenoid in both pulps . These cMediterranneibacter was the central genus in FMPF and FMB formulations. Representatives of this genus are Gram-positive, coccoid, or coccobacilli, non-motile obligate anaerobes belonging to the Lachnospiraceae family from the Firmicutes phylum. All type species were discovered in the human gut and the major metabolic products from carbohydrates were acetic, formic, and lactic acids [Mediterranneibacter, Faecalibacterium prausnitzii, and Lachnospira sp. preferentially use these organic acids as a carbon source for butyrate production. An increase in the abundance of the Mediterranneibacter genus as characterized by the FMPF and FMB formulations was also reported by Ruiz-Rico et al. [Mediterranneibacter faecis is considered a biomarker for improving health due to its capacity to produce several short-chain fatty acids.ic acids . In a coo et al.  when theF. prausnitzii has specialized enzymatic machinery to secrete enzymes that break down the fiber polymers into fructose, glucose, and other monosaccharides, which are available as a carbon and energy source for the human colonic epithelium or other bacteria in the community, thereby cross-feeding them and improving gut health. Moreover, F. prausnitzii can produce large amounts of SCFAs, especially butyrate, which is secreted into the intestinal lumen [Lachnospiraceae family were observed to maintain the integrity of the intestinal barrier in mice, and their abundance was negatively correlated with chronic kidney disease [al lumen . Bacteri disease .Mediterraneibacter, we hypothesize that the bacteria from this genus may competitively interact with Alistipes, Flintibacter, Parabacteroides, and Enterocloster. Mediterraneibacter and Flintibacter use the same substrates to produce SCFA, such as butyrate, while Parabacteroides utilizes polysaccharides and produces SCFAs via the metabolic pathway of fatty acid biosynthesis [Regarding the negative relationship of ynthesis . These iAlistipes is a member of the Bacteroidetes phylum, Rickenellaceae family, known to be an SCFA producer; its low abundance is associated to patients with non-alcoholic fat liver disease and liver fibrosis. However, it may be pathogenic in anxiety disorders, chronic fatigue syndrome, and depression, although it exerts a protective role in the health phenotype [Flintibacter genus is characterized by rod-shaped, Gram-negative bacteria from the Ruminococcaceae family. The type species for this genus is Flintibacter butyricus. It is able to produce butyrate and acetate not only from sugars but also from the amino acids glutamine and glutamate and is considered part of the beneficial microbiota [Mediterraneibacter glycyrrhizinilyticus and Enterocloster were linked to secondary bile acid metabolism. Bile acids are potent antimicrobials and play a key role in the innate immune defense within the intestines [henotype . The Flicrobiota ,107. Medtestines .Salmonella and Enterobacter genera was observed in the SHIME® system. Considering that bacteria from the Enterobacteriaceae family are the major groups present in the gut and have similar metabolic traits, it is possible that these bacteria use the same antibacterial antagonistic mechanisms, such as colicins, microcins, T6SS specialized protein secretion systems, and contact-dependent growth inhibition to counteract Salmonella in the gut. These bacterial interactions take place in a highly complex chemical environment, composed of chemical substances [A positive interaction between bacteria from the bstances ,110. Add® .Equivalent results for metabolites production after administering fermented milk were reported by Freire et al. . These aEscherichia-Shigella and Klebsiella were found after fermented milk containing probiotic strains  was administered for 28 days to patients under Helicobacter pylori eradication therapy [The correlation analysis between the microbiota at the taxonomic level of the family and the concentrations of SCFA A was per therapy .Veillonella genus, belonging to the Veillonellaceae family, are unable to break down carbohydrates and use lactate for growth [Veillonella might have used lactate formed by microorganisms during milk fermentation or by microorganisms from the gut microbiota to produce acetate. Moreover, bacteria from the Veillonellaceae family are known for their ability to use intermediate or end products from the bacterial digestion of polysaccharides to produce acetate [Microorganisms from the r growth ; a higher growth  Therefor acetate ,126. In Butyric and propionic acids were correlated with two and one families, respectively. For butyric acid, Xanthomonadaceae and Sphingobacteriaceae presented a negative correlation with this SCFA. The correlation between these two families and butyrate has not been previously observed. At this point, it is unclear whether this significant correlation is influenced by other variables and is, therefore, causal. The reduction in Xanthomonadaceae and Sphingobacteriaceae may have been caused by other factors, such as competition for attachment sites and for nutrients, and/or production of antimicrobial substances. Nevertheless, the increase in butyric acid may also have been the result of the metabolism of other bacteria present in the microbiota.Clostridium propionicum, can produce propionic acid from lactate fermentation [Clostridium may have produced propionate through this route.Clostridiaceae showed a positive correlation with propionic acid. It is known that some species from the Clostridiaceae family, such as entation . This saentation . ConsideThe correlation analysis between the microbiota at the taxonomic level of the family and the concentrations of ammonia pointed to a correlation with the abundance observed in nine families A, and thBacteroidetes, so the higher the concentration of ammonia in the environment, the smaller the population of Bacteroidaceae [There was a negative correlation between Bacteriodaceae and ammonia. Bacteroidetes belonging to the Bacteriodaceae family are considered the main bacteria that maintain a healthy state and homeostasis in the intestinal microbiota , althougoidaceae .On the other hand, there was a positive correlation between Ruminococcaceae and Enterobacteriaceae and ammonia. Ruminococcaceae and Enterobacteriaceae, as well as Lachnospiraceae and Clostridiaceae, are commensal microorganisms with a significant role in the digestion of amino acids and proteins, thus, justifying their positive correlation with ammonia production ,132,133.Enterobacteriaceae, in addition to being indicators of intestinal dysbiosis, are also known to cause disease to the host when present in inadequate proportions . The comAmong the SCFA, the number of families correlated with acetic, butyric, and propionic acid were five, two, and one, respectively A. As forStr. thermophilus, directly influencing the availability of types of substrates in fermented products, such as peptides, which can influence microbial abundance and, consequently, the production of SCFA and ammonia [Administration of functional products containing probiotics and prebiotics, resulting in increased SCFA and decreased ammonia, has been demonstrated in the literature over the years ,135,136. ammonia .® using healthy human microbiota after administering ice cream containing Lb. acidophilus and B. animalis and a decrease in ammonia after administering a dietary supplement containing Lb. acidophilus and B. animalis (both after 7 days of treatment).Freire et al.  showed tPhocaeicola genus was dominant in the samples from the formulations with fruit pulp, which resulted in a lower bacterial diversity (Gini–Simpson) if compared to the FMC. The Bifidobacterium genus was related to all FM formulations, while the Alistipes genus was related to FMPF and FMBP formulations, and the Lactobacillus and Lacticaseibacillus genera were related to FMC. Furthermore, the FM with added fruit pulp showed conservation of the central taxon and its relationships in the genera co-occurrence networks. Regarding metabolites, there was a correlation between ammonia and the three most abundant families of the microbiota. All results showed that the administration of probiotic fermented milk with fruit pulp, especially FMB, boosted the beneficial effects observed in the intestinal microbiota of healthy humans, as well as increased the production of SCFA in SHIME® and decreased ammonium ions, which could be related to the presence of bioactive compounds. Therefore, producing FM with fruit pulp can be considered a promising strategy for supplying probiotic FM with functional characteristics, even though additional studies should be carried out focusing on evaluating the sensory characteristics of the product, as well as the required shelf-life time to guarantee the desired beneficial effect to consumers.The viability of probiotic LAB in FM was ≥6 log CFU/mL and was affected by the addition of the fruit pulp. The Bacteroidetes phylum had greater abundance in the microbial communities associated with FM formulations, followed by Firmicutes. The


Infertility is a nationwide public health priority in the U.S. However, few studies have investigated the effects of dietary intake of polyunsaturated fatty acids (PUFAs) on female infertility. This study explored the association between PUFA intake and risk of infertility.A total of 1,785 women aged 20–44 years from three National Health and Nutrition Examination Survey cycles (2013–2018) were included in this cross-sectional study. The intake of PUFAs was obtained from a 24-h dietary interview on two separate days with a 3–10-day interval, and nutrient residue models were used. Fertility status was assessed by positive response to two relative questions via a questionnaire. Logistic regression models were used and some covariates were adjusted.Among all the participants, 340 (19.05%) women suffered from infertility. The intake of docosahexaenoic acid (DHA)  was slightly related to the risk of infertility. In contrast, women with higher α-linolenic acid (ALA)  and linoleic acid (LA) intake  presented with a relatively higher risk of primary infertility. Furthermore, in 20–34-year-old women, higher omega-6/omega-3 was significant associated with the risk of infertility .Our results suggest that PUFA intake is only slightly associated with infertility. The higher the DHA intake, the lower the risk of infertility regardless of age. In women with primary infertility, ALA and LA has negative effect. Infertility is characterized by the failure to establish a clinical pregnancy after 12 months of regular, unprotected sexual intercourse or impairment of a person's capacity to reproduce, either as an individual or with his/her partner . The patDiet is a modifiable lifestyle factor that plays an essential role in influencing human fertility . VariousPUFAs play a significant role in the regulation of body homeostasis. Omega-3 fatty acids, especially EPA and DHA, exert anti-inflammatory, vasodilatory, bronchodilatory, and platelet anti-aggregation effects , whereasFurthermore, PUFAs are considered vital for reproductive health. In males, PUFAs can affect fertility . Previouin-vitro and animal studies, but the potential impact on female fertility needs attention. The objective of this study was to determine whether omega-3 and omega-6 fatty acids in the diet are associated with infertility using a national population-based survey of three National Health and Nutrition Examination Survey (NHANES) cycles (2013–2018) in representative American civilian women.There has been growing concern about the effect of dietary intake of fatty acids, especially omega-3 and omega-6 fatty acids, on the prevalence of female infertility. However, clinical research on dietary intake of PUFA and female infertility is still lacking. Associations between omega-3 and omega-6 fatty acids and body health have been explored mainly in A cross-sectional study was conducted based on data from 2013–2018 cycles of the NHANES, which aimed to assess the health and nutritional status of adults and children in the United States. Combining interviews and physical examinations, NHANES is a multistage study that uses complex stratified sampling methods to collect a representative sample of the U.S. population in every 2-year cycle. The NCHS Research Ethics Review Board (ERB) approved all the NHANES protocols.n = 3,707) because we can obtain available data on reproductive health from these age groups. Women with a history of hysterectomy or removal of both ovaries were excluded from the study. Women with kilocalorie intake per day <600 or >6,000  was used, and additional foods not remembered earlier were asked repeatedly to ensure that the outcome was more reliable.https://msm.dife.de/) was used to estimate the usual dietary PUFA intakes of all participants from 24 h recall information. According to the User Guide of MSM, everyone was assumed as a habitual consumer. Age, race and BMI were included in MSM regression models as explanatory variables. There were five types of omega-3 fatty acids, including α-linolenic acid (ALA), stearidonic acid (SDA), EPA, DPA, and DHA, and two types of omega-6 fatty acids, including linoleic acid (LA) and arachidonic acid (AA). Nutrient residual models were used to estimate calorie-adjusted values for intake of each nutrient (The Multiple Source Method (MSM)  and P75 (75 percentile). Categorical variables were presented as percentages. χ2 , two independent sample t-tests  and Wilcoxon rank sum test  were conducted to test the significance of the differences between the infertility and control groups.All statistical analyses were conducted according to the NHANES analytic guidelines . The appThe association between PUFA intake and infertility was assessed using two logistic regression models: a crude model (no covariate adjusted) and an adjusted model containing all covariates. Considering the effect of age on infertility, we conducted a subgroup analysis stratified by age (35 years). In addition, because of the possible differences between primary and secondary infertility, we further analyzed data from women with primary and secondary infertility separately. Odds ratio (OR) and 95% confidence intervals (CI) were used to assess the strength of the association. All statistical analyses were performed using SAS software version 9.4.A total of 1,785 women aged 20–44 years were included in this analysis, of which 340 (19.05%) suffered from infertility. The characteristics of all the participants are shown in n = 3,707) and participants included in the final analysis  regarding PUFA consumption, infertility and baseline information were presented in Furthermore, the comparisons between eligible participants , we summarized and listed the top five foods that contributed to PUFA intakes in Further, according to the food codes provided by NHANES  or ask for additional foods not remembered earlier, repeatedly, to ensure that the outcome was more reliable. Also, we did not include dietary supplements in that we can solely get the available information about the total PUFA supplement intake, not separated fatty acid supplement intakes, and very few people in the final analysis had PUFA supplement consumption.via regression models. In this way, the estimation of usual dietary intake became more accurate. Additionally, the importance and functions of PUFAs nowadays are always considered in males, not females, and clinical studies are regarding the same are absent. Existing studies on PUFAs and female infertility have only focused on serum levels of PUFAs, or solely carried out basic research on animal models. However, it is obvious that with regard to dietary advice for women in their daily lives, the intake matters.Despite these limitations, our study had several strengths. First, we had a large sample size with nationwide representative samples, which can increase the strength of the evidence. Furthermore, the usual dietary intakes of PUFAs were estimated by MSM, which was a new statistical method for calculating intakes combining dietary intake data with supporting data such as age and sex via a daily diet or dietary supplements, these results may provide evidence and instruction. Despite these limitations, the clinical implications and conclusions of this study should be interpreted carefully.Infertility is a multifactorial disease with various etiologies. Our study concluded that DHA was a protective factor against infertility, and LA and total omega-6 fatty acids were risk factors for primary infertility in women. For women aiming to improve fertility https://www.cdc.gov/nchs/nhanes/index.htm.Publicly available datasets were analyzed in this study. This data can be found at: The studies involving human participants were reviewed and approved by the NCHS Research Ethics Review Board (ERB). The patients/participants provided their written informed consent to participate in this study.RW, YF, and FM contributed to conception and design of the study. RW and JC organized the database and wrote the first draft of the manuscript. RW performed the statistical analysis. RW, YF, JC, and YC wrote sections of the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the National Natural Science Foundation of China, China (31771662 and 31470797).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.