title: PeerJ description: Articles published in PeerJ link: https://peerj.com/articles/index.rss3?journal=peerj&page=139 creator: info@peerj.com PeerJ errorsTo: info@peerj.com PeerJ language: en title: Altered expression of miRNA profile in peripheral blood mononuclear cells following the third dose of inactivated COVID-19 vaccine link: https://peerj.com/articles/18856 last-modified: 2025-01-22 description: COVID-19 vaccination is the most effective strategy for preventing severe disease and death. Inactivated vaccines are the most accessible type of COVID-19 vaccines in developing countries. Several studies, including work from our group, have demonstrated that the third dose (booster vaccination) of inactivated COVID-19 vaccine induces robust humoral and cellular immune responses. The present study aimed to examine miRNA expression profile in participants who received a homologous third dose of the CoronaVac vaccine. Samples of peripheral blood mononuclear cells (PBMCs) were collected from healthcare volunteers both before and 1–2 weeks after the booster dose. miRNA microarray analysis in a discovery cohort of six volunteers identified 67 miRNAs with differential expression. Subsequently, the expression of six miRNAs related to immune responses was examined in a validation cohort of 31 participants via qRT-PCR. Our results validated the differential expression of miR-25-5p, miR-34c-3p, and miR-206 post-booster, with a significant correlation to the receptor binding domain (RBD)-specific antibody. Bioinformatic analysis suggested that miR-25-5p, miR-34c-3p, and miR-206 may target multiple pathways involved in immune regulation and inflammation. Therefore, our study highlights miR-25-5p, miR-34c-3p, and miR-206 in PBMCs as promising biomarkers for assessing the immune response induced by the booster dose of the CoronaVac vaccine. creator: Guanguan Qiu creator: Ruoyang Zhang creator: Huifeng Qian creator: Ruoqiong Huang creator: Jie Xia creator: Ruoxi Zang creator: Zhenkai Le creator: Qiang Shu creator: Jianguo Xu creator: Guoping Zheng creator: Jiangmei Wang uri: https://doi.org/10.7717/peerj.18856 license: https://creativecommons.org/licenses/by/4.0/ rights: © 2025 Qiu et al. title: Assessing the readability, quality and reliability of responses produced by ChatGPT, Gemini, and Perplexity regarding most frequently asked keywords about low back pain link: https://peerj.com/articles/18847 last-modified: 2025-01-22 description: BackgroundPatients who are informed about the causes, pathophysiology, treatment and prevention of a disease are better able to participate in treatment procedures in the event of illness. Artificial intelligence (AI), which has gained popularity in recent years, is defined as the study of algorithms that provide machines with the ability to reason and perform cognitive functions, including object and word recognition, problem solving and decision making. This study aimed to examine the readability, reliability and quality of responses to frequently asked keywords about low back pain (LBP) given by three different AI-based chatbots (ChatGPT, Perplexity and Gemini), which are popular applications in online information presentation today.MethodsAll three AI chatbots were asked the 25 most frequently used keywords related to LBP determined with the help of Google Trend. In order to prevent possible bias that could be created by the sequential processing of keywords in the answers given by the chatbots, the study was designed by providing input from different users (EO, VH) for each keyword. The readability of the responses given was determined with the Simple Measure of Gobbledygook (SMOG), Flesch Reading Ease Score (FRES) and Gunning Fog (GFG) readability scores. Quality was assessed using the Global Quality Score (GQS) and the Ensuring Quality Information for Patients (EQIP) score. Reliability was assessed by determining with DISCERN and Journal of American Medical Association (JAMA) scales.ResultsThe first three keywords detected as a result of Google Trend search were “Lower Back Pain”, “ICD 10 Low Back Pain”, and “Low Back Pain Symptoms”. It was determined that the readability of the responses given by all AI chatbots was higher than the recommended 6th grade readability level (p < 0.001). In the EQIP, JAMA, modified DISCERN and GQS score evaluation, Perplexity was found to have significantly higher scores than other chatbots (p < 0.001).ConclusionIt has been determined that the answers given by AI chatbots to keywords about LBP are difficult to read and have low reliability and quality assessment. It is clear that when new chatbots are introduced, they can provide better guidance to patients with increased clarity and text quality. This study can provide inspiration for future studies on improving the algorithms and responses of AI chatbots. creator: Erkan Ozduran creator: Volkan Hancı creator: Yüksel Erkin creator: İlhan Celil Özbek creator: Vugar Abdulkerimov uri: https://doi.org/10.7717/peerj.18847 license: https://creativecommons.org/licenses/by/4.0/ rights: © 2025 Ozduran et al. title: UPLC-MS metabolite profiling and antioxidant activity of Sanghuangporus sanghuang extract link: https://peerj.com/articles/18758 last-modified: 2025-01-22 description: BackgroundThe objective of the present study is to examine the total phenolic and flavonoid content of an ethanol extract of Sanghuangporus sanghuang and to evaluate its phytochemical properties, antioxidant activity, and capacity to protect DNA from damage. This pharmaceutical/food resource mushroom may serve as a novel substitute functional food for health-conscious consumers, given its promising source of phenolics and flavonoids.MethodsS. sanghuang ethanol extract (SEE) was evaluated for total phenolic and flavonoid contents, while UPLC-MS analysis was used for terpenoids, phenylpropanoid, flavonoids, steroidal, phenols identification, and function prediction. Antioxidant and anti-DNA damage activities were tested in vitro using ferric reducing antioxidant power (FRAP), 1,1-diphenyl-2-picrylhydrazyl (DPPH), 2,2′-azino-bis-3-ethylbenzotiazolin-6-sulfonic acid (ABTS), and DNA damage protection assay.Results and ConclusionTotal phenolic content (TPC) in SEE was 385.38 ± 1.36 mg GA/g extract, while total flavonoid content (TFC) was 298.22 ± 2.38 mg QE/g extract. The extracts exhibited high antioxidant and free radical scavenging activities with relatively stronger free radical scavenging activity. A total of 491 metabolites were investigated by Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Most of the top 20 compounds were predicted to have various functions like antioxidant, anti-cancer and anti-inflammatory. This study highlighted S. sanghuang was a beneficial source of phenolics and flavonoids. It contains potential natural antioxidant that could be used as a lead contender in the development of antioxidant medicines for the treatment of a wide range of oxidative stress-related illnesses. creator: Weike Wang creator: Na Lu creator: Cheng Jiang creator: Guanping Chen uri: https://doi.org/10.7717/peerj.18758 license: https://creativecommons.org/licenses/by-nc/4.0 rights: © 2025 Wang et al. title: A nomogram for predicting bladder dysfunction in patients with type 2 diabetes mellitus: a retrospective study link: https://peerj.com/articles/18872 last-modified: 2025-01-22 description: BackgroundDiabetic bladder dysfunction (DBD) is a common urinary complication in diabetic patients, significantly affecting their overall well-being and quality of life, and placing a considerable burden on healthcare resources. Early prevention is crucial; however, the absence of a simple and effective tool to predict DBD onset remains a significant challenge. This study aims to identify risk factors for DBD in patients with type 2 diabetes mellitus (T2DM) and to develop a predictive nomogram for clinical application.MethodsThis retrospective study included patients with T2DM treated at two hospitals. Data from patients treated at one hospital between January 2020 and August 2023 were used to create the training set, while data from patients treated at another hospital between March 2022 and October 2023 were used to create the validation set. Patients were classified into two groups based on the presence or absence of DBD: the DBD group and the non-DBD group. Significant factors identified via bivariate analysis (P < 0.05) were incorporated into multivariate logistic regression to construct a predictive model, and a corresponding nomogram was developed. The model’s performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and clinical impact plots (CIC), with validation performed through 1,000 bootstrap resamplings.ResultsA total of 1,010 participants were included in this study, with a DBD incidence rate of 38.81% (392/1,010). Multivariate logistic regression analysis identified HbA1c, PCP-2h, DPN, TCO2, PAB, T-Bil, I-Bil, IgE, URBC, UI and UR as independent risk factors for DBD. A nomogram was constructed based on these factors. Both internal and external validations demonstrated the good predictive performance of the nomogram. The area under the curve (AUC) for the training and validation datasets was 0.897 and 0.862, respectively. The calibration curve showed a high degree of consistency. Results from DCA and CIC indicated that the prediction model had high clinical utility.ConclusionsA predictive model and nomogram for DBD in T2DM patients were developed, demonstrating strong accuracy and clinical utility, aiding in early DBD risk assessment and intervention. creator: Yingjie Hu creator: Fengming Hao creator: Ying Wang creator: Ling Chen creator: Lihua Wen creator: Jue Li creator: Wei Ren creator: Wenzhi Cai uri: https://doi.org/10.7717/peerj.18872 license: https://creativecommons.org/licenses/by/4.0/ rights: © 2025 Hu et al. title: Priority effects, nutrition and milk glycan-metabolic potential drive Bifidobacterium longum subspecies dynamics in the infant gut microbiome link: https://peerj.com/articles/18602 last-modified: 2025-01-21 description: BackgroundThe initial colonization of the infant gut is a complex process that defines the foundation for a healthy microbiome development. Bifidobacterium longum is one of the first colonizers of newborns’ gut, playing a crucial role in the healthy development of both the host and its microbiome. However, B. longum exhibits significant genomic diversity, with subspecies (e.g., Bifidobacterium longum subsp. infantis and subsp. longum) displaying distinct ecological and metabolic strategies including differential capabilities to break down human milk glycans (HMGs). To promote healthy infant microbiome development, a good understanding of the factors governing infant microbiome dynamics is required.MethodologyWe analyzed newly sequenced gut microbiome samples of mother-infant pairs from the Amsterdam Infant Microbiome Study (AIMS) and four publicly available datasets to identify important environmental and bifidobacterial features associated with the colonization success and succession outcomes of B. longum subspecies. Metagenome-assembled genomes (MAGs) were generated and assessed to identify characteristics of B. longum subspecies in relation to early-life gut colonization. We further implemented machine learning tools to identify significant features associated with B. longum subspecies abundance.ResultsB. longum subsp. longum was the most abundant and prevalent gut Bifidobacterium at one month, being replaced by B. longum subsp. infantis at six months of age. By utilizing metagenome-assembled genomes (MAGs), we reveal significant differences between and within B. longum subspecies in their potential to break down HMGs. We further combined strain-tracking, meta-pangenomics and machine learning to understand these abundance dynamics and found an interplay of priority effects, milk-feeding type and HMG-utilization potential to govern them across the first six months of life. We find higher abundances of B. longum subsp. longum in the maternal gut microbiome, vertical transmission, breast milk and a broader range of HMG-utilizing genes to promote its abundance at one month of age. Eventually, we find B. longum subsp. longum to be replaced by B. longum subsp. infantis at six months of age due to a combination of nutritional intake, HMG-utilization potential and a diminishment of priority effects.DiscussionOur results establish a strain-level ecological framework explaining early-life abundance dynamics of B. longum subspecies. We highlight the role of priority effects, nutrition and significant variability in HMG-utilization potential in determining the predictable colonization and succession trajectories of B. longum subspecies, with potential implications for promoting infant health and well-being. creator: Nicholas Pucci creator: Joanne Ujčič-Voortman creator: Arnoud P. Verhoeff creator: Daniel R. Mende uri: https://doi.org/10.7717/peerj.18602 license: https://creativecommons.org/licenses/by/4.0/ rights: ©2025 Pucci et al. title: Three-dimensional analysis of facial morphology in nine-year-old children with different unilateral orofacial clefts compared to normative data link: https://peerj.com/articles/18739 last-modified: 2025-01-21 description: AimTo compare three-dimensional (3D) facial morphology of various unilateral cleft subphenotypes at 9-years of age to normative data using a general face template and automatic landmarking. The secondary objective is to compare facial morphology of 9-year-old children with unilateral fusion to differentiation defects.Methods3D facial stereophotogrammetric images of 9-year-old unilateral cleft patients were imported into 3DMedX® for processing. All images of patients with a right sided cleft were mirrored. A regionalized general facial template was used for standardization. This template was pre-aligned to each face using five automatically determined landmarks and fitted using MeshMonk. All cleft patients were compared to an age-and gender matched normative face using distance maps and inter-surface distances (mm). Average faces were created for five groups (unilateral cleft lip, alveolus, and/or palate (UCL/A/P), fusion and differentiation defects). The selected regions for the evaluation of facial morphology were: complete face, nose, upper lip, lower lip, chin, forehead, and cheeks.ResultsA total of 86 consecutive 3D-stereophotogrammetry images were acquired for examination. No statistically significant differences were observed among the UCL, UCLA, and UCLP groups for the complete face, cheeks, chin, forehead, lower lip, and nose. However, in the upper lip region a significant difference was observed between the UCLP and UCL groups (P = 0.004, CI [−2.93 to −0.48]). Further visual examination of the distance maps indicated that more severe clefts corresponded to increased retrusion in the midface and the tip of the nose, though these differences were not statistically significant across groups. For fusion vs differentiation defects, no statistically significant differences were observed, neither for the complete face nor for any of the individual regions.ConclusionThe findings demonstrate statistically significant differences in the upper lip region between children with UCL and those with UCLP, particularly with greater upper lip retrusion in the UCLP group. The use of color-coded distance maps revealed local variations and a trend of asymmetry in the nasal region, with increasing retrusion of the nose tip, upper lip, and cheeks correlating with the severity of the cleft. Although these trends were not statistically significant, they suggest a progressive facial retrusion pattern as cleft severity increases. For the secondary objective, no statistical differences were found between the facial morphology of children with fusion and differentiation defects, although a similar progression of maxillary retrusion was observed in the distance maps. creator: Marjolein Crins-de Koning creator: Robin Bruggink creator: Marloes Nienhuijs creator: Till Wagner creator: Ewald M. Bronkhorst creator: Edwin M. Ongkosuwito uri: https://doi.org/10.7717/peerj.18739 license: https://creativecommons.org/licenses/by/4.0/ rights: © 2025 Crins-de Koning et al. title: Attaching artificial Achilles and tibialis cranialis tendons to bone using suture anchors in a rabbit model: assessment of outcomes link: https://peerj.com/articles/18756 last-modified: 2025-01-21 description: ObjectiveThe purpose of this study was to investigate the timing and mode of failure of metallic screw-type suture anchors used to attach artificial tendons to bone in an in vivo New Zealand White rabbit model.Study DesignMetal suture anchors with braided composite sutures of varying sizes (United States Pharmacopeia (USP) size 1, 2, or 5) were used to secure artificial tendons replacing both the Achilles and tibialis cranialis tendons in 12 female New Zealand White rabbits. Artificial tendons were implanted either at the time of (immediate replacement, n = 8), or four/five weeks after (delayed replacement, n = 4) resection of the biological tendon. Hindlimb radiographs of the rabbits were obtained immediately after surgery and approximately every other week until the study endpoint (16 weeks post-surgery).ResultsAll suture anchors used for the tibialis cranialis artificial tendons remained secure and did not fail during the study. The suture anchor used to attach the Achilles artificial tendon to the calcaneus bone failed in nine of 12 rabbits. In all cases of suture anchor failure, the suture broke away from the knot, while the metallic screw remained securely embedded in the bone. Based on radiographic analysis, the mean estimated failure timepoint was 5.3 ± 2.3 weeks post-surgery, with a range of 2–10 weeks. Statistical analyses (Mann–Whitney U test and Fisher’s exact test) revealed no significant effect of tendon implantation timing or suture size on either the timing or frequency of suture anchor failure.ConclusionFor the suture anchors used to attach artificial tendons in this study, suture anchor failure was most likely due to suture wear or cutting against the eyelet of the anchor screw. Future studies are needed to test the effect of suture-eyelet interaction on suture strength under different loading conditions. creator: Obinna P. Fidelis creator: Caleb Stubbs creator: Katrina L. Easton creator: Caroline Billings creator: Alisha P. Pedersen creator: David E. Anderson creator: Dustin L. Crouch uri: https://doi.org/10.7717/peerj.18756 license: https://creativecommons.org/licenses/by/4.0/ rights: ©2025 Fidelis et al. title: Morphometric analysis revealed two different Mediterranean horse mackerel (Trachurus mediterraneus) stocks in the Adriatic Sea link: https://peerj.com/articles/18765 last-modified: 2025-01-21 description: Phenotypical differentiation among individuals of Mediterranean horse mackerel Trachurus mediterraneus in the Adriatic Sea was investigated through the analysis of several morphometric characters. Overall, 426 individuals of Mediterranean horse mackerels were sampled from the northern, central and southern Adriatic Sea during the summers of 2012 and 2013. Forty-six morphometric characters were measured for each individual and then compared using multivariate techniques (linear discriminant analysis). Based on the morphometric characteristics, at least two different Mediterranean horse mackerel were identified: one comprising the northern and central Adriatic, and the other formed by individuals from the southern Adriatic basin. The northern and central areas showed stable populations, overlapping both in space and time. The southern area seemed to be more variable over the years, with a low degree of overlapping both in space and time. A possible hypothesis for this, to be further investigated, could be the flow of individuals from the Ionian and Aegean Seas populations through the Otranto Channel. The main differences between the two stocks were associated with the head characters of the fish. In particular, the northern and central Adriatic Sea individuals had shorter and thicker heads than the southern ones. This could be due to different feeding habits: the former mainly feed on small fishes, the latter mainly on euphausiids. A short mouth could reduce the power of suction of bigger preys, while a long mouth could increase the volume of water to be filtered to feed on small planktonic crustaceans. From this study, it becomes clear that the Mediterranean horse mackerel should not be managed as a single stock in the Adriatic Sea as it was evident that at least two morphologically different stocks are present in the basin. creator: Claudio Vasapollo uri: https://doi.org/10.7717/peerj.18765 license: https://creativecommons.org/licenses/by/4.0/ rights: ©2025 Vasapollo title: Geometrical determinants of cerebral artery fenestration for cerebral infarction link: https://peerj.com/articles/18774 last-modified: 2025-01-21 description: PurposeFew data are available on the causality of cerebral artery fenestration (CAF) triggering cerebral infarction (CI) and this study aims to identify representative morphological features that can indicate risks.MethodsA cohort comprising 89 patients diagnosed with CAF were enrolled from a total of 9,986 cranial MR angiographies. These patients were categorized into Infarction Group (n = 55) and Control Group (n = 34) according to infarction events. These two groups are divided into two subgroups depending on fenestration location (basilar artery or other cerebravascular location), respectively, i.e., BA Infarction Group (n = 37), BA Control Group (n = 23), Non_BA Infarction Group (n = 18), Non_BA Control Group (n = 11). This study firstly defined 12 indices to quantify the morphological characteristics of fenestration per se and its connecting arteries. The data were evaluated using either the independent sample t-test or the Mann–Whitney U test. Conducting univariate and multivariate logistic regression analyses to ascertain potential independent predictors of CI.ResultsThe initiation angle φ1 and confluence angle φ2 at the fenestration in the Infarction Group are both smaller compared to the Control Group, but only the Infarction Group and BA Infarction Group have significant difference (p < 0.05). The maximum left fenestration axis (fAL) and the left tortuosity index (TIL) were greater in the Infarction Group for CAFs than those in the Control Group (p < 0.05). In contrast, the maximum right fenestration axis (fAR) and the right tortuosity index (TIR) were smaller than those in Control Group (p < 0.05). The logistic regression analysis revealed that φ2 (AUC = 0.68, p = 0.02), fAL (AUC = 0.72, p < 0.01), and fAR (AUC = 0.70, p < 0.01) serve as independent risk factors influencing the occurrence of CI. The regression predictive model achieved an AUC of 0.83, enabling accurate classification of 77.5% of cases, indicating a robust predictive performance of the model.ConclusionMorphological results demonstrated a left-leaning type of fenestration with more narrow fenestration terminals indicating a higher risk of CI occurrence. Furthermore, the regression predictive model established in this study demonstrates a good predictive performance, enabling early prediction of CI occurrence in fenestrated patients and facilitating early diagnosis of CI. creator: Yuqian Mei creator: Xiaoqin Chen creator: Yao Zhang creator: Yanling Wang creator: Bo Wu creator: Mingcheng Hu creator: Quan Bao uri: https://doi.org/10.7717/peerj.18774 license: https://creativecommons.org/licenses/by/4.0/ rights: ©2025 Mei et al. title: Development of metastasis and survival prediction model of luminal and non-luminal breast cancer with weakly supervised learning based on pathomics link: https://peerj.com/articles/18780 last-modified: 2025-01-21 description: ObjectiveBreast cancer stands as the most prevalent form of cancer among women globally. This heterogeneous disease exhibits varying clinical behaviors. The stratification of breast cancer patients into risk groups, determined by their metastasis and survival outcomes, is pivotal for tailoring personalized treatments and therapeutic interventions. The pathological sections of radical specimens encompass a diverse range of histological information pertinent to the metastasis and survival of patients. In this study, our objective is to develop a deep learning model utilizing pathological images to predict the metastasis and survival outcomes for breast cancer patients.MethodsThis study utilized pathological sections from 204 radical mastectomy specimens obtained between January 2013 and December 2014 at the Second Affiliated Hospital of the Medical College of Zhejiang University. The 204 pathological slices were scanned and transformed into whole slide imaging (WSI), with manual labeling of all tumor areas. The WSI was then partitioned into smaller tiles measuring 512 × 512 pixels. Three networks, namely Densely Connected Convolutional Network 121 (DenseNet121), Residual Network (ResNet50), and Inception_v3, were assessed. Subsequently, we combined patch-level predictions, probability histograms, and Term Frequency-Inverse Document Frequency (TF-IDF) features to create comprehensive participants representations. These features served as the foundational input for developing a machine learning algorithm for metastasis analysis and a Cox regression model for survival analysis.ResultOur results show that the Inception_v3 model shows a particularly robust patch recognition ability for estrogen receptor (ER) recognition. Our pathological model shows high accuracy in predicting tumor regions. The train area under the curve (AUC) of the Inception_v3 model based on supervised learning is 0.975, which is higher than the model established by weakly supervised learning. But the AUC of the metastasis prediction in training and testing sets is higher than value based on supervised learning. Furthermore, the C-index of the survival prediction model is 0.710 in the testing sets, which is also better than the value by supervised learning.ConclusionOur study demonstrates the significant potential of deep learning models in predicting breast cancer metastasis and prognosis, with the pathomic model showing high accuracy in identifying tumor areas and ER status. The integration of clinical features and pathomics signature into a nomogram further provides a valuable tool for clinicians to make individualized treatment decisions. creator: Hui Liu creator: Linlin Ying creator: Xing Song creator: Xueping Xiang creator: Shumei Wei uri: https://doi.org/10.7717/peerj.18780 license: https://creativecommons.org/licenses/by/4.0/ rights: © 2025 Liu et al.