title: PeerJ description: Articles published in PeerJ link: https://peerj.com/articles/index.rss3?journal=peerj&page=442 creator: info@peerj.com PeerJ errorsTo: info@peerj.com PeerJ language: en title: Multi-omics data integration reveals the complexity and diversity of host factors associated with influenza virus infection link: https://peerj.com/articles/16194 last-modified: 2023-10-09 description: Influenza viruses pose a significant and ongoing threat to human health. Many host factors have been identified to be associated with influenza virus infection. However, there is currently a lack of an integrated resource for these host factors. This study integrated human genes and proteins associated with influenza virus infections for 14 subtypes of influenza A viruses, as well as influenza B and C viruses, and built a database named H2Flu to store and organize these genes or proteins. The database includes 28,639 differentially expressed genes (DEGs), 1,850 differentially expressed proteins, and 442 proteins with differential posttranslational modifications after influenza virus infection, as well as 3,040 human proteins that interact with influenza virus proteins and 57 human susceptibility genes. Further analysis showed that the dynamic response of human cells to virus infection, cell type and strain specificity contribute significantly to the diversity of DEGs. Additionally, large heterogeneity was also observed in protein-protein interactions between humans and different types or subtypes of influenza viruses. Overall, the study deepens our understanding of the diversity and complexity of interactions between influenza viruses and humans, and provides a valuable resource for further studies on such interactions. creator: Zhaozhong Zhu creator: Ruina You creator: Huiru Li creator: Shuidong Feng creator: Huan Ma creator: Chaohao Tuo creator: Xiangxian Meng creator: Song Feng creator: Yousong Peng uri: https://doi.org/10.7717/peerj.16194 license: https://creativecommons.org/licenses/by/4.0/ rights: ©2023 Zhu et al. title: Dose-sparing effect of lapatinib co-administered with a high-fat enteral nutrition emulsion: preclinical pharmacokinetic study link: https://peerj.com/articles/16207 last-modified: 2023-10-09 description: BackgroundLapatinib is an oral small-molecule tyrosine kinase inhibitor indicated for advanced or metastatic HER2-positive breast cancer. In order to reduce the treatment cost, a high-fat enteral nutrition emulsion TPF-T was selected as a dose-sparing agent for lapatinib-based therapies. This study aimed to investigate the effect of TPF-T on lapatinib pharmacokinetics.MethodsFirst, a simple and rapid liquid chromatography tandem mass spectrometry (LC–MS/MS) method was developed to quantitatively evaluate lapatinib in rabbit plasma. The method was fully validated according to the China Pharmacopoeia 2020 guidance. Rabbits and rats were chosen as the animal models due to their low and high bile flows, respectively. The proposed LC–MS/MS method was applied to pharmacokinetic studies of lapatinib, with or without TPF-T, in rabbit and rat plasma.ResultsThe LC–MS/MS method revealed high sensitivity and excellent efficiency. In the rabbit model, co-administration with TPF-T resulted in a 32.2% increase in lapatinib exposure. In the rat model, TPF-T had minimal influence on the lapatinib exposure. In both models, TPF-T was observed to significantly elevate lapatinib concentration in the absorption phase.ConclusionCo-administration with TPF-T had a moderate effect on increasing exposure to lapatinib. Dose sparing using a high-fat liquid diet is potentially feasible for lapatinib-based therapies. creator: Junfeng Zhu creator: Gaoqi Xu creator: Dihong Yang creator: Yu Song creator: Yinghui Tong creator: Sisi Kong creator: Haiying Ding creator: Luo Fang uri: https://doi.org/10.7717/peerj.16207 license: https://creativecommons.org/licenses/by/4.0/ rights: ©2023 Zhu et al. title: Machine learning algorithms accurately identify free-living marine nematode species link: https://peerj.com/articles/16216 last-modified: 2023-10-09 description: BackgroundIdentifying species, particularly small metazoans, remains a daunting challenge and the phylum Nematoda is no exception. Typically, nematode species are differentiated based on morphometry and the presence or absence of certain characters. However, recent advances in artificial intelligence, particularly machine learning (ML) algorithms, offer promising solutions for automating species identification, mostly in taxonomically complex groups. By training ML models with extensive datasets of accurately identified specimens, the models can learn to recognize patterns in nematodes’ morphological and morphometric features. This enables them to make precise identifications of newly encountered individuals. Implementing ML algorithms can improve the speed and accuracy of species identification and allow researchers to efficiently process vast amounts of data. Furthermore, it empowers non-taxonomists to make reliable identifications. The objective of this study is to evaluate the performance of ML algorithms in identifying species of free-living marine nematodes, focusing on two well-known genera: Acantholaimus Allgén, 1933 and Sabatieria Rouville, 1903.MethodsA total of 40 species of Acantholaimus and 60 species of Sabatieria were considered. The measurements and identifications were obtained from the original publications of species for both genera, this compilation included information regarding the presence or absence of specific characters, as well as morphometric data. To assess the performance of the species identification four ML algorithms were employed: Random Forest (RF), Stochastic Gradient Boosting (SGBoost), Support Vector Machine (SVM) with both linear and radial kernels, and K-nearest neighbor (KNN) algorithms.ResultsFor both genera, the random forest (RF) algorithm demonstrated the highest accuracy in correctly classifying specimens into their respective species, achieving an accuracy rate of 93% for Acantholaimus and 100% for Sabatieria, only a single individual from Acantholaimus of the test data was misclassified.ConclusionThese results highlight the overall effectiveness of ML algorithms in species identification. Moreover, it demonstrates that the identification of marine nematodes can be automated, optimizing biodiversity and ecological studies, as well as turning species identification more accessible, efficient, and scalable. Ultimately it will contribute to our understanding and conservation of biodiversity. creator: Simone Brito de Jesus creator: Danilo Vieira creator: Paula Gheller creator: Beatriz P. Cunha creator: Fabiane Gallucci creator: Gustavo Fonseca uri: https://doi.org/10.7717/peerj.16216 license: https://creativecommons.org/licenses/by/4.0/ rights: © 2023 Brito de Jesus et al. title: Temporal and spatial patterns of small vertebrate roadkill in a supercity of eastern China link: https://peerj.com/articles/16251 last-modified: 2023-10-09 description: An assessment of animal roadkill can help develop road mitigation measures. This article is the first to report data on animal-vehicle collisions (AVCs) in Nanjing, a supercity in eastern China. The research was conducted on a 224.27 km stretch of nine roads in Nanjing. In the period, between November 2020 and October 2021, 26 fortnightly monitoring missions were conducted to gather roadkill carcasses so that we could analyze their temporal and spatial distribution patterns. A total of 259 carcasses were collected, comprising 22 different species, of which 46.42% were mammals and 48.81% were birds. Cats and dogs are the most roadkill mammals, and blackbirds and sparrows are the most roadkill birds. The temporal analysis demonstrated that the peak of vertebrate roadkill occurred from May to July. Spatial analysis showed that the distribution patterns of vertebrate roadkill on different roads varied with a generally non-random distribution and aggregation. By mapping accidents using kernel density analysis, we were able to pinpoint locations that were at high risk for roadkill. Due to the fortnightly survey, our results would underestimate the casualties, even if, our study suggests that the problem of car accidents due to animals should be a cause for concern, and the results of the analysis of temporal and spatial patterns contribute to the establishment of mitigation measures. creator: Qiong Wu creator: Taozhu Sun creator: Yumeng Zhao creator: Cong Yu creator: Junhua Hu creator: Zhongqiu Li uri: https://doi.org/10.7717/peerj.16251 license: https://creativecommons.org/licenses/by/4.0/ rights: © 2023 Wu et al. title: Effect of the index of cardiac electrophysiological balance on major adverse cardiovascular events in patients with diabetes complicated with coronary heart disease link: https://peerj.com/articles/15969 last-modified: 2023-10-06 description: PurposeTo investigate the prognostic value of the index of cardio-electrophysiological balance (ICEB) and its association with major adverse cardiac events (MACE) and cardiovascular death in diabetic patients complicated with coronary heart disease.MethodsA total of 920 diabetic patients were enrolled in this longitudinal study. Participants were categorized into three groups based on their ICEB levels: normal ICEB, low ICEB, and high ICEB. The primary outcome was the occurrence of MACE, and secondary outcomes included cardiovascular death, coronary heart disease (CHD), heart failure (HF), and sudden cardiac arrest (SCA). Patients were followed for a median period of 3.26 years, and the associations between ICEB levels and various outcomes were evaluated.ResultsOver the follow-up period, 46 (5.0%) MACE were observed in the normal ICEB group, 57 (6.2%) in the low ICEB group, and 62 (6.8%) in the high ICEB group. Elevated ICEB levels were found to be associated with a higher risk of MACE and cardiovascular death. A significant relationship between ICEB levels and the risk of MACE was observed for both genders. The risk of MACE increased with each unit increment in the ICEB index. However, the two-stage linear regression model did not outperform the single-line linear regression models in determining the threshold effect.ConclusionThis study demonstrates the potential utility of ICEB, derived from a standard non-invasive ECG, as a prognostic tool for predicting MACE and cardiovascular death in diabetic patients complicated with CVD. The associations between ICEB levels and the risk of MACE highlight the importance of understanding cardiac electrophysiological imbalances and their implications in CVD. creator: Yuan Lin creator: Fang Zhou creator: Xihui Wang creator: Yaju Guo creator: Weiguo Chen uri: https://doi.org/10.7717/peerj.15969 license: https://creativecommons.org/licenses/by/4.0/ rights: © 2023 Lin et al. title: Species catalogue of Drymaeus (Mesembrinus) Alberts, 1850 (Gastropoda: Bulimulidae) from Brazil and new data on morphology and distribution of Drymaeus (Mesembrinus) interpunctus (Martens, 1887) link: https://peerj.com/articles/16037 last-modified: 2023-10-06 description: BackgroundHerein, we attempted to obtain detailed data on the distribution of the species of Drymaeus (Mesembrinus) in Brazil, using biodiversity databases, malacological collections and literature as sources of occurrence records. We provided a catalogue of species, along with distribution maps. We also estimated the suitable distribution of Drymaeus (Mesembrinus) interpunctus using the maximum entropy approach. A detailed description of the anatomy of the soft parts of this species was provided, with new data on the pallial system.Materials and MethodsFor each species we provided information on the compiled data associated with museum collections and the literature. Distribution maps including geographic boundaries, Brazilian biomes and altitude were made with QGIS software 3.16.10 Hannover. For niche modelling, nineteen bioclimatic variables and a topographic variable were used as predictors. The models were performed with MaxEnt version 3.3.3k.ResultsMost of the species are represented by scarce material in malacological collections; for some species, these records correspond to type material, indicating that they have not been recollected. Most of the species were represented by shells making anatomical comparison and DNA analysis difficult, limiting our ability to provide new criteria for species delimitation. Our results allowed us to expand the known distribution area for three species, Drymaeus dutaillyi, D. gereti and D. oreades, with new occurrence records in Brazil. The MaxEnt model showed a thin area of high suitability to D. (M.) interpunctus in the Southeastern Brazil, corresponding to the Atlantic Forest. Minimum temperature of the coldest month and mean temperature of coldest quarter were the variables that most influenced the development of the model.DiscussionDrymaeus interpunctus was described based on specimens collected in Brazil without mention to the exact localities. Herein the new records from databases allowed to expand the known geographic distribution for this species and to infer its potential distribution. Although the type locality of D. interpunctus is in Brazil, the anatomy of the soft parts of specimens from this country was not previously described. The anatomy of the reproductive system of the specimens analyzed herein mostly corresponds to a previous description for specimens from Paraguay, except for the absence of penial sheath and the relative length of the bursa copulatrix duct. The results of niche modeling showed a thin area of high suitability for D. interpunctus and a vast area of moderate suitability, indicating that this species present a niche breadth that favors its occurrence in a range of different biomes, including less suitable areas.ConclusionThe small number of records obtained for most of the species and their restricted ranges associated with habitat destruction may indicate that they are of conservation concern. creator: Maria Isabel Pinto Ferreira Macedo creator: Ximena Maria Constanza Ovando creator: Sthefane D’ávila uri: https://doi.org/10.7717/peerj.16037 license: https://creativecommons.org/licenses/by/4.0/ rights: ©2023 Macedo et al. title: β-Glucosidase activity and antimicrobial properties of potentially probiotic autochthonous lactic cultures link: https://peerj.com/articles/16094 last-modified: 2023-10-06 description: BackgroundThe demand for lactic acid bacteria products, especially probiotics, has increased. Bacteria that increase polyphenol bioavailability and act as bio preservatives are sought after. This study aims to identify autochthonous lactic acid cultures from EMBRAPA that demonstrate β-glucosidase activity and inhibitory effect on microbial sanitary indicators.MethodsCell-free extracts were obtained by sonicating every 5 s for 40 min. The extracts were mixed with cellobiose and incubated at 50 °C. The reaction was stopped by immersing the tubes in boiling water. The GOD-POD reagent was added for spectrophotometer readings. Antimicrobial activity was tested against reference strains using the agar well diffusion method. Lactic cultures in MRS broth were added to 0.9 cm wells and incubated. The diameter of the inhibition zones was measured to determine the extension of inhibition.ResultsOnly L. rhamnosus EM1107 displayed extracellular β-glucosidase activity, while all autochthonous strains except L. plantarum CNPC020 demonstrated intracellular activity for this enzyme. L. plantarum CNPC003 had the highest values. On the other hand, L. plantarum CNPC020, similarly to L. mucosae CNPC007, exhibited notable inhibition against sanitary indicators. These two strains significantly differed from the other five autochthonous cultures regarding S. enterica serovar Typhimurium ATCC 14028 inhibition (P < 0.05). However, they did not differ from at least one positive control in terms of inhibition against S. aureus ATCC 25923 and E. coli ATCC 25922 (P > 0.05). Therefore, it is advisable to consider these cultures separately for different technological purposes, such as phenolics metabolism or bio preservative activity. This will facilitate appropriate selection based on each specific property required for the intended product development. creator: Isadora Kaline Camelo Pires de Oliveira Galdino creator: Miqueas Oliveira Morais da Silva creator: Ana Paula Albuquerque da Silva creator: Vanderlania Nascimento Santos creator: Raísa Laura Pereira Feitosa creator: Laura Cecília Nascimento Ferreira creator: Giordanni Cabral Dantas creator: Elainy Virgínia dos Santos Pereira creator: Tiago Almeida de Oliveira creator: Karina Maria Olbrich dos Santos creator: Antonio Silvio Egito creator: Flávia Carolina Alonso Buriti creator: Haíssa Roberta Cardarelli uri: https://doi.org/10.7717/peerj.16094 license: https://creativecommons.org/licenses/by/4.0/ rights: © 2023 Kaline Camelo Pires de Oliveira Galdino et al. title: Analysis of factors influencing the pathological complete remission (ipCR) in patients with internal mammary lymph node metastasis after neoadjuvant chemotherapy link: https://peerj.com/articles/16141 last-modified: 2023-10-06 description: ObjectiveTo investigate the factors impacting pathological complete remission (ipCR) of the internal mammary lymph nodes in patients with internal mammary lymph node metastasis (IMLN) after adjuvant chemotherapy.MethodsSixty-five cases of primary breast cancer (BC) with IMLN metastasis who had received neoadjuvant chemotherapy (NAC) were retrospectively analyzed. Postoperative pathology was used to divide the patients into ipCR and non-ipCR groups. Univariate and multivariate analyses were performed on ipCR after NAC. A receiver operating characteristic (ROC) curve was used to evaluate the predictive value of the factors related to ipCR and a Kaplan-Meier curve was used to analyze prognosis.ResultsTwenty-nine (44.62%) of the 65 female patients received ipCR after NAC. Significant differences in hormone receptor (HR) negative and axillary pathological complete response (apCR) rates between the ipCR and non-ipCR group (P < 0.05). Multivariate logistic regression analysis showed that HR (OR = 2.698) and apCR (OR = 4.546) were the most significant factors that influenced ipCR (P < 0.05). The ROC curves showed that the area under the curves (AUC) for HR and apCR for the prediction of ipCR were 0.744 and 0.735 respectively. The AUC for the combined detection was 0.905. The average disease free survival (DFS) for patients in the ipCR group was 94.0 months which was significantly longer compared to patients in the non-ipCR group (64.2 months) (χ2 = 4.265, P = 0.039). No significant difference in OS was detected between the two groups (P > 0.05).ConclusionsipCR after NAC is correlated with HR and apCR. HR combined with apCR has value in predicting ipCR. ipCR has prognostic value in patients with IMLN metastasis and may have the potential to inform clinical decision-making. Further validation of these findings is required through larger-scale prospective studies. creator: Yang Li creator: Yang Fei uri: https://doi.org/10.7717/peerj.16141 license: https://creativecommons.org/licenses/by/4.0/ rights: ©2023 Li and Fei title: A retrospective study: analysis of the relationship between lactate dehydrogenase and castration-resistant prostate cancer based on restricted cubic spline model link: https://peerj.com/articles/16158 last-modified: 2023-10-06 description: BackgroundDifferent prostate cancer patients take different amounts of time to progress to castration-resistant prostate cancer (CRPC), and this difference in time determines the patient’s ultimate survival time. If the time to progression to CRPC can be estimated for each patient, the treatment can be better individualized.ObjectiveCastration-resistant prostate cancer is a challenge in attacking prostate cancer, the aim of the paper is to analyze the correlation between lactate dehydrogenase (LDH) and CRPC occurrence based on the restricted cubic spline model, and to provide a theoretical basis for LDH as a prognostic biomarker for prostate cancer patients.MethodsWe retrospectively analyzed clinical and follow-up data of patients diagnosed with prostate cancer and treated with Androgen Deprivation Therapy (ADT) in our hospital from October 2019 to August 2022. Investigate the correlation between LDH and CRPC by COX regression, restricted cubic spline model and survival analysis.ResultsThe initial tPSA concentration, prostate volume, LDH and alkaline phosphatase levels in patients with prostate cancer with rapid progression are higher than those in patients with prostate cancer with slow progression. Multivariate COX regression showed that initial tPSA level and LDH level are independent risk factors for prostate cancer. Restricted cubic spline model further showed that LDH level is linearly correlated with the risk of CRPC in prostate cancer patients (total P < 0.05, nonlinear P > 0.05).ConclusionLDH was associated with the prognosis of prostate cancer and had a dose-response relationship with the risk of CRPC in prostate caner patients. creator: Ruiying Qiu creator: Ke Bu creator: Hengqing An creator: Ning Tao uri: https://doi.org/10.7717/peerj.16158 license: https://creativecommons.org/publicdomain/zero/1.0/ rights: ©2023 Qiu et al. title: KSFinder—a knowledge graph model for link prediction of novel phosphorylated substrates of kinases link: https://peerj.com/articles/16164 last-modified: 2023-10-06 description: BackgroundAberrant protein kinase regulation leading to abnormal substrate phosphorylation is associated with several human diseases. Despite the promise of therapies targeting kinases, many human kinases remain understudied. Most existing computational tools predicting phosphorylation cover less than 50% of known human kinases. They utilize local feature selection based on protein sequences, motifs, domains, structures, and/or functions, and do not consider the heterogeneous relationships of the proteins. In this work, we present KSFinder, a tool that predicts kinase-substrate links by capturing the inherent association of proteins in a network comprising 85% of the known human kinases. We also postulate the potential role of two understudied kinases based on their substrate predictions from KSFinder.MethodsKSFinder learns the semantic relationships in a phosphoproteome knowledge graph using a knowledge graph embedding algorithm and represents the nodes in low-dimensional vectors. A multilayer perceptron (MLP) classifier is trained to discern kinase-substrate links using the embedded vectors. KSFinder uses a strategic negative generation approach that eliminates biases in entity representation and combines data from experimentally validated non-interacting protein pairs, proteins from different subcellular locations, and random sampling. We assess KSFinder’s generalization capability on four different datasets and compare its performance with other state-of-the-art prediction models. We employ KSFinder to predict substrates of 68 “dark” kinases considered understudied by the Illuminating the Druggable Genome program and use our text-mining tool, RLIMS-P along with manual curation, to search for literature evidence for the predictions. In a case study, we performed functional enrichment analysis for two dark kinases - HIPK3 and CAMKK1 using their predicted substrates.ResultsKSFinder shows improved performance over other kinase-substrate prediction models and generalized prediction ability on different datasets. We identified literature evidence for 17 novel predictions involving an understudied kinase. All of these 17 predictions had a probability score ≥0.7 (nine at >0.9, six at 0.8–0.9, and two at 0.7–0.8). The evaluation of 93,593 negative predictions (probability ≤0.3) identified four false negatives. The top enriched biological processes of HIPK3 substrates relate to the regulation of extracellular matrix and epigenetic gene expression, while CAMKK1 substrates include lipid storage regulation and glucose homeostasis.ConclusionsKSFinder outperforms the current kinase-substrate prediction tools with higher kinase coverage. The strategically developed negatives provide a superior generalization ability for KSFinder. We predicted substrates of 432 kinases, 68 of which are understudied, and hypothesized the potential functions of two dark kinases using their predicted substrates. creator: Manju Anandakrishnan creator: Karen E. Ross creator: Chuming Chen creator: Vijay Shanker creator: Julie Cowart creator: Cathy H. Wu uri: https://doi.org/10.7717/peerj.16164 license: https://creativecommons.org/licenses/by-nc/4.0 rights: ©2023 Anandakrishnan et al.