The intra-tumoral microbiome as a potential biomarker of response to external beam radiation therapy in cervical cancer.

BACKGROUND: We aimed to determine the potential predictive value of the intra-tumoral microbiome as a marker of the response to external beam radiation therapy (EBRT) in cervical cancer (CC). METHODS: A prospective longitudinal trial of 36 CC patients receiving pelvic radiotherapy was designed to investigate microbial characteristic signatures and diversity (alpha and beta) of multiple sites (tumor, vaginal, gut, urethral, and oral) in the superior response (SR) and inferior response (IR) groups of CC patients by 16S rRNA sequencing. Utilized the least absolute shrinkage and selection operator (LASSO) logistic regression method to analyze clinicopathological factors that potentially influenced the efficacy of EBRT. LEfSe analysis highlighted the microbiome features that best distinguished the categorized patient samples. Selected parameters were validated with Spearman correlation analysis, receiver operating characteristic (ROC) area under the curve (AUC) analysis and Kaplan-Meier survival analysis. RESULTS: Firstly, in our cohort, LASSO logistic regression analysis revealed no association between clinicopathological factors and EBRT efficacy. Subsequently, we employed 16S rRNA sequencing to compare microbiome differences across multiple sites and their correlations with major clinicopathological factors. We discovered that the intra-tumoral microbiome was independent of clinicopathologic features and represented the most direct and reliable reflection of the microbial differences between the SR and IR groups. We found lower alpha diversity in the tumor microbiome of SR group and identified the most relevant microbiome taxa (Bifidobacteriaceae, Beijerinckiaceae, and Orbaceae) associated with the efficacy of the response to EBRT in CC patients. We then conducted ROC analysis, finding that specific microbial taxa had an AUC of 0.831 (95% CI, 0.667-0.995), indicating the potential of these taxa as biomarkers for predicting EBRT efficacy. Kaplan-Meier survival analysis showed a better prognosis for patients with lower alpha diversity and higher relative abundance of Bifidobacteriaceae. CONCLUSIONS: Our data suggested that intra-tumoral specific microbiome taxa and lower alpha diversity may play an important role in the CC patient sensitivity to EBRT and offer novel potential biomarkers for predicting the response to EBRT efficacy.

Altered microbial diversity and composition of multiple mucosal organs in cervical cancer patients.

OBJECTIVES: The aim of this study was to characterize the microbiome of multiple mucosal organs in cervical cancer (CC) patients. METHODS: We collected oral, gut, urinary tract, and vaginal samples from enrolled study participants, as well as tumor tissue from CC patients. The microbiota of different mucosal organs was identified by 16S rDNA sequencing and correlated with clinical-pathological characteristics of cervical cancer cases. RESULTS: Compared with controls, CC patients had reduced α-diversity of oral and gut microbiota (pOral_Sob < 0.001, pOral_Shannon = 0.049, pOral_Simpson = 0.013 pFecal_Sob = 0.030), although there was an opposite trend in the vaginal microbiota (pVaginal_Pielou = 0.028, pVaginal_Simpson = 0.006). There were also significant differences in the ß-diversity of the microbiota at each site between cases and controls (pOral = 0.002, pFecal = 0.037, pUrine = 0.001, pVaginal = 0.001). The uniformity of urine microbiota was lower in patients with cervical squamous cell carcinoma (pUrine = 0.036) and lymph node metastasis (pUrine_Sob = 0.027, pUrine_Pielou = 0.028, pUrine_Simpson = 0.021, pUrine_Shannon = 0.047). The composition of bacteria in urine also varied among patients with different ages (p = 0.002), tumor stages (p = 0.001) and lymph node metastasis (p = 0.002). In CC cases, Pseudomonas were significantly enriched in the oral, gut, and urinary tract samples. In addition, Gardnerella, Anaerococcus, and Prevotella were biomarkers of urinary tract microbiota; Abiotrophia and Lautropia were obviously enriched in the oral microbiota. The microbiota of tumor tissue correlated with other mucosal organs (except the gut), with a shift in the microflora between mucosal organs and tumors. CONCLUSIONS: Our study not only revealed differences in the composition and diversity of the vaginal and gut microflora between CC cases and controls, but also showed dysbiosis of the oral cavity and urethra in cervical cancer cases.

Protein function annotation based on heterogeneous biological networks.

BACKGROUND: Accurate annotation of protein function is the key to understanding life at the molecular level and has great implications for biomedicine and pharmaceuticals. The rapid developments of high-throughput technologies have generated huge amounts of protein-protein interaction (PPI) data, which prompts the emergence of computational methods to determine protein function. Plagued by errors and noises hidden in PPI data, these computational methods have undertaken to focus on the prediction of functions by integrating the topology of protein interaction networks and multi-source biological data. Despite effective improvement of these computational methods, it is still challenging to build a suitable network model for integrating multiplex biological data. RESULTS: In this paper, we constructed a heterogeneous biological network by initially integrating original protein interaction networks, protein-domain association data and protein complexes. To prove the effectiveness of the heterogeneous biological network, we applied the propagation algorithm on this network, and proposed a novel iterative model, named Propagate on Heterogeneous Biological Networks (PHN) to score and rank functions in descending order from all functional partners, Finally, we picked out top L of these predicted functions as candidates to annotate the target protein. Our comprehensive experimental results demonstrated that PHN outperformed seven other competing approaches using cross-validation. Experimental results indicated that PHN performs significantly better than competing methods and improves the Area Under the Receiver-Operating Curve (AUROC) in Biological Process (BP), Molecular Function (MF) and Cellular Components (CC) by no less than 33%, 15% and 28%, respectively. CONCLUSIONS: We demonstrated that integrating multi-source data into a heterogeneous biological network can preserve the complex relationship among multiplex biological data and improve the prediction accuracy of protein function by getting rid of the constraints of errors in PPI networks effectively. PHN, our proposed method, is effective for protein function prediction.

A tensor-based bi-random walks model for protein function prediction.

BACKGROUND: The accurate characterization of protein functions is critical to understanding life at the molecular level and has a huge impact on biomedicine and pharmaceuticals. Computationally predicting protein function has been studied in the past decades. Plagued by noise and errors in protein-protein interaction (PPI) networks, researchers have undertaken to focus on the fusion of multi-omics data in recent years. A data model that appropriately integrates network topologies with biological data and preserves their intrinsic characteristics is still a bottleneck and an aspirational goal for protein function prediction. RESULTS: In this paper, we propose the RWRT (Random Walks with Restart on Tensor) method to accomplish protein function prediction by applying bi-random walks on the tensor. RWRT firstly constructs a functional similarity tensor by combining protein interaction networks with multi-omics data derived from domain annotation and protein complex information. After this, RWRT extends the bi-random walks algorithm from a two-dimensional matrix to the tensor for scoring functional similarity between proteins. Finally, RWRT filters out possible pretenders based on the concept of cohesiveness coefficient and annotates target proteins with functions of the remaining functional partners. Experimental results indicate that RWRT performs significantly better than the state-of-the-art methods and improves the area under the receiver-operating curve (AUROC) by no less than 18%. CONCLUSIONS: The functional similarity tensor offers us an alternative, in that it is a collection of networks sharing the same nodes; however, the edges belong to different categories or represent interactions of different nature. We demonstrate that the tensor-based random walk model can not only discover more partners with similar functions but also free from the constraints of errors in protein interaction networks effectively. We believe that the performance of function prediction depends greatly on whether we can extract and exploit proper functional similarity information on protein correlations.

NPF:network propagation for protein function prediction.

BACKGROUND: The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, treating disease and developing new medicines. Various methods have been developed to facilitate the prediction of these functions by combining protein interaction networks (PINs) with multi-omics data. However, it is still challenging to make full use of multiple biological to improve the performance of functions annotation. RESULTS: We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. According to the comprehensive evaluation of NPF, it delivered a better performance than other competing methods in terms of leave-one-out cross-validation and ten-fold cross validation. CONCLUSIONS: We demonstrated that network propagation, together with multi-omics data, can both discover more partners with similar function, and is unconstricted by the "small-world" feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional information of similarity from protein correlations.

Nanoinstabilities of Cu2O porous nanostructured films as driven by nanocurvature effect and thermal activation effect.

In this work, the instabilities at the nanoscale (i.e. nanoinstabilities) of triangular pyramids-like Cu2O porous nanostructured films (PNFs) are studied by heating treatments under different atmosphere and temperature. It is found that the nanoscale building triangular pyramids turn round preferentially at the sharp angles and/or coalesce with their contacting ones by directional diffusion and plastic flow of atoms, which are driven by the nonuniformly-distributed surface nanocurvature. As a result, the triangular pyramids become quasi-sphere shape and the PNF evolves into a big, dense particles film. It is also observed that the heating or thermal activation effect efficiently promotes the reduction or oxidation of Cu2O pyramids and the crystallization or growth of the as-achieved Cu or CuO grains. The above physical and chemical instabilities or changes at the nanoscale of Cu2O PNFs can be well accounted for by the combined mechanism of nanocurvature effect and thermal activation effect. The nanocurvature effect can lower the energy barrier for the atom diffusion or plastic flow and lower the activation energy for the chemical reactions, while the thermal activation effect can supply the required kinetic energy or activation energy and make the atomic transportations and reactions kinetically possible. The findings reveal the evolution laws of morphology, crystal structure and composition of triangular pyramids-like Cu2O PNF during heating treatments, which can further be extended to other types of Cu2O PNFs. Also, the findings have important implications for the nanoinstabilities of Cu2O PNFs-based devices, especially those working at a high temperature.

Cu2O porous nanostructured films fabricated by positive bias sputtering deposition.

In this work, the authors fabricated Cu2O porous nanostructured films (PNFs) on glass slide substrates by the newly developed positive bias deposition approach in a balanced magnetron sputtering (MS) system. It was found that the surface morphology, crystal structure and optical property of the as-deposited products were greatly dependent on the applied positive substrate bias. In particular, when the substrate was biased at +50 and +150 V, both of the as-prepared Cu2O PNFs exhibited a unique triangular pyramids-like structure with obvious edges and corners and little gluing, a preferred orientation of (111) and a blue shift of energy band gap at 2.35 eV. Quantitative calculation results indicated that the traditional bombardment effects of electrons and sputtering argon ions were both negligible during the bias deposition in the balanced MS system. Instead, a new model of tip charging effect was further proposed to account for the controllable formation of PNFs by the balanced bias sputtering deposition.

Docosahexaenoic acid inhibits monocrotaline-induced pulmonary hypertension via attenuating endoplasmic reticulum stress and inflammation.

Endoplasmic reticulum (ER) stress and inflammation contribute to pulmonary hypertension (PH) pathogenesis. Previously, we confirmed that docosahexaenoic acid (DHA) could improve hypoxia-induced PH. However, little is known about the link between DHA and monocrotaline (MCT)-induced PH. Our aims were, therefore, to evaluate the effects and molecular mechanisms of DHA on MCT-induced PH in rats. Rat PH was induced by MCT. Rats were treated with DHA daily in the prevention group (following MCT injection) and the reversal group (after MCT injection for 2 wk) by gavage. After 4 wk, mean pulmonary arterial pressure (mPAP), right ventricular (RV) hypertrophy index, and morphological and immunohistochemical analyses were evaluated. Rat pulmonary artery smooth muscle cells (PASMCs) were used to investigate the effects of DHA on cell proliferation stimulated by platelet-derived growth factor (PDGF)-BB. DHA decreased mPAP and attenuated pulmonary vascular remodeling and RV hypertrophy, which were associated with suppressed ER stress. DHA blocked the mitogenic effect of PDGF-BB on PASMCs and arrested the cell cycle via inhibiting nuclear factor of activated T cells-1 (NFATc1) expression and activation and regulating cell cycle-related proteins. Moreover, DHA ameliorated inflammation in lung and suppressed macrophage and T lymphocyte accumulation in lung and adventitia of resistance pulmonary arteries. These findings suggest that DHA could protect against MCT-induced PH by reducing ER stress, suppressing cell proliferation and inflammation.

The role of autophagy in pulmonary hypertension: a double-edge sword.

Autophagy is a recycling process that degrades damaged or unneeded cellular components for renewal. Accumulating evidence suggests that dysregulation of autophagy is involved in pulmonary hypertension (PH). PH is a progressive disease characterized by persistent proliferation of apoptosis-resistant pulmonary vascular cells. However, reports on the role of autophagy in the development of PH are often conflicting. In this review, we discuss recent development in the field with emphasis on pulmonary arterial endothelial cells, pulmonary smooth muscle cells, right ventricular myocyte, as well as pharmacological strategies targeting the autophagic signaling pathway.

Research on radiotherapy at different times of the day for inoperable cervical cancer.

PURPOSE: To investigate the radiation effects and acute damage in inoperable cervical cancer patients irradiated at different times as well as the underlying mechanisms. METHODS: 67 patients were randomized to a morning group (MG, 9:00 - 11:00 AM) and an evening group (EG, 9:00 - 11:00 PM) and both received external beam radiotherapy (RT) (50 Gy in 25 fractions) at different times. Brachytherapy (36 - 42 Gy in 6 - 7 fractions) was also performed to enhance the radiation response twice every week in all patients at the same time. Clinical therapeutic effects and acute toxicities were evaluated after RT. Flow cytometry was analyzed before and after RT. RESULTS: Patients' response to radiation was similar in the two groups. Incidences of overall and high-grade (III - IV) diarrhea in the MG vs. the EG were 75.0% vs. 57.6% and 12.5% vs. 6.1%, respectively. The incidence of severe hematological toxicity in the EG was significantly increased compared to the MG group. Cell apoptosis in the EG was significantly higher at 9:00 - 11:00 PM than that at 9:00 - 11:00 AM after RT. No significant differences were found in Gap Phase 0/Gap Phase 1 (G0/G1), Gap Phase 2/Metaphase Phase (G2/M), and Synthesis Phase (S) phase between different times and groups, nor were expressions of Per1, Per2, and Clock. But expressions of Per1, Per2, and Clock were significantly negative with G2/M phase and positively correlated with cell apoptosis. CONCLUSION: RT at different time intervals results in similar efficacy. However, RT in the morning reduces severe hematological toxicity. Radiation responses may be associated with circadian genes by influence of cell cycles and apoptosis.
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Utilizing Ni(II) complex for metal drug-gel particles in cervical cancer treatment and designing novel drugs through machine learning methods.

In the present study, a novel coordination polymer (CP) based on Ni(II), namely, [Ni(L)(D-CAM)(H2O)]n (1) (H2D-CAM = (1R,3S)-1,2,2-trimethylcyclopentane-1,3-dicarboxylic acid and L = 3,6-bis(benzimidazol-1-yl)pyridazine), has been produced successfully through applying a mixed ligand synthesis method via reacting Ni(NO3)2·6H2O with 3,6-bis(benzimidazol-1-yl)pyridazine ligand in the presence of a carboxylic acid co-ligand. Hyaluronic acid (HA) and carboxymethyl chitosan (CMCS) are representatives of natural polysaccharides and have good biocompatibility. Based on the chemical synthesis method, HA/CMCS hydrogel was successfully prepared. SEM showed that the lyophilized gel presented a typical macroporous structure with three-dimensional connected pores, which had unique advantages as a drug carrier. Using paclitaxel as a drug model, we further synthesized a novel paclitaxel-loaded metal gel and evaluated its therapeutic effect on cervical cancer. Finally, novel drugs from the reinforcement learning simulation are suggested to have better biological activity against ovarian cancer due to low affinity energy and stronger interaction strength towards the protein receptor.

Effect of waiting time for radiotherapy after last induction chemotherapy on prognosis of locally advanced nasopharyngeal carcinoma.

BACKGROUND: The effect of radiotherapy waiting time after last induction chemotherapy (IC-RT) on prognosis of patients with locally advanced nasopharyngeal carcinoma (LANPC) needs further discussion. METHODS: Three hundred and six patients with LANPC diagnosed pathologically by induction chemotherapy (IC) and radiotherapy (RT) from 2013 to 2018 were selected for this study. RESULTS: The IC-RT was a risk factor for the post-treatment progression of LANPC (OR = 1.017 95%CI: 1.003-1.031), For patients with LANPC, the IC-RT > 40 days significantly reduced 5-year PFS (70% vs. 55%; p = 0.0012), 5-year OS (84% vs. 73%; p = 0.028), 5-year DMFS (80% vs. 66%; p = 0.003), 5-year LRFS (77% vs. 67%; p = 0.012). Indicating that patients with stage IVa who IC-RT > 40 days were found to be a significant predictor of aggravated PFS (HR = 2.69; 95%CI: 1.57-4.6), OS (HR = 2.55; 95%CI: 1.29-5.03), DMFS (HR = 3.07; 95%CI: 1.64-5.76) and LRFS (HR = 2.26; 95%CI: 1.21-4.21). CONCLUSION: The prognosis of patients will be adversely affected if the IC-RT exceeds 40 days, especially for stage IVa patients.

Qualitative assessments of density and background parenchymal enhancement on contrast-enhanced spectral mammography associated with breast cancer risk in high-risk women.

OBJECTIVE: To investigate the correlation between the risk of breast cancer for high-risk females and the density and background parenchymal enhancement (BPE) on contrast-enhanced spectral mammography (CESM). METHODS: Females at high-risk, without breast cancer history and received CESM from July 2016 to December 2017 were retrospectively enrolled. The longest follow-up time was 4.5 years, and patients who developed breast cancer with maximized follow-up time were classified as cancer cohort, while females who did not develop breast cancer were categorized as control cohort. These two cohorts were one-to-one matched in age, family and/or genetic history of breast cancer, menopausal status and BRCA status. The density and BPE at CESM imaging were assessed. Conditional logistic regression was applied to evaluate the relationship between imaging features and breast cancer risk. RESULTS: During the follow-up interval, 90 women at high-risk without history of breast cancer were newly diagnosed. Compared with minimal BPE, increasing BPE levels were associated with the risk of breast cancer among high-risk females in a time interval of 4.5 years (mild: odds ratio [OR]=3.2, p = 0.001; moderate: OR = 4.0, p = 0.002; marked: OR = 11.2, p < 0.001). In addition, females with mild, moderate or marked BPE were four times more likely to be diagnosed with breast cancer than females with minimal BPE in a time interval of 4.5 years (OR = 4.0, p < 0.001). CONCLUSION: Qualitative CESM BPE assessment may be useful in the prediction of breast cancer risk among high-risk females. ADVANCES IN KNOWLEDGE: • Qualitative CESM BPE assessment may be useful in the prediction of breast cancer risk among high-risk women during the follow-up period of 4.5 years. • The significance of breast density as an independent risk factor is not fully established for high-risk women during the follow-up period of 4.5 years.

Deep Learning-Based Diffusion-Weighted Magnetic Resonance Imaging in the Diagnosis of Ischemic Penumbra in Early Cerebral Infarction.

The prefiltered image was imported into the local higher-order singular value decomposition (HOSVD) denoising algorithm (GL-HOSVD)-optimized diffusion-weighted imaging (DWI) image, which was compared with the deviation correction nonlocal mean (NL mean) and low-level edge algorithm (LR + edge). Regarding the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), sensitivity, specificity, accuracy, and consistency, the application effect of the GL-HOSVD algorithm in DWI was investigated, and its adoption effect in the examination of ischemic penumbra (IP) of early acute cerebral infarction (ACI) patients was evaluated. A total of 210 patients with ACI were selected as the research subjects, who were randomly rolled into two groups. Those who were checked by conventional DWI were set as the control group, and those who used DWI based on the GL-HOSVD denoising algorithm were set as the observation group, with 105 people in each. Positron emission tomography (PET) test results were set as the gold standard to evaluate the application value of the two examination methods. It was found that under different noise levels, the peak signal-to-noise ratio (PSNR) of the GL-HOSVD algorithm and the root mean square error (RMSE) of the FA parameter were better than those of the nonlocal means (NL-means) of deviation correction and low-rank edge algorithm (LR + edge). The sensitivity, specificity, accuracy, and consistency (8.76%, 81.25%, 87.62%, and 0.52) of the observation group were higher than those of the control group (57.78%, 53.33%, 57.14%, and 0.35) (P < 0.05). Moreover, the apparent diffusion coefficient (ADC) of the DWI images of the observation group was basically consistent with that of the PET images, while the control group had a poor display effect and low definition. In summary, under different noise levels, the GL-HOSVD algorithm had a good denoising effect and greatly reduced fringe artifacts. DWI after denoising had high sensitivity, specificity, accuracy, and consistency in the detection of IP, which was worthy of clinical application and promotion.

Multimodal Imaging of Target Detection Algorithm under Artificial Intelligence in the Diagnosis of Early Breast Cancer.

This study aimed to analyze the diagnostic value of multimodal images based on artificial intelligence target detection algorithms for early breast cancer, so as to provide help for clinical imaging examinations of breast cancer. This article combined residual block with inception block, constructed a new target detection algorithm to detect breast lumps, used deep convolutional neural network and ultrasound imaging in diagnosing benign and malignant breast lumps, took breast density grading with mammography, compared the convolutional neural network (CNN) algorithm with the proposed algorithm, and then applied the proposed algorithm to the diagnosis of 120 female patients with breast lumps. According to the results, accuracy rates of breast lump detection (94.76%), benign and malignant breast lumps diagnosis (98.22%), and breast grading (93.65%) with the algorithm applied in this study were significantly higher than those (75.67%, 87.23%, and 79.54%) with CNN algorithm, and the difference was statistically significant (P < 0.05); among 62 patients with malignant breast lumps of the 120 patients with breast lumps, 37 were patients with invasive ductal carcinoma, 8 with lobular carcinoma in situ, 16 with intraductal carcinoma, and 4 with mucinous carcinoma; among the remaining 58 patients with benign breast lumps, 28 were patients with fibrocystic breast disease, 17 with intraductal papilloma, 4 with breast hyperplasia, and 9 with adenopathy; the differences in shape, growth direction, edge, and internal echo of multimodal ultrasound imaging of patients with benign and malignant breast lumps had statistical significance (P < 0.05); the malignant constituent ratios of patients with breast density grades I to IV were 0%, 7.10%, 80.40%, and 100%, respectively. In short, the multimodal imaging diagnosis under the algorithm in this article was superior to CNN algorithm in all aspects; according to the judgment on benign and malignant breast lumps and breast density with multimodal imaging features, the higher the breast density, the higher the probability of breast cancer.

An efficient strategy for identifying essential proteins based on homology, subcellular location and protein-protein interaction information.

High throughput biological experiments are expensive and time consuming. For the past few years, many computational methods based on biological information have been proposed and widely used to understand the biological background. However, the processing of biological information data inevitably produces false positive and false negative data, such as the noise in the Protein-Protein Interaction (PPI) networks and the noise generated by the integration of a variety of biological information. How to solve these noise problems is the key role in essential protein predictions. An Identifying Essential Proteins model based on non-negative Matrix Symmetric tri-Factorization and multiple biological information (IEPMSF) is proposed in this paper, which utilizes only the PPI network proteins common neighbor characters to develop a weighted network, and uses the non-negative matrix symmetric tri-factorization method to find more potential interactions between proteins in the network so as to optimize the weighted network. Then, using the subcellular location and lineal homology information, the starting score of proteins is determined, and the random walk algorithm with restart mode is applied to the optimized network to mark and rank each protein. We tested the suggested forecasting model against current representative approaches using a public database. Experiment shows high efficiency of new method in essential proteins identification. The effectiveness of this method shows that it can dramatically solve the noise problems that existing in the multi-source biological information itself and cased by integrating them.

Evaluation of the sphericity index of the fetal heart during middle and late pregnancy using fetalHQ.

OBJECTIVE: To explore the feasibility of the fetal heart quantitative technique (fetalHQ) for evaluating the sphericity index (SI) of the fetal heart during middle and late pregnancy. METHODS: Ninety-six normal fetuses in middle and late gestation who underwent systemic ultrasound examination in the Department of Ultrasound of the Maternal and Child Health Care Hospital of Hunan Province in November 2020 were enrolled, and dynamic images of the four-chamber view of the fetal heart were collected. The correlation between the global sphericity index (GSI), ventricular 24-segment SI, and gestational age (GA) was analyzed, and the differences between the left and right ventricular 24-segment SI were compared. RESULTS: The success rate of fetalHQ analysis was 93.75%. There was no significant linear correlation between GSI and ventricular 24-segment SI and GA (all ps > .05). The differences in SI between segments 1 and 9 and 15 and 24 in the left and right ventricles were statistically significant (all ps < .05), while the differences in SI between segments 10 and 14 were not statistically significant (all ps > .05). In segments 1-9, the SI of the right ventricle was smaller than that of the left ventricle, indicating that the right ventricle was significantly more spherical than the left ventricle. In segments 15-24, the opposite was true. CONCLUSION: FetalHQ is a simple and reliable method for evaluating the GSI and 24-segment SI of the left and right ventricles. It can provide some theoretical basis for the clinical quantitative evaluation of fetal heart geometry and lay a foundation for the quantitative evaluation of fetal heart function in cases of structural and functional abnormalities.

Correlation between Parturients' Uterine Artery Blood Flow Spectra in the First and Second Trimesters of Pregnancy and Fetal Growth Restriction.

Objective: To explore the correlation between parturients' uterine artery blood flow spectra in the first and second trimesters of pregnancy and fetal growth restriction (FGR). Methods: The data of parturients treated in our hospital from February 2018 to February 2020 were retrospectively analyzed, 50 parturients with FGR were selected as the FGR group, and other 50 healthy cases were selected as the control group. In the first trimester (11-12 weeks of gestation) and the second trimester of pregnancy (13-24 weeks of gestation), the parturients of the two groups accepted the color Doppler ultrasonography (CDS), their hemodynamics indicators of uterine artery were recorded, and the correlation between their uterine artery blood flow spectra in the two periods and FGR was analyzed with the Receiver Operating Characteristic (ROC) curve. Results: No statistical differences in the parturients' general information including age, gestational weeks, gravidity, and parity between the two groups were observed (P > 0.05); the newborn's body weight, Apgar scores, number of preterm infants, and the number of infants transferring to the neonatal intensive care unit (NICU) were significantly different between the two groups (P < 0.05); in the first and second trimesters of pregnancy, the uterine artery pulsatility index (UtA-PI), uterine artery resistance index (UtA-RI), maximal systolic flow velocity, and systolic/diastolic (UtA-S/D) ratio were significantly higher in the FGR group than in the control group (P < 0.05), and the time-averaged maximal velocity (TAMX) was significantly lower in the FGR group than in the control group (P < 0.001); in early pregnancy, the incidence of early diastolic notch at bilateral uterine arteries between the two groups was not significantly different (P > 0.05), and the unilateral and total incidence in the first trimester as well as the unilateral, bilateral, and total incidence in the second trimester were significantly higher in the FGR group than in the control group (P < 0.05); in the first trimester, the sensitivity of detecting FGR with a uterine artery blood flow spectrum was 0.820, AUC (95% CI) = 0.840 (0.757-0.923), and in the second trimester, it was 0.860, AUC (95% CI) = 0.900 (0.832-0.968). Conclusion: There is a correlation between uterine artery blood flow spectra in the first and second trimesters of pregnancy and FGR, and the sensitivity of spectrum in the first trimester is higher than that in the second trimester, presenting a better clinical application value.

A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization.

Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, there has been an increasing interest in using computational methods to predict essential proteins based on protein-protein interaction (PPI) networks or fusing multiple biological information. However, it has been observed that existing PPI data have false-negative and false-positive data. The fusion of multiple biological information can reduce the influence of false data in PPI, but inevitably more noise data will be produced at the same time. In this article, we proposed a novel non-negative matrix tri-factorization (NMTF)-based model (NTMEP) to predict essential proteins. Firstly, a weighted PPI network is established only using the topology features of the network, so as to avoid more noise. To reduce the influence of false data (existing in PPI network) on performance of identify essential proteins, the NMTF technique, as a widely used recommendation algorithm, is performed to reconstruct a most optimized PPI network with more potential protein-protein interactions. Then, we use the PageRank algorithm to compute the final ranking score of each protein, in which subcellular localization and homologous information of proteins were used to calculate the initial scores. In addition, extensive experiments are performed on the publicly available datasets and the results indicate that our NTMEP model has better performance in predicting essential proteins against the start-of-the-art method. In this investigation, we demonstrated that the introduction of non-negative matrix tri-factorization technology can effectively improve the condition of the protein-protein interaction network, so as to reduce the negative impact of noise on the prediction. At the same time, this finding provides a more novel angle of view for other applications based on protein-protein interaction networks.