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Aim: To clarify the concept of fear of childbirth among pregnant women and to examine its current measure tools. Background: Fear of childbirth is a psychological symptom, prevalent among pregnant women, which negatively impacts women's health and well‐being. It has become an increasingly concerning issue in perinatal mental health. However, due to its poor conceptualization, it presents difficulty in conducting reliable assessments and identifying risk factors. Methods: The Walker and Avant approach to concept analysis guided this review. Six bibliographic databases were systematically searched for published research from their inception date to May 2023. Additional records were identified by manually searching the reference lists of relevant studies. Quantitative and qualitative studies investigating fear of childbirth in pregnant women were included. Results: Three critical attributes have been identified: cognitive impairments, affective disorders and somatic symptoms. Antecedents include perceived a real or anticipated threat of pregnancy or its outcomes, low perceived self‐coping ability and unmet social support needs. Consequences include processing and avoiding behaviours. This study also identified the dimensions of fear of childbirth, including 6 primary categories and 14 subcategories. The content of five scales was analysed and none covered all domains. Conclusions: The current analysis provides healthcare providers with a more comprehensive framework to assess and identify fear of childbirth. Further research is needed to develop a suitable instrument that covers all the attributes and dimensions of this concept and assesses its severity. Impact: This conceptual analysis provides a comprehensive insight into the phenomenon of fear of childbirth. This will help family members, healthcare providers and policymakers to identify the psychological needs of pregnant women and improve the quality of antenatal care. Patient or Public Contribution: Not applicable as no new data were generated. [ABSTRACT FROM AUTHOR]

Anemia is a common complication in patients with progressive chronic kidney disease. This cohort study evaluated the prevalence, clinical features and prognosis of membranous nephropathy (MN) with anemia. We retrospectively analyzed a cohort of MN patients diagnosed using renal biopsy between February 2012 and February 2018. The clinical and pathological characteristics at baseline were recorded, and the outcomes (hemoglobin, proteinuria and renal function) during follow-ups were also evaluated. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors for anemia in MN patients. The MN patients were divided according to the therapeutic effect they experienced as follows: without-anemia, completely corrected anemia, standard anemia treatment and nonstandard anemia treatment groups. We compared the rate of complete remission of MN and renal end-point events among the four groups. The median age of 483 patients was 42.43 (26.59, 50.20) years at the time of MN diagnosis. The prevalence of anemia at baseline was 23.81%, and the cumulative prevalence was 50.72%. There were 133 cases of mild anemia, 103 cases of moderate anemia and 9 cases of severe anemia; in addition, there were 228 cases of normocytic anemia and 17 cases of microcytic hypochromic anemia. Multivariate logistic regression indicated that acute renal tubule injury >5% (OR = 1.634, 95% CI 1.034, 2.581; p = 0.035), total protein level (OR = 0.949, 95% CI 0.923, 0.975; p < 0.001), cholesterol level (OR = 0.833, 95% CI 0.749, 0.926, p = 0.001), hypokalemia (OR = 2.612, 95% CI 1.227, 5.560, p = 0.013) and hypophosphatemia (OR = 2.653, 95% CI 1.303, 5.403, p = 0.007) were independent risk factors for anemia in MN patients. The complete remission rate of MN patients without anemia was significantly higher than that of anemia patients who exhibited treatment failure. The incidence of renal endpoint events was different among the four groups. The anemia experienced by MN patients is mainly mild and moderate, normocytic anemia. The pathological features of acute renal tubular injury and clinical nutritional status are independent risk factors for anemia. There were differences in renal prognosis among anemia patients with different treatment outcomes. [ABSTRACT FROM AUTHOR]

Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique for studying gene expression patterns at the single-cell level. Inferring gene regulatory networks (GRNs) from scRNA-seq data provides insight into cellular phenotypes from the genomic level. However, the high sparsity, noise and dropout events inherent in scRNA-seq data present challenges for GRN inference. In recent years, the dramatic increase in data on experimentally validated transcription factors binding to DNA has made it possible to infer GRNs by supervised methods. In this study, we address the problem of GRN inference by framing it as a graph link prediction task. In this paper, we propose a novel framework called GNNLink, which leverages known GRNs to deduce the potential regulatory interdependencies between genes. First, we preprocess the raw scRNA-seq data. Then, we introduce a graph convolutional network-based interaction graph encoder to effectively refine gene features by capturing interdependencies between nodes in the network. Finally, the inference of GRN is obtained by performing matrix completion operation on node features. The features obtained from model training can be applied to downstream tasks such as measuring similarity and inferring causality between gene pairs. To evaluate the performance of GNNLink, we compare it with six existing GRN reconstruction methods using seven scRNA-seq datasets. These datasets encompass diverse ground truth networks, including functional interaction networks, Loss of Function/Gain of Function data, non-specific ChIP-seq data and cell-type-specific ChIP-seq data. Our experimental results demonstrate that GNNLink achieves comparable or superior performance across these datasets, showcasing its robustness and accuracy. Furthermore, we observe consistent performance across datasets of varying scales. For reproducibility, we provide the data and source code of GNNLink on our GitHub repository: https://github.com/sdesignates/GNNLink. [ABSTRACT FROM AUTHOR]

Structure-based drug design protocols may encounter difficulties to investigate poses when the biomolecular targets do not exhibit typical binding pockets. In this study, by providing two concrete examples from our labs, we suggest that the combination of metadynamics free energy methods (validated against affinity measurements), along with experimental structural information (by X-ray crystallography and NMR), can help to identify the poses of ligands on protein surfaces. The simulation workflow proposed here was implemented in a widely used code, namely GROMACS, and it could straightforwardly be applied to various drug-design campaigns targeting ligands' binding to protein surfaces. [ABSTRACT FROM AUTHOR]

This study aims to analyze the characteristics of idiopathic membranous nephropathy (iMN) with nondiabetic urine glucose during the follow-up. We retrospectively analyzed the data of 1313 patients who were diagnosed iMN. The prevalence of nondiabetic urine glucose during follow-up was 10.89%. There were significant differences between the patients with nondiabetic urine glucose and those without urine glucose in gender, hypertension ratio, proteinuria, N-acetyl-β-glucosaminidase, retinol binding protein, serum albumin, serum creatinine (Scr), cholesterol, triglyceride and positive anti-phospholipase A2 receptor antibody ratio, glomerular sclerosis ratio, acute and chronic tubular injury lesion at baseline. To exclude the influence of the baseline proteinuria and Scr, case control sampling of urine glucose negative patients was applied according to gender, baseline proteinuria and Scr. The proteinuria nonremission (NR) ratio was 45.83 versus 12.50% of the urine glucose positive group and case control group. Partial remission (PR) ratio of the two groups was 36.46 versus 23.96% and complete remission (CR) ratio was 19.79% versus 63.54%, respectively. Patients with urine glucose had higher risk of 50% estimated glomerular filtration rate (eGFR) reduction. Cox regression showed that urine glucose and baseline Scr were risk factors of 50% reduction of eGFR. Urine glucose remission ratio of the patients with proteinuria NR, PR, and CR was 13.33, 56.25, and 94.73% (p < 0.005). Patients who got urine glucose remission also had better renal survival. In conclusion, non-diabetic urine glucose was closely related to proteinuria. It could be applied as a tubular injury marker to predict renal function. [ABSTRACT FROM AUTHOR]

Simple Summary: R-Loops, finely regulated by R-Loop binding proteins (RLBPs), play pivotal roles in maintaining genomic stability. By integrated proteogenomic analysis, we identified two RLBPs subtypes with distinct prognostic and therapeutic differences in colorectal cancer (CRC). Cluster-I (CI), characterized by high expression of RLBPs, was associated with chromosomal instability (CIN), better prognosis, and sensitivity to drugs targeting genome integrity and EGFR. Cluster-II (CII), characterized by low expression of RLBPs, was associated with mucinous adenocarcinoma, right-sided colon cancer, and poor prognosis. High inflammatory signaling pathway and lymphocyte infiltration enriched in CII, indicating potential application of drugs targeting inflammatory and immune response. Our research might be helpful for the precision treatment of CRC. Chromosomal instability (CIN) covers approximately 65 to 70% of colorectal cancer patients and plays an essential role in cancer progression. However, the molecular features and therapeutic strategies related to those patients are still controversial. R-loop binding proteins (RLBPs) exert significant roles in transcription and replication. Here, integrative colorectal cancer proteogenomic analysis identified two RLBPs subtypes correlated with distinct prognoses. Cluster I (CI), represented by high expression of RLBPs, was associated with the CIN phenotype. While Cluster II (CII) with the worst prognosis and low expression of RLBPs was composed of a high percentage of patients with mucinous adenocarcinoma or right-sided colon cancer. The molecular feature analysis revealed that the active RNA processing, ribosome synthesis, and aberrant DNA damage repair were shown in CI, a high inflammatory signaling pathway, and lymphocyte infiltration was enriched in CII. In addition, we revealed 42 tumor-associated RLBPs proteins. The CI with high expression of tumor-associated proteins was sensitive to drugs targeting genome integrity and EGFR in both cell and organoid models. Thus, our study unveils a significant molecular association of the CIN phenotype with RLBPs, and also provides a powerful resource for further functional exploration of RLBPs in cancer progression and therapeutic application. [ABSTRACT FROM AUTHOR]

At present, deep learning-based medical image diagnosis had achieved high performance in several diseases. However, the black-box nature of the convolutional neural network (CNN) limits their role in diagnosis. In this study, a novel interpretable diagnosis pipeline using the CNN model was proposed. Furthermore, a sizeable melanoma database that contains 841 digital whole-slide images (WSIs) was built to train and evaluate the model. The model achieved strong melanoma classification ability (0.962 areas under the receiver operating characteristic, 0.887 sensitivity, and 0.925 specificity). Moreover, the proposed model outperformed the existing schemes in terms of accuracy that is 20 pathologists (0.933 vs 0.732 accuracy). Finally, the gradient-weighted class activation mapping (Grad-CAM) method was used to show the inner logic of the proposed model and its feasibility to improve diagnosis process in healthcare. The mechanism of feature heat maps which is visualized through a saliency mapping has demonstrated that features learned or extracted by the proposed model are compatible with the accepted pathological features. Conclusively, the proposed model provides a rapid and accurate diagnosis by locating the distinctive features of melanoma to build doctors' trust in the CNNs' diagnosis results. [ABSTRACT FROM AUTHOR]

In traditional hospital systems, diagnosis and localization of melanoma are the critical challenges for pathological analysis, treatment instructions, and prognosis evaluation particularly in skin diseases. In literature, various studies have been reported to address these issues; however, a prominent smart diagnosis system is needed to be developed for the smart healthcare system. In this study, a deep learning-enabled diagnostic system is proposed and implemented that it has the capacity to automatically detect malignant melanoma in whole slide images (WSIs). In this system, the convolutional neural network (CNN), sophisticated statistical method, and image processing algorithms were integrated and implemented to locate benign and malignant lesions which are extremely useful in the diagnoses process of melanoma disease. To verify the exceptional performance of the proposed scheme, it is implemented in a multicenter database, which has 701 WSIs (641 WSIs from Central South University Xiangya Hospital (CSUXH) and 60 WSIs from the Cancer Genome Atlas (TCGA)). Experimental results have verified that the proposed system has achieved an area under the receiver operating characteristic curve (AUROC) of 0.971. Furthermore, the lesion area on the WSIs is represented by its degree of malignancy. These results show that the proposed system has the capacity to fully automate the diagnosis and localization problem of the melanoma in the smart healthcare systems. [ABSTRACT FROM AUTHOR]

Background: The rate of carcinoma upgrade for atypical ductal hyperplasia (ADH) diagnosed on core needle biopsy (CNB) is variable on open excision. The purpose of the present study was to develop and validate a simple-to-use nomogram for predicting the upgrade of ADH diagnosed with ultrasound (US)-guided core needle biopsy in patients with US-detected breast lesions. Methods: Two retrospective sets, the training set (n = 401) and the validation set (n = 186), from Fudan University Shanghai Cancer Center between January 2014 and December 2019 were retrospectively analyzed. Clinicopathological and US features were selected using univariate and multivariable logistic regression, and the significant features were incorporated to build a nomogram model. Model discrimination and calibration were assessed in the training set and validation set. Results: Of the 587 ADH biopsies, 67.7% (training set: 267/401, 66.6%; validation set: 128/186, 68.8%) were upgraded to cancers. In the multivariable analysis, the risk factors were age [odds ratio (OR) 2.739, 95% confidence interval (CI): 1.525–5.672], mass palpation (OR 3.008, 95% CI: 1.624–5.672), calcifications on US (OR 4.752, 95% CI: 2.569–9.276), ADH extent (OR 3.150, 95% CI: 1.951–5.155), and suspected malignancy (OR 4.162, CI: 2.289–7.980). The model showed good discrimination, with an area under curve (AUC) of 0.783 (95% CI: 0.736–0.831), and good calibration (p = 0.543). The application of the nomogram in the validation set still had good discrimination (AUC = 0.753, 95% CI: 0.666–0.841) and calibration (p = 0.565). Instead of surgical excision of all ADHs, if those categorized with the model to be at low risk for upgrade were surveillanced and the remainder were excised, then 63.7% (37/58) of surgeries of benign lesions could have been avoided and 78.1% (100/128) malignant lesions could be treated in time. Conclusions: This study developed a simple-to-use nomogram by incorporating clinicopathological and US features with the overarching goal of predicting the probability of upgrade in women with ADH. The nomogram could be expected to decrease unnecessary surgery by nearly two-third and to identify most of the malignant lesions, helping guide clinical decision making with regard to surveillance versus surgical excision of ADH lesions. [ABSTRACT FROM AUTHOR]

Background: Cellulose powder (CP) has been reported as a safe and effective complementary treatment for allergic rhinitis (AR). Currently, CP has gained increasing application for clinical management worldwide, particularly in China. However, studies focusing on the effect of CP on normal human nasal epithelial cells (hNECs) and ciliary function are lacking. Here, we aimed to explore the adverse effects of CP on the activity and ciliary function of hNECs. Methods: We biopsied ethmoid sinus or middle turbinate tissues during surgical resection from control subjects who underwent endoscopic sinus surgery for diseases other than AR. Cells were isolated and passaged, followed by differentiation in an air-liquid interface (ALI). Flow cytometry and cell viability test (cell counting kit-8) were performed to detect the cytotoxicity of CP (effects on cell proliferation) on normal hNECs. By using the ALI culture model, we investigated the effects of CP on ciliary beat frequency (CBF). Results: There was a significant reduction in hNEC count at high concentrations of CP (2.5 mg/mL) at days 3 and 7 (both p < 0.05). As the concentration increased, cell death increased progressively from day 3 to day 7. However, these effects were not evident at low concentrations (0.25 mg/mL, p > 0.05). High-dose CP (2.5 mg) significantly reduced the CBF (p < 0.05). At lower concentrations (0.25–2.5 mg/mL), CP initially increased but subsequently reduced the CBF of hNECs compared with control group. Conclusions: Cytotoxicity and the suppression of ciliary beat at high concentrations justify more prudent use of CP for the management of AR. [ABSTRACT FROM AUTHOR]

New Delhi metallo-β-lactamase-1 (NDM-1) as a target for the development of anti-superbug agents, plays an important role in the resistance of β-lactam antibiotics and has received worldwide attention. Sulfhydryl propionic acid derivatives can effectively inhibit the catalytic activity of NDM-1, but the quantitative structure–activity relationship (QSAR) and inhibitor-target recognition mechanism both remain unclear. In this work, CoMFA and CoMSIA models of sulfhydryl propionic acid inhibitors with high predictive ability were obtained, from which the effect of different substituents on the inhibitory activity against NDM-1 were revealed at the molecular level. Then, two 120-nanosecond comparative molecular dynamics (MD) simulations for NDM-1 enzyme and NDM-1-inhibitor complex systems were performed to study the recognition and inhibition mechanism of sulfhydryl propionic acid derivatives. The binding of inhibitors alters the entire H-bond network of the NDM-1 system accompanied by the formation of strong interactions with I35, W93, H120, H122, D124, H189 and H250, further weakens the recognition of NDM-1 by its endogenic substrates. Finally, the results of free energy landscape and conformation cluster analyses show that NDM-1 underwent a significant conformational change after the association with sulfhydryl propionic acid inhibitors. Our findings can provide theoretical support and help for anti-superbug agents design based on the structures of NDM-1 and sulfhydryl propionic acid derivatives. [ABSTRACT FROM AUTHOR]

We designed and synthesized a simple, but highly effective photosensitizer (G-Mito-Pc), which can precisely target the mitochondria of epidermal growth factor receptor (EGFR)-overexpressing cancer cells, to achieve dual targeting function at both cell and organelle levels in cancer therapy. We further explored the possible molecular mechanism of the enhanced bioactivity of G-Mito-Pc compared to that of the reference photosensitizer using molecular dynamics simulations on their interactions with a physiologically relevant mitochondrial membrane model. [ABSTRACT FROM AUTHOR]

Effectively adjusting the phosphorescent quantum efficiency of transition metal complexes is of great significance for the development of new energy-saving organic light-emitting diodes devices. In this article, density functional theory (DFT) and time-dependent density functional theory (TDDFT) methods are employed to investigate the effects of electronegativity caused by introduction of azole moieties on the phosphorescence properties, radiative and nonradiative decay rate constants and photodeactivation mechanism of cyclometalated (CˆC*) platinum(II) NHC complexes. According to calculated results, by decreasing the electronegativity of NHC ligand, the maximum wavelength of the complex appears blue shift; the electron transition density of the lowest triplet state is concentrated to the Pt(II) atom, the singlet-triplet splitting energy and the spin-orbital coupling (SOC) matrix elements are obviously increased, leading to a significant increase of radiative decay rate constant. However, the decrease of ligand electronegativity results in a slight decrease in molecular rigidity and the SOC matrix elements between the ground state and the lowest triplet excited state increase, the energy barriers in photodeactivation pathway decrease, which eventually leads to the increase of nonradiative decay rate constant. Considering its prominent effect on the enhancement of the radiative decay rate constant, this method may still effectively improve the phosphorescence quantum yield of the complexes. This work has a certain theoretical reference value for the design of highly efficient phosphorescent complexes. [ABSTRACT FROM AUTHOR]

In this work, density functional theory and time‐dependent density functional theory were used to investigate the effects of π‐conjugation of the ligand on the photophysical properties, radiative/nonradiative processes and phosphorescence quantum efficiency of tetradentate cyclometalated Pt (II) complex with carbazolyl‐pyridine ligands PtNON. By simulating the absorption spectra and emission wavelengths, increasing the π‐conjugation of the ligand could cause the absorption and emission wavelengths to red‐shift. The results of the computation of key parameters in the radiative decay process, such as singlet‐triplet splitting energy, transition dipole moment and spin‐coupled matrix element between the lowest triplet and singlet excited states, showed that the expansion of π‐conjugation on the carbazole ligand of PtNON resulted in reduction of these parameters, thereby reducing the radiation rate constant. The analyses of the PtNON nonradiative pathway also found that the high activation energy of PtNON made it one of the reasons for the high phosphorescence quantum yield. At the same time, enhancing the molecular orbital delocalization of the ligand further enlarged the energy barrier of the nonradiative pathway, and was conducive to the improvement of phosphorescence quantum yield. [ABSTRACT FROM AUTHOR]