SLC7A11 is a potential therapeutic target and prognostic biomarker correlated with immune cell infiltration in cervical cancer.

Background: SLC7A11 is importantly in both ferroptosis and disulfidptosis which participated in human development and homeostasis. By utilizing bioinformatics and in vitro validation, we explored SLC7A11's role in cervical cancer.

Methods: From the TCGA database, we analyzed SLC7A11 expression profiles and validated its promoting role in cervical cancer by in vitro.

New Algorithms to Generate Permutationally Invariant Polynomials and Fundamental Invariants for Potential Energy Surface Fitting.

Symmetric functions, such as Permutationally Invariant Polynomials (PIPs) and Fundamental Invariants (FIs), are effective and concise descriptors for incorporating permutation symmetry into neural network (NN) potential energy surface (PES) fitting. The traditional algorithm for generating such symmetric polynomials has a factorial time complexity of , where is the number of identical atoms, posing a significant challenge to applying symmetric polynomials as descriptors of NN PESs for larger systems, particularly with more than 10 atoms. Herein, we report a new algorithm which has only linear time complexity for identical atoms.

PEG-rhG-CSF versus rhG-CSF in supporting neutrophil recovery after induction chemotherapy for patients with newly diagnosed acute lymphoblastic leukemia.

To compare the efficacy and safety of pegylated recombinant human granulocyte colony-stimulating factor (PEG-rhG-CSF) and rhG-CSF in the recovery of neutrophils after induction therapy in ALL patients, PEG-rhG-CSF was injected subcutaneously within 24 ~ 48 h after the end of intravenous infusion of daunorubicin/idarubicin during induction chemotherapy. In rhG-CSF group, patients were given rhG-CSF. The main outcome indexes were the incidence and duration of grade 4 chemotherapy-induced-neutropenia (CIN, ANC less than 0.

Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction model.

Background: Current models for predicting intraoperative hemorrhage in cesarean scar ectopic pregnancy (CSEP) are constrained by known risk factors and conventional statistical methods. Our objective is to develop an interpretable prediction model using machine learning (ML) techniques to assess the risk of intraoperative hemorrhage during CSEP in women, followed by external validation and clinical application.

Methods: This multicenter retrospective study utilized electronic medical record (EMR) data from four tertiary medical institutions.

Comparison of different tracers in sentinel lymph node detection for endometrial cancer: a systematic review and network meta-analysis.

Background: In the realm of endometrial cancer (EC) therapeutics and prognostic assessments, lymph nodes' status is paramount. The sentinel lymph node (SLN) detection, recognized for its reliability, has been progressively adopted as a standard procedure, posing a compelling alternative to conventional systematic lymphadenectomy. However, there remains a lack of agreement on the most effective choice of tracers for this procedure.

Robust Gaussian Process Regression Method for Efficient Tunneling Pathway Optimization: Application to Surface Processes.

Simulation of surface processes is a key part of computational chemistry that offers atomic-scale insights into mechanisms of heterogeneous catalysis, diffusion dynamics, and quantum tunneling phenomena. The most common theoretical approaches involve optimization of reaction pathways, including semiclassical tunneling pathways (called instantons). The computational effort can be demanding, especially for instanton optimizations with an ab initio electronic structure.

APLN promotes the proliferation, migration, and glycolysis of cervical cancer through the PI3K/AKT/mTOR pathway.

Apelin (APLN) is an endogenous ligand of the G protein-coupled receptor APJ (APLNR). APLN has been implicated in the development of multiple tumours. Herein, we determined the effect of APLN on the biological behaviour and underlying mechanisms of cervical cancer.

Effect of direct surgical treatment in pregnancy-related uterine arteriovenous malformation.

Background: Uterine arteriovenous malformation (UAVM) is a relatively rare but potentially life-threatening situations abnormal vascular connections between the uterine arterial and venous systems. Lack of recognized guidelines and clinic experience, there is a lot of clinic problems about diagnosis and treatment. By analyzing the clinical data of patients with pregnancy-related UAVM, we aim to confirm the safety of direct surgeries and the benefit of pretreatment (uterine artery embolization or medical therapy) before surgery, and to explore more optimal therapies for patients with pregnancy-related UAVM.

Identification of lymph node metastasis in pre-operation cervical cancer patients by weakly supervised deep learning from histopathological whole-slide biopsy images.

Background: Lymph node metastasis (LNM) significantly impacts the prognosis of individuals diagnosed with cervical cancer, as it is closely linked to disease recurrence and mortality, thereby impacting therapeutic schedule choices for patients. However, accurately predicting LNM prior to treatment remains challenging. Consequently, this study seeks to utilize digital pathological features extracted from histopathological slides of primary cervical cancer patients to preoperatively predict the presence of LNM.

Development and validation of a deep learning survival model for cervical adenocarcinoma patients.

Background: The aim was to develop a personalized survival prediction deep learning model for cervical adenocarcinoma patients and process personalized survival prediction.

Methods: A total of 2501 cervical adenocarcinoma patients from the surveillance, epidemiology and end results database and 220 patients from Qilu hospital were enrolled in this study. We created our deep learning (DL) model to manipulate the data and evaluated its performance against four other competitive models.

Cesarean Scar Ectopic Pregnancy Clinical Classification System With Recommended Surgical Strategy.

Objective: To establish a new cesarean scar ectopic pregnancy clinical classification system with recommended individual surgical strategy and to evaluate its clinical efficacy in treatment of cesarean scar ectopic pregnancy.

Methods: This retrospective cohort study included patients with cesarean scar ectopic pregnancy in Qilu Hospital in Shandong, China. From 2008 to 2015, patients with cesarean scar ectopic pregnancy were included to determine risk factors for intraoperative hemorrhage during cesarean scar ectopic pregnancy treatment.

Construction of a prognostic model based on eight ubiquitination-related genes machine learning and potential therapeutics analysis for cervical cancer.

Ubiquitination is involved in many biological processes and its predictive value for prognosis in cervical cancer is still unclear. To further explore the predictive value of the ubiquitination-related genes we obtained URGs from the Ubiquitin and Ubiquitin-like Conjugation Database, analyzed datasets from The Cancer Genome Atlas and Gene Expression Omnibus databases, and then selected differentially expressed ubiquitination-related genes between normal and cancer tissues. Then, DURGs significantly associated with overall survival were selected through univariate Cox regression.

Analysis of prognostic factors for cervical mucinous adenocarcinoma and establishment and validation a nomogram: a SEER-based study.

Up to now, there are no relevant studies on prognostic factors of cervical mucinous adenocarcinoma. Therefore, we explored the prognostic factors for cervical mucinous adenocarcinoma, and established and validated the prognostic model using the SEER database. We selected the independent factors through univariate and multivariate analyses.

Use of Nomogram to Predict the Risk of Lymph Node Metastasis among Patients with Cervical Adenocarcinoma.

Background: The objective of this study was to develop a nomogram that can predict lymph node metastasis (LNM) in patients with cervical adenocarcinoma (cervical AC).

Methods: A total of 219 patients with cervical AC who had undergone radical hysterectomy and lymphadenopathy between 2005 and 2021 were selected for this study. Both univariate and multivariate logistic regression analyses were performed to analyze the selected key clinicopathologic features and develop a nomogram and underwent internal validation to predict the probability of LNM.

Comparison of different treatment strategies in the management of endogenic caesarean scar pregnancy: a multicentre retrospective study.

Background: The aim of this study was to evaluate the effectiveness and safety of different treatment strategies for endogenic caesarean scar pregnancy (CSP) patients.

Methods: According to Vial's standard, we defined endogenic-type CSP as (1) the gestational sac growing towards the uterine cavity and (2) a greater than 0.3 cm thickness of myometrial tissue at the caesarean scar.

Integration of Quantum Confinement and Alloy Effect to Modulate Electronic Properties of RhW Nanocrystals for Improved Catalytic Performance toward CO Hydrogenation.

The d-band center and surface negative charge density generally determine the adsorption and activation of CO, thus serving as important descriptors of the catalytic activity toward CO hydrogenation. Herein, we engineered the d-band center and negative charge density of Rh-based catalysts by tuning their dimensions and introducing non-noble metals to form an alloy. During the hydrogenation of CO into methanol, the catalytic activity of RhW nanosheets was 5.

Optimality and stability of symmetric evolutionary games with applications in genetic selection.

Symmetric evolutionary games, i.e., evolutionary games with symmetric fitness matrices, have important applications in population genetics, where they can be used to model for example the selection and evolution of the genotypes of a given population.