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. Recently, machine learning has been applied to accelerate reaction-pathway optimization, showing great potential for a wide range of applications. However, previous methods still suffer from numerical and efficiency issues and were not designed for condensed-phase reactions. We propose an improved framework based on Gaussian process regression for general transformed coordinates, which has improved efficiency and numerical stability, and we propose a descriptor that combines internal and Cartesian coordinates suitable for modeling surface processes. We demonstrate with 11 instanton optimizations in three representative systems that the improved approach makes ab initio instanton optimization significantly cheaper, such that it becomes not much more expensive than a classical transition-state theory rate calculation.

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. The expression and survival curves of APLN were determined using Gene Expression Profiling Interactive Analysis. The cellular functions of APLN were detected using CCK-8, clone formation, EdU, Transwell assays, flow cytometry, and seahorse metabolic analysis. The underlying mechanisms were elucidated using gene set enrichment analysis and Western blotting. APLN was upregulated in the samples of patients with cervical cancer and is associated with poor prognosis. APLN knockdown decreased the proliferation, migration, and glycolysis of cervical cancer cells. The opposite results were observed when APLN was overexpressed. Mechanistically, we determined that APLN was critical for activating the PI3K/AKT/mTOR pathway via APLNR. APLN receptor inhibitor ML221 reversed the effect of APLN overexpression on cervical cancer cells. Treatment with LY294002, the PI3K inhibitor, drastically reversed the oncological behaviour of APLN-overexpressing C-33A cells. APLN promoted the proliferation, migration, and glycolysis of cervical cancer cells via the PI3K/AKT/mTOR pathway.

A Histopathologic Image Analysis for the Classification of Endocervical Adenocarcinoma Silva Patterns Depend on Weakly Supervised Deep Learning.

Twenty-five percent of cervical cancers are classified as endocervical adenocarcinomas (EACs), which comprise a highly heterogeneous group of tumors. A histopathologic risk stratification system known as the Silva pattern system was developed based on morphology. However, accurately classifying such patterns can be challenging. The study objective was to develop a deep learning pipeline (Silva3-AI) that automatically analyzes whole slide image-based histopathologic images and identifies Silva patterns with high accuracy. Initially, a total of 202 patients with EACs and histopathologic slides were obtained from Qilu Hospital of Shandong University for developing and internally testing the Silva3-AI model. Subsequently, an additional 161 patients and slides were collected from seven other medical centers for independent testing. The Silva3-AI model was developed using a vision transformer and recurrent neural network architecture, utilizing multi-magnification patches, and its performance was evaluated based on a class-specific area under the receiver-operating characteristic curve. Silva3-AI achieved a class-specific area under the receiver-operating characteristic curve of 0.947 for Silva A, 0.908 for Silva B, and 0.947 for Silva C on the independent test set. Notably, the performance of Silva3-AI was consistent with that of professional pathologists with 10 years' diagnostic experience. Furthermore, the visualization of prediction heatmaps facilitated the identification of tumor microenvironment heterogeneity, which is known to contribute to variations in Silva patterns.

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. METHODS: A total of 106 patients in Qilu Hospital of Shandong University from January 2011 to December 2021 diagnosed of pregnancy-related UAVM were involved in this study. Depending on whether preoperative intervention was performed, the patients were divided into direct surgery group and pretreatment group (uterine artery embolization or medical management). Clinical characteristics, operative related factors and prognosis were analyzed. RESULTS: The most common symptom of pregnancy-related UAVM was vaginal bleeding (82.5%), which could also be accompanied by abdominal pain. Pretreatments (uterine artery embolization or medical therapy) had no obvious benefit to the subsequent surgeries, but increased the hospital stay and hospital cost. Direct surgery group had satisfactory success rate and prognosis compared to pretreatment group. CONCLUSION: For pregnancy-related UAVM, direct surgery has good effects and high safety with shorter hospital stays and less hospital cost. What is more, without uterine artery embolization and other medical therapy, patients could remain better fertility in future.

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. METHODS: A deep learning (DL) model was trained using the Vision transformer (ViT) and recurrent neural network (RNN) frameworks to predict LNM. This prediction was based on the analysis of 554 histopathological whole-slide images (WSIs) obtained from Qilu Hospital of Shandong University. To validate the model's performance, an external test was conducted using 336 WSIs from four other hospitals. Additionally, the efficiency of the DL model was evaluated using 190 cervical biopsies WSIs in a prospective set. RESULTS: In the internal test set, our DL model achieved an area under the curve (AUC) of 0.919, with sensitivity and specificity values of 0.923 and 0.905, respectively, and an accuracy (ACC) of 0.909. The performance of the DL model remained strong in the external test set. In the prospective cohort, the AUC was 0.91, and the ACC was 0.895. Additionally, the DL model exhibited higher accuracy compared to imaging examination in the evaluation of LNM. By utilizing the transformer visualization method, we generated a heatmap that illustrates the local pathological features in primary lesions relevant to LNM. CONCLUSION: DL-based image analysis has demonstrated efficiency in predicting LNM in early operable cervical cancer through the utilization of biopsies WSI. This approach has the potential to enhance therapeutic decision-making for patients diagnosed with cervical cancer.

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. We tried to demonstrate a new grouping system oriented by survival outcomes and process personalized survival prediction by using our DL model. RESULTS: The DL model reached 0.878 c-index and 0.09 Brier score in the test set, which was better than the other four models. In the external test set, our model achieved a 0.80 c-index and 0.13 Brier score. Thus, we developed prognosis-oriented risk grouping for patients according to risk scores computed by our DL model. Notable differences among groupings were observed. In addition, a personalized survival prediction system based on our risk-scoring grouping was developed. CONCLUSIONS: We developed a deep neural network model for cervical adenocarcinoma patients. The performance of this model proved to be superior to other models. The results of external validation supported the possibility that the model can be used in clinical work. Finally, our survival grouping and personalized prediction system provided more accurate prognostic information for patients than traditional FIGO stages.

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. Univariable analysis and multivariable logistic regression analyses were used to explore the independent risk factors for hemorrhage (300 mL or greater) during a cesarean scar ectopic pregnancy surgical procedure. The model was internally validated with a separate cohort. Receiver operating characteristic curve methodology was used to identify optimal thresholds for the identified risk factors to further classify cesarean scar ectopic pregnancy risk, and the recommended operative treatment was established for each classification group by expert consensus. A final cohort of patients from 2014 to 2022 were classified according to the new classification system, and the recommended surgical procedure and clinical outcomes were abstracted from the medical record. RESULTS: Overall, 955 patients with first-trimester cesarean scar ectopic pregnancy were included; 273 were used to develop a model to predict intraoperative hemorrhage with cesarean scar ectopic pregnancy, and 118 served as an internal validation group for the model. Anterior myometrium thickness at the scar (adjusted odds ratio [aOR] 0.51, 95% CI 0.36-0.73) and average diameter of the gestational sac or mass (aOR 1.10, 95% CI 1.07-1.14) were independent risk factors for intraoperative hemorrhage of cesarean scar ectopic pregnancy. Five clinical classifications of cesarean scar ectopic pregnancy were established on the basis of the thickness and gestational sac diameter, and the optimal surgical option for each type was recommended by clinical experts. When the classification system was applied to a separate cohort of 564 patients with cesarean scar ectopic pregnancy, the overall success rate of recommended first-line treatment with the new classification grouping was 97.5% (550/564). No patients needed to undergo hysterectomy. Eighty-five percent of patients had a negative serum ß-hCG level within 3 weeks after the surgical procedure; 95.2% of patients resumed their menstrual cycles within 8 weeks. CONCLUSION: Anterior myometrium thickness at the scar and the diameter of the gestational sac were confirmed to be independent risk factors for intraoperative hemorrhage during cesarean scar ectopic pregnancy treatment. A new clinical classification system based on these factors with recommended surgical strategy resulted in high treatment success rates with minimal complications.

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

Introduction: Ubiquitination is involved in many biological processes and its predictive value for prognosis in cervical cancer is still unclear. Methods: 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. Machine learning was further used to select the DURGs. Then, we constructed and validated a reliable prognostic gene signature by multivariate analysis. In addition, we predicted the substrate proteins of the signature genes and did a functional analysis to further understand the molecular biology mechanisms. The study provided new guidelines for evaluating cervical cancer prognosis and also suggested new directions for drug development. Results: By analyzing 1,390 URGs in GEO and TCGA databases, we obtained 175 DURGs. Our results showed 19 DURGs were related to prognosis. Finally, eight DURGs were identified via machine learning to construct the first ubiquitination prognostic gene signature. Patients were stratified into high-risk and low-risk groups and the prognosis was worse in the high-risk group. In addition, these gene protein levels were mostly consistent with their transcript level. According to the functional analysis of substrate proteins, the signature genes may be involved in cancer development through the transcription factor activity and the classical P53 pathway ubiquitination-related signaling pathways. Additionally, 71 small molecular compounds were identified as potential drugs. Conclusion: We systematically studied the influence of ubiquitination-related genes on prognosis in cervical cancer, established a prognostic model through a machine learning algorithm, and verified it. Also, our study provides a new treatment strategy for cervical cancer.

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. LASSO regression analysis was conducted to identify potential risk factors. In conjunction with LASSO and multivariate analysis, the nomogram incorporated three variables, including age, tumour size, and AJCC stage for OS. The c-index was 0.794 and 0.831 in development and validated cohorts, indicating that this prediction model showed adequate discriminative ability in the development cohort. Besides, calibration curves showed good concordance for the development cohort, as well as the validation cohort. We constructed a first-of-its-kind nomogram to predict cervical mucinous adenocarcinomas OS and it showed better performance than AJCC and FIGO stages. Patients with cervical mucinous adenocarcinoma might benefit from using this model to develop tailored treatments.IMPACT STATEMENTWhat is already known on this subject? Cervical cancer has a variety of pathological types. The biological behaviour of each type is different, and the prognosis is quite different.What do the results of this study add? We analysed and explored the relevant factors affecting the prognosis of cervical mucinous adenocarcinoma.What are the implications of these findings for clinical practice and/or further research? Through the analysis of the SEER dataset, the prognostic factors affecting cervical mucinous adenocarcinoma were identified, and the first predictive model was created to predict the prognosis to help doctors develop individualised treatment plans and follow-up plans.

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. Results: Lymphovascular invasion (LVI), tumor size ≥ 4 cm, and depth of cervical stromal infiltration were independent predictors of LNM in cervical AC. However, the Silva pattern was not found to be a significant predictor in the multivariate model. The Silva pattern was still included in the model based on the improved predictive performance of the model observed in the previous studies. The concordance index (C-index) of the model increased from 0.786 to 0.794 after the inclusion of the Silva pattern. The Silva pattern was found to be the strongest predictor of LNM among all the pathological factors investigated, with an OR of 4.37 in the nomogram model. The nomogram developed by incorporation of these four predictors performed well in terms of discrimination and calibration capabilities (C - index = 0.794; 95% confidence interval (CI), 0.727-0.862; Brier score = 0.127). Decision curve analysis demonstrated that the nomogram was clinically effective in the prediction of LNM. Conclusion: In this study, a nomogram was developed based on the pathologic features, which helped to screen individuals with a higher risk of occult LNM. As a result, this tool may be specifically useful in the management of individuals with cervical AC and help gynecologists to guide clinical individualized treatment plan.

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. A total of 447 endogenic CSP patients out of 527 patients from 4 medical centres in China were enrolled in this study. A total of 120 patients were treated with methotrexate (MTX) followed by surgery, 106 received ultrasound-guided curettage directly and 221 received curettage combined with hysteroscopy. The clinical information and clinical outcomes of these patients were reviewed. Successful treatment was defined as (1) no additional treatment needed, (2) no retained mass of conception and (3) serum ß subunit of human chorionic gonadotropin (ß-hCG) level returning to a normal level within 4 weeks. The success rate was analysed based on these factors. RESULT: Among 447 patients, no significant difference was observed in baseline characteristics between groups except for foetal heartbeat. The success rate was significantly different (p<0.001) among the three groups. The highest success rate of 95.9% was noted in the hysteroscopy group, and the lowest success rate of 84.0% was noted in the curettage group. In addition, the MTX group reported the longest hospital stay and highest expenses, but the curettage group showed the shortest and lowest expenses, respectively. Nevertheless, no difference in blood loss was observed between the groups. CONCLUSION: The combination of curettage and hysteroscopy represents the most effective strategy. Pretreatment with MTX did not result in better clinical outcomes. Ultrasound-guided curettage directly should not be considered a first-line treatment choice for endogenic CSP patients.

Integration of Quantum Confinement and Alloy Effect to Modulate Electronic Properties of RhW Nanocrystals for Improved Catalytic Performance toward CO2 Hydrogenation.

The d-band center and surface negative charge density generally determine the adsorption and activation of CO2, thus serving as important descriptors of the catalytic activity toward CO2 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 CO2 into methanol, the catalytic activity of Rh75W25 nanosheets was 5.9, 4.0, and 1.7 times as high as that of Rh nanoparticles, Rh nanosheets, and Rh73W27 nanoparticles, respectively. Mechanistic studies reveal that the remarkable activity of Rh75W25 nanosheets is owing to the integration of quantum confinement and alloy effect. Specifically, the quantum confinement in one dimension shifts up the d-band center of Rh75W25 nanosheets, strengthening the adsorption of CO2. Moreover, the alloy effect not only promotes the activation of CO2 to form CO2δ- but also enhances the adsorption of intermediates to facilitate further hydrogenation of the intermediates into methanol.

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. In this paper, we review the theory for obtaining optimal and stable strategies for symmetric evolutionary games, and provide some new proofs and computational methods. In particular, we review the relationship between the symmetric evolutionary game and the generalized knapsack problem, and discuss the first and second order necessary and sufficient conditions that can be derived from this relationship for testing the optimality and stability of the strategies. Some of the conditions are given in different forms from those in previous work and can be verified more efficiently. We also derive more efficient computational methods for the evaluation of the conditions than conventional approaches. We demonstrate how these conditions can be applied to justifying the strategies and their stabilities for a special class of genetic selection games including some in the study of genetic disorders.