A bibliometric worldview of breast-conserving surgery for breast cancer from 2013 to 2023.

Over the last decade, significant advancements have been made in breast-conserving surgery (BCS) for breast cancer. However, there is a lack of analytical and descriptive investigations on the trajectory, essential research directions, current research scenario, pivotal investigative focuses, and forthcoming perspectives. The objective of this research is to provide a thorough update on the progress made in BCS for breast cancer over the preceding decade. Retrieved from the Web of Science database, the data span from January 1, 2013, to November 30, 2023. Utilizing a set of advanced analytical instruments, we conducted comprehensive bibliometric and visual analyses. The findings underscore the predominant influence of the USA, representing 35.77% of the overall publications and playing a pivotal role in shaping research within this field. Notable productivity was evident at various institutions, including the Memorial Sloan Kettering Cancer Center, the University of Texas MD Anderson Cancer Center, and the University of Toronto. Annals of Surgical Oncology contributed the most publications in this field. An examination of keywords indicated a change in the concentration of research attention, transitioning from molecular subtype, ultrasonography, and intraoperative aspects to SEER, male breast cancer, and adjuvant measures. By offering a comprehensive bibliometric assessment, this study enhances our understanding of BCS for breast cancer. Consequently, this benefits both experienced researchers and newcomers alike, providing prompt access to essential information and fostering the extraction of innovative concepts within this specific field.

Effect of neoadjuvant chemoradiotherapy with or without PD-1 antibody sintilimab in pMMR locally advanced rectal cancer: A randomized clinical trial.

Neoadjuvant chemoradiotherapy (NACRT) was the standard treatment for patients with locally advanced rectal cancer (LARC) with proficient mismatch repair (pMMR) proteins. In this randomized phase 2 trial (ClinicalTrial.gov: NCT04304209), 134 pMMR LARC patients were randomly (1:1) assigned to receive NACRT or NACRT and the programmed cell death protein 1 (PD-1) antibody sintilimab. As the primary endpoint, the total complete response (CR) rate is 26.9% (18/67, 95% confidence interval [CI] 16.0%-37.8%) and 44.8% (30/67, 95% CI 32.6%-57.0%) in the control and experimental arm, respectively, with significant difference (p = 0.031 for chi-squared test). Response ratio is 1.667 (95% CI 1.035-2.683). Immunohistochemistry shows PD-1 ligand 1 (PD-L1) combined positive score is associated with the synergistic effect. The safety profile is similar between the arms. Adding the PD-1 antibody sintilimab to NACRT significantly increases the CR rate in pMMR LARC, with a manageable safety profile. PD-L1 positivity may help identify patients who might benefit most from the combination therapy.

Effects of Proprioceptive Neuromuscular Facilitation Technique on Balance Function and Muscle Health in Older Adults With High Fall Risk.

PURPOSE: Older adults frequently encounter health challenges, such as impaired balance and muscle health, which increase risk of falls. The current study investigated the effectiveness of the proprioceptive neuromuscular facilitation (PNF) technique in improving balance and muscle health among older adults with high fall risk. METHOD: A total of 160 older adults with high fall risk were randomized into control and intervention groups. Over 6 months, the control group received standard interventions, while the intervention group received the same interventions and additional PNF training. RESULTS: Both groups demonstrated improvements in balance function over time, with the intervention group exhibiting significant improvements in Berg Balance Scale scores, Timed Up and Go test times, and 30-Second Chair Stand Test counts (p < 0.05). Bone density significantly increased in the intervention group compared to the control group (p < 0.05), although no substantial differences in lower limb muscle mass were observed. Satisfaction rates were higher and fall incidents fewer in the intervention group. CONCLUSION: The PNF technique is effective in enhancing balance function and muscle health in older adults with high fall risk, demonstrating potential in reducing fall risk and improving quality of life among older adults. [Journal of Gerontological Nursing, 50(8), 37-44.].

Pyrolytic Depolymerization of Polyolefins Catalyzed by Zirconium-based UiO-66 Metal-Organic Frameworks.

Polyolefins such as polyethylenes and polypropylenes are the most-produced plastic waste globally, yet are difficult to convert into useful products due to their unreactivity. Pyrolysis is a practical method for large-scale treatment of mixed, contaminated plastic, allowing for their conversion into industrially-relevant petrochemicals. Metal-organic frameworks (MOFs), despite their tremendous utility in heterogenous catalysis, have been overlooked for polyolefin depolymerization due to their perceived thermal instabilities and inability of polyethylenes and polypropylenes to penetrate their pores. Herein, we demonstrate the viability of UiO-66 MOFs containing coordinatively-unsaturated zirconia nodes, as effective catalysts for pyrolysis that significantly enhances the yields of valuable liquid and gas hydrocarbons, whilst halving the amounts of residual solids produced. Reactions occur on the Lewis-acidic UiO-66 zirconia nodes, without the need for noble metals, and yields aliphatic product distributions distinctly different from the aromatic-rich hydrocarbons from zeolite catalysis. We also demonstrate the first unambiguous characterization of polyolefin penetration into UiO-66 pores at pyrolytic temperatures, allowing access to the abundant Zr-oxo nodes within the MOF interior for efficient C-C cleavage. Our work highlights the potential of MOFs as highly-designable heterogeneous catalysts for depolymerization of plastics which can complement conventional catalysts in reactivity.

Maternal smoking during pregnancy could accelerate aging in the adulthood: evidence from a perspective study in UK Biobank.

BACKGROUND: Maternal smoking during pregnancy (MSDP) is significantly linked to the short- or long-term health of offspring. However, little research has examined whether MSDP affect the aging rate of offspring. METHODS: This study used questionnaires to determine out whether the participants' mothers smoked when they were pregnant. For evaluating aging rate, we used the following several outcome measures: telomere length, frailty index, cognitive function, homeostatic dysregulation score, KDM-age, age-related hospitalization rate, premature death, and life expectancy. RESULT: After adjusting for covariates, we found that the offspring of the MSDP group had significantly shorter telomere length in adulthood by 0.8 % (ß = -0.008,95%CI:-0.009 to -0.006) compared with non-MSDP group. Compared to the non-MSDP group, participants in MSDP group showed higher levels of homeostatic dysregulation (ß = 0.015,95%CI: 0.007-0.024) and were frailer (ß = 0.008,95%CI:0.007-0.009). The KDM age increased by 0.100 due to MSDP (ß = 0.100,95 % CI:0.018-0.181), and the age acceleration of KDM algorithm also increases significantly (ß = 0.101, 95%CI:0.020-0.183). Additionally, we found that the risk of aging-related hospitalizations was significantly higher than the non-MSDP group by 10.4 %(HR = 1.104,95%CI:1.066-1.144). Moreover, MSDP group had a 12.2 % increased risk of all-cause premature mortality (HR = 1.122,95%CI:1.064-1.182) and a significant risk of lung cancer-specific premature mortality increased by 55.4 %(HR = 1.554,95%CI:1.346-1.793). In addition, participants in the MSDP group had significantly decreased cognitive function and shorter life expectancies than those in non-MSDP group. CONCLUSION: Our findings indicated a significant association between MSPD and accelerated aging, elevated hospitalization rates, increased premature mortality rates, and reduced life expectancies in offspring.

Study on the establishment of air pollutant and carbon emission inventory and collaborative emission reduction potential of China's coking industry from 2012 to 2022.

Coking industry is usually regarded as a high pollution and high energy consumption industry. China is accelerating its efforts to reduce pollution and carbon emissions in the industrial sector, which has received little attention as the world's largest producer of coke. Therefore, in this study, the trend of air pollution and carbon emissions in China's coking industry and the path of coordinated emission reduction were studied. The results indicate that the average annual emissions of PM, SO2, NOx, VOCs, and CO2 in China's coking industry from 2012 to 2022 amount to 205.98, 69.47, 193.45, 599.80 Gg and 191.10 Tg, respectively. The main sources of PM, SO2, NOx, VOCs and CO2 in coking industry were coal preparation (51.5 %), charge and pushing (39.5 %), coke oven gas (99.8 %), byproduct recovery (47.0 %) and fuel combustion (87.5 %). The emissions from coking plants in central and southern Shanxi, eastern and southern Hebei, and central Shandong are the most concentrated. Ultra-low emission transformation and deep treatment of VOCs have greatly reduced pollutant emissions in key areas of air pollutant control, but the actual emission reduction effect of these measures has been weakened by the additional emissions caused by the increase of coke production in other non-key areas. The research on synergetic emission reduction path shows that there is a great synergistic benefit between air pollutants and CO2 emission reduction in coking industry. It is estimated that the APeq (air pollutants and carbon equivalent) of China's coking industry in 2025, 2028 and 2030 will decrease by 38.2 %, 63.5 % and 70.8 % respectively compared with 2022. With the continuous promotion of pollution reduction and carbon reduction measures, the emission reduction potential of China's coking industry will gradually shift from key areas to non-key areas.

Asynchronous Control of 2-D Markov Jump Roesser Systems With Nonideal Transition Probabilities.

This article intends to study the asynchronous control problem for 2-D Markov jump systems (MJSs) with nonideal transition probabilities (TPs) under the Roesser model. Two practical considerations motivate the current work. First, considering that the system mode cannot always be observed accurately, a hidden Markov model (HMM) is adopted to describe the relationship between the mismatched modes. Second, considering that the TPs information related to the Markov process and the observation process is difficult to obtain, the nonideal TPs (unknown or uncertain) are simultaneously considered on the two processes. Under the considerations, several new sufficient conditions are developed for concerned closed-loop 2-D MJSs with nonideal TPs, by which the asymptotic mean square stability is ensured with an H∞ performance index. A nonconservative separation strategy is utilized to decouple the system mode TPs and the observation TPs to facilitate the analysis of nonideal TPs. An unified LMI-based condition is finally developed for the concerned closed-loop 2-D MJSs with/without nonideal TPs, showing more satisfactory conservatism than that in the literature. In the end, we present two examples to validate the superiority of the proposed design method.

Advances in the study of ferroptosis and its relationship to autoimmune diseases.

Ferroptosis represents a novel mode of programmed cell death characterized by the intracellular accumulation of iron and lipid peroxidation, culminating in oxidative stress and subsequent cell demise. Mounting evidence demonstrates that ferroptosis contributes significantly to the onset and progression of diverse pathological conditions and diseases, including infections, neurodegenerative disorders, tissue ischemia-reperfusion injury, and immune dysregulation. Recent investigations have underscored the pivotal role of ferroptosis in the pathogenesis of rheumatoid arthritis, ulcerative colitis, systemic lupus erythematosus, and asthma. This review provides a comprehensive overview of the current understanding of the regulatory mechanisms governing ferroptosis, particularly its interplay with iron, lipid, and amino acid metabolism. Furthermore, we explore the implications of ferroptosis in autoimmune diseases and deliberate on its potential as a promising therapeutic target for diverse autoimmune disorders.

The short- and mid-term efficacy and safety of elagolix in the management of pain associated with endometriosis: A systematic review and meta-analysis.

BACKGROUND: Elagolix, an approved non-peptide GnRH antagonist, shows promise in relieving endometriosis-related pain, but its short- and mid-term efficacy and potential side effects are still under investigation. OBJECTIVE: The aim is to provide data for therapeutic applications by methodically evaluating elagolix's safety and effectiveness in treating endometriosis-related pain. METHODS: Databases such as PubMed, Embase, Cochrane Library, Web of Science, ClinicalTrials.gov, and others were thoroughly searched. The search time was from the establishment date to September 2023. The study included randomized controlled trials (RCTs) that compared the efficacy of elagolix versus placebo in treating endometriosis-associated pain. After data extraction and literature scanning, quality assessment was carried out using Quality evaluation was carried out using the bias risk assessment tool suggested by the Cochrane Reviewers' Handbook 5.1.0 after literature screening and data extraction. Stata 15.0 was used to do the meta-analysis. RESULTS: In total, five RCTs involving 2056 patients were included in the analysis. The meta-analysis demonstrated a significant superiority of elagolix over placebo in the management of endometriosis-related pain, specifically in endometriosis pain [WMD=-0.77, 95% CI (-1.00, -0.53), P<0.001], as well as in non-menstrual pelvic pain, daily assessment of dysmenorrhea (DYS), and dyspareunia (DYSP), all of which are associated with endometriosis. Regarding safety, no discernible variation was observed in the incidence of serious adverse responses between the elagolix and placebo groups [RR=0.90, 95% CI (0.58, 1.40), P=0.643]. Conversely, the elagolix group exhibited a significantly higher incidence rate of general adverse responses [RR = 1.34, 95% CI (1.18, 1.52), P<0.001] compared to the control group. CONCLUSIONS: The efficacy of elagolix in reducing pain in premenopausal women with endometriosis has been demonstrated over the short- to mid-term. However, careful monitoring for potential adverse effects is essential throughout the treatment duration.

Research progress in stroke risk perception assessment tool.

BACKGROUND: One of the main illnesses in the globe that causes impairment and death in people is stroke. In the globe today, it ranks as the second leading cause of death and the leading cause of death in China. OBJECTIVE: This paper analyses into the critical role of risk perception in developing individual awareness of stroke risk and encouraging proactive preventive health behaviors, essential for effective primary stroke prevention strategies and reduced stroke incidence. It discusses the concept of risk perception, the content and dimensions of global stroke assessment tools, and their application status, aiming to provide insights for their development and intervention research. METHODS: Risk perception encompasses subjective assessments of stroke likelihood and severity, influenced by personal experiences, knowledge of risk factors, beliefs about prevention effectiveness, and emotional responses. Global stroke assessment tools, like the Framingham Stroke Risk Score and CHA2DS2-VASc Score, evaluate stroke risk based on factors such as age, gender, blood pressure, and cholesterol levels. In order to improve risk perception and proactive health management and lower the burden of strokes, the paper assesses the advantages and disadvantages of these tools and makes recommendations for improving accessibility, customizing interventions, running educational campaigns, promoting multidisciplinary collaboration, and integrating technology. RESULTS: By combining the research tools of stroke risk perception, it is found that the evaluation tools are mostly single-dimensional evaluation tools centered on the two dimensions of onset possibility and susceptibility. CONCLUSION: Some scholars have developed multi-dimensional evaluation tools, but the evaluation population is relatively limited, and the evaluation system lacks comprehensiveness and systematization.

TNC upregulation promotes glioma tumourigenesis through TDG-mediated active DNA demethylation.

Gliomas represent the most predominant primary malignant tumor in central nervous system. Thymine DNA glycosylase (TDG) is a central component in active DNA demethylation. However, the specific mechanisms of TDG-mediated active DNA demethylation in gliomas remain unclear. This research indicates TDG expression is overexpressed in gliomas and correlated with poor prognosis. TDG knockdown suppressed the malignant phenotype of gliomas both in vitro and vivo. Notably, RNA-seq analysis revealed a strong association between TDG and tenascin-C (TNC). ChIP-qPCR and MeDIP-qPCR assays were undertaken to confirm that TDG participates in TNC active DNA demethylation process, revealing decreased DNA methylation levels and elevated TNC expression as a result. Silencing TNC expression also suppressed the tumor malignant phenotype in both in vitro and in vivo experiments. Additionally, simultaneous silencing of TNC reduced or even reversed the glioma promotion caused by TDG overexpression. Based on our findings, we conclude that TDG exerts an indispensable role in TNC active DNA demethylation in gliomas. The DNA demethylation process leads to alternations in TNC methylation levels and promotes its expression, thereby contributing to the development of gliomas. These results suggest a novel epigenetic therapeutic strategy targeting active DNA demethylation in gliomas.

Impact of multi-parameter images obtained from dual-energy CT on radiomics to predict pathological grading of bladder urothelial carcinoma.

OBJECTIVE: To investigate the effect of radiomics models obtained from dual-energy CT (DECT) material decomposition images and virtual monoenergetic images (VMIs) in predicting the pathological grading of bladder urothelial carcinoma (BUC). MATERIALS AND METHODS: A retrospective analysis of preoperative DECT examination was conducted on 112 patients diagnosed with BUC. This cohort included 76 cases of high-grade urothelial carcinoma and 36 cases of low-grade urothelial carcinoma. DECT can provide material decomposition images of venous phase Iodine maps and Water maps based on the differences in attenuation of substances, as well as VMIs at 40 to 140 keV (interval 10 keV). A total of 13 image sets were obtained, and radiomics features were extracted and analyzed from each set to achieve preoperative prediction of BUC. The best features related to BUC were identified by recursive feature elimination (RFE), the Minimum Redundancy Maximum Relevance (mRMR), and the Least Absolute Shrinkage and Selection Operator (LASSO) in order. A five-fold cross-validation method was used to divide the samples into training and testing sets, and models for pathological prediction of BUC grading were constructed by a random forest (RF) classifier. Receiver operating curves (ROC) were plotted to evaluate the performance of 13 models obtained from each image set. RESULTS: Despite the notable differences in the best radiomics features chosen from each image set, all the features selected from 40 to 100 keV VMIs included the Dependence Variance of the GLDM feature set. There were no statistically significant differences in the area under the curve (AUC) between the training set and the testing set for all 13 models. In the testing set, the AUCs of the models established through 40 keV to 140 keV (interval of 10 keV) image sets were 0.895, 0.874, 0.855, 0.889, 0.841, 0.868, 0.852, 0.847, 0.889, 0.887 and 0.863 respectively. The AUCs for the models established using the Iodine maps and Water maps image sets were 0.873 and 0.852, respectively. CONCLUSION: Despite the differences in the selected radiomic features from DECT multi-parameter images, the performance of radiomics models in predicting the pathological grading of BUC was not affected by the variations in the types of images used for model training.

Kinetic understanding of lithium metal electrodeposition for lithium anodes.

Lithium, a representative alkali metal, holds the coveted status of the "holy grail" in the realm of next-generation rechargeable batteries, owing to its remarkable theoretical specific capacity and low electrode potential. However, the inherent reactivity of Li metal inevitably results in the formation of the solid-electrolyte interphase (SEI) on its surface, adding complexity to the Li electrodeposition process compared to conventional metal electrodeposition. Attaining uniform Li deposition is crucial for ensuring stable, long-cycle performance and high Coulombic efficiency in Li metal batteries, which requires a comprehensive understanding of the underlying factors governing the electrodeposition process. This review delves into the intricate kinetics of Li electrodeposition, elucidating the multifaceted factors that influence charge and mass transfer kinetics. The intrinsic relationship between charge transfer kinetics and Li deposition is scrutinized, exploring how parameters such as current density and electrode potential impact Li nucleation and growth, as well as dendrite formation. Additionally, the applicability of classical mass-transfer-controlled electrodeposition models to Li anode systems is evaluated, considering the influence of ionic concentration and solvation structure on Li+ transport, SEI formation, and subsequent deposition kinetics. The pivotal role of SEI compositional structure and physicochemical properties in governing charge and mass transfer processes is underscored, with an emphasis on strategies for regulating Li deposition kinetics from both electrolyte and SEI perspectives. Finally, future directions in Li electrodeposition research are outlined, emphasizing the importance of ongoing exploration from a kinetic standpoint to fully unlock the potential of Li metal batteries.

Identification of Dementia & Mild Cognitive Impairment in Chinese Elderly Using Machine Learning.

OBJECTIVE: To assess the role of Machine Learning (ML) in identification critical factors of dementia and mild cognitive impairment. METHODS: 371 elderly individuals were ultimately included in the ML analysis. Demographic information (including gender, age, parity, visual acuity, auditory function, mobility, and medication history) and 35 features from 10 assessment scales were used for modeling. Five machine learning classifiers were used for evaluation, employing a procedure involving feature extraction, selection, model training, and performance assessment to identify key indicative factors. RESULTS: The Random Forest model, after data preprocessing, Information Gain, and Meta-analysis, utilized three training features and four meta-features, achieving an area under the curve of 0.961 and a accuracy of 0.894, showcasing exceptional accuracy for the identification of dementia and mild cognitive impairment. CONCLUSIONS: ML serves as a identification tool for dementia and mild cognitive impairment. Using Information Gain and Meta-feature analysis, Clinical Dementia Rating (CDR) and Neuropsychiatric Inventory (NPI) scale information emerged as crucial for training the Random Forest model.

Preliminary Study on Rapid Serial Visualization Presentation Multi-Class Target EEG Classification.

OBJECTIVE: Brain-Computer Interface (BCI) provides a direct communication channel between the brain and external devices. After combining with the Rapid Serial Visualization Presentation (RSVP) paradigm, the RSVP-BCI system can be utilized for human vision-based fast information retrieval. Currently only binary classification of single-trial EEG can be achieved, also the research on the multi-class target RSVP is few, which limited information transfer rate and the application scenarios of the system. In this paper, we focus on the RSVP multi-class target image retrieval task that contains two classes of targets for achieving triple classification for RSVP-EEG. METHODS: Designed two experiments, each containing two tasks with different task difficulties. We recruited 30 subjects to participate in the experiments, collected EEG data, and made the data publicly available. Moreover, we conducted behavioral analysis, ERP analysis, and proposed a model, MDCNet, for EEG classification to study the feasibility of multi-class target RSVP and the impact of task difficulty. RESULTS: The experimental results indicated that (1) RSVP-EEG classification that includes non-target and 2-class target is feasibility; (2) the different targets in the same task will evoke P300 with the same latency and different amplitudes, and the hit rate of the target in EEG classification is positively correlated with its amplitude; (3) the information hidden in the time dimension play an important role in EEG classification; (4) the harder the task is, the latency of P300 is longer. CONCLUSION/SIGNIFICANCE: The experimental analysis obtained meaningful results, which provided a theoretical basis for subsequent research.

Deep Learning and Histogram-Based Grain Size Analysis of Images.

Grain size analysis is used to study grain size and distribution. It is a critical indicator in sedimentary simulation experiments (SSEs), which aids in understanding hydrodynamic conditions and identifying the features of sedimentary environments. Existing methods for grain size analysis based on images primarily focus on scenarios where grain edges are distinct or grain arrangements are regular. However, these methods are not suitable for images from SSEs. We proposed a deep learning model incorporating histogram layers for the analysis of SSE images with fuzzy grain edges and irregular arrangements. Firstly, ResNet18 was used to extract features from SSE images. These features were then input into the histogram layer to obtain local histogram features, which were concatenated to form comprehensive histogram features for the entire image. Finally, the histogram features were connected to a fully connected layer to estimate the grain size corresponding to the cumulative volume percentage. In addition, an applied workflow was developed. The results demonstrate that the proposed method achieved higher accuracy than the eight other models and was highly consistent with manual results in practice. The proposed method enhances the efficiency and accuracy of grain size analysis for images with irregular grain distribution and improves the quantification and automation of grain size analysis in SSEs. It can also be applied for grain size analysis in fields such as soil and geotechnical engineering.