A comprehensive bibliometric analysis (2000-2022) on the mapping of knowledge regarding immunotherapeutic treatments for advanced, recurrent, or metastatic cervical cancer.

Background: Despite extensive literature on therapeutic strategies for cervical cancer, a bibliometric analysis specifically focused on immunotherapy for advanced, recurrent, or metastatic (A/R/M) cervical malignancies remains unexplored. This study aims to address this gap by presenting a comprehensive overview that includes general characteristics, research focal points, the trajectory of evolution, and current emerging trends in this under-researched area. Methods: A systematic search was conducted using the Web of Science Core Collection (WOSCC) to identify articles related to A/R/M cervical cancer published between 2000 and 2022. Citespace and VOS viewer were the primary tools used to identify research focal points, intriguing future patterns, and to evaluate contributions and co-occurrences among authors, institutions, countries, and journals. Results: A total of 1,001 original articles were identified, involving 6,387 authors from 66 countries and 1,474 institutions, and published across 366 academic journals. The United States contributed most significantly. The most productive researcher was Van der Burg SH from Leiden University Medical Center. The International Journal of Cancer and Cancer Research were identified as the most productive and influential journals, respectively. Analysis of co-citation clusters highlighted 25 clusters, primarily focusing on potential predictive biomarkers, dendritic cell-based tumor vaccines, therapeutic HPV vaccinations, peptide-based cancer vaccines, tumor immune microenvironments, and adoptive cell transfer (ACT). The latest significant trends in A/R/M cervical cancer immunotherapy research included ACT, CAR-T, and immune checkpoint inhibitors (ICIs), as revealed by keyword and reference burst detection. Conclusion: This pioneering study provides a detailed landscape of immunotherapy research in A/R/M cervical cancer. It underscores the importance of global collaboration, enriches our understanding of the immunology of A/R/M cervical cancer, expands on potential beneficiaries of immunotherapy, and explores clinical applications of various therapies, including therapeutic vaccines, adoptive cell transfer, and ICIs, particularly in combination with established treatments such as chemotherapy, radiotherapy, and targeted therapy.

Characteristics of germline DNA damage response gene mutations in ovarian cancer in Southwest China.

DNA damage response (DDR) pathways are responsible for repairing endogenous or exogenous DNA damage to maintain the stability of the cellular genome, including homologous recombination repair (HRR) pathway, mismatch repair (MMR) pathway, etc. In ovarian cancer, current studies are focused on HRR genes, especially BRCA1/2, and the results show regional and population differences. To characterize germline mutations in DDR genes in ovarian cancer in Southwest China, 432 unselected ovarian cancer patients underwent multi-gene panel testing from October 2016 to October 2020. Overall, deleterious germline mutations in DDR genes were detected in 346 patients (80.1%), and in BRCA1/2 were detected in 126 patients (29.2%). The prevalence of deleterious germline mutations in BRCA2 is higher than in other studies (patients are mainly from Eastern China), and so is the mismatch repair genes. We identified three novel BRCA1/2 mutations, two of which probably deleterious (BRCA1 p.K1622* and BRCA2 p.L2987P). Furthermore, we pointed out that deleterious mutations of FNACD2 and RECQL4 are potential ovarian cancer susceptibility genes and may predispose carriers to ovarian cancer. In conclusion, our study highlights the necessity of comprehensive germline mutation detection of DNA damage response genes in ovarian cancer patients, which is conducive to patient management and genetic counseling.

Real-Time Spatiotemporal Measurement of Extracellular Signaling Molecules Using an Aptamer Switch-Conjugated Hydrogel Matrix.

Cells rely on secreted signaling molecules to coordinate essential biological functions including development, metabolism, and immunity. Unfortunately, such signaling processes remain difficult to measure with sufficient chemical specificity and temporal resolution. To address this need, an aptamer-conjugated hydrogel matrix that enables continuous fluorescent measurement of specific secreted analytes - in two dimensions, in real-time is developed. As a proof of concept, real-time imaging of inter-cellular cyclic adenosine 3',5'-monophosphate (cAMP) signals in Dictyostelium discoideum amoeba cells is performed. A set of aptamer switches that generate a rapid and reversible change in fluorescence in response to cAMP signals is engineered. By combining multiple switches with different dynamic ranges, measure cAMP concentrations spanning three orders of magnitude in a single experiment can be measured. These sensors are embedded within a biocompatible hydrogel on which cells are cultured and their cAMP secretions can be imaged using fluorescent microscopy. Using this aptamer-hydrogel material system, the first direct measurements of oscillatory cAMP signaling that correlate closely with previous indirect measurements are achieved. Using different aptamer switches, this approach can be generalized for measuring other secreted molecules to directly visualize diverse extracellular signaling processes and the biological effects that they trigger in recipient cells.

Non-apoptotic cell death in ovarian cancer: Treatment, resistance and prognosis.

Ovarian cancer is mostly diagnosed at an advanced stage due to the absence of effective screening methods and specific symptoms. Repeated chemotherapy resistance and recurrence before PARPi are used as maintenance therapies, lead to low survival rates and poor prognosis. Apoptotic cell death plays a crucial role in ovarian cancer, which is proved by current researches. With the ongoing development of targeted therapy, non-apoptotic cell death has shown substantial potential in tumor prevention and treatment, including autophagy, ferroptosis, necroptosis, immunogenic cell death, pyroptosis, alkaliptosis, and other modes of cell death. We systematically reviewed the research progress on the role of non-apoptotic cell death in the onset, development, and outcome of ovarian cancer. This review provides a more theoretical basis for exploring therapeutic targets, reversing drug resistance in refractory ovarian cancer, and establishing risk prediction models that help realize the clinical transformation of vital drugs.

Real-time monitoring of drug pharmacokinetics within tumor tissue in live animals.

The efficacy and safety of a chemotherapy regimen fundamentally depends on its pharmacokinetics. This is currently measured based on blood samples, but the abnormal vasculature and physiological heterogeneity of the tumor microenvironment can produce radically different drug pharmacokinetics relative to the systemic circulation. We have developed an implantable microelectrode array sensor that can collect such tissue-based pharmacokinetic data by simultaneously measuring intratumoral pharmacokinetics from multiple sites. We use gold nanoporous microelectrodes that maintain robust sensor performance even after repeated tissue implantation and extended exposure to the tumor microenvironment. We demonstrate continuous in vivo monitoring of concentrations of the chemotherapy drug doxorubicin at multiple tumor sites in a rodent model and demonstrate clear differences in pharmacokinetics relative to the circulation that could meaningfully affect drug efficacy and safety. This platform could prove valuable for preclinical in vivo characterization of cancer therapeutics and may offer a foundation for future clinical applications.

MediMLP: Using Grad-CAM to Extract Crucial Variables for Lung Cancer Postoperative Complication Prediction.

Lung cancer postoperative complication prediction (PCP) is significant for decreasing the perioperative mortality rate after lung cancer surgery. In this paper we concentrate on two PCP tasks: (1) the binary classification for predicting whether a patient will have postoperative complications; and (2) the three-class multi-label classification for predicting which postoperative complication a patient will experience. Furthermore, an important clinical requirement of PCP is the extraction of crucial variables from electronic medical records. We propose a novel multi-layer perceptron (MLP) model called medical MLP (MediMLP) together with the gradient-weighted class activation mapping (Grad-CAM) algorithm for lung cancer PCP. The proposed MediMLP, which involves one locally connected layer and fully connected layers with a shortcut connection, simultaneously extracts crucial variables and performs PCP tasks. The experimental results indicated that MediMLP outperformed normal MLP on two PCP tasks and had comparable performance with existing feature selection methods. Using MediMLP and further experimental analysis, we found that the variable of "time of indwelling drainage tube" was very relevant to lung cancer postoperative complications.