Endometrial hyperplasia (EH) is a condition originating from uterine endometrial glands undergoing disordered proliferation including the risk to progress to endometrial adenocarcinoma. In recent ...years, a steady increase in EH cases among younger women of reproductive age accentuates the demand of therapeutic alternatives, which emphasizes that an improved disease model for therapeutic agents evaluation is concurrently desired. Here, a new hormone‐induced EH mouse model was developed using a subcutaneous estradiol (E2)‐sustained releasing pellet, which elevates the serum E2 level in mice, closely mimicking the effect known as estrogen dominance with underlying, pathological E2 levels in patients. The onset and progression of EH generated within this model recapitulate a clinically relevant, pathological transformation, beginning with disordered proliferation developing to simple EH, advancing to atypical EH, and then progressing to precancerous stages, all following a chronologic manner. Although a general increase in nuclear progesterone receptor (PR) expression occurred after E2 expression, a total loss in PR was noted in some endometrial glands as disease advanced to simple EH. Furthermore, estrogen receptor (ER) expression in the nucleus of endometrial cells was reduced in disordered proliferation and increased when EH progressed to atypical EH and precancerous stages. This EH model also resembles other pathological patterns found in human disease such as leukocytic infiltration, genetic aberrations in β‐catenin, and joint phosphatase and tensin homolog/paired box gene 2 (PTEN/PAX2) silencing. In summary, this new and comprehensively characterized EH model is cost‐effective, easily reproducible, and may serve as a tool for preclinical testing of therapeutic agents and facilitate further investigation of EH.
We report the establishment of a new, hormone‐induced endometrial hyperplasia (EH) mouse model using a subcutaneous estradiol (E2)‐sustained release implant, elevating E2 serum levels in mice to closely mimic estrogen dominance leading to EH in human. This model authentically recapitulates EH development and progression, hormones receptor alteration, leukocytic infiltration, and genetic aberrations. This is the first report comprehensively characterizing E2‐induced EH mouse model providing a tool for therapeutic evaluations.
Background
Both nonoperative and operative treatments for spinal metastasis are expensive interventions. Patients' expected 3‐month survival is believed to be a key factor to determine the most ...suitable treatment. However, to the best of our knowledge, no previous study lends support to the hypothesis. We sought to determine the cost‐effectiveness of operative and nonoperative interventions, stratified by patients' predicted probability of 3‐month survival.
Methods
A Markov model with four defined health states was used to estimate the quality‐adjusted life years (QALYs) and costs for operative intervention with postoperative radiotherapy and radiotherapy alone (palliative low‐dose external beam radiotherapy) of spine metastases. Transition probabilities for the model, including the risks of mortality and functional deterioration, were obtained from secondary and our institutional data. Willingness to pay thresholds were prespecified at $100,000 and $150,000. The analyses were censored after 5‐year simulation from a health system perspective and discounted outcomes at 3% per year. Sensitivity analyses were conducted to test the robustness of the study design.
Results
The incremental cost‐effectiveness ratios were $140,907 per QALY for patients with a 3‐month survival probability >50%, $3,178,510 per QALY for patients with a 3‐month survival probability <50%, and $168,385 per QALY for patients with independent ambulatory and 3‐month survival probability >50%.
Conclusions
This study emphasizes the need to choose patients carefully and estimate preoperative survival for those with spinal metastases. In addition to reaffirming previous research regarding the influence of ambulatory status on cost‐effectiveness, our study goes a step further by highlighting that operative intervention with postoperative radiotherapy could be more cost‐effective than radiotherapy alone for patients with a better survival outlook. Accurate survival prediction tools and larger future studies could offer more detailed insights for clinical decisions.
•Laboratory data is of prognostic value in predicting survival for spinal metastasis.•Machine-learning-based survival predicting model outperforms regression-based model.•Accurate survival prediction ...model aids patient-centered care for spinal metastasis.
Well-performing survival prediction models (SPMs) help patients and healthcare professionals to choose treatment aligning with prognosis. This retrospective study aims to investigate the prognostic impacts of laboratory data and to compare the performances of Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy (METSSS) model, New England Spinal Metastasis Score (NESMS), and Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) for spinal metastases (SM).
From 2010 to 2018, patients who received radiotherapy (RT) for SM at a tertiary center were enrolled and the data were retrospectively collected. Multivariate logistic and Cox-proportional-hazard regression analyses were used to assess the association between laboratory values and survival. The area under receiver-operating characteristics curve (AUROC), calibration analysis, Brier score, and decision curve analysis were used to evaluate the performance of SPMs.
A total of 2786 patients were included for analysis. The 90-day and 1-year survival rates after RT were 70.4% and 35.7%, respectively. Higher albumin, hemoglobin, or lymphocyte count were associated with better survival, while higher alkaline phosphatase, white blood cell count, neutrophil count, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, or international normalized ratio were associated with poor prognosis. SORG-MLA has the best discrimination (AUROC 90-day, 0.78; 1-year 0.76), best calibrations, and the lowest Brier score (90-day 0.16; 1-year 0.18). The decision curve of SORG-MLA is above the other two competing models with threshold probabilities from 0.1 to 0.8.
Laboratory data are of prognostic significance in survival prediction after RT for SM. Machine learning-based model SORG-MLA outperforms statistical regression-based model METSSS model and NESMS in survival predictions.
Patient-derived xenografts (PDXs) have clinical value but are time-, cost-, and labor-intensive and thus ill-suited for large-scale experiments. Here, we present a protocol to convert PDX tumors into ...PDxOs for long-term cultures amenable to moderate-throughput drug screens, including in-depth PDxO validation. We describe steps for PDxO preparation and mouse cell removal. We then detail PDxO validation and characterization and drug response assay. Our PDxO drug screening platform can predict therapy response in vivo and inform functional precision oncology for patients.
For complete details on the use and execution of this protocol, please refer to Guillen et al.1
Display omitted
•Protocol to establish long-term breast cancer organoids from patient-derived xenografts•Characterization and validation of PDxOs•Detailed steps for efficient PDxO drug screening•Adaptable for co-clinical functional precision oncology studies
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
Patient-derived xenografts (PDXs) have clinical value but are time-, cost-, and labor-intensive and thus ill-suited for large-scale experiments. Here, we present a protocol to convert PDX tumors into PDxOs for long-term cultures amenable to moderate-throughput drug screens, including in-depth PDxO validation. We describe steps for PDxO preparation and mouse cell removal. We then detail PDxO validation and characterization and drug response assay. Our PDxO drug screening platform can predict therapy response in vivo and inform functional precision oncology for patients.
Uncontrolled and sustained inflammation disrupts the wound-healing process and produces excessive reactive oxygen species, resulting in chronic or impaired wound closure. Natural antioxidants such as ...plant-based extracts and natural polysaccharides have a long history in wound care. However, they are hard to apply to wound beds due to high levels of exudate or anatomical sites to which securing a dressing is difficult. Therefore, we developed a complex coacervate-based drug carrier with underwater adhesive properties that circumvents these challenges by enabling wet adhesion and controlling inflammatory responses. This resulted in significantly accelerated wound healing through balancing the pro- and anti-inflammatory responses in macrophages. In brief, we designed a complex coacervate-based drug carrier (ADC) comprising oligochitosan and inositol hexaphosphate to entrap and release antioxidant proanthocyanins (PA) in a sustained way. The results from in vitro experiments demonstrated that ADC is able to reduce LPS-stimulated pro-inflammatory responses in macrophages. The ability of ADC to reduce LPS-stimulated pro-inflammatory responses in macrophages is even more promising when ADC is encapsulated with PA (ADC-PA). Our results indicate that ADC-PA is able to polarize macrophages into an M2 tissue-healing phenotype via up-regulation of anti-inflammatory and resolution of inflammatory responses. Treatment with ADC-PA around the wound beds fine-tunes the balance between the numbers of inducible nitric oxide synthase-positive (iNOS+) and mannose receptor-negative (CD206-) M1 and iNOS-CD206+ M2 macrophages in the wound microenvironment compared to controls. Achieving such a balance between the numbers of iNOS+CD206- M1 and iNOS-CD206+ M2 macrophages in the wound microenvironment has led to significantly improved wound closure in mouse models of diabetes, which exhibit severe impairments in wound healing. Together, our results demonstrate for the first time the use of a complex coacervate-based drug delivery system to promote timely resolution of the inflammatory responses for diabetic wound healing by fine-tuning the functions of macrophages.
Display omitted
•Evodiamine is a natural product with anti-proliferation properties.•We analyzed the impact of evodiamine treatment on human thyroid cancer cells by proteomic strategy.•Evodiamine ...dysregulated protein folding, cytoskeleton regulation and transcription control.
Evodiamine is a natural product extracted from herbal plants such as Tetradium which has shown to have anti-fat uptake and anti-proliferation properties. However, the effects of evodiamine on the behavior of thyroid cancers are largely unknown. To determine if evodiamine might be useful in the treatment of thyroid cancer and its cytotoxic mechanism, we analyzed the impact of evodiamine treatment on differential protein expression in human thyroid cancer cell line ARO using lysine-labeling two-dimensional difference gel electrophoresis (2D-DIGE) combined with mass spectrometry (MS). This study demonstrated 77 protein features that were significantly changed in protein expression and revealed evodiamine-induced cytotoxicity in thyroid cancer cells involves dysregulation of protein folding, cytoskeleton, cytoskeleton regulation and transcription control. Our work shows that this combined proteomic strategy provides a rapid method to study the molecular mechanisms of evodiamine-induced cytotoxicity in thyroid cancer cells. The identified targets may be useful for further evaluation as potential targets in thyroid cancer therapy.
Honokiol is a natural product extracted from herbal plants such as the Magnolia species which have been shown to exhibit anti-tumor and anti-metastatic properties. However, the effects of honokiol on ...thyroid cancers are largely unknown.
To determine whether honokiol might be useful for the treatment of thyroid cancer and to elucidate the mechanism of toxicity of honokiol, we analyzed the impact of honokiol treatment on differential protein expression in human thyroid cancer cell line ARO using lysine-labeling two-dimensional difference gel electrophoresis (2D-DIGE) combined with mass spectrometry (MS).
This study revealed 178 proteins that showed a significant change in expression levels and also revealed that honokiol-induced cytotoxicity in thyroid cancer cells involves dysregulation of cytoskeleton, protein folding, transcription control and glycolysis.
Our work shows that combined proteomic strategy provides a rapid method to study the molecular mechanisms of honokiol-induced cytotoxicity in thyroid cancer cells. The identified targets may be useful for further evaluation as potential targets in thyroid cancer therapy.
Display omitted
A swept-source optical coherence tomography system is used to clinically scan oral precancer and cancer patients for statistically analyzing the effective indicators of diagnosis. Three indicators ...are considered, including the standard deviation (SD) of an A-mode scan signal profile, the exponential decay constant (alpha) of an A-mode-scan spatial-frequency spectrum, and the epithelium thickness (T) when the boundary between epithelium and lamina propria can still be identified. Generally, in abnormal mucosa, the standard deviation becomes larger, the decay constant of the spatial-frequency spectrum becomes smaller, and epithelium becomes thicker. The sensitivity and specificity of the three indicators are discussed based on universal and individual relative criteria. It is found that SD and alpha are good diagnosis indicators for moderate dysplasia and squamous cell carcinoma. On the other hand, T is a good diagnosis indicator for epithelia hyperplasia and moderate dysplasia.
To investigate the clinical outcomes of patients with type 2 diabetes mellitus (T2DM) who initiated dapagliflozin in real-world practice in Taiwan.
In this multicenter retrospective study, adult ...patients with T2DM who initiated dapagliflozin after May 1
2016 either as add-on or switch therapy were included. Changes in clinical and laboratory parameters were evaluated at 3 and 6 months. Baseline factors associated with dapagliflozin response in glycated hemoglobin (HbA1c) were analyzed by univariate and multivariate logistic regression.
A total of 1,960 patients were eligible. At 6 months, significant changes were observed: HbA1c by -0.73% (95% confidence interval CI -0.80, -0.67), body weight was -1.61 kg (95% CI -1.79, -1.42), and systolic/diastolic blood pressure by -3.6/-1.4 mmHg. Add-on dapagliflozin showed significantly greater HbA1c reduction (-0.82%) than switched therapy (-0.66%) (
= 0.002). The proportion of patients achieving HbA1c <7% target increased from 6% at baseline to 19% at Month 6. Almost 80% of patients experienced at least 1% reduction in HbA1c, and 65% of patients showed both weight loss and reduction in HbA1c. Around 37% of patients had at least 3% weight loss. Multivariate logistic regression analysis indicated patients with higher baseline HbA1c and those who initiated dapagliflozin as add-on therapy were associated with a greater reduction in HbA1c.
In this real-world study with the highest patient number of Chinese population to date, the use of dapagliflozin was associated with significant improvement in glycemic control, body weight, and blood pressure in patients with T2DM. Initiating dapagliflozin as add-on therapy showed better glycemic control than as switch therapy.
Dramatic increases in the size and complexity of modern datasets have made traditional “centralized” statistical inference prohibitive. In addition to computational challenges associated with big ...data learning, the presence of numerous data types (e.g., discrete, continuous, categorical, etc.) makes automation and scalability difficult. A question of immediate concern is how to design a data-intensive statistical inference architecture without changing the basic statistical modeling principles developed for “small” data over the last century. To address this problem, we present MetaLP, a flexible, distributed statistical modeling framework suitable for large-scale data analysis, where statistical inference meets big data computing. This framework consists of three key components that work together to provide a holistic solution for big data learning: (i) partitioning massive data into smaller datasets for parallel processing and efficient computation, (ii) modern nonparametric learning based on a specially designed, orthonormal data transformation leading to mixed data algorithms, and finally (iii) combining heterogeneous “local” inferences from partitioned data using meta-analysis techniques to arrive at the “global” inference for the original big data. We present an application of this general theory in the context of a nonparametric two-sample inference algorithm for Expedia personalized hotel recommendations based on 10 million search result records.
PDF
