De novo sequencing of circulating miRNAs identifies novel markers predicting clinical outcome of locally advanced breast cancer

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) have been recently detected in the circulation of cancer patients, where they are associated with clinical parameters. Discovery profiling of circulating small RNAs has not been reported in breast cancer (BC), and was carried out in this study to identify blood-based small RNA markers of BC clinical outcome.</p> <p>Methods</p> <p>The pre-treatment sera of 42 stage II-III locally advanced and inflammatory BC patients who received neoadjuvant chemotherapy (NCT) followed by surgical tumor resection were analyzed for marker identification by deep sequencing all circulating small RNAs. An independent validation cohort of 26 stage II-III BC patients was used to assess the power of identified miRNA markers.</p> <p>Results</p> <p>More than 800 miRNA species were detected in the circulation, and observed patterns showed association with histopathological profiles of BC. Groups of circulating miRNAs differentially associated with ER/PR/HER2 status and inflammatory BC were identified. The relative levels of selected miRNAs measured by PCR showed consistency with their abundance determined by deep sequencing. Two circulating miRNAs, miR-375 and miR-122, exhibited strong correlations with clinical outcomes, including NCT response and relapse with metastatic disease. In the validation cohort, higher levels of circulating miR-122 specifically predicted metastatic recurrence in stage II-III BC patients.</p> <p>Conclusions</p> <p>Our study indicates that certain miRNAs can serve as potential blood-based biomarkers for NCT response, and that miR-122 prevalence in the circulation predicts BC metastasis in early-stage patients. These results may allow optimized chemotherapy treatments and preventive anti-metastasis interventions in future clinical applications.</p

Cancer-Secreted miR-105 Destroys Vascular Endothelial Barriers to Promote Metastasis

SummaryCancer-secreted microRNAs (miRNAs) are emerging mediators of cancer-host crosstalk. Here we show that miR-105, which is characteristically expressed and secreted by metastatic breast cancer cells, is a potent regulator of migration through targeting the tight junction protein ZO-1. In endothelial monolayers, exosome-mediated transfer of cancer-secreted miR-105 efficiently destroys tight junctions and the integrity of these natural barriers against metastasis. Overexpression of miR-105 in nonmetastatic cancer cells induces metastasis and vascular permeability in distant organs, whereas inhibition of miR-105 in highly metastatic tumors alleviates these effects. miR-105 can be detected in the circulation at the premetastatic stage, and its levels in the blood and tumor are associated with ZO-1 expression and metastatic progression in early-stage breast cancer

Breast-cancer-secreted miR-122 reprograms glucose metabolism in premetastatic niche to promote metastasis

Reprogrammed glucose metabolism as a result of increased glycolysis and glucose uptake is a hallmark of cancer. Here we show that cancer cells can suppress glucose uptake by non-tumour cells in the pre-metastatic niche, by secreting vesicles that carry high levels of the miR-122 microRNA. High miR-122 levels in the circulation have been associated with metastasis in breast cancer patients and we show that cancer-cell-secreted miR-122 facilitates metastasis by increasing nutrient availability in the pre-metastatic niche. Mechanistically cancer-cell-derived miR-122 suppresses glucose uptake by niche cells in vitro and in vivo by downregulating the glycolytic enzyme pyruvate kinase (PKM). In vivo inhibition of miR-122 restores glucose uptake in distant organs, including brain and lungs, and decreases the incidence of metastasis. These results demonstrate that by modifying glucose utilization by recipient pre-metastatic niche cells, cancer-derived extracellular miR-122 is able to reprogram systemic energy metabolism to facilitate disease progression

The Normal Body Mass Index (BMI) of Women with Polycystic Ovary Syndrome (PCOS) was Associated with IVF/ICSI Assisted Conception Outcomes

Background: One of the characteristics that is directly linked to polycystic ovary syndrome (PCOS) is body mass index (BMI), and there have been numerous studies that are pertinent to PCOS patients with high BMI. However, further research is needed to determine the precise impacts of normal BMI on PCOS patients’ metabolism and chances of becoming pregnant. Achieving a normal BMI may enhance glucose metabolism and lower the risk of gestational diabetes in pregnant PCOS women. By examining the reproductive results of PCOS patients with normal BMI, this study offers fresh suggestions for the management and alleviation of clinical symptoms in PCOS patients. Methods: From January 1, 2021 to April 30, 2022, 133 in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) cases with normal body mass index were enrolled in the Reproductive Medical Center of Hainan Women and Children’s Medical Centre, including 77 PCOS patients with normal BMI and 46 non-PCOS patients with normal BMI, the ovulation induction regimen was used as an antagonist regimen, and the waist circumference, body mass index, follicle-stimulating hormone (FSH), luteinizing hormone (LH), LH/FSH, anti-Mullerian hormone (AMH), blood lipids, homeostasis model assessment of insulin resistance (HOMA-IR), gonadotropin (Gn) dosage between the two groups were compared, Gn days of use, number of eggs obtained, normal fertilization rate, normal cleavage rate, number of available embryos, number of high-quality embryos, embryo implantation rate, clinical pregnancy rate and other indicators. Results: The endocrine situation between the two groups showed that the AMH, LH value, LH/FSH value, fasting insulin and HOMA-IR of PCOS group (group 1) were significantly higher than control group (group 2), and the data between the two groups were extremely significant (p 0.05). The results of kendall analysis showed that BMI, lipids, and AMH, and of PCOS patients with normal body mass index were significantly positively correlated with HOMA-IR (R > 0, p 0, p > 0.05), and the clinical pregnancy rate was negatively correlated with HOMA-IR (R 0.05). BMI was significantly negatively correlated with clinical pregnancy rate (R < 0, p < 0.05). Conclusions: Patients with PCOS with normal BMI should be treated with hyperandrogen control and insulin resistance therapy, and weight loss is recommended despite a normal body mass index. This study found that the Gn dose of PCOS patients with normal BMI should be lower than that of non-PCOS patients, which would be more conducive to pregnancy in PCOS patients

Evaluation and Prediction Model for Ice–Snow Tourism Suitability under Climate Warming

Studying the changes in tourist comfort is significant for improving the comfort of the tourism experience and for local tourism economies in the context of global warming. An evaluation and prediction model for ice–snow tourism suitability was constructed to objectively evaluate the suitability of ice–snow tourism environments and provided scientific tourism guidance for tourists. In this study, a comparative analysis was conducted on the monthly average temperature of the Jilin Province (China) over the past 40 years. The results show that in the last ten years, Jilin Province became hotter in the summer half-year and colder in the winter half-year. The corresponding climate comfort index (CCI) rose in the summer half-year and dropped in the winter half-year. The analysis showed that it was no longer suitable to evaluate the tourism experience in winter with the CCI alone. By comprehensively considering the CCI, the index of clothing, and the effects of precipitation, an evaluation and prediction model was constructed for an ice–snow tourism suitability index (ISTSI). The ISTSI comprehensively considered the influences of the environmental temperature, humidity, wind, and precipitation, as well as subjective human initiatives. The test results show that the ISTSI can quantify the degree of comfort of ice–snow tourism and objectively reflect the changes therein. The evaluation process was simpler than the previous methods

Evaluation and Prediction Model for Ice&ndash;Snow Tourism Suitability under Climate Warming

Studying the changes in tourist comfort is significant for improving the comfort of the tourism experience and for local tourism economies in the context of global warming. An evaluation and prediction model for ice&ndash;snow tourism suitability was constructed to objectively evaluate the suitability of ice&ndash;snow tourism environments and provided scientific tourism guidance for tourists. In this study, a comparative analysis was conducted on the monthly average temperature of the Jilin Province (China) over the past 40 years. The results show that in the last ten years, Jilin Province became hotter in the summer half-year and colder in the winter half-year. The corresponding climate comfort index (CCI) rose in the summer half-year and dropped in the winter half-year. The analysis showed that it was no longer suitable to evaluate the tourism experience in winter with the CCI alone. By comprehensively considering the CCI, the index of clothing, and the effects of precipitation, an evaluation and prediction model was constructed for an ice&ndash;snow tourism suitability index (ISTSI). The ISTSI comprehensively considered the influences of the environmental temperature, humidity, wind, and precipitation, as well as subjective human initiatives. The test results show that the ISTSI can quantify the degree of comfort of ice&ndash;snow tourism and objectively reflect the changes therein. The evaluation process was simpler than the previous methods

Water Quality Evaluation and Pollution Source Apportionment of Surface Water in a Major City in Southeast China Using Multi-Statistical Analyses and Machine Learning Models

The comprehensive evaluation of water quality and identification of potential pollution sources has become a hot research topic. In this study, 14 water quality parameters at 4 water quality monitoring stations on the M River of a city in southeast China were measured monthly for 10 years (2011&ndash;2020). Multiple statistical methods, the water quality index (WQI) model, machine learning (ML), and positive matrix factorisation (PMF) models were used to assess the overall condition of the river, select crucial water quality parameters, and identify potential pollution sources. The average WQI values of the four sites ranged from 68.31 to 77.16, with a clear trend of deterioration from upstream to downstream. A random forest-based WQI model (WQIRF model) was developed, and the results showed that Mn, Fe, faecal coliform, dissolved oxygen, and total nitrogen were selected as the top five important water quality parameters. Based on the results of the WQIRF and PMF models, the contributions of potential pollution sources to the variation in the WQI values were quantitatively assessed and ranked. These findings prove the effectiveness of ML in evaluating water quality, and improve our understanding of surface water quality, thus providing support for the formulation of water quality management strategies

Exploring Doctor-Patient Information Interaction Patterns in Online Health Community: Evidence from Chunyu Doctor via Content Analysis

As a new type of doctor-patient information exchange platform in the Internet medical era, online health community integrates abundant health information resources and provides online communication and interaction channels for patients and doctors. This study aims to understand the information interaction patterns in online health community by integrating the dyadic interaction between doctors and patients. Based on 1260 valid interaction information from Chunyu Doctor, a popular OHC in China, this study identified three information interaction patterns, including P-OHC-P (Pattern1), P-OHC-P-H(Pattern2) and H-P-OHC-P-H (Pattern3) via content analysis. Combining the dimensions of information source, information flow, user online and offline diagnosis, medical treatment status, interactive content topic and other dimensions, we summarize the different characteristics of each pattern. The findings of this study have several theoretical implications to the information interaction in online communities, as well as practical implications to managers of OHC. The limitations of this study is also given

Efficacy and safety of oritavancin for the treatment of acute bacterial skin and skin-structure infections: a systematic review and meta-analysis

ABSTRACT: Objectives: This study aimed to evaluate the efficacy and safety of oritavancin (ORI) versus comparators for the treatment of acute bacterial skin and skin-structure infections (ABSSSIs) based on available clinical studies. Methods: PubMed, Cochrane Library and Embase were searched from database inception to 28 July 2020 to identify clinical studies assessing the efficacy and safety of ORI and comparator antibiotics for the treatment of ABSSSIs. Primary efficacy outcome, investigator-assessed clinical cure, lesion size reduction ≥20%, additional post-treatment antibiotics, and 30-day emergency room (ER) visits and readmission were assessed as efficacy outcomes. Adverse events (AEs) and mortality were assessed as safety outcomes. I2 statistic was calculated for heterogeneity, and a fixed-effects or random-effects model was used for estimation of the risk ratio (RR). Results: A total of 9213 patients from two randomised clinical trials (RCTs) and four cohort studies were included in this meta-analysis. ORI was statistically non-inferior to control agents in all efficacy and safety outcomes. Moreover, ORI significantly reduced the occurrence of 30-day readmission (RR = 0.42; P = 0.0004) and drug-related AEs (RR = 0.78; P = 0.002). In the subgroup analysis, ORI also had a lower rate of 30-day ER visits in the outpatient setting (RR = 0.34; P < 0.00001). Conclusion: ORI was not inferior to comparators for the treatment of ABSSSIs. Meanwhile, it showed advantages in reducing the rate of readmission and drug-related AEs. More high-quality and large-scale RCTs are required to further confirm the efficacy and safety of ORI. [Trial registration: PROSPERO ID: CRD42020201942