Near-infrared spectroscopy based on colorimetric sensor array coupled with convolutional neural network detecting zearalenone in wheat.

Wheat is a vital global cereal crop, but its susceptibility to contamination by mycotoxins can render it unusable. This study explored the integration of two novel non-destructive detection methodologies with convolutional neural network (CNN) for the identification of zearalenone (ZEN) contamination in wheat. Firstly, the colorimetric sensor array composed of six selected porphyrin-based materials was used to capture the olfactory signatures of wheat samples. Subsequently, the colorimetric sensor array, after undergoing a reaction, was characterized by its near-infrared spectral features. Then, the CNN quantitative analysis model was proposed based on the data, alongside the establishment of traditional machine learning models, partial least squares regression (PLSR) and support vector machine regression (SVR), for comparative purposes. The outcomes demonstrated that the CNN model had superior predictive performance, with a root mean square error of prediction (RMSEP) of 40.92 µ g ∙ kg-1 and a coefficient of determination on the prediction (RP2) of 0.91. These results affirmed the potential of integrating colorimetric sensor array with near-infrared spectroscopy in evaluating the safety of wheat and potentially other grains. Moreover, CNN can have the capacity to autonomously learn and distill features from spectral data, enabling further spectral analysis and making it a forward-looking spectroscopic tool.

Quantitative detection of aflatoxin B1 in peanuts using Raman spectra and multivariate analysis methods.

Aflatoxin B1 (AFB1), among the identified aflatoxins, exhibits the highest content, possesses the most potent toxicity, and poses the gravest threat. It is commonly found in peanuts and their derivatives. This study employs Raman spectroscopy to monitor the AFB1 levels in moldy peanuts, providing a reliable theoretical basis for peanut storage management. Firstly, different degrees of moldy peanuts are spectrally characterized using a portable Raman spectrometer. Subsequently, a two-step hybrid strategy for feature selection is proposed, combining backward interval partial least squares (BiPLS) and variable combination population analysis (VCPA), aiming to simplify model complexity and enhance predictive accuracy. Finally, partial least squares (PLS) regression models are constructed based on different feature intervals and wavelength points. The research results reveal that the PLS regression model using the optimized feature intervals and wavelength points exhibits improved predictive capability and generalization performance. Notably, the BiPLS-VCPA-PLS model, established through the two-step optimization, selects nine wavelength variables, achieving a root mean square error of prediction (RMSEP) of 33.3147 µg∙kg-1, a correlation coefficient of the prediction set (RP) of 0.9558, and a relative percent deviation (RPD) of 3.4896. These findings demonstrate that the two-step feature optimization method, combining feature interval selection and feature wavelength selection, can more accurately identify optimal variables, thus enhancing detection efficiency and predictive precision.

Improve the accuracy of FT-NIR for determination of zearalenone content in wheat by using the characteristic wavelength optimization algorithm.

The research contributes a unique method to achieve high-precision quantification of zearalenone (ZEN) in wheat, significantly improving accuracy in the analysis. Fourier transform near infrared spectroscopy (FT-NIR) was employed to capture the spectral information of wheat with different mildew degrees. Three feature selection models, competitive adaptive reweighted sampling (CARS), support vector machine-recursive feature elimination (SVM-RFE), and multiple feature-spaces ensemble-least absolute shrinkage and selection operator (MFE-LASSO) were introduced to processed pre-processed near-infrared spectral data and established partial least squares (PLS) regression according to the selected features. The outcomes indicated that the optimal generalization performance was achieved by the PLS model optimized through the MFE-LASSO model. The root mean square error of prediction (RMSEP) was 18.6442 µg·kg-1, coefficient of predictive determination (RP2) was 0.9545, and relative percent deviation (RPD) was 4.3198. According to the results, it is feasible to construct a stoichiometric model for the quantitative determination of ZEN in wheat by using FT-NIR combined with feature selection algorithm, and this method can also be extended to the detection of various molds in other cereals in the future.

Association between systemic immune inflammation Index and all-cause mortality in incident peritoneal dialysis-treated CKD patients: a multi-center retrospective cohort study.

BACKGROUND: Chronic inflammatory disorders in peritoneal dialysis (PD) contribute to the adverse clinical outcome. Systemic immune inflammation index (SII) is the novel and convenient measurement that is positively associated with various diseases. However, scarce is known regarding the association between SII with all-cause mortality among PD patients. METHODS: In this multi-center retrospective cohort study, 1,677 incident patients with PD were enrolled. Eligible patients were stratified into groups based on SII level: tertile 1(< 456.76), tertile 2(456.76 to 819.03), and tertile 3(> 819.03). The primary endpoint was the all-cause mortality. Both Cox regression analysis and competing risk models were used to examine the association between SII and all-cause mortality. Subgroup analysis was performed to assess the influence of the SII tertiles on all-cause mortality in different subgroups. RESULTS: During the follow-up period of 30.5 ± 20.0 months, 26.0% (437/1,677) patients died, of whom the SII tertile 3 group accounted for 39.1% (171/437) of the deaths. Patients in the SII tertile 3 group had a higher all-cause mortality rate than patients in the SII tertile 1 and 2 groups (log-rank = 13.037, P < 0.001). The SII tertile 3 group was significantly associated with 80% greater risk (95% confidence interval:1.13 to 2.85; P = 0.013) compared with the SII tertile 1 group in multivariable Cox regression analysis. The competing risk model also indicated that the relationship between SII tertiles and all-cause mortality remains (subdistribution hazard ratio: 1.86; 95% confidence interval: 1.15 to 2.02, P = 0.011). Furthermore, the relationship between the log-transformed SII and all-cause mortality in patients with PD was nearly linear (P = 0.124). CONCLUSION: A close relationship was observed between the SII and all-cause mortality in patients undergoing PD, suggesting that more attention should be paid to the SII, which is a convenient and effective measurement in clinical practice.

The association between blood nickel level and handgrip strength in patients undergoing maintenance hemodialysis.

BACKGROUND: Progressive loss of peripheral muscle strength is highly pronounced in patients receiving maintenance hemodialysis (MHD), of which the pathological mechanism tends to be multifactorial. Plasma nickel was reportedly correlated with muscular strength in non-dialysis patients. However, scarce is known regarding the association between blood nickel level and handgrip strength among the patients undergoing MHD. METHODS: This cross-sectional study included patients undergoing MHD at our center in October 2021. Blood samples were collected before the hemodialysis sessions. Nickel level was measured using inductively coupled plasma mass spectrometry. Eligible patients were stratified into three groups by the blood nickel level: tertile 1 (≥ 5.2 ug/L); tertile 2 (< 5.2 ug/L and ≥ 4.5 ug/L); and tertile 3 (< 4.5 ug/L). Handgrip strength measurement was used to evaluate the muscle status. Spearman's analyses and multivariable linear regression analyses were performed to study the relationship between blood nickel level and handgrip strength. RESULTS: A total of 236 patients were enrolled, with an average age of 55.51 ± 14.27 years and a median dialysis vintage of 83 (IQR: 48-125) months. Patients in group with a higher blood nickel level (tertile 1) tended to be female, had longer dialysis vintage and higher Kt/V, but lower BMI, serum creatinine, hemoglobin, and handgrip strength level (all p < 0.05). After adjustment for confounding factors in multivariable models, for every 1ug/L increase in nickel level, the patient's handgrip strength decreases by 2.81 kg (ß: - 2.810, 95% confidence interval: - 5.036 to - 0.584, p = 0.014). Restricted cubic spline confirmed the relationship was nearly linear. CONCLUSIONS: Our study highlighted that blood nickel level was related to handgrip strength in patients undergoing MHD. Prospective studies with larger sample sizes are still needed to confirm the result.

Atherogenic index predicts all-cause and cardiovascular mortality in incident peritoneal dialysis patients.

BACKGROUND AND AIMS: Atherosclerosis, the main cause of cardiovascular disease (CVD), is prevalent in patients undergoing peritoneal dialysis (PD). Atherogenic index (AI) is a strong predictor of atherosclerosis. However, its prognostic value in CVD outcomes and all-cause mortality among patients undergoing PD remains uncertain. Therefore, we aimed to evaluate the association between AI and all-cause and CVD mortality in PD patients. METHODS: Calculated based on lipid profiles obtained through standard laboratory procedures, AI was evaluated in 2682 patients who underwent PD therapy between January 2006 and December 2017 and were followed up until December 2018. The study population was divided into four groups according to the quartile distribution of AI (Q1: <2.20, Q2: 2.20 to <2.97, Q3: 2.97 to <4.04, and Q4: ≥4.04). Multivariable Cox models were employed to explore the associations between AI and CVD and all-cause mortality was evaluated. RESULTS: During a median follow-up of 35.5 months (interquartile range, 20.9-57.2 months), 800 patients died, including 416 deaths from CVD. Restricted cubic splines showed non-linear relationship between AI and adverse clinical outcomes. The risks of all-cause and CVD mortality gradually increased across quartiles (log-rank, p < 0.001). After adjusting for potential confounders, the highest quartile (Q4) showed significantly elevated hazard ratio (HR) for both all-cause mortality (HR 1.54 [95% confidence interval (CI), 1.21-1.96]) and CVD mortality risk (HR 1.78 [95% CI, 1.26-2.52]), compared to the lowest quartile (Q1). CONCLUSIONS: AI was independently associated with all-cause and CVD mortality in patients undergoing PD, suggesting that AI might be a useful prognostic marker.

Quantitative analysis of aflatoxin B1 of peanut by optimized support vector machine models based on near-infrared spectral features.

This study designs a chemometric framework for quantitatively evaluating aflatoxin B1 (AFB1) in peanuts based on near-infrared (NIR) spectroscopy technique. The NIR spectra of peanut samples exhibiting diverse fungal contamination levels were acquired using a portable NIR spectrometer. Subsequently, appropriate pre-processing techniques were employed for data refinement. To streamline the analysis, the iterative variable subset optimization (IVSO) technique was employed to conduct an initial screening of the pre-processed NIR spectra, eliminating numerous irrelevant variables. Building upon this screening process, the beluga whale optimization (BWO) algorithm was utilized to optimize the selected feature variables further. Subsequently, support vector machine (SVM) models were developed using the refined near-infrared spectral features to test AFB1 in peanuts quantitatively. The results indicate that the SVM model significantly improves detection performance and generalization proficiency, particularly after secondary optimization using BWO-IVSO. Among the different models considered, the SVM model established after BWO-IVSO optimization exhibited the most extraordinary level of generalization, with a root mean square error of prediction of 24.6322 µg∙kg-1, a correlation coefficient of 0.9761, and a relative percent deviation of 4.6999. Overall, this investigation highlights the effectiveness of the proposed NIR spectroscopy model based on BWO-IVSO-SVM for quantitatively analyzing AFB1 in peanuts. The study contributes valuable technical and methodological insights that can serve as a reference for rapidly determining mycotoxins in cereal crops.

Combined influence of depression and low-grade inflammation on mortality in peritoneal dialysis patients.

BACKGROUND: The relationship between depression and systemic inflammation as risk factors for mortality is not well understood and requires further investigation. METHODS: Patients undergoing continuous ambulatory peritoneal dialysis (CAPD) between July 01, 2015 to December 31, 2019, were analyzed and followed up until December 31, 2020. According to their status of depression (PHQ-9 score ≥ 5) and low-grade inflammation (hs-CRP level ≥ 3 mg/L), patients were divided into four groups (G1, without depression, nor inflammation; G2, with depression, without inflammation; G3, with inflammation, without depression; G4, with both depression and inflammation). We performed Kaplan-Meier and multivariable Cox proportional analyses of mortality for the combined influence of depression and systemic inflammation in this cohort. RESULTS: During the mean follow-up of 36.3 ± 14.8 months, 73 deaths were recorded in 358 participants. Compared with patients in group G1, patients in group G2 and G3 carried 137% {hazard ratio (HR): 2.37, 95% confidence interval (CI): 1.06-5.23, p = 0.035} and 140% (HR: 2.40, 95% CI: 1.01-5.69, p = 0.048) higher risk of mortality. Patients in group G4 (with both depression and inflammation) showed the highest risks of all-cause mortality with 276% higher mortality risk (HR: 3.76, 95% CI: 1.73-8.15, p = 0.001), respectively. CONCLUSION: The combined of depression and inflammation is associated with all-cause mortality in peritoneal dialysis patients, suggesting a need for further study of depression and low-grade inflammation in PD patients and potential relationship between them.

Clinical significance of serum glucose to lymphocyte ratio as a prognostic marker in peritoneal dialysis patients.

BACKGROUND: The glucose-to-lymphocyte ratio (GLR), a glucose metabolism and systemic inflammatory response parameter, is associated with an adverse prognosis for various diseases. However, the association between serum GLR and prognosis in patients undergoing peritoneal dialysis (PD) is poorly understood. METHODS: In this multi-center cohort study, 3236 PD patients were consecutively enrolled between 1 January 2009 and 31 December 2018. Patients were divided into four groups according to the quartiles of baseline GLR levels (Q1: GLR ≤ 2.91, Q2:2.91 < GLR ≤ 3.91, Q3:3.91 < GLR < 5.59 and Q4: GLR ≥ 5.59). The primary endpoint was all-cause and cardiovascular disease (CVD) related mortality. The correlation between GLR and mortality was examined using Kaplan-Meier and multivariable Cox proportional analyses. RESULTS: During the follow-up period of 45.93 ± 29.01 months, 25.53% (826/3236) patients died, of whom 31% (254/826) were in Q4 (GLR ≥ 5.59). Multivariable analysis revealed that GLR was significantly associated with all-cause mortality (adjusted HR 1.02; CI 1.00 ∼ 1.04, p = .019) and CVD mortality (adjusted HR 1.02; CI 1.00 ∼ 1.04, p = .04). Compared with the Q1 (GLR ≤ 2.91), placement in Q4 was associated with an increased risk of all-cause mortality (adjusted HR: 1.26, 95% CI: 1.02 ∼ 1.56, p = .03) and CVD mortality (adjusted HR 1.76; CI 1.31 ∼ 2.38, p < .001). A nonlinear relationship was found between GLR and all-cause or CVD mortality in patients undergoing PD (p = .032). CONCLUSION: A higher serum GLR level is an independent prognostic factor for all-cause and CVD mortality in patients undergoing PD, suggesting that more attention should be paid to GLR.

Remnant cholesterol as a risk factor for all-cause and cardiovascular mortality in incident peritoneal dialysis patients.

BACKGROUND AND AIMS: Remnant cholesterol (RC) adversely contributes to cardiovascular disease (CVD) and overall survival in various diseases. However, its role in CVD outcomes and all-cause mortality in patients undergoing peritoneal dialysis (PD) is limited. Therefore, we aimed to investigate the association between RC and all-cause and CVD mortality in patients undergoing PD. METHODS AND RESULTS: Based on lipid profiles recorded using standard laboratory procedures, fasting RC levels were calculated in 2710 incident patients undergoing PD who were enrolled between January 2006 and December 2017 and followed up until December 2018. Patients were divided into four groups according to the quartile distribution of baseline RC levels (Q1: <0.40 mmol/L, Q2: 0.40 to <0.64 mmol/L, Q3: 0.64 to <1.03 mmol/L, and Q4: ≥1.03 mmol/L). Associations between RC and CVD and all-cause mortality were evaluated using multivariable Cox models. During the median follow-up period of 35.4 months (interquartile range, 20.9-57.2 months), 820 deaths were recorded, of which 438 were CVD-related. Smoothing plots showed non-linear relationships between RC and adverse outcomes. The risks of all-cause and CVD mortality increased progressively through the quartiles (log-rank, p < 0.001). Using adjusted proportional hazard models, a comparison of the highest (Q4) to lowest (Q1) quartiles revealed significant increases in the hazard ratio (HR) for all-cause mortality (HR 1.95 [95% confidence interval (CI), 1.51-2.51]) and CVD mortality risk (HR 2.60 [95% CI, 1.80-3.75]). CONCLUSION: An increased RC level was independently associated with all-cause and CVD mortality in patients undergoing PD, suggesting that RC was important clinically and required further research.

Association of a low ankle brachial index with progression to end-stage kidney disease in patients with advanced-stage diabetic kidney disease.

INTRODUCTIONS: The effect of a low ankle-brachial index (ABI) in patients with advanced-stage diabetic kidney disease is not fully understood. This study investigates the prevalence of a low ABI in patients with advanced-stage diabetic kidney disease, which was defined as a urinary albumin-to-creatinine ratio (UACR) ≥300 mg/g and an estimated glomerular filtration rate (eGFR) between 15-60 mL/min/1.73 m2. Furthermore, the association between a low ABI and end-stage kidney disease (ESKD) was determined. METHODS: This single-center, retrospective, cohort study included 529 patients with advanced-stage diabetic kidney disease who were stratified into groups according to the ABI: high (>1.3), normal (0.9-1.3), and low (<0.9). The Kaplan-Meier method and Cox proportional analysis were used to examine the association between the ABI and ESKD. RESULTS: A total of 42.5% of patients with a low ABI progressed to ESKD. A low ABI was associated with a greater risk of ESKD (hazard ratio (HR): 1.073). After adjusting for traditional chronic kidney disease risk factors, a low ABI remained associated with a greater risk of ESKD (HR: 1.758; 95% confidence interval: 1.243-2.487; p = 0.001). CONCLUSIONS: These results indicate that patients with a low ABI should be monitored carefully. Furthermore, preventive therapy should be considered to improve the long-term kidney survival of patients with residual kidney function.

Effectiveness and feasibility of self-sampling for human papillomavirus testing for internet-based cervical cancer screening.

Objectives: Worldwide, around 18.2% of cervical cancer occurred in China, mainly because of lower screening coverage and screening quality in regional disparities. To assess self-sampling for human papillomavirus (HPV) testing, combined with the internet, as a primary cervical cancer screening (CCS) method in low-resource settings, and to establish an internet-based self-sampling CCS-management model. Methods: The women who participated registered on a CCS website. We recruited 20,136 women, aged 30-59 years, from 13 provinces in China, to perform vaginal self-sampling for HPV testing as a primary CCS, based on the internet. A questionnaire was subsequently used to investigate the acceptability of self-sampling. Results: Of the 20,103 women with qualified samples, 35.80% lived in remote areas, 37.69% had never undergone CCS, 59.96% were under-screened, and the overall prevalence of a high-risk of HPV was 13.86%. Of 8,136 respondents, 95.97% of women felt that self-sampling was easy to perform, 84.61% had no discomfort when using a self-sampling brush, 62.37% women were more likely to choose self-sampling for CCS in future, and 92.53% were willing to introduce the concept to others around them. The reliability and ease of self-sampling were independent factors influencing selection of self-sampling (p < 0.05). Conclusions: The Internet-facilitated self-sampling for HPV testing and management model for cervical cancer prevention is feasible and effective. It can be used as a supplement to the conventional screening, particularly in outlying areas with few medical resources, to improve the coverage of CCS. Clinical trial registration: https://www.chictr.org.cn, identifier: ChiCTR2000032331.

Parameter Optimization of Support Vector Machine to Improve the Predictive Performance for Determination of Aflatoxin B1 in Peanuts by Olfactory Visualization Technique.

This study proposes a novel method for detection of aflatoxin B1 (AFB1) in peanuts using olfactory visualization technique. First, 12 kinds of chemical dyes were selected to prepare a colorimetric sensor to assemble olfactory visualization system, which was used to collect the odor characteristic information of peanut samples. Then, genetic algorithm (GA) with back propagation neural network (BPNN) as the regressor was used to optimize the color component of the preprocessed sensor feature image. Support vector regression (SVR) quantitative analysis model was constructed by using the optimized combination of characteristic color components to achieve determination of the AFB1 in peanuts. In this process, the optimization performance of grid search (GS) algorithm and sparrow search algorithm (SSA) on SVR parameter was compared. Compared with GS-SVR model, the model performance of SSA-SVR was better. The results showed that the SSA-SVR model with the combination of seven characteristic color components obtained the best prediction effect. Its correlation coefficients of prediction (RP) reached 0.91. The root mean square error of prediction (RMSEP) was 5.7 µg·kg-1, and ratio performance deviation (RPD) value was 2.4. The results indicate that it is reliable to use the colorimetric sensor array with strong specificity for the determination of the AFB1 in peanuts. In addition, it is necessary to properly optimize the parameters of the prediction model, which can obviously improve the generalization performance of the multivariable model.

Markov Transition Field Combined with Convolutional Neural Network Improved the Predictive Performance of Near-Infrared Spectroscopy Models for Determination of Aflatoxin B1 in Maize.

This work provides a novel approach to monitor the aflatoxin B1 (AFB1) content in maize by near-infrared (NIR) spectra-based deep learning models that integrates Markov transition field (MTF) image coding and a convolutional neural network (CNN) strategy. According to the data structure characteristics of near-infrared spectra, new structures of one-dimensional CNN (1D-CNN) and two-dimensional MTF-CNN (2D-MTF-CNN) were designed to construct a deep learning model for the monitoring of AFB1 in maize. The results obtained showed that compared with the 1D-CNN model, the performance of the 2D-MTF-CNN model had been significantly improved, and its root mean square error of prediction, coefficient of predictive determination, and relative percent deviation were 1.3591 µg·kg-1, 0.9955, and 14.9386, respectively. The results indicate that the MTF is an effective data encoding technique for converting one-dimensional spectra into two-dimensional images. It more intuitively reflects the intrinsic characteristics of the NIR spectra from a new perspective and provides richer spectral information for the construction of deep learning models, which can ensure the detection accuracy and generalization performance of deep learning quantitative detection models. This study provides a new analytical perspective for the chemometrics analysis of the NIR spectroscopy.

Determination of aflatoxin B1 (AFB1) in maize based on a portable Raman spectroscopy system and multivariate analysis.

Aflatoxin B1 (AFB1) is the most widely distributed, most toxic, and most harmful, and it is widely present in moldy grains. This study proposes a new method for quantitative and rapid determination of the AFB1 content in maize based on Raman spectroscopy. The Raman spectra of maize samples with different mildew degrees were collected by a portable laser Raman spectroscopy system. Three different spectral selection methods, which were bootstrapping soft shrinkage (BOSS), variable combination population analysis (VCPA) and competitive adaptive reweighted sampling (CARS), were applied to optimize the characteristic wavelength variables of the pretreated Raman spectra. The support vector machine (SVM) detection models based on different optimized characteristic wavelength variables were established, and the results of each detection model were compared. The results obtained showed that the performance of the SVM models established by optimized features was significantly better than the performance of the SVM model built by full-spectrum data. Among them, the SVM model based on the characteristic wavelength variables optimized by the CARS method had the best performance, and its root mean square error of prediction (RMSEP) was 3.5377 µg∙kg-1, the determination coefficient of prediction (RP2) was 0.9715, and the relative prediction deviation (RPD) was 5.8258. The overall results reveal that the rapid quantitative detection of the AFB1 in maize by Raman spectroscopy has a promising application prospect. In addition, the implementation of the characteristic wavelength optimization of Raman spectra in the model calibration process can effectively improve the detection accuracy of chemometric models.

miR-92a-3p Promoted EMT via Targeting LATS1 in Cervical Cancer Stem Cells.

miR-92a-3p (microRNA-92a-3p) has been reported to be dysregulated in several cancers, and as such, it is considered to be a cancer-related microRNA. However, the influence of miR-92a-3p on biological behaviors in cervical cancer (CC) still remains unclear. Quantitative real-time PCR was used to detect miR-92a-3p levels in CC stem cells. Here, Cell Counting Kit-8 (CCK8) assay, Transwell cell invasion assay and flow cytometry assay were used to characterize the effects that miR-92a-3p and large tumor suppressor l (LATS1) had on proliferation, invasion and cell cycle transition. The luciferase reporter gene assay was used to verify the targeting relationship between miR-92a-3p and LATS1. Western Blotting was used to investigate the related signaling pathways and proteins. Data from The Cancer Genome Atlas (TCGA) showed that miR-92a-3p was upregulated in CC tissues and closely associated with overall survival. miR-92a-3p promoted proliferation, invasion and cell cycle transition in CC stem cells. The luciferase reporter assay showed that miR-92a-3p bound to the 3'-untranslated region (3'-UTR) of the LATS1 promoter. LATS1 inhibited proliferation, invasion and cell cycle transition. Results measured by Western Blotting showed that LATS1 downregulated expressions of transcriptional co-activator with PDZ-binding motif (TAZ), vimentin and cyclin E, but upregulated the expression of E-cadherin. Re-expression of LATS1 partly reversed the effects of miR-92a-3p on proliferation, invasion and cell cycle transition, as well as on TAZ, E-cadherin, vimentin, and cyclin E. miR-92a-3p promoted the malignant behavior of CC stem cells by targeting LATS1, which regulated TAZ and E-cadherin.

Post-marketing safety surveillance and reevaluation of Motherwort injection: A clinical study of 10 094 cases.

OBJECTIVE: To investigate the safety profiles of Motherwort injection (MI). METHODS: A multi-center, prospective and drug- derived hospital intensive monitoring method was conducted to assess the safety of MI in real world applications. This study was based on a very large population after the injection was approved and marketed in China. All patients using the injection in participating hospitals were monitored to determine the incidence, pattern, severity and outcome of associated adverse events. RESULTS: The post-marketing surveillance was performed in 10 094 female patients from April to December, 2015. The incidence of adverse drug reactions (ADRs) was 0.79¡ë(8/10 094). Among the 8 patients, the reported adverse events mainly included systemic abnormalities, such as fever, chills and eyelid edema; skin and appendages disorders, such as pruritus and rash; gastrointestinal disorders, such as nausea, abdominal distension and pain; heart rate and rhythm disorders, such as palpitation and increased heart rate. All of these ADRs were mild in severity. CONCLUSION: In this study the ADRs incidence rate of MI is very low, which supports that it is generally safe for use in obstetric and gynecological diseases. However, the total number of 8 ADRs recorded over a relatively short time span seems limited, and the low number of reports could not represent an absolute guarantee of safety.

LATS1 suppresses proliferation and invasion of cervical cancer.

Loss of large tumor suppressor kinase 1 (LATS1)Y has been implicated in numerous types of human cancer. However, its involvement in human cervical cancer remains to be elucidated. The present study aimed to investigate the clinical significance and biological characteristics of LATS1 in human cervical cancer. The present study investigated the protein expression levels of LATS1 in tissues from 80 cases of cervical cancer using immunohistochemistry and demonstrated that LATS1 was downregulated in 45% (36/80) of cervical cancers. Transfection of LATS1 was performed in the SiHa cell line and LATS1 siRNA knockdown was performed in the Caski cell line. MTT assay and Matrigel invasion assay indicated that LATS1 overexpression inhibited cell proliferation and invasion. LATS1 overexpression upregulated p27 expression, and downregulated the expression of cyclin E and matrix metalloproteinase 9. In addition, LATS1 overexpression stimulated yes­associated protein 1 (YAP) phosphorylation. Depletion of LATS1 in Caski cells resulted in the opposite effects. The current study demonstrated that LATS1 was downregulated in cervical cancer and may suppress cell growth and invasion through regulating the expression of cyclin E, p27, MMP9 and YAP.

Experience and levels of satisfaction with the levonorgestrel-releasing intrauterine system in China: a prospective multicenter survey.

BACKGROUND: Although surveys conducted in Western countries have shown that the levonorgestrel-releasing intrauterine system (LNG-IUS; Mirena(®)) is well accepted by European women, its acceptance by Chinese women is not yet clearly known. The purpose of this study was to analyze the experiences and levels of satisfaction with Mirena among Chinese women living in 12 different cities. METHODS: In total, 1,021 women who attended 21 medical centers for insertion of Mirena were invited to complete a questionnaire regarding their contraceptive decision at baseline (preinsertion), and two further questionnaires on their experience and satisfaction with Mirena at 3-6 months and 1 year after insertion. RESULTS: At baseline, 36% of women self-reported heavy or very heavy menstrual bleeding, while 41% reported normal bleeding. The majority of women (98%) were satisfied with the preinsertion counseling, during which contraceptive reliability was identified as the most important reason for considering Mirena. Continuation rates for Mirena were 99% at 3-6 months and 93% at 12 months after insertion, and most women (92% and 93%, respectively) had less bleeding at these times. The percentage of women who rated Mirena as better than their previous contraceptive method was 63%. Overall, around 90% of respondents were very satisfied or rather satisfied with Mirena, and 64% stated that they would recommend it to their friends. CONCLUSION: These data suggest that continuation and satisfaction rates with Mirena were very high, and that the device is well accepted by Chinese women.