Predictors of urinary heavy metal concentrations among pregnant women in Jinan, China.

BACKGROUND: Toxic heavy metal exposure and insufficiency or excess of essential heavy metals may have negative effects on pregnant women's health and fetal growth. To date, the predictors of pregnant women's heavy metal exposure levels remain unclear and vary with different regions. The study intended to explore potential predictors of exposure to heavy metals individually and high co-exposure to heavy metal mixtures. METHODS: We recruited 298 pregnant women in first trimester from prenatal clinics in Jinan, Shandong Province, China, and collected spot urine samples and questionnaire data on their demographic characteristics, lifestyle habits, consumption of food and dietary supplement, and residential environment. All urine samples were analyzed for seven heavy metals: cobalt (Co), molybdenum (Mo), strontium (Sr), arsenic (As), cadmium (Cd), lead (Pb) and mercury (Hg). RESULTS: Factors associated with single heavy metal concentration were as follows: a) urinary As, Sr and Cd increased with women's age respectively; b) pregnant women with higher monthly household income per capita had lower Sr and Mo levels; c) pregnant women with intermittent folic acid supplementation and those not taking tap water as domestic drinking water had lower Sr concentrations; d) Cd was positively linked with consumption frequency of rice; e) Hg was adversely related to consumption frequency of egg and the women who took purified water as domestic drinking water had lower Hg exposure. In addition, pregnant women's age was positively associated with odds of high co-exposure to Co, As, Sr, Mo, Cd and Pb; while those with an educational level of college had lower odds of high exposure to such a metal mixture compared with those whose educational levels were lower than high school. CONCLUSION: Predictors of single urinary heavy metal concentration included pregnant women's age (As, Sr and Cd), monthly household income per capita (Sr and Mo), folic acid supplementation (Sr), rice consumption frequency (Cd), egg consumption frequency (Hg) and the type of domestic drinking water (Sr and Hg). Pregnant women with older age, lower educational level tended to have high co-exposure to Co, As, Sr, Mo, Cd and Pb.

Dual ensemble system for polyp segmentation with submodels adaptive selection ensemble.

Colonoscopy is one of the main methods to detect colon polyps, and its detection is widely used to prevent and diagnose colon cancer. With the rapid development of computer vision, deep learning-based semantic segmentation methods for colon polyps have been widely researched. However, the accuracy and stability of some methods in colon polyp segmentation tasks show potential for further improvement. In addition, the issue of selecting appropriate sub-models in ensemble learning for the colon polyp segmentation task still needs to be explored. In order to solve the above problems, we first implement the utilization of multi-complementary high-level semantic features through the Multi-Head Control Ensemble. Then, to solve the sub-model selection problem in training, we propose SDBH-PSO Ensemble for sub-model selection and optimization of ensemble weights for different datasets. The experiments were conducted on the public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, ETIS-LaribPolypDB and PolypGen. The results show that the DET-Former, constructed based on the Multi-Head Control Ensemble and the SDBH-PSO Ensemble, consistently provides improved accuracy across different datasets. Among them, the Multi-Head Control Ensemble demonstrated superior feature fusion capability in the experiments, and the SDBH-PSO Ensemble demonstrated excellent sub-model selection capability. The sub-model selection capabilities of the SDBH-PSO Ensemble will continue to have significant reference value and practical utility as deep learning networks evolve.

CMNet: deep learning model for colon polyp segmentation based on dual-branch structure.

Purpose: Colon cancer is one of the top three diseases in gastrointestinal cancers, and colon polyps are an important trigger of colon cancer. Early diagnosis and removal of colon polyps can avoid the incidence of colon cancer. Currently, colon polyp removal surgery is mainly based on artificial-intelligence (AI) colonoscopy, supplemented by deep-learning technology to help doctors remove colon polyps. With the development of deep learning, the use of advanced AI technology to assist in medical diagnosis has become mainstream and can maximize the doctor's diagnostic time and help doctors to better formulate medical plans. Approach: We propose a deep-learning model for segmenting colon polyps. The model adopts a dual-branch structure, combines a convolutional neural network (CNN) with a transformer, and replaces ordinary convolution with deeply separable convolution based on ResNet; a stripe pooling module is introduced to obtain more effective information. The aggregated attention module (AAM) is proposed for high-dimensional semantic information, which effectively combines two different structures for the high-dimensional information fusion problem. Deep supervision and multi-scale training are added in the model training process to enhance the learning effect and generalization performance of the model. Results: The experimental results show that the proposed dual-branch structure is significantly better than the single-branch structure, and the model using the AAM has a significant performance improvement over the model not using the AAM. Our model leads 1.1% and 1.5% in mIoU and mDice, respectively, when compared with state-of-the-art models in a fivefold cross-validation on the Kvasir-SEG dataset. Conclusions: We propose and validate a deep learning model for segmenting colon polyps, using a dual-branch network structure. Our results demonstrate the feasibility of complementing traditional CNNs and transformer with each other. And we verified the feasibility of fusing different structures on high-dimensional semantics and successfully retained the high-dimensional information of different structures effectively.

Tri-branch feature pyramid network based on federated particle swarm optimization for polyp segmentation.

Deep learning technology has shown considerable potential in various domains. However, due to privacy issues associated with medical data, legal and ethical constraints often result in smaller datasets. The limitations of smaller datasets hinder the applicability of deep learning technology in the field of medical image processing. To address this challenge, we proposed the Federated Particle Swarm Optimization algorithm, which is designed to increase the efficiency of decentralized data utilization in federated learning and to protect privacy in model training. To stabilize the federated learning process, we introduced Tri-branch feature pyramid network (TFPNet), a multi-branch structure model. TFPNet mitigates instability during the aggregation model deployment and ensures fast convergence through its multi-branch structure. We conducted experiments on four different public datasets：CVC-ClinicDB, Kvasir, CVC-ColonDB and ETIS-LaribPolypDB. The experimental results show that the Federated Particle Swarm Optimization algorithm outperforms single dataset training and the Federated Averaging algorithm when using independent scattered data, and TFPNet converges faster and achieves superior segmentation accuracy compared to other models.

MicroRNA-21 and its impact on signaling pathways in cervical cancer.

Oncogenic microRNA-21 (miR-21/miRNA-21) is a stable inhibitor of gene expression that is often upregulated in cervical cancer, a disease that affects the health of women and tends to transform and spread. Previous studies investigating miR-21 in biopsies and cells from cervical cancer patients have identified that miR-21 binds target mRNAs in signaling pathways or long non-coding RNAs (lncRNA). Furthermore, studies have elucidated the molecular mechanisms of two tumor necrosis factor α (TNF-α) signaling pathways that promote cell proliferation and inhibit cell apoptosis. miR-21 inhibits the TNF receptor 1 (TNFR1) signaling pathway and activates the TNFR2 signaling pathway. Moreover, miR-21 enhances cervical cancer cell proliferation by influencing the protein kinase B/mammalian target of rapamycin and RAS p21 protein activator 1 signaling pathways. The present review discusses the evidence that miR-21 may impact cervical cancer through inhibiting apoptosis and enhancing proliferation, and may therefore be a target for clinical intervention.

Amygdalin attenuates acute liver injury induced by D-galactosamine and lipopolysaccharide by regulating the NLRP3, NF-κB and Nrf2/NQO1 signalling pathways.

Acute liver injury (ALI) is a life-threatening syndrome accompanied by overwhelming inflammation. Amygdalin (AGD) has been reported to possess various biological activities, particularly anti-inflammatory activity. The current study was designed to assess the protective effects and underlying mechanisms of AGD against ALI induced by d-galactosamine (GalN) and lipopolysaccharide (LPS) in mice. The results indicated that AGD treatment effectively reduced the lethality, ameliorated the histopathological liver changes, reduced the malondialdehyde (MDA) and myeloperoxidase (MPO) levels, and decreased the alanine transaminase (ALT) and aspartate aminotransferase (AST) levels resulting from LPS/GalN challenge. Moreover, AGD significantly inhibited LPS/GalN-induced inflammatory responses in mice with ALI by reducing not only the secretion of tumour necrosis factor (TNF)-α, interleukin (IL)-1ß, and IL-6 but also the protein expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2). Additionally, our results demonstrated that the inhibitory effect of AGD was due to the suppressed activation of nuclear factor-kappa B (NF-κB) and nucleotide-binding domain (NOD-)like receptor protein 3 (NLRP3) inflammasome activity. Furthermore, AGD treatment substantially increased nuclear factor erythroid 2-related factor 2 (Nrf2) nuclear translocation and enhanced NAD (P) H: quinoneoxidoreductase 1 protein expression, which was reversed by a Nrf2 inhibitor, in HepG2 cells. In summary, our investigations suggested that the ability of AGD to ameliorate LPS/GalN-induced ALI may involve the inhibition of the NLRP3 inflammasome and NF-κB signalling pathways and the upregulation of the Nrf2/NQO1 signalling pathway.

Association of miR-182 expression in amniotic fluid with intrauterine infection and brain injury in premature infants.

Changes of micro-ribonucleic acid-182 (miR-182) level in cases of intrauterine infection were investigated to explore the association between miR-182 level change and brain injury in premature infants. A total of 257 preterm infants born in obstetrics department of Jinan Maternity and Child Care Hospital from February 2015 to February 2017 were enrolled in this study. These preterm infants were divided into infected group (n=113) and uninfected group (n=144) based on pathological diagnosis results. Quantitative polymerase chain reaction (qPCR) was employed to detect miR-182 level in amniotic fluid. Bregmatic sagittal and coronal plus lateral fontanel craniocerebral ultrasound, craniocerebral computed tomography (CT) and craniocerebral magnetic resonance imaging examinations were conducted in these preterm infants within one week after birth, and the results were recorded. The relationship between intrauterine infection and brain injury in premature infants was analyzed, and the association of miR-182 level with brain injury was explored. According to pathological diagnoses, brain injury was found in 61 of 113 infants in the infected group, with an incidence rate of 54.0%; and 28 of 144 infants in uninfected group, with an incidence rate of 19.4%; among them, 3 had placental infection caused by intrauterine infection in pregnant women, and all preterm infants had brain damage. Risk value of brain injury in premature infants due to intrauterine infection was hazard ratio (HR) = 2.2611, χ2=33.798, P<0.02. Infected group had a higher miR-182 level in comparison with uninfected group, and the difference in miR-182 level between infected group and uninfected group was statistically significant (P<0.05). Intrauterine infection can lead to an increase in miR-182 level; growth in miR-182 level is closely related to brain injury in premature infants.

Atractylodin attenuates lipopolysaccharide-induced acute lung injury by inhibiting NLRP3 inflammasome and TLR4 pathways.

Acute lung injury (ALI) arises from uncontrolled pulmonary inflammation with high mortality rates. Atractylodin (Atr) is a polyethylene alkynes and has been reported to possess anti-inflammation effect. Thus, we aimed to investigate the protective effect of Atr on lipopolysaccharide (LPS)-induced inflammatory responses ALI. The results indicated that Atr treatment not only significantly attenuated LPS-stimulated histopathological changes but also lessened the myeloperoxidase (MPO) activity, the wet-to-dry weight ratio of the lungs, protein leakage and infiltration of inflammatory cells. Moreover, Atr inhibited the tumor necrosis factor (TNF)-α, interleukin (IL)-6, IL-1ß and monocyte chemoattractant protein (MCP)-1 secretion in BALF. Further study demonstrated that such inhibitory effects of Atr were due to suppression of nucleotide-binding domain-(NOD-) like receptor protein 3 (NLRP3) inflammasome and toll like receptor 4 (TLR4) activation, likely contributing to its anti-inflammatory effects. Collectively, these findings suggest that Atr may be an effective candidate for alleviating LPS-induced inflammatory responses.

Combination therapeutic effects of high intensity focused ultrasound and Metformin for the treatment of adenomyosis.

Adenomyosis is a pathological gynaecological condition characterized by benign invasion of the endometrium into the myometrium. High intensity focused ultrasound (HIFU) is one of the most common therapeutic strategies for the treatment of adenomyosis. However, the efficacy of single HIFU therapy is insufficient for the treatment of patients with adenomyosis. In the present study, the efficacy of HIFU and metformin for the treatment of adenomyosis was analyzed in 274 patients with adenomyosis who were admitted to Jinan Maternity and Child Care Hospital. The results demonstrated that there was a significant reduction in menstrual flow and pain between patients who received HIFU and metformin, and those who received single HIFU treatment (P<0.01). Furthermore, the outcomes demonstrated that inflammatory cytokines levels were significantly lower compared with the baseline values (P<0.01). In conclusion, the outcomes of the current study indicate that combined HIFU and metformin treatment is more efficacious in patients with enlarged adenomyosis compared with single HIFU treatment.

Projection-based disparity control for toed-in multiview images.

The multiview images captured by toed-in camera array can reproduce the 3D scene vividly with appropriate positive, negative, and zero disparities. However, it is a challenging task to adjust the depth of the scene according to requirements of visual effects. In this paper, we propose a novel disparity control method based on projection to solve this problem. With the relationship between the world coordinate system and camera coordinate system, the zero disparity point in reference view is projected into other views. Thus, disparities of different views are obtained through matched corresponding points and views are shifted with calculated disparities. The proposed method is easy to implement, and the depth of toed-in multiview images can be adjusted as requirements. Experiment results show that the proposed method is effective in comparison to the conventional method, and the processed multiview images present desirable stereoscopic visual quality.

[Comparisons between NIR and HPLC fingerprint on quality stability of Shudihuang decoction pieces].

OBJECTIVE: To establish a new method for evaluating quality stability of Shudihuang decoction pieces with near-infrared spectrum. METHOD: Clustering analysis was used to distinguish different Shudihuang samples. And comparisons between NIR and HPLC fringerprint. RESULT: Results of clustering reflected the similar degree among these samples. Compared with HPLC fingerprint, NIR analytical process fast and convenient with exact results. CONCLUSION: NIR is a feasible and effective approach to examine the quality stability of Shudihuang decoction pieces with unique virtues.

Research on processing medicinal herbs with multi-steps infrared macro-fingerprint method.

How to apply rapid and effective method to research medicinal herbs, the representative of complicated mixture system, is the current study focus for analysts. The functions of non-processed and processed medicinal herbs are greatly different, so controlling the processing procedure is highly important for guarantee of the curative effect. Almost, the conventional criteria of processing are based on personal sensory experience. There is no scientific and impersonal benchmark. In this article, we take Rehmannia for example, conducting a systematic study on the process of braising Rehmannia with yellow wine by using the multi-steps infrared (IR) macro-fingerprint method. The method combines three steps: conventional Fourier transform infrared spectroscopy (FT-IR), second derivative spectroscopy, and two-dimensional infrared (2D-IR) correlation spectroscopy. Based on the changes in different types of IR spectra during the process, we can infer the optimal end-point of processing Rehmannia and the main transformations during the process. The result provides a scientific explanation to the traditional sensory experience based recipe: the end-point product is "dark as night and sweet as malt sugar". In conclusion, the multi-steps IR macro-fingerprint method, which is rapid and reasonable, can play an important role in controlling the processing of medicinal herbs.

[Identification of categories of Radix Rehmannia by IR spectroscopy and compare software].

OBJECTIVE: To identify the categories of Radix Rehmannia between the wild and the planting, different stored time, (gained in 2000,2002) , trueborn place (Henan) and no-trueborn places. METHODS: FTIR combined compare software, and the data was dealt with the means of normalization in computer. RESULT: The wild and the planting have evident difference. They can be identified for the different stored-time completely. The identification percentage between trueborn place and no-truedborn places is more than 90%. CONCLUSION: The method is fast, accurate.

[The correlation between 2D-IR and HPLC in dried Rehmannia glutinosa processed by different processing techniques].

OBJECTIVE: To establish one easier and higher efficient method of evaluating the quality of dried Rehmanwa glutinosa processed by different processing techniques in different producing areas. METHOD: Studies on the correlations between 2D-IR and HPLC in dried R. glutinosa processed by different processing techniques. RESULT: There are good correlations between their datas. CONCLUSION: It is properly to evaluate the quality of dried R. glutinosa processed by different processing techniques and different producing areas.