Toxicity effects and mechanism of micro/nanoplastics and loaded conventional pollutants on zooplankton: An overview.

Micro/nanoplastics in aquatic environments is a noteworthy environmental problem. Zooplankton, an important biological group in aquatic ecosystems, readily absorb micro/nanoplastics and produce a range of toxic endpoints due to their small size. This review summarises relevant studies on the effects of micro/nanoplastics on zooplankton, including combined effects with conventional pollutants. Frequently reported adverse effects include acute/chronic lethal effects, oxidative stress, gene expression, energetic homeostasis, and growth and reproduction. Obstruction by plastic entanglement and blockage is the physical mechanism. Genotoxicity and cytotoxicity are molecular mechanisms. Properties of micro/nanoplastics, octanol/water partition coefficients of conventional pollutants, species and intestinal environments are important factors influencing single and combined toxicity. Selecting a wider range of micro/nanoplastics, focusing on the aging process and conducting field studies, adopting diversified zooplankton models, and further advancing the study of mechanisms are the outstanding prospects for deeper understanding of impacts of micro/nanoplastics on aquatic ecosystem.

Large-scale uterine myoma MRI dataset covering all FIGO types with pixel-level annotations.

Uterine myomas are the most common pelvic tumors in women, which can lead to abnormal uterine bleeding, abdominal pain, pelvic compression symptoms, infertility, or adverse pregnancy. In this article, we provide a dataset named uterine myoma MRI dataset (UMD), which can be used for clinical research on uterine myoma imaging. The UMD is the largest publicly available uterine MRI dataset to date including 300 cases of uterine myoma T2-weighted imaging (T2WI) sagittal patient images and their corresponding annotation files. The UMD covers 9 types of uterine myomas classified by the International Federation of Obstetrics and Gynecology (FIGO), which were annotated and reviewed by 11 experienced doctors to ensure the authority of the annotated data. The UMD is helpful for uterine myomas classification and uterine 3D reconstruction tasks, which has important implications for clinical research on uterine myomas.

Can xenobiotics support the growth of Mn(II)-oxidizing bacteria (MnOB)? A case of phenol-utilizing bacteria Pseudomonas sp. AN-1.

Biogenic manganese oxides (BioMnOx) produced by Mn(II)-oxidizing bacteria (MnOB) have garnered considerable attention for their exceptional adsorption and oxidation capabilities. However, previous studies have predominantly focused on the role of BioMnOx, neglecting substantial investigation into MnOB themselves. Meanwhile, whether the xenobiotics could support the growth of MnOB as the sole carbon source remains uncertain. In this study, we isolated a strain termed Pseudomonas sp. AN-1, capable of utilizing phenol as the sole carbon source. The degradation of phenol took precedence over the accumulation of BioMnOx. In the presence of 100 mg L-1 phenol and 100 µM Mn(II), phenol was entirely degraded within 20 h, while Mn(II) was completely oxidized within 30 h. However, at the higher phenol concentration (500 mg L-1), phenol degradation reduced to 32% and Mn(II) oxidation did not appear to occur. TOC determination confirmed the ability of strain AN-1 to mineralize phenol. Based on the genomic and proteomics studies, the Mn(II) oxidation and phenol mineralization mechanism of strain AN-1 was further confirmed. Proteome analysis revealed down-regulation of proteins associated with Mn(II) oxidation, including MnxG and McoA, with increasing phenol concentration. Notably, this study observed for the first time that the expression of Mn(II) oxidation proteins is modulated by the concentration of carbon sources. This work provides new insight into the interaction between xenobiotics and MnOB, thus revealing the complexity of biogeochemical cycles of Mn and C.

Isolation, characteristics, and poly(butylene adipate-co-terephthalate) (PBAT) degradation mechanism of a marine bacteria Roseibium aggregatum ZY-1.

Marine microorganisms have been reported to degrade microplastics. However, the degradation mechanisms are still poorly understood. In this study, a bacterium Roseibium aggregatum ZY-1 was isolated from seawater, which can degrade poly(butylene adipate-co-terephthalate) (PBAT). The PBAT-PLA(polylactic acid, PLA) films, before and after degradation, were characterized by scanning electron microscope (SEM) and Fourier transform infrared spectrometer (FTIR), the weight loss rate and water contact angle were measured. The results indicate that ZY-1 colonized on PBAT-PLA film, changed the functional groups and decreased water contact angle of PBAT-PLA film. Moreover, liquid chromatography mass spectrometry (LC-MS) analysis reveales that PBAT was degraded into its oligomers (TB, BTB) and monomers (T, A) during 10 days, and adipic acid (A) could be used as a sole carbon source. The whole genome sequencing analyses illustrate the mechanisms and enzymes such as PETase, carboxylesterases, arylesterase (PpEst) and genes like pobA, pcaBCDFGHIJKT, dcaAEIJK, paaGHJ involved in PBAT degradation. Therefore, the R. aggregatum ZY-1 will be a promising candidate of PBAT degradation.

Interactive segmentation of medical images using deep learning.

Medical image segmentation algorithms based on deep learning have achieved good segmentation results in recent years, but they require a large amount of labeled data. When performing pixel-level labeling on medical images, labeling a target requires marking ten or even hundreds of points along its edge, which requires a lot of time and labor costs. To reduce the labeling cost, we utilize a click-based interactive segmentation method to generate high-quality segmentation labels. However, in current interactive segmentation algorithms, only the interaction information clicked by the user and the image features are fused as the input of the backbone network (so-called early fusion). The early fusion method has the problem that the interactive information is much sparse at this time. Furthermore, the interactive segmentation algorithms do not take into account the boundary problem, resulting in poor model performance. So we propose early fusion and late fusion strategy to prevent the interaction information from being diluted prematurely and make better use of the interaction information. At the same time, we propose a decoupled head structure, by extracting the image boundary information, and combining the boundary loss function to establish the boundary constraint term, so that the network can pay more attention to the boundary information and further improve the performance of the network. Finally, we conduct experiments on three medical datasets (Chaos, VerSe and Uterine Myoma MRI) to verify the effectiveness of our network. The experimental results show that our network greatly improved compared with the baseline, and NoC@80(the number of interactive clicks over 80% of the IoU threshold) improved by 0.1, 0.1, and 0.2. In particular, we have achieved a NoC@80 score of 1.69 on Chaos. According to statistics, manual annotation takes 25 min to label a case(Uterine Myoma MRI). Annotating a medical image with our method can be done in only 2 or 3 clicks, which can save more than 50% of the cost.

DMF mineralization and substrate specificity mechanism of Aminobacter ciceronei DMFA1.

N,N-dimethylformamide (DMF) is widely used in various industries, but its direct release into water poses high risks to human beings. Although a lot of DMF-degrading bacteria has been isolated, limited studies focus on the degradation preference among DMF and its analogues. In this study, an efficient DMF mineralization bacterium designated Aminobacter ciceronei DMFA1 was isolated from marine sediment. When exposed to a 0.2% DMF (∼1900 mg/L), strain DMFA1 exhibited a degradation efficiency of 100% within 4 days. The observed growth using formamide as the sole carbon source implied the possible DMF degradation pathway of strain DMFA1. Meanwhile,the strain DMFA1 possesses a broad-spectrum substrate degradation, which could effectively degraded 0.2% N,N-dimethylacetamide (DMAC) and N-methylformamide (NMF). Genomic analysis further confirmed the supposed pathway through annotating the genes encoding N, N-dimethylformamidase (DMFase), formamidase, and formate dehydrogenase. The existence of sole DMFase indicating its substrate specificity controlled the preference of DMAc of strain DMFA1. By integrating multiple sequence alignment, homology modeling and molecular docking, the preference of the DMFase in strain DMFA1 towards DMAc are related to: 1) Mutations in key active site residues; 2) the absence of small subunit; and 3) no energy barrier for substrates entering the active site.

Revolutionizing hysteroscopy outcomes: AI-powered uterine myoma diagnosis algorithm shortens operation time and reduces blood loss.

Background: The application of artificial intelligence (AI) powered algorithm in clinical decision-making is globally popular among clinicians and medical scientists. In this research endeavor, we harnessed the capabilities of AI to enhance the precision of hysteroscopic myomectomy procedures. Methods: Our multidisciplinary team developed a comprehensive suite of algorithms, rooted in deep learning technology, addressing myomas segmentation tasks. We assembled a cohort comprising 56 patients diagnosed with submucosal myomas, each of whom underwent magnetic resonance imaging (MRI) examinations. Subsequently, half of the participants were randomly designated to undergo AI-augmented procedures. Our AI system exhibited remarkable proficiency in elucidating the precise spatial localization of submucosal myomas. Results: The results of our study showcased a statistically significant reduction in both operative duration (41.32 ± 17.83 minutes vs. 32.11 ± 11.86 minutes, p=0.03) and intraoperative blood loss (10.00 (6.25-15.00) ml vs. 10.00 (5.00-15.00) ml, p=0.04) in procedures assisted by AI. Conclusion: This work stands as a pioneering achievement, marking the inaugural deployment of an AI-powered diagnostic model in the domain of hysteroscopic surgery. Consequently, our findings substantiate the potential of AI-driven interventions within the field of gynecological surgery.

The cryptic step in the biogeochemical tellurium (Te) cycle: Indirect elementary Te oxidation mediated by manganese-oxidizing bacteria Bacillus sp. FF-1.

Tellurium (Te) is a rare element within the chalcogen group, and its biogeochemical cycle has been studied extensively. Tellurite (Te(IV)) is the most soluble Te species and is highly toxic to organisms. Chemical or biological Te(IV) reduction to elemental tellurium (Te0) is generally considered an effective detoxification route for Te(IV)-containing wastewater. This study unveils a previously unnoticed Te0 oxidation process mediated by the manganese-oxidizing bacterium Bacillus sp. FF-1. This bacterium, which exhibits both Mn(II)-oxidizing and Te(IV)-reducing abilities, can produce manganese oxides (BioMnOx) and Te0 (BioTe0) when exposed to Mn(II) and Te(IV), respectively. When 5 mM Mn(II) was added after incubating 0.1 mM or 1 mM Te(IV) with strain FF-1 for 16 h, BioTe0 was certainly re-oxidized to Te(IV) by BioMnOx. Chemogenic and exogenous biogenic Te0 can also be oxidized by BioMnOx, although at different rates. This study highlights a new transformation process of tellurium species mediated by manganese-oxidizing bacteria, revealing that the environmental fate and ecological risks of Te0 need to be re-evaluated.

Research progress on the role of biofilm in heavy metals adsorption-desorption characteristics of microplastics: A review.

Microplastics (MPs) have been found to be widely distributed in aquatic environments, where they will interact with toxic heavy metals and result in more serious adverse effects on the aquatic environments and organisms. However, after entering the aquatic environments, MPs are quickly covered by biofilms, which significantly modify MPs properties and relevant heavy metals adsorption-desorption characteristics In order to better understand the adsorption behavior of heavy metals on biofilm developed MPs (BMPs), we comprehensively reviewed representative studies in this area. First, we summarized the formation process of biofilms on MPs. Subsequently, we reviewed the current understanding on the influence of biofilm formation on the properties of MPs and discussed the metal adsorption-desorption characteristics of MPs affected by these changes. Finally, based on the systematic literature review, some future research needs and strategies were proposed to further understand the interactions between MPs and heavy metals.

WA-ResUNet: A Focused Tail Class MRI Medical Image Segmentation Algorithm.

Medical image segmentation can effectively identify lesions in medicine, but some small and rare lesions cannot be well identified. Existing studies do not take into account the uncertainty of the occurrence of diseased tissue, and the problem of long-tailed distribution of medical data. Meanwhile, the grayscale image obtained from Magnetic Resonance Imaging (MRI) detection has problems, such as the features being difficult to extract and invalid features being difficult to distinguish. In order to solve these problems, we propose a new weighted attention ResUNet (WA-ResUNet) and a class weight formula based on the number of images contained in the class, which improves the performance of the model in the low-frequency class and the overall effect of the model by improving the degree of attention paid to the valid features and invalid ones and rebalancing the learning efficiency among the classes. We evaluated our method on an uterine MRI dataset and compared it with the ResUNet. WA-ResUNet increased Intersection over Union (IoU) in the low-frequency class (Nabothian cysts) by 21.87%, and the overall mIoU increased by more than 6.5%.

UWV-Yolox: A Deep Learning Model for Underwater Video Object Detection.

Underwater video object detection is a challenging task due to the poor quality of underwater videos, including blurriness and low contrast. In recent years, Yolo series models have been widely applied to underwater video object detection. However, these models perform poorly for blurry and low-contrast underwater videos. Additionally, they fail to account for the contextual relationships between the frame-level results. To address these challenges, we propose a video object detection model named UWV-Yolox. First, the Contrast Limited Adaptive Histogram Equalization method is used to augment the underwater videos. Then, a new CSP_CA module is proposed by adding Coordinate Attention to the backbone of the model to augment the representations of objects of interest. Next, a new loss function is proposed, including regression and jitter loss. Finally, a frame-level optimization module is proposed to optimize the detection results by utilizing the relationship between neighboring frames in videos, improving the video detection performance. To evaluate the performance of our model, We construct experiments on the UVODD dataset built in the paper, and select mAP@0.5 as the evaluation metric. The mAP@0.5 of the UWV-Yolox model reaches 89.0%, which is 3.2% better than the original Yolox model. Furthermore, compared with other object detection models, the UWV-Yolox model has more stable predictions for objects, and our improvements can be flexibly applied to other models.

An Instance Segmentation Model Based on Deep Learning for Intelligent Diagnosis of Uterine Myomas in MRI.

Uterine myomas affect 70% of women of reproductive age, potentially impacting their fertility and health. Manual film reading is commonly used to identify uterine myomas, but it is time-consuming, laborious, and subjective. Clinical treatment requires the consideration of the positional relationship among the uterine wall, uterine cavity, and uterine myomas. However, due to their complex and variable shapes, the low contrast of adjacent tissues or organs, and indistinguishable edges, accurately identifying them in MRI is difficult. Our work addresses these challenges by proposing an instance segmentation network capable of automatically outputting the location, category, and masks of each organ and lesion. Specifically, we designed a new backbone that facilitates learning the shape features of object diversity, and filters out background noise interference. We optimized the anchor box generation strategy to provide better priors in order to enhance the process of bounding box prediction and regression. An adaptive iterative subdivision strategy ensures that the mask boundary details of objects are more realistic and accurate. We conducted extensive experiments to validate our network, which achieved better average precision (AP) results than those of state-of-the-art instance segmentation models. Compared to the baseline network, our model improved AP on the uterine wall, uterine cavity, and myomas by 8.8%, 8.4%, and 3.2%, respectively. Our work is the first to realize multiclass instance segmentation in uterine MRI, providing a convenient and objective reference for the clinical development of appropriate surgical plans, and has significant value in improving diagnostic efficiency and realizing the automatic auxiliary diagnosis of uterine myomas.

Randomized Trial of First-Line Tyrosine Kinase Inhibitor With or Without Radiotherapy for Synchronous Oligometastatic EGFR-Mutated Non-Small Cell Lung Cancer.

BACKGROUND: Adding radiotherapy (RT) to systemic therapy improves progression-free survival (PFS) and overall survival (OS) in oligometastatic non-small cell lung cancer (NSCLC). Whether these findings translate to epidermal growth factor receptor (EGFR)-mutated NSCLC remains unknown. The SINDAS trial (NCT02893332) evaluated first-line tyrosine kinase inhibitor (TKI) therapy for EGFR-mutated synchronous oligometastatic NSCLC and randomized to upfront RT vs no RT; we now report the prespecified interim analysis at 68% accrual. METHODS: Inclusion criteria were biopsy-proven EGFR-mutated adenocarcinoma (per amplification refractory mutation system or next generation sequencing), with synchronous (newly diagnosed, treatment naïve) oligometastatic (≤5 metastases; ≤2 lesions in any one organ) NSCLC without brain metastases. All patients received a first-generation TKI (gefitinib, erlotinib, or icotinib), and randomization was between no RT vs RT (25-40 Gy in 5 fractions depending on tumor size and location) to all metastases and the primary tumor/involved regional lymphatics. The primary endpoint (intention to treat) was PFS. Secondary endpoints included OS and toxicities. All statistical tests were 2-sided. RESULTS: A total of 133 patients (n = 65 TKI only, n = 68 TKI with RT) were enrolled (2016-2019). The median follow-up was 23.6 months. The respective median PFS was 12.5 months vs 20.2 months (P < .001), and the median OS was 17.4 months vs 25.5 months (P < .001) for TKI only vs TKI with RT. Treatment yielded no grade 5 events and a 6% rate of symptomatic grade 3-4 pneumonitis in the TKI with RT arm. Based on the efficacy results of this prespecified interim analysis, the ethics committee recommended premature cessation of this trial. CONCLUSIONS: As compared with a first-line TKI alone, addition of upfront local therapy using RT statistically significantly improved PFS and OS for EGFR-mutated NSCLC.

Ginsenoside Rg3 increases gemcitabine sensitivity of pancreatic adenocarcinoma via reducing ZFP91 mediated TSPYL2 destabilization.

Background: Ginsenoside Rg3 and gemcitabine have mutual enhancing antitumor effects. However, the underlying mechanisms are not clear. This study explored the influence of ginsenoside Rg3 on Zinc finger protein 91 homolog (ZFP91) expression in pancreatic adenocarcinoma (PAAD) and their regulatory mechanisms on gemcitabine sensitivity. Methods: RNA-seq and survival data from The Cancer Genome Atlas (TCGA)-PAAD and Genotype-Tissue Expression (GTEx) were used for in-silicon analysis. PANC-1, BxPC-3, and PANC-1 gemcitabine-resistant (PANC-1/GR) cells were used for in vitro analysis. PANC-1 derived tumor xenograft nude mice model was used to assess the influence of ginsenoside Rg3 and ZFP91 on tumor growth in vivo. Results: Ginsenoside Rg3 reduced ZFP91 expression in PAAD cells in a dose-dependent manner. ZFP91 upregulation was associated with significantly shorter survival of patients with PAAD. ZFP91 overexpression induced gemcitabine resistance, which was partly conquered by ginsenoside Rg3 treatment. ZFP91 depletion sensitized PANC-1/GR cells to gemcitabine treatment. ZFP91 interacted with Testis-Specific Y-Encoded-Like Protein 2 (TSPYL2), induced its poly-ubiquitination, and promoted proteasomal degradation. Ginsenoside Rg3 treatment weakened ZFP91-induced TSPYL2 poly-ubiquitination and degradation. Enforced TSPYL2 expression increased gemcitabine sensitivity of PAAD cells and partly reversed induced gemcitabine resistance in PANC-1/GR cells. Conclusion: Ginsenoside Rg3 can increase gemcitabine sensitivity of pancreatic adenocarcinoma at least via reducing ZFP91 mediated TSPYL2 destabilization.

Functional roles of the SHCBP1 and KIF23 interaction in modulating the cell-cycle and cisplatin resistance of head and neck squamous cell carcinoma.

BACKGROUND: This study aimed to explore the functional roles of Shc SH2-domain-binding protein 1 (SHCBP1) and Kinesin Family Member 23 (KIF23) in HPV-negative head and neck squamous cell carcinoma (HNSCC). METHODS: Bioinformatic analysis was conducted using data from The Cancer Genome Atlas (TCGA) and GSE103322. HNSCC cell lines were used for in vitro and in vivo analysis. RESULTS: SHCBP1 upregulation was associated with unfavorable survival. SHCBP1 knockdown reduced cell proliferation and increased the cisplatin sensitivity of SCC9/SCC25 cells. SHCBP1 interacted with KIF23 via its Nesd homology domain (NHD) domain, which was important for its nucleus localization. SHCBP1 positively modulated KIF23 expression and activated phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt), extracellular signal regulated kinase (ERK)1/2, nuclear factor kappa B (NF/κB)-p65, and Wnt/ß-catenin signaling. KIF23 knockdown abrogated cisplatin resistance induced by SHCBP1 overexpression. CONCLUSION: SHCBP1 interacts with KIF23 and cooperatively regulates cell-cycle progression and cisplatin resistance of HNSCC tumor cells.

Polycystic ovary syndrome is an independent risk factor for hypertensive disorders of pregnancy: A systematic review, meta-analysis, and meta-regression.

PURPOSE: The risk of hypertensive disorders of pregnancy (HDP) in women with polycystic ovary syndrome (PCOS) is inconsistent in some studies. The aim of this meta-analysis was to examine the evidence regarding the strength of the association between PCOS and HDP. METHODS: PubMed, Web of Science, Embase, and the Cochrane Library were systematically searched to identify observational studies investigating HDP in patients with PCOS. The primary outcome was the pooled odds ratio (OR) of HDP, including pregnancy-induced hypertension (PIH) and pre-eclampsia (PE), in women with PCOS compared with the non-PCOS population. RESULTS: A total of 30 studies were eligible for meta-analysis. PCOS was associated with a higher risk of HDP (OR 2.02, 95CI% 1.83-2.22), including PIH (OR 1.94, 95CI% 1.70-2.21), and PE (OR 2.07, 95CI% 1.91-2.24). The association remained significant after adjusting for age, body mass index (BMI), and nulliparity (HDP: OR 1.48, 95CI% 1.48-1.60; PIH: OR 1.42, 95%CI 1.29-1.57; PE: OR 2.07, and 95%CI 1.91-2.24). The increased risk of HDP for the PCOS group remained significant in subgroups of BMI, Age, singleton pregnancy, multiple pregnancy, gestational diabetes mellitus (GDM), hyperandrogenism, and nulliparity, while the finding was not observed in subgroups of nonhyperandrogenic and non-GDM. In the meta-regression, BMI contributed significantly to the heterogeneity in the prevalence of HDP. CONCLUSIONS: PCOS is independently associated with a significantly increased risk of HDP. To prevent HDP during pregnancy, our findings highlight the importance of establishing supervision guidelines for PCOS patients, especially in the population with hyperandrogenism and GDM.

Single Micelle Vectors based on Lipid/Block Copolymer Compositions as mRNA Formulations for Efficient Cancer Immunogene Therapy.

Immunogene therapy provides a new strategy for the treatment of colorectal cancer. Compared to plasmid DNA, mRNA possesses several advantages as a therapeutic nucleic acid material and shows high potential in cancer therapy. Although efforts have been made to conquer the limited efficiency of mRNA delivery, most of the current mRNA vectors possess complex structures or compositions, which introduces additional toxicity and hinders their further clinical application. Hence, it is highly necessary to develop potent mRNA delivery systems with simple structures. Here, we report efficient mRNA delivery using the biodegradable micelle delivery system of DMP (DOTAP-mPEG-PCL). Biodegradable DMP micelles were simply prepared by the self-assembly of cationic lipid DOTAP and the diblock polymer monomethoxy poly(ethylene glycol)-poly(ε-caprolactone). With an average size of only 30 nm, we proved that these single-structured cationic micelles are highly potent in condensing and protecting mRNA molecules, with a delivery efficiency of 60.59% on C26 mouse colon cancer cells. The micelles triggered specific internalization pathways and were fully degraded in vivo. After binding with IL-22BP (interleukin-22 binding protein)-encoding mRNA, a strongly elevated IL-22BP mRNA level was detected in C26 cells. After intraperitoneal and intratumoral injection of the DMP/mIL-22BP complex, strong inhibition effects on C26 colon cancer models were observed, with high therapeutic efficiency and safety when systemically administrated. These data suggest that the DMP micelle is an advanced single-structured mRNA delivery system with high safety.

A prognosis-predictive nomogram of ovarian cancer with two immune-related genes: CDC20B and PNPLA5.

Ovarian carcinoma (OV) is one of the most lethal gynecological malignancies globally, and the overall 5-year survival rate of OV was 47% in 2018 according to American data. To increase the survival rate of patients with OV, many researchers have sought to identify biomarkers that act as both prognosis-predictive markers and therapy targets. However, most of these have not been suitable for clinical application. The present study aimed at constructing a predictive prognostic nomogram of OV using the genes identified by combining The Cancer Genome Atlas (TCGA) dataset for OV with the immune score calculated by the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm. Firstly, the algorithm was used to calculate the immune score of patients with OV in the TCGA-OV dataset. Secondly, differentially expressed genes (DEGs) between low and high immune score tissues were identified, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed to predict the functions of these DEGs. Thirdly, univariate, multivariate and Lasso Cox's regression analyses were carried out step by step, and six prognosis-related DEGs were identified. Then, Kaplan-Myer survival curves were generated for these genes and validated by comparing their expression levels to further narrow the range of DEGs and to calculate the risk score. Two genes were identified, cell division cycle 20B and patatin-like phospholipase domain containing 5, which were both shown to have higher expression levels in OV tissues and to be significantly associated with the prognosis of OV. Next, a nomogram was created using these two genes and age, and using the receiver operating characteristic (ROC) curve and calibration curve, the effectiveness of the nomogram was validated. Finally, an external validation was conducted for this nomogram. The ROC showed that the areas under the curve (AUCs) of the 3- and 5-year overall survival predictions for the nomogram were 0.678 and 0.62, respectively. Moreover, the ROC of the external validation model showed that the AUCs of the 3- and 5-year were 0.699 and 0.643, respectively, demonstrating the effectiveness of the generated nomogram. In conclusion, the present study has identified two immune-related genes as biomarkers that reliably predict overall survival in OV. These biomarkers might also be potential molecular targets of immune therapy to treat patients with OV.

MiRNAs expression profiling of rat ovaries displaying PCOS with insulin resistance.

PURPOSE: The present study established microRNA (miRNA) expression profiles for rat ovaries displaying polycystic ovary syndrome (PCOS) with insulin resistance and explored the underlying biological functions of differentially expressed miRNAs. METHODS: A PCOS with insulin resistance rat model was created by administering letrozole and a high-fat diet. Total RNA was extracted from the ovaries of PCOS with insulin resistance rats and normal rats. Three ovaries from each group were used to identify differentially expressed miRNAs by deep sequencing. A hierarchical clustering heatmap and volcano plot were used to display the pattern of differentially expressed miRNAs. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to explore the potential target genes of the differentially expressed miRNAs and identify their putative biological function. Nine of the differentially expressed miRNAs were selected for validation by Real-time Quantitative PCR (qRT-PCR). RESULTS: A total of 58 differentially expressed miRNAs were identified in the rat ovaries exhibiting PCOS with insulin resistance compared with control ovaries, including 23 miRNAs that were upregulated and 35 miRNAs that were downregulated. GO and KEGG pathway analyses revealed that the predicted target genes were related to metabolic processes, cellular processes, and metabolic pathways. Furthermore, qRT-PCR confirmed that miR-3585-5p and miR-30-5p were significantly upregulated and miR-146-5p was downregulated in the ovaries of PCOS with insulin resistance rats compared with the controls. CONCLUSION: These results indicate that differentially expressed miRNAs in rat ovaries may be involved in the pathophysiology of insulin resistance in PCOS. Our study may be beneficial in establishing miRNAs as novel diagnostic and therapeutic biomarkers for insulin resistance in PCOS.

Selective detection of cadmium ions using plasmonic optical fiber gratings functionalized with bacteria.

Environmental monitoring and potable water control are key applications where optical fiber sensing solutions can outperform other technologies. In this work, we report a highly sensitive plasmonic fiber-optic probe that has been developed to determine the concentration of cadmium ions (Cd2+) in solution. This original sensor was fabricated by immobilizing the Acinetobacter sp. around gold-coated tilted fiber Bragg gratings (TFBGs). To this aim, the immobilization conditions of bacteria on the gold-coated optical fiber surface were first experimentally determined. Then, the coated sensors were tested in vitro. The relative intensity of the sensor response experienced a change of 1.1 dB for a Cd2+ concentration increase from 0.1 to 1000 ppb. According to our test procedure, we estimate the experimental limit of detection to be close to 1 ppb. Cadmium ions strongly bind to the sensing surface, so the sensor exhibits a much higher sensitivity to Cd2+ than to other heavy metal ions such as Pb2+, Zn2+ and CrO42- found in contaminated water, which ensures a good selectivity.