The role of microRNA-142a in Toxoplasma gondii infection-induced downregulation of Foxp3: implications for adverse pregnancy outcomes.

BACKGROUND: Toxoplasma gondii (T. gondii) is capable of infecting nearly all warm-blooded animals and approximately 30% of the global population. Though most infections are subclinical in immunocompetent individuals, congenital contraction can lead to severe consequences such as spontaneous abortion, stillbirth, and a range of cranio-cerebral and/or ocular abnormalities. Previous studies reported that T. gondii-infected pregnancy mice unveiled a deficit in both the amount and suppressive functions of regulatory T (Treg) cells, accompanied with reduced levels of forkhead box p3 (Foxp3). Recently, accumulative studies have demonstrated that microRNAs (miRNAs) are, to some extent, relevant to T. gondii infection. However, the link between alterations in miRNAs and downregulation of Foxp3 triggered by T. gondii has been only sporadically studied. METHODS: Quantitative reverse transcription polymerase chain reaction (RT-qPCR), protein blotting and immunofluorescence were employed to evaluate the impact of T. gondii infection and antigens on miRNA transcription and Foxp3 expression. Dual-luciferase reporter gene assays were performed to examine the fluorescence activity in EL4 cells, which were transfected with recombinant plasmids containing full-length/truncated/mutant microRNA-142a-3p (miR-142a) promoter sequence or wild type/mutant of Foxp3 3' untranslated region (3' UTR). RESULTS: We found a pronounced increase in miR-142a transcription, concurrent with a decrease in Foxp3 expression in T. gondii-infected mouse placental tissue. Similarly, comparable findings have been experimentally confirmed through the treatment of EL4 cells with T. gondii antigens (TgAg) in vitro. Simultaneously, miR-142a mimics attenuated Foxp3 expression, whereas its inhibitors markedly augmented Foxp3 expression. miR-142a promoter activity was elevated upon the stimulation of T. gondii antigens, which mitigated co-transfection of mutant miR-142a promoter lacking P53 target sites. miR-142a mimics deceased the fluorescence activity of Foxp3 3' untranslated region (3' UTR), but it did not affect the fluorescence activity upon the co-transfection of mutant Foxp3 3' UTR lacking miR-142a target site. CONCLUSION: In both in vivo and in vitro studies, a negative correlation was discovered between Foxp3 expression and miR-142a transcription. TgAg enhanced miR-142a promoter activity to facilitate miR-142a transcription through a P53-dependent mechanism. Furthermore, miR-142a directly targeted Foxp3 3' UTR, resulting in the downregulation of Foxp3 expression. Therefore, harnessing miR-142a may be a possible therapeutic approach for adverse pregnancy caused by immune imbalances, particularly those induced by T. gondii infection.

Hepatitis C infection, related services, and barriers to HCV treatment among drug users in methadone maintenance treatment (MMT) clinics in Shanghai, China.

BACKGROUND: The purpose of this study was to document the prevalence of hepatitis C among MMT patients, hepatitis C virus (HCV) knowledge of patients and MMT staff members, and the barriers preventing them from receiving or delivering HCV-related services in MMT clinics of China. METHODS: Data were collected from 240 MMT patients and 58 staff members in Shanghai MMT clinics. Structured questionnaires (HCV Knowledge Scale and Alcohol Use Disorders Identification Test) and several self-developed questionnaires were used to assess (1) patient and staff HCV knowledge, (2) attitudes toward HCV-related services in MMT clinics, and (3) what type of HCV-related services the staff members have provided in their routine work. The HCV test results were based on the patients' medical records. RESULTS: The HCV seropositive rate was high (70%), and both patients and staff had limited HCV knowledge. The mean score of patient HCV knowledge was 6.8 out of 20 (SD = 3.7), whereas the mean score of staff HCV knowledge was 10.9 out of 20 (SD = 3.1). For HCV-positive patients, only 13.7% had accessed HCV medical treatment. Barriers included the cost of medical treatment, lack of HCV knowledge, lack of professional training for patients to receive HCV-related services from individuals or MMT clinics, and lack of an adequate policy-making system. CONCLUSIONS: HCV infection remains an important problem among MMT patients in China. Barriers to HCV-related services are attributable to individual, clinical, and policy-related factors. This study may provide evidence-based information for future work to optimize the resources of MMT clinics. TRIAL REGISTRATION: ClinicalTrials.gov NCT01647191 . Registered 17 April 2012.

AbdomenAtlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking.

We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and facilities. AbdomenAtlas provides 673 K high-quality masks of anatomical structures in the abdominal region annotated by a team of 10 radiologists with the help of AI algorithms. We start by having expert radiologists manually annotate 22 anatomical structures in 5,246 CT volumes. Following this, a semi-automatic annotation procedure is performed for the remaining CT volumes, where radiologists revise the annotations predicted by AI, and in turn, AI improves its predictions by learning from revised annotations. Such a large-scale, detailed-annotated, and multi-center dataset is needed for two reasons. Firstly, AbdomenAtlas provides important resources for AI development at scale, branded as large pre-trained models, which can alleviate the annotation workload of expert radiologists to transfer to broader clinical applications. Secondly, AbdomenAtlas establishes a large-scale benchmark for evaluating AI algorithms-the more data we use to test the algorithms, the better we can guarantee reliable performance in complex clinical scenarios. An ISBI & MICCAI challenge named BodyMaps: Towards 3D Atlas of Human Body was launched using a subset of our AbdomenAtlas, aiming to stimulate AI innovation and to benchmark segmentation accuracy, inference efficiency, and domain generalizability. We hope our AbdomenAtlas can set the stage for larger-scale clinical trials and offer exceptional opportunities to practitioners in the medical imaging community. Codes, models, and datasets are available at https://www.zongweiz.com/dataset.

The performance of deep learning on thyroid nodule imaging predicts thyroid cancer: A systematic review and meta-analysis of epidemiological studies with independent external test sets.

BACKGROUND AND AIMS: It is still controversial whether deep learning (DL) systems add accuracy to thyroid nodule imaging classification based on the recent available evidence. We conducted this study to analyze the current evidence of DL in thyroid nodule imaging diagnosis in both internal and external test sets. METHODS: Until the end of December 2022, PubMed, IEEE, Embase, Web of Science, and the Cochrane Library were searched. We included primary epidemiological studies using externally validated DL techniques in image-based thyroid nodule appraisal. This systematic review was registered on PROSPERO (CRD42022362892). RESULTS: We evaluated evidence from 17 primary epidemiological studies using externally validated DL techniques in image-based thyroid nodule appraisal. Fourteen studies were deemed eligible for meta-analysis. The pooled sensitivity, specificity, and area under the curve (AUC) of these DL algorithms were 0.89 (95% confidence interval 0.87-0.90), 0.84 (0.82-0.86), and 0.93 (0.91-0.95), respectively. For the internal validation set, the pooled sensitivity, specificity, and AUC were 0.91 (0.89-0.93), 0.88 (0.85-0.91), and 0.96 (0.93-0.97), respectively. In the external validation set, the pooled sensitivity, specificity, and AUC were 0.87 (0.85-0.89), 0.81 (0.77-0.83), and 0.91 (0.88-0.93), respectively. Notably, in subgroup analyses, DL algorithms still demonstrated exceptional diagnostic validity. CONCLUSIONS: Current evidence suggests DL-based imaging shows diagnostic performances comparable to clinicians for differentiating thyroid nodules in both the internal and external test sets.

Mapping soil pollution by using drone image recognition and machine learning at an arsenic-contaminated agricultural field.

Mapping soil contamination enables the delineation of areas where protection measures are needed. Traditional soil sampling on a grid pattern followed by chemical analysis and geostatistical interpolation methods (GIMs), such as Kriging interpolation, can be costly, slow and not well-suited to highly heterogeneous soil environments. Here we propose a novel method to map soil contamination by combining high-resolution aerial imaging (HRAI) with machine learning algorithms. To support model establishment and validation, 1068 soil samples were collected from an arsenic (As) contaminated area in Zhongxiang, Hubei province, China. The average arsenic concentration was 39.88 mg/kg (SD = 213.70 mg/kg), with individual sample points determined as low risk (66.9%), medium risk (29.4%), or high risk (3.7%), respectively. Then, identified features were extracted from a HRAI image of the study area. Four machine learning algorithms were developed to predict As risk levels, including (i) support vector machine (SVM), (ii) multi-layer perceptron (MLP), (iii) random forest (RF), and (iii) extreme random forest (ERF). Among these, we found that the ERF algorithm performed best overall and that its prediction performance was generally better than that of traditional Kriging interpolation. The accuracy of ERF in test area 1 reached 0.87, performing better than RF (0.81), MLP (0.78) and SVM (0.77). The F1-score of ERF for discerning high-risk points in test area 1 was as high as 0.8. The complexity of the distribution of points with different risk levels was a decisive factor in model prediction ability. Identified features in the study area associated with fertilizer factories had the most important contribution to the ERF model. This study demonstrates that HRAI combined with machine learning has good potential to predict As soil risk levels.

Remediation of mercury contaminated soil, water, and air: A review of emerging materials and innovative technologies.

Mercury contamination in soil, water and air is associated with potential toxicity to humans and ecosystems. Industrial activities such as coal combustion have led to increased mercury (Hg) concentrations in different environmental media. This review critically evaluates recent developments in technological approaches for the remediation of Hg contaminated soil, water and air, with a focus on emerging materials and innovative technologies. Extensive research on various nanomaterials, such as carbon nanotubes (CNTs), nanosheets and magnetic nanocomposites, for mercury removal are investigated. This paper also examines other emerging materials and their characteristics, including graphene, biochar, metal organic frameworks (MOFs), covalent organic frameworks (COFs), layered double hydroxides (LDHs) as well as other materials such as clay minerals and manganese oxides. Based on approaches including adsorption/desorption, oxidation/reduction and stabilization/containment, the performances of innovative technologies with the aid of these materials were examined. In addition, technologies involving organisms, such as phytoremediation, algae-based mercury removal, microbial reduction and constructed wetlands, were also reviewed, and the role of organisms, especially microorganisms, in these techniques are illustrated.