Magnetically actuated sonodynamic nanorobot collectives for potentiated ovarian cancer therapy.

Ovarian cancer presents a substantial challenge due to its high mortality and recurrence rates among gynecological tumors. Existing clinical chemotherapy treatments are notably limited by drug resistance and systemic toxic side effects caused by off target drugs. Sonodynamic therapy (SDT) has emerged as a promising approach in cancer treatment, motivating researchers to explore synergistic combinations with other therapies for enhanced efficacy. In this study, we developed magnetic sonodynamic nanorobot (Fe3O4@SiO2-Ce6, FSC) by applying a SiO2 coating onto Fe3O4 nanoparticle, followed by coupling with the sonosensitizer Ce6. The magnetic FSC nanorobot collectives could gather at fixed point and actively move to target site regulated by magnetic field. In vitro experiments revealed that the magnetic FSC nanorobot collectives enabled directional navigation to the tumor cell area under guidance. Furthermore, under low-intensity ultrasonic stimulation, FSC nanorobot collectives mediated sonodynamic therapy exhibited remarkable anti-tumor performance. These findings suggest that magnetically actuated sonodynamic nanorobot collectives hold promising potential for application in target cancer therapy.

Phosphoserine aminotransferase deficiency diagnosed by whole-exome sequencing and LC-MS/MS reanalysis: A case report and review of literature.

BACKGROUND: Phosphoserine aminotransferase deficiency (PSATD) is an autosomal recessive disorder associated with hypertonia, psychomotor retardation, and acquired microcephaly. Patients with PSATD have low concentrations of serine in plasma and cerebrospinal fluid. METHODS: We reported a 2-year-old female child with developmental delay, dyskinesia, and microcephaly. LC-MS/MS was used to detect amino acid concentration in the blood and whole-exome sequencing (WES) was used to identify the variants. PolyPhen-2 web server and PyMol were used to predict the pathogenicity and changes in the 3D model molecular structure of protein caused by variants. RESULTS: WES demonstrated compound heterozygous variants in PSAT1, which is associated with PSATD, with a paternal likely pathogenic variant (c.235G>A, Gly79Arg) and a maternal likely pathogenic variant (c.43G>C, Ala15Pro). Reduced serine concentration in LC-MS/MS further confirmed the diagnosis of PSATD in this patient. CONCLUSIONS: Our findings demonstrate the importance of WES combined with LC-MS/MS reanalysis in the diagnosis of genetic diseases and expand the PSAT1 variant spectrum in PSATD. Moreover, we summarize all the cases caused by PSAT1 variants in the literature. This case provides a vital reference for the diagnosis of future cases.

Influences of coexisting aged polystyrene microplastics on the ecological and health risks of cadmium in soils: A leachability and oral bioaccessibility based study.

Whether coexisting microplastics (MPs) affect the ecological and health risks of cadmium (Cd) in soils is a cutting-edge scientific issue. In this study, four typical Chinese soils were prepared as artificially Cd-contaminated soils with/without aged polystyrene (PS). TCLP and in vitro PBET model were used to determine the leachability (ecological risk) and oral bioaccessibility (human health risk) of soil Cd. The mechanisms by which MPs influence soil Cd were discussed from direct and indirect perspectives. Results showed that there was no significant difference in the leachability of soil Cd with/without aged PS. Additionally, aged PS led to a significant decrease in the bioaccessibility of soil Cd in gastric phase, but not in small intestinal phase. The increase in surface roughness and the new characteristic peaks (e.g., Si-O-Si) of aged PS directly accounted for the change in Cd bioaccessibility. The change in organic matter content indirectly accounted for the exceptional increase in Cd bioaccessibility of black soil with aged PS in small intestinal phase. Furthermore, the changes in cation exchange capacity and Cd mobility factor caused by aged PS explained the change in Cd leachability. These results contribute to a deeper understanding about environmental and public health in complicated emerging scenarios.

Single exposure to anesthesia/surgery in neonatal mice induces cognitive impairment in young adult mice.

BACKGROUND: The effects of a solitary neonatal exposure to anesthesia plus surgery (anesthesia/surgery) on cognitive function and the underlying mechanism in developing brains remains largely undetermined. We, therefore, set out to investigate the impact of single exposure to anesthesia/surgery in neonatal mice. METHODS: Six-day-old male and female mice received abdominal surgery under 3% sevoflurane plus 50% oxygen for 2 h. The new object recognition (NOR) and Morris water maze (MWM) were used to evaluate cognitive function in young adult mice. Western blot, ELISA and RT-PCR were used to measure levels of NR2B and IL-6 in medial prefrontal cortex and IL-6 in blood of the mice. We employed NR2B siRNA and IL-6 antibody in the interaction studies. RESULTS: The anesthesia/surgery decreased the ratio of novel time to novel plus familiar time in NOR and the number of platform crossings, but not escape latency, in MWM compared to sham condition. The mice in anesthesia/surgery group had increased NR2B expression in medial prefrontal cortex, and IL-6 amounts in blood and medial prefrontal cortex. Local injection of NR2B siRNA in medial prefrontal cortex alleviated the anesthesia/surgery-induced cognitive impairment. IL-6 antibody mitigated the anesthesia/surgery-induced upregulation of NR2B and cognitive impairment in young adult mice. CONCLUSIONS: These results suggest that a single neonatal exposure to anesthesia/surgery causes impairment of memory, but not learning, in young adult mice through IL-6-regulated increases in NR2B concentrations in medial prefrontal cortex, highlighting the need for further research on the underlying mechanisms of anesthesia/surgery's impact on cognitive function in developing brains.

PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images.

OBJECTIVE: To investigate the correlation between CT imaging features and pathological subtypes of pulmonary nodules and construct a prediction model using deep learning. METHODS: We collected information of patients with pulmonary nodules treated by surgery and the reference standard for diagnosis was post-operative pathology. After using elastic distortion for data augmentation, the CT images were divided into a training set, a validation set and a test set in a ratio of 6:2:2. We used PB-LNet to analyze the nodules in pre-operative CT and predict their pathological subtypes. Accuracy was used as the model evaluation index and Class Activation Map was applied to interpreting the results. Comparative experiments with other models were carried out to achieve the best results. Finally, images from the test set without data augmentation were analyzed to judge the clinical utility. RESULTS: Four hundred seventy-seven patients were included and the nodules were divided into six groups: benign lesions, precursor glandular lesions, minimally invasive adenocarcinoma, invasive adenocarcinoma Grade 1, Grade 2 and Grade 3. The accuracy of the test set was 0.84. Class Activation Map confirmed that PB-LNet classified the nodules mainly based on the lungs in CT images, which is in line with the actual situation in clinical practice. In comparative experiments, PB-LNet obtained the highest accuracy. Finally, 96 images from the test set without data augmentation were analyzed and the accuracy was 0.89. CONCLUSIONS: In classifying CT images of lung nodules into six categories based on pathological subtypes, PB-LNet demonstrates satisfactory accuracy without the need of delineating nodules, while the results are interpretable. A high level of accuracy was also obtained when validating on real data, therefore demonstrates its usefulness in clinical practice.

Learning how to detect: A deep reinforcement learning method for whole-slide melanoma histopathology images.

Cutaneous melanoma represents one of the most life-threatening malignancies. Histopathological image analysis serves as a vital tool for early melanoma detection. Deep neural network (DNN) models are frequently employed to aid pathologists in enhancing the efficiency and accuracy of diagnoses. However, due to the paucity of well-annotated, high-resolution, whole-slide histopathology image (WSI) datasets, WSIs are typically fragmented into numerous patches during the model training and testing stages. This process disregards the inherent interconnectedness among patches, potentially impeding the models' performance. Additionally, the presence of excess, non-contributing patches extends processing times and introduces substantial computational burdens. To mitigate these issues, we draw inspiration from the clinical decision-making processes of dermatopathologists to propose an innovative, weakly supervised deep reinforcement learning framework, titled Fast medical decision-making in melanoma histopathology images (FastMDP-RL). This framework expedites model inference by reducing the number of irrelevant patches identified within WSIs. FastMDP-RL integrates two DNN-based agents: the search agent (SeAgent) and the decision agent (DeAgent). The SeAgent initiates actions, steered by the image features observed in the current viewing field at various magnifications. Simultaneously, the DeAgent provides labeling probabilities for each patch. We utilize multi-instance learning (MIL) to construct a teacher-guided model (MILTG), serving a dual purpose: rewarding the SeAgent and guiding the DeAgent. Our evaluations were conducted using two melanoma datasets: the publicly accessible TCIA-CM dataset and the proprietary MELSC dataset. Our experimental findings affirm FastMDP-RL's ability to expedite inference and accurately predict WSIs, even in the absence of pixel-level annotations. Moreover, our research investigates the WSI-based interactive environment, encompassing the design of agents, state and reward functions, and feature extractors suitable for melanoma tissue images. This investigation offers valuable insights and references for researchers engaged in related studies. The code is available at: https://github.com/titizheng/FastMDP-RL.

By using machine learning and in vitro testing, SERPINH1 functions as a novel tumorigenic and immunogenic gene and predicts immunotherapy response in osteosarcoma.

Introduction: The most prevalent bone tumor with a relatively high level of aggressiveness and malignancy is osteosarcoma. The characteristics of the serpin family in osteosarcoma have not been defined. Methods: In this study, the predictive significance of the serpin superfamily was investigated in the osteosarcoma and Gene Expression Omnibus (GEO) databases from The Cancer Genome Atlas (TCGA). Results: It was discovered that SERPINH1 is a significant biological marker in osteosarcoma. According to the CCK-8, EdU, and Transwell assays as well as the IHC assay, SERPINH1 may promote osteosarcoma proliferation and migration. It is also more expressed in tumor samples than in healthy samples. SERPINH1 might forecast the effects of immunotherapy. Additionally, immune cells are interacted with through checkpoint, cytokine, and growth factor pathways in osteosarcomas with high SERPINH1 levels. The biological function, immunological characteristics, and treatment response (immunotherapy and chemotherapy responses) of patients with osteosarcoma were successfully predicted using a model related to SERPINH1. SERPINH1 and the SERPINH1-related score predict ferroptosis/pyroptosis/apoptosis/necroptosis in osteosarcoma. Discussion: The SERPINH1-related score was an effective method for identifying osteosarcoma patients who would respond to immunotherapy and chemotherapy, as well as for predicting the survival outcomes of such patients.

An immune infiltration-related prognostic model of kidney renal clear cell carcinoma with two valuable markers: CAPN12 and MSC.

Background: Since its discovery, clear cell renal cell carcinoma (ccRCC) has been the most prevalent and lethal kidney malignancy. Our research aims to identify possible prognostic genes of ccRCC and to develop efficient prognostic models for ccRCC patients based on multi-omics investigations to shed light on the treatment and prognosis of ccRCC. Methods: To determine a risk score for each patient, we screened out differentially expressed genes using data from tumor samples, and control samples mined from The Cancer Genome Atlas (TCGA) and GTEx datasets. Somatic mutation and copy number variation profiles were analyzed to look for specific genomic changes connected to risk scores. To investigate potential functional relationships of prognostic genes, gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were carried out. We created a prognostic model by fusing risk ratings with other clinical variables. For validation, the 786-O cell line was used to carry out the dual-gRNA approach to knock down CAPN12 and MSC. This was followed by qRT-PCR to verify the knockdown of CAPN12 and MSC. Results: For ccRCC, seven predictive genes were discovered: PVT1, MSC, ALDH6A1, TRIB3, QRFPR, CYS1, and CAPN12. The most enriched pathways in the GSVA study and GSEA analysis promote tumorigenesis and immune system modulation. The risk score derived from prognostic genes corresponds with immune infiltration cells and helps predict how well a medicine will work. The mutation of numerous oncogenes was also linked to a high-risk score. A prognostic model with a high ROC value was created for the risk score. An in vitro study demonstrates that the suppression of CAPN12 and MSC dramatically reduced the ability of 786-O cells to proliferate in the CCK-8 proliferation assay and plate clonality assays. Conclusions: A thorough prognostic model with good performance has been developed for ccRCC patients using seven prognostic genes that were discovered to be related to ccRCC prognosis. In ccRCC, CAPN12 and MSC were significant indicators and would make good therapeutic targets.

ß-Hydroxybutyrate alleviates cartilage senescence through hnRNP A1-mediated up-regulation of PTEN.

Senescence chondrocytes play an important role in Osteoarthritis (OA) progression. However, alleviating OA progression through senescent chondrocyte intervention still faces great challenges. ß-Hydroxybutyrate (BHB) exhibits anti-senescence effects in a variety of age-related dis-eases, but its role in osteoarthritis remains poorly understood. To explore the molecular mechanisms, gene sequencing was used to identify critical genes and potential cellular signaling pathways and male SD rats were used to generate an osteoarthritis model. Results showed that BHB attenuated the senescence of Osteoarthritis chondrocytes (OA-Chos) and alleviated OA progression. Gene ontology (GO) enrichment analysis revealed significant changes in cell cycle genes, with PTEN being the most significant differentially expressed gene. BHB up-regulated the expression of PTEN in OA-Chos, thereby alleviating chondrocyte senescence. Furthermore, BHB facilitated the expression of PTEN by binding to hnRNP A1 and inhibiting the phosphorylation of Akt. This study provided evidence that BHB mitigated chondrocyte senescence and delayed OA, and could thus be used as a novel therapeutic approach for osteoarthritis treatment.

The multi-BRCT domain protein DDRM2 promotes the recruitment of RAD51 to DNA damage sites to facilitate homologous recombination.

DNA double-strand breaks (DSBs) are the most toxic form of DNA damage in cells. Homologous recombination (HR) is an error-free repair mechanism for DSBs as well as a basis for gene targeting using genome-editing techniques. Despite the importance of HR, the HR mechanism in plants is poorly understood. Through genetic screens for DNA damage response mutants (DDRMs), we find that the Arabidopsis ddrm2 mutant is hypersensitive to DSB-inducing reagents. DDRM2 encodes a protein with four BRCA1 C-terminal (BRCT) domains and is highly conserved in plants including bryophytes, the earliest land plant lineage. The plant-specific transcription factor SOG1 binds to the promoter of DDRM2 and activates its expression. In consistence, the expression of DDRM2 is induced by DSBs in a SOG1-dependent manner. In support, genetic analysis suggests that DDRM2 functions downstream of SOG1. Similar to the sog1 mutant, the ddrm2 mutant shows dramatically reduced HR efficiency. Mechanistically, DDRM2 interacts with the core HR protein RAD51 and is required for the recruitment of RAD51 to DSB sites. Our study reveals that SOG1-DDRM2-RAD51 is a novel module for HR, providing a potential target for improving the efficiency of gene targeting.

Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases.

Background and objective: For patients with advanced colorectal liver metastases (CRLMs) receiving first-line anti-angiogenic therapy, an accurate, rapid and noninvasive indicator is urgently needed to predict its efficacy. In previous studies, dynamic radiomics predicted more accurately than conventional radiomics. Therefore, it is necessary to establish a dynamic radiomics efficacy prediction model for antiangiogenic therapy to provide more accurate guidance for clinical diagnosis and treatment decisions. Methods: In this study, we use dynamic radiomics feature extraction method that extracts static features using tomographic images of different sequences of the same patient and then quantifies them into new dynamic features for the prediction of treatmentefficacy. In this retrospective study, we collected 76 patients who were diagnosed with unresectable CRLM between June 2016 and June 2021 in the First Hospital of China Medical University. All patients received standard treatment regimen of bevacizumab combined with chemotherapy in the first-line treatment, and contrast-enhanced abdominal CT (CECT) scans were performed before treatment. Patients with multiple primary lesions as well as missing clinical or imaging information were excluded. Area Under Curve (AUC) and accuracy were used to evaluate model performance. Regions of interest (ROIs) were independently delineated by two radiologists to extract radiomics features. Three machine learning algorithms were used to construct two scores based on the best response and progression-free survival (PFS). Results: For the task that predict the best response patients will achieve after treatment, by using ROC curve analysis, it can be seen that the relative change rate (RCR) feature performed best among all features and best in linear discriminantanalysis (AUC: 0.945 and accuracy: 0.855). In terms of predicting PFS, the Kaplan-Meier plots suggested that the score constructed using the RCR features could significantly distinguish patients with good response from those with poor response (Two-sided P<0.0001 for survival analysis). Conclusions: This study demonstrates that the application of dynamic radiomics features can better predict the efficacy of CRLM patients receiving antiangiogenic therapy compared with conventional radiomics features. It allows patients to have a more accurate assessment of the effect of medical treatment before receiving treatment, and this assessment method is noninvasive, rapid, and less expensive. Dynamic radiomics model provides stronger guidance for the selection of treatment options and precision medicine.

Dual hydrogen production from electrocatalytic water reduction coupled with formaldehyde oxidation via a copper-silver electrocatalyst.

The broad employment of water electrolysis for hydrogen (H2) production is restricted by its large voltage requirement and low energy conversion efficiency because of the sluggish oxygen evolution reaction (OER). Herein, we report a strategy to replace OER with a thermodynamically more favorable reaction, the partial oxidation of formaldehyde to formate under alkaline conditions, using a Cu3Ag7 electrocatalyst. Such a strategy not only produces more valuable anodic product than O2 but also releases H2 at the anode with a small voltage input. Density functional theory studies indicate the H2C(OH)O intermediate from formaldehyde hydration can be better stabilized on Cu3Ag7 than on Cu or Ag, leading to a lower C-H cleavage barrier. A two-electrode electrolyzer employing an electrocatalyst of Cu3Ag7(+)||Ni3N/Ni(-) can produce H2 at both anode and cathode simultaneously with an apparent 200% Faradaic efficiency, reaching a current density of 500 mA/cm2 with a cell voltage of only 0.60 V.

Application of Deep Learning in Histopathology Images of Breast Cancer: A Review.

With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze studies related to the detection, segmentation, and classification of breast cancer in pathological images. After an analysis of 107 articles on the application of deep learning to pathological images of breast cancer, this study is divided into three directions based on the types of results they report: detection, segmentation, and classification. We introduced and analyzed models that performed well in these three directions and summarized the related work from recent years. Based on the results obtained, the significant ability of deep learning in the application of breast cancer pathological images can be recognized. Furthermore, in the classification and detection of pathological images of breast cancer, the accuracy of deep learning algorithms has surpassed that of pathologists in certain circumstances. Our study provides a comprehensive review of the development of breast cancer pathological imaging-related research and provides reliable recommendations for the structure of deep learning network models in different application scenarios.

A dynamic-static combination model based on radiomics features for prostate cancer using multiparametric MRI.

Objective. To propose a new dynamic multiparametric magnetic resonance imaging (mpMRI) radiomics method for the detection of prostate cancer (PCa), and establish a combined model using dynamic and static radiomics features based on this method.Approach. A total of 166 patients (82 PCa patients and 84 non-PCa patients) were enrolled in the study, and 31 872 mpMRI images were performed in a radiomics workflow. The whole prostate segmentation and traditional static radiomics features extraction were performed on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI,bvalue of 10, 50, 100, 150, 200, 400, 600, 800, 1000, 1500 s mm-2respectively), apparent diffusion coefficient (ADC), and T2-weighted imaging (T2WI) sequences respectively. Through the building of eachb-value DWI model and the analysis of the static key radiomics features, three types of dynamic features called standard discrete (SD), parameter (P) and relative change rate (RCR) were constructed. And the b-value parameters used to construct dynamic features were divided into three groups ('Df_', 'Db_' and 'Da_'): the front part (10-200 s mm-2), the back part (400-1500 s mm-2), and all (10-1500 s mm-2) of theb-values set, respectively. Afterwards, the dynamic mpMRI model and combined model construction were constructed, and the PCa discrimination performance of each model was evaluated.Main results.The models based on dynamic features showed good potential for PCa identification. Where, the results of Db_SD, Da_P and Db_P models were encouraging (test cohort AUCs: 90.78%, 87.60%, 86.3%), which was better than the commonly used ADC model (AUC of ADC was 75.48%). Among the combined models, the models using static features of T2WI and dynamic features performed the best. The AUC of Db_SD + T2WI, Db_P + T2WI and Db_RCR + T2WI model was 92.90%, 91.29% and 81.46%.Significance.The dynamic-static combination model based on dynamic mpMRI radiomics method has a good effect on the identification of PCa. This method has broad application prospects in PCa individual diagnosis management.

Comparative Analyses of EPA-Phosphatidylcholine, EPA-Lysophosphatidylcholine, and DHA-Lysophosphatidylcholine on DHA and EPA Repletion in n-3 PUFA-Deficient Mice.

Docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) play an important role in maintaining the physiological functions of tissues, and the beneficial effects of DHA/EPA in phospholipid forms have been widely reported. Although lysophosphatidylcholine (LPC) is considered to be the preferred form of DHA supplementation for the brain, the kinetics of DHA and EPA recovery and corresponding changes of n-6 docosapentaenoic acid (DPA) and arachidonic acid (AA) levels in different phospholipid molecules and different tissues after administration of EPA in phosphatidylcholine (PC) and LPC forms and DHA in the LPC form are not clear. Here, we measured the total fatty acids in tissues and fatty acid composition of different phospholipid molecules after gavage administration of equal molar amounts of EPA/DHA in mice with n-3 polyunsaturated fatty acid (PUFA) deficiency induced by maternal dietary deprivation of n-3 PUFA during pregnancy and lactation. The results showed that dietary supplementation with EPA-PC, EPA-LPC, and DHA-LPC exhibited different priorities for EPA or DHA accretion and supplementation efficiency curves in different tissues during the developing period. EPA-PC exhibited a more optimal efficacy in DHA and EPA repletion in serum and hepatic total fatty acids. In terms of DHA recovery in the brain, EPA-LPC and DHA-LPC showed great effects. Meanwhile, the DHA level in total fatty acids and major fractions of phospholipids (PC, PE, and PI + PS) in the heart and bone marrow with the supplementation of DHA-LPC displayed a relatively considerable increase compared with that of EPA supplementation groups. The study provides a reference for the time course of DHA or EPA recovery in phospholipid molecular species in different tissues after the supplementation of EPA-PC, EPA-LPC, and DHA-LPC.

Automatic CD30 scoring method for whole slide images of primary cutaneous CD30+ lymphoproliferative diseases.

AIMS: Deep-learning methods for scoring biomarkers are an active research topic. However, the superior performance of many studies relies on large datasets collected from clinical samples. In addition, there are fewer studies on immunohistochemical marker assessment for dermatological diseases. Accordingly, we developed a method for scoring CD30 based on convolutional neural networks for a few primary cutaneous CD30+ lymphoproliferative disorders and used this method to evaluate other biomarkers. METHODS: A multipatch spatial attention mechanism and conditional random field algorithm were used to fully fuse tumour tissue characteristics on immunohistochemical slides and alleviate the few sample feature deficits. We trained and tested 28 CD30+ immunohistochemical whole slide images (WSIs), evaluated them with a performance index, and compared them with the diagnoses of senior dermatologists. Finally, the model's performance was further demonstrated on the publicly available Yale HER2 cohort. RESULTS: Compared with the diagnoses by senior dermatologists, this method can better locate the tumour area and reduce the misdiagnosis rate. The prediction of CD3 and Ki-67 validated the model's ability to identify other biomarkers. CONCLUSIONS: In this study, using a few immunohistochemical WSIs, our model can accurately identify CD30, CD3 and Ki-67 markers. In addition, the model could be applied to additional tumour identification tasks to aid pathologists in diagnosis and benefit clinical evaluation.

Single-Cell Sequencing Reveals that DBI is the Key Gene and Potential Therapeutic Target in Quiescent Bladder Cancer Stem Cells.

Background: Drug resistance and recurrence often develop during the treatment of muscle-invasive bladder cancer (MIBC). The existence of cancer stem cells (CSCs) in MIBC makes the formulation of effective treatment strategies extremely challenging. We aimed to use single-cell RNA sequencing approaches to identify CSCs and evaluate their molecular characteristics and to discover possible therapeutic measures. Methods: GEO data sets GSE130001 and GSE146137 were used to construct an expression matrix. After cells were identified by type, malignant epithelial cells inferred by InferCNV were extracted for stemness evaluation. The subset of cells with the highest stemness was subjected to weighted gene coexpression network analysis (WGCNA) and pseudotime analysis to identify key genes. In addition, we predicted drug sensitivity relationships for key genes in CTD and predicted the correlation between drugs and survival through siGDC. Results: We found that there were some CSCs in MIBC samples. The CSC population was heterogeneous during tumor development and was divided into quiescent and proliferating CSCs. We identified DBI as the key gene in quiescent CSCs. Analysis of a TCGA data set showed that higher DBI expression indicated higher histological grade. In addition, we predicted that acetaminophen can reduce DBI expression, thereby reducing the stemness of CSCs. Thus, we identified a potential new use of acetaminophen. Conclusion: We systematically explored CSCs in tumors and determined that DBI may be a key gene and potential therapeutic target in quiescent CSCs. In addition, we confirmed that acetaminophen may be a candidate drug targeting CSCs, improving our understanding of CSC-targeting therapeutic strategies.

Genotoxicity, oxidative stress and transcriptomic effects of Nitenpyram on human bone marrow mesenchymal stem cells.

Despite of the global contamination and ubiquitous exposure to nitenpyram (NIT), little knowledge is available on the adverse effects to human health, with some evidence referring to its genotoxic potency to non-target organisms and esophageal squamous papilloma in rats. Human bone marrow mesenchymal stem cells (hBMSCs) was employed as an in vitro model more relevant to humans to assess the potential genotoxicity of NIT and to understand the underlying mechanisms at cellular and molecular levels. Noncytotoxic concentrations of NIT, 50-2500 µg/mL, dose-dependently elevated micronucleus (MN) and nuclear bud (NB) frequencies to 8.7-29‰ and 15-35‰, respectively. Additional metabolism by rat liver S9 fraction decreased chromosome impairment by 27-52% on MN frequencies and 63-76% on NB frequencies. A commercial NIT product, containing 20% of NIT and 60% of pymetrozine, caused higher cytotoxicity and chromosome impairment in comparison with NIT alone. Expressions of genes responses to DNA damage, ATM, ATR, p53, p21, Bax, H2AX, and GADD45A were disturbed by NIT treatment. Reactive oxygen species (ROS) amount and superoxide dismutase (SOD) activity were enhanced by NIT. Comet assay showed that lower concentrations of NIT, 12.5-100 µg/mL, induced the DNA damage. Transcriptomic analysis identified 468 differentially expressed genes (p < 0.05, |log2(Foldchange)| ≥ 1), from which 22 pathways were enriched. Multiple affected pathways were related to cancer including viral carcinogenesis and bladder cancer. NIT may produce genotoxicity via inducing oxidative stress and deregulating PI3K/Akt, AMPK and mTOR signaling pathways, associated with carcinogenetic potency. While environmental levels of NIT alone may pose little risk to human health, attention should be paid to the health risk arose from the synergistic or additive effects that may exist among NEOs and other types of pesticides.

Neonatal acute ethanol intoxication during the epidemic of COVID-19: a case report.

BACKGROUND: After the outbreak of COVID-19, many families equip with 75% ethanol to inactivate the SARS-CoV-2, which increases the risk of exposure to ethanol. CASE PRESENTATION: We reported a 25-day-old newborn who was diagnosed with neonatal acute ethanol intoxication with a presenting complaint of accidental consumption about 15 ml formula milk containing 75% ethanol. His main clinical manifestations were irritability, flushed skin, tachycardia, tachypnea, and toxicology analysis detected ethanol. After timely gastric lavage and intravenous fluid replacement, he was cured and discharged. CONCLUSIONS: During the COVID-19 epidemic, high concentration ethanol used for inactivating SARS-COV-2 should be placed reasonably and neonatal feeding safety should be emphasized. Timely diagnosis and symptomatic treatment are essential for the prevention and management of acute ethanol intoxication in newborns.

Accelerating Monte Carlo simulation of light propagation in tissue mimicking turbid medium based on generative adversarial networks.

PURPOSE: Monte Carlo (MC) simulation is the most frequently used method to numerically model the light propagation in biological tissues because of its high flexibility and precision. Although MC simulation is assumed to be capable of achieving any desired precision, larger number of photons are always necessary for more precise simulation, leading to its major limitation of intensive computation. In this work, the authors present a way to adapt generative adversarial networks (GAN) to accelerate MC simulation. METHODS: The pix2pix network, a variant of GAN, was investigated to reconstruct precise MC simulation results from the results roughly modeled by small amount of photons, thus the computation time was expected to be significantly saved. The proposed method was tested on single-layer embedded tumor models to derive the absorption distribution maps. RESULTS: The results demonstrate that the absorption distribution maps reconstructed from the simulation of only 10 000 photons were very similar to those modeled by using 1 000 000 photons, based on the criterion of peak signal to noise ratio (PSNR) and percentage difference of power coupling efficiencies, and the simulation process was proved to be accelerated by approximately 102 times. CONCLUSIONS: For the first time, GAN was adapted to save computation time of MC simulation of light propagation. By achieving MC simulation with acceptable quality, the proposed method can speed up the computation by hundreds of times.