Photoinduced Redox Cascade Reaction of Isatins and Aliphatic Carboxylic Acids to Access 6-Hydroxyindoloquinazolinones.

A visible-light-induced cascade reaction is described for the one-pot synthesis of 6-hydroxyindoloquinazolinones using isatins (or isatins and isatoic anhydrides) and aliphatic carboxylic acids. The method provides 36 desired products in 33-96 % yield, exhibiting broad substrate scope and good functional group tolerance. This approach utilizes inexpensive and commercially available starting materials, enabling the direct construction of high-value complex structures under mild conditions without the need for photocatalyst, showcasing significant applicability and environmental friendliness.

Photoredox-Neutral Radical-Radical Cross-Coupling of Isatins and Benzyl Carboxylic Acids.

A photoredox-neutral radical-radical cross-coupling is described for the synthesis of 3-hydroxy-3-alkyloxindoles using isatins and benzyl carboxylic acids as substrates and 2,4,5,6-tetra(9H-carbazol-9-yl)isophthalonitrile (4CzIPN) as the photocatalyst. The method features a broad substrate scope and good functional group tolerance, providing 30 sterically hindered alcohols with moderate to excellent yields. This approach utilizes inexpensive and commercially available starting materials, avoiding the use of transition metals, extra oxidants/reductants, and harsh reaction conditions, showcasing significant applicability and environmental friendliness.

First identification of microplastics in human uterine fibroids and myometrium.

Microplastics (MPs) pollution has received widespread attention in recent years as the use of plastics continues to increase. However, currently no studies have reported the finding of MPs in human uterine fibroids (UFs) and myometrium tissues. In this study, UFs tissues (n = 48) and myometrium tissues (n = 40) from 48 patients and myometrium tissues (n = 8) from healthy population were collected. Following digestion of the samples by 10% KOH and 30% H2O2, MPs were analyzed qualitatively and quantitatively using Raman spectroscopy. The 16 UFs and myometrium tissue samples contained an average of 1.5 ± 1.17 MP particles per gram of tissue. Notably, the abundance of MPs in the UFs tissues (2.13 ± 1.17 particles per gram) was higher than in the myometrium tissues (0.88 ± 0.78 particles per gram). In the same cohort of individuals with UFs, the quantities of MPs detected in the affected UFs tissue (2.63 ± 1.77 particles per gram) exceeded those detected in healthy tissue (1.08 ± 0.93 particles per gram), particularly in elderly patients. A correlation was observed between elevated MP levels and frequent consumption of takeout meals and bottled water among patients, indicating that MP ingestion through food sources might have contributed to the increased abundance and variety of MPs within UFs. Furthermore, UFs increased in size with higher concentrations of MPs, which may have been related to elevated levels of MPs-induced hormones. This study provides new insights into the assessment of the relationship between exposure to MPs and human disease risk.

Detection and analysis of microplastics in tissues and blood of human cervical cancer patients.

Microplastics (MPs) can enter the reproductive system and can be potentially harmful to human reproductive health. In this study, 13 types of microplastics (MPs) were identified in patient blood, cancer samples, and paracarcinoma samples using Raman spectroscopy, with polyethylene, polypropylene and polyethylene-co-polypropylene being the most abundant polymer types. Futher, cotton was also found in our study. The diversity and abundance of MPs were higher in blood samples than in cancerous tissues, and there was a significant positive correlation between diversity (p < 0.05). Furthermore, the diversity and abundance of MPs in cancerous tissues were higher than in paracancerous tissues. The dimensional sizes of MPs in these samples were also very similar, with the majority of detected MPs being smaller in size. Correlation analysis showed that patient's age correlated with the abundance of MPs in blood samples, body mass index (BMI) correlated with the abundance of MPs in cancerous tissues. Notably, the frequency with which patients consume bottled water and beverages may also increase the abundance of MPs. This study identifies for the first time the presence of MPs and cotton in cancerous and paracancerous tissues of human cervical cancer patients. This provides new ideas and basic data to study the risk relationship between MP exposure and human health.

Visible Light-Induced Hydroacylation of Benzylidenemalononitriles with Aroyl Chlorides Using Silane as a Hydrogen Donor.

A novel photoredox-catalyzed direct hydroacylation of benzylidenemalononitriles is described. In this method, aroyl chlorides are employed as a readily available and affordable source of acyl groups, while commercially available tris(trimethylsilyl)silane acts as both the hydrogen atom donor and electron donor. By eliminating the requirement for complex synthesis of acyl precursors and hydrogen atom-transfer (HAT) reagents, this approach offers a convenient and efficient strategy for the hydroacylation of benzylidenemalononitriles.

Carbon nanotube supported cobalt nickel sulphide nano-catalyst for degradation of chloroquine phosphate with peroxymonosulphate.

Carbon nanotubes supported cobalt nickel sulphide nanoparticles (nano-NiCo2S4@CNTs) were successfully prepared by a hydrothermal method as heterogeneous catalyst which can be used as an activator of peroxymonosulphate (PMS) for the degradation of chloroquine phosphate (CQP). Based on characterisation techniques, the prepared catalyst has excellent surface properties and structural stability. When different concentrations of CQP were treated with 0.2 g/L nano-NiCo2S4@CNTs and 1.0 mM PMS, the highest degradation rate could reach 99.86% after 30 min. Under the interference of pH, common anions and humic acid in the water environment, the reaction system can still achieve high degradation efficiency, showing excellent anti-interference ability and practical applicability. Furthermore, in the nano-NiCo2S4@CNTs/PMS system, according to the identification results of reactive oxygen species, the free radical and non-free radical pathway are responsible for the degradation of CQP, and the PMS mechanism activation was comprehensively proposed. Twelve intermediate products were detected in the degradation process, and the possible degradation pathways of CQP were proposed. This toxicity analysis demonstrates that the intermediate products formed during CQP degradation pose lower environmental risks compared to the original pollutant. In addition, after using the catalyst four cycles, the removal efficiency of CQP remains above 80%, indicating the excellent reusability and low metal ion leaching characteristics. Therefore, the nano-NiCo2S4@CNTs synthesised in this research has broad application prospects in activating PMS for wastewater treatment.

Long non-coding RNA NRSN2-AS1 promotes ovarian cancer progression through targeting PTK2/ß-catenin pathway.

As a common malignant tumor among women, ovarian cancer poses a serious threat to their health. This study demonstrates that long non-coding RNA NRSN2-AS1 is over-expressed in ovarian cancer tissues using patient sample and tissue microarrays. In addition, NRSN2-AS1 is shown to promote ovarian cancer cell proliferation and metastasis both in vitro and in vivo. Mechanistically, NRSN2-AS1 stabilizes protein tyrosine kinase 2 (PTK2) to activate the ß-catenin pathway via repressing MG-53-mediated ubiquitinated degradation of PTK2, thereby facilitating ovarian cancer progression. Rescue experiments verify the function of the NRSN2-AS1/PTK2/ß-catenin axis and the effects of MG53 on this axis in ovarian cancer cells. In conclusion, this study demonstrates the key role of the NRSN2-AS1/PTK2/ß-catenin axis for the first time and explores its potential clinical applications in ovarian cancer.

MicroRNA-Based Discovery of Biomarkers, Therapeutic Targets, and Repositioning Drugs for Breast Cancer.

Breast cancer treatment can be improved with biomarkers for early detection and individualized therapy. A set of 86 microRNAs (miRNAs) were identified to separate breast cancer tumors from normal breast tissues (n = 52) with an overall accuracy of 90.4%. Six miRNAs had concordant expression in both tumors and breast cancer patient blood samples compared with the normal control samples. Twelve miRNAs showed concordant expression in tumors vs. normal breast tissues and patient survival (n = 1093), with seven as potential tumor suppressors and five as potential oncomiRs. From experimentally validated target genes of these 86 miRNAs, pan-sensitive and pan-resistant genes with concordant mRNA and protein expression associated with in-vitro drug response to 19 NCCN-recommended breast cancer drugs were selected. Combined with in-vitro proliferation assays using CRISPR-Cas9/RNAi and patient survival analysis, MEK inhibitors PD19830 and BRD-K12244279, pilocarpine, and tremorine were discovered as potential new drug options for treating breast cancer. Multi-omics biomarkers of response to the discovered drugs were identified using human breast cancer cell lines. This study presented an artificial intelligence pipeline of miRNA-based discovery of biomarkers, therapeutic targets, and repositioning drugs that can be applied to many cancer types.

Expression-Based Diagnosis, Treatment Selection, and Drug Development for Breast Cancer.

There is currently no gene expression assay that can assess if premalignant lesions will develop into invasive breast cancer. This study sought to identify biomarkers for selecting patients with a high potential for developing invasive carcinoma in the breast with normal histology, benign lesions, or premalignant lesions. A set of 26-gene mRNA expression profiles were used to identify invasive ductal carcinomas from histologically normal tissue and benign lesions and to select those with a higher potential for future cancer development (ADHC) in the breast associated with atypical ductal hyperplasia (ADH). The expression-defined model achieved an overall accuracy of 94.05% (AUC = 0.96) in classifying invasive ductal carcinomas from histologically normal tissue and benign lesions (n = 185). This gene signature classified cancer development in ADH tissues with an overall accuracy of 100% (n = 8). The mRNA expression patterns of these 26 genes were validated using RT-PCR analyses of independent tissue samples (n = 77) and blood samples (n = 48). The protein expression of PBX2 and RAD52 assessed with immunohistochemistry were prognostic of breast cancer survival outcomes. This signature provided significant prognostic stratification in The Cancer Genome Atlas breast cancer patients (n = 1100), as well as basal-like and luminal A subtypes, and was associated with distinct immune infiltration and activities. The mRNA and protein expression of the 26 genes was associated with sensitivity or resistance to 18 NCCN-recommended drugs for treating breast cancer. Eleven genes had significant proliferative potential in CRISPR-Cas9/RNAi screening. Based on this gene expression signature, the VEGFR inhibitor ZM-306416 was discovered as a new drug for treating breast cancer.

Modeling and minimization for thermal stress birefringence of an optical voltage transducer.

The thermal stress birefringence (TSB) is a big issue that destroys the sensing stability of the optical voltage transducer (OVT), and the existing research on the theoretical modeling and solution are not good enough to handle it. This paper presents a mathematical model of the TSB based on the photo-elastic effect, and then it is quantitatively calculated through the multiphysics coupling simulation. It shows that the asymmetric radial stresses in the electro-optic crystal are the root cause for the TSB, which results in a phase delay greater than 3.153°. On this basis, a method for TSB minimization to use the polyurethane buffer layer to enwrap around the crystal is proposed, which eliminates the random radial stress and improves the symmetry of stress distribution. Finally, the effectiveness of the proposed method is verified by simulation and experiment.

Approaches in the Treatment of Cesarean Scar Pregnancy and Risk Factors for Intraoperative Hemorrhage: A Retrospective Study.

Background: Cesarean scar pregnancy (CSP) involves a rare form of placental attachment that often leads to life-threatening conditions. The best treatment for CSP has been debated for decades. We aimed to evaluate the different treatments for CSP and analyzed the risk factors for intraoperative hemorrhage. Methods: CSP patients treated at the Affiliated Hospital of Jiangnan University were reviewed retrospectively from January 2014 to 2020. CSP was classified into three types based on the location and shape of gestational tissue, blood flow features, and thickness of the myometrium at the incision site. The clinical characteristics, types, approaches of treatment, and clinical outcomes of CSP were analyzed. Results: A total of 55 patients were included in this study, 29 (52.7%) of whom underwent transvaginal curettage after uterine artery embolization (UAE) and 22 (40%) of whom underwent transabdominal ultrasound-guided hysteroscopic curettage (USHC) in type I and II. Four patients (7.3%) classified as type III underwent laparoscopic cesarean scar resection (LCSR). Intraoperative blood loss, blood transfusion rate, and scar diverticulum were significantly higher in type II than in type I (P < 0.05). Even though USHC showed no differences in intraoperative blood loss, length of stay, and scar diverticulum compared with curettage after UAE (P > 0.05), superiority was found in surgical time and hospitalization cost (P < 0.05). Furthermore, the type of CSP (OR = 10.53, 95% CI: 1.69-65.57; P = 0.012) and diameter of the gestational sac (OR = 25.76, 95% CI: 2.67-248.20; P = 0.005) were found to be risk factors for intraoperative hemorrhage. Conclusions: Transabdominal ultrasound-guided hysteroscopic curettage is an effective and relatively safe treatment option for patients with CSP. Type of CSP and diameter of the gestational sac were found to be associated with excessive intraoperative hemorrhage.

Nano-Mechanical Properties and Creep Behavior of Ti6Al4V Fabricated by Powder Bed Fusion Electron Beam Additive Manufacturing.

Effects of scanning strategy during powder bed fusion electron beam additive manufacturing (PBF-EB AM) on microstructure, nano-mechanical properties, and creep behavior of Ti6Al4V alloys were compared. Results show that PBF-EB AM Ti6Al4V alloy with linear scanning without rotation strategy was composed of 96.9% α-Ti and 2.7% ß-Ti, and has a nanoindentation range of 4.11-6.31 GPa with the strain rate ranging from 0.001 to 1 s-1, and possesses a strain-rate sensitivity exponent of 0.053 ± 0.014. While PBF-EB AM Ti6Al4V alloy with linear and 90° rotate scanning strategy was composed of 98.1% α-Ti and 1.9% ß-Ti and has a nanoindentation range of 3.98-5.52 GPa with the strain rate ranging from 0.001 to 1 s-1, and possesses a strain-rate sensitivity exponent of 0.047 ± 0.009. The nanohardness increased with increasing strain rate, and creep displacement increased with the increasing maximum holding loads. The creep behavior was mainly dominated by dislocation motion during deformation induced by the indenter. The PBF-EB AM Ti6Al4V alloy with only the linear scanning strategy has a higher nanohardness and better creep resistance properties than the alloy with linear scanning and 90° rotation strategy. These results could contribute to understanding the creep behavior of Ti6Al4V alloy and are significant for PBF-EB AM of Ti6Al4V and other alloys.

Identification of Prognostic and Chemopredictive microRNAs for Non-Small-Cell Lung Cancer by Integrating SEER-Medicare Data.

This study developed a novel methodology to correlate genome-scale microRNA (miRNA) expression profiles in a lung squamous cell carcinoma (LUSC) cohort (n = 57) with Surveillance, Epidemiology, and End Results (SEER)-Medicare LUSC patients (n = 33,897) as a function of composite tumor progression indicators of T, N, and M cancer stage and tumor grade. The selected prognostic and chemopredictive miRNAs were extensively validated with miRNA expression profiles of non-small-cell lung cancer (NSCLC) patient samples collected from US hospitals (n = 156) and public consortia including NCI-60, The Cancer Genome Atlas (TCGA; n = 1016), and Cancer Cell Line Encyclopedia (CCLE; n = 117). Hsa-miR-142-3p was associated with good prognosis and chemosensitivity in all the studied datasets. Hsa-miRNA-142-3p target genes (NUP205, RAN, CSE1L, SNRPD1, RPS11, SF3B1, COPA, ARCN1, and SNRNP200) had a significant impact on proliferation in 100% of the tested NSCLC cell lines in CRISPR-Cas9 (n = 78) and RNA interference (RNAi) screening (n = 92). Hsa-miR-142-3p-mediated pathways and functional networks in NSCLC short-term survivors were elucidated. Overall, the approach integrating SEER-Medicare data with comprehensive external validation can identify miRNAs with consistent expression patterns in tumor progression, with potential implications for prognosis and prediction of chemoresponse in large NSCLC patient populations.

Merging Visible Light Photocatalysis and l-/d-Proline Catalysis: Direct Asymmetric Oxidative Dearomatization of 2-Arylindoles To Access C2-Quaternary Indolin-3-ones.

A mild and effective method for asymmetric synthesis of C2-quaternary indolin-3-ones directly from 2-arylindoles by combining visible light photocatalysis and organocatalysis is described. In this reaction, 2-substituted indoles undergo photocatalyzed oxidative dearomatization, followed by an organocatalyzed asymmetric Mannich reaction with ketones or aldehydes. Products with opposite configurations are easily obtained in satisfactory yields with excellent enantio- and diastereoselectivity by employing readily available l- and d-proline as chiral organocatalysts.

CL-PMI: A Precursor MicroRNA Identification Method Based on Convolutional and Long Short-Term Memory Networks.

MicroRNAs (miRNAs) are the major class of gene-regulating molecules that bind mRNAs. They function mainly as translational repressors in mammals. Therefore, how to identify miRNAs is one of the most important problems in medical treatment. Many known pre-miRNAs have a hairpin ring structure containing more structural features, and it is difficult to identify mature miRNAs because of their short length. Therefore, most research focuses on the identification of pre-miRNAs. Most computational models rely on manual feature extraction to identify pre-miRNAs and do not consider the sequential and spatial characteristics of pre-miRNAs, resulting in a loss of information. As the number of unidentified pre-miRNAs is far greater than that of known pre-miRNAs, there is a dataset imbalance problem, which leads to a degradation of the performance of pre-miRNA identification methods. In order to overcome the limitations of existing methods, we propose a pre-miRNA identification algorithm based on a cascaded CNN-LSTM framework, called CL-PMI. We used a convolutional neural network to automatically extract features and obtain pre-miRNA spatial information. We also employed long short-term memory (LSTM) to capture time characteristics of pre-miRNAs and improve attention mechanisms for long-term dependence modeling. Focal loss was used to improve the dataset imbalance. Compared with existing methods, CL-PMI achieved better performance on all datasets. The results demonstrate that this method can effectively identify pre-miRNAs by simultaneously considering their spatial and sequential information, as well as dealing with imbalance in the datasets.

Multi-Walled Carbon Nanotube-Induced Gene Expression Biomarkers for Medical and Occupational Surveillance.

As the demand for multi-walled carbon nanotube (MWCNT) incorporation into industrial and biomedical applications increases, so does the potential for unintentional pulmonary MWCNT exposure, particularly among workers during manufacturing. Pulmonary exposure to MWCNTs raises the potential for development of lung inflammation, fibrosis, and cancer among those exposed; however, there are currently no effective biomarkers for detecting lung fibrosis or predicting the risk of lung cancer resulting from MWCNT exposure. To uncover potential mRNAs and miRNAs that could be used as markers of exposure, this study compared in vivo mRNA and miRNA expression in lung tissue and blood of mice exposed to MWCNTs with in vitro mRNA and miRNA expression from a co-culture model of human lung epithelial and microvascular cells, a system previously shown to have a higher overall genome-scale correlation with mRNA expression in mouse lungs than either cell type grown separately. Concordant mRNAs and miRNAs identified by this study could be used to drive future studies confirming human biomarkers of MWCNT exposure. These potential biomarkers could be used to assess overall worker health and predict the occurrence of MWCNT-induced diseases.

Differential gene regulation in human small airway epithelial cells grown in monoculture versus coculture with human microvascular endothelial cells following multiwalled carbon nanotube exposure.

Concurrent with rising production of carbon-based engineered nanomaterials is a potential increase in respiratory and cardiovascular diseases due to exposure to nanomaterials in the workplace atmosphere. While single-cell models of pulmonary exposure are often used to determine the potential toxicity of nanomaterials in vitro, previous studies have shown that coculture cell models better represent the cellular response and crosstalk that occurs in vivo. This study identified differential gene regulation in human small airway epithelial cells (SAECs) grown either in monoculture or in coculture with human microvascular endothelial cells following exposure of the SAECs to multiwalled carbon nanotubes (MWCNTs). SAEC genes that either changed their regulation direction from upregulated in monoculture to downregulated in coculture (or vice versa) or had a more than a two-fold changed in the same regulation direction were identified. Genes that changed regulation direction were most often involved in the processes of cellular growth and proliferation and cellular immune response and inflammation. Genes that had a more than a two-fold change in regulation in the same direction were most often involved in the inflammatory response. The direction and fold-change of this differential gene regulation suggests that toxicity testing in monoculture may exaggerate cellular responses to MWCNTs, and coculture of cells may provide a more in-depth assessment of toxicological responses.

Concurrent Asymmetric Reactions Combining Photocatalysis and Enzyme Catalysis: Direct Enantioselective Synthesis of 2,2-Disubstituted Indol-3-ones from 2-Arylindoles.

The combination of photoredox and enzymatic catalysis for the direct asymmetric one-pot synthesis of 2,2-disubstituted indol-3-ones from 2-arylindoles through concurrent oxidization and alkylation reactions is described. 2-Arylindoles can be photocatalytically oxidized to 2-arylindol-3-one with subsequent enantioselective alkylation with ketones catalyzed by wheat germ lipase (WGL). The chiral quaternary carbon center at C2 of the indoles was directly constructed. This mode of concurrent photobiocatalysis provides a mild and powerful strategy for one-pot enantioselective synthesis of complex compounds. The experiments proved that other lipases containing structurally analogous catalytic triad in the active site also can catalyze the reaction in the same way. This reaction is the first example of combining the non-natural catalytic activity of hydrolases with visible-light catalysis for enantioselective organic synthesis and it does not require any cofactors.

SIN-KNO: A method of gene regulatory network inference using single-cell transcription and gene knockout data.

As a tool of interpreting and analyzing genetic data, gene regulatory network (GRN) could reveal regulatory relationships between genes, proteins, and small molecules, as well as understand physiological activities and functions within biological cells, interact in pathways, and how to make changes in the organism. Traditional GRN research focuses on the analysis of the regulatory relationships through the average of cellular gene expressions. These methods are difficult to identify the cell heterogeneity of gene expression. Existing methods for inferring GRN using single-cell transcriptional data lack expression information when genes reach steady state, and the high dimensionality of single-cell data leads to high temporal and spatial complexity of the algorithm. In order to solve the problem in traditional GRN inference methods, including the lack of cellular heterogeneity information, single-cell data complexity and lack of steady-state information, we propose a method for GRN inference using single-cell transcription and gene knockout data, called SINgle-cell transcription data-KNOckout data (SIN-KNO), which focuses on combining dynamic and steady-state information of regulatory relationship contained in gene expression. Capturing cell heterogeneity information could help understand the gene expression difference in different cells. So, we could observe gene expression changes more accurately. Gene knockout data could observe the gene expression levels at steady-state of all other genes when one gene is knockout. Classifying the genes before analyzing the single-cell data could determine a large number of non-existent regulation, greatly reducing the number of regulation required for inference. In order to show the efficiency, the proposed method has been compared with several typical methods in this area including GENIE3, JUMP3, and SINCERITIES. The results of the evaluation indicate that the proposed method can analyze the diversified information contained in the two types of data, establish a more accurate gene regulation network, and improve the computational efficiency. The method provides a new thinking for dealing with large datasets and high computational complexity of single-cell data in the GRN inference.

A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder.

Drug targets are biomacromolecules or biomolecular structures that bind to specific drugs and produce therapeutic effects. Therefore, the prediction of drug-target interactions (DTIs) is important for disease therapy. Incorporating multiple similarity measures for drugs and targets is of essence for improving the accuracy of prediction of DTIs. However, existing studies with multiple similarity measures ignored the global structure information of similarity measures, and required manual extraction features of drug-target pairs, ignoring the non-linear relationship among features. In this paper, we proposed a novel approach MDADTI for DTIs prediction based on MDA. MDADTI applied random walk with restart method and positive pointwise mutual information to calculate the topological similarity matrices of drugs and targets, capturing the global structure information of similarity measures. Then, MDADTI applied multimodal deep autoencoder to fuse multiple topological similarity matrices of drugs and targets, automatically learned the low-dimensional features of drugs and targets, and applied deep neural network to predict DTIs. The results of 5-repeats of 10-fold cross-validation under three different cross-validation settings indicated that MDADTI is superior to the other four baseline methods. In addition, we validated the predictions of the MDADTI in six drug-target interactions reference databases, and the results showed that MDADTI can effectively identify unknown DTIs.