On-site monitoring of nandrolone in cattle farming samples by portable atmospheric pressure chemical ionization mass spectrometry with ambient sampling.

Nandrolone (NT) is a type of androgen anabolic steroid that is often illegally used in cattle farming, leading to unpredictable harm to human health via the food chain. In this study, a rapid detection method for NT in the samples of cattle farming was established using a portable mass spectrometer. The instrument parameters were optimized, including a thermal desorption temperature of 220 °C, a pump speed of 30 %, an APCI ionization voltage of 3900 v, and an injection volume of 6 µL. The samples of bovine urine, feed, sewage, and tissue were selected, and extracted using a solution of methanol:acetonitrile (1:1, v/v), followed by spiking a NT standard solution (1000 ng·mL-1) and ionization through the APCI ion source for detection. The results showed that NT could not be detected in beef and feed due to the complexity of the matrix, while clear signals of NT ions were observed in bovine urine and sewage samples, with LODs of 1000 and 100 ng·mL-1, respectively. Furthermore, quantitative analysis was attempted, and a good linear relationship (R2 = 0.9952) was observed for NT in sewage within the range of 100 to 1000 ng·mL-1. At spiked levels of 100, 500, 1000 and 2000 ng mL-1, the recovery rates ranged from 74.3 % to 92.8 %, with a relative standard deviation (n = 6) of less than 15 %. In conclusion, this detection method offers the advantages of simplicity, rapidity, strong timeliness, and specificity, making it suitable for on-site detection. It can be used for qualitative screening of nandrolone in bovine urine and quantitative analysis of nandrolone in sewage.

Synthesis of 2,2-difluoro-1,3-diketone and 2,2-difluoro-1,3-ketoester derivatives using fluorine gas.

Solutions of 1,3-diketones and 1,3-ketoester derivatives react with fluorine to give the corresponding 2,2-difluoro-1,3-dicarbonyl derivatives in the presence of quinuclidine. Quinuclidine reacts with fluorine in situ to generate a fluoride ion that facilitates limiting enolization processes, and an electrophilic N-F fluorinating agent that is reactive towards neutral enol species.

Quantification and visualization of cis-regulatory dynamics in single-cell multi-omics data with TREASMO.

Recent advances in single-cell multi-omics technologies have provided unprecedented insights into regulatory processes. We introduce TREASMO, a versatile Python package designed to quantify and visualize transcriptional regulatory dynamics in single-cell multi-omics datasets. TREASMO has four modules, spanning data preparation, correlation quantification, downstream analysis and visualization, enabling comprehensive dataset exploration. By introducing a novel single-cell gene-peak correlation strength index, TREASMO facilitates accurate identification of regulatory changes at single-cell resolution. Validation on a hematopoietic stem and progenitor cell dataset showcases TREASMO's capacity in quantifying the gene-peak correlation strength at the single-cell level, identifying regulatory markers and discovering temporal regulatory patterns along the trajectory.

nBu4N+[AgI(CF3)2]-: Trifluoromethylated Argentate Derived from Fluoroform and Its Reaction with (Hetero)Aryl Diazonium Salts.

The preparation of a well-defined trifluoromethylated argentate nBu4N+[Ag(CF3)2]- 1 from fluoroform was described. The complex was stable in the solid state and in solution under an inert atmosphere. Treatment of a variety of (hetero)aryl diazonium tetrafluoroborates with nBu4N+[Ag(CF3)2]- 1 generated trifluoromethylated (hetero)arenes in good to excellent yields. Preliminary experiments were conducted, and a reasonable mechanism of the reaction was proposed.

Unravelling spatial gene associations with SEAGAL: a Python package for spatial transcriptomics data analysis and visualization.

SUMMARY: In the era where transcriptome profiling moves toward single-cell and spatial resolutions, the traditional co-expression analysis lacks the power to fully utilize such rich information to unravel spatial gene associations. Here, we present a Python package called Spatial Enrichment Analysis of Gene Associations using L-index (SEAGAL) to detect and visualize spatial gene correlations at both single-gene and gene-set levels. Our package takes spatial transcriptomics datasets with gene expression and the aligned spatial coordinates as input. It allows for analyzing and visualizing genes' spatial correlations and cell types' colocalization within the precise spatial context. The output could be visualized as volcano plots and heatmaps with a few lines of code, thus providing an easy-yet-comprehensive tool for mining spatial gene associations. AVAILABILITY AND IMPLEMENTATION: The Python package SEAGAL can be installed using pip: https://pypi.org/project/seagal/. The source code and step-by-step tutorials are available at: https://github.com/linhuawang/SEAGAL.

Constructing interface engineering and tailoring a nanoflower-like FeP/CoP heterostructure for enhanced oxygen evolution reaction.

The inexpensive and highly efficient electrocatalysts toward oxygen evolution reaction (OER) in water splitting electrolysis have displayed promising practical applications to relieve energy crisis. Herein, we prepared a high-yield and structurally regulated bimetallic cobalt-iron phosphide electrocatalyst by a facile one-pot hydrothermal reaction and subsequent low-temperature phosphating treatment. The tailoring of nanoscale morphology was achieved by varying the input ratio and phosphating temperature. Thus, an optimized FeP/CoP-1-350 sample with the ultra-thin nanosheets assembled into a nanoflower-like structure was obtained. FeP/CoP-1-350 heterostructure displayed remarkable activity toward the OER with a low overpotential of 276 mV at a current density of 10 mA cm-2, and a low Tafel slope of only 37.71 mV dec-1. Long-lasting durability and stability were maintained with the current with almost no obvious fluctuation. The enhanced OER activity was attributed to the presence of copious active sites from the ultra-thin nanosheets, the interface between CoP and FeP components, and the synergistic effect of Fe-Co elements in the FeP/CoP heterostructure. This study provides a feasible strategy to fabricate highly efficient and cost-effective bimetallic phosphide electrocatalysts.

From Diabetes to Diabetic Complications: Role of Autophagy.

Diabetes and its complications reduce quality of life and are life-limiting. At present, diabetes treatment consists of hypoglycemic agents to control blood glucose and the use of insulin-sensitizing drugs to overcome insulin resistance. In diabetes, autophagy is impaired and thus there is poor intracellular environment homeostasis. Pancreatic ß-cells and insulin target tissues are protected by enhancing autophagy. Autophagy decreases ß-cell apoptosis, promotes ß-cell proliferation, and alleviates insulin resistance. Autophagy in diabetes is regulated by the mammalian target of rapamycin (mTOR)/adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) pathway and others. Autophagy enhancers can likely be used as a treatment for diabetes and its complications. This review examines the evidence linking autophagy to diabetes.

Automatic Classification of Mass Shape and Margin on Mammography with Artificial Intelligence: Deep CNN Versus Radiomics.

The purpose of this study is to test the feasibility for deep CNN-based artificial intelligence methods for automatic classification of the mass margin and shape, while radiomic feature-based machine learning methods were also implemented in this study as baseline and for comparison study. In this retrospective study, 596 patients with breast mass that underwent mammography from 4 hospitals were enrolled from January 2012 to October 2019. Margin and shape of each mass were annotated according to BI-RADS by 2 experienced radiologists. Deep CNN-based AI was implemented for the classification task based on Resnet50. Balanced sampler and CBAM were also used to improve the performance of the Deep CNNs. As comparison, image texture features were extracted and then dimensionality reduction methods (such as PCA, ICA) and classical classifiers (such as SVM, DT, KNN) were used for classification task. Based on Python programming software, accuracy (ACC) was used to evaluate the performance of the model, and the model with the highest ACC value was selected. Deep CNN based on Resnet50 with balanced sampler and CBAM achieved the best performance for both margin and shape classification, with ACC of 0.838 and 0.874, respectively. For the radiomics-based machine learning, the best performance for margin was achieved as 0.676 by the combination of FA + RF, while the best performance for shape was 0.802 by the combination of PCA + MLP. The feasibility for automatic classification with coarse labeling of the mass shape and margin was testified with the deep CNN-based AI methods, while radiomic feature-based machine learning methods achieved inferior classification results.

scGREAT: Graph-based regulatory element analysis tool for single-cell multi-omics data.

Motivation: With the development in single-cell multi-omics sequencing technology and data integration algorithms, we have entered the single-cell multi-omics era. Current multi-omics analysis algorithms failed to systematically dissect the heterogeneity within the datasets when inferring cis-regulatory events. Thus, there is a need for cis-regulatory element inferring algorithms that considers the cellular heterogeneity. Results: Here, we propose scGREAT, a single-cell multi-omics regulatory state analysis Python package with a rapid graph-based correlation measurement L. The graph-based correlation method assigns each cell a local L index, pinpointing specific cell groups of certain regulatory states. Such single-cell resolved regulatory state information enables the heterogeneity analysis equipped in the package. Applying scGREAT to the 10X Multiome PBMC dataset, we demonstrated how it could help subcluster cell types, infer regulation-based pseudo-time trajectory, discover feature modules, and find cluster-specific regulatory gene-peak pairs. Besides, we showed that global L index, which is the average of all local L values, is a better replacement for Pearson's r in ruling out confounding regulatory relationships that are not of research interests. Availability: https://github.com/ChaozhongLiu/scGREAT.

Unraveling Spatial Gene Associations with SEAGAL: a Python Package for Spatial Transcriptomics Data Analysis and Visualization.

Summary: In the era where transcriptome profiling moves towards single-cell and spatial resolutions, the traditional co-expression analysis lacks the power to fully utilize such rich information to unravel spatial gene associations. Here we present a Python package called Spatial Enrichment Analysis of Gene Associations using L-index (SEAGAL) to detect and visualize spatial gene correlations at both single-gene and gene-set levels. Our package takes spatial transcriptomics data sets with gene expression and the aligned spatial coordinates as input. It allows for analyzing and visualizing spatial correlations at both single-gene and gene-set levels. The output could be visualized as volcano plots and heatmaps with a few lines of code, thus providing an easy-yet-comprehensive tool for mining spatial gene associations. Availability and Implementation: The Python package SEAGAL can be installed using pip: https://pypi.org/project/seagal/ . The source code and step-by-step tutorials are available at: https://github.com/linhuawang/SEAGAL . Contact: linhuaw@bcm.edu.

Clinical application of Mycobacterium RT-PCR assay using various specimens for the rapid detection of lymph node tuberculosis: A diagnostic accuracy study.

To evaluate the diagnostic accuracy of the Capital Bio Mycobacterium real-time polymerase chain reaction assay Capital Bio assay for lymph node (LN) tuberculosis (LNTB), and to further compare the effect of different types of LN specimens on the detection capability of the test. We retrospectively analyzed the medical records of LNTB patients who met the inclusion criteria. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve of Capital Bio assay were calculated to evaluate its diagnostic accuracy compared with the final clinical diagnosis as reference standard. Three hundred sixty-four patients were included in the study. The overall sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve of the Capital Bio assay for LNTB were 74.4%, 100.0%, 100.0%, 34.9%, and 0.87, respectively. For the pus specimens, these values for Capital Bio assay were 93.2%, 100.0%, 100.0%, 27.3%, 0.97, respectively. For the core needle biopsy specimens, these values were 65.9%, 100.0%, 100.0%, 33.3%, and 0.83, respectively. For the fine-needle aspiration specimens, these values were 60.0%, 100.0%, 100.0%, 53.9%, and 0.80, respectively. For the tissue, these values were 59.3%, 100.0%, 100.0%, 33.3%, 0.80, respectively. The Capital Bio assay had good effective for the diagnosis of LNTB. Compared to LN fine-needle aspiration and core needle biopsy specimens and tissue specimens, pus specimens were more suitable for molecular testing and had the best diagnostic efficacy.

Study on the treatment of postmenopausal osteoporosis with quercetin in Liuwei Dihuang Pill based on network pharmacology.

BACKGROUND: Liuwei Dihuang Pill (LP) was verified to alleviate postmenopausal osteoporosis (PMOP) development. Nevertheless, the major constituent of LP and the related network pharmacology study remain unexplored. METHODS: Protein-protein interaction was established to identify the downstream target of LP in PMOP, and the related signaling pathway was investigated by bioinformatics analysis. MC3T3-E1 cells were added to ferric ammonium citrate (FAC) to mimic osteoporosis in vitro. The osteoblasts were identified by Alizarin red staining. Western blot was applied to evaluate protein levels. In addition, Cell Counting Kit-8 (CCK8) assay was applied to assess cell viability, and cell apoptosis was assessed by flow cytometry. RESULTS: Quercetin was the major constituent of LP. In addition, quercetin significantly reversed FAC-induced inhibition of osteogenic differentiation in MC3T3-E1 cells. In addition, quercetin notably abolished the FAC-induced upregulation of Bax, Caspase-3, FOS, JUN, TGFB1 and PPARD. In contrast, Bcl-2, p-mTOR/mTOR, p-AKT/AKT and p-PI3K/PI3K levels in MC3T3-E1 cells were reduced by FAC, which was restored by quercetin. Meanwhile, FAC notably inhibited the viability of MC3T3-E1 cells via inducing apoptosis, but this impact was abolished by quercetin. Furthermore, quercetin could reverse pcDNA3.1-FOS-mediated growth of FAC-treated osteoblasts by mediating PI3K/AKT/mTOR signaling. CONCLUSION: Quercetin alleviated the progression of PMOP via activation of PI3K/AKT/mTOR signaling. Hence, this study would shed novel insights into discovering new methods against PMOP.

Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss.

BACKGROUND: Single-cell omics technology is rapidly developing to measure the epigenome, genome, and transcriptome across a range of cell types. However, it is still challenging to integrate omics data from different modalities. Here, we propose a variation of the Siamese neural network framework called MinNet, which is trained to integrate multi-omics data on the single-cell resolution by using graph-based contrastive loss. RESULTS: By training the model and testing it on several benchmark datasets, we showed its accuracy and generalizability in integrating scRNA-seq with scATAC-seq, and scRNA-seq with epitope data. Further evaluation demonstrated our model's unique ability to remove the batch effect, a common problem in actual practice. To show how the integration impacts downstream analysis, we established model-based smoothing and cis-regulatory element-inferring method and validated it with external pcHi-C evidence. Finally, we applied the framework to a COVID-19 dataset to bolster the original work with integration-based analysis, showing its necessity in single-cell multi-omics research. CONCLUSIONS: MinNet is a novel deep-learning framework for single-cell multi-omics sequencing data integration. It ranked top among other methods in benchmarking and is especially suitable for integrating datasets with batch and biological variances. With the single-cell resolution integration results, analysis of the interplay between genome and transcriptome can be done to help researchers understand their data and question.

Region-specific denoising identifies spatial co-expression patterns and intra-tissue heterogeneity in spatially resolved transcriptomics data.

Spatially resolved transcriptomics is a relatively new technique that maps transcriptional information within a tissue. Analysis of these datasets is challenging because gene expression values are highly sparse due to dropout events, and there is a lack of tools to facilitate in silico detection and annotation of regions based on their molecular content. Therefore, we develop a computational tool for detecting molecular regions and region-based Missing value Imputation for Spatially Transcriptomics (MIST). We validate MIST-identified regions across multiple datasets produced by 10x Visium Spatial Transcriptomics, using manually annotated histological images as references. We benchmark MIST against a spatial k-nearest neighboring baseline and other imputation methods designed for single-cell RNA sequencing. We use holdout experiments to demonstrate that MIST accurately recovers spatial transcriptomics missing values. MIST facilitates identifying intra-tissue heterogeneity and recovering spatial gene-gene co-expression signals. Using MIST before downstream analysis thus provides unbiased region detections to facilitate annotations with the associated functional analyses and produces accurately denoised spatial gene expression profiles.

Nodakenin attenuates cartilage degradation and inflammatory responses in a mice model of knee osteoarthritis by regulating mitochondrial Drp1/ROS/NLRP3 axis.

Osteoarthritis (OA) is a common degenerative disease with few treatments. In traditional Chinese medicine (TCM), Radix Angelicae biseratae (RAB) is commonly used to treat OA. Nodakenin (Nod) is one main coumarin active component in RAB and exhibits anti-inflammatory, anti-oxidative, and anti-apoptotic effects. Reactive oxygen species (ROS) produced by mitochondria play a vital role in the pathogenesis of OA. We hypothesized that Nod might ameliorate cartilage degradation and inflammatory responses by regulating the mitochondrial Drp1/ROS/NLRP3 axis. With this, the effects of Nod on a mouse model of knee OA and activated primary chondrocytes were assessed. The results showed that Nod intervention improved bone volume, lowered trabecular separation, and increased trabecular number in the subchondral bone. Nod decreased the Osteoarthritis Research Society International (OARSI) scores and increased collagen II-positive areas in the articular cartilage of the tibial plateau. Compared with OA mice, Nod-treated animals exhibited lower levels of inflammatory factors in the serum and synovitis of the knee joint. In vitro results indicated that Nod suppressed dynamin-related protein 1 (Drp1) phosphorylation and massive ROS production by Drp1-dependent mitochondrial fission in lipopolysaccharide-stimulated chondrocytes. Moreover, Nod inhibited the mRNA levels of inflammatory cytokines (COX 2, IL-1ß, and TNF-α), nod-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome, and matrix metalloproteinase 13 expression in activated chondrocytes. In conclusion, Nod attenuates cartilage degradation and inflammatory responses in mice with OA by regulating the mitochondrial Drp1/ROS/NLRP3 axis, suggesting its potential for OA therapy.

Integrating multimodal data through interpretable heterogeneous ensembles.

Motivation: Integrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems. Results: We propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms and uses heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data and mortality due to coronavirus disease 2019 (COVID-19) from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling. Availability and implementation: Code and data are available at https://github.com/GauravPandeyLab/ensemble_integration. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Endoscopic-Assisted Trans-Lateral Ventricular Transchoroidal Fissure Trans-Aqueductal Approach for Evacuation of Severe Intraventricular Hemorrhage.

BACKGROUND: Intraventricular hemorrhage (IVH) is one of the most fatal types of intracerebral hemorrhage (ICH), especially when the third and the fourth ventricles are involved. The use of external ventricular drainage is limited for evacuation of hemorrhage in the lateral ventricles. Endoscopic surgery can provide visualized evacuation of the hemorrhage in the lateral and third ventricles. However, it is usually challenging to access the fourth ventricle using a routine endoscopic approach. METHODS: We have reported 3 cases of severe IVH with cast fourth ventricles treated using an endoscopic-assisted trans-lateral ventricular transchoroidal fissure trans-aqueductal approach. RESULTS: The average preoperative Graeb score was 11, and the average IVH volume was 75.12 mL. The IVH evacuation rate was 97.5%-100%. The average Glasgow coma scale score had increased to 12 at discharge from 6.6 at admission. At 3 months after surgery, the average modified Rankin scale score was 3. No cerebrospinal fluid shunt had been required and no surgery-related complication had occurred in any patient. CONCLUSIONS: Our results have shown that the endoscopic-assisted trans-lateral ventricular transchoroidal fissure trans-aqueductal approach is a feasible and safe endoscopic option that can achieve one-off complete removal of clots in all 4 ventricles in patients with severe IVH.

Integrating multimodal data through interpretable heterogeneous ensembles.

Motivation: Integrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities, but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems. Results: We propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms, and uses effective heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data, and mortality due to COVID-19 from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen (BUN) and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling. Availability: Code and data are available at https://github.com/GauravPandeyLab/ensemble_integration . Contact: gaurav.pandey@mssm.edu.

Planar Tetraindolodipleiadiene via Zirconium-Promoted Intramolecular Indolyl C4-H Homocoupling.

A novel N-rimmed PAH molecule containing a dipleiadiene core (TIDP) was designed and synthesized from indole, wherein a ZrCl4-promoted intramolecular C4-H homocoupling reaction of the indole moieties was the key approach. TIDP exhibited a nearly full planar structure and antiaromaticity of the two embedded heptagonal rings. The extremely stable radical cation TIDP•+·PF6- was isolated quantitatively by oxidation with AgPF6.