Iodine-Mediated α-C-H Amination of Carbonyl Compounds via Nitrogen-Directed Oxidative Umpolung.

A new synthetic strategy for direct C(sp3)-H amination of carbonyl compounds at their α-carbon has been established employing molecular iodine and nitrogen-directed oxidative umpolung. In this transformation, iodine acts not only as an iodinating reagent but also as a Lewis acid catalyst, and both the nitrogen-containing moiety and the carbonyl group in the substrate play important roles. This synthetic approach is applicable to a broad variety of carbonyl substrates, including esters, ketones, and amides. Its features also include no requirement for transition metals, mild reaction conditions, short reaction times, and gram-scale synthesis.

Development of a Fast Raman-Assisted Antibiotic Susceptibility Test (FRAST) for the Antibiotic Resistance Analysis of Clinical Urine and Blood Samples.

Human health is at great risk due to the spreading of antimicrobial resistance (AMR). The lengthy procedure of conventional antimicrobial susceptibility testing (AST) usually requires a few days. We developed a fast Raman-assisted antibiotic susceptibility test (FRAST), which detects single bacterial metabolic activity in the presence of antibiotics, using Raman single-cell spectroscopy. It was found that single-cell Raman spectra (SCRS) would show a clear and distinguishable Raman band at the "silent zone" (2000-2300 cm-1), due to the active incorporation of deuterium from heavy water (D2O) by antibiotic-resistant bacteria. This pilot study has compared the FRAST and the conventional AST for six clinical standard quality controls (four Gram-negative and two Gram-positive bacteria strains) in response to 38 antibiotics. In total, 3200 treatments have been carried out and approximately 64 000 SCRS have been acquired for FRAST analysis. The result showed an overall agreement of 88.0% between the FRAST and the conventional AST assay. The gram-staining classification based on the linear discriminant analysis (LDA) model of SCRS was developed, seamlessly coupling with the FRAST to further reduce the turnaround time. We applied the FRAST to real clinical analysis for nine urinary infectious samples and three sepsis samples. The results were consistent with MALDI-TOF identification and the conventional AST. Under the optimal conditions, the "sample to report" of the FRAST could be reduced to 3 h for urine samples and 21 h for sepsis samples. The FRAST provides fast and reliable susceptibility tests, which could speed up microbiological analysis for clinical practice and facilitate antibiotic stewardship.

Synthesis of cyclic gem-dinitro compounds via radical nitration of 1,6-diynes with Fe(NO3)3·9H2O.

Cyclic gem-dinitro compounds were obtained via nitration of 1,6-diynes using Fe(NO3)3·9H2O as the nitrating agent. The reaction proceeds at room temperature using a cheap nitrating agent. A number of gem-dinitro cyclic compounds were obtained in moderate to good yields. The reaction offers an easy and convenient protocol to prepare gem-dinitro compounds, which are not easily obtained via the known procedures.

Rhodium-catalyzed triazole-directed C-H bond functionalization of arenes with diazo compounds.

Heterocyclic compounds bearing 1,2,3-triazole scaffolds have found wide application both in medicinal chemistry and materials science. In this paper, rhodium(iii)-catalyzed triazole-directed alkylation reactions of arenes using diazo compounds as the alkylating agents are described. A number of polysubstituted arenes were provided from easily available materials in good yields under mild conditions. The reactions proceed via triazole-directed ortho C-H bond activation and subsequent carbene insertion originating from diazo compounds.

Evidence Construction of Chuankezhi Injection Against Chronic Obstructive Pulmonary Disease: A Systematic Review and Network Pharmacology.

Objective: Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease with high prevalence, morbidity, and mortality. Chuankezhi (CKZ) injection, a Chinese patent medicine, has been commonly used for treating COPD. This study evaluated the clinical efficacy of CKZ injections in COPD patients and explored potential underlying mechanisms by integrating meta-analysis and network pharmacology. Research Methods: Randomized controlled trials (RCTs) were search in database by Web of Science, Cochrane Library and PubMed as of November 2022 for literature collection, and the Review Manager 5.4 was used to analyze the data. Through the network pharmacology method, the chemical components and their targets, as well as the disease targets were further analyzed. Results: A total of 15 RCTs including 1212 patients were included. The results of meta-analysis showed that CKZ injection can significantly improve the clinical effective rate (RR = 1.25, 95% CI: 1.14 to 1.36), and the clinical advantage was that it can significantly reduced acute exacerbation rate (RR = 0.29, 95% CI: 0.12 to 0.70) and COPD assessment test (CAT) scores (MD =-4.62, 95% CI:-8.966 to-0.28). A total of 31 chemical compounds and 178 potential targets for CKZ injection were obtained from the online databases. Molecular docking revealed that most key components and targets could form stable structure. Conclusion: This systematic review with meta-analysis and network pharmacology demonstrates that CKZ could effectively improve the clinical efficacy and safety in the treatment of COPD. Such efficacy may be related to an anti-inflammatory effect and immunoregulation of CKZ via multiple components, multiple targets and multiple pathways.

Leveraging single-cell Raman spectroscopy and single-cell sorting for the detection and identification of yeast infections.

Invasive fungal infection serves as a great threat to human health. Discrimination between fungal and bacterial infections at the earliest stage is vital for effective clinic practice; however, traditional culture-dependent microscopic diagnosis of fungal infection usually requires several days, meanwhile, culture-independent immunological and molecular methods are limited by the detectable type of pathogens and the issues with high false-positive rates. In this study, we proposed a novel culture-independent phenotyping method based on single-cell Raman spectroscopy for the rapid discrimination between fungal and bacterial infections. Three Raman biomarkers, including cytochrome c, peptidoglycan, and nucleic acid, were identified through hierarchical clustering analysis of Raman spectra across 12 types of most common yeast and bacterial pathogens. Compared to those of bacterial pathogens, the single cells of yeast pathogens demonstrated significantly stronger Raman peaks for cytochrome c, but weaker signals for peptidoglycan and nucleic acid. A two-step protocol combining the three biomarkers was established and able to differentiate fungal infections from bacterial infections with an overall accuracy of 94.9%. Our approach was also used to detect ten raw urinary tract infection samples. Successful identification of fungi was achieved within half an hour after sample obtainment. We further demonstrated the accurate fungal species taxonomy achieved with Raman-assisted cell ejection. Our findings demonstrate that Raman-based fungal identification is a novel, facile, reliable, and with a breadth of coverage approach, that has a great potential to be adopted in routine clinical practice to reduce the turn-around time of invasive fungal disease (IFD) diagnostics.

Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy.

Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h.

Synthesis of 1,2-Fused/Disubstituted Benzimidazoles and Benzimidazolium Salts by I2-Mediated sp3 C-H Amination.

An intramolecular sp3 C-H amination reaction of aniline derivatives has been established. This reaction employs molecular iodine (I2) under transition-metal-free conditions and produces 1,2-fused or 1,2-disubstituted benzimidazoles. This operationally simple synthetic process works well with N-substituted aniline substrates, forming novel benzimidazolium frameworks. Under the optimal reaction conditions, a broad variety of 1,2-fused/disubstituted benzimidazoles and benzimidazolium salts, including several bioactive compounds, were synthesized from readily accessible precursors in an efficient and scalable fashion.

Experimental Study of Yield Surface in Polypropylene Considering Rate Effect: Stress Path Dependence.

In order to investigate the yield surface evolution of polypropylene (PP) under dynamic impact and the relationship between yield surface parameters and the strain rate, five shear-compression specimens (SCSs) with different inclination angles are designed and produced to explore the yield behavior of PP under dynamic loading. Dynamic combined stress loading paths with different compression-shear ratios are achieved by the split Hopkinson pressure bar (SHPB). The evolution laws of the compressive stress and shear stress in the measurement region during the PP SCS compressive deformation process are analyzed. In terms of mechanical response, PP under combined compression-shear loading is of visco-elasticity plasticity and its deformation undergoes a three-stage transition, namely "unyield→yield→failure". The yield characteristics of PP are found to be affected not only by the hydrostatic pressure but also by the stress path. According to the Hu-Pae yield criterion, the dynamic yield surface and model parameters of PP are obtained, and the relationship between the yield surface and the strain rate is ascertained. These findings contribute to deepening the research on the mechanical response characteristics of PP-based materials.

High-Speed Diagnosis of Bacterial Pathogens at the Single Cell Level by Raman Microspectroscopy with Machine Learning Filters and Denoising Autoencoders.

Accurate and rapid identification of infectious bacteria is important in medicine. Raman microspectroscopy holds great promise in performing label-free identification at the single-cell level. However, due to the naturally weak Raman signal, it is a challenge to build extensive databases and achieve both accurate and fast identification. Here, we used signal-to-noise ratio (SNR) as a standard indicator for Raman data quality and performed bacterial identification using 11, 141 single-cell Raman spectra from nine bacterial strains. Subsequently, using two machine learning methods, a simple filter, and a neural network-based denoising autoencoder (DAE), we demonstrated 92% (simple filter using 1 s/cell spectra) and 84% (DAE using 0.1 s/cell spectra) identification accuracy. Our machine learning-aided Raman analysis paves the way for high-speed Raman microspectroscopic clinical diagnostics.

Raman spectroscopy and machine learning for the classification of breast cancers.

Breast cancer is a major health threat for women. The drug responses associated with different breast cancer subtypes have obvious effects on therapeutic outcomes; therefore, the accurate classification of breast cancer subtypes is critical. Breast cancer subtype classification has recently been examined using various methods, and Raman spectroscopy has emerged as an effective technique that can be used for noninvasive breast cancer analysis. However, the accurate and rapid classification of breast cancer subtypes currently requires a great deal of effort and experience with the processing and analysis of Raman spectra data. Here, we adopted Raman spectroscopy and machine learning techniques to simplify and accelerate the process used to distinguish normal from breast cancer cells and classify breast cancer subtypes. Raman spectra were obtained from cultured breast cancer cell lines, and the data were analyzed by two machine learning algorithms: principal component analysis (PCA)-discriminant function analysis (DFA) and PCA-support vector machine (SVM). The accuracies with which these two algorithms were able to distinguish normal breast cells from breast cancer cells were both greater than 97%, and the accuracies of breast cancer subtype classification for both algorithms were both greater than 92%. Moreover, our results showed evidence to support the use of characteristic Raman spectral features as cancer cell biomarkers, such as the intensity of intrinsic Raman bands, which increased in cancer cells. Raman spectroscopy combined with machine learning techniques provides a rapid method for breast cancer analysis able to reveal differences in intracellular compositions and molecular structures among subtypes.

Stain-free Gram staining classification of pathogens via single-cell Raman spectroscopy combined with machine learning.

Gram staining (GS) is one of the routine microbiological operations to classify bacteria based on the cell wall structure. Accurate GS classification of pathogens is of great significance since it helps correct administration of antimicrobial treatment. The laborious procedure and low sensitivity results related to conventional GS have resulted in reluctance among clinicians. In this study, we integrate confocal Raman spectroscopy and machine learning techniques to distinguish Gram-negative (GN) or Gram-positive (GP) bacteria. A single-cell Raman database including seven most common clinical pathogens (three GP strains and four GN strains) was constructed. Machine learning algorithms including the support-vector machine (SVM), k-nearest neighbors' algorithm (k-NN), gradient boosting machine (GBM), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) were trained to achieve the binary classification for GS. With such a relatively small database, the SVM model achieved the highest accuracy of 98.1%. The molecular signatures of GN and GP embedded in their Raman fingerprints were identified with hierarchical cluster analysis (HCA). The results indicated that Raman peaks for peptidoglycan and teichoic acid were the most significant factors that contributed to accurate classification. The Raman machine learning approach could greatly enhance the diagnosis of pathogenic infections.

A unique pathway of PtNi nanoparticle formation observed with liquid cell transmission electron microscopy.

An understanding of nanoparticle growth is significant for controlled synthesis of nanomaterials with desired physical and chemical properties. Here we report the in situ study of platinum-nickel alloy nanoparticle growth using in situ liquid cell transmission electron microscopy (TEM). The observation revealed that Ni dendrites can form at the beginning and subsequently PtNi nanoparticles nucleate and grow by consumption of the Ni dendrites. The resulting PtNi alloy nanoparticles have a narrow size distribution with an average diameter of 3.7 nm, which are smaller than those obtained via classical solution growth. This work shed light on using such a unique growth pathway for the synthesis of novel nanoparticles.

Unbridged Rh(ii)-Rh(ii) complexes of N-heterocyclic carbenes and reactions with O2 to form dirhodium(µ-η1:η1-O2) complexes.

Dimeric rhodium(ii) complexes and rhodium(iii) peroxide complexes supported by N-heterocyclic carbenes were synthesized. The treatment of [Ag3(L)2](PF6)3 (L = bis(N-pyridylimidazolylidenyl)methane) with [Rh(COD)Cl] afforded [Rh(L)(CH3CN)]2(PF6)4 in which two [Rh(L)] moieties are bound together via an unsupported Rh-Rh bond. The substitution of axial acetonitrile by phosphines led to the insertion of O2 into the Rh-Rh bond and the isolation of [Rh(L)(PPh3)]2(µ-η1:η1-O2)(PF6)4 and [Rh(L)(PCy3)]2(µ-η1:η1-O2) (PF6)4.

Sterically hindered N-heterocyclic carbene/palladium(ii) catalyzed Suzuki-Miyaura coupling of nitrobenzenes.

Palladium-catalyzed denitrative Suzuki coupling of nitroarenes using 2-aryl-5-(2,4,6-triisopropylphenyl)-2,3-imidazolylidene[1,5-a]pyridines as the ligands is described. The key to success is the use of the NHC ligands which show strong donating ability and suitable steric hindrance allowing the successful oxidative addition of Ar-NO2 bonds. Both aromatic and aliphatic boronic acids are tolerated, and a variety of biphenyls and alkylarenes were obtained in good to excellent yields.

One-pot synthesis of 2,3-disubstituted dihydrobenzofurans and benzofurans via rhodium-catalyzed intramolecular C-H insertion reaction.

Intramolecular sp(3) C-H insertion reaction of α-imino rhodium carbene generated from N-sulfonyl-1,2,3-triazoles has been described. A number of 2,3-dihydrobenzofuran and benzofuran derivatives have been obtained in good to excellent yields.