Chiral Assembly of Perovskite Nanocrystals: Sensitive Discrimination of Amino Acid Enantiomers.

Chirality is a widespread phenomenon in nature and in living organisms and plays an important role in living systems. The sensitive discrimination of chiral molecular enantiomers remains a challenge in the fields of chemistry and biology. Establishing a simple, fast, and efficient strategy to discriminate the spatial configuration of chiral molecular enantiomers is of great significance. Chiral perovskite nanocrystals (PNCs) have attracted much attention because of their excellent optical activity. However, it is a challenge to prepare perovskites with both chiral and fluorescence properties for chiral sensing. In this work, we synthesized two chiral fluorescent perovskite nanocrystal assembly (PNA) enantiomers by using l- or d-phenylalanine (Phe) as chiral ligands. PNA exhibited good fluorescence recognition for l- and d-proline (Pro). Homochiral interaction led to fluorescence enhancement, while heterochiral interaction led to fluorescence quenching, and there is a good linear relationship between the fluorescence changing rate and l- or d-Pro concentration. Mechanism studies show that homochiral interaction-induced fluorescence enhancement is attributed to the disassembly of chiral PNA, while no disassembly of chiral PNA was found in heterochiral interaction-induced fluorescence quenching, which is attributed to the substitution of Phe on the surface of chiral PNA by heterochiral Pro. This work suggests that chiral perovskite can be used for chiral fluorescence sensing; it will inspire the development of chiral nanomaterials and chiral optical sensors.

Rapid Targeted Screening and Identification of Active Ingredients in Herbal Extracts through Ligand-Detected NMR and Database Matching.

Herbal extracts are rich sources of active compounds that can be used for drug screening due to their diverse and unique chemical structures. However, traditional methods for screening these compounds are notably laborious and time-consuming. In this manuscript, we introduce a new high-throughput approach that combines nuclear magnetic resonance (NMR) spectroscopy with a tailored database and algorithm to rapidly identify bioactive components in herbal extracts. This method distinguishes characteristic signals and structural motifs of active constituents in the raw extracts through a relaxation-weighted technique, particularly utilizing the perfect echo Carr-Purcell-Meiboom-Gill (peCPMG) sequence, complemented by precise 2D spectroscopic strategies. The cornerstone of our approach is a customized database designed to filter potential compounds based on defined parameters, such as the presence of CHn segments and unique chemical shifts, thereby expediting the identification of promising compounds. This innovative technique was applied to identifying substances interacting with choline kinase α (ChoKα1), resulting in the discovery of four new inhibitors. Our findings demonstrate a powerful tool for unraveling the complex chemical landscape of herbal extracts, considerably facilitating the search for new pharmaceutical candidates. This approach offers an efficient alternative to traditional methods in the quest for drug discovery from natural sources.

Real-Time NMR-Based Drug Discovery to Identify Inhibitors against Fatty Acid Synthesis in Living Cancer Cells.

Abnormal fatty acid metabolism is recognized as a key driver of tumor development and progression. Although numerous inhibitors have been developed to target this pathway, finding drugs with high specificity that do not disrupt normal cellular metabolism remains a formidable challenge. In this paper, we introduced a novel real-time NMR-based drug screening technique that operates within living cells. This technique provides a direct way to putatively identify molecular targets involved in specific metabolic processes, making it a powerful tool for cell-based drug screening. Using 2-13C acetate as a tracer, combined with 3D cell clusters and a bioreactor system, our approach enables real-time detection of inhibitors that target fatty acid metabolism within living cells. As a result, we successfully demonstrated the initial application of this method in the discovery of traditional Chinese medicines that specifically target fatty acid metabolism. Elucidating the mechanisms behind herbal medicines remains challenging due to the complex nature of their compounds and the presence of multiple targets. Remarkably, our findings demonstrate the significant inhibitory effect of P. cocos on fatty acid synthesis within cells, illustrating the potential of this approach in analyzing fatty acid metabolism events and identifying drug candidates that selectively inhibit fatty acid synthesis at the cellular level. Moreover, this systematic approach represents a valuable strategy for discovering the intricate effects of herbal medicine.

Comprehensive assessment of deep generative architectures for de novo drug design.

Recently, deep learning (DL)-based de novo drug design represents a new trend in pharmaceutical research, and numerous DL-based methods have been developed for the generation of novel compounds with desired properties. However, a comprehensive understanding of the advantages and disadvantages of these methods is still lacking. In this study, the performances of different generative models were evaluated by analyzing the properties of the generated molecules in different scenarios, such as goal-directed (rediscovery, optimization and scaffold hopping of active compounds) and target-specific (generation of novel compounds for a given target) tasks. In overall, the DL-based models have significant advantages over the baseline models built by the traditional methods in learning the physicochemical property distributions of the training sets and may be more suitable for target-specific tasks. However, both the baselines and DL-based generative models cannot fully exploit the scaffolds of the training sets, and the molecules generated by the DL-based methods even have lower scaffold diversity than those generated by the traditional models. Moreover, our assessment illustrates that the DL-based methods do not exhibit obvious advantages over the genetic algorithm-based baselines in goal-directed tasks. We believe that our study provides valuable guidance for the effective use of generative models in de novo drug design.

The translocon protein FtsHi1 is an ATP-dependent DNA/RNA helicase that prevents R-loop accumulation in chloroplasts.

R-loops, three-stranded nucleic acid structures consisting of a DNA: RNA hybrid and displaced single-stranded DNA, play critical roles in gene expression and genome stability. How R-loop homeostasis is integrated into chloroplast gene expression remains largely unknown. We found an unexpected function of FtsHi1, an inner envelope membrane-bound AAA-ATPase in chloroplast R-loop homeostasis of Arabidopsis thaliana. Previously, this protein was shown to function as a component of the import motor complex for nuclear-encoded chloroplast proteins. However, this study provides evidence that FtsHi1 is an ATP-dependent helicase that efficiently unwinds both DNA-DNA and DNA-RNA duplexes, thereby preventing R-loop accumulation. Over-accumulation of R-loops could impair chloroplast transcription but not necessarily genome integrity. The dual function of FtsHi1 in both protein import and chloroplast gene expression may be important to coordinate the biogenesis of nuclear- and chloroplast-encoded subunits of multi-protein photosynthetic complexes. This study suggests a mechanical link between protein import and R-loop homeostasis in chloroplasts of higher plants.

Self-Healing Flexible Sensor Based on Epoxidized Natural Rubber with the Synergistic Effect of Coordination and Hydrogen Bonds.

The use of highly tensile and self-healing conductive composites has gained considerable interest due to their wide range of applications in healthcare, sensors, and robotics. Epoxidized natural rubber (ENR), known for its ability to undergo highly reversible deformation, can be utilized in strain sensors to effectively transmit a broader range of signal changes. In this study, we introduced a self-healing ENR composite specifically designed for high-strain sensors. The rubber molecular chains were enhanced with hydrogen bonds and metal coordination bonds, allowing the matrix material to autonomously repair itself through these interactions. Following a repair period of 12 h at 45 °C, the composites achieve a repair efficiency exceeding 90%. Furthermore, by incorporating conductive fillers into the matrix using multistage layering, the resulting composite has good electrical conductivity, thermal conductivity, and hydrophobicity. In addition, this composite presents good sensitivity even at large strain (strain in the range of 50-200%, GF = 7.65). In conclusion, this self-healing nanocomposite, characterized by its high strain sensitivity, holds immense potential for various strain sensor applications.

A potent new-scaffold androgen receptor antagonist discovered on the basis of a MIEC-SVM model.

Prostate cancer (PCa) is the second most prevalent malignancy among men worldwide. The aberrant activation of androgen receptor (AR) signaling has been recognized as a crucial oncogenic driver for PCa and AR antagonists are widely used in PCa therapy. To develop novel AR antagonist, a machine-learning MIEC-SVM model was established for the virtual screening and 51 candidates were selected and submitted for bioactivity evaluation. To our surprise, a new-scaffold AR antagonist C2 with comparable bioactivity with Enz was identified at the initial round of screening. C2 showed pronounced inhibition on the transcriptional function (IC50 = 0.63 µM) and nuclear translocation of AR and significant antiproliferative and antimetastatic activity on PCa cell line of LNCaP. In addition, C2 exhibited a stronger ability to block the cell cycle of LNCaP than Enz at lower dose and superior AR specificity. Our study highlights the success of MIEC-SVM in discovering AR antagonists, and compound C2 presents a promising new scaffold for the development of AR-targeted therapeutics.

Association between delayed ambulation and increased risk of adverse events after lumbar fusion surgery in elderly patients.

PURPOSE: The relationship between delayed ambulation (DA) and postoperative adverse events (AEs) following transforaminal lumbar interbody fusion (TLIF) in elderly patients remains elusive. The aim of our study was to evaluate the effects of DA on the postoperative AEs including complications, readmission and prolonged length of hospital stay (LOS). METHODS: This was a retrospective analysis of a prospectively established database of elderly patients (aged 65 years and older) who underwent TLIF surgery. The early ambulation (EA) group was defined as patients ambulated within 48 h after surgery, whereas the delayed ambulation (DA) group was patients ambulated at a minimum of 48 h postoperatively. The DA patients were 1:1 propensity-score matched to the EA patients based on age, gender and the number of fused segments. Univariate analysis was used to compare postoperative outcomes between the two groups, and multivariate logistic regression analysis was used to identify risk factors for adverse events and DA. RESULTS: After excluding 125 patients for various reasons, 1025 patients (≤ 48 h: N = 659 and > 48 h: N = 366) were included in the final analysis. After propensity score matching, there were 326 matched patients in each group. There were no significant differences in the baseline data and the surgery-related variables between the two groups (p > 0.05). The patients in the DA group had a significant higher incidence of postoperative AEs (46.0% vs. 34.0%, p = 0.002) and longer LOS (p = 0.001). Multivariate logistic regression identified that age, operative time, diabetes, and DA were independently associated with postoperative AEs, whereas greater age, higher international normalized ratio, and intraoperative estimated blood loss were identified as independent risk factors for DA. CONCLUSIONS: Delayed ambulation was an independent risk factor for postoperative AEs after TLIF in elderly patients. Older age, increased intraoperative blood loss and worse coagulation function were associated with delayed ambulation.

Study on Quality Characteristic of Chebulae Fructus and Its Adulterants and Degradation Pathway of Hydrolyzable Tannins.

Chebulae Fructus (CF) is known as one of the richest sources of hydrolyzable tannins (HTs). In this study, ultra-performance liquid chromatography coupled with a photodiode array detector method was established for simultaneous determination of the 12 common phenolcarboxylic and tannic constituents (PTCs). Using this method, quantitative analysis was accomplished in CF and other four adulterants, including Terminaliae Belliricae Fructus, Phyllanthi Fructus, Chebulae Fructus Immaturus, and Canarii Fructus. Based on a quantitative analysis of the focused compounds, discrimination of CF and other four adulterants was successfully accomplished by hierarchical cluster analysis and principal component analysis. Additionally, the total contents of the 12 compounds that we focused on in this study were unveiled as 148.86 mg/g, 96.14 mg/g, and 18.64 mg/g in exocarp, mesocarp, and endocarp and seed of CF, respectively, and PTCs were witnessed to be the most abundant in the exocarp of CF. Noticeably, the HTs (chebulagic acid, chebulanin acid, chebulinic acid, and punicalagin) were observed to be ultimately degraded to chebulic acid, gallic acid, and ellagic acid during sunlight-drying of the fresh fruits. As a result, our study indicated that CF and its adulterants could be distinguished by the observed 12 PTCs, which were mainly distributed in the exocarp of the fruits. The HTs were prone to degrade into the three simple phenolcarboxylic acids during drying or processing, allowing us to obtain a more comprehensive understanding of the PTCs, with great significance in the improved quality of CF and related products.

Exploration of the Dynamic Variations of the Characteristic Constituents and the Degradation Products of Catalpol during the Process of Radix Rehmanniae.

Radix Rehmanniae (RR), a famous traditional Chinese medicine (TCM) widely employed in nourishing Yin and invigorating the kidney, has three common processing forms in clinical practice, including fresh Radix Rehmanniae (FRR), raw Radix Rehmanniae (RRR), and processed Radix Rehmanniae (PRR). However, until now, there has been less exploration of the dynamic variations in the characteristic constituents and degradation products of catalpol as a representative iridoid glycoside with the highest content in RR during the process from FRR to PRR. In this study, an ultra-performance liquid chromatography coupled with photodiode array detector (UPLC-PDA) method was successfully established for the simultaneous determination of ten characteristic components to explore their dynamic variations in different processed products of RR. Among them, iridoid glycosides, especially catalpol, exhibited a sharp decrease from RRR to PRR. Then, three degradation products of catalpol were detected under simulated processing conditions (100 °C, pH 4.8 acetate buffer solution), which were isolated and identified as jiofuraldehyde, cataldehyde, and norviburtinal, respectively. Cataldehyde was first reported as a new compound. Moreover, the specificity of norviburtinal in self-made PRR samples was discovered and validated, which was further confirmed by testing in commercially available PRR samples. In conclusion, our study revealed the decrease in iridoid glycosides and the production of new degradation substances during the process from FRR to PRR, which is critical for unveiling the processing mechanism of RR.

Identification of active molecules against Mycobacterium tuberculosis through machine learning.

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb) and it has been one of the top 10 causes of death globally. Drug-resistant tuberculosis (XDR-TB), extensively resistant to the commonly used first-line drugs, has emerged as a major challenge to TB treatment. Hence, it is quite necessary to discover novel drug candidates for TB treatment. In this study, based on different types of molecular representations, four machine learning (ML) algorithms, including support vector machine, random forest (RF), extreme gradient boosting (XGBoost) and deep neural networks (DNN), were used to develop classification models to distinguish Mtb inhibitors from noninhibitors. The results demonstrate that the XGBoost model exhibits the best prediction performance. Then, two consensus strategies were employed to integrate the predictions from multiple models. The evaluation results illustrate that the consensus model by stacking the RF, XGBoost and DNN predictions offers the best predictions with area under the receiver operating characteristic curve of 0.842 and 0.942 for the 10-fold cross-validated training set and external test set, respectively. Besides, the association between the important descriptors and the bioactivities of molecules was interpreted by using the Shapley additive explanations method. Finally, an online webserver called ChemTB (http://cadd.zju.edu.cn/chemtb/) was developed, and it offers a freely available computational tool to detect potential Mtb inhibitors.

Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?

Machine-learning (ML)-based scoring functions (MLSFs) have gradually emerged as a promising alternative for protein-ligand binding affinity prediction and structure-based virtual screening. However, clouds of doubts have still been raised against the benefits of this novel type of scoring functions (SFs). In this study, to benchmark the performance of target-specific MLSFs on a relatively unbiased dataset, the MLSFs trained from three representative protein-ligand interaction representations were assessed on the LIT-PCBA dataset, and the classical Glide SP SF and three types of ligand-based quantitative structure-activity relationship (QSAR) models were also utilized for comparison. Two major aspects in virtual screening campaigns, including prediction accuracy and hit novelty, were systematically explored. The calculation results illustrate that the tested target-specific MLSFs yielded generally superior performance over the classical Glide SP SF, but they could hardly outperform the 2D fingerprint-based QSAR models. Although substantial improvements could be achieved by integrating multiple types of protein-ligand interaction features, the MLSFs were still not sufficient to exceed MACCS-based QSAR models. In terms of the correlations between the hit ranks or the structures of the top-ranked hits, the MLSFs developed by different featurization strategies would have the ability to identify quite different hits. Nevertheless, it seems that target-specific MLSFs do not have the intrinsic attributes of a traditional SF and may not be a substitute for classical SFs. In contrast, MLSFs can be regarded as a new derivative of ligand-based QSAR models. It is expected that our study may provide valuable guidance for the assessment and further development of target-specific MLSFs.

Interleukin-17 receptor C is essential for the pro-inflammatory pathogenicity of granulocyte-macrophage-colony-stimulating factor-producing T helper cells in experimental autoimmune uveitis.

Autoimmune uveitis is an inflammatory disorder of the eye triggered by the responses of autoreactive T cells to ocular autoantigens. This study aims to understand the role of granulocyte-macrophage-colony-stimulating factor (GM-CSF)-producing T helper (ThGM) cells in the pathophysiology of mouse experimental autoimmune uveitis (EAU). We established an EAU model by immunizing mice with interphotoreceptor retinoid-binding protein (IRBP) 651-670. Splenic or eye-infiltrating ThGM cells were analyzed and enriched by flow cytometry according to the levels of an array of surface markers, transcription factors, and cytokines. Lentiviral transduction was conducted to silence or overexpress the target gene in differentiated ThGM cells. The adoptive transfer was applied to determine the pathogenicity of ThGM cells in vivo. We found that ThGM cells were present in the spleen and the eye after EAU induction. Both splenic and eye-infiltrating ThGM cells were phenotypically CD4+CCR7-CXCR3-CCR6-CCR10hi. Eye-infiltrating ThGM cells up-regulated interleukin-1ß (IL-1ß), interleukin-6 (IL-6), and IL-17 receptor C (IL-17RC) relative to splenic ThGM cells. IL-17RC overexpression enabled interleukin-17A (IL-17A)-induced up-regulation of IL-1ß and IL-6 production in ThGM cells. Adoptive transfer of IL-17RC overexpressing ThGM cells exacerbated EAU severity, as evidenced by a higher histology score as well as increased pro-inflammatory cytokines and inflammatory cells in the eye. However, IL-17RC-silenced ThGM cells did not impact EAU. Therefore, for the first time, this study unveils the essential pro-inflammatory role of IL-17RC-expressing ThGM cells in EAU pathophysiology. We discovered a novel mechanism underlying the pathophysiology of autoimmune uveitis.

Computationally guided discovery of novel non-steroidal AR-GR dual antagonists demonstrating potency against antiandrogen resistance.

As a major class of medicine for treating the lethal type of castration-resistant prostate cancer (PCa), long-term use of androgen receptor (AR) antagonists commonly leads to antiandrogen resistance. When AR signaling pathway is blocked by AR-targeted therapy, glucocorticoid receptor (GR) could compensate for AR function especially at the late stage of PCa. AR-GR dual antagonist is expected to be a good solution for this situation. Nevertheless, no effective non-steroidal AR-GR dual antagonist has been reported so far. In this study, an AR-GR dual binder H18 was first discovered by combining structure-based virtual screening and biological evaluation. Then with the aid of computationally guided design, the AR-GR dual antagonist HD57 was finally identified with antagonistic activity towards both AR (IC50 = 0.394 µM) and GR (IC50 = 17.81 µM). Moreover, HD57 could effectively antagonize various clinically relevant AR mutants. Further molecular dynamics simulation provided more atomic insights into the mode of action of HD57. Our research presents an efficient and rational strategy for discovering novel AR-GR dual antagonists, and the new scaffold provides important clues for the development of novel therapeutics for castration-resistant PCa.

CovalentInDB: a comprehensive database facilitating the discovery of covalent inhibitors.

Inhibitors that form covalent bonds with their targets have traditionally been considered highly adventurous due to their potential off-target effects and toxicity concerns. However, with the clinical validation and approval of many covalent inhibitors during the past decade, design and discovery of novel covalent inhibitors have attracted increasing attention. A large amount of scattered experimental data for covalent inhibitors have been reported, but a resource by integrating the experimental information for covalent inhibitor discovery is still lacking. In this study, we presented Covalent Inhibitor Database (CovalentInDB), the largest online database that provides the structural information and experimental data for covalent inhibitors. CovalentInDB contains 4511 covalent inhibitors (including 68 approved drugs) with 57 different reactive warheads for 280 protein targets. The crystal structures of some of the proteins bound with a covalent inhibitor are provided to visualize the protein-ligand interactions around the binding site. Each covalent inhibitor is annotated with the structure, warhead, experimental bioactivity, physicochemical properties, etc. Moreover, CovalentInDB provides the covalent reaction mechanism and the corresponding experimental verification methods for each inhibitor towards its target. High-quality datasets are downloadable for users to evaluate and develop computational methods for covalent drug design. CovalentInDB is freely accessible at http://cadd.zju.edu.cn/cidb/.

The implementation of enhanced recovery after surgery pathway in patients undergoing posterior thoracolumbar fusion for degenerative spinal deformity.

PURPOSE: The prevalence of degenerative spinal deformity (DSD) and the increased cost of correction surgery impose substantial burdens on the health care and insurance system. The aim of our study was to investigate the effects of the implementation of Enhanced Recovery After Surgery (ERAS) protocol on postoperative outcomes after complex spinal surgery. METHODS: A retrospective analysis of prospectively established database of DSD was performed. The consecutive patients who underwent open correction surgery for degenerative spinal deformity between August 2016 and February 2022 were reviewed. We extracted demographic data, preoperative radiographic parameters, and surgery-related variables. The ERAS patients were 1:1 propensity-score matched to a historical cohort by the same surgical team based on age, gender, BMI, and number of levels fused. We then compared the length of hospital stay (LOS), physiological functional recovery, and the rates of complications and readmissions within 90 days after surgery between the groups. RESULTS: There were 108 patients included, 54 patients in the ERAS cohort, and 54 patients matched control patients in the historical cohort. The historical and ERAS cohorts were not significantly different regarding demographic characteristics, comorbidities, preoperative parameters, operative time, and reoperation rate (P > 0.05). Patients in the ERAS group had significantly shorter postoperative LOS (12.0 days vs. 15.1 days, P = 0.001), average days of drain and urinary catheters placement (3.5 days vs. 4.4 days and 1.9 days vs 4.8 days, respectively), and lower 90-day readmission rate (1.8% vs. 12.9%, P = 0.027). The first day of assisted-walking and bowel movement occurred on average 1.9 days (2.5 days vs. 4.4 days, P = 0.001) and 1.7 days (1.9 days vs. 3.6 days, P = 0.001) earlier respectively in the ERAS group. Moreover, the rate of postoperative urinary retention (3.7% vs. 16.7%, P = 0.026) and surgical site infection (0% vs. 7.4%, P = 0.046) were significantly lower with ERAS protocol applied. CONCLUSIONS: Our study confirmed that the ERAS protocol was safe and essential for patients undergoing thoracolumbar deformity surgery for DSD. The ERAS protocol was associated with a shorter postoperative LOS, a lower rate of 90-day readmission, less rehabilitation discharge, and less postoperative complications.

Saturated fatty acids synergizes cadmium to induce macrophages M1 polarization and hepatic inflammation.

Exposure to the toxic metal cadmium (Cd) is a well-established risk factor for hepatic inflammation, but it remains unclear how metabolic components, such as different fatty acids (FAs), interact with Cd to influence this process. Understanding these interactions is essential for identifying potential preventative and therapeutic targets for this disorder. To address this question, we conducted in vitro and in vivo studies to investigate the combinatorial effect of Cd and saturated FAs on hepatic inflammation. Specifically, we assessed the cytotoxicity of Cd on macrophages and their polarization and inflammatory activation upon co-exposure to Cd and saturated FAs. Our results showed that while saturated FAs had minimal impact on the cytotoxicity of Cd on macrophages, they significantly collaborated with Cd in predisposing macrophages towards a pro-inflammatory M1 polarization, thereby promoting inflammatory activation. This joint effect of Cd and saturated FAs resulted in persistent inflammation and hepatic steatohepatitis in vivo. In summary, our study identified macrophage polarization as a novel mechanism by which co-exposure to Cd and saturated lipids induces hepatic inflammation. Our findings suggest that intervening in macrophage polarization may be a potential approach for mitigating the adverse hepatic effects of Cd.

[Variations of glucose content in Massa Medicata Fermentata during processing based on quantitative proton nuclear magnetic resonance].

A quantitative proton nuclear magnetic resonance(qHNMR) method was established to determine the glucose content in commercially available Massa Medicata Fermentata(MMF) products and explore the variations of glucose content in MMF products during processing. The qHNMR spectrum of MMF in deuterium oxide was obtained with 2,2,3,3-d_4-3-(trimethylsilyl) propionate sodium salt as the internal standard substance. With the doublet peaks of terminal hydrogen of glucose with chemical shift at δ 4.65 and δ 5.24 as quantitative peaks, the content of glucose in MMF samples was determined. The glucose content showed a good linear relationship within the range of 0.10-6.44 mg·mL~(-1). The relative standard deviations(RSDs) of precision, stability, repeatability, and recovery for determination were all less than 2.3%. The glucose content varied in different commercially available MMF samples, which were associated with the different fermentation days, wheat bran-to-flour ratios, and processing methods. The glucose content in MMF first increased and then decreased over the fermentation time. Compared with the MMF products fermented with wheat bran or flour alone, the products fermented with both wheat bran and flour had increased glucose. The glucose content of bran-fried MMF was slightly lower than that of raw MMF, while the glucose content in charred MMF was extremely low. In conclusion, the qHNMR method established in this study is simple, fast, and accurate, serving as a new method for determining the glucose content in MMF. Furthermore, this study clarifies the variations of glucose content in MMF during processing, which can not only indicate the processing degree but also provide a scientific basis for revealing the fermentation mechanism and improving the quality control of MMF.

A Remotely Controlled Nanosystem for Spatiotemporally Specific Gene Regulation and Combinational Tumor Therapy.

The dearth of technologies that allow gene modulation and therapy with high spatiotemporal precision remains a bottleneck in biomedical research and applications. Here we present a near-infrared (NIR) light-controlled nanosystem that allows spatiotemporally controlled regulation of gene expression and thus combinational tumor therapy. The nanosystem is built by engineering of an enzyme-activatable antisense oligonucleotide and further combination with an upconversion nanoparticle-based photodynamic system and a mitochondria localization signal. The system relies on photodynamic effect-induced translocation of a DNA repair enzyme from nucleus into mitochondria, which enables spatially selective gene regulation via enzymatic reactions. We demonstrate that the NIR light-induced mitochondrial photodamage and gene regulation enable enhanced antitumor effect. Our approach may enable the specific gene regulation and tumor treatment with high precision both spatially and temporally.

Chemokines, soluble PD-L1, and immune cell hyporesponsiveness are distinct features of SARS-CoV-2 critical illness.

Critically ill patients manifest many of the same immune features seen in coronavirus disease 2019 (COVID-19), including both "cytokine storm" and "immune suppression." However, direct comparisons of molecular and cellular profiles between contemporaneously enrolled critically ill patients with and without severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are limited. We sought to identify immune signatures specifically enriched in critically ill patients with COVID-19 compared with patients without COVID-19. We enrolled a multisite prospective cohort of patients admitted under suspicion for COVID-19, who were then determined to be SARS-CoV-2-positive (n = 204) or -negative (n = 122). SARS-CoV-2-positive patients had higher plasma levels of CXCL10, sPD-L1, IFN-γ, CCL26, C-reactive protein (CRP), and TNF-α relative to SARS-CoV-2-negative patients adjusting for demographics and severity of illness (Bonferroni P value < 0.05). In contrast, the levels of IL-6, IL-8, IL-10, and IL-17A were not significantly different between the two groups. In SARS-CoV-2-positive patients, higher plasma levels of sPD-L1 and TNF-α were associated with fewer ventilator-free days (VFDs) and higher mortality rates (Bonferroni P value < 0.05). Lymphocyte chemoattractants such as CCL17 were associated with more severe respiratory failure in SARS-CoV-2-positive patients, but less severe respiratory failure in SARS-CoV-2-negative patients (P value for interaction < 0.01). Circulating T cells and monocytes from SARS-CoV-2-positive subjects were hyporesponsive to in vitro stimulation compared with SARS-CoV-2-negative subjects. Critically ill SARS-CoV-2-positive patients exhibit an immune signature of high interferon-induced lymphocyte chemoattractants (e.g., CXCL10 and CCL17) and immune cell hyporesponsiveness when directly compared with SARS-CoV-2-negative patients. This suggests a specific role for T-cell migration coupled with an immune-checkpoint regulatory response in COVID-19-related critical illness.