Precise Post-Synthetic Modification of Heterometal-Organic Capsules for Selectively Encapsulating Tetrahedral Anions.

Post-synthetic modification plays a crucial role in precisely adjusting the structure and functions of advanced materials. Herein, we report the self-assembly of a tubular heterometallic Pd3Cu6L16 capsule that incorporates Pd(II) and CuL1 metalloligands. This capsule undergoes further modification with two tridentate anionic ligands (L2) to afford a bicapped Pd3Cu6L16L22 capsule with an Edshammer polyhedral structure. By employing transition metal ions, acid, and oxidation agents, the bicapped capsule can be converted into an uncapped one. This uncapped form can then revert back to the bicapped structure on the addition of Br- ions and a base. Interestingly, introducing Ag+ ions leads to the removal of one L2 ligand from the bicapped capsule, yielding a mono-capped Pd3Cu6L16L2 structure. Furthermore, the size of the anions critically influences the precise control over the post-synthetic modifications of the capsules. It was demonstrated that these capsules selectively encapsulate tetrahedral anions, offering a novel approach for the design of intelligent molecular delivery systems.

Visible-Light-Driven NO Release from Postmodified MOFs via Photoinduced Electron Transfer for Antibacterial Application.

Photoresponsive nitric oxide (NO)-releasing materials (NORMs) enable the spatiotemporal delivery of NO to facilitate their potential applications in physiological conditions. Here two novel metal-organic frameworks (MOFs)-based photoactive NORMs achieved by the incorporation of prefunctionalized NO donors into the photosensitive Fe-MOFs via a postmodification strategy is reported. The modified Fe-MOFs display superior photoactivity of NO release when exposed to visible light (up to 720 nm). Significantly, the visible-light-driven NO release properties are further corroborated by their efficient antibacterial performance.

Identification of key genes associated with persistent immune changes and secondary immune activation responses induced by influenza vaccination after COVID-19 recovery by machine learning methods.

COVID-19 is hypothesized to exert enduring effects on the immune systems of patients, leading to alterations in immune-related gene expression. This study aimed to scrutinize the persistent implications of SARS-CoV-2 infection on gene expression and its influence on subsequent immune activation responses. We designed a machine learning-based approach to analyze transcriptomic data from both healthy individuals and patients who had recovered from COVID-19. Patients were categorized based on their influenza vaccination status and then compared with healthy controls. The initial sample set encompassed 86 blood samples from healthy controls and 72 blood samples from recuperated COVID-19 patients prior to influenza vaccination. The second sample set included 123 blood samples from healthy controls and 106 blood samples from recovered COVID-19 patients who had been vaccinated against influenza. For each sample, the dataset captured expression levels of 17,060 genes. Above two sample sets were first analyzed by seven feature ranking algorithms, yielding seven feature lists for each dataset. Then, each list was fed into the incremental feature selection method, incorporating three classic classification algorithms, to extract essential genes, classification rules and build efficient classifiers. The genes and rules were analyzed in this study. The main findings included that NEXN and ZNF354A were highly expressed in recovered COVID-19 patients, whereas MKI67 and GZMB were highly expressed in patients with secondary immune activation post-COVID-19 recovery. These pivotal genes could provide valuable insights for future health monitoring of COVID-19 patients and guide the creation of continued treatment regimens.

Identification of key gene expression associated with quality of life after recovery from COVID-19.

Post-acute sequelae of COVID-19 (PASC) is a persistent complication of severe acute respiratory syndrome coronavirus 2 infection that includes symptoms, such as fatigue, cognitive impairment, and respiratory distress. These symptoms severely affect the quality of life of patients after their recovery from COVID-19. In this study, a group of machine learning algorithms analyzed the whole blood RNA-seq data from patients with different PASC levels. The purpose of this analysis was to identify the gene markers associated with PASC and the special expression patterns for different PASC levels. By comparing the quality of life of patients after the acute phase of COVID-19 and before the disease, samples in the dataset were divided into three groups, namely, "Better," "The Same," and "Worse." Each patient was represented by the expression levels of 58,929 genes. The machine learning-based workflow included six feature-ranking algorithms, incremental feature selection (IFS), and four classification algorithms. The feature ranking algorithms were in charge of assessing feature importance, whereas IFS with classification algorithms were used to extract essential genes and to construct efficient classifiers and classification rules. The expression of top genes in the results was associated with the immune response to viral infection, which is supported by the published literature. For example, patients with low CCDC18 expression and high CPED1 expression had good quality of life, whereas those with low CDC16 expression had poor quality of life.

Protocol to dissociate and isolate wide-diversity single cells by density gradient centrifugation from human hepatoblastoma tissue.

Single-cell transcriptome sequencing can characterize various cell types in human liver tissue and facilitate understanding of hepatoblastoma heterogeneity. Here, we present a protocol for isolating hepatocytes and immune cells from human hepatoblastoma samples with high viability. We describe steps for tissue processing, enzymatic digestion, Percoll density gradient separation, cell lysis, cell suspension quality control, and scRNA library construction. We then detail sequencing and data analysis. This protocol is applicable to preparing single-cell suspensions from other human liver tissue samples.

Tailor-Made Pdn L2n Metal-Organic Cages through Covalent Post-Synthetic Modification.

Post-synthetic modification (PSM) is an effective approach for the tailored functionalization of metal-organic architectures, but its generalizability remains challenging. Herein we report a general covalent PSM strategy to functionalize Pdn L2n metal-organic cages (MOCs, n=2, 12) through an efficient Diels-Alder cycloaddition between peripheral anthracene substituents and various functional motifs bearing a maleimide group. As expected, the solubility of functionalized Pd12 L24 in common solvents can be greatly improved. Interestingly, concentration-dependent circular dichroism and aggregation-induced emission are achieved with chiral binaphthol (BINOL)- and tetraphenylethylene-modified Pd12 L24 , respectively. Furthermore, Pd12 L24 can be introduced with two different functional groups (e.g., chiral BINOL and achiral pyrene) through a step-by-step PSM route to obtain chirality-induced circularly polarized luminescence. Moreover, similar results are readily observed with a smaller Pd2 L4 system.

Isolation of single cells from human hepatoblastoma tissues for whole-exome sequencing.

By combining single-cell processing with whole-exome sequencing, we have developed single-cell whole-exome sequencing to investigate the mechanisms of hepatoblastoma development and to provide potential targets and therapeutic approaches for clinical treatment. In the following protocol, we outline the steps involved in single-cell sorting, whole-genome amplification, amplification uniformity estimation, and whole-exome library construction. In addition to the cells we use, this protocol is also suitable for other cell lines and cell types.

Self-Assembly of Two Tubular Metalloligand-Based Palladium-Organic Cages as Hosts for Polycyclic Aromatic Hydrocarbons.

Herein we report two tubular metal-organic cages (MOCs), synthesized by the self-assembly of bidentate metalloligands with different lengths and PdII. These two MOCs feature Pd4L8-type square tubular and Pd3L6-type triangular cage structures, respectively. Both MOCs have been fully characterized by NMR spectroscopy, mass spectrometry, and theoretical calculation. Both cages can be employed for encapsulating polycyclic aromatic hydrocarbons and show high binding affinity toward coronene.

Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods.

The rapid spread of COVID-19 has become a major concern for people's lives and health all around the world. COVID-19 patients in various phases and severity require individualized treatment given that different patients may develop different symptoms. We employed machine learning methods to discover biomarkers that may accurately classify COVID-19 in various disease states and severities in this study. The blood gene expression profiles from 50 COVID-19 patients without intensive care, 50 COVID-19 patients with intensive care, 10 non-COVID-19 individuals without intensive care, and 16 non-COVID-19 individuals with intensive care were analyzed. Boruta was first used to remove irrelevant gene features in the expression profiles, and then, the minimum redundancy maximum relevance was applied to sort the remaining features. The generated feature-ranked list was fed into the incremental feature selection method to discover the essential genes and build powerful classifiers. The molecular mechanism of some biomarker genes was addressed using recent studies, and biological functions enriched by essential genes were examined. Our findings imply that genes including UBE2C, PCLAF, CDK1, CCNB1, MND1, APOBEC3G, TRAF3IP3, CD48, and GZMA play key roles in defining the different states and severity of COVID-19. Thus, a new point of reference is provided for understanding the disease's etiology and facilitating a precise therapy.

Identification of COVID-19 severity biomarkers based on feature selection on single-cell RNA-Seq data of CD8+ T cells.

The global outbreak of the COVID-19 epidemic has become a major public health problem. COVID-19 virus infection triggers a complex immune response. CD8+ T cells, in particular, play an essential role in controlling the severity of the disease. However, the mechanism of the regulatory role of CD8+ T cells on COVID-19 remains poorly investigated. In this study, single-cell gene expression profiles from three CD8+ T cell subtypes (effector, memory, and naive T cells) were downloaded. Each cell subtype included three disease states, namely, acute COVID-19, convalescent COVID-19, and unexposed individuals. The profiles on each cell subtype were individually analyzed in the same way. Irrelevant features in the profiles were first excluded by the Boruta method. The remaining features for each CD8+ T cells subtype were further analyzed by Max-Relevance and Min-Redundancy, Monte Carlo feature selection, and light gradient boosting machine methods to obtain three feature lists. These lists were then brought into the incremental feature selection method to determine the optimal features for each cell subtype. Their corresponding genes may be latent biomarkers to determine COVID-19 severity. Genes, such as ZFP36, DUSP1, TCR, and IL7R, can be confirmed to play an immune regulatory role in COVID-19 infection and recovery. The results of functional enrichment analysis revealed that these important genes may be associated with immune functions, such as response to cAMP, response to virus, T cell receptor complex, T cell activation, and T cell differentiation. This study further set up different gene expression pattens, represented by classification rules, on three states of COVID-19 and constructed several efficient classifiers to distinguish COVID-19 severity. The findings of this study provided new insights into the biological processes of CD8+ T cells in regulating the immune response.

Characterization of spleen and lymph node cell types via CITE-seq and machine learning methods.

The spleen and lymph nodes are important functional organs for human immune system. The identification of cell types for spleen and lymph nodes is helpful for understanding the mechanism of immune system. However, the cell types of spleen and lymph are highly diverse in the human body. Therefore, in this study, we employed a series of machine learning algorithms to computationally analyze the cell types of spleen and lymph based on single-cell CITE-seq sequencing data. A total of 28,211 cell data (training vs. test = 14,435 vs. 13,776) involving 24 cell types were collected for this study. For the training dataset, it was analyzed by Boruta and minimum redundancy maximum relevance (mRMR) one by one, resulting in an mRMR feature list. This list was fed into the incremental feature selection (IFS) method, incorporating four classification algorithms (deep forest, random forest, K-nearest neighbor, and decision tree). Some essential features were discovered and the deep forest with its optimal features achieved the best performance. A group of related proteins (CD4, TCRb, CD103, CD43, and CD23) and genes (Nkg7 and Thy1) contributing to the classification of spleen and lymph nodes cell types were analyzed. Furthermore, the classification rules yielded by decision tree were also provided and analyzed. Above findings may provide helpful information for deepening our understanding on the diversity of cell types.

A Comparative Study of Hip Arthroplasty and Closed Reduction Proximal Femur Nail in the Treatment of Elderly Patients with Hip Fractures.

Objective: To compare the clinical effect of hip arthroplasty and closed reduction intramedullary nailing of proximal femur in the treatment of elderly hip fracture patients. Methods: There are 90 elderly hip fracture patients being recruited in the present study. Fifty patients in Group A received closed reduction intramedullary nailing of proximal femur, and 40 patients in Group B received hip arthroplasty. All patients were followed up for 12 months after surgery, clinical outcomes included surgical indicators, visual analog scale (VAS) score, Harris score, quality of life, mental status, and complications. Results: The surgery time, bleeding volume, infusion volume of patients in Group A are all significantly lower than those in Group B (p < 0.05), while the weight-bearing activity time and first workout time of Group A are all significantly higher than those in Group B (p < 0.05). The VAS score in patients of Group A at 1 week postoperative is significantly lower than that in patients of Group B (p < 0.05). The Harris score in patients of Group A at 3, 6, and 12 months postoperative are all significantly higher than those in patients of Group B (p < 0.05), and the excellent and good rate of hip function recovery at 12 months postoperative in patient of Group A is significantly lower than that in patients of Group B (80% vs. 95%, p < 0.05). Furthermore, The score of SF-36 standardized physical component, SF-36 standardized mental component and Barthel in patients of Group A at 6 months postoperative are significantly lower than those in patients of Group B (p < 0.05), and the score of mini-mental state examination is significantly higher (p < 0.05), while there are not significantly different at 12 months postoperative (p > 0.05). The incidence of postoperative complications in Group A was significantly lower than that in Group B (10% vs. 27.5%, p < 0.05). Conclusion: Elderly hip fracture patients treated with closed reduction intramedullary nailing of proximal femur has less surgical trauma and lower complication rates, but slower postoperative recovery compared with hip arthroplasty.

Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using a Machine Learning Method.

SARS-CoV-2 shows great evolutionary capacity through a high frequency of genomic variation during transmission. Evolved SARS-CoV-2 often demonstrates resistance to previous vaccines and can cause poor clinical status in patients. Mutations in the SARS-CoV-2 genome involve mutations in structural and nonstructural proteins, and some of these proteins such as spike proteins have been shown to be directly associated with the clinical status of patients with severe COVID-19 pneumonia. In this study, we collected genome-wide mutation information of virulent strains and the severity of COVID-19 pneumonia in patients varying depending on their clinical status. Important protein mutations and untranslated region mutations were extracted using machine learning methods. First, through Boruta and four ranking algorithms (least absolute shrinkage and selection operator, light gradient boosting machine, max-relevance and min-redundancy, and Monte Carlo feature selection), mutations that were highly correlated with the clinical status of the patients were screened out and sorted in four feature lists. Some mutations such as D614G and V1176F were shown to be associated with viral infectivity. Moreover, previously unreported mutations such as A320V of nsp14 and I164ILV of nsp14 were also identified, which suggests their potential roles. We then applied the incremental feature selection method to each feature list to construct efficient classifiers, which can be directly used to distinguish the clinical status of COVID-19 patients. Meanwhile, four sets of quantitative rules were set up, which can help us to more intuitively understand the role of each mutation in differentiating the clinical status of COVID-19 patients. Identified key mutations linked to virologic properties will help better understand the mechanisms of infection and will aid in the development of antiviral treatments.

Identification of Cell Markers and Their Expression Patterns in Skin Based on Single-Cell RNA-Sequencing Profiles.

Atopic dermatitis and psoriasis are members of a family of inflammatory skin disorders. Cellular immune responses in skin tissues contribute to the development of these diseases. However, their underlying immune mechanisms remain to be fully elucidated. We developed a computational pipeline for analyzing the single-cell RNA-sequencing profiles of the Human Cell Atlas skin dataset to investigate the pathological mechanisms of skin diseases. First, we applied the maximum relevance criterion and the Boruta feature selection method to exclude irrelevant gene features from the single-cell gene expression profiles of inflammatory skin disease samples and healthy controls. The retained gene features were ranked by using the Monte Carlo feature selection method on the basis of their importance, and a feature list was compiled. This list was then introduced into the incremental feature selection method that combined the decision tree and random forest algorithms to extract important cell markers and thus build excellent classifiers and decision rules. These cell markers and their expression patterns have been analyzed and validated in recent studies and are potential therapeutic and diagnostic targets for skin diseases because their expression affects the pathogenesis of inflammatory skin diseases.

Self-Assembly of a Pd4Cu8L8 Cage for Epoxidation of Styrene and Its Derivatives.

Herein we report a discrete heterometallic Pd4Cu8L8 cage with a tubular structure, which was synthesized by the assembly of copper metalloligands and PdII ions in a stepwise manner. The Pd4Cu8L8 cage has been unequivocally characterized by single-crystal X-ray diffraction, electrospray ionization-mass spectroscopy, and energy dispersive spectroscopy. The cage showed excellent catalytic activity in the epoxidation of styrene and its derivatives under conditions without using additional solvent, providing potential material for catalyzing the oxidation reactions.

Predicting Heart Cell Types by Using Transcriptome Profiles and a Machine Learning Method.

The heart is an essential organ in the human body. It contains various types of cells, such as cardiomyocytes, mesothelial cells, endothelial cells, and fibroblasts. The interactions between these cells determine the vital functions of the heart. Therefore, identifying the different cell types and revealing the expression rules in these cell types are crucial. In this study, multiple machine learning methods were used to analyze the heart single-cell profiles with 11 different heart cell types. The single-cell profiles were first analyzed via light gradient boosting machine method to evaluate the importance of gene features on the profiling dataset, and a ranking feature list was produced. This feature list was then brought into the incremental feature selection method to identify the best features and build the optimal classifiers. The results suggested that the best decision tree (DT) and random forest classification models achieved the highest weighted F1 scores of 0.957 and 0.981, respectively. The selected features, such as NPPA, LAMA2, DLC1, and the classification rules extracted from the optimal DT classifier played a crucial role in cardiac structure and function in recent research and enrichment analysis. In particular, some lncRNAs (LINC02019, NEAT1) were found to be quite important for the recognition of different cardiac cell types. In summary, these findings provide a solid academic foundation for the development of molecular diagnostics and biomarker discovery for cardiac diseases.

Longitudinal immune profiling reveals dominant epitopes mediating long-term humoral immunity in COVID-19-convalescent individuals.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic and contagious coronavirus that caused a global pandemic with 5.2 million fatalities to date. Questions concerning serologic features of long-term immunity, especially dominant epitopes mediating durable antibody responses after SARS-CoV-2 infection, remain to be elucidated. OBJECTIVE: We aimed to dissect the kinetics and longevity of immune responses in coronavirus disease 2019 (COVID-19) patients, as well as the epitopes responsible for sustained long-term humoral immunity against SARS-CoV-2. METHODS: We assessed SARS-CoV-2 immune dynamics up to 180 to 220 days after disease onset in 31 individuals who predominantly experienced moderate symptoms of COVID-19, then performed a proteome-wide profiling of dominant epitopes responsible for persistent humoral immune responses. RESULTS: Longitudinal analysis revealed sustained SARS-CoV-2 spike protein-specific antibodies and neutralizing antibodies in COVID-19 patients, along with activation of cytokine production at early stages after SARS-CoV-2 infection. Highly reactive epitopes that were capable of mediating long-term antibody responses were shown to be located at the spike and ORF1ab proteins. Key epitopes of the SARS-CoV-2 spike protein were mapped to the N-terminal domain of the S1 subunit and the S2 subunit, with varying degrees of sequence homology among endemic human coronaviruses and high sequence identity between the early SARS-CoV-2 (Wuhan-Hu-1) and current circulating variants. CONCLUSION: SARS-CoV-2 infection induces persistent humoral immunity in COVID-19-convalescent individuals by targeting dominant epitopes located at the spike and ORF1ab proteins that mediate long-term immune responses. Our findings provide a path to aid rational vaccine design and diagnostic development.

Resveratrol analog, triacetylresveratrol, a potential immunomodulator of lung adenocarcinoma immunotherapy combination therapies.

Introduction: Resveratrol, an activator for longevity regulatory genes-sirtuin family (SIRTs) and Sirtuin 2 (SIRT2) is an important factor of SIRTs which demonstrated biological function in cancers, but the underlying mechanism is unrevealed. Methods: We investigated the mRNA and protein levels of SIRT2 in a variety of cancers and the potential role for clinical prognosis, as well as analysed the association between the gene and immune infiltration in various cancers. And an analysis of two types of lung cancer was conducted to construct a systematic prognostic landscape. Finally, putative binding site of the triacetylresveratrol bound to SIRT2 was built from homology modeling. Results and discussion: We concluded that higher mRNA and protein levels of SIRT2 affected prognosis in various types of cancers, especially in LUAD cohorts. In addition, SIRT2 is linked with a better overall survival (OS) in LUAD patients. Further research suggested a possible explanation for this phenotype might be that SIRT2 mRNA levels are positively correlated with infiltrating status of multiple immunocytes in LU-AD but not LUSC, i.e. SIRT2 expression may contribute to the recruitment of CD8+T cell, CD4+ T cell, T cell CD4+ memory resting, Tregs, T cell NK and positively correlated to the expression of PD-1, also excluding neutrophil, T cell CD8+ naïve and B cell plasma cells in LUAD. We found that triacetyl-resveratrol demonstrated the most potent agonist efficiency to SIRT2 and the EC 50 as low as 142.79 nM. As a result, SIRT2 appears to be a promising novel biomarker for prognosis prediction in patients with LUAD and triacetylresveratrol might be a potential immunomodulator of LUAD to anti-PD-1 based immunotherapy combination therapies.

Identification of Methylation Signatures and Rules for Sarcoma Subtypes by Machine Learning Methods.

Sarcoma, the second common type of solid tumor in children and adolescents, has a wide variety of subtypes that are often not properly diagnosed at an early stage, leading to late metastases and causing serious loss of life and property to patients and families. It exhibits a high degree of heterogeneity at the cellular, molecular, and epigenetic levels, where DNA methylation has been proposed to play a role in the diagnosis of sarcoma subtypes. Thus, this study is aimed at finding potential biomarkers at the DNA methylation level to distinguish different sarcoma subtypes. A machine learning process was designed to analyse sarcoma samples, each of which was represented by lots of methylation sites. Irrelevant sites were removed using the Boruta method, and remaining sites related to the target variables were kept for further analyses. Afterward, three feature ranking methods (LASSO, LightGBM, and MCFS) were adopted to rank these features, and six classification models were constructed by combining incremental feature selection and two classification algorithms (decision tree and random forest). Among these models, the performance of RF model was higher than that of DT model under all three ranking conditions. The specific expression of genes obtained from the annotation of highly correlated methylation site features, such as PRKAR1B, INPP5A, and GLI3, was proven to be associated with sarcoma by publications. Moreover, the quantitative rules obtained by decision tree algorithm helped us to understand the essential differences between various sarcoma types and classify sarcoma subtypes, providing a new means of clinical identification and determining new therapeutic targets.