Direct conversion of fibroblasts to neurons by reprogramming PTB-regulated microRNA circuits.

The induction of pluripotency or trans-differentiation of one cell type to another can be accomplished with cell-lineage-specific transcription factors. Here, we report that repression of a single RNA binding polypyrimidine-tract-binding (PTB) protein, which occurs during normal brain development via the action of miR-124, is sufficient to induce trans-differentiation of fibroblasts into functional neurons. Besides its traditional role in regulated splicing, we show that PTB has a previously undocumented function in the regulation of microRNA functions, suppressing or enhancing microRNA targeting by competitive binding on target mRNA or altering local RNA secondary structure. A key event during neuronal induction is the relief of PTB-mediated blockage of microRNA action on multiple components of the REST complex, thereby derepressing a large array of neuronal genes, including miR-124 and multiple neuronal-specific transcription factors, in nonneuronal cells. This converts a negative feedback loop to a positive one to elicit cellular reprogramming to the neuronal lineage.

A Complex of U1 snRNP with Cleavage and Polyadenylation Factors Controls Telescripting, Regulating mRNA Transcription in Human Cells.

Full-length transcription in the majority of human genes depends on U1 snRNP (U1) to co-transcriptionally suppress transcription-terminating premature 3' end cleavage and polyadenylation (PCPA) from cryptic polyadenylation signals (PASs) in introns. However, the mechanism of this U1 activity, termed telescripting, is unknown. Here, we captured a complex, comprising U1 and CPA factors (U1-CPAFs), that binds intronic PASs and suppresses PCPA. U1-CPAFs are distinct from U1-spliceosomal complexes; they include CPA's three main subunits, CFIm, CPSF, and CstF; lack essential splicing factors; and associate with transcription elongation and mRNA export complexes. Telescripting requires U1:pre-mRNA base-pairing, which can be disrupted by U1 antisense oligonucleotide (U1 AMO), triggering PCPA. U1 AMO remodels U1-CPAFs, revealing changes, including recruitment of CPA-stimulating factors, that explain U1-CPAFs' switch from repressive to activated states. Our findings outline this U1 telescripting mechanism and demonstrate U1's unique role as central regulator of pre-mRNA processing and transcription.

Hydroxyl-functionalized pillar[5]arene with high separation performance for gas chromatography.

Here we report the first example of employing hydroxyl-functionalized pillar[5]arene (P5A-C10-OH) as stationary phase for capillary gas chromatographic (GC) separations. The statically coated P5A-C10-OH capillary column possessed moderate polarity and column efficiency of 3233 plates per m determined by n-dodecane. As a result, the P5A-C10-OH column exhibited high-resolution capability for the mixture of 17 analytes from apolar to polar nature. Importantly, it exhibited advantageous performance for high resolution of the challenging isomers of bromonitrobenzene, chloroaniline, bromoaniline, iodoaniline and dimethylaniline with good peak shapes over the P5A-C10 and commercial HP-35 columns. In addition, eight cis-/trans-isomers with diverse types were baseline separated on the P5A-C10-OH column. And the application of detecting isomeric impurities in real samples gave strong evidence of its potential and feasibility for the viable GC analysis.

Ester-functionalized pillar[6]arene as the gas chromatographic stationary phase with high-resolution performance towards the challenging isomers of xylenes, diethylbenzenes, and ethyltoluenes.

This work presents the first example of the utilization of polar ester group functionalized pillar[6]arene (P6A-C10-OAc) as a stationary phase for capillary gas chromatographic (GC) separations. The statically coated P6A-C10-OAc column showed a high column efficiency of 5393 plates/m and moderate polar nature. Its resolving capability and retention behaviors were investigated for a mixture of 20 analytes and more than a dozen isomers from apolar to polar in nature. As evidenced, the P6A-C10-OAc column achieved high-resolution separations of all the analytes and good inertness. Importantly, it exhibited distinctly advantageous performance for high resolution of the challenging isomers of xylenes, diethylbenzenes, ethyltoluenes, and halobenzenes over the commercial HP-5 (5% phenyl dimethyl polysiloxane), HP-35 (25% phenyl dimethyl polysiloxane), and PEG-20M (polyethylene glycol) columns.

Progress of Clinical Studies Targeting Claudin18.2 for the Treatment of Gastric Cancer.

Claudin18.2 is a tight junction protein, highly selective, generally expressed only in normal gastric mucosal epithelial cells, which can effectively maintain the polarity of epithelial and endothelial cells, thus effectively regulating the permeability and conductance of the paracellular pathway. Abnormal expression of Claudin18.2 can occur in various primary malignant tumors, especially gastrointestinal tumors, and even in metastatic foci. It regulates its expression by activating the aPKC/MAPK/AP-1 pathway, and therefore, the Claudin18.2 protein is a pan-cancer target expressed in primary and metastatic lesions in human cancer types. Zolbetuximab (IMAB362), an antibody specific for Claudin18.2, has been successfully tested in a phase III clinical trial, and the results of the study showed that combining Zolbetuximab with chemotherapy notably extends patients' survival and is expected to be a potential first-line treatment for patients with Claudin18.2(+)/HER-2(-) gastric cancer. Here, we systematically describe the biological properties and oncogenic effects of Claudin18.2, centering on its clinical-pathological aspects and the progress of drug studies in gastric cancer, which can help to further explore its clinical value.

The gut microbiota composition and metabolites are different in women with hypertensive disorders of pregnancy and normotension: A pilot study.

INTRODUCTION: Hypertensive disorders of pregnancy (HDP) are one of the main causes of perinatal morbidity. Gut microbiota influences host inflammatory pathways, glucose, and lipid metabolism. However, there is a lack of studies available on gut microbiota in HDP. OBJECTIVES: We investigate the mechanistic and pathogenic role of microbiota in the development of HDP, and want to treat HDP with gut microbiota. METHODS: We performed a case-control study to compare fecal samples of HDP and normotensive pregnant women by 16S ribosomal RNA sequencing. Fecal samples, collected from pregnant women, were divided into groups P and C (pregnant women with HDP and normotension, respectively). There were six pregnant women in group P and nine pregnant women in group C. Age of pregnant women is from 18 to 40 years and gestational age is from 27 to 40 weeks. DNA was extracted from fecal samples; a gene library was constructed and analyzed using bioinformatics. Finally, we determined the changes in the microbiome by alpha diversity, beta diversity, classification abundance, and taxonomic composition analyses. RESULTS: Escherichia (10.48% in group P and 0.61% in group C) was the dominant bacterium in HDP patients by classification abundance analysis, which can lead to the development of preeclampsia through inflammatory response. We found that pregnant women with HDP had higher abundance of Rothia (p = 0.04984), Actinomyces (p = 0.02040), and Enterococcus (p = 0.04974) and lower abundance of Coprococcus (p = 0.04955) than pregnant women with normotension for the first time by taxonomic composition analysis. Based on the Kyoto Encyclopedia of Genes and Genomes database analysis, physiological and biochemical functions of HDP patients were significantly weakened, especially in energy metabolism. CONCLUSIONS: We found the effect of changes in gut microbiota on the development of HDP. In comparison with group C, group P contained more harmful bacteria and less beneficial bacteria, which are associated with HDP. Our research further provides a basis for a clinical application for HDP treatment using antibiotics and probiotic supplementation.

Resilience of weighted networks with dynamical behavior against multi-node removal.

In many real-world networks, interactions between nodes are weighted to reflect their strength, such as predator-prey interactions in the ecological network and passenger numbers in airline networks. These weighted networks are prone to cascading effects caused by minor perturbations, which can lead to catastrophic outcomes. This vulnerability highlights the importance of studying weighted network resilience to prevent system collapses. However, due to many variables and weight parameters coupled together, predicting the behavior of such a system governed by a multi-dimensional rate equation is challenging. To address this, we propose a dimension reduction technique that simplifies a multi-dimensional system into a one-dimensional state space. We applied this methodology to explore the impact of weights on the resilience of four dynamics whose weights are assigned by three weight assignment methods. The four dynamical systems are the biochemical dynamical system (B), the epidemic dynamical system (E), the regulatory dynamical system (R), and the birth-death dynamical system (BD). The results show that regardless of the weight distribution, for B, the weights are negatively correlated with the activities of the network, while for E, R, and BD, there is a positive correlation between the weights and the activities of the network. Interestingly, for B, R, and BD, the change in the weights of the system has little impact on the resilience of the system. However, for the E system, the greater the weights the more resilient the system. This study not only simplifies the complexity inherent in weighted networks but also enhances our understanding of their resilience and response to perturbations.

High Selectivity of A Novel Pillar[5]arene with Ester Units as a Gas Chromatographic Stationary Phase toward Aromatic Isomers.

This work reports the first example of employing ester-functionalized pillar[5]arene (P5A-C10-OAc) stationary phase for gas chromatography (GC) separations. The as-fabricated P5A-C10-OAc column achieved improved column efficiency of 4270 plates/m and separation performance in contrast to the P5-C10-Br column. The P5A-C10-OAc column showed good separation performance for a wide range of analytes such as alkanes, bromoalkanes, ketones, fatty acid methyl esters, aldehydes, alcohols, halobenzenes, anilines, phenols, naphthalenes, and showed sharp and symmetrical peak shapes for analytes that are liable to peak-tailing in GC analysis. As testified by the challenging isomer mixtures (bromonitrobenzene, chloronitrobenzene, bromobenzaldehyde, chlorobenzaldehyde, nitrobenzaldehyde), the P5A-C10-OAc column exhibited comprehensively higher separation capability than the P5A-C10-Br, P5A-C10 and commercial HP-35 columns. This work demonstrates the great potential of pillararene-based stationary phases as a new type of stationary phases for GC separations.

Preparation of DNA nanoflower-modified capillary silica monoliths for chiral separation.

Innovative chiral capillary silica monoliths (CSMs) were developed based on DNA nanoflowers (DNFs). Baseline separation of enantiomers such as atenolol, tyrosine, histidine, and nefopam was achieved by using DNF-modified CSMs, and the obtained resolution value was higher than 1.78. To further explore the effect of DNFs on enantioseparation, different types of chiral columns including DNA strand containing the complementary sequence of the template (DCT)-modified CSMs, DNF2-modified CSMs, and DNF3-modified CSMs were prepared as well. It was observed that DNF-modified CSMs displayed better chiral separation ability compared with DCT-based columns. The intra-day and inter-day repeatability of model analytes' retention time and resolution kept desirable relative standard deviation values of less than 8.28%. DNF2/DNF3-modified CSMs were able to achieve baseline separation of atenolol, propranolol, 2'-deoxyadenosine, and nefopam enantiomers. Molecular docking simulations were performed to investigate enantioselectivity mechanisms of DNA sequences for enantiomers. To indicate the successful construction of DNFs and DNF-modified CSMs, various charaterization approaches including scanning electron microscopy, agarose gel electrophoresis, dynamic light scattering analysis, electroosmotic flow, and Fourier-transform infrared spectroscopy were utilized. Moreover, the enantioseparation performance of DNF-modified CSMs was characterized in terms of sample volume, applied voltage, and buffer concentration. This work paves the way to applying DNF-based capillary electrochromatography microsystems for chiral separation.

Safety and efficacy of anlotinib hydrochloride capsules in advanced non-small-cell lung cancer: a multicenter, real-world study.

Objective: To investigate the safety and efficacy of anlotinib hydrochloride capsules in stage III-IV non-small-cell lung cancer (NSCLC). Methods: NSCLC patients received anlotinib monotherapy or combination therapy. The primary end point was adverse reactions during anlotinib treatment and the secondary end point was progression-free survival. Results: During anlotinib treatement, 41.85% (167/399) of patients experienced adverse reactions, and the monotherapy group had a lower incidence than the combination group (36.89 vs 49.68%; p = 0.012). The median progression-free survival of patients in the monotherapy group was significantly lower than that in the combination group (5 vs 6 months; p = 0.0119). Conclusion: Compared with anlotinib monotherapy, combination therapy resulted in longer PFS and a higher incidence of adverse reactions in patients with NSCLC.

A Bayesian network prediction model for gallbladder polyps with malignant potential based on preoperative ultrasound.

BACKGROUND: It is important to identify gallbladder polyps (GPs) with malignant potential and avoid unnecessary cholecystectomy by constructing prediction model. The aim of the study is to develop a Bayesian network (BN) prediction model for GPs with malignant potential in a long diameter of 8-15 mm based on preoperative ultrasound. METHODS: The independent risk factors for GPs with malignant potential were screened by χ2 test and Logistic regression model. Prediction model was established and validated using data from 1296 patients with GPs who underwent cholecystectomy from January 2015 to December 2019 at 11 tertiary hospitals in China. A BN model was established based on the independent risk variables. RESULTS: Independent risk factors for GPs with malignant potential included age, number of polyps, polyp size (long diameter), polyp size (short diameter), and fundus. The BN prediction model identified relationships between polyp size (long diameter) and three other variables [polyp size (short diameter), fundus and number of polyps]. Each variable was assigned scores under different status and the probabilities of GPs with malignant potential were classified as [0-0.2), [0.2-0.5), [0.5-0.8) and [0.8-1] according to the total points of [- 337, - 234], [- 197, - 145], [- 123, - 108], and [- 62,500], respectively. The AUC was 77.38% and 75.13%, and the model accuracy was 75.58% and 80.47% for the BN model in the training set and testing set, respectively. CONCLUSION: A BN prediction model was accurate and practical for predicting GPs with malignant potential patients in a long diameter of 8-15 mm undergoing cholecystectomy based on preoperative ultrasound.

A Bayesian network model to predict neoplastic risk for patients with gallbladder polyps larger than 10 mm based on preoperative ultrasound features.

BACKGROUND: Polyp size of 10 mm is insufficient to discriminate neoplastic and non-neoplastic risk in patients with gallbladder polyps (GPs). The aim of the study is to develop a Bayesian network (BN) prediction model to identify neoplastic polyps and create more precise criteria for surgical indications in patients with GPs lager than 10 mm based on preoperative ultrasound features. METHODS: A BN prediction model was established and validated based on the independent risk variables using data from 759 patients with GPs who underwent cholecystectomy from January 2015 to August 2022 at 11 tertiary hospitals in China. The area under receiver operating characteristic curves (AUCs) were used to evaluate the predictive ability of the BN model and current guidelines, and Delong test was used to compare the AUCs. RESULTS: The mean values of polyp cross-sectional area (CSA), long, and short diameter of neoplastic polyps were higher than those of non-neoplastic polyps (P < 0.0001). Independent neoplastic risk factors for GPs included single polyp, polyp CSA ≥ 85 mm 2, fundus with broad base, and medium echogenicity. The accuracy of the BN model established based on the above independent variables was 81.88% and 82.35% in the training and testing sets, respectively. Delong test also showed that the AUCs of the BN model was better than that of JSHBPS, ESGAR, US-reported, and CCBS in training and testing sets, respectively (P < 0.05). CONCLUSION: A Bayesian network model was accurate and practical for predicting neoplastic risk in patients with gallbladder polyps larger than 10 mm based on preoperative ultrasound features.

A Bayesian Network Prediction Model for Microvascular Invasion in Patients with Intrahepatic Cholangiocarcinoma: A Multi-institutional Study.

BACKGROUND: Microvascular invasion (MVI) has been reported to be an independent prognostic factor of recurrence and poor overall survival in patients with intrahepatic cholangiocarcinoma (ICC). This study aimed to explore the preoperative independent risk factors of MVI and establish a Bayesian network (BN) prediction model to provide a reference for surgical diagnosis and treatment. METHODS: A total of 531 patients with ICC who underwent radical resection between 2010 and 2018 were used to establish and validate a BN model for MVI. The BN model was established based on the preoperative independent variables. The ROC curves and confusion matrix were used to assess the performance of the model. RESULTS: MVI was an independent risk factor for relapse-free survival (RFS) (P < 0.05). MVI has a correlation with postoperative recurrence, early recurrence (< 6 months), median RFS and median overall survival (all P < 0.05). The preoperative independent risk variables of MVI included obstructive jaundice, prognostic nutritional index, CA19-9, tumor size, and major vascular invasion, which were used to establish the BN model. The AUC of the BN model was 78.92% and 83.01%, and the accuracy was 70.85% and 77.06% in the training set and testing set, respectively. CONCLUSION: The BN model established based on five independent risk variables for MVI is an effective and practical model for predicting MVI in patients with ICC.

Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic.

Predicting the evolutionary dynamics of the COVID-19 pandemic is a complex challenge. The complexity increases when the vaccination process dynamic is also considered. In addition, when applying a voluntary vaccination policy, the simultaneous behavioral evolution of individuals who decide whether and when to vaccinate must be included. In this paper, a coupled disease-vaccination behavior dynamic model is introduced to study the coevolution of individual vaccination strategies and infection spreading. We study disease transmission by a mean-field compartment model and introduce a non-linear infection rate that takes into account the simultaneity of interactions. Besides, the evolutionary game theory is used to investigate the contemporary evolution of vaccination strategies. Our findings suggest that sharing information with the entire population about the negative and positive consequences of infection and vaccination is beneficial as it boosts behaviors that can reduce the final epidemic size. Finally, we validate our transmission mechanism on real data from the COVID-19 pandemic in France.

High-Resolution Performance of Polycaprolactone Functionalized with Guanidinium Ionic Liquid for Gas Chromatography.

This work firstly reported a new polycaprolactone based material functionalized with guanidinium ionic liquid (PCL-GIL) as the stationary phase with high resolution performance for capillary gas chromatography (GC). It is composed of polycaprolactone (PCL) and guanidinium ionic liquid (GIL) with amphiphilic conformation. The PCL-GIL capillary column coated by static method exhibited high column efficiency of 3942 plates/m and moderate polarity. As a result, the PCL-GIL column exhibited high-resolution capability. For a mixture of 27 analytes with a wide ranging polarity and outperformed the PCL-2OH and HP-35 columns, showing its advantageous separation capability for analytes of diverse types. Moreover, the PCL-GIL column showed high resolving capability for various positional isomers and cis-/trans-isomers, including alkylbenzenes, chlorobenzenes, naphthalenes, bromonitrobenzenes, chloronitrobenzenes, benzaldehydes, phenols, alcohols, respectively. In a word, PCL derivatized by GIL units as a new type of stationary phase has a promising future in GC separations.

Construction of chiral capillary electrochromatography microsystems based on Aspergillus sp. CM96.

Novel chiral capillary electrochromatography (CEC) microsystems were constructed based on Aspergillus sp. CM96. As a newly discovered intrinsic characteristic of the cell, cell chirality occupies an essential position in life evolution. Aspergillus sp. CM96 spore (CM96s) was chosen as a proof of concept to develop chiral capillary columns. Interestingly, various types of amino acid (AA) enantiomers were baseline separated under the optimized conditions. Furthermore, the time-dependent chiral interactions between AAs and CM96s were explored in a wider space. Pectinases generated from Aspergillus sp. CM96 fermentation were immobilized onto graphene oxide-functionalized capillary silica monoliths for separating AA enantiomers. Molecular docking simulations were performed to explore chiral separation mechanisms of pectinase for AA enantiomers. These results indicated that Aspergillus sp. CM96-based CEC microsystems have a significant advantage for chiral separation.

Rationale and Design of the ADIDAS Study: Association Between Dapagliflozin-Induced Improvement and Anemia in Heart Failure Patients.

BACKGROUND: Heart failure (HF) is one of the most serious health concerns worldwide. Anemia is a highly prevalent comorbidity and outcome predictor in HF patients. Sodium glucose co-transport 2 (SGLT2) inhibitors have been demonstrated to reduce the risk of cardiovascular death and HF hospitalization in HF patients. PURPOSE: This investigator-initiated, interventional, prospective, double-blind, multicenter study is designed to investigate whether anemia correction is one of the prerequisites and determinants related to the beneficial effects of dapagliflozin in HF patients. METHODS AND RESULTS: Up to 2030 HF participants receiving standard care will be randomly assigned to either oral dapagliflozin 10 mg once daily or placebo 10 mg once daily for 12 months. The primary outcome is the composite incidence of hospital admission for HF and all-cause death. Secondary outcomes include change in the Kansas City Cardiomyopathy Questionnaire (KCCQ) score and change in 6-min walk distance and hemoglobin level. Patients will be followed for 12 months after randomization. CONCLUSIONS: The ADIDAS trial offers an opportunity to assess the hemoglobin change and association between hemoglobin change and readmissions due to heart failure and all-cause death in patients with heart failure treated with dapagliflozin or placebo. This study could highlight if dynamic hemoglobin change is related to the outcome for HF patients. TRIAL REGISTRATION: ClinicalTrials.gov ; NCT04707261. Registration date, 2020/12/01, "retrospectively registered".

A New Capillary Gas Chromatography Column Based on Poly(ethylene glycol) Methyl Ether-Functionalized Calix[4]arene.

In this work, a novel capillary column (C4A-mPEG) with a calixarene-based polymer stationary phase (poly(ethylene glycol) methyl ether-functionalized 4-tert-butylcalix[4]arene) was designed and used for gas chromatographic (GC) separations. The C4A-mPEG capillary column, prepared by the static coating method, showed moderate polarity and a column efficiency of 2332 plates/m, determined by 1-octanol at 120 °C. The separation features of C4A-mPEG stationary phase, resulting from its unique structure and multiple molecular recognition processes with analytes, including π-π, H-bonding, dipole-dipole, and van der Waals interactions, allowed to obtain high-resolution performances for a wide range of compounds and their isomers, especially benzaldehydes, phenols, and anilines. Moreover, compared with 4-tertbutyl calix[4]arene (C4A) and polyethylene glycol (PEG) stationary phases, a higher resolving capability was also observed for the separation of toluidine and xylidine isomers.

An Oversampling Method of Unbalanced Data for Mechanical Fault Diagnosis Based on MeanRadius-SMOTE.

With the development of machine learning, data-driven mechanical fault diagnosis methods have been widely used in the field of PHM. Due to the limitation of the amount of fault data, it is a difficult problem for fault diagnosis to solve the problem of unbalanced data sets. Under unbalanced data sets, faults with little historical data are always difficult to diagnose and lead to economic losses. In order to improve the prediction accuracy under unbalanced data sets, this paper proposes MeanRadius-SMOTE based on the traditional SMOTE oversampling algorithm, which effectively avoids the generation of useless samples and noise samples. This paper validates the effectiveness of the algorithm on three linear unbalanced data sets and four step unbalanced data sets. Experimental results show that MeanRadius-SMOTE outperforms SMOTE and LR-SMOTE in various evaluation indicators, as well as has better robustness against different imbalance rates. In addition, MeanRadius-SMOTE can take into account the prediction accuracy of the overall and minority class, which is of great significance for engineering applications.

Learn Quasi-Stationary Distributions of Finite State Markov Chain.

We propose a reinforcement learning (RL) approach to compute the expression of quasi-stationary distribution. Based on the fixed-point formulation of quasi-stationary distribution, we minimize the KL-divergence of two Markovian path distributions induced by candidate distribution and true target distribution. To solve this challenging minimization problem by gradient descent, we apply a reinforcement learning technique by introducing the reward and value functions. We derive the corresponding policy gradient theorem and design an actor-critic algorithm to learn the optimal solution and the value function. The numerical examples of finite state Markov chain are tested to demonstrate the new method.