Pharmacotherapy and cardiovascular challenges: a case report of olverembatinib-induced myocardial infarction with non-obstructive coronary arteries.

The anticancer drug of tyrosine kinase-inhibitors (TKIs) has significantly improved the prognosis of patients with specific leukemia but has also increased the risk of organ adverse reactions. Herein, we present a case of a patient diagnosed with myeloproliferative neoplasms who experienced recurrent chest pain after receiving treatment with Olverembatinib. Electrocardiography and coronary angiography confirmed the diagnosis of myocardial infarction with non-obstructive coronary arteries. This case serves as a reminder for clinicians to pay more attention and actively prevent the cardiac adverse reactions of TKIs when using such medications.

[Simultaneous detection of lower aliphatic amines and conventional cations in atmospheric PM2.5 particulates by ion chromatography].

Many amine pollutants exist in the atmosphere. Lower aliphatic amines promote the formation and growth of particles into PM2.5, which damages the heart, lungs, and kidneys of the human body. PM2.5, a common atmospheric particulate pollutant with complex compositions, is the main cause of haze weather. Therefore, measuring the contents of lower aliphatic amines and cations in PM2.5 is of great significance for monitoring environmental air quality and protecting human health. This study established a suppressed ion-chromatographic method with conductivity for the simultaneous detection of four lower aliphatic amines (methylamine, dimethylamine, trimethylamine, and ethylamine) and five cations (Na+, N[Formula: see text], and Ca2+ showed high concentrations. The contents of the four lower aliphatic amines were low; however, the ethylamine content in some samples was high. The results indicate that the proposed method meets the quantification requirements for cations and lower aliphatic amines in PM2.5, with simple processing, high sensitivity, and good accuracy. It can quickly and accurately detect a large number of samples and be used to assess the pollution of small particles in the air as well as trace pollution sources to protect human health.

SERT-StructNet: Protein secondary structure prediction method based on multi-factor hybrid deep model.

Protein secondary structure prediction (PSSP) is a pivotal research endeavour that plays a crucial role in the comprehensive elucidation of protein functions and properties. Current prediction methodologies are focused on deep-learning techniques, particularly focusing on multi-factor features. Diverging from existing approaches, in this study, we placed special emphasis on the effects of amino acid properties and protein secondary structure propensity scores (SSPs) on secondary structure during the meticulous selection of multi-factor features. This differential feature-selection strategy results in a distinctive and effective amalgamation of the sequence and property features. To harness these multi-factor features optimally, we introduced a hybrid deep feature extraction model. The model initially employs mechanisms such as dilated convolution (D-Conv) and a channel attention network (SENet) for local feature extraction and targeted channel enhancement. Subsequently, a combination of recurrent neural network variants (BiGRU and BiLSTM), along with a transformer module, was employed to achieve global bidirectional information consideration and feature enhancement. This approach to multi-factor feature input and multi-level feature processing enabled a comprehensive exploration of intricate associations among amino acid residues in protein sequences, yielding a Q3 accuracy of 84.9% and an Sov score of 85.1%. The overall performance surpasses that of the comparable methods. This study introduces a novel and efficient method for determining the PSSP domain, which is poised to deepen our understanding of the practical applications of protein molecular structures.

Explainable machine learning model for identifying key gut microbes and metabolites biomarkers associated with myasthenia gravis.

Diagnostic markers for myasthenia gravis (MG) are limited; thus, innovative approaches are required for supportive diagnosis and personalized care. Gut microbes are associated with MG pathogenesis; however, few studies have adopted machine learning (ML) to identify the associations among MG, gut microbiota, and metabolites. In this study, we developed an explainable ML model to predict biomarkers for MG diagnosis. We enrolled 19 MG patients and 10 non-MG individuals. Stool samples were collected and microbiome assessment was performed using 16S rRNA sequencing. Untargeted metabolic profiling was conducted to identify fecal amplicon significant variants (ASVs) and metabolites. We developed an explainable ML model in which the top ASVs and metabolites are combined to identify the best predictive performance. This model uses the SHapley Additive exPlanations method to generate both global and personalized explanations. Fecal microbe-metabolite composition differed significantly between groups. The key bacterial families were Lachnospiraceae and Ruminococcaceae, and the top three features were Lachnospiraceae, inosine, and methylhistidine. An ML model trained with the top 1 % ASVs and top 15 % metabolites combined outperformed all other models. Personalized explanations revealed different patterns of microbe-metabolite contributions in patients with MG. The integration of the microbiota-metabolite features and the development of an explainable ML framework can accurately identify MG and provide personalized explanations, revealing the associations between gut microbiota, metabolites, and MG. An online calculator employing this algorithm was developed that provides a streamlined interface for MG diagnosis screening and conducting personalized evaluations.

[Characteristics of drug resistance and biofilm formation in carbapenem-resistant Acinetobacter baumannii in hospitalized children]. / 住院患儿耐碳青霉烯类鲍曼不动杆菌的耐药及生物膜特征分析.

OBJECTIVES: To study the distribution, drug resistance, and biofilm characteristics of carbapenem-resistant Acinetobacter baumannii (CRAB) isolated from hospitalized children, providing a reference for the prevention and treatment of CRAB infections in hospitalized children. METHODS: Forty-eight CRAB strains isolated from January 2019 to December 2022 were classified into epidemic and sporadic strains using repetitive extragenic palindromic sequence-based polymerase chain reaction. The drug resistance, biofilm phenotypes, and gene carriage of these two types of strains were compared. RESULTS: Both the 22 epidemic strains and the 26 sporadic strains were producers of Class D carbapenemases or extended-spectrum ß-lactamases with downregulated outer membrane porins, harboring the VIM, OXA-23, and OXA-51 genes. The biofilm formation capability of the sporadic strains was stronger than that of the epidemic strains (P<0.05). Genes related to biofilm formation, including Bap, bfs, OmpA, CsuE, and intI1, were detected in both epidemic and sporadic strains, with a higher detection rate of the intI1 gene in epidemic strains (P<0.05). CONCLUSIONS: CRAB strains are colonized in the hospital, with sporadic strains having a stronger ability to form biofilms, suggesting the potential for forming new clonal transmissions in the hospital. Continuous monitoring of the epidemic trends of CRAB and early warning of the distribution of epidemic strains are necessary to reduce the risk of CRAB infections in hospitalized children.

Computational identification of DNA damage-relevant lncRNAs for predicting therapeutic efficacy and clinical outcomes in cancer.

OBJECTIVES: The role of long non-coding RNAs (lncRNAs) in cancer treatment, particularly in modulating DNA repair programs, is an emerging field that warrants systematic exploration. This study aimed to systematically identify the lncRNA regulators that potentially regulate DNA damage response (DDR). METHODS: Using genome-wide mRNA and lncRNA expression profiles of the same tumor patients, we proposed a novel computational framework. This framework performed Gene Set Variation Analysis to calculate DDR pathway enrichment score, which relies on weighting by tumor purity to obtain DDR activity score for each patient. Then, an in-depth differential expression profiling was conducted to identify pathway activity lncRNAs between high- and low-activity groups, utilizing a bootstrap-based randomization method. RESULTS: We comprehensively charted the landscape of DDR-relevant lncRNAs across 23 epithelial-based cancer types. Its effectiveness was validated by assessing the consistency of these lncRNAs within various datasets of the same cancer type (hypergeometric test P < 0.001), examining the expression perturbation of these lncRNAs in response to treatment and demonstrating its application in prioritizing clinically-related lncRNAs. Furthermore, leveraging 820 epithelial ovarian cancer patients from four public datasets, we applied these lncRNAs identified by DDRLnc to establish and validate a risk stratification model to evaluate the benefits of platinum-based adjuvant chemotherapy for the improvement of clinical treatment outcomes. CONCLUSIONS: Comprehensive pan-cancer analysis illustrates the utility of computational framework in identifying potentially lncRNAs involved in DDR, thereby offering novel insights into the complex realm of cancer research, and it will become a valuable tool for identifying potential therapeutic targets for cancer.

ACP-ML: A sequence-based method for anticancer peptide prediction.

Cancer is a serious malignant tumor and is difficult to cure. Chemotherapy, as a primary treatment for cancer, causes significant harm to normal cells in the body and is often accompanied by serious side effects. Recently, anti-cancer peptides (ACPs) as a type of protein for treating cancers dominated research into the development of new anti-tumor drugs because of their ability to specifically target and destroy cancer cells. The screening of proteins with cancer-inhibiting properties from a large pool of proteins is key to the development of anti-tumor drugs. However, it is expensive and inefficient to accurately identify protein functions only through biological experiments due to their complex structure. Therefore, we propose a new prediction model ACP-ML to effectively predict ACPs. In terms of feature extraction, DPC, PseAAC, CTDC, CTDT and CS-Pse-PSSM features were used and the most optimal feature set was selected by comparing combinations of these features. Then, a two-step feature selection process using MRMD and RFE algorithms was performed to determine the most crucial features from the most optimal feature set for identifying ACPs. Furthermore, we assessed the classification accuracy of single learning models and different strategies-based ensemble models through ten-fold cross-validation. Ultimately, a voting-based ensemble learning method is developed to predict ACPs. To validate its effectiveness, two independent test sets were used to perform tests, achieving accuracy of 90.891 % and 92.578 % respectively. Compared with existing anticancer peptide prediction algorithms, the proposed feature processing method is more effective, and the proposed ensemble model ACP-ML exhibits stronger generalization capability and higher accuracy.

Revision surgery for periprosthetic fracture of distal femur after endoprosthetic replacement of knee joint following resection of osteosarcoma.

Purpose: Periprosthetic fracture (PPF) is one of the severe complications in patients with osteosarcoma and carries the risk of limb loss. This study describes the characteristics, treatment strategies, and outcomes of this complication. Methods: Patients were consecutively included who were treated at our institution between 2016 and 2020 with a PPF of distal femur. The treatment strategies included two types: 1) open reduction and internal fixation with plates and screws and 2) replacement with long-stem endoprosthesis and reinforcement with wire rope if necessary. Results: A total of 11 patients (mean age 12.2 years (9-14)) were included, and the mean follow-up period was 36.5 (21-54) months. Most fractures were caused by direct or indirect trauma (n = 8), and others (n = 3) underwent PPF without obvious cause. The first type of treatment was performed on four patients, and the second type was performed on seven patients. The mean Musculoskeletal Tumor Society (MSTS) score was 20 (17-23). All patients recovered from the complication, and limb preservation could be achieved. Conclusion: PPF is a big challenge for musculoskeletal oncologists, particularly in younger patients. Additionally, PPF poses a challenge for orthopedic surgeons, as limb preservation should be an important goal. Hence, internal fixation with plates and endoprosthetic replacement are optional treatment strategies based on fracture type and patient needs.

A systematic-search-and-review of registered pharmacological therapies investigated to improve neuro-recovery after a stroke.

Background: Stroke burden is largely due to long-term impairments requiring prolonged care with loss of productivity. We aimed to identify and assess studies of different registered pharmacological therapies as treatments to improve post-stroke impairments and/or disabilities. Methods: We performed a systematic-search-and-review of treatments that have been investigated as recovery-enhancing or recovery-promoting therapies in adult patients with stroke. The treatment must have received registration or market authorization in any country regardless of primary indication. Outcomes included in the review were neurological impairments and functional/disability assessments. "The best available studies" based on study design, study size, and/or date of publication were selected and graded for level of evidence (LOE) by consensus. Results: Our systematic search yielded 7,801 citations, and we reviewed 665 full-text papers. Fifty-eight publications were selected as "the best studies" across 25 pharmacological classes: 31 on ischemic stroke, 21 on ischemic or hemorrhagic stroke, 4 on intracerebral hemorrhage, and 2 on subarachnoid hemorrhage (SAH). Twenty-six were systematic reviews/meta-analyses, 29 were randomized clinical trials (RCTs), and three were cohort studies. Only nimodipine for SAH had LOE A of benefit (systematic review and network meta-analysis). Many studies, some of which showed treatment effects, were assessed as LOE C-LD, mainly due to small sample sizes or poor quality. Seven interventions had LOE B-R (systematic review/meta-analysis or RCT) of treatment effects. Conclusion: Only one commercially available treatment has LOE A for routine use in stroke. Further studies of putative neuroprotective drugs as adjunctive treatment to revascularization procedures and more confirmatory trials on recovery-promoting therapies will enhance the certainty of their benefit. The decision on their use must be guided by the clinical profile, neurological impairments, and target outcomes based on the available evidence. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=376973, PROSPERO, CRD42022376973.

Investigation of the predictive value of a novel algorithm based on coronary CT angiography regarding fractional flow reserve and revascularization in patients with stable coronary artery disease.

Fractional flow reserve (FFR) has been established as a gold standard for functional coronary ischemia. At present, the FFR can be calculated from coronary computed tomography angiography (CCTA) images (CT-FFR). Previous studies have suggested that CT-FFR outperforms CCTA and invasive coronary angiography (ICA) in determining hemodynamic significance of stenoses. Recently, a novel automatical algorithm of CT-FFR called RuiXin-FFR has been developed. The present study is designed to investigate the predictive value of this algorithm and its value in therapeutic decision making. The present study retrospectively included 166 patients with stable coronary artery disease (CAD) who underwent CCTA screening and diagnostic ICA examination at Peking University People's Hospital, in 73 of whom wire-derived FFR was also measured. CT-FFR analyses were performed with a dedicated software. All patients were followed up for at least 1 year. We validated the accuracy of RuiXin-FFR with invasive FFR as the standard of reference, and investigated the role of RuiXin-FFR in predicting treatment strategy and long-term prognosis. The mean age of the patients was 63.3 years with 63.9% male. The CT-FFR showed a moderate correlation with wire-derived FFR (r = 0.542, p < 0.0001) and diagnostic accuracy of 87.6% to predict myocardial ischemia (AUC: 0.839, 95% CI 0.728-0.950), which was significantly higher than CCTA and ICA. In the multivariate logistic regression analysis, CT-FFR ≤ 0.80 was an independent predictor of undergoing coronary revascularization (OR: 45.54, 95% CI 12.03-172.38, p < 0.0001), whereas CT-FFR > 0.80 was an independent predictor of non-obstructive CAD (OR: 14.67, 95% CI 5.42-39.72, p < 0.0001). Reserving ICA and revascularization for vessels with positive CT-FFR could have reduced the rate of ICA by 29.6%, lowered the rate of ICA in vessels without stenosis > 50% by 11.7%, and increased the rate of revascularization in patients receiving ICA by 21.2%. The average follow-up was 23.7 months, and major adverse cardiovascular events (MACE) occurred in 11 patients. The rate of MACE was significantly lower in patients with CT-FFR > 0.80. The new algorithm of CT-FFR can be used to predict the invasive FFR. The RuiXin-FFR can also provide useful information for the screening of patients in whom further ICA is indeed needed and prognosis evaluation.

Prognostic significance of persisting DNMT3A, ASXL1, and TET2 mutation burden in acute myeloid leukemia patients with allogeneic hematopoietic stem cell transplantation during complete remission.

We retrospectively analyzed 155 AML patients with DAT mutations at diagnosis who underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT) at complete remission. Of the 155 AML patients with DAT mutations at diagnosis, 59 (38.1%) patients had persisting DAT mutations pretransplantation. Compared to patients with pretransplant DAT transitions, patients with persisting DAT mutation burden were shown to be older (p = 0.004), and fewer patients had TET2 mutations at diagnosis (p = 0.033). Patients with persistent DAT mutation burden had shorter overall survival (OS) (3-year OS: 59.3% vs. 83.0%, p < 0.001) and disease-free survival (DFS) (3-year DFS: 56.1% vs. 83.0%, p < 0.001) with a higher cumulative incidence of relapse (CIR) (24.6% vs. 17.4%, p = 0.002) than those with DAT transitions. Additionally, multivariate analysis confirmed that persisting DAT mutations were an independent adverse factor for relapse, OS, and DFS. Collectively, persisting DAT mutations prior to allo-HSCT at complete remission for AML correlated with negative outcomes.

GwasWA: A GWAS one-stop analysis platform from WGS data to variant effect assessment.

Using the accumulated whole-genome sequencing (WGS) data and assessing the functional effects of genetic variants, particularly non-coding variants, help identify new and rare variants and decipher the molecular mechanisms underlying diseases and traits but presents significant challenges. GwasWA is a comprehensive and efficient platform to identify causal variants and assess their functional effects based on WGS data. It covers the entire workflow from downloading and processing WGS data to detecting associated variants and assessing their functional effects with optimized configurations, standardized input/output formats, personalized analysis options, data visualization, and parallel processing capability that is crucial for large-scale studies. Applying GwasWA to real datasets identified three novel genes related to seed size and revealed the regulatory mechanism underlying the linkage between a human non-coding variant, rs80067372, and tumor necrosis factor levels. These results highlight the capability of GwasWA to detect novel variants based on WGS data and provide comprehensive insights into the molecular mechanisms underlying the association of variants with diseases and traits, thus contributing to medicine and biology. GwasWA and its documentation are freely available at https://github.com/unicorn-23/GwasWA.

Using TransR to enhance drug repurposing knowledge graph for COVID-19 and its complications.

MOTIVATION: The COVID-19 pandemic has been spreading globally for four years, yet specific drugs that effectively suppress the virus remain elusive. Furthermore, the emergence of complications associated with COVID-19 presents significant challenges, making the development of therapeutics for COVID-19 and its complications an urgent task. However, traditional drug development processes are time-consuming. Drug repurposing, which involves identifying new therapeutic applications for existing drugs, presents a viable alternative. RESULT: In this study, we construct a knowledge graph by retrieving information on genes, drugs, and diseases from databases such as DRUGBANK and GNBR. Next, we employ the TransR knowledge representation learning approach to embed entities and relationships into the knowledge graph. Subsequently, we train the knowledge graph using a graph neural network model based on TransR scoring. This trained knowledge graph is then utilized to predict drugs for the treatment of COVID-19 and its complications. Based on experimental results, we have identified 15 drugs out of the top 30 with the highest success rates associated with treating COVID-19 and its complications. Notably, out of these 15 drugs, 10 specifically aimed at treating COVID-19, such as Torcetrapib and Tocopherol, has not been previously identified in the knowledge graph. This finding highlights the potential of our model in aiding healthcare professionals in drug development and research related to this disease.

Assessing the length of hospital stay for patients with myasthenia gravis based on the data mining MARS approach.

Predicting the length of hospital stay for myasthenia gravis (MG) patients is challenging due to the complex pathogenesis, high clinical variability, and non-linear relationships between variables. Considering the management of MG during hospitalization, it is important to conduct a risk assessment to predict the length of hospital stay. The present study aimed to successfully predict the length of hospital stay for MG based on an expandable data mining technique, multivariate adaptive regression splines (MARS). Data from 196 MG patients' hospitalization were analyzed, and the MARS model was compared with classical multiple linear regression (MLR) and three other machine learning (ML) algorithms. The average hospital stay duration was 12.3 days. The MARS model, leveraging its ability to capture non-linearity, identified four significant factors: disease duration, age at admission, MGFA clinical classification, and daily prednisolone dose. Cut-off points and correlation curves were determined for these risk factors. The MARS model outperformed the MLR and the other ML methods (including least absolute shrinkage and selection operator MLR, classification and regression tree, and random forest) in assessing hospital stay length. This is the first study to utilize data mining methods to explore factors influencing hospital stay in patients with MG. The results highlight the effectiveness of the MARS model in identifying the cut-off points and correlation for risk factors associated with MG hospitalization. Furthermore, a MARS-based formula was developed as a practical tool to assist in the measurement of hospital stay, which can be feasibly supported as an extension of clinical risk assessment.

Tumor size combined with staging systems for thymoma recurrence prediction: A 28-year experience.

Background: We evaluated a new thymoma prognosis prediction model by combining current staging systems with tumor size. Methods: The clinical records of thymoma patients in a single center between January 1993 and December 2021 were collected, and data on tumor size and stage and recurrence-free survival (RFS) was obtained. The prediction model was designed by combining staging with tumor size. Results: During 28 years, 219 thymoma patients were enrolled. Twenty-seven patients had a median RFS of 8.2 years. Further, 153 patients were categorized into limited stage and 66 patients into advanced stage. The RFS was statistically different between these two groups (P = 0.022). The largest area under the curve (AUC) of receiver operating characteristic (ROC) was the dividing group as 5 cm (AUC: 0.804). Conclusions: Combining tumor staging and size improves thymoma recurrence prediction. Patients with advanced stage and tumor size >5 cm may show a poor prognosis.

Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis.

Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and established a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods, namely least absolute shrinkage and selection operator, extreme gradient boosting (XGBoost), random forest, and classification and regression trees with nested leave-one-out cross-validation were trained using ASV taxon-based data and full ASV-based data to identify key ASVs in each data set. The results revealed XGBoost to have the best predicted performance. Overlapping key features extracted when XGBoost was trained using the full ASV-based and ASV taxon-based data were identified, and 31 high-importance ASVs (HIASVs) were obtained, assigned importance scores, and ranked. The most significant difference observed was in the abundance of bacteria in the Lachnospiraceae and Ruminococcaceae families. The 31 HIASVs were used to train the XGBoost algorithm to differentiate individuals with and without MG. The model had high diagnostic classification power and could accurately predict and identify patients with MG. In addition, the abundance of Lachnospiraceae was associated with limb weakness severity. In this study, we discovered that the composition of gut microbiomes differed between MG and non-MG subjects. In addition, the proposed XGBoost model trained using 31 HIASVs had the most favorable performance with respect to analyzing gut microbiomes. These HIASVs selected by the ML model may serve as biomarkers for clinical use and mechanistic study in the future. Our proposed ML model can identify several taxonomic markers and effectively discriminate patients with MG from those without with a high accuracy, the ML strategy can be applied as a benchmark to conduct noninvasive screening of MG.

Real-Time Monitoring of Translocation, Dissipation, and Cumulative Risk of Maleic Hydrazide in Potato Plants and Tubers by Ion Exclusion Chromatography.

In this paper, a high-performance ion exclusion chromatographic (ICE) method was developed and applied for monitoring maleic hydrazide (MH) translocation in complex potato plant tissue and tuber matrices. After middle leaf uptake, most MH was trapped and dissipated in the middle leaf, and the rest was transported to other parts mainly through the phloem. Soil absorption significantly reduced the uptake efficiency of the root system, in which MH was partitioned to dissipate in root protoplasts or transfer through the xylem and persisted in the plant. Tuber uptake enabled MH to remain in the flesh and maintain stable levels under storage conditions, but during germination, MH was translocated from the flesh to the growing buds, where it dissipated through the short-day photoperiodic regime. The results demonstrated successful application of the ICE method and provided necessary insights for real-time monitoring of MH translocation behavior to effectively improve potato edible safety.

The clinical value of anal swabs for microbial detection in allogeneic haematopoietic stem cell transplantation.

The intestinal microbiota plays critical roles in allogeneic hematopoietic stem cell transplantation (allo-HSCT). Rapid and effective microbial detection methods have important guiding value for the selection of intervention strategies for allo-HSCT recipients. We evaluated the application of the anal swab test before transplantation in allo-HSCT recipients. A total of 120 patients who underwent anal swab testing before allo-HSCT were retrospectively analyzed and divided into 3 groups: sterile (aseptic growth-negative), G+ (gram-positive bacterial colonization), and G- (gram-negative bacterial colonization). On 16S rRNA sequencing, gram-negative bacteria predominated in the G- group before and after transplantation. Compared with the sterile group, the percentage of natural killer cells was higher and the percentage of T cells was lower after transplantation in the G- group at 1 month after transplantation. The percentage of CD4+ and CD4+CD8+ T cells was lower and the percentage of regulatory T cells was higher in the G- group. The plasma levels of proinflammatory cytokines (TNF-α, IFN-γ, IL-6, and IL-17A) at 2 weeks post-transplantation were lower in the G- group than in the sterile group, as was the cumulative incidence of grade III-IV acute graft-versus-host disease (GVHD). Gram-negative bacterial colonization before allo-HSCT was associated with low rates of bloodstream infections within 100 days post-transplantation and cytomegalovirus reactivation at 100 days to 2 years post-transplantation. Moreover, patients in the G- group had a higher rate of 2-year GVHD-free, relapse-free survival compared with patients in the sterile group. The detection results using anal swabs were consistent with the gram-negative or gram-positive bacteria abundance of 16S rRNA sequencing results and associated with immune homeostasis and clinical outcomes after allo-HSCT. Anal swab testing may have potential advantages as a simple and effective method for microbial detection in allo-HSCT.

Effect of PCSK9 on atherosclerotic cardiovascular diseases and its mechanisms: Focus on immune regulation.

Atherosclerosis is a basic pathological characteristic of many cardiovascular diseases, and if not effectively treated, patients with such disease may progress to atherosclerotic cardiovascular diseases (ASCVDs) and even heart failure. The level of plasma proprotein convertase subtilisin/kexin type 9 (PCSK9) is significantly higher in patients with ASCVDs than in the healthy population, suggesting that it may be a promising new target for the treatment of ASCVDs. PCSK9 produced by the liver and released into circulation inhibits the clearance of plasma low-density lipoprotein-cholesterol (LDL-C), mainly by downregulating the level of LDL-C receptor (LDLR) on the surface of hepatocytes, leading to upregulated LDL-C in plasma. Numerous studies have revealed that PCSK9 may cause poor prognosis of ASCVDs by activating the inflammatory response and promoting the process of thrombosis and cell death independent of its lipid-regulatory function, yet the underlying mechanisms still need to be further clarified. In patients with ASCVDs who are intolerant to statins or whose plasma LDL-C levels fail to descend to the target value after treatment with high-dose statins, PCSK9 inhibitors often improve their clinical outcomes. Here, we summarize the biological characteristics and functional mechanisms of PCSK9, highlighting its immunoregulatory function. We also discuss the effects of PCSK9 on common ASCVDs.

The efficiency of human umbilical cord mesenchymal stem cells as a salvage treatment for steroid-refractory acute graft-versus-host disease.

Acute graft-versus-host disease (aGVHD) is a life-threatening complication after hematopoietic stem cell transplantation (HSCT) and is primarily treated with steroids. However, there is no standard treatment for steroid-refractory acute graft-versus-host disease (SR-aGVHD). Although mesenchymal stem cells (MSCs) have proven effective for SR-aGVHD, few reports have focused on human umbilical cord blood-derived MSCs (hUCB-MSCs). Here, we report on the efficiency of hUCB-MSCs as the salvage therapy for SR-aGVHD in 54 patients. The overall response rate (ORR) reached 59.3% (32/54) 28 days later. Twenty-four patients achieved complete remission (CR), and 8 achieved partial remission (PR). The median follow-up time after the initiation of hUCB-MSC treatment was 19.3 (0.6-59.0) months. The probability of overall survival (OS) and progression-free survival (PFS) was 60.9% (47.4-74.4%, 95% CI) and 58.8% (45.3-72.3%, 95% CI), respectively, while that of GVHD/relapse-free survival (GRFS) was only 30.8% (17.86-43.74%, 95% CI). Multivariate analysis revealed that response on Day 28 was an independent favorable prognostic factor (OS, P < 0.001; PFS, P < 0.001; GRFS, P = 0.001), but an age of ≥ 18 years suggested an unfavorable long-term prognosis (OS, P < 0.001; PFS, P < 0.001; GRFS, P = 0.003). In addition, liver involvement was adversely associated with PFS (P = 0.021) and GRFS (P = 0.009). An infused MNC ≥ 8.66 × 108/kg was also detrimental to GRFS (P = 0.031). Collectively, our results support hUCB-MSCs as an effective treatment for SR-aGVHD.