Prognostic Capability of Clinical SYNTAX Score in Patients with Complex Coronary Artery Disease and Chronic Renal Insufficiency Undergoing Percutaneous Coronary Intervention.

Background: The SYNTAX score (SS) is useful for predicting clinical outcomes in patients undergoing percutaneous coronary intervention (PCI). The clinical SYNTAX score (CSS), developed by combining clinical parameters with the SS, enhances the risk model's ability to predict clinical outcomes. However, prior research has not yet evaluated the prognostic capacity of CSS in patients with complex coronary artery disease (CAD) and chronic renal insufficiency (CRI) who are undergoing PCI. We aimed to demonstrate the prognostic potential of CSS in assessing long-term adverse events in this high-risk patient cohort. Methods: A total of 962 patients with left main and/or three-vessel CAD and CRI were enrolled in the study spanning from January 2014 to September 2017. The CSS was calculated by multiplying the SS by the modified age, creatinine, and left ventricular ejection fraction (ACEF) score (age/ejection fraction + 1 for each 10 mL of creatinine clearance < 60 mL/min per 1.73 m 2 ). The patients were categorized into three groups based on their CSS values: low-CSS group (CSS < 18.0, n = 321), mid-CSS group (18.0 ≤ CSS < 28.3, n = 317), and high-CSS group (CSS ≥ 28.3, n = 324) as per the tertiles of CSS. The primary endpoints were all-cause mortality (ACM) and cardiac mortality (CM). The secondary endpoints included myocardial infarction (MI), unplanned revascularization, stroke, and major adverse cardiac and cerebrovascular events (MACCE). Results: At the median 3-year follow-up, the high-CSS group exhibited higher rates of ACM (19.4% vs. 6.6% vs. 3.6%, p < 0.001), CM (15.6% vs. 5.1% vs. 3.2%, p = 0.003), and MACCE (33.8% vs. 29.0% vs. 20.0%, p = 0.005) in comparison to the low and mid-CSS groups. Multivariable Cox regression analysis revealed that CSS was an independent predictor for all primary and secondary endpoints (p < 0 .05). Moreover, the C-statistics of CSS for ACM (0.666 vs. 0.597, p = 0.021) and CM (0.668 vs. 0.592, p = 0.039) were significantly higher than those of SS. Conclusions: The clinical SYNTAX score substantially enhanced the prediction of median 3-year ACM and CM in comparison with SS in complex CAD and CRI patients following PCI.

External validation of the SYNTAX score II 2020 in patients with chronic renal insufficiency.

BACKGROUND: The SYNTAX score Ⅱ 2020 (SSⅡ-2020) was created as a customized decision-making tool for individuals diagnosed with complex coronary artery disease (CAD). Nevertheless, there has been a scarcity of research investigating the long-term predictive significance of SSⅡ-2020 for patients with both CAD and chronic renal insufficiency (CRI) who undergo percutaneous coronary intervention (PCI). AIMS: We sought to showcase the prognostic capacity of SSII-2020 in evaluating long-term all-cause mortality (ACM) within this high-risk patient cohort. METHODS: A retrospective cohort comprising 1156 individuals diagnosed with CRI and exhibiting left main CAD, three-vessel CAD or both was included in this investigation. We categorized participants into three groups based on the optimal SSII-2020 threshold for predicting long-term ACM, determined using the X-tile software. RESULTS: At the median follow-up duration of 6.3 years, the ACM rates were determined to be 10% in the low, 17% in the moderate, and 28% in the high SSII-2020 groups (p < 0.001). Employing multivariate Cox regression analysis, it was observed that the high SSII-2020 group exhibited a 3.289-fold increased risk of ACM (95% confidence interval [CI]: 2.229-4.856, p < 0.001) compared with the low SSII-2020 group, whereas the high SSII-2020 group displayed a 1.757-fold (95% CI: 1.190-2.597, p = 0.005) in comparison to the median SSII-2020 groups. Compared with SSII, the SSII-2020 had an incremental value for predicting 7-year ACM (C-index: 0.662 vs. 0.534, p = 0.007; IDI: 0.016, p < 0.001). CONCLUSIONS: SSII-2020 enhances long-term ACM prediction, facilitates improved risk stratification, and improves clinical utility for PCI patients with complex CAD and CRI.

Identification of network interactions from time series data: An iterative approach.

The first step toward advancing our understanding of complex networks involves determining their connectivity structures from the time series data. These networks are often high-dimensional, and in practice, only a limited amount of data can be collected. In this work, we formulate the network inference task as a bilinear optimization problem and propose an iterative algorithm with sequential initialization to solve this bilinear program. We demonstrate the scalability of our approach to network size and its robustness against measurement noise, hyper-parameter variation, and deviations from the network model. Results across experimental and simulated datasets, comprising oscillatory, non-oscillatory, and chaotic dynamics, showcase the superior inference accuracy of our technique compared to existing methods.

Salt Restriction and Angiotensin-Converting Enzyme Inhibitor Improve the Responsiveness of the Small Artery in Salt-Sensitive Hypertension.

For salt-sensitive hypertension (SSH), salt restriction and angiotensin-converting enzyme (ACE) inhibitors are essential treatments, but their effect on the function of resistance arteries is unclear. Here, we present an intravital study to detect the effect of salt restriction and ACE inhibitors on the function of the mesenteric small artery (MSA) in SSH. Dahl salt-sensitive rats were randomized into the following groups: ACE inhibitor gavage, salt restriction, ACE inhibitor combined with salt restriction, and high-salt diet. After a 12-week intervention, the mesenteric vessels maintained their perfusion in vivo, and the changes in the diameter and blood perfusion of the MSAs to norepinephrine (NE) and acetylcholine (ACh) were detected. Switching from a high-salt diet to a low-salt diet (i.e., salt restriction) attenuated the vasoconstriction of the MSAs to NE and promoted the vasodilatation to ACh, while ACE inhibitor improved the vasodilatation more obviously. Pathologically, changes in local ACE, AT1R, and eNOS expression were involved in these processes induced by a high-salt diet. Our study suggests that salt restriction and ACE inhibitor treatment improve high salt intake-induced MSA dysfunction in SSH, and salt restriction is a feasible and effective treatment. Our findings may provide a scientific basis for the treatment of hypertension.

On-surface synthesis of Au-C4 and Au-O4 alternately arranged organometallic coordination networks via selective aromatic C-H bond activation.

Selective activation of the C-H bond of aromatic hydrocarbons is significant in synthetic chemistry. However, achieving oriented C-H activation remains challenging due to the poor selectivity of aromatic C-H bonds. Herein, we successfully constructed alternately arranged Au-C4 and Au-O4 organometallic coordination networks through selective aromatic C-H bond activation on Au(111) substrate. The stepwise reaction process of the 5, 12-dibromopyrene 3,4,9, 10-tetracarboxylic dianhydride precursor is monitored by high-resolution scanning tunneling microscopy. Our results show that the gold atoms in C-Au-C organometallic chains play a crucial role in promoting the selective ortho C-H bonds activation and forming Au-C4 coordination structure, which is further demonstrated by a comparative experiment of PTCDA precursor on Au(111). Furthermore, our experiment of 2Br-PTCDA precursor on Cu(111) substrate confirms that copper atoms in C-Cu-C organometallic chains can also assist the formation of Cu-C4 coordination structure. Our results reveal the vital effect of organometallic coordination on selective C-H bond activation of reactants, which holds promising implications for controllable on-surface synthesis.

In silico analysis and verification of critical genes related to vascular calcification in multiple diseases.

Identifying a functional molecular therapeutic target of vascular calcification (VC) that will not affect normal osteogenic differentiation is a challenge. To address this aim, we screened the differentially expressed genes (DEGs) in different VC conditions, including endothelial-osteogenic transition (EOT) (GSE167962), chronic kidney disease (CKD), and atherosclerosis (AS) (GSE159832). KEGG pathways, protein-protein interactions, and hub genes were also analyzed. The intersecting DEGs among the EOT, CKD, and AS groups were verified by quantitative reverse transcription polymerase chain reaction and immunohistochemistry in a DOCA-salt hypertension mouse model. The phosphoinositide 3-kinase-protein kinase B signaling pathway, ECM-receptor interaction, chemokine signaling pathway, and focal adhesion were enriched in EOT and AS-induced VC. ECM-receptor interaction, PPAR signaling pathway, apelin signaling pathway, AMPK signaling pathway, adipocytokine signaling pathway, and cholesterol metabolism were enriched in CKD and AS-induced VC. C4b, Cebpa, Lyz2, and Spp1 were also upregulated in EOT, CKD, AS, and hypertension. This study identified promising molecular targets for VC therapy.

Experience with the management of 2599 cases of congenital muscular torticollis and a multicenter epidemiological investigation in 17 hospitals in China.

BACKGROUND: Congenital muscular torticollis (CMT) is a common musculoskeletal disease affecting infants and young children. If CMT is not treated correctly and timely, it can lead to limited head and neck movements, head and neck deviation, and abnormal posture. In order to improve patients' symptoms and alleviate the negative impact of the disease on their lives, we are committed to exploring the treatment of CMT. METHODS: The general clinical and ultrasonographic data of 2599 children with CMT who received standardized treatment at Shenzhen Children's Hospital from 2004 to 2020 were retrospectively reviewed. According to given treatment, children with CMT were divided into the physiotherapy group, physiotherapy combined with glucocorticoid treatment group, and surgical treatment group. We divided children with CMT into local mass, uniform thickening, and atrophy according to ultrasound features. General clinical information, treatment, and ultrasound examination data in each group were compared. Additionally, electronic medical records of 2344 patients admitted due to CMT in 17 tertiary children's hospitals of China's Futang Research Center of Pediatric Development (FRCPD) from 2015 to 2019 were retrospectively analyzed. Data on sex, age, year of admission and discharge, and treatment costs during hospitalization were extracted from the first medical record pages according to the ICD codes. The data were assessed for normality using the Kolmogorov-Smirnov test. Depending on the data distribution, they were analyzed using parametric tests, such as the t-test, or non-parametric tests. Qualitative data are expressed as percentages (%) and analyzed using the chi-square or Fisher's exact probability test, with α = 0.05 as the test level. P < 0.05 was considered to be indicative of a statistically significant difference. RESULTS: Three types of CMT were defined based on sternocleidomastoid muscle ultrasound examination characteristics: local mass, uniform thickening, and atrophy. Age at first diagnosis was 69.21 ± 108.41 days in local mass type group, 216.85 ± 324.09 days in uniform thickening group, and 417.88 ± 739.05 days in atrophy- type group; while age at first physiotherapy use was 94.06 ± 206.49 days, 255.00 ± 430.62 days, 540.92 ± 1059.29 respectively. The children included in local mass type group have shown a high success rate of conservative treatment, with a rate of 7.5% of children underwent surgery. Age at first diagnosis was 112.44 ± 224.12 days in the physiotherapy group, 115.87 ± 144.86 days in the physiotherapy combined with glucocorticoid treatment subgroup, whereas the age at first physiotherapy use was 137.38 ± 312.11 and 196.91 ± 344.26 days respectively. In the observation period (2015-2019) the mean age at surgery for CMT in 17 tertiary children's hospitals of the FRCPD was 50 months. Overall, 663 children with CMT were 1-2 years of age, accounting for the largest proportion (28.3%). Followed by 417 individuals (17.8%) were 7-14 years of age, indicating that there are still more children with CMT receiving surgical treatment later. CONCLUSIONS: Early diagnosis and treatment are essential to improve the conservative treatment success rate and achieve good prognosis in children with CMT. Our team's concept for treating CMT is as follows: after diagnosing the children, we will adopt the standardized protocol of treatment, with physiotherapy combined with the injection of glucocorticoid drugs and SCM release surgery, when needed. This program has a high conservative treatment success rate and may facilitate the achievement of better prognosis and reduced teratogenicity rate.

Epidemiology and management of 10,486 pediatric fractures in Shenzhen: experience and lessons to be learnt.

PURPOSE: To explore and analyze the causes and related influencing factors of pediatric fractures, and provide theoretical basis for reducing the incidence and adverse effects of pediatric fractures. METHODS: This study retrospectively analyzed the epidemiological characteristics of fractures in pediatric aged ≤18 years old who were admitted to the our hospital between July 2015 and February 2020. RESULTS: A total of 10,486 pediatric patients were included in the study, of whom 6961 (66.38%) were boys, and 3525 (33.62%) were girls. For the fracture incidence, age group of the 3-6 years reached the peak. 5584 (60.76%) children were operated upon within 12 h after admission. The top three types of fractures were the distal humerus (3843 sites, 27.49%), distal ulna (1740 sites, 12.44%), and distal radius (1587 sites, 11.35%). The top three causes of injury were falls (7106 cases, 82.10%), car accidents (650 cases, 65.72%), and clipping (465 cases, 5.37%). Fractures predominantly occurred between July and November (4664 cases, 48.87%) and on Saturdays and Sundays (3172 cases, 33.24%). The highest number of hospital visits occurred between 20:00 and 00:00 (4339 cases, 45.46%). CONCLUSION: For pediatric fractures, we should take appropriate and effective preventive measures to reduce the incidence of children's fractures according to the distribution characteristics of age, gender, cause of injury, and fracture site.

Factors influencing and long-term effects of manual myotomy phenomenon during physiotherapy for congenital muscular torticollis.

PURPOSE: To investigate the factors influencing and long-term effects of manual myotomy (MM) occurring during physiotherapy for congenital muscular torticollis (CMT). METHODS: We retrospectively collected the clinical data of children with CMT receiving physiotherapy between 2008 and 2018. The children were divided into manual myotomy (MM) and non-manual myotomy (NMM) groups according to whether MM occurred during treatment. We assessed physiotherapy outcomes in children with CMT using craniofacial asymmetry parameters and the Cheng-Tang rating score. By measuring the ear-eye distance, ear-nose distance, eye-mouth distance, ear-mouth distance, half-head circumference, and half-head top at two sides to evaluate craniofacial asymmetry. Based on the Cheng-Tang assessment criteria, we recorded the range of rotation, range of lateral flexion, the status of the contracted muscle, the hardness of the mass, the extent of head tilting during activities and sleeping, the status of daily activities, face size, type of head shape, cranial changes, and subjective head tilting to assess the effectiveness of treatment. Clinical data and outcome indicators (craniofacial asymmetry parameters and Cheng-Tang rating score) were compared. RESULTS: The MM group had a significantly higher total Cheng-Tang rating score than the NMM group (P < 0.05). Age at initial physiotherapy session was the risk factor for MM during physiotherapy. CONCLUSION: Children with CMT developing MM during physiotherapy generally have a good outcome, although we do not recommend MM as a goal of treatment. Physiotherapists should understand this phenomenon, assess relevant factors to predict risk, and carefully observe treatment to prevent possible complications.

Deep Phenotyping of Chinese Electronic Health Records by Recognizing Linguistic Patterns of Phenotypic Narratives With a Sequence Motif Discovery Tool: Algorithm Development and Validation.

BACKGROUND: Phenotype information in electronic health records (EHRs) is mainly recorded in unstructured free text, which cannot be directly used for clinical research. EHR-based deep-phenotyping methods can structure phenotype information in EHRs with high fidelity, making it the focus of medical informatics. However, developing a deep-phenotyping method for non-English EHRs (ie, Chinese EHRs) is challenging. Although numerous EHR resources exist in China, fine-grained annotation data that are suitable for developing deep-phenotyping methods are limited. It is challenging to develop a deep-phenotyping method for Chinese EHRs in such a low-resource scenario. OBJECTIVE: In this study, we aimed to develop a deep-phenotyping method with good generalization ability for Chinese EHRs based on limited fine-grained annotation data. METHODS: The core of the methodology was to identify linguistic patterns of phenotype descriptions in Chinese EHRs with a sequence motif discovery tool and perform deep phenotyping of Chinese EHRs by recognizing linguistic patterns in free text. Specifically, 1000 Chinese EHRs were manually annotated based on a fine-grained information model, PhenoSSU (Semantic Structured Unit of Phenotypes). The annotation data set was randomly divided into a training set (n=700, 70%) and a testing set (n=300, 30%). The process for mining linguistic patterns was divided into three steps. First, free text in the training set was encoded as single-letter sequences (P: phenotype, A: attribute). Second, a biological sequence analysis tool-MEME (Multiple Expectation Maximums for Motif Elicitation)-was used to identify motifs in the single-letter sequences. Finally, the identified motifs were reduced to a series of regular expressions representing linguistic patterns of PhenoSSU instances in Chinese EHRs. Based on the discovered linguistic patterns, we developed a deep-phenotyping method for Chinese EHRs, including a deep learning-based method for named entity recognition and a pattern recognition-based method for attribute prediction. RESULTS: In total, 51 sequence motifs with statistical significance were mined from 700 Chinese EHRs in the training set and were combined into six regular expressions. It was found that these six regular expressions could be learned from a mean of 134 (SD 9.7) annotated EHRs in the training set. The deep-phenotyping algorithm for Chinese EHRs could recognize PhenoSSU instances with an overall accuracy of 0.844 on the test set. For the subtask of entity recognition, the algorithm achieved an F1 score of 0.898 with the Bidirectional Encoder Representations from Transformers-bidirectional long short-term memory and conditional random field model; for the subtask of attribute prediction, the algorithm achieved a weighted accuracy of 0.940 with the linguistic pattern-based method. CONCLUSIONS: We developed a simple but effective strategy to perform deep phenotyping of Chinese EHRs with limited fine-grained annotation data. Our work will promote the second use of Chinese EHRs and give inspiration to other non-English-speaking countries.

Epidemiology of skeletal trauma and skull fractures in children younger than 1 year in Shenzhen: a retrospective study of 664 patients.

BACKGROUND: Unintentional injury is one of the top three causes of death for infants. However, the epidemiological studies of skeletal trauma and skull fractures in infants younger than 1 year were poorly understood in China. Therefore, our study aimed to examine accidental and emergency attendance in infants under 1 year. It also tried to determine the prevalence and severity of accident types in infants. METHODS: A retrospective analysis was performed on the demographic characteristics of infants younger than 1 year with skeletal trauma and skull fractures who visited the Shenzhen Children's Hospital from January 1, 2016 to December 31, 2019. Age, gender, fracture site and type, mechanism of injury, length of visit, length of hospital stay, hospitalization cost, and treatment methods were analyzed. RESULTS: A total number of 675 fractures in 664 infants were included, the median age was 187days (IQR,90-273days), including 394 males and 270 females. The top three fracture sites were the skull (430 sites, 63.70 %), long bones of the limbs (168 sites, 24.89 %), and clavicle (53 sites, 7.85 %). The top three causes of injury were locomotion injuries (256 cases, 38.55 %), falls or trips from low height (from beds, tables, chairs, etc.) (130 cases, 19.58 %), and birth injuries (97 cases, 14.61 %). The greatest amount of fractures occurred in children 1-28 days of life (d) reached a top of 101 cases, followed by 331-365 days, accounting for 15.21 and 10.24 %, respectively. The number of fractures reached a trough of 29 cases in the 29-60d group (4.37 %). And increased again to 65 cases in the 151-180d group (9.79 %). The proportion remained relatively constant at 9 % in the 181-210d group (9.19 %) and 211-240d group (9.64 %). The interval between injury and visiting our hospital was ≤ 72 h in 554 cases. CONCLUSIONS: Special attention should be given to the demographic characteristics of fractures in infants under 1 year of age, and appropriate outreach should be implemented. For example, health education should be provided to aid in the prevention especially for frequently occurring locomotion injuries, and prompt access to specialist medical care should be recommended for skull fractures, which are prone to delayed treatment. In addition, multidisciplinary collaboration should be implemented in trauma care, while also promoting the establishment of trauma centers in specialist children's hospitals with a stronger capacity to treat pediatric trauma, and a regional system for pediatric trauma treatment.

Correction: Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study.

[This corrects the article DOI: 10.2196/26892.].

Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study.

BACKGROUND: Phenotypes characterize the clinical manifestations of diseases and provide important information for diagnosis. Therefore, the construction of phenotype knowledge graphs for diseases is valuable to the development of artificial intelligence in medicine. However, phenotype knowledge graphs in current knowledge bases such as WikiData and DBpedia are coarse-grained knowledge graphs because they only consider the core concepts of phenotypes while neglecting the details (attributes) associated with these phenotypes. OBJECTIVE: To characterize the details of disease phenotypes for clinical guidelines, we proposed a fine-grained semantic information model named PhenoSSU (semantic structured unit of phenotypes). METHODS: PhenoSSU is an "entity-attribute-value" model by its very nature, and it aims to capture the full semantic information underlying phenotype descriptions with a series of attributes and values. A total of 193 clinical guidelines for infectious diseases from Wikipedia were selected as the study corpus, and 12 attributes from SNOMED-CT were introduced into the PhenoSSU model based on the co-occurrences of phenotype concepts and attribute values. The expressive power of the PhenoSSU model was evaluated by analyzing whether PhenoSSU instances could capture the full semantics underlying the descriptions of the corresponding phenotypes. To automatically construct fine-grained phenotype knowledge graphs, a hybrid strategy that first recognized phenotype concepts with the MetaMap tool and then predicted the attribute values of phenotypes with machine learning classifiers was developed. RESULTS: Fine-grained phenotype knowledge graphs of 193 infectious diseases were manually constructed with the BRAT annotation tool. A total of 4020 PhenoSSU instances were annotated in these knowledge graphs, and 3757 of them (89.5%) were found to be able to capture the full semantics underlying the descriptions of the corresponding phenotypes listed in clinical guidelines. By comparison, other information models, such as the clinical element model and the HL7 fast health care interoperability resource model, could only capture the full semantics underlying 48.4% (2034/4020) and 21.8% (914/4020) of the descriptions of phenotypes listed in clinical guidelines, respectively. The hybrid strategy achieved an F1-score of 0.732 for the subtask of phenotype concept recognition and an average weighted accuracy of 0.776 for the subtask of attribute value prediction. CONCLUSIONS: PhenoSSU is an effective information model for the precise representation of phenotype knowledge for clinical guidelines, and machine learning can be used to improve the efficiency of constructing PhenoSSU-based knowledge graphs. Our work will potentially shift the focus of medical knowledge engineering from a coarse-grained level to a more fine-grained level.

Advanced Simultaneous Isolation, Culture, and Identification of Myoblasts and Fibroblasts From Sternocleidomastoid Muscle of Congenital Muscular Torticollis.

BACKGROUND: Congenital muscular torticollis (CMT) is a common pediatric disease caused by contracture of sternocleidomastoid muscle (SCM) that leads to neck stiffness and deformity. Based on the adhesion characteristics of different cells in affected SCM of CMT, myoblasts and fibroblasts can be isolated simultaneously by advanced culture conditions. Our study aimed to explore and optimize the isolation, culture, and identification of myoblasts and fibroblasts in SCM of CMT. METHODS: Myoblasts and fibroblasts were separated by combined digestion with trypsin and collagenase. With this improved method, the morphology of isolated myoblasts and fibroblasts was observed under the microscope, the cell growth curve was drawn, and the purity of myoblasts and fibroblasts was determined by immunofluorescence. RESULTS: The method allowed to satisfactorily culture myoblasts and fibroblasts. The cells could stably grow and be passaged, provided they were at least 80% confluent. Immunofluorescence of myoblasts and fibroblasts showed high rate of positive staining, and cell count showed excellent growth state. Moreover, according to the growth curve, fibroblasts grew at a higher rate than myoblasts. CONCLUSIONS: The isolated myoblasts and fibroblasts have high purity, intact structure, and relatively high vitality. This method can be used to establish a cell model with myoblasts and fibroblasts, which can be applied to investigate etiology of CMT or mechanisms of drug action.

High Sodium Intake Impairs Small Artery Vasoreactivity in vivo in Dahl Salt-Sensitive Rats.

The effects of high sodium intake on the functionality of resistance arteries have been repeatedly studied in vitro, but no study has focused on salt-sensitive hypertension in vivo. We studied the in vivo reactivity of mesenteric small arteries (MSAs) to vasoactive agents in Dahl salt-sensitive (DS) rats with various sodium diets. Twenty-four male DS rats were randomized into 3 groups: LS (0.3% NaCl diet), NS (0.6% NaCl diet), and HS (8% NaCl diet). After a 12-week intervention, the diameter changes of the MSAs after noradrenaline (NA) and acetylcholine (ACh) exposure were detected by a microscope, and changes in blood perfusion through the MSAs were measured by full-field laser perfusion imaging. HS enhanced the constrictive response of the MSAs to NA and attenuated the relaxing response to ACh. Low sodium intake reduced the response of the MSAs to NA and promoted ACh-induced vasodilatation. HS also aggravated NA-induced blood perfusion reduction and impaired ACh-induced hyperperfusion of the MSAs. Pathologically, HS was associated with arteriolar structural damage and fibrosis of the MSAs. We conclude that sodium intake affects the responsiveness of the MSAs to vasoactive agents in DS rats and might play important roles in modulating blood pressure in hypertensive individuals.

Identification of DNA methylation-driven genes in esophageal squamous cell carcinoma: a study based on The Cancer Genome Atlas.

BACKGROUND: Aberrant DNA methylations are significantly associated with esophageal squamous cell carcinoma (ESCC). In this study, we aimed to investigate the DNA methylation-driven genes in ESCC by integrative bioinformatics analysis. METHODS: Data of DNA methylation and transcriptome profiling were downloaded from TCGA database. DNA methylation-driven genes were obtained by methylmix R package. David database and ConsensusPathDB were used to perform gene ontology (GO) analysis and pathway analysis, respectively. Survival R package was used to analyze overall survival analysis of methylation-driven genes. RESULTS: Totally 26 DNA methylation-driven genes were identified by the methylmix, which were enriched in molecular function of DNA binding and transcription factor activity. Then, ABCD1, SLC5A10, SPIN3, ZNF69, and ZNF608 were recognized as significant independent prognostic biomarkers from 26 methylation-driven genes. Additionally, a further integrative survival analysis, which combined methylation and gene expression data, was identified that ABCD1, CCDC8, FBXO17 were significantly associated with patients' survival. Also, multiple aberrant methylation sites were found to be correlated with gene expression. CONCLUSION: In summary, we studied the DNA methylation-driven genes in ESCC by bioinformatics analysis, offering better understand of molecular mechanisms of ESCC and providing potential biomarkers precision treatment and prognosis detection.

HG-PerCon: Cross-view contrastive learning for personality prediction.

Personality prediction task not only helps us to better understand personal needs and preferences but also is essential for many fields such as psychology and behavioral economics. Current personality prediction primarily focuses on discovering personality traits through user posts. Additionally, there are also methods that utilize psychological information to uncover certain underlying personality traits. Although significant progress has been made in personality prediction, we believe that current solutions still overlook the long-term sustainability of personality and are constrained by the challenge of capturing consistent personality-related clues across different views in a simple and efficient manner. To this end, we propose HG-PerCon, which utilizes user representations based on historical semantic information and psychological knowledge for cross-view contrastive learning. Specifically, we design a transformer-based module to obtain user representations with long-lasting personality-related information from their historical posts. We leverage a psychological knowledge graph which incorporates language styles to generate user representations guided by psychological knowledge. Additionally, we employ contrastive learning to capture the consistency of user personality-related clues across views. To evaluate the effectiveness of our model, and our approach achieved a reduction of 2%, 4%, and 6% in RMSE compared to the second-best baseline method.

Insights into coordination and ligand trends of lanthanide complexes from the Cambridge Structural Database.

Understanding lanthanide coordination chemistry can help develop new ligands for more efficient separation of lanthanides for critical materials needs. The Cambridge Structural Database (CSD) contains tens of thousands of single crystal structures of lanthanide complexes that can serve as a training ground for both fundamental chemical insights and future machine learning and generative artificial intelligence models. This work aims to understand the currently available structures of lanthanide complexes in CSD by analyzing the coordination shell, donor types, and ligand types, from the perspective of rare-earth element (REE) separations. We obtain four sets of lanthanide complexes from CSD: Subset 1, all Ln-containing complexes (49472 structures); Subset 2, mononuclear Ln complexes (27858 structures); Subset 3, mononuclear Ln complexes without cyclopentadienyl ligands (Cp) (26156 structures); Subset 4, Ln complexes with at least one 1,10-phenanthroline (phen) or its derivative as a coordinating ligand (2226 structures). The subsequent analysis of lanthanide complexes in these subsets examines the trends in coordination numbers and first shell distances as well as identifies and characterizes the ligands and donor groups. In addition, examples of Ln-complexes with commercially available complexants and phen-based ligands are interrogated in detail. This systematic investigation lays the groundwork for future data-driven ligand designs for REE separations based on the structural insights into the lanthanide coordination chemistry.

Research Progress of Long Non-coding RNA-ZFAS1 in Malignant Tumors.

Long non-coding RNAs (lncRNAs), although incapable of encoding proteins, play crucial roles in multiple layers of gene expression regulation, epigenetic modifications, and post-transcriptional regulation. Zinc finger antisense 1 (ZFAS1), a lncRNA located in the 20q13 region of the human genome, exhibits dual functions as an oncogene or tumor suppressor in various human malignancies. ZFAS1 plays a crucial role in cancer progression, metastasis, invasion, apoptosis, cell cycle regulation, and drug resistance through complex molecular mechanisms. Additionally, ZFAS1 has a long half-life of over 16 h, demonstrating exceptional stability, and making it a potential biomarker. This review integrates recent studies on the role and molecular mechanisms of ZFAS1 in malignancies and summarizes its clinical significance. By summarizing the role of ZFAS1 in cancer, we aim to highlight its potential as an anti-cancer biomarker and therapeutic target.

Research progress on long non­coding RNAs in non­infectious spinal diseases (Review).

Spinal diseases, including intervertebral disc degeneration (IDD), ankylosing spondylitis, spinal cord injury and other non­infectious spinal diseases, severely affect the quality of life of patients. Current treatments for IDD and other spinal diseases can only relieve symptoms and do not completely cure the disease. Therefore, there is an urgent need to explore the causes of these diseases and develop new treatment approaches. Long non­coding RNA (lncRNA), a form of non­coding RNA, is abundant in diverse sources, has numerous functions, and plays an important role in the occurrence and development of spinal diseases such as IDD. However, the mechanism of action of lncRNAs has not been fully elucidated, and significant challenges remain in the use of lncRNAs as new therapeutic targets. The present article reviews the sources, classification and functions of lncRNAs, and introduces the role of lncRNAs in spinal diseases, such as IDD, and their therapeutic potential.