Relationship between HLA genetic variations, COVID-19 vaccine antibody response, and risk of breakthrough outcomes.

The rapid global distribution of COVID-19 vaccines, with over a billion doses administered, has been unprecedented. However, in comparison to most identified clinical determinants, the implications of individual genetic factors on antibody responses post-COVID-19 vaccination for breakthrough outcomes remain elusive. Here, we conducted a population-based study including 357,806 vaccinated participants with high-resolution HLA genotyping data, and a subset of 175,000 with antibody serology test results. We confirmed prior findings that single nucleotide polymorphisms associated with antibody response are predominantly located in the Major Histocompatibility Complex region, with the expansive HLA-DQB1*06 gene alleles linked to improved antibody responses. However, our results did not support the claim that this mutation alone can significantly reduce COVID-19 risk in the general population. In addition, we discovered and validated six HLA alleles (A*03:01, C*16:01, DQA1*01:02, DQA1*01:01, DRB3*01:01, and DPB1*10:01) that independently influence antibody responses and demonstrated a combined effect across HLA genes on the risk of breakthrough COVID-19 outcomes. Lastly, we estimated that COVID-19 vaccine-induced antibody positivity provides approximately 20% protection against infection and 50% protection against severity. These findings have immediate implications for functional studies on HLA molecules and can inform future personalised vaccination strategies.

Exhaustive Exploitation of Nature-Inspired Computation for Cancer Screening in an Ensemble Manner.

Accurate screening of cancer types is crucial for effective cancer detection and precise treatment selection. However, the association between gene expression profiles and tumors is often limited to a small number of biomarker genes. While computational methods using nature-inspired algorithms have shown promise in selecting predictive genes, existing techniques are limited by inefficient search and poor generalization across diverse datasets. This study presents a framework termed Evolutionary Optimized Diverse Ensemble Learning (EODE) to improve ensemble learning for cancer classification from gene expression data. The EODE methodology combines an intelligent grey wolf optimization algorithm for selective feature space reduction, guided random injection modeling for ensemble diversity enhancement, and subset model optimization for synergistic classifier combinations. Extensive experiments were conducted across 35 gene expression benchmark datasets encompassing varied cancer types. Results demonstrated that EODE obtained significantly improved screening accuracy over individual and conventionally aggregated models. The integrated optimization of advanced feature selection, directed specialized modeling, and cooperative classifier ensembles helps address key challenges in current nature-inspired approaches. This provides an effective framework for robust and generalized ensemble learning with gene expression biomarkers. Specifically, we have opened EODE source code on Github at https://github.com/wangxb96/EODE.

Docosahexaenoic Acid Supplementation for Neonatal Hyperbilirubinemia: A Double-Blind, Randomized Clinical Trial.

Docosahexaenoic acid (DHA) is an essential component for brain development during fetal and early postnatal life. Hyperbilirubinemia is characterized by abnormally high levels of bilirubin in the bloodstream, frequently leading to jaundice in newborns. In severe instances, this condition can progress to neurological damage or kernicterus, a form of brain damage. Initial cell-based experiments conducted by our research team revealed that DHA significantly enhances the survival rate of nerve cells treated with bilirubin and diminishes the oxidative stress indicated by reduced peroxide activity caused by unconjugated bilirubin (UCB). Further investigations through animal studies demonstrated that DHA effectively mitigates bilirubin-induced brain injury in neonatal rats. However, the potential of DHA to decrease the incidence of bilirubin-induced brain damage in clinical settings has not been previously explored or reported. Infants with neonatal hyperbilirubinemia (n = 30 per group) participated in a double-blind, randomized, placebo-controlled parallel study. They received either 100 mg/d DHA or placebo syrup immediately when they were diagnosed. The study found that the bilirubin level at 48 hours of treatment, serum neuron-specific enolase (NSE) levels, mean phototherapy duration, and abnormal rate of cranial magnetic resonance imaging (MRI) were lower in the DHA group than those in the control group (P < .05). These results suggested that DHA is effective as an adjuvant treatment for hyperbilirubinemia in children. It can reduce the incidence of neonatal hyperbilirubinemia brain injury and plays a certain protective role. Clinical study on protective effect of DHA on neonatal bilirubin injury is registered at Chinese Clinical Trial Registry as ChiCTR2300070250.

Terpenes based hydrophobic deep eutectic solvents for dispersive liquid-liquid microextraction of aliphatic aldehydes in drinking water and alcoholic beverages.

Aliphatic aldehydes are a class of organic compounds containing aldehyde groups, which are widespread, and closely related to people's daily life and health. In this work, a series of terpenes based hydrophobic deep eutectic solvents were designed and synthesized using hexafluoroisopropanol as hydrogen bond donor and menthol/thymol as hydrogen bond acceptor. Then they are used as extraction solvent in dispersive liquid-liquid microextraction for extracting and determining seven aliphatic aldehydes from drinking water and alcoholic beverage combined with high performance liquid chromatography-ultraviolet. Due to the fact that these hydrophobic deep eutectic solvents are liquid at the room temperature, a density greater than that of water, a lower viscosity (≤26.10 mPa s, 25 °C), after extraction and centrifugation, the microvolume DES-rich phase in the bottom is convenient for collection and direct analysis without further dissolution or dilution with organic solvents. Some factors affecting the extraction recovery were optimized by one-variable-at-a-time and response surface methodology. Under the optimal conditions, the enrichment factors for the seven aliphatic aldehydes were 48-56. The method had good performance: linear ranges of 1.0-200, 0.5-200, 0.2-200, 0.4-400, 1.0-400, 0.4-400 and 0.4-400 µg L-1 for seven aliphatic aldehydes (r2 ≥ 0.9949), limits of detection of 0.1-0.5 µg L-1, intra-day and inter-day precisions <4.9%. The recoveries of seven aliphatic aldehydes ranged from 76.0 to 119.0%. The proposed dispersive liquid-liquid microextraction method is simple, rapid, highly efficient, and green, which effectively reduces the amount of toxic chemical reagents used and their impact on the environment. Rapid and efficient detection of aliphatic aldehydes helps ensure a healthy diet and has great application prospects in food safety analysis.

Association of longitudinal trajectories of general and abdominal adiposity during middle age with mental health and well-being in late life: A prospective analysis.

Single measures of adiposity markers, such as body mass index (BMI) and waist circumference (WC), are associated with adverse mental health outcomes; however, long-term patterns of adiposity and their health effects remain unclear. The current study assessed adiposity trajectories during a 14-year span beyond middle age and their relevance to mental well-being in late life, and the contribution of genetic and lifestyle factors to the trajectories. Based on a nationally representative sample with longitudinal anthropometric measures, adiposity trajectories were identified by latent mixture modeling, and logistic regression model was used to estimate their associations with mental well-being, with adjustment for confounders. Of the 3491 eligible participants included (mean [SD] age, 69.5 [8.9] years), five discrete BMI and four WC trajectory patterns were identified over 14 years. Compared with the low-stable BMI group (range, 22.8 to 22.9 kg/m²; representing stable healthy body weight), the high-stable group (range, 34.3 to 35.4 kg/m²; stable obese) was associated with increased risk of depression (odds ratio [OR], 1.63; 95 % CI, 1.28-2.07) and low subjective well-being (OR, 1.35; 95 % CI, 1.02-1.79). Compared with the low-stable WC group (range, 75 to 79 cm healthy WC), the high-increasing group (range, 114 to 121 cm) was associated with increased risk of depression (odds ratio [OR], 1.64; 95 % CI, 1.19-2.25) and low well-being (OR, 1.48; 95 % CI, 1.01-2.16). The adiposity trajectories, especially the high-stable/increasing groups, were driven by genetic factors in a dose-response manner, whereas the high/moderate-increasing groups were also behaviorally related. This longitudinal cohort study reveals that stably high trajectory patterns of central and general adiposity during middle age were associated with higher risk of depression and low well-being in late life. The findings indicate the importance of weight management beyond middle age, such as adherence to a healthy lifestyle, in promoting mental health and well-being.

Long-term risk of psychiatric disorder and psychotropic prescription after SARS-CoV-2 infection among UK general population.

Despite evidence indicating increased risk of psychiatric issues among COVID-19 survivors, questions persist about long-term mental health outcomes and the protective effect of vaccination. Using UK Biobank data, three cohorts were constructed: SARS-CoV-2 infection (n = 26,101), contemporary control with no evidence of infection (n = 380,337) and historical control predating the pandemic (n = 390,621). Compared with contemporary controls, infected participants had higher subsequent risks of incident mental health at 1 year (hazard ratio (HR): 1.54, 95% CI 1.42-1.67; P = 1.70 × 10-24; difference in incidence rate: 27.36, 95% CI 21.16-34.10 per 1,000 person-years), including psychotic, mood, anxiety, alcohol use and sleep disorders, and prescriptions for antipsychotics, antidepressants, benzodiazepines, mood stabilizers and opioids. Risks were higher for hospitalized individuals (2.17, 1.70-2.78; P = 5.80 × 10-10) than those not hospitalized (1.41, 1.30-1.53; P = 1.46 × 10-16), and were reduced in fully vaccinated people (0.97, 0.80-1.19; P = 0.799) compared with non-vaccinated or partially vaccinated individuals (1.64, 1.49-1.79; P = 4.95 × 10-26). Breakthrough infections showed similar risk of psychiatric diagnosis (0.91, 0.78-1.07; P = 0.278) but increased prescription risk (1.42, 1.00-2.02; P = 0.053) compared with uninfected controls. Early identification and treatment of psychiatric disorders in COVID-19 survivors, especially those severely affected or unvaccinated, should be a priority in the management of long COVID. With the accumulation of breakthrough infections in the post-pandemic era, the findings highlight the need for continued optimization of strategies to foster resilience and prevent escalation of subclinical mental health symptoms to severe disorders.

Effects of acupuncture and moxibustion on ulcerative colitis: An overview of systematic reviews.

Ulcerative colitis (UC) is a gastrointestinal disease with an unknown etiology that severely affects patients' quality of life. Acupuncture and moxibustion therapies are effective in the treatment of UC, but existing systematic reviews (SRs) and meta-analyses (MAs) on this subject have variable methodological and outcome quality. Therefore, this study aimed to summarize and evaluate the evidence of existing SRs and MAs to provide more reliable evidence for clinical practice. Data were extracted from seven databases through systematic search and evaluated in terms of the methodological quality, reporting quality, risk of bias, and quality of evidence using the AMSTAR-2, PRISMA, ROBIS, and GRADE systems, respectively. Ten studies were finally included, and all of them showed many problems with the overall design and quality of outcomes. Because of the lack of high-quality evidence to support the findings from the existing studies, we should take this conclusion with caution and strictly implement the registration, design, and implementation of trials based on evidence to provide high-quality results in future studies.

Transition in metabolic health phenotypes across general adiposity categories and association with the risk of depression: a prospective analysis.

BACKGROUND: The association between obesity and depression may partly depend on the contextual metabolic health. The effect of change in metabolic health status over time on subsequent depression risk remains unclear. We aimed to assess the prospective association between metabolic health and its change over time and the risk of depression across body mass index (BMI) categories. METHODS: Based on a nationally representative cohort, we included participants enrolled at the wave 2 (2004-2005) of the English Longitudinal Study of Ageing and with follow-up for depression at wave 8 (2016-2017). Participants were cross-classified by BMI categories and metabolic health (defined by the absence of hypertension, diabetes, and hypercholesterolemia) at baseline or its change over time (during waves 3-6). Logistic regression model was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the risk of depression at follow-up stratified by BMI category and metabolic health status with adjustment for potential confounders. RESULTS: The risk of depression was increased for participants with metabolically healthy obesity compared with healthy nonobese participants, and the risk was highest for those with metabolically unhealthy obesity (OR 1.62, 95% CI 1.18-2.20). Particularly hypertension and diabetes contribute most to the increased risk. The majority of metabolically healthy participants converted to unhealthy metabolic phenotype (50.1% of those with obesity over 8 years), which was associated with an increased risk of depression. Participants who maintained metabolically healthy obesity were still at higher risk (1.99, 1.33-2.72), with the highest risk observed for those with stable unhealthy metabolic phenotypes. CONCLUSIONS: Obesity remains a risk factor for depression, independent of whether other metabolic risk factors are present or whether participants convert to unhealthy metabolic phenotypes over time. Long-term maintenance of metabolic health and healthy body weight may be beneficial for the population mental well-being.

Fecal calprotectin: A novel predictor of ulcerated esophageal injury after atrial fibrillation catheter ablation.

BACKGROUND: Atrial esophageal fistula (AEF) is a lethal complication that can occur post atrial fibrillation (AF) ablation. Esophageal injury (EI) is likely to be the initial lesion leading to AEF. Endoscopic examination is the gold standard for a diagnosis of EI but extensive endoscopic screening is invasive and costly. This study was conducted to determine whether fecal calprotectin (Fcal), a marker of inflammation throughout the intestinal tract, may be associated with the existence of esophageal injury. METHODS: This diagnostic study was conducted in a cohort of 166 patients with symptomatic AF undergoing radiofrequency catheter ablation from May 2020 to June 2021. Fcal tests were performed 1-7 days after ablation. All patients underwent endoscopic ultrasonography 1 or 2 days after ablation. RESULTS: The levels of Fcal were significantly different between the EI and non-EI groups (404.9 µg/g (IQR 129.6-723.6) vs. 40.4 µg/g (IQR 15.0-246.2), p < .001). Analysis of ROC curves revealed that a Fcal level of 125 µg/g might be the optimal cut-off value for a diagnosis of EI, giving a 78.8% sensitivity and a 65.4% specificity. The negative predictive value of Fcal was 100% for ulcerated EI. CONCLUSIONS: The level of Fcal is associated with EI post AF catheter ablation. 125 µg/g might be the optimal cut-off value for a diagnosis of EI. Negative Fcal could predict the absence of ulcerated EI, which could be considered a precursor to AEF.

Predicting recurrence and survival in patients with non-metastatic renal-cell carcinoma after nephrectomy: a prospective population-based study with multicenter validation.

BACKGROUND: Accurate prognostication of oncological outcomes is crucial for the optimal management of patients with renal cell carcinoma (RCC) after surgery. Previous prediction models were developed mainly based on retrospective data in the Western populations, and their predicting accuracy remains limited in contemporary, prospective validation. We aimed to develop contemporary RCC prognostic models for recurrence and overall survival (OS) using prospective population-based patient cohorts and compare their performance with existing, mostly utilized ones. METHODS: In this prospective analysis and external validation study, the development set included 11  128 consecutive patients with non-metastatic RCC treated at a tertiary urology center in China between 2006 and 2022, and the validation set included 853 patients treated at 13 medical centers in the USA between 1996 and 2013. The primary outcome was progression-free survival (PFS), and the secondary outcome was OS. Multivariable Cox regression was used for variable selection and model development. Model performance was assessed by discrimination [Harrell's C-index and time-dependent areas under the curve (AUC)] and calibration (calibration plots). Models were validated internally by bootstrapping and externally by examining their performance in the validation set. The predictive accuracy of the models was compared with validated models commonly used in clinical trial designs and with recently developed models without extensive validation. RESULTS: Of the 11  128 patients included in the development set, 633 PFS and 588 OS events occurred over a median follow-up of 4.3 years [interquartile range (IQR) 1.7-7.8]. Six common clinicopathologic variables (tumor necrosis, size, grade, thrombus, nodal involvement, and perinephric or renal sinus fat invasion) were included in each model. The models demonstrated similar C-indices in the development set (0.790 [95% CI 0.773-0.806] for PFS and 0.793 [95% CI 0.773-0.811] for OS) and in the external validation set (0.773 [0.731-0.816] and 0.723 [0.731-0.816]). A relatively stable predictive ability of the models was observed in the development set (PFS: time-dependent AUC 0.832 at 1 year to 0.760 at 9 years; OS: 0.828 at 1 year to 0.794 at 9 years). The models were well calibrated and their predictions correlated with the observed outcome at 3, 5, and 7 years in both development and validation sets. In comparison to existing prognostic models, the present models showed superior performance, as indicated by C-indices ranging from 0.722 to 0.755 (all P <0.0001) for PFS and from 0.680 to 0.744 (all P <0.0001) for OS. The predictive accuracy of the current models was robust in patients with clear-cell and non-clear-cell RCC. CONCLUSIONS: Based on a prospective population-based patient cohort, the newly developed prognostic models were externally validated and outperformed the currently available models for predicting recurrence and survival in patients with non-metastatic RCC after surgery. The current models have the potential to aid in clinical trial design and facilitate clinical decision-making for both clear-cell and non-clear-cell RCC patients at varying risk of recurrence and survival.

Composite healthy lifestyle, socioeconomic deprivation, and mental well-being during the COVID-19 pandemic: a prospective analysis.

The adverse psychological and social impacts of COVID-19 pandemic are well characterized, but the role of composite, modifiable lifestyle factors that may interact to mitigate these impacts is not. The effect of socioeconomic deprivation on these lifestyle risks also remains unclear. Based on a nationally representative, longitudinal cohort, we assessed the association between a combination of pre-pandemic lifestyle factors and mental health conditions during pandemic, and the contribution of deprivation to it. Composite lifestyle factors included BMI, smoking status, alcohol consumption, physical activity, sedentary time, sleep duration, and fruit and vegetable intake, with lifestyle scores and lifestyle categories calculated for each participant. Symptoms of depression and anxiety, and personal well-being were assessed by validated scales during the pandemic. Socioeconomic deprivation was characterized by both individual-level (income, wealth, and education) and group-level factors (Index of Multiple Deprivation). Of the 5049 eligible participants (mean [SD] age, 68.1 [10.9] years; 57.2% were female) included in the study, 41.6% followed a favorable lifestyle, 48.9% followed an intermediate lifestyle, and 9.5% followed an unfavorable lifestyle. Compared with favorable lifestyle category, participants in the intermediate and unfavorable lifestyle category were at increased risk of mental health conditions, with the hazard ratio (HR) for trend per increment change towards unfavorable category of 1.17 (95% CI 1.09-1.26) for depression, 1.23 (1.07-1.42) for anxiety, and 1.39 (1.20-1.61) for low well-being. A significant trend of lower risk for mental health conditions with increasing number of healthy lifestyle factors was observed (P < 0.001 for trend). There were no significant interactions between lifestyle factors and socioeconomic deprivation for any of the outcomes, with similar HRs for trend per one increment change in lifestyle category observed in each deprivation group. Compared with those in the least deprived group with favorable lifestyle, participants in the most deprived group adherent to unfavorable lifestyle had the highest risk of mental health outcomes. These results suggest that adherence to a broad combination of healthy lifestyle factors was associated with a significantly reduced risk of mental health conditions during the COVID-19 pandemic. Lifestyle factors, in conjunction with socioeconomic deprivation, independently contribute to the risk of mental health issues. Although further research is needed to assess causality, the current findings support public health strategies and individual-level interventions that provide enhanced support in areas of deprivation and target multiple lifestyle factors to reduce health inequalities and promote mental well-being during the ongoing COVID-19 pandemic.

Deriving aquatic PNECs of endocrine disruption effects for PFOS and PFOA by combining species sensitivity weighted distributions and adverse outcome pathway networks.

Perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA), as emerging endocrine-disrupting chemicals (EDCs), pose adverse effects on aquatic organisms. Conventional ecological risk assessment (ERA) not fully considering the mode of toxicity action of PFOS and PFOA, may result in an underestimation of risks and confuse decision-makers. In the study, we developed species sensitivity weighted distribution (SSWD) models based on adverse outcome pathway (AOP) networks for deriving predicted no-effect concentrations (PNECs). Three kinds of weighting criteria (intraspecies variation, trophic level abundance, and data quality) and weighted log-normal distribution methods were adopted. The developed models considered the inter/intraspecies variation and integrated nontraditional endpoints of endocrine-disrupting effects. The PNECs of endocrine disruption effects were derived as 2.52 µg/L (95% confidence intervals 0.667-9.85 µg/L) for PFOS and 18.7 µg/L (5.40-71.0 µg/L) for PFOA, which were more conservative than those derived from the SSD method and were comparable with the values in the literature based on the chronic toxicity data. For PFOS, the effect of growth and development was the most sensitive; however, for PFOA, the effect of reproduction was the most sensitive in the effects of growth and development, reproduction, biochemistry and genetics, and survival. The endocrine-disrupting effects of PFOS and PFOA are significant and need to be fully recognized in the ERA. This study provided an ERA framework that can improve the ecological relevance and reduce the uncertainty of PNECs of EDCs.

Percutaneous ballon compression for recurrent TN -a retrospective study of 33 cases.

Objective: To investigate the clinical efficacy of percutaneous microballoon compression in the treatment of recurrent TN. Methods: This retrospective study included 33 patients who underwent percutaneous microballoon compression for the treatment of recurrent TN from March 2019 to May 2022. Postoperative pain recurrence and facial numbness were assessed according to the Barrow Neurological Institute (BNI) pain score. Patients' anxiety and sleep status during follow-up were assessed according to the Self-rating Anxiety Scale (SAS) and Pittsburgh Sleep Quality Index (PSQI). Results: All patients (33 cases) were followed up for 12-38 months, with an average follow-up time of 23 months. On postoperative day 1, 31 patients (93.9%) reported no pain, and 2 patients were given drug treatment for pain relief, The total efficacy was 93.9%. Moreover, 2 patients (6.1%) reported significant pain relief 2 weeks postoperatively. There are many complications during and after PBC. The incidence of the trigeminocardiac reflex (TCR) during surgery was 100%, and the incidence of facial numbness, masseter muscle weakness, labial herpes and headache was 97, 60.6, 12.1 and 3%. No patient experienced severe facial numbness, hearing impairment, diplopia, injury to cranial nerves, Meningitis, intracranial haemorrhage or keratitis. 1 patient had recurrence of pain at 6 months post-op, which was relieved by oral medication. 81.8% suffered from anxiety and 54.5% had poor sleep quality before surgery. After the period of PBC, SAS and PSQI scores decreased continuously. There were significant improvements in anxiety and sleep status postoperatively compared with preoperatively. Conclusion: PBC is a safe and effective option for the treatment of recurrent TN. The arduous and demanding nature of the clinical course subjects the patient to severe pain, mental, and physical stress. Thankfully, it significantly improves the symptoms of anxiety, depression, and sleep quality.

Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet.

RNA-binding proteins play crucial roles in the regulation of gene expression, and understanding the interactions between RNAs and RBPs in distinct cellular conditions forms the basis for comprehending the underlying RNA function. However, current computational methods pose challenges to the cross-prediction of RNA-protein binding events across diverse cell lines and tissue contexts. Here, we develop HDRNet, an end-to-end deep learning-based framework to precisely predict dynamic RBP binding events under diverse cellular conditions. Our results demonstrate that HDRNet can accurately and efficiently identify binding sites, particularly for dynamic prediction, outperforming other state-of-the-art models on 261 linear RNA datasets from both eCLIP and CLIP-seq, supplemented with additional tissue data. Moreover, we conduct motif and interpretation analyses to provide fresh insights into the pathological mechanisms underlying RNA-RBP interactions from various perspectives. Our functional genomic analysis further explores the gene-human disease associations, uncovering previously uncharacterized observations for a broad range of genetic disorders.

Improving Lightweight AdderNet via Distillation From ℓ2 to ℓ1-norm.

To achieve efficient inference with a hardware-friendly design, Adder Neural Networks (ANNs) are proposed to replace expensive multiplication operations in Convolutional Neural Networks (CNNs) with cheap additions through utilizing l1 -norm for similarity measurement instead of cosine distance. However, we observe that there exists an increasing gap between CNNs and ANNs with reducing parameters, which cannot be eliminated by existing algorithms. In this paper, we present a simple yet effective Norm-Guided Distillation (NGD) method for l1 -norm ANNs to learn superior performance from l2 -norm ANNs. Although CNNs achieve similar accuracy with l2 -norm ANNs, the clustering performance based on l2 -distance can be easily learned by l1 -norm ANNs compared with cross correlation in CNNs. The features in l2 -norm ANNs are encouraged to achieve intra-class centralization and inter-class decentralization to amplify this advantage. Furthermore, the roughly estimated gradients in vanilla ANNs are modified to a progressive approximation from l2 -norm to l1 -norm so that a more accurate optimization can be achieved. Extensive evaluations on several benchmarks demonstrate the effectiveness of NGD on lightweight networks. For example, our method improves ANN by 10.43% with 0.25× GhostNet on CIFAR-100 and 3.1% with 1.0× GhostNet on ImageNet.

Advances in Phytochemistry and Modern Pharmacology of Saposhnikovia Divaricata (Turcz.) Schischk.

Saposhnikovia divaricata (Turcz.) Schischk (S. divaricata, Fangfeng) is a herb in the Apiaceae family, and its root has been used since the Western Han Dynasty (202 B.C.). Chromones and coumarins are the pharmacologically active substances in S. divaricata. Modern phytochemical and pharmacological studies have demonstrated their antipyretic, analgesic, anti-inflammatory, antioxidant, anti-tumor, and anticoagulant activities. Technological and analytical strategy theory advancements have yielded novel results; however, most investigations have been limited to the main active substances-chromones and coumarins. Hence, we reviewed studies related to the chemical composition and pharmacological activity of S. divaricata, analyzed the developing trends and challenges, and proposed that research should focus on components' synergistic effects. We also suggested that, the structure-effect relationship should be prioritized in advanced research.

False Tendons in the Left Ventricle: Implications for Successful Ablation of Left Posterior Fascicular Tachycardias.

BACKGROUND: The anatomical substrate for left posterior fascicular ventricular tachycardia (LPF-VT) is still unclear. OBJECTIVES: The purpose of this study is to describe the endocavitary substrate of the re-entrant loop of LPF-VT. METHODS: A total of 26 consecutive patients with LPF-VT underwent an electrophysiology study and radiofrequency ablation. RESULTS: Intracardiac echocardiography imaging observed a 100% prevalence of false tendons (FTs) at the left posterior septal region in all patients, and 3 different types of FTs could be classified according to their location. In 22 patients, a P1 potential could be recorded via the multielectrode catheter from a FT. In 4 patients without a recorded P1 during LPF-VT, the earliest P2 potentials were recorded from a FT in 3 patients, and from a muscular connection between 2 posteromedial papillary muscles in 1 patient. Catheter ablation focused on the FTs with P1 or earliest P2 (in patients without P1) was successful in all 26 patients. After 19 ± 8.5 months of follow-up, no patients had recurrence of LPF-VT. CONCLUSIONS: FTs provide an electroanatomical substrate for LPF-VT and a "culprit FT" may be identified as the critical structure bridging the macro-re-entrant loop. Targeting the "culprit FT" is a novel anatomical ablation strategy that results in long-term arrhythmia-free survival.

Reliable Identification and Interpretation of Single-Cell Molecular Heterogeneity and Transcriptional Regulation using Dynamic Ensemble Pruning.

Unsupervised clustering is an essential step in identifying cell types from single-cell RNA sequencing (scRNA-seq) data. However, a common issue with unsupervised clustering models is that the optimization direction of the objective function and the final generated clustering labels in the absence of supervised information may be inconsistent or even arbitrary. To address this challenge, a dynamic ensemble pruning framework (DEPF) is proposed to identify and interpret single-cell molecular heterogeneity. In particular, a silhouette coefficient-based indicator is developed to determine the optimization direction of the bi-objective function. In addition, a hierarchical autoencoder is employed to project the high-dimensional data onto multiple low-dimensional latent space sets, and then a clustering ensemble is produced in the latent space by the basic clustering algorithm. Following that, a bi-objective fruit fly optimization algorithm is designed to prune dynamically the low-quality basic clustering in the ensemble. Multiple experiments are conducted on 28 real scRNA-seq datasets and one large real scRNA-seq dataset from diverse platforms and species to validate the effectiveness of the DEPF. In addition, biological interpretability and transcriptional and post-transcriptional regulatory are conducted to explore biological patterns from the cell types identified, which could provide novel insights into characterizing the mechanisms.

Emerging roles of phase separation in plant transcription and chromatin organization.

Transcription is a core step in gene expression. Regulation of transcription is achieved at the level of transcription machinery, local chromatin environment as well as higher-order chromatin organization. Our understanding of transcriptional regulation was advanced by recent introduction of transcription and chromatin-associated condensates, which typically arise via phase separation of proteins and nucleic acids. While studies from mammalian cells are unveiling the mechanisms of phase separation in transcription regulation, those in plants further broaden and deepen our understanding of this process. In this review, we discuss recent progress in plants how phase separation operates in RNA-mediated chromatin silencing, transcription activity, and chromatin compartmentalization.

The P-body component DECAPPING5 and the floral repressor SISTER OF FCA regulate FLOWERING LOCUS C transcription in Arabidopsis.

Flowering is the transition from vegetative to reproductive growth and is critical for plant adaptation and reproduction. FLOWERING LOCUS C (FLC) plays a central role in flowering time control, and dissecting its regulation mechanism provides essential information for crop improvement. Here, we report that DECAPPING5 (DCP5), a component of processing bodies (P-bodies), regulates FLC transcription and flowering time in Arabidopsis (Arabidopsis thaliana). DCP5 and its interacting partner SISTER OF FCA (SSF) undergo liquid-liquid phase separation (LLPS) that is mediated by their prion-like domains (PrDs). Enhancing or attenuating the LLPS of both proteins using transgenic methods greatly affects their ability to regulate FLC and flowering time. DCP5 regulates FLC transcription by modulating RNA polymerase II enrichment at the FLC locus. DCP5 requires SSF for FLC regulation, and loss of SSF or its PrD disrupts DCP5 function. Our results reveal that DCP5 interacts with SSF, and the nuclear DCP5-SSF complex regulates FLC expression at the transcriptional level.