Functional Mapping of Plant Growth in Arabidopsis thaliana

Most traits important to agriculture, biology, and biomedicine are complex traits, determined by both genetic and environmental factors. The complex traits that change their phenotypes over different stages of development are called dynamic traits. Traditional quantitative trait loci (QTLs) mapping approaches ignore the dynamic changes of complex traits. Functional mapping, as a powerful statistical tool, can not only map QTLs that control the developmental pattern and process of complex traits, but also describe the dynamic changes of complex traits. In this study, we used functional mapping to identify those QTLs that affect height growth in 10th generation recombinant inbred lines derived from two different Arabidopsis thaliana accessions. Functional mapping identified 48 QTLs that are related to height traits. The growth curves of different genotypes can be drawn for each significant locus. By GO gene function annotations, we found that these QTLs detected are associated with the synthesis of biological macromolecules and the regulation of biological functions. Our findings provide unique insights into the genetic control of height growth of A. thaliana and will provide a theoretical basis for the study of complex traits

Efficacy and safety of Chinese herbal medicine in post-stroke epilepsy: a systematic review and meta-analysis

Background: Poststroke epilepsy (PSE) is a common complication of strokes that seriously affects the recovery and quality of life of patients, and effective treatments are needed. Chinese herbal medicine (CHM) adjunctive therapy is a viable treatment option, but current evidence is insufficient to support its efficacy and safety. This study aimed to evaluate the efficacy and tolerability of CHM adjunctive therapy in the treatment of PSE.Methods: A systematic search of eight databases was conducted to identify PSE-related randomized clinical trials from the inception of each database through October 2023. The methodological quality assessment was conducted by RoB 2.0, meta-analysis was conducted by RevMan 5.3 and Stata 15.1, and evidence quality was evaluated by GRADE.Results: Twenty-three RCTs involving 1,901 PSE patients were identified. We found that orally administered CHM plus conventional Western medicine (CWM) was superior to CWM monotherapy in increasing the 75% responder rate (RR 1.46, 95% CI: 1.31 to 1.62, p < 0.00001), decreasing the seizure duration (MD -1.01, 95% CI: −1.30 to −0.72, p < 0.00001), improving total responder rate (RR 1.29, 95% CI: 1.20 to 1.37, p < 0.00001), reducing epileptiform discharges (EDs) (MD -2.02.46, 95% CI: −2.64 to −1.40, p < 0.00001), and decreasing the number of leads involved in epileptiform discharge (MD -3.92, 95% CI: −5.15 to −2.68, p < 0.00001). Furthermore, intravenously administered CHM plus CWM was superior regarding 75% responder rate (RR 1.39, 95% CI: 1.24 to 1.56, p < 0.00001), total responder rate (RR 1.29, 95% CI: 1.20 to 1.39, p < 0.00001), EDs (MD -3.92, 95% CI: −5.15 to −2.68, p < 0.00001), and the number of leads involved in epileptiform discharge (MD -1.82, 95% CI: −2.62 to −1.02, p < 0.00001). However, regarding the 50%–75% responder rate, there was no statistically significant difference between the two groups for either oral (RR 1.00, 95% CI: 0.77 to 1.29, p = 0.98) or injectable CHM (RR 0.95, 95% CI: 0.67 to 1.33, p = 0.75). Both orally administered CHM plus CWM (RR 0.56, 95% CI: 0.35 to 0.90, p = 0.02) and intravenously administered CHM plus CWM (RR 0.64, 95% CI: 0.45 to 0.90, p = 0.010) caused fewer AEs than CWM. Furthermore, the levels of evidence ranged from low to high due to publication bias and heterogeneity.Conclusion: CHM adjuvant therapy may be an effective and safe therapy for PSE. However, due to the poor quality of clinical data, more well-designed RCTs are needed to confirm these findings.Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=364356, identifier PROSPERO (CRD42022364356

Aurora-A Induces Chemoresistance Through Activation of the AKT/mTOR Pathway in Endometrial Cancer

Endometrial cancer (EC) is the most common gynecological tumor all over the world, and advanced/metastatic EC remains a malignancy with poor survival outcome due to highly resistant to conventional chemotherapeutic treatment. Here, we report that Aurora-A, a serine-threonine kinase, plays a vital role in chemoresistance of EC. Aurora-A is overexpressed in EC tissues, compared with normal endometrium and Aurora-A expression is associated with decreased overall survival. Overexpression of Aurora-A in EC cell lines (Ishikawa and HEC-1B cells) promotes cell proliferation and induced paclitaxel- and cisplatin-resistance. Furthermore, Aurora-A activating AKT-mTOR pathway further induces chemoresistance in vitro, consistent with a positive correlation between Aurora-A and phosphorylated AKT/4E-BP1 expression in EC tissues. In summary, our study provides the strong evidence that Aurora-A controls the sensitivity of EC cell lines to chemotherapy via AKT/mTOR pathway, indicating that pharmacologic intervention of Aurora-A and AKT/mTOR in combination with chemotherapy may be considered for the targeted therapy against EC with overexpression of Aurora-A

Transcriptionally active HERV-H retrotransposons demarcate topologically associating domains in human pluripotent stem cells.

Chromatin architecture has been implicated in cell type-specific gene regulatory programs, yet how chromatin remodels during development remains to be fully elucidated. Here, by interrogating chromatin reorganization during human pluripotent stem cell (hPSC) differentiation, we discover a role for the primate-specific endogenous retrotransposon human endogenous retrovirus subfamily H (HERV-H) in creating topologically associating domains (TADs) in hPSCs. Deleting these HERV-H elements eliminates their corresponding TAD boundaries and reduces the transcription of upstream genes, while de novo insertion of HERV-H elements can introduce new TAD boundaries. The ability of HERV-H to create TAD boundaries depends on high transcription, as transcriptional repression of HERV-H elements prevents the formation of boundaries. This ability is not limited to hPSCs, as these actively transcribed HERV-H elements and their corresponding TAD boundaries also appear in pluripotent stem cells from other hominids but not in more distantly related species lacking HERV-H elements. Overall, our results provide direct evidence for retrotransposons in actively shaping cell type- and species-specific chromatin architecture

Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis

BackgroundRecent studies have reported that machine learning (ML), with a relatively strong capacity for processing non-linear data and adaptive ability, could improve the accuracy and efficiency of prediction. The article summarizes the published studies on ML models that predict motor function 3–6 months post-stroke.MethodsA systematic literature search was conducted in PubMed, Embase, Cochorane and Web of Science as of April 3, 2023 for studies on ML prediction of motor function in stroke patients. The quality of the literature was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effects model was preferred for meta-analysis using R4.2.0 because of the different variables and parameters.ResultsA total of 44 studies were included in this meta-analysis, involving 72,368 patients and 136 models. Models were categorized into subgroups according to the predicted outcome Modified Rankin Scale cut-off value and whether they were constructed based on radiomics. C-statistics, sensitivity, and specificity were calculated. The random-effects model showed that the C-statistics of all models were 0.81 (95% CI: 0.79; 0.83) in the training set and 0.82 (95% CI: 0.80; 0.85) in the validation set. According to different Modified Rankin Scale cut-off values, C-statistics of ML models predicting Modified Rankin Scale>2(used most widely) in stroke patients were 0.81 (95% CI: 0.78; 0.84) in the training set, and 0.84 (95% CI: 0.81; 0.87) in the validation set. C-statistics of radiomics-based ML models in the training set and validation set were 0.81 (95% CI: 0.78; 0.84) and 0.87 (95% CI: 0.83; 0.90), respectively.ConclusionML can be used as an assessment tool for predicting the motor function in patients with 3–6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022335260, identifier: CRD42022335260

Efficacy and safety of acupuncture in post-stroke constipation: a systematic review and meta-analysis

Background and objectivePost-stroke constipation (PSC) is a common complication of strokes that seriously affects the recovery and quality of life of patients, and effective treatments are needed. Acupuncture is a viable treatment option, but current evidence is insufficient to support its efficacy and safety. This study aims to evaluate the efficacy and safety of acupuncture in the treatment of PSC.MethodsA systematic search of eight databases was conducted to identify PSC-related randomized clinical trials from the inception of each database through May 2023. Methodological quality assessment was conducted by RoB 2.0, meta-analysis was conducted by RevMan 5.3 and Stata 15.1, and evidence quality was evaluated by GRADE. Moreover, reporting quality of acupuncture interventions was assessed using the Standards for Reporting Interventions in Clinical Trials of Acupuncture (STRICTA).ResultsThirty RCTs involving 2,220 patients were identified. We found that acupuncture was superior to conventional treatment (CT) in improving total responder rate [risk ratio (RR): 1.16, 95% confidence interval (CI): 1.09 to 1.25, p < 0.0001], decreasing constipation symptom scores [standardized mean difference (SMD): -0.65, 95% CI: −0.83 to −0.46, p < 0.00001], increasing serum P substance (SP) levels (SMD: 1.92, 95% CI: 0.47 to 3.36, p = 0.009), reducing the time to first bowel movement (BM) (SMD: -1.19, 95% CI: −2.13 to −0.25, p = 0.01), and lowing serum vasoactive intestinal peptide (VIP) levels (SMD: –2.11, 95% CI: −3.83 to −0.38, p = 0.02). Furthermore, acupuncture plus CT was superior regarding total responder rate (RR: 1.26, 95% CI: 1.17 to 1.35, p < 0.00001), serum SP levels (SMD: 2.00, 95% CI: 1.65–2.35, p < 0.00001), time to first BM (SMD: –2.08, 95% CI: −2.44 to −1.71, p < 0.00001), and serum VIP levels (SMD: –1.71, 95% CI: −2.24 to −1.18, p < 0.00001). However, regarding Bristol Stool Scale (BSS) score, acupuncture plus CT was superior to CT (SMD: -2.48, 95% CI: −3.22 to −1.73, p < 0.00001), while there was no statistically significant difference between acupuncture and CT (SMD: 0.28, 95% CI: −0.02 to 0.58, p = 0.07). Acupuncture causes fewer AEs than CT (RR: 0.13, 95% CI: 0.06 to 0.26, p < 0.00001), though there was no statistically significant difference between acupuncture plus CT vs. CT (RR: 1.30, 95% CI: 0.60 to 2.84, p = 0.51).ConclusionAcupuncture may be an effective and safe therapy for PSC. However, given the inferior quality of clinical data, additional well-designed RCTs are required to confirm these findings

Chinese Herbal Medicine Combined With Antiepileptic Drugs for Intractable Epilepsy: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background: Intractable epilepsy (IE) is still a major concern in neurology, and existing therapies do not adequately control symptoms. Chinese Herbal Medicine (CHM) has been widely used as an adjunct to antiepileptic drugs (AEDs) for IE. However, because of the contradictory findings reported in previous studies, it is uncertain if the present evidence is robust enough to warrant its usage. The purpose of this meta-analysis was to systematically evaluate the efficacy of the combination of CHM and AEDs for IE.Methods: From inception to September 2021, Medline, Ovid, Embase, Cochrane Library, Chinese Biomedical Database, China National Knowledge Infrastructure, VIP Database, and Wanfang Database were searched. Only randomized controlled trials (RCTs) that assessed the efficacy of the combination of CHM and AEDs for IE were included. We defined monthly seizure frequency as the primary outcome. The secondary outcomes included the abnormal rate of electroencephalogram (EEG), seizure duration, quality of life (QoL), and adverse events (AEs).Results: Twenty studies with 1,830 patients were enrolled. Most trials had poor methodological quality. The meta-analysis showed that the combination of CHM and AEDs was more efficient than AEDs alone in reducing monthly seizure frequency [MD = −1.26%, 95% CI (−1.62, −0.91); p < 0.00001], the abnormal rate of EEG [RR = 0.66%, 95% CI (0.53, 0.82); p = 0.0002], and improving the QoL [MD = 6.96%, 95% CI (3.44, 10.49); p = 0.0001]. There was no significant difference in seizure duration between groups. Moreover, the combination of CHM and AEDs significantly reduced the AEs [RR = 0.45%, 95% CI (0.32, 0.64); p < 0.00001].Conclusion: The combination of CHM and AEDs could improve seizure control by reducing monthly seizure frequency and abnormal rate of EEG with a decreased risk of adverse events in patients with IE. However, these findings must be interpreted carefully due to the high or uncertain risk of bias in the included trials. To provide stronger evidence for the use of CHM combined with AEDs in IE, high-quality RCTs will be urgently warranted in the future

Rationale and design of a multi‐center, prospective randomized controlled trial on the effects of sacubitril–valsartan versus enalapril on left ventricular remodeling in ST ‐elevation myocardial infarction: The PERI‐STEMI study

Background Angiotensin receptor neprilysin inhibitor (ARNI) sacubitril-valsartan has been recommended as one of the first-line therapies in heart failure with reduced ejection fraction. However, whether ARNI could benefit patients with ST-segment elevation myocardial infarction (STEMI) by improving left ventricular (LV) remodeling remains unknown. The primary objective of the PERI-STEMI trial is to assess whether sacubitril-valsartan is more effective in preventing adverse LV remodeling for patients with STEMI than enalapril. Hypothesis We hypothesize that sacubitril/valsartan is superior to enalapril in preventing adverse LV remodeling evaluated by cardiovascular magnetic resonance imaging at the 6-month follow-up. Methods PERI-STEMI is an investigator-initiated, prospective, multi-center, randomized, open-label, superiority trial with blinded evaluation of outcomes. A total of 376 first-time STEMI patients with primary percutaneous coronary intervention (PPCI) within 12 h after symptom onset will be randomized to sacubitril-valsartan or enalapril treatment. All the patients will receive a baseline cardiovascular magnetic resonance (CMR) examination at 4–7 days post-PPCI. The primary endpoint is the change of indexed LV mass at the 6-month follow-up CMR. Results Enrollment of the first patient is planned in November 2021. Recruitment is anticipated to last for 12–18 months and patients will be followed for 5 years after randomization. The study is expected to complete in June 2027. Conclusions The results of the PERI-STEMI trial are expected to provide CMR evidence on whether ARNI could benefit patients with STEMI, so as to facilitate the strategy of CMR-based risk stratification and therapy selection for these patients. PERI-STEMI is registered at ClinicalTrials.gov (NCT04912167)

A novel hybrid machine learning model for auxiliary diagnosing myocardial ischemia

IntroductionAccurate identification of the myocardial texture features of fat around the coronary artery on coronary computed tomography angiography (CCTA) images are crucial to improve clinical diagnostic efficiency of myocardial ischemia (MI). However, current coronary CT examination is difficult to recognize and segment the MI characteristics accurately during earlier period of inflammation.Materials and methodsWe proposed a random forest model to automatically segment myocardium and extract peripheral fat features. This hybrid machine learning (HML) model is integrated by CCTA images and clinical data. A total of 1,316 radiomics features were extracted from CCTA images. To further obtain the features that contribute the most to the diagnostic model, dimensionality reduction was applied to filter features to three: LNS, GFE, and WLGM. Moreover, statistical hypothesis tests were applied to improve the ability of discriminating and screening clinical features between the ischemic and non-ischemic groups.ResultsBy comparing the accuracy, recall, specificity and AUC of the three models, it can be found that HML had the best performance, with the value of 0.848, 0.762, 0.704 and 0.729.ConclusionIn sum, this study demonstrates that ML-based radiomics model showed good predictive value in MI, and offer an enhanced tool for predicting prognosis with greater accuracy

Group-Agent Reinforcement Learning

It can largely benefit the reinforcement learning process of each agent if multiple agents perform their separate reinforcement learning tasks cooperatively. Different from multi-agent reinforcement learning where multiple agents are in a common environment and should learn to cooperate or compete with each other, in this case each agent has its separate environment and only communicate with others to share knowledge without any cooperative or competitive behaviour as a learning outcome. In fact, this learning scenario is not well understood yet and not well formulated. As the first effort, we propose group-agent reinforcement learning as a formulation of this scenario and the third type of reinforcement learning problem with respect to single-agent and multi-agent reinforcement learning. We then propose the first distributed reinforcement learning framework called DDAL (Decentralised Distributed Asynchronous Learning) designed for group-agent reinforcement learning. We show through experiments that DDAL achieved desirable performance with very stable training and has good scalability