The correlation between rs2501577 gene polymorphism and biliary atresia: a systematic review and meta-analysis.

IMPORTANCE: Multiple studies indicate a possible correlation between ADD3 rs2501577 and biliary atresia susceptibility; however, a conclusive determination has yet to be made. OBJECTIVE: Investigate the role of ADD3 rs2501577 in biliary atresia susceptibility across diverse populations. DATA SOURCES: The study protocol has been registered on PROSPERO, an international platform for systematic review registration (PROSPERO ID: CRD42023384641). The following databases will be searched until February 1, 2023: PubMed, Embase, Cochrane, CBM, Web of Science, and CNKI. STUDY SELECTION: Eight studies were selected from seven papers to assess the data. A total of 7651 participants were included, consisting of 1662 in the BA group and 5989 in the NC group. DATA EXTRACTION AND SYNTHESIS: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed while conducting the systematic reviews and meta-analyses. Two authors independently assessed the quality of the included studies using the Newcastle-Ottawa Quality Assessment Scale. The significance of the pooled odds ratio (OR) was evaluated with a Z test, and statistical heterogeneity across studies was assessed using the I2 and Q statistics. Publication bias was assessed using Egger's and Begg's tests. MAIN OUTCOME(S) AND MEASURE(S): The primary study outcome was the development of biliary atresia. Subgroup analysis was performed based on race, region, and assessment of Hardy-Weinberg equilibrium (HWE). RESULTS: The studies indicate that the ADD3 rs2501577 susceptibility locus increases the risk of developing biliary atresia, regardless of allelic, homozygote, dominant, and recessive gene inheritance models. Furthermore, ADD3 has been found to be associated with apoptosis, cell cycle, and cell damage repair based on functional analysis. CONCLUSIONS AND RELEVANCE: The ADD3 rs2501577 polymorphic locus is associated with an increased risk of biliary atresia, particularly in Asian populations. This study recommends further investigation of the ADD3 rs2501577 locus in Asian populations to validate its role in the diagnosis of biliary atresia.

A Scalable Cross-Chain Access Control and Identity Authentication Scheme.

Cross-chain is an emerging blockchain technology which builds the bridge across homogeneous and heterogeneous blockchains. However, due to the differentiation of different blockchains and the lack of access control and identity authentication of cross-chain operation subjects, existing cross-chain technologies are struggling to accomplish the identity transformation of cross-chain subjects between different chains, and also pose great challenges in terms of the traceability and supervision of dangerous transactions. To address the above issues, this paper proposes a scalable cross-chain access control and identity authentication scheme, which can authenticate the legitimacy of blockchains in the cross-chain system and ensure that all cross-chain operations are carried out by verified users. Furthermore, it will record all cross-chain operations with the help of Superchain in order to regulate and trace illegal transactions. Our scheme is scalable and, at the same time, has low invasiveness to blockchains in the cross-chain system. We implement the scheme and accordingly conduct the evaluations, which prove its security, efficiency, and scalability.

The Application of Artificial Intelligence in Predicting Embryo Transfer Outcome of Recurrent Implantation Failure.

Background: Recurrent implantation failure (RIF) refers to that infertile patients have undergone multiple in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) cycles and transferred multiple embryos without embryo implantation or clinical pregnancy. Due to the lack of clear evidence-based medical guidelines for the number of embryos to be transferred in RIF patients, how to obtain the highest single cycle pregnancy success rate with as few embryos transferred as possible while avoiding multiple pregnancy as much as possible, that is, how to balance the pregnancy success rate and multiple pregnancy rate, is a great challenge for clinicians and RIF patients. We urgently need an effective and reliable assisted decision-making method to help clinicians find this balance, and an artificial intelligence (AI) system will provide an efficient solution. Design and Methods: In this research, we filtered out the RIF data set (n = 45,921) from the Human Fertilisation and Embryology Authority (HFEA) database from 2005 to 2016. The data set was divided into two groups according to the number of embryos transferred, Group A and B. Group A included 34,175 cycles with two embryos transferred, while Group B included 11,746 cycles with only one embryo transferred, each containing 44 features and a prediction label (pregnancy). Four machine learning algorithms (RF, GBDT, AdaBoost, and MLP) were used to train Group A and Group B data set respectively and 10-folder cross validation method was used to validate the models. Results: The results revealed that the AdaBoost model of Group A obtained the best performance, while the GBDT model in Group B was proved to be the best model. Both models had been proved to provide accurate prediction of transfer outcome. Conclusion: Our research provided a new approach for targeted and personalized treatment of RIF patients to help them achieve efficient and reliable pregnancy. And an AI-assisted decision-making system will be designed to help clinicians and RIF patients develop personalized transfer strategies, which not only guarantees efficient and reliable pregnancy, but also avoids the risk of multiple pregnancy as much as possible.

Live-Birth Prediction of Natural-Cycle In Vitro Fertilization Using 57,558 Linked Cycle Records: A Machine Learning Perspective.

Background: Natural-cycle in vitro fertilization (NC-IVF) is an in vitro fertilization (IVF) cycle without gonadotropins or any other stimulation of follicular growth. Previous studies on live-birth prediction of NC-IVF were very few; the sample size was very limited. This study aims to construct a machine learning model to predict live-birth occurrence of NC-IVF using 57,558 linked cycle records and help clinicians develop treatment strategies. Design and Methods: The dataset contained 57,558 anonymized register patient records undergoing NC-IVF cycles from 2005 to 2016 filtered from 7bsp;60,732 records in the Human Fertilisation and Embryology Authority (HFEA) data. We selected matching records and features through data filtering and feature selection methods. Two groups of twelve machine learning models were trained and tested. Eight metrics, e.g., F1 score, Matthews correlation coefficient (MCC), the area under the receiver operating characteristic curve (AUC), etc., were computed to evaluate the performance of each model. Results: Two groups of twelve models were trained and tested. The artificial neural network (ANN) model performed the best in the machine learning group (F1 score, 70.87%; MCC, 50.37%; and AUC score, 0.7939). The LogitBoost model obtained the best scores in the ensemble learning group (F1 score, 70.57%; MCC, 50.75%; and AUC score, 0.7907). After the comparison between the two models, the LogitBoost model was recognized as an optimal one. Conclusion: In this study, NC-IVF-related datasets were extracted from the HFEA data, and a machine learning-based prediction model was successfully constructed through this largest NC-IVF dataset currently. This model is universal and stable, which can help clinicians predict the live-birth success rate of NC-IVF in advance before developing IVF treatment strategies and then choose the best benefit treatment strategy according to the patients' wishes. As "use less stimulation and back to natural condition" becomes more and more popular, this model is more meaningful in the decision-making assistance system for IVF.

Virtual motor training to improve the activities of daily living, hand grip, and gross motor function among children with cerebral palsy: Meta-regression analysis.

BACKGROUND: Given that cerebral palsy is a lifelong condition, continuous motor training is necessary to correct abnormal posture and patterns to improve functional performance. OBJECTIVE: To explore the essential features of virtual motor training and identify its effects on the activities of daily living, hand grip function, and gross motor function among children with cerebral palsy. METHODS: Ten databases were systematically searched for randomized controlled trials from inception until October 15, 2019. Studies with a randomized controlled trials design, children with cerebral palsy, comparisons of virtual motor training with other interventions, and activities of daily living, hand grip, and gross motor function outcomes were included. Subgroup analyses and meta-regression were performed. The effect size was assessed using random-effects meta-analysis and evaluated by using Hedges' g. Random effects univariate meta-regression model was used to examine the association of covariates with intervention effectiveness. RESULTS: Seventeen randomized controlled trials with 853 participants were included. Meta-analysis indicated that the virtual motor training had significant positive effects on activities of daily living (g = 0.31, 95% confidence interval, CI = 0.10, 0.51), grip (g = 0.40, 95% CI = 0.08, 0.71), and gross motor (g = 0.71, 95% CI = 0.43, 0.99) function. Univariate random-effects meta-regression analysis indicated an increase in activities of daily living scores for motion capture systems that used depth imaging compared with those that used digital imaging. SIGNIFICANCE: Our systematic review indicated that virtual motor function may significantly improve ADLs, hand grip, and gross motor functions among children with cerebral palsy.

Global topology alteration of the brain functional network affects the 8-week antidepressant response in major depressive disorder.

PURPOSE: Previous studies have indicated that the global topology of the brain functional network in patients with major depressive disorder (MDD) differs from that of those with normal controls (NCs). However, the relationship between an altered global topology and the response to antidepressants remains unclear. Here, we investigated whether differences in global topology affect the efficacy of antidepressants in MDD patients. METHODS: 108 MDD patients and 61 NCs were recruited. A magnetic resonance imaging (MRI) scan was performed at the baseline, and the Hamilton Depression Scale-24 (HAMD-24) was assessed at baseline and after 2 and 8 weeks of antidepressant treatment. Seven global topological parameters of the brain functional network were measured and compared between groups. A correlation analysis was performed to identify the relationships between global topological parameters and antidepressant efficacy. RESULTS: The brain networks of MDD patients and NCs were both small-world networks. The clustering coefficient (Cp) and local efficiency (Eloc) were significantly smaller in MDD patients compared with those in NCs. The characteristic path length (Lp) were negatively correlated with the 8-week reductive rate of HAMD-24 in the MDD group. CONCLUSION: The present research found that the brain functional network of MDD patients still had a small-world organization but with a lower Cp and Eloc than the NCs. In addition, the brain network global topology might have an impact on the antidepressant response and thus had the potential to become a treatment predictor of MDD.

Stretching-Tunable High-Frequency Magnetic Properties of Wrinkled CoFeB Films Grown on PDMS.

We demonstrated a convenient method via applying uniaxial tensile strains to continuously tune the high-frequency properties of flexible magnetic films. CoFeB films were magnetron sputtered onto prestretched polydimethylsiloxane (PDMS) membranes. They exhibit a self-assembled periodic wrinkling surface structure because of the large mismatch of Young's moduli between the elastomeric PDMS substrates and the metal layers. The wrinkling morphology and the residual tensile stress caused by the Poisson effect can be continuously tuned by a uniaxial stretching strain less than the growth prestrain, which consequently results in changes in high-frequency performance. The initial permeability and the ferromagnetic resonance frequency of flexible CoFeB thin films can be monotonously tuned in wide ranges of about hundreds and 1 GHz, respectively. A good repeatability over thousands of stretching-relaxing cycles has been demonstrated without any obvious reduced high-frequency properties. This flexible CoFeB films with excellent stretching-tunable high-frequency performances are promising for application in flexible and tunable microwave devices.

Rehabilitation training based on virtual reality for patients with Parkinson's disease in improving balance, quality of life, activities of daily living, and depressive symptoms: A systematic review and meta-regression analysis.

OBJECTIVE: To examine the effectiveness of rehabilitation training based on virtual reality in improving balance, quality of life, activities of daily living, and depressive symptoms of patients with Parkinson's disease. DATA SOURCES: PubMed, EMBASE, CINAHL, Scopus, Cochrane Library, PsycINFO, ProQuest, Physiotherapy Evidence Database, IEEE Xplore, China National Knowledge Infrastructure, Wanfang, and VIP Information databases were searched from their inception to October 15, 2020. Trial registries, gray literature, and target journals were also searched. METHODS: Eligible randomized controlled trials included studies with patients with Parkinson's disease in rehabilitation training based on virtual reality. Comprehensive Meta-Analysis 3.0 software was used. Physiotherapy Evidence Database Scale and the Grading of Recommendation, Assessment, Development, and Evaluation system were used to assess the methodological quality of individual trials and the overall quality of the evidence, respectively. RESULTS: A total of 22 randomized controlled trials with 836 patients were included. Meta-analysis revealed that training significantly improved balance (g = 0.66, P < 0.001), quality of life (g = 0.28, P = 0.015), activities of daily living (g = 0.62, P < 0.001), and depressive symptoms (g = 0.67, P = 0.021) compared to the control group. Subgroup analysis indicated that training should utilize video game consoles. Meta-regression analyses showed that age, sessions, and frequency of training had statistically significant impacts on balance scores. Quality of individual trials was high and overall evidence ranged from very low to low. CONCLUSION: Virtual rehabilitation training could be adopted in healthcare institutions as supplementary training for patients with Parkinson's disease.

Novel pyrene-pyridine oligomer nanorods for super-sensitive fluorescent detection of Pd2.

Conjugated polymers (CPs) can be fabricated into conjugated polymer nanoparticles of various shapes, thus tuning the hydrophobicity and sensing performances of the parent polymers. Herein, two new hydrophobic oligomeric CPs containing pyrene-pyridyl moieties, P1 and P2, were directly prepared and conveniently converted into hydrophilic nanorods, i.e. P1NRs and P2NRs (about 4-21 and 6-20 nm in diameter), by a modified microemulsion method. Notably, separated P1NRs exhibit excellent stability while P2NRs tend to stack on each other perhaps due to their different rigidity of π-delocalized backbones, which may have a profound effect on their fluorescence properties. In addition, Pd2+ can coordinate with the pyridyl N atoms, thereby causing ultrasensitive fluorescence quenching of P1NRs and P2NRs owing to the aggregation of oligomeric CP nanorods. These two simple nanosensors can help to determine Pd2+ with detection limits as low as 1 and 70 nM, respectively. It is worth noting that biocompatible P1NRs with bright blue fluorescence can be employed for efficient imaging of trace level Pd2+ ions in live cells.

Generation of concentric perfect Poincaré beams.

We theoretically propose and experimentally verify a method to generate new polycyclic beams, namely concentric perfect Poincaré beams (CPPBs), by using an encoded annular phase mask. The proposed beams consisting of multiple polarization structured fields can be simultaneously generated in one concentric mode, which are respectively mapped by fundamental Poincaré sphere (PS), high-order Poincaré sphere (HOPS), and hybrid-order Poincaré sphere (HyPS). Moreover, the ring radius, numbers and polarization orders of the CPPBs at arbitrary positions on arbitrary PS are independently controlled. This work enriches the mode distributions of perfect vortex and introduces a new polarization degree of freedom, which has the potential to implement more information beyond the orbital angular momentum multiplexing in optical communication.

A novel fluorescent nanosensor based on small-sized conjugated polyelectrolyte dots for ultrasensitive detection of phytic acid.

A novel nanosensor is developed for selective and highly sensitive detection of phytic acid (PA) based on small-sized conjugated polyelectrolyte dots (Pdots) fabricated from a new conjugated polymer (P1) by a modified reprecipitation method. P1 featuring a π-delocalized backbone bearing meta-substituted pyridyl groups can be endowed with enhanced flexibility and hence is beneficial for the synthesis of ultrasmall Pdots (i.e. Pdot-1, ∼3.8 nm in average diameter) as well as for the binding of Fe3+, thus leading to the obvious fluorescence quenching of Pdot-1 (∼444 nm) in the presence of Fe3+ via an electron transfer (ET) process. In addition, phytic acid with six phosphate groups exhibits strong chelating ability. When phytic acid is added, phytic acid readily binds to Fe3+ and the fluorescence of Pdot-1 around 444 nm can be recovered, rendering the supersensitive and selective sensing of PA. Under the optimum conditions, this ultra-small Pdot-based nanoprobe favors the fluorescent determination of PA with the detection limit as low as 10 nM. Particularly, Pdot-1 with bright blue fluorescence exhibits low cytotoxicity. Furthermore, the small-sized and biocompatible Pdot-1 can be applied to the sensitive fluorescence assay for PA in cell extracts and the efficient imaging of PA in live cells.

The protein and mRNA expression levels of glial cell line-derived neurotrophic factor in post stroke depression and major depressive disorder.

Previous studies have indicated that the level of glial cell line-derived neurotrophic factor (GDNF) may be correlated with stroke and depression. Here, we investigated whether GDNF can be a discriminant indicator for post stroke depression (PSD). 159 participants were divided into four groups: PSD, stroke without depression (Non-PSD), major depressive disorder (MDD) and normal control (NC) group, and the protein and mRNA expression levels of GDNF in serum were measured. The results showed that only MDD group had statistical difference in protein and mRNA levels compared with the other three groups (Bonferroni test, P < 0.05). The results of receiver operating curve (ROC) analysis supported GDNF as general distinguishing models in PSD and MDD groups with the area under the curve (AUC) at 0.797 (P < 0.001) and 0.831 (P < 0.001) respectively. In addition, the Spearman analysis demonstrated that the GDNF protein level negatively correlated with the value of Hamilton depression rating scale (HAMD) in PSD patients (correlation coefficient = -0.328, P = 0.047). Together, these findings suggest the protein and mRNA expression levels of GDNF decreased in patients with depression. GDNF may serve as a potential biomarker for differential diagnosis of PSD from MDD patients.