Evaluation of the clinical efficacy of vaginal treatment options for persistent high-risk human papillomavirus infection after excisional treatment of cervical high-grade squamous intraepithelial lesions: a systematic review and Bayesian network meta-analysis.

BACKGROUND: To evaluate the clinical efficacy of different vaginal administration on cervical persistent high-risk human papillomavirus (HR-HPV) infection after excisional treatment for high-grade squamous intraepithelial lesions (HSIL). METHODS: Six databases (PubMed, EmBase, Cochrane Central, China Knowledge Network database, China Biomedical Literature Service, and WanFang database) were searched to collect randomized controlled trials (RCTs) of various types of vaginal administration compared to no treatment on persistent HR-HPV infection after HSIL excisional treatment, and comprehensive analysis of the clearance of different drugs on HR-HPV was performed using Bayesian reticulation meta-analysis. RESULTS: The study analyzed the efficacy of eight interventions, including Interferon, Baofukang, Paiteling, Bletilla striata Sanhuang Powder, Lactobacilli vaginal capsules, Fuanning + Interferon, Interferon + Lactobacilli vaginal capsules, and Interferon + Baofukang, on the clearance of HR-HPV after excisional treatment through pooling and analyzing data from 52 RCTs. The results of the study demonstrated that Interferon + Lactobacilli vaginal capsules [OR 16.0 (95% CIs 8.1-32.0)], Interferon + Fuanning [OR 16.0 (95% CIs 1.1-52.0)], and Interferon + Baofukang [OR 14.0 (95% CIs 6.8-28.0)] were all found to significantly improve postoperative HR-HPV clearance rates when compared to no treatment. Furthermore, when studies with high-risk bias were excluded, Interferon + Lactobacilli vaginal capsules [OR 8.6 (95% CIs 4.7-19.0)] and Interferon + Baofukang [OR 22.0 (95% CIs 8.7-59.0)] were still found to be positively associated with increased postoperative HR-HPV clearance rate. Additionally, the study´s results also indicate that Interferon + Baofukang was effective in enhancing the postoperative HR-HPV clearance rates, mainly when the studies were restricted to a follow-up period of at least 12 months [OR 9.6 (95% CIs 2.9-34.0)]. However, it is important to note that the majority of the trials (29 out of 52, 51.6%) were rated as moderate to high risk of bias, and the certainty of the evidence was moderate to very low. CONCLUSION: The application of various forms of vaginal administration, except for individual use of Lactobacilli vaginal capsules, is more efficacious than no treatment in patients with cervical persistent HR-HPV infection after excisional treatment. However, all of the estimates of the effect size for change in the efficiency of HR-HPV clearance are uncertain. Our confidence in effect estimates and ranking of treatments is low, which needs larger, more rigorous, and longer follow-up RCTs to resolve.

Identifying and distinguishing of essential tremor and Parkinson's disease with grouped stability analysis based on searchlight-based MVPA.

BACKGROUND: Since both essential tremor (ET) and Parkinson's disease (PD) are movement disorders and share similar clinical symptoms, it is very difficult to recognize the differences in the presentation, course, and treatment of ET and PD, which leads to misdiagnosed commonly. PURPOSE: Although neuroimaging biomarker of ET and PD has been investigated based on statistical analysis, it is unable to assist the clinical diagnosis of ET and PD and ensure the efficiency of these biomarkers. The aim of the study was to identify the neuroimaging biomarkers of ET and PD based on structural magnetic resonance imaging (MRI). Moreover, the study also distinguished ET from PD via these biomarkers to validate their classification performance. METHODS: This study has developed and implemented a three-level machine learning framework to identify and distinguish ET and PD. First of all, at the model-level assessment, the searchlight-based machine learning method has been used to identify the group differences of patients (ET/PD) with normal controls (NCs). And then, at the feature-level assessment, the stability of group differences has been tested based on structural brain atlas separately using the permutation test to identify the robust neuroimaging biomarkers. Furthermore, the identified biomarkers of ET and PD have been applied to classify ET from PD based on machine learning techniques. Finally, the identified biomarkers have been compared with the previous findings of the biology-level assessment. RESULTS: According to the biomarkers identified by machine learning, this study has found widespread alterations of gray matter (GM) for ET and large overlap between ET and PD and achieved superior classification performance (PCA + SVM, accuracy = 100%). CONCLUSIONS: This study has demonstrated the significance of a machine learning framework to identify and distinguish ET and PD. Future studies using a large data set are needed to confirm the potential clinical application of machine learning techniques to discern between PD and ET.

Dynamic multicolor emissions of multimodal phosphors by Mn2+ trace doping in self-activated CaGa4O7.

The manipulation of excitation modes and resultant emission colors in luminescent materials holds pivotal importance for encrypting information in anti-counterfeiting applications. Despite considerable achievements in multimodal and multicolor luminescent materials, existing options generally suffer from static monocolor emission under fixed external stimulation, rendering them vulnerability to replication. Achieving dynamic multimodal luminescence within a single material presents a promising yet challenging solution. Here, we report the development of a phosphor exhibiting dynamic multicolor photoluminescence (PL) and photo-thermo-mechanically responsive multimodal emissions through the incorporation of trace Mn2+ ions into a self-activated CaGa4O7 host. The resulting phosphor offers adjustable emission-color changing rates, controllable via re-excitation intervals and photoexcitation powers. Additionally, it demonstrates temperature-induced color reversal and anti-thermal-quenched emission, alongside reproducible elastic mechanoluminescence (ML) characterized by high mechanical durability. Theoretical calculations elucidate electron transfer pathways dominated by intrinsic interstitial defects and vacancies for dynamic multicolor emission. Mn2+ dopants serve a dual role in stabilizing nearby defects and introducing additional defect levels, enabling flexible multi-responsive luminescence. This developed phosphor facilitates evolutionary color/pattern displays in both temporal and spatial dimensions using readily available tools, offering significant promise for dynamic anticounterfeiting displays and multimode sensing applications.

Event-triggering load frequency control for multi-area power system based on random dynamic triggering mechanism and two-side closed functional.

By taking into account sampled-data mechanism and transmission delay, the novel event-triggering load frequency control (LFC) strategy involving random dynamic triggering algorithm (RDTA) is developed for multi-area power systems in this paper. Firstly, an improved multi-area LFC model considering sampling and transmission delay (STD) simultaneously is addressed. Secondly, a modified event-triggering mechanism (ETM) with RDTA is proposed, considering parameter disturbances and a dynamic adjustment mechanism of the triggering threshold. Thirdly, a more advanced Lyapunov-Krasovskii functional (LKF) is constructed, introducing the delay-dependent matrices, more variable cross terms and the two-sided closed functional. Furthermore, two less conservative stability criteria are obtained according to the designed approach. Finally, two multi-area LFC systems are presented to verify the progressiveness of the proposed approach.

Extended analysis on the global Mittag-Leffler synchronization problem for fractional-order octonion-valued BAM neural networks.

In this paper, a new case of neural networks called fractional-order octonion-valued bidirectional associative memory neural networks (FOOVBAMNNs) is established. First, the higher dimensional models are formulated for FOOVBAMNNs with general activation functions and the special linear threshold ones, respectively. On one hand, employing Cayley-Dichson construction in octonion multiplication which is essentially neither commutative nor associative, the system of FOOVBAMNNs is divided into four fractional-order complex-valued ones. On the other hand, Caputo fractional derivative's character and BAM's interactive feature are also properly dealt with. Second, the general criteria are obtained by the new design of LKFs, the application of the related inequalities and the construction of the linear feedback controllers for the global Mittag-Leffler synchronization problem of FOOVBAMNNs. Finally, we present two numerical examples to show the realizability and progress of the derived results.

Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning.

Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder(ASD), definitive neuroimaging markers remain obscured due to inconsistent or incompatible findings, especially for structural imaging. Furthermore, brain differences defined by statistical analysis are difficult to implement individual prediction. The present study has employed the machine learning techniques under the unified framework in neuroimaging to identify the neuroimaging markers of patients with ASD and distinguish them from typically developing controls(TDC). To enhance the interpretability of the machine learning model, the study has processed three levels of assessments including model-level assessment, feature-level assessment, and biology-level assessment. According to these three levels assessment, the study has identified neuroimaging markers of ASD including the opercular part of bilateral inferior frontal gyrus, the orbital part of right inferior frontal gyrus, right rolandic operculum, right olfactory cortex, right gyrus rectus, right insula, left inferior parietal gyrus, bilateral supramarginal gyrus, bilateral angular gyrus, bilateral superior temporal gyrus, bilateral middle temporal gyrus, and left inferior temporal gyrus. In addition, negative correlations between the communication skill score in the Autism Diagnostic Observation Schedule (ADOS_G) and regional gray matter (GM) volume in the gyrus rectus, left middle temporal gyrus, and inferior temporal gyrus have been detected. A significant negative correlation has been found between the communication skill score in ADOS_G and the orbital part of the left inferior frontal gyrus. A negative correlation between verbal skill score and right angular gyrus and a significant negative correlation between non-verbal communication skill and right angular gyrus have been found. These findings in the study have suggested the GM alteration of ASD and correlated with the clinical severity of ASD disease symptoms. The interpretable machine learning framework gives sight to the pathophysiological mechanism of ASD but can also be extended to other diseases.

Stability Evaluation of Brain Changes in Parkinson's Disease Based on Machine Learning.

Structural MRI (sMRI) has been widely used to examine the cerebral changes that occur in Parkinson's disease (PD). However, previous studies have aimed for brain changes at the group level rather than at the individual level. Additionally, previous studies have been inconsistent regarding the changes they identified. It is difficult to identify which brain regions are the true biomarkers of PD. To overcome these two issues, we employed four different feature selection methods [ReliefF, graph-theory, recursive feature elimination (RFE), and stability selection] to obtain a minimal set of relevant features and nonredundant features from gray matter (GM) and white matter (WM). Then, a support vector machine (SVM) was utilized to learn decision models from selected features. Based on machine learning technique, this study has not only extended group level statistical analysis with identifying group difference to individual level with predicting patients with PD from healthy controls (HCs), but also identified most informative brain regions with feature selection methods. Furthermore, we conducted horizontal and vertical analyses to investigate the stability of the identified brain regions. On the one hand, we compared the brain changes found by different feature selection methods and considered these brain regions found by feature selection methods commonly as the potential biomarkers related to PD. On the other hand, we compared these brain changes with previous findings reported by conventional statistical analysis to evaluate their stability. Our experiments have demonstrated that the proposed machine learning techniques achieve satisfactory and robust classification performance. The highest classification performance was 92.24% (specificity), 92.42% (sensitivity), 89.58% (accuracy), and 89.77% (AUC) for GM and 71.93% (specificity), 74.87% (sensitivity), 71.18% (accuracy), and 71.82% (AUC) for WM. Moreover, most brain regions identified by machine learning were consistent with previous findings, which means that these brain regions are related to the pathological brain changes characteristic of PD and can be regarded as potential biomarkers of PD. Besides, we also found the brain abnormality of superior frontal gyrus (dorsolateral, SFGdor) and lingual gyrus (LING), which have been confirmed in other studies of PD. This further demonstrates that machine learning models are beneficial for clinicians as a decision support system in diagnosing PD.

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning.

Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. This study proposed an ML framework based on coarse-to-fine feature selection. The proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. The method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. The experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. The identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.