Clinical Efficacy of Vestibular Rehabilitation Training Combined with Medical Wisdom Platform on Vertigo Caused by Vestibular Neuritis.

Objective: The clinical manifestation of vertigo caused by vestibular neuritis is acute and persistent vertigo, accompanied by nausea, vomiting, and dizziness. Low-dose glucocorticoid therapy is recommended in the acute phase, while drug therapy is not recommended in the recovery phase. Therefore, it is of certain clinical value to explore other treatment options. This study hopes to better fill the current research gap in non-drug treatment of vertigo caused by vestibular neuritis. Methods: The medical data of 96 patients with vertigo caused by vestibular neuritis in our hospital from May 2019 to May 2021 were retrospectively analyzed. According to different treatment methods, they were divided into the control group (routine treatment regimen) and the experimental group (vestibular rehabilitation training combined with the medical wisdom platform), with 48 cases in each group, and the clinical efficacy of the two groups was compared. Results: The total effective rate of treatment was 95.83% in the experimental group, which was significantly higher than 79.17% in the control group (χ2 = 6.095, P = .014). In the two groups, the scores of dizziness handicap inventory (DHI) and vestibular symptom index (VSI) decreased. In contrast, the scores of Tinetti performance-oriented mobility assessment (POMA) and functional independence measure (FIM) increased after treatment. After treatment, the experimental group had significantly lower DHI score (t = 12.942, P < .001), distinctly higher POMA score (t = 9.940, P < .001), overtly lower VSI score (t = 8.783, P < .001), and notably higher FIM score than the control group (t = 12.649, P < .001). Conclusion: The application of vestibular rehabilitation training combined with the medical wisdom platform is beneficial to patients with vertigo caused by vestibular neuritis, which provides reference for the subsequent treatment of this disease and has a certain clinical promotion value.

CABEAN: a software for the control of asynchronous Boolean networks.

SUMMARY: Direct cell reprogramming, also called transdifferentiation, has great potential for tissue engineering and regenerative medicine. Boolean networks, a popular modelling framework for gene regulatory networks, make it possible to identify intervention targets for direct cell reprogramming with computational methods. In this work, we present our software, CABEAN, for the control of asynchronous Boolean networks. CABEAN identifies efficacious nodes, whose perturbations can drive the dynamics of a network from a source attractor (the initial cell type) to a target attractor (the desired cell type). CABEAN provides several control methods integrating practical constraints. Thus, it has the ability to provide a rich set of control sets, such that biologists can select suitable ones for validation based on specific experimental settings. AVAILABILITY AND IMPLEMENTATION: The executable binary and the user guide of the software are publicly available at https://satoss.uni.lu/software/CABEAN/.

Controlling large Boolean networks with single-step perturbations.

MOTIVATION: The control of Boolean networks has traditionally focussed on strategies where the perturbations are applied to the nodes of the network for an extended period of time. In this work, we study if and how a Boolean network can be controlled by perturbing a minimal set of nodes for a single-step and letting the system evolve afterwards according to its original dynamics. More precisely, given a Boolean network (BN), we compute a minimal subset Cmin of the nodes such that BN can be driven from any initial state in an attractor to another 'desired' attractor by perturbing some or all of the nodes of Cmin for a single-step. Such kind of control is attractive for biological systems because they are less time consuming than the traditional strategies for control while also being financially more viable. However, due to the phenomenon of state-space explosion, computing such a minimal subset is computationally inefficient and an approach that deals with the entire network in one-go, does not scale well for large networks. RESULTS: We develop a 'divide-and-conquer' approach by decomposing the network into smaller partitions, computing the minimal control on the projection of the attractors to these partitions and then composing the results to obtain Cmin for the whole network. We implement our method and test it on various real-life biological networks to demonstrate its applicability and efficiency. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Crystallization Kinetics of Poly(ethylene oxide) under Confinement in Nanoporous Alumina Studied by in Situ X-ray Scattering and Simulation.

While a relatively complete understanding of the nucleation and orientation of polymers under confinement in one-dimensional nanochannels has been achieved, crystallization kinetics investigation of confined polymers is still rare. In this work, we investigated the crystallization kinetics of poly(ethylene oxide) confined in anodic alumina oxide templates with different pore sizes using in situ wide-angle X-ray scattering (WAXS). The crystallization kinetics results were fitted with the Avrami equation. The Avrami index was determined by both "isothermal step crystallization" and in situ WAXS. The crystallization process of polymers under one-dimensional nanopore confinement was simulated by a "one-dimensional lattice model". Based on this model, it is shown that homogeneous nucleation with the simultaneous growth of multiple crystal planes with drastically different growth rates could result in Avrami indexes lower than 1.

Complexity Analysis of Carbon Market Using the Modified Multi-Scale Entropy.

Carbon markets provide a market-based way to reduce climate pollution. Subject to general market regulations, the major existing emission trading markets present complex characteristics. This paper analyzes the complexity of carbon market by using the multi-scale entropy. Pilot carbon markets in China are taken as the example. Moving average is adopted to extract the scales due to the short length of the data set. Results show a low-level complexity inferring that China's pilot carbon markets are quite immature in lack of market efficiency. However, the complexity varies in different time scales. China's carbon markets (except for the Chongqing pilot) are more complex in the short period than in the long term. Furthermore, complexity level in most pilot markets increases as the markets developed, showing an improvement in market efficiency. All these results demonstrate that an effective carbon market is required for the full function of emission trading.

Target Control of Asynchronous Boolean Networks.

We study the target control of asynchronous Boolean networks, to identify interventions that can drive the dynamics of a given Boolean network from any initial state to the desired target attractor. Based on the application time, the control can be realised with three types of perturbations, including instantaneous, temporary and permanent perturbations. We develop efficient methods to compute the target control for a given target attractor with these three types of perturbations. We compare our methods with the stable motif-based control method on a variety of real-life biological networks to evaluate their performance. We show that our methods scale well for large Boolean networks and they are able to identify a rich set of solutions with a small number of perturbations.

Impact of liver metastases status on survival outcomes of first-line immunotherapy in extensive stage small cell lung cancer: a systematic review and meta-analysis.

PURPOSE: This study aims to assess the impact of liver metastases status on survival outcomes of first-line immunotherapy in extensive stage small cell lung cancer (ES-SCLC) patients. MATERIALS AND METHODS: Comprehensive searches were conducted in the Cochrane Library databases, Embase, PubMed, and abstracts from WCLC, ESMO, and ASCO from inception to December 2022. Randomized controlled trials reporting progression-free survival (PFS) and/or overall survival (OS) of first-line immunotherapy in ES-SCLC patients were included. RESULTS: Six trials involving 3501 patients were analyzed, comprising 1350 patients with liver metastases and 2151 without. The quality of the included trials was consistently high. Pooled results revealed that immunotherapy plus chemotherapy did not significantly improve PFS (hazard ratio [HR] = 0.82, 95% confidence interval [CI]: 0.68-1.00, P = 0.05) and OS (HR = 0.89, 95% CI: 0.79-1.00, P = 0.05) in ES-SCLC patients with liver metastases compared to chemotherapy alone. However, immunotherapy plus chemotherapy improved PFS (HR = 0.66, 95% CI: 0.57-0.77, P < 0.01) and OS (HR = 0.74, 95% CI: 0.67-0.82, P < 0.01) in ES-SCLC patients without liver metastases compared to chemotherapy alone. CONCLUSIONS: First-line immunotherapy plus chemotherapy significantly improved PFS and OS in ES-SCLC patients without liver metastases compared to chemotherapy alone. However, patients with liver metastases did not experience comparable benefits.

Efficacy of First-Line Immunotherapy Combined With Chemotherapy in Extensive-Stage Small Cell Lung Cancer Patients With Different Brain Metastases Status: A Systematic Review and Meta-Analysis.

Background: This study aims to evaluate the efficacy of first-line immunotherapy combined with chemotherapy in extensive-stage small cell lung cancer (ES-SCLC) patients with differing brain metastasis statuses. Methods: We conducted a comprehensive search in public databases, such as PubMed, EMBASE, and the Cochrane Library, to identify randomized controlled trials involving ES-SCLC patients, with or without brain metastases, who underwent first-line immunotherapy combined with chemotherapy. The primary outcome measure was progression-free survival (PFS), and the secondary outcome measure was overall survival (OS). Results: Our analysis incorporated seven high-quality randomized controlled trials, encompassing 398 patients with brain metastases and 3,533 without. Among patients without brain metastases, the combination of immunotherapy and chemotherapy led to significantly improved PFS (hazard ratio (HR) = 0.72, 95% confidence interval (CI): 0.62 - 0.84, P < 0.001) and OS (HR = 0.77, 95% CI: 0.67 - 0.88, P < 0.001) in comparison to chemotherapy alone. Conversely, for patients with brain metastases, the addition of immunotherapy to chemotherapy did not result in a significant improvement in PFS (HR = 1.03, 95% CI: 0.66 - 1.61, P = 0.887) or OS (HR = 1.03, 95% CI: 0.82 - 1.31, P = 0.776) when compared to chemotherapy alone. Conclusions: In ES-SCLC patients without brain metastases, first-line immunotherapy combined with chemotherapy demonstrated improved PFS and OS in contrast to chemotherapy alone. However, patients with brain metastases did not experience similar benefits.

Experiences and perspectives of healthcare professionals, patients and caregivers toward the serious illness conversation guide: protocol for a qualitative meta-synthesis.

INTRODUCTION: Exploring the experiences and perspectives of healthcare professionals, patients and caregivers toward serious illness conversations based on the Serious Illness Care Program is vital for improving communication with patients who are seriously ill, as has been shown in previous studies. However, few studies have carried out a systematic review to examine common themes, strengthen conclusions and identify gaps in the literature, the findings of which could help steer further research, policies and practice to improve more timely and person-centred conversations about the values and priorities of patients with serious illnesses. The objective of this qualitative meta-synthesis is to explore how healthcare professionals, patients and caregivers described their own experiences of the processes of serious illness communication through a secondary analysis of published qualitative data. METHODS AND ANALYSIS: Meta-aggregation will be used to conduct a systematic review of qualitative studies. We conducted an initial search on 10 October 2023; papers published in English will be searched using electronic databases, including PubMed (MEDLINE), Web of Science, Embase, Ovid and CINAHL. Studies that satisfy the eligibility criteria will be evaluated for methodological quality using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Qualitative Research. The meta-aggregative review will consist of the following: (1) extraction of findings of all included studies; (2) categorisation of the findings, with at least two findings per category; and (3) synthesis of one or more findings from at least two categories. Study eligibility screening, data extraction, analysis and JBI Critical Appraisal Checklist and Grading of Recommendations Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative research assessments will be undertaken independently by two authors. ETHICS AND DISSEMINATION: Secondary data analysis of published literature does not require ethical approval. The results will be disseminated in peer-reviewed journals and presented in conference papers and elsewhere. PROSPERO REGISTRATION NUMBER: CRD42022330859.

Towards Optimal Decomposition of Boolean Networks.

In recent years, great efforts have been made to analyze biological systems to understand the long-run behaviors. As a well-established formalism for modelling real-life biological systems, Boolean networks (BNs) allow their representation and analysis using formal reasoning and tools. Most biological systems are robust-they can withstand the loss of links and cope with external environmental perturbations. Hence, the BNs used to model such systems are necessarily large and dense, and yet modular. However, existing analysis methods only work well on networks of moderate size. Thus, there is a great need for efficient methods that can handle large-scale BNs and for doing so it is inevitable to exploit both the structural and dynamic properties of the networks. In this paper, we propose a method towards the optimal decomposition of BNs to balance the relation between the structure and dynamics of a network. We show that our method can greatly improve the existing decomposition-based attractor detection by analyzing a number of large real-life biological networks.

Microbial characteristics response to the soil quality of newly created farmland on the Loess Plateau.

Microbiome plays an important role in evaluating soil quality for sustainable agriculture. However, the suitability of biological indicators in reclaimed farmland is less understood. Using high-throughput sequencing, we evaluated the soil microbial community of the newly created farmland (NF) after reclamation with two local high-yield farmlands (slope farmland (SF), check-dam farmland (CF)) on the Loess Plateau. Soil enzyme activities and the amount of culturable microorganism were also quantified to assess the soil quality. Results showed that the microbial diversity, cultural microorganism abundance, and soil enzyme activities indicated poor soil quality in NF. The dominant bacterial phyla were Proteobacteria, Bacteroidetes, Acidobacteria, and Cyanobacteria. The abundance of Acidobacteria was significantly lower in NF (13.31%) than in SF (27.25%) and CF (27.91%). Soil enzyme activities had a significant correlation with the abundance of culturable microorganism, Proteobacteria and Bacteroidetes, soil organic matter, total nitrogen, cation exchange capacity, and pH, suggesting that soil microbes have driven the formation of nutrition and further mediated crop growth. Therefore, the application of bacterial fertilizers could be a potential way to improve the soil quality of reclaimed farmland for crop growth.

An Efficient Approach Towards the Source-Target Control of Boolean Networks.

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be perturbed in a single-step to drive its dynamics from an initial state to a target steady state (or attractor), which we call the source-target control of Boolean networks. Due to the phenomenon of state-space explosion, a simple global approach that performs computations on the entire network may not scale well for large networks. We believe that efficient algorithms for such networks must exploit the structure of the networks together with their dynamics. Taking this view, we derive a decomposition-based solution to the minimal source-target control problem which can be significantly faster than the existing approaches on large networks. We then show that the solution can be further optimized if we take into account appropriate information about the source state. We apply our solutions to both real-life biological networks and randomly generated networks, demonstrating the efficiency and efficacy of our approach.

Efficacy of weekly paclitaxel for the treatment of advanced ovarian cancer: A protocol for systematic review and meta-analysis.

BACKGROUND: This study aims to assess the efficacy and safety of weekly paclitaxel (WP) for the treatment of advanced ovarian cancer (AOC). METHODS: This study will systematically search bibliographic databases (MEDLINE, EMBASE, Cochrane Library, Web of Science, CINAHL, PSYCINFO, Allied and Complementary Medicine Database, CNKI, WANGFANG, and Chinese Biomedical Literature Database) and other literature sources from inception to the March 1, 2020 without language and publication time limitations. Two authors will independently complete all literature selection, data collection, and study quality evaluation. Any disagreements will be solved by a third author through discussion. We will analyze data by RevMan V.5.3 software. RESULTS: This study will systematically generate a comprehensive summary on the efficacy and safety of WP for the treatment of AOC. CONCLUSION: This study may provide beneficial evidence of WP for the treatment of AOC. SYSTEMATIC REVIEW REGISTRATION: INPLASY202040193.

Effect of melatonin for the management of endometriosis: A protocol of systematic review and meta-analysis.

BACKGROUND: This study aims to explore the effect of melatonin for the management of endometriosis. METHODS: We will search electronic databases (Cochrane Library, MEDLINE, EMBASE, CINAHL, Web of Science, Scopus, Allied and Complementary Medicine Database, Chinese Biomedical Literature Database, and China National Knowledge Infrastructure) from their inceptions to the February 29, 2020 without language and publication time limitations. The study identification, study quality assessment, and data extraction will be undertaken by two separate researchers. We will also appraise evidence quality of main outcomes by Grading of Recommendations Assessment Development and Evaluation, and statistical analysis performance by RevMan 5.3 Software. RESULTS: This study will summarize up-to-date clinical evidence to investigate the effect of melatonin for the management of endometriosis. CONCLUSION: This study may provide helpful evidence of melatonin for the management of endometriosis. SYSTEMATIC REVIEW REGISTRATION: INPLASY202040093.

Biochar can improve the soil quality of new creation farmland on the Loess Plateau.

The Loess Plateau is the most severely degraded soil area worldwide and represents one of the lowest areas of soil productivity. To solve the conundrum between increasing populations and decreasing agricultural acreage, enhancing the quantity of cultivated land, gully land consolidation projects has been implemented. However, the new creation farmland soil is not enough to satisfy the demand of agricultural production. An incubation experiment was conducted to determine the effects of biochar on the new creation farmland soil. Five levels of amendments (0, 1%, 2%, 5%, and 10% (wt%) biochar soil) were used, and the soil columns remained in the laboratory for approximately 2 months. The results show that biochar proportion was a more important factor than incubation time across all soils tested. The soil moisture content and particle size clearly increased as the amendment level increased; however, the soil pH decreased gradually with incubation time and tended to slow soil salinization. These findings will have to be verified under field conditions.

Algorithms for the Sequential Reprogramming of Boolean Networks.

Cellular reprogramming, a technique that opens huge opportunities in modern and regenerative medicine, heavily relies on identifying key genes to perturb. Most of the existing computational methods for controlling which attractor (steady state) the cell will reach focus on finding mutations to apply to the initial state. However, it has been shown, and is proved in this article, that waiting between perturbations so that the update dynamics of the system prepares the ground, allows for new reprogramming strategies. To identify such sequential perturbations, we consider a qualitative model of regulatory networks, and rely on Binary Decision Diagrams to model their dynamics and the putative perturbations. Our method establishes a set identification of sequential perturbations, whether permanent (mutations) or only temporary, to achieve the existential or inevitable reachability of an arbitrary state of the system. We apply an implementation for temporary perturbations on models from the literature, illustrating that we are able to derive sequential perturbations to achieve trans-differentiation.

Electrical stimulation on adverse events caused by chemotherapy in patients with cervical cancer: A protocol for a systematic review of randomized controlled trial.

BACKGROUND: This protocol of systematic review aims to investigate the effectiveness of electrical stimulation (ES) on adverse events (AEs) caused by chemotherapy in patients with cervical cancer (CC). METHODS: This systematic review of randomized controlled trials will be identified through searchers of PUBMED, PsycINFO, Scopus, Opengrey, Cochrane Central Register of Controlled Trials, EMBASE, Cumulative Index to Nursing and Allied Health Literature, Web of Science, Allied and Complementary Medicine Database, and Chinese Biomedical Literature Database. All the sources will be searched from the inception to the date of study search ran. Additionally, websites of clinical trials registry and reference lists provided in relevant studies and reviews will also be searched. Two independent reviewers will evaluate the eligibility criteria of all potential literature, extract the data, and determine the risk of bias for each included study. RevMan 5.3 software will be used to pool the data and to conduct a meta-analysis. RESULTS: This systematic review will assess the effectiveness of ES on AEs caused by chemotherapy in patients with CC. CONCLUSION: The findings of this study may summarize the latest evidence for the ES on AEs following chemotherapy for CC. PROSPERO REGISTRATION NUMBER: PROSPERO CRD42019120191.

ASSA-PBN: A Toolbox for Probabilistic Boolean Networks.

As a well-established computational framework, probabilistic Boolean networks (PBNs) are widely used for modelling, simulation, and analysis of biological systems. To analyze the steady-state dynamics of PBNs is of crucial importance to explore the characteristics of biological systems. However, the analysis of large PBNs, which often arise in systems biology, is prone to the infamous state-space explosion problem. Therefore, the employment of statistical methods often remains the only feasible solution. We present ${\mathsf{ASSA-PBN}}$ , a software toolbox for modelling, simulation, and analysis of PBNs. ${\mathsf{ASSA-PBN}}$ provides efficient statistical methods with three parallel techniques to speed up the computation of steady-state probabilities. Moreover, particle swarm optimisation (PSO) and differential evolution (DE) are implemented for the estimation of PBN parameters. Additionally, we implement in-depth analyses of PBNs, including long-run influence analysis, long-run sensitivity analysis, computation of one-parameter profile likelihoods, and the visualization of one-parameter profile likelihoods. A PBN model of apoptosis is used as a case study to illustrate the main functionalities of ${\mathsf{ASSA-PBN}}$ and to demonstrate the capabilities of ${\mathsf{ASSA-PBN}}$ to effectively analyse biological systems modelled as PBNs.

Predicting body weight by measuring weight underneath the buttock in supine position: A quantitative observation study.

BACKGROUND: Measuring body weight (BW) for bedridden patients often presents difficulty and challenge. OBJECTIVES: The present study aimed at providing a self-designed indirect method to predict BW by measuring weight underneath the buttock (WUB) of an individual in supine position, thereby providing an easy, safe and effective way of BW measurement for bedridden patients. METHODS: A total of 180 subjects participated in the present study and agreed to have their BW and WUB to be measured. BW was measured normally at the standing position through an electronic weighing machine without any special requirement. By placing the electronic weighing machine under the subject's buttock along with an additional hard board set under the electronic weighing machine, WUB was measured in subjects who were asked to assume a supine position in beds to simulate conditions of bedridden individuals. Measurement was repeated thrice to minimise the test error. RESULTS: Average BW (62.7 ± 11.7 kg) was 2.0 ± 0.1 (≈ 2) folds of WUB (31.4 ± 6.0 kg). Significant linear correlation was identified between BW and WUB in all subjects with a linear equation yielded (y= 1.8 x + 6.0). Further multiple regression analysis resulted in an equation of BW (kg) =-36.8 + 1.66*WUB (kg) + 29.0*height (m). Predicted BW (PBW) was calculated out based on the results described above: the multiple relationship (2 folds), the linear equation, and the multiple regression equation, and differed from the measured BW by 3.6 ± 2.8, 3.5 ± 2.7 and 4.2 ± 3.1 kg respectively. CONCLUSIONS: Predicting BW through WUB in supine position is effective and reliable because the latter can be easily measured and features a strong linear relationship with BW. This method provides clinical staff with remarkable benefits in BW determination for bedridden patients.

In situ synthesis of bilayered gradient poly(vinyl alcohol)/hydroxyapatite composite hydrogel by directional freezing-thawing and electrophoresis method.

Bilayered poly(vinyl alcohol) (PVA)/hydroxyapatite (HA) composite hydrogels with anisotropic and gradient mechanical properties were prepared by the combination of directional freezing-thawing (DFT) and electrophoresis method. Firstly, PVA hydrogels with aligned channel structure were prepared by the DFT method. Then, HA nanoparticles were in situ synthesized within the PVA hydrogels via electrophoresis. By controlling the time of the electrophoresis process, a bilayered gradient hydrogel containing HA particles in only half of the gel region was obtained. The PVA/HA composite hydrogel exhibited gradient mechanical strength depending on the distance to the cathode. The gradient initial tensile modulus ranging from 0.18MPa to 0.27MPa and the gradient initial compressive modulus from 0.33MPa to 0.51MPa were achieved. The binding strength of the two regions was relatively high and no apparent internal stress or defect was observed at the boundary. The two regions of the bilayered hydrogel also showed different osteoblast cell adhesion properties.