Search

Abstract Two important factors affecting the progress of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are the S-protein binding function of ACE2 receptors and the membrane fluidity of host cells. This study aimed to evaluate the effect of static magnetic field (SMF) on S-protein/ACE2 binding and cellular membrane fluidity of lung cells, and was performed in vitro using a Calu-3 cell model and in vivo using an animal model. The ability of ACE2 receptors to bind to SARS-CoV-2 spike protein on host cell surfaces under SMF stimulation was evaluated using fluorescence images. Host lung cell membrane fluidity was tested using fluorescence polarization to determine the effects of SMF. Our results indicate that 0.4 T SMF can affect binding between S-protein and ACE2 receptors and increase Calu-3 cell membrane fluidity, and that SMF exposure attenuates LPS-induced alveolar wall thickening in mice. These results may be of value for developing future non-contact, non-invasive, and low side-effect treatments to reduce disease severity in COVID-19-invaded lungs.

BackgroundTranscranial alternating current stimulation (tACS) can apply currents of varying intensity to the scalp, modulating cortical excitability and brain activity. tACS is a relatively new neuromodulation intervention that is now widely used in clinical practice. Many papers related to tACS have been published in various journals. However, there are no articles that objectively and directly introduce the development trend and research hotspots of tACS. Therefore, the aim of this study is to use CiteSpace to visually analyze the recent tACS-related publications, systematically and in detail summarize the current research hotspots and trends in this field, and provide valuable information for future tACS-related research.Material and methodsThe database Web of Science Core Collection Science Citation Index Expanded was used and searched from build to 4 August 2023. Using the CiteSpace to analyze the authors, institutions, countries, keywords, co-cited authors, journals, and references.ResultsA total of 677 papers were obtained. From 2008 to 2023, the number of publications shows an increasing trend, albeit with some fluctuations. The most productive country in this field was Germany. The institution with the highest number of publications is Carl von Ossietzky University of Oldenburg (n = 50). According to Bradford’s law, 7 journals are considered core journals in the field. Herrmann, CS was the author with the most publications (n = 40), while Antal, A was the author with the highest number of co-citations (n = 391) and betweenness centrality (n = 0.16). Disease, neural mechanisms of the brain and electric stimulation are the major research areas in the field. The effect of tACS in different diseases, multi-site stimulation, combined treatment and evaluation are the future research hotspots and trends.ConclusiontACS has research value and research potential, and more and more researchers are paying attention to it. The findings of this bibliometric study provide the current status and trends in the clinical research of tACS and may help researchers to identify hotspots s and explore new research directions in this field.

The early combustion stage in coalfield fire areas (CFA) is the initial phase of coal fire development and spread, making the effective identification of the combustion state crucial. To effectively identify the early combustion state of CFA, this study investigates the CFA in Xinjiang, China. Based on the abnormal characteristics of shallow soil and surface space, a data-driven evaluation method for identifying early combustion states of CFA is proposed. Construct a soil-gas dual-layer early combustion state assessment model of CFA. The evaluation model was applied and validated in the coal fire areas of Xinjiang, China.The results show that there are obvious differences in the spatial distribution of auto-ignition gas, temperature, humidity, surface vegetation coverage and other indicators in the shallow soil and surface space of the coal fire area. The early combustion states of coal fires in areas A, B, and C in the survey area are all in the combustion stage, the combustion state in area D is in the oxidation stage. The accuracy of the evaluation method is bidirectionally verified by the radon measurement method and the infrared thermal imaging detection method. It provides a theoretical basis for controlling the development and spread of coalfield fire areas.

Voltage-gated potassium channel 1.3 (Kv1.3) has emerged as a pivotal player in numerous biological processes and pathological conditions, sparking considerable interest as a potential therapeutic target across various diseases. In this review, we present a comprehensive examination of Kv1.3 channels, highlighting their fundamental characteristics and recent advancements in utilizing Kv1.3 inhibitors for treating autoimmune disorders, neuroinflammation, and cancers. Notably, Kv1.3 is prominently expressed in immune cells and implicated in immune responses and inflammation associated with autoimmune diseases and chronic inflammatory conditions. Moreover, its aberrant expression in certain tumors underscores its role in cancer progression. While preclinical studies have demonstrated the efficacy of Kv1.3 inhibitors, their clinical translation remains pending. Molecular imaging techniques offer promising avenues for tracking Kv1.3 inhibitors and assessing their therapeutic efficacy, thereby facilitating their development and clinical application. Challenges and future directions in Kv1.3 inhibitor research are also discussed, emphasizing the significant potential of targeting Kv1.3 as a promising therapeutic strategy across a spectrum of diseases.

In order to reveal the spatial and temporal distribution of COVID-19's economic impact on the Beijing-Tianjin-Hebei region, this study uses the NPP/VIIRS night light remote sensing data from January to September in 2020 to compare the development trend of COVID-19 and analyze its economic impact on the Beijing-Tianjin-Hebei region. At the same time, the regional economic resilience measurement algorithm is introduced by coupling the regional night light greyscale value to obtain the economic resilience data of various cities during the epidemic. The findings show that: 1. there are structural differences in the spatial distribution of COVID-19 outbreaks in the Beijing-Tianjin-Hebei region. Beijing-Tianjin-Hebei region present a ''core-adjacent-external'' structure and the spatial distribution pattern of Tianjin-Beijing-Shijiazhuang prominent in the inverted ''L'' shape. 2. There are differences in the economic resilience of the Beijing-Tianjin-Hebei region in the face of the epidemic, with high economic resilience in the core urban areas close to Beijing and Tianjin. Therefore, strengthening regional cooperation and establishing relatively stable economic ties with surrounding areas are the key to improving the overall economic resilience of Beijing-Tianjin-Hebei region.

PurposeDepression and anxiety are prevalent mental health challenges among college students. Music therapy has shown effectiveness in addressing depressive symptoms and enhancing psychosomatic functioning. This study aimed to evaluate the effectiveness of a 4-step structured music therapy program in improving mood and reducing symptoms of depression and anxiety among medical school students.Materials and methodsThe self-controlled study involved 45 medical school students (21 men and 24 women) aged 18–24 years to examine the prevalence of depression and anxiety, common mental health issues among medical school students. Participants underwent psychological assessment using the Symptom Checklist-90 (SCL-90), Self-Rating Anxiety Scale (SAS), and Self-Rating Depression Scale (SDS). An 8-week music therapy intervention, comprising four steps—sociality, interaction, music lessons, and creative expression—was administered.ResultsBefore-intervention, 55.6% and 15.6% students were identified as suffering from depression and anxiety respectively. Post-intervention, significant reductions in psychological distress, particularly in the Global Severity Index (GSI) and Positive Symptom Total (PST) on the SCL-90 scale, were observed (P < 0.05). Male students exhibited notable improvements in various psychological symptoms compared to females. Junior grade students demonstrated greater improvements, and clinical medicine students exhibited significant enhancements in specific areas post-intervention.ConclusionThe structured music therapy program showed promising results in improving mood and regulating emotions among medical school students. Music therapy holds potential as a holistic approach to address mental health challenges in this demographic.

With the continuous development and widespread application of network technology, network security issues have become increasingly prominent.Penetration testing has emerged as an important method for assessing and enhancing network security.However, traditional manual penetration testing methods suffer from inefficiency,human error, and tester skills, leading to high uncertainty and poor evaluation results.To address these challenges, an autonomous security analysis and penetration testing framework called ASAPT was proposed, based on attack graphs and deep Q-learning networks (DQN).The ASAPT framework was consisted of two main components:training data construction and model training.In the training data construction phase, attack graphs were utilized to model the threats in the target network by representing vulnerabilities and possible attacker attack paths as nodes and edges.By integrating the common vulnerability scoring system (CVSS) vulnerability database, a “state-action”transition matrix was constructed, which depicted the attacker’s behavior and transition probabilities in different states.This matrix comprehensively captured the attacker’s capabilities and network security status.To reduce computational complexity, a depth-first search (DFS) algorithm was innovatively applied to simplify the transition matrix, identifying and preserving all attack paths that lead to the final goal for subsequent model training.In the model training phase, a deep reinforcement learning algorithm based on DQN was employed to determine the optimal attack path during penetration testing.The algorithm interacted continuously with the environment, updating the Q-value function to progressively optimize the selection of attack paths.Simulation results demonstrate that ASAPT achieves an accuracy of 84% in identifying the optimal path and exhibits fast convergence speed.Compared to traditional Q-learning, ASAPT demonstrates superior adaptability in dealing with large-scale network environments, which could provide guidance for practical penetration testing.

AbstractContext Panax japonicus is the dried rhizome of Panax japonicus C.A. Mey. (Araliaceae). Saponins from Panax japonicus (SPJ) exhibit anti-oxidative and anti-aging effects.Objective We evaluated the neuroprotective effects of SPJ on aging rats.Materials and methods Sprague-Dawley rats (18-months-old) were randomly divided into aging and SPJ groups (n = 8). Five-month-old rats were taken as the adult control (n = 8). The rats were fed a normal chow diet or the SPJ-containing diet (10 or 30 mg/kg) for 4 months. An in vitro model was established by d-galactose (d-Gal) in the SH-SY5Y cell line and pretreated with SPJ (25 and 50 µg/mL). The neuroprotection of SPJ was evaluated via Nissl staining, flow cytometry, transmission electron microscopy and Western blotting in vivo and in vitro.Results SPJ improved the neuronal degeneration and mitochondrial morphology that are associated with aging. Meanwhile, SPJ up-regulated the protein levels of mitofusin 2 (Mfn2) and optic atrophy 1 (Opa1) and down-regulated the protein level of dynamin-like protein 1 (Drp1) in the hippocampus of aging rats (p

Abstract Household air purifiers have been widely used as an effective approach to improving indoor air quality. Air purifiers can automatically record indoor air quality parameters, providing valuable data resources for in-depth data-driven analysis. This work presents a one-year hourly indoor air quality dataset collected by household air purifiers in 100 residential homes in 18 provinces across 4 different climate zones in China. The data were collected from July 1, 2021, to July 1, 2022. The concentrations of formaldehyde, PM2.5, TVOC, temperature, relative humidity, on/off status and the airflow rate of air purifiers during operations were recorded hourly. The data were carefully screened with possibly missing values imputed using chained equation-based methods if any. The dataset provides a comprehensive and detailed picture of the indoor air quality in residential buildings, enabling evaluations on the cleaning effect of air purifiers, the impact of outdoor climate change on indoor air quality, and the future trends in indoor human behavior.

Abstract Purpose Currently, there are no accurate markers for predicting potentially lethal prostate cancer (PC) before biopsy. This study aimed to develop urine tests to predict clinically significant PC (sPC) in men at risk. Methods Urine samples from 928 men, namely, 660 PC patients and 268 benign subjects, were analyzed by gas chromatography/quadrupole time-of-flight mass spectrophotometry (GC/Q-TOF MS) metabolomic profiling to construct four predictive models. Model I discriminated between PC and benign cases. Models II, III, and GS, respectively, predicted sPC in those classified as having favorable intermediate risk or higher, unfavorable intermediate risk or higher (according to the National Comprehensive Cancer Network risk groupings), and a Gleason sum (GS) of ≥ 7. Multivariable logistic regression was used to evaluate the area under the receiver operating characteristic curves (AUC). Results In Models I, II, III, and GS, the best AUCs (0.94, 0.85, 0.82, and 0.80, respectively; training cohort, N = 603) involved 26, 24, 26, and 22 metabolites, respectively. The addition of five clinical risk factors (serum prostate-specific antigen, patient age, previous negative biopsy, digital rectal examination, and family history) significantly improved the AUCs of the models (0.95, 0.92, 0.92, and 0.87, respectively). At 90% sensitivity, 48%, 47%, 50%, and 36% of unnecessary biopsies could be avoided. These models were successfully validated against an independent validation cohort (N = 325). Decision curve analysis showed a significant clinical net benefit with each combined model at low threshold probabilities. Models II and III were more robust and clinically relevant than Model GS. Conclusion This urine test, which combines urine metabolic markers and clinical factors, may be used to predict sPC and thereby inform the necessity of biopsy in men with an elevated PC risk.

AIM: To analyze ultrasound biomicroscopy (UBM) images using random forest network to find new features to make predictions about vault after implantable collamer lens (ICL) implantation. METHODS: A total of 450 UBM images were collected from the Lixiang Eye Hospital to provide the patient's preoperative parameters as well as the vault of the ICL after implantation. The vault was set as the prediction target, and the input elements were mainly ciliary sulcus shape parameters, which included 6 angular parameters, 2 area parameters, and 2 parameters, distance between ciliary sulci, and anterior chamber height. A random forest regression model was applied to predict the vault, with the number of base estimators (n_estimators) of 2000, the maximum tree depth (max_depth) of 17, the number of tree features (max_features) of Auto, and the random state (random_state) of 40.0. RESULTS: Among the parameters selected in this study, the distance between ciliary sulci had a greater importance proportion, reaching 52% before parameter optimization is performed, and other features had less influence, with an importance proportion of about 5%. The importance of the distance between the ciliary sulci increased to 53% after parameter optimization, and the importance of angle 3 and area 1 increased to 5% and 8% respectively, while the importance of the other parameters remained unchanged, and the distance between the ciliary sulci was considered the most important feature. Other features, although they accounted for a relatively small proportion, also had an impact on the vault prediction. After parameter optimization, the best prediction results were obtained, with a predicted mean value of 763.688 μm and an actual mean value of 776.9304 μm. The R² was 0.4456 and the root mean square error was 201.5166. CONCLUSION: A study based on UBM images using random forest network can be performed for prediction of the vault after ICL implantation and can provide some reference for ICL size selection.

Wireless Local-Area Networks (WLANs), as a popular internet access solution, are widely used in numerous places, including enterprises, campuses, and public venues. As the number of devices increases, large-scale deployments will cause the problem of dense wireless networks, including a lot of energy consumption. Thus, the optimization of energy-efficient wireless AP devices has become a focal point of attention. To reduce energy consumption, we have proposed the active access-point (AP) configuration algorithm for WLANs using APs with a dual interface. This uses the greedy algorithm combined with the local search optimization method to find the minimum number of activated APs while satisfying the minimum throughput constraint. However, the previous algorithm basically satisfies only the average throughput among the multiple hosts associated with one AP, wherein some hosts may not reach the required one. In this paper, to overcome this limitation, we propose an enhanced active AP configuration algorithm by incorporating the throughput request satisfaction method that controls the actual throughput at the target value (target throughput) for every host by applying traffic shaping. The target throughput is calculated from the single and concurrent communicating throughput of each host based on channel occupancy time. The minimum throughput constraint will be iteratively adjusted to obtain the required target throughput and achieve the fair throughput allocation. For evaluations, we conducted simulations using the WIMNET simulator and experiments using the testbed system with a Raspberry Pi 4B for APs in four topology cases with five APs and ten hosts. The results show that the proposed method always achieved the required minimum throughput in simulations as well as in experiments, while minimizing the number of active APs. Thus, the validity and effectiveness of our proposal were confirmed.