A finite element analysis of a low-profile femoral neck system of screws in sleeves in a vertical femoral neck fracture model.

BACKGROUND: Femoral neck system (FNS) has exhibited some drawbacks, such as non-fit of the plate with the lateral femoral cortex, postoperative pain, and the potential risk of subtrochanteric fractures. We have developed a low-profile FNS system that addresses some compatibility issues in FNS. In this study, we conducted finite element analysis on the 1-hole FNS (1 H-FNS), 2-holes FNS (2 H-FNS), and low-profile FNS (LP-FNS) and compared their biomechanical performance. METHODS: After the mesh convergence analysis, we established three groups of 1 H-FNS, 2 H-FNS, and LP-FNS. The interfragmentary gap, sliding distance, shear stress, and compressive stress and the bone-implant interface compression stress, stiffness, and displacement were determined under the neutral, flexion, or extension conditions of the hip joint, respectively. The stress and displacement of the femur after the implant removal were also investigated. RESULTS: (1) There were no obvious differences among the three FNS groups in terms of the IFM distance. However, the LP-FNS group showed less rotational angle compared with conventional FNS (neutral: 1 H-FNS, -61.64%; 2 H-FNS, -45.40%). Also, the maximum bone-implant interface compression stress was obviously decreased under the neutral, flexion, or extension conditions of the hip joint (1 H-FNS: -6.47%, -20.59%, or -4.49%; 2 H-FNS: -3.11%, 16.70%, or -7.03%; respectively). (2) After the implant removal, there was no notable difference in the maximum displacement between the three groups, but the maximum von Mises stress displayed a notable difference between LP-FNS and 1 H-FNS groups (-15.27%) except for the difference between LP-FNS and 2 H-FNS groups (-4.57%). CONCLUSIONS: The LP-FNS may not only provide the same biomechanical stabilities as the 1 H-FNS and 2 H-FNS, but also have more advantages in rotational resistance especially under the neutral condition of the hip joint, in the bone-implant interface compression stress, and after the implant removal. In addition, the 1 H-FNS and 2 H-FNS have similar biomechanical stabilities except for the maximum von Mises stress after the implant removal. The femur after the LP-FNS removal not only is subjected to relatively little stress but also minimizes stress concentration areas.

A Dynamic Window Method Based on Reinforcement Learning for SSVEP Recognition.

Steady-state visual evoked potential (SSVEP) is one of the most used brain-computer interface (BCI) paradigms. Conventional methods analyze SSVEPs at a fixed window length. Compared with these methods, dynamic window methods can achieve a higher information transfer rate (ITR) by selecting an appropriate window length. These methods dynamically evaluate the credibility of the result by linear discriminant analysis (LDA) or Bayesian estimation and extend the window length until credible results are obtained. However, the hypotheses introduced by LDA and Bayesian estimation may not align with the collected real-world SSVEPs, which leads to an inappropriate window length. To address the issue, we propose a novel dynamic window method based on reinforcement learning (RL). The proposed method optimizes the decision of whether to extend the window length based on the impact of decisions on the ITR, without additional hypotheses. The decision model can automatically learn a strategy that maximizes the ITR through trial and error. In addition, compared with traditional methods that manually extract features, the proposed method uses neural networks to automatically extract features for the dynamic selection of window length. Therefore, the proposed method can more accurately decide whether to extend the window length and select an appropriate window length. To verify the performance, we compared the novel method with other dynamic window methods on two public SSVEP datasets. The experimental results demonstrate that the novel method achieves the highest performance by using RL.

The EC50 of propofol with different doses of dexmedetomidine during gastrointestinal endoscopy: A double-blind, placebo-controlled trial.

PURPOSE: The goal of this study was to evaluate the dose-response relationship between dexmedetomidine and propofol in sedating patients and to determine the optimal dosage of dexmedetomidine during gastrointestinal endoscopy. METHODS: One hundred fifty patients were divided into 5 groups, each receiving a loading dose of dexmedetomidine (0.4, 0.6, 0.8, 1.0 µg/kg) or saline, with propofol for sedation. The median effective concentration (EC50) of propofol was calculated using the modified Dixon up-and-down approach. Adverse effects, vital signs, procedure, and recovery times were recorded. RESULTS: The EC50 of propofol in groups NS, D0.4, D0.6, D0.8, and D1.0 were 3.02, 2.44, 1.97, 1.85, and 1.83 µg/mL, respectively. Heart rate in the dexmedetomidine groups decreased more than the NS group (P < .001). The mean arterial pressure (MAP) in the NS group experienced a decline compared to groups D0.8 and D1.0 when the plasma concentration and effect-site concentration reached equilibrium. Additionally, the respiratory rate was found to be lower in groups NS, D0.4, D0.6, and D0.8 (P < .05). Recovery time in groups D0.8 and D1.0 was longer than the NS group (P < .05). Bruggemann comfort scales score was higher in group D1.0 (P < .05). No significant difference was found in the incidences of hypotension and bradycardia, and the dose of ephedrine and atropine. Respiratory depression was significantly reduced in groups D0.8 and D1.0 compared to the NS group. CONCLUSION: A single dose of 0.6 to 0.8 µg/kg of dexmedetomidine should be recommended in combination with propofol for gastrointestinal endoscopy. And the EC50 of propofol is 1.97 to 1.85 µg/mL.

Organellophagy regulates cell death:A potential therapeutic target for inflammatory diseases.

BACKGROUND: Dysregulated alterations in organelle structure and function have a significant connection with cell death, as well as the occurrence and development of inflammatory diseases. Maintaining cell viability and inhibiting the release of inflammatory cytokines are essential measures to treat inflammatory diseases. Recently, many studies have showed that autophagy selectively targets dysfunctional organelles, thereby sustaining the functional stability of organelles, alleviating the release of multiple cytokines, and maintaining organismal homeostasis. Organellophagy dysfunction is critically engaged in different kinds of cell death and inflammatory diseases. AIM OF REVIEW: We summarized the current knowledge of organellophagy (e.g., mitophagy, reticulophagy, golgiphagy, lysophagy, pexophagy, nucleophagy, and ribophagy) and the underlying mechanisms by which organellophagy regulates cell death. KEY SCIENTIFIC CONCEPTS OF REVIEW: We outlined the potential role of organellophagy in the modulation of cell fate during the inflammatory response to develop an intervention strategy for the organelle quality control in inflammatory diseases.

Pd(II)-Catalyzed Desymmetrizing gem-Dimethyl C(sp3)-H Alkenylation/Aza-Wacker Cyclization Directed by PIP Auxiliary.

Desymmetrization of gem-dimethyl groups has been developed as an efficient pathway to achieve asymmetric C(sp3)-H functionalization. Herein, we described a Pd(II)-catalyzed desymmetrizing gem-dimethyl C(sp3)-H alkenylation/aza-Wacker cyclization directed by a bidentate 2-pyridinylisopropyl auxiliary. Chiral α-methyl γ-lactams were obtained in good yields (up to 82%) and high enantioselectivities (up to 91.5% ee).

Research Progress of Long Non-Coding RNA in Tumor Drug Resistance: A New Paradigm.

In the past few decades, chemotherapy has been one of the most effective cancer treatment options. Drug resistance is currently one of the greatest obstacles to effective cancer treatment. Even though drug resistance mechanisms have been extensively investigated, they have not been fully elucidated. Recent genome-wide investigations have revealed the existence of a substantial quantity of long non-coding RNAs (lncRNAs) transcribed from the human genome, which actively participate in numerous biological processes, such as transcription, splicing, epigenetics, the cell cycle, cell differentiation, development, pluripotency, immune microenvironment. The abnormal expression of lncRNA is considered a contributing factor to the drug resistance. Furthermore, drug resistance may be influenced by genetic and epigenetic variations, as well as individual differences in patient treatment response, attributable to polymorphisms in metabolic enzyme genes. This review focuses on the mechanism of lncRNAs resistance to target drugs in the study of tumors with high mortality, aiming to establish a theoretical foundation for targeted therapy.

RIscoper 2.0: A deep learning tool to extract RNA biomedical relation sentences from literature.

RNA plays an extensive role in a multi-dimensional regulatory system, and its biomedical relationships are scattered across numerous biological studies. However, text mining works dedicated to the extraction of RNA biomedical relations remain limited. In this study, we established a comprehensive and reliable corpus of RNA biomedical relations, recruiting over 30,000 sentences manually curated from more than 15,000 biomedical literature. We also updated RIscoper 2.0, a BERT-based deep learning tool to extract RNA biomedical relation sentences from literature. Benefiting from approximately 100,000 annotated named entities, we integrated the text classification and named entity recognition tasks in this tool. Additionally, RIscoper 2.0 outperformed the original tool in both tasks and can discover new RNA biomedical relations. Additionally, we provided a user-friendly online search tool that enables rapid scanning of RNA biomedical relationships using local and online resources. Both the online tools and data resources of RIscoper 2.0 are available at http://www.rnainter.org/riscoper.

Comprehensive Analysis of PKD1 and PKD2 by Long-Read Sequencing in Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is mainly caused by heterogeneous variants in the PKD1 and PKD2 genes. Genetic analysis of PKD1 has been challenging due to homology with 6 PKD1 pseudogenes and high GC content. METHODS: A single-tube multiplex long-range-PCR and long-read sequencing-based assay termed "comprehensive analysis of ADPKD" (CAPKD) was developed and evaluated in 170 unrelated patients by comparing to control methods including next-generation sequencing (NGS) and multiplex ligation-dependent probe amplification. RESULTS: CAPKD achieved highly specific analysis of PKD1 with a residual noise ratio of 0.05% for the 6 pseudogenes combined. CAPKD identified PKD1 and PKD2 variants (ranging from variants of uncertain significance to pathogenic) in 160 out of the 170 patients, including 151 single-nucleotide variants (SNVs) and insertion-deletion variants (indels), 6 large deletions, and one large duplication. Compared to NGS, CAPKD additionally identified 2 PKD1 variants (c.78_96dup and c.10729_10732dup). Overall, CAPKD increased the rate of variant detection from 92.9% (158/170) to 94.1% (160/170), and the rate of diagnosis with pathogenic or likely pathogenic variants from 82.4% (140/170) to 83.5% (142/170). CAPKD also directly determined the cis-/trans-configurations in 11 samples with 2 or 3 SNVs/indels, and the breakpoints of 6 large deletions and one large duplication, including 2 breakpoints in the intron 21 AG-repeat of PKD1, which could only be correctly characterized by aligning to T2T-CHM13. CONCLUSIONS: CAPKD represents a comprehensive and specific assay toward full characterization of PKD1 and PKD2 variants, and improves the genetic diagnosis for ADPKD.

Amide Proton Transfer-Weighted Magnetic Resonance Imaging for Application in Renal Fibrosis: A Radiological-Pathological-Based Analysis.

INTRODUCTION: Renal fibrosis (RF), being the most important pathological change in the progression of CKD, is currently assessed by the evaluation of a biopsy. This present study aimed to apply a novel functional MRI (fMRI) protocol named amide proton transfer (APT) weighting to evaluate RF noninvasively. METHODS: Male Sprague-Dawley (SD) rats were initially subjected to bilateral kidney ischemia/reperfusion injury (IRI), unilateral ureteral obstruction, and sham operation, respectively. All rats underwent APT mapping on the 7th and 14th days after operation. Besides, 26 patients underwent renal biopsy at the Nephrology Department of Shanghai Tongji Hospital between July 2022 and May 2023. Patients underwent APT and apparent diffusion coefficient (ADC) mappings within 1 week before biopsy. MRI results of both patients and rats were calculated by comparing with gold standard histology for fibrosis assessment. RESULTS: In animal models, the cortical APT (cAPT) and medullary APT (mAPT) values were positively correlated with the degree of RF. Compared to the sham group, IRI group showed significantly increased cAPT and mAPT values on the 7th and 14th days after surgery, but no group differences were found in ADC values. Similar results were found in human patients. Cortical/medullary APT values were significantly increased in patients with moderate-to-severe fibrosis than in patients with mild fibrosis. ROC curve analysis indicated that APT value displayed a better diagnostic value for RF. Furthermore, combination of cADC and cAPT improved fibrosis detection by imaging variables alone (p < 0.1). CONCLUSION: APT values had better diagnostic capability at early stage of RF compared to ADC values, and the addition of APT imaging to conventional ADC will significantly improve the diagnostic performance for predicting kidney fibrosis.

IFM calculator: An algorithm for interfragmentary motion calculation in finite element analysis.

BACKGROUND: Interfragmentary motion (IFM) is a complex state that significantly impacts the healing process of fractures following implant placement. It is crucial to fully consider the IFM state after implantation in the design and biomechanical testing of implants. However, current finite element analysis software lacks direct tools for calculating IFM, and existing IFM tools do not offer a comprehensive solution in terms of accuracy, functionality, and visualization. METHODS: In our study, we developed a Python-based algorithm for calculating IFM that addresses limitations. Our algorithm automatically calculated IFM distances, sliding distances, gaps, as well as the angles and rotation of the two fracture surfaces using all nodes on both sides of the fracture ends. Researchers could input data and selected desired parameters in the interface. The algorithm then performed the necessary calculations and presented the results in a clear and concise manner. The algorithm also provided comprehensive data export capabilities, allowing researchers to customize analyses based on specific needs.To provide a more intuitive demonstration of the calculation process and usage of IFM-Cal, we conducted simulations in Ansys using two rectangular blocks to compare the accuracy and function of three different methods (Point based method, contact tool and IFM-Cal). RESULTS: The point-based method and the contact tool could not accurately calculate IFA, while IFM-Cal could provide a comprehensive evaluation of IFA. In simulation 1, the IFM distances calculated using the point sampling method, contact tool, and IFM-Cal were 2.00 mm, 3.15 mm, and 2.00 mm, respectively. In simulation 2, both the point sampling method and contact tool failed to calculate the interfragmentary angle (IFA), while the IFM-Cal algorithm estimated an angle of -7.87°, which had a small error compared to the ground-truth value of 7.9°. CONCLUSION: We have developed an algorithm for computing IFM which can be utilized in finite element analysis and biomechanical experiments. By conducting comparative simulations with other existing algorithms, we have demonstrated the superior accuracy and expanded evaluation capabilities of our algorithm.

Transformation of 2D Flakes to 3D Hollow Bowls: Matthew Effect Enables Defects to Prevail in Electromagnetic Wave Absorption of Hollow rGO Bowls.

High-efficiency electromagnetic (EM) wave (EMW)-absorbing materials have attracted extensive scientific and technical interest. Although identifying the dominant EM loss mechanism in dielectric-loss materials is indispensable, it is challenging due to a complex synergism between dipole/interfacial polarization and conduction loss. Modulation of defects and microstructures can be a possible approach to determine the dominant EM loss mechanism and realize high-efficiency absorption. Herein, 2D reduced graphene oxide (rGO) flakes are integrated into a 3D hollow bowl-like structure, which increases defect sites (i.e., oxygen vacancy and lattice defect) and reduces the stacked thickness of rGO. Despite their lower stacked thicknesses, the hollow rGO bowls with more defects exhibit lower conductivities but higher permittivities. Accompanied by the transformation from 2D flakes to 3D hollow bowls, the dominant EM loss mechanism of rGO transforms from conduction loss to defect-induced polarization. Furthermore, the defect engineering and structural design endow rGO with well-matched impedance and strong EMW-absorbing capacity. A minimum reflection loss of -41.6 dB (1.3 mm) and an effective absorption bandwidth of 4.8 GHz (1.5 mm) is achieved at a filler loading of 5 wt%. This study will provide meaningful insights into the development of materials with superior EMW-absorbing performances via defect engineering and structural design.

Techno-economic analysis of municipal solid waste treatment for poly-generation system.

Municipal solid waste (MSW) is characterized by complex composition, low calorific value and high moisture content. Using a single treatment technology is costly and difficult to achieve optimal results. A poly-generation system that integrates classified waste pyrolysis and incineration is proposed, producing fuel, electricity, and steam. The system has been designed and optimized to find the optimal feed ratio and process model. However, the economic performance of the poly-generation system is still unclear. In this work, a techno-economic analysis (TEA) was conducted to evaluate the economic feasibility of the proposed MSW treatment poly-generation system. The annual electricity generated by the poly-generation system is 104.13 GWh. The annual steam generated is 4.91 kt, and the average annual diesel produced is 1.60 kt. The TEA results showed that the total capital investment of the system was 602.07 M¥, and the annual operating cost was 40.63 M¥. The net present value was 93.01 M¥, which is a positive value. The internal rate of return was 10.28 %, which is higher than the benchmark rate of return, indicating that the system is economically feasible. Sensitivity analysis indicated that the internal rate of return was extremely sensitive to fixed capital investment, price of electricity, product yield, and government subsidy. The TEA analysis of poly-generation system provides a fundamental theoretical basis for the feasibility of classified MSW treatment and offers valuable insights for policy makers and investors in the waste management field. Considering the complexity of the waste, future research can be carried out from the environmental aspect, combining both economic and environmental objectives for multi-objective optimization and conducting comprehensive evaluation of the industrial practicability of the poly-generation system.

The Research Progress in Transforming Growth Factor-ß2.

Transforming growth factor-beta 2 (TGF-ß2), an important member of the TGF-ß family, is a secreted protein that is involved in many biological processes, such as cell growth, proliferation, migration, and differentiation. TGF-ß2 had been thought to be functionally identical to TGF-ß1; however, an increasing number of recent studies uncovered the distinctive features of TGF-ß2 in terms of its expression, activation, and biological functions. Mice deficient in TGF-ß2 showed remarkable developmental abnormalities in multiple organs, especially the cardiovascular system. Dysregulation of TGF-ß2 signalling was associated with tumorigenesis, eye diseases, cardiovascular diseases, immune disorders, as well as motor system diseases. Here, we provide a comprehensive review of the research progress in TGF-ß2 to support further research on TGF-ß2.

[Spatio-temporal Characteristics and Influencing Factors of Ozone Suppression Events Under High Temperature in China].

Temperature is a key meteorological factor affecting ozone formation. In general, a positive correlation is observed between ozone and temperature, that is, ozone concentration increases with the increase in temperature. However, this relationship may change at extremely high temperatures. When the temperature exceeds a threshold value, the ozone concentration tends to decrease, which is referred to as an ozone suppression event. Ozone suppression events lead to greater uncertainties in the prediction of future air quality under climate change. Based on the national air quality monitoring data, reanalysis data, and meteorological observation data, this study used the Z test to systematically analyze the spatio-temporal characteristics of the critical temperature(Tx) and frequency of ozone suppression events in China during the warm season(April to September) from 2013 to 2020 and further analyzed the possible influencing factors for the occurrence of ozone suppression events. The results showed that approximately 18% of the sites in China experienced ozone suppression events in the warm season from 2013 to 2020. The sites with a high frequency of ozone suppression events were mainly distributed in the central and western regions of China, such as Sichuan, Xinjiang, and Shaanxi, with an average frequency of ten times per year. The critical temperature(Tx) ranged from 19.2 to 39.3℃, and the Tx of most sites showed an increasing trend from 2013 to 2020. The high values of Tx were mainly distributed in the central and western regions such as Sichuan, Chongqing, Hunan, and Hubei, whereas the low values of Tx were concentrated in the Qinghai-Tibet Plateau. Contrary to the interannual trend of Tx, the frequency of ozone suppression events decreased significantly in the Beijing-Tianjin-Hebei Region and exhibited a characteristic of "increase-decrease-increase" in the Fenwei Plain, the Yangtze River Delta, and the Chengdu-Chongqing regions. The most significant effect of extreme high temperature on ozone suppresion was found in the Pearl River Delta Region. In addition, ozone precursors(e.g., NO2) and meteorological conditions(wind speed and direction) were possible factors affecting the occurrence of ozone suppression events.

[Impact of Accelerated Electrification Under the Low Carbon Path in Dongguan City on the Coordinated Emission Reduction of CO2 and Pollutants].

Cities are the center of energy consumption. Electrification integrates urban energy structure and achieves the efficient use of clean energy. Exploring the urban impact of accelerated electrification under the low-carbon path is crucial to reducing urban pollution and carbon. Based on the Long-range Energy Alternative Planning System(LEAP-DG), this study set up three scenarios, including the baseline, low-carbon, and accelerated electrification scenarios, to evaluate the emission reduction potential of electrification under different power structures, quantify the contribution of key sectors, and discuss the coordinated emission reduction effect of Dongguan, a typical manufacturing city in Guangdong. The results showed that accelerated electrification under the low-carbon path would reduce the emission intensity of power pollutants, and in 2050, Dongguan will further reduce CO2, NOx, VOC, and CO by 7.35×106, 1.28×104, 1.62×104, and 8.13×104 t; SO2 and PM2.5 emission reductions on the consumption side and increased emissions on the production side had been balanced. Accelerated electrification in the industrial and transportation sectors would reduce CO2 and air pollutant emissions at the same time, and the transportation sector would benefit from the high conversion efficiency of fuel vehicles and electric vehicles, reducing CO2, CO, VOC, and NOx by 5.42×106, 7.76×104, 1.43×104, and 1.06×104 t, respectively, in 2050. In the building sector with high electrification rates, coal power was higher in extra electricity, increasing CO2 and pollutant emissions. Under the optimization of power supply structure, cities can reasonably adjust the electrification of different departments to achieve targeted pollution prevention and control.

Novel Information-Driven Smoothing Spline Linearization Method for High-Precision Displacement Sensors Based on Information Criterions.

A noise-resistant linearization model that reveals the true nonlinearity of the sensor is essential for retrieving accurate physical displacement from the signals captured by sensing electronics. In this paper, we propose a novel information-driven smoothing spline linearization method, which innovatively integrates one new and three standard information criterions into a smoothing spline for the high-precision displacement sensors' linearization. Using theoretical analysis and Monte Carlo simulation, the proposed linearization method is demonstrated to outperform traditional polynomial and spline linearization methods for high-precision displacement sensors with a low noise to range ratio in the 10-5 level. Validation experiments were carried out on two different types of displacement sensors to benchmark the performance of the proposed method compared to the polynomial models and the the non-smoothing cubic spline. The results show that the proposed method with the new modified Akaike Information Criterion stands out compared to the other linearization methods and can improve the residual nonlinearity by over 50% compared to the standard polynomial model. After being linearized via the proposed method, the residual nonlinearities reach as low as ±0.0311% F.S. (Full Scale of Range), for the 1.5 mm range chromatic confocal displacement sensor, and ±0.0047% F.S., for the 100 mm range laser triangulation displacement sensor.

Alignment-Enhanced Interactive Fusion Model for Complete and Incomplete Multimodal Hand Gesture Recognition.

Hand gesture recognition (HGR) based on surface electromyogram (sEMG) and Accelerometer (ACC) signals is increasingly attractive where fusion strategies are crucial for performance and remain challenging. Currently, neural network-based fusion methods have gained superior performance. Nevertheless, these methods typically fuse sEMG and ACC either in the early or late stages, overlooking the integration of entire cross-modal hierarchical information within each individual hidden layer, thus inducing inefficient inter-modal fusion. To this end, we propose a novel Alignment-Enhanced Interactive Fusion (AiFusion) model, which achieves effective fusion via a progressive hierarchical fusion strategy. Notably, AiFusion can flexibly perform both complete and incomplete multimodal HGR. Specifically, AiFusion contains two unimodal branches and a cascaded transformer-based multimodal fusion branch. The fusion branch is first designed to adequately characterize modality-interactive knowledge by adaptively capturing inter-modal similarity and fusing hierarchical features from all branches layer by layer. Then, the modality-interactive knowledge is aligned with that of unimodality using cross-modal supervised contrastive learning and online distillation from embedding and probability spaces respectively. These alignments further promote fusion quality and refine modality-specific representations. Finally, the recognition outcomes are set to be determined by available modalities, thus contributing to handling the incomplete multimodal HGR problem, which is frequently encountered in real-world scenarios. Experimental results on five public datasets demonstrate that AiFusion outperforms most state-of-the-art benchmarks in complete multimodal HGR. Impressively, it also surpasses the unimodal baselines in the challenging incomplete multimodal HGR. The proposed AiFusion provides a promising solution to realize effective and robust multimodal HGR-based interfaces.

Acupuncture Relieves Cervical Spondylosis Radiculopathy by Regulating Spinal Microglia Activation Through MAPK Signaling Pathway in Rats.

Purpose: Local acupuncture has been found to have a good analgesic effect in rats with cervical spondylosis radiculopathy (CSR), but it lacks a regulatory effect on traditional Chinese medicine syndrome types of CSR. We proposed "Invigorating Qi and activating Blood" (IQAB) acupuncture, compared with Fenbid, and local electroacupuncture (LEA), to observe whether it has advantages in the protection of the CSR rat model and to elucidate its mechanism through the MAPK (mitogen-activated protein kinase) signaling pathway. Materials and Methods: Male Sprague-Dawley rats were randomly divided into six groups: control, sham, model, Fenbid, LEA, and IQAB. The CSR model was induced by inserting nylon sutures to compress the C4-T1 nerve root. The Fenbid group was treated with ibuprofen sustained-release capsules (15 mg/kg·d, ig). The LEA group received electroacupuncture at both C5 and C7 EX-B2 once a day. The IQAB group received acupuncture at both ST36 and BL17 based on the LEA group's intervention. Mechanical allodynia and gait, morphological changes in the spinal cord, IL-6 and TNF-α levels, MAPKs phosphorylation ratio, monocyte chemoattractant protein-1 (MCP-1) levels in the spinal cord, and the expression of p-p38 in the spinal cord and its colocalization with neurons and glial cell activation markers were detected. Results: Mechanical allodynia, gait disorder, edema, reduced Nissl-positive cell numbers, and increased IL-6 and TNF-α levels in the spinal cord were observed in CSR rats. IQAB significantly alleviated these changes, and the effects were generally comparable to those of Fenbid. Meanwhile, the phosphorylation ratios of p38 and extracellular regulated protein kinase (ERK), co-expression of p-p38 with neuron/microglia, and MCP-1 levels in the spinal cord were markedly down-regulated by IQAB compared with those in CSR model rats. Conclusion: IQAB reduced p38-activation-related microglia activation and MCP-1 levels, thus alleviating pathological changes, inflammation levels in the local spinal cord, and pain behavior of CSR.

Safety and immunogenicity of the COVID-19 mRNA vaccine CS-2034: A randomized, double-blind, dose-exploration, placebo-controlled multicenter Phase I clinical trial in healthy Chinese adults.

BACKGROUND: The novel coronavirus pneumonia (COVID-19) is an infectious disease caused by the infection of a novel coronavirus known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which has resulted in millions of deaths. We aimed to evaluate the safety and immunogenicity of the COVID-19 mRNA vaccine (CS-2034, CanSino, Shanghai, China) in adults without COVID-19 infection from China. METHOD: This is a multicenter Phase I clinical trial with a randomized, double-blinded, dose-exploration, placebo-controlled design. The trial recruited 40 seronegative participants aged 18-59 years who had neither received any COVID-19 vaccine nor been infected before. They were divided into a low-dose group (administered with either the CS-2034 vaccine containing 30 µg of mRNA or a placebo of 0.3 ml type 5 adenovirus vector) and a high-dose group (administered with either the CS-2034 vaccine containing 50 µg of mRNA or a placebo of 0.5 ml type 5 adenovirus vector). Participants were randomly assigned in a 3:1 ratio to receive either the mRNA vaccine or a placebo on days 0 and 21 according to a two-dose immunization schedule. The first six participants in each dosage group were assigned as sentinel subjects. Participants were sequentially enrolled in a dose-escalation manner from low to high dose and from sentinel to non-sentinel subjects. Blood samples were collected from all participants on the day before the first dose (Day 0), the day before the second dose (day 21), 14 days after the second dose (day 35), and 28 days after the second dose (day 49) to evaluate the immunogenicity of the CS-2034 vaccine. Participants were monitored for safety throughout the 28-day follow-up period, including solicited adverse events, unsolicited adverse events, adverse events of special interest (AESI), and medically attended adverse events (MAE). This report focuses solely on the safety and immunogenicity analysis of adult participants aged 18-59 years, while the long-term phase of the study is still ongoing. This study is registered at ClinicalTrials.gov, NCT05373485. FINDINGS: During the period from May 17, 2022, to August 8, 2022, a total of 155 participants aged 18-59 years were screened for this study. Among them, 115 participants failed the screening process, and 40 participants were randomly enrolled (15 in the low-dose group, 15 in the high-dose group, and 10 in the placebo group). Throughout the 28-day follow-up period, the overall incidence of adverse reactions (related to vaccine administration) in the low-dose group, high-dose group, and placebo group was 93.33% (14/15), 100.00% (15/15), and 80.00% (8/10), respectively. There was a statistically significant difference in the incidence of local adverse reactions (soreness, pruritus, swelling at the injection site) among the low-dose group, high-dose group, and placebo group (P = 0.002). All adverse reactions were mainly of severity grade 1 (mild) or 2 (moderate), and no adverse events of severity grade 4 or higher occurred. Based on the analysis of Spike protein Receptor Binding Domain (S-RBD) IgG antibodies against the BA.1 strain, the seroconversion rates of antibodies at day 21 after the first dose were 86.67%, 93.33%, and 0.00% in the low-dose group, high-dose group, and placebo group, respectively. The geometric mean titer (GMT) of antibodies was 61.2(95%CI 35.3-106.2), 55.4(95%CI 36.3-84.4), and 15.0(95%CI 15.0-15.0), and the geometric mean fold increase (GMI) was 4.08(95%CI 2.35-7.08), 3.69(95%CI 2.42-5.63), and 1.00(95%CI 1.00-1.00) for each group. At day 28 after the full vaccination, the seroconversion rates of antibodies were 100.00%, 93.33%, and 0.00%, and the GMT of antibodies was 810.0(95%CI 511.4-1283.0), 832.2(95%CI 368.1-1881.6), and 15.0(95%CI 15.0-15.0), and the GMI was 54.00(95%CI 34.09-85.53), 55.48(95%CI 24.54-125.44), and 1.00(95%CI 1.00-1.00) for each group, respectively. Based on the analysis of CD3+/CD4+ cell cytokine response, the percentages of IL-2+, IL-4+, IFN-γ+, and TNF-α+ cells increased after 14 days and 28 days of full vaccination in both the low-dose group and high-dose group. The increase was most pronounced in the high-dose group. INTERPRETATION: At day 28 after the full vaccination, both the low-dose and the high-dose CS-2034 vaccine were able to induce the production of high titers of S-RBD IgG antibodies against the BA.1 strain. Adverse reactions in the low-dose and high-dose groups were mainly of severity grade 1 or 2, and no trial-limiting safety concerns were identified. These findings support further development of this vaccine.

Improving Generalized Zero-Shot Learning SSVEP Classification Performance From Data-Efficient Perspective.

Generalized zero-shot learning (GZSL) has significantly reduced the training requirements for steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). Traditional methods require complete class data sets for training, but GZSL allows for only partial class data sets, dividing them into 'seen' (those with training data) and 'unseen' classes (those without training data). However, inefficient utilization of SSVEP data limits the accuracy and information transfer rate (ITR) of existing GZSL methods. To this end, we proposed a framework for more effective utilization of SSVEP data at three systematically combined levels: data acquisition, feature extraction, and decision-making. First, prevalent SSVEP-based BCIs overlook the inter-subject variance in visual latency and employ fixed sampling starting time (SST). We introduced a dynamic sampling starting time (DSST) strategy at the data acquisition level. This strategy uses the classification results on the validation set to find the optimal sampling starting time (OSST) for each subject. In addition, we developed a Transformer structure to capture the global information of input data for compensating the small receptive field of existing networks. The global receptive fields of the Transformer can adequately process the information from longer input sequences. For the decision-making level, we designed a classifier selection strategy that can automatically select the optimal classifier for the seen and unseen classes, respectively. We also proposed a training procedure to make the above solutions in conjunction with each other. Our method was validated on three public datasets and outperformed the state-of-the-art (SOTA) methods. Crucially, we also outperformed the representative methods that require training data for all classes.