Layered double hydroxide-based electrode materials derived from metal-organic frameworks: synthesis and applications in supercapacitors.

Metal-organic frameworks (MOFs) have emerged as promising electrode materials for supercapacitors (SCs) due to their highly porous structures, tunable chemical compositions, and diverse morphologies. However, their applications are hindered by low conductivity and poor cycling performance. A novel approach for resolving this issue involves the growth of layered double hydroxides (LDHs) using MOFs as efficient templates or precursors for electrode material preparation. This method effectively enhances the stability, electrical conductivity, and mass transport ability of MOFs. The MOF-derived LDH exhibits a well-defined porous micro-/nano-structure, facilitating the dispersion of active sites and preventing the aggregation of LDHs. Firstly, this paper introduces synthesis strategies for converting MOFs into LDHs. Subsequently, recent research progress in MOF-derived LDHs encompassing pristine LDH powders, LDH composites, and LDH-based arrays, along with their applications in SCs, is overviewed. Finally, the challenges associated with MOF-derived LDH electrode materials and potential solutions are discussed.

High pressure-sensitive and stable fiber Fabry-Perot interferometer with nano-diaphragm assembled by H-O catalysis bonding.

Herein, a high pressure-sensitive and stable fiber Fabry-Perot (FP) interferometer with nano-diaphragm assembled by H-O catalysis bonding is proposed and demonstrated. In order to assemble a nano-diaphragm-based fiber FP interferometer by H-O catalysis bonding technique, a SiO2 film, introduced as a bridging layer on the nano-diaphragm, can be regarded as a solid adhesive to bridge hollow-core fiber end-face and nano-diaphragm. As thus, by depositing bonded layers on different diaphragm materials, this H-O catalysis bonding technology can be used to for assembling FP interferometer with different materials nano-diaphragms. Experimentally, Si nano-diaphragm is transferred to hollow-core fiber end-face to build a stable fiber FP interferometer without polymeric adhesive. Experimental results reveal that this Si nano-diaphragm-based fiber FP interferometer has a high (79.6 pm/kPa) pressure sensitivity and a low (17.3 pm/°C) temperature sensitivity. Besides that, different materials nano-diaphragm also can be assembled by using this H-O catalysis bonding technique, and the functional FP interferometer can be realized by using functional nano-diaphragm material. Thus, a Pd nano-diaphragm is successfully assembled to build a FP interferometer with a hydrogen concentration measurement capacity. Further investigation will focus on exploitation of multi-material nano-film patterning transfer and different nano-film integration by using this H-O catalysis bonding transfer.

Microstructure and Mechanical Properties of Refill Friction Stir Spot-Welded Joints of 2A12Al and 7B04Al: Effects of Tool Size and Welding Parameters.

To solve problems in dissimilarly light metal joints, refilled friction stir spot welding (RFSSW) is proposed instead of resistance spot welding. However, rotation speed, dwell time, plunge depth, and the diameter of welding tools all have a great influence on joints, which brings great challenges in optimizing welding parameters to ensure their mechanical properties. In this study, the 1.5 mm thick 2A12Al and 2 mm thick 7B04Al lap joints were prepared by Taguchi orthogonal experiment design and RFSSW. The welding tool (shoulder) diameters were 5 mm and 7 mm, respectively. The macro/microstructures of the cross-section, the geometrical characteristics of the effective welding depth (EWD), the stir zone area (SZA), and the stir zone volume (SZV) were characterized. The shear strength and failure mode of the lap joint were analyzed using an optical microscope. It was found that EWD, SZA, and SZV had a good correlation with tensile-shear force. The optimal welding parameters of 5 mm diameter joints are 1500 rpm of rotation speed, 2.5 mm of plunge depth, and 0 s of dwell time, which for 7 mm joints are 1200 rpm, 1.5 mm, and 2 s. The tensile-shear force of 5 mm and 7 mm joints welded with these optical parameters was 4965 N and 5920 N, respectively. At the same time, the 5 mm diameter joints had better strength and strength stability.

Study on the solid-liquid equilibrium and thermodynamic model of the L-phenylalanine + L-tryptophan + water system.

Amino acids hold significant importance in the diagnosis and treatment of various chronic diseases. Accurate solid-liquid equilibrium data are the key to drug synthesis and chemical production. However, the studies on the solid-liquid equilibrium of amino acids remain limited. In this work, the solid-liquid equilibrium of the L-tryptophan + L-phenylalanine + water ternary system under atmospheric pressure at temperatures of 278.15 K-318.15 K was explored via isothermal solution saturation. The isothermal equilibrium phase diagram of the ternary system was constructed. The obtained solid-liquid equilibrium data were correlated with a semi-empirical-model, yielding thermodynamic parameters pa, pb, pc, and k. Furthermore, the model can be used to effectively predict the solid-liquid equilibrium data of ternary systems at other temperatures, and the dY and dP are 0.005 and 4.34%, respectively. The solid-liquid equilibrium data and ternary equilibrium phase diagrams of the system were utilized for the separation and purification of an L-tryptophan and L-phenylalanine mixture. By employing thermodynamic models to calculate the relevant phase diagram data for mixtures with different proportions, effective separation and purification of the mixture could be achieved using the principles of variable temperature phase diagrams. These works are valuable for optimizing chemical processes and have practical implications in the field.

Enhanced matrix inference with Seq2seq models via diagonal sorting.

The effectiveness of sequence-to-sequence (seq2seq) models in natural language processing has been well-established over time, and recent studies have extended their utility by treating mathematical computing tasks as instances of machine translation and achieving remarkable results. However, our exploratory experiments have revealed that the seq2seq model, when employing a generic sorting strategy, is incapable of inferring on matrices of unseen rank, resulting in suboptimal performance. This paper aims to address this limitation by focusing on the matrix-to-sequence process and proposing a novel diagonal-based sorting. The method constructs a stable ordering structure of elements for the shared leading principal submatrix sections in matrices with varying ranks. We conduct experiments involving maximal independent sets and Sudoku laws, comparing seq2seq models utilizing different sorting methods. Our findings demonstrate the advantages of the proposed diagonal-based sorting in inference, particularly when dealing with matrices of unseen ranks. By introducing and advocating for this method, we enhance the suitability of seq2seq models for investigating the laws of matrix inclusion and exploring their potential in solving matrix-related tasks.

Mice with type I interferon signaling deficiency are prone to epilepsy upon HSV-1 infection.

Viral encephalitis continues to be a significant public health concern. In our previous study, we discovered a lower expression of antiviral factors, such as IFN-ß, STING and IFI16, in the brain tissues of patients with Rasmussen's encephalitis (RE), a rare chronic neurological disorder often occurred in children, characterized by unihemispheric brain atrophy. Furthermore, a higher cumulative viral score of human herpes viruses (HHVs) was also found to have a significant positive correlation with the unihemispheric atrophy in RE. Type I IFNs (IFN-I) signaling is essential for innate anti-infection response by binding to IFN-α/ß receptor (IFNAR). In this study, we infected WT mice and IFNAR-deficient A6 mice with herpes simplex virus 1 (HSV-1) via periocular injection to investigate the relationship between IFN-I signaling and HHVs-induced brain lesions. While all mice exhibited typical viral encephalitis lesions in their brains, HSV-induced epilepsy was only observed in A6 mice. The gene expression matrix, functional enrichment analysis and protein-protein interaction network revealed four gene models that were positively related with HSV-induced epilepsy. Additionally, ten key genes with the highest scores were identified. Taken together, these findings indicate that intact IFN-I signaling can effectively limit HHVs induced neural symptoms and brain lesions, thereby confirming the positive correlation between IFN-I signaling repression and brain atrophy in RE and other HHVs encephalitis.

Recent Advances on F-Doped Layered Transition Metal Oxides for Sodium Ion Batteries.

With the development of social economy, using lithium-ion batteries in energy storage in industries such as large-scale electrochemical energy storage systems will cause lithium resources to no longer meet demand. As such, sodium ion batteries have become one of the effective alternatives to LIBs. Many attempts have been carried out by researchers to achieve this, among which F-doping is widely used to enhance the electrochemical performance of SIBs. In this paper, we reviewed several types of transition metal oxide cathode materials, and found their electrochemical properties were significantly improved by F-doping. Moreover, the modification mechanism of F-doping has also been summed up. Therefore, the application and commercialization of SIBs in the future is summarized in the ending of the review.

Identifying Minimal Hepatic Encephalopathy: A New Perspective from Magnetic Resonance Imaging.

Type C hepatic encephalopathy (HE) is a condition characterized by brain dysfunction caused by liver insufficiency and/or portal-systemic blood shunting, which manifests as a broad spectrum of neurological or psychiatric abnormalities, ranging from minimal HE (MHE), detectable only by neuropsychological or neurophysiological assessment, to coma. Though MHE is the subclinical phase of HE, it is highly prevalent in cirrhotic patients and strongly associated with poor quality of life, high risk of overt HE, and mortality. It is, therefore, critical to identify MHE at the earliest and timely intervene, thereby minimizing the subsequent complications and costs. However, proper and sensitive diagnosis of MHE is hampered by its unnoticeable symptoms and the absence of standard diagnostic criteria. A variety of neuropsychological or neurophysiological tests have been performed to diagnose MHE. However, these tests are nonspecific and susceptible to multiple factors (eg, aging, education), thereby limiting their application in clinical practice. Thus, developing an objective, effective, and noninvasive method is imperative to help detect MHE. Magnetic resonance imaging (MRI), a noninvasive technique which can produce many objective biomarkers by different imaging sequences (eg, Magnetic resonance spectroscopy, DWI, rs-MRI, and arterial spin labeling), has recently shown the ability to screen MHE from NHE (non-HE) patients accurately. As advanced MRI techniques continue to emerge, more minor changes in the brain could be captured, providing new means for early diagnosis and quantitative assessment of MHE. In addition, the advancement of artificial intelligence in medical imaging also presents the potential to mine more effective diagnostic biomarkers and further improves the predictive efficiency of MHE. Taken together, advanced MRI techniques may provide a new perspective for us to identify MHE in the future. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Analyzing the Robustness of Complex Networks with Attack Success Rate.

Analyzing the robustness of networks against random failures or malicious attacks is a critical research issue in network science, as it contributes to enhancing the robustness of beneficial networks and effectively dismantling harmful ones. Most studies commonly neglect the impact of the attack success rate (ASR) and assume that attacks on the network will always be successful. However, in real-world scenarios, attacks may not always succeed. This paper proposes a novel robustness measure called Robustness-ASR (RASR), which utilizes mathematical expectations to assess network robustness when considering the ASR of each node. To efficiently compute the RASR for large-scale networks, a parallel algorithm named PRQMC is presented, which leverages randomized quasi-Monte Carlo integration to approximate the RASR with a faster convergence rate. Additionally, a new attack strategy named HBnnsAGP is introduced to better assess the lower bound of network RASR. Finally, the experimental results on six representative real-world complex networks demonstrate the effectiveness of the proposed methods compared with the state-of-the-art baselines.

Computed Tomography-derived intratumoral and peritumoral radiomics in predicting EGFR mutation in lung adenocarcinoma.

OBJECTIVE: To investigate the value of Computed Tomography (CT) radiomics derived from different peritumoral volumes of interest (VOIs) in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients. MATERIALS AND METHODS: A retrospective cohort of 779 patients who had pathologically confirmed lung adenocarcinoma were enrolled. 640 patients were randomly divided into a training set, a validation set, and an internal testing set (3:1:1), and the remaining 139 patients were defined as an external testing set. The intratumoral VOI (VOI_I) was manually delineated on the thin-slice CT images, and seven peritumoral VOIs (VOI_P) were automatically generated with 1, 2, 3, 4, 5, 10, and 15 mm expansion along the VOI_I. 1454 radiomic features were extracted from each VOI. The t-test, the least absolute shrinkage and selection operator (LASSO), and the minimum redundancy maximum relevance (mRMR) algorithm were used for feature selection, followed by the construction of radiomics models (VOI_I model, VOI_P model and combined model). The performance of the models were evaluated by the area under the curve (AUC). RESULTS: 399 patients were classified as EGFR mutant (EGFR+), while 380 were wild-type (EGFR-). In the training and validation sets, internal and external testing sets, VOI4 (intratumoral and peritumoral 4 mm) model achieved the best predictive performance, with AUCs of 0.877, 0.727, and 0.701, respectively, outperforming the VOI_I model (AUCs of 0.728, 0.698, and 0.653, respectively). CONCLUSIONS: Radiomics extracted from peritumoral region can add extra value in predicting EGFR mutation status of lung adenocarcinoma patients, with the optimal peritumoral range of 4 mm.

Optimization of preparation of NaA zeolite from fly ash for CO2 capture.

The green synthesis method of fly ash-based NaA zeolite was explored to reduce the synthesis cost and environmental hazards. For the prepared NaA samples, the effects of crystallization time, solid-liquid ratio, and Si/Al ratio were systematically studied. CO2 adsorption isotherm is used for adsorption model fitting analysis and adsorption selectivity determination. According to the experimental results, the optimized NaA zeolite synthesis conditions are as follows: the Si/Al ratio of NaA zeolite is 1.4, the solid-liquid ratio is 10, and the crystallization time is 6 h. The green synthesis method reported in this study can successfully prepare NaA zeolite and exhibit excellent CO2 adsorption performance, reaching 4.34 mmol/g, with high CO2 selective adsorption ability, reaching 89.2 for N2, 257.1 for O2, and 45.8 for CH4. The adsorbed CO2 can be released for further utilization, and NaA zeolite also has strong adsorption and regeneration performance, with a ten cycle adsorption capacity only decreasing by 1.17%. In addition, the use of cheap raw materials synthesis methods will promote the large-scale industry application of green synthesis technology in the future.

The Effect of Warm Acupuncture on EOS, IgE, Inflammatory Factors, and T Lymphocyte Subsets in Patients with Allergic Rhinitis of Lung Qi Deficiency and Cold-Type.

Objective: The purpose of this study was to investigate the effects of warming needle therapy on eosinophils, specific immunoglobulin E (IgE), inflammatory factors, and T lymphocyte subsets in patients with lung qi deficiency and cold-type allergic rhinitis (AR). Methods: A total of 155 patients with lung qi deficiency and cold-type AR from May 2021 to December 2022 were randomly divided into a study group of 76 cases and a control group of 79 cases. The control group received medication (chlorpheniramine and fluticasone), and the study group received medication combined with warming needle therapy. The efficacy, TCM syndrome score, eosinophils, IgE, inflammatory factors (interleukin-6 (IL-6), IL-8, and tumor necrosis factor-alpha (TNF-α)), T lymphocyte subsets (CD3+, CD4+, and CD8+), and rhinoconjunctivitis quality of life questionnaire (RQLQ) scores were evaluated after 2 weeks of treatment. Results: The total effective rate in the study group was 92.11%, which was higher than that in the control group (77.22%) (P < .05). The TCM syndrome scores of the study group were lower than those of the control group after 1 and 2 weeks of treatment (P < .05). The positive rate of eosinophils in the study group was lower than that in the control group after 1 week (47.37% vs. 64.56%, P < .05) and after 2 weeks (21.05% vs. 37.97%, P < .05) of treatment. The serum levels of specific IgE, IL-6, IL-8, and TNF-α in the study group were lower than those in the control group after 1 and 2 weeks of treatment (P < .05). The peripheral blood levels of CD3+ and CD4+ were higher and the peripheral blood level of CD8+ was lower in the study group than in the control group after 1 and 2 weeks of treatment (P < .05). The RQLQ scores of the study group were lower than those of the control group after 1 and 2 weeks of treatment (P < .05). Conclusion: Warming needle therapy can effectively improve the clinical symptoms of patients with lung qi deficiency and cold-type AR, reduce inflammation, and enhance immune function.

Causal Associations between Functional/Structural Connectivity and Stroke: A Bidirectional Mendelian Randomization Study.

Disruption of brain resting-state networks (RSNs) is known to be related to stroke exposure, but determining causality can be difficult in epidemiological studies. We used data on genetic variants associated with the levels of functional (FC) and structural connectivity (SC) within 7 RSNs identified from a genome-wide association study (GWAS) meta-analysis among 24,336 European ancestries. The data for stroke and its subtypes were obtained from the MEGASTROKE consortium, including up to 520,000 participants. We conducted a two-sample bidirectional Mendelian randomization (MR) study to investigate the causality relationship between FC and SC within 7 RSNs and stroke and its subtypes. The results showed that lower global mean FC and limbic network FC were associated with a higher risk of any ischemic stroke and small vessel stroke separately. Moreover, ventral attention network FC and default mode network SC have a positive causal relationship with the risk of small vessel stroke and large artery stroke, respectively. In the inverse MR analysis, any stroke and large artery stroke were causally related to dorsal attention network FC and somatomotor FC, respectively. The present study provides genetic support that levels of FC or SC within different RSNs have contrasting causal effects on stroke and its subtypes. Moreover, there is a combination of injury and compensatory physiological processes in brain RSNs following a stroke. Further studies are necessary to validate our results and explain the physiological mechanisms.

Deep Learning-Based Modified YOLACT Algorithm on Magnetic Resonance Imaging Images for Screening Common and Difficult Samples of Breast Cancer.

Computer-aided methods have been extensively applied for diagnosing breast lesions with magnetic resonance imaging (MRI), but fully-automatic diagnosis using deep learning is rarely documented. Deep-learning-technology-based artificial intelligence (AI) was used in this work to classify and diagnose breast cancer based on MRI images. Breast cancer MRI images from the Rider Breast MRI public dataset were converted into processable joint photographic expert group (JPG) format images. The location and shape of the lesion area were labeled using the Labelme software. A difficult-sample mining mechanism was introduced to improve the performance of the YOLACT algorithm model as a modified YOLACT algorithm model. Diagnostic efficacy was compared with the Mask R-CNN algorithm model. The deep learning framework was based on PyTorch version 1.0. Four thousand and four hundred labeled data with corresponding lesions were labeled as normal samples, and 1600 images with blurred lesion areas as difficult samples. The modified YOLACT algorithm model achieved higher accuracy and better classification performance than the YOLACT model. The detection accuracy of the modified YOLACT algorithm model with the difficult-sample-mining mechanism is improved by nearly 3% for common and difficult sample images. Compared with Mask R-CNN, it is still faster in running speed, and the difference in recognition accuracy is not obvious. The modified YOLACT algorithm had a classification accuracy of 98.5% for the common sample test set and 93.6% for difficult samples. We constructed a modified YOLACT algorithm model, which is superior to the YOLACT algorithm model in diagnosis and classification accuracy.

The prelimbic cortex regulates itch processing by controlling attentional bias.

Itch is a complex and unpleasant sensory experience. Recent studies have begun to investigate the neural mechanisms underlying the modulation of sensory and emotional components of itch in the brain. However, the key brain regions and neural mechanism involved in modulating the attentional processing of itch remain elusive. Here, we showed that the prelimbic cortex (PrL) is associated with itch processing and that the manipulation of itch-responsive neurons in the PrL significantly disrupted itch-induced scratching. Interestingly, we found that increasing attentional bias toward a distracting stimulus could disturb itch processing. We also demonstrated the existence of a population of attention-related neurons in the PrL that drive attentional bias to regulate itch processing. Importantly, itch-responsive neurons and attention-related neurons significantly overlapped in the PrL and were mutually interchangeable in the regulation of itch processing at the cellular activity level. Our results revealed that the PrL regulates itch processing by controlling attentional bias.

Genes Associated with Altered Brain Structure and Function in Obstructive Sleep Apnea.

Obstructive sleep apnea (OSA) has been widely reported to cause abnormalities in brain structure and function, but the genetic mechanisms behind these changes remain largely unexplored. Our research aims to investigate the relationship between sleep characteristics, cognitive impairments, genetic factors, and brain structure and function in OSA. Using structural and resting-state functional magnetic resonance imaging data, we compared cortical morphology and spontaneous brain activity between 28 patients with moderate-to-severe OSA and 34 healthy controls (HCs) utilizing voxel-based morphology (VBM) and the amplitude of low-frequency fluctuations (ALFF) analyses. In conjunction with the Allen Human Brain Atlas, we used transcriptome-neuroimaging spatial correlation analyses to investigate gene expression patterns associated with changes in gray matter volume (GMV) and ALFF in OSA. Compared to the HCs, the OSA group exhibited increased ALFF values in the left hippocampus (t = 5.294), amygdala (t = 4.176), caudate (t = 4.659), cerebellum (t = 5.896), and decreased ALFF values in the left precuneus (t = -4.776). VBM analysis revealed increased GMV in the right inferior parietal lobe (t = 5.158) in OSA. Additionally, functional enrichment analysis revealed that genes associated with both ALFF and GMV cross-sampling were enriched in gated channel activity and synaptic transmission, glutamatergic synapse, and neuron.

Predicting the risk of nodular thyroid disease in coal miners based on different machine learning models.

Background: Nodular thyroid disease is by far the most common thyroid disease and is closely associated with the development of thyroid cancer. Coal miners with chronic coal dust exposure are at higher risk of developing nodular thyroid disease. There are few studies that use machine learning models to predict the occurrence of nodular thyroid disease in coal miners. The aim of this study was to predict the high risk of nodular thyroid disease in coal miners based on five different Machine learning (ML) models. Methods: This is a retrospective clinical study in which 1,708 coal miners who were examined at the Huaihe Energy Occupational Disease Control Hospital in Anhui Province in April 2021 were selected and their clinical physical examination data, including general information, laboratory tests and imaging findings, were collected. A synthetic minority oversampling technique (SMOTE) was used for sample balancing, and the data set was randomly split into a training and Test dataset in a ratio of 8:2. Lasso regression and correlation heat map were used to screen the predictors of the models, and five ML models, including Extreme Gradient Augmentation (XGBoost), Logistic Classification (LR), Gaussian Parsimonious Bayesian Classification (GNB), Neural Network Classification (MLP), and Complementary Parsimonious Bayesian Classification (CNB) for their predictive efficacy, and the model with the highest AUC was selected as the optimal model for predicting the occurrence of nodular thyroid disease in coal miners. Result: Lasso regression analysis showed Age, H-DLC, HCT, MCH, PLT, and GGT as predictor variables for the ML models; in addition, heat maps showed no significant correlation between the six variables. In the prediction of nodular thyroid disease, the AUC results of the five ML models, XGBoost (0.892), LR (0.577), GNB (0.603), MLP (0.601), and CNB (0.543), with the XGBoost model having the largest AUC, the model can be applied in clinical practice. Conclusion: In this research, all five ML models were found to predict the risk of nodular thyroid disease in coal miners, with the XGBoost model having the best overall predictive performance. The model can assist clinicians in quickly and accurately predicting the occurrence of nodular thyroid disease in coal miners, and in adopting individualized clinical prevention and treatment strategies.

Association of coal mine dust lung disease with Nodular thyroid disease in coal miners: A retrospective observational study in China.

Background: Coal dust is a major risk factor for the occupational health of coal miners, and underground workers with coal mine dust lung disease (Coal miners with coal mine dust lung disease (CMDLD) may have a higher risk of developing Nodular thyroid disease (NTD). The aim of this study was to investigate the relationship between coal mine dust lung disease and the development of Nodular thyroid disease in coal miners. Methods: This was a clinical retrospective observational study that included 955 male coal miners from 31 different coal mining companies in Huainan, Anhui Province, China, who were examined in April 2021 at the Huainan Occupational Disease Prevention and Control Hospital to collect all their clinical physical examination data, including general conditions, laboratory test indices and imaging indices. Based on the presence or absence of Nodular thyroid disease, 429 cases with Nodular thyroid disease were classified as the diseased group and 526 cases without Nodular thyroid disease were classified as the control group. Logistic regression was used to analyse the correlation between the occurrence of Nodular thyroid disease in coal miners, and further single- and multi-factor logistic regression was used to screen the risk exposure factors for Nodular thyroid disease in coal miners. Results: Age, coal mine dust lung disease (CMDLD), red blood cells (RBC), mean red blood cell volume (MCV), albumin (ALB), albumin/globulin (A/G), indirect bilirubin (IBIL), globulin (GLOB), total bilirubin (TBil) and myeloperoxidase (MPO) were associated with the development of Nodular thyroid disease in coal miners (p < 0.05) The results of univariate and multifactorial logistic regression analysis showed that CMDLD (OR:4.5,95%CI:2.79-7.51) had the highest OR and CMDLD was the strongest independent risk exposure factor for the development of Nodular thyroid disease in coal miners. Conclusions: There is a strong correlation between coal mine dust lung disease and Nodular thyroid disease in underground coal miners, and clinicians need to be highly aware of the high risk of NTD in coal miners with CMDLD and adopt individualized clinical prevention strategies.

A strategy for anode modification for future zinc-based battery application.

In the past several years, rechargeable zinc batteries, featuring the merits of low cost, environmental friendliness, easy manufacturing, and enhanced safety, have, attracted much attention. Zinc (Zn) anodes for zinc metal batteries play an important role. In this review, the fundamental understanding of these batteries and modification strategies to deal with the problematic issues for Zn anodes, including dendrite growth, corrosion, and the hydrogen evolution phenomenon will be summarized. The practical application of Zn anodes can still lead to Zn dendrites, various side reactions, and serious safety risks. Therefore, metal-free anodes for "rocking chair" zinc ion batteries to replace Zn anodes are systemically reviewed. The performance and the zinc storage mechanism of metal-free anodes will be discussed. Subsequently, a "rocking chair" zinc ion battery prototype selected as a recent example is assessed to explore the merits and demerits of Zn anodes and metal-free anodes. To conclude, a perspective on the future of zinc metal batteries and "rocking chair" zinc ion batteries is presented. It is hoped that this review may provide for further improvement of commercial rechargeable zinc batteries.

[Warming-needle moxibustion with different lengths of moxa stick for asthenospermia with kidney deficiency and liver depression: a randomized controlled trial].

OBJECTIVE: To explore the clinical effect of warming-needle moxibustion with different lengths of moxa stick for asthenospermia with kidney deficiency and liver depression. METHODS: A total of 240 patients with asthenospermia of kidney deficiency and liver depression were randomly divided into a 4-cm group (moxibustion with 4-cm moxa stick, 60 cases, 3 cases dropped off), a 3-cm group (moxibustion with 3-cm moxa stick, 60 cases, 4 cases dropped off), a 2-cm group (moxibustion with 2-cm moxa stick, 60 cases, 2 cases dropped off) and an acupuncture group (60 cases, 3 cases dropped off). All patients were treated with warming-needle moxibustion with different lengths of moxa stick or conventicnal acupuncture at Zhongwan (CV 12), Guanyuan (CV 4), Zhongji (CV 3), Guilai (ST 29), Yaoyangguan (GV 3), Guanyuanshu (BL 26), etc., once a day, five times a week; 4-week treatment was taken as one course, a total of two courses of treatment were given. The semen routine indexes, seminal plasma biochemical indexes, sex hormone levels and TCM syndrome score were compared before and after treatment among the 4 groups. RESULTS: After treatment, the sperm density, sperm viability, ratio of grade A sperm, ratio of grade A and B sperm, seminal plasma fructose and neutral α-glucosidase were higher than those before treatment (P<0.05, P<0.01), and the sperm deformity rates were lower than those before treatment in the 4-cm group and the 3-cm group (P<0.01, P<0.05). The ratio of grade A sperm, ratio of grade A and B sperm, seminal plasma fructose and neutral α-glucosidase in the 4-cm group were higher than the other three groups (P<0.05, P<0.01), and the sperm deformity rate was lower than the other three groups (P<0.05). After treatment, except for dizziness and tinnitus score, each-domain score and total scores of TCM syndrome scale in the 4-cm group and the 3-cm group were lower than those before treatment (P<0.01, P<0.05). The each-domain score of depression, weak waist and knees, low sexual function and total score in the 4-cm group were lower than those in the other three groups (P<0.05, P<0.01). The total effective rate was 87.7% (50/57) in the 4-cm group, which was higher than 78.6% (44/56) in the 3-cm group, 77.6% (45/58) in the 2-cm group and 70.2% (40/57) in the acupuncture group (P<0.05, P<0.01). CONCLUSION: The warming-needle moxibustion with 4-cm moxa stick could effectively improve quality and motility of sperm and clinical symptoms in patients with asthenospermia of kidney deficiency and liver depression, which is superior to moxibustion with 3-cm, 2-cm moxa sticks and conventional acupuncture.