The development of the neurocritical care specialty in China based on the analysis of neurocritical care unit volume and quality.

PURPOSE: Through three neurocritical care unit (NCCU) surveys in China, we tried to understand the development status of neurocritical care and clarify its future development. METHODS: Using a cross-sectional survey method and self-report questionnaires, the number and quality of NCCUs were investigated through three steps: administering the questionnaire, sorting the survey data, and analyzing the survey data. RESULTS: At the second and third surveys, the number of NCCUs (76/112/206) increased by 47% and 84%, respectively. The NCCUs were located in tertiary grade A hospitals or teaching hospitals (65/100/181) in most provinces (24/28/29). The numbers of full-time doctors (359/668/1337) and full-time nurses (904/1623/207) in the NCCUs increased, but the doctor-bed ratio and nurse-bed ratio were still insufficient (0.4:1 and 1.3:1). CONCLUSION: In the past 20 years, the growth rate of NCCUs in China has accelerated, while the allocation of medical staff has been insufficient. Although most NCCU hospital bed facilities and instruments and equipment tend to be adequate, there are obvious defects in some aspects of NCCUs.

Clinical value and mechanistic analysis of HIIT on modulating risk and symptoms of depression: A systematic review / Valor clínico y análisis mecanicista del HIIT sobre la modulación del riesgo y los síntomas de la depresión: una revisión sistemática

Background: The exact causal mechanisms of depression remain unclear due to the complexity of the triggers, which has led to limitations in treating depression using modern drugs. High-intensity interval training (HIIT) is as effective as medication in treating depression without toxic side effects. Typically, HIIT requires less time commitment (i.e., shorter exercise duration) and exhibits pronounced benefits on depressive symptoms than other forms of physical exercise. This review summarizes the risk reduction and clinical effects of HIIT for depression and discusses the underlying mechanisms, providing a theoretical basis for utilizing HIIT in treating depression. Methods: A database search was conducted in PubMed, Embase, Web of Science, and Scopus from inception up to October 2022. The methodological quality of the included literature was evaluated by the physiotherapy evidence database (PEDro) scale criteria. The review focused on evaluating the changes in depression risk or symptoms of HIIT interventions in healthy individuals, patients with depression, and patients with other disorders co-morbid with depression. Consequently, the mechanisms associated with depression related HIIT were summarized. Results: A total of 586 participants (52 % female; mean age: 43.58±8.93 years) from 22 studies were included. Implementing HIIT using different exercise types alleviates depressive symptoms in individuals with depression and in individuals with depression who have exhibited comorbidities and reduced depression scale scores in subjects immediately after acute exercise. In addition, the long-interval HIIT and short-interval HIIT in the treatment of patients with cardiovascular or psychiatric disorders may reduce depressive symptoms via complex exercise-related changes on several levels, including by effecting the following measures: releasing monoamines, reducing neuronal death, inducing neurogenesis, modulating the functional...(AU)

Flexiplex: a versatile demultiplexer and search tool for omics data.

MOTIVATION: The process of analyzing high throughput sequencing data often requires the identification and extraction of specific target sequences. This could include tasks, such as identifying cellular barcodes and UMIs in single-cell data, and specific genetic variants for genotyping. However, existing tools, which perform these functions are often task-specific, such as only demultiplexing barcodes for a dedicated type of experiment, or are not tolerant to noise in the sequencing data. RESULTS: To overcome these limitations, we developed Flexiplex, a versatile and fast sequence searching and demultiplexing tool for omics data, which is based on the Levenshtein distance and thus allows imperfect matches. We demonstrate Flexiplex's application on three use cases, identifying cell-line-specific sequences in Illumina short-read single-cell data, and discovering and demultiplexing cellular barcodes from noisy long-read single-cell RNA-seq data. We show that Flexiplex achieves an excellent balance of accuracy and computational efficiency compared to leading task-specific tools. AVAILABILITY AND IMPLEMENTATION: Flexiplex is available at https://davidsongroup.github.io/flexiplex/.

Clinical value and mechanistic analysis of HIIT on modulating risk and symptoms of depression: A systematic review.

Background: The exact causal mechanisms of depression remain unclear due to the complexity of the triggers, which has led to limitations in treating depression using modern drugs. High-intensity interval training (HIIT) is as effective as medication in treating depression without toxic side effects. Typically, HIIT requires less time commitment (i.e., shorter exercise duration) and exhibits pronounced benefits on depressive symptoms than other forms of physical exercise. This review summarizes the risk reduction and clinical effects of HIIT for depression and discusses the underlying mechanisms, providing a theoretical basis for utilizing HIIT in treating depression. Methods: A database search was conducted in PubMed, Embase, Web of Science, and Scopus from inception up to October 2022. The methodological quality of the included literature was evaluated by the physiotherapy evidence database (PEDro) scale criteria. The review focused on evaluating the changes in depression risk or symptoms of HIIT interventions in healthy individuals, patients with depression, and patients with other disorders co-morbid with depression. Consequently, the mechanisms associated with depression related HIIT were summarized. Results: A total of 586 participants (52 % female; mean age: 43.58±8.93 years) from 22 studies were included. Implementing HIIT using different exercise types alleviates depressive symptoms in individuals with depression and in individuals with depression who have exhibited comorbidities and reduced depression scale scores in subjects immediately after acute exercise. In addition, the long-interval HIIT and short-interval HIIT in the treatment of patients with cardiovascular or psychiatric disorders may reduce depressive symptoms via complex exercise-related changes on several levels, including by effecting the following measures: releasing monoamines, reducing neuronal death, inducing neurogenesis, modulating the functional homeostasis of the HPA axis, and enhancing the level of inflammation in the body. Conclusion: HIIT is a relatively safe and effective antidepressant, which may involve multiple neurobiological mechanisms (release of monoamines, reducing neuronal death, inducing neurogenesis, modulating the functional homeostasis of the HPA axis, and enhancing the level of inflammation in the body), thereby reducing the risk or symptoms of depression in participants.

Split-half reliability estimates of an online card sorting task in a community sample of young and elderly adults.

Executive function is vital for normal social, cognitive, and motor functions. Executive function decline due to aging increases the risk of disability and falls in older adults, which has become an urgent public health issue. Fast and convenient neuropsychological tools are thus needed to identify high-risk groups as early as possible to conduct a timely intervention. Card sorting tasks, such as Wisconsin Card Sorting Task (WCST) and its variants, are popular tools for measuring executive function. This study investigated the reliability of an open-source, self-administered, online, short-version card sorting task with a sample of young (n = 107, 65 females, age: M = 30.1 years, SD = 5.5 years) and elderly Chinese (n = 113, 53 females, age: M = 64.0 years, SD = 6.7 years). We developed an automated scoring and visualization procedure following the recent recommendations on scoring perseverative responses to make the results comparable to the standardized WCST. Reliability estimates of commonly used measures were calculated using the split-half method. All task indices' reliabilities were reasonably good in both old and young groups except for "failure-to-maintain-set." Elderly Chinese adults showed compromised task performance on all measures compared with the young Chinese adults at the group level. The R script of automated scoring and estimation of reliability is publicly available.

SDS-Net: A lightweight 3D convolutional neural network with multi-branch attention for multimodal brain tumor accurate segmentation.

The accurate and fast segmentation method of tumor regions in brain Magnetic Resonance Imaging (MRI) is significant for clinical diagnosis, treatment and monitoring, given the aggressive and high mortality rate of brain tumors. However, due to the limitation of computational complexity, convolutional neural networks (CNNs) face challenges in being efficiently deployed on resource-limited devices, which restricts their popularity in practical medical applications. To address this issue, we propose a lightweight and efficient 3D convolutional neural network SDS-Net for multimodal brain tumor MRI image segmentation. SDS-Net combines depthwise separable convolution and traditional convolution to construct the 3D lightweight backbone blocks, lightweight feature extraction (LFE) and lightweight feature fusion (LFF) modules, which effectively utilizes the rich local features in multimodal images and enhances the segmentation performance of sub-tumor regions. In addition, 3D shuffle attention (SA) and 3D self-ensemble (SE) modules are incorporated into the encoder and decoder of the network. The SA helps to capture high-quality spatial and channel features from the modalities, and the SE acquires more refined edge features by gathering information from each layer. The proposed SDS-Net was validated on the BRATS datasets. The Dice coefficients were achieved 92.7, 80.0 and 88.9% for whole tumor (WT), enhancing tumor (ET) and tumor core (TC), respectively, on the BRTAS 2020 dataset. On the BRTAS 2021 dataset, the Dice coefficients were 91.8, 82.5 and 86.8% for WT, ET and TC, respectively. Compared with other state-of-the-art methods, SDS-Net achieved superior segmentation performance with fewer parameters and less computational cost, under the condition of 2.52 M counts and 68.18 G FLOPs.

Ab Initio Time-Domain Study of Charge Relaxation and Recombination in N-Doped Cu2O.

Cu2O is a good photoelectric material with excellent performance, and its crystal structure, electronic structure, and optical properties have been extensively studied. To further illustrate the charge distribution and the carrier transport in this system, the e-h recombination dynamics was studied. It is found that N doping induced a shallower impurity band above the VBM, leading to significant charge localization around the impurity atom. NAMD simulation reveals that the N doping system possesses a longer e-h nonradiative recombination time scale. Therefore, we demonstrate that the formation of the impurity band and charge localization play an essential role in suppressing e-h recombination in N doping systems. This work is conducive for understanding the carrier transport mechanism in N-doped Cu2O.

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification.

The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

[Electrocardiogram signal classification based on fusion method of residual network and self-attention mechanism].

In the diagnosis of cardiovascular diseases, the analysis of electrocardiogram (ECG) signals has always played a crucial role. At present, how to effectively identify abnormal heart beats by algorithms is still a difficult task in the field of ECG signal analysis. Based on this, a classification model that automatically identifies abnormal heartbeats based on deep residual network (ResNet) and self-attention mechanism was proposed. Firstly, this paper designed an 18-layer convolutional neural network (CNN) based on the residual structure, which helped model fully extract the local features. Then, the bi-directional gated recurrent unit (BiGRU) was used to explore the temporal correlation for further obtaining the temporal features. Finally, the self-attention mechanism was built to weight important information and enhance model's ability to extract important features, which helped model achieve higher classification accuracy. In addition, in order to mitigate the interference on classification performance due to data imbalance, the study utilized multiple approaches for data augmentation. The experimental data in this study came from the arrhythmia database constructed by MIT and Beth Israel Hospital (MIT-BIH), and the final results showed that the proposed model achieved an overall accuracy of 98.33% on the original dataset and 99.12% on the optimized dataset, which demonstrated that the proposed model can achieve good performance in ECG signal classification, and possessed potential value for application to portable ECG detection devices.

Friction properties of black phosphorus: a first-principles study.

Based on the first-principle, the friction anisotropy, structural super-lubricity and oxidation induced ultra-low friction of black phosphorus at atomic scale under different loads have been studied. The results show that the interface friction of black phosphorus is anisotropic, that is, the friction along the armchair direction is greater than that along the zigzag direction. Moreover, the friction between the black phosphorus interfaces shows a structural superlubricity property, and the incommensurate interface friction is approximately one thousandth of the commensurate interface friction, which is mainly due to the less electronic charge and the smaller amplitude of electronic charge change between the incommensurate interfaces during the friction process. In addition, the oxidation of black phosphorus is beneficial for lubrication between interfaces.

Reconstructing a long-term water storage-based drought index in the Yangtze River Basin.

Drought is a prolonged dry period in the natural climate cycle, and is one of the most costly weather events. The Gravity Recovery and Climate Experiment (GRACE) derived terrestrial water storage anomalies (TWSA) have been widely used to assess drought severity. However, the relatively short cover period of GRACE and GRACE Follow-On limit our knowledge about the characterization and evolution of drought over decades time scale. This study proposes a standardized GRACE reconstructed TWSA index (SGRTI) to assess the drought severity based on a statistical reconstruction method calibrated by GRACE observations. Results show that the SGRTI correlates well with 6-month scale SPI and SPEI, with correlation coefficients reaching 0.79 and 0.81 in the YRB from 1981 to 2019. Soil moisture can capture drought condition like the SGRTI, while cannot further reflect deeper water storage depletion. The SGRTI is also comparable to the SRI and in-situ water level. As a case study for the Yangtze River Basin, its three sub-basins experience more frequent droughts, shorter drought duration, and lower severity drought, as identified by SGRTI during 1992-2019 relative to 1963-1991. The presented SGRTI in this study can provide a valuable supplement to the drought index before the GRACE era.

The luminescent and reaction mechanisms of a fluorescent probe for the detection of hypochlorous acid: Insights from theory.

We have designed and synthesized a novel fluorescent probe BMH for detection of hypochlorous acid (HClO), which can increase dramatically the fluorescence intensity and had ultrafast response, a low detection limit and a wide pH range of application. In this paper, we further studied its fluorescence quantum yield and photoluminescence mechanism theoretically. The calculated results indicated the first excited states of BMH and BM (it was the oxidized product by HClO) were bright states with large oscillator strengths, however, due to more larger reorganization energy of BMH, the predicted internal conversion rate kIC of BMH was four orders of magnitude larger than that of BM; moreover, owing to the effect of heavy atom from sulfur atom in BMH, the predicted intersystem crossing rate kisc of BMH was five orders of magnitude larger than that of BM; meanwhile there was no significant difference found between both the predicted radiative rates kr, thus the calculated fluorescence quantum yield of BMH was nearly zero and that of BM was more than 90%, the data showed the BMH had no fluorescence but its oxidated produce BM possessed strong fluorescence. In addition, the reaction mechanism of BMH transforming into BM has been investigated too, according to the potential energy profile, we found that the course of BMH converting into BM consisted of three elementary reactions. The research results revealed the solvent effect can decreased the activation energy, which was more favorable for these elementary reactions.

Monte Carlo modeling of hepatic steatosis based on stereology and spatial distribution of fat droplets.

BACKGROUND AND OBJECTIVE: To model hepatic steatosis in adult humans with non-alcoholic fatty liver disease based on stereology and spatial distribution of fat droplets from liver biopsy specimens. METHODS: Histological analysis was performed on 30 adult human liver biopsy specimens with varying degrees of steatosis. Morphological features of fat droplets were characterized by gamma distribution function (GDF) in both two-dimensional (2D) and three-dimensional (3D) spaces from three aspects: 1) size distribution indicating non-uniformity of fat droplets in radius; 2) nearest neighbor distance distribution indicating heterogeneous accumulation (i.e., clustering) of fat droplets; 3) regional anisotropy indicating inter-regional variability in fat fraction (FF). To generalize the morphological description of hepatic steatosis to different FFs, correlation analysis was performed among the estimated GDF parameters and FFs for all specimens. Finally, Monte Carlo modeling of hepatic steatosis was developed to simulate fat droplet distribution in tissue. RESULTS: Morphological features, including size and nearest neighbor distance in 2D and 3D spaces as well as regional anisotropy, statistically captured the distribution of fat droplets by the GDF fit (R2 > 0.54). The estimated GDF parameters (i.e., scale and shape parameters) and FFs were well correlated, with R2 > 0.55. In addition, simulated 3D liver morphological models demonstrated similar sections to real histological samples both visually and quantitatively. CONCLUSIONS: The morphology of hepatic steatosis is well characterized by stereology and spatial distribution of fat droplets. Simulated models demonstrate similar appearances to real histological samples. Furthermore, the model may help understand MRI signal behavior in the presence of liver steatosis.

The development of neurocritical care in China from the perspective of evaluation and treatment of critical neurological diseases.

Objective: To understand the varieties, evaluation, treatment, and prognosis of severe neurological diseases using the third NCU survey in China. Design: A cross-sectional questionnaire study. The study was completed in three main steps: filling in the questionnaire, sorting out the survey data, and analyzing the survey data. Results: Of 206 NCUs, 165 (80%) provided relatively complete information. It was estimated that 96,201 patients with severe neurological diseases were diagnosed and treated throughout the year, with an average fatality rate of 4.1%. The most prevalent severe neurological disease was cerebrovascular disease (55.2%). The most prevalent comorbidity was hypertension (56.7%). The most prevalent complication was hypoproteinemia (24.2%). The most common nosocomial infection was hospital-acquired pneumonia (10.6%). The GCS, APACHE II, EEG, and TCD were the most commonly used (62.4-95.2%). The implementation rate of the five nursing evaluation techniques reached 55.8-90.9%. Routinely raising the head of the bed by 30°, endotracheal intubation and central venous catheterization were the mostprevalent treatment strategies (97.6, 94.5, and 90.3%, respectively). Traditional tracheotomy, invasive mechanical ventilation and nasogastric tube feeding (75.8, 95.8, and 95.8%, respectively) were more common than percutaneous tracheotomy, non-invasive mechanical ventilation and nasogastric tube insertion (57.6, 57.6, and 66.7%, respectively). Body surface hypothermia brain protection technology was more commonly used than intravascular hypothermia technology (67.3 > 6.1%). The rates of minimally invasive hematoma removal and ventricular puncture were only 40.0 and 45.5%, respectively. Conclusion: In addition to traditional recognized basic life assessment and support technology, it is necessary to the use of promote specialized technology for neurological diseases, according to the characteristics of critical neurological diseases.

The reciprocal interactions between microglia and T cells in Parkinson's disease: a double-edged sword.

In Parkinson's disease (PD), neurotoxic microglia, Th1 cells, and Th17 cells are overactivated. Overactivation of these immune cells exacerbates the disease process and leads to the pathological development of pro-inflammatory cytokines, chemokines, and contact-killing compounds, causing the loss of dopaminergic neurons. So far, we have mainly focused on the role of the specific class of immune cells in PD while neglecting the impact of interactions among immune cells on the disease. Therefore, this review demonstrates the reciprocal interplays between microglia and T cells and the associated subpopulations through cytokine and chemokine production that impair and/or protect the pathological process of PD. Furthermore, potential targets and models of PD neuroinflammation are highlighted to provide the new ideas/directions for future research.

Cascade of Denoising and Mapping Neural Networks for MRI R2* Relaxometry of Iron-Loaded Liver.

MRI of effective transverse relaxation rate (R2*) measurement is a reliable method for liver iron concentration quantification. However, R2* mapping can be degraded by noise, especially in the case of iron overload. This study aimed to develop a deep learning method for MRI R2* relaxometry of an iron-loaded liver using a two-stage cascaded neural network. The proposed method, named CadamNet, combines two convolutional neural networks separately designed for image denoising and parameter mapping into a cascade framework, and the physics-based R2* decay model was incorporated in training the mapping network to enforce data consistency further. CadamNet was trained using simulated liver data with Rician noise, which was constructed from clinical liver data. The performance of CadamNet was quantitatively evaluated on simulated data with varying noise levels as well as clinical liver data and compared with the single-stage parameter mapping network (MappingNet) and two conventional model-based R2* mapping methods. CadamNet consistently achieved high-quality R2* maps and outperformed MappingNet at varying noise levels. Compared with conventional R2* mapping methods, CadamNet yielded R2* maps with lower errors, higher quality, and substantially increased efficiency. In conclusion, the proposed CadamNet enables accurate and efficient iron-loaded liver R2* mapping, especially in the presence of severe noise.

Evaluation of Adenosine A2A receptor gene polymorphisms as risk factors of methamphetamine use disorder susceptibility and predictors of craving degree.

The adenosine A2A receptor (ADORA2A) is highly expressed in the central nervous system and plays vital roles in drug addiction. In this study, we aimed to explore the susceptibility of ADORA2A to methamphetamine use disorder (MUD) and the craving degree based on a two-stage association analysis. A total of 3,542 (1,216 patients with MUD and 2,326 controls) and 1,740 participants (580 patients with MUD and 1,160 controls) were recruited in discovery and replication stage, respectively. Significant SNPs identified in the discovery stage were genotyped in the replication samples. Serum levels of ADORA2A were measured using enzyme-linked immunosorbent assay kits. The genetic association signal of each SNP was examined using Plink. A linear model was fitted to investigate the relationship between craving scores and genotypes of significant SNPs. SNP rs5751876 was significantly associated with MUD in the discovery samples and this association signal was then further replicated in the replication samples. Significant associations were also identified between serum levels of ADORA2A and the genotypes of rs5751876 (P = 0.0002). The craving scores in patients with MUD were strongly correlated with rs5751876 genotypes. Our results suggest that polymorphisms of the ADORA2A gene could affect the susceptibility to MUD and its craving degree.

Functional Connectivity Increases in Response to High-Definition Transcranial Direct Current Stimulation in Patients with Chronic Disorder of Consciousness.

High-definition transcranial direct current stimulation (HD-tDCS) has been shown to play an important role in improving consciousness in patients with disorders of consciousness (DOCs), but its neuroelectrophysiological evidence is still lacking. To better explain the electrophysiological mechanisms of the effects of HD-tDCS on patients with DOCs, 22 DOC patients underwent 10 anodal HD-tDCS sessions of the left dorsolateral prefrontal cortex (DLPFC). This study used the Coma Recovery Scale-Revised (CRS-R) to assess the level of consciousness in DOC patients. According to whether the CRS-R score increased before and after stimulation, DOC patients were divided into a responsive group and a non-responsive group. By comparing the differences in resting-state EEG functional connectivity between different frequency bands and brain regions, as well as the relationship between functional connectivity values and clinical scores, the electrophysiological mechanism of the clinical effects of HD-tDCS was further explored. The change of the phase locking value (PLV) on the theta frequency band in the left frontal-parietooccipital region was positively correlated with the change in the CRS-R scores. As the number of interventions increased, we observed that in the responsive group, the change in PLV showed an upward trend, and the increase in the PLV appeared in the left frontal-parietooccipital region at 4-8 Hz and in the intra-bifrontal region at 8-13 Hz. In the non-responsive group, although the CRS-R scores did not change after stimulation, the PLV showed a downward trend, and the decrease in the PLV appeared in the intra-bifrontal region at 8-13 Hz. In addition, at the three-month follow-up, patients with increased PLV in the intra-bifrontal region at 8-13 Hz after repeated HD-tDCS stimulation had better outcomes than those without. Repeated anodal stimulation of the left DLPFC with HD-tDCS resulted in improved consciousness in some patients with DOCs. The increase in functional connectivity in the brain regions may be associated with the improvement of related awareness after HD-tDCS and may be a predictor of better long-term outcomes.

Discovery of Antibacterial Contezolid Acefosamil: Innovative O-Acyl Phosphoramidate Prodrug for IV and Oral Therapies.

New oral antibiotic contezolid (CZD) is effective against Gram-positive infections but unsuitable for intravenous (IV) administration due to its modest solubility. To address the medical need for an IV form of CZD, its isoxazol-3-yl phosphoramidate derivatives have been explored, and contezolid acefosamil (CZA, 8), the first representative of a novel O-acyl phosphoramidate prodrug class, has been identified. CZA exhibits high aqueous solubility (>200 mg/mL) and good hydrolytic stability at media pH suitable for IV administration. CZA rapidly converts into the active drug CZD in vivo. In a pharmacokinetic (PK) rat model, the exposure of active drug CZD after IV administration of the prodrug CZA was similar to or higher than that from the IV administration of CZD. The prodrug CZA is bioequivalent to or better than CZD in several preclinical infection models. CZA is likewise active upon its oral administration. To date, CZA has been evaluated in Phase 1 and Phase 2 clinical trials in the USA. It is advancing into further clinical studies including step-down therapy with in-hospital intravenous CZA administration followed by outpatient oral CZD treatment.