Lipidomics random forest algorithm of seminal plasma is a promising method for enhancing the diagnosis of necrozoospermia.

BACKGROUND: Despite the clear clinical diagnostic criteria for necrozoospermia in andrology, the fundamental mechanisms underlying it remain elusive. This study aims to profile the lipid composition in seminal plasma systematically and to ascertain the potential of lipid biomarkers in the accurate diagnosis of necrozoospermia. It also evaluates the efficacy of a lipidomics-based random forest algorithm model in identifying necrozoospermia. METHODS: Seminal plasma samples were collected from patients diagnosed with necrozoospermia (n = 28) and normozoospermia (n = 28). Liquid chromatography-mass spectrometry (LC-MS) was used to perform lipidomic analysis and identify the underlying biomarkers. A lipid functional enrichment analysis was conducted using the LION lipid ontology database. The top 100 differentially significant lipids were subjected to lipid biomarker examination through random forest machine learning model. RESULTS: Lipidomic analysis identified 46 lipid classes comprising 1267 lipid metabolites in seminal plasma. The top five enriched lipid functions as follows: fatty acid (FA) with ≤ 18 carbons, FA with 16-18 carbons, monounsaturated FA, FA with 18 carbons, and FA with 16 carbons. The top 100 differentially significant lipids were subjected to machine learning analysis and identified 20 feature lipids. The random forest model identified lipids with an area under the curve > 0.8, including LPE(20:4) and TG(4:0_14:1_16:0). CONCLUSIONS: LPE(20:4) and TG(4:0_14:1_16:0), were identified as differential lipids for necrozoospermia. Seminal plasma lipidomic analysis could provide valuable biochemical information for the diagnosis of necrozoospermia, and its combination with conventional sperm analysis may improve the accuracy and reliability of the diagnosis.

The EEG-Based Fusion Entropy-Featured Identification of Isometric Contraction Forces under the Same Action.

This study explores the important role of assessing force levels in accurately controlling upper limb movements in human-computer interfaces. It uses a new method that combines entropy to improve the recognition of force levels. This research aims to differentiate between different levels of isometric contraction forces using electroencephalogram (EEG) signal analysis. It integrates eight different entropy measures: power spectrum entropy (PSE), singular spectrum entropy (SSE), logarithmic energy entropy (LEE), approximation entropy (AE), sample entropy (SE), fuzzy entropy (FE), alignment entropy (PE), and envelope entropy (EE). The findings emphasize two important advances: first, including a wide range of entropy features significantly improves classification efficiency; second, the fusion entropy method shows exceptional accuracy in classifying isometric contraction forces. It achieves an accuracy rate of 91.73% in distinguishing between 15% and 60% maximum voluntary contraction (MVC) forces, along with 69.59% accuracy in identifying variations across 15%, 30%, 45%, and 60% MVC. These results illuminate the efficacy of employing fusion entropy in EEG signal analysis for isometric contraction detection, heralding new opportunities for advancing motor control and facilitating fine motor movements through sophisticated human-computer interface technologies.

EEG and fNIRS datasets based on Stroop task during two weeks of high-altitude exposure in new immigrants.

Maintaining sufficient cerebral oxygen metabolism is crucial for human survival, especially in challenging conditions such as high-altitudes. Human cognitive neural activity is sensitive to fluctuations in oxygen levels. However, there is a lack of publicly available datasets on human behavioural responses and cerebral dynamics assessments during the execution of conflicting tasks in natural hypoxic environments. We recruited 80 healthy new immigrant volunteers (males, aged 20 ± 2 years) and employed the Stroop cognitive conflict paradigm. After a two-week exposure to both high and low-altitudes, the behavioural performance, prefrontal oxygen levels, and electroencephalography (EEG) signals were recorded. Comparative analyses were conducted on the behavioural reaction times and accuracy during Stroop tasks, and statistical analyses of participants' prefrontal oxygen levels and EEG signals were performed. We anticipate that our open-access dataset will contribute to the development of monitoring devices and algorithms, designed specifically for measuring cerebral oxygen and EEG dynamics in populations exposed to extreme environments, particularly among individuals suffering from oxygen deficiency.

Compound heterozygous mutations in PMFBP1 cause acephalic spermatozoa syndrome: A case report.

BACKGROUND: Acephalic spermatozoa syndrome (ASS) is an extremely rare form of severe teratozoospermia, where in most of the sperm either appear to lack heads or have disconnected or poorly connected heads and tails. CASE SUMMARY: We reported the case of a male patient with secondary infertility whose sperm showed typical ASS upon morphological analysis. Whole-exome sequencing was performed on the patient's peripheral blood, which revealed two heterozygous variants of the PMFBP1 gene: PMFBP1c.414+1G>T (p.?) and PMFBP1c.393del (p.C132Afs*3). CONCLUSION: It is speculated that the compound homozygous mutation of PMFBP1 may be the cause of ASS. We conducted a literature review in order to provide the basis for genetic counseling and clinical diagnosis of patients with ASS.

Online Noninvasive Assessment of Human Brain Death by Near-Infrared Spectroscopy with Protocol of O2 Inspiration.

Brain death is the irreversible loss of all the functions of the brain and brainstem. Compared to traditional diagnostic methods of brain death, near-infrared spectroscopy (NIRS) is a noninvasive, objective, cost-effective, and safe way of assessment of brain death. Eighteen brain dead patients and 20 healthy subjects were studied by NIRS, with a multiple-phase protocol at varied fractions of inspired O2 (FIO2). We found that the changes in the concentration ratios of oxyhemoglobin to deoxyhemoglobin (Δ[HbO2]/Δ[Hb]) in the cerebral cortex of brain dead patients were significantly higher than those of healthy subjects, and its low-to-high FIO2 phase was most sensitive, with a recommended threshold in the range 1.40-1.50. Our study indicated that NIRS is a promising technology for assessing brain death. The success of this application potentially offers a supplementary technique for the assessment of brain death in real time in order to be able to promptly offer quality-assured donor organs.

Effect of brain alpha oscillation on the performance in laparoscopic skills simulator training.

BACKGROUND: Laparoscopic skill involves sensory processing and motor control, which is associated with high-level alpha oscillation of the brain. Neurofeedback (NF) has been reported effective in enhancing alpha oscillation. Our objectives were to assess the alpha oscillation during laparoscopic skills training, and to verify the usefulness of NF in improving the learning efficacy. METHODS: Sixty medical students without laparoscopic experience were recruited. Multi-channel electroencephalography (EEG) signals were recorded during training of peg transfer task. Training performance was assessed based on the task completion time. All subjects participated in the first experiment comprising eight training blocks and one testing block. Subjects were ranked based on performance: the top 20 subjects were classified as the good performance group and the bottom 20 subjects as the fair performance group. In the second experiment, the fair performance group were randomly divided into the NF and control groups. Spectral analysis of EEG signals was used to calculate alpha power and alpha band coherence. Training performance and EEG alpha powers were compared between the NF and control groups. RESULTS: In the first experiment, the completion time was significantly faster in the good performance group (62.5 ± 2.8 s) compared with the fair performance group (75.0 ± 5.6 s) (P < 0.05). EEG oscillations showed strong alpha power and alpha coherence in the posterior electrode clusters in the good performance group. In the second experiment, the NF group showed much stronger alpha activity power and coherence compared with the control group. Furthermore, the NF training led to a significant performance improvement from 75.1 ± 5.9 s in the first experiment to 64.3 ± 4.9 s in the second experiment (P = 0.003). CONCLUSIONS: The learning performance of laparoscopic skills varies among individuals. Subjects with good performance results had high alpha power and strong alpha coherence. The alpha enhancement NF increased alpha oscillations, leading to improved learning efficacy.

NDDN: A Cloud-Based Neuroinformation Database for Developing Neuronal Networks.

Electrical activity of developing dissociated neuronal networks is of immense significance for understanding the general properties of neural information processing and storage. In addition, the complexity and diversity of network activity patterns make them ideal candidates for developing novel computational models and evaluating algorithms. However, there are rare databases which focus on the changing network dynamics during development. Here, we describe the design and implementation of Neuroinformation Database for Developing Networks (NDDN), a repository for electrophysiological data collected from long-term cultured hippocampal networks. The NDDN contains over 15 terabytes of multielectrode array data consisting of 25,380 items collected from 105 culture batches. Metadata including culturing and recording information and stimulation/drug application protocols are linked to each data item. A Matlab toolbox named MEAKit is also provided with the NDDN to ease the analysis of downloaded data items. We expect that NDDN may contribute to both the fields of experimental and computational neuroscience.

Combined nonlinear metrics to evaluate spontaneous EEG recordings from chronic spinal cord injury in a rat model: a pilot study.

Spinal cord injury (SCI) is a high-cost disability and may cause permanent loss of movement and sensation below the injury location. The chance of cure in human after SCI is extremely limited. Instead, neural regeneration could have been seen in animals after SCI, and such regeneration could be retarded by blocking neural plasticity pathways, showing the importance of neural plasticity in functional recovery. As an indicator of nonlinear dynamics in the brain, sample entropy was used here in combination with detrended fluctuation analysis (DFA) and Kolmogorov complexity to quantify functional plasticity changes in spontaneous EEG recordings of rats before and after SCI. The results showed that the sample entropy values were decreased at the first day following injury then gradually increased during recovery. DFA and Kolmogorov complexity results were in consistent with sample entropy, showing the complexity of the EEG time series was lost after injury and partially regained in 1 week. The tendency to regain complexity is in line with the observation of behavioral rehabilitation. A critical time point was found during the recovery process after SCI. Our preliminary results suggested that the combined use of these nonlinear dynamical metrics could provide a quantitative and predictive way to assess the change of neural plasticity in a spinal cord injury rat model.

[Decreases of progressive motility, total motility, and acrosin activity of sperm from oligoasthenoteratospermia males at different time points after sperm activation].

OBJECTIVE: To investigate the progressive motility, (PR), total motility (progressive + non-progressive motility, PR + NP), and acrosin activity of sperm from normal and infertile men at different time points after sperm activation. METHODS: Based on the 5th edition of the WHO Laboratory Manual for the Examination and Processing of Human Semen and the results of modified Papanicolaou staining, we divided the semen samples into groups A (normal, n = 28), B (oligoasthenoteratospermia, n = 30), and C (asthenoteratospermia, n = 32). At 1, 24, and 48 hours after sperm activation, we detected sperm PR and PR + NP by CASA and chemical colorimetry, and determined sperm acrosin activity using the modified Kennedy method. RESULTS: Sperm PR and PR + NP were significantly decreased in all the three groups at 1-24 hours and even more significantly at 24-48 hours after sperm activation as compared with the baseline (P < 0.05). Sperm acrosin activity showed remarkable reduction in group A (P = 0. 013) , even more significant at 1-24 hours than at 24-48 hours after sperm activation, but not in groups B and C (P = 0.519 and 0.979). CONCLUSION: Sperm PR, PR + NP, and acrosin activity are all decreased with the extension of time after sperm activation, each in a specific manner. Examination of sperm acrosin activity should be applied as a routine tool in the assessment of male fertility.

Time-frequency patterns of somatosensory evoked potentials in predicting the location of spinal cord injury.

Somatosensory evoked potentials (SEPs) were found to exhibit different time-frequency patterns after acute spinal cord injury (SCI) at different levels, which implies that changes of these patterns may be associated with the location of SCI. Based on this finding, we propose the hypothesis that there are information regarding the location of SCI contained in the time-frequency patterns of SEPs. Purpose of the present study is to verify this hypothesis by comparing the time-frequency patterns of SEPs after acute and chronic SCI at the same level. The study examined the distribution patterns of the time-frequency components (TFCs) of SEPs before and after acute and chronic injury at C5 level in the spinal cord. Experimental results of SEP recordings from 24 adult rats show that there are common areas in the time-frequency distributions of SEPs. The TFCs from both the acute injury group and the chronic injury group are located in these areas with no TFCs from the normal group. Findings suggest that these areas are likely to possess information concerning the site of neurological deficits in spinal cord while independent of the modality of injury. This study provides basis for identification of stable time-frequency patterns of SEPs after different types and locations of SCI, which will guide the development of SEP-based SCI location detection.

Developing neuronal networks: self-organized criticality predicts the future.

Self-organized criticality emerged in neural activity is one of the key concepts to describe the formation and the function of developing neuronal networks. The relationship between critical dynamics and neural development is both theoretically and experimentally appealing. However, whereas it is well-known that cortical networks exhibit a rich repertoire of activity patterns at different stages during in vitro maturation, dynamical activity patterns through the entire neural development still remains unclear. Here we show that a series of metastable network states emerged in the developing and "aging" process of hippocampal networks cultured from dissociated rat neurons. The unidirectional sequence of state transitions could be only observed in networks showing power-law scaling of distributed neuronal avalanches. Our data suggest that self-organized criticality may guide spontaneous activity into a sequential succession of homeostatically-regulated transient patterns during development, which may help to predict the tendency of neural development at early ages in the future.

Spatial-temporal dynamics of chaotic behavior in cultured hippocampal networks.

Using multiple nonlinear techniques, we revealed the existence of chaos in the spontaneous activity of neuronal networks in vitro. The spatial-temporal dynamics of these networks indicated that emergent transition between chaotic behavior and superburst occurred periodically in low-frequency oscillations. An analysis of network-wide activity indicated that chaos was synchronized among different sites. Moreover, we found that the degree of chaos increased as the number of active sites in the network increased during long-term development (over three months in vitro). The chaotic behavior of the dissociated networks had similar spatial-temporal characteristics (rapid transition, periodicity, and synchronization) as the intact brain; however, the degree of chaos depended on the number of active sites at the mesoscopic level. This work could provide insight into neural coding and neurocybernetics.

Transient alterations in slow oscillations of hippocampal networks by low-frequency stimulations on multi-electrode arrays.

Slow oscillations in the hippocampus are correlated with memory consolidation and brain diseases. The characteristic firings of the hippocampal network in vitro are still poorly understood. Here, spontaneous oscillations(~0.004 Hz) were found in high-density hippocampal networks by multi-electrode arrays after 30 days in vitro.This kind of spontaneous activity was characterized by periodic synchronized superbursts, which persisted for approximately 60 s at long intervals. Additionally, 1-Hz stimulation (duration <120 s) could regulate these network wide oscillatory activities by triggering the next synchronized superbursts prematurely. The results demonstrated that the slow oscillatory activities in hippocampal cultures could be regulated by external stimulation, which indicates that multi-electrode arrays provide a well-suited platform for studying the dynamics of slow oscillations in vitro and may help to elucidate the mechanism of electrical stimulation therapy.