Normal Signal Research Articles

Myeloproliferative Neoplasms: Diseases Mediated by Chronic Activation of Signal Transducer and Activator of Transcription (STAT) Proteins.

Myeloproliferative neoplasms (MPNs) are hematopoietic diseases characterized by the clonal expansion of single or multiple lineages of differentiated myeloid cells that accumulate in the blood and bone marrow. MPNs are grouped into distinct categories based on key clinical presentations and distinctive mutational hallmarks. These include chronic myeloid leukemia (CML), which is strongly associated with the signature BCR::ABL1 gene translocation, polycythemia vera (PV), essential thrombocythemia (ET), and primary (idiopathic) myelofibrosis (PMF), typically accompanied by molecular alterations in the JAK2, MPL, or CALR genes. There are also rarer forms such as chronic neutrophilic leukemia (CNL), which involves mutations in the CSF3R gene. However, rather than focusing on the differences between these alternate disease categories, this review aims to present a unifying molecular etiology in which these overlapping diseases are best understood as disruptions of normal hematopoietic signaling: specifically, the chronic activation of signaling pathways, particularly involving signal transducer and activator of transcription (STAT) transcription factors, most notably STAT5B, leading to the sustained stimulation of myelopoiesis, which underpins the various disease sequalae.

Epileptic Seizure Classification Based on Random Neural Networks Using Discrete Wavelet Transform for Electroencephalogram Signal Decomposition

An epileptic seizure is a brief episode of symptoms and signs caused by excessive electrical activity in the brain. One of the major chronic neurological diseases, epilepsy, affects millions of individuals worldwide. Effective detection of seizure events is critical in the diagnosis and treatment of patients with epilepsy. Neurologists monitor the electrical activity in the brains of patients to identify epileptic seizures by employing advanced sensing techniques, including electroencephalograms and electromyography. Machine learning-based classification of the EEG signal can help differentiate between normal signals and the patterns associated with epileptic seizures. This work presents a novel approach for the classification of epileptic seizures using random neural network (RNN). The proposed model has been trained and tested using two publicly available datasets: CHB-MIT and BONN, provided by Children’s Hospital Boston-Massachusetts Institute of Technology and the University of Bonn, respectively. The results obtained from multiple experiments highlight that the proposed scheme outperformed traditional classification schemes such as artificial neural network and support vector machine. The proposed RNN-based model achieved accuracies of 93.27% and 99.84% on the CHB-MIT and BONN datasets, respectively.

Support Vector Machines Based Mutual Interference Mitigation for Millimeter‐Wave Radars

With the intelligent development of vehicles, the number of vehicles equipped with millimeter‐wave (mmWave) radars is increasing, and the possibility of interference between radars is rising dramatically. In automatic driving, it will be common for target detection to be affected by multiple interfering radars. Addressing the mutual interference challenges, an adaptive interference detection method based on support vector machines (SVMs) is proposed. First, a window selection is performed on the received signal and features describing the difference between the normal signal and the interference are extracted. Then, we use a nonlinear SVM to distinguish between the interference and the normal signal. After completing the localization of the interference, we use an autoregressive (AR) prediction model to reconstruct the target echo signal. Results from both multiple interference simulation scenarios and real experimental scenarios show that the accuracy of interference localization and the effect of interference mitigation of the proposed method outperforms the mainstream methods.

FBSE-based Approach for Discriminating Seizure and Normal EEG Signals

FBSE-based Approach for Discriminating Seizure and Normal EEG Signals

Cardiac Magnetic Resonance Visualization of the Myocardial Microstructure in Non-Ischemic Cardiomyopathies

Cardiac magnetic resonance (CMR), a non-radiation based type of examination, can achieve the simultaneous comprehensive multi-parameter, multi-plane, and multi-sequence evaluation of the anatomical structure of the heart; and at the same time, determine systolic and diastolic function, and blood perfusion and tissue characteristics. Traditional late gadolinium enhancement imaging based on CMR reflects focal replacement fibrosis, in contrast to normal myocardial signal intensity, but cannot effectively identify diffuse myocardial fibrosis. T1 mapping and its derived extracellular volume fraction can be used to quantitatively analyze the extracellular space in myocardial tissue and evaluate diffuse myocardial interstitial fibrosis that is invisible to the naked eye. Diffusion tensor imaging reveals the direction of cardiomyocyte aggregates by quantifying the anisotropy of water molecule diffusion, and can be applied to evaluate the integrity of myocardial tissue and arrangement structure of myocardial microstructural characteristics. On the basis of the micro-motion of myocardial tissue, feature tracking analysis decomposes myocardial deformation into three dimensions of micro-mechanical changes, and can identify early systolic and diastolic dysfunction before heart enlargement or ejection fraction reduction. This Commentary discusses current research advances in these new techniques, as well as their clinical application prospects and limitations for non-ischemic cardiomyopathies.

Whole-body fluorescence cryotomography identifies a fast-acting, high-contrast, durable contrast agent for fluorescence-guided surgery.

Imaging of tumor-specific fluorescent contrast agents to guide tumor removal has been shown to improve outcomes and is now standard practice for some neurosurgical procedures. However, many agents require administration hours before surgery, a practical challenge, and may exhibit inconsistent concordance with contrast-enhanced MRI (CE-MRI), the current standard for diagnosing and guiding glioma removal. A fluorescent agent that accurately marks tumor shortly after administration and is otherwise similar to CE-MRI would help overcome these shortcomings. Methods: We used whole-body 3-D fluorescence cryo-imaging and co-registered CE-MRI volumes to evaluate several fluorescent contrast agent candidates for diagnostic performance and concordance with CE-MRI. Mice with brain tumors were administered a cocktail of fluorescent agent candidates and a MRI contrast agent, and then imaged with MRI and fluorescence cryo-imaging at several timepoints after administration. The high-resolution 3-D cryo-imaging volumes of the fluorescent agents were used to determine diagnostic performance metrics and correlation with CE-MRI. Results: While all agents showed positive metrics, one agent, tetramethylrhodamine conjugated to a small polyethylene glycol chain (TMR-PEG1k), outperformed the others, exhibiting minimal normal brain signal, high tumor-to-background-ratio, diagnostic accuracy, and cross-correlation to CE-MRI at all post-administration timepoints (10-90 min) and tumor lines examined. Conclusion: These favorable properties establish TMR-PEG1k as a promising candidate for surgical guidance.

The crosstalk between miRNAs and signaling pathways in human cancers: Potential therapeutic implications.

MicroRNAs (miRNAs) are increasingly recognized as central players in the regulation of eukaryotic physiological processes. These small double stranded RNA molecules have emerged as pivotal regulators in the intricate network of cellular signaling pathways, playing significant roles in the development and progression of human cancers. The central theme in miRNA-mediated regulation of signaling pathways involves their ability to target and modulate the expression of pathway components. Aberrant expression of miRNAs can either promote or suppress key signaling events, influencing critical cellular processes such as proliferation, apoptosis, angiogenesis, and metastasis. For example, oncogenic miRNAs often promote cancer progression by targeting tumor suppressors or negative regulators of signaling pathways, thereby enhancing pathway activity. Conversely, tumor-suppressive miRNAs frequently inhibit oncogenic signaling by targeting key components within these pathways. This complex regulatory crosstalk underscores the significance of miRNAs as central players in shaping the signaling landscape of cancer cells. Furthermore, the therapeutic implications of targeting miRNAs in cancer are substantial. miRNAs can be manipulated to restore normal signaling pathway activity, offering a potential avenue for precision medicine. The development of miRNA-based therapeutics, including synthetic miRNA mimics and miRNA inhibitors, has shown promise in preclinical and clinical studies. These strategies aim to either enhance the activity of tumor-suppressive miRNAs or inhibit the function of oncogenic miRNAs, thereby restoring balanced signaling and impeding cancer progression. In conclusion, the crosstalk between miRNAs and signaling pathways in human cancers is a dynamic and influential aspect of cancer biology. Understanding this interplay provides valuable insights into cancer development and progression. Harnessing the therapeutic potential of miRNAs as regulators of signaling pathways opens up exciting opportunities for the development of innovative cancer treatments with the potential to improve patient outcomes. In this chapter, we provide an overview of the crosstalk between miRNAs and signaling pathways in the context of cancer and highlight the potential therapeutic implications of targeting this regulatory interplay.

EEG-Based Depression Detection in the Prefrontal Cortex Lobe using mRMR Feature Selection and Bidirectional LSTM

Depression can induce significant anguish and impair one's ability to perform effectively in professional, academic, and familial settings. This condition has the potential to result in suicide. Annually, the number of deaths resulting from suicide exceeds 700,000. Among individuals aged 15-29, suicide has emerged as the fourth most prevalent cause of mortality. Challenges in treating depression include limited accessibility to mental health care in rural regions and misdiagnosis resulting from subjective evaluations, wherein insufficient expertise can contribute to inaccurate diagnoses. Electroencephalography (EEG) has gained popularity as a tool for the identification and study of a number of mental illnesses in the past several years. Therefore, an automated technique is required to precisely distinguish between normal EEG signals and depression signals. This research focused on developing an EEG-based depression detection system in the prefrontal cortex lobe area (Fp1, Fpz, and Fp2). One of the developments carried out in this research is the implementation of Bidirectional Long Short-Term Memory (Bi-LSTM) as the model classification and minimum redundancy maximum relevance (mRMR) feature selection. Results suggest that the combination of mRMR feature selection with 25 features and the Bidirectional LSTM obtained 92.83% for the accuracy.

A Navigation Algorithm to Enable Sustainable Control of Insect-Computer Hybrid Robot with Stimulus Signal Regulator and Habituation-Breaking Function.

The insect-computer hybrid soft robots are receiving increasing attention due to their excellent motor capabilities, small size, and low power consumption. However, the effective control of insects is limited to minutes since the response from insects is reduced as the number of stimulus signal increase. This phenomenon is known as habituation, which causes the loss of control of robots and hinders their application in practical tasks such as search and rescue missions that require several hours. It has been shown that constantly switching the pattern of stimulus signals can slow down the onset of habituation. Moreover, when habituation occurs, applying a different stimulus signal can break the habituation. Based on this, we have designed a navigation algorithm that can extend the control time of insects to several hours. The algorithm is composed of a stimulation decision-making core responsible for deciding on the type of stimulus signal (left, right, acceleration), a stimulation parameters adjustment (SPA) core responsible for adjusting the stimulus signal voltage constantly to delay the occurrence of habituation, and a reactivation function (RF), as a different stimulus signal from the normal stimulus signal, is used to break insects' habituation to the normal stimulus signal. Experiments have shown that our SPA regulator and RF can significantly extend the control time of insects. Navigation experiments demonstrating effective control of the insects for up to 3 h verified the effectiveness of the navigation algorithm, which strikes a balance between control accuracy and control time.

B-Cell Receptor Signaling and Beyond: The Role of Igα (CD79a)/Igβ (CD79b) in Normal and Malignant B Cells.

B-cell receptor (BCR) is a B cell hallmark surface complex regulating multiple cellular processes in normal as well as malignant B cells. Igα (CD79a)/Igβ (CD79b) are essential components of BCR that are indispensable for its functionality, signal initiation, and signal transduction. CD79a/CD79b-mediated BCR signaling is required for the survival of normal as well as malignant B cells via a wide signaling network. Recent studies identified the great complexity of this signaling network and revealed the emerging role of CD79a/CD79b in signal integration. In this review, we have focused on functional features of CD79a/CD79b, summarized signaling consequences of CD79a/CD79b post-translational modifications, and highlighted specifics of CD79a/CD79b interactions within BCR and related signaling cascades. We have reviewed the complex role of CD79a/CD79b in multiple aspects of normal B cell biology and how is the normal BCR signaling affected by lymphoid neoplasms associated CD79A/CD79B mutations. We have also summarized important unresolved questions and highlighted issues that remain to be explored for better understanding of CD79a/CD79b-mediated signal transduction and the eventual identification of additional therapeutically targetable BCR signaling vulnerabilities.

AI-Enabled Compressive Spectrum Classification for Wideband Radios

Cognitive radio is a promising technology that emerged as a potential solution to the spectrum shortage problem by enabling opportunistic spectrum access. In many cases, cognitive radios are required to sense a wide range of frequencies to locate the spectrum white spaces; hence, wideband spectrum comes into play, which is also an essential step in future wireless systems to boost the throughput. Cognitive radios are intelligent devices and therefore can be opted for the development of modern jamming and anti-jamming solutions. To this end, our article introduces a novel AI-enabled energy-efficient and robust technique for wideband radio spectrum characterization. Our work considers a wideband radio spectrum made up of numerous narrowband signals, which could be normal communications or signals disrupted by a stealthy jammer. First, the receiver recovers the wideband from significantly low sub-Nyquist rate samples by exploiting compressive sensing technique to decrease the overhead caused by the high complexity analog-to-digital conversion process. Once the wideband is recovered, each available narrowband signal is given to a cyclostationary feature detector that computes the corresponding spectral correlation function and extracts the feature vectors in the form of cycle and frequency profiles. Then profiles are concatenated and given as input features set to an artificial neural network which in turn classifies each NB signal as legitimate communication with a specific modulation or disrupted by a stealthy jammer. The results show a classification accuracy of about 0.99 is achieved. Moreover, the algorithm highlights significantly high performances in comparison to recently reported spectrum classification techniques. The proposed technique can be used to design anti-jamming systems for military communication systems.

How to awaken a sleeping giant: antagonistic expression of Flowering locus T homologs and elements of the age-related pathway are associated with the flowering transition in Agave tequilana

Key messageAntagonistic expression of Flowering locus T proteins and the ageing pathway via miRNAs and sugar metabolism regulate the initiation of flowering in A. tequilana.Flowering in commercial plantations of Agave tequilana signals that plants are ready to harvest for tequila production. However, time of flowering is often unpredictable and a detailed understanding of the process would be beneficial in the field, for breeding and for the development of future research. This report describes the functional analysis of A. tequilana FLOWERING LOCUS T (FT) genes by heterologous expression in A. thaliana and in situ hybridization in agave plants. The gene structures of the Agave tequilana FT family are also described and putative regulatory promoter elements were identified. Most Agave species have monocarpic, perennial life cycles that can last over 25 years during which plants do not respond to the normal environmental signals which induce flowering, suggesting that the ageing pathway as described in Arabidopsis may play an important role in determining flowering time in these species. Elements of this pathway were analyzed and in silico data is presented that supports the regulation of SQUAMOSA PROMOTER BINDING LIKE proteins (SPL), APETALA2 (AP2) proteins and members of Plant Glycoside Hydrolase Family 32 (PGHF32) by interactions with miRNAs 156, 172 and 164 during the initiation of flowering in A. tequilana.

Multi-Modal Feature Integration in Machine Learning Predictions for Cardiovascular Diseases

Early detection and prevention of cardiovascular illnesses rely heavily on phonocardiogram (PCG) and electrocardiogram (ECG). A novel multi-modal machine learning strategy based on ECG and PCG data is presented in this work for predicting cardiovascular diseases (CVD). ECG and PCG features are combined for optimal feature subset selection using a genetic algorithm (GA). Then, machine learning classifiers are implemented to do the classification of abnormal and normal signals

The role of polymorphism of redox-sensitive genes in the mechanisms of oxidative stress in obesity and metabolic diseases

The review summarizes ideas about the role of polymorphic variants of redox-sensitive genes that regulate the development of oxidative stress in obesity and associated metabolic diseases. The concept of oxidative stress, activated oxygen metabolites (AOM), which include reactive forms of oxygen, nitrogen, and chlorine, is considered, and an idea of the antioxidant system and its enzymatic link is given. The important role of gene polymorphism of AOM-producing enzymes — CYBA, CYBB, MT-ND1/2/4L, MT-CO1/3, XOR, CYP, NOS2/3, MPO — in the induction of oxidative stress in obesity has been shown. The dualism of AOM in obesity is emphasized: on the one hand, they are necessary for normal adipogenesis and signaling, and, on the other hand, they play a trigger role in the development of oxidative stress. It has been demonstrated that an imbalance in antioxidant system in obesity and metabolic disorders may be associated with variability in the genes of key antioxidant enzymes and proteins — SOD1/2/3, CAT, GPX1-8, GSR, GSTP1, GSTM1, GSTT1, PRDX3, TXNIP, HMOX1, NQO1, NFE2L2, KEAP1. The critical role of polymorphism in the Nrf2 transcription factor gene, the main regulator of redox homeostasis under physiological conditions and in obesity, has been demonstrated. It has been demonstrated that disruption of redox homeostasis due to genetic variability of the prooxidant-antioxidant system contributes to the development of the pathological obesity phenotype. Understanding the genetic mechanisms underlying oxidative stress in obesity and metabolic diseases is necessary to expand knowledge about the mechanisms of pathogenesis of these diseases and to develop effective methods for their correction.

Resilient Event-Triggered Control of Connected Automated Vehicles Under Cyber Attacks

Dear Editor, This letter contributes to designing a resilient event-triggered controller for connected automated vehicles under cyber attacks, including denial-of-service (DoS) and deception attacks. To characterize the effect of DoS attacks, the effective intervals of the attack are redivided based on the sampling period. Then, a resilient distributed event-triggering mechanism is proposed to compensate for the sabotage of DoS attacks and reduce the amount of transmitted data. Since the communication channel transmits the data only at the trigger instant, deception attacks may occur at this instant and be transmitted to each vehicle in superposition with the normal signal. Therefore, we construct stochastic models satisfying Bernoulli distribution to describe the false information injected by the attackers. Based on the above framework, an attack-resilient control strategy is proposed to resist the impact of cyber attacks. Then, sufficient conditions are established to achieve stability of vehicular platoons, and a co-design strategy regarding the control gain and triggering parameter matrices is given. Finally, the simulation results are provided to substantiate the effectiveness of the proposed method.

Investigating lysosomal exocytosis as a pathway of beta‐amyloid release from neurons

AbstractBackgroundThe endosomal‐lysosomal system has long been linked to the production of beta‐amyloid (Aβ), but the specific intracellular compartments involved in Aβ secretion remain contentious. Conventionally, lysosomes are associated with Aβ degradation, yet studies have also shown that lysosomes are a site of Aβ accumulation. Lysosomal exocytosis is a major secretory pathway in non‐neuronal cells, but few studies have investigated this pathway in neurons. This project examined the role and mechanism of lysosomal exocytosis in neurons, and we hypothesized that lysosomal exocytosis is a pathway for Aβ secretion from neurons.MethodsWe have established human iPSC‐derived neurons in culture, including cells bearing the APP Swedish mutation. Cells were transduced with pH sensitive fluorescent (pHluorin/pHmScarlet) tagged compartment markers to examine neurosecretory granules (synaptophysin), early endosomes (Rab5), late endosomes (Rab9) and lysosomes (LAMP1). Cells were loaded with fluorescent exogenous Aβ or Amytracker to label endogenous Aβ. We then stimulated secretion with ionomycin and used live cell Total Internal Reflection Fluorescence (TIRF) microscopy to examine molecular events occurring within 250 nm to the plasma membrane. shRNA against proteins known to be involved with lysosomal secretion (Rab27b, Munc13‐4 and synaptotagmin‐7) were used as controls. Imaris software was used to analyze the number and timing of lysosomal secretory events and the secretion of Aβ.ResultsAs previously reported, exogenous Aβ in wild‐type cells and endogenous Aβ in APP Swedish cells accumulates in lysosomes, but not in early endosomes. TIRF microscopy captured videos of lysosomes approaching and fusing with the plasma membrane. Fusion appears as a reduction in normal fluorescent LAMP1 signal and an increase in pH sensitive fluorescent protein marker signal (reflecting the loss of luminal acidity due to fusion). Intracellular Aβ was released from neurons as lysosomes fused with the plasma membrane, seen as a simultaneous decrease in fluorescent Aβ labelling. Silencing of Rab27b significantly reduced lysosomal exocytosis and Aβ secretion, which confirmed that this is a lysosomal process.ConclusionOur results demonstrate that lysosomal exocytosis is a novel secretion pathway for Aβ in human neurons and identify potential pharmacological targets that regulate clearance of Aβ.

Fault Diagnosis of Rotating Machinery under Complex Conditions Based on Multi-Scale Convolutional Neural Networks

In complex operational scenarios involving variable speeds, burdens, and noise, rotating machinery necessitates extended maintenance. Extracting stable and effective fault-sensitive features under such intricate conditions presents a significant challenge. To tackle this issue, the paper introduces the Multi-Scale Convolutional Neural Network (MSCNN) model tailored specifically for such complexities. The approach in this paper simultaneously captures multi-scale vibration signal features using the innovative Multi-Scale Bifurcation (MSB) module and subsequently aggregates them through a multi-scale fusion layer. This model effectively addresses the common problem of low CNN accuracy in fault diagnosis. The paper validates methodology using a bearing dataset provided by Case Western Reserve University (CWRU) and demonstrates superior performance compared to classical CNN models and other alternatives, achieving an impressive 99.56% classification accuracy for normal signals.

Modeling the diverse effects of divisive normalization on noise correlations.

Divisive normalization, a prominent descriptive model of neural activity, is employed by theories of neural coding across many different brain areas. Yet, the relationship between normalization and the statistics of neural responses beyond single neurons remains largely unexplored. Here we focus on noise correlations, a widely studied pairwise statistic, because its stimulus and state dependence plays a central role in neural coding. Existing models of covariability typically ignore normalization despite empirical evidence suggesting it affects correlation structure in neural populations. We therefore propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability. We first show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we discuss how to infer when normalization signals are shared. We then apply our model to calcium imaging data from mouse primary visual cortex (V1), and find that it accurately fits the data, often outperforming a popular alternative model of correlations. Our analysis indicates that normalization signals are often shared between V1 neurons in this dataset. Our model will enable quantifying the relation between normalization and covariability in a broad range of neural systems, which could provide new constraints on circuit mechanisms of normalization and their role in information transmission and representation.

Developmental synapse pathology triggered by maternal exposure to the herbicide glufosinate ammonium

Environmental and genetic factors influence synapse formation. Numerous animal experiments have revealed that pesticides, including herbicides, can disturb normal intracellular signals, gene expression, and individual animal behaviors. However, the mechanism underlying the adverse outcomes of pesticide exposure remains elusive. Herein, we investigated the effect of maternal exposure to the herbicide glufosinate ammonium (GLA) on offspring neuronal synapse formation in vitro. Cultured cerebral cortical neurons prepared from mouse embryos with maternal GLA exposure demonstrated impaired synapse formation induced by synaptic organizer neuroligin 1 (NLGN1)–coated beads. Conversely, the direct administration of GLA to the neuronal cultures exhibited negligible effect on the NLGN1-induced synapse formation. The comparison of the transcriptomes of cultured neurons from embryos treated with maternal GLA or vehicle and a subsequent bioinformatics analysis of differentially expressed genes (DEGs) identified “nervous system development,” including “synapse,” as the top-ranking process for downregulated DEGs in the GLA group. In addition, we detected lower densities of parvalbumin (Pvalb)-positive neurons at the postnatal developmental stage in the medial prefrontal cortex (mPFC) of offspring born to GLA–exposed dams. These results suggest that maternal GLA exposure induces synapse pathology, with alterations in the expression of genes that regulate synaptic development via an indirect pathway distinct from the effect of direct GLA action on neurons.

Anterior direct decompression significantly relieves spinal cord high signal in patients with ossification of the posterior longitudinal ligament: a case-control study

BackgroundIn patients with cervical spondylotic myelopathy caused by ossification of the posterior longitudinal ligament, high cord signal (HCS) is frequently observed. However, limited research has investigated the variations in HCS improvement resulting from different surgical approaches. This study aims to explore the potential relationship between the choice of surgical approach and the postoperative improvement of intramedullary high signal in ossification of the posterior longitudinal ligament (OPLL) patients.MethodsWe extensively reviewed the patients' medical records, based on which demographic information such as gender, age, and body mass index (BMI) were recorded, and assessed the severity of the patients' neurological status preoperatively and postoperatively by using the Japanese Orthopedic Association score (JOAs), focusing on consecutive preoperative and postoperative Magnetic resonance imaging (MRI) T2WI measurements, to study the statistical correlation between the improvement of HCS and the choice of surgical approach.ResultsThere were no significant differences in demographic, imaging parameters, and clinical symptoms between patients undergoing anterior and posterior surgery (p > 0.05, Table 1). However, both improvement in JOAs (Recovery2) and improvement in HCS (CR2) were significantly better in the anterior surgery group two years after surgery (p < 0.05, Table 1). Multifactorial logistic regression analysis revealed that posterior surgery and higher preoperative signal change ratio (SCR) were identified as risk factors for poor HCS improvement at the two-year postoperative period (p < 0.05, Table 2).Table 1Differences in demographic, imaging parameters, and clinical symptoms in patients with anterior and posterior approachAnterior approachPosterior approachP-ValuesDemographic data Sex (male/female)10/126/170.175 Age58.59 ± 5.6861.43 ± 9.040.215 Hypertension14/814/90.848 Diabetes16/619/40.425 BMI25.58 ± 4.7226.95 ± 4.580.331 Smoking history19/316/70.175Preoperative measured imaging parameters Preoperative SCR1.615 ± 0.3691.668 ± 0.3560.623 CR10.106 ± 0.1250.011 ± 0.2460.08 CNR0.33 ± 0.0730.368 ± 0.0960.15 C2–7 Cobb angle8.977 ± 10.81813.862 ± 13.1910.182 SVA15.212 ± 8.02417.46 ± 8.910.38 mK-line INT3.694 ± 3.2914.527 ± 2.2270.323Imaging follow-up 6 months postoperative SCR1.45 ± 0.441.63 ± 0.3970.149 2 years postoperative SCR1.26 ± 0.191.65 ± 0.350.000** CR20.219 ± 0.14− 0.012 ± 0.2370.000**Clinical symptoms Preoperative JOAs10.64 ± 1.5910.83 ± 1.470.679 6 months postoperative JOAs11.82 ± 1.3711.65 ± 1.40.69 2 years postoperative JOAs14.18 ± 1.0112.52 ± 2.060.001** Recovery10.181 ± 0.1090.128 ± 0.1540.189 Recovery20.536 ± 0.1780.278 ± 0.3070.001***, statistical significance (p < 0.05). **, statistical significance (p < 0.01)BMI = body mass index. SCR = the signal change ratio between the localized high signal and normal spinal cord signal at the C7-T1 levels. CR1 = the regression of high cord signals at 6 months postoperatively (i.e., CR1 = (Preoperative SCR—SCR at 6 months postoperatively)/ Preoperative SCR). CR2 = the regression of high cord signal at 2 years postoperatively (i.e., CR2 = (Preoperative SCR—SCR at 2 years postoperatively)/ Preoperative SCR). CNR = canal narrowing ratio. SVA = sagittal vertical axis. mK-line INT = modified K-line interval. JOAs = Japanese Orthopedic Association score. Recovery1 = degree of JOAs recovery at 6 months postoperatively (i.e., Recover1 = (JOAs at 6 months postoperatively—Preoperative JOAs)/ (17- Preoperative JOAs)). Recovery2 = degree of JOAs recovery at 2 years postoperatively (i.e., Recover2 = (JOAs at 2 years postoperatively−Preoperative JOAs)/ (17−Preoperative JOAs))Table 2Linear regression analyses for lower CR2 values95% CIP valueUni-variable analysesDemographic data Sex (male/female)− 0.010.2210.924 Age− 0.0150.0030.195 Hypertension− 0.0710.2040.334 Diabetes− 0.1950.1350.716 BMI− 0.3750.4220.905 Smoking history− 0.2490.0770.295 Surgical approach− 0.349− 0.1130.000#Preoperative measured imaging parameters C2–7 Cobb angle− 0.0090.0020.185 SVA− 0.0080.0080.995 mK-line INT− 0.0430.0050.122 Preoperative SCR0.0920.4450.004# CR10.1560.7840.004# CNR− 0.760.8440.918Multi-variable analyses Surgical approach− 0.321− 0.1180.000** Preoperative SCR0.1270.410.000** CR1− 0.0180.5010.067#, variables that achieved a significance level of p < 0.1 in the univariate analysis*statistical significance (p < 0.05). **statistical significance (p < 0.01)BMI = body mass index. SCR = the signal change ratio between the localized high signal and normal spinal cord signal at the C7-T1 levels. CR1 = the regression of high cord signals at 6 months postoperatively (i.e., CR1 = (Preoperative SCR—SCR at 6 months postoperatively)/ Preoperative SCR). CR2 = the regression of high cord signal at 2 years postoperatively (i.e., CR2 = (Preoperative SCR—SCR at 2 years postoperatively)/ Preoperative SCR). CNR = canal narrowing ratio. SVA = sagittal vertical axis. mK-line INT = modified K-line intervalConclusionsFor patients with OPLL-induced cervical spondylotic myelopathy and intramedullary high signal, anterior removal of the ossified posterior longitudinal ligament and direct decompression offer a greater potential for regression of intramedullary high signal. At the same time, this anterior surgical strategy improves clinical neurologic function better than indirect decompression in the posterior approach.