Design and study of mine silo drainage method based on fuzzy control and Avoiding Peak Filling Valley strategy.

Coal is a non-renewable fossil energy source on which humanity relies heavily, and producing one ton of raw coal requires the discharge of 2-7 tons of mine water from the ground. The huge drainage task increases the cost of coal mining in coal mines significantly, so saving the drainage cost while guaranteeing the safe production of coal mines is a problem that needs to be solved urgently. Most of the fuzzy controllers used in the traditional dynamic planning methods applied to mine drainage are two-dimensional fuzzy controllers with limited control effect, so the traditional two-dimensional fuzzy controllers are improved by introducing the rate of change of gushing water to form a three-dimensional fuzzy controller with three-dimensional control of instantaneous section-water level-rate of change of gushing water, and at the same time, the optimized dynamic planning method is designed by combining the Avoiding Peak Filling Valley strategy and the optimal dy-namic planning method is used in conjunction with the un-optimized dynamic planning method. The optimized dynamic planning method is applied to the same coal mine water silo gushing water experiments; experimental comparison found that the pumping station system before the optimi-zation of the electricity consumed is 52,586 yuan/day, while after the optimization of the electricity consumed is reduced to 41,692 yuan/day, the cost per day consumed compared with the previous reduction of 20.69%, a year can be saved 3,969,730 yuan. Therefore, the mine water bin drainage method based on fuzzy control and Avoiding Peak Filling Valley strategy proposed in this paper can be used as an improvement method of the existing mine drainage method, which can further ex-pand the economic benefits of coal mines and realize safe production while realizing cost savings.

Hallmark guided identification and characterization of a novel immune-relevant signature for prognostication of recurrence in stage I-III lung adenocarcinoma.

The high risk of postoperative mortality in lung adenocarcinoma (LUAD) patients is principally driven by cancer recurrence and low response rates to adjuvant treatment. Here, A combined cohort containing 1,026 stage I-III patients was divided into the learning (n = 678) and validation datasets (n = 348). The former was used to establish a 16-mRNA risk signature for recurrence prediction with multiple statistical algorithms, which was verified in the validation set. Univariate and multivariate analyses confirmed it as an independent indicator for both recurrence-free survival (RFS) and overall survival (OS). Distinct molecular characteristics between the two groups including genomic alterations, and hallmark pathways were comprehensively analyzed. Remarkably, the classifier was tightly linked to immune infiltrations, highlighting the critical role of immune surveillance in prolonging survival for LUAD. Moreover, the classifier was a valuable predictor for therapeutic responses in patients, and the low-risk group was more likely to yield clinical benefits from immunotherapy. A transcription factor regulatory protein-protein interaction network (TF-PPI-network) was constructed via weighted gene co-expression network analysis (WGCNA) concerning the hub genes of the signature. The constructed multidimensional nomogram dramatically increased the predictive accuracy. Therefore, our signature provides a forceful basis for individualized LUAD management with promising potential implications.

Prediction of road dust concentration in open-pit coal mines based on multivariate mixed model.

The problem of dust pollution in the open-pit coal mine significantly impacts the health of staff, the regular operation of mining work, and the surrounding environment. At the same time, the open-pit road is the largest dust source. Therefore, it analyzes the influencing factors of road dust concentration in the open-pit coal mine. It is of practical significance to establish a prediction model for scientific and effective prediction of road dust concentration in the open pit coal mine. The prediction model helps reduce dust hazards. This paper uses the hourly air quality and meteorological data of an open-pit coal mine in Tongliao City, Inner Mongolia Autonomous Region, from January 1, 2020, to December 31, 2021. Create a CNN-BiLSTM-Attention multivariate hybrid model consisting of a Convolutional Neural Network (CNN), a bidirectional long short-term memory neural network (BiLSTM), and an attention mechanism, Prediction of PM2.5 concentration in the next 24h. Establish prediction models of parallel and serial structures, and carry out many experiments according to the change period of the data to determine the optimal configuration and the input and output size. Then, a comparison of the proposed model and Lasso regression, SVR, XGBoost, LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM models for short-term prediction (24h) and long-term prediction (48h, 72h, 96h, and 120h). The results show that the CNN-BiLSTM-Attention multivariate mixed model proposed in this paper has the best prediction performance. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) of the short-term forecast (24h) are 6.957, 8.985, and 0.914, respectively. Evaluation indicators of long-term forecasts (48h, 72h, 96h, and 120h) are also superior to contrast models. Finally, we used field-measured data to verify, and the obtained evaluation indexes MAE, RMSE, and R2 are 3.127, 3.989, and 0.951, respectively. The model-fitting effect was good.

Deciphering the immune heterogeneity dominated by natural killer cells with prognostic and therapeutic implications in hepatocellular carcinoma.

Belonging to type 1 innate lymphoid cells (ILC1), natural killer (NK) cells play an important role not only in fighting microbial infections but also in anti-tumor response. Hepatocellular carcinoma (HCC) represents an inflammation-related malignancy and NK cells are enriched in the liver, making them an essential component of the HCC immune microenvironment. In this study, we performed single-cell RNA-sequencing (scRNA-seq) analysis to identify the NK cell marker genes (NKGs) and uncovered 80 prognosis-related ones by the TCGA-LIHC dataset. Based on prognostic NKGs, HCC patients were categorized into two subtypes with distinct clinical outcomes. Subsequently, we conducted LASSO-COX and stepwise regression analysis on prognostic NKGs to establish a five-gene (UBB, CIRBP, GZMH, NUDC, and NCL) prognostic signature-NKscore. Different mutation statuses of the two risk groups stratified by NKscore were comprehensively characterized. Besides, the established NKscore-integrated nomogram presented enhanced predictive performance. Single sample gene set enrichment analysis (ssGSEA) analysis was used to uncover the landscape of the tumor immune microenvironment (TIME) and the high-NKscore risk group was characterized with an immune-exhausted phenotype while the low-NKscore risk group held relatively strong anti-cancer immunity. T cell receptor (TCR) repertoire, tumor inflammation signature (TIS), and Immunophenoscore (IPS) analyses revealed differences in immunotherapy sensitivity between the two NKscore risk groups. Taken together, we developed a novel NK cell-related signature to predict the prognosis and immunotherapy efficacy for HCC patients.

Research on fault diagnosis system for belt conveyor based on internet of things and the LightGBM model.

As an equipment failure that often occurs in coal production and transportation, belt conveyor failure usually requires many human and material resources to be identified and diagnosed. Therefore, it is urgent to improve the efficiency of fault identification, and this paper combines the internet of things (IoT) platform and the Light Gradient Boosting Machine (LGBM) model to establish a fault diagnosis system for the belt conveyor. Firstly, selecting and installing sensors for the belt conveyor to collect the running data. Secondly, connecting the sensor and the Aprus adapter and configuring the script language on the client side of the IoT platform. This step enables the collected data to be uploaded to the client side of the IoT platform, where the data can be counted and visualized. Finally, the LGBM model is built to diagnose the conveyor faults, and the evaluation index and K-fold cross-validation prove the model's effectiveness. In addition, after the system was established and debugged, it was applied in practical mine engineering for three months. The field test results show: (1) The client of the IoT can well receive the data uploaded by the sensor and present the data in the form of a graph. (2) The LGBM model has a high accuracy. In the test, the model accurately detected faults, including belt deviation, belt slipping, and belt tearing, which happened twice, two times, one time and one time, respectively, as well as timely gaving warnings to the client and effectively avoiding subsequent accidents. This application shows that the fault diagnosis system of belt conveyors can accurately diagnose and identify belt conveyor failure in the coal production process and improve the intelligent management of coal mines.

Hallmark-guided subtypes of hepatocellular carcinoma for the identification of immune-related gene classifiers in the prediction of prognosis, treatment efficacy, and drug candidates.

Hepatocellular carcinoma (HCC), accounting for ~90% of all primary liver cancer, is a prevalent malignancy worldwide. The intratumor heterogeneity of its causative etiology, histology, molecular landscape, and immune phenotype makes it difficult to precisely recognize individuals with high mortality risk or tumor-intrinsic treatment resistance, especially immunotherapy. Herein, we comprehensively evaluated the activities of cancer hallmark gene sets and their correlations with the prognosis of HCC patients using gene set variation analysis (GSVA) and identified two HCC subtypes with distinct prognostic outcomes. Based on these subtypes, seven immune-related genes (TMPRSS6, SPP1, S100A9, EPO, BIRC5, PLXNA1, and CDK4) were used to construct a novel prognostic gene signature [hallmark-guided subtypes-based immunologic signature (HGSIS)] via multiple statistical approaches. The HGSIS-integrated nomogram suggested an enhanced predictive performance. Interestingly, oncogenic hallmark pathways were significantly enriched in the high-risk group and positively associated with the risk score. Distinct mutational landscapes and immune profiles were observed between different risk groups. Moreover, immunophenoscore (IPS) and tumor immune dysfunction and exclusion (TIDE) analysis showed different sensitivities of HGSIS risk groups for immune therapy efficacy, and the pRRophetic algorithm indicated distinguishable responses for targeted/chemotherapies in different groups. KIF2C was picked out as the key target concerning HGSIS, and the top 10 small molecules were predicted to bind to the active site of KIF2C via molecular docking, which might be further used for candidate drug discovery of HCC. Taken together, our study offers novel insights for clinically significant subtype recognition, and the proposed signature may be a helpful guide for clinicians to improve the treatment regimens.

Identification of a Tumor Immunological Phenotype-Related Gene Signature for Predicting Prognosis, Immunotherapy Efficacy, and Drug Candidates in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the predominant subtype of primary liver cancer and represents a highly heterogeneous disease, making it hard to predict the prognosis and therapy efficacy. Here, we established a novel tumor immunological phenotype-related gene index (TIPRGPI) consisting of 11 genes by Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) algorithm to predict HCC prognosis and immunotherapy response. TIPRGPI was validated in multiple datasets and exhibited outstanding performance in predicting the overall survival of HCC. Multivariate analysis verified it as an independent predictor and a TIPRGPI-integrated nomogram was constructed to provide a quantitative tool for clinical practice. Distinct mutation profiles, hallmark pathways, and infiltration of immune cells in tumor microenvironment were shown between the TIPRGPI high and low-risk groups. Notably, significant differences in tumor immunogenicity and tumor immune dysfunction and exclusion (TIDE) were observed between the two risk groups, suggesting a better response to immune checkpoint blockade (ICB) therapy of the low-risk group. Besides, six potential drugs binding to the core target of the TIPRGPI signature were predicted via molecular docking. Taken together, our study shows that the proposed TIPRGPI was a reliable signature to predict the risk classification, immunotherapy response, and drugs candidate with potential application in the clinical decision and treatment of HCC. The novel "TIP genes"-guided strategy for predicting the survival and immunotherapy efficacy, we reported here, might be also applied to more cancers other than HCC.

Design of PM2.5 monitoring and forecasting system for opencast coal mine road based on internet of things and ARIMA Mode.

The dust produced by transportation roads is the primary source of PM2.5 pollution in opencast coal mines. However, China's opencast coal mines lack an efficient and straightforward construction scheme of monitoring and management systems and a short-term prediction model to support dust control. In this study, by establishing a PM2.5 and other real-time environmental information to monitor, manage, visualize and predict the Internet of things monitoring and prediction system to solve these problems. This study solves these problems by establishing an Internet of things monitoring and prediction system, which can monitor PM2.5 and other real-time environmental information for monitoring, management, visualization, and prediction. We use Lua language to write interface protocol code in the APRUS adapter, which can simplify the construction of environmental monitoring system. The Internet of things platform has a custom visualization scheme, which is convenient for managers without programming experience to manage sensors and real-time data. We analyze real-time data using a time series model in Python, and RMSE and MAPE evaluate cross-validation results. The evaluation results show that the average RMSE of the ARIMA (4,1,0) and Double Exponential Smoothing models are 12.68 and 8.34, respectively. Both models have good generalization ability. The average MAPE of the fitting results are 10.5% and 1.7%, respectively, and the relative error is small. Because the ARIMA model has a more flexible prediction range and strong expansibility, and ARIMA model shows good adaptability in cross-validation, the ARIMA model is more suitable as the short-term prediction model of the prediction system. The prediction system can continuously predict PM2.5 dust through the ARIMA model. The monitoring and prediction system is very suitable for managers of opencast coal mines to prevent and control road dust.

Characterization of cellular senescence patterns predicts the prognosis and therapeutic response of hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is a prevalent malignancy with a high mortality rate. Cellular senescence, an irreversible state of cell cycle arrest, plays a paradoxical role in cancer progression. Here, we aimed to identify Hepatocellular carcinoma subtypes by cellular senescence-related genes (CSGs) and to construct a cellular senescence-related gene subtype predictor as well as a novel prognostic scoring system, which was expected to predict clinical outcomes and therapeutic response of Hepatocellular carcinoma. Methods: RNA-seq data and clinical information of Hepatocellular carcinoma patients were derived from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). The "multi-split" selection was used to screen the robust prognostic cellular senescence-related genes. Unsupervised clustering was performed to identify CSGs-related subtypes and a discriminant model was obtained through multiple statistical approaches. A CSGs-based prognostic model-CSGscore, was constructed by LASSO-Cox regression and stepwise regression. Immunophenoscore (IPS) and Tumor Immune Dysfunction and Exclusion (TIDE) were utilized to evaluate the immunotherapy response. Tumor stemness indices mRNAsi and mDNAsi were used to analyze the relationship between CSGscore and stemness. Results: 238 robust prognostic differentially expressed cellular senescence-related genes (DECSGs) were used to categorize all 336 hepatocellular carcinoma patients of the TCGA-LIHC cohort into two groups with different survival. Two hub genes, TOP2A and KIF11 were confirmed as key indicators and were used to form a precise and concise cellular senescence-related gene subtype predictor. Five genes (PSRC1, SOCS2, TMEM45A, CCT5, and STC2) were selected from the TCGA training dataset to construct the prognostic CSGscore signature, which could precisely predict the prognosis of hepatocellular carcinoma patients both in the training and validation datasets. Multivariate analysis verified it as an independent prognostic factor. Besides, CSGscore was also a valuable predictor of therapeutic responses in hepatocellular carcinoma. More downstream analysis revealed the signature genes were significantly associated with stemness and tumor progression. Conclusion: Two subtypes with divergent outcomes were identified by prognostic cellular senescence-related genes and based on that, a subtype indicator was established. Moreover, a prognostic CSGscore system was constructed to predict the survival outcomes and sensitivity of therapeutic responses in hepatocellular carcinoma, providing novel insight into hepatocellular carcinoma biomarkers investigation and design of tailored treatments depending on the molecular characteristics of individual patients.

Integrative analysis identifies key mRNA biomarkers for diagnosis, prognosis, and therapeutic targets of HCV-associated hepatocellular carcinoma.

Hepatitis C virus-associated HCC (HCV-HCC) is a prevalent malignancy worldwide and the molecular mechanisms are still elusive. Here, we screened 240 differentially expressed genes (DEGs) of HCV-HCC from Gene expression omnibus (GEO) and the Cancer Genome Atlas (TCGA), followed by weighted gene coexpression network analysis (WGCNA) to identify the most significant module correlated with the overall survival. 10 hub genes (CCNB1, AURKA, TOP2A, NEK2, CENPF, NUF2, CDKN3, PRC1, ASPM, RACGAP1) were identified by four approaches (Protein-protein interaction networks of the DEGs and of the significant module by WGCNA, and diagnostic and prognostic values), and their abnormal expressions, diagnostic values, and prognostic values were successfully verified. A four hub gene-based prognostic signature was built using the least absolute shrinkage and selection operator (LASSO) algorithm and a multivariate Cox regression model with the ICGC-LIRI-JP cohort (N =112). Kaplan-Meier survival plots (P = 0.0003) and Receiver Operating Characteristic curves (ROC = 0.778) demonstrated the excellent predictive potential for the prognosis of HCV-HCC. Additionally, upstream regulators including transcription factors and miRNAs of hub genes were predicted, and candidate drugs or herbs were identified. These findings provide a firm basis for the exploration of the molecular mechanism and further clinical biomarkers development of HCV-HCC.

Mining TCGA Data for Key Biomarkers Related to Immune Microenvironment in Endometrial cancer by Immune Score and Weighted Correlation Network Analysis.

Background: Endometrial cancer (EC) is one of the most lethal gynecological cancers around the world. The aim of this study is to identify the potential immune microenvironment-related biomarkers associated with the prognosis for EC. Methods: RNA-seq data and clinical information of EC patients were derived from The Cancer Genome Atlas (TCGA). The immune score of each EC sample was obtained by ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) was used to identify the interesting module and potential key genes concerning the immune score. The expression patterns of the key genes were then verified via the GEPIA database. Finally, CIBERSORT was applied to evaluate the relative abundances of 22 immune cell types in EC. Results: Immune scores were significantly associated with tumor grade and histology of EC, and high immune scores may exert a protective influence on the survival outcome for EC. WGCNA indicated that the black module was significantly correlated with the immune score. Function analysis revealed it mainly involved in those terms related to immune regulation and inflammatory response. Moreover, 11 key genes (APOL3, C10orf54, CLEC2B, GIMAP1, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GYPC, IFFO1, TAGAP) were identified from the black module, validated by the GEPIA database, and revealed strong correlations with infiltration levels of multiple immune cell types, as was the prognosis of EC. Conclusion: In this study, 11 key genes showed abnormal expressions and strong correlations with immune infiltration in EC, most of which were significantly associated with the prognosis of EC. These findings made them promising therapeutic targets for the treatment of EC.

The SARS-CoV-2 spike L452R-E484Q variant in the Indian B.1.617 strain showed significant reduction in the neutralization activity of immune sera.

To assess the impact of the key non-synonymous amino acid substitutions in the RBD of the spike protein of SARS-CoV-2 variant B.1.617.1 (dominant variant identified in the current India outbreak) on the infectivity and neutralization activities of the immune sera, L452R and E484Q (L452R-E484Q variant), pseudotyped virus was constructed (with the D614G background). The impact on binding with the neutralizing antibodies was also assessed with an ELISA assay. Pseudotyped virus carrying a L452R-E484Q variant showed a comparable infectivity compared with D614G. However, there was a significant reduction in the neutralization activity of the immune sera from non-human primates vaccinated with a recombinant receptor binding domain (RBD) protein, convalescent patients, and healthy vaccinees vaccinated with an mRNA vaccine. In addition, there was a reduction in binding of L452R-E484Q-D614G protein to the antibodies of the immune sera from vaccinated non-human primates. These results highlight the interplay between infectivity and other biologic factors involved in the natural evolution of SARS-CoV-2. Reduced neutralization activities against the L452R-E484Q variant will have an impact on health authority planning and implications for the vaccination strategy/new vaccine development.

GLS1-mediated glutaminolysis unbridled by MALT1 protease promotes psoriasis pathogenesis.

Psoriasis is a severe disease associated with the disturbance of metabolism and inflammation, but the molecular mechanisms underlying these aspects of psoriasis pathology are poorly understood. Here, we report that glutaminase 1-mediated (GLS1-mediated) glutaminolysis was aberrantly activated in patients with psoriasis and in psoriasis-like mouse models, which promoted Th17 and γδ T17 (IL-17A-producing γδ T) cell differentiation through enhancement of histone H3 acetylation of the Il17a promoter, thereby contributing to the immune imbalance and development of psoriasis. We further demonstrate that mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1) protease was constitutively active in psoriatic CD4+ and γδ T cells, thereby supporting GLS1 expression by stabilizing c-Jun, which directly binds to the GLS1 promoter region. Blocking the activity of either GLS1 or MALT1 protease resolved Th17 and γδ T17 cell differentiation and epidermal hyperplasia in the psoriasis-like mouse models. Finally, IL-17A enhanced GLS1 expression via the MALT1/cJun pathway in keratinocytes, resulting in hyperproliferation of and chemokine production by keratinocytes. Our findings identify the role of the MALT1/cJun/GLS1/glutaminolysis/H3 acetylation/T17 axis in psoriasis pathogenesis and reveal potential therapeutic targets for this disease.

Antiapoptotic properties of MALT1 protease are associated with redox homeostasis in ABC-DLBCL cells.

Mucosa-associated lymphoid tissue lymphoma translocation protein-1 (MALT1) protease presents crucial antiapoptotic properties in activated B cell-like diffuse large B-cell lymphoma (ABC-DLBCL); however, the mechanism is unclear. Here, we reported that inhibition of MALT1 protease in ABC-DLBCL cells led to cell apoptosis, along with elevated mitochondrial reactive oxygen species production and a reduced oxygen consumption rate. These alterations induced by MALT1 protease inhibition were associated with reduced expression of glutaminase (GLS1) and glutathione levels. We further show that MALT1 protease was required for the activation and nuclear translocation of c-Jun, which functions as a transcription factor of the GLS1 gene by binding directly to its promoter region. Taken together, MALT1 protease maintained mitochondrial redox homeostasis and mitochondrial bioenergetics through the MALT1-c-Jun-GLS1-coupled metabolic pathway to defend against apoptosis in ABC-DLBCL cells, which raises exciting possibilities regarding targeting of the MALT1-c-Jun-GLS1 axis as a potential therapeutic strategy against ABC-DLBCL.

Inhibition of MALT1 paracaspase activity improves lesion recovery following spinal cord injury.

Spinal cord injury (SCI) is a devastating traumatic injury that causes persistent, severe motor and sensory dysfunction. Immune responses are involved in functional recovery after SCI. Mucosa-associated lymphoid tissue lymphoma translocation 1 (MALT1) has been shown to regulate the survival and differentiation of immune cells and to play a critical role in many diseases, but its function in lesion recovery after SCI remains unclear. In this paper, we generated KI (knock in) mice with a point mutation (C472G) in the active center of MALT1 and found that the KI mice exhibited improved functional recovery after SCI. Fewer macrophages were recruited to the injury site in KI mice and these macrophages differentiated into anti-inflammatory macrophages. Moreover, macrophages from KI mice exhibited reduced phosphorylation of p65, which in turn resulted in decreased SOCS3 expression and increased pSTAT6 levels. Similar results were obtained upon inhibition of MALT1 paracaspase with the small molecule inhibitor "MI-2" or the more specific inhibitor "MLT-827". In patients with SCI, peripheral blood mononuclear cells (PBMC) displayed increased MALT1 paracaspase. Human macrophages showed reduced pro-inflammatory and increased anti-inflammatory characteristics following the inhibition of MALT1 paracaspase. These findings suggest that inhibition of MALT1 paracaspase activity in the clinic may improve lesion recovery in subjects with SCI.

Glutaminolysis Mediated by MALT1 Protease Activity Facilitates PD-L1 Expression on ABC-DLBCL Cells and Contributes to Their Immune Evasion.

Previous studies have demonstrated that programmed death-1 ligand 1 (PD-L1) expressed in an aggressive activated B-cell (ABC)/non-germinal center B cell-like (GCB) subtype of diffuse large B-cell lymphoma (DLBCL) is associated with inhibition of the tumor-associated T cell response. However, the molecular mechanism underlying PD-L1 expression in ABC-DLBCL remains unclear. Here, we report that MALT1 protease activity is required for ABC-DLBCL cells to evade cytotoxity of Vγ9Vδ2 T lymphocytes by generating substantial PD-L1+ ABC-DLBCL cells. While, NF-κB was dispensable for the PD-L1 expression induced by MALT1 protease activity in ABC-DLBCL cells. Furthermore, we showed that GLS1 expression was profoundly reduced by MALT1 protease activity inhibition, which resulted in insufficiency of glutaminolysis-derived mitochondrial bioenergetics. Activation of the PD-L1 transcription factor STAT3, which was strongly suppressed by glutaminolysis blockade, was rescued in a TCA (tricarboxylic acid) cycle-dependent manner by glutamate addition. Collectively, MALT1 protease activity coupled with glutaminolysis-derived mitochondrial bioenergetics plays an essential role in PD-L1 expression on ABC-DLBCL cells under immunosurveillance stress. Thus, our research sheds light on a mechanism underlying PD-L1 expression and highlights a potential therapeutic target to vanquish immune evasion by ABC-DLBCL cells.

Magnetic resonance imaging analysis of surgical trans-sacral axial L5/S1 interbody fusion.

BACKGROUND: Trans-sacral axial L5/S1 interbody fusion (AxiaLIF), a novel surgical procedure, recently adopted in clinical practice, has excellent clinical outcomes. However, there is inadequate data on the feasibility of the approach in all adult patients and the optimal surgical approach is currently unclear; therefore, further studies are required. In order to enhance the surgical approach for AxiaLIF, prospective anatomical imaging optimization is necessary. The objective of this study was to investigate the ability of magnetic resonance imaging (MRI) to achieve an optimal procedural setting. METHODS: The subjects (n=40) underwent lumbosacral MRI examination. The median sagittal MRI images were analyzed and four measurement markers were defined as follows: the center of the L5/S1 disc (A), the anterior margin of the S1/2 disc space (B), the sacrococcygeal junction (C), and the coccygeal tip (D). The measurement markers were connected to each other to produce five lines (AB, AC, AD, BC, and BD), as reference lines for surgical approaches. The distance between each reference line and the anterior and posterior margins of the L5 and S1 vertebral bodies was measured to determine the safety of the respective approaches. RESULTS: In all patients, Lines AB and AC satisfied the imaging safety criteria. Line AB would result in a significant deviation from the median and was determined to be unsuitable for AxiaLIF. Line AD satisfied the imaging safety criteria in 39 patients. However, the anal proximity of the puncture point proved to be limiting. For lines BC and BD, the imaging safety criteria were satisfied in 70% and 45% of patients, respectively. CONCLUSIONS: The AxiaLIF procedure is a safe technique for insertion of fusion implants in all subjects. Line AC is a favorable reference line for surgical approach and safe for all subjects, while line BC is not suitable for all subjects.

Is the transsacral axial interbody fusion a candidate surgical approach for fusing both L5/S1 and L4/5?

BACKGROUND: Previous clinical and basic research of axial lumbar interbody fusion (AxiaLIF) all focused on the L5/S1. However, there is no data on the feasibility of this approach for the fusion of both L4/5 and L5/S1. This study aimed to explore whether transsacral axial interbody fusion is a candidate for the fusion of both L4/5 and L5/S1. METHODS: The subjects (n = 40) underwent lumbosacral magnetic resonance imaging (MRI). The median sagittal MRI images were analyzed and five measurement markers were defined as follows: the center of the L4/5 disc (A), the center of the L5/S1 disc (B), the anterior margin of the S1/2 space (C), the sacrococcygeal junction (D), and the coccygeal tip (E). The measurement markers were connected each other to produce nine lines (AB, AC, AD, AE, BC, BD, BE, CD and CE) as the reference lines for surgical approaches. The distance between each reference line and the anterior and posterior margins of the L4, L5 and S1 vertebral bodies were measured to determine the safety of the respective approaches. RESULTS: Twenty subjects were capable of finding one reference line to fuse both L4/5 and L5/S1 via transsacral axial interbody fusion approach. The surgical approach reference line was AE or CE line. In the other 20 subjects, it was failed to find a reference line which met the safety criteria for fusing both L4/5 and L5/S1. CONCLUSIONS: About half of subjects were capable of finding a suitable AxiaLIF reference line to fuse both L4/5 and L5/S1. In some subjects, it was difficult to find a suitable AxiaLIF reference line to fuse both L4/5 and L5/S1.