Radiomics Nomograms Based on Multi-sequence MRI for Identifying Cognitive Impairment and Predicting Cognitive Progression in Relapsing-Remitting Multiple Sclerosis.

RATIONALE AND OBJECTIVES: To build radiomics nomograms based on multi-sequence MRI to facilitate the identification of cognitive impairment (CI) and prediction of cognitive progression (CP) in patients with relapsing-remitting multiple sclerosis (RRMS). MATERIALS AND METHODS: We retrospectively included two RRMS cohorts with multi-sequence MRI and Symbol Digit Modalities Test (SDMT) data: dataset1 (n = 149, for training and validation) and dataset2 (n = 29, for external validation). 80 patients of dataset1 had a 2-year follow-up SDMT. CI and CP were evaluated using SDMT scores at baseline and follow-up. The included DIR sequence aided in identifying cortical lesions. Lesion radiomics and structural features were extracted and selected from multi-sequence MRI, followed by the computation of radiomics and structural scores. The nomogram was developed through multivariate logistic regression, integrating clinical data, radiomics, and structural scores to identify CI in patients. Moreover, a similar method was employed to further construct a nomogram predicting CP in patients. RESULTS: The nomogram demonstrated superior performance in identifying patients with CI, with area under the curve (AUC) values of 0.937 (95% Conf. Interval: 0.898-0.975) and 0.876 (0.810-0.943) in internal and external validation sets, compared to models solely based on clinical data, lesion radiomics, and structural features. Furthermore, another nomogram constructed in predicting CP also exhibited outstanding performance, with an AUC value of 0.969 (0.875-1.000) in the validation set. CONCLUSION: These nomograms, integrating clinical data, multi-sequence lesions radiomics, and structural features, enable more effective identification of CI and early prediction of CP in RRMS patients, providing important support for clinical decision-making.

Comparative analysis of tubular retractors and hook retractors in oblique lumbar interbody fusion at the initial stage of the learning curve.

PURPOSE: Oblique lumbar interbody fusion (OLIF) still has a steep learning curve that many spinal surgeons who want to develop are hesitant. The purpose of this study is to provide reference for beginners through the comparative analysis of the application of two kinds of retraction devices in the early stage of learning curve. METHOD: We prospectively included the first 60 patients with lumbar degenerative diseases treated with OLIF by a surgeon in our department. According to the application of different retraction devices during the operation, the patients were divided into hook retractor group and tubular retractor group. The clinical effects and complications of the two groups were compared. RESULT: The average age of hook retractor group was 62 years old, the average age of tubular retractor group was 65 years old. There was no significant difference in age, sex, operative segment, follow-up time and blood loss between the two groups. The operation time in hook retractor group was less than that in tubular retractor group. The incidence of complications in hook retractor group (11.8%) was significantly lower than that in tubular retractor group (38.5%). CONCLUSION: The tubular retractor group has a higher risk of neurovascular injury in the initial stage of learning, as well as the risk of vertebral fracture. In contrast, the hook retractor group has the advantages of simple method, high fault tolerance and relatively low incidence of complications. Therefore, we believe that the application of hook retractor in the early stage of OLIF learning curve is easier to increase the operator's confidence and make OLIF more acceptable.

Enhanced choroid plexus segmentation with 3D UX-Net and its association with disease progression in multiple sclerosis.

BACKGROUND: The choroid plexus (CP) is suggested to be closely associated with the neuroinflammation of multiple sclerosis (MS). Segmentation based on deep learning (DL) could facilitate rapid and reproducible volume assessment of the CP, which is crucial for elucidating its role in MS. PURPOSE: To develop a reliable DL model for the automatic segmentation of CP, and further validate its clinical significance in MS. METHODS: The 3D UX-Net model (3D U-Net used for comparison) was trained and validated on T1-weighted MRI from a cohort of 216 relapsing-remitting MS (RRMS) patients and 75 healthy subjects. Among these, 53 RRMS with baseline and 2-year follow-up scans formed an internal test set (dataset1b). Another 58 RRMS from multi-center data served as an external test set (dataset2). Dice coefficient was computed to assess segmentation performance. Compare the correlation of CP volume obtained through automatic and manual segmentation with clinical outcomes in MS. Disability and cognitive function of patients were assessed using the Expanded Disability Status Scale (EDSS) and Symbol Digit Modalities Test (SDMT). RESULTS: The 3D UX-Net model achieved Dice coefficients of 0.875 ± 0.030 and 0.870 ± 0.044 for CP segmentation on dataset1b and dataset2, respectively, outperforming 3D U-Net's scores of 0.809 ± 0.098 and 0.601 ± 0.226. Furthermore, CP volumes segmented by the 3D UX-Net model aligned consistently with clinical outcomes compared to manual segmentation. In dataset1b, both manual and automatic segmentation revealed a significant positive correlation between normalized CP volume (nCPV) and EDSS scores at baseline (manual: r = 0.285, p = 0.045; automatic: r = 0.287, p = 0.044) and a negative correlation with SDMT scores (manual: r = -0.331, p = 0.020; automatic: r = -0.329, p = 0.021). In dataset2, similar correlations were found with EDSS scores (manual: r = 0.337, p = 0.021; automatic: r = 0.346, p = 0.017). Meanwhile, in dataset1b, both manual and automatic segmentation revealed a significant increase in nCPV from baseline to follow-up (p < 0.05). The increase of nCPV was more pronounced in patients with disability worsened than stable patients (manual: p = 0.023; automatic: p = 0.018). Patients receiving disease-modifying therapy (DMT) exhibited a significantly lower nCPV increase than untreated patients (manual: p = 0.004; automatic: p = 0.004). CONCLUSION: The 3D UX-Net model demonstrated strong segmentation performance for the CP, and the automatic segmented CP can be directly used in MS clinical practice. CP volume can serve as a surrogate imaging biomarker for monitoring disease progression and DMT response in MS patients.

An Adaptive Sampling Algorithm with Dynamic Iterative Probability Adjustment Incorporating Positional Information.

Physics-informed neural networks (PINNs) have garnered widespread use for solving a variety of complex partial differential equations (PDEs). Nevertheless, when addressing certain specific problem types, traditional sampling algorithms still reveal deficiencies in efficiency and precision. In response, this paper builds upon the progress of adaptive sampling techniques, addressing the inadequacy of existing algorithms to fully leverage the spatial location information of sample points, and introduces an innovative adaptive sampling method. This approach incorporates the Dual Inverse Distance Weighting (DIDW) algorithm, embedding the spatial characteristics of sampling points within the probability sampling process. Furthermore, it introduces reward factors derived from reinforcement learning principles to dynamically refine the probability sampling formula. This strategy more effectively captures the essential characteristics of PDEs with each iteration. We utilize sparsely connected networks and have adjusted the sampling process, which has proven to effectively reduce the training time. In numerical experiments on fluid mechanics problems, such as the two-dimensional Burgers' equation with sharp solutions, pipe flow, flow around a circular cylinder, lid-driven cavity flow, and Kovasznay flow, our proposed adaptive sampling algorithm markedly enhances accuracy over conventional PINN methods, validating the algorithm's efficacy.

irGSEA: the integration of single-cell rank-based gene set enrichment analysis.

irGSEA is an R package designed to assess the outcomes of various gene set scoring methods when applied to single-cell RNA sequencing data. This package incorporates six distinct scoring methods that rely on the expression ranks of genes, emphasizing relative expression levels over absolute values. The implemented methods include AUCell, UCell, singscore, ssGSEA, JASMINE and Viper. Previous studies have demonstrated the robustness of these methods to variations in dataset size and composition, generating enrichment scores based solely on the relative gene expression of individual cells. By employing the robust rank aggregation algorithm, irGSEA amalgamates results from all six methods to ascertain the statistical significance of target gene sets across diverse scoring methods. The package prioritizes user-friendliness, allowing direct input of expression matrices or seamless interaction with Seurat objects. Furthermore, it facilitates a comprehensive visualization of results. The irGSEA package and its accompanying documentation are accessible on GitHub (https://github.com/chuiqin/irGSEA).

Supercritical CH3OH-Triggered Isotype Heterojunction and Groups in g-C3N4 for Enhanced Photocatalytic H2 Evolution.

The structure tuning of bulk graphitic carbon nitride (g-C3N4) is a critical way to promote the charge carriers dynamics for enhancing photocatalytic H2-evolution activity. Exploring feasible post-treatment strategies can lead to effective structure tuning, but it still remains a great challenge. Herein, a supercritical CH3OH (ScMeOH) post-treatment strategy (250-300 °C, 8.1-11.8 MPa) is developed for the structure tuning of bulk g-C3N4. This strategy presented advantages of time-saving (less than 10 min), high yield (over 80%), and scalability due to the enhanced mass transfer and high reactivity of ScMeOH. During the ScMeOH post-treatment process, CH3OH molecules diffused into the interlayers of g-C3N4 and subsequently participated in N-methylation and hydroxylation reactions with the intralayers, resulting in a partial phase transformation from g-C3N4 into carbon nitride with a poly(heptazine imide)-like structure (Q-PHI) as well as abundant methyl and hydroxyl groups. The modified g-C3N4 showed enhanced photocatalytic activity with an H2-evolution rate 7.2 times that of pristine g-C3N4, which was attributed to the synergistic effects of the g-C3N4/Q-PHI isotype heterojunction construction, group modulation, and surface area increase. This work presents a post-treatment strategy for structure tuning of bulk g-C3N4 and serves as a case for the application of supercritical fluid technology in photocatalyst synthesis.

EMPT: a sparsity Transformer for EEG-based motor imagery recognition.

Introduction: Transformer network is widely emphasized and studied relying on its excellent performance. The self-attention mechanism finds a good solution for feature coding among multiple channels of electroencephalography (EEG) signals. However, using the self-attention mechanism to construct models on EEG data suffers from the problem of the large amount of data required and the complexity of the algorithm. Methods: We propose a Transformer neural network combined with the addition of Mixture of Experts (MoE) layer and ProbSparse Self-attention mechanism for decoding the time-frequency-spatial domain features from motor imagery (MI) EEG of spinal cord injury patients. The model is named as EEG MoE-Prob-Transformer (EMPT). The common spatial pattern and the modified s-transform method are employed for achieving the time-frequency-spatial features, which are used as feature embeddings to input the improved transformer neural network for feature reconstruction, and then rely on the expert model in the MoE layer for sparsity mapping, and finally output the results through the fully connected layer. Results: EMPT achieves an accuracy of 95.24% on the MI EEG dataset for patients with spinal cord injury. EMPT has also achieved excellent results in comparative experiments with other state-of-the-art methods. Discussion: The MoE layer and ProbSparse Self-attention inside the EMPT are subjected to visualisation experiments. The experiments prove that sparsity can be introduced to the Transformer neural network by introducing MoE and kullback-leibler divergence attention pooling mechanism, thereby enhancing its applicability on EEG datasets. A novel deep learning approach is presented for decoding EEG data based on MI.

Clinical efficacy and imaging analysis of oblique lateral lumbar interbody fusion in the treatment of different types of lumbar intervertebral foramen stenosis.

PURPOSE: To analyze and study the clinical efficacy and imaging indexes of oblique lateral lumbar interbody fusion (OLIF) in the treatment of lumbar intervertebral foramen stenosis(LFS) caused by different causes. METHOD: 33 patients with LFS treated with OLIF from January 2018 to May 2022 were reviewed. Oswestry Dysfunction Index (ODI) and visual analogue scale (VAS) were calculated before and after operation. Segmental lordotic angle (SLA), lumbar lordotic angle (LLA) and segmental scoliosis angle (SSA), disc height (DH), posterior disc height (PDH), lateral disc height (LDH), foraminal height (FH), foramen width (FW) and foraminal cross-sectional area (FSCA) were measured before and after operation. RESULT: The VAS and ODI after operation were significantly improved as compared with those before operation. Compared with pre-operation, the DH, PHD increased by 67.6%, 94.6%, LDH increased by 107.4% (left), 101.7% (right), and FH increased by 30.2% (left), 34.5% (right). The FSCA increased by 93.1% (left), 89.0% (right), and the FW increased by 137.0% (left), 149.6% (right). The postoperative SSA was corrected by 74.5%, the postoperative SLA, LLA were corrected by 70.2%, 38.1%, respectively. All the imaging indexes were significantly improved (p < 0.01). CONCLUSION: The clinical efficacy and imaging data of OLIF in the treatment of LFS caused by low and moderate lumbar spondylolisthesis, intervertebral disc bulge and reduced intervertebral space height, degenerative lumbar scoliosis, articular process hyperplasia or dislocation have been well improved. OLIF may be one of the better surgical treatments for LFS caused by the above conditions.

Discrete wavelet transform assisted convolutional neural network equalizer for PAM VLC system.

With the deepening of research and the further differentiation of damage types, and to compensate for both linear and nonlinear damage in visible light communication systems (VLCs), we propose a novel discrete wavelet transform-assisted convolutional neural network (DWTCNN) equalizer that combines the advantages of wavelet transform and deep learning methods. More specifically, wavelet transform is used in DWTCNN to decompose the signal into diverse coefficient series and employ an adaptive soft-threshold method to eliminate redundant information in the signal. The coefficients are then reconstructed to achieve complete signal compensation. The experimental results show that the proposed DWTCNN equalizer can significantly reduce nonlinear impairment and improve system performance with the bit error rate (BER) under the 7% hard-decision forward error correction (HD-FEC) limit of 3.8 × 10-3. We also experimentally compared DWTCNN with the Long Short-Term Memory (LSTM) and entity extraction neural network (EXNN) equalizer, the Q factor has been improved by 0.76 and 0.53 dB, and the operating ranges of the direct current (DC) bias have increased by 4.76% and 23.5%, respectively.

A crazy trio in Parkinson's disease: metabolism alteration, α-synuclein aggregation, and oxidative stress.

Parkinson's disease (PD) is an aging-associated neurodegenerative disorder, characterized by the progressive loss of dopaminergic neurons in the pars compacta of the substantia nigra and the presence of Lewy bodies containing α-synuclein within these neurons. Oligomeric α-synuclein exerts neurotoxic effects through mitochondrial dysfunction, glial cell inflammatory response, lysosomal dysfunction and so on. α-synuclein aggregation, often accompanied by oxidative stress, is generally considered to be a key factor in PD pathology. At present, emerging evidences suggest that metabolism alteration is closely associated with α-synuclein aggregation and PD progression, and improvement of key molecules in metabolism might be potentially beneficial in PD treatment. In this review, we highlight the tripartite relationship among metabolic changes, α-synuclein aggregation, and oxidative stress in PD, and offer updated insights into the treatments of PD, aiming to deepen our understanding of PD pathogenesis and explore new therapeutic strategies for the disease.

Abnormal phosphorylation / dephosphorylation and Ca2+ dysfunction in heart failure.

Heart failure (HF) can be caused by a variety of causes characterized by abnormal myocardial systole and diastole. Ca2+ current through the L-type calcium channel (LTCC) on the membrane is the initial trigger signal for a cardiac cycle. Declined systole and diastole in HF are associated with dysfunction of myocardial Ca2+ function. This disorder can be correlated with unbalanced levels of phosphorylation / dephosphorylation of LTCC, endoplasmic reticulum (ER), and myofilament. Kinase and phosphatase activity changes along with HF progress, resulting in phased changes in the degree of phosphorylation / dephosphorylation. It is important to realize the phosphorylation / dephosphorylation differences between a normal and a failing heart. This review focuses on phosphorylation / dephosphorylation changes in the progression of HF and summarizes the effects of phosphorylation / dephosphorylation of LTCC, ER function, and myofilament function in normal conditions and HF based on previous experiments and clinical research. Also, we summarize current therapeutic methods based on abnormal phosphorylation / dephosphorylation and clarify potential therapeutic directions.

Improving water efficiency is more effective in mitigating water stress than water transfer in Chinese cities.

The interactions between human and natural systems and their effects have unforeseen results, particularly in the management of water resources. Using water stress mitigation as an example, a water resources management effect index (WRMEI) was created to quantitatively evaluate the trends of water management effects. This revealed that the WRMEI was decreasing due to the impact of the water resources management process. The findings demonstrate that water resources management has unintended effects: there was a gap between the expectation of water stress to be mitigated and the actual results of water stress increasing. That is caused by human activities in water utilization: (1) increasing available water resources from water transfer was not utilized sparingly in the receiving cities-increased water transfers from external sources increase domestic water consumption per capita; (2) improving water efficiency has a positive effect on mitigating water stress, but the population growth decreased the efficiency. It was concluded that much greater attention needs to be paid to water conservation in residential and living use to counter these unintended water management effects.

Multi-level feature extraction and reconstruction for 3D MRI image super-resolution.

Magnetic resonance imaging (MRI) is an essential radiology technique in clinical diagnosis, but its spatial resolution may not suffice to meet the growing need for precise diagnosis due to hardware limitations and thicker slice thickness. Therefore, it is crucial to explore suitable methods to increase the resolution of MRI images. Recently, deep learning has yielded many impressive results in MRI image super-resolution (SR) reconstruction. However, current SR networks mainly use convolutions to extract relatively single image features, which may not be optimal for further enhancing the quality of image reconstruction. In this work, we propose a multi-level feature extraction and reconstruction (MFER) method to restore the degraded high-resolution details of MRI images. Specifically, to comprehensively extract different types of features, we design the triple-mixed convolution by leveraging the strengths and uniqueness of different filter operations. For the features of each level, we then apply deconvolutions to upsample them separately at the tail of the network, followed by the feature calibration of spatial and channel attention. Besides, we also use a soft cross-scale residual operation to improve the effectiveness of parameter optimization. Experiments on lesion-free and glioma datasets indicate that our method obtains superior quantitative performance and visual effects when compared with state-of-the-art MRI image SR methods.

How to Choose Surgical Corridor in Left Oblique Approach Lumbar Interbody Fusion at the L5-S1 Segment: A Prospective Cohort Study.

OBJECTIVE: There are 2 surgical corridors to L5-S1 lumbar interbody fusion via the left oblique approach: anterior to psoas-oblique lateral interbody fusion (ATP-OLIF) and oblique-anterior lumbar interbody fusion (O-ALIF). The aim of this study was to evaluate criteria to guide the selection of surgical corridors for L5-S1 lumbar interbody fusion via the left oblique approach. METHODS: According to the structure of L5-S1 segment left common iliac vein (LCIV) in axial magnetic resonance image, the LCIV was divided into 6 types. O-ALIF was performed for type I and type II. ATP-OLIF was performed for type A and type B. For sexually active men, ATP-OLIF was chosen. Between April 2020 and April 2022, 22 patients were assigned to ATP-OLIF or O-ALIF based on the type of LCIV. Clinical outcomes and radiographic outcomes were assessed. RESULTS: There were 11 cases in O-ALIF group (type I, n = 10; type II, n = 1) and 11 cases in ATP-OLIF group (type A, n = 8; type B, n = 3). No differences were observed in clinical outcomes (Oswestry Disability Index, VAS, and complication rate); radiographic outcomes (mean disk height and segmental lordosis angle); length of hospital stay; operation time; and blood loss. No vascular injury occurred in either group. CONCLUSIONS: This may be an appropriate criterion to guide the selection of surgical corridor for L5-S1 lumbar interbody fusion through the left oblique approach. O-ALIF was performed for type I and type II. ATP-OLIF was performed for type A and type B. For sexually active men, ATP-OLIF was chosen. According to this standard, the operation can be performed safely and with good clinical results.

Is Kidney Stone Associated with Thyroid Disease? The United States National Health and Nutrition Examination Survey 2007­2018

BACKGROUND: Kidney stones and thyroid disease are two common diseases in the general population, with multiple common risk factors. The associations between kidney stones and thyroid disease are unclear. AIM: This study aims to assess the association between 'once had a thyroid disease' and the odds of kidney stones. METHODS: Adult participants from the National Health and Nutrition Examination Survey (NHANES) 2007-2018 with reliable kidney stone and thyroid disease data were included. Adjusting for age, gender, race, education level, and marital status, diabetes, hypertension, gout, angina pectoris, stroke, and asthma, logistic regression was used to examine the relationship between kidney stones and thyroid illness. RESULTS: Using stratified analysis, the association between thyroid illness and kidney stones was investigated further. Among the participants, 4.9% had kidney stones, and 10.1% had thyroid disease. Kidney stone was associated with thyroid disease (OR=1.441, (95% CI:1.294-1.604), p <0.01), which remained significant (OR=1.166, (95% CI:1.041-1.305), p <0.01) after adjustments with age, gender, race, education level and marital status, diabetes, hypertension, gout, angina pectoris, stroke, and asthma. Stratified by blood lead, blood cadmium, and blood urea nitrogen levels in the human body, the odds of kidney stones still increased with once having a previous thyroid disease. CONCLUSIONS: In this large nationally representative survey over 10 years, kidney stone was strongly associated with thyroid disease. In this cross-sectional study, we explored the association between thyroid disease and kidney stones, which may help clinicians intervene in them early.

Ligand-Nondestructive Direct Photolithography Assisted by Semiconductor Polymer Cross-Linking for High-Resolution Quantum Dot Light-Emitting Diodes.

The photolithographic patterning of fine quantum dot (QD) films is of great significance for the construction of QD optoelectronic device arrays. However, the photolithography methods reported so far either introduce insulating photoresist or manipulate the surface ligands of QDs, each of which has negative effects on device performance. Here, we report a direct photolithography strategy without photoresist and without engineering the QD surface ligands. Through cross-linking of the surrounding semiconductor polymer, QDs are spatially confined to the network frame of the polymer to form high-quality patterns. More importantly, the wrapped polymer incidentally regulates the energy levels of the emitting layer, which is conducive to improving the hole injection capacity while weakening the electron injection level, to achieve balanced injection of carriers. The patterned QD light-emitting diodes (with a pixel size of 1.5 µm) achieve a high external quantum efficiency of 16.25% and a brightness of >1.4 × 105 cd/m2. This work paves the way for efficient high-resolution QD light-emitting devices.

Prediction of recurrence risk factors in patients with early-stage cervical cancers by nomogram based on MRI handcrafted radiomics features and deep learning features: a dual-center study.

PURPOSE: To establish and validate a deep learning radiomics nomogram (DLRN) based on intratumoral and peritumoral regions of MR images and clinical characteristics to predict recurrence risk factors in early-stage cervical cancer and to clarify whether DLRN could be applied for risk stratification. METHODS: Two hundred and twenty five pathologically confirmed early-stage cervical cancers were enrolled and made up the training cohort and internal validation cohort, and 40 patients from another center were enrolled into the external validation cohort. On the basis of region of interest (ROI) of intratumoral and different peritumoral regions, two sets of features representing deep learning and handcrafted radiomics features were created using combined images of T2-weighted MRI (T2WI) and diffusion-weighted imaging (DWI). The signature subset with the best discriminant features was chosen, and deep learning and handcrafted signatures were created using logistic regression. Integrated with independent clinical factors, a DLRN was built. The discrimination and calibration of DLNR were applied to assess its therapeutic utility. RESULTS: The DLRN demonstrated satisfactory performance for predicting recurrence risk factors, with AUCs of 0.944 (95% confidence interval 0.896-0.992) and 0.885 (95% confidence interval 0.834-0.937) in the internal and external validation cohorts. Furthermore, decision curve analysis revealed that the DLRN outperformed the clinical model, deep learning signature, and radiomics signature in terms of net benefit. CONCLUSION: A DLRN based on intratumoral and peritumoral regions had the potential to predict and stratify recurrence risk factors for early-stage cervical cancers and enhance the value of individualized precision treatment.

Multilevel Laser-Induced Pain Measurement with Wasserstein Generative Adversarial Network - Gradient Penalty Model.

Pain is an experience of unpleasant sensations and emotions associated with actual or potential tissue damage. In the global context, billions of people are affected by pain disorders. There are particular challenges in the measurement and assessment of pain, and the commonly used pain measuring tools include traditional subjective scoring methods and biomarker-based measures. The main tools for biomarker-based analysis are electroencephalography (EEG), electrocardiography and functional magnetic resonance. The EEG-based quantitative pain measurements are of immense value in clinical pain management and can provide objective assessments of pain intensity. The assessment of pain is now primarily limited to the identification of the presence or absence of pain, with less research on multilevel pain. High power laser stimulation pain experimental paradigm and five pain level classification methods based on EEG data augmentation are presented. First, the EEG features are extracted using modified S-transform, and the time-frequency information of the features is retained. Based on the pain recognition effect, the 20-40[Formula: see text]Hz frequency band features are optimized. Afterwards the Wasserstein generative adversarial network with gradient penalty is used for feature data augmentation. It can be inferred from the good classification performance of features in the parietal region of the brain that the sensory function of the parietal lobe region is effectively activated during the occurrence of pain. By comparing the latest data augmentation methods and classification algorithms, the proposed method has significant advantages for the five-level pain dataset. This research provides new ways of thinking and research methods related to pain recognition, which is essential for the study of neural mechanisms and regulatory mechanisms of pain.

Clinical efficacy and biomarker analysis of neoadjuvant camrelizumab plus chemotherapy for early-stage triple-negative breast cancer: a experimental single-arm phase II clinical trial pilot study.

BACKGROUND: Triple-negative breast cancer (TNBC) is associated with a dismal prognosis. Immune checkpoint inhibitors have shown promising antitumor activity in neoadjuvant settings. This single-arm, phase II trial aimed to evaluate the efficacy and safety of camrelizumab plus chemotherapy as the neoadjuvant therapy (NAT) in early TNBC. METHODS: Patients received eight cycles of camrelizumab plus nonplatinum-based chemotherapy. The primary endpoint was total pathological complete response (pCR). Secondary endpoints included the breast pathological complete response (bpCR), adverse events (AEs). Multiomics biomarkers were assessed as exploratory objective. RESULTS: Twenty of 23 TNBC patients receiving NAT underwent surgery, with the total pCR rate of 65% (13/20) and bpCR rate of 70% (14/20). Grade ≥3 treatment-related AEs were observed in 14 (60.9%) patients, with the most common AE being neutropenia (65.2%). Tumor immune microenvironment was analyzed between pCR and non-pCR samples before and after the NAT. Gene expression profiling showed a higher immune infiltration in pCR patients than non-pCR patients in pre-NAT samples. Through establishment of a predictive model for the NAT efficacy, TAP1 and IRF4 were identified as the potential predictive biomarkers for response to the NAT. Gene set enrichment analysis revealed the glycolysis and hypoxia pathways were significantly activated in non-pCR patients before the NAT, and this hypoxia was aggravated after the NAT. CONCLUSION: Camrelizumab plus nonplatinum-based chemotherapy shows a promising pCR rate in early-stage TNBC, with an acceptable safety profile. TAP1 and IRF4 may serve as potential predictive biomarkers for response to the NAT. Aggravated hypoxia and activated glycolysis after the NAT may be associated with the treatment resistance.