Objective: This study aims to investigate the anatomical relationship among the nerve roots, intervertebral space, pedicles, and intradural rootlets of the cervical spine for improving operative outcomes and exploring neuroventral decompression approach in posterior endoscopic cervical discectomy (PECD). Methods: Cervical computed tomography myelography imaging data from January 2021 to May 2023 were collected, and the RadiAnt DICOM Viewer Software was employed to conduct multiplane reconstruction. The following parameters were recorded: width of nerve root (WN), nerve root-superior pedicle distance (NSPD), nerve root-inferior pedicle distance (NIPD), and the relationship between the intervertebral space and the nerve root (shoulder, anterior, and axillary). Additionally, the descending angles between the spinal cord and the ventral (VRA) and dorsal (DRA) rootlets were measured. Results: The WN showed a gradual increase from C4 to C7, with measurements notably larger in men compared to women. The NSPD decreased gradually from the C2–3 to the C5–6 levels. However, the NIPD showed an opposite level-related change, notably larger than the NSPD at the C4–5, C5–6, and C7–T1 levels. Furthermore, significant differences in NIPD were observed between different age groups and genders. The incidence of the anterior type exhibited a gradual decrease from the C2–3 to the C5–6 levels. Conversely, the axillary type exhibited an opposite level-related change. Additionally, the VRA and DRA decreased as the level descended, with measurements significantly larger in females. Conclusion A prediction of the positional relationship between the intervertebral space and the nerve root is essential for the direct neuroventral decompression in PECD to avoid damaging the neural structures. The axillary route of the nerve root offers a safer and more effective pathway for performing direct neuroventral decompression compared to the shoulder approach.

Objective: This study aims to investigate the anatomical relationship among the nerve roots, intervertebral space, pedicles, and intradural rootlets of the cervical spine for improving operative outcomes and exploring neuroventral decompression approach in posterior endoscopic cervical discectomy (PECD). Methods: Cervical computed tomography myelography imaging data from January 2021 to May 2023 were collected, and the RadiAnt DICOM Viewer Software was employed to conduct multiplane reconstruction. The following parameters were recorded: width of nerve root (WN), nerve root-superior pedicle distance (NSPD), nerve root-inferior pedicle distance (NIPD), and the relationship between the intervertebral space and the nerve root (shoulder, anterior, and axillary). Additionally, the descending angles between the spinal cord and the ventral (VRA) and dorsal (DRA) rootlets were measured. Results: The WN showed a gradual increase from C4 to C7, with measurements notably larger in men compared to women. The NSPD decreased gradually from the C2–3 to the C5–6 levels. However, the NIPD showed an opposite level-related change, notably larger than the NSPD at the C4–5, C5–6, and C7–T1 levels. Furthermore, significant differences in NIPD were observed between different age groups and genders. The incidence of the anterior type exhibited a gradual decrease from the C2–3 to the C5–6 levels. Conversely, the axillary type exhibited an opposite level-related change. Additionally, the VRA and DRA decreased as the level descended, with measurements significantly larger in females. Conclusion A prediction of the positional relationship between the intervertebral space and the nerve root is essential for the direct neuroventral decompression in PECD to avoid damaging the neural structures. The axillary route of the nerve root offers a safer and more effective pathway for performing direct neuroventral decompression compared to the shoulder approach.

Objective: This study aims to investigate the anatomical relationship among the nerve roots, intervertebral space, pedicles, and intradural rootlets of the cervical spine for improving operative outcomes and exploring neuroventral decompression approach in posterior endoscopic cervical discectomy (PECD). Methods: Cervical computed tomography myelography imaging data from January 2021 to May 2023 were collected, and the RadiAnt DICOM Viewer Software was employed to conduct multiplane reconstruction. The following parameters were recorded: width of nerve root (WN), nerve root-superior pedicle distance (NSPD), nerve root-inferior pedicle distance (NIPD), and the relationship between the intervertebral space and the nerve root (shoulder, anterior, and axillary). Additionally, the descending angles between the spinal cord and the ventral (VRA) and dorsal (DRA) rootlets were measured. Results: The WN showed a gradual increase from C4 to C7, with measurements notably larger in men compared to women. The NSPD decreased gradually from the C2–3 to the C5–6 levels. However, the NIPD showed an opposite level-related change, notably larger than the NSPD at the C4–5, C5–6, and C7–T1 levels. Furthermore, significant differences in NIPD were observed between different age groups and genders. The incidence of the anterior type exhibited a gradual decrease from the C2–3 to the C5–6 levels. Conversely, the axillary type exhibited an opposite level-related change. Additionally, the VRA and DRA decreased as the level descended, with measurements significantly larger in females. Conclusion A prediction of the positional relationship between the intervertebral space and the nerve root is essential for the direct neuroventral decompression in PECD to avoid damaging the neural structures. The axillary route of the nerve root offers a safer and more effective pathway for performing direct neuroventral decompression compared to the shoulder approach.

Objective: This study aims to investigate the anatomical relationship among the nerve roots, intervertebral space, pedicles, and intradural rootlets of the cervical spine for improving operative outcomes and exploring neuroventral decompression approach in posterior endoscopic cervical discectomy (PECD). Methods: Cervical computed tomography myelography imaging data from January 2021 to May 2023 were collected, and the RadiAnt DICOM Viewer Software was employed to conduct multiplane reconstruction. The following parameters were recorded: width of nerve root (WN), nerve root-superior pedicle distance (NSPD), nerve root-inferior pedicle distance (NIPD), and the relationship between the intervertebral space and the nerve root (shoulder, anterior, and axillary). Additionally, the descending angles between the spinal cord and the ventral (VRA) and dorsal (DRA) rootlets were measured. Results: The WN showed a gradual increase from C4 to C7, with measurements notably larger in men compared to women. The NSPD decreased gradually from the C2–3 to the C5–6 levels. However, the NIPD showed an opposite level-related change, notably larger than the NSPD at the C4–5, C5–6, and C7–T1 levels. Furthermore, significant differences in NIPD were observed between different age groups and genders. The incidence of the anterior type exhibited a gradual decrease from the C2–3 to the C5–6 levels. Conversely, the axillary type exhibited an opposite level-related change. Additionally, the VRA and DRA decreased as the level descended, with measurements significantly larger in females. Conclusion A prediction of the positional relationship between the intervertebral space and the nerve root is essential for the direct neuroventral decompression in PECD to avoid damaging the neural structures. The axillary route of the nerve root offers a safer and more effective pathway for performing direct neuroventral decompression compared to the shoulder approach.

Objective: This study aims to investigate the anatomical relationship among the nerve roots, intervertebral space, pedicles, and intradural rootlets of the cervical spine for improving operative outcomes and exploring neuroventral decompression approach in posterior endoscopic cervical discectomy (PECD). Methods: Cervical computed tomography myelography imaging data from January 2021 to May 2023 were collected, and the RadiAnt DICOM Viewer Software was employed to conduct multiplane reconstruction. The following parameters were recorded: width of nerve root (WN), nerve root-superior pedicle distance (NSPD), nerve root-inferior pedicle distance (NIPD), and the relationship between the intervertebral space and the nerve root (shoulder, anterior, and axillary). Additionally, the descending angles between the spinal cord and the ventral (VRA) and dorsal (DRA) rootlets were measured. Results: The WN showed a gradual increase from C4 to C7, with measurements notably larger in men compared to women. The NSPD decreased gradually from the C2–3 to the C5–6 levels. However, the NIPD showed an opposite level-related change, notably larger than the NSPD at the C4–5, C5–6, and C7–T1 levels. Furthermore, significant differences in NIPD were observed between different age groups and genders. The incidence of the anterior type exhibited a gradual decrease from the C2–3 to the C5–6 levels. Conversely, the axillary type exhibited an opposite level-related change. Additionally, the VRA and DRA decreased as the level descended, with measurements significantly larger in females. Conclusion A prediction of the positional relationship between the intervertebral space and the nerve root is essential for the direct neuroventral decompression in PECD to avoid damaging the neural structures. The axillary route of the nerve root offers a safer and more effective pathway for performing direct neuroventral decompression compared to the shoulder approach.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Nucleotide-binding oligomerization domain (NOD)-like receptor protein 3 (NLRP3) is a cytosolic pattern recognition receptor that recognizes multiple pathogen-associated molecular patterns and damage-associated molecular patterns. It is a cytoplasmic immune factor that responds to cellular stress signals, and it is usually activated after infection or inflammation, forming an NLRP3 inflammasome to protect the body. Aberrant NLRP3 inflammasome activation is reportedly associated with some inflammatory diseases and metabolic diseases. Recently, there have been mounting indications that NLRP3 inflammasomes play an important role in liver injuries caused by a variety of diseases, specifically hepatic ischemia/reperfusion injury, hepatitis, and liver failure. Herein, we summarize new research pertaining to NLRP3 inflammasomes in hepatic injury, hepatitis, and liver failure. The review addresses the potential mechanisms of action of the NLRP3 inflammasome, and its regulation in these liver diseases.
Humans ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Inflammasomes/physiology* ; Animals ; Liver Diseases/metabolism* ; Liver/metabolism* ; Reperfusion Injury/metabolism*

In this experiment, self-assembled nanoparticles(SANs) were prepared by the pH-driven method, and Her-HCP SAN was constructed by using herpetrione(Her) and Herpetospermum caudigerum polysaccharides(HCPs). The average particle size and polydispersity index(PDI) were used as evaluation indexes for process optimization, and the quality of the final formulation was evaluated in terms of particle size, PDI, Zeta potential, and microstructure. The proposed Her-HCP SAN showed a spheroid structure and uniform morphology, with an average particle size of(244.58±16.84) nm, a PDI of 0.147 1±0.014 8, and a Zeta potential of(-38.52±2.11) mV. Her-HCP SAN significantly increased the saturation solubility of Her by 2.69 times, with a cumulative release of 90.18% within eight hours. The results of in vivo unidirectional intestinal perfusion reveal that Her active pharmaceutical ingredient(API) is most effectively absorbed in the jejunum, where both K_a and P_(app) are significantly higher compared to the ileum(P<0.001). However, the addition of HCP leads to a significant reduction in the P_(app) of Her in the jejunum(P<0.05). Furthermore, the formation of the Her-HCP SAN results in a notably lower P_(app) in the jejunum compared to Her API alone(P<0.001), while both K_a and P_(app) in the ileum are significantly increased(P<0.001, P<0.05). The absorption of Her-HCP SAN at different concentrations in the ileum shows no significant differences, and the pH has no significant effect on the absorption of Her-HCP SAN in the ileum. The addition of the transporter protein inhibitors(indomethacin and rifampicin) significantly increases the absorption parameters K_a and P_(app) of Her-HCP SAN in the ileum(P<0.05,P<0.01), whereas the addition of verapamil has no significant effect on the intestinal absorption parameters of Her-HCP SAN, suggesting that Her may be a substrate for multidrug resistance-associated protein 2 and breast cancer resistance proteins but not a substrate of P-glycoprotein.
Nanoparticles/metabolism* ; Polysaccharides/pharmacokinetics* ; Intestinal Absorption/drug effects* ; Animals ; Rats ; Particle Size ; Drugs, Chinese Herbal/pharmacokinetics* ; Male ; Rats, Sprague-Dawley ; Drug Carriers/chemistry* ; Drug Compounding ; Cucurbitaceae/chemistry*

The traditional Chinese medicine(TCM) industry is a crucial part of China's pharmaceutical sector and plays a strategic role in ensuring public health and promoting economic and social development. In response to the practical demand for high-quality development of the TCM industry, this paper focused on the bottlenecks encountered during the digital and intelligent transformation of TCM production systems. Specifically, it explored technical strategies and methodologies for constructing the best TCM production mode. An innovative artificial intelligence(AI)-centered technical architecture for TCM production was proposed, focusing on key aspects of production management including process modeling, state evaluation, and decision optimization. Furthermore, a series of critical technologies were developed to realize the best TCM production mode. Finally, a novel AI-driven TCM production mode characterized by a closed-loop system of "measurement-modeling-decision-execution" was presented through engineering case studies. This study is expected to provide a technological pathway for developing new quality productive forces within the TCM industry.
Artificial Intelligence ; Drugs, Chinese Herbal ; Medicine, Chinese Traditional/methods* ; Humans

Mycophenolic acid (MPA), the active moiety of both mycophenolate mofetil (MMF) and enteric-coated mycophenolate sodium (EC-MPS), serves as a primary immunosuppressant for maintaining solid organ transplants. Therapeutic drug monitoring (TDM) enhances treatment outcomes through tailored approaches. This study aimed to develop an evidence-based guideline for MPA TDM, facilitating its rational application in clinical settings. The guideline plan was drawn from the Institute of Medicine and World Health Organization (WHO) guidelines. Using the Delphi method, clinical questions and outcome indicators were generated. Systematic reviews, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) evidence quality evaluations, expert opinions, and patient values guided evidence-based suggestions for the guideline. External reviews further refined the recommendations. The guideline for the TDM of MPA (IPGRP-2020CN099) consists of four sections and 16 recommendations encompassing target populations, monitoring strategies, dosage regimens, and influencing factors. High-risk populations, timing of TDM, area under the curve (AUC) versus trough concentration (C₀), target concentration ranges, monitoring frequency, and analytical methods are addressed. Formulation-specific recommendations, initial dosage regimens, populations with unique considerations, pharmacokinetic-informed dosing, body weight factors, pharmacogenetics, and drug‍-‍drug interactions are covered. The evidence-based guideline offers a comprehensive recommendation for solid organ transplant recipients undergoing MPA therapy, promoting standardization of MPA TDM, and enhancing treatment efficacy and safety.
Mycophenolic Acid/administration & dosage* ; Drug Monitoring/methods* ; Humans ; Organ Transplantation ; Immunosuppressive Agents/administration & dosage* ; Delphi Technique