Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

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.

Background::Programmed death 1 (PD-1) blockade plus chemotherapy has become the new first-line standard of care for patients with advanced non-small-cell lung cancer (NSCLC). Yet not all NSCLC patients benefit from this regimen. This study aimed to investigate the predictors of PD-1 blockade plus chemotherapy in untreated advanced NSCLC.Methods::We integrated clinical, genomic, and survival data from 287 patients with untreated advanced NSCLC who were enrolled in one of five registered phase 3 trials and received PD-1 blockade plus chemotherapy or chemotherapy alone. We randomly assigned these patients into a discovery cohort ( n = 125), a validation cohort ( n = 82), and a control cohort ( n = 80). The candidate genes that could predict the response to PD-1 blockade plus chemotherapy were identified using data from the discovery cohort and their predictive values were then evaluated in the three cohorts. Immune deconvolution was conducted using transcriptome data of 1014 NSCLC patients from The Cancer Genome Atlas dataset. Results::A genomic variation signature, in which one or more of the 15 candidate genes were altered, was correlated with significantly inferior response rates and survival outcomes in patients treated with first-line PD-1 blockade plus chemotherapy in both discovery and validation cohorts. Its predictive value held in multivariate analyses when adjusted for baseline parameters, programmed cell death ligand 1 (PD-L1) expression level, and tumor mutation burden. Moreover, applying both the 15-gene panel and PD-L1 expression level produced better performance than either alone in predicting benefit from this treatment combination. Immune landscape analyses revealed that tumors with one or more variation in the 15-gene panel were associated with few immune infiltrates, indicating an immune-desert tumor microenvironment.Conclusion::These findings indicate that a 15-gene panel can serve as a negative prediction biomarker for first-line PD-1 blockade plus chemotherapy in patients with advanced NSCLC.

Aim To investigate the effect of total glu-cosides of paeony on inflammatory injury and TLR4/NF-κB/NLRP3 pathway in autoimmune thyroiditis(AIT)rats.Methods The experiment was divided into control group,model group,total glucosides of pae-ony(TGP),TLR4 inhibitor group and TGP+TLR4 ag-onist group,with 10 animals in each group.Except for the control group,the rats in other groups were subcu-taneously injected with thyroglobulin and Freund's ad-juvant to induce the AIT rat model.After six weeks of administration,thyroid histopathological changes were observed using hematoxylin-eosin(HE)staining;ser-um levels of TPOAb,TgAb,TSH,T3,T4,TNF-α,INF-γ,IL-1 β and IL-1 β were detected by enzyme-linked immunosorbent assay(ELISA);TLR4/NF-κB/NLRP3 pathway mRNAs and proteins expression in thyroid tis-sues were detected by RT-qPCR and Western blot.Re-sults Compared with the control group,the thyroid follicular epithelium of rats was significantly damaged,and the serum levels of TPOAb,TgAb,TSH,T3,T4,TNF-α,INF-γ,IL-1 β and IL-1 β increased(P＜0.01).The expression of TLR4/NF-κB/NLRP3 path-way mRNAs and proteins increased in the model group(P＜0.01).Compared with the model group,the damage of thyroid follicular epithelium was alleviated,and the serum levels of TPOAb,TgAb,TSH,T3,T4,TNF-α,INF-γ,IL-1 β and IL-1 β were reduced(P＜0.01),the expression of TLR4/NF-κB/NLRP3 path-way mRNAs and proteins were down-regulated in the TGP group and TLR4 inhibitor group(P＜0.01).Compared with TGP group,the damage of thyroid follic-ular epithelium was aggravated,and the levels of serum TPOAb,TgAb,TSH,T3,T4,TNF-α,INF-γ,IL-1 β and IL-1 β were elevated(P＜0.05 or P＜0.01),the pro-tein expressions of TLR4/NF-κB/NLRP3 pathway mR-NAs and proteins were up-regulated in TGP+TLR4 ag-onist group(P＜0.05 or P＜0.01).Conclusions TGP may play a protective role in thyroid by inhibiting the TLR4/NF-κB/NLRP3 pathway and improving the inflammatory injury of thyroid tissues.

Objective To investigate the effect of Polyetheretherketone(PEEK)rod semi-rigid pedicle screw fixation sys-tem in lumbar spine non-fusion surgery.Methods A total of 74 patients with tow-level lumbar degenerative diseases who un-derwent surgery from March 2017 to December 2019 were divided into PEEK rod group and titanium rod group.In the PEEK rod group,there were 34 patients,including 13 males and 21 females,aged from 51 to 79 years old with an average of(62.4±6.8)years old;There were 1 patient of L1-L3 segments,7 patients of L2-L4 segments,20 patients of L3-L5 segments and 6 pa-tients of L4-S1 segments.In the titanium rod group,there were 40 patients,including 17 males and 23 females,aged from 52 to 81 years old with an average of(65.2±7.3)years old;There were 3 patient of L1-L3 segments,11 patients of L2-L4 segments,19 patients of L3-L5 segments and 7 patients of L4-S1 segments.The general conditions of operation,such as operation time,intraoperative blood loss,postoperative drainage was recorded.The visual analogue scale(VAS)for low back pain and Os-westry disability index(ODI)were compared in preoperatively and postoperatively(3 months,12 months and last follow-up)between two groups.The change of range of motion(ROM)was observed by flexion and extension x-ray of lumbar Results All patients successfully completed the operation.The follow-up time ranged from 22 to 34 months with an average of(26.8±5.6)months.The operative time(142.2±44.7)min and intraoperative blood loss(166.5±67.4)ml in PEEK group were lower than those in titanium group[(160.7±57.3)min、(212.8±85.4)ml](P＜0.05).There was no significant differences in postoperative drainage between the two groups(P＞0.05).At the final follow-up visit,in PEEK group and titanium group VAS of low back pain[(0.8±0.4)points vs(1.0±0.5)points],VAS for leg pain[(0.7±0.4)points vs(0.8±0.5)points]and ODI[(9.8±1.6)％vs(12.1±1.5)％]were compared with preoperative[(5.8±1.1)points vs(6.0±1.1)points],[(7.2±1.7)points vs(7.0±1.6)points],[(68.5±8.9)％vs(66.3±8.2)％]were significantly different(P＜0.05).There was no significant difference in VAS scores between the two groups at each postoperative time point(P＞0.05).At 3 months after surgery,there was no difference in ODI between the two groups(P＞0.05).There were significant differences in ODI between PEEK group and titanium rod group at 12 months[(15.5±2.1)％vs(18.4±2.4)％]and at the last follow-up[(9.8±1.6)％vs(12.1±1.5)％](P＜0.05).The total range of motion(ROM)of lumbar decreased in both groups after surgery.At 12 months after surgery and the last follow-up,the PEEK group compared with the titanium rod group,the total range of motion of lumbar was statistically significant(P＜0.05).The range of motion(ROM)of the fixed segments decreased in both groups after surgery.The ROM of the fixed segments in PEEK group decreased from(9.5±4.6)° to(4.1±1.9)° at the last follow-up(P＜0.05),which in the titanium rod group was de-creased from(9.8±4.3)°to(0.9±0.5)° at the last follow-up(P＜0.05).The range of motion(ROM)of upper adjacent segment increased in both groups,there was statistical significance in the ROM of upper adjacent segment between the two groups at 12 months after surgery and the last follow-up,(P＜0.05).There was no screw loosening and broken rods in both groups during the follow-up period.Conclusion The PEEK rod semi-rigid pedicle screw internal fixation system used in lumbar non-fusion surgery can retain part of the mobility of the fixed segment,showing comparable short-term clinical efficacy to titanium rod fu-sion.PEEK rod semi-rigid pedicle screw internal fixation system is a feasible choice for the treatment of lumbar spine degener-ative diseases,and its long-term efficacy needs further follow-up observation.

This study aimed to determine the drug resistance phenotype and genetic characteristics of Listeria monocytogenes ST5 LK100 isolated from a neonate,which provided a basis for the diagnosis and treatment of L.monocyto-genes infection and to enhance the understanding of the genomic characteristics of this strain.A suspected L.monocytogenes strain was isolated from the gastric juice sample of an infected neonate,and identified with a VITEK2 Compact automatic mi-crobial identification instrument and 16S RNA sequencing.Five drug sensitivity tests were conducted on the identified strain with the E-test method.Additionally,the whole genome of the strain was sequenced using a third-generation sequencing plat-form.The antibiotic resistance elements of the strain were identified by BlastN with the CARD antibiotic resistance gene data-base.The multilocus sequence typing(MLST),serotyping,and virulence genes of the strain was determined by Pasteur da-tabase,the virulence gene distribution was analyzed using the virulence analysis website.The prophages of the strain were predicted and annotate by PHASTER online website.The strain(LK100)isolated from the neonate was identified as L.monocytogenes.This strain was sensitive to penicillin,ampicil-lin,meropenem,erythromycin,and trimethoprim-sulfame-thoxazole antibiotics.The MLST type and serotype was ST5 and 1/2b-3b,respectively.The total length of the chromoso-mal genome of LK100 was 3 032 582 bp with a GC content of 37.91％,and it contained a complete circular plasmid with a se-quence length of 52 822 bp.The strain LK100 carried complete InlA protein,LIPI-1 pathogenicity island,SSI-1 stress survival island,and an LGI2 genomic island.The intrinsic antibiotic resistance genes were mainly located on the chromosome.Five prophage sequences were predicted in the LK100 genome.This study identified a strain of ST5 L.monocytogenes LK100 from an infected neonate and characterized its genome and antibiotic sensitivity,laying the foundation for further research on ST5 L.monocytogenes.