Neoadjuvant therapy has become the standard treatment for locally advanced resectable esophageal cancer, significantly improving long-term survival compared to surgery alone. Neoadjuvant therapy has evolved to include various strategies, such as concurrent chemoradiotherapy, chemotherapy, immunotherapy, or targeted combination therapy. This enriches clinical treatment options and provides a more personalized and scientific treatment approach for patients. This article aims to comprehensively summarize current academic research hot topics, review the rationale and evaluation measures of neoadjuvant therapy, discuss challenges in restaging methods after neoadjuvant therapy, and identify the advantages and disadvantages of various neoadjuvant therapeutic strategies.

Objective: To assess the effectiveness of vertebral cement augmentation (VCA) at upper instrumented vertebra (UIV) and UIV+1 in preventing proximal junction complications in correction surgery for adult spinal deformity patients. Methods: A literature search was conducted on Web of Science, PubMed, and Cochrane Library databases for comparative studies published before December 30th, 2024. Two reviewers independently screened eligible articles based on the inclusion and exclusion criteria, assessed study quality with Newcastle-Ottawa scale, and extracted data like study characteristics, surgical details, primary and secondary outcomes. Data analysis was performed using Review Manager 5.4 and Stata software. Results: Of all 513 papers screened, a meta-analysis was conducted on 7 articles, which included 333 cases in the VCA group and 827 cases in the control group. Patients in the VCA group had significantly older age and lower T score than patients in the control group. Although there was no statistically significant difference in the incidence of proximal junctional failure between the 2 groups, the results of the meta-analysis showed that the incidence of proximal junctional failure and the need for revision surgery were reduced by 36% and 71%, respectively, in the VCA group. One study reported 2 clinically silent pulmonary cement embolism and 1 patient requiring surgical decompression for cement leak into the spinal canal. Conclusion This meta-analysis supported the use of VCA in corrective surgery for spinal deformities patients, especially in patients with advanced age and osteoporosis.

Objective: To assess the effectiveness of vertebral cement augmentation (VCA) at upper instrumented vertebra (UIV) and UIV+1 in preventing proximal junction complications in correction surgery for adult spinal deformity patients. Methods: A literature search was conducted on Web of Science, PubMed, and Cochrane Library databases for comparative studies published before December 30th, 2024. Two reviewers independently screened eligible articles based on the inclusion and exclusion criteria, assessed study quality with Newcastle-Ottawa scale, and extracted data like study characteristics, surgical details, primary and secondary outcomes. Data analysis was performed using Review Manager 5.4 and Stata software. Results: Of all 513 papers screened, a meta-analysis was conducted on 7 articles, which included 333 cases in the VCA group and 827 cases in the control group. Patients in the VCA group had significantly older age and lower T score than patients in the control group. Although there was no statistically significant difference in the incidence of proximal junctional failure between the 2 groups, the results of the meta-analysis showed that the incidence of proximal junctional failure and the need for revision surgery were reduced by 36% and 71%, respectively, in the VCA group. One study reported 2 clinically silent pulmonary cement embolism and 1 patient requiring surgical decompression for cement leak into the spinal canal. Conclusion This meta-analysis supported the use of VCA in corrective surgery for spinal deformities patients, especially in patients with advanced age and osteoporosis.

Objective: To assess the effectiveness of vertebral cement augmentation (VCA) at upper instrumented vertebra (UIV) and UIV+1 in preventing proximal junction complications in correction surgery for adult spinal deformity patients. Methods: A literature search was conducted on Web of Science, PubMed, and Cochrane Library databases for comparative studies published before December 30th, 2024. Two reviewers independently screened eligible articles based on the inclusion and exclusion criteria, assessed study quality with Newcastle-Ottawa scale, and extracted data like study characteristics, surgical details, primary and secondary outcomes. Data analysis was performed using Review Manager 5.4 and Stata software. Results: Of all 513 papers screened, a meta-analysis was conducted on 7 articles, which included 333 cases in the VCA group and 827 cases in the control group. Patients in the VCA group had significantly older age and lower T score than patients in the control group. Although there was no statistically significant difference in the incidence of proximal junctional failure between the 2 groups, the results of the meta-analysis showed that the incidence of proximal junctional failure and the need for revision surgery were reduced by 36% and 71%, respectively, in the VCA group. One study reported 2 clinically silent pulmonary cement embolism and 1 patient requiring surgical decompression for cement leak into the spinal canal. Conclusion This meta-analysis supported the use of VCA in corrective surgery for spinal deformities patients, especially in patients with advanced age and osteoporosis.

Objective: To assess the effectiveness of vertebral cement augmentation (VCA) at upper instrumented vertebra (UIV) and UIV+1 in preventing proximal junction complications in correction surgery for adult spinal deformity patients. Methods: A literature search was conducted on Web of Science, PubMed, and Cochrane Library databases for comparative studies published before December 30th, 2024. Two reviewers independently screened eligible articles based on the inclusion and exclusion criteria, assessed study quality with Newcastle-Ottawa scale, and extracted data like study characteristics, surgical details, primary and secondary outcomes. Data analysis was performed using Review Manager 5.4 and Stata software. Results: Of all 513 papers screened, a meta-analysis was conducted on 7 articles, which included 333 cases in the VCA group and 827 cases in the control group. Patients in the VCA group had significantly older age and lower T score than patients in the control group. Although there was no statistically significant difference in the incidence of proximal junctional failure between the 2 groups, the results of the meta-analysis showed that the incidence of proximal junctional failure and the need for revision surgery were reduced by 36% and 71%, respectively, in the VCA group. One study reported 2 clinically silent pulmonary cement embolism and 1 patient requiring surgical decompression for cement leak into the spinal canal. Conclusion This meta-analysis supported the use of VCA in corrective surgery for spinal deformities patients, especially in patients with advanced age and osteoporosis.

Objective: To assess the effectiveness of vertebral cement augmentation (VCA) at upper instrumented vertebra (UIV) and UIV+1 in preventing proximal junction complications in correction surgery for adult spinal deformity patients. Methods: A literature search was conducted on Web of Science, PubMed, and Cochrane Library databases for comparative studies published before December 30th, 2024. Two reviewers independently screened eligible articles based on the inclusion and exclusion criteria, assessed study quality with Newcastle-Ottawa scale, and extracted data like study characteristics, surgical details, primary and secondary outcomes. Data analysis was performed using Review Manager 5.4 and Stata software. Results: Of all 513 papers screened, a meta-analysis was conducted on 7 articles, which included 333 cases in the VCA group and 827 cases in the control group. Patients in the VCA group had significantly older age and lower T score than patients in the control group. Although there was no statistically significant difference in the incidence of proximal junctional failure between the 2 groups, the results of the meta-analysis showed that the incidence of proximal junctional failure and the need for revision surgery were reduced by 36% and 71%, respectively, in the VCA group. One study reported 2 clinically silent pulmonary cement embolism and 1 patient requiring surgical decompression for cement leak into the spinal canal. Conclusion This meta-analysis supported the use of VCA in corrective surgery for spinal deformities patients, especially in patients with advanced age and osteoporosis.

ObjectiveFunctional near-infrared spectroscopy (fNIRS), a novel non-invasive technique for monitoring cerebral activity, can be integrated with upper limb rehabilitation robots to facilitate the real-time assessment of neurological rehabilitation outcomes. The rehabilitation robot is designed with 3 training modes: passive, active, and resistance. Among these, the resistance mode has been demonstrated to yield superior rehabilitative outcomes for patients with a certain level of muscle strength. The control modes in the resistance mode can be categorized into dynamic and static control. However, the effects of different control modes in the resistance mode on the motor function of patients with upper limb hemiplegia in stroke remain unclear. Furthermore, the effects of force, an important parameter of different control modes, on the activation of brain regions have rarely been reported. This study investigates the effects of dynamic and static resistance modes under varying resistance levels on cerebral functional alterations during motor rehabilitation in post-stroke patients. MethodsA cohort of 20 stroke patients with upper limb dysfunction was enrolled in the study, completing preparatory adaptive training followed by 3 intensity-level tasks across 2 motor paradigms. The bilateral prefrontal cortices (PFC), bilateral primary motor cortices (M1), bilateral primary somatosensory cortices (S1), and bilateral premotor and supplementary motor cortices (PM) were examined in both the resting and motor training states. The lateralization index (LI), phase locking value (PLV), network metrics were employed to examine cortical activation patterns and topological properties of brain connectivity. ResultsThe data indicated that both dynamic and static modes resulted in significantly greater activation of the contralateral M1 area and the ipsilateral PM area when compared to the resting state. The static patterns demonstrated a more pronounced activation in the contralateral M1 in comparison to the dynamic patterns. The results of brain network analysis revealed significant differences between the dynamic and resting states in the contralateral PFC area and contralateral M1 area (F=4.709, P=0.038), as well as in the contralateral PM area and ipsilateral M1 area (F=4.218, P=0.049). Moreover, the findings indicated a positive correlation between the activation of the M1 region and the increase in force in the dynamic mode, which was reversed in the static mode. ConclusionBoth dynamic and static resistance training modes have been demonstrated to activate the corresponding brain functional regions. Dynamic resistance modes elicit greater oxygen changes and connectivity to the region of interest (ROI) than static resistance modes. Furthermore, the effects of increasing force differ between the two modes. In patients who have suffered a stroke, dynamic modes may have a more pronounced effect on the activation of exercise-related functional brain regions.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

INTRODUCTION: Randomised controlled trials (RCTs) have informed guideline recommendations for the management of stable coronary artery disease (CAD). However, the real-world impact of contemporary guidelines and trials on practising physicians in the Asia-Pacific region remains uncertain. We aimed to evaluate the knowledge, attitudes and practices among cardiovascular physicians in the region regarding stable CAD management. METHOD: An anonymised cross-sectional electronic survey was administered to cardiovascular practitioners from the Asia Pacific, assessing 3 domains: 1) baseline knowledge on recent trials and society guideline, 2) attitudes towards stable CAD, and 3) case scenarios reflecting management preferences. Correlations among knowledge, attitudes and practice scores were assessed between physicians from developed and developing countries using Pearson correlation. RESULTS: Overall, 713 respondents from 21 countries completed the survey. The mean knowledge score was 2.90±1.18 (out of 4), with 37.3% of respondents answering all questions correctly, while 74.6% noted that guidelines have significant impact on their practice. Despite guidelines recommending optimal medical therapy, majority chose revascularisation (range 53.4- 90.6%) as the preferred strategy for the case scenarios. Practitioners from developed regions had higher knowledge scores and lower attitude scores compared to developing regions, while practice scores were similar in both groups. Weakly positive correlations were noted between knowledge, attitude and practice scores. CONCLUSION Variations exist in knowledge and attitudes towards guideline recommendations and correspondingly actual clinical practice in the Asia Pacific, with most practitioners choosing an upfront invasive strategy for the treatment of stable CAD. These differences reflect real-world disparities in guideline interpretation and clinical adoption.
Humans ; Coronary Artery Disease/therapy* ; Cross-Sectional Studies ; Practice Patterns, Physicians'/statistics & numerical data* ; Asia ; Health Knowledge, Attitudes, Practice ; Surveys and Questionnaires ; Male ; Practice Guidelines as Topic ; Female ; Attitude of Health Personnel ; Middle Aged ; Developing Countries

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control