OBJECTIVE: To investigate the effectiveness of basal osteotomy of the first metatarsal combined with first metatarsophalangeal joint replacement for the treatment of hallux valgus combined with hallux rigidus. METHODS: A retrospective analysis was conducted on the medical records of 15 patients (16 feet) with hallux valgus combined with hallux rigidus who underwent first metatarsal basal osteotomy combined with first metatarsophalangeal joint replacement between May 2019 and December 2024. The patients included 1 male and 14 females, aged 60-80 years, with an average age of 68.5 years. According to the Mann classification of hallux valgus, 2 feet were moderate and 14 feet were severe. According to the Hallux Rigidus Coughlin grading, 12 feet were grade 3 and 4 feet were grade 4. The evaluation indicators included hallux valgus angle (HVA) and intermetatarsal angle (IMA) measured on weight-bearing X-ray film at preoperation and last follow-up; the American Orthopaedic Foot and Ankle Society (AOFAS) score and visual analogue scale (VAS) score were used to evaluate the improvement of walking function, metatarsophalangeal joint function, and pain relief in patients. RESULTS: All patients underwent surgery successfully without complications such as vascular or nerve injury during operation. One foot developed a superficial incisional infection postoperatively, which healed after dressing changes; the remaining 15 feet had primary incision healing. All patients were followed up 5-55 months, with an average of 17.5 months. All osteotomies achieved bony union, with a union time ranging from 8 to 13 weeks, averaging 11.8 weeks; no elevation of the first metatarsal was observed. At last follow-up, no complications such as prosthesis loosening, dislocation, fracture, toe shortening, bone dissolution, synovial reaction, or metatarsalgia was found. The appearance and function of the affected feet significantly improved, and the pain significantly relieved. At last follow-up, the HVA, IMA, AOFAS score, and VAS score showed significant differences when compared with preoperative values ( P<0.05). CONCLUSION Basal osteotomy of the first metatarsal combined with first metatarsophalangeal joint replacement for the treatment of hallux valgus with hallux rigidus can correct deformities through osteotomy, restore the normal flexion-extension axis of the metatarsophalangeal joint, relieve pain, and preserve a certain degree of mobility, achieving good short- and mid-term effectiveness.
Humans ; Male ; Female ; Hallux Valgus/diagnostic imaging* ; Osteotomy/methods* ; Aged ; Middle Aged ; Metatarsophalangeal Joint/surgery* ; Retrospective Studies ; Hallux Rigidus/diagnostic imaging* ; Aged, 80 and over ; Metatarsal Bones/surgery* ; Treatment Outcome ; Arthroplasty, Replacement/methods*

Lung cancer remains the leading cause of cancer-related incidence and mortality worldwide. Among its histological subtypes, non-small cell lung cancer (NSCLC) accounts for the majority of cases, representing the predominant pathological type. Notably, in the elderly population, NSCLC continues to be a major contributor to cancer-related deaths. With the global ageing population, immunosenescence has emerged as a key factor influencing the occurrence, progression, and the efficacy of immunotherapy of NSCLC. Immunosenescence refers to the age-related decline in immune system function, which manifests as alterations in both the quantity and functionality of immune cells. These include thymic involution, T cell exhaustion, epigenetic modifications, weakened immune responses, and a chronic low-grade inflammatory state. This review comprehensively analyzes the role of immunosenescence in elderly patients with advanced NSCLC and proposes potential therapeutic strategies to intervene in the immunosenescence process. By targeting immunosenescence, these strategies aim to inhibit the progression of NSCLC and improve the effectiveness of immunotherapy.
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Humans ; Carcinoma, Non-Small-Cell Lung/genetics* ; Immunotherapy ; Lung Neoplasms/genetics* ; Immunosenescence ; Aged

Objective:To investigate the value of CT radiomic model based on analysis of primary gastric cancer and the adipose tissue outside the gastric wall beside cancer in differentiating stage T1-2 from stage T3-4 gastric cancer.Methods:This study was a case-control study. Totally 465 patients with gastric cancer treated in Affiliated People′s Hospital of Jiangsu University from December 2011 to December 2019 were retrospectively collected. According to postoperative pathology, they were divided into 2 groups, one with 150 cases of T1-2 tumors and another with 315 cases of T3-4 tumors. The cases were divided into a training set (326 cases) and a test set (139 cases) by stratified sampling method at 7∶3. There were 104 cases of T1-2 stage and 222 cases of T3-4 stage in the training set, 46 cases of T1-2 stage and 93 cases of T3-4 stage in the test set. The axial CT images in the venous phase during one week before surgery were selected to delineate the region of interest (ROI) at the primary lesion and the extramural gastric adipose tissue adjacent to the cancer areas. The radiomic features of the ROIs were extracted by Pyradiomics software. The least absolute shrinkage and selection operator was used to screen features related to T stage to establish the radiomic models of primary gastric cancer and the adipose tissue outside the gastric wall beside cancer. Independent sample t test or χ2 test were used to compare the differences in clinical features between T1-2 and T3-4 patients in the training set, and the features with statistical significance were combined to establish a clinical model. Two radiomic signatures and clinical features were combined to construct a clinical-radiomics model and generate a nomogram. The area under the receiver operating characteristic curve (AUC) was used to evaluate the efficacy of each model in differentiating stage T1-2 from stage T3-4 gastric cancer. The calibration curve was used to evaluate the consistency between the T stage predicted by the nomogram and the actual T stage of gastric cancer. And the decision curve analysis was used to evaluate the clinical net benefit of treatment guided by the nomogram and by the clinical model. Results:There were significant differences in CT-T stage and CT-N stage between T1-2 and T3-4 patients in the training set ( χ2=10.59, 15.92, P=0.014, 0.001) and the clinical model was established. After screening and dimensionality reduction, the 5 features from primary gastric cancer and the 6 features from the adipose tissue outside the gastric wall beside cancer established the radiomic models respectively. In the training set and the test set, the AUC values of the primary gastric cancer radiomic model were 0.864 (95% CI 0.820-0.908) and 0.836 (95% CI 0.762-0.910), and the adipose tissue outside the gastric wall beside cancer radiomic model were 0.782 (95% CI 0.731-0.833) and 0.784 (95% CI 0.702-0.866). The AUC values of the clinical model were 0.761 (95% CI 0.705-0.817) and 0.758 (95% CI 0.671-0.845), and the nomogram were 0.876 (95% CI 0.835-0.917) and 0.851 (95% CI 0.781-0.921). The calibration curve reflected that there was a high consistency between the T stage predicted by the nomogram and the actual T stage in the training set ( χ2=1.70, P=0.989). And the decision curve showed that at the risk threshold 0.01-0.74, a higher clinical net benefit could be obtained by using a nomogram to guide treatment. Conclusions:The CT radiomics features of primary gastric cancer lesions and the adipose tissue outside the gastric wall beside cancer can effectively distinguish T1-2 from T3-4 gastric cancer, and the combination of CT radiomic features and clinical features can further improve the prediction accuracy.

Objective:To investigate the effectiveness and feasibility of a 3D U-Net in conjunction with a three-phase CT image segmentation model in the automatic segmentation of GTVnx and GTVnd in nasopharyngeal carcinoma.Methods:A total of 645 sets of computed tomography (CT) images were retrospectively collected from 215 nasopharyngeal carcinoma cases, including three phases: plain scan (CT), contrast-enhanced CT (CTC), and delayed CT (CTD). The dataset was grouped into a training set consisting of 172 cases and a test set comprising 43 cases using the random number table method. Meanwhile, six experimental groups, A1, A2, A3, A4, B1, and B2, were established. Among them, the former four groups used only CT, only CTC, only CTD, and all three phases, respectively. The B1 and B2 groups used phase fine-tuning CTC models. The Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95) served as quantitative evaluation indicators.Results:Compared to only monophasic CT (group A1/A2/A3), triphasic CT (group A4) yielded better result in the automatic segmentation of GTVnd (DSC: 0.67 vs. 0.61, 0.64, 0.64; t = 7.48, 3.27, 4.84, P < 0.01; HD95: 36.45 vs. 79.23, 59.55, 65.17; t = 5.24, 2.99, 3.89, P < 0.01), with statistically significant differences ( P < 0.01). However, triphasic CT (group A4) showed no significant enhancement in the automatic segmentation of GTVnx compared to monophasic CT (group A1/A2/A3) (DSC: 0.73 vs. 0.74, 0.74, 0.73; HD95: 14.17 mm vs. 8.06, 8.11, 8.10 mm), with no statistically significant difference ( P > 0.05). For the automatic segmentation of GTVnd, group B1/B2 showed higher automatic segmentation accuracy compared to group A1 (DSC: 0.63, 0.63 vs. 0.61, t = 4.10, 3.03, P<0.01; HD95: 58.11, 50.31 mm vs. 79.23 mm, t = 2.75, 3.10, P < 0.01). Conclusions:Triphasic CT scanning can improve the automatic segmentation of the GTVnd in nasopharyngeal carcinoma. Additionally, phase fine-tuning models can enhance the automatic segmentation accuracy of the GTVnd on plain CT images.

To summarize the nursing care of a very low birth weight premature infant with severe type Ⅱbronchopulmonary dysplasia(BPD)during the transition period from hospitalization to home.The care of the infant was provided one-on-one by a BPD specialist nurse throughout the period.The key points of transitional care from hospitalization to home include:implementing tracheotomy and mechanical ventilation care to ensure stable blood oxygen saturation of the infant;providing nutritional support to improve the nutritional status of the infant;implementing step-by-step rehabilitation measures to improve the neuromotor development of the infant;implementing family integrated care to promote the primary caregivers of the infant to master nursing knowledge and skills;conducting personalized discharge follow-up with a multidisciplinary team to improve the quality of home care for this infant.After being hospitalized for 106 days,the infant was successfully discharged with a tracheotomy tube.At the age of 2 years and 6 months,a tracheotomy closure surgery was performed.After the surgery,the infant was able to breathe autonomously without symptoms of breathing difficulties and returned to normal family life.

Gastric ulcer is a common digestive system disease,and the long-term use of non-steroidal anti-inflammatory drugs(NSAIDs)is the second most important cause.NSAID-induced gastric ulcer animal models are key experimental tools for studying the pathogenesis,corresponding treatment method,and effective mechanisms of NSAID-induced gastrointestinal injury.However,there are currently a lack of reviews on NSAID-induced gastric ulcer animal models.This review summarizes and compares the relevant literature on animal research into indomethacin-and aspirin-induced gastric ulcers in the past 10 years,including the selection of experimental animals,drug solvents,and specific modeling method.The limitations of current models,such as the cumbersome modeling method,incomplete modeling details,inadequate models for clinical use,and lack of comparative drug research,are discussed.Feasible solutions are proposed with the aim of providing an effective reference for research in this field.

In recent years,with the increase of social pressure,the incidence of"psycho-cardiological disease"increases.Western medicine mainly uses conventional cardiovascular drugs combined with antidepressant or anxiety drugs.TCM has the characteristics of multi-pathway,multi-target,multi-mechanism,integration and low toxicity.It has unique advantages in the intervention of"psycho-cardiological disease"and has significant clinical efficacy.However,the pathogenesis,clinical diagnosis,syndrome differentiation and treatment of this disease are not the same.This article summarized relevant literature and reviewed the etiology,pathogenesis,experience in syndrome differentiation and treatment,and clinical research of this disease from the perspective of TCM,in order to provide reference for diagnosis of disease,precision treatment and improvement of curative efficacy of"psycho-cardiological disease".

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.

Objective To investigate the research hotspots and trend in pain management with virtual reality from 2013 to 2023 therefore to provide nursing administrators and researchers with insights into leveraging information technology and to improve nursing quality.Methods Literatures in virtual reality and pain management were retrieved from the Web of Science core collection and China National Knowledge Infrastructure databases.Bibliometric analysis using CiteSpace software were conducted to examine the trend of annual publications,countries and institutions of the authors,leading authors,cited journals and cluster keywords.The researched time was from January 2013 to September 2023.Results A total of 2 503 English and 328 Chinese articles were included.It was found that the publications in relevant topics were on the rise annually in number and peaked twice in 2014 and 2020.Some authors from different institutions conducted certain level of collaborations in the researches.The journal Pain received the highest citations.Eight cluster keywords were identified:rehabilitation,virtual screening,distraction,quality of life,surgery,phantom limb pain,social pain and virtual reality.Four research hotspots were summarised,including current application of VR technology in pain management,application effect and research in rehabilitation,research in innovation of VR technology and personalised mental health interventions.Conclusions Research about VR technology in pain management is growing with technological advancements and policy supports.However,collaborations should be further improved between the scholars from different institutions.The domestic research emerged relatively behind the foreign institutions.Future studies should focus on intervention trials of virtual reality,particularly its impact on different participants groups and its role in rehabilitation.Innovative technical methods and personalised and accurate pain management strategies are crucial for promotion of the advanced VR technology in pain management.

Objective:This study aims to explore the predictive value of T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and early-delayed phases enhanced magnetic resonance imaging (DCE-MRI) radiomics prediction model in determining human epidermal growth factor receptor 2 status in breast cancer.Methods:A retrospective study was conducted, involving 187 patients with confirmed breast cancer by postsurgical pathology at Zhenjiang First People's Hospital during January 2021 and May 2023. Immunohistochemistry or fluorescence in situ hybridization was used to determine the HER-2 status of these patients, with 48 cases classified as HER-2 positive and 139 cases as HER-2 negative. The training set was used to construct the prediction models and the validation set was used to verify the prediction models. Layers of T2WI, ADC, and early-delayed phase DCE-MRI images were used to delineate the volumeof interest and 960 radiomic features were extracted from each case using Pyradiomic. After screening and dimensionality reduction by intraclass correlation coefficient, Pearson correlation analysis, least absolute shrinkage, and selection operator, the radiomics labels were established. Logistic regression analysis was used to construct the T2WI radiomics model, ADC radiomics model, DCE-2 radiomics model, DCE-6 radiomics model, and the joint sequence radiomics model to predict the HER-2 expression status of breast cancer, respectively. Based on the clinical, pathological, and MRI image characteristics of patients, univariate and multivariate logistic regression analysis wasused to construct a clinicopathological MRI feature model. The radscore of every patient and the clinicopathological MRI features which were statistically significant after screening were used to construct a nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of each model and the decision curve analysis wasused to evaluate the clinical usefulness.Results:The T2WI, ADC, DCE-2, DCE-6, and joint sequence radiomics models, the clinicopathological MRI feature model, and the nomogram model were successfully constructed to predict the expression status of HER-2 in breast cancer. ROC analysis showed that in the training set and validation set, the areas under the curve (AUC) of the T2WI radiomics model were 0.797 and 0.760, of the ADC radiomics model were 0.776 and 0.634, of the DCE-2 radiomics model were 0.804 and 0.759, of the DCE-6 radiomics model were 0.869 and 0.798, of the combined sequence radiomics model were 0.908 and 0.847, of the clinicopathological MRI feature model were 0.703 and 0.693, and of the nomogram model were 0.938 and 0.859, respectively. In the training set, the combined sequence radiomics model outperformed the clinicopathological features model ( P＜0.001). In the training and validation sets, the nomogram outperformed the clinicopathological features model ( P＜0.05). In addition, the diagnostic performance of the nomogram was better than that of the four single-modality radiomics models in the training cohort ( P＜0.05) and was better than that of DCE-2 and ADC models in the validation cohort ( P＜0.05). Decision curve analysis indicated that the value of individualized prediction models was higher than clinical and pathological prediction models in clinical practice. The calibration curve showed that the multimodal radiomics model had a high consistency with the actual results in predicting HER-2 expression. Conclusions:T2WI, ADC and early-delayed phase DCE-MRI imaging histology models for HER-2 expression status in breast cancer are expected to provide a non-invasive virtual pathological basis for decision-making on preoperative neoadjuvant regimens in breast cancer.