Following spinal fusion surgery, the mechanical properties of macroscopic bone regenerative tissue and microscopic bone cells during daily activities remain unclear. This study employed a submodel approach to establish connections between bone regenerative tissue and bone cells, simulating human physiological activities to obtain stress-strain data at both macro- and micro-scales across various stages and working conditions. Results indicate that vertical external forces significantly impact bone healing. Patients should minimize large-amplitude forward flexion and right rotation movements during the early healing phase. Once healing is largely complete, appropriate activity is safe, though caution should still be exercised to avoid large-amplitude forward flexion, left rotation, and right lateral flexion movements. This study investigated healing variations in regenerated bone tissue across different end-face orientations, regions, and operational conditions during the healing process. It provides a theoretical basis for developing movement guidelines that promote healing during the postoperative recovery phase for patients who have undergone spinal fusion surgery.
Objective
Through a retrospective study on esophageal function changes and symptom relief after video-assisted thoracoscopic surgery treatment for achalasia of cardia (AC) to assess the clinical value of this operation.
Methods
We reviewed the data of 34 AC patients who received modified Heller operation by video-assisted thoracoscopic surgery in the Affiliated Hospital of Guizhou Medical University from March 2012 to September 2014. There were 11 males and 23 females with a median age of 35 (11–67) years. These patients were divided into four groups according to the time of treatment and follow-up: preoperative group, postoperative one-month group, postoperative three-month group and postoperative six-month group. Changes of symptoms, radiography and esophageal dynamics before and after therapy were collected. These different groups were analyzed based on statistical methods.
Results
There was no statistical difference in ages and genders among groups (P>0.05). The surgery was successful and no complication or death occurred. Symptoms of patients showed different degrees of relief and the postoperative grade of clinical symptoms decreased (P<0.05). After surgery, lower esophageal sphincter pressure (LESP), lower esophageal sphincter resting pressure (LESRP) and esophageal body pressure (EBP) decreased significantly, while lower esophageal sphincter relax rate (LESRR) increased (P<0.05). While there was no significant difference in length of lower esophageal sphincter (LESL,P>0.05). Angiography of upper digestive tract revealed that compared to the preoperative group, the maximum width in postoperative three-month group decreased significantly (P<0.05). During the follow-up, 3 patients suffered gastroesophageal reflux, 2 patients esophageal perforation and 1 patient empyema due to esophago-pleural fistula. No massive hemorrhage of upper digestive tract and hiatal hernia occured.
Conclusion
Sugery can significantly ameliorate the clinical symptoms of the patients with AC, and improve esophageal dynamics. And it is simple and easy to perform with less complications and better long-term outcomes. Improved Heller operation by video-assisted thoracoscopy is a less invasive procedure when compared with the traditional thoracotomy. Moreover, esophageal manometry can objectively assist in the diagnosis and degree of the disease and effect of therapy.
Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language model technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. The paper systematically analyzes that Large Language Models (LLMs) demonstrate exceptional potential in lung cancer auxiliary diagnosis, tumor feature extraction, automatic staging, progression/outcome analysis, treatment recommendations, medical documentation generation, and patient education. However, they face critical technical and ethical challenges including inconsistent performance in complex integrated decision-making (e.g., TNM staging, personalized treatment suggestions) and "black box" opacity issues, along with dilemmas such as training data biases, model hallucinations, data privacy concerns, and cross-lingual adaptation challenges ("data colonization"). Future directions should prioritize constructing high-quality multimodal corpora specific to lung cancer, developing interpretable and compliant specialized models, and achieving seamless integration with existing clinical workflows. Through dual drivers of technological innovation and ethical standardization, LLMs should be prudently advanced for holistic lung cancer management processes, ultimately promoting efficient, standardized, and personalized diagnosis and treatment practices.
