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 models (LLMs) 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 the exceptional potential of LLMs 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.

Objective To summarize the clinical efficacy of robotic-assisted left thoracic small-incision minimally invasive direct coronary artery bypass grafting(MIDCAB).Methods A retrospective analysis was conducted on the procedures and treatment outcomes of robotic-assisted MIDCAB in the Army Medical Center of PLA from October 2016 to June 2023.Baseline clinical information,MIDCAB-related data,perioperative conditions and data during follow-up were collected and analyzed.Results There were 23 patients subjected,including 21 males and 2 females,with a mean age of 58.17±7.49 years,and a body mass index(BMI)of 23.99±3.25 kg/m2.All of them experienced angina pectoris,and 1 had a history of myocardial infarction,1 had dilated cardiomyopathy,2 patients had chronic obstructive pulmonary disease(COPD),and 10 had a history of percutaneous coronary intervention(PCI).Robotic-assisted MIDCAB procedure was successfully completed.No internal mammary artery injury or transformation of the procedure occurred in these cases,and excellent bridging vessel flow was achieved after anastomosis of the internal mammary artery to left anterior descending branch.The incision length in the left chest was 8(8,8)cm,the operation time was 380(300,465)min,the intraoperative bleeding volume was 300(100,400)mL,the length of ICU stay was 3(2,3)d,the amount of thoracic drainage was 780(525,1 040)mL,and the postoperative length from surgery to discharge was 11.17±2.38 d.No mortality was observed during or within 30 d of hospitalization,and 1 patient was readmitted due to pericardial effusion within 30 d,and was discharged after symptomatic treatment including pericardiocentesis and drainage.No deaths,major adverse cardiovascular and cerebrovascular events(MACCE),or re-revascularization occurred in all patients during outpatient and telephone follow-up.Conclusion Robotic-assisted internal mammary artery dissection is a delicate and safe technique,and coronary artery bypass grafting in minimally invasive small-incision off-pump is effective,safe and feasible,with satisfactory short-and mid-term outcomes.The technique is suitable for minimally invasive coronary artery disease surgery and is worthy of popularization and application.

Objective:To investigate the risk factors for delirium after free flap reconstruction surgery in elderly patients with head and neck cancer.Methods:This study retrospectively collected clinical data from 309 elderly patients with head and neck cancer who underwent radical resection and free flap reconstruction surgery in the Department of Operation & Anesthesiology, College & Hospital of Stomatology, Guangxi Medical University from January 2018 to December 2022. Among them, there were 197 males (63.8%) and 112 females (36.2%), with an average age of (66.8±5.5) years old. Based on the delirium diagnostic criteria outlined in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders, patients were evaluated for postoperative delirium within one week after surgery. They were then divided into a delirium group ( n=75) and a non-delirium group ( n=234). Perioperative indicators potentially related to postoperative delirium, including age, gender, past medical history, tumor characteristics, hematological tests, perioperative sleep disturbances, surgical procedures, intraoperative fluid intake and output, anesthetic dosage, postoperative pain, etc, were collected from the relevant medical record system.Variables with P<0.05 were included in a multivariate Logistic regression model to screen for independent risk factors for delirium. Results:The incidence of delirium after free flap reconstruction surgery for head and neck cancer in elderly patients was 24.3% (75/309). Multivariate Logistic regression analysis showed that male gender ( OR=2.802, P=0.005), perioperative sleep disturbances ( OR=7.104, P<0.001), and moderate-to-severe postoperative pain ( OR=6.903, P<0.001) were risk factors for postoperative delirium. Conclusions:Male gender, perioperative sleep disturbances, and moderate-to-severe postoperative pain are independent risk factors for delirium in these patients.

Mental disorders pose a significant challenge to global public health,profoundly affecting the quality of life of a vast number of individuals and imposing a heavy health burden on society.Nonetheless,there remains a substantial gap between the current societal capacity to provide prevention,diagnosis,and treatment for mental disorders and the existing demand for such services.In recent years,the development and application of artificial intelligence(AI)technologies have provided unprecedented opportunities to enhance mental healthcare services.As one of the fastest-growing fields of AI,generative AI has played a pivotal role in analyzing diverse forms of data,including medical image processing,protein structure prediction,clinical document generation,auxiliary diagnostic discrimination,and clinical decision support.These advancements have significantly strengthened capabilities in clinical diagnosis,data reconstruction,and adjunctive therapeutic interventions.This review highlights the potential applications of generative AI in advancing fundamental psychiatric research,identifying early risk factors for mental disorders,and assisting clinicians in diagnosis and treatment.Additionally,it addresses the challenges and limitations currently facing the application of generative AI to mental healthcare,including biases,privacy breaches,and insufficient interpretability.Finally,the review summarizes strategies to enhance AI's capacity to deliver mental health services,aiming to leverage new technologies to reduce the global burden of mental disorders and improve the quality of life of affected individuals.

Intraluminal thrombus(ILT)at the vertebral artery origin is rare and often missed due to atypical symptoms.However,it poses a high risk of artery-to-artery embolism and severe posterior circulation ischemia.This article reports a case of missed diagnosis of ILT to improve the diagnostic ability.The patient,a 49-year-old male was admitted to emergency department with sudden dizziness accompanied by nausea for 12 hours.Head CT suggested posterior circulation ischemia.Patient was given aspirin 0.2 g/d and atorvastatin 20 mg/d orally and discharged from the hospital.Head MRI in the outpatient department showed a recent infarct in the right cerebellar hemisphere next day.On the 28th,dizziness recurred,accompanied by unsteady walking and right-sided hemisensory numbness.The symptoms persisted and did not improve.He was admitted to the hospital on the 29th.Cervical CTA showed severe stenosis at the origin of the right vertebral artery,filling defect in the distal lumen combined with the Donut sign.Thus,ILT was diagnosed.The drug treatment regimen was adjusted to aspirin 100 mg/d combined with clopidogrel 75 mg/d for a total of 15 weeks.High-resolution MRI of the cervical blood vessels in the outpatient department showed occlusion of the V1-V2 segment of the right vertebral artery.On November 2,endovascular treatment was performed under local anesthesia.At 90 days and 6 months of follow-up after the operation,no recurrence of cerebral ischemia events occurred,and the mRS score was 0.This case highlights the need for vigilance toward ILT in posterior circulation ischemia when initial imaging is inconclusive.Early dynamic CTA(5-7 days)combined with dual antiplatelets may prevent thrombus progression and improve outcomes.

Objective:To develop an artificial intelligence (AI)-guided intravitreal injection robot system to accurately detect the injection point on the ocular surface and guide the robotic arm to complete the intravitreal injection positioning task through 3D position calculation.Methods:The Dikablis subset of the TEyeD dataset was used.Training set, testing set, and validation set were constructed by using equal interval sampling strategy.The system read the ocular surface color RGB image with an RGBD camera, then used a PatchCrop-Transformer-based injection point detection algorithm to detect and locate key points such as the pupil, iris, and eyelid in the image.Next, it extracted the local 3D point cloud data near the injection point based on the depth information obtained by the camera.Through principal component analysis (PCA) of the local area point cloud data, the injection point and injection direction were determined.The key information was then passed to the robotic arm system.The end of the robotic arm adopted a remote center of motion (RCM) mechanism.After solving the forward and inverse kinematics, the joint movement path was obtained, and the robotic arm was controlled to move to 2 cm above the injection point.After confirmation by the doctor, the insertion, injection, and withdrawal operations were completed to ensure the stability and repeatability of the injection process.The mean square error (MSE) of key points localization and the success detection rate (SDR) within different pixel error ranges (2, 5, and 10 pixels) of the study method were compared with those of the NFDP, SLPT, and StarLoss methods, and the effects of random weight enhancement, fixed weight enhancement, and no enhancement methods on the MSE of key points localization were evaluated.The repeatability and absolute positioning accuracy of the robotic arm system were also evaluated.Results:After adding random weight enhancement, the model of this study outperformed the fixed weight enhancement and no enhancement methods in both MSE and SDR.The MSEs of the model proposed in this study for overall eye, pupil, and iris localization were 4.25, 2.41, and 1.54, respectively, which were lower than those of the NFDP, StarLoss, and SLPT methods.Within the error ranges of 5 and 10 pixels, the SDRs of the model proposed in this study were 72.09% and 92.68%, respectively, which were higher than those of the NFDP, StarLoss, and SLPT methods.The single-axis repeatability errors and absolute positioning errors of the robotic arm were within ±5 μm.Conclusions:The AI-guided intravitreal injection robot system integrates RGBD images to achieve automatic recognition of the ocular injection point and high-precision motion control through RCM mechanism design and corresponding kinematic solution methods.

Objective:To investigate the risk factors for delirium after free flap reconstruction surgery in elderly patients with head and neck cancer.Methods:This study retrospectively collected clinical data from 309 elderly patients with head and neck cancer who underwent radical resection and free flap reconstruction surgery in the Department of Operation & Anesthesiology, College & Hospital of Stomatology, Guangxi Medical University from January 2018 to December 2022. Among them, there were 197 males (63.8%) and 112 females (36.2%), with an average age of (66.8±5.5) years old. Based on the delirium diagnostic criteria outlined in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders, patients were evaluated for postoperative delirium within one week after surgery. They were then divided into a delirium group ( n=75) and a non-delirium group ( n=234). Perioperative indicators potentially related to postoperative delirium, including age, gender, past medical history, tumor characteristics, hematological tests, perioperative sleep disturbances, surgical procedures, intraoperative fluid intake and output, anesthetic dosage, postoperative pain, etc, were collected from the relevant medical record system.Variables with P<0.05 were included in a multivariate Logistic regression model to screen for independent risk factors for delirium. Results:The incidence of delirium after free flap reconstruction surgery for head and neck cancer in elderly patients was 24.3% (75/309). Multivariate Logistic regression analysis showed that male gender ( OR=2.802, P=0.005), perioperative sleep disturbances ( OR=7.104, P<0.001), and moderate-to-severe postoperative pain ( OR=6.903, P<0.001) were risk factors for postoperative delirium. Conclusions:Male gender, perioperative sleep disturbances, and moderate-to-severe postoperative pain are independent risk factors for delirium in these patients.

Soil cadmium pollution can adversely affect the cultivation of the ornamental plant, Coleus scutellarioides. Upon cadmium contamination of the soil, the growth of C. scutellarioides is impeded, and it may even succumb to the toxic accumulation of cadmium. In this study, we investigated the effects of arbuscular mycorrhizal fungi (AMF) on the adaptation of C. scutellarioides to cadmium stress, by measuring the physiological metabolism and the degree of root damage of C. scutellarioides, with Aspergillus oryzae as the test fungi. The results indicated that cadmium stress increased the activity of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), and the content of malondialdehyde (MDA) and proline (Pro) within the cells of C. scutellarioides, but inhibited mycorrhizal infestation rate, root vigour and growth rate to a great degree. With the same cadmium concentration, the inoculation of AMF significantly improved the physiological indexes of C. scutellarioides. The maximum decrease of MDA content was 42.16%, and the content of secondary metabolites rosemarinic acid and anthocyanosides could be increased by up to 27.43% and 25.72%, respectively. Meanwhile, the increase of root vigour was as high as 35.35%, and the DNA damage of the root system was obviously repaired. In conclusion, the inoculation of AMF can promote the accumulation of secondary metabolites, alleviate root damage, and enhance the tolerance to cadmium stress in C. scutellarioides.
Cadmium/toxicity* ; Mycorrhizae/physiology* ; Plant Roots/drug effects* ; Soil Pollutants/toxicity* ; Stress, Physiological ; Superoxide Dismutase/metabolism*

Objective:To develop an artificial intelligence (AI)-guided intravitreal injection robot system to accurately detect the injection point on the ocular surface and guide the robotic arm to complete the intravitreal injection positioning task through 3D position calculation.Methods:The Dikablis subset of the TEyeD dataset was used.Training set, testing set, and validation set were constructed by using equal interval sampling strategy.The system read the ocular surface color RGB image with an RGBD camera, then used a PatchCrop-Transformer-based injection point detection algorithm to detect and locate key points such as the pupil, iris, and eyelid in the image.Next, it extracted the local 3D point cloud data near the injection point based on the depth information obtained by the camera.Through principal component analysis (PCA) of the local area point cloud data, the injection point and injection direction were determined.The key information was then passed to the robotic arm system.The end of the robotic arm adopted a remote center of motion (RCM) mechanism.After solving the forward and inverse kinematics, the joint movement path was obtained, and the robotic arm was controlled to move to 2 cm above the injection point.After confirmation by the doctor, the insertion, injection, and withdrawal operations were completed to ensure the stability and repeatability of the injection process.The mean square error (MSE) of key points localization and the success detection rate (SDR) within different pixel error ranges (2, 5, and 10 pixels) of the study method were compared with those of the NFDP, SLPT, and StarLoss methods, and the effects of random weight enhancement, fixed weight enhancement, and no enhancement methods on the MSE of key points localization were evaluated.The repeatability and absolute positioning accuracy of the robotic arm system were also evaluated.Results:After adding random weight enhancement, the model of this study outperformed the fixed weight enhancement and no enhancement methods in both MSE and SDR.The MSEs of the model proposed in this study for overall eye, pupil, and iris localization were 4.25, 2.41, and 1.54, respectively, which were lower than those of the NFDP, StarLoss, and SLPT methods.Within the error ranges of 5 and 10 pixels, the SDRs of the model proposed in this study were 72.09% and 92.68%, respectively, which were higher than those of the NFDP, StarLoss, and SLPT methods.The single-axis repeatability errors and absolute positioning errors of the robotic arm were within ±5 μm.Conclusions:The AI-guided intravitreal injection robot system integrates RGBD images to achieve automatic recognition of the ocular injection point and high-precision motion control through RCM mechanism design and corresponding kinematic solution methods.

Amorphous calcium phosphate（ACP）is a component of urinary stones，primarily forming mixed stones with calcium oxalate，while pure ACP stones are relatively rare. This article reports a case of a patient with an ACP bladder stone who was admitted due to progressive dysuria over 5 years，which had worsened in the past months. Upon admission，tPSA was 29.63 ng/ml. CT and enhanced MRI revealed multiple bladder stones and prostatic hyperplasia. The patient underwent ultrasound-guided prostate biopsy and transurethral cystolithotripsy with pneumatic lithotripsy. Postoperative infrared spectroscopy confirmed the stone composition as ACP，and prostatic adenocarcinoma was diagnosed by prostate biopsy pathology. Endocrine therapy was administered postoperatively，and follow-up imaging at 3 months showed no stone. This article presents the first reported case of an ACP bladder stone coexisting with prostate cancer，providing important clinical insights into the etiology of such stones and the rare local manifestations of prostate cancer.