A 71-year-old male presented with esophageal cancer and severe aortic valve regurgitation. Treatment strategies for such patients are controversial. Considering the risks of cardiopulmonary bypass and potential esophageal cancer metastasis, we successfully performed transcatheter aortic valve implantation and minimally invasive three-incision thoracolaparoscopy combined with radical resection of esophageal cancer (McKeown) simultaneously in the elderly patient who did not require neoadjuvant treatment. This dual minimally invasive procedure took 6 hours and the patient recovered smoothly without any surgical complications.

Objective To investigate the population density and seasonal fluctuations of Aedes albopictus in Guangzhou City, Guangdong Province, from 2021 to 2023, so as to provide insights into A. albopictus control and management of dengue fever. Methods The surveillance of A. albopictus density was performed in all surveillance sites assigned across all streets (townships) in Guangzhou City during the period from January to December from 2021 to 2023. The surveillance frequency was twice every half month from May to September, and once every month for the rest of a year. In each surveillance period, A. albopictus mosquito larvae were captured from indoor and outdoor small water containers in residential areas, parks, medical facilities, schools, other government sectors and social organizations, construction sites, special industries and others for mosquito species identification. Adult mosquitoes were captured using electric mosquito suction apparatus for species identification and gender classification. Adult mosquitoes and mosquito eggs were collected with mosquito and egg traps at the breeding and dwelling places of Aedes mosquitoes for identification. The mosquito oviposition index (MOI), Breteau index (BI), adult mosquito density index (ADI) and standard space index (SSI) were calculated. The A. albopictus density was classified into grades 0, 1, 2 and 3 in each surveillance site, with Grade 0 density defined eligible, and the eligible rate of A. albopictus density was calculated at all surveillance sites each year from 2021 to 2023. In addition, the changing trends in MOI, SSI, BI and ADI of A. albopictus were analyzed in Guangzhou City from 2021 to 2023. Results The eligible rates of A. albopictus density were 61.69%, 68.75% and 55.15% in surveillance sites of Guangzhou City from 2021 to 2023 (χ² = 297.712, P < 0.001), and appeared a tendency towards a reduction followed by a rise each year, which gradually reduced since January, maintained at a low level during the period between May and October, and gradually increased from November to December. The MOI, SSI, BI and ADI of A. albopictus all appeared a tendency towards a rise followed by a reduction in Guangzhou City during the period between January and December from 2021 to 2023. The BI of A. albopictus peaked in the first half of June in 2021 (4.03), the first half of July in 2022 (3.89) and the last half of August in 2023 (5.02), and the SSI of A. albopictus peaked in the last half of June in 2021 (0.93), the last half of May in 2022 (0.59), and the last half of June (0.94) and the first half of September in 2023 (1.12). In addition, the MOI of A. albopictus peaked in the first half of May in 2021 (8.64), the first half of June in 2022 (8.96), and the last half of May (10.21) and the last half of June in 2023 (10.89), and the ADI of A. albopictus peaked in the first half of June in 2021 (3.41), the last half of June in 2022 (4.06), and the first half of July in 2023 (3.61). Conclusions The density of A. albopictus is high in Guangzhou City during the period from May to October, and the risk of local outbreak caused by imported dengue fever is high. Persistent intensified surveillance of the density and seasonal fluctuation of A. albopictus is recommended and timely mosquito prevention and control is required according to the fluctuation in the A. albopictus density.

Objective To investigate the setup error in patients with spinal bone metastasis who underwent radiotherapy under the guidance of kilovoltage cone-beam CT (KV-CBCT). Methods A total of 118 patients with spinal metastasis who underwent radiotherapy, including 17 cases of cervical spine, 62 cases of thoracic spine, and 39 cases of lumbar spine, were collected. KV-CBCT scans were performed using the linear accelerators from Elekta and Varian’s EDGE system. CBCT images were registered with reference CT images in the bone window mode. A total of 973 data were collected, and 3D linear errors were recorded. Results The patients with spinal bone metastasis were grouped by site, height, weight, and BMI. The P value of the patients grouped only by site was P<0.05, which was statistically significant. Conclusion When grouped by site in the 3D direction, the positioning effect of cervical spine is better than that of thoracic and lumbar spine. The positioning effect of the thoracic spine is better in the head and foot direction but worse in the left and right direction compared with that of the lumbar spine. Instead of extending or narrowing the margin according to the BMI of patients with spinal metastasis, the margin must be changed according to the site of spinal bone metastasis.

ObjectiveTo explore the characteristics of traditional Chinese medicine （TCM） syndromes in the patients with concurrent knee osteoarthritis （KOA） and postmenopausal osteoporosis （PMOP） and provide a scientific basis for precise TCM syndrome differentiation， diagnosis， and treatment of such concurrent diseases. MethodsA prospective， multicenter， cross-sectional clinical survey was conducted to analyze the characteristics of TCM syndromes in the patients with concurrent PMOP and KOA. Excel 2021 was used to statistically analyze the general characteristics of the included patients. Continuous variables were reported as x¯±s， and binary variables were reported as percentages. UCINET and Gephi were used to construct a complex "isease-syndrome-symptom" network for in-depth mining. Topological structure indicators， edge connection information between nodes， and weighted adjacency matrices were calculated by UCINET and Python. Gephi was used for complex network visualization. ResultsA total of 157 patients with concurrent PMOP and KOA from 12 TCM hospitals or community health service centers were selected for the collection of TCM syndrome and symptom information. A total of 4 main TCM syndromes were obtained， which were kidney-Yang deficiency （47.13%）， liver-kidney Yin deficiency （36.94%）， spleen-kidney Yang deficiency （8.92%）， and kidney Yin-Yang deficiency （7.01%）. In addition， 10 concurrent syndromes （mainly Qi stagnation and blood stasis， Qi and blood deficiency， spleen Yang deficiency， and cold-dampness） were identified. Based on the results of comprehensive frequency statistics and complex network analysis， as well as TCM theoretical knowledge and relevant guidelines， the general characteristics of concurrent PMOP and KOA and the main TCM syndromes in the dimensions of physical symptoms， pain， tongue and pulse manifestations， and defecation and urination were obtained. ConclusionThe main TCM syndromes in the patients with concurrent PMOP and KOA may be kidney Yang deficiency， liver-kidney Yin deficiency， spleen-kidney Yang deficiency， and kidney Yin-Yang deficiency， among which kidney Yang deficiency and liver-kidney Yin deficiency are the core TCM syndromes.

ObjectiveTo explore the characteristics of traditional Chinese medicine （TCM） syndromes in the patients with concurrent knee osteoarthritis （KOA） and postmenopausal osteoporosis （PMOP） and provide a scientific basis for precise TCM syndrome differentiation， diagnosis， and treatment of such concurrent diseases. MethodsA prospective， multicenter， cross-sectional clinical survey was conducted to analyze the characteristics of TCM syndromes in the patients with concurrent PMOP and KOA. Excel 2021 was used to statistically analyze the general characteristics of the included patients. Continuous variables were reported as x¯±s， and binary variables were reported as percentages. UCINET and Gephi were used to construct a complex "isease-syndrome-symptom" network for in-depth mining. Topological structure indicators， edge connection information between nodes， and weighted adjacency matrices were calculated by UCINET and Python. Gephi was used for complex network visualization. ResultsA total of 157 patients with concurrent PMOP and KOA from 12 TCM hospitals or community health service centers were selected for the collection of TCM syndrome and symptom information. A total of 4 main TCM syndromes were obtained， which were kidney-Yang deficiency （47.13%）， liver-kidney Yin deficiency （36.94%）， spleen-kidney Yang deficiency （8.92%）， and kidney Yin-Yang deficiency （7.01%）. In addition， 10 concurrent syndromes （mainly Qi stagnation and blood stasis， Qi and blood deficiency， spleen Yang deficiency， and cold-dampness） were identified. Based on the results of comprehensive frequency statistics and complex network analysis， as well as TCM theoretical knowledge and relevant guidelines， the general characteristics of concurrent PMOP and KOA and the main TCM syndromes in the dimensions of physical symptoms， pain， tongue and pulse manifestations， and defecation and urination were obtained. ConclusionThe main TCM syndromes in the patients with concurrent PMOP and KOA may be kidney Yang deficiency， liver-kidney Yin deficiency， spleen-kidney Yang deficiency， and kidney Yin-Yang deficiency， among which kidney Yang deficiency and liver-kidney Yin deficiency are the core TCM syndromes.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

OBJECTIVE To build a Tibetan-language medication service platform based on “internet+” and evaluate its effect on improving medication compliance and safety of Tibetan patients with chronic disease. METHODS Medication guidance contents of commonly used drugs in the outpatient department were summarized， translated and recorded in Tibetan-language or video to form a “text-audio-video” multi-dimensional “internet+ ” Tibetan-language medication service platform. A total of 387 Tibetan outpatients with chronic disease in our hospital after the implementation of “internet+” Tibetan-language medication service platform （from January 2024 to June 2024） in our hospital were selected as the intervention group， and 387 Tibetan outpatients before the implementation （from January 2023 to June 2023） were selected as the control group. Patients in the control group received conventional window-based Chinese-language medication services， while patients in the intervention group received both conventional window-based Chinese-language medication service and “internet+ ” Tibetan-language medication service. The medication compliance of patients was evaluated using the 12-item Medication Compliance Scale. A six-level causality assessment was conducted as the principles for analyzing adverse drug reactions （ADR） set by the National Center for ADR Monitoring. Additionally， statistics were compiled on the occurrence of ADR that were assessed as “definite”“probable” or “possible” in the causality assessment. RESULTS The proportion （31.0%） of patients with good medication compliance and compliance scores [39.0 （37.0，42.0）] of patients in the intervention group were significantly better than control group [7.0%， 21.0（19.0， 23.0）]（ P＜0.05）. There were no statistically significant differences in the incidence of various types of ADR or the overall incidence between the two groups （P＞0.05）. CONCLUSIONS The “internet+” Tibetan-language medication service platform is constructed successfully； the service can effectively improve the medication compliance of Tibetan-language patients， but its effect on improving the medication safety of patients is limited.

Prostate cancer (PCa) ranks as the second most prevalent malignancy among men worldwide. Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients' survival rates. The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. The introduction of the foundation model has revolutionized the application of AI in medical treatment and facilitated its integration into clinical practice. This review emphasizes the clinical application of AI in PCa by discussing recent advancements from both pathological and imaging perspectives. Furthermore, it explores the current challenges faced by AI in clinical applications while also considering future developments, aiming to provide a valuable point of reference for the integration of AI and clinical applications.
Humans ; Prostatic Neoplasms/diagnosis* ; Male ; Artificial Intelligence ; Deep Learning ; Prognosis

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.