With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective:Based on the consultation records of the online medical consultation platform, the chronic kidney disease (CKD) health information needs of users are analyzed, so as to provide a decision-making basis for the development of personalized CKD health information services, management and intervention.Methods:From September 15th to December 30th, 2022, the combination of manual retrieval and web crawler was used to obtain the question records of "kidney disease" in the three online medical platforms including Good Doctor Online, Dingxiang Doctor and Chunyu Doctor. Also, the hot question records were classified according to the content of users' questions. The top 50 high-frequency keywords were calculated by word segmentation and term frequency-inverse document frequency (IF-IDF) algorithm. The Gephi software was used to visualize the co-occurrence matrix of keywords.Results:A total of 78 405 valid consultation records were collected in the obtained question content. The classification results showed that the users' CKD health information needs can be classified into five categories, including basic knowledge (etiology and risk factors, symptoms and signs, classification and grading of diagnosis, complications or comorbidities, drug effects and adverse reactions), treatment plan, social life impact (physical impact, family impact, social interaction impact, economic burden impact, work impact), self-management (self-care or home care, emergency response) and social support (family support, professional medical team support).Conclusions:The information demand for CKD based on the online medical consultation platform presents a wide range of content and diversified subjects. This suggests that targeted and personalized health information guidance should be formulated when providing CKD health information services for different subjects.

Objective To explore the risk factors for portal vein thrombosis(PVT)in patients with cirrhosis in the decompensated stage and construct a risk prediction model for PVT so as to improve the early diagnosis rate of decompensated liver cirrhosis PVT.Methods The clinical data of patients with cirrhosis in the decompensated stage admitted to Department of Gastroenterology,The First Affiliated Hospital of Xi'an Jiaotong University between June 2018 and June 2023 were collected and divided into cirrhosis PVT group(n=135)and cirrhosis non-PVT group(n=225)according to whether or not portal vein thrombosis was formed.We made a univariate analysis of the general data,laboratory indexes,liver function scores and imaging findings of the patients in the two groups,and indexes with statistically significant differences were included in binary Logistic regression for multifactorial analysis to screen out independent risk factors.A predictive model of binary Logistic regression was established based on the independent risk factors.The clinical data of the validation set were incorporated into the model,the accuracy of the prediction model was evaluated by receiver operating curve(ROC),and the practicability of the model was evaluated by consistency curve to complete the validation and evaluation of the constructed prediction model.Internal stability of the model was verified with Bootstrap method.Finally,R software(4.3.1)was used to draw a nomogram of the prediction model to visualize the model.Results Univariate analysis revealed statistically significant differences between patients in the PVT and non-PVT groups in the following five aspects:history of splenectomy,history of endoscopic varicose vein treatment,portal vein diameter,and neutrophil-to-lymphocyte ratio(P＜0.05).Binary Logistics regression analysis showed that a history of splenectomy(P=0.002,OR=3.012,95％CI:1.500-6.047),a history of endoscopic varicose vein treatment(P=0.001,OR=2.276,95％CI:1.400-3.698),widening of portal vein diameter(P=0.007,OR=1.942,95％CI:1.202-3.136),increased neutrophil-to-lymphocyte ratio(P=0.009,OR=1.886,95％CI:1.170-3.041),and elevated D-dimer(P＜0.001,OR=3.725,95％CI:2.149-6.485)were independent risk factors for the formation of portal vein thrombosis in patients with cirrhosis in the decompensated stage of cirrhosis chemically presented(P＜0.05).The area under the ROC curve of the predictive model and the model after internal validation was 0.760 and 0.7494,respectively.The model still had good prediction ability and accuracy in the verification set.Conclusion A history of splenectomy,history of endoscopic varicose vein treatment,widening of portal vein diameter,increased neutrophil-to-lymphocyte ratio,and elevated D-dimer concentration are independent risk factors for the formation of portal vein thrombosis in patients with decompensated cirrhosis.The Logistic prediction model and visual nomogram constructed based on the above independent risk factors have a good ability to predict the occurrence of PVT in patients with decompensated cirrhosis and have important clinical guiding significance for early screening patients with PVT in decompensated cirrhosis.

Objective To establish an efficient and stable model of pelvic inflammatory disease in rats via a non-surgical method,and to evaluate its application in pharmacodynamic testing.Methods Female Sprague-Dawley rats were divided randomly into the following groups:control group;model group with phenol for 7 d;model group with phenol for 10 d;treatment group modeled with phenol;model group with low concentration of bacteria;model group with high concentration of bacteria;and treatment group modeled with bacteria.Rats in the model and treatment groups were injected with 25%phenol gel and 2×107 or 2×108 Escherichia coli and Staphylococcus aureus mixture via a non-surgical method,to construct a rat model of pelvic inflammatory disease.Rats in the treatment groups received the Chinese patent medicine Jingangteng capsules by gavage,and rats in the control group received the same volume of solvent solution.The health status,weight changes,and uterine appearance were monitored and the uterine coefficient was calculated.Pathological changes in the uterus and fallopian tubes,endometrial thickness,and number of glands were detected by hematoxylin and eosin staining.Serum levels of interleukin(IL)-1β,IL-6,and tumor necrosis factor(TNF)-α were detected by enzyme-linked immunosorbent assay.Protein expression of the macrophage marker CD68 was detected by immunofluorescence.Expression of Toll-like receptor 4(TLR4)/nuclear factor(NF)-κB pathway-related proteins in the uterus was detected by Western blot.Results The mortality rate in the model group was only 5%.Compared with the control group,model rats showed decreased body weight,increased uterine coefficient,pathological changes in the uterus and Fallopian tubes,thinner endometrium,fewer glands,significantly higher serum levels of IL-1β,IL-6,and TNF-α and more macrophages in the uterine tissue,and activation of the TLR4/NF-κB signaling pathway.The 7 d phenol and low-concentration bacterial solution models were judged to be mild pelvic inflammatory disease models,and the 10 d phenol and high-concentration bacterial solution models were considered severe pelvic inflammatory disease models.Treatment with Jingangteng capsules relieved the pathological symptoms in the uterus and fallopian tubes,in line with the efficacy evaluation of clinical pelvic inflammatory disease.Conclusions We established rat models of pelvic inflammatory disease using phenol and a mixed bacterial solution via a non-surgical method,to simulate the different pathological states of pelvic inflammatory disease caused by different factors.These models will be suitable for evaluating drug efficacy and elucidating the pathological mechanism of pelvic inflammatory disease.

Objective To evaluate the application value of nucleic acid mass spectrometry in detecting ERG11 gene mutations associated with azole resistance in Candida tropicalis(C.tropicalis),thereby providing a scientific basis for the rational clinical use of azole antifungal agents.Methods Clinical isolates of C.tropicalis were obtained from the Affiliated Hospital of Xuzhou Medical University between July 2023 and November 2024.For each isolate,specific ERG11 mutations(A395T and C461T)were analyzed using Sanger sequencing and nucleic acid mass spectrometry.Sanger sequencing was used as the reference standard to evaluate the performance of mass spectrometry in mutation detection.Results The established nucleic acid mass spectrometry method effectively identified four genotypes of ERG11(A395T and C461T),with distinct spectral peaks for each mutation and no cross-interference observed.Among 88 clinical isolates,the sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NPV)for the A395T mutation were 86.7%,100%,100%,and 97.3%,respectively.For the C461T mutation,the corresponding values were 81.3%,100%,100%,and 96.0%,respectively.Kappa statistics demonstrated a high level of agreement between mass spectrometry and sequencing results.Conclusions Nucleic acid mass spectrometry exhibits high sensitivity and specificity in the detection of ERG11 mutations,enabling rapid,accurate,and adaptable high-throughput analysis.This makes it a highly effective method for identifying mutations associated with fungal resistance.

Objective To investigate the prevalence of specific IgE antibodies against Alternaria alternata and Aspergillus fumigatus in serum samples from clinical allergy patients across selected provinces in China.Methods Data on specific IgE antibodies for Alternaria A.and Aspergillus F.were collected from 20 hospital laboratories in 17 cities spanning 11 provinces.The study analyzed the levels of specific IgE and their variations across different provinces and seasons.Results A total of 27 471 cases of Alternaria A.and 32 843 cases of Aspergillus F.specific IgE data were included.The national average positive rate of Alternaria A.IgE was 10.40%,with the highest rate of 22.68%in Jiangsu and the lowest rate of 2.06%in Guangxi.For Aspergillus F.specific IgE,the average positive rate was 4.24%,with Hubei province having the highest rate(7.25%)and Hunan province the lowest(1.23%).The difference in IgE levels for both Alternaria A.and Aspergillus F.among provinces were statistically significant(H=9 955,16 993,all P<0.0001).Among patients,5.85%had Alternaria A.specific IgE levels at grade 3 or above,while only 0.57%had Aspergillus F.specific IgE levels at this level.When examining seasonal variations using data from Liaoning,Hunan and Anhui provinces,significant seasonal changes were observed for both Alternaria A.and Aspergillus F.IgE antibodies(HAlternaria A=347.6,338.0,401.3,HAspergillus F=196.6,133.7,231.7,all P<0.0001).Conclusion The sensitization to Alternaria A.and Aspergillus F.exhibits distinct geographical characteristics and vary significantly with seasons.Given the relatively high IgE levels associated with Alternaria A.,it should be given adequate clinical attention.

Immune checkpoint inhibitors(ICI)are monoclonal antibodies that could restore and improve the ability of T lymphocytes to specifically recognize and kill tumor cells by inhibiting immune checkpoint activity,this therapeutic strategy is a breakthrough in the treatment of malignant solid tumors in recent years.ICI-associated myocarditis(ICI-M)is one of the immune-related adverse events with rapid onset,severe symptoms and rapid progression.However,the clinical understanding of ICI-M is insufficient at present.This article reviews current research progress on epidemiology,pathogenic mechanism,diagnosis and treatment of ICI-M.

Objective:To investigate the effect of a blended teaching mode based on medical knowledge graph and artificial intelligence (AI) teaching assistant on students' learning effectiveness and systematic thinking ability in teaching of pathogens and immunology.Methods:A controlled experimental design was employed, involving 114 clinical medical students ("5+3" integrated) enrolled in 2023 at Tianjin Medical University. They were divided into experimental group ( n=57) and control group ( n=57) based on their classes. A blended online-offline teaching mode based on medical knowledge graph and AI teaching assistant was used in the experimental group, and a blended online-offline teaching mode based on the online resources of this course was used in control group. Teaching effectiveness was assessed by comparing the scores of four chapter quizzes and the final exam between the two groups, as well as by analyzing student questionnaire responses. Data were analyzed using t test and χ2 test. Results:The experimental group achieved significantly higher scores in all four chapter quizzes and the final exam compared to the control group [(78.77±19.65) vs. (69.47±22.95), (84.56±14.02) vs. (76.49±16.20), (81.89±13.60) vs. (73.13±16.52), (81.56±21.28) vs. (73.16±16.27), (69.75±13.30) vs. (64.10±14.93), all P<0.05]. The questionnaire survey showed that 74.07% students ( n=40) believed that this blended teaching model stimulated their learning interest, 74.07% ( n=40) students believed that this blended teaching model enhanced their learning initiative, 81.48% ( n=44) students believed that this blended teaching model could help them construct the relationship between the old and new knowledge and thus improved memory retention, and 81.48% students b( n=44) elieved that this blended teaching model helped them integrate and summarize knowledge and construct systematic knowledge framework to improve their learning effectiveness. Conclusions:The blended teaching mode based on medical knowledge graph and AI teaching assistant is beneficial for cultivating students' systematic thinking ability and is helpful to improve their learning efficiency and effectiveness.

Objective:To investigate the effectiveness of the crisis management theory of reduction, readiness, response and recovery (4R) in the prevention of postoperative complications in patients with lower extremity varices.Methods:Convenience sampling was used to select 172 patients who underwent surgical treatment for lower extremity varices in the Hefei First People's Hospital from October 2022 to November 2023 as study subjects. Patients admitted from October 2022 to June 2023 were recruited in control group ( n=88) with routine nursing. Patients admitted from July to November 2023 were included in observation group ( n=84) , and nursing interventions based on the 4R crisis management theory were implemented on the basis of control group. Postoperative ulcer surface healing time, time for calf numbness to subside, pain scores and postoperative complications were compared between the two groups. Results:The time for disappearance of ulcer surface and the time for calf numbness to subside of the patients in observation group were shorter than those in control group, and the differences were statistically significant ( P<0.05) . Before the intervention, the pain score was (3.58±0.61) in observation group and (3.72±0.57) in control group, and the difference was not statistically significant ( P>0.05) . After the intervention, the pain score was (2.33±0.55) in observation group and (2.50±0.50) in control group, and the difference was statistically significant ( P<0.05) . The incidence of postoperative complications was 4.76% (4/84) in observation group and 19.32% (17/88) in control group, and the difference was statistically significant ( P<0.05) . Conclusions:Nursing interventions based on the 4R crisis management theory can effectively alleviate the pain of patients undergoing lower extremity varicose vein surgery, shorten the time of ulcer healing and calf numbness, and reduce the incidence of postoperative complications.

Objective To evaluate the application value of nucleic acid mass spectrometry in detecting ERG11 gene mutations associated with azole resistance in Candida tropicalis(C.tropicalis),thereby providing a scientific basis for the rational clinical use of azole antifungal agents.Methods Clinical isolates of C.tropicalis were obtained from the Affiliated Hospital of Xuzhou Medical University between July 2023 and November 2024.For each isolate,specific ERG11 mutations(A395T and C461T)were analyzed using Sanger sequencing and nucleic acid mass spectrometry.Sanger sequencing was used as the reference standard to evaluate the performance of mass spectrometry in mutation detection.Results The established nucleic acid mass spectrometry method effectively identified four genotypes of ERG11(A395T and C461T),with distinct spectral peaks for each mutation and no cross-interference observed.Among 88 clinical isolates,the sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NPV)for the A395T mutation were 86.7%,100%,100%,and 97.3%,respectively.For the C461T mutation,the corresponding values were 81.3%,100%,100%,and 96.0%,respectively.Kappa statistics demonstrated a high level of agreement between mass spectrometry and sequencing results.Conclusions Nucleic acid mass spectrometry exhibits high sensitivity and specificity in the detection of ERG11 mutations,enabling rapid,accurate,and adaptable high-throughput analysis.This makes it a highly effective method for identifying mutations associated with fungal resistance.