With the rapid development of minimally invasive techniques in surgery, arti-ficial intelligence (AI), particularly deep learning, is playing an increasingly important role in mini-mally invasive surgery. By automated analysis of surgical videos, AI can efficiently perform key tasks such as instrument recognition, surgical phase identification, action analysis, anatomical structure recognition, intraoperative diagnosis, adverse event monitoring and smart desmoking. These appli-cations provide essential support for real-time monitoring, surgical navigation and skill assessment during surgery. The authors summarize the current research progress of AI in minimally invasive surgery, including its applications in the fields of hepatobiliary and pancreatic surgery, as well as gastrointestinal surgery. It also explores the potential of AI in enhancing surgical safety, efficiency and skill assessment. By synthesizing the latest research achievements of AI technology in the field of surgery, as well as analyzing its technical challenges and risks, it aims to provide guidance for future innovations and clinical applications, promoting the advancement and implementation of AI in minimally invasive surgery.

Objective To explore the objective facial appearance characteristics of patients with coronary heart disease(CHD).Methods From April 7,2019 to December 1,2022,313 patients with CHD were recruited from Dongzhimen Hospital,Beijing University of Chinese Medicine,Dongfang Hospital,Beijing University of Chinese Medicine,and the First Affiliated Hospital of Hunan University of Chinese Medicine,together with 293 healthy controls.Standardized facial images were obtained using the tongue-face diagnostic instrument.The face was divided into six regions:the forehead,left cheek,right cheek,nose,lips,and chin.Nine color parameters were extracted from each region,including red(R),green(G),blue(B),hue(H),saturation(S),value(V),lightness(L),red-green axis(a),and yellow-blue axis(b).Comparisons between groups were performed.Results Compared with the healthy group,in the forehead region,values of R,S,V,a,and b were higher in the coronary heart disease group,whereas B was lower(P<0.05);in the left cheek,nose,and chin regions,R,G,B,V,and L decreased,whereas S,a,and b increased(P<0.05);in the right cheek region,R,G,B,H,V,and L decreased,while S,a,and b increased(P<0.05);in the lips region,R,G,B,H,V,L,and a decreased,whereas S and b increased(P<0.05).Conclusion Compared with healthy individuals,patients with CHD present with a darker,more saturated facial complexion with reduced brightness,overall manifesting as"dark red complexion"and"dense but not bright color,"suggesting the pathogenesis of qi and blood circulation stagnation and internal blood stasis retention.The objective expression of facial features may have greater application value in syndrome differentiation and auxiliary diagnosis in traditional Chinese medicine.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

Objective To explore the influencing factors of different types of small intestinal bacterial overgrowth(SIBO).Methods A total of 539 patients who were hospitalized in the Department of Gastroenterology,the Sixth Medical Center of PLA General Hospital from June 2021 to December 2021 and who underwent methane-hydrogen breath test were retrospectively selected.Based on breath test results,patients were divided into SIBO-negative group(n=300)and SIBO-positive group(n=239).The clinical data were compared between two groups.According to the specific values of breath test results,SIBO-positive patients were further divided into hydrogen-producing bacterial overgrowth(hydrogen-positive,n=103),intestinal methanogen overgrowth(methanogen-positive,n=80),and simultaneous methanogen and hydrogen-producing bacterial overgrowth(double positive,n=56)groups.Multivariate logistic regression analysis was employed to identify influencing factors of different SIBO types.Additionally,SIBO-positive patients were categorized by age into<45 years(n=23),45-60 years(n=82),60-75 years(n=124),and≥75 years(n=10)to compare SIBO positivity rates across age groups.Results The patients in SIBO-positive and double positive groups were older and had a lower body mass index(BMI)than those in SIBO-negative group,with statistically significant differences(P<0.05).Compared with the patients in SIBO-negative group,those in hydrogen-positive group showed a higher proportion of history of coronary heart disease,those in methanogen-positive group were older,and higher proportion of statin use,with statistically significant differences(P<0.05).Multivariate logistic regression analysis revealed that,among different SIBO types,a history of coronary heart disease served as an independent risk factor for hydrogen-producing bacterial overgrowth(OR=2.728,95%CI 1.271-5.857,P=0.010).For methanogen overgrowth,increasing age was identified as an independent risk factor(OR=1.040,95%CI 1.009-1.063,P=0.010),while the application of statin played the role of an independent protective factor(OR=0.420,95%CI 0.236-0.754,P=0.003).As for the simultaneous overgrowth of methane-producing and hydrogen-producing bacteria,increased BMI was found to be an independent protective factor(OR=0.870,95%CI 0.786-0.964,P=0.008).In SIBO-positive group,it was found that for patients aged<45 years,both the methane-positive rate and the double-positive rate were significantly lower than the hydrogen positivity rate(P<0.05).Moreover,among patients aged 45-60 years,the double-positive rate was significantly lower than the hydrogen positivity rate(P<0.01).When it comes to the hydrogen-positive rate,it was significantly lower for patients aged 45-60 and 60-75 years compared with that of patients aged<45 years(P<0.05).In contrast,the methane-positive rate and the double-positive rate were significantly higher for patients aged 45-60 and 60-75 years than those of patients aged<45 years(P<0.01).Conclusion A history of coronary heart disease and increasing age are independent risk factors for intestinal hydrogen-producing bacterial overgrowth and methanogen overgrowth,respectively.The application of statins and increased BMI are independent protective factors for intestinal methanogen simultaneous overgrowth of methanogen and hydrogen-producing bacteria,respectively.

Objective: To understand the dynamic temporal evolution pathways of Internet addiction among university students and to identify the core driving nodes, so as to provide theoretical evidences for the precise implementation of targeted interventions. Methods: Using a convenient cluster sampling method, a total of 1 066 full time freshmen and sophomores were recruited from three universities in Guizhou, Jiangxi, and Guangdong Provinces for a follow up survey (T1:January-March 2024; T2:January-March 2025). The Revised Chen Internet Addiction Scale (CIAS-R) was employed to assess the status of Internet addiction among university students, and cross sectional as well as cross lagged panel network models were constructed to analyze Internet addiction and its multidimensional influencing factors. Results: The T1 network comprised 19 nodes and 114 non zero edges, while the T2 network comprised 19 nodes and 126 non zero edges. Cross sectional network analysis revealed the strongest association between "insufficient sleep" and "daytime fatigue"; the core nodes were "first thought upon waking for going online" and "feeling low after disconnection" (characteristics of psychological dependence) at T1, while the core nodes shifted to "impaired health" and "excitement when online" (characteristics of functional impairment and addictive psychodynamic features) at T2. Cross lagged network analysis further indicated that "reduced leisure" directly predicted "sleep compression", and a bidirectional relationship was observed between "needing more time to achieve satisfaction" and "academic decline". Conclusions Internet addiction among university students exhibits dynamic evolutionary characteristics. Stage specific targeted interventions focusing on core driving nodes are needed, integrating behavioral regulation and academic support to break the vicious cycle and enhancing the ability to cope with real life demands.

Background and Objectives: The Fractional Flow Reserve and Intravascular UltrasoundGuided Intervention Strategy for Clinical Outcomes in Patients with Intermediate Stenosis (FLAVOUR) trial demonstrated non-inferiority of fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) compared with intravascular ultrasound (IVUS)-guided PCI. We sought to investigate the cost-effectiveness of FFR-guided PCI compared to IVUS-guided PCI in Korea. Methods: A 2-part cost-effectiveness model, composed of a short-term decision tree model and a long-term Markov model, was developed for patients who underwent PCI to treat intermediate stenosis (40% to 70% stenosis by visual estimation on coronary angiography).The lifetime healthcare costs and quality-adjusted life-years (QALYs) were estimated from the healthcare system perspective. Transition probabilities were mainly referred from the FLAVOUR trial, and healthcare costs were mainly obtained through analysis of Korean National Health Insurance claims data. Health utilities were mainly obtained from the Seattle Angina Questionnaire responses of FLAVOUR trial participants mapped to EQ-5D. Results: From the Korean healthcare system perspective, the base-case analysis showed that FFR-guided PCI was 2,451 U.S. dollar lower in lifetime healthcare costs and 0.178 higher in QALYs compared to IVUS-guided PCI. FFR-guided PCI remained more likely to be cost-effective over a wide range of willingness-to-pay thresholds in the probabilistic sensitivity analysis. Conclusions Based on the results from the FLAVOUR trial, FFR-guided PCI is projected to decrease lifetime healthcare costs and increase QALYs compared with IVUS-guided PCI in intermediate coronary lesion, and it is a dominant strategy in Korea.

Background and Objectives: The Fractional Flow Reserve and Intravascular UltrasoundGuided Intervention Strategy for Clinical Outcomes in Patients with Intermediate Stenosis (FLAVOUR) trial demonstrated non-inferiority of fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) compared with intravascular ultrasound (IVUS)-guided PCI. We sought to investigate the cost-effectiveness of FFR-guided PCI compared to IVUS-guided PCI in Korea. Methods: A 2-part cost-effectiveness model, composed of a short-term decision tree model and a long-term Markov model, was developed for patients who underwent PCI to treat intermediate stenosis (40% to 70% stenosis by visual estimation on coronary angiography).The lifetime healthcare costs and quality-adjusted life-years (QALYs) were estimated from the healthcare system perspective. Transition probabilities were mainly referred from the FLAVOUR trial, and healthcare costs were mainly obtained through analysis of Korean National Health Insurance claims data. Health utilities were mainly obtained from the Seattle Angina Questionnaire responses of FLAVOUR trial participants mapped to EQ-5D. Results: From the Korean healthcare system perspective, the base-case analysis showed that FFR-guided PCI was 2,451 U.S. dollar lower in lifetime healthcare costs and 0.178 higher in QALYs compared to IVUS-guided PCI. FFR-guided PCI remained more likely to be cost-effective over a wide range of willingness-to-pay thresholds in the probabilistic sensitivity analysis. Conclusions Based on the results from the FLAVOUR trial, FFR-guided PCI is projected to decrease lifetime healthcare costs and increase QALYs compared with IVUS-guided PCI in intermediate coronary lesion, and it is a dominant strategy in Korea.