With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

BACKGROUND:Parkinson's disease is a multifactorial neurological disorder characterized by progressive loss of dopaminergic neurons,and dimethyl fumarate(DMF)has potent neuroprotective and immunomodulatory effects in neurodegenerative diseases. OBJECTIVE:To explore the neuroprotective mechanism of DMF in a mouse model of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine(MPTP)-induced Parkinson's disease. METHODS:Twenty-four C57BL/6 mice were selected and randomly divided into control group,model group,low-dose DMF,and high-dose DMF groups.An animal model of Parkinson's disease was established in the latter three groups by intraperitoneal injection of 30 mg/kg MPTP,once a day for 5 consecutive days.Intragastric administration was given 30 minutes after each injection of MPTP.Mice in the low-dose DMF group(30 mg/kg)and high-dose DMF group(50 mg/kg)were intragastrically administered once a day for 7 consecutive days.The control and model groups were initially administered the same dose of normal saline.Behavioral testing,western blot,oxidative stress marker detection,and immunohistochemical staining were used to analyze the regulatory effects of DMF on oxidative stress and Keap1/Nrf2 signaling pathway in MPTP-induced Parkinson's disease mice,as well as the protective mechanism of DMF on degeneration of dopamine neurons. RESULTS AND CONCLUSION:Compared with the model group,mice in the low-dose DMF group exhibited significant improvements in motor retardation and postural imbalance(P<0.01),with even more remarkable improvements observed in the high-dose DMF group(P<0.01).Compared with the control group,the model group showed a significant increase in the oxidative stress marker malondialdehyde and a decrease in superoxide dismutase expression(P<0.01).Compared with the model group,the low-dose DMF group reduced malondialdehyde production and increased superoxide dismutase expression(P<0.01),and similar improvements were observed in the high-dose DMF group(P<0.01).Immunohistochemical and western blot assays demonstrated a significant decrease in the number of dopaminergic neurons and tyrosine hydroxylase protein expression in the substantia nigra of mice in the model group compared with the control group(P<0.01).However,in the low-dose DMF group,there was an increase in the number of dopaminergic neurons and tyrosine hydroxylase protein expression in the substantia nigra(P<0.01),with even more significant improvements in the high-dose DMF group(P<0.01).Western blot results revealed that the model group exhibited elevated Keap1 protein expression and decreased Nrf2 protein expression.In contrast,the DMF groups showed reduced Keap1 protein expression and increased Nrf2 protein expression compared to the model group(P<0.01).To conclude,DMF regulates the Keap1/Nrf2 pathway in the substantia nigra of mice with Parkinson's disease,and this regulatory effect is positively correlated with the dose of DMF(P<0.01).Therefore,we infer that DMF exerts neuroprotective effects through the Keap1/Nrf2 signaling pathway.

Patients with alcoholic cirrhosis often experience varying degrees of malnutrition， and the patients with malnutrition are more susceptible to complications such as infections and ascites， which may lead to a poor prognosis. Therefore， it is particularly important to conduct nutritional risk screening for patients in clinical practice， and appropriate nutritional assessment tools should be used to evaluate the nutritional status of patients and develop individualized nutritional supplementation regimens， thereby promoting disease recovery and improving prognosis and quality of life. This article elaborates on the specific methods for nutritional screening， assessment， and management in patients with alcoholic cirrhosis and points out that systematic nutritional screening and assessment can help to identify the patients with malnutrition in the early stage and provide timely intervention. Individualized nutritional supplementation regimens should be adjusted based on the conditions of patients， so as to meet their nutritional needs， promote the recovery of liver function， improve overall health status， and enhance long-term quality of life.

Sesquiterpenes are natural terpenes containing 15 carbon atoms. They are widely used in the perfume, pharmaceutical, and biofuel industries due to their remarkable biological activities. The traditional production of sesquiterpenes relies on chemical synthesis or plant extraction, which has the disadvantages of low yields and waste of resources. The construction of microbial cell factories for the efficient synthesis of sesquiterpenes by means of synthetic biology provides a new option. In recent years, with the development of metabolic engineering and synthetic biology, the heterologous synthesis of a variety of sesquiterpenes has been successfully achieved by metabolic engineering of the oleaginous yeast, Yarrowia lipolytica. In this paper, we review the research progress in the heterologous synthesis of different sesquiterpenes by Y. lipolytica, discuss the synthetic biology strategies commonly used in this field, and make an outlook on the research directions and engineering approaches to further enhance the sesquiterpene yield in this host. This paper provides a reference for strategies such as synergistic optimization of synthetic biology and metabolic engineering, enhanced precursors, and opens up new directions for the application of synthetic biology in green chemistry and sustainable production.
Yarrowia/genetics* ; Sesquiterpenes/metabolism* ; Metabolic Engineering/methods* ; Synthetic Biology/methods*

Objective To develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia(AVNRT)and atrioventricular re-entrant tachycardia(AVRT)using 12-lead wearable electrocardiogram devices.Methods A total of 356 samples of 12-lead supraventricular tachycardia(SVT)electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model,and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October,2021 to March,2023 were selected as the testing set.The changes in electrocardiogram parameters before and during induced tachycardia were compared.Based on multiscale deep neural network,an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated.The 3-lead electrocardiogram signals from Ⅱ,Ⅲ,and V1 were extracted to build new classification models,whose diagnostic efficacy was compared with that of the 12-lead model.Results Of the 101 patients with SVT in the testing set,68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study.The pre-trained model achieved a high area under the precision-recall curve(0.9492)and F1 score(0.8195)for identifying AVNRT in the validation set.The total F1 scores of the lead Ⅱ,Ⅲ,V1,3-lead and 12-lead intelligent diagnostic models in the testing set were 0.5597,0.6061,0.3419,0.6003 and 0.6136,respectively.Compared with the 12-lead classification model,the lead-Ⅲ model had a net reclassification index improvement of-0.029(P=0.878)and an integrated discrimination index improvement of-0.005(P=0.965).Conclusion The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.

Objective:To analyze the epidemic characteristics of reported tuberculosis incidence in Kashgar from 2015 to 2022, and use the seasonal autoregressive integrated moving average (SARIMA) model to predict the incidence, providing references for the local control of pulmonary tuberculosis.Methods:The reported incidence data of tuberculosis in the Kashgar area of Xinjiang from January 2015 to August 2023 were collected through the"Infectious Disease Monitoring System", a subsystem of the "Chinese Disease Prevention and Control Information System". The epidemic characteristics of reported incidence in this area from 2015 to 2022 were analyzed. Two SARIMA models of monthly reported incidence number and rate were established. The prediction performance of the two models was evaluated using the reported incidence data of tuberculosis from January 2023 to August 2023. The χ2 test was used to analyze population characteristics, and the Cochran-Armitage trend test was used to analyze annual incidence. Results:From 2015 to 2022, 133 972 cases of pulmonary tuberculosis were reported in Kashgar, with a yearly reported incidence rate of 383.64/100 000, showing a rising trend ( TCA=77.03, P<0.001) and then a declining trend ( TCA=176.16, P<0.001). The proportion of pathogenic positive pulmonary tuberculosis had increased yearly ( TCA=132.66, P<0.001). The reported onset time was concentrated from January to June each year, with a peak in April. Yengisar County, Zepu County and Yopurga County had the highest reported incidence rate in Kashgar. The sex ratio of men to women was 1.03∶1, and the reported incidence rate of men was higher than that of women ( χ2=27.04, P<0.001). The reported incidence rate of the group aged 60 years and older was the highest. The patient′s occupation was mainly farmers (84.99%). The average relative errors of the SARIMA ( 1, 1, 2) ( 0, 1, 1) 12 model and SARIMA ( 0, 1, 1)( 0, 1, 1) 12 model in predicting the reported monthly incidence number and rate were 11.67% and -9.81%, respectively. Both models had good prediction accuracy (MAPE=33.55%, MAPE=38.22%). Conclusion:The average reported incidence rate of pulmonary tuberculosis in the Kashgar area shows a rising trend first and then a declining trend. The patients are mainly men and farmers, and attention should be paid to the prevention and control of tuberculosis among the elderly in winter and spring. The SARIMA ( 1, 1, 2) ( 0, 1, 1) 12 model and SARIMA ( 0, 1, 1)( 0, 1, 1) 12 model can fit the trend of reported tuberculosis incidence in the Kashgar area well and have good predictive performance.

Objective To investigate the sleep disorders and its effects on the changes in quality of life in patients with liver cancer from the hospital admission to 6 months after surgery and to analyse the correlation between the sleep disorder and quality of life.Methods A total of 214 patients who underwent surgery for liver cancer for the first time were included in the study.Demographic questionnaire,Pittsburgh sleep quality index(PSQI),and functional assessment of cancer therapy-hepatobiliary(FACT-Hep)were used for the investigation at admission and at 1,3 and 6 months after surgery.Multiple linear regression was employed to analyse the correlation between the sleep disorders at the admission and its effect on quality of life up to 6 months after surgery.Results Toally 214 patients finished the study at admission and 209 finished the study 1 month after surgery,and 208 finished the stuoly 3 months after surgery,and 205 patients finished the study 6 months after surgery.The scores of both of PSQI at admission and the quality of life at 6 months after surgery varied across the tested time points with a statistically significant difference(both P<0.001).The overall level of sleep disorder in the patients showed a characteristic pattern with initially increasing and then decreasing,and the quality of life presented a characteristic tendency of starting from high to low and then gradually increasing.It showed that the sleep disorder at admission was attributive to the poorer quality of life at 6 months after surgery.The hierarchical regression analysis showed that among the patients at BCLC Stage A,sleep disorder at admission was the influencing factor of the quality of life at 6 months after surgery.Conclusions The sleep disorder and quality of life in the patients who had surgical operations for hepatocellular carcinoma both changed dynamically from admission to the 6 months after surgery.The quality of life was poor in the patients with sleep disorder at admission.Therefore,medical staff should enhance the sleep management at admission,conduct dynamic assessment of the sleep disorder and quality of life of the patients,and then develop continuity nursing measures to improve the quality of life after surgery.

Objective:To analyze the epidemic characteristics of reported tuberculosis incidence in Kashgar from 2015 to 2022, and use the seasonal autoregressive integrated moving average (SARIMA) model to predict the incidence, providing references for the local control of pulmonary tuberculosis.Methods:The reported incidence data of tuberculosis in the Kashgar area of Xinjiang from January 2015 to August 2023 were collected through the"Infectious Disease Monitoring System", a subsystem of the "Chinese Disease Prevention and Control Information System". The epidemic characteristics of reported incidence in this area from 2015 to 2022 were analyzed. Two SARIMA models of monthly reported incidence number and rate were established. The prediction performance of the two models was evaluated using the reported incidence data of tuberculosis from January 2023 to August 2023. The χ2 test was used to analyze population characteristics, and the Cochran-Armitage trend test was used to analyze annual incidence. Results:From 2015 to 2022, 133 972 cases of pulmonary tuberculosis were reported in Kashgar, with a yearly reported incidence rate of 383.64/100 000, showing a rising trend ( TCA=77.03, P<0.001) and then a declining trend ( TCA=176.16, P<0.001). The proportion of pathogenic positive pulmonary tuberculosis had increased yearly ( TCA=132.66, P<0.001). The reported onset time was concentrated from January to June each year, with a peak in April. Yengisar County, Zepu County and Yopurga County had the highest reported incidence rate in Kashgar. The sex ratio of men to women was 1.03∶1, and the reported incidence rate of men was higher than that of women ( χ2=27.04, P<0.001). The reported incidence rate of the group aged 60 years and older was the highest. The patient′s occupation was mainly farmers (84.99%). The average relative errors of the SARIMA ( 1, 1, 2) ( 0, 1, 1) 12 model and SARIMA ( 0, 1, 1)( 0, 1, 1) 12 model in predicting the reported monthly incidence number and rate were 11.67% and -9.81%, respectively. Both models had good prediction accuracy (MAPE=33.55%, MAPE=38.22%). Conclusion:The average reported incidence rate of pulmonary tuberculosis in the Kashgar area shows a rising trend first and then a declining trend. The patients are mainly men and farmers, and attention should be paid to the prevention and control of tuberculosis among the elderly in winter and spring. The SARIMA ( 1, 1, 2) ( 0, 1, 1) 12 model and SARIMA ( 0, 1, 1)( 0, 1, 1) 12 model can fit the trend of reported tuberculosis incidence in the Kashgar area well and have good predictive performance.

Objective:In the context of the"dual-channel"management of national medicine,it analyzes the drug purchase selection and influencing factors of local employees in retail pharmacies,and puts forward policy suggestions for optimizing the"dual-channel"of national negotiation drugs.Methods:Using the fsQCA method,it analyzes the combination of individual factors(economic level,health status,et al.)and policy factors(policy promotion efforts,policy implementation degree)on patients'retail pharmacy purchasing selects.Results:Three combinations of conditions were obtained,namely"the type of individual and policy factors working together","the type of policy promotion and implementation driving together",and"the type of policy implementation leading".Conclusion:Firstly,improving the ability of prescription circulation and drug supply in designated retail pharmacies;secondly,strengthening the information release of the"dual-channel"management policy;thirdly,building a collaborative management system centered on insured individuals.