Background: Visceral white adipose tissue (vWAT) inflammation is a critical pathology of obesity-caused heart damage and is closely associated with adipocyte endoplasmic reticulum (ER) dysfunction. Serine (or cysteine) peptidase inhibitor, clade A, member 3C (Serpina3c) has been identified as an adipokine with anti-vWAT inflammatory effects. However, it remains unclear whether Serpina3c deficiency promotion of vWAT inflammation involves adipocyte ER dysfunction and whether it further contributes to heart damage in obesity. Methods: Wild type and Serpina3c knockout (Serpina3c–/–) mice were fed a high-fat diet (HFD) for 12 weeks. An adeno-associated virus (AAV) was injected locally into epididymal white adipose tissue (eWAT) of Serpina3c–/– mice to induce eWAT-adipocyte- specific overexpression of Serpina3c (AAV-Serpina3c) or knockdown of hypoxia-inducible factor 1α (AAV-shHIF1α). In vitro experiments were performed in 3T3-L1 adipocytes. Results: Serpina3c–/– mice exhibited more severe eWAT, serum and heart inflammation after HFD feeding. Consistently, these adverse phenotypes were mitigated in AAV-Serpina3c and AAV-shHIF1α mice. Mechanistically, ER oxidoreductase 1α (Ero1α) and protein disulfide isomerase (PDI) family members PDIA3 and PDIA4 were found to be target genes of HIF1α. In the obese mice, Serpina3c deficiency caused adipocyte more hypertrophy, and activated HIF1α-Ero1α/PDI mediated ER overoxidation and ER stress in eWAT. Subsequently, this led to increased adipocyte apoptosis and chemokine production and decreased adiponectin expression, which promoted macrophage infiltration and M1 polarization in eWAT, thus exacerbating eWAT inflammation and ultimately facilitating serum and distal heart inflammation. Conclusion These findings indicate that Serpina3c is a significant regulator of adipocyte ER redox homeostasis, thus highlighting Serpina3c as a potential therapeutic target for obesity-related eWAT inflammation and heart damage.

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 To explore the safety and feasibility of 5G remote robot-assisted thoracoscopic anatomic lobectomy and segmentectomy. Methods A retrospective analysis was conducted on patients who underwent 5G remote robot-assisted thoracoscopic anatomic lobectomy or segmentectomy between July and September 2024. The surgeries were performed collaboratively by Shanghai Chest Hospital, Affiliated Hospital of Yangzhou University, and The First Affiliated Hospital of Nanchang University. Perioperative outcomes were recorded and analyzed. Results Ten patients were included, comprising 4 anatomic lobectomies and 6 segmentectomies. The median age was 61.0 (56.0, 72.0) years. The median intraoperative bi-directional network latency was 52.5 (39.0, 54.0) ms. There were no instances of network interruption, robot-related adverse events, or conversions to open thoracotomy. The median postoperative chest tube drainage volume was 500.0 (375.0, 600.0) mL, and the median hospital stay was 5.5 (4.0, 6.0) days. No complications of Clavien-Dindo grade Ⅱor higher occurred. All patients were recurrence-free and alive during the 6-month postoperative follow-up period. Conclusion 5G remote robot-assisted thoracoscopic surgery demonstrates high safety and operational stability. This technology shows promising potential for clinical application and is worthy of further development and utilization.

This study reports a new species of Culicoides(Avaritia)isolated from Xunke County, Heilongjiang Province, China. The new species, Culicoides(Avaritia)dongshanensis Liu et Wang, sp. nov., was identified and illustrated based on female adults. Its diagnostic characteristics are as follows: eyes contiguous over a short distance, with short interfacetal hair, antennal ratio(AR)of 1.20, palpal ratio(PR)of 2.16, wing length of 0.83 mm, mandible with 15 teeth, one pale spot in base cell M1, one pale spot in cell M4, two pale spots in cell A, and two unequal spermathecae. Type specimens were deposited at the Center for Disease Control and Prevention of the Northern Theater Command.

Background Long-term exposure to ambient fine particulate matter (PM_2.5) is a significant risk factor for cardiometabolic disorders. However, the mechanisms of its interaction with the endogenous circadian system remain incompletely understood. Objective To investigate whether chronic PM_2.5 exposure interferes with the rhythmic expression of the cardiac circadian clock, thereby disrupting downstream antioxidant defenses and metabolic homeostasis, and ultimately driving cardiometabolic dysfunction. Methods Seventy-two male C57BL/6 mice were randomly divided into a PM_2.5 exposure group (PM group) and a filtered air control group (FA group). Whole-body exposure was conducted for 8 weeks in a meteorological environmental animal exposure system. Samples were collected at six distinct zeitgeber time (ZT) points post-exposure. The 24 h ambulatory blood pressure and serum lipid profiles were monitored. Rhythm parameters were derived via cosinor analysis to compare differences in Midline statistic of rhythm (Mesor), amplitude, and phase between the two groups. The rhythmic expression of core circadian clock genes and antioxidant genes in the myocardium was detected by quantitative polymerase chain reaction (qPCR). Myocardial reactive oxygen species (ROS) levels and downstream pathway protein expression were analyzed by immunofluorescence and Western blot (WB), respectively. The expression changes of the clock gene retinoic acid receptor-related orphan receptor α (RORα) were assessed at both the mRNA and protein levels. Finally, Spearman correlation analysis was used to explore the relationships among myocardial RORα expression, lipid profiles, and oxidative stress indicators. Results Compared to the FA group, mice in the PM group exhibited a blunted circadian rhythm in blood pressure, characterized by sustained elevation throughout the day. Chronic PM_2.5 exposure showed a significant interaction with ZT on systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) (F-interaction=9.11, 5.70, and 6.02, respectively; P<0.05), as well as on serum triglycerides (TG), total cholesterol (T-CHO), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) (F-interaction=16.32, 11.12, 15.39, and 28.09, respectively; P<0.05). Cosinor analysis further revealed that the Mesor values of T-CHO, TG, and LDL-C were significantly increased (P<0.05), while that of HDL-C was significantly decreased in the PM group (P<0.05). The oscillation amplitudes of SBP, DBP, and MAP showed a decreasing trend, whereas those of TG and LDL-C were significantly increased (P<0.05). Furthermore, SBP, T-CHO, and HDL-C all exhibited a significant phase delay (P<0.05). Mechanistically, PM_2.5 exposure significantly suppressed the expression of the positive circadian regulator RORα in the myocardium, leading to disordered rhythmic expression of core clock genes (Bmal1, Clock, Per1/2, and Cry1/2). This exposure also inhibited the rhythmic expression of antioxidant genes (GPX1, SOD2, and CAT), resulting in increased ROS generation and elevated expression of calcium/calmodulin-dependent protein kinase II (CaMKII) and reduced nicotinamide adenine dinucleotide phosphate (NADPH) proteins. Correlation analysis further revealed that myocardial RORα expression level was negatively correlated with T-CHO, TG, and LDL-C (r=−0.55, −0.63, and −0.51, respectively; P<0.001), and positively correlated with HDL-C (r=0.37, P=0.010), and antioxidant genes GPX1, SOD2, and CAT expression (r=0.34, 0.35, and 0.56, respectively; P < 0.001). Conclusion Chronic PM_2.5 exposure induces cardiometabolic dysfunction by suppressing myocardial RORα expression. This suppression disrupts the cardiac circadian clock and the diurnal balance of oxidative stress, triggering oxidative damage and elevating expression of CaMKII/NADPH pathway proteins. Collectively, these alterations precipitate the loss of cardiac metabolic rhythms and subsequent functional impairment.

Compared with matrix-assisted laser desorption ionization mass spectrometry(MALDI-MS)using organic small molecule matrix,surface-assisted laser desorption ionization mass spectrometry(SALDI-MS)based on nanomaterial matrix is more effective in analysis of small molecule compounds.Ion sputtering instruments have obvious advantages for applying inorganic nanomatrix.In this work,the carbon-platinum material was sputtered onto a glass cover slip using an ion sputtering instrument to form a carbon-platinum(C-Pt)composite nanomatrix,and an SALDI-MS analytical method was thus established based on the prefabricated C-Pt composite matrix.The experimental results showed that the C-Pt composite nanomatrix could significantly improve the signal intensity and signal-to-noise ratio of the mass spectrum peaks of the components to be measured.The ratio of carbon to platinum,the duration of ion sputtering,and the laser power in mass spectrometer were investigated to select the optimal C-Pt matrix prefabricated conditions and SALDI-MS experimental conditions.Using the prefabricated C-Pt composite matrix,the melittriose and daidzein sample solution were applied as sample to examine repeatability.The results showed that the intra-point repeatability(RSD)was≤4.8%and the inter-point repeatability(RSD)was≤6.4%.The quercetin and melitriose were applied as model samples,and a linearity between MS peak intensity and respective concentration in the range of 0.05-1.0 mg/mL was found,with linear correlation coefficients(R2)greater than 0.994,showing good potential for quantitative and imaging analysis.Then,the prefabricated C-Pt composite matrix was applied to SALDI-MS analysis of the 50%ethanol extract of soybean,and 15 kinds of compounds including oligosaccharides and triglycerides were identified from the mass spectra.Furthermore,the C-Pt matrix was employed in SALDI-MS imaging for the compositions in corn tissue section,and the results showed that diglycerides and triglycerides were mainly distributed in the corn embryo and around the embryo,and the distribution of oligosaccharides was relatively even.

Hereditary liver diseases refer to liver metabolic disorders caused by gene mutations. The existing clinical treatments are mostly symptomatic therapies，delaying liver damage and improving patients' lives. With the development of gene therapy technology，it has become possible to treat genetic diseases by modifying known gene mutations. This review briefly introduces several common gene therapies（including recombinant adeno-associated virus vectors，CRISPR/Cas9，antisense oligonucleotides，lipid nano particle-mRNA），and discusses the latest research progress，ongoing challenges and potential solutions of gene therapy for hereditary liver diseases through the relevant preclinical studies and drug clinical trials of hereditary liver diseases. In order to promote the further development of gene therapy and provide reference for the treatment of other complex diseases.

Objective To construct a medical consumables selection indicator system based on analytic hierarchy process(AHP)and fuzzy cluster method.Methods Firstly,a medical consumables selection indicator system was established preliminarily with the literature research results and actual situation of hospital consumables management;secondly,13 experts in related fields were selected to execute two rounds of online questionnaires,the expert weights were determined with AHP,the importance of each selection indicator was scored by the expert evaluation method and fuzzy cluster method,and consistency analysis was carried out on the two rounds of expert evaluation results;finally,the final medical consumables selection indicator system was built with the 100-point scale.Results The constructed medical consumables selection indicator system was composed of 5 primary indicators,12 secondary indicators and 38 tertiary indicators.The primary indicators included consumables quality,clinical demand,cost-effectiveness,supply capacity and after-sales service,with the percentage-based scores being 27,25,14,26 and 8,respectively.Conclusion The medical consumables selection indicator system based on AHP and fuzzy cluster method with high reliability provides effective and reliable references for medical institutions to select medical consumables.

Objective To investigate the utility of dual-energy computed tomography(DECT)virtual non-contrast(VNC)images instead of true non-contrast(TNC)images in the quantitative analysis of emphysema.Methods A retrospective selection was con-ducted on 59 patients who underwent chest CT plain scan plus dual-phase enhanced scan on APEX-CT.VNC images of arterial phase(VNCart)and venous phase(VNCven)were generated on AW4.7 workstation.Volume CT dose index(CTDIvol)and dose length product(DLP)were recorded respectively.In a double-blind manner,two physicians graded the severity of each patient's emphysema according to the Fleischner Society's emphysema visual classification system.The"digital lung"detection and analysis platform was used to quantitatively measure emphysema at three thresholds(-950 HU,-930 HU and-910 HU),and the difference in meas-urement results between VNC images and TNC images was compared.Quantitative differences in low attenuation volume(LAV),percentage of low attenuation area(LAA%)and mean lung density(MLD)at the-950 HU threshold were compared using Bland-Altman plots.Results Using TNC images as the standard,there was no significant difference in the results of the visual classifica-tion evaluation of emphysema between TNC and VNC images(χ2=2.80,P=0.247).In quantitative measurement,there was no significant difference in total lung volume(TLV)(χ2=3.26,P=0.196)between the three groups images.Compared to TNC ima-ges,there were no statistically significant differences in LAV,LAA%and MLD of VNCven images at 15th percentile lung density(Perc 15%)and different thresholds(P>0.05).Compared to the TNC mode,the VNC mode could reduce the effective dose(ED)by approximately 32.6%.Conclusion The use of DECT VNCven images on chest has the potential to replace TNC for the quantitative analysis of emphysema,thereby streamlining scans and reducing radiation dose.

Artificial intelligence(AI)is accelerating the digital transformation of the healthcare service system,with policy systems playing an increasingly prominent role in guiding technological innovation and governance reform.However,there is still a lack of systematic theoretical explanation for how AI technologies achieve deep integration under the collaborative effect of diverse policy objectives and policy tools.Drawing on national-level AI healthcare policy texts in China from 2015 to 2025,this study proposes an analytical framework of"policy goals-policy instruments-embedding pathways".It systematically reveals the staged evolution and underlying mechanisms of AI technology integration in healthcare.The findings show:first,China's AI healthcare policy system has progressed from strategic guidance and scenario-driven development to regulatory governance,demonstrating a dynamic adaptability to the integration of complex technologies;second,a dynamic coupling mechanism has formed between policy goals and instruments,achieving a coordinated balance among technological innovation,risk governance,and social value;third,the embedding pathway has evolved from exogenous,relationship-based integration to endogenous,structure-based integration,characterized by a progressive mechanism of"institutional empowerment-structural coupling-regulatory internalization".This research deepens the theoretical understanding of adaptive governance and institutional transformation for complex technologies,offering theoretical insights and practical references for optimizing AI healthcare policies and enhancing digital governance capacity.