ObjectiveTo explore the relationship between acne vulgaris and gut microbiota. MethodsA total of 29 clinical cases diagnosed with moderate-to-severe acne vulgaris and 26 healthy individuals as control subjects were recruited. Fecal specimens were collected from all participants， and further analysis of gut microbial communities was performed by leveraging high-throughput sequencing techniques that target the hypervariable regions of 16S rRNA genes. ResultsAssociations between acne vulgaris and alterations in gut microbiota were identified. At the phylum level， the relative abundance of Bacteroidota exhibited a statistically significant elevation in the acne vulgaris cohort when compared with the healthy control group （P<0.01）， while Cyanobacteria was significantly lower in the acne group （P<0.01）. At the genus level， the top five different bacterial taxa in both groups were Bacteroides， Escherichia⁃Shigella， Klebsiella， Roseburia， and Parabacteroides. Among them， Bacteroides， Roseburia， and Parabacteroides were more abundant in acne patients. Linear discriminant analysis identified five biomarkers all belonging to the Bacteroidota phylum in the acne and control groups. These biomarkers belong to the phylum Bacteroidetes. ConclusionThere are significant differences in the composition of intestinal microbiota between acne patients and healthy people. Changes in the richness of specific bacterial genera may become new targets for the diagnosis and treatment of acne.

Objective To optimize the extraction, separation and purification process of Actinoside E. Methods Single factor experiment combined with orthogonal test was used to determine the optimal extraction process of Actinoside E using its content as an index. The extracts were separated and purified by optimizing the chromatographic conditions of macroporous resin, silica gel and ODS column. Results 25 times amount of 55% ethanol with heating reflux at 95℃ for one hour were used as the optimal extraction process of Actinoside E. The optimum separation and purification process was as follows: D101 macroporous resin column was eluted with 7 BV of 50% ethanol, silica gel column was eluted with 5 BV of ethyl acetate-ethanol（10∶1）and 50% methanol eluted fraction was purified repeatedly by ODS column to obtain Actinoside E. The transfer rate of Actinoside E in the whole process was 53.70%, the yield was 0.35%, and the purity was 99.9%. Conclusion The process is stable and viable, which can provide material foundation for the development and utilization of Actinoside E.

Metabolic dysfunction-associated fatty liver disease （MAFLD） has become one of the most prevalent chronic liver diseases worldwide， posing a serious challenge to public health. In this context， the integration of artificial intelligence （AI）， especially intelligent diagnosis and treatment and digital health interventions based on machine learning， can break through the limitations of traditional methods， realize efficient screening of multi-dimensional data such as key genes， biomarkers， and biochemical metabolites， and achieve revolutionary breakthroughs in risk prediction， subtype identification， and therapeutic effect assessment for MAFLD. This article systematically reviews the ground-breaking application of machine learning models in driving the innovation of clinical diagnosis and precise risk prediction of MAFLD， conducts a comprehensive comparative analysis of digital health practice cases of MAFLD in China and globally， and deeply analyzes their advantages and limitations in terms of research subjects， interventions， and management team. Studies have shown that the deep integration of digital health and long-term management of MAFLD is becoming the key engine driving the transformation of disease management modes towards an intelligent， individualized， and precise era， but there are various ethical and technical issues that need to be addressed urgently.

Background: and Purpose Live cell-based assays (LCBA) are increasingly used for serological antibody detection due to their ability to preserve antigen conformation, offering moderately higher sensitivity than fixed cell-based assays. However, the clinical necessity of prioritizing LCBA for the detection of aquaporin-4 immunoglobulin G (AQP4-IgG) in neuromyelitis optica spectrum disorder (NMOSD) remains unclear, especially when compared to its established role in diagnosing myelin oligodendrocyte glycoprotein antibody-associated disease. Methods: We compared the performance of live cell-based assays using immunofluorescence (LCBA-IF) and fixed cell-based assays using immunofluorescence (FCBA-IF) in detecting AQP4-IgG in 90 cases of NMOSD meeting 2015 International Panel for Neuromyelitis Optica Diagnosis criteria, alongside 40 controls. Additionally, we further investigated the relationship between AQP4-IgG titers as measured by LCBA-IF and FCBA-IF and clinical parameters in NMOSD patients. Results: Results showed 96.9% agreement between LCBA-IF and FCBA-IF (Cohen’s κ=0.935), with a strong Spearman correlation (0.977, p<0.001). Both methods demonstrated 100% specificity, with LCBA-IF showing slightly higher sensitivity compared to FCBA-IF. Within LCBAIF-tested groups, statistically significant differences in annualized relapse rates were observed across all pairwise comparisons (low-titer vs. moderate-titer, low-titer vs. high-titer, and moderate-titer vs. high-titer; all p<0.050). However, this association reached statistical significance in some FCBA-IF-tested groups. Conclusions Overall, there is a strong concordance between LCBA-IF and FCBA-IF in detecting AQP4-IgG, where LCBA-IF shows slightly higher sensitivity. Furthermore, there is a potential link between elevated AQP4-IgG titers and an increased risk of relapse, and this correlation may appear more pronounced when using LCBA-IF.

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.

AIM: To compare objective visual quality and vision-related quality of life after implantation of different presbyopia-correcting intraocular lenses(PC-IOLs).METHODS:A prospective, non-randomized controlled study. Cataract patients who underwent phacoemulsification combined with PC-IOL implantation at Jinan Mingshui Eye Hospital from September 2023 and December 2025 were included. These patients were divided into three groups according to the type of IOL: the Trifocal IOL group,the Vivity group, and the Synergy group. Follow-ups were conducted at 1 wk,1 and 3 mo postoperatively. Uncorrected distance visual acuity(UDVA), uncorrected intermediate visual acuity(UIVA), and uncorrected near visual acuity(UNVA), objective scatter index(OSI), modulation transfer function cut off(MTF-cut off), and Strehl ratio(SR), as well as contrast visual acuity(CVA)at 100%, 20%, and 9% contrast were assessed. Postoperative defocus curves were plotted. Vision-related quality of life and spectacle independence were evaluated using questionnaires.RESULTS: This study included 101 cataract patients(101 eyes),aged 30-70(53.8±9.7)y,with 59 males and 42 females.The Trifocal group consisted of 33 patients(33 eyes), aged 30-71(51.7±11.3)y, with 21 males and 12 females; the Vivity group consisted of 37 patients(37 eyes), aged 36-71(54.2±9.0)y, with 18 males and 19 females; and the Synergy group consisted of 31 patients(31 eyes), aged 30-73(55.9±8.5)y, with 20 males and 11 females.No significant differences were observed among the three groups in UDVA or UIVA(all P>0.05). Significant differences were found in UNVA among groups(P<0.05). UDVA differed significantly across different time points(P<0.05), while there were no significant differences in UIVA and UNVA(all P>0.05). At 3 mo postoperatively, significant differences were found among the three groups in OSI, MTF -cut off, SR, and CVA at all contrast levels(all P<0.05). The defocus curve at postoperative 1 mo showed that the Trifocal group had a smooth and continuous profile from +1.0 D to -3.0 D, providing good distance, intermediate, and near vision. All groups demonstrated smooth transitions in the intermediate range(-1.0 D to -1.5 D), with LogMAR visual acuity better than 0.2. In the near range(-2.5 D to -3.5 D), the Trifocal and Synergy groups performed significantly better than the Vivity group. No significant differences were observed in VF-14 questionnaire scores among groups(P>0.05). For night driving(motorized or non-motorized), the Vivity group performed better than the Trifocal and Synergy groups. Spectacle independence rates were 92.3% in the Trifocal group, 54.1% in the Vivity group, and 80.0% in the Synergy group(P<0.05).CONCLUSION: All types of PC-IOLs can effectively improve postoperative full-range visual acuity in cataract patients. There are discrepancies in the objective visual quality between Trifocal IOLs and extended depth of focus(EDOF)IOLs. Some patients suffer from postoperative optical disturbances, which result in certain difficulties with night driving, although patients generally report favorable subjective visual perceptions.

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.

ObjectiveTo analyze the epidemiological characteristics and influencing factors of clustered influenza-like illness (ILI) outbreaks in Yangpu District, Shanghai from 2012 to 2022, and to provide a scientific basis for formulating specific prevention and control measures of influenza in this district. MethodsData on clustered ILI outbreaks from 2012 to 2022 were collected, and the epidemiological pattern of the cluster outbreaks of influenza and its influencing factors of prevalence and duration were explored using statistical methods such as descriptive epidemiology, χ² test and logistic regression analysis. ResultsA total of 60 clustered ILI outbreaks were reported in Yangpu District, Shanghai from 2012 to 2022, with a cumulative number of 543 cases and an annual average incidence rate of 16.96%. The male-to-female ratio was1.06∶1. The largest number of ILI were reported in 2019, mainly from November to January of the next year, with December being the peak of outbreaks. The Streets of Changhai Rd. in Yangpu District had the largest number of reported outbreaks, accounting for 30.00% of the total outbreaks. Influenza cluster outbreaks occurred in schools, with the majority of outbreaks and cases reported in primary schools (accounting for 65.00% and 62.06% of the total, respectively). The outbreaks were mainly caused by influenza B virus, accounting for 50% of the total number of outbreaks. The prevalence rate of influenza cluster outbreaks varied among different schools in terms of their affiliated streets, school attributes and school types, with those in Kongjiang Rd. Subdistrict, private schools, and childcare institutions being relatively high. The duration of outbreaks reported for >2 days was longer than those reported for ≤2 days (OR=8.604, 95%CI: 1.509–49.067). ConclusionThe ILI outbreaks in Yangpu District present a seasonal pattern, with primary schools and Changhai Rd. Street as the targeted places for implementing prevention and control measures. Timely detection and reporting and active disposal can prevent a further spread of the outbreaks.

Objective To investigate the characteristics of attentional bias towards cancer-related stimuli in breast cancer patients with fear of cancer recurrence(FCR)under subthreshold and suprathreshold stimulus conditions.Methods A total of 94 female breast cancer patients admitted in First Affiliated Hospital of Army Medical University from September 2022 to March 2023 were recruited to complete the Fear of Cancer Recurrence Inventory-Short Form(FCRI-SF)and the dot-probe task.According to the FCRI-SF cut-off score of 13,they were divided into clinical and non-clinical FCR groups,with 47 cases in each group.One-sample t-test and repeated-measures ANOVA were used to statistically analyze the subjects in the 2 groups in terms of attentional bias score,attentional orienting score and attentional disengagement difficulty score.Results The patients in the clinical FCR group showed a significant attentional bias toward cancer-related negative words(P<0.05).Under the subthreshold stimulus condition,its main component was attentional orienting to cancer-related negative words(P<0.05).In the suprathreshold stimulus condition,the main components were attentional orienting to neutral words matched by cancer-related negative words and difficulty in attentional disengagement from cancer-related negative words as well as attentional avoidance for cancer-related positive words(P<0.05).Conclusion Breast cancer patients with clinical FCR have an attentional bias toward cancer-related negative stimuli.Reducing their attention to cancer-related negative stimuli may be an effective measure to reduce FCR level in the patients.

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.