ObjectiveTo investigate the mechanism of topical treatment of allergic contact dermatitis （ACD） mice with Portulacae Herba based on the nuclear factor E₂-related factor 2 （Nrf2）/heme oxygenase-1 （HO-1）/nuclear factor-κB （NF-κB） signaling pathway. MethodsA total of 70 6-week-old specific pathogen free （SPF） female Kunming mice were adaptively fed for 1 week and randomly divided into blank group， model group， compound dexamethasone acetate cream group （2.075×10^-2 g·g^-1）， blank matrix cream group， low-dose Portulacae Herba cream group （0.1 g·g^-1）， high-dose Portulacae Herba cream group （0.2 g·g^-1）， and Portulacae Herba + inhibitor group （0.2 g·g^-1 + 30 mg·kg^-1 ML385）， with 10 mice in each group. One day before the experiment， the mice were shaved on the neck and back. Except for the blank group， the mice in the other groups were treated with 2，4-dinitrochlorobenzene （DNCB） to establish an ACD model. After respective administration， the skin lesion of the mice was scored， and the histopathological changes of the skin were stained with hematoxylin-eosin （HE）. Enzyme-linked immunosorbent assay （ELISA） was used to detect the levels of interleukin-6 （IL-6）， interleukin-1β （IL-1β）， reactive oxygen species （ROS）， superoxide dismutase （SOD） activity， and malondialdehyde （MDA） in serum of mice. The expression of Nrf2/HO-1/NF-κB signaling pathway-related proteins in mouse skin tissue was detected by immunohistochemistry （IHC）， Western blot， and real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）. ResultsCompared with the blank group， the mice in the model group had an increased skin lesion score （P<0.01）， severe pathological damage to skin tissue， increased content of IL-1β， IL-6， ROS， and MDA in their serum （P<0.01）， and decreased content of SOD （P<0.01）. In addition， the mRNA and protein expression levels of Nrf2， HO-1， and nuclear factor-κB inhibitor α （IκBα） in skin tissue were up-regulated （P<0.01）， while the protein expression levels of phosphorylated （p）-IκBα and p-NF-κB p65 and the mRNA expression of NF-κB p65 were down-regulated （P<0.01）. Compared with the model group and the blank matrix cream group， the mice treated with Portulacae Herba had a decreased skin lesion score （P<0.01）， reduced pathological damage to skin tissue， decreased content of IL-1β， IL-6， ROS， and MDA in their serum （P<0.01）， and increased content of SOD （P<0.01）. Additionally， the mRNA and protein expression levels of Nrf2， HO-1， and IκBα in skin tissue were down-regulated （P<0.05，P<0.01）， and the protein expression levels of p-IκBα and p-NF-κB p65 and the mRNA expression of NF-κB p65 were up-regulated （P<0.05，P<0.01）. Compared with the Portulacae Herba + inhibitor group， the high-dose Portulacae Herba cream group had a decreased skin lesion score （P<0.01）， alleviated pathological damage to skin tissue， decreased content of IL-1β， IL-6， ROS， and MDA in the serum of mice （P<0.05，P<0.01）， and increased content of SOD （P<0.01）. The protein expression levels of Nrf2， HO-1， and IκBα and the mRNA expression of Nrf2 and HO-1 in skin tissue were up-regulated （P<0.05，P<0.01）， and the protein expression levels of p-IκBα and p-NF-κB p65 and the mRNA expression of NF-κB p65 were down-regulated （P<0.05）. ConclusionPortulacae Herba can improve DNCB-induced ACD skin damage in mice by regulating the Nrf2/HO-1/NF-κB signaling pathway.

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.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

Surgical treatment is one of the key approaches for non-small cell lung cancer (NSCLC). Regular postoperative follow-up is crucial for early detection and timely management of tumor recurrence, metastasis, or second primary tumors. A scientifically sound and reasonable follow-up strategy not only extends patient survival but also significantly improves quality of life, thereby enhancing overall prognosis. This consensus aims to build upon the previous version by incorporating the latest clinical research advancements and refining postoperative follow-up protocols for early-stage NSCLC patients based on different treatment modalities. It provides a scientific and practical reference for clinicians involved in the postoperative follow-up management of NSCLC. By optimizing follow-up strategies, this consensus seeks to promote the standardization and normalization of lung cancer diagnosis and treatment in China, helping more patients receive high-quality care and long-term management. Additionally, the release of this consensus is expected to provide insights for related research and clinical practice both domestically and internationally, driving continuous development and innovation in the field of postoperative management for NSCLC.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

OBJECTIVE To study the effects and mechanism of Portulaca oleracea cream on skin pruritus and barrier function in allergic contact dermatitis （ACD） mice. METHODS Low-concentration and high-concentration P. oleracea creams were prepared， with the P. oleracea extract solution （1 g/mL， calculated by crude drug） concentrations of 10% and 20%. Sixty BALB/c mice were randomly allocated into blank group， model group， Mometasone furoate cream group （positive control）， blank matrix cream group， P. oleracea low-concentration and high-concentration cream groups. Except for blank group， ACD model was induced in each group using 2，4-dinitrochlorobenzene solution. The blank group and model groups received normal saline， while the remaining groups were treated with their respective creams， once a day， at a dose of approximately 0.5 g per application， continuously for 14 days. At 24 h post-final administration， skin lesions of mice were observed and scored； pathological changes of skin tissue were observed； serum levels of immunoglobulin E（IgE） and tumor necrosis factor-α （TNF-α） were quantified. mRNA expression of MAS-related G protein-coupled receptors （including MrgprA3， MrgprC11， and MrgprD） in dorsal root ganglion （DRG） was assessed； while protein expressions of skin barrier function-related proteins Claudin-1 and Occludin in skin tissues were determined. RESULTS Compared with blank group， mice in the model group exhibited severe skin damage， characterized by loss of epidermal architecture， hyperkeratosis of the skin tissue， and the infiltration of a large number of inflammatory cells. The skin injury scores， as well as the serum levels of IgE and TNF-α， and the mRNA expression levels of MrgprA3， MrgprC11， and MrgprD in DRG， were all significantly elevated compared to the blank group （P＜0.05 or P＜0.01）； in contrast， the protein expression levels of Claudin-1 and Occludin in the skin tissue were markedly reduced （P＜0.05）. Compared with model group， mice in P. oleracea low-concentration and high- concentration cream groups demonstrated significant alleviation of skin damage， as evidenced by reduced epidermal hyperplasia， mitigated spongiosis in the dermis， and decreased infiltration of inflammatory cells； these quantitative indicators were almost significantly reversed （P＜0.05 or P＜0.01）. No significant differences were observed in the aforementioned skin injuries， pathological alterations， or quantitative indicators between the blank matrix cream group and the model group. CONCLUSIONS P. oleracea may ameliorate skin lesions and restore skin barrier function of ACD mice， the mechanism of which may be associated with downregulating mRNA expressions of MrgprA3， MrgprC11 and MrgprD in DRG， and up-regulating the protein expressions of Claudin-1 and Occludin in skin tissue.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.