Bronchiolar adenoma (BA) is a rare type of lung tumor characterized by bilayered epithelial cells having a continuous basal layer and a luminal layer. It resembles mucinous adenocarcinoma (MA) on frozen section, with difficulty in distinguishing the basal layer. Immunohistochemistry is the best choice for verifying the diagnosis. This study aimed to comprehensively characterize three cases of BA-combined carcinoma using clinical, histopathological, and genetic features. BA and carcinoma sections were subjected to next-generation sequencing, respectively. It was hypothesized that while different mutation forms matched different regions, BA and lung adenocarcinoma shared the same gene mutation when they co-occurred in the same location. BA with extensive carcinoma is extremely rare and presents diagnostic challenges due to its overlap with conditions such as MA. Because of its distinctive morphological characteristics, BA may be regarded as a low-grade malignancy, particularly during a confusing evaluation. A multifaceted examination of clinical, radiological, immunohistochemical, and genetic data is necessary for an accurate diagnosis.

Aim To investigate the mechanism of ultra-fine garlic powder(UGP)in ameliorating dyslipidemia and aortic inflammation and fibrosis in atherosclerotic(AS)mice.Methods A 10-week ApoE-/-mouse AS model was constructed,cardiac index was meas-ured,and aortic histopathological changes were ob-served by oil red O staining.Serum inflammatory factor levels were detected by ELISA,and the expression of JNK,NF-κB,ERK and their phosphorylated proteins were detected by Western blot.Results Cardiac in-dex and other indicators as well as aortic lesions were worsened in the AS group,as compared with the normal control group.Compared with the AS group,the UGP treatment group and the traditional garlic grinding pow-der(TGP)treatment group significantly decreased total cholesterol(TC),triglyceride(TG),low-density lipo-protein cholesterol(LDL-C),atherosclerosis index(AI1,AI2),and coronary cardiac index and restored high-density lipoprotein cholesterol(HDL-C)levels,and the area of aortic lesions,inflammation and fibrosis were significantly improved,and at the same time,sig-nificantly inhibited the expression of TNF-α,IL-1β,and IL-6,as well as the expression of p-JNK,p-NF-κB and p-ERK proteins.The therapeutic effect of the UGP group was superior to that of the TGP group.Conclu-sion UGP can significantly inhibit the formation of aortic endothelial AS plaques,reduce the levels of in-flammation and fibrosis,and regulate blood lipids in a-orta of AS mice.

This study analyzed the epidemiological characteristics of imported malaria in Yunnan Province from 2020 to 2023,to provide scientific evidence for formulating measures to decrease imported malaria and prevent re-establishment of malaria transmission.Malaria data reported by the China Disease Prevention and Control Information System were analyzed to determine parasite species;sources of infection;temporal,spatial,and population distributions;and importation routes.A total of 828 malaria cases were reported in the province.Plasmodium vivax and Plasmodium falciparum accounted for 89.98％and 8.33％of cases,respectively.A total of 47.58％of cases were imported from Myanmar,and all P.falciparum malaria ca-ses were from Africa.Thirteen(81.25％)prefectures or municipalities reported malaria,among which Dehong,Baoshan,Kunming,and Lincang reported 94.32％of cases.A total of 52.54％of cases were in young men.The proportion of cross-bor-der personnel flow,land input,and aircraft input were 88.89％and 11.11％respectively.A total of 98.19％of patients sought medical care within 7 days after fever onset,and 82.85％initiated diagnosis for malaria,and 84.90％of diagnoses were con-firmed by health facilities at or below the county level.Imported malaria is a major challenge in preventing re-establishment of transmission in Yunnan.Most imported cases involved cross-border malaria transmission of mainly Plasmodium vivax between China and Myanmar.To achieve malaria elimination,vigilance of health staff in malaria diagnosis and treatment should be pro-moted,and intensive malaria health education should be provided to people traveling to malaria endemic territories,to enable individual protection,and timely diagnosis and treatment after return from endemic countries.

This study analyzed the epidemiological characteristics of imported malaria in Yunnan Province from 2020 to 2023,to provide scientific evidence for formulating measures to decrease imported malaria and prevent re-establishment of malaria transmission.Malaria data reported by the China Disease Prevention and Control Information System were analyzed to determine parasite species;sources of infection;temporal,spatial,and population distributions;and importation routes.A total of 828 malaria cases were reported in the province.Plasmodium vivax and Plasmodium falciparum accounted for 89.98％and 8.33％of cases,respectively.A total of 47.58％of cases were imported from Myanmar,and all P.falciparum malaria ca-ses were from Africa.Thirteen(81.25％)prefectures or municipalities reported malaria,among which Dehong,Baoshan,Kunming,and Lincang reported 94.32％of cases.A total of 52.54％of cases were in young men.The proportion of cross-bor-der personnel flow,land input,and aircraft input were 88.89％and 11.11％respectively.A total of 98.19％of patients sought medical care within 7 days after fever onset,and 82.85％initiated diagnosis for malaria,and 84.90％of diagnoses were con-firmed by health facilities at or below the county level.Imported malaria is a major challenge in preventing re-establishment of transmission in Yunnan.Most imported cases involved cross-border malaria transmission of mainly Plasmodium vivax between China and Myanmar.To achieve malaria elimination,vigilance of health staff in malaria diagnosis and treatment should be pro-moted,and intensive malaria health education should be provided to people traveling to malaria endemic territories,to enable individual protection,and timely diagnosis and treatment after return from endemic countries.

Aim To investigate the mechanism of ultra-fine garlic powder(UGP)in ameliorating dyslipidemia and aortic inflammation and fibrosis in atherosclerotic(AS)mice.Methods A 10-week ApoE-/-mouse AS model was constructed,cardiac index was meas-ured,and aortic histopathological changes were ob-served by oil red O staining.Serum inflammatory factor levels were detected by ELISA,and the expression of JNK,NF-κB,ERK and their phosphorylated proteins were detected by Western blot.Results Cardiac in-dex and other indicators as well as aortic lesions were worsened in the AS group,as compared with the normal control group.Compared with the AS group,the UGP treatment group and the traditional garlic grinding pow-der(TGP)treatment group significantly decreased total cholesterol(TC),triglyceride(TG),low-density lipo-protein cholesterol(LDL-C),atherosclerosis index(AI1,AI2),and coronary cardiac index and restored high-density lipoprotein cholesterol(HDL-C)levels,and the area of aortic lesions,inflammation and fibrosis were significantly improved,and at the same time,sig-nificantly inhibited the expression of TNF-α,IL-1β,and IL-6,as well as the expression of p-JNK,p-NF-κB and p-ERK proteins.The therapeutic effect of the UGP group was superior to that of the TGP group.Conclu-sion UGP can significantly inhibit the formation of aortic endothelial AS plaques,reduce the levels of in-flammation and fibrosis,and regulate blood lipids in a-orta of AS mice.

This study systematically investigated the molecular mechanisms underlying the involvement of mesoderm development-associated genes in melanoma progression through integrated bioinformatics analy-sis and experimental validation.Utilizing the GSVA(gene set variation analysis)algorithm to perform enrichment analysis of 7 752 biological functions in 406 skin cutaneous melanoma(SKCM)cases,we i-dentified for the first time the significant activation of mesoderm development pathways during SKCM pathogenesis.Four core regulatory genes(SMAD4,NODAL,BMPR1A,and ZFP36L1)were screened using LASSO-COX regression analysis and a prognostic risk-scoring system was established.Gene Ontolo-gy(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analyses revealed predomi-nant enrichment of these genes in mRNA metabolic processes and TGF-β signaling pathways.Experimen-tal validation through Quantitative Polymerase Chain Reaction(qPCR),Western blotting,and immuno-histochemistry(IHC)demonstrated that:(1)Downregulation of SMAD4 and BMPR1A in tumor tissues was significantly correlated with poor prognosis(P＜0.05);(2)NODAL promoted tumor invasion and metastasis by regulating epithelial-mesenchymal transition(EMT);(3)High ZFP36L1 expression was associated with enhanced chemotherapy sensitivity.Further analyses revealed significant correlations be-tween core gene expression levels and tumor immune infiltration characteristics as well as immune check-point molecules.By integrating multi-omics analysis with experimental validation,this study elucidates the critical roles of mesoderm development-associated genes in SKCM progression,particularly clarifying the molecular mechanisms through which SMAD4/NODAL/BMPR1A/ZFP36L1 influence tumor biologi-cal behaviors via immune microenvironment regulation and EMT processes.These findings provide novel theoretical foundations for molecular subtyping and targeted therapy in melanoma.

This study systematically investigated the molecular mechanisms underlying the involvement of mesoderm development-associated genes in melanoma progression through integrated bioinformatics analy-sis and experimental validation.Utilizing the GSVA(gene set variation analysis)algorithm to perform enrichment analysis of 7 752 biological functions in 406 skin cutaneous melanoma(SKCM)cases,we i-dentified for the first time the significant activation of mesoderm development pathways during SKCM pathogenesis.Four core regulatory genes(SMAD4,NODAL,BMPR1A,and ZFP36L1)were screened using LASSO-COX regression analysis and a prognostic risk-scoring system was established.Gene Ontolo-gy(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analyses revealed predomi-nant enrichment of these genes in mRNA metabolic processes and TGF-β signaling pathways.Experimen-tal validation through Quantitative Polymerase Chain Reaction(qPCR),Western blotting,and immuno-histochemistry(IHC)demonstrated that:(1)Downregulation of SMAD4 and BMPR1A in tumor tissues was significantly correlated with poor prognosis(P＜0.05);(2)NODAL promoted tumor invasion and metastasis by regulating epithelial-mesenchymal transition(EMT);(3)High ZFP36L1 expression was associated with enhanced chemotherapy sensitivity.Further analyses revealed significant correlations be-tween core gene expression levels and tumor immune infiltration characteristics as well as immune check-point molecules.By integrating multi-omics analysis with experimental validation,this study elucidates the critical roles of mesoderm development-associated genes in SKCM progression,particularly clarifying the molecular mechanisms through which SMAD4/NODAL/BMPR1A/ZFP36L1 influence tumor biologi-cal behaviors via immune microenvironment regulation and EMT processes.These findings provide novel theoretical foundations for molecular subtyping and targeted therapy in melanoma.

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.