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.

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.

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 evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

OBJECTIVE: Lipid oxidation is involved in the pathogenesis of atherosclerosis and may be contribute to the development of Ischemic stroke (IS). However, the lipid profiles associated with IS have been poorly studied. We conducted a pilot study to identify potential IS-related lipid molecules and pathways using lipidomic profiling. METHODS: Serum lipidomic profiling was performed using LC-MS in 20 patients with IS and 20 age- and sex-matched healthy controls. Univariate and multivariate analyses were simultaneously performed to identify the differential lipids. Multiple testing was controlled for using a false discovery rate (FDR) approach. Enrichment analysis was performed using MetaboAnalyst software. RESULTS: Based on the 294 lipids assayed, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models were used to distinguish patients with IS from healthy controls. Fifty-six differential lipids were identified with an FDR-adjusted P less than 0.05 and variable influences in projection (VIP) greater than 1.0. These lipids were significantly enriched in glycerophospholipid metabolism (FDR-adjusted P = 0.009, impact score = 0.216). CONCLUSIONS Serum lipid profiles differed significantly between patients with IS and healthy controls. Thus, glycerophospholipid metabolism may be involved in the development of IS. These results provide initial evidence that lipid molecules and their related metabolites may serve as new biomarkers and potential therapeutic targets for IS.
Humans ; Pilot Projects ; Lipidomics ; Male ; Female ; Biomarkers/blood* ; Middle Aged ; Ischemic Stroke/blood* ; Aged ; China ; Lipids/blood* ; Adult ; Case-Control Studies ; East Asian People

OBJECTIVE: Recombination events are common and serve as the primary driving force of diverse human adenovirus (HAdV), particularly in children with acute respiratory tract infections (ARIs). Therefore, continual monitoring of these events is essential for effective viral surveillance and control. METHODS: Respiratory specimens were collected from children with ARIs between January 2022 and December 2023. The penton base, hexon, and fiber genes were amplified from HAdV-positive specimens and sequenced to determine the virus type. In cases with inconsistent typing results, genes were cloned into the pGEM-T vector to detect recombination events. Metagenomic next-generation sequencing (mNGS) was performed to characterize the recombinant HAdV genomes. RESULTS: Among 6,771 specimens, 277 (4.09%, 277/6,771) were positvie for HAdV, of which 157 (56.68%, 157/277) were successfully typed, with HAdV-B3 being the dominant type (91.08%, 143/157), and 14 (5.05%, 14/277) exhibited inconsistent typing results, six of which belonged to species B. The penton base genes of these six specimens were classified as HAdV-B7, whereas their hexon and fiber genes were classified as HAdV-B3, resulting in a recombinant genotype designated P7H3F3, which closely resembled HAdV-B114. Additionally, a partial gene encoding L1 52/55 kD was identified, which originated from HAdV-B16. CONCLUSION A novel recombinant, P7H3F3, was identified, containing sequences derived from HAdV-B3 and HAdV-B7, which is similar to HAdV-B114, along with additional sequences from HAdV-B16.
Humans ; Adenoviruses, Human/isolation & purification* ; Respiratory Tract Infections/epidemiology* ; Child, Preschool ; Child ; Recombination, Genetic ; Male ; Beijing/epidemiology* ; Infant ; Female ; Phylogeny ; Adenovirus Infections, Human/epidemiology* ; Acute Disease ; Genome, Viral

Primary ciliary dyskinesia (PCD) is a clinically rare, genetically and phenotypically heterogeneous condition characterized by chronic respiratory tract infections, male infertility, tympanitis, and laterality abnormalities. PCD is typically resulted from variants in genes encoding assembly or structural proteins that are indispensable for the movement of motile cilia. Here, we identified a novel nonsense mutation, c.466G>T, in cilia- and flagella-associated protein 300 ( CFAP300 ) resulting in a stop codon (p.Glu156*) through whole-exome sequencing (WES). The proband had a PCD phenotype with laterality defects and immotile sperm flagella displaying a combined loss of the inner dynein arm (IDA) and outer dynein arm (ODA). Bioinformatic programs predicted that the mutation is deleterious. Successful pregnancy was achieved through intracytoplasmic sperm injection (ICSI). Our results expand the spectrum of CFAP300 variants in PCD and provide reproductive guidance for infertile couples suffering from PCD caused by them.
Adult ; Female ; Humans ; Male ; Pregnancy ; China ; Ciliary Motility Disorders/genetics* ; Codon, Nonsense ; East Asian People/genetics* ; Exome Sequencing ; Homozygote ; Infertility, Male/genetics* ; Kartagener Syndrome/genetics* ; Pedigree ; Sperm Injections, Intracytoplasmic ; Cytoskeletal Proteins/genetics*

Objective To explore the risk factors for 28-day mortality of sepsis-associated acute kidney injury(SA-AKI)patients and to develop a nomogram risk prediction model.Methods A retrospective cohort study was conducted,involving 184 patients with SA-AKI admitted to the intensive care unit(ICU)of the 940th Hospital of Joint Logistic Support Force of PLA between January 2017 and December 2022.Patients were categorized into survival(n=135)and non-survival(n=49)groups based on 28-day mortality.Clinical data were collected,and statistically significant risk factors were preliminarily screened.Multivariate stepwise logistic regression analysis was performed to identify independent risk factors for 28-day mortality of SA-AKI patients.A nomogram predictive model was constructed using these factors,and internally validated with the Bootstrap method.The receiver operating characteristic curve(ROC curve)was drawn,and the area under the ROC curve(AUC)was calculated to verify the predictive value and accuracy of the model.Results The 28-day mortality rate among 184 SA-AKI patients was 26.6％(49/184).Multivariate stepwise logistic regression analysis identified multiple organ dysfunction syndrome(MODS)(OR=16.393,95％CI 4.317-62.254,P＜0.001),high acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ)score(OR=1.097,95％CI 1.036-1.161,P=0.002),low oxygenation index(OR=0.992,95％CI 0.986-0.998,P=0.015),low neutrophil count(OR=0.912,95％CI 0.860-0.968,P=0.002)and low fibrinogen concentration(OR=0.733,95％CI 0.549-0.978,P=0.034)as independent risk factors.The prediction model equation was P=1/1+e-logit(P),logit(P)=-1.626+2.797×MODS+0.092×AP ACHE Ⅱ+(-0.311)×fibrinogen+(-0.092)×neutrophil count+(-0.008)×oxygenation index.Internal validation with 1000 Bootstrap resamples showed high consistency between predicted and actual values.ROC analysis showed an AUC of 0.911(95％CI 0.868-0.955,P＜0.05)for the model,with 93.9％sensitivity and 78.5％specificity at a cut-off of 0.194.The Hosmer-Lemeshow test confirmed good calibration(P=0.62),and decision-making curve analysis demonstrated clinical utility within the high-risk threshold range(0.1-0.9).Conclusions MODS,high APACHE Ⅱ score,low oxygenation index,low neutrophil count,and low fibrinogen concentration are independent risk factors for 28-day mortality in SA-AKI patients.The developed nomogram risk prediction model may provide important guidance for predicting 28-day mortality in SA-AKI patients.